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@leouieda
Created February 19, 2014 19:12
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Prototype for an L-curse regularization parameter selection for Fatiando a Terra
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"worksheets": [
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"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import numpy\n",
"from fatiando.mesher import SquareMesh\n",
"from fatiando.seismic import ttime2d, srtomo\n",
"from fatiando.inversion.regularization import Smoothness2D\n",
"from fatiando.vis import mpl\n",
"from fatiando import utils"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"area = (0, 500000, 0, 500000)\n",
"shape = (30, 30)\n",
"model = SquareMesh(area, shape)\n",
"model.img2prop('logo.png', 4000, 10000, 'vp')\n",
"seed = 0 # Set the random seed so that points are the same everythime\n",
"src_loc = utils.random_points(area, 80, seed=seed)\n",
"rec_loc = utils.circular_points(area, 30, random=True, seed=seed)\n",
"srcs, recs = utils.connect_points(src_loc, rec_loc)\n",
"tts = ttime2d.straight(model, 'vp', srcs, recs)\n",
"tts, error = utils.contaminate(tts, 0.01, percent=True, return_stddev=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mesh = SquareMesh(area, shape)\n",
"datamisfit = srtomo.SRTomo(tts, srcs, recs, mesh)\n",
"regul = Smoothness2D(mesh.shape)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class LCurve(object):\n",
" def __init__(self, datamisfit, regul, regul_params):\n",
" self.regul_params = regul_params\n",
" self.datamisfit = datamisfit\n",
" self.regul = regul\n",
" self.regul_param = None\n",
" self.objectives = None\n",
" self.corner = None\n",
" self.estimate_ = None\n",
" self.p_ = None\n",
" self.dnorm = numpy.empty(len(regul_params))\n",
" self.mnorm = numpy.empty(len(regul_params))\n",
" \n",
" def fit(self):\n",
" self.objectives = []\n",
" for i, mu in enumerate(self.regul_params):\n",
" solver = (self.datamisfit + mu*self.regul).fit() \n",
" p = solver.p_\n",
" self.dnorm[i] = self.datamisfit.value(p)\n",
" self.mnorm[i] = self.regul.value(p)\n",
" self.objectives.append(solver)\n",
" corner = self.select_corner(self.dnorm, self.mnorm)\n",
" self.corner = corner\n",
" self.regul_param = self.regul_params[corner]\n",
" self.p_ = self.objectives[corner].p_\n",
" self.estimate_ = self.objectives[corner].estimate_\n",
" return self\n",
" \n",
" def select_corner(self, dnorm, mnorm):\n",
" # Uses http://www.sciencedirect.com/science/article/pii/S0168927401001799\n",
" x, y = numpy.log(dnorm), numpy.log(mnorm)\n",
" n = len(dnorm)\n",
" corner = n - 1\n",
" dist = lambda p1, p2: numpy.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)\n",
" cte = 7.*numpy.pi/8.\n",
" angmin = None\n",
" c = [x[-1], y[-1]]\n",
" for k in xrange(0, n - 2):\n",
" b = [x[k], y[k]]\n",
" for j in xrange(k + 1, n - 1):\n",
" a = [x[j], y[j]]\n",
" ab = dist(a, b)\n",
" ac = dist(a, c)\n",
" bc = dist(b, c)\n",
" cosa = (ab**2 + ac**2 - bc**2)/(2.*ab*ac)\n",
" ang = numpy.arccos(cosa)\n",
" area = 0.5*((b[0] - a[0])*(a[1] - c[1]) - (a[0] - c[0])*(b[1] - a[1]))\n",
" # area is > 0 because in the paper C is index 0\n",
" if area > 0 and (ang < cte and (angmin is None or ang < angmin)):\n",
" corner = j\n",
" angmin = ang\n",
" return corner \n",
" \n",
" def predicted(self, p=None):\n",
" return self.objectives[self.corner].predicted(p)\n",
" \n",
" def residuals(self, p=None):\n",
" return self.objectives[self.corner].residuals(p)\n",
" \n",
" def plot_lcurve(self):\n",
" mpl.title('Corner regularization parameter: %g' % (self.regul_param)) \n",
" mpl.loglog(self.dnorm, self.mnorm, '.-k')\n",
" ax = mpl.gca()\n",
" vmin, vmax = ax.get_ybound() \n",
" mpl.vlines(self.dnorm[self.corner], vmin, vmax)\n",
" vmin, vmax = ax.get_xbound() \n",
" mpl.hlines(self.mnorm[self.corner], vmin, vmax)\n",
" mpl.plot(self.dnorm[self.corner], self.mnorm[self.corner], '^b', markersize=10)\n",
" mpl.xlabel('Data misfit')\n",
" mpl.ylabel('Regularization')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tomo = LCurve(datamisfit, regul, [10**i for i in range(-10, 10, 1)]).fit()\n",
"tomo.plot_lcurve()\n",
"mpl.grid(True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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huHz5MpKSklBcXIzk5GQkJSUhOTkZCQkJePHihSpjfCv6Pg5hGD16NB4+fIiI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\n5P8BRsU7YEededsAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x4875e90>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"estimate = tomo.estimate_\n",
"residuals = tomo.residuals()\n",
"mesh.addprop('vp', estimate)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mpl.figure(figsize=(14, 5))\n",
"mpl.subplot(1, 2, 1)\n",
"mpl.axis('scaled')\n",
"mpl.title('Vp synthetic model of the Earth')\n",
"mpl.squaremesh(model, prop='vp', cmap=mpl.cm.seismic)\n",
"cb = mpl.colorbar()\n",
"cb.set_label('Velocity')\n",
"mpl.points(src_loc, '*y', label=\"Sources\")\n",
"mpl.points(rec_loc, '^r', label=\"Receivers\")\n",
"mpl.legend(loc='lower left', shadow=True, numpoints=1, prop={'size':10})\n",
"mpl.m2km()\n",
"mpl.subplot(1, 2, 2)\n",
"mpl.axis('scaled')\n",
"mpl.title('Tomography result (damped)')\n",
"mpl.squaremesh(mesh, prop='vp', vmin=4000, vmax=10000,\n",
" cmap=mpl.cm.seismic)\n",
"cb = mpl.colorbar()\n",
"cb.set_label('Velocity')\n",
"mpl.m2km()\n",
"mpl.figure()\n",
"mpl.grid()\n",
"mpl.title('Residuals (data with %.4f s error)' % (error))\n",
"mpl.hist(residuals, color='gray', bins=10)\n",
"mpl.xlabel(\"seconds\")\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.text.Text at 0x5402410>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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5md15AUE7DrGCXNpxEDmrX2JtuAMLTsPnZVSz3zTNno9SvHC3M9+f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vWZ2CKbMcHV22RzLKN89AWoEIV5WjTVtJGRDoxEIpFIzBlj2qm7l22/++67hIeHY2trC0Dz0vTl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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x4a5e510>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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69Nl0yalDrxr0yKnLOpec7tdkUT927Bjr1q3jV7/6FUopADIyMkhISAAgISGB\nNWvWAJCenk58fDy+vr6YzWaCg4PJyclpxvhCCCEu12RRnzdvHi+//DI+Pv+etKysDKPRCIDRaKSs\nrAyA4uJiTCaTfTqTyURRUZGrM7uNLn02XXLq0KsGPXLqss4lp/u1djTyk08+oUePHoSFhTX6pg0G\ng70t09j4hsyePRuz2QxAQEAAVqvV/idQ3bw8PVynpeRpbDg3N7dF5KlTVxTr2hhXFsnGxreU4dLS\nUqem99T7sdls5Obmenx9e9NwS12eNpuN5cuXA9jrZVMMqq6n0oCkpCTeeecdWrduzfnz5zl9+jTT\np09n165d2Gw2goKCKCkpISoqiv3799t764mJiQBMmDCBhQsXEhERUX+mBgMOZis0ZDAYSE5Odvt8\nk5OTb7r5yu/OzcuZ2umw/fLiiy9SWFhIQUEBq1atYsyYMbzzzjtMmTKFtLQ0ANLS0pg6dSoAU6ZM\nYdWqVVRXV1NQUMDBgwcJDw930dsRQgjRlGs6T72ulZKYmMiGDRsICQlh06ZN9j1zi8VCXFwcFouF\niRMnsnTpUoetmZZOlz6bLjl16FWDHjl1WeeS0/0c9tQvFxkZSWRkJACBgYFkZ2c3OF1SUhJJSUmu\nSSeEEOKayBWlDuhy7qouOXU4/xv0yKnLOpec7idFXQghvIgUdQd06bPpklOHXjXokVOXdS453U+K\nuhBCeBEp6g7o0mfTJacOvWrQI6cu61xyup8UdSGE8CJS1B3Qpc+mS04detWgR05d1rnkdD8p6kII\n4UWkqDugS59Nl5w69KpBj5y6rHPJ6X5S1IUQwotIUXdAlz6bLjl16FWDHjl1WeeS0/2kqAshhBeR\nou6ALn02XXLq0KsGPXLqss4lp/tJURdCCC8iRd0BXfpsuuTUoVcNeuTUZZ1LTveToi6EEF5EiroD\nuvTZdMmpQ68a9MipyzqXnO4nRV0IIbyIFHUHdOmz6ZJTh1416JFTl3UuOd3PYVE/f/48ERERWK1W\nLBYLzz33HADl5eVER0cTEhJCTEwMp06dsj8nJSWF/v37ExoaSlZWVvOmF0IIUY/Dot6uXTs2b95M\nbm4u33zzDZs3b+azzz4jNTWV6Oho8vPzGTt2LKmpqQDk5eWxevVq8vLyyMzMZM6cOdTW1rrljTQH\nXfpsuuTUoVcNeuTUZZ1LTvdrsv3SoUMHAKqrq7l06RJdunQhIyODhIQEABISElizZg0A6enpxMfH\n4+vri9mPMXUtAAAUAElEQVRsJjg4mJycnGaML4QQ4nJNFvXa2lqsVitGo5GoqCgGDhxIWVkZRqMR\nAKPRSFlZGQDFxcWYTCb7c00mE0VFRc0Uvfnp0mfTJacOvWrQI6cu61xyul/rpibw8fEhNzeXiooK\nxo8fz+bNm+uNNxgMGAyGRp/f2LjZs2djNpsBCAgIwGq12v8EqlvAnh6u01LyNDacm5vbIvLUqSuK\ndW2MK4tkY+NbynBpaalT03vq/dhsNnJzcz2+vr1puKUuT5vNxvLlywHs9bIpBqWUcmpK4E9/+hPt\n27fnf//3f7HZbAQFBVFSUkJUVBT79++399YTExMBmDBhAgsXLiQiIqL+TA0GrmG2QgMGg4Hk5GS3\nzzc5Ofmmm6/87ty8nKmdDtsvJ06csJ/Zcu7cOTZs2EBYWBhTpkwhLS0NgLS0NKZOnQrAlClTWLVq\nFdXV1RQUFHDw4EHCw8Nd8V6EEEI4wWFRLykpYcyYMVitViIiIoiNjWXs2LEkJiayYcMGQkJC2LRp\nk33P3GKxEBcXh8ViYeLEiSxdutRha6al06XPpktOHXrVoEdOXda55HQ/hz31wYMHs3v37qseDwwM\nJDs7u8HnJCUlkZSU5Jp0QgghrolcUeqALueu6pJTh/O/QY+cuqxzyel+UtSFEMKLSFF3QJc+my45\ndehVgx45dVnnktP9pKgLIYQXkaLugC59Nl1y6tCrBj1y6rLOJaf7SVEXQggvIkXdAV36bLrk1KFX\nDS07p4+Pj/3WHO78CQgIuK68umybuuR0RpP3fhFCtBy1tbUkJydTUFDg1jaRJ26JIK6P7Kk7oEuf\nTZecOvSqQY+cOmQEfbZNXXI6Q4q6EEJ4ESnqDujSZ9MlZ0vuVV9Oh5w6ZAR9tk1dcjpDiroQQngR\nKeoO6NJn0yWnLn1gHXLqkBH02TZ1yekMKepCCOFFpKg7oEufTZecuvSBdcipQ0bQZ9vUJaczpKgL\nIYQXkaLugC59Nl1y6tIH1iGnDhlBn21Tl5zOkKIuhBBeRIq6A7r02XTJqUsfWIecOmQEfbZNXXI6\no8miXlhYSFRUFAMHDmTQoEEsWbIEgPLycqKjowkJCSEmJoZTp07Zn5OSkkL//v0JDQ0lKyur+dIL\nIYSop8mi7uvryyuvvMLevXvZsWMHf//739m3bx+pqalER0eTn5/P2LFjSU1NBSAvL4/Vq1eTl5dH\nZmYmc+bMoba2ttnfSHPQpc+mS05d+sA65NQhI+izbeqS0xlNFvWgoCCsVisAnTp1YsCAARQVFZGR\nkUFCQgIACQkJrFmzBoD09HTi4+Px9fXFbDYTHBxMTk5OM74FIYQQda6pp37kyBG+/vprIiIiKCsr\nw2g0AmA0GikrKwOguLgYk8lkf47JZKKoqMiFkd1Hlz6bLjl16QPrkFOHjKDPtqlLTmc4fT/1yspK\n7rvvPl577TX8/Pzqjau7kX5jGho3e/ZszGYzAAEBAVitVvufQHUL2NPDdVpKnsaGc3NzW0SeOnUF\np65FcGUBamx8SxkuLS11anpPvp/S0lK3L586nt7em2M4Nze3ReWpG7bZbCxfvhzAXi+bYlBKqaYm\nunjxIpMnT2bixInMnTsXgNDQUGw2G0FBQZSUlBAVFcX+/fvtvfXExEQAJkyYwMKFC4mIiPj3TA0G\nnJit0IjBYPDIFykkJyfLfN00X/md9TxnameT7RelFI8//jgWi8Ve0AGmTJlCWloaAGlpaUydOtX+\n+KpVq6iurqagoICDBw8SHh5+I+9DCCGEk5os6tu3b2fFihVs3ryZsLAwwsLCyMzMJDExkQ0bNhAS\nEsKmTZvse+YWi4W4uDgsFgsTJ05k6dKlDlszLZkufTZdcurSB9Yhpw4ZQZ9tU5eczmiyp37XXXc1\nekpidnZ2g48nJSWRlJR0Y8mEEEJcM7mi1AFdzl3VJacu51brkFOHjKDPtqlLTmdIURdCCC8iRd0B\nXfpsuuTUpQ+sQ04dMoI+26YuOZ3h9HnqQg8BAQFUVFR4OoYQwkOkqDugS5/t8pwVFRUeO4+5Kbr0\ngXXIqUNG0PN3SHfSfhFCCC8iRd0BXfpsuuTUpQ+sQ04dMoI+26YuOZ0hRV0IIbyIFHUHdOmz6ZJT\nlz6wDjl1yAj6bJu65HSGFHUhhPAiUtQd0KXPpktOXfrAOuTUISPos23qktMZUtSFEMKLSFF3QJc+\nmy45dekD65BTh4ygz7apS05nSFEXQggvIkXdAV36bLrk1KUPrENOHTKCPtumLjmdIUVdCCG8iBR1\nB3Tps+mSU5c+sA45dcgI+mybuuR0hhR1IYTwIk0W9cceewyj0cjgwYPtj5WXlxMdHU1ISAgxMTGc\nOnXKPi4lJYX+/fsTGhpKVlZW86R2E136bLrk1KUPrENOHTKCPtumLjmd0WRRf/TRR8nMzKz3WGpq\nKtHR0eTn5zN27FhSU1MByMvLY/Xq1eTl5ZGZmcmcOXMa/X5TIYQQrtdkUR81ahRdunSp91hGRgYJ\nCQkAJCQksGbNGgDS09OJj4/H19cXs9lMcHAwOTk5zRDbPXTps+mSU5c+sA45dcgI+mybuuR0xnX1\n1MvKyjAajQAYjUbKysoAKC4uxmQy2aczmUwUFRW5IKYQQghn3PCBUoPBgMFgcDheV7r02XTJqUsf\nWIecOmQEfbZNXXI647q+zs5oNFJaWkpQUBAlJSX06NEDgF69elFYWGif7tixY/Tq1avB15g9ezZm\nsxn4+Xs1rVar/U+gugXs6eE6LSVPY8O5ubn1hut+4ev+RHfXcJ3rHd9ShktLS13yfptzuLS01GPr\n19Pbe3MM5+bmtqg8dcM2m43ly5cD2OtlUwxKKdXUREeOHCE2NpZvv/0WgPnz59O1a1cWLFhAamoq\np06dIjU1lby8PGbMmEFOTg5FRUWMGzeOQ4cOXbW3bjAYcGK24joYDAaPfUepzNe75yu/s57nTO1s\nck89Pj6eLVu2cOLECXr37s0LL7xAYmIicXFxLFu2DLPZzPvvvw+AxWIhLi4Oi8VC69atWbp0qdbt\nFyGE0E2TPfWVK1dSXFxMdXU1hYWFPProowQGBpKdnU1+fj5ZWVkEBATYp09KSuLQoUPs37+f8ePH\nN2v45qZLn02XnLr0gXXIqUNG0Gfb1CWnM+SKUiGE8CJS1B3Q5dxVXXLqcm61Djl1yAj6bJu65HSG\nFHUhhPAiUtQd0KXPpktOXfrAOuTUISPos23qktMZUtSFEMKLSFF3QJc+my45dekD65BTh4ygz7ap\nS05nXNcVpUKIm4uPj49Hrjnx9/evd2tv0TQp6g7YbDYtPsF1yVlQUKDFHqYOOd2dsba29rquZL3R\nnO66elaX3yFnSPtFCCG8iBR1B3T55NYlZ0vf+62jQ04dMoI+OXX5HXKGFHUhhPAiUtQd0OXcVV1y\n6nJutQ45dcgI+uTU5XfIGVLUhRDCi0hRd0CXPpsuOXXpr+qQU4eMoE9OXX6HnCFFXQghvIgUdQd0\n6bPpklOX/qoOOXXICPrk1OV3yBly8VEzCQgIoKKiwtMxhBA3GSnqDtxIn62iosJj3yXZUunSX9Uh\npw4ZQZ+c0lMXQgjRIjVLUc/MzCQ0NJT+/fvz0ksvNccs3EKXPpsufUvJ6To6ZAR9curyu+4Mlxf1\nS5cu8eSTT5KZmUleXh4rV65k3759rp6NW+Tm5no6glNKS0s9HcEpktN1dMgI+uTU5XfdGS7vqefk\n5BAcHIzZbAbgoYceIj09nQEDBrh6Vk169dVX2bp163U/f9++fdf1fF9f3+ue5/U4f/68W+d3vSSn\n6+iQEW48pztv+Ttv3jz7/3W+5a/Li3pRURG9e/e2D5tMJnbu3Onq2Tjlvffeo3379nTv3v26nm8w\nGPDxufY/Zm7kg0QI8W/Xe8vfa7V582aioqLswy35hIOmuLyoe+JG+o1p3bo1x44d4+TJk9f1/JKS\nElq3vvZFVFlZeV3zu1667FFITtfRISPom1PrLwVRLvbFF1+o8ePH24dffPFFlZqaWm+afv36KUB+\n5Ed+5Ed+ruGnX79+TdZgg1JK4UI1NTX84he/YOPGjfTs2ZPw8HBWrlzpkZ66EELcbFzefmndujV/\n+9vfGD9+PJcuXeLxxx+Xgi6EEG7i8j11IYQQnuPxK0oXL16Mj48P5eXlno7SoN///vcMHToUq9XK\n2LFjKSws9HSkBj377LMMGDCAoUOHMn369BZ535l//etfDBw4kFatWrF7925Px7mKDhfNPfbYYxiN\nRgYPHuzpKA4VFhYSFRXFwIEDGTRoEEuWLPF0pAadP3+eiIgIrFYrFouF5557ztORGnXp0iXCwsKI\njY11PKFLj5Jeox9++EGNHz9emc1m9dNPP3kySqNOnz5t//+SJUvU448/7sE0jcvKylKXLl1SSim1\nYMECtWDBAg8nutq+ffvUgQMH1OjRo9VXX33l6Tj11NTUqH79+qmCggJVXV2thg4dqvLy8jwd6ypb\nt25Vu3fvVoMGDfJ0FIdKSkrU119/rZRS6syZMyokJKRFLk+llKqqqlJKKXXx4kUVERGhtm3b5uFE\nDVu8eLGaMWOGio2NdTidR/fU/+u//ou//OUvnozQJD8/P/v/Kysr6datmwfTNC46Otp+Tn1ERATH\njh3zcKKrhYaGEhIS4ukYDbr8ojlfX1/7RXMtzahRo+jSpYunYzQpKCgIq9UKQKdOnRgwYADFxcUe\nTtWwDh06AFBdXc2lS5cIDAz0cKKrHTt2jHXr1vGrX/0K1UTH3GNFPT09HZPJxJAhQzwVwWnPP/88\nffr0IS0tjcTERE/HadJbb73FpEmTPB1DKw1dNFdUVOTBRN7jyJEjfP3110RERHg6SoNqa2uxWq0Y\njUaioqKwWCyejnSVefPm8fLLLzt1MWSz3no3Ojq6wXs/LFq0iJSUFLKysuyPNfXp05way/niiy8S\nGxvLokWLWLRoEampqcybN4+3337bAymbzgk/L9s2bdowY8YMd8cDnMvYErWki+a8SWVlJffffz+v\nvfYanTp18nScBvn4+JCbm0tFRQXjx4/HZrO1qFvxfvLJJ/To0YOwsDCnbjzWrEV9w4YNDT7+3Xff\nUVBQwNChQ4Gf/7QYNmwYOTk59OjRozkjNaixnFeaMWOGR/eAm8q5fPly1q1bx8aNG92U6GrOLsuW\nplevXvUOghcWFmIymTyYSH8XL17kvvvuY+bMmUydOtXTcZrk7+/PPffcw5dfftmiivrnn39ORkYG\n69at4/z585w+fZpHHnmEf/7znw0/wS0d/ia05AOl+fn59v8vWbJEzZw504NpGrd+/XplsVjU8ePH\nPR2lSaNHj1Zffvmlp2PUc/HiRXXbbbepgoICdeHChRZ7oFQppQoKClr8gdLa2lo1a9YsNXfuXE9H\ncej48ePq5MmTSimlzp49q0aNGqWys7M9nKpxNptNTZ482eE0Hj+lEVr2n77PPfccgwcPxmq1YrPZ\nWLx4sacjNeipp56isrKS6OhowsLCmDNnjqcjXeWjjz6id+/e7Nixg3vuuYeJEyd6OpLd5RfNWSwW\nHnzwwRZ50Vx8fDx33nkn+fn59O7d22OtwKZs376dFStWsHnzZsLCwggLCyMzM9PTsa5SUlLCmDFj\nsFqtREREEBsby9ixYz0dy6Gm6qVcfCSEEF6kReypCyGEcA0p6kII4UWkqAshhBeRoi6EEF5EiroQ\nQngRKepCCOFFpKgLcYNsNluLvgWCuLlIURdCCC8iRV1or6qqinvuuQer1crgwYN5//33+eqrrxg9\nejTDhw9nwoQJ9puMHTp0iHHjxmG1Whk2bBgFBQXAz18yMnjwYIYMGcL7778PYL+x0wMPPMCAAQOY\nOXOmfZ6ZmZkMGDCAYcOG8dFHH9kf37Jli/0Kyttvv53Kyko3LgkhaBn3fhHiRnzwwQfqiSeesA9X\nVFSoO++8U504cUIppdSqVavUY489ppRSKjw8XK1Zs0YppdSFCxfU2bNn1QcffKCio6NVbW2tKisr\nU3369FElJSVq8+bNyt/fXxUVFana2lp1xx13qO3bt6tz586p3r17q0OHDimllIqLi7N/cUFsbKz6\n/PPPlVI/f/lCTU2N25aDEEq1kHu/CHEjhgwZwoYNG0hMTOSzzz7jhx9+4LvvvmPcuHGEhYWxaNEi\nioqKqKyspLi4mHvvvReANm3a0L59e7Zv386MGTMwGAz06NGDyMhIdu3ahcFgIDw8nJ49e2IwGLBa\nrRQUFLB//3769u1Lv379AJg5c6b91tEjR45k3rx5vP7665w8eZJWrVp5bLmIm1Oz3npXCHfo378/\nX3/9NWvXruV3v/ud/bsxP//883rTnTlzptHXUFfcAqnupklt27a1P9aqVStqamquuqHS5c9dsGAB\nkydPZu3atYwcOZJPP/2UX/ziF9f93oS4VrKnLrRXUlJCu3btePjhh/l//+//kZOTw4kTJ9ixYwfw\n83298/Ly8PPzw2Qy2b+m7sKFC5w7d45Ro0axevVqamtrOX78OFu3biU8PLzBL24xGAyEhoZy5MgR\nvv/+ewBWrlxpH3/48GEGDhzI/PnzGTFiBAcOHHDDEhDi32RPXWjv22+/5dlnn8XHx4c2bdrw3//9\n37Rq1Yqnn36aiooKampqmDdvHhaLhXfeeYdf//rX/OEPf8DX15cPPviAadOm8cUXXzB06FAMBgMv\nv/wyPXr0YN++fQ3e5rRt27a88cYb3HPPPXTo0IFRo0ZRVVUFwGuvvcbmzZvx8fFh0KBBLer2wuLm\nILfeFUIILyLtFyGE8CJS1IUQwotIURdCCC8iRV0IIbyIFHUhhPAiUtSFEMKLSFEXQggvIkVdCCG8\nyP8HGHrete51dc8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x4a43390>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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