lstm-legacy.ipynb

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   "source": [
    "Copyright (c) 2023 wcyat\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy\n",
    "of this software and associated documentation files (the \"Software\"), to deal\n",
    "in the Software without restriction, including without limitation the rights\n",
    "to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
    "copies of the Software, and to permit persons to whom the Software is\n",
    "furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all\n",
    "copies or substantial portions of the Software.\\\n",
    "THE PREDICTED DATA MUST NOT BE USED TO SUBMIT TO THE COMPETITION AI Challenge – \n",
    "Weather Forecasting Competition (2023) ORGANIZED BY HKEDCITY, MICROSOFT AND OTHERS.\n",
    "<https://www.edcity.hk/coding/en/WeatherForecasting>\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
    "AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
    "OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
    "SOFTWARE."
   ]
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     "text": [
      "2023-07-05 23:44:50.558660: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-07-05 23:44:50.668219: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-07-05 23:44:50.842795: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-07-05 23:44:50.843444: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-07-05 23:44:51.781138: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "import random\n",
    "import joblib\n",
    "import seaborn as sns\n",
    "from datetime import timedelta\n"
   ]
  },
  {
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    },
    "tags": []
   },
   "source": [
    "Load the data"
   ]
  },
  {
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   "execution_count": 2,
   "id": "7882fefe",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘data’: File exists\n"
     ]
    }
   ],
   "source": [
    "!mkdir data\n",
    "!if ! [ -f \"data/processed_data.csv\" ]; then wget https://github.com/sdip15fa/weather-predict/raw/master/data/processed_data.csv -O data/processed_data.csv; fi;\n",
    "\n",
    "input_file = 'data/processed_data.csv'\n",
    "df = pd.read_csv('data/processed_data.csv',\n",
    "                 parse_dates=['DateTime'], index_col='DateTime')\n",
    "df = df.resample('60T').mean()\n",
    "# df[\"DateTime\"] = pd.to_datetime(df.index)\n",
    "df = df.reset_index()"
   ]
  },
  {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Convert the 'Date' and 'Time' columns to integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "caa96d9d",
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   "source": [
    "df[\"Temperature\"] = df[\"Temperature\"].astype(float)\n",
    "df[\"Wind Speed\"] = df[\"Wind Speed\"].astype(float)\n",
    "df[\"Rainfall\"] = df[\"Rainfall\"].astype(float)\n",
    "df[\"Wind Direction\"] = df[\"Wind Direction\"].astype(float)\n",
    "df[\"Humidity\"] = df[\"Humidity\"].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20916779",
   "metadata": {
    "id": "KO9gyf6zo0Fz",
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    },
    "tags": []
   },
   "source": [
    "Filter and replace outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32818586",
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   "source": [
    "df['Temperature'] = df['Temperature'].replace(3276.7, np.nan)\n",
    "df['Wind Speed'] = df['Wind Speed'].replace(32767, np.nan)\n",
    "df['Rainfall'] = df['Rainfall'].replace(32767, np.nan)\n",
    "df['Wind Direction'] = df['Wind Direction'].replace(32767, np.nan)\n",
    "df['Humidity'] = df['Humidity'].replace(32767, np.nan)\n",
    "df['Temperature'] = df['Temperature'].mask((df['Temperature'] > 40) | (df['Temperature'] < -10), np.nan)\n",
    "df['Wind Speed'] = df['Wind Speed'].mask((df['Wind Speed'] > 200) | df[\"Wind Speed\"] < 0, np.nan)\n",
    "df['Rainfall'] = df['Rainfall'].mask((df['Rainfall'] > 500)| df[\"Rainfall\"] < 0, np.nan)\n",
    "df['Wind Direction'] = df['Wind Direction'].mask((df['Wind Direction'] < 0) | (df['Wind Direction'] > 360), np.nan)\n",
    "df['Humidity'] = df['Humidity'].mask((df['Humidity'] < 0) | (df['Humidity'] > 100), np.nan)"
   ]
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       "      <th></th>\n",
       "      <th>DateTime</th>\n",
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       "      <th>36</th>\n",
       "      <td>2008-01-02 12:00:00</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>14.0</td>\n",
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       "      <td>27.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.7</td>\n",
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       "      <td>31.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
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       "      <th>134727</th>\n",
       "      <td>2023-05-15 15:00:00</td>\n",
       "      <td>2023.0</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>265.0</td>\n",
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       "      <td>2023.0</td>\n",
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       "      <td>29.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>41.0</td>\n",
       "      <td>273.0</td>\n",
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       "      <td>13.0</td>\n",
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       "      <td>12.0</td>\n",
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       "      <td>93.0</td>\n",
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       "<p>12089 rows × 11 columns</p>\n",
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      "text/plain": [
       "                  DateTime    Year  Month  Date  Time  Minute  Temperature  \\\n",
       "36     2008-01-02 12:00:00  2008.0    1.0   2.0  12.0     1.0          NaN   \n",
       "38     2008-01-02 14:00:00  2008.0    1.0   2.0  14.0     1.0         11.2   \n",
       "39     2008-01-02 15:00:00  2008.0    1.0   2.0  15.0     1.0         10.7   \n",
       "59     2008-01-03 11:00:00  2008.0    1.0   3.0  11.0     1.0         11.9   \n",
       "111    2008-01-05 15:00:00  2008.0    1.0   5.0  15.0     1.0         16.0   \n",
       "...                    ...     ...    ...   ...   ...     ...          ...   \n",
       "134727 2023-05-15 15:00:00  2023.0    5.0  15.0  15.0     1.0          NaN   \n",
       "134728 2023-05-15 16:00:00  2023.0    5.0  15.0  16.0     1.0          NaN   \n",
       "135060 2023-05-29 12:00:00  2023.0    5.0  29.0  12.0     1.0          NaN   \n",
       "135109 2023-05-31 13:00:00  2023.0    5.0  31.0  13.0     1.0         27.4   \n",
       "135180 2023-06-03 12:00:00  2023.0    6.0   3.0  12.0     1.0         27.4   \n",
       "\n",
       "        Wind Speed  Wind Direction  Rainfall  Humidity  \n",
       "36             NaN             NaN       NaN       NaN  \n",
       "38            19.0             NaN       0.0      27.0  \n",
       "39            13.0             NaN       0.0      31.0  \n",
       "59            15.0             NaN       0.0      36.0  \n",
       "111           10.0             NaN       0.0      29.0  \n",
       "...            ...             ...       ...       ...  \n",
       "134727        19.0            26.0       0.0       NaN  \n",
       "134728         1.0           265.0       0.0       NaN  \n",
       "135060        41.0           273.0       0.0       NaN  \n",
       "135109        13.0             NaN       0.0      88.0  \n",
       "135180         7.0             NaN       0.0      93.0  \n",
       "\n",
       "[12089 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2acfbeca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(method=\"ffill\").fillna(method=\"bfill\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c385face",
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    {
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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def show_heatmap(data):\n",
    "    plt.matshow(data.corr())\n",
    "    plt.xticks(range(data.shape[1]), data.columns, fontsize=14, rotation=90)\n",
    "    plt.gca().xaxis.tick_bottom()\n",
    "    plt.yticks(range(data.shape[1]), data.columns, fontsize=14)\n",
    "\n",
    "    cb = plt.colorbar()\n",
    "    cb.ax.tick_params(labelsize=14)\n",
    "    plt.title(\"Feature Correlation Heatmap\", fontsize=14)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "show_heatmap(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62affd06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:28.653708Z",
     "iopub.status.busy": "2023-07-04T02:30:28.653422Z",
     "iopub.status.idle": "2023-07-04T02:30:28.658673Z",
     "shell.execute_reply": "2023-07-04T02:30:28.657870Z"
    },
    "id": "fYTgYEvho0Fz",
    "papermill": {
     "duration": 0.021772,
     "end_time": "2023-07-04T02:30:28.660772",
     "exception": false,
     "start_time": "2023-07-04T02:30:28.639000",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "time_steps = 144  # Number of time steps for the LSTM model\n",
    "scaler = StandardScaler() # MinMaxScaler(feature_range=(0, 1))  # Scale the data to [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91b13c48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:28.687932Z",
     "iopub.status.busy": "2023-07-04T02:30:28.687640Z",
     "iopub.status.idle": "2023-07-04T02:30:28.691471Z",
     "shell.execute_reply": "2023-07-04T02:30:28.690630Z"
    },
    "id": "y-zPsENY_V2J",
    "papermill": {
     "duration": 0.019628,
     "end_time": "2023-07-04T02:30:28.693414",
     "exception": false,
     "start_time": "2023-07-04T02:30:28.673786",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "features_keys = ['Temperature', 'Wind Speed',\n",
    "               'Rainfall', 'Humidity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55632654",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:28.720598Z",
     "iopub.status.busy": "2023-07-04T02:30:28.720350Z",
     "iopub.status.idle": "2023-07-04T02:30:28.736126Z",
     "shell.execute_reply": "2023-07-04T02:30:28.735124Z"
    },
    "papermill": {
     "duration": 0.031847,
     "end_time": "2023-07-04T02:30:28.738291",
     "exception": false,
     "start_time": "2023-07-04T02:30:28.706444",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[features_keys].copy()\n",
    "scaler.fit(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b2997eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:28.766043Z",
     "iopub.status.busy": "2023-07-04T02:30:28.765780Z",
     "iopub.status.idle": "2023-07-04T02:30:28.773429Z",
     "shell.execute_reply": "2023-07-04T02:30:28.772526Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\npretrained_model: tf.keras.Model = None\\ntry:\\n    pretrained_model = tf.keras.models.load_model(\"lstm.keras\")\\nexcept:\\n    pass\\n\\nif pretrained_model:\\n    for col in features_keys:\\n        indices = df[df[col].isna() == True].index.values\\n        if len(indices) > 0:\\n            input_data = []\\n            for i in indices:\\n                input_data_i = df[features_keys].iloc[i-time_steps-1:i-1].values\\n                if len(input_data_i):\\n                    input_data_i = scaler.transform(pd.DataFrame(input_data_i, columns=features_keys))\\n                    input_data.append(input_data_i)\\n                else:\\n                    indices = np.delete(indices, i)\\n            if len(input_data):\\n                input_data = np.array(input_data).reshape(len(input_data), time_steps, len(features_keys))\\n                predicted = pd.DataFrame(pretrained_model.predict(input_data), columns=features_keys, index=indices)[col].values\\n                df.loc[indices, col] = predicted\\n'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "pretrained_model: tf.keras.Model = None\n",
    "try:\n",
    "    pretrained_model = tf.keras.models.load_model(\"lstm.keras\")\n",
    "except:\n",
    "    pass\n",
    "\n",
    "if pretrained_model:\n",
    "    for col in features_keys:\n",
    "        indices = df[df[col].isna() == True].index.values\n",
    "        if len(indices) > 0:\n",
    "            input_data = []\n",
    "            for i in indices:\n",
    "                input_data_i = df[features_keys].iloc[i-time_steps-1:i-1].values\n",
    "                if len(input_data_i):\n",
    "                    input_data_i = scaler.transform(pd.DataFrame(input_data_i, columns=features_keys))\n",
    "                    input_data.append(input_data_i)\n",
    "                else:\n",
    "                    indices = np.delete(indices, i)\n",
    "            if len(input_data):\n",
    "                input_data = np.array(input_data).reshape(len(input_data), time_steps, len(features_keys))\n",
    "                predicted = pd.DataFrame(pretrained_model.predict(input_data), columns=features_keys, index=indices)[col].values\n",
    "                df.loc[indices, col] = predicted\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7ec22d",
   "metadata": {
    "id": "nW3SJd_do0Fz",
    "papermill": {
     "duration": 0.013386,
     "end_time": "2023-07-04T02:30:28.848650",
     "exception": false,
     "start_time": "2023-07-04T02:30:28.835264",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Prepare the data for LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b62f28",
   "metadata": {
    "id": "ry9s2DQgo0GE",
    "papermill": {
     "duration": 0.013316,
     "end_time": "2023-07-04T02:30:28.875803",
     "exception": false,
     "start_time": "2023-07-04T02:30:28.862487",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Remove rows with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a43b27d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:28.904899Z",
     "iopub.status.busy": "2023-07-04T02:30:28.904100Z",
     "iopub.status.idle": "2023-07-04T02:30:28.916004Z",
     "shell.execute_reply": "2023-07-04T02:30:28.915082Z"
    },
    "id": "rux_VYPWo0GE",
    "papermill": {
     "duration": 0.028987,
     "end_time": "2023-07-04T02:30:28.918094",
     "exception": false,
     "start_time": "2023-07-04T02:30:28.889107",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.dropna().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1a86f6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:28.947506Z",
     "iopub.status.busy": "2023-07-04T02:30:28.946586Z",
     "iopub.status.idle": "2023-07-04T02:30:28.972762Z",
     "shell.execute_reply": "2023-07-04T02:30:28.971936Z"
    },
    "id": "f0e4-F66o0GE",
    "outputId": "777aaec3-334c-47c1-bcb4-e75caeadea06",
    "papermill": {
     "duration": 0.042854,
     "end_time": "2023-07-04T02:30:28.974694",
     "exception": false,
     "start_time": "2023-07-04T02:30:28.931840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>DateTime</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>102.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>135869</th>\n",
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       "    <tr>\n",
       "      <th>135870</th>\n",
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       "      <td>2023.0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>20.9</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>47.0</td>\n",
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       "      <td>160.0</td>\n",
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       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135874 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index            DateTime    Year  Month  Date  Time  Minute  \\\n",
       "0            0 2008-01-01 00:00:00  2008.0    1.0   1.0   0.0     1.0   \n",
       "1            1 2008-01-01 01:00:00  2008.0    1.0   1.0   1.0     1.0   \n",
       "2            2 2008-01-01 02:00:00  2008.0    1.0   1.0   2.0     1.0   \n",
       "3            3 2008-01-01 03:00:00  2008.0    1.0   1.0   3.0     1.0   \n",
       "4            4 2008-01-01 04:00:00  2008.0    1.0   1.0   4.0     1.0   \n",
       "...        ...                 ...     ...    ...   ...   ...     ...   \n",
       "135869  135869 2023-07-02 05:00:00  2023.0    7.0   2.0   5.0     1.0   \n",
       "135870  135870 2023-07-02 06:00:00  2023.0    7.0   2.0   6.0     1.0   \n",
       "135871  135871 2023-07-02 07:00:00  2023.0    7.0   2.0   7.0     1.0   \n",
       "135872  135872 2023-07-02 08:00:00  2023.0    7.0   2.0   8.0     1.0   \n",
       "135873  135873 2023-07-02 09:00:00  2023.0    7.0   2.0   9.0     1.0   \n",
       "\n",
       "        Temperature  Wind Speed  Wind Direction  Rainfall  Humidity  \n",
       "0               3.6       107.0            49.0       0.0      37.0  \n",
       "1               3.0       103.0            42.0       0.0      35.0  \n",
       "2               2.3       101.0            39.0       0.0      37.0  \n",
       "3               2.4        92.0            47.0       0.0      33.0  \n",
       "4               2.0       102.0            47.0       0.0      32.0  \n",
       "...             ...         ...             ...       ...       ...  \n",
       "135869         21.5        52.0           192.0       0.0     100.0  \n",
       "135870         22.0        67.0           194.0       0.0     100.0  \n",
       "135871         20.9        64.0           213.0      10.0     100.0  \n",
       "135872         22.0        47.0           196.0       0.0     100.0  \n",
       "135873         22.5        21.0           160.0       0.0     100.0  \n",
       "\n",
       "[135874 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e94cff87",
   "metadata": {
    "id": "ksanizXmbeZ6",
    "papermill": {
     "duration": 0.013563,
     "end_time": "2023-07-04T02:30:29.002064",
     "exception": false,
     "start_time": "2023-07-04T02:30:28.988501",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Check if any hours are missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5410fd10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:29.031592Z",
     "iopub.status.busy": "2023-07-04T02:30:29.030857Z",
     "iopub.status.idle": "2023-07-04T02:30:29.037400Z",
     "shell.execute_reply": "2023-07-04T02:30:29.036470Z"
    },
    "id": "KBnSzgpObeZ6",
    "outputId": "1ccc2b43-d41a-4c65-b9fc-987cb0566818",
    "papermill": {
     "duration": 0.023501,
     "end_time": "2023-07-04T02:30:29.039328",
     "exception": false,
     "start_time": "2023-07-04T02:30:29.015827",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# Get the minimum and maximum DateTime values in the DataFrame\\nmin_time = df['DateTime'].min()\\nmax_time = df['DateTime'].max()\\n\\n# Create a complete sequence of hours using pandas date_range\\ncomplete_hours = pd.date_range(start=min_time, end=max_time, freq='H')\\n\\n# Extract the unique hours from the DataFrame\\nexisting_hours = pd.to_datetime(df['DateTime']).unique()\\n\\n# Find the missing hours by comparing the complete sequence with the existing hours\\nmissing_hours = [hour for hour in complete_hours if hour not in existing_hours]\\n\\n# Print the missing hours\\nprint(missing_hours)\\n\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "# Get the minimum and maximum DateTime values in the DataFrame\n",
    "min_time = df['DateTime'].min()\n",
    "max_time = df['DateTime'].max()\n",
    "\n",
    "# Create a complete sequence of hours using pandas date_range\n",
    "complete_hours = pd.date_range(start=min_time, end=max_time, freq='H')\n",
    "\n",
    "# Extract the unique hours from the DataFrame\n",
    "existing_hours = pd.to_datetime(df['DateTime']).unique()\n",
    "\n",
    "# Find the missing hours by comparing the complete sequence with the existing hours\n",
    "missing_hours = [hour for hour in complete_hours if hour not in existing_hours]\n",
    "\n",
    "# Print the missing hours\n",
    "print(missing_hours)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52bcb829",
   "metadata": {
    "id": "yH8sRnrZo0GE",
    "papermill": {
     "duration": 0.013684,
     "end_time": "2023-07-04T02:30:29.066963",
     "exception": false,
     "start_time": "2023-07-04T02:30:29.053279",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Scale the temperature, wind speed, rainfall, and wind direction values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7b5acc76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:29.096846Z",
     "iopub.status.busy": "2023-07-04T02:30:29.095484Z",
     "iopub.status.idle": "2023-07-04T02:30:29.106038Z",
     "shell.execute_reply": "2023-07-04T02:30:29.105219Z"
    },
    "id": "ioRT6PB7o0GE",
    "papermill": {
     "duration": 0.027027,
     "end_time": "2023-07-04T02:30:29.107881",
     "exception": false,
     "start_time": "2023-07-04T02:30:29.080854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "features = df[features_keys].copy()\n",
    "scaled_features = scaler.transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "446cfbbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:29.137156Z",
     "iopub.status.busy": "2023-07-04T02:30:29.136414Z",
     "iopub.status.idle": "2023-07-04T02:30:29.142935Z",
     "shell.execute_reply": "2023-07-04T02:30:29.142013Z"
    },
    "id": "3HWJi4kH_V2J",
    "outputId": "ea4b5554-f96a-4d4b-b8f3-4b8433c7012f",
    "papermill": {
     "duration": 0.023028,
     "end_time": "2023-07-04T02:30:29.144830",
     "exception": false,
     "start_time": "2023-07-04T02:30:29.121802",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.63797805,  0.83337701, -0.14938301, -3.2800311 ],\n",
       "       [-2.75394614,  0.74031892, -0.14938301, -3.40438779],\n",
       "       [-2.88924224,  0.69378987, -0.14938301, -3.2800311 ],\n",
       "       ...,\n",
       "       [ 0.7057684 , -0.16699743,  0.34439569,  0.63720472],\n",
       "       [ 0.91837655, -0.56249431, -0.14938301,  0.63720472],\n",
       "       [ 1.01501662, -1.16737187, -0.14938301,  0.63720472]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43969cbb",
   "metadata": {
    "id": "MtfyrlIso0GE",
    "papermill": {
     "duration": 0.013731,
     "end_time": "2023-07-04T02:30:29.172571",
     "exception": false,
     "start_time": "2023-07-04T02:30:29.158840",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Create sequences of input data and corresponding target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "162f3c54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:29.202146Z",
     "iopub.status.busy": "2023-07-04T02:30:29.201406Z",
     "iopub.status.idle": "2023-07-04T02:30:29.346865Z",
     "shell.execute_reply": "2023-07-04T02:30:29.345934Z"
    },
    "id": "w_vv2_l_o0GE",
    "papermill": {
     "duration": 0.16251,
     "end_time": "2023-07-04T02:30:29.349212",
     "exception": false,
     "start_time": "2023-07-04T02:30:29.186702",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = []\n",
    "target = []\n",
    "for i in range(1, len(scaled_features) - time_steps):\n",
    "    # exclude the target\n",
    "    data.append(scaled_features[i:i+time_steps])\n",
    "    # Only the temperature is the target\n",
    "    target.append(scaled_features[i+time_steps])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "acdbf556",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:29.380253Z",
     "iopub.status.busy": "2023-07-04T02:30:29.378840Z",
     "iopub.status.idle": "2023-07-04T02:30:29.839609Z",
     "shell.execute_reply": "2023-07-04T02:30:29.838614Z"
    },
    "id": "iMnszm71o0GE",
    "papermill": {
     "duration": 0.478679,
     "end_time": "2023-07-04T02:30:29.842373",
     "exception": false,
     "start_time": "2023-07-04T02:30:29.363694",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = np.array(data)\n",
    "target = np.array(target)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bde62c5",
   "metadata": {
    "id": "pm3RIBnoo0GE",
    "papermill": {
     "duration": 0.014449,
     "end_time": "2023-07-04T02:30:29.871359",
     "exception": false,
     "start_time": "2023-07-04T02:30:29.856910",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Split the dataset into training and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "42e4fcd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:29.901480Z",
     "iopub.status.busy": "2023-07-04T02:30:29.901167Z",
     "iopub.status.idle": "2023-07-04T02:30:30.138708Z",
     "shell.execute_reply": "2023-07-04T02:30:30.137745Z"
    },
    "id": "nk58ReQ2o0GE",
    "papermill": {
     "duration": 0.25549,
     "end_time": "2023-07-04T02:30:30.141107",
     "exception": false,
     "start_time": "2023-07-04T02:30:29.885617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "split = 0.05\n",
    "random_state = 42\n",
    "\n",
    "random.seed(random_state)\n",
    "np.random.seed(random_state)\n",
    "\n",
    "val_index = (pd.Series([random.random() for _ in range(len(target))]) < split).values\n",
    "\n",
    "train_data, train_target = data[~val_index], target[~val_index]\n",
    "val_data, val_target = data[val_index], target[val_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73615e08",
   "metadata": {
    "id": "sRzJzu0-o0GE",
    "papermill": {
     "duration": 0.01544,
     "end_time": "2023-07-04T02:30:30.171211",
     "exception": false,
     "start_time": "2023-07-04T02:30:30.155771",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Build the LSTM model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9b23319e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:30.201116Z",
     "iopub.status.busy": "2023-07-04T02:30:30.200809Z",
     "iopub.status.idle": "2023-07-04T02:30:33.431908Z",
     "shell.execute_reply": "2023-07-04T02:30:33.430939Z"
    },
    "id": "rvOWUL_Co0GE",
    "papermill": {
     "duration": 3.248738,
     "end_time": "2023-07-04T02:30:33.434260",
     "exception": false,
     "start_time": "2023-07-04T02:30:30.185522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-04 23:52:30.446500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-07-04 23:52:30.448520: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-07-04 23:52:30.449642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.LSTM(512,  input_shape=(time_steps, len(features_keys))),\n",
    "    tf.keras.layers.Dense(64, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(len(features_keys))\n",
    "])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f20ab16c",
   "metadata": {
    "id": "cVjVLc9no0GE",
    "papermill": {
     "duration": 0.014029,
     "end_time": "2023-07-04T02:30:33.462893",
     "exception": false,
     "start_time": "2023-07-04T02:30:33.448864",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Compile the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c2c8714b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:33.498860Z",
     "iopub.status.busy": "2023-07-04T02:30:33.498247Z",
     "iopub.status.idle": "2023-07-04T02:30:33.516762Z",
     "shell.execute_reply": "2023-07-04T02:30:33.515995Z"
    },
    "id": "sk3yxZIio0GE",
    "papermill": {
     "duration": 0.041953,
     "end_time": "2023-07-04T02:30:33.518877",
     "exception": false,
     "start_time": "2023-07-04T02:30:33.476924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
    "model.compile(loss='mae', optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9798949d",
   "metadata": {
    "id": "mlmltGMRo0GE",
    "papermill": {
     "duration": 0.013797,
     "end_time": "2023-07-04T02:30:33.546764",
     "exception": false,
     "start_time": "2023-07-04T02:30:33.532967",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Define early stopping and learning rate scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "63988c8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:33.576692Z",
     "iopub.status.busy": "2023-07-04T02:30:33.575934Z",
     "iopub.status.idle": "2023-07-04T02:30:33.581298Z",
     "shell.execute_reply": "2023-07-04T02:30:33.580479Z"
    },
    "id": "-FzJGi_jo0GE",
    "papermill": {
     "duration": 0.022338,
     "end_time": "2023-07-04T02:30:33.583285",
     "exception": false,
     "start_time": "2023-07-04T02:30:33.560947",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "    monitor='val_loss', factor=0.2, patience=5, min_lr=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c89e78d",
   "metadata": {
    "id": "eQuMJC2xo0GE",
    "papermill": {
     "duration": 0.013748,
     "end_time": "2023-07-04T02:30:33.611134",
     "exception": false,
     "start_time": "2023-07-04T02:30:33.597386",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Train the LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4a75e898",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:30:33.643017Z",
     "iopub.status.busy": "2023-07-04T02:30:33.642376Z",
     "iopub.status.idle": "2023-07-04T02:59:58.080668Z",
     "shell.execute_reply": "2023-07-04T02:59:58.079498Z"
    },
    "id": "FPNW8kgSo0GE",
    "outputId": "5a832a5f-ebca-4d34-d24c-9b7e67d7fa19",
    "papermill": {
     "duration": 1764.458288,
     "end_time": "2023-07-04T02:59:58.083358",
     "exception": false,
     "start_time": "2023-07-04T02:30:33.625070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/75\n",
      "32/32 [==============================] - 29s 732ms/step - loss: 0.1034 - val_loss: 0.0434 - lr: 0.0010\n",
      "Epoch 2/75\n",
      "32/32 [==============================] - 23s 722ms/step - loss: 0.0407 - val_loss: 0.0409 - lr: 0.0010\n",
      "Epoch 3/75\n",
      "32/32 [==============================] - 23s 727ms/step - loss: 0.0327 - val_loss: 0.0294 - lr: 0.0010\n",
      "Epoch 4/75\n",
      "32/32 [==============================] - 23s 730ms/step - loss: 0.0315 - val_loss: 0.0305 - lr: 0.0010\n",
      "Epoch 5/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0303 - val_loss: 0.0290 - lr: 0.0010\n",
      "Epoch 6/75\n",
      "32/32 [==============================] - 23s 722ms/step - loss: 0.0277 - val_loss: 0.0264 - lr: 0.0010\n",
      "Epoch 7/75\n",
      "32/32 [==============================] - 23s 719ms/step - loss: 0.0272 - val_loss: 0.0280 - lr: 0.0010\n",
      "Epoch 8/75\n",
      "32/32 [==============================] - 24s 737ms/step - loss: 0.0272 - val_loss: 0.0250 - lr: 0.0010\n",
      "Epoch 9/75\n",
      "32/32 [==============================] - 23s 724ms/step - loss: 0.0244 - val_loss: 0.0239 - lr: 0.0010\n",
      "Epoch 10/75\n",
      "32/32 [==============================] - 23s 723ms/step - loss: 0.0232 - val_loss: 0.0211 - lr: 0.0010\n",
      "Epoch 11/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0219 - val_loss: 0.0216 - lr: 0.0010\n",
      "Epoch 12/75\n",
      "32/32 [==============================] - 23s 725ms/step - loss: 0.0229 - val_loss: 0.0220 - lr: 0.0010\n",
      "Epoch 13/75\n",
      "32/32 [==============================] - 23s 723ms/step - loss: 0.0218 - val_loss: 0.0238 - lr: 0.0010\n",
      "Epoch 14/75\n",
      "32/32 [==============================] - 23s 723ms/step - loss: 0.0208 - val_loss: 0.0221 - lr: 0.0010\n",
      "Epoch 15/75\n",
      "32/32 [==============================] - 23s 720ms/step - loss: 0.0210 - val_loss: 0.0230 - lr: 0.0010\n",
      "Epoch 16/75\n",
      "32/32 [==============================] - 23s 720ms/step - loss: 0.0192 - val_loss: 0.0184 - lr: 2.0000e-04\n",
      "Epoch 17/75\n",
      "32/32 [==============================] - 23s 720ms/step - loss: 0.0182 - val_loss: 0.0183 - lr: 2.0000e-04\n",
      "Epoch 18/75\n",
      "32/32 [==============================] - 23s 723ms/step - loss: 0.0181 - val_loss: 0.0182 - lr: 2.0000e-04\n",
      "Epoch 19/75\n",
      "32/32 [==============================] - 23s 724ms/step - loss: 0.0180 - val_loss: 0.0182 - lr: 2.0000e-04\n",
      "Epoch 20/75\n",
      "32/32 [==============================] - 23s 725ms/step - loss: 0.0179 - val_loss: 0.0180 - lr: 2.0000e-04\n",
      "Epoch 21/75\n",
      "32/32 [==============================] - 23s 721ms/step - loss: 0.0179 - val_loss: 0.0181 - lr: 2.0000e-04\n",
      "Epoch 22/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0178 - val_loss: 0.0180 - lr: 2.0000e-04\n",
      "Epoch 23/75\n",
      "32/32 [==============================] - 23s 723ms/step - loss: 0.0179 - val_loss: 0.0178 - lr: 2.0000e-04\n",
      "Epoch 24/75\n",
      "32/32 [==============================] - 23s 731ms/step - loss: 0.0178 - val_loss: 0.0184 - lr: 2.0000e-04\n",
      "Epoch 25/75\n",
      "32/32 [==============================] - 23s 720ms/step - loss: 0.0179 - val_loss: 0.0180 - lr: 2.0000e-04\n",
      "Epoch 26/75\n",
      "32/32 [==============================] - 23s 720ms/step - loss: 0.0177 - val_loss: 0.0177 - lr: 2.0000e-04\n",
      "Epoch 27/75\n",
      "32/32 [==============================] - 23s 724ms/step - loss: 0.0178 - val_loss: 0.0180 - lr: 2.0000e-04\n",
      "Epoch 28/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0175 - val_loss: 0.0176 - lr: 2.0000e-04\n",
      "Epoch 29/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0177 - val_loss: 0.0179 - lr: 2.0000e-04\n",
      "Epoch 30/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0177 - val_loss: 0.0175 - lr: 2.0000e-04\n",
      "Epoch 31/75\n",
      "32/32 [==============================] - 23s 725ms/step - loss: 0.0174 - val_loss: 0.0183 - lr: 2.0000e-04\n",
      "Epoch 32/75\n",
      "32/32 [==============================] - 23s 723ms/step - loss: 0.0174 - val_loss: 0.0180 - lr: 2.0000e-04\n",
      "Epoch 33/75\n",
      "32/32 [==============================] - 23s 721ms/step - loss: 0.0174 - val_loss: 0.0174 - lr: 2.0000e-04\n",
      "Epoch 34/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0173 - val_loss: 0.0173 - lr: 2.0000e-04\n",
      "Epoch 35/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0172 - val_loss: 0.0172 - lr: 2.0000e-04\n",
      "Epoch 36/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0172 - val_loss: 0.0174 - lr: 2.0000e-04\n",
      "Epoch 37/75\n",
      "32/32 [==============================] - 23s 721ms/step - loss: 0.0174 - val_loss: 0.0181 - lr: 2.0000e-04\n",
      "Epoch 38/75\n",
      "32/32 [==============================] - 23s 720ms/step - loss: 0.0173 - val_loss: 0.0174 - lr: 2.0000e-04\n",
      "Epoch 39/75\n",
      "32/32 [==============================] - 23s 727ms/step - loss: 0.0172 - val_loss: 0.0173 - lr: 2.0000e-04\n",
      "Epoch 40/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0169 - val_loss: 0.0170 - lr: 4.0000e-05\n",
      "Epoch 41/75\n",
      "32/32 [==============================] - 23s 731ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 4.0000e-05\n",
      "Epoch 42/75\n",
      "32/32 [==============================] - 23s 723ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 4.0000e-05\n",
      "Epoch 43/75\n",
      "32/32 [==============================] - 23s 724ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 4.0000e-05\n",
      "Epoch 44/75\n",
      "32/32 [==============================] - 23s 719ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 4.0000e-05\n",
      "Epoch 45/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 4.0000e-05\n",
      "Epoch 46/75\n",
      "32/32 [==============================] - 23s 721ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 8.0000e-06\n",
      "Epoch 47/75\n",
      "32/32 [==============================] - 23s 734ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 8.0000e-06\n",
      "Epoch 48/75\n",
      "32/32 [==============================] - 23s 727ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 8.0000e-06\n",
      "Epoch 49/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 8.0000e-06\n",
      "Epoch 50/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 8.0000e-06\n",
      "Epoch 51/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.6000e-06\n",
      "Epoch 52/75\n",
      "32/32 [==============================] - 23s 724ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.6000e-06\n",
      "Epoch 53/75\n",
      "32/32 [==============================] - 23s 727ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.6000e-06\n",
      "Epoch 54/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.6000e-06\n",
      "Epoch 55/75\n",
      "32/32 [==============================] - 23s 724ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.6000e-06\n",
      "Epoch 56/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 57/75\n",
      "32/32 [==============================] - 23s 720ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 58/75\n",
      "32/32 [==============================] - 23s 725ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 59/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 60/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 61/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 62/75\n",
      "32/32 [==============================] - 23s 726ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 63/75\n",
      "32/32 [==============================] - 23s 725ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 64/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 65/75\n",
      "32/32 [==============================] - 23s 724ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 66/75\n",
      "32/32 [==============================] - 23s 714ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 67/75\n",
      "32/32 [==============================] - 23s 720ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 68/75\n",
      "32/32 [==============================] - 23s 720ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 69/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 70/75\n",
      "32/32 [==============================] - 23s 723ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 71/75\n",
      "32/32 [==============================] - 23s 728ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 72/75\n",
      "32/32 [==============================] - 23s 724ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 73/75\n",
      "32/32 [==============================] - 23s 724ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 74/75\n",
      "32/32 [==============================] - 23s 718ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n",
      "Epoch 75/75\n",
      "32/32 [==============================] - 23s 730ms/step - loss: 0.0168 - val_loss: 0.0170 - lr: 1.0000e-06\n"
     ]
    }
   ],
   "source": [
    "batch_size = 4096\n",
    "epochs = 75\n",
    "history = model.fit(train_data, train_target, batch_size=batch_size, epochs=epochs, validation_data=(val_data, val_target),\n",
    "                    callbacks=[early_stopping, lr_scheduler])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3706d31d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:59:58.485493Z",
     "iopub.status.busy": "2023-07-04T02:59:58.485131Z",
     "iopub.status.idle": "2023-07-04T02:59:58.788673Z",
     "shell.execute_reply": "2023-07-04T02:59:58.787662Z"
    },
    "id": "jNgFvXyyvbR1",
    "outputId": "030b071f-c098-4e59-fc5f-0a28585c6cd8",
    "papermill": {
     "duration": 0.505952,
     "end_time": "2023-07-04T02:59:58.790912",
     "exception": false,
     "start_time": "2023-07-04T02:59:58.284960",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Access the loss values from the training history\n",
    "train_losses = history.history['loss']\n",
    "val_losses = history.history['val_loss']\n",
    "\n",
    "# Plot the loss values\n",
    "epochs = range(1, len(train_losses) + 1)\n",
    "plt.plot(epochs, train_losses, 'b', label='Training loss')\n",
    "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e552ec",
   "metadata": {
    "id": "da_mp26Po0GV",
    "papermill": {
     "duration": 0.199308,
     "end_time": "2023-07-04T02:59:59.192681",
     "exception": false,
     "start_time": "2023-07-04T02:59:58.993373",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Make predictions using the trained LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c43cba85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T02:59:59.643633Z",
     "iopub.status.busy": "2023-07-04T02:59:59.642677Z",
     "iopub.status.idle": "2023-07-04T03:00:02.598427Z",
     "shell.execute_reply": "2023-07-04T03:00:02.597441Z"
    },
    "id": "_fk4WnEOo0GV",
    "outputId": "90e48217-ec57-4d65-af64-17840d1d5ede",
    "papermill": {
     "duration": 3.210832,
     "end_time": "2023-07-04T03:00:02.601020",
     "exception": false,
     "start_time": "2023-07-04T02:59:59.390188",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "210/210 [==============================] - 2s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(val_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50516a90",
   "metadata": {
    "id": "lyT2YUwfo0GV",
    "papermill": {
     "duration": 0.203384,
     "end_time": "2023-07-04T03:00:03.005760",
     "exception": false,
     "start_time": "2023-07-04T03:00:02.802376",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Rescale the predictions back to the original range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "51af6d8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:03.410381Z",
     "iopub.status.busy": "2023-07-04T03:00:03.409951Z",
     "iopub.status.idle": "2023-07-04T03:00:03.418600Z",
     "shell.execute_reply": "2023-07-04T03:00:03.417734Z"
    },
    "id": "16ZjScEeo0GV",
    "papermill": {
     "duration": 0.214312,
     "end_time": "2023-07-04T03:00:03.420635",
     "exception": false,
     "start_time": "2023-07-04T03:00:03.206323",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaled_predictions = pd.DataFrame(predictions, columns=features_keys)\n",
    "predictions = pd.DataFrame(scaler.inverse_transform(\n",
    "    predictions), columns=features_keys)\n",
    "predicted_temperature = predictions[\"Temperature\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cd99bbd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:03.894115Z",
     "iopub.status.busy": "2023-07-04T03:00:03.893751Z",
     "iopub.status.idle": "2023-07-04T03:00:03.908281Z",
     "shell.execute_reply": "2023-07-04T03:00:03.907421Z"
    },
    "id": "lxqz2lcsAMk0",
    "papermill": {
     "duration": 0.261848,
     "end_time": "2023-07-04T03:00:03.910535",
     "exception": false,
     "start_time": "2023-07-04T03:00:03.648687",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "val_target = pd.DataFrame(val_target, columns=features_keys)\n",
    "val_target_rescaled = pd.DataFrame(scaler.inverse_transform(val_target), columns=features_keys)\n",
    "train_target = pd.DataFrame(train_target, columns=features_keys)\n",
    "train_target_rescaled = pd.DataFrame(scaler.inverse_transform(train_target), columns=features_keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ecba1a3",
   "metadata": {
    "id": "-1vqJau8o0GV",
    "papermill": {
     "duration": 0.201501,
     "end_time": "2023-07-04T03:00:04.322492",
     "exception": false,
     "start_time": "2023-07-04T03:00:04.120991",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Calculate MSE and MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "02649ea3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:04.724498Z",
     "iopub.status.busy": "2023-07-04T03:00:04.724074Z",
     "iopub.status.idle": "2023-07-04T03:00:04.732619Z",
     "shell.execute_reply": "2023-07-04T03:00:04.731727Z"
    },
    "id": "OK9H9h3Do0GV",
    "papermill": {
     "duration": 0.212437,
     "end_time": "2023-07-04T03:00:04.734799",
     "exception": false,
     "start_time": "2023-07-04T03:00:04.522362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mse = mean_squared_error(val_target_rescaled[\"Temperature\"], predictions[\"Temperature\"])\n",
    "mae = mean_absolute_error(val_target_rescaled[\"Temperature\"], predictions[\"Temperature\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "41361c1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:05.140147Z",
     "iopub.status.busy": "2023-07-04T03:00:05.139792Z",
     "iopub.status.idle": "2023-07-04T03:00:05.144818Z",
     "shell.execute_reply": "2023-07-04T03:00:05.143923Z"
    },
    "id": "sC-MqFKgo0Gl",
    "outputId": "91215d09-7186-4b3a-f9c7-76cfcc5c5104",
    "papermill": {
     "duration": 0.210994,
     "end_time": "2023-07-04T03:00:05.147320",
     "exception": false,
     "start_time": "2023-07-04T03:00:04.936326",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 0.5612677803801317\n",
      "Mean Absolute Error (MAE): 0.5097256619359687\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "print(\"Mean Absolute Error (MAE):\", mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a78c081",
   "metadata": {
    "id": "N1UNa22do0Gl",
    "papermill": {
     "duration": 0.199268,
     "end_time": "2023-07-04T03:00:05.546163",
     "exception": false,
     "start_time": "2023-07-04T03:00:05.346895",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Visualize the actual vs. predicted temperatures for the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6c6da9c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:05.946800Z",
     "iopub.status.busy": "2023-07-04T03:00:05.946018Z",
     "iopub.status.idle": "2023-07-04T03:00:06.349689Z",
     "shell.execute_reply": "2023-07-04T03:00:06.348691Z"
    },
    "id": "rLzZRHyLo0Gl",
    "outputId": "c0001062-bc02-47c2-b918-199089a489f4",
    "papermill": {
     "duration": 0.606523,
     "end_time": "2023-07-04T03:00:06.351806",
     "exception": false,
     "start_time": "2023-07-04T03:00:05.745283",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(len(val_target)), val_target_rescaled[\"Temperature\"], label='Actual')\n",
    "plt.plot(range(len(val_target)), predicted_temperature, label='Predicted')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Actual vs. Predicted Temperatures (Validation Set)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a84d756",
   "metadata": {
    "id": "zirXhmtjo0Gl",
    "papermill": {
     "duration": 0.200024,
     "end_time": "2023-07-04T03:00:06.800369",
     "exception": false,
     "start_time": "2023-07-04T03:00:06.600345",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Visualize the actual vs. predicted temperatures for the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "baf1ff61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:07.209077Z",
     "iopub.status.busy": "2023-07-04T03:00:07.208707Z",
     "iopub.status.idle": "2023-07-04T03:00:49.702621Z",
     "shell.execute_reply": "2023-07-04T03:00:49.701673Z"
    },
    "id": "NlVyqCTyo0Gl",
    "outputId": "faf8d271-2b7e-4832-f825-ff89a2db4f08",
    "papermill": {
     "duration": 42.941779,
     "end_time": "2023-07-04T03:00:49.947619",
     "exception": false,
     "start_time": "2023-07-04T03:00:07.005840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4034/4034 [==============================] - 28s 7ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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Lly7tkcy2rh0ALF68GM2aNUNAQADkcjkUCgWOHDni8rXL/041atQIAGw+E/np0aMHZDKZ3WPv3buH6Oho9O3b1+K4ypUro23bti7J9+abb+LZs2f4999/MWbMGDRo0AA7duzAW2+9ha+++spYbvfu3XjppZfQpEkTi/vXrVs3MAxjfE9doXTp0tDr9TbdaSgUCsVdqJJOoVAoNnj48CFOnDiBHj16gBCC5ORkJCcn49133wVgivgOAHFxcV4JOnb+/Hl07doVgMFf8/Tp07hw4QJ+/PFHAHB54G/O4MGDkZ2djY0bNwIADhw4gBcvXmDQoEHGMu3bt8eOHTug1WoxYMAAVKxYES+99JJL/rT2CAwMxIULF3DhwgVcv34dycnJ2LNnj1XAOFt+pTExMdi1a5dRyc/716BBAwBAfHw8AINvapkyZayOL1u2rFP58kyCPb2vMTExSE5OhlKptJIzOjraQkZbMsnlcuMkiafExMQAAN59910rGX777TcQQpCYmGhxTPHixS1+K5VKFCpUCAEBAVbbbaXLs3Vty5YtazzPPJnGjBljJdPw4cMBmO5fHraegdGjR2PChAno06cPdu3ahXPnzuHChQto3LixR++BK+SXw51zWbBgAb7//nvs2LEDnTp1QvHixdGnTx+LyT5b5J1L/uvvqcwAMHfuXHzxxRdo1aoVtm3bhrCwMFy4cAFvvPGGy9cu/7OZF0DPleOdHZv3rNh6d21ts0dgYCD69OmDWbNm4fjx43j48CHq16+Pv/76C7du3QJguIfXr1+3un9FihQBIcTqWXRE3j0S6vmjUCgFC+qTTqFQKDZYsWIFCCHYunUrtm7darV/9erVmDZtGmQyGUqVKoWIiAiP2woICDCuZJuTf4C4ceNGKBQK7N6922LQbit3r6vUr18fLVu2xMqVKzFs2DCsXLkS5cuXN04G5NG7d2/07t0bOTk5CAsLw4wZM9C/f39UrVoVrVu3drtdlmXx8ssvOy1nK5hbyZIl0ahRI/zyyy82jylfvjwAgzJgKwCbKytdeX7xERERHkVrzguotX//fpv7ixQpYpQxTybzCQqtVuvUX9kVGQDgjz/+sBul2x2lxxVsXdvo6GjjeebJNG7cOCt/9jzq1Klj8dvWM5Dnaz99+nSL7fHx8S4HWHP1vbMnhzvnUrhwYUyePBmTJ09GTEyMcVW9V69euHv3rl0Z89pITEy0GwjNEfauXceOHbFo0SKL7XzEQOCDvGclbxLEHC6r1JUrV8Znn32GkSNH4tatW2jQoAFKliyJwMBAi0lXc/KuvyvkTXi5cwyFQqHYgyrpFAqFkg+dTofVq1ejRo0aWLZsmdX+3bt3Y86cOdi3bx969uyJ7t27Y+3atbh3756VgpGHo5WmqlWrYsuWLcjJyTGWS0hIwJkzZyyCE+WlTzI3Fc3KysLatWs5ne+gQYPwxRdf4NSpU9i1axdGjx5t0Ub+8+jQoQNCQkJw4MABXLlyxSMlnQs9e/bE3r17UaNGDRQrVsxuuU6dOmHz5s3YuXOnhXnu+vXrnbbRtWtXyGQyLFq0yOH5qVQqm/e0Z8+e2LhxI3Q6ncOUfXlButatW4fmzZsbt2/evNkqUr27tG3bFiEhIbh9+7aFia+QbNiwAaNHjzYqh0+fPsWZM2cwYMAAAAaltVatWrh27ZqVgu0ODMNYpT/bs2cPIiMjUbNmTeM2Pt47e3h6LmXKlMEnn3yCa9euYf78+cjMzEShQoVslq1bty4A4NGjR0ZLEa7YunbXr1/H2bNnJZE+rE6dOihbtiw2b96M0aNHG7c/e/YMZ86cMU7C2SMtLQ0MwyAoKMhqX545f14dPXv2xPTp01GiRAlUq1bNYb323vU8Hj9+jBIlSvA+8UWhUAomVEmnUCiUfOzbtw9RUVH47bffbEY6fumll/Dnn39i+fLl6NmzJ6ZMmYJ9+/ahffv2GD9+PBo2bIjk5GTs378fo0ePRt26dVGjRg0EBgZi3bp1qFevHoKCglC+fHmUL18eH3/8MZYsWYKPPvoIQ4cORUJCAmbOnGmlKPTo0QNz585F//798dlnnyEhIQGzZ8/mnKv5gw8+wOjRo/HBBx8gJyfHKrr2xIkTERERgS5duqBixYpITk7G77//DoVCgQ4dOhjLyeVydOjQwSq9F99MmTIFhw4dQps2bTBixAjUqVMH2dnZCA8Px969e7F48WJUrFgRAwYMwLx58zBgwAD88ssvqFWrFvbu3YsDBw44baNq1aoYP348pk6diqysLHzwwQcIDg7G7du3ER8fb0wF17BhQ2zfvh2LFi1C8+bNjRYC77//PtatW4c333wT33zzDVq2bAmFQoGIiAgcO3YMvXv3xv/+9z/Uq1cPH330EebPnw+FQoHXXnsNN2/eNEYQ50JQUBD++OMPDBw4EImJiXj33XeN0b2vXbuGuLg4q9VUrsTGxuJ///sfhg4dipSUFPz8888ICAjAuHHjjGWWLFmC7t27o1u3bvjkk09QoUIFJCYm4s6dO7h8+TK2bNnitJ2ePXti1apVqFu3Lho1aoRLly5h1qxZVu4JfLx3jnD1XFq1aoWePXuiUaNGKFasGO7cuYO1a9eidevWdhX0vOMCAwMRFhZm5QfuKT179sTUqVPx888/o0OHDrh37x6mTJmCatWqcZ4Y4gOWZTF58mQMGzYM7777LgYPHozk5GRMnjwZ5cqVs0qhlp979+6hW7dueP/999GhQweUK1cOSUlJ2LNnD5YuXYqOHTuiTZs2AICRI0di27ZtaN++PUaNGoVGjRpBr9fj2bNnOHjwIL799lvjJFvDhg0RGhqKXbt2oVy5cihSpIjFpGxYWBg6dOhg03qBQqFQ3EbcuHUUCoUiPfr06UOUSiWJjY21W+b9998ncrmcREdHE0IIef78ORk8eDApW7YsUSgUpHz58qRv374kJibGeMyGDRtI3bp1iUKhsIqavHr1alKvXj0SEBBA6tevTzZt2mQzuvuKFStInTp1iEqlItWrVyczZswgy5cvJwDIkydPjOVcie5uTv/+/QkA0rZtW6t9u3fvJt27dycVKlQgSqWSlC5dmrz55pvk5MmTFuUAuNRmXnR3Z1SpUoX06NHD5r64uDgyYsQIUq1aNaJQKEjx4sVJ8+bNyY8//mgRXTkiIoK88847JCgoiBQpUoS888475MyZM06ju+exZs0a0qJFCxIQEECCgoJI06ZNLY5LTEwk7777LgkJCSEMw1jUodFoyOzZs0njxo2Nx9etW5cMGzaMPHjwwFguJyeHfPvtt6R06dIkICCAvPLKK+Ts2bOkSpUqnKK753H8+HHSo0cPUrx4caJQKEiFChVIjx49LMrlnX9cXJzFsfbuVYcOHUiDBg2s2l67di0ZMWIEKVWqFFGpVKRdu3bk4sWLVsdfu3aN9O3bl5QuXZooFApStmxZ0rlzZ2MmBUJMkcTzR94mhJCkpCTy6aefktKlS5NChQqRV199lZw8edLmc8/1vbMVPd3dc/nhhx/Iyy+/TIoVK2Z8d0eNGmXMoOCIjz/+mNSvX9/ufkfR3fM/C4QYnrcxY8aQChUqkICAANKsWTOyY8cOm/1N/utl757ktXfs2DHjNnvR3W1dx/ztEELI0qVLSc2aNYlSqSS1a9cmK1asIL179yZNmza1ey0IMTwb06ZNI507dzb2WYULFyZNmjQh06ZNI5mZmRbl09PTyU8//UTq1KlDlEolCQ4OJg0bNiSjRo0y9u+EEHL16lXStm1bUqhQIau+7uHDhzazOVAoFIqnMIQQ4s1JAQqFQqFQKP5FaGgoOnXqhC1bthiDK1L44eLFi2jRogXCwsIcuk74O8nJyahduzb69OmDpUuXii2OBRMmTMCaNWvw6NEjzlkZKBQKBaDR3SkUCoVCoVAky8svv4y+ffti6tSpYoviNaKjo/H1119j+/btOH78ONasWYNOnTohLS0N33zzjdjiWZCcnIy//voL06dPpwo6hULhDdqbUCgUCoVCoUiYOXPmYPny5UhLSzNmBvBnVCoVwsPDMXz4cCQmJqJQoUJ45ZVXsHjxYt4C6PHFkydPMG7cOPTv319sUSgUih9Bzd0pFAqFQqFQKBQKhUKRCNTcnUKhUCgUCoVCoVAoFIlAlXQKhUKhUCgUCoVCoVAkAlXSKRQKhUKhUCgUCoVCkQgFLnCcXq9HVFQUihQpAoZhxBaHQqFQKBQKhUKhUCh+DiEEaWlpKF++PFjW8Vp5gVPSo6KiUKlSJbHFoFAoFAqFQqFQKBRKAeP58+eoWLGiwzIFTknPS13y/PlzFC1aVGRpKBQKhUKhUCgUCoXi76SmpqJSpUoupdIscEp6nol70aJFqZJOoVAoFAqFQqFQKBSv4YrLNQ0cR6FQKBQKhUKhUCgUikSgSjqFQqFQKBQKhUKhUCgSgSrpFAqFQqFQKBQKhUKhSASqpFMoFAqFQqFQKBQKhSIRqJJOoVAoFAqFQqFQKBSKRKBKOoVCoVAoFAqFQqFQKBKBKukUCoVCoVAoFAqFQqFIBKqkUygUCoVCoVAoFAqFIhGokk6hUCgUCoVCoVAoFIpEoEo6hUKhUCgUCoVCoVAoEoEq6RQKhUKhUCgUCoVCoUgEqqRTKBQKhUKhUCgUCoUiEaiSTqFQKBQKhUKhUCgUikSgSjqFQqFQKBQKhUKhUCgSQVQl/cSJE+jVqxfKly8PhmGwY8cOp8ccP34czZs3R0BAAKpXr47FixcLLyiFQqFQKBQKhUKhUCheQFQlPSMjA40bN8aff/7pUvknT57gzTffRLt27XDlyhWMHz8eI0aMwLZt2wSWlEKhUCgUCoVCoVAoFOGRi9l49+7d0b17d5fLL168GJUrV8b8+fMBAPXq1cPFixcxe/ZsvPPOOwJJSaFQKBRKweBmZAoqlyiEogEKsUWhUCgUCqXA4lM+6WfPnkXXrl0ttnXr1g0XL16ERqOxeUxOTg5SU1Mt/lEoFAqFQrHkxP049PzjFF6fe1xsUSgUCoVCKdD4lJIeHR2NMmXKWGwrU6YMtFot4uPjbR4zY8YMBAcHG/9VqlTJG6JSKBQKheJT7LsZDQCISc0RWRIKhUKhUAo2PqWkAwDDMBa/CSE2t+cxbtw4pKSkGP89f/5ccBkpFAqF4p+M2HAFX62/LLYYFAqFQqFQ/BifUtLLli2L6Ohoi22xsbGQy+UoUaKEzWNUKhWKFi1q8Y9CoVAoFHdJydRg57Uo7L7+AvHpdLWZQqFQKL7JmYfxGLv1GlKzbbsLU8THp5T01q1b49ChQxbbDh48iJdffhkKBQ1yQ6FQKBTh0OdabgGA2Z8UCoVCKYBkqXVYdfoJIpIyxRbFbfovO4fNFyPwxT+XxBaFYgdRlfT09HRcvXoVV69eBWBIsXb16lU8e/YMgMFUfcCAAcbyn3/+OZ4+fYrRo0fjzp07WLFiBZYvX44xY8aIIT6FQqFQKBSKJHiemIlHcelii0GhFBh+238Xk3bdxhvzT4otisecfpggtghuo9bqcSE8ERqdXmxRBEVUJf3ixYto2rQpmjZtCgAYPXo0mjZtiokTJwIAXrx4YVTYAaBatWrYu3cvQkND0aRJE0ydOhULFiyg6dcoFAqFQqEUaNrNPIYuc44jJYuar1Io3uD0Q0PQ6vQcrciSFCx+2nED7y0+i0k7b4ktiqCImie9Y8eOxsBvtli1apXVtg4dOuDyZRq0h0KhUCgUCiU/L1KyEBxIXQApFIp/svliBABg3bln+OV/DUWWRjh8yiedQqFQ/IGwxwnoPCcUZx/5nplZQYa6ofsI2anAlkHAnd1iS0KhUCiSh1oCSBOqpFMoFIqXeX9pGB7HZeCDv8PEFoXiIXayfvo0hbVJCFWOwleyf8UWhRsn5wC3tgObPhRbEgrFyK/77mLV6Sdii0FxA41Oj3Hbr2PntSixRRGUdWFPxRaBYgOqpFMoFAqFQkHH2DWoysZgjGKL2KJwIz1GbAlE4T1ZKIbI9ogtBsUGd16kYvHxR5i067bYolDcYNulCGw4/xwjNlyxud9frKv8PQCbr0KVdAqFQhEBGXRii8CZ5Ew1LoQnOowtQvEdWOL7zyQAEL8ZOrvHLMVS/KRYB0XqM+eFKV4lg5oT+yTx6TliiyAgBC2YuyiGVIuUojciUhCTmi2eWBQjVEmnWJGp1kKtpbNqFIpQvMGexz3VQPRkz4otCie6zDmO9xafxcHbBXPl0hfJUuuQo7WnjPuHDX9UcsEeYDJqmoaNQqE4pit7EVtUUxCqGm3cdi86Db3+PIVW04+AEILUbJopQkyokk6xIFOtRf2JB9D2t6Nii0Kh+C2LlfMhZ/T4U/mH2KJwIiFDDQA4VECUdF+3GMjR6lBv4n60mHbY58/FEVHJWWKLQKE4JjUKiLgothSUAsxncoNrTDCTabQ9uvwsybj/838uodGkg7gVlSKCdBSAKumUfNx5kQYAiEvzZxMfCkU6dJ4diueJmWKLQXETX1xzfppgeM5Ss02mt6tOP0HnOaF4kUIVW3/BF59Nf8dqSmxuPWBZF+DFdTHEobjBK+xtlEe803K+5tLwMnvf4f4DtwyT76vPhHtBGootqJJOoVAoIvI4PgNTdtNgQhTvkreQPmnXbTyOy8DM/feQmEEnZ32JBzFp+HLdZdyPSfNrywh/Rv/8vNgiUBxQNvkqNiqn4UzACKdlN5z3/VgQjF6LJYq5+Ey2S2xRKKBKOoVCoYiOr8eAoPqB73HuSaLFb7VOjwS/DpLkf/RbGoY9N17g/aVhuPo82bRD4vkBd1+PwsFb0WKLIQluR6WKLQLFAeVTr7pcVqf33Q/hlSeG97FS9GF0k13EeMUG4z76fRcPqqRTLJD4t51C8TvmKhaisI4O1HwBHx6DWZHuY6aZFGsSc2NCJGaofeZ+Jmao8dX6K/hs7aUClfbJnqKTpfGN+0bxbyo/MaTdlOusXe+iJOgKJYcWzZj7fpElxxFysQWgUCiUgszbslMgkSoAr4ktCsUJ68+ZzBl9XV+3Mo/29RMq4Jiv4mVKWGFPM4sWradLdBSJw/hpx5ij1UFl9jsEhowQttbpTj9M8IpM7vAwYAAAYLX2dQBviSuMgNCVdAqFQhGZUrpYsUWguMDOa5Hoxl5AN9Y3/UjNB2D+OfQsuJgr6UlZ0k2bFJNqcqmITS1Y7hUd2atoxjgO1kWRFv7aT74y/YjFb4bxzTMdKD8ktgiCQpV0iiWE4A/FAvwo/0dsSQQhJjUbHWcdw9ITj8QWheIGK049QYdZxxBJUytJEuK3Q5l8qDOxRDkPS5TzfTYXdWv2FvrKjokthmhka3To/vtJTNnlX8EazV3VzBeoLz1NQttfj0rGB/zoXdOEZOi9gjM5qcyIwirlTGxXTcq3h/oYShnGT609kjJtT+RtvxJpc7uUAlNKSRahoUo6xYJCiXfQSxaGofK9dstEp2Tj87WXEPZYeiYwzph36D7CEzIxfe9d6PUEY7dewz9hT8UWi2KH0w/jMWrNCUzZfRtPEzLx2767YotEMUMJDaoyL8QWQ1CSM9X4ct1lHLsbi4QUU+wARmPy3Zt36D5+2+8bz+YG5S+YqfgbgdGOczTPO3QfM33knBwxfe8d/HHkgfH3rmtRuPMiFStOPxFRKv5hzJS98iGBxr8/WXEekclZ+GztJTHEouSizLA3SVJwFA7fxHR/YlKzrff6icKY13uYu6CEIA0tmTsAiDH+BcW7UCWdYgGjN/MXsxMl6ftt17H/VjTeXxrmLbF4Q20WqObwnRhsvhiBn3bcFFEiiiPurfoS8x73Qnv2GgBAq/ffQEMZOVqceRiPNWfDxRbFZf5VTkSo6lvUSXes8Pkyv+2/iz03XmDQqgs292epdfj9yAMsCn2EuDSJm+/qTb7KZc5OtthFQCCH6f36/cgDLPSFc3LC0hOPMefQfeP3zJcjMDsiNcs0iJazJoU9x8czR/g7qZka/HHkAc2sIFXMDB3Gb79htVtGtHiNvYQQpHlRKO9wTPUtNqumoit7EQyNKi0KVEmnWEDMXsRtlyNslolIso7+6IukZks3uA7FwGD5fgDA9/KNIksiLAwIft13F/2XncPE/27hnI9YqTRgDVYozVMOiCyJcESnWK+emGM+cZT39/WIZDyOk545vCLFZDVUWmu9sveh/IjVNn+JwJ0XodifVHQFtGjF3IECWkz875bZHt8YUF8ITypALky2n7xj9+Mw59B9fLPxqsX2x3HpuB6RLLxYFIeYm7vHJSVb7X9fvR3LlHOwTTnJe0JxxNWJymKM4RvWhb3iIz2K/0GVdIpdXtw4LrYI/GOnb9Lo9D5vzkMI8flVL2fkfS9TsjTI1vhP6o2STAouPU0y/i44A1f/IzY1G2/9eRqd50i7/3QWtXi6/G/MU/xlsS0uLcdnzTtf/c3gh++j4ttkteJXbFJNxRXVZ0jJlm6wOHMYBjimHIXwgP7YeS0SbX89KrZI3oHYnuzKew9PPYy32N55znG89edpmybWFO+g1xNkqk3jjIHZ1rGaOmlPAQBqsL7j9rXSTVcfBoSmZxYJqqRTLDD3axv67Fs7ZYA6zDMo4D8r0bV+3IdmUw/hzgvfzVc988A9tPjlsEWaKH8kNVuDxpMPosW0w2KLwhv12Od4Re177iMm/EjzsQGbawZurtjGJSYDsD7zZ4nStTS6EJ5o8XvZycd2y/aXH8P/ZKfBphkCCf13NRItfjmMn3fesnuMVJDyPeCTNjJD8LsgJhvF7JnbSmxwXVgdj2psDACgu49mSfAIO0o6cXKDniYUjGdZiny14TKuPDNNnr+ssY7r4IvuJDuvRdnYaviS2XsaGal1JAUEqqRT7BJIbM/gdtccwgHVD/hbMcfLEvGAk35m2yXbJv6+wKJQQ8T6SbukP4h2l7zbxjCme5Qm4VzAnvBx1lqxRfAYf/58l9M8x03Vpxgl32qxPfnsagCWK7NSGMg8TchAmxlHbK6WrD9vmsDTEWDanjvG3/ZWmJlc5eLX3KCNa876dqDNiPhUrFHMsLqfvk5D1ux+S3TZS68n2HDmofF3OSbRQemCR6vph3HmUbzzghSvsPdGtMMePd2PxiAB8B1LUn+yhnIGVdIpFrgSHOJt9S4AQEfZNaHF4YX9N6MxbO1FpGRpwBAd3mFP+HVEarUPzuy6il4PTPaz1En+zKYLzzBiwxWf9GvO1ugwbO1FdHr+FwoxOfhGvt1iP0ukOUCbtPMWolKynb4nBqsAgmrMCziyhJiw4yb+u2o7LY9vQNCMuY8iMKxIhp/ehPayG1b309fxhUH2g1jLOA3OXC78CbXavisak2upE5Oag/5/n7PY509uXf7G1ovPLX4zxHfv1TD5HgBAUWRY7SvLJEp+Jv7Oi1QMWX3Bp61hbUGVdIpDCCH4+8RjnH3kG4GsbPH5P5dw4FYM5h26j1bJezFHuRihKtum/BTxeRyXjvmH7yPVzMeyPvsU/yl/wq17dyGDDjuUP2GB4g8RpaSYY2+o/f22G9h5LQrb7QShlDJrzz7FgVsxFtsUMA3C8sxU1Vo9GOT+yx3IlEGiaNF+tQ6DAjEWf42Sb8Mx1bcYJ19v94jbL1Ktglr5Er3Ys9iumoTdyvEAfEOZ9QRfCK5JQBDI+HfcFHtknVtpc3t39jyuq4biK9m/WKX4DXUZS3e11WfCvSCdMOj1BF+uu4yBK8775EQtYDmRlL9n1RHLbbVj9nhFJiH5UWH9LWgvuyFV4xwjfZecxeE7sei7+KzYovAKVdILOLeiUnAzMsXu/sN3YvHL3jv44G+Tv6yvzn3HpeegWqZlCo1SSMZ8xZ94mTEof756bv5E13knMP/wA0zK5/vamH2MUcx6NGUeoAn7GG/J/Ksz9ulVJSeip2RpcOxeLGLTfCcIUkp6Gn5X/InXZSY/xF5mz1ze/Vp+8jH+VU7EAeX3YPQ6yHJScC7gK1wNGOZ1mZ2R/xnLW03OW0WxRZ5poUZHjL75UoQQguP34xCTmo0ajMnnsqfM8O2qwsaKJZpXsBe4qgwSMF2+DLWZ5zb3exMGjPF+GHC9z3sYm45LT33XPL5Eju2JyrayWyjCZGGMYgs6yq5hnfIXi/2Jmb47qbTrehT23HiB4/fjfHqyIY/8VorRKZYBXgvlxHlTHK/CqK1X2KVEWm62Jn9zg5SLLQBFPHK0OvRYYIhMeXtKNxRSWj8OTxOk/WK6S0qWZQTcXxTL0VV2CX1kZ5BDFFiSswlAfXGEowAwrQReNot2nkdhZEvd6qpA4uye7LgShdsvUhGokOHO1De8IhNXmsZsRxfZGYttXdjLFr/1eoINp+/iB4UhAFt8WgQCUqRrNeBIJbLn53dCNRJ/6vrgREYjnFZNxzTthwB6CCEeJw7djsFnaw0TKuEBj4zbu8kuWpSz96zGpmajRJAKMtYfehjTOSyQzUdT5gHekx0H8Ll4IuXC2PnbFmqtHqnZGpQMUuG1uYZsCad/6IwKIYGCyScUrIsTEiUY3823HZOajVJBKrC579CT+Ayw0KM40nA/xjfPi2Hs37e/Tz7BO0qzsn7sLM1q0gGEiC1GgYOupBdgstWmWcHUrLzZJ8vP5pXnyVbHOYtGKmXMlXRCCCozptUVFaNB62jrFBu+QgmkoBt7HjJzk1w/+2j48rPnCh3Yaxgi2+N7kVGc3JbbuX5iWT7kXxmoSbbalhdNGwDOPknC1xuvQG1lxukb9y4vB64zZAzBN/J/MV+xECpGg6mKVcIK5iH5U1i5w5mH8Wg5/QiGrL7Ao0TSoB7CAQAKRvx3L7/JbCE4Nn1/c8FJvDztMJ7EmxYLbvho7vAcjWmFz9U81b7E4dsxaDX9CL7ecMVi+1rFDFwM+ALhV0PFEYwjzkYcdVlzCxXp39e8MaFY7lgU96BKOsWSfD3SnusvIIcWlp2PjypKBJIw+ROK3aofsUQ5H8NkuwEAB29Fo8Uvh3Gaw+BVarDQY7FynvG3vwUJWa38DT8p1qFUvG+lY5P+0MR9EtKd+87uuZ7PxFgCXaOj4J9cxHO0oiQFXJnXIoTYPI8Vp8MBAMfu+a+5qlSoY/YNHi7/z2HZh7mB5vbfjDZuuxft2uSS1DA/76N3/c/1Ysgag8XKnhumPnH+4QdoKzO4rfVlfC9l6seygxYBJmuzjgNoSt1l7VlCJl6ZcQTXI1LcdseSkk+6tK8yv1AlnWJJvjexGFJxQzUEixXzxZGHBxjoUQyp2HPjhcWHEsg/C+pDZKcAd3YDWpMikZfOZqxiEwDgs7WXEJ+uxofLztmsQuqE28gP2569bmEO+N1W38gw4ApqrdlKl4RNpm3iR19NtVaP4esu4amTXNu2xiyGFGzSvRiOLFGIE7krMr4/2TdgRQHJy517myftvCUp6yM2JxVvyEzWCgGMxm7ZtWG+neovP3LGZHEzdM1FByUt8TWjKl8k7HECPll5Hs/yjTnctRq6EZmCL/65hByt+FYrtpix7w5iUj0L3Cip59CGMDLo0Ih5ZGFJ6g9QJZ3ikLdkZxHIqC0+rFL66LvCEsU8XAn4HM2ZexaSF023ziOckRwHaLKstkuO9f2ATR8CByeILYlXyT+o00s3jpWRvAwJofdcXz05mC+quC+QrdFh3qH7uBFhCkQ5QrYdO5Q/IRC+ETDuzotUvPrbUey9Ee1RP3c7KgVbLkp34k/uZwMYcwgIXmVvIDygv90ypx/43wqmbRg8T8zEKokF65Jnum6pMGHHTePfxCLCtpS0BeEYIDuAcfJ1EtOOHMPkCyxp/lvKp/H+0jCE3ovD1xuvOC8M4NrzZMw/fN9qe0pmDvbdjMbas9KcYOLiZnH8vrStjH6S/4OdqgmYJF8ttii8QpV0it/TNTc68zbVZAu/vJAU6zzCHTUnoZ9Zw2uyecyz3CjT1zbYLVKNeYGBsgNQwv5qhRSpxrzASPlWm/k6fZGzjxLwy947+GSl6/6u2RKdibcHA2BR6CP8fuQBev1pCEaZkaPFaMVWNGEf433ZMZ94Drv/fhKxaTn4XLYTX8t3OCybN9wJNntOx/17E9efWwc8lAofkZ1iiyAYhAD/KGc4LFMUGZI3SeUDAkgyt73UXSakxBTFagyT70F17SPnhSXAENkeXFV9ZuFS2Is1zwok/cWd/NHa7dH7r9OYf/gBWjB3LbbnneGv++5aH+TjZOVIe0wySH4AAPCx3PfcKhxBo7tTLGBcmLfR+sLyJQdYjX8oh8dyc8EHIQspmT0ABggOVIgslTWEEEQkZaFisUAwDIP9yu+hYrSoykQ7P9gHiErJRlFkIBMql4/xhQFNfvLHB7jyLBmv5v5dn3mK+wEDsU7bBVKMDA6Y5Gehxw8K5zmn8+7RCDOfRUB8t3RH7bfQXxNfQJHpyVrHe5ARLfrLjuCM3j8ye+gJQURcMhYr5iGQkU4KL1tDh69l2+FOn8AUsAc4gPiAZR+AnxTrAABTFSuRl0WgMmOyCKtslgIxW6NDarYGpYsEeFVGW7xwUTG3RV32Wb4thkkorUQDAyZncZgol/Brl5qtQVGxhRAIupJOcUj+VYfolGy/jEzq6zjKP/267BJaT9mJ1pN3QmsViVp85h9+gHYzj+H3Iw8AACrGEAW3OfNATLE8wlY0fVVOPK4HDMUR5RiX6/G11T5b0pqb2r4nPwEA+FB+xHSMxOwf77xIRZjqSzwO+Mil8nn3qCYbZbFdLoEo2p4gdXNGZ7jyNI2Tb0AH2XWr7a+lbsN0xXKE5k5s+jqTd91B99ilFm5qUmDS7ltW275VbHXp2CBkohSSjebuUus/hOJRrO8Fyrv2PNnq/rzC3jH+/epvR9HylyOISHIc90Norj5PRusZR23ui0q2rbwfvm3fFa0iE4f3ZKG5wZalRbZGh/NPEj0+XsqTY40mHRRbBMGgSnoBZ7R8M36Qr3e5/CszjvjkKp8t1p+Tpt+QO+TN2F54Yt/Etgn7CLcDBuN2wGBkZHsWNERI8pTz+YfdV8qlNEwLj89Ai18OY8lxS/PE0nGGlbsqbEHxhzVw+I79wcxPO26g3cxjSM+RzmCmeMJllGVcN1VnYEh72JK9Z9x2SjUSG5S/CCAdPzjqubM10pvAcwfiwuRxP3moze21s60Vd18mIikTHRI2iy2GFRFOgjHaQ6cjuBkwBBcChkOlScGsA3fxyowjDienpUyAk9RzKZmmFc8ijLiKrLsURjZ6/3UaA1acR4idNI/x6QbrDrEzz2y7ZD9A66kHtmWbtMt6oimPd2SnMEuxFENleznLxjfRKc7fFb2DPlRS0d2J5bfqM9kukSQRHqqkF2Q0mRgh34HP5bvBZthWIGowUVbbpKQYFXSy1Vr8dewhRm266lJ5JjtZUHncZc3ZcOPfpZEE3Nph/F2JdWFlT0KrKdP23EZ8uhozPPRHM0/vQsDgr2MP+RJNcFKzNLhy+y52Kn9EP9kxAJamjvn5J+wZIpKysP2ydKLYF8p64byQGXWY59il+lEgaTynhDYGB5Xf4QPZEeeF/YjGyf7li+iMgmDRVgSZqMFE4trzZOO2YyeO469jjxCTmoPFoY/FE44DrJNR1D9mCwjLlHOEFscmB25FY8CK8y5NhEzaaVJcX2LDAQAnH8SjPJPg8DhRP986w0RICNLA5ga4c0WeyKQM5I2C7S1YdZK5FoBOLCoytsdWTxJ809VzvMJ+bCZfhyrpBRm9mVmm3tBh5V9L8bcgDP7IrAP3oJagGbsrTPzP9HE/rBoDbBno1vE91Pv4Fslj+BxwBECNWQfuOZzZFpvHcaZVkpRsDcbKN6ER+wS/Kf4GAEyRrxJJMs/Q6Ny71l1ll1Ce8dx8UCg+SFqC2mwkZiiWiy2KV6mR7npaq/z4mnuJrzJPsdBpmbOPEjDvkCFy9inVCBxRfYeWSbuFFs2rOHveyqTe8JIk9hm29hJO3I/DtN13HJZLSM+RXBYBZ2Q8PgdMLYk374zF1YBhWG/D+sleFoHv5RtxTvUlPpHtt1u/uXWVVDA/m7WK6aLJQXEPqqRTLAi95Th9UGPmIRrmzpT6Or8qloktgmDYS/clJZOl/BRl3A/g8maO9MzK+OBzucF8a88N91Z3vcGjuHR8s/EK4tIsTTYLMZYrLh1lznPYS8gQAlsuSTd1mjsoHZjSBvtJxgS+yPPR1Gh9b5LTF32yG7HWaU/NiU3Lxgd/hxldoIJzTb1rJB63Wd5X07E5U9JbPXE+meEtEjNsBx4khODQ7RiEO1h9rerAmkpMEraOAgC0zjkNwNJf3hmfy3ejDJOMSYo1gsgmFOb9RTXW/fsi4aGjX0OV9ALMw7g0q221Iv91eMx/qolCieN1pBT11lPsfertpvvywYGdr8AQPTqw11ACKc4LO6EiY/CHi0iSXmTfLnOO47+rUei31LfS6zjDR41R3KIQI72YFFzJ1ugMQZ486NpCl/2AhPQcpGX5/rfAH+g274TTMsFMhlU+bimTpdZZRRCXOZFf7QOTRkfuxGLomot4Z9FZq30d2SuowUSiPus47o9Yo5FMtXVwz1HyLQiGaUzsylBJIcEAcfbwq5FfARrH0hRsBZiHselonm+bo3Qfej2hszoSo0ju6nNZWPp+vcWetlleiqpUGSTiIz9wq2iTfhCDlbORSIIA9Ddu9/SaD5AdQI62Fi+yCY3VytADx/czGOkoxSQDaCCYTBT3eId1riBJkS5zjiMyOQvLQtxPLzRWsQmPIr+UvLm7Xk/Asq73JL1ltvt/qZOUafsemt+dv5VzcU5fFyPUX4GQql6RiwvtZx1DXFoOws2yjY2Ub3N4TLZWWhkibD1/F57ad/VZpZwltEi88438XzTVRgD4HwDg1MN4vO/kmEEOTN6liqOghWyG40B+7vZDFO5QnYuSi+HFy2/Cak67mce8JQzFDRjo8TJ732LbAuVftguz0nvl96rG4Wv5DrHF4EyTLMOKQnE7EW0BAIteBXJcS6kzRbEaCQ7eR7GoxUTgJ/laFIdlXvQesvOmH+vecVjHJdXnOKwai5A06aTZK+hDjznKxWKL4BGRuamSEtI9Ww1nNRmSVtJnHbiL5tMO2U0JZYsRftCfmpP/3WzF3sW5gK9QIeO2KPK4g60x1SD5ASdHid8bdWSv4m/FHNx9+AjtZh5Dho1MHHkWDY78sx0htQXRxsRk9h5/03ngzcquBLeVCIQA7djruBswyG6ZgUtsp6MDgH03X6Dx5IM4+8hxMEBvILHHRlCkN2KneI0AtWkmNM+3y9FgJdKNQYJYrDj1BENWX+BsLpYjsZlsR2xSTsWfyj9cKstI7asIoARj7XbhTxDzQAAxN7Dqz8kuH1s/VXqrm4dUYzFEvg8zFUs8rkPOGN7PconnnZT0HlLMbUtxHU97NkZ6XaIFfx17hKRMDf44apntQeJie4XGib63kukrrFLOxOuyS/hZsRqRyVnYdzPaYn9hTRLOqb7Cr/KlPuef7cokSD3Gd1P0pmRq8PHyc/mypxCsVf7q8DhH4//gyFCk5Wjx2RrPA3TywdZLEXh7oW9aCnkCVdILMKWSr1ptc5YaROpM2X0bh+/EYsfVSOeFHfDJCjs+3RJEipFEKfaJSnR9UqJm6jkBJeFGY9aUD/419rJnlRDp+F4OkB8SWwSf4WYk97gLfFEEmajJeJ7Kj2FgTMEkJVIyNWg9o2Cl0QMMq7NFkYE6zDPjtsqM7UCovj1a8Q1KM8kADLEfzGnxYgNKM8l4Xx7qdp2vsZcwSr7VKt+1lPhZsVZsETxmwdEHOPkgHqM3Ow/e6irO0ukJwcPYdEzbfRvx6SZrlDFbruH2i1QHR/kX1Ce9AGO+qMrkziw6CsiyRTlJYIn4I9OGaZY7NHy6CsArvMhCEY4cjR6XniaieZXiYotS4CjFmD6URdyIzJ+eo0VQ7t9ZGmlYrMSn56CJ2aSDL+ONIH49/ziF8F97CN6OK5xWjUDR3CjgnhB6LxYDZbecF/Qyk3bdwosUBzmqJazgcOEfxQy0zXc/arG2J92l7KbAhZRs6Vn1LAp9hI9eqWLawKGbycv9HhrbEUBVLmJ5hE6CFoV8kmwjtgNfp+yommN3Y1EuJAB1yxblpa0eC04iR6vH/dh0rBnckpc6fQ26kk4xkGuSq3Rg8tkin9+zlOHaH41XbOBFDqkh5RRsnvLOorM+EQ3XM/xvMLHylCkNk7u5yYUiy0a0X4pvwEVBB4AtF22n3lNr9QiPFy9lXX5rBQuvmdRsZN4P9a5AXqADe81KQfdFniVkWq08u4NWIv2iOUK4OwZmi+PTbe/eSO+q84cr58Zl0ut2VCoGrbqAN+af9LiO/OTkjuuuRyTzVqevQZX0Aoz5C5nnk/66zEOzVQpFBPJWDdUSzZ+l0xPkX3JwZ57EHyf8T943DcwYUOXYF6lox/zYF1HBdkTx95eeRcfZoTh6V7xcz0HIRAMmHJ3YK4bO4OERZJ74E1/MWITt65e6Xd9yswkyqaHTE6xW/ia2GJy5/CwJ7WcdQ/ffPVdW8geC9V/E+cA5sjbS6YlFTnF/wZVTWqD40+6+lxjHfceDWO/EFiqPeHRlxfWL9ybU3J1CKUBk5GhRSGwhzGjCPHReyAH12ad4jb0EoBs/AvHI1efJ6LfkLAYHx6KV2fZx7lhp+KHpg8osBUyRHN+JjusJManZKFM0wHlBH2ON4lcA9qME+xLbVLYDOV5+lgwA2Hj+OTrXLeNFiUzcDBhi/HtjcmHgn0koBGC7yrP6pu6+jU9frcaPcDyy98YLjNx4FfcVYkvCnZ1XowAATzhYYRRipJPVoxV7Fz/LV+NX7QcW2/W8zItLSxlmCMH2ae9DXbI+PhRbGJ5hs5OclmnE2lfEX5NdgZ35TK9yJmCE2CJ4FbqSTgFg8kmn+DeHbom3KmSLHaqJnOtYppwDaKSXeeD7rdeRo9UjIslz2Som+04AQ09ZcOQBhq65mGt1ICy3olLw7qIzOP/Efo5fPllxWrorl1yozkY7L0ThlRpp3PsCRzFnxGT4usuStYaiGFLGDZfvtNgWnSqdiQS+CGYy8J5+Pz6MnSu2KG6Tlq3Bh8vCsO6c7aj0jJabW1Ae6RzjPVHcgyrplFz8Q0l/iXmMd2XH8TQuDX9u3oMXyfx0TBSJo5XCgIH/d6i8Lor3OsXGXBVnQDD30H0cuh2DY3eFN6EesPw8Lj5NQt8lZy226/3KvNH2c/jXMW5WKwWFvTdeWPwW68koobeMphyb5iCInIt0Z6WT8tAcFdRo4MSc1iZSfG8JQSvmDkohmbcqvTWp6IjajO3YDVwQK/CfP7oT/H3yCU4/TMCP/960uZ/wMgluvw5GQKs/Kb7m3oIq6RQAJp90X2e36ifMVizBR5f74avb/XFk8bdiiyQpFIT7QI/iHt6Itu2rmPc6OV4I/peQoba5PfTCVWEalEC3mpCeg4n/3cSsAzRVoysMXyeNuCwjs/+y+J2h5v5+LFQugPqp9BT1zcop2KP60e3j6qScEkAabtRMO4dNqqkIU33JW535JxXF5HpEMo7d82xCVZPPWkIC3aPfkO4kIwAf13qX8kcURToPNVFchSrpBRh/UR3SsjV4mmDp/1WTNaxAfpS9XgyRJEv16H1ii+DHkHy/6BDEFtlaU7A4qaRQik8RL4o33wTqLc/lg7/DsOasbRNIin3qMs8wXPYfZDrbEztCk38lXc+TO4hy5eu81MMnjdnHHh0XrJFeAMMaqecAADKGIMnOpKAv89afpzFo5QWkZrvvoGxlsSSN7r9AwPCwHN2QDcefij94kMY1OrDXsEc5DnUQ7rU2pQYNHFcAIIRApyeQy/xzTqbV9CPIVOtweHR71BRbGInDR0ctSSQWYE0roH+lRqeHQqR3maRGcZ7cuxmZCuTGUpOOlYEw90uMt61Ozg2L3/dj6MqHO+T5be9X/QAA+C8lAEAbr8uh1uotllGCGf+ZSOKT54mZqFRcSuFQTTSdegilkIwACQWC85S8vnqmfAlqsFG4oK7jtgZhPfzw0/GIyNhaIOBr0aC97IbN7UJ8yfOyPcwnvwH4TIAWpI9/am0UCz5efh6tph+xygUcn276cPhy4LjM3PM69SBeZEmkD5GYMutfmK5th1mhyM5Row17E0EMf0Ht5hy8h9o/7cOtqBTnhQWAmVuP3woJAUAgEzkVG30rKHn8q/zZ4ndltTT8V5uyNKaALTZeeCa2CBbk70suBAzHSdUoUWThk8IwuMr1lR9Hc/YB2rK2fZ/dwW8XDSSINy51V/YCXmI8s4pxRBEU3AlKqqQXAE49jEdChhpnH1sqscfN8hX7g2luTJrvz1YLj/jqyH9XI/HOojO4F81fXk1CxI8MbD7pFZmchW5p27FeOR2/Kpbx1sYfRx+CEODXfXd5q9PbBML8PSVYoZiFS6rPIdOIt+IbrBEmFZw33jZCCL5afxmTd93yQmv+TxP2kdgi2EQu8kSWp6Rma9B3yVn8/B93pc4Wfx2T1v3y/ZGUbTrIrlv8bsiGc67TX68VAK/nWmeJFj/I1+NV1vZKt9AUTbmLpcp52K36SZT2/RWqpFN8HhXUKI0kLAqV1sdakkhgJf2bjVdx6WkSus0/wVudm/6eiRO7/+GtPk/I7zL6roy/8/MnDqnGWvzuLLuKECYDpWOEv15lkIif5atRnbGMmt8w6ZAg7QVnR2LbpQgsOPJAkPoB4G50GnZff4GVp8PtlCAohSQopZDkluIxJZlUsUXwiGUnHuP8k0SsLoBxERTgJ11VF/YSL/WIzcMnlhH8pRKTRAiuPE/2anvN4v7D5/Ld+Ec5w6vt5lE4PVyUdv0dqqRTAAC7r73A4zjf9F08ohqD8wFfohrzwnnhgg4j/ivfgb2GvxTzUQz8DTqHZCxF+4v8RdP1BDafXzNfAzR/Q0xl4y/lAgySH8AO5USvtNc8diu+3XINcw/dx81IYVwUtDrHA90f5etwIeBL3A8YKEj7gCGC/Mbzz5DhxRy6aq0em7xg6swAOPkgDmcfJTgtS7EmPUdYC4AqTLSg9XOhPXuNl3qWK+fwUo/YBP7T0+K3GCq6t1a4nfXLfBOitp2udfOF58jR6ry+ss8n4i8tiQcNHFeAuBWZio61S4NlDY88IaZHf8mJx5h6IgnhAWJJ5zkVGYMZfz9ZqKhy+AIZOTrRA+3kBQPJgg8+bA7oKrNc7ajO8jN4jEvLQakiKl7qKug0ZgzWNkWZTIvtgg0CzAZGKVnirGQPle8VvI2Plp/HnRepOP8kEXP7NRG8PQBYcvwR/j0cin4CvBopWRoE5/6t1enx8XJD2rL707pDKRd/opNiYrx8PYBPxRbDJs1Z4SxofJEaLF1I4Up0iiE2QNlg++MnlmgxXLYDp/QNMXYbEJGUibcLCeNuQhEW+rUpMBDMOXQf8w7bDoLjqdnR9WfSCdb2uXwXr/WpvZC32S6EADr+V6UuPYxCu5nHkJwpfmqY0kgSWwSfoMUvh8UWQUC8O7tvvzVh5JDrTe+ZDy9kOOXOC4N1xL6b3lvVPPkwHkNlewSpO80svZTGbEVMq/fSN0GvQwO24JmHe4L0LJZMw+rh8p0iyiFRtGYxSUToFNUCZl4xR5XK//ur1urxyowjeGXGEeRo7VuotE3cgbGKzdipmgAAOP4gHkGRp3iXxxxGQFdKP/50OoUq6QWAuYqFOKQcCyU0+OOo/QixnpiLx5xYwUU0SVP7p31YfSZcnMa3DARm1wKy+TWRfU9+HADwJL7gRsukSIc7IkWpz49QvpEskZoC4V8Idd+It5Rxe5z+Xdz2fYjOsqtii2CBP/tZ88Ke0aI1Pf7fG6jz036vtKXM5N9qwNydKDXL/relbA7/EdYp4kCV9ALA27JTqMVGokM+/6gQxuSDrmQ0WKiY73bdRXJiuIrHiT8EDMgEAD/vFCli8u3/gKxEw/88IhMoHzTFkvCA/oLWf80LQWm0Oj3Wz/8O29cvAWDwy+WbXrKzvNfpCHt52YUaWJvXK4GYjRQXkaU+N/6tF2G1TxO21OttCoU/ZI6h8MgV8wCv3n021p+TVro+ocjvD5+Zo7X77fMFfFdy7lAlvQDzpXyH8e+TqlGoxz63X1hCbLn4HHMP3gMAzDkkjRy2gsHzAJHk+19MfPmj4W0exFimq0vNFn6F9sqZQ+ifvBRv3zdEY8/zy+WT4mYThYUyIniv3xmEEPy2/y6eJQhjWWJukiiUrkf0WmxUTsVsxWJhGnABBbRoxtwH60XTY2EDIZn6pky18PcwP0mZ/hOJP++a1WAixRVEQPbfjMbU3beh0xP6XaOIgr2J5mbMfTyITRP8uaTPvTBQJd0PIYRg++UI3I5KRdJd+2mNCoN7XvFYEXKTR/z7M2qf/BpXnyV6vW2xIYRg6yXPlZnyTCIqMrE8SuQ5tFN3jdmKxejz12kAQB3mGeYqFqIyI7wFC5Nu8i++GZmCIGQ6KM2d6veXC1q/LU4/TMCi0EdQa4XRvp56wa0kMP46XmHviJryb65iIbarJmEks9FrbV4IFzKmhfemMa89T8aua4bIzPHpOfgn7Cl0Ypvb88idR+EID+iPI6rvxBZFMD7/5xKWn3qCndf8dyJCEPw5UIdAVGTiUMnG99/chdH8qm5XTcIo+VY8FvhbREdzwkCju/shoffiMHqzwbT9umqIg7eHewf5JN77adtGKbYBAK4+OQFA4bV2CSG4HpGCOmWLIEAh81arFr/23YzGmC3X8C6HwOinVCOxNqIj6pYtgkJK8boAf/o8a3V6wTrTd2UnMCb7cwDADuVEBDLq3CjlgwVq0cCph/F4Offvnn+cQnjAEEHb8w6WnWFCRk7uVoF80hnhn3KGiK/Q9ZKFAQAGM7uRkqlBcCHv9MtCDQwZbbbx75JMKpoz93InFbvx3lbv3Am4isUCMeG/m7gZmYrOfpTM4edU76Q7lAKxqTkoS7UVSZGWrcGzxEw0KB/svLDU0WlwSvUNACBO8zYAQ0dxPyYNaTk6M43O8iH8Rv4vjuT8T2Dh6IMvBHQl3Q+5/cKUh9g81VD+lUsV49tBjVidd1fx/wl7it5/ncaglRe82i4hBNkag8nljcgUqMA9MvvGXXvxv7/OcK7HXZ4nmp7HSgz/Ps7x6d637ACAqbtve6WdQMZw772RyuZRrP8EF9Tpic1sDXkRaQVT0qHH6+xFfCjzzwj92Rrr/LutZvj+uTJ6k7l5caRim2oytqsmAWbKOx88jDVNcj+Jz8DNSMO325+sjAojS2wRvIaoGWG8AP8uJtb15Wh10Ov5a+e1ucfRY8EpnH4onUxEnsJoTOMnNscUdPVZQqZFj+FPCyCA4Xz0euIwor2/QpX0AgQDQoMXceCfMEPQkbOPE7za7vfbrqPuhP14EJ2Kt++Nwb2ATzjXGYAc3Mvn5+wNrpoFPKvJRvFev/mg15usPkvTJXFFL+CKcNd5x9F82iGb+4ogEz1zV4L55nX2Ev5WzsUvihUITLGfWcMXeRyXjroT9uPbzZYBSbM1/qWoBDEmJZNo+FXSL4SbXLZG57uO/oO/qQz2mXPoPp4mCOsWJCa/r1jDb4X5Ho2MHC0aTjpotC7hg5hUw8T9fi+mhxSDKmYm8HqtdUwLX574YwC8vzQM9Sd6JzK/lKBKegGDfxcg8V58YQMHgZcVa84Qgs0XDT7ouw/uR62kk7xU6600MbFp2ej1xymvRVUNTDFLPaLTAmGLgRjhI/SXg3cnbryBt6MyC7kK9SguA2nZWptntEQx12ihwDdFzBQ8RbYIz8i/nwtWdec5hnSO26+Y/HC9PRCsIIA1Tn4UEG71hrrk+h/eCOopFiOfjxC0/vPhiVBr9bgRyV9qzubMPYyWbwarV6McEtCN9Y4lpBDvtr1vsjIrFt1kF42/E9OtrVd8vat5O+JX7Jf7b1wLe1AlvQDxEvsEQr2qR+/GYPy/N/zKHOVewCcYLNsnqgwvUkwrN7EJ/AXKK8GkgvFCOrZZ++/hRmQKxv97A4DwHwp5djzC4zMwZss1RB3+E9j/PbCoDS91a3V6TNhxE/tvWpuar1TO5KUNqeKv5toMgDYy77gqCJbT21G11zYI0qYUUEKDV2XCTMDFpJr6XXOXMb5N0V6kZGGMfBM2KydDCf+J6G6J767gOeLysyT8tPEMnuWunI+Wb8aHssMoBu9bqPkqqVn5JkcF6CK3qSZjhHwHWsVvw9mAr7FEOY//RmzC78k8jkvHzztumDaY9UVFku+4UIMw7+GtqBSM2XINSZnCLmq9Lw9FLbbgBWakgeMKEN/I/0U6CQTQk8daDR3R4FWGWTxCgBlvN+SxfmctC8tExVoAfwIAWKJDZ/YyLutreaFlA6dC92G4rCzW6zojKi4RUPJT7xLl/Ny/+JuxtkWmJt+kjcBLRwyAD5edQ2RyFlorDuEdHuP7bb0UgbVhT7E27CnCf+2BiKRMHLsbi/deroS6PpK+0B1eYUwK0A9y4ZU9Ht0Q7WK+0rv5wnM8TcxAL+GbBQA8jEuHd3rGgsH/ZKcEq3v6nlvYZGM739ZbekLwlfw/AMCb7Dns0L9qaMdPFVuhWHM2HO82r+jVQKgblkzHLMVSTL3xIabLo9BffsxrbfsL3rTWKpnt2znSP/g7DDmp8UBu0GB3r5xQV7rHAkM/nMZG4DWexqcUE1RJ90McTfb/qFgPvX4hWJbfQUBRpKMKE4sN57kr6c8TM6HTE1QtWdhxQS/b7/RR78Iw5XI805cC8L5X2nxPdhyQAWMVm3BOX9crbfKK2T2Kv38OZS/8I3hzkcnCBCrKn26w67wTyFTr8CwxEz8K0qL7aHR6XI9IRqOKIVDIuBlK9WRMSpC52bZQeMsFI4+x264DAL7jkCnBHTZdeI7/CRFgV6RAIwpoMUq+FSf0jYzb5IweQ2W7AfQQvP0PZEcFqzs9R5cXODkf/F5rxqw+RW4g15ICT5zmJzI5C2qtHtWcfW89xQs2/RP/u4W70WmY/j/vTYPNUiwFAExQrPNam34HAZCTBlxeA9TrBWL7peMFb39f+J5oi0nNQYjFFlP9rkwexqcL7b5JJxaFgJq7F0DWhvEf5CpUNRq7VD/hVfaG88IO0OsJ2s08ho6zQ5GR49i3iwCC5242p53GoLRUZoX3g7RFK/auKO1ypSjSARCUXN8VLSN5DjwjIplqg5XA6YfC+xoHwrWAVRN23MQ7i856Ldq8r8BADzms+5NGzCOvyqHzhrmAl/hYdgjD5TuxUTnNYvuPivVeab8J6917JzQMCEbJt+JiwBeoyHgnEjUhBG1/PYpOs0ORmi2Mub3WS8/8ifvifJf9kWdeCn6n0emB/eOAA+OBJe2hM/PAy1T7r28/n2Sqtdhzw3m2lwy1/7iiOsWPgn1QJd0PcfZ8brzAn2muLNevuThjiKrdhb3MqT6N3tRLO0unRQjBzNzZbIo0yXl6HtcDPsNCxe9eapGx8Zfv05B54lK5vHd7DQ/R5r19/VQC+uTuVY5HmOoryPMFAavjRTeFz2W7DINSnjFfSPfmwLYq47/RkvM/J0Z4tlowr46AwTfy7bzW7w7RKfxGrqf4Ltcikr3STnLEPWgf5lrEZCVZBA9NEHzl1/ewsAZgGBy9G4P6Ew/gUZzzrDZ8ruybx+yQJBEXnZfxEaiSXgAprOcvTdVX8v9welpX3upzlzdl573Wlr/nQBWCPln/AvDefSICmv6KqfR/LD+EtWfDvdqmt80DWQEDGdZjn6EkkwoFY6l86Yn3PoEdZdew4fQ9/is2m5XNy7PtDYox/hsg61v5Fpvb+X4jimWZJokCpJBNhAc2nn+GN+afQJRAbkcU/4GFHhk5tifEfH0xVIjvJ5Pv18T/XA+cWY1xvtruKruumdLntmVvYLtyImoxEbzVzxm1OKl4hYAq6QWQQYlzseQwN7N0c9pqzxn//lB2WPDUaGKh99Pz8i/MV9K9c7+80UovWRgmuPFB9kXEmATRe7nVxfsv8V6nWN1SHcb/giXm0V7G3/fRERXSrpraZK97pc38VGGiUZt5zttz9MP2G7gbnYZf9hgiTpdivOtj7w2SMvxjQsUe3gpzYesb3ZM9i1bMHTyO51nR8nJHefqRd1xWbGN9rq/LuFm52mOdcgaasQ8xRmF7YlMM/EkHoUq6H8IwwOvsRdRgbKcr6CE7j6En2wnStpLRIdRLvmHeehH3XOdvBpIiLOaPhNBKehUmGp/LdiKA0BUjPsi/yu0NvK2kH1SN9Wp7/srSE/7ljw5433IFAEAIjqtG46Dqe7A5ybxWnZ2b2cMihZ2fIHbMj7DHCdhxRcB0VCIpOUGpj/Cn8g9sUk3FJys9z2eelq3B8lNPRLXmOHzbMA5+npiJFaeeIIsHn3DzPsJyIsW737GbV8MkHQPiQaz3LMqEhirpfkj5pAv4WzkXR1Tf2S3DMsJ1wimZ/PiWOv9OeOdD8uX6y9R0z0NKFFZ4t0EvRro+ohyDHxQb8b94keIiqDOBtBhx2vYhbkbaX8mTC2hib4tgM4Xl6vNk3lfkIp495LU+R4iRJowQgnWHL2DGXhoYkW/kGbQvcZVnieJOPLy/NAwjN13F3WhhlJFykQcEqdcZAVlRzgu5wKSdtzF19230+eu02VZxJh56zt4L2f7vsHQd9zSm+c3d7VGDR9N2W5SMPoUBK7znauoumX4UJI8q6X5ISNJNUdvnkvuScWfg56U+tyITi6yHp6CS85h0myIMRA+AYLZiMfrIzgjalJwxKHgtWAH8jF2AzGsAzKkNpEjIF0xi3I1ORc8/7OfTzkt75W3OPIrH//46ida/HuFUT1q2xmJi6uI175hpA+Io6ScPbMOHp17DYsV8r7ctBEqdafK3PJPo9faFXDD1H4NTa0TKemjF80T+Fw80Oj00t3bzXq8tmrEPwAgwUXrigWGVN3/aVDEYw27AQPkhfPP0S071FEUG+smOmTZYPISWb1slJpZTW76ORF5PXqBKup/x17GHOPNI+JRQjlCoveOD5q1BwCnVSNTY/S5q6MO91CLFUwgYVGJi8a7shNiiCA6TZRjUp98VJl+0KOa3PHMhPMnhfjEUTQC4cPMuLqq+wFiyyuM6pu6+jYaTDuLKs2TjtjKxJ7kLJ2GCry4CAHSTiRS9l2ftjDWLIl+f5T81akGhG3sBMr1w2SHy49ZigoAMXcPDe6DNMVhl5dJh5jEUTfOORU5tNhJF1eYKpem61vbxmBcN2HAAQHWeVrUXKP7Ed4rNZlsY4yRbScbSosJr37UMMf3u7SONt5MfRFfSFy5ciGrVqiEgIADNmzfHyZOOBxnr1q1D48aNUahQIZQrVw6DBg1CQoK4SqmUmHXgnmgDzzzKRR9zXsgOBARvsWcsZwwlQgDEn5X1NcRQ9JQ28mFz4cCtaHSaHYpbUdL0c7oXLUyUbbH7EaHpJzuGcow4346W0RtQgknDYPl+j+tYfsqQlm/D+WfGba1Z75mBizKF4/vzRhb49xvmPZYo52GQZqPX2pPKSjpnCEHW9GrA9HIgGsOqfFRKtiQidXdkr/Jan7fHIhMVa/HOIktrvp5/nPQ4TWZH2TWL33kK+meyXZitWGKxL7/Szjd5VzIt2TvWPyq3M1/4z4dCVCV906ZNGDlyJH788UdcuXIF7dq1Q/fu3fHs2TOb5U+dOoUBAwbg008/xa1bt7BlyxZcuHABQ4YM8bLkFEdwMqHT67BA+Sd+U/wNmQ0fOfPUD/7zGlKkzLC1l/AkPgP7b/lvXmhb+MNKuqOx9G+KvzFSxLzUQlBI4T2XHH8MCOYcnrUzCWl7xMvxGfimi9Z7ViQSum2c0OkJAvUZAICIR6YJPvF6fn4ubHq2OG5M+Xkrci5CGFOU+puRqdh6iacJEIYBwwDjFdx93T1l0yXvWDso4J6PuZ+8ngBEVtLnzp2LTz/9FEOGDEG9evUwf/58VKpUCYsWLbJZPiwsDFWrVsWIESNQrVo1vPrqqxg2bBguXvSfxPVcGSzbhx8V60WWgkMXT0wDBVZjvUL49YYrfLRCEZDkTDWWHH+E6JRsr7c984AY/uHeeRI/k+2ys8e3B9cU/mC9ODqp4GUrhNB7sUjiKSipp/hRZh8KB0Qzd4+5DaQKExSMWPwtzvmZxzMaKD/ocT1ZGmkEDhsoP4QG+dxYtDq+OhHxVFEGAAM9Dt2W5sIFVdJ5QK1W49KlS+jatavF9q5du+LMGdsBn9q0aYOIiAjs3bsXhBDExMRg69at6NGjh912cnJykJqaavHPn5moWCu2CLzhbEAUKXKEVYptvt18DTP23cUHf4d5/WOfmeP9GXRvnaGYM+be4mlCBq5HJIsthhfg/tR0ZK9gk3JKgQkSxCUlk3QRdzgpxJxDM+Y+JsrXIEDvv99nVoSR84N7t4BFrYG5dXmrkxB7E7ziPJeM3qRc8z8J6F8zbGJac/ykWIcjyjFIz5Bm1qOcwuXEFoE3RFPS4+PjodPpUKZMGYvtZcqUQXS07dmZNm3aYN26dejXrx+USiXKli2LkJAQ/PHHH3bbmTFjBoKDg43/KlWqxOt5UGzAoS90Z4Z6zw2av1yKHLtnUBqexGd43WR6kHw/6ggYcOYl5jGKw3KiT8GzD7z7+M+8cYdZoXjrz9N4HJfuvLAL+ItZqi1WKWehFXsXcxUmyzO9nlpVSJ1MtRY52lxlxA+fz+2qSRgs348+yavEFkUwxFhJn716i6D1M7md5Uj5VqgYkSxWiP0V8JQsDfR6z8cTfKUGlgwMg5Qs8c6pOhuNlow0U2HqlEXFFoE3RA8cx+QbRRFCrLblcfv2bYwYMQITJ07EpUuXsH//fjx58gSff/653frHjRuHlJQU47/nz307YqQvIJRaRqitoc8RSLy7mtJLFoaFygX8VJYWY2Fa2Ih5hN2qn3A5wLK/qcGKPVkkzHtRhBFvlpzPIH0yN/3ZfI28VIAATMqfn1JYxGcSAOdXLUutQ/2JB9BqOrfUe75AOY13x1oM473xgT9P/okZp4MwtlWSBzFpaDz5IAaudD03d0mkoBHzyPibyZRGJHK+ntIFhx8gTSK+9xThkIvVcMmSJSGTyaxWzWNjY61W1/OYMWMG2rZti++++w4A0KhRIxQuXBjt2rXDtGnTUK6ctYmDSqWCSqXi/wQodvFk9VSnJ3h/6VlUClZgrp0yegIEwuTnXIpJ9kxACi8cuh2DKbtvYX6/pmhepZjNMimZGsAH08sTnQbMnNoAABVWIQdKvMreFFkqirsoNam4ovpMbDGs8PfI+UIggw4t2Ptii8GJezGGOCvJuat6knoKeNZx/XFO/dzjBIzZeg2axAggwLttm1/OwbJ9qM08xw/aodzqlNhNsifPhvOGCZ+TD1xTtBnocTHgC4ttTVjvpJXzFs+uHcUahefZQfhAqnZb9hZ6fRHRVtKVSiWaN2+OQ4cOWWw/dOgQ2rRpY/OYzMxMsPmcgWQygwYgtc6mIKMnBN9tuYbNF12fSb/8LAkXwpPw3zXTqqQtZV9u1i3I/XyFTOoMXXMRzxOz8Em+2e3qzAssV8xCE8Z3P4qpaaaghcVhHcDwVlSKN8XxLqG/AZdWi9a8Cmr8vnEXL+aJVV4cQFGxV19t4A+R813l1qZJePpra6gz3H9n5hy8h3mHDIp5d9b1VTRfQU/EGUxmRRomHP84+kDQdi6GeydFk7f44O8wpCfGYKZiqahyTFSsxfvyULRhb/FYq/8oNu/KTogtguCsVM5Ce9kNUWWQ6nfMf55kkc3dR48ejWXLlmHFihW4c+cORo0ahWfPnhnN18eNG4cBAwYYy/fq1Qvbt2/HokWL8PjxY5w+fRojRoxAy5YtUb58ebFOg5KP68+TsOVSBMZuve7yMRqtQfk2f+ltvf4N2HCz/f70KvouORrL+dSlitnoIruCHaqJIklkm8O3Y7DjSqRrhZ18e/73l+3glmJTBokYJd+C0kjyrIKYW0DodGDXCH4Fc4NNyqk4rBqL3ZuXOC/sDNpFiE6DO/NQJfs2bu6Y7dZxCek5+OPoQ/x+5AFSszWSSPmm5sHn/xPZfvRmTwEAHsfzE3vBXfYs+RExqdmYf8RMSec7uxwI3l18lt9KHVCREd6cuSyJx5WAz0VXjvIoDCcZVHLSgQz710Vq3WOmmp+12aY2FgiKM+K8a/6MQidcBh8u8Qf8CdHM3QGgX79+SEhIwJQpU/DixQu89NJL2Lt3L6pUqQIAePHihUXO9E8++QRpaWn4888/8e233yIkJASdO3fGb7/9JtYpUGyQoXZ/hXvbZVvKk/UnZKNymgcSUbxJRUgz2vSQNYZUjS2rFUf5kECXj5MxOtRHuMU2tU4PKPiUjh9WKWeiHvsMXdgrAD5y61hCCG4+DEdDYURzmSaswY/wpZhdAL7iVplEv/NCTTBKdWUDAHJy3BvQaXQEAEEA1FgX9sxpeW/wz9mnGNWjuMfHK1OfYZJiTe6vGXiakClKP0L0eny9/kq+jfy2kZip5rdCCdBZdsV5IYGw1Wc4fd9nVDD8/304EGjDJe2FeOdji7VhT9FFKbYUwuJPVr+fyvcJVvfemy/Q08NjPdFBpIqoSjoADB8+HMOHD7e5b9WqVVbbvv76a3z99dcCS0XhQk5OjtvH3I+xNil2Rk3GxVVRiaLV6ZGeo0VIIf/6Kkltdj4/yZkat5T0uYpFaMneQyopJKBUnmP+za/HGpSZl8wsTlzlv6tR2LjnNjZK5nHkPpgpaNY2/na2cxSL8I7sFGY8XY1iNtxOvM2dF6nIVBuCNRVSuj98kqstTf5NCrv3OS+0KToB2rLSWHH2B8rZSEnm6qRc0uMrKNags/WOlAhTXX7kx0vxDsHIEKzuh7GeWz7k9dH+gOjR3Sn+x1jtEgTAfUXdXQLh2zP1fRaeRpMph/A0IV9H5+MzrVJezfOEluw9AJCEua0t+EpFePB2tN/l3OZr3HlCJ7Z9gR/gZr8WlZKFd2QGs/C3Y37Hd4rNQkjlFowuB/UnHkD9iQeg88AcMzJZOPNQd7D5XvD83SFgsE45g9c6xUbMSb/P5busti1Rznfp2Jg0289dBk/m5Xxh7/rS+QNpEshIdQzuP2NQqqRTBOEPhf3c9bbI64TNO2lnyl4X2WW35ZISNyMNaabyK1kHbomd0osb/qakSx3e0rAQwnMgIvdZorCX20FcXpASvNZ35Xkyr/Xl4U/v3qHbMca/62RKo6/vmGiaKEj34L07dk8ak2CtmDsAgOaMb0fL9zYt2Ltii2CFy3FWbPA41sw6RQKKcBUm2uZ29+eP/KcfLKj8fviex8dWcsNSUupQJZ0iCK/nKtArTz9xqTxDCN6ThaIuY/I9dNbN+mt09wn/HPVaW8PWXkRMKsfVHcbhT8lB6AfcJg0zzuJ/stOiytBNdtH4d2qWBl/8cwmHzZQ19+H+NKqJDKmQpqtDfvL8+cUmW+OffXM1XTi3CsyXBHkIQucpldg4yKHFNtVkwdrwpwmjPPrIpBcwdOSmq2KLwBtTFPxkFanDRjgvRJE0XdmLzgvZwZ/GeFRJ9weenAAWNDP8LzEm77rtUrk26lOYpViKPaofXa7bX/1N1ymne62tA7di8OO/1G/QCv98tBzSMu2w2CJYoNYR7LsZbQz4JxZhbFPs1bUSVQZfIyLJ2jXEH5U2d2HNlgSJXly/SQXytc+DTXGO1j8nZ6QAl7vD2Hv1GLs/fI7niZn4/fADJGeq0ZwVNrUgRXiq2bGqcAkfdxk1hyrp/sDqXkDiI8P/PkoNra0VIMuPRv6omMX8JKUGk+88a7HeDYgXxbOfJGt3ROBDODmF1iKbhRcE+FDq+FjRLVq9BUoU9R/zOW/wzh/HONchRZWB6zPZItM0ka7XS0uh5WNc60djY7/C7m0hLpVyyNs5kzw6zh3M54+eJ9qODdPrz1OYd/g+vt/meupfbzNQdoC+JC7ykdzzRQPiR+nbqJJOoYiMmEFRurEXQADEcjV5L2BsUP4itghG/HWFMpjhHjlWrdFwriMh5CXUKFmYcz3mfCnfafrh5UFbFlGie46wAb1yNP4TXdce8RnuB0ctozWZ4eo0Ugu65J/9CACotXokZRiud0J6DjQ6aQVMc4VyDIdo/GZjjGyNDimZ1v2ip8OQy6QWLupre3i0+yRm2H5vknPP6dwTgbMWcGCyYjUqJZwUWwyfgIXn72hwIQnmx/UQqqRTRCVvdbyCruD6EOV9HInW+4O2hYr5uPMiFS2nH8HasKe811+d9e0geAUBqeZtbc4+QCCywUV5CFLHc5ZDE1ITaWxRzvXYJdK7QdHGaz7FHVJF0Db8deLInC5zjuNRnHvWXOaK0MIjrrmCCYW/uovZovOcUDSdegjnHieg+bTDePP3gqsotZh2GI2nHDQo6maPAAGDu9GpHtTI4F31JITqGvMmIxd0Omn3PUUznjkvROFESIDo2cV5gyrpFNFISM9Bu5nHMPfgPQTrU2yUkHZny5WiyEAtxjA58SBsD5hppXBj26+iyTPVxfgB+XE01KvIcFeSKI7hMth+npiJNr8exewD9/AiRXrWFHcCBmO6fLlo7U8pPQ+dW7dEvKI8vtcMFaQNnca7110PBrXLBAnahs30mO5OBhHprcbnf9P2XHdvEtL8XV1/RhpB/kxwV9rNb3Er9g7n+jzm/kHg7h7jz4ikLADAuNz4Kw845GD2ddJyDO/V9chkq2/H9kvuLZZs0bY3/v2DZgh34ezgTteRd35CMEw9SrC6KZYUhecpb/1Jc6BKOkUwPpYddLh/6YnHiEjKwoKjD11aefGnFw8ATqlG4JBqLEqn3ULQvq8AAA1veDevbMFZS6HYYvreO3iRko0/jz0UWxS79Jd7L9tBfiYOHwylnEWjisHYpOskSBsPvawwELBY+6mwgfB+VPzDuY5Aja2JW7EhqMM8Qx3Gs9Uwc6VIyXB3xcjjub4Ub3W5y9qwpxix4Qq0+UzIA3g8P1f5dNUFnLobAax/D9jYH8hKBgBUYmK4xxHhGJh3tPpzPNBX4CaDp7g4eGoa969b1a7UvWH8Oxr8pqmUIof0zTnXURCsjJzxy57bqPrDHpx5aH8RJ4jxfPJaT3zPncUeVEmnCMZUxSqH+3VmwR28qSxGkeJebM0+RRnD7H7lhNPc/M14wtO0FX75yfGh2YtmjOeRbOPT3fer9SX4MulVyPj7VG44n1/B8+6AQg8GeoFdHHqy5wStXyyU0OCA6gccUP0AlS1rASeYP43K/NHVOTBZO4BzHZ48EWqtHhN23MTOa1HYc+OFqPFVAODI3Vh8seqsaYPaMAF2UjUKG5S/oLaOw2Qkh8C8l/S1sF3fHp9qxnjevgOeJ2Zi7sF7HPtzBk1fbHHrCE4RuN2AYQC5jfflzMN4LDn+iJPLVgZRuVxWT1UmXvj7pCE1c/9llt+JPddfYPOF55zrl6oLnyfQJ44iCTLVDgYseflkeXrxJmkG8lJPQacIMvEmG+bRYNXbhN6LFVsEweCy0qyRuP8eV/g6uzzlI4EU4VzXuO3ipjzUg0GQSlifPZXNVVT37oYUBydBxGT1EIQst48nZkqsVQo0L8PHBNaK00+Mf6cLaGbsDuYrlUfvxlpEA6+j98zF4NQDfty2npEyvNRjzkj5Vry7+AwWHH2IkRuvunwcIdZz0ZlZ7j3Tw7u/jPPju6BxxWC3jnOXojnRuKMahF/lSy229192DjP23cXRu3nfd/d7/I/U410u+02XWm7Xnx///uK6jgyW2S0IIfhy/WWM3XYd0Vxd7/zoIkvxO0gpgOhtDhgYkOtboJ9REXh4hLe2NPCfoBJc4ZIu7W/lHCxULsBEdiWPEglD7NohxskEP5pk5Q0GevSUhQlW/1fqrwWrGwCikrOgt5F2he+FvTdyfuO5Ru8/j4M+eB9FAvwn+q1YXFANR2BOgptHmYZcndmrvMnSuFIIb3W5w73oNCihQQmkgBDx+9ZqzAuLwf/4f2+i3Uxu6QAvPU3CR8u5WYaYTxxEEn7NwkfKtyMm1bCCHvbY8Dxma3SWq+o2OkIdsbSdYxigOuveynhQ8TIoXTQA64e+gtnvCRc4rmXsZigYHd6Xh9rcH5GUhd7sKVxTDUUrxr1YCDGKii6V65rzG9rX5sOthA5AurIX8CjgYwyUHTA+s+akZnNzlRHaUsybUCWdIgls+ekQQsBsHwJWkwH9P+/y1pZ5S3z4iHliWsO1E5ICr+QGBurNcPPV8wZ95ccxWLbf9QME7uMP6ZoJ24CL5K0Q92Q9V9BfuOA+ckbfwOP6nbHrWhTa/HoUIzddtdpHeLK/LZy78hyHEF7qs8Q7A4qjuiZY1mo/WjQU7l5wxbIvld5Aq6Q2xvg3yxA0jtpo/O3ud+B7xUbnhVzk64/68laXOxBCcFg5BpcCvkDhTGEytHyn+QxHdE1dKntM9S2uBHxu/P2n8g+8LzNZGpmPM/67GulSnVefJ7smqIts07XjtT5z8s6u3cxjeHmadZ5p82d08KoLnNvTBRQDYOgf323umrIrFL8rFyKYycQK5Uy3jpv3gWvf4sRCNdC8SjFPRKPkY6lyHgBDSrpLT5Os9seHc4sfoddTn3SKH9M2+3evtOPO+JkQwpufSQQxzYYSADt0bTyua+7Be2g946jbecbvR6cZ/xY7kEhRpOMt9jQCiPSie/NJacbwMRDbbxIAYok0PvZ5l6IChyj8CcR5erJECJfC7M+jBj/TndeibOzl52a/06wiOvCyiiIemQhAlrKk2GLY5XZUKlr8chjrzvGfCpIvQpgMm9vHbb+OTrNDHbttCcWoW0CRst5vN5fKbBwAoELcKc513dFXNv59QtcQb+VMxRZdR1T6apdH9b3M3sevimU29+X1G96GeGHYHZeWzzedGILqVRu3FwBQDKm54ylT/7j7uvv+5dqg8ha/kxlhzN7d6cVlbsb4qFq8sEvl6pXn5xt25E4slp18zEtd/krC1d2cjpfe9K7nUCVdoqTnaDFk9QVsv+z9/OFSDI7Bp171kJhmfGUyFmH6+h7XteDoQ0SnZmNhqNTS6bjO38q5WKD8C5NlKzyu42J4Ij5efs7rkardoTIjHb90Ld/vWI541z2NFHJapkxR14PzuEuOVmd3H18r6QEKGVYPbslLXfnxZr5qcSeoHA+dxmy5hvh0NX7896YLpd2gu3sra56w4fxzhCdkYreTlGyCDB6D+VrBdF864uCXJ6Qh0Pj315qvEVukAe5MeQO1y3CPBSE2P3Svi5WftICO8D++Ko94/KFYgCYOgogeyfXbfpm5iysBn2OxYr7FftXlvznLsb/UIM512ML8yWJzrLM+TNltSh/rblaBUiVdm3z9qYfn40RzqjLRmLbnjkXgZLF4qqwJtBMmmKGraHTWkyovJ+6xUdJ1CF1JpwjN0uOPcPhOLEZvvub1tsXvOmxz+0Uqb3Xt0r0CAFgjewdZRMm5Pk6r/CIv7bZi7wIAerOer4S8u/gsTj6IR5+/TvMlFu90kV2BDDrR/SYBIIrwvKI5owKgdT+yb6baoOAG21khdAVd/T4O96/UdkPnuqU9rt8Z4Qme51PNj8fuL/X7IKVQFY8O9aYlDSNqX+O47fx+hHxcly3a9kCrYZzrsYcUrHL4wlG/GHovFotCraNo10/nN46FwsyffNuX7XD6h84IVMp4q5+BIf4GYH+cc/phPP448sAY44KPW1xYJcfnHWqgU93S6FCX/+Bx85QL0UsWhm2KiU7LDpUbVtPfkF2wuAafyB2nzHWF8q99ZXP7T5pBuKuvhHA993NXpVtH/+ai8LIOMndkFTJZCtQpy89E0UdyQ3ylQ7djnJQUlhgUR+VxF4EuE0SV49Lx3fht/11EJJmCFrqTaciWu13xwv4Tc4Uq6RIlKVM8n+V0s9lsrjRgnjgvZAfzARAB8L+F/CmAIzRfoW3275A36Yf9emFWyBxhEbDF663zjekMpBLh1x6nVN+A0bkQjV7gm1K+60je69Qkum8mfDfX7eILuWfmpADAyh1/EKtVKA3xJrbdu5GuDA0aZf+NZtmLLTf2XY0HfUNdauMlxtLUUQqTRlLEk1cw9q31wJuzjb8fk/IOSnOHEAA6Df5WzMFnMlfeIQE7lsEHOFZgX7ZPVl7Ab/vv4mS+KOfDIn4wO5r7g2yeZoswMshYfq9XUZKGC6rhmC5fBkIIjtyJMUV/JwRQZ+LDZecw59B9zNh3B9eeJ/MyEWNRBcvfpEMeTlOhMcCnsr0YK9+I5ux9Tm3FkBAAwF/at1CisOUCR7tatief/9G9jjfUv+EC+FmNBoAHMWnOC3HkyQcnkU0UuKuv5LBcVs0ebtdt7hqj1uqx78YLJGV4L1POelkfkSdtDTTS38Gi0Efot8SUOlGld931Ukus3ydVoO9b3uRBlXSKFSN7NOetrj2qH+3uM+8gnH3gZQyxmGXnStFAFca+/zq+61YHh8d2c/v42NRsZKn5kkcao3Txu2vX6ZEzHb1yprl9XDkmEarEuwJI5B4D2tfhv1Iijibs7EOvksvdmhnnF3eVdOflU1HYpo/9y1WdB9ADgN2qnyx+y7MTXROOByoW428C1n3cfQbcf2a0QeWAlkPxTs7P+Ev7Fpbruht2vD7VWCbFBfcMd3h6ch1el13CeMUGp2U9dW3QEwb39E7M2iu/gj45U1yuc7bCcqIpLj3HpumpOS9S7Kfo4uMNL1uhGqJJMTzTl0KhwvwPtN9UH0BJJhX95UfxKC4Dn66+aIr+vm0IML0cajCGgHJ/n3yC3n+dhlqrRxv2JseWza4Ow7+S7uzaEwJMUPyD4fKdKMmYLBI9eRr/07VFo+ylmKV9HyWCLN2YnH0LyoYEedBiPjlzT/b1ecIHrC1drAguf3gT6qGO20p99Scs1vbyuJ35h+/ji3WX0W/pWeeFeUIe5Nr3yltEmaVdK6Fx7DZkjvmz/4NmCKJa/ggEcw8ILRWokk6xok9T7z3gjIMgH/lXmFYp+El/NOTVatgz4lX0blIBAQoZKhV3f9DWcvoRtPzFOnqqR7g5ujmlk250ZiGIsmHOdItUxQ1SXQRp+EGIGWxXarTlllEW7qaRcq9lpUzE6R83r3McETbfry2U6a5FmeYKAdCrke2V5VXarl6RwVtcInUwS/s+SoXkTqa0HYH+6vG4pa/iVl5kV3gR5/r74+lrXz1nHbqpZyJyuOPYJ9fc6BN75Uu5OGnnLfRdwk1J4DoZp2v4Hl7N+R0d1fNQpmiAxb6v1bZNqXnj5lYAwCj5NtRjTFZJwYnXsV45nbdm+IqTISapsK9sn9A1tNo28rVaWPlJC2QxnsUm4Tv2gaswANrULotGlRwrtPoi5TBb1xf9cjwzHc+LZXE/xnuxZZrWE2ChgCd0cH0iy3zi83+fjkf5N8cKIZJoUCXdBzh2z7sBrxgAx+SvCt5OYU0CLqq+wAT5WpfKt5Hddl7IBX7qWR8Vi3FfTUnjybT7+IM4t8rv1HsejV4I+Ap8tTs3TkB+CpVwbGrmPuKs6kbw7YeeD2dxEc48ikfjyQctoqAroEVYALcc5pVjjyKxaD27+xtVDIYEYuS4xIq8lVc7LP6oOdrWLIFz47vw1yjjnc9wqSIBYO2YD6/R2VbSd/XhuoLoOkJMXHWoYwoK9e2woZhacQnvk3txad7LilGhtGUf8pvmfYvfVYt7bilRhknC9WeeT9gxhAB6z7+JvXOmILPmW9BCDj1Yq156l5e+ez1lYdinGofGjCH6e9k0ft8BYqaA3NJ7FsfCuk7H7469vZ50y86sHtfprPvGka/VRqe6paFqO9ytttRa60Ucb85x2OqT4m1kM2HkgSCMHJdILZfr9qab0yZtR4vfaSQQCWWkNY4EgP6yI/hCthORgW5cR7Onu1X1EkKIJSpUSfcBBq284HJOT0/4L18KMoZhMD/wS8Hay6Nl9AaUYNLwqXyf4G2JgV5P8PWGK/jzqI2Iq2amyYTwb5ILALfdHACwjGdfDb6+NUd1TWxuD6n8ktW2tjU974zFWsc42H47ACCxGTel2C5yx6sUA5afR2q2FiM2XDFuKwLuQddKJl1Flsp+hFyFjEXfl/meaLFN/pgI7t7rLDi+hm+8VBbrhrxitcrHBcLKeavLYTsO9k3rY736NUD9PXo14e++MSI73zevUhwbP2uNTnX4TaV39hFXSxTPmKL5GIt0b1lsYx1oMI/05RzWt1b5K9Yrf+EkU2K65xMW10hNVC0ZhEYVg9G6egnI800ojeteFyu17rummePOhPKrnE3cTZh/WonZpNwk7UCLcltk7vs2A0BZxjrXtEtyefA1LBWkQpEAOX5/v4nN/Y1e+wjjNZ/a3KdVupfG7Mwjx6lB6zPhGCnfigC4HzTVU3a13YrvNJ9ZbCtTVAV3wyccvhODj5adQ3SK8JN8V0hNi99rFO/h9QbipW20x3TFcnyv2IgAHX/BYH0dqqT7CN9svCpY3dn5opsXCZBjYKdGgrUnRb5Qf4N0wt/AGwDOPk7ArmtRmH3QFKhl+akn+N/C07gWdsS4jfFQOXaGzqe8zB0MoLr9Arz8KfC6yd/yn09b4drPHprouuK7LcB0/eAujaGfmIzib1n60ud4aAJohcr5AEgFy8A0hRjuAwSG6BFR1sHKcpU2KBIgnCKqhAaLFPPQX3YES45bmgPrfeAdIF5aSXdEtVKW5qv/6drghL4xpzpjUy2fLW+o6NpCzqNHr/ikBW/thSc4zopw6WkiZu6/a0wTyGe6PQKgYQVL94zx3e2bsKpVxZzWmZfpw1PmHrzH6XiWZfDfl22xfmgrq1XMYR1qYHI+pdZddGYTRUpoMEexCD1Zxyb+fFt4EDOf9MewjDOwWDmAewM6DVYpfsNXsn+dy+JB9eVDAnFtYlf0bmLbLfLLzrWwS2GaTElrZErLVqWke3EGCqWFA7B+b5IzDd+xvarxGCnfjhFy5+fqCbbu/KCurTBmhGXaMoZhMLZbXbfq3nczGqcexuPnnTcFtw7In7Xki65NUEhp+iYv1Xo2OSQUcuL6pIvcQXR+f8C/z64A8IzH9EMA8Jl6FBQyFm834yv3qmuIPZSOqdgNDXOW8Vpn/sBy92PSMHX3LbwfNRO1bv/pcb3xLvrNyh34+9sjId17M9L5uUGq2d4RWAzoORdoMwJo9y3QZzEYhkFwoIdpNlxZ0RNo1c+WuXF82Q5okb0Q/XIm4Ka+KkaoPbNicSZyV+Y87gV8gsEyk+XK17IdHrVljq24EmpGBYy8CXy4Daj5Guc2HNFXForusguYrliOc08sg7B5Mwe5pxDGOyvpJbQO3KbyjRI36Tpybu/L9Zc5Ha/QuT+BpA8IcVqGT6UrNi1ff5nvJXxn0VksDH2EZSfzspxwb/uszhAhO7h5X2z7wtIKrou9VIclavHStqM+hiUaJGVyj07NMIxgUafNs0x8KDuMd2Qn8afyD0Hasof5pFwSsZwcKxnCQ0yM2/+ho+waxii2mNq0U7TGPfdzoxMQu24zeWz8zOS6FlS7g/HvYoXdm5AOyFXS85vY/7zzlsXv+mYxBPjEXvo/W6c/pF017B7Rzu02Eh1EdH+emIl9N15wS/ELQ4yOr9VfYaO2I1DnTbDNPrLYP137Iaf6rciXDlavJ9h74wWikq0DT9o6My3j+tjOF77xXKBKukRR6LNxQDkWD1QfoxpjP9Jh+1nHeG33oN7xKsNpXQO8njOT1zbtIYia1P47m5uXfPwyCIfXwRWf24R0NQbL9qOfPBQdZNeN2z+UuReA7gzbDIu1PfG1+iss1L5lt5zMg2j4Y/5a7/Yx5lRmPM/9+QhOJoYYBugyEWjygcdtGJCWg3T5Bm0hDy6Lc6QeeqqnY6e+rYc1OT6v+XLDYHSiwhADIlujQ1XWSeoeO9wuYbJiYGxYJtwPaASEVAJqCaugA0BRM5P9/AMeX4jRRARIyWSLCvoou/uYfKv5d/SVObd36amlCa67t6JB/F7OMnib0gnnbG5/FGcICMVHz9NfMx51s1cipEwlKOX5vln2niVlYcEHs83vzUXf5BWCtnFsTEdOx1diTfFfSphFOXcEH9dtWVGTL3ZSEZO1Q/7n4dWaPMQt0bo+uRWSdN15IQ9QyFh0zJmDEeqvgPq9jduLBPJjrXgjMoWXepwRoLDzPtn4sDAMg6olCrtU7xzFQrAuLKK0m3kMX6y7bAwsx4Vd+jb4QfsZ8MEGm65xUzUGxf0L9Tec28rKtLQw2nopAiPWnUebX49alQ1hDGXN9Zy7hVu53NYjlp+4DlKFKukSpU3sRtRhI6BgdFij+FWwdsZpPsUWXXsAwDV9dSz6sJndsnEkGB9qfsQD4v4qu96GFmv+8RNabdISFhh5A+hkOyVcqSIqzH7PffPOCohDN/YC1oaFOy2ryHxhVJAs2nZxsJDHN6/Vxq/a/tilb4M52vcwSWPbRE7mwUr6yqyRbh9jfh9XKjyfwDk0qj3UNnJe8o0tpVIUhocB3WaAeWU4Ng9rzb0+N8+r7oT9Hjd1vsLHxr/luiwkF7EM9HI3oInFbytlgkf6yE4Z/7ZecfABLd1LK+lobT86dv6rlGQjxZy7lIdjf1JnsMT9SUYxpt/MV/kKZwoTO+bLTjWw8yvD5B0Bi2yobK82szLgizPAZ6H5hGR4yWPujO7p2wWtv1pJ15QgPrnPMR/395qheKSobfydUKwxPlePxJs5060sE4a25x7UMCFd2FzbT4NdcxcJJ+UMQW7NnlOlUungCGvsTZA8jnPsZiI4dmd/XfvevCM7henyZVimmIVgrfMUnOefOC/DhX8+bYXNirdwsM9V7NO7riDbI0Nr+WDfv3MVd1SDMFG+xkoXeFt2EgBQlTEtGOjc+CbOUX6ONdrX0dODlLy+AFXSJUphrWkVwnz2l2826LogsOareL/wMuxvtQbdG9oPLqPz8HG59DQRL006gH/C3DNJ4t3iOKSyw6W1d5u7P/lwOuAbLFHOQ082zGpf/qYKx/MTiOatxuXxZsOymNCzPnSQYZXuDZvl5DzmlXdEIGMaFNRgPZ/xrVm6CHLg3kfcMySykl66HtB6OCBXolLxQjg7rjPa1SqJFZ+87FF17Jl5bpXvxZ7xyAf1uK6RRcCh+BItkBjSEEPVo/Gp+lt8p/kMh4LfsTimmosrDJ5Qm7WvGPFhfis03lpJD2xvf4XEXOGLISG8tLdIMZeXerjgjVh17ii/nlp2fNetLhpVDHGtrjINgPJNPWvICbxZpjTh2bxWQJ4lcYvbYWvctF/fErdJVbAMcCbXfQFwsHLrBvdjuU0qOONRsHsTyhbvoNt9HZPvf9uUYLitrK/Rvu5WebsB99x4Qd6Xh+I12RUMTftL9KnkV2uVxI1J3dC1iR2XQ468Hr8WCkaHwfL96PnHKYt9JZlUyGEZ8NXVVI5bde2RwgRjonYQbvpwSl5HUCVdonhj1juPtZ+2wsbv3sP3PSwj/CYVrmHxm4BB6SIqfN3ZMlKkM0ZsuIpMtQ4/7bBUUhk7f+eRmMHNP/pEN+9Fjbfn19adPYeXGYMiVOzOP7y0JWdZLPywOT591XGHKmO8t2JcGkn4ROb5ymweXvEvEiPKdP8tTouUCw7E2k9boXNd58GvbCE/t8it8n8oPYuLMFDzvcV9ulf3cwDAIf3LOKJvji26jtDCciZcLLPz65HuDVirirBSZx5IyhmbLzzHh8vCkJqtcbsdxpEfqdmuW/qqbtdti4ZsuOcHh59ClZQLvMghBitPP8EnK88jBGloydwx9jl89G8fvVIZFYsFuhc3pkpbXvvWyOQs9OOSU73nPGDgLt7k8YSv5P+J2j5gWMHUQHhLGq4+zRa40JnXLBWExhWD0bFOKaf+6w6bsvrDNpz6GgA5cDO+jb1rwLCYrXnPrapC9M5XyZ/Ee89yYPJbDbhXku9xM9dnbr+wthxtzd62MP/vnLjZpWYiy3XFHx80RfHCSsx8xz+DXXvJzo7iixT7+hhI1BUwaww+RQTAufFdDKsuZ/htqyn7kN8KAWQEmZRYLWQuPewxJARlmGRe2i+U+giLlL/n/voWZWNP8lJv8cLWq819cqZgh2qixbb/dG3wpXwnL206Y5NyCqqxnvuj5+GNaNyM3oXVVb61ytoeRqLnEf6GaQwaVwoBct0ZCWv9PIplq5C/3ZZuWgqw5ZsB7nmfcMYdJX3sNsNFXxz6CGPdbsn+M82Yzddngt8sFyYcPxVyokFP9iwu6WsDq/rDccIw6WA5oW74e/Ku2wCAi6rvUJJJxao0OQB+Vren9WkIQoh7wdU6/wRcDOWlfQD48d8bhiCNnj4qchVQrT1v8giBHDqP4rrkJ3+K1Y65KQBLF1GhTc2SOM65hfyIvS5rCJC640tPY6uYIHnPuESM3/KwJw7DAMf1jTEGzifl3eHpo1twkh3ULufKD8CUhg0w8b9bzgsDGNimKnDQs7bycHUlPA8GBA08DP7XuFIILv30mmDBJsWGrqQXcJpWDrG/MyAYTPWOxp87C/fl9CKURQKQ7obpPseOuXKJQhimHoVoUgyDdeNdOmZiyXnQu5m33B6BmfYDNXHB3L/3f00NqTWu5suDuVXXHherD0fcIGszfCHgQ0EH3FfSPfFhV6Y+c15I5JzOwmC6thfCufm4lSxiGjEwDIOXyltGJe5kL8q0l3ld5nqE8frZK1CzkvdzxxLW/SwFadla54Xy4bDvNlvtiibOU3V5hJNXqm/2Fvyp/AMHVO5PP2zXvYr+6vFWE5hta5Zwuy53eU9mX80qmRtvpEHqKfx3NRJZGn6sm9z5DmsZOaAI5KXdPJ7ynFVGioxSbMOjgI/RkHnivLAbVCxWCOd/7IITYzvxWm8eehvfLkbvvuWNPVpVK+5SOT6i9DvqMtqx/AW9q1fOvdRw9ibxGXgW+8HZdarIeB7fQwopPl2x5LhD3A9WWj03dai/KugAVdIliytBcxylbnCVVYNauly2QqWqHrdTGFkIC/gamF3TDQWIm6JUrWRhHNC3wCs5f+Ei6rl0zMwhvfCnrrfzgi7gDT2vdhnbH5egfkvxx4ctUKqKa+ctNo/0hnWz/P57z/WlHB43RvO5B60Jd2PiignjC8o37y3mYKqaD5Zh0bBiMNYNaYWtn7fG3wNeRv+Wlh9cb31Eg0i6x8dmIgAvVQhGJDEodvlzywpF/kHUw9g0qLXWyhyvJqs2+ET9Hf7VtcV87TvOCwtAG41hQrEoY52mxxmjNcNxRv+SMS3j2XGdsXzgy+jhIMYKXzRizZU42895QlIyXmz9HhUzueUh9wQZy98wL+8R9Kb5rdj0k4fyXmfpIgG8+J/b4sKTBKttzAX3U63Z4gjbBp3qeHEC1kGXt1bJX0BluZvfJz6/ZgwIMnLcn3R1vX6gYjF+J+mcku9b5ex6fSg7gmKM+7EUqpcs5PYxvgY1d5co1dMvWfyuy1iv/jWbeohzO+7kmubiJ1+esf5wCEkCKYISSvcf7+BAhSHIlZM4JEWRjqx8tn5anR5ymXfnvQa/WhU5Wh3mH36A3bpW6Ck7h8v6mnjjJdPg9C/tW14ze/eUwsrcbtxMYamTvQo5UCDcwXEPifuKFGNP2Qn91fBx6TQO0SnZ8CRjrWbAXuB3qRrq8qnkmT67mRrDhGJbPtIHceTDnC0AenKq4wP1T/hUthd/63rilPPinInPNE3I/nc1Et9svIpXa5bEP0Mso+zuuGoKkOeuOaEBR+buDEL1TRGqF26SiWG8Z51SLjgQ5YK9PDAFYO8de0Mmnn+9q369jmjCPEQtNgJAQ6dlfRGdnkCo8I0E3rXWVie/QH4Xa2UGP1Z9hYqV8e6qpZ22bI2HubXDTzUeud8TIDbdFH+J93Ekw6BTndL4oXtdNCjPPWuHK1gPsRy/Ad1kF9FNdtH9hiRgJSA0/n+GPgqbzzxpv+oHkSQxwfeHJi3HmbWA5z3nBX1di99yN3rPkkHOnX+uB3yGY6rRFtuaTjlkYd3gjW+ZSi7DyNcM6V1+0AzFWM1QDFZb5oKfpX1feEE4UqqwYUJFb9YlGSK9O76IHgVDspWqLCsZCJ0BHP8VyEzE5efJ7tcL2LzpC7R9PKtLQMrAM3P3c7nvFWO2OheT6lqAxx81gz1q0x0CCbdIzADwjJTBz9pBiCCOrTj44k6saeV41ZlwAMCph9bmjStOhQsnhB+bCzoijfCsyEvJS6ZEblrEWnnxMDy/xztUEzFLsRSlE84DAIogE++wJzgKaMmmz17htT53aDX9iGht8x0k+Fs5v/7Q5hwtI3wfbkFuv5T/O5+XtktIcpQh9nfa6S/lMhZNK3kyvW+i/s8HEJ5rqSKDDqWQzKk+wGDJ9nmHGmhXyzvfNL/0FhQJqqRLFLVGOPMXz+FpIJf7Bj+MdWyayug8j+7+cg1DhOy/+jdDySCVW2b9rp5mfj+htBwttl+OcL0dnklHIWzWdcJvH3cUTQZP0VfvAgBIhnu+YU0ru+8/q0q0YXKqM02KDV0VhqR07speHjWaduGtLi6oGFOfci7Afs5sR+RF/jZPQeOqfpdAPJ/F/27LNZfKVdeHe9wGIFLIJRdXAwgIXmVv4Gf5aii0HvgEO7xRpn0KGYOSQUos+bi5Valk4nn0+/wBtKTAJl1HXuvLcDrx7EUG7gK6zQB6/8VblUUzwgEACxR/YI5yMW/1AkCr6sLHD7BHfDq3TDKOcDaRzKc7AgDIbFms8KQ0DXnDjXEUH9jpMz6T7+G1GfNW1mpfAwAcfs1Rthr79/SjV6q43X4w0jFOvg51mGdQa/VYcPQBAGCDchouBAxHY+aR23XmkT8vuTfwzNKLYguqpEsUZ494nn9iU+aB8MLkwmWI5ckrK1d7nvuyZBHDCkmPRuVw4ccuaOlisBPBWN3LK82wDNCtgWXwq5qlg9yqI9zM3/DvE4+x8TzPpmU20FU0fPy/Z0bhmr46PlV/69JxnoRhCn6y1+H+y8+SsTDU849ifrzuDyYgxlUfi/yJrn1GuKwYbbkUgZhU5xMnxUmSx20A4iyE1i1nWnm55sCCgxDgH+UMDJIfwBv3fuJZCtMNfaV6CVz48TWrfgQAeqqn47re01y60hu4cU1Llj+Nl6QGp0XLAa2HA4Ehbh2W5kJ6v04y1ybNKMBlUsvh/iIBwnud8rVaX6aoUJkfnCHMe7Vbl2u9YTaBOUE7GFWz10OrtD+pbG7y/4LkG1u6vYxMME2xAsPke3Agn8VsS/YeAKCvLNTNOk3ci3Hf15srQsdPMbbjlVbEhSrpEsXVocO/qp8FlcMcr+SwFgBv+lCZt2W+2ogn/JoGutJ+Hvu+aedWHX1z899GJmfhl7138MP2G7zI5og8P/GWLV5Bb/U0HNFbr+LZonYZ91dn5Tm2FDli9hfj8bPuTowHX2SjzhCR2HIl3f1r1TB7mdvH6FxYEeDbdNQRCYFVeamnWGHTwDf/KV56mogT960zYpTS8ZNNwQjLoHRuxP6uDcravaeEANmwTrknNju/aovH0990+7inpAyvcuTdvlHyrdirHMdr3d5i1oF7YovgH3z3GF1yZuEpcZYxQvg+y1fHbkIb35zRG3KC61sOAwBs05nGSvXLuTa2WK/tzFmOl3jOIGCOTsdPRgl3iEnjzxKxoEMDx0kUZ4NNX/b5IEQPBqwLnybfO0mxP4W22le4GYQkNs1g+idkxFFrDPd6TNc6aFm1OF6uWgwLjjzEu80rOjyqdhn3rATskanWgo84oYVV1l0q49F6vzS5l5smhbGI8eDaU39dX934d/4o/nyh49gxuvP+xlfujhL3FnFqDwAcZeV6Z5Fhwuz8j11AuJotOjF33/dNO1yPSEH72pZ+i4kkCMUZz6Pmu9Q8RxpVDHGrfN+cCWgru4UNus6YpljJuzzfyLfzXqe3eBRn/17z8UVOb9Af/PTa/MFHPnQrCpfAo9zApo0qcvNTLqiYJheE6Txeq2eIVN+6WVOcKnQbbcoVR6hGj5jUbNSykzkHyL8YIzzV2WiPj/Wk3/1X1xb/k532uM2Pl53HjV/NzP59WWERGbqSTvEKtmZynfUdCSneN9Mx4Hm3W0DjL3EntxNXylm8Vr8MQgopMbFXfdR3Go2UnwueoTZNSGxQTkM15gUv9QIAYf1xLtR03V2NyRiJUuicMxvNshcLNv2m9+J8SK23J+B6xQ9w940NnOrRm19L6NGevYaisFSU4tPU6JB9lFM7jmByklEiSIVOdUtDlu+G/mUW+PD7N+p43IYzn3RvrvadJ/UwT/suPnjFU9N928h4zEfNN1IZJqc3GSq2CFa8IxPG0u3gqPYY/XptjOlq/71Ryb3x3PNw9z/ewb0ON2EE7hOKBpqsgl6tWwHlggNRtWRhp/ERhJYrMsn9NJR8MkPTHzf1VfG9ZihQrgkAQFOmscvHe2thoiDo/v44evQLvGm26TJeVkBDrnBfpfI2fOSulyKEEIHdBjx83nkKumMea6cOG4HdKv58fnUysfz4hOHP/k3BMKbo8IyLPuk1ShXGo7jyAICOVQsDbi4OeKNHdOcRZ1WF0WgI9+BZCrnpM/yZbDd+UGxEuL4MgH7G7c+TMvF65i7BptWJwr4dibny3KxKMSQqZPBs4dHxHczW6Ly+bFA0gF/3lEBNMq/18YmrjzZrK/sFn3KYTQI90FdALTbSQWnvUM7DbBfOqF2mCGo7WJEFgGolCwMCp53n+uXWEQayGp14kcUdhO7zPa7f7EPR9tWO+erkLvUVT7PL2MCTex+LYuipng4A+K3/GODyWiiafQzMcW2Sdp5iEcy/X0G6ZA+kcIECsCpGV9IliiMl/XpEMt76yxsZfPkhMjnL4nyI1mBOXaes449XLc19zxut0tbzYzmw7ZJ40d0B/vqsfkvOIkutgxIayOEFs3cPp0T5mtEWdDAgwfk2T7kxqSt6NipvMTlCXHzoFnzQFOWCAzDnvcb460Nh8nFzeRqaVApBk0ruZwvgilxmytD8g2IjAKAqa+lz/veJx7y3+6vGlJqRqELslrP4FhHDFiHwdGL6G/Vwt48Z0aUWqpQohCHtqjsv7A4CK7jewLHpN/d7b95np8DzbAH+gpLPnNj+imDKmKf1mo6r1eBlzhKYT4T+LF+NACJctgFXsIitU6Qs0OE7w/8u0kV2xeJ3/axLfIlW4KC9gw/y4bJzuBmZ6vV2q5dyLz2WOR1ZUzRY8uAgACBQ4diQQwkOpoPNBnh+rIcsV8yCiovMEuLck0RsOvsIV1Sf4WHAAGBVDyDdOoAVf3iopPvAROqjOIGXSbxIkdyVR/PVc1ctLBqUD8bZcV3wTvOKnG8cIQS/7ruLPdct3RIqsZ4/ozu+bGtl6u0NGA8G6XxYWj0npU0/VPbdSvgzQxfm2lZ7uZvbx4x+vTaOf9cJxQvzGwRPhGxHLuOqaI7u971o7uMO81e/kJIac6JUXbElcIpYgeeMz6zZJD4jMRtnvjM6DJIfwEh2M2/1efKpbVHV+5PVnpBW/CWxRRAcqqRLFEeDsLRsbiubMzQfAABGqN3LlVyppOd5jiszsca/H8W49qGXMxxWJViZ8zI800V2BT31R0wbfECBtEVX9gIAICjrOQozhhld5ulp4MhkXurXEhvdjocrUIVV/Jircg7K5YASQdKLhs0V8w+/J24Qnhxjntbl6N1YLD7+CF+uv+x2Pc7o/pJhxaDvy46DFvIFwzjvq6yeTh4e12qlTf25o5Q5JN8vTwfsj+LScDPS87Sa9vioVWVOx6cSPkJGGngYyz3AnlC4bO5uK892LproOzw4gppP8HleSyZUHOWQCJ1/Al75Evj0sGBNcJ3Uuwdu75g5j0p2cb2w4Pq4Zw+gxXPL8X2wdfRgGX954D2799IfvDbPXgRNgOPYAf4AVdIlipCvyHZdO9TKXoMjctdScyU1+wppFdqj6EvdPWpvtHwzFGYm03lRw/USmxE14fnVL4xMHuVwH3vm32nE9VzdS5XzANj49uTwY72ht9ntePYsBCj4mowR7lkM8ce0bGaKpScDba5uCvHpwpkDzu3bBCsHtcCU3t6ZpWdduIAB+iw0Yx8af2t03KNRV23zDq7pq2ON9nWoXHyPuLwlKVka9PxDADctjlYZOvA3oZup9mZGDHfhPqoYJD8AXF7DTQozaxUuk6y+mlbMClUR4I3pQKUWYktily+13/JWV0yw6wHItHqCm5Epwn2dPe07HBxHPFhw4BK93RlFA9y3VmlYQbhsBNdVQ3ipJwHBKFPUTybqHEBtjQogWrDQQI4QG+mibFHsrV+stl3W17QYNDpihHxHvi2GLvdqRApe9bMnUOxZr8olbK8K9VFPwUeyw4ZBlovItNarQjlaHVRyboNam5H+PfXldDFomTP4NlkzRxZUXLC6xcM8BY33n3q1Vjjf30ClDJ3qlHZekCcYF6x+emVapvTiQz0JCAhAb/U0AMAABxNJ5nnRNYVKu6UcqbV649FfyHdBDTmAHpaFCOGoaEt1sldapGZrXPpA5d2JHK0OShlrbfVyjluwRPMJugohgYCbxgf/aLvgI/kR/In38T1WcZIlj1GKbbzUk8csTV98x2uN3OCykv6jZjCiUMp5QQGYe+gBzh2UY5w8BW1zx4quxkBxBc8nesxNycQe9TmmbLD7gWuHdagOhZzh9B289DQJzatYm80XZfhZyPr17YaoW9Zz615fQdpPVwHGUQoDFno0ZB57HNArL0exnIP/5QjN1x4fK/W8CU9LvMpLPbGp2bzU4wo7v2qLNxqUxd8DbAcxeUQqYLJ2oMv1BSMdP0R8abEtLVuLOj/tx4//3uAkq627Twp5ZrYkbMR5fqjTsKXYIvAOYx5V36OldPeOYaA33usL4YmY8N8t99v0YYKIpSbj7qC7VfafHrfdv5UpTRmRqRAmdz1QUtMpBy1+fyP/17KAJgtY+Arw31f8DJg9QNpfI/5wlgIvj9MP45GSqUHDnw/iw2Xn+BfETAxZu1GGP+r3dvnwCdpBaJczD1vkvXgWjB9SSSDe+GK22GJYwHJIicWAQCnnT1XwJO6H+TvKr0+6p1llZFiv7YzdulbQBVflUR5pEKCQYXjHmqhXznMl+OjdGOeFOPB+S/5cMKQMVdIliqNubLR8C3apfsKvimUe1U1ybzuXIEmqklU9PjYPSaaZA9C9W0+PjzW/pPtvCdtJmdOoYggWf9zckM6FB1qyd6223YoymLuvO/eMU922BuO62m9yqtNdzl63PD9HPrkUW5jnSRfeJ32fchyIzjApOXmXcwV9w/ln6P2nb2TA8GSOoyYb5Vb5GFhbc7jqctCgguVA7Tnruq9+htqxWb729m4g7i5wZa3LdeZH6JzF7jJ26zXnhSQMA4JDd2Kg1ulx5lE8vp82jef6zYaddd4Avr0HvLvK5eMXfvgySEhVLBvILaq2UOzVtULDisKZC3sCF9eAIJUcqwfzN9HcpJLr14bAezm33YEBg/HaIfhK843Vyr6DkA5u8f3W67zUI9VxNsU1qJLug3wl/w8A8K7shEfHv928EgBg/Jv1PJaB/1AZ0iG4kOd+cv7SIeptfNQTMvjxAw5kbOSSZ73r9xCy9V2L3/5x1yxZuP8q1p17KkjdFrnRPRn/yZTIIa7f87rsc8jSXM+nPG77DVyL4D9ImRDYmx+KS+PP796Wj2HrGgbrlcrFHQdOM7+9FUJcj20BAB/LDjrc/+AuPwNRLvDp26zV67H5orhpOLmS9w1rxDzCSeVI/KadZbGfcyyZ/IsDRcpapHR0RveG5XDq+85oUimEmxwFCC7P+Lev17ZptuwpKjdW5T+UH8FN1ad4mTWl4/1HoG+aWzi4nO6+HfYWCDZdfO5mTfaQ1iQmxT2oki5RhFT2xnavjysTXsebDct5XMe7zT2PfEyM//tf5+EvZySzMXstxXPz1Ny9Hmv5ARR0JV2kVfqUk4vx4783hanc7Lp7tJLJMPhQPZ5HgWzhG1Mve25EGYNpmvPDNv4U2B1ftrXaVrywEtcndcWRbzs4PNZ8QiZAIUM247qP41TFKuuNelPfUu3RP8a/WU/vF2eXF/56trJF3ff/lBp5V2OnaoLNlIYJGTYmWSlGpDiu4WLuLia9ZWdQmMmxUNIvPU3mrX4ppHPL1nAPAuoY8c+R4jlUSZcoQirpDMuiGMf8sMPa1+BJGv/COF68uxdzcvhJWcYHVewElLNHXoR3IUgiQfxV5gM+6WKJqILG+HdyJr8Da2K2GqYg7q/4ylnG7cGsLuWF80JmtGSsXTakyL4bLzB1922r7Q94TOdlz7WpaIACCjfztJeo/Qo3YaKuADAElQtQJxo312fFWSG7heq81VW6iO9HG67IWCvm5iSkc+tLiJ8POzewPZwXcpEDAZ5l1OETfQVpuRXwOTbmoy4J6PmShF4XfvDv3tKHEVRJ5+G2c/FnByHI1ujQnT3PWQ5bpDT8RJB6XcG4GrTxA9FksMXGz17B92/UFVsMAICW5XO1iZsGnJXrMyvsjLo4XyvzPiQlS+OgpGe151Ek033zXrmMRZ8m7lnyVPy3j0vl8u5pCYaflIHe4Ni9WLFFcJkBratyq4AY7s8P23nyueQ4CTYBw7FR25EXWaQc2yIowDU3rqmKlcIKoskQtn6RWKnthnrZK9CmtWupbe1xt1Q349+PW4k/0U+K8zeJBQCoxHGSj1c8szBw3Oe41wcowfe32RLpL2NQHEGVdIki9Eq6EJzQNXSp3Joz4ag7YT9qsO6tjLlKSiN+8jB6glQ7xHLBgfiiozSsH26pmlht83RsyyW6+83IFNSbuB/jtnOLVu8UT9PLcSSQUZsyQPCuPJibu3tWd8tqnkX0d8aRqW+iOHxHQbeHkGkB3aJWV8P/JWoBABIyOQ4qNVkAgO2XI5FBxF95TmSCMVX7MS91SdHUOQ9Xu8pWNoKGmlOX5eYry6j5ScEkNSqEBKJbk+r4qnNNTvWkK03pzga15VlB9gie+yGO+eD5fMeEGGe7+6nN73pnVhNnWSi+D1XSCyIC2d8u1XkeFZ1PxEzL5QPW16KzNehD3uricq8XHHkAwBAJXEjE8nv7XL4LJ1UjAQCyDL4zDZgmHuS6LI9qqFm6CF/CWNBTFoYJCveihS/S8pPOydWJSnMcPcGlkIyKjMir7EXKAN8/BYafBQDo9ByfZ51JyS/M8Bccjwt8DfyFGPRf1UtBUeMPbTFpTBbzTaXihTD//aYIUnELgnq2iiH1qZ4wCFB6HsTWHC7PZUDhEF5kkCIRZV7nXIf1ZCo/fcAO5URe6hFr4nBh6CN8s/GKKG37E1RJlyiCvlaMMLfd1eAkgkdAp5qy4HAx6xzYvo51fZ7K4Qv3WqSVdAAoxxh8fvm4SuaKrMXEg4f3gJPLjBPKMwkoBNcVwF261ry0mwDP88ra4kLAcJxSjURhIrKJcGAIIDMoDFVLcEvzqNYLHSjJPRhY9j8zNB/gJ80gscSxwt98uP3tfPJIz+Hnue7zclVUzV6P98vt46U+zkjsG8vn+DFb5VnUem9ckSbsI17qESvjkApq/Hc1SgBXu4KFf/aWFIcItdLsanRef0lTZguPIxT7CBWZOCigxf4bnrsqNK9inbPZU/jKkSzoOEREJd3QPkHRDdytXMJJWdMPRmb8Uy/zrsnyswTn5rKt2LuYo1zscp11y/GT1/hAha9xQtcQUYT7My43e24KZfCVjoc77qRQssW6sGd4GJvGkzTcv2cDWle1WG3ao2+Ff3SerbCpdfy/63x9UaRiil80kFvQWqnC1/WtVLwQbkzqio2f8ee77c9jLopjFK0+E6Xdj2WHDH/QR48TVEmXKIyNVenUbJ5mpATSSFz9EEhlsCAEEpt05pVX2Ns4pfoGW5STkZLouQkuY2MFleg9Hdx6fsG9ca+iSTEwCnHTMqWHrUaR7CjO9fRqXMH4t3lcC4/N+UvX8+gwTXY6hsv+w+vsRYSAH2Xvm9dq8VJPmrwYBmjG4Wv11y4fw4DY9GP8QLuNF5n4xtb76w6h9+Lw2twTeIl5zJNE3Bj5Wi0s7N+cl7oqRx9GDSaSl7ry8LfvZaCcjtqdUSRAAVZASyOKAWE80aT1fDdr0kSUdosz/E3EFmS4Oc9QBMNW95yl1qFogAJ6woBlPO8I+Fp9zM9zUsp5IXhjVpf7+cUFVEWp7HARWpYuxRlDSqgm7CPwn9zKs2eCq6LNQA8CFk/iMlDWeXG3eTXnd9wRyL3EVfTPz/FSj8wiVZfpwgcqPLwJAZ6tXn8n34zB8v2etWkHpYyfT2GlYoUAJPCiWHXVhHKuQ8rsVE4QWwQAhkwDLWuYghiO7VYbbPFqwHb366rFRuKI6jsepfM/JT3PbcLvkPBtknDSAVHx9LLIzSap86ewlELudYr/QJV0ieKL5knyMvWAZOflhP6W8VH/0yJNPVLS/d3cnQ8YW0qrhx82LqaupTSRuKYahpW67rgROQj8eCVbovWrLtYsorvZda9RMtCrUjRn7/NeJ9fV4Tx+6F4Xap0e4Ve4y6jTE0naunGd5B0h345kTWFOE818ygMAYE3vacs6VVC2XHmPlHQhUMokrP15gkqYgJHi42f3qQAg97DfD1TK8P0bdaHW6lEySPwMFRT/RYJDAApgW0nn6xMglJnvSJ5MRqWAMsgzn9I6Wv7XmCnC8L+EZSjKZOEb+XZIzURNmjA2/9YHV/K+KDzD12plSCEl5vZtghpuRK63NyFrvr0qE81ZNv7gdq2asw/wn4qfyMUGeHh3WTk+UY/FMPVI6AOFSQ3oKTYnNT2riad6KDaRsK+bLy76eINejct7fOwXHWvYdJOiV9oSAoI7et8fI4gFVdJ9CR0/PulCfUpcXQxleFpBMSe5rZnpJA8fy4pt+np0XBPNVc5tF0Q8zQmtDwjhVxAB0Its/pbCNa91Lhqd2XkoTRG+NeVb8lK/q/jEgNONPmivajxqkKcOy5RhkjkKxB9S00X4kKeQQoZrAS1wSt4apYtIa2UsUClzXshHWKF9Q2wRBIOaOfseAQoB3i36HFB4hCrpEsXWQJTNlNJqijWudk1CDLIza/GT5ziP4kHeNeH1NbikYDNX8Mwq9EwOBbd0UN7A3H9NDDydAMlP6P040w+ZAq9k/4E22QtA5N59V4ToP/gyd88jSu7eysEv6pnc3ikH3GV5tnCSmpbOw7QzyzI4N/41XJ74OuQyaQ2LShTmKRq6BG7bOl0XsUUQDI1M3AChFIqUMP+aSaDr8Vmk9TWiGLFt7m541LkO5YRIwbZL9woqFy+E7bpXnZatzHgeGdwevJ+SREx4d+jaiC2CTTjlJ7ehpHuqoHARwzyevFAfkZNjO3mUD7xLziwAwJ/a3tyF4En3S8rSGv8OVMgQjRKIQkmvr/QJlZuCTzSsexMXRZhMnHwQz6sM23Wv4gfNEOxoMJ/XeqUGX4FQlXIWKrn0Vq1Dmr/NSz1ZrLgTmt9rhuLXYe+KKoOgsNINiEeVJIq3sXSOo9YFnkKVdB+Crwedrw77/+3dd3wUZf4H8M9syab3HkISeiBAIBRpUqVbzgaKiAIqVhQrdrGep4jeiZ4FPDvnWX4WvBMretZDUBS7KIggoFJDyu4+vz8iyW6ym20z88zMft6vly/JZnbmu5nZmef71F2JHZr/XXbGClSVZGBbZr+Q7zvX8ZxKEfjwSfICLV8XseRsPNj3ydj3Y1Epdb+ou0NvlEl6DIf8ZU99yw8atWKWZidH9b7vRAk61T2K29zTYo5BrfuG13dG9wQ7lp8yEA/OGoBUV/ST4x1bfzVub4ys4K5JS7rKM/Dboqg9+n7HPlVjyMnKRuWUc3He4eqtt2xEdqtNrNaK0kGd4SQvpsV+L4nFCs9oFKZbt7XZbrAeGP6skyQZPeEzdnT6U6AY/pwZmZHvKnEtULHDaEWRbWm9m//dp0MmAKC6NEtKLPa635r/bTvwWztbhu+EKYepsp9YlGYbszt3wa51qu7PK1SoWImY0b5R/rwq3Z5rG9yhNwqDEP5/r9E98jG2siCmfc6dMQN/8xwV0Xs0OWsqd8VxOGLfX6wFmxSXHbOGliM5Qd0VBrToiRWLFJdxWzCNJJKWdLu7VsNIrEmrpW3JXJiQkpqYpBtUwC+6Sq19qpWxDFRYU2w+3RRVSvjULtxGo1eRVZer8RftlW20hMGI1m3ercp+tCh6TKwqxFVTekb0Hm1a0tW9jvp0iG4NeFWxrGgNKl2bkaxg8KeXB6pyTCK1ne94WnYIFAG15sSJV0zSDSv4ha3WckGxEga6fITSkqQrUlpltaHJ7KMGlJoUfTfI4+pVWM5JxSRtcv1NWOfthDniqtAbB5HtO1lURkcVooqdVvedqX2LIo5EdSon6XYV9ldhU3lIiUrUHhoQO2M8DzWTbKwl4aKVlexEUYa1urs3ipbns1HKZVY32v6J7BAoQvxmRM9oT1v6gy3QxHEqFSTV2k+gycNkfRl9uwbv3FsnKQrjaxDqJP1q1446HNF1WVUAfCR64IyG82M6/vfb98b0fl8bRDmOargB74sqdXY4f506+4mRVoXQ/NTICu6Vts2qx6Co/CiMdG8KgD116gxLaBEnLRipebIjUNWXhUe0/HDE34CczursWHKvow8uH2e4mfNj5TeDtYEzkTi5ExgCV2Dz1fTH4BCA6FnrjmlxB58BsRSWX/CoN4mQb+t1pF5KWKhaHIB/ku72eFTb72pP79AbaanXn1Td3Q5FrVYZdUsktiiXwGopGEX+ft/Hxtvfqjezdpf8VADAuJ6xjdduZrPjF5Gpzr5iYOXHrNoF7MqS7Ijfs3jV16rGsDW5u6r7O8jIyYgVpJTXoE/dfSivewzoP1O1/cocM3114ywkOKxX3ExQ1CtraIlfWdLbWY7n8ZjzJvxv429M0mNgvbumhSlK7LVST3jGqBVOwMqCcJfm6mX7UbU4AAA+FQZ7auvb2TAyJzdGXpnw2/4G1Y6P7pPxddUF6u3PgI/rHSJddghIhnq9Lx4/bTCuP7IXbjhKpZZ0xLbWuloPSM26c0rK+n4Suc3/jmlJwQAmVBXiFU9N2NtrUYT5IbVag70CRryHWElpUSGunz4cz50dejnTSCSJ/aruLxInnHODtGMTkCDUKxMRhWuY/XNc+sjrssMwNSbpBhWou7vRCkciwNhEWREKnyRm+54DkqJo0v/6VertTFGwJ0u9ZO8V+6Eq7Um9tOK3GJL0g61DsUbziuvSGPfQIj8tETOHlCMtUb1Zp1Ni2JcWS7BZwUrP4OZ/q93KqCgKfhJyu2EnaNS12FpXgQGlFeLI6hJUl2aqutsEt7pL/EWiskh+RWw8yxXqrHhDFCnO1RAbJulm8se1HstFX54T3brNgQRK0mXxncTObrWuNSq28i13HI+5DRdiuXtCTPv58Vf1WmUCV0hFxggPggfckzTb99buJ2u27/DJ/xurya/SIcrhFu3ZA/XutdE4rGe+Njtmf3dtaTSo9ffaRk32S0QtRJwOSn/CPVp2CJZknCyLVOUVgQtSMwaXqXcQAyXp8LZMumRTrHaTVK9Q7LU58Kq3Br+L2JZ2++xn9SZai4VR8oVPvJ1wg1u98aOtFU6Ofg4Htb4Ph/ctUWU/RtHXr6VS/QupWyeVJvyKklZjgNubePRpzwhNjkmxG9+rUHYIqvu7e4rsEDS3TWTJDsGSduYP0WS/8Tr+eqH7tKC/i9e/iRoMlGVRuMK53Lf2PSfwe23qLekVcAk2SVmT4/fvmv+tRsvsQScPUbFSI0pq/klnD6sAYKxJwNT4fNG0pPdU1JkX4UdvPuY1qDlvAHDaiE4AgIl/FKyTEuz4lye6oQrH2N9WJabyvBRV9mMcis+/5D4KDVLXFLNfDTC/hPlpc3fOSXFpsl+ZbnbPkB2C5sbX3yo7BEv6qeJ4bXYcpy3pwSgQTNJj4JAdAIVvd20Dwi0CORxBTm2QFvZoGGmddHdyy/hPO9RbyujqqT2BtZG9xwZ112lXswB/6rByDKrIxq8r3wS2qLjjGHht0Y+3Tk9qem80j4DOtq1RH9fXyIYlANQdA3zGoZ0womsuuhe29HjYJWQnyVZJJZv4DtdRNOjubpReHmprb/w+i2Kkt8fdo3Gi4w3ZYUCr++MeyL7vE4XWuyQD+FV2FNZjnCyLQqqtb0o+w1r2I8jyaF4VS1Fqz4gcC3d6S4u3Tai3LEo067oeY1+t2vHVpigKqkoyYDfQerXPpURfo52R5MTDswepOklbNFIS7Hj9opGq7c9mazpPTp/ztLPDYartPzrWmt3dvyVd/RgiuXfkKntUP75Q82Yf7jEtVpEjhVYtcQZ6Xqvp8na62ZIxBRuOaQXxWlH54KwBQX9n3bOtPekl9aVLl6KiogKJiYmoqanB22+33zWzvr4eV1xxBcrKyuByudC5c2csW7ZMp2gli+TbH2TJJqHiLcRILelen278v6X1UHXfB5AY0fZ/cd6n6vG1YKTC9D5bbF1kD+2Wh17FGSpFE51ThpWjQ5a2E4XZVRyqEo32xiKbnRYVjqkJ1uyo1n6vA+tcI1ZOJKziydMPQYesJNlhAOC4W4pvWSkJQX9XbvtFx0isRWqWtWLFCpx//vm44oorsHbtWowYMQKTJk3Cpk2bgr7n+OOPx2uvvYYHH3wQX331FZ544gn06KFuUmYJQSZ1UzM5C1ywlVSw8ek5oHZ525FprQmzAODXlK5Sj98o1E04bTGsIx6rgeVZOGOk9pOEqVnBFg2PV70eKtE6dfmH6u3M50ahSQVEhPtUe5hMY2YnVfcXby53z5F0ZCZ74TqkUw7euXSM7DAAAGU735Qdgmms8tbIDoFUFuxpl60YY5Jhs5KapC9evBhz5szB3LlzUVlZiSVLlqC0tBT33HNPwO3//e9/46233sLKlSsxbtw4lJeXY9CgQRg6dKjOkcsRUZkvSHf3ykIVJ/Yx0Ozu/kuwqZtMOGV/TA0SiB1pvWJ6f6ytBjX19/r8FHuh1CaxlfepeUORrkN3+2GdczU/Rnv2fvay1OMDwBtf7VBxb9p2d4/UVNt76u5Qo54X7VVoWCm93CVSZYegKiudGyNyNao/ZMWq0jM4Y328mGpX+bkGYIV7lOr7NKqo0o9du3bhgQcewMKFC/Hbb78BAD7++GNs2RL+TFQNDQ1Ys2YNxo8f7/f6+PHj8e677wZ8z/PPP48BAwbg1ltvRUlJCbp164aLLroIBw4cCHqc+vp67Nmzx+8/s/rvdzvD3zhIQcqlYsZpqHXSfWLplBNZ9/Qw9q7y/uQbUBHbQzLWHhkH7OoWgG0aTPxlND1L5HbptxugJV1VvvcvDSp5fk2vjGj7PGW3qseXcdfyGqCyQy0eWW0YGp24z382b9nHDGyC69CHSzjULqMZh8dmvVUUYjHf8azq+/xMlKu+T6OK+Cn06aefolu3bvjzn/+M2267Dbt27QIAPPvss1i4MPy1fHfu3AmPx4OCggK/1wsKCrBt27aA7/n+++/xzjvv4LPPPsOzzz6LJUuW4F//+hfOPvvsoMe5+eabkZGR0fxfaWlp2DEazUPv/hD+xsEKncX9VYkFCDwmXVaLVFZKy00/O1nu2F21uZ2xrWkeSGmM4/hOtf87pvevucp3ErTYrxmZ3d31kpUmd5ZfI00UqTYturv/llkV0fbpyn7VY9BbvQg+LtFsJvXpIDsEVbU3ZpRiZ/eqt6qM1b2cewq+9Jq3LN6efXn9ZIcghZ5z1uyWvtKNfiIu2S5YsACnnHIKvvnmGyQmtiRGkyZNwurVkc9q3frECiGCnmyv1wtFUfDYY49h0KBBmDx5MhYvXoyHHnooaGv6woULsXv37ub/Nm/eHHGMRhFRF+NgrdxO9WowA7akSyrH+ybpNrVnx5W87uWezCp4VJ7EKNb7aazLl6ndPdxmoF4dmlGxgi0aRpooUm2KBtdPflpk91q1WxxSdJi47rcM/2Ez//YOVP0YX3vlzAmSliyrtU+b503H7Pgp2MqgVUv6WxeP0mS/MmUXdMTEhj9LjkKrVRRs+Idb9kos1rZexM98KxGXTD766COcccYZbV4vKSkJ2gIeSG5uLux2e5v3bN++vU3r+kFFRUUoKSlBRkZLt8/KykoIIfDTTz8FfI/L5UJ6errff/FB+2x5W3q15scIl28rn02oXaMtubu7ouDP7ulyYzA4xUBLymlGUYCr5C1Emujdp9m+5zZcqNm+w6JBK8D4ngX4l+dQ1fcbrsIM7ZPM7zse5/dzeqr6KxxsFEX43FsWekO1SV5NQW2NKYHLVaQOu1ebJL0sJwXI6aLJvmU5c1RnzBleITUGLdtevBau0A5Gr/a5kxoWYqMo0ulo8kV8JSUmJgYc1/3VV18hLy8v7P0kJCSgpqYGq1at8nt91apVQSeCGzZsGH7++Wfs29dSWPz6669hs9nQoYO1uqYF8qbrQrjd4Y0LVXTo/rslezBObbgYI+rv0PxYkfCk5MsOQRUf5R2j2b6NMFGWmmwWK1AHZZe3rJfT26DZvj/0dtds30Fp3D3PZlNwaeNpmFh/C5a6j9D0WLK07uCjxX1FQJGyZKQIMvmq5hzajGltTCrUZL/URNMx6WeruKqFASQ67bhqak/ZYWhCQMRlkq6Xd7y9sXSG3F6Feor4SjryyCOxaNEiNDY23ZAURcGmTZtw2WWX4ZhjIksqFixYgAceeADLli3DF198gQsuuACbNm3CvHnzADR1VT/55JObtz/xxBORk5ODU089FRs2bMDq1atx8cUXY/bs2UhKMsZamVr7cv1HYW6pfaFm2sBS/C9hIAb1k7+chk0BLs68AxclXofSjiovh9VB/S6c4Xg/f5pm+5Y5G3prE6tiLzx2youXHjLyaDkm3Q0ZlQ8+s7trNPGgB3Z8KTpaakI1X60/1bge2lSQuhz6F3qlJellwzXZrc1uzWvQKDZla7jKULxUQutIyzHUVr3fG8EPt0zB5N5sSQ/qtttuw44dO5Cfn48DBw5g5MiR6NKlC9LS0nDjjTdGtK9p06ZhyZIlWLRoEaqrq7F69WqsXLkSZWVNXdu2bt3qt2Z6amoqVq1ahV27dmHAgAGYMWMGDj/8cNx1112RfgzT8jrC7E6owxjd7JQErL3qMNx+fN+Ww2p+1MAURcGt80/Fny+ZD4faXZ8n/0Xd/YXpYG+sPh3Un9XbQDk6slWY0MjhbJvkPeieFPN+SR8rzhknOQJtvhDH1TT18JLREqyH5AP+w9VSk9SvbJE22MgmqdeKRr3guhewIlNLP2YNkR0CRUSbO0uPwnR5K0NIZKQypZVE/BRKT0/HO++8g9dffx0ff/wxvF4v+vfvj3HjoitknXXWWTjrrLMC/u6hhx5q81qPHj3adJGnAAIk6UvdRyDwXzp6bRJiid9URVGgSWNBkpw1PSsLm2Z1z09PxKnnXAmx/Hko3aeos3PJk+GprT6jbe+JFJcDsNiqYTJpOXFchzBXGxikfKFNABrdt245pg9OP7QTtjz7ChD+lC2m0fObe1u9otX9X0Z3d2sVtDm7u3q+8nbA+95KzHK0lEUV4ZUYEUVKqyJQdkqCZStlSX8RJelutxuJiYlYt24dxowZgzFjxmgVFwVR1xjmpGitCp0D6+7GDmSqnqSTdgaUt1QOFBV1AC7dpNqYZKuNSfcm52J0/e2oFS58kHgOACAnNQFQd+lp0khCmN2Z/+m6XrVjbk/t0fxvLWZ3BwC7TUHXgjRsiZOWFS3uK52UrQD0X3u4wZGq+zG1ZLV7vkwTGm4FACbpFBC7u5NaIio5OBwOlJWVweNh85Qse+vDTdL9T+0OZEFeZ3SKRpsxU2pOGqbRGNxoCFvsy7ElOu3YKIrwC7KbX9vq6BjzfkkfKS79uxbvdrWMa9N8joY46Av4gbeHJh+zu+0nKV3ePYq6y0RKZ7GeAYYjWC6mJt9KWjZSJj3XSY8nEd+1r7zySixcuBC//fabFvFQCNc8/3lY27XumjqoIhtPn8kxU2aiyF76TWN3u4/Avz0DsSc/9on5+nfMxBF9i3H26M44pv4a3NZ4HN5O45h0Co/2BQzrF2C2i0zN9i2j+6hX1sRxWmEhWltsSTeVAymlmu37Oe8wzfZN8SXi5ou77roL3377LYqLi1FWVoaUlBS/33/88ceqBRfPdvU7C5lrl7Z5PdzErfUSbP88Q6cEnbX1qtGySGWE8tpf/lj7fYUK14yiKLjrhH4AgPI3vsMaT3eMs1ohWzKPxeYx8E3MNU/SjfCF08DutC7I2Put5se5Lek83LTvKnzjLcHQgQPxy8cvogC/anpMy3VZteg1aBTs7m4eFzScifHZvTXbv5bzt1B8iThJP+qoozQIg1rLHDwDCJCkUxzRsFClWDiBHVSejQ9/+A0zBncENsqOxjocBhoioQrfSgfNk3RrFtqE0lKEUKDdKJof7OUYWL8UgIIfjpgC98cva3MgHwlpeZofg8wrrdUQHSbp5vGsdwTGa7j/nkXpwO8aHoDiRsRJ+jXXXKNFHNRaYRU+K/wTqrY9G937WWtuelpO9COSczTbt2yPnTYYW3fVoWNOmMsVUlgUi7Wkd8huuT5sWi+3ZdH7sWi1frO2PRKUIP/Whs2ZqPkx9NaQUoKE/Vtkh6G6R91jMdL2KUptO3Q53nsLxyAjyQnc1PKawqVE6A8Xju8GrJAdBVmBNav3LWJ3ctuJr8ItmnAmVwvQ8BRqPlFWBNQu2DvtNiboGrBWig50yGxZ9q318CD16ft9m9twoS7H8Sq+lRvaXSFVJRl+P7uhfU8gA90iVfPjmL/JDkETV7rnYETDErgdKaE3VkFRRhKSE9iSbmZ5adqtGDGmR75m+6b4EnHJxGazwW63B/2P1NS2lHCz44Ew3yupSG3Bgo0V2a3WdZm0Z7GsxbdngFZLsB20O62Lpvv3tdbbBb91GKvLsYTPsBktJ7q89vBeOGNkJ/z7/BEAgKscC7BVZOPChnmaHTM5wXrlGatORrr8lIG48LDusGte2RbcZ3lTpR2bInPpxB4YUJ4desMoGWWm8w+8PUJvRIYWcR+/Z5/1737d2NiItWvX4h//+Aeuu+461QKjwIbZw5vd3fcWsdIzCJO1Cafd41JstCxQJTqtVwAljVmsu7svxabt96EgLx/4QtNDNPtdpKI8R58WRdF6mIBGhdOMZCcWTqps/vlrWycMqW9qFb5dkyMCI7tZrzXMqs/n0T3yMbpHPpB0KfDKlbodt1444VIaAQC7XYW6HZdic+aozrJD0EWjYDnP7CJO0o888sg2rx177LHo1asXVqxYgTlz5qgSGMW47IzPWyvy0mIPhiznisbZuNG5THYYZBLWWzfa558at6Tvz+yq6f59fS066NaSI5RWXX51Oao+LNnbyIIfyc+Qc3RN0n2rLU8dVqHbcYnCYfWvezxQrWQyePBgvPrqq2rtjoDYWiV8Wr2cdk49QG1N6sdCBYXvp0T9umzrwbeniqJxQqZ398dpA7VbA9iX16+7u+VGRFiO1SZ/bEPnC9DrU4TOTdVujDNRNPQa3nJjhj4TiouE+GtwVCV7O3DgAP7617+iQ4cOauyOVCEC/EsPLKWpRevWPU4uSJHY1+sk2SGoqiG9rPnfWifReidHgyq0G2/py7e7uwLBe4rBsRJFXRav8tBcrWDFhpZyUxN0OU5m9RG6HEeZ/4kuxzGSiLu7Z2Vl+RVohBDYu3cvkpOT8eijj6oaXLyLpcDjXyjU88nMUgCRFfXtUhZ6IxPxOFLQv+5eNMKB9RofKytFv6ECnfP0GY8OtJ44zjgTJlFgPDtkJLuRgmTUyw7DwvSpRjr90E66HAeJ6focx0AiTtLvuOMOvwexzWZDXl4eBg8ejKysLFWDi3cihgKPcLSs8Sp0rO9t8HCtUNOQUGK7pnEWWk8v6XJwOIYpWCwBczlt+A36PPTLsvVbEjAzSZ/WE6BtS7peLHYp6ohtv2oSXMWYDEyvezKH1Gon4iR9zJgxKC0tDVhjvmnTJnTs2HZtb9JfY2pJ87+9Ok745Nge3uzzFJrW5dCYJiaM0n60VB4tOKwbNv9Wiz4dMtp5B5E2uuan4oRBHZGnR5dAi44FbvC2FM4UCDbVGlxDbi/ZIViKNb/VZBWK1S5QJf5mq484Sa+oqMDWrVuRn++/PMmvv/6KiooKeNiSqh7Vmgv0+6Z2+/0t3Y5lfdqeNxnl6SOrW+atOG+sfjNeU3R25A5C3s4PZYehupq6e7BGUXDz0b11OqKepSX9jrVlTyMOTj+pQMDGtMXYXPE38ZKWZFR0a2GXSEGmsr/tL3pou/b7bpGCIuU3TY8Rz2zuAOfUzGzx12If8ScWQVoE9u3bh8TExIC/o2jFsgRby3u3pfVRIZbwZDf8rNuxrE7zLp0S+oxWVfbQ/ZgUvR8qpjX/uzxXvy7bWvsVOvfesGhLep3S8szvmJ2MhMY9uhx3wWHdAADTdZrF3iqskVIax62iaTLN5e4JkiOJzTddTg38i6Pv0/S4v9s4RFZLndzfyQ6BYhR2S/qCBQsANE0Mc/XVVyM5uaXA5vF48MEHH6C6ulr1AClaLY/jLRn9JMahg+Rc2RFQmDyFFr8WLSw5IeKOV9RMzyRdv1TsO1cvjNv3AgCge0Eqvtep4u+4AaUY1iUXRRlsGIhEXhpn01bT02IsVtX1wS/IQpA01xQSEoJcFwnaTkKZ4LABbk0PEdd+EZkokB0ExSTsUtfatWsBNLWkr1+/HgkJLeP4EhIS0LdvX1x00UXqR0hR8pmBX2IUFAObtnMJ6N2qssbbFeXsbmloP1RMQ/nGFT6vWLPt7e1LRut6vGA90MzO67tMpPDqeuzizCRdj2cFmcn6TSoYL36BPssdainBKee6cNjtTNI19JPIY5JucmEn6W+88QYA4NRTT8Wdd96J9PT4mwpfb7E0SvjODG/5FjCrTvXr0LrVQ7+/W3ndYwCATxzxN/GHmZTO/DuwyCdJt+h3q1TH2dYB6J7A6iXB6VORWD4c2CcvFiK9WeX26LtKg572K6lSjktkFhF/M5cvX65FHKShsT2LZIdA0bBrm6Rn7vtW0/37UzBvZGdkJOm30gCpwKItwHoTXv3+jnqesf7lOcDxnwAb3wb6Tofy6iodj04U3A6RjjxFnzkSzE7Y5FSeP5E9D46ftsELGw6xfSElBitLTmCjiNlFVX320Ucf4amnnsKmTZvQ0NDg97tnnnlGlcAIiGJev4AcXIfanFza9lYp3PGOpvtv7bJJnDSO4pPQsSVd1/XKbXYgq7zpPyDg0qxkLOu8nVBt+152GJrbIDphpLJO02NY5mqX1JJ+wZ8OxYn334wTbK/ikDqdkvQrtulzHAOw8X5sehFnb08++SSGDRuGDRs24Nlnn0VjYyM2bNiA119/HRkZXO/YKITveoIZXLvejBS7trWgu2obNd0/WYDCCj41CIvODCLicN1as/tTwyLZIWjqpIaFeNXTD/dlzNf8WFdN7QkAmDO8IsSWxmZzyBmT3ikvFe8tHIO+PXWswHfG01wW6j53jqu/WtX9UWgRV5/ddNNNuOOOO3D22WcjLS0Nd955JyoqKnDGGWegqIjdqlUVUy1Yy3sVp8VnwC0bKjsCTThs2taCetmVmVphvbtGdB2TruNZbFWJwzqd2OwWyZovDihU6qFnVO94e+Mdb2/0sGs/Sen0QR0xpke+6WfNV+zyhqEpioKf80fpcqxdCYXI1OVI1vSRYG9IvUV8t/7uu+8wZcoUAIDL5cL+/fuhKAouuOAC3HeftmsqUvh80y/F6kXvw++UHYEmHHZtC1PC6tcFkUF4E/SbaFWPqrenPcOxwVuGX/OtWUEqy6T6W2SHYBl6dfXNT080/zAPSd3dD1JsCobVaV+O+yJnnObHMBS2w5hexFlAdnY29u7dCwAoKSnBZ599BgDYtWsXamtr1Y2OoufTpGH250dISVmyIzApq18YFDvrXSO77fovmeRNzMTpDRfoflytXNh4FiY33ITkZP+uo5avENbYz8jV/Bj3nzxA82MYAZfoC58iO0kHsAV5GFZ3J3rXPaDdgSy6ykYwP6f2lB0CxSjiJH3EiBFYtappBtfjjz8e8+fPx2mnnYYTTjgBY8eOVT1AItIGK1kpFCumXN8lV+t+TCGAV7wDdT+uVq6e2hMnDOqIIZ1yZIdiKU/NG6L5MQ7rqe/KyQegb1fwx+YOxoReBbjpT1W6HtfMZHZ3BwD7H0P7tiAPe6Hd8pg24dFs30bzfNapqJp5m+wwKEYRV5/97W9/Q11dHQBg4cKFcDqdeOedd3D00UfjqquuUj1Aio7i07rMoccUiMfjtmYWRlGzfK8bAELCh9TzFqzH7O6zg0yUZfpuv5INLNe/l4fW9B5WNaxLLoZ10b5HgpUodrkt6VP7FOPif32q+XG8Fp+PwdcR85foc6BkVtRqKaIr1u1244UXXoDN1vQ2m82GSy65BM8//zwWL16MrCx2O1ZVDAWevNKuuLLxVCzwnAMXl2CjAIqxU3YIZHCWrN+TUGspdDwm55owl7XeLrJDoHgnuSU9KcGOaw/Xrmv2s55h+NZbjHcLZ2p2jHjhFj75xJTbgTNWywsmDkSUvTkcDpx55pmor6/XKh7yE31hK8Fhw8Krb8MNV10Hm8azhJM5WTIBizN3u4/ANmG9ytHtIlN2CKrStV5AYms2x6RH7syGpqXC/uU5VHIk2oin1kuzskluSQeAbgXazcZ/r/twjGu4Dfsd1ntW6ikzuVVlzsC5QEYHOcHEiYjvnoMHD8batWu1iIVUluJyIDlB/s2X6L2FY2SHYEl/cU9HrdBuzKes7sv3e6Zotu+MJLmtRpqTOb6JFcIR24YcdKl7GBc1zpMdiiZYGWx8sieOawpCu13b/rgKuxemaneQOPD6haPYU0tnEX8zzzrrLFx44YX46aefUFNTg5SUFL/f9+nTR7XgiF8G0o6eN9uiDM60q5Yv0oehcs9/m3/WcgyyrAL2AZGg2b7LBx+u2b6D4bwg1B535EUx07D6uuxWIHtMOgCkurSL4ZzRnbA1qRuOrSnV7BjxIDslAY2yg4gzEd89p02bho0bN+K8887DsGHDUF1djX79+jX/n9Rjxjl4PMKEQcct/3P1mJurM5jBLmfL7MxT+xSpuu/WLee6fptPegYo7gec/aF2x5j+OBz9Zmi3/yBEnLQnMh2LnN6zrbd2XsM5mu7falf+4sZjZYegOpvNLjsE9C7JwClDy3HllErV9901LxVzR3RqnkXeapa4j8Zx9VfLDoM0EHHV1caNG7WIgyyiqXXWao/l+KDA2muIPu8ZgiPs78kOI2a+xYy/ndgfP2j4bNZ1JvQuY5v+01IP7brRt0fPlvSNmUMwSL/D+TFjxbJsiU65CdLPQtsZ5a3WPXb/kAulHNcrFNgUbW4kgVrS73NPwemaHC1IDIqCa4/o1fTDa+ru2+qVpG94qvGJ0GcCSmv/JY0n4iS9rKxMizjIIvR4IF/YMA+3J9yr+XHiTbf8VOA32VFoZ59Qv8v9LyITctvBAJsFK1cGd8oBNsuOQj16Fmw2ZwzQ8Wj+PCzBRWzu8Aq88MnPmNirUHYomrBakn7VVO1mIW/PgPp70FHZjhGjJuDC9waruu9ASfpN7hm6JulaEsJ6z0hfCoCsZCcsWBSIe1H1TnvkkUcwbNgwFBcX48cffwQALFmyBP/3f/+nanAUnXsq/ibt2HqU0bwWe+jL0vpcyW7RMZtl7omY4VwiOwxLfhtqyqw1C29uqnZj7I2EY+8j17c0E59cMx73nNRfyvG1TqKtlqTL8hvSsU500aS7is0IE8dpyeI3psdOG4wPLh/X/PMmb56GR+P3WU8RJ+n33HMPFixYgMmTJ2PXrl3weDwAgMzMTCxZskTt+OJblDfj2TNOVDmQSGj/Bf4d5p6h801PX9khAGhbeLJ6jq52YTGjcgz+tUBO92lfikZdIGXKS9VuxnoZ0hKdeOWCQ+HVYc4OmV07rdbd/RtviS7HyUhy6rqSwjuepm7F//EMQLpSq+2xbPJ6dlB4bHZrP/yFxZP0RIcdCY6WdO4Rz2GaHYuVbvqKOEn/61//ivvvvx9XXHEF7D5f7AEDBmD9+vWqBkfRfRlcDnk3XD2+wHPH9Nb8GFpy2EN/7X6EuhOCBdLmXFn8Qaa2bgUpyEzWv4VUz3HiiqypwCxYDuhWkIbNNn2SPllsFsvSJzbcIjsETZzVeD4ubJiHCxvnoVDRdozTEsdsTfcfb3JS1H/mGGF2dy25k3Jkh6ArL6fwtIyIz+TGjRsDzuLucrmwf/9+VYIi89IjzctLa3lINbi0nfRGC2lhrNP8kyJhpLPFx21ZpQrCq/hXwqm1BNu6Q+8P8KpV/mrGYJvxpOwQKAIeWLOFcQ9S8LT3UOxDMg6p0PYZWgsuv6mGu0/sj+NqOuDEwerPC2VT2qYCd0wzRo8/NbhtibJD0FarytGhnXM1OxRLBPqKOEmvqKjAunXr2rz+8ssvo2dPORNqWJYJWyX0rsE7kF6h6/GiUSdaJ+Whz+sDiv7LvHhY+2oKb+efjI3eAtzWeBwA9ZL06jHHt3lN1hrHSqO2XXBlKe1i7l5AIZnwmRXv8tKsNbTEqqb0KcJfjuuLBIetebiCWmwBWtL/1K+DqseQyerd3VtLcpq7LPe1rbPsEAwj4jN58cUX4+yzz8aKFSsghMCHH36IG2+8EZdffjkuvvhiLWIkE9HnVmjDOUm34GXPQNiPfUCXI8YiUWmM+D210L7mt3V3953J+izhoaeDa7+/69GgAlHSg/+YQ6sxuuEOfNV9HgDAZsW6ba+1e3Vo6biaUtkhEDWz+vJXMsxvPAe7RIp6OzTAOulaKmBFlGr0GNLa7eqPNT+GWUQ8EOXUU0+F2+3GJZdcgtraWpx44okoKSnBnXfeienTp2sRI5mKPi0pSy46Aw2e05CcYMaxVMZsbbJiWrTIPROrvb3xrrcKlzqeUHXfHbLkdOPsWpCGz66bgJSEpoKVWi3pgUhrGBUeSQc2t3rhQHmuioX3CBnzzkatHVVdjOfW/YxThpZD1G7Q9FgnadA9O979igzc5j4eNziXq7I/xeITx2VrMI7fyLQsE5C+ouoTcdppp+HHH3/E9u3bsW3bNmzevBlz5sxRO7a4t+HnPbJDiJg+Mz8KOOw2kybo+k781Z7W58qKPcLqkYD/eAdhL5JV33dWcui5BbSS6nI0zwat5dUk65pQ7PFVqLIKY9zZ1PPB5WNlh6CJPx/bBytOPwRXTKmE1v3fzhptrh5aX/W/WnYIIY3unqdqWcvqS7AlpVlrSc/WlNbXgkHKmLH4S2PT8LsXPYdIjkSuqAcubN++HV988QW+/vpr7NixQ82Y6A89dq2WHULELJjnqS6s26cO2VGbIyRmaH5MUp8Va81Fp1GyQ6BoWKBw6Ksg3ZoTTrkcdgzulANnGCuNxMpuM9c1UTj8ZNkhhLRkWtvJm2OhKNZuSYfTWpMXqj0nQST0WoLtbs9RqKxbhn97BupyPKOK+A69Z88ezJw5E8XFxRg5ciQOPfRQFBcX46STTsLu3bu1iDFuleFn2SFEjGsohhbe30iPxMs/jtIiCTPKm5pRrnXtrhWbpDXYHQWchDQasitsZB+fIhdvk2qFUu8x/t8jI9mJMT3Ue163XoJtfafTVNs3qW+TaP/ca9q7TsN9t3ZAh7mZjC7iJH3u3Ln44IMP8NJLL2HXrl3YvXs3XnzxRfzvf//Daafxiy1bI+R1wQX0SdJFgOVCKAqtl0kH8IR7tJRQTMku97t2kJYTx0krrlp83V6tyJ5EsE23SzK8uka37BAMxhzXcKNXxe+6zb9MVZeQqd6+SXWhytlWqix93dsPv4lUvOZRt/eIWUSc7bz00ktYtmwZJkyYgPT0dKSlpWHChAm4//778dJLL2kRI4Xp7+4p+L9D5K7Dq88SbGa/ARmzEOAVCha6T8NJDQtlh6KaK6dUAgDunF6tfgVSl3Hq7i9K2j6Q5V2raxMGSDu2WV3gOU9uAMa8tVE7bBYbohArRVFMUVn9yWb1eq7aWs/ubvYiVrzJ1HNFD33vF7VIxMD6ezCn8SJdj2sUEWdUOTk5yMhoO3Y1IyMDWVnWnpxBb5HcJ8fW/wU3u2dgV0onzeIJh1eXL7DJCxUGCb910ur9o9ujPudQH3NHdMKGRRNwZHWJ+km6QSbb0bb1VGJpzTqXoS661z2EjiNOkBqDMMh3gsLHr1lbTxZehOq6v8sOo11uFZepbL1OupVaYmWpFU3LvrmFDg1XKXnaH0NnVSXpAIBhXXLggR3xeqeK+Oq58sorsWDBAmzdurX5tW3btuHiiy/GVVddpWpw8c4dwdio70QJAKBLfqpW4YSFY9Lb8orI/ya9SzLVD6SV1leX94+HidXO4cFVAKxa8LDq57Kqyxrn4v88Q1Xdp1vY8PiZo7DgsO6q7jdS1rpzxActz9lPKVUa7l0rCp48fQiWnzVBdiDt2luv3jKVisXXSZfhqIZF+JfnUMzVoAX4W1Hc/gYa9o7Runy4QzQ1Av/zjCF49qyhOLamg6bHM7qIq73vuecefPvttygrK0PHjh0BAJs2bYLL5cKOHTvw97+31D5+/DEXpNfLi+cOxxdb92BkN7k1at+hA3LBCQTbE85NbtpAPbsvNXFklQDYpftxKTbaPjLlpV1WrXx40jMGT3rG4Ej7u6rt84PikzCsjD3ZKHKKUC/Za81rwlnDHXYFSQl29OvY9vvkTUjTZUCf3lp3d7daRb0MX4tSXNQ4D52VLarve5W3Btfgkeaf9RyxovVTWdibeiAkJzjQr2MW9tTF95wZESfpRx11lAZhUCCR3CirSjJQVSJ/Ca2nlMMwGJ9rfBRzPUB+Rg46YGdE79FjaZzW11e3vkOBJ1c2t6iTOWibzMpLlL93dEV1/RppxzeTgwUb2cx1ZyYAgLdBw50b44rwCiXslSrSEttOCPpN9aXo6v0BtuEXqB1a1FwOdSpAGm1JUEy2TB7JO1/viL6YpLyPn0U2QrTnRyUn1f9ZdmjXXMwdXoFef3R/jzcRJ+nXXHONFnGQRTRGfklZXp1IaHVPNcYDsXUUyh/VsdZsv7Ru64BVW5zXJA3B0fvlToRpFnsStSguRYGTkJlObZ12SbowyPWww5aLArEj6vcfSO0IjLtcxYhiN7pHAfBV7PtZ2/EUVFvs2Xin+2jMdzwjOwzdfe8tRCfbNmzKG4XBGh3jGpyBNY2d8ZLnELzX6nc/evNRZtse0/7tre4ZiqLgyqnxuyRrTE1m+/btw549e/z+o/gWayL0okerW4s80SRRQofEK9C5qi7NZDIb9g6N8XeSvewWReZQlYck3en+EzbkGmT8rEG+E2QMhnmWRHJdBtjWiEsLprjUaRDpWZyO1g3pda4cVfYtQ1XdA7jDfazsMHDdEb0AADMPKdPtmBMabkVN3T3Ym9xRs2PsQzIe8EzBVrS9RlT5vhvvqyZVxEn6xo0bMWXKFKSkpDTP6J6VlYXMzEzO7k4YUBbbzf3cxnMxtO4ulaIxhuhuXHKS9GfOHIpzx3bV/NgyGKbAqDJNW9KZ/6vuH6cOxJfXT4z6/R+ljvH7+Q73cRhdaZCWdDIdRag3S7hV6VFpHim1IkpNdFpqGb59SJYdAgBg1tByfHn9RIzsmqvbMRvhwK/I0LSutL1dpyv7Yz9Apn6VGmYQcVXcjBkzAADLli1DQUFBcxdZUp8Zk4peJenAj9G/PynBiZ8b2r+pGe9x2b5ozqOsmnubTYFTpbFuRlOUmQTskx2F+qzaku5W2o4NtQJFUZDojP475rD7v/fl+SNQWRSf4/VIBULL+4cxyjCRPIMDl2mN8Tl8ue2J6uxIsUFRgDMazsffE5YAMF8Zy6ia7vP6/zW7F6Zptu/xvQrx7NotAVeS+lVkIFuJsZD1p3tje7/FRJykf/rpp1izZg26d5e71AtZ06oFI/HWVzuAl9vbyngPTDMK/uiw5sRxVq1PVKBhS5jEv9kmRyd5BzcRIyXoFv2KWZx2SYRRxqRH9AkdSW1esmRj1CFnA1+8AAyYDUVR8B/vIJ9fWvDzyqJBT5WrplYCq9q+/uK5w/Hdjn0Y2lm71vvrj6rCgPIsjO9Z2OZ3HjXKjhnxveRaaxH/RQcOHIjNmzdrEQu14rDH342yJDMJJw7WbjwN+Qp8fRmlYKU6izYPqHG2lrvbjmneIdKl/s0443AQBv6zGHHsLrVP24knzXU97BDpAWtzG3J7SYgmhFhP28SbgPM/BZKsNUz1hEEd8eK5w2WH0UyL71eXfP+K2YOVSFUlGTiyukT14/lKdTkwY3AZ8tLarihixt6/RhdxS/oDDzyAefPmYcuWLaiqqoLT6d8lsU+fPqoFF+/MWODRI7+zYq32p5OeQZ+Xj27+2WuT2eXcmi3pVmVToSW9Y37bgtp3ogR2meukW+9rroqE5Exgl+woAuMpMx8m6cHdWf0idmzfhkW9jVeuVWeSrpZ9TOxVCHz3x8smrtG++ejeskPwp+lwEmPxxnhN1sEFlQZxWEbESfqOHTvw3Xff4dRTT21+TVEUCCGgKAo8Ho+qAcYzM361zVixoLVwzqMnoWWN++c9Q9AtS/vhJL4P+c+9ZTjYVmDZlnR+rKACzRSclODEzwXD8Y23BOtFBY4O8D4t8V4S2H5XvuwQgkp2WXM+CyvTcuK4xARjLMkabUI7/6gRKkeiHrUf09cfVQXcru4+o7XEfTTOt8gSakKD4WhGLaLFmqR/4ByEkSrFYhUR30Fnz56Nfv364YknnuDEcRozZdcRHa4HK1ZM+v7ZFjSeiRf1+Dv6/Pt7UdScpEOxZkt6bb01KxBtihpfiLbXW1ZKIn6yJ+CwhlsBKLon6eW5ycBPOh/UBPKrRgEb75YdRkAsD5iPlq2mxrkewo/DLKNsHHZ1n9PpSS3pgCuGiS3VsFukSD2+qjSoBDNqGbgx8pTSX2qBOoFYSMR/0R9//BHPP/88unTpokU8ZHZ63D1M8hA9KNJw9fp4QY+jcpK+oGEeFqu6x+h4vAZ9shlBgMJ0WtLBoUxyvnCZSQlSjmt05TXjgRdkR0FWoeW3uzZZ2/Gx4Qr3zq/ASBUL7VN72TSXz6oudsk1FaZsoApCMWpGrYFdou2M75GIZdUTq4q4ND5mzBh88sknWsRCrZjkWeFHj5urx2GMdTDD1XrGy8Ddyf1fS9Ghm6DvuSrMSAz4uhqe8R6q6v5IH0KxISeViTKRlWm5OsTelHLN9h2OR9zjAAAPJZ4c1vammqtXwwKi7LTyRc8QyRGoSfZfUz8exJZkf1gwTaVIrCPiTODwww/HBRdcgPXr16N3795tJo474ogjVAsu3lmpNlFN9dn6L//nEQrsUXYrDuddvrX3F0/ojtJs7SsihKI0B2f3Ob5V10m3qrmNF+EB520x7SNQbb+ADYMrsnHemC7oUqDduqvx6jeRGvuasgZjllZIaqFld/df8odptu9wXOU+FUvcxyAvswNQ++eQ28dLOuXJ7R5jOqWtncgIvZFJiDhqSd8o2i7LFonG9FKVIrGOiJP0efPmAQAWLVrU5necOI60LqQtc09EHwn3vKYKk2gPHNnf5PQReq0PHTiuHkXqPiDPGtVZ1f1Fy6qPyte8/TGt/iqscF2v7o4VGxRFwYLx+leKxQMrVsJa7xMBP4lcdFB2yg5DM1om6fUu7dZrDo+CX5GBPMlRaCOWb1v771VlmhNqokGSbtTTE2tcp42oUCUOK4k4Sfd6tesaReanVa3hs55h+N5bhAc8k/GIJkfQTjiF8YaUopYfbPrPiCt8vteKTd0x6ZdM7KHq/qgtT4zpUaAhGMKiEwgahRpJ+n89vSC3rdL6xtbfhmzsxXuJ58oORRPbXWWa7XtkD+OuRGB2VSXWaW22sngakx6rtERn6I3iTEzZQF1dHRITuaodae+Z8mvw9jfmbM0IZ1kKYXehd90D8MCGDSonyeH4PrEnag7+oBi5I1z0Yl0exKgUaNSCySRdU2oU3d7wVhssSbdegbQeCdiKHNlhaKbRpl0ZLj3A0o4yhJsnmal3S2K3MVG/V6QVhd6IVKHlnA9GY55vj3lEXArzeDy4/vrrUVJSgtTUVHz//fcAgKuuugoPPvig6gHGs0aP+Qo8WnV3f3DWQE32q4dwzqIQAnuRjFroV+nlm7R+ldKcolt2fWqbBRMILZmpwGpO1vv7xsuQ9C3COkm7lt3d4+aCkCEpM+q31o+9sd3fe21s0VSLFr1LjVpG0/ReEqciTtJvvPFGPPTQQ7j11luRkNAy82/v3r3xwAMPqBpcvDNhjq6ZBEfLpZqRZK4HiDfyr5lOWm70fs8Ri7agWvnrpMXDkd3dtaVGJUi3fKOtJxz4M+0Wybio8QydY9FOifKr7BAoAj2K4mviy8fcY/Hnxul42H1YwN+L5MCVTHe6j8Zbnj7YVhR9Kz3502KIsDBoaYZJuvoiLoU9/PDDuO+++zBjxgzY7S3dYvv06YMvv/xS1eCIfN0xrS8WTuqBrhJmmo6lQJ3UZjm1AON/o967OnyT9MBLxJlflcea96e8NFfs6V6gcy45SbfoZaiqPw3vKzuEVgKftGfFSAw79jydY1HH8lMGGmbyS4rMi+cOx+xhFbjuiF6yQ9HVD6IA93iOwCT7h0G2CFziuMN9LGY1XgavRYe8ySBE/HR3J/VFXArbsmULunTp0uZ1r9eLxsZGVYKiJsWsrffzp34dcMZIOYUl69cQ+jxIJIyJ10O+d4fsEDTx0KmDonrfd17fcYmcOM4M3nYM9fvZmW+smfeDVaz0KcnAn/p10DcYlYzukY9LJvbA/7zdZIeiCW0rw+TWtFWVZODqw3siMzkh9MbQZCJuKQ42KuQpuyVHQl5v/Kx4xXp19UVcCuvVqxfefvvtNq8/9dRT6NevnypBUZMU1MkOgVTwW2HoqZ1k3Nx8ewcc3rekJRaLJmdG7SIWq8qidFw4IfIE4jHPuHZ/zzHp2ormauzQsdz/hUxzrCtrhUrOR9ztf1/MqizbaEMm5LHxlmcI955UE3qjMNUJucMj9+X0AdC0lCNRpMKeenP27Nm48847cc0112DmzJnYsmULvF4vnnnmGXz11Vd4+OGH8eKLL2oZa9wx5WzUFu2jGm3C4lm4FZ4Vt4Sxf7mykn0eZJZN0q15bQKIqgmoFq72N7DodWAU0VyP7oMdXs76AKjfA6QVqhuURjwWaE0Shp1bJDZOu4b3RYuWB4wu5NPA4F0GJlYVAv+SHYU6GmzJqKxbhkY4sMixHCc63oh5n0Yty1ihMtZown7q/OMf/8CBAwdw+OGHY8WKFVi5ciUURcHVV1+NL774Ai+88AIOOyzwJBUUHaN+ESl8dldyWNvJeGYGu760mqFfNis/QGL9bI32ttep7O7u/TpmSj2+1qK5v+8v/GNoQ34PoDS6YQ4yfL6F3W4B4MrGU/G7SJUdBgVhnTJXqM/R/vOia358TbSnJY8QOIBEuOHA5e7TZIejKSuXsWQJuxTmu4zAhAkT8NZbb2Hfvn2ora3FO++8g/Hjx0cVwNKlS1FRUYHExETU1NQE7EofyH//+184HA5UV1dHdVxzsMoDw/xiu/WEPo9piS2dWrRYsiMSikXHpFvZrt2xJUEb8ia3ec2dkBHTPmM1unu+1OMbUVKWOVrOW0tR6mWHEDO17spZyj6V9kSkrv+cfyiWnzIQPYvTZYdiGVlhzodAFEhEpXG1W9hWrFiB888/H1dccQXWrl2LESNGYNKkSdi0aVO779u9ezdOPvlkjB07VtV4jMaMdVLWqYlWU+gzWZCeiEVH9sJtx/XVryU7yGGMugZnrKx8be7YF3kSlOizrKEnwLq4e3KqYwkpZlbt0REbMz4VgGPtq2WHELOCjMSY91GcmaRCJGZiru+w1+DdwNUS7FN2L0zD6B7mqxy91z016O9kX4GVRem4fHIP/PUE9ebs4qMxfkSUpHfr1g3Z2dnt/heJxYsXY86cOZg7dy4qKyuxZMkSlJaW4p577mn3fWeccQZOPPFEDBkyJKLjmY1x19cmtQkInDykHMfWGGAGZJs1l19hV6zIWHUMrlFY8Wq0cuExOSH2+2L/jln43l6hQjTq+T2zSnYIpDIrV0gH0q9Ubq+vUE4/tDMO71us2v7stcZcqYZlLPWFPXEcAFx33XXIyFDny9DQ0IA1a9bgsssu83t9/PjxePfdd4O+b/ny5fjuu+/w6KOP4oYbbgh5nPr6etTXt7Qy7dmzJ/qgdWbGy31fhjWXqYlNGA9MKSe7JS53ckvtuVVnd0+AW3YImomm98PAimzgh6Z/T+5d1Ob3Ir7KebqLt4K0+bV/vh51j8VJjtdC7qVBib1FXk21KaU4rP5W7BKp+CjxrIjeWy+ccCnWWXo3xSV3JnC1hJp0WHjNWLpsR4I55nmYVFUIfBv7fhS3MYcPMUlXX0RJ+vTp05Gfr05XmJ07d8Lj8aCgoMDv9YKCAmzbti3ge7755htcdtllePvtt+FwhBf6zTffjOuuuy7meOUwXyGuIbkIk+pvxm6RgncTz5MdjkGEvnGFu46rmtITnUBt0789SS3Lg1i1m7Hddy14i4lmHgPflsFUV6D7qTWvA6OIanb3hEz1A1GRla+YUJ9tSeKZOMkdOkk3WvW7ogDfiOh6cL3l7YPx9jUqRySPlhPd66kO8TUOel3JiXB//w5e9g5C6KY7eZbO6A+okY4YtCElN9UFrhytrrDPtFYF99b7FUIEPJbH48GJJ56I6667Dt26hd9au3DhQuzevbv5v82bN8ccs17M2tLyhSjDz4h8Tci1iYM1iEYdWp+LBIf+N936opa1SP2+cgZ9AMTKrN+ncOSmRNEC5JMrBLrnCotW1phZXXal7BBCsO41862zR7u/f+fSMWHtx7e16fB6I6cUob3prZYdAgXwpqev7BB01ehIwYzGK/Cox9grTKmVR8leeSWYzGRr9EQxkrBb0tWecTo3Nxd2u71Nq/n27dvbtK4DwN69e/G///0Pa9euxTnnnAMA8Hq9EELA4XDglVdewZgxbR+SLpcLLleI9YANyozdTWO5Sr5x9YR6U2sYiUFPpC1ISyqTM9PJiKonRuBva71wwKW4sSfHmt9GM2MhSJ4dzkKMrr8du0QK1ibOa/P7RGfoMeu7k0qR7fO9Wy86qRqj3kJ1qzbbs6QhIRPmLC3684RsfzNWb45YWbX3XzCGbXCIk4kX9RR2dYzX61WtqzsAJCQkoKamBqtWrfJ7fdWqVRg6dGib7dPT07F+/XqsW7eu+b958+ahe/fuWLduHQYPNm4rLIXH5TDuhGX3e6bIDkF9PjdU31mHrfrAS7bAMlBBidi68vue8X7192Fg3d1oSMqLLSad/STa773zpWLuhAgAOucZe+ylFreOA8IYXXcVABtFEX5H9MtTbc0ebNTiNQH4LW+g7BD0EUfJlDXHSRvzLmLMqMwtojHpaluwYAFmzpyJAQMGYMiQIbjvvvuwadMmzJvXVEu9cOFCbNmyBQ8//DBsNhuqqvxnIc3Pz0diYmKb160iDQdkhxAx3y/pTyIXHZSdYb+3bwfjrs252H0cXvf0wzOua2WHojmrJulWFk1PJxGku3stElGLRMuV4xpgrFZoIRTLlWqUENfM7Y3H4kLnvyLap80g9yNVwlAUwyUNvN+3MGwLpdqMdQnGLO4uYYN2d1dCPQAoYlKT9GnTpuHXX3/FokWLsHXrVlRVVWHlypUoKysDAGzdujXkmulWtkOkI08xz2z0sbLbjHnjAZqWw/tYRDtzvWj1kzGeKMG6KrLQFi/8snR5YVDcWCu6Rvweo8yNoEZlwTE1HbD9DRWCUdH4ngWoyE1BTVkW8Hlk7zVahQPFpxmDyvDoez9iQlWh7FB0YdQy2i6Her2tqYnUJB0AzjrrLJx1VuBlPx566KF233vttdfi2muvVT8og2g0WMuP1vLSjLU0jdVt23UAHQO8btQHAAUXc0u6irEYldHSiXWiM0phzPVuo6bBvcMolZpqSEt0oj6nAPjlGwDAvJGdJUfUNJb+9QtHNt33I0zSrSY/zQoj0uNPRrIT/71sTJCyi9Hu/LEz6id6J3caJm2/X3YYlmLcpksy5XrVXfKjHzOZ6DTf5w2PMQuZniBrpZrxuot30bRo+Y5iD1S2MVt3dxFips2+4iudIgmP0brfqyLEre7yyZHPTm+UJL1nkTrDsXJPuAf1JYdgx5TluHRid1X2GatoK2aPqi4Jteeo9iuL77KUViYMm+ZFj40L8rltrORSG0vjBhb2Mgvp0a1xqoXy3BQ8efoheHXBoTHtZ51XfguDeoz5QNzvyAz4Oh925mO2hJoAo94XtGS3mffe0r0wLeQ28xrOBwAsx5HBN8rsCNdp/0HewKNNf68NdT6tmAxagiOeei2a+zsWiFG/V10LjD2xqRkxSTewkMubHHTqS9oGEqFDOuWgS37oAk0bvY9v/ucZDReoGFHsCtNjeKgZ836K75ODrKXKlnTTieahrfjMCB9ovpcki7UqLbcfKzsEioLXIMWUcBLqf3sHobzucXyTZ+z1mtVixjHptYKtfSIpS3YIZEGzhpbLDsFyjPH0o4BEuKcnq1zTOHSTWdr8z1+QLTGQth6eMwgju+XhFU9NxO81ysRHrQVL7MzeuhOXomhKT2rc1fxvpe635n8vnNQDR/cvwZBOOWpEpptQrXqh1w7Wm/W+Z763jusbZ7T5vVG6rlN4vkKZ7BBUZ75qhcg1yp9uykCsd8ZFkKGKsjntRnvGmh//ogbmZYumYXQrSMM/Zg/CDpEZ8XuVVgnU/nqPSlHFKtjs7rzuzGZ7TuTr+3bNTw74+hkjO2Px8dWwma1rcojKJSaIemj5Gz/vGabKHo1y3owRhb7c3vZ/L6DALcz2vGjnTBb20S8Mlb3n6YlbGqfj1sbjcfrEAcjjJHgWZswkndRntrtrXDFKNz8ZLjHIhDqtOe2xF9V21zWqEEnsKouDTITElnTTOaRXV1TVPYCzG84L+z02Z1Lzv+PjlMfFh5SspfD48JzBEuNQ3/CuuQDanwG8qqTpnjqquzWWIgo1ueH23MH4r7cq6O9DTeYo2zueXv4v9DtJTiAqqIUL93qOwFLPUZg2oBQfXj426Lacw8TcSjKTQm9ElhC/WaApGPsBp6WzRnWRHULMfkUmAON2d89ISgjyG2PGS8EVZiRi9VVHYkp1aeiN/3CgdETzv3nGSW2KrW3xIppWcafDGMWUgvREfHzVYXj70tFBt3n2rGH46IpxKM8J3EvFbDZ42+/uXp/Q/thmI+aCKUpd87/v9hzV/O+PvN0Am3nn4chM9q9Q4bC1Jkb+KxxWf2tU70uy7EpI1BrPtIGxu7u51StNSXDr7u6Gx4e7KWWnJETURd23p47JrtCA3J72++bmpAarlJKjEL+F3shkFJ8icaBhM9FMNObKDr/iSWvZKQlwOYInck677Y9uxta4h1rhvtCensoPskOQwurn1Sy+EcZZmYmMiVmggYU9cRyZigNGGZMejDUKmPEo2vogK7S6dLTtaPf3XXr21ymS8Ayzfy47BI2pc00pTnbtlMVhb79lOcUVYoIyg99XamzfyA5BCmG2hoMYGPWT7o9llYHc7tiUHHyYiRm9nzZedgiGxCzQwIwyYQ4Ft1uE7tbYurv7JPtHWoWjDoMXrCi4SO4ZHoPOEKuF6xtPwu/lU2SHYXl+lT0BLsU4yg0swam0X6F8SIgVIIyeDHZXNjf/2+xPPTMuh6cHo57Xr0WUPYQu2wy4UrHTZZweRmqwBRgeRUzSDU2wu7vhDai/V3YI6mOSblreMArFD7onYXz9n7F114GW94WYxdnsPiicjqFdcmWH4ed1T7XsEFTnd+uIo/vInlaVtVZJl4aktd87xSYaTdOYcEXjbHzuLcN9OLr5tb1o6aVhkKkPdJGdYqyhP/HoZ+Shb4eMyN+Y2DQ5pVm+d+FiJVNgcXRbMh92dzci/xtJe+uRNo9FD3HvMd8SNmRUu1yhx7hd7575Ry1+y4Vp9frAF88dYbg1XJ3tjG22BpUKkcMvUGc/GrJK8fKsVqtD2A32nYnFY55xmNJwM7a605pfy8lq6QmQHEeTcVlheJNZHV9/FZ7xDMezhfPxf+cMlx2OgVjlLqqu+LkrmdB2R6HsECiEz6+bEPR34TwG73dPRnX9feoFFBE+qK1mZ0rnsLdtdLQUVqFYPWE0nlRX+8tbmV3ARCCa5KCHCYcpmDQJWuk9JMJ3KHC0syypzYB/B6fPnDDFfUbJC0RtzHECMloL7YeiEgsaz8IeW6bsUAzFdBMs64RJuoE9lTZTdggUQnsT54Rzy7nRfRL2Qc5yPQYsP1GMOuWltvv7CfW3AGharqfe5bN8ki3EBFBEEQo0u3u8DEo3+ljscIXTmy+tnWegEceZ+o6zFxUjW/4tIxgV+XZ6cDlZ6Wp0IsYrzqhL++4TiW1eO67+6jDeafZvoDaMdwelZtudJbJD0E2DsO5D5efdB0JvJEFtakfZIZDKpvYuavf3X4mOuGh8N7xwznB40fKdUxwco6i3FzKmyw5Bde6UlmeWUNomb+kHNukZjm5ajw81a3HzpfNad79tPxFQAvx+uXuizwbGK2J6fSbMFHmVeNQ9FgDwZMrJskJShV1R8LcT++GvJ/RDaqhZ9y1inzMbmLNKdhhSeE2Uvu1H28SdwhMf32STMmJXMa084RmDWbKDiNALnkNweDu/P9jN6pvt+wz5TWtILsIx9ddgD1LQ+jFXXvcYfkicISUurfwiMlGg7JIdhqZCrZN+x7S++FO/pnHrn/yUiCXuo+FCI2amGntoTYOwIyHETNNm862rFx50T8Icx8uyQ1FPQhJq6u6BG3Y8FaAVtbj7QOAdCXHpzpxpeq9i/4msahPzInr/mPrb8AOKcOr4gYAjEXDEsMyURvy6P9vsuNI9G392n4BuLnM3iiiKwNQ+xbLD0NW7HU7D+NJBssOISqydbYyaHwSa0M6YkZqDeapi4pA9RIGb5PpetN9qaYZi2hrRHd+ItpONHVtjreU9AOAjb4+Y3n9Ow7mm7/FxMEEHAK8AlriPxZ/dJ0iMKDxWvRO2nhXcCn5FBnYjNXBhzYAtq1qwSG93bM49NKLtvxfFABTg0IuAoedoE1SUrpxSifKcZHTMaf2dU7AXyaYfoiCEVe+SwRltvLmeKnv0kh1CBOL3PMUqPp6YJpWXarxa6HgXyeQWRn9ktlcRe9txffULxCRe9A7BpyL8idmMzrdQavRrNV79J2G87BCiFvD+YrfmsAqrLYd0UDiVKq3Hxhp15vC5IzrhzYtHI91l7orWYOIxYQ3nSrPqdzNl1HxgwGxgxr9kh+In0FVoC+faNHklmVaYpBvY+F4FskOISSQ3R7PcRiO5jeRgFwCgIidFk1iMZn3mWNkhaOrTa8cjOcGiBTyzfAEtJJw/+Z0p54XeyECUdn4CABT21ikSba1wj2p/A4uUN8P5GGablVmB1+eHlmvUXJ+CgPDuoXslTcwbTOe8pvLg4X1jHJrgTAKm3gF0PUyFqNTU9qzYfL9zFBEm6QY2ttLcSXokNbvZKeZrYSnNSmr39064AQBlucZ6SGilcqCxk/RYC2HpiU7TVCaFIyWhZaIEow+tiaaV6H1vJQDgTY8xe4VUl2ZatpUHCL4E27vFZpt9pK3QVyNTPqNSgvQgqumY1XZjE3kvzWjJmvbam+H8Atc1+NxbhrtLbtUxotCePXsYHp87GDMPKZMdim7CecrFY0+QcDBJN7BwLuwNXnN80fvX3dvu7/fXu3WKRD2BZrY1E7VngHUccrqq+yNtZSS3rNPtchi7h8BOZEb8ngW4CAsb5+CmpIvUD0gFZ47qjNy09oc0dQhREWg0iT5LP1m5yBVqNnerFDijqUQy/FOxVYCvXzgSCyf1wIXju8uJRyXbHeae+C4a7X3PLjnrTLw99lmce9KxOkYUWnqiE0O75Iac5NWsWs86/6h7LFvSY8Ak3exMMhHPvMntz8CZiHqdIomN70Mh/IJY8JvxUdXFuG9mTYxRRae6NBOnDC3HNYf3VGeHdmfobUzOqDOqhsNrwomFTm64FG97qnCZ98yI37vsrPHY0/MkLJ07RoPIYpfotKOmrP3Wuxv+VKVTNOoozU7GmaM649KJPUxfidmeX5ztd1U1WQ/woMJZi7n1NkkGX6O79SfqlJeKM0Z2RpLJhzI57Nb9vgXTXhmsKCMJ80Z2Rmay+Xpp+gq07riRzVOu8Pv5SvccSZFYgwEXhqKDwpmAxWXoB2JL/Kcf2hl4PfiWR+JN7cOR5MuiozDsuzsC/m7J9H46R9NCURRce4SZZgiVrywnGdgmO4rotC7OmCGRWO3ti9Xevujm3BHxe3sUpuPuGf01iEo9oVoq89PMVUADgEsnNq2i8M3WXUG2MH8y8YzzCCTX7sLpjpcC/t4EX60whT5XvluU5yRj8bRqzaJRg1V6OQDAX91H4VzHcwCAKXG2/BoACK91zmUwB5CAVNQBAIRiM/zds2PPIcAG/9fC+85Z/1xGwxzNsGRKVnoYBhLq0+37Y8KSCTXdtA/GqJLMPc6vtURDV4q1z+gP9/bUwporXXgV6/Y+UaLozvmo29jzWhx0+RHVuMk9o/lnm0l6tEUsxOcSEH7PwTcvHo3+Bh/bHXJyQxPZKVrWtU9OsOg12I4vf9krOwRN3NTYtCzqdY0z/V5XhPG7jZ86vEJ2CJYSf99q0pG5H4ChhErSU1ELAOiQFR8Tx7XmTS0EZr0oO4xmVp6kKxpmWhc4mjHpZnAgMVd2CBqKrHhRJ8wzMePEqkJ8ck17y+OZ57sVO7OctYOsc25qbF/LDkEqMz3DInGf53CU1z2O5Z5JMNv3q2t+quwQLIVJOlGUzHXr1J9txj+BQnONqQ1p4GlN/y8fITeOKFizOENGFWy0Vnv3TTP1vspICt4Lwkyfo33hrURNcrjQKDsEqbKSrdsTCQBKMpP8Ghd+6cXx3fGGY9JJM1Z8dFun8KUHY1VjqNKS3vvYprWeszvFvi+dhZqR2sgSnXZYc4JYM52FCAXJ0tufjMycf4823y1zfow2Qk4cJ8zXP8k3XsVm7naq70T8jUP3VZppzWFQB7mcNuBAy8916eXSYglXoFtfZpLDos9v7Zn7DkUwa6HGCqxSEFPTp1ntdQG1AEUB8nsADnPPGGs2FbkpskPQhoXvIZEmb2ZL9tpjlW644UxeS/Ks9vaRHYJUZl5tJSzCfI+I1re+o/uX4IRBHUO+7/WsaRpFZG5M0g3M7Lcfs8cfmtlun9o74EiXHQIFYcbWvjunV6NLfir+cmxf2aGQDoY4v5UdAvkI5x5htrZ0K00c975XpeVTySRMcL22umksPr4aqa62E+6e2nAxtogcnNJwCUbWL4a9VM5SxEbHJJ0oSibIceQyWC13vJ6vpe4jAADXuU+WHEnkjqwuwasLRqKLZSejse5VGawVNvhdQaBCbNYqHE1Z+CzKDoDa4Tcm27oXYXAWvzwF/D/i5t9qZYUSNhHmhfiGtx+G1f8Vb3qr8aMo1Dgq82KSbnJGrsWOx2cGkdHc6p6GQXV341HPYX6vh/swJQ2ZoTuDyoJ9Ys73YTyWPCPGLTJF7JZjrNvd/cKGeXjfW4nvvEVBtxEw75Ko4RDChE9pqy5HKQn/miZn6IKNhR6GB1nwI6nKyJVG8UvBdgRYu9jAtw4yv2jGpK9LPVSLUHRnma9WiN5QZvycVnpCWemztPa091BMb7gKuxC8F1VmhvWH1/mXqYx/xu1hTsbYvSDN7+fKwrQgW8Y3zu5OmtmLFAA7Wl444q/A8+dKi0cNop2fqJW0+J551iiSnHYcaPSgW4FVu4yTEQVfgi14QbPelqRRNBpr9WGF1xpTGYeudDX5M9BgQ7IilZmcgPc8PVGs7MSBXIstdxqGjkUFskPQVFN3d3N9xxz2AEl6gB5jj8wdhJc+3Yoehen44df9mFjFLu+BsCXdwMJ6fhj4+3sZzsd6bzlOa1jQ9EL/kzG2/i9yg1KTgf/28vhctCk58sKgZs+dPQxH9y/BAycP9Hudl68BxGF392CtQWYrjMYHcyexoZj90w0sz8IJjVdgdMNiwGatNcMfOnUgpg8sbXcbb2rwrvBWIIR/RVnIJRENKlDY+WmJOHVYBYZ0zsEJgzpyJYkg2JJOmvlBKcHhDTf5vWalghi7dptLvJ6v7oVpWHx8tewwKADr3A3VUdfobv737IaL8KtIx/9JjCdcbe8t1jizoT6FEOa7p1rpOdCU2CjwWugzHTSqez5Gdc/Hmk8C//4lzyAM7jBI36B08K6nZcZ+AeF3vZr3LJs3ctmYpJuckZNeK1aMterUKCkKotjFZSOuwexJ7SQ7BM201609mJ376nFwLqjXvf1VjkhHFvluiTA6W5ot6bViuSQeLXNPwiGyg1DBwvIV2Pb1/7DO2xlH2N/D/3mGBt2Wl278YXd3oohYpPSlFQOXgMxWmCTr25kZ/vrv7c1ybES+t4IXPD7F6aBj1XlvNR+znzPrPBNKskw6n0OUBBRLdJG+adYE3HPdQvyOdPzDMwG7kIZDu+UBAE4+pBxFym+SIySZ2JJuckYeoxIosoFdOwCbdA+FdGL2IlswpzZcjOWyg1BZcWai7BAIwB6RhHTlQMjtzFzJdEC4fH4y7+cIpvV9z6r3QcsxcPkpXO8tHIP6Ri8ykqw1Jv0gq1feKYqCRKf/UnL3zazBl9v2ok9JBvC638b6BqcSRbH2OdQSk3SzM/C1H6iWMyu30NRJuu8DQ7C/sKm87umHo+3vRPXeN7z9VI5Gvk55qbhnRn/kprlCb0waCq/g5XSYd03g/LTwEgjfv8TSGf2Rb9Zr0yrPBnPmBO2y2kcqyoivFnRfVjuXByU67aguzZQdBhkAu7sbWDhdeYxcyxgo+jnDK3SPgwgAXvQegm+8JbLDMJRJvYswsDxbdhhxSxj4/h0rh73lCdApNyXk9k1TYLX8PSb3LsIAs16bwhpLsHlDTQxn3cuXDKK9HkQ2m1XT9MBsZm1Jlx2AiTFJJ10luczdecPMXU71Ydy/z/iehXjHG+Zaspdt1jYYIosrTG8ZTuEMo6RhM3GXSKs+Fxo95j0n4TBpzhNXEh2Bbx5luSmW7eJPdBCTdNLMbcc3TYp0+eQeLS86k7FdZMoJSAW+LT2FcdzNLBgjz5EQUbfuxHTtAiH6w+Sq8CeDe99pruWG/HqC+XT/tmLaZ80F2AC7vf37uUhI0ykSilfBWsvPHdNV50i0deuxfQAAd0xrZzJRA5ev2mWV4T8SmLtZkwxtdPd8fHXDRLh8xlK6HHbMbJiPp13XSYxMHeG0DhERBZOVkoA9YW671t4H0zSNRjsKwuv+bTdrIbSVeCiSzmy4DMtSkmWHETH/S8wa1xuZ3/EDSnFkdbFfebk1s16tVpiFXxamGSb3SMrJskNoV+sbjt2mYP64bpKiUUP4xa91xWYtUkfPqxi33q+9ytwvvaX6BUKq2ynM2/Mh3K7Spk78fMZot/dpC9PNOVFcm3NokTHp7bno2NFw2m0wb+pAZhDs6lIseN21l6Cbm6mfXlIxSTexPnX3YU3CQNlhRCw7JUF2CKpoXTBb5anBnIYLm3+2JWXqHJF87xXMwPfeQixuPFZ2KAF5A9zy1nvLMbnhZgnRkFqMPIGmWnJSzJnAAsD+tJYJQ408JEY1AWoE/9J4vIRAYtNexWZe8/Ah63/3SKYg11c83EdaMeu905xRG4Nxm70opD1IlR1CHAp+u3nSMwqbRX7zz2XZ8bcO9QFHBsY0LAYALJAcS2upLnvAx/1WkRMweV/n7Yxq23d41jNM++CIQhhTmR96I4Pandtfdgi68p21f6vIxn+9Vfi6yxyJEanPduB32SHEzqRJD8UnXq3xh0k66c/UD0YR5N9N3GjpruRNytEhHgrX2aO74L532050FOxqPKphEXKwB78iQ9vAKGZmXZqmtb+5j0QaajHLsarN75wmXm5I+Hb/joNJhBSfz3hewzn4SPTAOJu1urI69/woOwQVmPc7FT8CnyMrL18ZjFmvVrPGbQTs7k66M/NYIr/YWz0jvLDB4/OVEs7QawOTfjKTE+DqfXgE71CYoJvER0qYS+sZ3G3uadgH660a4Zu0tnf3t8ywBZ9Kid9g0RnQm8+puZ7nvsPUOKGV8QW7IwgRf+euIs+sZUqL3NclYJJucqZslDDxgzEruW3nk8fdo/GJtxPe9vb2/0UcTB7U2uTehQCA4gyjdvUP1Jplxi8R+brJNk92CKoJNpFcfWoHnSPRRrBxlftt5k1mW99BFIvc+9srX5i1QsW8pQ+Kdwl2a/XGodDY3Z10Z+IcHYl+s282FVIud5/m84qJP5wKBpRn49UFI1GcacwkvSIvBfjS/7Vghc2vb5iE855Yi39/vk2HyCgW+xTzLQV1UOurr/U95Kj6RchVduPk9AqYV+iE7vt+lwFfvaZDLNrz/bQ/ibym18yZ07bDch+IDChoicrMBckoeRPM2ZIeh6dKNWxJNzle/PKETsjjsxDTJT8VyQnGrP/rXZLZ5rVgZzHBYcNR/Uo0jYdi96h7rOwQNLVOdMGr3hpz3+vDGJPekJgDq9wzhc9nrIc1VjNp44/PaL5ex6YLOM5Z456ghsaSQbJDIJ0xSSdSkd/jxHpNJ5a0SZh31mwC9iEZWcnWSYQacdYKIQAAM+tJREFUReAKLnNPjhfemHSzal1hK+Lg3q8oZv2MvkMRrHg1BrbeWy47BIqZOa7Xz0XrXl/miNuImKST7vh1JWkCJDrPcYk10+lbd5/fz/ecZI0lvgaWZ+GXyln42luCOxqP8fudqe+bPklr0LV+hUCtacelt/pMwiMnDD3FQUWElXzgrZQdgqri8fIzy6TLTrt/aqnE48lSCZN0k9gmsgK+buVr/0XPIQCA1Z7eIbbUU9s/+LAuOT6/VdrdlozHCxsuPKyb7DAoAruR2vzv3h0y0SXfrMmdv6fmDUVWTj7GN/wFd3qOCf0Gs/BbuTLofM14IedU/NfTC/MbztIjqpg97h4DAFjmOtH/Fz6fcWqfIiQ4bLh0Ug89Q1OFNZ9g5kh01Bafn9pqzPGN5LWmHibpJvGjKJAdgnrC/AZf0ng6zms4B2c3ztc2nih9md00FvbROYPRv2Nm2w3McT+Ne4MrsnDu2K6yw6C45X9DDNqr3dQln3C6uyuodWRgRuMV+D/vcD2Citnl7jkYXPc3vJ7gPy+Cbz3EheO7Y8N1E9CtwBoVSQeZdwZ7U3+RombW2fiDzf0Tj+ukm0Xr3lL7ss1XQWkUTNLJsEb0KsPz3qGo7tpRdigB/ZLSlNgpigK77eBNyXcddT5EjCbQ475HobUKz2Ru/UozA75ulq6OgfgmCEG7u5uSgl+Q3aZiRWnV3d1hN2dRq91H2B8f2mxD0xttrpYfLHUtts+sSbpZ49aCWa9Wtysbg+rulh2GKRlzCmayuPBuNbcfX40Jn2/DuJ7m6UXgW6hpyGXtoRn06xh4KAmRDIf1LMDSGf1RWZTu97qp84kwZnc3c9ej1pP6+U4cZ+bT5hv8CvcoTHO82fKCSSuhv8kYisN+vuePn0x9dije2My7Tvp2sJwVDXNW75LJhfdwT3U5cHT/DkhPdGocT/i61H3W/O80V0sdV6DZpetzqnSJicIXKNFhTT0ZiaIomNy7CBW5/mviuhxmflyH/o4pEMhIMs69PhJKqxvLnqxezf922M2bCPqetZe9/ss/iZSm9d/Nlud6TZzoxCeTXWAaEilciSbesCXdJFx2W8ByDsfl6CvXva3536cOa1lmYtGRVdhT14gxhSnAxzIio3AEGt9m0gYhihPnjO6CTb/VojpIN3gzUMJsWb5ofHf8+Gstpg0s1T4oFdlafShPahEOrb8De0US1mYlywlKdf43yoaDazbz/kkS8LlN8YBJuknUuXKAOtlRkK8Un5b0woxEPHn6EHz11QafJJ1PEbMb1T0PJZlJ6F2SITsU8nHn9GrgOdlR6OOiCd1lh6ACEeBfbeWkuvD4aYdoH45Krp7aE4tXfY1bju7j97oQApusNNkrAlWumLNnx/DOucDnsqOgWMVl6coknQpan5shnXMCbkehMUk3uGn1VyFD2YcjU36UHYqKTHKniUrLZ2MvB+MJPK43+HlKdNrx9iWjzT0e2IKOrC6JmyTdEsJp9jLh7XL28AqcMrQctlZN6Sb8KIH5fJAhFVnAFp/f/XFTzKjoD3zxvr5xxSDJ6VO5EEc39v8pvXEq/iM7jIgdUJJkh2AYZpk8tHWULgeHmESLSbrBfSAqAQGckLAF2C87GrWY40YTK8H+WJbQugBOxpLgqZUdAqnCnPdLK98ffM/IwPJMvyT9YH7b6eirsWtVElL6HAFzzigQH+yVUzDrMzu+9JbiA9nBRODTvtfA+eH5qLL94P+LOCxfpbiY7MYbc/ZXiiMvnDMcT5x2CJISrHSq2t5cv8kdG2A78wm2pqfVrU0fIzuEMAU6P/F5zqxi4I6nZYdAQfzTPRLfeYuwrdjn/h4HrZdWzB/anrU/XnEmIXPy1XB2qNY3oKhZ8OSEQ1HwlrcvfkG27EgicuKkkfjh2Jdlh2EIyQnmbVddffFo2SGYknnPeJzo3aFpLOz7QZ4rOSmuwL8wmQZ7SuiNTMC3/BlPRQFzV07E05mynu1JncA5b43pEvcZAASW2hNbXgySwXKVBeNprxO/me/4ZB5Ouw1T+xQDz8iOhMIVqDzYMccqE2jqy0rNs3FneJdc3Hx0b9lhRMG6rZl+S/F4Wegk/dWL+Op0ujFtoOwQqF3WuLdHxrz3/rc8fQK+7rW1XmY0Hs+ruXydOaL53+a9IgOz2uchCoRJumn435I8QsGjcwejONMak2qYuyW2RYqrJUEqzLBGL4dwmPn8HWzYe9PTV24gRHEg6J3CQn3EzfxRvhdFAV//rWC4/wuKiT/kQRYfelFnT5UdgmbM/B0jCheTdJNa7D5OdgiqMsv9dp9IDL0RmdL73krZIRBZkm+BOvi93ixPgfjh1xnMxkmrzOa/hScDAB53m2XOmEjwfkHWxyTdNPxvSD/bCyXFEd9e9LS/hq/fmHR2dzcJq50nq30eInOJh2+gWZaDai2eWmB3JpWjW90/cLl7rknPVnBxdBopjjFJN6m+f0woZxVm7i7tS7F497lgTDPpU4Dzk+ri/JlmtjF9gOwQKISC9NBDfzKSrDOXQjwlgmajxNnJabDswnjxWdYyA6uU542ApVOzaPVcMfNXIFAea5kvtd+Hi6/CgCkEKKAVpjcNYbDMNRgnBtXdjW62n9AjY5jsUCiIB2cNwPc79mNAecuyT8G+ZZ1yrbHCBwAkm3g947KcFGB36O1MUzHbWhzd5jN9Kr5MerbaYb1PRNQaW9JNQ7Tzk7mYuSI7dCLX8nth5g8aIXMnuNY6T6YtPEdoO7Lwjrc3FJuZrz1gNfoBAH4SuZIjUd/YygKcdmgnv9eCLcCGTqN0iEgfI7vm4cjqYlw+uYfsUCK2oeBwAMAab1e/1+O0k5ipzRlRgXGVBbj9OOtNihpHxSvL2OTNkx2C6bAl3awsdoMyd5LXwr8d3WInyQLaG44QL8mtVZw9ujOe+XgLzhjZWXYoMblBzMbH7gq87BmED2QHI8Enh9yBvmNPBJzWmZTTZlNw5/R+ssOIyrbkrhhQdw9+Ryr+gv1Bt7PGE9vakhMceGBW03CgN7/eITkadcVTI4hVrHf2RkfP67LDMBUm6SZh9QTCLEm60xGq84k5PgdZV7xcgRdP6IGLxnc3/TwQtUoKHvJMlB2GRIqlEnSzU6BgJw7OedNS7jD514wsxtolYrMLfLPYr1hnSJNepHd3X7p0KSoqKpCYmIiamhq8/fbbQbd95plncNhhhyEvLw/p6ekYMmQI/vOf/+gYrXGY+QYV6GHvsZmjvqgkM7nd38drQcbUlUjpxQDMU1FELcyeoJO5n2VW115jpVm/e94E664d3p5R3Zq6Gic5zTtfgi82pJuPqcuJkkhN0lesWIHzzz8fV1xxBdauXYsRI0Zg0qRJ2LRpU8DtV69ejcMOOwwrV67EmjVrMHr0aBx++OFYu3atzpEbgEWu9Vsbp+E7bxHeKz5FdijhCVEu8Su48CliON7UlqULf+hzATDjaSCjg8SI1PdPzyjZIVAEzJnqqKc2taPsECgq5ny+NaSU4LrGmbiwYZ7sUHT1p34lWHbKALx18SjZoVAE9gn2MopnUpP0xYsXY86cOZg7dy4qKyuxZMkSlJaW4p577gm4/ZIlS3DJJZdg4MCB6Nq1K2666SZ07doVL7zwgs6R66/149Ccj8e2lnqOxNiG21HrzJIdiipEq1HpBz3tGQEAeMQ9TueI9GGWVmhnYkt3q715/YCu1jsfi9wzZYdAESjJSpIdgjSXNp6GfVm9ZIdBPrJSEpr/3d68Kua44we23DMJT3sPlR2Grmw2BWN6FCA/nUmfmXjkd3hWjd1unc+iF2l/sYaGBqxZswbjx4/3e338+PF49913w9qH1+vF3r17kZ2dHXSb+vp67Nmzx+8/M7L62p7m+XjtF02CNaQvbJyLafVXYZH7ZI3ionCUZLYkRA0ZndrZ0ry89tBrUpNx3HtSDcb2yMe/5g2RHYoufO+gL3ji4zObyRmHdsLEXoW4c3o1hDBzKk6WZpoyY2xmN1yMHSID5zScKzuUmBWygihi0pL0nTt3wuPxoKCgwO/1goICbNu2Lax93H777di/fz+OP/74oNvcfPPNyMjIaP6vtLQ0prjlsdIdyboPfr9P5m05Zw1w4gNRiUYTz9V4R+MxskNQxcj6xTii/no0pBTLDkUTj8wZLDsEikB5bgoePGWg31riViaC/JuMIcXlwL0za3BkdQk8Xm/Q7Uw6JJ0swmuelp2YrBHdMbB+KV70mqdCM9iZ4S0jctL7HrSefEQIEdaEJE888QSuvfZarFixAvn5+UG3W7hwIXbv3t383+bNm2OO2QistrzXn/qVyA4hTCEHpesThgQrvf7JX60wZ4vtj6IQn4q2y3Y57NY4d4d0ypEdAhFZwJgeLY0o/UqtMSSNrCG+lmAzV9nkU2dvAIBb+KeY3QrSZIRjatKa9XJzc2G329u0mm/fvr1N63prK1aswJw5c/DUU09h3Lj2x5S6XC64XOZMJtpj7m5oLTfXf8wehF7F6chNNck5iuDPLhC8FcLsxtf/GT+KAnyVeIrsUKLWuj5laKcc4AcpoRDFjda3UBbcjCs/veW57DtWHWhaqo1IlnhK0c3m8cQT8PneFLzhrYbvel05ZinnG4i0lvSEhATU1NRg1apVfq+vWrUKQ4cODfq+J554Aqeccgoef/xxTJkyReswDSsnNSH0RiZQlJFongQdCP1kiJPZ3TeKItTD3Ndg6+vOumfLWj7w9pAdAqlk2SmDUJ7LtXMNK7mdXjkWfr6R8VmtN2kwE3q132hpRI2KCw97JmCzMF/sRiN1gOyCBQswc+ZMDBgwAEOGDMF9992HTZs2Yd68pqUxFi5ciC1btuDhhx8G0JSgn3zyybjzzjtxyCGHNLfCJyUlISMjQ9rnkKFHYbrsEGLQksharxWl5bNZqQxzXaP/jOFT+xZjanVHYIWkgGLw4KwB2LG3Hl3y43O9XLMzy0oCFFp1aabsEKg9Bb2A8TcA6QHm7zDp1zApwRrrhMe7PbUNskPQRVGG+Vb/sFDRVzqpSfq0adPw66+/YtGiRdi6dSuqqqqwcuVKlJWVAQC2bt3qt2b63//+d7jdbpx99tk4++yzm1+fNWsWHnroIb3D15eFMj6TPtsBhE4QgnUBdNgUuL3mPIffeEuw3DMJXZWfml+7/di+sDnN2ZI+tjJI7a6F5xOwEnMP9SFf/MqZwNDAs0qb9dx1K0jDKUPLkZdmoh581Ib3p/8BsH5v2gvGdcP2vXU4qtos8za1o3Qw8P5S2VGYivSpps866yycddZZAX/XOvF+8803tQ/ILEz6gARMXssW0Zj0Fivnj8Cpyz/C/HFdgRdVj0pTSoAzZtYCGpnXHpGMdKUWb6MfzDPPLbWhBP2BSBfXHtFLdggUIzs8skPQRUayE0tn1MgOIyJB7+o9jwSOewgo7KNjNOYmPUmncJk6tY0fSRn40ZsPu+JFcnJu88vdCtLw38vGNP1giSTdfzqLrS5rrjlOxjGu/i8YbPsCOztOlB0KxYSJuSU4uOYxyZOZzJ4QpqMoQK8/yY7CVJikm5Z5Czq1Ob3wubcMW0U22p+b34BCThxnw+iGxVAg8JHN+mPfptbfgFG2T7At+zhMlR1MjHakcEIyI0vJ7YAXdmbh+r6lskMhIqf5xsqSdWzfUyc7BAriT/1KsGHrHvQotNqcU/pjkm4Sv7n8x6OYuV1dsTkwpeEmAOZb8UqE6Oed5LTD+8eiCS6HtMUTdPOZ6ITPPJ3wJ5s5x6f7+ilrEOY1nI9vRTFelR0MtfHcWcOwfstuDO0ceh34Je6jcb72IVEUOPGfNSgc80QS1TZad4lbs5s9vAKVRenoUxpfE3prgUm6Sbyfeywm/Xx3889mTtKbWPMBn+JyYOmM/hCi6d9kLv/2DpIdAgWRkezE8K65oTcEsMR9LJN0g/Ifkm7N5wARaYuVfcZltylhP6upfdZv6rMIt+LE6PrbfV7hDUqO0H/3yb2LMKVPkQ6xSMTCNRFFwfwVzASwBEKy8U5C1sck3SQEgI3C4okfGdYF47rJDoGILIZ1fUQUDWGhZYmJgmGSTrrr/sdkEtkp5hvH/GbuCdgrkvCge5LsUHTjsAHzx3UNOMv7+J5Na46fMrRc56iIguP1aFwKl2CzhEDPAyK9sLs7xQMOmiXdJSc4sGHRBDhs5qsjGtC3D/p+ej9SExMwR3YwOvF6A03Q0vSA/PvMGuypcyMjyalvUETtuObwnrJDoCBYuCaiWP1u55hnsj4m6SaV+vsG2SHEJDnBnJfe+J4FePKMYeiSnyo7FN0FKlwrisIEnQyHM08bGc+NFSg8jyRRQw6XTCXrM2emREio3SY7hLikKAoGVWTLDkNX8VIUY15HpC9FMV9vKiKSr2O3vrJDINIcn5AmkWBvdao4HIyIgvAK1jiQQbE2zBIUhYUQkmdwRY7sEIg0xyTdJM4d08Xv5/2ZnG2bJGJB29AOwHyTMhKRiXB2bdLR194Sv58FW6ooDjBJN4mcVBeemjek+ef6ZC7HRkSBcXIuMq6Wa5N1fWbGJIn0c0TDDbJDINIdk3QTGVjeMha6MSFLYiQUTw4utZOcVSg5Em0N68LZYon0xSzdTO5oPKblBxFo1Q8ibRTnZaNetExSm57ICWvJ+pikm8x5DefgbvcR+L1gSOiNiVRwMEl/9LzJ+GrSkxBzX7dkE1i/jll4/pxhWHPlONmhxGyx+zgAwOPu0ZIjIWrFgveOePGMd7jsEChO/d/Zw/x+TnTaJUVCpB/O7m4yz3uHAt6hWMZyDuksLdGJ7oMnyQ5DU306ZMoOQRXLPBPxmrcfNol8nCg7GCKyHo5JJx2lJToBpVF2GES6Yks6kc7MNvO2uaKlJgp+FIUQcXSL/4f7MNkhUIS4nr25+M91wSSdiEhL8VOCsxhWYhNRMC5H/N3aPxMVskOgsDAxN6t9IqnlBxs7YhIRaYl3WSKdma1+hevhkhl86u0kOwQKA+8m5rULaZjfcBbccOB2R6LscIiILC3+mluIJHveOxQAsNbbRXIkRNbxleiIY+qvwfD6O2WHQmFSFBZBzOb/vMPxkvcQOO08d0REWmJLOpHOrmicg7c9vfG6tx/WyQ6GLGlczwK89OlWdMlPlR2KrtaI7rJDoFA4Dt30OuWmwG7jeSQi0hKTdJNKT+IakWZVi0Q84z1UdhhkYTcf3RuDK7Ixscraa9uT+Sgck256QzrnyA6BiMjy2F/JZG49tg/mjeyMAWVZskMhIoNKT3Ti5CHlyE/juFEyMLaqm8qTpx+C6QNLccnEHrJDoTh0a+PxAIC73UdIjoRIH2xJN5njB5TKDoHijMKpnshE2A3X4HwSc54pczmkUw4O6cRWdJJjqedIPO8dip9EHs6WHQyRDtiSTqSzu0/sDwC47ohekiMhso55IzsDaGrtI+NilR8RRUfBTyIfrN6jeMGWdCKdTelThLGVE5HotMsOJSxsSSczuGxSD5w/rqtpvldEREREwbAlnUgCJhJE6uP3ymzYIkZERBQIk3QiaheL0USkHp8x6by5EBERBcQknYjaZVfY3Z2IVMLMnIiIKCQm6UTUPoW3CSLSABN2IiKigFj6JqJ2ceI4IlKL8OvuziSdiIgoECbpRERERERERAbBJJ2I2seGdCJSCdvOiYiIQmOSTkRERDphmk5EkSvLSQYAdMhKkhwJkT6YpBNRQMvdEwAA/0g5VXIkRGQVguPQiSgKj8wejJMO6YjH5g6WHQqRLhyyAyAiYyqf8Vec8tpHuPL4UbJDISKLYIpORNHomJOMG47qLTsMIt0wSSeigEZXFmB05VTZYRARERERxRV2dyciIiJdsLs7ERFRaEzSiYiISCdM0omIiEJhkk5ERERERERkEEzSiYiIiIiIiAyCSToRERERERGRQTBJJyIiIl1w3jgiIqLQmKQTERGRLgQnjiMiIgqJ66QH4fF40NjYKDsMipLT6YTdbpcdBhER+WHbABERUShM0lsRQmDbtm3YtWuX7FAoRpmZmSgsLITC/pVERERERGQSTNJbOZig5+fnIzk5mQmeCQkhUFtbi+3btwMAioqKJEdEREREREQUHibpPjweT3OCnpOTIzscikFSUhIAYPv27cjPz2fXdyIiIiIiMgUODvNxcAx6cnKy5EhIDQfPI+cWICIyCPZOIyIiColJegDs4m4NPI9ERERERGQ2TNKJiIhIJ6w8JSIiCoVJOmlOURQ899xzssMgIiIiIiIyPCbpFvPuu+/Cbrdj4sSJEb2vvLwcS5Ys0SYoIiIiIiIiCguTdItZtmwZzj33XLzzzjvYtGmT7HCIiIiaCc4VQkREFBKT9BCEEKhtcEv5TwgRUaz79+/HP//5T5x55pmYOnUqHnroIb/fP//88xgwYAASExORm5uLo48+GgAwatQo/Pjjj7jgggugKErzhGvXXnstqqur/faxZMkSlJeXN//80Ucf4bDDDkNubi4yMjIwcuRIfPzxxxH/nYmIyPo4oScREVFoXCc9hAONHvS8+j9Sjr1h0QQkJ4R/ilasWIHu3buje/fuOOmkk3DuuefiqquugqIoeOmll3D00UfjiiuuwCOPPIKGhga89NJLAIBnnnkGffv2xemnn47TTjstohj37t2LWbNm4a677gIA3H777Zg8eTK++eYbpKWlRbQvIiKyNrcnsspnIiKieMQk3UIefPBBnHTSSQCAiRMnYt++fXjttdcwbtw43HjjjZg+fTquu+665u379u0LAMjOzobdbkdaWhoKCwsjOuaYMWP8fv773/+OrKwsvPXWW5g6dWqMn4iIiKzk3e92YpTsIIiIiAyOSXoISU47NiyaIO3Y4frqq6/w4Ycf4plnngEAOBwOTJs2DcuWLcO4ceOwbt26iFvJw7F9+3ZcffXVeP311/HLL7/A4/GgtraW4+GJiKiN3XUewCk7CiIiImNjkh6CoigRdTmX5cEHH4Tb7UZJSUnza0IIOJ1O/P7770hKSop4nzabrc24+MbGRr+fTznlFOzYsQNLlixBWVkZXC4XhgwZgoaGhug+CBERWVZ/5RvZIRARERkeJ46zALfbjYcffhi333471q1b1/zfJ598grKyMjz22GPo06cPXnvttaD7SEhIgMfj8XstLy8P27Zt80vU161b57fN22+/jfPOOw+TJ09Gr1694HK5sHPnTlU/HxERWUN3G3tZERERhWL8JmIK6cUXX8Tvv/+OOXPmICMjw+93xx57LB588EHccccdGDt2LDp37ozp06fD7Xbj5ZdfxiWXXAKgaZ301atXY/r06XC5XMjNzcWoUaOwY8cO3HrrrTj22GPx73//Gy+//DLS09Ob99+lSxc88sgjGDBgAPbs2YOLL744qlZ7IiKyPsG2ASIiopD4tLSABx98EOPGjWuToAPAMcccg3Xr1iE9PR1PPfUUnn/+eVRXV2PMmDH44IMPmrdbtGgRfvjhB3Tu3Bl5eXkAgMrKSixduhR33303+vbtiw8//BAXXXSR3/6XLVuG33//Hf369cPMmTNx3nnnIT8/X9sPTEREpuQFl2AjIiIKRRGRLsZtcnv27EFGRgZ2797t1yIMAHV1ddi4cSMqKiqQmJgoKUJSC88nEZGxfHT1QAy0fd30w7W75QZDRESko/by0NbYkk5ERES68LLYQUREFBKflkRERKQLM6yWQkREJBuTdCIiItKHwmIHERFRKHxaEhERkS7Y3Z2IiCg0Pi2JiIhIFx6F3d2JiIhCYZJOREREuvjS2VN2CERERIbHJJ2IiIh04VHsskMgIiIyPCbpREREpAtFdgBEREQmwCSdiIiIdPGhaygAYJM3T3IkRERExsUknSJy7bXXorq6uvnnU045BUcddZTucfzwww9QFAXr1q3T/dhERBSdrc4OGFR3Nw5r+IvsUIiIiAxLepK+dOlSVFRUIDExETU1NXj77bfb3f6tt95CTU0NEhMT0alTJ9x77706RWpsp5xyChRFgaIocDqd6NSpEy666CLs379f0+PeeeedeOihh8Lalok1ERFtRxbqkSA7DCIiIsOSmqSvWLEC559/Pq644gqsXbsWI0aMwKRJk7Bp06aA22/cuBGTJ0/GiBEjsHbtWlx++eU477zz8PTTT+scuTFNnDgRW7duxffff48bbrgBS5cuxUUXXdRmu8bGRtWOmZGRgczMTNX2R0RE1tUlP1V2CERERIYnNUlfvHgx5syZg7lz56KyshJLlixBaWkp7rnnnoDb33vvvejYsSOWLFmCyspKzJ07F7Nnz8Ztt92mXZBCAA375fwnREShulwuFBYWorS0FCeeeCJmzJiB5557rrmL+rJly9CpUye4XC4IIbB7926cfvrpyM/PR3p6OsaMGYNPPvnEb5+33HILCgoKkJaWhjlz5qCurs7v9627u3u9Xvz5z39Gly5d4HK50LFjR9x4440AgIqKCgBAv379oCgKRo0a1fy+5cuXo7KyEomJiejRoweWLl3qd5wPP/wQ/fr1Q2JiIgYMGIC1a9dG9LchIiL5CtOTZIdARERkeA5ZB25oaMCaNWtw2WWX+b0+fvx4vPvuuwHf895772H8+PF+r02YMAEPPvggGhsb4XQ627ynvr4e9fX1zT/v2bMnskAba4GbiiN7j1ou/xlISIn67UlJSc2t5t9++y3++c9/4umnn4bd3rQEzpQpU5CdnY2VK1ciIyMDf//73zF27Fh8/fXXyM7Oxj//+U9cc801uPvuuzFixAg88sgjuOuuu9CpU6egx1y4cCHuv/9+3HHHHRg+fDi2bt2KL7/8EkBToj1o0CC8+uqr6NWrFxISmro73n///bjmmmvwt7/9Df369cPatWtx2mmnISUlBbNmzcL+/fsxdepUjBkzBo8++ig2btyI+fPnR/13ISIiOXLT2M2diIgoFGlJ+s6dO+HxeFBQUOD3ekFBAbZt2xbwPdu2bQu4vdvtxs6dO1FUVNTmPTfffDOuu+469QI3iQ8//BCPP/44xo4dC6CpUuSRRx5BXl7TjLqvv/461q9fj+3bt8PlcgEAbrvtNjz33HP417/+hdNPPx1LlizB7NmzMXfuXADADTfcgFdffbVNa/pBe/fuxZ133om//e1vmDVrFgCgc+fOGD58OAA0HzsnJweFhYXN77v++utx++234+ijjwbQ1OK+YcMG/P3vf8esWbPw2GOPwePxYNmyZUhOTkavXr3w008/4cwzz1T7z0ZERBo6rqYUH/+4C4d2y5UdChERkWFJS9IPUhT/VVOFEG1eC7V9oNcPWrhwIRYsWND88549e1BaWhp+gM7kphZtGZzJEW3+4osvIjU1FW63G42NjTjyyCPx17/+FUuXLkVZWVlzkgwAa9aswb59+5CTk+O3jwMHDuC7774DAHzxxReYN2+e3++HDBmCN954I+Dxv/jiC9TX1zdXDIRjx44d2Lx5M+bMmYPTTjut+XW3242MjIzm/fbt2xfJyS1/jyFDhoR9DCIiMoYEhw23H99XdhhERESGJi1Jz83Nhd1ub9Nqvn379jat5QcVFhYG3N7hcLRJNg9yuVzNLcVRUZSYupzrafTo0bjnnnvgdDpRXFzs1/0/JcX/M3i9XhQVFeHNN99ss59oJ4JLSop8rKHX6wXQ1OV98ODBfr872C1fRDg2n4iIiIiIyKykTRyXkJCAmpoarFq1yu/1VatWYejQoQHfM2TIkDbbv/LKKxgwYEDA8ejxJiUlBV26dEFZWVnIv0f//v2xbds2OBwOdOnSxe+/3NymboiVlZV4//33/d7X+mdfXbt2RVJSEl577bWAvz84Bt3j8TS/VlBQgJKSEnz//fdt4jg40VzPnj3xySef4MCBA2HFQUREREREZFZSZ3dfsGABHnjgASxbtgxffPEFLrjgAmzatKm5i/XChQtx8sknN28/b948/Pjjj1iwYAG++OILLFu2DA8++GDAZcaofePGjcOQIUNw1FFH4T//+Q9++OEHvPvuu7jyyivxv//9DwAwf/58LFu2DMuWLcPXX3+Na665Bp9//nnQfSYmJuLSSy/FJZdcgocffhjfffcd3n//fTz44IMAgPz8fCQlJeHf//43fvnlF+zevRsAcO211+Lmm2/GnXfeia+//hrr16/H8uXLsXjxYgDAiSeeCJvNhjlz5mDDhg1YuXKltjP6ExERERERSSJ1TPq0adPw66+/YtGiRdi6dSuqqqqwcuVKlJWVAQC2bt3qt2Z6RUUFVq5ciQsuuAB33303iouLcdddd+GYY46R9RFMS1EUrFy5EldccQVmz56NHTt2oLCwEIceemjzcINp06bhu+++w6WXXoq6ujocc8wxOPPMM/Gf//wn6H6vuuoqOBwOXH311fj5559RVFTUXOnicDhw1113YdGiRbj66qsxYsQIvPnmm5g7dy6Sk5Pxl7/8BZdccglSUlLQu3dvnH/++QCA1NRUvPDCC5g3bx769euHnj174s9//jPPOxERERERWY4i4mzA7549e5CRkYHdu3cjPT3d73d1dXXYuHEjKioqkJiYKClCUgvPJxERERERGUF7eWhrUru7ExEREREREVELJulEREREREREBsEknYiIiIiIiMggmKQTERERERERGQST9ADibC49y+J5JCIiIiIis2GS7sPpdAIAamtrJUdCajh4Hg+eVyIiIiIiIqOTuk660djtdmRmZmL79u0AgOTkZCiKIjkqipQQArW1tdi+fTsyMzNht9tlh0RERERERBQWJumtFBYWAkBzok7mlZmZ2Xw+iYiIiIiIzIBJeiuKoqCoqAj5+flobGyUHQ5Fyel0sgWdiIiIiIhMh0l6EHa7nUkeERERERER6YoTxxEREREREREZBJN0IiIiIiIiIoNgkk5ERERERERkEHE3Jl0IAQDYs2eP5EiIiIiIiIgoHhzMPw/mo+2JuyR97969AIDS0lLJkRAREREREVE82bt3LzIyMtrdRhHhpPIW4vV68fPPPyMtLQ2KosgOp1179uxBaWkpNm/ejPT0dNnhkES8FuggXgt0EK8FOojXAh3Ea4EO4rVgPEII7N27F8XFxbDZ2h91Hnct6TabDR06dJAdRkTS09P55SIAvBaoBa8FOojXAh3Ea4EO4rVAB/FaMJZQLegHceI4IiIiIiIiIoNgkk5ERERERERkEEzSDczlcuGaa66By+WSHQpJxmuBDuK1QAfxWqCDeC3QQbwW6CBeC+YWdxPHERERERERERkVW9KJiIiIiIiIDIJJOhEREREREZFBMEknIiIiIiIiMggm6UREREREREQGwSTdoJYuXYqKigokJiaipqYGb7/9tuyQKEw333wzBg4ciLS0NOTn5+Ooo47CV1995beNEALXXnstiouLkZSUhFGjRuHzzz/326a+vh7nnnsucnNzkZKSgiOOOAI//fST3za///47Zs6ciYyMDGRkZGDmzJnYtWuX3zabNm3C4YcfjpSUFOTm5uK8885DQ0ODJp+d2nfzzTdDURScf/75za/xWogvW7ZswUknnYScnBwkJyejuroaa9asaf49r4f44Ha7ceWVV6KiogJJSUno1KkTFi1aBK/X27wNrwVrWr16NQ4//HAUFxdDURQ899xzfr832nlfv349Ro4ciaSkJJSUlGDRokXgnNPqaO9aaGxsxKWXXorevXsjJSUFxcXFOPnkk/Hzzz/77YPXgoUJMpwnn3xSOJ1Ocf/994sNGzaI+fPni5SUFPHjjz/KDo3CMGHCBLF8+XLx2WefiXXr1okpU6aIjh07in379jVvc8stt4i0tDTx9NNPi/Xr14tp06aJoqIisWfPnuZt5s2bJ0pKSsSqVavExx9/LEaPHi369u0r3G538zYTJ04UVVVV4t133xXvvvuuqKqqElOnTm3+vdvtFlVVVWL06NHi448/FqtWrRLFxcXinHPO0eePQc0+/PBDUV5eLvr06SPmz5/f/Dqvhfjx22+/ibKyMnHKKaeIDz74QGzcuFG8+uqr4ttvv23ehtdDfLjhhhtETk6OePHFF8XGjRvFU089JVJTU8WSJUuat+G1YE0rV64UV1xxhXj66acFAPHss8/6/d5I53337t2ioKBATJ8+Xaxfv148/fTTIi0tTdx2223a/YHiSHvXwq5du8S4cePEihUrxJdffinee+89MXjwYFFTU+O3D14L1sUk3YAGDRok5s2b5/dajx49xGWXXSYpIorF9u3bBQDx1ltvCSGE8Hq9orCwUNxyyy3N29TV1YmMjAxx7733CiGabs5Op1M8+eSTzdts2bJF2Gw28e9//1sIIcSGDRsEAPH+++83b/Pee+8JAOLLL78UQjQ9AGw2m9iyZUvzNk888YRwuVxi9+7d2n1o8rN3717RtWtXsWrVKjFy5MjmJJ3XQny59NJLxfDhw4P+ntdD/JgyZYqYPXu232tHH320OOmkk4QQvBbiRevEzGjnfenSpSIjI0PU1dU1b3PzzTeL4uJi4fV6VfxLUKAKm9Y+/PBDAaC50Y7XgrWxu7vBNDQ0YM2aNRg/frzf6+PHj8e7774rKSqKxe7duwEA2dnZAICNGzdi27ZtfufY5XJh5MiRzed4zZo1aGxs9NumuLgYVVVVzdu89957yMjIwODBg5u3OeSQQ5CRkeG3TVVVFYqLi5u3mTBhAurr6/262JK2zj77bEyZMgXjxo3ze53XQnx5/vnnMWDAABx33HHIz89Hv379cP/99zf/ntdD/Bg+fDhee+01fP311wCATz75BO+88w4mT54MgNdCvDLaeX/vvfcwcuRIuFwuv21+/vln/PDDD+r/Aahdu3fvhqIoyMzMBMBrweqYpBvMzp074fF4UFBQ4Pd6QUEBtm3bJikqipYQAgsWLMDw4cNRVVUFAM3nsb1zvG3bNiQkJCArK6vdbfLz89scMz8/32+b1sfJyspCQkICryedPPnkk/j4449x8803t/kdr4X48v333+Oee+5B165d8Z///Afz5s3Deeedh4cffhgAr4d4cumll+KEE05Ajx494HQ60a9fP5x//vk44YQTAPBaiFdGO++Btjn4M68NfdXV1eGyyy7DiSeeiPT0dAC8FqzOITsACkxRFL+fhRBtXiPjO+ecc/Dpp5/inXfeafO7aM5x620CbR/NNqSNzZs3Y/78+XjllVeQmJgYdDteC/HB6/ViwIABuOmmmwAA/fr1w+eff4577rkHJ598cvN2vB6sb8WKFXj00Ufx+OOPo1evXli3bh3OP/98FBcXY9asWc3b8VqIT0Y674FiCfZe0kZjYyOmT58Or9eLpUuXhtye14I1sCXdYHJzc2G329vUSm3fvr1NDRYZ27nnnovnn38eb7zxBjp06ND8emFhIYC2NY++57iwsBANDQ34/fff293ml19+aXPcHTt2+G3T+ji///47GhsbeT3pYM2aNdi+fTtqamrgcDjgcDjw1ltv4a677oLD4QhaC81rwZqKiorQs2dPv9cqKyuxadMmALw3xJOLL74Yl112GaZPn47evXtj5syZuOCCC5p73PBaiE9GO++Bttm+fTuAtq39pI3GxkYcf/zx2LhxI1atWtXcig7wWrA6JukGk5CQgJqaGqxatcrv9VWrVmHo0KGSoqJICCFwzjnn4JlnnsHrr7+OiooKv99XVFSgsLDQ7xw3NDTgrbfeaj7HNTU1cDqdftts3boVn332WfM2Q4YMwe7du/Hhhx82b/PBBx9g9+7dftt89tln2Lp1a/M2r7zyClwuF2pqatT/8ORn7NixWL9+PdatW9f834ABAzBjxgysW7cOnTp14rUQR4YNG9ZmOcavv/4aZWVlAHhviCe1tbWw2fyLYHa7vXkJNl4L8clo533IkCFYvXq131Jcr7zyCoqLi1FeXq7+H4D8HEzQv/nmG7z66qvIycnx+z2vBYvTZ346isTBJdgefPBBsWHDBnH++eeLlJQU8cMPP8gOjcJw5plnioyMDPHmm2+KrVu3Nv9XW1vbvM0tt9wiMjIyxDPPPCPWr18vTjjhhIBLrHTo0EG8+uqr4uOPPxZjxowJuKxGnz59xHvvvSfee+890bt374DLaowdO1Z8/PHH4tVXXxUdOnTg0joS+c7uLgSvhXjy4YcfCofDIW688UbxzTffiMcee0wkJyeLRx99tHkbXg/xYdasWaKkpKR5CbZnnnlG5ObmiksuuaR5G14L1rR3716xdu1asXbtWgFALF68WKxdu7Z5xm4jnfddu3aJgoICccIJJ4j169eLZ555RqSnp3PZLZW0dy00NjaKI444QnTo0EGsW7fOrzxZX1/fvA9eC9bFJN2g7r77blFWViYSEhJE//79m5fvIuMDEPC/5cuXN2/j9XrFNddcIwoLC4XL5RKHHnqoWL9+vd9+Dhw4IM455xyRnZ0tkpKSxNSpU8WmTZv8tvn111/FjBkzRFpamkhLSxMzZswQv//+u982P/74o5gyZYpISkoS2dnZ4pxzzvFbQoP01TpJ57UQX1544QVRVVUlXC6X6NGjh7jvvvv8fs/rIT7s2bNHzJ8/X3Ts2FEkJiaKTp06iSuuuMKv8M1rwZreeOONgGWEWbNmCSGMd94//fRTMWLECOFyuURhYaG49tprueSWStq7FjZu3Bi0PPnGG28074PXgnUpQvwx6p+IiIiIiIiIpOKYdCIiIiIiIiKDYJJOREREREREZBBM0omIiIiIiIgMgkk6ERERERERkUEwSSciIiIiIiIyCCbpRERERERERAbBJJ2IiIiIiIjIIJikExERERERERkEk3QiIiJqdu2116K6ulp2GERERHFLEUII2UEQERGR9hRFaff3s2bNwt/+9jfU19cjJydHp6iIiIjIF5N0IiKiOLFt27bmf69YsQJXX301vvrqq+bXkpKSkJGRISM0IiIi+gO7uxMREcWJwsLC5v8yMjKgKEqb11p3dz/llFNw1FFH4aabbkJBQQEyMzNx3XXXwe124+KLL0Z2djY6dOiAZcuW+R1ry5YtmDZtGrKyspCTk4MjjzwSP/zwg74fmIiIyISYpBMREVG7Xn/9dfz8889YvXo1Fi9ejGuvvRZTp05FVlYWPvjgA8ybNw/z5s3D5s2bAQC1tbUYPXo0UlNTsXr1arzzzjtITU3FxIkT0dDQIPnTEBERGRuTdCIiImpXdnY27rrrLnTv3h2zZ89G9+7dUVtbi8svvxxdu3bFwoULkZCQgP/+978AgCeffBI2mw0PPPAAevfujcrKSixfvhybNm3Cm2++KffDEBERGZxDdgBERERkbL169YLN1lKvX1BQgKqqquaf7XY7cnJysH37dgDAmjVr8O233yItLc1vP3V1dfjuu+/0CZqIiMikmKQTERFRu5xOp9/PiqIEfM3r9QIAvF4vampq8Nhjj7XZV15ennaBEhERWQCTdCIiIlJV//79sWLFCuTn5yM9PV12OERERKbCMelERESkqhkzZiA3NxdHHnkk3n77bWzcuBFvvfUW5s+fj59++kl2eERERIbGJJ2IiIhUlZycjNWrV6Njx444+uijUVlZidmzZ+PAgQNsWSciIgpBEUII2UEQEREREREREVvSiYiIiIiIiAyDSToRERERERGRQTBJJyIiIiIiIjIIJulEREREREREBsEknYiIiIiIiMggmKQTERERERERGQSTdCIiIiIiIiKDYJJOREREREREZBBM0omIiIiIiIgMgkk6ERERERERkUEwSSciIiIiIiIyiP8HTxX6cdErFN4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(len(train_target)), train_target[\"Temperature\"], label='Actual')\n",
    "plt.plot(range(len(train_target)), pd.DataFrame(model.predict(\n",
    "    train_data), columns=features_keys)[\"Temperature\"], label='Predicted')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Actual vs. Predicted Temperatures (Training Set)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5ca4559",
   "metadata": {
    "id": "Kl1Dcsauo0Gl",
    "papermill": {
     "duration": 0.242212,
     "end_time": "2023-07-04T03:00:50.440729",
     "exception": false,
     "start_time": "2023-07-04T03:00:50.198517",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Save the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "090d547c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:50.928208Z",
     "iopub.status.busy": "2023-07-04T03:00:50.927847Z",
     "iopub.status.idle": "2023-07-04T03:00:50.976875Z",
     "shell.execute_reply": "2023-07-04T03:00:50.975869Z"
    },
    "id": "juUFGBK3o0Gl",
    "papermill": {
     "duration": 0.295164,
     "end_time": "2023-07-04T03:00:50.979029",
     "exception": false,
     "start_time": "2023-07-04T03:00:50.683865",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.save(\"lstm.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "4ce96a0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:51.472504Z",
     "iopub.status.busy": "2023-07-04T03:00:51.472139Z",
     "iopub.status.idle": "2023-07-04T03:00:51.481843Z",
     "shell.execute_reply": "2023-07-04T03:00:51.480851Z"
    },
    "papermill": {
     "duration": 0.257421,
     "end_time": "2023-07-04T03:00:51.484028",
     "exception": false,
     "start_time": "2023-07-04T03:00:51.226607",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scaler.save']"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(scaler, \"scaler.save\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ab3f386d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-05 23:45:11.342244: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:1579] failed to query PCI bus id for device: UNKNOWN ERROR (801)\n",
      "2023-07-05 23:45:11.342262: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:970] no PCI bus ID for device ordinal: 0\n",
      "2023-07-05 23:45:11.343933: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2023-07-05 23:45:11.698622: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-07-05 23:45:11.701036: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-07-05 23:45:11.702417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model(\"lstm.keras\")\n",
    "scaler = joblib.load(\"scaler.save\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c5445e29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df[df[\"Year\"] == 2022]\n",
    "# df = df[df[\"Month\"] == 7]\n",
    "features = df[features_keys].copy()\n",
    "scaled_features = scaler.transform(features)\n",
    "data = []\n",
    "target = []\n",
    "for i in range(1, len(scaled_features) - time_steps):\n",
    "    # exclude the target\n",
    "    data.append(scaled_features[i:i+time_steps])\n",
    "    # Only the temperature is the target\n",
    "    target.append(scaled_features[i+time_steps])\n",
    "data = np.array(data)\n",
    "target = np.array(target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfb535b1",
   "metadata": {},
   "source": [
    "Test random samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad2d1e0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-05 09:46:55.061227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-07-05 09:46:55.061975: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-07-05 09:46:55.062878: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean mse: 0.8785829137258339\n",
      "Mean mae: 0.7045396378040314\n"
     ]
    }
   ],
   "source": [
    "total_samples = 100\n",
    "sample_steps = 16\n",
    "\n",
    "def test_samples(total_samples: int = 20, sample_steps: int = 16):\n",
    "    mse = []\n",
    "    mae = []\n",
    "    sample_results = []\n",
    "    sample_datum = []\n",
    "    sample_targets = []\n",
    "    for _i in range(total_samples):\n",
    "        rand_index = random.randint(0, len(data) - 1)\n",
    "        sample_data = data[rand_index]\n",
    "        sample_datum.append(sample_data)\n",
    "        sample_target = target[rand_index:rand_index+sample_steps]\n",
    "        sample_targets.append(sample_target)\n",
    "\n",
    "        sample_predicted = []\n",
    "\n",
    "        for i in range(1, sample_steps + 1):\n",
    "            scaled_prediction = model.predict(sample_data.reshape(1, time_steps, len(features_keys)), verbose=0)\n",
    "            prediction = scaler.inverse_transform(scaled_prediction)[0]\n",
    "            # print(f'{df[\"DateTime\"].max() + timedelta(hours=i)}:', list(map(lambda x: round(x, 6), prediction)))\n",
    "            # print(prediction's [0], data, np.append(data, prediction[0]))\n",
    "            sample_data = np.vstack((sample_data, scaled_prediction))\n",
    "            sample_data = np.delete(sample_data, 0, axis=0)\n",
    "            sample_predicted.append(prediction)\n",
    "\n",
    "        sample_result = pd.DataFrame(sample_predicted, columns=features_keys)\n",
    "        sample_results.append(sample_result)\n",
    "\n",
    "        mse.append(mean_squared_error(\n",
    "            sample_result[\"Temperature\"], scaler.inverse_transform(sample_target)[:, 0]))\n",
    "        mae.append(mean_absolute_error(\n",
    "            sample_result[\"Temperature\"], scaler.inverse_transform(sample_target)[:, 0]))\n",
    "        \n",
    "        if _i == total_samples - 1:\n",
    "            print(\"Mean mse:\", np.array(mse).mean())\n",
    "            print(\"Mean mae:\", np.array(mae).mean())\n",
    "            return sample_datum, sample_targets, sample_results, mse, mae\n",
    "    \n",
    "sample_datum, sample_targets, sample_results, mse, mae = test_samples(total_samples, sample_steps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "13e79328",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error of 1 sample: 18.443495892177523\n",
      "Mean absolute error of 1 sample: 4.1049758911132805\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error of 1 sample:\", mean_squared_error(\n",
    "    sample_results[0][\"Temperature\"], scaler.inverse_transform(sample_targets[0])[:, 0]))\n",
    "print(\"Mean absolute error of 1 sample:\", mean_absolute_error(\n",
    "    sample_results[0][\"Temperature\"], scaler.inverse_transform(sample_targets[0])[:, 0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8e9b9af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(len(sample_results[0])), sample_results[0][\"Temperature\"], label='Predicted')\n",
    "plt.plot(range(len(sample_results[0])), scaler.inverse_transform(sample_targets[0])[:, 0], label=\"Actual\")\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Predicted Temperatures')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1f67bce1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:51.974584Z",
     "iopub.status.busy": "2023-07-04T03:00:51.974033Z",
     "iopub.status.idle": "2023-07-04T03:00:55.003514Z",
     "shell.execute_reply": "2023-07-04T03:00:55.002604Z"
    },
    "id": "MyoS6kB6o0Gl",
    "outputId": "a432fb93-ed6e-4522-d9b5-baa1a9d14a5c",
    "papermill": {
     "duration": 3.27716,
     "end_time": "2023-07-04T03:00:55.005524",
     "exception": false,
     "start_time": "2023-07-04T03:00:51.728364",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 328ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-05 23:45:25.218249: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-07-05 23:45:25.219129: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-07-05 23:45:25.219975: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-07-02 10:00:00: [22.889942, 23.01441, 0.005554, 99.531555]\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "2023-07-02 11:00:00: [23.432518, 22.043661, 0.033023, 98.72486]\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "2023-07-02 12:00:00: [23.92079, 22.393965, 0.076055, 97.8544]\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "2023-07-02 13:00:00: [24.239912, 23.679502, 0.009348, 97.13786]\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "2023-07-02 14:00:00: [24.360909, 25.60296, -0.035506, 96.831566]\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "2023-07-02 15:00:00: [24.26706, 27.48805, -0.018941, 97.0038]\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "2023-07-02 16:00:00: [23.97762, 28.957823, 0.011659, 97.6078]\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "2023-07-02 17:00:00: [23.539467, 30.060907, 0.142823, 98.30513]\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "2023-07-02 18:00:00: [23.056044, 31.671843, 0.030741, 98.925354]\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "2023-07-02 19:00:00: [22.70476, 33.7533, -0.017413, 99.275475]\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "2023-07-02 20:00:00: [22.493942, 35.64438, -0.062612, 99.46559]\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "2023-07-02 21:00:00: [22.382887, 37.290707, -0.068715, 99.60592]\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "2023-07-02 22:00:00: [22.321106, 38.800842, -0.085299, 99.6854]\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "2023-07-02 23:00:00: [22.276506, 40.1047, -0.091852, 99.749054]\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "2023-07-03 00:00:00: [22.231262, 41.25083, -0.050697, 99.8173]\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "2023-07-03 01:00:00: [22.181845, 42.289734, -0.022935, 99.8649]\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "2023-07-03 02:00:00: [22.117517, 43.23652, -0.002442, 99.904594]\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "2023-07-03 03:00:00: [22.043404, 44.082874, -0.01063, 99.93102]\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "2023-07-03 04:00:00: [21.969545, 44.70048, 0.003595, 99.96026]\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "2023-07-03 05:00:00: [21.914799, 44.920715, 0.013317, 99.99418]\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "2023-07-03 06:00:00: [21.90371, 44.502953, 0.014309, 100.03769]\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "2023-07-03 07:00:00: [21.962563, 43.16356, 0.003185, 100.09505]\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "2023-07-03 08:00:00: [22.132296, 40.82805, -0.002516, 100.12789]\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "2023-07-03 09:00:00: [22.42904, 37.742996, 0.023955, 100.12438]\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "2023-07-03 10:00:00: [22.895227, 34.840782, -0.054132, 99.822556]\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "2023-07-03 11:00:00: [23.46271, 32.266273, -0.14406, 99.13035]\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "2023-07-03 12:00:00: [23.97847, 30.717138, -0.093676, 98.18504]\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "2023-07-03 13:00:00: [24.34163, 30.490156, -0.042156, 97.36614]\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "2023-07-03 14:00:00: [24.479055, 31.2079, -0.01283, 96.96919]\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "2023-07-03 15:00:00: [24.377167, 32.287945, 0.019393, 97.085655]\n"
     ]
    }
   ],
   "source": [
    "test_data = scaled_features.copy()[-time_steps:]\n",
    "predicted = []\n",
    "predict_steps = 30\n",
    "\n",
    "for i in range(1, predict_steps + 1):\n",
    "    scaled_prediction = model.predict(test_data.reshape(1, time_steps, len(features_keys)))\n",
    "    prediction = scaler.inverse_transform(scaled_prediction)[0]\n",
    "    print(f'{df[\"DateTime\"].max() + timedelta(hours=i)}:', list(map(lambda x: round(x, 6), prediction)))\n",
    "    # print(prediction's [0], test_data, np.append(test_data, prediction[0]))\n",
    "    test_data = np.vstack((test_data, scaled_prediction))\n",
    "    test_data = np.delete(test_data, 0, axis=0)\n",
    "    predicted.append(prediction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "84cfba5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:55.565473Z",
     "iopub.status.busy": "2023-07-04T03:00:55.565108Z",
     "iopub.status.idle": "2023-07-04T03:00:55.584589Z",
     "shell.execute_reply": "2023-07-04T03:00:55.583752Z"
    },
    "id": "ruBB1-8ZG2N_",
    "papermill": {
     "duration": 0.276639,
     "end_time": "2023-07-04T03:00:55.586602",
     "exception": false,
     "start_time": "2023-07-04T03:00:55.309963",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame(predicted, columns=features_keys)\n",
    "results.insert(0, \"DateTime\", [df[\"DateTime\"].max() + timedelta(hours=i) for i in range(1, predict_steps + 1)])\n",
    "results.to_csv(\"predict.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02572f64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-04T03:00:56.092890Z",
     "iopub.status.busy": "2023-07-04T03:00:56.092523Z",
     "iopub.status.idle": "2023-07-04T03:00:56.432474Z",
     "shell.execute_reply": "2023-07-04T03:00:56.431613Z"
    },
    "id": "qmiz_9Meo0Gl",
    "outputId": "5386ee5d-ad41-4dbd-af44-33a8338df91b",
    "papermill": {
     "duration": 0.594013,
     "end_time": "2023-07-04T03:00:56.434444",
     "exception": false,
     "start_time": "2023-07-04T03:00:55.840431",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(results[\"DateTime\"], np.array(predicted)[:, 0], label='Predicted')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Predicted Temperatures')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96c45018",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 1853.764924,
   "end_time": "2023-07-04T03:00:59.993226",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-07-04T02:30:06.228302",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}