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@alexlib
Created April 21, 2014 20:39
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auto- and cross-correlation functions of turbulent time signals of streamwise and transverse velocity components
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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# load the data\n",
"u = np.loadtxt('u.dat')\n",
"v = np.loadtxt('v.dat')\n",
"\n",
"# estimate fluctuations\n",
"u -= np.mean(u)\n",
"v -= np.mean(v)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(u,v,'.')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"[<matplotlib.lines.Line2D at 0xa3de1d0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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58KmesfH6LOmEDRZbfLgR3UkWeIGhzqTUspi5tVVdjVl7APFNeYnCgPvDZb+4un6KzMgI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\n",
"text": [
"<matplotlib.figure.Figure at 0x9988070>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# estimate correlation coefficient of the two signals\n",
"np.corrcoef(u,v)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"array([[ 1. , -0.11021129],\n",
" [-0.11021129, 1. ]])"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot a portion of the signal\n",
"t = np.r_[0:len(u)]\n",
"plt.plot(t[:50],u[:50],'r-o',t[:50],v[:50],'b--x')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"[<matplotlib.lines.Line2D at 0xa494570>,\n",
" <matplotlib.lines.Line2D at 0xa494750>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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gjRpVUlLuL7KyiNq21UAAgbOHl2GqLU6YQPT22zpPakDuu4/oo48kn6Zbhc6Z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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xa34e530>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Faster correlation functions definition through FFT\n",
"def autocor(x):\n",
" s = np.fft.fft(x)\n",
" return np.real(np.fft.ifft(s*np.conjugate(s)))/np.var(x)\n",
"\n",
"def crosscorr(x,y):\n",
" s = np.fft.fft(x)\n",
" w = np.fft.fft(y)\n",
" return np.real(np.fft.ifft(s*np.conjugate(w)))/(np.std(x)*np.std(y))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# estimate the three correlation functions \n",
"Ruu = crosscorr(u[:100],u[:100]) # equivalent to autocorr(u[:100])\n",
"Rvv = crosscorr(v[:100],v[:100]) # equivalent to autocorr(v[:100])\n",
"Ruv = crosscorr(u[:100],v[:100])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure()\n",
"plt.plot(Ruu[:len(Ruu)/2]/Ruu[0],'r-')\n",
"plt.plot(Rvv[:len(Rvv)/2]/Rvv[0],'g--')\n",
"plt.plot(Ruv[:len(Ruv)/2]/Ruv[0],'b-.')\n",
"plt.xlabel('$\\\\tau$',fontsize=16)\n",
"plt.ylabel('$\\\\rho$',fontsize=16)\n",
"plt.legend((u'$\\\\rho_{uu}$','$\\\\rho_{vv}$','$\\\\rho_{uv}$'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"<matplotlib.legend.Legend at 0xa0e1830>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Qb70lRKtWQty9q98Pio0VwttbiCFDhEhIKN65Dh8WmmbNxeBKW8X1raf0E58Q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\nSAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xa3f4970>"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Direct correlation function from the definition, slow for long signals\n",
"def autocorrelate(x,r,dr):\n",
" N = x.shape[0]\n",
" R = np.zeros((r.shape[0],1))\n",
" for i in r:\n",
" R[i] = 0.0\n",
" for n in np.arange(N-i*dr):\n",
" R[i] += x[n]*x[n+i*dr]\n",
" \n",
" return R/R[0]\n",
"\n",
" \n",
"def crosscorrelate(x,y,r,dr):\n",
" N = x.shape[0]\n",
" R = np.zeros((r.shape[0],1))\n",
" for i in r:\n",
" R[i] = 0.0\n",
" for n in np.arange(N-i*dr):\n",
" R[i] += x[n]*y[n+i*dr]\n",
" \n",
" return R/R[0]\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Ruu = autocorrelate(u[:100],np.arange(100),1)\n",
"Rvv = autocorrelate(v[:100],np.arange(100),1)\n",
"Ruv = crosscorrelate(u[:100],v[:100],np.arange(100),1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure()\n",
"plt.plot(Ruu[:len(Ruu)/2],'r-')\n",
"plt.plot(Rvv[:len(Rvv)/2],'g--')\n",
"plt.plot(Ruv[:len(Ruv)/2],'b-.')\n",
"plt.xlabel('$\\\\tau$',fontsize=16)\n",
"plt.ylabel('$\\\\rho$',fontsize=16)\n",
"plt.legend((u'$\\\\rho_{uu}$','$\\\\rho_{vv}$','$\\\\rho_{uv}$'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"<matplotlib.legend.Legend at 0xad005f0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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HQ1mP5MxknLt1Dm1c2wAA2rc3aCHAINRqNdRqtf4nKF0lm+E9ali/dOlSoQ3r\nycnJ4tdffy27DetPyskRol07IRYvLvSwj3d/LHyW+Yjb926bKDBSXGioHB2+erXSkZAWB68eFDXm\n1xChZ0OLPthMlPTZaXZPWrVaLTw9PYWHh4dYnPvQXLZsmVi2bFneMR988IFo0KCB8PX1FadPn9Z6\nnjKVRIQQ4uxZ2RtGR5dmIWSj3pQdU0SbH9oUe6oLKsic2kaj46NFyJEQ7TvXr5eNpFFRpg2KSiTy\nWqSo+W1NsS7GhNPAlILFJxFDUSKJpKYK8c03RrzAf/4je2sV0qUyR5MjRm8eLcZtGWfEQMq2Dh2E\n0PHdxKT2X9kvasyvIbae2Vpw508/CeHiIsTx46YPjErseOJx8eyCZ8WKwyvybb95U3aEMydMIrmU\nSCJZWULUqyfEYWPNkajRyMFdH35Y6GHZOdkiOSPZSEGUbadPC/Hss0JkZysbx664XaLG/Brijwt/\nFNwZHCyEq2uhpVIyP+eSz4kGixqIXXG78rZ9/LEQffua17yRJX12ctoTA5s/X0518NNPRrrAv//K\nVd/WrpXzKZBBffcdcOMGsGCBcjGEngvFmC1jsHHQRrSv3z7/zu++k5N57dkDuLkpEyDp7VbmLTg5\nOMFGJfv4PnwIdOokpx37v/9TOLhcXE8kl1JJJCVFdpbp0MGIF9m+Xfb3Pn7c+tZMMDIh5KSaSq1U\nnJmVCf+V/gjpFwL/Ok90xRRCzqe2bp1cK6IkXXjJrN24IZcTyu1wqjgmkVwWPQFjcUycKMcG/Ppr\nsQ5/dC+UHlNDRcvWZKOczRO97x8tNhQeLpeYLeXyp0SF4VTw1uK77+Ra1GvWFOvw+QfmY/a+2UYO\nigwhXwLJzgbGjZNjhNRqJpAyKCEtAf9m/Kt0GHpjErFUjo6yFPLuu8CVK0UePsp7FNaeXIsFBxWs\n7KeSuX9fLi8bHy/bQEqxbjaZry1ntqBtSFvEpcQBkAVPS8IkYkTnzxv5A+HrC7z3HjB8uFwBpxC1\nKtXC7uG7ERQdhBX/rDBiUJbp1CnZlmXy6/57SvuO1FTZ2mpvL9eEqFTJtIGRybzt9zamvzAdL/7w\nIjaf2Yzx42Wh01IwiRiJRiNnKklMNPKFZsyQs7kVo2uHaxVX7B6xG7P3zcbamLVGDsyynDsnGzdN\nadnhZei1phfSHqTl33Hzppw8sWlTWV1p1Hl1yBy87fc2tg7dind3vovstnPg/+JDpUMqNjaslwX/\n/iu7+45bdX4MAAAW8klEQVQeLRtgi3Dy35OYHj4dO17fkb/+nUwmODoY8w/Ox94Re+FR7YkZqC9e\nlHNgjRghvxiwI4RVSbmXgtFbRuNVz1cx0nukIjGwd1Yuq0oiAHD9upzdbcYM2XOrCEII9tRSSFBU\nEBZGLsTeEXvh5vTEWI/jx4FevYBPP5VLmZJVUronZUmfnfwaWlbUqSMbXzt0ACpWlNOCF4IJRBnB\n0cFYGLkQf478Ew2qNpAbs7Lk6MZHAwkHDVI0RlLW0/9vpqYCVaooFEwxsCRS1pw5I9eFWLxY9uwh\nsxKbFAsnByfUrlRbbjh6VDaeOTvL1c0aNFA0PjIvN28CPj7A/v3A9XIRaFyjMZwdnY16TY4TMUPb\nt8tnukl4egJhYcCkSfLCxaQRGqw6sgpZOVlGDM78hIcDK1ea7npeNbxkArl/H/jkEzlMeepUGQgT\nCD2lVi3gs8+Al18G/nd0L54Leg5Tw6biyp2iu/WbCpOICXh4AHPnAvfumeiCLVoAW7fKhva9e4v1\nloc5D/G/2P+h/2/9kfEww8gBmo+QEAUueuCAnP/s7FnZJWzkSDagk05vvSWbyD56cRZOTjwJ+3L2\n8Fnug+GbhuNY4jGlw2N1lqn06QO88gowZowJL7pvHzBwoHxIDRkCtGxZ6MMqKycLb257E2eSzyB0\nWKjRi81Ky8wEXFyAuDhZm2RIF1Iu4H+x/8PMtk/0ljt8WLZ97NsHBAUBAwYY9qJkNe7cv4Olh5bi\ndPJp/PzKzwX2n7t1DvP+moeHOQ/zvbycvbCge+EDjo3aOys9PR2VLGTQk7klkZs35YBjo6zFXphz\n54Cff5az/qpUMpkMHQo0bqz1cCEEPtn7CdaeXIu1A9bihbovmDhg08nOlk0Sfn6GO2dWTha+j/4e\nc/+ai1kdZ2Fiy7egCg0FFi4ELl8GpkyR05iYc0spWbyEtASEnQ+DfTl72Nnawc7WDva29qhRsQZa\nPduq0PcaJYn8888/GDx4MC5evAgXFxeMHj0an376KSooNdVpMZhbElGcEMA//8hk8ttvMqP16QM8\n88zjdbTt7GSWs7PDZsTix8y/sXnEDqg42K1Y9lzcgyk7p6DuM3WxuOM8eG77G1i0SCaM996TpcJy\n7BBJpZedbbyPklGSyLhx4zBu3DhkZ2cjJiYGK3NbIg8cOAAHBwf9ozUiJpFCaDTAX3/JKcXv35eL\nGmRl5f+ZnCzrea5fB559FmjYsODL3V25OdPNTMjREHyhnoPAqoPRb18iVNt3AO3aAdOny59s8yAD\nefgQaN3aeNOpGSWJLFy4ENOnT8+3bf369Thw4AAWm6zbUckwiRjIw4dygse4OODChcev8+fl9lq1\ngEaNZFLx8ABq1wZq1pSvWrVkY0NZLsnExwPbtiEt9H8o93ckHPzayBJe375AvXpKR0dl1M2bxpvQ\n2SiDDe/cuYNTp06hSZMmedsGDRqE48ePlzxCK5eaKnveDhumdCTFZGcnk0SjRgCAuw/uwrG8o5wu\nJTsbuHpVJpTz5+WUHceOyWlY/v0XSEqSr8qVZWnG3V2+PDzky91drs5nb2/yPyslRc9vcRqNrBYM\nDZUTI169CvTqhcqjxgNrN8rqQSIjM6cVAYqVRGbMmIE+ffqgZcuWeP3119GyZUsIIVDe5K3Els/G\nRjbmDh1qmTUciyIXYe+lvfht4G+oVanW48Sga1k2jQa4c0d+Y794UZZoYmPlQ/jiRfkQdnYG6teX\n39zr13/877p1ZcWvEAVfDg56J6CcHFkdEB0NODkV4w3p6bLqb9s2PAgLRUKdynDr9Kps72jThu0c\npLiMDPkxVOD7WPF7Z6WmpmLBggUIDg4GAFSpUgWvvfYaxo8fDw8PjyLebXqszjKOHE0OZu+bjZCj\nIVj/2nq0cW1TuhNmZwMJCTKZXLny+OeVK7I9JidHZtunXxkZwLVrcrqX556Tr0aN5M/ateV6K49e\nFSs+7haXmQnEx0NzNR4216/J5HbtmqwfeNQ+9PChXCP30c/r14HWrXGkRwuMsN2KHo374rtu35X+\nZhIZyIoVQGCg/Nm+fenOZfQJGLOzsxEVFYXdu3djz549iIqKQp06dTBkyBDMnTu3xAEbC5OIcYWe\nC8WYLWPwWcfP8I7fO8rMxZWVJbvNnjuX/5WUJJNMZqZ8ZWTIxGNvL5NW3bry5er6+GetWrJ0Y2cn\nj3v0094eWbVr4usTwfg++nss7L4Qrzd7nXOPkVkRAti0SfYg37hRlrT1ZfJZfDMyMhAREYHTp0/j\nvffeK82pDIpJxPjiUuIwYP0AzGgzA280f0PpcAqXlSWnDKhcuUT1iGeSz2D4puGo7lAdq/quQt1n\n6hoxSKLSSU+XBe8nP+I5OTLJFLfWlVPB57KEJKLRyC/Gltx5KTMrE+VtyqO8bdlsH9twegOSMpLw\nVqu3WPogi3T4sJye7cCB4h3PJJLLEpLI9OmyNuWp3tNkRFu2yDpjLldO1iQnB7C1Ld6xTCK5LCGJ\nHDkC9OsnOyxZcmnEUjyaK+vCBaBGDaWjITJPnAregvj6Av7+pl/b29j+zfgXkfGRSodRQFiYvN+6\nEsitzFumDYioDGASUdiGDUCrwudDszhnk8+i79q+UF9WKx1KPjVqyNWDnyaEQHB0MHyW+1jVNPhE\nhsDqLDKKPy/9iUEbBmHtgLXo4t5F6XB0unP/DsZvG49zt85h46CN8KhmfmOeiEyJ1VlkFjq5dcLG\nQRsxbOMwhF8IVzocrQ5cPQDvZd6oXak2IsdFMoEQ6YElETKqg9cOov+6/tg7ci+a1myqdDh50h6k\noeWKlljQbQH6PN9H6XCIzAZ7Z+WytCQSGysXu/vPf5SOxPAu3r4It6puZjfOIluTLSeSJKI8TCK5\nLC2J3L8vJ4dt21bpSMqezZuB48eBzz9XOhIi88ckksvSkggZT3zSXdxPrYyGDc2rJERkjtiwThbh\n8p3L2Hd5n9GvE3Y+DC/+0gQ37Q4a/VpE1shskkhaWhr69euHevXqoX///khPT9d6XIMGDdC8eXP4\n+PjA39/fxFGSoVy/ex2DNwzG99HfG6XEmHo/FeO2jsPEHRPx3/7/Rdt6rCckMgazSSJLly5FvXr1\ncP78edStWxfLli3TepxKpYJarcbRo0cRHR1t4ihN4+RJOetmWda2Xlv8PfZvrDqyCr4rfDH/wHxc\nuXPFIOfeFbcLzZc1Rzmbcjjx1gl0cutkkPMSUUFmk0Sio6MxduxY2NvbY8yYMYiKitJ5bFlu6xAC\n6N8fOHRI6UiMz83JDf+M/wcLuy1EXEocXvrvS8jKySrVOTVCg6WHl2Jln5UIfGkZsjMrGyhaItLG\nbBrW69evj7Nnz6JChQrIzMyEl5cXrlwp+M3U3d0dlStXhpubG8aMGYO+fftqPZ8lN6x/8QWQmAjk\nLiJpNYQQWrsBp9xLwdXUq/By9oJ9ueKv/7l2LbBmjVwKnYiKp6TPTpN2ku/atSsSExMLbP/qq6+K\nHfSBAwfg4uKC2NhY9OnTB/7+/qhdu7ahQ1XUiBHAd1a4+qqucSSnk07jrdC3EHc7Dg2qNkCzms1g\na2OL56o9h9mdZus83/r1wMCBxoqWiAATJ5Fdu3bp3Ld69WrExsbCx8cHsbGx8PPz03qci4sLAMDL\nywt9+/bFtm3b8Oabb2o9dtasWXn/DggIQEBAgN6xm1L9+nLgIUnt6rXDyYkn8SD7Ac7eOouYmzHI\nzMosdKS5RgOkpsqp9olIN7VaDbVarff7zaY6a/78+bh27Rrmz5+PGTNmwM3NDTOemnI1MzMTOTk5\nqFy5MpKSkhAQEICdO3fC1dW1wPksuTqLiEgpFjtO5O2338bVq1fx/PPP4/r163jrrbcAAAkJCejd\nuzcAIDExEe3bt4e3tzeGDBmC9957T2sCISIi0zCbkoihsSRCRFRyFlsSoYISE4ExY5SOgohINyYR\nM1azJvDaa2V/4KGhBQUBN24oHQWRdeA82GbMxgbo2VPpKCxTxYpKR0BkHdgmQkREedgmQkREJsMk\nYiFSUpSOgIioICYRC5CRAXh4MJEQkflhErEAFSsC3bsD69YpHYl5GzgQOHJE6SiIrAuTiIUYPRqI\ni1M6CvN14wawZw/QuLHSkRBZF/bOojJhxQrgwAFg9WqlIyGybCV9djKJUJkgBJCWBjzzjNKREFk2\nJpFcTCJERCXHcSJERGQyTCIWRghgyBAgOVnpSIiImEQsjkoFvPMOUKmS0pGYhxs3gFOnlI6CyHox\niVig9u2BChWUjsI8nD4NbN6sdBRE1osN60RElIcN60REZDJMIhbs1i3g/HmloyAia8YkYsG2bwcm\nTlQ6CiKyZkwiFmzIENmwfPy40pGY3vHjwJIlSkdBRFwe14LZ2QHz5gHp6UpHYno//ww4OCgdBRGx\ndxZZHI0GqF8f2LkTaNJE6WiIyhb2zqIyTwggJIQJhMgcsCRCRER5WBKxYteuAZ98Ir+pExGZApNI\nGVKtGuDtLefXIiIyBVZnkcVISZE90erVUzoSorKL1VlUZh0+zLEhROaGJZEyTAhg716gc2dWcRFR\n8bAkQnkyM4GPPwY8PIAvvzTceadOBW7fNtz5iMhyccR6GVaxIhAZCRw7Bpw4UfL3p6bKgX1OTo+3\naTRAq1ZAlSqGi5OILBdLImWcSgX4+AAjRxbc9+uvwMqVBbcfOAAMGiQbsP/4I/8+Gxtg+HD580kn\nTwL+/sD164aLnYjMH5OIFWvXTraXPC0rC+jaFbh0CRg8uHjn8vIC+veX70tKMmycI0YAcXGGPScR\nGQYb1smgliwBXn0VqFvXMOeLipKJ7MIFoBwrX4mMrqTPTiYRMmujRsnquKlTlY6EyDpYbO+s33//\nHU2aNIGtrS2OHDmi87iIiAh4eXmhUaNGCAoKMmGEpITgYGD8eKWjICJdzCaJNGvWDJs2bUKHDh0K\nPW7q1KlYvnw5du/ejeDgYCQnJ5soQtJHYiLw8KH+769YkeuGEJkzs0kinp6eeO655wo9JjU1FQDQ\noUMH1K9fH926dUNUVJQpwiM9ffEFEB6udBREZCxmk0SK49ChQ/D09Mz7vXHjxoiMjFQwIipKUBDQ\np0/xj9+3D/jxR+PFQ0SGZdL+Ll27dkViYmKB7XPnzkWfkjxpimnWrFl5/w4ICEBAQIDBr0GFe3o8\nCQBcvAjY2wN16hTc9+yzgK2t8eMiIkmtVkOtVuv9frPrndWpUycsWLAAvr6+BfalpqYiICAAR48e\nBQBMnjwZPXr0QO/evQscy95Z5iskBJgxQ45T+fJLoHlzpSMiokcstnfWk3T9AVVy59qIiIjA5cuX\nsWvXLrRu3dqUoZEBjBkDXL0qByfa2SkdDRGVhtkkkU2bNsHV1RWRkZHo3bs3evbsCQBISEjIV9JY\ntGgRJkyYgC5dumDixIlwdnZWKmQqhUqVZDJ5oomLiCyQ2VVnGQqrs4iISq5MVGcREZFlYBIhIiK9\nMYkQEZHemESIiEhvTCJERKQ3JhEiItIbkwgREemNSYSIiPTGJEJERHpjEiEiIr0xiRARkd6YRIiI\nSG9MIkREpDcmESIi0huTCBER6Y1JhIiI9MYkQkREemMSISIivTGJEBGR3phEiIhIb0wiRESkNyYR\nIiLSG5MIERHpjUmEiIj0xiRCRER6YxIhIiK9MYkQEZHemESIiEhvTCJERKQ3JhEiItIbkwgREemN\nSYSIiPTGJEJERHpjEiEiIr0xiRARkd7MJon8/vvvaNKkCWxtbXHkyBGdxzVo0ADNmzeHj48P/P39\nTRghERE9zWySSLNmzbBp0yZ06NCh0ONUKhXUajWOHj2K6OhoE0Vn2dRqtdIhmA3ei8d4Lx7jvdCf\n2SQRT09PPPfcc8U6Vghh5GjKFv4P8hjvxWO8F4/xXujPbJJIcalUKnTu3Bn9+/fH1q1blQ6HiMiq\nlTPlxbp27YrExMQC2+fOnYs+ffoU6xwHDhyAi4sLYmNj0adPH/j7+6N27dqGDpWIiIpDmJmAgADx\nzz//FOvYadOmiRUrVmjd5+HhIQDwxRdffPFVgpeHh0eJntkmLYkUl9DR5pGZmYmcnBxUrlwZSUlJ\nCA8Px7Rp07Qee+HCBWOGSEREMKM2kU2bNsHV1RWRkZHo3bs3evbsCQBISEhA7969AQCJiYlo3749\nvL29MWTIELz33ntwdXVVMmwiIqumErq+9hMRERXBbEoihhIREQEvLy80atQIQUFBSodjUmPGjEGt\nWrXQrFmzvG1paWno168f6tWrh/79+yM9PV3BCE3n2rVr6NSpE5o0aYKAgACsWbMGgHXej/v376N1\n69bw9vbGCy+8gMDAQADWeS8eycnJgY+PT16HHmu9F9oGb5f0XpS5JDJ16lQsX74cu3fvRnBwMJKT\nk5UOyWRGjx6NnTt35tu2dOlS1KtXD+fPn0fdunWxbNkyhaIzrfLlyyMwMBCnTp3Chg0b8OmnnyIt\nLc0q70eFChXw559/4tixY9i3bx9++OEHnD9/3irvxSOLFy9G48aNoVKpAFjv/yfaBm+X9F6UqSSS\nmpoKAOjQoQPq16+Pbt26ISoqSuGoTKd9+/ZwcnLKty06Ohpjx46Fvb09xowZYzX3o3bt2vD29gYA\nODs7o0mTJjh06JDV3g9HR0cAQHp6OrKzs2Fvb2+19yI+Ph47duzAuHHj8jrxWOu9AAp2ZCrpvShT\nSeTQoUPw9PTM+71x48aIjIxUMCLlPXlPPD09rXKqmAsXLuDUqVPw9/e32vuh0WjQokUL1KpVC5Mm\nTUK9evWs9l5MmzYN3377LWxsHj/+rPVeaBu8XdJ7YZZdfMlwrL3fRFpaGgYPHozAwEBUqlTJau+H\njY0Njh8/jsuXL6NXr15o27atVd6L0NBQ1KxZEz4+PvmmOrHGewFoH7xd0ntRpkoifn5+OHPmTN7v\np06dwgsvvKBgRMrz8/NDbGwsACA2NhZ+fn4KR2Q6WVlZGDBgAIYPH45+/foBsO77AciG1F69eiEq\nKsoq78XBgwexdetWuLm5YejQodi7dy+GDx9ulfcCAFxcXAAAXl5e6Nu3L7Zt21bie1GmkkiVKlUA\nyB5aly9fxq5du9C6dWuFo1JW69atERISgnv37iEkJMRqkqoQAmPHjkXTpk3x7rvv5m23xvuRnJyM\nO3fuAABu3bqFP/74A/369bPKezF37lxcu3YNly5dwrp169C5c2f8/PPPVnkvMjMzkZaWBgB5g7d7\n9OhR8ntRovHtFkCtVgtPT0/h4eEhFi9erHQ4JjVkyBDh4uIi7OzsRN26dUVISIi4e/eu6Nu3r3B1\ndRX9+vUTaWlpSodpEvv37xcqlUq0aNFCeHt7C29vbxEWFmaV9+PEiRPCx8dHNG/eXHTr1k2sXr1a\nCCGs8l48Sa1Wiz59+gghrPNeXLx4UbRo0UK0aNFCdO7cWfzwww9CiJLfCw42JCIivZWp6iwiIjIt\nJhEiItIbkwgREemNSYSIiPTGJEJERHpjEiEiIr0xiRARkd6YRIiISG9MIkREpDcmESIi0huTCBER\n6Y1JhMgEduzYAW9vb5QvXx62trYoX748ypcvj6pVq1rVEs5U9jCJEBlZTEwM9u/fjz179uDevXt4\n9913kZWVhaysLNy5cwfOzs5Kh0ikN65sSGRkFSpUwNdffw0A+OOPP+Du7q5wRESGw5IIkZE1atQo\n799btmxBy5YtFYyGyLC4ngiRiQgh4O7ujtjYWFSoUEHpcIgMgiURIhM5fPgwHB0dmUCoTGESITKR\nI0eOoFevXkqHQWRQrM4iIiK9sSRCRER6YxIhIiK9MYkQEZHemESIiEhvTCJERKQ3JhEiItIbkwgR\nEemNSYSIiPTGJEJERHr7f+PSAnQ1nTPcAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x996b5f0>"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
}
],
"metadata": {}
}
]
}
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