location_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn.cluster as cluster\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler as sc\n",
    "import hdbscan\n",
    "\n",
    "big_data = pd.read_csv('airbnb_coded.2.0.csv', parse_dates=['host_since'])\n",
    "sc = sc()\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "plot_kwds = {'alpha' : 0.25, 's' : 80, 'linewidths':0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13.4462961  52.48141516]\n",
      " [13.41708604 52.60610923]\n",
      " [13.50276362 52.44614449]\n",
      " ...\n",
      " [13.29098912 52.4976456 ]\n",
      " [13.38508885 52.46504928]\n",
      " [13.58954646 52.46768108]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [-1])], remainder='passthrough')\n",
    "var = 'bathrooms'\n",
    "var2 = 'accommodates'\n",
    "var3 = 'cleaning_fee'\n",
    "var4 = 'bedrooms'\n",
    "var5 = 'beds'\n",
    "var6 = 'minimum_nights_x'\n",
    "var7 = 'maximum_nights'\n",
    "var8 = 'number_of_reviews_y'\n",
    "var9 = 'listing_class'\n",
    "var10 = 'price_x'\n",
    "var11 = 'bed_code'\n",
    "var12 = 'cancellation_policy_mapped'\n",
    "var13 = 'room_code'\n",
    "var14 = 'superhost'\n",
    "var15 = 'instant_book'\n",
    "var16 = 'review_scores_location'\n",
    "var17 = 'review_scores_rating'\n",
    "var18 = 'host_total_listings_count'\n",
    "var19 = 'availability_90'\n",
    "var20 = 'room_code'\n",
    "var21 = 'extra_people'\n",
    "var22 = 'property_type'\n",
    "var23 = 'review_scores_value'\n",
    "var24 = 'review_scores_cleanliness'\n",
    "var25 = 'review_scores_checkin'\n",
    "var26 = 'review_scores_communication'\n",
    "var27 = 'review_scores_value'\n",
    "var28 = 'guests_included'\n",
    "var29 = 'security_deposit'\n",
    "var30 = 'host_since'\n",
    "var31 = 'longitude_x'\n",
    "var32 = 'latitude_x'\n",
    "var33 = 'amenities'\n",
    "\n",
    "# big_data = big_data[big_data.price_x < 500]\n",
    "\n",
    "data = big_data[[var, var2, var3, var4, var5, var6, var7, var8, var11, var12, var13, var14, var15, var16, var17, var18, var19, var20, var21, var22, var23, var24,\n",
    "                 var25, var26, var27, var28, var29, var30, var31, var32, var33, var9, var10]]\n",
    "\n",
    "\n",
    "\n",
    "data[var21] = data[var21].str.replace(',', '')\n",
    "data[var21] = data[var21].str.replace('$', '')\n",
    "\n",
    "big_data[var21] = big_data[var21].str.replace(',', '')\n",
    "big_data[var21] = big_data[var21].str.replace('$', '')\n",
    "\n",
    "data[var29] = data[var29].str.replace(',', '')\n",
    "data[var29] = data[var29].str.replace('$', '')\n",
    "\n",
    "big_data[var29] = big_data[var29].str.replace(',', '')\n",
    "big_data[var29] = big_data[var29].str.replace('$', '')\n",
    "\n",
    "\n",
    "data[var30] = (big_data.host_since - big_data.host_since.min())\n",
    "data[var30] = data[var30].values.astype('timedelta64[D]').astype('float')\n",
    "\n",
    "big_data[var30] = (big_data.host_since - big_data.host_since.min())\n",
    "big_data[var30] = big_data[var30].values.astype('timedelta64[D]').astype('float')\n",
    "\n",
    "\n",
    "data[var] = data[var].fillna(data[var].median())\n",
    "data[var2] = data[var2].fillna(data[var2].median())\n",
    "data[var3] = data[var3].fillna(data[var3].median())\n",
    "data[var4] = data[var4].fillna(data[var4].median())\n",
    "data[var5] = data[var5].fillna(data[var5].median())\n",
    "\n",
    "data[var6][17990] = data[var6].median()\n",
    "data[var6][6215] = data[var6].median()\n",
    "data[var6][11299] = data[var6].median()\n",
    "data[var6][12737] = data[var6].median()\n",
    "data[var6][14598] = data[var6].median()\n",
    "# data[var7][872] = 8000\n",
    "# data[var7][2372] = 8000\n",
    "# data[var7][4391] = 8000\n",
    "# data[var7][17952] = 8000\n",
    "# data[var7][17990] = data[var7].median()\n",
    "# data[var7][6351] = data[var7].median()\n",
    "# data[var7][8341] = data[var7].median()\n",
    "\n",
    "\n",
    "data[var6] = data[var6].fillna(data[var6].median())\n",
    "data[var7] = data[var7].fillna(data[var7].median())\n",
    "data[var8] = data[var8].fillna(data[var8].median())\n",
    "data[var10] = data[var10].fillna(data[var10].median())\n",
    "data[var11] = data[var11].fillna(data[var11].median())\n",
    "data[var12] = data[var12].fillna(data[var12].median())\n",
    "data[var13] = data[var13].fillna(data[var13].median())\n",
    "data[var14] = data[var14].fillna(data[var14].median())\n",
    "data[var15] = data[var15].fillna(data[var15].median())\n",
    "data[var16] = data[var16].fillna(data[var16].median())\n",
    "data[var17] = data[var17].fillna(data[var17].median())\n",
    "data[var18] = data[var18].fillna(data[var18].median())\n",
    "data[var19] = data[var19].fillna(data[var19].median())\n",
    "data[var20] = data[var20].fillna(data[var20].median())\n",
    "data[var21] = data[var21].fillna(data[var21].median())\n",
    "data[var22] = data[var22].fillna('Apartment')\n",
    "data[var23] = data[var23].fillna(data[var23].median())\n",
    "data[var24] = data[var24].fillna(data[var24].median())\n",
    "data[var25] = data[var25].fillna(data[var25].median())\n",
    "data[var26] = data[var26].fillna(data[var26].median())\n",
    "data[var27] = data[var27].fillna(data[var27].median())\n",
    "data[var28] = data[var28].fillna(data[var28].median())\n",
    "data[var29] = data[var29].fillna(data[var29].median())\n",
    "data[var30] = data[var30].fillna(data[var30].median())\n",
    "data[var31] = data[var31].fillna(data[var31].median())\n",
    "data[var32] = data[var32].fillna(data[var32].median())\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# data[var6][17990] = data[var6].median()\n",
    "\n",
    "# data = data.drop(data.index[17990])\n",
    "# data = data.drop(data.index[14598])\n",
    "\n",
    "data = data[[var31, var32, var10]]\n",
    "\n",
    "X = data.iloc[:, :-1].values\n",
    "# y = data.iloc[:, -1].values\n",
    "# X = ct.fit_transform(X)\n",
    "# # X = X.todense()\n",
    "# X = np.array(X)\n",
    "# X = ct.fit_transform(X)\n",
    "# # X = X.todense()\n",
    "# X = np.array(X)\n",
    "# X = ct.fit_transform(X)\n",
    "# # X = X.todense()\n",
    "# X = np.array(X)\n",
    "# X = ct.fit_transform(X)\n",
    "# # X = X.todense()\n",
    "# X = np.array(X)\n",
    "# ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [-4])], remainder='passthrough')\n",
    "# X = ct.fit_transform(X)\n",
    "# # X = X.todense()\n",
    "# X = np.array(X)\n",
    "# ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [-5])], remainder='passthrough')\n",
    "# X = ct.fit_transform(X)\n",
    "# X = X.todense()\n",
    "# X = np.array(X)\n",
    "# ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [-6])], remainder='passthrough')\n",
    "# X = ct.fit_transform(X)\n",
    "# # X = X.todense()\n",
    "# X = np.array(X)\n",
    "\n",
    "# plt.scatter(data[var6].values, data[var7].values, s=100, c='red', alpha=0.5)\n",
    "# plt.show()\n",
    "\n",
    "print(X)\n",
    "# X[:,-8:] = sc.fit_transform(X[:,-8:])\n",
    "# X = sc.fit_transform(X)\n",
    "# print(X[0])\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1)\n",
    "\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_clusters(data, algorithm, args, kwds):\n",
    "    labels = algorithm(*args, **kwds).fit_predict(data)\n",
    "    palette = sns.color_palette('deep', np.unique(labels).max() + 1)\n",
    "    colors = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in labels]\n",
    "    plt.title(\"Location Distribution Clustered n=4\")\n",
    "    plt.ylabel('longitude')\n",
    "    plt.xlabel('latitude')\n",
    "    plt.scatter(data.T[0], data.T[1], c=colors, **plot_kwds)\n",
    "    plt.savefig('loc_clustered_1.png')\n",
    "    frame = plt.gca()\n",
    "    frame.axes.get_xaxis().set_visible(False)\n",
    "    frame.axes.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XbTDSm7vi66d1JrbH2EKTg/155hseTSckbykUipmaw2hPjkMDxWt9iyk3AKnguMW50oF3I0N42w9XAvYqLY9a22e+7lJpuliGTs3xcXyLUs6k0vJWrrfde9lITfb8+HLqvnsRam2fe/aUqLUDjk3XafsBKEVGF94w2s19+zYsj5OSsoZUcNzCXGzgBbY0iHc8doA1wX4db6ixhSb9RZvFpocXRoQh5C2NvoLFQNFmbKHJAyMWZxebPPbKLGEU0ZOzCEJFpeVdVAhspibrtHmrs5Eg7sqaeEHEW2/vo7+Y4dxSCy+IONCX431H9qQCN2VLpILjFmazgffoRIVa26fS8vHDCFPXmFxu8fY7+i8YWDoeO2MLzQ29oTqqLKXgjv4iIoJSipoTkDV1zi23qDs+f/PiNDnL4GBfniBSjC81GenJX1QIbEVNdquyelJgaMLLU1W+/Nocdwx2EUQRA0WbB/Z387qhrgui+lNSLkUqOG5hNht4v35mCUODUtYiZ1m4QcjEcoujE5UNPW5i4bHxoNNRZRUyBm4QYpsGbhBRyMRuutPVNsstHVQ82D1xssVQl42IMLHcYrg7t+lq51JqsluZzqQgjBTfmKjQcAKaXkBzvMJt/XncbJxaPXV7Trkc0pQjtzBdWZOxhSZffHmaT399nC++PM3YQpOlhkeXba5Ui7NNgy7b5MRcY9vXONiXp+UFsSrMC6i2XNp+/HlsscnB3gK1dkDTC4kUZAyNE/MN5usOLTckY2ib1uPotN3yApRSK3+nqcLPpxk5OVdntuqgidCds3DDiOWWC8A7Dw+kK42UyyIVHLcwbS/gL549x7HpKn4QstBw+R/HZhENQNYdLZflqtlRZfUULAZLWbKWwWCXTU/BYk/Jpr+Yoe0FlHMmCqg7AYIAGg035La+wqb5lDptW4bGcsvDMrR0IEzorMbGl1oUMgaWoROEUM5e/iQgJaVDqqq6Ram0PL74yix7u7MoBZW2T9MN6c6bzFZdChmDoa4sGUPDDSKqjs89w12Xda2OKmt9sB/EqqVcxqDthfQVLObrDoKgieJAbyFJfKg2tVtcTE12o3E1XYs7TgteEGHpGl4Q4QYhQ+V4NZbGa6RcCemK4xZlbKFJ2wsZ7LLpLmSwzdjTKWtoGDp4SeK7WtsnVBH7urNX3VWzo2oq2gZ7SjamoZHPGNwxWOT1+7tX4jxuBbvF1S6Z21mNHezLs9DwiFTESG8OXYSq46fxGilXRLriuEWptX36ixlabkjd9bF0HUMTlloedwwUuH9/NwsNl6GSvWOBdetzKO0tZbl/X5mxhQZeGLGnZK/YLQ4Pdd/UwX474Vpczll814P76DsxT7XtEwSKtucThBF1J+D58eWbqg9Tdo90xXGL0pU1uWOgSMP1WW55aBK7yEaR4t69ZfqLGYZK9o4bUDs5lH7wrQe4Z2+JIFJYhhYXHZqt4Qbhiorras7IryWd1cWXjs+tPMP6+iQQe7jV2v4VXaucs3jHHf3cM1yivyuDpgl37ymxvzt3Q/dhyrUlXXHcohzsy1Npebzptj6+fGKO2bpHwTJ4/5E97ClnaXnBrqqHyjmLg33xoHrPcHlNMCHcPMF+mwVd6pqsuBbXHZ+pSpvFpkc5uza6/nJYbQfqK2Ru+D5MufakguMWZXXg3rv1QaarbQ72FmL11Sr10E6yXvVUd/xNhcPNEuy3XgCGkWJyuU2l7WObGv0Fm8WmiwaYWjzQbyWNylbUeDdLH6Zce1LBcRNzqcFktbdT59jdqoWx0cz7hfEKbxhdK6w6A9vNEOxXaXk8N76MBhRtk66syeRyi4yhUbAM9vfk+NtXZ4iUomib7O/JkbV0dE02XBV03tmJ2TqvTFcpZy32lrObpmvp9GEYKaYqbRpuiKHDSM+VJ5tMubVIBcdNynYTAO62W+tGqqeefBwU+Pp95++j7YeIQN3x+drpRaJIUbItunIGpazJO+7o37V7vhw6g/tUpc3p+QYLDRcU5DKxSupgXwERjYItZC2dMIrozWe4Y7ALNwg5PlPjzsEijh9e0O7z48tEERybrkEEDSeg5gQ03JCRntwFwuZgX54vn5hnYrlNyTaxdKglnnNXqg5LubVIBcdNythCkyiCc0stGm5AIRMLkOtFn72R2uRgX55nzy7R8oKVyoGvTNfiyPO8iY7ghRGn5usUMgZ5OxYe12ut7Dhx4wxhBAv1NktNH0sXDEMnDBXTlRYqAtvS6S1kOLvYImeZtAO1ErEP8bu8Z29pTdsdwXtuqUWkoLuQwQ8UdcdnsCvLUtPF0NcGcZZzFqWsSbXl44URhYzBffsKm65oUlI2IxUcNylTlTaz1XZS+8LEDULOLjZwguyGgXi7zUaqJ0PXuD+JBD+33GK62iZjaPTmc5yrtGi7AXvLOYJQkc8Y7CvbjC+2CCN1WZ5fO+neW2l5PPbKLIYIPQWLZ88s0nQDCrZF1lLkuuIiSsdma3zg3j105zIcnazi+AGGruH4cboVpeKiWOvTqHQEb8MNKWUN/CDCNDSaSZqW8USgdGq8d55NKbhvXxmR80LlYgGWKSkbsaPuuCJyRkReFJEXROSZZNsvi8gxETkqIo+KyIZRZSJSFpHPJMe+KiJvSbb3iMhjInIi+X3tR8HrkIYToCWz1s7sVROh4QTX+taAzfNM3bevzAMj3QyXs9y7t4xl6NimHqdjzxicnG9QyBgEkcI249+bpSS5GGcXm3zm2XM8PbbEXM1hqeFdVdfUsYUmYRTRlbVo+SFVJ0BEiFREEEVEUUR/MUMpZ5GzTJpeQK3tMVt1EQQ/DGO1k+dTsHW+ca6y5v7OJ4/U6crGiSibjk/W1JiptpmutukrZC5wXe6ct5obzVaUcu3ZjTiOdyul7ldKPZR8fgw4opS6D3gN+NlNzvs14PNKqdcBrwdeTbb/c+DvlFJ3AH+XfE5ZR8E2iADHD1FK4fghUbL9eqDj1eUGIV8/u8TLUxV07fwsuBPXUMjouEFEztIAoeb4ILHR3A3irLvbjXdYvRroL2QIIuIZesS2BdBm1No+Pbl4QF+oOwwWs4DQcEMKlo4gzNUc3jjajR+GHJuuU86a7O3JEqEIIkV3zqTthoz2FC4QAKuTR4pAd94iUArRhLmGy9sO9TGQZBnu2JLGFpppYsiUq8KujyJKqS+u+vgk8N3rjxGREvAI8OHkHA/oTAW/A3hX8vcfAf8A/MyO3OwNzHA5i23EHkk1J6CQ0Rko5ukpXF967DBSHBkurcRtdAz4nZnxcDnL8Zk6Rdvk7GITQ4SG6zPSk8PxQ0Z781uaMa9WS81UHRqOz0hPPlmNxYF3G9kFNjp/K2qtrqxJEMZ1RSrtgOFyhobr0fYiLCP2lMpaBl6geHV6mVBF7O/O0ZPP4AYRNSfgpakqI905llseWUunaMfPGNsjulfcqZ0gpOEEjPbmGS5nmaq02d+91lOq4512qfopKSlbYacFhwK+KCIK+G2l1CfX7f8o8OkNzjsIzAN/ICKvB54FfkIp1QQGlVLTyXEzwODO3PqNTSfAb39Pbk0w3fUys6y0PL7w0gyVtkdvPsNwOQvAZKXN6YUmdwwUqLZ9Boo2dw4WGFtskrcNBks2DScgY+gc7Muja3JBzMlqT6aGEyACNSfgYG+e/mKGYzM1am2f5ZZLT94G4nTu8w2XQ4PFC4RET97i9HxjWyVqO/0/0pNnseGx3PYYLmfZ153DNg28IMQLIyaWm3hBQD5jcmahSSFj8LY7+lluupiaFkd4h4rjM3UODxUpZIwVe8Qlk0du4rp8MyWGTLk27LTgeLtSalJEBoDHROSYUupxABH5eSAAPrXJfT0I/LhS6ikR+TVildS/XH2QUkolQukCRORjwMcARkZGrtoD3ShczzPLjitppe3Tl8/ghREvjC+DQDlroQEZQ0fwcYMQpeCe4RIfvG+Ycs6i0vI4OlHh2GwdQa1J2NfxZGo4sc2gr2Cz0HDpK2QYX2oSKkXDCak5PvPjLvePlOnOZai1PXQNevIWT5yYX1P9sNLyuGtPaVsR16v7/67hrjUBlm0/5MXJCoOlWGC+Nluj6UYUbJOibXJ6vklv3mSgGPeNbRosNlwefe4cmqbRW8hwoC/PaO/Gk4BLlfNNSblSdlRwKKUmk99zIvIo8DDwuIh8GPgg8B6l1EYD/wQwoZR6Kvn8Gc7bMmZFZI9SalpE9gBzm1z7k8AnAR566KFbMof09Tqz7LiS9uYtvDA2cjfcFgDdOYuibZKzDPqL9kqNjdVU2z6vzTZW6pP7geL58WVu6y/w2CuzeEHEVKVNzfFZbPoYulC0TTKmzlNjiwyXsrQ8k6oETC63qTsBBdvkvXcPcXaxycRya031w/GlFgXbYKDLXrmH1RHXm6mxOv1/sC/P0YkKJ+YanF1qcGigyJ5SlrmaQylrce/ebs4uNeP0537IXL1Nd97k3r1lJistFhsuL05UCYHRniyFjM5fPjfBdz64b0PhcT1PGlJuDnZMcIhIHtCUUvXk728GfkFEvgX4aeCdSqnWRucqpWZE5JyIHFZKHQfeA7yS7P5r4IeAX0p+/9VOPUPKztBxJe3YLwD8MMILoxW7BWycDiM2bM8kbq52MrDH9cmfPLVIww1oOD4tP06G6PgRs9U2lh77gYQR9OQzAORaHgXboJy1eN+RIco5i799dRZDE2aqLm0/JGvq5Cyd0/NN3jDaC8TBiKfnG3hhHJwYZxq26c5ZzNddnjgxnxilNYZKWaJI0V+0eeNoz8rsv2gbLDc13CAknzEY7ckzVWnjRRG9BZvRnjwDXTYF2+DR584RoujNZzjYXyRvGVRbLk+eWtx01XG9ThpSbg52csUxCDya+IsbwJ8qpT4vIieBDLHqCuBJpdTHRWQY+F2l1AeS838c+JSIWMBp4CPJ9l8C/lxEfhg4C3zPDj5DylXg7GKTJ08tMl936C/aFLMGthkbew8PFZmqtHGDiKxpcHioa8UIvJHRO3ZzhZ6CteJmvNhw+ZuXppiuOpiGMFjMUszohFEsfDKmQcMJ0TSX3pyF44doIrzl9j4KGYNzy61kxVDh9FyNIBL6CzZ5S8cPY480L4xoeQFBGPHiZAWFcN/eEmOLTRpOnJb+eK3G0ckqiw2XnKnTV8jw9Ngyoz053nk4s+LhBOAGIaYuvDRZjQ3lpoFpaDx0oIf79pU5Pd+g5cWBm5qmMdqTWxEaEKcsma62d/dFpqQk7JjgUEqdJnajXb/90CbHTwEfWPX5BeChDY5bJF6BpNwAnF1s8odPjLHQ8PDCAEs3KNo69+0vc7CvQCET52jKmBoC6JqglNpULx+7uZq4QYRt6szXHV6crBGqiL0lm9m6y2uzdQ4NFPGDkFYEedNguNvGCSI0TTB1YbS3SNE2mas5TFcdbENnqekyU/NYanmYukZ/MV6ZaJrG7T05LEPjpakqBdvktr64OmGtXWViqcX/ODaPrkMQKLxAMRu5KOIVkh+GDHdneXCkB4AgjHhqbJHpipPYVjS68yZ7yznu21dmtDdPKakHv9zy6C1kKGT0FaEBMFt3aPnRBQF+KSm7wfXh1J+yq+xmQaQ/+doYT5ycx9A1irZBzlJMVFo03BBBKNgGw+XsSs6pS+nlz7u5xlrOk3N1RMWR5IeGunDDalyQqtFmsJSj7vgMl3Mc7Mvz5tt7V7yjsqZOywsYW2wwULQZX2pimzr7ylmiKOLUXANNIG8bDJftFftG2w0wxODkXJ2GG/KNc8ucWWxQawf4YUTTCdA1KGQz2IZGPqNTafmcmKvz4EgPdcfn6TNLnJptsr8nx56STdMNGOiyOTzYxVLTY7Q3v0bVdKAvz18+N0G15VK0TWbrDq/N1Hn36wa37OWVknI1SQs53WJc7RKlF+PsYpO/OzaHpWuUbBPXjziz0ERUxGytzYG+PDlLX3ER3oowO9iXR9PijK6GBjNVF9MUjgyX6C/Y3LO3hKELi804ivrevSWO7O3ifUeGGO3N80CS0mS55WEZGntKWVw/xDZ1bNOgnIvtCL15C8vQuL2vwEhPnmrbxwsiCrbB8dkGp+abVFoedcdjfLGN64d4QYQTQsOHWtvlzEKL6apHECkWGy5KKU7PN1hqeXTnTddQ290AACAASURBVAoZg4xhUMiY1J2Apaa7YSDjaG+e73xwH1nLYLoaZ7V99+sGua2/cEGAX0rKbpCuOG4xNiuIdHSikqhert4q5MlTi5i6RsbSEU0jVCGWrtPwIroNfeXaXz21wFTFWfGS2iwtOKz1GDJ04ci+EoWMTl/BXnmegm2ii4YgTCzH9pN7k0SInYJRHSHVcALGFppkDI22H+IFIWcWW3hBhB+FDHRlcP2IuxN3XBHB0gVL15irOUxWXDRNaPkRUaTouO+5Puh6yGKjjWCTt2JbyumFOo6bxJa4ASXbxDQ0XMdnqeVxaHDjWuCjvefdb790fC6tq5FyTUkFxy3GRllpKy2Pfzg+x4G+Aj0586ID93aYrzvc1ptnbLEZ518KQkhSpL/pYM/KtR99doKBUmyMnqk5eH7EaF8OXRMeuXPggnYvpsYZm2/gh4rbB4oMl7NkDI1aO04x8t1v2Aewkm4+9p5yeOLEPHtKNsPdWc4tNpmvewyWMoQRPHdmmTCCLtsga+koBYcGiszXHWquz3LTQ0URCkHXQI8gJPkJI3TdoOX5CPDSZJXFZkDT8TF1jcVGnQP9RWxdiBTomralAM2boTZJyo1NKjhuMdYPOnXH54lTC3HcRCFOdzG+1NqwnsN2yWcMilkTQxMaLR8viogU9BcyvOPOgfjaJxbQNKGYMTi90KTpxon7Wm5ApeVfMmV6R43z5KlFpqttFBEHenIEkWJsoUnW1OkrWrhesKLKyVkGYaR4bbaBG4TcPdzFueU2r0zWyFgaA8UMtaaPEsHUNTSJODHXJFKJcNCErqxFb94iUoowAk0HP4gFRoesZRIpcIOIyYqDpgkNx+fsQgNEyFs6ugjlvMVIT4733j1IOWdd4IX25tt717jdpgF+Kdea1MZxi7E+yd3phQZukoqkk7fJNvVN9e1bpWMzma60MXSNgIhAKWxL5zse2Eve0vnS8TnOLMaD+4nZOrM1l5rjs9T0qDk+9bbP0YnKJa812pvnQw+P8GPvuZN3HR5kpuYiAnlLJ4wUJ+fq2IZGre2vJE+cqrSxTZ0gUgyXs9w5WGSglMHxQtqeT90NKWYM8hmNtqc4s9Cg7cd9Vm17HJ+tEYSKjCGEEXi+IlgVZmoKoBSmLvTmM3HakaaHG4R02bHwnlhq89JUjSN7u/j+txxgtDfP2cUmf/ncBG0vYE8pS9sL+MvnJji7eN5+0VHXrbbVpIbxlN0kXXHcYqyPKnaDiLuHS2sy067O23S5HJ2ocGaxScMLWW55aEB31uLefUWGyzmePbuEE4TcNVSk6vh8faxBd8Ekb+nU3RBdhIGizYm5xhp11VY8wjKmhh+EWIYGKFDgBmpFldP2QxpuSJdtkLPi1U13zqScNVhu+WhA1lJoGlSdAD+KUEpnqeES5Szu21/m5FyDmaqDoWuYZkgYQBRfCgFsU1Y8npRSNL2AuZpgmgY1JyRrGAwUM7T9iJen6rzzsE85Z/HkqUVKWZNSLnYF7vxeH+x3owX47aYnX8rOkwqOW5DVg05X1mSp4TG+FM9oM4a+krfpchIidgaIzzwzwbGZGj05k9v7C1SbATXH59yyy0y1zZtu6+PcUotq22e27pK3DYIwouXFhudDA0VaXkje1te0falyuErB2w/18fSZJZZbHqWkdvdSy2O62mZsPvaIansh5ZyJbenUWj6DpSwohS5x6nbb0JiveyiBLtukYBvUnIBHDg/wyJ0DfOGlGZZbPi03xPMhAnQBTSBjCkU7gwIcPyJv6nTnLJZaHi23TcbQKWQNogh6chaGLiuCYb7usKeUXdOnN3qw33bLGKdc/6SC4xZndRbXpabLfMNB1zTee/fQlr7Uq2eSIqxktF1qxeqidhDh+D6WqZHLaMw1XL50fC4pQmTg+hE502Coy2Kq6tBbMBnpydL2A8aXmrzJ7uHx1+ZQCmaqDn2FzBqPsIYT8IWXZhgq2TS9gGfOLNJ0oyTFiGKx6bLc8gijiK+djKvwjS83qTsBYRAx1JOlO2txYq6O60eM9uRoegETFQc3jMiZsbqr5YWUcueNz1PVNmcXG0QKTB28EAIFuoJixiBrSFJsSlCaUG27NN2QhheieQEhimyXQV8hQ2/OYr7uUGl5tPyI584t01/I0FeMvbHqTpzS5EZlM0++tFztjUsqOG5itqIeWO/eemiwuGU1wvqZ5NGJCnU3IKPrtL0Qz49ouSG2aWCZGn4UB9AFOZOXJ6uM9ubRNY2CrTNQypJNEh8utTx0hIGuDLW2z2Mvz7InKROra8K+nhz9hQxdWZOJpSZ+BOWcyd8fm0NFCl0TpqstlIIH9ndzcr7B2HyDnG0yudyi6YY4YYilCU0vxDQ0LEPjwZEypqGxtOzh+wGljIZhaIktI44krzsBlZbH6fkGSuLcV6xLodnwQuZqPkqgkInPd4IQ149IHMtwvZBKy+POoQK2pWObGs+PL3N4sMiTpxeoND3qjk9vIYMfRLz7rsEdU/fstBqp1vYxNOH4TC0uZJXR2VOycdZVIky5cUgFx03KdtQDl6svXz+TDCKFpQlPjS2wpyv2bJquxsFxBVvH9UIsXTjQl2eh7hCEEQ03oDefoZAxOHhHP6/N1BFNwwsCZqoOL5yrkjE0hio2moAbhjS9gNmcxWLd5WBfgb6ixd++MpMMSgYNN6Scy9BwA07MNVhsujS8kLGlJi0nIiAevJuAISGlnEEY6Tw3XqGUNejN2yw2PcJIYRmx2uxNB3sx9bhq3/Pjy4SRQoUQRGv7JARqToROrLZquRHV5LOpQ94S2r7CDUO8xP7hBxEH+vJEUZzssb9gM11t4UeQMQ0+9Mb9lLLmjqh7dkON1PQCvnZ6ER2NUlYnjCzmGy73DHddlfZTdp9UcNykHJ2oMLncJogUhUyc1qMTXbyRkLicWef6mJBCJk7FESp4/UiJlu8nSQLjqGtEcf/+HspZi3xS8tUNPaptnx962wGWmh7ji036xOLJsRqvTNfjkrcRvDpdp6dgkLdM5uoer99XouYGHJ1Y5t59ZZZbAX0Fk1DFg/tDB3vY153lGxMVmk5AwwtoubHQgPOLhEDBUjOgZCvcMKLuBsxWHSIlmEYcRHhiro4fhty3r5uMqRNFYGkabnRBl6wQAiqKbR+dz2EIfqRiF10fHDOi1gr4zv9pHy9NVjm71CRrGhzoy7OnHKvrBrtsRnvzK4P71Vb37LQaqdLyOD3XIAojunLGSpneci6NObmRSQXHTUil5fHC+DJ9hQxdtokbhByfqXHnYHFD9cDlzjrXx4QMl7M8PbZIbz5Dbz7Dg6PdvDhRo+F6lGyLQsaglIvvZ7SvQN4yGCgGOEGwEhldd3z+8CtjTFUc2m5ISOwzHgELjYCWEbC/nKPpRkRRhFKKMFL0FkzcIHZ/LWYNFuouuiaMJ1Hg9bZHuElVFgVUnRDbVGgoREApRcY0KNomYQTzDY/x5Rb7u3N89dQ8nrqI1EjY6IjOPYSAUhGmoVHKmjScAA1Wytjapo7rBzScWNRtFLh5NaLF17dbd3xem61xfLbO8+NLHBooXjKW5mKMLTQxDZ3X7Smx2HBpeSE5S6c7Z7JhJZ6UG4JUcNyEjC006SlkENFWUo93tt+zt7Th8Zcz61wfiKZrspLptuYEDHVlefCtPbS9kIWGy0LDYabmcqA3T87UcfyQquNzz3DXyorn6ESV8aUWlbaL1vGmXTXAeAHU/diwUMgYzNZcml7A7f0FnhpbwtSEke4cx2Zr1JyQoqXjaLDYuHifKcAPIpSKB/yMHgf6ZQyNhuMzXW0zvtjCNHRMTdFwr0w/L8SBgY4fMrbQpGAbNF0fx4/L4rpBSKQUBTt+F6uFdN3xmaq0WWy6lLNxRcTLHdjXt/vCuWUml1uUbRPbMHh5qka17fOOO/ov6xqdbMZBBKO9BSAWyvMNJ410v4FJBcdNSK3tc7A3z2uz8WiZMTSUglNJ8aGvnJhfE5F8ubPZjSrNffv9e9dkoG37IZoG7zsyBMCXT8xTTQLxDEPY151do4rJW7Hr6rnFVhwXoUCEFd1SAARhyMm5uAKgGyrOLDQJI8Weko0gVJ2AlhvSm49XC+1mnILdD0P8i8xyVwfwuSFkwriSYNOLztu/vUuvNLaCAqIIpmpt/vyZcbKmRimbQdc13CCikDEY6C3QU4jfS0dIN5yAs4sNtCSq3TZ1PvPsOfaUsgyXs9s2bK8W/pPLLRYbHoausbc7TzbJzVVt+5etulqfzbiTAmar6VVSrk9SwXET0pU18YJopUhSzQmotl0qLY/Rnhx7Slnqjr9SfvRKch9tZFhfXUtifXr0d9zRf4EtZfWKp2ib3DlQWKnDLVpsZNaSCb6e3JvrB9imznLL57XZOkXbpK9gcWKuwem5OpW2v1J+tuEE+KHCTNKCbAUFOG54UTvGlRKEMFd1YK9iuJTj2GydSGV5+EAPhq7RSiL64byQ/sJLM/gR9OZNumyTyUobQ4SWG6xkOr6tvxBH32/BXlXOWdzWX+DJU4s8PbaIF0Qc2Vcin4n/F+KBPrzsLALn3b1zibu3i66xZXfvlOuTVHDchHRmkVEUqwVmam2+cmKeXMbAMuocGiisZJN98tQi7zsydFVzH13MS2ujfbV2ZWXFM1zOMlfLcWS4xNNjcYJBBWSM2PXVtjQKlgEiOGFEOWviBCFHz1Xxw5CZerxKEkBFAU7QIIrA0ATDEAq64HpxzqxLKZt2UmhA/Fzxyi7ANg3uGupiru5wbLbOgyPdK/3//PjyihAo2AZ37elCJHZvtU09HtwdfyWu5bFXZrh3bxlDE16eqvLl1+a4f6Sb+/aVgbXp63vyFqfnGxzoy6MJHJupM1Vpk7dM8hkDN4gwDLlstdKVuHunXL+kguMmpDOLfOyVWRYbLqdmayjASpLsPXtmiTcc6KEnZzFdbW+octqoiNJOsXrFU7RN7h/pxtDjALqJ5TZVJyBragwWYxdbEdjXnWex6dBwAlw/pNZ21wz0mhYP/JoXr1hylk7ONujPZ5hcbrHU8uMYjGtI3tbozmUwNI35ustobw5dhP6uOM3IV04uMF11ONibp7+Yoe2HnJ5vcHKuTt0JmVhuMtqTo2hbuEHEs2eXWKi7GLqsJHG0kxK244stpisOx2drTFccIhUxVMqSzxi8YaSHnGWwtzvHfMNlcrnF5HKT/T15qo7Pvu7sFamVbrT0KCmXJhUcu8xu5exZanrcu7fEEycWONhXQKHhBAFBFA+iJ+ca3DVUXIlIvpZf7o2M7HcMFnnfkT2cno9XDEtNl8lKmydOzJMxdfZ125xbivsRuXB10HF6ir2X4jxSvbkM5ZzJVEXwr7XQsITevIUfReSteJXnBhFeFDKdRMi33ABDYHypSdbSabgBJ+cbnJlv0luwmK+7vDxZJVRw794So0GOE3N1TNFougFDpSy2qaOUxmzd4fhMjfGFJiN9eQzNYLbqUGl7FDMGA112LLT3n1/lVB2fQwOFK/KqSrk5SQXHLrKbOXs6Bu/llktfIcP+nhyvTlepOT7lbJa5Wpvhks277xq8qte9HC624unYSzpqjocP9vB7XxljvuHi+iEKCDfws43W/e0FETM1B0Ni991rjeMplls+GSPk5akKlZZPEEUgGgf78/jJcmiwaOMGIU+PLfLM2WWml1s0/YixxRZ+EOIFCl3gK84Cr85YGJqwv5zjhYkKe+oO9+4tU8gYtNyQuaqDZcaxMABaVmh4AS9OVHn7HecTSYoI77xzgPv2lVdWOaWsecX/o2miw5uHVHDsIruZs6ej/unOZZLsrxaH+otM1drM1V3yGYs7hoqcWWiy1PSu+Zd4sxXPRtuLtslfPDfBydk6fhgRBJd2jW36EU3fTYzqBqYWR31fKxESApV2XPJpth4bnjMaGDq8OuVzer5OORvX6ejNZ3h5qoaIou4GVNt+nLpEksBCBZ4bEUUOPQWbyWqb3rxFGEY8P17hyN4uchkdL4oo2ue/8oYu5EydlhfbtLKmzumFBoJaU5YWrvx/tDNp6qwej83UefbsEu+9e2gl628qWG4cUsGxi+xUENdGdNQ/dwwW+NrJebwgwjY1Hhrppu74DHTZLDU8ZkMHU9eYXG7x9sv01d9t7t1XZn9PDl3gS8fn8SKFHoSXNHYDLLdD+kSwTY36Tlu/t4kbxT+GH+EHijCM1YpnFlpU2x5tL6DlqfPPuU7qtXzIugFZwyJjaoSRoFREztIxNC02cEcKP4wwNKHtRyDCG287X9vDDSLu3VumaJ83hgdhxEtT1Ssa0McWmkRJ1Lht6vQXMptWZkwz6F7/pIJjF9nNkp+r1T/ugR7GFloIit5ChoGuDAsNF9vUyVkWbhAysdzi6ERlw1Kt1yPlnMUPvvUg44vxoFrbxrkLrS365F4jAiDwFYHyOTEb4CZForayOlpsBfiR4o6BIncPl3CCkIYTcN/+Mu841Mfz4xVcP6AZKfxQMVy2+eB9wyuz/k6a/elqnJBQE8Viw6W/y76sAb2zivjKiXmcIKI7Z61Ex3dlLeYbDkcnKkxXHCptn968xXA5uyK40gy61yep4NhFdqrk52ZL/I6a54GRte3/3hOn6bLNlYhy2zRQSl1QNOl6Z7Q3zz96w36Oz9XRmsGGKT5uZNwA3MtQpjluiBeETFXavOm2XkZ6czyQuOMOlrJ8/cwSrh9yaKDAe+4aWlMgqidv8fjxOUpZk6Jt8sp0LYnuL29bdbXapjfYZfONc8s4XkjG1MlbBm4QkjE0XhhfJmPq9OUzeKHi+Eydw0NFChljR1bjKVfOjgoOETkD1IlVsYFS6iER+WXg2wAPOAV8RCl1QX3Qjc5Ntn8C+BFgPjn055RSn9vJ57ha7ITb63YM7p1a1k8cn6ect7hruIv+QqfOgyDXQON/pXrtt9zey2DRZrHh0vSuvdH7ekAJ+KFiX0+W+YbD/SPllViQPaUsP/GeOzft46Wmx5G9ZZZbHvXE9fmuwSI1x2cPcYGprapXV9v09nbnODZTIwgj5msOepJW3Y8iegoZbEPHC6OVycxUpc3+nlyaluQ6ZTdWHO9WSi2s+vwY8LNKqUBE/i3ws8DPbPHcDr+qlPqVq32ju0E5Z3Gw73wQ1thCk4N9bGuwXD3YblTcCC6cEX7xpWl+5/HTuEGEiKLphdTaHg/f1kuXba3kjNpNroaXWTlnce++MqfmGzS961sFtRvYGpTzBrou9BcyIKykgFnfx8AFQrvW9ukvZhjoiicUhYyBF4RrcnNtVb262qZXtE3edLCPJ08vMFNrs687y0hPnpenqtyzJ78S0Ahg6RoLTY/egsVQqbAmADI1mF8f7LqqSin1xVUfnwS+e7fvYbfZrEre5eqMVw+2x2bqNF2frBXrjacqbeqOT0Ssdlhqenzt1CJ/+uQZMoZOb9EiihRzdQdLz/LyRI2Hb49TkHcii3eLq+Vl9l0P7OWrJxdYal0ik+FNjg7kbYPDQyXuGS4z0pvnTGKUPj5T48RcnWorQNOEv31llq6syd4kv1UnXYmuyQUZj49OLFOwTZRS21Kvrrfp7SlneduhfhYaLgNdNl1Zk/tHyhi6Rs4yODzUxVSlzUKSvPG2/sKmQi8VHteWnRYcCviiiCjgt5VSn1y3/6PApy/j3B8TkR8EngF+Uim1vP5kEfkY8DGAkZGRK3yMi3MxdctmVfJ685mL6ow3ahPgCy/NrDEi9uYtGo7Pibk6UVLrYaHustTyeG2mzv37yzxxcgFFnO01CBUiQl/Boh2EmIZwz3DpsmdyV6JqulpeZvfuK/NP33snH/+T5246O8d20DXY35OjN29xx2CBlhermo7N1JhYblFr+ShRTC22WXYCerIWfV0Zjs3UeeTOfkrZOOV9K1m5dYIx93XHKqPtqlc3sul1El6u/35AvLrZ35Ojt3BepZuWnL0+2WnB8Xal1KSIDACPicgxpdTjACLy88QOJJ/a5rm/BfwisWD5ReDfEwugNSSC5pMADz300I4pvy+lbtmoSl7JNpmqtDk8FC/31w+WG7X5xIl5FFBpe2uMiHvLWWqOz5m5BiM9Oc4ttUHi5HQLTZdHn59gqR5HB4cKvECRy2gAOF7Ewwd7LzCeX61nvxTb9TK7mJD65iN7eNsdPXzlxNItKTxsDQ4NFHhwtMxde0rs78lxsC/PF16aYanl0XQDnDBipuowV2/T9kJcPyRCEUYRj782x/uP7InL7a6zw12um/ZmNj1Ym38rY2h84eUZxuYb5DMG7zrcD2xtYpHGflwbdlRwKKUmk99zIvIo8DDwuIh8GPgg8B6lNi7nstm5SqnZzjEi8jvAf9/JZ7gU6wVDGCkml9uMLTR4YKQnNvJ151aO34rOeKOZVqUVp9bozWfWGBFrjs9oT55zyy1mqg6aFqfXeHmqRldWJwgiNE0IlCKIFMqHvKWx1PQp50zefHvvVXv2TpK9L7w0w1DJvuQXeTteZlsRUt/78CjzNZdjs83LfqYbERP46Dtu4733DF0wCSjYBpWWx3TVIWvqzNUd6u14ReGHEdWWh21ozIqzab2WK2F9AOf69zi20OQLL02TzejctacLPwh56vQiYQR7yvZFJxa7mYkhZS07JjhEJA9oSql68vc3A78gIt8C/DTwTqVUazvnJvv2KKWmk0O/C3hpp55hK6yeFdUdn+MzNTKGhoaGF0RMV9vYhr5ibNyKznijmZYfRrT9kDCjeG2mRlfOYrjLpuZE9BYs3n14gOfOVmi4AfWWS87SWGx4NJyQrBUXTbJ0DU0XFlseAvzII7etccWE7c3gNqoed3apiR9G7OvO8vJklS+/Ns/9I+UN8x1tx8tsK2qLt97ex0uTVerOFJNV55Lv7mbAAH7graO88WDPBYkIX5yo8FfPT/D1s8s4foihCWEYoWuCZWiEUex91XQDNE1jqenRk7eueDBe/z/UsbVt5Mxxcq6OEGcztk0D24xrgJyeb7KnbK9Rm63/rqSqrGvHTq44BoFHRaRznT9VSn1eRE4CGWL1E8CTSqmPi8gw8LtKqQ9sdm7S7r8TkfuJVVVngB/dwWe4JKvVLVOVdhLcJBTs2H5xsLfA2GJc4W2rOuONVDh+FDFdaXP3cIm79pSYrLR5cbLK6/YUV2aZf39sFiJoexF+qAhCRXfWwAsV/V0Zluoe3VmLnrzF971phLce6l/zLNudwa2/z6lKG8cPWWp6fO7FKbpyFn05i6+dXOSPvzpGK4jozmZ41+F+vunuwZVYk614mW3VHnJkb4lXpmpU2w6NmzwEoGRrfM9DI3zgvuELBPyLExV+5QuvcmqugevGyS3byeJeQ6Fpipylk7V0Ku2AwZLN/SNllpreFQ3GZxebPPbKLGEU0ZOzqLZ9Hj8+x5G9ZfqLmTXOHEU7/g6YhrA6a0wuYzBfdzZUm63+rnT+J6arbV6cqDJTaxMp6E7qmadqq51jxwSHUuo08PoNth/a5Pgp4AMXOzfZ9wNX8TavmNXqlrrjr5T9HO0tAtBfzOAE4UpKh63ojA/25XnixDxTlTaz/z97bx5k15me9/2+s59z1763dwANNDYS4DZDcjTD0cwonNFIsiTbijKSSpaTSHZZZcmV2IkTJ1WuSqz8pdhxReV/HCuOU5bkOJZkj2bGUkbiaOOMhuQMV5BDgsTSQKP37rvfsy9f/jj3NrobDXSDBEiA7KcK1bjLOfe79577vd/3vs/7PN2AJJX0ooTRggkIHENlplagbOucHC9tnuuh6QpvLXdpeRGGJjg6WsANUooin+SPjxb4qSdnbvqDut0V3M5U00LLY7XjE6WSphtxYc0lilI6QcRIwaRia0iZ8jsvzvPWcofHj9YoWdq+WGZ71UOGQa9eMPnZ75shlRnfvNDc9/d4P8HW4cHJEj/3ydnNADzEcLX/z77xNm8u9QiSlFSyzW9dIRd9rFiCkqnhmPCxIzUePVzltWvtd01YaHsRz7y5giYEtWIuznh+oU29YNDyIsbL1iaZY1jjG3EMWv0I27x+Hi9MUBWFlU4AtCnbOo8duXHHWrZ1rjRcnr+0gaYqxElGEKV0vZBrTY+2Fx2kre4SDjrH3yO2plsyIEPywGRpUzLBj1Omq/ZtF6D7YcLFNRc3jJESelFMx435/XPLNN0QRQgO12wWmzVKVp5SmihbtNyYuYaLF8RUHZPTEza6rjBWMJio3Hoct8ty2plqilKJpqq8PN9AUQRCSpY6AWkmqToGHT+hG6QstbzcZ8OPOVJzMDR1T5bZXvWQrUHPMTR+8Mzkh6pQbqqgCYXDdYcnjlZ5eLpKkGwPslcbLl99dTGXj7nWwYsSBnJUKALEwLxKU6Fo6Zu+HeUBm+o/vHwNgWCmVthMrcL++zbmNlzSDGpFY9PrPs2g5YY03Yh+mKAIcKOUMM2QUnJyvMSltX7uuBgnxEnKWi9kpGAwWjRvuZiYHS3w1VcWMVQFP84QQmAbGiVL5cJqn8+cGj1IW90lHASOO4BhAXA4uflRumkWNLTJvB3kaZtcrXS8ZKFrgm9dWOOF+SaJzPPBGZILqy69IObsdIWFlocXpmQy44GJEl6QsuGGtP2IR+pVDledTf/qm+HdaGltLX6udgN+58V5hCCn/SKI0wyB5NJAmtsNUxCQRhlXNlyWOyFPzdZuyTK7/jrb0xZppvDrz15mvuGy1PFxDAUhBG03zs2KbutTv3ehAiVTo2zpzI46lCwdS1fpBgmOoW2m97766iJXGrnasRSSaJD+GdJPNj8PARNlAyEUJioWuiq4vNZnToCuKsw3XD5zanzTPGq/fRtdP6bm6IRJ7vHuRgltL+DSmsvJyRLH6gVURRAlKbaRp6lm6g6/9PRJXrzS5OJqD1PXOD1V5sGJ8mbwutliouoYOKaKKgSrfZeKpTFSMDE1wUY/vGsCogc4CBx3FFud94Y53lrBvG0/g64fs9YNKJoaf6PMWAAAIABJREFUhpZTZxeaPkNFDUPNJ5M4hY1uxLPvrPP40REa/YhqQSdJJV6cMlWxmanZPDhZ3uZffTO8Vy0tKcHUNZIspw1buoplaPSDGA1JEGekWb4ENnQNqeQuf2+t9Cja1z+bmwWrYZBqexG/8tU3+NpryyQfAZWRFDB1lbJtcKxeIJMQJhlFU92cHOc2XC6v91nthnQGRem2l24GC0FeFNQUsDSNE2MlpARFFYRRSsnWiZKE5W7ASjdAKApPHB1humrvu2+jbOfX3nzTw4sSlts+bpinaUu6zpUNl6mqjWVonBwv8bnT45upteNjRT42M8LsaOGm6bJrrZxLs5W4MVMr4EcJp4VCmkkMTaEfRIw45l0TED3AQeC44xg6721dtXtRcltb5rKtE6UZ5kBFFKAT5p4Nw3uEEOiKJM3g0nqPWlFHVxUmSiWqg8K7Y6j0ggRDU/b143+vWlpFS6Nq68RJymy9kHtHaBE9madLemGCKiQgqNo6tqZSc3SuNj2eUus3ZZm1vYhzC20urvXwooxXrjb547d3U6L58KLjhTx1vE6aZuiaSsuNUITgWxcvM7/Rp+EltNyQkqXj6CpCKugqbGF9oylQtlRGCwaOoTJasri81qdkG0ig4ydYmkqSZSRpRiYlvSDmtWvtffVIzI4WaHsRMzWHV661COIEBfjcqTEyKegEMW0v4lPH6ySZvCkZY2f3OsB6L2S5E2Bp6jY/j7NTZb4718TUcop5P8gIk5SHD1fviIDoAXbHQeC4w9irTnAzuutOWZKipbHa8ZECwihDDqqbqpbnuiWQIlGAOJM4uoqhqZu55REn7yn5/lNjt1VfeS8WstNVm+8/OcqXX1mkHyZULZ2oaJEkEl1XaboBiqJgagqWoWFrKkGcM8CefXudP35zFSEEG/2AbpBQMjWefnCclY7Pn5xfox8kZBKiD0sO6jbQj+Fbl9ZxdJ3Jipk7GvYCOl60mRIMEuiGIZYK9aKFY2ikfoJjCsq2gaooKAIO1WyeOFajF8TMCWAQIDRFASmJM4lk4PfuxZtOgHvRcrcuPCxN4bEjIzTciE4Qk6ZQsfIdqKYqOKZyUzLGzu51P06Za/RBwm+/OE/LizBVlcmKiR+lfP7MBG8udXGjBENXeWj0egPkQWH87uAgcNxh3KpOcLMV1m6aPEVD49V2QJCmOLrK1IjDlQ2PNAGhZqRZ3uhXMAQjjs61Zm4putIN8uK8qW86+71fGK44f+Jjh/j2pQ3cKCGTko/NjKAIQZQWWekEJJkkSVLCNC+UC6HQ9SJaXsR8y8NQFQ6P2CQS/tVfXMYN5Qfm1HcvYaUbU3My2n5Iz0+3eHRs/3TCFJpuQMnSKdsaIwWDY6MFRosGR0YKVJ2cjdcLYs4v9+l6Ef0oxdIEXpRhaoKul6CKgIYqOD5W3Lc/xtaFR7Mfsd4P8cOEgqnhRRlNNxc4/OypsZumpII4vWHnqymCP3t7HS9IKNm5CsKVpkc/THjsSJWf+b53Lyt00H1++zgIHHcYt6oT3GyF9fylBsdGC9u6z5c7PsfHi9QLBp0goWRpeGHCei8iSgeCdoZgtl7AMjRqRZNjts5KJ+C5Sxt8/EiNjx+9kcJ4NzFccVYdl4qj0w8S3lntIaWkVjDoBTFBnNH1Y4qOTtHQUFUFEMy1PFa7+a7MTzJ6Ky6GBsGB4O02NL29fQ4l+edmZRkFS6doanzsSJV6wcg9553r2mdLbZ+X51t0w4R+lGJrKiXbGFyn+XfzbvwxZkcLvHS1haOrTFdtzq/0aLkRM7UCmqJQdYxbLrJ27nyfeXOVfphQsXV0TUUnt/71opQLa30ePVzd1+S/W3PigZDi7WNfgUMI4QB/H5iRUv4tIcQp4AEp5Qcq93Ev4lZ1gq7f3tZlvtT288l1rcvhEXvzHEttH1UR6KrKsdEi6/2Aa02Xh6ZLtP2EXpCQZBmff3CcWsECJC03RldUjo8VafVDWn70ntRu3+0qbKd51CvzLZr9fDehqgoPTZfpBjEFQydIErI1yXLHu8FLI+MgaLwXSKDjpUwUTcq2xotzTR45UuX7Zmvbuvh/5OGpgZpBh4WWh6kqjBVNLjdc3ln1OTFeIpNyX/4YO6+ZsqXhR/niAXIBRsdQOLfQ4qkT9VsusnaeK0pSVCRpJtHJm1vVwU7Lj9I9G1eHdbJX51vUiiaz9VwR+Jk3V5itFw+6z28T+91x/N/AS8BTg9uLwO/wAetE3avYrU7Q9iJWOgHnV3rYukI/SBkpGBiaoGTqvL7Y5tHDI5QsnX6YYOsaeeM8XFrrUbJ0kjTjkcMjHK0X6XghtpF32E5VbHRF4eJ6n26QUDRVytb+WVw7cbXh8rVXF1lo+bhhgmOqHB5x+CsfO3SDRMleGKavRhyDrh9xqGozhc3RWoFvvLVMox/SPzBguivIgPNrHpc2PE6MOxx27W1e4pBfq587Pb7JcPqt56+w1PKp2DoFU0NmkqW2Ry/QqReNXSf14e5l5+TdDRLcQe0kyfKdZsfPm2SHNsW7LbI6fswzb67kPSFOztRKsozJqsOGG5GECY6uYOkqQhEUDPWWjavDFPFiy2e0aCKEwjurfR6YLJFm0HRDbENlqe2z1gvxooS2H/LMm6sIJDO1Ap86Ub/ta//DjP0GjhNSyp8RQvwsgJTSE2I4rR1gLwwv3NGiiRvGXG16JGmGqQsUIXjiaI1LG30ur+dbbk0R6JqCpSsEcUrHTzA1gaoIRks5t71k6Sx3fAqmxivXWix3AjIJjq7SdiPabsTvn1tieuC3sN8gcrXh8s/++ALvrHaJ04wRx6SUaKRZHkz++lPHbisgDXdgX355gW9fanBxvU8YZ4wUdA5VbDrB3qmXA7w3xBmcX/FIEsnnH5y8aUd11TEYK1oUDY2KY+JGCRu9gPV+hJnKbbvI3ZhQOyfv2XqB33j+CpaqULZ1dE3BjxIUITm30OFzp8d3FUF85s1VNCEwjbyA/uq1NmVLYyOLmCqbZFLiRRlhmvLZ2VHGyxb2FgYibKfvvjLfxFBVemHCRMliOHUttX1qjs5iJ6Af5vW4phvRdEMur/U5M12mYGg0+iG/9/ICP/H44YPgMcB+A0ckhLAZVOGEECeA8K6N6kOGrbUN21BZ6gQgoe3FPHVilJKlUzBV3l7tbjZFVRwdR9cGXeL5dvwTx+oUBj/MXhBTMPNzXmm4hFGKY2qsdkP8WHJ8rMBGLxeU22/Otu1FfO3VRS5v9NEEGIaOH2VkMsHUFNZ7IecWcpffC2t9BJKSpdMLYhr9CIlgdtTh5Hhpm7CdEPlk89pCCzdMSFNo9vIfZ3oPbTZUQBXwYd0AXW36/M5L84yXLOY2+vzk40duuCaKloYbJQRxTsqYKNuUbR1FwP/1zTm+cX6FOMk4OVbihx+e5OR4Lq3zxlKHTxytbTvXWMlEASxdkGRgaoIjtQJJktC8iZBY3n2eYRkab6908eOUIM7wooSypXK4VuRay6Xq6Dx5rM6nT+Td4bvTd/MdhkLOJtvoh6SppB/mDaJhmnJmssJax6c6WaLrJygI1nsRE5V8gWbpGmEiGXF0nr/UOAgcA+w3cPzPwNeBI0KIfwN8P/Dzd2tQHzbstNB8cLJMlKREKZupA01V+PhMbXNVN0wHaKrgxx+b5vVrbXRVkGUZvSDf8p+eLFMrGExVHda7AVGakSE4PGIxUytwreXz+NFcNn0/Odu5DZf1Xoijq7ihxFAVUkWSZilNL6Ji6zx3qUHFzruYG27EH59fp+7oVGwdVQhevtIkSSVr3YBjo0XaXsh/PLfES1c7N3Zy30ObDU3AVNkizjK6QYQXf9AjuvOIM5hveBwfLfLWcp9vXljnszt006arNpaW08eHaU8pVb7y2iKvX2vhDepOl9c9Xplv8hOPH+bkWBE/SnctdE9X7U013n6QcK3pkmUZs4OAsxN597nBm8tdWl6MY2iUTIWWH1O0dR4+VOaXn94udzc7yg21krmGu1m7KFoacSoZsQ1evNrE0hRMXcVUBQstF0NXEAIabkS9oGNoCiOOQRBLdFXgRikzls1yx7/D38j9i30FDinlM0KIl4FPkTeh/t2beIEfYBfsZI/sJa2+M4f82JEqD01XeP5Sg+WOz1jJ4ukzE1zZcLF1lYqlMV4sY2oal9f7gzbh68vm/UovdP0YTRXUHIN+mBIN/MmbboKixFQsHVVRKBgary10eG2hTRilXGtA1TY4OVGgaGq8NN9mumzxB68vca3pcmHNveflPxQBx8YKtNyIgqFypeHfS3HtjmGu6VKeb5PKlLm/6PGHb6zwY49O89SJ+kCpOK9JHak5m5Pwbz43x+sL14NGLnkDq/2YP31rlamyhZSS9V7AWMkiSTPmNnLpk0NVm9VewFLby3uMNJV+mPHOSpd/8vXzzNScbfWDsq3T8WPeXOoghELZytDVvPfn2IjDxbUenzs9vu09DdOh5xbafG+pjUQQxCmWnqsuTFdt3l7p0eiHuEFMoCpIP+bMZIkjtQJr3RBVVfjYkSpxKlnphvQHtN84ldi6Si+IGStZHCDHLQOHEOLxHXcNfTBmhBAzUsqX786wPlzYyR65lbT6zl6P9V7IS1dbTFUsTk+WttluNt0IP045UnOYW+8jEKgKg+14wonxIrB/kbqyrTNRtnDDlJqjsdoJ6EcpQggOVxx0XeFa0+WNxRaKkvt9pEmKl6S03JDFjps7uqkKvbrDajfk6oZLcq9HDaBka1RsjaM1hytNl/mmf0+l0e4Ugkjy3StNCoaKZSi03Ii2H/Hy1SaphI4XUXE0zkxVGC/lZlxLbX/bDmzrx3K16SERnJ2qoGuCMEl5db6FZahUbI2Wl0vlT5RMRhyThhdu+qAvtTyOjBQ4v9zl5z8zy9F6gVrB4Nm313BMDTJJL0yQmeTTJ0exDRV/l4vpOmOqTa1gMFYyeflqm6+8usjDhyqcHC9xqGrzrQtrCCE4WnMo2gaKAENTqDgazX7IQ9NVrjZdJkoWr7VblGyNIE6oFXJ5+KfPTNz9L+g+wV47jn86+GsBTwKvkS84HiX3+37qJscdYAt2o+juJq3e9qKBp3hEvWBStnUWWx6ayKWmoyTbVq8YBqTDVYd+ENN0c5dAw1CoFgxOjOW+0/uVXpgdLbDY8nDDBEtXcMNcknu0bG56d3zltUVUoGgKZJbRj1LSDBIlQ1UVGv0Qc9DB3nJD/CSvG9zLmlK6gJ/5xFF+8XPH6fgx//jr5zE1QRzfw4N+l5DkEuvdMKUbpjSIudLw+fbFJkfqDk/NjrDSCXlraYGffOIwjx2p4sc3j/xJyrYej7Kt89B0lfmml6seFxVURXC16YEQXFztE6cZVccgk5JOENNbTfj9c8v88tMnaboRDx+q0g8T5jb6HCpZVGwdP0npDgQ9t6LtRXzzwjpvLHYI44zWYOE1O1rEjxPmGy5JmpFkMFoysXUNN84QIm9KXG77TFdtZuq5eGSQpBQMjVrRYK0XIpDUi+YBq2oHbhk4pJRPAwgh/gPwuJTy9cHth4F/dNdH9yHCXlIew51G248HnuIZL8w1mK7YOZV1oIQK1+sVWwPSsdEio8WEoqVRsq77mzvm/nSqhmP8zKkxyrbOhbU+UZpxeqLE6YkyJUvn5fkmKpAN0muJzDvYAdIM3CAhlZDp0FJC/IE2yL28cjcVODlR5Gc+cWTTWOrpB8Z55WqLfnxr/ofxISikb03HXW14rLQ9Rgaqs//xtWVsXUVXbk6gNNS8TudFCWVbp+vHNN0QS1dJpWS+4VF3TEw1FyFsuCE1x0BT84J1wdSJ4oRXBzvyrh8zVjJ5+sEJRq4Z9IOEXhCz3PY5OqD9tr1o83rONcz6LLQ8NEWh68ekWa6zVbJ01noRRctgveczVrQQIqTnJwRJSi+I0FXB2enyZm/L7doffFSx3+L4A8OgASClfEMIceYujekjiSHzql4wiFK56WXQ9fMcQduLeOlqi4Kh4Jja5gW+VdJ9WBcZ+nPsJ1jsxskf8vpfmW8RJdlmwJISjtYdGm5EGKfE6UAra3CubDCJ9uMMhCRK5F2vbZQs6O3DJfZQxaBkaiy1Q2IyHFWh6pjUCjr/zQ89wNF6YTPl8efvrLPe25s0eL8Hjd0QprDSiyiFChu9iKKZp1ZVducyOEZeVyvb2qY6wvmVHmNFk3MLba42Xbpe3rBasDRUIfDiFF1TKTkGmiLoZ2APanLDeiBA0dRYbgcstn3GyyaTZZO3lnu8s9rji2cnOVovcG6hQ8uNUIVKL4xZ6gSEcUrbjzk1XiKVkq4fESYZo0WDyarNtaa7aZA2UTH3NFY7wI3Yb+A4J4T4l8BvDW7/HHDu7gzpo4kh82pYyAMomyqLbY/1nsqJ8SJlS6PrR3SCZNuq63YsX4eT44W1PkGUiwaenapsei/slgqDvDajKYLDNQdLV9FUwXzL2xSj2zmxeNHdDxoC+C+eOsGLVxu8cLl90+epgKWp/Lc/9CBTVZv/740VVjs+ExWbv/TwJEdqDs++s8Zzlxq8tdTi9YUeH8Is1W2hF2XYGrwy32aqYjFe0lnrxZvfsQDqRY2aY/L2ao9f/NzxTRvgl642eWu5y/OXNpAiZwLqmspSK8DUVbwoYaKioAnoRzFRnPDYqVEgT5d+88I6F1b7dP2IS+s9+kFClGYcrjpMVx26ft7r8aUnDtPsR8gsI8kyoiRFIMmyXLNtsmxTLxq8tdJjsR3Q9WMePVLlgYkyx+p5KmuibB0EjXeB/QaOXwB+Cfi7g9vPAv/8rozoI4rhSqtk6TwwWWKp7aMoAj/OOD5aZMQxCJMUCczWC9votbtpYPWDhD98Y4XJirWpyXO14fLcpQb9MOFozaEfpKz1A1a7AYdGHMYGjmu7pcK29pdICc++s4atqQgSVHFjOur9qIdL4MW5Zu47UvVZaIe7vq6p5+P79Wcv8YufO8EXz05s7rB6QcyvfeMdrjU9mm7E5fU+SfYRjxoDxCl0g5CRokHB0NHUGEdVsAwVVVGYKls8cqiMoyubk2/VMfjkbJ1f+dobpBlIJLahIiUcHrFoezGlsokiJU0/QpGSBydL/Nhj05vHq4pgpePTdCOEANtQafZi/uyddZ48OsLhEYdwYFVQKxq8Mt8nSSVIQZTm+lWOpYGUXFzvowJTZYMMODffJpqWzNQcxkuFPc3NDrA79kvHDYD/ffDvAHcBW1f3RVPjSM2hXjQ4NGKDhG4QUzQ1jtYLN4jNbe0T6QUxF9Z6vL3cQddUDo/YXGt6/Ma35+gGCX6UULV1BLkTWxCmpGnu1RynGVcbfYLEviEVNsRwx6JrChMlg0xCkCR0vPQDoa++cKXFeFFnomximzqLTRc3vq6mqwJZBhtuSMsL+d+eOc8vfPo4iy2fyxs9zi10MDSVKElpuBFJKrcymT/SSCT0I2j0Aoqmjq5ClGaoicAwFY6OFpis2tQL5vbjMsnhkQKOHrLWDYmlRBMCL8opsk+dGMUNU8I44eREic+fmdhWeF7p+NQLJuMlm+WOx/mVHpYuiJKMjX6EH2fMjjp0/ZjjYwW+dWEdQ1VQFShbGnGacqhisdQJqRcNqgVjs4l2smSSZZIjNWdf5mYH2B37FTmcY6d2MyClPH7HR/QRxc3EEec23G11BmCzEDnEcLeSZpK3V7qs9UIKpo6uqry20GZuo08vSPDjnFrbDVOSzCdIUmqOiRA5ZdfSNcI4pb9DXXCnV0jHj3loukLXi7EMnTdXuh9on8ZaPyaIU544WuPv/eBpvnOlwb997ipBlqfP0hSGVXwv9Pjnf3oRATT8CDfMUICipZKkklsQiD6SUMgVDkxNpWyZ9KOE0YLJoZpN0VSJk4xPnahvO6brxxwesen5EbWSQZxmZGl+3c7UbB6cKvNjj07f9DUleR2kVlCRQ4q5zH1ogiSjaEqiNKNs6wgBMzWHMJEoAioDJ8mipZMRM1YySFOoF0y0EhiqwmLb37e52QF2x35TVU9u+b8F/BRQu8lzD/AusRvzareu2J302uFuZbHlY2oKfpSiqQrTVZultsdiK2C6ahN3QzQtn0gTKfGilLKVoSoCe6CLlZHLTgyxs35ybqFNL0w4MVrEMXTeXu2Tpjf6ZQx5OO/X4r0bZjx3cYOuF3Kt7RPcJADEEubbAdoWinAKB5pZu0Ahb4wMU2j7MY6hMmLpIGCsZGNoYlf9prKtc2q8xHcuN1EGDaVulKBrOqcnyzcsTHbi1HiRKxsubhAjpeRQ1eZK06OgKzi6wpERBzdKN21mv3Bmgu/MNUkzqNgqR2o2ry92KJo6cSIZK1kIAYdrBZIk5dCIc8Ceeo/Yb6qqseOuXxNCvAT8T3d+SAfYiv3YuV5/Th8FhZKlUXVyddM4zchkXjyslQzCJKMTh6RxLiliacqmN7iuihvyvjvrJ0kmqVg63SDG0VXWOj5plqGR1zWG87VCbhc7ZHK+H659QQYvLfT29dx7ua/kXoCmgKYI0gFzLssyhNDwkwRN1/nsqVFm6s6uvQ3D7vOz0yUurvVZ7QakmeRYvYAbJtxKHrU9SMHqisJ8yydJMgqmxoMTZQqGyqGag2monJosbXp6WLrKF85M5DYFYYqmwkOH8n6PZ95YQQrJ0ZpDkqQHjXx3CPtNVW3tIFfIdyB7HiuEuAL0yBd1iZTySSHEPwH+MhABl4BfkFLeQInZ7djB/TXg3wHHgCvAT0spW/t5H/cr9mPnmj+nRpRkmymrIE7QFIWKnZsozdaLaJrC1SxjrRdyrGZxtF7k7FR5k1W1M++70wq3aGpEScp6P+T8aoeCoZHIDF2XJElGEEsSQFNzjr6lKRQMlZW+jxfID6WMx4cRRVMlyyReKilaKoaqUDR1kjSn5z53aZ2PzZza9djhQmatG2IZGostf1Pq340SXplvMlmxtvmCwPVmvo4fM1oySGVGox+iqoIHJ0s8NF1BUxW8KNn0mhnuth1D4/REafMaHjIDh1I9QzmUp3fUUw7w7iDkPiqBQog/3XIzAeaAfyqlfHuP464AT27VtRJC/BDwJ1LKRAjxvwJIKf+H/Rw7uP8fA00p5a8KIf5HYGS347fiySeflC+++OKtnvKhwNa00lAvaLHto4h8pyDIC4QZ8PB0mR9+eArgloZNO3s5ekHMuYUWLS/m5SstljseGQJH12h5IXGa4ccZliao2AYnxgucGC8RJxnfeGuFphsTHkSPexoKYKr57lEABVPDHnz/ApgoW3zm1Cg/8MD4LVM+bS/id19aQBOgqgoXV3MdtSMjFpaucWjE3kYZf/adNb631KVqG5iaQphktP2Io3WHqYp902v0wPr17kEI8dJw0b4V+61x/E0p5eUdJ5x9NwORUv7RlpvPA1+6zVP8VeA/Gfz/XwN/BtwycHxUsDWtFcQpDx2q8OMDmuOwd0MgOTle2rbau9Vu5mY6W0nmUSvobPRV4lTSi3K5k9wmVuOh6SqPHq5StjVOjpc4d63N2akKL8w1CO/lVvKPMAS5lAxSDFKcUDCUgbqtwNBUbF1lxNEZcfTN5tSboeoYTFUsvDDh7dUeBVNjumrjGGqe6jS0bbTyi2s9KlaeegKwdJWK1Fnp+PzoIzcvpu9nR36AO4v9Bo7fBXYKHv4u8MQex0ngj4QQEvgXUspf3/H43yBPO93OsRNSyqHY4gqwa8JSCPGLwC8CzMy8eyP7+w03+xENu8Hf3flu1Nk6NOLmRcoX51nqBHhejMzANnTOTJU5MVbEUFUurPapFyy+e6XJ85cbB7uNexhjRQ0/zkhlhiIEuiZQhUKcZKRpRiwFmZQcqliEA1bTXqt9IeDiWp9L6/1Bn5COquQpz52qzfmeeOeiQg7uP8C9hL3UcR8EHgIqQoif3PJQmZxdtRc+I6VcFEKMA88IIc5LKZ8dnPsfkqe9/s3tHjuElFIOAssNGASaX4c8VbWPsR7gJrgZ22uh5TFWsljtBggEqSKxNJXFgXf1O6s92m7Ey/NNLq95H/lu7HsZZVPBjzMEoKt5fSNOMlKR7zxURVBxVBxdZaUXIK91ODJSYKHlMV6ydlUsuNpwOXctZ+GNDJhVL8xtcGaqwlMnRm9QbT41XhzIqQtMTSW8ibDhAT547LXjeAD4caBKXtAeogf8rb1OLqVcHPxdE0J8Gfg+4FkhxM8PzvsFeZMiy82OBVaFEFNSymUhxBSwttc4PgwYNt5dXOshEZwaL95QXHy/IQBDF5RNlZYvUDNJkqZIIXjxSpOyo1M0NBaaB0HjXkXBEJwcK7Ha9QlT8MMYS8/7JxAQJmAZgoKlMVa2aPQCsgxUIfjqa0uULJUffHASp6rdIML5/KUGk2WLwzWVhabLlUaukHtlw6Vo5TuPL56d3BzLo4erdP2YthfT8SN0VeHwiLNZCD/AvYO91HG/AnxFCPGUlPK52zmxEKIAKFLK3uD/PwT8L0KIHwH+AfADUkrvdo4dPPxV4L8EfnXw9yu3M677EUO2yULLp2LpgOTNpQ5dP/7ABNrOLbRp+zEdL6bpJ0yWLXRVYaMX0gsSJBJNUVBVQXhr2v4B7jIU8iC/NUto6zBVsTk2WmC2XuTP315DTzK8MMaLUkxDRcnyAnmUSJbaAUudAAVwTIWZmo0ioOPGPHthjc+dGqcbxPSCmIy8NrbeC5iq2CiKwgOTFWoFk9cX26z3I3p+jKYpPPPmKl88mzOdhurMB4Xuex97par+gZTyHwN/TQjxszsfl1L+17c4fAL48sAYXgP+Hynl14UQFwGTPP0E8LyU8m8LIaaBfyml/NGbHTs4768Cvy2E+JvAVeCn9/9270/Mbbh0/JiqbWwWDoUQtAcmOe93YbDtRbw632a0aAJ5ITV3XFOxDZVemGCpAkNVUBAHEh4fMLa20BgK6JqgXrT4zMkxSpZOP0yYGXW4uuFhawpenCGz6130cZb/CCV5bjnzMzbciNNawtZVAAAgAElEQVRFk1SB1W7AC3MbnBwvYWoqGTJn95m5c17FySVJ3DClaGog4dCIjampA8HCFb70xHVZ+4NC972PvVJVbw3+3jaXdcDCemyX+0/u8nSklEvAj97q2MFjDeALtzue+xU5xbbJ5TWXetFgrGxTMDRMTaXjR3syW+4G5jZcagUDIcDSFWqFvAek7cVUBwVTCRQsldGCxYX1Pr2DqvgHDp1cFUBTcvbbaifAUAUTJYuqreOHKf0oIZHxZvOmMjhWKIDMtb8Alts+ZdtASkmUpByq2kDuAPjAZAlVEUxWbC4MlJ5Llk6j77PRC3hspoal51NP2TZY74cfyALoAO8ee6Wqvjb4+6/fn+EcYCuGfRmGqlKyNPxIcnXD5ehoAVWArir7soS90+j6MbOjBd5Z7eEYGqqQeGGCqgpGCw79yKDpRliqSslSefRQiecut+953/EPEzTywJ2mMqfWMhTPLDBi60gBRVtjsmozWy9yrenx6RN1oiSjoan4UUbRkKz3I1SR73CFAuHAY6XjJ7hhjKoIWv2I5XbA8bEiD0yWKFk6UkoKhsZPPH6Y5y81WO74lB2Tkm0wXbE3xxkmGbV9UHsPcG9hv53jX+NGnlyHfCfyLwbquQe4wxjKfRwfK9L1Y+abHh0v4ntLbSxd5cxUhc+eHnvfx1W2daIk49CIw6X1PhXHRFUU+mHCSi/k5FiZY2ds4lTS6McEieTUuM2GG9Nyk30HEF1wUFS/TVhavlOoOib1gsmp8SLnljrYmkK9YDJSMPCilIeny4RphiLgG+dX6PkxR0YcPn1qjPWujxumXN7o0+jnu0ddFYRb/L4zCQstn5PjJWbHC7mY4JYvdsiYOlovbHZqDxsCu35E2c7lb4I4Zabm7GsBdNDod+9gv30cl4Ex4N8Obv8MObPqNPB/Av/5nR/aAYZyH0IITo6XmNvos94PQcDpiRKaAq8vtKnY+vv6Axo2Ba51A85OlZkdLbLU9qkXTcqWxkw9Z8LMbbi8Mt/EDWPOTFW41vK4sNKjG0SbRk/DVMjOYKIJMD6kvt93E0VTp2Lr1ByD2fEiD0yUSGTGQsvHjRLGyyaPHKrQDWKWWh5+bFPUNVSh8Oq1NifHi6z1I9Y6PmGcYRjQDyGOchXhrd+GpasoAr5vts58w0UgWWx5zNQLu/rcVx2DL56d4Jk3V1jvh9QcnZmag6Kwp7z57ZiVHeDuY7+B49NSyk9suf01IcR3pZSfEEJ8724M7ADX5dIdQ6MbxIyVbEYcE8dUOVovEsTJB1Ig3ymqWLHzpr9hiuJay9tcGUoE9aLOei9gvRcwXrYoWTob/YCml2Br+eo1SLdPSroKinIQNG4XG27M5x8cQ1MUvCTj1fkWqqYwVbE4PVGibOnoqsLlRh/TULE1DV0TmIkkQ/Ktixv4Ua73lAFhlE8SCddZWZoCRUOhXsh3npqi8MBkGVtXWe0GnJwo3VSy/Gi9wJeeOHLbO4fdzMqG9x/URt5/7DdwFIUQM1LKeQAhxAxQHDwW3fywA7wXbJX76AcJXhSjKYLRUr46+yAL5FtFFR1DoxfEvDLf5M2lLhv9kO87XuOhqQqmphClkssb/YGLnCBK09xfAQgSGCloEKb4iUQl10lyTJ32Qd77XeHbF5s8fqxKP0ywNJVRS0PXTBQhGC9bTFdtXrnWYqJkYWj5ns/QBGSSpZaPUEAVCkma3eDsCJBkEKYZG72II3WHpbbPF85MoCqCkxOlPfWr3k26aafYJnBD5/kB3j8oez8FgL8PfEsI8adCiD8Dvgn8d4Mei4984Xy4jf7zt9d4Zb61KQ39XjFc2RuaQkaGqalMVR0Kg9VWmKQfWIEc8sDmRQlr3YBX5lucX+nR8WOmKjaLrYDXrnUoDNgzXS+m64csNj06XkzBUJmo5MwsTVU4VrepORqKArapUTBUbENBv4/UJjTg2IjFaFGlZqs4Gh+IWIYXJXxvsctsvYhjaCx3Q5I0v36mq7mwYNnWidPtCcL1fkSY5WMWAzvgrVL5W+HH0HBDbEOlXsjtXvdy1Bv+Tpr9iLVuwHfmGvzuSwtcbbh7vqfh7nv7GNIP7Nr/qGO/fhx/IIQ4BTw4uOvtLQXxX7srI7tPcLXh8sybq6RZRs0xSFJJ24vuWO51yGufHS1sNgH6UQJIukHM4RHnA7O/HAa2P3xjhdVuQMXO0yC1gkGcSNb7AQ034PR4kUurXZa7AV6cUXdyO88wSTF1lTBOaXop01WbQxUbL0m41sy9GDJyCui9TuadKpn8nc+f4K8/NcvVhsvzlxp850qDFy83udp+/7gjji6wDYV+mNJwwwEjT6fj5y6JQ0XaTxyr8afnVxEIHFOl6eaTuQJECahKhhAg5PUUosKNQaTpRiy2PR5OKnte8+cW2lxY7XO10adiG0xVbdI029bHcTPsFNvczdDsAO8f9puqglzQ8NjgmMeEEEgpf+OujOo+QdvLm5c0IagVLcIkZb7pMlMr3PHca9Ux+OypsW2yI2enKx+47EjVMZisWHhRQsU2mG+6xEmGriq03JBqwaRgGnzxoUP8/utLaEqEqgpAYJsaHz8ywno/ZLJsMTtWpGhqbPQjvtJfxNTy7Pq92Hl+pGrxuVNjfOb0GE+dqG/7DoZMoqKl8c0L758ijqUJqraBo6u0g4jvzDXpBwmZzHB0jdlRh8MjDgCfPjFKz4+5vO6y0PJpuhGmrpJkCVmW7zh2Sg4OA4k2eCADpis2R0cLpNmt61HDplE/SqnaBkIozDe93PY1Svf8vWwV27zW8ugHCUUrV9edHeWgQP4+Y7903N8ETgCvcn3xJ4GPdOCY23BJM6gVc+bTsKmp6YZo6p1PUlQd412r3N5NlAc7jTBJGS1ZXN3oEyUZUSKxNZUgTnn0cIXL6z3eWu7SCWKsLKViGKz1AppexKdP1Hn8aO5GfP7VBTQhUZRccM9P7q39RkEXfPHsOH/5Y4dvmc/vBwkbvbsX9R6ZLrLRi1jrRehq7nNRMDTW3Rg3SJEyIcnyH6ofxaiKzwtzDX6sMc3ReoEffnhqk/m22gnJsow3ljr0ggQhBEi5udtTyYviuqoQpxlJCgVV5eMzVQxVvUEifSeGTaMXvX7OFBwk8ZbbAbOjzr7qdFXHYHY0D0KjRRNbV5nbcPnKq4sUDI2ZmsOnTtQPjJreB9yO5/jZmwkSflTR9WNqjk6YZJtSIKamst7PmSUfFcyOFlhseSy0PMqWzmTZ4krTwzRURor6ZlPY8bFirpyrCWQmWe749IMEXYVvXtjYpB1Dro8EkL3Pl9xuwt5bYSpg6RqvXuvwxLH6LQNH0dLuatPjD56Z5NCIw7lrbc4ttgnTjMM1Bz/uERgqXpRsFrcFuQlXL0z447dW+BufObGZBu36MQoK9YJBy0touAG9ICZJwTJVKpZGoxchFJBZzoLTVPjkiTqlAUtrr0L1sGl0rpF7iRcGDLy1fkC1oNENk30Vy7eyq5bbPi9c3sBQFVQBfpTwey8v7OqDfoA7i/0Wx98AJvd81kcIbS9ipROw2Pa5uNaj6YZIKen6EaqifGB1hw8CQ3G6s9MV/CRDKPDFsxP8vR88zanxXH5CSokiBKNFE0vTWO3H+HFKEKdEqeSt5S7PXWpwfqVLmkm6QQRSot+FnRvkAWAnrH0Us6MsnwRXewH94Na7iemqfcvH3yt++7vX6PgxUZqRZBI/yNNAUZJRMNRcY0oBUxdoCnixZKXt84dvrGwjcZRtHU0TlGyDLzw4zvHRIqfGyzxxrMbPfmKGzz8wzmNHqjiGihSSoqnx5LERPnW8RhDntam9CtVlW0dTFT45WycFWl6MF8UDP3PJgxMloiTbk1zS9WPswSLt9cU2RVNnpGgSJJKKY1KxdZ6/1LiTH/MBdsF+dxyjwJtCiO8A4fBOKeVfuSujusex1Rt5rRugKILLaz3GyjZFS+WLZyc/cjnXm6XRKra+aQKlqoIffXSaf/UXl7ENlVY/pGhqOJaGJuDV+SYdP+ZKw8UNU8IkI7rDWSpThb/9Ayf4ySeO8PylBi/Pt+j5MS9cbpAiSdOUW6XrJXk3+0o75DtXGkxVb/TOHmJ2tIAmILlLm6aVbsg33lplvuGiKQJNUfj2xQ1WuwG6pjJg2pJmuVChBJa6EUvdiF/6ze/y6JER/qvPn2J2NPfVuLjWp+tFGKqCS0LZ0nn0cJWz02WuNlwurPXxoxSBpB+mmJrK7Ghhk1F1q0L1sLhdsXU+/8A4cxsu31vqcHK8yMOHqpSs60FnmPJqexHPXWrw3bkmYZxwcqLERMXC0vPUWGuQsooTiWPkb7Zk6Sx3/LvzgR9gE/sNHP/obg7ifsO5hTYLLZ+qbXBmusJy22ejH1KxNf7Txw+/b0HjfpBg2Kp2WrZ1mv2IJJXITGIZGqqAOM7QDIUgkTR6AUmSoSqCJL112uh24ejw+NG89wRgsmLx5NERukHCtabLQttHVSEdbCQkN3/9FHh1vk3B0Oj4MZ/dRd6+6hicGCvw9tredNN3gxR4a6mTqw8LKBg6uiooWTotN0YoEKeQ7fIuVnsxL1xuEiXn+ZW/+giPHq7y1nKPi+suaZYbcq10fL788jWWO3W+cGZy26Jg67XnmMpNG/6G2M3WeKRgcGTEYaCCDVzvzbjacPnt717jxasNDEVgGxobvZBa0eBjMyMcGy1SdQzaboSuqUxW8x1+L4gZK+3HY+4A7wX7SlVJKf8cOA+UBv/eGtz3kcRWb+SiqTM94lAvmFxc7TG34d6xPo5bYciJj5KMEcfY1zb/g8Ywxz1azNV0VZEzcwxdZb0XYWmCdpgQphlJlt3x+oClqbhBynov5E/eWqVs69QKJoqAU5NlTowVckrxLoyi3XCt4fHtixu8udRhbmP34PDXPnV3bYvjVKKpuWOeG0VEaZor4Gr5ZHyzz1ACnSDhraUuz19q5KwqVaFkali6QtuPEQj6QczldY/fe3l7v8UwEPzAA+P7pp7vPGaY4toKP04RAp55c4WLq30UQFVUwiRD1XLa8EY/xNAUZmoOfpIxUtCxNYWOF9LxYz51ov4uP80D7Bf7ChxCiJ8GvgP8FLn/xQtCiC/dzYHdy9jqjexGCVc3+iRpim1o79sEvrVIKITY/P/NJrB7AVXHYKpi8cBECZB4UZozdJKUKM3y1FScYWgqfiTv6G4DIJMCRRFkwMXVHrOjBRQFZmoFpis20yMOJVMnE/vrG4kkrPZCvvrqEr/13BX+3Xfmt02uVxsubnj3yuMFHQqmhlAEmqqQZYMCeJBgqOreJwAa/Zh3VnsstX3eXOqAkCx3AvwwoxPERKmkH8Z3vHbQ9iJ6QcwLlzd4baFF18/7SF5f7HBuocO1hkfDDXAMDcvQ0FWVOMnQNIWlls/HZ0b46U/M8MtPn6ReMFnu+NgDNd6Dwvjdx35TVf8Q+ISUcg1ACDEGfAP43bs1sHsZW72R17sBAghTODHu7FtD572mme5XCYaSpeGYKj/y8DTPXd4gGhTHFQUURWG0ZNL347ti/tSPEqSUeGFM1S5sS5/M1B2+eWGdKxset0OgDaKUKElxo5h31nrMbfT52U8eBeD/feEqa/1wjzO8e0yPOFi6xrVBsBIiI4xBESmjRZMrjb3fSQK0/IjVrs+6G+JGKeudEKFkpL5grRvQ9GKW2x5+LPGihM+fmXhPk/NWwcInjtaY23D51sUNHEPl7FSFt1d6dIOYlW5AmGSMlxV0RdCPUhxDxdSvT1tb1Xe3nvteTt9+GLDfwKEMg8YADfbPyPrQYas38kY/oGwZTFS0TSrpXhP4nVD63CqAOMT9IsEgyWm3EyWT86s9Lq/1KdsaBT2nr3rB/qXXb+t1s7zTuerofGI27xkZ1mBeX2hxab2/vxzVtvdCXpgWKh0vZqMX8BvfnuPQiE0vSLhyF3aApgoFU+X4aJHDtQI1R+ed1R79BExNUHWMTQvXvaACjqay0AzoeTENL8KPE5IUlEGNpB14XN7In9voB3z74gY//PAUYyXzXU3OW3fLvSD3OHfDhCyTpJmk4YaUTIPxksVaL0BKScXOae8NMo7Ui7wy37rhdQ8UdN8/7DdwfF0I8Ydsl1X/g7szpHsfW72RwzTD1BSOjxY3mSF7TeB3QunzfpVgkBIePVRhuRNg6BqfPjHKyfEijX6IrWu8PN+kG8Z3PE0Feb7fj2OePFrjqR158K+/uZpThhVuS99EV3Pv7tV+QNnK9Z9eW+gw3/RJ04zlzp2TG6k7KqamUXF0fu6TMyy2fOI04/REiSM1h1evtTEUhYWWy7q7v31TxVFp+xGOqVGwdHphjDuQGckL69eRAoudiPXeOpfW+vz0J2Y4NVG6bYmd4W65F8S8vdLF0lXMQQPpC3MbjDgGHT/h7FSZVEqiOGOlE3CoZnNmqsJTx+ubKeGtr3ugoPv+Yb9aVf+9EOI/A75/cNevSym/fPeGde9jq4bUK/OtzV6FW03gw/TUX1xYZ6JscWjE2Qw2t5tm2ppmaXkRZVvfk9nyQWKo3/TqQouypfPETI0HJssABEle67ja9LB0ldGCQRj5pDLvm7hT0AQ8Mj3Cw4crN3xOLTdCAW6nSV0HbFMlTnMbVrLcz9tUlVxSPsuvhzuhtaULeOTwCGenK/ylhyd55HB18zNd7wWcHC9xeqLIH725mqf+uHlhfAhDgbNTZTRVQQCqkntsVCwI05ROsPsZogzmWz7/x7MXqTom01WT2XqRH354cl8SOMPd8lLbx9JVLF1D1xR0VLw4IU4zjo4WWGx7PHIo/66WOwEfn6luW6DB9qBwv6Zv70fsW6tKSvnvgX9/F8dyX2K/E3guhrhCmkGQZKz3Q/phwgOTuY/Fu0kzbaW6fpDYq15zteHyey8vULF1zk6WOb/S5Y/eWuGLZyaoOgYVW6flRji6SkuCUBQKloYXJnc0cJQsjSeOjexaPymbGoam0ov3X+GIAVNV8JKEoq7gJyktP+XJAeX3nbUeQrx3SrECHB21+YXvn+UHHrhOid2Z33/2nTVGC20kuwsSboWuwFMn6vzcJ4/xnSsNBIKJssV6NyQDxB5nkBL8KCXLAuIkQUrBS1dbN6Umb8VwsdVwQ0YLJkGcM8H8KKPj5cV6IVv0woTpqsVYyWJ21OHRQ9VdqbtD3M/p2/sNtwwcQogeu1/3ApBSyvJdGdV9hr0m8FwMcXUghmigKYJLa30O1+xbOqbdD9hPXvn5Sw0qto6mqWz0AjRVpeuHPPvOGl96cobPnhpDEYKOl+e7Z0YcjtYcXrrawkvujNaTpcJ42UJRrsvQt71oUzQyzjKU/RGRtsHQNHQkUkKSZhwesanYBoqQZAJ0Ieh43W0qs7cDTcDMiM3RemHPCVBKOD1R5sJaj/mGixflr7h1t2PrcGq8xN95+hTT1VzwcKaWWwDbusqIoxP2Mrre/8/emwVJlp7nec9/9iX3rL2qq3qdnq0HM8AMARAgFiJAgiDIQEiMsC8om5IcCvnKEZYsm+EIXyhCFw7d+NYM01Y4LMt0yKQkRnARKIHAgMBggMEsmOmZ3tfaKyv3s5/z++LPzK6qruruGUwPeqD+bmYqO/Nk5slzvu//3+973/fe2lGmAZoQaJoGUpBkBWlW0A3vbyw2Xmxt9WJ2hglN32K+4vLXF7d483aH7X6EZei4hsYwSrm02eeTy3W252JmKnc4GgeLwkH4drsfc601ZL7qTP79Ud2RH4xHnaN1z8IhpfxPR3DpIYYSQyxolByEEDR8G2agHST3dUx71ONBcOXtvpJcv7EzxDZ0Gr6Fb+msd6PJDbFQc2kHCZahZvZbw1iNGgPtMPvAq3ZDQNnRcQ0dTYMfXWuR5jkb3YhumNAOUqqOyXzFRUOyM+g/8HvpwGzZwTV1gizDNgzOzpWJs5yqY3Bqyud4w0PX4eLmgDSXFIXEMlTCLSQM4vzI9/NNjYWai6kLXFOnH6V0guTI66TimlQ8gy+dneWv39tkvRcSxjl5AYYBn1pp8jufOkaU5vuOcXqmTGsQ0w1SZisua90IcZ9ej4ZgsvgX40a6JMvkAwsWfuZUk2+d3+B2O+DNm21udQJ6kXrtMMqINQ3haXiOzk9udqi4Jr/2zPyRPb2DCrrr3ZDpkkMQZ7x6bZfXbuzy1afnHvlx3Y9Dk//9yKo/jvvEUauEtU5IP8xY73aouSa+ZTCIU7b7MZ9crj9yq4n7xd7veWW7z9nZOxvPfpSy2g7Y7Kmm8Ikpn+myw8WtPq5hTBznslwyU7YnBWYse7HVUyOhZVvH0CS5LLB11Xu4397D1UHTIRyhF4YAz9HRdcXdyAv1njv9mLdXe9imxnOLNVzLYKnuE6Y5vjVgkDxY6XBMgWUIVqZ8kjwnSSVRkuOYOmdmy5xbqvHKlRYvnZii7Fl0hwnr3RBN05guqYJzYaN3aGG0BNR9E6GpgvCrT86SZpJ//dot5qsuCzX3rutmfA77UcaXzs7w6rUWN9oBFVtpUP3SySaaBs8v17i6PVDnzNTRNcGZ2TKWoXFhfcBM1cazdNpByiDO7wKsbB3V0xPqU5u6RpKqYj+etLpfjHd7RQFXtga8tzEgzjM0TUNDYOqQFgVRlnPMc9gdJlzeHvDFTOmbHQUJ7939O4bOhc0egyijH2V0wpTzaz2++cLiz92O4F7xcWjyP9TCIYS4DvRRa5dMSvmiEOKfA7+Fspy9AvxdKWXniNfrwI+BVSnlN0aP/Qvgi0B39LTfk1K+8TC/x4PEUauEk9Ml1rshnq0TZTm7w4R313o0Sha+pTFVsh+51cRhMS4Wlzb7vLvWQwL9OGOnH/LqtTa//swcsxWHCxs9BDBbcbjZCvi3b9wmKwrevt3jidkSCzWPIM4ZxClfPDszWZ2O/Uaqrsmr11pc3OhTckw6UcZ02WJnkJBnd8M9Omq1W3Z1KARpLvFtiW0INKH80EuWTi7Bd0xc26RiG2z0IlZ3A+bKDg3fplmyePN2jqHr3L9EqZjybWxTwzQEMxUXy9CIkoLnl2uTxFR1TV6/2ebFlQa7w5grO0POr3Z5/lgd39YZJhnRdn8f4VEAaFAU8NJKg5PTJQZxRjfMMIRQvZ9DpopAaYPdaAWEWcYXn5zh1HQZiWSjG3J9d8iZmRJV17yrL/f5M9NUXJPXb3bIckmSF2ijnUQxqhza6FwjQWgSz9SREoSQ1D0DIaA9jGn497+OFUQ4YLsfc3WnTztM0DWBa6nOihACgSTJctIcSq6JoWmUHfOeisTj6IUpt9tDNrsxeSGVT0whSbN8H89DSh45KOjj0OT/KHYcX5ZS7uz5+1vA70spMyHE/wz8PvDfH/Ha/wZ4FzjYS/nvpJSPFPnwqFXCK1daTJcc3rrdIUhytnoBWSEZxDpfPjvLTEWZID1Kq4mDMRZ1XO+GvHa1TZyrAjhfc/Ftk2Gc8qdvrvLS8QauqSNRgnvfvbRFmOYYAuqeyXubA6IMVhouTy002e7HJHk+kv6w2B0m9KOMmmfz4nGLU8OEN293uLjRI9vTIxgjJKYOliZwTJ1+mGFoCj5xLJ0gypGMGOmOScUxMTVBe6jkKrIC+lHG26tdjjV8hnEOSOarLlkxZHAfxvdi2WK6YrPc8FmsOcxWDt8F7IVPDF15cv/muXnOr/XY7kd84YlpNrshYZLuo5AUBfTjlEGsksj59R6npkvYhkYvSu9ahe5duLxwrMa11pDdQYxEwWPPLNQmEM+44IwT8FhM8K/Ob3B9Z0g7TOhFGRSqb2IbCpryHQNT1/BNnSgvaLomQhfUPYulmseZ2RKLNY/dYXJfOOit211u7g65vhOQZuqbJ6nSMNMEpCOlyfGk4lRJ7UrXOkrA8H7Yf8U1uTqCiN9e65EXI5VgQ+eNW+pejBJlvZzkBdNlh99+fuGRgLE+Dk3+jxyqklL++z1/vgIcKl0ihFgCfhP4Z8B/+xF8tAeOwyCpXphiaIILGz0GcUbJNpivOtzcDWj6Jgs1h16YsT2IqFhKVXR+JLv9qK0mDsZY1LEfZpimxk4QkxWSYZJj6RpJBlJmvHqtxdfPLbBY9/j2e5sMw4yyZ5HmOadmysjNHp6p4VsGf/rWGgAvrdR5d63HW7fbzFVdskIJury91qU9TOiFCWGao6Ea3HGudhm6DoYuKNkGaQG2qSOAJC8YRAV5Limk8mUfRjktI2YQW6x2Qma7ESXbwLU0tvsx376wiQbsDJReU821COPoSIh/qWazWHV56WSD/+pXTgFMrofDHOkOG544t1QD4PWbbf7VqzfRUA3nkV4heQ5xWnBha4BnmdxqD0myHN8xmBs1iPdeN+OFS15ILm4O1FhzyebNmx3qvtLkGkvTjJ8/Ljjfu7TND6/t4pkazZI6R/skpDJwTEV0nC5ZLNd9qq7Brz07r0yZ9kw6SSkf6FreHSSstUPSQlJ2DUqBSS9KyaXEtnTSWH0A3zGZr9nUXIuTUyXWuxFTJfuuXf3uMJncjw1fcUQubQ5oDWKSrEBognZQYOmCrCjY7sU4ts4nFquUHZONTsC/e2OVv/PZ4z/3ncfHgaP1sAuHBP69EEIC/6uU8g8O/PvfA/7oiNf+L8A/QYkqHox/JoT4n4D/APwPUsqHp+twII6CpKK0mHgpVxyTOMv56WqHME3RhEXVs2n4AIJuEO8bCX3UVhPjGBfIv/zpOhXXIkhzSrZBlBVYhsZ6J1Sy2jrMlD12hjEN36bsmNxqB5QcE4G6+CuOxWLN4821Llv9CMtQMhI/uLLLzjDGNjSut4YUUk3rDKKUtW5IEOfE+Z2ECiA05TOR5ZIwy9GkBkKS5lKp6hYF6Z7zq0nlRdEappQdye4wZmeQIJHUXBNNSLYHKcMow/QtskLimoJBenivoxukPDlX4VjdpxumXN0efOBGZi9MQYIuwBiNdWVFQSHVpFa7H/Hdi5uAYKsXc2amTNlWjGtdE5PrZgxvXCU3A+0AACAASURBVNzsj7gROlJqDJOMlabHWifk7NwdztCtdgDA31ze4dLWgJ1+RBCltMIM0xAUUpKMznuBcmK0dQ3fNkmznNOz9Z9pZaxpkqvbQ3Rd4Fk6NVfdM3Gq9Mpmqy55LnlqvsyT8xXOzJTZHkScaJb27eoHUca3zm9wbrFG3bPY7sf829dvI0fnZK0bKl8XQ8fSdaIkQxMAKRXXoDVMma8Z1HxbTWE9Ajv/jwNH62EXjs9LKVeFEDPAt4QQ70kpvwsghPgfUWDyvzz4IiHEN4AtKeVrQogvHfjn3wc2AAv4AxTM9U8POcY/AP4BwPLyh6dQehQkdX2nOxI/vDNqIhG4lkkBRGmObWhUXIOtfkhNvz9h8KOKw3ZQ3TDl372xynY/5sr2kJqvehFTJRtTE/SDFE0XZLlEExolS8fUHV6+vMV81WMQ5RSFxLMN5stq7HMQq6Z3zbfY6cckmWStE7LVD/Esg6myTZoXtAcxmtAI0xy5B6Ia8xNGqujoAigEQpdMeTb9OGMQZhz0VxoXnTAtMLScMC6Yrtg0XItzS1VAkhdDXFPj8taAvFDGSEfFMCkm5LVvnd+4K5nBgzUyx2Zgti4IdEjzHE3cAawKIEgKHGss95ETZ5KKq2PogjMz5cl1M07igzin4qjPEGf5CCsXDPYYt2/3Y9a7IY6hs9YO6IUpW72QTqjMtQSg64KSIdA1QZzmJEWBIQSOobFQd/nVp2Yn/Rt48JVxJ0j4/pUd3lnrKVhK14jTglRKyraJaxZYusanVupUHIOpksPJaZ+FmouhC6bL9r7j7Q5j8uLOeb+41edaK6DmWjimwNZ1wiQlyQry0W5W04SC4EwdS9foDGMano01Im4+rNg7/i0RnJkpHdmkf1Q4WkfFQy0cUsrV0X+3hBB/AvwS8F0hxO8B3wC+coQd7eeA3xZCfB1wgIoQ4v+SUv6ulHJ99JxYCPF/AP/4iPf+A1Rh4cUXX/zQFCyOalwN44wXluusdyN6UUbJ1nluscqFzR4rDZ92oHDjqmvymZNTRGn+SKwmDttBfe/SNufX+2x1Q6RQDm1Xtwejpqfk+FSJH17Zpqxb5FKyULUZJsoJTtc1Gp7JVNni6vaQp+YtbF0wiDJaQcJ0WZHMkjwnTApawxApIU7UiKxvGWR5QS4LNKGRyf29hoIRua0Ax9IoOwY13yIaFZks3/9Tj9OwACxdMF12CGOVnMquQcW1GEQpwzjjybkyFzeVX3p+jytGQ2HwaV6QFyp57eUXHIQeDyvMoGCqqZLNc8dq/PBqizjPlVWuBCHA19WOwtAE/SRHyoI0y7nRUruFX39mfnLdjOENQ1eLFCHUf88t1riyM6BsG5OFyrXWkBPNErc7AWFesNlT12yeqzOV5hKkxDZV/0IpF+fM1z2+8MT0PpHD+62M9353IaAbprx9u8vp6TJBnLE2gg1JJXGeUXMtVqZ8Vhqe+t5RymYvZKHmUnaMu3Y4u0FCY8/7vbvWG+0iBYUUNMs2qx0FezmGcjBMsoKaZxKnBboG/SjH0jMW6+5D2/mP+4S32yGWJtgeRLx1q8Of/3SdF5brzI24Jo9is/6weGiFQwjho8QR+6P//zXgnwohvoaCoL4opQwOe62U8vdROwtGO45/LKX83dHf81LKdaGA1W+ibG0/sjhqez5ddjB0bSKjARAkGadnyuSF5FjD27cq++LZe7NrP6o4bAfVCVIubHRxTfX4saZH0VIrU1OHlYbPc8dq6ELHdzRmKh6+beAYgpJjcnauwmLd4zsXNtnsx+z0Y3zH4LmlKsM4YxjnDOOMMMmQhUDXpMK2NUWiK6SCSjxLIysKPB3i5A6tQKCgqqmyTZKp6R9D0zCE6n2Myd/anudrAoWl2wZNX0PTBHmhkkgB1D2b1U6EbQjqjkeYZax147umuASKEyGlcsFreCa7I4nwtU7IIM4xdEWqg6OhTV27I4X/tz55DITg/FqXIMmYKtkEccaYdh7nBa6pkaYQZhn9OKcTZPyL71/jy2dnJqvWF5br6JrgjZsdGr7FE7NlDF0bkRLNSXKfrzo4psbFjT4N11Ligrkc9ZcU1OfogkKq6aYn5yt8alnJnexlrsPRK+Px6nr8WU5M+VzbGdKPM3aDmMWax6dWmhi3FNscoYrwUtPlzGyZQZxxqzWk7tvMV12SEblQoIyaxveSrmmKFzWKJFc7e9vQcC3FfalYBkkuqXgmeS4pl9WuN8lz2sMY1zap+SbzNeehWT5f2xnSDVMsXeNGazjhcKVZwYXNPpauUfMsfu3pWRxTf+QnLR/mjmMW+JNR48wA/m8p5V8IIS4DNgq6AnhFSvkPhRALwP8mpfz6fY77L0ey7gJ4A/iHD+0bHBJHNa4+c6q5bzZ+/Ph4cuVRxSsP7qDWOyE/vNri/FqPkm1QdS10IdQYplCmOl87N0/Dt3jrdodumJJlkqs7A2bLNgt1bzIgMF9zKbsm5xZrk6blH/3oJpauEneY5GiagvcsXd3sudK7oGSblB2dJC9AgKEXmKhi4NgGNc/CNBSJzjF1giQhl1D1LJy0YBBlEwtYBU+AbxloSHzboJAFSS4ppGSuYlF2DL71zgZV1yZOM8Isx9T2a2XpqFyuCYHMQROShm+z0Yt583aHqmNi6dCL0pF6cnIktPn2WpeXVpRC73zN5b/47PEJ/+VzZ6b53793lXdWuxRIikyCVFyUNFOyHC+t1CkKyfm1Lr0w5fMjmY8vPKEKyV53vsMkQN5Z7VJxTfQRIbUXpRQCLKFhGWoqDpRL4gvLdZ6YrRy5Gj+4o2r4Fle3B1zaVFazl4MB11pDSrbBbNkBCUGsSIgvnWyy0Q1Z70QEacbp6TKeabAeBJiG+v1Ljjp/M2WHOMuxDG1yL3316Vmubg8IkgzX1JmtONzYGTA96zBTcdjqxei6RsmE6ZJFmOTMVlx8W2e54bPeC1mqezy3VP3QuB1HDc9kmWSrF7LRjWgNEoZxSmuYYBoaJ6d8DA3+4p0NfueTx6h65iPRbzkqHlrhkFJeBT5xyOOnj3j+GnBX0ZBS/jXw13v+/tUP7UN+gLhX42qvv/bBAvFRXgDvR65g7w5qvRPy1xc2iTLlzhcmOd1oyHTJUU1uQ6fh34FaBIAEicQ1DXpxxuXNPnVfDQj0wgTH1PnEsTs35GdONrENndblbTRdsNTw6AwTwiQniHMKWWAbqlkaxgryS3OJKTSmKzY1z6Q9SJmp2Oz0ExquQS/IMIRG07foRxmZBrNlm16UEOdqvNMxDUqWwVLTJ8kkrmVyZrpMyTXYHcS8sFwfTeL06QSCXpThmBoyKcikKhoFqFU4YJkat9shdd+aqPsmeUHJNnhuqYSuiclvcBi0KZD7dq5lx2S56XN6tjwala2x2o7YDWJ0TUMWStLD0DWWGy6eZaJrgqpr0Qn2y3yMdwHj6+DNW51918GJKZ+XL24z5Vusd0OqnoHT1ak6OgjBQtXjVjvAtXRONH2enK2gaRy6Gt+7ozI0wTtrXd5e7VL3LH662kEWajfo2TpSSsq2wXzVY2egCKKGLmiW1M7xuVqVJCuI00wxyKXaKS7smT6M0vwuHsfe++7TJ5toKMjHNzUqthoUODmtvNHTXNLwTabLDp87PfWhQ0J7NekcQ9AJUv7fH0WTXfGNXTWOn0sIR0MmEuiGGctNn81uyLfOb/D0QoWCR1cm5TFz/APEUdvzR6GhdRg08vKlbaoHyE63dgP+/O0NbraGxGnO6dkyV7f69OKcMMnxHYX9FwVs9iJ800B4Jpau85dvb1B2DTphSj/OCOIM29S4uNlnyjcpuyYXVzusdZQr23rnIp9cabBQU5pLeSE53vT5s5+uKnjI1CiEJIxybEtnvuyyWHNoBSloYAiNJM0Yjpb/v/rUDG/e6ihV3UwwXbXJpURHOeLJQtKNUuJMp+LpGCjhxGgkgX9uscL13SGZlDyzUN13c/q2gurKrs5WL6Y1TJASkiwjSsA04dRMidmKM4JK1FTQ4lLt0LHUo6DN0zNlgkRhaoc1lr/y1Bw/vd2l4mjs9BPaYYqvaSzWHWYrDnGes1L1sQ2dbpjc1dQ9LKG/fHGL55frPLdU4/nlmhoVLzvEWcGqratEpgs0HZ5bqnKs4VF2DBojDsVhCWy8o+oGKd+9uEU7yFjrDBnEasc3V3EwDdWrKCRc2upzbqnGM4sVfnKjzXo34txShU+t1LF1ndVOwM3dAF1oVDyDJ2bL97UrOHjfPbNQnagGz9dchklO1bVoeCOrYI2HAgPt1aSzLY13VjtsD2IWqh76iEN0ozVkumxj6JIwySm7BkJCmBQESUaSF7TDBNvQKZA/E2T1MPWuHheOX7DYP8/fZ6sfc31HTQwdn/IxdY0fXN7htZu7aEKjHyas9yLeut0BoW50ITRmyzatYYyhQYHEtQUSyYkpn/VexCtXBpyaLrHRixhEOVGupCCut1J2hindMMUxBP0oY7sfUfctHEOnEySTufuvPDXHn729Tjpi9zUbNiXboO7ZeI5Os2RzeavPei/Edw0+faKOpmm8fGkHx9A4M1PiZjtgvRNRdo0RWU3w/HKd719rcWLaxjZ0OlGKrWukueTNGx2eWajytafnyAq5b/U6Nuha64bc3B3gWyaOaaBJyW6Q4dc15usep6Z8VqZKaMBGN+SF5caRY6lHQZv3gzBXmj5/7/Mn+f9+cpvZak7F0elHGVv9mIprstLw8W2DKM0wde2uhPrW7Q6rnZBemNEaxCzUXKZKNjdbAXkhOTldIi8kRQG2oVH3bM6vd6n5aphhpuIgRoZb90o4Y/7Sdy9usd6JGGQpt9ohvTDFtjWCJKc2+myWoTFMcpYbHlLC187NT449LnRn5yqcmi7xzlqP8+tdorSgFyYYuvbA04eHuQJ+FIKBezXpbu4OyQpJxbFI8wLXsnjuWJ2buwG9MKHqWZQcQxVqBFkh2e7HmJqaVHxnrcvZuTJF8cGkRh623tXjwvELFuMbeUwC6wQJ3SBhMytwTI04l7xyZQchBE3fJsoKTE0nlDl5UeBaJuEIKlisOgyTAl1AxTGJ8py/em+TkmNQsU1+cLVFkhdUHGUCtNOP2ewpH43ZsosuNDRNwQatQUo7SDjWUMziF0YaXdd2BkpOPUgwhEZeSExdsNmJuLyjGL+GJgj7Od86v8VM2aJsG2i6oDWMRw1HQZiqBBUlOZ0gQUegCUmc50RxRjstsA1Icri1G7JY82iU7tY5Ght06ZpGnkscUyW7v7ncwtJhumSzMlXCtwzCJEUi7knYut9M/r0SwrmlGsca3r6ppPVOyGonYrUdEKYZeSF5drG6D0bqBAlv3OwwVbKJ0xxdaGx0I1aaHlkh8Sxj8hv85dsbpHnBctPj2aUq692Qd1YVs/oLZ6YxdO2uhLM3EW90I7pRwmYvphMm7AbK10QCSVLQTmKKoiDOJJ6pUXK0uxL7+Ny/sFzf11D/jWfn2e7HvHajzfPLtQ+c9D4qJKAXpjQ8izjLCZKcrADX0OgnOXNVjbpn8cxihUGcs1B1WeuEvLfRR+gaJ5oOgzijNUg4t1TlyfkKhia40RoQZe4DyazsjYetd/W4cPyCRcU1eWetOyGBbfUjRYqTBa1hynRZEZ2iNCdMMoZxgW0qnLUo1Ly/b+ustwPmay6DKKNSsumOVFmFgCKDq10Fax1vegihLvC1TkiY5KS5REo1pXOs7jFTcXhvo8N6N2Cx5lLfM2WzWPcQCAyhsRvEWKZGmGZc2ekzjHNMXadk6+QFtIYR24OYp+fLlDSTvFCrWFGAo2u8dKLJrZ0hQZrzxGyJV661EFIQJBlZIbFMwVzF49JWH0OD/3zkDb43Dhp0FaNx28W6w1Y34ljdwzN1olFxfXqhet/i8LMkLtWTYDL/3xomrHdCTEOnZOks1F18e/9tfG1nSMNXv1WYFvijPtFqN+TUVGkyLlzzLOaqDkt1l/VuxJXtAe+udwnjnF6UcaXmcnqmjGcZh0qb1D2LbLQQ6YQxvSjFGA0+VFxFzsulYvNXPQsdsHTjSIZ2zbMoOyafPtmcJLqFmkeQZFiG9khi/Xuj4ppkudLF0jWBrimr4iAt2B2mxFmf6bLLbAXqvrLGna+5XNnsq6lAXeMzp5o8vVCbHDNOcwYHiUkPEA9b7+px4fiYxl73t+myw2dONVlp+qPG5xZTJRspNdIctocxjqFxoxWw2g0nzOQgyYnzAikLipGg3fRoFW6ZGqau8+xSnVvtIZ5rUXINqq5NP0pwTYMgKdA1xdG41QrJRhwEfSSxXciCrX6EAIZxzkLVQxMam92Yly9townBiaaaOAkStXrWNUk/zhkkObpQ2lPDRH1eIRV/Qym2CgbxCIKSBVMlh6KAxbrL9VZAlBXIQjnZ5bJA1zSEFJiawLd1gjTnr97d5MxMCbh7fr7mWZycLk0anU/Plam5Jpv9CE1TO7C6p8ZAv3Nh66FBIHvn/6uOSZHHuKbOYt3j+WN1yo55l9ZZL0w5MeUrH/Io4dJmxHDUT3lipryvVyAE/HS1g6nrrHVCVndDDF1jpuxwZXvAIMr4xLEaUap2cn/59gadMMU11dRVIaHsmmQt9RtXXAPPNgiTHEGGEkMWmLri3JyYLt2Tof1xEPg7Kk5M+XSChOWGjxBwvTVkvRvx1HyJkq3TCVOklPzmcwtkhSK/ZrLgxZUVpss2//HCFpvdkN1hTN2ziEej4iXn/afph6139bhwfAxjr6PefNWlH6X8m5/c5pufXGKl6fP8cp2bLeVt0PBMrreG2IZGluW0+il5rvykgzjHsTTiTCI0mHItUllwanQMpOTZxRp//vYaYZKDEMyUbYZRSo4ak20HKbvDhDgvFG9eU6Ocaicg6Mc5moiZrdjUfBsEHB8x09OsoB0kCCEwDIFZwOYghpFvRV5IfEOnKCQgSQrF31AFK1eENaEmVOZqDstNjyzLySRsdEJOz5S4ujO8w5mwdUxDo+FZCARxkvPOWg+B5Nxi7S7F2d1hwrnF2uTmOxOlXN0ZEGcK2umGKbah3yUe+H6Lx70w+PH8v2JC6+RSUHPV9NhYRuQwJzxFcrO4OZKqN3WNsm3w7QtbfO5Uk19/dn7yfImgNYgJ4oySY5BkygK3bFsMYlWUlpser99sK39yU+fy1gCE8vJYbnhc2howVVbnNS9ybEOMjlVQtg0Wqg5TZQfX0InhSIb2x0Hg76g4KGhZSMmTc2X6kRLbfHKuzGJNwYUnpnwubvTJCzVE4Vo6SzUXSxcKatU0SrbOTNm/C1J9kHjYelePC8fHMMaOelXP5lZryJurHbb7Ma/f7vCPvnqW55Zq5CMsWyC51R4yCFO6ccowzWj4Fp0oJc1zolTtECqOxXTVIo5zWsOUlSmfl443Jw0+x8jwbYtCgmfrWJHGXFXhsmGqxmgNTVCybfJCUfXSAoqiIMnUVIshBMcaHp6ls9EL6UcZVdcYGSk5XNjKqLsWTd+iF2Vs9mK0VI3F5kq8Ft81mC0rpniY5sQjFvDV7T7Xd4ZMlS2OT5UU6Wy6xPRI3j0fFR9T03BNE0NXCUm54EnWu9GEvDleDR9c/ZYdk+cWa7SDhLJjYhv6z4wh36+JOZ7/91y1wndNnX6c0hpE9EemR3XP2pdcxknjjZttSpZBdUYVGtfSiJKcneEdMygp4bnFKt++sM0wyWiUbKUXlUkMHTqBUkJebnp4lkHTt7myPRhNOgl2+glzVYfPnmxyabOv/D9cA00YlByTYZRxfNpXU02+ySC+N0N7/Nk3uhGXtvps92NcS+Nvf/LYA5/Tn2cchCUPE4G81Q5YbQecX+ti6mpkd2vkG98LVdH45HJtkuw/CCnxYetdPS4cH8PY7kfMV11utYZ85+IWrm0wU7JoBSl/+PJV/v6vnJxcNP0o49MnpnjjVpswl+iahmdqaLpQ8thpgWlqnGh6WIbOXMXlC09MYegaOyN+w3/5ueOTHY6CRlIKKTjR8Lm42VOJgjsMZ0PT6EUZug5PNMo0PAvfMrjRHgIwU7Hphumk//HT212u7wbsDGJcw2CppjNfdQmTjCBT2lKebbDcVMcRmlrNCgHDWPUaWv0Y09SYq0xTdQ22+xGtQUzTs4mSnNYgRghBydaVtIXnIDSBPTKWGjvP7V2932v1+2FBKvdrYgoB692Q660hNc9E0wTvrvfoRym+qdENMqZKFr/3+ROTY46Txr969QamUPpoy1M+rqF6M6vtO4INFddkd5Co301o6AIavkmaSzqBGrN+frmGlOr7LdRcXr/ZVgRMHTphSs0z+dRKnVMzJYSAH13fpRdmuIYgzCTDOMUxNExNw/c15msODd/iuxe3uLQ1QCCZq7qUbDUZtzOI+f7lHTzbYLZs41g6//HdTTVN9gjInj9oHHX9bPZCkqzAt42R7whs9SJ822ClWWJnEH8oyf5hDgU8Lhwfw5guO/SjlDdXO7i2Qck2iVKVQGquyZ+/vcE/+dqTk4tmd5Cw2g3YGUQUhfKHni3bgCCVCQ3PZKbiUPMsPn2iOZmbh3ECq/PNTy7xypUW692QQkrOLVYwDR1D05j2LUqWwrVzKUeKtjknmiUWGx5xmhNlObapsdENaQUxZVtnumyz2g5YqDlEaYlhlNJP1ERKwzepeU3WukPSTNIs2Sw3XWxDoxvk3OoMidOCTpQhJLimRtmxeGety1zNZb7qst4JMHWU8mqe45kGVddkseby7GKNtU5InBUESUonSHntRnufXMi9tvvXdoYfCqRyWAHK8oIfXd/lby7vcHV7QCEljqHRGhT89HZ3MpJbrljoQjXvz6/19iXVmmexWPcwhKDs3Dl+mhfY5h1z9YZv8d0LW7iWTs032egq7bAXjtXRDSVXMmaih2lO2TF5Yq7MRi+iE+SUHZ2zc2V0TbBQU9M/Xz+3MDn+WHpkXCBOz5RZafr8dPRYnOV0g5Rvv7dNo2RxdrbMxY0+ui54ar6KPzq/3SDmlSutj1XhOOr6CZKchmdRsi1u7A6xdR3P0rmyNWC54fGZU82JTPxhMv2PQjwuHD/n+CAz5p851eTf/OQ22/2YmZJFlGZEac6J6SoVW+d2O5w8d5wYHF3jeLNEP8xoBTGOYdCLEuquxclpdTOautj3PntX0HtHKP/o1ZuESUbVsykKuN0JyPKCMMnQdI3brYBayebFlRppoRKqYxi0g4g4z3lmSY0abvfj0fSXgW8baLrOtG9ScU1qvs+t1pAzM2Vmyg6fPTXFtdaA9jBloaaz2hmyPYxGmLrSsSprAm1Edvuvv3ia128JXMtgumLzGzPzPLfHA0PXBPNVhx9e22W9G/DkbGUiF7LeifjuxS2kVBap8SF2pSemmCSFLC94Z73Lte0Bi3WPfpSy0vT3eUTcy3BobwHqRymvXmuxG6aUTIOmZzJICoZxRj9OaAUJddfg9FwFIVDGU1nGj67t8hvn5vcd+6XjDf7i7Q22BjFCqZYo/sSzc5Pn7A4Tnh3Bb0KMTLHilN0w5ktnZycyHHu/7+npEmGSUXEEzy1W0TVxJH4+lkH5whN3NK5ev9lmrRsqoqVtEqeRErnshMyVHQZJhq0Jbu0OeXKuCiiYcL0b3nX8RzmOgouu7QwAoXYYDZ/tfswgLDB0ODld4q3bHdY7EVu96JEzmRrH48Lxc4hxsVjrhKx3I040fabL9gM3WFeaPt/85BKv3+7QClLljjZdpe5a7A4iZqvu5LnjxLDaCehEXQwDnp6vMEhyluoO3miU8+ZuyO4wwdA0Pnd6Gjh6BV1yDGV5muYsNVzFXI4SZiouTd/mE0s1mr6FqesKijFVU/rs3BSmrvHEbJlb7YArW4PR9Jek7Ji4hgZCJc+Gb1JylKJrzVcY/unZOV650uInN3fpxzlpJtE1kEKgSxhGKaausdMTRGnB505PHzr/Pr6ZozSn5hmUnDK6rmEZOienHa5u90mLgucW7+DM499k3JPohcoPozWM+entLoNY6Sy5ls5rN9r84PIOLx5v3vd3Pbgqvbo9YHeYMltxuLzVByEwNNVofWquwiBMiDNJN0yVtSuCuapNcAhEdqzukecFO/0YIZSO10LV4ZmF6uQ5vVCNaM9UHM6OHhuz3vcm+71JMEpznh4dIysknq29L0ilF6ZsjlRxLUNjN0gpuwbDJOfdjR5IQSBzit1gUjj6kRI3/LjFYXDR6ZmyGsoQSnVgrurgWBrPLFS40RpyeWtAZ5hQsg1sU3+kTKbG8bhwfMSxtxkaxBmGgJu7Q1xLn0BEb93uUHbMe65WV5o+/+irZ/nDl69Sc00qts7uIKITpvztF+80EvcmhtMzZS5v9bnZCrjdDjm2WGW1E9H0bU5MeVzbCfjepe3J++1dRe7dGQ2ijKZv0w0Tbu4Gyieh5DBdsfEsnbOzFYQQXNjoK/JemnG9FbDdj3lirsx2P57IZN/cDSZy87/9/CKvXN2hF6qidHLaZ6Hq8vkzqpCNZci3ejF5nmNoQiVWIYjzHJkIKq6g6hq8vdrhm59cOvQ3GK+gr+0MaQ2UZavvqNHU9zZ6ShtrkHByqjT5TcaQwcFG9sXNPnXPYrnh44wgoCQPSXKVfGcqzj0b5wdXpUmeK+Xazb7itejaZLXt2zpSCNJC2fFKJLe7AUIoEuDB6+yH11qcnC7h2wa7QYKla5yZreyzdr1XH+ew3fD7JaIdFhVX9VAc84728CBRBc4xDI5PedzeDYiSnEGUkBeqUH75qdmf+b0fhXhuqUY3VOoKvTDHMMQEEvzjn9wiSQvKjollqOvpUTKZGsfjwvERx0TbJ0x5+dIOSZ7jmSZxJvnc6SmyvOCNm20+fXLqvlIB55Zq/P1fOcmfv72h5vw9g8+dmWJ3qIrTiSl/X2IoOyYvLDc4O1fhzM6QqzsDmr49mRM/VnfZ7Gn88Oouv/X8wmQVeRjp68fXW1iGxlzFpjVI2RlpOs1XYIQeIAAAIABJREFUPQxdw7MMzs6VJ06INc9kueFxczfgjRttvvrsHM8sVCfTX2MM+FfOTFM5oKtV86wJGa8dJHSjFF3XqZccdgcxmSyQEvI8R9dMPnNymmcXq0d6X4/tUte6IZc2uhimTtlWboU7g4jlho+l61zY6HN2rkzJNmgfoXSbF7DdU3am48gziUDJro9jDPsdBU2OC1mQFLy92sUxNQxNsNGL2eolOBasthUBsTWISIuCXEoMoJCSF483JjuhimvSj1J2+glrnZBCQtOzsU2Ny1t9qp45KQBH4fBz1dKh014HbVo/CHflxJTaYW90Q2ou2LrGpY2+UsCtWFiGhufo+JbO+Y0ezy/V+fIeD5CPe9Q8i18ZKRQcPI8SQZBm+6TikTx0k6n3G48Lx0ccvTAlSnO+c2GLNFc2md1gyPWdAbMVm+1BRGuoxNJAstzwWKx5R642zi3VOLdU25fc9/IKTk6XDpV7/8ypJq9c3WG+6iCRpFmBEPBLJ+p0w3TfyvJgwpypONRLFqvtkJ1BStVVXhv9MOXli9vUfJOFmsuJpk/JNnhhuY4AfnKzjaHpTFdMbrcDXFOfJKIxBvz5QyTAAdY6IZtdJZo4W7bREAhSGiOpbOXDoPOlJ2b49KnGJNkfFspDPaAbZhxr+mz2Eq63hkyXLHzHZKMX8cunptE1wVon5FjDO3KSyjEEq52QrJDUPJOpsoNuCPJUTXCNIxyZKx2VjMf2s2VbpwBut0NmKg7TvsXWIGYQK6fFZxcq5LLMext92sOEU7MljjVddgbxvuP+4EqLa9t9TMOg4hhkhaQXKULeXiby0Tj83UVS2bRucm6xeteiZnydHDSrOiw51jyL335+kT8dOUwiJBXfpGwZ6JpGJ1S7o7mKw1zNo+QYvHKlxdurXXVdPYKKse+3V3nUxNOZmRLXd4YMoxTfMUkzed8R5p9HPC4cH0Ec1PY5v9bF0DWEEKSFxDZ08iLj//z+NYZxjmfrHKu7zFQcrmypkdrj94EJjhrrHGsSHTbPfW6pymo7IisyhbXWSmRZfheWfFjCNDU1XvnkXBln5IC4G6QIDWbKNkh47UYbXYNnFiq8em2XlUYJ39FJsoIbOwGnpsqTz3e/8/bOWo+SrVP3jZHCLgzTDLvQqNdMTE1wbqnG2bnqvmR/WFzaGlBxTHaHKRXXwjIMdoOY1iDmuaU6rWGMrinHwJ1hQrNkHTpJ1Y8U+bHsGkgkwzijPezimEq8ru5Z++yB95o37f2NXrnS4viUj2cZSNQuDikJ4oKqZ/DUXBlN16g6Bralk+Xwy6emlOOiJrh+4LfPC8l6J2K9F1FzlUtjnhd0R2KBmrb/fByWxHph567fXNm0FncVkz/+yS0GkeKAnGj6JFnBy5e2EagJwMN2zitNn9/97HGu7Qz5m0vbLDc8gjhnox+r60vXOL/WY5AoK1vf0kdjvUp/7VEyOfowBQWfW6qx3ol4e61LPFRky4dtMvVB4nHheMhxGMxzfr2HpWsgBLqQDJOCNJds9WNqnjlaLWcUMma+5rAbJExF9j3f5168gqNWN195am4fP6MfpYdiyYfh4Kau0Y8zNrohYVrQCRIcU2PKV2O+nzhWJ0gyru8M2R7E5BIqjo5Aza1XXJPdYYwxYsoeZa06Pm+WrnGrFWAbSm5jqx8jC/Adg2N1DwEca/h3JfvDQozMZD1LJ80KXFNnvuIQpZLjUz4LdVc12YcxNdeaJICDk1TfvbjNbpCyUPHYDWJu9UI0Ac8sOHzjE4v7dlJn5+q8eauDu2cUdvwbbfcjnppX5MOSreNZJvNVQVbAmdkSwygnkzln5ypMl+19u8ogUWzvLC+4sNFjexBzux3QDmLipGCoK0XdsmvgGBp1X3FoOkFyz6R22G++G6T7bFr7UcqN1oAbrYCzc2WE0Li4OeDsXFm5+klYaSpJl8P6PHuvy91BwrcvbGIKQdmz2OzHmJbOIMrYGcQsLNWJ0mwilPmo4P2dIOFPfnKbaztDLENjueHdpe/1fqLmWfz6s3PM15x9I8wflsnUhxWPC8dDjrG8dZarpFBxTMqOwVonZK7ioukaZqEMa5olJT2uawIhxMSX3NbFffVqDrvRt/sxO4P4SC2l8XTWmJ8xXXYOxZIPw8FNXSPOCrZ6IQUoXaQ4wzN1PFtntR3w4vEGJcfgylYf1xAkI1mSOMvVuGqQMFt17mutCrBUvyPH4FoGnqVzvOGhaYLlhoeua5i6mCT7k9OlUTHq3PXdx1MtZcdkvRMo+9hCEqYZr9/c5ekFBcU0S9a+VeNBBdcwzTk97bPWjdCE4HOnp9CFYGcQUz2ErHYv2+Hx4ws1l3fXe4CSXGkPU3QNPrFUZ7npTcQh9xakt253eGu1S821aA9jWv2YJJUs1F1aQ9Vc9kydlaaS1T/RLO1Lap0g4ftXdvjx9RZhWnBmpsRLx5t0DviF6Br7sPe1TogmBKah4ZjGhCG91lGy6q1BTFZISrb6XkfBh2ONpywv8BxFSk3SnKcWKuz0Y3aHahdiGzq9KH1ktKvGOmLvrveYKtkIBFe2lBfJJ5aUvtcHicNGmB+1eFw4jogPQ8N/LG/tW+qCv76T0RomHG/6atzOMSg7Ble3B3TClOOjbX7FswjjjH6c4iQ6Z5erExe0o+Jgct/ux7y92uHZxdo9t8+HSVwfjMNw8Pmaw6dPNPj2hS2yvCBKc/pRSpIVLNRdbu0GbPZjvvbsHM8v13lvvceNVkDVVU53Wa6EB4H7WquCcoHrRSm6Lmh4Fp6psVBzkUDDs5BS+YY8MVve1zM47LvvnWpplGw2uxFJVvCJY1UlzZ5JrrUGfPXpuSMVXJ9ZqPLGrQ5vrfbQhUQCr91ImK+51I+w/XwQ22HlIFjjB1e2Wai5LNbcifnQ+Bo8bBWrdlGSzV6EYxkUEjRNMFdxSfOcQaJ8Oz59Qo0IjxNvJ0j445/c4nsXd7AMDd/SeWe1x3Yv5usjMb47Nq1z+2xaW0OlgbXc8IizHMc0sA2NzX7EeifEMtVCKc5yLmz0WG4crrs0vr5evrSFkxvUGqba9WkafTOdJOA4yynZxiOjXTXWEZsq2WhCYBk6QijRz2ut4b6x51+0eFw4DomD8NLYE2C+6ryv5ty1nSGOpXGrHVCyTeq+xUZPcTdeOtFkrRuyM4jxbR3TVHr9BQrb9iwdx9RGs/fuffHNg8l9ZxDz7GKNmYrqV/ysevwHE9Z3LmxhaIJPn2hycbPHRi/Ct5XgXlFIfNtimKRsdEP+1iePkReSU9Nldocxu4FaRX/16bmRydTd8M1h1qorDZ+dQcxmL2K24vDESFtqrRMqhznucDTuJeNxcKql6dtMlezJuQIIkuzQqaxOkPAf3t3k4mYPKQVb/QhTE9imhqXrDOKMNC+4sNGnH6X7IIYHtR1ebno8v3zmgaeXpIRzizXWuxFRVlC2NE5OlwgTpefViyRlx+UrT81O1HTHjfq/ubzNy5d3qNnKHS8rJEGS0Y8zzq/1+M9+aXnfe/XClD95/RZXtwPSPOe5xRovrDQmMiZSQmeYUPdt5f+SKSvgOM251hryqeOHw4c1z+JLZ2d5Z61HzbXIikIJKaKY/90gppCSmWbpQxXr+1lirCM2X3O52VJyOoYuaAc5u4P4kepJfNjxuHAcEnsTTz9Kubk7xBAQxNldCqr3il6YYukaUunGAuBbBruDhMW6yxfPzrDWCbk9UjCdKttUHIPVdsCNdkDdtfjlU1N89lTzgQrV3uT+nQtbD1WeuuKavLfRZ7pkU3Vtqo7JlK8RZTmDpKDhC0q2QxDnd6mGnp694yq3O0we2FpV0+DXn53j2s6QJLvTpD07Z+7zbDissXvwu3+Qc6Vkxdf50fUWRQ5lzyBKcxIhyAtBt4hZqLqUbJNCSs6vdemF6b5JsfdjO/yg46djNdyzcxWGSca17QHFaMpr2XV5b6PHmVk1VhwkypFRoqCf9jAhijK24pxhWuCbOo6l0wsTtvvRvve50RryZ2+tEacFJ6d81jsh3720w1ov4otnpgkSJYhYcQ2eP1ZHCDWV1osyfFvJrd/rOt67E8xzyWLdIU4tZisOUiri6b1sbD/qqLgmhiHQhcbKVImdvuJROYbO849Q8/5hxOPCcUjsbTSvdUIcUx/hq9n7WrlXXJM4U5jxziBmGOdUXRPH0AjTgpJtcKzh0SwpTF6xRvssNX2+/NTsz9QQe9jy1CemfF67sUsvHAnk6RpJIVmoubimTtN3CLNsMqF1VML8INaqe5vUh0lGv9/v/qDPf+t2h7fXepQsU3ma5Ex0PLKiwDN15XkuJWXboupadIL0oTdy957D06Px5t1hQs1TCr4vrjSYr7mT81hx7yj7xlmBFIJMStIsJzc0WoMYSxd3Tde9cqVFkktcy2BnkFB2LVY0Qasf8+r1Xb5+bp5vfGJhX2E/O6fO4biw3yvGO8GxtpWhCZ48VnvkGsPjODHlc7sdTLxSZspql7VU9ybyNr+o8bhwHBJ7E8kgzkY4bTGZy3/QlbtKrm2yvGC54RNnqhfQ8K1J43tvQlxp+h9aQ+xh6/HXPIuvPj3Ht85vIgTMVmzWuhFhnNP0LHaDmIpj8JlTzfse5/1aq97vNe/3uz/o8y9v9dGA6bJNlOUEcYFtCLJCYOoCfaS2W7JNSo5abHTD5KETtw7KgXxqRX3ugyTKcXznwtYEHnRNnYpn0OonDNOckqMTphmGdvdvt92PFLExyrF0DVPXqDqKrHliyqfsmA9U2PfGwV5iw7eUHe5C9Wf2Ofmw4qh+595Cd3mrj0Tw9EL1kS10H2YIKeX9n/UxjxdffFH++Mc/fuDn7+1x3GwNGcYZEsHZufIEI7YM7YHkF260hhMXuYZnThqdH8WN8GE0+B/0PdY6Idd3lH2sBE7PlPjKU3M/N7bv+/3uD/L8P/zeVbZ7EVkh2RkoklqU5qy2h8yUHWarDpauOB0rUz66UJyKZxarH4pUx4cVr99sT3YE33png4tbPTa6EYMoo+yYzJQtPnt6mt/73Il9r/ujV29ycbPPZj/GNjQGUUo3ytAFPDVfpuZafO7M9D3Jf3vjIGl1ux/zvUvbVD2Tpbo3kqV5f/fbhx2HEWv3apf9oocQ4jUp5YsHH3+84zgk9q7gPNugG6UTFrSSRX7wlftK0+d3PnXsoSfww+Jh6vEffI9HKTHC+//uD/L8MzMlxfwPEpq+RTdI6YUpx6dKfPZUU1mtJhnH6x4aSl9pqe49ck3S8Q5rEGUMEgXLWoaGb5t4ls4nlmocG0nLj6MTKKLjejdksxuBUJBVEKUUUvLuRp+n5srsDpIJQe9+18RhvcSkUO6P6YiXcnauck8VgIcd9xu0+E81HmrhEEJcB/oo1etMSvmiEOKfA78FJMAV4O9KKTtHvF4HfgysSim/MXrsBPD/AE3gNeDvSCk/9Ktqb0Icr0Y/qLnKR5HAH8fDj+eWavTClLVR8vRsnU9N1fnt5xdZafoT74nLW33CrHhkYYvxwugv396g4pgIBKdnytQ9i/+/vXsPjuuuDjj+Pft+SKuVJfkhK37EdgIhMapjQgjB1KHQNIUknYSGNhRSMpOBlLYzHV6ZzHQoLR0Y2kJnMi3NQEtpQ8uUNmmTQGoDyYQETHBcO7ZDcBw7iWU7lqy3tO/d0z/uXbFWdqVdSbvSSuczs6Pdvffunl1f77n3d3+/8xtLZugfT7Prkq6p9YtH3R3RIDf1rudbP32VY+fGiAR8+HweutuC+Dwezo6m+OEL59jYEcHrkVmbXctdS+yM+hlN5dns9009P1MVgHpr5jnQ66kRZxy7VfV8yeO9wD2qmhORLwL3AJ+usO0fAz8HYiXPfRH4sqr+u4h8FbgT+Ps6xD3FfvgNOPvBtRWK0xWXL/WBW0XxSIC1bSHeuC7GRDo3Y++n0qPufEHpag0Q9rVxdjxNOptnYNwZZFgowLp4HlUllc3PmjTLXUuMhQNMpJOkss51lPOT6RmrANRbM8+BXk8zd3OoA1Xdo6rFKmv7gLK1r0WkB/hN4GslzwlwHfAd96l/Bm6uX7TGXKh4tP7OS1c3fTt38UexNeTn0rUxrtzYzoaO6OsGm44ls1MX04/3j5PMFAi5zVrxSMApO5Nx5jZRdUrqqAonz0/O+P6bO6NTTb9RtwuwR5yxQX63dExpyZfFUBqjqk7dX2rNj41W7zMOBfaIiAL/oKr3T1v+EeDbFbb9CvApoLXkuQ5gpCTx9AHry20sIncBdwFs2LCh3CpLRiMuYhszXbW9yUqPul8dSrKmLUT/WIqQ38vQZAYPziyJ0aCfglu0czydrdibrLRJL5HJEwl4aQn6yalOTWrWFvG/ruTLYpitB99KVe/Eca2qnhaR1cBeEXlBVZ8EEJF7gRzwwPSNROS9QL+qPisivzqXN3aT1P3g9Kqa6weot4WsrGmWtqV2gFDtj2JpgkEVL8qqaIDOliCnhpJEQ8UJrAp4RHjjulbGU7myzTnF+k59w0n8HmEylePVwQQbVkV456WrLyhxslR+oK2p+vXqmjhU9bT7t19EHgSuAp4UkTuA9wLv0vL9gd8O3CgiNwAhICYi/wr8HhAXEZ971tEDnK7nZ6g367WxtJUbZzBTKZDi0fT0yqZQfi6OxT5AqOZHsTTBxKMBzo2l2NQRpT0S4PxEmtPDSbweWBsLOVPlpnOEA96yzTnF+k4Br4fXxlIEvV66WoIMJ7L89OQQt17ZsySSxUJbagcN81W3axwiEhWR1uJ94D3AERG5HqcJ6kZVTZTbVlXvUdUeVd0EfAD4oap+0E0yjwO3uqt+GPjven2GRihtPy4K+71Laravlap4NpjJFWiPBBiayPDQgT6GJjK0RwJT5WdGSgoGPvXiAM+fGSXs8xDy+Th6ZowfvTjA3udfY9+JQR4+dIa9z7/GaCI7VXq7GRSTxwev3shbN68i6PMwmsywbU0r69pCvG1LJ91tIcZSOZK5ArfsKJ8AivWdxpJZgl4vAZ+HgN+LRyBfKDTN91GL6fvR9P2mGdXz4vga4CkROQQ8Azyqqo8B9+Fct9grIgfdnlGISLeIfLeK1/008CcichznmsfX6xN+YxTbj0tZr42lofRsUEQYTmRoC/sZTmQQ+WXZ9+KP3cnzk4wksrSFA4QDfsIBH/FwgBP9kzx86AyFAnS2BEhnlSd+0c9Iov6jyhdasWfZm9a3cXFXC7su6eJju7fS0x4h4POwfX0bd+/eyhUVSm4U6zuNpnL4vU79tmyugM/rYVUk0HTfRzWm70fT95tmVLemKlU9Aby5zPNbK6x/BrihzPNPAE9Me92rFirOxVbv0iBm7qb34Z9IOyOrx6fNJX7KrQz79IsDDCcybO5smVoe9Hk4PjBOxOcl6PPgEQ8tIed47cCrw1x/+boGfZqFU655q1KimK5Y3ymfLzCRzhHwCZPpHKtjIVZFg8vygGk5jgVpeHdcc6FiE0DA52E4kZkqrdDM7Z/LxfSzwZagM8K5dC7xgfE0Z935PNbEQiDC8f5xJjM5JjM5jvePc2owSTAgjKWyZHJ5FMXnVfrHUiuuW2exvtPbtnYwkc5OTYt86ZrY1Jwjy81ybFWwkiNLgPXaWJqmnw22RwKcHk5w0aro1FziJwcn2NzRQiTgY317xJ26Ncnxc2MUFPL5AqtjQfIF4di5cXIFJRLwsiYW4oqethV5gBCPBLjhim6u2dK5rC4YV7IcWxWsyKExM5itV9WZkSTxsJ+zoykm0jk8Aol0ngOnhlgbC7N1dQupXJ6H/+8sYb+H1rAXv8fLaCrLH75rG9ds7Zo9CDNvi92rabHff66syKExczDbBEvjqSyHT4/QFg5MTZOK5NjeE2fXttWICHuOvsZl61s5P5FhNJnl4s4w29a2cmoo2eiPsyLVOlaqHj/yy61VwRKHMfOkCIlMntdGk4wmc+RVWdsWmhptPZxIs64tTFdLGK9H2NQZpVAocHbUEkcjVDtWqjgG5ycvDVIoKG0RZ9KrvuEE7yiZxdFY4jBmXlTh4s4oz5wcZCSRZTiZIZ9XTg1OEPF7uay7jXg4wMhkBr/fy8Y252xlPJV93Qx7pj6q6dVUHIPzs5NDnBycJJNTMvk8HdEA7dEAXo9wwxXdjQ59ybJeVcbMQyzsZ2A8TXskwEQmT1soQFdLiEjIz6G+UYYmM2zsjJDK5WkP+wn7PYwm0owms7POjmgWRjW9mp7rG6FvOMGZkSQT6SwD4ymGJjIMjKcZGEvx/Z+fa+oBewvNEocx87C5M8rQZIaXBiYJ+734vR5yqmxcFSUadCoAvH/nBj62exsdLUHOjiYJB3zcvKNn0WZHXGmqqXD7Yv8EsZCfiUyOkcksfq+HSNBLIlMgmckz5s4dbxzWVGXMPMQjAXo3xHmub4Ro0IfXA+taw3iBjkiAgfEU4FxQt0SxOKop5igo4Ixkz6vgEXHaIRXwePAKy3JU+1xZ4jBmnrb3xOlZFYYCtLcEyeYKpHN52qMBOqLBxQ7PMHuvpq2rWzl6ZoyWoI9oMEs2WyCnSlvITyzoJR4NNPWAvYVmTVXGzFM8EuCWHReRzBXoH0/hEWiPBsjmCnYdo0ls74nT0x6mOx4mHvYR9HuIh/1ctCpMLOxfknPHLyY74zBmAVzRE+fu3X72vTTIwHiKjmiQq7d0WPNUkyiWQmkL+4kGfUymcuQLitcjdLUGeV/v+pq741Yqsb8cuvVa4jBmgdh1jOZWnDN+e0983gMAi917+4YTxEJ+QDh6ZozRZHZZjAmxxGGMMSUWYpR3aYn9kN/5mRURRpPZZTFJm13jMMaYBTaWzJLNFwj6fllJOejzTE1i1ewscRhjzAKLhf34vR6ndpkrnSvg88my6J1licMYYxaYc13Ez2gyQzKTJZnJMZJ0ZpBcDr2zLHEYY8wCK06xe1l3G8lcgVQux5u6Y8viwjjYxXFjjKmLYi+tXZesXuxQFpydcRhjjKmJJQ5jjDE1scRhjDGmJpY4jDHG1KSuiUNEXhaRwyJyUET2u899SUReEJHnRORBEYmX2S4kIs+IyCEROSoif1ay7BsictJ9zYMi0lvPz2CMMeZCjTjj2K2qvaq60328F7hcVbcDx4B7ymyTBq5T1TcDvcD1InJ1yfJPuq/Zq6oH6xq9McaYCzS8O66q7il5uA+4tcw6Cky4D/3uTesfnTFmpXllcHKqqnFXa8iqGleh3mccCuwRkWdF5K4yyz8CfK/chiLiFZGDQD+wV1V/WrL4825T15dFpOxMOSJyl4jsF5H9AwMD8/0cxphl6JXBSR460EffcIJXByd57OhZPvfwUX583H4zZlLvxHGtqu4AfgP4AxHZVVwgIvcCOeCBchuqal5Ve4Ee4CoRudxddA/wBuAtwCrg0xW2v19Vd6rqzq6urgX7QMaY5WPfS4PkCsqxc+NkC7A2FsIvwtefPsErgzbHeCV1TRyqetr92w88CFwFICJ3AO8FbnebpWZ6jRHgceB69/FZdaSBfyq+pjHG1GpgPMW50SSRgI9IwIdHPLS3BCjknaRiyqtb4hCRqIi0Fu8D7wGOiMj1wKeAG1U1UWHbrmJvKxEJA+8GXnAfr3P/CnAzcKRen8EYs7x1tYYYmMwQ8v/ypzCVVTpbgwyMpxYxsqWtnmcca4CnROQQ8AzwqKo+BtwHtAJ73e60XwUQkW4R+a677TrgcRF5DvgZzjWOR9xlD4jIYeAw0An8RR0/gzFmGbt6SwceEYYnMhS0QCKTJ5nJsS4Wpqs1tNjhLVkyS0vRsrBz507dv3//YodhjFmCfnx8gK8/fYJCHjpbg6yLhfF64OYdPSu+d5WIPFsylGKKVcc1xqxo12ztYn17xLrk1sAShzFmxdvYEbVEUQOrVWWMMaYmljiMMcbUxBKHMcaYmljiMMYYUxNLHMYYY2qyIsZxiMgA8MocNu0Ezi9wOI3QrHFD88berHFD88berHFD88S+UVVfV+xvRSSOuRKR/eUGvyx1zRo3NG/szRo3NG/szRo3NHfsYE1VxhhjamSJwxhjTE0scczs/sUOYI6aNW5o3tibNW5o3tibNW5o7tjtGocxxpja2BmHMcaYmljiMMYYU5MVmThE5B9FpF9EjpQ89+ci8pw7udQeEemusO1jIjIiIo+UW15Pc41bRHpF5CcictRd97bGRj6v2DeKyAF3naMi8tFmiLtk3ZiI9InIfY2J+IL3ns9+nnfXOSgi/9O4qOcd9wZ3+c9F5HkR2dSouN33n+t+vrvk+z4oIikRubmRsddEVVfcDdgF7ACOlDwXK7n/R8BXK2z7LuB9wCPNEjdwCbDNvd8NnAXiTRJ7AAi691uAl4HupR53yfK/Bb4F3Ncs+4u7bKLR8S5Q3E8A7y7ZXyLNEnvJOquAoUbHXsttRZ5xqOqTOP8wpc+NlTyMAmV7DajqD4Dx+kVX2VzjVtVjqvqie/8M0A+8bjRoPc0j9oyqpt2HQRp8ljyffUVErsSZQnlP3QKcwXxiX0xzjVtELgN8qrrX3WZCVRP1jHW6BfrObwW+1+jYa2ETOZUQkc8DHwJGgd2LHE7VaolbRK7COYp/qQGhzaqa2EXkIuBRYCvwSTf5LarZ4hYRD/DXwAeBX2tsdDOrcn8Jich+IAd8QVUfalR8lVQR9yXAiIj8F7AZ+D7wGVXNNy7K8mr8bfkA8Dd1D2oeVuQZRyWqeq+qXgQ8AHx8seOpVrVxi8g64F+A31fVQqPim0k1savqKVXdjpM4PiwiaxoZY4WYZov7buC7qtrX2MhmV+X+slGdkhi/C3xFRLY0LMAKqojbB7wD+ATwFuBi4I6GBTiDGv+PXgH8b6NimwtLHOU9ANyy2EHMQcW4RSSGc9R+r6rua2hU1Zn1O3fPNI7g/DgsFZXifhvwcRF5Gfgr4EMi8oVGBlaFit+5qp52/57AuW7wK40La1aV4u4DDqrqCVXGnOOzAAADCElEQVTNAQ/hXG9YSmbbz38beFBVsw2KZ04scbhEZFvJw5uAFxYrllpUE7eIBIAHgW+q6ncaFdtsqoy9R0TC7v124FrgF42JsLxq4lbV21V1g6puwjkC/qaqfqZBIVZU5XfeLiJB934n8Hbg+cZEWF6V/z9/BsRFpHj97joWOW6o+bfld4B/q29EC2Cxr84vxg3nH+YskMU5SrkT+E+co9nngIeB9e66O4GvlWz7I2AASLrb/vpSjxunnT0LHCy59TbDdw68211+yP17VzPEPe017mBxelXN9Tu/BjjsfueHgTubIe5p+8th4BtAoIli3wScBjyN3ldqvVnJEWOMMTWxpipjjDE1scRhjDGmJpY4jDHG1MQShzHGmJpY4jDGGFMTSxzGzIGITMyyPC4id5c87haR77j3e0Xkhjm852dF5BO1R2vMwrLEYUx9xHHKjgDOqHdVvdV92AvUnDiMWSoscRgzDyLSIiI/cOcMOSwiN7mLvgBscedW+JKIbBKRI+4o/s8Bt7nLbpt+JuGut8m9f6+IHBORp4BLS9bZIs7cMM+KyI9E5A0N+9BmxbPquMbMTwr4LVUdc8tz7HMnPvoMcLmq9gIUE4GqZkTkT4Gdqvpxd9lny72wW5b9AzhnKD7gAPCsu/h+4KOq+qKIvBX4O5wSG8bUnSUOY+ZHgL8UkV1AAViPMwfHQngHTsG7BEBxJj4RacEpC/IfIlJcN7hA72nMrCxxGDM/t+NMinWlqmbdarihGl8jx4XNxrNt7wFGimczxjSaXeMwZn7agH43aewGNrrPjwOtFbaZvuxl3PLfIrIDZxIigCeBm0UkLCKtOFMWo86McidF5P3uNiIib164j2TMzCxxGDM/DwA7ReQwzgxvLwCo6iDwtHuh+0vTtnkcuKx4cRyneuoqETmKM8nPMfc1DgDfxqlS+z2csuFFtwN3isgh4ChOuW5jGsKq4xpjjKmJnXEYY4ypiSUOY4wxNbHEYYwxpiaWOIwxxtTEEocxxpiaWOIwxhhTE0scxhhjavL/08eABd3MxLwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Location Distribution Unclustered\")\n",
    "plt.ylabel('longitude')\n",
    "plt.xlabel('latitude')\n",
    "plt.scatter(X.T[0], X.T[1], alpha=0.2)\n",
    "plt.savefig('loc_unclustered.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label = hdbscan.HDBSCAN(min_cluster_size=450, min_samples=15).fit_predict(cluster_data)\n",
    "label = cluster.KMeans(n_clusters=10).fit_predict(X)\n",
    "max(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 1 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-757145b683a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_clusters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcluster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKMeans\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'n_clusters'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\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-38-00e944076878>\u001b[0m in \u001b[0;36mplot_clusters\u001b[0;34m(data, algorithm, args, kwds)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kwds\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      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'loc_clustered_1.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\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[0;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clusters(X[:,:-1], cluster.KMeans, (), {'n_clusters':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude_x</th>\n",
       "      <th>latitude_x</th>\n",
       "      <th>price_x</th>\n",
       "      <th>loc_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.446296</td>\n",
       "      <td>52.481415</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.417086</td>\n",
       "      <td>52.606109</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.502764</td>\n",
       "      <td>52.446144</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.473918</td>\n",
       "      <td>52.516235</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.383814</td>\n",
       "      <td>52.511494</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude_x  latitude_x  price_x  loc_class\n",
       "0    13.446296   52.481415     35.0          0\n",
       "1    13.417086   52.606109     80.0          1\n",
       "2    13.502764   52.446144     50.0          2\n",
       "3    13.473918   52.516235     80.0          0\n",
       "4    13.383814   52.511494    180.0          1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label = hdbscan.HDBSCAN(min_cluster_size=450, min_samples=15).fit_predict(cluster_data)\n",
    "clusterer = cluster.KMeans(n_clusters=4)\n",
    "X = clusterer.fit_predict(X)\n",
    "# clusterer.fit(X)\n",
    "# Clusters = clusterer.predict(X)\n",
    "data['loc_class'] = X\n",
    "big_data['loc_class'] = X\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13.4462961  52.48141516]\n",
      " [13.41708604 52.60610923]\n",
      " [13.50276362 52.44614449]\n",
      " ...\n",
      " [13.29098912 52.4976456 ]\n",
      " [13.38508885 52.46504928]\n",
      " [13.58954646 52.46768108]]\n"
     ]
    }
   ],
   "source": [
    "data = data[[var31, var32, 'loc_class', var10]]\n",
    "X = data.iloc[:, :-2].values\n",
    "print(X)\n",
    "y = data.iloc[:, -2].values\n",
    "# X = ct.fit_transform(X)\n",
    "# X = X.todense()\n",
    "# X = np.array(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.9935340846111214\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors=65, weights='distance')\n",
    "classifier = classifier.fit(X_train, y_train)\n",
    "print(classifier.score(X_train, y_train))\n",
    "print(classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.9961204507666728\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators=500, min_samples_leaf=1)\n",
    "classifier = classifier.fit(X_train, y_train)\n",
    "print(classifier.score(X_train, y_train))\n",
    "print(classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(max_depth=2)\n",
    "classifier = classifier.fit(X_train, y_train)\n",
    "print(classifier.score(X_train, y_train))\n",
    "print(classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(classifier, X_train, \n",
    "                          y_train, scoring='accuracy', cv=5)\n",
    "print(scores)\n",
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After clustering of property types\n",
    "big_data.to_csv('airbnb_coded.2.0.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor as RFR\n",
    "rf = RFR(n_estimators=500, min_samples_leaf=1)\n",
    "rf = rf.fit(X_train, y_train)\n",
    "rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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