ml_code/Model_Building_95_with_pipelines.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9d2fe4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
    "\n",
    "\n",
    "# Logistic Regression, SVM, Decision Tree, Boost, and a NN\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb\n",
    "# import tensorflow as tf\n",
    "# from tensorflow.keras import Sequential\n",
    "# from tensorflow.keras.layers import Dense\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "thresh_ub = 0.95\n",
    "thresh_lb = 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1713874",
   "metadata": {},
   "source": [
    "# Cleaning and helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8633139e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
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      "text/plain": [
       "      Unnamed: 0   Latitude   Longitude  Solar_farm_present  land_cover  \\\n",
       "0              0  38.638561 -121.322253                   0         1.0   \n",
       "1              1  39.146968 -121.473261                   0         1.0   \n",
       "2              2  33.632707 -115.734961                   0         1.0   \n",
       "3              3  32.850543 -115.583953                   0         1.0   \n",
       "4              4  33.886911 -118.237378                   0         1.0   \n",
       "...          ...        ...         ...                 ...         ...   \n",
       "4995        4995  34.414872 -120.502496                   0         0.0   \n",
       "4996        4996  39.029643 -122.875477                   0         0.0   \n",
       "4997        4997  35.157928 -120.739794                   0         0.0   \n",
       "4998        4998  33.613153 -114.526898                   0         0.0   \n",
       "4999        4999  38.208370 -122.336163                   0         0.0   \n",
       "\n",
       "      slope  elevation feature_violated  solrad_annual  \\\n",
       "0       2.0       37.0              NaN       5.732396   \n",
       "1       0.0       27.0              NaN       5.648400   \n",
       "2       2.0      561.0              NaN       6.640421   \n",
       "3       0.0      -18.0              NaN       6.666664   \n",
       "4       1.0       27.0              NaN       5.956900   \n",
       "...     ...        ...              ...            ...   \n",
       "4995    0.0        0.0       land_cover       5.789799   \n",
       "4996    0.0      404.0       land_cover       5.817975   \n",
       "4997    0.0        0.0       land_cover       5.749070   \n",
       "4998    2.0       83.0       land_cover       6.543961   \n",
       "4999    1.0        1.0       land_cover       5.545259   \n",
       "\n",
       "                                             ac_monthly  \\\n",
       "0     [84883.06914835521, 110107.3007904001, 142457....   \n",
       "1     [78866.77428118252, 106080.1644945415, 124079....   \n",
       "2     [142033.6075897373, 135669.0721331215, 169827....   \n",
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       "4     [123194.0549525465, 121968.199271841, 148417.1...   \n",
       "...                                                 ...   \n",
       "4995  [134294.313590256, 118366.24124287, 149754.498...   \n",
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       "4997  [113917.7039648926, 111119.0424322115, 148308....   \n",
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       "4999  [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "0     [104.6620668695758, 137.671252552197, 181.5298...   \n",
       "1     [99.45905129543517, 133.9026315700545, 159.580...   \n",
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       "4     [154.0335887228755, 155.190735588352, 191.3896...   \n",
       "...                                                 ...   \n",
       "4995  [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "4996  [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "4997  [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "4998  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "4999  [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "0     [3.376195705470185, 4.916830448292751, 5.85580...   \n",
       "1     [3.208356493401135, 4.782236841787659, 5.14776...   \n",
       "2     [5.706598457761423, 6.062095675076274, 7.13041...   \n",
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       "...                                                 ...   \n",
       "4995  [5.329879132134445, 5.185863067001377, 6.07044...   \n",
       "4996  [3.504699058170492, 5.022799443149995, 5.40866...   \n",
       "4997  [4.514956006137627, 4.956770937932831, 6.00852...   \n",
       "4998  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "4999  [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \\\n",
       "0     [89177.54404926489, 115410.2864073284, 149285....  1.598632e+06   \n",
       "1     [82933.84836181221, 111212.6071361726, 130119....  1.547354e+06   \n",
       "2     [148606.6602928677, 142527.3934126461, 177911....  1.835391e+06   \n",
       "3     [148922.2969679596, 152201.7710591838, 177685....  1.802739e+06   \n",
       "4     [128993.2732027951, 127672.1091778474, 155507....  1.671830e+06   \n",
       "...                                                 ...           ...   \n",
       "4995  [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06   \n",
       "4996  [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06   \n",
       "4997  [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06   \n",
       "4998  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06   \n",
       "4999  [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06   \n",
       "\n",
       "      min_distance  \n",
       "0           9.1889  \n",
       "1          18.0916  \n",
       "2          51.7247  \n",
       "3           9.6227  \n",
       "4           1.6499  \n",
       "...            ...  \n",
       "4995       79.3772  \n",
       "4996      106.8099  \n",
       "4997       50.5146  \n",
       "4998      167.7547  \n",
       "4999       13.9789  \n",
       "\n",
       "[5000 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_without_dist_thresh = pd.read_csv('../new_data/threshold_95percentile/ca_data_version_5.csv')\n",
    "#solar_without_dist_thresh.drop(columns='Unnamed: 0', index=1, inplace = True)\n",
    "solar_without_dist_thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68e47c35-4440-4b6d-bcf7-0e6347b39f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "376.63874999999996"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_thresh = solar_without_dist_thresh['min_distance'].quantile(0.95)\n",
    "distance_thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d09ec333-0f01-4561-be7e-93c50598e0ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>35.412132</td>\n",
       "      <td>-115.389800</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>864.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.527775</td>\n",
       "      <td>[141434.1413381812, 136124.0954096352, 166817....</td>\n",
       "      <td>[173.2232671149272, 168.5816319217624, 213.397...</td>\n",
       "      <td>[5.587847326287974, 6.02077256863437, 6.883804...</td>\n",
       "      <td>[148162.7379302353, 142869.4070327808, 174890....</td>\n",
       "      <td>1.832379e+06</td>\n",
       "      <td>405.5617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>36.878690</td>\n",
       "      <td>-117.849072</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.386666</td>\n",
       "      <td>[128492.3018898137, 127811.1756327208, 158042....</td>\n",
       "      <td>[158.4554965959694, 159.1760700017676, 200.688...</td>\n",
       "      <td>[5.111467632128046, 5.684859642920272, 6.47383...</td>\n",
       "      <td>[134516.1812690618, 134014.1473248255, 165970....</td>\n",
       "      <td>1.777724e+06</td>\n",
       "      <td>442.5785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>161</td>\n",
       "      <td>35.509902</td>\n",
       "      <td>-115.993832</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.607981</td>\n",
       "      <td>[129683.3698839003, 139460.1122901668, 169655....</td>\n",
       "      <td>[159.6131821379072, 173.5768544970778, 217.711...</td>\n",
       "      <td>[5.148812327029266, 6.199173374895635, 7.02295...</td>\n",
       "      <td>[136065.5127620298, 146416.8431842005, 177869....</td>\n",
       "      <td>1.842138e+06</td>\n",
       "      <td>410.2260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>164</td>\n",
       "      <td>35.568565</td>\n",
       "      <td>-116.166412</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.505455</td>\n",
       "      <td>[134326.0023383891, 134152.3462391439, 164120....</td>\n",
       "      <td>[167.8295481256568, 169.7495607428846, 213.110...</td>\n",
       "      <td>[5.413856391150221, 6.062484312245878, 6.87454...</td>\n",
       "      <td>[140555.8381742158, 140672.1768397138, 171810....</td>\n",
       "      <td>1.789185e+06</td>\n",
       "      <td>500.2115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>204</td>\n",
       "      <td>35.685889</td>\n",
       "      <td>-115.497663</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.480138</td>\n",
       "      <td>[135530.092523047, 133629.5490529925, 164006.9...</td>\n",
       "      <td>[165.7934031766128, 165.7932777545079, 208.392...</td>\n",
       "      <td>[5.348174296019769, 5.921188491232424, 6.72232...</td>\n",
       "      <td>[142044.2965130362, 140297.3994964414, 172144....</td>\n",
       "      <td>1.816785e+06</td>\n",
       "      <td>425.1674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>1942</td>\n",
       "      <td>36.448500</td>\n",
       "      <td>-116.490000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.518310</td>\n",
       "      <td>[127434.48650282, 127146.3200349195, 166299.66...</td>\n",
       "      <td>[157.3039646189393, 158.295784593015, 213.6623...</td>\n",
       "      <td>[5.074321439320622, 5.653420878321966, 6.89233...</td>\n",
       "      <td>[133494.7490928563, 133395.7948534102, 174265....</td>\n",
       "      <td>1.808813e+06</td>\n",
       "      <td>387.6072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2043</th>\n",
       "      <td>2043</td>\n",
       "      <td>36.076972</td>\n",
       "      <td>-116.036977</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.660173</td>\n",
       "      <td>[143665.4488511992, 133907.9983662198, 168385....</td>\n",
       "      <td>[177.7666822999402, 168.8681547613739, 217.454...</td>\n",
       "      <td>[5.734409106449684, 6.031005527191923, 7.01465...</td>\n",
       "      <td>[150281.5655128679, 140817.5299301871, 176460....</td>\n",
       "      <td>1.843130e+06</td>\n",
       "      <td>548.5578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2089</th>\n",
       "      <td>2089</td>\n",
       "      <td>36.311621</td>\n",
       "      <td>-116.856734</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-83.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.525715</td>\n",
       "      <td>[133666.2755004521, 135875.8489995141, 156723....</td>\n",
       "      <td>[167.9954245754158, 172.5019522388283, 205.393...</td>\n",
       "      <td>[5.419207244368253, 6.160784008529583, 6.62560...</td>\n",
       "      <td>[139845.2619451468, 142101.8083229327, 164201....</td>\n",
       "      <td>1.775751e+06</td>\n",
       "      <td>440.7516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>2101</td>\n",
       "      <td>36.213851</td>\n",
       "      <td>-116.468428</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.551510</td>\n",
       "      <td>[136397.2879423771, 135006.4917563437, 163593....</td>\n",
       "      <td>[168.5101375027884, 169.3148019333693, 210.483...</td>\n",
       "      <td>[5.435810887186722, 6.046957211906046, 6.78977...</td>\n",
       "      <td>[142762.4383323136, 141765.4960606013, 171427....</td>\n",
       "      <td>1.823559e+06</td>\n",
       "      <td>379.9231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2108</th>\n",
       "      <td>2108</td>\n",
       "      <td>36.018309</td>\n",
       "      <td>-116.187985</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>643.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.518612</td>\n",
       "      <td>[141469.6221096348, 131754.7160368475, 164883....</td>\n",
       "      <td>[176.3470005051659, 165.3940000143925, 213.014...</td>\n",
       "      <td>[5.688612919521479, 5.906928571942588, 6.87144...</td>\n",
       "      <td>[148036.9041841091, 138093.5832088081, 172705....</td>\n",
       "      <td>1.805058e+06</td>\n",
       "      <td>558.8744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0   Latitude   Longitude  Solar_farm_present  land_cover  \\\n",
       "6              6  35.412132 -115.389800                   0         1.0   \n",
       "140          140  36.878690 -117.849072                   0         1.0   \n",
       "161          161  35.509902 -115.993832                   0         1.0   \n",
       "164          164  35.568565 -116.166412                   0         1.0   \n",
       "204          204  35.685889 -115.497663                   0         1.0   \n",
       "...          ...        ...         ...                 ...         ...   \n",
       "1942        1942  36.448500 -116.490000                   0         1.0   \n",
       "2043        2043  36.076972 -116.036977                   0         1.0   \n",
       "2089        2089  36.311621 -116.856734                   0         1.0   \n",
       "2101        2101  36.213851 -116.468428                   0         1.0   \n",
       "2108        2108  36.018309 -116.187985                   0         1.0   \n",
       "\n",
       "      slope  elevation feature_violated  solrad_annual  \\\n",
       "6       2.0      864.0              NaN       6.527775   \n",
       "140     0.0      639.0              NaN       6.386666   \n",
       "161     2.0      860.0              NaN       6.607981   \n",
       "164     1.0      232.0              NaN       6.505455   \n",
       "204     2.0      877.0              NaN       6.480138   \n",
       "...     ...        ...              ...            ...   \n",
       "1942    0.0      677.0              NaN       6.518310   \n",
       "2043    0.0      769.0              NaN       6.660173   \n",
       "2089    0.0      -83.0              NaN       6.525715   \n",
       "2101    2.0      780.0              NaN       6.551510   \n",
       "2108    0.0      643.0              NaN       6.518612   \n",
       "\n",
       "                                             ac_monthly  \\\n",
       "6     [141434.1413381812, 136124.0954096352, 166817....   \n",
       "140   [128492.3018898137, 127811.1756327208, 158042....   \n",
       "161   [129683.3698839003, 139460.1122901668, 169655....   \n",
       "164   [134326.0023383891, 134152.3462391439, 164120....   \n",
       "204   [135530.092523047, 133629.5490529925, 164006.9...   \n",
       "...                                                 ...   \n",
       "1942  [127434.48650282, 127146.3200349195, 166299.66...   \n",
       "2043  [143665.4488511992, 133907.9983662198, 168385....   \n",
       "2089  [133666.2755004521, 135875.8489995141, 156723....   \n",
       "2101  [136397.2879423771, 135006.4917563437, 163593....   \n",
       "2108  [141469.6221096348, 131754.7160368475, 164883....   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "6     [173.2232671149272, 168.5816319217624, 213.397...   \n",
       "140   [158.4554965959694, 159.1760700017676, 200.688...   \n",
       "161   [159.6131821379072, 173.5768544970778, 217.711...   \n",
       "164   [167.8295481256568, 169.7495607428846, 213.110...   \n",
       "204   [165.7934031766128, 165.7932777545079, 208.392...   \n",
       "...                                                 ...   \n",
       "1942  [157.3039646189393, 158.295784593015, 213.6623...   \n",
       "2043  [177.7666822999402, 168.8681547613739, 217.454...   \n",
       "2089  [167.9954245754158, 172.5019522388283, 205.393...   \n",
       "2101  [168.5101375027884, 169.3148019333693, 210.483...   \n",
       "2108  [176.3470005051659, 165.3940000143925, 213.014...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "6     [5.587847326287974, 6.02077256863437, 6.883804...   \n",
       "140   [5.111467632128046, 5.684859642920272, 6.47383...   \n",
       "161   [5.148812327029266, 6.199173374895635, 7.02295...   \n",
       "164   [5.413856391150221, 6.062484312245878, 6.87454...   \n",
       "204   [5.348174296019769, 5.921188491232424, 6.72232...   \n",
       "...                                                 ...   \n",
       "1942  [5.074321439320622, 5.653420878321966, 6.89233...   \n",
       "2043  [5.734409106449684, 6.031005527191923, 7.01465...   \n",
       "2089  [5.419207244368253, 6.160784008529583, 6.62560...   \n",
       "2101  [5.435810887186722, 6.046957211906046, 6.78977...   \n",
       "2108  [5.688612919521479, 5.906928571942588, 6.87144...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \\\n",
       "6     [148162.7379302353, 142869.4070327808, 174890....  1.832379e+06   \n",
       "140   [134516.1812690618, 134014.1473248255, 165970....  1.777724e+06   \n",
       "161   [136065.5127620298, 146416.8431842005, 177869....  1.842138e+06   \n",
       "164   [140555.8381742158, 140672.1768397138, 171810....  1.789185e+06   \n",
       "204   [142044.2965130362, 140297.3994964414, 172144....  1.816785e+06   \n",
       "...                                                 ...           ...   \n",
       "1942  [133494.7490928563, 133395.7948534102, 174265....  1.808813e+06   \n",
       "2043  [150281.5655128679, 140817.5299301871, 176460....  1.843130e+06   \n",
       "2089  [139845.2619451468, 142101.8083229327, 164201....  1.775751e+06   \n",
       "2101  [142762.4383323136, 141765.4960606013, 171427....  1.823559e+06   \n",
       "2108  [148036.9041841091, 138093.5832088081, 172705....  1.805058e+06   \n",
       "\n",
       "      min_distance  \n",
       "6         405.5617  \n",
       "140       442.5785  \n",
       "161       410.2260  \n",
       "164       500.2115  \n",
       "204       425.1674  \n",
       "...            ...  \n",
       "1942      387.6072  \n",
       "2043      548.5578  \n",
       "2089      440.7516  \n",
       "2101      379.9231  \n",
       "2108      558.8744  \n",
       "\n",
       "[61 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Look for locations that don't have solar farms and have no feature violated\n",
    "long_distance_constraint = ((solar_without_dist_thresh['Solar_farm_present']==0) & (solar_without_dist_thresh['feature_violated'].isnull()) \\\n",
    "                                            & (solar_without_dist_thresh['min_distance'] >= distance_thresh))\n",
    "long_distance = solar_without_dist_thresh.loc[long_distance_constraint]\n",
    "long_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12f75e79-e2f3-41ca-8a69-6d428a10beb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_without_dist_thresh.loc[long_distance_constraint,'Solar_farm_present'] = 0\n",
    "solar_without_dist_thresh.loc[long_distance_constraint,'feature_violated'] = 'min_distance'\n",
    "solar_without_dist_thresh.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "caeb751b-07cc-4715-8e29-9f000d1d0412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 15)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "solar_without_dist_thresh[solar_without_dist_thresh['feature_violated'] == 'slope'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d18ba6-04b2-4ed0-a950-d6c0b82f7993",
   "metadata": {},
   "source": [
    "### Remove all rows where no feature is violated for solar farms are not present and save this file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "597d5b0b-af6c-41a1-b21e-e21b9ee343b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2059, 15)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_feature_violated = solar_without_dist_thresh.loc[(solar_without_dist_thresh['feature_violated'].isnull()) & (solar_without_dist_thresh['Solar_farm_present']==0)]\n",
    "no_feature_violated.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64a4363d-400e-4cbf-a115-2256b18eb86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3729</th>\n",
       "      <td>3729</td>\n",
       "      <td>36.177601</td>\n",
       "      <td>-120.109001</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.988686</td>\n",
       "      <td>[94729.03706566579, 112899.4317807716, 148815....</td>\n",
       "      <td>[117.2264253417953, 143.5962623979706, 193.969...</td>\n",
       "      <td>[3.781497591670815, 5.128437942784665, 6.25709...</td>\n",
       "      <td>[99456.86078053594, 118253.1308946665, 155783....</td>\n",
       "      <td>1.643483e+06</td>\n",
       "      <td>51.2865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3730</th>\n",
       "      <td>3730</td>\n",
       "      <td>33.265202</td>\n",
       "      <td>-116.328003</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.663122</td>\n",
       "      <td>[145947.9535344609, 142834.661946108, 172511.5...</td>\n",
       "      <td>[184.4958804592433, 182.799235309969, 226.3683...</td>\n",
       "      <td>[5.9514800148143, 6.528544118213177, 7.3022034...</td>\n",
       "      <td>[152913.6057181737, 149334.5490715083, 180501....</td>\n",
       "      <td>1.818351e+06</td>\n",
       "      <td>101.7398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3731</th>\n",
       "      <td>3731</td>\n",
       "      <td>34.823002</td>\n",
       "      <td>-118.348000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.696370</td>\n",
       "      <td>[141910.6800494553, 137028.811976832, 173116.9...</td>\n",
       "      <td>[174.5395287168338, 171.1071960003887, 218.391...</td>\n",
       "      <td>[5.630307377962379, 6.110971285728167, 7.04489...</td>\n",
       "      <td>[148793.0135197195, 144002.1003871498, 181749....</td>\n",
       "      <td>1.878859e+06</td>\n",
       "      <td>32.3959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3732</th>\n",
       "      <td>3732</td>\n",
       "      <td>34.707401</td>\n",
       "      <td>-118.305000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>748.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.660706</td>\n",
       "      <td>[128241.1482835144, 137774.940553894, 173170.4...</td>\n",
       "      <td>[160.4580289181927, 171.7472105871453, 223.264...</td>\n",
       "      <td>[5.176065448973959, 6.133828949540904, 7.20209...</td>\n",
       "      <td>[134241.0719142978, 144436.8702194193, 181533....</td>\n",
       "      <td>1.851904e+06</td>\n",
       "      <td>13.7756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3733</th>\n",
       "      <td>3733</td>\n",
       "      <td>34.708599</td>\n",
       "      <td>-118.274002</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.708849</td>\n",
       "      <td>[142734.0003740294, 138850.805634565, 173172.2...</td>\n",
       "      <td>[176.3520191843734, 174.3075934660958, 223.422...</td>\n",
       "      <td>[5.688774812399141, 6.225271195217706, 7.20717...</td>\n",
       "      <td>[149444.0213297507, 145674.4399659933, 181504....</td>\n",
       "      <td>1.868110e+06</td>\n",
       "      <td>10.6077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>4995</td>\n",
       "      <td>34.414872</td>\n",
       "      <td>-120.502496</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.789799</td>\n",
       "      <td>[134294.313590256, 118366.24124287, 149754.498...</td>\n",
       "      <td>[165.2262530961678, 145.2041658760386, 188.183...</td>\n",
       "      <td>[5.329879132134445, 5.185863067001377, 6.07044...</td>\n",
       "      <td>[140523.2672581649, 124183.2297143774, 156954....</td>\n",
       "      <td>1.682095e+06</td>\n",
       "      <td>79.3772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>4996</td>\n",
       "      <td>39.029643</td>\n",
       "      <td>-122.875477</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.817975</td>\n",
       "      <td>[86549.00961829678, 108134.5256253014, 127573....</td>\n",
       "      <td>[108.6456708032853, 140.6383844081999, 167.668...</td>\n",
       "      <td>[3.504699058170492, 5.022799443149995, 5.40866...</td>\n",
       "      <td>[90875.1327292863, 113331.488332072, 133713.53...</td>\n",
       "      <td>1.562668e+06</td>\n",
       "      <td>106.8099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>4997</td>\n",
       "      <td>35.157928</td>\n",
       "      <td>-120.739794</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.749070</td>\n",
       "      <td>[113917.7039648926, 111119.0424322115, 148308....</td>\n",
       "      <td>[139.9636361902664, 138.7895862621193, 186.264...</td>\n",
       "      <td>[4.514956006137627, 4.956770937932831, 6.00852...</td>\n",
       "      <td>[119359.1192386094, 116425.779373396, 155596.2...</td>\n",
       "      <td>1.642594e+06</td>\n",
       "      <td>50.5146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>4998</td>\n",
       "      <td>33.613153</td>\n",
       "      <td>-114.526898</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>6.543961</td>\n",
       "      <td>[139332.6846621154, 142410.7072380826, 166391....</td>\n",
       "      <td>[177.3764960027499, 181.0924057287071, 217.431...</td>\n",
       "      <td>[5.721822451701609, 6.467585918882397, 7.01391...</td>\n",
       "      <td>[145754.6466105258, 148919.70615459, 174081.44...</td>\n",
       "      <td>1.787126e+06</td>\n",
       "      <td>167.7547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>4999</td>\n",
       "      <td>38.208370</td>\n",
       "      <td>-122.336163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.545259</td>\n",
       "      <td>[93175.08528784048, 103355.3357037224, 129479....</td>\n",
       "      <td>[113.3388156294827, 129.7969652194793, 164.969...</td>\n",
       "      <td>[3.656090826757508, 4.63560590069569, 5.321594...</td>\n",
       "      <td>[97740.49794099826, 108382.4085881702, 135709....</td>\n",
       "      <td>1.560007e+06</td>\n",
       "      <td>13.9789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2941 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0   Latitude   Longitude  Solar_farm_present  land_cover  \\\n",
       "3729        3729  36.177601 -120.109001                   1         1.0   \n",
       "3730        3730  33.265202 -116.328003                   1         1.0   \n",
       "3731        3731  34.823002 -118.348000                   1         1.0   \n",
       "3732        3732  34.707401 -118.305000                   1         1.0   \n",
       "3733        3733  34.708599 -118.274002                   1         1.0   \n",
       "...          ...        ...         ...                 ...         ...   \n",
       "4995        4995  34.414872 -120.502496                   0         0.0   \n",
       "4996        4996  39.029643 -122.875477                   0         0.0   \n",
       "4997        4997  35.157928 -120.739794                   0         0.0   \n",
       "4998        4998  33.613153 -114.526898                   0         0.0   \n",
       "4999        4999  38.208370 -122.336163                   0         0.0   \n",
       "\n",
       "      slope  elevation feature_violated  solrad_annual  \\\n",
       "3729    0.0      119.0              NaN       5.988686   \n",
       "3730    1.0      164.0              NaN       6.663122   \n",
       "3731    0.0      768.0              NaN       6.696370   \n",
       "3732    0.0      748.0              NaN       6.660706   \n",
       "3733    0.0      731.0              NaN       6.708849   \n",
       "...     ...        ...              ...            ...   \n",
       "4995    0.0        0.0       land_cover       5.789799   \n",
       "4996    0.0      404.0       land_cover       5.817975   \n",
       "4997    0.0        0.0       land_cover       5.749070   \n",
       "4998    2.0       83.0       land_cover       6.543961   \n",
       "4999    1.0        1.0       land_cover       5.545259   \n",
       "\n",
       "                                             ac_monthly  \\\n",
       "3729  [94729.03706566579, 112899.4317807716, 148815....   \n",
       "3730  [145947.9535344609, 142834.661946108, 172511.5...   \n",
       "3731  [141910.6800494553, 137028.811976832, 173116.9...   \n",
       "3732  [128241.1482835144, 137774.940553894, 173170.4...   \n",
       "3733  [142734.0003740294, 138850.805634565, 173172.2...   \n",
       "...                                                 ...   \n",
       "4995  [134294.313590256, 118366.24124287, 149754.498...   \n",
       "4996  [86549.00961829678, 108134.5256253014, 127573....   \n",
       "4997  [113917.7039648926, 111119.0424322115, 148308....   \n",
       "4998  [139332.6846621154, 142410.7072380826, 166391....   \n",
       "4999  [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "3729  [117.2264253417953, 143.5962623979706, 193.969...   \n",
       "3730  [184.4958804592433, 182.799235309969, 226.3683...   \n",
       "3731  [174.5395287168338, 171.1071960003887, 218.391...   \n",
       "3732  [160.4580289181927, 171.7472105871453, 223.264...   \n",
       "3733  [176.3520191843734, 174.3075934660958, 223.422...   \n",
       "...                                                 ...   \n",
       "4995  [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "4996  [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "4997  [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "4998  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "4999  [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "3729  [3.781497591670815, 5.128437942784665, 6.25709...   \n",
       "3730  [5.9514800148143, 6.528544118213177, 7.3022034...   \n",
       "3731  [5.630307377962379, 6.110971285728167, 7.04489...   \n",
       "3732  [5.176065448973959, 6.133828949540904, 7.20209...   \n",
       "3733  [5.688774812399141, 6.225271195217706, 7.20717...   \n",
       "...                                                 ...   \n",
       "4995  [5.329879132134445, 5.185863067001377, 6.07044...   \n",
       "4996  [3.504699058170492, 5.022799443149995, 5.40866...   \n",
       "4997  [4.514956006137627, 4.956770937932831, 6.00852...   \n",
       "4998  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "4999  [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \\\n",
       "3729  [99456.86078053594, 118253.1308946665, 155783....  1.643483e+06   \n",
       "3730  [152913.6057181737, 149334.5490715083, 180501....  1.818351e+06   \n",
       "3731  [148793.0135197195, 144002.1003871498, 181749....  1.878859e+06   \n",
       "3732  [134241.0719142978, 144436.8702194193, 181533....  1.851904e+06   \n",
       "3733  [149444.0213297507, 145674.4399659933, 181504....  1.868110e+06   \n",
       "...                                                 ...           ...   \n",
       "4995  [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06   \n",
       "4996  [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06   \n",
       "4997  [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06   \n",
       "4998  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06   \n",
       "4999  [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06   \n",
       "\n",
       "      min_distance  \n",
       "3729       51.2865  \n",
       "3730      101.7398  \n",
       "3731       32.3959  \n",
       "3732       13.7756  \n",
       "3733       10.6077  \n",
       "...            ...  \n",
       "4995       79.3772  \n",
       "4996      106.8099  \n",
       "4997       50.5146  \n",
       "4998      167.7547  \n",
       "4999       13.9789  \n",
       "\n",
       "[2941 rows x 15 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_solar_farms0 = solar_without_dist_thresh.loc[solar_without_dist_thresh['Solar_farm_present']==0]\n",
    "solar_farms0    = solar_without_dist_thresh.loc[solar_without_dist_thresh['Solar_farm_present']==1]\n",
    "#\n",
    "no_solar_farms_nandrop = no_solar_farms0.dropna(subset =['feature_violated'])\n",
    "solar_final0    = pd.concat([solar_farms0,no_solar_farms_nandrop])\n",
    "solar_final0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07da8176-3e1c-44d9-9946-01506db8a466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2884, 14)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Make sure that there is no NaN for any feature\n",
    "feature_list = ['slope','elevation','land_cover','ac_annual','min_distance']\n",
    "solar_final0 = solar_final0.dropna(subset = feature_list)\n",
    "solar_final0.drop('Unnamed: 0',axis=1,inplace=True)\n",
    "solar_final0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4b74d0e-162e-479d-aff5-3687f806798f",
   "metadata": {},
   "outputs": [],
   "source": [
    "solar_final0.to_csv('../new_data/threshold_95percentile/ca_data_version_6.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3257f91c-c1f8-46b6-8cec-9539564b4e27",
   "metadata": {},
   "source": [
    "### Now proceeed with model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0dacf079-24b8-4a76-aab9-a776842e88e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.177601</td>\n",
       "      <td>-120.109001</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.988686</td>\n",
       "      <td>[94729.03706566579, 112899.4317807716, 148815....</td>\n",
       "      <td>[117.2264253417953, 143.5962623979706, 193.969...</td>\n",
       "      <td>[3.781497591670815, 5.128437942784665, 6.25709...</td>\n",
       "      <td>[99456.86078053594, 118253.1308946665, 155783....</td>\n",
       "      <td>1.643483e+06</td>\n",
       "      <td>51.2865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.265202</td>\n",
       "      <td>-116.328003</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.663122</td>\n",
       "      <td>[145947.9535344609, 142834.661946108, 172511.5...</td>\n",
       "      <td>[184.4958804592433, 182.799235309969, 226.3683...</td>\n",
       "      <td>[5.9514800148143, 6.528544118213177, 7.3022034...</td>\n",
       "      <td>[152913.6057181737, 149334.5490715083, 180501....</td>\n",
       "      <td>1.818351e+06</td>\n",
       "      <td>101.7398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.823002</td>\n",
       "      <td>-118.348000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.696370</td>\n",
       "      <td>[141910.6800494553, 137028.811976832, 173116.9...</td>\n",
       "      <td>[174.5395287168338, 171.1071960003887, 218.391...</td>\n",
       "      <td>[5.630307377962379, 6.110971285728167, 7.04489...</td>\n",
       "      <td>[148793.0135197195, 144002.1003871498, 181749....</td>\n",
       "      <td>1.878859e+06</td>\n",
       "      <td>32.3959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.707401</td>\n",
       "      <td>-118.305000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>748.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.660706</td>\n",
       "      <td>[128241.1482835144, 137774.940553894, 173170.4...</td>\n",
       "      <td>[160.4580289181927, 171.7472105871453, 223.264...</td>\n",
       "      <td>[5.176065448973959, 6.133828949540904, 7.20209...</td>\n",
       "      <td>[134241.0719142978, 144436.8702194193, 181533....</td>\n",
       "      <td>1.851904e+06</td>\n",
       "      <td>13.7756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.708599</td>\n",
       "      <td>-118.274002</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.708849</td>\n",
       "      <td>[142734.0003740294, 138850.805634565, 173172.2...</td>\n",
       "      <td>[176.3520191843734, 174.3075934660958, 223.422...</td>\n",
       "      <td>[5.688774812399141, 6.225271195217706, 7.20717...</td>\n",
       "      <td>[149444.0213297507, 145674.4399659933, 181504....</td>\n",
       "      <td>1.868110e+06</td>\n",
       "      <td>10.6077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>34.414872</td>\n",
       "      <td>-120.502496</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.789799</td>\n",
       "      <td>[134294.313590256, 118366.24124287, 149754.498...</td>\n",
       "      <td>[165.2262530961678, 145.2041658760386, 188.183...</td>\n",
       "      <td>[5.329879132134445, 5.185863067001377, 6.07044...</td>\n",
       "      <td>[140523.2672581649, 124183.2297143774, 156954....</td>\n",
       "      <td>1.682095e+06</td>\n",
       "      <td>79.3772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2880</th>\n",
       "      <td>39.029643</td>\n",
       "      <td>-122.875477</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.817975</td>\n",
       "      <td>[86549.00961829678, 108134.5256253014, 127573....</td>\n",
       "      <td>[108.6456708032853, 140.6383844081999, 167.668...</td>\n",
       "      <td>[3.504699058170492, 5.022799443149995, 5.40866...</td>\n",
       "      <td>[90875.1327292863, 113331.488332072, 133713.53...</td>\n",
       "      <td>1.562668e+06</td>\n",
       "      <td>106.8099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2881</th>\n",
       "      <td>35.157928</td>\n",
       "      <td>-120.739794</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.749070</td>\n",
       "      <td>[113917.7039648926, 111119.0424322115, 148308....</td>\n",
       "      <td>[139.9636361902664, 138.7895862621193, 186.264...</td>\n",
       "      <td>[4.514956006137627, 4.956770937932831, 6.00852...</td>\n",
       "      <td>[119359.1192386094, 116425.779373396, 155596.2...</td>\n",
       "      <td>1.642594e+06</td>\n",
       "      <td>50.5146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2882</th>\n",
       "      <td>33.613153</td>\n",
       "      <td>-114.526898</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>6.543961</td>\n",
       "      <td>[139332.6846621154, 142410.7072380826, 166391....</td>\n",
       "      <td>[177.3764960027499, 181.0924057287071, 217.431...</td>\n",
       "      <td>[5.721822451701609, 6.467585918882397, 7.01391...</td>\n",
       "      <td>[145754.6466105258, 148919.70615459, 174081.44...</td>\n",
       "      <td>1.787126e+06</td>\n",
       "      <td>167.7547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2883</th>\n",
       "      <td>38.208370</td>\n",
       "      <td>-122.336163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.545259</td>\n",
       "      <td>[93175.08528784048, 103355.3357037224, 129479....</td>\n",
       "      <td>[113.3388156294827, 129.7969652194793, 164.969...</td>\n",
       "      <td>[3.656090826757508, 4.63560590069569, 5.321594...</td>\n",
       "      <td>[97740.49794099826, 108382.4085881702, 135709....</td>\n",
       "      <td>1.560007e+06</td>\n",
       "      <td>13.9789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2884 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Latitude   Longitude  Solar_farm_present  land_cover  slope  elevation  \\\n",
       "0     36.177601 -120.109001                   1         1.0    0.0      119.0   \n",
       "1     33.265202 -116.328003                   1         1.0    1.0      164.0   \n",
       "2     34.823002 -118.348000                   1         1.0    0.0      768.0   \n",
       "3     34.707401 -118.305000                   1         1.0    0.0      748.0   \n",
       "4     34.708599 -118.274002                   1         1.0    0.0      731.0   \n",
       "...         ...         ...                 ...         ...    ...        ...   \n",
       "2879  34.414872 -120.502496                   0         0.0    0.0        0.0   \n",
       "2880  39.029643 -122.875477                   0         0.0    0.0      404.0   \n",
       "2881  35.157928 -120.739794                   0         0.0    0.0        0.0   \n",
       "2882  33.613153 -114.526898                   0         0.0    2.0       83.0   \n",
       "2883  38.208370 -122.336163                   0         0.0    1.0        1.0   \n",
       "\n",
       "     feature_violated  solrad_annual  \\\n",
       "0                 NaN       5.988686   \n",
       "1                 NaN       6.663122   \n",
       "2                 NaN       6.696370   \n",
       "3                 NaN       6.660706   \n",
       "4                 NaN       6.708849   \n",
       "...               ...            ...   \n",
       "2879       land_cover       5.789799   \n",
       "2880       land_cover       5.817975   \n",
       "2881       land_cover       5.749070   \n",
       "2882       land_cover       6.543961   \n",
       "2883       land_cover       5.545259   \n",
       "\n",
       "                                             ac_monthly  \\\n",
       "0     [94729.03706566579, 112899.4317807716, 148815....   \n",
       "1     [145947.9535344609, 142834.661946108, 172511.5...   \n",
       "2     [141910.6800494553, 137028.811976832, 173116.9...   \n",
       "3     [128241.1482835144, 137774.940553894, 173170.4...   \n",
       "4     [142734.0003740294, 138850.805634565, 173172.2...   \n",
       "...                                                 ...   \n",
       "2879  [134294.313590256, 118366.24124287, 149754.498...   \n",
       "2880  [86549.00961829678, 108134.5256253014, 127573....   \n",
       "2881  [113917.7039648926, 111119.0424322115, 148308....   \n",
       "2882  [139332.6846621154, 142410.7072380826, 166391....   \n",
       "2883  [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "0     [117.2264253417953, 143.5962623979706, 193.969...   \n",
       "1     [184.4958804592433, 182.799235309969, 226.3683...   \n",
       "2     [174.5395287168338, 171.1071960003887, 218.391...   \n",
       "3     [160.4580289181927, 171.7472105871453, 223.264...   \n",
       "4     [176.3520191843734, 174.3075934660958, 223.422...   \n",
       "...                                                 ...   \n",
       "2879  [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "2880  [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "2881  [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "2882  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "2883  [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "0     [3.781497591670815, 5.128437942784665, 6.25709...   \n",
       "1     [5.9514800148143, 6.528544118213177, 7.3022034...   \n",
       "2     [5.630307377962379, 6.110971285728167, 7.04489...   \n",
       "3     [5.176065448973959, 6.133828949540904, 7.20209...   \n",
       "4     [5.688774812399141, 6.225271195217706, 7.20717...   \n",
       "...                                                 ...   \n",
       "2879  [5.329879132134445, 5.185863067001377, 6.07044...   \n",
       "2880  [3.504699058170492, 5.022799443149995, 5.40866...   \n",
       "2881  [4.514956006137627, 4.956770937932831, 6.00852...   \n",
       "2882  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "2883  [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \\\n",
       "0     [99456.86078053594, 118253.1308946665, 155783....  1.643483e+06   \n",
       "1     [152913.6057181737, 149334.5490715083, 180501....  1.818351e+06   \n",
       "2     [148793.0135197195, 144002.1003871498, 181749....  1.878859e+06   \n",
       "3     [134241.0719142978, 144436.8702194193, 181533....  1.851904e+06   \n",
       "4     [149444.0213297507, 145674.4399659933, 181504....  1.868110e+06   \n",
       "...                                                 ...           ...   \n",
       "2879  [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06   \n",
       "2880  [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06   \n",
       "2881  [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06   \n",
       "2882  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06   \n",
       "2883  [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06   \n",
       "\n",
       "      min_distance  \n",
       "0          51.2865  \n",
       "1         101.7398  \n",
       "2          32.3959  \n",
       "3          13.7756  \n",
       "4          10.6077  \n",
       "...            ...  \n",
       "2879       79.3772  \n",
       "2880      106.8099  \n",
       "2881       50.5146  \n",
       "2882      167.7547  \n",
       "2883       13.9789  \n",
       "\n",
       "[2884 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_final = pd.read_csv('../new_data/threshold_95percentile/ca_data_version_6.csv')\n",
    "solar_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9947a425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2884.000000</td>\n",
       "      <td>2884.000000</td>\n",
       "      <td>2884.000000</td>\n",
       "      <td>2884.000000</td>\n",
       "      <td>2884.000000</td>\n",
       "      <td>2884.000000</td>\n",
       "      <td>2884.000000</td>\n",
       "      <td>2.884000e+03</td>\n",
       "      <td>2884.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.103096</td>\n",
       "      <td>-119.598448</td>\n",
       "      <td>0.163315</td>\n",
       "      <td>0.741678</td>\n",
       "      <td>4.633495</td>\n",
       "      <td>710.074549</td>\n",
       "      <td>5.974094</td>\n",
       "      <td>1.673572e+06</td>\n",
       "      <td>146.301482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.713619</td>\n",
       "      <td>2.461154</td>\n",
       "      <td>0.369717</td>\n",
       "      <td>0.437788</td>\n",
       "      <td>8.214975</td>\n",
       "      <td>680.743993</td>\n",
       "      <td>0.584004</td>\n",
       "      <td>1.537223e+05</td>\n",
       "      <td>133.951538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.537677</td>\n",
       "      <td>-124.450274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-85.000000</td>\n",
       "      <td>4.113513</td>\n",
       "      <td>1.194338e+06</td>\n",
       "      <td>1.123700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.766846</td>\n",
       "      <td>-121.451689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>5.605303</td>\n",
       "      <td>1.582380e+06</td>\n",
       "      <td>35.118600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.644041</td>\n",
       "      <td>-119.978377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>5.950324</td>\n",
       "      <td>1.655665e+06</td>\n",
       "      <td>94.283400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.342509</td>\n",
       "      <td>-117.567972</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1220.000000</td>\n",
       "      <td>6.533265</td>\n",
       "      <td>1.818790e+06</td>\n",
       "      <td>235.533525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42.001868</td>\n",
       "      <td>-114.268027</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>3699.000000</td>\n",
       "      <td>6.973360</td>\n",
       "      <td>1.963854e+06</td>\n",
       "      <td>600.921800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Latitude    Longitude  Solar_farm_present   land_cover        slope  \\\n",
       "count  2884.000000  2884.000000         2884.000000  2884.000000  2884.000000   \n",
       "mean     37.103096  -119.598448            0.163315     0.741678     4.633495   \n",
       "std       2.713619     2.461154            0.369717     0.437788     8.214975   \n",
       "min      32.537677  -124.450274            0.000000     0.000000     0.000000   \n",
       "25%      34.766846  -121.451689            0.000000     0.000000     0.000000   \n",
       "50%      36.644041  -119.978377            0.000000     1.000000     1.000000   \n",
       "75%      39.342509  -117.567972            0.000000     1.000000     4.000000   \n",
       "max      42.001868  -114.268027            1.000000     1.000000    44.000000   \n",
       "\n",
       "         elevation  solrad_annual     ac_annual  min_distance  \n",
       "count  2884.000000    2884.000000  2.884000e+03   2884.000000  \n",
       "mean    710.074549       5.974094  1.673572e+06    146.301482  \n",
       "std     680.743993       0.584004  1.537223e+05    133.951538  \n",
       "min     -85.000000       4.113513  1.194338e+06      1.123700  \n",
       "25%      34.000000       5.605303  1.582380e+06     35.118600  \n",
       "50%     670.000000       5.950324  1.655665e+06     94.283400  \n",
       "75%    1220.000000       6.533265  1.818790e+06    235.533525  \n",
       "max    3699.000000       6.973360  1.963854e+06    600.921800  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_final.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e1401c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Latitude                0\n",
       "Longitude               0\n",
       "Solar_farm_present      0\n",
       "land_cover              0\n",
       "slope                   0\n",
       "elevation               0\n",
       "feature_violated      471\n",
       "solrad_annual           0\n",
       "ac_monthly              0\n",
       "poa_monthly             0\n",
       "solrad_monthly          0\n",
       "dc_monthly              0\n",
       "ac_annual               0\n",
       "min_distance            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_final.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c8dfb58",
   "metadata": {},
   "outputs": [],
   "source": [
    "solar_existing     = solar_final[solar_final['Solar_farm_present']==1]\n",
    "solar_non_existing = solar_final[solar_final['Solar_farm_present']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8877a823-bacb-433b-8c8b-c95675de25d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 471     min_distance\n",
       "472     min_distance\n",
       "473     min_distance\n",
       "474     min_distance\n",
       "475     min_distance\n",
       "            ...     \n",
       "2879      land_cover\n",
       "2880      land_cover\n",
       "2881      land_cover\n",
       "2882      land_cover\n",
       "2883      land_cover\n",
       "Name: feature_violated, Length: 2413, dtype: object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_non_existing.feature_violated.unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98d3e5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "solar_non_existing_sample = solar_non_existing.sample(500, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba9fdb62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
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       "      <th>ac_annual</th>\n",
       "      <th>min_distance</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>41.552124</td>\n",
       "      <td>-120.157335</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>5.708481</td>\n",
       "      <td>[90824.11839699467, 102829.6556834618, 143499....</td>\n",
       "      <td>[107.8519946084084, 124.0311720672096, 178.041...</td>\n",
       "      <td>[3.47909660027124, 4.429684716686057, 5.743285...</td>\n",
       "      <td>[95389.38922327725, 108338.5711922057, 150954....</td>\n",
       "      <td>1.631080e+06</td>\n",
       "      <td>270.3715</td>\n",
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       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>34.199777</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.548646</td>\n",
       "      <td>[119388.0465199253, 119892.8236904812, 136898....</td>\n",
       "      <td>[151.1261468250299, 153.6178619026519, 175.816...</td>\n",
       "      <td>[4.875036994355805, 5.486352210808996, 5.67151...</td>\n",
       "      <td>[125031.9986245192, 125504.6847665692, 143415....</td>\n",
       "      <td>1.549850e+06</td>\n",
       "      <td>7.0732</td>\n",
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       "    <tr>\n",
       "      <th>2633</th>\n",
       "      <td>41.825881</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.358604</td>\n",
       "      <td>[81777.94319949651, 88208.8431930181, 119106.0...</td>\n",
       "      <td>[101.1324268757145, 112.3246005397691, 156.127...</td>\n",
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       "      <td>1.439828e+06</td>\n",
       "      <td>216.0598</td>\n",
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       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>38.051937</td>\n",
       "      <td>-122.120438</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.578596</td>\n",
       "      <td>[89869.58539348899, 105640.842545473, 134581.3...</td>\n",
       "      <td>[110.0128495733674, 130.8310216054974, 169.867...</td>\n",
       "      <td>[3.548801599140885, 4.672536485910622, 5.47959...</td>\n",
       "      <td>[94349.4240644087, 110747.6585552315, 141041.7...</td>\n",
       "      <td>1.581137e+06</td>\n",
       "      <td>15.8505</td>\n",
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       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>34.434426</td>\n",
       "      <td>-120.437778</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.893005</td>\n",
       "      <td>[108458.3620016632, 119036.1827334248, 153523....</td>\n",
       "      <td>[134.6389190773575, 146.861415405863, 191.7419...</td>\n",
       "      <td>[4.343190937979274, 5.245050550209392, 6.18522...</td>\n",
       "      <td>[113696.0251851082, 124712.4168489491, 160857....</td>\n",
       "      <td>1.706744e+06</td>\n",
       "      <td>81.2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>1644</th>\n",
       "      <td>33.163409</td>\n",
       "      <td>-116.835161</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>507.0</td>\n",
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       "      <td>6.133979</td>\n",
       "      <td>[131130.4989592696, 121180.6818841291, 141020....</td>\n",
       "      <td>[163.2696125959735, 153.3947864982667, 181.791...</td>\n",
       "      <td>[5.266761696644306, 5.478385232080953, 5.86423...</td>\n",
       "      <td>[137260.8498054741, 126925.2806200898, 147750....</td>\n",
       "      <td>1.710004e+06</td>\n",
       "      <td>38.3799</td>\n",
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       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>41.786773</td>\n",
       "      <td>-121.732132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>5.647868</td>\n",
       "      <td>[94911.62383536712, 100898.2624121443, 137732....</td>\n",
       "      <td>[114.8911393100053, 122.8726025850188, 171.294...</td>\n",
       "      <td>[3.706165784193718, 4.388307235179242, 5.52563...</td>\n",
       "      <td>[99583.63434035424, 105898.473017372, 144839.2...</td>\n",
       "      <td>1.598426e+06</td>\n",
       "      <td>205.2744</td>\n",
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       "    <tr>\n",
       "      <th>2082</th>\n",
       "      <td>38.658115</td>\n",
       "      <td>-119.898464</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2784.0</td>\n",
       "      <td>slope</td>\n",
       "      <td>6.264465</td>\n",
       "      <td>[99844.13610653757, 108395.0268360737, 151716....</td>\n",
       "      <td>[122.1171688476623, 135.4605597777643, 195.551...</td>\n",
       "      <td>[3.939263511214913, 4.837877134920153, 6.30810...</td>\n",
       "      <td>[104980.0458990117, 114493.4969472504, 160785....</td>\n",
       "      <td>1.732883e+06</td>\n",
       "      <td>171.5859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>41.552124</td>\n",
       "      <td>-122.336163</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>5.545210</td>\n",
       "      <td>[91905.51549225205, 92629.21799297993, 131613....</td>\n",
       "      <td>[112.6000742039042, 115.2211056464884, 165.666...</td>\n",
       "      <td>[3.632260458190458, 4.115039487374587, 5.34409...</td>\n",
       "      <td>[96423.42967688404, 97220.22276542906, 138053....</td>\n",
       "      <td>1.539033e+06</td>\n",
       "      <td>131.4506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>37.465314</td>\n",
       "      <td>-118.496248</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1528.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>6.527113</td>\n",
       "      <td>[126527.3440376545, 137889.4121649166, 165871....</td>\n",
       "      <td>[151.927794714278, 169.7683745071795, 207.3324...</td>\n",
       "      <td>[4.900896603686387, 6.06315623239927, 6.688143...</td>\n",
       "      <td>[132869.3347614637, 146361.0663233372, 175469....</td>\n",
       "      <td>1.854792e+06</td>\n",
       "      <td>369.8214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Latitude   Longitude  Solar_farm_present  land_cover  slope  elevation  \\\n",
       "1236  41.552124 -120.157335                   0         1.0    1.0     1385.0   \n",
       "2858  34.199777 -119.272860                   0         0.0    0.0        0.0   \n",
       "2633  41.825881 -122.961768                   0         0.0    2.0      494.0   \n",
       "2304  38.051937 -122.120438                   0         0.0    0.0        1.0   \n",
       "2285  34.434426 -120.437778                   0         0.0    0.0        0.0   \n",
       "...         ...         ...                 ...         ...    ...        ...   \n",
       "1644  33.163409 -116.835161                   0         1.0   21.0      507.0   \n",
       "674   41.786773 -121.732132                   0         1.0    1.0     1297.0   \n",
       "2082  38.658115 -119.898464                   0         1.0   22.0     2784.0   \n",
       "645   41.552124 -122.336163                   0         1.0    2.0      931.0   \n",
       "891   37.465314 -118.496248                   0         1.0    1.0     1528.0   \n",
       "\n",
       "     feature_violated  solrad_annual  \\\n",
       "1236        elevation       5.708481   \n",
       "2858       land_cover       5.548646   \n",
       "2633       land_cover       5.358604   \n",
       "2304       land_cover       5.578596   \n",
       "2285       land_cover       5.893005   \n",
       "...               ...            ...   \n",
       "1644            slope       6.133979   \n",
       "674         elevation       5.647868   \n",
       "2082            slope       6.264465   \n",
       "645         elevation       5.545210   \n",
       "891         elevation       6.527113   \n",
       "\n",
       "                                             ac_monthly  \\\n",
       "1236  [90824.11839699467, 102829.6556834618, 143499....   \n",
       "2858  [119388.0465199253, 119892.8236904812, 136898....   \n",
       "2633  [81777.94319949651, 88208.8431930181, 119106.0...   \n",
       "2304  [89869.58539348899, 105640.842545473, 134581.3...   \n",
       "2285  [108458.3620016632, 119036.1827334248, 153523....   \n",
       "...                                                 ...   \n",
       "1644  [131130.4989592696, 121180.6818841291, 141020....   \n",
       "674   [94911.62383536712, 100898.2624121443, 137732....   \n",
       "2082  [99844.13610653757, 108395.0268360737, 151716....   \n",
       "645   [91905.51549225205, 92629.21799297993, 131613....   \n",
       "891   [126527.3440376545, 137889.4121649166, 165871....   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "1236  [107.8519946084084, 124.0311720672096, 178.041...   \n",
       "2858  [151.1261468250299, 153.6178619026519, 175.816...   \n",
       "2633  [101.1324268757145, 112.3246005397691, 156.127...   \n",
       "2304  [110.0128495733674, 130.8310216054974, 169.867...   \n",
       "2285  [134.6389190773575, 146.861415405863, 191.7419...   \n",
       "...                                                 ...   \n",
       "1644  [163.2696125959735, 153.3947864982667, 181.791...   \n",
       "674   [114.8911393100053, 122.8726025850188, 171.294...   \n",
       "2082  [122.1171688476623, 135.4605597777643, 195.551...   \n",
       "645   [112.6000742039042, 115.2211056464884, 165.666...   \n",
       "891   [151.927794714278, 169.7683745071795, 207.3324...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "1236  [3.47909660027124, 4.429684716686057, 5.743285...   \n",
       "2858  [4.875036994355805, 5.486352210808996, 5.67151...   \n",
       "2633  [3.262336350829501, 4.011592876420324, 5.03637...   \n",
       "2304  [3.548801599140885, 4.672536485910622, 5.47959...   \n",
       "2285  [4.343190937979274, 5.245050550209392, 6.18522...   \n",
       "...                                                 ...   \n",
       "1644  [5.266761696644306, 5.478385232080953, 5.86423...   \n",
       "674   [3.706165784193718, 4.388307235179242, 5.52563...   \n",
       "2082  [3.939263511214913, 4.837877134920153, 6.30810...   \n",
       "645   [3.632260458190458, 4.115039487374587, 5.34409...   \n",
       "891   [4.900896603686387, 6.06315623239927, 6.688143...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \\\n",
       "1236  [95389.38922327725, 108338.5711922057, 150954....  1.631080e+06   \n",
       "2858  [125031.9986245192, 125504.6847665692, 143415....  1.549850e+06   \n",
       "2633  [85920.33108380224, 92608.6086730362, 124939.9...  1.439828e+06   \n",
       "2304  [94349.4240644087, 110747.6585552315, 141041.7...  1.581137e+06   \n",
       "2285  [113696.0251851082, 124712.4168489491, 160857....  1.706744e+06   \n",
       "...                                                 ...           ...   \n",
       "1644  [137260.8498054741, 126925.2806200898, 147750....  1.710004e+06   \n",
       "674   [99583.63434035424, 105898.473017372, 144839.2...  1.598426e+06   \n",
       "2082  [104980.0458990117, 114493.4969472504, 160785....  1.732883e+06   \n",
       "645   [96423.42967688404, 97220.22276542906, 138053....  1.539033e+06   \n",
       "891   [132869.3347614637, 146361.0663233372, 175469....  1.854792e+06   \n",
       "\n",
       "      min_distance  \n",
       "1236      270.3715  \n",
       "2858        7.0732  \n",
       "2633      216.0598  \n",
       "2304       15.8505  \n",
       "2285       81.2001  \n",
       "...            ...  \n",
       "1644       38.3799  \n",
       "674       205.2744  \n",
       "2082      171.5859  \n",
       "645       131.4506  \n",
       "891       369.8214  \n",
       "\n",
       "[500 rows x 14 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_non_existing_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5eeda41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "solar = pd.concat([solar_existing, solar_non_existing_sample])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "548b2d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>9.710000e+02</td>\n",
       "      <td>971.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.482999</td>\n",
       "      <td>-119.231411</td>\n",
       "      <td>0.485067</td>\n",
       "      <td>0.834192</td>\n",
       "      <td>3.031926</td>\n",
       "      <td>565.375901</td>\n",
       "      <td>6.094958</td>\n",
       "      <td>1.698638e+06</td>\n",
       "      <td>108.740940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.471374</td>\n",
       "      <td>2.340868</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>0.372100</td>\n",
       "      <td>6.702362</td>\n",
       "      <td>606.876661</td>\n",
       "      <td>0.528262</td>\n",
       "      <td>1.405410e+05</td>\n",
       "      <td>122.008707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.618301</td>\n",
       "      <td>-124.450274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-83.000000</td>\n",
       "      <td>4.242356</td>\n",
       "      <td>1.241787e+06</td>\n",
       "      <td>2.540700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.645849</td>\n",
       "      <td>-121.048905</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>5.724602</td>\n",
       "      <td>1.609009e+06</td>\n",
       "      <td>22.380400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.980701</td>\n",
       "      <td>-119.294433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>6.052929</td>\n",
       "      <td>1.674875e+06</td>\n",
       "      <td>58.239900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.130154</td>\n",
       "      <td>-117.492956</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>951.000000</td>\n",
       "      <td>6.610528</td>\n",
       "      <td>1.837796e+06</td>\n",
       "      <td>155.783600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>41.982314</td>\n",
       "      <td>-114.311172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>3568.000000</td>\n",
       "      <td>6.926908</td>\n",
       "      <td>1.951958e+06</td>\n",
       "      <td>600.335400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover       slope  \\\n",
       "count  971.000000  971.000000          971.000000  971.000000  971.000000   \n",
       "mean    36.482999 -119.231411            0.485067    0.834192    3.031926   \n",
       "std      2.471374    2.340868            0.500035    0.372100    6.702362   \n",
       "min     32.618301 -124.450274            0.000000    0.000000    0.000000   \n",
       "25%     34.645849 -121.048905            0.000000    1.000000    0.000000   \n",
       "50%     35.980701 -119.294433            0.000000    1.000000    1.000000   \n",
       "75%     38.130154 -117.492956            1.000000    1.000000    2.000000   \n",
       "max     41.982314 -114.311172            1.000000    1.000000   38.000000   \n",
       "\n",
       "         elevation  solrad_annual     ac_annual  min_distance  \n",
       "count   971.000000     971.000000  9.710000e+02    971.000000  \n",
       "mean    565.375901       6.094958  1.698638e+06    108.740940  \n",
       "std     606.876661       0.528262  1.405410e+05    122.008707  \n",
       "min     -83.000000       4.242356  1.241787e+06      2.540700  \n",
       "25%      56.000000       5.724602  1.609009e+06     22.380400  \n",
       "50%     321.000000       6.052929  1.674875e+06     58.239900  \n",
       "75%     951.000000       6.610528  1.837796e+06    155.783600  \n",
       "max    3568.000000       6.926908  1.951958e+06    600.335400  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "377cef42",
   "metadata": {},
   "outputs": [],
   "source": [
    "solar.to_csv('ML_data_95.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "40327880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Latitude', 'Longitude', 'Solar_farm_present', 'land_cover', 'slope',\n",
       "       'elevation', 'feature_violated', 'solrad_annual', 'ac_monthly',\n",
       "       'poa_monthly', 'solrad_monthly', 'dc_monthly', 'ac_annual',\n",
       "       'min_distance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a1c4bdca",
   "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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>9.710000e+02</td>\n",
       "      <td>971.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.482999</td>\n",
       "      <td>-119.231411</td>\n",
       "      <td>0.485067</td>\n",
       "      <td>0.834192</td>\n",
       "      <td>3.031926</td>\n",
       "      <td>565.375901</td>\n",
       "      <td>6.094958</td>\n",
       "      <td>1.698638e+06</td>\n",
       "      <td>108.740940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.471374</td>\n",
       "      <td>2.340868</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>0.372100</td>\n",
       "      <td>6.702362</td>\n",
       "      <td>606.876661</td>\n",
       "      <td>0.528262</td>\n",
       "      <td>1.405410e+05</td>\n",
       "      <td>122.008707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.618301</td>\n",
       "      <td>-124.450274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-83.000000</td>\n",
       "      <td>4.242356</td>\n",
       "      <td>1.241787e+06</td>\n",
       "      <td>2.540700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.645849</td>\n",
       "      <td>-121.048905</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>5.724602</td>\n",
       "      <td>1.609009e+06</td>\n",
       "      <td>22.380400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.980701</td>\n",
       "      <td>-119.294433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>6.052929</td>\n",
       "      <td>1.674875e+06</td>\n",
       "      <td>58.239900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.130154</td>\n",
       "      <td>-117.492956</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>951.000000</td>\n",
       "      <td>6.610528</td>\n",
       "      <td>1.837796e+06</td>\n",
       "      <td>155.783600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>41.982314</td>\n",
       "      <td>-114.311172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>3568.000000</td>\n",
       "      <td>6.926908</td>\n",
       "      <td>1.951958e+06</td>\n",
       "      <td>600.335400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover       slope  \\\n",
       "count  971.000000  971.000000          971.000000  971.000000  971.000000   \n",
       "mean    36.482999 -119.231411            0.485067    0.834192    3.031926   \n",
       "std      2.471374    2.340868            0.500035    0.372100    6.702362   \n",
       "min     32.618301 -124.450274            0.000000    0.000000    0.000000   \n",
       "25%     34.645849 -121.048905            0.000000    1.000000    0.000000   \n",
       "50%     35.980701 -119.294433            0.000000    1.000000    1.000000   \n",
       "75%     38.130154 -117.492956            1.000000    1.000000    2.000000   \n",
       "max     41.982314 -114.311172            1.000000    1.000000   38.000000   \n",
       "\n",
       "         elevation  solrad_annual     ac_annual  min_distance  \n",
       "count   971.000000     971.000000  9.710000e+02    971.000000  \n",
       "mean    565.375901       6.094958  1.698638e+06    108.740940  \n",
       "std     606.876661       0.528262  1.405410e+05    122.008707  \n",
       "min     -83.000000       4.242356  1.241787e+06      2.540700  \n",
       "25%      56.000000       5.724602  1.609009e+06     22.380400  \n",
       "50%     321.000000       6.052929  1.674875e+06     58.239900  \n",
       "75%     951.000000       6.610528  1.837796e+06    155.783600  \n",
       "max    3568.000000       6.926908  1.951958e+06    600.335400  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a38146c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = solar[feature_list]\n",
    "y = solar['Solar_farm_present']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d75bcd0",
   "metadata": {},
   "source": [
    "# Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c8050f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6981947a",
   "metadata": {},
   "source": [
    "# Confusion Matrix Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3db8d495",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred,ml_method):\n",
    "    \n",
    "    figsize = (10, 10)\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
    "    cax = ax.matshow(cm, cmap=plt.cm.Blues) # https://matplotlib.org/3.2.0/api/_as_gen/matplotlib.axes.Axes.matshow.html\n",
    "    fig.colorbar(cax)\n",
    "\n",
    "    ax.set(title=\"Confusion Matrix : \" + ml_method,\n",
    "           xlabel=\"Predicted label\",\n",
    "           ylabel=\"True label\",\n",
    "           #xticks=np.arange(n_classes),\n",
    "           #yticks=np.arange(n_classes),\n",
    "           #xticklabels=labels,\n",
    "           #yticklabels=labels\n",
    "          )\n",
    "\n",
    "    ax.xaxis.set_label_position(\"bottom\")\n",
    "    ax.xaxis.tick_bottom()\n",
    "\n",
    "    ax.xaxis.label.set_size(20)\n",
    "    ax.yaxis.label.set_size(20)\n",
    "    ax.title.set_size(20)\n",
    "\n",
    "    threshold = (cm.max() + cm.min()) / 2.\n",
    "\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
    "               horizontalalignment=\"center\",\n",
    "               color=\"white\" if cm[i, j] > threshold else \"black\",\n",
    "               size=15)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caaacfff",
   "metadata": {},
   "source": [
    "# Imputing some features and using std scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "922d3737",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_transform = ['slope', 'elevation','ac_annual', 'min_distance']\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('pipeline', Pipeline([\n",
    "            ('imputer', SimpleImputer(strategy='mean')),\n",
    "            ('scaler', StandardScaler())\n",
    "        ]), features_to_transform)\n",
    "    ],\n",
    "    remainder='passthrough'  # Features not specified in transformers will be passed through\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "167e6e8b",
   "metadata": {},
   "source": [
    "# Logistic Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ca39aac7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 600 candidates, totalling 3000 fits\n"
     ]
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('preprocessor', preprocessor),  # Preprocessing step\n",
    "    ('classifier', LogisticRegression(max_iter=3000, random_state=104))  # Logistic Regression model\n",
    "])\n",
    "\n",
    "# Specify hyperparameters for the logistic regression model\n",
    "param_grid = {\n",
    "    'classifier__C': np.logspace(-1, 1, 100),\n",
    "    'classifier__solver': ['lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga']\n",
    "}\n",
    "\n",
    "# Create GridSearchCV to search through hyperparameter combinations\n",
    "lr_cv = GridSearchCV(pipeline, param_grid, scoring='accuracy', n_jobs=-1, verbose=1, cv=5)\n",
    "\n",
    "# Fit the pipeline with hyperparameter tuning\n",
    "lr_cv.fit(X_train, y_train)\n",
    "\n",
    "# # Access the best model with its hyperparameters\n",
    "best_model = lr_cv.best_estimator_\n",
    "\n",
    "# Make predictions\n",
    "y_pred_train_lr = best_model.predict(X_train) \n",
    "y_pred_test_lr = best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d144c437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(classification_report(y_train, y_pred_train_lr, output_dict=True))\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "ax.axis('off')\n",
    "table = ax.table(cellText=df.values, colLabels=df.columns, rowLabels=df.index, loc='center', bbox=[0, 0, 1, 1], fontsize=14)\n",
    "plt.subplots_adjust(left=0.1, right=0.8, top=0.9, bottom=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b0b14dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_train, y_pred_train_lr,'Logistic Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "54d78235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(classification_report(y_test, y_pred_test_lr, output_dict=True))\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "ax.axis('off')\n",
    "table = ax.table(cellText=df.values, colLabels=df.columns, rowLabels=df.index, loc='center', bbox=[0, 0, 1, 1], fontsize=14)\n",
    "plt.subplots_adjust(left=0.1, right=0.8, top=0.9, bottom=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "390795c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred_test_lr, 'Logistic Regression' )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4533fe46",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e3a3c078",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4332 candidates, totalling 21660 fits\n"
     ]
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('preprocessor', preprocessor),  # Preprocessing step\n",
    "    ('classifier', DecisionTreeClassifier(random_state=406))  # Logistic Regression model\n",
    "])\n",
    "# Specify hyperparameters for the logistic regression model\n",
    "params = {\"classifier__criterion\":(\"gini\", \"entropy\"), \n",
    "          \"classifier__splitter\":(\"best\", \"random\"), \n",
    "          \"classifier__max_depth\":(list(range(1, 20))), \n",
    "          \"classifier__min_samples_split\":[2, 3, 4], \n",
    "          \"classifier__min_samples_leaf\":list(range(1, 20))\n",
    "          }\n",
    "\n",
    "# Create GridSearchCV to search through hyperparameter combinations\n",
    "tree_cv = GridSearchCV(pipeline, params, scoring='accuracy', n_jobs=-1, verbose=1, cv=5)\n",
    "\n",
    "# Fit the pipeline with hyperparameter tuning\n",
    "tree_cv.fit(X_train, y_train)\n",
    "\n",
    "# # Access the best model with its hyperparameters\n",
    "best_model = tree_cv.best_estimator_\n",
    "\n",
    "# Make predictions\n",
    "y_pred_train_tree = best_model.predict(X_train) \n",
    "y_pred_test_tree = best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "57d7f492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(classification_report(y_train, y_pred_train_tree, output_dict=True))\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "ax.axis('off')\n",
    "table = ax.table(cellText=df.values, colLabels=df.columns, rowLabels=df.index, loc='center', bbox=[0, 0, 1, 1], fontsize=14)\n",
    "plt.subplots_adjust(left=0.1, right=0.8, top=0.9, bottom=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "61450aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_train, y_pred_train_tree, 'Decision Tree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1a288a6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UvjRXr14d5ubmOH78uJL+5s2bqtikF1fhvDxz585VOnQKO3mio6OV+XgAwN/fH0lJSahZs6bOn4WFhU7ePj4+uHTpkupH60ePQ0NZWFgok0MXatCgAfLz83H16lWdshT+IO7r66t64AAAZfRNUX755Rd4enpi4sSJaNiwIby8vPDrr7/qpOvXrx8iIiIQExOD8+fP44033lDV+8SJE6r0T1LvFwU7eeiFMmbMGCxZsgQrV65EcnIyRowYoTwJgohKlpubi4SEBCQkJAB4OBFeQkKCasgt0b+Ro6MjnJ2dsXjxYpw7dw779u3DmDFjlPV9+vRBxYoV8dprr+Hw4cM4f/48fvzxR2Xi/ilTpmDdunWYMmUKkpOTkZiYiM8++0x5f8uWLfHtt98iPj4eJ06cwPDhw3VGQujj5eWF3bt345dffkFycjLefvtt1cW9paUlxo0bhw8//BDff/890tPTceTIESxbtkyVz1tvvYVZs2ZBRPh0PHouvLy8cOLECezcuROpqamYPHmyzpe0qVOnYs6cOfj666+RlpaGuLg4fPPNN8XmO3bsWKxYsQILFy5EWloavvzyS2zcuFGZSLaosqxatQrJyck4evQo+vXrp3ckUd++ffHdd99h9+7dyq1awMMOqYEDB2Ls2LGIiorCmTNnMGTIEJiYmCgjJujF5ejoiHr16mHNmjVKh87LL7+MuLg4pKamqkbyjBs3Dr/88osykXFaWhq2bNlS5MTLbdq0QY0aNTBw4ECcOnUKhw8fxqRJkwCgVMde1apVkZubi7179+LPP//E7du34e3tjX79+mHAgAHYuHEjMjIycOzYMcycORPbtm0DALz77rvYsWMHZs+ejbS0NHz77bfYsWNHsdvy8vLCxYsXsX79eqSnp+Prr7/Gpk2bdNJ1794dOTk5GDFiBF555RW4u7sr695++22cPXsW48aNQ2pqKjZs2KA8nZIxp8ezniDoRcOJl//5vvnmG/Hw8BALCwtp3LixHDly5HkXiR7zok3Q928SFRUlAHT+Bg4c+LyLRo9hHJXe7t27xdfXV7RardSrV0+io6NVk1leuHBBevToIXZ2dmJtbS0NGzaUo0ePKu//8ccfpX79+mJhYSHly5eX7t27K+syMzOlbdu2YmNjI15eXrJ9+3a9Ey/Hx8erynTt2jXp2rWr2Nraiqurq0yaNEkGDBggXbt2VdLk5+fLp59+Kp6enmJubi4eHh4yY8YMVT45OTlibW0t77zzzlPdZ8aOcaRfSEiIzgTDj04OW+jR+Ll7964MGjRI7O3txcHBQUaMGCHjx48XPz8/1Xu+++478fHxEXNzc3Fzc5P//Oc/evN71IIFC6R69epibm4u3t7eqgmT9YmLi5OGDRuKpaWleHl5yQ8//KC3/ElJSQJAPD09paCgQLXur7/+kr59+4q1tbVUrFhRvvzyS2ncuLGMHz++2G2/iF7EOBo1apRq4mARET8/P6lYsaJO2mPHjkmbNm3E1tZWbGxspF69ejJ9+nRl/ePHZnJysjRt2lQsLCykVq1a8tNPPwkA2bFjh4job09u3LghACQqKkpZNnz4cHF2dhYAMmXKFBERuXfvnvz3v/+VqlWrKjHYrVs3OXXqlPK+ZcuWSeXKlcXKyko6d+4ss2fPLnHi5bFjx4qzs7PY2tpK7969Ze7cuXrf06tXLwEgy5cv11m3ZcsWqVmzpmi1WmnRooUsXLhQACgTVBu70vQzaEQeu0GcyiQuLg4BAQGIjY2Fv7//8y4O0b/SmjVr0L9/f8YRURkwjuhRFy5cQI0aNXD8+HEeD6XAOCJD3bp1C5UqVcKcOXMwZMiQ512cfxTG0bN1+PBhNGvWDOfOnUONGjWed3H+NtOnT8d3332HS5cuPe+i/C1K08/AiZeJiIiIjNT9+/dx7do1TJo0CU2aNOEXLKKnJD4+HmfPnkXjxo1x8+ZNTJs2DQDQtWvX51wyMnabNm2Cra0tvLy8cO7cOYwaNQpNmzY1+g6eBQsWoFGjRnB2dsbhw4fxxRdfFHlb24uOnTxERERERurw4cN45ZVX4O3tjf/973/PuzhERmX27NlISUlRHu5x8OBBlC9f/nkXi4xcTk4Oxo0bh4sXL6J8+fJo3bq16klVxiotLQ2ffvoprl+/Dg8PD7z//vuYMGHC8y7WPxI7eYiIiIiMVIsWLXQe3U5EZdegQQPExsY+72LQC2jAgAEYMGDA8y7G327u3LmYO3fu8y7GvwKfrkVEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATYyUNEREREREREZATMDE148eJF/Pnnn8+yLEYhOTkZALB9+3blfyIqncOHDwNgHBGVBeOIqOwYR0RlxzgiKruMjAyD02pEREpKdPHiRfj6+uL27dtlKtiLwsTEBAUFBc+7GET/aowjorJjHBGVHeOIqOwYR0RlZ2pqioMHDyIoKKjYdAaN5Pnzzz9x+/ZtrF69Gr6+vk+lgMZq+/btmDx5MvcVURkwjojKjnFEVHaMI6KyYxwRlV1ycjL69+8PrVZbYlqDb9cCAF9fX/j7+z9xwV4EhUMQua+InhzjiKjsGEdEZcc4Iio7xhHR34sTLxMRERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGQF28hARERERERERGYF/fSdPdHQ0NBoNsrOzn2paIiIiIiIiIqJ/k399J09wcDCysrJgb2//VNMSGbMDBw6gc+fOcHd3h0ajwebNm0t8T3R0NPz9/aHValGzZk2sWLHimZfzn2j+/PmoWrUqLC0tERgYiGPHjhWZ9v79+5g2bRpq1KgBS0tL+Pn5YceOHaXOc/HixWjRogXs7OyK7KiOi4tDmzZt4ODgAGdnZwwbNgy5ubk66VasWIF69erB0tISrq6uCAsLU60/deoUmjdvDktLS1SpUgWff/65an2LFi2g0Wh0/jp16qSkyc3NxciRI1G5cmVYWVmhdu3a+O677/TuIxFBhw4ddI7Da9euoX379nB3d4dWq0WVKlUwcuRI/PXXX0qarKws9O3bF97e3jAxMcHo0aN18t+4cSMaNmwIBwcH2NjYoH79+li1apUqjb76aDQafPHFFwCACxcuYMiQIahWrRqsrKxQo0YNTJkyBffu3VPyuHDhgt48jhw5otr3j6+3tLTUu1+M3bOIo0KzZs2CRqPROR5+//13hIaGomLFirCxsYG/vz9+/PFHZX3hD0H6/o4fPw4AuHv3LgYNGoS6devCzMwMr732ms72Bw0apDePOnXqKGlycnIwevRoeHp6wsrKCsHBwco29Bk+fDg0Gg2++uor1fLU1FR07doV5cuXh52dHZo1a4aoqChlvb5jrvDv6tWrAAyLI0Pi/sqVKxg0aBDc3d1hbW2N9u3bIy0tTW99iop7ADh+/DhatWoFBwcHODo6ol27djh58qSyfurUqXrLYmNjU+T+M1ZPO4707dtatWqp0ty9exdhYWFwdnaGra0tevTogStXrqjS7N27F8HBwShXrhwqVqyIcePG4cGDB6o0O3fuRJMmTVCuXDm4uLigR48euHDhgipNSdcdCxcuRL169WBnZwc7OzsEBQXh559/VqVJT09Ht27d4OLiAjs7O/Tq1UtVXkPO74bEfVHnj99//12VLjMzE/3794ezszOsrKxQt25dnDhxQllvaPsZExODli1bwsbGBnZ2dnj55Zdx584dg+vEOFJ7HrH09ttvo0aNGrCysoKLiwu6du2Ks2fPKusNOX8/6vDhwzAzM0P9+vVLXRag+GMKAK5fv45+/frBzs4ODg4OGDJkiM51ZknXkADw1VdfwcfHB1ZWVqhSpQree+893L17V5XG0M+juLak0LVr11C5cmWd6+eNGzeiTZs2yrkhKCgIO3fu1JsHUPS1RWnK8rcSA8TGxgoAiY2NNSS5wfLy8p5qfv8Eq1evfib7iuhp2r59u0ycOFE2btwoAGTTpk3Fpj9//rxYW1vLmDFjJCkpSb755hsxNTWVHTt2PJPy/VPjaP369WJhYSHLly+XM2fOyNChQ8XBwUGuXLmiN/2HH34o7u7usm3bNklPT5cFCxaIpaWlxMXFlSrPuXPnysyZM2XmzJkCQG7cuKHaTmZmpjg6Osrw4cPl7NmzcuzYMQkODpYePXqo0s2ZM0fc3d1lzZo1cu7cOTl58qRs2bJFWX/z5k2pUKGC9OvXT06fPi3r1q0TKysrWbRokZLm2rVrkpWVpfydPn1aTE1NJTw8XEkzdOhQqVGjhkRFRUlGRoYsWrRITE1NVdsq9OWXX0qHDh10jsPr16/LggUL5Pjx43LhwgXZs2eP+Pj4SJ8+fZQ0GRkZ8u6778rKlSulfv36MmrUKJ38o6KiZOPGjZKUlCTnzp2Tr776SufYfbQ+WVlZsnz5ctFoNJKeni4iIj///LMMGjRIdu7cKenp6bJlyxZxdXWV999/X1UWALJnzx5VXvfu3VPShIeHi52dnWr977//rlPmp+VFiqNCx44dk6pVq0q9evV0joc2bdpIo0aN5OjRo5Keni6ffPKJmJiYKPnk5eXpHAtvvfWWVKtWTQoKCkREJDc3V4YPHy6LFy+Wdu3aSdeuXXXKkJ2drcrj0qVL4uTkJFOmTFHS9OrVS2rXri379++XtLQ0mTJlitjZ2cnly5d18tu4caP4+fmJu7u7zJ07V7XOy8tLOnbsKCdPnpTU1FR55513xNraWrKyskRE5Pbt2zp1ateunYSEhCh5GBJHJcV9QUGBNGnSRJo3by7Hjh2Ts2fPyrBhw8TDw0Nyc3N18isq7nNycsTJyUkGDRokZ8+eldOnT0uPHj2kQoUKSizl5OTo1Kl27doycOBAne08DS9SHE2ZMkXq1Kmj2rd//PGHKp/hw4dLlSpVZO/evXLixAlp0qSJBAcHK+sTEhLEwsJCPv74Y0lLS5Po6GipVauW6nx5/vx50Wq1MmHCBDl37pzExsbKyy+/LA0aNFClKem6IzIyUrZt2yapqamSkpIiH330kZibm8vp06dF5GG8Vq9eXbp16yanTp2SU6dOSdeuXaVRo0aSn58vIoad3w2J+6ioKAEgKSkpqv1XuB2Rh+2ap6enDBo0SI4ePSrnz5+XnTt3yrlz55Q0hrSfv/zyi9jZ2cnMmTPl9OnTcvbsWYmIiJC7d+8aXCfG0f95XrG0aNEi2b9/v2RkZEhsbKx07txZqlSpIg8ePBARw87fhW7cuCHVq1eXtm3bip+fn2qdIWUp6ZgSEWnfvr34+fnJkSNH5ODBg1KzZk3VNZkh15Br1qwRrVYra9askYyMDNm5c6e4ubnJe++990SfR1FtyaO6du2qpHn0+nnUqFHy2WefybFjxyQ1NVUmTJgg5ubmpb62KE1Zyqo0fTJPtZMnJCREwsLCJCwsTOzs7MTZ2VkmTZqkXBx5enrKtGnTJDQ0VMqVK6ecSA4ePCjNmjUTS0tLqVy5svznP/9RXRTcvXtXPvzwQ6lcubJYWFhIjRo1ZOnSpSLyfyfVwg/twoUL8uqrr4qDg4NYW1tL7dq1Zdu2bXrTioj873//k9q1a4uFhYV4enrK7NmzVXXy9PSU6dOny+DBg8XW1laqVKmiOlgf908+iRHpY8jJ6MMPP5Q6deqolvXu3VvatWv3TMr0T42jxo0bS1hYmPI6Pz9f3N3dZebMmXrTu7m5ybfffqta1r17d+nXr98T5anvHCby8ELB1dVVdTF56tQpASBpaWki8vDi0srKSvbs2VNk/RYsWCCOjo6qDvhx48aJj49Pke+ZO3eulCtXTnXOrlOnjkybNk2Vzt/fXyZOnKhaFh8fL5UqVZKsrCyDjsN58+ZJ5cqV9a4LCQkpsuF9XIMGDWTSpElFru/atau0bNmy2Dw+//xzqVatmvK6sJMnPj6+yPeEh4eLvb29QWV8Gl6kOBJ5+KXFy8tLdu/erfd4sLGxke+//161zMnJSZYsWaJ3u/fu3RMXFxedY7nQwIED9X7Ze9ymTZtEo9HIhQsXROThhbupqals3bpVlU5fjFy+fFkqVaokp0+fFk9PT1Unzx9//CEA5MCBA8qyv/76SwDI7t279Zbl6tWrYm5urrMfChkaR4/HfUpKigBQvlyLPPxcXVxcdPZvcXF//PhxASAXL15Ulj1+LntcQkKCzn54ml6kOJoyZYrOF8RHZWdni7m5ufzwww/KsuTkZAEgMTExIiIyYcIEadiwoep9kZGRYmlpKX/99ZeIiPzwww9iZmamarMiIyNFo9EonXlPet3h6OiofEfYuXOnmJiYyM2bN1V10Gg0RcaIiO75/VFFxX1R7fOjxo0bJ82aNSu2/Ia0n4GBgcW2YfoUVyeRFzeORJ5PLOlz8uRJAaDq9HtUcefv3r17y6RJk/Ru15CylHRMJSUlCQA5fvy4suznn38WjUYjmZmZImLYNWRYWJjO9dWYMWOkadOmymtDPw9DriEXLFggISEhsnfv3hLjU0Skdu3a8vHHH6uWlXRtYWhZnobSdPI89du1Vq5cCTMzMxw7dgzz5s3Dl19+iaVLlyrrZ8+eDT8/P8THx2Py5MlIT09H+/bt0aNHD5w6dQoRERE4dOgQRo4cqbxnwIABWLduHb7++mskJydj0aJFsLW11bv9sLAw5OXl4cCBA0hMTMRnn31WZNrY2Fj06tULb7zxBhITEzF16lRMnjxZZzjonDlz0LBhQ8THx+Odd97BiBEjkJKSUvadRfQvERMTg9atW6uWtWvXDjExMc+pRH+/e/fuITY2VrUfTExM0Lp16yL3Q15ens7tOFZWVjh06NAT51nUdiwsLGBi8n+ndCsrKwBQtrV7924UFBQgMzMTvr6+qFy5Mnr16oVLly4p74mJicHLL78MCwsLZVm7du2QkpKCGzdu6N32smXL8MYbb6iGeAcHByMyMhKZmZkQEURFRSE1NRVt27ZV0ty+fRt9+/bF/PnzUbFixRLr+Ntvv2Hjxo0ICQkxcK/oEhHs3bsXKSkpePnll/WmuXLlCrZt24YhQ4YUm9fNmzfh5OSks7xLly5wdXVFs2bNEBkZqbM+NzcXnp6eqFKlCrp27YozZ848WWX+pZ5FHBUKCwtDp06ddM5VhYKDgxEREYHr16+joKAA69evx927d9GiRQu96SMjI3Ht2jUMHjy4FDXUtWzZMrRu3Rqenp4AgAcPHiA/P7/EOhUUFCA0NBRjx45V3epVyNnZGT4+Pvj+++9x69YtPHjwAIsWLYKrqysCAgL0luX777+HtbU1Xn/99TLX6dG4z8vLAwBVnUxMTKDValV1KinufXx84OzsjGXLluHevXu4c+cOli1bBl9fX1StWlVvWZYuXQpvb280b968THX6N3mWcZSWlgZ3d3dUr14d/fr1w8WLF5V1sbGxuH//vmq7tWrVgoeHh7LdorZz9+5dxMbGAgACAgJgYmKC8PBw5Ofn4+bNm1i1ahVat24Nc3NzAKW/7sjPz8f69etx69YtBAUFKWXRaDTQarVKOktLS5iYmOjU+1FFnd8NUb9+fbi5uaFNmzY4fPiwal1kZCQaNmyInj17wtXVFQ0aNMCSJUtUaUpqP69evYqjR4/C1dUVwcHBqFChAkJCQoqtjyF1ehHjCHh+sfS4W7duITw8HNWqVUOVKlX0pinq/B0eHo7z589jypQpReZfXFkMOaZiYmLg4OCAhg0bKstat24NExMTHD16VElT0jVkcHAwYmNjlduvzp8/j+3bt6Njx44ADP88DLmGTEpKwrRp0/D999+rro+LUlBQgJycHJ04KenaorTXs3+bp9lrFBISIr6+vsrIHZGHPXi+vr4i8nBUzGuvvaZ6z5AhQ2TYsGGqZQcPHhQTExO5c+eO8utQUT3uj/ec161bV6ZOnWpQ2r59+0qbNm1UacaOHSu1a9dWXnt6ekr//v2V1wUFBeLq6ioLFy7Uu41/ck81kT4woMfZy8tLZsyYoVq2bds2ASC3b99+6mX6J8ZRZmamAJBffvlFtXzs2LHSuHFjve/p06eP1K5dW1JTUyU/P1927dolVlZWYmFh8UR5FvVL4enTp8XMzEw+//xzycvLk+vXr0uPHj0EgPK5zZw5U8zNzcXHx0d27NghMTEx0qpVK/Hx8VF+dWnTpo3O+fjMmTMCQJKSknTKc/ToUQEgR48eVS2/e/euDBgwQACImZmZWFhYyMqVK1Vphg0bJkOGDFFeF3UcvvHGG2JlZSUApHPnznLnzh2dNCLFj0DIzs4WGxsbMTMzE61WK8uWLdObTkTks88+E0dHxyK3IyKSlpYmdnZ2snjxYmXZH3/8IXPmzJEjR47IsWPHZNy4caLRaHSG2K9cuVLi4+MlOjpaXn31VbGzs5NLly4Vua2yeFHiSERk3bp18tJLLymfm77j4caNG9K2bVvluLSzs5OdO3cWWdYOHTpIhw4dilxvyEiezMxMMTU1lYiICNXyoKAgCQkJkczMTHnw4IGsWrVKTExMxNvbW0kzY8YMadOmjWo09OO3a126dEkCAgJEo9GIqampuLm56R1qXsjX11dGjBhR5HpDRvLoi/t79+6Jh4eH9OzZU65fvy55eXkya9YsASBt27ZV0hkS94mJiVKjRg0xMTERExMT8fHxUUZBPe7OnTvi6Ogon332WbFlLosXKY62b98uGzZskJMnT8qOHTskKChIPDw8lBE4a9asUaUv1KhRI/nwww9F5P9Gz6xdu1YePHggly9flubNmwsAWbt2rfKe6OhocXV1FVNTUwEgQUFBqrbN0OuOU6dOiY2NjZiamoq9vb0yel/k4cgHOzs7GTVqlNy6dUtyc3Nl5MiRAkCnrSuk7/z+qKLi/uzZs/Ldd9/JiRMn5PDhwzJ48GAxMzNTHTdarVa5TS0uLk4WLVoklpaWsmLFCiVNSe1nTEyMABAnJydZvny5xMXFyejRo8XCwkJSU1OfqE4vahyJPL9YKjR//nyxsbERAOLj41PkKB4R/efv1NRUcXV1lZSUFBHRP2qnpLIYckxNnz5d1T4VcnFxkQULFoiI4deQ8+bNE3NzczEzMxMAMnz4cGWdoZ9HSW3J3bt3pV69erJq1SoRMWykXeH136O3hRlybWHo9ezT8FxH8jRp0gQajUZ5HRQUhLS0NOTn5wOAqgcQAE6ePIkVK1bA1tZW+WvXrh0KCgqQkZGBhIQEmJqaGvzr7bvvvotPP/0UTZs2xZQpU3Dq1Kki0yYnJ6Np06aqZU2bNlWVFwDq1aun/K/RaFCxYkW9E14RET1q3rx58PLyQq1atWBhYYGRI0di8ODBBv2iUBp16tTBypUrMWfOHFhbW6NixYqoVq0aKlSooGyroKAA9+/fx9dff4127dqhSZMmWLduHdLS0lQTtZbGsmXLULduXTRu3Fi1/JtvvsGRI0cQGRmJ2NhYzJkzB2FhYdizZw+Ah79m7tu3T2cSWX3mzp2LuLg4bNmyBenp6RgzZkypy1muXDkkJCTg+PHjmD59OsaMGYPo6Gi9aZcvX45+/foVOSFyZmYm2rdvj549e2Lo0KHK8vLly2PMmDEIDAxEo0aNMGvWLPTv31+ZvBl42B4OGDAA9evXR0hICDZu3AgXFxcsWrSo1HV6kZQUR5cuXcKoUaOwZs2aYieynjx5MrKzs7Fnzx6cOHECY8aMQa9evZCYmKiT9vLly9i5c2eJI7pKsnLlSjg4OOhM1Lpq1SqICCpVqgStVouvv/4affr0UeoUGxuLefPmKRNv6iMiCAsLg6urKw4ePIhjx47htddeQ+fOnZGVlaWTPiYmBsnJyWWuk764Nzc3x8aNG5GamgonJydYW1sjKioKHTp0UOpkSNzfuXMHQ4YMQdOmTXHkyBEcPnwYL730Ejp16qSaALTQpk2bkJOTg4EDB5apTi8CQ9qjDh06oGfPnqhXrx7atWuH7du3Izs7Gxs2bDB4O23btsUXX3yB4cOHQ6vVwtvbW/mVvnBbv//+O4YOHYqBAwfi+PHj2L9/PywsLPD666/j4Xckw/n4+CAhIQFHjx7FiBEjMHDgQCQlJQEAXFxc8MMPP+Cnn36Cra0t7O3tkZ2dDX9/f73tcFHnd0PL8fbbbyMgIADBwcFYvnw5goODMXfuXCVNQUEB/P39MWPGDDRo0ADDhg3D0KFDVRMrl9R+FhQUAHg4ae/gwYPRoEEDzJ07Fz4+Pli+fPkT1YlxVDpPM5b69euH+Ph47N+/H97e3ujVq5fOJMSA/vN3fn4++vbti48//hje3t5FlrekspT2mCqL6OhozJgxAwsWLEBcXBw2btyIbdu24ZNPPjE4D0PakgkTJsDX1xf9+/c3KM+1a9fi448/xoYNG+Dq6grAsGuL0lzP/t3+9qdrPT5re25uLt5++20kJCQofydPnkRaWpoy43hpvPXWWzh//jxCQ0ORmJiIhg0b4ptvvilTmQuHjhbSaDRKQBC9CCpWrKjz9IwrV67Azs6u1DH6b1W+fHmYmprq3Q9FDc90cXHB5s2bcevWLfz66684e/YsbG1tUb169SfOsyh9+/bF77//jszMTFy7dg1Tp07FH3/8oWzLzc0NAFC7dm1V+cqXL68M2y3qcy5c96hbt25h/fr1Ol8Y79y5g48++ghffvklOnfujHr16mHkyJHo3bs3Zs+eDQDYt28f0tPT4eDgADMzM5iZmQEAevTooXPrTMWKFVGrVi106dIFixYtwsKFC/V+gS2OiYkJatasifr16+P999/H66+/jpkzZ+qkO3jwIFJSUvDWW2/pzee3337DK6+8guDgYCxevLjE7QYGBuLcuXNFrjc3N0eDBg2KTWNsnkUcxcbG4urVq/D391eOp/379+Prr7+GmZkZ8vPzkZ6ejm+//RbLly9Hq1at4OfnhylTpqBhw4aYP3++zjbDw8Ph7OyMLl26PHFdRQTLly9HaGioavg6ANSoUQP79+9Hbm4uLl26hGPHjuH+/ftKnQ4ePIirV6/Cw8NDqdOvv/6K999/X7ltad++fdi6dSvWr1+Ppk2bwt/fHwsWLICVlRVWrlypU56lS5eifv36Rd7KZYii4h54eAtOQkICsrOzkZWVhR07duDatWtKnQyJ+7Vr1+LChQsIDw9Ho0aN0KRJE6xduxYZGRnYsmWL3jq9+uqrqFChwhPX6d/oWcSRPg4ODvD29lbOURUrVsS9e/d0nvD4+HbHjBmD7OxsXLx4EX/++Se6du0KAMq25s+fD3t7e3z++edo0KABXn75ZaxevRp79+5Vbv0w9LrDwsICNWvWREBAAGbOnAk/Pz/MmzdPWd+2bVukp6fj6tWr+PPPP7Fq1SpkZmbq1Lu053dDNG7cWHV+d3NzU7XBAODr66u0wYa0n/ra8sfzKW2dXtQ4Ap5fLBWyt7eHl5cXXn75Zfzvf//D2bNnsWnTJp336zt/5+Tk4MSJExg5cqRyTp02bRpOnjwJMzMz7Nu3z6CyGHJM6Rvg8ODBA1y/fl3ZT4ZcQ06ePBmhoaF46623ULduXXTr1g0zZszAzJkzUVBQYNDnYUhbsm/fPvzwww/K+latWgF4+Hk/flvb+vXr8dZbb2HDhg2qW7IMubYozfXs3+2pd/IUnpwLHTlyBF5eXjA1NdWb3t/fH0lJSahZs6bOn4WFBerWrYuCggLs37/f4DJUqVIFw4cPx8aNG/H+++/r3O9ayNfXV+d+2cOHD8Pb27vI8hK9iIKCgrB3717Vst27dyv3vb8ILCwsEBAQoNoPBQUF2Lt3b4n7wdLSEpUqVcKDBw/w448/Khe8ZcmzKBUqVICtrS0iIiJgaWmJNm3aAIAyavHR+cSuX7+OP//8U5krJCgoCAcOHMD9+/eVNLt374aPjw8cHR1V2/nhhx+Ql5en8yvJ/fv3cf/+fZ1fSU1NTZXO8fHjx+PUqVOqzn3g4aid8PDwIutW+P7C+T+eVEFBgd48li1bhoCAAPj5+emsy8zMRIsWLRAQEIDw8HCDRmMlJCQoF0/65OfnIzExsdg0xuZZxFGrVq2QmJioOp4aNmyIfv36KaOBb9++DQDFHpeFRATh4eEYMGCAzo88pbF//36cO3eu2JEzNjY2cHNzw40bN7Bz506lTqGhoTox4u7ujrFjxyqPeC2qTiYmJjp1ys3NxYYNG8o8iqeouH+Uvb09XFxckJaWhhMnTih1MiTub9++DRMTE9XopcLXj9cpIyMDUVFRZa7Tv9GziCN9cnNzkZ6erpyjAgICYG5urtpuSkoKLl68qLNdjUYDd3d3WFlZYd26dahSpQr8/f0B/N/n/KjC6+7Cz/lJrzuKOr+XL18eDg4O2LdvH65evarqwH2S87shHm8DmjZtqjOnZ2pqqtIGG9J+Vq1aFe7u7sXmU5o6vchxBDy/WNJHHj4QSef4Ler8bWdnp9P2DR8+XBndFhgYaFBZDDmmgoKCkJ2drcyrBTzsSCkoKFC2Y8g1ZHGxLyIGfR6GtCU//vgjTp48qawvnB/44MGDCAsLU/Jet24dBg8ejHXr1qFTp06qchlybfGk17N/i6d5/1dISIjY2trKe++9J2fPnpW1a9eKjY2NfPfddyKi/37ykydPipWVlYSFhUl8fLykpqbK5s2bVbNqDxo0SKpUqSKbNm2S8+fPS1RUlHJ/++P32I0aNUp27Ngh58+fl9jYWAkMDJRevXrpTRsbGysmJiYybdo0SUlJkRUrVoiVlZXqUcD6yuzn56d6FOqj/qn3nBI9KicnR+Lj4yU+Pl4AyJdffinx8fHy66+/iojI+PHjJTQ0VElf+CjTsWPHSnJyssyfP/+FfYS6VquVFStWSFJSkgwbNkwcHByUx2CHhobK+PHjlfRHjhyRH3/8UdLT0+XAgQPSsmVLqVatmuqe4JLyFHn4iO/4+HhZsmSJ8vSL+Ph4uXbtmpLmm2++kdjYWElJSZFvv/1WrKysZN68earyd+3aVerUqSOHDx+WxMREefXVV6V27drK00yys7OlQoUKEhoaKqdPn5b169eLtbW13icKNmvWTHr37q13P4WEhEidOnUkKipKzp8/L+Hh4WJpaanct60PHruHedu2bbJ8+XJJTEyUjIwM2bp1q/j6+qqewCAiynEcEBAgffv2lfj4eDlz5oyyfsaMGbJr1y5JT0+XpKQkmT17tpiZmek88efmzZtibW2td761y5cvS82aNaVVq1Zy+fJl1WNIC61YsULWrl0rycnJkpycLNOnTxcTExNZvny5kubjjz9WHmkbGxsrb7zxhlhaWqrK+zS9SHH0uMfvm793757UrFlTmjdvLkePHpVz587J7NmzRaPRqObwEBHZs2ePAJDk5GS9eZ85c0bi4+Olc+fO0qJFC+UYfFz//v0lMDBQbx47duyQn3/+Wc6fPy+7du0SPz8/CQwMVGJRH31P13J2dpbu3btLQkKCpKSkyAcffCDm5uaSkJCgeu/SpUvF0tKyyH1WUhwVKi7uN2zYIFFRUZKeni6bN28WT09P6d69e5H1EdGN++TkZNFqtTJixAhJSkqS06dPS//+/cXe3l5+++031XsnTZok7u7uyuOGn5UXKY7ef/99iY6OloyMDDl8+LC0bt1aypcvL1evXlXSDB8+XDw8PGTfvn1y4sQJCQoKkqCgIFXZPv/8czl16pScPn1apk2bJubm5qrPee/evaLRaOTjjz+W1NRUiY2NlXbt2omnp6cy344h1x3jx49XHkF96tQpGT9+vGg0Gtm1a5eSZvny5RITEyPnzp2TVatWiZOTk4wZM0ZZb8j5XaTkuJ87d65s3rxZ0tLSJDExUUaNGiUmJiaqJ1oeO3ZMzMzMZPr06ZKWliZr1qwRa2trWb16tZLGkPZz7ty5YmdnJz/88IOkpaXJpEmTxNLSUpnPxdA6iTCORJ5PLKWnp8uMGTPkxIkT8uuvv8rhw4elc+fO4uTkpPOo8JLO34/SNyePIXFd0jEl8vAR6g0aNJCjR4/KoUOHxMvLS/UIdUOuIadMmSLlypWTdevWKe1fjRo1lO/qhnwe+jzeljxO35w8a9asETMzM5k/f74qRrKzs4vMx5B560oqS1k810eov/POOzJ8+HCxs7MTR0dH+eijj4qdNFDk4UmvTZs2YmtrKzY2NlKvXj2ZPn26sv7OnTvy3nvviZubm1hYWEjNmjWVC+fHP7SRI0dKjRo1RKvViouLi4SGhsqff/6pN63I/z1C3dzcXDw8POSLL75QlY2dPGSMCmPh8b+BAweKyMOJBUNCQnTeU79+fbGwsJDq1aurOkOftn9yHH3zzTfi4eEhFhYW0rhxYzly5IiyLiQkRNmHIg8nlvT19RWtVivOzs4SGhqqPGrS0DxFHjaK+j6vRz+D0NBQcXJyEgsLC6lXr57eR2zevHlT3nzzTXFwcBAnJyfp1q2b6lHFIg873ps1ayZarVYqVaoks2bN0snn7NmzAkB1If2orKwsGTRokLi7u4ulpaX4+PjInDlzVJPyP+7xRnHfvn0SFBQk9vb2YmlpKV5eXjJu3Didixx9+8XT01NZP3HiRKlZs6ZYWlqKo6OjBAUFyfr163W2v2jRIrGystLbuIeHh+vdzqO/k6xYsUJ8fX3F2tpa7OzspHHjxqrHDIuIjB49WvmcK1SoIB07dix2ktyyetHi6FH6LsRSU1Ole/fu4urqKtbW1kXGSZ8+fSQ4OLjIvD09PYs9FkQeXuxaWVkVOdFpRESEVK9eXSwsLKRixYoSFhZW7IVl4XYfvx45fvy4tG3bVpycnKRcuXLSpEkT2b59u857g4KCpG/fvkXmXVIciZQc9/PmzZPKlSsr11OTJk1SPUq3qO0+fjG8a9cuadq0qdjb24ujo6O0bNlSeTx3ofz8fKlcubJ89NFHxeb/NLxIcdS7d2/lWrtSpUrSu3dvnYlg79y5I++88444OjqKtbW1dOvWTafz4JVXXlHO3YGBgXqPyXXr1kmDBg3ExsZGXFxcpEuXLjodqyVdd7z55pvi6ekpFhYW4uLiIq1atdI5PseNGycVKlQQc3Nz8fLy0mmLDDm/i5Qc95999pnUqFFDLC0txcnJSVq0aCH79u3TqfdPP/0kL730kmi1WqlVq5bOOcLQ9nPmzJlSuXJlsba2lqCgIDl48GCp68Q4+j9/dyxlZmZKhw4dxNXVVczNzaVy5crSt29fOXv2rE7ZSjp/P0pfJ48hcS1S/DElInLt2jXp06eP2Nraip2dnQwePFhycnJUaUq6hrx//75MnTpViZUqVarIO++8o3NtV9J18eOepJMnJCSk2O9C+vybOnk0/78wxYqLi0NAQABiY2OVoZb6tGjRAvXr1/9HTj70d1mzZg369+9f4r4ioqIxjojKjnFEVHaMI6KyYxwRlZ2hfTLAc5h4mYiIiIiIiIiInj528hARERERERERGQGzp5lZdHT008yOiIiIiIiIiIgMxJE8RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGwKw0ibdv347k5ORnVRajcPjwYQDcV0RlwTgiKjvGEVHZMY6Iyo5xRFR2GRkZBqfViIiUlCgmJgbNmzdHfn5+mQr2ojAxMUFBQcHzLgbRvxrjiKjsGEdEZcc4Iio7xhFR2ZmamuLgwYMICgoqNp1BI3m0Wi3y8/OxevVq+Pr6PpUCGqvt27dj8uTJ3FdEZcA4Iio7xhFR2TGOiMqOcURUdsnJyejfvz+0Wm2JaUt1u5avry/8/f2fuGAvgsIhiNxXRE+OcURUdowjorJjHBGVHeOI6O/FiZeJiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIzAv76TZ+rUqahfv77yetCgQXjttdeeW3mIiIiIiIiIiJ6Hf30nDxlu/vz5qFq1KiwtLREYGIhjx44Vmfb+/fuYNm0aatSoAUtLS/j5+WHHjh2lyvP69ev4z3/+Ax8fH1hZWcHDwwPvvvsubt68qcpj7969CA4ORrly5VCxYkWMGzcODx48UNbfvXsXgwYNQt26dWFmZqa3E2/jxo1o06YNXFxcYGdnh6CgIOzcuVMnXWZmJvr37w9nZ2dYWVmhbt26OHHihFLncePGoW7durCxsYG7uzsGDBiA3377TZXH9OnTERwcDGtrazg4OOhs49q1a2jfvj3c3d2h1WpRpUoVjBw5En/99ZeSJjo6GhqNRufv999/V9Lk5ORg9OjR8PT0hJWVFYKDg3H8+HHVZ1RSeS9cuIAhQ4agWrVqsLKyQo0aNTBlyhTcu3dPSZOSkoJXXnkFFSpUgKWlJapXr45Jkybh/v37qv3bsGFDODg4wMbGBvXr18eqVat06k70Ijlw4AA6d+4Md3d3aDQabN68ucT3REdHw9/fH1qtFjVr1sSKFSueeTn/iZ5Fe1Ro1qxZ0Gg0GD16tGr577//jtDQUFSsWBE2Njbw9/fHjz/+qKwv6rys0WiUc68h7dGgQYP05lGnTh0lTUnn98cNHz4cGo0GX331lWp5amoqunbtivLly8POzg7NmjVDVFSUsn7FihVF1unq1asAgKysLPTt2xfe3t4wMTHR2W8A0KJFC715dOrUSUlz5coVDBo0CO7u7rC2tkb79u2Rlpamtz4igg4dOuiNm+PHj6NVq1ZwcHCAo6Mj2rVrh5MnTyrrp06dqrcsNjY2Re4/Y/W040jfvq1Vq5YqzeLFi9GiRQvY2dlBo9EgOzu7yG3m5eWhfv360Gg0SEhIUJZfuHBB72d45MgR1fuzs7MRFhYGNzc3aLVaeHt7Y/v27cr6hQsXol69erCzs1Ou/X7++edSl/f69evo168f7Ozs4ODggCFDhiA3N1eVRkQwe/ZseHt7Q6vVolKlSpg+fbqy/mnGfXJyMrp06QJ7e3vY2NigUaNGuHjxYqnqVNK1qiHnhhcF2yO2R8bYHj3TTp5Hv0jS8xUREYExY8ZgypQpiIuLg5+fH9q1a1fkiXzSpElYtGgRvvnmGyQlJWH48OHo1q0b4uPjDc7zt99+w2+//YbZs2fj9OnTWLFiBXbs2IEhQ4YoeZw8eRIdO3ZE+/btER8fj4iICERGRmL8+PFKmvz8fFhZWeHdd99F69at9Zb3wIEDaNOmDbZv347Y2Fi88sor6Ny5s6q8N27cQNOmTWFubo6ff/4ZSUlJmDNnDhwdHQEAt2/fRlxcHCZPnoy4uDhs3LgRKSkp6NKli2pb9+7dQ8+ePTFixAi9ZTExMUHXrl0RGRmJ1NRUrFixAnv27MHw4cN10qakpCArK0v5c3V1Vda99dZb2L17N1atWoXExES0bdsWrVu3RmZmpsHlPXv2LAoKCrBo0SKcOXMGc+fOxXfffYePPvpISWNubo4BAwZg165dSElJwVdffYUlS5ZgypQpShonJydMnDgRMTExOHXqFAYPHozBgwfr7UgjelHcunULfn5+mD9/vkHpMzIy0KlTJ7zyyitISEjA6NGj8dZbb71wcfQs2qNCx48fx6JFi1CvXj2ddQMGDEBKSgoiIyORmJiI7t27o1evXko+wcHBqvNxVlYW3nrrLVSrVg0NGzYEYFh7NG/ePFUely5dgpOTE3r27KmkKen8/qhNmzbhyJEjcHd311n36quv4sGDB9i3bx9iY2Ph5+eHV199VfnBoHfv3jp1ateuHUJCQpT2Ji8vDy4uLpg0aRL8/Pz01mnjxo2qPE6fPg1TU1OlTiKC1157DefPn8eWLVsQHx8PT09PtG7dGrdu3dLJ76uvvoJGo9FZnpubi/bt28PDwwNHjx7FoUOHUK5cObRr10754eGDDz7QqVPt2rVV+/dF8KziqE6dOqp9e+jQIdX627dvo3379qrriKJ8+OGHeo/bQnv27FFtKyAgQFl37949tGnTBhcuXMD//vc/pKSkYMmSJahUqZKSpnLlypg1axZiY2Nx4sQJtGzZEl27dsWZM2dKVd5+/frhzJkz2L17N7Zu3YoDBw5g2LBhqjSjRo3C0qVLMXv2bJw9exaRkZFo3Lixsv5pxX16ejqaNWuGWrVqITo6GqdOncLkyZNhaWlZqjqVdK1qyLnhRcD2iO2R0bZHYoDY2FgBILGxscWmCwkJkbCwMBk1apQ4OztLixYtJDExUdq3by82Njbi6uoq/fv3lz/++EN5T35+vnz22WdSo0YNsbCwkCpVqsinn36qrP/www/Fy8tLrKyspFq1ajJp0iS5d++esn7KlCni5+envB44cKB07drVkGo9E6tXrzZoX/3dGjduLGFhYcrr/Px8cXd3l5kzZ+pN7+bmJt9++61qWffu3aVfv35PnKeIyIYNG8TCwkLu378vIiITJkyQhg0bqtJERkaKpaWl/PXXXzrvL83nW7t2bfn444+V1+PGjZNmzZoZ9N5Cx44dEwDy66+/6qwLDw8Xe3t7g/KZN2+eVK5cWXkdFRUlAOTGjRt609++fVtMTU1l69atquX+/v4yceLEJypvoc8//1yqVatWbHnfe++9EvdVgwYNZNKkScWmeVL/1DgiKgoA2bRpU7FpPvzwQ6lTp45qWe/evaVdu3bPpEz/1Dh6Fu2RiEhOTo54eXnJ7t27JSQkREaNGqVab2NjI99//71qmZOTkyxZskTvdu/duycuLi4ybdo0vesNbY82bdokGo1GLly4ICKlO79fvnxZKlWqJKdPnxZPT0+ZO3eusu6PP/4QAHLgwAFl2V9//SUAZPfu3XrLcvXqVTE3N9fZD4X07Td95s6dK+XKlZPc3FwREUlJSREAcvr0aSVNfn6+uLi46Ozf+Ph4qVSpkmRlZenEzfHjxwWAXLx4UVl26tQpASBpaWl6y5KQkKCzH56mFymOHr+mLk5J1zHbt2+XWrVqyZkzZwSAxMfHK+syMjJ0lj1u4cKFUr16ddU1vyEcHR1l6dKlBpc3KSlJAMjx48eVZT///LNoNBrJzMxU0piZmcnZs2cNLseTxn3v3r2lf//+Bm2jpM9AxPBr1ZLODWX1IsWRCNsjEbZHz4KhfTIiIk99JM/KlSthYWGBw4cPY9asWWjZsiUaNGiAEydOYMeOHbhy5Qp69eqlpJ8wYQJmzZqFyZMnIykpCWvXrkWFChWU9eXKlcOKFSuQlJSEefPmYcmSJZg7d+7TLrZRu3fvHmJjY1W9vCYmJmjdujViYmL0vicvL0/1qwEAWFlZKb/oPEmeAHDz5k3Y2dnBzMys2O3cvXsXsbGxpavoIwoKCpCTkwMnJydlWWRkJBo2bIiePXvC1dUVDRo0wJIlS4rN5+bNm9BoNHqHuhrqt99+w8aNGxESEqKzrn79+nBzc0ObNm1w+PBhZfmDBw+Qn59f7GfwpOW9efOmar887ty5c9ixY4fe8gIPe8j37t2LlJQUvPzyy0XmQ0RqMTExOr+2tWvXrthzprF5Fu1RobCwMHTq1KnIXzSDg4MRERGB69evo6CgAOvXr8fdu3fRokULvekjIyNx7do1DB48uBQ11LVs2TK0bt0anp6eAAw/vxcUFCA0NBRjx45VDa0v5OzsDB8fH3z//fe4desWHjx4gEWLFsHV1VU1IuJR33//PaytrfH666+XuU5vvPGGMiQ9Ly8PAFR1MjExgVarVdXp9u3b6Nu3L+bPn4+KFSvq5Ovj4wNnZ2csW7YM9+7dw507d7Bs2TL4+vqiatWqesuydOlSeHt7o3nz5mWq07/Js4yjtLQ0uLu7o3r16ujXr5/qNiFDXblyBUOHDsWqVatgbW1dZLouXbrA1dUVzZo1Q2RkpGpdZGQkgoKCEBYWhgoVKuCll17CjBkzkJ+frzev/Px8rF+/Hrdu3UJQUJDBZY2JiYGDg4MyOgIAWrduDRMTExw9ehQA8NNPP6F69erYunUrqlWrhqpVq+Ktt97C9evXi8z3SeK+oKAA27Ztg7e3N9q1awdXV1cEBgYadCtwWT2tc8O/CdsjtkdG3R49zV6jkJAQadCggfL6k08+kbZt26rSXLp0SQBISkqK/PXXX6LVaovstdTniy++kICAAOU1R/KULDMzUwDIL7/8olo+duxYady4sd739OnTR2rXri2pqamSn58vu3btEisrK7GwsHjiPP/44w/x8PCQjz76SFm2c+dOMTExkbVr18qDBw/k8uXL0rx5cwEga9eu1cnD0M/3s88+E0dHR7ly5YqyTKvVilarlQkTJkhcXJwsWrRILC0tZcWKFXrzuHPnjvj7+0vfvn31ri/p15E33nhDrKysBIB07txZ7ty5o6w7e/asfPfdd3LixAk5fPiwDB48WMzMzFTHTVBQkISEhEhmZqY8ePBAVq1aJSYmJuLt7f1E5RURSUtLEzs7O1m8eLHOuqCgINFqtQJAhg0bJvn5+ar12dnZYmNjI2ZmZqLVamXZsmVFbqes/olxRFQcGDCSx8vLS2bMmKFatm3bNgEgt2/ffupl+ifG0bNoj0RE1q1bJy+99JJyntX3C+CNGzekbdu2AkDMzMzEzs5Odu7cWWRZO3ToIB06dChyvSHtUWZmppiamkpERIRquSHn9xkzZkibNm2koKBARETnl1ORh9dUAQEBotFoxNTUVNzc3CQuLq7I8vj6+sqIESOKXG/IL6dHjx4VAHL06FFl2b1798TDw0N69uwp169fl7y8PJk1a5YAUF0HDhs2TIYMGaK81hc3iYmJUqNGDTExMRETExPx8fFRfnV+3J07d8TR0VE+++yzYstcFi9SHG3fvl02bNggJ0+elB07dkhQUJB4eHjoHVld1CiSgoICad++vXzyyScion/Uzh9//CFz5syRI0eOyLFjx2TcuHGi0Whky5YtShofHx/RarXy5ptvyokTJ2T9+vXi5OQkU6dOVW3v1KlTYmNjI6ampmJvby/btm3TW/+iyjt9+nS911UuLi6yYMECERF5++23RavVSmBgoBw4cECioqKkfv368sorr+jd1pPGfeFoAmtra/nyyy8lPj5eZs6cKRqNRqKjow2u06MMHclT0rmhrF6kOGJ7xPboWXmuI3ke7a07efIkoqKiYGtrq/wVTuCWnp6O5ORk5OXloVWrVkXmFxERgaZNm6JixYqwtbXFpEmTnuhXBSqdefPmwcvLC7Vq1YKFhQVGjhyJwYMHw8TkyQ6Zv/76C506dULt2rUxdepUZXnbtm3xxRdfYPjw4cqkeh07dgSAJ97W2rVr8fHHH2PDhg2q+4oLCgrg7++PGTNmoEGDBhg2bBiGDh2K7777TieP+/fvo1evXhARLFy48InKMXfuXMTFxWHLli1IT0/HmDFjlHU+Pj54++23ERAQgODgYCxfvhzBwcGqUWqrVq2CiKBSpUrQarX4+uuv0adPH737xZDyZmZmon379ujZsyeGDh2qsz4iIgJxcXFYu3Yttm3bhtmzZ6vWlytXDgkJCTh+/DimT5+OMWPGIDo6+on2DRGRoUpqjy5duoRRo0ZhzZo1Or9GPmry5MnIzs7Gnj17cOLECYwZMwa9evVCYmKiTtrLly9j586dqjnknsTKlSvh4OCgMyFmSef32NhYzJs3T5moUh8RQVhYGFxdXXHw4EEcO3YMr732Gjp37oysrCyd9DExMUhOTi5znZYtW4a6deuq5iMxNzfHxo0bkZqaCicnJ1hbWyMqKgodOnRQ6hQZGYl9+/bpTNb5qDt37mDIkCFo2rQpjhw5gsOHD+Oll15Cp06dcOfOHZ30mzZtQk5ODgYOHFimOr0IDLmu69ChA3r27Il69eqhXbt22L59O7Kzs7FhwwaDt/PNN98gJycHEyZMKDJN+fLlMWbMGAQGBqJRo0aYNWsW+vfvjy+++EJJU1BQAFdXVyxevBgBAQHo3bs3Jk6cqHPN5uPjg4SEBBw9ehQjRozAwIEDkZSUVIo9U7KCggLk5eXh+++/R/PmzdGiRQssW7YMUVFRSElJ0Un/pHFfUFAAAOjatSvee+891K9fH+PHj8err76q91r1aXla54YXAdsjtkf/lvboqXfyPDqbdG5uLjp37oyEhATVX1paGl5++WVYWVkVm1dMTAz69euHjh07YuvWrYiPj8fEiRM5oXMplS9fHqamprhy5Ypq+ZUrV/QOTwMAFxcXbN68Gbdu3cKvv/6Ks2fPwtbWFtWrVy91njk5OWjfvj3KlSuHTZs2wdzcXLV+zJgxyM7OxsWLF/Hnn3+ia9euAKBsqzTWr1+Pt956Cxs2bNAZIunm5obatWurlvn6+up0GhZ2mPz666/YvXs37OzsSl0OAKhYsSJq1aqFLl26YNGiRVi4cKHeE12hxo0b49y5c8rrGjVqYP/+/cjNzcWlS5dw7Ngx3L9/X2e/GFLe3377Da+88gqCg4OxePFivduvUqUKateujT59+mDWrFmYOnWqali0iYkJatasifr16+P999/H66+/jpkzZ5Z2txC9sCpWrKj3nGlnZ1die2gsnkV7FBsbi6tXr8Lf3x9mZmYwMzPD/v378fXXX8PMzAz5+flIT0/Ht99+i+XLl6NVq1bw8/PDlClT0LBhQ70TZ4eHh8PZ2Vln4v3SEBEsX74coaGhsLCwUK0r6fx+8OBBXL16FR4eHkqdfv31V7z//vvKMPF9+/Zh69atWL9+PZo2bQp/f38sWLAAVlZWWLlypU55li5divr16xc5dN4Qt27dwvr16/VemAcEBCAhIQHZ2dnIysrCjh07cO3aNaVO+/btQ3p6OhwcHJQ6AUCPHj2UWxTWrl2LCxcuIDw8HI0aNUKTJk2wdu1aZGRkYMuWLXrr9Oqrr6pu838RPIs40sfBwQHe3t6qa5OS7Nu3DzExMdBqtTAzM0PNmjUBAA0bNiz2y09gYKBqO25ubvD29oapqamyzNfXF7///rvqe4CFhQVq1qyJgIAAzJw5E35+fpg3b57B5a1YsaLOJLsPHjzA9evXlX3p5uYGMzMzeHt7q8oCQOcasixxX758eZiZmRl0rfo0PY1zw78R26OH2B4ZZ3v0TJ+u5e/vjzNnzqBq1aqoWbOm6s/GxgZeXl6wsrLC3r179b7/l19+gaenJyZOnIiGDRvCy8sLv/7667MsslGysLBAQECAaj8XFBRg7969Jd63bGlpiUqVKuHBgwf48ccflQ4YQ/P866+/0LZtW1hYWCAyMrLIXm2NRgN3d3dYWVlh3bp1qFKlCvz9/UtVz3Xr1mHw4MFYt26d6jF6hZo2barzi0tqaqpyXyrwfx0maWlp2LNnD5ydnUtVhqIU/jpTeJ+oPgkJCXBzc9NZbmNjAzc3N9y4cQM7d+5UPgNDy5uZmYkWLVogICAA4eHhBo2QKigowP3795VyF5WmuPoQkVpQUJBOe7d79+5SzR/xb/cs2qNWrVohMTFR9WNSw4YN0a9fPyQkJMDU1BS3b98GoDtC1NTUVOc8JyIIDw/HgAEDdH6UKI39+/fj3Llzxf5SWdT5PTQ0FKdOnVLVyd3dHWPHjlWexlZUnUxMTHTqlJubiw0bNpT5V9MffvgBeXl56N+/f5Fp7O3t4eLigrS0NJw4cUKp0/jx43XqBDwc9RoeHq7UycTERPVrceHrx+uUkZGBqKioF3L0wbOII31yc3ORnp6u99qkKF9//TVOnjypfMaFjzyPiIhQPXL8cY9fAzVt2hTnzp1Tfe6pqalwc3PT+ZL6qNJemwQFBSE7O1s1D+S+fftQUFCAwMBApSwPHjxAenq6qiwAVNeQQNni3sLCAo0aNSrxWvVpelrnhn8jtkdqbI+MrD16mvd/PX7vXGZmpri4uMjrr78ux44dk3PnzsmOHTtk0KBB8uDBAxERmTp1qjg6OsrKlSvl3LlzEhMTo8yKv2XLFjEzM5N169bJuXPnZN68eeLk5KS6t5Rz8hhm/fr1otVqZcWKFZKUlCTDhg0TBwcH+f3330VEJDQ0VMaPH6+kP3LkiPz444+Snp4uBw4ckJYtW0q1atVU9/2WlOfNmzclMDBQ6tatK+fOnZOsrCzlr/DzF3n4tKdTp07J6dOnZdq0aWJubq5zT+SZM2ckPj5eOnfuLC1atJD4+HjV/d1r1qwRMzMzmT9/vmo72dnZSppjx46JmZmZTJ8+XdLS0mTNmjVibW0tq1evFpGH93B26dJFKleuLAkJCap88vLylHx+/fVXiY+Pl48//lhsbW2VsuTk5IjIwzk2li9fLomJiZKRkSFbt24VX19fadq0qZLH3LlzZfPmzZKWliaJiYkyatQoMTExkT179ihpduzYIT///LOcP39edu3aJX5+fhIYGKg8acKQ8l6+fFlq1qwprVq1ksuXL6vSFFq9erVERERIUlKSpKenS0REhLi7u6ueFDBjxgzZtWuXpKenS1JSksyePVvMzMxKNZ9WafxT44joUTk5OUr8A1DmUCh8ut348eMlNDRUSX/+/HmxtraWsWPHSnJyssyfP19MTU1lx44dz6R8/9Q4ehbt0eMevx65d++e1KxZU5o3by5Hjx6Vc+fOyezZs0Wj0ejM4bFnzx4BIMnJyXrzLqk9KtS/f38JDAzUm0dJ53d99D3NxNnZWbp37y4JCQmSkpIiH3zwgZibm0tCQoLqvUuXLhVLS8si91lhHQICAqRv374SHx8vZ86c0UnXrFkz6d27t948NmzYIFFRUZKeni6bN28WT09P6d69e5H1EdGdAyE5OVm0Wq2MGDFCkpKS5PTp09K/f3+xt7eX3377TfXeSZMmibu7u+p64ll4keLo/fffl+joaMnIyJDDhw9L69atpXz58nL16lUlTVZWlsTHx8uSJUuUp8jEx8fLtWvX9JZT35w8K1askLVr10pycrIkJyfL9OnTxcTERJYvX66kuXjxopQrV05GjhwpKSkpsnXrVnF1dVU9gXf8+PGyf/9+ycjIkFOnTsn48eNFo9HIrl27SlXe9u3bS4MGDeTo0aNy6NAh8fLykj59+ijr8/Pzxd/fX15++WWJi4uTEydOSGBgoLRp00anvmWN+40bN4q5ubksXrxY0tLS5JtvvhFTU1M5ePBgqepU0rVqoZLODU/LixRHj2N7xPboaSnNnDzPtJNHRCQ1NVW6desmDg4OYmVlJbVq1ZLRo0crEzfl5+fLp59+Kp6enmJubi4eHh6qiSnHjh0rzs7OYmtrK71795a5c+eyk+cJffPNN+Lh4SEWFhbSuHFjOXLkiLIuJCREBg4cqLyOjo4WX19f0Wq14uzsLKGhocqjJA3Ns3BCOH1/GRkZSrpXXnlF7O3txdLSUgIDA2X79u062/H09NSbz6Pl17f+0TqJiPz000/y0ksviVarlVq1aqkmIC68ENH3FxUVpaQbOHBgsWn27dsnQUFBSp28vLxk3LhxqpPZZ599JjVq1BBLS0txcnKSFi1ayL59+1RljYiIkOrVq4uFhYVUrFhRwsLCVJ1WhpQ3PDy8yDSF1q9fL/7+/mJrays2NjZSu3ZtmTFjhmqi6IkTJ0rNmjXF0tJSHB0dJSgoSNavX6/zOT0t/+Q4IipU1Dmu8LwzcOBACQkJ0XlP/fr1xcLCQqpXry7h4eHPrHz/5Dh6Fu3Ro4q6Hunevbu4urqKtbW11KtXT++jW/v06SPBwcFF5l1SeyTycKJ6KysrvZPci5R8fi9qu49PdHn8+HFp27atODk5Sbly5aRJkyZ629CgoKBiJ+XXVx9PT09VmrNnzwoA1RfoR82bN08qV66sXMtNmjRJ9QNJUdt9/EedXbt2SdOmTcXe3l4cHR2lZcuWEhMTo0qTn58vlStXVj3I4Vl5keKod+/e4ubmJhYWFlKpUiXp3bu3nDt3TpVmypQpeo+Xos5lRXXy+Pr6irW1tdjZ2Unjxo3lhx9+0HnvL7/8IoGBgaLVaqV69eoyffp01ZeoN998Uzw9PcXCwkJcXFykVatWOsenIeW9du2a9OnTR2xtbcXOzk4GDx6s0xmSmZkp3bt3F1tbW6lQoYIMGjRIp2PracX9smXLlGsuPz8/2bx5c6nrVNK1aqGSzg1Py4sUR49je6TG9ujJlaaTRyMioju+Ry0uLg4BAQGIjY0t9S00L5o1a9agf//+3FdEZcA4Iio7xhFR2TGOiMqOcURUdqXpk3mmc/IQEREREREREdHfg508RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGgJ08RERERERERERGwKw0ibdv347k5ORnVRajcPjwYQDcV0RlwTgiKjvGEVHZMY6Iyo5xRFR2GRkZBqfViIiUlCgmJgbNmzdHfn5+mQr2ojAxMUFBQcHzLgbRvxrjiKjsGEdEZcc4Iio7xhFR2ZmamuLgwYMICgoqNp1BI3m0Wi3y8/OxevVq+Pr6PpUCGqvt27dj8uTJ3FdEZcA4Iio7xhFR2TGOiMqOcURUdsnJyejfvz+0Wm2JaUt1u5avry/8/f2fuGAvgsIhiNxXRE+OcURUdowjorJjHBGVHeOI6O/FiZeJiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIwAO3mIiIiIiIiIiIzAU+3kEREMGzYMTk5O0Gg0SEhIeJrZExERERERERFREZ5qJ8+OHTuwYsUKbN26FVlZWfjrr7/QuXNnuLu7Q6PRYPPmzU9zc1RK8+fPR9WqVWFpaYnAwEAcO3asyLT379/HtGnTUKNGDVhaWsLPzw87duwoMv2sWbOg0WgwevRo1fLff/8doaGhqFixImxsbODv748ff/xRlSYuLg5t2rSBg4MDnJ2dMWzYMOTm5irrV6xYAY1Go/fv6tWrqvr5+vrCysoKPj4++P7771Xb0ZePpaWlKs2VK1cwaNAguLu7w9raGu3bt0daWpoqzeLFi9GiRQvY2dlBo9EgOztbZ3+kpqaia9euKF++POzs7NCsWTNERUUp669du4b27dvD3d0dWq0WVapUwciRI/HXX3+p8snLy8PEiRPh6ekJrVaLqlWrYvny5cr6Fi1a6N0vnTp1UtJMnToVtWrVgo2NDRwdHdG6dWscPXpUp8yF26tfv75OJ+3UqVP1bsfGxkZvPsbsnxpHhYr6DC9cuKD3Mzxy5IiSZuPGjWjYsCEcHBxgY2OD+vXrY9WqVTrbSE5ORpcuXWBvbw8bGxs0atQIFy9eVNbfvXsXYWFhcHZ2hq2tLXr06IErV66o8jh+/DhatWoFBwcHODo6ol27djh58qSyPjo6Gl27doWbm5tSljVr1hS579avXw+NRoPXXntNtbyoc8cXX3xh8L4zpCxLlixB8+bN4ejoqMTZ48dGbm4uRo4cicqVK8PKygq1a9fGd999V2SdjNk/MY6io6OLPF6OHz8O4OGxPWjQINStWxdmZmY6xxsADBo0SG8ederUUdLk5ORg9OjR8PT0hJWVFYKDg5Vt6DN8+HBoNBp89dVXquUltTWGtJ9ZWVno27cvvL29YWJiorPfAMPaGkPaz0Iigg4dOui9Nizp3MD26P8YcxwBJV8DAUB2djbCwsLg5uYGrVYLb29vbN++vVT7yZA26+LFi+jUqROsra3h6uqKsWPH4sGDB3rLffjwYZiZmaF+/fqq5TNnzkSjRo1Qrlw5uLq64rXXXkNKSkqpymLoNfGaNWvg5+cHa2truLm54c0338S1a9dU2/rqq6/g4+MDKysrVKlSBe+99x7u3r2rt07G7N8aRykpKXjllVdQoUIFWFpaonr16pg0aRLu37+v5HPmzBn06NEDVatW1duGGFret99+GzVq1ICVlRVcXFzQtWtXnD17VpVGX1nXr1+vdztFxUhhOR//CwsLU6WLiYlBy5YtYWNjAzs7O7z88su4c+eOKs22bdsQGBgIKysrODo6FnmeuXbtGipXrqz3O11J3y1Lu3//VmKA2NhYASCxsbHFpvvmm2/Ew8NDeb19+3aZOHGibNy4UQDIpk2bDNnc3yovL++p5rd69WqD9tXfbf369WJhYSHLly+XM2fOyNChQ8XBwUGuXLmiN/2HH34o7u7usm3bNklPT5cFCxaIpaWlxMXF6aQ9duyYVK1aVerVqyejRo1SrWvTpo00atRIjh49Kunp6fLJJ5+IiYmJkk9mZqY4OjrK8OHD5ezZs3Ls2DEJDg6WHj16KHncvn1bsrKyVH/t2rWTkJAQJc2CBQukXLlysn79eklPT5d169aJra2tREZGKmnCw8PFzs5Olc/vv/+urC8oKJAmTZpI8+bN5dixY3L27FkZNmyYeHh4SG5urpJu7ty5MnPmTJk5c6YAkBs3bujsEy8vL+nYsaOcPHlSUlNT5Z133hFra2vJysoSEZHr16/LggUL5Pjx43LhwgXZs2eP+Pj4SJ8+fVT5dOnSRQIDA2X37t2SkZEhv/zyixw6dEhZf+3aNVV9Tp8+LaamphIeHq6kWbNmjezevVvS09Pl9OnTMmTIELGzs5OrV6/qlPvdd9+VDh06CACJj49Xlufk5Oh8BrVr15aBAwfq5PE0MI5GqdaVFEePKuozzMjIEACyZ88e1ed47949JU1UVJRs3LhRkpKS5Ny5c/LVV1+Jqamp7NixQ0lz7tw5cXJykrFjx0pcXJycO3dOtmzZotoHw4cPlypVqsjevXvlxIkT0qRJEwkODlbW5+TkiJOTkwwaNEjOnj0rp0+flh49ekiFChWU8kyfPl0mTZokhw8fVspiYmIiP/30k06dMzIypFKlStK8eXPp2rWrat3jx+3y5ctFo9FIenq6wfvOkLL07dtX5s+fL/Hx8ZKcnCyDBg0Se3t7uXz5spJm6NChUqNGDYmKipKMjAxZtGiRmJqaypYtW3TK8jQwjkap1pUUR3l5eTrHy1tvvSXVqlWTgoICERHJzc2V4cOHy+LFi6Vdu3Y6x5uISHZ2tiqPS5cuiZOTk0yZMkVJ06tXL6ldu7bs379f0tLSZMqUKWJnZ6c6Xgpt3LhR/Pz8xN3dXebOnataV1JbY0j7mZGRIe+++66sXLlS6tevr7PfREpuawxtPwt9+eWXSqw9em1oyLmB7dFDxh5HIiVfA+Xl5UnDhg2lY8eOcujQIcnIyJDo6GhJSEgo1X4qqc168OCBvPTSS9K6dWuJj4+X7du3S/ny5WXChAk6Zb5x44ZUr15d2rZtK35+fqp17dq1k/DwcDl9+rQkJCRIx44ddWKkpLIYEtOHDh0SExMTmTdvnpw/f14OHjwoderUkW7duilp1qxZI1qtVtasWSMZGRmyc+dOcXNzk/fee0/vZ1FWjKNRqnVPI47S09Nl+fLlkpCQIBcuXJAtW7aIq6ur6rg8duyYfPDBB7Ju3TqpWLGiThtiaHkXLVok+/fvl4yMDImNjZXOnTtLlSpV5MGDB0oaABIeHq4q8507d3S2U1yMXL16VfX+3bt3CwCJiopS0vzyyy9iZ2cnM2fOlNOnT8vZs2clIiJC7t69q6T53//+J46OjrJw4UJJSUmRM2fOSEREhN56d+3aVWmPHv1OZ8h3y9Ls36fB0D4ZEZGn1skzcOBAAaD8eXp6qjdkYCdPQUGBTJkyRapUqSIWFhbi5uYm//nPf5T1d+/elQ8//FAqV64sFhYWUqNGDVm6dKmyPjo6Who1aiQWFhZSsWJFGTdunNy/f19ZHxISImFhYTJq1ChxdnaWFi1aiIhIYmKitG/fXmxsbMTV1VX69+8vf/zxhyG7R+WfehJr3LixhIWFKa/z8/PF3d1dZs6cqTe9m5ubfPvtt6pl3bt3l379+qmW5eTkiJeXl+zevVtCQkJ0Tgo2Njby/fffq5Y5OTnJkiVLROThScPV1VXy8/OV9adOnRIAkpaWprdsV69eFXNzc1W+QUFB8sEHH6jSjRkzRpo2baq8Dg8PF3t7e715ioikpKQIADl9+rSyLD8/X1xcXJTyPioqKkpvJ88ff/whAOTAgQPKsr/++ksAyO7du4vc/rx586Ry5crK659//lns7e3l2rVrRb7ncXPnzpVy5crpvagudPPmTeXL/qO2b98utWrVkjNnzuh8yX1cQkKCTh2fJsbRKNX6kuKoUHGfYWEnT3Gfqz4NGjSQSZMmKa979+4t/fv3LzJ9dna2mJubyw8//KAsS05OFgASExMjIiLHjx8XAHLx4kUlTUlxLyLSsWNHGTx4sGrZgwcPJDg4WJYuXSoDBw4s8stCoa5du0rLli11lpfm+C+qLI+Xq1y5crJy5UplWZ06dWTatGmqdP7+/jJx4sRit/WkGEejVOsNjaNC9+7dExcXF53PrJAhx5uIyKZNm0Sj0ciFCxdE5OGXNFNTU9m6dasqnb5j4fLly1KpUiU5ffq0eHp6qi4gn6St0dd+PkrfftPn8bamNO1nfHy8VKpUSbKysnSuDZ/k3MD26CFjiyNDroEWLlwo1atXV/1Y8biS9pMhbdb27dvFxMRE9cPgwoULxc7OTufH4t69e8ukSZNkypQpOl9gH3f16lUBIPv37ze4LPryeDymv/jiC6levboq3ddffy2VKlVSXoeFhem0hY9fNz9NjKNRqvVPO44Kvffee9KsWTO96x5vQ0pT3sedPHlSAMi5c+eUZYZ+1y9NjIwaNUpq1KihdGyJiAQGBqquSx93//59qVSpkqp/oCgLFiyQkJAQ2bt3r853OkO+Wz6quP37tJSmk+ep3a41b948TJs2DZUrV0ZWVlaxw46L8+OPP2Lu3LlYtGgR0tLSsHnzZtStW1dZP2DAAKxbtw5ff/01kpOTsWjRItja2gIAMjMz0bFjRzRq1AgnT57EwoULsWzZMnz66aeqbaxcuRIWFhY4fPgwvvvuO2RnZ6Nly5Zo0KABTpw4gR07duDKlSvo1avXk++Qf5B79+4hNjYWrVu3VpaZmJigdevWiImJ0fuevLw8nVuZrKyscOjQIdWysLAwdOrUSZX3o4KDgxEREYHr16+joKAA69evx927d9GiRQtlOxYWFjAx+b9D0crKCgB0tlXo+++/h7W1NV5//fUSy3vs2DHVsMXc3Fx4enqiSpUq6Nq1K86cOaPKA4AqHxMTE2i12iLLoo+zs7MypO/WrVt48OABFi1aBFdXVwQEBOh9z2+//YaNGzciJCREWRYZGYmGDRvi888/R6VKleDt7Y0PPvhAZzjio5YtW4Y33nijyGHr9+7dw+LFi2Fvbw8/Pz9l+ZUrVzB06FCsWrUK1tbWJdZx6dKl8Pb2RvPmzUtMayz+yXEEGP4ZdunSBa6urmjWrBkiIyOLTCci2Lt3L1JSUvDyyy8DAAoKCrBt2zZ4e3ujXbt2cHV1RWBgoOp2i9jYWNy/f19Vl1q1asHDw0PZTz4+PnB2dsayZctw79493LlzB8uWLYOvry+qVq1aZJlu3rwJJycn1bJp06bB1dUVQ4YMKfJ9ha5cuYJt27bppC3t8V9UWR51+/Zt3L9/X5UmODgYkZGRyMzMhIggKioKqampaNu2rUHbNAb/9Dh6VGRkJK5du4bBgweXooa6li1bhtatW8PT0xMA8ODBA+Tn55dYp4KCAoSGhmLs2LGqW70KPUlbo6/9fNI6PdrWGNp+3r59G3379sX8+fNRsWJFnXyf5NzA9ughY4sjQ66BIiMjERQUhLCwMFSoUAEvvfQSZsyYgfz8fACG7SdD2qyYmBjUrVsXFSpUUNK0a9cOf/31l+o6Mjw8HOfPn8eUKVMMquPNmzcBQGknDCnL4/TFdFBQEC5duoTt27dDRHDlyhX873//Q8eOHZU0wcHBiI2NVW5NOn/+PLZv365KY+yMLY7OnTuHHTt2qL5LGKqk8j7q1q1bCA8PR7Vq1VClShWdfMqXL4/GjRtj+fLlEBHV+tLEyL1797B69Wq8+eab0Gg0AICrV6/i6NGjcHV1RXBwMCpUqICQkBDV/o+Li0NmZiZMTEzQoEEDuLm5oUOHDjh9+rQq/6SkJEybNg3ff/+96ntoIUO/W/5jPc1eo7lz5+qM4CkEA3v35syZI97e3np75Qt/KSrqF6qPPvpIfHx8VL198+fPF1tbW2WkSEhIiDRo0ED1vk8++UTatm2rWnbp0iUBICkpKSWW+VH/xJ7qzMxMASC//PKLavnYsWOlcePGet/Tp08fqV27tqSmpkp+fr7s2rVLrKysxMLCQkmzbt06eemll5ShePp6fm/cuCFt27YVAGJmZiZ2dnayc+dOZf3p06fFzMxMPv/8c8nLy5Pr169Ljx49BIDMmDFDb9l8fX1lxIgRqmUTJkyQihUryokTJ6SgoECOHz8uFSpUEADy22+/icjD4X0rV66U+Ph4iY6OlldffVXs7Ozk0qVLIvKwl9zDw0N69uwp169fl7y8PJk1a5YA0Dk+RIoeySPy8PgJCAgQjUYjpqam4ubmpnco5xtvvCFWVlYCQDp37qwa1tiuXTvRarXSqVMnOXr0qGzbtk08PT1l0KBBevfL0aNHBYAcPXpUZ91PP/0kNjY2otFoxN3dXY4dO6asKygokPbt28snn3wiIiWP+Lhz5444OjrKZ599pnf908A4GqXKp6Q4MuQz/OOPP2TOnDly5MgROXbsmIwbN040Go3OrULZ2dliY2MjZmZmotVqZdmyZcq6wl/era2t5csvv5T4+HiZOXOmaDQaiY6OFpGHQ8AfrV+hRo0ayYcffqi8TkxMlBo1aoiJiYmYmJiIj4+PMtJBn4iICLGwsFCNFDh48KBUqlRJGXVZ0siKzz77TBwdHVVxVtrjv6iyPG7EiBFSvXp11bbu3r0rAwYMUD5HCwsL1Uifp41xNEqVT0lx9LgOHTpIhw4dilxvyEiezMxMMTU11RkmHhQUJCEhIZKZmSkPHjyQVatWiYmJiXh7eytpZsyYIW3atFGuafT9SmhoW1NIX/v5KEN+wdXX1hjafg4bNkyGDBmivNZ3bViacwPbI+ONI0OugXx8fESr1cqbb74pJ06ckPXr14uTk5NMnTrV4P1kSJs1dOhQnevAW7duCQDZvn27iIikpqaKq6ur8r2hpFEK+fn50qlTJ9WoAEPbz0cVFdMbNmwQW1tbMTMzU64xH/9uNW/ePDE3N1fSDB8+vMjylhXjaJQqn6cZR0FBQaLVagWADBs2THV3xKOKGmliSHlFHn6ntrGxEQDi4+OjGsUjIjJt2jQ5dOiQxMXFyaxZs0Sr1cq8efOU9aWNkYiICDE1NZXMzExlWUxMjAAQJycnWb58ucTFxcno0aPFwsJCUlNTlfoAEA8PD/nf//4nJ06ckD59+oizs7MyMvDu3btSr149WbVqlYjo/05nyHdLQ/bv0/RcRvI8iRkzZsDW1lb5u3jxInr27Ik7d+6gevXqGDp0KDZt2qRMbJaQkABTU9MieyiTk5MRFBSk9PYBQNOmTZGbm4vLly8ryx7/hevkyZOIiopSlaVWrVoAgPT09Kdd7X+FefPmwcvLC7Vq1YKFhQVGjhyJwYMHKz2dly5dwqhRo7BmzRqdXs5HTZ48GdnZ2dizZw9OnDiBMWPGoFevXkhMTAQA1KlTBytXrsScOXNgbW2NihUrolq1aqhQoYLeXtWYmBgkJyfr/BI/efJkdOjQAU2aNIG5uTm6du2KgQMHAoCST1BQEAYMGID69esjJCQEGzduhIuLCxYtWgQAMDc3x8aNG5GamgonJydYW1sjKioKHTp00FuWoogIwsLC4OrqioMHD+LYsWN47bXX0LlzZ2RlZanSzp07F3FxcdiyZQvS09MxZswYZV1BQQE0Gg3WrFmDxo0bo2PHjvjyyy+xcuVKvaN5li1bhrp166Jx48Y661555RUkJCTgl19+Qfv27dGrVy9lgr5vvvkGOTk5mDBhgkH127RpE3JycpT9S0X7u+LIkM+wfPnyGDNmDAIDA9GoUSPMmjUL/fv315mAuFy5ckhISMDx48cxffp0jBkzBtHR0QAeHpMA0LVrV7z33nuoX78+xo8fj1dffbVUEwjfuXMHQ4YMQdOmTXHkyBEcPnwYL730Ejp16qT32I6KisLgwYOxZMkSZURDTk4OQkNDsWTJEpQvX96g7S5fvhz9+vVT7evSHv/6yvK4WbNmYf369di0aZPOto4cOYLIyEjExsZizpw5CAsLw549ewza9ovq74qjR12+fBk7d+40aIRYcVauXAkHBwedCR9XrVoFEUGlSpWg1Wrx9ddfo0+fPkqdYmNjMW/ePGWSVX1K09YARbefpaWvrTGk/YyMjMS+ffuKnZiytOcGtkeG+7fFkSHXQAUFBXB1dcXixYsREBCA3r17Y+LEiX/7hPb5+fno27cvPv74Y3h7exv0nrCwMJw+fbrIiWkNUVRMJyUlYdSoUfjvf/+L2NhY7NixAxcuXMDw4cOVNNHR0ZgxYwYWLFiAuLg4bNy4Edu2bcMnn3zyxOV5EfwT4ygiIgJxcXFYu3Yttm3bhtmzZxtcH0PLCwD9+vVDfHw89u/fD29vb/Tq1Us1UffkyZPRtGlTNGjQAOPGjcOHH36oXGM+SYwsW7YMHTp0gLu7u7Ks8Dr07bffxuDBg9GgQQPMnTsXPj4+yqTshWkmTpyIHj16ICAgAOHh4dBoNPjhhx8AABMmTICvry/69+9f5PYN+W75j/Y0e41KO5Ln2rVrkpaWpvwVzp1z+/ZtiYyMlP/85z9SsWJFCQoKknv37klkZKSYmpoWee9tt27ddEY5FN6r/euvv4qI/t7J9u3bS/fu3VVlKfwrbm4Tff6JPdV5eXliamqqs/8HDBggXbp0Kfa9d+7ckcuXL0tBQYF8+OGHUrt2bRF5OMcAADE1NVX+ACi/Jj548EDOnTunc4++iEirVq3k7bff1tnW77//Ljk5OZKbmysmJiayYcMGnTRvvvmm1K9fv8jy3rt3Ty5duiQPHjxQJswqqkdbROT111+XN954Q2d5dna2MjFx48aN5Z133tFJU9RInj179oiJiYncvHlTtbxmzZpF3uMr8nBUAh7pHR4wYIDUqFFDlSYpKUkAKL3VhXJzc8XOzk6++uqrIvN/vCyFI6W6du0qJiYmOp+lqampDBgwQOe9LVu2lNdee82g7TwpxlHp4qi0n2Ghb7/9VipWrFhs2YcMGaL8gpmXlydmZmbKqJdCH374oTIxpL77mkVEPDw85MsvvxQRkaVLl+rMxZWXlyfW1taybt061fuio6PFxsZGFi1apFoeHx+vs+80Go2y7x7/henAgQMCQDUhp0jp9l1RZXnUF198Ifb29nL8+HHV8tu3b4u5ubnOPCxDhgyRdu3aFZlfWTCOnrw9mjZtmri4uBQ710dJI3kKCgqkZs2aMnr06CLT5ObmKuf8Xr16SceOHUXk4fVUYfkfrZOJiYlynVXatqak9lOk5JE8hrQ1RbWfo0aNKrJOhZPGlubcIML2yJjjyJBroJdffllatWqlSrN9+3YBIHl5eQbtJ0ParMmTJ+uMODh//rwAkLi4OLlx44be9qhw2d69e1XvDQsLk8qVK8v58+dVyw0py6OKiun+/fvL66+/rlr2+DVms2bNdOYbWbVqlVhZWRV73fykGEfPNo4KFX6Gj06IXEjfSBNDyqtP4Xl57dq1RZZl69atAkDu3r1b6hi5cOGCmJiYyObNm1XLC+OucAROoV69eknfvn1FRGTfvn0CQA4ePKhK07hxY/noo49ERMTPz0917WdiYqKU5b///a/qfYZ+t+RInkc4OTmhZs2ayp+ZmRmAh/e7de7cGV9//TWio6MRExODxMRE1K1bFwUFBdi/f7/e/Hx9fRETE6O6/+/w4cMoV64cKleuXGQ5/P39cebMGVStWlVVnpo1axrFIzktLCwQEBCAvXv3KssKCgqwd+9eBAUFFfteS0tLVKpUCQ8ePMCPP/6Irl27AgBatWqFxMREJCQkKH8NGzZEv379lBFXt2/fBqDb22lqaqr0sj6qQoUKsLW1RUREBCwtLdGmTRvV+tzcXGzYsKHYX4TMzc1RuXJlmJqaYv369Xj11VeL7G3Nz89HYmIi3NzcdNbZ29vDxcUFaWlpOHHihFJvQxRVbxMTE731LlS4rnBug6ZNm+K3335TPU4+NTUVJiYmOsfzDz/8gLy8vGJ7pB/fVuF2vv76a5w8eVL5HAsfPRoREYHp06er3peRkYGoqKgy/xL8b/RPjqPSfIaPSkhI0Hv8P+rRY8XCwgKNGjXSeeRramqqMt9IQEAAzM3NVfspJSUFFy9eVPbT7du3YWJiohqhUPj60RiJjo5Gp06d8Nlnn2HYsGGqbdaqVUtn33Xp0kUZtfb4feLLli1DQECAai6q0uy74spS6PPPP8cnn3yCHTt2oGHDhqp19+/fx/379w0+Hxqrf3IcFRIRhIeHY8CAATA3N3/iuu7fvx/nzp0r9nxpY2MDNzc33LhxAzt37lTqFBoailOnTqnq5O7ujrFjx2Lnzp0AStfWGNJ+GsKQtqao9nP8+PE6dQIejmgNDw9X6mTIuQFge2TscWTINVDTpk1x7tw51bZTU1Ph5uYGCwsLg/aTIW1WUFAQEhMTVY8o3717N+zs7FC7dm3Y2dnp7Lvhw4fDx8cHCQkJCAwMVPbJyJEjsWnTJuzbtw/VqlVT1dmQshQqLqYL4+hRpqamShkMTWPsjDGOCgoKcP/+fYOvKwwprz7y8OFNyvWhPgkJCXB0dIRWqzU4RgqFh4fD1dUVnTp1Ui2vWrUq3N3dS7wO1Wq1qjT379/HhQsXlDQ//vij6tpv6dKlAICDBw/qPK69NN8t/1GeZq/R4yN5cnJyJD4+XvnFtXAOh8JRNfqEh4fL0qVLJTExUdLT02XSpEliZWUlf/75p4iIDBo0SKpUqSKbNm2S8+fPS1RUlHKv++XLl8Xa2lrCwsIkOTlZNm/eLOXLl1c9tlTfr1SZmZni4uIir7/+uhw7dkzOnTsnO3bskEGDBhXZg1mUf2JPtcjDRwRqtVpZsWKFJCUlybBhw8TBwUF5UkBoaKiMHz9eSX/kyBH58ccfJT09XQ4cOCAtW7aUatWq6Z1/ptDj+/bevXtSs2ZNad68uRw9elTOnTsns2fPFo1GI9u2bVPSffPNNxIbGyspKSny7bffipWVleoezkJLly4VS0tLvWVISUmRVatWSWpqqhw9elR69+4tTk5OkpGRoaT5+OOPZefOnZKeni6xsbHyxhtviKWlpZw5c0ZJs2HDBomKipL09HTZvHmzeHp6Svfu3VXbysrKkvj4eFmyZIng/z/RIz4+XrnP848//hBnZ2fp3r27JCQkSEpKinzwwQdibm6ujCLYtm2bLF++XBITEyUjI0O2bt0qvr6+qnuzc3JypHLlyvL666/LmTNnZP/+/eLl5SVvvfWWTv2bNWsmvXv31lmem5srEyZMkJiYGLlw4YKcOHFCBg8eLFqttsj5RIqbk2TSpEni7u5e6rgoLcbRKOW1oXH0KH2f4YoVK2Tt2rWSnJwsycnJMn36dDExMZHly5craWbMmCG7du2S9PR0SUpKktmzZ4uZmZnqaQ8bN24Uc3NzWbx4saSlpck333wjpqamql9Mhg8fLh4eHrJv3z45ceKEBAUFSVBQkLI+OTlZtFqtjBgxQpKSkuT06dPSv39/sbe3V35l3Ldvn1hbW8uECRNUj9Ms7kkrRf0ifPPmTbG2tpaFCxcW+d7i9p0hZZk1a5ZYWFjI//73P1WanJwcJU1ISIjUqVNHoqKi5Pz58xIeHi6WlpayYMGCEsv1JBhHo5TXpYmjPXv2CABJTk7Wm/eZM2ckPj5eOnfuLC1atFCucx7Xv39/CQwM1JvHjh075Oeff5bz58/Lrl27xM/PTwIDA4v9pVbf07VKamsKFdd+iohSh4CAAOnbt6/Ex8er2sZCRbU1Ioa1n4/DY6O8DTk3FGJ7ZNxxZMg10MWLF6VcuXIycuRISUlJka1bt4qrq6t8+umnBu8nkZLbrMJHqLdt21YSEhJkx44d4uLiovcR6oX0zTcyYsQIsbe3l+joaFU7cfv2bYPLUqi4mA4PDxczMzNZsGCBpKeny6FDh6Rhw4aqeWamTJki5cqVk3Xr1innoRo1akivXr2KrFNZMI5GKa+fVhytXr1aIiIiJCkpSdLT0yUiIkLc3d1VT/rKy8tTYsvNzU0++OADiY+PL/ZJpo+XNz09XWbMmCEnTpyQX3/9VQ4fPiydO3cWJycn5VHzkZGRsmTJEklMTJS0tDRZsGCBWFtb64yKeVRRc/Lk5+eLh4eHjBs3Tu/75s6dK3Z2dvLDDz9IWlqaTJo0SSwtLVUjuEeNGiWVKlWSnTt3ytmzZ2XIkCHi6uoq169f15unvrszDPlu+ST7tyyeyyPURXQ7eQp32ON/AwcOLDKPTZs2SWBgoNjZ2YmNjY00adJE9ajnO3fuyHvvvSdubm5iYWEhNWvWVH1JMeQR6vqGIqempkq3bt3EwcFBrKyspFatWjJ69GjVJM6G+KeexEQedqZ4eHiIhYWFNG7cWI4cOaKsCwkJUX0u0dHR4uvrK1qtVpydnSU0NFQ18ZU++vZtamqqdO/eXVxdXcXa2lrq1aun88jA0NBQcXJyEgsLC73rCwUFBSlD8R6XlJQk9evXFysrK7Gzs5OuXbvK2bNnVWlGjx6t1L9ChQrSsWNHnQkqCx9jbm5uLh4eHjJp0iSdx2NOmTJF73EdHh6upDl+/Li0bdtWnJycpFy5ctKkSRNlcj6Rh18ag4KCxN7eXiwtLcXLy0vGjRun00gkJydL69atxcrKSipXrixjxoxRXQyIiJw9e1YAyK5du3T2y507d6Rbt27i7u4uFhYW4ubmJl26dFFNvPy4ojp58vPzpXLlyspQx2eJcTRKtcyQOHpUUZ08vr6+Ym1tLXZ2dtK4cWPVY1pFRCZOnCg1a9YUS0tLcXR0lKCgIFm/fr1O/suWLVPS+fn56QynvXPnjrzzzjvi6Ogo1tbW0q1bN8nKylKl2bVrlzRt2lTs7e3F0dFRWrZsqXpE7MCBA/XGWeFtHfoU1cmzaNEisbKykuzs7CLfW0jfvjOkLJ6ennrTPPojQ1ZWlgwaNEjc3d3F0tJSfHx8ZM6cOaVuZwzFOBqlWmZoHPXp00e5/VCfoj7rR2VnZ4uVlZUsXrxYbx4RERFSvXp15VolLCysxONT31DwktqaQsW1nyKitz6P335fXFsjYlj7qW+7j98qUdK5QYTtUSFjjyNDroF++eUXCQwMFK1WK9WrV5fp06frdPwVt59EDGuzLly4IB06dBArKyspX768vP/++6rvF4/T9wVWX30fv340pCwiJcf0119/LbVr1xYrKytxc3OTfv36yeXLl5X19+/fl6lTp0qNGjXE0tJSqlSpIu+8806xnRVlwTgapVr2NOJo/fr14u/vL7a2tmJjYyO1a9eWGTNmqB74UHhNU5prqcfLm5mZKR06dBBXV1cxNzeXypUrS9++fVXfs37++WepX7++UhY/Pz/57rvvir31r6hOnp07dwpQ/MOPZs6cKZUrVxZra2sJCgrSuTXr3r178v7774urq6uUK1dOWrduXezDMvR18hjy3fJJ9m9ZlKaTRyNS8pi8uLg4BAQEIDY2Fv7+/iUlf6GtWbMG/fv3574iKgPGEVHZMY6Iyo5xRFR2jCOisitNn8y/4IYyIiIiIiIiIiIqCTt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMADt5iIiIiIiIiIiMgFlpEm/fvh3JycnPqixG4fDhwwC4r4jKgnFEVHaMI6KyYxwRlR3jiKjsMjIyDE6rEREpKVFMTAyaN2+O/Pz8MhXsRWFiYoKCgoLnXQyifzXGEVHZMY6Iyo5xRFR2jCOisjM1NcXBgwcRFBRUbDqDRvJotVrk5+dj9erV8PX1fSoFNFbbt2/H5MmTua+IyoBxRFR2jCOismMcEZUd44io7JKTk9G/f39otdoS05bqdi1fX1/4+/s/ccFeBIVDELmviJ4c44io7BhHRGXHOCIqO8YR0d+LEy8TERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvIQERERERERERkBdvL8f1WrVsVXX331vItBVKwDBw6gc+fOcHd3h0ajwebNm1XrRQT//e9/4ebmBisrK7Ru3RppaWmqNF26dIGHhwcsLS3h5uaG0NBQ/Pbbb8Vu9+7duwgLC4OzszNsbW3Ro0cPXLly5WlXj+gfIycnB6NHj4anpyesrKwQHByM48ePAwDu37+PcePGoW7durCxsYG7uzsGDBhQYhwBwPz581G1alVYWloiMDAQx44de9ZV+UcqzX64f/8+pk2bhho1asDS0hJ+fn7YsWNHkelnzZoFjUaD0aNHq5b//vvvCA0NRcWKFWFjYwN/f3/8+OOPyvro6GhoNBq9f4Wf/d27dzFo0CDUrVsXZmZmeO2113S2P2jQIL151KlTR0lT3PGlz/9r796DorzOP4B/l9sKFJXlIhdngahcHBFHKApYjQVdraYiiUYhSK2mtSVGpaWaVqrRzNiYTgiitlaRhhQCMoFoyQUR0CQOt3ARGBGV4o2QaGJIBOS2nN8f/nibdZebi1XW72dm/9j3PXv2nLPvwx6ePe/7btiwATKZTGuecvHiRSxbtgy2trYYO3Ys5syZg8LCQmn/P//5z377dPPmTQBAc3MzwsPD4e7uDiMjI61xA4Cnn35aZx1LliyRynz11Vf4xS9+AScnJ1hYWGDRokVa3z99hBBYvHixzu+xsrIyBAcHY/z48bC2toZKpcK5c+ek/Tt37tTZFktLy37Hj+hxNdi8bihxpSs+N2zYMOD7DmW+SDRaMI60PfFJnq6urkfdBKIha2trg4+PDw4cOKBz/969e7Fv3z78/e9/R0lJCSwtLaFSqdDR0SGVmT9/Po4dO4b6+nq89957aGhowHPPPTfg+27ZsgX//ve/kZmZiTNnzuCLL75AWFjYiPaN6HGyfv165OXl4Z133kFNTQ0WLlyIkJAQNDU1ob29HRUVFYiLi0NFRQWysrJQX1+Pn//85wPWmZGRgZiYGOzYsQMVFRXw8fGBSqWS/tl+Ugx3HLZv345Dhw4hMTER58+fx4YNG7B8+XJUVlZqlS0rK8OhQ4cwffp0rX1r1qxBfX09Tpw4gZqaGoSFhWHlypVSPYGBgWhubtZ4rF+/Hm5ubvDz8wMAqNVqmJub4+WXX0ZISIjO9iYkJGjUcf36dSgUCqxYsUIqM9Dxdb/s7GwUFxfDyclJa9/SpUvR09ODgoIClJeXw8fHB0uXLsWXX34JAHj++ee1+qRSqTBv3jzY29sDADo7O2FnZ4ft27fDx8dHZ5+ysrI06qitrYWxsbHUJyEEQkND8Z///AfHjx9HZWUlXFxcEBISgra2Nq363nrrLchkMq3tra2tWLRoEZRKJUpKSvDZZ5/BysoKKpUK3d3dAIDf//73Wn2aOnWqxvgSjRYDzeuGE1cvvviiRkzs3bt3wPcdynyRaLRgHOkghqC8vFwAEOXl5QOWy8zMFNOmTRNjxowRCoVCBAcHi9bWVjFv3jyxadMmjbLLli0TUVFR0nMXFxexa9cusWrVKmFhYSGcnJzE/v37NV4DQBw8eFAsWrRIjBkzRri5uYnMzEyNMtXV1WL+/PlSG1588UVx584daX9UVJRYtmyZeO2114Sjo6NwdXUV8+bNEwA0Hg/qX//615DGikhfAER2drb0vLe3Vzg4OIg33nhD2tbS0iLkcrl49913+63n+PHjQiaTia6uLp37W1pahKmpqUas1dXVCQCiqKhI/47owDiiR6m9vV0YGxuLnJwcje0zZ84Uf/rTn3S+prS0VAAQV69e7bdef39/ER0dLT1Xq9XCyclJ7NmzZ2Qafp/HNY6GOw6Ojo5a84GwsDARERGhse3OnTtiypQpIi8vT+e8w9LSUqSkpGhsUygU4vDhwzrft6urS9jZ2Yldu3bp3N83nxhMdna2kMlk4sqVK0KI4R1fN27cEM7OzqK2tla4uLiI+Ph4ad+tW7cEAPHJJ59I277//nsBQOTl5elsy82bN4WpqanWOPTRNW66xMfHCysrK9Ha2iqEEKK+vl4AELW1tVIZtVot7OzstMa3srJSODs7i+bmZq3vsbKyMgFAXLt2TdpWXV0tAIhLly7pbEtVVZXWOIykxzWOyPDcHw9Djauhxm2fB50v6oNxRP8rhhxHQ83JCCHEiK3kaW5uxurVq/HLX/4SdXV1OH36NMLCwiCEGHIdb7zxBnx8fFBZWYlt27Zh06ZNyMvL0ygTFxeHZ599FufOnUNERARWrVqFuro6APeyeCqVCtbW1igrK0NmZiZOnTqFl156SaOO/Px81NfXIy8vDzk5OcjKysLEiROxa9cuKXNHNNo0Njbiyy+/1Ph1edy4cZg1axaKiop0vub27dtITU1FYGAgTE1NdZYpLy9Hd3e3Rr2enp5QKpX91ks0mvX09ECtVmPMmDEa283NzfHZZ5/pfM13330HmUyG8ePH69zf1dWF8vJyjTgyMjJCSEjIExVHDzIOnZ2dQ/osoqOjsWTJkn5X2AQGBiIjIwO3b99Gb28v0tPT0dHRgaefflpn+RMnTuCbb77B2rVrh9FDbUlJSQgJCYGLiwuAoR9fvb29iIyMRGxsrMapXn1sbGzg4eGBlJQUtLW1oaenB4cOHYK9vT18fX11tiUlJQUWFhaDrt4cSp9WrVolnSLV2dkJABp9MjIyglwu1+hTe3s7wsPDceDAATg4OGjV6+HhARsbGyQlJaGrqwt3795FUlISvLy84OrqqrMtR44cgbu7O37yk5/o1Seix81Q4woAUlNTYWtri2nTpuGVV15Be3t7v/U+yHyRaLR6UuPIZKQqam5uRk9PD8LCwqSJjLe397DqCAoKwrZt2wAA7u7uOHv2LOLj47FgwQKpzIoVK7B+/XoAwO7du5GXl4fExEQcPHgQaWlp6OjoQEpKijTx2L9/P5555hm8/vrrmDBhAgDA0tISR44cgZmZmVSvsbExrKysdE46iEaDvuX5fcd5nwkTJkj7+mzduhX79+9He3s7Zs+ejZycnAHrNTMz0/rnVVe9RIbAysoKAQEB2L17N7y8vDBhwgS8++67KCoqwuTJk7XKd3R0YOvWrVi9ejXGjh2rs86vv/4aarVaZ3xeuHDhofTjcfQg46BSqfDmm29i7ty5mDRpEvLz85GVlQW1Wi2VSU9PR0VFxYDXtTl27Bief/552NjYwMTEBBYWFsjOztb5mQL3EhkqlQoTJ058gJ7e88UXX+Cjjz5CWlqatG2ox9frr78OExMTvPzyyzrrlslkOHXqFEJDQ2FlZQUjIyPY29vj448/hrW1db99Cg8Ph7m5+QP3qbS0FLW1tUhKSpK29SX+X3nlFRw6dAiWlpaIj4/HjRs3NH4427JlCwIDA7Fs2TKddVtZWeH06dMIDQ3F7t27AQBTpkxBbm4uTEy0p6wdHR1ITU2V5o5EhmSocRUeHg4XFxc4OTmhuroaW7duRX19PbKysnTWO5z5ItFo96TG0Yit5PHx8UFwcDC8vb2xYsUKHD58GN9+++2w6ggICNB63rdKZyhl6urq4OPjo3HxvaCgIPT29qK+vl7a5u3trZHgIXrSxMbGorKyEidPnoSxsTHWrFkzrFV3RIbunXfegRACzs7OkMvl2LdvH1avXg0jI82vze7ubqxcuRJCCPztb397RK01bAkJCZgyZQo8PT1hZmaGl156CWvXrpU+i+vXr2PTpk1ITU3VWh3zQ3FxcWhpacGpU6fw+eefIyYmBitXrkRNTY1W2Rs3biA3Nxfr1q3Tq+1vv/02xo8fr3WB5sGOr/LyciQkJEgXTtZFCIHo6GjY29vj008/RWlpKUJDQ/HMM8/oXJFcVFSEuro6vfuUlJQEb29v+Pv7S9tMTU2RlZWFixcvQqFQwMLCAoWFhVi8eLHUpxMnTqCgoGDAm1zcvXsX69atQ1BQEIqLi3H27FlMmzYNS5Yswd27d7XKZ2dn486dO4iKitKrT0SPo6HEFQD86le/gkqlgre3NyIiIpCSkoLs7Gw0NDQ8wtYTPR6e1DgasSSPsbEx8vLy8NFHH2Hq1KlITEyEh4cHGhsbYWRkpPUPZN8F9B4F3oGBDFHfKrT773r11Vdfaa1Qs7W1hbu7OxYsWID09HR8+OGHKC4u7rferq4utLS0DFovkaGYNGkSzpw5g9bWVly/fh2lpaXo7u7GU089JZXpS/BcvXoVeXl5/a7iAe7FnLGx8ZDi05A9yDjY2dnh/fffR1tbG65evYoLFy7gRz/6kfRZlJeX4+bNm5g5cyZMTExgYmKCM2fOYN++fTAxMYFarUZDQwP279+Po0ePIjg4GD4+PtixYwf8/Px0XqgxOTkZNjY2g15MeyBCCBw9ehSRkZFaPywNdnx9+umnuHnzJpRKpdSnq1ev4ne/+5102lJBQQFycnKQnp6OoKAgzJw5EwcPHoS5uTnefvttrfYcOXIEM2bM6PdUrqFoa2tDenq6zkSRr68vqqqq0NLSgubmZnz88cf45ptvpD4VFBSgoaEB48ePl/oEAM8++6x0ylxaWhquXLmC5ORk/PjHP8bs2bORlpaGxsZGHD9+XGefli5dqvVLKpGhGCyudJk1axYA4PLlyzr3D2e+SGQInsQ4GtG7a8lkMgQFBeHVV19FZWUlzMzMkJ2dDTs7O41fldRqNWpra7Vef/8/mcXFxfDy8hpyGS8vL5w7d07jStlnz56FkZERPDw8Bmy7mZmZxtJvotHGzc0NDg4OyM/Pl7Z9//33KCkp0VoB90O9vb0A/nvO6v18fX1hamqqUW99fT2uXbs2YL1EhsDS0hKOjo749ttvkZubK51m0pfguXTpEk6dOgUbG5sB6zEzM4Ovr69GHPX29iI/P/+JiiN9xmHMmDFwdnZGT08P3nvvPemzCA4ORk1NDaqqqqSHn58fIiIiUFVVBWNjY+m8+vtXYhkbG0t/A/sIIZCcnIw1a9b0e62yoThz5gwuX7484MqZ/o6vyMhIVFdXa/TJyckJsbGxyM3NBYB++2RkZKTVp9bWVhw7dkzvVTyZmZno7OzECy+80G+ZcePGwc7ODpcuXcLnn38u9Wnbtm1afQKA+Ph4JCcnS30yMjLSWL3U9/z+PjU2NqKwsFDvPhGNBv3FlS59seXo6Khz/4POF4lGuycpjkbsmjwlJSXIz8/HwoULYW9vj5KSEty6dQteXl6wtLRETEwMPvjgA0yaNAlvvvmm1qoA4F5CZu/evQgNDUVeXh4yMzPxwQcfaJTJzMyEn58f5syZg9TUVJSWlkrnhUdERGDHjh2IiorCzp07cevWLWzcuBGRkZGD/srj6uqKTz75BKtWrYJcLoetre1IDQ3RiGltbdXIKDc2NqKqqgoKhQJKpRKbN2/Ga6+9hilTpsDNzQ1xcXFwcnKSThUoKSlBWVkZ5syZA2trazQ0NCAuLg6TJk2S/iA1NTUhODgYKSkp8Pf3x7hx47Bu3TrExMRAoVBg7Nix2LhxIwICAjB79uxHMQxED11ubi6EEPDw8MDly5cRGxsLT09PrF27Ft3d3XjuuedQUVGBnJwcqNVq6fxrhUIhrdoIDg7G8uXLpYv/x8TEICoqCn5+fvD398dbb72FtrY2vS/sO9oMNg5r1qyBs7Mz9uzZA+De362mpibMmDEDTU1N2LlzJ3p7e/GHP/wBwL3ruEybNk3jPSwtLWFjYyNt9/T0xOTJk/HrX/8af/3rX2FjY4P3339fugHDDxUUFKCxsVG6/t/9zp8/j66uLty+fRt37tyRJoIzZszQKJeUlIRZs2ZptQ0Y+PgC7l1U+f7EoampKRwcHKQfrQICAmBtbY2oqCj8+c9/hrm5OQ4fPozGxkYsWbJE47UZGRno6enpNznT14fW1lbcunULVVVVMDMzw9SpU7X6FBoaqjOpmZmZCTs7OyiVStTU1GDTpk0IDQ3FwoULAdz71VPXr5tKpRJubm4AgAULFiA2NhbR0dHYuHEjent78Ze//AUmJiaYP3++xuuOHj0KR0dHLF68WGefiEaDweZ1g8VVQ0MD0tLS8LOf/Qw2Njaorq7Gli1bMHfuXEyfPl2q19PTE3v27MHy5cshk8kGnS8SjSaMIx1G6nZd58+fFyqVStjZ2Qm5XC7c3d1FYmKiEOLebUh/85vfCIVCIezt7cWePXt03kL91VdfFStWrBAWFhbCwcFBJCQkaLwHAHHgwAGxYMECIZfLhaurq8jIyNAoM9RbqN+vqKhITJ8+Xcjlct5CnR5bhYWFAoDWoy+Went7RVxcnJgwYYKQy+UiODhY1NfXS6/viw+FQiHF0IYNG8SNGzekMo2NjQKAKCwslLbdvXtX/Pa3vxXW1tbCwsJCLF++XDQ3Nz+0fjKO6FHLyMgQTz31lDAzMxMODg4iOjpatLS0CCH+GyO6Hj+MGxcXF7Fjxw6NehMTE4VSqRRmZmbC399fFBcXP7Q+PM5xNNA4zJs3T2N+cPr0aeHl5SXkcrmwsbERkZGRoqmpacD6dd0K9eLFiyIsLEzY29sLCwsLMX36dJ23El+9erUIDAzst24XFxedn/0PtbS0CHNzc/GPf/xDZx0DHV8Dve8Pb6EuxL1bji9cuFAoFAphZWUlZs+eLT788EOt1wYEBIjw8PB+69bVHxcXF40yFy5cEADEyZMnddaRkJAgJk6cKExNTYVSqRTbt28XnZ2dA/YJ993qVgghTp48KYKCgsS4ceOEtbW1+OlPfyqKioo0yqjVajFx4kTxxz/+ccD6R8LjHEc0+g02rxssrq5duybmzp0rzesmT54sYmNjxXfffafxPgBEcnKy9Hyw+eJIYxzRw/SkxNFwbqEu+/8GD6iiogK+vr4oLy/HzJkzHzCdNDBXV1ds3rwZmzdv7reMTCZDdnb245Ed60dqaipeeOGFhzpWRIaOcUSkP8YRkf4YR0T6YxwR6W84OZkRvSYPERERERERERE9GkzyEBEREREREREZgBG78LK+rly5MmiZIZxZRkRERERERET0ROJKHiIiIiIiIiIiA8AkDxERERERERGRAWCSh4iIiIiIiIjIADDJQ0RERERERERkAJjkISIiIiIiIiIyAEzyEBEREREREREZACZ5iIiIiIiIiIgMAJM8REREREREREQGgEkeIiIiIiIiIiIDwCQPEREREREREZEBYJKHiIiIiIiIiMgAMMlDRERERERERGQAmOQhIiIiIiIiIjIATPIQERERERERERkAk+EUrqure1jtMBiNjY0AOFZE+mAcEemPcUSkP8YRkf4YR0T6G078yIQQYrBC165dg5eXF9rb2/Vq2JPC2NgYarX6UTeDaFRjHBHpj3FEpD/GEZH+GEdE+rOwsEBdXR2USuWA5YaU5AHuJXq+/vrrEWmcoevs7IRcLn/UzSAa1RhHRPpjHBHpj3FEpD/GEZH+bG1tB03wAMNI8hARERERERER0eOLF14mIiIiIiIiIjIATPIQERERERERERkAJnmIiIiIiIiIiAwAkzxERERERERERAaASR4iIiIiIiIiIgPAJA8RERERERERkQFgkoeIiIiIiIiIyAD8H+f+GNEwGnD2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(classification_report(y_test, y_pred_test_tree, output_dict=True))\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "ax.axis('off')\n",
    "table = ax.table(cellText=df.values, colLabels=df.columns, rowLabels=df.index, loc='center', bbox=[0, 0, 1, 1], fontsize=14)\n",
    "plt.subplots_adjust(left=0.1, right=0.8, top=0.9, bottom=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "71a50961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred_test_tree, 'Decision Tree')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1c180e",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cad1bad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n"
     ]
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('preprocessor', preprocessor),  # Preprocessing step\n",
    "    ('classifier', SVC(kernel='rbf', gamma=0.1, C=4.0, random_state= 113))  # Logistic Regression model\n",
    "])\n",
    "\n",
    "# Specify hyperparameters for the logistic regression model\n",
    "params = {\"classifier__C\":(0.1, 1, 10), \n",
    "          \"classifier__gamma\":(0.01, 0.1, 1), \n",
    "          \"classifier__kernel\":('linear', 'rbf')}   \n",
    "\n",
    "# Create GridSearchCV to search through hyperparameter combinations\n",
    "svm_cv = GridSearchCV(pipeline, params, scoring='accuracy', n_jobs=-1, verbose=1, cv=5)\n",
    "\n",
    "# Fit the pipeline with hyperparameter tuning\n",
    "svm_cv.fit(X_train, y_train)\n",
    "\n",
    "# # Access the best model with its hyperparameters\n",
    "best_model = svm_cv.best_estimator_\n",
    "\n",
    "# Make predictions\n",
    "y_pred_train_svm = best_model.predict(X_train) \n",
    "y_pred_test_svm = best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8956ee75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(classification_report(y_train, y_pred_train_svm, output_dict=True))\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "ax.axis('off')\n",
    "table = ax.table(cellText=df.values, colLabels=df.columns, rowLabels=df.index, loc='center', bbox=[0, 0, 1, 1], fontsize=14)\n",
    "plt.subplots_adjust(left=0.1, right=0.8, top=0.9, bottom=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "976e41b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_train, y_pred_train_svm, 'SVM ,Train' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "22197224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nbdiwgXbt2kVjxowpd7+MVbXw8HBq06YNffLJJ9SsWTNydXUVTWxeo0YNcnZ2rvCE4B4eHhQeHq62r/r165eZl8mTJ1PPnj2FSZ7/+ecfUZo2bdpQrVq1aPfu3bRz50768MMPhS/NderUIX19fbp48aKQ/uHDh6LYZO+u4nl5AgIChAad4kaesLAwYT4eIqLmzZtTcnIyubq6qv3J5XK1bbu7u9ONGzdEP1qXvA51JZfLhcmhizVr1owKCwvpzp07ankp/kHcw8ND9MABIhJ632hz/vx5cnJyorlz55Knpye5ubnRn3/+qZZu6NChtHv3boqIiKCrV6/SRx99JDru6OhoUfpXOe53BTfysHfKtGnTaMOGDbR161ZKSUmhiRMnCk+CYIyVLzc3l+Li4iguLo6IXk6EFxcXJ+pyy9i/kZmZGVlYWNAvv/xCGRkZ9Pvvv9O0adOE9YMHD6aaNWvSBx98QOHh4XT16lXat2+fMHH/ggULKCgoiBYsWEApKSmUmJhI3377rfD+Tp060U8//USxsbEUHR1NEyZMUOsJoYmbmxudPHmSzp8/TykpKfTxxx+Lbu6VSiXNmjWLZs6cSdu2baPMzEy6cOECbdq0SbSd//znP7Rs2TICwE/HY2+Em5sbRUdH04kTJygtLY3mz5+v9iVt4cKFtHLlSvrxxx8pPT2dLl26RKtXry5zuzNmzKAtW7bQunXrKD09nb7//nvav3+/MJGstrxs376dUlJSKDIykoYOHaqxJ9GQIUPo559/ppMnTwpDtYheNkiNHDmSZsyYQaGhoXT58mUaO3YsSaVSoccEe3eZmZlR48aNaefOnUKDTocOHejSpUuUlpYm6skza9YsOn/+vDCRcXp6Oh06dEjrxMtdu3YlFxcXGjlyJCUkJFB4eDjNmzePiKhC156zszPl5ubS6dOn6Z9//qG8vDyqW7cuDR06lEaMGEH79++nrKwsioqKoqVLl9KRI0eIiGjy5Ml0/Phx+u677yg9PZ1++uknOn78eJn7cnNzo+vXr1NwcDBlZmbSjz/+SAcOHFBL169fP3r8+DFNnDiR3nvvPbKzsxPWffzxx3TlyhWaNWsWpaWl0Z49e4SnU3LMafC6Jwh61/DEy2+/1atXw9HREXK5HK1atcKFCxfedJZYKe/aBH3/JqGhoSAitb+RI0e+6ayxUjiOKu7kyZPw8PCAQqFA48aNERYWJprM8tq1a+jfvz+MjY1hYGAAT09PREZGCu/ft28fmjZtCrlcDktLS/Tr109Yl52djW7dusHQ0BBubm44evSoxomXY2NjRXm6d+8e/Pz8YGRkBGtra8ybNw8jRoyAn5+fkKawsBDffPMNnJycoK+vD0dHRyxZskS0ncePH8PAwACffPJJlZ6z6o7jSDMfHx+1CYZLTg5brGT8PHv2DKNGjYKJiQlMTU0xceJEzJ49G02aNBG95+eff4a7uzv09fVha2uLTz/9VOP2Slq7di3q1KkDfX191K1bVzRhsiaXLl2Cp6cnlEol3NzcsHfvXo35T05OBhHByckJRUVFonWPHj3CkCFDYGBggJo1a+L7779Hq1atMHv27DL3/S56F+NoypQpoomDAaBJkyaoWbOmWtqoqCh07doVRkZGMDQ0ROPGjbF48WJhfelrMyUlBW3btoVcLke9evXw66+/gohw/PhxAJrrkwcPHoCIEBoaKiybMGECLCwsQERYsGABAOD58+f48ssv4ezsLMRg3759kZCQILxv06ZNcHBwgEqlQu/evfHdd9+VO/HyjBkzYGFhASMjIwwaNAgBAQEa3zNw4EAQETZv3qy27tChQ3B1dYVCoUDHjh2xbt06EJEwQXV1V5F2BglQaoA4q5RLly5RixYtKCYmhpo3b/6ms8PYv9LOnTtp2LBhHEeMVQLHESvp2rVr5OLiQhcvXuTroQI4jpiunjx5Qvb29rRy5UoaO3bsm87OW4Xj6PUKDw+ndu3aUUZGBrm4uLzp7PzPLF68mH7++We6cePGm87K/0RF2hl44mXGGGOMsWrqxYsXdO/ePZo3bx61bt2av2AxVkViY2PpypUr1KpVK3r48CEtWrSIiIj8/PzecM5YdXfgwAEyMjIiNzc3ysjIoClTplDbtm2rfQPP2rVrqWXLlmRhYUHh4eG0YsUKrcPa3nXcyMMYY4wxVk2Fh4fTe++9R3Xr1qX/+7//e9PZYaxa+e677yg1NVV4uMfZs2fJ0tLyTWeLVXOPHz+mWbNm0fXr18nS0pK6dOkielJVdZWenk7ffPMN3b9/nxwdHenzzz+nOXPmvOlsvZW4kYcxxhhjrJrq2LGj2qPbGWOV16xZM4qJiXnT2WDvoBEjRtCIESPedDb+5wICAiggIOBNZ+NfgZ+uxRhjjDHGGGOMMVYNcCMPY4wxxhhjjDHGWDXAjTyMMcYYY4wxxhhj1QA38jDGGGOMMcYYY4xVA9zIwxhjjDHGGGOMMVYNcCMPY4wxxhhjjDHGWDXAjTyMMcYYY4wxxhhj1QA38jDGGGOMMcYYY4xVA9zIwxhjjDHGGGOMMVYNcCMPY4wxxhhjjDHGWDXAjTyMMcYYY4wxxhhj1QA38jDGGGOMMcYYY4xVA9zIwxhjjDHGGGOMMVYNcCMPY4wxxhhjjDHGWDWgp2vC69ev0z///PM681ItpKSkEBHR0aNHhf8ZYxUTHh5ORBxHjFUGxxFjlcdxxFjlcRwxVnlZWVk6p5UAQHmJrl+/Th4eHpSXl1epjL0rpFIpFRUVvelsMPavxnHEWOVxHDFWeRxHjFUexxFjlSeTyejs2bPk7e1dZjqdevL8888/lJeXRzt27CAPD48qyWB1dfToUZo/fz6fK8YqgeOIscrjOGKs8jiOGKs8jiPGKi8lJYWGDRtGCoWi3LQ6D9ciIvLw8KDmzZu/csbeBcVdEPlcMfbqOI4YqzyOI8Yqj+OIscrjOGLsf4snXmaMMcYYY4wxxhirBriRhzHGGGOMMcYYY6wa4EYexhhjjDHGGGOMsWqAG3kYY4wxxhhjjDHGqgFu5GGMMcYYY4wxxhirBriRhzHGGGOMMcYYY6wa4EYexhhjjDHGGGOMsWqAG3kYY4wxxhhjjDHGqgFu5GGMMcYYY4wxxhirBriRhzHGGGOMMcYYY6wa4EYexhhjjDHGGGOMsWqAG3kYY4wxxhhjjDHGqgFu5GGMMcYYY4wxxhirBriRhzHGGGOMMcYYY6wa4EYexhhjjDHGGGOMsWrgX9/IExYWRhKJhHJycqo0LWOMMcYYY4wxxti/yb++kadNmzZ069YtMjExqdK01dGaNWvI2dmZlEoleXl5UVRUlNa0L168oEWLFpGLiwsplUpq0qQJHT9+XJSmsLCQ5s+fT7Vr1yaVSkUuLi709ddfEwAhze3bt2nUqFFkZ2dHBgYG1L17d0pPT1fbX0REBHXq1IkMDQ3J2NiYOnToQE+fPhXWX7p0ibp27UqmpqZkYWFB48ePp9zcXNE2rl+/Tr169SIDAwOytramGTNmUEFBgbB+1KhRJJFI1P4aNGggpFm6dCm1bNmSatSoQdbW1vTBBx9QamqqaD+//PILdezYkYyNjctsNDxy5Ah5eXmRSqUiMzMz+uCDDzSmu3fvHjk4OGjc1po1a8jDw4NUKhW5u7vTtm3bNG6DiCg4OJgkEonafnJzc2nSpEnk4OBAKpWK6tevTz///LPGbQCgHj16kEQioYMHD6qt37JlCzVu3JiUSiVZW1uTv7+/1vxUV1UdR7pcc3///TcNHz6catasSYaGhtS8eXPat2+fKI2zs7Patb1s2TJh/cKFCzVe/4aGhkKay5cvU//+/YVt/fDDD2Wei2XLlpFEIqGpU6cKy+7fv0+ffvopubu7k0qlIkdHR5o8eTI9fPhQ9N6LFy9S586dydTUlMzMzMjX15fi4+OF9deuXdOY3wsXLghpOnbsqDFNr169hDT79++nbt26kYWFBUkkEoqLi9N6PNqu//j4eBo8eDDVqlWLVCoVeXh40KpVq9Tev3PnTmrSpAkZGBiQra0tjRkzhu7duydKk5OTQ/7+/mRra0sKhYLq1q1LR48eLfM8v6uqOtY0xUC9evVEaXSJtfLqoy1btmi8LiUSCd25c0ct7+Hh4aSnp0dNmzYVLV+3bh01btyYjI2NydjYmLy9venYsWOiNJpiYMKECaI05cUaEdGePXuoadOmZGBgQE5OTrRixQrRel3qz5I0lQ1ERB9//DG5uLiQSqUiKysr8vPzoytXrojSaNpPcHCwKE1F6sbq7E3ESGZmJvXt25esrKzI2NiYBg4cSLdv3xalWbx4MbVp04YMDAzI1NRULS/37t2j7t27k52dHSkUCqpVqxZNmjSJHj16JKS5desWDRkyhOrWrUtSqVTtWiq2d+9eqlevHimVSmrUqJFaeaotFkte4+XVn8+ePaNRo0ZRo0aNSE9PT+P9XFXlt6QJEyZorIvLyy/Ry/rsu+++o7p165JCoSB7e3tavHixKE1+fj7NnTuXnJycSKFQkLOzM23evFlrfljZqjoeiYiys7Np2LBhZGFhQSqViho1akTR0dHCegD05Zdfkq2tLalUKurSpYva96y0tDTy8/MjS0tLMjY2pnbt2lFoaKgojS7lbjFtddbjx49p6tSp5OTkRCqVitq0aUMXL14UpdFUl3Tv3l1Yf+3aNRo7dqzou+WCBQvo+fPnou3ocn2HhYVR8+bNSaFQkKurK23ZskW0XlMcSSQS0Xeb8upYXcqyNwY6iImJAREhJiZGl+Q6y8/Pr9LtvQ127NjxWs5VZQUHB0Mul2Pz5s24fPkyxo0bB1NTU9y+fVtj+pkzZ8LOzg5HjhxBZmYm1q5dC6VSiUuXLglpFi9eDAsLCxw+fBhZWVnYu3cvjIyMsGrVKgBAUVERWrdujfbt2yMqKgpXrlzB+PHj4ejoiNzcXGE758+fh7GxMZYuXYqkpCRcuXIFu3fvxrNnzwAA2dnZMDMzw4QJE3DlyhVERUWhTZs26N+/v7CNgoICNGzYEF26dEFsbCyOHj0KS0tLzJkzR0iTk5ODW7duCX83btyAubk5FixYIKTx9fVFYGAgkpKSEBcXh549e6rlNyAgAEuXLsXSpUtBRHjw4IHa+fu///s/mJmZYd26dUhNTcXly5exe/dujefaz88PPXr0UNvW2rVrUaNGDQQHByMzMxNBQUEwMjJCSEiI2jaysrJgb2+P9u3bw8/PT7Ru3LhxcHFxQWhoKLKysrB+/XrIZDIcOnRIbTvff/+9kJcDBw6I1q1cuRJ2dnbYuXMnMjIyEB8fr3EbVeFdiiNdrrmuXbuiZcuWiIyMRGZmJr7++mtIpVLRdpycnLBo0SLRNV5yG48fPxatu3XrFurXr4+RI0cKaaKiojB9+nQEBQWhZs2aCAgI0HouoqKi4OzsjMaNG2PKlCnC8sTERPTr1w8hISHIyMjA6dOn4ebmJorXx48fw9zcHKNGjcKVK1eQlJSE/v37w8bGBs+fPwfw8pomIpw6dUqU5+L1AHDv3j3RuqSkJMhkMgQGBgpptm3bhq+++gobNmwAESE2NlbrMWm7/jdt2oTJkycjLCwMmZmZ2L59O1QqFVavXi2kOXfuHKRSKVatWoWrV6/i7NmzaNCgAfr27Sukyc/Ph6enJ3r27Ilz584hKysLYWFhiIuL05qnynhb40gXryPWFixYgAYNGoiumbt374q2U16s6VIf5eXlqcWar68vfHx81PL94MED1KlTB926dUOTJk1E60JCQnDkyBGkpaUhNTUVX3zxBfT19ZGUlCSk8fHxwbhx40T7evjwobBel1g7evQo9PT0sG7dOmRmZuLw4cOwtbUVXd+61J/FtJUNALB+/XqcOXMGWVlZiImJQe/evVGrVi0UFBQIaYgIgYGBov09ffpUWF+RurEqvK1x9CZiJDc3F3Xq1EHfvn2RkJCAhIQE+Pn5oWXLligsLBTSffnll/j+++8xbdo0mJiYqOXl/v37WLt2LS5evIhr167h1KlTcHd3x+DBg4U0WVlZmDx5MrZu3YqmTZuqXUsAEB4eDplMhuXLlyM5ORnz5s2Dvr4+EhMThTSlY3Hz5s2QSCTIzMwU0pRXf+bm5mLChAn45Zdf4Ovrq3afVZX5LbZ//340adIEdnZ2anVxefkFgE8//RTu7u44dOgQrl69iujoaPz222+iNH369IGXlxdOnjyJrKwsnD9/HufOnVPLS1V4W+OoqryOeLx//z6cnJwwatQoREZG4urVqzhx4gQyMjKENMuWLYOJiQkOHjyI+Ph49OnTB7Vr1xaVmW5ubujZsyfi4+ORlpaGTz75BAYGBrh165aQprxyt1hZddbAgQNRv359nDlzBunp6ViwYAGMjY1x8+ZNIc3IkSPRvXt30X7u378vrD927BhGjRqFEydOIDMzE4cOHYK1tTU+//xz0b7Ku76vXr0KAwMDTJs2DcnJyVi9ejVkMhmOHz8upLlz544oHydPngQRITQ0VEhTXh2rS1lWlSrSJlOljTw+Pj7w9/eHv78/jI2NYWFhgXnz5qGoqAjAfwul4cOHo0aNGsKXi7Nnz6Jdu3ZQKpVwcHDAp59+Kiqsnj17hpkzZ8LBwQFyuRwuLi7YuHEjACA0NFT05fjatWt4//33YWpqCgMDA9SvXx9HjhzRmBZ4+WW8fv36kMvlcHJywnfffSc6JicnJyxevBijR4+GkZERatWqhfXr12s9B29rIdaqVSv4+/sLrwsLC2FnZ4elS5dqTG9ra4uffvpJtKxfv34YOnSo8LpXr14YM2aM1jSpqakgItENaWFhIaysrLBhwwZhmZeXF+bNm6c17+vXr4e1tbXoBiIhIQFEhPT0dAAvb1KlUin+/vtvIc26detgbGystTHxwIEDkEgkuHbtmtZ937lzB0SEM2fOqK3TdD0BwIsXL2Bvby9co2VZu3YtfHx8cPr0abVteXt7Y/r06aL006ZNQ9u2bUXLCgoK0KZNG2zcuBEjR45Uu/lo0KABFi1aJFrWvHlzzJ07V7QsNjYW9vb2uHXrltqX3Pv370OlUuHUqVPlHlNVeJfiqDRN15yhoSG2bdsmSmdubi6KIycnpzIbZUqLi4sDEeGPP/7QuL6s7T1+/Bhubm44efIkfHx8NN7MlrRnzx7I5XK8ePECAHDx4kUQEa5fvy6kKR3TxY08ZTXKlBYQEIAaNWqo3ezqsr2yrn9NPvnkE7z33nvC6xUrVqBOnTqiND/++CPs7e2F1+vWrUOdOnVEDVWv09saR7p4HbG2YMECtZvS0sqLNV3qo9Lu3LkDfX19te0CwKBBgzBv3jyd8gYAZmZmorqlvPjTJdYGDx6MAQMGiN73448/wsHBQbh/K01b/VnRsiE+Ph5EJPrSUl786Vo3VpW3NY7eRIycOHECUqlU9CUnJycHEokEJ0+eVEsfGBiosZFHk1WrVsHBwUHjOm3X0sCBA9GrVy/RMi8vL3z88cda9+Pn54dOnTqJllWk/tR0n1XV+b158ybs7e2RlJSkMW/l5Tc5ORl6enq4cuWK1jTHjh2DiYkJ7t27V+axVJW3NY6qyuuIx1mzZqFdu3Za91lUVISaNWtixYoVwrKcnBwoFAoEBQUBAO7evat2r/fo0SMQkShmdbnvAbTXWXl5eZDJZDh8+LAofenvG7rET2nLly9H7dq1hde6XN8zZ85EgwYN1PLu6+ur9T1TpkyBi4uLqN7TpR4rrayyrLIq0shT5cO1tm7dSnp6ehQVFUWrVq2i77//njZu3Cis/+6776hJkyYUGxtL8+fPp8zMTOrevTv179+fEhISaPfu3XTu3DmaNGmS8J4RI0ZQUFAQ/fjjj5SSkkLr168nIyMjjfv39/en/Px8+uOPPygxMZG+/fZbrWljYmJo4MCB9NFHH1FiYiItXLiQ5s+fr9ada+XKleTp6UmxsbH0ySef0MSJE9WGU7zNnj9/TjExMdSlSxdhmVQqpS5dulBERITG9+Tn55NSqRQtU6lUdO7cOeF1mzZt6PTp05SWlkZEL4c0nDt3jnr06CFsg4hE25FKpaRQKITt3LlzhyIjI8na2pratGlDNjY25OPjI9pPfn4+yeVykkr/e7mqVCoiIiFdREQENWrUiGxsbIQ0vr6+9OjRI7p8+bLGY9y0aRN16dKFnJycNK4nImGIibm5udY0pV26dImys7NJKpVSs2bNyNbWlnr06EFJSUmidMnJybRo0SLatm2b6NiKafsMoqKi6MWLF8KyRYsWkbW1NY0dO1Zjftq0aUMhISGUnZ1NACg0NJTS0tKoW7duQpq8vDwaMmQIrVmzhmrWrKm2jZMnT1JRURFlZ2eTh4cHOTg40MCBA+nGjRs6n5d/u9cVR6VpuubatGlDu3fvpvv371NRUREFBwfTs2fPqGPHjqL3Llu2jCwsLKhZs2a0YsUK0XDF0jZu3Eh169al9u3ba02jjb+/P/Xq1Ut0Lsry8OFDMjY2Jj09PSIicnd3JwsLC9q0aRM9f/6cnj59Sps2bSIPDw9ydnYWvbdPnz5kbW1N7dq1o5CQkDL3s2nTJvroo49EQ9B0Ud71r+2YSn5G3t7edOPGDTp69CgBoNu3b9P//d//Uc+ePYU0ISEh5O3tTf7+/mRjY0MNGzakJUuWUGFhYYXyW929zlhLT08nOzs7qlOnDg0dOpSuX78uWl9erOlSH5W2bds2MjAwoAEDBoiWBwYG0tWrV2nBggVlnI2XCgsLKTg4mJ48eULe3t6idTt37iRLS0tq2LAhzZkzh/Ly8oR1usSatnN38+ZN+vPPPzXmR1v9WZGy4cmTJxQYGEi1a9emWrVqqW3H0tKSWrVqRZs3bxYNA9e1bqzO3lSM5Ofnk0QiIYVCISxTKpUklUrLrNfK89dff9H+/fvJx8enQu+LiIhQu9Z8fX21noPbt2/TkSNHNN4vVaT+fFW65LeoqIiGDx9OM2bM0Docsrz8/vrrr1SnTh06fPgw1a5dm5ydnek///kP3b9/X0gTEhJCnp6etHz5crK3t6e6devS9OnTRVMlMN28rngs/ow+/PBDsra2pmbNmtGGDRuE9VlZWfT333+L9mtiYkJeXl7Cfi0sLIQhrU+ePKGCggJav349WVtbU4sWLUT7L6vcJSq7ziooKKDCwkKdypiwsDCytrYmd3d3mjhxotqw9tJK32/pcn1XtGx4/vw57dixg8aMGUMSiUS0rqw6trRXLctei6psNfLx8YGHh4eoBWzWrFnw8PAA8LLl+YMPPhC9Z+zYsRg/frxo2dmzZyGVSvH06VOhN4imXwgA9d4UjRo1wsKFC3VKO2TIEHTt2lWUZsaMGahfv77w2snJCcOGDRNeFxUVwdraGuvWrdO4j7expTo7OxtEhPPnz4uWz5gxA61atdL4nsGDB6N+/fpIS0tDYWEhfvvtN6hUKsjlciFNYWEhZs2aBYlEAj09PUgkEixZskRY//z5czg6OuLDDz/E/fv3kZ+fj2XLloGI0K1bNwBAREQEiAjm5ubYvHkzLl26hKlTp0IulyMtLQ0AkJSUBD09PSxfvhz5+fm4f/8++vfvDyIS9jdu3Dhhm8WePHkCIsLRo0c1nhOZTKZ1CFXx8fXq1Uvrr4PaevIEBQWBiODo6Ij/+7//Q3R0NAYPHgwLCwvhF5Nnz56hcePG2L59u9ZtzZkzBzVr1kR0dDSKiopw8eJF2NjYgIjw119/AXgZK/b29kJ3ak0t5M+ePcOIESNARNDT04NcLsfWrVtFacaPH4+xY8cKr6lUi/7SpUuhr68Pd3d3HD9+HBEREejcuTPc3d1fy7DLdymOStJ2zT148ADdunUTPkNjY2OcOHFClGblypUIDQ1FfHw81q1bB1NTU3z22Wca9/P06VOYmZnh22+/1Xq82n4pDAoKQsOGDYVuvOX9ynH37l04Ojriiy++EC1PTEyEi4sLpFIppFIp3N3dRb0C7t69i5UrV+LChQuIiooSyhptQwQjIyNBRIiMjNS4vqyePOVd/6WFh4dDT09P7TPYs2cPjIyMoKenByJC7969Rb123N3doVAoMGbMGERHRyM4OBjm5uZa66zKehvjSBevK9aOHj2KPXv2ID4+HsePH4e3tzccHR3x6NEjIU15saZLfVSah4cHJk6cKFqWlpYGa2trpKamAtDegyIhIQGGhoaQyWQwMTEReiYXW79+PY4fP46EhATs2LED9vb2oiGCQPmxtn79ehgYGODUqVMoLCxEamoq6tWrp/EzALTXn7qWDWvWrIGhoSGICO7u7qJePACwaNEinDt3DpcuXcKyZcugUCiEYeCAbnVjVXob4+hNxcidO3dgbGyMKVOm4MmTJ8jNzcWkSZNARGr38kD5PXk++ugjqFQqobzUNDwE0H4t6evrY9euXaJla9asgbW1tcbtfPvttzAzM1PbT0Xqz8r05NElv0uWLEHXrl1FoyBK18Xl5ffjjz+GQqGAl5cX/vjjD4SGhqJp06ai3qe+vr5QKBTo1asXIiMjceTIEWFo0OvwNsZRVXld8ahQKKBQKDBnzhxcunQJ69evh1KpxJYtWwC8vBfRVO59+OGHGDhwoPD6xo0baNGiBSQSCWQyGWxtbUXDwoDyy11d6ixvb2/4+PggOzsbBQUF2L59O6RSKerWrSukCQoKwqFDh5CQkIADBw7Aw8MDLVu2FA3ZLSk9PR3Gxsb45ZdfhGW6XN9ubm5qdfKRI0dARMjLy1Pbz+7duyGTyZCdnS1arksdC+hellXWGx2uNXr0aNGygwcPQk9PDwUFBXBycsI333wjWu/p6Qm5XA5DQ0Phz8DAAESE5ORk4aRr695e+svxhg0boKenhzZt2uDLL79EfHy81rTNmjVTu7k+ePAg9PX1hYvNyckJy5cvF6Vp3LgxvvrqK435eRsLsVcpfO7cuQM/Pz9IpVLIZDLUrVsXn3zyCZRKpZAmKCgIDg4OCAoKQkJCArZt2wZzc3Oh8AGA6OhoNGnSBEQEmUwGX19f9OjRA927dwfw3wKq5Nw5wMvGutmzZwuvd+7cCRsbG8hkMsjlckyfPh02NjZYtmwZgIo38ixZsgQWFhZlNlBMmDABTk5OuHHjhsb12hp5du7cCSISDet79uwZLC0t8fPPPwMAPvvsMwwaNKjMbeXl5WH06NHQ09ODTCaDnZ0dZs6cCSLC33//jUePHsHZ2Vl0fJpuPlasWIG6desiJCQE8fHxWL16NYyMjISG00OHDsHV1RWPHz8W3lP6S+7ixYtBRKIvO3fu3IFUKhWNb60q71IclaTtmps0aRJatWqFU6dOIS4uDgsXLoSJiQkSEhK05nfTpk3Q09MT5rYqadeuXdDT0xMNbyxN043l9evXYW1tLSpXy2rkefjwIVq1aoXu3buLyvC8vDy0atUKI0aMQFRUFCIiItC/f380aNBAY+VbbPjw4Vq7Lo8fPx6NGjXS+l5tjTy6XP8lJSYmwtLSEl9//bVo+eXLl2Fra4vly5cLX5AaNWokGtLq5uamNv/IypUrUbNmTa35roy3MY508b+INeBlg46xsbFo+JMusVZefVTS+fPnQUSIjo4WlhUUFMDT01P0Y5G2Rp78/Hykp6cjOjoas2fPhqWlJS5fvqz1mIqH/hY3nOgSa0VFRZg5cyaUSiVkMhnMzMywcOFCEBEuXLigtg9N9WdFyoacnBykpaXhzJkz6N27N5o3b17mDfH8+fNFXd/Lqxur2tsYR28yRk6cOIE6deoIXxiHDRuG5s2bY8KECWrvL6+R59atW0hJScGhQ4dQv359tcbQYlXVyOPu7o5JkyZpzU+xsurP19nIEx0dDRsbG9EXTV2GkpXO77hx40BEwhdy4L/f5YqHuHTt2hVKpRI5OTlCmn379kEikZRZD7+qtzGOqsrrikd9fX14e3uL3vfpp5+idevWAHRr5CkqKkKfPn3Qo0cPnDt3DjExMZg4cSLs7e3LbBQvWe7qWmdlZGSgQ4cOwne+li1bYujQoahXr57W/WRmZgrzL5Z28+ZNuLi4iH6EA3S7vivayNOtWze8//77WvNZrHQdW0zXsqyy3upGntIFVb169fDpp58iPT1d7S8/Px8hISEVauQBXt5srFu3Dn379oW+vj5+/PFHjWl1beQpnecmTZponGwQeDsLsfz8fMhkMrUvLSNGjECfPn3KfO/Tp09x8+ZN4SawZC8nBwcHtfGkX3/9Ndzd3dW2k5OTgzt37gB4OW71k08+AfByYiwiEnq0FBs4cCCGDBmitp2///4bjx8/Rm5uLqRSKfbs2QPgZWFUurAp3nbp1uqioiK4urpi6tSpWo/b398fDg4OuHr1qtY02hp5fv/9dxARzp49K1reqlUroTdDkyZNhIJdJpNBKpUKheKXX34pet/z589x48YNFBQUCBNOFhYWIjY2VnhP8Z9EIhFuvDIyMpCXlwd9fX21MbJjx44VxqVOmTJFeE/xHxFBKpUKE4Vu3rwZRKTW+GBtbS1qXa8q71IcFdN2zWVkZKjNbQUAnTt3LnPegaSkJFGFV1KnTp3UelWWpqnsO3DggNo1R0TC9VOy8eLRo0fw9vZG586d1b7Abdy4UW1ek/z8fBgYGAjjyDX56aefNDaI5ObmwtjYGD/88IPW92pr5NHl+i92+fJlWFtbq/VKAoBhw4apzWty9uxZ0Q1Yhw4d0LlzZ1Gao0ePgojemR5xunjdsVaSp6en8INCRWNNW31U0pgxY9C0aVPRsgcPHmgsu4uXnT59Wmt+O3furLHHRLHc3FwQkdD4XpFYKygowM2bN5Gfny9cl8X1djFt9WdFyoaSivNS+otvSYcPHwYRqX3h1lY3VrW3MY7eVIyUdPfuXeH+x8bGRu0HUaBic/KULi9L0tZoUqtWLbV66ssvv0Tjxo3V0v7xxx8gIp0mui+r/qxMI095+Q0ICNBaHzk5Oemc3y+//BJ6enqiNHl5eSAiYXLaESNGwMXFRZQmOTkZRCT0pK9Kb2McVZXXFY+Ojo5qDRxr166FnZ0dgP82kJS+r+nQoQMmT54MADh16pTaPFoA4OrqqnW+IEBc7la0zsrNzRXieODAgejZs2eZ56Dkj+DFsrOz4ebmhuHDh6uV67pc3+3bt1eLwc2bN8PY2Fht/9euXYNUKsXBgwfLzGfxsZWsYzUpqyyrrDc6J09kZKTo9YULF8jNzY1kMpnG9M2bN6fk5GRydXVV+5PL5dSoUSMqKiqiM2fO6JyHWrVq0YQJE2j//v30+eefi8YvluTh4UHh4eGiZeHh4VS3bl2t+f03ksvl1KJFCzp9+rSwrKioiE6fPq02tr80pVJJ9vb2VFBQQPv27SM/Pz9hXV5entpcMjKZjIqKitS2Y2JiQlZWVpSenk7R0dHCdpydncnOzk5tjqO0tDSNc+XY2NiQkZER7d69m5RKJXXt2pWIXs6HkZiYKHo87cmTJ8nY2Jjq168v2saZM2coIyND45hsADRp0iQ6cOAA/f7771S7du0yz48mLVq0IIVCITqmFy9e0LVr14Rj2rdvH8XHx1NcXBzFxcUJ81adPXtW7bHk+vr65ODgQDKZjIKDg+n9998nqVRK9erVo8TERGEbcXFx1KdPH3rvvfcoLi6OatWqRS9evKAXL16U+TnNnj2bEhISRNshIgoICKDAwEAiImrbti0RkeiY7t+/T//880+ZcxpVJ68rjsq75orH/uoaa8Xi4uJIKpWStbW1aHlWVhaFhoZqncOpLJ07d1a75jw9PWno0KEUFxcnlJuPHj2ibt26kVwup5CQELUx2sVlR8lxz8WvyzsmW1tbteV79+6l/Px8GjZsWIWPSZfrn+jl4+Xfe+89GjlypNpjOkseU0nF5wP/f1x727ZtKSMjQ3SMaWlpZGtrS3K5vMJ5r65eV6yVlpubS5mZmcI1VdFY01Yfldz+nj171GLN2NhYLY4mTJhA7u7uFBcXR15eXlrzXFRUJMx3p0nx9VvymHSNNZlMRvb29iSXyykoKIi8vb3JyspKlEZb/alr2VAaXv7YWO4xmZmZieaBIdJeN74L3lSMlGRpaUmmpqb0+++/0507d6hPnz6vfkD/P/9EVOa1UJq3t7foHBC9vPfTdA42bdpELVq0oCZNmpS7XW31Z2WVl9/hw4er1Ud2dnY0Y8YMOnHihM75bdu2LRUUFFBmZqaQpnj+zOJ7trZt29Jff/1Fubm5ojRSqZQcHByq5oDfEa8rHtu2bVvm96PatWtTzZo1Rft99OgRRUZGCvvVVq9JpdJy77eKy92K1lmGhoZka2tLDx48oBMnTpRZxty8eZPu3bsnKmOys7OpY8eO1KJFCwoMDFTLuy7Xd0XKhsDAQLK2tqZevXppzWfJ80JEGsvEYq9Slr0WVdlq5OPjAyMjI3z22We4cuUKdu3aBUNDQ6F1TtMvw/Hx8VCpVPD390dsbCzS0tJw8OBB0Qzlo0aNQq1atXDgwAFcvXoVoaGhwljw0r0ppkyZguPHj+Pq1auIiYmBl5eX0GWtdNqYmBhIpVIsWrQIqamp2LJlC1Qqlejxu9WhJw/w8tF+CoUCW7ZsQXJyMsaPHw9TU1Oha/Pw4cNFv9RcuHAB+/btQ2ZmJv744w906tQJtWvXFvVaGTlyJOzt7YVHqO/fvx+WlpaYOXOmkGbPnj0IDQ1FZmYmDh48CCcnJ/Tr10+Ut4CAABgbG2Pv3r1IT0/HvHnzoFQqRV3hVq9ejZiYGKSmpuKnn36CSqUSjRUtfoR6t27dEBcXh+PHj8PKykptGBjw8hd3Ly8vjedp4sSJMDExQVhYmOhxeSW79t26dQuxsbHCI5n/+OMPxMbGip5QMGXKFNjb2+PEiRO4cuUKxo4dC2tra9FjAkvS1CsoNTUV27dvR1paGiIjIzFo0CCYm5sjKytL4zYAzb8w+fj4oEGDBggNDcXVq1cRGBgIpVKJtWvXat0OaRiu4ufnhwYNGiA8PByJiYl4//33Ub9+/dfypKB3KY7Ku+aeP38OV1dXtG/fHpGRkcjIyMB3330HiUQizM9x/vx5BAQEIC4uDpmZmdixYwesrKwwYsQItWOYN28e7OzsNP6ynp+fj9jYWMTGxsLW1hbTp09HbGys1qcGAeq/WD58+BBeXl5o1KgRMjIyRMdUvM+UlBQoFApMnDgRycnJSEpKwrBhw2BiYiL88rFlyxbs2rULKSkpSElJweLFiyGVSrF582a1PLRr1040/LGke/fuITY2VuimGxwcjNjYWNGjQ0srff0nJibCysoKw4YNEx1PyV4OgYGB0NPTw9q1a5GZmYlz587B09NT1FX7+vXrqFGjBiZNmoTU1FQcPnwY1tbWasOYq8rbGke6eB2x9vnnnyMsLAxZWVkIDw9Hly5dYGlpKXyOusQaUH59VGzjxo1QKpVqvT010dT1ffbs2cLjxhMSEjB79mxIJBLhl8qMjAwsWrQI0dHRyMrKwqFDh1CnTh106NBB2IYusXb37l2sW7cOKSkpiI2NxeTJk6FUKjXOb1VW/Vla6bIhMzMTS5YsQXR0NP7880+Eh4ejd+/eMDc3Fx4zHBISgg0bNiAxMRHp6elYu3YtDAwMRD1cX6VurIy3NY7eRIwAL38Nj4iIQEZGBrZv3w5zc3NMmzZNlLc///wTsbGx+Oqrr2BkZCTUK8XDYo8cOYLNmzcjMTERWVlZOHz4MDw8PNTmoyt+X4sWLTBkyBDExsaKhisWz4/23XffISUlBQsWLND4SPKHDx/CwMBA43yautafly9fRmxsLHr37o2OHTsKeXsd+S2p9PcQXfJbWFiI5s2bo0OHDrh06RKio6Ph5eUlmof08ePHcHBwwIABA3D58mWcOXMGbm5u+M9//qM1L5XxtsZRVXkd8RgVFQU9PT0sXrwY6enp2LlzJwwMDLBjxw4hzbJly2BqairMc+Pn5yd6hPrdu3dhYWGBfv36IS4uDqmpqZg+fTr09fWFXm26lLulaaqzjh8/jmPHjuHq1av47bff0KRJE3h5eQnfEx4/fozp06cjIiICWVlZOHXqFJo3bw43Nzehp+bNmzfh6uqKzp074+bNm6J7rmK6XN/Fj1CfMWMGUlJSsGbNGrVHqBdvy9HREbNmzVI7Rl3qWF3LsqryRodrffLJJ5gwYQKMjY1hZmaGL774oszJw4CXF3HXrl1hZGQEQ0NDNG7cGIsXLxbWP336FJ999hlsbW0hl8vh6uoq3OiX/nI8adIkuLi4QKFQwMrKCsOHD8c///yjMS3w30eo6+vrw9HRUfQYOm15/jc28gAvb0wdHR0hl8vRqlUr0Vh7Hx8f4ZH2ABAWFgYPDw8oFApYWFhg+PDhapNRPXr0CFOmTIGjoyOUSiXq1KmDuXPnioYdFD9Grvj8zps3T+OwhKVLl8LBwQEGBgbw9vZWG+o0fPhwmJubQy6Xo3HjxhofRXvt2jX06NEDKpUKlpaW+Pzzz4VHNhfLycmBSqXSOsSIiDT+lWz4W7BgQblpnj9/js8//xzW1taoUaMGunTpojYEoCRN12ZycjKaNm0KlUoFY2Nj+Pn5lfm4QEBzI8+tW7cwatQo2NnZQalUwt3dHStXrtT6aNzi81C6kefhw4cYM2YMTE1NYW5ujr59+4oey1uV3qU40uWaS0tLQ79+/WBtbQ0DAwO1GChu0DYxMYFSqYSHhweWLFmiNryhsLAQDg4OGocbAf8d0lT6r/SwpZJKf5ErvpY1/ZX8Evbbb7+hbdu2MDExgZmZGTp16oSIiAhh/ZYtW+Dh4QEDAwMYGxujVatW2Lt3r9r+r1y5IuqiW1pgYKDGvGgrwwH1619bzJfuPv/jjz+ifv36UKlUsLW1xdChQ3Hz5k1RmvPnz8PLywsKhQJ16tTB4sWLtQ5lqay3OY50UdWxNmjQIOE+wt7eHoMGDVIbV19erAG61UfAy0koNQ071kTTDfOYMWPg5OQEuVwOKysrdO7cWXSdX79+HR06dIC5uTkUCgVcXV0xY8YMtW755cXa3bt30bp1a2FOxM6dO2uci6e8+rO00mVDdnY2evToAWtra+jr68PBwQFDhgwR1WvHjh1D06ZNhfvBJk2a4OeffxZ113+VurEy3uY4ehMxMmvWLNjY2EBfXx9ubm4a7ydGjhypscwMDQ0F8HJYu7e3t1Bnubm5YdasWWoNorqUu3v27EHdunUhl8vRoEEDtcnJgZeTp6pUKtH8M8V0rT+dnJw05ud15Lf0fkt+D9E1v9nZ2ejXrx+MjIxgY2ODUaNGqT0uPSUlBV26dIFKpYKDgwOmTZv2WubjAd7uOKoqVR2PAPDrr7+iYcOGUCgUqFevnlr5W1RUhPnz58PGxgYKhQKdO3cWzVUDABcvXkS3bt1gbm6OGjVqoHXr1qL5PHUpd0vTVGft3r0bderUgVwuR82aNeHv7y+Kuby8PHTr1g1WVlbQ19eHk5MTxo0bJ5pLTds9W+lY0+X6Lp6QWS6Xo06dOqL76mInTpwAEamdM0C3OlbXsqyqVKSRRwKUej6aBpcuXaIWLVpQTEwMNW/eXGu6jh07UtOmTemHH34ob5PV1s6dO2nYsGHlnivGmHYcR4xVHscRY5XHccRY5XEcMVZ5urbJEBG9G4OXGWOMMcYYY4wxxqo5buRhjDHGGGOMMcYYqwb0qnJjYWFhVbk5xhhjjDHGGGOMMaYj7snDGGOMMcYYY4wxVg1wIw9jjDHGGGOMMcZYNcCNPIwxxhhjjDHGGGPVADfyMMYYY4wxxhhjjFUD3MjDGGOMMcYYY4wxVg1wIw9jjDHGGGOMMcZYNcCNPIwxxhhjjDHGGGPVADfyMMYYY4wxxhhjjFUD3MjDGGOMMcYYY4wxVg1wIw9jjDHGGGOMMcZYNcCNPIwxxhhjjDHGGGPVADfyMMYYY4wxxhhjjFUD3MjDGGOMMcYYY4wxVg1wIw9jjDHGGGOMMcZYNaBXkcRHjx6llJSU15WXaiE8PJyI+FwxVhkcR4xVHscRY5XHccRY5XEcMVZ5WVlZOqeVAEB5iSIiIqh9+/ZUWFhYqYy9K6RSKRUVFb3pbDD2r8ZxxFjlcRwxVnkcR4xVHscRY5Unk8no7Nmz5O3tXWY6nXryKBQKKiwspB07dpCHh0eVZLC6Onr0KM2fP5/PFWOVwHHEWOVxHDFWeRxHjFUexxFjlZeSkkLDhg0jhUJRbtoKDdfy8PCg5s2bv3LG3gXFXRD5XDH26jiOGKs8jiPGKo/jiLHK4zhi7H+LJ15mjDHGGGOMMcYYqwa4kYcxxhhjjDHGGGOsGuBGHsYYY4wxxhhjjLFqgBt5GGOMMcYYY4wxxqoBbuRhjDHGGGOMMcYYqwa4kYcxxhhjjDHGGGOsGuBGHsYYY4wxxhhjjLFqgBt5GGOMMcYYY4wxxqoBbuRhjDHGGGOMMcYYqwa4kYcxxhhjjDHGGGOsGuBGHsYYY4wxxhhjjLFqgBt5GGOMMcYYY4wxxqoBbuRhjDHGGGOMMcYYqwa4kYcxxhhjjDHGGGOsGuBGHsYYY4wxxhhjjLFq4F/fyLNw4UJq2rSp8HrUqFH0wQcfvLH8MMYYY4wxxhhjjL0J//pGHqa7NWvWkLOzMymVSvLy8qKoqCitaV+8eEGLFi0iFxcXUiqV1KRJEzp+/Lgozbp166hx48ZkbGxMxsbG5O3tTceOHRPW379/nz799FNyd3cnlUpFjo6ONHnyZHr48KFoO9evX6devXqRgYEBWVtb04wZM6igoEBYf+7cOWrbti1ZWFiQSqWievXqUUBAgGgbhYWFNH/+fKpduzapVCpycXGhr7/+mgAIaXJzc2nSpEnk4OBAKpWK6tevTz///LNoOx9//DG5uLiQSqUiKysr8vPzoytXrmg8R/fu3SMHBweSSCSUk5Ojdq49PDxIpVKRu7s7bdu2TbR+y5YtJJFIRH9KpVKUZv/+/dStWzeysLAgiURCcXFxGvNBRASAevToQRKJhA4ePChaV3o/EomEgoODRWny8/Np7ty55OTkRAqFgpydnWnz5s2ivHh6epKpqSkZGhpS06ZNafv27VrzU51VdRzpcu3evn2bRo0aRXZ2dmRgYEDdu3en9PR00XZ++eUX6tixIxkbG2u8JomI+vTpQ46OjqRUKsnW1paGDx9Of/31lyhNQkICtW/fnpRKJdWqVYuWL1+u9fiCg4NJIpGoNawvXLiQ6tWrR4aGhmRmZkZdunShyMhIjdvIz8+npk2bql3jCxcu1HjtGhoait7/ww8/CGVMrVq16LPPPqNnz54J65cuXUotW7akGjVqkLW1NX3wwQeUmpqqlo+IiAjq1KkTGRoakrGxMXXo0IGePn0qrL9//z4NHTqUjI2NydTUlMaOHUu5ubmibZw4cYJat25NNWrUICsrK+rfvz9du3ZN7XjLijX2X1Uda5quqXr16onS/P333zR8+HCqWbMmGRoaUvPmzWnfvn2iNJcuXaKuXbuSqakpWVhY0Pjx40XXgqbyvfjvzp07ankPDw8nPT090Q9WROXXsUREHTt2VNvHhAkTRGkuXrxInTt3JlNTUzIzMyNfX1+Kj48XpdmzZw81bdqUDAwMyMnJiVasWCFaP2rUKI3H06BBAw2fBtGyZctIIpHQ1KlTRct1qWN1qbPKq2PfFVUdI0RE2dnZNGzYMOGeq1GjRhQdHS2sB0Bffvkl2drakkqloi5duqjVR87Ozmqf4bJly0RpdCkvi2mLEV3yWxX1ka75La981xSvEomEevXqVaHzW15dHhYWRn5+fmRrayvcs+3cuVO0DV3uQ9l/cX3E9dG/rj6CDmJiYkBEiImJ0SW5ID8/v0LpX8WCBQvQpEkT4fXIkSPh5+f32verzY4dO17pXL1uwcHBkMvl2Lx5My5fvoxx48bB1NQUt2/f1ph+5syZsLOzw5EjR5CZmYm1a9dCqVTi0qVLQpqQkBAcOXIEaWlpSE1NxRdffAF9fX0kJSUBABITE9GvXz+EhIQgIyMDp0+fhpubG/r37y9so6CgAA0bNkSXLl0QGxuLo0ePwtLSEnPmzBHSXLp0Cbt27UJSUhKysrKwfft2GBgYYP369UKaxYsXw8LCAocPH0ZWVhb27t0LIyMjrFq1Skgzbtw4uLi4IDQ0FFlZWVi/fj1kMhkOHTokpFm/fj3OnDmDrKwsxMTEoHfv3qhVqxYKCgrUzpGfnx969OgBIsKDBw+E5WvXrkWNGjUQHByMzMxMBAUFwcjICCEhIUKawMBAGBsb49atW8Lf33//Ldr+tm3b8NVXX2HDhg0gIsTGxmr7ePH9998LeTlw4IBoHREhMDBQtK+nT5+K0vTp0wdeXl44efIksrKycP78eZw7d05YHxoaiv379yM5ORkZGRn44YcfIJPJcPz4ca15qox3KY7Ku3aLiorQunVrtG/fHlFRUbhy5QrGjx8PR0dH5ObmCtsJCAjA0qVLsXTpUrVrstj333+PiIgIXLt2DeHh4fD29oa3t7ew/uHDh7CxscHQoUORlJSEoKAgqFQqUawVy8rKgr29Pdq3b69W5u7cuRMnT55EZmYmkpKSMHbsWBgbG+POnTtq25k8ebJw7Za8xh8/fiy6Zm/duoX69etj5MiRov0oFArs3LkTWVlZOHHiBGxtbfHZZ58JaXx9fREYGIikpCTExcWhZ8+eaufu/PnzMDY2xtKlS5GUlIQrV65g9+7dePbsmZCme/fuaNKkCS5cuICzZ8/C1dUVgwcPFtZfvXoVCoUCc+bMQUZGBmJiYtChQwc0a9ZMdLzlxVpVelvjSBevI9YWLFiABg0aiK6pu3fvirbTtWtXtGzZEpGRkcjMzMTXX38NqVQqbCc7OxtmZmaYMGECrly5gqioKLRp00ZUr+Xl5aldu76+vvDx8VHL94MHD1CnTh1069ZNdC8DlF/HAoCPjw/GjRsn2tfDhw+F9Y8fP4a5uTlGjRqFK1euICkpCf3794eNjQ2eP38OADh69Cj09PSwbt06ZGZm4vDhw7C1tcXq1auF7eTk5Ij2cePGDZibm2PBggVqxxQVFQVnZ2c0btwYU6ZMEa3TpY4tr87SpY6tSm9rHL2OGLl//z6cnJwwatQoREZG4urVqzhx4gQyMjKENMuWLYOJiQkOHjyI+Ph49OnTB7Vr1xZ9Rk5OTli0aJHoMyxZ5upaXgJlx4gu+a2K+qiqyvd79+6JzklSUhJkMhkCAwMrdH7Lq8sXL16MefPmITw8XLhnk0ql+PXXX4U0utyHVqW3NY50wfUR10dvS31UkTaZKm3k8fHxgb+/P6ZMmQILCwt07NgRiYmJ6N69OwwNDWFtbY1hw4aJLuLCwkJ8++23cHFxgVwuR61atfDNN98I62fOnAk3NzeoVCrUrl0b8+bNEy4EgBt5dNWqVSv4+/sLrwsLC2FnZ4elS5dqTG9ra4uffvpJtKxfv34YOnRomfsxMzPDxo0bta7fs2cP5HI5Xrx4AeBlMEulUlHFsm7dOhgbG5fZSNi3b18MGzZMeN2rVy+MGTOmzPw2aNAAixYtEqVp3rw55s6dq3U/8fHxICLRDQPwMqh9fHxw+vRptS/U3t7emD59uij9tGnT0LZtW+F1YGAgTExMtO63pKysrDIbeWJjY2Fvb49bt25pbeQpvaykY8eOwcTEBPfu3dMpP8WaNWuGefPmVeg9unqX4qi8azc1NRVEJKpICwsLYWVlhQ0bNqjtMzQ0VGsjT2mHDh2CRCIRytS1a9fCzMxMFHuzZs2Cu7u76H0FBQVo06YNNm7cqFOZ+/DhQxARTp06JVp+9OhR1KtXD5cvXy63ITMuLg5EhD/++ENY5u/vj06dOonSlY610u7cuQMiwpkzZ4RlXl5eZV7LycnJICJcvHhRWHbs2DFIJBJkZ2cDAPbu3Qs9PT0UFhYKaUJCQkTn91Vj7VW9rXGki9cRa6XvFzQxNDTEtm3bRMvMzc2FWFu/fj2sra1Fn3NCQgKICOnp6Rq3eefOHejr66ttFwAGDRqEefPm6ZQ3QL2O9fHxUbtxLenixYsgIly/fl1rfgcPHowBAwaI3vfjjz/CwcEBRUVFGrd74MABSCQSXLt2TbT88ePHcHNzw8mTJ8vNG6C5ji2vztKljq1Kb2scvY4YmTVrFtq1a6d1n0VFRahZsyZWrFghLMvJyYFCoUBQUJCwzMnJCQEBAVq3o0t5WaysGCkvv5q8Sn30usr3gIAA1KhRQ2gA0/X8lla6LtekZ8+eGD16tPC6IvehVeFtjSNdcH2kGddH//v6qCKNPFU+XGvr1q0kl8spPDycli1bRp06daJmzZpRdHQ0HT9+nG7fvk0DBw4U0s+ZM4eWLVtG8+fPp+TkZNq1axfZ2NgI62vUqEFbtmyh5ORkWrVqFW3YsEFtqA4r2/PnzykmJoa6dOkiLJNKpdSlSxeKiIjQ+J78/Hy1bpsqlYrOnTunMX1hYSEFBwfTkydPyNvbW2teHj58SMbGxqSnp0dEL4dINGrUSPSZ+/r60qNHj+jy5csatxEbG0vnz58nHx8fYVmbNm3o9OnTlJaWRkRE8fHxdO7cOerRo4coTUhICGVnZxMACg0NpbS0NOrWrZvG/Tx58oQCAwOpdu3aVKtWLWF5cnIyLVq0iLZt20ZSqXoIaTt3UVFR9OLFC2FZbm4uOTk5Ua1atcjPz0/r8ZYlLy+PhgwZQmvWrKGaNWtqTefv70+WlpbUqlUr2rx5s2goUEhICHl6etLy5cvJ3t6e6tatS9OnTxcNVSkJAJ0+fZpSU1OpQ4cOFc7zv9XriqPyrt38/HwiItF2pFIpKRQKrfGoi/v379POnTupTZs2pK+vT0Qv47FDhw4kl8uFdL6+vpSamkoPHjwQli1atIisra1p7Nix5e7n+fPn9Msvv5CJiQk1adJEWH779m0aN24cbd++nQwMDMrdzsaNG6lu3brUvn17YVmbNm0oJiZG6DZ99epVOnr0KPXs2VPrdoqHi5qbmxMR0Z07dygyMpKsra2pTZs2ZGNjQz4+PqJzGxERQaampuTp6Sks69KlC0mlUqHbf4sWLUgqlVJgYCAVFhbSw4cPafv27dSlSxfh/FY01t5Vr7POSk9PJzs7O6pTpw4NHTqUrl+/Llrfpk0b2r17N92/f5+KioooODiYnj17Rh07dhT2I5fLRWW/SqUiItIaj9u2bSMDAwMaMGCAaHlgYCBdvXqVFixYUMbZeKmsOnbnzp1kaWlJDRs2pDlz5lBeXp6wzt3dnSwsLGjTpk30/Plzevr0KW3atIk8PDzI2dlZOCZN5+7mzZv0559/aszPpk2bqEuXLuTk5CRa7u/vT7169RJ9dtpoq2OLt6OtztK1jq3OXleMFJdRH374IVlbW1OzZs1ow4YNwvqsrCz6+++/Rfs1MTEhLy8vtf0uW7aMLCwsqFmzZrRixQrRMHxdykui8mOkvPxqOm+vUh+9rvJ906ZN9NFHHwnDkCtyfotpqss1efjwoVDvFauK+9DqjusjdVwf/Uvqo6psNfLx8RF1Xfz666/RrVs3UZobN26AiJCamopHjx5BoVBo/DVamxUrVqBFixbCa+7JU77s7GwQEc6fPy9aPmPGDLRq1UrjewYPHoz69esjLS0NhYWF+O2336BSqSCXy0XpEhISYGhoCJlMBhMTExw5ckRrPu7evQtHR0d88cUXwrJx48apXSNPnjwBEeHo0aOi5fb29pDL5ZBKpWo9cgoLCzFr1ixIJBLo6elBIpFgyZIlojTPnj3DiBEjQETQ09ODXC7H1q1b1fK5Zs0aGBoagojg7u4uatF99uwZGjdujO3btwPQ3Gtizpw5qFmzJqKjo1FUVISLFy/CxsYGRIS//voLwMvhIVu3bkVsbCzCwsLw/vvvw9jYGDdu3FDLT1k9ecaPH4+xY8cKr0lDi/OiRYtw7tw5XLp0CcuWLYNCoRANY/P19YVCoUCvXr0QGRmJI0eOCN2fS8rJyYGhoSH09PSgUCiwadMmtfxUlXcpjsq7dp8/fw5HR0d8+OGHuH//PvLz87Fs2TIQkVrsAOX35Jk5cyYMDAxARGjdujX++ecfYV3Xrl0xfvx4UfriXzWTk5MBAGfPnoW9vb3QI1Nbmfvrr7/C0NAQEokEdnZ2iIqKEtYVFRWhe/fu+PrrrwGU31vt6dOnMDMzw7fffqu2btWqVdDX14eenh6ICBMmTNC4DeDlue7Vq5foF5aIiAgQEczNzbF582ZcunQJU6dOhVwuR1paGoCXXd/r1q2rtj0rKyusXbtWeB0WFgZra2vIZDIQEby9vUWfg66xVlXexjjSxeuKtaNHj2LPnj2Ij4/H8ePH4e3tDUdHRzx69EhI8+DBA3Tr1k2oJ4yNjXHixAlhfVJSEvT09LB8+XLk5+fj/v376N+/P4hIrc4p5uHhgYkTJ4qWpaWlwdraGqmpqQC0/6pbXh27fv16HD9+HAkJCdixYwfs7e3Rt29fUZrExES4uLhAKpVCKpXC3d1d9Ivn+vXrYWBggFOnTqGwsBCpqamoV6+exs8AePn5yGQy7N69W7Q8KCgIDRs2FLqya/vltKw6Fii/ztKljq1Kb2Mcva4YUSgUwrCkS5cuYf369VAqldiyZQsAIDw8XON5/vDDDzFw4EDh9cqVKxEaGor4+HisW7cOpqamomG0QPnlpS4xUl5+i1VFfVTV5XtkZCSICJGRkcIyXc8vUHZdXtru3bshl8tFPYIrch9aFd7GONIF10f/xfXRm6+P3uhwrf/85z/C6wEDBkBfXx+Ghoaiv+Iv8MUF3NWrV7VuMzg4GG3atIGNjQ0MDQ2hUChgZWUlrOdGnvK9SgF1584d+Pn5QSqVQiaToW7duvjkk0+gVCpF6fLz85Geno7o6GjMnj0blpaWuHz5str2Hj58iFatWqF79+6i7qQVaeS5evUqEhIS8Msvv8Dc3By7du0S1gUFBcHBwQFBQUFISEjAtm3bYG5uLqroV6xYgbp16yIkJATx8fFYvXo1jIyMcPLkSdF+cnJykJaWhjNnzqB3795o3ry5UEh89tlnGDRokJBW0xfqvLw8jB49Gnp6epDJZLCzs8PMmTNBRFrHOz9//hwuLi4ah4xou+E4dOgQXF1d8fjxY2GZpkae0ubPnw8HBwfhddeuXaFUKpGTkyMs27dvHyQSCfLy8oRlhYWFSE9PR2xsLL777juYmJggNDS0zH29qncpjnS5dqOjo9GkSRMQEWQyGXx9fdGjRw90795dbZ/lNfLcvXsXqamp+O2339C2bVv07NlT6AJbXiPPo0eP4OzsLIpNbWVubm4u0tPTERERgTFjxsDZ2VkYv75q1Sq0bdtWGPdcXiPPrl27oKenpxY/oaGhsLGxwYYNG5CQkID9+/ejVq1aao3AxSZMmAAnJyfRTWzxTXXJecAAoFGjRpg9ezYA3Rp5bt26BTc3N8yYMQOXLl3CmTNn4OPjg86dO4vOry6xVlXexjjSxeuss0p68OABjI2NRd3NJ02ahFatWuHUqVOIi4vDwoULYWJigoSEBCHNzp07YWNjA5lMBrlcjunTp8PGxgbLli1T28f58+dBRIiOjhaWFRQUwNPTE+vWrROWabup1rWOLVY8hLj4RjUvLw+tWrXCiBEjEBUVhYiICPTv3x8NGjQQrrmioiLMnDkTSqUSMpkMZmZmWLhwIYgIFy5cUNvHkiVLYGFhIRrWef36dVhbWyM+Pl5Ypu2muqw6VpPSddar1LGV8TbG0euKEX19fdHcLgDw6aefonXr1gAq1ghR0qZNm6CnpyfMc1ZeealrjJSX32KVrY9eR/k+fvx4NGrUSLSsIue3rLq8pN9//x0GBgYaf9Qsqaz70KrwNsaRLrg++i+uj958ffRGG3lKnsDu3bujX79+SE9PV/vLzc0VxuFpa+Q5f/48ZDIZvvnmG1y8eBFpaWlYtGiRaAwpN/KULz8/HzKZTO3L/4gRI9CnT58y3/v06VPcvHlTCLr69euXmb5z585qXxIfPXoEb29vdO7cWS1w5s+fr1aQXL16FUQkmqCstK+//lr0pcvBwUFt/OvXX38tzCWSl5cHfX19HD58WJRm7Nix8PX11bqf/Px8GBgYCA1KTZo0EQptmUwGqVQqfPH+8ssvRe99/vw5bty4gYKCAmFirpLjZksbMGAAPvroI7Xl2r4AT5kyBRKJRMhL8a9LUqlU44RqxQ4fPgwiEm62RowYARcXF1Ga4jlIinsyaDJ27FiNPUmqwrsUR+VduyXl5OQIk0W2atUKn3zyiVqaiszJU9yzsvjmZfjw4Wrl5++//w4iwv379xEbGytc78V/EolEuA5L/wJSkqurq/DLUsmbn5LXrkwmw4gRI9Te26lTJ3zwwQdqy9u1a6c2Fnr79u1QqVRqsebv7w8HBwe1+qa4vCnunVds4MCBGDJkCICXX1BMTU1F61+8eAGZTIb9+/cDAObNmwdPT09RmuLzGxERAeDVY+1VvY1xpIv/ZZ3l6ekpNOZlZGSASs1/Bbys1z7++GO19/799994/PgxcnNzIZVKsWfPHrU0Y8aMQdOmTUXLHjx4oDGOipedPn1aa3411bEl5ebmgoiESfE3btyoNmdDcb1Weo6PgoIC3Lx5E/n5+Th69CiISG1y2qKiIri6umLq1Kmi5QcOHFA7JiISygZNDy8omZeSP9qUVrrOKlbROvZVvY1x9LpixNHRUdQ7GHg5V5udnR0AIDMzU+P9SIcOHTB58mSt+0xKSgIR4cqVKwDKLy91jZHy8qtNReujqi7fc3NzYWxsjB9++EG0/FXPb+m6vFhYWBgMDQ01PjxBE233oVXhbYwjXXB9xPVRSW+6Pnqjc/KU1Lx5c7p8+TI5OzuTq6ur6M/Q0JDc3NxIpVLR6dOnNb7//Pnz5OTkRHPnziVPT09yc3PTOh6PaSeXy6lFixai81xUVESnT58uc/4copfzgNjb21NBQQHt27eP/Pz8ykxfVFQkzCFCRPTo0SPq1q0byeVyCgkJURu36O3tTYmJiaLH+J08eZKMjY2pfv36Ou8nLy9PbX4cmUxGRUVFRPTycYYvXrwoM40meNkQKuxr3759FB8fT3FxcRQXF0cbN24kIqKzZ8+Sv7+/6L36+vrk4OBAMpmMgoOD6f3339c4hw/Ry/GtiYmJZGtrqzUvpc2ePZsSEhKEvBQ/7jMgIIACAwO1vi8uLo7MzMxIoVAQEVHbtm3pr7/+Ej1yMS0tjaRSKTk4OGjdTunPoLp7XXFU3rVbkomJCVlZWVF6ejpFR0eXG4/lKd5H8efo7e1Nf/zxh2gc8cmTJ8nd3Z3MzMyoXr16lJiYKLrm+vTpQ++99x7FxcWpjWMuva/i/fz444+iODp69CgREe3evZsWL14sel9WVhaFhoZqnP9H27kjImHMNACaNGkSHThwgH7//XeqXbu2KL2zszPZ2dmpPVY9LS1NGN/t7e1NOTk5FBMTI6z//fffqaioiLy8vMrNS/F5ftVYe9f8r+qs3NxcyszMFMrd4rkDdI1HGxsbMjIyot27d5NSqaSuXbuqbX/Pnj1q166xsbFaHE2YMIHc3d0pLi5OuKY0Ka/cLa4HSh6TVColiUQipCl+XfqYZDIZ2dvbk1wup6CgIPL29iYrKytRmjNnzlBGRobaMXXu3FntmDw9PWno0KEUFxcnxEJppetYbcdUss4qVpE6trp5XTHStm3bMsvC2rVrU82aNUX7ffToEUVGRpa537i4OJJKpWRtbU1E5ZeXusZIefnVpqL1UVWX73v37qX8/HwaNmyYaPmrnt/SdTnRy8eo9+rVi7799lsaP358meeD6NXuQ98FXB9xfVT6mP419VFVthqV7smTnZ0NKysrDBgwAFFRUcjIyMDx48cxatQooRVt4cKFMDMzw9atW5GRkYGIiAihq9qhQ4egp6eHoKAgZGRkYNWqVTA3N+eePK8gODgYCoUCW7ZsQXJyMsaPHw9TU1OhK9nw4cOF1mMAuHDhAvbt24fMzEz88ccf6NSpE2rXri3qHTB79mzh0XMJCQmYPXs2JBIJfvvtNwAvh2h5eXmhUaNGyMjIED1+rvjzL36Eerdu3RAXF4fjx4/DyspKNHTip59+QkhICNLS0pCWloaNGzeiRo0aoqdijRw5Evb29sJjqPfv3w9LS0vMnDlTSOPj44MGDRogNDQUV69eRWBgIJRKpTDcIjMzE0uWLEF0dDT+/PNPhIeHo3fv3jA3N9f6mERNvSZSU1Oxfft2pKWlITIyEoMGDYK5uTmysrKENF999RVOnDiBzMxMxMTE4KOPPoJSqRR1e7x37x5iY2Nx5MgREBGCg4MRGxuLW7duaf2cqdRwrZCQEGzYsAGJiYlIT0/H2rVrYWBgIOp19PjxYzg4OGDAgAG4fPkyzpw5Azc3N9HQyyVLluC3335DZmYmkpOT8d1330FPT69C82lVxLsUR7pcu3v27EFoaCgyMzNx8OBBODk5oV+/fqK83bp1C7GxsdiwYQOIXj6FKjY2VnjSx4ULF7B69WrExsbi2rVrOH36NNq0aQMXFxfhF4mcnBzY2Nhg+PDhSEpKQnBwMAwMDMr8FbB0mZubm4s5c+YIj3eNjo7G6NGjoVAo1H6RKlbWcK158+bBzs5O4y8vCxYsQI0aNRAUFISrV6/it99+g4uLi6hb+8SJE2FiYoKwsDBRGVSy+3xAQACMjY2xd+9epKenY968eVAqlaKeSd27d0ezZs0QGRmJc+fOwc3NTfQI9dOnT0MikeCrr75CWloaYmJi4OvrCycnJ2FfusRaVXpb40gXryPWPv/8c4SFhSErKwvh4eHo0qULLC0thV8Hnz9/DldXV7Rv3x6RkZHIyMjAd999B4lEIpp7YPXq1YiJiUFqaip++uknqFQq0Rj9Yhs3boRSqdSpV52m7vHl1bEZGRlYtGgRoqOjkZWVhUOHDqFOnTro0KGDsI2UlBQoFApMnDgRycnJSEpKwrBhw2BiYiIMCbl79y7WrVuHlJQUxMbGYvLkyVAqlaK5QooNGzYMXl5e5R4PoH5PqEsdq0udpUsdW5Xe1jh6HTESFRUFPT09LF68GOnp6di5cycMDAywY8cOIc2yZctgamqKQ4cOISEhAX5+fqJHfJ8/fx4BAQGIi4tDZmYmduzYASsrK1EvTV3Ky9I0xUh5+a2q+qiqy/d27dqJhv6XVN751aUuLx6iNWfOHFG9V/LJX7rch1altzWOdMH1EddHb0t99NYM1wJeTubUt29fmJqaQqVSoV69epg6daowbrSwsBDffPMNnJycoK+vD0dHR9FkUTNmzICFhQWMjIwwaNAgBAQEcCPPK1q9ejUcHR0hl8vRqlUr0dhGHx8fjBw5UngdFhYGDw8PKBQKWFhYYPjw4cKjgouNGTMGTk5OkMvlsLKyQufOnYVgB/7bAKLpr+TFf+3aNfTo0QMqlQqWlpb4/PPPhUesAy8fndegQQMYGBjA2NgYzZo1w9q1a0Xd4B49eoQpU6bA0dERSqUSderUwdy5c0VjNG/duoVRo0bBzs4OSqUS7u7uWLlypXAtZmdno0ePHrC2toa+vj4cHBwwZMgQoXuxJpoaeZKTk9G0aVOoVCoYGxvDz89PbRtTp04VPgsbGxv07NlTbXhaYGCgxnO3YMECrfkp3chz7NgxNG3aFEZGRjA0NESTJk3w888/q3UhTElJQZcuXaBSqeDg4IBp06aJbrTmzp0LV1dXKJVKmJmZwdvbG8HBwVrzUVnvUhzpcu2uWrUKDg4OQhk5b9480XrgZVmo6XoJDAwE8HLCvPfeew/m5uZQKBRwdnbGhAkTcPPmTdF24uPj0a5dOygUCtjb22sc111S6TL36dOn6Nu3L+zs7CCXy2Fra4s+ffqIJrosTVsjT2FhIRwcHESTtZf04sULLFy4EC4uLlAqlahVqxY++eQTUTxqK4OKz0uxpUuXwsHBAQYGBvD29sbZs2dF6+/du4fBgwfDyMgIxsbGGD16tGg+LODl/ErNmjWDoaEhrKys0KdPH6SkpIjSlBdrVeltjiNdVHWsDRo0CLa2tpDL5bC3t8egQYPUhhimpaWhX79+sLa2hoGBARo3bqz2qNnhw4fD3Nwccrlc4/pi3t7ewpC/8mi6qS6vjr1+/To6dOggxLSrqytmzJiBhw8firZTPGeHiYkJzMzM0KlTJ2GICfDyprp169YwNDSEgYEBOnfurHHug5ycHKhUKvzyyy86HZOmH/7Kq2N1qbN0qWOr0tscR1UdI8DLSYobNmwIhUKBevXqqX3eRUVFmD9/PmxsbKBQKNC5c2dhwlbg5XcGLy8vmJiYQKlUwsPDA0uWLFEb3qBLeVmStnlCyspvVdZHVVW+X7lyBUQkiuWSyju/utTlI0eO1FjvlRzKr8t9aFV6m+NIF1wfcX30NtRHFWnkkQAlngOmxaVLl6hFixYUExNDzZs3r2hnoXfKzp07adiwYXyuGKsEjiPGKo/jiLHK4zhirPI4jhirvIq0ybwbg5cZY4wxxhhjjDHGqjlu5GGMMcYYY4wxxhirBriRhzHGGGOMMcYYY6wa4EYexhhjjDHGGGOMsWqAG3kYY4wxxhhjjDHGqgFu5GGMMcYYY4wxxhirBriRhzHGGGOMMcYYY6wa4EYexhhjjDHGGGOMsWqAG3kYY4wxxhhjjDHGqgFu5GGMMcYYY4wxxhirBriRhzHGGGOMMcYYY6wa4EYexhhjjDHGGGOMsWqAG3kYY4wxxhhjjDHGqgFu5GGMMcYYY4wxxhirBriRhzHGGGOMMcYYY6wa4EYexhhjjDHGGGOMsWqAG3kYY4wxxhhjjDHGqgG9iiQ+evQopaSkvK68VAvh4eFExOeKscrgOGKs8jiOGKs8jiPGKo/jiLHKy8rK0jmtBADKSxQREUHt27enwsLCSmXsXSGVSqmoqOhNZ4OxfzWOI8Yqj+OIscrjOGKs8jiOGKs8mUxGZ8+eJW9v7zLT6dSTR6FQUGFhIe3YsYM8PDyqJIPV1dGjR2n+/Pl8rhirBI4jxiqP44ixyuM4YqzyOI4Yq7yUlBQaNmwYKRSKctNWaLiWh4cHNW/e/JUz9i4o7oLI54qxV8dxxFjlcRwxVnkcR4xVHscRY/9bPPEyY4wxxhhjjDHGWDXAjTyMMcYYY4wxxhhj1QA38jDGGGOMMcYYY4xVA9zIwxhjjDHGGGOMMVYNcCMPY4wxxhhjjDHGWDXAjTyMMcYYY4wxxhhj1QA38jDGGGOMMcYYY4xVA9zIwxhjjDHGGGOMMVYNcCMPY4wxxhhjjDHGWDXAjTyMMcYYY4wxxhhj1QA38jDGGGOMMcYYY4xVA9zIwxhjjDHGGGOMMVYNcCMPY4wxxhhjjDHGWDXAjTyMMcYYY4wxxhhj1QA38jDGGGOMMcYYY4xVA1XayAOAxo8fT+bm5iSRSCguLq4qN88YY4wxxhhjjDHGtKjSRp7jx4/Tli1b6PDhw3Tr1i169OgR9e7dm+zs7EgikdDBgwercnfsFaxZs4acnZ1JqVSSl5cXRUVFaU374sULWrRoEbm4uJBSqaQmTZrQ8ePH1dJlZ2fTsGHDyMLCglQqFTVq1Iiio6OF9QDoyy+/JFtbW1KpVNSlSxdKT08XbeP+/fs0dOhQMjY2JlNTUxo7dizl5uaK0iQkJFD79u1JqVRSrVq1aPny5Wp52bt3L9WrV4+USiU1atSIjh49KlrPeXn1vCxevJjatGlDBgYGZGpqqraPd0VVx9DChQtJIpGI/urVqyesv3btmtr64r+9e/cK6S5evEidO3cmU1NTMjMzI19fX4qPjxfta8+ePdS0aVMyMDAgJycnWrFihda8h4eHk56eHjVt2lS03NnZWWNe/P39hTQff/wxubi4kEqlIisrK/Lz86MrV66o7WPLli3UuHFjUiqVZG1tLdoGEdGJEyeodevWVKNGDbKysqL+/fvTtWvXKpTfpUuXUsuWLalGjRpkbW1NH3zwAaWmporS/PLLL9SxY0cyNjYmiURCOTk5atsvLxa1fU4XLlwQ0mzYsIHat29PZmZmZGZmRl26dCnz+nnX/a9jjYjo77//puHDh1PNmjXJ0NCQmjdvTvv27ROluXTpEnXt2pVMTU3JwsKCxo8fL7oWtmzZojVm79y5o5Z3bdfuunXrqHHjxmRsbEzGxsbk7e1Nx44dE6Xp2LGj2j4mTJggSlMVZcOoUaM0Hk+DBg00fBpEy5YtI4lEQlOnThUt16Vs0LSf4OBgUZo1a9aQh4cHqVQqcnd3p23btmnMR3X3b42Rku7du0cODg5qZa8u15wuMUJEFBERQZ06dSJDQ0MyNjamDh060NOnT4X1aWlp5OfnR5aWlmRsbEzt2rWj0NDQCuV3//791LVrV7KyshLycuLECdF7//jjj3K/F+lyP6apHl62bJmwPiwsjPz8/MjW1pYMDQ2padOmtHPnTtE2NJUdEomEevXqpfG4q7M3EUeZmZnUt29f4XoZOHAg3b59W5Smqr4D5OTkkL+/P9na2pJCoaC6deuKvgfocl9XDAD16NFD7frVtd7Lz8+nuXPnkpOTEykUCnJ2dqbNmzcL6/fv30+enp5kamoqXLvbt28X5SE3N5cmTZpEDg4OpFKpqH79+vTzzz+L0pRX18THx9PgwYOpVq1apFKpyMPDg1atWqV2vOXlV5fz+8ZABzExMSAixMTElJlu9erVcHR0FF4fPXoUc+fOxf79+0FEOHDggC67+5/Kz8+v0u3t2LFDp3P1JgQHB0Mul2Pz5s24fPkyxo0bB1NTU9y+fVtj+pkzZ8LOzg5HjhxBZmYm1q5dC6VSiUuXLglp7t+/DycnJ4waNQqRkZG4evUqTpw4gYyMDCHNsmXLYGJigoMHDyI+Ph59+vRB7dq18fTpUyFN9+7d0aRJE1y4cAFnz56Fq6srBg8eLKx/+PAhbGxsMHToUCQlJSEoKAgqlQrr168X0oSHh0Mmk2H58uVITk7GvHnzoK+vj8TERM5LFeTlyy+/xPfff49p06bBxMRE4zVTVd7WOHodMbRgwQI0aNAAt27dEv7u3r0rrC8oKBCtu3XrFr766isYGRnh8ePHAIDHjx/D3Nwco0aNwpUrV5CUlIT+/fvDxsYGz58/B/CyPNbT08O6deuQmZmJw4cPw9bWFqtXr1bL94MHD1CnTh1069YNTZo0Ea27c+eOKC8nT54EESE0NFRIs379epw5cwZZWVmIiYlB7969UatWLRQUFAhpVq5cCTs7O+zcuRMZGRmIj4/HoUOHhPVXr16FQqHAnDlzkJGRgZiYGHTo0AHNmjWrUH59fX0RGBiIpKQkxMXFoWfPnnB0dERubq6QJiAgAEuXLsXSpUtBRHjw4IHaPsqLxaysLBARTp06JTo/xecfAIYMGYI1a9YgNjYWKSkpGDVqFExMTHDz5k21/VWFtzWOdPEmYg0AunbtipYtWyIyMhKZmZn4+uuvIZVKhe1kZ2fDzMwMEyZMwJUrVxAVFYU2bdqgf//+wjby8vLUYtbX1xc+Pj5q+S7r2g0JCcGRI0eQlpaG1NRUfPHFF9DX10dSUpKQxsfHB+PGjRPt6+HDh8L6qiobcnJyRPu4ceMGzM3NsWDBArVjioqKgrOzMxo3bowpU6aI1ulSNhARAgMDRfsrWRetXbsWNWrUQHBwMDIzMxEUFAQjIyOEhISo5aUqvK1x9G+OkZL8/PzQo0cPtbJXl2tOlxg5f/48jI2NsXTpUiQlJeHKlSvYvXs3nj17JqRxc3NDz549ER8fj7S0NHzyyScwMDDArVu3dM7vlClT8O233yIqKgppaWmYM2cO9PX1RedXl+9FutyPOTk5YdGiRaLzU7JOW7x4MebNm4fw8HBkZGTghx9+gFQqxa+//iqkuXfvnuj9SUlJkMlkCAwM1Pg5VRbH0X/jKDc3F3Xq1EHfvn2RkJCAhIQE+Pn5oWXLligsLBTSVcV3gPz8fHh6eqJnz544d+4csrKyEBYWhri4OCGNLvd1xb7//nvh+i95/epa7/Xp0wdeXl44efIksrKycP78eZw7d05YHxoaiv379yM5OVm4dmUyGY4fPy6kGTduHFxcXBAaGoqsrCysX78eMplMdA9ZXl2zadMmTJ48GWFhYcjMzMT27duhUqnU7onLy68u57cq6domAwBV1sgzcuRIEJHw5+TkJN6Rjo08RUVFWLBgAWrVqgW5XA5bW1t8+umnwvpnz55h5syZcHBwgFwuh4uLCzZu3CisDwsLQ8uWLSGXy1GzZk3MmjULL168ENb7+PjA398fU6ZMgYWFBTp27AgASExMRPfu3WFoaAhra2sMGzZMrWLTxdtaiAFAq1at4O/vL7wuLCyEnZ0dli5dqjG9ra0tfvrpJ9Gyfv36YejQocLrWbNmoV27dlr3WVRUhJo1a2LFihXCspycHCgUCgQFBQEAkpOTQUS4ePGikObYsWOQSCTIzs4G8PKmzszMTNQoN2vWLLi7uwuvBw4ciF69eon27+XlhY8//pjzUsm8lBQYGPjONvK8jhhasGCB2pe78jRt2hRjxowRXl+8eBFEhOvXrwvLEhISQERIT08HAAwePBgDBgwQbefHH3+Eg4MDioqKRMsHDRqEefPm6ZS3KVOmwMXFRW0bJcXHx4OIhMbf+/fvQ6VS4dSpU1rfs3fvXujp6YlueEJCQiCRSEQNJxXN7507d0BEOHPmjNq60NBQjY08usRicSNPbGxsmfsvqaCgADVq1MDWrVt1fk9FvK1xpIs3FWuGhobYtm2baJm5uTk2bNgA4OWNo7W1tei6LB1rpd25cwf6+vpq2wUqdu0CgJmZmeiex8fHR60hpaSqLhuKHThwABKJBNeuXRMtf/z4Mdzc3HDy5Mly8waolw1A+feL3t7emD59umjZtGnT0LZt2zL39are1jiqDjGydu1a+Pj44PTp01ob2Itpu+ZKKx0jXl5emDdvntb0d+/eBRHhjz/+EJY9evQIRISTJ0++cn4BoH79+vjqq680rtN0net6P+bk5ISAgIAy911az549MXr0aK3rAwICUKNGDVFjUVXiOPqvEydOQCqVihrkc3JyIJFIhGuuqr4DrFu3DnXq1FG7byqLtvu62NhY2Nvb49atW+WW05rqvWPHjsHExAT37t3TOS8A0KxZM1EMN2jQAIsWLRKlad68OebOnat1G5rqmtI++eQTvPfeexXK76uc38qoSCNPlQ3XWrVqFS1atIgcHBzo1q1bdPHixVfazr59+yggIIDWr19P6enpdPDgQWrUqJGwfsSIERQUFEQ//vgjpaSk0Pr168nIyIiIXg4b6tmzJ7Vs2ZLi4+Np3bp1tGnTJvrmm29E+9i6dSvJ5XIKDw+nn3/+mXJycqhTp07UrFkzio6OpuPHj9Pt27dp4MCBr35C3jLPnz+nmJgY6tKli7BMKpVSly5dKCIiQuN78vPzSalUipapVCo6d+6c8DokJIQ8PT3pww8/JGtra2rWrBlt2LBBWJ+VlUV///23aL8mJibk5eUl7DciIoJMTU3J09NTSNOlSxeSSqUUGRkppOnQoQPJ5XIhja+vL6WmptKDBw+ENCX3U5ymeD+cl1fPC3t9MURElJ6eTnZ2dlSnTh0aOnQoXb9+XWs+YmJiKC4ujsaOHSssc3d3JwsLC9q0aRM9f/6cnj59Sps2bSIPDw9ydnYuMy83b96kP//8U1gWGBhIV69epQULFpR9QujlOdmxYweNGTOGJBKJxjRPnjyhwMBAql27NtWqVYuIiE6ePElFRUWUnZ1NHh4e5ODgQAMHDqQbN24I72vRogVJpVIKDAykwsJCevjwIW3fvp26dOlC+vr6r5RfIqKHDx8SEZG5ublO6Yl0i8Viffr0IWtra2rXrh2FhISUud28vDx68eJFhfLyLniTsdamTRvavXs33b9/n4qKiig4OJiePXtGHTt2FPYjl8tJKv3v7ZNKpSIiUttXsW3btpGBgQENGDBAtLwi125hYSEFBwfTkydPyNvbW7Ru586dZGlpSQ0bNqQ5c+ZQXl6esK4qy4aSNm3aRF26dCEnJyfRcn9/f+rVq5danaOJprKh5HYsLS2pVatWtHnzZgIgrNOW36ioKHrx4kW5+60OqkOMJCcn06JFi2jbtm2itNpou+aKaYqRO3fuUGRkJFlbW1ObNm3IxsaGfHx8RPmwsLAQhvw9efKECgoKaP369WRtbU0tWrR45fwWFRXR48ePK1S+V+R+bNmyZWRhYUHNmjWjFStWUEFBQZnbfvjwYZl52bRpE3300UdkaGioc37/7d5UHOXn55NEIiGFQiEsUyqVJJVKhe1U1XeAkJAQ8vb2Jn9/f7KxsaGGDRvSkiVLqLCwUOs50XRfl5eXR0OGDKE1a9ZQzZo1Nb63JE31XvF3xuXLl5O9vT3VrVuXpk+fLho6WRIAOn36NKWmplKHDh2E5W3atKGQkBDKzs4mABQaGkppaWnUrVs3jdspq64pqXSM6JLfip7f/6mqbDUKCAhQ68FTjHTsybNy5UrUrVtXY4tYamqqxpb1Yl988QXc3d1FLY9r1qyBkZGR8IuCj4+PWnf/r7/+Gt26dRMtu3HjBogIqamp5ea5pLe1pTo7OxtEhPPnz4uWz5gxA61atdL4nsGDB6N+/fpIS0tDYWEhfvvtN6hUKsjlciGNQqEQhlRcunQJ69evh1KpxJYtWwC8HCpERPjrr79E2/7www8xcOBAAC+7ldatW1dt/1ZWVli7di2Al92Dx48fL1p/+fJlEBGSk5MBAPr6+ti1a5cozZo1a2Btbc15qWReSnpXe/K8rhg6evQo9uzZg/j4eBw/fhze3t5wdHTEo0ePNG5z4sSJ8PDwUFuemJgIFxcXSKVSSKVSuLu7i37xXL9+PQwMDHDq1CkUFhYiNTUV9erVEx1TWloarK2thXKvvF+jdu/eDZlMJvyqVNKaNWtgaGgIIoK7u7vo15OlS5dCX18f7u7uOH78OCIiItC5c2e4u7uLfpEKCwuDtbU1ZDIZiAje3t6iX04rmt/CwkL06tVL6y/+2nry6BKLd+/excqVK3HhwgVERUVh1qxZkEgkou7DpU2cOBF16tQRdcOvSm9jHOniTcbagwcP0K1bNxAR9PT0YGxsjBMnTgjrk5KSoKenh+XLlyM/Px/3799H//79QURYsmSJxrx5eHhg4sSJomW6XrsJCQkwNDSETCaDiYkJjhw5Ilq/fv16HD9+HAkJCdixYwfs7e3Rt29fUZqqKBtKys7Ohkwmw+7du0XLg4KC0LBhQ+F61taTp6yyAQAWLVqEc+fO4dKlS1i2bBkUCgVWrVolrJ8zZw5q1qyJ6OhoFBUV4eLFi7CxsdFYj1WFtzGO/u0x8uzZMzRu3Bjbt28HoL3sLXm8mq45oOwYiYiIABHB3NwcmzdvxqVLlzB16lTI5XKkpaUJ6W7cuIEWLVpAIpFAJpPB1tZWNPymovkFgG+//RZmZmZah/1o+l6k6/3YypUrERoaivj4eKxbtw6mpqb47LPPtOZl9+7dkMvlomFsJUVGRoKIEBkZqXUblcVx9N84unPnDoyNjTFlyhQ8efIEubm5mDRpEohIuKevqu8A7u7uUCgUGDNmDKKjoxEcHAxzc3MsXLhQ4/Fpu68bP348xo4dK7wu73u9pnrP19cXCoUCvXr1QmRkJI4cOSJM+VFSTk4ODA0NoaenB4VCgU2bNonWP3v2DCNGjBDKILlcrrFHdHl1TUnh4eHQ09MTlWW65Lei57ey3khPnlexZMkSMjIyEv6uX79OH374IT19+pTq1KlD48aNowMHDgit03FxcSSTycjHx0fj9lJSUsjb21vU8ti2bVvKzc2lmzdvCstKtswTvZx8KTQ0VJSX4gmyMjMzq/qw/zVWrVpFbm5uVK9ePZLL5TRp0iQaPXq06BeMoqIiat68OS1ZsoSaNWtG48ePp3HjxqlNgMXYu0iXGOrRowd9+OGH1LhxY/L19aWjR49STk4O7dmzR217T58+pV27dol68RQvHzt2LLVt25YuXLhA4eHh1LBhQ+rVq5fwi8O4ceNo0qRJ9P7775NcLqfWrVvTRx99REQvf7kqLCykIUOG0FdffUV169bV6fg2bdpEPXr0IDs7O7V1Q4cOpdjYWDpz5gzVrVuXBg4cSM+ePSOil+XGixcv6McffyRfX19q3bo1BQUFUXp6ujDZ5d9//03jxo2jkSNH0sWLF+nMmTMkl8tpwIABBOCV8uvv709JSUlqk7hWBUtLS5o2bRp5eXlRy5YtadmyZTRs2DCtk1svW7aMgoOD6cCBA2q/CrKKq6pYmz9/PuXk5NCpU6coOjqapk2bRgMHDqTExEQiImrQoAFt3bqVVq5cSQYGBlSzZk2qXbs22djYaPx1PyIiglJSUkQxW5Fr193dneLi4igyMpImTpxII0eOpOTkZGH9+PHjydfXlxo1akRDhw6lbdu20YEDB4R7l6ooG0rbunUrmZqa0gcffCAsu3HjBk2ZMoV27txZ7vVcVtlQ/Bm0bduWmjVrRrNmzaKZM2eK4mj+/PnUo0cPat26Nenr65Ofnx+NHDlSa37ZS29TjMyZM4c8PDxo2LBhOuVd0zVXrKwYKSoqIqKXk7COHj2amjVrRgEBAeTu7i5MngqA/P39ydrams6ePUtRUVH0wQcfUO/evenWrVuvlN9du3bRV199RXv27CFra2ud3lMR06ZNo44dO1Ljxo1pwoQJtHLlSlq9ejXl5+erpQ0NDaXRo0fThg0btE6UvmnTJmrUqBG1atWqyvNa3VRFHFlZWdHevXvp119/JSMjIzIxMaGcnBxq3rx5lZdhRUVFZG1tTb/88gu1aNGCBg0aRHPnztX6PU3TfV1ISAj9/vvv9MMPP+i0T031XnFeJBIJ7dy5k1q1akU9e/ak77//nrZu3SrqHVOjRg2Ki4ujixcv0uLFi2natGkUFhYmrF+9ejVduHCBQkJCKCYmhlauXEn+/v506tQp0f7Kq2uKJSUlkZ+fHy1YsEDUG0iX/Fb0/P5PVWWrUUV78ty7dw/p6enCX/HcOXl5eQgJCcGnn36KmjVrwtvbG8+fP0dISAhkMpnWcW99+/ZVaw2Mi4sDEeHPP/8EoPmXpe7du6Nfv36ivBT/VXRs6tvYUg28nBhKJpOpfQYjRoxAnz59ynzv06dPcfPmTRQVFWHmzJmoX7++sM7R0VHUsgu8HCNqZ2cHAMjMzNQ4T0WHDh0wefJkAC8nvzI1NRWtf/HiBWQyGfbv3w8AGD58OPz8/ERpfv/9dxAR7t+/DwCoVauW2hjlL7/8Eo0bN+a8VDIvJb2rPXleVwxp4unpidmzZ6st37ZtG/T19XHnzh3R8o0bN6rNgZCfnw8DAwO1eZUKCgpw8+ZN5Ofn4+jRoyAi3LlzBw8ePAARQSaTCX8SiURYdvr0adF2rl27BqlUioMHD5Z5LCXzUtyjbPPmzSAi3LhxQ5TO2toav/zyCwBg3rx58PT0FK0v7mEZERFR4fz6+/vDwcEBV69e1ZpPbb/O6hKLmvz000+oWbOm2vIVK1bAxMRENNb+dXgb40gXbyrWMjIyQERqv3Z37txZmMOspL///huPHz9Gbm4upFIp9uzZo5ZmzJgxaNq0qWhZRa/d0nkp/cttSbm5uSAiYZLKqigbSioqKoKrqyumTp0qWn7gwAG1YyIioWdEyYmVSypdNmhy+PBhEJFoolwAeP78OW7cuIGCggJhMuaSx1lV3sY4+rfHSJMmTSCVSoVrRSqVCtfPl19+KdqGtmtOm5IxcvXqVRCR0AOn2MCBAzFkyBAAwKlTp9TmRwEAV1dXYV6WiuS3ePLbw4cPl5lPTd+LKno/ViwpKQlEhCtXroiWh4WFwdDQUDQRb2m5ubkwNjbGDz/8UGZ+K4vjSPN93d27d4V7DhsbGyxfvhxA1X0H6NChAzp37ixKU1y+l374kLb7uilTpghlecnyXSqVanyggKZ6D3h5Xl1cXETLiuceKtmzrrSxY8cKI27y8vKgr6+vFl9jx46Fr6+v1m1oq2suX74Ma2trfPHFF6+U34qc36rwr+nJY25uTq6ursKfnp4eEb0c29i7d2/68ccfKSwsjCIiIigxMZEaNWpERUVFdObMGY3b8/DwoIiICNHY7fDwcKpRowY5ODhozUfz5s3p8uXL5OzsLMqPq6trtRmbKpfLqUWLFnT69GlhWVFREZ0+fVptfH9pSqWS7O3tqaCggPbt20d+fn7CurZt26o9jjgtLU0YM127dm2qWbOmaL+PHj2iyMhIYb/e3t6Uk5NDMTExQprff/+dioqKyMvLS0jzxx9/iMbbnzx5ktzd3cnMzExIU3I/xWmK98N5efW8sNcXQ6Xl5uZSZmYm2draqq3btGkT9enTh6ysrETL8/LySCqVinoxFr8u/iWzmEwmI3t7e5LL5RQUFETe3t7CIzwTExMpLi5O+JswYYLwK2nxNVcsMDCQrK2tdXrcKl5O8i/8yti2bVsiIlHZcf/+ffrnn3+EsqP4mErnnejledc1vwBo0qRJdODAAfr999+pdu3a5ea3NF1iUZO4uDi1z3H58uX09ddf0/Hjx0Vj7dl/valYK57LRtN1VzqOiIhsbGzIyMiIdu/eTUqlkrp27aq2/T179qj9mlnRWCupqKhI46/1xeLi4oiIRMdU2bKhpDNnzlBGRobaMXXu3FntmDw9PWno0KFCL2xNSpcN2o7JzMxMNH8FEZG+vj45ODiQTCaj4OBgev/999+Znjz/9hjZt28fxcfHC9fKxo0biYjo7Nmzao9t1nbNaVMyRpydncnOzq7M+1RtxySVSoVj0jW/QUFBNHr0aAoKCnqlR5G/6v1YXFwcSaVSUa+hsLAw6tWrF3377bc0fvx4re/du3cv5efn69xLqTp5G+7rLC0tydTUlH7//Xe6c+cO9enTh4iq7jtA27ZtKSMjQxSfaWlpZGtrK5rLh0j7fd3s2bMpISFBVL4TEQUEBFBgYKDasWqq94rz8tdff4keA5+WlkZSqbTM7+klY/rFixf04sULncugYprqmsuXL9N7771HI0eOpMWLF79Sfityfv/nqrLVqHRPnsePHyM2NhaxsbEgInz//feIjY0VetVoEhgYiI0bNyIxMRGZmZmYN28eVCoV/vnnHwDAqFGjUKtWLRw4cABXr15FaGioMEb35s2bMDAwgL+/P1JSUnDw4EFYWlqKHreoqSdPdnY2rKysMGDAAERFRSEjIwPHjx/HqFGjtP76pM3b2FJdLDg4GAqFAlu2bEFycjLGjx8PU1NT/P333wBetgiXbGW+cOEC9u3bh8zMTPzxxx/o1KkTateuLfqVOyoqCnp6eli8eDHS09Oxc+dOGBgYYMeOHUKaZcuWwdTUFIcOHRIeE6jpUeHNmjVDZGQkzp07Bzc3N9FjAnNycmBjY4Phw4cjKSkJwcHBMDAwUHtUuJ6eHr777jukpKRgwYIFGh8Vznl5tbz8+eefiI2NFR7fXRzbxY/xrkpvaxy9jhj6/PPPERYWhqysLISHh6NLly6wtLRU+wU9PT0dEokEx44dU8tXSkoKFAoFJk6ciOTkZCQlJWHYsGEwMTERxvbfvXsX69atQ0pKCmJjYzF58mQolcoyx+BrmyeksLAQjo6OmDVrltq6zMxMLFmyBNHR0fjzzz8RHh6O3r17w9zcXDQ3gZ+fHxo0aIDw8HAkJibi/fffR/369YWemqdPn4ZEIsFXX32FtLQ0xMTEwNfXF05OTsjLy9M5vxMnToSJiQnCwsJEj/YsuY1bt24hNjYWGzZsEJ6yEhsbK3qiQnmxuGXLFuzatQspKSlISUnB4sWLIZVKsXnzZiHNsmXLIJfL8X//93+ivLyOGALe3jjSxZuItefPn8PV1RXt27dHZGQkMjIy8N1330EikYjm+Vi9ejViYmKQmpqKn376CSqVSjRnTLGNGzdCqVSW+wQeQPO1O3v2bOERsAkJCZg9ezYkEgl+++03AC97VSxatAjR0dHIysrCoUOHUKdOHXTo0EHYRlWXDcOGDYOXl1e5xwOo32/pUjaEhIRgw4YNSExMRHp6OtauXQsDAwNRb4nU1FRs374daWlpiIyMxKBBg2Bubo6srCyd8lVRb2scVYcYKVbWHDdlXXPlxQjw8ruJsbEx9u7di/T0dMybNw9KpVKYn+Pu3buwsLBAv379EBcXh9TUVEyfPh36+vpaH4OsKb87d+6Enp4e1qxZIyrfc3JyhDS6fC8q737s/PnzCAgIQFxcHDIzM7Fjxw5YWVlhxIgRwjZ+//13GBgYYM6cOaK8aHpKULt27TBo0CCNx1mVOI7E93WbN29GREQEMjIysH37dpibm2PatGmivFXFd4Dr16+jRo0amDRpElJTU3H48GFYW1vjm2++Ee2rrPs6TUjLnDxl1XuPHz+Gg4MDBgwYgMuXL+PMmTNwc3PDf/7zHyHNkiVL8NtvvyEzMxPJycn47rvvoKenJzy9D3hZtzRo0AChoaG4evUqAgMDoVQqhbmKdKlrEhMTYWVlhWHDholipORnpEt+dT2/VeWNPEIdUG/kKS4ES/+NHDlS6zYOHDgALy8vGBsbw9DQEK1btxY9Zvfp06f47LPPYGtrC7lcDldXV9FNtC6PUNc0EWBaWhr69u0LU1NTqFQq1KtXD1OnTi3zscCavK2FWLHVq1fD0dERcrkcrVq1woULF4R1Pj4+os8mLCwMHh4eUCgUsLCw+H/t3XtcVGX+B/APtxlmQC6DXGUFFUHyAkkrIpsWYOSrksKUBFGzG6W7ue624qvItC0tt1rFW5uKeSnEVbJaIcnUykXipqKMiISQCmolrXcMvr8/+jnbYYaLDq4yft6v1/wx5zzznOc5M98zz3znOedIcnKyyQusfvLJJzJgwABRq9XSr18/w+kWVzU3N0taWpp4enqKWq2W6Ohoowta//DDDzJ+/HhxdHQUJycnefzxx41++Ozbt09+97vfiVqtlh49esj8+fON2pKVlSWBgYGiUqmkf//+RhepZFuuvy2TJk0yGc87duww2p65buU46uwYSkhIMBzPevToIQkJCSYvDjdr1iz5zW9+0+rpCNu2bZPIyEhxdnYWV1dXiYqKkvz8fMP606dPy9ChQ8XBwUG0Wq1ER0cr2m5Ka0mezz77rNUL0x8/flxGjRolHh4eYmdnJ76+vpKYmGg0jfynn36SKVOmiIuLi+h0OnnkkUcUt3kW+WXa+5133ikODg7i7u4uo0ePFr1ef03tNfWZBSAZGRmK17VXpr1YXL16tQQHB4tWqxUnJycZMmSIbNy4UdEWPz8/k9v59R8RnelWjqOOuBmxdvjwYYmPjxcPDw/RarUyaNAgo9tFJycni06nE5VKZXL9VREREYZTQtpj6rM7ZcoU8fPzE5VKJe7u7hIdHa348VpbWyvDhw8XnU4narVaAgIC5IUXXjA67aSzjg0NDQ2i0WiMvuNb03K81ZFjQ05OjoSGhoqjo6M4ODhISEiILF++XHHcKy8vl9DQUNFoNOLk5CRxcXFGx5fOdCvHUVePkataS/K095lrL0aumjdvnvj6+opWq5WIiAj56quvFOsLCwvlvvvuE51OJ926dZOhQ4fK1q1br6m9I0aMaPc3T0d+F7U3HisuLpbw8HBxdnYWe3t7CQ4Oltdff11xOmNr47WWp9YcOnRIAJjcZ52NcaSMo5kzZ4qnp6fY2dlJ37595a233jL6zdlZvwH+/e9/S3h4uKjVaundu7e89tprRpMY2hrXmdJakqe97z29Xi8xMTGi0WjE19dXZsyYofjT7cUXX5SAgACxt7cXV1dXiYiIkMzMTEUddXV1MnnyZPHx8RF7e3sJCgpS7L+OfNe0Nu5redmZ9tor0rH921muJcljJfKrc5taUVJSgrCwMBQXF2Pw4MHtFb+trV+/HhMmTOC+IjID44jIfIwjIvMxjojMxzgiMt+15GRuj5OXiYiIiIiIiIgsHJM8REREREREREQWgEkeIiIiIiIiIiILwCQPEREREREREZEFYJKHiIiIiIiIiMgCMMlDRERERERERGQBmOQhIiIiIiIiIrIATPIQEREREREREVkAJnmIiIiIiIiIiCwAkzxERERERERERBaASR4iIiIiIiIiIgvAJA8RERERERERkQVgkoeIiIiIiIiIyAIwyUNEREREREREZAGY5CEiIiIiIiIisgBM8hARERERERERWQAmeYiIiIiIiIiILIDttRTeunUr9Hr9jWqLRdi9ezcA7isiczCOiMzHOCIyH+OIyHyMIyLzVVdXd7islYhIe4Xy8/Nx9913o6mpyayG3S6sra3R3Nx8s5tB1KUxjojMxzgiMh/jiMh8jCMi89nY2OCrr75CREREm+U6NJNHrVajqakJ69atQ3BwcKc00FJt3boVaWlp3FdEZmAcEZmPcURkPsYRkfkYR0Tm0+v1mDBhAtRqdbtlr+l0reDgYAwePPi6G3Y7uDoFkfuK6PoxjojMxzgiMh/jiMh8jCOi/y1eeJmIiIiIiIiIyAIwyUNEREREREREZAGY5CEiIiIiIiIisgBM8hARERERERERWQAmeYiIiIiIiIiILACTPEREREREREREFoBJHiIiIiIiIiIiC8AkDxERERERERGRBWCSh4iIiIiIiIjIAjDJQ0RERERERERkAZjkISIiIiIiIiKyAEzyEBERERERERFZACZ5iIiIiIiIiIgsAJM8REREREREREQWgEkeIiIiIiIiIiILwCQPEREREREREZEFYJLn//n7++Pvf//7zW4GUZu+/PJLPPTQQ/Dx8YGVlRU++ugjxXoRwcsvvwxvb29oNBrExMSgsrJSUWb06NHo2bMn7O3t4e3tjeTkZJw4caLN7V66dAlTp06Fm5sbHB0dMWbMGJw8ebKzu0d0yzh79iymT58OPz8/aDQaDBs2DIWFhQCAK1euYObMmRg4cCAcHBzg4+ODiRMnthtHALBkyRL4+/vD3t4e4eHh+Oabb250V7qsa9lXV65cwdy5c9GnTx/Y29sjJCQEubm5ijKvvPIKrKysFI9+/fopytTX1yM5ORleXl5wcHDA4MGDsWnTJkWZkpISjBw5Ei4uLnBzc8PTTz+Nc+fOGdavXr3aaDtXH6dOnTJq++7du2Fra4vQ0FDF8mXLlmHQoEFwcnKCk5MTIiIikJOToyhzzz33GG0jJSVFUaawsBDR0dFwcXGBq6srYmNjsW/fPkWZrKwshIaGQqvVws/PDwsWLFCsnzx5ssn+9O/f38S7AcyfPx9WVlaYPn26YvkzzzyDPn36QKPRwN3dHXFxcTh06JCijKntZGZmKsosWbIEwcHB0Gg0CAoKwpo1a0y2g+hW19647uTJk5g8eTJ8fHyg1Wpx//33G43rOnIcaKkj40WiroJxZOy2T/I0Njbe7CYQddj58+cREhKCJUuWmFz/5ptvYtGiRVi+fDkKCgrg4OCA2NhYXLp0yVDm3nvvRVZWFioqKrBp0yZUVVXh0UcfbXO7f/zjH/HJJ59g48aN2LVrF06cOIH4+PhO7RvRreTJJ59EXl4e1q5di7KyMtx3332IiYnB8ePHceHCBZSUlCAtLQ0lJSXYvHkzKioqMHr06Dbr3LBhA2bMmIHZs2ejpKQEISEhiI2NNfnD/3Z3rfvqpZdewrvvvov09HSUl5cjJSUFjzzyCEpLSxXl+vfvj7q6OsPj66+/VqyfOHEiKioq8PHHH6OsrAzx8fEYN26coZ4TJ04gJiYGAQEBKCgoQG5uLg4ePIjJkycb6khISFBso66uDrGxsRgxYgQ8PDwU22toaMDEiRMRHR1t1CdfX1/Mnz8fxcXFKCoqQlRUFOLi4nDw4EFFuaeeekqxrTfffNOw7ty5c7j//vvRs2dPFBQU4Ouvv0a3bt0QGxuLK1euAABycnKQlJSElJQUHDhwAEuXLsU777yDxYsXG+pZuHChYhvfffcddDodxo4da9TuwsJCvPvuuxg0aJDRurCwMGRkZECv1+Ozzz6DiOC+++5DU1OTolxGRoZiew8//LBh3bJlyzBr1iy88sorOHjwIObMmYOpU6fik08+Mdoe0a2urXGdiODhhx/Gt99+iy1btqC0tBR+fn6IiYnB+fPnFWXbOg6Y0pHxIlFXwTgyQTqguLhYAEhxcXGb5TZu3CgDBgwQe3t70el0Eh0dLefOnZMRI0bI888/rygbFxcnkyZNMjz38/OTuXPnymOPPSZarVZ8fHxk8eLFitcAkKVLl8r9998v9vb20qtXL9m4caOizP79++Xee+81tOGpp56Ss2fPGtZPmjRJ4uLi5K9//at4e3uLv7+/jBgxQgAoHtdr3bp1HdpXROYCINnZ2Ybnzc3N4uXlJQsWLDAsa2hoELVaLR9++GGr9WzZskWsrKyksbHR5PqGhgaxs7NTxJperxcAkp+fb35HTGAc0c104cIFsbGxkU8//VSxfPDgwfLiiy+afM0333wjAKSmpqbVeocMGSJTp041PG9qahIfHx+ZN29e5zS8ha4cR9e6r7y9vY3GDPHx8ZKUlGR4Pnv2bAkJCWlzuw4ODrJmzRrFMp1OJ++9956IiLz77rvi4eEhTU1NhvX79+8XAFJZWWmyzlOnTomdnZ1RvSIiCQkJ8tJLL3WobSIirq6usmLFCsNzU+OrXyssLBQAUltb22p7x48fL48++qjidYsWLRJfX19pbm42WW92drZYWVnJ0aNHFcvPnj0rffv2lby8vHbbJiKyb98+ASBHjhwxLGv53dZSRESE/PnPf1YsmzFjhkRGRra5revVleOIupaWn/2KigoBIAcOHDAsa2pqEnd3d8MxSaT940BL1zteNAfjiP5XLDmOOpqTERHptJk8dXV1GD9+PKZMmQK9Xo+dO3ciPj4eItLhOhYsWICQkBCUlpYiNTUVzz//PPLy8hRl0tLSMGbMGOzbtw9JSUl47LHHoNfrAfySxYuNjYWrqysKCwuxceNGfP7555g2bZqiju3bt6OiogJ5eXn49NNPsXnzZvj6+mLu3LmGzB1RV1NdXY36+nrExMQYljk7OyM8PBz5+fkmX/Pjjz9i/fr1GDZsGOzs7EyWKS4uxpUrVxT19uvXDz179my1XqKu7Oeff0ZTUxPs7e0VyzUajdHMj6t++uknWFlZwcXFxeT6xsZGFBcXK+LI2toaMTExjKMWrmdfXb58uUPvV2VlJXx8fNC7d28kJSWhtrZWsX7YsGHYsGEDfvzxRzQ3NyMzMxOXLl3CPffcY9iOSqWCtfV/h08ajQYAWv1srFmzBlqt1mjGZEZGBr799lvMnj27jb3xi6amJmRmZuL8+fOIiIhQrFu/fj26d++OAQMGYNasWbhw4YJhXVBQENzc3LBy5Uo0Njbi4sWLWLlyJYKDg+Hv72/ok6l9d+zYMdTU1Jhsz8qVKxETEwM/Pz/F8qlTp+KBBx5QvHetOX/+PDIyMtCrVy/85je/Maqne/fuGDJkCFatWqUYS7bW3m+++cYwO4nIEly+fBkAFJ93a2trqNVqo+NNW8eBlq5nvEjUVd2ucdSpSZ6ff/4Z8fHx8Pf3x8CBA/Hcc8/B0dGxw3VERkYiNTUVgYGB+P3vf49HH30U77zzjqLM2LFj8eSTTyIwMBCvvvoq7rrrLqSnpwMAPvjgA1y6dAlr1qzBgAEDEBUVhcWLF2Pt2rWK64c4ODhgxYoV6N+/P/r37w+dTgcbGxt069YNXl5e8PLy6pydQvQ/VF9fDwDw9PRULPf09DSsu2rmzJlwcHCAm5sbamtrsWXLljbrValURj9eTdVLZAm6deuGiIgIvPrqqzhx4gSampqwbt065Ofnm/wT4NKlS5g5cybGjx8PJycnk3V+//33aGpq6lB83u6uZ1/Fxsbi7bffRmVlJZqbm5GXl4fNmzcr3q/w8HCsXr0aubm5WLZsGaqrq3H33Xfj7NmzhjJZWVm4cuUK3NzcoFar8cwzzyA7OxsBAQEAgKioKNTX12PBggVobGzEmTNnkJqaCgCt/kG0cuVKJCYmGpJBwC/JptTUVKxbtw62trat7ouysjI4OjpCrVYjJSUF2dnZuOOOOwzrExMTsW7dOuzYsQOzZs3C2rVrMWHCBMP6bt26YefOnVi3bh00Gg0cHR2Rm5uLnJwcw3ZjY2OxefNmbN++Hc3NzTh8+DDeeuutVvt04sQJ5OTk4Mknn1Qsz8zMRElJCebNm9dqfwBg6dKlcHR0hKOjI3JycpCXlweVSmVYP3fuXGRlZSEvLw9jxozBc889ZxjnXW3vihUrUFxcDBFBUVERVqxYgStXruD7779vc9tEXcnVP9RmzZqFM2fOoLGxEW+88QaOHTumiM32jgMtXct4kairu13jqNOSPCEhIYiOjsbAgQMxduxYvPfeezhz5sw11dHy36mIiAjDLJ2OlNHr9QgJCYGDg4NhfWRkJJqbm1FRUWFYNnDgQMWAguh288ILL6C0tBTbtm2DjY0NJk6ceE2z7ogs3dq1ayEi6NGjB9RqNRYtWoTx48crZnAAv1zwd9y4cRARLFu27Ca1lhYuXIi+ffuiX79+UKlUmDZtGh5//HHF+zVq1CiMHTsWgwYNQmxsLLZu3YqGhgZkZWUZyqSlpaGhoQGff/45ioqKMGPGDIwbNw5lZWUAfrmmz/vvv4+33noLWq0WXl5e6NWrFzw9PY0+GwCQn58PvV6PJ554wrCsqakJiYmJmDNnDgIDA9vsV1BQEPbu3YuCggI8++yzmDRpEsrLyw3rn376acTGxmLgwIFISkrCmjVrkJ2djaqqKgDAxYsX8cQTTyAyMhJ79uzB7t27MWDAADzwwAO4ePEigF+uQTBt2jQ8+OCDUKlUGDp0KB577DEAMNmn999/Hy4uLorr5Hz33Xd4/vnnsX79eqNZNi0lJSWhtLQUu3btQmBgIMaNG6e4fkFaWhoiIyNx5513YubMmfjLX/6iuBB0WloaRo0ahaFDh8LOzg5xcXGYNGlSq+0l6qrs7OywefNmHD58GDqdDlqtFjt27MCoUaMUn/X2jgNEt7PbNY467dvQxsYGeXl5yMnJwR133IH09HQEBQWhuroa1tbWRj8gb+aU2l8ngYgsxdUZaC3venXy5Emj2Wndu3dHYGAgRo4ciczMTGzduhV79uxptd7GxkY0NDS0Wy+RpejTpw927dqFc+fO4bvvvjOcCtK7d29DmasJnpqaGuTl5bU6iwf4JeZsbGw6FJ+3u+vZV+7u7vjoo49w/vx51NTU4NChQ3B0dFS8Xy25uLggMDAQR44cAQBUVVVh8eLFWLVqFaKjoxESEoLZs2fjrrvuUlzMMTExEfX19Th+/Dh++OEHvPLKKzh9+rTJba1YsQKhoaEICwszLDt79iyKioowbdo02NrawtbWFnPnzsW+fftga2uLL774wlBWpVIhICAAYWFhmDdvHkJCQrBw4cJW+xQeHg4Ahj598MEHOHr0KDIyMvDb3/4WQ4cOxQcffIDq6mrDDE4rKyu88cYbOHfuHGpqalBfX48hQ4YAgFGfRASrVq1CcnKy4s+y4uJinDp1CoMHDzb0adeuXVi0aBFsbW0VF1Z2dnZG3759MXz4cPzzn//EoUOHkJ2d3Wafjh07Zphyr9FosGrVKly4cAFHjx5FbW0t/P390a1bN7i7u7daD1FXFBYWhr1796KhoQF1dXXIzc3FDz/80OaxreVxoKVrGS8SWYLbMY469S8PKysrREZGYs6cOSgtLYVKpUJ2djbc3d0V06Gamppw4MABo9e3/JG5Z88eBAcHd7hMcHAw9u3bp7hS9u7du2FtbY2goKA2265SqYzu7kDUlfTq1QteXl7Yvn27Ydl//vMfFBQUGM2A+7Xm5mYA/z1ntaWwsDDY2dkp6q2oqEBtbW2b9RJZAgcHB3h7e+PMmTP47LPPEBcXB+C/CZ7Kykp8/vnncHNza7MelUqFsLAwRRw1Nzdj+/btjKMWzNlX9vb26NGjB37++Wds2rTJ8H6Zcu7cOVRVVcHb2xsADOfet5wNYmNjYzhO/pqnpyccHR2xYcMG2NvbY+TIkUb1Z2VlKWbxAICTkxPKysqwd+9ewyMlJcUwa+fqwNKU5ubmVo/VALB3714AUPTJ2toaVlZWhjJXn7fsk42NDXr06AGVSoUPP/wQERERRkmTXbt24ciRI0Z9io6ONurTXXfdhaSkJOzduxc2NjYm2ysiEJF2++Tq6gq1Wq1YbmdnB19fX9jY2CAzMxMPPvggZ/KQxXJ2doa7uzsqKytRVFTU5rGt5XGgpesdLxJ1dbdVHHXWlZz37Nkjr732mhQWFkpNTY1kZWWJSqWSrVu3yvLly0Wr1cqnn34qer1ennrqKXFycjK6u5aTk5O88cYbUlFRIYsXLxYbGxvJzc01lAEg3bt3l5UrV0pFRYW8/PLLYm1tLQcPHhQRkfPnz4u3t7eMGTNGysrK5IsvvpDevXsrtnP17lotjRw5UkaPHi3Hjh2T06dPd2S3mMSrx9ONdPbsWSktLZXS0lIBIG+//baUlpYa7ugzf/58cXFxkS1btsj+/fslLi5OevXqJRcvXhSRX+I0PT1dSktL5ejRo7J9+3YZNmyY9OnTRy5duiQiIseOHZOgoCApKCgwbDclJUV69uwpX3zxhRQVFUlERIRERETcsH4yjuhmy83NlZycHPn2229l27ZtEhISIuHh4dLY2CiNjY0yevRo8fX1lb1790pdXZ3hcfnyZUMdUVFRkp6ebniemZkparVaVq9eLeXl5fL000+Li4uL1NfX35A+dOU4am9fJScnS2pqqqH8nj17ZNOmTVJVVSVffvmlREVFSa9eveTMmTOGMn/6059k586dUl1dLbt375aYmBjp3r27nDp1SkREGhsbJSAgQO6++24pKCiQI0eOyN/+9jexsrKSf/3rX4Z60tPTpbi42DBW0Wg0snDhQqM+rFixQuzt7RVtaI2pu2ulpqbKrl27pLq6Wvbv3y+pqaliZWUl27ZtExGRI0eOyNy5c6WoqEiqq6tly5Yt0rt3bxk+fLihDr1eL2q1Wp599lkpLy+XAwcOyIQJE8TZ2VlOnDghIiKnT5+WZcuWiV6vl9LSUvnDH/4g9vb2iu+AqyZMmCDh4eHt9kfE+E4lVVVV8vrrr0tRUZHU1NTI7t275aGHHhKdTicnT54UEZGPP/5Y3nvvPSkrK5PKykpZunSpaLVaefnllw31VFRUyNq1a+Xw4cNSUFAgCQkJotPppLq6ukPtulZdOY7o1tfeuC4rK0t27NghVVVV8tFHH4mfn5/Ex8cbXt+R44CISFBQkGzevNnwvL3xYmdjHNGNdLvE0bXcXavTkjzl5eUSGxsr7u7uolarJTAw0DC4bWxslGeffVZ0Op14eHjIvHnzTN5Cfc6cOTJ27FjRarXi5eVlNGgCIEuWLJGRI0eKWq0Wf39/2bBhg6JMR2+h3lJ+fr4MGjRI1Go1b6FOt6wdO3YIAKPH1Vhqbm6WtLQ08fT0FLVaLdHR0VJRUWF4/dX40Ol0hhhKSUmRY8eOGcpUV1cLANmxY4dh2cWLF+W5554TV1dX0Wq18sgjj0hdXd0N6yfjiG62DRs2SO/evUWlUomXl5dMnTpVGhoaROS/MWLq8eu48fPzk9mzZyvqTU9Pl549e4pKpZIhQ4bInj17blgfunoctbWvRowYoRhD7Ny5U4KDg0WtVoubm5skJyfL8ePHFfUlJCSIt7e3qFQq6dGjhyQkJChu3S0icvjwYYmPjxcPDw/RarUyaNAgo1ufJycni06nE5VKZXL9VREREZKYmNihvppK8kyZMkX8/PxEpVKJu7u7REdHGxI8IiK1tbUyfPhww/E8ICBAXnjhBfnpp58U9Wzbtk0iIyPF2dlZXF1dJSoqSvLz8w3rT58+LUOHDhUHBwfRarUSHR1t8nPZ0NAgGo1G/vGPf3SoTy2TPMePH5dRo0aJh4eH2NnZia+vryQmJsqhQ4cMZXJyciQ0NFQcHR3FwcFBQkJCZPny5Ypb1peXl0toaKhoNBpxcnKSuLg4RR2dravHEd3a2hvXLVy4UHx9fcXOzk569uwpL730kuLPhI4eBwBIRkaG4Xl748XOxjiiG+l2iaNrSfJY/X+D21RSUoKwsDAUFxdj8ODB1ztpqE3+/v6YPn06pk+f3moZKysrZGdnKy72d6tZv349JkyYcEP3FZGlYxwRmY9xRGQ+xhGR+RhHROa7lpwMT14mIiIiIiIiIrIATPIQEREREREREVkA25vdgKuOHj3abpkOnFlGRERERERERHRb4kweIiIiIiIiIiILwCQPEREREREREZEFYJKHiIiIiIiIiMgCMMlDRERERERERGQBmOQhIiIiIiIiIrIATPIQEREREREREVkAJnmIiIiIiIiIiCwAkzxERERERERERBaASR4iIiIiIiIiIgvAJA8RERERERERkQVgkoeIiIiIiIiIyAIwyUNEREREREREZAGY5CEiIiIiIiIisgBM8hARERERERERWQDbayms1+tvVDssRnV1NQDuKyJzMI6IzMc4IjIf44jIfIwjIvNdS/xYiYi0V6i2thbBwcG4cOGCWQ27XdjY2KCpqelmN4OoS2McEZmPcURkPsYRkfkYR0Tm02q10Ov16NmzZ5vlOpTkAX5J9Hz//fed0jhLd/nyZajV6pvdDKIujXFEZD7GEZH5GEdE5mMcEZmve/fu7SZ4gGtI8hARERERERER0a2LF14mIiIiIiIiIrIATPIQEREREREREVkAJnmIiIiIiIiIiCwAkzxERERERERERBaASR4iIiIiIiIiIgvAJA8RERERERERkQVgkoeIiIiIiIiIyAL8HxahlLLjuxpnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(classification_report(y_test, y_pred_test_svm, output_dict=True))\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "ax.axis('off')\n",
    "table = ax.table(cellText=df.values, colLabels=df.columns, rowLabels=df.index, loc='center', bbox=[0, 0, 1, 1], fontsize=14)\n",
    "plt.subplots_adjust(left=0.1, right=0.8, top=0.9, bottom=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c4ef5dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred_test_svm, ' SVM , Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "582b9974",
   "metadata": {},
   "source": [
    "# Save our model pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1886bdd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "with open('lr.pkl', 'wb') as file:\n",
    "    pickle.dump(lr_cv, file)\n",
    "    \n",
    "with open('decision_tree.pkl', 'wb') as file:\n",
    "    pickle.dump(tree_cv, file)\n",
    "    \n",
    "with open('svm.pkl', 'wb') as file:\n",
    "    pickle.dump(svm_cv, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f31e13",
   "metadata": {},
   "source": [
    "# Load model pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2a2d3f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('lr.pkl', 'rb') as file:\n",
    "#     lr_cv = pickle.load(file)\n",
    "    \n",
    "# with open('decision_tree.pkl', 'rb') as file:\n",
    "#     tree_cv = pickle.load(file)\n",
    "    \n",
    "# with open('svm.pkl', 'rb') as file:\n",
    "#     svm_cv = pickle.load(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb491b4d",
   "metadata": {},
   "source": [
    "# Classification for any (lat,lon) point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2950bc",
   "metadata": {},
   "source": [
    "## Need to update these functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "589953f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_slope_from_lat_lon(lat, lon):\n",
    "    slope,elevation,land_cover,ac_annual,min_distance = np.array(X_test.iloc[100])\n",
    "    return slope\n",
    "\n",
    "def get_elevation_from_lat_lon(lat, lon):\n",
    "    slope,elevation,land_cover,ac_annual,min_distance = np.array(X_test.iloc[100])\n",
    "    return elevation\n",
    "\n",
    "def get_land_cover_from_lat_lon(lat,lon):\n",
    "    slope,elevation,land_cover,ac_annual,min_distance = np.array(X_test.iloc[100])\n",
    "    return land_cover\n",
    "\n",
    "def get_ac_annual_from_lat_lon(lat,lon):\n",
    "    slope,elevation,land_cover,ac_annual,min_distance = np.array(X_test.iloc[100])\n",
    "    return ac_annual\n",
    "\n",
    "def get_min_distance_from_lat_lon(lat,lon):\n",
    "    slope,elevation,land_cover,ac_annual,min_distance = np.array(X_test.iloc[100])\n",
    "    return min_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bd48f0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_features_from_lat_lon(lat, lon):\n",
    "    slope      = get_slope_from_lat_lon(lat, lon)\n",
    "    elevation  = get_elevation_from_lat_lon(lat, lon)\n",
    "    land_cover = get_land_cover_from_lat_lon(lat,lon)\n",
    "    ac_annual  = get_ac_annual_from_lat_lon(lat,lon)\n",
    "    min_distance = get_min_distance_from_lat_lon(lat,lon)\n",
    "    \n",
    "    feature_list = [slope, elevation, land_cover, ac_annual, min_distance]\n",
    "    return feature_list\n",
    "    \n",
    "def classify_solar_site(lat,lon,model):\n",
    "    \n",
    "    features_list = get_features_from_lat_lon(lat, lon)\n",
    "    slope,elevation,land_cover,ac_annual,min_distance = np.array(features_list)\n",
    "    dict_ = {'slope':[slope], 'elevation':[elevation], 'land_cover':[land_cover],\n",
    "             'ac_annual':[ac_annual], 'min_distance':[min_distance]}\n",
    "\n",
    "    features = pd.DataFrame.from_dict(dict_)\n",
    "    \n",
    "    if model=='Logistic_Regression':\n",
    "        estimator = lr_cv\n",
    "    elif model=='Decision_Tree':\n",
    "        estimator = tree_cv\n",
    "    elif model=='Support_Vector_Machine':\n",
    "        estimator = svm_cv\n",
    "        \n",
    "    # # Access the best model with its hyperparameters\n",
    "    best_model = estimator.best_estimator_\n",
    "\n",
    "    # Make predictions\n",
    "    classification = best_model.predict(features) \n",
    "    \n",
    "    return classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34ba05b",
   "metadata": {},
   "source": [
    "# Find classification output for any (lat,lon) point for any model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1ade65d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat = 45345\n",
    "lon = 0\n",
    "model = 'Logistic_Regression'\n",
    "\n",
    "classify_solar_site(lat,lon,model)"
   ]
  }
 ],
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