GPC for schools dataset.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##convert coordinates\n",
      "# latitude    = 41.145556; // (\u03c6)\n",
      "# longitude   = -73.995;   // (\u03bb)\n",
      "# mapWidth    = 200;\n",
      "# mapHeight   = 100;\n",
      "def convertCoordinates(latitude, longitude, mapWidth, mapHeight):\n",
      "    x = (longitude + 180) * (mapWidth/360)\n",
      "    latRad = latitude * math.pi / 180\n",
      "    mercN = math.log(math.tan((math.pi/4) + (latRad /2 )))\n",
      "    y = (mapHeight/2)-(mapWidth*mercN/(2*math.pi))\n",
      "    return [x,y]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "dlon = lon2 - lon1 \n",
      "\n",
      "dlat = lat2 - lat1 \n",
      "\n",
      "a = (sin(dlat/2))^2 + cos(lat1) * cos(lat2) * (sin(dlon/2))^2 \n",
      "\n",
      "c = 2 * atan2( sqrt(a), sqrt(1-a) ) \n",
      "\n",
      "d = R * c (where R is the radius of the Earth)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate distance\n",
      "def deg2rad(deg):\n",
      "    return deg * (math.pi/180)\n",
      "\n",
      "def getDistance(lon1, lat1, lon2, lat2):\n",
      "    R = 6371\n",
      "    dlon = deg2rad(lon2 - lon1)\n",
      "    dlat = deg2rad(lat2 - lat1)\n",
      "    a = (math.sin(dlat/2))**2 + math.cos(deg2rad(lat1)) * math.cos(deg2rad(lat2)) * (math.sin(dlon/2))**2\n",
      "    c = 2 * math.atan2( math.sqrt(a), math.sqrt(1-a))\n",
      "    d = R * c\n",
      "    return d\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "getDistance(-77.037852,38.898556,-77.043934,38.897147)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "0.5491557912038084"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from numpy import *\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import scipy\n",
      "import math\n",
      "from numpy import matrix\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\n",
      "v0 = 1\n",
      "alpha = 1\n",
      "v1 = 0\n",
      "v2 = 1\n",
      "\n",
      "#Calculate Covariance Matrix C\n",
      "def kernelFunction(x1, x2):\n",
      "    c = 0.;\n",
      "    for i in range(len(x1)):\n",
      "        c = getDistance(x1[0],x1[1],x2[0],x2[1])\n",
      "#        c += l[i] * ((x1[i] - x2[i]) **2 )\n",
      "\n",
      "    c = v0 * math.exp(- 0.5 * alpha * c)+ v1\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[0,:]) + np.var(data[1,:])\n",
      "\n",
      "#calculate inversion of Matrix A\n",
      "def getInversionMatrix(data, C):\n",
      "    size = len(data[0,:])\n",
      "    var = getVariance(data)\n",
      "    return  (C + v2 *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\n",
      "\n",
      "#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] < 0.0000001 and (x_new[1] - x_old[1])/x_old[1] < 0.0000001):\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\n",
      "\n",
      "\n",
      "\n",
      "def getAdjacencyMatrix(sepList, maxVar, pointsNumPerDistanceUnit, data, invA):\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 = getDistance(sepList[i,0], sepList[i,1], sepList[j,0], sepList[j,1])\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)\n",
      "\n",
      "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": 200
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Schools Dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#init\n",
      "dataset = loadtxt(\"dataset\\school.txt\", skiprows=1)\n",
      "data = array([dataset[:,0], dataset[:,1]])\n",
      "size = len(data[0,:])\n",
      "# for i in range (size):\n",
      "#     point = convertCoordinates(dataset[i,1],dataset[i,0], 1000., 100.)\n",
      "#     data[0,i] = point[0]\n",
      "#     data[1,i] = point[1]\n",
      " \n",
      "\n",
      "X = data[0,:]\n",
      "Y = data[1,:]\n",
      "\n",
      "dimension = 2\n",
      "\n",
      "plotScatter(X,Y,\"Scatter Plot\", 'g')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s3LgReXl5eP7551FYWIixY8eioaEBWq0W5eXlSE1NRXV1da+eytWrV5GTk4ND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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7500a58>"
       ]
      }
     ],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate Covariance Matrix\n",
      "alpha = 0.5\n",
      "C = getCovarianceMatrix(data)\n",
      "invA = getInversionMatrix(data, C)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 201
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grid_x = [149. + 0.005 * i for i in range (40) ] \n",
      "grid_y = [-35. - 0.01 * i for i in range (50) ] \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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zHs4zHc6wJOtEhCT4TPoLmPIi1VqbStpBxdqZGXNy50nCRRPoBkFvIjptnp6s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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x75a2ef0>"
       ]
      }
     ],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print alpha"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.5\n"
       ]
      }
     ],
     "prompt_number": 189
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get SPEs\n",
      "SEPs = array([getEquilibriumPoint(data[:,i], data, invA, 1, 1000) for i in range (size) ])\n",
      "sepList, sepIndexMap = reduceSEPs(SEPs, 0.9)\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:  4 x0:  [ 149.076661  -35.254993] xt:  [149.07678245421366, -35.254898976625071]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 149.156836  -35.286717] xt:  [149.15646970337735, -35.287230773766098]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 149.081951  -35.327679] xt:  [149.08198139489051, -35.32800471579101]\n",
        "iteration:  1 x0:  [ 149.092732  -35.339376] xt:  [149.09272524326374, -35.339344649285408]\n",
        "iteration:  1 x0:  [ 149.148034  -35.33483 ] xt:  [149.14795326986919, -35.334821769180493]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18 x0:  [ 149.126375  -35.259648] xt:  [149.12634392123496, -35.259834686475536]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 149.041555  -35.240755] xt:  [149.04130906441529, -35.240983135533803]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [ 149.084391  -35.366936] xt:  [149.08485569830944, -35.366857016283546]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [ 149.128788  -35.338847] xt:  [149.12912407778325, -35.338816579321609]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 105 x0:  [ 149.055559  -35.351057] xt:  [149.05469476682714, -35.355138225715208]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 149.154682  -35.24641 ] xt:  [149.15485044754578, -35.246536106599606]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 51 x0:  [ 149.124674  -35.330919] xt:  [149.12475093938536, -35.332076960765164]\n",
        "iteration:  1 x0:  [ 149.115115  -35.31699 ] xt:  [149.11511888912358, -35.316938378288633]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 116 x0:  [ 149.097963  -35.356475] xt:  [149.09757708586983, -35.35794726449452]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.138695  -35.253518] xt:  [149.13878868409864, -35.253523933852961]\n",
        "iteration:  2 x0:  [ 149.137037  -35.266525] xt:  [149.13699059626774, -35.266537917239937]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [ 149.14765   -35.323929] xt:  [149.14747755894561, -35.323997849224831]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13 x0:  [ 149.088697  -35.357959] xt:  [149.08896211673624, -35.358163909845729]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 48 x0:  [ 149.144242  -35.32282 ] xt:  [149.14745131048539, -35.323988738206189]\n",
        "iteration:  3 x0:  [ 149.029638  -35.205121] xt:  [149.02972463598886, -35.205116197497702]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 149.049819  -35.333941] xt:  [149.05009720644588, -35.333540887739744]\n",
        "iteration:  1 x0:  [ 149.061289  -35.213525] xt:  [149.06127753863819, -35.213473303685085]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40 x0:  [ 149.040615  -35.223979] xt:  [149.0408042875311, -35.224381379100109]\n",
        "iteration:  2 x0:  [ 149.051977  -35.382449] xt:  [149.05202001939386, -35.382425039533672]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 149.080086  -35.394049] xt:  [149.08025636981429, -35.394106063568437]\n",
        "iteration:  1 x0:  [ 149.041696  -35.225664] xt:  [149.04166979528324, -35.225636610920823]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 32 x0:  [ 149.12805   -35.248056] xt:  [149.12795079930098, -35.248773128143561]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.086051  -35.408265] xt:  [149.08620473434351, -35.408252618682269]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 x0:  [ 149.058949  -35.327858] xt:  [149.05880892673122, -35.328062537775104]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.109707  -35.223474] xt:  [149.10975739093206, -35.223563246558619]\n",
        "iteration:  1 x0:  [ 149.089323  -35.243503] xt:  [149.08928776996973, -35.24349446420571]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2 x0:  [ 149.116552  -35.410301] xt:  [149.11651891601988, -35.41030913233535]\n",
        "iteration:  2 x0:  [ 149.139632  -35.273729] xt:  [149.13969664410064, -35.27368603497542]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 149.147097  -35.335969] xt:  [149.14706739245801, -35.335839330881967]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [ 149.119593  -35.440799] xt:  [149.11900919682782, -35.440926589479169]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.134385  -35.313804] xt:  [149.13422729884081, -35.313918773058518]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.033656  -35.334219] xt:  [149.03404661196154, -35.334296883851849]\n",
        "iteration:  1 x0:  [ 149.122047  -35.418155] xt:  [149.12203114716897, -35.418141191745853]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8 x0:  [ 149.095654  -35.403473] xt:  [149.09539259149102, -35.403518849190739]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 107 x0:  [ 149.059794  -35.21254 ] xt:  [149.06088334813447, -35.213199959552895]\n",
        "iteration:  1 x0:  [ 149.154143  -35.248452] xt:  [149.15413671165487, -35.248378798558193]\n",
        "iteration:  1 x0:  [ 149.130653  -35.252379] xt:  [149.13062248009598, -35.252365648052077]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90 x0:  [ 149.094282  -35.323849] xt:  [149.09245446348942, -35.326542370094032]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [ 149.103234  -35.306948] xt:  [149.10355692977924, -35.307256082664466]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.147123  -35.278101] xt:  [149.14701595785164, -35.278180265485965]\n",
        "iteration:  1 x0:  [ 149.156099  -35.290271] xt:  [149.15601282770146, -35.290127928745065]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 149.122499  -35.264552] xt:  [149.12283512283571, -35.264497126796876]\n",
        "iteration:  1 x0:  [ 149.126081  -35.264995] xt:  [149.12605696431427, -35.264940391066432]\n",
        "iteration:  1 x0:  [ 149.146358  -35.255113] xt:  [149.14625983792351, -35.255048797202861]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 149.124974  -35.25148 ] xt:  [149.12542988763948, -35.251383270512505]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3 x0:  [ 149.126171  -35.315084] xt:  [149.12621557287824, -35.315111396886202]\n",
        "iteration:  1 x0:  [ 149.132893  -35.339741] xt:  [149.13286173785153, -35.339623991430834]\n",
        "iteration:  1 x0:  [ 149.150698  -35.329016] xt:  [149.15052703142118, -35.32901062914398]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 149.103287  -35.344681] xt:  [149.1032105108188, -35.344665011956543]\n",
        "iteration:  1 x0:  [ 149.093456  -35.332248] xt:  [149.09341953185202, -35.332245619010656]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [ 149.076243  -35.341633] xt:  [149.07633244756354, -35.341559598037826]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11 x0:  [ 149.088321  -35.34036 ] xt:  [149.08859696784128, -35.340228991175287]\n",
        "iteration:  1 x0:  [ 149.097655  -35.358505] xt:  [149.09757978915948, -35.358447342400638]\n",
        "iteration:  1 x0:  [ 149.089434  -35.363475] xt:  [149.08941374397585, -35.36347801442686]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 149.090949  -35.370046] xt:  [149.09091158368327, -35.369989306776091]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.043079  -35.356878] xt:  [149.04339565111781, -35.356817912592163]\n",
        "iteration:  1 x0:  [ 149.079914  -35.255999] xt:  [149.07981608778724, -35.255890015834829]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 149.039298  -35.246957] xt:  [149.03898140667027, -35.24707726638416]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 149.074517  -35.251302] xt:  [149.07467768431036, -35.251532650401167]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14 x0:  [ 149.031897  -35.244317] xt:  [149.03228097384377, -35.244454216674768]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8 x0:  [ 149.034593  -35.250284] xt:  [149.03497970656753, -35.249741291153462]\n",
        "iteration:  1 x0:  [ 149.059393  -35.250069] xt:  [149.05938623403418, -35.25004202108655]\n",
        "iteration:  1 x0:  [ 149.047137  -35.251826] xt:  [149.04713670233934, -35.251717667279891]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2 x0:  [ 149.036203  -35.198682] xt:  [149.03624205559029, -35.198713305084127]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9 x0:  [ 149.043375  -35.191795] xt:  [149.04318266699426, -35.192171511158776]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [ 149.024452  -35.232764] xt:  [149.02463671691029, -35.232641710227433]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3 x0:  [ 149.012022  -35.222654] xt:  [149.01221894532836, -35.22250978925409]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9 x0:  [ 149.034298  -35.220157] xt:  [149.03427328466222, -35.220311958242512]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 x0:  [ 149.012349  -35.213112] xt:  [149.01252816393705, -35.213350045543194]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15 x0:  [ 149.055621  -35.208737] xt:  [149.05584792535464, -35.208992515397476]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 149.075906  -35.20707 ] xt:  [149.07580483130374, -35.207207409282013]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [ 149.094449  -35.213717] xt:  [149.09446146106998, -35.213774203527059]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.1012    -35.225083] xt:  [149.10136220659456, -35.225085457627863]\n",
        "iteration:  1 x0:  [ 149.11404   -35.224835] xt:  [149.11388637018678, -35.224822538508931]\n",
        "iteration:  1 x0:  [ 149.102261  -35.235115] xt:  [149.10224167270326, -35.235071722169565]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 x0:  [ 149.066961  -35.213295] xt:  [149.06690276236083, -35.213259479290329]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.064261  -35.378318] xt:  [149.06415707972693, -35.378393438104929]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3 x0:  [ 149.063336  -35.394483] xt:  [149.06342860518708, -35.394415781869569]\n",
        "iteration:  2 x0:  [ 149.086363  -35.394992] xt:  [149.08635061564212, -35.395002606119498]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 x0:  [ 149.095898  -35.393069] xt:  [149.09575798861883, -35.393163576546421]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 149.118175  -35.404286] xt:  [149.11793095669421, -35.40477468935125]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9 x0:  [ 149.110565  -35.413608] xt:  [149.11085569493164, -35.41350646864403]\n",
        "iteration:  1 x0:  [ 149.089411  -35.414879] xt:  [149.0894054435189, -35.414856840676919]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [ 149.113943  -35.428859] xt:  [149.11392874695957, -35.428884175104102]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 38 x0:  [ 149.051068  -35.332247] xt:  [149.05075653045239, -35.332719920053876]\n",
        "iteration:  1 x0:  [ 149.090709  -35.439222] xt:  [149.09071871927145, -35.439186426871608]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 38 x0:  [ 149.096766  -35.45791 ] xt:  [149.09526432316369, -35.460219767339787]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 149.074161  -35.239149] xt:  [149.07419948323243, -35.239279921317888]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [ 149.068195  -35.413803] xt:  [149.0684212428267, -35.41371942404858]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.109254  -35.438412] xt:  [149.1093956305734, -35.438398135806061]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25 x0:  [ 149.117635  -35.441052] xt:  [149.11838433513933, -35.440979102255781]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 x0:  [ 149.15501   -35.238509] xt:  [149.15433919479048, -35.2391326809284]\n",
        "iteration:  1 x0:  [ 149.059719  -35.355017] xt:  [149.05961518657952, -35.35498032569069]\n",
        "iteration:  1 x0:  [ 149.052092  -35.226519] xt: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " [149.05202807177005, -35.226505016492837]\n",
        "iteration:  1 x0:  [ 149.135582  -35.419488] xt:  [149.135333320277, -35.419491526195287]\n",
        "iteration:  1 x0:  [ 149.089497  -35.428716] xt:  [149.08948285459908, -35.428712251344074]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17 x0:  [ 149.101809  -35.180246] xt:  [149.10151683089447, -35.180478017057567]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3 x0:  [ 149.120303  -35.1975  ] xt:  [149.12034573656092, -35.197345822185291]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 x0:  [ 149.112366  -35.265224] xt:  [149.11271487569761, -35.26512978244736]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8 x0:  [ 149.05436   -35.355429] xt:  [149.05454011945383, -35.355239401272605]\n",
        "iteration:  1 x0:  [ 149.098449  -35.464883] xt:  [149.09837228922086, -35.464655380688612]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [ 149.079926  -35.331662] xt:  [149.08002633210157, -35.33158799101254]\n",
        "iteration:  1 x0:  [ 149.12396   -35.447667] xt:  [149.12377347202349, -35.447527145843623]\n",
        "iteration:  1 x0:  [ 149.092398  -35.326599] xt:  [149.09241205357009, -35.326593269494467]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9 x0:  [ 149.086978  -35.455517] xt:  [149.08752473113921, -35.455584808940145]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 41 x0:  [ 149.09402   -35.337447] xt:  [149.09370228289097, -35.33780191416858]\n",
        "iteration:  1 x0:  [ 149.038363  -35.232767] xt:  [149.03835372755285, -35.232761446192825]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8 x0:  [ 149.082604  -35.431846] xt:  [149.08311099240277, -35.43175145369819]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [ 149.09507   -35.460389] xt:  [149.09524195708187, -35.460269634723687]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3 x0:  [ 149.111784  -35.169196] xt:  [149.11194713652355, -35.169274686584636]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12 x0:  [ 149.092808  -35.424911] xt:  [149.09264378594659, -35.425052620674272]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 149.142818  -35.253355] xt:  [149.14286875339016, -35.253429456010281]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22 x0:  [ 149.100262  -35.181275] xt:  [149.10136083848533, -35.180589165832231]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.123672  -35.188529] xt:  [149.1235768031934, -35.188620923716861]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4 x0:  [ 149.035968  -35.380778] xt:  [149.03631930557978, -35.380732323390284]\n",
        "iteration:  2 x0:  [ 149.107114  -35.378739] xt:  [149.10694389410133, -35.378751150851627]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2 x0:  [ 149.02911   -35.224913] xt:  [149.02913017475299, -35.224921381080328]\n",
        "iteration:  1 x0:  [ 149.123922  -35.166843] xt:  [149.12387424441124, -35.166761597409291]\n",
        "iteration:  1 x0:  [ 149.15938   -35.246571] xt:  [149.15910260863498, -35.246546673808261]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 37 x0:  [ 149.125588  -35.16392 ] xt:  [149.12397397527192, -35.166599188730004]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 33 x0:  [ 149.15037   -35.240588] xt:  [149.15156786447019, -35.240489141069965]\n",
        "iteration:  2 x0:  [ 149.1516423  -35.1975306] xt:  [149.15147136165606, -35.197533044703711]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6 x0:  [ 149.147091  -35.285316] xt:  [149.14740105294334, -35.285213352230471]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5 x0:  [ 149.110759  -35.312126] xt:  [149.1107640407692, -35.312186484384917]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10 x0:  [ 149.133153  -35.339388] xt:  [149.1329229064701, -35.339517459715374]\n",
        "iteration:  1 x0:  [ 149.090983  -35.405251] xt:  [149.09099053344855, -35.405251389959524]\n",
        "iteration: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3 x0:  [ 149.124691  -35.332344] xt:  [149.12475016691192, -35.332260336321653]\n",
        "size of reduced SEPs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 105\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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MTISvry9WrVoFAMjJycGJEycwd+5cHDhwALW1tZg8eTJOnToFX9/2eY6jrZyEQziJPIrL\nR1u1JQ4AaGhoQFhYmEXlk5KS5IQwZswY1NTUAAAKCwsxZ84cBAQEICIiAkOHDkVZWZm1YZKteGZH\nrsZ+N0VuA5v6PNLT0xEeHo6CggK59gAABoMBkiRBq9WipKSk29d57733kJycDAA4f/481Gq1/Jha\nrUZtba0tYZICf3hEZmO/myK3QZf/JJiUlISLFy92uD8rKwspKSlYv3491q9fj5ycHCxbtgz5+fkY\nMGAAqquroVKpUF5ejunTp6OioqJdTeV+69evx49+9CPMnTvXZBw+Pj6d3r927Vr5ularhVar7erj\neK+2Hx7Q+sPjv66RO2G/m03bQKfTQafT2T8mYQdVVVUiJiam08e0Wq04ePBgp4/l5+eL8ePHi++/\n/16+Lzs7W2RnZ8u3p0yZIkpLSzuUtVPo3mHqVCEAIRIShLh2zdXREFnm2jUhZs3y7t+uHbeBvY6d\nVjdb6fV6+XphYSEkSQIA1NXVoaWlBQBw9uxZ6PV6DBkypEP5L7/8Em+++SYKCwvRo0cP+f5p06Zh\nx44daGpqgsFggF6vx+jRo60NkwAOZyT3xn43RW6DLputurJ69WpUVlbCz88PkZGR2Lx5MwCguLgY\na9asQUBAAHx9fbFlyxaE/PCB09LSsGTJEsTHx+Oll15CU1MTkpKSAADjxo1DXl4eoqOjMXv2bERH\nR8Pf3x95eXkmm6280vPPtzZD9ezZmhTM+TG1/fCIiOyECyO6G632//svZs1iUnA2a5I3kYK4fKgu\nuYindh66y4gwBY56IXIFJg9346n9F+5yUPbU5E1kITZbkTIkJ7cmjoQEZSdGzrgnN8c/g3JW8mAb\nt3NYelC25Hvxlu/QWz4n2YTJw1nJgx3UymTJ9+It36G3fE6yCTvMnYVt3MpkyffiLd+ht3xOUgTW\nPLrDNm5lsuR78Zbv0Fs+J9mEzVbsMDeNbd9EZAKbrcg0dxn2SkRui8mjK+4yce1BbPsmIgdj8uiK\nks7gLUlk1k4kdNdkSbazx3fP349XYfLoipLO4C1JZNauwOnIZMkDi7LZ47tX0skWORyTR1eUtBSI\nMxKZI9+DBxZls8d3r6STLXI4jrZyF84YhunI93CX5Ue8lT2+ew4VdgscquttycPd8cBCpAhMHp6U\nPFwxL4NzQYi8Eud5eBJX9AewD4KIbMDkoQSu6Ghk5yY9iCPiyAJstlICV/QHsA+CHsRVeb0C+zw8\nKXkQKQFHxLkfK/ou2edB5Cze0pyjpHlNZB4X9l36O/XdSPk4Cqujth0UaN0+ntqc07YyAbkPF/Zd\nsuZB7XEUVkeuHFzgLbUeso4La4tWJ4+MjAxoNBrExcUhMTER1dXVAACj0YjAwEBIkgRJkrB06dJO\ny69YsQIjRoyARqNBamoqbty4YVF5uo89DzAchdWRK5tzmMypK9auY2cPwko3b96Ur2/cuFE899xz\nQgghDAaDGDlyZLfli4qKREtLixBCiJUrV4qVK1daVN6G0D3PhAlCAK2XWbNse61r11pf49o1u4RG\nNpo6tfV7TUjgd0KdS0trPQZMnWrWb8Rex06rax5BQUHy9YaGBoSFhVlUPikpCb6+rW8/ZswY1NTU\nWBsKWVtb6KzG4sozGeqIndjUHRfVTm3q80hPT0d4eDgKCgqwatUq+X6DwQBJkqDValFSUtLt67z3\n3ntITk62urzXM3WA6a45y52aRLy17Z/J3Hae/ttxUVNzl/M8kpKScPHixQ73Z2VlISUlRb6dk5OD\nyspK5Ofno6mpCbdu3YJKpUJ5eTmmT5+OioqKdjWV+61fvx7l5eX49NNPAcDs8j4+PsjMzJRva7Va\naLVaiz68x+tu0pc9xvU7a3QWJ7CRtTz9t9PNhF+dTgedTiffXrdunX3myNmj7auqqkrExMR0+phW\nqxUHDx7s9LH8/Hwxfvx48f3335t8bVPl7RS6Z+uuvdwe/Rv27G/pCtv+yVqmfjsW9hV4CnsdO61+\nlVOnTsnXN27cKObNmyeEEOLy5cuiublZCCHEmTNnxMCBA8W1Tr6YPXv2iOjoaHH58uV295tbnsnD\nDM7o/HbWQV1pHfleeuBxS6Z+O/Y48XHD34HLk8fMmTPFyJEjhUajEampqeLSpUtCCCE+/fRTERMT\nI+Li4kR8fLz44osv5DKLFy+WaxFDhw4V4eHhIi4uTsTFxYklS5YIIYT45JNPTJZvFziThzIo7aDu\nLM6qcZHj2OPEx9bfgQuSj72OnVzbisgaXAfK/dljcVBbfwcu6I/hwohMHuRKXJWYANt/By44CWHy\nYPIgIlPcZY02F5yEMHkweRCRKZ4+PNcGXJKdiMgUrtHmcKx5EJFjOaIJqbvXZJ+USWy2YvIgcg+O\naEJyVbOUu/SldIHNVkTkHhzRhOSqZil3Wg/OwZg8iMixHLEysKtWG2ZfiozNVkRE5vKAvhT2eTB5\nEJFSuFFfCPs8iIiUwgv7Qpg8iIhs5YV9IWy2IiKylS19IU5u8mKfB5MHEXkCJ89ZYZ8HEZEncNMm\nL9Y8iIhcycnDf9lsxeRBRGQxNlsREXmK559v7ftITm6tibgBJg8iIlez5zwRJyUiJg8iIlezZ6e5\nkyYsMnkQEbmaPRd6dNLoLXaYExF5km5Gb3G0FZMHEZHFXD7aKiMjAxqNBnFxcUhMTER1dTUAwGg0\nIjAwEJIkQZIkLF261KLyAJCdnY2oqCgMHz4cRUVF1oZIREQOYnXNo76+HkFBQQCATZs24ciRI9i6\ndSuMRiNSUlJw7Ngxq8qfOHECc+fOxYEDB1BbW4vJkyfj1KlT8PVtn+dY8yAispzLax5tB34AaGho\nQFhYmF3KFxYWYs6cOQgICEBERASGDh2KsrIya8MkIiIH8LelcHp6Oj744AP07NkTpaWl8v0GgwGS\nJCE4OBivv/46fv7zn3dZPjAwUE4Q58+fx9ixY+XnqNVq1NbW2hImERHZWZfNVklJSbh48WKH+7Oy\nspCSkiLfzsnJQWVlJfLz89HU1IRbt25BpVKhvLwc06dPR0VFRbuaxoPuL//SSy9h7Nix+Jd/+RcA\nwOLFi5GcnIzU1NT2gfv4IDMzU76t1Wqh1WrN/uBERN5Ap9NBp9PJt9etW6ec0Vbnzp1DcnIyjh8/\n3uGxiRMnIjc3F/Hx8WaVz8nJAQCsWrUKAPDkk09i3bp1GDNmTPvA2edBRGQxl/d56PV6+XphYSEk\nSQIA1NXVoaWlBQBw9uxZ6PV6DBkyxOzy06ZNw44dO9DU1ASDwQC9Xo/Ro0dbGyYRETmA1X0eq1ev\nRmVlJfz8/BAZGYnNmzcDAIqLi7FmzRoEBATA19cXW7ZsQcgPE1XS0tKwZMkSxMfHmywfHR2N2bNn\nIzo6Gv7+/sjLy4OPj48dPioREdkLJwkSUUdO/mtUch6XN1sRkQdz0uJ65L6YPIioIzf9a1RyHjZb\nEVFHtvw1Kpu8FI0LIzJ5ECmTVtva5AW0LjP+8ccuDccqHpwA2edBRMqkhCYvW/9Nj30+3WLyICL7\nMuePjRz9V6m2HvyVkAAVjsmDiOwrJKS1qaqrph5Hn9nbevC35z/7eSj2eRCR8yUntyaOhATHHKBt\n6fD3cOwwZ/Igcl88uLsMkweTBxGRxTjaiojIUzh6AIEDMHkQuSs3POCQCW44NJjJg5TP3Q+Sjorf\nDQ84ZIIbDg1m8iDlc/eDpKPid8MDDplgamiwgk+cmDxI+dztIPngDu+o+DkXwXOYmhuj4BMnjrYi\n5XO3YZ0Pru30zjuOjd+D12GyiCduBwfMh+FQXSYP05SwEykhBldx9AS4B3nCQoT24O7bobN9xgEn\nThyqS6YpoaqrhBhcxdnNSe7WrOco7r4dOttnzFnqxUWYPDyREnYiJcTgKs7e4dn30crdt4Ob7TNs\ntvJESugjUEIMns6bmwatpeRt5qR9hn0eTB7k7dy9jd8VuM3Y50HkMkoZe+9mzRyKwG1mN6x5EFlK\nKWevbBq0HLeZ62seGRkZ0Gg0iIuLQ2JiIqqrqwEARqMRgYGBkCQJkiRh6dKlDilP5DJKOXt1VMe8\nUmpWjqDg0UvuxuqaR319PYKCggAAmzZtwpEjR7B161YYjUakpKTg2LFjDi3Pmge5jKefvSqlZkUO\nYa9jp7+1BdsO/ADQ0NCAsLAwp5Yncpm2s1dPpZSaFSmaTR3m6enpCA8PR0FBAVatWiXfbzAYIEkS\ntFotSkpKHFaeiBzA3edLkFN02WyVlJSEixcvdrg/KysLKSkp8u2cnBxUVlYiPz8fTU1NuHXrFlQq\nFcrLyzF9+nRUVFS0q2k8yJryPj4+yMzMlG9rtVpotVpLPjsRkcfT6XTQ6XTy7XXr1ilnnse5c+eQ\nnJyM48ePd3hs4sSJyM3NRXx8vF3Ls8+DiMhyLh9tpdfr5euFhYWQJAkAUFdXh5aWFgDA2bNnodfr\nMWTIELuXJw9h7sgeTx4BROSGrO4wX716NSorK+Hn54fIyEhs3rwZAFBcXIw1a9YgICAAvr6+2LJl\nC0J+aDdNS0vDkiVLEB8fb1V5cgFHL+fQthhc23uZ6og293lE5BScJEhdc/SwTXOXL3f2MudKYa/k\nreQ1ncipXN5sRV7C0cM2zR3Z460jgOy1tL03L5FPDsGaB3XN0yfEKZ29alzeWnOjDriqLpMHeQN7\nJW+eBNAPmDyYPMhbsL+C7Ih9HkTegv0VpEBMHkSuYu7cFa41RQrE5EHkKubWKLx1pBkpmtWTBInI\nRubWKO5fxZf9H6QQrHkQuYo1NQr2f5BCsOZB5CrW/C8I+z9IIThUl8idcL4G2YjzPJg8iIgsxnke\nRJ3h0u2Ox21MYPIgT8MOZcfjNiYweXgHbzpTZIey43EbE9jn4R0c/Z8c5nDW/AR2KDset7FbY4c5\nk4f5lLActxISGBGxw5wsoITlLdjUQeRRWPMg52BTB5EisNmKyYOIyGJstiIiIpdh8iAiIosxeZBn\nUNJcFiXEooQYyKNZnTwyMjKg0WgQFxeHxMREVFdXAwCMRiMCAwMhSRIkScLSpUu7fJ3c3Fz4+vri\n6tWr8n3Z2dmIiorC8OHDUVRUZG2I5E2UNOvZmljsfbBX0vYgzySsdPPmTfn6xo0bxXPPPSeEEMJg\nMIiRI0ea9Rrnzp0TU6ZMEREREeLKlStCCCEqKiqERqMRTU1NwmAwiMjISNHS0tKhrA2hkyeaOlUI\nQIiEBCGuXXO/WCZMaC0DCDFrlmtiIK9gr2On1TWPoKAg+XpDQwPCwsIsfo3ly5djw4YN7e4rLCzE\nnDlzEBAQgIiICAwdOhRlZWXWhkneQglzWWyJxd7zYJS0Pcgj2dTnkZ6ejvDwcBQUFGDVqlXy/QaD\nAZIkQavVoqSkpNOyhYWFUKvVGDVqVLv7z58/D7VaLd9Wq9Wora21JUzyBm1/rKSEA6U1sdj7YK+k\n7UEeqct/EkxKSsLFixc73J+VlYWUlBSsX78e69evR05ODpYtW4b8/HwMGDAA1dXVUKlUKC8vx/Tp\n01FRUdGuptLY2IisrCzs3btXvk90Me7Yx8en0/vXrl0rX9dqtdBqtV19HCLlsuZfBYnMoNPpoNPp\n7P66dpkkeO7cOSQnJ+P48eMdHps4cSJyc3MRHx8v33fs2DFMnjwZPX+oqtfU1GDgwIH47rvvkJ+f\nDwByTebJJ5/EunXrMGbMmPaBc5IgUeectQgluSWXTxLU6/Xy9cLCQkiSBACoq6tDS0sLAODs2bPQ\n6/UYMmRIu7KxsbG4dOkSDAYDDAYD1Go1ysvL8ZOf/ATTpk3Djh070NTUBIPBAL1ej9GjR1sbJpFn\nMWdUljuPtOIQY7fRZbNVV1avXo3Kykr4+fkhMjISmzdvBgAUFxdjzZo1CAgIgK+vL7Zs2YKQH858\n0tLS8MILL+CnP/1pu9e6v1kqOjoas2fPRnR0NPz9/ZGXl2ey2YrI67QlBqD1QNtZU5c7L0Jpzucj\nReDaVkRKYG5TkznL67vzIpRK+PsAD8eFEZk8yJOY+38n7pwYzOHpn08BmDyYPMiTePMZNzv4ncrl\nHeZEZEfePKnPnTv4vZjVHeZEbkuJZ7qeNM/D0u3rzh38Xow1D/I+7nam627DVy3dvt5c63JjrHmQ\n93G3M11vBxG6AAAOD0lEQVR3G75q6fb1pFqXF2HNg7yPu53puluyc/T2dXZNzN1qfk7C0VZESsfh\nq+2ZO6zZXd/Pwex17GSzFZHSsVmnPWfXxNyt5uckrHkQkXtxdk3Mw2p+nCTI5EHeTolDjknxOEmQ\nyNu525Bj8ihMHkRKZ2q0jzu2xXPkksdg8iBSOlM1DHcbcgywtuRBONqKyFms7aMwVcNwx1FY7lhb\nok6xw5zIWaydL+BJo3086bO4KY62YvIgd+PNy66TYjB5MHmQu+FZNykAkweTBxGRxTjPg8jbKHmY\nq5JjI4dg8iByF0oe5mrv2JiMFI/Jg8hdKHmYq71jU3KiJABMHkTuQ8mTAu0dm5ITJQGwIXlkZGRA\no9EgLi4OiYmJqK6uBgAYjUYEBgZCkiRIkoSlS5d2+Tq5ubnw9fXF1atXrSpP5DXaJgUqLXEA9o9N\nyYmSANgw2qq+vh5BQUEAgE2bNuHIkSPYunUrjEYjUlJScOzYsW5fo7q6GmlpaaisrMTBgwfRp08f\ns8tztBURcWVhy7l8tFVb4gCAhoYGhIWFWfway5cvx4YNG6wNgYi8HftGXMamPo/09HSEh4ejoKAA\nq1atku83GAyQJAlarRYlJSWdli0sLIRarcaoUaM6PGZOeSIi9o24TpfNVklJSbh48WKH+7OyspCS\nkiLfzsnJQWVlJfLz89HU1IRbt25BpVKhvLwc06dPR0VFRbuaSmNjIyZOnIi9e/eid+/eGDx4MP7x\nj38gNDTUrPJAa9UrMzNTvq3VaqHVam3ZFkTkbjhrv1s6nQ46nU6+vW7dOuXMMD937hySk5Nx/Pjx\nDo9NnDgRubm5iI+Pl+87duwYJk+ejJ4/nDXU1NRg4MCBKCsrQ79+/botD7DPg4jIGi7v89Dr9fL1\nwsJCSJIEAKirq0NLSwsA4OzZs9Dr9RgyZEi7srGxsbh06RIMBgMMBgPUajXKy8vRr18/s8oTeTRO\nkCM3YPX/eaxevRqVlZXw8/NDZGQkNm/eDAAoLi7GmjVrEBAQAF9fX2zZsgUhP1Qn09LS8MILL+Cn\nP/1pu9fy8fGRr3dVnsgrtHUCA62JxN3+s4O8AhdGJFIaLt1ODsRVdZk8yFPZ2gnMuQ/UBSYPJg+i\nzln7j4XkFVzeYU5ECsW5D+QErHkQeRpTzV5sziKw2YrJg8hSbM4isNmKiCzF5iyyI9Y8iLwFl/Ig\nsNmKyYOIyApstiIiIpdh8iAiIosxeRARkcWYPIiIyGJMHkREZDEmDyIishiTBxERWYzJg4iILMbk\nQUREFmPyICIiizF5EBGRxZg8iIjIYkweRERkMSYPIiKymNXJIyMjAxqNBnFxcUhMTER1dTUAwGg0\nIjAwEJIkQZIkLF26tNPya9euhVqtlp+3Z88e+bHs7GxERUVh+PDhKCoqsjZEIiJyFGGlmzdvytc3\nbtwonnvuOSGEEAaDQYwcObLb8mvXrhW5ubkd7q+oqBAajUY0NTUJg8EgIiMjRUtLS4fn2RC6U33z\nzTeuDsEsjNO+GKd9uUOc7hCjEPY7dlpd8wgKCpKvNzQ0ICwszJrE1eG+wsJCzJkzBwEBAYiIiMDQ\noUNRVlZmbZgup9PpXB2CWRinfTFO+3KHON0hRnuyqc8jPT0d4eHhKCgowKpVq+T7DQYDJEmCVqtF\nSUmJyfKbNm2CRqPBc889h+vXrwMAzp8/D7VaLT9HrVajtrbWljCJiMjOukweSUlJiI2N7XD5/PPP\nAQDr16/HuXPnsGjRIixbtgwAMGDAAFRXV+PQoUP4j//4D8ydOxf19fUdXnvJkiUwGAw4fPgwHn74\nYbzyyism4/Dx8bHlMxIRkb3Zo+2rqqpKxMTEdPqYVqsVBw8e7LL8/f0k2dnZIjs7W35sypQporS0\ntEOZyMhIAYAXXnjhhRcLLpGRkTYc7f+fP6yk1+sRFRUFoLWfQpIkAEBdXR1UKhX8/Pxw9uxZ6PV6\nDBkypEP5Cxcu4OGHHwYAfPbZZ4iNjQUATJs2DXPnzsXy5ctRW1sLvV6P0aNHdyh/+vRpa0MnIiIb\nWZ08Vq9ejcrKSvj5+SEyMhKbN28GABQXF2PNmjUICAiAr68vtmzZgpCQEABAWloalixZgvj4eKxc\nuRKHDx+Gj48PBg8ejC1btgAAoqOjMXv2bERHR8Pf3x95eXlstiIiUhgfIToZ8kRERNQFRc4w//LL\nLzF8+HBERUXhjTfe6PQ5v/3tbxEVFQWNRoNDhw5ZVNbVcVZXV2PixImIiYnByJEjsXHjRkXG2aal\npQWSJCElJUWRMV6/fh3PPPMMRowYgejoaJSWlioyzuzsbMTExCA2NhZz587FnTt3XBbnyZMnMW7c\nOPTo0QO5ubkWlVVCnErbh7ranoBz9iFb47R4P7JLz4kdNTc3i8jISGEwGERTU5PQaDTixIkT7Z7z\n17/+VUydOlUIIURpaakYM2aM2WWVEOeFCxfEoUOHhBBC1NfXi2HDhikyzja5ubli7ty5IiUlRZEx\nLliwQGzbtk0IIcTdu3fF9evXFRenwWAQgwcPFrdv3xZCCDF79myxfft2l8X5z3/+Uxw4cECkp6eL\nt956y6KySohTafuQqTjbOHofskeclu5Hiqt5lJWVYejQoYiIiEBAQAB+9atfobCwsN1zdu3ahYUL\nFwIAxowZg+vXr+PixYtmlXV1nJcuXUL//v0RFxcHAOjVqxdGjBiB8+fPKy5OAKipqcHu3buxePHi\nTid1ujrGGzduYP/+/Xj22WcBAP7+/ggODlZcnL1790ZAQAAaGxvR3NyMxsZGDBw40GVx9u3bFwkJ\nCQgICLC4rBLiVNo+ZCpOwDn7kK1xWrMfKS551NbWYtCgQfLtziYJmnrO+fPnuy3r6jhramraPcdo\nNOLQoUMYM2aMouJse86yZcvw5ptvwtfXcT8VW7alwWBA37598etf/xrx8fFIS0tDY2OjouKsra1F\nnz598MorryA8PBwDBgxASEgIJk+e7LI4HVHWUvZ6LyXsQ11xxj4E2BanNfuR4pKHuSOrHJnBzWFt\nnPeXa2howDPPPIM//vGP6NWrl13j6+z9uvJgnEIIfPHFF+jXrx8kSXLo9rZlWzY3N6O8vBxLly5F\neXk5HnroIeTk5DgiTJt+m2fOnMEf/vAHGI1GnD9/Hg0NDfjv//5ve4cIwLZJtc4c2WiP91LSPtQZ\nZ+1DgG1xWrMfKS55DBw4UF6hF2jtGLt/uZLOnlNTUwO1Wm1WWVfH2dZUcffuXcycORPz5s3D9OnT\nHRKjrXF+++232LVrFwYPHow5c+bg66+/xoIFCxQVo1qthlqtxs9+9jMAwDPPPIPy8nK7x2hrnP/4\nxz8wfvx4hIaGwt/fH6mpqfj2229dFqcjylrK1vdS0j5kirP2IVvjtGo/skdHjT3dvXtXDBkyRBgM\nBnHnzp1uOyX/53/+R+6UNKesEuK8d++emD9/vnj55ZcdEpu94ryfTqcTTz/9tCJjfOyxx0RlZaUQ\nQojMzEzx6quvKi7OQ4cOiZiYGNHY2Cju3bsnFixYIN5++22XxdkmMzOzXcep0vYhU3EqbR8yFef9\nHLkP2SNOS/cjxSUPIYTYvXu3GDZsmIiMjBRZWVlCCCH+9Kc/iT/96U/yc1588UURGRkpRo0a1W75\nk87KKi3O/fv3Cx8fH6HRaERcXJyIi4sTe/bsUVyc99PpdA4dKWJLjIcPHxYJCQli1KhRYsaMGQ4b\nbWVrnG+88YaIjo4WI0eOFAsWLBBNTU0ui/PChQtCrVaL3r17i5CQEDFo0CBRX19vsqzS4lTaPtTV\n9mzj6H3I1jgt3Y84SZCIiCymuD4PIiJSPiYPIiKyGJMHERFZjMmDiIgsxuRBROQEO3fuRExMDPz8\n/LqdQ9HZQopHjhzBuHHjMGrUKEybNq3Tf2i93+HDhzF+/HiMHDkSGo0GH3/8sV0+RxsmDyIiO9Pp\ndPj1r3/d7r7Y2Fh89tlnePzxx7st/8c//hHR0dHtZo0vXrwYGzZswNGjRzFjxgy8+eabXb7GQw89\nhA8++ADHjx/Hl19+iZdffhk3b9607gN1gsmDiMjOOlsqZPjw4Rg2bFi3ZU0tpKjX6/HYY48BACZP\nnoxPP/0UQGstZcWKFRg9ejQ0Gg3eeecdAEBUVBQiIyMBAA8//DD69euHy5cv2/zZ2jB5EBHZmS3T\n50wtpBgTEyOvkrtz5055KZJt27YhJCQEZWVlKCsrw7vvvguj0diubFlZGe7evSsnE3tg8iAispOx\nY8dCkiSkpaVh165dkCQJkiShqKjIrPJdLaT43nvvIS8vDwkJCWhoaMCPfvQjAEBRURHef/99SJKE\nsWPH4urVqzh9+rRc7sKFC1iwYAHy8/Pt90Fhw3+YExFRe23/vrdv3z5s377d4gN220KKu3fvxu3b\nt3Hz5k0sWLAA77//Ph599FH87W9/AwCcOnUKu3fvlsu9/fbbSEpK6vB6N2/exNNPP42srCyMHj3a\nhk/WEWseRER21l2zlanHs7KyUF1dDYPBgB07dmDSpEl4//33AUDur7h37x5ef/11vPDCCwCAKVOm\nIC8vD83NzQBaE0tjYyOampowY8YMLFiwAKmpqfb6aDImDyIiO/Px8enQaf7ZZ59h0KBBKC0txVNP\nPYWpU6cCAM6fP4+nnnrK5Ou0+ctf/oJHH30UI0aMgFqtxqJFiwC0jsKKjo5GfHw8YmNjsWTJEjQ3\nN+Pjjz/G/v37sX37drn57OjRo/b7jFwYkYiILMWaBxERWYzJg4iILMbkQUREFmPyICIiizF5EBGR\nxZg8iIjIYkweRERkMSYPIiKy2P8Bh2LJ8wX4++0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x74ad160>"
       ]
      }
     ],
     "prompt_number": 190
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = getAdjacencyMatrix(sepList, 0.20, 5, data, invA)\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, 2, 3, 3, 4, 2, 1, 3, 4, 5, 2, 4, 4, 3, 2, 2, 4, 3, 6, 7, 1, 1, 8, 9, 2, 9, 7, 1, 1, 10, 2, 10, 4, 7, 10, 9, 2, 3, 4, 2, 2, 2, 4, 4, 3, 3, 3, 3, 3, 5, 1, 1, 1, 1, 1, 6, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 9, 9, 9, 10, 10, 9, 10, 9, 11, 1, 12, 10, 2, 5, 1, 10, 9, 13, 14, 2, 11, 3, 10, 11, 1, 9, 13, 9, 14, 8, 15, 1, 13, 16, 2, 4, 9]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T3+DflUytrVfxxx8/0aZNG6Kjo2nVqu29u5hcrK3TOH36WIHuYGbP/pC5czej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\nQAqCEEKIB6QgCCGEAOC/pFybYXXF8mUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x736df98>"
       ]
      }
     ],
     "prompt_number": 207
    },
    {
     "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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CBIvdJCUWzIl8mCO71nKfKZIjJg8iL3BkJhX3mSI54pUEibzAkd7E4F18udiP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\nkcOYPIiIyGH/D0qid+XSwpVsAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x759d6a0>"
       ]
      }
     ],
     "prompt_number": 208
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=open('results\\\\schools_Var=0.20.txt','a')\n",
      "for i in range (len(results)):\n",
      "    print >> f, results[i]\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 209
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = getAdjacencyMatrix(sepList, 0.30, 5, data, invA)\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, 2, 3, 3, 3, 2, 1, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 1, 3, 1, 1, 3, 3, 2, 3, 3, 1, 1, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 4, 4, 2, 3, 3, 3, 3, 1, 3, 4, 3, 4, 3, 3, 1, 4, 5, 2, 3, 3]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Wf2cytbBYw2+//Urbtm2JiIigVat2ZGbaArlYWKRx/vzpQt3BfPTRx8yfvwOt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\nxH1SEIQQQgBSEIQQQtwnBUEIIQQA/w+GYGavYp5qhAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7b98ba8>"
       ]
      }
     ],
     "prompt_number": 211
    },
    {
     "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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BIiJymOpqHkREpH5MHkRE5DAmDyIichiTBxEROYzJg4jIAw4cOIDo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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x736db38>"
       ]
      }
     ],
     "prompt_number": 212
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=open('results\\\\schools_Var=0.30.txt','a')\n",
      "for i in range (len(results)):\n",
      "    print >> f, results[i]\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 213
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = getAdjacencyMatrix(sepList, 0.40, 5, data, invA)\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, 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]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ggTtt2vixb1/JPXYq2//+9zWBga5YW3+Gs/NPfPXVihqTDADi4+NRKNwoSgZQ1DVRT05O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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7822630>"
       ]
      }
     ],
     "prompt_number": 214
    },
    {
     "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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4+OOPUV5eDgD2nsxPf/pTbNy4ETNnznQMnAcJEo3MVwtRkub4/SBBq9Vqv1xZ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\nmDyIiEg2Jg8iIpKNyYOIiGT7f4lWYo5hMMjFAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x759eb70>"
       ]
      }
     ],
     "prompt_number": 215
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=open('results\\\\schools_Var=0.40.txt','a')\n",
      "for i in range (len(results)):\n",
      "    print >> f, results[i]\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 216
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = getAdjacencyMatrix(sepList, 0.10, 5, data, invA)\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, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 8, 14, 15, 5, 8, 16, 17, 18, 19, 20, 21, 14, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 22, 14, 4, 32, 33, 6, 14, 29, 5, 34, 4, 35, 8, 8, 36, 7, 7, 7, 37, 38, 39, 40, 41, 42, 19, 43, 18, 44, 45, 46, 47, 18, 48, 49, 21, 50, 26, 26, 51, 52, 53, 54, 55, 56, 57, 11, 10, 58, 59, 60, 61, 62, 63, 54, 3, 64, 65, 66, 60, 67, 60, 68, 69, 70, 19, 71, 72, 73, 13, 22]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PFfQQTPcJS8uaD10tqQyKOnYWOey0S5cuJCUlPbJ+wYIF+Pn5FS4vWrSIqKgo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\nyUw6BNbdAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7500a90>"
       ]
      }
     ],
     "prompt_number": 217
    },
    {
     "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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6yMnJQWZmpv1+bm4u6urqUFBQgK6uLty4cQPR0dGorKzE4sWLcf78+X41jTu5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\nskCi74gTjwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7470240>"
       ]
      }
     ],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=open('results\\\\schools_Var=0.10.txt','a')\n",
      "for i in range (len(results)):\n",
      "    print >> f, results[i]\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 219
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}