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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ferguson/anaconda/envs/iraf27/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy import stats\n",
"import ofiltsky\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a 1D array of flux values drawn from a Gaussian distribution with a fairly small sigma. Add a small constant offset to each flux (equivalent, for example to a bias level for a CCD). Then quantize and return both the floating point and the quantized versions of this array."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def realization(mean,sigma,offset,nsamp=1000000):\n",
" floatmean = stats.norm(loc=mean+offset,scale=sigma).rvs(nsamp)\n",
" intmean = (floatmean+0.5).astype(np.int32)\n",
" return floatmean,intmean"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f,i = realization(100.,0.1,0.)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1180e7950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"foo = plt.hist(f,np.arange(99.,101.,0.02),alpha=0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For convenience, run ofilter and just return its estimate of the central value."
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def ofil(data,binsize=0.1):\n",
" return ofiltsky.fitsky_ofilter(data,sigclip_sigma=None,binsize=binsize)[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now compare the estimates from three different techniques, operating on each of the floating point and the integer versions of the array: \n",
" * An unclipped mean\n",
" * An unclipped median\n",
" * ofilter"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean of floating point versions: 100.000737257 100.100024274 100.200292341\n",
"Mean of integer versions: 100.000314 100.099384 100.200408\n",
"Median of float versions: 100.001914986 100.101023652 100.19948506\n",
"Median of integer versions: 100.0 100.0 100.0\n",
"Ofilt of floating point versions 99.949881912 100.050940841 100.145858354\n",
"Ofilt of floating point versions 100.000989093 99.9852017423 100.184095627\n"
]
}
],
"source": [
"f0,i0 = realization(100.,1.,0.)\n",
"f1,i1 = realization(100.,1.,0.1)\n",
"f2,i2 = realization(100.,1.,0.2)\n",
"print \"Mean of floating point versions: \", f0.mean(),f1.mean(),f2.mean()\n",
"print \"Mean of integer versions: \",i0.mean(),i1.mean(),i2.mean()\n",
"print \"Median of float versions: \",np.median(f0),np.median(f1),np.median(f2)\n",
"print \"Median of integer versions: \",np.median(i0),np.median(i1),np.median(i2)\n",
"print \"Ofilt of floating point versions\", ofil(f0),ofil(f1),ofil(f2)\n",
"print \"Ofilt of floating point versions\", ofil(i0),ofil(i1),ofil(i2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Results\n",
"----\n",
"\n",
"All three estimates look comparable on the floating point data. \n",
"The standard-error of the mean for one million samples if the standard deviation \n",
"is 1.0 should be 1/sqrt(1e6) = 0.001. \n",
"The estimate from the mean is within that for all three values of the offset, \n",
"for both the floating point and integer versions.\n",
"\n",
"The estimate from the median is okay for the floating point version, but (as expected) \n",
"offset for the versions where there was a non-integer constant offset.\n",
"\n",
"The estimate from ofilter is systematically low for the floating point version. \n",
"It is certainly closer than the median for the integer version, but is still systematically low.\n",
"\n",
"Is there a trend with the offset?\n",
"--------\n",
"\n",
"Repeat the same test, varying the offset and plot the trend."
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def repeat(realization,sigma=1.,nsamp=100000,val=100,binsize=0.1,plotmed=True):\n",
" offset = np.arange(-1.,1.,0.02)\n",
" fmean = offset*0.\n",
" imean = offset*0.\n",
" fmedian = offset*0.\n",
" imedian = offset*0.\n",
" fofilt = offset*0.\n",
" iofilt = offset*0.\n",
" for n,o in enumerate(offset):\n",
" f,i = realization(val,sigma,o,nsamp=nsamp)\n",
" fmean[n],fmedian[n],fofilt[n] = f.mean(),np.median(f),ofil(f,binsize=binsize)\n",
" imean[n],imedian[n],iofilt[n] = i.mean(),np.median(i),ofil(i,binsize=binsize)\n",
" plt.plot(offset,fmean-offset,'o',label='mean',alpha=0.3)\n",
" if plotmed:\n",
" plt.plot(offset,imedian-offset,'o',label='int median',alpha=0.3)\n",
" plt.plot(offset,fmedian-offset,'s',label='float median',alpha=0.3)\n",
" plt.plot(offset,fofilt-offset,'d',label='float ofilt',alpha=0.3)\n",
" plt.plot(offset,iofilt-offset,'d',label='int ofilt',alpha=0.3)\n",
" plt.legend()\n",
"# return offset, fmean, imean, fmedian, imedian, fofilt, iofilt"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Tac6EjGMxcQmnurr6mG6iU6XvXLly5aSMbNPleG1dqEilIJlVTLQxFEYKOBUn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\nZ8vFm23nzpImbMniu5OzzOaFmfXNoVurebXrBnP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xVVv+ku3Tj7Je36djR6LzNb4lH0Md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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a284e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results = repeat(realization)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The trend is certainly systematic that the ofilt is always low. \n",
"\n",
"Is it unacceptably low?\n",
"--------\n",
"Compare the offset to the expected standard-error-of-the-mean $\\sigma/\\sqrt(N)$.\n"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def repeat_relative(realization,sigma=5.,nsamp=100000,val=100,binsize=0.1,plotmed=True):\n",
" offset = np.arange(-1.,1.,0.02)\n",
" fmean = offset*0.\n",
" imean = offset*0.\n",
" fmedian = offset*0.\n",
" imedian = offset*0.\n",
" fofilt = offset*0.\n",
" iofilt = offset*0.\n",
" stderr = sigma/np.sqrt(nsamp)\n",
" for n,o in enumerate(offset):\n",
" f,i = realization(val,sigma,o,nsamp=nsamp)\n",
" fmean[n],fmedian[n],fofilt[n] = f.mean(),np.median(f),ofil(f,binsize=binsize)\n",
" imean[n],imedian[n],iofilt[n] = i.mean(),np.median(i),ofil(i,binsize=binsize)\n",
" plt.plot(offset,(fmean-val-offset)/stderr,'o',label='mean',alpha=0.3)\n",
" if plotmed:\n",
" plt.plot(offset,(imedian-val-offset)/stderr,'o',label='int median',alpha=0.3)\n",
" plt.plot(offset,(fmedian-val-offset)/stderr,'s',label='float median',alpha=0.3)\n",
" plt.plot(offset,(fofilt-val-offset)/stderr,'d',label='float ofilt',alpha=0.3)\n",
" plt.plot(offset,(iofilt-val-offset)/stderr,'d',label='int ofilt',alpha=0.3)\n",
" plt.ylim(-50,50)\n",
" plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try first with the default binsize of 0.1 (hypothesizing that the offset is due to this \n",
"pixilization of the histogram and how you define the center). "
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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EgycOkm3KpsRagjuZks/ZMO+KX5KkzwL/H6k9A/+qKMo/T/e356LAAcJhRf1N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\nQzp+iUQiOWT8/84uL1zbZamCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b1c3b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"repeat_relative(realization)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yes. It is unacceptably low (by nearly a factor of 20). \n",
"Now try with a smaller binsize"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GG27gnXfemXhco9HwL//yLyiVSm688UZMJtMpv64zIQL/JerwAhu6lI7FsxdPOQMQK36E\ns83n81FTUzNx22g04nA4Joqer169mpaWFmw2GzabjWg0OmWK5WS4XK6J3/V6/RG3J5dpfPHFF7Hb\n7djtdmw2G++99x7Dw8MThdoPlXoEpvT7cOvXr2fJkiU4HA5sNhvr16+f0m+HwzGl1OS5LtUovty9\nhB2a9oGPa6uq/ABTL/Qqt09M+4gLvYTp5vF46Ovrm7idSCQYHx+noqKCd999l5/85Ce89dZbtLS0\nAGC32yeWTE73CpqqqiruvfdefvGLXxzxWH9/P6FQiFQqNRH8+/v7j1onOJvNcuutt/L8889z8803\no1AouOWWWy6opZ5ixC8Ax7/QK5fN0RPu4Zd/+qUY/V9ElE4n7SrVET9Kp/OctnE8d955J8888wy7\ndu0ik8nw8MMPs2TJEqqrq4nFYqjVahwOB9lslscff5xYLDbxXJfLNVH7djp86UtfYu3atbz++usU\ni0XS6TQbN27E5/NRXV3N/Pnz+f73v08ul+Pdd99l7dq1U55/qB/ZbJZsNovT6UShULB+/Xpef/31\naenjdBEjfgH4ZOpnZ8dO2oJt2MvteJu8yEWZLXu3oC5Tk1FmxOj/InKyyy3PdhvHK414/fXX88QT\nT/CFL3yBcDjM0qVL+a//+i8AVqxYwYoVK2hqasJkMvHQQw9RVVU18dzbbruN559/HofDQV1dHVu3\nbj2lfR+usrKSl19+mW9/+9vceeedqFQqFi5cyM9//nPg4HcA9913Hw6HgyVLlnDfffcRDoePaNtk\nMvH0009PfBG9atUqbr755lP6G51tIlePcITJ+X32te8joo+ABCX5EmZ5Z1EoFHBlXWK9/wVE5OqZ\nuUSuHuGcmDztk8qmQIJcOIfX4wXEen9BuNiJwC8cYfKKH0vagiVjmZLfR6z3F4SL2xkHfkmSKiVJ\nelOSpL2SJO2WJOnvP77fJknS65IktUuS9JokSdYz765wrhxa8fOVL3yFWkMtKtXBr4PEen9BuPid\n8Ry/JEnlQLksyzskSTIBHwE3A/cD47Is/1iSpH8EbLIsf/cozxdz/Bc4Udjlwifm+Geui6IQiyRJ\nfwJ++vHPtbIs+z8+OGyQZXnWUbYXgf8iIgq7XJhE4J+5LvgvdyVJqgWuADYBLlmW/QCyLI8AZdO5\nL+H8EIVdBOHiN23r+D+e5nkJ+IYsy3FJkg4/RInhyAxwrPX+h2f4jKajWHQWMfUjCBegaQn8kiSp\nOBj0fyPL8ssf3+2XJMk1aapn9FjPf/TRRyd+X758OcuXL5+ObglnyeRUD4emfeCTDJ+l9tKDK34K\n4mIvQZguGzZsYMOGDdPS1rTM8UuS9BwQkGX5Hybd969AUJblfxVf7s5MyWRyorCLUqlkb9teAtnA\nlGRv4mKvc+NCnuPv6Ojg9ttvp7u7mx/84Ad89NFHVFVV8fjjj5/XfqXTaW677TbeffddbrjhBm6+\n+Waee+45Xn31VQAUCgWdnZ3U1dWd136ejTn+Mx7xS5J0NXA3sFuSpO0cnNJ5GPhX4EVJkv4X0Af8\nzZnuS7iwTJ72iaajBzN8zll8xIqftmAbgJj2OU+KxSLb2tq4sqXlqEnFznYbh0ovHkp/fP/9959W\nHw732GOP0dXVxXPPPXdaz3/ppZcYGxsjGAxOpEy46667Jh6fnEbh/vvvvyAOVtPljAO/LMvvAcpj\nPPxXZ9q+cGETGT4vfJv37qVbq6XQ1saiOXPOeRsXaunFvr4+mpqajpkn50I9g5oO4spdYdqIDJ8X\nnj6fj16VCrPdTo9SSZ/Pd07bON+lF/fv3891112HzWZj7ty5Exk1H330UR5//HF++9vfYrFYeOaZ\nZ/j1r3/NsmXLjtq/F154gR//+MdYLJYTJly7GIjAL0ybY9X0PZThM2aKETFFRJqHcySRSPCBz4ex\ntBQAY2kpH/h8JBKJc9bG+Sy9mM/nWbVqFZ/5zGcYGxvj6aef5u677+bAgQM8+uijPPzww9xxxx1E\no9GJ6aejjf4feOAB7r77br7zne8QjUZ5+eWXj9jmYiMCvzCtjlbTt6e/B3WZGiQwaAxivf85srWj\nA11l5ZT7dJWVbO3oOKdtHM/ZLL24adMmEokE//iP/4hKpeK6667jpptumkj7fCkTgV84K0SGz/Nv\nflMT6cHBKfelBweZ39R0Tts4nrNZetHn803J3w8HyyUeavtSJgK/cFaIDJ/nn9FoZInHQ2JsDIDE\n2BhLPB6MRuM5beN4Tqb04ksvvUQoFCIUCmGxWE669KLH42FgYGDKff39/VRUVJxyP891oZSzTQR+\n4awRGT7PvxqPh9p8nlgwiLdQoMbjOS9tHMvZLL24aNEiDAYDP/7xj8nn82zYsIFXXnnltFYYuVwu\nuru7T/t1XmhE4BfOusmjf01Ic3C9/+yp6/07Ojt4a8dbYtrnLFh42WU0ZbMs+Lhg+blu42RLL1ZU\nVNDT03PU0oterxeDwXBE6UVZlnE4HMyfP/+I/arVatauXcu6detwOp187Wtf4ze/+Q2NjY2n3O8v\nf/nL7N27F7vdzhe+8IVTev0XIlF6UTjnRIbP6XchX7krnJkLPjunIJwMkeFTEM4vMeIXzotDxV3e\n2vEW6nI1Xo934otfUdjl1IkR/8x1URRiOeUOiMB/SRPTPtNDBP6ZS0z1CDOOmPYRhHNPjPiF8+5Y\n0z5wcMXP4J5BWhpaRGGX4xAj/plLTPUIM9rkaR+YWtilpaGFQqEgpn6OQQT+mUtM9Qgz2uRpHxAX\negnC2SJG/MIF5dC0TzQdZV/3PirnVB5R2EWs+DmSGPHPXGLEL8x4h9I8rFi8gkUtiybSPBxa8RPS\nhlCXq0V+n4vInDlzePvtt8/Jvt5//32ampqwWCysWbOGlStX8pvf/AbgmPn2L0Ui8AsXLFHYZXoU\ni0X2bN1DsVg8L23s2bOHT33qUye1rdfr5c033zzlfRzyyCOP8Pd///dEo1E+97nPsW7dOu65556J\nxyenYVAoFDMq/86pEIFfuGCJwi7TY+/mvWg6NLRtaTuvbZwLfX19tJxkPqGZlnHzVIjAL1zQRGGX\nM+Pr86HqVWE321H2KPH1nXrpxTNtY/Io/rHHHuP222/nvvvuw2KxMHfuXLZt2wbAvffeS39/P6tW\nrcJisbB69eqjtnd4qcaRkREAGhoa6Onp4aabbsJisZDL5bjuuuv4j//4jyPauPbaa5FlmcsvvxyL\nxcLvf//7U3pNFzsR+IWLgijscuoSiQS+D3yUGg+WTSw1luL74NRLL55pG4dbu3Ytd911F5FIhFWr\nVvHggw8C8Nxzz1FdXc0rr7xCNBrlW9/61hHPPVqpxttvvx2Azs5Oqqqq+POf/0w0GkWtVh+zDxs3\nbgRg9+7dRKNRbrvtttN+PRcjEfiFi4Io7HLqOrZ2UKmbWjaxUldJx9aTL5s4HW0c7pprrmHFihVI\nksQ999zDrl27pjx+vNVJJyrVeKLnH+5SXQklAr9w0RCFXU5N0/wmBtNTyyYOpgdpmn/yZROno43D\nlZeXT/xuMBhIp9Mn/aXxiUo1CidHBH7hoiMKu5wco9GIZ4mHscTBsoljiTE8S0699OKZtnEqTqac\n4tFKNVYeVhBeOD4R+IWLkljvf3I8NR7ytXmCsSAFbwFPzamXTZyONo5n8nRLeXn5cZdYHq1U4+LF\ni48oqn4yTrSvmUwEfuGiJzJ8Ht9lCy8j25SlZcHpl148kzZONIqf/Ph3v/tdnnjiCex2O0899dQR\n2x6tVONvf/vbY+7rePt+9NFHuffee7Hb7bz00ksn+3JmBJGyQZgRLvXCLiJlw8wlsnMKwglcqoVd\nROCfuUSuHkE4ATHtIwgnJkb8woxzKRZ2ESP+mUtM9QjnXOeWLRQCgSPuVzqdNCxYcB56dPIupcIu\nIvDPXGcj8KvOuFfCjFYIBGjO54+4v/0oB4MLTWtTK2s/XIvWpUWpVE650GtsVwfKcIxischrbUO0\n1M0GLo4DmiCcKRH4Z7jpGLFnMhm6ukbZ1zXG7PpS6uvLQHXhv3UOXeh1qLCLLqVj8ZyDF3opwzFq\nIjH2d/TQF86j7xph6VWtDJ/vTgvCUSSTyWk9K70gPr3t69cDZ2e0dbKB71QCZDKZYufOAaJRGYtF\norW1CoNBP639PtzpBvAzHbGnM1k2b/Yz6JeJD5eyMyMzPu7HsvTiuFLy0IVeABadBb/KDxy84vPt\nLTtRW63ozQ78BSW/e/UNWm9acT67e9pqamou6TTDM1l5RTlrP1w7rVOSF0TgPxSYzsb0wYkC36Eg\n3v3GDuZKKXK5PG/s62PlFY00NpbTe9jzkskUa9f2oNU2o1Qq8fsLrF3bzqpV3pMO/qcTxE8lgE8+\nMCX3DeF2GRgcDJ/WiL27ewx1wkDIH6NY5iHvH0Kr1bL5jd1s6Vcx+6p65s2rOesHvukweepn//4e\n3FYrhXSRVEaDRV8gJuf4z3V/ZDCfoFJlZGgoRjIpYzBI1NWVYqzwXLDTQL29vee7C8JpKhaLbH9v\nOx3ZDvRuPR2dHYS0IQqxwkQiwkQ8wfN/fp6aqpqJRQln4oII/Pv3D+H1Ok8YjE531JvJZDhwYIR1\nOw5MBHRUqilBPJncx9Z9/XzkH6bC08qabYMsC0lYllZO2e/br71Lyq9BoVBQ0FsoqWyiWCzyaqKN\nL/yvW0/q9R4K4sVikbYuHy31HhQKBe2BwDFf41BHB811dSds+/AD0+joXn63YTdaUwmZwKmP2EOh\nNNmeLNir0emNBJRq+t7YTNBQoLbDydiOQfa6YMnKFuZe4GXtfHv34h2F99Z9yPimLiqMZopYyCWU\n7A3upbTBSCyfJhA6wPiHfurL/opNuzaywFbKgfe3YWwpnfjfnOiMMZ3J0t09RlxtpWF+6zk5Kzxd\nF3rfO7dsITHko7PTT0/nKN6GMqqqShhMKDBWzD1nZ92n4lizAoff39jo5PU/fEBgaxxrqxaX0Xqw\n6FC5HW/TJxchbtu/Db1Bj9vmxl84mIbkTJz1wC9J0meA/4eD1wz8uyzL/3r4NsFQGWNjwycMRpNH\nvZ0dHRRiMQB6lcpjfiAPTVW80z2OQprHmm39EwF9586BSSP3MYqxAnpDIzqljXShwIGxBLbuMVq0\nGmoTCXp6ArBrgPkKOyUleoaLBcyFg/054AtNvEG7u8dOOFLMZDL8z4Z9qIYd9Pfs5vprZ4FKdcyR\n/UA0SiaToacnMNH20Q6Wk18TwNhYEHOukkBvAHNdFXn/EGaTiUD3GMcbMxx6g360ewhdthaHwYxC\nUhACCvpyLIkwltIylL4hPHoTnVvaTynwn4/pssSQj8xHARapF+DP76IkqWA8bidbaUdKZ4hFxzDY\nNIyOxaiyu1i/510MigYGfHtYqDEwvLkH2ezG63UecSY4+f+WyWTYvNmPW11Bu1qL319/zLPCyUF3\noKMDolEAFBYLFU0HM2Ce7S+cJw9EduzrIzYu4dZUHbfvhx8sDvV9cr8P9d1z2Zwz+l8nhnxE3x8k\n55e4LFpPR882BiLDyNV12FuM+IpFdq9567QHH6fyuT2Z9+2xZgXq7MNsf7MTtboShULBjp7d/HrX\nR1i0zZgaryT4yiBjbV3ULrBhqHZPfIZ3vfE2UsqPGjPBwMHpvDMpowlnOfBLkqQAfgpcD/iALZIk\nvSzL8v7J221t28wVjVfS3T2G8Tij+skKsRh16TR+fxRVTkFGcQBZhj0Msmt3H5XGIkNDUXb95R3U\nIciYGih1aonm3RMBPT6QpTjeBcDY/g/JpSxUO+eSiYcp81TTNbKditEidXWlbN7sR62uQFJYSCX1\nJBMJipVKhoc6yEXH6c8OEWzbgX04isNRjcniwOyuIzoyCEuBBZ+8ado39fDWjj7KirPwhYNofAWe\n6/wzlQuqUatVaNJphqJ5/BkD9VVWJEnBvn0jBPZDRcVstFot4XCBzZuHjjhYRqPyxBsGIB5PUxzL\noiqrQKc3kiopY/e2NuIhifIPOo76xt39zjt8sK4NpcpDUoaAOoocSGL3OMiozMSRKbtsDjq9kazd\nzWBPPxq7Bpj6wdBoMsgy5HLaKR+SQ+0f+gAc64N7tJFeQ4PrhFMux/pwdneP4VZX0Dvkw6iawxZf\nGyVNdURmB0oZAAAgAElEQVTyKdA4CG86QOk8E8MH9pFOqkkVm7FYFYyl7HwUjTKaiaLYOsSeD3dh\nbCmdEuyK0SiadJqRkSi9gSzqkkW43UVQf5wmWtvMzp1dLFkyNZ3xlAN9KERzoQDAYChE5UlOgR4v\nGJ3sAfbQQGTwgxjjUohZXj8xzcFrH452RlsIBCYGQ8mkDP0HWFqiY2fvKB17fegLCVwuC4MqNW/+\ncRtqdSWytYxU/ZVTDiQn079D042GqJpoNo9yEFxaO8VQHO/HA6+iwsWmP7yKJh6feN5ARwe58SC+\nWA6dwzsloE8+GIW3b6FsNItLV82A34/L5ZryuT3kZN+3hw++Dv3/3137J5aYW6CYp2/Ih7G3k9nR\nUhJVNsp0erL2cujpJe/KkKnNTKxGK46P05jNYFGVUIj40WokXC7Lcd8TJ3K2R/wLgQOyLPcBSJL0\nW+BmYErgD0eKbOl6i+S4i2RSZq6UQpahUFBPjGx7P9720Kh3//4R/MMxHI4qMkolW7dKgJ7i7DLi\nHXnG2/Yya9ZlGMIqRjNmak1O5GwKR0kFO3reR5sYw1NuZL6uCaVaye9H+rm++vNo5QJJ+eCbqbyk\nmU071hEJpmnMlFPqytKRClNZyKJU2NnbN0xFVQHrQB9zK2tYv6+Dz2kbkAcivK3u50a3F7W6gu7u\nMRonjQK6e/6CdaSGvmKGTKmOpkCSVL+OYO4A9goLsXCeAwkjDv0cXm3vxGmrYP9QDFP1dezvCOKX\n+vl0y1UTbU8euSeGdhPp7UShOHhR9njvbhLUo1MVSEXGSYYiZHNGMl397Hp5D6/+/EWqW+toWTp/\n4kPXuaWdyxUu+geH+Lzcwnr1LkySlXQ4QTi8F0uZg+rSamKxcaR8llQiR3rLDtb9ZDXtW/qQzPXk\nCnn8B3oALZ6mCmxVsyY+JIfap5jno+0fUKaRiISj/PeOD+j86/aJD+fkkZ6n14CvfRcBQmTdJVTO\n33nUUdnkg9aYfxirzT7xGsc6xlAoJIIjeVI2F7XFSjI6mVg2g9VkxN14A5rCIIlwO2MJLe4qB5li\nBofdzbauQTxpM6qRAA4lDG/uoaRifCLY+RM5Bj5+PxYiMnmtnY6OIIVZBz9iuVyWTZv6ThiAc7kc\nfn+Ud0cCWHtGQIYetLTtG6KiwnLE9AZwzO+cjvfY5H2nM1nW/eUAipFq4qkOGgse8p0+mhpcpPrb\nkFIxBntDtLs/ScXcvWcPo2MG1OoKFAol4cgA7+zuRa72IqvVlGcgcKCbD8f9eFytWB0D+DRalLEA\n6XSKn+58i+ZF17Br1xhNTUsxGAxT+ufbu2diFL7p5beoDDox6V2EPC5kSUcyAVl9ih7fIDVuDwqF\nklwwPuVglOveR0U4j1LnJhTNTwT03pK9EwcjhUJB13v7SSQUFK3DVOiuoDczhEap5L31bQSt3onB\nS9uf32G+rgm18mDgrnF7QOE64my3c+vOiQFlaKgDokFC4TAjPXvw1ASI5bOkUmW0jwaps89HqT34\nha3GaCaVLGWgq4uvPHgPf1z/PwSiMdKDMjltCRljKZJCQSJRpL197MTR9zjOduCvAAYm3R7k4MFg\ninFHJXu7d6BvUFOeLGPrvn5AT3OznWxWMWVku3mzH6XKzZ7uJIuUVQwPJxnX51A7D374+/xRIE2V\nZi5jY1Ha81k8thaCCjXBUBBFLIMkVdPd9yFu2U37wJ/o1ptoqv4Ubw9+QGvNUtBpCUcC7G9/i5Ym\nD/3bDlCuS/HWR12YbQ3sU/dRlvSRiRvpV3VyY2U9L46PoTO0sinWiywXKdiaebNtB7OtbnYPBjnw\nv/+DErkCrbaPwO6PqEw24i8zI6dS9Km1uLNuXuvxsahgJRoGvaaE4VQEW76OPv8Q2XEnW0uCjGZG\nKTVX8buuNubUzcE3kMbwWvvE6DraN4q+a3zizGD0sjp6fBmspWUkE0lKTTX0K9tRJGKkNv6FUqWD\nfv92FBHrRGBOJmWC0Si6sIlUQWapXM8mgw+ntoZ0U46ScBaFQkLKZ7Hl8/ilYXKxKOMbdnKDtoKR\nZJiesQDzpRLyxSKv7tjMYk8dio8/JMmkjEKhpHtggPSATCw3SL2thmQoROH9bt5f8yY+rRKdRsKb\nt6NIVhBLpCkLa1FYHBRVKtzjzolRWSefnCVu+eOr1MWs9AfaMERshA1D1JW2MtCWJJ0xs61tgNKm\nFpSZKCEpiyujJ2tIY9WW4FMP4hv3o07labE3IcsqijLEUnGq1C2M6xN0+9JkLEp65Sw1RjPv7O6j\nUFXLnoFBFimrGPIlaI9048p/hEKS8Cf2ISPT3j6Ezl3FUDFK79gAr/78RbxeO0VfO5JNx0hS5s87\nD7ACHa7SGgrxIKpQEdDSUGUmvG2U91/cMDG90ZtO8erPX6RoNWKq/CL19XmUSiVKpRJZrub5598j\nkZDJZpvxerOMD3Zhr/DS0yPxy1++z6JFVRMHn337BpH9JVBUEit3kBxOQ7KEdCBCiUpFXSaFHA+S\n2Nw7MRB7r2OQlrLrUCgOjmrD4Sg2VQ2BSAqVVWZkOEJ/zE4uoEarzGNKRtAZi+gU24l0DRLS6Nm0\nx0c+pGLra+txN3pw1LRMnF3U29VE3x/Era7AHFPhSjsZsmjRKCTGnVaqhjS0BwbxDsh0ZnrRKdX4\nR4L893/vm3jvDw3uQZVQ068PIYfcdGcG0CnVvPf2Nq5uXomrPIUvMEZ5toA1qyA0oCBUmSY8WKBQ\nZmIkO8Sff/ouYGBWs53RXcMcyI6St6qRCl76ij68lZUHz3gmMeUiuHNqkCQ2d+1nVrKIUmFnF0oU\n43liUSXFJgeZcSv9yiCVShsAhUKBbnmMQV8H/+8//IKKihYqtBbigyXEQr0oG2UMlR8XoUlc2CP+\nk/Je7ztkYgnye/YRHN7OdcYFGHUOXt76PivnzScwKvHe+ja0Wpn5uib6fSMUw3Z8ugQek4l1Bz5g\nYaYUlUJBMpAAskh5G5FYkZsXXceft3XhLb+GfDBGhamGnpFN/FVtA24pTdqoQ8pbmDOrCUkVJ6kc\nxum6nP6hDlZcMRuzpOdAPEp/pJsy5yyUSi2RnJt0Kob5sgZSPg2bRv1I+gYcBhu+aBC1UodF0jLq\nSxHp3YGhzEyobxhn9gClDS7MqiKbDX5q9G4CsTAacwUvym/idc9n6/hOlmW8xAsSw2Wg6D1ALpFF\nWVVKdGgQo92J3uqlUNTz9odbaKpppH/jftrbhwADHkuK+Z5mBgZ3o9AVuM7rRF2VIzQyRLGoJUmE\nvNqHx2/BppOptEBqLMjsYn5i9KJUZokM5KFgZtxmxBmRqAmq2NvUzWeaqomFE6QHBpHlDL7oKIl8\nkLrELLriPtyGJMVKFfk8ICl4Lz5MUVvP2nfXscRTwfBACJ0Wekf24xs3EbO7UA3JBNMJ1IU4JUNB\n4vESGnBwgG4y6TzYcgxbjOgSSorxIPtj+1FYtETCUXy7t1BZaeUam47RFPi2D2LXpCimLcRtVvIR\nBTFznIxOQpHPIRsdZIpqPCUu9hX6iQUzkCigdBro6evmMtMieoqv0ZcdxWQ2k8tn0QXzFNVaNAaZ\nrMnD9oF2TEY7HX3DLNHUsLdvlGL04PtRlouo4mUYFD3UlFSTGAuS3/4uVeEsqPxEx8AyEMCrsKEO\n+dFqjfTs7OVtjYaiNI+3w90szsZ5O+DjVpcXlULBaDhJTA4zV1HFyHiUnp3v4ZJLqVO1sLF9A66x\n3Wza/AYllhTqYpxA1wCy0Y6syKFKvE9XaBBL0cgOZYSSklqKRtgfnD9xoB/oDNJcaGTQrMKishLM\n5KkMlNAT6sHgyDI0OIC6qpYPtvZRqpHYuO4desZHMLqUWK16hjJFsjkZhVJFLpchFo5CTkcsp6VQ\naScczxLOjJMN2xiJdLBE48alzhCKRmjGBugYHRzCW3lwGuyAL0R3WMKtrgBJQqFXsrkwSJ1+Icl0\nDpPNxobhbVRoXQR0aqIftmE0ZckVw6STGdq730G2a8nlIZpX4h+VUNSriW3eh8miZsw3TkzVQ8dH\nPkyGKgK5CMakm5RKy0AxgSmRItfpI1fYxjz3UgBU2R7qVTLmaJGOSB7rvHJGB/qJRvfSWxzgd0/+\nfwDk82oSXTvQRPL40jYiPi1DqiwVpjhxlZKBcByFuxUZFTU1c+ga2ITV5sBcKNDt7yeojOLNW6lL\nziK/P4DKkqSyoKBRdzl/3vMhmz7aRaXVgUXvOKOYe7YD/xBQPel25cf3TTGveRm+6AEK2mHKgxnk\n5HY+6N2Aw7mMP7z/IQsrriVRMBMoaOk8MIQjL5GorqFvwM972QGsjgWEYoNcYy4nEw9iNOuoyGcI\nFDO4bKVc1RSie3gfkpQnmuqkuiSPKmSlPdVLGhOV5bWUl1soL1/OK++uoaQQQG0ap8JyOb0HwmRr\nvPj2F/ConIwWYuhSVoK6EpQOK6FkKT09eZouryKeiKDChl6ykU2NM8tSR/doEFsiiEGrowYTO9u6\n0GvribvSJFCB1kJPchy993IsZVWE94zTk9EQKS9BUhZRoMJtraGYHwWVDlfKTDobQ6M0YkgpmWeU\nGdr5AXXRg3PsQz1t5JpjZMbClCod6Fr03FJm5xeKAAbZxkikj/pIKYkKFWGfn/DYMEg2+oZ8VJSV\nsn//KGQylPmNJKrtGPRGxvMF6vNWFMoASxctYqizk7/IfqRgAb9qjJqQk2JtI8WOFKGMTDGcRKWC\n/ZFRJGMtDq0WvcpAdmyAeTUaFFYdbR8FkVxelEUFlFUy4NuDpM8SyekwZkvwK2UUSRMydkYMepRq\nLWGnBnrHkdMOYvsHqLfVoCto0YV09Ozspc9gIxMpZ0cyg7XWjUKpQeF0MT7ci2S3EffYSCszFOQ4\nBZ2B0lIze9UdWHI6RqI7uMK+GG9ZI0qFRCj/EVlDDrWsJ6OWiVcV0KZNjGbSoCgn7bBDuEiPJkwi\na6ZQW8+OA+2oJB25ynLM2RgD0W70UhmR8V4+V93AsEKiZyyAV1FCEdg2NIjVpsOfNZLXeLAYnBTz\nCv57fAca03zej/u5xuKhfzyC0axDoVSxZXwMVaGcgeAuWq0hDIF2ZmlKGI6EQTaR0/r4jMbNSD7C\n+8EBPiVVMJK14bdoaIlqqEyGyGsV6Ab3IycjbHmuDaVCwZ8iW6jRXk4um6Lc5WBDeisudZhUKsPs\nymq2RJNYI3aSyQM0WKpJpsdwJrT0+nwk1S4iqgRFjQWlVqZ3LEgi6YTyCoyKJBGS5ActKJrc5Ecz\njBsC2CpcxEJZyBcpItE/HsGdSTHqDzCgiwBQZrMzMDJCOO1krDqNIjWKXNRTKEbQ1paS1ngwFuJU\neLzoo910R5NUmWEgnmcspCamj5CL25Dddci5HC5XI4Ge7cwrVBMbG8aIg7jZRrLfSCErE/OaUUkK\nNA4j1SMpMnkl1cjki0X+3N/JldWVDEXCFD0tZLJJQqksuZ4edKUBEmsOnp15Gt0YMw600W5ScTV5\nTyXJET/DhAmFbehKKzGZVYSLSWxOLYulZjYld5MbLjJUGGSZUkNPaASpuIuxcBBkE3ltApWuBDQV\nfP6yvwVpkOWNc3jm/d+fdmA+29k5twANkiTVSJKkAe4A1hy+UT7jR60b5HPOaqyxFJlYhPKS2aiK\nGRRFJ3s6X0cxtgtDdozCaJ6EpYKSpivpttsoSHPQldYQ1bv5k/8ASqpIFSCf78No1vJG+zZmu2vQ\n6/vR6tKgGMQml5MpK6drOI9TW4NS+cmFL3+14K854NvEVbOr6e0cBbuHeDZNU8On0WQU5MlSXjmH\nyxYsB00JWZ2aqvKlZOIFfKEALvts9CY7Y9kcSU0ejcXNgN9MugDjqWHiMTPWqibK0waGNTtJFf2M\ny6NUVdTi0lq4rGUp+3UJFI5yCiix2OrxWYok9AUa3K1otWpUqVGUyhRzG65ky0A3/mEfzXKR2chU\n5ApoY1Gq4zbCkQzSiBF1DuaWGxkvHaZkOIypzItOpyNqMjMeMiBXVzE6kGHL9g6yOTeJgoX2kgyZ\nQgpZDmFxSAyUx1i6eDZNixZx3d13c9Pnr6fLEcGdK8UxeymW0koMVfUMRRLEkhkUWhVdRXAYS9Fo\nVDhN5WwJ5hgKxNi6exid24vRqkStyqAzqtF4atg9liAWU5NUqIm67VhLnOwqjoJSjVIBqWKWbgoo\naiuJ5T0EUnHGkhGCoQjRvJOxMQ1xawlKdS1FSUs+n0elN1DQ6wmOdLFo8RdhdhXWpjLmXDMP7+JZ\nVH26CrkpizohU1N6sGZvTdllFNNWMqpOgooU/Q1+tPU2MOsoJJTIXg8pt5WC00X3SBFKPThdHhR6\nN1pDAyqzg4ynkWKulspPLUan8hBIpQ6+1z8+ExqIhCBpoyOYZLvCQtFUQUSrpR8NCUULOW0JMWM9\na0b7KeRcBBMJ2mIjaE0NWLUWPEYvUnyYy0xa9PF+9BkdOaeLdMzMeCrA/kwEU0kr7/nHoKwShVIL\nZZX0jMfZHh+mkIjQlMti7g+i0xjRtdjRapWo9WaSJh3FWTZs5a0kckpC2SyGhJ1AIoMxV0c4l0Ey\nlLApPUJ3vpReZwURVTW/T3WzU61hJKRnWFFCQqtBUitRFrQYXc3I2QT6qnISkoZMsUiyWCCXD9AX\nDlKIW3h30x6iUQ1Ox2Vksw42b+thbCiHu/EqnBkPmWoV5morOa8GbfksvPPmkjPqUZQ66MJMJlnF\nnrFhIlkTaXcVsaiemMaM1qinUISUnMeirCes0JAKaUlbnChVOkze2XygjpBzqEiosmRNGjaXRkhp\nZIrFAu9FhzBY5/JO7yBaTz2mEgW5xBhVZjPeilqKYzG8CjtV6PnLrm2ksjmGQjYUZbWUlVnIlDrx\nDdtIVdTSKxUJIpG36cFlp8ejQTfLSKd1gIVGNQ1GK9VaDcZkGH1GT87pQqmq5veBfSRMDWCrICCX\ns9a37YwC81kN/LIsF4CvAa8De4HfyrK87/DtQkY/i01G4sEMxVQN22Imsjoz4WISu85EuACzrTqq\nUklcBi0KSYlKb8JsLMNTMQudRklVYxVaQz2FWg9F9HCFnfXxXiLKUl4Z/pDPfmkZipI+XDkVcokb\nnd6Ip24uO3r3kdCpGFcoGFco2B08wFe+fgfkC2RVOlQGI7UV9Yym+9EbbViMOpJlWUIJH3WtDTRe\nPpcu5X7Gy2TKmusYpp+cOUvFrEowm8iECnjmLSQa1TOSU6OrcmK1KvA6bXgtVnyGQeyNc1GjJaxS\nM1TIops3m1CiC8kkE7AMo52jp8TjYCzVhrJcwlhXgsOhxxdqo1JnRkraGYyGKRYLDCcTBANKorKa\nsNtJJAW+/SmaHE4q42kub7oci0NCqU5TSKWwVl5OPlUkIEuMdPWR7t5KoTtJjzbMcDbCWC5ByJIn\nfW01vXwyl7l47lw8kQhOixeN0QyAzmxB66knmBtkXd8+WuqasdsKQIZUMkpjxQI+GlcwYixjsxwn\nY9Zh9JSQ1cvsLo6RxU0mriVo12GxmvE77Sjrq+mL95PQZPGND8FcL0mTGcoq2T4cQM446B+L4ItL\nyI5qVCUlRN1aCtEUmVyYfKodX76D4nCS7lf/k6rRvQR1nUTiIcbMY3z15s9iTOVZsPBaEvokQVWc\nlCnDvEUrMZvLKZQMop6vxu2yY9DIFFVmhkoyRItJfPkkOVsTEYWW7oAfs6MerBYKqRByLIqz6nJS\nkRyG6nq6hmMkMmkCySij8TChlApFnZdQQo/HdiXqknIs9XMoSlZqvZ9F1pagMTrJ5atQNreQzljY\nl05TWVqNUgGlRjfdRSVZpUxCMqIsd2IwFNFXldOeSiOpayGnwO5YQIIUSmUOnVGNz2ikmG1g09AA\nQ0NBMjkbAR84NEZ6lAeIF8dpN3VhzBpR1bfQHzWy3VcgoTITLHOQVGqJR5UYzXZKSmdjbpqN2l5C\nzV8t56obP4O7pgX1vMsZNsikHQ786SxKjZtoSRF9o5OK2nLSRicfdQ2Qj9jxyRliuRzFqlKkrAFH\nuYRao8buKCEcCJLQ2jGbS3AZyymMhknUxTAurMU210N5czMmVylJpZqswkr6stl0htUMWxzEDXri\n1XUcUCaJW2TCxEnFMsRLNPQ79RgdzeiyaiRi6EoNOK++gqIamq+aRcEukW4oB+1sNo7sQTZU4XF6\nKFrr2KIYRlVeQs6op2DS02vJ0K0sUETivbgftWEubx7oJmOqQKk1oNJCKpJD42zBrbZDuZ1oJEJp\njQfrnDpqb72KH/+f1Szwepg/+1MY6+eR1FnpC0aRnR70eh3Kci+oqqif3YypsoSm+Vdgr511RrH5\nrOfjl2X5VVmWm2VZbpRl+cmjbbOotozmqhIKYQVhk4lG6xWobUWwqCk6NXgaWtibLpKyq8hVKjDo\nYwx1baK2rBZjtRp1pR4pWKChdhG5YpHaKxfTOW6iqu5aPnXTZ2icu4JitZerWlpxeRuwOCQUiij1\njZU0z/ESCI3jaKgna5JY+X9dz+IVK0jblaiaFOi1CcxGmdIKGFUNYKp1EZCGUdVLGEwmTNVOym4q\np77FzcIbllHaakM/S4O71sNwRxsl1RVYrQ5CCi2JbDmuCifl5RYam+uxZa1Uz/ZSUhKharYHU2UJ\n9XO8GBxpWr58Jd5aO5HmMAp9hoXNXmy1CkYU/ZTbSsgUgjg0frJBDeq6BoJJmZ2jPaSz5fRF9bSX\nqJGdJezTGwjnbQwHsjiuqKPLIUOZlWghhc1ZT6QkT9IhodcVcTiclPv1XGap5IpCDcXSHGmFAu21\nVXjnzOLzn/vcJ28chYLGT89lvynIGBIBSUFIpeUjS5ydLiNNs6/jreE2Qg4zSb2GgtnG/vAByg0W\nPt24GE2ljYCigK2pkVAp5FVa7HPmsE0TJGfUgkZPbUUjI9oQ5pUeJLMCdUstiionWr2GcWUatbGZ\nmNFFKKwkrrahMxkoKykjbioQ1GvJZPMoDRG02Qx19V5c4+PcMbuEVpuKIaefxS1VeCsrefj73yRj\nTtO8eB5zrplH8+J55Epy/PP//h7f+OpXWLl0Ln7LCBmrln1VQ+hrNShrTagvK8PXlEY3rwZNlZOE\nO4+puYSiTYnNUEXaAimLCr3FgrGygT2DnagyRvYH/Bjr6tAbjDTOuorB3vfRasDf3UaDZw6RdBuO\nehtyNkZFfSu+eBCfRkGtvRWlNofeUEShiNBQOZt1Q2HSpkrMNgNutwVrmYWQ5EGXN1PUacmUKMlk\n1FhLtATTI8g6B5pyF3m5gvZkkvZsDI2qgXyfH09tHaGGNLWOGuYsuJaaK+ehq7+MaM7z/7d358Fx\nnvdhx7/P3veJPYBdLO6DIMBLIimSEg3ZFiVZ1hHfqeMcTlq3aWfSmTbTuk4n1LSZSa9Jp+10WieZ\nNodTTyZTJ3adxJZj0UmsUJZMkSLFAzxAkMQNLM69d/H0DxAwAAI8QWCB/X1mNAIWL9/32Xef9/c+\nz+999nm4Hq0m53BwI+hmumTjBwODFG0hApEokYiDmoYwk5dKWM0+XnjlGNFDzZQyExD1c82XpNDq\nJNzYQMpkoj+QImtxkwm7GRi14WttwR/w4W1sZrBvilw+y+hUGl3byowHkqZZzHE3Ve4IcyUDLzQ3\n4W2zkEyPYXcaGem9QinqxW9yUtNwBDs23GEXe589SujJDjw1QYIRL15HkOm4i2BNPT3+PAaLh5LZ\nRLg+xqg1zc0dJTK5WXTdFK6RFNZIkLQpQW3Qg9GY5cjOXWiPkbRL4YqEmHWYmTJkaKGDvx3uRTnr\nqfKGmPM28iPGmTAZuDE5i8FTx5C3wGjUTDRWRTKkGeofYtQ9yotP7MbtdnPsYBel4iC9N28y2m+i\n5I5jcxax24uMzt5kR+0RrLkijU1B4vEAzYm9jxSXjcePH3+kHTyq119//fjr/+CX6fmrPmyxNmxu\nK6NTM3hyTmwhKw6bmf5kD0Fy7K3dwaXCFcLhKF1P7KHn8mks9QH08AieXIgpPU1Te5iSnuNyzywe\nV5Cq5gRul5/33zvHjDvFcNGE1xfB6LaB00bSW2DMcguPNYKrepZjL3QDYMrnOJXsA1w4/QFKHgMX\nCmdIBOqp2WFmLuHCPGNixDPCFz7+LKPjN5kcyVPdbsQXtXDhBx/gs1ipa46jdY7JuVlSJhNFrTDH\nI2SUYsQ+gyWY57WXuxnrnySRiJHJj7LrUBBbay0DyV4aqqzk8km8RRfZ8DQhwwSpnAOsfbQZguSc\nATQlMJfIjmtMzbUMF7J07O3AEo0Q3tnBpcIAz3/+w+zdu4fLpQmsys9sbo7+3BTGWg/NzQnGJydI\njs+AzYFK1OGZzjM7l2e0doq2o7s5HA4TCi5/oNSxo50b5inmMkacXj8qCKXGLPsSH6br4IfIU6Rg\nNWO1h8ikUzhMGbyleorGDA3eACeLfSSqGxkYuMG+w8+iXHYytUGyw1MYqn1MWSaw7LeTrXJQCJvR\nVX4ONnbisELq6gRFXwRD3EfOYGBwZhRLKII5nMBsdXG6dAF70IXOp9ndtZ/6xgRmp5vJgVESO2uo\nb29i/86dKKVwOp0YrDmunh/A7fIzOHSNJ5+N0draTNATpO9WL9qW59zsLaa8SaKGIK21zZgdJc67\nr9LmifNERyfXZq+Rc5Xw2J3cSo1TDFmIhqtIpdP0Zm+gSi6K3ipspTjFoIFpi8IRC2K0FpiYGqa2\ncwfXr5xk97OdOM0av0qQdxbY88I+/JEQfTcHcAaDWHwWfG4L3zv/Azy+OBPGGXbX1lIwmrjQd5OA\nJ07eWyRotzCjp/BaAgwkh8mVNI6ol4jVhMVk5ersGCZHgnisg2LeQGHiOkOFPC32JmqbmwHwhIL0\nDQ3hCfmxeW1YjAbeTV/GHWllsJCkxuvDFXGTHB4hNQmWthBVwSB+t4e+iQESPh99DZPsathBvK6Z\nodQYo2PDtOw6zPj0FGZjA7NFxZg5Rw4zE1Mpxkd7yAatzCgDsZoIHQe6qIpXU4gY8UQ1n37+GGGP\ni3vxoBgAABrOSURBVB8PXmH47y6hzBYaaqpIz2VImwrUeGowWLM0799JyjxJr3OEYMHG+dRFuuL1\nhNxupsxpUipFbE8LtsYIhoYCO5/ZT9/wRQxXUzRFWxhKjlHt3UM+m2eu2o4rXo3XXsXbI+9RH6jm\n0vi71E8EyXl9jI3lCXuj2HwOYtE2LkxepKahFl9DM6Ok8XTEOHR4P/HmBpLmJIN1OT7+VBedt8/z\nVG8vfpWi51ySQaeTyVyWqngAHXQTqItz+tY5XM0JotVRAHpvneYvv/8nHD9+/PWHibtlEfif33MU\ne6kes8NNPp+jaMmhZx2UilmMzhSzyR9jGbMxZ7nFy0+18HZxipCnngHPDaqMLgrawPVkP1X1fix+\nH++dv4DVGYNa1+KJcjuCZItJ7LuiWJWfYCRO1jaHqcPEr/zi57k5fJFPfPqlxfHvkbo6GhrrOZ8d\nx2MOM1NX4B/+wifIzI3zMz/7SRQ5zmcHOdxeS2dzM+0dzVy7eZZPfPolOjpb+eZb38ZZG8Pnr8bo\ntuEPVXF2rg9LTYgdu5+YP/ZOM//4pz9DQ2MdPdfOMjmSx1U9yyc/+RKp4WFGzBDLZAj6HJxhgpaw\nk4N7O+jLDBALRBib8mKL15DKTpMfLWLztJA2ZTF31ZHtHcce9pJ3KkxHwpRS07QkEoTcTn48eIUa\new1n7Ffpqp+/IHuvnMWWM+BPtOJwecgbDbjGxsg2mHi6s5mdTU13fHYWi4Vqn5ue2X7CZg9j7hvY\nR0y0NT8BQE28gXfffZNiCQZmruOaizEbjjE+Ok3GZ8ddM0ff1CBxWx2JlhYCgQAFk5mMbY6AzUbg\n5QYaOxrYH41ia6xlevIGIUOAoZ4+SNuZqC7hdTnwRf2krEW0miPS3obBX8LaOcdMcoBWax2J+Px4\nd5PVSjo1RzI3ynOffnnZpGaxWPWyz2ChAWA2m2mMNGItwI2+C7S1BEmbzLiyDgb8Q+xu8XLm1jVM\naTMjzgHccSetwZZl53YoNcbIyBB17QdJ261Mmix4DF4i+xqo69qBTjjJ+8epDdZijiVxO6vIzRno\nG+2jritBIFyFt6qKEcswWZuJmqoaem9eJBqppbOpnd6ZCWaKeQweH2nHHB9M99DW1IHF78ZsNPL2\nxGnyhQKWaC1VIQ+2kI/x2RmK0y5MZjfBqjBOu5ex5CjTlpvUde3DZ50fYjg4PMrNUAanx0qiOsr1\n5GXSbh9ef4SJfBKjUxGJ19GfHWfIMcW+ll0YjUZsdjvDxX5uRGd45cguxlUG84yJq8lzdMT2E+/q\nJGc2kbaaqHLXYzLl8TsdzDpKRMM2nvFU05u7iMfhoSoaJ5keoxBL8fMvPo/VasXndqPnMvxF7yks\nLjsNNY24/R6G56ZJ5mYxJwJYvCZUY5EjnU185+opnF1hArjxBKsoGDOcjSSxlVzMNc7x2Q8fwpbL\ncfbcNezjLvy1tTgDPvpHhvC46tBOA7VPdDFRHKT7tX2cHjhP62gRXyjBtaFbxAO7KeVnmPaAOeSg\nNhrj3fEz7Gndy4/VedrCCYKeIMn0GJYWxUd3tXJ49+7FOnjz7DmunxzGHG6iIVHLqCGPYRp8TXHs\nTdWkE1NU28J4vVWLDZPf/p2vbu3A/+IXPsuNVAmvJ0YGA6aAm7OT75MzGphN/YiYtQ57Qzum/BwF\nBnHVRBiOWHj+8C5KhUlM2seg7xbtHU8SjMQxBsxcnrnMkQNHF78913vrNJ/+wkepj4b58eCVxdb6\nK4f2EAmF2NnZvhj0FyxUroUAv6u1dXG7WDiMVecXW40Gg2HxbwaDge7uI1yZm1y8yeSdCkubwhXz\nYE1bF4+90IpeeuMwGAzUhEJY8nk+8vzzdD7xBP6Am4++8AKR9nYOPvMUt+bSJLFTXd3Ird4b2PLV\nTPuNqFonWKyMzKUozWSwPB3E5TTwbHs7Fotl8T1dLI5ypCOxeEH26Ys4KVATrEbrHA6XYtoxSm3C\nzEuvvLLmzI9Lz1Hm2k12N3ZjNP5ksFiitoVzN/6KWvdO7LVP4ozWoTwhsrPD/NKv/hT9syOY7H58\nVj9Gkwmr1lw3JJlsyNHY1cjhcJjdra3MjY/zTNdOTg1dpTCluOma4pmDh6lvqyeciJH35BmLZ2kP\n1ZKJT/NzL36IKz1n8Dn8+My+xfIMqDGKoRL7njp4x3tZ+RksMJvNJKoTBNwe/HEPWuXpLSSpjRiZ\nyg5gdmjG3UWaWu1QmCHls3K0q37VYBeK1jCq0xgyVuwOCwV3iULNLF/63KvcHL7IL37p81y5/gGl\ntJWb9is0xFuxmx0k02NYO8yEXZqbV0a5PniDXfuexuB2EwxEODVylWiiHufRGj7U3UGx5Ka2rZOS\nS9HSXYN5TxxDscD+p5/DF4vRc+kic3kXroZ6CmYLKQCrlUCohGdXlNIE2M0OcnMz9HmGqd0RwzBe\n5Nr4MO17D/FEVzuN9U281/cB8ViCQiKLK27DnrMvlnchwB07fBh0lvPZQTwBA8rqwWf143G66J8d\nZSqVxOA3E3AXSVp6KI0M8USjg4NtVfwwl8RridxxvQDEwmGiATdzURdWg59QTR3KDhc8t3gisZMx\n3xivHNrDnrY2wl4H/voExZATR8mDcb8fZ9iKrcbO0zvr6WxupiYUomegF2eolprqJnzRapRTcSs5\nhK8jQTYzzpPPxvjQoYOcfeOHOAyN2OI1+KJVDCVHCPobsVVZaHvmSSZLQ3S/to8+08yy62zEM8Kr\nh/eyu61t2TX11nffZijpQgeryJlM2Gx2eqaG0NkixU7jsqzCQsPk9ddf39qB/4++9rXFFER1rIG+\n2QE4qBgbPI1Heyi4W3BGoqQtLm4Mj5Kyp3AGrFQHPDx96CADY5f55CeO8d7wNcwzJibD0xzcW8PY\nzZk7uu4rg/lCV2stKwP8AqUUNeHwmgHRYrEstq4XPvDPfvgQfo911WMvvXGs3P9qx6oOBnl//Bql\nCShiYjA/ia0hyO7dXXg9bm4502Q6jIvBc+UFY9V5nj1wYPGCVBO3qNvTiHFGE/L5yRpmse5zYzYp\n9txjnpiF/X3k6AHe+pt3CPhqFv92feAMRw7swGkIYXCb0XNpnF6I+uxoNc0Lx45ydrx3MdCkdYpx\n+y26juynDdjZ1LT4/v0eD3ouQ59hCvwFwsbgYpBZaNldLo5yuL2WrpYWAtNTDMwOUkqZsJscJLPj\nFHxj7G9uoHrHjjvex8rPYKWgJ8j5S+cJxbyYdZ654gwZleHpPfuxqiKJWBVTmVHGblxnf8cOgj4P\nPcXRZcHOaDLhttl4d+IMVq+LfDx7R+OjvaOZvsHzy+r0QuA7cvhJ/vTb3+C5Yz+LOxzGEQjgq47i\ndgQ4NX2a154/wEefObKs9/L5z72K22qgrTXKtQuDuF1+5kxZso4J6lo7MTptuMMOcI/zS//0M8sa\nR2OBJC/taaMQ8vK3f/0m9Q0HqfN4cdjt2Ox27AYHbw38kE++dIQjO3csq+9LA9xCHTl28CBnk/Of\nt8vuoahTvG+9SoPyknYN4k1nCLp2MaVHONrRzIjO0V9lXPVaVUpRF4sRXnKdTYWneaorwS3TzOK/\nWdjOoxQjVpgyz+CJ+fl4SwvVXsfita2UorOxcVl9nLNpekrvE3fG8MbSHHuhG6UUMzbFhJpveN3t\nBrHyOlsr5twopRYbcguf6bhpeUpoZcNkywf+3/iN31gMkta0lWw0x/5ENZPDE7hdXdR07cIW9GH0\nexieSjHtSXLg+T3MGme5ePkiH//Ii0SqqpYF9JWVf6HrDmsH89XcK8DfzWo3mQc59t0svbEEtW9Z\nd3K14LnWe1ooz85wmAvJUYatBfKTBgaqZylZShxubydSV3fXsizsz+VyrZorz7iMDM0USIRq8Acc\neDx2ho1J7LVGOpqa7rhBfuG5p3HncjzZ0XHHOYqFwziMcxzd1cmpoavLguKetrZl5zY1O4u5lOft\nmWHmMnauOQc40BAl1NBAIBZ74HO+kPZJjabwWhxMjU3RsbMDm82G027h3fPvUgqUcHotuMNupsZH\n6IjW8bEjhxeD3cLNbTo0jj3hXGxtLrVwAwp4vXfUH4PBwL4DnXfcYMemr/Dsx3Zx9MknUUotCxJG\no5GacJh4vGbxmgjUFTj8kS5Gb02QSMRIpQc58JHaVRtHh/fsITU8jGquZuyd92mt71j1uAs35tUC\n3EIdsVqtyz7vhUB9KXmVmtQEplw90ao445MlSnqURGM9saa6u14vK8v70QMHVr3GfG43qeFhMkEv\nTaXSfCt/xbW9WoPti596jsmZG8t6gksbXne7Qay8ztZ6Hyv3t1pKaGXD5FECf1mtufvWmTO8daWf\nIy1xDu3axW/9we9y60aBhHt+3pHR8TGu5ntwVM/w2vPPAvNfc47kIxzac4i5uTlOnT/Pvo4ODAYD\nxWKRb/zJt/mpT72EaZNWjFpZpvW2cM4afVb6RjJEp6sZ8g7x2tO7GZ+cfKDj9g0M8MOJCaYGJ/BW\n+zni91NXU3Pvf7jC1//wz8iM+HBGp/jM33uFVCrFH3z/r3ENBQk4qkimx5iNjvOFDx/F6XQuex8L\nn/2DvPd7/ZuH2ff9WLqm74VLF5iyT4ECX9FHe0P7srrZNzDA//3BqQf+fNaqP+/86BTv/02S6mgj\ng0PX2PVMgP0H9t2zzCuviZWf1VrHXfi9NJvj3A8n1jzu/db3pZ/Jwc5O/vdv/WdmLuRoif1kX1f6\n3+OJj9Rz5Kc/d8/3db/HfZjyrVVnVn6mrxzu4tRb7z10vFm5v08c3XvX62/bLLa+8kMZHR3lK7//\n21Tn6wg4q7h44zy9nkv8zLFn8Po9ZDNZem/0UkwWeXbPs+xu3b3lF81+UEvP2cmzZx85wJ08e5Zr\nVitN+TwHOzsfah+r3XDvVakf5ga53hf7g0qn04sLu5w5f4a8L09hssCB1gPoOX1H3Tx9+fK63oDW\nCtoP4mEaR+tx3JWfyX/41/+RIB2YTebFbQrFAuOc51f/zT9/qGM8ivW8QTyIB9nftgn8q7l49Sr/\n88++S3g6zAfFszx1tJq6uhqymSzvfPAOxiojAR2gJdFCbji3rsuTbTXrEeAeZw/lcbW8N1M6neZM\nzxlOnjtJ3p+nqbYJPadXrZsv7X+Ji9evr9u53awe7eM47sjICF//nRM01z25+NqVvnf53C91Ew6H\n1+UYj8N6Xy8Psr9tHfjhJwFjXyLE6Ow1rBHrqsuTLe1ai/LzuNNem2lp61/q5sN52PRVpdr2gX9p\nwMhms5zpOcObp9/EHDXTUPOT5ckqPe0jNtdC639l3QTIZrLcOneLjuaOxTVTpX7eaT3SSJVi2wf+\n1Sx9sCZpH1FOltZNmA/6b599m1AgREdzB6VSSernGsphQMZW8SiBf8v2t3e37iY3nKNUKtF7oxdj\nlZHSTImGmob5pc4iVs70nNnsYooKtLRuAlztvQoWaKqdH1Yr9XNtJpOJT3/uVQn6j9mWbfGDdK1F\n+Vqom9PZaS5cu0C8M76sbkpaUjyqikz1LCVda1HOJC0pHoeKTPUsJV1rUc4kLSnKzbZo8YN0rUV5\nk7SkWG8Vn+pZSbrWolxJWlKsl4pP9awkXWtRriQtKcrBtmzxw9pda0n7iM0maUmxHiTVcxeS9hHl\nTOqneFgS+O/iXnOopGZTTF+bpq62Th6siQ13t/qp5zRXe69iy9g42HFQ6qZYRnL8d+FwOHj54MtE\n8hGKySIBHVgM+tlMllMXT9Fv6ifvzzNsGeZbb3+LdDq92cUWFWKt+rkww+eMa4Yp15TUTbGuKuJ7\n0Q6HY3FWxKUjKhYe/Lq0C1j+YE1mURQbZbX6eeHSBcxhMyhwWBxSN8W62vYt/qVWjqhIZVOLo31g\n/sFaz5Ue3jz9Jn93+u+kdSU21NL6mclnQEFhsrBYPwv5AifPneQ7J78j9VM8km2f419p6YiKvpt9\neJu9OBwOebAmysJqC7sspCVlvL9YSh7uPiRZPEOUq6V102g08sH5DxjLj/HUjqcWh35K/axsEvgf\ngXyVXpQrGe8v7kYC/zqQr9KLcibj/cVKMpxzHchX6UU5k2lIxHqSFv8S0rUW5UymIRFLSarnMZCu\ntShXUjcFSKrnsZCutShXUjfFo5IW/11I11qUKxmNJiTV85hJ11qUKxmNVrkk1fOYSddalCsZjSYe\nxiO1+JVS/x54GcgBV4Ff0FpP3/7bl4EvAkXgV7TW311jH2Xf4gfpWovyJaPRKtOmpXqUUh8Fvq+1\nnlNK/SagtdZfVkp1AF8D9gNx4HtAy2oRfqsE/gXStRblTNKSlWPTUj1a6+9predu/3qS+SAP8Arw\nda11UWt9HbgMHHiUY5WLe3WtC/kCvZO9fPVPvyozKIoNd7e0pNRNsWA9c/xfBP789s8x4OaSv/Xf\nfm3LW7pwhmXCgi1jW5w4a6GFJYtniM0iC7uI+3HPhViUUm8AkaUvARr4itb6W7e3+QpQ0Fr/n8dS\nyjKzdOEMj83DsGkYmF/YRRbPEJtNFnYR93LPwK+1fu5uf1dK/TzwMeDDS17uB2qX/B6//dqqjh8/\nvvhzd3c33d3d9ypW2djduntx+txMPgOO24tntDYsPlg7nzy/uK3kVsVGuVvdhJ8s7DKdnZZBCVvA\niRMnOHHixLrs61Ef7r4A/CfgqNZ6fMnrCw93DzKf4nmDbfJwdzWrLZ6x0LWWB2tiM8nCLtvXZo7q\nuQxYgIWgf1Jr/cu3//Zl4BeBAttgOOf9kIVdRLmShV22H/nmbhmR8f6iXMl4/+1FAn8ZkvH+opzJ\neP+tT6ZsKEPyVXpRzmQaksomLf7HSLrWopzJ7LNbm6R6tgDpWotyJXVza5JUzxYgXWtRrqRuVh5p\n8W8g6VqLciWj0bYeSfVsMdK1FuVKRqNtHZLq2WLu1bXWHs0ffvsP+c7J78gsimJDyeyzlUEC/yZY\nawbFhbTPqYun6Df1k/fnZRZFsaFk9tnKcM9J2sTjsdoMisBiD8ClXQAyi6LYcDL77PYnLf5NtrJr\nncqmFtM+MJ9j7bnSw5un35SutdhwS+tnJp8BdXuGz5oGqZtbmDzcLQNLv+jVd7MPb7MXh8MhD35F\nWZDZZ8uTjOrZRmSGT1GupG6WFwn824yM9xflSsb7lw8J/NuUjPcX5UrG+28+Gce/TclX6UW5ktln\ntzZp8Zc56VqLciWzz24uSfVUAOlai3ImacmNJ6meCiBda1HOJC25tUiLfwuRrrUoZzIabWNJqqcC\nSddalCupmxtDUj0VSLrWolxJ3Sx/Evi3qLvN8AnzOf/p7PQml1JUIqmb5U9m59zC1prhE6BUKuGx\neTaraKLCSd0sb9Li3wZWjvhZGNq5u3X3JpdMVDqpm+VJHu5uE0tH/MiXuUQ5kbr5eMioHiGEqDAy\nqkcIIcR9k8AvhBAVRgK/EEJUGAn8QghRYSTwCyFEhZHAL4QQFUYCvxBCVBgJ/EIIUWEk8AshRIVZ\nl8CvlPpnSqk5pVRgyWtfVkpdVkpdUEodW4/jCCGEeHSPPDunUioOPAf0LXltB/AZYAcQB76nlGqR\nuRmEEGLzrUeL/7eAX13x2qvA17XWRa31deAycGAdjiWEEOIRPVLgV0q9AtzUWp9d8acYcHPJ7/23\nXxNCCLHJ7pnqUUq9AUSWvgRo4NeAf8V8mueRHD9+fPHn7u5uuru7H3WXQgixrZw4cYITJ06sy74e\nelpmpVQn8D0gzfzNIM58y/4A8EUArfVv3t72L4Ff11q/vcp+JPUvhBAPqCzm41dK9QL7tNYTSqkO\n4GvAQeZTPG8Aqz7clcAvhBAP7lEC/3quuauZb/mjtT6vlPpj4DxQAH5ZorsQQpQHWYFLCCG2IFmB\nSwghxH2TwC+EEBVGAr8QQlQYCfxCCFFhJPALIUSFkcAvhBAVRgK/EEJUGAn8QghRYSTwCyFEhZHA\nL4QQFUYCvxBCVBgJ/EIIUWEk8AshRIWRwC+EEBVGAv82s15Ls4l5cj7Xj5zL8iGBf5uRi2t9yflc\nP3Iuy4cEfiGEqDAS+IUQosKUxdKLm1oAIYTYoh526cVND/xCCCE2lqR6hBCiwkjgF0KICrPhgV8p\n9Sml1DmlVEkpte8u272glLqolOpRSv2LjSzjVqKU8iulvquUuqSU+o5SyrvGdteVUmeUUu8ppX60\n0eUsZ/dT15RS/0UpdVkpdVoptWejy7iV3Ot8KqU+pJSaVEqduv3fr21GObcCpdTvKqWGlVLv32Wb\nB66bm9HiPwv8FPCDtTZQShmA/wY8D+wEflop1b4xxdty/iXwPa11G/B94MtrbDcHdGut92qtD2xY\n6crc/dQ1pdSLQJPWugX4EvA/NrygW8QDXLt/rbXed/u/f7uhhdxa/hfz53JVD1s3Nzzwa60vaa0v\nA3d7Gn0AuKy17tNaF4CvA69uSAG3nleB37v98+8Br62xnUJSe6u5n7r2KvD7AFrrtwGvUiqyscXc\nMu732n2o0SiVRmv9t8DEXTZ5qLpZroEgBtxc8vut26+JO4W11sMAWushILzGdhp4Qyn1jlLq729Y\n6crf/dS1ldv0r7KNmHe/1+6h26mJbyulOjamaNvSQ9VN0+MoiVLqDWDpXUcxH3i+orX+1uM45nZ2\nl/O5Wm50rfG5R7TWg0qpEPM3gAu3WxNCbLQfAwmtdfp2quJPgdZNLlNFeSyBX2v93CPuoh9ILPk9\nfvu1inS383n7wU9Eaz2slIoCI2vsY/D2/0eVUt9gvksugf/+6lo/UHuPbcS8e55PrfXskp//Qin1\n35VSAa11coPKuJ08VN3c7FTPWnm+d4BmpVSdUsoCfA745sYVa0v5JvDzt3/+OeDPVm6glHIopVy3\nf3YCx4BzG1XAMnc/de2bwM8CKKWeAiYX0mviDvc8n0tz0EqpA8x/kVSC/toUa8fKh6qbj6XFfzdK\nqdeA/wpUAf9PKXVaa/2iUqoa+G2t9ce11iWl1D8Bvsv8zel3tdYXNrqsW8S/A/5YKfVFoA/4DMDS\n88l8mugbt6fHMAFf01p/d7MKXE7WqmtKqS/N/1l/VWv950qpjymlrgAp4Bc2s8zl7H7OJ/AppdQ/\nAgpABvjs5pW4vCml/gjoBoJKqRvArwMWHrFuypQNQghRYTY71SOEEGKDSeAXQogKI4FfCCEqjAR+\nIYSoMBL4hRCiwkjgF0KICiOBXwghKowEfiGEqDD/HwrHdvlRx+8xAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c4cf150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"repeat_relative(realization,binsize=0.01)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, this looks much better, but I don't think the solution is to just use a smaller binsize...I think OFILT is probably return a value that is systematically `0.5 * binsize` too low. Check this in the two next cells:\n",
"\n",
" * `binsize=0.1, sigma = 10`: off by 0.5\n",
" * `binsize=0.1, sigma = 20`: off by 1.0\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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sv5uBw5HkRaPRaDSRE+k6hZ+LyAHgBeALSqk+4AERaQIOAaeUUk8DiMhcEXkR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\nUmEbUVJEswAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1189df7d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"repeat(realization,sigma=10.,binsize=0.1,plotmed=False)"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JSGoEfdFoNBqNCYS9yY5S6oSIfBP4M3AZeBsYBz4GfF9EvgBsB0Z9XHct8Eml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\nPf8BBFl3JnzG4hUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119e26510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"repeat(realization,sigma=20.,binsize=0.1,plotmed=False)"
]
}
],
"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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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