treeNeuronClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "#change these paths to where they are located on your computer\n",
    "sys.path.append(\"/Users/anirudhnatarajan/Documents/UPenn/McNeuron\")\n",
    "import McNeuron\n",
    "import pickle\n",
    "from random import randint\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tree_data = pickle.load(open('.././trees/tree_data.csv', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trees = tree_data.sort_values(['location'])\n",
    "trees = trees.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def splitTrees(location) :\n",
    "    \"\"\"\n",
    "    splitTrees is used to quickly and efficiently split the data into usable chunks\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    location: string\n",
    "        the name of the location where the tree is from, matches exactly with the trees dataset, and is a string\n",
    "\n",
    "    Return:\n",
    "    -------\n",
    "    splitTrees returns 2 lists of equal size that contain proper training and testing data\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    ind = np.where(trees['location']==location)[0]\n",
    "    \n",
    "    train = [0] * size\n",
    "    test = [0] * size\n",
    "    rand = np.linspace(0,totalTrees-1,totalTrees)\n",
    "    \n",
    "    for i in range (0,size) :\n",
    "        r = randint(0,len(rand)-1)\n",
    "        neuron = McNeuron.Neuron(trees['swc'][ind[int(rand[r])]])\n",
    "        rand = np.delete(rand,r)\n",
    "        neuron.setNormalizedFeatures()\n",
    "        neuron.geometrical_features()\n",
    "        neuron.motif_features()\n",
    "        train[i] = neuron.getNeuronVector()\n",
    "    \n",
    "#     print('half')\n",
    "    for i in range (0,size) :\n",
    "        r = randint(0,len(rand)-1)\n",
    "        neuron = McNeuron.Neuron(trees['swc'][ind[int(rand[r])]])\n",
    "        rand = np.delete(rand,r)\n",
    "        neuron.setNormalizedFeatures()\n",
    "        neuron.geometrical_features()\n",
    "        neuron.motif_features()\n",
    "        test[i] = neuron.getNeuronVector()\n",
    "        \n",
    "    return train,test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "neurons = pd.read_pickle(\"/Users/anirudhnatarajan/Documents/UPenn/neuronData/neurons.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "help1 = np.where(neurons['Secondary Brain Region']=='CA1')[0]\n",
    "help2 = np.where(neurons['Species Name']=='mouse')[0]\n",
    "index_to_see = np.intersect1d(help1,help2)\n",
    "\n",
    "indA = np.where(neurons.iloc[index_to_see]['Secondary Cell Class']=='GABAergic')[0]\n",
    "indP = np.where(neurons.iloc[index_to_see]['Secondary Cell Class']=='pyramidal')[0]\n",
    "indS = np.where(neurons.iloc[index_to_see]['Secondary Cell Class']=='Somatostatin (SOM)-positive')[0]\n",
    "indB = np.where(neurons.iloc[index_to_see]['Secondary Cell Class']=='basket')[0]\n",
    "indT = np.where(neurons.iloc[index_to_see]['Secondary Cell Class']=='perisomatic targeting')[0]\n",
    "\n",
    "\n",
    "def getSWC(ind) :\n",
    "    length = len(ind)\n",
    "    swc = [0] * length\n",
    "    for i in range(0,length) :\n",
    "        swc[i] = neurons['swc'][index_to_see[ind]][index_to_see[ind][i]]\n",
    "    return swc\n",
    "\n",
    "swcA = getSWC(indA)\n",
    "swcP = getSWC(indP)\n",
    "swcS = getSWC(indS)\n",
    "swcB = getSWC(indB)\n",
    "swcT = getSWC(indT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getVectors(swc, size) :\n",
    "    train = [0] * size\n",
    "    test = [0] * size\n",
    "    rand = np.linspace(0,len(swc)-1,len(swc))\n",
    "    \n",
    "    for i in range (0,size) :\n",
    "        r = randint(0,len(rand)-1)\n",
    "        neuron = McNeuron.Neuron(swc[int(rand[r])])\n",
    "        rand = np.delete(rand,r)\n",
    "        neuron.setNormalizedFeatures()\n",
    "        neuron.motif_features()\n",
    "        neuron.geometrical_features()\n",
    "        train[i] = neuron.getNeuronVector()\n",
    "\n",
    "#     print('half')\n",
    "    for i in range (0,size) :\n",
    "        r = randint(0,len(rand)-1)\n",
    "        neuron = McNeuron.Neuron(swc[int(rand[r])])\n",
    "        rand = np.delete(rand,r)\n",
    "        neuron.setNormalizedFeatures()\n",
    "        neuron.motif_features()\n",
    "        neuron.geometrical_features()\n",
    "        test[i] = neuron.getNeuronVector()\n",
    "    \n",
    "#     print('done')\n",
    "    return train,test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n",
      "done\n",
      "done\n",
      "done\n",
      "done\n",
      "done\n",
      "done\n",
      "done\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "totalTrees = 20\n",
    "size = int(totalTrees/2)\n",
    "\n",
    "trainG, testG = splitTrees('Gabon')\n",
    "print('done')\n",
    "trainH, testH = splitTrees('Ghana')\n",
    "print('done')\n",
    "trainU, testU = splitTrees('UK')\n",
    "print('done')\n",
    "trainW, testW = splitTrees('Wytham Meteoc (UK)')\n",
    "print('done')\n",
    "\n",
    "trainA,testA = getVectors(swcA, size)\n",
    "print('done')\n",
    "trainP,testP = getVectors(swcP, size)\n",
    "print('done')\n",
    "trainS,testS = getVectors(swcS, size)\n",
    "print('done')\n",
    "trainB,testB = getVectors(swcB, size)\n",
    "print('done')\n",
    "trainT,testT = getVectors(swcT, size)\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn import neighbors\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def subsetData(arr,q):\n",
    "    \"\"\"\n",
    "    subsetData is used to split the data into lists with only 1 classifying feature in order to test how important each feature is individually for classification\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    arr: array or list\n",
    "        the master array that contains all the features\n",
    "    q: int\n",
    "        the index of the desired feature\n",
    "\n",
    "    Return:\n",
    "    -------\n",
    "    subsetData returns an array with only 1 classifying feature\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    x = [0] * len(arr)\n",
    "    for i in range(0,len(arr)) :\n",
    "        x[i] = arr[i][q]\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6 0\n",
      "0.6444444444444445 1\n",
      "0.7333333333333333 2\n",
      "0.8666666666666667 3\n",
      "0.5555555555555556 4\n",
      "0.4777777777777778 5\n",
      "0.4777777777777778 6\n",
      "0.9222222222222223 7\n",
      "0.6 8\n",
      "0.7 9\n",
      "0.7666666666666667 10\n",
      "0.5333333333333333 11\n",
      "0.5555555555555556 12\n",
      "0.6444444444444445 13\n",
      "0.8222222222222222 14\n",
      "0.8222222222222222 15\n",
      "0.7888888888888889 16\n",
      "0.7888888888888889 17\n",
      "0.8 18\n",
      "0.8222222222222222 19\n",
      "0.8333333333333334 20\n",
      "0.8 21\n",
      "0.8333333333333334 22\n",
      "0.6888888888888889 23\n",
      "0.6777777777777778 24\n",
      "0.7111111111111111 25\n",
      "0.7333333333333333 26\n",
      "0.6666666666666666 27\n",
      "0.9555555555555556 28\n",
      "0.9555555555555556 29\n",
      "0.5777777777777777 30\n"
     ]
    }
   ],
   "source": [
    "for q in range(0,31) :\n",
    "    a = subsetData(trainA,q)\n",
    "    b = subsetData(trainP,q)\n",
    "    c = subsetData(trainS,q)\n",
    "    d = subsetData(trainB,q)\n",
    "    e = subsetData(trainT,q)\n",
    "    f = subsetData(trainG,q)\n",
    "    g = subsetData(trainH,q)\n",
    "    h = subsetData(trainU,q)\n",
    "    i = subsetData(trainW,q)\n",
    "    j = subsetData(testA,q)\n",
    "    k = subsetData(testP,q)\n",
    "    l = subsetData(testS,q)\n",
    "    m = subsetData(testB,q)\n",
    "    n = subsetData(testT,q)\n",
    "    o = subsetData(testG,q)\n",
    "    p = subsetData(testH,q)\n",
    "    r = subsetData(testU,q)\n",
    "    s = subsetData(testW,q)\n",
    "\n",
    "    train = np.expand_dims(np.concatenate((a,b,c,d,e,f,g,h,i)),axis=1)\n",
    "    test = np.expand_dims(np.concatenate((j,k,l,m,n,o,p,r,s)),axis=1)\n",
    "\n",
    "    y = np.concatenate(([0] * size*5,[1] * size*4))\n",
    "    clf = ExtraTreesClassifier()\n",
    "    clf.fit(train,y)\n",
    "    z = clf.predict(test)\n",
    "\n",
    "    sum = 0\n",
    "    parts = 2\n",
    "    length = 0\n",
    "    for i in range (0,parts) :\n",
    "        for j in range (0,len(np.where(y==i)[0])) :\n",
    "            if z[j + length] == i :\n",
    "                sum += 1\n",
    "        length += len(np.where(y==i)[0])\n",
    "    accuracy = sum / len(z)\n",
    "    print(accuracy, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getFeatureImportance(length, featureNum) :\n",
    "    \"\"\"\n",
    "    getFeatureImportance uses the subsetData function to generate a matrix with a large number of individual feature accuracies.\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    length: int\n",
    "        how many times the function will get new data and test the accuracy of individual features \n",
    "    featureNum: int\n",
    "        the number of features needed to be tested\n",
    "\n",
    "    Return:\n",
    "    -------\n",
    "    getFeatureImportance returns a matrix of shape (featureNum,length) with all values filled with the accuracy of each feature\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    masterFeature = np.zeros([featureNum, length])\n",
    "    for t in range (0,length) :\n",
    "        \n",
    "        trainG, testG = splitTrees('Gabon')\n",
    "        trainH, testH = splitTrees('Ghana')\n",
    "        trainU, testU = splitTrees('UK')\n",
    "        trainW, testW = splitTrees('Wytham Meteoc (UK)')\n",
    "        print('done')\n",
    "\n",
    "        trainA,testA = getVectors(swcA, size)\n",
    "        trainP,testP = getVectors(swcP, size)\n",
    "        trainS,testS = getVectors(swcS, size)\n",
    "        trainB,testB = getVectors(swcB, size)\n",
    "        trainT,testT = getVectors(swcT, size)\n",
    "        print('done')\n",
    "        \n",
    "        for q in range(0,featureNum) :\n",
    "            a = subsetData(trainA,q)\n",
    "            b = subsetData(trainP,q)\n",
    "            c = subsetData(trainS,q)\n",
    "            d = subsetData(trainB,q)\n",
    "            e = subsetData(trainT,q)\n",
    "            f = subsetData(trainG,q)\n",
    "            g = subsetData(trainH,q)\n",
    "            h = subsetData(trainU,q)\n",
    "            i = subsetData(trainW,q)\n",
    "            j = subsetData(testA,q)\n",
    "            k = subsetData(testP,q)\n",
    "            l = subsetData(testS,q)\n",
    "            m = subsetData(testB,q)\n",
    "            n = subsetData(testT,q)\n",
    "            o = subsetData(testG,q)\n",
    "            p = subsetData(testH,q)\n",
    "            r = subsetData(testU,q)\n",
    "            s = subsetData(testW,q)\n",
    "\n",
    "            train = np.expand_dims(np.concatenate((a,b,c,d,e,f,g,h,i)),axis=1)\n",
    "            test = np.expand_dims(np.concatenate((j,k,l,m,n,o,p,r,s)),axis=1)\n",
    "\n",
    "            y = np.concatenate(([0] * size*5,[1] * size*4))\n",
    "            clf = ExtraTreesClassifier()\n",
    "            clf.fit(train,y)\n",
    "            z = clf.predict(test)\n",
    "\n",
    "            sum = 0\n",
    "            parts = 2\n",
    "            length = 0\n",
    "            for i in range (0,parts) :\n",
    "                for j in range (0,len(np.where(y==i)[0])) :\n",
    "                    if z[j + length] == i :\n",
    "                        sum += 1\n",
    "                length += len(np.where(y==i)[0])\n",
    "            accuracy = sum / len(z)\n",
    "            masterFeature[q][t] = accuracy\n",
    "        print(t)\n",
    "    return masterFeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "totalTrees = 20\n",
    "size = int(totalTrees/2)\n",
    "\n",
    "featureNames = ['Width X', 'Height Y', 'Soma Radii', 'Soma Surface Area', \\\n",
    "                'Soma Volume', 'Skewness X', 'Skewness Y', 'Skewness Z', \\\n",
    "                'Euclidian Skewness', 'Length', 'Surface Area', \\\n",
    "                'Section Area', 'Volume', 'Average Radius', 'Tips', \\\n",
    "                'Stems', 'Branch Pt', 'Segments', 'branch branch', \\\n",
    "                'branch die', 'die die', 'global angle', 'local angle', \\\n",
    "                'distance from root', 'path_length/euclidean', \\\n",
    "                'mean Contraction', 'branch angle', 'side branch angle', \\\n",
    "                'mean segmental neural length', 'mean segmental euclidean length', \\\n",
    "                'segmental branch angle']\n",
    "featureNum = len(featureNames)\n",
    "\n",
    "masterFeature = getFeatureImportance(3, featureNum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This cell uses the getFeatureImportance funtion to get a large amount of data and graph the\n",
    "results on a bar graph\n",
    "\"\"\"\n",
    "\n",
    "values = np.zeros([3,featureNum])\n",
    "for i in range(0,featureNum) :\n",
    "    values[0,i] = masterFeature[i,:].mean()\n",
    "    values[1,i] = masterFeature[i,:].std()\n",
    "featureNames = np.vstack(featureNames)\n",
    "index = np.argsort(values[0,:])\n",
    "values[2] = index\n",
    "featureRange = np.linspace(0,featureNum-1,featureNum)\n",
    "plt.bar(featureRange,values[0,index], yerr = values[1,index])\n",
    "plt.xticks(featureRange,featureNames[index,0], rotation='vertical')\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "\n",
    "plt.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/featureImportanceTreeVsNeuron.eps', dpi=300, bbox_inches = 'tight')\n",
    "plt.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/featureImportanceTreeVsNeuron.png', dpi=300, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Try out any of the following classifiers by uncommenting a line!\n",
    "\"\"\"\n",
    "\n",
    "#clf = svm.SVC()\n",
    "#clf = RandomForestClassifier()\n",
    "clf = ExtraTreesClassifier()\n",
    "#clf = DecisionTreeClassifier()\n",
    "#clf = GradientBoostingClassifier()\n",
    "#clf = BaggingClassifier()\n",
    "#clf = BaggingClassifier(ExtraTreesClassifier())\n",
    "#clf = neighbors.KNeighborsClassifier()\n",
    "#clf = SGDClassifier()\n",
    "#clf = MLPClassifier()\n",
    "#clf = GaussianNB()\n",
    "#clf = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 90\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Train the classifier and print the accuracy\n",
    "\"\"\"\n",
    "\n",
    "train = np.concatenate((trainA,trainP,trainS,trainB,trainT,trainG,trainH,trainU,trainW))\n",
    "test = np.concatenate((testA,testP,testS,testB,testT,testG,testH,testU,testW))\n",
    "\n",
    "accuracy = 0\n",
    "while accuracy<1 :\n",
    "    y = np.concatenate(([0] * size*5,[1] * size*4))\n",
    "    clf.fit(train,y)\n",
    "    z = clf.predict(test)\n",
    "\n",
    "    sum = 0\n",
    "    parts = 2\n",
    "    length = 0\n",
    "    for i in range (0,parts) :\n",
    "        for j in range (0,len(np.where(y==i)[0])) :\n",
    "            if z[j + length] == i :\n",
    "                sum += 1\n",
    "        length += len(np.where(y==i)[0])\n",
    "    accuracy = sum / len(z)\n",
    "    print(accuracy, sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature 23 (0.181318)\n",
      "2. feature 22 (0.105150)\n",
      "3. feature 8 (0.103627)\n",
      "4. feature 16 (0.099210)\n",
      "5. feature 28 (0.064222)\n",
      "6. feature 1 (0.062528)\n",
      "7. feature 15 (0.052818)\n",
      "8. feature 20 (0.050710)\n",
      "9. feature 29 (0.041997)\n",
      "10. feature 9 (0.033693)\n",
      "11. feature 26 (0.024698)\n",
      "12. feature 21 (0.024304)\n",
      "13. feature 4 (0.024127)\n",
      "14. feature 18 (0.021812)\n",
      "15. feature 2 (0.021379)\n",
      "16. feature 10 (0.017699)\n",
      "17. feature 30 (0.015975)\n",
      "18. feature 27 (0.014507)\n",
      "19. feature 24 (0.012028)\n",
      "20. feature 31 (0.011350)\n",
      "21. feature 3 (0.005109)\n",
      "22. feature 17 (0.004179)\n",
      "23. feature 6 (0.003948)\n",
      "24. feature 25 (0.003000)\n",
      "25. feature 11 (0.000614)\n",
      "26. feature 7 (0.000000)\n",
      "27. feature 5 (0.000000)\n",
      "28. feature 12 (0.000000)\n",
      "29. feature 14 (0.000000)\n",
      "30. feature 19 (0.000000)\n",
      "31. feature 13 (0.000000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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sXga3axf17p3+bt6sEElfwu3d80gqJoWE28obJS8aJ2lJYGeg2bBxL2ADM3s+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185533358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureNames = ['Width X', 'Height Y', 'Soma Radii', 'Soma Surface Area', \\\n",
    "                'Soma Volume', 'Skewness X', 'Skewness Y', 'Skewness Z', \\\n",
    "                'Euclidian Skewness', 'Length', 'Surface Area', \\\n",
    "                'Section Area', 'Volume', 'Average Radius', 'Tips', \\\n",
    "                'Stems', 'Branch Pt', 'Segments', 'branch branch', \\\n",
    "                'branch die', 'die die', 'global angle', 'local angle', \\\n",
    "                'distance from root', 'path_length/euclidean', \\\n",
    "                'mean Contraction', 'branch angle', 'side branch angle', \\\n",
    "                'mean segmental neural length', 'mean segmental euclidean length', \\\n",
    "                'segmental branch angle']\n",
    "featureNames = np.array(featureNames)\n",
    "\n",
    "importances = clf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in clf.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(train.shape[1]):\n",
    "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f]+1, importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(train.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(train.shape[1]), featureNames[indices], rotation='vertical')\n",
    "\n",
    "plt.xlim([-1, train.shape[1]])\n",
    "plt.axis([-1,32,0,1])\n",
    "# plt.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/featureImportanceTreeVsNeuronNew.eps', dpi=300, bbox_inches = 'tight')\n",
    "# plt.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/featureImportanceTreeVsNeuronNew.png', dpi=300, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This histogram gives you the breakdown of what the classifier predicted on the test values\n",
    "\"\"\"\n",
    "\n",
    "plt.hist(z)\n",
    "print(len(np.where(z == 0)[0]))\n",
    "print(len(np.where(z == 1)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "X_embedded = TSNE(n_components=2, verbose=2).fit_transform(train)\n",
    "plt.scatter(X_embedded[:,0], X_embedded[:,1], c=y, marker='x')\n",
    "#plt.savefig('/Users/anirudhnatarajan/Documents/UPenn/Misc/treeNeuronClassificationTSNE.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(train,y)\n",
    "X_embedded = pca.fit_transform(train,y)\n",
    "\n",
    "fig,ax = plt.subplots(1)\n",
    "ax.scatter(X_embedded[:,0], X_embedded[:,1], c=y, s=10)\n",
    "ax.set_yticks([])\n",
    "ax.set_yticklabels([])\n",
    "ax.set_xticks([])\n",
    "ax.set_xticklabels([])\n",
    "fig.dpi=300\n",
    "\n",
    "# fig.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/treeNeuronClassificationPCA.eps')\n",
    "# fig.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/treeNeuronClassificationPCA.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "valuesT = pickle.load(open('featureImportanceTree','rb'))\n",
    "valuesN = pickle.load(open('featureImportanceNeuron','rb'))\n",
    "valuesTN = pickle.load(open('featureImportanceTreeNeuron','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSZIkTcYwJUDvU83yu3LsfURsGBF7Am8E1gFWmJ7wJElt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YT4Fennn226zLNC0i3AeNvfkjQ9sfFsXAM2BH5je/5qVQ4MSesTnT/jgH8wecFMNkFmSecS3/PL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\niIjocSsCtwFIejmwAbC57cskLQicWGe4iIiIiIgYNScAp0kCmAo82nyD7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x188262f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from collections import namedtuple\n",
    "\n",
    "featureNames = np.array(['Width X', 'Height Y', 'Soma Radii', 'Soma Surface Area', \\\n",
    "                'Soma Volume', 'Skewness X', 'Skewness Y', 'Skewness Z', \\\n",
    "                'Euclidian Skewness', 'Length', 'Surface Area', \\\n",
    "                'Section Area', 'Volume', 'Average Radius', 'Tips', \\\n",
    "                'Stems', 'Branch Pt', 'Segments', 'branch branch', \\\n",
    "                'branch die', 'die die', 'global angle', 'local angle', \\\n",
    "                'distance from root', 'path_length/euclidean', \\\n",
    "                'mean Contraction', 'branch angle', 'side branch angle', \\\n",
    "                'mean segmental neural length', 'mean segmental euclidean length', \\\n",
    "                'segmental branch angle'])\n",
    "\n",
    "n_groups = len(featureNames)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(25, 10))\n",
    "\n",
    "index = valuesTN[2].astype(int)\n",
    "bar_width = 1/4\n",
    "\n",
    "opacity = 0.4\n",
    "error_config = {'ecolor': '0.3'}\n",
    "\n",
    "def getInd(start) :\n",
    "    ind = [0] * n_groups\n",
    "    for i in range(0,n_groups) :\n",
    "        ind[i] = start + i*bar_width*4\n",
    "    return ind\n",
    "\n",
    "rects1Ind = getInd(0)\n",
    "rects2Ind = getInd(bar_width)\n",
    "rects3Ind = getInd(bar_width*2)\n",
    "\n",
    "rects1 = ax.bar(rects1Ind, valuesTN[0][index], bar_width,\n",
    "                alpha=opacity, color='b',\n",
    "                yerr=valuesTN[1][index], error_kw=error_config,\n",
    "                label='Tree Vs Neuron')\n",
    "\n",
    "rects2 = ax.bar(rects2Ind, valuesT[0][index], bar_width,\n",
    "                alpha=opacity, color='r',\n",
    "                yerr=valuesT[1][index], error_kw=error_config,\n",
    "                label='Tree')\n",
    "\n",
    "rects3 = ax.bar(rects3Ind, valuesN[0][index], bar_width,\n",
    "                alpha=opacity, color='g',\n",
    "                yerr=valuesN[1][index], error_kw=error_config,\n",
    "                label='Neuron')\n",
    "\n",
    "# rects4 = ax.bar(np.arange(n_groups) + bar_width*3, [0]*n_groups, bar_width,\n",
    "#                 alpha=0, color='w',\n",
    "#                 yerr=[0]*n_groups, error_kw=error_config)\n",
    "\n",
    "plt.rc('legend', fontsize=14)\n",
    "\n",
    "ax.set_xlabel('Classifier', fontsize=20)\n",
    "ax.set_ylabel('Accuracy', fontsize=20)\n",
    "ax.set_title('Feature Importance For Each Classifier', fontsize=20)\n",
    "ax.set_xticks(np.arange(31) + bar_width)\n",
    "ax.set_xticklabels(featureNames[index], rotation='vertical', fontsize=16)\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# plt.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/featureImportanceMaster.eps', dpi = 300)\n",
    "# plt.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/featureImportanceMaster.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "valuesT = pickle.load(open('featureImportanceTNew','rb'))\n",
    "valuesN = pickle.load(open('featureImportanceNNew','rb'))\n",
    "valuesTN = pickle.load(open('featureImportanceTNNew','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iSZIkSZIkSZIkNUTdJUDJzNOB0ye9dkjP49uB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\n0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hLWpi0UFaiU9BNgLts/bHPuCOBh2wd1X9mQORLYRtIFztw6yvabklYFDq9aywhTXDxB\n0iRdnWw/1A0tI0UpycwGkj5OdAA2OgHnIvYTN1apa4B8zva/qxYxubD9kqTlga8SY19mIYo5xgHn\n53gvl7QdYds8azq0BHCTpLOJe1xPzwouDdvrVq2hZvDUFqAFI+lEYpOxh6SfAnsRm6u3iZbwf9je\nqEqNg0HSasD8to+W9BHgHODz6fSDwDq2b61M4AjQUkG1MlGYWCfqKyLZ6I623W/4s6Rvp3M9nUxP\nFn8A9xOLvsuJWX9ZWcZNKSTLu5WJDXzb2a0NK+fckDQ77Tsas9nAS7oH+A/wtebNk6SFiSDLf22v\nWpW+wSLpMuD7tu+sWkvNlImk6YnN/GO5VpAmS9C3gC/YzrEYrVgk3Q9sb/vcqrWMFCWsTVtJNoxF\nBCol3QkcYvuYNue2AHaxvWj3lQ2dZF/4VWKe1PMtp2177+6rGhqSLgDG2v5l1VomFyXEE9L+daLf\n/RztmwvZC/2BuK7mIz6jW+iznr3cdqurSE3NsJD0PaIw9VjgIuB00ppb0s7A2rbrufU1NZMgq4VA\nzaDZB/hY+vkgIsDyDcLj/h9Av8rEXiZVizd+fkLSkmTu2d8g8wqqkpkTeLjDucYcpl5nI6LD7/Gq\nhdQMiFHEvbsxePyRlPAbm6xjdiCjTqDU3bM/EYSYqcPLctrAjyKsp48GtgKQtBBwKXAXaZZmLthe\nqWoNNZMmBYy+RXtbYNv+bvdVDQ9JaxL2foulQ41K3j8SlbynVCaupiQOBbaTdH5B9qwlrE2B9ywZ\nvw9ckpK0JSRq5wbu6XDufmCeLmoZNpLWALYj5tAu1OYlJroycmFn4GxJLxPzQR+nJdHU2o2eC4XF\nE7agfwJwViIRPT+wX9cVDZEC90LLEG41lxExhtaigJqakeZHRGHNT9rM4b6TsHLNCkkrEHu7uWm/\nt1ul+6pqSqdOABZMs81ASo7tnP4UQQme/R0qqE6nrqDqFZ4n7HLHtjm3ANDzn4/t3GZ9TumUNrt1\nRyJw9Cti83sAYTm5cfo7qwps2/enYNhYSU8BxxPJv/uAr9p+rUp9Q0HSDMAadN6AZBNkKZGUYL6G\nCA59kJgb07DHe57oEM4KSesCfyMsc39CzGJp8ADhUtGzCUBJ87ccagQj5pT0f3hLSQoAACAASURB\nVK2vz3HGT0F8mEha3C7pYqKzrJmsupcS2a9NGyRLxl8SxTWl8Cqdk7Bzkd8cyl8D1xNruTtzLrhN\n3Jb+Ppz2VqA5zC+bgBLjCbaP73Dq18nquPU53MuUthf6TNUaavoj6R1gGdvXDaCDNreu4PmIEVDt\neIXOifWeRNLWREfjs0TBUOu6QP3+UU3NCFBbgBaMpPcD07Qbjirpg8CbuS3i03ysnYjKozmBR4Gr\ngMNs9wu69DqSbiOCx3UFVQ+SNhjLAks3D4eWNAdwNXC17Y2r0ldTHs12ZZLuAE60/Yt07vvA/rZn\nneh/0kOke9zxwGE0WeSl59NFxH1vdHUKh4akVYjq11eBO4Av5ziIXNKXCDvtThsn52ixVBKS/kF0\nXqxLbHK/ANxKdALvA6xp+5bqFA4eSTcDN9reUtLUwJv03RvWAX5ru2e7mDoEVtTmGJCnTVkpNNmg\ndyK7e1xpa1NJNwFHTCTgnxWSziKClUs1zwuW9AGimOMh2+tUpW+wSHoVWNf2RVVrGQkkjWbS1pL7\ndEfNyDClxRMkjQKOs/2xSb64Byh4L/RZYHmiM/Po5NC1ADGHNrukc+5I2hs4xvZjpd3nJD0C7GX7\n2Fbb/ZRM29X2J6pVOXAk3Q1cB2xh+82q9dRMOeSU9a8ZPH8E3k9YALZyNBFw2aKrioaBpMWI2QMz\nEhuo24E5iOHw20paxfZtE/kveo66gqrn2ZOoer1H0rn0WSutSVTq/KxCbTVlcgVR4HAucBKwt6R5\naZrdWpmyoTE/cEPqaHwbmBaiK13SYcCRwOgK9U0SSSu3OWxiM/81wmJ7qRhhBLYv7Zq44XMYMB74\nHnBbvQnpSZYAtqGvOnQq228Dx0qajfgMc7NyXQTYNf3cGqB4nggm9TKbVy2gZmDYnqpqDZOB0tam\newGHS7oxt31cB0YTxal3S/ozUaw6J/Bt4t62WWXKhsbN9I0UyZ4cEy2TYgqMJ7Sdo9fDZL8XaiYV\nM/yZ2AM1ip/OAZ4gHB3uBnarTOAUSnNCr8D73DnAXpLGAg+mY077oJ0IO+ecmJMoYqj33TVdpU4A\nls1KdPZD/gcRtMyJI4g26S/Ybtz4ScHxC4jF04pVCBsudQVVb2J7vKQliDlFqxGfzzPAWcDezddh\nTc0IUdTsVsKesLFJf4ywYrsy/T41YWXY64whNrfNdhzNv/8t/d3YBOfUTbIIsGGaM1nTm0wPPGf7\nXUkvALM1nbuBCJ7nxotM+D6amRd4untSBo/tE6rWUDPlUuDa9CfEfe5mSePpP5PNtleoQthQsH2L\npJWAg4n3NhVh83cFsH5uHdvA9sAJku6xfeUkX11TGSXFEyQt3+bwNMCngd2Bf3VX0bAoYS/UzAHA\nqsB3gIuBJ5vOnQ9sS50ArBRJxwL72X6gzbl5iLVCNo0gRGHTysB/gGuJNcIRwMLAU8R6KCduJAoD\nLqlaSM2URZ0ALJvZiRtiO54muudyYglg09aNbdoI7w0cV42soVNXUPU+tscTVms1NZOdAme33gws\nSvj2XwjsI+k1oqPxAOCmCrUNlNy6qwbDQ4S9ZE3vMh74SPr5LuDrRNETRMdPdvbnRMBod0nn0zev\nzGlN9AMigFRTU9OBwtam7xCuLsVg+zpgeUnTAjMDz+c4IzhxNjADcLmkV+j/zLHtebova3BI+hDw\nRcIdaaztl9OM3dHAZ4nYyBG2z6xO5dAoNJ4wlvZW2wDjgO93Vc3wKGEv1My3gJ/ZPiXZMTbzAFHI\nlRXJVnYbIjnbbh56NvaSic2A3xOfRyuzEa5C2SQAbT8r6QvEPM1RRKxkauA3wKG2X6xS3xDYHjhZ\n0l22L69aTM2UQ50ALJungM8QfvCtfIbopsuJZ+k8OP118ns/UFdQ1dTUJCRNQ2zWN7Odm9VnJw4j\nKtwA9gY+B5ycfn+QCPb3NLbHVa1hMrIPsJukSzLcPE0pXEx0+ZwB/Bo4VdKyROBoYWIdkRt7ELMv\n7gL+SQT5diOCsDMS8w5rakYESVsRweKFaFPwkNsMwBKQNAPwkoMVq9YzuUhJv1wTfw0uYRKzpHod\nSQsSbg5zEkmkJyStRey1BdxPdJadIWmU7TGViR0aJcYT2hXfvQ48aPuJbosZJtnvhVqYlZh/3o6p\nyKywUNIaRMJ8DLGuvoBw3vkS8fnk1G3aTKf79kfI8LmUupj3S3+yQ9LDTPiZzAhclubsts5tzaKw\npiY/6gRg2ZwL7ClprO1bGwclfYYIvpxVmbKh8Tvgx5Iusv1642CqrtwFOKoyZUOnuAqq0pC0AvE5\nzU37irBVuq+qpkRsv5lmQ7w+yRdngu2Lm35+QtKSwCeIjdUdqcsxWyQtSlT1Pp6pNdaahBvAA5Ku\nBp5rOW/bm3ZfVk0Tu5OCKbZPT1XjDVvgw4FjKtQ2JJJzw+eIBPQoogNoeSLospftx6rUV1MOkjYh\nRgScACwGHEt0AK1NdPyc3Plf9y4FrE2fJ+YdXyfpUmBb23dWrGnEkDQ/sCGdP5/vdl/V0LC9WdUa\nRoD9iLX1l4mu858TnY03A+vYfl3SdETsZDciEZATxcUTci++aylyKG0v9ABx/24383xJorgrJ/Yk\n4og7AW8R36WbUuHAhWTiSiFpPWC9pkP7SHqm5WXTAssRFpQ13SX7Ypqa/KkTgGWzF1E1fqOk6+kb\nEr8k8eDu+SHxkpr9nAXMAzwk6Z9EddscwBpEFct03Vc4bIqqoCoNSVsTiedngXvo34Gqfv+opmZ4\nnA1sAFxUtZDhkjoaTyOsOS6HiHoB91YqbJBI2ghY3fZ3Wo4fDWzZ9PtVwFdsv9JlicNhWWIz8iLw\nqTbn641KBUj6NfG9eRhYiiZ7KNvnEJXKWWP7ESCbIHhNtuwI/IJIAGwJ/DYF9mYmLOaycw8pZG36\nJpGIhZjfPkN1UkYWSesQHdtTEW48rZ9P/VztPl8CdrN9CYCkHwL/JRLPrwPYflXSkcR3KzeKjSdk\nPNewY5FDjnuhFk4EfppmtjYsc51mn+5E2OrmxMJE3PRd4v48NYDtuyWNJhKEp1embuDMTST3IN7H\n/9L/+fMGcBVRXJgN6TvUiXeJOZs3An+y/eREXlsZhRTT1GSO4vlTUyqSZgJ+xIRD4i8iAksvVKlt\nIEh6dxAvd242PpLuAP5s+4BUsfcW8IUUnPgZMSx+8WpVTrlIupuwKdvC9ptV66kpn1S9dwQx4Pps\n4HFagkW2J7YI7ikkvQSsZXts1VqGSppT9rTtTZqOfZ1Ibl5CfF6LEAPID7K9ZyVCa4ohrX2Wtn2d\npHeAZdJMqSKRNCPwSeCJlBisqRkR0jNoHWIcwlvAsravSec2BA6w/ckKJQ6aEtamkm4FHiYSZccS\nz8/7O73e9oldkjZsJN1GrN02tv101XpGguQetDewAjHT8Dkigb6f7dsqlDYgkrvGcravTr9PQ3QE\nLmH7xqbXLQ1cWccTqqfDXMMl0ns6E7jbds/amia3hlVtX9m8pqta10iQrrGTiS7nN4gE82tEt/Op\ntjeuUN6gkfQssIHtyyQ9AfzA9l/TudWAv9vOqtFA0gPAerb/XbWWkUDSZcCCwEeJRpZGI8h8xPP2\nSWIv/jKwgu2i5grX1IwUdQdg4dj+P6KiZa+qtQwF21NVrWEyU1oFVWnMCRyXa4ClJkv+lv7+WvrT\nwPRtgHMKTFwJLE0EinLl08BPW45tRGwy1k+z885JBTfrEZWiNTXD4WkiIXYdfd/7rJE0ClipNWAn\naQ9ijTp1+v00YBPbb3dfZU2BvAZMZdspsDc/cE069zLwscqUDZ0S1qZ7EMH91Yn728T2qSb2S7kw\nP7BzQcm/JYBxxHfpH8Ss6o8AawFflbR8cxKtR5mKsJpu0Pi59dma67O2xHhC7nMN7yE+kzPS72tI\nWrjTi3MqcrD9DvBNSUcRNu6zEx3pF2Rq3XoXfTa5NwA7SrqSmLW9MzC+GllDx/Z8VWsYYX5NzNL8\nvO2bGwclfZ7oztyH6AC8iLh3rNfuP+kVJB0KzNbqLpTOnUR0OO/SfWU1pVMnAKcAJM1CWBDMQnQA\nXmu7dc5PTTUcSMwkOQn4Yzp2BX0VVEdWJawGiIXE/ESXT01NN2g39D5ndgbOlvQynTsaB9PpXQWz\nEUPgm1kJGJuSfw3GATt0TdUIkebebEFU9s9CbOLHAsfbfrVCaVMyFwHHSTog/X62pFYbnwa2/Yku\n6RoO29Dy3U+V1fsBtxFroEWArYln7yHdFlhTJLcBCxAzvf5FBGQfIAJ7o4Ec585lvza1fU7an85F\nVPNvANxSraoR407CdacUfgH8B1il2XJR0oeI79UviNl6vc6caTYj9BXSzSnp/5peM1eXNY0UJcYT\ncp9rWGyRg6RpbL9p+1/Ec7X1/EdtP16BtKFyMrH+hOh0HkOMToIoFtioClHDQdJPgLls/7DNuSOA\nh20f1H1lQ2Z/YHRz8g/A9o2S9gH2t/0ZSQcBB1eicHCsTefCjAvTuToBWDPi1AnAwpG0PxGAbfZ+\nf0PSwbnalElakwkDleNsn1etqqFRYAVVaWwPnCzprsYMs5qayUmB3/uGNdTh6U8r781a6GGeI543\nwHtWWDMQNq3NvEZm1eOSPkIk+xYkkpyNDpkNgB9KWrFXZykUztbArcCiwCbp52cqVTR8FieSfc1s\nTtiwjbL9BIAkiGBLnQCsGQn+QNzTILqzxxCBcYCXgHWrEDVMilibpj3Qgyl4d43tx6rWNELsChwm\n6VrbHW1NM2Jp4Dut89ZsvyTpV8AJ1cgaNH9tc+zslt+z7LgvNJ6Q9VzDNkUOXweKsGMETpH0dbeZ\nJSXpo4Tldsdux17D9lFNP9+Y9nmrA9MCYzK1k9yczuvofxPJpZwSgAvSeR/0NFHoBXAf8MGuKBoe\ncxI26O14JJ2vqRlxej3oVjMMJO1I2Jb9iahAalh2fJuogH3a9hEVShwUqdLwXGK47dvEwnZWYGdJ\n/wLWtP1yhRIHTYEVVKVxDhHov0zSq8RA72Zse57uy6opHUkzENaTcwKPAv9p6TbLhX3JMJjSwq2E\nBVEjULQh8Z4uaHndQkSHY04cSMzzWc72lY2Dkr5I2NH+CtisGmlTLqnz8iAASZsCexcwO2Z2YmPe\nzGrAFY3kX+I84vtWUzNsbJ/W9PO9kj5FuKJMB1xlO8fEelFrU9v7VK1hhBlNSl5IuocoImrGtlfo\nuqqhM6k1XA5rvM2rFjA5KTSe8ABxr24393xJwraxp2kqcjiCKHJ4tGpNI8SywG+A7ZoPpqLCy4i5\ngNmSZlEfU7WOYTI3YUPbjvuBbNYIifHAloT9bytb0WfTOhsRI+51nieSlmPbnFuAKFCrqRlx6gRg\n2WwDHG57p6ZjdwHjkh3btkA2CUDg58DniMDQqbbfSZYQ3wR+l85vX6G+oVBUBVWBXEIeG9uagpC0\nF9G5PT1RjQzwkqSDbO9fnbLBY3t01RpGgF8Cl0q6HniKqK7+V5uZN98grNlyYnXgJ83JPwDbV0n6\nGfHeaypC0jTEOq2E59BLNFXlSvokESS/puV1L5LXnNOajLD9CtEFmDP12rS3eYcMkhOD4FqicHhM\niwXoB4Gf0P8e3nPYzqVLcaiUGE8oYq6hpKmBHxD37VISgF8lClCeahRwSJqDSNa+DaxSpbihorCg\n+ChhnTsBGXZzv0rnLrK5yC9Juy/wZ0m3EgWqTxGFhesTBdMNm9ZV6e/Q04uMAfaQdE6z0076Hv2U\nmHtaUzPiqM06oaYQJL1OdMX12+hKWhU413a/B1yvIukx4Fe2+9nISdoB2NV2Vu3Skp4A/ma7XQXV\nWOAN24tVoa2mpqb7JCusPYkZHqcSQ+/nIGZhbAHsV0hSLSskrUsEHGYBrgN2s/100/m5gH8Sn88Z\n1agcPJJeA9az3drNiKRRwNm2p+2+spoGkl4BVs/Z6g9A0jjgLttbpd93ITpMV7B9RdPrNgH2sT1f\nNUprSiMlKr4LLE8knbeyfY+kbwL/tp3jHMCamq4gaUliT/o64cTzOOEo9FXCIm9F29dXJrCmyHhC\nKvI+mXDdeIOw/HyNvrmGG1cob1BIehT4nu1/Vq1lpJD0ZaIb/YdEgnYcYc2a3egASbMCRwHr0aFB\nxnZWhWmSzgLmA5ay/UbT8Q8QRRsP2V6nKn1DIc0N3wf4PPB+4C3gBsIlZUx6zf8A79h+qzKhA0DS\nvMD1xH3tXPpsP9ck7ndL2X6gKn015VInAAsmLTYOsn1Ym3PZJcwkvUEkNPtVRKQHwjk5JTQBJH2e\nqMo7pKWC6jLgXWIRlaM9UU1NzRBIhQ4n2/5xm3MHAxvZ/lj3lQ2N1M04MWy7dS5YTZeQ9G/gv+0C\nKZJOAj5te/HuK6tpIOlK4JTmGSU5kpLoZ6Y/TxLWsvcAizd3LaSghWznOJutpseQ9HEiAD4XcCdR\nKb6E7ZskHQ28z/aWFUqsqel5JH0W2IsYwzELYWs6jih6um1i/7Zm8lNyPEHScmQ+1zDNyvyk7a9V\nrWUkkbQxcByRvHiTuM6emPi/6j0knQ2sRBTf3km8lwnIrYtY0mLAVcTcvD8T3adzEqOgZgW+ZPuW\n6hQOHUlTEVafz9h+t2o9QyUlAfclxiHMSnxWFxEJzQerU1ZTMrUFaNmcBewn6VmiUuqtZEPwdeJm\nk9WDjPCCX5P2LdFrpPNZkQYNbwCcI+lxIjB2KWH7t0qui/XSSIuohWhvCXFi9xXVFMyMwIUdzl0A\nfL+LWkaC0RM51wj61wnA6jgYODEFik6hr7L/m4SNSj2LrXp2Bv4i6UHgvHYWXzlg++w0m3pnIoB8\nDbBNS/JvLiIIs2s1KmsK5BCimvqTwGNMGNgbRyY2cu2o16a9jaSZieuu3eeTVUe37VuBDarWUdOe\nUuIJkr5FrHPem3neaa5hZowHNkqjBP5OrLUnWMvZPrYCXQNG0vxtDl8NHE2MQFgfmK7xuswsM1cC\ndrB9fNVCRgrbtyS73IMJq+apiGKAK4D1c03+AaSk31NV6xgutscDm1Sto2bKou4ALBhJHyIsyb5E\nzCN4jgi6vI+4+a9h++XqFA4OSTsRG/njCEuI5kDllsCP2tmD5kApFVSlIWkm4Dxg6cah9Pd7N87c\nLCFqehtJlwIX2/5Fm3O7A6vZXrn7ykaOFBRbi0gErFtbXFSLpK2IoqDZmw4/Cexl+5hqVNU0kPQw\nURjwQWK2ylNMGDiy7Xmq0FZT0+tIep6w/DwjWcq9BXwhdQCuAPzT9gcn/r/0FiWvTSVNT1TCP9br\nFl6dSBZkxxLWhWr3mlw/HwBJMxKJzSdsP1K1npo+co8nSPo/ImE+jihk/4ftx6pVNXwkTapLyb1+\nT0jvoVPgWK3nev39NCPpAWBb2+dXrWVyIGlaYGbgeduvVa1nqKS56KvTvvCpdhSqqRkAdQdgwdh+\nSdLyhEd/q2XH+blVkds+VNKHiTlMm6XDIip7f5lL8q/wCqrS+DkRiFieqD5cD3iBmMW2DJF8rqkZ\nFsnKosH2wFmS3gbOoG8G4IbEdZeVX387bD9PdJ01Zi6sUbGkKRrbf5D0R2JD1Vgn3JWzrUphXELn\noEtNTc3EmQZ4qcO5GYmEYG4UtzaVtCZRiLIYcb9bErgpPZsutX1KlfoGyZ7AisCmwEnAdsT8vM2A\njwI7VCVsoKQZwCvZ3q3l+B6EFejU6ffTgE1sv919lVM2hcYTPky4T6xDfI+OknQDcDYxk/qOKsUN\ngxJmGm9etYDJyJHANpIuyC0+OkDeR8zMy/Y+LeljRAPLvMQaoV/hE5k5CqUitG8Bc9M+oblK91XV\nlE7dAViTHal7ZGn6ApXXpIByFpRcQVUaku4jhg2fTASJlrB9Yzr3O+CDtuvW/Zph0eae0O8+0HT8\nXdtFFO9IWpmo7p2+ai1TGskh4C3br3c4/z/A+213CpzX1NTU9DySrgb+Y/t7bToAfwcsmFuQpbS1\naZoP+jei2OEi4ED6PqM9gOVtj6pS42CQdCdwGHAMTddbOncG0d3Y00nANIvVzTPLJK1GWNTfRszK\nWgTYGtjV9iGVCJ2CmRLiCZKWIZKB6wALAvcSycCzbF9TpbaaspB0MNE0MQZojSva9t7dVzU82hXW\npOdqdoU1kk4GFiAKGx7i/9m793hLx/r/46/3KOczySHHSPXVr68idDAk5FCkIpVzSZGUkg7GoKR0\n8FXRNyUd+KqEEjkMURKKckgkx8TQOB8ap3n//rju3Sxr1prZs2fvda977ffz8ZjHrHVfdx7v3Zq9\n1r3uz3V9LtgA+Bdl4tNOwBZN2jdP0geA4yn7mt5MWdDyHLY37XWuGHwDcRMxxpeq2NfkJfqDPINq\n0KwA3Gr7WUnTgcVaxk4HTq0nVgyYwxmfK3y2pVy8Rw9J2oTyBffN1d+dvB44V9LGti/rVbaIiFF2\nNHCaJCj7nAK8XNJ2wF7AW+sKNg8G7dr0UOB7tt9X7VX/pZax64EP1RNrxFYB/lK9Pk9T2jcPOZHS\norGvC4DAusy6mmIPykrGLYfaSla/V++mbNERvTXw9xNs/56yqvFgSS+lrHbeDvi4pPuAn9vep86M\n0fy2wJK2pqzUXoDSDaWdKZ9TjdE2seaTPPdz9TbKCvXGFAAp3ew+TtnLGcqE6NuBSdXkrmNpVpek\nAyn//+9p+6k5nRwxWlIAHDBzmA3Wzv2+kqRqYTpsTdhU3fb3684QwzYVWLJ6fAeltdLF1fM16wgU\ng8f25LozjBVJnTa1nx9YB3gFDftCNSA+AJxlu1vxD9tTJJ1J+UKcAmCNJM1xJY/tH/QiS0TT2D5d\n0oeAoygzxQF+QGkLup/tc2sLN3KDdm36MuCg6nH7d9gHKe1Om+R+YKizwT8oqy9+Wz1fFliojlBz\naTnglrZjmwOXtu0pdzawS89SxX+Mt/sJtm8EvgB8QdIKwPY0ZAJHtcfcbO/P2e7U0rVvzKYt8Kcp\n3+Wa3Bb4q8AfKN95bmzq/rNtBm1izdDewDMkPU7Z03DIRcB+9cQasZUor0+Kf9FTfV38iREZtJUk\nFzO8n2eo1UXj2lu0avoMqgF0KeXGyi8p+3gcKmk1Sg/13YBf1JYsohneyKzv4dMpNy2PARp3A6Pa\nT2VHuvfs36v3qebK65l5s3V2TuO5XxijHid1Od76e5UCYEQXtr8l6YeU67nlKAWayxrc4njQrk0f\noRTGOlmN5nUKuJyygu5XlBUYR1Rtt5+hzPq/tMZsw/UoLSsXJa1FuQHb3nbxERr+3XsQDfr9BNv3\nUNrnHV93lmG6hFm/Cy0DvBZ4jFLA6Hf70PYzVG2BP8esbYGvolmrglcB9rd9Xd1BRtGgTay5i5nX\nCbcAWzCzi81rKPcWmuQqYA3KCs2InkkBcMAM4EqSget9POAzqAbNYcCK1eOjKRdLOwELU26wfLim\nXBGNYHu1ujOMpqpt3E+BCcB9zNqzvwkTcF5IWZUwJ3dV50a9Vu9wbBlKC913A+/tbZyI5rH9ON1b\nHjfNoF2bXgB8StKvKIUnAEtagDKrv2nbPnyRckMZys3xNSkTdOejFNA+WFOuuXEjpZ3a2dXz7SjX\nN+e3nbc6cG8Pc0VlPNxPkDQR2JnuE+4asX+r7d07HZe0JHAuzfhsGuS2wH9i5mfqoBi0iTW/BiZS\n9gD9X+Cbkv6bss/ultWxJtkfOFnSTU3oYBeDQ3YT7lVFDI5srB4R0UySrgPuAd5ju2lfngCQNI2y\n58BsV4lIeiulPUnTZomOG5IOBda2/e66s0T0i0HcPmCQVasXr6QUmM4BdqWsQP9/wBLAerbv7va/\nb4KqmLmA7UfqzjIc1f5Rp1d/7gV2B24G1nXLzaPqO61sb19HzvFs0O8nSPoAZYXf/ZR/e+0T7rDd\n+Inikt4OfN72S+vOMjuSngC2tn1xy7F/AX+yvUXLsW2AU20vNut/pT9JejWlI80HbP+u7jyjQdLJ\nlK02NqZMrHkaeDVwA6Ul9Z9t711fwrkjaVlgadt/q55/mJkTn84FDrfdmFWAkv4BLE5pF/4EZVVm\nK9tetefBYuBlBWBE7w3yDKqBIWl+yj4ru8/pRnlEdCdpJUrbq40pKxXeYvt6SQcAv7d9Ra0B584a\nwIFNLf5V/kxZPTan97W3VOdG//ot8LG6Q0T0mYuZuRpbzHlldmNaGA7itant2yW9irKycUvgWcr1\nwrnApCYV/6rX53LgYNv/WS1n+0k6FDD6le0zq2u0A4GlKT/TPm3FvxdROvUMp6V4jL5Bv59wIHAK\nZcLaIO+TNR14Ud0hhmGQ2wKfSSnG/KbaX+6htvEmFmM+Q5lYcxNlYo2Bg5k5saZpkzaepmwfAoDt\nrwNfry/OPLuQZnQNigGTAmA0hqTZ9UefATxM6af8Xdv93I4kG6s3gO2nJD1D83qKR/QNSf9FKVI8\nC/yecsNi/mp4VUrf/iatXrqR5u2b0O57wPcknWP7zE4nSNqBspfU7r0MFnNtQ8r+MRExU+uqkCUp\nN4muB06lrGZ6IaWt3H8B+/Y83TwY1GvTap+yft8/d46q12d1yn5/jWb7WODY2YzfRfn9inoM+v2E\nlShdKAay+CfpecA6wGTgL/WmGZZBbgs8cMWYAZtY8zzKSuC3AWfVHGdUdGsLHDHWUgCMJhHwEmAF\n4DZmfolfndKS7V5ga+CjkibavqGuoHMwyDOoBs2ZwDuY9eI2IobnK8BfKV8+pgOtX+Qvo+yV0yQH\nAcdIusL2rXWHGQnbJ1ftvX4m6ZeUL1NDsypXpaz82xY4zfYpNcWMiqRJHQ7PT7lxtA3wjd4miuhv\nti8ZeizpJOB82+9rO+0Hkr4L7EDzbigN1LWppBcASw219mobewnwgO1pvU82YhcAWwCzm7gaMa8G\n/X7CVZSuGxfWHWReSZpB9wLTI5RruX73NeB0SUszsy3wdUB7y8y3Adf0Ntq8GdRizABNrHlG0r2U\nImZEzIMUAKNJvgocA7za9p+GDlZ9u39CmeFyFeUL8ecpFyD9aJBnUA2aks2+DwAAIABJREFUXwHH\nSjqNcsPlHtou4G3nC35Ed68Hdrb9mKT2GxD3AsvXkGleTKbcYPmrpJuBB9rGbXtiz1PNvZ2AT1Pa\nR76F57bLewiYBHyhnmjRZnKHY09SirafJ69TxOxsB+zYZezHlFWBTTNo16bHUT5LP9Bh7KOUz9xu\nr2E/+jrwo2rVQrfXp5ETiKKvDPr9hP2BkyXdNAD7tB7OrAXA6ZTruF/Zfrj3keZO2gI3m6QlgLWA\nqVVhsGl+BLyP0s50YEh6JbA2sGD7mO0f9D5RDDq1vGdH9DVJ1wBftv3DDmO7Ap+w/QpJe1Tn9WWb\ntmys3hzVjL1OTLWvjO0mzqqM6AlJjwDvsX1WVQB8GljP9tVVm8kT+vW9uhNJFzOHNjG2N53deD+R\n9HxgPWDl6tA/gD/afrq+VBERo0PSw5T92I7vMLYvcKTtJXqfbOQG7dpU0j3AvrZP7zD2NuAbtlfq\nfbKRaXt9Ol4vNOn1if40iPcTJP2D5/7OLAEsCjwBPNh2ehP3ZYs+JekVwKHARGApyqSUi4EjbF9X\nY7Rhk7QlsKntg9uOf4YysXNo8c+PgV1tN6ZVtaQPUiauTgV+TueJNSfWEG1EJC1Jmbyx4dCh6u//\n/Ey5ToixkBWA0SQvAbq1gPkXsGb1+BZaWmL0m8ygapTG3MiP6FNXAnvQucXajszaOqav2d6k7gyj\nqSr0/b76ExExaM4GviBpGnC67WeryShvBz4H/LLWdCMzaNemS1H2ce/kEZq37+4edQeIwTeg9xMG\nbi+2TgZgNdZAkbQ+cAnwb+AXlCLT8pQOKdtI2tj2VTVGHK59aPv9kbQ5cASlXet3gJdRVttfRdmm\noym+Wf29EvDqDuMGGlMABI6kXNtsDPyW0rnuYWBPYCPgXfVFi0GWFYDRGJL+Ctxg++0dxs4AXmr7\nZdWqkq83abZoRMQgkjQRmAL8GjgF+C7wKeC/KBe3G9u+or6EEf2nWpm5kO1H2o6/APgE8HLgbuA4\n23+uIWJEI0haFjgDeB3wDGUVyVKUSbCXAm+zfX99CUPS34ETbR/ZYezTwPttr977ZBERIzPIq7EG\njaQpwOLAZrYfbTm+GOU77MO2t6gr33BJup2yYvG7LcdOAbYH1rA9tTp2HLCB7U6FtL4kaY6rfW3f\nMadz+oWkWyjbV51M6Y60/lCRWdLxwCK2d60xYgyorACMJjmcsqfCtcDPgPuA5SizeNcB3l2d9yYg\nN5Rj1EhanPJvbCXgn8D17TdmI2JWti+p2hQdw8yZeUcBtwPbN7X4J2kpyuzdTj37m75XSdTvaGBb\nZnY2GJoxfjXlc+hBSmus90h6XYqAEZ3Znga8oZoFvyGwAqV11O9tT6k13DwaoGvT04BPS7rG9tB+\nZkjaBjgYmKV9a1NIWpHq9bF9d915IppC0iTgO51+byStQJkYcHjvkw3bIK/GGjQbAru0Fv8AbD8q\n6YvA9+uJNdeWo3RCa7U5cOlQ8a9yNrBLz1KNgiYV94ZpBeDWqivFdGCxlrHTaeb+1NEAKQBGY9j+\nv6qFz2GUHtDPp8yY+COwRcsX+Y8Bz9aTMgZN9QXkQMoeBEP9uR+VdLTtz9WXLKIZqht6Z0tak/Ll\n5H7bN9Uca0QkLUgpZO7IzPeDdunZH/Pq9ZRZoa0OoNxI3s/2cdUNsIsoK2p36nG+iEaxfQFwQd05\nRsuAXZseTmmD9QtJUynFzJUoLdgup3zva5Rqb/rDgFVajt0JHGL7R7UFi2iOQ4FzKd0O2q1Yjfdz\nAXBdSrGv1R7AdGDLltVYUCaxpwBYnzm1xGtKy7xHadkGSdJalDaTl7ed9wgN/a4q6f9RrheWAf7X\n9tTq/sK97QXcPjcVWLJ6fAel7efF1fM1O/0PIkZDCoDRKENf4CVNAJYFptme0XbO9FrCxcCRdBhw\nCGWW3qmUTdZfCOwMHCbpebYn15cwojls/x34e9055tEhwCbAbsAPgX0pX+Z3p8zm+0hdwWKgrAq0\nr+rbFrjN9nEAtu+R9FVgco+zRUSNBu3a1PYTVbvwXSirFZahXCucD/yoaa3xJO0HHEtpHXcEz319\nvi9pCdvfnM1/IiK6T7KD0sb5yV4FGaGBXY01gK6grEKf0tYCdBHgk8xaQOtXNwLbUf5NUT025bO0\n1eqUz6XGkLQA8CNgB8p7g4GzKIW0LwF/o3QMaIpLKUW/X1LuJxwqaTVKq/rdKHtRRoy67AEYEdGF\npLuBk21/osPYl4F3216x98kimqNqU7Y1ZSZ8e8tM226fIdu3JN1IaWd6AmUF+nq2r67GfgrcbTtF\nwJgnkp4AtrJ9SfV8Ycrm8D+wvVfLeRsDF9heoJ6kEf1H0rPARravlDSD2c/et+1GTYjNtWl/k3Qb\n8Gvbe3YYOwmYmD0NI2YlaRPgjdXTzwLfo6wIbrUQsA3wuO31e5du7ki6F9hzqK1xtRrrJuBztie1\nnPcG4DzbC9eTNCS9hrL6ajqlIHMPZQX6NpR/b5vY/kNtAYep2nLj9OrPvZTJqTcD67rlpr+kMyh1\ngO3ryDkS1bXNXpSJtxdQfr71bF8t6f3Ah2yvW2fGuSHpxcCKtn9b7ft+FKWby8KUlc8fzv7UMRYa\n9YUnIqLHlgDO6zJ2LvDBHmaJaBxJr6PM0Fuyyylm1hY5/WwV4C9Vz/6naWm1QmkN+j0auApQ0vJ0\nLtBmT8N6/ANYG7ikev56SrueS9vOW5jSyiciZjocuKvl8aDNds21aX9bnu7795xCaSEeEbOaSCn8\nQXnf3qPDOU8BNwD79yrUCA3saqxBU00W2hCYBGwJLA08QGmzf4Tt6+rMN1y2z5R0AKU9+NKUlYv7\ntBX/XgRsChxUT8oR2xn4rO1TJLW3L70NWK33kUbO9i1UK4RtP015zQ6sNVSMCykARkR0dwWwPqWN\nT7v1q/GI6O4Y4Hbg/cB1tp+qN848u5+y5xKUIs0rgd9Wz5elzBRtDEkrUVqqbNxpmHKzopH7RDTc\necBBki6j3Bg6mHLT6+y28zYE7uxxtoi+ZvuwlseTa4wyVgbq2lTS/JS9THemTERpX9HctFWa1wEv\n7jK2FnB9D7NENEb13n0YQLV6e0PbV9abasS+BpwuaWlmrsa6Dvhd23lvA67pbbRoZ/ta4B1155hX\nto+ltKDuNn4X3Sfl9rNlgL92GZvArNcNfau65pkK7G47rT6jp5p0MR0RMeaq/SWH7A+cIekZ4KfM\n3MdjR2BPymy+iOjuZcCOtq+qO8gouRxYF/gV8DPgCEmLUXr2H8isK7T63fHAOpSZoNfR/3uqjBef\nA97KzJtCAibZvm/ohGoG7B7Aab2PFxG9NODXpkdT2nr9itK6rOmfQx8BTpU0DTi96hgwH/B24BPA\nu2pNF9EAtifM+az+NeCrsQaapCUokzWmVgWzqN9tlD3zLuow9hpKe91GsP1Udf02ve4sMf5kD8CI\niBYd9osRndtHCZjRsFnJET0l6QbgM7bPqDvLaJC0HrCK7dOrwt9JlELNfJQv9++y3ZgVWZIeBPa3\n/cO6s8RzVf++3gksBVxp+7dt4y8E3gv80nZjvvhGjDVJk+Z81n80Yh/aQb42lfRP4Djbn687y0hJ\n+gfPfT2WoHQLeBZ4kPI+Ph/wGPCQ7VV7HjKiQSStMpvhGcDDth/tVZ4YLJK2BDa1fXDb8c9QWoEO\nfYb+GNjV9jM9jhgtJH0K+DSwD2Wi0OPAqymrGU8DJtv+en0J546kbwPY3rvuLDG+pAAYEdFC0mTm\nYr+Y1lZTEfFcknYCPgZsbnsg9yqTtACwQBN/Pkl3A3vY7rafVEREo1TFsuGy7b5vczzI16aSHgbe\nZrvTzP5GkHQSc/f6dNrbLCIqHSY9dHIr8CXbJ/QgUgwQSWdQPv93aDm2OaUF/3XAdyhdbD4AHGT7\nK7UEDeA/XU9OpnQ6eJLS8vPflL3rT7X9nhrjzTVJb6O0ar0COBO4h7b3uyZfE0X/SgEwIiIixoSk\nHwJvABYDfk/ZVL2Vbe/W82ABgKTDgBfbfm/dWSIiYvyR9CPg7wO6X2NEjICkvSkrfh6itNy/F1ie\n0kp3CeA4yv7VWwF72T6pnqTRRJJuB46w/d2WY6cA2wNr2J5aHTsO2MD2q2sJGs8h6Q3AlsBywP3A\nubYvqTfV3JvNRDVTdXhowuS0aJ4UACMiImJMSLptDqfY9ho9CTNCknYFzrZ9f/V4tmz/oAexRkV1\ng+Vg4HbgHGYt0GL7xB7HioiIcULSBsAPKLP7u30O3drrXBFRH0lfBlaz/Y4OYz8D7rD9sWqi4Tq2\n1+15yGgsSU8AW9u+uOXYv4A/2d6i5dg2lBVmi/U+ZQwqSRPndE4TC5vR/1IAjIiIiOiimqW3oe0r\nh9FarlEz9gbt54mIaCVpW8pN5G90GNsXuM32Ob1PFkPaPoc63pjI51DE+CJpKrC77XM7jG0FnGT7\nhZLeSinQLNzzkNFYku4F9rR9dvV8LeAm4HO2J7Wc9wbgvPz7qpekqykThf7P9r1154loqsZsEB4R\nERFRg9UpvfmHHg+SQft5IiJaHQKc3mVsoWo8BcB67clc7J8XEePCYsALuoy9AFi0evwI8GxPEsUg\nuRHYDji7er4d5XPo/LbzVqe0n4163Qt8CfiSpCmUYuCZtqfXG2veSFocWAdYCfgncL3tR+pNFYMs\nBcCIiIiILmzf0enxIBi0nycios1Lgau7jP0Z+GwPs0QH2bsrIjq4BDhS0g22rxo6KGk94PPAr6tD\nawF31pAvmu1rwOmSlqYUl3YHrgN+13be24Brehst2tneStJywLuB9wKnAI9KOg34ke1fz/Y/0Ick\nTQIOpExmUHX4UUlH2/5cfclikE2oO0BERERERETEKJvAzJUi7RYDnt/DLBERMTz7Ak8BV0q6TdIV\n1b7iVwBPAh+uzlsU+GZNGaOhbJ8JHACsD+wKXA680y37Y0l6EbAp6RLQF2zfZ/sY2+sB/0X5vd8M\nmCKpURNaJR0GTAZ+DGwOvAJ4E/AT4DBJk2sLFwMtewBGREREdFHdcBj2xZLtNcYwzqiTtCWwD7A2\nsGD7eNN+nkE0h9fItl/c+1QR/U/SpcBU2+/oMHYasJLtjXqfLFpVM/t3pvt73F69TxURdZL0fGAP\nYANgBUo7/ssp+/89XWe2iKiXpAWBtwNHASs2aa9gSXcDJ9v+RIexLwPvtr1i75PFoEsL0IiI2ZC0\nLmWPmI2BJYHX2L5a0pHAbzptTh4RA+USnlsA3Ax4IaVNzL3V49cBU4ELe55uHkjaGjgLmEJplXcu\nsDDl57kD+G196QLyGkXMo68AP5P0U+AE4C7KXit7U1p7vbPGbAFIWptyU38+YBFgGrB09fxB4OH6\n0g2PpI3n5nzbvxmrLBGDoiryfbv6ExGBpDcCuwA7UFYAXwl8odZQc28J4LwuY+cCH+xhlhhHUgCM\niOhC0uspN11vpfQa369leAZlRUYKgBEDzPbuQ48l7U2Zifxa23e1HF+ZciH/+54HnDeHUFqofBR4\nGvhsNcHhJZSf51d1hgsgr1HEiNk+Q9JHKHtG7VAdFvAYsL/t02sLF0OOptzA2x54HNgKuJbSlu0w\nSqG2313M8DoFqDqvMSsVIiIi6iRpHcref++mTOK6A/gf4Ie2b64z2whdQWk/O6XD2PrVeMSoSwvQ\niIguqtZR91NuSsxH2Ytgverm6w7AMbZXqTNjRPSOpJuBT9v+aYexHYEjba/Z+2QjI+lBYEfKF5Bn\ngI1sX1mN7QZ83PYraow47uU1iph3khYDXgssQ1lhdpntx+pNFQCS7qFMqDuL8h73Gtt/rMYOAray\nvWmNEedI0sS5Od/2JWOVJWIQSJof+BSlNfAqwAJtp9h2FjNEjAOSZlC6AfyUUvRrXPcTSRNanr4c\nOIOyuvmnzOwotCPwfmA72zf0PGQMvHxoRkR09ypgB9uW1D5bYhrwghoyRfS16iJ9uLOLmvYF/kXA\n9C5jT1JmJTbJDOCZ6j3uX5SbLFdWY3cD2VuufnmNIuaR7Ufp3m4p6rUo8IDtGZIeBpZtGfsjMKme\nWMOXgl7EqDsa2JfS5eB0yjV2RIxPOwG/sN3k94FneO79EVH2Lzyq7TxRuiA06f5INET+UUVEdDed\nstdSJyvQgH1JImpwOMMvADbNDcAnJF1g+z+FQEkLAZ+oxpvkJmC16vEfgQMk/Y7yJeVA4PZ6YkWL\nvEYRIzScvdmyH1vtbgeWrx7fRNmXcai9/rbAQzVkioh6vQM41Pbn6w4SEfXq1HlnSLUCfzfbe/Yw\n0kgM8v2RaIgUACMiuruUcrP15y3Hhj649wIu6n2kiP5me3LdGcbQQcDZwJ2SzmFmy46tKRt6b1Vj\ntpE4GXhZ9fhQSpvJob0Nn6XstRD1ymsUMXIXM+cbLtmPrV4XAJtT2mB9FTi12oP7GeCllP0bG6Xa\nr2gvYG1gwbZh296s96kiGmVRmrevdkT0gKQ1KfsE7wKsCjwB9HUBcMDvj0RDZA/AiIguJL0S+B1l\ndvJpwCHA14FXAq8G1rd9U20BI6LnJL0M+CywIWUl8D2UmxSfs31jndnmlaQXUYqYCwFTsv9A/8lr\nFDF8XfZmW4aysmwisJ/tX/U2VbSStACwgO1HqudvobT7WpiyEvAEN+iGhaQNgEso3x3WorTyWorS\nvvku4O+231hbwIgGkPQjyu/K5LqzRET9JC1BuTbYFdioOnwN8L/A/w1dQ0REdykARkTMhqRXUfYh\n2JgyS3wG8FvgY7b/VGe2iCaQND+lYNFtJvwRvU8VERHjmaSvUQpPH6o7SwwOSRdSugPsAjwNrGf7\naklvBH4I7GI7HUQiZqMqpP+A0gXhHOCB9nNs39rrXBHRO5ImAG+mFP3eSrmPcDdlX9B9gU3Txj1i\n+FIAjIgYBkkLAksDD9l+ou48EU0gaUVKK93VKG3YVA395+LDdtqv1UiSgLdQJjksA0y2fUe1cuZm\n23fXGjCA/7xOKzBrET03wSJGQNKbgFNtL1t3lvFM0q3A22xf02FsHeAXttfofbKRkfQvYDfK6sVn\ngA1s/6Ea+yCwu+0NaowY0fckzWh52vGGZb4/RAwuSV8G3gMsB0wHzgS+T9kKYXHKpIBNUgCMGL7s\nARgRMQy2p1NmHEXE8B0N/ItSXLoT2KB6vieljccW9UUbHkknAkfYvq16PDu2vVcvco0GSUtRZlZv\nADwCLEZpc3wH8H7Kl6v9awsYSFoG+CbwNrpft+cmWMTcW5vS1SHqtRqwQJexBSn7+zTJ84HHbc+Q\n9ABl4saQm4B16okV0Sh7Muf9WyNicH2M8h5wDmXizP1DA5Ly3hAxAikARkS0kDRpLk5P+8KI2XsD\n8HFmFs9n2L4dmCRpPuBYYLuasg3XpsD/VI/fyOxvSDTtC8nRwMrA64A/AE+1jE0BPlFHqHiO71L+\nDX4DuJHnvkYRMRuSdu1weH5KEWYvShupqF+3z871gId6GWQU3AKsVD2+FthT0i+r53sAU2tJFdEg\ntk+qO0NE1OpE4B3ANsBNkk4FfmD7ynpjRTRXCoAREc81ue15a9vC9uMAKQBGdLcMcHc1E/5xYKmW\nsYuA/eqJNXy2V295vFqNUcbCdsDHbf++Ksi2upNSHIx6bQp8JDfDIkbkpC7HnwR+DHykd1FiiKSP\nAh+tnho4S1L75IaFKK33T+1ltlFwFrAJcApwJHA2ZYX9s8CiZFV9xLBVe4C9nPJ94o+2H685UkT0\ngO33SdoP2IHSVnsf4IOS/gacQfMm3UbULgXAiIgWticMPZb0cuAXwLcpNyDuBV4I7Expj7dtHRkj\nGuQuYGh/pVsoLT+nVM9fQ+npH/VZFPhnl7EF6Tz5IXrrAcpnT0TMvdU7HJtuO79T9boVuLB6vBvw\nR0p78FZPAjcA3+lhrnlme3LL4ymSNgTeDiwMnGv7/LqyRTSJpH2BQynfIwysD1wt6UzgItvH1pkv\nIsZWtQXPKcApklYAdgV2AQ6uTjlK0nHAadW5ETEbslM4j4joRNJFwPm2j+ow9ingTbY3632yiGaQ\n9C3g37Y/KumDlL3MpgBPA1sC/2t73zozzomkVebmfNt3jlWW0Sbpz5T3uIOqFYBPA+vZvlrSF4E3\n2H5tvSnHN0kfAyYC2zsX7RExYCR9Dzjc9m11Z4mI/iDp/cDxlDaA5wM/Yeb16YHAW21PrDNjRNRD\n0vqUyUM7UVYHP2x7qdn/ryIiBcCIiC6qloXb2Z7SYexNwM9tL9L7ZBHNIGlZYGnbf6uef5hysb4w\ncC7lpl9fz9iTNIO5aDNiu72VZt+qbrB8EziMMsPyFuBNwKqUPef2tn1yfQkDQNKXKXtgTAEebBu2\n7UN7nyqiGSQJeAuwMeVG0WTbd0iaCNxs++7Z/gei5yQtTVm9eb3tJ+vOMzckPQts1GmfIkmvBq5s\n0nVCRB0k/RX4he1Pdpigtg3wXdvL15syIuok6fmU67tdbW9fd56IfpcWoBER3T0MbM7MloWttqjG\nI6IL29OAaS3Pvw58vb5EI7InA7rPgO0TJL2YUgA8vDp8ATAD+FKKf/WTtDWwL7AAsHaHU0xpkRUR\nbSQtBZwDbEDZh20xymfQHZRW7g+QPdlqJemzwCK2P1U93xj4JbAI8E9Jm9m+uc6Mc2l2rbPnY0Cv\nJyJG2erAeV3GHgeW7GGWiOhDtp8GTq/+RMQcpAAYEdHdicCnJC0K/JSZewDuCOwNHFljtoi+V7XR\n/ZDtGzuMvQT4lu039j7Z8Nk+qe4MY8n2wZKOp0xqeAFwP3CB7VvrTRaVrwJ/oBQBb6y+7EbE8BwN\nrAy8jvJ79FTL2BTgE3WEiud4L/CVludfAq6p/p4EHAG8q4Zcc0XSBGYW/yZUz1stBGxFy6SoiOhq\nGrBal7G16b5/dURERHSQAmBERHeTKDN1DwD2qY6JMvPwSGByPbEiGmMTYPEuY4tR9jZrDEkvAJYa\namnaNvYS4IFq1WOj2L4DOKHuHNHRKsD+tq+rO0hEA20HfNz276s2cq3upBQHo14rATfDfz5j1wc2\ns32xpPmBY+sMNxySDqV8Z4DyveF3szn9uLFPFNF4ZwGTJF1MWbEN4GprgY8CZ9YVLCIioolSAIyI\n6ML2DOAQSV8BXgGsANwDXGs77T8jhqdbu6sXA4/1MsgoOI7SMu4DHcY+StlfaseeJhoFklam3Ahf\nsH3M9kW9TxQt/gSsWHeIiIZalO4rRRZk9u0aozeeBeavHm8MTGdmAe1fwNJ1hJpLF1d/i1II/C5w\nV9s5TwI3UNqbRsTsfRZ4I3A9cAXlu8SxwEuB+5jZtj4iIiKGIQXAiIg5sP0Q8Nu6c0Q0gaQ9gD2q\npwa+LenRttMWAtYBLuxltlHwekorxk7OB77RwyzzTNIawMnAa4YOVX+7emzKnkVRn/2B70u62fbs\nVpVExKxuorQ37rSX80QgK2vr9xfgvZIuo+y5e0lLq+OVKTf7+5rtS4BLACQZOMH23fWmimgu2/dL\nWo/ShWdL4BbKvctvAF+z/Uid+SIiIpomBcCIiBaSNgautv1Y9Xi2bP+mB7EimmQGZUY/lCJS6/Mh\n9wPHA1/sYa7RsBTQbfXvI5QVgE3yHUqLyQOAG3nu/ljRH86ktNH9jaTHgYfaxm171d7HimiEbwLf\nlPQwcEp1bMlqosp+lP2co16HAz8H3gM8TbnZP2Rr4Oo6Qo2U7cPqzhAxCGw/StkD9Ii6s0RERDSd\n7G6duSIixh9JM4ANbV9ZPe72JinKjdesjonoQtKvgQ/avrHuLKNB0t+BE20f2WHs08D7ba/e+2Qj\nU63M3N32z+rOEp1JOonun0MA2N5jduMR45mko4CPU67bhlY2zwC+ZPszdWaLQtLqwKuAP9u+peX4\nB4BrbF9eW7gRkDQR2Jkywaa9tbZtb9b7VBERERExXqUAGBHRovrSflW1AnDinM6v2v5ExDhQ3Uje\nD9jJ9tktx7cB/g843vYn68o3tyT9FTjI9ll1Z4mIGCuSVgU2B5ajrEC/wPat9aaKQVQVLY+n/Du7\nmbL333PY3rTXuSL6naS52XM6hfSIiIi5kAJgREREjBlJi1PaeHWbCd+Y1j6SFqbsJbUBMBX4J7AS\nsDxwObC57SfqSzh3JO0CfADY0vbjdeeJiIjBN8jt9iX9DbgS2NN22mpHDJOki5lDx4NWKaRHREQM\nXwqAERFdSJoATLD9TMuxLYF1gIts/6m2cBENIOl1wFnAkl1OaVwbXUnPB3ahrCZZBpgGnA/8qPW9\noikkfZ6yD9blwINtw7a9W+9TRStJrwAOBSZS9qF8ALgYOML2dTVGi4iYa4Pcbr/aq/Wtti+sO0tE\nREREBKQAGBHRlaQfA0/a3rV6vg9wXDX8NLCN7Sl15Yvod5L+AMwHvB+4LrPh+4uk3YETgWeB+4D2\n18e21+h1rphJ0vrAJcC/gV9QVp4uD7wFWAjY2PZV9SWMiJg7g9xuX9JvgB/aPqHuLBHjQTVh9+/A\nW2z/pe48ERER/SgFwIiILiTdAXzS9qnV81uAC4EDgW8Dy6f9SER3kh4DdrR9Tt1ZYlbVe9wfgb1s\nP1R3npiVpCnA4sBmth9tOb4YpR3tw7a3qCtfRETMJOm/gZOBDzapdWlEU0majzIxdz3bV9edJyIi\noh89r+4AERF9bDnKHl9IWhNYHfiG7UclfQ84pc5wEQ1wJ7BA3SFGi6TbmP3+JLb94l7lGQXLAMel\n+NfXNgR2aS3+AVSfQ18Evl9PrIiI6OAsyqSNX0t6gs6ttVftfayIiIiIGK9SAIyI6O4Ryg1ygE2A\nabavrZ4/CyxYR6iIBjkMOFjShbYfqTvMKLiEWQuAywCvBR4DLup5onlzKfAyysrm6E9zatWRVh4R\n0SiS5uaz0rY3G7Mwo+9C8r4cEREREX0kBcCIiO4uoxQvngEOAFrbGK4J3FVLqojm2BZ4IXCbpN8D\nD7SN2/ZuvY81MrZ373Rc0pLAuZSWjE3yEeAnkh6k5G9fqYDtGT0c/BPeAAAgAElEQVRPFa2uAD4t\naUpbC9BFgE8Cl9eWLCJiZCbw3CLZ2pS9TW8H7qVcN6wG3APc1ONs86TbdUJERERERF2yB2BERBeS\n1gLOphT7bgXeZPv2auwi4A7be9SXMKK/VS0zZ8e21+hJmDEm6e3A522/tO4swyVpqLjX7WLQtjNZ\nrEaSXgNcDEwHfkm5Ib48sA2wELCJ7T/UFjCiz1Tva8P9gpv3uJpJ2h74H+Cdtq9sOb4B8GPgI7Z/\nXle+iOhv2QMwIiJizvKFJyKiC9s3Ay+RtIzt+9uGPwJMrSFWRGPYXr3uDD00HXhR3SHm0uGkVVlf\ns32lpA2BScCWwNKUlbQXAUfYvq7OfBF9KO9rzXIEcEhr8Q/A9hWSJgOfAxpVAJS0LnAIsDGwJPAa\n21dLOhL4je1zaw0YEREREeNKCoAREXPQofhHbrpGBICk5wHrAJOBv9SbZu7Ynlx3hpizau/Zd9Sd\nI6IJ8r7WOGsB/+oydh+lC0djSHo9pR34rcApwH4twzOAfSgttyMiIiIiemJC3QEiIiJicElaRNL+\nkk6T9OuqtS6S3iWpMe0yobSWk/Rs6x/gSeAqyk3Kj9abMAadpCUkrSepaatNIyI6uQ34QJexD1D2\nBWySo4DzgP8CPtY2djXwqp4nihhsM4DDgLvrDhIREdGvsgIwIiIixoSklSn7l70IuJGyUm6xanhT\n4E3A+2oJNzKdWstNB+4AfmX74d5HikEjaUtgU9sHtx3/DKUV6POq5z8GdrX9TO9TRjSDpPmBrYC1\ngQXbhm37iN6nihaHASdLuh44DbgXeCFl1fNLgffUmG0kXgXsYNuS2q8XpgEvqCFTRCNJWh5YhVnf\nu7H9m+pvU95HIiIioosUACMiImKsfIWyQm4tyszcp1rGLqG0zWyMtJaLHtmHtkKzpM0pe2VdB3wH\neBlldcxVlN+ziGgjaUXgUmA1yu+UqqHW368UAGtk+1RJ0yg38D8FPB94GvgDsKXtC+vMNwLTgYW7\njK0AZKJQxBxIWgn4EWUfzVmGKe/h8/U0VERERIOlABgRERFjZXNgb9t3Smr/ov5PYKUaMo0JSROA\nJW0/UHeWaLx1mbUosQflxvKWtqcCSAJ4NykARnRzNGV/uY2BO4ENqud7AjsBW9QXLYbYngJMqT5H\nlwWm2Z5Rc6yRuhQ4QNLPW44NFZz3Ai7qfaSIxjme0jXkIMrEpyfrjRMREdFsKQBGRETEWJkfeLTL\n2BKUWf59TdIDwJtsX109F/Bz4ADbt7acuj5wGZmRHPNuOeCWtmObA5cOFf8qZwO79CxVRPO8Afg4\nM/eGmmH7dmBSNSnlWGC7mrJFm6rod1/dOebRIcDvgGsoLU0N7Cbpq8CrKdcKETF7bwD2t/3DuoNE\nREQMggl1B4iIiIiBdS3w9i5jW1HaF/a7JXnuhKkJwLbV8Yix8CiwyNATSWsBywCXt533CCk4R8zO\nMsDdVWHpcWCplrGLgE3qCBWDy/Y1lBWn9wKfobQr3K8anmj7prqyRTTIv2n+ZICIiIi+kRWAERER\nMVaOBk6rWhWeUh17uaTtKK2w3lpXsIg+diNlVdLZ1fPtKKtIzm87b3XKTeaI6OwuSktJKKtqtwCm\nVM9fQ2mrGzGqqo4Bm0laEFgaeMj2EzXHimiSEygdDs6rO0hERMQgSAEwIiIixoTt0yV9CDiKsucS\nwA8oK5z2s31ubeEi+tfXgNMlLU0p8O1O2QPnd23nvY3SZi4iOvs1MBE4E/hf4JuS/pvSfnrL6ljE\nmLA9nZntZyNiNiTt2fL0LmAXSRcB5wCz7K9t+8ReZYuIiGg62Z7zWREREREjJGkRYCPK3mb3A5fZ\n7rY3YF+RNAPY0PaV1fP5KDeP1xvaF7A6vgHl50pLxphnkvYHDqSsHrkS2Mf2zS3jLwKuBw6y/e16\nUkb0N0nLAkvb/lv1/MPATsDCwLnA4VWRJmLUSHoZ8A5gZWDBtmHb3q33qSL6W3W9PVzO9XZERMTw\npQAYERER0UV1Q+LtzFxpNR9wE6Ut419aTl0X+EluSERERIxPknYFTqS0bb4PeKrtFNteo+fBIvqc\npFXn5nzbd4xVloiIiEGTAmBERESMGUnPo6z+6zQTvu9b+FQFwPaLJXU7lgJgRER/qNrHfcj2jR3G\nXgJ8y/Ybe58sBpWkm4Frgb1sP1R3noiIiIiI7AEYERERY0LSq4AzgBdRCmTtTJkp38/2qDtARESM\nyCbA4l3GFqPsDxgxmpantGxO8S9ihCQ9C2w01H6/bezVwJWZcBcRETF8KQBGRETEWPkW8BiwPXAj\ns7bC6nu2v193hoiIGLFu7W5eTPl8ihhNvwNeBlxYd5CIBus0aXDIfHR/X4+IiIgOUgCMiIiIsfJy\nYEfb59QdJCIiBp+kPZi5ctvAtyU92nbaQsA6pEgTo28/4HRJ9wPnAw+2n2B7Rs9TRTSApAnMLP5N\nqJ63WgjYCpjW02ARERENlwJgREREjJW/AYvUHSIiIsaNGcCz1WO1PR9yP3A88MUe5orx4S7gT8CP\nuoyb3IOJmIWkQ4FJ1VNTVtN2c9zYJ4qIiBgcsrN6PiIiIkafpDdTbrC+xfaddeeJiIjxQ9KvgQ/a\nvrHuLDE+SPohsBNwFl1an9s+rNe5IvqdpImUfVtFKQR+l1JQb/UkcAPwy6ykjYiIGL4UACMiImLM\nSDoK+ChlNWB7Kyzbntj7VBERERGjS9IjwCG2/6fuLBFNVa0GPMH23XVniYiIGARpPxERERFjQtLB\nwEHAv4BHmLUNW0RExJiRtDiwNbAKsGDbsG0f0ftUMcAep6xQiogRyirZiIiI0ZUVgBERETEmJE0F\nzgD2s53iX0RE9Iyk11FaMS7Z5RTbnq+HkWLASToSWMH2HnVniWgyScsBOwNr03nyxl69TxUREdFM\nWQEYERERY2Vh4Kcp/kVERA2OAW4H3g9cZ3uW/dgiRtkdwM6SLgDOZdbW59g+seepIhpE0trA5cB8\nwCLANGDp6vmDwMP1pYuIiGierACMiIiIMSHpx8C1tj9fd5aIiBhfJD0G7Gj7nLqzxPggacYcTsmq\n04g5kPQLYAFge0pb3fWAa4FdgcOAbW1fU1/CiIiIZskKwIiIiBgrxwAnSYLuM+Fv7XWoiIgYF+6k\n3ESO6JXV6w4QMQDWB/YBnqyeT7D9DHCipGUp3y82rStcRERE02QFYERERIyJtpnwHS84MhM+IiLG\ngqSdgI8Bm9t+pO48ERExZ5IeBba2/VtJDwI72z63Gnsj8Avbi9YaMiIiokGyAjAiIiLGyp50KfxF\nRESMsW2BFwK3Sfo98EDbuG3v1vtYERExG7cDy1ePbwLeSekkAuV9/aEaMkVERDRWVgBGRERERETE\nQJF02xxOse01ehImxoXq31y3GywzgIeBq4BjbV/fs2ARDSLpq8CitveWtCNwKnAz8AzwUuDztifV\nmTEiIqJJUgCMiIiIiIiIiJgHkk6i7E22HHAZcC9lFerrgKnA1cBGwJLAZrYvqydpRP+StACwwFDr\nZknbAu8CFqasBDzBuZEZERExbCkARkRExJiQdOJshltnwp9ue3pvUkVERESMPkl7AR8BtrA9teX4\nCsB5wNeBU4ALgUdtb15L0IgGkLQ4sA6wEvBP4Prs5xoRETH3UgCMiIiIMVG1wlqCMtP9GWAasCxl\nD+Kh/TuWBG4BNrV9Vx05IyJiMElaBNgL2BhYBtjb9s2S3gX82faNtQaMgSLpb8CnbZ/WYWxH4Ejb\na0raGfiW7SV6HjKiASRNAg4EFgVEaa37GHC07c/VmS0iIqJpJtQdICIiIgbWuymr/N4OLGh7RWBB\n4J3AI8C2wAbVsS/UFTIiIgaPpJWBa4GjgbUoRcDFquFNgY/XFC0G18rAk13GplNWMkFZzTR/TxJF\nNIykw4DJwI+BzYFXVH//BDhM0uTawkVERDTQ8+oOEBEREQPra8AXbZ8xdMD2DOBnkpYDjrH9Gklf\nACbVFTIiIgbSVyjFmLWAu4GnWsYuodxgjhhNfwUOlHS+7f8UAiUtSCk4/7U6tCJlf8CImNX7ga/Y\n/kTLsb8AF0l6GNibvH9HREQMWwqAERERMVZeSWnv2cktlH09AG4AlupJooiIGC82p7T8vFPSfG1j\n/2TmaqyI0XIQ8EvgTknnAPcBywFbU1qeb12d91rg/FoSRvS/JSh7ZnZyLvDBHmaJiIhovLQAjYiI\niLEyFXhHl7F3MnP2++LAgz1JFBER48X8wKNdxpYAnu5hlhgHbE8BXgVcSGk5++Hq7ynAf9u+sDpv\nf9t71xY0or9dAazfZWz9ajwiIiKGKSsAIyIiYqwcA3xN0orAacycCf9Oyiz4A6rz3gD8qZaEEREx\nqK6l7EF7boexrYCrehsnxgPbN1D2QI6IkdkfOEPSM8BPKRMGXwjsCOwJbCfpP4sZqu0FIiIiogvZ\nrjtDREREDChJ76Ps7/eilsN3AYfZ/m51zmrAv21nP5yIiBgVknagTD75LnAKZVXWrpQ9AT8FvNV2\np+JgxDypihMvB5YB/mj78ZojRTSGpKGCXqeblWo7bttZ2BARETEbKQBGRETEmJIkSgFwBeAe4C7n\nAiQiIsaYpH2Ao4DFKDeOobQF/YTtb9cWLAaWpH2BQ4FlKYWK9W1fLelM4CLbx9YaMKLPSZpM5+Jf\nR7YPG7s0ERERzZcCYERERERERAwkSYsAG1FaUN8PXGa7296AESMm6f3A8cCJwPnAT4D1qgLggZRV\npxPrzBgRERER48uEOZ8SERERMTKS1pV0uqRpkp6R9Krq+JGS3lx3voiIGGy2H7c9xfYpts9L8S/G\n0MeAr9jeGzijbexGYO3eR4qIiIiI8Sy9siMiImJMSHo9MAW4lbL/0n4twzOAfYDsvxQREWNC0vMo\nq/9WBhZsH7d9Ys9DxSBbHTivy9jjwJI9zBIRERERkQJgREREjJmjKDfCtgfm47kFwKuBXesIFRER\ng69acX4GZQ9adTjFlFaNEaNlGrBal7G1gX/2LkpERERERAqAERERMXZeBexg25LaNx2eBryghkwR\nETE+fAt4jDIJ5UbgqXrjxDhwFjBJ0sXAHdUxS1oW+ChwZl3BIiIiImJ8SgEwIiIixsp0YOEuYysA\nD/cwS0REjC8vB3a0fU7dQWLc+CzwRuB64ArKKtNjgZcC9wGH1xctIiIiIsajCXUHiIiIiIF1KXCA\npPlajg2tBNwLuKj3kSIiYpz4G7BI3SFi/LB9P7Ae8AXg+cAtlEnX3wA2sp2JTxERERHRU7LbO3JF\nREREzDtJrwR+B9wOnAYcAnwdeCXwamB92zfVFjAiIgaWpDcDXwTeYvvOuvNERERERET0WgqAERER\nMWYkvQo4GtgYmA+YAfwW+JjtP9WZLSIiBpukoyh7r/0NeLBt2LYn9j5VDCpJE4AJtp9pObYlsA5w\nUa57IiIiIqLXUgCMiIiIMSdpQWBp4CHbT9SdJyIiBpukg4EjgX8Bfweeaj/H9qa9zhWDS9KPgSdt\n71o93wc4rhp+GtjG9pS68kVERETE+JMCYERERPSMpKWB1YHrbT9Zd56IiBhMkqYCZwD72X627jwx\n+CTdAXzS9qnV81uAC4EDgW8Dy6foHBERERG9NKHuABERETGYJH1W0hdanm9M2Q/wSuBmSWvVlS0i\nIgbewsBPU/yLHloO+CeApDUpE56+YftR4HvAK2rMFhERERHjUAqAERERMVbeC9za8vxLwDXA9sC9\nwBF1hIqIiHHhV8BGdYeIceURYJnq8SbANNvXVs+fBRasI1REREREjF/PqztAREREDKyVgJsBJL0A\nWB/YzPbFkuYHjq0zXEREDLRjgJMkAZwLPNh+gu1b249FzIPLgIMlPQMcAJzTMrYmcFctqSIiIiJi\n3MoegBERETEmJD0E7Gj7fElvB34ALGn76aod6Hm2F6o3ZUREDCJJM1qedvzSa3u+HsWJcaBqbX42\npdh3K/Am27dXYxcBd9jeo76EERERETHeZAVgREREjJW/AO+VdBmwJ3CJ7aersZWB+2pLFhERg25P\nuhT+IsaC7ZuBl0haxvb9bcMfAabWECsiIiIixrGsAIyIiIgxIWlL4OfA84GngS1t///27j1as7K+\nD/j3x4w6UC/gJfHCLdg0UExtUbRGRS2NKDWVXGgaDQE0TfBSpIXVtBURd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1854672e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from collections import namedtuple\n",
    "\n",
    "featureNames = np.array(['Width X', 'Height Y', 'Soma Radii', 'Soma Surface Area', \\\n",
    "                'Soma Volume', 'Skewness X', 'Skewness Y', 'Skewness Z', \\\n",
    "                'Euclidian Skewness', 'Length', 'Surface Area', \\\n",
    "                'Section Area', 'Volume', 'Average Radius', 'Tips', \\\n",
    "                'Stems', 'Branch Pt', 'Segments', 'branch branch', \\\n",
    "                'branch die', 'die die', 'global angle', 'local angle', \\\n",
    "                'distance from root', 'path_length/euclidean', \\\n",
    "                'mean Contraction', 'branch angle', 'side branch angle', \\\n",
    "                'mean segmental neural length', 'mean segmental euclidean length', \\\n",
    "                'segmental branch angle'])\n",
    "\n",
    "n_groups = len(featureNames)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(25, 10))\n",
    "\n",
    "index = valuesTN[2].astype(int)\n",
    "bar_width = 1/4\n",
    "\n",
    "opacity = 0.4\n",
    "error_config = {'ecolor': '0.3'}\n",
    "\n",
    "def getInd(start) :\n",
    "    ind = [0] * n_groups\n",
    "    for i in range(0,n_groups) :\n",
    "        ind[i] = start + i*bar_width*4\n",
    "    return ind\n",
    "\n",
    "rects1Ind = getInd(0)\n",
    "rects2Ind = getInd(bar_width)\n",
    "rects3Ind = getInd(bar_width*2)\n",
    "\n",
    "rects1 = ax.bar(rects1Ind, valuesTN[0][index], bar_width,\n",
    "                alpha=opacity, color='b',\n",
    "                yerr=valuesTN[1][index], error_kw=error_config,\n",
    "                label='Tree Vs Neuron')\n",
    "\n",
    "rects2 = ax.bar(rects2Ind, valuesT[0][index], bar_width,\n",
    "                alpha=opacity, color='g',\n",
    "                yerr=valuesT[1][index], error_kw=error_config,\n",
    "                label='Tree')\n",
    "\n",
    "rects3 = ax.bar(rects3Ind, valuesN[0][index], bar_width,\n",
    "                alpha=opacity, color='r',\n",
    "                yerr=valuesN[1][index], error_kw=error_config,\n",
    "                label='Neuron')\n",
    "\n",
    "# rects4 = ax.bar(np.arange(n_groups) + bar_width*3, [0]*n_groups, bar_width,\n",
    "#                 alpha=0, color='w',\n",
    "#                 yerr=[0]*n_groups, error_kw=error_config)\n",
    "\n",
    "plt.rc('legend', fontsize=14)\n",
    "\n",
    "ax.set_xlabel('Classifier', fontsize=20)\n",
    "ax.set_ylabel('Accuracy', fontsize=20)\n",
    "ax.set_title('Feature Importance For Each Classifier', fontsize=20)\n",
    "ax.set_xticks(np.arange(31) + bar_width)\n",
    "ax.set_xticklabels(featureNames[index], rotation='vertical', fontsize=16)\n",
    "ax.legend()\n",
    "\n",
    "plt.axis([-1,32,0,.3])\n",
    "fig.tight_layout()\n",
    "\n",
    "# plt.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/featureImportanceMasterNew.eps', dpi = 300)\n",
    "# plt.savefig('/Users/anirudhnatarajan/Documents/UPenn/Images/featureImportanceMasterNew.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
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