Specifications.ipynb

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    "- creer le predicteur pour chacun des timeframes (30mn - 1H - 4H - 1D/ repertoires stephane_fresh\n",
    "- utiliser le modele pour predire un max de perte qui soit 2 fois inferieur au profit.\n",
    "- A chaque fin de chandelle (4h ou 1h) , faire une prediction pour :\n",
    "- si la chandelle qui se termine est negative (cad close<open) :\n",
    "  - probabilite que le LOW de la prochaine chandelle soit au moins 20 points plus bas que le CLOSE de la chandelle qui s'est terminee.\n",
    "  - probabilite que le HIGH del la prochaine chandelle soit maximum 18 pips au dessus du CLOSE\n",
    "- si la chandelle qui se termine est positive (cad open<close) :\n",
    "  - probabilite que le HIGH de la prochaine chandelle soit au moins 20 points plus haut que le CLOSE de la chandelle qui s'est terminee.\n",
    "  - probabilite que le LOW soit max 18 pips en dessous du CLOSE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ramon/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/ramon/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier,ExtraTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "import xgboost as xgb\n",
    "from datetime import datetime, timedelta\n",
    "from service import files_service\n",
    "from model.models import TradeModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set folder for data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PATH = \"data/test_all_periods/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read data and convert to TradeModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EURUSD_UTC_1H_Bid_2010.01.01_2017.02.18.csv', 'EURUSD_UTC_30M_Bid_2010.01.01_2017.02.18.csv', 'EURUSD_UTC_4H_Bid_2010.01.01_2017.02.18.csv', 'EURUSD_UTC_1D_Bid_2017.01.01_2017.02.20.csv']\n"
     ]
    }
   ],
   "source": [
    "files = files_service.get_files(PATH,extension='.csv')\n",
    "print files\n",
    "files = [files[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EURUSD_UTC_30M_Bid_2010.01.01_2017.02.18 \"30M\"\n"
     ]
    }
   ],
   "source": [
    "TradeModel_dict={}\n",
    "for f in files:\n",
    "    name = f.split('.csv')[0]\n",
    "    frequency =  '\\\"' + f.split('_')[2] + '\\\"'\n",
    "    print name, frequency\n",
    "    path = PATH+f\n",
    "    tm = TradeModel(path,name,frequency=frequency)\n",
    "    TradeModel_dict[name]= tm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tm =TradeModel_dict['EURUSD_UTC_30M_Bid_2010.01.01_2017.02.18']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>KC_M_1</th>\n",
       "      <th>KC_U_1</th>\n",
       "      <th>KC_D_1</th>\n",
       "      <th>KC_M_2</th>\n",
       "      <th>KC_U_2</th>\n",
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       "      <th>DOWN_next_Low_under_Close</th>\n",
       "      <th>DOWN_next_High_over_Close</th>\n",
       "      <th>UP_next_High_over_Close</th>\n",
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       "      <th>2010-01-01 11:30:00</th>\n",
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       "      <td>1.43324</td>\n",
       "      <td>1.43336</td>\n",
       "      <td>1.43267</td>\n",
       "      <td>1.43293</td>\n",
       "      <td>2021.0</td>\n",
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       "<p>5 rows × 36 columns</p>\n",
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      "text/plain": [
       "                        Open     High      Low    Close  Volume     KC_M_1  \\\n",
       "date_time                                                                    \n",
       "2010-01-01 11:30:00  1.43324  1.43334  1.43267  1.43306   1633.8       NaN   \n",
       "2010-01-01 12:00:00  1.43321  1.43344  1.43258  1.43295   1542.4       NaN   \n",
       "2010-01-01 12:30:00  1.43324  1.43336  1.43267  1.43293   2021.0  1.433000   \n",
       "2010-01-01 13:00:00  1.43284  1.43334  1.43207  1.43276   1567.1  1.432900   \n",
       "2010-01-01 13:30:00  1.43316  1.43352  1.43287  1.43331   1953.1  1.432981   \n",
       "\n",
       "                       KC_U_1    KC_D_1    KC_M_2    KC_U_2  ...   \\\n",
       "date_time                                                    ...    \n",
       "2010-01-01 11:30:00       NaN       NaN       NaN       NaN  ...    \n",
       "2010-01-01 12:00:00       NaN       NaN       NaN       NaN  ...    \n",
       "2010-01-01 12:30:00  1.433740  1.432260  1.433000  1.433740  ...    \n",
       "2010-01-01 13:00:00  1.433840  1.431960  1.432900  1.433840  ...    \n",
       "2010-01-01 13:30:00  1.433851  1.432111  1.432981  1.433851  ...    \n",
       "\n",
       "                     DOWN_next_Low_under_Close  DOWN_next_High_over_Close  \\\n",
       "date_time                                                                   \n",
       "2010-01-01 11:30:00                          0                          0   \n",
       "2010-01-01 12:00:00                          0                          0   \n",
       "2010-01-01 12:30:00                          0                          0   \n",
       "2010-01-01 13:00:00                          0                          0   \n",
       "2010-01-01 13:30:00                          0                          0   \n",
       "\n",
       "                     UP_next_High_over_Close  UP_next_Low_under_Close  \\\n",
       "date_time                                                               \n",
       "2010-01-01 11:30:00                        0                        0   \n",
       "2010-01-01 12:00:00                        0                        0   \n",
       "2010-01-01 12:30:00                        0                        0   \n",
       "2010-01-01 13:00:00                        0                        0   \n",
       "2010-01-01 13:30:00                        0                        0   \n",
       "\n",
       "                     DOWN_next_Low_and_High  UP_next_Low_and_High  hour  day  \\\n",
       "date_time                                                                      \n",
       "2010-01-01 11:30:00                       0                     0    11    4   \n",
       "2010-01-01 12:00:00                       0                     0    12    4   \n",
       "2010-01-01 12:30:00                       0                     0    12    4   \n",
       "2010-01-01 13:00:00                       0                     0    13    4   \n",
       "2010-01-01 13:30:00                       0                     0    13    4   \n",
       "\n",
       "                     month  year  \n",
       "date_time                         \n",
       "2010-01-01 11:30:00      1  2010  \n",
       "2010-01-01 12:00:00      1  2010  \n",
       "2010-01-01 12:30:00      1  2010  \n",
       "2010-01-01 13:00:00      1  2010  \n",
       "2010-01-01 13:30:00      1  2010  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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3/+JPjuDtL7Lw1pbjOH5Rg4Mn+E1AU13XhFqG0qM1ca6q/3v3kMNt9v+eh+95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\nct69zDU7xHlsHkJiooJx751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lzYWItdlWuI6csCXQX4F6iyDWrYMKp67cttr2/+7tj4fG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\npJALIYQQGvYX4/EkdF4DaGsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcfdcad2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = tm.df.cs_body_size.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#tm.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save TradeModel  to .csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tm.df.to_csv('tradeModel_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute models and predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_test_split(df, date, target):\n",
    "              \n",
    "    predictors =tm.get_predictors_columns()\n",
    "    #predictors =['Open','High','Low','High','KC_M_3','KC_U_3','ATR','EMA12','EMA30']\n",
    "\n",
    "    df_train = df[df.index<date]\n",
    "    df_test = df[df.index>=date]\n",
    "    \n",
    "    tm.X_train,tm.y_train = df_train[predictors].values,df_train[target].values\n",
    "    tm.X_test, tm.y_test  = df_test[predictors].values,df_test[target].values  \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def cv_optimize(clf,  tm,parameters={}, n_folds=5):\n",
    "    \n",
    "    gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds, n_jobs=-1)\n",
    "    gs.fit(tm.X_train, tm.y_train)\n",
    "    \n",
    "    return gs.best_estimator_    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_next(tm,target,clf, parameters={}):\n",
    "    \n",
    "    best_clf = cv_optimize(clf,tm, parameters)\n",
    "   \n",
    "    prediction =  best_clf.predict_proba(tm.X_test)\n",
    "    \n",
    "    return prediction,best_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compute_prediction(trade_model,target,last_index,single=True,\\\n",
    "                       clf=xgb.XGBClassifier(max_depth=12,n_estimators=300),\\\n",
    "                      params={}):\n",
    "\n",
    "    prediction_list = []\n",
    "\n",
    "    df = tm.df.copy()\n",
    "    df = df.dropna()\n",
    "    \n",
    "    \n",
    "    if not single:\n",
    "        for dt in df.tail(last_index).index:\n",
    "            print dt\n",
    "\n",
    "            train_test_split(df,dt,target)\n",
    "            predictions = predict_next(tm,target,clf,params)\n",
    "            prediction_list.append((dt,tm.y_test[0],predictions[0][0],predictions[0][1]))\n",
    "    else :\n",
    "              \n",
    "  \n",
    "        dt_range =  df.iloc[-last_index:].index\n",
    "        dt = dt_range[0]\n",
    "\n",
    "        train_test_split(df,dt,target)\n",
    "        predictions,clf = predict_next(tm,target,clf)\n",
    "\n",
    "        prediction_list = zip(dt_range,tm.y_test,predictions[:,0],predictions[:,1]) \n",
    "    \n",
    "    result ={}\n",
    "        \n",
    "    result['df'] =  pd.DataFrame(prediction_list,columns=['date','observed',target+'_FALSE', target+'_TRUE'])\n",
    "    result['clf'] = clf\n",
    "    result['tm'] = tm\n",
    "    \n",
    "\n",
    "    pickle.dump(result, open('data/'+trade_model.name+'_'+target+'.p', \"wb\"))\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DOWN_next_Low_under_Close',\n",
       " 'DOWN_next_High_over_Close',\n",
       " 'UP_next_High_over_Close',\n",
       " 'UP_next_Low_under_Close',\n",
       " 'DOWN_next_Low_and_High',\n",
       " 'UP_next_Low_and_High']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TARGETS = [col for col in tm.df.columns if '_next' in col]\n",
    "TARGETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>ratio_TRUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DOWN_next_Low_under_Close</th>\n",
       "      <td>7366</td>\n",
       "      <td>4170</td>\n",
       "      <td>36.147712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOWN_next_High_over_Close</th>\n",
       "      <td>11508</td>\n",
       "      <td>28</td>\n",
       "      <td>0.242718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UP_next_High_over_Close</th>\n",
       "      <td>7364</td>\n",
       "      <td>4172</td>\n",
       "      <td>36.165049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UP_next_Low_under_Close</th>\n",
       "      <td>6909</td>\n",
       "      <td>4627</td>\n",
       "      <td>40.109223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOWN_next_Low_and_High</th>\n",
       "      <td>11508</td>\n",
       "      <td>28</td>\n",
       "      <td>0.242718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UP_next_Low_and_High</th>\n",
       "      <td>10313</td>\n",
       "      <td>1223</td>\n",
       "      <td>10.601595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               0     1  ratio_TRUE\n",
       "DOWN_next_Low_under_Close   7366  4170   36.147712\n",
       "DOWN_next_High_over_Close  11508    28    0.242718\n",
       "UP_next_High_over_Close     7364  4172   36.165049\n",
       "UP_next_Low_under_Close     6909  4627   40.109223\n",
       "DOWN_next_Low_and_High     11508    28    0.242718\n",
       "UP_next_Low_and_High       10313  1223   10.601595"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = tm.df[TARGETS].apply(pd.Series.value_counts).T\n",
    "summary['ratio_TRUE']=summary[1]/summary.sum(axis=1)*100.0\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compute_all_predictions(trade_model,last_index=1000,clf=None,params=None):\n",
    "    for target in TARGETS:\n",
    "        predictions = compute_prediction(tm,target,last_index,clf=clf,params=None,single=True)\n",
    "        #!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
    "        #break\n",
    "        #!!!!!!!!!!!!!!!!!!!!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-4810eb516734>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcompute_all_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-21-182404ad70db>\u001b[0m in \u001b[0;36mcompute_all_predictions\u001b[0;34m(trade_model, last_index, clf, params)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_all_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrade_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlast_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mTARGETS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m         \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlast_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msingle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0;31m#!!!!!!!!!!!!!!!!!!!!!!!!!!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0;31m#break\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-18-79571769a188>\u001b[0m in \u001b[0;36mcompute_prediction\u001b[0;34m(trade_model, target, last_index, single, clf, params)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_next\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mprediction_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-17-89288ea3dea0>\u001b[0m in \u001b[0;36mpredict_next\u001b[0;34m(tm, target, clf, parameters)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpredict_next\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mbest_clf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv_optimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mbest_clf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-16-b7441203549a>\u001b[0m in \u001b[0;36mcv_optimize\u001b[0;34m(clf, tm, parameters, n_folds)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramon/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    827\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    828\u001b[0m         \"\"\"\n\u001b[0;32m--> 829\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    831\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramon/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.pyc\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, parameter_iterable)\u001b[0m\n\u001b[1;32m    617\u001b[0m                 **best.parameters)\n\u001b[1;32m    618\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 619\u001b[0;31m                 \u001b[0mbest_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    620\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    621\u001b[0m                 \u001b[0mbest_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramon/anaconda2/lib/python2.7/site-packages/xgboost/sklearn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose)\u001b[0m\n\u001b[1;32m    443\u001b[0m                               \u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m                               \u001b[0mevals_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevals_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m                               verbose_eval=verbose)\n\u001b[0m\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjective\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb_options\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"objective\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramon/anaconda2/lib/python2.7/site-packages/xgboost/training.pyc\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, learning_rates, xgb_model, callbacks)\u001b[0m\n\u001b[1;32m    203\u001b[0m                            \u001b[0mevals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m                            \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m                            xgb_model=xgb_model, callbacks=callbacks)\n\u001b[0m\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramon/anaconda2/lib/python2.7/site-packages/xgboost/training.pyc\u001b[0m in \u001b[0;36m_train_internal\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)\u001b[0m\n\u001b[1;32m     74\u001b[0m         \u001b[0;31m# Skip the first update if it is a recovery step.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_rabit_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0mversion\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ramon/anaconda2/lib/python2.7/site-packages/xgboost/core.pyc\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m    804\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    805\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 806\u001b[0;31m             \u001b[0m_check_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_LIB\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBoosterUpdateOneIter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    807\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    808\u001b[0m             \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "compute_all_predictions(tm,clf=xgb.XGBClassifier(),params= {})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def load_predictions(tm_name):\n",
    "    predictions_dict={}\n",
    "    files = files_service.get_files('data/',extension='.p',filter_on=tm_name)\n",
    "    for f in files:\n",
    "        prediction_result = pickle.load(open('data/'+f, \"rb\"))\n",
    "        predictions_dict[f.split(tm_name)[1][1:-2]]=prediction_result\n",
    "        \n",
    "    return predictions_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_roc(name, clf, ytest, xtest, ax=None, labe=10, proba=True, skip=0):\n",
    "    initial=False\n",
    "    if not ax:\n",
    "        ax=plt.gca()\n",
    "        initial=True\n",
    "    if proba:\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.predict_proba(xtest)[:,1])\n",
    "    else:\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.decision_function(xtest))\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    if skip:\n",
    "        l=fpr.shape[0]\n",
    "        ax.plot(fpr[0:l:skip], tpr[0:l:skip], '.-', alpha=0.6, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n",
    "    else:\n",
    "        ax.plot(fpr, tpr, '.-', alpha=0.6, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n",
    "    label_kwargs = {}\n",
    "    label_kwargs['bbox'] = dict(\n",
    "        boxstyle='round,pad=0.3', alpha=0.2,\n",
    "    )\n",
    "    for k in xrange(0, fpr.shape[0],labe):\n",
    "        #from https://gist.github.com/podshumok/c1d1c9394335d86255b8\n",
    "        threshold = str(np.round(thresholds[k], 2))\n",
    "        ax.annotate(threshold, (fpr[k], tpr[k]), **label_kwargs)\n",
    "    if initial:\n",
    "        ax.plot([0, 1], [0, 1], 'k--')\n",
    "        ax.set_xlim([0.0, 1.0])\n",
    "        ax.set_ylim([0.0, 1.05])\n",
    "        ax.set_xlabel('False Positive Rate')\n",
    "        ax.set_ylabel('True Positive Rate')\n",
    "        ax.set_title('ROC')\n",
    "    \n",
    "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def analysis_result():\n",
    "    \n",
    "    \n",
    "    ### Load data\n",
    "    dico_pred = load_predictions('EURUSD_UTC_4H_Bid_2010.01.01_2017.02.18')\n",
    "\n",
    "    \n",
    "    for key in [k for k in dico_pred.keys() if 'and' in k]:\n",
    "        print '--------------------'+key+'------------------'\n",
    "        df_test =dico_pred[key]['df']\n",
    "        \n",
    "        df_test['predicted'] =( df_test[key+'_TRUE']>0.17)*1\n",
    "        \n",
    "        ## print Confusion Matrix\n",
    "        \n",
    "        print confusion_matrix(df_test.observed.values,df_test.predicted.values)\n",
    "\n",
    "        df_test['result_OK']= np.where(df_test.predicted==df_test.observed,1,0)\n",
    "        df_test =df_test.sort_values(by=key+'_TRUE')\n",
    "\n",
    "        ### Compute Baseline\n",
    "        df_test[df_test.observed==1]\n",
    "\n",
    "        \n",
    "        print 'accuracy baseline : {}%'.format(df_test.observed.value_counts()[0]*100.0/df_test.observed.count())\n",
    "\n",
    "        ####  Accuracy and Precision\n",
    " \n",
    "\n",
    "        accuracy = sum(df_test.result_OK)*100.0 /df_test.observed.count()\n",
    "        print 'Accuracy model : {} %'.format(accuracy)\n",
    "        #precision = TP/(FP+TP)\n",
    "        precision = np.sum((df_test.predicted==1) & (df_test.result_OK==1)) *100.0 /np.sum(df_test.predicted==1)\n",
    "\n",
    "\n",
    "        print 'Precision model : {} %'.format(precision)\n",
    "        \n",
    "        print dico_pred[key]['clf']\n",
    "        \n",
    "        make_roc(key,dico_pred[key]['clf'], dico_pred[key]['tm'].y_test,dico_pred[key]['tm'].X_test)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------DOWN_next_Low_and_High------------------\n",
      "[[998   0]\n",
      " [  2   0]]\n",
      "accuracy baseline : 99.8%\n",
      "Accuracy model : 99.8 %\n",
      "Precision model : nan %\n",
      "XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n",
      "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
      "       min_child_weight=1, missing=nan, n_estimators=100, nthread=-1,\n",
      "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
      "       scale_pos_weight=1, seed=0, silent=True, subsample=1)\n",
      "--------------------UP_next_Low_and_High------------------\n",
      "[[897  51]\n",
      " [ 36  16]]\n",
      "accuracy baseline : 94.8%\n",
      "Accuracy model : 91.3 %\n",
      "Precision model : 23.8805970149 %\n",
      "XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n",
      "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
      "       min_child_weight=1, missing=nan, n_estimators=100, nthread=-1,\n",
      "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
      "       scale_pos_weight=1, seed=0, silent=True, subsample=1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ramon/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:33: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "image/png": 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hKhKZgoPve/iBREgvJQMA3Z5JSFepienk8x4FRyIW0mjpvUNaMD0yBYvaRJie\nTJFExCASUrBMhWTeJ1sEkIiHNWZPS7CjK08qsAhHdRqrI+zszgPQ0hDDdzSMUJhYWOONh5WGtSzF\nwCFiqHgBvLI9RU/OQpVl6qvCnLx8OiFDwXUDpCBg0440ml+6GDM0jVzRxcia1MQMkjmL2niIRHR3\n/X3LAMxvreJNS1twPZ/OZJF80cHxfOrCUdpzpTvgfhAQNnSqq8LUJUp1HbWoiVTuZbrTJookcciM\n0ozbT2XS+FLvhZeqEDVUZjTGWDqvnmeVLl7elsRxfVRN5ZAZNUiyxJa2yraYxSKm5dPWbZK3XCQk\nAijVVa9x4tIWVLXUdU6RIJN3MLImtfEQsXApZlva0hXHV1GymdEUJ/AD1hfyBDJIskRVVGdB73qh\nNHP7mk1deEFQEes+fevMFZ3yPlNVCV1yaKirQZVKyXFVVC+fQ47rD/l5f69s05G1MKblVeyH1roo\nVTF98L4LAjRNpS5R2magvN/74gClRDgW1nj7EY1Ma6ob9vxNRA12dmapiuqcduwsNm4xeGpztiK2\niajGsYub+efaXeXjs/86+mIxUpwANhoWc2Y1o2mVX6f9j+f+x0P/c6J/XSOtY2C9igTpdMCs1rF9\ntwGsrZF54uUMmbyDpsk4tkfOdMrvFcTDGnNaS0+HtuxIkSuW4tHX/r421VRHSabyFe0OaxLvPGYW\nruuQcyrj0b5WR3MTHHfo7qGsb42EOaQ1Sl1jM4+vT9KpBViGQX0iTDSscvj8eh4Kh5jVoDNnbul7\n2N4VpTOsccSCemSplOD0fdeZtkeoWiYWT1TsRyUwmTOjiZEUTJe2zhzZQikBPetN80o3W4b43b+/\ncSYPv9BJ3nSpiYKqheh5udT2eEgDAhRFRlF1dNljZmOM5QvqSW9WUOwYS+fW4nkBsgTPagZyEBAx\ndHY8tZWauhqqW+J4VVGOOu8dqNKBMYGjIAhCf1OSYEyfPp1cLsf27dtpbm5m9erVfO9735vQdWiq\nzLzWKizbHfMfYE2VOf34ORy3pBkCqK8u3R0+Yek0CCAS0tjVk2dGY5xISOXkI1s5PFf6oxsLa+R6\n71hWx0p38LJFm7CuDvp82fw68kUHXXIIlBDZYg1+7x10WZGJGCoFy0XyLBrqqgnpKt2ZInWJMI7n\noQYuXVkb25MwNIVc0aZouVTFDHIFh9pE6U51V3eK6dPqqI4ZdHSlSOZdNF0n3nuXsmC5GJqCpqs4\ntovleET9UoPNAAAgAElEQVQMFccukojHyyOaKIpMdczA9XxefnUn8Xipa0b5Dm1Yw3FMaqrixMN6\n+ULkjYe1kC3axMOlYS6Xz6/HdlxM26OlPla+4FVViTcta2XpvHry2S4a6xvQNL0cv6Ltlv/vX3/f\nMn2xVVUJVVU456QFtCcL1CXC7OrsQlIi9GSL6JqC5FosnNNcUde5Jy+krStHJKRRX1Xa5y01Ejlb\nJWyoKJKErMjEwxqKInHUwgYWtCZKF4SGzyGzp5W3r39bFBWWza9n0WwJ3/OJxAwKOQtZkamLSuXt\nUFWpHKv+29m/zr7jq7O7h1is9BRgTpNONBojGlIxNLW8XoC3HN7K0rm15ItORaz79F9n/5j2nQfv\nXDmbVM6iOmaUz6HhPh9KyKjcD47nDbvvYmGtYpuHikNfOzV1+GE8NVXmfW9dwMtbukuxUCQUVR4y\ntgO3v/86+sdipDgFDE4CXlrzDLfc9FPMYpGqmgbO/dBlzJ7ZWnFOPPX0E9z1219yt2VS39DExZd+\nsbyOYrqLb3z5EmKxBF/+RuV3Y19b0pkISxfOGNN3W2n5yljkik7FqEV95zmUjjvP88ufDZXwVBy3\nqlS6CTL4FYYRR6tSFInD59fTs7mW9iDF8oX1RAwVRZGwbQvDGDwqVN8yBcutSA6rQz51tTUV+ypw\nBi0+SCSk8ukzl7GtI1v+bh/ud2YxxxsPayFnOmQyWWQ1RPKVUtvfcmRpwtKIofLsnz2OPWwm8+c3\noChSxehxfe2/O3CJ+jqP3vQguc4Mqqbx9Su/RnNjDa6lUl09isYLgiDsZyYtwXjxxRf57ne/y44d\nO1BVlfvvv5+TTjqJ6dOnc8opp3DFFVdw+eWXA3DaaacxZ86cCW+DIktEQtqY/wBD6SJlWl1ln+r+\nP1fFdo8Jryky9dW7uwMMvIiriRtDft5UW3r83tHVjYtEdaxynHkAXdPRJYn66lKyEIvEe3+jYpo+\nslTqegFQq+3u+hLr12ViZnOcxrrSuhpqwgRy5RwMfcsnYgaZrI+uleIVqHJ5vf2pqsKMhhh5V0KS\nKtsdeLu3d3d5qeKzoersX7a+OkRMiyHJQcWyffEb6iJ5qDpDhsKs5lK8qqIaDgqtoVL3Lccq/b5/\nXSFDYW5rZb/++qowtfLQ7VUUidpEiNpECAWnfPE1sC2yLCPLlOOUiIfJ9HaBU6TKi4ehtndgnSFD\nwbMNvN5+97XxELNah+5frqpS+TgbzsD9A/Q+YymdBw3Vgy/uhvu8rF/Py/77YeBXzsB913+bh4pD\nTdwodRPbwyg7mqpUtE8Khj6W+trQf/sHxmK4cn0CfOR+7el7mfkLX/sOc+Yu5P57bueO3/+Yz3/l\nqnIZ1zW56YbvVpT51Q3X8vmvXEUx084Prv5PFi1eSseutmHbUpcIj+u7beC2DDyf+hvpXB2qPtu2\nSt0uh9DSOoN/Pbq6/HP/0apM00FRJKbPmMX6Fx4rfyeVR7RrbBmyTkWRymWB3q5v9qB9NdpRmSIh\nlUUza/b4O7O4e5vz+Rxyv7b3nefFYh6zmGPO7FnlLq8NTdN5/qn/K9dZzKfJ59L8358fgUAmFotx\nySWXUhOqojFWhx/xmT8jPrgxgiAI+7lJSzCWLFnCzTffPOzvV6xYwR/+8IfJWv0+ZegKjr3ni57h\nhEM6qbyLogzeHZ7nYUSH3k2GEQI/CwxOTHYLKi5CjFCIoCcH2kjLlGjK8Bcv0WiEVGcGfcCdRV2b\nmMf5sXCot7vYxAiFdMx+MR7thZmmSpjenvZtMGKsdN1A8jMwoE+653mEouObK0DXZApuqV0TFfM+\nrutSHd+7OQwMTWGy7rvalkliD/3pB9L1yetmosgBirq7/rUvPktD0zTmzC2NdnTiSe/glpt/SrFY\nIByO7LGMrut8+YprWPPck8MmGCPJ2Tk2p7cS02Ko8uDt7rF6CNyhk04v8MnZOUAipkdRJHnIzwBy\nUohsrnJCO9vMo1fVksplKDpGxbJVs6bR3rGTx555hDkLD+GBP61i0bLDea3Yhuwp7OzKkJjRQk93\nO8+99E+mz13IP+77E/MWH07GL0CxgB/4dBV7MF2TjkIXsiTjBz4FpwhARAujSza2XvmkRKGIlLEY\nDdf3SFvpUpuNxJAxTOcy5EwNSZLoMJOkHYvY9OYR2w5QPXN6uQyqwc0/uoYgMJBVjSUnLOcjZ3wE\nVVHw7FJ5xzKpSkwbVbsFQRD2J1PSRepgU1tdReq1XWjh8d1pikVjJFO7QEkM/qVnE4sO/TKrJElE\nwgqm66AoQ18QulaRqprdfdVlWSYaUrB8F1kefve7jk1dYvi7l5quY6gBnu8j975M6dp2uTvZ3nCs\nIk2NVWi5AjnTRdH2/jCNRWMk0+2gxHu3bXTtrK6qYvvOLrTeJx9DcW2T6qbhL3glSSIaVikO2E+S\nZxGNNox+I/pJJBJkdnYiKcaI+2lc3L2/qKmtjrOtIz0oAd1bvu8T0UEb4yRutYkwHSmzYvLIieDY\nNvFwZeK4q207TU2777iHwmFisQTtO3cwe+6CUZcZV3t8l9+su5WOYje6rDE93oIsVSa/ZsGikJVR\nB8TCD3za851k7dK7bnE9TmOkno5CV8VnTdEGZEnGyKhY9u6bAJ7roipFduyMkEsXcP3ooPqOOfdd\n3PKrn+DaDvG6Gua/eyV3/vPPbFn9FItOfyuGmuDwM07n7j/+As+2idbWcuR7/o2XU5vZ/ORTvPzP\nx3BMC992uP5bn6OudQZzTj2RvFN6DyyqRak1VOLe7vPVsQokqmU2te35mPEDn22ZHSStFAA1oWpm\nxFsHxbCQKeJ4UfzA5+XOV7AKMoYeYdl73j2o7f966e+sW72a43sHOln+7//G3X/8Ba5lIfkW0Vnz\nWPquYwlHI7zYvZbDGg4txdPziIbkQTN+C4IgHAjEN9cEUBSF2dMb6epJU7TdUu+QMT7NaK6Pk0yl\nsRyPwA+QJRldV2iojeO7xWGXq01ESKUyFM0cnr97+B2J0pOVupoYgW/Rf2LY2qrSMgWzd/Se3j7k\nrlrEK5goqkxNxCCkg+cMv+6GmijJVBrb9JAlidpYCEMLRlxmJFIQYGgKTY1VhMMhwuEQWjJFtmD2\njvCzd5NPtdbHSWez6GGN0Bja2VQbIpVOYzteb8ehyhjXVg2O8UA1iTBSKkOxmMVVwyiuQ2NdYsR9\nOxIZmNEYw3UcVD0o3fHcm/gEAbIkEdJVZsxo2uuJvsLhEDMaoSeVLR3TeztxWBCgyBLxkEZ93Z4H\nhBiouqoKWc6Szhax3b0/lghKTwYbqkK4buWNBcuy0PTKJ4S6bmBZ5pjKjEfaypDvPaYSRpxDahZQ\nZQy+cVEsFMlkLdzekeeg9OSjO9MGbu+oY0qYOjVKj7ez4rMZoTpieox4PEQ2a5b2jSIRDilUV5fW\nldTTdBU80laGolvappAaYtnhR3Ly0af2ri/P2p4NJGpqiEyrodqowit4zFm0jBWLjx20jxqOO4na\nN8wkY5dGjkrocWbGp/Nadht4pedliuLREmqgJd4MBOiKTKIqTCg0dLe3oeLXXewh75aeIETUyJAx\nTIey5C2NzmIXrxrbkWUX1ywwf96hHH3ZcZWVNsLyBUcC8Njf/49UMsmHLysN1ZvJplFDGpvTr2LI\nKjkzSa7QQ0xWSBgBDfVjP9YFQRD2ByLBmCCapjGtaegnDaM3dD/jPdrD6CijXaahIT6mkboAmDl4\nhuyJVFtTTe3QXaLHaQq7G/TGfFxxPgCFwyFawxP8dGUvJOJxEvGJ78++sztX8fNILzOPpcx4VBkJ\nQopB3ikwPTaNZY1L0IZ6UjnEOeX4Lv6WIuu6NwCwuG4ep8x6Mw+8SsVnp845Hk1WRzyOu/weakMS\nnWY3Vm9y0hprZmHtvHKXI9f36DZ7yDo5gsCnOpQgHo9xbMuyIbslub5H0GaxJbUVgLnV0zmm+Uj+\nuYuKz948YzmtTeO7MHd8lx35XeWkaE7VzCFj2EOKtCnRHG1kY3ITeaVINBbmxAVHYaiDu59u376d\nL3/58+zauROASy/4MHX19eXtktps8m6BqBphQct0JNeiYYzdAAVBEPYnIsEQBEGYQCO9zDyWMuOh\nySpvqF3I1sxrnDLzzUMnFyMse9qct3JM8xEA1IVrh/1sTyQJVFnh+JajWVJ3CMCg9xlUWWFlywpy\ndp6wGqLomsT06JDJRV/5oeob+JkijW9285FiMNT2ARiqzpkLTqe90ElTpGFQcuH7AT/60Q+46647\nIADDMLjs8i+Wk4uBcejbfndyp6cSBEGYdCLBEARh0vi+T0dXEtN28fzRD4Tg+m6/C65RfE0FpXkH\nIiGVhrrJufPr+C7dxdIEfkNdeJZeDs7QNG82nZ3tbFi3hkVvOIy/3L2K5UceW/F0YvGhy/l519VD\nlumrJ2fnCdi3F5qarNIcbdzjZ3uiazpe3kbVVOrCwz+CVGWF6lCp+9FQd/6HKj+wvv6f+f7uUfDG\nazTbq+s6bqbQ+y6azszE4MQwmUxx0UUXkEmX5kE59tjj+PrX/wvdGLyd/eMAoI4waIQgCMKBYNJn\n8p5IY+1WctsjmwE488R5k9Gcg87rpevOVHs9xXnzq23IenRM73S4vse9r/yVbjOJrqi0xga/qDwc\n3/OQpQItM2opFOw9LzBKfuCzObWFzmI3kgT1oTrmVc9BlmTa2wpooTDbs20krRSSBN6OPJvv/xeu\n7ZCor+PN555JLpniqXsf5LSPfwiAtpdf4bE/3YNr2+UyoXiUxx7+K6889iye5eBbLrGaahpnTuct\n5723ok2ek6euMTKonRuSm7A8m8MbDuUdc04Z01OMsdjTcbxxaxv6CIMjTAazmGfhrKbywBOT6eWt\nbSMO/uD7AWe/7z14vs8V37iSpcsOH1W9vu8T1TyaGkqDczQ0iGFqBUE48IgnGIIgTIpcLk8gG2N+\nYTxn50laKdzAJfACLM/CUEb3ki6yhGnKuK6DH4y/q8xARbdI3i3gBT4EUHCLFN0iYTWMH5TaWPRM\n/CCAAKIz63jnZRdXtDsUj3Lqxy4st6t5/mz+/QuXVqzH8iwajphHtHem86gWZka8FUMxBm2PH/iD\nPiu6RbzAI6qFyTl50laG+vDU9OVvaahiZ0cKFAN1jKN+jZXnuvieTUt9bJ8kFwCtjTXs6EiCrJe3\n75G/rea+++7h29/5HpIEP/3pjVTX1CDLEnu6lxcEAY5lEjEkmhrG8V6dIAjCfkQkGIIgTIpcwRzX\nhWVMj6IpOm7gsqB6LitbVgzbL38ovl+anMz3Jm7uC8d3uXfLg/1edl7EO+a8FU1WWSe3oYXCPNr2\nRL+XjWdzXMvRY2o3lJ7ejLoe12T29MquPI7vcv/Wh8nYWRJ6fMgRpPaVWDTKgjlR8oUCtm0zmc/K\ndS1ENFq716OfjUUkEmbB7DD5QoGOjnYuueQjPP/8swA89e63cfLJp1DV2teda88bL8sSscZ6MSyt\nIAgHBfFNJgjCPvXSmme45aafYpkm9Q1NXHzpF6mr2z0fiCoryG0mz91xD2sDlUcbmstlXNfl5l/9\niLVrniUIAhYvWc4FH/lUxUWZLMsEgQ9MXIKxp5d/9/RC82jtbT2arPL22SeRtjJUGYlJ6x41FtFI\nhGhk5NnkD2Q3/fpGrrzyP3FdF0VR+OIXv8J7z3rvnhcUBEE4iE39Xx9BECaF4zhksjlGc/d0/CRi\n0QiGMbouTKZZ5Pprv8UXvvYd5sxdyP333M6vbriWz37pyvJL3a5t8+BNv+PED7+P97/xXB76y5/5\n1Q3X8vmvXMW9d/6RTDrFd6+9EdfzuOqKy1j94D2ccuq/TeI2lgz38m/fPfOhXkAej9HXM/R+1WR1\nyrpFvZ74vs+b3nQsGzeuB2Dp0sP53e9uo6FhfJNnCoIgHExEgiEIB5kgCNi2s4OiDboRmtRuI0EQ\n0J1Joyses6Y373Fda198loamacyZuxCAE096B7fc9FNWv/J3skEOQwkhvVbAqI5i10o83vYkx7/l\nbdxy808pFgscsngpR688EVlR0BWFhYsOZWfbtknbvtEwtIl7UnIgrFcokWWZ6upqQqEQ3/veD3jf\n+86d6iYJgiDsN8RYeIJwkOno6sHFwAiFJ71PuiRJ6KEQgRphZ3vXHsvvattOU9PuCSVD4TDRWJwt\nr71Mt5miLb+L5zc/g1ETQ0Im7xZwZZ9YLEH7zh0sPGQJzb1zRSST3Tz/7BMsP/LYSdu+0aitimDv\n5QzcY2Wbeepqpu79iterp59+klNOORG7d5LEW25Zxfr1W0VyIQiCMIB4giEIB5mi5SKrkztqz0CS\nJFGwvT2WsywLTa+cB8AwDCSv1N2nKdKAoqcphjI0hOuIqhFiehRdN7D6XcRf+fXP8MrmDZz27vey\nZOmRE7sxY1SViCPLEulsEcf1d/eZmmBBECBLEoam0NJSh67ved4IYWLYts3HPvYR7r77zwBcccVX\nueqqq0kkRJInCIIwFJFgCMJBxvWDKTmxPW/Pw8IaoRCOXTk/hW1bzK2bQz7s8abWlTxWnaXQneKE\n1pXlmY1t26yYqO7rV15HoZDn59f/P/7wm59zzvkfnfDtGYt4LEY8tm/nfBD2jXvuuYtPfvKj5PN5\nJEninHPO41vf+u5UN0sQBGG/JhIMQTjIDHUDfU8jN422zHXfu4JsJs3Xvnnt4PWOojtWS+sM/vXo\n6vLPhXyOfC5HXVMzjtOFIivlMn0zG/eVaZrWytNPPMqsOfOpb2giEolywlvezqrf/2rKEwzh4HTB\nBedw3333AtDaOp3f/nYVixcvnuJWCYIg7P/EOxiCcJDrG7npoo9/nu/98CaWH7mSX91w7R7L3HjD\n90mZGVzfw/U9/v74g2zatA7bs9mSeo2CUyz/fjiu75EyM1iuTcrMsHDxUrq62lm79nm6i0luv+M3\nLDviGFRdw3YtPN9j8aHL6epqZ8O6NQD85e5VLD/yWEKhME8/+Si3//HX+L5PEAQ89/S/mDlz7qTG\nT3j9mjNnPrKscPnlX+TZZ9eK5EIQBGGUxBMMQTjIDTlyU++oTOFwZMgyx7/lbfzmph/zx5duIxSK\n4FkOj/7vKhqOWUD7mldZtelOFBSqjDi6otMSbca1i7zo7O7GlOrOsaOQw/ZsLM8ipIbQZI1jz3k3\n/3P9lTi2TagmxuFnvJWHnn6ATaufJHpplBOnH8+ln/06v/7FdZiWSVNzK5dc+h8AnHvBx/jfX1zH\nFz/7QQI/YPqM2Xz4ks/t44gKB6tt217lgx88jxtuuJH58xfwjW98k09/+rPU1dVPddMEQRAOKCLB\nEISD3FAjN/WNyjR77oIhy7iyjxY2yHenUaZpvLz6CeqWzEKr6k0gggBf8vACH8/3CIIAXVYxlN0v\nHgdBgNf7dMPtLeP5Hg1zWlnxsfeQdwqlcopKLFbH0Reeju055Ow8i5cczlXX/GLQtsTiCT75ua9P\neIwE4b/+6+v85Cc/xPd9Pv3pj3PvvQ8iy7JILgRBEMZBJBiCcJAbauSmgaMyDSwT06MomgpOQG0x\nSuHVLpZ95J3s3LwFCdBlHU3RmB6bRlyPs7JlBb5tsnD2tHId2yMdrN7+HFknR8pMUR2qJq7FOLr5\nCP6562m2pLYCMLtqJrIkU3CL5VGjBGFfee65Zzn//PfR3t4OwAknnMivf/27KW6VIAjCgU0kGIJw\nkBt65KbKUZkGllFlBdyA5uom1ty5mksu+SLNc2exwXmB+yI7OX3eO2iKNmB7zu6RngasV5UVVras\nIGfnCashiq5ZLnt8y9EsqTsEgCqj9DJ330zeqrx3E8gFwWTOXC4cTP77v/+L6667BoB4PMHPfvYr\nTj75lClulSAIwoFPJBiCcBBzfY+qhjp2Pbqj/Fn/UZn6DDW6k1UoYoTDvPbqK/z42itL9bkuplnk\nZ9/8Jt/+/i+IaLuTlKGoslIeDcpQ9YrP68I1FWX7yu0t3/dRFAV3z6PmCq9zRx65AkmSOOuss7nu\nuh+jquJPoiAIwkQQ36aCcJByfY+HX/s7PfEUbbu2sfrJB5iz8A089Oc/sXDpMnZZXWCVykZnNNDe\nsZO/PfUQsxcs5ME7bqN+4UzSusmF3/4aK1tWoMoKa198jtv/+Oshh6kdKB4Nk+0uDOqeNdkc2yQR\nb6Wnp7BP1yvs/3K5HOeffzbnnXcBZ555NqeeehobNrxKdXX1VDdNEAThoCISDEE4SOXsPNtzbXiB\nz6IzT2DVr3+J57jE62s47uzT+cfzf+P5+x7h5I+eC8Cx576bVb/+Oa7tEKurYt67j0GWJPJugZyd\nH/MThmg0gpbM4PsqsrxvRsT2HJeqiI6i7F03K+Hg8+tf38hXv/pFbNtmw4b1nHnm2QAiuRAEQZgE\nIsEQhINUTI+iSgoSMocddhQfeMsFKFLlKX/Kke/Y/UPDUt6x8nQAvMDlha61eL5X8eL14iWHs3jJ\n4aNuw6zWJrp7khQtG88PRjUZ33gEQYCiyFTFdWqqqyZlHcKBqa1tB+ec8++sX78OgEWLDuH3v79t\nilslCIJwcBMJhiAcpFRZoTnahCqrvGn6cWN+ebox0rDXL15LkkR9Xe24lhWEvXX77bfyiU9cjO/7\naJrOlVd+mw9/+OKpbpYgCMJBTyQYgnCQ6T+GkiRJhNTwuBKE/i9oj2q9YvQmYT9zzDHHIkkyK1eu\n5Oab/0AiMTEDCQiCIAgjEwmGIBxkVFnC9T1ydh4v8DDdIq7v7fXwr3uiKfvmPQtBGE7fJHmJRIKr\nrrqa1tYZbNiwVSQWgiAI+5hIMAThIOPJJr996Q7swMXyLGJ6jMfbniyPBDUp6/Q8oiFtUuoWhNFY\nvfpBLr74g2QyGRRF4T//80pCoZBILgRBEKaASDAE4SBT1AuYfheereEEAaqmkC5mSOaTEzbXRJ8g\nCPA9l3hYpbmxYULrFoTRKBQKXHjh+3nkkdI8Lo2NTdx88+8JhUJT3DJBEITXL5FgCMJBJqZFqaqJ\nEdUiFItF6kIGVYbBguY6VHliT3lFljEMY58NQysI/a1du5a3v/1ELMtClmUuueSTfOMb3xTHoyAI\nwhQTCYYgHGRUWWV6vIUF1XNZ2nAoeadAlZFAm+DkQhCm2sKFCzEMgxkzZvG7361i1qzZU90kQRAE\nAZFgCALZbJaCaQOTM0cDvTXHYmEi4fBe1+X4LmkrM6qkQZVV6sNimFjh4HHNNd/hiSf+xR/+8CdU\nVeW559YTi8WmulmCIAhCPyLBEF63HMfh1e0dSGoYRZv8F5ST7VkMNU19/fgvhhzf5Y5N99BR6CSs\nRjiycSnKgBe3e8wk27NtZOwsncVu3j77JPH0QjjgrV+/jnPPPYvt27cB8OKLL7BkyVKRXAiCIOyH\nxFWH8Lq1qzOJGo7vs/UZoRC+79PR2YMs6eOqI21l2JrZRtE1gRQQEFIrX2Y1XRPbd1AkmYydJW1l\nxFMM4YDl+z6XX/5pbrnlZoIgIBqN8sMf/pQlS5ZOddMEQRCEYYgEQ3jdKtoe+j4eaEaWZfJFm3hk\nfAlGlZFAlzVMTBbUzOONLccMenHb9V0ea3sCN3BJ6HGqDDFMp3BgMk2TZcsOIZnsAeCd7zydG264\nEV0f3/kjCIIg7BsiwRBet3x/amae3pvVarLKgpp5dBS6OHPBu4ft+nTGgneN+j0NQdhfhUIhpk2b\nhizL3HTT71ix4pipbpIgCIIwCuLKQxD6eWnNM9xy00+xTJP6hiYuvvSL1NU1jKrMbX/4X/563x3E\n41Xlsu/7wEWsOOaEiuX3Nq1RJJmwGhoxcdDEy93CAeq22/7A//zPtaxe/RiyLHPvvQ8RCoXE0LOC\nIAgHEJFgCEIv0yxy/bXf4gtf+w5z5i7k3rtv5Wc/+X9c/uVvl2fAtswiP7r2W3zhq99m9tyFPHDv\n7dx4w/e5/MtXEQBvPfU9nH7WeaStNABVRhV+UJlS+IGPH/jjbqcX+BRdE8d3xdMJ4aDR2dnJueee\nxfPPPwvAjTf+jIsu+hiRSGSKWyYIgiCMlbg6EYRea198loamacyZuxDX90jNDFh707Pc9Nxvaalt\nRZZktr+0EaM6ykZlBxtf3YEzP8qam57kng33sTm1hYCA3667laydBSCux2mKNiBLu+++qlhEqsd3\n6vmBz/ZsG47vcv/Wh8UIUcJB4Qc/uIbvfve/8TwXVVX5yle+wUUXfWyqmyUIgiCMk7gyEYReu9q2\n09TUAkDOzmPKDlrEwErliTVFCashXk0VqWtspC5UU1ooBKFoFDnrElHDbF2/gW3rNmIVilQvaOEN\npxxPTCst28fQXGKR8Q2Lm3cK7JIVYnpUjBAlHBTe/ObjWLv2RQCOOOIofvvbP1JXVz/FrRIEQRD2\nhkgwBKGXZVlovaPTxPQoqqyiaCoNWi3HtRyNKits119ETrisaF5eXu6WUIz5sdk0L61ldv1sosta\n2dyxied//wCZp17luA+/v9zFCqA6LlMdHd/Y/Y7vIvUOPytGiBIOBocdtpQtWzbzgx/8mDPOOHOq\nmyMIgiBMAJFgCEIvIxTCsW0AVFlhWrQJXJ8jpy8vJwj9y/SxbZNQKMyiNxzGkYDreyxrWsI8s5nV\n99xZkVzsLU1Wefvsk8QIUcIB65//fIxPf/rj3HnnfTQ3T+O6637M1Vf/gFBoH48ZLfx/9u47PIqy\na+Dwb2s22fQKCS1BIiUCoQsIglH88LWDgjRfioKAikiRIiJNEUSKYAGUEpo0pRkUEBERaQKhgwFC\ngPS6yfb9/sjLSihJQFKAc19XLrI7z8ycZ7Ihc+ZpQghRYmRaDnFfs9ptpOalk2RIwd3fh8uXE5zb\nzHlGTLlGKlQIcb4XHFKZxKvK5BpyMOTkEFQxhMuXEsjNNaBWqvBz9cFN7YpafecTgCszRElyIe4m\nRqORbt068cwzT3L2bByjRg0H8teGkeRCCCHuLZJgiPuWzWFj89mtrDixliXHV3JYE0fC5fNs/mMj\nx6CLW1kAACAASURBVFJOsvvnn/EPr8z+tFisdhsAtetEkpKSyIljhwHYtH4lkQ2bodO5smr5N3y3\ndB4OhwOz2czWzeuo36BZWVZRiHLh++9X8+CD1YiJ2YhCoaBHj5589dU3ZR2WEEKIEiKPQMV9K9ts\nID77Ink2IzaHDYfCQfjzLfl+8QJsFgsabz31XniMc3+fYvu8FYwa8ylaFxf6vz2aBXOnYzQZCaoQ\nwuv9hwHQ9b/9mf/Fp7w7sDtKpZJ6DZrS/pmOZVxLIcpWnz6v8v33qwGoUqUqS5asJDz8wTKOSggh\nREmSBEPctzy0erQqLWa7GTVq3NQ6qtSrSe8nXgdgf9IhLHYLHhp3OrV8xblf7Yj6TJw697rjeXn5\nMGjYuFKLX4i7Qdu2j7Fhww8MGfIegwYNKetwhBBClAJJMMR9S6VQUUEfgE5dmTq+NVEpVXi5eDoH\nZbep3JIcs+F/M0rduYHaQtzL4uL+pkuXjowb9xGPPfY4nTt34/nnO8o4CyGEuI/IGAxxX1MqlOg1\nbgTq/fFz9SmQSKiVKrx1nnc8uVDc0aMJUT7Y7XZGjBhCs2aRnD59ijFjRji3SXIhhBD3lxJtwZg4\ncSIHDx5EoVAwYsQI6tat69wWHR3NDz/8gFKpJCIigpEjR5ZkKKKcSk5NI8tgxmJzgMNRoufSaVX4\neunx9Mhfg0KtLpv8Wq2SFEPcW/bs2U2PHq+QkpIMwGOPPc68eYvKOCohhBBlpcQSjD///JNz586x\nfPlyzpw5w4gRI1i+fDkAOTk5zJs3j82bN6NWq+nZsyd//fUX9evXL6lwRDmUlJJGdh6otG6UVgek\nxNRclEoF7no97joNdpu9lM6cz2I24e0ZgMVcdFkh7gaffTaViRPHAuDl5c3cuQto3bpNGUclhBCi\nLJVYgrFr1y6ioqIAqF69OpmZmeTk5ODu7o5Go0Gj0ZCbm4ubmxt5eXl4eXmVVCiinMrKNaPWupXq\nOTU6HZlZubjr9VQI9EN52oDZqMWYl4uiBDsvOXCgVoGvhw5vLw+Sk7NL7FxClKYnn2zPJ59M5KWX\nOjN16gyUSul5K4QQ97sSSzBSUlKoU6eO87Wvry/Jycm4u7vj4uJC//79iYqKwsXFhaeeeorQ0NCS\nCkWUU1arHbW29M9ruarVwtPXjQA3Dx4I9sdRgl20lEolKpUMFBd3v4yMDLp2fYmoqLa8/fZwatas\nRVzcJbTaMvhlFkIIUS6V2ixSV9+85eTk8OWXX/Ljjz/i7u5Ojx49OH78ODVr1iz0GAEBHrd0Tjc3\n7W3tdz8rzWt1Oc0NVzd9gfcOHtjLvC9nkJeXS2BQRQYNGYV/QFCxy/z+2y988/VMbDY71R8IZ9CQ\n0bjp3Que2KZ01lN3Tote70LFij4lV9EbkM9kyZNrfOfNnDmTwYMHY7FYOHo0lnHjxkmLhRBCiOuU\nWIIRGBhISkqK83VSUhIBAQEAnDlzhsqVK+Pr6wtAo0aNiI2NLTLBuNVuJbm55tva734VEFC6XXfS\nM3Ixmv+5OTEa8/ho3EiGjPqI0LBwYjasZtonE3l3xMRilUlKvMSszz7m7VETqFa5OssWfsG2rVtp\n/shjBc7rsOaRrM+vZ26ukSRrOhcT09EoSyffLu3rfD+Sa3xnxcefo1OnFzl16iQAdepEsHlzDKmp\nhjKO7N4nibIQ4m5UYo+eWrRoQUxMDABHjhwhMDAQd/f8J8khISGcOXMGo9EIQGxsLNWqVSupUMRd\n4mjsAQKCKhIaFg5A67b/x+FDe8nLyy1WmR3bN1Ohdhh7jUdY/3cMkf95DO9alTiacuKar2PsvXyA\n3Rf3Ep99kZPpZ4g5uxWL3Vom9RaiPNuy5ScaN67HqVMncXFxYdq0WWzb9jvBwcFlHZoQQohyqsQe\n2TZo0IA6derQqVMnFAoFY8aMYfXq1Xh4ePD444/Tq1cvunfvjkqlIjIykkaNGpVUKOIucfniBYKC\n/rlp0bm64u7uSeKlBKqF1SiyTFzcSYxqMzvnr8aUkUPFGqE0efZJ1FpNgfPYLAYydakYrUbMdgse\nai1Z5mwyTVn4u/qWTmWFuEu0aPEIrq5uNGrUmAULluLmVroTMwghhLj7lGifkHfffbfA66u7QHXq\n1IlOnTqV5OnFXcZkMqG5ZqCoVuuCyWQsVhmz0UhaUgIPdXucML8w9kRvIOPPOJ59qWuB8g5rHlVC\n/LHarfxyYSdmmxlPrQdeLp4lVzkh7hJWq5U33uhDTk4OS5Z8h06n49Sp86jVpTZkTwghxF1O/mKI\ncsNFp8NiLrhAhNlsRKdzLVYZvZs74fXrUcm/MlFhbfB7Usu6tUvx6Nq3QHmHUoW3S/60yM9Wb0+m\nKQsvF89SG4MhRHm1efOP9O3bi5ycbBQKBcnJyQQEBEhyIYQQ4pbI9B+i3AgOqUzi5QTn61xDDoac\nHIIqhhSrjF9AEBajCRe1C2qlCuX/vgqjUarxd/WV5ELc17KysnjuufZ07foSOTnZVKwYzJYtO5wT\ncwghhBC3QhIMUS6YrGb8QyuTnHyZg4f+JD4rgUUrvuTBevWwKe0YrUaMViNhNWuRnJzI4dh9GK1G\n1v2wjLoNGoNaQWTT5pzad5D01BTMVjO/bN1InYcalHXVhCjXLl++RO3aYfz++28olUrefvtdDh48\nTkRE3bIOTQghxF1KHtuKMmeymlh+Yi0mu5kHnmvBnC8nYzaZ0ProqfZUY77e+jUXfj1M1GtdAGjS\n+Sm+/PITrGYLHv4+NH/5GX658Dt2nZ2qrevx0+zF/Kr5jnoRjXj6+c5lXDshyrcKFSoSHByCTqdj\n2bLVBAeHFL2TEEIIUQhJMESZSzdmYrKbUSvU+IdVwv31Z8kyZ2F32FEqlHj6+9BhwBu4afLHYgTX\nq0CDes2uO06uJY+wh+tTs0Vj3LXuNKvQqMD4DSFEvgkTxrJp0wZ+/fUPlEolv/++T8ZZCCGEuGPk\nL4ooc3qNGxqFGq1aS4h7MDa7lSMpxzHbzbiotYR5VaVRhfqoixhPYbXbsNgtGKy56NVuuGv1hZYX\n4n4TG3uIV17pyOXLlwDYuvVnoqKekORCCCHEHSV/VUSZUfzvX5VSTaA+AD+dDw2D6gMQ4VeLbHMO\nHlp3fF19ikwuANRKFQ8HNybHbMBdq7/5Po47VAEh7hJWq5U33+zHypXLAXB39+CLL+YRFfVEGUcm\nhBDiXiQJhrgpm81GWkYmZrMNFAqsdivppkxMVhO5llw8XTydC9OlmzIB8HHxQl3MGZlSUjPQ5lrz\nB3AbTDhc8u/81UoVgXp/AvX+txyzWqnCW1f4ehZKpaLQ7ULcS+x2Ow89FE5qagoAzz/fgc8//0pa\nLYQQQpQY+QsjbijPaCT+UhoanR6FQo3VbuPn879xJj0OgzUXyJ/iNVhfAQUKUozpKIAAVz8e8A5D\nqSx6grLUnCxUJncsNgtJOXmkZJ7CbDXTvFLTYrVY3A6Hw4GrtmSOLUR5pFQqadSoCX/9tY9Fi1ZQ\nv35kWYckhBDiHicJhrihlLQstK7uztc5ZgOXchIx2Uw4cKAArA4rycZUlCgx2/MXv0s1pqHN1qBV\nuxR5DovKQnZaKgq1GjsO3HSepGRkkBNoKLIV4nZYrVYUNiMBQRXu+LGFKE+ioxfy8ccT+PXX3Xh7\ne7NgwZJiJf1CCCHEnSAJhrihPLMNre6f1+5aPa5qHZkoUSnyWwC0Ki3h3tVRKpScz85f/K6aZ2Wa\nVGiAqpgtEHa7nfTMTPZfPozJZkZjd+DrokGtsNzR+ihw4OOpxdOjIgqFdJES96bLly/RqdOLHD0a\nC8DMmdMYPXqsJBdCCCFKlSQY4obs9oIjodVKFaFeVdCpXWgcFInRanIOwAbINGUB4OXiecvdm/QB\nblTwCyTHbMAVqFIx6I7UQYj7yeTJE5k27RNsNhsajYaxYyfQu3ffsg5LCCHEfUgSDHFDN3rKn3Dy\nb7atXsMO+1ICAirQp/9Q1Pr8ZMLvf4nGkcP7WbLwC0xGI/4BQfTpPxQ/vwBmfzaBuL9POo+Vm2sg\n/ME6vDVkLPDP4Gy7Ja8UaifEvaVduzYcOLAPgKZNH2bRouV4e3uXcVRCCCHuV5JgiGIxGvPY9M0i\nHu39Mp1bvMJPG9fwzZfTeHfExAJlPp82niGjPiI0LJyYDaudZd54e2SB430yYTiPtGlX2tUQ4p70\n9NPPcfLkCWbN+pKnnnq6rMMRQghxn5OOuaJYjsYewMvfF79KFQFo3fb/OHxoL3l5uQXKBARVJDQs\n/KZlAA7u343FYqFBo+alVwEh7iHbt28jIuIB9u3bA8CAAW9x5swFSS6EEEKUC5JgiGK5fPECXv7/\nrEuhc3XF3d2TxEsJBcoEBQUXWgZg1Ypveb5j95IPWoh7TG5uLp06vUDHjs+SlJTEpEnjnNtkILcQ\nQojyQv4iiWIxmUyoNAV71Gm1LphMxgJlNFptoWWOxh7A4YBadeqVbMBC3GO++24ZNWtWY+vWn1Eo\nFPTp05cVK9aWdVhCCCHEdWQMhigWF50Om8Va4D2z2YhO51qgjMVsLrTM7zu28HDLtiUbrBD3mEGD\nBhAdvRCAsLDqLF26itDQsDKOSgghhLgxacEQRbLabXgF+JGenIzZasJqt5FryMGQk0NQxRBnueCQ\nyiRe/qc71I3K/LV/N/UbNC3V+IW4273ySjd0Oh0ffDCeP/44IMmFEEKIck0SDFEoi83Khr83c9o1\nkfSUZE6cOMIfl/awYd13RDZsVqB1onadSFJSEjlx7DAAm9avLFAmMzOdrMx0KlSsVCZ1EeJucfr0\nKRo1eog5c2YB0LhxU86evcwbb7xZxpEJIYQQRZMEQxQq3ZhOUl4KRsyEPteM0xt3sWTcFE6eiKVH\n77c4c+oYH48bCoDWxYX+b49mwdzpvDOgK6dPHaNH77ecx0pLTcbD01sGowpxE3a7nXfffYsWLRpx\n/vw5vvpqtnOb/N4IIYS4WygcDoej6GLlQ3Jy9i2VX7X9DAAvtq5eEuHccwICPJzX+ETcJVxc9aTl\nZfDDmU1oVVrs2PHVeeOh8eDh4Ma3vGJ3cdgteYRVvrdX8r76OouScTde499//43//rcr6elpADz5\nZHvmzl2I9pqJE8qLu/Ea340CAjzKOgQhhLhlMshbFMrmsBGoD6CSewgP+lQnz2rEXasvkeRCiPvV\n/PlfM3z4YAB8fX359tslNGsm68QIIYS4O0mbu7ghhSL/X7PdjFKhxFfnjYtai7fOs4STi7umQU2I\nO+aFFzri7u7Bq6/24ujRvyW5EEIIcVeTFgxBtjmbREMKFpcgsvJMABgsOeQ4LCRkX8JoNaFSlE4u\nqlVLy4i496Wnp9G5cweCg0OYP38R3t7enD4dL+MshBBC3BMkwbjP5VnymHPwG4w2M25xLlRwrYBS\noSQ700B8Wha5jvxF8tzUOgLc/Eu09cJiNOIfoC+x4wtRHsyaNZ2JE8ditVo5cuQwZrMZrVYryYUQ\nQoh7hiQY97nLuUkYbWZcVFrcXfRU9gjBQ+tOtnsOybm7ycnKReFQkZWTRmpWEt4uniUQhQOdVk2Q\nnxvuekkwxL3pzJnTvPJKB+Li/gagfv0GREd/V24HcQshhBC3SxKM+5xWqUGr1KDXuPGgfxjNA5qg\nUaqx2K1kmLI4lnoCgHC/ijQIrY5GKR8ZIW7VX38doF27R3E4HOh0rkydOp2OHTuVdVhCCCFEiZC7\nxfuc1WGjkkcwD/o8wOO1HiYjLb9LlEappn1oFE0rNADAz9VXkgshblPduvUICQmhZs06zJ+/CJ1O\nV9YhCSGEECWmWHeM6enpXLhwgYceegi73S59hcsJk8lEWkYOFpsNUBR7P6vdSrbZgIdWz7msSyQn\nZxOmgAsJqaSl515TWoECB2m5mfj7eqNSySBsIYpiNpt5/fWe/P33abZv/wOlUsnevbHyf6cQQoj7\nQpEJxvr165kxYwZarZb169czbtw4ateuTceOHUsjPnETOQYDCcnZuOjcQKkp9n5Wu42Y87+SZkxH\no9RgtVvJsxmJzTjPg1VqY1e63HC/XKuDv+MTqRYSgEZT/PMJcb9Zv/57Bg7si8FgQKFQcPz4MWrW\nrCXJhRBCiPtGkX/xvvnmG77//nt8fHwAGDZsGCtWrCjxwEThUjMM+cnFLcoxG0jKTcFoM5FpysJg\nMaBSKLHarWSZcm66n0KhQKNzJyUt89+ELcQ9KyMjg//85wl69uyGwWCgUqXKbN/+BzVr1irr0IQQ\nQohSVWQLhoeHB66urs7XOp1OnmCXA0azFRfXostdy12rR6vS4LA5CPerDkCeNQ+9Wo+nizvZJlOh\n+5ssttsJV4h7Wm5uLg89VAOTyYRKpeKdd4YyZMh7ZR2WEEIIUSaKTDB8fHxYs2YNJpOJI0eOsHHj\nRnx9fUsjNlEIxw0WvD5yeD9LFn6ByWjEPyCIPv2H4ucXUKDMiSMH+XPeGsxGE4kVq9HrjXfR+ruh\ntiuZOXUisYcPYrPaeLHTq7Rs9XixzivE/c7NzY2GDRuTlZXJ0qWrCQoKKuuQhBBCiDJTZBepsWPH\ncvjwYQwGA6NGjcJkMjFhwoTSiE0URlFwULfRmMfn08bTu9+7TJm5kMiGD/PNl9NuWKb1y8/z8shB\nNGjUnIVfz8Bb58n6VUsw5hmZ/Nm3jBr3GcsWfUVS4qXSrJEQdw273c6YMSOoVSsMozF/5rW1azey\ndetOSS6EEELc94pMMHbs2MH777/Phg0bWLNmDaNGjWLjxo2lEZsopnRjBht/+x4PPx/0Qb6k5aVT\nt3kzDh/cy8W0i6TlpZOWl87ufTvwCQjAOyQIk81EizZPcPjQXvLycjl8aC9R7Z5CqVTi5xdAwyYt\n2L9nZ1lXTYhy58CBfdStG86cObNITU1h1SoZkyaEEEJc7aZdpI4ePcqRI0eYP38+eXl5zvetViuf\nf/45nTt3LpUAReFyzbksPvYd8SdiyXO1sj5uMxX0gSgVSjRuOn459gu+IRUAOHpqL3hqOZ91AQdw\nQB2Lu7sniZcSUCgU2O1253F1OlcSLyeUUa2EKH+sViv9+vXm++9XA+Dp6cXXX39DmzZRZRyZEEII\nUb7cNMFwcXEhNTWV7Oxs9u3b53xfoVAwdOjQUglOFO1SbhIWuxW71YZSrUKtUBLiXgG9Ro/ORUeQ\n1p+q3qEAJKhisbh64KZxxUXlgsGai1qjwWQyElG3Ieu/X8mA6hFkZaazd/dv1KxTr4xrJ0T50bBh\nBJcuXQQUvPxyZ6ZPny1TzwohhBA3cNMEo3r16lSvXp1mzZpRv379AttiYmJKPDBRPAE6PzQKNSqN\nBovNQRXPytTxq4VaqcJhtfGAf5gzwTjpXZG81Cwq6itgsOaiV7thMZvR6Vx5rkM3li/+ghGDexNU\nIZh6kU1Qq2XlbiGueOGFjqxZs5Lo6O+oUyeirMMRQgghyq0i7yADAwOZPHky6enpQP4Ktbt376Zd\nu3YlHpwomlatJcy7Gh5hCk6e/ZOWIc1QK1XkGnIw5OQQVDHEWTY4pDK7d27j4eDG5JgNKK0wz5Bf\nRqdz5e13R5GeYQDgq88nSwuGuK998808xo9/n02bthIe/iBjxoxjzJhxZR2WEEIIUe4V2b4/dOhQ\nvL29+euvv4iIiCA9PZ3JkyeXRmyiGGx2Gxa7hdAHa5KRmsqZE0cB2LR+JZENm6HT/bNYRu06kaSk\nJHLmxFG8dZ78tGG1s8y6NUuZ+8V0ABLiz3Lk8H4aNm5RJnUSoiwlJMTzyCNNGDZsENnZ2Xzxxedl\nHZIQQghxVymyBUOlUvHaa6+xY8cOunTpQocOHXjnnXdo3rx5acQnCmG129h+YSfnsy5wUamhVdcX\n+HbudEwmI0EVQni9/zDOnDrGymXfMGz0ZLQuLvR/ezQL5k7HeFUZgFZt2vHFzIkMeqMLWq2W1wcM\nR693L+MaClG6JkwYy8yZ07Db7Wi1WiZMmEyPHj3LOiwhhBDirlJkgmEymbh8+TIKhYL4+HiCg4NJ\nSJDZhcqDHLOBVGMGduw4sONW2Y9hEz7FW+fpLOPt48uw0f+0ONWOqM/EqXOvO5aXty8fTZ3j7CIl\nxP3mhRf+w2+//QpAy5atWLhwGe7ukmQLIYQQt6rILlK9e/dm165d9OrVi2effZZmzZoRGRlZGrGJ\nIrhr9eg1OpQocVXr8NZ54a7Vl3VYQtyV3njjTby9vVmyZCWrV6+X5EIIIYS4TUW2YERF/TPH+59/\n/onBYMDLy6tEgxLFo1aqCPepgVKhokFAXYI9KqJWqso6LCHuClu2/ETfvj0ZN+4jOnXqQlTUE5w8\neb6swxJCCCHuejdtwbDb7Sxbtoxx48axfv16ANRqNVqtlrFjx5ZagOImHA4AlAolOrUOb5136SQX\nipI/hRAlKScnhxdffJrOnV8kMzOTb7+dV9YhCSGEEPeUmyYY48aN488//6Rq1aosW7aMRYsWsWvX\nLp555hl0Ol1pxihuwEVTNi0VLmppIRF3r8WLv6VWrVB27NiOUqmkf/+3+PHHrWUdlhBCCHFPuWkX\nqWPHjrFs2TIAOnToQJs2bQgJCWHatGlERMgiU2XNx1NHUoaxVM9pNuURFCjd48TdacyYUcyZMwOA\n8PAHWbp0JZUrVy3jqIQQQoh7z00TDI1G4/zezc2N0NBQoqOjUamK/wR74sSJHDx4EIVCwYgRI6hb\nt65z26VLl3jnnXewWCzUrl2bDz/88DarcH/y9vJCqczmdKYBiykHm9mAVVHkmP1CWS1gNefecJtO\noyIwwBNXV2m9EncXu92OUqnk9df7sXTpIoYNG0mvXq+VdVhCCCHEPeumCYZCUbCzvVarvaXk4s8/\n/+TcuXMsX76cM2fOMGLECJYvX+7c/tFHH9GzZ08ef/xxxo4dy8WLFwkODr6NKty/PD08CAzyIkOb\nQrXKAfjovP/V8QICPEhOzr5D0QlRto4ePcorr7xI69ZtmT79c4KDQzh58lxZhyWEEELc826aYCQl\nJbFy5Urn6+Tk5AKvO3ToUOiBd+3a5ZyBqnr16mRmZpKTk4O7uzt2u519+/bx6aefAjBmzJh/VQkh\nhLjCbrfTs2dPvv32WxwOB5s3byrrkIQQQoj7yk0TjMjISPbt2+d8Xb9+/QKvi0owUlJSqFOnjvO1\nr68vycnJuLu7k5aWhl6vZ9KkSRw5coRGjRoxePDgf1OP+5bZaibTlInRWrrjMYQoj7Zv30bv3j3I\nzMwA4Omnn2POnOsXlhRCCCFEyblpgjFp0qQ7eiLH/6ZVvfJ9YmIi3bt3JyQkhNdee41ffvmFRx99\ntNBjBAR43NI53dy0t7Xf3cJgymXDzs2Y7RaWnV7NqNYDcdO6/atj3qvXqryR63znrVq1io4d8x98\nBAYG8sMPP9C0adMyjureJp9jIYQQN1LkQnu3KzAwkJSUFOfrpKQkAgICAPDx8SE4OJgqVaoA8PDD\nD3Pq1KkiE4xbHR+Qm2u+rf3uFsdST2Ky5dcxy2jgr7OnqOETdtvHkzEYpUOu8511ZRB3y5ZRhIRU\n4qmnnmHOnJmkphrkOpcg+RyXDknihBB3o3837VAhWrRoQUxMDABHjhwhMDAQd3d3IH/BvsqVK3P2\n7Fnn9tDQ0JIK5Z5VySMYjUKDWqHGU+NOJQ8ZJC/uH8nJyTz22CM8/nhrAJRKJQcOHGX8+I9QKkvs\nvzYhhBBCFKHEWjAaNGhAnTp16NSpEwqFgjFjxrB69Wo8PDx4/PHHGTFiBMOHD8fhcBAeHk7btm1L\nKpR7lk6to5ZfOK4qHf+p3g5XtUwhK+4Pn346mU8+mYTNZkOj0ZCenoaPj29ZhyWEEEIIipFgHD9+\nnBEjRpCbm8uPP/7I559/TsuWLalXr16RB3/33XcLvK5Zs6bz+6pVq7J06dLbCFlcTa1UE+DmL8mF\nuC+cPHmCzp1fJD7+PACNGjUhOnqFJBdCCCFEOVJkP4IPP/yQiRMnOsdPtG/f/o4PABfFk2XK5q/k\nWE5nxHE26zznsy5gd9gxWAxY7NayDk+IEpWQEM8jjzQhPv48bm5ufP31t2zc+LMkF0IIIUQ5U2QL\nhlqtLtDyEBoailpdYj2rxE1Y7FYWHVtBqjEdrVLjHG9xIfsiSbnJOIB21dqiUcrPRtxbrgziDgmp\nTMOGjQkICODrrxeg1WrLOjQhhBBC3ECxEoz4+Hjnyt7bt28vMOWsKB2ZpixyrXkAeOu8qOUbDkCO\nxYCb2pUsczaZpiz8XeVprrg3GI1GevXqzv79ezh8+BRqtZqNG38u67CEEEIIUYQiE4xhw4bxxhtv\nEBcXR8OGDQkJCWHy5MmlEZu4ipeLJzqVC3lWI5Xcg6kbkL+I4SVDIlnmbDy1Hni5eJZxlELcGatX\nf8egQQPIy8tDoVCwc+cOWrduU9ZhCSGEEKIYikwwNBoN69atIy0tDa1W65xqVpQujVJNuG91ErIv\nFegK1a5aWzJNWXi5eEr3KHHXS01N4ZVXOnLgwD4AqlatxtKlq3jggRplHJkQ4ooLFy7w9NNPExER\nAYDZbCY8PJwPPvgAlUpFXl4ekyZN4tChQ6jVavz9/RkzZgwVK1YE4OzZs0ycOJG0tDTsdjuRkZEM\nGzasTLs99u3bl9zcXBYuXHhL+w0fPpwjR47g7e2NxWIhIiKCwYMH4+rqCsDOnTuZOXMmDocDk8nE\nSy+9xCuvvEJMTAw///wzn3zyCQAbN25k/vz5rFy5EsifYGfMmDFMnTqVxx9/nDVr1ji7q69evRqA\nF1544U5V/zoxMTG0a9eu0Hq3a9eONm3K14Ofjz/+mBo1atz02sycORMfHx+6du3qfK9t27asW7eO\nmJgY50ynN1KcOhsMBvr168fMmTPx8vL6d5X5Fy5dusTQoUOx2WwEBATwySefFPj9stvtjBkzhlOn\nTqHRaPjggw+oXr06e/bs4dNPP0WtVuPm5sbkyZPZv38/v//+OyNHjrylGIoc5N2vXz86dOjANzDk\n3wAAIABJREFU+vXrMZvNt15LcceoFCp0al2BREKjVOPv6ivJhbjr2e12GjaM4MCBfahUakaN+oA9\new5JciFEORQaGsqiRYtYtGgRy5cvx2KxsG7dOgAmTZpEYGAga9euZeXKlfTp04fevXtjsViw2WwM\nHDiQ3r17s3LlSlatWgXA559/XpbVYd++fbecXFzxzjvvsGjRIpYsWYK3tzcjRowAICEhgXHjxjF1\n6lSWL1/OihUr2LVrF9999x2NGzdm3759Bc6fkZGBwWAAYO/evTRt2hSABx54gKlTp/7LGhbfhQsX\n2LBhQ6mdr7x44YUXbppcFNesWbN46aWXyjS5AJgxYwavvPIKS5YsoWrVqs7E9YotW7aQnZ3NsmXL\nmDBhgrNn0qRJk5gwYQKLFi0iMjKS5cuX06ZNGxISEjh06NAtxVDkXWlMTAyxsbFs2rSJTp06ERoa\nyrPPPkv79u1v6URCCHEjVwZxK5VKXnjhJY4cOcySJd/h5+df1qEJcc+wWG2kZhrx89KhUavu+PHr\n1q3LuXPnyMnJYceOHfz000/ObQ0bNqRu3bps2bIFNzc3wsLCaNKkCQAKhYIhQ4ZctzimxWJh+PDh\nJCQk4OLiwuTJk9m5cyenTp1i2LBhGAwGnn76abZu3coTTzxBq1at8PPzY+3atc5FftesWcPx48fp\n2bMnI0eOxGKxoFKpGD9+PMHB/yxM+9FHH5Gbm0vv3r2ZM2cO77//PvHx8ZjNZt58801atmxZ4Bz9\n+vW74TVQKpW88cYbtG/fnsTERJYuXUq3bt0ICQkBQKvV8t577/H666/TsWNH3N3dSUhIICQkhCNH\njtCuXTsOHDhAy5Yt2bt3Lx07dgSgTp065OXlsWvXLh5++OEifxYzZ84kKyuLuLg44uPjGTFiBK1b\nt2bz5s3Mnz8ftVpNREQEw4cP5/PPP0en09GrVy9mz56NWq1m7969HDp0iFmzZjFgwIBb+BTgfOJt\ns9no0qULgYGBxMTEMHbsWNatW8dXX33FunXrSEpKYvDgwSxatOiGx/nhhx9YvHgxSqWSGjVqMG7c\nOFavXs2+fftITU3l7Nmz9OrVi44dO/L9998zd+5cgoKC0Ol01Khxew+lrrRuvPzyywwZMoSLFy8S\nGRnJpk2b+PXXXwHYvXs3ixcv5tKlS0yZMoXatWs79zeZTMTExDiXaJg/fz4xMTHY7XZat27NgAED\nmDlzJvHx8Vy4cIFFixYxY8YM9u7di81mo2vXrvznP//h+PHjjB07FrVajVKpZPr06Xh7ezvP8913\n3/HDDz8UiP2NN94o8NnYvXs3Y8eOBaBNmzbMnz+fV155xbn97Nmz1K1bF4AqVapw8eJFbDYbPj4+\nZGRkAJCZmUlYWBgAXbt2ZeHChUyZMqXY17NYj70jIiKIiIjg1VdfZfbs2QwdOlQSDCHEv2K32xk5\ncijR0Yv444/9BAeH8OmnM8o6LCHuKjsPXeTMhYxCy9jsdo6fTcdosqJzUVOzmg+qQla7r17JmxZ1\ng2+6/VoWi4UtW7bQuXNn4uPjCQsLu262yVq1ahEXF4erqyu1atUqsE2nu34dp7Vr1+Lv78/UqVPZ\nsGEDW7ZsuWE5AKvVSqtWrWjVqhV//PEHp06dokaNGmzZsoWePXsyffp0evbsSfPmzdm+fTuzZ89m\n/Pjxzv2HDx/OmjVrmDt3LmvXrkWr1bJ48WISExPp3r07MTExBc5RGKVSSe3atfn777/5+++/eeyx\nxwpsDw4OJj09HbvdTtOmTdmzZw+enp5otVqaNGnCnj17aNmyJYcOHWLSpEmkpqYCMGjQIIYNG0az\nZs0KPf8ViYmJzJ07l19//ZVly5bRqFEj5syZw/Lly9Fqtbz11lvs27ePPn360KVLF1q2bMkvv/xC\ndHQ09erVIzo6+paTiz179nDq1CmWLVtGbm4uzzzzDGvWrGHatGkA7N+/H19fX7Kzs9m/f7+zheZG\n8vLymDt3Lp6ennTp0oUTJ04AcPLkSZYtW8bZs2d555136NChA9OmTWPVqlV4enoWq9vYwoULnUko\nQHJycoHtO3bswGQysWLFCrZt28aCBQuc2xQKBfPmzWPZsmWsWbOmQIJx6NAhwsPDUan+SeCXLFmC\nUqnkscce49VXXwXyf1+WLFnC3r17SUhIIDo6GrPZzPPPP09UVBSpqamMHj2a2rVrM336dNatW0e3\nbt2cx+zYsaMz+Szs+l3pEuXn53ddHcPDw1mwYAE9evTg3LlzxMfHk56ezogRI+jatSuenp54eXkx\nePBgIH/x7Cstc8VVZIKRlJTE5s2b+fHHH0lLS6N9+/b3ZdOZEOLO+fPPP+jRo7Pzj+eqVSsYOHBQ\nGUclxL3JaLJhNFn/970Vo8mG3rXIHtKFiouLc970nDhxgt69exMVFcXx48ex2WzXlXc4HKhUKhQK\nxQ23X+vIkSPOJ7JPPfUU8M/Ygxu58jT2iSeeYNu2bVSpUoVTp04RGRnJyJEjiYuLY86cOdhsNnx9\nbz7bYmxsrPPGNygoCK1W63yie+UcRTEYDCiVykLrqlAoaNq0Kb/88gs+Pj40aNCAyMhI5s6dy7lz\n56hQoYJzHAdAtWrVqF27Nhs3bixWDA0aNACgQoUKZGdnc/r0aS5evEivXr0AyM7O5uLFizRs2JB3\n3nmHLl268MUXX6DRaIp1/BuJjY2lcePGALi5ufHAAw9w/vx5tFoteXl5XLx4kccff5yDBw+yf//+\nQrsjeXl58cYbbwBw5swZ58+gfv36qFQqZ73S09PR6/X4+fkVqHdhunfvft0YjKudOXPGeZzWrVsX\nSJYbNmwI5H82Dh48WGC/pKQkKlSo4Hyt0+no2rUrarWa9PT06z5H+/fv5+DBg87fI7vdTnJyMn5+\nfkyZMgWj0UhSUhJPP/10kXUqzI1mfm3dujX79++nS5cuPPjgg4SFheFwOBg3bhyzZs2iYcOGfPzx\nxyxZsoTu3buj0+mcXRyvTqAKU2SC8eKLL9K+fXuGDRvGQw89dOs1E0KI/zGbzfTp04NNm/IfUnh7\n+/DNN4tp0eKRMo5MiLtTi7rBRbY2WKw2Vvx8ivRsIz4eOl6KqvGvu0ldGYMB8OabbxIaGgpApUqV\niIuLw2w2FxhUevz4caKiotBqtURHRxc4ltls5uzZs4SHhzvfU6lU2O32AuWuTJcP+a0WV7tyYxwV\nFcXbb79NjRo1eOSRR1AoFGg0GqZPn05gYGCx6nb1DZnZbHZ23yrOzbfVanW2oISFhREbG0ujRo2c\n2xMSEggICEChUNC4cWM+++wzvL29adq0KZ6enphMJnbv3n3Dp/v9+/enV69edOnSpcj1yK7drtFo\niIiIYN68edeVTU5OxtPTk8uXLxdZv8Jc/fOB/Cf1SqWShg0bsmvXLvR6PfXq1WP79u0cPXrU2ZXo\nWmazmQ8//JDvv/+egIAAXn/99ZvWCyjQve5OLKNwJRm+UZ2uvrm+0bmulE9ISODbb79lzZo16PV6\n/vOf/zjLXPkcabVaOnToUKB+AN26daNPnz60atWKefPmkZubW2B7cbpIubm5YTQa0el0JCYm3vCz\nP2jQPw/1oqKi8PPz48SJE84kqnnz5s5xVbfjpo8wkpKSgPympK5du+Lt7U18fLzzq7yz2WwYcrLJ\nzsokJTWtRL5S09LIzcsrsTo4HA4yMjO5lJTEyYt/k5qWQWpaOpeSk28pzuSUNNIzMor11EiIkvTI\nI03ZtGkDCoWCrl1f5fjxOEkuhChhGrWKl6Jq0KFtjTuSXFxryJAhTJkyhby8PNzd3WnTpg2zZs1y\nbt+/fz9Hjx7l0UcfpUWLFiQkJLB161Yg/6ntJ598ct2T+Yceeog//vgDgG3btvHFF1/g7u7uvDe5\neoD01YKCglAoFKxfv945C1K9evX4+ef8NXR27dpV6E3TQw89xO7du4H8mXiUSiWensWfAn7mzJm0\nbt0aX19fOnfuTHR0NOfPnwfyb7g/+ugjevToAeQ/pddqtezZs4fIyEgAateuzapVq26YYPj7+xMV\nFcWyZcuKHc8VoaGhnDlzxtlqPGPGDBITE8nOzmbBggWsWLGCuXPnkp2djVKpvC6BK46IiAjntTMY\nDJw/f56qVavSuHFjFi5cSN26dalZsyYHDx5Ep9PddNYwg8GASqUiICCAS5cuERsbi8ViuWFZb29v\nsrOzycrKwmKxsH///luO+1pVqlQhNjYWgN9++63Y906BgYHOJC09PR1fX1/0ej1HjhwhISHhujrU\nrVuXbdu2YbfbMZlMjBs3DoCMjAyqVKmC2Wxm+/bt1+3XsWNH5wQLV76uHZvTvHlzZzewzZs388gj\nBf/OHj9+nPfeew+AX3/9ldq1a6NUKvH39+f06dMAHD58mKpVqwL561Kp1epit15AIS0YH3/8MVOn\nTqVXr14oFIoCmZpCoWDLli3FPklpu3Q5maw8G2Z7/gCZHEvJzbCUmp2D0pFO1ZCAf9W0eK2MzEwu\npxpQaLRsT9hFpjmb5NxU1Co1P587wMPBjVEri/+DtpvtJGUk4+mqoWKQ3x2LU4iiXBnEDTB48BA+\n/ngi0dHfUbNmrSL2FELcKRq1igp++hI5duXKlWnXrh1z5szhnXfeYcSIEUydOpVnnnkGrVaLr68v\n06dPd96czJs3j/fff59Zs2ah1Wpp3rz5df3927dvz++//+7sYvLxxx+j1+uZM2cO3bp1o3Xr1tc9\nXb6ibdu2LFy40DkF7IABAxgxYgQbNuQ/3Jg0adJN6/LUU0/x559/0q1bNywWCx9++GGR9f/000+Z\nP38+GRkZ1K9f39lXPTg4mClTpjBkyBAcDgdms5lnnnmG5557zrlv06ZN2b17t3MJgIYNG7J69Wpn\nwnGtnj17snTp0iJjuparqysjRoygT58+aLVaateuTWBgIB9++CH//e9/8ff3p2vXrnz66acMHDiQ\no0ePMnHixEL73V+pN0D16tX54IMPiIiIoEuXLlitVgYPHoybmxsNGjSgX79+vP3222g0GnJzc2nR\nosVNj+vj40OLFi148cUXqVmzJr1792bSpEnOxOxqSqWSAQMG0LVrV0JCQm57gPfV2rRpw6pVq+jc\nuTNNmjQpMMC6MHXr1uXEiRPYbDZq1aqFXq+nU6dONGzYkE6dOjF27Fhn6wDkd+dq2rQpL7/8Mg6H\nwzkIu2vXrvTv35/KlSvTrVs3PvzwQ9q3b++cprg4Bg4cyLBhw1i+fDnBwcHOz9ygQYOYNGkS4eHh\nOBwOOnTogIuLi3Pw9tixYxk1ahQajQYvLy8mTpwIwIEDBwq0xBWHwlFEe9KZM2eoXr16gfcOHDhw\n0w9/SUpOzi6yTGZWFimZVlQaNVv3XQCgbcNKJR0aCmsuVStVKLpgMdhsNk6dT0Lnqic5N5Xvz2zE\nZreTZ8vDTe1GFc9KNKvQCG/drS+sZ7NY8fdS43WDJzIBAR7Fusbi37mfrvOXX37O+PEfMG/eIp54\n4slSO+/9dI3Lilzj0hEQ4FHWIQhxX8nIyGD37t20a9eOxMREevTowY8//lisfSdNmkS9evXuuYmQ\nBgwYwGuvvVbscUhQSAtGVlYWGRkZjBgxosC0VFemjrt6BH55km0wodK4lPp5jRYHDofjpk9UbkVa\nRiYuOjcA3NSuqBVqtGoFWpUWP503erUb7trbexKl0qjJNpjwkkW/RQk6d+4snTu/yOnTpwBYuXJ5\nqSYYQghxrxswYACZmZkF3nN3d2fOnDl35PiHDh1ytgJd7f/+7/8KTHla3o5/8eJFhg0bdt37jRs3\n5s033yxyf71ez6ZNm5g3bx52u93Zlag4Bg4cSP/+/WnRokWZr4Vxp/zyyy9UqFDhlpILKKQFY/v2\n7SxYsIC9e/fi7//PfPRKpZKWLVvywQcf/KuAb0dxnpadS0jCocqfyq40WzCMebnUqBJwS/3TbuZy\nUhpGe37uZ7Sa2Bq/A18XbyID65JnNeKu1d9S96hrKe0mqgQHXPe+PJEsHffydbbb7XzwwUi+/HIO\nDocdFxcdn3wyjU6dupRqHPfyNS4v5BqXDmnBEELcjW7agtG6dWtat27N0qVL6dy5c2nG9K8oFApu\nlDEdObyfJQu/wGQ04h8QRJ/+Q/HzCyh2mcTLCcyYOhZ3d0/eG1P8hUbuBKVCiZvGldPHYousg9Vq\nZXn012xa9x3Tv1x+3XYhStJ//9vFOUNUmzaP8c030bi5uZVxVEIIIYQoTTedRWrVqlVA/mIt06dP\nv+7rbmI05vH5tPH07vcuU2YuJLLhw3zz5bRil7mYcJ6pk0YS9kDxB9jcKTa7DaPVSHp2OrOKqAPA\ntI9HodO53uBIQpSMq2cbGTnyAypUqMiKFWtZvnyNJBdCCCHEfeimCcaVWV+uTEt17dfd5GjsAQKC\nKhIalj/Hduu2/8fhQ3vJy8stVhmtVst7H0ylRnjtGx6/pFjtNrbE/8r5rAvs2LsVVx8PKlerftM6\nADzXoRsvvvxqqcYp7l+bNm2gRo0qjB07GoDw8Ac5dOgEjz7atog9hRBCCHGvumkXqeeffx7IH0SU\nk5ODu7s7KSkpnD17tlgrJZYnly9eICjon4WIdK6uuLt7kngpgWphNYpdprTlmA1kGrOwYycvLQud\njzs5ZgPeOs+bxlfjwTplEqu4v2RlZdG160v88cfvAGzbtoUxY8aVcVRCCCGEKA9u2oJxxbhx49i0\naRMZGRl06tSJxYsXl8kA73/DZDKhuWZBF63WBZPJeEtlSpu7Vo9e44oSJUobuLoUnD2qrOMT96f5\n87+mVq0w/vjjd5RKFYMGDeWXX34v67CEEKXkwoULREZG0q1bN7p168bLL7/M6NGjnQuS5eXl8f77\n7/Pcc8/RoUMH+vbty6VLl5z7nz17ltdee40OHTrwwgsvMG7cOMxmc1lVB4C+ffvSvXv3W96vW7du\nnDx5ssB7VxbJW716Na1bt6Zbt2507dqVbt26ORcxKylFTae6evVqPv744xKN4XZs27aN4cOH33T7\n7t27r5sBavjw4Wzbto1jx44xY8aMm+5b3DoPGTKEAwcOFD/oEmCxWBg8eDCdO3ema9euN1zYetq0\naXTq1ImXX36Zr7/++qb7Xb58mZ49e950kcKSVmSCcfToUTp27MimTZt4/vnn+eyzzzh37lxpxHbH\nuOh0WK75z8tsNhYYq1CcMqVNrVQR7vMAVTwrUSvwQXw0XgVmjyrr+MT9Z+bMaQwfPhiLxUzt2nXY\nvz+W994bVdZhCSFKWWhoqHMV4eXLl2OxWJwrZE+aNInAwEDWrl3LypUr6dOnD71798ZisWCz2Rg4\ncCC9e/dm5cqVzvGen3/+eVlWh3379rFw4cI7ftz27duzaNEiFi9ezMCBAxk/fvwdP8fVvvrqqxI9\nfnlUq1atYk0/W5ht27bh6upaJmu8XW39+vV4enqydOlS+vbty9SpUwtsP3nyJLt372bZsmUsXbqU\n1atXk5ycfMP9KlSoQKtWrUrkc10cRS5xfWUW219++YW3334boMyfNFzLYrWTkpmH0WTl1Lk0PD29\n8PdyxeYAi8VKUIXK7N65zVk+15CDISeHoIohzveCQ4ouUxbsdhtWu5VKlUI5se+fzLq8xCfuD1ar\nFbVaTZ8+/fj223m89dZgunf/b1mHJYQoJovNQnpeJj6uXmhUmjt+/Lp163Lu3DlycnLYsWMHP/30\nk3Nbw4YNqVu3Llu2bMHNzY2wsDCaNGkC5M/8OGTIEOe4T2e8/1tzKyEhARcXFyZPnszOnTs5deoU\nw4YNw2Aw8PTTT7N161aeeOIJWrVqhZ+fH2vXrnWu07VmzRqOHz9Oz549GTlyJBaLBZVKxfjx4wkO\n/qdL9EcffURubi69e/dmzpw5vP/++8THx2M2m3nzzTdp2bJlgXP069fvtq5RvXr1Cn1Au3v3bqKj\nowGIi4ujXbt2DBgwgNOnT/Phhx+iUCjQ6/V89NFHnDhxgnnz5vHFF1+wd+9evvjiC5o1a8aJEycY\nMGAAs2bNuqXYNm7cyLfffotKpaJOnToMHjyYl19+mR9++IHExEQeffRRdu7cia+vL8888wwrV65E\ne02vD4Djx48zduxY1Go1SqWS6dOnk5OTw/Dhw6lcuTInTpygVq1aTJgwgRMnTjBs2DC8vLyoUqXK\nrV3MG1y3GTNm8NVXX7FhwwYqV66M1Wrlv//N/zuVlJTEwIEDOX36NL169aJDhw4FjrFgwQLn2hm/\n//4706dPR6PR4OnpyWeffcaBAweYP38+ubm5DBs2jIsXLzJ//nzUajUREREMHz6cnJwcBg8eTG5u\nLkajkdGjRxdYOyI2Nva6lpS2bds6YwTYtWuXc9Xt5s2bX7eSuoeHByaTCbPZjM1mQ6lU4urqetP9\nXnrpJZ599ll69ep129f3dhWZYISGhtK+fXt8fX2pVasWa9euLVeLh1isdr78PpaT8ekYTDYsRgM6\nvTuB3q7Y7Q5wgLZiEMnJiZw4dpgHaz3EpvUriWzYrMDT/9p1Ivk65ZNCy5Q2k9XMb5f2YLabyHbz\nKLIOQtxphw79RZcuL1GtWijr1sWg0+nYty+2rMMSQvzPH/H7+Tv9fKFlbHY7J1P/xmQ14aJ2Idwv\nDJXy5h0Ywnyq0Kxy8cdaWiwWtmzZQufOnYmPjycsLAy1uuDtRa1atYiLi8PV1ZVatWoV2KbT6a47\n5tq1a/H392fq1Kls2LCBLVu23LAc5D8AadWqFa1ateKPP/7g1KlT1KhRgy1bttCzZ0+mT59Oz549\nad68Odu3b2f27NkFWhKGDx/OmjVrmDt3LmvXrkWr1bJ48WISExPp3r07MTExBc5xu7Zt28ZDDz1U\naJlDhw6xadMm7HY7bdu2ZcCAAYwbN44PP/yQatWqER0dTXR0NP369WPVqlXs3LmT2bNnM2nSJKpU\nqcLXX399y8mFwWBg2rRprF27Fr1eT9++fTl48CDu7u5kZWWxf/9+GjVqxF9//UX9+vXx8fG5YXIB\nkJqayujRo6lduzbTp09n3bp1tGnThiNHjjBt2jT8/Pxo1aoVWVlZzJ49mwEDBhAVFcWYMWOKjPPP\nP/+kW7duztd///037dq1c77OyMggOjqamJgYcnJyeOKJJ5w37/Hx8SxdupRz584xaNCgAgmGxWLh\n5MmT1KyZP1NoZmYmU6ZMoXLlygwdOpTffvsNvV7PyZMniYmJwWKxMHr0aJYvX45Wq+Wtt95i3759\n+Pr60rFjR6Kioti1axdff/01M2fOdJ4nIiKCRYsWFVrHlJQUfH19gfzJlhQKBWaz2Xm9K1asyJNP\nPkmbNm2w2Wz079/fOUb6Rvu5ubnh5+fH2bNnqVatWpHX+E4qMsEYP348J0+epHr1/NmLHnjgASZP\nnlzigRVXRo6Jy2m5mG0OHPn5BA6bA0OuGY1GjYebBqNVwat9h7Ng7nSMJiNBFUJ4vf8wzpw6xspl\n3zBs9GS0Li70f3v0dWUAtsT8wI8bVpGba8CYl8uQN3tQ/YGa9H2z+Ks73o7LOYmY7SYALAobL73e\nr9A6ZGakMf79Qc79J4wZhEqp4r0xU/CV9TDELbBarQwY8DqrV38HgMlkdLZiCCHuLkarEZM1/2+J\nyWrCaDWi1/67KaTj4uKcN3snTpygd+/eREVFcfz4cedYjKs5HA5UKhUKheKG26915MgRHn74YQCe\neuopIL8v/c1ceVL8xBNPsG3bNqpUqcKpU6eIjIxk5MiRxMXFMWfOHGw2m/NG7EZiY2OdYyiCgoLQ\narVkZGQUOEdRFAqF8/uNGzcSGxuLw+EgICCAkSNHFrpv7dq1cXUt+ODw0KFDjB6dP1Of2Wx2JilD\nhw6lQ4cOvPjii/+qBeDs2bNUrVoVvT5/jGeTJk04duwYjRo14uDBg+zfv58ePXrw119/Ybfbady4\n8U2P5efnx5QpUzAajSQlJfH0008DUKVKFQIC8u9DAgMDyc7O5syZM85Jg5o2bcqvv/5aaJxNmjQp\nMNbi2jEb58+fJzw8HJ1Oh06nK/DzqlevHiqViqCgILKzCy4QmpGRgbe3t/Pn5uvry6hRo7DZbMTH\nx9OsWTP0ej0PPvggWq2WY8eOcfHiRWerQHZ2NhcvXiQ8PJzZs2czb9485839v3XtWtjx8fH89NNP\n/Pzzz1itVjp16kT79u0L3S8oKIhLly6VvwTDaDSydetWpk+fjkKhoH79+jzwwAOlEVuxeLu74KpT\nozUowKHAYQGdi5pa1fxQqxTkmqy4u2po8FAjmlzzS+Ht48uw0f8kS7Uj6jNx6tzrzvFYu2d4rN0z\nJV6XawXo/VEr1Ngcdtw1bjxcuxWPNokqUObqOnh5+/LJjAWlHqe4t/z882Zef70n2dlZALzwQkdm\nzfpSkgshyqFmlRsU2dpgsVlYc+xHMvKy8Hb15PlaT/7rblJXxmAAvPnmm4SGhgJQqVIl4uLiCjx1\nhfyuM1FRUWi1Wmc3oP9n776jq6rSPo5/b08PCaTQe0cQUJQiCIIgIo6K0lVUbGDhVVGKRhRQBFSK\nCioygiggYkPECurQRaRZgIChh4SQclNuf//ImDESEsAkN+X3WYs13FP2fs42Q85zd/uT0+nkjz/+\noEmTJnnHTCYTXq8333V/fXH/6/47ABZL7vP07NmThx9+mMaNG3PFFVdgMBiwWCzMmjWL6Ojoc3q2\nv76cOZ3OvOFbf9bxVxEREaSnp+d9TklJyXuRhtw5GH8OvTkXBf07GxgYyKJFi/I9P4Ddbsdms5GY\nmHjO5RfEYDDke2aXy4XNZqNDhw78/PPPJCQkMG7cOD744APcbjc9epx9GfIpU6YwcuRIunbtyoIF\nC8jKyl1K/+/bG/h8Pnw+X94z/f2/9YXw+Xz5htr9tb2K+v3112vHjx/P66+/TsOGDXnmmWfyjv/5\n82yxWGjVqhULFizIV8bcuXOJiYlh+vTp7Nq164wv489liFR0dDRJSUk0a9YMl8uFz+dtxFSBAAAg\nAElEQVTL9/+jXbt20aZNm7wktGnTpuzdu7fI+/yhyEneTz75JHa7nUGDBnHLLbeQnJzMxIllZ1Kn\nxWykca0qtG8azeND2jG4ZxOG9mpCj3a16NqmJp0vqk6Xi2pgNhuKLqyMsZqs1A+vS8uqTbmpcX9s\nZv/+sEjFt3XrZoYMGUBGRjqxsdX5+uvvmTdvgZILkXLMYrJwQ/M+/Kt572JJLv7uscceY8aMGWRn\nZxMSEkL37t3zDdP56aef+OWXX7jyyivp3LkzR48e5dtvvwVyXyynT5/O6tWr85V50UUXsWnTJiB3\naNG8efMICQnh5MmTQO6k7ILExMRgMBhYtWpV3vCZNm3a8PXXXwO5Y9z/nIxekIsuuojNmzcDcPz4\ncYxGI2FhYWe9vmPHjnzyySd5n99///1/NIyqIM2aNcv7dv+zzz5j48aNQO4Ik5deeomTJ0/y888/\nA2d+430u6tWrlzd/BnKHIrVq1Yq2bduybds2bDZb3rCbX375pdCenNTUVOrUqYPT6eS7774rdAWj\n+vXrs3t37pDbP9v8n6hZsyb79u3D5XKRkpKSV3ZRqlSpQmpqal7b2e12qlevTnp6Ops3bz7jGerX\nr098fDynTp0CYPbs2SQmJnL69Om8nqSvv/76jPv+HCL11z9/TS4AOnfunLcS2Nq1a/N60/5Up04d\ndu/ejdfrzRvaVbt27ULvS0xMJDY29pzaojgVmWAkJyfz+OOPc+WVV9K9e3cmTJjwj7Pl4mYyGggJ\nslK/RjhN60QQWzUIs9mA2WwgItRWLpOLP5mMJiICqii5kBL158INl156Ge3atWf06IfZufN3Wre+\n2M+RiUhxsJgsRIdUK5EJ3rVr16Z379689tprQO43wA6Hg/79+zNgwADmzZvHrFmzMJlMGI1GFixY\nwPLly7nxxhsZMmQIoaGhZ6wC1LdvX7Kzsxk2bBhvv/02N9xwAx07dswbmnXgwIEzvtH/U48ePdi6\ndSvt27cHcvfz+uabbxg6dCivvPIKF1989n/Xrr32WjweD8OHD2fMmDH5vsEuyMCBAwkNDWXQoEEM\nHTqUo0ePMnr06PNpviJNmDCB+fPnM2zYMFauXEnz5s35/PPPiY2NpVmzZowdO5Znn30Wt9tN8+bN\nz5jA/HerV6/OW2J4+PDhmM1mxo4dy1133cWQIUNo0aIFl1xyCcHBwWRnZ+cNyWrcuDFGo7HQb8aH\nDRvGqFGjePDBBxk+fDgffvhhXuLyd/fddx/Tp09n5MiRBfYOna9q1arRr18/br75ZqZMmULr1q3P\naWNoi8VCo0aN+P333wEYMmQIgwcP5sknn+Suu+5i/vz5JCUl5V0fGBjI+PHjGTlyJIMGDSI1NZXo\n6Giuv/56Fi5cyB133EHr1q1JSkrKWyXtXPXt2xev18vgwYNZsmQJjzzyCJC7Otj27dtp1aoVnTt3\nZsiQIQwfPpwBAwZQq1ats96XnZ1NcnJyXg9jaTL4ikh3b775ZhYtWpTXHZOVlcXtt9/O8uXLSyXA\nv0pKyijw+AffxQNwU7eGHD2ehMtgK82wAHBk22lav0bRF56DlNOnSc70kuGys+n4j4TbQrm8+qX5\nlqj9J6w4qRFb7YzjUVGhZ21jKT5lqZ1PnDjOoEE3kpycxM6de89YyaW8KkttXFGpjUtHVFSov0MQ\nKTdWrlxJv379MJvNXHfddSxYsOCcvr3/5ptv+P7775k0aVIpRFl63n77bZxOJyNHjiz1uosc9zBw\n4ECuueYaWrVqBeROvHrooYdKPLALFRYayImUbCzW0ksyfD4fQQHFN4QkODSY17YuJAs3Do+DMFsY\nPh90rPHPkwynI4eqVf/5xCMp/557bjKzZs3E6/VgsVg5cCCeRo38s2u9iEhlMXfu3AKHBE2dOpXa\ntWv/4/KdTmeBy5LWr1+/yB4Zf5c/evRo0tLS8h0LCQnJ6x0rSnJyMrfccgtWq5XrrrvunIcGXXXV\nVaxZsyZvpayK4MSJE6xbt4758+f7pf4iezAgdxzinj17MBgMtGrVipiYmNKI7Qzn0oMBkJxymtPp\nOXgxYTSV3DeyPp8Pr8dNoNVI7epRxfbtb3J2CnM3v4E9zYvT56N6SCyhtjAui21HlYCzjwUtjNfj\nxYiHyPBAqkZUKfAafSNZOvzdznv27Gbo0Js5duwoAJdf3ol33lle6Djj8sbfbVwZqI1Lh3owRKQ8\nKvRr9++++44DBw7Qvn17evbsWdilZUq1yAiqRYLD4Tin5fAulMFgyJv8VJzCbWEEhwQREOTB7DMT\nabURbrXRpHo1LMYL6ykxmUzYbKU/dEzKFrvdTs+eV+DxeAgODuHVV9/gmmuu9XdYIiIiUoGc9W11\nzpw5rF+/nrZt2zJx4kTuuusu+vcv/aVa/4ny+kJtMZqpG1YbI0aubXA1ma4swm1hF5xciOTk5BAQ\nEEBISAjXX38jHo+H1157U6tDiYiISLE769vFf/7zH5YsWYLZbCYjI4MHHnig3CUY5YnL6ybNkX5G\nImE2mqkWePZNgUQKk7sowxA2blzPzp2/ExERybx5C4q+UUREROQCnXVsj9Vqzft2MzQ0tESHGlV2\nLq+bT+I/Z+nvK1n8yzK2nfiZg2mHiE87yBd/fIvL6y66EJG/Wbp0CU2b1mPdum9xOl18//06f4ck\nIiIilcBZezD+vr702dabln8uzZFOQvph7K4sUkjF4XHg8DgJNAeQ7swgzZGuXgw5Z4mJiQwZchO7\ndu0EoFGjxrz33gfUrVvPv4GJiIhIpXDWBCM+Pp6xY8ee9fPft0CXCxduC8NmsmF3ZdG4Sn06Vb+U\nTSe24fK6CLOGEm6rOKv7SMnr2rUDp0+fxmKx8NRTz3DPPaP8HZKIiIhUImdNMB599NF8nzt27Fji\nwVRWFqOZ5pFN+CP9EP9q2JdASyA1QmsUOCdDpCB/TuIG+L//G8snn3zEO+8sIyJCPV8iIiJSus76\n5nrDDTeUZhyVnsloIsAcgPm/yYRFk7vlHHi9Xh555EGWLl3Cxx9/TocOl3PPPaPUayEiIiJ+o6/G\nS1l6hp3T6VnkON1A7rwWj8/D3mMnOJ2dxm+mI1jNAcVQkw+r2USV0AAiqoQXQ3lS1qxf/wMjRgwj\nNfU0AF9//SUdOlzu56hERESkslOCUYrSM+ycSMnCagvAFph7zO31sObAlySkHwZgW+pvdKl5OWaj\nqVjqPJXhBNKUZFQgOTk53HnnrXz11RoAqlatyttvv6fkQkRERMqEc9qC+vTp0+zatQvIHZIhFybN\nno3Vlr93wu7M5FTOabx4MRlNpDsysDszi61Os8VKmj2n2MoT/+vX72q++moNBoOBO++8mz174pVc\niIiISJlRZIKxatUqBg4cyLhx4wB49tlnef/990s8sIrI5T4zOQu2BGHGiNlgJtgcRJWAcEKswcVa\nr7OAeqV8ycrKyvv7tGkzadKkKRs2bOO552ZgNJ7T9wQiIiIipaLIN5OFCxfy8ccfExERAcDjjz/O\n8uXLSzywishXwLHdu35k6xsf8+vrX3Do/c00s9U/Y3iU2+1myduvMWxAD06dSsp3LvHEUSY8djfP\nTcq/6pdUHLNnv0ijRrWYM+clANq3v5T//GcrDRs28nNkIiIiImcqMsEIDQ0lMDAw73NAQAAWi6VE\ng6oM3F4PiaknmTfreS65sTcPTXmOyzp0ZdEbs8+49qVpEwkICDzj+LGjh5j53AQaNGpWGiFLKYuP\n38+ll7Zm8uSncbvdbNmy2d8hiYiIiBSpyAQjIiKCDz/8EIfDwZ49e5g+fTqRkVo+9Z9wez18feg7\n3lu3GGOYFUekifj0BDp3v5pdO38kOzsr3/X/GjCcmwbefkY5VquVcU/PpHGTFqUUuZQGr9fL448/\nQqdO7UlI+IPAwEDmzVvA4sVL/R2aiIiISJGKTDAmTZrErl27yMzMZOLEiTgcDiZPnlwasVVYdmcm\nx+zHOXUiEUuVIHw+L16vF7fRS0hIGInHj+a7vnHTlgWWUy0qloiIqqURspSiRx55kIUL38Dn83H1\n1dfw++8J3Hjjzf4OS0REROScFLlMbVhYGE899VRpxFJphFiDMRvM4PERaA2kRnAsYdZQQqzBWK02\nHA6t+lTZOJ1OILdXatKkqWzZsokXXniJzp2v8HNkIiIiIuenyASjW7duGAyGM46vW7euJOKpFMxG\nE9WDY0gNrYrBGcYVNTvmJh1GE05nToHzLaTi+vjjlTz00P1cccWVLF68lLCwMNav/9HfYYmIiIhc\nkCITjHfffTfv7y6Xi40bN+JwOM6p8KlTp7Jjxw4MBgPjx4+ndevWZ1wzc+ZMfv75ZxYvXnweYZd/\nRoORqNga/PbLJqoEhAGQlWkn024npnpNP0cnpeH06RSGDLmZbdu2ArmTukVERETKuyLnYNSsWTPv\nT7169Rg8eDA//PBDkQVv2bKFhIQEli1bxpQpU5gyZcoZ1+zfv5+tW7deWOTlgMPj5Lj9BFmuLJwe\nJy6PC5fHTY47hyx3NlXq1yApKZHff83dxPDzVSto2/5y9WBUAq+9NpeWLRuxbdtWTCYTTzwxkQ0b\n1GshIiIi5V+RPRgbN27M9/nEiRMcOnSoyII3btxIz549AWjYsCFpaWnY7XZCQkLyrnn++ecZM2YM\nc+fOPd+4yzyX182rPy8gw5WJ1WihVmgNUpOcGC2BJGYmkeHMwGg00XXYjfz7zVk4HDnExNbknlGP\nE7/vV1YsXcjjT75AWmoKk58ak1fulLgxmIwmxsXNYPuPG1nz2QdkZWWSk53FYw/eRsNGzbj3wXF+\nfHIpypIlS4iLGw/ARRe14d13VxATE+PnqERERESKR5EJxquvvpr3d4PBQEhICJMmTSqy4OTkZFq2\n/N/qR5GRkSQlJeUlGCtXrqRDhw7UrFkxhwOlOdLJcGViMhixmCxEBkRgC/TiNBo5mZWEwWjAZDAS\nVLsqj095MW+YFECViEgef/IFAMKrRDJ99tsF1nFV7/5c1bt/qTyP/DNerxe73U5YWBhDhw7lxRdf\n5vbb72TQoKH+Dk1ERESkWBWZYDzxxBP5EoUL5fP9bx/r1NRUVq5cycKFC0lMTDznMqKiQgs8HhRk\nLfS8P1TxBBD0qw2z0Uyb6s25oXkfDh5Kwmu0Yjlg4PfkAwDEVqlG7egozMYi/1NcMEeO75zbpiy1\nYUWxefNm+vfvT0hICPHx8QB58y6k5OhnueSpjUVEpCBFvtVOmzaNRYsWnXfB0dHRJCcn530+efIk\nUVFRAGzatImUlBSGDh2K0+nk0KFDTJ06lfHjxxdaZlJSRoHHs7KchZ73l9jAWEKtIXSJ6kJqSg4p\np7MwWXy0i2xHw+BGAITbwshIdwDnNnH+Qjhzss6pbaKiQstcG5ZnTqeT++67i08//QgAh8PBvn2H\nady4ttq5hOlnueSpjUuHkjgRKY+KTDBq1KjB8OHDadOmDRaLJe/4Qw89VOh9nTt3Zs6cOQwaNIg9\ne/YQHR2dNzyqT58+9OnTB4AjR44wbty4IpOLssTldZPmSCfcFoblPHoeDOT24piNJqoGRpRUeAXU\nK6Xts88+ZfTou8nMzMRgMDBo0DBeemkORmOR6yqIiIiIlGtFvh3XqlWLWrVqnXfB7dq1o2XLlgwa\nNAiDwUBcXBwrV64kNDSUXr16XVCwZYHL6+azA19yKOMIASYbF1VrgcloOuM6j9fDkYxjmIwmTAYT\nvev1INBqxuEroNASFmArueFXcqaEhD8YMSJ3bkXNmrVYsmQFLVq08HNUIiIiIqXjrG+en3zyCf37\n92f06NEXXPijjz6a73OzZs3OuKZWrVrlag+MNEc6xzMTSXWkA2BO2UuAOeCM63LcOTi9LkLNNtKd\nGaQ50omqWoU/DiditAWXyjfZPp8PV04mdWpULfG6JHduUZUqVahbtx79+99A48aNefzxif4OS0RE\nRKRUnTXBWLFiBf37a4Wivwu3hRFkCYRsqBNai+saXF3gBG231826I+txeByEWUMJt4VhNpppULc6\nqWlp5DiclGRnhgGwWI1ERsdgMp3ZwyLF5+jRwwwceCOHDh3it98OEhQUxJtvFrzyl4iIiEhFp7Ez\n58liNHNpTDs8Xg9da15O1cDIs17bv+E1Z8zVMBqNREaU3vwLKVmTJj3Ja6/Nwev1YrXa+PXXPbRv\nf6m/wxIRERHxm7MmGNu3b+fKK68847jP58NgMLBu3boSDKtsMxtNBJgDilxa1mI0U62QBETKr507\nf2bo0FtITDwBwBVXdOPtt9/Lt5GkiIiISGV01jfkFi1a8OKLL5ZmLCLlgtfrpW/fXjidDkJDw3j9\n9YVcdVX5XbhAREREpDidNcGwWq0VdpdtkQtx+nQKERGRGI1GHnxwDH/8cZDZs1/DbNZIQxEREZE/\nnfXNqHXr1qUZh0iZZbfbGT58IBs3rufbbzfQokULxo4tP/u2iIiIiJSms66V+thjj5VmHCJl0qJF\nC2nWrD7r1/8AwPbtP/o5IhEREZGyTWM7RApw7NhRBg++iV9//QWApk2bsXTpB9SsWdvPkYmIiIiU\nbZU+wcjIyCAj04H3v6tjFcXtdbMvOYHE06kcNSZjyjpzk72/8/l8mE1GIsJDsNlsxRG2lLBrr+3F\n0aNHsFisPPvsc9xxx0h/hyQiIiJSLlTqBOPQ0RO4vFZMFmvuznRFcHs9fJnwHQfSEvB6vdiS44kK\nqYvZWMRGdgZwe+GP46nERARQJTy8eB5AitWpU8lUrVoNgBkzZjF79ossXryMsLAwP0cmIiIiUn6c\ndQ5GRWe3Z+L0WjBZzj3HsjszOZGZiNvrxouXbHcOdmfmOd9vCwgk6XTWhYQrJcjr9fLAA/fSokVD\nlixZBMBVV/Xi448/V3IhIiIicp4qbQ9GRmY2Zov1vO4JsQZjMwVgNGQTagkhKqgaIdbg8yrDZzCT\nnZ1NYGDged0nJWPdum+5667bSE9PA+D333/1c0QiIiIi5VulTTC8Xt85DYv6K7PRRO3QmlSxhXN5\n9UsIt4UVPTzqb4xGI2635/wqlmKXlZXFbbcN5rvv1gIQHR3D4sVLadu2vZ8jExERESnfKm2CcbYJ\n3Xt2/cS7i+bhyMmhWlQMI0eNpWrVqLzzRoMRm8HCmuVL+fzT95k1f1ne+VdfnsLBA3vzrs3KyqRJ\n05Y89Nikv1ZcMg8k5+XP5MJoNHLPPaOJi3sGo7HSjhgUERERKTaVNsEoSE5ONq+8NJnHJj5P/QZN\n+OzT5bz26vM88Fgcpv/2VHi8br5YsIQOrTqecf/9D0/I93n6lCe4onvvUoldipaUlERERARms5k5\nc+Zx221DeP31hdStW8/foYmIiIhUGPrK9i9+2b2dqJjq1G/QBJfHTVItN7/v2sH7ez7k+6Mb+f7o\nRg6mHaJG5+bEdmlRaFk7ftqMy+Wi3SWdSil6Kcz06c/RunUTHn54FACxsdX54ou1Si5EREREipl6\nMP7ixLEjxMTUAMDuysRl9mAJsuFOyyGqZu7ypWmOdOo2akqmu/DVoD5Y/m8GD7+3xGOWwv32268M\nGTKAI0cOA5CcnOTniEREREQqNiUYf+FwOLBYc1eWCrEEYzGYMVnMRJrDaRt9EQA57hwy3VkEm4PO\nWs4vu7fj80Hzlm1KJW45k9fr5ZFHHuTddxfj8/kICgpi7tz59Ot3vb9DExEREanQlGD8hS0gAJfT\nCYDZaCY6OArcPtrXapu3WlTHGpdid2YSYg1m/lnK2fDDN3Ts0qOUopaCzJjxfN6eFtde25/589/C\naj2/ZYlFRERE5PxpDga5O3Sn5qQTU70miSeO5h13ZufgyMomNrZm3jGz0USVgMKXp/35p81c3O6y\nEo1ZzpSTk8OpU8kAPProE1x2WUdWrfqShQvfUXIhIiIiUkoqfYLh9nr49tD3fHbwC/YFHOd44lE+\nXf8R2xJ/Zts366jWtA4/pezG7T23vSvS0k6Tnnaa2Oq1Sjhy+av3319KkyZ1uPHGfkDufiOffvoF\nHTpc7ufIRERERCqXSj9Eyu7MJDn7FFnuHAA6DOrLF0uX4nY6sVQJpt2A3iQc2Md3C5YzMe5F0lJT\nmPzUmLz7p8SNwWQ0MS5uBpFVo0g5lURoWBXtqVBKkpKSGDJkADt2bAdy59G43W7M5kr/oy0iIiLi\nF5X+LSzEGozVZCPTnU3D8Hp0an4pt/W8E4/PzeYTP5HtzibYHMygLkMACK8SyfTZb5+1vPoNmvDK\nmytKK/xKbfbsF3nuucl4PLkJxfjxcYwe/ZC/wxIRERGp1Cp9gmE2mmgQVpcjdjOXV7+EAHMAABbM\ndK5xWd6E7sLmXEjp27RpA5MnPw1Au3aXsGTJcqpWrebPkEREREQEJRgAmIxGrGYbJkP+JOLPCd3F\nyefzYjAairXMysLr9XL8+FFq1qzN5Zd34rrr/kW/ftdzww03+Ts0EREREfmvSptgWC1Gchxe3D4v\nJzJPkuNx4PGd20Tuf8LrdhEUGFni9VQ0mzZt4Pbbh+Byufn99z8wm80sWLDI32GJiIiIyN9U2pnI\nkRFVyMlO57ODX/L76f0cSj/M5hPbznm1qAvh9XoJshk1Afw8OJ1Obr11EP379yElJQWz2czx40eL\nvlFERERE/KLS9mAYjUbCq1pIiz+GIycDi8HKqfREUkJPlsCwKB9mk5Egm4XY6OhiLbsi+/jjlTz4\n4P1kZ2dhMBi49dYRTJv2ohI0ERERkTKs0iYYANFhUcTGRJAVkE64LZAm1arTtn5DLMZK3SxlQnp6\nOvfccwder5c6dery7rsraNKkqb/DEhEREZEiVOo3aYvRTJuoVpiNZrrX7kKdsNpKLvzs8OEEateu\nS1hYGPff/yChoaGMGfOYv8MSERERkXNUqd+mXV43GS47YCAioIqSCz86ePAAgwffxB9/HGTr1h3U\nrl2Xp556xt9hiYiIiMh5qrSD2V1eNyv3fcrXCd+x9/R+vjn0PS6v299hVTper5cJE8Zy+eVtOXAg\nHqvVSnx8vL/DEhEREZELVGm/sk9zpHMk4zhevACkOzNIc6RTTUvIlpqtWzdz221DSE5OAqBHj568\n9dY7BAUF+TkyEREREblQlTbBCLeFEWoNITHLSJgtlKjAaoTbinf1KCnc4ME3kZ6eTnh4Fd588226\ndevu75BERERE5B+qtAmGxWimXXRrjAYDXWpcRsOIBpqDUQoOHjxA/foNAHjhhZf44YfvmDlztpae\nFREREakgKt0btdvtxul04gNcDhcmj5lgQzDOHCdOnMVSh8Vsxmq1FktZFUVqairDht3Cli2bePvt\n97jmmmu58cabufHGm/0dmoiIiIgUo0qTYDidTo6cOIXLa8Tt83Ii6yQnM9NIzfRyLCibnEBbsdXl\ndXsxGtzEVA0jLDSk2Motr958cx5xcRNwuVyYTCaOHdNO3CIiIiIVVaVJMA4fP4XJFozJ6+GbhLUc\nyTiO0+PAaraxO/U3uoV0xmw0FU9lltz/OZaUTlBgAGZzpWnmfA4fTmDgwJvYv38vAC1btuK99z4g\nNra6nyMTERERkZJSKQa+OxwOPL7c5MHuzCTLlYXP5wUDhFlCcHld2J2ZxV6vLTCY06npxV5ueTF8\n+GD279+LzWbjpZfmsnbtBiUXIiIiIhVc5UgwnA5MltxuhRBrMAHmAAwGI8HmYMJsoQSbgwmxBhd7\nvQaDAZfbW+zllmX79+/D68195gULFtGzZ29+/z2BoUNv9XNkIiIiIlIaKkWC4fP97+9mo4kWkU2J\nDapGvcyqfD93KR9Ne4UZk5/g1KmkM+51u90sefs1hg3oke98TnY281+ZxqMP3sqY+4fyn++/Krhy\ng6G4H6dMcrvd3H33CDp1as+kSRMBaNiwEe+++772tRARERGpRCpFgvF3Xp+XrJwsls57hXtGjWXm\nnMW0bd+RhfNfOuPal6ZNJCAg8IzjH61YjCMnhxde/jcTn32ZpYtf52Ti8dIIv8xZs2Y1TZrU5aOP\nPgAgJ8fh54hERERExF8qXYLhcDv5MmEdv+3ZgSkskBp16gHQrcc17Nr5I9nZWfmu/9eA4dw08PYz\nytm180e6du+N0WikatUo2nfozE9b15fCE5Qd6enpXH/9Ndx66yDs9gyqV6/Bt9/+h2nTZvo7NBER\nERHxk0qXYCRmJeHyOslJycAWEUJiVu6wp4DAQEJCwkg8nn8J1cZNWxZYjsFgyJtrABAQEEjiicq1\n/OqECWPZuHE9RqORhx56hB07fqNVq9b+DktERERE/KjSJRgxQVFYjBZ8Lg82q5WYoKi8c1arDYcj\n55zKadW6PV+t+Rin00lyUiI/bv4PTperpMIuM44dO0pqaioAM2fOpnv3q/jppz1MmBDn58hERERE\npCyodAmGzWylSURDokKjqRlYHZv5fztuO505Bc63KMi/BgwnMrIa4x+5i4Wvv0Sbth0IDir+lajK\nkilTJtGuXUuGDbsFAKvVyrJlH1KjRk0/RyYiIiIiZUWl3AHOZDQRW7MW+37fkncsK9NOpt1OTPVz\ne1kOCAhk5P2P5X1+/ZUXaNayTbHHWhbs3r2ToUNv4fjxYwAEBp5bEiYiIiIilU+l68Hw+nxkubIJ\nrhNFUlIiv/+6C4DPV62gbfvLz7kH49MP32PJ268BcPTwH+zZ9RPtL+1cYnH7g9frZfToe+jRowvH\njx8jJCSUd95Zzvvvf+zv0ERERESkjCrRHoypU6eyY8cODAYD48ePp3Xr/00A3rRpEy+++CJGo5H6\n9eszZcoUjMaSz3eOZhznYFoCHp+H5jd1ZeEbL+N0OoiJrck9ox4nft+vrFi6kMeffIG01BQmPzUm\n794pcWMwGU2Mi5tB1+69mfvSZMbcPxSr1co9o58gODikxOMvTcuXv8fy5e8B8K9/3cSrr76B2Vwp\nO71ERERE5BwZfL6/bkNXfLZs2cKCBQuYP38+8fHxjB8/nmXLluWdv/rqq1m0aK207PgAAB9TSURB\nVBGxsbE8+OCD3HTTTXTr1q3QMpOSMgo8/sF38QDc1K1hgefT0tNJzvBgMpn4KXEn3x/dgMVowWqy\n0rtuD+qEldwcAqvBRY2YqiVWfnGKigolISGR48eP0bBhIwDuvnsE9903mrZt2/s5uoojKir0rD/L\nUjzUxiVPbVw6oqJC/R2CiMh5K7Eug40bN9KzZ08AGjZsSFpaGna7Pe/8ypUriY2NBSAyMpLTp0+X\nVCj5RNgiMBvMGA1Ggs2B+VaRquwWLFhA06Z16dfv6rwleF9/faGSCxERERE5ZyWWYCQnJxMREZH3\nOTIykqSkpLzPISG5w4lOnjzJ+vXri+y9+CcMhv/93Wa2UD+8LpfFtOemxv3zrSJVIkqmg6hYnThx\nnO7dO3HXXXfhcDiIjIwkKyur6BtFRERERP6m1AbUFzQS69SpU9x7773ExcXlS0bO5mxdxUFB1kLP\nh4ZayPGcxmS14MrJAaOHyPAwqkWGYjaWbBMEWaxluov76aefZvLkyXg8HiwWCzNnzuSBBx7wd1gV\nXln+mago1MYlT20sIiIFKbG36+joaJKTk/M+nzx5kqio/w1HstvtjBw5kocffpguXbqcU5lnG++b\nleUs9DzAieSTfHbke5KyTuHyOjmafpJDp47TqUYHzEbTOdV/vhw52dSvEVFmxynHx+9n0qRJAFx2\nWUfWrFmNy2Uqs/FWFBq7XvLUxiVPbVw6lMSJSHlUYkOkOnfuzBdffAHAnj17iI6OzhsWBfD8889z\n22230bVr15IKIR9PgJ3ktGM4XA58gMfnITUnDbszs/jr8nhwZGcSFW7Dai3hIVjnyev1snv3TgAa\nNmzEffc9yFtvLebTT7+gSpUqfo5ORERERMq7EuvBaNeuHS1btmTQoEEYDAbi4uJYuXIloaGhdOnS\nhY8++oiEhARWrFgBQL9+/Rg4cGBJhUN0eDWqxQaRZk/H6XBhMxmpFmQmNiQAi9FdrHWZTCbCw2Ix\n/HXyRxnwww/fceedw7Hb7ezZs5+IiEgmTZrs77BEREREpAIp0QkIjz76aL7PzZo1y/v77t27S7Lq\nM5iNZmqH1aRzzQ40CK+H2WimamAklhKeg1EW5OTkMGLEUL755isAqlWLIikpiYiISD9HJiIiIiIV\nTaXaydtoMBIRUIVaoTWIDY6uFMnF++8vpUmTOnzzzVcYDAZGjryX3bv30aRJU3+HJiIiIiIVUMV/\nw/6vDFcmWa4sTuek4vK6K0Vy4Xa7GTNmNE6nkwYNGvLeex9Qv34Df4clIiIiIhVYxX/LBlxeN6sO\nfMkx+3EO24+R6kjnmvo9K2ySsX37Ntq2bY/ZbGbq1Omkp6czevRD/g5LRERERCqBivmG/TdpjnSy\n3dl48WLEQHJ2CmmOdKoFVqw5CPv372PQoBs5dCiBjz5aTadOXbj11hH+DktEREREKpFKMQcj3BZG\nsCUII0asJivVAiMJt4X5O6xi4/V6eeyxh+nc+RIOHUogMDBIO3GLiIiIiF9Uih4Mi9FMi8imBJkD\nuaZeL6KDq1WY4VEbNvyHO+4YRkpKCgB9+vTl9df/TUBAgJ8jExEREZHKqGK8Zf+Ny+smzZFOuC0s\nXyJhNBipGhhRYZILgFGj7iYlJYXIyEjeeusdOnU6t13RRURERERKQsV50/4vl9fNv/e8S1L2KWxG\nK/XD6wKwL/UADo+TrxLW0qecT/DeunUzl156GQALFy5hyZK3mTbtRYzGSjHiTURERETKsAr3Rprm\nSOdkdjIerwen14XTk/vH6/MSYgkiw2UnzZHu7zAvyKlTyfTp051rr+3Fyy/PBODii9syffrLSi5E\nREREpEwov1/jn0W4LQyb0YobN62qtaB3vR4AfPHHt6Q7MwizhpbLCd5z585i6tRJuN1uTCYTgYGa\nYyEiIiIiZU+FSzAsRjP1wuvg9XrpXa9H3lCo3vV6FDgvo6w7ePAAgwbdxMGD8QBcfHE7lix5n6io\nKD9HJiIiIiJypgo5rsZoMBJkCcyXSFiMZqoFRpar5ALg8cf/j4MH4wkICOSVV17nyy/XKbkQERER\nkTKrQiYY5d327dvIyckB4M03F3HjjTezd28CN988yM+RiYiIiIgUTglGGeJ0OhkxYhi9e3fnjjuG\nAxAWFsa8eQu0r4WIiIiIlAtKMMqIVas+pmnTunz22ScYDAZq1arl75BERERERM5b+ZqQUAGlpqYy\nbNgtbNmyCYBatWrz7rsraNasuZ8jExERERE5f+rB8LMFC+axZcsmTCYTjz02jp9+2qPkQkRERETK\nrQrVg3EyOYXMbBfJidlYDE72m0+USD1GIMBqJiYqApPJdN73Hz6cgNPpomHDRjzyyBMcOnSY8eOf\nIiYmpviDFREREREpRRUmwUg9fZoMRyQmSyBmaxBmgxmzNajE6nMCBw+foGHdGhgMhnO6x+v1MmnS\nRObPf5WaNWuxbdtuAGbNeqXE4hQRERERKU0VIsHwejy4vL4L6k34R8yBpKWnUyU8vMhLf/55O8OG\n3cLJk4kANGjQEK/Xi9GoUWoiIiIiUnFUiATD4XRgMttKvV6z2Uy2w0WVQq5xu93cf/9IPvroAwDC\nwsJ5442FdO/es3SCFBEREREpRRUiwfB5vRgMZ/YE7Nn1E+8umocjJ4dqUTGMHDWWqlXz74K9bet6\nPlj2b9wuFyGhYYy4ewy169TH7XazeOFcftm1HZ/PR4tWbbn1zgcwm/M3mc9XeGybN2/8b3JhYODA\nwcya9ap6LURERESkwqqQb7oer4dTGaeY+9Kz3HXfo8yYs4i27TuycP5L+a5LOZXE/LnTGPXQBF6Y\n9W86drmKt+a/CMDqT5aTnpbKtJfeYurMNzmUEM/arz87p/rT09Pzlp3t3PkKRo9+mLVr1zNnzjwl\nFyIiIiJSoVW4t90sVw6/p+xnx86tmMICqFGnHgDdelzDrp0/kp2dlXetyWxm1MMTqVk795qmzVpx\n9PAfADRr0ZqBQ0diNJmwWq00adqS48cOF1n/woULaNGiATfd1J+srNy6nnrqGVq2bFWszykiIiIi\nUhZVuATj99P7cPvc5KSkY40IJjErCYCAwEBCQsJIPH4079rw8AjatO2Q93nH9i00bJy7B0WTZq2I\nrV4TgNOnT7Fj+xbatr/8rPUePXqYLl068PjjY3A6nTRo0CBfMiMiIiIiUhlUuAQj3BqO2WAGtw+r\nxUpM0P/mXFitNhyOnALv273zJ9Z8toJht9+f7/izTz7E/40ayiWXdaFV6/YF3vvss3G0b38Re/f+\nhtVqZfr0l/nuu01UrVqt+B5MRERERKQcqHAJhs1soX54XRpUrU/NwOrYzNa8c05nDgEBgWfc8+OW\n//D6K9N45ImpecOl/vTks7N45c0POHbkEMveeeOMe9PTUnn11dl4vV46d76CX345wG233VHszyUi\nIiIiUh5UuAQDwGQ00aBuE5ITj+cdy8q0k2m3E/PfYU9/2r1zG4vfmsvjT75Ag0ZN845v27Ke5KTc\nPSuCgoK5ontvdu7YCoDH7eHHH7cAEBZehcmTp/HOO8v58MPPCAsLK+nHExEREREpsypcguHxeshx\n51Ctfi2SkhL5/dddAHy+agVt21+erwfD4cjh9Vde4OHHnqFmrbr5ytm2dT0rl7+N1+vF5/Px87bN\n1KnTgC1bNnPjTdcx7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BH69OmTb/lfgPHjx9O8efMC75kzZw5ut5sxY8ZccL2bN2/m/vvv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08440c9110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis_result()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}