EEG_all_classifiers_basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = load(root + name,'exp_data_aligned.cls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAEsCAYAAAC41gacAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4lFXa+PHvmUwmmbRJJUwqJBTpvYsIKEQERRTXrruu\nu7725VWwrQIu2NDftldXXQFhVYrKShEQFRQp0pcuLX3S6ySZtJnz+yPJbCIQApmW5Hyuay6SZ2ae\n554kzD2n3UdIKVEURVGU9k7j7gAURVEUxRVUwlMURVE6BJXwFEVRlA5BJTxFURSlQ1AJT1EURekQ\nVMJTFEVROgSV8BRFUZQOQSU8RfFgQogUIYRFCFHW6PZ3IcQDQgjrL46XCSGiGj33DiHET0KIciFE\nbv3XjwghhDtfk6K4i0p4iuL5pkkpAxrdHqs/vusXxwOklCYAIcT/An8B3gQ6A5HAw8AYQOeOF6Eo\n7qZ1dwCKojiWEMIAzAfuk1J+3uiug8Dd7olKUdxPtfAUpf0ZBfgAX7o7EEXxJCrhKYrn+7cQorjR\n7aH64yN/cfxs/fFwIF9KWdtwAiHEzvrHWIQQ17j8FSiKB1Bdmori+aZLKb9pfEAI8QCwW0p59QUe\nXwCECyG0DUlPSjm6/nkZqA+6Sgel/vAVpf3ZBVQBN7s7EEXxJKqFpyjtjJSyWAgxD3infgnCZqAc\n6A/4uzU4RXEjofbDUxTPJYRIoW5JgbXR4S3UTUj5ELD84injpZR76597N/Ak0Je6hHeu/jlLpZTV\nzo1cUTyPSniKoihKh6DG8BRFUZQOQSU8RVEUpUNQCU9RFEXpEFTCUxRFUToElfAURVGUDqHdr8ML\nDw+XXbp0cXcYiqIoigPt378/X0oZcTnPafcJr0uXLuzbt8/dYSiKoigOJIRIvdznqC5NRVEUpUNQ\nCU9RFEXpEFTCUxRFUToElfAURVGUDkElPEVRFKVDUAlPURRF6RBUwlMURVE6BJXwFEVRlA6h3S88\nVzxLWVkZZWVl+Pv7ExgY6O5wFEXpQFTCU1yitraWHTt2cODAAYQQSCnp1q0b1113HXq93t3hKYrS\nAaiEpziNlJKcnBwyMjI4ceIEeXl5REdH4+XlhZSS5ORkNm7cyE033YRWq/4UFUVxLvUuozhUfn4+\nW7ZsQQhBdXU1eXl5eHt7ExgYSFxcnP1xQgiioqJITU3lm2++4frrr8fLy8uNkSuK0t6phKc4zJkz\nZ1i/fj1+fn7odDqEEMTHxzf7nJiYGI4fP47NZmPSpEktaumVlpaSl5eHlBI/Pz+MRiNCCEe9DEVR\n2imV8BSHOHbsGBs3biQyMvKyxuSEEMTGxnLq1ClKS0tJSkoiODgYqOsSbUhkUkrS0tLYvXs3JpPJ\nftxmsxEWFsbIkSNJSEigpqYGKSX+/v6Of5GKorRpQkrp7hicaujQoVJtD+RcR44c4euvv6Zz5874\n+Phc8Xny8/MpLy9Hq9UihMBqtZKQkMCAAQPYt28fqampGAwGe0JsUFZWRlFRkT0JarVaJk2aRI8e\nPVr1uhRF8VxCiP1SyqGX9RyV8JTWOHr0KJs2bSIqKgqdTueQc0op7a27goICKioq8PPzIzw8vNnn\n2Ww2NBoNFouFnJwcRo4cyejRo9Fo1HJTRWlvriThqS5N5Yrl5eWxZcsWhyY7qOvmbGitXSrJNdaQ\n2PR6PbGxsfz0008UFxczevRoQkNDHRafoihtk0p4yhWx2Wx8++23+Pv7OzTZOYqXlxdxcXGkpKRw\n6tQpjEYjvXr1IjExUS14V5QOSvX1KFfkxIkTmEwmj245CSHo3LkzsbGxVFZWsm3bNpYsWcKBAwew\nWq3uDk9RFBdTLTzlslVXV7N9+3YiIiLcHUqLCCEIDAwkMDCQ6upqtm7dSkpKCpMmTSIgIMDd4SmK\n4iIe18ITQgwQQuwSQhwRQqwTQgQ1uu85IcQZIcTPQojJ7oyzIzt58iQWi6VNlgTT6XTEx8djMpn4\n97//rVp6itKBeFzCA/4JPCul7AesAZ4BEEL0Bu4A+gBJwDtCCFWaw8Wqq6vZsWNHi1t3NTU1To7o\nynTu3Jnc3FyOHj3q7lAURXERT+zS7AH8UP/1FmAz8EfgZmCFlLIKSBZCnAGGA7vcEmUHdfz4cSor\nK5tNeDU1NXzxxRfs3LmTrKwsBg8ezI033ki/fv1cGOmlRUZG8sMPPxAdHU14eDg2mw2r1Yq3tzfw\n32UOiqK0D56Y8I5Rl9z+DcwEYuuPRwO7Gz0uo/7YeYQQvwN+BzSp36i0TmVlJTt27KBTp04XfUxR\nURGLFi3i1KlT9O/fnwEDBrBr1y7mz5/PqFGjeOCBBzxmoouPjw8+Pj4sX74cf39/LBYLXl5eDB06\nlKqqKv7zn/8QHBxMVFQUUNcqjIuLIygo6BJnVhTFE7ll4bkQ4hug8wXuegH4GfgrEAasBZ6QUoYJ\nIf4O7JZS/qv+HB8CG6WUnzV3LbXw3HH27t3Ljh07iImJueD9NTU1PP/882RlZfHoo48yatQo+/G1\na9fy+eefo9Vqueuuu5g8ebJH1b+sqqrC29sbq9VKXl4eGo2G8PBwqqursVgsAFgsFqSUDBw4kMGD\nBxMUFORRr0FROpI2s/BcSnndJR4yCUAI0QO4sf5YJv9t7QHE1B9TXMBsNrNr165mW3efffYZKSkp\nzJ49m2HDhtmPe3t7c+uttzJ69Gg+/PBDPvzwQ/z9/Rk7dqwrQm+RhpJoGo3G3qKDujJlfn5+9u+t\nVitHjhzh0KFD+Pj4EBAQgI+PD126dCE+Pp7Onc//HNe4JqiiKO7jcaXFhBCdpJS5QggNsBTYJqVc\nLIToA3xC3bhdFPAt0F1K2ew0O9XCc4yNGzdy9uzZC76hQ91OCc8//zzXXnstjzzyyEXPY7VaeeGF\nF8jPz+fPf/5zm14WUFtbS01NDbW1tZjNZmpqaujZsydGo5Hc3FzMZjMVFRWUlpai1WoJCwsjOjqa\nqKgoYmJiPHLBvqK0FW2mhXcJdwohHq3/+gtgCYCU8pgQYhVwHKgFHr1UslMcIz09nePHj190PNRm\ns7F48WKCg4O5//77mz2Xl5cXv//975kzZw6ffPIJv/vd75wRsktotVr7dkaBgYH2HR3Onj2Ln58f\n3t7eaLVaOnfujNVqpaKigv/85z/s3bsXb29vgoKC0Ol0jB8/nsjISDe/GkVp/zwu4Ukp/wL85SL3\nLQAWuDaijs1ms7Ft2zZCQkIu2i23fft2Tp8+zWOPPdaibXm6du1KUlISmzZtYsqUKRcdE2xrhBAX\n7fLVaDT2jXDhv61Ds9nMp59+yuTJk+nRo4faBFdRnEjNue6AbDYbNputRY89e/YseXl5F52ZaDab\n+fjjj+nWrdtljcndeuut+Pr68umnn7b4Oe2JVqtFr9cTEhJCeHg4mzZt4sMPP+T48eNqMbyiOInH\ntfAUxysrK+P48eMkJydTUFBAVVUVISEhTJkypdlJKFVVVXz//feEhYVd8H4pJe+99x6lpaXMmTPn\nstasGQwGpk2bxqpVqzh9+jTdu3e/7NfVXvj6+hIbG4vFYmHTpk3s27ePa6+9ltjYWDXZRVEcyOMm\nrThaW560UllZSXZ2NpWVlfj7+xMbG3vpJzVSU1PDgQMH2L27bvmiwWDA19cXrVZLSUkJpaWl9O7d\nm4SEBMLDwzEYDGg0GnJzczl8+DDHjx9Ho9FcdKLKt99+yz/+8Q/uuecebr755ib3WSwWCgoK0Ov1\nBAcHX7CrzmKx8PjjjxMTE8PLL7+s3tzrlZaWUlRURKdOnejWrRu9e/fGYDC4OyxF8SjtZdJKh2az\n2TCZTBw9epSff/4Zm82GEILa2lomTpzIwIEDL5kYrFYr586d44cffsBsNtO5c2f75IoGBoMBf39/\nUlJSOHHihH3qvJeXFzU1Nfj4+BAZGXnRMaX8/Hw++ugj+vbty7Rp0857DTk5OVxzzTVkZ2dz6tQp\n4uLizmsB6vV6br31VhYvXsyhQ4cYNGjQFfzE2p+goCCCgoIoLy9nz549HD58mJkzZ3rMgn1FaatU\nC8+DnD59mh9//JHCwkL8/PwIDQ21J5yamhoyMzMxGAzExMTQv39/oqKizkt+NTU1rFu3juTkZMLD\nw1s0iaQxq9V6yYkTUkpeffVVjh8/zltvvXXeDMPMzEz69u3LxIkTkVKybds2Dh48SExMzHlJr6am\nhqeeego/Pz9ef/11VcrrAoqKiqitrWXmzJmXtSGuorRnV9LCU+8uHiIlJYW1a9ei0WiIj48nIiKi\nSeLx9vamS5cu+Pn5kZqayooVK1i/fn2T4sy1tbVs2rSJtLQ04uPjLzvZAS2aJbhz504OHjzIXXfd\ndV6yM5vN+Pv7c/XVVwN1MxfHjRvHgAEDSE1Npbq6usnjvb29ueOOO0hJSWHnzp2XHW9HEBISgk6n\nY8WKFWRmqloLinKlVMLzAOXl5WzcuLFFLTIfHx/Cw8OJj4/n7NmzbNq0CYvFgsViYe3atZw9e9ap\n0/xra2v59NNPiYuLY/Lkpjs0Wa1WCgsLSUpKslcugbop+RMmTOCGG24gNzeXoqKiJs8bM2YM8fHx\nrFixwmN3V3C3hi7oFStWsGrVKpKTk9VsTkW5TCrhuZnVamXz5s1YrdbLbpFFR0dz7tw53nvvPT74\n4AMyMzOdvqZt69at5OTkcOedd57XGszJyWHw4MEXjEEIQd++fbnnnnvw9fUlNzfXfp9Go+Guu+4i\nJyeH7777zqnxt2WBgYHEx8dTWlrKmjVrWLJkCfv27aOkpMTdoSlKm6ASnhtJKfnhhx9ISUm56EzI\n5gghiI6OJiYmhk6dOmE0Gp0Q5X+VlZWxevVqevTowZAhQ5rcV1VVhUajYeTIkc2eIzw8nKlTp1JZ\nWdmkhTJo0CB69+7NJ598orrtLiE4OJi4uDh8fHzYsWMHS5Ys4fvvv6e8vNzdoSmKR1MJz41SU1PZ\nv3+/Q1plv5yF6Wg2m42///3vlJaW8sADD5w3WSYvL49Ro0a1aBf0kJAQ+vfvT15env2YEILHHnsM\nb29vXnvtNcxms8NfQ3uj1+uJjo7GaDRy+PBhlixZwtGjR8nLy8NsNlNVVeXuEBXFo6iE5yZSSnbv\n3k1ISEibmJm4Zs0a9u/fz/3333/eInGLxYKPj89lbfA6dOhQampqmrTyIiIieOaZZ8jPz2fBggWU\nlZU5LP72zMvLC6PRSHBwMFu2bOHjjz/mww8/5J133mHDhg2q5aco9Tz/nbadys7Oti8z8HQbN25k\nxYoVjB07lqSkpCb31dbWkpuby+TJky+r+n9wcDB9+vQhPz+/yfGePXvy9NNPk5qayiuvvHLeBBfl\n4hoqtsTExNhv586dY9myZZw9e9bd4SmK27Uo4QkhQoQQfYQQCfXb9iittH///ib7rHmiqqoqli9f\nzuLFixk2bBiPPPJIk65MKSWZmZlcffXVJCQkXPb5Bw4cSGVl5XnHhwwZwtNPP01mZiZz5szh+PHj\nrXodHZUQAqPRiL+/P2vWrGHFihV89913nD59WnV3Kh3SRReeCyEMwKPAnYAOyAN8gUhgN/COlHKr\ni+K8Yp648LyiooL333+fqKgoj+3OTE1N5a233iIrK4uJEyfy4IMP4u3t3eQxGRkZ9OjRg6SkpCt+\nHStWrKC8vPyCLd3U1FTefPNNcnJyGDZsGNdffz29e/dusuRBaRkppX1cr7KyEi8vL6Kjo+nVqxdX\nXXWVx/4dKsrFXMnC8+YS3hZgGbBOSln8i/uGAPcCR6SUH15hvC7hiQnv9OnTrF+//rJrY14Jm81G\nSUkJhYWF5Ofnk5yczMmTJ/H29iYiIoKIiAj8/f2x2WyEh4cTFBTE4cOHWbNmDf7+/jz++OMXHJvL\nzs7GaDRy0003nZcIL0dycjJffPEF8fHxF7zfYrHw1Vdf8eWXX2KxWBBCEBwcTEhICKGhoSQkJNCv\nXz+6devm9Ik77YnVaqWsrIzi4mJiY2MZP348ERER7g5LUVrMoQmvvfDEhLd27VpycnIICQlx2jXK\nysrYsmUL69atazLjUQhB165dkVKSl5d30YkhAwYM4NFHH71gjLW1tWRnZ/Pggw/a93e7UjabjZUr\nV1JWVtbsz6OqqoqTJ09y8uRJCgsL7Qk8MzMTKSW+vr7Ex8cTGBhIQkICAwcOJCEhQe0v1wIFBQWU\nlZXRt29frr766iuq0KMoruaUhCeE+BZ4S0r5VaNj70sp28RW1Z6W8KqqqnjvvfeaLcx8pfLz8/nm\nm2/YuXMnWVlZQN36tsGDBxMWFkZoaChGo7HJ2KHFYqGyshIhBDk5ORQWFnLVVVc1m3xMJhMDBw7k\nmmuucUjcOTk5fPzxx0RHR1/2z8RsNnPs2DGOHDlCZmYmpaWlZGRkIKUkICCAAQMGMGTIELp3735e\nuTblvxoKfut0OsaNGweATqezt/oVxdM4a7eErsAcIcQwKeW8+mOXdRHlv7KyslpUoPlypKWl8e9/\n/5sdO3YgpaR///6MGzeOgQMHkpiY2Oxz9Xq9fe1ccHDwJa9ltVqx2WwMHDjQIbEDREZGMnDgQI4e\nPUpUVNRlPTcwMJCRI0c2WfBeWlrK4cOHOXToEAcPHmTHjh1AXUUXX19fIiIi6N+/P6NGjaJbt25q\nWyLqfjZGo5GysjI2bdpk3z1DSsnIkSMZPny4+rCgtHktaeEdAIYDfwVigXuArVLKwc4Pr/U8rYW3\nZcsWzpw506rxEpPJxOHDhykqKiIlJYUDBw7g4+PDddddd8lNXVvLZDLRv39/rr32Woee12KxsGzZ\nMnx8fAgICHDYeW02G8nJyaSmppKTk4PFYiE9PZ2TJ09SW1tLdHQ0w4YNY/DgwfTo0UO9qf+C1Wol\nMzOTsLAwBgwYQEJCgmrxKR7BWV2aB6WUg+q/fgD4XyBESuncoo0O4kkJr7a2lnfffZfw8PAWTfRI\nSUnhu+++4/jx49hsNrRaLfn5+fYxuYYJHNdffz1JSUmtHk+7FIvFgtls5oEHHnDKkoq0tDRWrVp1\nwb3zHK28vJxdu3axfft2fv75Z6xWK35+fvYu0NGjR7dqMk570zDBBaBPnz6MGjXKoR9MFOVyOSvh\n/V5K+V6j74cAj0opf3NlYbqWJyU8k8nEypUrWzQ786effuIvf/kLAFdddRV6vZ6amhr7TgmDBg0i\nIiLCZd1xUkpSU1OZOnUqV111ldOus2nTJs6dO+fUVuovlZeXc+TIEQ4ePMjBgwcpKioiLCyMqVOn\nMmLECDV7sRGr1UpeXh5eXl5MmDCByMhIDAaD6hZWXM7RyxKa3V5ZSll4ORdyF09KeNu3b+fQoUOX\nLPL81VdfsXTpUrp168acOXPcXo3FarWSkZFBv379uO6665za+ioqKmLp0qVERUW5pXtRSsnhw4dZ\ntWoVp06dAqBTp04kJiaSmJhIt27dSEhIaFHN0PasoqKCgoICoO4D2XXXXadaxIpLOXrSyn5AAgIw\nAqb6r6k/fvmlNTowKSUnTpwgNPTinyOklCxfvpx169YxbNgwnnzySY9YZJ2ZmcnQoUMZO3as07sa\nQ0JCGDBgAMePH7+iHSRaSwjBgAEDGDBgACaTif3793Pq1CnOnDnDrl277I8xGAyEhIQQEhKC0Wik\nX79+xMfHExIS0iHGAf38/PDz80NKyc8//4zZbGb69OmXVV5OUVztoglPStm14evG43jKlSkoKKC8\nvLzZhPfFF1+wbt06Jk+ezK9//WuPeOMsKCggJibGJcmuwdChQzl8+LDDZ7NerqioqCazRktKSjh3\n7hznzp0jLy+PwsJCCgoKOHLkCBs2bACwF9EeMWIEI0eOxNfX113hu4QQgpiYGDIyMti7dy9jxoxx\nd0iKclEtLU3Rvlenu0B2dnaz4xw//vijvUDzgw8+6BFjIg07qc+cOdOlpaeCgoLo27cvJ0+edEsr\n72IMBgODBg1i0KCmn/2qq6s5ffo0JpOJ1NRUDhw4wL59+1i8eDETJkzgpptuavaDTntgNBr56aef\n6NatG5GRke4OR1EuSNVicpHk5OSLVrA4ceIE//d//0evXr34n//5H5clO6vVitlsxmw2o9Vq8fHx\nQafTUVVVRXl5OX5+fkyZMsUtb9ZDhgzh8OHD2Gw2j6/zqNPp6NOnD3369AHquqZPnjzJ119/zcaN\nG9m4cSPdu3fn6quvZvLkyR7xYcbRvLy8MBgMrFu3jpkzZ7p93FlRLqS5SSuzGn07C3i78f1Syrdp\nAzxh0orNZuPdd98lNDT0vIH9rKwsnn/+eQIDA1mwYIHTlxZIKSkuLqasrAwvLy/i4uLo2rUrlZWV\nFBUVUVJSQnBwMN26dSMuLs6tExE2bdrEmTNnPKqVd7lycnL47rvvOHjwIMnJyQwfPpxHHnmk3Zbv\napjIMnHiRIKCgggICECv17fLJK+4l6Nnab7c3BMbVV3xaJ6Q8AoLC1m2bNl5O5ubzWaef/55ysvL\nWbhwoVPf2G02G3l5eVRXVxMXF8egQYOIjo72iEkxF1NWVsbHH3+MTqdz+gcBZ5NSsmHDBpYvX45e\nr2f69OlMnTq1XRa8LikpabJW1MfHhz59+jBgwAC1aF1xGEfP0jwNbJZSFrQuLCU3N5dffrCwWq28\n+eabFBQU8PLLLzs12ZnNZgoLC+nVqxcjRowgLCzMaddypICAAKZNm8bKlSvx8vLy+P0DmyOEYOrU\nqfTp04cVK1bw8ccfs3fvXp566ql2t87PYDA06dKsqqri4MGD/Pzzz6q7U3Gr5lp4c4DJgDfwLbAR\n2CPb2PYKntDC27RpE6mpqU0Szbp161i2bBmPP/54q4owFxYWYrPZCA0NtY91lZeXU1FRQXV1NTab\nDYPBwKRJk85rYbYVycnJfPXVV2g0mnaTHHbs2MF7772HlJIZM2Zw4403tvsp/Q3dncOGDSMiIgKr\n1UpAQABhYWGqy1O5bM6qtBIIXAckUVdT8wSwibrWX84Vxuoy7k54Ukree+89goKC7G9oubm5zJo1\ni759+zJnzpwr/s9uMpkICwsjODiY06dP268XHh5OdHQ0QUFBREVFOWVnBlcrKSlhw4YN5OXlERUV\n1S7eIHNycvjoo4/Yu3cvnTp14t5772XEiBHt4rVdTHl5OcXFxfYeDyklYWFhXH/99ZcsyKAojblk\nPzwhRG/gBmCSlHLyZT3ZDdyd8EpKSli8eHGTcmKLFi3i0KFD/L//9/+uqMVSW1tLZmYm3bp1Iykp\nCR8fHyorK5tskNoeVVdXs3XrVk6cOGFP7O2h4smRI0dYsmQJ6enpdO/enbvuuou+ffu6OyyXKSkp\nobi4mFGjRtG7d+92+/erOJbDE54QQgtYpZRSCBELjADOSikPtjLQmcBcoBcwXEq5r9F9zwEPAlbg\nCSnl5vrjQ4ClgB74CniyJd2r7k54Z8+eZd26dfbuxOzsbJ544gmmT5/OXXfd1aJzVFVVUV1dTUBA\nAGazmaKiIq6++mqGDRvm8VP2naGiooLU1FS2bduG1Wpt07M4G1itVrZt28bq1aspKCigf//+PPDA\nAy2qu9oe1NbWkpubi9VqpUePHowaNardr11UWudKEt5F3y2FEA8BuUBq/dffArcBK+rH91rjKDAD\n+OEX1+wN3AH0oa4L9R0hRENf3LvAQ0D3+ltSK2NwiYyMjCZjMxs3bkSj0ZCUdOnwq6qqSE1NxWw2\n4+PjQ3p6OkII7rjjDkaMGNEhkx3UlbXq1asXDzzwAJ06dbJvdtuWeXl5MXHiRP76179y3333ce7c\nOebMmcOGDRuwWq3uDs/ptFotUVFRxMTEkJqaytKlS/n00085ceKEu0NT2pHmZmk+BSQCgdSN28VL\nKfOFEH7AXuD1K72olPIEcKGxipuBFVLKKiBZCHEGGC6ESAGCpJS765+3DJhO3UQaj5acnGyfUl9e\nXs53333H6NGjL/nptaysjMLCQqZMmULPnj3RaDSUlZXh4+OjivTW0+v1TJs2jc8++4yMjAygLnEE\nBASg0+nQ6XRN/sYaNjWFumUaVVVV6HS6JuObtbW1mM1m9Hq9W8qC6XQ6pk2bxtixY3n33XdZunQp\nmzdv5tZbb+Xqq69u82OxlyKEoFOnTkgpKSsrY/369ZSVlTFs2DB3h6a0A80lvGopZRFQJIQ4I6XM\nB5BSVgghqp0UTzSwu9H3GfXHauq//uVxj1ZRUUFxcbG9O/OHH36gsrKSG2+8sdnn5ebmIoTgV7/6\nFdHR/32Zav+x8+n1em6//XZKSkqwWCyYTCbS0tIwm83k5eU12bkbsH/dMNaZn59PTU2N/Xze3t7E\nxMSQmZlJQUEBgYGB+Pv7n5donF0BJjg4mGeffZY9e/awevVq/v73v/P5559z2223MWbMmA6R+AID\nA9Hr9Xz//ffYbDaGDx/erif0KM7XXMLTCyEGUdftqav/WtTfLvnRVwjxDXChwZUXpJRfXkmwLSWE\n+B3wO4C4uDhnXqpZBQUFTdbfbd26la5du5KYmHjBxzfsLh0TE0NSUpJapNtCPj4+9v3z4uPjGTVq\nFFD3gSM3N5fy8nI0Gg2xsbEIISgsLCQsLMy+rq+yspLy8nJ8fX3x8/NDCIHFYuHYsWOkpqZiMpmo\nra1tkjihLjlGRkY67U1YCMGIESMYNmwYe/fuZfXq1fztb3/js88+6zCJT6vVEhsby/bt25FSMnjw\n4Ha/fENxnubW4W2jmaLRUsrxrb543TWebpi0Uj9hBSnlq/Xfb6ZucksKsFVKeVX98TuBa6WUv7/U\nNdw5aWXPnj3s3r2bqKgoUlJSeOaZZ/jNb37DDTfccN5jrVYraWlpDB8+nDFjxrTLChxtVXV1NRkZ\nGVRVVaHX6wkPD8dms/Hjjz9y4sQJ/P39CQgIwGq1UlRUBIBGo6FTp04O/T3abDZ74ktNTcVgMNCj\nRw9GjBjBqFGj2nUisFqtmEwmhBD07t2biRMntvtkrzTPoZVWpJTXtjqiy7cW+EQI8TYQRd3klD1S\nSqsQolQIMRL4CbgP+Jsb4msxKSVHjhwhJCQEqGvdabVarr766gs+3mQyMXz4cMaNG+fKMJUW0Ol0\nJCScv/3DPrLpAAAgAElEQVTjlClTGDJkCPv27SMvLw+NRsO4ceMICgoiKyuLPXv2EBwc7LCWukaj\nadLi27NnDydOnGDv3r189NFHTJgwgUmTJrl0t3hX8fLyIjY2FpvNxtGjR9FoNEycOFF1cSqX5aIJ\nTwgxo7knSim/uNKLCiFuoS5hRQAbhBCHpJSTpZTHhBCrgONALfColLJhitoj/HdZwkY8fMJKbm4u\npaWlxMbGUlNTw/bt2xk6dOgFa0IWFhYSERHB6NGj3RCp0hqRkZEXHJNt2CF9w4YNmEwmjEajw96c\nGxLfiBEjkFJy9OhRNm3axNq1a1m7di2DBw/muuuuY+DAge2up0Cj0RATE8OhQ4eorq5m7Nixbb7O\nquI6zf1vmFb/bydgNPBd/ffjgZ3AFSc8KeUaYM1F7lsALLjA8X1Am1mNe+rUKftsygMHDmA2mxk/\n/vxeYLPZTE1NDVOmTFGzL9sZo9HI3XffzbZt2zh58iRQt6O7IycfCSHo168f/fr1Iz8/ny1btvDt\nt9+yf/9+goKCGDNmDOPHj6dr166XPlkbodFoiI+P59y5c5w5c4bRo0fTv3//dt2lqzhGS0qLfQ3c\nL6XMqv/eCCxtC1VWwD1jeFarlffee4/g4GB0Oh2vvfYa586d4913320y7mCxWCgoKOBXv/qVKqvU\nzlVXV5OVlcWmTZuorq526u+7traWQ4cO8f3337Nv3z5qa2vp3bs3U6dOZfDgwe1q7Ku6uprc3Fw6\nderE7bff3u5atMrFOXq3hAaxDcmuXg7gvqmPbYDJZLKv8SoqKuLgwYNMmzbtvPVeOTk5zJgxQyW7\nDkCn0xEfH899993H119/zZkzZ4iOjnbKG7RWq2Xo0KEMHToUs9nM1q1b2bhxI2+88QaRkZFMmTKF\n8ePHt4uybDqdjpiYGNLT0zlw4ADDhw93d0iKB2vJQqJvhRCbhRAPCCEeADYA3zg3rLbt8OHD9jeT\n7du3Y7PZzuvOzMzM5JprrrngZAil/dLr9UydOpVRo0aRl5dHWloa5eXlTrteYGAgN910E3//+9/5\nwx/+QFBQEEuWLOHJJ5/k2LFjTruuqxmNRn788cd2UXVHcZ4WFY+un2TSsIfND/VjcG2Cq7s0y8vL\n+eCDD+wV/WfNmoVer2fhwoX2xxQXF+Pv78+vfvWrDlseTKlr5aemprJp0ya8vLwIDw93yXVPnjzJ\nO++8Q3Z2NlOnTmXmzJntorVnNpsxm83ccsstHaYGaUfm6Fqa9illUso1Uso/1N/WXOgxSp2zZ88C\ndQPrZ86cISMj47zWXUlJCWPHjlXJroPTarUkJiZy77334ufnR25urkuue9VVV/H6668zYcIE1q1b\nx5NPPsm5c+dccm1nCgwMxGAwsGrVKr777jtKSkrcHZLiYZp7x90qhHhcCNFkvE4IoRNCTBBCfATc\n79zw2p79+/c3WXun0+maLDcoKioiLi6uSckwpWMLCgpixowZ+Pn5kZ+f75Jr6vV6Hn74YRYuXIhW\nq2Xu3LntolCzv78/MTExHDt2jGXLltk/gCoKNJ/wkqjboudTIYRJCHFcCJEMnAbuBP4spVzqghjb\njLKyMkpKSvD396eqqoodO3YwYsQI/P397Y8xm82MHj1aLZhVmggICOCWW27BarVSUVHhsut2796d\n+fPnExwczNy5c1m2bBmVlZUuu74zeHl5YTQaCQ4OZs2aNRw6dMjdISke4qIJT0pZKaV8R0o5BogH\nJgKDpJTxUsqHWrsnXntUWFho/3rv3r1UVFQ06c4sLS0lMjKSqKgod4SneDiDwcANN9xgr//pKuHh\n4SxcuJDx48ezbt06nn/+eXJyclx2fWfR6/VER0fzzTffkJyc7O5wFA/QokEkKWWNlDJLSlns7IDa\nspycHHvLbevWrURERNCnTx/7/Q27OqvWnXIxCQkJTJs2DZvNRkZGBhkZGWRlZTXZ0cEZAgICePjh\nh3nxxRcpLCzk2WefJTU11anXdIWGAt9r1qxhy5YtZGZmujskxY3UrAkHSktLIyAggPz8fI4cOcK4\ncePsE1MsFgtBQUHEx8e7OUrF0/Xs2ZP777+fe++9l9tuu40BAwaQm5tLcbHzP28OGDCAV199FW9v\nb1577TV7Mey2TK/XYzQaOXPmDCtWrGgXE3SUK6MSnoPYbDZMJhMBAQF8//33SCm59tpr7fcXFha2\nuyoXivNoNBrCwsKIiYnhmmuu4e6776a6utolCchoNPLss89iNpt59dVXXTqm6Cze3t5ERETQqVMn\nvvzyS7Zv386JEyeora11d2iKC10y4dXP1AxxRTBtWXFxMVarFY1Gw7Zt2+jTpw+RkZH2+202W7uq\nZ6i4Vnh4ODNnzsTX15f09HQyMjJIT08nPT2d7OxsKioqaMma2pZKSEhg1qxZpKWl8dprr1FVVeWw\nc7uTXq8nIiKCw4cPs27dOvuHU6VjaEldo0hgrxDiALAY2CzVX8h5CgsLkVJy7tw5srOzueWWW+z3\nlZeXExYWZl+uoChXIiIignvvvZeioiKsViv+/v7k5+eTkpJCcnIy6enp9s1wHTFOPHjwYJ544gn+\n/Oc/M3/+fJ555hmCg4Md8Ercy9fXF19fX2w2GwcPHsTX15dBgwbZNwRW2q+WVloRwCTg18BQYBXw\noZTS4xe5uKrSyvbt2zl8+DDffvstX375Jf/85z/t25ZkZGQwbtw4Bg0a5PQ4lI4rPz+f7du3k5mZ\nSefOnR123t27d/O3v/2NoKAgXnzxxXa1hrS2tpasrCy8vLzo2bMnPXr0IDQ0lMDAQFUYwsM5tNJK\nY/Utuuz6Wy0QAnwmhHjjsqNsp7Kzs/Hz82PPnj307t27yR5dUkq6dOnivuCUDiE8PJzJkyfj7e1N\nWVmZw847cuRI5s+fT01NDS+//HK7mL3ZQKvVEhsbS2RkJMnJyXz55ZcsXryYpUuXkpGR4e7wFAdr\nyRjek0KI/cAbwA6gn5Tyf4AhwK1Ojq/NyMvLo6CggMzMzCYV2y0WC8HBwao7U3EJPz8/brzxRgoK\nChw6ISMxMZF58+bh5eXFyy+/zOnTpx12bk/QUMs0JiaG2NhYhBCsXLmSPXv2qDG+dqQlLbxQYEb9\njuSrpZQ1AFJKGzDVqdG1ERaLherqag4erFuL3zjhFRcX07NnT3eFpnRAMTExjB071uFrzqKjo5k/\nfz4BAQHMnz+/XZQiu5jAwEBiYmL44Ycf2LVrFzabzd0hKQ7QkoSXIKVs0ochhFgOIKVsv3/xl6Gh\n++jgwYMkJiYSFhZmv89qtaq1d4rLDR06lISEBNLS0jCZTFitVoecNzIyknnz5hEaGsrChQvbXUuv\nMS8vL2JjY9m1axerV68mLy/P3SEprdSShNen8TdCCC/qujOVeqWlpdTW1nLmzBl69eplP261WhFC\n0KlTJzdGp3REGo2GadOmcccddzBgwADS0tLsPRGt7aILCwvjpZdewmAwsGDBgnZdtsvLy4v4+HhK\nSkr4+OOPOXXqlLtDUlqhue2BnhNCmIH+QojS+psZyAW+dFmEbUBhYSEmk4mamhquuuoq+/HS0lK6\ndOmCt7e3G6NTOiqtVktUVBTjxo1jypQplJeXYzabSU9Pb3VrJSwsjJdffhlfX19eeeUV0tPTHRS1\nZwoNDSU8PJy1a9fy7bffUlxcrMb22qBLLksQQrwqpXzORfE4nCuWJWzYsIGPPvqI1atX8/7779sn\nqKSnp5OUlNSk1aco7paXl8fWrVvJycnBaDS26lxZWVm8/PLLWK1Wnn32Wbp37+6gKD2T1WolNzeX\nmpoaNBoNcXFxTJ06FZ1O5+7QOhxHbwDb0FRZLYQY/MtbqyJtZ/Ly8khOTiYyMvK82ZhqZwTF00RE\nRDBt2jQMBkOrW3pGo5G5c+fi6+vL3LlzccWaV3dq2HqoYU/L9PR0Nm/e7LAxUsW5mhvDm1X/71sX\nuC1yclxthtVqpbi4mDNnztCjRw/78YqKCkJDQzEYDG6MTlEuTK/XM336dLy9vSkoKGjVuaKioliw\nYAGxsbG88cYbbN68uUN09wkhiIqK4vTp02zZssXpO1oordfcfni/q/93/AVuE1wXomcrKysjPz+f\n4uLiJuN3xcXF9O7d242RKUrzgoKCmDlzJl5eXq1Oeg0byA4aNIh//vOfvPjiixw7dsxBkXouIQSx\nsbGcPHmSL774gkOHDpGWltYhEn5b1JKF5zOFEIH1X78ohPhCCKFqZNUrLS21V55o3MJTyxGUtsBg\nMDBz5kxsNhulpaWtOpevry+zZ8/mt7/9LYWFhcybN4+tW7c6KFLPJYQgJiaG4uJivv/+e1atWsXX\nX39t/3nm5+eTlpbm5igVaFnx6D9KKVcLIa4GrgPeBP4BjHBqZG1EcXExmZmZ9hJFANXV1fj6+hIe\nHu7m6BTl0gwGAzNmzGDlypVUVlYSERFxxcWnvby8mDx5Mtdccw2LFi3inXfeoaCggBkzZrT72pQN\n629tNhunTp3i+PHj+Pr6YrFYsFqtjBkzhpEjR7b7n4Mna8lPvmE09kbgfSnlBkBNSaqXlZVFTk4O\nsbGx9r3uioqK6Nmzp/rDVtqMyMhI7rnnHuLi4khJSWn1dkB6vZ5nn32WsWPHsnLlSl599dVWtyDb\nCo1Gg9FoJDY2FoPBQFxcHHFxcezatYslS5awZ88eKisr3R1mh9SSd+RMIcR7wK+Ar4QQPi18XoeQ\nlZWFyWQiLi7Ofqy6uprExEQ3RqUoly80NJSpU6cydepUcnJyWr3xq7e3N48//jgPPfQQR48eZfbs\n2R1u4XbDcoWGBew6nY6dO3fyySefkJub6+boOp6WJK7bgc3AZCllMXW1NZ9xalRtRG1tLRkZGRQX\nF9vH62w2GxqNxqHbsyiKK/Xq1YsZM2aQl5fX6gLUQggmTZrEn/70J7y8vHjppZfYsGFDh53Uodfr\niYmJQUrJihUryMnJcXdIHcolE56UsoK6yirlQog4wBs46ezA2gKz2YzJZAKwt/DMZjMxMTH4+Pi4\nMzRFaZUuXbowZswY+993ayUmJvL6668zePBgli5dyqJFixy6hVFbYzAYCAgI4LPPPnPYz1i5tJbM\n0nwcyAG2ABvqb+udHFebUFJSYv9jbWjhlZaWqt0RlHZh2LBhdOnShezsbIecLyAggGeeeYb77ruP\n/fv3M3v2bH7++WeHnLstCgoKQq/X8+mnn7JmzRoyMzM7bMvXVVrSpfkk0FNK2UdK2a/+1t/ZgbUF\nhYWFZGVlYTAYCA4Oth9vTztCKx2Xl5cXSUlJ+Pv7U1hY6JBzCiGYNm0ar7zyCkIIXnrpJdatW9dh\n3+iDgoKIi4sjNzeXFStW8Pnnn7d67FS5uJYkvHSgxNmBtEVZWVlkZ2fbuzOrq6vx8/NrkvwUpS3z\n8/Nj+vTp1NTUUF5e7rDzdu/enTfeeINhw4axbNkyli9f3mGTnhCCsLAw4uPjyc7OZtWqVQ7d0kn5\nr5aswzsHbBNCbADsc5WllG9f6UWFEDOBuUAvYLiUcl/98TDgM2AYsFRK+Vij5wwBlgJ64CvgSenm\n/yGZmZmYTCb69esH1I3fxcfHX/EaJkXxRCEhIUyfPp2VK1ei1WodNj7t7+/PrFmzWLJkCevWrSM/\nP5+HH34YPz8/h5y/LercuTMFBQWsXLkSLy8vvL29CQgIYOjQoXTr1k3tvNJKLWnhpVE3fqcDAhvd\nWuMoMAP44RfHK4E/Ak9f4DnvAg8B3etvSa2MoVWqqqpITk6mpqbGPn5XUVGhqqso7VJ0dLR9uYLZ\nbHbYeTUaDb/5zW+45557+Omnn5g9ezZnz5512PnborCwMGJjY+3F6K1WK1999RXr1q2jurra3eG1\naS2ZpTlPSjkPeLPh6/rvr5iU8oSU8rzRailluZTyR+oSn50QwggESSl317fqlgHTWxNDa5WUlJCV\nlQX8d8KKEIKIiAh3hqUoTtOjRw/uvPNOKioqHNq9KYTg5ptvZv78+dTW1vLCCy906KULDby8vNBq\ntQQEBBAfH096ejrr169vdVGAjqwlszRHCSGOU78UQQgxQAjxjtMjayoayGj0fUb9MbcpLi7GZDKh\n0WiIjo7GarWi0WjO2x5IUdoTo9HIDTfcQH5+vsMTUs+ePXnzzTcZOHAgS5cu5dVXX6WkRE0faNCw\nHdGXX36pKrVcoZZ0af4ZmAwUAEgp/wNcc6knCSG+EUIcvcDt5taFfGlCiN8JIfYJIfa1dr+vi2ko\nKWY0GtHpdJSVlREdHW0vL6Yo7VXXrl3p0aOHUyqFBAYGMmfOHB588EF7dZbTp087/DptVXR0NDk5\nOSxevJiNGzc6bMlIR9GiEmFSyvRfHLrk9CEp5XVSyr4XuH15BXFmAjGNvo+pP3axa78vpRwqpRzq\nrC7GrKwssrKy7N2Z5eXldOnSxSnXUhRPIoRg3LhxaLXaVm8rdLHzJyUlsWDBArRaLS+99BIHDx50\n+HXaKqPRSHBwMKmpqaxYscK+W4tyaS1aliCEGA1IIYS3EOJp4IST42pCSpkFlAohRoq6KZD3UVf9\nxS1sNhtpaWnk5eU1KSmmyokpHUVQUBB33HEHOp2u1bumX0zXrl157bXXiImJYdGiRZw8qQo8NdDp\ndISHhxMaGsrnn39OcnKyu0NqE1qS8B4GHqVuzCwTGFj//RUTQtwihMgARgEbhBCbG92XArwNPCCE\nyBBCNOyi+gjwT+AMcBbY2JoYWqO0tJSMjLohxfj4ePtYhtoOSOlIDAYDt912GzqdziktPajr4nzx\nxRcJCwtj/vz5rFu3Tq1Pa8TPz4+IiAjWrFmjkl4LtGSWZr6U8m4pZaSUspOU8h4pZav+uqWUa6SU\nMVJKn/rzTm50XxcpZaiUMqD+Mcfrj++r7xJNlFI+5s41eMXFxfYZmnFxcZSXlxMZGanqZyodTmBg\nILfeeitCCLKysrDZbA6/hsFgYP78+fTv359ly5bxxhtvYLFYHH6dtkqv19uT3rlz59wdjkdrNuEJ\nIcbX73B+rP72mRDiWhfF5rHy8/PJysrCz8+P8PBwSktL1XZASocVHBzM3XffTY8ePUhLS3NKMgoO\nDrZPZjl48CDz5s1z6HrAtq5x0lNdvxd30YQnhLgRWAysA+4C7qauwsliIcQU14TnmTIyMsjOzrZX\nVbHZbBiNRneHpShu4+/vz+TJk7nlllvsXf5FRUUOXbrQMJll9uzZpKWlsWDBAtXSa0Sv19O5c2fW\nr1/P3r17qampcXdIHqe5Ft4zwHQp5RIp5X+klIeklIupW/A9xzXheR4pJWlpaaSnp5OQkICUEiml\nWnCuKNRtA3Tfffdx3XXX2WcSOnrN2NChQ/nDH/5AcnIyr7zyin14QQEfHx9iYmL48ccf+cc//sHW\nrVs79DZMv9Rcwutcv+auCSnlYSDSeSF5NrPZTEZGhn1X84qKCiIiIvD19XV3aIriEQIDA+nbty+3\n3XYbN954I0VFRQ6fyTls2DD+8Ic/kJGRwdNPP8369evVZJZ6Wq2W2NhYIiIiOHLkCEuWLOHHH38k\nMzOzw/+Mmise3VztIMfVFWpjCgsLSU+vW5aYkJCA2Wxm0KBBbo5KUTyPEIJevXoRFRXF5s2bMZlM\nREVFOez8I0eOpHv37nzwwQd89NFH7Ny5kyeffJLIyA77ebwJrVZLVFQU1dXVHDx4kL179+Lj48OA\nAQMYPHgwer3e3SG6XHMtvEQhxNoL3NYBCa4K0NPk5uaSmZmJXq/HaDRSW1vr0P/EitLeGAwGpk+f\nTmhoKPn5+Q49d1hYGHPmzOGJJ57AZDIxe/ZsfvrpJ4deo63T6XQYjUZiY2MJDg5m3759LFmyhJSU\nFHeH5nLNJbybgbcucFuEmws3u1NaWhqZmZl07doVjUaDlJKwsDB3h6UoHk2n0zFt2jSklBQVFTn0\n3EIIxo4dyxtvvIHRaGTRokX89a9/VWNXF6DT6YiOjiYgIIAvvviiw63du2jCk1J+39zNlUF6CpvN\nRkZGBunp6SQmJlJTU4Ovry+Bga3dLUlR2j+DwcDtt9+ORqMhOzvb4cWnO3XqxCuvvMLMmTPZuXMn\nL774osNblO1Fw4L1zz//nC+++IITJ05QWlrq7rCcrkW1NJU6JSUlZGRkUFNTQ0JCAmVlZcTExKgN\nXxWlhcLCwrjzzjuJjY0lLS3N4VvdeHt7c/vtt/PHP/6RwsJCXnjhBU6ccGklxDZDr9cTFxdHYWEh\nmzdv5sMPP2TDhg0O3frJ06iEdxkaT1hpmKGpCkYryuXx8/Pjpptu4vrrrycrK4va2lqHX6NPnz7M\nnz8frVbLyy+/zPLly9XmqReg0WgIDg4mJiaGmJgYzp07x/Lly9ttV6dKeJchIyPDXmElMjISKaWq\nn6koV0AIQf/+/bnmmmvIyMhwymavXbp0YdGiRUycOJG1a9fy7LPPtts3ckcQQmA0GtHr9Xz++ed8\n88037a6azUWXJdTPxrzoX6GU8ianROTBUlJSzpuwEhoa6u6wFKXNGjZsGIWFhZw6dcops531ej2/\n//3vGTZsGP/4xz947rnnmDlzJtOnT1d7V16Ev78/er2e48ePc/z4cSIjI9FqtfTq1YvExMQ2XTO4\nuXV4i1wWRRtgsVjIy8sjLS2NKVOmUF1dTUBAQJv+5SuKuwkhuPbaa8nMzKSkpASDweCU6wwePJi3\n3nqLDz/8kBUrVrBnzx4effRR4uLinHK9tk6j0RAVFUVNTQ0WiwWr1crmzZsJDAxk+vTpbbZnS83S\nbKHCwkKys7Opra0lMTERi8WiyokpigP4+voybdo0SkpKHD6JpbHAwECeeuopZs2aRX5+PrNnz+bz\nzz/v8NVHmuPt7U1AQAAGg4HY2FgAPvnkE/v2aG3NJcfwhBDd63dJOC6EONdwc0VwniQ7O9v+S05I\nSKCyslIlPEVxkE6dOjFp0iRMJpNTthhqbNSoUbz99tsMHz6cFStW8Pzzz5OWlubUa7YXwcHBGAwG\nPvvsszZZw7Qlk1aWAO8CtcB4YBnwL2cG5YlSUlLIysrC39+fyMhIqqur22yzXlE8UZ8+fRg8eLBL\nWg8Gg4FZs2ap1t4V8Pf3tye97Oxsd4dzWVqS8PRSym8BIaVMlVLOBW50bliexWq1YjKZyMjIICEh\nASEEQgi14FxRHEgIwbhx4+jSpYvLWg+/bO0999xznD592iXXbssCAwMJCAhg5cqVnD59GpPJ1CbW\n77Uk4VUJITTAaSHEY0KIW4AAJ8flURrGFlJTU0lIqCsjqhKeojieVqtlypQphIWFkZmZ6ZTlCr/U\n0Nr73//9X4qLi3nhhRf417/+pVp7lxAYGEhwcDDr169n9erVfPDBB3z//fcevQ9fSxLek4Af8AQw\nBLgHuN+ZQXmagoICsrKysFqtJCYm2scY/P393RyZorQ/vr6+3HrrrSQmJpKenu6SpAd1uy/8+c9/\nZuLEiXz55ZcsXLiw3a1DczR/f39iY2OJjo6mc+fO7Nu3j507d7o7rItqNuEJIbyAX0kpy6SUGVLK\nX0spb5VS7nZRfB4hIyODnJwcoG4xa2VlJWFhYWg0at2+ojiDj48PU6ZMoXfv3i6dEejn58fvf/97\nHn74YY4fP85zzz1Hamqqy67flmm1WmJiYti7dy8nT56kuLjYKVV0WqPZd2wppRW42kWxeKy0tDSy\ns7PR6/VERkZisVjUhBVFcTKNRsPEiROJj48nNzfXpdeeOHEi8+bNo7q6mj/+8Y+cOnXKpddvq7y8\nvOjUqRNfffUVS5cuZfHixRw7dszpM29bqiVNlIP1++DdK4SY0XBzemQeoqqqisLCQtLS0ujSpQsa\njYbKykq1yaSiuIC3tzfjx4+nqqrK5W+aPXr04NVXXyUoKIg//elPHDt2zKXXb6v8/PyIi4sjJiYG\nvV7PV1995THdnC1JeL5AATABmFZ/m+rMoDxJYWEhUsomE1aklAQHB7s5MkXpGEJCQrjqqqvcstVP\nWFgY8+bNIyQkhHnz5vHJJ5+oySyXoWFHht27d/Of//zH3eE0W1qswT+llDsaHxBCjHFSPB4nPz+f\n3Nxcqqur6dq1q/24SniK4jpDhw7lxIkTSCldvh1XWFgYr732GkuWLGHNmjWkpaXx1FNP4evr69I4\n2iovLy+io6PZsmULer2eHj16uC2WlrTw/tbCY+1SZmamffyga9eu2Gw2hBAEBQW5OTJF6TgiIiLo\n27evS2dtNqbX63nkkUd46KGHOHDgAHPnzqW4uNjlcbRV3t7edO7cmfXr13P69Gm3/A6hmYQnhBgl\nhPhfIEIIMavRbS7QYcqM5+TkkJWVhbe3N9HR0fYamqrSuqK41oQJE+jevbtb6zhOmjSJ2bNnk5GR\nwQsvvEBmZqbbYmlrfH19iYiIYO3ataxdu9Yt+xM218LTUbfAXAsENrqVArc5PzT3q62tpbi4mPT0\ndOLj4/Hy8qK8vJzo6Gh3h6YoHY5WqyUpKYmEhAS3tfSgrnt17ty5VFZW8uKLL3Ly5Em3xNEWNYzp\nnTt3jv3797v8+pfaLWEeMFJKOa/R7W0pZYeovWM2m7HZbCQnJ9vH76qqqjAajW6OTFE6Jm9vb6ZM\nmUL37t3dmvS6devGggULCAwMZP78+eze3aGWJrdKw0azu3fvprCw0KXXbskY3j+FEPYZGkKIECHE\nZifG5DFKS0spKCigoqLCnvCEEGrCiqK4kbe3N0lJSfTq1Yu0tDS3rfHq3Lkzf/rTn+jatStvv/02\nGzZscEscbZFWq8XX15dvvvnGqVtC/VJLEl64lNI+OiulLAI6OS8kz1FUVITJZALqtgSSUqolCYri\nAbRaLZMmTWLAgAGkpaW5rX5jUFAQL730EsOGDWPp0qUsXbrUYxZZe7qIiAiysrJYv369y5JeSxKe\nTQhh3xZYCBEPuKcfwcWys7PJzs5Go9EQGxtLZWUlISEh6HQ6d4emKB2el5cXEydOZMKECWRlZZGX\nlwgLey0AACAASURBVOeWLk4fHx9mzZrFDTfcwIYNG3jttdcoLS11eRxtUXR0NOnp6fzwww8uuV5L\nEt4LwI9CiOVCiH8BPwDPOTcsz5CdnU1mZiaxsbHodDoqKirU+J2ieBAhBIMHD+a+++7DaDSSmpqK\nxWJxeRxeXl78+te/5re//S1Hjhzh6aefVpVZWigqKorDhw+7ZMbrJROelHITMBhYCawAhkgp2/0Y\nXm1tLUVFRaSmptKlSxcALBaLSniK4oHCw8O5+eabufnmmykrKyM9PZ3i4mKXdi8KIZg8eTILFy7E\n19fXXpnFHQm4LdFoNISEhPD11187vWv6kglP1JU1SAIGSynXA35CiOGtuagQYqYQ4pgQwiaEGNro\n+PVCiP1CiCP1/05odN+Q+uNnhBB/FU4ut2A2myktLaWkpMReUgxUhRVF8VRCCLp3786DDz7I1KlT\nCQ0NxWQykZmZ6dJyYF27duX111/nmmuuYc2aNTz22GNs2bJFje01IygoiOLiYvbt2+fU67SkS/Md\nYBRwZ/33ZuD/Wnndo8AM6rpHG8sHpkkp+1G3597yRve9CzwEdK+/JbUyhmaZzWb7AtfGJcVUhRVF\n8Ww6nY7u3bszY8YMHnroIYYMGUJmZqZLd+TW6/U89thjLFy4kOjoaN5//33mzp1LUVGRy2Joa4xG\nIzt37iQ7O9tp12hJwhshpXwUqAT7LM1Wzdr4/+3de3hU5bX48e9KMrmQC5AbIQl3AoqCKEFEBQER\n0arhKlLU4wWttf6qoljRY0EB+VkV0aqn1dOKnGKl1oOgVouKAtUKci0gIIgICYEkJIFcCWHW+WMm\naYBcJiGZmSTr8zzzMLP37L1XXpGV/e73fZeq7lDVXdVs36SqB90ftwNhIhIiIh2BKFX9Wl1PpRcB\nY84mhroUFBRUNnznzp0rlxSzKufGNB/h4eFcfvnlTJo0iby8PK8mPYCUlBSefPJJfv7zn7N3716e\neOKJytqa5lRBQUG0a9eOjz76qMlWYfEk4Z1wF4JVABGJA7xxbz4e2Kiqx4EkoOp6QunubdUSkbtF\nZL2IrM/Ozm7QxXNzc8nJyaFt27aEh4dTWlpKdHS0LSlmTDOUlJTEhAkTKCgoICMjw6uFSUWEESNG\nMHPmTAoLC/nVr37Fe++959X5Z81F27ZtOXr0aJNN5Pck4b0ELAU6iMhc4B/A03UdJCKfisi2al5p\nHhx7HvAM8DMP4juDqr6mqqmqmhoXF9eQU3DkyBGys7NJTEwEXANWrAaeMc1XcnIyd9xxR2UXZ3Fx\nsVevn5KSwty5c+nVqxeLFy9m9uzZXr/jbA46duzIN998UzkHWlXJz89vlLu+OssDqepiEdkAXOne\nNEZVd3hw3MiGBCQiybgS7K2q+r17cwaQXOVrye5tTebIkSMcPnyYQYMGAa6El5CQ0JSXNMY0sTZt\n2nD55ZeTlJTEhx9+SGFhIXFxcV4rOZSUlMRjjz3GP//5T1588UVmz57N9OnTiYmJ8cr1m4PAwEAi\nIiJYt24dY8aMISMjg3feeQcRIT4+nn79+lWOnK8vT+7wANrgqpAQAIQ16EoecC9h9iHwaNUafKqa\nCRwTkUvcozNvBZY1VRwnT57k0KFDFBYWnrJQtI3QNKZl6NatG7feeitJSUns37+fo0ePevX6gwcP\nZvr06aSnp/PQQw+xZs0an60L6o/at2/PDz/8wLFjx9i4cSNt2rQhOTmZ48eP88knn/D6668DhNf3\nvJ5MS/g18CYQDcQCb4jIf9b3Qqedc6yIpOMa/flhlbU57wN6Ar8Wkc3uV8UyZvcC/w3sAb4HPjqb\nGGpTVFRExbO/ii5NcPUvG2NahqioKG644QYmTJhAWFgYP/74o1dL1gwYMIBnn32W5ORkXnrpJZ5/\n/nlbocUtICAAEWH9+vV8//33REdHAxAZGUmnTp0qPofU97yeVDyfAlygqqUAIvL/gc3AnPperIKq\nLsXVbXn69jk1nVdV1wPnN/Sa9VFUVFQ5kioxMbFyhGZERIQ3Lm+M8RIRoUuXLnTq1ImtW7eycuVK\nYmJiaNOmjVeu37FjR5588kk++OAD3n77bWbMmMGMGTNITk6u++AWLiYmhi1bthAUFNRoXc6edGke\nBKrWsg+hiZ+f+VphYSFZWVkEBgYSHx9PaWkpMTExNkLTmBYqICCACy64gDFjxpCVleXVieqBgYGk\npaXx1FNPcfz4cR5//HHWrVvntev7q7CwMBwOBw0deFgdTxLeUWC7iCwUkTdwTRrPd6928lKjReJH\n8vPzyc7OJiEhgcDAQFtD05hWolu3bgwaNMgnlcxTUlKYN28eCQkJPPvss7zyyis0dFpVS5GQkEBQ\nkCcdkZ7x5Eyndz9+0WhX91NHjhwhJyensluhtLTURmga00oMHjyYrKwsDh486PVfdOPi4pgzZw5L\nlizhgw8+YM2aNYwYMYKxY8c26p1Oa+XJtIQ3AUTEgesZWoaqZjV1YL6UnZ1NdnY2F1/87yVDbYSm\nMa1DUFAQ1157Le+88w45OTnExsZ69foOh4Obb76Zq6++mvfee4/PPvuMlStXct111zF+/HjCwpps\noHyLV2OXpoj8zj0BHBFpC2zBtaTXJhGZXNNxzZ3T6WT37t2Ul5dXjtAUEVtD05hWJCwsjLS0NFTV\nZyMn4+LiuOuuu3j55ZcZMmQIy5Yt48EHH2THjjqnQZsa1PYMb4iqVhR0uh34zr2o8wDgkSaPzEdy\nc3Mr19BMTEzk5MmTBAQE2AhNY1qZtm3bMm7cOAoLC8nPz/dZHLGxsfziF79gzpw5OBwOZs6cyaJF\niygtLfVZTM1VbQmv6oSUq4D3AFS16Zay9gOHDx8mK8vVY5uYmEhJSQnx8fFeW4nBGOM/OnTowE03\n3URwcDDp6eleHb15ut69e/Ob3/yGK6+8kvfff58HHniAVatW+TSm5qa2hJcvIteJyIXAZcDHACIS\nRBOutuJr33//PXl5eURERBAVFUVxcbENWDGmFYuPj2fy5MkMGDCA9PR0n65/GRYWxs9+9jNmz55N\nVFQUL7/8Mg8//DBr1661lVo8UFvC+xmulU/eAB6ocmd3Ja7lv1qckydPsn//fnJyciqf35WVlVnC\nM6aVczgcDBkyhBtvvJHCwkKf17U755xzeOaZZ5g2bRpOp5PnnnuOp556qtVPY6hLjQlPVb9T1dGq\n2l9VF1bZ/ndVfcgr0XlZbm4u5eXlZGZm2pJixpgzdOrUicmTJyMi5OTk+DQWEWHw4MHMnz+fqVOn\nsmfPHh566CHefvttr68N2lx4unh0q3D48GFKS0vJy8sjMTERVUVVK9dxM8aY6OhobrzxRkJCQnye\n9MC1UsvVV1/Nc889R9++fXn33Xf55S9/yeeff27dnKexhOd24sQJ1q5dWznyqWLASmxsLMHBZ1Xg\n3RjTwkRFRTFhwgS/SXrgGmAzffp05s+fT5cuXXj11Vd5+umn/SY+f1BrwhORABG50VvBeNPpv/ls\n2bKFY8eOVfbNJyYmUlhYSOfOnX0RnjHGz0VGRvpd0gNXt+usWbO444472LFjB9OmTWPLli2+Dssv\n1JrwVNVJC51zt3LlSnbv3g3AsWPH+PLLL0lISCAjIwMRISEhgbKyslOe5RljTFWRkZGMHz8eh8Ph\nV0kvICCAa665hueff574+HjmzZvHl19+WfeBLZwnXZqfisjDItJJRKIrXk0eWRMqLCxk06ZNlUN5\nN2/ejIjgcDg4ePAg8fHxOBwOAHt+Z4ypVVRUFBMnTiQ4OJiDBw/idDp9HVKlDh06MGvWLHr27MmC\nBQt45plnKucZt0aeJLxJwC+A1cAG92t9UwbV1Hbu3InD4SArK4u9e/eyadMm4uPjcTqdfPvtt/Ts\n2bNyhRVbQ9MYU5eoqCgmT57MOeecw/79+zlx4oSvQ6oUERHBzJkzufnmm9m6dSsPP/wwX3zxRasc\n0FJnwlPVbtW8unsjuKZQXl7Ohg0biImJISQkhDVr1gCuBWP37dvH0aNHufDCCysnnFsNPGOMJ8LC\nwhg1ahTDhg0jPT3dr5Kew+EgLS2N+fPn07VrV1555ZVWWWG9zoQnIm1E5D9F5DX35xQRua7pQ2tc\nxcXFbNu2jcWLF1NaWkpoaCixsbEcPHiwsuzGpk2bAOjfv7/VwDPGNEhqairDhw/3u6QHrlVjKu72\n1q9fz7Rp01izZk2rudvzpB7eG7i6MS91f84A3gE+aKqgGlNBQQFLliwhMzMTgPbt25OUlAS45q/0\n6tWr8rubNm2iR48etG3bloKCAq+XBTHGtAypqamICCtXriQqKoqoqKjKcQG+VlFhvX///vzud7/j\npZde4osvvmDq1Kkt/pd8T57h9VDV3wAnAFS1GGg2KykXFRVV3q0lJycTHh5e7fcKCgr47rvv6N+/\nf+U2q5BgjGmoAQMGMH78eDp37kxmZibFxcW+DukUXbp0Yc6cOUydOpXdu3fz0EMP8de//tXv7kob\nkycJr0xEwgAFEJEewPEmjaoRiQiRkZF1PovbvHkzqsqFF15Yuc0SnjHmbHTv3p1rrrmG8ePHk5OT\nQ0lJia9DOkXFKi0LFiwgNTWVJUuWMH36dLZv3173wc2QJwlvFq5KCZ1EZDHwGS1wbt6qVauIiYmh\nZ8+egGtiuiU8Y0xj6NKlC2PGjCEnJ4eCggJfh3OG6Ohopk2bxmOPPUZZWRmzZs1i7ty5fPnll353\nZ3o26nyGp6orRGQDcAmursz7VdV/Zlg2giNHjvCvf/2LcePGERgYSFlZGREREX7T526Maf66d+/O\nTTfdxLJly8jLyyM6Otrvfqm+8MILeeGFF1i+fDmfffYZCxYsIDAwkP79+zNx4kR69Ojh6xDPSp0J\nT0T+BKwC1qjqzqYPyftWrVqFqjJs2DAASktLiYmJ8W1QxpgWp2PHjtx22238+OOPfPzxxzgcDkJC\nQnwd1ilCQkKYOHEi48aNY9euXWzYsIHPP/+cRx99lCuuuILbb7+9xrEQ/s6TLs0/AB2B34rIXhF5\nV0Tub+K4mtShQ4d44403KCsrw+l08sUXX3DuuedW1r0rLS2tnKpgjDGNKTQ0lN69ezN8+HAOHz7s\n63BqFBgYSJ8+fbjlllt4+eWXGTt2LGvWrOHBBx9k6dKl5Ofn+zrEevOkS/NzEVkNDASGA/cA5wEv\nNnFsTeYvf/kLa9asISoqivbt25OZmcmkSZMq95eVldkdnjGmSZ133nls2bKFvLw82rdv7+twatWm\nTRt++tOfMmjQIN58803eeustli5dyh133MEVV1yBSPMYuO9Jl+ZnQDjwT2ANMFBVm+1ibLm5uXz1\n1Vc4HA6WLl1KcHAwvXv35tJLLz3le5GRkT6K0BjTGgQGBjJ69GgWL15MaGgoYWFhvg6pTj169OCp\np54iPT2d1157jVdeeYUVK1Zw1VVXMXjwYEJDQ30dYq086dL8F1AGnA/0A853T1NollasWIHT6WTG\njBk4nU4KCwuZOnXqGb+h+NvDZGNMyxMXF8f1119PVlaW301ZqE1ycjIzZ87kzjvvpKioiFdffZW7\n776bhQsX+uUo1AqedGk+CCAikcBtuFZeSQD860mrBw4dOsSKFStITU2lb9++3HfffZSWltK1a9fK\n75w8eRIRsYRnjPGKHj16cMMNN7By5Upyc3Pp0KEDQUGeLILlWxV3qFdffTU7duzg008/5W9/+xur\nV69m7NixjBw50u/uWj3p0rwPGAIMAPYBf8TVtdms7Ny5k+eeew6g8nnd6d2Y4Ory7NWrl01JMMZ4\nTUpKCp07d2b9+vWsW7fulB6niIgIv67aIiL06dOHPn36kJaWxsKFC1m0aBHvvvsuV111Fddee63f\nPKOUuhYNFZGHcSW4Dapa7pWoGlFUVJQmJiaya9cu4uLiePzxxyvX0qzO/v37uemmm6zwqzHGJ3Jz\nc8nLy0NEKCkpYd26dc1uIN3u3btZvnw5a9euJSwsjAcffPCUZRvPVlFREbfddtsP9a3cU2fCAxCR\nC3Dd5YFrPl6zqRcvIpqcnMyVV17J8OHDa50/UlRUhNPp5Lbbbms2o46MMS1bfn4+f/7znwkKCvKb\nOyVPHTx4kPnz57N//34mT55MWloaAQGeDB2pXUMTniflgX4JLAbi3a8/icj/a1iYleecKCLbRcQp\nIqlVtl8sIpvdry0iMrbKvgEislVE9ojIS+JhRkpOTuaFF17guuuuq3OyZF5eHgMHDrRkZ4zxG+3a\ntWPixIkEBQWRnp7uVxXV65KYmMjs2bO55JJLeOutt3j66ad9On/Pk1Q7FRikqr9W1V/jWmLsrrO8\n7jZgHK4q6qdvT1XV/sBo4PciUvGc8b/c101xv0afZQynqBis0tyXzjHGtDyxsbFMmTKFc889l/T0\n9GZVv66iS/Puu+/m22+/Zfr06WzdutUnsXiS8AQ4WeXzSc6yPJCq7lDVXdVsL67ynDCUf1do6AhE\nqerX6vovvQgYczYxnK5isIq/jSoyxhiA4OBgRo4cSUpKCvv376e8vPkMqRARrrrqKubNm0d4eDhz\n5sxhxYoVXo/Dk4T3BrBWRGaJyCzga1zLjTUJERkkItuBrcA97gSYBKRX+Vq6e1ujKSkpoW/fvo15\nSmOMaVQV5XwGDhxIZmYmeXl5vg6pXrp06cK8efPo378/r7/+OosWLfJqF22dCU9V5wO3A7nu1+2q\nuqCu40TkUxHZVs0rrY7rrVXV83AtZTZDROo9dV9E7haR9SKyvqioqNrvFBYWUlZWBrjWzgwPD2/x\n1X6NMc1fcHAwQ4cO5ZZbbqG4uLjy37HmIiwsjEceeYTRo0fz/vvv8/zzz3vtZ6hxHp470dwD9MR1\nt/VqfaYlqOrIswlMVXeISCGuFV4ygOQqu5Pd22o69jXgNYBOnTqd0dntdDo5cuQIIkLnzp3Jyclh\nyJAhdRaJNcYYfxEbG8uwYcP45JNPiI6OBsDhcBAaGur3A+8CAwO58847SUhIYOHChfz+97/nvvvu\na/K4a5t4/iZwAtccvGuAc4EHmjIYEekGHFDVchHpApwD7FPVHBE5JiKXAGuBW4HfNvQ6OTk59O7d\nm8zMTPLz83E4HJx//vmN80MYY4yX9O3bl7y8PPLy8lBVjh07xoEDBwgLCyM2NtbvE99PfvITSkpK\nWLJkCZ07dyYtrdYOwLNWW8Lro6p9AUTkD8C6xrqoe7rBb4E44EMR2ayqVwOXA4+KyAnACdxbpdjs\nvcBCIAz4yP2qN1WlpKSEiy++mCNHjvD2228zZswYv6tJZYwxdQkICKis41khOzubf/zjH+zdu5fE\nxES/XzVq/PjxHDhwgMWLF5OUlERqamrdBzVQbc/wTlS8aewVVlR1qaomq2qIqnZwJztU9X9U9TxV\n7a+qF6nqe1WOWa+q56tqD1W9Txs4Ljc7O5sePXoQHx9PSkoKw4YNo0+fPo31oxljjE/FxcWRlpbG\nFVdcQUZGht+P5hQR7r33Xrp168aLL77I/v37m+xatSW8C9zdiMdEpADoV/FeRI41WURNqKioCBFh\nxIgRAAQFBTF8+HC7uzPGtCgBAQGkpqYydOhQMjIy/H7eXkhICI888gihoaEsWLCAEydO1H1QA9SY\n8FQ1UFWj3K9IVQ2q8j6qSaJpIiUlJRw6dIi8vDyuv/56oqKaVfjGGNMgqamp9OrVi4yMGsf4+Y2Y\nmBjuueceDhw4wDvvvAO4RtA/8cQTLFu2rFGu4f81KM5SUFAQqkq/fv3o06cPsbGxvg7JGGO8IiAg\ngFGjRvHxxx+zd+9ekpKSGmUty6YyYMAAhg0bxrJly+jRowfbt29n586d7N69m4suuohOnTqd1fk9\nWjy6OUtNTdX169f7OgxjjPGZ8vJyVq1axaZNm4iJiSEyMhKA48eP+90jnaKiIubOncuePXtQVYYO\nHcrGjRtJTk7mySefJCAgoOkWjzbGGNO8BQUFceWVVzJhwgQCAgJIT0/nwIEDFBQUcODAAb96xhce\nHs7MmTO59NJL6datG3fddRe33HILO3fu5IMPPjirc7f4Lk1jjDEuXbt2pWvXrhQUFCAihIaGsnLl\nSrZu3UpgYCBOp5O4uDifrykcEhLCAw88gKoiIgwfPpyNGzeyePFievbsSZcuXRp0XuvSNMaYVszp\ndJKbm4vD4SAjI4PVq1fjdDqJj4/3dWinKC4uZsaMGRw7doz777+fuXPnNk0B2ObMEp4xxniusLCQ\n5cuXk5WVRceOHf1qycVDhw7x9NNPk52dTXl5uT3DM8YY03ARERGMGzeOfv36cfDgQTIyMqhpEX5v\nS0hIYO7cuXTvXq88V8nu8IwxxlQrLy+PPXv2sHXrVgoLC0lMTPR1SAAcPXqUqVOn2h2eMcaYxtG+\nfXsGDhzIlClT6NSpE/v27SMzM5PS0lKfxhUU1LDxljZK0xhjTK1CQkK4/vrr+eGHHzhy5AhfffWV\nX4zmrC9LeMYYY+oUFBRESkoKKSkpxMfHs2zZMhwOB23btm02ic+6NI0xxtRL9+7dmTJlCgMGDKC8\nvJwDBw6QkZHB8ePHfR1arewOzxhjTL3Fx8cTHx/P4MGDKSgoYNeuXaxdu5aTJ0/SoUMHv5rOUMES\nnjHGmLMSGRlJamoq5513Hl9//TVbtmwBIDg4mJiYGL9JfpbwjDHGNIqwsDCGDx/OwIEDyc/PZ8eO\nHWzbtg2Hw0F8fLzPKzVYwjPGGNOoIiIiiIiIIDk5mYEDB7Ju3Tq2bt1KTEwMERERPovLBq0YY4xp\nMu3atWPUqFFMmjSJ48ePk5WV5bNYLOEZY4xpcsnJyUyZMoWwsDCOHDnikxgs4RljjPGKinU6wbVs\nmbdZwjPGGOM1bdu25cYbb8TpdHr9Ts8SnjHGGK9q3749kyZNIjg4mIMHD3qt4rolPGOMMV7Xrl07\nJk+eTPfu3Tlw4ECtSa+xFqu2hGeMMcYnQkNDueaaa+jTp0+NSe/o0aPs27evUZYts4RnjDHGZwID\nAxk5ciRdu3YlJyfnjP0FBQWce+65jTKdwRKeMcYYnwoKCmLYsGGUlJRw8uTJM/ZffPHFhIeHU1xc\nfFbXsYRnjDHG56KjoxkwYACHDx+u3OZ0OhEREhISGDlyJNnZ2WeV9CzhGWOM8QsDBw5ERCgrKwOg\nqKiIxMREHA4H3bt3Z/z48eTm5lJSUtKg81vCM8YY4xfatGnDJZdcUvm8rqCggG7dulXu79q1K2lp\naeTm5jbo/JbwjDHG+I2+ffsSFBTE0aNHOX78OImJiafs79atG5dddhlAvSfvWcIzxhjjN0JDQxk6\ndCghISEMHjyY+Pj4M75z6aWXAhTU99xWHsgYY4xf6devH/369atxv8PhACiv73l9cocnIhNFZLuI\nOEUktZr9nUWkUEQerrJtgIhsFZE9IvKSiIh3ozbGGNOc+apLcxswDlhdw/75wEenbfsv4C4gxf0a\n3WTRGWOMaXF8kvBUdYeq7qpun4iMAX4AtlfZ1hGIUtWv1bX2zCJgjFeCNcYY0yL41aAVEYkAfgU8\nedquJCC9yud09zZjjDHGI002aEVEPgUSqtn1uKouq+GwWcALqlp4No/oRORu4G6Azp07N/g8xhhj\nWo4mS3iqOrIBhw0CJojIb4B2gFNESoF3geQq30sGMmq59mvAawCpqaneKbRkjDHGr/nVtARVHVLx\nXkRmAYWq+rL78zERuQRYC9wK/NYnQRpjjGmWfDUtYayIpAODgQ9F5O8eHHYv8N/AHuB7zhzFaYwx\nxtTIJ3d4qroUWFrHd2ad9nk9cH4ThmWMMaYFk9rKqrcEIlIAVDsFwpwhFjizAqOpjrWV56ytPGdt\n5bneqhpZnwP86hleE9mlqmes5mLOJCLrra08Y23lOWsrz1lbeU5E1tf3GL+ah2eMMcY0FUt4xhhj\nWoXWkPBe83UAzYi1leesrTxnbeU5ayvP1butWvygFWOMMQZaxx2eMcYY03ITnoiMFpFd7vp5j/o6\nHn8iIn8UkSwR2VZlW7SIfCIiu91/tvdljP5CRDqJyOci8q27huP97u3WXqcRkVARWSciW9xt9aR7\nu7VVDUQkUEQ2icgH7s/WVtUQkX3ueqibK0ZnNqStWmTCE5FA4BXgGqAPMFlE+vg2Kr+ykDPrCT4K\nfKaqKcBn7s/GVVX5IVXtA1wC/ML9d8na60zHgRGqegHQHxjtXg7Q2qpm9wM7qny2tqrZcFXtX2Xa\nRr3bqkUmPOBiYI+q7lXVMuBtIM3HMfkNVV0N5J62OQ140/3+TazeIACqmqmqG93vC3D945SEtdcZ\n1KXQ/dHhfinWVtUSkWTgJ7iWTKxgbeW5erdVS014ScCBKp+tfl7dOqhqpvv9IaCDL4PxRyLSFbgQ\n1wLm1l7VcHfRbQaygE9U1dqqZguARwBnlW3WVtVT4FMR2eAu/wYNaKvWsNKKqSdVVRGx4btVuIsT\nvws8oKrHqtZrtPb6N1U9CfQXkXbAUhE5/7T91laAiFwHZKnqBhEZVt13rK1OcbmqZohIPPCJiOys\nutPTtmqpd3gZQKcqn2utn2cAOCwiHQHcf2b5OB6/ISIOXMlusar+r3uztVctVDUf+BzXs2JrqzNd\nBtwgIvtwPXIZISJ/wtqqWqqa4f4zC1fhgYtpQFu11IT3DZAiIt1EJBi4CVju45j83XLgP9zv/wOo\nqSp9qyKuW7k/ADtUdX6VXdZepxGROPedHSISBlwF7MTa6gyqOkNVk1W1K65/n1aq6s1YW51BRMJF\nJLLiPTAK2EYD2qrFTjwXkWtx9ZEHAn9U1bk+DslviMifgWG4VmY/DMwE3gP+AnQGfgRuVNXTB7a0\nOiJyObAG2Mq/n7U8hus5nrVXFSLSD9fggUBcv0z/RVWfEpEYrK1q5O7SfFhVr7O2OpOIdOff5eSC\ngLdUdW5D2qrFJjxjjDGmqpbapWmMMcacwhKeMcaYVsESnjHGmFbBEp4xxphWwRKeMcaYVsES7Xke\n6wAAAkdJREFUnjFeJiIn3au+V7y6NtF1HhCRW93vF4rIhNP2F1Z/JIhIsIisFhFbjcm0GPaX2Rjv\nK1HV/jXtFJEgVS0/mwu4E9UdwEUNOV5Vy0TkM2ASsPhsYjHGX9gdnjF+QERuE5HlIrISV6kTRGS6\niHwjIv+qqC3n3v64iHwnIv8QkT+LyMPVnHIEsNGTxCkiT1W528wQkTfcu94DpjTCj2eMX7A7PGO8\nL8xdUQDgB1Ud635/EdBPVXNFZBSQgmvNQAGWi8hQoAjXUlT9cf3/uxHYUM01Lqtm+7Mi8p+nf1FV\nfw382r0s2BrgZfeubcDABv6MxvgdS3jGeF9NXZqfVFkaaZT7tcn9OQJXAowElqpqMYCI1LRGbEdO\nLSwKMF1V/1rxoeozPPeaoX8C5qvqBnBVPhCRMhGJdNcCNKZZs4RnjP8oqvJegHmq+vuqXxCRBzw8\nVwkQWo9rzwLSVfWN07aHAKX1OI8xfsue4Rnjn/4O3OGuw4eIJLlrga0GxohImHsF+etrOH4H0NOT\nC4nI9cBI4JenbY8BclT1RAN/BmP8it3hGeOHVHWFiJwL/NNdbLYQuFlVN4rIEmALrvpf39Rwio+A\n//HwctOAJGCd+1rL3c/1hgMfNvynMMa/WLUEY5oxEZkFFKrqc9XsWwo8oqq7G3ju/wUeVdXvzi5K\nY/yDdWka03I9imvwSr25Cye/Z8nOtCR2h2eMMaZVsDs8Y4wxrYIlPGOMMa2CJTxjjDGtgiU8Y4wx\nrYIlPGOMMa2CJTxjjDGtwv8B9ogWJYkxTjcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3e6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(432x288)\n"
     ]
    }
   ],
   "source": [
    "data  = converter_mne(test)\n",
    "epochs = data.train_epochs()\n",
    "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": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAABZCAYAAAA9x+IKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZhVdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9d2f60>"
      ]
     },
     "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": 24,
   "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": 25,
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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"
     ]
    }
   ],
   "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",
    "LDA_scores = []\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",
    "    LDA_scores.append(svc.score(X_test, test_list[i]['Y_test']))\n",
    "\n",
    "print \"MAX:\", np.max(LDA_scores)\n",
    "print \"MEAN:\", np.mean(LDA_scores)\n",
    "print \"MIN:\", np.min(LDA_scores)\n",
    "print \"DISP:\", np.std(LDA_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Piermann classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyriemann.classification import TSclassifier\n",
    "from pyriemann.estimation import covariances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "array must not contain infs or NaNs",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-7aa4d0da80ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\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      6\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\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[0mtest_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Y_test'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mPierman_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\pyriemann\\classification.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    382\u001b[0m         \u001b[0mts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTangentSpace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtsupdate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtsupdate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pipe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_pipeline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pipe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\sklearn\\pipeline.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[0mThis\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m         \"\"\"\n\u001b[0;32m--> 268\u001b[0;31m         \u001b[0mXt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\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[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\sklearn\\pipeline.pyc\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    232\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"fit_transform\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m                 \u001b[0mXt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params_steps\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m                 \u001b[0mXt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params_steps\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\pyriemann\\tangentspace.pyc\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_reference_points\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m         self.reference_ = mean_covariance(X, metric=self.metric,\n\u001b[0;32m--> 168\u001b[0;31m                                           sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    169\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mtangent_space\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreference_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\pyriemann\\utils\\mean.pyc\u001b[0m in \u001b[0;36mmean_covariance\u001b[0;34m(covmats, metric, sample_weight, *args)\u001b[0m\n\u001b[1;32m    338\u001b[0m                \u001b[1;34m'harmonic'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmean_harmonic\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m                'kullback_sym': mean_kullback_sym}\n\u001b[0;32m--> 340\u001b[0;31m     \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovmats\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    341\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\pyriemann\\utils\\mean.pyc\u001b[0m in \u001b[0;36mmean_riemann\u001b[0;34m(covmats, tol, maxiter, init, sample_weight)\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m             \u001b[0mtmp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCm12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcovmats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCm12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m             \u001b[0mJ\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlogm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mcrit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mJ\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'fro'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\pyriemann\\utils\\base.pyc\u001b[0m in \u001b[0;36mlogm\u001b[0;34m(Ci)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \"\"\"\n\u001b[0;32m---> 41\u001b[0;31m     \u001b[0mD\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mV\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meigh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m     \u001b[0mD\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0mOut\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mV\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mV\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\scipy\\linalg\\decomp.pyc\u001b[0m in \u001b[0;36meigh\u001b[0;34m(a, b, lower, eigvals_only, overwrite_a, overwrite_b, turbo, eigvals, type, check_finite)\u001b[0m\n\u001b[1;32m    286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m     \"\"\"\n\u001b[0;32m--> 288\u001b[0;31m     \u001b[0ma1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_asarray_validated\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_finite\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    289\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0ma1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0ma1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'expected square matrix'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\scipy\\_lib\\_util.pyc\u001b[0m in \u001b[0;36m_asarray_validated\u001b[0;34m(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)\u001b[0m\n\u001b[1;32m    226\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'masked arrays are not supported'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0mtoarray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray_chkfinite\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcheck_finite\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mobjects_ok\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'O'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\User\\Anaconda3\\envs\\py27\\lib\\site-packages\\numpy\\lib\\function_base.pyc\u001b[0m in \u001b[0;36masarray_chkfinite\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m   1031\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchar\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtypecodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'AllFloat'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1032\u001b[0m         raise ValueError(\n\u001b[0;32m-> 1033\u001b[0;31m             \"array must not contain infs or NaNs\")\n\u001b[0m\u001b[1;32m   1034\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1035\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: array must not contain infs or NaNs"
     ]
    }
   ],
   "source": [
    "X_train = epochs.get_data()[:test_list[0]['Y_test'].shape[0]]\n",
    "X_train = data_prep(X_train, 1000, 6, 16)\n",
    "\n",
    "clf = TSclassifier()\n",
    "# compute covariance matricestest_list[i]['Y_test']\n",
    "data_cov = covariances(X_train)\n",
    "clf.fit(data_cov , Y_train[:test_list[0]['Y_test'].shape[0]])\n",
    "\n",
    "Pierman_scores = []\n",
    "for i in range(0, len(test_list)):\n",
    "    X_test = data_prep(test_list[i]['X_test'], 1000, 6, 16)\n",
    "    Pierman_scores.append(clf.score(covariances(X_test),test_list[i]['Y_test']))\n",
    "    \n",
    "print \"MAX:\", np.max(Pierman_scores)\n",
    "print \"MEAN:\", np.mean(Pierman_scores)\n",
    "print \"MIN:\", np.min(Pierman_scores)\n",
    "print \"DISP:\", np.std(Pierman_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX: 1.0\n",
      "MEAN: 0.802777777778\n",
      "MIN: 0.533333333333\n",
      "DISP: 0.136394263302\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\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 = GaussianNB()\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",
    "Bayes_scores = []\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",
    "    Bayes_scores.append(svc.score(X_test, test_list[i]['Y_test']))\n",
    "\n",
    "print \"MAX:\", np.max(Bayes_scores)\n",
    "print \"MEAN:\", np.mean(Bayes_scores)\n",
    "print \"MIN:\", np.min(Bayes_scores)\n",
    "print \"DISP:\", np.std(Bayes_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-nearest neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX: 0.966666666667\n",
      "MEAN: 0.866666666667\n",
      "MIN: 0.766666666667\n",
      "DISP: 0.0561083607687\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\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 = KNeighborsClassifier()\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",
    "KNN_scores = []\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",
    "    KNN_scores.append(svc.score(X_test, test_list[i]['Y_test']))\n",
    "\n",
    "print \"MAX:\", np.max(KNN_scores)\n",
    "print \"MEAN:\", np.mean(KNN_scores)\n",
    "print \"MIN:\", np.min(KNN_scores)\n",
    "print \"DISP:\", np.std(KNN_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification and Regression Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX: 0.933333333333\n",
      "MEAN: 0.786111111111\n",
      "MIN: 0.6\n",
      "DISP: 0.0957024065386\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\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 = DecisionTreeClassifier()\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",
    "Trees_scores = []\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",
    "    Trees_scores.append(svc.score(X_test, test_list[i]['Y_test']))\n",
    "\n",
    "print \"MAX:\", np.max(Trees_scores)\n",
    "print \"MEAN:\", np.mean(Trees_scores)\n",
    "print \"MIN:\", np.min(Trees_scores)\n",
    "print \"DISP:\", np.std(Trees_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM (Support Vector Machines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX: 1.0\n",
      "MEAN: 0.844444444444\n",
      "MIN: 0.666666666667\n",
      "DISP: 0.109994388185\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\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 = SVC()\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",
    "SVM_scores = []\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",
    "    SVM_scores.append(svc.score(X_test, test_list[i]['Y_test']))\n",
    "\n",
    "print \"MAX:\", np.max(SVM_scores)\n",
    "print \"MEAN:\", np.mean(SVM_scores)\n",
    "print \"MIN:\", np.min(SVM_scores)\n",
    "print \"DISP:\", np.std(SVM_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX: 0.833333333333\n",
      "MEAN: 0.65\n",
      "MIN: 0.5\n",
      "DISP: 0.0787635937709\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\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 = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(8, 10), random_state=1)\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",
    "NN_scores = []\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",
    "    NN_scores.append(svc.score(X_test, test_list[i]['Y_test']))\n",
    "\n",
    "print \"MAX:\", np.max(NN_scores)\n",
    "print \"MEAN:\", np.mean(NN_scores)\n",
    "print \"MIN:\", np.min(NN_scores)\n",
    "print \"DISP:\", np.std(NN_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}