EEG_decoding_strategies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pickle, os, glob\n",
    "from EEG.info import info_exp\n",
    "from EEG.converter import converter_mne\n",
    "import mne\n",
    "import numpy as np\n",
    "import sys\n",
    "from mne.decoding import UnsupervisedSpatialFilter, Vectorizer\n",
    "from sklearn.decomposition import PCA, FastICA\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Thu Mar 30 09:51:35 2017\n",
    "\n",
    "@author: Egor Mikheev\n",
    "@email: mail@nebesa.me\n",
    "@package: github/thisroot/eeglab\n",
    "\"\"\"\n",
    "\n",
    "import pickle, os, glob\n",
    "import numpy as np\n",
    "import neo\n",
    "import mne\n",
    "from EEG.info import info_exp\n",
    "\n",
    "class converter_mne:\n",
    "    def __init__(self,data):\n",
    "        self.obj = data\n",
    "        self.list_tests = self.obj.info.list_tests\n",
    "        self.num_tests = self.obj.info.num_tests\n",
    "        self.labels = self.__get_labels()\n",
    "        self.tmin = 0;\n",
    "        self.tmax = (self.obj.data['time'][0]-1.)/1000\n",
    "        \n",
    "    def mne_info(self):\n",
    "        return mne.create_info(ch_names = self.obj.info.chanels_names, \n",
    "                               sfreq = self.obj.info.frequency,\n",
    "                               ch_types = self.obj.info.ch_types, \n",
    "                               montage = self.obj.info.montage)\n",
    "        \n",
    "    def train_raw(self):\n",
    "        raw = np.concatenate(self.obj.data['data_train'],axis=1)\n",
    "        return mne.io.RawArray(raw, self.mne_info()).set_eeg_reference(self.obj.info.chanels_names)\n",
    "    \n",
    "    def test_raw(self,idx):\n",
    "        if(idx >= self.num_tests):\n",
    "            print idx, \" out the range: \", self.num_tests - 1\n",
    "            return False\n",
    "        raw = np.concatenate(self.obj.data['tests'][idx]['data_test'],axis=1)\n",
    "        return mne.io.RawArray(raw, self.mne_info()).set_eeg_reference(self.obj.info.chanels_names)\n",
    "    \n",
    "    def __get_labels(self):\n",
    "        labels = dict.fromkeys(self.obj.info.labels_names)\n",
    "        for idx, i in enumerate(labels):\n",
    "            labels[i] = idx\n",
    "        labels.pop(u'удалено', None)\n",
    "        return labels\n",
    "        \n",
    "        \n",
    "    def train_events(self):\n",
    "        events = []\n",
    "        for i in range(0, len(self.obj.data['start'])):\n",
    "            events.append([int(self.obj.data['start'][i]),0,int(self.obj.data['states_train'][i])])\n",
    "        return np.array(events)\n",
    "        \n",
    "    def train_epochs(self):\n",
    "\n",
    "        #return mne.EpochsArray(np.array(self.obj.data['data_train']), info=self.mne_info(), events=np.array(self.train_events()),\n",
    "        #                 event_id=self.__get_labels())\n",
    "        return mne.Epochs(self.train_raw(),np.array(self.train_events()),event_id=self.labels,add_eeg_ref=False, preload = True, tmin = self.tmin, tmax = self.tmax, baseline = None)\n",
    "        #pass\n",
    "    \n",
    "    def test_events(self,idx):\n",
    "        if(idx >= self.num_tests):\n",
    "            print idx, \" out the range: \", self.num_tests - 1\n",
    "            return False\n",
    "        events = []\n",
    "        for i in range(0, len(self.obj.data['tests'][idx]['start'])):\n",
    "            events.append([int(self.obj.data['tests'][idx]['start'][i]),0,int(self.obj.data['tests'][idx]['states_test'][i])])\n",
    "        return np.array(events)\n",
    "    \n",
    "    def test_epochs(self,idx):\n",
    "        if(idx >= self.num_tests):\n",
    "            print idx, \" out the range: \", self.num_tests - 1\n",
    "            return False\n",
    "        \n",
    "        #return mne.EpochsArray(np.array(self.obj.data['tests'][idx]['data_test']), info=self.mne_info(), events=np.array(self.test_events(idx)),\n",
    "                        # event_id=self.__get_labels())\n",
    "        return mne.Epochs(self.test_raw(idx),np.array(self.test_events(idx)),event_id= self.labels, add_eeg_ref=False, preload = True, tmin = self.tmin, tmax = self.tmax, baseline = None)\n",
    "        \n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "root = \"C:\\\\eeg\\\\01exp\\\\\"\n",
    "name = \"20161129_DBS_001\"\n",
    "\n",
    "def load(path,name):\n",
    "    with open(path + '/' + name, 'rb') as input:\n",
    "        data = pickle.load(input)\n",
    "        return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = load(root + name,'exp_data_aligned.cls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n",
      "Experiment status:  True ; Num tests:  12\n",
      "Align status:  True\n",
      "Respondent name:  Ольга Ветрова\n",
      "Description: Эксперимент с обратной связью\n",
      "Labels_names: [ удалено, отдых, левая, правая ]\n",
      "Frequency: 1000\n",
      "Count train activities:  45\n",
      "Count tests activities: [ 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30 ]\n",
      "Time of activity: 4500\n",
      "Type_chanels: eeg  Montage: standard_1005\n",
      "------------------------------------------------------------------\n",
      "Chanels_names:\n",
      "------------------------------------------------------------------\n",
      "FC5        FT7        FC3        FCz        FC4        FT8       \n",
      "T3         C3         Cz         T4         C4        \n",
      "TP7        CP3        CPz        CP4        TP8       \n",
      "FC1        FC2        FC6        P4         C5        \n",
      "C1         C2         C6         P3         CP5       \n",
      "CP1        CP2        CP6        POz       \n",
      "------------------------------------------------------------------\n",
      "List tests\n",
      "------------------------------------------------------------------\n",
      "20161129_173025 20161129_173919 20161129_174505\n",
      "20161129_175011 20161129_175517\n",
      "20161129_180043 20161129_180632\n",
      "20161129_181557 20161129_182114\n",
      "20161129_182715 20161129_183224\n",
      "20161129_183740\n",
      "==================================================================\n"
     ]
    }
   ],
   "source": [
    "test.getinfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data  = converter_mne(test)\n",
    "epochs = data.train_epochs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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LbGwsSUlJ1NTUsGbNGmbNmsWuXbu89gxCRfFGKuG5QU1NDStXriQ6Ovq6ezNardbly9Wl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb657278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(432x288)\n"
     ]
    }
   ],
   "source": [
    "raw = data.train_raw()\n",
    "raw.filter(6,16,phase='zero',filter_length='auto', fir_window='hamming',l_trans_bandwidth='auto', h_trans_bandwidth='auto')\n",
    "print epochs.plot_psd(area_mode='range', tmax=4.499,fmax=50.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IjB/dVr081qSbl8rL1M15JH3QTSJyLYDW+pB0PDoL+KXW+o4ttK85hEQ33wc+KyKfTVdT\nXlFsbLvmRTSzUzpm3QYcShKiNaSjNY1PwO+AbdNxTmmt7ybZIPxa4FxJMlWsuldi9TZubvsgUq/l\nVSTeiBO11tV0oPo+cIrW+sZRbeB6Mt109WdK01c7/q7ex+5f21JVxvohIq8Dfg2cpbX+Sfqf5CfA\nROAYrfXKUW3gepBpZtOQrih8gyQTx+Fa64XpBPx64Bta65eU2mdzIdPNpkFEZpDEVl9PUgpYk2Ry\neQeJjv4zis1bb9akm0wzGx8ReSeJ8+F4rfXd6UTqdyQb4t+3ygbpzZotva/ZrGKMRWQi8HuSAP4L\ngd3T5f8VJDFIl4nIp7TWPxzFZq4Xhsh6zcAz1h8ROYEkXvoTJEsnh5BMrC4g8YjfLSKHDdsNvVmT\naWbkSZf6fwpMAM4AZonINiQx9u8GvpmGcH3slbL8n+lm5BGR+cC1JKtwN5Ms/wqJh9wn8Ry/UWv9\n51Fr5HqS6WZkST3CnwdOIUlvZ6djVMDzsdXXi8ibtNaVUWvoerCla2bUDWMR6QQOcRznEMMwjo/j\nONdeLs0Z19l+RNFztBKRZhDSW6kZi7tXOGEYXuLl8u9uNurXAzeSxK1tXm7vYSgl5L1Rf8xbFGlH\nsz1wWM51DhfY21ASd+W989tsO/IcSyvPpi6WdPdVzZ6eHg/k38pyrtGhfxNwo9Z62SjfxouSaWZk\nEJEcsJ8o81AxrDfqKBxrOF7La+u8xvIKsRimBL6PX6sof6DH0mHrg4hxhIi6GvSNwF2bs1cn083I\nkE6QDsewDgY5AlDKK31MOYUPiZGE7uvIJ25VjbjR74G+VUznFiL/RuAPWusnRrP96yLTzcYnjWvd\nHeRQDPt4In9rlBWY+fafKScfG7ajlWh02FBBZaUVNGqOYRgLLNu+KgyCm4A/pqkzN0u2dM2Myp2l\nXuAjc7n8Bx3H3XuvvfcKjjzkwPxu220lO07uwFy5OBcue5bWyj6ipo9VzOGMm4A5bS5PR3nu/s/C\n3W7+02073fT76z9Z6e+rKqUu1lpfrLVePBr3szZECVbeXveJGetERLpA3pXL508v5HMdx+y/l9pv\nRpc7I2qSf3aFWvlEb6E14GPnLcbvNJ6ph++Ntdex/LNR4vxrbvf+cvtf3rzsobuPbjz3kKks9+86\nbH0b+O3mZuxkmtl4pJOo3U3b+R/Dso/tnDY7mLnr/rnxc3Y0Jmw1lzFjOr2ia3qeZRBpTbUZ8tzK\nOk9111j8xDOs+O+jW1We+utHm889+P6ovlLEMH9GHH1Xa/3v0b63Vcl0s/FIJ1FvUZZ3hrK9rYsz\n58elrebncuNn446ZAqh8HCf+GK01g2GiQozft5hm91OHDTz59336H//rOYaTeyb2G98FfrI5egQz\n3Ww8RGS2MswzlGWfapfHGm1b7+G4E+ZZRnkKymt3AMcwBDdvU2z3mDGhyDYTS0xwYirPPt756D/u\nf88dt9xw8qP/fNCyXe+GoNX8NnDH5ub829I1s0kNY0nK870ll8+fM2nS5PzHPv6x0huOOlyVjMgx\naj3EvUsJn32M6oKn6H9yEdUlfQQ1HytvU56xiDHVPmbM24Nph+7L4Uce5XR/6WvOXfc9kL/ysks+\nctcfrjkrVyj+qlGrfkprvdlUylGG4JS2XAFtCkRknJvLf96ynVMPPuKo4EMfPKO0zzaT4akHqT18\nP8vu+w/PPdTN44sqLG9F2ErYsbdJbkIHE+fuQse4+czYdgeebHaKPW3/fL2/n+ZzD+458PC12wZ9\ni7+jDPMzOo5+pLUOR/teIdPMxkJE9s0Vit+ybGfWkae+13ntIcfZblunG0TJGGMZQsE2KTsmOcvA\nMoSKHzGh5FLOWRhKcIodVGftbDZrjWLQv5T6grtOG/j3DW+zc4X7gkbtY1rrB0f5NofIdLPhiEhO\nTOtDyrQ/UZy6TTT1gLe0d2zzWkzLwkiN30hr4lij09fQd5UkBsPUDgy1PbZ5XM5SmpX/fWDr/970\n8y8ueeT+r1qud0HYan5tczKQM91sOCKyjVconme7uf13PPSNetJex+Ra+fFUqi1ajYCgFREGEXGs\nMQyF5Rh4nsXYksP4osPWYwp0zZ7IMQftqz78kY8UFyzp5oorrjjqNz/87gH1anWxJOnKbthcDOQt\nXTObzDAWkdfkc7nLZ86YMePcc76c32/vPZEoQFpVVKNCvHIp0fJFtJY8x8CCJfQ+0c3AcxUavU3s\nvEXYDFGWyZhSJ3axg1KnQ5yz2Xf3+czf+TXFFZ/9Eldc+p23XHHZpW80TfPLURSdtzl4AsVQOGVn\n3SdmrIaIGIicYTvulw4+7kTn9I+caW09fZI3Lmeilj9BWO2jsbyPWneVFd11FtQCBsIkHa+3os7U\nZ7qJepbiTVIUXRPbMbFcAycqkttuX5m85yHtqraApTdc/K0VCx8/U0RO01r/ZZRvO9PMBiIik7xc\n7uIxY8fu99HPfDF3wJHH04qhHkTUg5ggiom0xjUNyo5Ju2fhmgpLQWessZQMeY+bQYQyBdszYUyR\nifO2dQtvfT+lBXfse+tlF/zFc90rmq3WR4fl7x29+850s0GIyNGG7VzaNXd+8TUnnuF1TZsNgKEE\nUwmGSgzjVhgTxXroNXjO4MsxFfbQyyA3/SCOOPLQtkb3c/zukm98/IE7bzldRD4AXLE5GDqZbl4+\nIlKwbPsrXi7/rje/53/cPY9/m/SGBkv6Gizpa+JHMYMrC6IEnRrGpmVQcE3acjbtnkW7Z1J2FLah\naJnCtAljedu73me+/g1vK910/XWl6y/8ylXNSv99IvJOrfVT62jWiLOla2bEDWMRUaahPp333LPO\n/8In3Le9+U2Gsmxo9iNhgIRNdH2AuNJLVOml1VdNXgMtmv0tmo0QHWmavQ1afVXigR50ZSXKK1Mq\nTUBrsA2hbfI4vvzlL5tnf/B95jtPfdtZDz78r7eIyHFa68fX3coRvH8l2HlrNJvwikREprie95tp\nM7ea89nzLylsNWcOBdvANRQS+UjYJG7UCJstgkaIH2v8+IVjTNgI0c0anil05W06yy61gaSCpGEo\nHNdk4vSdOenYX+f6Hrht5vmf+dhNtm1fFATBJ0dzUpVp5uUjIie4jnPp6R84wzrjo2d52rRphho3\n1igRlES0wsTAcUxFzjJwTME2hME0nMXUi9xVchlX9dGxxnKSrrJQcJg9vsABB71fvn3W+7zPfeDt\nb7riD386UkRO0FrfPkq3DWS6ebmISMG0nUtybZ1HHvbRc4pTd9htyMgdxFDPZzb1w+gFRnHyeXK+\nbRrYpsIxFZ5tYClFzkp+LnZuzeu//yNn4b//4Zx9+jsuWrlixVtE5BStde8mveFVyHTz8hCRXV3X\nvXr/gw8tnv3lr3uSK7Os5tOstPBsE8828CyD0DJesLJgGArHsyjnLMqeRdE2sQ3BUoKlQGvBNRRl\nx6Qz57DP4Ucxa7d9c7f98gd73HvlRQ+JyIdJ0q6O2qRqS9fMiBrGIlIqOtb1s8Z37vybr52dn7rt\n9kirHx25IAqJQyRsEftNdBhAGKCj5wtwGbaB1QwxXRNRijgICWoNrEovqjQGZecpOCVMBUoEzxTG\njMlzx+Xnl799/nfyn/7R1Q+mOz1vGMn7XBvJksOWO7MaCUTk9blc7vozPvJx95TTP2LHJAOUrQRT\ngUQhpCXURSmUofAMoWwpIj/CMxQTXROnlKQwtg1hTM5mcodHT38z/R5Yjkk5Z1FwTHY/5nh14pEH\n5T9w6knvfeifD+8nIgdrrVctm7xJyDSz/oiIUfKcb4/vbD/tmssvye30ugNoikMtSL17OgmdsGJF\nrAdDKRSD9o7WGq2FSINSyWdF16SzYBPFiffYNhUT2ly2nVhmVkeO8Tmfiz90cmEfmoUzrr/rD7ao\nzwfo80ZrwMp0s/6IyEwvX7h1+7327zrhrK/kxHaHDN5olYn24M+2qdboLR7yFBuJIWylx3KWQc4y\nKNomJddg8h67cfffHyz93/+efcCVV17xiIgc8FLK1I4UmW7WH9d135PPF86/4MKLnIOOOEbVgpj+\nZpgYt4YMTY5sU6FMhRE9P7EyLYNy3qazYFOwDRxTMTyTsUgyzrmmouyaVHyTcW0FjnzHGc6O+xzs\n/PLT772gWRs4QEROHS0HzpaumREr8CEiYz1Rfztw0rjdrnn7UYVxNIh6u9H1KhI2kcgHHaNFEGUg\npoXYLqZrY5dyuO0euTEehXF5cmM8nLKD6dooQ0Ecp17DFkbUIm8pcobGaPQjtZVEPUt49x5zzcuO\n2SefN4zfKJHTR+o+1/kclMLKu6u9MtaMiBxfyHk3/uqnPy6dffbZdt4xsI1Bj56gBnsQZaK8PHYp\n0Ue5w2N6zmLrosM2RZuuKSVyEzoQN4/omIJjMrbk0lZ0cPMWpm1guyaOqRJPItDV1cWNV19ReOeb\njto+5zr/EJHZo/IMMs2sFyJi50zj2tlj20974Pufyb12zmSkVRvSjIigNcQxaRgFRDp5H0SaVqhp\nRppGGNOKNPUgCbWwjcTr15az6Co5jC87TBuTZ0LRoeQoVLOfqNrPXhO7+N7EOV5RjM+ZyKWjlbA/\n0836ISI7uZ53/wfO/NSUL1xwcW7KmDa68jZtnkXJNSk4idfPNtUaPMiDHuLEAMrZBp5tkLcNCnZi\nCBcck6JtUrAMym5iFHumwjOFzmKOC7/5Ne/b53xhnOe694rIQaP2HNagm4w1IyKSy+W+2NHR8a1b\n7/yzd+Qxx65mQyl5YeiNSvsgUYJpJeNOwTUpuFYygTKS82JgcC6mBEzR5CyDsmtRdk3aPJPtttuO\nL115S3729q851svlb5cXVqvdZGzpfc2IGMYi0mkhD+7hlmZ+Ys5sJx6oU+vt57tXXMvy7mWI1iCK\na/94F7/9092ofAlVbOeRFVWu+e9CvK52ipPaWF4UbtUVSpOLFCaUWKQ15/z+biJRicsP+MQnP8Xf\n77sXCZpIUOfyX/6KH/36d7T6qmznuXxx6izPQL6tRN4/Eve6zmdhKOxifrVXxuqIyBtc2/rltRee\n4x209644hvDIQw9y8/W/HZpRN4OQc8//Lk8sWYkqdZLrauOOSoWlnYopEwrM6vRYWIp4pkMoTOrC\nKHdy5a9/w503XIudLm/2P/EAi2792dBmmjiO+frnzuZf//oXhuVw3v9+yDps950mWKZxv4isXhB+\npJ9DppmXjIiYDnLrlJx34K/edniurCLiRo1fXHklj/w7SRwRac19f72XX/3sMupBRLUVsnhpN1f+\n+BJWVOr0t0IWL+/lou9cwJIVvVRbIZV6k+su+SYDy57FUELONnjspp/z9H23UnZMPFNx4023cO6V\nyWLUBM/jo7mpORM51Uiq521y4zjTzUtHRHawbfsv//vZz5ff+/7TVVfeIVy5lLuv/UXixTMSg+Wv\n11/JgocfIIo1rTDmsfvv4T/33I6fxhkv/Nf9PHLXTRhKsJTiyQf/yh2/+SmOqXANRX/3Yr7z+TOJ\n/SamSib4F13wTW763bWIX+OUow+WT7zvlKJpGL8Xkd1H5VmsQTcZa8ZxnK/n84Uzb7vzrtxWs+eg\nNfzx5hu55647k02ZGroXPcutP/8+QZA4c6MoZsk91+H3L0MpwTYUC/98LZUlC7AMhSHC5T+8hLv+\n/GcG1yiuvupKrrj8R9hGoqOVzy3g19/4LJ6KGdNW4ms/+KU3eeq01zqOc7+MQnXXLb2v2eihFCLi\nWcitc1Su63V20YyDmLDpQxhRdB1s20ErA226TJs1C601sVtGKYP8+Cm0LVtJcWonYijGRRETDU1x\ncgdOW4FZHSV26+jEKrWjTReUycknn8Q2284DItAxB++xM62FRcKliwkbIXPx+J/8JON7tUXfEJGn\nN3VYhRgKq5TblL/yFYmI7J23zZ++57C97a0mj0PiECXguR75QpEkwkZjiDBzzly6Jk3HDHqI+5bT\nNXkshXyFznEQNkOmNh3GTR+HO348Uupk623HoFc0WZouf5ZnzMMstBNEMa0wJojh0De+hVlz5qKV\nj1YGP/jMB42vXvQT78Lr77xDRHbWWg9ssmeRaeYlISLioH482bR3efOMKY4lgo6TWhzFQhHHdQlj\njR9pch2hMD+gAAAgAElEQVRj6ZhUo7cR0IpiGjGEpkt/KyIQHxWBVeqgEkEQh/goJmy9A065i2as\nMZQwd88DmTt9IpYhKIHX7bozkyrPoP67ANMz6DBN3m1PMn8ZLD1wQIefBr64SZ9HppuXhIhMznne\nbaecfJK33z6vF89SBDGUCzlKhSKWoQhjDRGMmTIDr72Luh/hhxGxYRObmoYfJl5jJ4dVSGKOY60Z\nP20mhZyHIYJSwviJkzn2pFPJ5/IoksIIRx9zDFM6ikPOnLNOPoqpBcv6wHmX3iQi8zf1vphMNy8N\nz/NOb29vf/973/tep7Ori0hrgljj5PKI5SZjSRTjFttonzQTPxb8MEbHGmV7oJ6PyfWKbeTyhSHn\nzPY77MTs2TOHPt9nv/3oqzURSULApkyfyYFvOJm86+CYirxr8aOrrrO+eOYHJ9/7l7t+LSJHa63j\n1Ro9QmzpmtnohrGF/GAK3tZvc8db2+aSiYwYCssyOeXwfTE7OtBWjtgtMm+XPUApojhGgiKzdykw\nZ+5cwmXPUPLybNtZYs420wGwSznMjjEcvvsMpNhB5JWJ3SLb7rgLsQYtMdq3mThhAn5tOX2LniMK\nYuIgZqryOE6N834dL7tKRHbalAnXB5ccMl4cEZnkmcbvLn3Lod4xR++PPXbM0Mx5q7nbMH7m1jTT\nbBORqXjdwUfgeAZRxcCcMpujjohoPPM01UXLiRo+h5RylKZPwJwwnTjXzqTyWJa4FR5/to9qMySM\nDMzyZFqNgGozpNoKmTlnO7QotGmDMsnlcnzurUeZS1b0Trnm3oevEJEjNlXsaKaZl4aC95eVcfx5\nU+e4M2dPwHQdxLQR0+KoIw6n4XawohElsX8d4+hy21laaVFphoDJ1L2PZHE1oBAkS5bbH3A0AyE0\nw5CBZsDYeXvQU/XxwwhDCZ1jJmHligSRJoqhrVRm261msGzREkzXxFXCRNPhXWpS/jutZz4pIn/f\nlBPxTDfrRkScYs676ezT39525plnSlzoohIm+xAmTpjAIce+iYof0gCiGKbMm09fPaDaDGj4EfbE\nrTFiTU/VxzEVzrgZdLomfhhjm4pi51jGjZ8wlNItVsLseTuiGVwq18yZMwerNYBUV6CbdVSzzom7\nzmXgbYcVP/WzG24RkXmbsrhDppt1IyJ7FYvFr//xllu8KTNm4adhWH6k2XHXPam2IlY2k0l3bLnM\n2HU/lvQ1aQQRURjTMW8/tNZEUYwfxUydvx/FMSUgCb3Y5bW7UXSeX8AfO3Yc+SCm6iehX4ZhMHOb\n7VFKsNIY9raxXfz48p95Rxy47/7/efTRT7EJJ+JbumY2qmEsIm/OYxx1rNnllCwD0zWxPBPTtbHy\nLipXRHKlxKjNd9KIdDIzFzBdDy/fifLKGG4BVWzD6F1OVEk27Covjyp1osZMIip0Eec7qQYxrShC\nSALV83YebBexLMRQ6CgmjmL8WDNGO+xKm30ffb8Vkddsqpy1otQWtcSwsRER5RrqynfsOMfdf7uZ\niGklu58k8eL4kaYZxvQ2AoI4xklTbMVouooTsAFbGYhlYRdzhE0f07Uxx0/FGDuZIN9JpRqxou6z\npK9B/0CTRtUnCCIcL2aF16Kn2qLaCvEjDcpEi0IMAzFMzn/XsfZfH1u4V6Wx/L3ARZvkmWSaWSci\nMs9Czv1UeWquozOHU0pi3FS+CI6HdvI0wiReeEXdp7vqs6ivwfKBJn31gCjWiSHjphswXYucnZQ4\nrfsR1WZA90CL/npiEA3GC3YWfPpbDo0wRts5JFfEynvYeRvPUNgqpozJ6flJ3gW1534hInM3VZXF\nTDfrxjKMr+6+3VZTzn77CUoHyV4XUzkoSeKGlRJinWy0C+KYSjOk2gxS4zgcCqEYjDEuuMkQaqgk\nDtk1k82dcawJRKPSEAxDIkxlEOk0jlRUssfGbxI3a8TVPt6yw1Zy29bTOm/491PfIyllv0nIdLN2\nRKTsus7Vl196oTdrxjR8IIzBjzWtKKYeRFT8ME0HGVH3Ixp+lOilGRK0QvxWspI1mMe44UeJlnSy\n0iBCuqLw/O/VqVaCKCaIE++00po4TZ9jiFDIeVz90x94O+6539kicovW+t5N8ky2cM1sNMNYRMYY\nyEXHGeOKHVbiPbELFlY+NYrTOOLYKxPn2un3YyqtCD9OvC+mElxTKNplih0ehmFj5kqogR504CNe\nHlUeQ1ToIsp30t+KGfAj/EijBHKxxvZyKDuPuHkMO7m1INJDm23mUbSeoj5lGa0zga9urHtf63Mx\nFGahsCl+1SuVd03K5+Z9cPd5jmGZoAzEtIkNmzDteHobASvqPlU/QgkUHJMJgYPWJMaxKCxlIG4e\ns14B08LsmkSca6euLXrqdZ7tbfBMT51aX5OBlQ3iMCBoudiOQU/Vpx5EBLEeil0fxLFMrjj7tNKe\nZ55/noj8Xmv97Eg/kEwza0dEDBv55SmFcc7WnSW8dg+nrYDTVkDlSmi7QGi4NFohfc2AnnrAMytr\nPLeywdL+Jo1akoLNMJO0SV1Fh85CRME1MZSi4Yf01QNWVn0qtWQSpZQQxZqiazK2mGS70Lk8Kl/C\ncG0M23jBoLaVmeOoti77dwMrLgGO2STPJdPNWhGRXYuu/Z4ffey0PEELiaNkR2YyH0rCIWI9tETe\n8KMXaKHaCIjCmCiKUelGqlUN5cEUbUoGfcQM5bG1DMFJwzScwX4mDNCNGv5ADb+/xhf33aVwx3+f\nOU5Efq61vnmTPJdMN2vFdezzTzz68NzhB+xLDEP6aIWaRhBTbSUGcSOIaIYxDT8cMo7DIKLVCPFb\niR8uDg2UEvo9i0ozIEizcA1OzETHSLr1K4o1zfD5awepreOamjgNmlACk8d2cOnnP+q9/VNfu0JE\n5mit/ZF+Jlu6ZjaaYewgX9/BKNqzLY+CqShbBnbexim5OG1FVLENKbQRuWWqIfQ1I1bUA/pbyUzL\nEKFgG4wrOHR6Fp3tkzFMC2W7SBSgTYco105c6EqWR1vJ9+tBhKWEDs/CUkKXW0J5eSTNwxTpJL9t\nDAjCfnSWr2LJp0Xkcq31oo11/y+KUoi35c6sNgQR6XCU+vrX9tqpaDs2yjYRywLLRhsWfqSp+qm3\nt9qie6BFFMcUXYtqKyRIY4bH5sfhApZhDK0wSKmTyGtnwI9YUmmxcEWN/hV1+roHqPcsJg597GI7\nljOR5QMt6kHMYKCE6Jg48NGhD3HM1pPG8uE3HGxfeN1tFwJHjfiDyTSzVgx45yTTmXZsR5fptju4\n7S5OexGjrQNV7iR2i9TDZBlyRT2gu9riuZUNFvXUqaxs0Kj7xGGMMhW5gpMYO6mWbDPx5vTXAyo1\nn9pAizCI0LEmaIUsNBUdBYfZnXlmlPO4uSKmayPG81XR/NS4emt5XO72Rt8BaTquP434g8l086KI\niCrY1o+/9uaDvA7XQIcBg/OYQc9crBMv8eDeg3rq9RvUQrPuE/qJYZxkGFCEXhI3OljQww9NAjNG\nRRBroRXFKBGaqQHkmIp8pMhZNohCRxHabxLWm7R6K5iVBv/32nnFs+7+xw9EZNYmSceV6eZFEZH5\n5ULuhG/+7wdzg5m0oljhR5pWpKn4ibe46qcGbOoJbvgRfhARtKKhyndRGBOkq1JV16e/HiTFYrRG\nAFOAKMQw7MRTHEO1FVH1k3AwP4pxTQPHjAjiOClFDkgYcNyeO6qLtprcefdjCz/GpnD6beGa2SiG\nsYhs5SAnHuF0umVDUTIVXt7CKTlDnhyj3EnslQnsAn3VgEUDLRZVmiyv+fTVk//7RdekK28zuzMP\nbQ6dpQloZSKhj7ZzxPlOljciumsBzw006a75Q0b12LyNbRTobCtj5ooo2yTyYxqRpjEsN3IJiz3N\nsv23aOBLwNs3xv2v9dkoA5UblYwqmz2OUp88ZPJ4Z7sJnSjbRFkmksb4Yto0/SQ3ZHfN5/GlFZ5b\n2aDhR3i2wZIOj6of0QpzRBomFccBoCwPiUNir0zLzDFQ81nY12DBkgq9y6pUljxJbfkz6DjCrnRg\nuQUqE0s0w2SpizQNoG7W0X4T4giwOOutx9gX/+HOA9IwnBEtA5xp5sUREc8Vdc6Hx08t5sfkyI3J\nkRtbwutqwyh3IsUOYq9MtRHTk640PLeyzuLeBv2pYVzv6yHymxi2i9/WCYCRGjY526DSDOlvBNSr\nLWoDTfxGSBxrWo2kn3rcNdlmXIHqGA/X8VCWiY502tfooWIzNsLZr9k2/+WHHv2uiGw70jHqmW7W\nynHjC96047adqbTfTFYhdYw2TKJ0g2Y99fi1wiQOtOGHVJohA6kWGlWfoBkRRTGGoTBsRRw+n8vY\nMRUF18KzDCCGKFmxhMRbHMdJLHveSoo3ABAnhrE/UKPVV6XWXWVnybFVLtf58EDlFOCHI/1gMt28\nOOWce8GX3nF8rqRiCFqITmJ+W1FMfzOgtxHQ3wyoBzHNMEq0k74Sb3GA3whpVfuJQh/T9tBxEcsx\n6akmq6BBlKw4JGOPjzaS3On1IIlbXlH3X+AUck01pCtDgMgnrlc45w37Ffb9yk8+JSIXaq37R/K5\nbOma2SiGsYV8+hC3Q421LCwRCqbCLli47S52KY9RakOVOgndMgOtiJXNkEWVJo8vr7G0r5FucImH\nEujHOokZFiw6Cl1I0CS2c/Q2I1bUQ55cWWdhX4MlfQ366gGOqejvyFF2TSYVi7Q5HjqKCZtJieDe\n4HnD2FXCYW6neU+t/0QR+V+t9ZKN8QxelC18ZvVyEZGypdTp79tuK1sMhbLMJEe1aaGViTZs/Cii\nvxXyXG+dp7prrFhexW+EmJbByv4mlXQWHekkFGd8YRyWYSdV8dwyFT/iuYEmT3VX6VteY2DJ01SX\nLUCn61CtgRU0epfht8YTxRpTCcqvE6VVGON6HQDDdsnlPD59xjvsz3zzki8AR4/ow8k0szZOnZsv\nqB3GtuO2u+TGeHidJeyONoz2scRemYY2qPrpgFJpsaSvSX2gSb2/RW1lN/WexYSNKsq0kwmSNxE3\nb+HnbQwlibenGdKsBTQqLVrVleg4ImwWECUsydksqbRohHESkx7H+DWfgTCmmnqAIp14hg6ZPoHv\nPP70xP5680DglhF9Mplu1oiIiGeoL31o3uy8319N/l+HQRI2pUz8IFkarwdR6vmL0zCK5NVqBKkW\nfPx6lSj0MUwb080Tp5uCjTTndcH1hwo7RLFOs1nEGEoIipqCY9LumUnBGVHoMCBqtvAH6tR76tRX\nNGgNtDi1OD73qUrt/0TkMq11NKIPKNPNGhGR+e05d4eT588i7u/BKHZAHBPGSQjFykZAd63FQBp7\nHsZJtpKhcuGpcezXq/i1ASK/QWQ30XFEzTFY3t+kvxEMlRgXFSdx50Ar0sn1qz4LV9SGnId+GFNw\nTZpRnATqxCEqaBBX+piIsNfYzvhPS7rfD5wzog9nC9fMBhvGItJuiZxweK7DtrVgKyHnmNh5O3kV\nc6h8Ee0WiJ0C9UrAinrA0kqL51bWWbSyQb3uE/oRylRUmyHltH54UnzBxDY8ao2IvmbEM/0NFvY1\neKq7ypK+JtVWmNQXD2OmtnvJBiogbPo0epus9F/Yp3iGMM4yOWhsV3hDd/d7gc9v6DNYK1v4zGoD\nOHX3sR3+lPZCbtAoFqUQZSQDliiiOImtWln16etrMLCiQbPWQJkWfitkYZpf1k035NmGUHbbsSWm\nEQu9w5bR6wMtmv3Lh4ziQcJmFR1rPMvANQVp9BH3Lieu9BHUm5iuncQ92y5vP+kE49PfuPhAEZms\ntX5uxJ5Mppk1IiLiGurMd8yaVnZKDk7JwS7mksl3sR0pthM5Rap+TF8zYKAZ0lNt0d8I8FsRfisk\nqA0Q1PoJ6gMo0yKodRC0xhGmRhBAw48IWiFhEBE2q4SNGjqO0HFM081Tr7boqfqEaV8T1Jo0075m\nIIxRJCEVhmVgezZnnXBw/uwfX/9xRtwwznTzIuzpiTFxvlPEH6gT1JvYYRKGGaHwo5BKK6S/GaZe\n42jIoG34EVGQ6MGvV/HrA8SBT2TZaWrAEqYdYDUM+p2kcEMSVmHgh9HQhr1BxuZtgjjJd4uOIQwI\nmz5BvUmrv0V9RYNmpcWUyGCMMr1FkX8o8PsRfTqZbtaIrdRH377dLNtf1ovb0YPRNQl0TKyh4kfJ\n3peqT1/dH4ozHySKNTrWxKEm9BuEzWRCpeMIMQz8RplWI0j6p1ZIK9JoN6kI3Ag1tXTT8NKBZmLn\nNMOh64/1o6HYZIlD4kaNaKCPZs8AJ5bHFu9csvyjInLeiE6otnDNbAyP8Zt2LpT88ZblVcM4Kdvr\nmpiuieHamHk32RBjeWl1KU1/MxHE8oEW9bpPsxYkGxpEMAzFkr4GS9o8zNjnkaXPsO32O1APkljR\nBb11nuqusnBFnWrNJ4piQsugkbNScULcatDqq1Bf0WB564XJJ8qWwRjX5ORtZhZv7+19r4h8YSSX\nOEUZqHxppC7/iiVvmqe/desZbWIIYgxmgXjhxjdNkpGi0gzxGyH1/n4avctST99ETNvgqXQwavdM\nXDOZIC1euAAzV2KleCzsbdDb36BVqxD5jdXaYdgebj6ZiBVtherpwV++iGbPADqOk/hR20W8PIXO\nsRx7/PHRFVdc8VZGcEaeaeZF2TlnmmN3mzAmCX0oWEnFpWIOVWgjdorJ5HsgoL+ZbLzrqweEfkTo\nR8RhMJTnWJkWyrQBkvhhP2Lxow/RNnM7glZEGMQEfkQcr3lsMZRgGoKELfyBGrWe5/uamKR8uV2w\nsEs53rz/64yP//Da14tIl9Z6+Ug9nEw3a8ZG3nWI0+Y1ltdpdlXwB+rk/KQ0fKShGWr6WyH9rZC+\nRjCUiaLuRzSDiNCPCYOYKPSJA584NXAAYr9G3DAwrekoU7FUCa0gIu+YL1hWt01FIU3nNTTapFkp\ngloTf6BBs69JbaDFSj+iGWv2tdvLv2kufx8jbBhnulkdESlYyLH7W0V7YMESvM4S5thJSGcLLR71\nIGJ5LQnT6qsHL6iCWPeTvNZRFA+VnwfSyXVEHPjUlz1Jq3MbugdadNdadNdsDBFMZVAPQrqrPj31\ngOUDTfrrAc0gwk03e7YGVylEEL9B3N9DbelKKov6GNsbUxLT7tHBvsCI7WvY0jWzwZXv2mzrnYeN\nGVs2LSMZDJRg2ApzKE2bh3h5tOXiR5pGGofT8CNafohfr6PT2VV96eMsvPkyGn7E8mqLu+65j19d\nfTWP99R5bEWN2+/9Oz/88qd5urtKpdrCbwb0PfEQojSdhcS48UxFXOnDH6gz4EcEw0zevCGMsQ2K\nEwrstv10SnmvCOy0oc9grYigTXe116sZEZkhwrTdJ3dhDPcWG0aSqm0YsdZDnUwUJoNSWO+nVeuj\nUWlRrba485orueG6a1nQ1+CZ/ha/uf73XH/Ln3g6XVl49s5rqf73jsQbDeiwiW70YuVKtE3Ziqld\neSaVXApGTNSzhNbKPoJaMnAaroPKF1GlDmKnyPEnvqVQLJbeMcIPKNPMGshZ5knHz5nqOgU7nXzb\nGMP7GDs3lKKtv5XEhzb8iCCI0FqjowDD9rDyZex8iXjZgxhGMhkPqn0svucP1FeuJAwiglaT7tsv\nJq73YjguppdHWn24XkShzWNKu0feUkSVXlp9VfqDeKivcZXQbhnkx+bJje+kPHk6Bx98iGaks1Nk\nulkNETFieONMnbd6F1epLK7S7OknrlUgDocyDPQ3AyqtwQwULfrqQbI5qlYnaLbSMSqitew/NBbc\n/byB89xDVJ74G82aT32gxVP3/Il7rvohC1fUWT7QYmV/jWX/fQhINuiZShBJYkMlDtGBT9T08WsB\nfjVgpR+xwo9Y6UdsJTkVan3QiFc2W4NuMjhkquFG4ZMD9DzWTf+CJYTdi1DNCqaCVhjTU03CtJb1\nN1k+kDj6eqrJprrKwACD5TZEGTSfvpt4YPHQxWtP/oW+BY+wtL/JE91VzvvaOfzimt+zoK/Fgr4m\nDzzyKE8tWEg9XcVSq5Qgd02VrHA2Bwh7llB5Zhn9C/t5uh6wlc4XbdQpI/p0tvC+ZoM8xiKSM5Xs\ntFtnO/Hy5lC1HzEEZQjKNsG0kDRuNIw1QZR0RADL/3kHK59ZwLi9TkSUgTVhOo6TGELVZkDnnJ3Y\nd/v5LOhNZmUVbzyFHQ+mf6CV7PIMmvT95y5mbD2LyR2TmFxysfwq8UAPYWrYKBIPjgImeRYTyw7l\naWVK08ZzzL67Gxf/9uZDgRHbTKVFoW1vpC7/SuWQ100dH9pOkm86MYoVKCN5DUuZpiTpEEQJhmmj\nTJvqf25AN7Yl334AfiPEmbgN9sQxLK0k+YjnH/s2+pshTy2vsrSvSW7qzjTNZcTVAQy/QVhdgiER\nHTN3pGtyiXmTy4zNW6hmJdlwByjbxMy5qEIau+qWid0S8+bvQbPZmDqS3r9MM2vGMNQx+8+cZBq2\nQRxpxFAYtpX0MbaLVmaaRinJLNDwn1/Gbiz9L33330Rp5zejLAutNY4JhfHTsT0Tr9jBVm/4CEAa\n1mXTscPBUJiCjiKUaVFd/FdUzWPrCXOYWnYpWgpdq6CjZKUsbwi1SDPJMxnX4VGaXKI4dRyqfTwH\nHX6U98fbbnsD8IORej6ZbtbIzi4qrjbhWSOgsKRK2/I+ooE+JEzGrChO8ukPeoorzXAoVd+iu64l\njEzsafskY1R5EmIlFb90HOFM3BHLzdOs+egYjI655MuTGOhrYNoGjaVP0vfIXczaYReKrkXOMrCU\nJJum4jCJMfYDIj/Cr/nUoiROvRHFeIZirLIai2P/9cCIpW7LdLM6JnL0uMgt/GughXpkBbkxOYpT\nx+Fs3Y9bnoaTxpA30tWokAjfVNRaIWGzwWNXfpOuPd8C0oZh2jjj54JVHFqlKu1wDFZ7B9W+Bo9a\nBh3b7o2aNot/dVcAuOeuP9OIDbp2PhDPNvAwKLgmnQWbznSFs2ApVLOf1tKlVBb1sWJRhWfqAdPw\n1P30HzaSz2dL18yGhlLMn1LMVwuO1VFRLQwBZajEA5huqBJlpBuqDO69526ifCd4YzCUMPk1r8Ob\nvB2S7tC1LIfc+F3wbIMo7agGk+33VH2W9vlQnEKzHqBjjWm5bH3Ch5k1pcyUNo8Oz+Q755/LVrqX\nuUDJNpjkmVTDmLJlMD1n0T6jjbYZXeQmj+fAA8a5P7/l7gMZyfQmotDWliugl4NrGvvvPWV8YWjT\nnW0meadTr7FOE+FHMTQbDZ79259Qna/BsD0MxyM383XYbePQqSfZ6pxM4OVZ0t+kmCbc7xmMLa77\niNuOmQ+wwhjDtDHGzSDXOZGOiR3MntqGW13Gt/7vAr71vx/ENm2ctiQFl1kqY7SPRcpdRLl2BvwY\nH4NpW29XffyfD+wBXDciDyjTzGqISMky1NTXTB2HrjTAX0OIg6ihZepnnniMZd39kJ+CUkJh8taI\nUwa7jSgsYZgKc+Jk3Pz/s/eeUZZd5Zn/b4eTb76VO6rVygIkIQFCyBhjogk2/mMb22BssBmw5z/2\nOI9xGIK9HMCE8YDBAQMmm5wNmGQRBJIASSig1Lm7uqqr6qZzT9h7Puxzb3cLBEZ064OoZ61avVRq\nhbv7rb3f8LzP4xPGHl6gUVq6mJJOdaC29WyKrMQa8ELF/OOfybalBucuNViqh7ztTf9M8a1reXTo\n0+lG7M5LSgvbIk3nrDbNM7egF3dS1ue54NKHkWfZw4QQ4rRRtzbj5jvhEVsISY1lzzBn5uiQ1oE+\n6eoG0bCPLyHQAl3xN2/54qfwtj6AgfXJxgWN8x7FOC3Jxq7A0vUZhH986ciWJUVF0TKFjxdG+NEM\no36GH2nixd1sPfdCZyQTeYRK8LIX/zH/7Zd+gQd1Tg4DU6mZjCpTKl9azlZxcMBkV3AaE+PNuPmO\n+NFFQvaOCnw5pnbzCs0z9hOfu4d48QI6oUe3FlALNWsHDzA4vIfm7oud3nmpWPzRZ4HfphwWSO0T\nzp9NOU4xFbe9yEaMRxm9NYkxllGnS3kwpbVe4mvJjkc8eUrDUbHHkRu/zOqBb3HJr7yAxUrSNrRj\nyuUDDA+tsLF3g72jgkFpaeEB1E7rLsz9PGbuVWIshGgBvwlsv2Shq5WnUL5Cpsf5vGJCRK/G1wjJ\nHbd9i2ShINg561yDkpjMavJKML1W8UXr1QJDaQyjzHBkY8yh9ZT+2sjpilaLel6gaEQeO2YStjdD\nOqHixy67kNlDNzE6cpTafMKZubtkmp6kc0aLzlldmru34G05k0t2zZPn+YOFEH8NfPS0aI0KifXu\nPyOGHwRCiIcDT4s8/fCLts2hQt8VUl4l1eaHCD/EaJ9s0sVZX6d/eC/+zAPQvkb5IaI1j/YjhBRY\nYylyt+Sy2h9Pt4LXhjlHe2PSQU6RG6RUeEkDqX3iVpfWbML2bU0uO6PDZYs1dqknE2gJUYJqzyHL\nEllvoRe2UyRdxn6D3iBndZSz+6KHxLd+/ZqfFUI8BHjlKe8cb8bMFEIIBfwGsHtHt9mPkqiTpjnm\nBAlGjHGyeqZAaSeNtX5kP+uH1wnO3oFSEj/0EHNLlMVxcwY/0viBJo48fO2mFJMFPGssQeihPYWQ\ngjD2mJtJOHepwc5WRDNQXHHxhRTxkODO2+ie1caLNKY01BfrzF64heZ5u9Hbz2EUtfE7PgjpA38j\nhPiatfaNp/6wNuNmAiHEmcCvSbhkgbABMCgte0cF8/s2GBxcoXHsCCLdINIxsafwlWB1353EySJl\nPE+Rl0gvQWQ5kCGlwkiJCiKnP1xxRstxiclzhFJu0SqrYZKoihtHoaiFmpqvaIY+j3viT3D2mTvh\n2B0I7aHDwLnEhhp/mKOE6ygrIditouiqfP3xQogYeIO19oZTf1ibcTOBEOLpwGXAUsclmNw2yJm5\na53WzQeZueg2/LOWmYln2NaK2NcK2Z+tk67sp7HrIorKAMarzVLkJUKC9n2sOW6G4Wg4OenGMmVW\nI9FeAnsAACAASURBVBvVSQc5/bURUS2gmfg0Y4966PKhyNdse8hlFKtb2d6M2N6KaIcK2TtIubbM\n8MgawxN2HASCLt7wMNkfCiHuAl5xyk0/7ucxc287xi8DloBHLbXrgY40ypeoSrxayIpKoaTrAlbJ\n8c/8/LPYuzHm1pUBvlbTCwP4NntWLcWUi9xPC4a9MYONMf1jIyexFIdIKZhrBCzWA+ZrPmEx4AFL\nHdKjkjz0SeYSvDzn4GjI7q0dmjuaNM/cQri0hJ7bytLc2WRZXgceBPyyEOLB1tq7ftBDPQn388rq\nvwohxCzwXuCLo7xY2r3UReVmOg5XoY8MIkSYgA6d611hSLqznPekZ3PnvnWUKlDVKEpINS2+jLFk\nlSA/OP7X+jCvhNULiur7ftwkqCV05hO2b2ly2a4O58wkzDdCHvQjV8Dafrdp213gy9ffzFI4w9ao\nhYmaTqeytPTHBbWZhQj4/4CPAX8PPO3UHtZmzJyA5wHPAnbMtOqRX0/IBym2NFMTH3CPDaZAS4Gn\nJJdc+eM0jg649VAPqSXac45TAF6gCSKPZuTRjI9bQWfVoue6FFO3Mm0lXqCp1wJ2zSXs6sZsbYTU\nfMni0hx2vJ1heozO2QvodsB/HjzKEy/eRve8nXjbz6asz3MsLVke5vhxTRTj0ZOBpwkhjlhrP3pK\nT2ozbgBn5gG8C7jNwCPrE2s74M5hznkrI9KVDUxvDT3uEYU16r4mDjSX/OSzuevokPWN1C3cnbBA\nJaRC+5FTF5ASaxS2LDFFRpm5d0lqnzJLgRm80P13a6GmFfs0K9vxyy9/OJ7OQfmIKGFPNuaQzWh2\nI+YHOaWFUWmoaUlNBuQjewmwF3ivEOKC05PkbMaNEOJhwCuBIx6ilIhpfnTbIGfnHWv09x5ipr9M\nc3aexVrAYiti+3kPJFo6m/Eod7sMhmnMaF9VXOPj5zvpHJfZiHywQbruM0waDBsdaq2cohPha0m3\n5jPXCGnHHo2gTvOsrczEHo1AEsuSj3/8Y1yq1zF5gS0tShxXxYhQdeCZwNcBD3jpqT2s+3fM3Nvl\nuyuB/wnc2KnFx1UoPOV+9RU3bvR4zRe+4ZLiE3ijk4CZCKI77UfHnZlthMw1Qsehif1p5xggH5ek\nA1dlpRtHKbISIQSdms9iPXQV1GAFs75CkWZITxPPRGy0FAcjS3NHk9qWGeI5ZwRg/IRChcRJUgB/\nCFwFPPgHPM9vg0VQSu/bvn4IcSFwE/AbvpJlEIWoyC1PqdCfqj/883s/yoc+8R9kpSUtzdRy1Z7w\nOEmppsXWBBPtyBM3wU1hyHOnRgBOjzhpBHS6CQ/a3uLsmYStjZBmoBDZ0MknBRGy0eX6A6vcfrSH\nDRuU8rjEUmktUb2JDqKjwK8BP3KqD2ozZk7ClcDfAu/fOj8TeUmIl4ToJET5GiEla4MRL3zVP3N0\nedntOQjwpBuPK1ntPUiB8hRh7NNqhGxpR+yaSzhzrsaOmYStnZiFVkS35jvJrVDjBXqaRC+0QrZ2\nYhbrAfVAEWmBMCUiCPHbbRpnLFIuznA7EJ+5BX/bmcjOgrMlz50ZgF/vKOD1wBuqz3VKsRk3U9SA\nc4CnSyj9uz1zo9JSpDkfveor/NO//Cu+cm/RJLEojcUUxtFqql+FlMhqx0H7EdqPUH6I9Nz5lkVG\nPuoz7q2S9VbJhuvT6UQ91DRCj9iTaOn2JqzUWB2iml3uGubsFdDY1mBmLmZn7LEt8lgKNdtaCYAA\nng7EwOKpPqzvFDc/pHgE8HbgFdHdYuZoVpKujxmv9XjZ372eb3z1atqRRzv2acUevne8UQPuz1hq\niVISL1R4gcILXdxIz8Oakjztk64vM1jeQ+/AbfQO76W/lk6NhJqx4xMv1AIW6yHziU8r1CSeRGRD\nrr3+RvYeXUOFPmE7YCE83ueMXZvy8zj1pM036vvEve0Yp7gqZHTRGVvw0ww/8TGVkYYONbtnYoIk\ndBqwldqAwVJW9pulubtjkEuOW6FH5EmMdSPRYVaipKAsDeN+j9GxwwD4cQOp22xtxyzVfRo2Rawd\npDy2jMkLFyzdOlc0Ih4hJUG7RrLQRXdmkPU21o/ILWzZtn14yzdviCaf5wc90LvDVJJjm5jGTGO2\nURuFSVQvqwLGS0Jn4x3GPPjii4i3nTcV3J/oiRpjT5K+uSdoKSilOK5kkRnKbIQxJUpLoprPuUt1\nzp+vc3Y3YT7RxHaMyEdVFRyDF/Mrz/4lrB9j/YjCWOwJMbt193lIpQybMXNfYBI3/Yc98HyCVo0i\nzabSfjryCZOIR1x8AZ1Wg6IaQcsTHilj7PSvta/o1HyX5DZDmqFGS+FoO2N310wKq/XCWf82I49u\nzWe+FjCXBG7ppXBLmiJMUN0FYj/k3MV5XvTwS1DNLnp+O2XSZWwVw9w5XDV2nm/X9tycAU024+Z0\nIgU8a62VQug2Jz/YlXs35y3NsH3XeSjh3ho5SYytK8QnSfEEoirIp5J/psRIhcldIlNmI3fXFBk6\ndKPzINB0agHtSNMM3CTUWEspNDJqorqLPPVJjye74yZWbrgD5UniIwOyfo5QgqARIPcISmtDNu+b\n043JXWNnCHIgOPFvWmMxWcGDd+/mgrN3saqkK5J9N/2WUjDZfBBSoBAnJchCCoxJKCuesckz8uE6\n1hjy4YaLmyjBzCbUQs1CI2B7M2QuCQi1xFfCfUmBKCx/+OvPJb/5q6ysb9Da0eSMY2NKa9k/Klgq\nAm5jmJaYzZi5F7i3ifEYCIGw1krwBxq/llLmJba06EjTSCKuOHubW6YSwm0xWvdI5caQVbJtE2Hs\nyNfUA00j1NR9t3zlScEw9oiqUWeRjRivL2NNSdxdIog8tjRCFhIPfeRW8v23UaytYkuDl0Qo33OV\nvu+Sr6DTQtXbyLhOqZ183C3fvKGFq6jC6nOdUljrXGw2QYo74yD0tdEVv1hIiRc7brEIIi7ccSbj\n9g4O9POKSuMkt4rcdYCLamQJYE08lfo7EarqFDr9/JI8HbjvK0mrFXHhUpNzZxKWapow7yHyEcIU\noDRG+1NHLKsDrA4pK9vXyZi+GPXJhv2t1edJT/VBbcbMSZjETRi1O+jOLEF6fJKsQh/lhzz+igc6\nCSEBskqO4Xj3r8jKaUcn9hVzjYC5xKcZaqQQ5MaiZY6xdqpM0B+75DvyFXONsBpjKmq+Qoz7ICQi\naaC1h2l2wRiE9pBJgzJqYeJ2JR/njGoOfOUTAfAc3CLVKbds3YybKXJAVfx0rTheJDW0dImFEmyb\n6xCcfw4DKpq6dROnsjCUhZ2OxSf3y4S6o/TxaVVZ+JRFhhyqqQnMJMlRWtKp+Sw0AuaTgMRXSKAw\n0M8NtagN1qC3ObrhjJIErTqDQyvkPee6qeOQ8pNWAfNs3jenG9O7Rp0ojQS0PYny3J/7lQ88F1tL\nWB8znUp9JwjprOYBjFclxoXFFDWKkXuTTjScyvrHKEYDtC/Z2onZ1Y7Z3gyJPemEDYST+gOwXoDx\nXVHe2LlItjHElBZ96zFmjo24ulcyzsxPAe9kM2a+b9zbxNjHXT6FigI8BF7ik48qrovvzBomHOMJ\njIXcGPLSTu02S2OndAlPCm679ktc8MAHEcQ1Dtx2M1d/6QsEFz0BKQRF7zDF2j5EPIP2I5baEbs7\nMVFvP8UdN5Dtv4tsY4hQkqBVm45ape/knGTSQMR1CCKX+FiIkqQYDQY3Az9afa5TCmuZuvH9kMMH\nCpzYBCr0p1Jt0vcQgUuOrfan8klp4WIkyytJnCyjzFJs6RLjIisoS49s0OPQwRuoXfwwlBTccdXH\nGHlNbGM3xljSAzfgz5yB8iVbOxG7OjFb6h7h4DAy7blOsR9VWowBVmmQGoSkyolR0l2CsadoNqaO\nPwWbMXO6MYmbwngRstnFT48/Kjr0Edpz94w1CEBUyTE43nCel+RjlxgXgbtzlBAcvv1m5Eybxa3b\nWVk9xkf/9Q1c+uRnEPkakW4wuO1GZs69lHqoaUUeM7FPI5D4NnNSW9rDyiYEdUTdOiqOkJTawwR1\nCumTlsWU3tM84wHFyg1XfQZ3d27GzemDBkrA4p6daeoSKYEXatREMUk6e+bcGEZ5WRXh1VdWUpYn\nd8WEsIz2XUdtx8UIqRgdvIHx6iFkYxtCKsxwGaFCp0QQ+2ztRGyph8wmPoEWGGBUGNLS/doMO8Re\nhFI+QRAi63cQzR1hvNavGjzTBacem3FzujG9a6yLnSkiJdGRchK0d8tpymrHxRg73U0Q0mkPSyVR\nWjJaPUS+tobf2E5ZBhzb8yXwmwgpMWWBXd+LaG5DRwlxI+Ss2YQzWiGzsSZQgsJCWhgKA2lpUV6I\nl3TRs9vwzxjQNQbpa4JGQP1gjwtuG/KfB9Zvza3djJl7gXubGM8Ay8BggMRvJPjJBvkgo8zMSVyb\nKYR04ykLWWkqGbZi2jEeZQXj0nDTN64jCgPOedCDiRpN6rMLZIDUAsZ9PE8Rzu+kOd9hZwPmYgl3\n3Mro9psZHVnDS0L8eoxqtJBRcjyIK1tfETjbRVuVXjPzi8O9t39rD3C0+lynFAbHld3ECTEzzpUO\nfaYaJtqrNIwltnqoSsN0+XLyUJXZiHI8mjpPaT8iT32KlT2kh26gf96lrkOYdBEyorRuhG5HK2ix\ng3orZHs3JikHhKMUvXHIbZfHLawOMVGTzLo4xTCtzoVwSbGvBHVfEQhLVG/eMeqtL7MZM6cb07jZ\nyCyqPYdNB/jGYNIU6XvHlW8ArJmyA4vqwcrHBaOh6zIrLVgb5vTHBXd941r6S0vMb9mGCgI684to\n30cVhvHqIcZH7qR5yeV0agGhydGmINIhYuwaMNaLsco7rrvt2ouu6K4MjUrjRvOFsXhJswBuwHFF\nt53qg9qMmym6wKq11ighshwTBtUCXqRc52+ise+kId1YeFg1a4rqzilLW/GMXSEupMTmI0YHv0m8\ndDY6aqDjBl5usH6EDmsUxRAVJkTteZJGwKxXsFgPqPkuRga5YT11xVKgJcNI0woCOt2daKnxwwRV\n34/fOwZFjlE+UghjrF3n+Bt19FQe1mbcTDG9a3LM3RLjiaNvgPB8jNInvVFZ7qzDzaSTgptKCCkQ\nQpAu72G0eoTmzBloTxK0t1CqBEZDsvXDFOkxkp2X0pyf46y5iCWvZLHm4Q1XQEh03MYCaWkojUtK\nG35C1FhEby3AlMyEAX49IeouMzNeJzpy4Kt5Xmy+UfcC33diLIQQHP/hPLScGrxW4lQFPCe8P4Ep\nDTbPEbbqpiCmCgJZYRiNCw5d8x+EvqL7uCexkRY88mefQy3Q9LKSuDPHmZc9kuv3reMFmpkHPIJk\n52VE9YCdZ3a55cNv4qr8Cp5Qcz7hQknCyrpRdRcRYQzGYIsMWz1mk6UtYUqEhrWVoxo4jPuBOOUB\ndH+vrL4PTGNmrT8M0R5SFScpC0xgrJ22eiajzYnnfJmNKPKU4a2fwp51JTp8AFH7DGZ3nEuvP2bk\nKfTCuZhxQd7P0J5i7vJnuEdqtsZ430286l/fzz+95Hcx4xEiqmGiJnnUZn1ckpkCScXlUuAr4Tir\ngPUkpVWUvRW0UkeAAW5kG1trh6fqoDZj5iTM4n42D+1fWc9FvePJwQY2z0FuON5ntQA1uWdURXnR\nFde8yA3jQc5g/w2spQeJnvhz7FsdcsGPP41u7LE6yjFoLn3sU1lPC2DMjvMfyNzuC4h8Rbfm858f\neBt7Z5r8j+c9xxXWQYLRIaPiuGGRV3EABa7dVFozXRoNtGS8diTH3TV14OJTfVCbcTPFNHnUiLUh\nZmGSGE+cWaWnEdoHpclLR3dZ7Y9ZH2SM04I8PW4jvvHNj+O3thJvexDST5h7+DMR0vFIo5mdeM2S\nbNhHKEXUfgphc4bu1i5bWpr3v/pFXPynf8bsmbsY5IaDvTH7N1LWxwVKCLqxx/ZmRNbwWejuRHkB\nOmmgBhvYLOXwWo8g8EfDUVoIIZZxPw83ncrD2oybKWaBW4FDA07O+mpa4kXHZUVRPrnJGOYla5UC\n0nhUTBcu841DrN34KbY/+llI6dE596GUpSvSg8ijc94jSAcjsuE6Zm476oIrac7Pse3MLvKOq/no\n1w9y5W89Fzk8hg3rlNYVVcdGJf2scEVVoOlGdeqdnSjlI+ttWrUWtS0zjG7fZzJrT1vD7/4eM/em\nY1wHMmttKoS4/VvLG5l31rzvJSHK72PyckpSN1nhktI8Q5QFQvhTa0NwF0syvxPtS3ppwUp/jJKO\n76eEmFbxSgqixMfOJjQ6Ec12zKW7Olzx0N/g4VsTxI2fob59HqRCL+5Eb9mFidpYpRHZCDXuUXIM\nO06xRY4scygz8mLEsN/zgP24AJo9dUfrYKxl8J3MCH74MAssW2t7YeCPDvfT+nyosaU5QYfWIExx\n0hJMaSymkkyyxmBMCcYikllQEflwA6VbyIq/pavOTJG5xRkvVITKI2mFLLZCzjv7Ep7y0AsxSQMp\nJCaokYZtlgcFa2lBXlpqgaLhKyf8j0GUGUpItO8mUscO7LHpsP81a62tHqsZYM+pOqjNmDkJkyTn\n9q9df2OvTLodPTOeOhRiyuMdY+viRwkPr7JPndw1xlhU1Eb4HoONlLuOem5S1YlpVDxjYx19B5j+\ns7XQI/EVj33az7KzXaOfG0KduOXQYckgNxTGoiREWhJ7klC5LpEAtBJucUZLRkf2GuA2YAubd83p\nxKSYAvjWBvnCZAFvwi8G3FK4kBSGuyU4Odm4cLQtU+K1tqLrc44PKo9LkgopUL6oVhIaBLU6Skni\nZkBnvsbOxQ6P/t0/ZX7rDjaykqPDnNuPDfnGvnX2rY5QUrBjJqa/WAI1lBDMNBZROkQEdYQp+dae\n6/B9/87qs5yeqeZm3EwwA3wBuH2D/CTZI08IpKecEZX2KC2kRcn6KGd1kJGNCifXZqyTqw3bxAvn\nYKtmoKyoFRPZSKklfqQpywSlJGHisbTY4GG7uzxs6SfZHpUIk4HysV5MPzesDAtuWx2yPi4ItWQu\n8clNSBnHNLs7UUFC0Oyijh7klsGHN9KivBn3c7B513yfuDeJsQSsEKIG3PDFG2/reY+7uOuWYCTj\nDQtlSZHm5MMRNh1i0gGizPFkgBkNWd9/B7E/Q+gp2LYLWW2C99MCX+dT3vFEqivQkk7i4yv3wOyY\nidnRjlhaaKICg7d0Bv1xwavf+RGe/2uX0m5uwSRdMAVysILIh1Dk2HSAzVKE9hBRi1tv24sfBHcN\nB4NSCFHnNCzflRb69+MA+j5ggAaAp+RN1+87ctn87kX32JjSFS1ZCkWGrwRaOs65qkZR+bF92LIS\n2Qe8md1Y67rIeRqhKpWCsjx+nxljnVxO4IwcfC0pLYTNGTakJmrV+MSn/oOb77iLhz7x6Rzpjwm0\nJNARQkCoBHLorGMBpBdTD5vccO1X1vMs+7oQwsMta5xSbMbMSbC4uLnh+uuv90zcxqQ9ZL3lfqaL\nzHGMwU2lygItPdcxFpbBvpuRYsEV5HEb5S+SDnJW11xiUhpLpxZMtYwnsn9KSpR0nV4pBCpMsF7A\n+rhkKAX//PrXcs6DLmPLORcyzEvqvmIu8QmURWiJJwEkfmmdeYQtSNePRriO1LncjcN4KrAZN1NM\n75oM++Vlxg/ZQexDpThRWmxpsPnxJc7JSDwfl6Sry4z7A/AiR9lqbz9BjeJ4YqyUUyKYOLcKKfAD\nRdxwlK1W7NHoNN1Ewli+cM21/PvHP4F4wJNYXxmiPffGRb5mJvZpRxpfCRpxG6k8sIav3bbfDEfp\nNdX/Zp3NuDmdmNw1+3KsGlESVX/e6kR2qDFYcJOGrHTFVJrT23sLfmcHyndOmrVdl7qkuGK5K+XU\nKYyxeJOYEQI/1GxpR5y31OC8uRpL7YTFuocdrGCl5i3v/yhl1GTmwodx++qQlf6YWuhNVSGkCLGh\nptXcgtUhMqhz7e37DXAjmzFzr/B9J8bW2jUhxGdw29XhZ7/6taZInoOXRG7ZDiizknxUkG0MyXt9\nvEEPkQ/xgzq3XPdlbvrKNZzx+GeyNsyn/97SuCDzU/e9oFrIK42djjNblULFjpmEZuihhKBQIbI+\nR+1Mn/MvWSZZPMNtg5eWSGmEKbCjPuX6Cqa35jjHpkTX27z7HW+zo9GoJoTYDfwi8Ogf9EDvDmPd\nmG4TvAP4uhDir4CZt/zHV/JH736yZ0oD6Rj0ADPYQGcDtC0IlCT2FLGv0L5ieNeXKb0uqrbkusYc\ndxAqsxGZnuiJmukmsBAVx6sae2aFYXWUs3cjxRISKIHXXqQ+VhzspfSzkhnl4ylB4knU8Biyv4xI\n++6/FzXwgE987CMxsAt4BnCTdSOrU4bNmDkJbwFeALxpY30t+ead+7lgoY1q9B0Hs9ruFlJNOb5S\nCDwpOLb/Lu666kM0H/7L072HMktJhwqlJSvV97LCOO1iLVEnUHsm3ebJqP3oMCMvPTwlmNl2Jl5r\nhmOjnNK65FcIR73xKRDjFF/7JJ5H7CnuuO6LuAEk5+JMS95wqg9qM26m+DwwK4R4JHDOzQzKS2kD\nkBlLPioohqlr2ORjtA+ekk4v3Vj6d11LkaYE2y+dKuCcyGMX0incaF+hPZfsSC3xPEVcaV4vtiIa\nocYYy3qaM8xL8qhN2VhivJEy6o3xAk1/lNNPc/pZwSg3jDyDEhD6NQTwD298c5rn+ZlCiEtwRlSn\n1hSGzbg5AW8B/hT4nIXyLoaci1u0Li3Y0lJWU/CJxB+4BkyR5Wx885PUz3880ex2TGEolUFXShai\nkm3zlJtE+ZWHQz3UTj6yHbOr41Qo2qFClJmjbOmA7bt20ydkdZRzZCNlpZ9NHTo9KfCqO6u0inrU\nJc0sd+0/2MYpmfxc9blOKe7vMXNvl+9eCrwREL7n9245Nmpvb8So0AcGmLxkvDHGX+uTrmwQrK/g\npT3i2iKPf8Lj2XnpldxydMB6lRhnVeWTVVW7e5D0NIDA2SKqSiy9EzmxdGPdZq9Kukgv5ief/nNO\nVQCIpEUOVlH9o2QH7yTffyfp6gbS04RZimrP8fWvfW2AtVcD1+HsNr/5gx3nt8MY55b2ww5r7T4h\nxGuArwKfu3Hvoa5QyjN5QZlWlW+4gm2vIevr1Pw2M7EzfanXArb+6M+zfnTIYPUQAKbIpl2cssiQ\nWUohIzQKMFVH5wQt28KwNszZtzrEk5JhXhIqCe1F5hrzjAtDM9As1gJmIk1S9FBreyn2344d9kB7\nqGaXAwcOUhZFCTwW53j3a6f6rDZj5iS8Dng38G9+GF37sU9++sFnP/eXEOM+suYGPLYsT05cKrm2\nhZ27ueyZv8tte9Yq+3AXK2KonPRRNY1wXePjhkITuT8lFWUlLznpjqSFwVOS3ZdeQV7REGNP0Y08\nmoGiJgtUfwWRj7HaI6jNkniKvTd+zQohP21t+XngszhntlOKzbhxsNaWQoj/hTNruHVIqUosCsGo\ntIwGOcOjQ8ZHV4nSdWrtnbQjj7lGwKHEo3vRYxiup5TjgYuXSr9YSIXULgnWviKoTGD8UFML9FSL\nv1ML6CY+cTU2z41llBtMUGfuwss5eKRPmPhoTxFVkywpREUdc9xNiyEvDHfddZfA6dB+HPgTa+0p\nl97ajJspPgr8CnA18MmjZI/D6RpjgGyQkfUGmN4actyn5vt0az5B5BEmIQuPeh6molLcHbLae6hV\nNs8uIQ7o1nxmYp/FWsCWRkArVNQ1yHQ0Vbl5wEUXs28jZ/1Ib3o/ZZWUaT9zBbuUYho7n/n0VdTq\n9Zv7vd7rgX04l9ZTivt7zNyrxNha+0UhxDkAUqnXve/qG5/9O1fs1l7spsrOVMGQHktJV9apb6xA\nf5WgkxJ7mk7s044zmrHH+G4i0ZNRJhzXCIx9NX2sYk8RewpPSkprGRUWz/cIIt+NvI1BjPuIfITq\nHSa7/XqGt93Kxh0HyXpDJv+Po8YBvnT11R7wS8CanWzJnGKUFnr345HD9wNr7R8JIV4IBLfuO3zs\n8MaQZmko0gxbGqS3glw5iGrO0ZztMlfz2dmJObQ2oj/ISEc5RdYBoBy7pPjuDnjGWoRhejk5nWPI\n85LVQTYdnw8y19EJlHQGM75iPglYqvu0GeKt3MH4m19htG8vZZo5w5ilJd7/xVtMFAYfy7LsadVn\nOi1jqs2YcbDWHgQuF0KI8Wj4S+965zte9Qu/+Iv1uaSLKMaO11XRo6x0MnuTZNZY51ZoplrGI8px\netygQQu055KckxNiMe0cF8ZOuzMTndtAS0KtCLScJsXdSNFUBWr9IHLjCDZLUUkDhCSO5vn8h9/d\nt6b8c+Bzm3fN6Ye19k1CiDdba20g1PX7SS/YTsSoNKxmJd3DA/r7l6kd2kPQPYNtjZCzFuoc2RhP\n9fhTIRCZ80aQ2kdphVICP9AEoXNErCc+zdijFXvOqewEk6rYU6hKI1sKp5/drfmMsogg8ogCzY6Z\nmK2dmGagCSt95NJCkVu+9KUvY4xZtdb+uBBCbMbN6YW1tsDZtQvgwtsYPuoKOp5AMCoN+SBnfKxP\neewI3vAYzWiJrY2Q3fM1bqp00k/kGSstp9NKWXWIm7EzC+rWAuZqAd3YYz4JmIk1nUgTyxI5OIbI\nU6fSpDy0Jwi0oBFoFlvRSXSvUV7Sz0q8iq4zKhTvfNe/DUfD4euBv92MmXuHe9sxniYEQoh/et07\nPvgzv/cTL2n4DWcPDVCkBVk/Z+XoOh//+Jd41o7zkOMBkdem7itqvqYWelPJthOd8CZffrW04kmJ\np9x41HFAHbfLVJuRaWkdV0dqZD5AZH0+/+lPcuu1X+Hpu2ZY/eZdrN9xlKyfk8wn+I2YD9zxmVIr\n/ZnUjo+dioO8JziS+v23svp+UcVNGofBe976n9/46edffr5vsoKyMm2QyTKqd5Trbt3P4TJgbut5\nbOnEHNkYM+pn5OMSa5pkOB1bISVK+1VHx3WJZRUfEytgayxl7rhgyxwvvjq1gNlaQOwpOpHHkTmb\nZwAAIABJREFUTKx575v/kct2tLjIG7L2jW/S23sYWxqCVg0hJa9563vX1zd6rz9dFw5sxsx3QrXo\n+J7rvvT513xr/xFqZyyQRE2kNUjPx0qF0T5vf8/7uegRjyY1IaPcuSYWuXEa2ON06kwmpCIPg6lx\nTGmsk+TTEl+rqbY6uHiZaK6X1Y0ZakWoJJ3I48hdt/L+T32M33v2TyOO7iFf3g9Fjmx0kF7ELXce\nZXX5cAp8fjNu7jtMzjrDvPZGei/ZTtTMjGWjMAyPjhgeXKU4vIe99qt8/fCYM859GAfnEtZHOdm4\nxBrIpJzeM9pztC4vUNOkuFPzmWsEdGsBjdCjGWpqftW8qaiF46KktJp2aVhoRdP9mQNf+QRFr8Pc\nWY9jJvYJlEDJSt6vtLz1TW/oDQeD1534WU4HNuPmZFR3zfUF9vBBxruWCMmMJRvkjFb6FKtHCUbH\nuOaaWyijWXbNdVgb5hy0bqelqCgGEw66Uo5CMaFOtCqr527ssVgP6ISaVqhIGCP7xxBpz1FAlY+Q\nmjBsEGl316xu9Ln+/f/E+Y/+Sfx4HnBTrH5WkJeG1Y0xn/jwB0RZlm/bjJl7j3udGJ+AL671+suf\n3bveuHz7PINDq4w3xpSZcwEajMYcGg4p0xEqGxKGnWk1HfvOTnGYnbwJ7mv39xJfE3suqLxqFDFJ\njpVgagSRlRZPWnwJlBky7bHkW0wsWL9tP+t3HGXl1mPY0iKUoDEa8zdv/1hvMBy+6hR8/u+K0tgp\nZWQTxzEaZ//nVR/47JNe8MgHuqWYLCfrDfHXVtHHllk9uE4adTmj5rPUDDnUClnrjcnHxdSJapLg\nSO3jhQFe4LijuvKtn6hbAJSFxZjcSb+dIFHZij0CLWmFHq1Qce6WDgumR/+WW1m54U429m0gpKS+\nNObmwYi7Di7nuLHmacNmzHxnWGvXlR+85zWvfe1Pv/hPXuhvqc/iS41NNwAwXsKBI0fZurZBP/Sc\ne11akI8L8rRPkfbJR32k56OCaGr5K6Qgqu6iWuhNJ1TAdAF4IuLvvu/uoVqgSDxJa/sWNnYuIdYO\nUhy8k/zwfqwx+ICaT3n1K1/Zy8bjv7PWnlbhz824uUe8ZR+jv0opSdCMSsN6L2Nj3wbDfQdZz0JG\n/YDzWxFH5mqsD3Oy1N0zUguKvOKth4og9KZJ8WwjYLEVMdcI6EQeNV/TDDS1QBGe4Lc3KiSBdt1j\nT0rm6gGltcxn59OpJ2xtOGvyQAskrsN8dG2dD73nXdpa+0+n+3A24+bbUSXHr7iO9RctEbZKC+mo\nYHh0yODgKuHyAY7u3UtzV8Kuha30qgbfshiRjwvK0sm2Kc+9SZOkuBn7NCOPdmUY1Ao19YqCJfsr\nTp5t4JwTZZi4iZifEHsx3dij36px1tnnsHV+Dqtd+pYbQ28MQ1nyuXe/tUSIz1prD5zO87m/x8y3\ni8h+n7DW2uFw+Od/+vdv7QVnnEPzzC20drRI5hPCdsTSbIvfePzlaK2mGqOqWo6ZJML9w3u54/Mf\nIjghgFqRq75dAHnV1q773ur+u3jFn/0BaZo6Xpa1mKo4EmUG4yE7GgEXdZuM1/rTRB3AizRfOLzK\nodX1HvCRH/Tzfy+UxtJLi2/72gRfGGb5nR+8cQ9u0uBs6cuswGYpj7viEp76xCfQDNzG9kIrolUP\nCBOPMPHw45hs+WbK3gHCJKq+7xPVfMLYI4w9/EjjBcqZwwBHrvl3Dnzhg6SDjPVhPh2Rh1oSaUHN\nkzz6knOZt0MGB1foHx4wPDoiG2Tko4K//vcv9dMs/8tq5HbasBkz9wyTZ3/1vje8trh2zzIH+jmD\noEXZXMQ05iniNr/w3OdTm1vi2ChntT9mNMqdJu1oQD7qkw+OMbr9PylHPQC0p6hFbry52IpYaATM\nJj6ziRuJ1wPNx/7+Lzhyh5OOVcJNsyb0m8STzDVjnnzlpZj1Fcxgg3yYYkoDUrL/yCrvfc97tLX2\nNaf7bDbj5jvDWrsqEG++WfWySEmUEFUHMGO81uNB8w2e9VNPoBUqttRDdswkzLYj4kZAmLj7JD92\nG6M9XyVIPOq1gMVqwW6xGTKX+Mwljic6V3PJzrWf/xRv/L8vp+ZLWqGmG3nMJT5bGgHbmiHbmxGX\nX3Yplz7wQlqhh69cUlxay7g0/OPrXlsKKT9srd13us/nO8XNJgB4wwHGaiQK/InsY146nvHGCs98\n8mO48vKHsFgP2N6J2NqJ6DRDwsTxjpWnOHrtxxmv7p9Ov+NqmhB7ikagibSk5klk2kOO1vmbV7+W\nD3zww5hjy5THjiBGG8hxj8RzUpBztZBHP/EptOvxlHqTFYaNNOfw+pD3/cMrR1k6+ovTfTD397vm\nVHSMAd50/U23vPTTB0b1R154AcrThN1lbGmI5toEnZaTV6rsfsGR0SePjO95hGEw7di0Y49moGmG\n+jinWB3P4aNdu/jJpz+DMAwx1u16T3uAlaEHEx6hV9kkLln8xKe2rc2LP/DZwWCU/t7p7uBAVVmN\n7r+V1b1FVZH/zv9680fe86SX/4849LTrsjUS54QHUGb4StIMNN3EjSzXBxll5S40jiP8ekhU9wki\ntwijqwpdTZZZCjO1d23uuhhblpjSTBenlBAEWuEriciG2FEfm6XY0qB8hZd4+InPLeMBV915sLTw\n2tN9Npsxc8+w1l6no/rn/+7Vr/qx//l7f6B3d2LqgcSTimxc0MucicKR/piVfkaW5hR5iSkyR6Mo\nc5AKFUQEkSaq+Sy2QrZ2YrY0Q9qhV02kBGlpGOYlj3rST9PZeTYoxyuOqjvJ8dMFIk9dQV7p3Pr1\nGBklyEaXP3nlP6ba8/7RjkZHTvfZbMbNPaPAvuiacuOZT4w6zGhFO1BT2p/NUmQ2JE7arhFT0SPW\nK75oriRFHEBREFWd4tlGOE2KJ02b2HPx4EvBj1xxObu2byH2pHM9lBYl3Xs2LgyltdXd42QBrXWT\nz8xY9h5c5s2vfVU2Hg1feF+czWbcfGdYa3uekC/7kjj2+1fqWhRGGukphJLOuKxIiappYzv0mGuE\n9NLCLcbh9luUH+B73snUUCUIKwMiXwkUxu1HpQMe/+ALmPMsZrDhusUT5RRbEFb7MLGn3DJwbqYJ\n6igr+OqH32GzcXqTtfYzp/ts7u8xc0oSY2ttLoR4/nP/4MX/cvOn3ttoNDrE2/e6pZgwRncXka05\niqBOZqoOr7FI4WgT3aWtzG3dRi306CY+7dB1iJuhJvKkcyuayC0ZS6A8HnzZpRjrEpuTICVC+wjt\n49dj4rkWQklqixlBq8b7j6wUe1Y2bsXJh512lMayNsy+92/84cS/r/WHX/67z95w+W8/5ZGBLTKX\nUNRbWOWDkNPHo+YrOjWfZuJTFgYhIXzg5Sgt8AK3CNOMnJyfr92lM64cFvtpQX+UI/UCpjBILQk9\n9/sCLatLCjBVxas9/EZMbT5BhxoZKP7k36/aSIviN0+lw909YTNmvjvKtP/rV73t77921pVP0pc/\n6GwWawG1QJOXhvVxwaHemH2rQ5Z7Y7JxSZFl00VN5ceEu64gbC2QNEIWujFnLdQ5oxOzWAtoRxot\nBdZCbiz9cUn9oZc5G9/SEHmKZqBpBE5z1pMCURXhIkpQzS4yqSNrLa49MuTt7/1QmqbpH98n57IZ\nN/cIa+0+X8q//WBx9Nf/bHZXI56J3M93EoH2EKZ0GsKBph16zDZC1oY5h41F6YLorAvwQ81sPWCu\nEZyUFM8mvjN20YJAiaqYbzLXfgAWtwejhK3UUiyeElPDYYFz+ExLS15a+lnBX/7vP+qXRfEWa+0p\ndbi7J2zGzT2jwP717Wb0/AOMot3dFvFMjBdHzm3TGnQl0VgLNM3Iox5q+lXBleYlSw95DJF3nJoF\nIIXbf5ECpMCpTxjXqDlv51bK9RVnjGZKd29VMpSieg+VcP+ssU7itp/mHDq8zOfe8LJhkQ5/9b44\nl/t7zJyqjjHA+44sL1/1u69604+8/EX/K/a3HXHGGkJivZgybpPqhMG4ZJSbqY1qoCXN2MfXckpI\nn4l95mo+UTXi9pWYOlOV1m2K58ZOTRu1dC5TCOkSqiBGtmfxioxWGFIfDhFSctD4/OGrXzwepuNn\n3xfdYnDB278fjRhOJaqu8XNf+i/vve6nnvITwTk7590jlbQowzpWagwFnnJqJN1awGIrR0nBenX5\nRJ6aboVPjBomi1NZ5Zy4PsxY6Wf002IqBzjbCNzv99TUxAELMohQ9TbxQheAOM143VdvGu/tDW60\nljfeF+eyGTPfHdbabwkh/+odL/rN39r3+3/fPHuxzmwjREvBMCtZ3kjZtzpiWHHSAZQfEdQ7CKmI\nu0u0F5osbmlwyc42D1yos60Z0gmrnQbpkpXMWIahopNrBpnrHishqnGoS4Qqlg5Wh6hmFxHGCO0z\nUgk/83NP2UjT9DestWv3xblsxs13R27tS76WDX7x+hlbe9IFszLZMkOy0EHGdaxUSNxb4u4an/WG\no3f10wIlBc3YSbpt7cSOPlELmIk9ar4k0s7xMFDCFdhVooPUUE07jRXYSkQnN7ZaHnfd43Fh6GUl\nn/v0f/C5j7wvLbLxH9xX57IZN/cMa+1QCPGcl/cPvPVRC2fWa4storkWMm5glY/FmX9Mdp8muwqT\n3QTgpKS4NI72mVd/7nlpwdNY7UMQIaIEWbjpkwhjZJhglAdSYwuw2Gm8jHL3th3ZGPPJv/vTjTLP\n3mCtve6+OJf7e8ycssS4SnJ++R9e99obL3zYj8RPevxjqPluSaU0TlatP3SjztVRPhWHnowXEl/T\njvQ0KW5Wiy2RFogyQ1iDVRor9XTklJcWC9MlhxKJ8CJM1EQIiRc30PPbAcilz9N/7vn9cV681Fr7\ntVP1ub8XjLGk9+ORww8Ka+1tUsrffup//5OXfeVTH64lUYDRISasO7vd8ngB1Qj1dKt7rgicVbiv\naMU+MzWfmq8rKb/KmME4EfJ+VnC0n9FL8+kjVws95uoBNV+hJxeX9jF+guouEAF+q8GXb93D337m\nmmyUFT9/nxVTmzHzX4D988GBW5929Rv+z/lHHvMsnbRC/MpNqiwMWZqTDhy/WEiFnzTwkwZe3KA1\nl7Bla5OH7e5y8VKDc2Zi5mON6i8jez1EmWGlxvcj4rBJq1ZjWBhGucZgkYjKnbGSBFQeJqxjvQDq\nEuPFvOC3fj9dPrryaU6DuP49YTNuvjuqJOdn/vfXvvnJRz32wfG2c3fgLZ2B6i5S+jHVVTMdV3dq\nLjFuxV6VGDuKxd2T4prnEmNlC0SWHZ88CYnVoJSPEFTLwNXisIVhVjLMS9LSsJ7m3LnvIK/43ecN\nimz8C9ba06qWdCI24+a7w1r7oVCrd73kllt/9s2PuSgO5heRzS42SMgN07hx1NCTZR8ni7uT5d3J\nn/kwdw3CSEtGpSUO6m7hrg0ijMEYZJRggjo2bJBZSVo6E5h+5iTaVvpjjmyM+epH3mmOfvPqFVsW\n910xdT+PmVPZMcZae0gI8bTf+tVnfaT/5g+GD77wfGpBpeVYuUf1KlHqflaQG7eM5ytHLK/7zrwj\n8aSrwkWJHPZgkhhLjfUiAj9GCInAUBimneRxacGvobzKa76WgzUYqfnV//7b4zv3H/xyWZZ/dSo/\n8/c8E2MZj+6/ldWpgLX29UdXjz32GS/4nZ9429vfHgqpyErDMD9eAAEESk5VJICqoHJLDJ3Ioxk4\nRYGJ0lZh3OOzmuY0As3GuCCtKvlQK+qBmjqWGWuxVUKuZrYgwoT9ueLpf/MXo3Fe/KK19o777Dw2\nY+Z7oqJvPXnt2n/7RlaKRufCxxI2WmhPHpfoK51+sZCSoNZ0i3atkLnFOhfvbHPhQp2zuzGLfoFa\nvhO7vIfi2BEocoQfIustdGceGbXRcZtaXCMrLZPqSAnh0hw/dh0fIbFS88r/+xrz7n9799HRaPjM\n0ymZ9G1nshk33xPW2i/Gof/Hz/jnD734qn/8yzjaciam1sWEdbLCdePALeTWq6lUVhh87e6eTnT8\nK/Fk1byRKJM5rrkpp8YMdtopdnswzsDDcYmHWcn6OKeXlWykBQdX13nF///MYVEUr7LWnlbVm287\nk824+Z4Yl+YFX9h35OGv/uKNZ77wYVdqGjMYP2FcGrLSTbBzYyiNOSkZLq2dGpf10oLIzxmEmn5W\nsp4WBBXtTwUxQQJW+y53sZZSe1i/Rhk16aUlw9w1FdfTnCM9lxTffM2XuOVdrxqZfPwEa+3ovjqP\n+3vMnNLEGMBa+2mh9K//0bN+6tVPfekb47PPOnPqKDUJlnGVnEy2u/2JyUKgKyk3lxR/6/prue7q\nL/OMJzmnZqt8THXpeH5MYVwFXlooCssYy6CqzAURVkZkpeGlf/zH2bvf977bR4PBU++rrt/x82A6\nzt3Ed0Y1bfiFz33us597zvNe8MCXvPxVgcUtrQyqCttYZ9taP6ErHGhJM/TohB6NUFHzFIEWfOpj\nH2V+YYELHnQR40DRCBUbqTd9iPLSTLnLnpLYydi8sMRJF5TP4X7Bjz3/TwaDNPuz0pj337fnsRkz\n/xVYa/cIIR47/Pq7PjXur8f+zJn49Q5+3EQFYWXK4KPDBD/SxLWA7nyNi3e2uXhLk/NmExZDg3fo\nZla+/mX+5Z0f5Gln78CzFi+JiOda+IvbULNb0J0FyqRLGNSxXkiJpKx01A2CwrhJ1jve+Tb7Zy98\n4cY4HT3SWrtx357HZtz8VzAa5397cK133k/84cuf8bH3vCOJmov0jUtWxsVxmp+vjifHvpJOJSl0\nUluxFkRa8o1rvkL/2FEe/8grXKe4ep+mX8qnKF0ynBZucroxLjg2yjmW5iwPMg6sbPDmP3re8NiB\nPR8ux6M/uq/PYzNuvjestakQ4lEve9+nr1t4wINnnvvfHirzoEF/WLAxLuiPCwZZyeiErywvscZS\nwLTgmtAqvvqx9/CQyx/OA84+C2t9SmupeSFRLUZVpbcVkrS09Ecl6+OSI/2MQ/0x+9dT/l975x4m\nV1Ul+t86p6q6qqv6kQfpPEgIBDUgw0tABQZ1eFxHRgb1iiOiIjJ6cUZkhgs6uYNXQV4Kw0u4M1EE\nMYAXJDwM8hCCJEAgYEgknZAHeXbSSb+rux7n1Klz1vyxTzWVphNCkn6Q7/y+r76uOnX2qbN3r7P3\n2muvvVZLV4HlS19nyR2XFQLPOVtVVw1ve+zfMrPPFWMA9cu/EjtWP/f751094yvX1Y6dPoNkbcJk\nmLKtHfKEN4TLVIGaSASVUG7imZ3Clu9AsQ8sG0kESLyGim7rq+L4AW45nLH5GnZsAZ6vZJ0S/++a\nK9wFjz6w2S3mT1HV3FDUd1cE+/nMal+hqq6InPb4o3MXdOWKH7rk6puSlh0L/69Bf0i+uC2kLJN5\nrKHGhPNLJyzqEzZ1CYuY71AbFEn7eRqtEuVUsn+neG3CJuOWKXg+XqDYYvzDyoFS9BTwKdkW27f1\n8tkzv5jv7O65qeg4Nwx3W0Qys/uo6isicpa/7o+PlQK/tuxMocBm7ESSRN1YkvUHhApyDam6BAeN\nr2XG+DSHjq1lYjpOvG0FTvMr9Lz+Btm3trKl3SWOmEg2B46hsbdAbTFPzCthN0GgAQFgJWpxfcUp\nB7i+UiwHzLnn18GN//cHfSWneIqqrhvutojkZvcIJ+LfXrl2ffpTnzv3rDkPPZKOpxvJhgprLlxZ\nAvrDi1aikNTGbVJhFJuELcREsdXfUSm2YqgdR0MZyXsBxbL525Ev0eV4dBZKbOlxWL+1g8evvTif\n3bTq+cAtnjucKwwVIrnZPVS1VUROuuzqmxZlYw1jzvvH79g9jknJ3O14dOZK9BSMu57rlim5ZQI/\nQEQouWXKJb/fetzZV2Jrr0tDtkhfqdwfbCBpCzG7EmggwPUDeh2f7XmXtnyJzd1F1rXlaF78Mn+Z\nfXkxKDlfUdVnhrst9neZGRLFGED98s0iVu+auy6+PXPMPyTrPnASyfrxJNMmQHqy1sSdPaDBpGg2\nPsZGYanMrj548EHMnNRIUMwjCbt/qVLjyf6OJlcK6C4aS2DW8ciVfPrcMlvbunjw6kudrSuXNJed\n/OnD6bO1QzsEul/PrPYlqtorIie/9OyT8zZu2nTCN6+5I1XfOBZLTEYoS4RkrCpSRY3ZBFVxvYm5\nJubjmSceZa7X1048WU99qgEjUj4Vka+ETKrsBHfLFk7ZYvmfX+K753+5WCwUfui6zn+MSDtEMvOe\nUNVnReQMf8P8J3TScWlr/EzLLzkUO7eivk8slQYyxOImssmEdIK6Goukm8Xfvom+NesortnKJ/JJ\n2jd2YAukx6UIPB87HsNOJkykibpGSKRBA7wAimXT/3QVXG74yY/L8+6/q6tkLMXDEk3gHe0Qyc1u\no6qBiJy3Zs3qW08/5eQLfvKL+1Jjp84gVzIT53yY6RDo38xrhdY+kbdfJxx/PFIq9IcHVctGYwl8\nK4Fb1n4ZyZWMAtWWL9GeL9HSVWDFyrUsuOmSotvT/nBQKn5jqOOj77QtIrnZbczGXznu6quuWrBk\n+ZuTzr30R/E2N2BLj0NrT5H2Xpe+nItb9PqzaoJJC10qlnGLHoVinLrDP0U7Ncj2PibUJ+kuejQk\n4+HmXyNvXhBQ8Iz/eUVmWrqK/OXph/Wth28uqOd+QVWfGpF22M9lZsgUYwDV4Fcisi635L65hU2v\n11iTjq1NjZ1MasxEasdNJjMmhZMv9WeUMn7GNpmERV1thqCUR8oOVgo0VmM2uNTUUVSb3pJPrxvQ\n2ufQ2ufSVfRo7SnS2uOwaumfWfaLWX1lp/A79ZyLVNUdynruug0UtzhiP/++Q1VzInLaxpV/ufHK\nL516wWcu+1nmgx85kVQiRm3Cpj4Z689oZ0LXmJ3kMQLEKyJuHnHMwoDU1BLYMSSRImHHSdgWpcDv\nj3HsBUrBNwOaXy7z4OxbnLl33l4quc65qvr4CLZBJDPvEVV9UUSODVpfe0L7WprsaX9dJ7EkTs92\nEukGvHEH4JcDbOvtEH3iFvB7Oym2dZNr7aOjrcB2t0zKtmjqLFI7PoVXcCgXHNR1zA/ZlYl5QK8b\nsGzVWmZ95xu9W9avbS4VC2er6pDHK95FG0Ry8x4I3er+2Y7Fl170udNvPvviK2qOPOMLsaJnfEUB\nbMu4U8Qti5qq1chymHXVD1PSU9kDE24Od7ygXynuLnp0Fb1+i19LV4HFTz3mr7z/ekd9/0fqezeO\nhKW4qh0iuXkPqOpGETlq3twHHnzhhYUn/P2sW+qLtU1s63HI5VyKuVKYbdPH99/23LRtCzthkSh4\nOIUSffkS7b0urRmHcZkE4zIml0Mi9E0ver7ZI5Nzae1x2NbeRfOc6/u6Vy7Katn9jKq+MYJtsF/L\nzJAqxhD6HIscFnSsvCvoWXdyYerJdU7PFLrXL6Nu0gwyTdMp5krkXBMYO9Awzh/C2EwT8UQa8T2z\n87smQ9YN6Mp5tOU9NmeLbOou8lZbji1dRdo3t7Ju3uxCduVzJYLyhar60FDX713r75fx8tmRvo33\nFaHl5Hsi8sSjV/3zPROP+UTtx776r+kDD5zM2EwNB2TMzvGC51Mbt41lR+x+vz6xTUykQN5OChOE\nGRL9gP7EDbnQf3nlksX85urLerPt25d5rvMVVd08QlUHIpnZU0JrzpHat/Xa8orfXWhPOSElYw+1\n+lrXkhrThDM+RbZQ6l8tULGMi5ZtIZZJM5+whLiYjHhW3MYKLcaSSiOpDH5NHYXAprU7xy233uzd\ne8dNJa/kXoPqDarqj2j9I7nZI/yy90sReXnuLVc+8MxD90059uuz6humzOiPmNRYG6dQ6xNoDZZA\n3LL6I5L4ASRs4wbo+0qp5Ie+xAFZp0xHoURnwaMt57KxI88by9/kjTnX9/VtWtGh5dKXVPXVkaw7\nRHKzJ6hqj4icsW3D2ot+8U+f++mBp5wTH/+xLyZKJSuMhuPiOTmCcgkNKlZji1gihZ1IEUskSKRi\n9KYTdCRNHP6amhipMB19JYqF4/k4eZeti5/RzU/cng8850GC8iWq2jei9d/PZWbIFWMAVd0uImfi\nFT7rb5j/n0GyMWNNPLauV5W+1rfITZpBvvdwCuFOy57pY+gel2ZKfZK6RA0xO4nvQaFglqE2ZYts\n7C6yZlsfm7bnaFu3mdaFD7i9K59SVO8nKF+uql3DUbd3QzXAc/bMtVlEjgHuAepUdXp4bDIwDzgc\nyFSW30QkC7weFv18df1F5AfAp8OPxwNTgSOBXwPrgU2q+rWq8/8F+IKqnrxHN76PUNUnReTQbUsX\nXvXI0gXfOvCkz+pxZ5+f+sCh0wHe3kBnxUjGLGpTDaZcOFBpPIkm0pQkQbHsUywH9DgeHYUSXYUS\nS15+kT/88ua+bWubS55T/C7w25G03FTYG5kBEJEG4F4gAzyiqjeLSAyYAzQBr6rq5QPKfAL4KSae\n1N2q+p8icj7wb0ArsLhSRkS+D5yO6T/+Zrg3tO6KcGf2JSLyG3/L4jvZtnSGPenYdM+msZJqHMPG\njgKtOZcZbi3jMmOwx02kbloTY9p6KDtl6juKiC1kmmppmFpP3bQmkpMnE2uahl/XRJsr/NcdPy/f\ndsP1pZLnLfJc53+p6tqRrjfsdV/zDpkJj38N+DpgA19R1S1VZS4ArgBeVNXzBlzvUeANVf33UPbu\nAg4G5qnqdXt0k0OIqi4XkSO716/8zvyrLri68QPHMfXUr2bGHDyT9lScsZkEOccjV5/ELZuJ9ZiU\nSRleiYLjB1Asm2gDFX/lrVmHLV0FVjav4M3H7sp3Ni8UDYLrCMo3juRKZjX7Wm5E5NNAJXTYh4CL\nVPWRqjJHA7cDATBLVReKyLeAC8JTblXV+0a73IRjxR0i8vuWhXNvb1k497S6D58Zj08+JhYEAV6x\nohj7/QmGrFgCK54glkgRS2aIpdLEkxniyRriNTZiSb/LjueUyK5aRMfLc/r8Qs9WLTt3wZd1AAAK\nOElEQVQXquoLI1trwzDqNbcBfwWsA/6x2vgw2PgUHjs/POUo4FNAGZiN8aVcC1zwbuP8sCjG0C9E\nj4nIH7TQcZ6/Yf7/wYpPsMZ/KNVb7IrnO1rIdx1NtnMyWzoLzJxSz0Hj00zI1BC3zQwq65Zp6Sqw\nri1PS8t2tr72Iu2vzcu621fFQe9Dg2tHYtPLrlDf35uZ1VrgY0C1H1EXcCrw8IBz31DVTw56D6ZD\nuU5ExgO/U9UuMe4Iv1HVHdKOikgNcPSe3vC+JtzZ/z0R+dnmBY9cvvmFRy8Yd8gR3nFnntP4ydPP\ngKmT+mPp+8kYtekDsGvDjTB2gqJvspdlXZ/2fImly1fw3FOP+88/NCdXyHb3lkvONcBdo2WQgr2W\nGYBvAfeq6v0i8pCIzMF0EMtU9VoRuU1EjhoQz/tS4ItAC7CIt1Nf/0xVf1k5SUROwHRcp+3NDQ41\nqvpnETmGwDvDb1l0Ra7l5aM3b3rRsvo+n2o+4G+Y1pBkTDLDuKYZpD/UzgQgUV9LsbMXy7aoacxQ\nN62J9CHTCSbN4OkVLdxzzZ35Rx552AKZ7zrFq1T1lZGuZzV7KTeDyUwN8AlVPXUnZR4DFgA/qj4o\nIkcCqapDZwFvqupXRWSeiExU1W17eqNDRTgY3yoid3evfOmfela/emmicaI94bgzG5qO/WvpmnqQ\n2WDVmGRCfZLGZJxMwiYeLn17vvEl7iuVaet12bBhE8sWztf1f5qbLbZtRIPyrajeMlqMNhX2tdyo\n6pPAkwAi8gowcHPYlcCXMGPZXIzR5mlVnS0iceBlTPzv94vcbAbOEpGjsssevoKlcz8j9VPL1thD\n66RuEmLtXM2KJdNGQa5JYcUSiB2j3L0ZZ8vrrrvpVU/VX4Xv/QR4dDQYbSoMh14jIscDCVX9pIhc\nCvwd8OiAa+0wPqnq3cDd4aTqNWAZEFPVE8Nr3gUcB+xypWbYFOMKYedzt4j8mqD80aBt+flBW/Pn\ny5DetnJ8sa1uUt2WqUck3pp8MOmxjSTTNUCZUm8P+fZWzW5c1Zvb1ByUcx21WLE/47t3Ag+p6qi0\n62vgUyrsWdSmynKJVKW9VlUHcKqPhRwmIguBF4F/28lDdBZmMKvw5dBSeIeq3h8e+ybGknzlHt30\nEKGqLcDFIjKrY/XrZz+7Zd03n77thx9vGN9UmHn0cXLU0cc0HHrwdJk4YTyZ2hSqAb19OVpbt7Nm\n7RpvyWuv9q564/WaslcuqcjvvWL+V8ALo8naWWFvZCbkEOAP4fsVmFWCQ4C/hMeWAidiOo0Kq4AG\noB3IVx2/JLQa/lhVn8V0TuNF5DngOVUdVXJSTfgMPAU8JSIfdDa+/LXV9y/98pp7Z0156NCZuY9/\n7OOpkz5yZO3B9TYNifHIgT5OJklP3mFbwaN5QXPu5dmPu2+u21CXydSt6clm7wl8/95qq+loYi/l\nZjCZmQTYIvJseOySaouNqnaISGaQa10M3IEZgMAMgr8L3z8HnMCO/dCoIpyMXysiN7idm07b8tw9\nF7Y8c+fpdk2t1k07rNw448MNE6ZMs8dNmEA6nSYRs/Bch96uDtq3bPa3rl6e7XzrDdsr9MYkVvNc\n4OTuBJ4YTZPvaoZAbp4AEJFDgO36zmhQY8L+HBFJi0hKVTeE35XDF7z/5GYZ8D9FpEmzG/7Bz7V+\ng8A7jJr6PklPSEhybJ0kasGuMa5/BJR6PUpeAXWyjubb8lrsTGPZbQT+/0f9e1R1+UjXazCGSa8Z\nOGadwTsV44HjU4VTgAXhGFCdicQF3tVVUkbDJERMa0zDPAgfxjRIE2Z5JgYUgU5gC/AmsARYEjbm\nqEZEngTGD/JVEqi+/9mqOnsn13hhoFuDiPwJOK1qyWEs0I2x9D2uqu/oQETk98DFqro+HNBcIIGZ\n0Z8F9GBm/+cM9pujDRFJYKzbHwFmAgcCB2Da1gcKwHbMMswK4BVg3WiaeQ/G3sqMiFwClID/AuZj\nZKIInBguN80BmlX12qoyJwIPYNrtx6r6KxFpBHqBccDTGEXnDqBTVWeJyG+Bn6rqkr2u9DAiIgdg\n+pojgA9iFMA6zLPgYp6DrcBqzORh8WideFezN3KzE5mZDvyVqp4rItcDr6jq3AHlpgM/qbhSiMhM\njOvFU5j+6d9FZDbwH6r6pohcCJRUdVjSq+8rRMQCDsMoZ4dhxqsmjGXcwvQ1bZhBdyXGIrVcR9jv\nfHfYidzscV9TMbKIyP/G9BV3DSgzF/ghpm9eDRyuqq3hdxdhFOdr9hO5yWBk5iiMW8lkYCxmNaYE\n5DCuAGuBZmDRaLSKD2Q49JrQ5WZWqI9cDYxX1W9XnfuO8anyvInIzzEG0+fCz2cB1wBrgHNUdZdp\n+4bdYjwYoaKyMXztV6jqp9/9rB0RkcuAMzG+nnfv5u90hWUfAY5hwMxaROowgrU+PL8yi/dEZAHw\nAYxyOWwpbPcWVS0Bi8PXfsOeyAzsIDcPYizCZ2MUvO3A88CpofVvQ3ismuuAj4fH/ygiv1XVnvC7\ndhFZjVEEsuG1wFhxDsNMVN83qGo78Pvwtd+wl33NYDIzhrf/1/N52wK8K/4Vo/TMrDqWBerD9/UY\nJeB9Rbiy1By+9iuGQG4qfBb4/CDFfwD8HOjDWAQ7wmt+FPhMeC3YP+Qmh3l25o/0vexLhkOvUdWl\nIrI8XJ1czoAxayfj09bQ0Hoy8L2qcx/DuPLehln1HOiKugPWrr6MGBlU9Weq+sndVYrD5Sg7/HgS\n8NYgp/0t4RJXWKY+/Gtjlr82EG6UCGeDHxaR7+5xJSKGnSq5uT204P0t5hlfpKq+qn439Bf12dG/\ni/BYTzjZCIB4lYykMBOnduAlzMZNMNb6YUuVHbHv2ZXMsGf/64OAuzEbOSuuWosw/oNgfN1HPBJD\nxN7xLnKDiEzEWHg7Bym7WlXPAL6N2fjticgU4Ebg61VW9khu9iPeq14TlrlSVT+F8RjYIYTqTsYn\nMPrMkirrcU1VsV7M6ukuiRTjUYyITBWRZ4AjROQZEZkuIvHw2FEY38mPYoTi1dDyO5XQLyucHVX4\nHGajQ4VzRGQxxif5UVXdqqrfV9X/Ec4Gm1W1unzE+wQR+Ug4y/4jZmNhUUSmiMifRGQ+8FLFT7ZK\nRq4HnhGRRRjf4SzwL+HnPwHXhctP84DDReR5wFLVl4a5ehFDwGAyo6pLgWK4vHk8A/oVEfk7TKST\nU0XkIYCq/uNy4H5VfR5jmT9CRF7ATNJah7l6EUPEYHITfvX3DPAHrZKbb4Zl7sGsLhD+bQLmhv1U\nikhu9kt2V68RESuUhWcxk6xXwvKVMWuw8Qneqet8WkSeD8esJozbxa7vcZS7W0ZEREREREREREQM\nC5HFOCIiIiIiIiIiIoJIMY6IiIiIiIiIiIgAIsU4IiIiIiIiIiIiAogU44iIiIiIiIiIiAggUowj\nIiIiIiIiIiIigEgxjoiIiIiIiIiIiAAixTgiIiIiIiIiIiICiBTjiIiIiIiIiIiICAD+G5Ubl5eE\nZhVdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb594898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(720x108)\n"
     ]
    }
   ],
   "source": [
    "print epochs.plot_psd_topomap()\n",
    "picks = mne.pick_types(info=data.mne_info(), meg=False, eeg=True, misc=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mne.set_log_level(verbose = \"ERROR\")\n",
    "# get test list\n",
    "test_list = []\n",
    "for i in range(0,data.num_tests):\n",
    "    epochs_test = data.test_epochs(i)\n",
    "    Y_test = epochs_test.events[:,-1]\n",
    "    X_test = epochs_test.get_data()\n",
    "    test_list.append({'Y_test':Y_test,'X_test':X_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy.signal as spsig\n",
    "def data_prep(data, sfreq, fmin, fmax):\n",
    "    [b_high, a_high] = spsig.butter(4, float(fmin) / (sfreq / 2), 'high')\n",
    "    [b_low, a_low] = spsig.butter(4, float(fmax) / (sfreq / 2), 'low')\n",
    "\n",
    "    chunk_high = spsig.lfilter(b_high, a_high, data)\n",
    "    chunk_low = spsig.lfilter(b_low, a_low, chunk_high)\n",
    "    return chunk_low\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    #print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    pass\n",
    "\n",
    "def plot_mult_conf_matrices(conf_matrices,classes,method='mean', mode = 'plot', normalize = True, title = 'Confusion matrix'):\n",
    "    if(len(conf_matrices) < 2):\n",
    "        print \"is not a matrices set\"\n",
    "        return 0\n",
    "    if(conf_matrices[0].shape[0] != conf_matrices[0].shape[1]):\n",
    "        print \"is not a square matrix\"\n",
    "        return 0\n",
    "\n",
    "    n = conf_matrices[0].shape[0]\n",
    "    \n",
    "    if(method == 'mean'):\n",
    "        matrices = np.mean([confusion_matrix[n-j] for j in range(1,n)], axis = 0)\n",
    "    elif(method == 'max'):\n",
    "        matrices = np.max([confusion_matrix[n-j] for j in range(1,n)], axis = 0)\n",
    "    elif(method == 'min'):\n",
    "        matrices = np.min([confusion_matrix[n-j] for j in range(1,n)], axis = 0)\n",
    "    else:\n",
    "        print \"incorrect method\";\n",
    "    if(mode == 'plot'):\n",
    "        plt.figure()\n",
    "        plot_confusion_matrix(matrices, classes,\n",
    "                      title=title, normalize = normalize)\n",
    "    else:\n",
    "        return matrices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SCP decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX: 1.0\n",
      "MEAN: 0.802777777778\n",
      "MIN: 0.566666666667\n",
      "DISP: 0.129427752858\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       1.00      1.00      1.00        10\n",
      "          2       0.83      1.00      0.91        10\n",
      "          3       1.00      0.80      0.89        10\n",
      "\n",
      "avg / total       0.94      0.93      0.93        30\n",
      "\n",
      "Normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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APwSOkTRT0jHNFXNj7DJ4KK/Omc28ua/x0Ucf8fe/jOHQw0auUWb4iFGMuetO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\nM8vDSdLMLA8nSTOzPP4/y9krdsKT0BYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfde3710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "from mne.decoding import CSP\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "Y_train = epochs.events[:,-1]\n",
    "X_train = epochs.get_data()\n",
    "X_train = data_prep(X_train, 1000, 6, 16)\n",
    "\n",
    "# train classifier\n",
    "svc = LinearDiscriminantAnalysis()\n",
    "csp = CSP(n_components=4, reg='ledoit_wolf', log=True)\n",
    "\n",
    "\n",
    "X_train = csp.fit_transform(X_train, Y_train)\n",
    "svc.fit(X_train, Y_train)\n",
    "\n",
    "scores = []\n",
    "classification_report = []\n",
    "confusion_matrix = []\n",
    "for i in range(0, len(test_list)):\n",
    "    X_test = data_prep(test_list[i]['X_test'], 1000, 6, 16)\n",
    "    X_test = csp.transform(X_test)\n",
    "    scores.append(svc.score(X_test, test_list[i]['Y_test']))\n",
    "    classification_report.append(metrics.classification_report(test_list[i]['Y_test'], svc.predict(X_test)))\n",
    "    confusion_matrix.append(metrics.confusion_matrix(test_list[i]['Y_test'],svc.predict(X_test)))\n",
    "\n",
    "print \"MAX:\", np.max(scores)\n",
    "print \"MEAN:\", np.mean(scores)\n",
    "print \"MIN:\", np.min(scores)\n",
    "print \"DISP:\", np.std(scores)\n",
    "print classification_report[0]\n",
    "\n",
    "\n",
    "plot_mult_conf_matrices(confusion_matrix,[u'Rest',u'left',u'right'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Riemann tangent space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyriemann.classification import MDM, TSclassifier\n",
    "from pyriemann.estimation import covariances, XdawnCovariances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found array with dim 3. LinearDiscriminantAnalysis expected <= 2.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-eecb0a24e598>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[1;31m# compute covariance matricestest_list[i]['Y_test']\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mdata_cov\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcovariances\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'oas'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_cov\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda2\\lib\\site-packages\\sklearn\\discriminant_analysis.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, store_covariance, tol)\u001b[0m\n\u001b[1;32m    440\u001b[0m                           DeprecationWarning)\n\u001b[1;32m    441\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtol\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 442\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    443\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munique_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.pyc\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    519\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    520\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    522\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.pyc\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    403\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n\u001b[0;32m--> 405\u001b[0;31m                              % (array.ndim, estimator_name))\n\u001b[0m\u001b[1;32m    406\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m             \u001b[0m_assert_all_finite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found array with dim 3. LinearDiscriminantAnalysis expected <= 2."
     ]
    }
   ],
   "source": [
    "Y_train = epochs.events[:,-1][:test_list[0]['Y_test'].shape[0]]\n",
    "X_train = epochs.get_data()[:test_list[0]['Y_test'].shape[0]]\n",
    "X_train = data_prep(X_train, 1000, 6, 16)\n",
    "\n",
    "# train classifier\n",
    "clf = TSclassifier(clf=LinearDiscriminantAnalysis())\n",
    "# compute covariance matricestest_list[i]['Y_test']\n",
    "data_cov = covariances(X_train, estimator='oas')\n",
    "clf.fit(data_cov , Y_train)\n",
    "\n",
    "\n",
    "scores = []\n",
    "classification_report = []\n",
    "confusion_matrix = []\n",
    "for i in range(0, len(test_list)):\n",
    "    X_test = data_prep(test_list[i]['X_test'][:test_list[0]['Y_test'].shape[0]], 1000, 6, 16)\n",
    "    X_test = covariances(X_test, estimator='oas')\n",
    "    Y_test = test_list[i]['Y_test'][:test_list[0]['Y_test'].shape[0]]\n",
    "    scores.append(clf.score(X_test,Y_test))\n",
    "    classification_report.append(metrics.classification_report(Y_test, clf.predict(X_test)))\n",
    "    confusion_matrix.append(metrics.confusion_matrix(Y_test,clf.predict(X_test)))\n",
    "\n",
    "print \"MAX:\", np.max(scores)\n",
    "print \"MEAN:\", np.mean(scores)\n",
    "print \"MIN:\", np.min(scores)\n",
    "print \"DISP:\", np.std(scores)\n",
    "print classification_report[0]\n",
    "\n",
    "\n",
    "plot_mult_conf_matrices(confusion_matrix,[u'Rest',u'left',u'right'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ICA decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX: 0.566666666667\n",
      "MEAN: 0.341666666667\n",
      "MIN: 0.233333333333\n",
      "DISP: 0.089365044123\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.22      0.20      0.21        10\n",
      "          2       0.25      0.20      0.22        10\n",
      "          3       0.31      0.40      0.35        10\n",
      "\n",
      "avg / total       0.26      0.27      0.26        30\n",
      "\n",
      "Normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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HNxJcLB/vY4JaaP18/gr8NRNldM5FR5sPms45l5Jox0wPms65CJHXNJ1zLiUeNJ1zLklC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\n55xLmlDEa5pRn3z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m+yy3RpI6m1l5rssRBeEo+R8I52BK+g8wm2DwpwPBQiYfAoOA24Dj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c9c7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_train = epochs.events[:,-1]\n",
    "X_train = epochs.get_data()\n",
    "X_train = data_prep(X_train, 1000, 6, 16)\n",
    "\n",
    "vect = Vectorizer()\n",
    "X_train = vect.fit_transform(X_train)\n",
    "\n",
    "\n",
    "ica = FastICA(n_components=30)\n",
    "X_train = ica.fit_transform(X_train, Y_train)\n",
    "\n",
    "svc = LinearDiscriminantAnalysis()\n",
    "svc.fit(X_train, Y_train)\n",
    "\n",
    "scores = []\n",
    "classification_report = []\n",
    "confusion_matrix = []\n",
    "for i in range(0, len(test_list)):\n",
    "    X_test = data_prep(test_list[i]['X_test'], 1000, 6, 16)\n",
    "    X_test = vect.fit_transform(X_test)\n",
    "    X_test = ica.transform(X_test)\n",
    "    scores.append(svc.score(X_test, test_list[i]['Y_test']))\n",
    "    classification_report.append(metrics.classification_report(test_list[i]['Y_test'], svc.predict(X_test)))\n",
    "    confusion_matrix.append(metrics.confusion_matrix(test_list[i]['Y_test'],svc.predict(X_test)))\n",
    "    \n",
    "\n",
    "print \"MAX:\", np.max(scores)\n",
    "print \"MEAN:\", np.mean(scores)\n",
    "print \"MIN:\", np.min(scores)\n",
    "print \"DISP:\", np.std(scores)\n",
    "\n",
    "print classification_report[0]\n",
    "\n",
    "plot_mult_conf_matrices(confusion_matrix,[u'Rest',u'left',u'right'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX: 0.533333333333\n",
      "MEAN: 0.333333333333\n",
      "MIN: 0.2\n",
      "DISP: 0.0757676760944\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.25      0.20      0.22        10\n",
      "          2       0.25      0.20      0.22        10\n",
      "          3       0.36      0.50      0.42        10\n",
      "\n",
      "avg / total       0.29      0.30      0.29        30\n",
      "\n",
      "Normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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mWQzaPo93v1ua7PAi06JJFitjEv+qdRto2SR7s3L9OuRy6b49OH3PLrw8bdHG\nHQ5nDerKhXt1q/bLZVtkcW51cfcF7j4lfFwETAe61FQ+ac1kMxsLnObuK2op8wFwtbtP2uT1gUC+\nu49t2CgbxucTPmLsi89w3zNjKl+779kxtO/YmeVLC7n63BPotkNvBgzeJ8Ioo3fGoC489/mCRl7v\n2TLTF69m+uLVdG/dlKG92/LEpAIAHv1sLkXrN9A8J5OzBnVlyZoS5ixfG3G0W6F+nYbtzCw2V4x0\n95HVHtasB7A78GlNB0tKMjQzA4a5+5Z2bAwEBgEpkwzbdezM4gUbm72FC+fTrmPnzcrN/PZr7rrh\nCu4Y+Rx5rdtUvt4+LNu6bXv2P/QoZkyd0qiT4fLiUto0y6l83qZZNsuLS6uU6dm2KRfv1x2AFttl\nMqBLC8rLncnzViU11mQqWreBvCYb/wxbNsli1brSGsvPWb6W1k2zaZadQXFpOUXrg1rlmpIypi9a\nTde8Jtt2MqReV6AscfdBdR7PLBd4EfiNu9f4y9RgzWQz62Fm35rZk8A0oMzM2oX7bgj3jTOzf5nZ\n1TE/eqKZfWZm35nZ/maWA9wKnGxmX5jZyQ0Vc3303XV3CubMYsG8OZSWlPDe2JfYZ+gRVcosmj+P\nGy89m9/9+UG279mr8vW1xWsoXl1U+XjS+A/ouVO/pMafbLOWFtOpRQ7tm+eQmWHs1aMVU+ZVHSm+\n8uUZXPnydK58eToTf1zJPz8raNSJEKBg1TraNMumVdMsMg127dySGYvXVCnTJqY7oXOL7cjKCBJh\ndqaRkxkkjuxMC/plV1cdkNrWGMEgWjxbXMczyyZIhM+4+79rK9vQNcPewFnuPsHMZofBDQaOBwYA\n2cAUYHJsTO4+xMyOAm5y90PN7EZgkLtfUt1JzGw4MBygY37XBnszsTKzsrjshju49rwTKS8v58jj\nT6Nn7768+tzjABx7yjk8+eCdrFqxjHtvvTb4mXAKzfKlhdxwyVkAlJVt4NBhxzNk/0NqPFdjUO7w\n5MQCrjlkBzIMPpy5jIKV6xnaOxhNf+9/6dNPGKvcYcz0Qn65Z1cyDKYUrKJwTQmDwv6/SfNWsnPH\nXAbmt6Ss3NlQ7oz+MmiR5OZkceruQb91hsHUBUV8v6Q4sveSKImaWhO2SB8Dprv73XWWd2+YHpqw\njf6+u/cMn88maOqeAbR295vC1+8G5rv7XWGf4e/dfbyZdQTGu3svMzubWpJhrD79B3o6zNnbGo9N\nnBd1CCktCqRHAAALQklEQVSvZ/umUYeQ8h699DjmfzctodMC+w/Yw194c1xcZfvlN59cWzPZzPYD\nPgK+Aiq66K6vaeyhoWuGa+ouspmKen4ZmgcpknYSdQWKu4+jHhXNKKbWjAeOMbMmYcfmsDh+pgho\n0bBhiUgqSNTUmvpKejJ094nAq8BU4A2CKmxd11y9D+ycSgMoItJAIsqGDdYMdffZQP+Y5z1idt/l\n7jebWTPgQ8IBFHc/KKb8EqBH+HgZMLihYhWR1JCOi7uODC+LaQI8UTFLXETSXD2mzSRaJMnQ3U+L\n4rwikvrSKhmKiFRPi7uKiACqGYqIaHFXEZFKqhmKiKTf1BoRkWqpz1BERDeRFxGpoGayiKS5isVd\no6BkKCIpRVNrRERQzVBEBNDUGhERQDVDEZF63fku0ZQMRSSlqJksIgK6NllEBDS1RkQEUJ+hiAiG\nkRFRNozivskiIilHNUMRSSlqJouIoKk1IiLpd99kEZHq6IZQIiIhi6hqqGQoIiklqmayptaISEqx\nOLc6j2P2DzNbbGbT4jmvkqGIpJZEZUP4J3BEvKdVM1lEUkqipta4+4dm1iPu87p7Qk6cKsysEJgT\ndRwx2gFLog5iG6DPqW6p9hl1d/f2iTygmb1J8D7j0QRYF/N8pLuP3OR4PYDX3b1/XQdrdDXDRP/n\nbC0zm+Tug6KOI9Xpc6pbOnxG7h53szbR1GcoIoKSoYgIoGSYDCPrLiLoc4qHPqN6MLN/AZ8Afcxs\nnpmdV2v5xjaAIiKyJVQzFBFByVBEBFAyFBEBlAwlRZhZTsxj/V5K0umXLknMrIOZ7Rs+PsvMdok6\nplRhZs2AwWbW1MyOBgZGHVMqM7MmZtY6fNw59otEtpxGk5PEzNoCLwJrgdbA8e5eEG1U0TOzg4Gd\ngGbAoUAvYJC7F0UaWIoKa81DgL2BYmAf4Gp3L4w0sEZANcMGZuFKle6+FHiY4Bf5A3cvMLOs2DLp\nxsx2Ax4EXgPKgb2AlyINKsW5ezkwHzgQuBV4x90L1bWw9fQBNiAzMw+r3ma2N7AYOBwYZmbXu/uG\nsGibqGKMWAfgS2Bw+Px8oBS4zMx2BDCzjmaWGVF8KaUi4bn7j8B04D1gFzPrFybJtP1iTQQ1k5PA\nzK4EjgN+6e6zzKwv8ALwD2BNuO9n7r6ulsM0Smb2DbAD0Mfd55jZYcCRwEIgF+gCXOruxRGGGSkz\ny4hJdv0JfmfmA02Bawk+p5uAFkAf4D+uP+x6U82wgZnZUcDJwNAwEVb8Mh8C7ElQU7w2XRJhRc0l\npln3JvAR8H9mluXu7wCvEzSb9wNGpHki7Ar82gKHA+8AfyL43Ax4ACgCngbGA8VKhFtGNcMEi20a\nh89/QtD8mw00J+jwLgOuI2giZqbLH/sm3Qb7AcuAue5eZGavAevc/cSY8k3dfW1E4aYEM+tDkOhe\nBdoCo939YzO7EziI4Mt0BcGKzmvc/cOoYt3WKRkm0CZ/7P0I/tjzCDq7jwX+AvwPGA587u6vRRVr\nMlXzBXENcBQwj+DzudzdfzCzVwgW7DzK3cuiiTZ1mFmmu5eZ2U7ACIJm8eXu/mW4/y8Ev1cHuvui\nCENtFNRMTqCYRHgNcA9wH3AK8DJwrLt/RPBtfjwwI6Iwo5AJwR+3mQ0E9nP3g4G54f4CAHf/GUEt\np1MkUaaYmES4lqB1sRY4wszywv3XAmOA3tFF2XgoGW6lTUfvwnlzh4Ur9mYQdGgvAXLN7CDgcuAM\nd/9fsmONgpm1A743szZhba8EmGVmdxNMrj7B3UvCyda4+8npPv8ypl/1J8DNwI0Ey9tfBAwFhldM\nunb3q9x9XEShNipKhltv09n/2wHvh7XDXODcsMbYHfgaGObuXyU5xsi4+xLgUuATM2vt7t8QTKkZ\nDJwTJsLzgZvDxJnWKroUzOxI4O8EteedgSsJph1dAPwMuLBinqokhvoMt0I4uncR8AUwzd1fDKfN\nPABkA4e4e6mZXUZwdcUp6TJYsqnwj/s+YHeCieenEfQPzgF+Dpzk7l9HF2G0zKyzuy8IHzcjmKD/\njLu/aWaDCbpbsgmm0LQB2rr7Z5EF3Ajpm2ULmdkRwC3AkwQ1nSPMbArBN/l4gjlfvzWzZcB5wOnp\nmggB3P0NM7scmEgwpehbgpHQbII5lt9HGV+UwmbxrWZ2j7t/4+7FZrYaOMzM3nH3ieHlnHcTzL+8\n191nbjowJVtHzeQtYGZtgLHAbe7+APAIQXO5s7uvAf4MvEWQELsBp7r7tKjiTRXuPha4miAhrnX3\nx919ZLonQg+cD7Q3s8fDXU+H/54U/vsDwRftkcCusHHAThJDzeQtFHb4/wXY291XmdkYgmkiUwhG\nike5+1J9e2/OzH5GMDCwJ8HfdNp+PjF9hHsTXJHUAngF+C1wIsH8wbbA9gTTkc4DvnH3p2s4pGwh\nJcOtENMP9ibBaisjgfbAr4BpwG/cfVV0EaYuM8t199VRx5EKwlHjPxPOITSzj4FJBIMmTQkWsPgO\n6AE8Chzh7jMjCrfRUjLcSmZ2KPA2QRN5UfhaBtAmHEkVqVU4EPcG8Ad3v93MmhB8wRa4++lhmX4E\nX7xXqMulYajPcCu5+3+Aowmm03QIXytXIpR4ufvbBIt1nG1mp4fXqf8U6Bkuc4a7TwdOViJsOBpN\nToBwpDQHeNPMBlWsMCISL3d/xcxKgT+aWY67P25m+4b9iZnuXubuy6KOszFTMzmB1A8mWyscXLqd\nYF7qIl2jnTxKhiIpxszau5bxTzolQxERNIAiIgIoGYqIAEqGIiKAkqGICKBkmJbMrMzMvjCzaWb2\nfLhk1JYe6yAzez18fKyZXVdL2VZm9ustOMfNZnZ1vK9vUuafZnZCPc7Vw8w0sTkNKRmmp7XuPtDd\n+xOsPH1h7M7wTmz1/t1w91fd/Y5airQC6p0MRZJByVA+AnqFNaJvzexJgkUmtjezw83sEzObEtYg\ncyFYy9HMZoTrNx5XcSAzO9vM7g8fdzSzl8zsy3DbB7gD2DGsld4ZlrvGzCaa2VQzuyXmWL83s+/M\nbBzBrRNqZWbnh8f50sxe3KS2e6iZTQqPNywsn2lmd8ac+4Kt/SBl26ZkmMbCZeOPBCpuQ9AbeNDd\ndyG4t/MfgEPdfQ/CVVTCRQQeAY4hWIKrpps33Qf8190HAHsQ3PLgOmBmWCu9JlygoDfBytcDgT3N\n7AAz25NgZeeBBMtWDY7j7fzb3QeH55tOsNRVhR7hOY4GHgrfw3nASncfHB7/fDPrGcd5pJHStcnp\nqamZfRE+/gh4DMgH5rj7hPD1vQjuvTE+WIiZHOAToC/wQ8UNrczsaYJbn25qKPBLCO7yBqy08CZG\nMQ4Pt8/D57kEybEF8FLFyuBm9moc76m/md1G0BTPJVhct8Lo8Hrx/5nZrPA9HA7sFtOfmBee+7s4\nziWNkJJhelrr7gNjXwgT3prYl4B33P3UTcpV+bmtZMDt7v7wJuf4zRYc65/Az8P1AM8muCVrhU0v\ns/Lw3Je6e2zSxMx6bMG5pRFQM1lqMgHY18x6AZhZcwvu4TsD6GFmO4blTq3h598luFlWRf9cHlBE\nUOur8BZwbkxfZJdwGbQPgZ+bWVMza0HQJK9LC2CBmWUDp2+y70Qzywhj3oHg/itvAReF5TGzncys\neRznkUZKNUOplrsXhjWsf5nZduHLf3D378xsODDGzIoJmtktqjnE5cBIMzsPKAMucvdPzGx8OHXl\njbDfsB/BbUQBVhPcU3qKmY0CvgQWE9wzpS43AJ8CheG/sTH9CHwGtAQudPd1ZvYoQV/iFAtOXkhw\nlz5JU1qoQUQENZNFRAAlQxERQMlQRARQMhQRAZQMRUQAJUMREUDJUEQEgP8Hq6KOVlwXgygAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d17c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_train = epochs.events[:,-1]\n",
    "X_train = epochs.get_data()\n",
    "X_train = data_prep(X_train, 1000, 6, 16)\n",
    "\n",
    "vect = Vectorizer()\n",
    "X_train = vect.fit_transform(X_train)\n",
    "\n",
    "pca = PCA(n_components=30)\n",
    "X_train = pca.fit_transform(X_train, Y_train)\n",
    "\n",
    "svc = LinearDiscriminantAnalysis()\n",
    "svc.fit(X_train, Y_train)\n",
    "\n",
    "scores = []\n",
    "classification_report = []\n",
    "confusion_matrix = []\n",
    "for i in range(0, len(test_list)):\n",
    "    X_test = data_prep(test_list[i]['X_test'], 1000, 6, 16)\n",
    "    X_test = vect.fit_transform(X_test)\n",
    "    X_test = pca.transform(X_test)\n",
    "    scores.append(svc.score(X_test, test_list[i]['Y_test']))\n",
    "    classification_report.append(metrics.classification_report(test_list[i]['Y_test'], svc.predict(X_test)))\n",
    "    confusion_matrix.append(metrics.confusion_matrix(test_list[i]['Y_test'],svc.predict(X_test)))\n",
    "    \n",
    "print \"MAX:\", np.max(scores)\n",
    "print \"MEAN:\", np.mean(scores)\n",
    "print \"MIN:\", np.min(scores)\n",
    "print \"DISP:\", np.std(scores)\n",
    "\n",
    "print classification_report[0]\n",
    "\n",
    "plot_mult_conf_matrices(confusion_matrix,[u'Rest',u'left',u'right'])"
   ]
  },
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   },
   "outputs": [],
   "source": []
  }
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