GP Clustering for spiral dataset.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Algorithm Implementation\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import scipy\n",
      "\n",
      "#plot the scatter plot\n",
      "def plotScatter(X,Y,title,c):\n",
      "    fig = plt.figure()\n",
      "    ax = fig.add_subplot(111)\n",
      "    ax.plot(X,Y,c+'.')\n",
      "    ax.set_title(title)\n",
      "    plt.show()\n",
      "\n",
      "def plotCluster(X,Y,clusters,title):\n",
      "#    fig = plt.figure()\n",
      " #   ax = fig.add_subplot(111)\n",
      "    \n",
      "    colors = np.arange(len(clusters))\n",
      "    t = [colors[(int)(clusters[i])]  for i in range (len(clusters))]\n",
      "    plt.scatter(X, Y, c=t)\n",
      "    plt.show()\n",
      "    \n",
      "def plotCluster2(X,Y,clusters,title):\n",
      "    fig = plt.figure()\n",
      "    ax = fig.add_subplot(111)\n",
      "\n",
      "    colors = ['r','g','b','c','m','y']   \n",
      "    c = [colors[((int)(clusters[i])-1) % len(colors)]  + '.' for i in range (len(clusters))]\n",
      "    \n",
      "    for i in range (len(X)):\n",
      "        ax.plot(X[i],Y[i],c[i])\n",
      "    ax.set_title(title)\n",
      "    plt.show() \n",
      "\n",
      "def plotHeatMap(heatmap, extent):\n",
      "    plt.clf()\n",
      "    plt.imshow(heatmap, extent=extent)\n",
      "    plt.colorbar()\n",
      "    plt.show()\n",
      "    \n",
      "#init parameters for the model\n",
      "# need to be determined for different dataset\n",
      "alpha = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Step 1. Construct a variance function and compute the level value $r^* = max_{x_i} \\sigma ^2 (x_i) $"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Calculate Covariance Matrix C\n",
      "def kernelFunction(x1, x2):\n",
      "    c = 0.;\n",
      "    for i in range(len(x1)):\n",
      "        c += alpha * (x1[i] - x2[i]) **2\n",
      "\n",
      "    c = math.exp(- 0.5 * c)\n",
      "    return c\n",
      "\n",
      "#calculate the whole covariance matrix \n",
      "def getCovarianceMatrix(data):\n",
      "    size = len(data[0,:])\n",
      "    C = [[ 0. for i in range (size)] for j in range (size)]\n",
      "    for i in range (size):\n",
      "        for j in range (size):\n",
      "            C[i][j] = kernelFunction(data[:,i], data[:,j])\n",
      "    return matrix(C)  \n",
      "\n",
      "\n",
      "#TODO check how to calculate the variance\n",
      "#calculate variance of dataset\n",
      "def getVariance(data):\n",
      "    return np.var(data)\n",
      "\n",
      "#calculate inversion of Matrix A\n",
      "def getInversionMatrix(data, C):\n",
      "    size = len(data[0,:])\n",
      "    var = getVariance(data)\n",
      "    return  (C + var * matrix(np.eye(size)) ).I \n",
      "\n",
      "#calcalate the variance for a given point x\n",
      "def varianceFunction(x, data, inv):\n",
      "    size = len(data[0,:])\n",
      "    k = matrix( [kernelFunction(x, data[:,i]) for i in range (size)]).T\n",
      "    result = kernelFunction(x,x) - k.T * inv * k\n",
      "    return result[0,0]\n",
      "\n",
      "\n",
      "\n",
      "def getMaxVariance():\n",
      "    maxVars = 0.\n",
      "    maxIndex = 0\n",
      "    for i in range (size):\n",
      "        if(i == 0):\n",
      "            maxVar = varianceFunction(data[:,i], data, invA)\n",
      "            maxIndex = 0\n",
      "        else:\n",
      "            tmp = varianceFunction(data[:,i], data, invA)\n",
      "            if(tmp > maxVar):\n",
      "                maxVar = tmp\n",
      "                maxIndex = i\n",
      "    return maxVar, data[:,maxIndex]\n",
      "\n",
      "def getMaxVariances(num):\n",
      "    maxVars = [ 0. for i in range (num)]\n",
      "    variances = array([varianceFunction(data[:,i], data, invA) for i in range(size)])\n",
      "    sorted_vars = sort(variances)\n",
      "    for i in range (num):\n",
      "        maxVars[i] = sorted_vars[-1-i]\n",
      "    \n",
      "    return maxVars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Step 2. Compute Stable Equilibrium Points\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#nabla_variance_function\n",
      "def nablaVarianceFunction(x, data, inv):\n",
      "    size = len(data[0,:])\n",
      "    k = matrix( [kernelFunction(x, data[:,i]) for i in range (size)]).T\n",
      "    nablaK_x1 = matrix([ - kernelFunction(x, data[:,i]) * (x[0] - data[0,i]) for i in range (size)]).T\n",
      "    nablaK_x2 = matrix([ - kernelFunction(x, data[:,i]) * (x[1] - data[1,i]) for i in range (size)]).T\n",
      "    delta_x1 =  nablaK_x1.T * inv * k + k.T * inv * nablaK_x1 \n",
      "    delta_x2 =  nablaK_x2.T * inv * k + k.T * inv * nablaK_x2\n",
      "    return [delta_x1[0,0], delta_x2[0,0]]\n",
      "\n",
      "#gradient descent iteration\n",
      "def gradientDescentIteration(x, data, inv, ita):\n",
      "    delta_x = nablaVarianceFunction(x,data,inv)\n",
      "    #print \"delta_x:\" , delta_x\n",
      "    return [ x[i] + ita * delta_x[i] for i in range (len(x))]\n",
      "\n",
      "def getEquilibriumPoint(x,data,inv,ita,maxIteration):\n",
      "    x_old = x\n",
      "    iteration = 1\n",
      "    for i in range(maxIteration):\n",
      "        x_new = gradientDescentIteration(x_old,data,inv,ita)\n",
      "        if( (x_new[0] - x_old[0])/x_old[0] < 0.00001 and (x_new[1] - x_old[1])/x_old[1] < 0.00001):\n",
      "            break\n",
      "        else:\n",
      "            x_old = x_new\n",
      "            iteration += 1\n",
      "    print \"iteration: \" , iteration, \"x0: \", x, \"xt: \", x_new\n",
      "    return x_new\n",
      "\n",
      "def isExistInList(sepList, point, min_accepted_covariance):\n",
      "    for i in range (len(sepList)):\n",
      "        covariance = kernelFunction(sepList[i], point)\n",
      "        if(covariance > min_accepted_covariance):\n",
      "            return i\n",
      "    return -1\n",
      "\n",
      "def reduceSEPs(seps, min_accepted_covariance):\n",
      "    sepList = []\n",
      "    sepIndexMap = {}\n",
      "    for i in range (len(seps)):\n",
      "        index = isExistInList(sepList, [seps[i,0],seps[i,1]], min_accepted_covariance)\n",
      "        if index == -1 :\n",
      "            index = len(sepList)\n",
      "            sepList.append([seps[i,0],seps[i,1]])\n",
      "        sepIndexMap[i] = index\n",
      "    return array(sepList), sepIndexMap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Step 3. Construct Adjacency Matrix A"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getGeometricDistance(x1, x2):\n",
      "    d = 0.\n",
      "    for i in range (len(x1)):\n",
      "        d += (x1[i] - x2[i])**2\n",
      "    return math.sqrt(d)\n",
      "\n",
      "def getAdjacencyMatrix(sepList, maxVar, pointsNumPerDistanceUnit):\n",
      "    A = [[ -1 for i in range (len(sepList))] for j in range (len(sepList)) ]\n",
      "    for i in range (len(sepList)):\n",
      "        for j in range (len(sepList)):\n",
      "            if(i == j ):\n",
      "                A[i][j] = 1\n",
      "            elif( i < j ):\n",
      "                isConnected = True\n",
      "                delta = sepList[i] - sepList[j]\n",
      "                distance = getGeometricDistance(sepList[i], sepList[j])\n",
      "                pointsNum = pointsNumPerDistanceUnit * distance\n",
      "                for m in range ((int)(pointsNum)):\n",
      "                    testPoint = sepList[j] + (m+1) * delta/pointsNum\n",
      "                    testVar = varianceFunction(testPoint, data, invA)\n",
      "                    if(testVar > maxVar):\n",
      "                        isConnected = False\n",
      "                        break\n",
      "                if isConnected is True:\n",
      "                    A[i][j] = 1\n",
      "                else:\n",
      "                    A[i][j] = 0\n",
      "            elif (i > j ):\n",
      "                A[i][j] = A[j][i]\n",
      "    return array(A)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Step 4. Assign cluster Labels to training data Points"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getSEPsClusters(adjacencyMatrix, sepList):\n",
      "    clusters = [ -1 for i in range (len(sepList))]\n",
      "    clusterIndex = 1\n",
      "    for i in range (len(sepList)):\n",
      "        isNewCluster = True;\n",
      "        clusterNo = clusterIndex;\n",
      "        for j in range ( len(sepList)):\n",
      "            if adjacencyMatrix[i][j] == 1 and clusters[j] != -1:\n",
      "                isNewCluster = False\n",
      "                clusterNo = clusters[j]\n",
      "                break\n",
      "        for j in range ( i , len(sepList)):\n",
      "            if adjacencyMatrix[i][j] == 1:\n",
      "                clusters[j] = clusterNo\n",
      "        if isNewCluster:\n",
      "            clusterIndex += 1\n",
      "        \n",
      "    return clusters    \n",
      "\n",
      "def getPointClusters(sepsClusters, sepIndexMap):\n",
      "    clusters = [-1 for i in range (len(sepIndexMap))]\n",
      "    for i in range (len(sepIndexMap)):\n",
      "        clusters[i] = sepsClusters[sepIndexMap[i]]\n",
      "    return clusters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Algorithm Test"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 1. Test with Spiral Dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#init\n",
      "dataset = loadtxt(\"dataset\\spiral.txt\")\n",
      "data = array([dataset[:,0], dataset[:,1]])\n",
      "clusters = dataset[:,2]\n",
      "size = len(clusters)\n",
      "\n",
      "X = data[0,:]\n",
      "Y = data[1,:]\n",
      "dimension = 2\n",
      "alpha = 1\n",
      "plotScatter(X,Y,\"Scatter Plot\", 'g')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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flX2ZJCB3GRtvNGLY2mG4cP2C7bHknsk4+rujUpWTKvvvvrW9Fbt+3CV9n1Ij\ncxt2xZfYqanUCpXDPgr5OpnLqI/W4/gTx20TnwBQd62OUywBQnlpoT2Z23CgaeqEICrXFaaQr5N5\n9Qrw88Rn0otJaO1oBQDsObMH9/75XlKjL5lYR7DKpYVU65JCPwsUTY3IqaBw5CDz6hUrfbQeE2+f\nCADoGdUTF5svYsfJHRjx+ghpJ7tlopzIpLq00BkK/SxQNDUip5ITsz8xSNayyr56xeqD2R+gbHsZ\nLM0W2wRo3bU6DF07FHn98qSsW1kor8dtP4KlGPzU+j+FI/RA0NRkJ6UZdtnLap3l79GtB6ovV0u7\nw7FqvNGIEa+PQN21OsTeFoumliYAPAmq5Goi0/o8hZUdamTvU56K+MlOSjkx2ctqHc1UX64mMWFk\nzZkXZRThrv532R63js5lPrcglFxNZFI4oc4V2ftUMGlqRE5prSiVslJZ0mnP2eg8MSYxotItaqkG\nit+np6j0KXcifh05RbLn9amlWKys5bbmzu0Depo+DQN7DyTzWXyllmrQSrCzkr3/+IIDOUFU8npU\nyqmkDOh5/fLQvWt3280ztBTUlUFt7l/manb0bUW1XboS8TeWsEdlT00lr2ctZ0JMAs5fPU9mvbY1\n72s/Ep37l7kAYAvq1kBwx7o7SAV1ZRtXrkLZPGuzpkbfaqj0n2DT7Iicyp6ayqGutZznr56X9lZw\nnlIGdeuoVfaRujJwF75X6NDGm1qaND8CV6LSf7zBqRU7Wp7UCSet1asnQb0oowj6aL0tiCbGJAZl\nvkAZqJdULfEqcFu3oaWgZo/KUba/OJDbobanptJIqU5+esJZUK8qrnIIookxifjp+k8AOo/clcHX\n/nflDsBdoL5w7UJEB24lKkfZ/uJAThi1RkqtvN5SDgTsj0T03fWqE6dqwdf+d/sdgCeBWrkzASIr\ncCtp7WjQmYg/IUiJwo0mrKhN2lArr7eUJ8fYX7fjg9kf2H5WXspAWS/2v2cbsl2+VnltEOXv1E/Y\n8YZa342ka6d4Tbjx29/+ViQlJYmRI0faHrt48aK45557xJAhQ0RBQYGwWCyd3ufBpoNu4oaJAiYI\nmCCKKorCXRyXLM0WUVRRJCzNnetSRvblXbBtgZi4YaKY+u5UMuUPFOX35up3d69lP6PUdwPNl9jp\nNrWyZ88exMbG4pFHHsGhQ4cAAEuWLEFCQgKWLFmCVatWwWKxoLy83OF9MqRWqB2KUcmTK2k9zcJC\nj1rfDaQn+e1HAAALWElEQVSgpFYmTJiA+Ph4h8e2bduGkpISAEBJSQkqKyu9+qOhQu1QjOqF8O1T\nBD269SCTzmJy4DSK/3zKkdfX18NgMAAADAYD6uvrA1qoQKGWU6Sad7bvdFQussXkoTaAodZ3w83v\nMzt1Oh10Op3qcyaTyfaz0WiE0Wj0989pGtUz8eyv+0x1Z8TCJ9LbjNlshtls9msbHi0/PH36NKZP\nn27LkQ8fPhxmsxnJycmora1Ffn4+jh075rhhCXLk1FHMmSuX7VH8DCy4lG3C+hi1AUywhGz54YwZ\nM7Bp0yYAwKZNm1BYWOjLZkKO0nJEgGbOXHlITPEzsOBStglOo/jPbSCfM2cOxo0bh+PHj2PAgAHY\nsGEDnn76aVRVVWHo0KHYvXs3nn766VCU1W/UgooWDjl5IpQpaaFdyyaizuyktqSJ2mUG1Nh/BuUp\n6LxMMTJpoV0HE5+i7wb1BkQ930xtR8r8Q729hgsHco2jfuKN2o6UO7t2UW+v4cI3ltA46rlF+2WK\nVsqbIdhfLpYDOw3OdsbU2yslmr5oltZo8Ww3ZWenNiHNnC8i0GJ7lRWnVlhYubpcLAcAuTgbefN3\nFlicI48wWswvcx5dXs5y3tQXEciGA3mEiZTJJOXn5Dx68KntPHnkHRp8Y4kIEymTSZxHDz21Oo60\nnDelM8E5kBNm37GWVC0h0+i8pQwgajswSp1OJs7qTa2OI+1UekoDBk6taESkpFkA9Zys2ufn3PrP\nnNUF572dC1cqiVMrESxS0iyA+shQ7fNTGlEFm7O6cNZuIm30rYZSKolH5BoR6SMotc/vbERFfaTu\nrPyuPpezuojkdiNrO+BVK6wTWRtrKDgLUtTTMM7SIa7Sa5EcsJ2RNR3Jp+izTpSnwMvSWENB7ZIA\ngOs0DNC5ntSCvLejYlc7Cm/f4ywd4iq95qwuIpmW0pGcI9c4LTXWQFHLfbqqJ7X8srOcs7eP+/Ie\nZ7lbSjldGWipvnhErnHO7gNKKZUQaGqjU1f3S1UL8t6Oil3tKLx9j7PRNY+61Tlr65qqLxEkQdw0\nC4CJGyYKmCBggiiqKAp3caRmabaIoooiYWm2uHzMl8d9fQ/zHLW27kvs5MnOCMWnW7NIQa2t86oV\n5jFexcAiBbW2zoGc+SWS8+aMyYKXHzK/RPJSRUZbpA9CePkhs+GlioyqSL8cA4/ImQ0vVWSyctcG\nI30Q4leOPDU1Fb169ULXrl0RFRWFb7755ucNc45cM2Q9lZlFDndtkNqEpishz5HrdDqYzWb84he/\n8GczTHKRPtph4eeuDWrq5B4f+J0j51G39jk7lZlv5sBCRUun0weDX6mVQYMGoXfv3ujatSsee+wx\nLFiw4OcNc2pF8zjlwgKF52F+FvLUyr59+9C3b1/89NNPKCgowPDhwzFhwgTb8yaTyfaz0WiE0Wj0\n588xyXDKhQVKJC99NZvNMJvNfm0jYCcErVixArGxsVi0aNGtDfOIXPNcTTDxCIt5g9pp9MEU0lu9\nXb9+HVevXgUAXLt2DZ9++imysrJ83RwjyNXtwCJ9XS9z5G4+hXPg/vE5tVJfX4/7778fANDW1obf\n/OY3mDx5csAKxmhzlXbh0XrkcZc6ifRVJ/7ia62woHCVduFJUu1xt3Pm1InnQppaYcwVV2kXd6N1\nXtJIj7tUGqdOgosDOQs5V52ac+ty8XTH6ukJOxzEg4MDOQs5X0frAI/YQ83THSuPuMOLc+RMKu6u\nmeEuv84Tqd5zVWec2w49zpEz8twdgrsbsXNqxnuu6oxH2jTwZWwZKa7udg/4v+xRSyN6Tz+Lqzrj\nZYE0cGqFaYq/yx5lWRoZiJ2Op59FS5eA1QK+1RuLeK5GkJ5cG8aTyVZ/A6wn2/Dk2iPuXuPptXB4\n1E0f58hZxPAk3+vuNZ7k4N29xpNtBGKnw/ntyMGpFca84MkqDnev8WQbnqQ7OCWiTb7ETg7kjHkh\nEAGWAzBzhQM5Y4wRx+vIGWMsAnEgZ4wx4jiQM8YYcRzIGWOMOA7kjDFGHAdyxhgjjgM5Y4wRx4Gc\nMcaI40DOGGPEcSBnjDHifA7kO3fuxPDhwzFkyBCsWrUqkGVijDHmBZ8CeXt7O373u99h586dOHLk\nCLZs2YKjR48GumxhZTabw10Ev3D5w4vLHz6Uy+4rnwL5N998g8GDByM1NRVRUVF46KGH8NFHHwW6\nbGFFvTFw+cOLyx8+lMvuK58C+blz5zBgwADb7ykpKTh37lzACsUYY8xzPgVynU4X6HIwxhjzlfDB\nV199JaZMmWL7/YUXXhDl5eUOr0lPTxcA+B//43/8j/958S89Pd3rmOzTjSXa2towbNgwfPbZZ+jX\nrx9Gjx6NLVu2YMSIEd5uijHGmJ+6+fSmbt3w+uuvY8qUKWhvb0dpaSkHccYYC5Og3eqNMcZYaATl\nzE7qJwulpqZi1KhRyM3NxejRo8NdHLceffRRGAwGZGVl2R67dOkSCgoKMHToUEyePBmNjY1hLKFr\nauU3mUxISUlBbm4ucnNzsXPnzjCW0Lmamhrk5+cjMzMTI0eOxGuvvQaATv07Kz+V+r9x4wbGjBmD\nnJwcZGRk4JlnngFAp/6dld/r+vdlstOVtrY2kZ6eLn788UfR0tIisrOzxZEjRwL9Z4IqNTVVXLx4\nMdzF8NiXX34pvv32WzFy5EjbY4sXLxarVq0SQghRXl4uli5dGq7iuaVWfpPJJF566aUwlsoztbW1\n4sCBA0IIIa5evSqGDh0qjhw5Qqb+nZWfSv0LIcS1a9eEEEK0traKMWPGiD179pCpfyHUy+9t/Qd8\nRK6Vk4UEoYzThAkTEB8f7/DYtm3bUFJSAgAoKSlBZWVlOIrmEbXyAzS+g+TkZOTk5AAAYmNjMWLE\nCJw7d45M/TsrP0Cj/gEgJiYGANDS0oL29nbEx8eTqX9AvfyAd/Uf8ECuhZOFdDod7rnnHuTl5eGt\nt94Kd3F8Ul9fD4PBAAAwGAyor68Pc4m8t3btWmRnZ6O0tFTaQ2N7p0+fxoEDBzBmzBiS9W8t/113\n3QWATv13dHQgJycHBoPBliaiVP9q5Qe8q/+AB3ItnCy0b98+HDhwADt27MAbb7yBPXv2hLtIftHp\ndOS+l8cffxw//vgjDh48iL59+2LRokXhLpJLTU1NmDVrFl599VXExcU5PEeh/puamvDggw/i1Vdf\nRWxsLKn679KlCw4ePIizZ8/iyy+/xOeff+7wvOz1ryy/2Wz2uv4DHsj79++Pmpoa2+81NTVISUkJ\n9J8Jqr59+wIAEhMTcf/99+Obb74Jc4m8ZzAYUFdXBwCora1FUlJSmEvknaSkJFsHnD9/vtTfQWtr\nK2bNmoXi4mIUFhYCoFX/1vI//PDDtvJTqn+r3r1747777sO//vUvUvVvZS3//v37va7/gAfyvLw8\n/Pvf/8bp06fR0tKC999/HzNmzAj0nwma69ev4+rVqwCAa9eu4dNPP3VYTUHFjBkzsGnTJgDApk2b\nbB2UitraWtvPW7dulfY7EEKgtLQUGRkZWLhwoe1xKvXvrPxU6r+hocGWdmhubkZVVRVyc3PJ1L+z\n8lt3QoCH9R/4OVghPvnkEzF06FCRnp4uXnjhhWD8iaA5deqUyM7OFtnZ2SIzM5NE+R966CHRt29f\nERUVJVJSUsSf/vQncfHiRTFp0iQxZMgQUVBQICwWS7iL6ZSy/OvXrxfFxcUiKytLjBo1SsycOVPU\n1dWFu5iq9uzZI3Q6ncjOzhY5OTkiJydH7Nixg0z9q5X/k08+IVP/3333ncjNzRXZ2dkiKytLrF69\nWgghyNS/s/J7W/98QhBjjBHHt3pjjDHiOJAzxhhxHMgZY4w4DuSMMUYcB3LGGCOOAzljjBHHgZwx\nxojjQM4YY8T9PxhnDP4dWY4UAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x81d0b70>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get Covariance Matrix\n",
      "C = getCovarianceMatrix(data)\n",
      "invA = getInversionMatrix(data, C)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grid_x = [0 + 0.25 * i for i in range (140) ] \n",
      "grid_y = [35 - 0.25 * i for i in range (140) ] \n",
      "variances = [[0 for i in range (len(grid_x)) ] for j in range (len(grid_y))]\n",
      "\n",
      "for i in range (len(grid_y)):\n",
      "    for j in range (len(grid_x)):\n",
      "        variances[i][j] = varianceFunction([grid_x[j], grid_y[i]], data, invA)\n",
      "        \n",
      "heatmap = variances\n",
      "extent = [grid_x[0], grid_x[-1], grid_y[-1], grid_y[0]]\n",
      "\n",
      "plotHeatMap(heatmap, extent)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IyCcvgn6xC6u0yRFAK7qbNagfh2AZKqsmPuUmIyws5rP0PhEg099Z7nsJuD3w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\nTCc/pgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x81ad588>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# to determin the cut level\n",
      "maxVars = getMaxVariances(100)\n",
      "print \"max variances: \", maxVars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "max variances:  [0.97490444899545936, 0.97411301339900247, 0.97406845750171667, 0.96797844548560041, 0.96668257275444025, 0.96637209365816867, 0.96606399595003767, 0.96605157260199914, 0.96577493717251905, 0.96554529691310709, 0.96548941049802928, 0.96534119519984474, 0.96523694209543454, 0.96515720761872537, 0.96510537437534016, 0.96493133713192136, 0.96488301755260764, 0.96485989372817693, 0.96465930203622885, 0.96465341479794198, 0.96455361801742279, 0.96447677940322618, 0.96438898965708675, 0.96429581669821651, 0.96418577313591003, 0.96405864833872434, 0.96399111868651988, 0.96399076553063379, 0.96398192794424831, 0.96395985074580026, 0.9639540036169113, 0.96355137561466297, 0.96346561622545879, 0.96339842517804719, 0.96332053142833174, 0.96314028943689123, 0.96310191078441887, 0.96309480097337485, 0.96299549668041995, 0.96273950589806379, 0.962727457492631, 0.96262281456432974, 0.96256917900217642, 0.9625381258096305, 0.96245480698489028, 0.96208970111850445, 0.96186219776760906, 0.9617556920058915, 0.96147967111607013, 0.9614321571548583, 0.96115195460300729, 0.96111022554211245, 0.96104592614913908, 0.96100238148787487, 0.9609759283826852, 0.96084290465211109, 0.96072861522310538, 0.96070171454464881, 0.96060425944652506, 0.96058361410618276, 0.9604334178218884, 0.96013685318993958, 0.95973171156955672, 0.95971477016537921, 0.95970471966544291, 0.95959275659028198, 0.95950650049325548, 0.95950506205962593, 0.95935615046696487, 0.95932966542255571, 0.95918955766384628, 0.95913788006455047, 0.95905970377609584, 0.95897876204835786, 0.95861669868835098, 0.95853741169116624, 0.95809154996549029, 0.95806499004892609, 0.95802502760200758, 0.95793870925841162, 0.95770460939768121, 0.95739568830254451, 0.95703425904572714, 0.95700242551570225, 0.95671797836867989, 0.95643892059583224, 0.95621970533273126, 0.956187296789617, 0.95612800804578379, 0.95611218169366641, 0.95610185128035696, 0.95595736807057818, 0.95587288144841542, 0.95586862356755919, 0.95576160223941398, 0.95576029479024327, 0.955615189154843, 0.95559676416769379, 0.95554436194779369, 0.95550600301004651]\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Get SEPs\n",
      "SEPs = array([getEquilibriumPoint(data[:,i], data, invA, 0.5, 10000) for i in range (size) ])\n",
      "sepList, sepIndexMap = reduceSEPs(SEPs, 0.99)\n",
      "print \"size of reduced SEPs: \" , len(sepList)\n",
      "plotScatter(sepList[:,0],sepList[:,1],\"reduced SEPs Scatter Plot\" ,\"r\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "iteration:  1 x0:  [ 31.95   7.95] xt:  [31.94428683625468, 7.9453296250555798]\n",
        "iteration:  1 x0:  [ 31.15   7.3 ] xt:  [31.149332395836719, 7.298798034464598]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 44 x0:  [ 30.45   6.65] xt:  [30.445361199463001, 6.6561731403732098]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 39 x0:  [ 29.7   6. ] xt:  [29.663972057590204, 6.0166853660464641]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1249 x0:  [ 28.9    5.55] xt:  [29.33096388421065, 5.8061292945071958]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 28.05   5.  ] xt:  [28.035354598628899, 5.0045943471880703]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 29 x0:  [ 27.2    4.55] xt:  [27.189651467672164, 4.5544783877672916]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2682 x0:  [ 26.35   4.15] xt:  [26.821773902245262, 4.3846091376151968]\n",
        "iteration:  1 x0:  [ 25.4    3.85] xt:  [25.398989311560683, 3.8497733971918144]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 726 x0:  [ 24.6   3.6] xt:  [25.048242423212145, 3.741507481125895]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 28 x0:  [ 23.6   3.3] xt:  [23.553429665976662, 3.3079514444625211]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 88 x0:  [ 22.75   3.15] xt:  [22.752308778736921, 3.1671972122671725]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1500 x0:  [ 21.85   3.05] xt:  [22.664857452837332, 3.15534035381283]\n",
        "iteration:  1 x0:  [ 20.9   3. ] xt:  [20.89956263712233, 2.9996454950407023]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 116 x0:  [ 20.    2.9] xt:  [19.987012234765153, 2.9526058352685145]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 82 x0:  [ 19.1   3. ] xt:  [19.114955718117809, 3.0237705313174379]\n",
        "iteration:  1 x0:  [ 18.2   3.2] xt:  [18.199070970436672, 3.1989594540264501]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 781 x0:  [ 17.3    3.25] xt:  [16.912102504167279, 3.3940330248393438]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 610 x0:  [ 16.55   3.5 ] xt:  [16.806923095577773, 3.4201409459189605]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1492 x0:  [ 15.7   3.7] xt:  [16.806891033754447, 3.4201490819010387]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1138 x0:  [ 14.85   4.1 ] xt:  [14.162546362044367, 4.4054569152108716]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 79 x0:  [ 14.15   4.4 ] xt:  [14.148755226039967, 4.4114687271453459]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2355 x0:  [ 13.4    4.75] xt:  [14.047272805878993, 4.459676991174474]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2998 x0:  [ 12.7   5.2] xt:  [11.533723562389641, 6.0976844566848909]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1893 x0:  [ 12.05   5.65] xt:  [11.533734012830646, 6.0976755753746144]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 44 x0:  [ 11.45   6.15] xt:  [11.460541432618536, 6.1574576896109789]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1183 x0:  [ 10.9    6.65] xt:  [11.428454413208801, 6.1880625368223603]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1072 x0:  [ 10.3    7.25] xt:  [9.4806288218288355, 8.1991146023533901]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 635 x0:  [ 9.7   7.85] xt:  [9.4805846998897216, 8.1991745149361801]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 384 x0:  [ 9.35  8.35] xt:  [9.444715270569862, 8.2483378730701258]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 39 x0:  [ 8.9   9.05] xt:  [8.9088593462994243, 9.0570953298835484]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2092 x0:  [ 8.55  9.65] xt:  [8.0287762686634352, 10.793730017824299]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1038 x0:  [  8.15  10.35] xt:  [8.0287723232760868, 10.79374101755845]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 74 x0:  [  7.95  10.95] xt:  [7.9751105213742672, 10.946610572783245]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1516 x0:  [  7.75  11.7 ] xt:  [7.5498593252718988, 12.524538783829602]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 681 x0:  [  7.55  12.35] xt:  [7.5498772884352299, 12.52444987058721]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 44 x0:  [  7.45  13.  ] xt:  [7.4628254180193334, 13.006883931081278]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 622 x0:  [  7.35  13.75] xt:  [7.3417844630254256, 14.262057544686275]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 67 x0:  [  7.3   14.35] xt:  [7.3388345041002578, 14.332271751180988]\n",
        "iteration:  1 x0:  [  7.35  14.95] xt:  [7.3499876745975499, 14.94812743715417]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1234 x0:  [  7.35  15.75] xt:  [7.7021514189409057, 16.996817852374342]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 932 x0:  [  7.55  16.35] xt:  [7.7021728208044093, 16.996900619061634]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 204 x0:  [  7.7   16.95] xt:  [7.7021731707514238, 16.996901277387082]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 44 x0:  [  7.8   17.55] xt:  [7.8386241936653036, 17.505821682492524]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 28 x0:  [  8.05  18.15] xt:  [8.0604079083219418, 18.131566301807247]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1809 x0:  [  8.3   18.75] xt:  [8.9965528812799178, 19.878502878075079]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1088 x0:  [  8.65  19.3 ] xt:  [8.9966088883705311, 19.878585849134218]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 318 x0:  [  8.9   19.85] xt:  [8.9965483875251309, 19.878496224287169]\n",
        "iteration:  1 x0:  [  9.3  20.3] xt:  [9.2995040124549178, 20.299089090113487]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [  9.65  20.8 ] xt:  [9.6677373675474652, 20.753768811545491]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1872 x0:  [ 10.2   21.25] xt:  [11.767690462563968, 22.554026146095612]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1111 x0:  [ 10.6   21.65] xt:  [11.767708432662012, 22.554037027657088]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 763 x0:  [ 11.1   22.15] xt:  [11.767732687187292, 22.554051714534335]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 514 x0:  [ 11.55  22.45] xt:  [11.767676689889967, 22.55401780617121]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16 x0:  [ 11.95  22.7 ] xt:  [11.955798303480092, 22.678982311757359]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 768 x0:  [ 12.55  23.  ] xt:  [13.347411236336773, 23.306859543047903]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 500 x0:  [ 13.05  23.2 ] xt:  [13.347449445751986, 23.306873061294869]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 13.45  23.4 ] xt:  [13.453939021536977, 23.370748485473975]\n",
        "iteration:  1 x0:  [ 14.    23.55] xt:  [13.99905857218857, 23.546720427391339]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 14.55  23.6 ] xt:  [14.540421432286937, 23.604656743553669]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1360 x0:  [ 15.1   23.75] xt:  [16.551147549543394, 23.784791202663175]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1026 x0:  [ 15.7   23.75] xt:  [16.551160614850541, 23.784790860679379]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 772 x0:  [ 16.15  23.85] xt:  [16.551178526425009, 23.784790391811896]\n",
        "iteration:  1 x0:  [ 16.7  23.8] xt:  [16.699846774807096, 23.798731439666938]\n",
        "iteration:  1 x0:  [ 17.15  23.75] xt:  [17.150088472959339, 23.749955721340761]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1207 x0:  [ 17.75  23.75] xt:  [18.702601255092677, 23.450291274398442]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 931 x0:  [ 18.2  23.6] xt:  [18.702637728874539, 23.450279830302161]\n",
        "iteration:  1 x0:  [ 18.65  23.5 ] xt:  [18.649668680852606, 23.497902688839638]\n",
        "iteration:  1 x0:  [ 19.1   23.35] xt:  [19.099660171453653, 23.34770437695035]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 616 x0:  [ 19.6   23.15] xt:  [20.47584517542046, 22.614192594758535]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 464 x0:  [ 20.    22.95] xt:  [20.475942909399279, 22.614125711296193]\n",
        "iteration:  1 x0:  [ 20.4  22.7] xt:  [20.399613318976822, 22.697756371533227]\n",
        "iteration:  1 x0:  [ 20.7   22.55] xt:  [20.696004630788078, 22.546051059687723]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 563 x0:  [ 21.    22.15] xt:  [20.568160395312919, 22.549659092779418]\n",
        "iteration:  1 x0:  [ 21.45  21.95] xt:  [21.446800407391603, 21.943618015700494]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1606 x0:  [ 21.75  21.55] xt:  [23.128268100144158, 18.306824996996806]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1529 x0:  [ 22.    21.25] xt:  [23.128249490645178, 18.307299868681863]\n",
        "iteration:  1 x0:  [ 22.25  21.  ] xt:  [22.248793715671479, 20.994526295357897]\n",
        "iteration:  1 x0:  [ 22.5  20.7] xt:  [22.49443462808852, 20.69421491981986]\n",
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        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 23.    19.35] xt:  [22.997236859514498, 19.346407363698965]\n",
        "iteration:  1 x0:  [ 23.1  19. ] xt:  [23.095729102794746, 18.994558160217696]\n",
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        "iteration:  1 x0:  [ 23.2  17.8] xt:  [23.190279510356223, 17.797422306792932]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 23.1   17.45] xt:  [23.093048654965902, 17.446179704533215]\n",
        "iteration:  1 x0:  [ 23.05  17.15] xt:  [23.038321989541728, 17.144050073276834]\n",
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        "iteration:  1 x0:  [ 22.6  16.2] xt:  [22.58311900953581, 16.185305390649837]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 22.55  16.05] xt:  [22.528807373402241, 16.039796094675228]\n",
        "iteration:  1 x0:  [ 22.4   15.95] xt:  [22.38861993978664, 15.936241103758906]\n",
        "iteration:  1 x0:  [ 22.35  15.8 ] xt:  [22.334600491768072, 15.793830998690792]\n",
        "iteration:  1 x0:  [ 22.2   15.65] xt:  [22.192874105297619, 15.647246530195611]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [ 22.15  15.55] xt:  [22.111498380872209, 15.565958230534505]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 71 x0:  [ 22.   15.4] xt:  [22.095636593317703, 15.555915912936953]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80 x0:  [ 21.9  15.3] xt:  [22.095634975617305, 15.555913990909588]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 83 x0:  [ 21.85  15.25] xt:  [22.095635377235723, 15.555914492830153]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 87 x0:  [ 21.75  15.15] xt:  [22.095568274319092, 15.555831653427784]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 91 x0:  [ 21.65  15.05] xt:  [22.095628203892911, 15.555905641886836]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 93 x0:  [ 21.55  15.  ] xt:  [22.095606013897385, 15.555878249611002]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 95 x0:  [ 21.5  14.9] xt:  [22.095598187145644, 15.555868584429032]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3220 x0:  [ 19.35  31.65] xt:  [21.779373627982306, 30.998966234856209]\n",
        "iteration:  1 x0:  [ 20.35  31.45] xt:  [20.35013715031894, 31.448997343118247]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 686 x0:  [ 21.35  31.1 ] xt:  [21.779322200809389, 30.998981466256414]\n",
        "iteration:  1 x0:  [ 22.25  30.9 ] xt:  [22.2488213326248, 30.898598922054045]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 508 x0:  [ 23.2   30.45] xt:  [23.556011523307511, 30.261725341424647]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 221 x0:  [ 23.95  30.05] xt:  [23.770789387614979, 30.155855170126188]\n",
        "iteration:  1 x0:  [ 24.9   29.65] xt:  [24.899418506613955, 29.647530177239716]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [ 25.6   29.05] xt:  [25.602408841615631, 29.053714210511835]\n",
        "iteration:  1 x0:  [ 26.35  28.5 ] xt:  [26.349695838119988, 28.500093725376733]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 462 x0:  [ 27.15  27.9 ] xt:  [27.469305371702305, 27.575054792297529]\n",
        "iteration:  1 x0:  [ 27.75  27.35] xt:  [27.748126912394444, 27.349509652153209]\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 993 x0:  [ 28.3  26.6] xt:  [27.605946120545632, 27.437092844572064]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1064 x0:  [ 28.95  25.85] xt:  [29.653503122148823, 24.885930197198682]\n",
        "iteration:  1 x0:  [ 29.5   25.15] xt:  [29.499833399179074, 25.148659729982551]\n",
        "iteration:  1 x0:  [ 29.95  24.45] xt:  [29.949471547851836, 24.449640320493078]\n",
        "iteration:  1 x0:  [ 30.4  23.7] xt:  [30.397641931695951, 23.698611132182826]\n",
        "iteration: "
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      {
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       "stream": "stdout",
       "text": [
        " 303 x0:  [ 30.6  22.9] xt:  [30.593739124922532, 22.984058680171543]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [ 30.9  22.1] xt:  [30.903516640296544, 22.098594826472119]\n",
        "iteration:  1 x0:  [ 31.25  21.3 ] xt:  [31.247739367970919, 21.298370444149128]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 608 x0:  [ 31.35  20.55] xt:  [31.307267399023424, 20.778533789114029]\n",
        "iteration:  1 x0:  [ 31.5  19.7] xt:  [31.499061552652414, 19.699227067698274]\n",
        "iteration: "
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      },
      {
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       "stream": "stdout",
       "text": [
        " 8 x0:  [ 31.55  18.9 ] xt:  [31.552791393038532, 18.89412563785384]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 592 x0:  [ 31.65  18.15] xt:  [31.596169039984265, 18.314522172677901]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2455 x0:  [ 31.6   17.35] xt:  [31.596167041193524, 18.314577101746988]\n",
        "iteration:  1 x0:  [ 31.45  16.55] xt:  [31.449582271460084, 16.549370898374971]\n",
        "iteration:  1 x0:  [ 31.3  15.8] xt:  [31.299645645525292, 15.799512104345803]\n",
        "iteration:  2 x0:  [ 31.15  15.05] xt:  [31.146657719632064, 15.050304779938202]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 30.9   14.35] xt:  [30.894823022276547, 14.351030576295738]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [ 30.6   13.65] xt:  [30.595388699842914, 13.65190405995274]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1956 x0:  [ 30.3  13. ] xt:  [30.510317892342986, 13.484808269288893]\n",
        "iteration:  5 x0:  [ 29.9  12.3] xt:  [29.892612368732788, 12.300926150637915]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 631 x0:  [ 29.5   11.75] xt:  [29.579970009447322, 11.893895267357955]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 29.    11.15] xt:  [28.9963957035731, 11.150708771340653]\n",
        "iteration:  1 x0:  [ 28.5  10.6] xt:  [28.498616008127737, 10.599470408157952]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 153 x0:  [ 28.   10.1] xt:  [28.01734465647009, 10.125527628585266]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 770 x0:  [ 27.55   9.65] xt:  [28.017386800881596, 10.125574857143963]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 26.9   9.1] xt:  [26.848880418953076, 9.1184788056459158]\n",
        "iteration:  1 x0:  [ 26.25   8.8 ] xt:  [26.249292469412261, 8.7976370428047712]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [ 25.7   8.4] xt:  [25.691345410759574, 8.4018005356895493]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 59 x0:  [ 25.15   8.05] xt:  [25.124240592646576, 8.0891366194366459]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400 x0:  [ 24.5    7.75] xt:  [24.593841134076506, 7.8553227730677886]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1085 x0:  [ 23.9    7.65] xt:  [24.593840024403615, 7.8553223461594754]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 28 x0:  [ 23.15   7.4 ] xt:  [23.107748346821818, 7.4135638813990159]\n",
        "iteration:  1 x0:  [ 22.5   7.3] xt:  [22.498979619734431, 7.2986470962009973]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 380 x0:  [ 21.9   7.1] xt:  [22.050709309978821, 7.1824964967528482]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1026 x0:  [ 21.25   7.05] xt:  [22.050563826935193, 7.1824686150431818]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 29 x0:  [ 20.5   7. ] xt:  [20.442297970501127, 7.0068863489969955]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 432 x0:  [ 19.9    6.95] xt:  [19.326499784305241, 7.0483141626042087]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 47 x0:  [ 19.25   7.05] xt:  [19.233040953763581, 7.0582192096356211]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 645 x0:  [ 18.75   7.1 ] xt:  [19.067752730291833, 7.0795016100225538]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50 x0:  [ 18.05   7.25] xt:  [18.046268244427985, 7.2622779706489835]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3371 x0:  [ 17.5    7.35] xt:  [15.246049225671225, 8.4468628986820296]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2215 x0:  [ 16.9   7.6] xt:  [15.246012428606907, 8.4468860969850859]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1772 x0:  [ 16.35   7.8 ] xt:  [15.246024857043581, 8.4468782616046081]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1396 x0:  [ 15.8    8.05] xt:  [15.246094530151066, 8.4468343370925449]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 722 x0:  [ 15.4    8.35] xt:  [15.246085584455246, 8.4468399767736084]\n",
        "iteration:  1 x0:  [ 14.9   8.7] xt:  [14.899291299246594, 8.698631607701099]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 55 x0:  [ 14.45   8.9 ] xt:  [14.486912534003077, 8.9450048216532974]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5569 x0:  [ 13.95   9.3 ] xt:  [11.575483513104587, 14.247681599641917]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4999 x0:  [ 13.6    9.65] xt:  [11.575484364823559, 14.247642951941868]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4472 x0:  [ 13.25  10.1 ] xt:  [11.575482797587023, 14.247714078903185]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4122 x0:  [ 12.95  10.55] xt:  [11.575482120579823, 14.247744820049954]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3871 x0:  [ 12.65  10.9 ] xt:  [11.57548411430934, 14.247654317713678]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3583 x0:  [ 12.35  11.4 ] xt:  [11.575482140685033, 14.247743906984446]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3353 x0:  [ 12.2   11.75] xt:  [11.575483403860643, 14.247686557817172]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2704 x0:  [ 11.95  12.2 ] xt:  [11.575483846978514, 14.247666447912893]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1176 x0:  [ 11.8   12.65] xt:  [11.575482417595337, 14.247731332148566]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 645 x0:  [ 11.75  13.05] xt:  [11.575481508304691, 14.247772630240478]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 438 x0:  [ 11.55  13.6 ] xt:  [11.575481742504815, 14.247761991682504]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 316 x0:  [ 11.55  14.  ] xt:  [11.575481939354106, 14.247753050656797]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 37 x0:  [ 11.55  14.35] xt:  [11.573094722373419, 14.326952671472304]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 33 x0:  [ 11.55  14.7 ] xt:  [11.578770217936732, 14.62279378195648]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3115 x0:  [ 11.6   15.25] xt:  [13.570949975888977, 18.798226345497731]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2776 x0:  [ 11.65  15.7 ] xt:  [13.570900177902844, 18.798185873181563]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2658 x0:  [ 11.8   16.05] xt:  [13.570975335519918, 18.798246955631825]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2450 x0:  [ 11.85  16.5 ] xt:  [13.570969932242852, 18.79824256433394]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2185 x0:  [ 12.    16.75] xt:  [13.570967497384361, 18.798240585495854]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1763 x0:  [ 12.15  17.2 ] xt:  [13.57101829759679, 18.798281870911811]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1570 x0:  [ 12.3  17.6] xt:  [13.570929743401136, 18.798209902073296]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1434 x0:  [ 12.55  17.85] xt:  [13.571011169799002, 18.798276078203887]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1297 x0:  [ 12.8   18.05] xt:  [13.57101051742479, 18.798275548023192]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1059 x0:  [ 13.1  18.4] xt:  [13.570913813935853, 18.798196955697382]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 841 x0:  [ 13.3  18.6] xt:  [13.570969329574851, 18.798242074538813]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 59 x0:  [ 13.55  18.85] xt:  [13.590620181876368, 18.814435077030303]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 764 x0:  [ 13.8   19.05] xt:  [15.290122002904235, 19.704195492471648]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 521 x0:  [ 14.15  19.25] xt:  [15.290190841633608, 19.704213809083075]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 442 x0:  [ 14.45  19.5 ] xt:  [15.290171145637087, 19.704208568505191]\n",
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 372 x0:  [ 14.85  19.55] xt:  [15.290164938304269, 19.704206916877769]\n",
        "iteration: "
       ]
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      {
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       "stream": "stdout",
       "text": [
        " 320 x0:  [ 15.   19.7] xt:  [15.290124953779877, 19.704196277667563]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 137 x0:  [ 15.25  19.7 ] xt:  [15.290103149866271, 19.704190479910288]\n",
        "iteration:  3 x0:  [ 15.55  19.85] xt:  [15.550808720046152, 19.827376717478728]\n",
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 595 x0:  [ 15.95  19.9 ] xt:  [17.333046613922214, 19.786461318431446]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 297 x0:  [ 16.2  19.9] xt:  [17.333143660878189, 19.786444856174356]\n",
        "iteration: "
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      {
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       "stream": "stdout",
       "text": [
        " 210 x0:  [ 16.55  19.9 ] xt:  [17.333012029501642, 19.786467184460502]\n",
        "iteration: "
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 168 x0:  [ 16.85  19.9 ] xt:  [17.333149967406403, 19.786443786470894]\n",
        "iteration: "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 112 x0:  [ 17.2  19.9] xt:  [17.333119807455518, 19.786449076342517]\n",
        "iteration:  1 x0:  [ 17.4  19.8] xt:  [17.397634728223704, 19.797604373553501]\n",
        "iteration:  1 x0:  [ 17.65  19.75] xt:  [17.637398876676247, 19.749745232311223]\n",
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 74 x0:  [ 17.8  19.7] xt:  [17.370624525802601, 19.779898123804298]\n",
        "iteration: "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        " 83 x0:  [ 18.   19.6] xt:  [17.370455288106427, 19.779923264856794]\n",
        "iteration: "
       ]
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      {
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       "stream": "stdout",
       "text": [
        " 89 x0:  [ 18.2   19.55] xt:  [17.370775394755459, 19.779866517232598]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 802 x0:  [ 3.9  9.6] xt:  [3.4114607659327918, 11.184673680861188]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 557 x0:  [  3.55  10.65] xt:  [3.4114693323726191, 11.184641607224483]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 414 x0:  [  3.35  11.4 ] xt:  [3.4001026778221446, 11.22744971651913]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2262 x0:  [  3.1   12.35] xt:  [3.0950550372565995, 13.159002079641059]\n",
        "iteration:  1 x0:  [  3.1   13.25] xt:  [3.0996713951391421, 13.250018871510092]\n",
        "iteration: "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 58 x0:  [  3.05  14.15] xt:  [3.0532188179647179, 14.12292572156707]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1979 x0:  [  3.   15.1] xt:  [3.1320084015863334, 16.204596366927088]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 684 x0:  [  3.1  16. ] xt:  [3.1320087584625687, 16.204599098354901]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 51 x0:  [  3.2   16.85] xt:  [3.2212624731461492, 16.797637127594346]\n",
        "iteration:  1 x0:  [  3.45  17.75] xt:  [3.4498473654041564, 17.74942064494364]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1410 x0:  [  3.7  18.7] xt:  [4.1177513717255776, 19.80615335852168]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 804 x0:  [  3.95  19.55] xt:  [4.1177607527118507, 19.806174169415229]\n",
        "iteration:  1 x0:  [  4.35  20.25] xt:  [4.3489101725114994, 20.249670898587286]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1453 x0:  [  4.7  21.1] xt:  [5.0583979818946299, 21.643307660662177]\n",
        "iteration:  1 x0:  [  5.15  21.8 ] xt:  [5.1499725234962712, 21.799594228993044]\n",
        "iteration:  1 x0:  [  5.6  22.5] xt:  [5.5997229449268859, 22.498279876001785]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1361 x0:  [  6.2  23.3] xt:  [6.9472988324594249, 24.011509896120582]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 780 x0:  [  6.8   23.85] xt:  [6.9472844948600834, 24.011495985457632]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [  7.35  24.45] xt:  [7.3554067690791616, 24.422562151861996]\n",
        "iteration:  1 x0:  [  8.05  24.95] xt:  [8.0496126941991157, 24.949190113869207]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2221 x0:  [  8.8   25.45] xt:  [10.211773604510057, 26.359087608871022]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1401 x0:  [  9.5  26. ] xt:  [10.211800383876145, 26.359101840585968]\n",
        "iteration:  1 x0:  [ 10.2   26.35] xt:  [10.200076344284284, 26.350156139079697]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 10.9   26.75] xt:  [10.904717118789735, 26.723602280158193]\n",
        "iteration:  1 x0:  [ 11.7  27. ] xt:  [11.699796050465219, 26.999942317392353]\n",
        "iteration:  1 x0:  [ 12.45  27.25] xt:  [12.44938987907689, 27.250097113362159]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 705 x0:  [ 13.3  27.6] xt:  [14.178083574811891, 27.650227379520061]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 331 x0:  [ 14.05  27.6 ] xt:  [14.178172575863091, 27.650237777243035]\n",
        "iteration:  1 x0:  [ 14.7   27.75] xt:  [14.69806422125364, 27.747796867324634]\n",
        "iteration:  1 x0:  [ 15.55  27.75] xt:  [15.54966933978643, 27.749704596161596]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2437 x0:  [ 16.4   27.75] xt:  [18.051381237121916, 27.72086209410395]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1751 x0:  [ 17.1   27.75] xt:  [18.051426231053711, 27.720859493358834]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 470 x0:  [ 17.9   27.75] xt:  [18.051383664450249, 27.720861953813216]\n",
        "iteration:  1 x0:  [ 18.55  27.7 ] xt:  [18.548781082817339, 27.699183141875533]\n",
        "iteration:  1 x0:  [ 19.35  27.6 ] xt:  [19.349636237954194, 27.597597670843506]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 593 x0:  [ 20.1   27.35] xt:  [20.397553227401932, 27.216059080520196]\n",
        "iteration:  1 x0:  [ 20.7  27.1] xt:  [20.699596651117908, 27.100019388282206]\n",
        "iteration:  1 x0:  [ 21.45  26.8 ] xt:  [21.450079382132831, 26.798851498028114]\n",
        "iteration:  1 x0:  [ 22.05  26.5 ] xt:  [22.0492140214348, 26.499561528389219]\n",
        "iteration:  1 x0:  [ 22.7   26.15] xt:  [22.699062202465569, 26.14822777194318]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 595 x0:  [ 23.35  25.65] xt:  [23.904326477410134, 25.179580089858867]\n",
        "iteration:  1 x0:  [ 23.8  25.3] xt:  [23.799962671829419, 25.29855714654942]\n",
        "iteration:  1 x0:  [ 24.3   24.85] xt:  [24.298994041534915, 24.849315238126973]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1459 x0:  [ 24.75  24.35] xt:  [25.468504937012653, 23.632945687108904]\n",
        "iteration:  1 x0:  [ 25.25  23.95] xt:  [25.248703726065983, 23.947726215031409]\n",
        "iteration:  1 x0:  [ 25.65  23.45] xt:  [25.648902447063481, 23.449202629966585]\n",
        "iteration:  1 x0:  [ 26.05  23.  ] xt:  [26.044826755943046, 22.997741682588789]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1617 x0:  [ 26.2  22.3] xt:  [27.048882759810954, 20.331935567191195]\n",
        "iteration:  1 x0:  [ 26.6  21.8] xt:  [26.597250517832357, 21.796748913846734]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 858 x0:  [ 26.75  21.25] xt:  [27.048962903246402, 20.33161444362721]\n",
        "iteration:  1 x0:  [ 27.   20.7] xt:  [26.997612511182179, 20.698060691475835]\n",
        "iteration:  1 x0:  [ 27.15  20.15] xt:  [27.147033324341436, 20.148444917362845]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40 x0:  [ 27.15  19.6 ] xt:  [27.193227815527457, 19.611210902252751]\n",
        "iteration:  1 x0:  [ 27.35  19.1 ] xt:  [27.345812223160348, 19.099456899208192]\n",
        "iteration:  1 x0:  [ 27.35  18.45] xt:  [27.348926428097446, 18.449024143916422]\n",
        "iteration:  1 x0:  [ 27.4  18. ] xt:  [27.39558211292033, 17.99968538900951]\n",
        "iteration:  1 x0:  [ 27.3  17.4] xt:  [27.297073893465864, 17.399827537425807]\n",
        "iteration:  1 x0:  [ 27.15  16.9 ] xt:  [27.149964268760492, 16.899237849741159]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 301 x0:  [ 27.   16.4] xt:  [27.053311854403702, 16.463343246388334]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1074 x0:  [ 27.   15.9] xt:  [27.053303979472311, 16.463309855446209]\n",
        "iteration:  1 x0:  [ 26.75  15.35] xt:  [26.747556888977307, 15.349484955232732]\n",
        "iteration:  1 x0:  [ 26.55  14.85] xt:  [26.545218770431941, 14.849646721017704]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 26.3   14.45] xt:  [26.279661814583765, 14.451829289815823]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20 x0:  [ 25.95  14.1 ] xt:  [25.964179921898896, 14.081372364405757]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 25.75  13.7 ] xt:  [25.708922241272695, 13.714586058961554]\n",
        "iteration:  1 x0:  [ 25.35  13.3 ] xt:  [25.347370063086451, 13.29856539111006]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 x0:  [ 25.05  12.95] xt:  [25.028663443304556, 12.952721397174841]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 132 x0:  [ 24.8  12.7] xt:  [24.795585631017062, 12.774612346370626]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 408 x0:  [ 24.4   12.45] xt:  [24.795556634901718, 12.774592024138185]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 547 x0:  [ 24.05  12.2 ] xt:  [24.79556428950913, 12.774598611743542]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 23.55  11.85] xt:  [23.523533277725235, 11.864335435338052]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 23.2   11.65] xt:  [23.169203337456278, 11.658338515688573]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23 x0:  [ 22.75  11.4 ] xt:  [22.683552185369773, 11.43121538427939]\n",
        "iteration:  1 x0:  [ 22.3  11.3] xt:  [22.297101726165604, 11.29913390150074]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 21.9  11.1] xt:  [21.793539907006856, 11.141642435379548]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 21.45  11.05] xt:  [21.385070976006119, 11.06256975200959]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24 x0:  [ 21.1  11. ] xt:  [21.03703402532059, 11.019338945309595]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 41 x0:  [ 20.7   10.95] xt:  [20.658211356364713, 10.995118535539486]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 42 x0:  [ 20.35  10.95] xt:  [20.340446074325801, 10.990971874145794]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 19.95  11.  ] xt:  [19.935113268234861, 11.006386132668393]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1132 x0:  [ 19.55  11.  ] xt:  [16.010530373874737, 13.272959655363488]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 928 x0:  [ 19.15  11.05] xt:  [16.010527194635163, 13.272965738448928]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 786 x0:  [ 18.85  11.1 ] xt:  [16.010523060301761, 13.272973649042788]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 556 x0:  [ 18.45  11.25] xt:  [16.010536589389623, 13.272947762840293]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 404 x0:  [ 18.15  11.35] xt:  [16.010503262624244, 13.273011530565515]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 312 x0:  [ 17.85  11.5 ] xt:  [16.010537488995979, 13.272946041579949]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 260 x0:  [ 17.5  11.7] xt:  [16.010542065789071, 13.272937284624721]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 232 x0:  [ 17.2   11.95] xt:  [16.010508672578904, 13.273001178844153]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 219 x0:  [ 17.    12.05] xt:  [16.01053918650797, 13.272942793655989]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 204 x0:  [ 16.75  12.2 ] xt:  [16.010530339625856, 13.272959720894331]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 195 x0:  [ 16.65  12.35] xt:  [16.010499929691889, 13.273017908043251]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 183 x0:  [ 16.5  12.5] xt:  [16.010515606696053, 13.272987910852917]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 168 x0:  [ 16.35  12.7 ] xt:  [16.010485591438005, 13.273045344359451]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 155 x0:  [ 16.2  12.8] xt:  [16.010539991898284, 13.272941252670154]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 141 x0:  [ 16.15  12.95] xt:  [16.010550817065095, 13.272920540671475]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 112 x0:  [ 16.   13.1] xt:  [16.010491546624905, 13.273033947880073]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 26 x0:  [ 15.95  13.25] xt:  [16.007124058429724, 13.277813635492627]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 83 x0:  [ 15.9  13.4] xt:  [16.003516621486774, 13.286467372545367]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 101 x0:  [ 15.8  13.5] xt:  [16.00356647808923, 13.2863707908107]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 112 x0:  [ 15.8   13.65] xt:  [16.003584127988994, 13.286336586094428]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 122 x0:  [ 15.75  13.85] xt:  [16.003469606388034, 13.286558591236044]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 131 x0:  [ 15.65  14.05] xt:  [16.003604139218908, 13.286297798978129]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 136 x0:  [ 15.65  14.25] xt:  [16.003535399805642, 13.286431042305386]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 142 x0:  [ 15.65  14.5 ] xt:  [16.003552601590833, 13.286397697057962]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 144 x0:  [ 15.65  14.6 ] xt:  [16.003519879044227, 13.28646112988867]\n",
        "size of reduced SEPs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 188\n"
       ]
      },
      {
       "metadata": {},
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I15HWM0cqsIPTni+xs1OQykIUfBK1sqRlX8f/93+WDnBZWuFW9jNH6nSq7PtR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\niEhy/w82QP+hZtqmtgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f99518>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get adjacency matrix\n",
      "A = getAdjacencyMatrix(sepList, 0.98, 5)\n",
      "sepsClusters = getSEPsClusters(A, sepList)\n",
      "print sepsClusters\n",
      "plotCluster(sepList[:,0],sepList[:,1],sepsClusters,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1GjRvHsSUKZ9Yza73Li4urNm4EYC4uDgqli6NQ/YeoWrAWa222Ha4iQ8Tca/k\nnnO7YCVPLiVFKpgob8gz8ucQGxtLYM2auKWmogbiHR05fPx4vtwHc/fu3XTs+Bbm38t2QHkA7Oy2\nMmVKRz7++GMl41mV06dP06BBM7TansB6oAhQCSenizRsWIodO7ZY3dJ4IQT1a9eGc+eomZVFtErF\niQIFuHT1qkXOyNqyZQv9RvSj0x8d0BRyYnu/nbSv2Z4Z0yx3GnCuN82SzKZ+8gml7t+nW1oaXdLS\nqPbwIeOfsjuSNZs9ey5abcPsW2459xsMLqSkpCoTykqZ+xGVA7SAEegIVEWn68T+/QdyNmmxJiqV\niq27dlGqbVu2FyuGtm5dwg8etMgiDtCuXTumjpvK5g5bWRKwjKY+TfnqM+tp2fu8ZCF/Dndv3cLb\nYMi5XdRo5O7t2womyjs2NjaYRz4rA9uBBOAGjo4RdOzYQdFs1qZs2bLAHSCTR3/VVFb9jtXT05NV\n69dz4+5d9h069FzTOZU0bMgwYm7F8PD+Q4IbBVOnYW2q+Ffm25nfWu334P+Thfw5NGnRggiNBh3m\nCXQnnJxoYoWtP5/HmDEfoNEcADwwTzb7FS+vMJYvX0j9+vUVTmddGjduzLvvdsHJKRS1OgXYAlzF\n0XEztWrVokQJcy/Mhw8f0q1bT4oVK0u9ekFcvHhR0dz5VWhoKMNGD8P3y+rUm1uX2b/NZtbsWUrH\nyhVyjPwfJCUl0e/dd9kbFoanhwez585l26ZNzF+4EIAeb73FgiVLsLOze8aRrNPBgwf59tsfMBpN\njBw5JN/suq6UiIgIIiMjWb9+M9HRd6hbtzbTpn2BRqNBCEHduo04fdpAZmYtVKqbeHgc48qVixQq\nZD3d+wwGAx99+CErli/Hwd6eT7/4ggEDBigd6xG9+vcisdZ9ag+rBUD0vmgufBLJyYMnFU72KLlE\nPxdkZGRQuWxZ3GJiCAHigc0aDQeOHcPHxwchRL4t4NKrZ56WWIrMzLH89QbZzW0tS5d+QceOHZUN\n9wImjhvHup9+orVWixbYoNGwZM0a2rRp88znvipDPxjKpUIXaTzFfA3owqqLxP96n/27Dyic7FFy\n+mEuGDNyJLdiYhgPOGDerLaSwcDevXtly1kp1zk6OmIyGQE9oAFMmExpaDQahZO9mI1r1xKs1WY3\nR4DaWi0b162zqEI+6v1R1GtUD2OGATtXe07NimDtirVKx8oVcoz8/9m6aROOwIPs2wK4bzLh4eGh\nYCopv3LJ5uxiAAAgAElEQVR2dmb48OFoNCuBQzg6/kGlSt4W3S72n7i5u/Pwb7dTbG0pYGFrLXx8\nfDh68CiBxnpUj/fltwW/sWTFEhqFNGLCJxPIyMh49kEslBxa+X8CqlbF4dIlLgB+QAyQWaQIV2/e\nxNHRUeF0Un4khGD58uXs33+IChXK8v7771vVRgiQvbFDmzb46vXobW256+bGiTNnKFasmNLR/lFK\nSgq+NX0p070UxRsV58zPZ6msqcK639cpHU2OkeeGPXv20LVDB0plZHBfpSJDo+HshQs5Mwwk6VU7\nd+4c778/mtjYeFq1as4333xlkatCz507x/r163FycqJ3794WvYnDpk2bGDd7HG/t7gqAQW/gu4I/\nEB8Tj5ub2zOenbfkGHkuaNasGfuPHGHr1q04OzvzzjvvWOxih7/o9XpOnjyJra0ttWrVssp+HtI/\nu3PnDg0aBJOWVg8h6nD7dijx8fGsXGl5nRd9fX1fqpe8ElQqFcJoIjUmjWvbrmEymhAmYbX95eUZ\nuZWLi4ujbt3GJCZmIkQWlSqV5M8/d1v8Di7S8/n1118ZNWpediMyLbAZuI6vry+LF/+XmjVrPuMI\nykhJSWHge++xd+9eCnl68tP8+TRt2lTpWDnS09OpWqMqcYlxlG9ZjoyUDOKP3efKhSt4e3srmk0u\n0X8NjRgxhrt3i5Ca2oe0tP5cvKjn88+/VDqWlEvs7e1Rqf66CLcGcAaGcO5cSYKDQ4iJiVEw3ZO9\n8+abXN+2jXeTkwmIiqJL+/ZcvnxZ6Vg5nJ2dqVy1Mo0/bUjXVZ15e3sPqveuxpfTrfN357Uu5FlZ\nWVZ7pfrQoUMsW7aMU6dOk5VVAfMqTDV6fVnOn89/3d1eV506dcLDIw1b282YN7dog3l9cTRarYrZ\ns2crG/AfmEwmQvfsoWVGBh6AD1DRYGDPnj1KR3vEg6QHeNf839l34YDCxMZb5h/GZ3lqIb99+zZN\nmjShWrVqVK9ePeeH5sGDB4SEhFCpUiVatGhBUlLSKwmbW0wmEyPffx8XjQZXZ2fe7NTJInc5eZIR\nI0bTokVnhg6dRVRUFLa2ZzD3RzHg5HSZwMBaSkeUcom7uzsREccYPrwxKpXAvKH0b0BhjMYGzJo1\nlzVr1iic8lFqtRqNgwNJmLvMnAOuZ2Zy8IBlLbwJaRLCsa+Po0/SkxqTRsT3pwlp0kLpWC/nabtO\nxMTEiIiICCGEEKmpqaJSpUri4sWL4qOPPhLTp08XQggxbdo0MX78+Mee+4xDK+qnH38UZTQaMQ7E\nxyCqOzmJ0SNGKB3ruZw5c0ZoNAUFjM/eqWaQUKkchJNTAeHo6C5at+4gMjIylI4p5YHJk6cIW1tX\nAU0FvCuggoCiokSJckpHe0y3rl2FBoQbiBIgKoOwU6nEsWPHlI6WIzMzU/Qd1FfYO9oLJ2cnMf7j\n8S+0i1ReeZna+ULP6Nixo9i1a5fw8fERsbGxQghzsffx8cmVMK+CTqcTIUFBoiOIqdn/3gNRs1o1\npaM9lVarFePHTxL+/nWEvX3Zv205NlU4OXmK8PBwcfPmTYv4QcxtBoNBhIaGit9//13cvn37pY+j\n0+lEXFycVX+NmjQJEVBbgLOArgJ6CpXKTSxcuEjpaI/47LPPhBeIqiCmZP+edQbxhr+/0tEeYzQa\nLepn4mVq53PPWomOjiYoKIjz589TqlQpHj58+NcZPZ6enjm3/2KJs1Zu3bpFk4YNSYyNpVxWFn91\nsjikVuMQEsKm0FBF8z2JEIKgoBCOH7+PXl8S2A28A5QCzlK48GHu3r2Z73rAbNmyhe07t7Nzz04y\n7DPwLFeAm/tvsWX9lhfebej7Od8zccJEbB1s8XD3wMXdBb1eT5eOXZj2xTSr+dodOnSIxo1DMBqD\ngEAgHThH9erxnDtnOc2fzpw5Q71atWhoNPLXdyoe2OrtTbQFXqANDw9n+47teBbwZNDAQYqu5M6z\neeRpaWl07dqVH374AVdX18c+6ZN2OZk6dWrO/wcHByu+7HjQe+9R9t49uhmNLAQWAC4ODiQ6O3Ng\nzhxFsz3NtWvXOHnyNHr9MMy7ursDS7GxUVO4sBehodutphA9r1mzZzHth2kUqu2J3kNPnz/fRW2j\nJnL9ZfoP7U/k2ee/oHvw4EG++PYLBl7sT3pcGitaraLhj/VxK+XGlg83Y5xo5PsZ3+fhq8k99evX\nJzi4MXv2ZAHnga2AKxcuJPH776vo0aO7wgnN/Pz8GD95MjOnTuU85iLuAARZ4Dzz35b/xqhxo/Ad\nXJ3ks8nMqzuPU0dP4e7u/uwn54KwsDDCwsL+1TGeeUaelZVFu3btaN26NaNGjQKgcuXKhIWF4e3t\nTUxMDE2aNCEy8tFfLEs8Iy9TtCjtYmMpjPm6/1bAu3lzlq9cadEtQyMjI6lduzHp6UMxX58WODv/\nypYtywkKCrK67cKeh0dBd9453JMLqy5h0Blo+lUwAOnx6cyvsojkxOTnPtasWbNYE7WK5rObsW9y\nOABNPg8CIOFyIhtbbeZulPXs1nPq1CkaNgxGp8sE+mI+H7uBg8Of3L5945mbZL8qRqORogUL4pec\nzBvADSDUxYXIa9csatVniXIlaLmyOcUDiwOw8a3NDGw8iPfff1+RPLk+j1wIQf/+/alatWpOEQfo\n0KEDS5YsAWDJkiV06tTpJeK+elWrVeOijQ1gfuFajYaub75p0UUcoGLFivj4lMfBYRtwDXv7HZQs\nWYgGDRrkyyIuhECn1ePs5UyJesW58PtFkm+nIEyCozOOE1gv8IWOV7p0ae4djsWQYcBOY0favf9t\nWZcem4bG2bo6DdasWZO5c3/Ezs4d8yyWRUAkGRmZ/PjjTwqn+5+7d+9iyMykIWCPec+pYjY2nDxp\nOUNAAOmp6biV/N+yfJdSLqSlpSmY6CU8bQB9//79QqVSCT8/P+Hv7y/8/f3F9u3bRWJiomjWrJmo\nWLGiCAkJEQ8fPsyVAfu8duvWLVGhVClR3NVVFHByEm917iwMBoPSsZ5LcnKyGDRoqKhVq754770B\nIjExUelIear7u28J367VxeDzA4Vvr+pCbasWDs4Oonb92jkX2p+X0WgUXbp3FsUqFxVVWlYW9s52\novbgWqLJV8HCs5inWPn7yjx6FXknPj5eODi4CNAIGC1gioBBwsHBWSQlJSkdTwhh/pl1tLMTo0H0\nyZ654qpWi2+++UbpaI/oM6CPqNaxqhh+dYjoua27cC/snjNbTwkvUzvzrNpaYiEXQoiMjAxx+vRp\ncfXqVYu6Ui09Kj09XQwcNlCU8SkjajeoLcLDw0VycvJLH89kMonw8HCxbt06cerUKTFl6hQxaswo\nERYWloupX62PP/5YgLeAt7MLuo1QqRzF5s2blY6W4+svvxTuDg7CEUQHEF1AeGo04o8//lA6Wg6t\nViv6De4nChQpILxLeou5c+cqmudlaqfstSLliXPnzjFm0hji78fTomkLvpj6Bfb29krHylfu3btH\nuXI+ZGQYgZ5ASeAkRYue5c6dKItpANU8KAiHP//kjezbF4G4wEDCjhxRMlYOo9FI205tuBx3hcLV\nC3Fly1UW/XcRnTt1ViSP7LUiWYTbt28T3DwIuzY2BMysweaITQwaPkjpWPlOsWLFGDlyGFAYuAPM\nB86TkJBAXFycsuH+xlIuvj7Jhg0buHL/Cu8c6kGrhS3ourkTg4Za18+rLORSrtu2bRtlW5Wh1tCa\nlGxQknYr2vD78t/lO7Q80KVLF2xtHwJngRDgDbKyDBbVoGr4qFHsd3BgBbACCHV0ZOT48UrHyhEX\nF0dhv8Kobc3l0DvAmwf3H2AymRRO9vxk42op19nb25OZmpVzOzM1E1s7+aOWF9544w0cHR1IS2sH\n/LX5SSJr1vyh+LqNv3h7e6OyscET81zye0JQoEABpWPlqF+/PpOmTkTloiLxciLpsVr8a/tbzNDU\n87CepPmYEIKVK1cyduxHzJs3D4PBoHSkf6Vz584kn01h14jdRMw/zbq26xk7dmy+nCqpNJVKlb0Y\n7K+mbwLQYUlf6pnffENNnY5WQBOgRUYGUyZMUDpWDn9/f+q+UY+b+24S0N+f8i3Lcuf2bRISEpSO\n9tzkaZIFGDZsBL/9tpH09IpoNJtYu3YTO3Zstqozgr/z8PDg+KHjTJ8xndiDsXw57it6v9tb6Vj5\nkhCClJQEYC3mVsZ6wB4PjybKBvubtJQUHIVgN+YVnnaAXfLzL+jKayaTibC9Ybx/ayiaQhoqd/Yh\n9Uoamzdvpm/fvkrHey7WWSmeQ1ZWFjExMRZ/dpuQkMDChQtJT38baIxW250jRyI4fvy40tH+lSJF\nijDzm5ksX7ScPr36yLPxPPK/6w4CqA+0AqqwdOnvyoX6f9565x3C1WoeAAGY/9w8TE62qN/N/3/9\nRqW2rll3+bKQh4aGUtjTkyrlyuFdqBD79+9XOtITpaWlYWvrCDhm32ODjY2b9a0skxShVqupXbsO\n5p+fS0AscIXbt6MsZtMUHx8fHGxt6QpUAboAmampnD17VuFkZmq1mgGDBrC+8yYub7rC/s8OEnMo\nlvbt2ysd7bnlu0IeHx9Pz27d6JqWxod6Pa2Sk+ncrh3p6elKR3uMwWBg27Zt2NvbolbvBR6gUh3H\n1jaJWrXk5hDS8wkKaoh5wKID5h2E3gHUFjNvXwiBra0tf70nU2X/s6Qz3tnfzWZgh4EkzE2keFQJ\njuw/YvHTJv8u342RR0ZGUsjWllLZtysCYUIQFRVF9erVlYz2CJPJRNu2nThw4CparQ9q9RkcHE7j\n61uDpUv3KdpGU7IuUVE3gWTMe3pmAG+hUpkwmUzYZPcWUpKbmxsqW1tmYO7bWcjODq/SpalRo4bS\n0XLY2Ngw4aMJTPjIci7Cvoh8V8hLlixJfEYGqYAr8ABIysykaNGiCid71KlTpzh48ARa7SDABpOp\nAfAjmzevVXwXb8l6nD9/no0btwOdAU/gIbASf/86FlHETSYTrZs3x0enIwC4DvypVnN+69Z813pZ\nSfluaKVs2bJM+PhjFmk0/OHmxlInJ2Z89x0FCxZUOtojUlJSACfM/cUBHLC1dZRj49IL2bt3L1lZ\nWcB+YCXmHTKzWLp0vrLBst27d4+bUVE0y8qiIPAGUNzBwaIWLOUH+a7XihCCzMxMrly5wrVr16hS\npQqVK1d+5TmeJiMjg0aNmnL8+EkgGKiEjc1Zypa9z6VLZ7C1zXdvlKQ8oNfrKVeuEjExAqgB+APL\ncHBIQKtNtYjpq4cPH6ZpgwaUEYKqQDVgoYsLq3fsoH79+krHs0h5tkOQtViyZAnvDx2KPiODAF9f\nNmzbRrFixZSO9ZiffvqJ8+eTgAHAduAAzs7OhIdHyCIuPRej0UjTpq2IiXEAfDDvFnQHqESrVrUt\noojfuHGDti1b0kAI3IG9wEFbW3xr1SIw8MV6yktPp/x3O5ecOHGCD4cNo7dOxySTCc3587zZseOz\nn6iAK1euo9OVwNzsqDfQB43GySL/6EiW6eTJk5w6dRbzudhtoBlwFUfHSDp2bKdsuGxLlyyhilZL\nI8zvF94ETM7ObNu1yyLG7/+SlZXFihUrmDVrltWu38g3hfzw4cNUNpkogvlFNTQaOXrqlEVNcfpL\n/fqBaDSXAR1gws4ugjfeqK10LMmKfPfdLDIytIA3UAz4HTDRokUd+vTpo2y4bFevXuW80chMYANg\nApycnCzqIqfBYCCkbQifzv2UNVGraNmhJUuXLVU61gvLN4Xc29ubGLUaY/bte0BBDw+LXFHYq1cv\nevVqh53dbBwdv6dq1SwWLJirdCzJSnzwwYesWrUZ8+TaU4ABqE2BAu6sX7/WIoZVzp8/z+b162kP\n9AMygdVqNUMV2gfzSTZt2sTttFv02PcmzWc3482dXflgxAcWeQL4NMp/x3PJzagoYvV65gKrgDX2\n9izI3lfU0qhUKubO/ZH4+HvcuBFJRMQxi983VLIM8+bN48cf52Bekn8Nc+va/UAW3bp1sYgiDrBz\n506qm0xUBAoA7YAstZrxkyYpnOxRCQkJFKzsidrG/HUrVLkg6anpGI3GZzzTsljGd/1funLlCl9O\nncpgk4nmmEeehVpN06ZNlY72VB4eHhQtWtQi3zVIlmfu3LkMGfI+5lWctkBBYBvghKPjOYYPH6Jo\nvr9zc3Mj9W8X7pMAN1dXi/tZb9SoEVc2X+Pmn7fITMskfOKf1Gtcz+omHeSLQn7jxg2K2ttTAPP1\n+6aAxsaG2NhYhZNJUu4YPHgYQ4eOwTwm7pn9zw1QY2urJzR0C35+fopm/LsePXpgLFqUPxwd2Qes\n02j45rvvlI71mEuRl6heozrru25kZsHvsT/vyLqV65SO9cLyxTzyqKgoAqpV412djsLADWCzqyv3\n4uNxdHR81tMlyWIJIZg06WOmTZuF+Qy8AuZxcRugOnCC3bu30KxZMyVj/qPU1FQWLFhAwv37hLRo\nQVBQkNKRHrF46WI++vQj6k+tS3q8lpPfnuLw/sOKrzt5mdqZLwo5wOJFi/hg2DDc7OzQAWs3bLD4\noRVJepZJkybz7be/YjA0BhKA45jHxXcDbjRrVp3du0MVzWitqteqRs0Z/pRpUgaAsE/CqZMZyMxv\nZiqa67XefPm9vn25HRPDzsOHuR0TI4u4lC/MmfMjBkMPzGsigzCfkd8DsqhSpSAbN1rWMMClS5do\n1bQpfj4+jBg2DJ1Op3SkJzIajdg4/G8sXO1gY3UXOf/yzELer18/vLy88PX1zblv6tSplChRgoCA\nAAICAggNtYwzAg8PD6pVq4azs7PSUSQpV5jPzP7/BcJzvPvum5w7d8qiftbj4uIIql8fdVgYda5c\nIWzRInr16KF0rCca1G8wOwfu5uq2a5xZfJbTs8/Q+x3r3MnqmYW8b9++jxVqlUrF6NGjiYiIICIi\nglatWuVZwPwkNTWVQ4cOcenSJaubpyopY+jQwWg0G4DLwAHU6kt8+ulHLF26xKJWR4J5ymFxg4FA\nISgJdNDr2bR1K5mZmUpHe8y5c+dYuWYl6ffT2TNkH8krU9myfgs1a9ZUOtpLeeYcm0aNGhEdHf3Y\n/bIQvZjz588THNycrCxnsrKS6Nq1A0uXLrS46ViSZZk+/Wu8vIrwxx9bKFKkENOnn8HHx0fpWI9J\nS0tjy6ZN3NbpOAHUxNwZXaVSWdwfnMTERJq1bErgZ29Qt3kdTv18muTwZBo0aKB0tJf20mPkc+bM\nwc/Pj/79+5OUlJSbmfKl7t17kZj4BikpvdHphrB+/T7WrbOs8U0wvz2+fPlydmtUSWlqtZqxY8dw\n6NA+NmxYY5FFPDMzk+D69bm8eTN1jUZOAr8BqzUaRo4caXGF/OjRoxSqVoiAAf54lPGgyfQgbty4\nYdXTlV9q1vvQoUP59NNPAZg8eTJjxoxhwYIFjz1u6tSpOf8fHBxMcHDwS4XMD6KirmOebQBgj05X\nyqJ6Mgsh+GjiR8ydOxeXgi5obDXs3r6bcuXKKR1NsnDbtm0jISqK9zIyUAG+wAyViu+nT2fY8OFK\nx3uMq6srKfdSMBlMqG3V6B7oyNRlKna9ISwsjLCwsH91jOeafhgdHU379u05d+7cc39MqX7klsrf\n/w3Oni2IEHUBHc7Oy1m+/Ec6WkiHxk2bNjF04lDe3t8dJ08njsw4SurmNA6HH1E6mmShHj58SKe2\nbTly7BgGo5GGQBPMzbG+s7fn5r17FrehS1xcHBs2bOCHn34go0AGpUJKcHXVdXq268m3X3+rdDzg\nFfYjj4mJydk6bf369Y/MaJH+2aJF86hfP5iMjENABq1adaJDhw5Kx8px9uxZyrYvg5OnEwC+farz\n65cLFU4lWbIh/fujP3mSCUYjWmAR5uYBSQ4O1KlVC09PT4UTPioqKoq6jepSvHExHKrZc2/3PUKq\nhjB8ygd07dpV6Xj/yjPHyHv27En9+vW5fPkyJUuWZOHChYwfP54aNWrg5+dHeHg4s2bNehVZnygm\nJobGdeviYGdHCS8vi5kO+XeTJk3BZCqLEG0Qogk7duzk1q1bSsfK4eTkxOVNV8jSmcfGr26+houH\ni8KpJEsjhGDBggW0atKE0G3bKJuZiRpwwXyB84ybG77durFx+3aLu5D/yX8+ptrgKrRb0Yb2K9sS\n8IE/2sx0unXrZnFZX9Qzz8hXrlz52H39+vXLkzAvq2ObNjidP89Yg4G78fH06NqV46dPU7FiRaWj\nAeaex7t2bcdoHI/5nAWESGD37t30799f2XDZUlJTsLFX80vlebiWcOPh9Yc4qZ2UjiVZmO+/+44Z\nn35KQ62WesBGzE3qPIEYR0cmfPopY8aMUTbkE8Tej6Nw58I5twv7FiL2RJyCiXKPdbX4+gd6vZ7T\n584x0WhEDZQFKqjVHDp0yGIKuY2NDba29hiN6YAHIFCpUi1qMYeDgwOl6pfCf7AfGUl6hBCEDdiv\ndCzJQggh+PPPP/nqyy8J0mqpmn1/CrDGzg6NgwOFK1Rg2LBhSsZ8qlZNWzH3m18oXte8E9eJb08y\n/C3L6o/+sqx+ib69vT32dnYkZt82AolgURdZVCoVU6dOQaNZCRzAwWEDxYrZWdQYea93exG1PprL\n667w8HoSOwfuYdzocUrHkiyAEILB/fvTo21bCiUlsR04nf0xtVpNw/bt+XHVKvYfPYqTk+W9i/th\nzg94FvFkypQpOGqd+KXcf5lb/lc61O/IhyM+VDpersgXTbPmz5/PuBEjqGw0Em9nR/k6ddi2e7fF\nzV/dtGkTu3btpUSJogwfPhwXF8sag75x4wbTZ04nKeUhXdp3pftb3ZWOJFmAY8eO0b5pU/qnp+OA\nuXXXPMwzVI45O3Pk5EmLnN8OMG3aND775jPU9mpK1C+OSqeiaaVm/Pj9jxY7Lv5adz88evQohw4d\nomjRonTr1s3qGsNLz08IwerVqzl88CBly5dn8ODBsl1xHli7di3zf/qJh0lJJFy5Qm+tNudj09Rq\nmoeE8MX06RbVB/3voqKiqOZfjXaL21C0dlEOfHmQ+xcTsE+059qFa0rHe6JXNv3QEt28eZOl//0v\nWVlZxNy9y6jRoy32L67070z46CNWzJ1L5fR0djk5sWbFCsIOHpR/vHPRypUrGTFgAMFaLQ6Yh1JO\nYp6Zcgrw8vZm47ZtFrO13D8JCwujfMtyVO5sfrfQak4Lpmm+pWagdfZTeRrL/S68gNDQUIb27Uv1\nyEgCr19n5qef8uOcOUrHeozBYOCjjyZQokQ5KlaszoYNG5SOZHW0Wi0//PADPdPTaQh00+m4c/Ei\n4eHhSkfLF86ePctnn33GhLFjaajVUh1z8W4C7LGx4WtbWy6WKsW2XbssuoiDebs53T1dztltyu0U\nUMH3079XOFnus+zvxHNavmgR9bRaKgClgKZaLUvnz1c61mMmTvyEn39ex927Lbl2LYC33+7LwYMH\nlY5lVXQ6HbZqNX9dUlMDrmo1aWlpSsayehcvXmTChAnUe+MNdk6dSoGYGHYBqX97zJvdu/MgKYlr\nN29StWrVJx3KYrRv3x5PCrKu3XrCPv2TZUErmTh+olU3x3qSfPFe1MnZmXsqFWT/5dUCGo1G2VD/\nYOXK1Wi1LTHvuwg6XU3WrPkjX/5g5RVPT098fX3ZefYstbOyuKlSEadWy6/hv7ByxQqGDRxIUb0e\nJ5OJNKAT5m2dtwAVgSMaDTtGjLCoKbPPYm9vz5+7/2ThwoXExMYweeGnhISEPPuJVihfFPIPP/qI\nBmvWYEhPx14Ijjs5sfaLL5SO9RgnJw3w9zPHVDIz9UrFsUoqlYotO3YwqG9fNhw9SsmSJdmzaBGF\nChVSOppVMplMDOzfn156Pd5AFuYZKVGYF/ncL1YMu4AAtk6cSGBgoKJZX4ajo6NFz23PLflm1srl\ny5eZ98svZOj19HrvPerWrfvKPvfz2rBhA926vYPRWBfzm9bzuLo6cvXqJby8vJSOJ72GUlNTKezp\nyQSDIWcfotVAEeC8RsNv69ZZzcYxhw4dIjo6Gj8/P6pVq6Z0nJf2Wk8/tAYGgwEHB0dMplqAE1AL\nZ+e9/PTTKPr06aN0POk1VaV8ecpERREoBDHAYqBQ4cJ8MW0afS2sHceTjBo7ipV/rKB4neJEhUcz\n4+sZ9O9rGe0vXtRrPf3QGqhUKtRqG0ymxpjbDIFKlYm9vb2ywaTX2padO2nfqhV7oqJwcnBg+ZIl\ndOvWTelYzy0iIoJlq5fR71wfHN0dSbySyIjaI+jZvadFXivLC7KQv0I2NjaMGDGCuXNXo9UGYGcX\nR4ECetq1a6d0NCkfMRgMzJs3jwtnz+JXsyYDBgx46irn8uXLc/HqVbRaLU5OTlaz/iImJobRE0YT\ncSYCtaMKtY15El7BSgVxcHEgMTHxtSnkcmjlFRNC8OuvvxIaupdSpYrzyScT5YU66V8zGo2cOnUK\nvV7P9C+/5Or+/ZTVarmu0VCzVSt+X7vWagr080hPT6dGrRoU71yUMi1Kc/zHk6TFpvHegd6cW36B\n45+c4Oa1m1a5SEyOkUvSa0iv19OqaVOunDuHnRDEabUMFgJPzLNQfnJy4uT58/lq2749e/YwePIg\n3j7UAwCTwcS3Bb5DZVRRpGgRtqzfQo0aNRRO+XLkGLkkvQbu3LnD+4MHc+XSJQJq16ZMhQokRkQw\nUK9HDewG9gBvYv4Fd7KxQfu3Pin5gY2NDYYMI0IIVCoVxiwjdjZ2RJyKoEKFCvnq3cfzyJdn5CaT\n6f/au/O4qOr9j+OvGRg22VQWUTAUQVRWN1xScUEzcwtTK82tcr3acrPl+ku9lWK7mdrV0ExzLbfc\n0mQ3zVTcQFzBBQFB2QcYmPn+/kDn5k1TEBgHvs/HYx4POefMmfd8cT6cOed7vl9OnTpFcXEx/v7+\nckAlyahlZ2czasQIIqKisLO2RigUtMnNpYVWy2mVigtWVnTMzaXD7e2vAT8CLwCJJiakNW3KyaQk\nowGzM9AAAB+uSURBVL+oXlpaiqmpKQqFAo1GQ8cnO2LSWknTEDfOfJ+Eb0M/Nq3dZOiYj6wytbNW\n3KL/ZyUlJfTr2ZN+XbsyvG9f/Ly9uX79uqFjSXVEQUEBmZmZ+g+iEAKdTgeUn8cuLS2fSq+oqIhT\np06Rnp5Oeno6ixcvZtGiRSQnJzNt0iRsrKyob2PDvA8+YPTIkWRFR/NaaSkDs7PJu3ULT62WJkBI\naSkatZoECwtKKZ/4+LRKhY2TE7tcXKjXqxf7Y2ONuojn5eXx9JCnsapnRT2benzy+Sf6uzZ7ufRG\nbFcwptdY1q5aa+ioBlPrjsgXhIXx/b//TWhRESZApKkpDZ96ip9+/rnGszwMnU7HF198yZYtO3Fy\nciAs7AO8vLwMHUuqICEEM994g68XL8ZEqaRNq1a079SJFStWoNPpaNWyJYlJSQD0Dg7mWHw8qtJS\nckpKUCqVeAImQpAIOCgUDCsuRgNstrIis6SE17Va/fgyOwE74EnKz4F/ZWFBjx49iI6Oxkyp5Inm\nzdkbFfVYTa7yKF4c+wJnxBmeWtaXgrQCNvT5kfAvw2ttby95jhxIPHmS5reLOIBXWRlxCQkGzfR3\n3n77XRYv3kBRUWcgk1272nH27GmeeOIJQ0eTKmDjxo2sX76c6aWlWAC/nDrFDydPMlWnwwRYm5BA\nR6AHsGH/fuwpP/VRQPkt8f7AE5RPBHgBsLm93/ZqNZFmZmRqtTQFBJCpVHLdxASL0lLOWVnR/3av\nlGvXrqHRaHB3d3/sJlV5FNExMQz+ZSCm5qbYu9vTZkJrImMia20hr4xaV8j927Vj2dat+BcVYQok\nqlT4BQYaOtZ9LVnyDUVF4yn/CHtTUpLJiy+OJi4uxtDRpAo4eOAA3oWF3Om13EGrJZH/FuRg4ABg\nBnQH9t5ebg14ADcoL+ROwMk/7femqSlPBgezOTaWVlot2SoVDZo1Y1BoKJfOnWNqUBBTp01DoVDg\n5uZWze+yZly/fp0ffviBEk0Jw0KH4eTsRNrRNBp6NkAIQdaxLBrfnndTKlfrCvn0GTOIjYxkcUQE\n5kolzm5uLPrmG0PHui+tVkv5cdYdCn7//ZCh4kiV1LxFC/ZZWqIrKkIJpACqP61PA+6MG5gK+nFN\nioBLQHMgFzhgYUG+VssupZJSpZL0evX4Y8UKbty4QWRkJA0bNmTEiBG19gL+5cuX6dilA00HNEVl\no+LTbp/yWdhn/HP6P7my8yr5qflY5lsy6btJho76WKl158ih/HxlSkoKxcXFeHp6PtY3BTz11NP8\n8stRyo/ZsoDfsbAQFBUVGjaYVCElJSWE9OjB5YQErJVKMgClqSkuxcUohSCxuBg3S0tsFApSbg+5\n3NDUlCyNhtY+PiQmJiKE4OWXX2bK9Ons3LkTU1NTnnvuORwdHQ399mrMtNemccLyOD3n9wDg+MoT\nFG4qInxJOBEREVhbWzNw4MDHcpLnqlItNwSNHz+enTt34uTkxKlTpwC4desWI0aM4PLly7i7u7Nx\n40bs7e0fOUxdlJGRQfPmLVGrzYF6WFoWM336OMLCPjJ0NKmCysrKiImJobCwkM6dO6NQKNi+fTta\nrZbg4GCOHDmCRqOhb9++WFhYkJCQgJOTE56enoaObjBCCH788UfOnTuHj48PG7ZsIK9rDm1fKT8d\nejn6MqfeTeTYb8cMnLTmVEshj42NxdrampdeeklfyGfOnImDgwMzZ85kwYIFZGdnExYW9shh6qor\nV64we/YHXL+eweDB/Zk8eVKdu6FBqnuEEIx7dRyRxyJxC3ElZUcKAS0C+eP0Hwza9AzmNmbsHvML\nYweM4//e+z9Dx60x1XaLfkpKCgMHDtQXcm9vb6Kjo3F2diY9PZ3g4GCSbnetepQwkiTVHWfOnOHJ\nPl155dwEzOqZUZxTzDcey5n55ky++fYbSjWlvDT6JcI+DKtVvXAepMa6H2ZkZOgnQnB2diYjI6My\nu5EkqQ67desWdk3sMatXfrOShb0FNo42DB08lFnvzTJwOuPyyFcBFQrFfU8DzJkzR//v4OBggoOD\nH/XlJEmqJfz8/FCnqTkefgKvwZ6c/iER0zJTWrRoYehoNSoqKoqoqKhH2kelT61ERUXRqFEj0tLS\n6Nmzpzy1IklShZ0+fZpRE0Zx8exFWvu2Ys2KH+r0xV+owbFWBg0axKpVqwBYtWoVQ4YMqcxuJEmq\npbRaLXl5eQ/czsfHh+O/Hyc/J5/fYw/X+SJeWQ8s5M8//zxdunTh7NmzuLm5sXLlSt555x327duH\nl5cXERERvPPOOzWRtc7S6XQsXPgV/fsPZuLEKaSnpxs6kiTd14rvVmBrb4tTIyd82vpw+fJlQ0eq\n9WrlDUG1zbRpM1i58mfU6raYmqbj6HiFM2dOYWdnZ+holSaE4MP5H/Llwi8oK9MyYcIEPpn/SZ3q\nnVBblJWV6W+6O3r0KCEDQxgZ8RwNWzbkt/mHyN2Rx5Hfjhg4pfGQw9jWQlqtlv/85xvU6ucAX8rK\nQsjPt2PHjh2GjvZIwleG85/1/+H5AyMYe/wltv22ldlzZ7Nu3TrWr19PTk6OoSNKD5CQkICXjxfm\n5uY0cW9CbGwsv//+O54DW+Dg7YBCoaDTWx2JPxx/eygKqbrIQv6YE0Lc/uts8qdlJkb/wdi5dyft\nZ7ajQYsG2LnZ0un9IBZ+s5B56z7iwzUf4NvWV44j/xjTaDT0e6Yf3m948V7p2/RY2o3BoYOxtrYm\n/UgGWk35/8/UQ6k4NHKQ37Sq2eM7CIkEgKmpKcOHP8/WrVsoKuqAUpmGuXk6/fv3N3S0R+LYwJGz\nZ//b0+lm0k3qNbZi6PbBAES+E0XXnl1o4NCQ3j1688HsDzA3NzdU3DovPz+f1atXk52dTd++fbG3\nt6dUUUrAeH8AWvT3wLG1Iy4uLrRr0Y7v263BsbUjlyKS6/SEDzVFFnIjsGrVt7z//lx++WU/rq6N\n+eKLAzg6OiKE4NKlSxQWFuLt7W1Us8D86+1/0bFrRwquFGBiYcLJtacIDuuhX9+oYyMu7LuEz/zW\n7FjwM9cnXWfNyjUGTFx3FRQUEPRkR0w8TLBrYcunAz/ly4+/pCCrgLzUfGyb2FCSV0LWhSxcXFz4\ncd2PREREkJGRQad5nfDw8DD0W6j15MVOI6XVahk5chQ7d+7B1NSKBg2siI2NMKoxqdPT09m0aRNa\nrZYr166w8/gOhmwZhNAJ1vZbj3eoN13e6kRJfglfOH5FUWGR/IpeA44cOcKeX/ZgZ2vHmDFjWLNm\nDUt/XcLQzeXflq7EXiFyfAxTJ03l44Uf07xvM67FpTL0qaEs/nKxgdMbv2oba6WmwkgPLzw8nOnT\n56NWjwDMMDGJoXt3FRERewwdrVLKysp4Zcor/LDqB3RCR313eyaeeQWliZKC9AKWNP8P6gI1SqW8\nrFPVtFqt/g/k1q1bGTdxHG3GtiI/uYCihGJGho4kovhXen3cE4D8tAK+8/ue7MxsDh48yMmTJ/Hw\n8KB37961ZrC3M2fOsHz5t2i1OsaOfYnAGpycRhbyOmTatBksXnwG6Hp7SRZOTlvJyLhqyFiPrLS0\nlNzcXDp06UCjp5xwautE/FcneL7/8yz4aIGh49UqGRkZDHthGL9F/4ZtfVuWLFrCrLmz6LqkE+49\n3QHYNuJnQtz6Er4mnEEbn6GBZwMi34jCW9WK9d+vN2j+6nLy5Em6dOlBYaEfYIKV1TH27t1J165d\nH/jcqiC7H9Yhfn5tsLJKpnz6XTAxScLb29uwoaqASqXCwcGBwwcO08msCxb7rfi/qf9H2IdhD36y\nVCHDRw1HtNXyjvothu19likzJnMz8yb1Perrt7H1sMXG1obwJeFETYjhO7/vaWXWmm+XfGvA5NVr\n3rxPUKs7Ar2AHqjVPXj//Q8NHetvySNyI6XVahk6dDj790djYlIPW1slcXGRuLu7GzqaZAA5OTlc\nvnyZpk2bUr9+/QduL4TAzNyMt/LewNSivM/D3im/oosXFDcpotfCnuRcymbbczvYvXU3nTp1qu63\n8NgYMGAou3YBBNxecpagoFQOHYqukdevsWFsJcMzMTFh27YfSUpKorCwkDZt2tTq6a+qk06nIyEh\nAY1Gg6+v72Pd++fQoUMcOHCARo0aMXz4cFQqFT9t+YnxL4/H1sWW3Ou5fLvsW4YPG/63+1EoFNR3\nrE/68QxcOzVBp9WRdTKL9yfP5tfofaxuuwYbO1uWfb2sVhZxIQRbtmzh0qVLBAQE0KdPH/26ceNe\nJCpqCmq1HeWnVqIZN2624cI+BHlELtVpJSUlDAwdyPGE45jXM8NaaU3k3vKRPe9FrVajUqlQqVT3\nXF+Vbty4wXuz3+PS5Ut0at+Jpq5N+dfcf9HyOS8y4zNxtXBjw+oNeLbyZPi+Ybi0bUT68Qw29N7E\nhaQLD5zrc/OWzYyfOJ6Wg73ISsiiqc0T7Nu577Ge47YqCCEYOXI0O3fGotG4YWZ2gTffnMLcue/r\nt1m58jvmzfsUrVbL9OmTmDFjeo1dyJUXOyWpguYvmM+auNUM2TwIpamSqHeicUltwsY1G+/aLi8v\nj9DnQ4mJiEEIgbOrM0qFgm5PdmPJwqXY2trec/8nTpwg/LtwBILxL41/6N4PhYWFBHQIwLGfA017\nuXFq+WkuRScz9tBLOLZyQKfVsbbrBqY+N5WvVn/FmOOj9M9d3X4t6xevJygo6IGvc/r0aeLi4nB0\ndGTw4MG1vogDHDt2jO7dn6Kw8FVABRRgZraE9PRrD3VaqrrJi52SVEEJZxNoNsgdE5UJCoUCr2c9\nSTiT8Jftpr42lWynm7yV/wbTLk+mSFWE12QvkjjDsBeG3XPfR44coUefHpxqcIIEh1P07NuTQ4cO\nPVSu2NhYaCDo80UvvAZ6MnjjQEpLSrFsWH76TGmipGHLBpiYmJB9JZsbCZkAZJ7J4ualmw99rcTH\nx4dJkyYRGhpaJ4o4wM2bNzE1bUh5EQewRqWyIjs725CxHknd+M1J0n34tfZj5eYV+I/xQ6lSkrTh\nLL5tfP+yXdxvcfT7KQSlqRJrZ2vavhJA7uVc+i3ryyc2n1NUVPSXaxQLvlhA59lBdJjWHgBLB0vC\nPp/P1o3bHpir/Kis/DTAna/0SqWSA/8+QPC8HqQdTeP8rgs8/a+ncXByYGr3KTh4OpJ5PpNFCxfp\np2Ks6y5cuMDhw4dxdnamV69eKBQKAgMDESITSABaoFTGU7++LU2bNjV03EqThVyq016f8ToxB2L4\nptkyzKzMcLB1ZNGeRX/ZrkmTJqQeTMWpTfnQCNcOpeLSzgV1VhEKheKe58zVRWr9ETSAlYMVOUUP\nnmwBoFu3bpjkmrD/tQhce7py+ttEQvqGUHyxmIWNvsaxkSPrV6/Hy8sLLy8v+vTqw8WLF2nevDku\nLi6Vb5BaZMeOHYwYMQoTk+YIcYPevTuzefMGHBwc2LdvFyNGjCY1dTutWvny00+/GPU3EnmOXKrz\nhBCcP38ejUZDy5Yt71mUT5w4Qe9+vWjSuQk3UjIpyCig/dR2nPvhHK++MJHZs/7aq2HDxg38451/\n0O/bEBRKBXtf+ZVP5nzC6BdHP1SuzMxMZs2ZxaUrl+jcvjOz3p31WPeoedzY2zuQmzsYaAqUYW39\nPevWLeaZZ54xdLS/JS92SlI1Sk9PJzo6GqVSyYWLF0i/kU73rt0JDQ2973NWfLeChUu+RCcE/5j4\nD159+dUaTFx3lZWVYWZmjhCzuHMp0MpqF59/Pp6JEycaNtwDyEIuVZpWq2XOnA/48cet2Nvb8/nn\n8+ncubOhY0nS3xJCcPToUbKzs2nXrh0NGjTQr2vTJpCkJEd0ui5AFlZWa4mL21+j46ZUhizkUqW9\n/vo/WbZsK2p1dyAbK6tIjhw5SKtWrQwdTZLuSafT8eyzI/j111hMTeujUGQREbFXX6hTUlLo23cA\nyckXMTExYenSrxk3bpyBUz+YLORSpdWv70ROzvNA+RGNicle5szpz6xZswwbTJL+RAjBRx+F8dln\nX1BSUkJpqQllZZMBM+AE3t4XOXPmxF3PycvLo169ekYzBLLsRy5VmkplBhTrf1YqNVhYWBgukCTd\nw8qVK5k/fzE5OcMpKhpLWZk5cGdi5xZcuZLyl+fY2toaTRGvLFnIJQBmz34PK6utwB+YmPyKjc1V\nRo0a9cDnSVJN2rRpG2p1EOBI+bfH3sBZQKBUxuPj42fQfIZivB0npSo1deoUGjd2YdOmrTRs2IKZ\nM9fed7wRSTIUR8eGKJXJ6HR3ltxEocjA0vJrnJwasnHjXkPGM5hHOkfu7u6u/9qiUqk4fPjwf3cs\nz5FLklTFkpOTads2CLX6CYQwwdz8HLt3b6dJkyY0bdq0VpxCqfGLnc2aNePo0aN3dfl5lDCSJNUt\n+fn5TJkyg5iYONzc3Fi+/OsH9pS6fv06GzZsQKvVEhoaSrNmzWoobc0wSCE/cuQIDRs2rJIwkiTV\nLT179uXgwZuUlHRAobiKnd0fnDuX8MAheGuzGu+1olAo6NOnD+3bt2f58uWPsitJkuqYgoIC4uJi\nKCkZALggREe02kZERUUZOprReaSLnQcOHMDFxYXMzExCQkLw9vamW7du+vVz5szR/zs4OJjg4OBH\neTmpltDpdCgUiloz47p0fxcuXCA09HmSkhJwc3Nn48Y1tG3bFuD2mDY6oASwAgRCqOvcTFdRUVGP\n/Merym4Imjt3LtbW1rz55pvlO5anVqT/odFoGD9+Ihs2rEWpNGHGjBksWDBPFvRaZsmSpYSFfYZW\nq6OgIJe8vA5AIHAOO7sYkpPP6SdweO21f7J8+SbU6jaYm6fRvLng2LFDdfoehho9taJWq8nPzwfK\nZzPZu3cvvr5/HcdZku6YNWs2mzcfoqzsdTSaKSxevFaekqtl1q5dx1tvzeXq1WCuXw8hLw/Kv/hb\nAH5AA44fP67f/osvPmHJkn8zZkxj3nvvWQ4fjqvTRbyyKn1EnpyczNChQ4HykcZefPFF3n333f/u\nWB6RS//D17c9p0/7AHd6GRxn0CBTtm3bZMhYUhUqn4FeAfjfXnIO+A0YC2iwslrGb7/9ir+///12\nUedVpnZW+hx5s2bN7vrLKkkP0rixCwkJGQhRXshNTTNxdW1r4FTS/eh0Ov1M84GBgXfNNH8/trY2\nKBRp/LcO5aFU3kSp3Ie5+VUGDeqPn1/dvPuyOslBs6Qac+bMGTp37k5paVMUijJsbLI5fvwPOS3Z\nY2T37t3MnRuGRqNBqdSRlJRBSYkr5uZ/nWn+XhITEwkKehK1ujVCKLGyOslHH82luLiYFi1a8Oyz\nz8prIg8gRz+UHnvXr19n9+7dmJqaMnjwYOzt7Q0dSbotMjKSAQOepagohPKJiX8GegDtKZ9pfjEZ\nGdcf+Du7cOEC3323irKyMkaPHkWbNm2qP3wtIgu5JN2WnZ3N0qVLuXHjJgMGPEVISIihI9U4rVaL\nUql86CPgkSNHs2HDTSDo9pLzQBxQPoa3ldUiEhKO4O7uXg1ppTvkMLaSBOTm5uLv3565c39i4cIT\nDBnywkP1jjl//jzbtm3j1KlTNZDy0eXm5nLjxo2/fOhv3bpF9+59MDMzx9ranvDw8Ifan7m5GVD2\npyWllPfxLkap/A0HB3tcXV2rKr5UlUQ1qcZdS9LfWrp0qbC09BMw5/ZjomjQwPlvn7N8+bfC0tJO\n2Nr6Ciur+mLu3A/vuV1qaqqIj48XBQUFlc6n0+lEYWFhpZ+v1WrF+PGvCpXKQpibW4ugoG4iJydH\nvz4k5GmhUnUSMEvAFGFp2UDExcU9cL/x8fGiXj07ASECBghzczvh5NRYqFQWIiCgo7h48WKlM0sP\nrzK1Ux6RS7VOYWEhWm29Py2xoahIfd/tc3JymDZtBkVFo8nLC0WtHk9Y2CecP3/+ru1mz/43zZu3\npEePQbi5Nefo0aN3rddoNLz99nt06PAkw4Y9z5UrV/7yWjt37sTOriG2tvY0b+5NUlJShd/f8uXf\nsn79PkpLX6Ok5A3i44uZPHm6fn1cXCylpd0o75TmhEbTmujo6AfuNyAggNjYSEaNasKwYbbs2PEj\nGRmpaDRFxMf/TvPmzSucVaoZcjxyqdbp378/77//ARpNU8ABC4tohgwZct/t09PTUalsKClxuL3E\nBjOzRly9ehVPT0+gfDiKTz/9mpKSSZSUWAOnGTRoGKmpyfr9vPjiGHbuPEFRUTvi41OJje1MUtJp\n/V2MKSkpDB/+Imr1MMCVlJQj9OnTnytXLqJUPvwxVWzsQdTqVpTfZAMaTSCHDsXo1zdo0JDU1HTA\nAxCYm2c9dM+gwMBAVq9e+dBZpMeDPCKXap3WrVuzY8cWWrVKwtn5Z154oQsrViy77/ZPPPEESmUp\n5TevAFyjtDT9ruFUExMTKb+RyfrOq5CWdgWNRgNAcXExW7b8RFHRUKAFWm0P1Or67Nu3T7+PY8eO\nYWr6BOAGKBCiA1lZN8nMzKzQ+2vZ0gMLi2uUj1MCSmUKzZq569eHh3+DldV2rKx2Ua/eD3h728vZ\nnmo5eUQu1Uo9e/YkMfHhbliztLRk165tPPPMEEpKdqFQ6Fi3bjUuLi76bVq2bIlCcRlQUz7A01mc\nnBpjZmYG8Kcjat2f9qy7a6IDFxcXtNoMQEP5ZMFZgLbCXTDffPMNNm/ezoULq1AqLTEzy2XZslj9\n+n79+nH06CGio6Oxt7dn6NCh+pxS7SS7H0rSbWVlZWRkZODo6HjPwjdz5rssWrQEM7OGQB6//LKD\nTp066dePG/cKGzdGoVYHoFKl4ex8ncTEE9jY2ADlM8CPHfsyP/20B2iMTneBr776lJdfnlDhrKWl\npcTGxlJcXEyXLl1kf/xaRPYjl6RqlpycTHp6Oq1bt8bOzu6udVqtlk8//Zxff43G3d2Njz6ai5OT\n013bCCGIiIjgypUrtGvXTt6uLv2FLOSSJElGTt4QJEmSVAfJQi5JkmTkZCGXJEkycrKQS5IkGTlZ\nyCVJkoycLOSSJElGThZySZIkIycLuSRJkpGThVySJMnIyUIuSZJk5CpdyPfs2YO3tzeenp4sWLCg\nKjNJkiRJFVCpQq7Vapk2bRp79uwhMTGRdevWcebMmarOZlBRUVGGjvBIZH7DkvkNx5izV1alCvnh\nw4dp0aIF7u7uqFQqRo4cybZt26o6m0EZ+38Gmd+wZH7DMebslVWpQp6amoqbm5v+Z1dXV1JTU6ss\nlCRJkvTwKlXIFQpFVeeQJEmSKktUwsGDB0W/fv30P8+bN0+EhYXdtY2Hh4cA5EM+5EM+5KMCDw8P\njwrX5EpNLFFWVkbLli3Zv38/jRs3pmPHjqxbt+6uyWolSZKkmlGpyZdNTU35+uuv6devH1qtlgkT\nJsgiLkmSZCDVNtWbJEmSVDOq5c5OY79ZyN3dHT8/PwIDA+nYsaOh4zzQ+PHjcXZ2xtfXV7/s1q1b\nhISE4OXlRd++fcnJyTFgwr93r/xz5szB1dWVwMBAAgMD2bNnjwET3t/Vq1fp2bMnbdq0wcfHh6++\n+gownva/X35jaf/i4mKCgoIICAigdevWvPvuu4DxtP/98le4/StzsfPvlJWVCQ8PD5GcnCw0Go3w\n9/cXiYmJVf0y1crd3V3cvHnT0DEeWkxMjDh27Jjw8fHRL3vrrbfEggULhBBChIWFibfffttQ8R7o\nXvnnzJkjPvvsMwOmejhpaWkiPj5eCCFEfn6+8PLyEomJiUbT/vfLbyztL4QQhYWFQgghSktLRVBQ\nkIiNjTWa9hfi3vkr2v5VfkReW24WEkZ0xqlbt27Ur1//rmXbt29nzJgxAIwZM4atW7caItpDuVd+\nMI7fQaNGjQgICADA2tqaVq1akZqaajTtf7/8YBztD2BlZQWARqNBq9VSv359o2l/uHd+qFj7V3kh\nrw03CykUCvr06UP79u1Zvny5oeNUSkZGBs7OzgA4OzuTkZFh4EQVt2jRIvz9/ZkwYcJj+9X4z1JS\nUoiPjycoKMgo2/9O/k6dOgHG0/46nY6AgACcnZ31p4mMqf3vlR8q1v5VXshrw81CBw4cID4+nt27\nd7N48WJiY2MNHemRKBQKo/u9TJ48meTkZI4fP46LiwtvvvmmoSP9rYKCAkJDQ1m4cCE2NjZ3rTOG\n9i8oKGDYsGEsXLgQa2tro2p/pVLJ8ePHuXbtGjExMURGRt61/nFv///NHxUVVeH2r/JC3qRJE65e\nvar/+erVq7i6ulb1y1QrFxcXABwdHRk6dCiHDx82cKKKc3Z2Jj09HYC0tDScnJwMnKhinJyc9B/A\nl19++bH+HZSWlhIaGsro0aMZMmQIYFztfyf/qFGj9PmNqf3vsLOzY8CAARw9etSo2v+OO/mPHDlS\n4fav8kLevn17zp8/T0pKChqNhg0bNjBo0KCqfplqo1aryc/PB6CwsJC9e/fe1ZvCWAwaNIhVq1YB\nsGrVKv0H1FikpaXp/71ly5bH9ncghGDChAm0bt2a1157Tb/cWNr/fvmNpf2zsrL0px2KiorYt28f\ngYGBRtP+98t/548QPGT7V/01WCF27dolvLy8hIeHh5g3b151vES1uXTpkvD39xf+/v6iTZs2RpF/\n5MiRwsXFRahUKuHq6ipWrFghbt68KXr37i08PT1FSEiIyM7ONnTM+/rf/OHh4WL06NHC19dX+Pn5\nicGDB4v09HRDx7yn2NhYoVAohL+/vwgICBABAQFi9+7dRtP+98q/a9cuo2n/kydPisDAQOHv7y98\nfX3Fxx9/LIQQRtP+98tf0faXNwRJkiQZOTnVmyRJkpGThVySJMnIyUIuSZJk5GQhlyRJMnKykEuS\nJBk5WcglSZKMnCzkkiRJRk4WckmSJCP3/xZew1CtozsUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8046f98>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get clusters assignment\n",
      "results = getPointClusters(sepsClusters, sepIndexMap)\n",
      "plotCluster2(X,Y,results,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Hi64COYm7A0l+2XDzYGWUysXvL0o3szMhAfjyS6BfP+XGSvv2AePHczAPJ/vD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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x81c9e80>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotCluster2(X,Y,clusters,\"Scatter Plot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gAqOpQE5hmL706TmJGzsBWzBXgglgS7H8atevwlgibXM+pihc9aqSvgYVGE11\nPyTXDUpGBDoz57yVg/3f7bf/HRcZhzO/OUMrsBChmWOKwH6t0H0/8tl/mo2KsxXIHJopdY1B8vEI\nNhIXsulGE8ZtHIcL1y/Yl8UPiMepJ09J+51T4ti1sK29Dfu/2y/9MaVK4n3YHX9ip6ZSK1Qu+0ik\n6yQupLGfEWd+c8be8AkA9dfqOcUSJKS7FjqSeB8ONk3VyKkgcd8GiYfqK5puNCHuhTi0dbQBAG7v\nfzvuGnaXlIPBKFBq4l//8DUarzfSrIU7InGgdaf7GjkV0jd4AlL3XlEY+xmR9dMsAMCAiAG42HIR\nFWcrMOG1CdI2dsvEuSGTbNdCV0gcaMGhqUBOodcKQGRgkOS9VxTvPvwu8pPzMSVhin1Z/bV6jN04\nVvr9INycR0KT7VqocO4rrsGBP65oKrVCqYVd8nl7Olv5+/e3jfaUvMGo6UYTJrw2AfXX6hF1WxSa\nW5sBcCOoM3cNmcrzm+dsprm9pD+ovKP71IrsQ/QdSd+tVanNVFeTaDBSBgvlJ+fj7mF325dz7bwr\ndw2ZFAbUuSX9QRU6mqqRN91oIlOjINOtlWCDkavaeWxkLDKHZuqmMVRtNlAqXXT9Quagck/3/cgp\nkr6rK7EUi0I5qVtbrNj/3f4uAX2kcSRGDBoh7XTHwaKWaqRU2fGK9AeQ7ziQE0QmrUemoF05B/TM\noZno27svDtfYbp6hpaDuXANf+NeF2q19K4jul+7o/sYSjmS/yYSCTFpPKejgwUBtrS3lQqAGpOR9\nHWuiC/+6EADsQV2ptd6x6Q5SQd15H3e8+XHxnmKUzS/TVu1bDZkDKLQ0WyOn0oOFTFpPKWhtrbS3\ngvOWc1BXaq2y19SdA3fen/O67OPNrc3ar4E7I3MAeY9TKw403agTTgQbP93xJqjnJ+fD2M9oD6Kx\nkbGovlwd9CDvHKhX7FvhU+BW1qHZGrgG8+FqOJA7oNaoQ2YfJdr46Q1XQX1fwb4uQTQ2MhY/XP8B\nQPeau3Pwdfzb+QTgKVBfuHZB34HbmQbz4Wo4kBNGbh8lV2DfOFcEHK/wjH2Nqg2nasHX8W/HE4A3\ngdr5ZALoLHA709jVoCt+xU4RIiFctdeW7F4isrZmidy3c4W1xRru4riVmysEIERmphBWuYtqQ67A\ngbG2WEV+eb6wtli7PM59O1egBCJzc6bHv+/dfq/b1zqu1/l/6s6SJUJkZdn2M2X/slqFyM/X/P7m\nT+z0+I5BZm0hAAAMD0lEQVRf/epXIi4uTkycONG+7OLFi+Lee+8VY8aMETk5OcKqsmFlCORZW7ME\nSiBQApFfnh/u4rhFbh91LLDaQacTnoKvqxOA2muZg6wsW0UBsO1nOuJP7PSYWjl06BCioqLw2GOP\n4fjx4wCAFStWYPDgwVixYgXWrVsHq9WK0tLSwC8PgoxagyeZPLkzjadZWBjoJI2iJiRzrUyfPh0x\nMTFdlu3evRuFhYUAgMLCQuzatcunf9pTqNxoQkF2HnzHvrz9+2v6buUsBNTucK+jKWiDwa9Jsxoa\nGmAymQAAJpMJDQ0NQS1UsFCbBIjs2AbHg47IJFtMImo1GB1NQRsMAY/sNBgMMBgMqs+VlJTYH5vN\nZpjN5kD/naaVlREd26AcdADhsxELG53vMxaLBRaLJaB1eNX9sKqqCnPmzLHnyMePHw+LxYL4+HjU\n1dUhOzsbp0+f7rpiCXLk1JHMmTuPtCP5IVhIOe8TyjJyNZjQ6LH5yOfOnYvt27cDALZv3468vDx/\nVtPjqNxBSEEyZ+58SUzyQ7CQct4nOI0SMI+BfMGCBZg6dSrOnDmD4cOHY+vWrXj66aexb98+jB07\nFgcOHMDTTz/dE2UNmPOtrWSniStObghlzjSxY8tFVyM7qXVH1MR8QI4fIi+PuykyjezYocND9D2g\nNv+KM/LpZh33DdYl8jtseHAg1zjy427UamJ8sGsX+R02PPjGEhpHPrXo2E1RoTR8AZ0NXxzYaXF1\nMia/w9LhV68VFh6aHOzmfLBzLxd6XH1nmtxh5cSpFRZezukWzqPLy1XNm7+zoOIcuc5oMr3MeXR5\nucp5cy+UoOJArjO6aUty/qCcRw89tZMn17x7RI+N7GRy0E1bEufRe57aNtZZzlttUkZZcSAnzPG4\nWrGCzk7nM+cAonYGo3TUycTVdlPbxjobSk+qvuDHDSy8EsJVMxW6uqGK2u2U1DaAju9c1I2rbeFq\nxyF3y6rgC9fdDP2JnVwj1wjdpFkA9Zqh2gYgVaUKMVfbwtWOo7PatxpKmSQO5BpBaacLCbUN4CpI\nUU/DuCq/u8/lalvoeMfxtBuQOpeF4MpACMGpFVnoOrvgKj1APQ3jKh3iLr/GqZJuZE1H+hM7eYi+\nxjmPgNdsF0U1alMCAO7TMED3DaXWFc9V33Zfl/vzHle1a3f5NVfbQsc0lY4MwQlFCME1clmEq8FG\namq1U3cbSq3q5mut2F31z9f3uKpdc63bJ7JuLn9iJwdyjXO1s1LKJPQId0e1WpB3Ffh9Xe7ve5jX\nqO3rHMiZ12TND0pJLcj7Wit2d6LgGnZIUdvX/YmdPERfp3i0NdMLavs6z7XCvMbzHDG9oLavcyBn\nAeFJBhkLP540iwWEB0IyqqiP8QoUB3Jmp6l+tUxX9F4J4UDO7FyN1tZ7bYeFn6d9UO+VkIBy5ImJ\niRg4cCB69+6NiIgIfPHFF50r5hy5ZujmBhZMWp72QWoNmu74EzsDGqJvMBhgsVjwk5/8JJDVMMnp\nvbbDws/TPqj3GQgCTq1wrVv7OOXCwk3HkzR6JaDUyqhRozBo0CD07t0bv/71r7FkyZLOFXNqRfM4\n5cKChbu+durx1Mrhw4cxZMgQ/PDDD8jJycH48eMxffp0+/MlJSX2x2azGWazOZB/xyTDKRcWLHqe\npdNiscBisQS0jqANCFqzZg2ioqKwbNky24q5Rq557hqYuIbFfEFtGH0o9eiAoOvXr+Pq1asAgGvX\nruHDDz9Eamqqv6tjBLm7g4re+/Wyrjy1p3AOPDB+p1YaGhrwwAMPAABu3ryJX/7yl5g5c2bQCsZo\nc5d24dq6/nhKnei910mgeK4VFhLu0i7cSKo9nk7OnDrxHs+1wqThLu3iqbbOXRrp8ZRK49RJaHEg\nZz3O3UHNuXW5eHti9XbADgfx0OBAznqcv7V1gGvsPc3bEyvXuMOLAzmTiqeA4CmwcKD3nbtt5u1Y\nAa5xhxcHciYVTwHBU2Dh1Izv3G0zrmnTwIGckeIpsATakKqlGn0w8ttc06aBux8yTQm026MsXSO9\n6Wvv6TXefhYtTQGrBT0+1wpjsnE3sMSbfK83ja2BBlhv1uHN3COeXuNrfpsRJkIkhKtmzC9WqxD5\n+bbf/r4mK0sIwPaTn+/fa7xZR26u7fnMTNdl8fQabz4vk48/sZNTK4z5wJsRip5e4806vEl3cEpE\nm/yJnRzIGfNBMAIsB2DmDgdyxhgjjudaYYwxHeJAzhhjxHEgZ4wx4jiQM8YYcRzIGWOMOA7kjDFG\nHAdyxhgjjgM5Y4wRx4GcMcaI40DOGGPE+R3I9+7di/Hjx2PMmDFYt25dMMvEGGPMB34F8vb2djz5\n5JPYu3cvTp48iR07duDUqVPBLltYWSyWcBchIFz+8OLyhw/lsvvLr0D+xRdfYPTo0UhMTERERAQe\neeQRvPfee8EuW1hR3xm4/OHF5Q8fymX3l1+B/Pz58xg+fLj974SEBJw/fz5ohWKMMeY9vwK5wWAI\ndjkYY4z5y59bEX322Wdi1qxZ9r+ff/55UVpa2uU1SUlJAgD/8A//8A//+PCTlJTUM7d6u3nzJsaN\nG4ePPvoIQ4cOxV133YUdO3ZgwoQJvq6KMcZYgPr49aY+ffDaa69h1qxZaG9vR1FREQdxxhgLk5Dd\n6o0xxljPCMnITuqDhRITEzFp0iRkZGTgrrvuCndxPHr88cdhMpmQmppqX3bp0iXk5ORg7NixmDlz\nJpqamsJYQvfUyl9SUoKEhARkZGQgIyMDe/fuDWMJXaupqUF2djZSUlIwceJEvPrqqwDobH9X5aey\n/W/cuIHJkycjPT0dycnJeOaZZwDQ2f6uyu/z9vensdOdmzdviqSkJPHdd9+J1tZWkZaWJk6ePBns\nfxNSiYmJ4uLFi+Euhtc++eQT8eWXX4qJEyfaly1fvlysW7dOCCFEaWmpWLlyZbiK55Fa+UtKSsSL\nL74YxlJ5p66uThw9elQIIcTVq1fF2LFjxcmTJ8lsf1flp7L9hRDi2rVrQggh2traxOTJk8WhQ4fI\nbH8h1Mvv6/YPeo1cK4OFBKGM0/Tp0xETE9Nl2e7du1FYWAgAKCwsxK5du8JRNK+olR+g8R3Ex8cj\nPT0dABAVFYUJEybg/PnzZLa/q/IDNLY/AERGRgIAWltb0d7ejpiYGDLbH1AvP+Db9g96INfCYCGD\nwYB7770XmZmZeOONN8JdHL80NDTAZDIBAEwmExoaGsJcIt9t3LgRaWlpKCoqkvbS2FFVVRWOHj2K\nyZMnk9z+SvnvvvtuAHS2f0dHB9LT02EymexpIkrbX638gG/bP+iBXAuDhQ4fPoyjR4+ioqICr7/+\nOg4dOhTuIgXEYDCQ+16eeOIJfPfddzh27BiGDBmCZcuWhbtIbjU3N2P+/Pl45ZVXEB0d3eU5Ctu/\nubkZDz30EF555RVERUWR2v69evXCsWPHcO7cOXzyySf4+OOPuzwv+/Z3Lr/FYvF5+wc9kA8bNgw1\nNTX2v2tqapCQkBDsfxNSQ4YMAQDExsbigQcewBdffBHmEvnOZDKhvr4eAFBXV4e4uLgwl8g3cXFx\n9gNw8eLFUn8HbW1tmD9/PgoKCpCXlweA1vZXyv/oo4/ay09p+ysGDRqE++67D//+979JbX+FUv4j\nR474vP2DHsgzMzPx3//+F1VVVWhtbcU777yDuXPnBvvfhMz169dx9epVAMC1a9fw4YcfdulNQcXc\nuXOxfft2AMD27dvtBygVdXV19sc7d+6U9jsQQqCoqAjJyclYunSpfTmV7e+q/FS2f2Njoz3t0NLS\ngn379iEjI4PM9ndVfuUkBHi5/YPfBivEBx98IMaOHSuSkpLE888/H4p/ETLffvutSEtLE2lpaSIl\nJYVE+R955BExZMgQERERIRISEsQf//hHcfHiRTFjxgwxZswYkZOTI6xWa7iL6ZJz+bds2SIKCgpE\namqqmDRpkpg3b56or68PdzFVHTp0SBgMBpGWlibS09NFenq6qKioILP91cr/wQcfkNn+X331lcjI\nyBBpaWkiNTVVrF+/XgghyGx/V+X3dfvzgCDGGCOOb/XGGGPEcSBnjDHiOJAzxhhxHMgZY4w4DuSM\nMUYcB3LGGCOOAzljjBHHgZwxxoj7/x8LKZMUgQC/AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x80468d0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}