cbpe_QSO_selection_MNRAS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NannyML CBPE applied to SDSS quasar identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
     ]
    }
   ],
   "source": [
    "import os, sys, time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from numpy_ext import rolling_apply\n",
    "\n",
    "try:\n",
    "    from sklearnex import patch_sklearn, get_patch_names\n",
    "    patch_sklearn('random_forest_classifier')\n",
    "\n",
    "except:\n",
    "    print('Intel Scikit-Learn extension not installed. Falling back to standard Scikit-Learn')\n",
    "    \n",
    "\n",
    "import sklearn\n",
    "from sklearn.metrics import f1_score, r2_score, mean_absolute_percentage_error\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "import dask\n",
    "import dask.dataframe as dd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from NannyML\n",
    "#https://github.com/NannyML/nannyml/blob/main/nannyml/performance_estimation/confidence_based/cbpe.py\n",
    "\n",
    "def estimate_f1(y_pred: pd.Series, y_pred_proba: pd.Series) -> float:\n",
    "    tp = np.where(y_pred == 1, y_pred_proba, 0)\n",
    "    fp = np.where(y_pred == 1, 1 - y_pred_proba, 0)\n",
    "    fn = np.where(y_pred == 0, y_pred_proba, 0)\n",
    "    TP, FP, FN = np.sum(tp), np.sum(fp), np.sum(fn)\n",
    "    metric = TP / (TP + 0.5 * (FP + FN))\n",
    "    return metric\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data\n",
    "path = ''\n",
    "df = dd.read_csv(path+'data.csv')\n",
    "\n",
    "# drop rows with missing values\n",
    "df=df.replace(-9999.0,np.nan).dropna(how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "351089\n"
     ]
    }
   ],
   "source": [
    "# undersample to make model training more tractable\n",
    "df = df.loc[::10]\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['objid' 'ra' 'dec' 'Redshift' 'fiberMag_u' 'petroMag_u' 'modelMag_u'\n",
      " 'psfMag_u' 'fiberMag_g' 'petroMag_g' 'modelMag_g' 'psfMag_g' 'fiberMag_r'\n",
      " 'petroMag_r' 'modelMag_r' 'psfMag_r' 'fiberMag_i' 'petroMag_i'\n",
      " 'modelMag_i' 'psfMag_i' 'fiberMag_z' 'petroMag_z' 'modelMag_z' 'psfMag_z'\n",
      " 'class' 'modelMag_r-fiberMag_r' 'modelMag_u-modelMag_g'\n",
      " 'modelMag_u-modelMag_r' 'modelMag_u-modelMag_i' 'modelMag_u-modelMag_z'\n",
      " 'modelMag_g-modelMag_r' 'modelMag_g-modelMag_i' 'modelMag_g-modelMag_z'\n",
      " 'modelMag_r-modelMag_i' 'modelMag_r-modelMag_z' 'modelMag_i-modelMag_z']\n"
     ]
    }
   ],
   "source": [
    "# some rudimentary feature engineering:\n",
    "# create a feature that is indicative of the size of a source on the sky:\n",
    "df['modelMag_r-fiberMag_r'] = df['modelMag_r']-df['fiberMag_r']\n",
    "\n",
    "# create colours:\n",
    "bands = ['u','g','r','i','z']\n",
    "for i,b1 in enumerate(bands[:-1]):\n",
    "    for b2 in bands[i+1:]:\n",
    "        df['modelMag_'+b1+'-modelMag_'+b2] = df['modelMag_'+b1] - df['modelMag_'+b2]\n",
    "\n",
    "print(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         GALAXY\n",
       "10        GALAXY\n",
       "20        GALAXY\n",
       "30           QSO\n",
       "40           QSO\n",
       "           ...  \n",
       "161460    GALAXY\n",
       "161470       QSO\n",
       "161480       QSO\n",
       "161490      STAR\n",
       "161500    GALAXY\n",
       "Name: class, Length: 351089, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_ml = [x for x in df.columns.values.tolist() if 'Mag_' in x]\n",
    "df = df.compute()\n",
    "df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Redshift</th>\n",
       "      <th>fiberMag_u</th>\n",
       "      <th>petroMag_u</th>\n",
       "      <th>modelMag_u</th>\n",
       "      <th>psfMag_u</th>\n",
       "      <th>fiberMag_g</th>\n",
       "      <th>petroMag_g</th>\n",
       "      <th>...</th>\n",
       "      <th>modelMag_u-modelMag_g</th>\n",
       "      <th>modelMag_u-modelMag_r</th>\n",
       "      <th>modelMag_u-modelMag_i</th>\n",
       "      <th>modelMag_u-modelMag_z</th>\n",
       "      <th>modelMag_g-modelMag_r</th>\n",
       "      <th>modelMag_g-modelMag_i</th>\n",
       "      <th>modelMag_g-modelMag_z</th>\n",
       "      <th>modelMag_r-modelMag_i</th>\n",
       "      <th>modelMag_r-modelMag_z</th>\n",
       "      <th>modelMag_i-modelMag_z</th>\n",
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       "      <td>23.81548</td>\n",
       "      <td>22.47039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.25837</td>\n",
       "      <td>2.29204</td>\n",
       "      <td>3.48837</td>\n",
       "      <td>4.19817</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351089 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      objid          ra        dec  Redshift  fiberMag_u  \\\n",
       "106310  1237679504318922907    0.001661  20.397911  0.169639    23.19317   \n",
       "85930   1237679477470265897    0.001699  22.313928 -0.000842    22.40629   \n",
       "88680   1237679478007202593    0.003054  22.654332  0.687130    23.72759   \n",
       "122220  1237656495113633949    0.004698  14.666738  0.494372    24.64123   \n",
       "178342  1237657191978959047    0.004742   0.708960  0.652078    22.79361   \n",
       "...                     ...         ...        ...       ...         ...   \n",
       "206160  1237680297281127272  359.996771  21.785129  0.403197    25.55326   \n",
       "245641  1237663306917741344  359.997392  29.784564  2.181215    22.86479   \n",
       "86230   1237663784197357819  359.997663   0.144871  1.355421    20.82982   \n",
       "238840  1237669768154054658  359.997837   8.470280  2.683349    18.47355   \n",
       "226751  1237663235523477803  359.999954  34.747133  2.397724    21.85833   \n",
       "\n",
       "        petroMag_u  modelMag_u  psfMag_u  fiberMag_g  petroMag_g  ...  \\\n",
       "106310    24.06787    23.09053  22.82628    22.08305    22.35545  ...   \n",
       "85930     21.80065    21.93820  21.91749    21.26552    21.00201  ...   \n",
       "88680     20.56767    22.96340  23.26116    23.81548    22.47039  ...   \n",
       "122220    27.72672    24.16196  24.22452    22.79229    21.48647  ...   \n",
       "178342    22.60482    20.88348  22.85150    22.60140    20.66000  ...   \n",
       "...            ...         ...       ...         ...         ...  ...   \n",
       "206160    23.06392    26.63804  26.12176    22.29978    21.04331  ...   \n",
       "245641    22.65849    22.53520  22.41355    21.84419    21.55383  ...   \n",
       "86230     20.60065    20.50475  20.45739    20.79091    20.53729  ...   \n",
       "238840    18.20370    18.16550  18.17316    18.54374    18.27373  ...   \n",
       "226751    21.53611    21.66895  21.70696    21.22537    20.99523  ...   \n",
       "\n",
       "        modelMag_u-modelMag_g  modelMag_u-modelMag_r  modelMag_u-modelMag_i  \\\n",
       "106310                0.93142                1.18984                1.11208   \n",
       "85930                 1.03176                1.42015                1.58159   \n",
       "88680                 0.25837                2.29204                3.48837   \n",
       "122220                2.14622                3.96156                4.89292   \n",
       "178342                0.56076                1.28619                2.00339   \n",
       "...                       ...                    ...                    ...   \n",
       "206160                5.63014                7.61149                8.32248   \n",
       "245641                1.11071                1.41677                1.49492   \n",
       "86230                -0.01320                0.21841                0.22166   \n",
       "238840               -0.06948               -0.36008               -0.60637   \n",
       "226751                0.77329                1.09625                1.44160   \n",
       "\n",
       "        modelMag_u-modelMag_z  modelMag_g-modelMag_r  modelMag_g-modelMag_i  \\\n",
       "106310                1.57977                0.25842                0.18066   \n",
       "85930                 1.58350                0.38839                0.54983   \n",
       "88680                 4.19817                2.03367                3.23000   \n",
       "122220                5.30700                1.81534                2.74670   \n",
       "178342                1.88960                0.72543                1.44263   \n",
       "...                       ...                    ...                    ...   \n",
       "206160                8.74460                1.98135                2.69234   \n",
       "245641                2.14227                0.30606                0.38421   \n",
       "86230                 0.16939                0.23161                0.23486   \n",
       "238840               -0.78399               -0.29060               -0.53689   \n",
       "226751                1.77464                0.32296                0.66831   \n",
       "\n",
       "        modelMag_g-modelMag_z  modelMag_r-modelMag_i  modelMag_r-modelMag_z  \\\n",
       "106310                0.64835               -0.07776                0.38993   \n",
       "85930                 0.55174                0.16144                0.16335   \n",
       "88680                 3.93980                1.19633                1.90613   \n",
       "122220                3.16078                0.93136                1.34544   \n",
       "178342                1.32884                0.71720                0.60341   \n",
       "...                       ...                    ...                    ...   \n",
       "206160                3.11446                0.71099                1.13311   \n",
       "245641                1.03156                0.07815                0.72550   \n",
       "86230                 0.18259                0.00325               -0.04902   \n",
       "238840               -0.71451               -0.24629               -0.42391   \n",
       "226751                1.00135                0.34535                0.67839   \n",
       "\n",
       "        modelMag_i-modelMag_z  \n",
       "106310                0.46769  \n",
       "85930                 0.00191  \n",
       "88680                 0.70980  \n",
       "122220                0.41408  \n",
       "178342               -0.11379  \n",
       "...                       ...  \n",
       "206160                0.42212  \n",
       "245641                0.64735  \n",
       "86230                -0.05227  \n",
       "238840               -0.17762  \n",
       "226751                0.33304  \n",
       "\n",
       "[351089 rows x 36 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by RA\n",
    "df=df.sort_values(by=['ra'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['isStar'] = np.where(df['class']=='STAR',1,0)\n",
    "df['isQSO'] = np.where(df['class']=='QSO',1,0)\n",
    "df['isGalaxy'] = np.where(df['class']=='GALAXY',1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df.iloc[::40]\n",
    "df_drift = df[~df.index.isin(df_train.index)]\n",
    "\n",
    "ra_lims=[150,200]\n",
    "\n",
    "train = df_train[(df_train.ra > ra_lims[0]) & (df_train.ra < ra_lims[1])]\n",
    "val = df_drift[(df_drift.ra > ra_lims[0]) & (df_drift.ra < ra_lims[1])]\n",
    "\n",
    "# set up a dictionary of ML models\n",
    "model_dict = {'rf': RandomForestClassifier(n_estimators=100,max_depth=4,n_jobs=3),\n",
    "            'xgb': xgb.XGBClassifier(n_estimators=300, max_depth=4, tree_method='hist',\n",
    "                                     eval_metric='map',reg_alpha=0,reg_lambda=1,\n",
    "                                     nthread=3),\n",
    "            'lgbm': lgb.LGBMClassifier(n_jobs = 3,colsample_bytree = 0.9,\n",
    "                                       n_estimators=1000,max_depth = 10,\n",
    "                                       learning_rate = 0.01, subsample = 0.8)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model: rf\n",
      "model: xgb\n",
      "model: lgbm\n",
      "CPU times: user 9min 46s, sys: 9.37 s, total: 9min 55s\n",
      "Wall time: 10min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# define several different data subsets\n",
    "\n",
    "df_train = df.iloc[::40]\n",
    "df_drift = df[~df.index.isin(df_train.index)]\n",
    "\n",
    "# define RA range used to train the models\n",
    "ra_lims=[150,200]\n",
    "\n",
    "# 1. train set\n",
    "train = df_train[(df_train.ra > ra_lims[0]) & (df_train.ra < ra_lims[1])]\n",
    "\n",
    "#2. validation set (same RA range as train, but no examples are in train set)\n",
    "val = df_drift[(df_drift.ra > ra_lims[0]) & (df_drift.ra < ra_lims[1])]\n",
    "\n",
    "# 3. test set (examples from outside the train / validation set regions)\n",
    "df_drift_no_val = df_drift[~df_drift.index.isin(val.index)]\n",
    "\n",
    "# 4. subset of examples in an RA range containing the Milky Way\n",
    "df_mw = df_drift[(df_drift.ra > 260) & (df_drift.ra < 320)]\n",
    "\n",
    "# data structure fo hold model scores\n",
    "scores_dict = {}\n",
    "\n",
    "# loop through the models\n",
    "for key in model_dict:\n",
    "    \n",
    "    print('model: '+key)\n",
    "    scores_dict[key] = {}\n",
    "    \n",
    "    # train a classifier which will return isotonically calibrated probabilities\n",
    "    clf = model_dict[key]\n",
    "    clf = CalibratedClassifierCV(base_estimator=clf,method = 'isotonic')\n",
    "    clf.fit(train[cols_ml],train['isQSO'])\n",
    "\n",
    "    # predict class probabilities (calibrated) on different data subsets \n",
    "    preds = clf.predict(df_drift[cols_ml])\n",
    "    preds_proba = clf.predict_proba(df_drift[cols_ml])[:,1]\n",
    "    \n",
    "    preds_ref = clf.predict(val[cols_ml])\n",
    "    preds_proba_ref = clf.predict_proba(val[cols_ml])[:,1]\n",
    "    \n",
    "    preds_no_train_no_val = clf.predict(df_drift_no_val[cols_ml])\n",
    "    preds_proba_no_train_no_val = clf.predict_proba(df_drift_no_val[cols_ml])[:,1]\n",
    "    \n",
    "    preds_mw = clf.predict(df_mw[cols_ml])\n",
    "    preds_proba_mw = clf.predict_proba(df_mw[cols_ml])[:,1]\n",
    "    \n",
    "    # add F1-scores to scores_dict\n",
    "    scores_dict[key] = {'F1_ref': f1_score(val['isQSO'],preds_ref),\n",
    "                        'F1_all': f1_score(df_drift['isQSO'],preds),\n",
    "                        'f1_all_estimated': estimate_f1(preds,preds_proba),\n",
    "                        'F1_no_val': f1_score(df_drift_no_val['isQSO'],preds_no_train_no_val),\n",
    "                        'F1_no_val_estimated': estimate_f1(preds_no_train_no_val,preds_proba_no_train_no_val),\n",
    "                        'F1_mw':f1_score(df_mw['isQSO'],preds_mw),\n",
    "                        'F1_mw_estimated': estimate_f1(preds_mw,preds_proba_mw),\n",
    "                        }\n",
    "    \n",
    "    # estimate F!-scores in rolling window\n",
    "    df_drift['F1_estimated_'+key] = rolling_apply(estimate_f1,1001,preds,preds_proba,n_jobs=3)\n",
    "    df_drift['F1_estimated_'+key].shift(-500)\n",
    "    \n",
    "    # calculate actual F1-scores in the rolling window\n",
    "    df_drift['F1_score_'+key] = rolling_apply(f1_score,1001,df_drift['isQSO'],preds,n_jobs=3)\n",
    "    df_drift['F1_score_'+key].shift(-500)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model F1_ref F1_notRef F1_notRef_est F1_mw F1_mw_est\n",
      "rf 0.7691 0.7619 0.7642 0.6412 0.5446\n",
      "xgb 0.7954 0.7783 0.7354 0.6527 0.4978\n",
      "lgbm 0.7953 0.7804 0.7431 0.6732 0.4971\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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20F6qi2GdxYIgCIKGCAJBEASDI4JAEATB4IggEARBMDgiCARBqJGic6h1g7yXulYd99VXX61zc65pJGpIEOoBcVlZlGUlOcpWfN3cqLWvb7WKzu3bt49Wr159we9CQpk58cteVOY7HrOo22NURCMQhHoAhECgu3uVhy1CxLLoHBLBUHYBCVvIwkWpCRRyvOyyy1SiWOmCb9AskOSF5C9kyyKrFyUqMMwlnFFkDtnAuEXmLfjoo4+KC7xB0FgrAmcJ3otMYJTAWL9+vZojCtMh8QssWLBAZQkj9h7aC5g8ebJK7kLSGOaPbGVz9WFkDk+cOLG4MkB9zfIXjUAQhApBrauFCxeqXTZ2/KjTBVMKFnckLb3yyiuq7Sseo2ImXhsVFaUWcXTlQ08PVPNEFjCqaKKODp5DeQUICZRdRp/wxMREtXijdMSXX36p6vL8+OOPqt4Wyizg+1Gf5/HHH1efh+xgVAw2VwoFqOyJKqNIrkJtLyz8aOgEsxaqg6JSKb4XQs1cz98yHv+6665TGhDMXJgLQP0kaELoYw5hB2FT3xBB4CBOn8uhA6cyaGDrUEdNQRBsAgsjCq1hcUTROSz+WPTNCym66FliLviGnT9q85gXanOROJR6QDtW7OqtFWkzF61DGQe8x1yYDqYecxE4lF1AqYg+ffqUKd1gzrDFd0B7ABA+0CbwWXgeA/V8LDl48GBxqQYU2IOWYC6GB8zzqo+IIHAQryzaSwu2J9C2p0dSgLeHo6ZhHKDSX6BRiWBb0TncolYQOvkBFEQrvRiXLviGXb3ZZo+2sL1791YdAC3fX7owGwrLobia2d6PW2tF4Cyx9AugKim+A4s+vsPDw0N9BjoAmqujWitk16lTJ9q0aZMSfOhTYksRvLqO+AgcQHZeIS3elUiFRSbaeqx+7jCcgvWfEqUc0oTArNFEfz5BlKNVvaxx6ukCYa3oHEpEo2AbNALs6s32dzPYhcNvAB8BBsxDlqBcNXwHsMXj/YjasQZehwJsKLKG16FzGIrAwXGNz0WJ5tLtXC2BFmP2J6BPOHj00UfV96LwHf4OS6az3wCaDPwWKCQH34RRsGvROZak/Oujd3lAt5vJ3/VqqeeD+OYbHlG6dvI/fk3Jq6oUrLKhaYSdZlw7LGRN4N45moPsvmFt6D+j2jl4RnWUxB18Mu8nOneK6M41RD7B55/b9yfRnOuI+nOEy9DHiX7n3emOH4gCI+nQyDfItVVJ+3ClMRVRwNYvyfvISvJKjKX8kFZ06rq5fFHX3N4qnXe7MWzfLg/LomL2jBqqT0CDQFVU+Diw8Fe7s5cT4xRF5/hLsfh/wGMkD/Rm28jHFvBEdlu87G4eaKNzOT8Xxvf38e23/DjPXvNyBmASesb3F7rc9Dfdd7hkXwTBBop4wfvndaJV/+PFvwHbAG4+LwSWPE3kzfuLte8TNe5CNOwpIg9voqs+Iep9K9Gvd1L0T9dTytCnKb1v2agTa7inHiXPxO2UG9mHCgMa8/cXUOgf/yH/nT9QfoNoymnah1JGvFCjQqCyGGERrwlgKoKjGr0AZsyY4ejpGMJHAC9OHC/qh3TBwNslGsfDUhBAHQlw0Yxw/jxSeJQ0+tmDXb8SHVtH1LA1z/K2ks8lHyBa9AhRvzuJ2l5S+c8uZHvnqZ1ETc53P7MkLSuf1u6Lp888NPtoUvw+yi/sRx5uYqWzmZUsAP5h5bLLtURjXuPtrN6Ug+2/dGApe+J3EXny5XTNbE0ImGnGl+Qdqynt+8lqEU/vxf/2brp/piCXvE9spJzmWkhhyNKnyPsY27Vz08k9XesxmxfanhKmLqfgVa+r958d9AilDfhPse/BNSeN/2+iIm8LzYRxSz+hhERhQEmnqiX+27+j7JaDqTCwacknCvnnsOlz9lheY1MDGaFiYCIym4mE2hEE6LRgacDDr6lvqdfwto0W8ECfNujA17Hg4F9zSVhOTOcbDBWWVi22fad2heTuwwtD7/OCAAIgg00Mc29kOzLb7a+owm4hk51Pm9iytfwlokc44sCvbETQHztP0hhaW/w4uvAI7UpIp27NSi4eQjnAlJl5WhMCV39W8jk4Cu/ic5txWhPIQZGUk19Iv8WepG/XH6XjKdn014ODKWH0WxTg6VMsBNxTDlKjebeQR0ocxU9fRwUNWvJOvzm5px0lk6sHpfW5k/LCO2kLPo/03rdTPm8iMjvzHHRccjMo8tP+lNFpAp0d/lzxcY/kfRT5+RAq8I+g+Ds2nBc85vflZZJLQQ6F/P0MmVhYpA55gvLCOlARNJ2glvw3sWJ9eKV2TQ15jLWaaTxhT4vTYSrhzCxxnsQ5blhMlTT523Mbai1Eo/TssOVG5kgTHt14vM8XdWCZN5lM6OzcCwMOpKqw52Q6/fTtx1T06z10JLA3rZ2wmeiWhfqTv7ERi3eLX13JWzN2IN2/nSiYBc7Zo0RLn+WtYOaFv2A7KzwzevCur4n2Zx5YYvVl87clUGZgGzL1nEIp1y2glUUxtOkIFCHBGok7V9LB1wdT1gp2NWWlaIvbpW8SXfmRev7omUx2uJ/va6vwD1dCIP5sFg19YwU9/ON2pYklZ+TSl2uPUJFXIJk8fNQi3GD58xTx1VhyzU6h0+O/oIKgZuojzvWcRqev/oqSxn/O929l809fyuUBinwblhACwOTlT9mtRlDAti/JDYJIJz+kNeWFtCH3jJOsRfxY4j3u7Mhu9kE38jq5hRImL6GCkGhq+Nd/KeLbKyhy5kXkm7BZ+3uHPcnbKo6SWcy+jm+u4g/NVu9HFExOTk7Zk5bGGkjSXk1DEgwpBM6cOVOppjr21AigAWi/Kg3ovCU7NBNN4fEqTxwCIo6FADpQt+fBW6ea5ejpVOq5/22KpZY0Kfkuypi9jd5mX+K4bqy4RLPjsMckbUc/jpUU7MZAGv8Jq98mCu9IFFPyh18CRKawM9IUEUO3bGpB77s1pOD97KzsNrHEy7AQ/Xv4DDuIh5DLyNsIBo2wkOUsCM7StItq+i+uB2SfJe/5t1JQXir5rGDbP8a0v/lK4kXRzV0Jgas+XEtnMvPoojah9Mgl7SimqaZZZeYW0G1fbVa339zalwa2bkjTv95Ms1kQDOgRrrJpG6x4gQK3zlY78NNXf8lCwLq2uZc1tqe+30F3jWpNF3c6H2mylf/dmjX0pdAAL/U4dcCD5LdrHkV+0ldpJ0njPqPs6GGUMG0lRXw9hoLXvkUZMPG4aTv64HXv8GKdT7mNu1GRXxidvOk38jizn9zOJZJbVhLl+7FAA+Hs8LvpZ02bnc9utR8ns+78jYqDR3ROCaBQ47qFv6L8skBCPcebhQB6KTuDINjIow0v7qzfEio6wTB3Q6nXHOMxnMcqfh1+YQifUT6FmmZ0V/6Rt1jG2yhfWucWSNO+3EQPfL+N8gqKaEIvlleXI7ipFFH9Nc1g+5zyBQFkGCJX+Ie3NuYVWv3jCVrs2ZWujVvKKn9eCTV+4+EUutZ1OY1spNuKj2+kR/z/oOeOjixfxTcw+QdXknf+Wbrb80U6kWGi16K3U9ekPUoQQKhOmrWBivi8PTiiLX257ghd8f4aJRCmDmpJczcco32J6TRrcm8axMfAnUNb0ZLdp+ivrSfp5oEtKPWi/6ode0bMRDJ5lmzzZybhbDY9/t12OpuZTx8sjqP+bUPJ28ONYo+m0oNfbqUQf0969Yau1DYiQJmUkse+o6KIYOYpUNohw/+uqYMepUY/3kD+O+ZSRrdJ5H72iBIa6axtQAgo2AyUz0IJQ/39ljHyuDa6s9mSzUjKB8WxGMFZhyk4gbXalkN4M6P3sN04kzUHjpC6jQVmZAfNsQ7zkiA4MHwUaXrv6OGjs/i7XuJjd+A5vv8x38cvhT16hJXRRdcOEE5q9/BRxPJP/3oTrTqQTNGhftQtKpj8vdxp54k0Osa25PtHtKGb+zUn+vslLTrlQXZABuo/bEu2fqN2aUVj36LL1rWlQ8kZNKhwI830ZPPFzeyUbnU+e/Ktecvp/tjxRAMfILdRbHJaxa9Z9jzF5HxG8x++lFryPITz/LkzkZ785m/635QR9NPmePavJKpdvxsvivO3n6C40xn03W39qEdUAzqXk09frTuqTD+nz+Wq9z95aQfWtKJLnNLrP11HB5IyaO79A8p10J9IyVLXBwTzsz/upFTWOKaPaEVv/raPbhsWTRP6R9G0TzZQLvsfcNnC7PT0NZ1oAAuJcuHfWcC2ryizwzjyPryCfQLPsTnqDC25bCmdMgVT71YNycfTrcLwUfNvtXjDsI03KL/yz8nNSznBKbQN0UcDlbZE0//RNIh/2YQ2jc2UbAoTjI2Lo3oW85cu4ptFpY59bHEfpqJR9pxDeeBH99mkXvQ1Lx4b2Ea/cn8SOxaLqGOTQIoK8aGnft3JgsGNxndlRWYlhyruYPvuQN75l+ZkrNIcFnldQrtPbqfXru5Cry3Mo1mt36OpLbQIFDPBcb/yIsY/5l5shgKNY9RNe5djtJHnIIJAJy+LnQOx9MsWN2XvH9Q6lHo2b0B7E8/Rq3+w7Zvx5X+/9yb2UEIAIDv77otb02288MMhn5adrwnyUtw5tDXdwprE4u2JdFmPsoJ91Z4keuqHHcWPISzevLkbxTQPpn8PnKFvVx+llIw8OpacRa+xJtA6wp81hlj6vzmxdNNFLeiWoS3I3VrVS168z3W/Rd0t8gmh3a6t6K3sm2nF97CgxtPEAVF0+0iOYisHCL3pX22iER0b0RNj9djwLhO0jcaH/TSBMPo1LWIK2i2ERRCbBk7x3/LL7exYn6UJiGqyhf0xs1YfphOJp+mRtBfJNOgBGjiSzV2CTefu8Xk76LVrYpwuOMTQJSag4t82OFoNy90WIk2mzt7ITsZYCripJ42A6p17zvqHjH2dtfVMeuuDjdQm3J+u6dlMmR9mJwTSFFf3Yo/5uew8Gpi5jBKCYqgJOwUVjbQaJj28TyiH8bUwURkcmOo8d87jWLJ76EzBCzSu3why58U4gMei+y5SJqEAb3fy83RnM3xZU5qnu6vm9ymHwWwmah8ZSB8uPkBdooKouYUWhn/39/7cT9HhfjR5aEve8RdRS77furG2K58+vBVN+WgDzdsQT8M6h1PfNlo457uTe9CMP/bT16uOKL/Bk1d1pMbB5e/As5pfRJMyXCi6uT+9MTCKfubPW7A5gW4e0oIFXNmfJK6NaSwEUlnz+HnLCXpsdHvtb8fCjryGsayxzruV6Mgqosm/s36NuAvSzEWjWKP96//YPMTC4KpPq20mevOvfbT9eBq9G/gdDXDZQct2sGlKBMEFwfry6qK9tO/UOTZLb6Rf7hpIzUKcJ/dDgtd1IAAwzALiU9YWOrF2AD9C9vU/a5EblsAWm8i2WmZp3Dk6lJRJD41qS278Ax3SNozyUo5T2vz/cvTGPvWafdvXUjvX45TdwWL3hB8xR6AM9EugTUdLRb4Y8Ify3MJd1OXZxZSw7EM649uKNhVE0/jukSUW+Sa8wGL3b00I2AL+jR+5qr36rCfn7qCMnPN2+G94IT+dnksPjG1HgzuE08iYxsVCADQP86PxfSKpgZ8n3X0Jm2EstMv/jutAT7EAOHQqg81GG+mf3VrkEMqIbD6UQkf4+jCz90S6WtTHdo9QJiFoEnBq/8m+i9Ln5IdNx+mGmespxNeTHmBzJQThbo6AK0Hnq4k6XaX5D6CFcgRTMQPuIRrBZsidP7FwvbdK58xMBs9xA/u5Hu6UTsPPIeqbL9+0OCW8hYpZd/CMsjxMYd8UztcU3mjCpOgsiCAoB/gLsPPCxb+czUaKf97gFf0PLSENJQs2fKIOL9tzigJ5lzqigxZRMrRdOBWwsuW74xvNkcwOu8QDWynN5EcRAywiiSB4WCto5XZKCZIz/CM3KjNXHaYv1hyhMQ0TqUnmbno3bRC1bRSghHFNExboTc9d21k5gl+Yt4t2x6epBfz7tcdoRJdGygxUHvewAJhzf39q6K9FClkyvEtj+uz23tSUTYvPsG/hMXYyX/fOGnro623K4QyhANbtP0OQY304kgl0ahrEI5B+Wn+8+DXn2LT1+i976NGfYtn8FUw/3TmAbtJNXcv3ng9PLb6Orv6c6OInrE960INahrWlj6sKvsE1ccmUX2ii7i143q2G0abBs+nJvFtos8E3MZb8yWbJhFQtvNdSoL+9ZB/d6reWnkx5nKPUG6qIt6cXaBtJZ0AEQQX0aRlCoRwV8lssuzLyebe1mwXAHPYZ/MRRr037EI15g0O1TbSCBcVg1gJgwgBQ+QLDmtCs4Hu1DGZ2Cn+Z2ZduDfuOfIP1kEAz135FJ67UsoyN+oP6PfYkvbRoD13aJYLebrWVity9qaDzBNaw2tktkqor+xzuG92G1sedobs+30xTP96g/v3urMBODzAfaIzlEcn/9u9N7UnXs80fkUVtIwLpOnYun0zNYR+DVu1yHd92bhZEgT7nk8vggE44m0Mr95ymhZtP0OQP19O/+87Qo6Pb0bfT+lEIayGhLHxiWGjgeiuDFb8EFqD9bIooKOQd++CHWVCwiQggx+U9DsFdzXEcSfuJ9v/F1+hb7O/aXuHfvmLfabVB6tiTw61v/pnaDbiMjro0pZUHrMzHoKXl7/hmC72zlM+pBZu3bqYHEx6mpwrfJzc233VbMZVu7uJD//C/I9YPZ8DQPoILgYVhTOcI+nHzccos6kp+01fwr/gDLdNz/CeqfMEu3k0mcZTKxawFWALz0Nvru9Ft3a4lt+UvkWvBc9R1gJWSFT7B1KVpIXnyd0EQjOrE5iIDgegcJHz14oX5TY68cflwObl0uYZeHmf/xIpxvZtSP4702Z9wjuLYEd2Bd+UN9ZyA6gAH8x0sUDBAQVER/b3rlPIFQDDEJWYof4Mlg9qHsl/Bm57/aZfKuoSgeGJCJ7q6XUmHNrTN9/8+wKalPApmc5E14Cj/fuMxmrvxuNI0J/VvTs+PY3+UpVBF0t3SZ7QBogYQ9UfpL+tAqBzfvYE+CF5JHoVsfnLzowBTBj0Suo427mH/GWvPRmcNa0xg5f7kEuHgZ5a+Tf3djlH+mLfII7IrO/bvot6N3GjWtiyOMsyk1uxbdDSiEVyAy2IiVDTRMqjjKA/AURLYDZG/Fvu9nHdJ+Pce0q5kxjMEQy7bAh/P0SJF3nN7m/rwYleGrBTy/v0+ujkszpB+gp0JaZTNTlrE+Ht78SJ8z0a2aZ8v0WBvGgV500UdwmjKxdHUT883qGkQRTSuVyT7Cs7S3LVH1bH+bRuWeQ1CUzuzA/ul67vQe1N6UJsmZSuPDuXrDJvIlbp2URrYn5Ff8TI7JhuwoBjePlyF1cK2X0ybkURT/yS6a70WYTSZA/smciiqO5//7FStPEcp9sUnsRnoHeqTs0bVZVJwAMXt6TMoPHm92gwZnVUsAEBieg4d4CgvcJxDkW9PuYHmDF5GHn3Zod+UozfvWkdtOmlNerZY+83DbLfhM62qbi0hguAC9G4RQuG8S/xte+mkaCoWBMhmhdpuCRKb7hvWmn7Zk06Dct+lqXkPUy82NZUBxdFi59JwnwO0g7ULRK4Yie3HtX4MXRsWaaUTUCTOSo2mug5CVaEpzFsfr3b+LdjxbM3H8N6UnjSQF/vyTGJd+Vpr4OtBK3Q/AZzHKZnni/W+sXivOqfv39Cd5rFf4T2+bdrAh/47L7bstRXOu/iek9nBPFCr3prNi9InrIn9/WKZ781Z/By152CHnLEzzhf5C2pGhR5+1MYlnlbHGds8ZOLFG8K5D68X4J992vn4eyOHlzNju1lkrXPkVrT7GXrWew7tPVQqMxyc2Ey0iE15/35Ysvgg8kZQZsUOiCC40Alir95Ytl3DLoukJUvg3N3GP7qLS2kDAD9k9BlAyGNUdHtq1K6fdVUemccBEdTC4yzlsS13xwlUsDQOOH+RHAkUuvwRos+Ga5mw9ZBgtvEP76KZD/tx2GlVfR+ISoM/ChuQu7/dQn1fXkb9X1lG/1u8T/W5+Iyd7rewKeiyGM2khHDUV6+KocNsgniF/TAVBiSgtAo7gWnNO1r9LTMcIBET/y397jWWGnS99Pxx/htcw9pRR/cEZQ4xMnvZtAihfG3vZhzk4K/s/yhkedO6MXRf2NYyoaKuuak0mRZS0GEr/RD26alXcUvPHzu+XssVgVnaDoggsIHLu0YolRuZrZbASQYtrrR/wJI2HPmC7NfPJ/cu/wtYEISZNEmPukNGYnt8Kt3UYDcvPByP3uXqel0OYUK/KBW2Oqxzyc5YlWUYm3vOcujharZJT+VwxNGdG9P7y+Po3jlbVZTV4+aEMx2U2JjYJ4q+ZBNRzxeX0sBX/+YorUPWK1SOfpU93my+mDdN25myGajot4doZ1FLOtD1sTIvdwnvQO3dTtAq/i2YI56MyCrdYQ5LwOA2YcoUl/zPx2zGc6FG3dCfqxSNYzhEOpoGZC0rG0aKWlS+oVrRS5jqwH6OVnT10AS1HRBnsQ10b9aA2nM8+Wt/7FVO4EYcfgj+3pukooq6RKLRWjUIjCCPU7soms0Fm49CIJR0JNZXsDs9kZJJNxW9zzGdbKboX704d2enFe8U/3h8iNrVV4fLebcfxqbI7lENistSTB7QguZuOE53XdzKalTTi1d2piu6NmGNM5Wjf5Loxd/30FbWxl6/Oob8OBKoGJSigL9gJmtn33GE3G3LaOPgWXTPvDh6t52VRD3WCIIKv6W8nBQV1orMZyOykjWiduz0xdoAf+HhtT+R1865tLioNw3vpSWOloC1qYx2V1PvrW/Qv3u2U7+eFpUfhj6mddaDQDDXKkPHPeSIeNd8ODUQjcCWk8Q/XNhcszjC5f65W9XO59OVB1VY6ciOjauc3FQMMo35B4jIGTiMjZKgE8s+kdYuJygg56RWvsOiQF99pbpCAOB6G9A6tERtIggFlC5o3tCv3O/t36ohTR/cisNR+9JjY9rTHztO0qi3V9KULzbQA3xdrz905nxE0Q0/aqWv2Yf1b1pDSnZpUFzZtQQ9JlH+w4fIJ7AhffWv5gg3Gtm8Luw/cpQ+K3qatagt1CcghT73fFNFVW0KHU+NOSDBGmH9b1K3hdt+sPgw1gCgqSEp0Px7OHOQxwGidmPs9jeIILCR1uEB9ALvqv49lEKXzlilojLGsEr+9GUdq/+vgMzPO1arhDRknF7x/mrVP8EI/oGurnqx2aYVmM6EGgX+iTuGtKKvpvZVWmgSa2bLWUu4+7stKsO52JF8w1zlGI5l8x3KpyCHwJpfwcO/Id3Qp7mq1wVfhNHYeDCRZri+RU2zdqs2pl6N2tKrjd6gJ/OnUKve5Xc59A1vQRs8elNSqoVf8HsWDt9cTavZ8Zy7naMTZ47UzKXYKIkgcA6u6dmUruoRqRxDiAh6f2KPMhUjqwNyCD6/pRc7nfKUMJi/DdW76y9YYHY0HMsho2yLDjGGOcyZgO/g61v70m/3XkRfTOmtrrsv1qAlyHngR4Afx6o2YGb12zTFdzWHwLrQN/VIK9jJgRt7Ey+8IctbPYP6ue6hgsvf19qhsqCN7jWGfnEbTaO7WKlYbMFvnd6m/0sbT4VI+kNE0NG1lN6gI930+Xr6axf7JOM3EKWzxjzyeUr1tF+OkWgElQQ21aX/GaIigqptEjKTHEf05RVEx9bTcNYK0E6xVZg/ffyPXVozOAXaApNGMageGtraamasUHugiis00k9WHlLJamZOpGYrAdG1omqZuxdQYNyvdAlryD9uOq5MJfWBJ37ZoZIdKwKZwb4JGyjesyV5dr+u+PiEXk1pw/+NoLALJCj2aB5CmXy+Ti3nUNHPLuYfRiHt8OdwXuaTE3rI6ZKnKC83l0ayGe+1P7XquzWN/PqqkG1c45mAWAQP/8N2QBYIDMoJXNGtiTIPnUq30oqwHhB/NpsyMzNoeurb/KD6/SWE6vPwJW1Vba2P/mGbtA4qjYJuFWkEcPSf3kuT+jWn9JyCeqPJxvM1in7ilnkapYFPr1XRISpqpJWUtzS/lXDClwPKq4MjqQVah8SAJrQqS6sptfOMvtGM30jr1y5TSXsoe2MPRBA4Axw+qjh3PmltaFstJFXFI9dT/0AHl2PUOv4X/rtLVt0UHEP7xoE0jiOLZq85UrwBgVkIIa/tLKqwliGiK1FGIvUJTlc+hwXlJF/WJXLyC5UAgN8WlUPNfL3uiOpVYg69/W37CXq06B4KG35flb4HyX5IWP2+YAjRvWwinfoH7Uw4R1EhvsrUtjD6adXvZMaeQA4E8KUhHJpqD0QQOAMI2UMyD2yBOh0iAtQFUl8FARrx9HTXTV+IThGcggdHtqUCNnd8opslkaXcMSJQCYNyaafFybvs/U0FUKw/nEJnK9hF1wUS085r4msOaslyKN737rID9DX7QRaz/R7Rg4t2niLfdsPIp4XVxl8XBJpDrxYNtPyhgMZkCm6ukkoHcIQXchJeTehOOy/5njYeS1ONlmrMHF0KEQTOAvrbWuyMcYEgZ2EVCwJVPbIeARvyr1tP0KhgNiH4N7beAlRwCAg/vbJbJH27/qjSCrAoXbCbVoMWWtQXmzZGd4pQC+TSPbVXJ8ceJKRppaSDfT1orV5MDgl88JcEsMnn+YW7VTXWqMwdNDWM7fbVaPnbk/0E8MVA+MAchaKBnSODVHY4jj/2cyz5eLjRhJ72a1wlgsBZQDEqNKqxAIkpsLnCjFKfWMDqNP6uGBe2RUdqxbcE5+EejojL580HeiEgd6ZrMxsSJqf+xZEtz/ECFqhKhmDHXJc5mappBEjCO3ImixfoLLV5CfLxoE8m9WRBkUMPfr+NJnsspZ67XipZ2bWSIH8IoPqwucQMklRHdmqkNLGdJ9Lpyu6RFMRCyV6IIHAWrphBdBk7Ti24qHWYamBSn8xDsK2iGmbncG8tA1bMQk4Hemej3af5ukOhuwuiR325FBVwGHQjVYCtOCehDpKo+0iu7tFU3aL97OJdp+jSmAg224SqUHJsZnp5x5Nr4y7V+i70SceOf9PRFCUI4BuATybQ24N9hZpPAKXE7YkIAicGOwBkjKIkQH1hy7FUFYlx/YDW5HIvRwtd9JCjpyRY4e6L+d+HNyHoD92inGzlMvx0K9F319ElnRqr7PiKrls0zNl6rGRdLbzHMnTVkSSwSQZVXtEICCGg7yw9oMqlX6W3TkVmdr9mPhSRf5yomoIAVWmhdUEjQO4COvOZy4Q8ckk7enl8F/YZ2qe0hBkRBM7C/sVa16i0+BKHsSPALgGVDesDiLqAjXV8Z703g506kAnVAyHSUwe2VAufzQ7KoEgVBt27kSs15BDoP8sxD+1KSKOrP1xLV3+0VjlewcGkDLrsvVV00WvLVTl2R5PIpp/GQT7KVzeQHbew2yPCxxzuibLzc68MJhdTYbUFAejFfgJskOCct6xdhqKVN/S1KGFtJ0QQOA0uWh5BesnQu54cUQD2njzniEnVKNjtLdqRSDd2DSK/T/tpzTcEp+WpyzrSc+hsZisdrlAlFty2fEGj2jekpWxOufObzTR19kZ69Y+9qkkLevXeMmsj+bOmcRGHQiIUE1VTx72/RnPE8vFJs9YrjcGRJLAgaKLXCEJdJzCehWKJ8uGJO7TbRpU4R+WA3zmc7DA3dWYtpLaR6qPOQqCeS1BKEDRroNUxP5GaVdszqnGg+qLnwtScL7UIKakvVL9o0kPbHS97jp73/oB2B7+ndvpu7D+AvwGFGgPY7o21dO70/srk9BxH30ArQObyRzf2UE7qCR+vo5tmrleNdUrX8a8tTnLUUM/mmm/kko6NaU23ZLqxbyk7fbcbiaJ4Q9OgZY1kduO8IPio2tWMq4AIAmcKHwWlkqtQuRCa+QkOK6vrbGX/QB+3fRS+/zutzG6Tbo6eklCTuLKBYdrfRHFLyCNhK80fNlY7npVCCXk+KiQVAuEF1jJQxBE8P64TXdurmXKOmnMVvpnWl81Eq2n22iNKK3FEeHNqVj5FsGnI7Kt79/ru2pP57ETOz9K6tLm5qzLcNQGikdryOYHgRMn72kYEgbOgLiyvMhoBHEmNA71VfHFdZ/uxZPqf9ywif7Z5XvyEo6cj2AOUTm5/qTbA8Y3sGLqSmoz7gB2fV/Io2eQeppYupUwhcJZ2Zw1h/eHzGb21rQ2ACGvlo3//D9G27zRNIJUdxVfPJGrev+zrqsD4HpG0m/0E1vpJ2BvxETgL0As7Xakl55SiKZuH4jmKoS4D+2da/F6KKuQfz5BHiDxtjEQR6jYhbDYJZpPKP69V6m19oxuqRTG9VHvY2uCknlVs1ghKcOgf9hS30SqFpseXKAtTXVAafMZEXfOoZUQjcCau+tTq4UiOVkDru7oMVN7Y3Mb0y1WbaXyM/TIkBSfDjx2tXa8jWvK0pu3amEXer2UIzWB7+aYjKTSsfSMHCQLvkk8UFXG484PaZq31CE0YoDRMPUA0gjoAMjWR4FKXS01sY/8AiGnZmLcfFZfmFeoZWDRB3DKb34L8GQ83F1p/qPY3QCd17btMZzH4QHpPO//3wJxbT8KfRRA4Eyv/R/R6dJm6JYhfhmnFnO1YF9l6/CwN9j5E0ZteYG9cyUQioZ4T3lGrsHvQdkGAhk/IaP7XAZpwAmsEKAVfxlZ/Mpbo7JFan09tIILA2aqQZrGDLP1EGdMQqMuRQ4gYGtUggVzWf6yp2IJxwK4ZTtWRvAmoBH2jQ1SmLXok1CaJ7Cy26ij+479a9nQ9RASBM4GepK7stlnzbhnTEKirkUP4ISNBKNpf/0F7136ctOBgWgxip3HlfEN9WzZUmjDyT2rbRxBR2lFcyE5rDomtr7kvIgiciRA2C3W/iT1kX3Bo2rHiw010QYCStHW1NzH/nqmZbx5HCwVo8deC8djyNdHOn21+Oco5uLm6cKDEmVqvMxRRWiM4tYuoIFurElwPEUHgbAx+RFOlLbQC2CpR+KqumobMZbQbefD8fWyoZCnUT7Z8SbTuA5tfjlaPyLKtTYdxJmuvKPMQEVxKEJzQ26mKRlB5XFxcRvPYxyOOx2PlvGYoj208dvHgIF2DE9SU6PrviIY9VcY8FF9Hy0ygiFiLhr7kSYUiCIxMq+G8oG5mW+Fpm9/SL7qhapdZW727T+qho01Km4bQV9svnM1b9i8AV680Al7U4XKH+GfDNyFPfCIfK5Evzo+xPfyQxxUmk6kT306w13zqFG1Gagsm4pQLcosdxnVVIziTmUfhgbzDuuZzottXOXo6gqPoeAWvOLwsfMFLQtI+m95yQ58ocuH/Xv/TttfXVFZx49KmoeFPE133db0JF61N01AfHnG8wB/igSLjc3mMK/WaG3j8zM8rgzjf2r5VqO9AAHwxlqMUprKjqkCFkCak5nDATdVb4jmKc6xqB3rrfoF6+kMSbKAR7/UmLSDKSePV4EaOHmMN8QJEsSZ560Utad6W+Frp1Jd0Ttt4oV845WUSHfuXnVw/auVfUFainmJPQYAODsctHqPQvtbV4TxteTRgzWAFj808Jln7ID4+nccmjKSk+tOkpUKQdNVzMtHe34gW3ENNeYeCyp1JdbAvwbmcfNVtiX57kB0Gcxw9HcGRtBhINJ0twFd/pmkHNjbJgY/suYW7VIc7e5KenV9cBI6+v5lo1iW8VZ3GqsI2u35vfRYE1rZ+pf8VsU3syQMVqviM01O82Lct8yaT6VMevTDCwrTWbYag3x1EQzh2efsc6py/vc6GkEIjQJ152j73fA13wbiggU2T7lo+ydr3+QKpuNG9PzuNH72kncpFmb+t5mr7WCMtWwtxDoQgOHuYKHooS6INRM1ZgNVj7CkIoAFYBg6j+WeCldf8yQt8Jo9kvr+SR1c7zqnuMeA+/lfyoOZn16mHaKJtCWq8PzN/pyNmZhPYwSGPIMiTtPK9EjUkmMFC+/eLRD/ewqbQiltUoncw2kaiwU1Wnv0SzNJYI/DzdFNVfymTw1ZD22mlpj2sJJjVI+wpCDbyaMM7/JY8sAxcz4MNhCWYz+Mift6dBzpQ9OWxx45zqnt4+RO1HEwBLjllcgnmbDhGLy/aS1+uO0qnnbT8RFZeoUoKauiuz9tbwkcFnYat2GvIGsEx3uT8wWHTFZh90C7z6cs6qjIrH/9zyG6nMJ3NmMosBP8FIvisVAM2rCCAuYbHMh5q68m3MTyevMBOEGL7Hh6L9cX9Bz6GENE7MPTX4PifPGJ5sP5FM/mY825vHcVN88hj3LsU7OtRHDmEBh9P/rqzuKn1Cn7srGYhEOKqazKiEQiWdLmGaBD7jjbPvmDr0l4tQujyrk3ok38O2i25Mo01AmUWgv/irrVE/e8yxL+XrRoB/oUe56E8KbxYx+o7/Arh1y3i0ZZHKx4v6cc+xrB4zRs8OvLozOOdyv8JBkCPtGkW7EUL2EZ68f9W0G1fbaI24f70w+39qFGgF63Yd9ppHcUg0IN3WP6NiHwbOnhGgtMxjEMz23JI6ZKn+IKx3vDezGNjtMY2MBHZVRAYDFsFgS8v0tixW1K7laCMDofbve87k/q0DFHZloivnj2lj+oBe3G7cFq1P1n1e3U2kKUJTGjw/fB+otbDHTwjwelAeWdEEU1iS3FA4wpfisTK2wdH08LtCRR3OsMuUUNBEATHebmbOZLotDEs1bYKgmQ257QyR/3wfdbnqGRzXcG+ePpT85Q19PmknqqL0bNXdCpOehnKguAcO2Q3HTnrtBoBBJYglItXwPk4/YyKzZwT+2rZvUt2VxxtVFVBEIhrFbW+4rH3NUbei62C4G4en/Boz0IANZIf4KHs/EItgZ00SlRbiWce1CZUNfFwRvOQ2UcQcXIp0XdsTcxJd/CMBKdm3YdEM7pVWIYClUE7NQmkZXtO2cU0FASNAL81c4c1A+BqY6mIO9k0hLY8COJvz/cH8Thq99kJ54m+WLu10twDcdYwGS13YkEQkB5HtP8P6U4mVEybUeyJzNaaNFXA8A6NaMuxs3SmBhMs89m0mslRbkoQZCZr2kA9aUVZbUHAC36hnvRFerz/ObvPSiiLP8vgCN4pxf5QXH/IEvgJ9p/KKJNn4CymIe8CvmzcfUQQCBUT2pqox81Em2ZV2A1sJAsCVFtZvi/JDlnF7qwRJGutKG3Mfq73gkBnK2sGC3jczOMq87DrzISyoCJpPw5nc0NaRkmGttMyrlcfwE7GuTQC1JR3z2eTkISOCrYwRC9UvPHzcl/SOTJQRcvVpHkoXddeVdRQYJN6n01sCYs+m2DRSDCaDbM4Bsex7V0mhOrTRm+aDQoLSjR4aRnqT7zeOl3zGmgEMF255KRKMplgG4ERvAgP0Jrdj7Le3pI3ojSsfSMOpz5BuQWF5OXuViP+AaBMQz0ewV3DYJMgYHPQFHtPRKgEe3/nkAmOvb7lN+1Hw2DX3dDfq7h6otPVGUL+QLit+w7B8Fz6FjtqK845GdEhXGXXo3HN4LZhNSsIDIZNpiGWvk15/MLjNI9TPObhmL0nJ5QDmmOkc/TuTI4kSjgfRRTqhIIA6rYKHb38XaIJsx09HaEu+Qou4Kgd2DqUvD1ca8w8lG4pCD4ZfEGHtRF9BF/odYKa6KWkF+rHBEfQuAvRrYtZQvM/36zRRLtRsolUqd5kJytTDdOQ0ggEobJs/ZZo6XPlPo0Wrj2iGtC2+LQaObdpuiAI9HbTquQWOGf9LkcKgjA2D32B+kH6wNbOQPWgnVQY3PY333YmmncbR1gcZY3A0+k0guKmNLMvu2AtGUEoQWIs0b8fEuWVHwnXOtyfDiVl1EifgjSzRkAZbA8vYnOmMXIIKptZfBNyCvRxEx/TMy4Eh+EfzuaWLzmW7nkV5aBpBHl2b95R2WqOgV58mR1ZpcdmC0IlWrZiV45rpxxahfmrzUZNNGxKZ0Hg6e5K3nlnDZVMVhlBMJXHtTwS9dIS1+jHBGdo8oEGNm4eFMY+AnQxS9ebazgD+JGGeeg/Uu8gx05GqFs0H0Tk4Ut04K9yXxId5qduD57OrJlNi7c5mYwMVSDRJkGAnsI80GAeJqJwHldKZrGTsWchjdt+O3kQdkc5TtWUJtRdSlALVQDNYNAhbP/icvsbQyMAB9k8VBOmoSAkk3n6adVQEZRhEGyNGvqSR3FHEb6PPsOz7DctodK4skaQvIEmuS1mP0HF3Z5quylNiJs0pRGqSJcJWoOYXOs1qhoHepOvp1sNCgIPDonpRnTDXK1xjkGw1TQUw7u7VPMDvg8jWnf7TEmoEu1GU2azoXS/+y+Udqbimu61XWfIx8uLqGmf4pwHQbCZTuOJJv9ebigpOpfBPHQoKbPmBIEBsVUQuEILMD/g+8g0lphAJ6NgxIvkSzkUFfuuo6dSos5QYThHNk1bojUsF4TKNmVCvR/Y7X+ebrXRfXSof41oBOnsW1PlJRb/H9F7vQz172SrIHiTx1oWAC9g4D6P1+03LaEqBDTrTHOLhlP7+J8qLNhVW5hrt0gegVBtMk5p+TI/Ti7T6B5+ApRWyWZTZI1oBOdYoy5ynoALZ3IWf8U3V/OAOEat46v42Nf2nJhQeaAmf+19I30R9RI7upo7jUbQ8sj3RB/2J8p3Die2UAdp1InoCjS65z3o7w+WaHTfKtxPPTycXHXzUBH7soob16PyqIFCRyvjLIbX5CAv/vwvQTt4jLB0HgvOg2dgGK1y6Vnc59gpehHkJBAlH5AS1EL1iGHH8eBHiLZ+Q7TugxqNHMrIK1DCRAsfPWOoZLLKmIbm8Sjkxb81387k0ZLHd3ablVBlkFTmmnqUd00PESXtcwpB4F2QoZWgdgLhJNRxhj5B1OFyNk7PIMrWEr9ahvqpS6s6giAty6LOkNIIjJNDQJVw+BahtITeg+Bdvv8e399qz4kJVQNlJg7FZxFtZHkdyZpBWDuHm4Y8Czj0z1sUSKGGGt2P/5Rveely9yyuOYSm9tWJHEoz1xmCIOg4TvvtGAhbBUE+L/wT+XYSDxbHCmPGWdUBjWB+VgMyebuTS/J+p2hK45afoTUnF4SawNNXu0WSGew5bu7KPFQdjSDdsvLomNdqYpb10jSEfgTs7aOXWBs4zEIBpiE21AnOBkpR5xa5UWFwNJuG9jtHU5qIrkQtjNPtSagFUo+xbaIb0a5f1EMIAmgEcPpWhXRdew30ctGaPhkMW6OGdvO4j8ccFgI9IAx4vGrvyQlV0whAdhALAifQCFTo6PCniUa96NC5CPWMwKaamWiz1uMCSWXZ+YV0Mj2nWqahsPSdRC+Eat3RDIStGoElcBYLTgoKz4GzviwICvPKrdFSq01pBKGmgRDocQvR0dW84YlT5ajBgVPnqiUI/AvR28BkuP7aVREEEvrhxITqGsHmVncRPRCrZWU6uinNxxcR/fWkw+Yh1FO63ag5jbfMpg6NA9Wh3Set1ySyRRC48srmk59quMqjVRUE5bcMEpzGNJScoe1wnKIpTdpxSSYTap6ARlp10gNLKMjXg5o28KFdCenVKi/hkp2iHTBYHkGl6wWxb+BX3LKvoD3f31vzUxKqQwA7Z73cXSn5HIeQzrlBa+7RC77+2udcLjSCAK3DlIePQ+Yg1HMG3EuUq5mDOjUJpN0JVdcIglQOwRmOQvLSSlEbiOoUjkO3COMU7K4jsIBWkUOnMzjy4eR2Dtv0d5wggEagojByDffDEmoJaAQ6nZoE0eJdp3STpEfVBEHUABYEnoZLfnS/wKIyo7yneBjLm1KHKG5iH9rGYZFDaEoDQdDAUw/FQ6cpQbAHJ7aw1plJnSPbqod7Tp6jPi1RILly4aOBEB7tRmvDYFzIR4CtJMdT0eZSYxMP5+h+IpQBGoFqYo+sYtT4cUAPY3NTGpiqqMu1ROEd5F9KsA8IRFjytNIIwK4ERP5Q1TSCzGRD+rMuZBrayGMn7+5Qdrq0tvCsfaYk1IRGsO34WU0jyMvg7U6C1tvYEXWG/FlxHP5ZrX63YDCi+hOtfpvCvfJViZWqOIzPmXNeZl1C1DiGaMIXdpho3dUI0KR+m7UnWDggu1hwQsJZEJzJzKP80E5aZzDdmeaIOkOSRyDYneYsCEyF5BK/kTqyVlAVQZCTX6hqFilnscFCR20RBP684Oudx4W6AsLoYA06ERCjdQYLb++wpjSRmbuIXoogOri81ucgGARsdlx4KTu6jjpz5BCSynILKpdImZtfRD7u/KPJThVBYAUVKqqbglCKulLwe0bz2McjjsdjFbyuNw+UuYYGIlSTqBDNMXssRZfhRUW1fk4zcvVeBC7sq8jnebhJhrFgJ7wD2ZzThej4v8pPUMC+qQOnbC9AV8ivzyssomAXVC9lYSAaQRksY6iiK/Nvw4s6UlrRPWIMj448JvKxjuW8DuX+Flfm84XyiWpoIQiWsivn44EOMw35ueqJbRI1JNiTCbN5hfle5RJU1mGcq2sPQSbdpORbuYgjI5iGLMNNKht6wvoaxbFp6RAPRBjN5THOyuvu5TFPb4Ep1ACNArzJ082Vjp/N0nY3p3drfVhrETjfgK+LHoEheQSCPQmJVuWpoQ2j4m1l/ATZ5l7H3g2IRj5PFNHNTpOsu1FDXXnHnq5rBj76fdIf8/pu0sSvdRCmctzicTyPvpYv4M/Da8bzGMajd3kfxK+bzjcYFBUlOWy29C6Gn+A4NIKYAdrBo2uIOqPtdO2QYY4aMrFpCEhmsWBvVr9DrkUF1DFiIG2Pt10jyCnQTaf+4bx9vd9Ok6vDGgEv9G5Y7HkE8HDX75sfVyQEgLXUvNJaxTs8/sufVaFnh5//lEcvjLCwsAt8rQCa8c5ImYYad+Wdkj8LgjIRwHY3DSE50yu8jVYlUjqUCfYmMZZo3fs0KDqQYuNT6bSNJalzOGIIBBax8Eg57BCfWl0sOmcr0ACaWTxuyoMD2kvQi8dc3vEf4Vs4ij/k+1facU6GASry8ZRs1b2JmrEidoQ1glqOGoKK7tqS/RNXzNAceoJgT2KuU32Mrwncq6Lm/tiZWClB0Pr4T0QzumklUQyGPQUBktHaoJsZDzQXvZ7HgtK5CDxaYPBD/legu8xF7YTq0SzER2VLqqbcPW5mI9/1tZphjKghlVWMbk8G3GEJDqDVMFU1tMmx+dSuUQD9HnvSprflcOgo8EZvbQ8/Q5ox7SYI0Oyeb+7Ro4H28PiBj+1ioXAHhr2+VygZQqocxp3YDTPogVotpFVc+GvpM0SvQBkUBDuDEOUubFjY9yeN7+hPG4+mUGLahc1DubpG4I1eBAYMHbW3RgBhsIhHWx6teLykH/sYw8prJ/OAViDUkI+gRC5BVgrRKY4eqiWKU/aRQ2DAHZbgQPNQ6xE0upW3bh66sFaQo4ePeuWdNWToqN0FgeB4QaAih8APk4h+ub1WTUP+EAToRcBhfYJQK0T2IJr4HbVo3ZHaN7bNPJSjm4Y8lSAQjUCoRwSyWSbY1+O8RtCcnbaJO7QU+lrTCDx0jUAEgVDLcPTP675f0bajSXQyjYMmbHAWp/dm82m/u2pjdk6HaAT13E9QLAiih/D/WFc+tKLWBAGihkQQCA7hyGqKSfiRXvWYSSv3nbJJIzC1HcPhLSNqY3ZOhwiCem4eKjYNoTCXVxBR3JJacxarfsUdriDqdkOtfKcgFMORckVDHqdr3FaS5/7fL6gRuFEh+SWu1/oRGBARBPWYZg186URqtiqqpfIJWl1MdGCp3cNI8wqKKJeHchb3vIWoz212/T5BsIbr4IcplzzJP3nrBZ3FIZROAXN407J7viFPZnV6Fgt1wDSUX2iixPQcigzmyJ2hj/Gvw73WKo8q0xCilRA1JJFDQm3Dm5+Tns2pYUZchS/LyWNB4KL37BBnsVBvy1Gf0c1DaBeJrmV2zico0ZTmk8FEvz9k1+8ThPLYEnE9zc0bqHpoV1RrqJE7SlAzIgiE+phdXJxUZgamoX9er5XKo8o0lMc/MIkaEhxEetur6Ye8AZSckVehjyDcTQSBUE9pwuYgN1cXOpKsX+Tg2FqiFa/aNYzULAhUHoEklAkOpHmIN7VyOUEn4lHOrHxBEOYmpiGhnuLh5kqdI4NoTZxFJETrkaq/qz3DSM2moUBPjkUoyJFeBILDaOmbS8u8HiHaOa/C8NGtnr2IJnwppiGhfjKyQ7iqzX7KXJK3aW8ibw4jPbDE/qYhN3N3MikxITiGJpFRlGwKJPek3RVqBKleTYg6XalF1xkQCR+t54zs2FjdLt2jJ9XgQodWsP9PoqLKNfiudNSQjxfR8Ge0rGZBcACe7q502K0lBaTvr9BZHEP8/PENtTgz50IEQT2nbSN/FT20ZLdFdmW7MZpWcM62Mr1VNQ35BwQQXfQf1kLQdkIQHEOybytqnHO43I1PDmsEN2Z9S/Tn47U8M+dBBEE9x4VDRUd2bERr484U79Sp01VE920hCrJPeehz/D3YiXmZ8rWOT/kV13oRBHuS2aA9eVGedi2WU4Y6yHTOsKGjQASBAYAgyCssolX7k7QDrvo/O5rG2MlHoMpLnNqpdXw6vNIu3yMItpAbNYSm5T1EqW4h5TqLA01pIgiE+k2v5g1UJdIS5iHkE7wRTXTmoH0LzgHJIxAcSFiTFrS0qCcdzbC+783Lz6OgAo6sC4yo5Zk5D6IRGAB3DiMd1i6c/t53mgpYM1CEtuatEO+C9v1R49+XYe5Ohl4EQPoRCA6keUM/6u5ygLJ3/mb1ef/8M+RG/LsIsmyxbixEEBiEoe3DKTUrn/ac1BNnGrQgCu9kF0FwvjuZnsgmGoHgQBAscav7Iuqw/WWrzyfk+9FnbT/RgigMiggCg9CTzUNg6/Gz5w/iwkemMQrD2cU0pDuJRRAIDsTH042OerahoJwTVstMnytwo+TgrkQBWqi1ERFBYBCaBHlTWIAXbT1mUVoCCTQmVok3fV6j34XoJGUaiuSw0dGvEfmF1ujnC0JlSQzuod05tq7E8aIiE3Uu3Evdzi62e3l2Z0YEgYHCSLs3C2ZBYKERNO5CNOpFoo4sEGqQdOUjYI0gvD1RvzukxITgcAoaxVAOeRIdLSkI0DfjSrc1dPHht+xeldeZEUFgILpHNaAjZ7IoJdOiEuOAe7XS1DUEyv1qGgELgvQEotN7auyzBaGqNA1rQNuLoqmwVPYwkskiXZIp08e4ZiEggsBAdI8KVrfbLP0EIDmO6Ofb+VeRXvZNB//WUu9tzDnIzCtUGrYSBOs+IPpsWHWnLQjVpgVHDj2QdzftHz2nTHeyJiwIsn2MGzoKRBAYiJimQeTK2m8JPwHIZQEQO5fo7xc0x3GRHmIKljxD9PlILedg/1+2l5fw8tBLUGvNcQTBkTRv6EsnqSEdSS0ok0wW6XKGcv2aOGhmzoEIAgPh6+lO7RsHskZQShBEsiOtywSiDZ/ygt+K6G2Elf6pPTdpPtGE2byys+q86CE2tuZW+B0ZJZrSiCAQnIMoFgRsuKRGGzl4YctXxcfzMs5SoEsW5ftFOm5yToAIAoPRjc1D21gjQLRECcazELhtOdFFD2stLRFNBHxDOLpoPNEY/gGlHmNh8VmFn59uKQigEUgymeAEBHIUW0M/LwpP4nDp2B+Kj2e5+FDfnPcpqQ1vhAyMMYtvGxhEDn23/hgdTMqgNo0Czj+B+kPQDDCs0epioh63EAVH2div2NydTExDgvNoBbGZHalp/B+s2XLAhLsn+wiITlEIufkbt+AcEI3AgJFDoIyfwBaumMGhpldU+BJzhVOVRzDgPqKhxi3tKzifw3hVbhuta97JbeqYV8I6usttPvm46E2UDIoIAoMRHeqnKoNuscwnqCAUFOF1Jcg9R7T2Pd76J164cX30EKK2o6o9Z0GoKYfxksyW2oMjq9RNcPwKesD9J/LyMnYXPREEBsOVw4a6snmojMPYihB44PttNOrtlZSVZxFpkXGaI4meJlr/8QWihlgQxG+yS3VTQaiqIEg2BVFW5MDioAePzARKNIWQNwdSGBkRBAY1D+0/de58oxor/L7jJM3flkDHUrJo5iqLhh4NOaqoA5uHNs6ymneAqCEkaPrhh/XjZKJVb9rjTxCEKlUhBesG8rV78RPqvlfmSUqgUFWPyMiIIDBoYhmChmLjrWsFyRm59PT8XdS1aRCN6tiIPv7nIJ0+x3ZVMwPvZxNRGtFmDiu1EjXkz0IAmgflZUrjesGpfATgSIpeDJE3Mr7ZJ+mEKZS83UUQCAajW1NzhrF1QfAMCwHs7N+Y0JUeH9uB8gqK6J2lB86/AJFFLQcT/fuhFn1hQXF5CYDqoxI1JDgJDXw9KIBNlkfP8AZl3jSi2ZeSd+4ZFgQNydtDBIFgMBr4eVJLdhpbixzaeCRFmYXuHdaa2nJ4KV53U7/mNHfDMTrA5qRiBj6g1SjK1NtfWvgIVMQQspMLWBB4arswQXCGwovNQ31ZEHBYc+MYosRY+rr7HPqwYBx5uRvbOOJq5xM/msc+HnE8HrPy/I08YvWxlkdXe85HOE833WEMp7Als1YfpiAfD5p2UXTxsfuGtyEPN1f6dv2x8y9sPZzoloVEQZFlexGYcwiAh7GjMQTn8xMojaDjOO1x0t9U5O6jmTINjN0EAS/q0LU+4IG2Px15TORjuLUEXsghvBixeKYXeHxqr/kIZf0ESedy6USqbi9l4s9m0eJdiTSxT1QJ51kIaxCDWofS0j2nyggOyjxTojZRcXcydy/+F59L1P4yOfWC09A8xJev82wqCNTaUg47/gGFu+ubFgNjT42gD484XjgO8YAhmVcF0sSwDh9fy8Mc0P4vj6Z2nI9gQfdmZRPLvlp3VKnPk/o3L3OuRrDTGD+gfZbmoV2/asXokverhxAS2G01CWYtwM1D64CGKCNBcCKHcQFHSiSkcvDDoAfVMRd3bwfPqn4LAtgMjls8jtePlcetPKw20OXFaTqPTRhJSSVt0kLVaB8RoOyiZocxcgXgBxjdqbG2kJdieIdwdbt096mSjW0A2l0yp9JzVdRQ+8YBRNmpWj/kDPn3EpwrlwAcgXlo+DP0aNs/yEXqYdlVEFgzulntBccL/MW6IPivted5p/kpj14YYWFhNThF4wKbf5fIoOKOZXM3HFeL+JSBLay+PjzAW/kVllgKghDWBvxYQByDMkfF2gKczCqRbM71RAlb7PuHCEIVcgmUn4C13/RCL44YMrajGNjzDEAD0AxxGjD7JFgRAvAPzOQxjhd6NjgLtQUW9p0J6TTty430/G+7VYN7c5N7a4xk89D2+DTe+es5Bcgca96/uP3fvkQtwawdBIE5msjT365/gyBUhvAAL/JkTfg4mznNjWm8DR46am9BsJFHG17oW/Lw5Pu8PaQFli/g4yhl+TOPm1kIaIZmodbAoo8cgQ2HU+ihkW1p9pTeykdQkSAAy/acPn8wagBRGkcTpcWzIMhQPzSEp9KuX4i8gsqvZioIDgDRQZFs+jxhFgT5LAgMnkwG7FZggxf2Al5U7uG7i3ngTM/iY7v42B368yhW8zQP1H/9UF+ACmD+sdechJKMYn/ARzf2oAEcEYSQ0QvRJtyfojjqYsnuRLqhr16Ouu0lWoSQpx/tP3WE2sE/gMJ0e1jmx1wr4aOC0wFBEK9Hy2XnF9l07dd37FppiRf1RXyzqNSx4mplfH8a32AIDsCNd0djutjeqxXCekSHRvTN+qPnE8dCWqpRyJEYqF90cz+OODqyRssj6HqDHWcvCFUXBMv2alptLjSCAN7IGBzxkgiV4tKYxsqchJyCYtgslLzhR8rl422hEbQbTXT/dvYQIYJYEJyLyAY+qp4WzELKNOQhpiERBEKl8w+wo1q4/eT5g9vmUKM/b6Phrpu10FHQgKOPKvA3CIKjwPULEtg8hOb13hI1JIJAqLyz7bKYCFq5P4lSs/SCc91uoGTfaPrc803q/MsIjkW9kajQ2B2fBOfWCACy6iVqSEM0AqHSXN61icrO/HOn3qUsKJKeb/Ixfe4xkVxTjxLlZWiZxYLgxBoBIofENKQhgkCoNJ2aBKqqpAtjz6eF7D6dQ+ubsd///liiCWX7FAiCs9A4yJtQYw4lU5RpyF2WQTkDQqVB9NDlbB5ad/CMKlyXW1BIh5MztdDRQI5C8ik/KU0QnCGrvnGgNx1GdjHjJc5iEQRC1c1D6HL29Pyd9NXaoyp8VJWWEIQ64ic4lKQJAm8RBCIIhKrRhhd9lKv+m+OxX1q0Rx3rEBEop1OoE6Cw4qEk9mUx3hI1ZN+EMqF+88pVXei5KzrRroQ0OssRRK3Dpa6QUHccxsh7AT6iEYggEKoHCnh1jxKfgFA3Q0iBtwgCMQ0JgmDcEFLgLaYhEQSCIBiPppYagbuUmJDwUUEQDIdlFz4vMQ2JIBAEwXj4erpTCPpmMN5iGhJBIAiCsf0E3qIRiCAQBMGYiCA4j/gIBEEwdAipt9QaEkEgCIIx6RHVgBoFemmd9gyOZBYLgmBILo2JUEMQjUAQBMHwiI9AEATB4IggEARBMDiujp6AIAiC4FhEEAiCIBgcEQSCIAgGRwSBIAiCwRFBIAiCYHBEEAiCIBgcEQSCIAgGRwSBIAiCwRFBIAiCYHBEEAiCIBgcEQSCIAgGRwSBIAiCwRFBIAiCYHDsKghcXFxG89jHI47HY1aeBzP052N59LDnfARBEIRaFAS8qLvxzQc8xvDoyGMiH8OtJXiujT6m8/jIXvMRBEEQal8j6MMjzmQyHeKRx/fn8hhX6jV4/BU/D/7l+8EsLKR3nCAIQj3pWRzJ47jF43gefW14DY6dtHwRCwdoCxggA+amas4tlEdyNT+jNqgr8wQyVzmndeFaNfJ12twRgsDFyjFTFV5DrC18yjcYNQILkk38mb1q6vPsRV2ZJ5C5yjmtC9eqXKe1bxrC7r6ZxeOmPBKq8BpBEAShjgqCjTzasARuycOT71/PY0Gp1+DxJD16qB/fT+NdRQmzkCAIgmBf7GYa4gW9gBf3e/juYh6IIJrFx3bxsTv05z/mm0U8xvKI45HFY4q95lOKGjMz2Zm6Mk8gc5VzWheQ69QKLrwgWzksCIIgGAXJLBYEQTA4IggEQRAMjqEEwYVKXjgantMRHjt4bEOYm34shMcSHgf02wYOmtssHqd57LQ4Vu7c+P7j+nnG+b7EwfN8lscJ/bxijHWCeTbjsZzHHh7wnd3vxOe0vLk643n15rGBx3Z9rs8543l1KX+ejjmn8BEYYegO64M8onkgimk7j45ONscjPEJLHXudx2P6fQiv1xw0t8E8UAtq54XmhvOqn18vHi318+7mwHk+y+NhK6915DyRQd9Dvx/AY78+H2c8p+XN1RnPK3KT/PX7HjzW8+jnbOe1gnk65JwaSSOwpeSFM4I5fqnfx+2VjpgEn7OVfJNi49xwfC6/J5fHYT0qrI8D51kejpznSR5b9Pvn+GaPnlXvjOe0vLmWhyPnCjL0h1hgMUzOdl5N5c+zPOw6TyMJgvLKWTgTuBD+YrVvMw9zSY1G5twK/TbcYbMrS3lzc8ZzfY9e4XaWhVnAKebJ82nBN931XaFTn9NSc3XK88rzcINZhe+e5rGEz6NTnlcX6/N0yDk1kiCwqZyFgxnIF0MPvSrr3XwRwMxRF3G2c42qtq14dOOBReBNZ5kn/xv78808Hg/wv316RS91wrk65XnluRXy6KZXKujD8+5cwctdrH2EfWZm0zwdck6NJAicvpwFXxRqPnyLHcIvuup3ylyRVb/Fc85CeXNzqnPN5/OU/qMr4oefWajUDp0nnzMPfWH9luf2szOfU2tzddbzaobnlco3K3iMdtbzWnqejjqnRhIEtpS8cBg8Jz8eAeb7fDOKx059jrfoL8PtfMfM0CrlzQ3Hr+e/wwvnW+83scEB81OYFwCd8Tx2Onqe/H3Y4X3OYw//6N9y5nNa3lyd9LyG8QjW7/vwzQgee53tvLqUM0+HnVP+hzXMYMbqEQ/wuP+fk80tWo8KwNhlnh/TkMcyHgf02xAHzW+Orqrm67uTWyuaG+avn2eUDB/j4Hl+zWMHj1j9BxXhBPMchJ+fPifYibfp16czntPy5uqM5zWGx1Z9TlhEn9aPO9V5rWCeDjmnUmJCEATB4BjJNCQIgiBYQQSBIAiCwRFBIAiCYHBEEAiCIBgcEQSCIAgGRwSBUG/heOtCvYLjTh4LzXHbFs+j8uOcCt4/m8dhc1c9K8+ba8XUOvzdD/I4xuN9R81BqD+IIBDqM9kcI92NR2e9EN3d5id4Ae2gX/+D9QS+8nhEb6tqN/j7K90yluf0Nt88bYfpCAZEBIFgFNaVKtJ1g5688xePK2z5AGR08ljHYyOPF0o994h+PNZcW14//hSPvXoN/Dk8HtaPr+DxMo9/+OH9fNsT9/WCg4vNGaZ824rHn/rxVTzaV+80CEItNq8XBGeBF0/0ohiul0kwcx2PkTza8biHR7kmIgve5fER78a/4s+01C5G6Sn/qAuDcgwL9IKBWTyu1qt14reGUs6bLT4vmD9riF7HBwJhHD9O4seY20s8purN1u/g42io0pfvf8hjWGXPgSBUhAgCoT7jAx8B37bQF+AlOMjHevNNEi+uR/k+ylCocr/8+OwFPm+gvrCTrk28pt+HIBillwwA/rpgQO2o+fy52fr3Liz1ed/rtxBGMF9Ba8BjCK6TfB+fM4DHj/px0huTCEKNIoJAqPc+Al5Eg/j+bzywi5/BYyKP9nwcHeFAoL7Az7ThM62V/sUq/Qp/1yelHboX+KxMi/fv4vf3L/V+zCsVf4MN8xKEKiM+AqHewwtpGt/cx+NhVG/k2wk8Yvh4Cwy9+xOEw4VYo1etBTdaHF/MY6q+g8cCHskDjU9W87hc70+L5y4t53NRRAzVKJUggKmIRye95j+iliaYq4AyXSvxpwuCTYggEAwBL6pb9cqu1/I4wY9PWDyN9pYdS5UAtsb9esMglDQPsvhsOJy/4wFHMipH/sQjgI9v1CtI4ntRw38TjzQrc0Pr1Gt4vIaQVr7dppuEzALnVv34rjrSXlWoY0j1UUGoII+Ab37jhfqnKv/AWBPg92fwra8ucKab+/9WF/7MyXzTiz8Pzm5BqDKiEQhC+WD3/kJ5CWU28qnusMbiP68GhQD8D4/zqKi9pSDYdj3pDQ8EQRAEgyIagSAIgsERQSAIgmBwRBAIgiAYHBEEgiAIBkcEgSAIgsH5fzRo7tBlKYpFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eaJiHljFGp4GSNIorTcQiEIRisPGg1up440Gtb4zgOuix8/vvv6tVNlb86PsFVwomdxRBoSsntpHFYzR9w3ttG7+h9xY6kWLnvtOnT6vXli1bppQEqnHRmO3kyZNq8sauf1999ZWqvkW3UtQrVGSLBd//f//3f6rxGz4PVcLoQIz7BmjngG6j6H+EXmGY+NGrCG4tVDSj+Ry+F0rNdl8DVBmjcRwsILi5MBajQyosIbSVgLKDsvE2ogi8xNFzGbT3VBpd3kJaTpdHNsRrCuBEciadOHeBalbzP/eQp8DE+OKLL6rJEV07Mflj0jcmUqObqG3jN6z8UX1sTNRGq2q0fEDraKzqLUGrCrScQMdfTNTo/ou/gRIAcPXgs59kVw76+KBlhG1bavT7gRIA+A5YDwDKB9YEPguvQ2JiYqz+Fj2EjP5iaEVt9E8ymugZ4/IFRBF4gXPp2TT2s9V05OwFWvRIP2paPcIbw/B7oGgX7jjJ5jdRbEQItaldRUlJgTm/If4sNYgNp0NJ6bSBrYLhogjcAhMoJmBsBoVbtJQwNnRBYzdL7DV+w6re8Nljm1m0esdOZZZ/b9voDRvZoMmb4e/HLT57PLt+4N4Btk3rLOMCaDeB78Ckj+9A/x98BhraYUKHcrAE3wdrBJ2G169frxQf9j2x14DO24giKGNyLubRpO820qnULKoUVIGmLount67zvmnoL6RcyKEf1x9hOUp7WBFYguvvt0m9iapoK8LicowVeCL/fo8MrU9fc4xgExRBp+L1gTczmISxqkbXTDRug0WASRI+dMsmcfYav1luNBMbG6tiB/DFQ8HgfWjOZgveN2bMGLXnCDqSwsWE5nV38XfBn49JHxvIQLHYA1YMxoLJH/3/0QTuX//6l/pe7CNg2/0Tn4t9TvCZ2AERsQkoAl/Eo03n+EcdzDfvseDK+5y/6w2b17E8w24O9XSl9Da/R4vCOIBNrHxoV1/lyNkMNeHUqWY/Y+KFOTto+qpD9DZP/tuPpdCMNQm0/F+XU40qlcp4pOUDHKNPWVk+O6wlxUU6PkaJqZn0/l/76acNR+lCzkXqWK8qje5Qm4a0rUGRlYLoZEomjfl4FTWJq0xPj21NVXg15yq4RrYdTqHmtSIoJKgi/bbuKL0zby99+0APmr70oHIT/fJY7xL1g7dHKq9O27E/uiywbVDmyawhfyJXb5SHGAcmfmxu5Sv4RNM5/lJM/h+yXMlylGUdPzeHB7LT4m2TWHbycyP4tVi+v4dvZ/DjbE+Ny5Ngwrht+jqqHBJIs7HytAGTEZTA+B716NrOdah7wyj6ZnUCTVt5kJ4a6t3Oj77ImfNZdNfX6+k4Hzco2Fl39WAryno1fz4rl77iY/rhkv3K2sLkf0uvBpdcQNgInFd6DWLC6eErm9Gzs7fT2n1n6cpW9mMzJ5Mv0Ouzd9G4PvWpexOtr/43yw/Rl0sO0oDWcfTcmNbKFVS9SgjVZndQp4bV6M9tp+ggu4gasZJxl0xWWlP+2Esju9SmFrUurXK9jRkm8dLgJ3YVIVCNjqnoAlpe8aRrCFGX/Tw5xuMBT/Cz+GYUi6UigDkSEaAtpXAVIRWj8M4SHgIX4Vn214cHB1JEpUCeL0q2ott0JJn2J55XFkFyRjZVDdP2UTXYfVLbY3REO82NUDcqjIa1rclWwWGadHkTtXIVNHJ5Un9g5iY6w7/PozyB/3fRXnrql2302tVt6X/bTtDvW47TPnb9QEmAq1pXp6eGtFQTfgFJezhfbzibZ7wIGv4u3di1Lk1npTv9r3ga0DyWAisWzp6Gq2dLQrKyAB4b3pyyc/OUEqgbHUZLdiRSxwbVVKZQvxaxygKAIgAb2SpoxDED9eO7wferDtO8TSdox5EU+uKebnbHJPguN954o5LyjicVQW2WSzu2a1aBdcNsog9Y5rBgnzbYwDew4uAlnDV8wcFhqJyGxmYM7vLPgTPsOthHGdkXWXJ5tZmtJplL30FUq0oodWCXQqd61WhMp9qFJvKigF8awNu2Ov4MDW5Ts1DwEjSzCA7f1a8RzeFJbcbqw3Rv/8bF+rf5G7Cs3vhjN63i3+yta9vRdV3qEnT02wv30h/bT7ACz6MGPDF3bxRNjXny7cG3XRpEWX/IBc7q+eYatgh4XXHgL6KPulPQ8HdYWfSiO9jKmMUT8Pi+2r60BifYGliw5SQN6VCTTqdl0Vu/a/7c3s1j6IXr2tATM7bQu/P2UB7/vp0bVqWwXb9R/fBYalE1h4auvoODiLdTetsb7P578DcVbRYacGfNXJlA9WPCKOF0Bv205ijd2Kt453dp4M52h8YiKio8uJCVJngfd13+nlQE9s4o29FdxYJKD0SBMAsu4hNxOf8jUq3+KD8f2/5MNWIExRlMPv+Xxas7rPyrR4aoyb5W1VCVTQLlgBV8/Ol02nw4mf639QR9+vcBemNMW5fTO3FhzOUJfWT7WrR41ylasf90IUWw5+R5iuPvq8YXjwFcGH2axLDL6CBN7NOQggPNvSLE7/D4j1vpTz6GN/eor5QAmDSgCb+Wo5Q3Vvbd2K0WkJtFtPV7zh/k9UZGW6LqnJYXwMevEruFQnml3p3XDo04JTGI3Ry/3qMUwsARo6knT+zz/lpLF/h3n3h5I1Yy2qk6c8VhpXBu69+AoiqH0IcL9vNkl0VPX92Kgnil/gzfTvxkLWVnpNHo+Jcpes8vdL7VGGrS8H4K2nmGLiz+gD5P7EyXsXXStIam7NPZdfX4N5upMp93k29qbzXRfr44ntjwoTfGtqWls2dxvIEDnW3irGIhK/k8msvnYyt2G8GV2JTdT6UdiwBIs0S7h+joaKefD/cbXJz4nXAhQmr7YWV1eVcC+C3xm/qCIsDy2HK/uTostjs038byBg8c59N+PgEP8i12k15b2oPp1TiGet1rnefriG1HU3gy2kK3T19PE3rWp5dGaXm/zljAaYppfNHf2K2uuvhX7i/cQwUWQXN9grDkTrYKbvlyrXJ3jOHYgVmB6+z2aesoiWMDzw1vRbf1vrRix+T0b37Oiots0S38N1EWLC2L9cEVLxD1eYSX8g9dem7iQmUl4HPeaL6dah5+ksaufIpePteXJvRrQOE8Uf+1+RBNi/uJuk6dQFk1O9C/W46m3Cp1eeKPpouRtah6zlH6tckcijyyjKL2HKZzvR+nlF6P0JBj52nuoZH0yIWPaM+qP+nH1S3ouWtbUzeOMTz3wzbKOL6X0jhf4q/tNWggB6/BrmOptHDrSbqJYxFNjs+jnknPUPuAHvTxr4/SqMvaUoO4cJrKLqz8Tb/SxKD5NHtDL3rl4mXUuXFN+nh8Z6oSWrpuRGwaj+IpR/v4WsZtMnlBFc5xsBy+PcuXbqqTIL7ZuMimHxYTRSnrVM5uw1siPOQOhhLAb+r1rCE+EFAye1kGsqAD0zqWcfx9Oyze8zHfnOLnXuD7WHqjNrw9P7ZOyPVC1lBW7kV6Ze4uFcydcUd36s2rdmfc/MUaOsgWxbL/G6ACwi/N3UkrnhhQkD2UxydIq+fn003d69OzNhMafoPB7y5XJ8YfD/X1yIrP14FFNWzKckrNzKUvbulC7epUdfzmI7xOqMkpt4EhROcSOLoUx34WDj0l7uIX4ePryNaBjdKwYEfiYWo6kyf5tCQanvE8peVVooyQGDWGjQ2mUMWqtSk4cQcFn9ZcQ2eueJXSOk+kSgeXUtzsOygvJJJOD5tCmfW1lgIgIDud6nzUiVLr9qWxZ++mvSfSqGOtIBp0ajrdyRN5KoXTLRVfo9cfGE2pbNk88tUmqpSTQp/d1JACqzehKms+osjlkyk9L4iW5HWkBRe70B95PejhrqH0YPzdFJCeSBeCqtELF26gjVFDadptXR1mpp2Yx8l5Cauoxl2/UEDF0lvrJZxJp/5vL6UHLm+q4jZTFu+jd/7cS5ufHURVwiS+lcEu5x6vLaZ72MV7X/8mDo/jgaTzdMV//6YmsZVp0aOXldrvUxTOsoY85ofgL0TQF92kFrDgCv0BSoAHcw9Ef9vLLL348Ta+XczyhDMlUCac5xXR7w9RSOphXoG25LhBJZq8YI9Tn9tx9i3DFTSmY22qsPIduqyONpGvsrAKjpzLUL7t5naKxzDx39G3Ia+I02j5Pu/+873FW3yMD3DmzX+vb+9cCaSwofk15xwseEZ7XK0+u37YNVG7M1HH8Sw3OVUC4CK7jRKvm8GpoIG0KOzftDr0IepWM4Bu6deQzt30M50e8SEdn7iUjt2+lE6MZfdPu7Hq7zIb9qfDD++jo/dusFICID84nM63H0dVD/xB719TgybV2kXvn76b7g78H2W0uppCQsOoWkYCvf/HPqUEUi/k0rSmS6nxjKuoAlsqKT0fpJO3LqDcVsPpqtDd9ET0Mnp9Qnt6aMzlFPA4r6dunUehNVvSm4GfUP/U2XT1R6uUC9P2vNxy8CTVXPs61Tz1N/1r+kJVvAhO80oe1ewl4VteFFXkc/Wm7locA+45fP36BK3dhtlZwdcuFjJwLzsDGW44bgmcCYcFot8XlPFJOo9v5tk894nFfbiKBnlyDHY5ukHzLXcYx6vHDsS2Pw+GnbW86qJvx3DKCvueh/6HQjgX+sGBTelJzlb5c1eiw5TDnzl3HT/szeGrOcrxIjUaGcOxh1q08sBpup792WAPT/KgmeEa2jOf86dmE12tHY6RHWqpyfCz5fHUrxkyac0DAvlfrDioYgJ9m1r82/M4j33HrxxI4d/EsJKgAPBb9XqgRN+ZW60Bnbr2W6rGq/ALPMG/0L4D5fNK35KcWHgpbXBiraV1uo1y+XcPrlyNxvWuS5VWN6LjV0yj7LrdOZ6RSSFz42nN5j3UKeQo3TWoOdVf/B2d5+ByHgec1ffFtaacke9TKv+7K2acoVaIdRjf2YDTkSfw+fLT7TSxZhtavilYFSZiMkb8pBMnORxPzqTvv/6AUJ44p+379NvGAPr73WUUFlyRDp3JoFAO6i5nKzWG4x/ugnjK9+uO0FVtanCMTXMFdahblWMnAbT24Fka2FJapSzmOQLs48xBRxxir8Fvm4+r2GQSJyScSsukmpyk4m3MV1l8il0IX3FKIUBaIRTB0teItvOEczFLm3zGsZKASX3uEF1beTt9Eh1O/1m4hwa2iCuUYgqN/iMrgssbVKKYf17lz+tEAe1uoEE7V9ECthKMTAwjYwjBPsVMPbvkKv7usCgKCayo8t+hDLByc2T2+6NP9V8/b1FZQE8NtZh4M86qSY/ilxBhoqzfi5cU/6cpzwHPaJZACcnm3+rUDchqLh1yq9SjtC53qvtZLUcqMcgPrER3X9GE2qYsp/tPvsILBj532JJJ7sP/JlsqVKSLcHfZtDtQrrAbvqUafD79r28+7fjuSdpw4CQ9P60vJVBNPocq0KzAPymnSkMaefV4atLpOC2a8y1tr3YFLzRqK1cOVvUPX9HM7X/bnC3H1Gp3AitrA2QLtWfrbe0hsQjy+Dz+a09igQsNbkZ72VQfLd2vsseeGNxCxSETWEH7giIwV4rKBTbZvmf3QQivyh/gcES767XnW4zgs5pXg2zeq6BiXb3x1F+vUODPt9NTvSOU22Yepy7agovgMJt4T4b/zm6lU2xJvEX0fme6I+srNsezaRf7isGeU+d5cg9VQTbF9V9rt2cOFHzW5axowOr4s6YKEKPnEiyvsGD92MC8+pon0YSVRCOmcOYP+1GPrOEIEh8zXslTL62lb3kjqnIwjbzhRjox7ldKGv4hnbr+e57w3VxJ6xYJJpN2oWfo1oC5tCjiJXqpRwDd0DKU2gYeoaBut6kiulaHvqGHzr1On/VOUz59nF/f/JOgJiljwnrvz30qHuYMLGa+WpVALdiahQWi/fE/7G/apx4juQL+cTOz7ViKWuFjsQhvD+IAtsdw+b4k+mXjMRrXrR51qa/VnxxmReALmEcRoMIUKYTJHFy87iuiSIvUznrdie5dxRENdhnFNL30/MDn+X/5NChpukr7XLiDJ3o7tQPtQ05Q04PfsH/6Zs3KqNOV6p9Zwa4l9jSt4e9j9rIisYoPxOl+7LOXFAFer8pBtzXxjndt8jc2JmhdPLta1gEc5byCk9s0pdr5Fu25BuyTv48nn1vmckyg/Gap5LG7J6tuT0pvPYay6lh3unSbMZ9TwAMbKCgknG7e9zC9OLgeVXycA9xd2JICcJ/FNCf6jhc8W3+kOzg9Gem3c9g1gZ5Mt3KGFoK9k+fvcfo1O0+kKhnP1oBKZIDrdDZfS9/dQP1i0iiXZ75NRfjF/Z3FnO4MZwEyAME+XvgZbD2aTDdOXc0JJWuVW+2eyxqrqnQo84Sz6d4aslkVQY626h/E7pv6l/qNF4AT3DbDoir797tMpIDNM2h47fO0jlf/lsG582kpNG/bCWrbtiMFNB2kpS2C5kOoQkYSPdzqPP3EigLtEbBCUPEB/P2312qrXXThOKO1pgVwO3XjCXH1QfMogg2sCKBkYS0VsO1HdoPwZN/6Gus3xzbXfhPhErCQboZbk8/vmeO0mglYvCCE3ZC3cywKCueXO6hnwsfUtnolFY95eNYmdV72bx6rHi/V3Rr2WLZXS2AYZMTI1k5VblNKO0ld/7lPTYBr9P0ZzMri3YmqNgmCCX5fYlqBywhp6EiEeGFEK1r82GWqrxjqUlB/AdeQL2AeRQD/6tWfEnW/272/6/sY/20oTbgwg06kZLL//kJB+4KLn/an/JwMuqYbrwLGzmRftp5i2uQKVdg0rtouNhPzVWsErJqURXBiM9F+dhCDW9id1FXzKRugShauEmQimYH1rAg6s5lslTLb6RbVEkIpbqFo4ji2Mv4nPmbvqPiCFRx/UoqCM5oCtv5At/RuqLqyLt9zgl4Y2Zo+Gd9ZuXzgr4Zrwx7L9iap96hCN8RulrGl1uRKdS1VPBdPbWuG0ToTK4ITKRdox/FUFTBHQSjiXXt1iwCWFDK2nhnWgm7lY28ZN6jP74Nb2RcwjyIAmGzczdGvzIHKPg9TlWgOFFOesgqIc8bpB56s0s9Qu5gA6sjZE4UuPs4UqXJ4MV3NKaVILS1oLcEmOlXgnOtWo7RMkAhrH3H3RpqLZI0JrIJTqZpihSKwokYbTknRUjYFF0H6rD1LFwQG8yLoY6K7ltCITvWpdVwIrYt8gm469TZVOr2dPri+FaVxIPiJn7cWSkeF7x/poSqTDYkUcx/RCvgGceY3sqrycumqGhdo4+Fzyt1kRv5iawAMbBlXcJ2jDxZYxnEBYK8OqV5UmEOLYBMfz7KMu5hLERSXy/5FVa79gMIrBVPSjiWqziA/aTfdl3UfXd61nf0CsMv/TTRksuofBNMZ5mKjaLZKtv/MqUODtBYIyGBa9b7mLtJpUSOSIisF0uoDZ00TH7BSBBs4fnOI3WZC6cMWK7LT5t7Tiaq1G0IBcMF92o+aTG1CG8MfpNr7vlV9ryxZw4kLORfzqR/SetG+o2o9zQUa15IVgZZ9NKJWqmrfgi66/kR6Vq5Lk/Fydp3BzWNkBGKjKdQIICiP11rWjGT3Z+G4FiwCKM+UjJxCrddRJzJpxsYyqzMQReDqgeKJvFfdSjT2wJPKh72qzh30T35buoZX/HZBcJNXaI1iK6uW0+3rVKFKO/nCO39SK3wCh1dpLRLSLmUjQWF0axhtCosA8QGkPLaupefLw9Ka/5RW4yF4jABYrHAjPcoLkdGf8ELnCQpt1p+OVb+cXvx9p2omZ4AVbSiHzrpWTdWsaVgCRuuOGE0R1L14RHV/RazBnlWwii3i/brPvDxx1zfrOZaibXrvCEzUiOn1bHypR1Oz6pXV2g4TOs7xvk3tdyWoF6V1yrUNGM9ce1jdLtmTpPbiKAtEEbhB+8a1aXzWE3S+/8v0ROIgZS472yxFVcH+fCe9MTCKfrynl7aKQkyAg8mKaL0M3SJgDHqwewgFQGju5c9sYPMXeegFjfZ2/kaUk34prVfwLFAIcMENeIoqjPmMnrj+ckrPzKL537ytZdnp8YG7a+yjkOlsxZ63CSgj3XrUh2zGDlPpv3AvWVoFqGp+cOYmGvf5Gnr0hy3lrg36+kPnVBdhZ6vyXZz+jGaIvVgRGBjdhVGzkc2foxRBGmccLnlN74t1ySIAlu4hWCDI6sICc1i7mvT2wj2q8SCOJeqLLJV0aWK+grISgIyeyfmNaXJKfTqamkBPDCuiqRN8qjtnUwX4aHHBwI8LMbBUBA37WQWMAayCUR0cWBzlHJjNWDFN7KOl21HqCa1quEY7Xiqx0hTKHDRE/G+bBBq2502a/30EtR1yh8p2GVN7gRbXCrVp9Q1067a1nlUEqwDV4Wi3gNRpTGCoet7I6aVIgEDH3/LAfs7yg7sLcvBMOjVmy95RVTyARWDQIDqcAtmyR9dYLHJ6HP2SaNbbWmYXemSx4qSDy6heNW0zKsuAMdqGaM0r67FLKYJ2chD6JlakBnA1oxittBGLwA3asnsHPywa0aGdtaOWEwWg+hUWwKZvib65WpvsLImopTKSLIvKAHyK+HyjZN1fC3Dge1bxAaw+kZeew5lSY75QxVCCdxh03d10IqQhNd71Md32xT9UN+AU1TnzD2dyTSicXg2w0t31u1r0GFYBtgR9d/FeahgTTr/d37tgT+6FO9gtWk7YdjSl4P6WI45rJKAI8O+0rA7GHIHnkCl4WX1OFV0+mbVDX6IHNmhKQE82Cf/9btXuA4V9BrPWHVEbHHXlqSUiJJC+mdiNnhzSgp7n1NPJ17ZTG1l5Arni3ACBNvRXgf9vRPtarm3I0e9x7Rabo8BVZHX0+fBHNy7kGkKcYCyvCBC4W++n5fswuwFWi8r33GyIVkCmByAF7xAUGEg1hj9LTSsco6ZnltAdocu03weKwB5752vV+smH1d4ab1zTll69ug2teXog/XB3TxX/wWoae0UvsFOQ6avs4JU4ejSFszhSBHAfoX7CsOAtMdxDY6IPapsjceahutYNl1rnW4nil1KrahcLXEPINEJM4U42rwImNyRa+5lqNYMCtNs49fT6LnUvbcFayogicBNsDgIQAHbZD3vDDC3Tom7Xwq/f9OOldhMWPMSrK3Q+ffrXbWq7RH8CLQ2+W5tA7djCikYDNEw0Pdgi6HSzt4cmMAGtR6uK5Leif6dxQUspAEq6igMXpdGY7/RebetGXsCg1bptlszg1jWUq9NTPm5PWKyta0UqL8BmC+vAku2sLLBntmV8wKApB4xBhzB2HaGZIaeTW9F8KGcLXqRBQVuVawhpux8vPaCa+I3O0ft0IqurjBBF4CZoDAcTrVDtgDNaDtc2SrFHJNxDhbtBoicRNsRBYcrnK8omc6Cs+G7NYVU099ig5kRH1rF9/ZFmLgu+AYrSOGU6LKIqBY3m9Ob+nCnnCMOC43RqZ1zFigAxV+w8Z+CpvVBKoxHiTp7ksfpuz9f5Lr5vrx/TqgNafVCBRYB/z4ElqugOFv1Lo1pT9avYI4A24rbXOGKF4bHUNXstnUzNpNfm7aJfNh2jSb2rU+j2mZcq6csIUQRuAp8eTLRS2zzm9D6iP54o7DZiruAYBNLy0BgM+9v6A2mZOfT+X/upd5Nozk2P0VY9i1/iM1HyFnwKtPe4kye1liM4wMkBfEegHiY8jhWBZhE4ok3tSJVrjzgBeu+M+mAFjf9ijc/047ckngPFFziZoQ27tTpwVhsyf4zmkbbxAaSKoqW0uo6/4mP1DVtTcx5QPYUm9GygzRPYL8MWuIWbXUV107eqtuqfLT/IyqMuPdQrTkscuWMxJ5hgS3edlGMcrJjF5rTjFtclQRSBt8Em62s4l/tUwcZtVtzN/kFkLmxyErAqT0zlvGi4B5D5oC6Sg39rLabtWEWCF8FE5epiByvX084b1+G3HsSLGuTGj/5wJe1PPK+2c/19q+3utb7hFgJwC3VADIuxjRMg6w1xLmyBq/YW+ZjP4RNbtfYy+xZyYgj/uzZ+QzSdvQGZ9l1LdMVLFH/jCsrnaXg4p4q+MrotBaCX1o0ztOaVAJlGYPtPRL/ezdaGZzauEkXgbRzUEtgGnXDhlHcS0zLpc1754KRXu5AhiwouBbSZFsovg98gumZqkW9DuxUkQozrXo9WPTlQWQlv/rFbbXrjS2w/lsqJIBWoEWf+1OCVPZoibrZRBJ/8fUBZDYPb1ND2zm7BE/4D6zl4OI3o/nWayxeBdDTns9nwqIDwaGpdL0YF1d+5oQNVPLXVOoMQO/H9ovci2zFb24IVTQY9gNjj3gbB5GCe7JOP2H25MscKanLQ2B8UwUdLDigzW8UGAKwB0Ki/9wYllBz0hnIBKP9dLw1WygA8O6wV3TB1NS8O4ukBTo7wFbazRdCKU7gDK2rrZMQJLC0CdBLGuTySMwe1+MBIolaXNiFSzRKxkt+7QNsF0YllFcDunm47fuEF0SAOoLygzQcPbtEsssqsZA4s1vblPr6RLYgXPfVPFovAJ+CVgTOTD6l35V0RHEu+oILE13G2FXKsFTjB4V+u3ta7gxNKzs45WruUIjCUAOjOkyiyiT7m1fWS3YlFbpBTFuRxzGLH8RSrNE2kjMefTlc9gRDgfoYz+ULYYsCe5qoGJt1OO5gPumit7xtf7vwLczI0V9K8x9U+JjRhzqU6GjSlTE8iWjZZe4xsLg8hriFfAJNhruNgcNO4CKUIcJKWV6ZwwBtYrfz6c5D8ke1SQOYPnOLfcRUHN8+613gO25Oi39Rt09dRx5cWqXbY3uxievBMOqWzq8pSEXTU4wR9Jv9F13y8Su0giCIvlSKbwpb8W420olFLut6hVWMX5fZsxZN7s8HsWvtMaxce1dC6XxnAZ9fs4DG3EBBF4AvcvkDtRevMIoA/Eqvq8shBXk39tPGo8g0jc8QKCRL7B51v09JO13/h1p/Vjw6nf54aSNNu7araqczmFMqRnFGEVbk32HNSyw6Ca8igR8Noeu/GDhzbqkUZWRfVdpRju9azqp+gKL1VigF2h3vmpJZVVZQ3AHuko7+WrQupGiuFyNpsCVyt7V3iQSRG4AsU0VLBKE5B/5O6UeVvU/upyw5QMPtbJw3QA+NGu2mkjmJDH2NHLaH8gq1fETBFpkz/pznu5fp5igr9ATy5Qq7tXJsmzdik2jBDOdjr4+9JzuoFbwgQW3YehpKy2/crSc+WwpagtthryeEOUAzoTABl4uFNmsQi8AXQq+VHXlE5KLBpoje82m+xD2p5Ij4pXaXiqXxrgH/nhula++1g+828hHJIN85wyeSg6sbClfKu0rl+FP3vwT5UPTKE/rPQeUqqJ0jO0BRBlTB267gCLIKwaG1l7wmw/zQsAg8jisAXgF8VmQPZ9if6auHBFFM5uNwGjOHzrRpqcWHFL9WyIGA+l1ZhnuB96nNws+11vDrW40AosrL1nbsA2o7c1quh6ljqrOGbJ0jmgDB6DKGvmMuKwJ41UM4QReALYEUB0p1nDhkbYpc3cHFVtVxhLf8PUQS7EtrLdpR+RQAr9TGf88k6UHv8DwePf5ukVY672U7iui51VMO36asOeWCgjkm2XbQUBYLC7u6D7oOIIvAFjE3vM84UmULqq/1ZnJF8IZsVQbD2AL2FDi3XrAEJFPs3Q98m6nSLpviXvOrWn0ZUCmJlUJfmbj1epu1VknnRUsU4V12h7bUeTessK0QR+AJhMUVaBEghTc3MpaS0LMq5mEdfrjhI983YQJe9tYQmfLnWZxUESvEzc/KoirHKimtBdNXr2gQh+DcV+Tcf8Z5WMIg6g2I0eERP/xlrtK0byypGUNVVi+B8ktZWwmgDUY4RReArFkHl6loBihOLAOxjq+CtBXvopbk7VSk8TlpsJ2ivKZYvkKrnhBe4hpAh1PM+vpUgsWncRdhxDr50i20aXQGFhwOax7EiSCizVuzJfL5WC3dREezmJI9P+1rtOV5eEUXgC2AnM7SqRadHBzTVFcFny+NV47bxPerRsn8NoGm3dVPb4s3efKysRuv2hQWURYAOiqs/0W4F8wD3yfif2EJww+WiM65bPTp9PptW7OfVd1m5hkJdHCc6rgZxmmyki3uT+DCiCMoJSL3E9pVL9ySpZl3/HtZKPR/FGUX9edX0GysC9FH3NXBhgaq4uJJ2Ec2333Jb8GOwMxe6chYjJtSvWayyJn/b7PkupfnsXk1R8SxXU0f3aBlSfrC1avn/F/gLczh4uuwtp218W9SIUMrgo3GdrbbJRFfHU6lZBRtp+xJGXra6uC7oqYBFVVsK/kfCP1q9jJtg/9+hbWvSwh2nKCM71wMDuwRaS2AfbZdjBEn+kToKRBH4Csc3axk1TnhjTDv66Z5eVC/aumpzYMs4tdH1r5uO+bZrCHsvAFEE5mP1Ry41pbPHqPa1VIuVRTtPlcmipZorWUOId6Qe9Zs9tkUR+FLAuIhNJ7AJeHO2CmyBdYBV0/ztJ3yutzs6Nl6yCAxF4MY2n4J/ULuT1ps/46zbf9q1QZTav9vT7qFk/Vx1qao4KJxowm9aWw0/QBSBL6WQOkkfLYrR7B6Cabt4t2dXTcWpKkbrYeyroBQB9l5AWqFgLmqxIgDHN7n9p+j1M6JDLZUdZ/QC8gQpRoabK64hxAWQFhvX0mPj8RtFwH7twSx7WPaz2N0Bm5/vz7KZZQeLvlOJWS2C4vv4uzTQ/O4Hk9J9rpgMbiG1LeWAZ4juW+XtIQneoGZ77RatRYrBqPa1VU0BCsw8bRFULco1lHyYaMEz2naUfoLHFAFf+IhmfsgyhAUpLmP5uVY274GPgJ2HNJIj9q359jpPjcfnQRtbbFtZzOKUoIoVVCD5jAdXTMW9uApWWKgdqKq37xXMBdyBOL9RgFUMWtaMUDuFTVm8j86czyrlwWmcs0xscMbmmVr7DD8oJCsLi6Aby36e4ONZcIRnsYyyec84ll/4dVU6yLeJHhyP73duvPvvErlNkEpqnMy+AsztAp/rus+Jtv/s3QEJ3uPm2UTXflmsP4VFOZmTJVIv5NIzv273SCV9imVigyOwI9nmGUQN+2n1P36CJxUBmndbbsSL5HHbht4IuVfjH3kpywaWCfY+iJ+/i2U9JCmpbApLyiPIdvCkD7XEFsGaqUQ7OcAmmJOqdbWFTjEncSRKPDqoGc3fcdIjBZTJvIgK5cQLy9TsQhxm12ZyAlGH8aX+/f6qCOz1F7Y9A7BzQ2eWYSxXsTzLk32hfCzW/lNZukBiY2NLf6S+QOIuok8v43zrVX5nERT4XBEsltRR8wIFsOh5ormPFPsj7uzbiLrUr0bP/baDzpXyoifZtkuuI7cQEh6cdAEoj3hSEcAC4CVAAajDPm7nPfN5gk9nQcrMMhY9qmQyKrBOPLG5RFW3ShGk+5bfEqssZWpjEhBFYG6QMBDAU86GaURbvi/WRyAD7YWRrSktM7fUA8fnVHuJIhQBdl7rMNatHdjMrghQHdWUV/gNWbAkvJHFtgUh/AR9+fVAFhzZ7iy8NDYhLuxJ4IoiOJPumUBacUDLC3RMVRdXdrrWVE8sAnODzDFsYDP3YbaCdxfrI1rXiqTm1SPYPVS6iiDFlfYSQ9/SxM9wSRHAXcOymGW7/rgdi9MyQV7hox78fpYF+uT+Az+HFNF7IPp78Px8FqQSrGX5nJ9T32E6KnFWBRKtiigqKypGgJbPvlJUZtV5VKqKBWMf3zFf8Io6nOjnibxayC1W4HhUx1q0IeEcHTmbUaquoWrOUkfRdtpPcdUi+IzlKRZ1ZfNkvVVf4TuF3zePpRlLYxa1MwXffgKxeM9bLK1Y2rC86/4/wU9AgQqsghJZBNpq5qyPxAmSLRUBAoXPnCRqe72XRyX4xEb3w/5DdGY/u0O3FOsjRravpW7RbLE0z9eqjiwCKKyPumv1AyZWBGE8SWPFbolnO0CZkcYDSpSSZqxmSjuIVpL4QEHnURAUylLJiyMSfIaWI4ke5JhYHeSKuE+damHUrUGUcg+VRippPjqPOmtBnbBSK/isC++1eRXBaTbHGvOtOuJ8/1q+Kf+7Mfga13B6Zd/HShQjAL6SQlqQl41V1rENRH886dfmteBm4BiWASbxUzuKdejgHsL2rTuOp5b40GewOzX7Yp5jiwBpz9h7AO20TawIJrF8ytKClQBsMY70kPLzC76DryoCVUdwchvRmo+d7sImmJD1XxJ93LtYPYiGta1JQRUDaHYpdN1NdtZnKO+i1kK76SC/yxZyWRHorSLuZdMJqhBJ/C34fh+WhKL+VnCTZW8TfdjdbxQBgm+FWlAjKC4IBm3GaNu0/srryhz3NqlHfUqX+lG0joPGJT9Xsws+sxAH/uLYXSJRK9vGCCZSBDzhIwWls34f+f6+uTmuP4DeJUm7i5VJASIrBRGnWftMUVkhRVAxRIsTCIJlD6JRH2rn/V8vu31cmlavTAfYPQQff6m1S7el0QBtjH5WRFYc19AmtgzmsNzMco0hHh2ZGakcp92mFs/URbteBIx9xiLgvGxsmBNYscKlYjL4hgXBkqbsbOh6h9bI7SBqSl0He3mfz8pVO/SVtJjMriKAWwgprx3H+3X7dFcVQRQLeiRfzgK1CPGPHRl8CSMj4dCKYn9ENR9qM6GyMIwLK5cv1DCcRoJghytf0s5/N91DjVkRAASNS7poscpwA8lHiKZ0JIr3/+746PVTJGx23ebpgQhM9dZE4WwVxC/hFchNxTokUWwRnDnvI4rAMi8bGVHo3CgI9kCB2e0L3LYYmxQogjTq0zSmxG7MqpYWwZJXOcuNYwNRDYv9uf5WWVyH5VeWRJZTLD/jOU8PznTgIuh5X4lylauFB/mMRYBMDKveLSiaEwRn5z/iYyvfc7n5YmzlEI6NBdK+EloEKXyuVgqqYN15FJlMTQaaYg8NV6/MaXqfoFp6K+nf9eeE0qbPI9reBMUkKjyEYwS+kaKJTIwCUxsdJ9G5URCccTFLSyn97X52E11wqd0ErIISu4YyLM5Vg/QkLaPJBLiqCGLZPTSNJVeX6XjOkwMzNReSOSJzoNhtJmARlDSLojTAKqsgRrBlFtEpc7aREtx0EY2YwjnQBzSF4AJQBAeSzpc4WFzV0i0EyyTjLLtqzTHNuVNZPB41BbpgV4bib7ArOGfaEKJ5jxfrKCFryOj66U2giFR/d7iGEADMyZDOo4JrNLqMKKY50QGOlbmoCE5zXMyoBSgOKbYtqFH42HMSUb0exf5Mf1QEt7OgW9hJvbXEtfpzgifANnjwkbqZQeFLRWXp2RfVZuNqlZXJFo6RMy4IrtCgN9Hh1S7V1DSNi1C3JXEPJdu2oEa9y1Wvav2/TEAFF1d3h1mwwTxcRHEso6Wy2IOggCWXlcARvhCKkT7qC4oAbiGg/K7SglpwF7RzaMLZ6pkpLmcOlSRgnGKb2IBFGPbQMAmuZg19xVKwnOP72Ge4eLtQC0XToA//MnxS7ppbrPRRX+hAapjpkco1dEELuoUVP71PMBnN2T16/dfso9c3bHJC7aqhKuOnJBbBBbZgw4Itsul387X3GufGJO0p9mf6o2uoHVsAyRYWApp7dPTMkAQK4RVO2+s4y+Y7ojR444rhGiqBv7Q0gM8VqFVW7U5Ej+/VfL+C4A6GNVlERX2jmJJlDmXm5FmnjiJjCEiw2PpYwwowHvD9KFeL0YRiMoJzqScuIIqoUSxF4G2LIC1L8+1GcI63IBQLbHT/XnutzUMRlCSFNPdinmpBHWqrCNBv0yRNEl21CP7DsooVwMsQ3GeZ7LlhCRTIE3qNtpqvcsN0l9v0hgVXpODACl6PEaRbKoLtvxDNHFus4LdgYuJaaTECF/YrgCI4lnyBMrLdz5bLzNUq3kODK1grgvAY0xRBuhosZmcdjWE5xZLIcg0/940nByYYB59XQwufI1rJudUugAIbxAm8rQjQCAyEhwRqF/Jetm4CQ7w6JqEcZg4Zu4O5GDA+kOh+gDczR7M4rC2CM6ZxC7kTLMbuZAd48v+Ab7exXGEZPBY8XGDT6WYOHM8hSj1RbhrPGYqgMhSB6jzKp4t0HhXcoUodTjur71ITxmbVNUWw91RasQLFIMRSEbTldW+3uy499nNctXt+ZrnIk38Tvv2cBV2YOJIplAlo0Qs/qYuVlqgu9rpFkJlLgRzECwm0aEEtCMXJoENNTRENCxtEhyuX6J5iKIJMexYBNszpfIvbn+XviiAPrSX4FnsQvMf3H+Hbmp4blmAFuh82u4pjBdO0ds4u9Bsy+qt7M0ZQmeMDcFVRViovtyK9Oh6hnNLhJqIhHI7Md64IsOcF9ibYdcL9/Ysv2FMEp/fzeVuythX+qAhy+ILmaB9NYDGS2/13lwZfBGZqlbqcjlO0eygqLIjOnC/ZRh2lkTUUbuRlV+bMp2gYk4JQjDhBu+u0zWGKoHmNCNpzsviuoVBOtFCg7uWDzkRrsU27OXBVEWA/gp4sr7I1cJCVAlxD33puWEIhGl9OdNcSDgA0cMkiQK+hbD0bwluuoYLU0dEfcqrBZ14bi1DOQS3Nqg+0rVyd0IIVQWJalttu0Uz9OkFRmiL9tHYrwWJrePLfyfIgy0xWAp2gDFjecOtoCyXDCLQinS47w+lbYyO07JwkL1oF6ZzGpwLFglBSkDq98Jkim9C1qKG5H3efTC2WRVDJcA0ZxWQmqoQvTpIsgsWCN0Br6smNiHbOdvq26pGaIkhMzfSqRaBSR8Es9vMu/6/XxiKUcxoP1Aq7tv9UpEUA3HUPZdrGCMQicAnZfdxbRDXSzNU9fzh9W1xEJXULM9mb6aMIFivQRTLliNfGIvhBcWWrkbzU/59TaxiWcDWOj7mrCC7k2MQICtpLiEXgjBfdOspC6bqHmg1mE/kvp9lDcYZF4G1FYASLEXwLCvPaWAQ/AL23sjmLZ+98h29BhhoCxrvdVQTZNhZBna5EQ992u72LqVxDHBuYrR/0FqU/HKFImg/VLohDyx2+JTo8WOmMJC+6htKzLmoWAXZKw6Y06O8uCMWlPmcPRdbhtM69RcYJUFSWl+f6Dn2ZuTYxgthm2naxJjpnS9JIY2GpjUJwb9MarK73zHeaUx3NmUOnUr1jEeAihEWgYgTKcsk31UUleIAKPEk/uJGo/5NFxgkyeIV/5JzzhApLMnWLQBU/gsTdrHD2FXuo5RGnaR286p/iJE4gLSa8QRD7/6/+RGvIVUTAODHNOxZBhu5zjYAiwJZ/dboRVannlbEIfoTRqwpVxg6awcE1BOAeqh8d7nKMIJStAVX8CBY8rVXDI13bJAS6UD/wGIu9pSUKzARv0GpUkW+J48CZt2IEyBgCyiII4QvzjkVeGYfgh/xwi2ZdYjFkh2bVL2UOXdW6huuKINimBXWEuRonFKUI1rFs57gA2k5bwdrzBc8MSXAJBIzR1bPXAw4zh7Yfd7/cvjQ4n6UV/hRkDQlCaW7atHOO5nK0080Wi4/60WFu1RJk5tjuRXCaqEa70hit38QIsEn9ZnsvsHJAdbHgLZBC+ifr4uQjDjOH0GYCm26UNec5UFzgGoK/9cMeRAcdB7cFwWVajtJ6Vx1c5vAtbWtXoeV7T9PhMxkuWwQhRlUxkhuMvQhMRFGKoDJP+K5HXYSyo9eD2u2qKQ5dQ0icOOOFLqRWriFUQiftIrro3d5Hgp+A7U6D2f2z8zeHb3licAuVNXfvjA0FxWJFBYtDDYsA5yviWiZqL+GKIpht4QpCK2q34L8ZzLKHZT+Lw3A/v9aVBW2uYYEIrlC1LlH7G4k2fs0zL/YKsiYuUi8q80LmkNVeBEgdBUGuBe4EwSlwB6ETL4rLLtrfjaxuVBi9c0MH2sGu0Rfm7HA5WKx9Pl83N8zQ6nVMRAU3qogbufPBPKnjyH7IMoQFKS5j+blWDt73JssCdz5fYPo8yhcDr/j/wX5BhS0C4I3MIWtFcEF7UtJHhdKi60Sivjj3HS9yBrasTvf1b0yz1h2hRTuxsaKLweIgVgQthxPFmKtbblGKwLIqw/UKDQ3OGaT97FqKZ4F/YhaLvXQXRDthbRRe1grOiW5M1HMSWwf1HFsEXsgcMvYrVsHiAotAKouFUqJ+Ly1JArv3OeHRK5tRDb4OvluTUGSwuJJhESDmtv/PSwsYk1CUImjPK/ZUFtRst9Pvq8e4LeJva7NYRjKP6s8VwJ+Bx1ez2M8Fu/S+u1jWQ5KS9D4ggsagV7QdzGyIraxZBKe8UF18ab9ivrjCotmWHEBUqUqZj0PwY7CncPzfTt+CwsprOtWmZftOO23AmMkWQYEigBL4doxWR2AinCoCXslXZIlkiWAJ1O8bjyOL0ZzO1qp4l+UJ/iynER1+fSpLF0hsrLmCOC6Rk6kFz5DxoINt+9CAyxsWARRBMF+EIYF8cTVmJTCBQ00R1ct8HIIfs5KnjhkcUsx2vln9mM516CJnTczefMxpr6FQI2sogxUMCI0qrZH6fYuJooAFwBHNAuqwHLd5TxeWWbzSP8S3CBR/xPdHe3BM/sl29qz9MIEoYaXV09XZLPZKsJizhqSGQPB49hDiY4f/cfq2xrGVqWO9qvTThqNYUBYdLM44qyU2IFZgIjypCFCM1hS7mbEE831OcaE5trUILA0g/BDNxu8zmtoJbtDmGm1z+LVTC7XlTfJSsFi5hcBKTm99t52VtSIIJaZeT569gop0D4Fr2SrYe+o8bT+W6tg1FFzxkkUAd6bJ8Jgi0De7v1/PBuJEcvqBn9vBSuEeiKe+15QgI6fjeD7Kc4lSYIhdqi72lmuocoi+pTWKc5DeavRxEYTSAIHiupyPcrBoRTC8XS3lKv1pwxG7DRKzci0qi5UiMJdbyNMWAZTBPJZmLI1ZXtWf+wRi5723sjjfgkhwTJeJ2mT7SR+iE1sKqouTWBG405K31FxDhkWg9iKQzqOCB2jI7qETWzV3jhOqhAbRoFbVac6W44Uq7Qu1oB70MtEw8+2m51FFIJQhUQ2Jbl+o7VcQ26KgliCXlcDZjGzv7Vcsm9IInqITx8Ue2KC5RYtgWNuadC4jh9YnnHO+KU1cS45mdi71ofo6ogj8iTp8Ao/+iC0D/lkPraAGFU+rp8s6YGy1X7FsSiN4isiaWi2NC27Hfs1ilXvItrisYJvKIF0RbJ6pNXM0GaII/JFMDopNH0ZNE+d7pboYMYIIo/NorY6cMjCoTL9fMBH7FxMter7It2Fh0rtxtFIEltlDKCYDKliMjqazOXy5Zx6eMhWiCPyRcM56iGtN0Ulr1cOyDhirrCFjv+LeDxINfq1Mv18wESc2azUFKDArgitb1aDDZzNUBpFlxlCBRWDEGiRrSPAbGvShSifXUxDlOq2qLG1QvIOtAqWOQCizvYxBEfUEYGDLOHW7aOfJQq6hSigoM4rJRBEI/qQIAtg/3zvssFoFlWWgGBQEiz+/guhXyRYWPARcjxVDiBIK7Z1VCBRYtq9b1SpOYBUszhBFIPjpSmloxAGHhTSe3IugQBHg4sqz3y5YEEqlLXWdroWq6h2BNNItR1MKenAZrqFKoggEv40T3PU3JTSfyD7RNJc26Cj1zqNA6giEsuhGinYTF7UtUp1xJSsCsHhXonXWUDBbBE2vJLpnBVE1822+KMFif6ZWB2pdN0bVEmAz77IgraDzqGX6qLSgFjxI/6eI7uMYQUW9mt0JTeMqq3hAfNL5whZBSARRjbam6zMERBH4M2mnqE/8u9QyIIG2H08pU4tA7VcMxCIQPE0F16exgIAAiqkcQqfPZxWOEez/k2jTDI8M0dcRReDPVAikyE2f0NCQLRwnSCn7/YqRr91+LFFt81VqCmXMwmeJZvK55gKaItCq7S/odQShUARbZhEtf9tjQ/RlRBH4e5ygdhcaGbSWtpWVIrDcphIVnyOnELUcUSbfLZiZfG1Fj705igBdeQ2LwHANhQTq6aMmTB0Fogj8nXY3UP2ceMo/uYOy9AZbZaEIVGUxLAJpPy2UVZYcAsZHtSJKZ1i6hrTdySqwd4kXLemnRREI/rtXQV5ARRoesIL2nrxUUVkmrqFzh4herKqZ3ILgSRr05VRSDvLutNryxC6xlYPpbHq2Kn5E1lAlo88QKotNtjOZgVgE/k54DF1oOpLyKKBM3EPns3OVmR1UscKlDcCR6y0IniSkspb+uYsVQZ5zyzeGXUPozA5loG1TWVF7QbmGzKkI9NQOwZ8JGzuNPn1xIQ0rC0Wg9iKwyBgCkj4qlAWdbuVZvhlRbqa2cY0T1xCAe8hqm8pHtmude02IKAITgJS5NrUi6eSRA/yI86Q9nD5aUEyWaygC2ZhGKAOaXqFJEVgqAnQfrWQoAraezYo51Z8JeSD/W3rv7L2UnZlRdp1HxSIQyprcbC17KM96JzJLYjhGcEkRsEWAquLkw0R/vcL+oviyGqlPIYrAJAQ1GUCRARkU/89sz+9XbFgEVeoQ9biPU4hqevQ7BaGA3b8TfTuG6MhqpzECcDqNYwR61hCd3ke07C1VhGlGRBGYhFa9h9PZ/AjK2fqzR78nzTJGgG3/Br/OCqG2R79TEApoepWWPbTD8YIHVe/YrUzFCIxgcYZ59yIAoghMQlilSrQ1sj81Obec8rM8l0Z6IiWTalTRe7WguCebXVFSSyCUZfYQ9u3eMI2tg/85jJnFcpwgCa6hXD199IIoAsEkBLS5hkIpiw6v/tUjn5+WmaNS8upF6U3m1n1O9Bq7hbLKpuGdICiG/1drHvfDBKI9fziME6DNRGaBRXCGnw0gCq1qyoMoFoGJaN97CE3KeZh+TW/jkc9POKMFousbiqAgWCxZQ0IZElqN6GZ2DbW+WlMIjqqL0/T00WDdNYS/q6BnEJkMUQQmomrlUDrXYAjN3e2ZjWqO6Duh1Ys2FAE/rhDkUntgQShVKkUSjflcS1hw0maioLJ4KAeKH95m2h9BFIHJGNIyiq488x2dWPdbqX92gqEILC0CKSYTvMnehUR/Ty70dExEMJ1hN2ZBHUFAgBZfMCmiCEzGFW1q002Bf1LOqo894hqKCg+miEpBFpvSiFtI8CKHV7EieFNLWrCxCNBrCIRCESx5jWjzTG+M0CcQRWAyalYNpxVhA6nOuTVEqcdL9bMPn02/ZA2AZpzK1+v+Uv0OQXCLer20PbOPbbBbXQxCUUew4SuX9z32R0QRmJDTjcfwD59HuZu+K9XPPcyuofpGfAC0GMaK4IFS/Q5BcIu6Xfl/7PY5vNqhIggLZMsgPdHUhY+iCExIkxbtaU1eC8rd8E2p5fjnXMyj48mZlzKGwPkkzs8+VyqfLwjFAplAca1YEfxj9XQsxwgMquTxOZqfRxQpikAwEV0bVKNZuQMoMbAWUWZyodfz2HeKTWwysnML/KhFcezcBfXeupaKAHnc399cWsMWhOJRr4dWMGax6LG0CKrkooaAieDrwaRI91ETEs0XwdbowfR8xDU0DSsmmxTQoe8tpzR9p7FWNSPpl/t6XerQWETGUP1oi/a/CBZXrl66gxcEdxnCWUMVrae6KqFBFFQxgC3ZfArPO89JDXzeRtQw7bEV15BJ6dYwitYnnKOL545YZVRMX3VI5VY/dmUzuueyxrTzRKp6zpX4ALCKEaj0UckaEryMjRIw2kxEh2tWQWb9/kRPH+NMivZlPTKfQRSBiRVBbNZhqjClHdFfLxfsJfDDuiM0tG1NemBgU3pySAsa2CKOPvhrf8Eer444fCZd7UyGHi5WisDJBiGCUGbMfYTl0UK1BKBSoF5HADEpoghMStcGURSfX4t217meaPVHRPFL6ZeNR5VL6NbeDQre9/Swlqpn+zuL9hZZQ4DUUbUJuIHUEQi+QmYq0Z55duMENXd8QjT/aW+NzP8VAZtfg1n2sOxnedLO6zexbNVlFYt5bbMypk61MKpVpRJ9GnILBw2aUv7s++inldupfZ0q1LHupcZbjWMr0/ge9Wnm2sO052Sa66mjYMBTRC1HeuqfIAiu06A3UdoJojPYpc9aEVQ+upzo6FpTH02PKQKe1BFd/JBlCAvnb9FYfg63lhxkuSw/P5/9EwT/xFRPjUcoTFd2D61KyKD8a6ZSftopeiRlMt3aq57yn1ryELuJQth8/nZ1gt3DyL+fUgT1omzcQF3vIGp0mRx6wfs01M/Dg0sLKYLA9JOmriHwtEXQjWU/TxLxLNl8fxbLKMs38POrWIxEc1R82O8QJXjMPZSYlkWdvjhN/86+hQ4GNqKh7QpvIlMtPJj6N4+lBTtOqtRSW9DONyP7IisCi8Bw3kWixF2ctlc4PVUQypyoRkSRPL0cXFbwVOtakaoddUUogkjzpo56WhFgRuGUlAKO6s85YiKL3ebhvEK9i2U9JCkpqRSHaG6GtKlBozrUosFtalL1AfdSz7unqJU/Jawi+vlOq2KwwfxeKI2Nh8/ZbS1RKHU0M4XoI87f3vq9x/8dglAksHI7sxu0BpwPGsPb1aR1/9eDArBfholTRz1dR2AvBG+3Ookn+AG6Iuhj73W2GqYabqMuXbqUTimsoOoJ3ruxY+EjkbiTaMcvRIdWEI3mQHLjAXQ5Zw8FV6xA87adpC5sSVhyICnduv20ESgGkj4q+AqX/cvqoXKBIogc04zN3ksJEmbEkxYBLIC6Fo/h9inU5Yx/DKjoz1lG8YSvl/gJXgW+/Tv+JAqJIJpxLWdbzFcdRfs1i1HuIcQELFm1/zTnZAdTQ6tiMmNTGpsAsiB4k1xOg07loLEB9tO+f522iY2J8aQi4KNLTXmib8iChN0bWeZYvoGfr8c3vPSkm3lycZ6fKJQttTpqysDY8u/UDuVCOpZ8gbYeZbePDmIGy/edpr5NYwqnjgKxCARf4vOBRL8/5O1RmEcR8MSOHgXoQbyAhaOG9AM/t4Mn/3sg+tueY4lm+Yif24wYgKfGIxRzl6fxv2gmdWxLurJldQrkyX7e9ksrKlQeY4OPfs1irf9WtqkUfJE63bR20xdztMcbv+Zy+uFsKSCfxbx4tI6AJ/55LM1YGrO8qj/3CUS/fwdLNZYOunTx5HiEYhDG8YB+j/OZUoGqBOVSz8bR9Me2S+6hv/dqwfu+TW0UAfyuI6aoGgVB8Bka9iPKPs9O6k3aY7Z06cQWjpZe6kZqRqSyWHANXDjvtKa76x1XNQMIGoNlrAjQmC42wqK1hKFAkKVR1TJMJAg+oAiQx7J/sfYYmzNFmDtjCIgiEFwjtgWfLYHU++jn1Kx6ZXp74R5KycihDQnnCruFAFJQT++Xoyv4Flig1O+lZcXBqkW1cYS5i8mAKALBNRD07fMIBSSsoNc6ptDB0+n02I+bKZeDxcgmKsRvk4gWvyhHV/A9Bj5PdLXyTrMikGIyIIpAcJ3Ot6r9BTof/IS61K9Gf+5KVBt/d+b7VmSnE509SFS9jRxdwfeo152odmetyCyupVWRmVkRRSC4aRU8ylbBSnqpq9aWGsFjVY1sSeJu/h+b3dVtW0sJgo9wYivRwmeJxv3AJ/F93h6N15EdygT36HIbT/CtqVXDvvRi9iFqb9GptIBEzsQA/D5B8EmQLbSKs9pacOooLASTIxaB4B6BnB3ESgDc0r02dbCnCE7t1Lb+q2rusn3Bh2kxTLv9chBR0h7vjsUHEEUgFI/VHGz7uLf9QhwOKtP4n1XtgSD4bLGkQXBl743DR5ArVSgeMU04PZRXUuvQJsqGiOqcotdTjqzg29y1lKjTLZI+yogiEIpH44GaLHmNKOXYpeczzhKtZN/rOfub2AiCT/XTGsnnqliuogiEYoLUu2H/IcrLJfrjX9YVyIs4GyNZFIEglBfEIhCKT1RDov5PEu2dzwG3vZf2MgBxkjEkCOUFSR8VSkbPSUTNhxLFNiM6G0+0/0+iyjWIwtFUVhCE8oBYBELJqBikKQGwZRZR/FJtDwNBEMoNYhEIpUePe4lqd2HF0FyOqiCUI0QRCKVHaDWiZoPkiApCOUNcQ4IgCCZHFIEgCILJEUUgCIJgckQRCIIgmBxRBIIgCCZHFIEgCILJqeDtAQiCIAjeRRSBIAiCyRFFIAiCYHJEEQiCIJgcUQSCIAgmRxSBIAiCyRFFIAiCYHJEEQiCIJgcUQSCIAgmRxSBIAiCyRFFIAiCYHJEEQiCIJgcUQSCIAgmx6OKICAgYDDLHpb9LE/aeR1M0V/fytLJk+MRBEEQylAR8KRekW8+ZBnC0oplLD+HW0vwWlNd7mL52FPjEQRBEMreIujGsj8/Pz+eJZvvz2IZZfMePP6aXwer+X5VVhY1PTgmQRAEwYZAm8elSW2WIxaPj7J0d+E9eO6E5ZtYOcBagIDzcDeVcGwxLKdL+BllQXkZJ5CxyjEtD+eqmc/T+t5QBAF2nssvxnuIrYWpfAMpFViRrOfP7FJan+cpyss4gYxVjml5OFflPC171xBW93UtHtdhOV6M9wiCIAjlVBGsY2nKGrghSzDfv5Fljs178HiCnj3Ug++n8KrCyi0kCIIgeBaPuYZ4Qs/lyf1+vruABRlEX/JzO/i5e/TXP+GbeSxDWfazZLDc5qnx2FBqbiYPU17GCWSsckzLA3Ke2iGAJ2Q7TwuCIAhmQSqLBUEQTI4oAkEQBJNjKkVQVMsLb8NjOsSyjWUz0tz056JYFrHs02+reWlsX7Iksmy3eM7h2Pj+U/pxxvG+ysvjfIHlmH5cIUN9YJx1WZaw7GJB7OwhHz6mjsbqi8e1Estali36WF/0xeMa4Hic3jmmiBGYQfSA9QGWRizIYtrC0srHxniIJcbmucksT+r3obze9NLY+rGgF9T2osaG46of3xCWhvpxr+jFcb7A8rid93pznKig76Tfj2DZq4/HF4+po7H64nFFbVJl/X4QyxqWHr52XJ2M0yvH1EwWgSstL3wRjPEr/T5uR3tjEHzMlvHNWRfHhudn8d9ksRzUs8K6eXGcjvDmOE+wbNTvp/HNLr2q3hePqaOxOsKbYwXn9YeYYCH5vnZc8x2P0xEeHaeZFIGjdha+BE6EhWz2bWAxWmpUN2or9Ns4r42uMI7G5ovH+n69w+2XFm4Bnxgnj6cB33TUV4U+fUxtxuqTx5XHURFuFb6byLKIj6NPHtcA++P0yjE1kyJwqZ2Fl+nNJ0MnvSvrJD4J4OYoj/jasUZX28YsHVgwCfzHV8bJv3FlvvmZ5WH+7VOdvdUHx+qTx5XHdpGlg96poBuPu42TtwfY+wjPjMylcXrlmJpJEfh8Ows+KdR4+BYrhF910++U0ZFVv8VrvoKjsfnUsebjeUq/6PL44WcWJrVXx8nHLEifWGfw2H7x5WNqb6y+elwNeFzJfLOUZbCvHlfbcXrrmJpJEbjS8sJr8JjCWSKM+3wziGW7PsZb9Lfh9jfvjNAujsaG52/kf0cIjre+38RaL4xPYUwAOlezbPf2OPn7sML7gmUXX/T/9eVj6misPnpcY1mq6vdD+eYKlt2+dlwDHIzTa8eUf1jTCDNUz3hAxP0ZHxtbIz0rALLDGB8TzbKYZZ9+G+Wl8c3UTdUcfXUy0dnYMH79OKNl+BAvj/Mblm0sW/ULqqYPjLMPLj99TPATb9bPT188po7G6ovHtR3LJn1MmESf05/3qePqZJxeOabSYkIQBMHkmMk1JAiCINhBFIEgCILJEUUgCIJgckQRCIIgmBxRBIIgCCZHFIHgt3C+9UW9g+N2lt+NvG2L19H5caaTv5/OctDYVc/O60avmDKHv/sRlsMsH3hrDIL/IIpA8GcucI50B5Y2eiO6ScYLPIG21M//fnoBnyP+T99W1WPw97u9ZSyP6R2+ec4DwxFMiCgCwSz8Y9Oka5xevLOQZaQrH4CKTpZ/WNaxvGzz2v/pz281esvrzz/LslvvgT+T5XH9+aUsr7H8zQ8f4tvOuK83HFxgVJjybWOW+frzy1lalOwwCEIZbl4vCL4CT57Yi2Kg3ibB4AaWK1mas9zP4tBFZMF7LB/zavxr/kxL62KQXvKPvjBoxzBHbxiYwTJG79aJaw2tnDdYfF5V/qzL9D4+UAij+HESP8bYXmW5Xd9s/R5+HhuqdOf7H7Fc7u4xEARniCIQ/JlQxAj4toE+AS/Ck/xcV75J4sk1ge+jDYVq98uPzxXxeb31iZ10a+JN/T4UwSC9ZQCorCsG9I76jT/3gv69v9t83vf6LZQR3FewGvAYiusE38fn9GL5UX+e9I1JBKFUEUUg+H2MgCfRKnx/LgtW8VNYxrK04OexIxyI1Cf4z134THutfzFLv87f9altQLeIz0q3+Psd/Pc9bf4e40rGv8GFcQlCsZEYgeD38ESawjcPsjyO7o18ex1LO36+AUTf/QnKoShW6l1rwU0Wzy9guV1fwWMCr82CjU9WsIzQ96fFa8McfC6aiKEbpVIEcBWxtNZ7/iNr6TqjCyjT3o1/uiC4hCgCwRTwpLpJ7+x6PcsxfnzM4mVsb9nKpgWwPR7SNwxCS/MqFp+NgPN3LAgko3PkTywR/Pw6vYMkvhc9/NezpNgZG7ZOvZblTaS08u1m3SVkKJyJ+vM7ysn2qkI5Q7qPCoKTOgK+mcsT9U/FvsDYEuC/P8+3YbrCucvY/7ek8Gfeyjdd+PMQ7BaEYiMWgSA4Bqv3lx0VlLnIVD1gjcn/51JUAog/PMXibHtLQXDtfNI3PBAEQRBMilgEgiAIJkcUgSAIgskRRSAIgmByRBEIgiCYHFEEgiAIJuf/AeL44Li0FV2lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# determine scores in reference  window\n",
    "\n",
    "names_dict = {'rf': 'RandomForest',\n",
    "             'xgb': 'XGBoost',\n",
    "             'lgbm': 'LightGBM'}\n",
    "\n",
    "print('model','F1_ref','F1_notRef','F1_notRef_est','F1_mw','F1_mw_est')\n",
    "for item in scores_dict:\n",
    "\n",
    "    print(item,scores_dict[item]['F1_ref'].round(4),scores_dict[item]['F1_no_val'].round(4),\n",
    "         scores_dict[item]['F1_no_val_estimated'].round(4),\n",
    "          scores_dict[item]['F1_mw'].round(4),scores_dict[item]['F1_mw_estimated'].round(4))\n",
    "\n",
    "resampled_ra = []\n",
    "ra_step = 3\n",
    "ra_edges = np.arange(0,360+ra_step,ra_step)\n",
    "for ra in ra_edges[:-1]:\n",
    "    resampled_ra.append(ra-(0.5*ra_step))\n",
    "\n",
    "# plot F1-scores\n",
    "for key in model_dict:\n",
    "    resampled_f1 = []\n",
    "    \n",
    "    # resample\n",
    "    for ra in ra_edges[:-1]:\n",
    "        resampled_f1.append(df_drift['F1_score_'+key][(df_drift.ra >=ra) & (df_drift.ra < ra+ra_step)].mean())\n",
    "    \n",
    "    plt.plot(resampled_ra,resampled_f1,label=key+' actual')\n",
    "\n",
    "plt.axvspan(ra_lims[0],ra_lims[1], alpha=0.125, color='c',label='Reference region')\n",
    "\n",
    "plt.xlabel('RA [degree]')\n",
    "plt.ylabel('F1-score')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "for key in model_dict:\n",
    "    \n",
    "    resampled_f1 = []\n",
    "    for ra in ra_edges[:-1]:\n",
    "        resampled_f1.append(df_drift['F1_estimated_'+key][(df_drift.ra >=ra) & (df_drift.ra < ra+ra_step)].mean())\n",
    "    plt.plot(resampled_ra,resampled_f1,label=key+' estimated')\n",
    "\n",
    "plt.axvspan(ra_lims[0],ra_lims[1], alpha=0.125, color='c',label='Reference region')\n",
    "\n",
    "plt.xlabel('RA [degree]')\n",
    "plt.ylabel('F1-score')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# show estimated vs actual score:\n",
    "for key in model_dict:\n",
    "    \n",
    "    resampled_f1 = []\n",
    "    for ra in ra_edges[:-1]:\n",
    "        resampled_f1.append(df_drift['F1_score_'+key][(df_drift.ra >=ra) & (df_drift.ra < ra+ra_step)].mean())\n",
    "    plt.plot(resampled_ra,resampled_f1,label=names_dict[key]+' (actual)')\n",
    "    \n",
    "    resampled_f1_est = []\n",
    "    for ra in ra_edges[:-1]:\n",
    "        resampled_f1_est.append(df_drift['F1_estimated_'+key][(df_drift.ra >=ra) & (df_drift.ra < ra+ra_step)].mean())\n",
    "    plt.plot(resampled_ra,resampled_f1_est,label=names_dict[key]+' (estimated)',ls='--')\n",
    "\n",
    "    plt.axvspan(ra_lims[0],ra_lims[1], alpha=0.125, color='c',label='Reference region')\n",
    "\n",
    "    plt.xlabel('RA [degree]')\n",
    "    plt.ylabel('F1-score')\n",
    "    plt.legend(loc='upper right',fontsize=8)\n",
    "    plt.ylim([0,1])\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(resampled_f1,resampled_f1_est)\n",
    "    r2 = r2_score(resampled_f1,resampled_f1_est)\n",
    "    plt.annotate('MAFE = '+str(mape.round(2))[:5],(0,0.93))\n",
    "    plt.annotate('$R^2$ = '+str(r2.round(2))[:5],(0,0.88))\n",
    "    \n",
    "    # uncomment the next two lines (and comment out plt.show() to save as a .pdf)\n",
    "    #plt.savefig(path+key+'_F1_actual_vs_estimated.pdf')\n",
    "    #plt.close()\n",
    "    \n",
    "    plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:intel]",
   "language": "python",
   "name": "conda-env-intel-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
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 },
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}