Activity_Rec_System.ipynb

{
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   "cell_type": "markdown",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.pipeline import Pipeline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Input, Dense, Dropout\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras import optimizers\n",
    "import keras_tuner as kt\n",
    "from keras_tuner.engine.hyperparameters import HyperParameters\n",
    "from keras_tuner import RandomSearch\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "import hickle as hkl\n",
    "import pickle \n",
    "from sklearn.model_selection import ParameterGrid\n",
    "import collections\n",
    "from numpy.random import seed\n",
    "tf.random.set_seed(1234)\n",
    "\n",
    "import utils\n",
    "import preprocessing_agent as prep\n",
    "import LR_aval_use_agent as LR\n",
    "import RF_aval_use_agent as RF\n",
    "import NN_aval_use_agent as NN\n",
    "import activity_agent as act\n",
    "import eval_metric_act_agent as eval_act\n",
    "import cold_start_NN as cs\n",
    "import emissions_price_agent as emp\n",
    "import recommendation_agent as rec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 2. Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_h5 = pd.read_csv('CLEAN_House5.csv', parse_dates=True,index_col=0)\n",
    "#data_h1 = pd.read_csv('CLEAN_House1.csv', parse_dates=True,index_col=0)\n",
    "#data_h2 = pd.read_csv('CLEAN_House2.csv', parse_dates=True,index_col=0)\n",
    "#data_h3 = pd.read_csv('CLEAN_House3.csv', parse_dates=True,index_col=0)\n",
    "#data_h4 = pd.read_csv('CLEAN_House4.csv', parse_dates=True,index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unix</th>\n",
       "      <th>Aggregate</th>\n",
       "      <th>Appliance1</th>\n",
       "      <th>Appliance2</th>\n",
       "      <th>Appliance3</th>\n",
       "      <th>Appliance4</th>\n",
       "      <th>Appliance5</th>\n",
       "      <th>Appliance6</th>\n",
       "      <th>Appliance7</th>\n",
       "      <th>Appliance8</th>\n",
       "      <th>Appliance9</th>\n",
       "      <th>Issues</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2013-09-26 09:56:09</th>\n",
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       "      <th>2013-09-26 09:56:16</th>\n",
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       "      <th>2013-09-26 09:56:23</th>\n",
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       "      <th>2013-09-26 09:56:36</th>\n",
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       "      <td>2</td>\n",
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      "text/plain": [
       "                           Unix  Aggregate  Appliance1  Appliance2  \\\n",
       "Time                                                                 \n",
       "2013-09-26 09:56:09  1380189369        275           2           0   \n",
       "2013-09-26 09:56:16  1380189376        273           2           0   \n",
       "2013-09-26 09:56:23  1380189383        273           2           0   \n",
       "2013-09-26 09:56:30  1380189390        273           2           0   \n",
       "2013-09-26 09:56:36  1380189396        278           2           0   \n",
       "\n",
       "                     Appliance3  Appliance4  Appliance5  Appliance6  \\\n",
       "Time                                                                  \n",
       "2013-09-26 09:56:09           0           0          11           0   \n",
       "2013-09-26 09:56:16           0           0          11           0   \n",
       "2013-09-26 09:56:23           0           0          11           0   \n",
       "2013-09-26 09:56:30           0           0          11           0   \n",
       "2013-09-26 09:56:36           0           0          11           0   \n",
       "\n",
       "                     Appliance7  Appliance8  Appliance9  Issues  \n",
       "Time                                                             \n",
       "2013-09-26 09:56:09           0           0           0       0  \n",
       "2013-09-26 09:56:16           0           0           0       0  \n",
       "2013-09-26 09:56:23           0           0           0       0  \n",
       "2013-09-26 09:56:30           0           0           0       0  \n",
       "2013-09-26 09:56:36           0           0           0       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_h5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "code_folding": [],
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for NANs\n",
    "data_h5.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7430755, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_h5.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 3. Predefined Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Features for the Availability Agent\n",
    "aval_features = ['Month', 'Day_of_week', 'Hour', 'User_aval_t-1','User_aval_t-1week','User_aval']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Usage and activity threshold for the Activity Agent Evaluation\n",
    "usage_threshold = 0.2\n",
    "activity_threshold = 0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Individual values for House 5\n",
    "num_devices = 8\n",
    "num_activities = 5\n",
    "activity_vector = 'Activities_vector_h5.csv'\n",
    "aval_off = True\n",
    "on_threshold = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Individual values for House 1\n",
    "num_devices = 5\n",
    "num_activities = 4\n",
    "activity_vector = 'Activities_vector_h1.csv'\n",
    "aval_off = True\n",
    "on_threshold = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Individual values for House 2\n",
    "num_devices = 7\n",
    "num_activities = 4\n",
    "activity_vector = 'Activities_vector_h2.csv'\n",
    "aval_off = True\n",
    "on_threshold = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Individual values for House 3\n",
    "num_devices = 7\n",
    "num_activities = 4\n",
    "activity_vector = 'Activities_vector_h3.csv'\n",
    "aval_off = True\n",
    "on_threshold = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Individual values for House 3\n",
    "num_devices = 6\n",
    "num_activities = 4\n",
    "activity_vector = 'Activities_vector_h4.csv'\n",
    "aval_off = True\n",
    "on_threshold = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 4. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Preprocessing pipeline for House 5\n",
    "preprocessing_pipeline = Pipeline(steps=[\n",
    "                        ('hourly_grouping',prep.hourly_grouping()),\n",
    "                        ('time_features',prep.time_features()),\n",
    "                        ('app_labels_h5',prep.appliance_label_house5()),\n",
    "                        ('aval_features',prep.availability(on_threshold)),\n",
    "                        ('device_usage',prep.device_usage(num_devices,on_threshold))\n",
    "                        ])\n",
    "data_prep = preprocessing_pipeline.fit_transform(data_h5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save the data preprocessing of House 5\n",
    "data_prep.to_pickle('H5_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Load the data preprocessing of House 5\n",
    "data_prep = pd.read_pickle('H5_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14373, 38)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prep.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
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       "      <th>Television_Site_usage_t-1week</th>\n",
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       "      <th>Kettle_usage_t-1week</th>\n",
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       "    <tr>\n",
       "      <th>2013-10-03 11:00:00</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
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       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Month  Day_of_week  Hour  User_aval_t-1week  \\\n",
       "Date                                                               \n",
       "2013-10-03 09:00:00     10            3     9                  0   \n",
       "2013-10-03 10:00:00     10            3    10                  0   \n",
       "2013-10-03 11:00:00     10            3    11                  0   \n",
       "2013-10-03 12:00:00     10            3    12                  0   \n",
       "2013-10-03 13:00:00     10            3    13                  0   \n",
       "\n",
       "                     User_aval_t-1  User_aval  Tumble_Dryer  Washing_machine  \\\n",
       "Date                                                                           \n",
       "2013-10-03 09:00:00              0          0             0                3   \n",
       "2013-10-03 10:00:00              0          0             0                3   \n",
       "2013-10-03 11:00:00              0          0             0                3   \n",
       "2013-10-03 12:00:00              0          0             0                3   \n",
       "2013-10-03 13:00:00              0          0             0                3   \n",
       "\n",
       "                     Dishwasher  Desktop_Computer  ...  Kettle_usage_t-1  \\\n",
       "Date                                               ...                     \n",
       "2013-10-03 09:00:00         273                 7  ...                 0   \n",
       "2013-10-03 10:00:00           0                 7  ...                 0   \n",
       "2013-10-03 11:00:00           0                 7  ...                 0   \n",
       "2013-10-03 12:00:00           0                 7  ...                 0   \n",
       "2013-10-03 13:00:00           0                 7  ...                 0   \n",
       "\n",
       "                     Toaster_usage_t-1  Tumble_Dryer_usage_t-1week  \\\n",
       "Date                                                                 \n",
       "2013-10-03 09:00:00                  0                           0   \n",
       "2013-10-03 10:00:00                  0                           0   \n",
       "2013-10-03 11:00:00                  0                           0   \n",
       "2013-10-03 12:00:00                  0                           0   \n",
       "2013-10-03 13:00:00                  0                           0   \n",
       "\n",
       "                     Washing_machine_usage_t-1week  Dishwasher_usage_t-1week  \\\n",
       "Date                                                                           \n",
       "2013-10-03 09:00:00                              0                         0   \n",
       "2013-10-03 10:00:00                              0                         0   \n",
       "2013-10-03 11:00:00                              0                         0   \n",
       "2013-10-03 12:00:00                              0                         0   \n",
       "2013-10-03 13:00:00                              0                         0   \n",
       "\n",
       "                     Desktop_Computer_usage_t-1week  \\\n",
       "Date                                                  \n",
       "2013-10-03 09:00:00                               0   \n",
       "2013-10-03 10:00:00                               0   \n",
       "2013-10-03 11:00:00                               0   \n",
       "2013-10-03 12:00:00                               0   \n",
       "2013-10-03 13:00:00                               0   \n",
       "\n",
       "                     Television_Site_usage_t-1week  Microwave_usage_t-1week  \\\n",
       "Date                                                                          \n",
       "2013-10-03 09:00:00                              0                        0   \n",
       "2013-10-03 10:00:00                              0                        0   \n",
       "2013-10-03 11:00:00                              0                        0   \n",
       "2013-10-03 12:00:00                              0                        0   \n",
       "2013-10-03 13:00:00                              0                        0   \n",
       "\n",
       "                     Kettle_usage_t-1week  Toaster_usage_t-1week  \n",
       "Date                                                              \n",
       "2013-10-03 09:00:00                     0                      0  \n",
       "2013-10-03 10:00:00                     0                      0  \n",
       "2013-10-03 11:00:00                     0                      0  \n",
       "2013-10-03 12:00:00                     0                      0  \n",
       "2013-10-03 13:00:00                     0                      0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for NANs\n",
    "data_prep.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Features for the Usage Agent for House 5\n",
    "columns = list(data_prep.columns)\n",
    "columns = columns[0:6]+columns[-num_devices*2:]+columns[6+num_devices:6+(num_devices*2)]\n",
    "\n",
    "usage_features = columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Preprocessing pipeline for House 1\n",
    "preprocessing_pipeline = Pipeline(steps=[\n",
    "                        ('hourly_grouping',prep.hourly_grouping()),\n",
    "                        ('time_features',prep.time_features()),\n",
    "                        ('app_labels_h1',prep.appliance_label_house1()),\n",
    "                        ('aval_features',prep.availability(on_threshold)),\n",
    "                        ('device_usage',prep.device_usage(num_devices,on_threshold))\n",
    "                        ])\n",
    "data_prep = preprocessing_pipeline.fit_transform(data_h1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save the data preprocessing of House 1\n",
    "data_prep.to_pickle('H1_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Load the data preprocessing of House 1\n",
    "data_prep = pd.read_pickle('H1_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_prep.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_prep' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-4667723a9b0a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata_prep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'data_prep' is not defined"
     ]
    }
   ],
   "source": [
    "data_prep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_prep.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Features for the Usage Agent for House 1\n",
    "columns = list(data_prep.columns)\n",
    "columns = columns[0:6]+columns[-num_devices*2:]+columns[6+num_devices:6+(num_devices*2)]\n",
    "\n",
    "usage_features = columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Preprocessing pipeline for House 2\n",
    "preprocessing_pipeline = Pipeline(steps=[\n",
    "                        ('hourly_grouping',prep.hourly_grouping()),\n",
    "                        ('time_features',prep.time_features()),\n",
    "                        ('app_labels_h2',prep.appliance_label_house2()),\n",
    "                        ('aval_features',prep.availability(on_threshold)),\n",
    "                        ('device_usage',prep.device_usage(num_devices,on_threshold))\n",
    "                        ])\n",
    "data_prep = preprocessing_pipeline.fit_transform(data_h2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save the data preprocessing of House 2\n",
    "data_prep.to_pickle('H2_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Load the data preprocessing of House 2\n",
    "data_prep = pd.read_pickle('H2_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_prep.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data_prep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_prep.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Features for the Usage Agent for House 2\n",
    "columns = list(data_prep.columns)\n",
    "columns = columns[0:6]+columns[-num_devices*2:]+columns[6+num_devices:6+(num_devices*2)]\n",
    "\n",
    "usage_features = columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Preprocessing pipeline for House 3\n",
    "preprocessing_pipeline = Pipeline(steps=[\n",
    "                        ('hourly_grouping',prep.hourly_grouping()),\n",
    "                        ('time_features',prep.time_features()),\n",
    "                        ('app_labels_h3',prep.appliance_label_house3()),\n",
    "                        ('aval_features',prep.availability(on_threshold)),\n",
    "                        ('device_usage',prep.device_usage(num_devices,on_threshold))\n",
    "                        ])\n",
    "data_prep = preprocessing_pipeline.fit_transform(data_h3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save the data preprocessing of House 3\n",
    "data_prep.to_pickle('H3_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Load the data preprocessing of House 3\n",
    "data_prep = pd.read_pickle('H3_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_prep' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-3d26a6ce8581>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata_prep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'data_prep' is not defined"
     ]
    }
   ],
   "source": [
    "data_prep.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_prep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_prep.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Features for the Usage Agent for House 3\n",
    "columns = list(data_prep.columns)\n",
    "columns = columns[0:6]+columns[-num_devices*2:]+columns[6+num_devices:6+(num_devices*2)]\n",
    "\n",
    "usage_features = columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### House 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Preprocessing pipeline for House 4\n",
    "preprocessing_pipeline = Pipeline(steps=[\n",
    "                        ('hourly_grouping',prep.hourly_grouping()),\n",
    "                        ('time_features',prep.time_features()),\n",
    "                        ('app_labels_h4',prep.appliance_label_house4()),\n",
    "                        ('aval_features',prep.availability(on_threshold)),\n",
    "                        ('device_usage',prep.device_usage(num_devices,on_threshold))\n",
    "                        ])\n",
    "data_prep = preprocessing_pipeline.fit_transform(data_h4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save the data preprocessing of House 4\n",
    "data_prep.to_pickle('H4_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Load the data preprocessing of House 4\n",
    "data_prep = pd.read_pickle('H4_data_prep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_prep.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data_prep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_prep.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_prep' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-017bcc7c793e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Features for the Usage Agent for House 4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_prep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mnum_devices\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnum_devices\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_devices\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0musage_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data_prep' is not defined"
     ]
    }
   ],
   "source": [
    "# Features for the Usage Agent for House 4\n",
    "columns = list(data_prep.columns)\n",
    "columns = columns[0:6]+columns[-num_devices*2:]+columns[6+num_devices:6+(num_devices*2)]\n",
    "\n",
    "usage_features = columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 5. Data Graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true,
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Get device consumption data from preprocessing dataset\n",
    "hh = data_prep.iloc[:,6:6+num_devices]\n",
    "# Subset representing a day of data\n",
    "hh_day= hh['2013-10-05 00:00:00':'2013-10-05 23:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a83d04eb8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3X6cPvLDxukLF9FHamAbP+AghujaknpQyu/HdaUNa6fj5uHHjeP311+nTpw9arZbQ0FCKi4s5cuQIy5cvZ+XKlURERODt7c2WLVtIT7cGM8vOziY0NJS7776boKAgVqxY0eS+jB8/nk8//ZR58+aRl5fH1q1bufPOO5vUZklJidP+T5o0iZkzZ/LEE08QFhZGYWGhw6xPY4wePZqHHnqIlJQU+vXrR2VlJZmZmQwYMKBJ7SpKSykvtg58oryOsJ+rnNbJSC4k60QR3QZ08Pj9E/5vNVK6H48nX59FN3oBYEgvxXiuAu/OgZ7unqI0ijt7fDJp2B4f1x9NlHrpdDry8/MdBhjnl43Cw8O56667uPHGG4mJiSE6OpqrrrL+MExKSuKpp55Co9Hg7e3N+++/3+S+3HrrrWzevJmoqCgGDBjAqFGj7MtJjeWq/0OGDOGFF15g4sSJaLVarr766iYP3jp27MiKFSuYM2cO1dXWXyBLlixRAx/lkmCpNlFWXI2vv5ZeFdtJ7xhFQZ7zGdtf1p3ilqdGePT+FcVFJDtJRtoQaWcP0c2nl/15+S9n6XBTP091TVGaRDR0OUMIsYTaAx8voC8wHTgE/Cil/INHe9gMYmJiZEJCgkPZ+RNTiqPy8nKCgoIoKChg5MiR7Nq1i86dO7d2t5qd+n5QWlt1dhkfL0mgQ7iW27R3c7BgAXsMY13W//VDQ4nUhXvs/ju+WMHedY0MGSEEdwx6Dllltj711dLlhVFofNTnYqX5CCH2SSlj6qvnzubm39dxs/7ALmB3Q9tTLg3Tp0+nuLgYg8HAH/7whyti0KMobUFloTWGT5C/EW11Cb38D3EqdDw555wvPf2y/hS9osJc7pVzh6GqkoObvm98A1Jiam9Ba9vqI6vNVB3MIzBW/fxQWp87S10uSSlPCiE+AP6ENcKzcpnYunVrrbKZM2dy+rRj8LS//e1vTJ06tYV6pSiXvwrb/p5gnwqohg4B2xiQO4AcrnVaPz+jnJR9ufSPafoJqoObfqC6omkxgoplPmGE2Z+X/3JWDXyUNsEjAx+bTCDKg+0pbdSaNfWHrlcUpWkqiq2pKoK8rDGtNFJPj5BkuvtdS6aLIyR7/+80fYdHoNE0ftbHbDKx/7t19VesR1bBMcK4EGnemFmOIascn25BTW5bUZrCk8fZZwDFHmxPURTlilVVYo3hEywvRD9u77ONflkbwMW4pjinkmN7mhYt+ejO+pORNkTq6X2gdeyoiuSstAXuHGd/3sWlUGASMBR43Y32Psa6KTpXShllK/s7cCNgAFKB30gpi23XngPuA8zAo1LK/9nKpwFvYz1N9i8pZdvPvKkoilKPSlty0mDThRxZAhPdI04S6W0iLdv5j+/4DacZOLIz2kZES5ZSkvB/zoOcustgrIIwLeReOIlWmZhHyK97o/H15GKDorjHne++JXVcy8e6v+cvbrS3AngX+KxG2Y/Ac1JKkxDib8BzwDNCiMHAHcAQoCuwSQhx/kzyP4HJWJfa4oUQ66WUDY+vriiK0sZIs6Si1LbUZTjpcK2dZjt9M3txxusWpwlMywurObw9i2GTerh931P74xucjLQhyr1KCSLA/lwazFQeyCNodBeP3UNR3OXOwKe/kzIJFJ6flXGHlHK7ECLyorKNNZ7+DJxPynQT8JWUsho4LYRIAUbarqVIKU8BCCG+stVt8sBH96ln898kzUuqt45Wq0Wn02E0GvHy8mLevHk8/vjjaDQaEhIS+Oyzz/jHP/7h9LVbt27ltdde49tvv/Vovy+2YsUKEhISePfdd5v1PopyJTMX6ykqMeDrr8XfmO5wTQhJt64p9BGVpJz1d/r6fT+kMXh8V7x93Ts+7m56ivqcKztFv4u2flb8clYNfJRW5c5x9tTm7IgT92JNjArQDetA6LxMWxlAxkXlo5w1JoRYACwA6Nmzp0c76in+/v4kJiYCkJuby5133klJSQl/+tOfiImJISam3vAEbZ7ZbEarVbE8FKUuxoIqCkoMdAjXorXk1trTE8xu+mT15LTvHMzG2sfbq8qMHNycQcwNkQ2+Z/aJY2QdO9LEnjtKPbOPfh0cBz7GsxVUnynFt2fz5RdTlLo0anOzEGKoEOJm22OopzslhHgBMAErzxc5qSbrKK9dKOWHUsoYKWVMx44dPdPRZhQREcGHH37Iu+++i5SSrVu3Mn36dAC2bdtGdHQ00dHRXH311ZSVlQHWYIOzZs3iqquu4q677kJKyd69e+1JStetW4e/vz8GgwG9Xk+fPn0AWL58ObGxsQwbNoxbb72VyspKAP7zn/8QFRXFsGHDiIuLs/ctOzubadOm0b9/f55++ml7+caNGxkzZgzDhw/ntttuo7y8HIDIyEhefvllxo8fz3/+85/m/+IpyiWussQawyfY34irsDzdup+gf1iRyzYSN51BX2Fs8D09PdsDUFx8DtHeu1Z5xS8qf5fSetwa+AghrhNCnAQOAP+1PQ4IIU4IIa7zRIeEEPOwbnq+S14IK50J1Fyw7g5k11F+WejTpw8Wi4Xc3FyH8tdee41//vOfJCYmsmPHDvz9rdPdBw4c4K233iI5OZlTp06xa9cuhg8fzoEDBwDYsWMHUVFRxMfH88svvzBqlHVy7JZbbiE+Pp6DBw8yaNAgeyLPl19+mf/9738cPHiQ9evX2++fmJjIqlWrSEpKYtWqVWRkZJCfn8+SJUvYtGkT+/fvJyYmhjfeeMP+Gj8/P3bu3Mkdd9zRrF+zS0lJysn6KylXpCrbxuYg33KXdQIs++lzboPLBKbVlSYObGzYfp3C7CxSE35xv6MNoA+snWG+6lAeFr3z9BuK0twaPPARQowBNgARwHvAItvjn7ayb4UQo5vSGdsJrWeAGVLKyhqX1gN3CCF8hRC9se432gvEA/2FEL2FED5YN0Cvv7jdS5mzlCLjxo1j8eLF/OMf/6C4uBgvL+uK5ciRI+nevTsajYbo6GjS0tLw8vKiX79+HD16lL1797J48WK2b9/Ojh07mDBhAgCHDx9mwoQJ6HQ6Vq5cyZEjR+z3mT9/PsuXL8dsNtvvP2nSJEJCQvDz82Pw4MGkp6fz888/k5yczLhx44iOjubTTz+1JyAFmD17dnN+mS5JKcs/pDhHffJVaquybWwO1hbWWa9z1yP0D3b9We/QlgwqbW3VJeHbxiUjbYiC6qxaZdJooXJfTrPcT1Hq486Mz4tAHjBYSvmIlPID2+NRrKet8m11GkQI8SWwBxgohMgUQtyH9ZRXMPCjECJRCLEMQEp5BPga66blH4CHpJRmKaUJeBj4H3AU+NpW97Jw6tQptFotERERDuXPPvss//rXv6iqqmL06NEcO3YMAF9fX3sdrVaLyWT9RDVhwgS+//57vL29ue6669i5cyc7d+60L1/Nnz+fd999l6SkJF588UX0eusntGXLlrFkyRIyMjKIjo6moKDA5X2klEyePJnExEQSExNJTk62zxwBBAaqzMw1mfV6/H7czMb338JsUp98FUdVpdYlqpoxfJzxtyTTJ/9/+AU6365pMlhI+C6tzjYqiotI3v5To/rZEGnZh5yWl+9Vg36ldbgz8BkFfCClrDV8t5V9AIxpaGNSyjlSyi5SSm8pZXcp5UdSyn5Syh5SymjbY2GN+n+WUvaVUg6UUn5fo/w7KeUA27U/u/F+2rS8vDwWLlzIww8/XCv3TmpqKjqdjmeeeYaYmBj7wMeVuLg43nrrLcaMGUPHjh0pKCjg2LFjDBkyBICysjK6dOmC0Whk5cqV9telpqYyatQoXn75ZcLDw8nIyHB1C0aPHs2uXbtISUkBoLKykhMnTjT27V/2TGfPoqmswq+qmp1ffVb/C5QrSlWZNV1FkCm9nprQqcshBvqecnn9yM4sSguqXF7f//16zMaG7wVyV0Z2MsK/9nKcKaeS6rSSZruvorjiznF2X6Cu79ISwKdp3Wk7GnL83NOqqqqIjo62H2efO3cuixcvrlXvrbfeYsuWLWi1WgYPHsz111/Pnj17XLY7atQocnJy7DM8Q4cOJSIiwj6geuWVVxg1ahS9evVCp9PZN0s/9dRTnDx5EiklkyZNYtiwYfZTZxfr2LEjK1asYM6cOVRXW39oL1myhAEDBjitf6UznrN+2u2SlcP29BR66aKJHDa8lXultAXmcgPlFdZZwGBDSr31fS2p9C7dTGrIA5SX1J49tJgk8d+eZtK8wbWuGfRVHPzxu6Z3ui5SYmov7QlLa6r45Ry+kSHNe39FuYhwtofEaUUh9gOVwEQppfmia1pgGxAgpWzzP71jYmJkQkKCQ9nRo0cZNGhQK/VIaWua+/uh6L9rOPfC80gh2DkxBrPFwry/v0tASPtmu6dyaahOL2XdB0mU6U3Ma38fXpa8el9j1HQjofwR9pcOdHpdaARz/jiSDp0dl5wTvl3Dtn9/5PQ1nnTdmPsJOxdW+4KXhq7Pj0QTUPvkl6K4SwixT0pZb9wXd5a6PgDGYt1/M1UI0cP2mAZsxLrM9X7juqsoVxbjWeuGVCElke3CqCwp5vt/vuF0M7tyZTEV6iksNhAUJBo06AHwtmTRu3onIaHOBxDSIvllveNymDUZacucBckqcLEcb7JQoTY5Ky2swQMfKeUHwJvANcB3QJrtsQH4FfCmlHK5x3uoKJch49kLGzsjkq3H2tMO7ifh2zWt1SWljagqrcZskQQFuLfvJjz0Zwbq411eTz2QR96ZMvvzY7u2UVbQsIFVUzlLWHpehdrkrLQwt+L4SCl/h/UE1++Bj4CPgReAIVLKJz3fPUW5PJnPXfiU65uSSscu1kDkO7/8jHOpKr7PlUxfZjvR5eM6ho8zXjKfXsQT1tHF1k0JP6+7EIDfU8lIG8KesNQJU14V+lS3sx4pSqO5nSJXSnkU69FxRVEa6fzm5vN6Ch/yAIvZxIZ/vMrcpW/j4x/g/MXKZU1fZovhoylw+7VhITsZUBDFHhcHbM8cKST7ZDH68pPkZ9R/YsyTLk5YWlPFL2fx66v2tykto1EpKxRFaTxpkRjPOcZnCYs/gMaWw6z43Fk2faS2y12p9OXWGZ8g6o7h44xWltLL/yCdOrvOh/fzulQS1rfcbM9558pcH7mvOlKAubz+QIuK4gnupqyIFUJ8KoTYLYQ4bktVUfNxvLk6qiiXC3NBEbLCcRnDK7+A7t172Z8f3bGFI9s2t3TXlFZmMZipqrQtdTUgho8zHQK2MSB3o8vrZ1NKKM13fzapqVLP7HN90SypSFCbnJWW0eClLiHE3cCngBlIAXLrfsWl7ehVnj3KPOhY/auDWq0WnU5nj+Mzb948Hn/8cTQa9yfmgoKC7ElCz0tLS2P37t3ceeedbrdXl3PnzvH4448THx+Pr68vkZGRvPXWW60Sw+cvf/kLzz//fIvf1x0XL3Od1620kpqZlTZ/vIyuA66ig23/j3L5MxfqKa2yRgsJqq4/ho8zGqmnR/tjdPOdRJaLSaN2EQMoyW3ZtIbFxecQvb2Rxc43bVfEnyN4YvdaAVsVxdPc+Y36e+Ak0FdKOVhKOcHZo5n6eUXw9/cnMTGRI0eO8OOPP/Ldd9/xpz/9yWPtp6Wl8cUXX3isPbDmEps5cybXXHMNqampJCcn85e//IWcnNb59PaXv/zF7dfUzEPWElwNfEJ+TnDY12PUV/Ht269iNjVfVF2lbTEV6CkoNeAXoMWvkTM+AO19ttL/7AZwMYawyO6NbrspnCUsPc9coKc6RW1yVpqfOwOfSOA9KaXrvAWKx0RERPDhhx/y7rvvIqXEbDbz1FNPERsby9ChQ/nggw8AOHv2LHFxcURHRxMVFcWOHTsc2snPz2fMmDFs2LCBZ599lh07dhAdHc2bb76JXq/nN7/5DTqdjquvvpotW7YAsGLFCm666SamTZvGwIED6xx8bdmyBW9vbxYutGcXITo6mgkTJiCl5KmnniIqKgqdTseqVasA2Lp1KxMnTuT2229nwIABPPvss6xcuZKRI0ei0+lITbWePJk/fz4LFy5kwoQJDBgwgG+//dbev4cffth+v+nTp7N161aeffZZe/Tru+66C4DPP/+ckSNHEh0dzW9/+1v7ICcoKIg//vGPjBo1qs6diLJcAAAgAElEQVSo182h5lH2mjT6aiI7O87u5J5OZccXK1qgV0pbYCyyxfAJFnjJ/Ea3IzDRveNJIrs4zwNXmh9q31PWkpwlLK2p4mf39zUpirvcOdWVBajwmi2oT58+WCwWcnNzWbduHSEhIcTHx1NdXc24ceOYMmUKq1evZurUqbzwwguYzWYqKy8ktc/JyWHGjBksWbKEyZMnExgYyGuvvWYfQLz++usAJCUlcezYMaZMmWLPr7V3714OHz5MQEAAsbGx/PrXvyYmpnZAzMOHDzNixAin/V+9ejWJiYkcPHiQ/Px8YmNj7WkzDh48yNGjRwkNDaVPnz7cf//97N27l7fffpt33nmHt956C7DOUm3bto3U1FR+9atf2XOBObN06VLeffdde1qNo0ePsmrVKnbt2oW3tzeLFi1i5cqV3HPPPVRUVBAVFcXLL7/s7j9Lk5nqyMje5cxZLs5wtu+79fTSXU3vq+sNSKpc4qptMXyC/Q3genKkQdppttM3sxdnvG7BYnEMjGk0eBHarQ/5Z1o2dEJa9iG6+vR0eb3qaCHmUgPadpdN9iOlDXI3cvNdtvQUSgs5H8l348aNfPbZZ0RHRzNq1CgKCgo4efIksbGxfPLJJ7z00kskJSURHBwMgNFoZNKkSbz66qtMnjzZads7d+5k7ty5AFx11VX06tXLPvCZPHkyYWFh+Pv7c8stt7Bz5063+75z507mzJmDVqulU6dOTJw4kfh4a4C12NhYunTpgq+vL3379mXKlCkA6HQ60tLS7G3cfvvtaDQa+vfvT58+fepNyFrT5s2b2bdvH7GxsURHR7N582ZOnbKeLNFqtdx6661uvydPMJ51vQwYcOAQwR0uCu0vJd+/9yYVxUXN3DOltVXbcnQFuRnDxxkhJN26ptCnU6XT6wHt+zb5Hu5ylbDUziKpiFcBDZXm5XLgI4QYW/MB7AaqgT1CiHuEEBMurmOrp3jIqVOn0Gq1REREIKXknXfeITExkcTERE6fPs2UKVOIi4tj+/btdOvWjblz5/LZZ9ZM315eXowYMYL//e9/LtuvKz3CxRsMXW04HDJkCPv2OT+tUVf7vr6+9r9rNBr7c41Gg8l0YXreWT+8vLywWCz2Mr3e+UdjKSXz5s2zf82OHz/OSy+9BICfnx/aVpjqBzC52OMD51NYdKhVXlVawnfvvq5SWlzmqitsJ7oaEcPHmWB20yf7W7TetX/UGw1dPXIPt9gSltal6nDjl/gUpSHqmvHZCeyo8dgGjAJigE+ArRddP19f8YC8vDwWLlzIww8/jBCCqVOn8v7772M0Wn8wnjhxgoqKCtLT04mIiOCBBx7gvvvuY//+/YB1gPDxxx9z7Ngxli5dCkBwcLA98zpAXFwcK1eutLd35swZBg60Jjn88ccfKSwspKqqirVr1zJu3Din/bz22muprq5m+fIL2Uri4+PZtm0bcXFxrFq1CrPZTF5eHtu3b2fkyJFufR3+85//YLFYSE1N5dSpUwwcOJDIyEgSExOxWCxkZGSwd+9ee31vb2/712jSpEl888035OZaDyAWFhaSnt6yQducMdr649u7h9PrEUcuXuyyOpOUyN513zRbv5TWJS2S6ipbVnY8N+vRrdsx+ofVni0szmuHt5+fx+7TUCWy7jQZxpxKpLFlDxwoV5a69vg80GK9aIMacvzc085vzD1/nH3u3LksXrwYgPvvv5+0tDSGDx+OlJKOHTuydu1atm7dyt///ne8vb0JCgqyz/iAdTnnq6++4sYbb6Rdu3Y88MADeHl5MWzYMObPn8+iRYtYuHAhOp0OLy8vVqxYYZ95GT9+PHPnziUlJYU777zT6f4esA6w1qxZw+OPP87SpUvx8/OzH2ePi4tjz549DBs2DCEEr776Kp07d3ZruWrgwIFMnDiRnJwcli1bhp+fH+PGjaN3797odDqioqIYPny4vf6CBQsYOnQow4cPZ+XKlSxZsoQpU6ZgsVjw9vbmn//8J7169arjjs3LXGnAdDYTgIjRkozTtev4pp4mYtpEcm31atr99ef0HDKULv2dZ+FWLl3m4moqDLbN90040XWxAMt++pzdwCn/uRj0FwYU0qIhrPtAzqUc9Ni9GiKz4Dih1LE4YJEYssrxjQxpuU4pVxRxJU6dx8TEyISEBIeyo0ePMmiQZ2P3XKpWrFhBQkIC7777bqv2Y/78+UyfPp1Zs2a1+L2b6/uh+nQGp66fgqZdMANvOE7aoTiqkmtv2M6dNomEs2lO2wiJ6MTcv72Db4BKaXE50acUs+u/Jzl2uoz5PV4h0LjfY21XaQbzi/ExjuRFOJRH9EjlzKF1HrtPQ/h4+zOz12Ngdv27J+TXvQme0DpH7pVLlxBin5Sy3lMgKmWForQgoy05qU+ncABCdc5/+IfGH0CjdT4hW5Kbw4/LW3dQqnieqbCKwlID/oFe+JrSPNq2vyWZPvk/4Bfo+D1VVdHZo/dpCIOxCkLr3l9nyCir87qiNIXbSUqVy9/8+fOZP3++Q1lBQQGTJk2qVXfz5s2EhYXVKveEFStWNEu7rclkG/h4h1lna4LZjXfXYRizHfd0eBUU0n3cCM6kO89vdHz3dnoNjUb3qynN22GlxZgK9RQUGwgN1+IlCz3efqcuhxioP8XBigvHycsKAwkI6UBlScueGKzwLiXQRcJSUAMfpXmpgY/SIGFhYfb4OErjGW0xfLxDrP/1hDATOjKcnLW1N7N2L3FMYXGxLZ98SNcBgwjr5nyTtHJpMZQbMZslQf7VTY7h44yvJZXIks2ktHuAitILJyc7dBlIZcnPnr9hHc6VnaYvQ1xeNxdVYy43oA1S8XwUz1NLXYrSgky2VB7eARd+8YT470ETFFirbruf4+vcx2Os1rPhH3/HZFQpLS4HhvMnujwQw8eVTp32MVAkO5RpfFp+s3/KmYR66xgym+/roFzZ1MBHUVqQKcd6lN3H98IPda0so/3YfrXqaqoN9IqoO0FpXtoptq/82LOdVFrF+YFPkKb2Mpf0CfLIPbwtWfSu2oF/jb0+5YXhHmnbHcXF5xDt604EYDhT2kK9Ua40DRr4CCE0QoiuQoj2zd0hRbmcGW0DH29NHuagLvby0PDD4CSgYpeM+nMXHfj+/0jd94vnOqm0OHOFkWqjNShnsKz9b/7Xdr9Hevl75F7hYT/TqX21/XlVhS/tO7X8CarqOhKWgprxUZpPQ2d8fIAzwIJm7IuiXNakyYLxXDZg/eT9XsAipC19trclg+CRtY/PBxw4RLvQ+j+R//D+2xj0VZ7tsNJizIV6yk3WE35BZscYPtI3mA8ze/JlxOMeuZeXzKezl+PuseCO/T3Stjvy60lYasxUG5yV5tGgzc1SSr0QogC4Yobg/1z4k0fbe2jZtXVer3lq6ty5c2i1Wjp27AhYE4b6+NTe5Ne9e3cOHz5M+/buTcSZzWauueaaWpnca5o6dSrffPONPfdXU+3atYvFixdTXV1NdXU1d955J3/4wx9Ys2YNKSkpPPXUU6xevZrBgwdz1VVXeeSebY2pqAxLQQ7asFCEJpe3Mnpza5+pdM36AYDQ3ucouyhRvJCSyKD2HCqsO4y/vqyUwoxMOvdv+V9gStOZCqso1dv2+OiPO1yrDuwGJfD8KR3D+8/mqoxVTb5fp+pEoLf9uUXWvaTaHNLPJtHV23XCUkulCWN+Fd7hnpnpUpTz3DnV9T1wA/BeM/Xlilbz1NRLL71EUFAQTz75ZLPcS6vV1jnoAerM8dUY8+bNY+3atURFRWE2mzl+3PrDfebMmfY6q1evRqPRXL4DH3sMn1DMgZ0wV2p4uugm/q3ZhLCYCLAcwn/QBKqOpjq8LuLwMWhX/3/V0pwcNfC5RJkK9NYYPkFe+JodZ2PK/C7k1Jp5ajrx3VIIynWeH6+hOohkQkJvp6TQujG+JD8MjVaLxdxyqSLOZB1h7FUzkHrX9zRmlKmBj+Jx7mxufgroIYT4SAgxWAhR9840xWM+/fRTRo4cSXR0NIsWLXJI0FlXnXfeeYfnn3/eXudf//oXTzzxBCaTyT5LlJWVxfjx44mOjiYqKordu3cD1tmk4uJiAF599VWioqKIiorinXfeASAlJYWoqCjuu+8+hgwZwvXXX+8yWShYc4917mwNlqbVahk8eLC9T48//jg7duzgu+++44knniA6Opq0tDROnjzJ1KlTGTFiBHFxcfbM8Zeq88ELvUMD0PtbvxY7C0NI6XZh8Beqq/06n9PpdOpa/x6MUtv+IeXSYyqyxvAJCgQv6RhTJ197IdpylVnLbYUPYg6MuLgJt/jLI0T4lVy4v0FLaPcWztYuJeYOdWcOUPF8lObgzsAnGxgM/AZIAvRCCMNFj+q6m1DcdfjwYdasWcPu3btJTEzEZDLx1VdfNajObbfdxjffXEhquWrVKmbPnu3w2s8//5wbb7yRxMREDh48yNChQx2u7927l5UrV7J371727NnDe++9x6FDhwA4fvw4jz/+OEeOHMHf35+1a9e6fB+PP/44/fv355ZbbmH58uVUVzt+q0yYMIEbbriBN998k8TERCIjI1mwYAHvvfce+/bt469//SsPP/xwo76GbYUx1zbwaSco8e5oL3/07FSkty2godiNd+dOtV7bU9afSb48T2W1vlQZK02YzZJg/9o/QjNlR4fnR8sDeMn3KaSm8Z89NVTRSToGxwwIaeGBD1As6/6eVQMfpTm4s9S1CrjyEnu1sk2bNhEfH29PElpVVUWPHj0aVOfOO++ke/fuJCQk0LNnT06fPs2oUaMw15jOjo2N5be//S16vZ6bb76ZYcOGObS9Y8cObr31VgJs8WRuvvlmdu7cyZQpU+jXrx86nXWKYsSIEaSlpbl8H3/605+YO3cuGzdu5LPPPmPVqlVs2rTJZf3i4mJ+/vlnbr31VnuZyWRyWf9SYLZlZff2r+ac5sKeiqPlAezvP5sRGZ8ghJkOoyPIXZvj8NrQvQfQREZgMbv+GpTnq4HPpcpo298T5FNeK3jhKWPtyOj/zu7GmH4PcUPmW42+Z0T1QYQmCmmbQDZUd637Bc0gq/A4oYxxed1wthxptiC0KvKK4jkNHvhIKe/25I2FEB8D04FcKWWUrSwU6wArEkgDbpdSFgkhBPA21j1GlcB8KeV+22vmAb+3NbtESvmpJ/vZ2qSU3HvvvbzyyiuNqjN79my+/vprIiMjufXWW7F+KS+49tpr2bp1Kxs2bOCuu+7iueee46677nJo25XzmdzBunxV38CkX79+9OvXjwceeICwsDBKSkpc1pVSEh4efllFiz5/lN3Hp4xsGepw7eGMiezyW4tGX0R7/z3kB3bCUlFhv64tLKLH+FjS0x33/9RUVlCANFkQXuqXxKVEGi0YDbaj7JqCWteT9c4PLyxKGcm2fjPolbm+Ufdt55NOeLiWvFzrB6GSvHb4+AdgqKpsVHuNkXI6AV3Psa4TlpokxrMV+HT3zCELRYHWDWC4Aph2UdmzwGYpZX9gs+05wPVAf9tjAfA+2AdKLwKjgJHAi0KIDs3e8xZ03XXX8fXXX5Nv+zRfUFDAmTNnGlxn1qxZrF69mq+++qrWMhdAeno6nTt3ZsGCBcyfP58DBw44XI+Li2PNmjVUVVVRXl7OunXrmDBhgtvvY8OGDfZB1IkTJ/D19a11Yiw4OJiyMuvUdocOHejSpQtr1qwBwGKxcPDgQbfv25aY7DF8cjljcvw2Pav3YUtH64BTK0tpP672JuVuRXVP++flpWMsUEfaLzWmIr19kieI2jF8Dpa2c/naGemzqAqLatR9/cUROmou7AuTUhDafUCj2mosg6FSJSxVWpxbubqEEBrgDmAK0Al4Vkp50BbY8AZgq5QyuyFtSSm3CyEiLyq+CbjG9vdPga3AM7byz6T1N+fPQoj2Qogutro/SmnN6CeE+BHrYOpLd96XM/UdP28pOp2OF198keuuuw6LxYK3tzfLli2jZ8+eDaoTFhZGv379SE1NZfjw4bXa37x5M2+88Qbe3t4EBQXx+eefO1wfOXIkc+bMITY2FoAHH3wQnU5HSkqKW+9jxYoVPPHEEwQEBODt7c0XX3yBRuM47p4zZw6//e1vef3111m7di1fffUVDz74IC+99BIGg4G777671lLcpUJKiTEvF7RavM1ZnNTX/mX2+OmRJLbvirY8mw7hhynUaqHGsmTIzwn4xgyiurKi1msBjEY9FXmF+HSqnf5CabtMNWL4BBtPO1yTPoGklfq5fG2J0Yu55Y/wtd+zaPTuJRrVyEoiDCkkc2Epzcc/EmjZWdYK7zICcX1yy5BRRh2rYYriNlHXUoZDRSH8gR+ACVhXoX2ByVLKn4QQXkAm8KGU8o8Nvrl14PNtjaWuYill+xrXi6SUHYQQ3wJLpZQ7beWbsQ6IrgH8pJRLbOV/AKqklK85udcCbAEYe/bsOSI93TFI2NGjRxk0qHYAOeXK5OnvB1OZgZS4sXiFBNHvmgP82ns5R8pqD1Be63OQWdl/AyAzbQplPx92uJ560zSOp510eZ/ZD75C92uu9li/leZXtiuLvTuzOZZWxvzuLxJoPGS/Vt1hIAPPvlhvGw/2SOPp/N8jZO0Tn3U5V3Ej66rvx2RbagvqUEn+qWXuvYEmih12I31KB7u87tXRn86/i2nBHimXKiHEPillvd8s7ix1vQSMBm7DugfHvllESmkCVlN76cpThJMyWUd57UIpP5RSxkgpY84HBlSUlmLOK0BWVeAdEYLUeHGs3Pkn3OfShmLoYF3mCu1d+3h65/S6o92WF7j3qV9pfebzMXwCvfAzXRzDp4uLVzl6PyOSHd1/6/a9g4LO0Snswo/M8qIAAtuH1vEKzzuZXnfCUlN+FRb9pX2wQWlb3Bn43IZ1Rue/gLOIUyexDoiaIse2hIXtz/M/+TOBmkeZumM9Xu+qXGklCxcuJDo62uHx2WeftXa3Wp09R1cHP2vwQun8v57RIvjIx3qOIEAm4neV4xHjwMQkQsJcD9wri9TA51JzPoZPcJBEK4sdruV71Q5t4Mq8lPGc6zrZrXsHcIRwg+Ngq0OXgW610VTFxWcRIXUczZdqn4/iWe4MfLoBde0urQBc78JrmPXAPNvf5wHrapTfI6xGAyVSyrPA/4ApQogOtk3NU2xlSitZtmwZiYmJDo977rmntbvV6kznj7IHYw9e6Mrf0vtT0TEagFBd7UnNXoEhLl9bVVTs8prSNpn01hg+QX6GWteyaPjstJSCGVl3Y2jf8Hg8GllJeGWyQ5nwcp1GorlUB9WXsFQNfBTPcWfgUwjUNe86GJwcSXBBCPElsAcYKITIFELcBywFJgshTgKTbc8BvgNOASnAcmARgG1T8ytAvO3x8vmNzorSlpwf+Pj4V1PiXX/U3dctcwBop92NVyfHX34dDx91+bqS0jxMJSqO6KVCSmnfXxPsU/uX+ymje8tOudXeLDQuRvoENfg1oaTjH3ThnEtZUctvBSiornui3nBGDXwUz3Fn4PMT8BvbJmcHQohewL24MdsipZwjpewipfSWUnaXUn4kpSyQUk6SUva3/VloqyullA9JKftKKXVSyoQa7Xwspexne3zixvtRlBZjn/HxKiJfU3+29Y+zelDQJQ6BidDRjssdvqfP0KlrD6evy81Pw5TbcnFYlKYxlxowaqyzekHa2jF8jla5H53jp4IOfNCh4Xn+AkIK6NTuwoyLvsKH9p2df381l7Szh+q8rmZ8FE9yZ+DzJyAM2Iv1dJTEOjvzCrAfMAJ/8XgPFeUyYLKlk/AWuZyVDfsU/2L5LCSC9gG/oLFFzj6vp8XZvn4oKMrCkO/8uLvS9pgLquwxfIKdRAI5WNa43QNL0wewv8e8+iti3ecTVuKYBy84vGWT3Z7JOoLwcx3Px1JmxFSsZjIVz2jwwEdKeQLr8pMA/mz78xngBeAc1qPtZ1y3oNRHCMHcuXPtz00mEx07dmT69OkArF+/nqVLl7p6udKGGXNzET4+eJnPkm5s2Kf4b/PCyex+A1pZQshYx19EoXsPoPVyEoZLSqqKXEfEVtoWU6GeCnsMH8cQG9I7gNTKxmcmn506haLO4+qtp6GCsDLHsAkWWX9SXI9qUMLS0hbqjHK5cyuAoZRyLxAlhIgGBmEd/JwEEmRDAwJdIl6fPd2j7f1u1bf11gkMDOTw4cNUVVXh7+/Pjz/+SLduF3I6zZgxgxkzZjToflJKpJS1ggQqLc9iMGM8l4l3p44IkcbJatebky/2ZP6NfKXZSGinZIo0GrBY94Noi4rp0W0kaU5SWOhL1C+IS4WpQE9JlQkEBBmOO1wzBHWDJqzwGC2CmTn3sSk4Ha+yzDrrhgQWE+LvTUmhEYDivA5otF515obztBLy6YDr2VBDRjkBOhWKRGm6Rv1WlFImSim/lFJ+IaWMv9wGPa3p+uuvZ8OGDQB8+eWXzJkzx35txYoV9gzlOTk5zJw5k2HDhjFs2DB2795NWloagwYNYtGiRQwfPpyMjAy+/PJLdDodUVFRPPPMMwB8/fXXLF68GIC3336bPn36AJCamsr48eMBePnll4mNjSUqKooFCxYgpeTo0aOMHDnS3p+0tDR7Nvd9+/YxceJERowYwdSpUzl7tsH73C975sIKzGfT8e5oXbZILm/4xtNfittxvNst+JjTCYp1DKjYtcj5AEdfWt74ziotylSop7DMQECQF76mDIdr5X5NTxqaVuXH7zRPIr1cR38GCAgpJML3QigEs1FLWPeWzdaeWXC8zuvqSLviKW4PfIQQnYQQ9wkh/mx73CeEqPt8rtJgd9xxB1999RV6vZ5Dhw4xatQop/UeffRRJk6cyMGDB9m/fz9DhgwB4Pjx49xzzz0cOHAAb29vnnnmGX766ScSExOJj49n7dq1xMXFsWPHDsCafT0sLIysrCx27txpz8P18MMPEx8fb5+B+vbbbxk0aBAGg4FTp04BsGrVKm6//XaMRiOPPPII33zzDfv27ePee+/lhRdeaIGv1qXBmJcPUuLd3gep8eZ4hXvLF49mT0F6BxLWx3Hza8jP+/ALrD2IqqosUwHfLhGmQmsMn6BA0ErHJUp3YvjUZV1OBF9FPFFnnQBxhA45SQ5l/iEtO/BJOZ0AWud71wCMWWVIi/qMrTSdWwMfIcRzQDrwIfCc7bEcSBdCqN90HjB06FDS0tL48ssvueGGG1zW++mnn3jwwQcBa2b0kBDr8kmvXr0YPXo0APHx8VxzzTV07NgRLy8v7rrrLrZv307nzp0pLy+nrKyMjIwM7rzzTrZv386OHTvsA58tW7YwatQodDodP/30E0eOHAHg9ttv5+uvvwasA5/Zs2dz/PhxDh8+zOTJk4mOjmbJkiVkZtY9tX4lOZ+c1CfYgjkwwmXwQldOVPizt/McAuR+/Ab2sZcLo5HIiNoRJkoqczHlqWSllwJztRmzWRLsX3vjbjb1n/5rqOdO6Tjeo3aS4vM0spzQkiNoNBcGHgZ9w6JGe0p9CUulwaJOLCoe0eCfwEKIB7Fuaj6MNbhgDBBr+/th4GUhxKLm6OSVZsaMGTz55JMOy1wNFRh4If9TXSuQY8aM4ZNPPmHgwIFMmDCBHTt2sGfPHsaNG4der2fRokV88803JCUl8cADD6DXW8+ezJ49m6+//poTJ04ghKB///5IKRkyZIg9YGFSUhIbN250/41fpkx5tqPsvlVU+Tful8ljZ8Zj8Q8jdJjjtrzOp2unsCgozcKofkG0eRa9ifOH84KcxfAxhdUqa4qbT02nPGKEy+vBHS2EdbzwK6E4vx0+/gEu6zeHCidfh5rUcpfiCe589HwMSADGSCk/l1Lul1Luk1L+G2vu3AO2OkoT3Xvvvfzxj39Ep9O5rDNp0iTef/99AMxmM6Wltfd7jBo1im3btpGfn4/ZbObLL79k4sSJAMTFxfHaa68RFxfH1VdfzZYtW/D19SUkJMQ+yAkPD6e8vJxvvvnG3mbfvn3RarW88sorzJ5t/QQ5cOBA8vLy2LNnDwBGo9E+Q6TUOMruVUipd+M2Z56r9mFT2F200+zCK+JCGwEHa6ewyMk9rWZ8LgGmAj0mb+sMR7Amv9b1Y42I4VOXKrOW2wofxBzoPIBmQPtCOspzFwqkIKx7y6avyCk7Xed1NfBRPMGdgU8k8IWU0njxBSmlAfgcaPlY55eh7t2789hjdY8h3377bbZs2YJOp2PEiBFOBxpdunThr3/9K7/61a8YNmwYw4cP56abbgJgwoQJZGRkEBcXh1arpUePHvaNze3bt+eBBx5Ap9Nx8803Exsb69Du7Nmz+fzzz7n99tsB8PHx4ZtvvuGZZ55h2LBhREdHs3v3bk98KS4Lprw8ALxldoOCF7ryRFos5uAIOox23FIXGeAY66WyshhDsYrl09aZCqqoOj/jY3EWw6fhp/8a6mh5AC/5PoUUtX/0+4tkQs44Jgz19o/0eB/qknpmX53X1cBH8QTR0ANZQogTwKdSyj+7uP4CME9KOcCD/WsWMTExMiHB8T/40aNHGTRokItXKFcaT34/nFn4JFXxWxl440l+6P4YC1Ocb1hviL/2SeL2s+9zcm0Ysso6q2Po1YNN7X0c6t0x+UW63R/rrAmljSjdmsGJg/n8crSI2/p+RkTFGvs16eVH7/KPsEYM8bxDvd6mXc4vtcpPJV3Lpu6PYay2hk0Ial9F/un3m6UPrtwR/XtkSa3P11Ya6PrSWDQ+rvcCKVcuIcQ+KWVMffXcmfF5D1gghKh11MCWSf23wLtutKcolz1pkZhyc/DuZN2v0dDgha78/vQQTB060X7chc8XPukZdO7mONlq1OuRZnUCpi0zF9aI4VN90uGaMagbzTXoAdjlPcZpeWB3XyJCLfbn5cX+BHbw3CbrhqgOqiNCswWMWSpcg9I07gx88myP40KIt4UQDwohFgoh/gEkA5F6iHoAACAASURBVDlAgRDizpqP5ui0olwqzKUGjJmn8A4PBnAreKHT9qSGD73uIjTiGNQITtnzotPrVZYKTAVqn09bZiqouhDDx+z5GD51eT93MNLJwCqwQxFhVWkOZR26tOwkfkF17Q37NanlLqWp3Inc/O8af3/EyfURF9UBaz6vL9ztlKJcLkz5xVhKC/EOsc7IuBO80JXXz/Rjfs8wgmI6UL7Xurerw979aPt2xmyyjoDKDYWY8irxjmjZUzlKw5mKqikoNhDWUVM7ho9384ZGO1QaRGX3oQTmH3Qo9xdHaHdyJ3SMtJcJbU+g5fbs5QsLXQTW3x5OqISlSlO5M/CZ3Gy9UJTL1PkTXT6BpkYFL3TlVdNsnu27ivK91ufa4hLah+koyLFGzC6uyMWYV4Vn7qZ4mjRbsJgtthg+Brhoci6b5k/N8Iv/eK7FceCjlWW0M+UQEOxFZZl1EF1aGA5CQAsE6A/r0ZdTJ7ugGxgAOc5DMhjOqIGP0jQNHvhIKTc3Z0cU5XJkyred6PKrsAYvrPRM7rR/Z3fj0T7B+PaPpPpkGgDt/AM5H9s5v+iMCvbWhpmKqpF+1h+/wT6ltQY+aR6O4ePM8vwornVSHhAZTKfgKk6XeQNQXelDh849KDrbvDmoNVovtL6ToFyQ56VxOfQzF1djLjegDfJxUUNR6qYyWCpKM7LH8NEUNDp4oSsvlN5KaPSFHEzBNT6Q5+alYcpXe3zaKnNBlT2GT5CzGD769s3ehz1FIehDa59cDOhQTGjRMYeyoLD+zd6f7kOupbTAuhR8+Ew5aFxv7lb7fJSmUAOfNiQo6ML+j++++47+/ftz5ozrT1lbt251iJezdu1akpOT7c/nz5/vEHxQaXnnY/j4yMxGBy90ZWN+KIU9Q/DqaJ0dCCi/MMNjMhkwVdZxOkZpVaZCvT2GT7CTGD6JzRDDx5n9gRNqlQWIZAKTNjmUWWS3Zu1Hu45dyD872P68tMyI7Ox6f5oa+ChN4c4enytK5rM7PNpe96W1f8C4snnzZh555BE2btxIz56uY0Ju3bqVoKAgxo4dC1gHPtOnT2fw4MEuX6O0LFNePtr27dHIbPK1nj8W/HT+NN4bs56C9QX45xZAjfAmZq0Zc2k12na+Hr+v0jSmAj0VJusUXZApzeGa1Pp6bC9YfVYU6Rh7UZlWlhAUYKZ9mBfFBdZ9PsV5HdB6edk3z3taUPhUCs85fg7PtoCr4ZYa+ChNoWZ82pgdO3bwwAMPsGHDBvr2tWZHzsvL49ZbbyU2NpbY2Fh27dpFWloay5Yt48033yQ6Oppt27axfv16/p+9Nw9z5Crv/T9HW2vtfZnVjO3xbo/HC17A2EkwSQyXwL0EAmS7ISzh3hB+cBNnIQRCgLAvhiRgMIEkJtjYBhsDNhgMXrA9Y8/i2ae7p/dWb1JLrV2qqvP7o6QuqaVWt2a09tTnefoZ1TlH0pnukuqtd/m+f/VXf8Xu3bsZHh4ueN0XXniBW265hWuuuYbf+q3fwu/3N+K/d9ahBBawD3QDMK11V/31nw/7mDh/M8LppG200DuYtqbIzJnhrmZEyWr4CAHe1FDBXMa7BSlrp+GTz08Wekh3nlc07j63nX7H4vKxmrHSs6024a5tl76c4EzxZ+PwSATspS9R6clo2V6EJiblMA2fJiKVSvG6172O73//+1x88cXL4+9973t53/vex969e7n//vt5+9vfzo4dO/izP/sz3ve+93HgwAFuueUWfud3fodPf/rTHDhwYNloAr131nve8x7uu+8+XnjhBd72trfxgQ98oBH/xbMKLZ5B9U9i79Zd9mcqXrgat8/eiPfll2GJRvG0GyGShIyizJsJzs3IsoaPr1jDJ+aqrYbPSg75bikac3eG6fQXVnw524sNpDPF5etgabF049RkSkUZKB3ukgnFzGEzOW3MUFcTYbfbednLXsZdd93FF7/4xeXxxx57rCB3Z2lpiUhk/a7eEydOcPjwYV71Kl2RQFVVNm+ubqKtSTFKKEl6agjfxXoC6VCyfY1nnB7DcRdHLr6Yc36+j472TmJLuibMUnLBbFbapKghXcOnt79YwydQYw2flfzX0pWsND3cliO490ewXPMKNE33rKSS1f/O6N3xauYnV78MTSQ1zl1lLj0Zxd5n6lSZVI7p8WkiLBYL9957L3v37uXjH//48rimaTzzzDMcOHCAAwcOMDU1hc/nW/frSim57LLLlp9/6NAhfvKTn9Tiv2CSh7IQhEwau1vvO3QkVhvDB+Dv5y/HtmUz7XajxDe45CdjlrQ3HWokDQ5LVsOnOAF9mtLd02vF92b7UXzbCsasMoy7v52+PuMSEV7w0eb2VO19N+3cxfxkeWPqyOgSwlm6L1fGzPMxOU1WNXyEED85jZ9H67n5jYjb7ebhhx/m7rvv5q677gLgN3/zN/nyl402aAcOHADA5/MVeH5WHue46KKLmJ+f55lnngH00Fepbu4m1WW5K7sjgrTYORlzrvGM02c+bUfpH8CTMpJP5wOjZqirCVECCaRb18jx2paK5keV6ueCrcWxzhLhrnPb6dXycgGloHvbRVV5P3ubk1T6pjXXqYoktYpXJ2UaPianSTmPz6XAJSt+rgZuzf7ckP3JHV+dXWNyhnR3d/PII4/w0Y9+lAcffJA77riD559/nl27dnHppZfyla98BYDXvva1fO9732P37t08+eSTvPnNb+bTn/40V111VUFys8Ph4L777uOv//qvufLKK9m9e3dBGbxJbVAXsho+1nlUzwCqrK2DNdrVh2vRCJsEF6dR4wpaSq3p+5pUhhJIomS7i/uspTR8apMLVo57Y7uLxtxdYdpHCju4252rBZ4qY/NFv00isr4bgZFo6U7tGX8UqWgl50xMyrFqcFVKWeD7FELsAB4H/hv4pJRyMju+Dfgb4NVQUgi0Jamk/LxaRKNG1+Ht27czMjKyfHzPPfcUrb/wwgt58cUXC8byc4G++c1vLj/evXs3TzzxRBV3a7IWysICCIFdnSLmuqLm7xf0dbFp6Ch0GeXrwmtFmY/j2Lb+0KhJbVGCSZLZx16tuLryxWjtQqKr8W3/Zj7c3Y81Nrc85rYcwXnQj+OVryed1I3n2NKZh+F6tp3H3MT6DagToxEu3upGrjSAFEnGH8Ox3Ty3TSqjklvQzwN7pJTvyRk9AFLKSSnlnwPPZ9eYmJig5/jY+nqwkCLsqH3exrSzE/vkNNa8PB/VqZIxE5ybCjWYJKrqCcO+zGjBnLQ6OBKtXh7NuvckLQx1F4a7rDKM8yWb6e82PIaxsAtv9+kLcVqsVqzOW6FEZ/jVkBLi3aW9Q6aej8npUInh8+voHp/VeDy7xsTEBN3jY+/TwxYBUfveSyM2H0LT6OgyckSSImH27GoylECCpWUNn5OFc3XU8FnJA4nisnLPeR30xEcKxjo3nX6ez7ZLjbYUq3HulnTR2MlAaRVy0/AxOR0qMXwEcHGZ+XJzJiZnFVLRUBZmcXTrCrzTsvYJq8eFfkHpcBkeg5gaMg2fJkMJJglGMrh9NhxaYzV88vnm9DY0Z2F+kbtrCd/RXxQutG4/rddv7x0gMFNeVb6zx85L7vsbevoKszBGp2KIzmIF8vSkafiYVE4lhs9PgXcLId66ckII8fvAnwGPFT3LxOQsRA2lyEwOY2/X795rJV6Yz6GEC0t7O968MEI4Pm+GupoILaWixTIshFL4vBKrLLxwB2z11fDJJ6VZGOstDHe5LUdwDO3D024YIpFgL4jKvVLe/ttQldKl6QAIuGT8ASzpFFvVkaLpJV9xN3ZlIYGWqE0bDZONSyWGz/uAGeA/hRATQoifCSEeE0JMAP8BzALvr8amhBDvE0IcEUIcFkL8txDCKYQ4VwjxnBBiUAhxjxDCkV3blj0eys7vqMYeTEzOBGUxhjo/hT2r01Ir8cJ8EqoVNg3giRmGTiA0qZdPa6a8fzOgBJMIjx1VkXidyaJ5v6huI9tKeSh9bcGxVYZoO3c7A17Da5iK2+navHoPwVJsu+RGgv7yXs+dmxK49j4CQOdz9xXZVsdmShjw0vT6mFTOug0fKeUEsBv4LBAHXgHcnH38WWC3lHL1VuLrRAixFfgL4Fop5eXobRffDHwS+LyU8gJgEfjT7FP+FFiUUu5ET67+5JnuwcTkTFHms6Xsdl2npZbihfmkevpxzhkl0nMLI6BKlGDxRdak/qiBBNKja/j47MUX7DG19rlg5bhz+iVIR2EOjvu8TrqCxwrGvD3r79vl8rWzFHpp2TVun41tP/rE8rFtaoj+gcLLk38+gegtbt5q5vmYVEpFwiJSykUp5e1SyouklI7sz0XZsWAV92UDXEIIG+AG/Oil8vdl578FvD77+HXZY7LzrxTiNPywDSYQCLB79252797Npk2b2Lp16/JxOl2c7Fcp3/jGN5iZmanCTk3WgxoIAOAQszUXL8wn3NFD26iRNxKPLyFcVjPPp0lQgoaGj9fSHBo++cQUK1P9NxeMebojeA4WKr2r6mp904vp2fFq0sny3ZEuTTyDJVz4+9gcO160LuAsfh3T8DGplKbr1SWlnBJCfAYYBxLAT4AXgJCUMhfMnQRyn7ytwET2uYoQIgz0AAWfIiHEO4F3Apxzztpu2g9/+MNn+l+p6PV6enqWFZk//OEP4/V6+cu//Muqvf83vvENrr76ajZtWn8OgaIo2GxNd4q0BEogADYbNnUKtX0zarw+3WHmPV30RiK42y8gnu3ZJb0iq+DcWG+CSaGGj09OF82/GO0oGqs3P1au5R38aPnYbT2CbcZN18tsLAb0r+DwfCdWux01U1pcMMfA+VewMFk+YXvbFmj/9jeLxjufvgfLVf+Aphph2iOTUV4hgLzIrWn4mFRKRd/GQoitQog7hRCjQoiEEOLXsuN92fHSbXYre48udC/OucAWwAPcVmJp7tQv5d0pSmiQUt4ppbxWSnltX19j4+iV8qlPfYrLL7+cyy+/nC996UvL46997Wu55ppruOyyy/j6178O6MbKH/7hH3LFFVdw+eWXc8cdd3DPPfdw4MABfu/3fm/Zg7R3715uueUWrrnmGm677TZmZ2cBuOmmm/jABz7AzTffXNAmw6QylIUA9v5ehFBJuOqXsDrepl84O9o7l8cy9gyZOTPBuRlQAollDR9vujCBV1rsHI40vunmV6bPR9qMkJJVW6TtvHPotxtOfVWx0rOtfLjL3uYko5QXgrW3WTjvyTtKzlmCM2zuL/wqD4bSMFCoc6RFMyiLZijXZP2s+3Y+mzT8LLohsgfYTtZwklLOCyFuBFR078yZcCswIqWcz77vA8DLgE4hhC3r9dkG5G6XJrN7mcyGxjqAaobdGsqePXu4++672bNnD6qqct1113HLLbewa9cuvvWtb9Hd3U08Hufaa6/lDW94AydPnmRhYYFDhw4BEAqF6Ozs5Etf+hJf/vKX2b17N6lUive+97089NBD9Pb2cvfdd/PBD36QO++8E9C7v5sqz2eGEghg79eNkHqIF+YYEh5+C2i3OchpAieJ4TF7djUFSjDJkgbCAt70YOGcd0vdPIPlCKTtzG17OQPTRpGu+7wuOqb3g8Wo+nL6zgOOlngFnc0X/SZzE+VDvJe4TmGbOLHq/KbgAaYobKcxaxEMrFiXnohg66pPONmk9ankU/ax7L+XA79HsaflR+jJzmfKOHCDEMKdzdV5Jfqn63Hgd7Nr/hh4MPv4oewx2fmfSyk3TAnLk08+yRve8Abcbjc+n4/Xv/71PPXUUwB8/vOf58orr+TGG29kcnKS4eFhdu7cyYkTJ3jve9/Lo48+SkdHsev82LFjHDlyhFtvvZXdu3fziU98gokJIy/kzW9+c93+fxsRKSVqMICjU/8irod4YY5DihtsNrxpo8Q3kl40S9qbAKlJ1MUUwUgGj89Gm1qo4RN3le9UXk8e47qCY3dXGPe+R7FYja/9VHL1/XZvPZe5yfPLvkdvv42ehz5Xdo3v6XuxOQovU4fHImAtvPyYlV0mlVCJ4fMq4F+llGOUCCUBo+iemDNCSvkcepLyPuBQdo93An8NvF8IMYSerHBX9il3AT3Z8fej9w3bMKxmwz322GM88cQTPPvssxw8eJBdu3aRTCbp6enhxRdf5KabbuKOO+7gXe96V8nX3LVrFwcOHODAgQMcOnSIH//4x8vzHk/9JfM3ElokgzIzjt2n/+2mZf0Mn7GUC2t/H66w0fU7FJ1BJhTUyJknyZucPmooBVKyEErh9UgsMlowH7Q3TsNnJf86fQHSYl8+dluPYYkt0ddnXDLC817aPMUqzMJiwea+FcooUFssgouO/RdCK99A1xINs7W38LyNxhS0TYXfUelx0/AxWT+VGD7tGOGlUtipUrK0lPJDUsqLpZSXSyn/UEqZklKeklJeJ6XcKaV8o5QylV2bzB7vzM6fqsYemoWbb76Z733veyQSCaLRKA8++CCveMUrCIfDdHd343K5OHLkCHv37gVgfn4eKSVvfOMb+cd//Ef27dsHgM/nIxLRvxwuvfRSpqam2LNnDwDpdJojR4405j+4AVHDKdITg9idupdlLNO5xjOq/P79AzinjOaX84u6ZyFjVnY1FCWQWNbw8ZXU8KlfSHQtppJtLPbfsHxsk0Hazt1Or5J/CRB0by1uX7H9st9gaaF849AL+xdpO/TkuvYyMP1c0diUUnhDmJmOmlpVJuumEsNnEiinN34DMHxm2zFZyXXXXcdb3vIWXvrSl3LDDTfw7ne/myuuuILXvOY1xONxrrzySj7ykY9w/fXXAzAxMcHNN9/M7t27ecc73sHHP/5xAP7kT/6Et7/97ezevRshBPfddx/vf//7ufLKK7nqqqt47rniLxeT0yMTCCHjERz2EACDyfpW6sS6erFPTGG163fs8/MjYAGlgeGuDRR9Pm2UYHJZw8drXyqaH22whs9KfmG9oeDYfV4XHaeeKRizO3cUHPt6+gnMXlb2dX2ddjb9YP1ya+5f3U+bu1Dx+fDIEiIvBCbTGplZ07A3WR+VeGgeAN4phPgaMJcdkwBCiNcBbwL+sbrbaxzVLmc/k/e+/fbbuf322wvGnE4njz76aMnn79+/v2jsTW96E29605uWj6+++urlXKF8So2ZVIa6oGv42KXudTkSK3/3W22Cvm48mkZHVw/BuRlUVUF4bdmS9sYQ/tFPaNu5A9dFp9/gstXJ1/DxiWINnxMN1vBZyb/4L+Z/CitC6uEod0+EtseP4HjVG0gn9bFouNBL5Ru4jaC/TFsK4NLAT7DEig2/1bCkU2zrjDEcN5KX02mNzIAHW14pe3piCcdmM0xvsjaVJjf7gb3ogoES+CshxJPoRtFh4DNV36GJSYuhBgMIpxObnENa7AzWSbwwh9+lh9ba85qVai7Z0FBX4tAx/B/+JzRNa9geGo0aSCxr+HhLaPgcijVewyef4biLpX6jhYXbegyhqQx0G4nz8SUXvh7d+Nl6yQ0E/eW9Vju2KHievK/smlL0nfpl0dh4vLBHV2YiWrTGxKQUlbSsCAM3ohs9L0Ov6vptYBd68vEtUkqzdMTkrEdZCGIf6AVA9QygyvqWKI/Y9PYYvrzCy7Qt1bBQl9QkqaFxUvtfYOHb323IHpoBJZBc1vDxZVZq+Ng4FClOFG40T9tftvzYpgVo27GNnlhhGmXHwIU4ve1EwuXbUrS5rbzksc+e1j5ce36I21cYoDg6EkG4jTFTyNBkvVTasiIkpfy/6FVVW9GruLqllO/OGkYmJmc9ymIQe59ufNRTvDDHcaF7evKblcbVCGo4hZYuX0VTC9JTEZLH9ST7wB1fIDE1W/c9NANKMMlSQkFYwJM6WTCnejeT0Zqv085X5i5B5hnQ7vO68R55vHCR5Rz6zr2NdMJOOS4Vh7DOnV47R6GpbPcUyrOpmiTZYwgtZuZiDTm/TVqP07oVlTp+KeW0lHLDnGlmAqYJnPl5oAaCODr1i0DYXv9KnUMJDxavF2c21whgKbkAsjEJzonBSeRC1thZCjH96c+ddZ81NZZBplQWlzV8JgvmY67ybR0axYtLXuJ9Vy4fu3ujOIb242k3PC3B2X7mJ8v37tq0yUrXD//ljPbSc/wnRWPDS3ml7hpkJs1wl8narNvwEUK8bI2fG4UQu4Woo1pbFXE6nQQCgbPuC9mkECklgUAAp/P08nK0lIKyMIPdo+eyLFh6q7m9dRFTLYhNA7SNGRfXYETPKWlEgnNqtDCfJf3IQ8w9UpyzsZFRAgkQMJ/T8CFWML/YRBo+K3nOedPyY7dF79I+4DX2rynlLyNWm+CC5796xvtwHnic9q5Cr9LgeBTR7lg+NoUMTdZDJVVdT1FauLAIIcR+4G+llD89rV01gG3btjE5Ocn8/Hyjt2LSYJxOJ9u2nZ4WpxpKkfGfwn6ubvj4ZXc1t7ZuUr392IaGcV9+HvHIEnMLY9BVfy0fLaWQ8ReHtoKf/RTt11+Dq7u+FW+NQg0mdQ2fxYyu4bPiz9BMGj4r+drCZfxG9rFNLuDYcT1dgWOwopXEalzcNYP9seJK09Nhm32ao+T1WpQQ7WzDk/X8mHk+JuuhEsPnncC7gIuA/wZyDVYuBt4MHMuOXwS8FfihEOJVUsqWuLWz2+2ce+65jd6GSYujLCZQpkZwWH2g1V+8MMdSRy/dQEdHF/HIEqGQH9FvqXuoKzUcJuUv0YtpcoSpf/13dv79X9R1P41CCSSR7tU1fMbU+nsG18szix0kt16KM6D35fKc301i/6Nw2e41b4U7e+z0P/ipqu2l+8DDsOVPCsZOzie5KvvYNHxM1kMlOT42oB+4SEr5Linl57I/70Q3fjYDCSnlu9GFDsPA31V9xyYmTYwSWARNxS6ngPqLF+aY8+iaMD6bEQbAZ6m7xyc5tEjyeGlxzNR37mL2V4fqup9GUaDhYyn2Kp9MNcZAXi/73Hnhrp4o1rlxunvWuG8WcMn4A4h09TqnO04+T09f4fuO++OIbj00rYZSZmsWkzWpxPD5/4CvSin9KyeklNPAV4H3ZY+ngK/Bik53JiYbHDUYwOLzYc0WOdZbvDDHRJtucHkzhtaJ0qaiBBJ1lfZP+UNYJktX8liUNIEvfp5kNFW3/TQKJZAgmS2O8mmTRfOHos2l4bOSf1/ctfzYbTsOQJ8tsNpyAHZuSuDa+0jV97JFHS0aC3mM3B/T62OyFpUYPi+BFRl5hcSya3KcAuqr3GZi0mDU4CL2AT2/vxHihTmGLLomjDtkhFVSIgGKRF2s3h14OZTFJFqi/EVIHHyGiW99ry77aSRKMEks21/Ku1LDR1h5sQk1fPL56UI36U6927pNm8fxkm10Tq2et+P22dj2o0/UZC9dex+AFZX/R6eNS5Np+JisRSWGzzjwFiFEkVhDduytwETe8Fag/C2BickGQwku4ujRL2KNEC/McUj1gMVC2/TM8lhM0XuH1SvclRxcREmurdmT/uaXmD04WvsNNQiZUdEiacJJBYtF4EkOFcyr3k2ktMacJ5VwyHfz8mP3+T249j2K1VZae+jSxDNYwsVtOdZCdHUy/0c3lV1jmzjBQH9hW4y5QAr63YBZ2WWyNpV82u5AD109I4R4mxDipuzPnwLPAtdk1+T4X+jtLUxMzhrUYAB7h/6l3AjxwhwjSSfW/j4cE1NYbXpORCiut9irV4JzajBEeqZEYvMKLJEgc3d+nXRSWXNtK6IEkyBhMZLB7bPRJgtDXXF3eQ2cZuE/lww9H09vDEtsib7eYsNn2xZof+ybFb++ds1lLL59My9THsB68fll125OHC8aC9j1z116ImrKkpiUpZKWFf8CfBC4HD1/55fZn68BlwEfklJ+GUAI4URPbL699KuZmGw8pCpRg0HsHv0C3gjxwnzU/gGEqtLRrVcMBUJ6wnU9PD5Sk6ROhUgOvrCu9baf3c/I/b+o7aYahBJILmv4+DwaFln4+29mDZ98vj/bj+LTZR5yeT69SqFGk73NwrlP3VH03LLY7YTf+nI6rzvOjRM/w6YpWK8sLwfX8fQ9WKyFRtfhiQhYQCYVlAWze5LJ6lTasuJj6G0q/hD4B+BD2cfbpJQfzVuXlFL+UEo5WM3Nmpg0M2o4hTI3jr1NV49thHhhPvFuXe8k16x0dl7vsVQPj09mKopsA8vI8Lqfk/73LzJ3cuPpaCk5DR9F4nUW51fNNLGGz0qOdd4CgE2bw3HOVtqHf1Uwf4nrFPbxtb18OSwv2cbSn+/iOnk/WxeNTAlH9whYV+/ybg342dxf6NUJLWWQA/q5bub5mJSj4sCylHJBSvltKeXHpJQflVLeLaWsPJhrYrLBUEMp0pODOKz6x6FR4oU5Fn36+3uzmaDJZBThttVFvTl5chHZlkJU0I3dNjmE/z/vRc1srA7uSiCxrOHjsxe3NBxTW0fs/jvRq5Yfu3f20nbwFzhcuoHS22+j56HPrfu11Fddi+O1Ua73/wCLNP7miZ7L2L54EOtVF5d9/qbFg0VjuYw20/AxKUfzZ9SZmLQISiiKtjiPXdNDSqOZrobuZ9qla8N4YoaXQXpAiyuo0dpqnSQHF1FTRmKzaG8vewefw37/vzH4o3213FrdUdfS8Ek31kCuhG/PbEb16B4qd28coals6spgsQguOvZfCG3t1o2ivZ3kO25k56afcu7C0eVxKSw8tf1d7Pb/LapnE5ZL3WVfx/fUvdgchZewwyMRsFlMw8ekLBUZPkKIc4UQXxBCPCOEOC6EOLniZ/0+ThOTDYYaCGLt6V7O4RhMtjd0P6N2/f1dec1KM/YMUNtwl5ZSSE9ESM4bke7QS3fy3T+7GNFR/nciNJXUf36FhYlideNWRQkmlzV8vCU0fA5HG3ueVIKUgqHuXwPAbdO/7rujw1zYv0jboSfXfL5l18XY/6iTqyL348wY56Bs6+ALfR/hPkwAOgAAIABJREFUDwZvIaVZONjz23hcRxCu1eUgLNEQW3szBWPxpIo64CbjjyGVjeU5NKkelTQpvQzYD7wbaAcuBBSgE9gJWIG5GuzRxKQlUBYXcfQbXp6jDRIvzHEC/Y7ZMW5cbJNZKa5aJjinhsMgJclTRihius/Gd9tP8LG3t8N555R9vuPw04x/58dodRRarBVSkwUaPr4iDR8LB5tcw2cl9yf0cJddm8GxfSsdex9k0w8+Wf5JNhviDTew6ZoDnL/wbMFUqusi/sDyCb44ft7y2BcDL2VzeAjx0kvKvmy/v1gVfCqjgSpJT5ud2k1KU4nH5yPohs5VwC3ZsT+XUvYD/xfwAe+o7vZMTFoHNbiIvVs3NqTFzomYq6H7OZDyYvF4sC2GcPl0r8JSKgjU1uOTHFxEdNqxnzQcwCe69Pc74Jjh3W8Mkbxx12pPB6Dtns9z8rHikuVWQ11KgSp1DR+rwJMq1PDRPJtIqGuHAJuJb01vR3PqBr57Zy9W/wiW2OoeOuuWTTjediEX2x+gIxEsmPNv/S1uCvwdTy8WKlf/MtBFtO9qLBeUN369T99Pm6vw93d4JIJos5Ixw10mq1CJ4fMK4E4p5VGM1nQCQEr5b8CjwBpmv4nJxkUNLWJv1z9SqncTUpYWd6sXMcWK2DQAQGeHnu8TiukdZ2qZ4JwaDKE5k4iMEYbY6zWcwQFLnD++5Shjb7gOROnfkSUaJn7/fxOarW9vsWqjBPT8qsVIBo/PhkNdqeGzpRHbOiNSmoWxPv3e191b/u9jv/kq+m6b5/ylxwrGpbDwi+3/hxuH/5j5dJEmLgA/cbySLrEfS9fqfcxEOsm2rsKGApmMRrrfTXrS9PiYlKYSw6cdyN2u5DIjPXnzTwPlJTdNTDYw6uIidrf+0Ug4Bxq8G51Uj56I6rO3ATAf0EuGaxXqUhaTKAsJlLSRxCva2xlc0ddJCvirC/fx2NuvRLhKe8Zcv/wug/c/1dJidGrQ0PDxulUsFHraFh2toeGzkodS1wLgtp8sOS88Hjy/fxXnbf0xXbGJgjnN2cVnej/K/x4sf7n41ORl9CRDcP0FZdf1jRbnFo3GMmaCs8mqVGL4zAIDAFLKCHpvrvwzsgO9g7uJyVmHGsugLMzgcOgu/7CjOQyfpU5dS8ib1kUV5xdGwSJQQylkZu0KnEpJDi7q/y4Y+j3KS1a/uN/Ze5gvv2sLYnPp35fru1/g5FNj1d1kHVEChoaPz1XcjHW2hTR88rlz+iVIhxe75sexrdBr5bj4fAbe5OAc9YcFZeoAye5LeAv/zL9M7FjzPWZSDiY3vRLrjvJGuuvZH+D2FV56jo1GURMKWjyzyrNMzmYqMXwOAtfmHT8J/IUQ4mVCiJvQ83xerObmTExaBTWUQvGfwi50T8eCaA5tlgWPHiZwh/S7X01TEV4rSMjUIM8nNRgCiyA5dmh5LLS1fNXSL11j/L8/VFGvvKhozuofIfzwD4kutmYHdyWYp+FjCxXNj6mNFbk8XWKKlan+bLjrAl0oE4uF9ldfzTlXP09X4kjRcya3vZob5/+W50Lrr2L7z+RNbEruwbJt9ZCg0FS2excLxqQmifc4zXCXSUkqMXy+A2wSQuT80h8EetANoF9mH3+gutszMWkNlFCSjH9kWcNnWjaH4TPh1JNGnX6jWanm1kNH1U5wlpokORRCdNlw5CU2j/etnes0bg3xv28bJfDb1xbNeR/6Fw5/b32tL5oNJZhEadOTb72iWMNnsIU0fFbyIyUb7uqNY+vvZcubt7K1/WHsWuF5JYWVx7a/h5uG/oDFTGVBga9Nbcdp98JLt5dd13P8p0Vjw4tpM9xlUpJKenV9W0r5cillInv8AnqPrr8C3g9cKaV8ojbbNDFpbtRgCGuHB4HuWh/NrJ6QWU+GLHpJvS2vWWnKqifcVjvPJzMVRSYUVGcCS8xIOD3asb6LT0qovPuqA7zwR9eCzbhACilx/eBrDO31V3W/9UAJJMnJR/qyRnE+h2Oto+Gzkq9On4e0ufDaDnLerZN0aMWl5Zqrh3/u+RhvH7zxtN5DSsELnbdh2Roou865/2e0dxUmSQ9PREkGzJ5dJsWsy/ARQjiyIa2ClrlSyjEp5eeklF+UUg6t9nwTk42OuhjC0WcYO0MNFi/McVj1gsWCRVFo79K9UHFVN0SqXdmVPKmHG9Lpwg42e9yzpZavyie3HuDud1+AyKvmcRx7ltlHniAZbZ2cDS2eQSYUYqruYfOu1PBBcGCpOc6T0yGQtjPX/3KsMoRVCxbNJ3ou53e1j3PnZHndprX43Py1nBPag2WNju1b7cWG8UK4tgrlJq3Jej0+Ej2c9Zoa7sXEpGVRF0PYuw2V2UaLF+YYSjqx9up5JB1uXShvKaGHXKod6solNqcWTy2Pid4e/NbKww0Pegf58NvccMG5y2OdD3yG/d8/fOYbrRNKUPf1LGU1fLypwp7NmmeAmNraXYN+yvUlx8e3vZYb5m5nX/jMPwfPhdpJ9exC7u4ru67n4MNFYycnYyiLxY1hTc5u1vWpk1Jm0Ku66iJMIoToFELcl22LcUwIcaMQolsI8VMhxGD2367sWiGEuEMIMSSEeFEIcXU99mhiko8SCmH36R+PZhAvzEcd0Cumcs1Kg5Gsls9CAlkldeRcmwqsguSE0X8pveP0y7WPOOZ4xxvmid28GwBLIorj8fsYPdQaPZFzhk8wksHjs2Jf0a4i4dnaiG1VlX+ZvgBpdSwfS4uNH297LzcPvYVwhfk85fiR9ZXY+ifK9ntznNhLd2/he07NxglOmnk+JoVUcrtxH/C7QqyiOFZdvgg8IqW8GLgSOAb8DfAzKeUFwM+yxwC3oZfVXwC8E/i3OuzPxKQANbSIw6Vf6JpBvDCfRLd+p+yJ6/ubm9dDLjKjoYaqUy2VGg6DKqHLhmPIiHovbC7faHItwiLJn7z8MENv0sUO3U8/wOgjz5NOKme65ZqjBHQNn8BiGp9Hw0Kh5yHUoho++fiTDoL9NwCguXv5x86P8+6h0l6gM+HTkxdzbvg4Ynf5ju1bZbH0wfBguOr7MWltKjF8/g3wAo8KIW4TQuwUQmxZ+XOmGxJCtAM3A3cBSCnTUsoQ8DrgW9ll3wJen338OuA/pM6zQKcQYvOZ7sPEZL3IjIq2GMCeLVduFvHCHEGfXjnkmtfzMMJLc4hsV+tMlfJ8cmEupS2BLWDke4z2VKdR5N+dv48fvWsXwuOh96HP8uIjzZ9SqAQSCI+djKLhdRaHW1pVw2clv7DcQLx3F/9L+TjfnN5Wk/cIpO2M970SLveUXde59/6iuMT8ULGMgMnZTSWGzzFgF3Ar8DBwApgo8XOmnAfMA/8uhNgvhPi6EMIDDEgp/QDZf3PfGltXvO9kdszEpC4ooRTK/AR2oSfxNot4YY4Zt95XqW0872Pi1UMGSpUqu1KD+sUloxSGoQ63V6/L+je7jvC5d/Zha9Nw7S8uX2421GDS0PCxl9Dw0VpTw2cln5y8jOtn/pIDS7VttvrN+Mtxek+W7dhuHz/BQH9hOGx2Ikp6obVbn5hUl0qCsB/H6NFVS2zA1cB7pJTPCSG+iBHWKkWpmELRPoUQ70QPhXHOOWdWZWBiko8aSpGZG8N2ga6V0yzihTlGbT6uAayLIVyX7iARjaC0KVipToJzrk0FQCI0ynJRsRDscVe3BP0Z5yTveauXjwzPITWJsDRPSHElSjCJ4tV/G15Kafh01XtLNWEuVbrXVrX51vRW/n4gztBLL4cn9q26bnPiBLPsXD5OZzQWB0MM9J5Z2NVk47Buw0dK+fe13Egek8CklDInCnEfuuEzK4TYLKX0Z0NZc3nr89WttgHTK19USnkncCfAtdde27rNf0yaDiUUx0ISgR7WaRbxwhwn8lrqdXR2kYhGSIo4HlxV0fLJhbmwCpLTJ5YNH7F1M2Ext+rzTpcZa5Qfbx3i0vkYHQO19TKcLlLRUMMpkj498dcnizV8jsSaQ+upldjT/lt0XzQNZRTjOn51L5ZdH0DLS9wPjS0xcGPrNYQ1qQ1NV0sppZwBJoQQOf36VwJHgYeAP86O/THwYPbxQ8AfZau7bgDCuZCYiUk9UBdD2HuNst1mES/McTDjWW4E2p5tVhpT9NBLNbR8cmEuuqy0jRhaNfHttQvl7G2bIDFWbEw0C8piEiTLGj6+9KmCeYlgf41DQxuRz85dQ4ftUNmO7daFKVa2fgvPJsy+XSbLVGT4CCG8Qoi/E0L8IltmfkN2vDc7fmGV9vUe4G4hxIvAbvQw2yeAVwkhBoFXZY8BfgScQu8c/zXg/1RpDyYm60INhbF3tS0fDyU7GribYpYyNiyb9AoiT1pvTBqK6Z4YLaagxk7/gpBrUwGQaUvimDScrfObVs/FOFMmbWFCc9VIKawNKzV8PKnCZGzN00dEMXs6V8q+sBdX21bU68tfagYWDxYcTwWSJE8srrLa5Gxj3Z88IUQP8BR62fgIehKyG0BKuSCEeDvQDfzlmW5KSnmAwoaoOV5ZYq1Eb5BqYtIQ1MUQdq9RvXQk2nx38um+fqwjI3jCerJxIDQJ9isB3etj9ZyesZaejCATeml5Si1MbB7uqe0d9kzcT/ni5sahBgwNH2+7rVjDx70VyndhMFmFH4hf45rz9pUVlWt/6h5sN+5GyWTDz/MJ4kOLuK/aGJV0JmdGJR6fj6JXS92Y/Vl53n0fveLLxOSsQg2HcDj15F5pdXAy3jzihTkinXrYyenXK89mF4yQ1JkkOC+HuYBEuFBD5aCvtmXEM7GZtRc1CCWQWNbw8bpVLBTqJYXaTMWN0+WzkxfRzzEsW1f/HVqiIbb25RneEhILSaRaHXkFk9amEsPntcC/Sin3Urq6a4TCJGMTkw2P1CRqOIzdprvRVc9AU4kX5pj36BVEtvFJLFYbqWQM4dEdvmeS4Lyc2GwTpGbyWjLY7bzQVlRjUFX8iUky2dBds6EEkssaPj5nsWE5J8q3XzBZnXDGRshzLep1O8qu65/ZU3Ac1SSpU6aYoUllhk8fMFhmXiEb+jIxOVvQImnUgB97tpAw7mxONd7JNj2UZVEU2rt1QUOZLfY6XY/PcpsKgC4rzom8cM72LaREbY2SIeswS5PFzTGbgcx0dFnDx2srvtiOa6bhcybcFb0Rub18zo736ftxuAxNn/mEQvJYc54vJvWlEsNnFj2vZzWuAsbPbDsmJq2FEkqhhqaxaXrCxpKjOXMIhq1G1VmHW3+csemdq09XvXm5TQWQakvgODW6PBfdXnuNmj1tUyTHilsUNBo1mkZdSqNk1bF9luKS/sF0d723taH4jn8zm+UC4qLVO7aLVILtXca5PTqTIHHcNHxMKjN8fgT8qRCiSJZWCHEt8EfopeUmJmcNaiiFyBi5LPOW5lTjPay5Idtmzyf0j31CRoGswrBSee7DcpgLSCoBhGa8hn/AUeopVSUpFOaWahtOOx0y0zEAktnft1ctpeHTXJV/rchB5ytQry5/o9E7agj+RKIZNE2SmYnVemsmTU4lhs9H0HN79gP/lH38B0KI/0Sv9prFKDE3MTkrUEMRrHkBXr/WnGq8J5MurL26sKI7poe2ltLZu1/JsvJyJRQkNkcKnb2DXdVpfroW/ljzSXalp3WDclnDJ3OqaM3+iK9ozKQyPjOzGzbNlu3Y7n72B7i9RvFyxucgccwspzvbWbfhI6WcRq/m2g+8C72q638DbwUeB14hpTTPKJOzCjUUxt5pSPaPZprT8JFSoPXrzlr3gm7whCJGVVSlCc75bSqwCZLzhRf3fd7iFg21YKYJDZ9M1vAxNHwKUyM1dy/hjKnhc6YciXjosTigTMd2oals8xmeybCGmedjUpmAoZRyVEr5GqAXeDlwE7BZSnmblNLM7zE561DDYRweZfl4sMnEC/OJ9+gJtW3jehLyXNDIj6m0WWl+mIsuK+484ULhcnHIXv1WFaWYyowQC9fHu7RecqGunIaPQysMdSXcZg/lavGw+gqUK8p7zzr9hpihfylNeiKCGknXemsmTcy6DR8hxLJGuJRyUUr5jJTyV1LK+nzDmZwxMp1G13s0qRZKKIy9zcgZOBJt3hBGyKeHuqzBRZweLwsL42DV81AyFVZ25Ye5ko44zsHh5WNtx1bqVdF/0DFOvIlaV2hptUDDx+dRERReZMOmhk/V+PzkTiw98wjn6irhriNPLj8e88fAIkiaSc5nNZV4fPxCiHuFEK8RQqweVDVpWpLHjpEeGW30NjYUWjiM3apHeKXVwWC8dm0azpQZt9HfqLOzGyk1hFf/KFfSsyu/TQVAQg0gMoZYXHhb/bxeJ+wLLPmbx9mc8cdAgnDrGj7ethIaPpbmrPxrRWKKFYt1C+p1l6y6xjo/iS8bjlYUCT0uEma466ymEsPnYXQRw4eAKSHEZ4UQu2uzLZNa4PcPEhw70ehtbBi0pIIaDmDPdt5uVvHCHGP29uXH7Q69t5jq0j2Aynxi3d7A/DYVAPFIYc+syb769j5upgTnXH6P9OgXWp+9hIaPbM7Kv1blq+EbUS+2l13T4zYM0ITTSmpwEZkxVZzPVipJbn4jsAm9Cegw8D7gBSHEQSHE+0qVuZs0FyPhEUaXRhu9jQ2DsphCxuexSl3Er1nFC3McF57lx96MLi6Ytug9pWRGQw2tL1cmP8yF3UIqWKilc7zzzDu+V8JMtHkMn/SUbvjkNHy8YrZozZCp4VNVvj/bj6U7huhc3dPYHjfENQMpDZnRSA7XtqWKSTGpoaG1F9WBSpObw1LKr0opXw7sRO/f5QE+C0wIIR6uwR5NqsRoeIyxpeYTfGtV1FASoRpJvs0qXpjjYNq3nAvhCuvGWkxbWp5fr4JzQWJzpwX3TOHF/XlPfdP+ZuJ+tCbpwZTx6/leKUtWM6mkhk9n0ZjJmeFXLkG54aJV573jB5YfT2SrEZNHzSLkeqJGIsx88QuN3gZQoeGTj5TylJTyQ1LKncAfAHHgtqrtzKSqKIuLHFUnGI2OI7XmuEi0OmooCYphBDSreGGOUMaKZZPumM01K11KGGXn6ylpL2hTAcQdMVwnjcRm0dXJKVv5VgLV5pQcYmlmae2FNUaqGplZ3fCJKjkNn+GidQdMDZ+q8yn/lWR2rv691nbkKSzZRP65QArhtpE4HjSLPerI3Oc+x2PXNIeMw2kbPkIItxDij4QQjwH/AbQDZgJJkxI9epgnXOPssU+RPlUsqGZSOWp4CXveNaxZxQvzSffpXin7+CQWq5Vg2ChDX0+Cc36bCoC4Mo8lZlS1KefUP9y3xznZFK0rMnMJyBo8wZiC1W7BnSp07WuuHgLp8vkoJpUzGHMh2x2ILaXPP5FK0N1j1OSonU60pTSZbGjSpLbE9+1jdGGQh7sa/zmF0zB8hBC3CiH+A12p+ZvAbuArwPVSykuruz2TajERPEVSKAzaAyxMlOs1a7Je1PASDrdRzdSs4oX5RDt1r5Qlk6G9q4fZhZHluczc2qGugjAXEI8VhnKCW7xV2GVlhC1JFoKNL2nPJTZjEYz6Y3h9VhxaYUuNpMfU8KkVz8auIH39uavOd1mNRPNo1vtjVnfVHplOM/25z/Chq5rnhrsSHZ9PCCEmgEeB3wN+DvwuuoDhn0sp99ZojyZVYDQ8uvx4LO+xyemjhsPY24w7xmYWL8wx7zESazs8PiKRBUSb/jWwLo9PXmKzsFtIhQorusYb1HS8GSq7coaP6HaSTmt43Yqp4VNHvjR5HrHzVw9dtS8a3rfZmH7DYub51J6FO7/GT3cL/NbI2ovrRCUen9uBOfRqrq1SytdJKR+QUmbWeJ5JEzCSV81lJjhXBzW8hF0YOTLNLF6YY6rNMM5yzUrJavlo0QxafPWPsxJMFvT0kl0WPHOFF44jHY0JHfhjjW9WmuvRlXTpeQy+Uho+1uZOgG9lEqqVtL0bLjyv5Lxr8Lnlx6N+XWQy44+hrLOa0aRyUqdOMbX/Kf6178VGb6WASgyfXVLKa6SUd0gpF2q2I5Oqkx4dZa/dKOcciY0jVbWBO2p9pKqhRcPYNf332uzihTmG85KS3HG9lF1pM4ydcgrOyaEVYS57FNfwSMHYHtcMjWA6OUE6T1uo3kgpl1tVBLKd7r0lNHwmtOZOgG91vrOwi9S1pb1qjuGDON26kR9PKIhOXcsqaTYtrQlSSqY/9jE+cUPzNXeoRMfncC03YlI7gsPHOOAwLkjPOSZJDRdXm5isHzWUgnQIC/rdourZ1NTihTmOaYaWjyvbrDSJEeIqF+4q0O8BIpl5bAEjR0IM9DFnbYzH54h9hMh4Y4wuADWYRKb0m4nJBf2c8IniL/zhdE9d93W28eP5XoI77GApfWnr6TAqv9JeB2Dm+dSK0D338sJLFI44ms/wqbi2LKvWfD3QRbHhJKWU/1yNjbUqWiKBxeVq9DYKGA0X3pWfsi2yMDnItgsvbNCOWh8llEKoxhdm3Nka+p3HEk6sPT2ogYDerHRbF1ElhAc3sHqC88o2FQCx2FT2WTqpc/qB+pay5zhonyE2OUrPRdsa8v65MJdwWvHP6r9DnzpRtO5IvPnzwFqdYGYLA7svxrLvaNFcpzLLFHq4MaRJ+oDUqRBaSsXSZnZiqhaZuTn8Dz/Ax2493uitlGTdho8Qwgl8F3g1IACZ/Ze8xxI4qw2f+MGDeG+4odHbKGA0PFpkoo6GR2nMJWJjoIZSkDHye8JNLl6YQ5UWtIEBCASwBYI4L9pOKDbHAFuA1T0+K9tUCIeFdKSwkmphi2fl0+qGFLqC8zkNev9cmEvrdsKMHkL0pou9qvuX2ovGTKrLlycv5bNXjuPeVzznnTkK2TyrqcUUfQCKJHlyEfcVZhiyWsz88z/zbzfFUERzasZVkuPzQXSj55PAreiGztuA3wGeAfYCV1R7g63G5OSxRm+hAC2dZihRnMxsJjifGWoojs1iiOYtiNb50kz2GKVXHR1dBBYNz8Rq6s0rw1yy04I3WJjDMtLd2LwxfwNbV+QquqJW/SvVarfgXmH4aM4u5k0Nn5ozmnAyub2zZMf2/E7tkzMJsOt/LzPPp3pEfvYzTjiC/MLVvNeYSgyfNwL3Syn/DjiYHRuXUj4M/DrgBH6/yvtrOU6mJ9DyBN0aTer4cZ5wFrvcR2LjSKVxyaCtjrq0hN1lVINMy9bpvxRqN/JM2h1OZudGln23SjCBVIrv0lbq98TsEVyjhV3RD7UXJ/PWk0ZWdqWzHp+5uP6ZKqXhkzI1fOrGydA5ZEp0bLcEZ2jv0o1PVZPQrRtHyRNBpGaqOJ8pajTG9F138g8XH2n0VspSieFzDvB49nHu1s4BkC1p/zbwluptrTV5pjtA7MCBtRfWCf/0yZL6Cc+2TZkJzmeAurSE3W54fEYzrdN/acZl7NWrqKQzCYQ7G/XWQAkUen20lEJ6vPAcCqdmcUzmXdgtFva4GltSPq4MEwvWt0EqgBpJo0XSIGB0Rn9/n1tBUCgNYGr41I87J89l6fLSgqI9LuMcibfp570WU0iPN77tSasz//nPc++NGhFLc0sEVGL4RDBygqKABuR/kkPo3dvPaoLWJBP+5knoGlmR2JxjzBZibuJknXezcdCWlrDndd5uBfHCHOMOI8/EnW1WKvPSc1YmOKeGw7DibjiaKDRyxLbNREWhWF+92eucJDFSf/f6snBhZxvRWNbj4ywOGc7bWiMBfiOQ0QRDvQMlO7a3xwwP+ELS8Hqb1V1nRnz/fsZmj3Nve/Nc/1ajEsPnFHABgJRSAY4Cb8ibfz3QeN34JmA41DyelNEyKs1jkeaNwTY7ajSKXTUu/kej9W/VcLqcEIaV45zRS03TNsNoWZngvDLMJRwWMtHC0vHY9sbnOM1YowQWisO6tSYX5sqVRwP4bKGidZOmhk9deXzyPJIlOrZ7xoys5/E8I9/M8zl9ZCbD9Gc/zYeuLn2j3WxUYvg8BrxBiJzcK3cCrxZCnBRCnAB+E/j3am+wFTmRGEVLrN33qNYoi4scUSdXnR9bGl91zmR11GgaoUYQQo/46uKFzSVhUI4XM15Emy7eZh+bwGK1kpCG/o6yokv7ysRmrUvgWyrMY5vb1Faj3VbGTLT+4bacxyeU17zVl+cNzDGUbp08sI3AL4JdTF9SXG3pOPorrNmk5kAojfDqOT/KXKJAmdxk/QS+/nV+fqVgytoa4cJKDJ9Pohs3FgAp5ZeBvwGSQBz4B87yUvYcP/GMEj94cO2FNSZ69DBPulY3bk7FxpAZs+NIpaiLKYRmuMVbRbwwx3zajmVAD7vozUq7iaSMu9189eaVbSoA4tYI7olC5+5Qd2PDXDmmG9CzK+PXjcDpsPE76I4fKlp3NNE6eWAbhaNtWxBbCzMwLOkU3d3G51XtMIx2M9xVOalTI0ztfYIv9TdXW4pyVKLcvCSlPJINc+XGPiWl3CWlvEpK+TEpZdXS4oUQViHEfiHEw9njc4UQzwkhBoUQ9wghHNnxtuzxUHZ+R7X2cLr4rRGmphpf1j4eHCYpVq/c+pVzktSg2am9UpRQCqEYXVtaRbwwn0y/cSfc7vaxmBe6UuYT5D7KK8NcAMGkH8ep0YKxA77GCBeuZDo+iVqiKq1WaClFTwa3W5jw656y9i4bHZniz7+p4VN/7h/dQfS684vGuy2GF3PJYhhBZrirMqSU+P/5Y3zyxuZTZy5HJR6fevNeIP/b45PA56WUF6DLw/5pdvxPgUUp5U7g89l1DacZ8nzGwuVzeKasS8xOD5VdY1KMGkpiSRuehVYRL8wn2mnkm/iElbmAca7ItIqa9V6khopzVSKJaYRmGBfC4WCfo/Hd0QFOihGiU/W7eGX8MV22tdupl0cDA94obVphgqds62Am5SjxCia1xJ90MLa9WKrVFzBu+GYjhtc7NbqE1sCeb61G6L772Lc1w6EmbEtRjqY0fIQQ24DXAF++Wk5DAAAgAElEQVTPHgvgN4D7sku+hZ5MDfC67DHZ+Vdm1zeUk7FRtHRj3f/5HdlXY3Qda0wKUZei2B2tKV6YI+A18k08ySSBwARYjY+NMh8v2aZCtFlREvMFY/IlW5pGofX5tmkSY/VLsMxM6fk9sbx2B5tsYwgKfx9Jr6mT3iietm9BeApVxV0nn11+POqPGz0INEnyhBnuWg/K/Dz+B7/LP+1oHvmW9dKUhg/wBeB2WP726AFCeWG2SSCnBrYVmIDlarNwdn1DecwzRvLFxsU8V3ZkXw1Twbly1KUIdqsR2mkl8cIck04j7OJaWERKDeEzOtgoc/GiNhUAWif4YsmCscjW0nopjSAtVPxL9SsuzVV0BZKGavVA+vmidUumhk/DeHaqD+3ywnCXffQITo9+vidTKqLbUHlOHDXDXeth5hOf4KsvTzTNTU8lNJ3hI4T4H8CclPKF/OESS+U65vJf951CiOeFEM/Pz8+XeEp1GbOFmBovbpJXL1Z2ZF+NU/EJZIM9U62GFokUaPi0knhhjhGrb/lx27huIKtO4+KdmU8UVXMBxKxLeKYKz6vpgeZqw1BPBedcRddYtiy6q8dGpyy+4Zm3tl4e2EZhOukgsLPY49bbYRj1KbdxDidPLiLV1ruY15PI449zwrrAz92jjd7KadF0hg/wcuB3hBCjwHfQQ1xfADqFELlb0m1A7tttEtgOkJ3vAIp8lVLKO6WU10opr+3r61s5XRMameezsiP7ajztnCB50kxwrgQtFsOmtqZ4YY6j0tAdsi0EaHN7SFkNT44yFy+Z2BxITOMcLDyvT3Yni9Y1knr17JKKRmYujvDaCYb0m4d+T4Q2rVgYdFK2Xjh0I3Gkv9jw7MgYBnwwT4pAJlVSI41tv9LMqNEY/jv/jQ9d2rgb+zOl6QwfKeXfSim3SSl3AG8Gfi6l/H30dhm/m132x8CD2ccPZY/Jzv+8mtVlZ8JgdKRh5eIjZYQL85mxRpnxm4bPetHSKkKNIYRxirWSeGGOwwkX1m4jRNXZ2U1cMb7sMzOxojYVAOHYFGLFOf2Cu/Ye1EqYSI2SjNf+c5eZi4MqUTqNcuhN1lMl1w5nWi8cupF4UetF9BUan77pw8uPp4KFxnvSLGtflfkvfIHv3igJi+a64amEMzZ8hBBXCiF+PVdeXkP+Gni/EGIIPYfnruz4XUBPdvz96NpCTcHPPGMkDh1ee2GV0dJphuPrz90xFZzXjxpKYcEIAbWaeGEOVVqQA4a+SbvDSThhlOhrcaWoTYVwWlFShRcE4fFw1NFchs/+tnESp2ovzpkLcy3lou0CBlLF+T0AR+PNkwd1NvLLqV4yl59bMOY8/ORyosTUbALhMBLUTT2f0iQOHmTMf4zvdDR/W4pyrNvwEUK8Twjx/RVj/wHsQ1d1PiSEqGpdr5TyF1LK/5F9fEpKeZ2UcqeU8o1SylR2PJk93pmdL33L1QCGbUH8Y/XvUps8epRflhEuXImp4Lx+1FAKoRqlm60mXphPoscI+XoVlcAaScFaB3SkCj0p6o4tNdnbmXDKtkhotvbGfCab2OyP6GGu7h4b7aJ0QcP+iK/kuEl9iClW/Du2FoxZwgt0duu5PVKCzEtwVoNJMrOF6uRnOzKTYfozn+Yj17T+jXIlHp+3YuTVIIT4NeAPgO8CH0LPu7m9mpvbCDQiz8fvH2TGGl17YZbh+Dhaqrm76TYLymISS9owEOKu1u3LG+4wih/dkShz8+XzwqLWJTxzCwVjoa3Nmd80Hal9gnN6OgoWwdi0Llw44A7Rphbfd8m2dqaTzdHS42zmQE9xnk93m2HcxByFl8PEUdPrk0/grm/w+BUwbi0uemg1KjF8zqVQUPB/AjPAW6WUHwW+Ary2invbEAxFR5BKfQWxKtXmedo5SfLEidpsZoOhhhJYFMPwCdvrkyhfC2ZdRjVa2/Qc0WgQ4bSuun4hNoHrZKEhP9nXnN6u6Rr37JJSkvHHEN1O0hm9Ami1/J6UZ2vJcZP6ciTSi9ixvWCsPWp4L+YTasGcqeJskB4dZeq5x/niQONbMVWDSgwfD5Dv+/sN4DEpZa7u7wi618ckj8ddYySP1jf7vVxH9lLMWaPMzDZeaboVUCNRHFbjC3FetK7hM+YwvDVtY+MIiwXpWd2QWYxNYokWehKPdcZXWd1Y/PEpalnjoASSyJRKwqUXmgoL9Cf3lFy75DQ1fJqBJ+Y6SV56TsGYe9RQTRmbLTyX0xMR1Kgp9QEw/fGP86mXLay9sEWoxPCZBq4AEEKcA1wGPJE33wWY8ZIVHLcvMHOqfgnOa3VkX41KjaWzFS0axSENb8K0bN2k1UFhqNmKTIb2rh6UttLeSeGyomWKS3yfdxd3IW8GTqmniM3VriQ5l9gcVHTjqqd39fweU8OnOZBSMLqt0PvmOPoctmyn9tBSBtGeV6MjIXncDHeF7r+f/VtSvOhozs/66VCJ4fMw8G4hxBeAe4E08MO8+cuA1s96qgH1zPNZqyP7apgKzutDJmJYpRHjHs20ruFzMONBOIwv+g6PjySlEzrVDkn7iuaforuLMVtzxvv3OP0kRkZr9vq5xObJBb2kd8AVxKFNlFw7JVvXK7jR2N/ZBzZDodyipOnJ69SutBfmYp3teT7x6Tn8D9zDR3dsjBBXjkoMn38EngP+ArgKeL+U0g8ghHCi5/z8otob3AgMR0aQWn2UQNfqyL4aw8lJtGTr6jLUA6lJUBIFY4PJ1u24PZt2YBkwCjF9FivRTGlDJmZZwrtQKGiYeUnzJnZHLCnmFksbItUgPR1FuGz4F/TzYZNYXQvrlKnh0zQcmOuFiwrL2jvz9G5X+ghTQ4vIzNmp4jw/EeHEp77OnTclSQt17Se0EOs2fKSUQSnlLegaOu1Syn/NmxbArcBHq7y/DcETznFSx+uje3C6IaunXBMk67THVkVdSmORhYbB0WhrlykrfUYYxhNPEYqV7rI8Hx3HNVxY9RXY0tzCjdPx2vXsykxH0braQILFKuhL7V117dFE67U02agcDPmIXlSYitoeMJS2Z5YKc3pkWiM53JxezVoyPbjI3v96npDtOI+569f0t15UouPzd0KIS6WUizkNnRxSygQQB95V7Q1uBF50zDI3dKjm7yOl5FRk9LSeG7DEmZ4zE5zLoYaSWKRhGEhrG0NxZ5lnND/RLkPN1hUIsrCKl2R+aQzbQmGVy3hvUwikr8pUjUra1aU0WjRD1Kp/ffb2WmgXq3++9y+1rldwI3JyS6H2lOvEM8uPx/xxsBQm+J9t1V0jB+f58VcOsfXwf/HBy46t/YQWpJJQ10eB3WXmdwH/dGbb2bjUI88nPTrK89bKE5tzVFoGf7ahhlJYM8bFVGlh8cIcAZ8RhmmbmNK1fFb8l4TLiqYV60Id7ihuadFMTMUnUTLVd9Gns4nNszE9pDzgDGDXShtZ0uFlItnaxvFGY5+zB+Ey1NZtEydw+/S8n3RGQ/QU/r3OpvYVx5/x88hXD3ORY5gfXxchbNmY6Q/V7NXVBtRXsKaFGAqfqml5LcDi8DEOtp1+5v24qeBcFiWUwpo23L4JV+tX60w5jZJ22/wCFrsF4bEVrFE7JJ1yxVeFEOxx1acZ6Oly2DFOfKz6e8xMR0HA6Ixe/rxJFDclzZHymho+zcbeqT60y3cWjPX4DEXyhHPF+b+UJj3Z3EZ+NTjw2Dg/+49jdPdacSz+jLs7N6a3B9YwfIQQXiHEFiFEzjfYmTte8XM5urLz6bsbNji/apsgVeMu6GfqsRlMTaDFm1OXpRlQwzFsinGKt7J4YY4Ra2GOUmdnD9JduCZqCeMLFaZ9ik39BCzNfa4csc0SmRyt+utmpqOIzjZicQWr3UJ/6rlV10bamq+lx9nOeMJFcGdhnk9nyjCQcxIF+Wz03l3PfH+Yp+8bwiIEF47dx0deurGrfNfy+Pw/YCL7I4Ev5R3n/xwEfhO4s2Y7bXGeb5smcLK0zke1WG9H9tV40jlBwkxwXpXM1BJ2zUiYbWXxwhxH8RQc+xxO0vZCOa75yBiukUJvYPKcqrblqwlSwFSk+vdiaX+MtFeXAejrEXjL5Pcs2FrfK7gROTJQ+HfxTBt/w5xEQT4bNc9HapLH7z7Ovkd0Q+fCvuD/396Zx8dVl/v//cyWSSZ72iRtKd0otS0iggICIiK4IILL1evV68XrdV+u6PWHu4ILqFxR9HpV3Bf0ooAIKktZS1uge9O0SdMmzd7sySSzb9/fH2fSbDOT2ZJMJt/36zWvnDlz5jnPZM4585zv93k+Dwc2D+WsTEW2sMzy+nbgVoxZ/y8AfwWmq/EpwAU8r5TajiYuzcMnmKuB79Md2TMotHGafHT3t7CR87PnWJ4Q6BhD+f2YJuncLGbxwnHqvQ7M5eWER4wLXXEogicyho2JSqSe4RaqO6fmsPSvWBwd6bPduiLiCxEe8jEcnQ6psfdj8cXvTt/F4g+O85E6tYwrqiqJDBojOfb6HcjLXodScGrAi9TYUb6J/LBgt5uQ04+lLH96roVDEbb98gjN+43jt7TCSmXb/dz46rm9Qc8FEgY+SqmngKcARGQN8COl1PPz4Vg+csLZwuVzZDvVjuzxaBtrZWMW/Mk3PHX9mG1Tp3YWs3jhOMGIoGpqIBr4OEZdjPkGKY8GPlJkQY16Z7yvpWpxpPN1u7Nb0h7sdoOC7mFjVKyWxHkQWsMnN9netYyPnLMO8zNG4GMaG6K80sLwYAgURCrsyKmpYp6+hkGKL86PqcuAL8TDPzlMZ+OENteWocf43qXxg/h8IpXk5gZgNN6LIrJZRL6QuUv5y/O2DvzNc1PdlWpH9ni0OvN7bjcdlFJ46wYwmabmuSxm8cLJ+KomRiXsvb0Mu3pOPw+XRSg3WWe8p65kcQyFtwZO4nNmLxcp0O0Cq4nOHi8Wmymhfg9Ag3fxB8f5yFjIQu+6qXk+lQUT10+XdeZPY75Ud/lcQf76vQNTgp71KwN01rRzwJbbBQvZIpXA5+vocvaMeM7eyWDj3Eh/Z6sUvSnQScQdu23BUiXQNkrY6cccmpovstjFC8dxllWdXra2ddA/SctnTEYoGZt2PFgs7C1YHBfIPYXd+LLYuiLY7YJKO+GIorpKURynP9c4B0fz4xjJRw5WTc3zKZ10De1zzxzR9DWPEAksbgXjsSEf9//3PvraJqrU7A4Lq47dzdfW51dbikSkEvjMJliiy9mToGX4xJzYPenMjrrmTnsHnsb8LWNMB8+hfsRuRvVPCJ3lg3jhOL1FE6MSJn+AkPKCxTjd+8facHRMnS6SVbV4TEEWAwMmN/392ZNpCHa7cReYAagp6MMcGY67rbI5OOldHLlQS5F6zzLkzImsS0fL3tPLbb0zp3cJKfxN8b/vXGe4x839t+9juGfqCOjWyH5+e9lo3rWlSIQuZ59nmkdasm4zNDSUVkf2WIya/HQNZN/HxYqKKLz1A5gdTootB0+vzwfxwnE6CqZO2ZUWFSPFxo97V18jtpbWKa+7zlzGYqLLlZ08HxWKEOz3MBBNeq1VRxNuH3CckfB1zcKyvacc/5Y1p59bG3djLTB+EsdcQaR8ZiLzYi1r72sb5f7/3o9reGrF5qoVgt+0n0eLltY1X5ezzzN7LJ0EWluzatPVcIQdhdlryJhuv698xN/iJDIWRFx7sEQm2lXkg3jhOE0ytRSwWMyECyOIw4IyB5FpDXZ7a2wsJrIV+AR7PRBWtPV6sNnNLJslv2fMnrtNXDUQVibaJo34SCRMVcXEzUyweOZx7mscMpoVLyI6G4d44HsH8LmmjtJabCbWNf2eL5+z9CRMdDn7PLOjsJ2ho4eoXbs2azbT7cge155WcD6Nt64fc7mFQs+eKZO9Tmvu69gky6GgAywWCBnHULHPj9/kxVZaSIVr5nTeicrAjHW5TJe7ExVRiCmzEbpgtwspsTLc4eGMleBQh42rXxwGLDrwyXX2ly1no9kMYWMUr1wN0IMx9TsCTB/bjLiDBDrGKFizOAobmg/0se0XRwmHZnaY31LcxmPr3QybYkzr5Tm6nH2eCaNoGTpBNi+J2R6haQp2Ena5MBfndvftuUaFjWkuk62DYtNUdd5+qYrzrsXHqUAB5ppqwl2G5k3h4DDudaMETD5KvTPF3A4ULy4xt0ZTG55T/ThWZRasBrpdhEoLAA81th7MvrhFroDW8FkM1A0s45/PXgcNRu5lycAx4GIATo0EZgQ+AGPbOyl4z5b5czJNju7o5uk/HIs5QlW13IK9fxu/3XRkATxbeJJOblZKvUcHPdmhxZm9kvZMOrLHY0dhB56GxPkLSwH/iWEi3hDmsR2YmDo33q3yJ/ABCFVPTN3ZOrpwevroHW3F0dkzZTux2zlY0DP97TnNAVsP7tbMcxiC3W5GxRg1qo1MH/ieyclgfh0j+cj+4VLcm1affl7YsPP0cluP+3SS/2R8RwZx702/J+J8sO+RVp76fWPMoEdMsKn9vrxvS5GIlJuUiohJRDaJyMUicsn0x1w4mW/sNXcQ6MxO3kGgtZU9luzmlLskQNdgdqrEFjOeugEsVeAoODjjtdZAeYx3LF7cFROjE9a+fsa8A3T0HMV+fGqQHlmzknCi+Z0cJCQRukczO0dURBE85eLUaICCIjPLgntnfU+jt2zWbTQLz4lVK04vW7pbcJQaEyHhkILK2FV5Iw81ExrKzc7lu+47wfMPxA/0N1U7qds8RGuet6VIREqBj4j8F9AHHAV2As/GeGhm4Rl7OyP1B7Jia6ilgTpb9u8+2kaX7t0AGBU83iMD4DtKUWSmvsVx/+KY40+WwZKpCsNjvh6UJYQEpyZEjq5anAFfpgnOoUEvKqRoO+WhpiJEEfH7c41zcEwHPouB/YVVSOFELltV8UQOm9dujvke5Q8z9KfY00gLyZHtLRzYFj9Hs6TcSlXrvXy3dubN3FIi6cBHRP4duB1DwfmrGKmePwS+h5EHthf44Bz4mHeEJELLUHY6tbfNUQVWtgQRFyu+pmGIKCyeXTFfPzKWX8J03fapAU2R2UKl3TFzu+rZ6iFyk64Me3YFu11IpZ1AMEKN9RQmlVgNWlmLOOHRGj6LgT3dNUS2bDj9vMw3cawMBmYmBY8TaB1l7JncUXDpPdnNrvsTT+luGXmCOy8ZIE+UONImlRGfjwK7lVKvBH4cXfegUuozGKrN64Clo4CUIS0j2cnzyZZw4XQag52ERxMnb+YznkP9mEvdlNlnTmkocwHN3vwQLxznpHlqIFccVpT6Z4oUHqtcnBUgHd42gr70RReD3W68RUbQVxuevYljsHiu2hFrss1Jj53hjROaS8WdEyO8Hf2Jj/fRx9sIdGXeKihTvGOjPHtPPQFf/J/0dSuDnFp+kr0F2W3cuxhJJfDZAtwTXR4f3zMDKKW6gJ8CN2bPtfzmAJ0EezJLEo34/ZzwzE3p+S57J+7GpZngrIJhfA1DiPsA1sjMO7p8Ei8cp0Gmju4Ujboo7huYsd3eor4Z6xYDews68bakP30b6HYxFFAUFluoCu2bdfsx+4pZt9HkDg01E3W2BUd2YIpKH/QN+pGiBKOcYcXQPY2o4MLd86tIhF33PkNva3w/7Q4LZxy7m1s2LJ22FIlIJfAJY+j1AIw375lcttAKurF3sjxZ1Irz0P6MbPiONrA9Cx3ZY+ExBelcognO3oYhpEAoCMYuYvQW5p8+y2FvEabSibwl+6leCpumjkpKSQlN1sVVyj5Op8XJcE9r2u8PdrvpGPRSU+anUM2e3zNgyR+By6XAIVmGVBjTvSb3KOVVE7k94fLEo7uhPi/Oh1vn0r2E7PvHo5zYn9jHLeogv790FP8SakuRiFQCnw6M6SyUUn6M9hSXTnr9AoxcH00SBCTMycHM+nad6slOR/Z4LNU8H29dPybLKUotL8R8fcSaf/os/ogJaid+rO3HmzG5ph5bobWLexQj3QTnsNOPiih6+n3UWLtmSBvEoltr+CwqdnQtJ3TO+tPPKy0TTTxd5tlHd13PdRt5gfPMyYP7aNrrJRSMnYQNsGKFEGQvDzuyJ6Oy2Ekl8NkOXDPp+b3AR0TkLhH5OfAB4OFMHRKR1SLylIg0iMgREflkdH2liGwTkePRvxXR9SIiPxCREyJSJyLnZ+rDfNGcoZ7PXAcm7WNLT8E54g/hOzaMaWwXJmJ3qR/II/HCyfirEgv8jaxa3JVs6QY+gW4XkQrjjro2nNxUwbFA/ih7LwWGgxZ6103kZZWOTox297qTyA1TMHRvExHP/DXvHe3v4+BjexnuiV9pabGZ2NB0N1958bF582sxkErgcyfwMxEZL1X4CvAY8H7gfcDTwGez4FMI+C+l1GYMCc2PicgW4HPAE0qpjcAT0ecAb8CYYtuIUVX245kmc5NDkXZC/f1pv3+uEpvHaQh2Eh5ZWoN4vqNDmMvAYY0vN5Bv4oXjOMsSNx9tX1y9SWfQ5UqvAifY7WbMLDhKLVSGZpehCBev5IedZ6W1L83CcXj5xBR2UfPu08utp7xT2tXEIzIaYPgvmY3iJ0soGOTJ3/yKnvYNCbfbXNLB4y93MWhKXIW41EhFublRKfUjpZQ3+tyllLoGo51JhVLqaqVUxgkASqlTSqn90eUxjPL5VcD1wG+im/0GeHN0+Xrgt8rgeYwO8otiTP5xRyujaeb5ZLMjezx2FXbiOtYwp/vINTyH+sHfRFEkfgJrvokXjtPrqEj4ekN57BGwxcLxSDu+QWfK7wt0u+h1h6gp9VGoZldsfqz87bjDKWvDahaYw74qJCpmaG3ajy2q4ePxhmJ2ao+F9/AA7n1zr+r89G9/jsu5iUgo/nFWudxC4cCj/LpiabalSETCs1NEKhO9DqCUGlJKpX41SQIRWQu8FHgBqFFKnYru8xQwPpa8CiP/aJzO6LqcxychWgfSu0NwNdTz7BwlNo/jkxAdQ5lL/S8WIt4Q/pNOLK4diMQXJss38cJxOgoSf649Rbkt0z8bu+1deJtTn14OnnLT1uOhxtqBkHgqI1K0jM+1XZCui5oFZPupKgJb1wDRTu3lE9eAQIxO7fEYeXBuVZ2Pbn+S4Z4Qzv7456uY4EUdf+GbF3bE3WYpM9ttSb+IHBCR74vI9SIyb7e6IlIM3AfcqJRKJCgTaxByxq+WiHxQRPaKyN7+DKaXsk1zmno+7YMt85Khv5Q6tXvrBzCX+igpSjwKl2/iheOckPhNaWVZJV3mxa3r5JMQp0ZSO54j3hARpXB7QtQGZ5/m2l71DpzBxSnyuNQJRoSOMyf0fCrUxO/EcHwdwxnMpapzf3sre//+N/q7Nifc7uzqUeo3DdBsGcq6D/nAbIHP48AG4D+B+4EBEdkvIt8VkTeJyJxosouIFSPouVspdX90de/4FFb077igSCewetLbzwBmKDQppe5SSr1MKfWy5ctzp+LicLiN0HDq1QAn56niailVdnnq+hH3AQrC8Ue58lG8cJy6UBFYYv9oB9bkRwl/Z4oKzoFuF4FiGyXlVirCiQOfiL2cm9ovysQ9zQJzoHw5mIyfxeLextPru4dTG8EJtI4ytj27qQh+j4e/33k7YrkCFYn/011cZmV5y5+5fcXSbkuRiISBj1LqdUAFcAnwRYyk4rOATwEPYARCe0TkdhG5RkQyvhUWEQF+ATQope6Y9NKDwA3R5RuAv05a/2/R6q6LAef4lNhiYJujlbEDswuiTUYpResctaqYztFwF6Gh/L9rCLuDBHtcWP2xtXvGyUfxwnE6/HbM1bFvCgZXzGxfsRjpGkvtxyjY7WY4AtUlbuyRxPluu5f/E31+aybuaRaYuqHlyMa1ABQe3XF6fWePF6yp5W2NbsuuqvOjP/4+jsoXMzoYf2QWYMvYU/zgssEl35YiEbN+k0qpsFLqeaXUtyYFQpcCXwKeBF4EfBp4CJgp9Zo6lwLvAa4UkYPRxzXAt4CrReQ4cHX0OcA/gBbgBPAzjNYaiwaXBGjrT61vV+BkK7tt89Mj5jl7J65j+a/g7D08gMnaR5kttnbP6e3yULxwMuHlsYX3WpelMNafw3R6OoiEk/8swW4X3cM+ai3tSIKpZWVzcFPnJdlwUbOA7B4qxf0iYwLB3NdOcZkRyIYjCipTHOkNK4buOYYKZn7u7HnofoZ7+unrTKwRvG5lkL6qZnYXZNaUN99JufQgGgg9p5S6DXgL8E6MruwCZDy5rZTaoZQSpdS5Sqnzoo9/KKUGlVKvUUptjP4dim6vlFIfU0ptUEq9WCk1s7lSjpNqns9Qy9E56cgei4CE6RhunZd9LSTeun5k7AXMs+Tp56N44WTclbFr1utLF3d+zziHrB1425L/UQj2e+jo8VIbSDwqe6jmbbTn6RToUqN51crTy1WOiSkuT0HqP2+hPg/OhzOTHelsqOf5+/4PZb6KRMM4hQ4Lqxt/x80bZu8lt9RJKfARkUIRuUpEviEiO4FhjKmmrRhTX5+eAx/zniPBNsLO5Avj2kbT7zmUDvme5xMeDRB2unCYZ5cWGDAtcjGbWRgsiVHIKcILhYtm9jghx6yDODuTu9FQwQjBsKKkzEK5ii9cqCx2bup+VbZc1CwwBxzLkAKjfL3MOzGyPuALpWXP9Vw3vuPpqTq7R4b5253fofbs1+EeKUy47dbgC/zmlWP4JD0/lxKzlbPbReRKEfmaiDyLEeiMixZ2YQQ6L1FKLVdKvVUpdefcu5x/PFacWp7PXAsXTiffK7s8h/sh2IxD7Z51267wrAoPi5pu+8zCTVlZi9M0d+W5801nkgrOwV43bquJmuIxCsKNcbdrXPFmmtyJf5Q0i4fd3ctRWw1hQEfHRMDb3pe4U3tcFAz9OXVV50g4zN++/20c5bX0tq9NuO3qlQq3vZ5Hi5aO/EgmzDbiMwxsw1Bm7sTovr5FKVWrlHpHVNBwdkUvTUKc4qO9L7k8n4jfzwnv/AYi9aqT0Mrc8uEAACAASURBVEA20rdyE+/hfsyunQlzOMZpDSYW+VvstFpmaoN4z8ytUS6TmLjDupaqgvS+i2RbVwS6XQx4w9RYWuPqOimTlc/3XpmWH5rcpNldxMhZhhRcQf0OTNFeXYMjAaQ4veT1dFSdn/3jb+hrbSEUfjWSQDraVmhm7dHf8KXNWqgwWWYLfAowurIfBPZHH01z7dRSJNk8H9/RBp4unN+prucLuhhryk8F59CIj4h7hBL77BotAMd9+anhM06jFM1Y11ebW6MZN5Rt5eqm7dwcSu+7SLayK9jtpq3fS02C/J6WlW/i4GjiKhvN4qOx1lBwNvncVFRO6tRelpyCcyy8hwdw708uN/P47l3sfeh+aje+Ec9Y4n2eYzrMfZe5GTPN3jxXYzBb4HMJRk8uC/Bl4DlgREQeFpHPicgrRESrdWWBBn8rYdfspY/dPU30mea3dUBIIrQPz+/02nzhPTQA7sPYI/GnMiZzxJXfgU+dz4GpZOpnbKnKnZyBs4vP5BN12wC44sQO3lrx4pRttAbaCbpnn7YIDPvAHKGc2Pk9Ssx8afDqlPevyX0OmpchZcboZ6VlIv9y1JRZjfjIX5sJzaIJNNzTzaM/vpPas15CX8fqhNuuXCGEwy9wf4kej0iF2XR8xsvYX49Rxn4ZcBtGBdcXgJ0YgdA2EfmSiLxyzj3OUx4rbsN9YPbk2oVKNJ6PhGpv4xAqnH2108T77Mfm35XUtspcQLMnt0Y/so03bIaaqSXth0rSS8zMNjaTjVv7BrCGA6fX3XRkO6uKYpfgx+N5exe+E4lHWFVE4fOFqXaMUhCOPQ3duer1PDc8JxqumgVmV9dywi828nxKJ43G945l1n19NlXnYMDPQ9+9FTGZ8PkS/5xaC0xsaPwdX35JanIomtSalJ4uY58UCF0CfAMoBG4BnpobN/OfQZOH9p5js27XOtI6987EYK4DH3/bKIO/O5px6WcqhAa8REZPUWqbPakZIFS8KHrfZox/WfXEE4uFvQWpqR3PFR93nMWm3qkjcw7/GN8ci2CS5AtUnSYfvQOJj+fQgBdnWFFrip0sqhC+NvKGpPepWVz0B6z0rTPyfIqOT+7U7klDBGYqgZOjuJ6NPd36+M9+RH97K8vXXovPnbg/2JbCZh652M3APM8A5ANpfYUiUghcCbwJuA54OcYokNaKzIAWZ+KM/NDQEEci8yNcOJ3DdBHs7Zt9wzQIuwIM3d0AYYVrRxeeuvnppeap60dcu7Go5BK3PfbURhYWK6PlVRNPzlw5Lz3hZuNlZRu54fBjMV+7oH0fN5RtTclepyvxeRTsdtEzFqAmuCfm670rr2LbQH5X+C11DkfFPC3NBykoNPJ8fP4wUpG5XpPzsTYC3VNTG+oef4Sj259k5aaX09+V+CarusaM1fkkvyvPf3HZuSCpwCda1v4aEfm6iOzAqPZ6BPg8cB6wAyMX6PI583QJ0OhpJeLxxH19Pjqyx2O3vYvR49k/yVREMfSHRsKjE9MXw/ceJ9gX//+QLfwneikkeb1Lp7V69o3ygL6iiWop1+qqBFvOD8VWB99sO45JxVfA/Xjd42wsPjNpm7MFPv5Tbsb8fsrU4Ziv3+Z+Y9L70ixO6gPLMK2sRZSiqnzi2Ouzm1ErbbDMijgs6Q0fTFN17m05wZO//ilFpeW4Rl+R8K1mq4mzT/yBr768NY0da2AWpWURuQV4NXAhYMUY0QkAL2BMaz0FPKeUCsQ1okmaR0tO8pGDBym5JLb0/Xx1ZI9FGEXHSBvZ/hl0PtqKv2WqeKMKhBn8/VGqP/ZSTAXmOO/MjGCvm/DgCUokcYuKyfTLwgcB80FHQRnjvZ97aha+99TnzCtZOfxEwm1sYT+39Q/xLw4rwcjseRizVXZ5RwNU2oaxBWZOvQ6uuJy/nlwaQfBS5pnuKj6+ZQ3W7h7Kw310Y6i27zoyAkBxpYciRw9BUxthlxNHYTlljmU4Citw2EqxmxxYVQGWkAXxg/JEYFL7ilCvB+cjJyl4dTUP3nEb4WCQyrPfxEB34nqhLaWd7FgzRpc5P9TUF4LZKrK+DISAPUwEOjuVUvmjZpZD9JncdHY3spnYgc98dWSPR9toG+dl0Z63fgDXM7F/gEJ9Xobva6LqXZtjvp4pnrp+zK5diDX5mL1LLY3A54SpmNdGl5sqFvZUv7piK9fvfzipbTf1HOWjL7mGO0dnlxY7aurCf6qXghWxpy/dowFqzLEToO/wXZeUP5rFjT9ionPNKtYBxb0NIFPb1biGinANrQfWI6YIZvsoYXM3nb37GOxqQUVmjlAWFZVRUb6CspJqSgorcZzo5fie/Yz293LGlksZ6E7cEqdquQXHqX/wk7Njj0RqkmO2wOcNwA6llM6emieaR5qJ9VOvlDICnwVskt02lr0E52C/h6E/Jy7B9NYNMHZmFyWXrcrafsfxN3dQUnAAkuwf6F5+Hl9te0nW/chFDocdYDZDOMwBx+CC+bGsoJKvNCY/IgfwvrpH2P6SKzjgTCwWd6igh9G2ZpbHCHxCI35GPEHW+2YmvTtrLuLutpUz1mvyk4OV1awTobB+O7w4fiaHipgY7i0HyoEtFFeHKasaROjA2dfEaL/R8sXjceLxOOliapJ+ceVyRgZfltAXk0nY1P5nbrlcNyDNlNnK2R/VQc/8cszbSsQ38y47cLKV3daOBfBogjrpItjTk7GdSCDM4O8bUP7Zp+2c/ziJvzX5PmbJEOh2Eek/QmEkubumQPlZvHHgPxkOLg3JqlafHfPy5Uhh4bw1w43FLcEiyj1DKb3HpCJ8s6OZIstMIcbJhFF0j8Y+n4LdLsb8bkpl5vHxv+G3pOSPZnFTP7wM2bAG8+ApSiuSn/YN+s0MdFfT330BgdC/UHnmxzjz3Lez8kUvx148TR1dhJKaawkFEk/rv2j5AIc2j9BsSe2c0Mwkw8I8TbbZ5mjFc2imYNrQyQYO2+amqipZ9hZ0M3oiOaG/RAzff5xQb5LJyxHF4N0NhMeyl0bmPdyD1bszqW3DxSt4p/cmWpdY5+1wdQ2RtSsTNYOeU95e8WIub05OX2k6qwfb+IwtsfAbQGecPB9frwdzeBBrZGpg5F5+Hj/tTD6BWrP42TVQjmezcSxVFqbZqwvwjBXQ17Gaod5Xoqz/QfVZH+DMc99EzfqtrD7nSoZ7ErdfKa+yUtF2P99dcTBtHzQT6MAnx+gyj9LVObM9ROvIwisnh1G0ZeiHa1c33oOplatHxoIM3t2QNXFD/7EmSu2zV3NF7OV8mC+y37n0WhK4K5fhXDWzYel8sMaxks/UP52Rjbcf2cbl5YnzwzrGYo/4jA77qJGZ07C/NL0tI580i5OTZ5wBQJknOyPugjA6WEJfx0acw6+jvzPxFLoIbO7+K9++bH5kPpYCOvDJQZqdM5MqW+e5I3s8MhEy9LeNMvL39LoHB1pHsyJu6G8fRQ0dwBpJLMqnLIV8teiLS1arZbikiq7q+b88mMXMrSNeigKZz7DfcuIgFbb4ysrt3k4igZkVYGPDPqp9U/V7fFVbuKN9fcY+aRYf+xzLwGrF0ZZcP79ss7F2jBMb+qm3Lty0c76hA58cpMl1kkhgYmrH6Mi+sPk947SmGfhMFilMl2yIG3rrO7BHEis1KzHzP5Vf4Hfd2U+qXix0F5bTWJb+0H66vL90C+d2xu6NlSrLxnr5soo/hbC3oBt/y9RgOuIJEnA5KTFNze+52/ZPqIWa99MsKPt7l8GWDRQc2YnZMr/HQEm5lZrj93Dr6oUJuvIVHfjkIE842vAdnrjw+o428HTR/HZkj8chUxfBrtSqCmKJFKZLJuKGSin8Rw9TYk4c+Ny34jN8t31DWvvIF1otJewtnt+csq2l6/hw3aNZtXl103aurTgn5munzGP09bVOWRc45SLo78UaOTWxrnwDt7aenVW/NIuHxjEHzrNXIwEflZVzoysWj61Dj3Hn5UOEmd8ehvmODnxykFbLCF1tR04/X4iO7PHYbzuFs3n2nmKTiSVSOB2xmXCucCKViSsnxsUNI0lUhE3H3+pERnZjUvEDp2dXf5jPtCyNsvVEHLUUocoTV0ZlE7u5gNu6O7FEst8J/gtHd1JbGFsfpWtans9Yr5eKyNT8nvuK3kFY6UvlUqax1mghUWEembd9bljpo6u2nd0Funw92+izOUdpntQReKE6ssdCCbQ5W5PePpFI4TjhFSaeGPwjj+z6CX8++B2GVwxBgiHlcXHDVPEeaKDYFn/IuGH1O3nPcd11BWDvWAnvG3kFHy8/n0LL3Hekv7FwPev6E3dMT5cSn5Ove81IjFaC0xOcRwbcVPsnRgRDpau5uTW1PmCa/OOQpQopKaFkKLE+VLYoKrGwqvFublmfnWlfzVR04JOjnHCdRAWNxMuTOZLYPE6yCc6ziRSKw8LJ0gbu3XUb/QOtAITDAR7b9TN2uP+Cqok/rOytG2BsR/J3Qiqi8DcepDAcO/DpXvV6rj1xbdL28p2wMvHJ4xfwl8PX8j31cq6qmLsf/0vKN/GuOA1Is8XFJ3fzrvIXz1g/vWdXqLeHYupOP/97yT/jj+jL5FLnue7lhF+8nqKm5+dlf+d4d3DX5U4COdAgOB/RZ3SO8qSjHd+RIwvakT0erUkoOCcUKRTwrQryt9b/ZfehB2O+v6v7GH96/lY6K1uRwtgBUCrihv4TA1jG9iAyc658pPYSrm79Vz2dEYNGVxHvrr+GirYruL3oAs4sStw1OlXKbKV8vaUemYcchhvrn2CdY2rCelO4m9CwMX2hgmHEdQqLGgAg7KjlC0tErVuTmB6/jf71q7CePIy9aG7zfNasDDFcfIynC3MjrzMf0Vf6HOW4ZZCek0dwNdSzw54bFV3jHDB3EehMHIzFEymUCit1ll38dccduFzDs+5r5757eLjrFwRXxugtkYK4oXv3HkoL981Y7606h9ee+hDusD4VEvHLrtV86uBb+djYxXy4/KXYzQVZsfslqqh2npp9wyxgD3q5bdiFRSYUuPcWdONpOQ6Ar8eDzTch0PlExTtwh+Y3mVWTu9QvN4L+qvK5G4WxOyyc2fBbvrxJ9+KaS/TVPodpdjYvaEf2eBy09TDSEl/BOaZIoVlwrnByb913aGh6NqX9OZ293L/zdhqL9iNlU9tGJCNuqMIRQi0HsUVap6wPlq3l+pFP0edf+A7kiwFv2MxHmy7ikfpr+b5cyKtmEQicjWsqzuH1x57JknfJsbXrMB8snfA7IGG6RowbC2f3CFUBQ9gyUljFZ9sS907SLC2OhKqQmmo2PPEdzrfXsX5lAEdpdtvYbA3v4w+XufGYZupLabKHDnxymONjJ2kdTk/wb65pG2mNuT6mSGG1hZ3eB3lk108IhdIvaT90ZBt/afw+rpWeKUfubOKG3oYObIGpSs2RouX8W+CzNLnnPnE33zg85uDdh9/I6s4r+FbRBawqit3hPBG1hcv5YkNybUOyzQfqHuWc0nWnn3dEW1cE2k9SjJFMumPZO5ZMbzZNcjzdXUXwnLVYetsof+SnrP3Dp7jowQ9xWftPeWlJE2tWhrE70j9mzlgJfvMB/l48PwnUSxl9Zucwz9jbuXwgCMsW2pOZtI228fJp66aLFIrdTFdxK8/u/iOoLLWb8Ln5+84fsn7tBVy4/A2oAePOyLWjC9uZJRSdO7Ns2bPjGSqsexhPI1G2Yj5p+RLP9cVX9dXMzo871uDoXs13z3qBY5Xt/Hb0CIHI7IGtIHzDa6LUm93ms8liiYS4tbuTd5QV4Av7T1d2mQabMasRVEEZN7VftCC+aXIXb9hM15pVrJm23tZSh62ljgpgvQjBzRczevZlDBWtpW/ITMA3+4i9zW5mXcOv+OhrZ7Yr0mQfHfjkMEdsffRXpCfWN9dMT3CeLlIYWglPHf01Q8OJW0OkS0vrPlrbD3LFhe+hemgFKhhh+N7jWGsdWKsn9GdUKEKk6xBm86jx3Gzjm6Vf5qHO2LoumtRwh018+NgrOL/sHH54ZiG/LR1i50jiRrbvLj+Hiw78fZ48jM26/mZurH0d33I10OHpIBKOYHIauT57qv+JnuO2BfVPk5scrKqeEfhMRpTCdvQ5lh19jmXARrMF/7lXMLr+IgZtZ9A/AKHgzHzFrdYGHnyFG6fJN2e+aybQU105Tp/ZtdAuxGS/pYtA20TwMxoVKZQSC03Fddy389tzFvSME4mEefL5X/PkyP8RWWGKKW7o2XuIIrORKKjExC+Wf56fd87euVuTGvudJbzr8HVs6noV33BcQE1h7GHKDcVncOPhJ+bZu9i86/BjXFy+iQOWbkb31FPsO4CyOrip89KFdk2To9SPLEPWJwp9piLhEPYDj1N93zfZ/MeP8Mrtn+Zi7yNsqR6gusaMySTU1prAu5N7yhLfMGiyhx7x0aTFYVsfwyePUbNmDd4jA4w924lnpY9t+3+Fzzs6r7709Z3kz323cdF5b2adeyvD9zVR9S4jgdWz6ymWqT0g8I9Vn+QbJzbNq29LjTvb11HSvZo7znqeQ5Wd3O2sJxRVY7aYLNw2MEJBKDfuagXF108e5a3VZQw/+Q/O5DCHa99K63H7QrumyVF2DpSz500bKDtVQ5Ef7L4IBb4wVm8QqzeAye1HPF5we1BuD0Smju6I30vRCw9R9MJD1AKR4nKoreU/3zY/lY0ag7wJfETk9cCdgBn4uVLqWwvsUt7T5myjcsDL0OPN7FdPcXxn/B5YZquVQkcxhYVF2K027CYLBUphC4Wx+QNY3V4sYy7C9gKCxQ6C9gICNgt+kwm/iuALBfH5vXjcLgLe2M0zXzj4AEeLn+XKsvdi21FC0cuWw0A9YouwZ/X7+Njx6VlJmrlgLGThA42X8YoKJz9cXcivSgbZ7Wzio8Wb2Nz88EK7N4XakS6+UHs2I94W1hYE+eypVy20S5ocRinBUVJFSYULvynCgITxmcK4TBZcJisuHLhE4VJh3JEQloCiIAAFAaHADyVBM2VBGyVBE8V+E0V+2LXKlTMtiZYKeRH4iIgZ+BFwNdAJ7BGRB5VSRxfWs/ymbaiF4gce5nDXM9gENq/diC0cwRYIYvV6sY65sTidWAaGMY+NTXmv2O1YyksxlxVjKbFjLrZiKbcRDijCHiehQT/hUQ+hkVEio1PfGymwEa6qIlReRrC0hKCjkEBBAQGLCb8I+zru5wzvi1h3vIKqwjqaV76Ntx+/aj7/NRrgueEynhu+npvWHOcdVXauqsutoGecaxufoqPqpTSVXE/D8fnrT6ZZnHy6/iJgavJ7lS1IjS1AtS3Acpuf1VYflWY/FSYfZSYvJeKh2O4jUhQkaIoQtETwmyL4JML5pgI2mLYyYDExIIoBFaQ/5GEwMMpIYBSlG5RmnbwIfIALgRNKqRYAEfk/4Hpg3gOf97GSa4vOxSdh/KLwmky4TYIH8IjCg8KjwngiQTwqhDvsxxP24Q358IZzYwogWbqHWznnrgc4F8BsxlxWiqWsBHNpIZZiG+blJiyryxF7OWG7hZDdQrDAgt9mwmUpYiBSSm+4mO5QMW2+Yjp8hVRaA6y1e1hlc1FrGWO5jFEecWEPhrD6jYfZG0J8EcJ+EyGvn5DTTXjMa/wddqL8fuBx/JvW43zjmby++a0L/J9a2nynbSOOrvWcX3oNawrcnGFzU2txUW0apRInpREnjtAwhYEhLN4BxDcyL0rOkzljtJ63eT88r/vU5A+DASuDAStHcaT1/gJThLMdXs4qcrO1wMUZVie1plFKZZSANYzbGsJlCjJmDjFkFgZMQj9hBsI+BoJjDPhHCEa09k+y5EvgswqYLG/cyfSQfJ74u+dt7O8xFIlNQLklSJU1QLklwHJzgDJzgFKzj2LxUyw+HPgoNPuwm7xYlZ+QKWzcEZgi+CVCRHI33i90wKr/ujwazNgYM5fSRyn9kVJ6wiV0+h20+ooYChVM3LUowB99TMZmPAYxHvuCwPTz2BJ9OMBuirDW7mG1zc0Kq4sa8xhVOClXTuwBL1Z/iJDZysc9b2PTCq2+mwsMU8EwcDAExGrCbgIcYCtWrLF7WF3gZuX4dytOyiNOSsIjFAUGsQVGkCxe6DsrLibQu4pzirNmUqNJCUU5x4HjYSBOBbwg1BZ4WW93c7FtjBVmJ8tlhIqCYcImPy5ziIApnMO/GdUL7QKQP4FPrFbeU757Efkg8MHoU5eIHJsjX5YBA9pertj7n2zay/HPqu2lz7PL4PYc9U3byzF7uezbIrB3a7b9m0xSJXf5Evh0ApNrlM8AptRSK6XuAu6aa0dEZK9SKmta99pe7tjLZd+0vdyxpe3lt71c9m0p2kuHfNHx2QNsFJF1ImID3gnEbvut0Wg0Go1myZIXIz5KqZCIfBx4FKOc/ZdKqSML7JZGo9FoNJocIy8CHwCl1D+Afyy0H2R/Ok3byx17ueybtpc7trS9/LaXy74tRXspIypLzSM1Go1Go9Focp18yfHRaDQajUajmRUd+Gg0Go1Go1ky6MAni4jI60XkmIicEJHPZWjrlyLSJyL1WfJttYg8JSINInJERD6ZgS27iOwWkUNRW7dkyUeziBwQkb9lwVariBwWkYMisjcL9spF5F4RaYz+D1+Rga1NUb/GH6MicmOG/n0q+l3Ui8gfRSTtTpsi8smonSPp+hXr+BWRShHZJiLHo38rMrD19qh/ERFJqTQ2jr3bo99tnYj8RUTKM7T39aitgyLymIiszMTepNc+IyJKRJZl6N/NItI16Ri8JlP/ROQT0evfERH5Tga+3TPJr1YROZiJbyJynog8P34tEJELM7T3EhF5Lnp9eUhESlOwF/M6nM65kcBWWudGAntpnRsJ7KV9bmQNpZR+ZOGBUU3WDKzH0CE+BGzJwN7lwPlAfZb8WwGcH10uAZrS9Q9DMLI4umwFXgAuzoKPnwb+APwtC7ZagWVZ/H5/A7w/umwDyrN43PQAazKwsQo4CRRGn/8JeG+ats4B6oEijOKHx4GNadiZcfwC3wE+F13+HPDtDGxtBjYBTwMvy4JvrwUs0eVvJ+tbAnulk5b/E/hJJvai61djVK62pXJsx/HvZuAzaR4jsey9OnqsFESfV2fyWSe9/l3gKxn69hjwhujyNcDTGdrbA7wquvw+4Osp2It5HU7n3EhgK61zI4G9tM6NBPbSPjey9dAjPtnjdL8wpVQAGO8XlhZKqe3AULacU0qdUkrtjy6PAQ0YP5jp2FJKKVf0qTX6yChLXkTOAN4I/DwTO3NB9I7ucuAXAEqpgFJqJEvmXwM0K6XaMrRjAQpFxIIRtHTPsn08NgPPK6U8SqkQ8AzwllSNxDl+r8cIIIn+fXO6tpRSDUqptNTX49h7LPp5AZ7HEEHNxN7opKcOUjg/Epz73wNuSsXWLPbSIo69jwDfUkr5o9v0ZeqbiAjwDuCPGfqmgPFRmTJSODfi2NsEbI8ubwPeloK9eNfhlM+NeLbSPTcS2Evr3EhgL+1zI1vowCd7xOoXllZgMdeIyFrgpRgjNenaMEeHoPuAbUqptG1F+T7GRT2SoZ1xFPCYiOwTo11JJqwH+oFfiTEV93MRSa8b4UzeSQoX9lgopbqA/wbagVOAUyn1WJrm6oHLRaRKRIow7pBXz/KeZKlRSp0C46II5Ebjnpm8D8i4lbyIfFNEOoB3A1/J0NZ1QJdS6lCmfk3i49Eph18mO+2YgLOBV4rICyLyjIi8PAv+vRLoVUodz9DOjcDt0e/iv4HPZ2ivHrguuvx20jw/pl2HMzo3snFNT9JeWufGdHvZPDfSQQc+2WPWfmG5gIgUA/cBN06LvFNCKRVWSp2HEf1fKCLnZODTtUCfUmpfujZicKlS6nzgDcDHROTyDGxZMIa7f6yUeingxhiOzggxVMavA/6coZ0KjDvGdcBKwCEi/5qOLaVUA8Zw9jbgEYwp21gtRfMSEfkixue9O1NbSqkvKqVWR219PAOfioAvkt0fiB8DG4DzMILl72ZozwJUABcD/w/4U3TEJhP+hQxvCqJ8BPhU9Lv4FNGR2wx4H8Y1ZR/GFE4gVQPZug5n21Yie+meG7HsZevcSBcd+GSPWfuFLTQiYsU4AO9WSt2fDZvRKZ+ngddnYOZS4DoRacWYIrxSRH6foV/d0b99wF8wpiLTpRPonDSqdS9GIJQpbwD2K6V6M7RzFXBSKdWvlAoC9wOXpGtMKfULpdT5SqnLMYb5M73jHqdXRFYARP8mNR0yX4jIDcC1wLtVNAEhS/yBFKZDYrABI6g9FD1HzgD2i0htugaVUr3Rm5cI8DMyOz/AOEfuj06D78YYuU06AXs60SnbtwL3ZOgXwA0Y5wQYNxkZfValVKNS6rVKqQswArPmVN4f5zqc1rmR7Wt6PHvpnhtJ+JfpuZEWOvDJHjndLyx69/ULoEEpdUeGtpaPZ/aLSCHGD29juvaUUp9XSp2hlFqL8X97UimV1ohF1CeHiJSML2Mk56VdHaeU6gE6RGRTdNVrgKPp2ptEtu5o24GLRaQo+j2/BmM+PS1EpDr690yMH59s+AjG+XBDdPkG4K9ZspsxIvJ64LPAdUopTxbsbZz09DoyOz8OK6WqlVJro+dIJ0bSaE8G/q2Y9PQtZHB+RHkAuDJq+2yMAoBMOnBfBTQqpToz9AuMG9BXRZevJMNAftL5YQK+BPwkhffGuw6nfG5k85qeyF6650YCe1k7N9JmerazfqT/wMiHaMK4A/hihrb+iDEEHcS40P1HhvYuw5h6qwMORh/XpGnrXOBA1FY9KVRdJGH7CjKs6sLIyTkUfRzJ9LuI2jwP2Bv9zA8AFRnaKwIGgbIs/d9uwbiA1AO/I1pdk6atZzECu0PAa9K0MeP4BaqAJzB+eJ4AKjOw9Zbosh/oBR7N0LcTGDl64+dGKlVYsezdF/0u6oCHMJI607Y37fVWUqvqiuXf74DDUf8eORWxZgAABjtJREFUBFZkaM8G/D76mfcDV2byWYFfAx/O0nF3GbAvejy/AFyQob1PYlznm4BvEe2AkKS9mNfhdM6NBLbSOjcS2Evr3EhgL+1zI1sP3bJCo9FoNBrNkkFPdWk0Go1Go1ky6MBHo9FoNBrNkkEHPhqNRqPRaJYMOvDRaDQajUazZNCBj0aj0Wg0miWDDnw0Gs2iQ0TWitGl/OaF9iUVROTXIqJLaTWaBUQHPhrNEkNErogGDZ9JsI0Skb/Np18ajUYzH1gW2gGNRqNJgzagkCXUR0yj0WQHHfhoNJqcI9rjx6yU8sV6XRnKqzFf02g0mkToqS6NRpM0IvJmEdkpIq7oY6eIXB9jOyUiv46x/r3R166YtO7m6LqtInKHiHRiBDUXJ/BjRo7P5HUicq2I7BERn4icEpHbo40vk/mM/ywiD4pIu4j4RWRARB4QkXOTeX/Uhj26z24R8YrIbhF5bZxtL4zm/jSJiEdExqL/17dM2+4H0c+3MYaNFSISEpFMO49rNHmPHvHRaJYuRSKSdAdtEfko8COMnmDfwOjD817gARH5kFLqrgz9uRvwAt+N2j6Vpp1rgI9iNI/8JXA98BlgGLg1ifd/HKMr/V1AD0Z39A8CO0XkfKVUMk0u/wi8GaMX0aNRG/cDJ2Ns+xbgRcCfMKbwqoh2FBeRdyul/hDd7qfAJ4D3AZ+fZuMGwIzRFFKj0SRA9+rSaJYY0dGWp5LY9O9KqWuj76nAaFTYg9EZfDS6vhSjYW01sFopNRJdr4DfKKXeO23f7wV+BbxaKfV0dN3NwFeBZ4CrlFKz5u2IyFqMIOIWpdTN09Z5gK1KqdboesFoyFmllFoxw9hM2w6llHvaus0YTRZ/oZT66Czvfy1GsDPl84vIm4G/ACilZJb9FWH8X8NKqS2T1u8C1gJnTv4/iUgTEJq8rUajiY2e6tJoli53AVfHeUznasAB/GA86AGILv8QKAauytCf7ycT9CTBA+NBD5zOB3oKqBWR4tnePB6EiEFpdFSsHzgGXJTE/t8c/Xv7NLsPRG3E3F90n0UiUgUUAU8Cm6PB5Th3ASuAN0x6z+XARvRoj0aTFHqqS6NZuhxXSj0e6wVjkGQK66J/j8TYvD76d32G/jRl+P5xWmKsG4z+rQJcid4sIi8Fvg5cgRHsTSbWVNV01gMRYn+eBmDTtP1VY0wdXo8xcjadcmA82LwH+D7wHxjTaESXA8Bvk/BNo1ny6MBHo9Ekw4xIKE0SXXM8WdpHOMFrCT+HiJwJbMcINL6OMULjxsg5+j7GyNZsJNrHlNei03CPAZuBHwB7ACfGZ/h34F1MGplXSnlF5PfAh0SkFuN/9k/Ag0qp/iR802iWPDrw0Wg0ydAc/bsVeGLaa+N5JZNHWoaAyhh2Mh0VmmveghHcXKeUmpIHFZ2C8idhoxl4LXA2M0fIXjTt+bnAS4CvKaW+Om1/749j/y7gY8C/YQRJRehpLo0maXSOj0ajSYZtGCMfnxCRkvGV0eVPYEwfbZu0fRPwimiS7vi2FRijGLnM+GjR9JGZDwC1Sdr4a/Tv/5tm481Mm+ZKsL9zMIKwGSil6oDdGNVd/wG0Y4waaTSaJNAjPhqNZlaUUiMichNGOfsLkzR63gucBXxIKeWc9Jb/AX4PPCkiv8PIU/kARrl2sgHEQvAwxvTR70TkfzBK4C/FKJFvJolrplLqURF5CLhBRCqBRzDK2T+EkQ91zqTNGzBGhW6KBonHMEaKxrc9P85u7gJ+Hl2+RSkVSeVDajRLGT3io9FokkIp9b/AW4ERjPLzr0aX3zJdw0cpdTdwE0YF0h3AvwJfw9DWyVmUUs0YFVMngS8A38KYsnsV0JmCqX/G+NwXYugSXQ68Ddg3bX9h4I0Yico3AHdG93UDE8nLsfg/jDykCIY8gEajSRKt46PRaDSLDBEpwBB43KOUet1C+6PRLCb0iI9Go9EsPt4NVGCoOWs0mhTQIz4ajUazSBCRNwFrgJuBXuDc6HSZRqNJEh34aDQazSJBRFqBlRi5Qu9XSsUSlNRoNAnQgY9Go9FoNJolg87x0Wg0Go1Gs2TQgY9Go9FoNJolgw58NBqNRqPRLBl04KPRaDQajWbJoAMfjUaj0Wg0S4b/D4BK2b7t0BshAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set size of plot\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(9, 5)\n",
    "# Stackplot\n",
    "ax.stackplot(list(range(24)), np.transpose(hh_day.values), labels=hh_day.columns.values)\n",
    "plt.xlabel('Hour in a day', fontsize=18)\n",
    "plt.ylabel('Watts averaged per hour', fontsize=18)\n",
    "plt.xticks(np.arange(0, 24, 1.0))\n",
    "ax.legend(loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true,
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Subsets representing a week of data\n",
    "hh_week = hh['2013-10-21 00:00:00':'2013-10-27 23:00:00']\n",
    "hh_week2 = hh['2013-10-28 00:00:00':'2013-11-03 23:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a7c3159e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set size of plot\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(9, 5)\n",
    "# Stackplot\n",
    "ax.stackplot(list(range(166)), np.transpose(hh_week.values), labels=hh_week.columns.values)\n",
    "plt.xlabel('Day of the week', fontsize=18)\n",
    "plt.ylabel('Watts averaged per hour', fontsize=18)\n",
    "plt.xticks(np.arange(0, 166, 24.0),['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday'])\n",
    "ax.legend(loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a6e59ae80>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set size of plot\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(9, 5)\n",
    "# Stackplot\n",
    "ax.stackplot(list(range(168)), np.transpose(hh_week2.values), labels=hh_week2.columns.values)\n",
    "plt.xlabel('Day of the week', fontsize=18)\n",
    "plt.ylabel('Watts averaged per hour', fontsize=18)\n",
    "plt.xticks(np.arange(0, 168, 24.0),['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday'])\n",
    "ax.legend(loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true,
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lauraloschmann/anaconda3/lib/python3.7/site-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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27drRvXt3OnXqRIcOHejevTsvvvgi48aN46233qJt27a89NJL9O/fnzZt2jTOp3v37jz++ONcddVVGGO4/PLLGTNmDAAXXXQRv/3tb7n44ovp1asXxx57LMOGDaN79+50796dcePGcc455zBo0CDOPPNMOnbs2FjuTTfdxH//93+zffv2xmH//ve/ueuuuyguLqauro7f//73TS66DOjUqRM//OEPbVmmAT6fj+a/p5s653WGldCjR48mcXVd3xXK4fTTT+fkfie7F2AUvLZM04kuW+fosnWOLltn6HJ1Rqou16Ql3caYb4G+gfcikgOMNMbsF5EPgTtE5B2siyaLjTG7RWQW8FjQxZMXA/clK2a7lZWVtRiWlZXVuOKMHTuWsWPHAlZr/QcffNBi/LVrm94M5qabbuKmm25qMd5xxx3XpPtJ8+4gjzzyCI888kjIOMePH8/48eObDDv11FOZP39+yPGVUkoppVRkTt4y8G1gEXCCiOSJyG0RRv8U2AZsASYDvwPwX0D5MLDU//dQ4KJKpbzKhO4BpZRSSqkM5uTdSyJeJWiMGRT02gC3hxnvNeA1W4NTygES8hIEpZRSSil9DLxSSimllFKOy6ikO9It9lTidPkqpZRSSoWWMUl3p06dKCws1MTQIcYYCgsL6dSpk9uhKKWUUkp5TjJvGeiqgQMHkpeXx759+xwpv6qqKuMTzk6dOjFw4EC3w1BKKaWU8pyMSbrbt2/P4MGDHSvf5/PZfn9qlZr0bIpSSimlmsuY7iVKOc3/0CellFJKqRY06VZKKaWUUsphmnQrpZRSSinlME26lVJKKaWUcpgm3UoppZRSSjlMk26lbGbQu5copZRSqilNupWyiaB3L1FKKaVUaJp0K6WUUkop5TBNupVSSimllHKYJt1KKaWUUko5TJNupZRSSimlHKZJt1I20buWKKWUUiocTbqVUkoppZRymCbdStlEbxmolFJKqXA06VZKKaWUUsphmnQrpZRSSinlME26lVJKKaWUcpgm3UoppZRSSjlMk26lbGaM3jpQKaWUUk1p0q2UTUT07iVKed3ByoNU1la6HYZSKgNp0q2UUipjHP7k4YycPNLtMJRSGcixpFtEXhORvSKyNmjY30Rkg4isEZH3RKRX0Gf3icgWEdkoIpcEDb/UP2yLiPzJqXiVUkplhnX71rkdglIqAznZ0j0VuLTZsDnAScaYHwCbgPsAROT7wBjgRP80L4pIWxFpC7wAXAZ8H7jRP65SSimllFIpw7Gk2xgzHzjQbNhsY0yd/+1iYKD/9TXAO8aYamPMdmALcIb/b4sxZpsxpgZ4xz+uUkoppZRSKcPNPt23Ap/5Xw8AcoM+y/MPCzdcKc8y6N1LlFJKKdVUOzdmKiJ/AeqAtwKDQoxmCH1QEDKjEZFxwDiAfv364fP5Eg80BmVlZUmfZ6bw2rJdV2L1By0pKWkSV3lZOQDLli2jpHuJG6FFzWvLNJ3osnWOnctWf6OmdL11hi5XZ6Tqck160i0itwBXAheaQzc0zgOODhptIJDvfx1ueBPGmEnAJICRI0earKwsG6Nunc/nI9nzzBReW7ad8zrDSujRo0eTuLpt7AZlMHLkSEb0H+FegFHw2jJNJ7psnWPLsp1n/dPfqCldb52hy9UZqbpck9q9REQuBe4FrjbGVAR99CEwRkQ6ishgYAjwDbAUGCIig0WkA9bFlh8mM2allFJKKaUS5VhLt4i8DWQBfUQkD3gA624lHYE5/geJLDbG/MYY852IzADWYXU7ud0YU+8v5w5gFtAWeM0Y851TMSullFJKKeUEx5JuY8yNIQa/GmH8R4FHQwz/FPjUxtCUUkoppZRKKn0ipVI2O3SpgopFg2lAJgj3zrnX7VCUUkop22nSrZRN/F2mVJzqG+oBeHrx0y5HopRSStlPk26llFIZZ+qqqW6HoJTKMJp0K6WUyjifbfms9ZGUUq6ZlzOPP8z6g9th2EqTbqWUUkop5SlZr2elXXdDTbqVUkoppZRymCbdSimllFJKOUyTbqVUWvrHsn/w9rdvux2GUkopBTj4cByllHLTbz75DQA3nhzqOV1KqUyx9cBWurTvQv/u/d0ORWU4TbqVUkoplbaO/7/jATAP6IPLlLu0e4lSSimllFIO06RbKaWUUkoph2nSrTJCVV0Vp7x8CvN3zHd8XgY9hamUUkqppjTpVhlh4/6NrClYw52f3enYPARxrGyvmL11Nte/e73bYSillFIpRy+kVEpF7ZI3L3F8HsbomQKllFLpR1u6lVKeIJL+ZwqU8pJzXzuXw5843O0wlMoYmnQrpTyl3tSzt3yv22GE1eHhDtz5qXPdlJRKlq9zv+Zg1UFby9ywfwPZO7JtLVOpdKFJt1LKc/r9vZ/bIYRV21DL80ufdzsMlSDtxuSM4S8MZ9TUUW6HoZQnadKtlFJKKaWUwzTpVspm2oKmlFLed95r5/HmmjfdDkNlEE26VUZxMiHOpAsB9cBCKZXqFuYu5Ofv/dztMFQG0aRbZYRMSohTlSbyKlrPLn6WXSW73A5DKaViokm3UkqplLGjaAe/n/V7rn7nardDUUqpmGjSrZRSKmXUNdQBUFRVlFA5evZLKZVsmnQr5YBvdn3D5sLNboehlFJKKY/QpFspmxkMZ75yJkOfH+p2KEqpMGZ8N4MpK6e4HYZSKoM4lnSLyGsisldE1gYNO1xE5ojIZv//w/zDRUSeE5EtIrJGREYETXOLf/zNInKLU/EqlSghc05XG/SiR5X67vjsDrdDUEplECdbuqcClzYb9ifgS2PMEOBL/3uAy4Ah/r9xwEtgJenAA8CZwBnAA4FEXSmllFJKqVThWNJtjJkPHGg2+Brgdf/r14Frg4ZPM5bFQC8R6Q9cAswxxhwwxhwE5tAykVcqaunWQjt11VTWFKxxOwyllFJKtaJdkufXzxizG8AYs1tE+vqHDwByg8bL8w8LN1ypmKRr149ffPALAMwDqX8woXeTUEoplc6SnXSHE2pvayIMb1mAyDisrin069cPn89nW3DRKCsrS/o8M4Udy3Zr2VYAysvLEy5rXck6AEpKSpqUVVpaCsDy5csbhyVrnYh1PokuU5/PRxux90RZvalvMQ87OPUbhCtX6wLnlJWVsXjJYgCqKqsSXs4N9Q36W2Gty3avt15cruFicjJWrQ8SF2r5pepyTXbSXSAi/f2t3P2Bvf7hecDRQeMNBPL9w7OaDfeFKtgYMwmYBDBy5EiTlZUVajTH+Hw+kj3PTGHHsj284HBYDt26dku4rM55nWEl9OjRo0lZ3Td3h1I47bTTYKU1zPF1Yl5884l7mQbNz+6ku66hDuYfep/wsotz2SRartYFzvH5fAz/wXD4Bjp17pTQOgzQpm0bx36r6rpqOrbr6EjZtglal21bb53a7hIRLqYkxKr1QQIi/D6pulyTfcvAD4HAHUhuAT4IGn6z/y4mZwHF/m4os4CLReQw/wWUF/uHKaVcpI9sVyq8jzd9TKdHOyETtMuUUuoQJ28Z+DawCDhBRPJE5DbgceAiEdkMXOR/D/ApsA3YAkwGfgdgjDkAPAws9f895B+mlFJKedJnmz9zOwSlqG+o57jnjmP62uluh6L8nLx7yY3GmP7GmPbGmIHGmFeNMYXGmAuNMUP8/w/4xzXGmNuNMccZY042xiwLKuc1Y8zx/j99koFSKe6VFa8gE4TiqmK3Q1FKKVds3L+RL7d96eg8Kmor2HZwG7/86JeOzkdFzysXUiqlMsTExRMByCvJo2enni5Ho1KVdnFSqWzYC8OA9LjzlIqePgZeZZR0u0+3UplGby2plEpVmnSrjJDM+3RrC5xSSqlYzdoyi7V717odhnKQJt0qKuU15WzYv8HtMDxNW+CUUs3pQbiK1qVvXcrJL53sdhjKQZp0u6SkuoTL3rqM3OLc1kf2gGunX8vwF4brDkQB2k1HKaWUipUm3S6ZvnY6n2/5nIfmPeR2KFH5YtsXboeglFJ64J9kMkG45f1bWh9RKdUqTbqVJ8kEYfzs8W6HkdKKq4qZumqq22FETZMpFYt07c5VWFGITBBPbbvTVk9zOwSl0oIm3cqznlr0lNshpLTbPryNX3zwC1bsXuF2KEp5khcP9LYc2ALAS8tecjkSlS68uJ5nKk26lbKZV/o77y7bDUBlbaXLkSiVubxSH6jMk65ng1KZJt0qozh5xJ/M2xIqpRKnSYlSKpk06VYZQXeu9tLTlUopL6itr+UPs/5AYUWh26Eo1SpNupWjaupr+N+v/pfymnK3Q3FFYUUhe8r2uB2GUkqlpfc2vMfTi5/m7ll3ux2KUq2KmHSLSBsRuT5Zwaj088qKV3gk+xEemf+I26G4os/f+tD/qf5uh6FU2tGzLfaZuGgic/fOdTuMuNQ31ANQ21DrciRKtS5i0m2MaQDuSFIsGemL7V8gE4R1+9a5HYojquuqAaiqq3I1Dt1BK5UeUqGrWCrEGOye2ffw0PrUeGaElzWYBubvmO92GMrDouleMkdExovI0SJyeODP8cgyRE5RDgDZO7LdDSRDeGlnuKtkFzJB+GTTJ26H4gq9q4NKV8EH+XrAnzme/+Z5Rk8dzUcbP3I7FOVR0STdtwK3A/OB5f6/ZU4GpVQqi3Ynuyzf2owmrZjkZDgpK7c4l4raCrfDUMp1dQ11bD2w1e0wVCs27t8IwM7inUmbZ15JHl/nfh3yMz3g855Wk25jzOAQf99LRnBK2c3RWwZ6qBU9FbR2i8VjnjmGy9+6PEnRKOVd931xH8f/3/FJTeaUpa6hjoKyArfDCGvo/w3l3NfOjWrcd9a+Q/+n+lPXUOdwVCqcdq2NICI3hxpujNHnwiYgHU6t3/25dbX4xEsnuhyJisSJdS1Z6++8HfOSMh+lvOyrnK8A2Fe+j2N6HuNyNJnl7s/v5vmlz1N0bxE9O/V0O5wWKuvCP/yseUPQbz/5LUVVRZRWl3JY58OcDk2F0GrSDZwe9LoTcCGwAtCkO8M9s+QZIDWS7kxshdaH9SiVurRrgDe8v/F9AEqqSzyZdKvU0mrSbYy5M/i9iPQE3nAsIqWUUipNxHpWKFIDQTqcIXWKHqSoVBDPw3EqgCF2B6JSg1b64Wmlr5Rygp61Ci8Tz2Kq1NVq0i0iH4nIh/6/T4CNwAfOh6aUSoSbB0ibCzcz7Plh7CvfF3ack146KaqyGkxD451elHJCXUMdr654tfFBK0op5YRoWrr/Djzl/3sMGGWM+ZOjUWWA5i0XerSe+rz6G4ZqJfvTt3/iwmkXOjbPv3/9dzYWbuTf6/8d9TTPLH4m5PCJiyZy+uTT8eX4bIpOqaaeXfwsv/zol7yy4hW3Q9EzZh6lZ3mVHaLp0z1PRPpx6ILKzc6GpJRy2pIDS+BA/NM7kRj88Ys/hhy+Zu8aAHYU7bB9nir12LnuBcraX7EfgAOVCWwUNgt1EK8JefJp1x5lp2i6l1wPfAP8BLgeWCIi/+V0YCq9eKWVwCtxpCrdASmv8OqZJSdkynf9YMMHfP+F7+t9pG2m+z3viKZ7yV+A040xtxhjbgbOAP7X2bAyT7q2YHhlZ6HJolLpxYk6889f/ZkG02Brmelatzvh1g9vZf3+9RRXFbsdSqNYElavJbe63/OeaJLuNsaYvUHvC6OcLiwRuVtEvhORtSLytoh0EpHBIrJERDaLyHQR6eAft6P//Rb/54MSmbdSXpNKO2Wv7VRU5nH6QL68ptzR8hOh219Lyao/NYFVdogmef5cRGaJyFgRGQt8Anwa7wxFZABwFzDSGHMS0BYYAzwBTDTGDAEOArf5J7kNOGiMOR6Y6B9PpYhUSijtEu2O0StnAZTKVKmyDWrC1zqnf8to6nX9nVRrWk26jTH/DUwCfgCcAkwyxtyb4HzbAZ1FpB3QBdgN/Acw0//568C1/tfX+N/j//xCSZWaUjXKhMrIze844h8j+GTTJyE/c7N1TFvmlFPyS/O5Z9Y9nrzNn+6iks+pBp5U3Hed+9q5TF87PeznWi+7J6puIsaYfxlj7jHG3G2MeS+RGRpjdmHdhnAnVrJdDCwHiowxgasn8oAB/tcDgFz/tHX+8XsnEoMXaSWtErFyz0rGfjC2yTAndxb2Ufm7AAAgAElEQVStVdqR1ufguIqqithZvNO2uFTmqK6vZuLiiczNmet2KI4IlURm4pnD1ui+s6Wvc79mzL/GtBieigcQ6abVWwaKyHVYXTr6AuL/M8aYHvHMUEQOw2q9HgwUAe8Cl4UYNVC7hFpLWtQ8IjIOGAfQr18/fD5fPOHFraysLKZ5bsjf0OT9xo0b8ZVGP71bfD4fbaRNi2HhbMnbAkBeXl5cv4nP54t52YayvXw7AOXl5QmXta5kHQAlJSVNyiopKQFgxYoVLaYJNc81+61b4RUWFtqyvtbW1jYpp7jYuhhp5cqV1G0LfTeAWOcrCAbD/Pnz6dCmQ9jx8vPzAdi0aRO+sqbzKCsva3w9ZOIQ9tfsZ+7o8InTnj17AFi/YT2+otjihdi/Y6Ll2rG+qtDKyspYvHhxk2GrVq2i3c5Wd2UhNdQ34PP52Lnz0IFfdnY2Xdp1SSjOXbt2Nb6eN28e7dpEH9/6kvUAlJaWNq5HpaWlACxfsZzKLZUJxRZJpPU21nU6nm2grtaqpxYsXEDP9j2jmmbdXqs+3rt3b6vzDPd5pOmqq6sBWLRoEds6bYtY/q5863fftHkTvgqrzNbqg1CfxbuvbG5+9nzg0HpeW1cLwIIFC+jRPq4UzhWhvluq1rPR1ARPAlcZY9bbNM8fAduNMfsAROTfwDlALxFp52/NHgjk+8fPA44G8vzdUXoS4g7DxphJWN1gGDlypMnKyrIp3Oj4fD5imeem5Zua3PH8hBNOIOu06KdPunnWv6ysrENJd9CwcFYuWglbYeDAgTEtn+CyY122ofTZ2weWQdeuXRMuq3NeZ1gJPXr0aFJWj609oARGjBgBq5pOE2qexRuK4Tvo06dP/DHNO/Syffv2Tcrpua0nlMAPf/hDzjvmvJDTxTxf/3SjRo2iU7tOYUd7p+wd2A1Dhw4la2TTeXRd1xUqrNf7a/YfimMeIR155JFQAMOHDSfr1Bjijfc7JliuHeurCs3n8/H9U75v3cTW75RTTiHruKzoCwlaz9q0bUNWVhaz6mb5z6fC+eefT/eO3ROK893ydxv3YKNGj6JD2/AHqM112dUFVkL37t0b16Pum7tDKZw24jTOHHhmQrE10WybC7nexrodJbDdtfumHdTBeeeeR+8u0Z3Q3rN2D6yHvn37hp9nuJiiiLXjyo5QDWeffTbH9DwmYiyB333okKFknWGVGbY+CDXveJZdhHJGnT8KFhxaz9stsZbv+eedz2GdD4t+Hm6JsDxStZ6NpntJgY0JN1jdSs4SkS7+vtkXAuuAuUDg/t+3cOhR8x/63+P//Cuj59iUSpqK2gqmrZ6mp7aV66rqqzzZh1spL9K+294TtqXb360EYJmITAfeB6oDnxtjon++cxBjzBIRmQmsAOqAlVgt1J8A74jII/5hr/oneRV4Q0S2YLVwt+yopFQrktmXza3k1Kn53v353UxaManVVh6lnHbZgss4fdvprY+YZrTfslLpIVL3kquCXlcAFwe9N0BcSTeAMeYB4IFmg7dhPXin+bhVWE/DVGlk3b51rClYw5iT0ucYKl13jPll1nny0upSlyNRCpbmL3U7hEZLdy3lmJ7H0K9bP7dDUaoFvXDSe8Im3caYXyQzEJVZTnzxRIC0Srrd5sWk347Wd+3WorzqjFfOoG/XvhSML3A7FKVUCkjoyZJKRUv7liWfk8s81kR4Wf4yZIKwpmBN3PP04kGF0+bvmM+oKaOora91OxQVxt7yvRE/t/OgMd5tuqymjNMnn57Q9ud1XtjHeCEG5W2adKuYxLoDycREyS3hfhu7TjHGs0MJ/P7/WvcvgLAP8Ilq/hnY4n3L+7eQvTObvJI8t0NxxLL8ZVz/7vV6cWQrEt2G5+XMY1n+Mu778j6bIvKOZHWhiKX+0f1echz11FFM8E1wO4yYREy6RaSNiFyfrGAyifa1UgFeTybdXld1B5a+fvLuT3h33buOPiCptLqU4qpix8pX9vNSi7HWP961u2w3D8570O0wYhIx6TbGNAB3JCkWpdJCtDuMZFXmXtqBKZVsvZ/sTa8nerkdRtQiHYR7/QBdWfR3UuFE071kjoiMF5GjReTwwJ/jkamUMn/HfLYf3O52GK1ysjK0u0V4X/k+dpfujnt6N7uVKOUVtQ3h+8OnSnKUaa2tbp9dCxZTtxIPxR1JtHX6svxlfLf3O4ejySzRPJHyVv//24OGGeB79oejUlFOUQ6jp44GwDzgzZ1YKu60+v69L+CdZWrHDkUTeKUis7uumrVlFpV1zj06PlOk4j4kIHDgEGsdfvpk6574XtkHpYNWk25jzOBkBKJS1+BndRXJBMEJc7TJc2Nln8I7LOVtXj+QszO+WMtaU7CGS9+6lP7d+kc1fmVtJZ3bd44nNNck6/dPlbMiwbTe9Z5Wu5f4H9f+PyIyyf9+iIhc6Xxo6c3rOwq7pGJFpZoKbh2JthJv3qISvB6kyilYpeJld7IT7zZzsPIgYF1wFo1hLwyLqfz6hnqW5y+PenxjDKOnjubDjR/GNJ9oOFWvhPotK2srqWuoc2R+Kr1F06d7ClADnON/nwc84lhEGSrWCmPO1jnsr9jvUDT200RLga4HKrS95Xvjrs90nbJP87vI/PqjX0cc/0Hfg4ycPJIVu1dEVX5NfQ3zd8znJ++m9kOmuzzWhR9P/7Ft5flyfAldv5Mp5m6fy3vr33M7jIREk3QfZ4x5EqgFMMZUgtZybqqtr+XiNy/mR9N+5HYoLTSYBn778W/T+iEMrdlRtCOq8VLxLECiMWfKGR4Vm7NePYsj/nZE0uebaqffk11nTFoxKeLnK/ZYybYXEsZk1y0fb/rYtrIueP0CTpt0mm3lhZPq9e9/TPsPrptxndthJCSapLtGRDpjXTyJiBwHVDsalYqowTQAsH7/epcjaWlXyS5eXv4yV/zzCrdDcc3P3vtZTOOn2o4/HsHfMdUrfqWSLRPqiHh56UxHInVbtF2A7OClZZZpokm6HwA+B44WkbeAL4E/OhqVUhls6qqpyAStFJVKB3a0TsdbhpeT9eKqYmaum2l7uZW17t+pxcvLXbmr1aTbGDMHuA4YC7wNjDTG+JwNK/Oke+tfot9vc+Fme3ZeKbCcn1j4RNLnebDyIJe/dTkFZQVRjR/vb/G3r/+Wkt1q4vHwvIfp/1R0d41QKhqxJnNe3tbGfjCWn7z7Ezbu32hbmR9t/Iguj3Xhm13f2FamUnaKpqUbYDRwIXABcL5z4ahYeLlCDbDjiH9DyQaGPj+U55Y8Z0NE9lpTsIbOj3ZmV8muuMtw+neMpvxJyyfx2ZbPeHrR0xHHi3mn7z/ICcRQVFUUc7eoVFjPQ7nfdz97yvbYUpbTB4vGGO7+/G5HHoTx+qrXyS3Otb1cO9ndMunUOpuq20IoOUU5AFTUVthW5uytswFYkrfEtjKVslM0twx8EfgN8C2wFvi1iLzgdGAqvEw7dbWrykpoF+9anHBZgtBgGpi8fDLVdYlfmvDCNy9QVVcV10U16fw7Br7b7Z/ezsKdC20tU9kvrySPZ5Y8wyVvXmJruRW1FYz9YCwXvH5ByM+1b2l0dN1XKj1E09I9GrjEGDPFGDMFuBzIcjSqDKQ7n+SZvnY64z4exyPzvXnny3jXhXCtoXbtsGNpZQsed/yc8bbEEG7+zyx+BpkgjRcYp6Nk1Q9OtfiGu0jMK9290qkFOZidv6dMEN7f8L5t5UXilfUiFpsLN1NVVwWk7/qkEhdN0r0ROCbo/dFA5t4PTqW8oqoigJS6z7mb3G5la23+935xL2DdSlN5SxuxdjHpfEDklFCJZ3V9dUJd2RI1Yd4E/nPGf7KvfJ9tZQZ/T7sOLp1I2iOVWVFbwdDnhzJl1RTb5+uEVDyoSRfRJN29gfUi4hMRH7AOOEJEPhQR+x8rleb2V+z3zA5o1pZZrNqzKqZp3NxY7W49eHn5y7b+FnYlp24nuc1pq03msPu3DqzLXqnz3BBrnRkp8RwzcwwDJw5MNKS4rdqzin+v/zdPLnzSkfIT3b84UXdGcyBgR1fFZNAz6u5rF8U49zseRYbYV76Pvn/vy33n3cfgXoMTLi/4IrVpq6dxw0k30Kldp6inv/StS63pH3AvqdpXvo8jukZ+KIaTFcVba97i56f83LHyk8npCtVrBwPKPk79toF1MpOTbjvtq7CvhdlO5bXlcU8bqt6Kpy4zxmgDgfK8aG4ZOC/SXzKCTBd7y/cC8MHGDxI6om9eIX2y+RPGfjCWP3/554Tic8OApwe4Ov+ymjJX5x9Kosnzhv0bHNn5BJfZ8/Getpev3ON0sqJJd/RC7Ru83kJ5w8wbKK0udWXeoQ4Y7Vxe2hVD2SnaWwYqDyuuKgagoDy6eyx7SW1DcvvhpnMFajBk78hm+AvDeXnZy1FN8+aaN1sdJ9ROrd7Uxxyf8j4RobymnJ/++6e29tt1UiLbtJdbRpflL/PEg15CCbXMA9fK2FFWopwoM1Iir2cBQztt0mlM8E1wOwxP0aQ7BaVz4hiJXWcHgstJt2W5qXATYO2wo/Hz95zpWqM7odRkjGHqqqn889t/8qDvQbfDyXh3fnan2yE4xonWe613nFdYUYhMEF5d8Wqr467YvYIH5z3ofFApJJr7dP+/aIap5PP6KUcVHy/tOEqrS9lyYIvbYSiHeWmdi5UX68Hg5ZlIa3qsD5LKRE6drSiuKmbbwW2H5hNDA00qb0+tCSyTl5c3PZtaXlPO9LXT3QgppUTT0n1LiGFjbY4jo4SqJOLZSFOhldZrp2+bx2NnfF7c+SfqwmkXsm7fuoTLSaVlU1hRSGFFodthpBWv1QMqMaG251j3R3bvv+xOdEdOHslxzx0XVd3l9fqt8aYLDuYMt396O2P+NYZvdn3j2DzSQdi7l4jIjcBNwOBmtwbsDugeyWax7JS8voF7UTq3PLQmkYRnaf5SGyNJDX3+1gew/64+5TXl1DXU0bNTalyEmgoH9Soyp/YVmbBupPMZPifWi9ySXADXLqhNFZFuGfg1sBvoAzwVNLyUBB+OIyK9gFeAkwAD3Ir1EJ7pwCAgB7jeGHNQrGzpWawnYVYAY40xKxKZv9Mm+CaQV5LH5Ksnh/w8kxLATPquXqPL3luOfeZYCisL40rmk5nk2LneZEJylgzx/iZeXv7aeKQyUdjuJcaYHcYYHzC/2W0CVwCPJjjfZ4HPjTHDgFOA9cCfgC+NMUOAL/3vAS4Dhvj/xgEvJThvxz0470FeWfmK22EkZEfRDs597VwOVB5wOxRHeXmn5EV2P6I+qmnTpGtCYWXsJwjdSEySubw18YpOKmwD4X7L+ob6pNwy0s1lpA0cKlrR9Om+KMSwy+KdoYj0AEYBrwIYY2qMMUXANcDr/tFeB671v74GmGYsi4FeItI/3vl7gV2Vg5OVzOMLHufr3K9T4sKId9a+w1+z/xrVuKmQZGsiElom7tjcXF8zYT0MrFOZuG7ZLdy62u7hdpz96tmOzbe13+7pRU87Nm+lYhU26RaR34rIt8AwEVkT9LedxLqXfA/YB0wRkZUi8oqIdAX6GWN2A/j/9/WPPwDIDZo+zz8s5QQqh53FO/n1x792OZrUE+4g48Z/3cifv4r8YKBMSCCctm7fuqjvz918eevyT0yyll+yb6eZCgfBAF0e7cKoKaPimtbOx8DHyivbXawX1yW6XgTvK/4w+w8JlZXo/L0oVba7dBSpT/c/gc+Av3KoqwdAqTEmkT4H7YARwJ3GmCUi8myz8psLVWu0WGNEZBxW9xP69euHz+dLIMTYlZWVtZhn8/fby7cDoR+Zu3HTRnylvhbDARYVLqKqvooL+l4AQF1DHWBt2D6fj3UF1t0l9hTsiet7h5omPz8fgE2bN+ErP/T5vHnzaCttw5a1aNEiAKqrq/H5fGzJsy5GycvLCxtbpJiHPjWUK/pcAcDefXu54h9XMKdgDrNHzY5YzsqDK+ndsTfHdDkGgJ0VOwGorKhk06ZNjeNt3rwZX0X4+YeyrsRa3iUlJeQ3WMtp8drFFBcXh50m1Hf8dv+3ABQWFjb5vLy86foR7W9aV1vHho0bANi9ZzdFFdbDKlauXEn99vDJss/nY9tO6zZQO3N3hp1f8wObSHHtyt/V+LqkuIQdO3dEjD1SWXsK9gCwfv16fAdbjmcarOpg3vx5dGjTIaay440p0ufN64LW6oVIqqqqAFi8eDE7OkdehonYV209CKemuobNmzcD1m+YaD1a01DT+DpUWYHvF2mc1qxevZr2ue1bHS9U2Q31Dfh8PnbsOLRss7Oz6dKuS5PxKusqyd6ZHXUdtmvXofU/Ozubzm07txpfwIaSDY2vi4uL8fl8LeqWuXPnRtU6v6aoaftY87omlGh+g9zcXHw+H4WFh7pMLVq0iG2dDt1iL9R6H1gfTIOhrMx6GvCyZcso6V4CWHUYwMKFC+nZProLjr/b+x1g7R/qi616bvOWzfiqWn6PePZBwdtgTqeckNOV1zWtszdt3tS4XwmVG7Q2b7v24/PnzwegocFaz+vrrOWzYMECerTvEVfZgfWztLS0yfCDBw8CsGr1KtrubJojJFqPhFqXAuuPHeUnU9ik2xhTDBQDNwKISF+gE9BNRLoZY3bGOc88IM8Ys8T/fiZW0l0gIv2NMbv93Uf2Bo1/dND0A4H8EPFOAiYBjBw50mRlZcUZXnx8Ph+N85xn/WsewxF7j4AwzywZdsIwskZkhfzsgglWsj3heuvJTrX1tZBttZxnZWWxa80u2AD9+vZrMc+ARbmL2FG8gzEnjTk0MEycANPLpsNuGDpkKFmnZzWOO3r0aNq1addk+mBnn302LIGOHTuSlZXFqsWrYCsMHDiw6XyCpg0Zs//zzWWbyeuZB0DfI/ry7rp3W04T4nsEllngorUN+zfAUujcpTNDhw4F/4XpQ4YMIeuMEPOPoHNeZ1gJPXr04Kh+R8FumLpjKqOOHWVtMSGE+o4lG0vgO+jdu3eTz7tt6AblkadtFLQc27Vvx7AThsEm6H9kf0oKS6AERowYwXnHnBd2uqysLJYsWALb4Zijjwn7O0XznQJmlM9o3Ep79OzBscccCxFqjKysrLDzOrLfkVAAw4YNI+vUlvOUBQL1MHrUaDq269gi9lGjRyFI9F0IImwX0XzeWBc0H6+1ckPotLoTVMFZZ53F4MMGRz1drHaV7ILF0KFjh8btY8BRA2KKNZSquirItlrWQpUV+H4Brc4vxDpyyimnkHVchOnC/A4Abdq2ISsri1l1sxrPp55//vl079g9chmtDH+3/N3G9f/c886lR8foE5yuu7rCSut1z549ycrKotf2XlByaJysrKyo1ue2O9rC6kPvm9c1oZZnNNv/0UcfTVZWFr3ze4O/Ce7ss8/mmJ7HRFzvq+uqrX1XG6Fbt25QBiNHjmRE/xEAtP+mPdTBueeeS58ufVrMt8E08Oj8R7n9jNs5vPPhAOz9bi+st/YPR3Y7EnbBkOOHkHVW0/m3+G7NYmvB/1mnTp2g2toGB/UaFHK6kuoSWHho0qFDhjbuV5rkBuHm3Ww5x7TdhfoOgbpv1ChYAG3aWOt5uyXtoA7OO++8xuUXa9mB9bN79+5Nhh+28zAoglNPOZWs72WFjy0WEdal4EQ72fleIqJ5OM5VIrIZ2I71lXOwWsDjYozZA+SKyAn+QRcC64APOXRP8FuAD/yvPwRuFstZQHGgG0o6qW+ot3Z8CYhUCZ/z2jnc+K8bEypfJYdXTgl7RaL9bds+1JYr/nmFTdEc8ssPf9n4BNBUp6ebndPz8Z5sP7jd7TDSwqwts7jfdz+3f3p7xPEyuY9+cNcWr3dzyUTRXEj5CHAWsMkYMxgrSV4YeZJW3Qm8JSJrgFOBx4DHgYv8Cf5F/vcAnwLbsNomJwO/S3DennS/734GThxIXkle1NNk2o4ynu+7p8zqmtDkMfBaKWWcz7bE3U4Q1qsrX9UDWRUVOx4w5SV21ptN6uNW6viaeqt7SkVtRdRl2iVSmdpQovvSaEXq0x1Qa4wpFJE2ItLGGDNXRJ5IZKbGmFXAyBAfXRhiXANEPqxNEZGOvveWW71pCsoKGNhjYLJCSnv9n+pv+0NOvEorveRLl2WuSUP0AtfUNHazU3GJtD+Md310Yj2Op9U8k1vaM/m7RyOaWqNIRLoB87Fap/cCdc6GlZ5su1Wgiy3cxhjeWvNWzKfVK2oreHjew/zpvD/Rvm3rFz05JRWSJLsrrebf+cttX9pavlJuSnZ92O/v/aitr6XkvpLWR05ToeqoWOrWmvoaVuz29DPuGqXCPsNLdHlFFk3SfQ3WpS53Az8FegIPORmUio5brVM/e+9nMU8zeYX1dM6+Xfvy65HJv11iuEQ2nbvohPvOP3rjR87PO0NaTu1Yf6rrqnlx6Yvceeadnmo9Dew8jTHsLtvNUd2PirmMfeX77A7LdU4/MCyd66RQ7GpkcPp2lxFb5rV1V5dBlCLdp/v3InI6UG2MqTfG1BljXjfGPGeMif3Rasp2qVg5V9VVtT6SiotWeqnpiYVPcM/se3h1xatuh4IxpsUB08vLXmbA0wNYvWd1mKnCO+aZY+wKLaxEDvCS1SpnMOQU5fDxpo/jmj7u7hbN6oQFOxfEVU5zoZZb3I+qT/A3yJQD/Hh0+2s3t0NQzUS6kHIg1uPa94qIT0QeE5ErRCSK+8yoUDIxKXKqQtxRZM89i71wKiwVD55iEdPt+lKMHetPcZV1n8mymrJWxkyO5uvjVzlfAaTNnVqSIdT6fvJLJ3PV21e5EM0hJdWZ0SXGif2OF/YViUr3fU0qCJt0G2PGG2POAY4E/ox1N85bgbUikl6XYquUU1RVFNd0Xqp07N4xeHWnYDCejS1ZcotzWx8pgmStt8HJYvPE0ZFT9hnUSunGQVUytzu3tnGnt41oGgxSYT2uqa9p3G+mQrzpKppbBnYGemD15e6Jdcv/JRGnUK7wYmJjZ4WYyPdLpUrGzVif/PpJHst+LOrxX1/1OgVlBQ5G5M31OiDa9TuabhahynJzXQgs90AM4X6HPWV70u6WeF43ZdUUTn35VLfD8JQmt4VNcuOKlxpzwpm7fa7bIUSlrqGO+Tvmux2GYyL16Z4kIguB6cDZwNfAT4wxI40xv0hWgOnEqeQhXU/dBzj5/Vy9E4xHK+pnlzwb1Xj5pfmM/WAsV79ztcMRWdJ1PY/0vZKePITo093ach/0zCBOfPFEJ8NynBfWLWMMU1ZOsZ443IrbPryN1QWx97FPda1tD078jjlFObaXmWzNcw+v7nsAJvgmMHrqaBbuTPRxMN4UqaX7GKAjsAfYhfU49vjO6WeoF755wdHyvdwCGI6XN/ZEeenesskQuDNF4AFEwepNfZP3yUhqOj3aKTW3iShidnodieb3CbftVtdXA9ZB2IXTLuSad66Jer7pXB/E6t1173Lrh7fy8PyHY5pu8vLJ3D/3/pCfuXkwMWrKKP5rxn/FNW2qrRfNt8/ANl3XUMe8ffNSsl5yy7r91lmzUPuVdBD2/lTGmEvF2mJPBM4B/gCcJCIHgEXGmAeSFGPKGj9nPLefcei5Pk5VgLpBR6/5svJiwpvIepLMndWp/wh/ejuQiGWi8rpy2+4S4QWtdS8JGPD0AFvm98c5f2Rp/lLm3pIap8PtcrDyIHDoQWkQXf007uNxADx0gbfu5Ju9MzvmaVr7vq197rV94eMLHufBdQ9y8oaTuW74dW6H4yivLXuvinhTWP/TINeKSBFQ7P+7EjgD0KTbY7xwirQ1Cd3eK4GEMhPv062cF2pH88B3D7B84fKoy/D6dpvs+P729d+SOj8v0/optQUuoHbifvV5JXkhuyK5vc54vT5zW6Q+3XeJyDsikov1NMorgY3AdYDeNjCFjJk5xu0QGlXWVbJh/4aYprH9Lh9pvCNrcjGRiy0PmdzqsaV8i9shOOKmf9/kdggZIRXqJ1svkI+jrG0HtzXeajO4HK8kfMmI4+iJR/O9577n+HwCquuqeWvNW55ZP5/b/JzbIcQlUp/uQcBM4AxjzPeMMT83xrxojFltjGlITnjpxe5EJNqVf/p3022dbyLu+/I+hr8w3LX5e6XCiMSLXV68wIuJfCqsT4lye30sKCuI6v7SG/dv5Lrp11FdF33XpkBy5MV1y8u2Htzq6vzX7l3L2a+eDbi/fmaK++fez8/e+1njA56W5S9r0hUqwK5tqbW69b3892yZT7JFuk/3PcaYmcaY3ckMKJ04VRkkcwfRYBriuiWcnTGG2vhiTXbC/RbpvLP1SqtPotLle6j4HPnUkRz/3PGtjvebT37Dexve4+vcrxOanxPrWyz1TCokkXbWm/He6m/9/vW2xRCNzQc2M32tdxqwki2/LB9o+oyMm9+7ufG1XdtNKqz/iYjmPt0qTs0rELsr89ZWzseyH0MmxPloXn/sD81/iCOfOjKuMjKNJoeZJ90O2oJP0XupFX9fhf19YsOx4zeNp4zA8l65Z2WLz+KtW1bubllWKgi1b9t+cHvUd7RwYru85M1LGPMv73TV9ILSmlLHyk7Xp6dGvJBSpbZYbz0VSqjTR9GwMwENVQHHejTspQQiEzR5smGat1ykg0i/UfBvaYx3+s0GpMu2HU+iGOlJpwVlBdz1+V2JhBSWG326k9l/Od2k4jZy24e3uR2CI7SlW4VkV6LkWBcbmyoRL1RGXojBbm62AC/cuZBPNn2SlHnF89uN+2hc6LLcvPA16Hsk47aahRWFPPX1U62O98W2L2yfd7S+2fUNv/n4N1H9Lu9veD/h+cVzMBPpSafJbin0Uj3m5IFheU15zNO4vWyS2fDxozd+1KQLSqzcXlZO06TbQU6v6M1XzmTstFN1g/Bia2uyY+r/VH9++I8fJnWe4O+ykKTvajCcN+U8rnz7yqTMLx6TV0yOeRontrs/zvkjN/7rxibDBOGxBY8lXPqiEZAAACAASURBVHaXR7twwesXhP38lx/9kvFzxrf6tL+L3rio1Xk5tW5d8PoF/GP5P6iorWh13PX7ktu/OFPsKdvDun3rHJ9PUVURNfU1NJgGnv/meSprK8OOO3LyyKjL9dpZoWT5tuDbpM7Pl+NLma5+2r3EY+LZSMNN48VEMxHBG5Vd382LG2q8FXVridmesj1Je8pX8++QqgdrybSqYBV1DXW0a3OoWnZyGw7cD/vt/3y7cZjBkFeS12S8eNbHyrpKfDm+sJ8n0hKWCnT9j16kdfzoiUdT11CHeSC25RdrvX7YE4dxxZArGHvqWO787E62H9wedtxQt7wN9/t6Zf+SjPUvmd91Wf6yJu8veP0CJl81mV+O+GXSYoiXtnQ7KJ4V3SsbqZeEqpR1J6bSzT+//Sd/+fIvrsw7Yp/uOO8uEY8GD96N1s26xqsNJ07fnSqgrqEu+nJMYmfUPtn8CaXV1oWB+yv3x11OKF79Hb0ommV1+uTTWwzbesDd21hGS5PuNBCuAszUU1uRtOivmgLL6PEFj3Pqy+EfuR4sHQ9G0vVAdMZ3M1i3b12Tncyy3U1bcLzweyZzG2n7UNukzQsOrVuhvqOdiVK8ZQV+f03awnNi/Zy2elpC0zevs3JLwl/wqjKLJt0OSnZF6cUE0qmr3GNdtmEfA+/Q/WbtLOe+L+9jdcHqJsNmb53doq+jl3fMdsTmyfU7gfXnhpk3cOKLJ0Y1rpd/23SV7AOeVPqNnd4WY1n2wdugFw5SQ3k0+1Fmb50d9fibCzfbOv90bbhIRZp0p7FUqsRj5dXKNVkuefOSkAmbnb/56j2rWx8pjHRe91JZZW1lyD6pkTTpXpLAzvt/vvofT3YficU7a9+Ja7po6qtMr9MgsXrDSwfloWKJ5Z7pQ58fGvM8b37vZo6eeHTM06nk0qQ7jWkl3pLBeOaoP/D7fLzp41bv4hBLedEOj+Sr7V9x6j+i69Ji1zyV825+/2aGvzCcspqypM/70exHI15c6TWhEsDmd3tJFt2eopPJB/tvrHmDvJI8z+zfVGiadHtMXkmeLbdI2lS4KapbXYXj9Uo+5u4l4R4DH+X3bDANPLv42Yi3kkrED176ge1lJrIDsvuiFK+vT/FKte/11favAKiuq3Zl/vUN9a7MV9kvpyiHTzd/6kjZS3ctdaRcgNdWvpZxj3NPRj3lpTMNXqa3DPSYa6dfCxDxFknRbEAnPH+CbTHFI68kj5r6GsfKT+SAIh7vfvcuv5/1e3YW7+T6E68POU4iFVvw43Ttqry8cteFTG59SgUz182koKwAoMktJSNdZJgqZm2Z5XYIALz17VsJTe/Fbejm926OeZpo66TL/3l5zLcJjFbgSYc3nHRDi8/sWtf3le9DJgjdOnSzpTzVulRpANGW7hS0o2hHyOHL8pchE4SN+zcmPA87Kvm7P7874TLCOee1cxwrO5TA6fhUvL+w0zvsncU7WwxrXgHaEUO6njZt/pj1ZPvJuz/hjs/uCPt5Mm8ZaLdL37o0qvGcXu4zvpvR5H1tfa1r6/ML37zAnrI97K+I/7Z4xhhPrwvxxmbXb7LpwCYAW7pxvbP2HWSCJP0Jo8oZriXdItJWRFaKyMf+94NFZImIbBaR6SLSwT+8o//9Fv/ng9yKOVF2JT/DXhgWcvimQmtDd+qUX6ya3/7MC5pXqtFUshMXTYyqpcqLrVFusfV2azG0PnkhMY/1u3shZnWI0y37ZTVldHikAw/Ne8jR+YRzx2d30P+p/hzxtyPiLiNUUhvvfkfX/8j+uuCvABEf2NOauoa6Jo0jC3cuTDgur0mV/a+bLd3/Dwh+du4TwERjzBDgIHCbf/htwEFjzPHARP94KSFZp2W9fPrXKxtCIsvontn3MDdnro3RWLyybEKJdXmV15Q3nd7D381pibQAemlbNhjGzx7PlgNb3A4lqeI5MI/FwcqDALyy8hVby3Xb7Z/ennAZiWw7BysP8sySZ4D0qn/sWv/GfTyu8fXV71xtS5nB9OApOq4k3SIyELgCeMX/XoD/AGb6R3kduNb/+hr/e/yfXyhe2jPFIFmn47x82i8RulF7V7e/hu+76NX1sb6hnikrpyR0cV8062TgDFQi3FiGWw5s4alFT5G9M/tQHB7cBgPLJp7YEtmV1NbXxj1tqnPrTFak6b/c/qUd4XhWiqY9MVuwc4HbITjKrZbuZ4A/AoGbtvYGiowxgWe+5gED/K8HALkA/s+L/eN7XrIv9ktXdlY2zZMXryaEkewt3+t2CK1yYgdRb+qZ4JtgW9/Gl5e9zK0f3soLS1+wpbxwWruoOdKysiu5aTANtiT/6ai2vpbHFz4e83SDnx1saxyhfut0arFNpmTX6148GE1VBeUFbofgqKTfvURErgT2GmOWi0hWYHCIUU0UnwWXOw4YB9CvXz98Pl/iwcagrKyM92e/z56qPU2GB8eRU54TdXmh4g837LuC75oM27Il/OngaMvNz88POf28efPClg2waNGixtclJSVs3tL6k7Va+61qa6wWpf37m17403y6cOX4fL7G36W6qrpJTFu3bsVXG3n+wV5b9Rq5+dYjfUtKSshvOLSciorCX2QZKrZv938bcpzSktIW4wZ/vqtyV8jP6+rq2LDRevDJ7j27Ka4oBmDlypXUbw/fmuvz+di2c1vj+9qaWjZujP1i3ODvmL/70HIpKS5hx47QF/+Gmra5PXus327SgkksObCEZZuW8Yehf2j83DQcqg5888KX03wey3Ksaw6Wr1+Or7LldNHUIRUVFS3Gi2YHHDzNzp2H+loePHiwyWdVVVUALF68mJ2dW16wGq1pO6YxJWdK4/sFC1tvTQq1Ps+bN492beLfbaxevZr2ue0pOtj6BcnRbt9rVq+hQ24Hious9X3VqlXIjpa7jVDTN9Q3cN+M+5oMy87Opku7LjQ0WG1CL3wc+qBsV+mh7XDb9m346g+Vn7crL+Q0cKiOrK6ubhLTxvyW21xRcRE+n6/xu0XS/PvlVYSPIZZyQiksLKSsruVFgpGm9fl81DS0vKPVsmXLKOthlVVXW9dk/NbK9fl8fLfvuxbDt2zZgq+65TStlRmoP6MVKKOirmkDW0FBy8Rx27Zt+OoixxRqeHm51XVv6dKlHOh2oPWYItSB0cw33LgFe6zvFLxuFxcXN5YTXF803+ckmo9FM/3OnTuTnvfFw41bBp4LXC0ilwOdgB5YLd+9RKSdvzV7IBDYa+cBRwN5ItIO6Am0WPOMMZOASQAjR440WVlZTn+PJnw+H2NXjWVHcdPkIjiO9fvWQ5TXFjaJf16zYfOajrdrzS4IqiuOP/54OJRDRV9ukOll02F3y+lHjx4N2S2HB5x99tmwxHrdo0cPhhw/BFq55XPI3yroO7bv0B6APn36QGGI6UJ9j2bLaEfRDlgCHTt1tGLyH5ccd9xxZJ0bYv5hYgGYs3cOYH2/o/od1bicevXqBWFyiVDfsXRjKXzXcpweW3pAiLw78HlOUQ580/Lztm3bcsIJJ8Am6H9kf0oPlEIJ/PCHP+T8Y89v8T2Cy12yYAn4r9Np36F9YzmxuHrR1Zx21GnMvWUubxS/Af7jzx49e3Ds/2/vzqPkqO5Dj39vb9Mzo9m0jYR2IQzIYTMyFgFMmyReiBN4jn2C7TgcP9vYz8ZLcJKHncOzCTh2vIBNQiA88IuJgwXGdsAsBgwaEAghgQCtSBq0jkbL7NMzPdPrfX90z6inp7unl6ququ7f55w500vVrdu3q+791a3bt5YtgzwxYyAQyJm/BQsWwAmY1TIL+qF1XuuU8lQvKkjV74HLA/BC9nQufe+lUwLG5zueh0OwfNnygo+L9PcB6hvqpy2nNs7cO5n+eZcuXZqs5YC2trYp6fnf9MM4rF27lpVtK2dMN5fv/vy7U55fesmlsDH/Otn258svvxyv2zv1xfzn4VOcd955BE4P0HqoNXnNMo/MfSLXPpKZ5vnnn09gRWBa3rLVny63ixWnr5iyr1922WU01TXhftkNcfjGjqlBeTYrV6wkcFlg8vkvR395qgXLMFFH1tXVTW0ftuyGjD6KlpYWAoEiyivNvr59UMJ017nq0XRz5szBPe6GjItO+Y7jQCDAeGx8Wvtx4YUX8u5F7wbAs9kDselp5Uo3EAjQt6sPMm5tMXvR7JztZb40j9VlafTymNhGMByEtN8mtre3Q8YFyZUrVxK4NHDqhQI+G0DD7gYYhc++9ln2Xr+XM+acMWX9aevlqQOzpZ9XWh7vG7gPTsLiRYshdb45sX8CyX00VV9ktjlFxWN5yiJfXbN06dLitmORig8v0Vp/Q2u9WGu9HLgGeE5r/UlgPfDR1GLXAo+kHj+aek7q/ee0Ta/lZAbcVjGzeIq5la3RyrlkWE03B3Cp3IftxOXol468NDk2rlKXWoOR4OQdBw0dEmTQ/vyp33zKkHRqmROHY80k3/FUqlL2f6eN2S0lv2YMccqWj+9s+E5Jd/n9z23/aUCOjJVe/9396t0AvNb9mlXZsS2n1E12mqf7fwM3KKU6SY7Zvi/1+n3AnNTrNwA3WpQ/Aay9b63VWTDULS/cYnUWppmpMfN7/DOut6ev/Lna7abcoGTdjnXJnjYbmzZzhkMaEifLtl9F41FD5lgWp2itOe/u8yq2vVz3s7CDcuZIB/LOqy/szdI7UmqtO4CO1OP9wEVZlhkHPlbRjDmcET0mTm7sfbf4pr2W6wYfTmxYnfDjqsw82qUX76MPfZTHPvFYSev2jPYw/4fzZ1zOjO/Hiu/cyXVAMbL1dH/td1+zICfOYda+4YS6zQilzpFu52PS7Dr+yNCR/Nt3yL4jt4GvQjYdfVO2Qg+qaKK6p/LKVfGW+r0fHZ7+w0xDpwSrYGU4U6P0+L7HS057/8D0H0rs7dvLeGw859WHQqSXj1kNl1HfQbXVLVrrrEH30/uftiA3uTkloLCC1WWzf2C/rY4LOwfm5Vj646VWZ8EQdhpeUvUq1dsXjGSf/SKXq9ddzfoDU2/+YlgjbWAFYFZlctNzN/GTTT8xJe1CffmiL08+rnQjsvj2xYanmbmvz9Qo7el17nCYz/32c1OeF7ufVmsj6RRWB23C2U6/43RW/csqq7NhOTudeNiZBN1VIDPAufn5m4ta/5E9j3D1g1fPvKBFjGwUs1UMt264la89VfnLyenBVoO3ofD1qrByO+vOs6zOQsk2dW2yOguC6jwu7EzKW4jiSdBdQW8cf8PqLADJsalSYU4XDAdRN6uier3TT3jseHXALuwyptsMnf2d9I/NPH9uLun7zVu9b6FuVtz6wq1GZK0s0gNcWfnKe+ORGeZ3FLZi5I84s7UHlT4279t6X1l1nDhFgu4K+fdX/52P/+rjVmeDfX37mP/D+dzxyh2mb0trbXovtZGOjyQnlv7XLf9a8Drl5KnUsskVlFdzYGt333z2m4ak0x1MTu580/qbLJ9pJdt+ZtQJoSk/NnXY/j9Rd3z1ya/yvRe/lze4CsfDFc3bTEr5/kz78WWO793KO0K/dOSlmRdykM/+9rM8se8Jq7ORl1M6qyTorpAvPP4Fq7MAJHvlAH739u8szknhJir4Qn4E1xfqy/u+XQ/M9OC95GA8xwmA1T2WRpW5na/OmJG3eCL3XUQrwc7lbaTMoM3s4D0z/Ts238E3nv2Go8pbo4supyW3LzEpN9ld+cCVkyex1cJJ+4jIToJuAZhzMBvVeE0EbQmdmHHZuT+YW/72bF6x5cufXU8qyuGkHsxYIsZQdOZbdhci/bustu+12j5PMYqpX+xaTqXUkYUEwOmf9w/+7Q+K3kYmO8/VXSi7t0eiODJlYBWQg1KUK3O8XrmBbnrv+qauTcxrKG1eWqf5ypNfMTQ9o65SGHYCbMO6xujAtFKfsdI97HaV6/vb2bNz8nG+edOtvpIn7MEp+4H0dAvbs+Jg6hntyTp/tVnSG56ZGt98QUa2siokKPn+xu/PuEw5frv3t6ambweD44Pc9epdhqVnxwBXCCv85BVjpnS18ndVpZwc7ji5g929u4taZ6abyFSKoT8mraK6UHq6a1TmTjxtXmWbXtYsV76DN70MJu48qL9VeDnUak9Vpk1dmzg2csySbVtVOfeO9bL23rWWbNssTtmfndLDVU2UUrYJhIrZT9ftWDf52C75z+fvnvm7Kc8n2uV8n3nlHStNzVOh3vGv77A6C7YkQbcQDlNsY1HpxuXi+y42ND0nNI6/3v3ropYv5PcJojil7id2Dto3HtnISGTE6mxMk6usd5zcUXRadi5/Ixn1OQ8NHrLlFJKZJwKReKSo9dcfWM8V919hZJZsSYaX1Khi7xhYTez4WY2YvSRn2lVy1cIpPa+FuHPznQUtZ+V3l+04qZZ9aYKV+1Sh9dDXn/q6yTkp3pOdTzI4Pjjt9XPuOqfotMrdp2olaIfkZ/30I5+2OhuT0vfhctvVx/Y+Vm52HEGCbpFVLVVkmSoVWOwf2F/SetUW+NSiiak78wlFQxwcPAhYczyauZ/ZaZ7uSgfexW6vf9yeNyVJ/6GjUWq53SlE50An6w+utzobtuSUdlGC7ipQTT2AZrPTgVmpW8/bsWdfnJLr+P3IQx+pcE7Md82vrjH9B8obDm/gd532vQ+BHI+iVEb2BhuxH0rsUTwZ021TWhd/84Fi0zfb5qObOXf+uaZvxy7KKdOiZi/JsR07nVCI8hk5brOUHkQzeh37x/q58dkbDd1Xn+x8kvf//P0smLUAgG91fAso/EfQVvWu1vIQPyNJ4CecRHq6a4zZFVTmravvff1eU7dXqEIa+ZICExPK0+jGt5QARy7zmsupJ0iZ++bDux62KCenPLTzIQCOjxy3OCdCFG+muyiLwjilzZKg26bMapTN7k2ptWmCpvwAskI9Lrn2DadUOsV6e+Btq7MgcvjYLz9mdRZsqTfUW/Q60mNb3XLV28t/sjznOtVap9cyGV5iU1prMo+3216+jTNmn1FWun//+78HpIK3s3K+m2wVu9MvW08MswiGgxbnxDjFTjFotEJO6p3aG18Ms46NB3c+mPO9R/c8aso2hTNZNSWkRktQbwEJum0qW4P39aezTx1VTMNRyjyqVqv0CYLdg1S7589KTgkUjwaL+zFh2dOqGXUb+LR8nBg5UVoaBu+/xZZlJqPKZuuxrZw550wafY15l7v+yesBOY6N4pTA0Sn5FOaS4SUOF46FS1pvpgrfKcFLur946C+szoKlnPidCWsYEQAs+NECS7dvJ4Pjg1x4z4X81W/+yuqsTHrq7aeszkJRwrEwfWMyvlmUxintnwTdNlVoL4j/O/6qGvO6/sB6+scKm5d2Yg7jCYVcsndC79JMAUm+yiXbuk6pjGaS+X2bTX6YZw67DW0z4gRgLDYGwMtHXi47LaN8+ckvW52Fonzz2W9anQUhTCdBt00VEyjt6tll2Haj8ahlv6YejYxyxf1X8OEHPlzQ8mPRMUO3b7dgINMHfv4Bq7NgqROjpQ1nKNXCHy2s6PZqxWhk1NY3+KjUSard65tK6wp2lbSelGNp8nVA3f7y7Vx838UVzE3tkKDbpqzqkf3krz/J3B/MteTybywRA6aPO3fKpWiz8/n0208DufeN4fAwe/r2THvdCb37orLuevWuktYzYl/6zVu/KTuNanFo8NDkYzlOhV3c8PQNbOraZHU2qpIE3VWglDP9XOv8ctcvy81O0ULRUN73cwWzpfZIVctwi2x+sPEHhqQjvUeVY8VJ5U3rb5pxGQkCC1fqd5hvujhRGKd0yghzOWU/kKDbpswODDMbVCsD0YnhJJXIgxOmSpvxjpRF5s/qzyOEmE5OapImysEpQZMQ5ZCg26ZqqUIudXynVNLmqaX9zwnK3deNOlasPIG7/eXbTUnXyKs65ZSPXF0qjZRbaYw6lp8/+Ly0F0WoeNCtlFqilFqvlNqtlNqplPpq6vXZSqlnlFL7Uv/bUq8rpdQdSqlOpdQ2pdS7Kp1nK1jdO2nF9osNDKwuo3JkVlJbj20loRMlrWv08kLY0Q1P32B1FnKqpsBv+4ntROKRim2vmsqu1mw8spHAzwLcueVOU7fj5LY+kxU93THg61rrs4G1wJeUUquBG4FntdZnAM+mngN8CDgj9XcdUNovgCxk9g8S1u1YV/Q6mXO42qHXOOftzY3sicoTgJZSBkblbf3B9fzgJWPGYxvBzg3hi4dfRN2s6BqePtuBnGCIYhlZ9/WP9Tt+lqFz7z6Xv/nd31idDdM5IZCzcz2cbl/fPquz4IjvEywIurXWx7TWW1OPg8BuYBFwFfCz1GI/A65OPb4KuF8nbQJalVKOmsurlKl3rA4erNx+ZkVjhxOCXNLLqZgKMlsF8caJN0pe18jlT46etHz/y2divu7nDz5vbUaEqX76+k8NTU9rzZ2bze2RiyVik7MMOdmmo5WbuaLcusbO7YMQmSwd062UWg5cALwCtGutj0EyMAfmpxZbBBxJW60r9VpVu2n9Tezu2V2x7WUGZlacNbb9c1ty2xmVsFSq5SmlUStkdgthDKf0ZkFlT8Y/8+hnDE3vzRNvTt6CfYKVZe+k791Mu3p2sezHy+gJ9RS97m0v32ZCjuzDzOPN6LSd0tNsNY9VG1ZKzQJ+BXxNaz2cpwLK9sa0b1cpdR3J4Se0t7fT0dFhUE4LMzIykvX1UvNx+6bbuX/r/Tx88cNl5Kpw8Xh8Sl67u7srst0J6duOx+NT3otEso8v3Lx5MycbTxaU9vHx45NpdXZ2Zl1u06Zk787Y2NiUdXMZHh6mO3GqnPr7c99JMzOdbOO3T544SUdHB0ODQ3nT6QoVdxOJbdu2UX+0vqh1jgaPFrW8FXbt3kVHfwc6cao6eP6FmXu/J76LgwcPJv8fOpjze05/fdewcTehSnfo0KEZl3l508scqp95uVz6+oq/4dXQ0PT9cMOLG5jlmVVyPiop8zvduHnjtGUS8QS7dk/9Xjds2DClDihEV1fxN3YJh8NTnn/799/OulzPycKCUSPbvGAwWLE29Obf38zh0GEODx0uet1/+P0/8JkVuU/Otm7dmvM9Iz5fMWns37+fjljhy3d0dORtU8r1/PPP43EVHwJ2HZ2+r3cd7WJwZHDy+euvvz7l/ULK6UjXkZzvFbL+4cOHKx73lcKSoFsp5SUZcP+X1nri3t0nlFILtdbHUsNHJqKpLmBJ2uqLgWkRodb6HuAegDVr1uhAIGBW9rPK9WUHAgEo8Sq48ihGTxstOU/FcLvdU/K6YOECqOBdsNO3HYpPnbfb5/NlXeeiiy5i9bzVM5ZvIBBIDkl4Bbw+L6tWrYK3py+3du1a2Ax+vx/C0/OVqbm5mdPaT4NjyeezZ8+Ggdx5SBdPxOGFqcvMb59PIBCg9WAr5Ii7A4FAcvzcluzvZ3POuecQOCNQ8n5oV2effTaBcwOoFxWkztMuf+/lsCH/emsvXUtvqJflLIdDsHzZ8qnfT1o5pb9e31UPU9sSQyxdunTqtbws1q5dy8q2lSVvY073HCiy/W5paZm2H1526WW0+FuST2y+P2Ueu/4l/mnfn8vt4p2r3wlvnXrtsssuo353PRQRdy9evBiKPE/1+XyT9QzAiXD2O67Omz8PemdOb1qbV8b309TUVFbbVYzGxkbIf6uGnMYT49z5du4hQ+9617sgx6i9QCAwrQ4uVjFltHLlSgKXBpJPClgnEAiUdNwW6vLLL8fr9mZ/M0/+Fi+avq8vXrSY/pP9k/XFBRdcAG+eer+QeOyxyGPJaC+LQr6rZUuXFbQdq1kxe4kC7gN2a63Trw09Clybenwt8Eja63+dmsVkLTA0MQyl2vWGevnwLwq7JbrTqZvNu9T66Uc+bVraVpDLeEmhaIiNRzYWPdPCNQ9fw5Lbl8y8oI08ue9Jq7MAOHvfM3q4SjoZAidqXbnHgJ1/R2QkK8Z0XwJ8CrhCKfVG6u9K4HvAnyil9gF/knoO8ASwH+gE/i/wRQvyXHOq6QD4jzf+o6DPs/3k9grkJilb8DJ5kwgZ61mQzz/2eS756SVFr/fInkdmXqiCCvm+M8ciV4KTA+xCaa0lYLZYNbU1TlILx7cdVXx4idb6RbKP0wb4oyzLa+BLpmZKTPPTN4ydOaAcRgahCsXOkzuzvnfVuqvKTrsSpJESxZITOSFEumprR5xyEiF3pBQ15cToCe59/V6rs5GVWYFRtVWuRnFKJS3MlXnclXK8yElN6aTsrCH1nzUk6Ba2l6sH2axgshKVUba8Tw4vmaHHXCrL3KRszCUncNl1HOwoeh2jg80XD79oaHqiutntWK6Vky8Juk0mFWH5ZMxleSQQtbfvvvhdq7MgyvT68eKntTE66Lns/10GwND4EA/tfMjQtEX1KbVd2Nu31+CcJJV7PGzp3sK/bfk3g3JjHgm6TTZREdqd3c56C2HWmXExQX6pechW4RWaVrHflRO/20qopXKRE9face1/X8tfPvyXVmdDGMDM47bU+u+pt58yOCfGeO7Ac3zpCfv//E+CbuFYTh5eIoQwV6Ez21TbCUkpN5mxkpnl7+TpYjcf3czj+x43LX2j2zlpNwsjQbcA7D2eys55s6K39J9e/KeilpfKUBjFSfvSxiPT70BZC4yqL4fDw4akMxMz96k9fXtMS9ts77n3Paamn+2uyMJ8EnQL26u2nigoL1j/+bafG5iT6vLM288Uvc7Dux9G3azoH5t+67dYImZEthyplobfWOFosMhbWFZQKBqi5XstVmdDmMhux7edO9eMJEG3qAmbujZZnYWCaK3ZP7Df8DRrxdUPXl3wshM9bLt6dgGwp3d6r5j3lhy3SRZVo9oaeyM6KYLhoAE5KczE8Scqy25XrWqlnZKgWwDVv8Pf9epdVmdhRlpr7txyJ4eGDlmdlZpkt0bIjqq9nqgGRpxE2LkXXhjD6GO53JO92zbdZlBO7E2CbuFY7ek/KwAAFA9JREFUxQRJdguocuXnpSMvVWxbte6WF26Z8lwCyqmqrQe4Fsg4XVEoM9uFt3rfMjS9aqqbJegWtlfxm+NYdIArpapy/LqwXikBdDU1dLXige0PSB1SoFrfv838/Nc9dt2U58dHjtMX6is5vWrqOPJYnQFhD9KrVb5iyjDXHSlnSqOUirKWGpda+qzCGHYPUov5cXD/WL/U5aIglQxkF/5oYXKb3yptm8VcwRmPjeP3+EvaTiVIT7ewPSMaETOCsc1HNxua3guHXjA0vQkfeegjpqRrR+U0JMPhYcKxsIG5qT5mDH+ykhMC1KHwUMHLJnTC9icRdlFMuVYjJw1FKqb9fnyveXObG0GCbgE46wCcUEyA5YRg4djIMX6x4xd5l6mmy2x2c+UDV/KHP/1Dq7NhG9n2tavWXWVBTsyVLfA+OHiw8hkxQEInHHEiYQdffPyLVmfBUkZ2RN2x+Q5T2yYnxie5SNAtAHtflq/0mO5KKLWCcvJndoKtx7ZanQVTSO9ncZw6P3s1BSdm6wn1WJ0FSxl+R0oT26Zq6mySoFsAMBYbs+1c1jmDbhsciJXuVbLDZxbOI/tNdtmC1Cc7n7QgJ8boGe2xbT1uN27ltjoLljI6SDbzhK+aTiYl6BaTLr7vYquzUJTjI8dNSdeMACUzr6VWeNLTLSqlFnrHszXmO0/utCAnxvj+xu9bnQXHcLtqPOg2uJ2zS9Bt9w4GCbqFY33ovz5kdRYKDoInfr1d9vZsXqEIeyolgK6FfS0Sj7Cvb5/V2RAWqIWTynyM7sAx83dTLd9rMS3tSpOgW9hevsb/ntfuqWBOpkvPWzGV+IIfLShte9LTLYShvvncN63OgrCAS9V2+FOtJ9V2P5mq7b1OOEK+S0uff+zzFczJdKUGwSORkZLW29O3p6T1bnu5Nm6xa4aETnDHK3cwHhu3OisVlWvfVjcrHtj+QIVzI4SxHt9n76nlzGbFOOlcdYqRU7Xa/WRCbo4jbG/vyF6rs5BTXMcrur3z7j6vpPW+/vTXDc5J7XD/Y22O/czXeH3y15+sYE4q69YNt1qdBVGF7BYMWnHV9P437+fa86+d9rr/O/a9mY3RpKdb2F5HT4fVWcipmn5VLUS6UDRkdRaEECax4iTAqfPfG0mCbiEyFNMDUOmebiEqZduJbVZnQYiqYbexxvL7IGtI0C1EGaSnWziB3KVQCJHObsNdaoUE3UKUIZ441dMtgY0QQggnkJ5ua0jQLUQeT+x7Iu/7D+58sEI5EUIIIYwhv9mwhmOCbqXUB5VSe5RSnUqpG63Oj6gNf/rAnxqepvQwCCGEsJIE3dZwRNCtlHIDdwIfAlYDH1dKrbY2V0KURsbSCSGEsNJodNTqLNQkp8zTfRHQqbXeD6CUWgdcBeyyNFeiKh0NHi1pvZ0nd+Z9/9E9jwLy40tRmon9pxQz7ZtCiOIVc0xu7NpY1jFstPUH1pd8k7ZSVaIMjgwdMTX9ciknXOpWSn0U+KDW+rOp558C3qO1vj5tmeuA6wDa29svXLduXUXzODIywp+99mcV3aYQQgghhEj6xJJP8LmVn6v4dt/3vve9prVeM9NyTunpzjYtxJSzBa31PcA9AGvWrNGBQKAC2Tqlo6ODN7/wJttObCMcC+NxeWj0NdI13MVpTafh9/hJ6ASD44O0N7YTTUTxuDw0eBsYjYwSiUc4PHSY5rpm2me1Mx4bp83fxsD4AOFYmBZ/C4Pjg8xvnM9weJjeUC+nt53O9pPbWdy8GI/Lg9flJa7jzG+cz96+vcxvnE9fqI++sT6WNC/B7/Gzt28vC5sWEgwHqffWs3DWQnb37sbj8rCidQUel4eh8BCt/laC4SDRRBStNfMb53M0eJRYIobX5WWWbxbHR44zv3E+zXXNHBw8iN/jp7mumUg8glKKsegYO3t2csGCC2jxtxAMB2n0NdIz2sNZc8/i8NBhOvs7mdc4j0g8Qm+ol3kN82jwNhDXcZp8TTTVNfHq66+y4swVDIwP0FLXgtftJRKPsKJ1BdFElIGxAcZj47T6WxkYH0BrTaOvkVgiRs9oD811zYzHxmnxt7CsZRkHBw+ysGkhQ+NDaDQtdS1Acs7thE7Q6G2c/K5m189mNDpKvaeeaCJKKBpCoYglYvjcPlzKxeD4IHMb5lLvracv1Mfs+tkopYgn4mg0e3r3sGr2Kuo8dZP7S/9YPy7lon+sn5VtK9nXt49oIgqAz+1Da004HqalroWh8BA+t486dx2NvkaOjxxnactSToycwO1yo1D4PX66g90sbFrI3Ia5k/tIS10Lblfyjopt/jbGY+OMREZ4Y9sbrDprFS7losWf/PzdwW6afE14XB7qPHWMRkZprmumb6wPl3Ixr2EevaFejgwfocnXRDQRpb2xHY1mYGyA2fWz8bg8k2UH4Pf4J7/PWCJGPBFnJDIyuT831zUzHB5maHyIWb5ZzGucR6M3+d25lAu3y43X5eW1Y6/x7tPeTTQRZTSSvCw6Gh1lTv0cookoR4eP0j/Wz7LWZYxGRhkcH+TMuWfi9/iJxCMMjQ/hdrlp87cxOD5IW30bA2MDk9+T1+3F4/LQXNfMSGRk8vud+AwTBscH8bl9KBShaIhgJEiTr4k6Tx3jsXE8Lg87d+7k7NVnE0/E8Xv8xBIx6jx1jEXH0Gjcyk0kHsHr9tJc14zf4+fw0GHm1M+ZvAoSiUdwKRcaTaO3kea6Zuq99WXVUd3BblzKRTAcpNXfSoO3gf0D+2nxt+BSLkYiI2itececd3Bo6BAr21bSP9aPx+XhwMABWvwtzPLNIhQN0d7YjsfloSfUM1m258w/hz19e3ApF2PRMWKJGBqN3+Nnlm8WLuVi4ayFdAe76Qn1EE/EmdMwh2A4yNyGuZN13UQd11zXjEYzp34OBwYPoFDs2b2Hs1efTf9YP811zQyFh2hvbGc0OsqK1hXs7NlJq78Vt3LT4G3A7XIzEhlJfuf+NnpDvclj3NdIk69pct8MRUMcCx4jruOcv+B8tNYcHjqMRif317R6p85dR0+oh5VtK3ErN2/1vsXqeasZDg/j9/g5PnKc2fWzCUaCROIRGr2Nk/tRKBrC7XITjoUJRUMsal7EaGR0cj/pDfVy+uzT6Qv14XP7Jo/dofEhzpx7Jj2jPUQTUXxuH8eCxybrW42mzd/GidETKBRLWpZMHocjkRH8Hj9el5ex2BhNviZGIiOEoiGWtCyhL9RHLBGjq7OLVWetYnb9bGbXz6Yn1EM0nqzz4jrOaGQUt8uNS7mY5ZvFglkLODBwgEZfI36Pf3Ifcis30USUhE7Q4G2gN9TLoqZFuF1uguEgsUQMt8vNvIZ59IR6aPQ2MhQeormumcHxQRI6gUIxMD7AitYVjMfGJ+u5iWNjKDyEQtHoS9bVE3VVMBKkua55sq6Z05A8pvrH+hmLjrGwaSHhWDj5vfgak3Wdu469fXtpn9VOQicmj/toPEp3sJtGXyNz6ufgdrlJ6AThWBiv28vCWQvxur0Mh4fpDnYTiUfwe/w0ehtp9bey+ehmAssDrN+0njPfeSaLmhdNHosnR0/SHewmloixonUFI5EREjqB2+UmnogzFhvj7Llnc2DwAOFYmHpvPQmdQGuN25WsP4bDw5zedjrheJj+sX5a/a14XV5GIiOTbU04Fp78TBP730Q911TXlLOuiCViHBw8yLKWZXQNd03W33XuOqKJKF6XF5dysaRlCZ39ncQTceo8dfjcPsKxMMdGjtHmb5tsSwAavY3Ma5yXdXsjkRGOBY/hc/toq0/W0d3Bbprrmmmpa2EsNkajt5HB8UHiOk6rv5Xt27dzxtlnMLdhLgpFd7Abv8dPYHmAOQ1zyqorzeSUnu6LgW9rrT+Qev4NAK31d7Mtv2bNGv3qq69WMIfJoLvSgX6tkLI1npSpeaRszSNlax4pW3NIuZrDbuWqlCqop9sRP6QEtgBnKKVWKKV8wDWAfQZHCSGEEEIIkYcjhpdorWNKqeuBpwA38FOttfwySAghhBBCOIIjgm4ArfUTQP47lQghhBBCCGFDThleIoQQQgghhGNJ0C2EEEIIIYTJJOgWQgghhBDCZBJ0CyGEEEIIYTIJuoUQQgghhDCZBN1CCCGEEEKYTIJuIYQQQgghTOaI28AXSynVAxyq8GbnAr0V3matkLI1npSpeaRszSNlax4pW3NIuZrDbuW6TGs9b6aFqjLotoJS6lWt9Rqr81GNpGyNJ2VqHilb80jZmkfK1hxSruZwarnK8BIhhBBCCCFMJkG3EEIIIYQQJpOg2zj3WJ2BKiZlazwpU/NI2ZpHytY8UrbmkHI1hyPLVcZ0CyGEEEIIYTLp6RZCCCGEEMJkNRt0K6WWKKXWK6V2K6V2KqW+mnp9tlLqGaXUvtT/ttTrZymlXlZKhZVSf5uWjl8ptVkp9WYqnZvzbPN3SqlBpdRjGa+vUEq9ktrmg0opn1mfuxKMKtu09NxKqdczyy1jmWtT6e5TSl2b9vp3lFJHlFIjZnzWSrFTmaa9/6hSaoeRn9MKdilbpVSTUuqNtL9epdSPzfrclWBR2Uo9W2TZKqUOKqW2p/a7V/Ns84NKqT1KqU6l1I1pr1+fek0rpeaa9ZkrwU7lmvb+v0gbNiWtcvfXDWn1bLdS6r/N+tzTaK1r8g9YCLwr9bgJ2AusBr4P3Jh6/Ubgn1OP5wPvBr4D/G1aOgqYlXrsBV4B1ubY5h8BfwY8lvH6Q8A1qcd3A//L6vKxQ9mmpXcD8EBmuaW9PxvYn/rflnrclnpvbSo/I1aXS7WUaer9j6TW32F12VRb2aYt9xrwXqvLx0llm1pG6tkiyxY4CMydYXtu4G1gJeAD3gRWp967AFheSDp2/7NTuabeXwP8J9KGGVquacv9CvjrSpVDzfZ0a62Paa23ph4Hgd3AIuAq4GepxX4GXJ1a5qTWegsQzUhHa60nzkC9qb+sA+W11s8CwfTXlFIKuAJ4OHObTmVU2QIopRYDfwrcm2eTHwCe0Vr3a60HgGeAD6bS3qS1PmbIB7OQncpUKTWLZPBzqwEfzXJ2Ktu0dM4g2ehsKOOjWc6CspV6toSyLdBFQKfWer/WOgKsS20LrfXrWuuDpX4WO7FTuSql3MAPgL8vMW3bsFO5TlBKNZGsFyrW012zQXc6pdRykmfqrwDtE0Fa6v/8AtZ3K6XeAE6SbExfKWLzc4BBrXUs9byL5I5YFcotW+DHJCucRJ5lFgFH0p5XVRlmskGZ3gL8CAgVk28nsEHZTvg48KBOdcVUgwqVbS5Sz+angaeVUq8ppa7LsUxN1bNgi3K9Hni0GjqO0tmgXCf8D+BZrfVw4bkvT80H3aleu18BXyu14LXWca31+cBi4CKl1B8Uk4VsSZaSD7spt2yVUh8GTmqtX5tp0SyvVUUZZrK6TJVS5wOrtNa/KXbbdmd12WY8vwb4RbF5sKsKlm3OJLK8VhV1hBFtGHCJ1vpdwIeALyml3pttU1leq4oyzMbqclVKnQZ8DPiXErdtS1aXa8bzj1Pheramg26llJfkl/9fWutfp14+oZRamHp/Icne64JorQeBDuCDSqn3pA3U//M8q/UCrUopT+r5YqC7yI9iOwaV7SXAnyulDpK8NHSFUurnWcq2C1iStl5VlGEmm5TpxcCFqfVfBN6hlOow5ANayCZlO5GX8wBPGQGmrVS4bHORejYPrXV36v9J4DckO4+WpJXtF6iRehZsU64XAKuAztR+36CU6jTkA1rEJuU6kZc5JIegPF7+JytczQbdqTF+9wG7tda3pb31KDAxU8O1wCMzpDNPKdWaelwP/DHwltb6Fa31+am/R3Otn7p8vB74aKHbtDujylZr/Q2t9WKt9XKSPX/Paa3/KkvZPgW8XynVppK/fH5/6rWqYZcy1VrfpbU+LbX+pcBerXXAwI9acXYp27SkKt77YhYLyjbX+lLP5k6nUSXHtqKUaiS5P+7QWh9JK9u7gS3AGSo5C4yP5PeQs8ydyi7lqrV+XGu9QGu9PLXfh7TWq4z8rJVkl3JNS+pjJH9sPW7E5yuYtsGvWq34IxkwaGAb8Ebq70qSY/+eBfal/s9OLb+A5JnTMDCYetwMnAu8nkpnB/B/8mxzA9ADjKXW/0Dq9ZXAZqAT+CVQZ3X52KFsM9IMkH/Ggv+ZKr9O4NNpr38/lV4i9f/bVpeP08s07f3lVMfsJbYqW5KzmZxldbk4uGylni2uDVtJcmaHN4GdwD/k2eaVJGedeDt9OeArqfRiJHsT77W6fKqhXDOWcfrsJbYqV1KjEipdDnJHSiGEEEIIIUxWs8NLhBBCCCGEqBQJuoUQQgghhDCZBN1CCCGEEEKYTIJuIYQQQgghTCZBtxBCCCGEECbzzLyIEEIIJ0rdAOLZ1NMFQJzkdHqQnPf3Dy3JmBBC1CCZMlAIIWqAUurbJOf6/aHVeRFCiFokw0uEEKIGKaVGUv8DSqnnlVIPKaX2KqW+p5T6pFJqs1Jqu1Lq9NRy85RSv1JKbUn9XWLtJxBCCGeRoFsIIcR5wFeBc4BPAe/QWl8E3At8ObXMT4DbtdbvBv4i9Z4QQogCyZhuIYQQW7TWxwCUUm8DT6de3w68L/X4j4HVSqmJdZqVUk1a62BFcyqEEA4lQbcQQohw2uNE2vMEp9oJF3Cx1nqskhkTQohqIcNLhBBCFOJp4PqJJ0qp8y3MixBCOI4E3UIIIQrxFWCNUmqbUmoX8AWrMySEEE4iUwYKIYQQQghhMunpFkIIIYQQwmQSdAshhBBCCGEyCbqFEEIIIYQwmQTdQgghhBBCmEyCbiGEEEIIIUwmQbcQQgghhBAmk6BbCCGEEEIIk0nQLYQQQgghhMn+P/TPKZ+bZAgNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the energy consumption data per device over the whole period\n",
    "columns = hh.columns.tolist()\n",
    "for col in columns:\n",
    "    #plt.rc('font', size=12)\n",
    "    #fig, ax = plt.subplots(4,2)\n",
    "    #fig.set_size_inches(6, 6)\n",
    "    fig, ax = plt.subplots(figsize=(12, 5))\n",
    "    ax.plot(hh.index, hh[col],color='green', label=col)\n",
    "\n",
    "    ax.set_xlabel('Time')\n",
    "    ax.set_ylabel('Watt per hour')\n",
    "    ax.set_title('Energy Consumption Data')\n",
    "    ax.grid(True)\n",
    "    ax.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 6. Availability Agent Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Logistic Regression Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "LR_auc = {}\n",
    "validation_split = 0.2\n",
    "\n",
    "# Perform and evaluate the predictions of the Availability Agent using a logistic regression over a period of one year\n",
    "# starting with 28 days of data\n",
    "for i in range(28,394):\n",
    "    data = data_prep.iloc[:24+24*i,:]\n",
    "    \n",
    "    # Get indices and split value for splitting the data into training and test set\n",
    "    data_size = len(data)-24\n",
    "    indices = list(range(data_size))\n",
    "    split = int(np.floor(validation_split * data_size))\n",
    "    \n",
    "    # Build the Availability Agent pipeline using the logistic regression model\n",
    "    aval_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                    ('LogisticRegression',LR.Availability_Agent_Logistic_Regression(indices,split))\n",
    "                    ])\n",
    "    auc_score, aval_pred, X_test = aval_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Save auc score in a dictionary\n",
    "    LR_auc.update({i:round(auc_score,2)})\n",
    "    \n",
    "LR_auc_avg = utils.list_average(list(LR_auc.values()))\n",
    "print(LR_auc_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Random Forest Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# number of decision trees in the forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 400, stop = 1000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 50, num = 11)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = np.linspace(0.1, 0.6, 10, endpoint=True) #[2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = np.linspace(0.1, 0.3, 5, endpoint=True)\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "RF_auc = {}\n",
    "validation_split = 0.2\n",
    "\n",
    "data = data_prep.iloc[:24*28,:]\n",
    "\n",
    "# Get indices and split value for splitting the data into training and test set\n",
    "data_size = len(data)-(24+24*21)\n",
    "indices = list(range(data_size))\n",
    "split = int(np.floor(validation_split * data_size))\n",
    "\n",
    "# Build the Availability Agent pipeline for the first random forest model\n",
    "aval_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                    ('RandomForest',RF.Availability_Agent_First_Random_Forest(indices,split))\n",
    "                    ])\n",
    "auc_score, params = aval_pipeline.fit_transform(data)\n",
    "\n",
    "# Save auc score in a dictionary\n",
    "RF_auc.update({28:round(auc_score,2)})\n",
    "\n",
    "cut = 0\n",
    "\n",
    "# Perform and evaluate the predictions of the Availability Agent using a random forest over a period of one year\n",
    "# starting with 28 days of data\n",
    "for i in range(29,394):\n",
    "    data = data_prep.iloc[:24*i,:]\n",
    "\n",
    "    # Get indices and split value for splitting the data into training and test set\n",
    "    data_size = len(data)-24\n",
    "    indices = list(range(data_size))\n",
    "    split = int(np.floor(validation_split * data_size))\n",
    "    \n",
    "    # Tune the model's hyperparameter after every 4 months and cut off the oldest two months (=60 days) of data\n",
    "    if i%120==0:\n",
    "        cut+=60*24\n",
    "        aval_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                    ('RandomForest',RF.Availability_Agent_Random_Forest(indices,split,params,True,random_grid))\n",
    "                    ])\n",
    "        auc_score, params, y_pred, X_test = aval_pipeline.fit_transform(data)\n",
    "    else:\n",
    "        aval_pipeline = Pipeline(steps=[\n",
    "                        ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                        ('RandomForest',RF.Availability_Agent_Random_Forest(indices,split,params,False,random_grid))\n",
    "                        ])\n",
    "        auc_score, params, y_pred, X_test = aval_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Save auc score in a dictionary\n",
    "    RF_auc.update({i:round(auc_score,2)})\n",
    "\n",
    "RF_auc_avg = utils.list_average(list(RF_auc.values()))\n",
    "print(RF_auc_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Neural network evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Availability agent\n",
    "NN_auc = {}\n",
    "validation_split = 0.2\n",
    "\n",
    "data = data_prep.iloc[:24*28,:]\n",
    "\n",
    "# Get indices and split value for splitting the data into training and test set\n",
    "data_size = len(data)-(24+24*21)\n",
    "indices = list(range(data_size))\n",
    "split = int(np.floor(validation_split * data_size))\n",
    "\n",
    "# Build the Availability Agent pipeline to tune a neural network model \n",
    "aval_tune_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                    ('NeuralNetwork_Initalized_Tuning',NN.Availability_Agent_Neural_Network_tuning(indices,split))\n",
    "                    ])\n",
    "model = aval_tune_pipeline.fit_transform(data)\n",
    "\n",
    "cut=0\n",
    "\n",
    "# Perform and evaluate the predictions of the Availability Agent using a neural network over a period of one year\n",
    "# starting with 28 days of data\n",
    "for i in range(28,394):\n",
    "    data = data_prep.iloc[cut:24*i,:]\n",
    "    \n",
    "    # Get indices and split value for splitting the data into training and test set\n",
    "    data_size = len(data)-(24+24*21)\n",
    "    indices = list(range(data_size))\n",
    "    split = int(np.floor(validation_split * data_size))\n",
    "    \n",
    "    # Tune the model's hyperparameter after every 4 months (=120 days) and cut off the oldest two months (=60 days) of data\n",
    "    if i%120==0:\n",
    "        cut+=60*24\n",
    "        model = aval_tune_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Build the Availability Agent pipeline \n",
    "    aval_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                    ('NeuralNetwork',NN.Availability_Agent_Neural_Network(indices,split,model))\n",
    "                    ])\n",
    "\n",
    "    auc_score, y_pred, X_test = aval_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Save auc score in a dictionary\n",
    "    NN_auc.update({i:round(auc_score,2)})\n",
    "    \n",
    "NN_auc_avg = utils.list_average(list(NN_auc.values()))\n",
    "print(NN_auc_avg)\n",
    "#plot_dict(NN_auc,'Availability Agent - Neural Network','days','AUC','NN_auc_H4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 7. Usage Agent & Activity Agent Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "columns = usage_features[-num_devices:]\n",
    "validation_split = 0.2\n",
    "LR_use = pd.DataFrame(columns = columns)\n",
    "LR_equal = {}\n",
    "\n",
    "# Perform and evaluate the predictions of the Usage Agent using a logistic regression and the Activity Agent \n",
    "# over a period of one year starting with 28 days of data\n",
    "for i in range(28,394):\n",
    "    data = data_prep.iloc[:24*i,:]\n",
    "\n",
    "    # Get indices and split value for splitting the data into training and test set\n",
    "    data_size = len(data)-24\n",
    "    indices = list(range(data_size))\n",
    "    split = int(np.floor(validation_split * data_size))\n",
    "    \n",
    "    # Build the Usage Agent pipeline\n",
    "    usage_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(usage_features)),\n",
    "                    ('Logistic_Regression_usage',LR.Usage_Agent_Logistic_Regression(num_devices,indices,split))\n",
    "                    ])\n",
    "    auc_scores, usage_pred = usage_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Save auc scores in a dataframe\n",
    "    LR_use = LR_use.append(auc_scores)\n",
    "    \n",
    "    # Build the Activity Agent Evaluation pipeline\n",
    "    activity_pipeline = Pipeline(steps=[\n",
    "                      ('activity',act.Activity_Agent(num_devices, num_activities,activity_vector)),\n",
    "                      ('activity_evaluation',eval_act.Activity_Agent_Evaluation(usage_threshold,activity_threshold,num_devices,num_activities, activity_vector))\n",
    "    ])\n",
    "    EQUAL = activity_pipeline.fit_transform(usage_pred)\n",
    "    \n",
    "    # Save EQUAL score in a dictionary\n",
    "    LR_equal.update({i:EQUAL})\n",
    "    LR_use = LR_use.reset_index(drop=True)\n",
    "    \n",
    "# Get the average auc scores per device\n",
    "LR_auc_dict = {}\n",
    "for col in columns:\n",
    "    l = LR_use[col].tolist()\n",
    "    print(col[:-6])\n",
    "    auc = round(utils.list_average(l),2)\n",
    "    print(auc)\n",
    "    LR_auc_dict.update({col:auc})\n",
    "    #hkl.dump([LR_auc_dict], 'LR_auc_dev')\n",
    "    \n",
    "# Get the average EQUAL score    \n",
    "EQUAL_avg = utils.list_average(list(LR_equal.values()))\n",
    "print(EQUAL_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# number of decision trees in the forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 400, stop = 1000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 50, num = 11)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = np.linspace(0.1, 0.6, 10, endpoint=True) #[2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = np.linspace(0.1, 0.3, 5, endpoint=True)\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "columns = usage_features[-num_devices:]\n",
    "validation_split = 0.2\n",
    "RF_use = pd.DataFrame(columns = columns)\n",
    "RF_equal = {}\n",
    "\n",
    "data = data_prep.iloc[:24*28,:]\n",
    "\n",
    "# Get indices and split value for splitting the data into training and test set\n",
    "data_size = len(data)-(24+24*21)\n",
    "indices = list(range(data_size))\n",
    "split = int(np.floor(validation_split * data_size))\n",
    "\n",
    "# Build the Usage Agent pipeline to tune a random forest model \n",
    "usage_tune_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(usage_features)),\n",
    "                    ('RandomForest',RF.Usage_Agent_Random_Forest_tuning(num_devices,indices,split,random_grid))\n",
    "                    ])\n",
    "auc_scores, usage_pred, params_dict = usage_tune_pipeline.fit_transform(data)\n",
    "\n",
    "# Save auc scores in a dataframe\n",
    "RF_use = RF_use.append(auc_scores)\n",
    "\n",
    "# Build the Activity Agent Evaluation pipeline\n",
    "activity_pipeline = Pipeline(steps=[\n",
    "                      ('activity',act.Activity_Agent(num_devices, num_activities,activity_vector)),\n",
    "                      ('activity_evaluation',eval_act.Activity_Agent_Evaluation(usage_threshold,activity_threshold,num_devices,num_activities, activity_vector))\n",
    "    ])    \n",
    "EQUAL = activity_pipeline.fit_transform(usage_pred)\n",
    "\n",
    "# Save EQUAL score in a dictionary\n",
    "RF_equal.update({28:EQUAL})\n",
    "\n",
    "# Perform and evaluate the predictions of the Usage Agent using a random forest and the Activity Agent \n",
    "# over a period of one year starting with 28 days of data\n",
    "for i in range(29,394):\n",
    "    data = data_prep.iloc[:24*i,:]\n",
    "    \n",
    "    # Get indices and split value for splitting the data into training and test set\n",
    "    data_size = len(data)-(24+24*21)\n",
    "    indices = list(range(data_size))\n",
    "    split = int(np.floor(validation_split * data_size))\n",
    "    \n",
    "    # Tune the model's hyperparameter after every 4 months (=120 days) and cut off the oldest two months (=60 days) of data\n",
    "    if i%120==0:\n",
    "        # Fit the Usage Agent pipeline for tuning a random forest\n",
    "        auc_scores, usage_pred, params_dict = usage_tune_pipeline.fit_transform(data)\n",
    "    else:\n",
    "        # Build the Usage Agent pipeline\n",
    "        usage_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(usage_features)),\n",
    "                    ('Random_Forest_usage',RF.Usage_Agent_Random_Forest(num_devices,indices,split,random_grid,params_dict))\n",
    "                    ])\n",
    "        auc_scores, usage_pred = usage_pipeline.fit_transform(data)\n",
    "        \n",
    "    # Save auc scores in a dataframe    \n",
    "    RF_use = RF_use.append(auc_scores)\n",
    "    \n",
    "    # Fit the Activity Agent Evaluation pipeline\n",
    "    EQUAL = activity_pipeline.fit_transform(usage_pred)\n",
    "    \n",
    "    # Save EQUAL score in a dictionary\n",
    "    RF_equal.update({i:EQUAL})\n",
    "    RF_use = RF_use.reset_index(drop=True)\n",
    "\n",
    "# Get the average auc score per device\n",
    "RF_auc_dict = {}\n",
    "for col in columns:\n",
    "    l = RF_use[col].tolist()\n",
    "    print(col[:-6])\n",
    "    auc = round(utils.list_average(l),2)\n",
    "    print(auc)\n",
    "    RF_auc_dict.update({col:auc})\n",
    "    hkl.dump([RF_auc_dict], 'RF_auc_dev')\n",
    "\n",
    "# Get the average EQUAL score \n",
    "EQUAL_avg = utils.list_average(list(RF_equal.values()))\n",
    "print(EQUAL_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "columns = usage_features[-num_devices:]\n",
    "validation_split = 0.2\n",
    "NN_use = pd.DataFrame(columns = columns)\n",
    "NN_equal = {}\n",
    "\n",
    "data = data_prep.iloc[:24*28,:]\n",
    "\n",
    "# Get indices and split value for splitting the data into training and test set\n",
    "data_size = len(data)-(24+24*21)\n",
    "indices = list(range(data_size))\n",
    "split = int(np.floor(validation_split * data_size))\n",
    "\n",
    "# Build the Usage Agent pipeline to tune a neural network\n",
    "usage_tune_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(usage_features)),\n",
    "                    ('NeuralNetwork_Initalized_Tuning',NN.Usage_Agent_Neural_Network_tuning(num_devices,indices,split))\n",
    "                   ])\n",
    "model_dict = usage_tune_pipeline.fit_transform(data)\n",
    "\n",
    "cut=0\n",
    "\n",
    "# Perform and evaluate the predictions of the Usage Agent using a neural network and the Activity Agent \n",
    "# over a period of one year starting with 28 days of data\n",
    "for i in range(28,394):\n",
    "    data = data_prep.iloc[cut:24*i,:]\n",
    "    \n",
    "    # Get indices and split value for splitting the data into training and test set\n",
    "    data_size = len(data)-(24+24*21)\n",
    "    indices = list(range(data_size))\n",
    "    split = int(np.floor(validation_split * data_size))\n",
    "    \n",
    "    # Tune the model's hyperparameter after every 4 months (=120 days) and cut off the oldest two months (=60 days) of data\n",
    "    if i%120==0:\n",
    "        cut+=60*24\n",
    "        # Fit the Usage Agent pipeline for tuning a neural network model\n",
    "        model_dict = usage_tune_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Build the Usage Agent pipeline\n",
    "    usage_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(usage_features)),\n",
    "                    ('NeuralNetwork',NN.Usage_Agent_Neural_Network(num_devices,indices,split,model_dict))\n",
    "                    ])\n",
    "    auc_scores, usage_pred = usage_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Save auc scores in a dataframe\n",
    "    NN_use = NN_use.append(auc_scores)\n",
    "    \n",
    "    # Build the Activity Agent Evaluation pipeline\n",
    "    activity_pipeline = Pipeline(steps=[\n",
    "                      ('activity',act.Activity_Agent(num_devices, num_activities,activity_vector)),\n",
    "                      ('activity_evaluation',eval_act.Activity_Agent_Evaluation(usage_threshold,activity_threshold,num_devices,num_activities, activity_vector))\n",
    "    ])\n",
    "    EQUAL = activity_pipeline.fit_transform(usage_pred)\n",
    "\n",
    "    # Save EQUAL scores in a dictionary\n",
    "    NN_equal.update({i:EQUAL})\n",
    "    NN_use = NN_use.reset_index(drop=True)\n",
    "\n",
    "# Get the average auc scores per device\n",
    "NN_auc_dict = {}\n",
    "for col in columns:\n",
    "    l = NN_use[col].tolist()\n",
    "    print(col[:-6])\n",
    "    auc = round(utils.list_average(l),2)\n",
    "    print(auc)\n",
    "    NN_auc_dict.update({col:auc})\n",
    "    \n",
    "# Get the average EQUAL score \n",
    "EQUAL_avg = utils.list_average(list(NN_equal.values()))\n",
    "print(EQUAL_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 8. Cold Start Problem Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Availability Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "total_days = 413\n",
    "test_begin = 393\n",
    "\n",
    "data = data_prep.iloc[:24*total_days,:]\n",
    "cut=0\n",
    "day = 28\n",
    "NN_aval_auc = {}\n",
    "\n",
    "# Build the Availability Agent pipeline to tune a neural network model\n",
    "aval_tune_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                    ('NeuralNetwork_Initalized_Tuning',cs.Availability_Agent_Neural_Network_tuning_cold_start(day,cut))\n",
    "                    ])\n",
    "model = aval_tune_pipeline.fit_transform(data)\n",
    "\n",
    "# Perform and evaluate the predictions of the Availability Agent using a neural network over a period of one year \n",
    "# starting with 28 days of data and using the same test set\n",
    "for day in range(28,394):\n",
    "    if day%120==0:\n",
    "        cut+=60*24\n",
    "        # Fit the Availability Agent pipeline for tuning\n",
    "        model = aval_tune_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Build the Availability pipeline\n",
    "    aval_pipeline = Pipeline(steps=[\n",
    "                ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                ('NeuralNetwork',cs.Availability_Agent_Neural_Network_cold_start(day,test_begin,total_days,model,cut))\n",
    "                ])\n",
    "    auc_score = aval_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Save auc score in a dictionary\n",
    "    d = day-27\n",
    "    NN_aval_auc.update({d:round(auc_score,2)})\n",
    "    \n",
    "# Get the average auc score\n",
    "auc_avg = utils.list_average(list(NN_aval_auc.values()))\n",
    "#plot_dict(NN_aval_auc,'Cold Start: Availability Agent - Neural Network','days','AUC','NN_aval_cs_H5',0.79)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Usage Agent & Activity Agent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "total_days = 413\n",
    "test_begin = 393\n",
    "\n",
    "columns = usage_features[-num_devices:]\n",
    "NN_usage_auc = pd.DataFrame(columns = columns)\n",
    "NN_act_equal = {}\n",
    "\n",
    "data = data_prep.iloc[:24*total_days,:]\n",
    "cut=0\n",
    "day = 28\n",
    "\n",
    "# Build the Usage Agent pipeline to tune a neural network model\n",
    "usage_tune_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(usage_features)),\n",
    "                    ('NeuralNetwork_Initalized_Tuning',cs.Usage_Agent_Neural_Network_tuning_cold_start(num_devices,day,cut))\n",
    "                    ])\n",
    "model_dict = usage_tune_pipeline.fit_transform(data)\n",
    "\n",
    "for day in range(28,393):\n",
    "    if day%120==0:\n",
    "        cut+=60*24\n",
    "        # Fit the Usage Agent pipeline for tuning\n",
    "        model_dict = usage_tune_model_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Build the Usage Agent pipeline\n",
    "    usage_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(usage_features)),\n",
    "                    ('NeuralNetwork',cs.Usage_Agent_Neural_Network_cold_start(num_devices,day,test_begin,total_days,model_dict,cut))\n",
    "                    ])\n",
    "    auc_scores, usage_pred = usage_pipeline.fit_transform(data)\n",
    "    \n",
    "    # Save auc scores in a dataframe\n",
    "    NN_usage_auc = NN_usage_auc.append(auc_scores)\n",
    "    \n",
    "    # Build the Activity Agent Evaluation pipeline\n",
    "    activity_pipeline = Pipeline(steps=[\n",
    "                      ('activity',act.Activity_Agent(num_devices, num_activities,activity_vector)),\n",
    "                      ('activity_evaluation',eval_act.Activity_Agent_Evaluation(usage_threshold,activity_threshold,num_devices,num_activities, activity_vector))\n",
    "    ])\n",
    "    EQUAL = activity_pipeline.fit_transform(usage_pred)\n",
    "    \n",
    "    # Save EQUAL score in a dictionary\n",
    "    d = day-27\n",
    "    NN_act_equal.update({d:EQUAL})\n",
    "    NN_usage_auc = NN_usage_auc.reset_index(drop=True)\n",
    "\n",
    "# Get the average auc scores per device\n",
    "NN_auc_dict = {}\n",
    "for col in columns:\n",
    "    l = NN_usage_auc[col].tolist()\n",
    "    print(col[:-6])\n",
    "    auc = round(utils.list_average(l),2)\n",
    "    print(auc)\n",
    "    NN_auc_dict.update({col:auc})\n",
    "    #plot_dict(NN_usage_auc[col],'Cold Start: Usage Agent - Neural Network','days','AUC','NN_usage_auc',0.79)\n",
    "    \n",
    "# Get the average EQUAL score \n",
    "EQUAL_avg = utils.list_average(list(NN_act_equal.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 9. Recommendation System Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Check for complete data\n",
    "# Dataset is complete if there is data for every hour of every day in the dataset\n",
    "temp = data_prep.copy()\n",
    "temp['date'] = temp.index.to_frame()\n",
    "temp['date'] = pd.to_datetime(temp['date']).dt.date\n",
    "temp.groupby(\"date\").size().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Add missing data rows (= hours not existent in the dataset)\n",
    "data = data_prep.copy()\n",
    "\n",
    "begin = data.index[0]\n",
    "end = data.index[-1]\n",
    "\n",
    "d0 = begin\n",
    "d1 = end \n",
    "delta = d1 - d0\n",
    "periods =delta.days*24\n",
    "\n",
    "# Create empty dataframe with hourly datetime index from 2013-09-26 to 2015-07-06\n",
    "index = pd.date_range(str(begin)[:10], periods=periods, freq='H')\n",
    "col = data.columns\n",
    "df = pd.DataFrame(index=index, columns=col)\n",
    "\n",
    "# Forward data from dataset to empty dataframe\n",
    "temp = df.merge(data,how='left',left_index=True, right_index=True)\n",
    "\n",
    "temp.fillna(0.01,inplace = True)\n",
    "\n",
    "data_new = temp.iloc[12:,(temp.shape[1]//2):]\n",
    "\n",
    "columns = data_new.columns\n",
    "\n",
    "for col in columns:\n",
    "    data_new = data_new.rename(columns={col: col[:-2]})\n",
    "    \n",
    "data_new = data_new.astype(int)\n",
    "\n",
    "data_pr = data_new#.iloc[4320:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Get the input for the Recommendation Agent from all other agents\n",
    "validation_split = 0.2\n",
    "cut = 0\n",
    "rec_dfs = {}\n",
    "\n",
    "data = data_pr.iloc[:24*28,:]\n",
    "\n",
    "# Get indices and split value for splitting the data into training and test set\n",
    "data_size = len(data)-(24+24*21)\n",
    "indices = list(range(data_size))\n",
    "split = int(np.floor(validation_split * data_size))\n",
    "\n",
    "# Build the Availability Agent pipeline for tuning a neural network\n",
    "aval_tune_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                    ('NeuralNetwork_Initalized_Tuning',NN.Availability_Agent_Neural_Network_tuning(indices,split))\n",
    "                    ])\n",
    "model = aval_tune_pipeline.fit_transform(data)#\n",
    "\n",
    "# Build Usage Agent pipeline for tuning a neural network\n",
    "usage_tune_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(usage_features)),\n",
    "                    ('NeuralNetwork_Initalized_Tuning',NN.Usage_Agent_Neural_Network_tuning(num_devices,indices,split))\n",
    "                   ])\n",
    "model_dict = usage_tune_pipeline.fit_transform(data)\n",
    "\n",
    "# Perform the predictions of the Availability Agent and Usage Agent using a neural network, the Activity Agent, \n",
    "# the Emissions Agent and the Price Agent over a period of one year starting with 28 days of data\n",
    "for i in range(29,394):\n",
    "    data = data_pr.iloc[cut:24*i,:]\n",
    "    \n",
    "    # Get indices and split value for splitting the data into training and test set\n",
    "    data_size = len(data)-(24+24*21)\n",
    "    indices = list(range(data_size))\n",
    "    split = int(np.floor(validation_split * data_size))\n",
    "    \n",
    "    # Tune the models' hyperparameter after every 4 months (=120 days) and cut off the oldest two months (=60 days) of data\n",
    "    if i%120==0:\n",
    "        cut+=60*24\n",
    "        data = data_pr.iloc[cut:24*i,:]\n",
    "        \n",
    "        # Get indices and split value for splitting the data into training and test set\n",
    "        data_size = len(data)-(24+24*21)\n",
    "        indices = list(range(data_size))\n",
    "        split = int(np.floor(validation_split * data_size))\n",
    "        \n",
    "        # Fit the Availability Agent pipeline for tuning a neural network\n",
    "        model = aval_tune_pipeline.fit_transform(data)\n",
    "        # Fit the Usage Agent pipeline for tuning a neural network\n",
    "        model_dict = usage_tune_pipeline.fit_transform(data)\n",
    "\n",
    "    # Build Availability Agent pipeline\n",
    "    aval_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(aval_features)),\n",
    "                    ('NeuralNetwork',NN.Availability_Agent_Neural_Network(indices,split,model))\n",
    "                    ])\n",
    "    auc_score, aval_pred, aval_X_test = aval_pipeline.fit_transform(data)\n",
    "    availability_prediction = pd.DataFrame(aval_pred, columns=['aval'], index = aval_X_test.index)\n",
    "    \n",
    "    # Get recommendation horizon\n",
    "    date_begin = availability_prediction.tail(24).index[0]\n",
    "    date_end = availability_prediction.tail(24).index[-1]\n",
    "    date_begin = str(date_begin)\n",
    "    date_end = str(date_end)\n",
    "    \n",
    "    # Workaround for the mismatch of emissions and price data availability \n",
    "    if date_begin[:4]=='2014':\n",
    "        year1 = '2019'\n",
    "    else: \n",
    "        year1 = '2020'\n",
    "    \n",
    "    if date_end[:4]=='2014':\n",
    "        year2 = '2019'\n",
    "    else: \n",
    "        year2 = '2020' \n",
    "    \n",
    "    # Convert format of the dates and hours for the Emissions Agent and the Price Agent\n",
    "    date1_ci = year1+date_begin[4:10]\n",
    "    date2_ci = year2+date_end[4:10]\n",
    "    begin_ci = year1+date_begin[4:10]+'T'+date_begin[-8:-3]+'Z'\n",
    "    end_ci = year2+date_end[4:10]+'T'+date_end[-8:-5]+'30Z'\n",
    "    date1_pr = date_begin[8:10]+'.'+date_begin[5:7]+'.'+year1\n",
    "    date2_pr = date_end[8:10]+'.'+date_end[5:7]+'.'+year2\n",
    "    begin_pr = date_begin[-8:]\n",
    "    end_pr = date_end[-8:]\n",
    "\n",
    "    # Build the Activity Pipeline (Usage Agent + Activity Agent)\n",
    "    activity_pipeline = Pipeline(steps=[\n",
    "                    ('FeatureSelector',prep.FeatureSelector(usage_features)),\n",
    "                    ('NeuralNetwork',NN.Usage_Agent_Neural_Network(num_devices,indices,split,model_dict)),\n",
    "                    ('activity',act.Activity_Agent(num_devices, num_activities,activity_vector))\n",
    "                    ])\n",
    "    activity_prediction = activity_pipeline.fit_transform(data)\n",
    "    activity_prediction.set_index(aval_X_test.index[-24:], inplace=True)\n",
    "\n",
    "    # Build the Emissions Agent pipeline\n",
    "    ca_pipeline = Pipeline(steps=[\n",
    "              ('carbon_intensity', emp.Emissions_Agent(date1_ci,date2_ci,begin_ci,end_ci))\n",
    "              ])\n",
    "    ca_intensity = ca_pipeline.fit_transform(X=None)\n",
    "    ca_intensity.set_index(aval_X_test[-24:].index, inplace=True)\n",
    "\n",
    "    # Build the Price Agent pipeline\n",
    "    pr_pipeline = Pipeline(steps=[\n",
    "              ('price_value', emp.Price_Agent(date1_pr,date2_pr,begin_pr,end_pr))\n",
    "              ])\n",
    "    price_table = pr_pipeline.fit_transform(None)\n",
    "    price_table.set_index(aval_X_test[-24:].index, inplace=True)\n",
    "\n",
    "    # Merge the output dataframes from the Availability Agent, the Activity Agent, the Emissions Agent and the Price Agent\n",
    "    rec_data = ca_intensity.merge(price_table['price'],left_index=True,right_index=True)\n",
    "    rec_data = rec_data.merge(availability_prediction[-24:], left_index=True, right_index=True)\n",
    "    rec_data = rec_data.merge(activity_prediction.iloc[:,-num_activities:],left_index=True, right_index=True)\n",
    "    rec_data = rec_data.merge(activity_prediction.iloc[:,:num_devices],left_index=True, right_index=True)\n",
    "    \n",
    "    # Store data for the Recommendation Agent in a dictionary\n",
    "    rec_dfs.update({i:rec_data})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save rec_dfs\n",
    "with open(\"H5rec_dfs.pkl\", \"wb\") as pkl_handle:\n",
    "    pickle.dump(rec_dfs, pkl_handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Load rec_dfs\n",
    "with open(\"H5rec_dfs.pkl\", \"rb\") as pkl_handle:\n",
    "    rec_dfs = pickle.load(pkl_handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "emissions_ratio = 1.0\n",
    "aval_threshold = 0.2\n",
    "dev_avg_con = {}\n",
    "\n",
    "# Set the parameters for grid search\n",
    "params = {'aval_threshold':[0.15,0.25,0.35,0.5,0.65],\n",
    "          'activity_threshold':[0.15,0.25,0.35,0.5,0.75]}\n",
    "          #'emissions_ratio':[0.0,0.5,1.0],\n",
    "          #'aval_off':[True,False]}\n",
    "\n",
    "# Set the estimator\n",
    "estimator = rec.Recommendation_Agent(num_activities, num_devices,aval_off,emissions_ratio,activity_vector,dev_avg_con,aval_threshold,activity_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ParameterGrid(params)\n",
    "recs = {}\n",
    "save = {}\n",
    "best = 0\n",
    "cut = 0\n",
    "\n",
    "for g in ParameterGrid(params):\n",
    "    for i in range(29,394):\n",
    "        print(g)\n",
    "        estimator.set_params(**g)\n",
    "        if i%120==0:\n",
    "            cut+=60*24\n",
    "        df = data_pr.iloc[cut:24*i,:]\n",
    "        devices = data_prep.columns[6:6+num_devices].tolist()\n",
    "        dev_avg_con = {}\n",
    "        \n",
    "        # Get average consumption per device\n",
    "        for dev in devices:\n",
    "            count = 0\n",
    "            sum_con = 0\n",
    "            for j in range(len(data)):\n",
    "                if data[dev][j] > on_threshold:\n",
    "                    count +=1\n",
    "                    sum_con = sum_con + data[dev][j]\n",
    "            dev_avg_con.update({dev:round(sum_con/count,2)})\n",
    "        \n",
    "        g.update({'dev_avg_con':dev_avg_con})\n",
    "        print(g)\n",
    "        estimator.set_params(**g)\n",
    "        rec_dict, save_dict, rec_cook, rec_work, rec_et, rec_laund, rec_clean = estimator.fit_transform(rec_dfs[i])\n",
    "        day = i-28\n",
    "        recs.update({day:rec_dict})\n",
    "        save.update({day:save_dict})  \n",
    "        \n",
    "    ems = 0\n",
    "    prs = 0\n",
    "    for j in range(1,365):\n",
    "        ems = ems + save[j]['em_savings'][0]   \n",
    "        prs = prs + save[j]['pr_savings'][0]\n",
    "    score = ems + prs\n",
    "    print('Best: ',best)\n",
    "    print('Score: ',score)\n",
    "    if score > best:\n",
    "        best = score\n",
    "        best_grid = g\n",
    "\n",
    "print(\"Savings: %0.5f\" % best) \n",
    "print(\"Grid:\", best_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Evaluation of the Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Set best parameters from grid search\n",
    "aval_off = True\n",
    "aval_threshold = 0.15\n",
    "activity_threshold = 0.15\n",
    "emissions_ratio = 1.0\n",
    "\n",
    "recs = {}\n",
    "save = {}\n",
    "\n",
    "recs_cook = {}\n",
    "recs_work = {}\n",
    "recs_et = {}\n",
    "recs_laund = {}\n",
    "recs_clean = {}\n",
    "\n",
    "cut = 0\n",
    "for i in range(29,33):#394):\n",
    "    if i%120==0:\n",
    "        cut+=60*24\n",
    "    df = data_pr.iloc[cut:24*i,:]\n",
    "    devices = data_prep.columns[6:6+num_devices].tolist()\n",
    "    dev_avg_con = {}\n",
    "\n",
    "    for dev in devices:\n",
    "        count = 0\n",
    "        sum_con = 0\n",
    "        for j in range(len(data)):\n",
    "            if data[dev][j] > on_threshold:\n",
    "                count +=1\n",
    "                sum_con = sum_con + data[dev][j]\n",
    "        dev_avg_con.update({dev:round(sum_con/count,2)})\n",
    "    \n",
    "    # Build the Recommendation Agent pipeline\n",
    "    rec_pipeline = Pipeline(steps=[\n",
    "              ('Recommendation', rec.Recommendation_Agent(num_activities,num_devices, aval_off,emissions_ratio,activity_vector,dev_avg_con,aval_threshold, activity_threshold))\n",
    "              ])\n",
    "    rec_dict, save_dict, rec_cook, rec_work, rec_et, rec_laund, rec_clean = rec_pipeline.fit_transform(rec_dfs[i])\n",
    "    \n",
    "    day = i-28\n",
    "    \n",
    "    # Save recommendations per day\n",
    "    recs.update({day:rec_dict})\n",
    "    # Save total emissions and energy costs without shifting and emissions and energy costs savings through shifting per day\n",
    "    save.update({day:save_dict})\n",
    "    # Save recommendations per activity per day\n",
    "    recs_cook.update({day:rec_cook})\n",
    "    recs_work.update({day:rec_work})\n",
    "    recs_et.update({day:rec_et})\n",
    "    recs_laund.update({day:rec_laund})\n",
    "    recs_clean.update({day:rec_clean})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Compute total emissions savings of a year\n",
    "ems = 0\n",
    "for i in range(1,365):\n",
    "    ems = ems + save[i]['em_savings'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Compute total energy costs savings of a year\n",
    "prs = 0\n",
    "for i in range(1,365):\n",
    "    prs = prs + save[i]['pr_savings'][0]\n",
    "prs = round(prs,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Compute total emissions without load shifting\n",
    "emt = 0\n",
    "for i in range(1,365):\n",
    "    emt = emt + save[i]['em_total'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Compute total energy costs without load shifting\n",
    "prt = 0\n",
    "for i in range(1,365):\n",
    "    prt = prt + save[i]['pr_total'][0]\n",
    "prt = round(prt,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "print('Total emissions without load shifting: ',emt,'gCO2')\n",
    "print('Total energy costs without load shifting: ',prt,'€\\n')\n",
    "print('Total emissions savings with load shifting: ',ems,'gCO2')\n",
    "print('Total energy costs savings with load shifting: ',prs,'€\\n')\n",
    "print('Total relative emissions savings:',round(ems/emt,2),'%')\n",
    "print('Total relative energy costs savings:',round(prs/prt,2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Get number of recommendations\n",
    "lh = collections.defaultdict(list)\n",
    "recom_num = 0\n",
    "for i in range(1,4):#365):\n",
    "    d = recs[i]\n",
    "    for j in range(24):\n",
    "        if len(recs[i][j]):\n",
    "            freq = len(recs[i][j])\n",
    "            recom_num = recom_num + freq\n",
    "            lh[j].append(freq)\n",
    "print('Total number of recommendations: ',recom_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "freq_lh = {}\n",
    "for i in range(24):\n",
    "    freq_lh.update({i:sum(lh[i])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "utils.plot_dict_bar(freq_lh,'availability: 0.15 | activity: 0.15 ','hour','frequency','H5_15_15_00')"
   ]
  }
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