Ships_Arrivals.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import linregress\n",
    "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
    "from statsmodels.tsa.api import Holt\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Ships')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('BIE_BIE2020.csv')\n",
    "df.set_index(['Year','Month'])['Total Arrivals'].plot(figsize= (20,6), color= 'red', rot=45, xticks=np.linspace(6, 10*11 + 4, 10), grid=True)\n",
    "df.set_index(['Year','Month'])['Cabotaje Arrivals'].plot(figsize= (20,6), color= 'deepskyblue', rot=45, xticks=np.linspace(6, 10*11 + 4, 10), grid=True)\n",
    "plt.xlabel('Period')\n",
    "plt.ylabel('Number of Ships')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Arrivals Forecast for 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Ships')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index(['Year','Month'])['Total Arrivals'].plot(figsize= (20,4), color= 'red', rot=45, xticks=np.linspace(6, 10*11 + 4, 10), grid=True)\n",
    "plt.xlabel('Period')\n",
    "plt.ylabel('Number of Ships')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### By plotting the ships arrival data, we can see there's an incremental trend throughout the years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving Averages for Total Arrivals "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BIE_BIE2020.csv')\n",
    "\n",
    "df['t_beginning'] = df['Year'] - df['Year'].min()\n",
    "\n",
    "reg_model = linregress(df['t_beginning'], df['Total Arrivals'])\n",
    "trend_exists = reg_model.pvalue < 0.05\n",
    "\n",
    "if trend_exists:\n",
    "    df['dt_lag'] = df['Total Arrivals'].shift(1)\n",
    "    df['delta_dt'] = df['Total Arrivals'] - df['dt_lag']   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ls = df.iloc[:-4,:] \n",
    "\n",
    "ma_estimators = list([])\n",
    "t_fl = data_ls.shape[0] #gives us the length of the learning set \n",
    "\n",
    "for p in range (1,t_fl+1):\n",
    "    ma_estimators.append((\n",
    "        data_ls['delta_dt'].iloc[-p:-1].sum() +\n",
    "        data_ls['delta_dt'].iloc[-1:].sum()) /p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_dic_2019 = list()   \n",
    "e_jan_2020 = list()\n",
    "e_feb_2020 = list()\n",
    "e_mar_2020 = list()\n",
    "\n",
    "for p in range (1, t_fl+1): \n",
    "    e_dic_2019.append(8539 - ma_estimators[p-1])\n",
    "    e_jan_2020.append(8128 - ma_estimators[p-1])\n",
    "    e_feb_2020.append(7137 - ma_estimators[p-1])\n",
    "    e_mar_2020.append(7344 - ma_estimators[p-1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors_df = pd.DataFrame({'p': range(1,t_fl+1),'ma(p)': ma_estimators,'error December 2019': e_dic_2019,'error January 2020': e_jan_2020,'error February 2020': e_feb_2020,'error March 2020': e_mar_2020})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-668.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors_df['error December 2019'] = errors_df['error December 2019'] ** 2\n",
    "errors_df['error January 2020'] = errors_df['error January 2020'] ** 2\n",
    "errors_df['error February 2020'] = errors_df['error February 2020'] ** 2\n",
    "errors_df['error March 2020'] = errors_df['error March 2020'] ** 2\n",
    "\n",
    "errors_df['MSE'] = errors_df[['error December 2019','error January 2020','error February 2020','error March 2020' ]].mean(axis=1)\n",
    "errors_df['RMSE'] = np.sqrt(errors_df['MSE'])\n",
    "\n",
    "delta_dt_hat = df['delta_dt'].iloc[1]\n",
    "delta_dt_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Forecast for next 4 months\n",
    "fc_dic_2019 = 7029 + delta_dt_hat\n",
    "fc_jan_2020 = fc_dic_2019 + delta_dt_hat\n",
    "fc_feb_2020 = fc_jan_2020 + delta_dt_hat\n",
    "fc_mar_2020 = fc_feb_2020 + delta_dt_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = [\"2019\",'December', str(round(fc_dic_2019)),0,0,0,0,0,0,0,0]\n",
    "row1 = [\"2020\",'January', str(round(fc_jan_2020)),0,0,0,0,0,0,0,0]\n",
    "row2 = [\"2020\",'February', str(round(fc_feb_2020)),0,0,0,0,0,0,0,0]\n",
    "row3 = [\"2020\", 'March',str(round(fc_mar_2020)),0,0,0,0,0,0,0,0]\n",
    "\n",
    "df.loc[119] = row\n",
    "df.loc[120] = row1\n",
    "df.loc[121] = row2\n",
    "df.loc[122] = row3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total Arrivals</th>\n",
       "      <th>Total Passengers</th>\n",
       "      <th>Cruise Arrivals</th>\n",
       "      <th>Cruise Passengers</th>\n",
       "      <th>Cabotaje Arrivals</th>\n",
       "      <th>Cabotaje Passengers</th>\n",
       "      <th>t_beginning</th>\n",
       "      <th>dt_lag</th>\n",
       "      <th>delta_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>2019</td>\n",
       "      <td>November</td>\n",
       "      <td>7029</td>\n",
       "      <td>1620305</td>\n",
       "      <td>280</td>\n",
       "      <td>827315</td>\n",
       "      <td>6749</td>\n",
       "      <td>792990</td>\n",
       "      <td>9</td>\n",
       "      <td>7344.0</td>\n",
       "      <td>-315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>2019</td>\n",
       "      <td>December</td>\n",
       "      <td>6361.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>2020</td>\n",
       "      <td>January</td>\n",
       "      <td>5693.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>2020</td>\n",
       "      <td>February</td>\n",
       "      <td>5025.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>2020</td>\n",
       "      <td>March</td>\n",
       "      <td>4357.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year     Month Total Arrivals  Total Passengers  Cruise Arrivals  \\\n",
       "118  2019  November           7029           1620305              280   \n",
       "119  2019  December         6361.0                 0                0   \n",
       "120  2020   January         5693.0                 0                0   \n",
       "121  2020  February         5025.0                 0                0   \n",
       "122  2020     March         4357.0                 0                0   \n",
       "\n",
       "     Cruise Passengers  Cabotaje Arrivals  Cabotaje Passengers  t_beginning  \\\n",
       "118             827315               6749               792990            9   \n",
       "119                  0                  0                    0            0   \n",
       "120                  0                  0                    0            0   \n",
       "121                  0                  0                    0            0   \n",
       "122                  0                  0                    0            0   \n",
       "\n",
       "     dt_lag  delta_dt  \n",
       "118  7344.0    -315.0  \n",
       "119     0.0       0.0  \n",
       "120     0.0       0.0  \n",
       "121     0.0       0.0  \n",
       "122     0.0       0.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential Smoothing for Total Arrivals "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BIE_BIE2020.csv')\n",
    "df['t_beginning'] = df['Year'] - df['Year'].min()\n",
    "\n",
    "reg_model = linregress(df['t_beginning'], df['Total Arrivals'])\n",
    "trend_exists = reg_model.pvalue < 0.05\n",
    "\n",
    "if trend_exists:\n",
    "    df['dt_lag'] = df['Total Arrivals'].shift(1)\n",
    "    df['delta_dt'] = df['Total Arrivals'] - df['dt_lag']   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ls = df.iloc[:-4,:] \n",
    "\n",
    "es_estimators = list([])\n",
    "es_estimators_val_set = list([])\n",
    "t_fl = data_ls.shape[0] #This gives us the number of rows in the learning set\n",
    "\n",
    "delta_dts = data_ls['delta_dt'].values\n",
    "delta_dts[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for alpha in np.linspace(0,1,11):\n",
    "    delta_dt_hat = list()\n",
    "    delta_dt_hat.append(delta_dts[0])\n",
    "    for t in range(1, len(delta_dts)):\n",
    "        delta_dt_hat.append(alpha * delta_dt_hat[-1] + (1-alpha) * delta_dts[-1])\n",
    "    delta_dt_hat_val = alpha * delta_dt_hat[-1] + (1-alpha) * delta_dts[t]\n",
    "    print(delta_dt_hat) #tienden al mismo numero igual q alpha\n",
    "    #print(alpha)\n",
    "    # Every time it produces the exponential smoothing forecasts, store in LS dataframe\n",
    "    data_ls['delta_dt_hat_es_a{}'.format(alpha)] = delta_dt_hat\n",
    "    es_estimators_val_set.append(delta_dt_hat_val) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_dic_2019 = list()\n",
    "e_jan_2020 = list()\n",
    "e_feb_2020 = list()\n",
    "e_mar_2020 = list()\n",
    "\n",
    "for p in range (0, 11): \n",
    "    e_dic_2019.append(8539 - es_estimators_val_set[p])\n",
    "    e_jan_2020.append(8128 - es_estimators_val_set[p])\n",
    "    e_feb_2020.append(7137 - es_estimators_val_set[p])\n",
    "    e_mar_2020.append(7344 - es_estimators_val_set[p]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors_df = pd.DataFrame({'alpha': range(0,11),\n",
    "                          'ES(alpha)': es_estimators_val_set,\n",
    "                          'error December 2019': e_dic_2019,\n",
    "                          'error January 2020': e_jan_2020,\n",
    "                          'error February 2020': e_feb_2020,\n",
    "                          'error March 2020': e_mar_2020})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors_df['error December 2019'] = errors_df['error December 2019'] ** 2\n",
    "errors_df['error January 2020'] = errors_df['error January 2020'] ** 2\n",
    "errors_df['error February 2020'] = errors_df['error February 2020'] ** 2\n",
    "errors_df['error March 2020'] = errors_df['error March 2020'] ** 2\n",
    "\n",
    "errors_df['MSE'] = errors_df[['error December 2019','error January 2020','error February 2020','error March 2020' ]].mean(axis=1)\n",
    "errors_df['RMSE'] = np.sqrt(errors_df['MSE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alpha = 0.8 is best estimator. Thus, apply to full dataset.\n",
    "delta_dts_full = df['delta_dt'].values\n",
    "delta_dt_hat_full = list()\n",
    "delta_dt_hat_full.append(delta_dts_full[1])\n",
    "\n",
    "alpha = 0.8\n",
    "\n",
    "for t in range(2, len(delta_dts_full)):\n",
    "    delta_dt_hat_full.append(alpha * delta_dt_hat_full[-1] + (1-alpha) * delta_dts_full[t-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_dic_2019 = 7029 + delta_dt_hat_full[-1]\n",
    "fc_jan = fc_dic_2019 + delta_dt_hat_full[-1]\n",
    "fc_feb = fc_jan + delta_dt_hat_full[-1]\n",
    "fc_mar = fc_feb + delta_dt_hat_full[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = [\"2019\",'December', str(round(fc_dic_2019)),0,0,0,0,0,0,0,0]\n",
    "row1 = [\"2020\",'January', str(round(fc_jan)),0,0,0,0,0,0,0,0]\n",
    "row2 = [\"2020\",'February', str(round(fc_feb)),0,0,0,0,0,0,0,0]\n",
    "row3 = [\"2020\", 'March',str(round(fc_mar)),0,0,0,0,0,0,0,0]\n",
    "\n",
    "df.loc[119] = row\n",
    "df.loc[120] = row1\n",
    "df.loc[121] = row2\n",
    "df.loc[122] = row3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total Arrivals</th>\n",
       "      <th>Total Passengers</th>\n",
       "      <th>Cruise Arrivals</th>\n",
       "      <th>Cruise Passengers</th>\n",
       "      <th>Cabotaje Arrivals</th>\n",
       "      <th>Cabotaje Passengers</th>\n",
       "      <th>t_beginning</th>\n",
       "      <th>dt_lag</th>\n",
       "      <th>delta_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>2019</td>\n",
       "      <td>November</td>\n",
       "      <td>7029</td>\n",
       "      <td>1620305</td>\n",
       "      <td>280</td>\n",
       "      <td>827315</td>\n",
       "      <td>6749</td>\n",
       "      <td>792990</td>\n",
       "      <td>9</td>\n",
       "      <td>7344.0</td>\n",
       "      <td>-315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>2019</td>\n",
       "      <td>December</td>\n",
       "      <td>6846.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>2020</td>\n",
       "      <td>January</td>\n",
       "      <td>6662.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>2020</td>\n",
       "      <td>February</td>\n",
       "      <td>6479.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>2020</td>\n",
       "      <td>March</td>\n",
       "      <td>6296.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year     Month Total Arrivals  Total Passengers  Cruise Arrivals  \\\n",
       "118  2019  November           7029           1620305              280   \n",
       "119  2019  December         6846.0                 0                0   \n",
       "120  2020   January         6662.0                 0                0   \n",
       "121  2020  February         6479.0                 0                0   \n",
       "122  2020     March         6296.0                 0                0   \n",
       "\n",
       "     Cruise Passengers  Cabotaje Arrivals  Cabotaje Passengers  t_beginning  \\\n",
       "118             827315               6749               792990            9   \n",
       "119                  0                  0                    0            0   \n",
       "120                  0                  0                    0            0   \n",
       "121                  0                  0                    0            0   \n",
       "122                  0                  0                    0            0   \n",
       "\n",
       "     dt_lag  delta_dt  \n",
       "118  7344.0    -315.0  \n",
       "119     0.0       0.0  \n",
       "120     0.0       0.0  \n",
       "121     0.0       0.0  \n",
       "122     0.0       0.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Double Exponential Smoothing for Total Arrivals "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BIE_BIE2020.csv')\n",
    "df['t_beginning'] = df['Year'] - df['Year'].min()\n",
    "ls = df['Total Arrivals'].iloc[:-4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha = 0.2, Beta 0.2 | MSE = 143533.54684784223, RMSE = 378.8582147028651\n",
      "Alpha = 0.2, Beta 0.5 | MSE = 1789577.7848248496, RMSE = 1337.7510175009584\n",
      "Alpha = 0.2, Beta 0.8 | MSE = 521143.91483821534, RMSE = 721.9029815967069\n",
      "Alpha = 0.5, Beta 0.2 | MSE = 189899.16527188628, RMSE = 435.7742136380792\n",
      "Alpha = 0.5, Beta 0.5 | MSE = 397954.9988289949, RMSE = 630.8367449895376\n",
      "Alpha = 0.5, Beta 0.8 | MSE = 630512.776664281, RMSE = 794.0483465534583\n",
      "Alpha = 0.8, Beta 0.2 | MSE = 389296.0874323658, RMSE = 623.9359642081596\n",
      "Alpha = 0.8, Beta 0.5 | MSE = 1162300.2247729462, RMSE = 1078.1002851186647\n",
      "Alpha = 0.8, Beta 0.8 | MSE = 2733444.0928107984, RMSE = 1653.3130655779619\n"
     ]
    }
   ],
   "source": [
    "#fit the estimator and produce the forecast\n",
    "for alpha in [0.2, 0.5, 0.8]:\n",
    "    for beta in [0.2, 0.5, 0.8]:\n",
    "        fit1 = Holt(ls).fit(smoothing_level=alpha, smoothing_slope=beta, optimized=False)  #smoothing_level=ALPHA, smoothing_slope= BETA\n",
    "        fcast1 = fit1.forecast(4)\n",
    "        #compute errors on validation set \n",
    "        e_dic_2019 = 8539 - fcast1.iloc[0]\n",
    "        e_jan_2020 = 8128 - fcast1.iloc[1]\n",
    "        e_feb_2020 = 7137 - fcast1.iloc[2]\n",
    "        e_mar_2020 = 7344 - fcast1.iloc[3]\n",
    "        mse = np.mean([e_dic_2019 ** 2, e_jan_2020 ** 2, e_feb_2020 ** 2, e_mar_2020 ** 2])\n",
    "        rmse = np.sqrt(mse)\n",
    "        \n",
    "        print('Alpha = {a}, Beta {b}'.format(a=str(alpha), b=str(beta)) + ' | MSE = {m}, RMSE = {r}'.format(m=str(mse), r=str(rmse)))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit2 = Holt(df['Total Arrivals']).fit(smoothing_level=0.2, smoothing_slope=0.2, optimized=False)\n",
    "fcast2 = fit2.forecast(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = [\"2019\",'December',str(round(fcast2[119])),0,0,0,0,0,0]\n",
    "row1 = [\"2020\",'January',str(round(fcast2[120])),0,0,0,0,0,0]\n",
    "row2 = [\"2020\",'February',str(round(fcast2[121])),0,0,0,0,0,0]\n",
    "row3 = [\"2020\", 'March',str(round(fcast2[122])),0,0,0,0,0,0]\n",
    "\n",
    "df.loc[119] = row\n",
    "df.loc[120] = row1\n",
    "df.loc[121] = row2\n",
    "df.loc[122] = row3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total Arrivals</th>\n",
       "      <th>Total Passengers</th>\n",
       "      <th>Cruise Arrivals</th>\n",
       "      <th>Cruise Passengers</th>\n",
       "      <th>Cabotaje Arrivals</th>\n",
       "      <th>Cabotaje Passengers</th>\n",
       "      <th>t_beginning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>2019</td>\n",
       "      <td>November</td>\n",
       "      <td>7029</td>\n",
       "      <td>1620305</td>\n",
       "      <td>280</td>\n",
       "      <td>827315</td>\n",
       "      <td>6749</td>\n",
       "      <td>792990</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>2019</td>\n",
       "      <td>December</td>\n",
       "      <td>6928.0</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",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>2020</td>\n",
       "      <td>January</td>\n",
       "      <td>6655.0</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",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>2020</td>\n",
       "      <td>February</td>\n",
       "      <td>6382.0</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",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>2020</td>\n",
       "      <td>March</td>\n",
       "      <td>6109.0</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",
       "</div>"
      ],
      "text/plain": [
       "     Year     Month Total Arrivals  Total Passengers  Cruise Arrivals  \\\n",
       "118  2019  November           7029           1620305              280   \n",
       "119  2019  December         6928.0                 0                0   \n",
       "120  2020   January         6655.0                 0                0   \n",
       "121  2020  February         6382.0                 0                0   \n",
       "122  2020     March         6109.0                 0                0   \n",
       "\n",
       "     Cruise Passengers  Cabotaje Arrivals  Cabotaje Passengers  t_beginning  \n",
       "118             827315               6749               792990            9  \n",
       "119                  0                  0                    0            0  \n",
       "120                  0                  0                    0            0  \n",
       "121                  0                  0                    0            0  \n",
       "122                  0                  0                    0            0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Arrivals')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cast to numeric\n",
    "arrivals = df.set_index(['Year','Month'])['Total Arrivals']\n",
    "arrivals_casted = pd.to_numeric(arrivals)\n",
    "# plot\n",
    "arrivals_casted.plot(figsize= (20,6), color='purple', rot=45, xticks=np.linspace(2, 10*12 + 2, 6), grid=True)\n",
    "plt.xlabel('Period (w/Double Exponential Smoothing)')\n",
    "plt.ylabel('Total Arrivals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For Total Arrivals, we chose the Double Exponential Smoothing method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cabotaje Arrivals Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Ships')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('BIE_BIE2020.csv')\n",
    "df.set_index(['Year','Month'])['Cabotaje Arrivals'].plot(figsize= (20,6), color= 'deepskyblue', rot=45, grid=True)\n",
    "plt.xlabel('Period')\n",
    "plt.ylabel('Number of Ships')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving Averages for Cabotaje Arrivals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('BIE_BIE2020.csv')\n",
    "df['t_beginning'] = df['Year'] - df['Year'].min()\n",
    "\n",
    "reg_model = linregress(df['t_beginning'], df['Cabotaje Arrivals']) #regression for trend\n",
    "trend_exists = reg_model.pvalue < 0.05\n",
    "\n",
    "if trend_exists:\n",
    "    df['dt_lag'] = df['Cabotaje Arrivals'].shift(1)\n",
    "    df['delta_dt'] = df['Cabotaje Arrivals'] - df['dt_lag'] \n",
    "trend_exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ls = df.iloc[:-4,:] \n",
    "ma_estimators = list([])\n",
    "t_fl = data_ls.shape[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for p in range (1,t_fl+1):\n",
    "    ma_estimators.append((\n",
    "        data_ls['delta_dt'].iloc[-p:-1].sum() +\n",
    "        data_ls['delta_dt'].iloc[-1:].sum()) /p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_dic_2019 = list()    #checar lo de p-1\n",
    "e_jan_2020 = list()\n",
    "e_feb_2020 = list()\n",
    "e_mar_2020 = list()\n",
    "\n",
    "for p in range (1, t_fl+1): \n",
    "    e_dic_2019.append(8371 - ma_estimators[p-1])\n",
    "    e_jan_2020.append(7961 - ma_estimators[p-1])\n",
    "    e_feb_2020.append(6968 - ma_estimators[p-1])\n",
    "    e_mar_2020.append(7091 - ma_estimators[p-1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors_df = pd.DataFrame({'p': range(1,t_fl+1), 'ma(p)': ma_estimators,'error December 2019': e_dic_2019,'error January 2020': e_jan_2020,'error February 2020': e_feb_2020,'error March 2020': e_mar_2020})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors_df['error December 2019'] = errors_df['error December 2019'] ** 2\n",
    "errors_df['error January 2020'] = errors_df['error January 2020'] ** 2\n",
    "errors_df['error February 2020'] = errors_df['error February 2020'] ** 2\n",
    "errors_df['error March 2020'] = errors_df['error March 2020'] ** 2\n",
    "\n",
    "errors_df['MSE'] = errors_df[['error December 2019','error January 2020','error February 2020','error March 2020' ]].mean(axis=1)\n",
    "errors_df['RMSE'] = np.sqrt(errors_df['MSE'])\n",
    "\n",
    "errors_df\n",
    "delta_dt_hat = df['delta_dt'].iloc[1]\n",
    "#delta_dt_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Forecast for next 4 months\n",
    "fc_dic_2019 = 6749 + delta_dt_hat\n",
    "fc_jan_2020 = fc_dic_2019 + delta_dt_hat\n",
    "fc_feb_2020 = fc_jan_2020 + delta_dt_hat\n",
    "fc_mar_2020 = fc_feb_2020 + delta_dt_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = [\"2019\",'December',0,0,0,0,str(round(fc_dic_2019)),0,0,0,0]\n",
    "row1 = [\"2020\",'January',0,0,0,0,str(round(fc_jan)),0,0,0,0]\n",
    "row2 = [\"2020\",'February',0,0,0,0,str(round(fc_feb)),0,0,0,0]\n",
    "row3 = [\"2020\", 'March',0,0,0,0,str(round(fc_mar)),0,0,0,0]\n",
    "\n",
    "df.loc[119] = row\n",
    "df.loc[120] = row1\n",
    "df.loc[121] = row2\n",
    "df.loc[122] = row3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total Arrivals</th>\n",
       "      <th>Total Passengers</th>\n",
       "      <th>Cruise Arrivals</th>\n",
       "      <th>Cruise Passengers</th>\n",
       "      <th>Cabotaje Arrivals</th>\n",
       "      <th>Cabotaje Passengers</th>\n",
       "      <th>t_beginning</th>\n",
       "      <th>dt_lag</th>\n",
       "      <th>delta_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>2019</td>\n",
       "      <td>November</td>\n",
       "      <td>7029</td>\n",
       "      <td>1620305</td>\n",
       "      <td>280</td>\n",
       "      <td>827315</td>\n",
       "      <td>6749</td>\n",
       "      <td>792990</td>\n",
       "      <td>9</td>\n",
       "      <td>7091.0</td>\n",
       "      <td>-342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>2019</td>\n",
       "      <td>December</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6136.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>2020</td>\n",
       "      <td>January</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6662.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>2020</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6479.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>2020</td>\n",
       "      <td>March</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6296.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year     Month  Total Arrivals  Total Passengers  Cruise Arrivals  \\\n",
       "118  2019  November            7029           1620305              280   \n",
       "119  2019  December               0                 0                0   \n",
       "120  2020   January               0                 0                0   \n",
       "121  2020  February               0                 0                0   \n",
       "122  2020     March               0                 0                0   \n",
       "\n",
       "     Cruise Passengers Cabotaje Arrivals  Cabotaje Passengers  t_beginning  \\\n",
       "118             827315              6749               792990            9   \n",
       "119                  0            6136.0                    0            0   \n",
       "120                  0            6662.0                    0            0   \n",
       "121                  0            6479.0                    0            0   \n",
       "122                  0            6296.0                    0            0   \n",
       "\n",
       "     dt_lag  delta_dt  \n",
       "118  7091.0    -342.0  \n",
       "119     0.0       0.0  \n",
       "120     0.0       0.0  \n",
       "121     0.0       0.0  \n",
       "122     0.0       0.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential Smoothing for Cabotaje Arrivals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BIE_BIE2020.csv')\n",
    "df['t_beginning'] = df['Year'] - df['Year'].min()\n",
    "\n",
    "reg_model = linregress(df['t_beginning'], df['Cabotaje Arrivals']) #regression for trend\n",
    "trend_exists = reg_model.pvalue < 0.05\n",
    "\n",
    "if trend_exists:\n",
    "    df['dt_lag'] = df['Cabotaje Arrivals'].shift(1)\n",
    "    df['delta_dt'] = df['Cabotaje Arrivals'] - df['dt_lag']   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ls = df.iloc[:-4,:] \n",
    "\n",
    "es_estimators = list([])\n",
    "es_estimators_val_set = list([])\n",
    "t_fl = data_ls.shape[0] #This gives us the number of rows in the learning set\n",
    "\n",
    "delta_dts = data_ls['delta_dt'].values\n",
    "delta_dts[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[0.0, 79.49999999999999, 151.04999999999995, 215.44499999999994, 273.4004999999999, 325.5604499999999, 372.5044049999999, 414.75396449999994, 452.77856804999993, 487.00071124499993, 517.8006401204999, 545.5205761084499, 570.4685184976048, 592.9216666478443, 613.1294999830599, 631.3165499847539, 647.6848949862786, 662.4164054876508, 675.6747649388857, 687.6072884449972, 698.3465596004975, 708.0119036404477, 716.710713276403, 724.5396419487627, 731.5856777538864, 737.9271099784978, 743.634398980648, 748.7709590825832, 753.3938631743249, 757.5544768568924, 761.2990291712032, 764.6691262540829, 767.7022136286746, 770.4319922658071, 772.8887930392265, 775.0999137353039, 777.0899223617735, 778.8809301255961, 780.4928371130366, 781.943553401733, 783.2491980615597, 784.4242782554037, 785.4818504298634, 786.433665386877, 787.2902988481893, 788.0612689633704, 788.7551420670334, 789.3796278603301, 789.941665074297, 790.4474985668674, 790.9027487101806, 791.3124738391625, 791.6812264552462, 792.0131038097217, 792.3117934287495, 792.5806140858746, 792.8225526772871, 793.0402974095584, 793.2362676686026, 793.4126409017424, 793.5713768115681, 793.7142391304113, 793.8428152173702, 793.9585336956333, 794.06268032607, 794.1564122934631, 794.2407710641168, 794.3166939577051, 794.3850245619345, 794.4465221057411, 794.501869895167, 794.5516829056503, 794.5965146150853, 794.6368631535768, 794.6731768382191, 794.7058591543972, 794.7352732389575, 794.7617459150617, 794.7855713235556, 794.8070141912001, 794.8263127720801, 794.843681494872, 794.8593133453849, 794.8733820108464, 794.8860438097618, 794.8974394287856, 794.907695485907, 794.9169259373163, 794.9252333435846, 794.9327100092262, 794.9394390083036, 794.9454951074732, 794.9509455967259, 794.9558510370533, 794.960265933348, 794.9642393400131, 794.9678154060118, 794.9710338654106, 794.9739304788695, 794.9765374309826, 794.9788836878844, 794.980995319096, 794.9828957871864, 794.9846062084678, 794.986145587621, 794.9875310288589, 794.9887779259731, 794.9899001333757, 794.9909101200382, 794.9918191080344, 794.992637197231, 794.9933734775079, 794.9940361297572, 794.9946325167815, 794.9951692651034]\n",
      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "for alpha in np.linspace(0,1,11):\n",
    "    delta_dt_hat = list()\n",
    "    delta_dt_hat.append(delta_dts[0])\n",
    "    for t in range(1, len(delta_dts)):\n",
    "        delta_dt_hat.append(alpha * delta_dt_hat[-1] + (1-alpha) * delta_dts[-1])\n",
    "    delta_dt_hat_val = alpha * delta_dt_hat[-1] + (1-alpha) * delta_dts[t]\n",
    "    print(delta_dt_hat) \n",
    "    # Every time it produces the exponential smoothing forecasts, store in LS dataframe\n",
    "    data_ls['delta_dt_hat_es_a{}'.format(alpha)] = delta_dt_hat\n",
    "    es_estimators_val_set.append(delta_dt_hat_val) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_dic_2019 = list()\n",
    "e_jan_2020 = list()\n",
    "e_feb_2020 = list()\n",
    "e_mar_2020 = list()\n",
    "\n",
    "for p in range (0, 11): \n",
    "    e_dic_2019.append(8371 - es_estimators_val_set[p])\n",
    "    e_jan_2020.append(7961 - es_estimators_val_set[p])\n",
    "    e_feb_2020.append(6968 - es_estimators_val_set[p])\n",
    "    e_mar_2020.append(7091 - es_estimators_val_set[p]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors_df = pd.DataFrame({'alpha': range(0,11),\n",
    "                          'ES(alpha)': es_estimators_val_set,\n",
    "                          'error December 2019': e_dic_2019,\n",
    "                          'error January 2020': e_jan_2020,\n",
    "                          'error February 2020': e_feb_2020,\n",
    "                          'error March 2020': e_mar_2020})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>ES(alpha)</th>\n",
       "      <th>error December 2019</th>\n",
       "      <th>error January 2020</th>\n",
       "      <th>error February 2020</th>\n",
       "      <th>error March 2020</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>5.739578e+07</td>\n",
       "      <td>5.135156e+07</td>\n",
       "      <td>3.810593e+07</td>\n",
       "      <td>3.963962e+07</td>\n",
       "      <td>4.662322e+07</td>\n",
       "      <td>6828.119745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>5.739578e+07</td>\n",
       "      <td>5.135156e+07</td>\n",
       "      <td>3.810593e+07</td>\n",
       "      <td>3.963962e+07</td>\n",
       "      <td>4.662322e+07</td>\n",
       "      <td>6828.119745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>5.739578e+07</td>\n",
       "      <td>5.135156e+07</td>\n",
       "      <td>3.810593e+07</td>\n",
       "      <td>3.963962e+07</td>\n",
       "      <td>4.662322e+07</td>\n",
       "      <td>6828.119745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>5.739578e+07</td>\n",
       "      <td>5.135156e+07</td>\n",
       "      <td>3.810593e+07</td>\n",
       "      <td>3.963962e+07</td>\n",
       "      <td>4.662322e+07</td>\n",
       "      <td>6828.119745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>5.739578e+07</td>\n",
       "      <td>5.135156e+07</td>\n",
       "      <td>3.810593e+07</td>\n",
       "      <td>3.963962e+07</td>\n",
       "      <td>4.662322e+07</td>\n",
       "      <td>6828.119745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>5.739578e+07</td>\n",
       "      <td>5.135156e+07</td>\n",
       "      <td>3.810593e+07</td>\n",
       "      <td>3.963962e+07</td>\n",
       "      <td>4.662322e+07</td>\n",
       "      <td>6828.119745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>5.739578e+07</td>\n",
       "      <td>5.135156e+07</td>\n",
       "      <td>3.810593e+07</td>\n",
       "      <td>3.963962e+07</td>\n",
       "      <td>4.662322e+07</td>\n",
       "      <td>6828.119745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>5.739578e+07</td>\n",
       "      <td>5.135156e+07</td>\n",
       "      <td>3.810593e+07</td>\n",
       "      <td>3.963962e+07</td>\n",
       "      <td>4.662322e+07</td>\n",
       "      <td>6828.119745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>5.739578e+07</td>\n",
       "      <td>5.135156e+07</td>\n",
       "      <td>3.810593e+07</td>\n",
       "      <td>3.963962e+07</td>\n",
       "      <td>4.662322e+07</td>\n",
       "      <td>6828.119745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>794.995652</td>\n",
       "      <td>5.739584e+07</td>\n",
       "      <td>5.135162e+07</td>\n",
       "      <td>3.810598e+07</td>\n",
       "      <td>3.963967e+07</td>\n",
       "      <td>4.662328e+07</td>\n",
       "      <td>6828.124076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.007364e+07</td>\n",
       "      <td>6.337752e+07</td>\n",
       "      <td>4.855302e+07</td>\n",
       "      <td>5.028228e+07</td>\n",
       "      <td>5.807162e+07</td>\n",
       "      <td>7620.473525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    alpha   ES(alpha)  error December 2019  error January 2020  \\\n",
       "0       0  795.000000         5.739578e+07        5.135156e+07   \n",
       "1       1  795.000000         5.739578e+07        5.135156e+07   \n",
       "2       2  795.000000         5.739578e+07        5.135156e+07   \n",
       "3       3  795.000000         5.739578e+07        5.135156e+07   \n",
       "4       4  795.000000         5.739578e+07        5.135156e+07   \n",
       "5       5  795.000000         5.739578e+07        5.135156e+07   \n",
       "6       6  795.000000         5.739578e+07        5.135156e+07   \n",
       "7       7  795.000000         5.739578e+07        5.135156e+07   \n",
       "8       8  795.000000         5.739578e+07        5.135156e+07   \n",
       "9       9  794.995652         5.739584e+07        5.135162e+07   \n",
       "10     10    0.000000         7.007364e+07        6.337752e+07   \n",
       "\n",
       "    error February 2020  error March 2020           MSE         RMSE  \n",
       "0          3.810593e+07      3.963962e+07  4.662322e+07  6828.119745  \n",
       "1          3.810593e+07      3.963962e+07  4.662322e+07  6828.119745  \n",
       "2          3.810593e+07      3.963962e+07  4.662322e+07  6828.119745  \n",
       "3          3.810593e+07      3.963962e+07  4.662322e+07  6828.119745  \n",
       "4          3.810593e+07      3.963962e+07  4.662322e+07  6828.119745  \n",
       "5          3.810593e+07      3.963962e+07  4.662322e+07  6828.119745  \n",
       "6          3.810593e+07      3.963962e+07  4.662322e+07  6828.119745  \n",
       "7          3.810593e+07      3.963962e+07  4.662322e+07  6828.119745  \n",
       "8          3.810593e+07      3.963962e+07  4.662322e+07  6828.119745  \n",
       "9          3.810598e+07      3.963967e+07  4.662328e+07  6828.124076  \n",
       "10         4.855302e+07      5.028228e+07  5.807162e+07  7620.473525  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors_df['error December 2019'] = errors_df['error December 2019'] ** 2\n",
    "errors_df['error January 2020'] = errors_df['error January 2020'] ** 2\n",
    "errors_df['error February 2020'] = errors_df['error February 2020'] ** 2\n",
    "errors_df['error March 2020'] = errors_df['error March 2020'] ** 2\n",
    "\n",
    "errors_df['MSE'] = errors_df[['error December 2019','error January 2020','error February 2020','error March 2020' ]].mean(axis=1)\n",
    "errors_df['RMSE'] = np.sqrt(errors_df['MSE'])\n",
    "errors_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alpha = 0.3 is best estimator. Thus, apply to full dataset.\n",
    "delta_dts_full = df['delta_dt'].values\n",
    "delta_dt_hat_full = list()\n",
    "delta_dt_hat_full.append(delta_dts_full[1])\n",
    "\n",
    "alpha = 0.3\n",
    "\n",
    "for t in range(2, len(delta_dts_full)):\n",
    "    delta_dt_hat_full.append(alpha * delta_dt_hat_full[-1] + (1-alpha) * delta_dts_full[t-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = [\"2019\",'December',0,0,0,0,str(round(fc_dic_2019)),0,0,0,0]\n",
    "row1 = [\"2020\",'January',0,0,0,0,str(round(fc_jan)),0,0,0,0]\n",
    "row2 = [\"2020\",'February',0,0,0,0,str(round(fc_feb)),0,0,0,0]\n",
    "row3 = [\"2020\", 'March',0,0,0,0,str(round(fc_mar)),0,0,0,0]\n",
    "\n",
    "df.loc[119] = row\n",
    "df.loc[120] = row1\n",
    "df.loc[121] = row2\n",
    "df.loc[122] = row3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total Arrivals</th>\n",
       "      <th>Total Passengers</th>\n",
       "      <th>Cruise Arrivals</th>\n",
       "      <th>Cruise Passengers</th>\n",
       "      <th>Cabotaje Arrivals</th>\n",
       "      <th>Cabotaje Passengers</th>\n",
       "      <th>t_beginning</th>\n",
       "      <th>dt_lag</th>\n",
       "      <th>delta_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>2019</td>\n",
       "      <td>November</td>\n",
       "      <td>7029</td>\n",
       "      <td>1620305</td>\n",
       "      <td>280</td>\n",
       "      <td>827315</td>\n",
       "      <td>6749</td>\n",
       "      <td>792990</td>\n",
       "      <td>9</td>\n",
       "      <td>7091.0</td>\n",
       "      <td>-342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>2019</td>\n",
       "      <td>December</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6136.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>2020</td>\n",
       "      <td>January</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6662.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>2020</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6479.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>2020</td>\n",
       "      <td>March</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6296.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     Year     Month  Total Arrivals  Total Passengers  Cruise Arrivals  \\\n",
       "118  2019  November            7029           1620305              280   \n",
       "119  2019  December               0                 0                0   \n",
       "120  2020   January               0                 0                0   \n",
       "121  2020  February               0                 0                0   \n",
       "122  2020     March               0                 0                0   \n",
       "\n",
       "     Cruise Passengers Cabotaje Arrivals  Cabotaje Passengers  t_beginning  \\\n",
       "118             827315              6749               792990            9   \n",
       "119                  0            6136.0                    0            0   \n",
       "120                  0            6662.0                    0            0   \n",
       "121                  0            6479.0                    0            0   \n",
       "122                  0            6296.0                    0            0   \n",
       "\n",
       "     dt_lag  delta_dt  \n",
       "118  7091.0    -342.0  \n",
       "119     0.0       0.0  \n",
       "120     0.0       0.0  \n",
       "121     0.0       0.0  \n",
       "122     0.0       0.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Double exponential Smoothing for Cabotaje Arrivals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BIE_BIE2020.csv')\n",
    "df['t_beginning'] = df['Year'] - df['Year'].min()\n",
    "ls = df['Cabotaje Arrivals'].iloc[:-4] #LS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha = 0.2, Beta 0.2 | MSE = 153263.2213025412, RMSE = 391.48846892666097\n",
      "Alpha = 0.2, Beta 0.5 | MSE = 2454696.192833787, RMSE = 1566.7470098371934\n",
      "Alpha = 0.2, Beta 0.8 | MSE = 728046.5421353086, RMSE = 853.2564339841268\n",
      "Alpha = 0.5, Beta 0.2 | MSE = 240366.78500586914, RMSE = 490.27215401842795\n",
      "Alpha = 0.5, Beta 0.5 | MSE = 421591.2117313964, RMSE = 649.3005557762879\n",
      "Alpha = 0.5, Beta 0.8 | MSE = 662013.031856123, RMSE = 813.6418326611059\n",
      "Alpha = 0.8, Beta 0.2 | MSE = 325887.61259915517, RMSE = 570.8656694872755\n",
      "Alpha = 0.8, Beta 0.5 | MSE = 1064304.5802055667, RMSE = 1031.6513850160657\n",
      "Alpha = 0.8, Beta 0.8 | MSE = 2475749.016578801, RMSE = 1573.4513073428109\n"
     ]
    }
   ],
   "source": [
    "#fit the estimator and produce the forecast\n",
    "for alpha in [0.2, 0.5, 0.8]:\n",
    "    for beta in [0.2, 0.5, 0.8]:\n",
    "        fit1 = Holt(ls).fit(smoothing_level=alpha, smoothing_slope=beta, optimized=False)  #smoothing_level=ALPHA, smoothing_slope= BETA\n",
    "        fcast1 = fit1.forecast(4)\n",
    "        #compute errors on validation set \n",
    "        e_dic_2019 = 8539 - fcast1.iloc[0]\n",
    "        e_jan_2020 = 8128 - fcast1.iloc[1]\n",
    "        e_feb_2020 = 7137 - fcast1.iloc[2]\n",
    "        e_mar_2020 = 7344 - fcast1.iloc[3]\n",
    "        mse = np.mean([e_dic_2019 ** 2, e_jan_2020 ** 2, e_feb_2020 ** 2, e_mar_2020 ** 2])\n",
    "        rmse = np.sqrt(mse)\n",
    "        \n",
    "        print('Alpha = {a}, Beta {b}'.format(a=str(alpha), b=str(beta)) + ' | MSE = {m}, RMSE = {r}'.format(m=str(mse), r=str(rmse)))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit2 = Holt(df['Cabotaje Arrivals']).fit(smoothing_level=0.2, smoothing_slope=0.2, optimized=False)\n",
    "fcast2 = fit2.forecast(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = [\"2019\",'December',0,0,0,0,str(round(fcast2[119])),0,0]\n",
    "row1 = [\"2020\",'January',0,0,0,0,str(round(fcast2[120])),0,0]\n",
    "row2 = [\"2020\",'February',0,0,0,0,str(round(fcast2[121])),0,0]\n",
    "row3 = [\"2020\", 'March',0,0,0,0,str(round(fcast2[122])),0,0]\n",
    "\n",
    "df.loc[119] = row\n",
    "df.loc[120] = row1\n",
    "df.loc[121] = row2\n",
    "df.loc[122] = row3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total Arrivals</th>\n",
       "      <th>Total Passengers</th>\n",
       "      <th>Cruise Arrivals</th>\n",
       "      <th>Cruise Passengers</th>\n",
       "      <th>Cabotaje Arrivals</th>\n",
       "      <th>Cabotaje Passengers</th>\n",
       "      <th>t_beginning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>2019</td>\n",
       "      <td>November</td>\n",
       "      <td>7029</td>\n",
       "      <td>1620305</td>\n",
       "      <td>280</td>\n",
       "      <td>827315</td>\n",
       "      <td>6749</td>\n",
       "      <td>792990</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>2019</td>\n",
       "      <td>December</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6708.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>2020</td>\n",
       "      <td>January</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6437.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>2020</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6165.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>2020</td>\n",
       "      <td>March</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5893.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year     Month  Total Arrivals  Total Passengers  Cruise Arrivals  \\\n",
       "118  2019  November            7029           1620305              280   \n",
       "119  2019  December               0                 0                0   \n",
       "120  2020   January               0                 0                0   \n",
       "121  2020  February               0                 0                0   \n",
       "122  2020     March               0                 0                0   \n",
       "\n",
       "     Cruise Passengers Cabotaje Arrivals  Cabotaje Passengers  t_beginning  \n",
       "118             827315              6749               792990            9  \n",
       "119                  0            6708.0                    0            0  \n",
       "120                  0            6437.0                    0            0  \n",
       "121                  0            6165.0                    0            0  \n",
       "122                  0            5893.0                    0            0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df.set_index(['Year','Month'])['Cabotaje Arrivals']\n",
    "b = pd.to_numeric(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cabotaje Arrivals')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b.plot(figsize= (20,6), color= 'deepskyblue', rot=45, xticks=np.linspace(2, 10*12 + 2, 6), grid=True)\n",
    "plt.xlabel('Period (w/Double Exponential Smoothing)')\n",
    "plt.ylabel('Cabotaje Arrivals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We chose Double Exponential Smoothing for Cabotaje Arrivals because it had the lowest RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cruise Arrivals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Triple Exponential Smoothing for Cruise Arrivals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Arrivals')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('BIE_BIE2020.csv')\n",
    "df.set_index(['Year','Month'])['Cruise Arrivals'].plot(figsize= (20,6), color= 'mediumorchid', rot=45, grid=True)\n",
    "plt.xlabel('Period (w/Triple Exponential Smoothing)')\n",
    "plt.ylabel('Number of Arrivals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend = additive, Seasonality additive, MSE = 1070.065398789262, RMSE = 32.711854101980556\n",
      "Trend = additive, Seasonality multiplicative, MSE = 1065.6741865409365, RMSE = 32.64466551430626\n",
      "Trend = multiplicative, Seasonality additive, MSE = 1395.329007275748, RMSE = 37.35410295102464\n",
      "Trend = multiplicative, Seasonality multiplicative, MSE = 1269.337064250296, RMSE = 35.62775693543303\n"
     ]
    }
   ],
   "source": [
    "ls = df['Cruise Arrivals'].iloc[:-12].astype(np.float64)\n",
    "vs = df['Cruise Arrivals'].iloc[-12:].astype(np.float64)\n",
    "\n",
    "for tr in ['additive','multiplicative']:\n",
    "    for seas in ['additive','multiplicative']:\n",
    "        fit1 = ExponentialSmoothing(ls + 10, trend=tr, seasonal=seas, seasonal_periods=12).fit()\n",
    "        predictions = fit1.forecast(12)\n",
    "\n",
    "        error_vector = vs - predictions\n",
    "        mse = np.mean(error_vector ** 2)\n",
    "        rmse = np.sqrt(mse)\n",
    "        \n",
    "        print('Trend = {a}, Seasonality {b}'.format(a=str(tr), b=str(seas)) + ', MSE = {m}, RMSE = {r}'.format(m=str(mse), r=str(rmse)))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119    368.832824\n",
       "120    376.890563\n",
       "121    331.857570\n",
       "122    360.559082\n",
       "123    313.062962\n",
       "124    209.402031\n",
       "125    165.305897\n",
       "126    166.910671\n",
       "127    170.881511\n",
       "128    181.317621\n",
       "129    252.837161\n",
       "130    303.126921\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit2 = ExponentialSmoothing(df['Cruise Arrivals'].astype(np.float64)+10, trend='additive', seasonal='multiplicative', seasonal_periods=12).fit()\n",
    "fit2.forecast(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c1cb1bc18>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PfH7YOYO0w+P0RAY1V+LyQqHTh9I1gltAiiooTjdX/TUXKoAAjOHoXbevb2KTnDMmIiIiIqL2qtkuStM2UhsCtDcaOJPE6qUyahV3n48s3JZeKUAfUJE+fH9hTzUiiI9rzEEKEX8T23mOsa0LbgFJE17Rp4lwOXOhgthgFIp69/92YkpHtezCXqm2+jCJiIiIiIjuUrxlAfJOV8u9Bs4k4ToSq5fK+3xk4VWruMi+UcDwE2kIRWx6n/RhAwVuYguNA/1xpHSVOUgbBLeAFBVwbQkr23iIWXnRuW98DfA6kACgNMMxNiIiIiIiai8/R2ezETYA6D8Vh4gIjrHto+WzRbi2xMhT9+cf+VKHDFg5B5U8Gw/CQFEETk6kcX52rdOH0jUCW0CKaN6hN7OJzVyowBiJ3nd7cooFJCIiIiIi2h+FGybURASx4fvfmwCAGosgc9xAjgWkfbP4Sh5qXEH/qfiW90kf9jrG8sxBCo1T42lcnCug1sTkUy8LbAFJqReQSg3mINVsF5XV6qYdSFqfCjWuMEibiIiIiIjarnDdQvpQDEJsPioFAINnkihct1ApsNul3aQrsfRqAUOPp+6LO9koVe8YK3ATW2icnkjDdGq4ni11+lC6QmALSEIBtH614aKPuVjfwDZyfwFJCOFtYmMHEhERERH1oKpVw+X/Oo/sN7hNqNPcqkTxlrVlgLZv4JEkIIGV83zj2m6rb5XhFGoYeXLr8TUAiCYiMEajDNIOkdMTGQDgGFtdYAtIgDd61ugIm7ngFZDim3QgAfVNbE1udQub7DcKuPjbs9xWR0RERBQgxWkLL//sNdz8H1nc+utcpw8n9EqzNlxHrnezbCV9xIBqKMid5Rhbuy2+kodQBQYfTe543/QhAwWOsIXGoaEEAGBmlc85EPACklf0sRsqaKx3IG1ZQIqhslaDU2SL6Fbmv7KG6c8tY/Uit0EQERERBcH8V9fw8s9eg1OsIn3E8LZ/UUf5xYetNrD5lIhA/+kEg7TbTEqJpVfyGDyTgGpEdrx/6rABc9GBU6ztw9FRpxlaBAktgmyh0ulD6QrBLiBNxVCzXNi5nTexlRccRAwF0dTmLwoJP0ibOUhbsrLeD82tz/LKFREREVE3c6su3vq9Obz5n24j+WAMT3/8KEafScNervKNb4cVblhQdIHEhL7jfQceScJccFBe4JvXdinetmEuOBjeYXzNlz7sdY5xjC08BpM6ciXWCYCAF5D87WmNjLF5G9i0LYPqmvlcYWUuOYDwWjzNJf4SIyIiIupG1rKDV3/uBm59NoeDHxrEE//nIcQGokgc8DpeirfZhdRJ+esmUgdjEMrWAdq+wTPeSBW7kNpn6eU8IIDhJ1IN3d/PriqwgBQag0kN2SLrBEDAC0iJA413DZmLlS3zjwAgNhSFogkGaW9BuhL2soPxb8lACOD2c8udPiQiIiIiusfyuSK+9lNXULxp4cy/PICHvn8ciuoVKlIH6xdMOcbWMdKVKNywdsw/8sUnNOgDKgtIbbT4Sh6Z43HofdGG7q+lVcSGoshf489RWAwldeSKbKAAAl5A0lIqtExkx64h6cp6B9LWLwpCEeuZSnQ/e9mBrAF9JxIYfiqNmS8so2a5nT4sIiIiIoJ3vnv9z5fw9f/7BrSUiqf+3WGMvStz1330wSjUuILibZ7vdoq5WEHNdHfMP/IJITD4SBLL50qQLhfZtJqZraBw3cJIg91HvvRhgx1IITKU1JBlAQlAwAtIgJeDtNP2NHu1CteRWwZor3+uSZ0dSFsws17OVGw4ioMfGkS15GLuy6sdPioiIiIiAoCZ51dw5Y8XMPrODJ7694eRnLq/QCGEQPJAjB1IHVS47n3v0w12IAHAwJkknEINhRvbP281y0Wtwgu8zci97nV2DT/RWP6RL3UohvJcBdUy88TCYCipY7lko8Yibg8UkBrYxLbTBrb1zzWlw8o6qFp8IbiXtVQvIA1F0fdQHKlDMdz661xDG/CIiIiIqL3W3jYRTUdw5iemoMa23iSVPBhD8bbFc7gOyV83ISJA8sDOAdq+gTPeGvHNxtiq5RrmvrSK13/xJl74oYt4/eM3W3asYWDOVyBUgfj49u8T77UepL1DUY96w2BCgyuB1TK7kAJfQEpO6aiWXdgr1S3vY9a3FhgjO3cgAUBphv8w7mXVQ7ONIS+I/OB3DqJ028byuVKHj4yIiIiIyjM2EpP6lgtjfMkDOqolF/by1ufO1D6F6xYSUzEo0cbfhul9USQP6MjVC0hOuYbZ/7mCb3z8Jl744Us496vTyF81ERuOrnc4UWOsZQexAbWhQPON/AyrwjWOsYXBYNKrE+RK4agT/Okrt7b8WFsKSEKImBDiZSHEG0KI80KIn6vffkgI8ZIQ4ooQ4k+FEFr9dr3+31fqH3+w0a+VmNo5SNtcqAACMIa3D0bzW31LM3zhvZeZdRBNRRCJef9kRt+VQTQdwa3P5jp8ZERERERUmrXXL4ZuJ3mwvomNY2z7TkqJ/HUT6QbzjzYaOJPE6sUyvvELN/HFH76E8/95BoUbJg58YABP/vwhvPuTD2Hy2QE4xRqcEqcpGmXlHOiDjYVnb6T3qdAHVORZQAqFoXoBKVvo/bgbs1LDz3zmzS0/3q4OJBvAs1LKdwB4FMAHhRDPAPg4gF+WUh4FsALgh+r3/yEAK/Xbf7l+v4Yk6+tIt8tBMhcdxAajO1b6jVENItLYVrewsZYcxDYU4CKagqn3DyD7WgHleX6/iIiIiDqlkq/CKdSQmGikgFTfxHabBaT9Zq9U4eRrDW9g22j4iRRcR6J408KB7xjAk//2MN79aw/hoe8fR9+JBIQiYIx65+p+fAftzM557xN3I3XIYMdXSAwlvUmmbAg6kC4vFLDdhHNbCkjS4w/pRut/JIBnAfxZ/fbfB/Dd9b9/V/2/Uf/4e8VO/bf+J09HEE1tv4nNXKjsmH8EAIoqEB/jJrbNWFkHxtDd38Op9w9AKMDt55Y7dFREREREVJr1zl0b6UCKJlXo/SqKt3i+u9/8rV2NbmDbaODhJN79Gw/hW379OB76vnH0HY/fN3blx3WwgNQY6UpYy1XEBnZXQEofiqE0azM/NwQGQ9SBdGk+v+3H25aBJISICCFeB7AI4PMArgJYlVL6A9fTACbrf58EcBsA6h9fAzC4yef8ESHEq0KIV5eWlvzbkJjavuhTXqzAGGnshSExxU1s95JSwlyq3NWBBACxgShGn8lg9u9WUDX5wklERETUCf65ayMFJOBOkDbtr8J1CxBA6oHmC0iAd+693TV2/4K5ueDs6vOHTSVfg6xK6EO7LCAdNgAJFBmk3fP6jCgiikCu1Pt1gotzBcS1rRcxtK2AJKWsSSkfBTAF4CkAJ1rwOX9TSvmElPKJ4eHh9dsTkzqKtzffxFazXVRWqg11IPmfqzxfgetwBabPKdTgVuSmGVIHPzSIquli7ourHTgyIiIiIirP2FCiArEG3wgnD3gXX90aN7Htp/x1E/ExDaqx9ZuzvYjGI1ATEXYgNcherm+Z3mUHUsrfxMYxtp6nKAIDCQ25Yu//bF2az+OhsdSWH2/7FjYp5SqA5wG8E0CfEEKtf2gKwEz97zMADgBA/eMZAA2nMyenYqiWaqis3b9NwlxqbAObLzGlAxIoz/X+P45GmUv1F9eh+7+HmWNxpI8auPU3OUiXJyFERERE+600W0F8Qm94k1TyYAyuI2HO83x3PxVuWLvKP2qGMRplAalBVs57j7ObEG0A0PtVaBkGaYfFUFJHttjbHUhSSlyaL+DEWHrL+7RrC9uwEKKv/ncDwPsBXIRXSPqe+t2+D8B/r//9L+v/jfrH/05u1k60he02sZkL3gtovMEOpGT9c22XqRQ2Vtb7Ht47wuY7+KFBlGcryJ0tbvpxIiIiImqf0oyNxERj57rAhk1sHGPbN06xCmvJQerB3Y2vNSo+oq2//6Ht2fUCUmxQ3eGemxNCIHU4tp5ttZ25L69i5vmVXX0d6g5DSQ3ZHu9AWsjbWC07ODm+/x1I4wCeF0KcBfAKgM9LKf8KwE8D+KgQ4gq8jKNP1e//KQCD9ds/CuBnmvliiW2KPv4LaKMjbPEJHRBgDtIGVr0DabMRNgAYfSYNrU/Frb9uuGmMiIiIiFqgVnFhLlYazj8C6ufOAijeYgFpv/hjTunDbe5AGtFgLjmcDGiAlXMgIgJaencFJABIHzJQmrZRq2wef1KruDj/69M49yvTuPDJGSyf4wX3oBpMaD2fgXRxzgvQ3q4Dafc/LduQUp4F8Ngmt1+Dl4d07+0WgH+w26+n96tQE8qmHUjlBQeRmIJoqrFZ44imwBiJojTNX6g+K+t9D9XE5t9DRVUw9YEBXPv0IkqzdkMrZImIiIho78z5CiDrF0EbFNEUxMc0bmLbR/6693Z3IBmjGmRVwl6p7no9fVhYOQf6gNrw6Odm0ocNSBco3rSQORa/62PleRtnP3EbhRsWDn14GAtfW8P5X5vBM//hKKJbvK+i7jWU1JEt9HYH0sX6BraOZiDtB28TW2zToo+5UIExqm27seBeickYO5A28Dewbfc9nHrfAIQqcPtv2IVEREREtF9Ks81tYPMlD3AT234q3DChD6p76nZphL95mmNsO7OXnT0X2VKHvILgvTlIi6/m8dLPXIW55ODRn3kAR/+3UTz8L6Zgrzi49KnZPX1N6ozBpA7TqaFcuT93uVdcmitgss9Axtj656InCkgAkKxvYruXuVhpOP/Il5jSUZqtcDNFnZV1dtzqofepGHtXBrMvrMIp1/bpyIiIiIjCzb/omRhvsoB00Ns8vNXoDbVW4bqFdJsDtIE7sR0M0t6Zldt7ASk2FEU0FVkfUXRrEm//0Tze+MVbMMY0PPPxIxh+3OvmyByN49D3jGD+y2uY/+rano+f9tdg0vvZ6uUupEvz+W3zj4AeKiAlpnQ4hRoq+TsVQSml14E00twLQ2JSh6xKvvDWWUsOjE02sN3r4IcGUbNczH1xdR+OioiIiIhKMzZiQ1FEYs2d1icPxgC5+RIaaq2a5aI0a7d9fA3wChoQLCDtREoJK1fd9QY2nxACqUNekLa9WsVr//YGbvxFFpPv68eTP3/4vk3ghz48jMwxAxd/axbWsrOnr037azjpFemzPZqDZFdruLpU2jb/COixAhJwd5B2ZbUK15ENB2ivf656CzDH2ICqVYNTrG25gW2j9GEDxqiGlfOlfTgyIiIiIirN2k2PrwEbNrExSLvtCrcsQAKpfehAUlQFscEoR9h24BRqkFWJ2MDec6LShw0Ub1l46aevYO1yGaf/+SRO/cgkItr9b7WViMDpH5+CW/XCtRl2Hhx+B1KuRzexXVksouZKnAhLB1KyXkDamIPU7AY2X2L9c7GA5G9g22mEzZc5ZmDtarmdh0RERERE8LooyjOVpgK0ffExDUpUsIC0D/JXvHwcPy+n3YxRDeYiu1u2Y+W8748+uPdMqvRhA7IGKJqCpz52GBPv6d/2/olxHQ99ZBzLZ0u4/dzynr8+7Y9BvwOp2Js1gktzBQDbb2ADeqiApA9GETHu3sRWrrdu3ts6uJNoPAK9X2UBCYBZLyAZw419DzNHDdi5KlsyiYiIiNrMXq6iZrtITDZ3rgsAQhFITG2eIUqttfT1POLj2r5tRTNGouvvg2hzfgGpFc/J8BNpnPrRSTz9C0eQerCxLrPJ9/Vj6PEU3v7DeRS5/TsQBhN+B1JvvmZems9DVxU8OBjf9n49U0ASQiA5pd9V9DEXHEAARgPjV/dKTOkcYQNgZb1fPo2MsAFA+qj3Dy5/1dzhnkRERES0F+sB2rvoQALqm9jYgdRWlUIVK+dLGHk63dRW6L0wRjRUVqoMSN+G3cICkqIKTD7bj2gi0vBjhBA49aMTiMQUnPvVabhVPlfdLhaNIKWryPboCNul+QKOj6agRrYvEfVMAQnwsos2XkUxFyqIDUShRJv/30xMegUkKcM9l2otORARAb2vsfbO1IMxiMidVl0iIiIiao/SbL2AtIsMJMDLQbJXqnCKvbuWutOWXi1AusDI05l9+5rcxLYzK+dARAAts/cRtt3S+6I49U8nUbhu4dr/u9Sx46DGDS5Vqz8AACAASURBVKX0nh1huzhXwImx7fOPgF4rIE3FUFmrolLwfgmaCxUYo7urKiemdNQsd706HVbmkoPYkAqhNHbFJKIpSB6MYe0Kc5CIiIiI2qk8Y0M1FGgNXui7V/KgV3gq3OrNN0TdYPGlPGLDUaQP70/+EXAnvoM5SFuzl6vQB6INv8dpl5Gn0pj49j5c/4slrF7iIqJuN5jQejJEe6lgI1u0cWJ8+/wjoOcKSHdvTysvVprOP1r/XPVW4NJc7/0DaYaVrSA21Nz3MH3EQP6qya0CRERERG1UmrURn9R3PRrlb2IrcYytLarlGnJnixh5av/G14CNBaRwv4/ZjpVzWrKBrRUe+v5xGMNRnPu1aVTNWqcPh7YxmNR6sgPp0nweAHByhw1sQI8VkJIbtqfVbBeVlSqMsd0VkPwrOU4+3C291pLTdIZU5mgc1bKL8jx/aRERERG1S2nG3nX+EQDo/SrUhOKtmaeWW3qtAFmVGH1656v6raRlIlB0sb6Rmu5n5Rzo+xRqvhPViOD0P5+CueBg7ktrnT4c2sZQUkeu1Hs/V41uYAN6rIAUG4pC0QVK0zbMpd1tYPNpaa+AVCmEtwrsVl3Yq1XEhpotIHnbBzjGRkRERNQeVbMGe7m66/wjoL6E5mAMJW5ia4vFl/LQ+lVkjm+/1ajVhBAwRjR2IG1BSgk75yA20Ln8o3v1nYxD0QXKs/xZ7GaDSR0r5Qqqtd4KPb84n8doWsdAYufaSU8VkIQikJyKoXjbWq+4x0d3V0BSExFAhLsDycpVAQnEhpv7HiamdER0hUHaRERERG1SmvXOdfdSQALqm9huW6FfHNNqNdtF9vUCRp5MdyRnxysgMQNpM06hBteRTV8kbychBOJjeqgmOK5/ZjFwDQdDSQ1SAsvl3nqeLs0VGuo+AnqsgATUt6dN2zAXvBdMY5cFJCUiEE1E4IS4A8nyu7iaHGETikD6SAxrLCARERERtUW5nvkZn9jdua4veVBHtezCCvnimFbLvl6Aa0uMPrO/42s+Y0SDuVBhYXAT9rL3b13vkgwkX3xUC83YoblYwZU/WcTyuWAFhw8lvYJ9LwVpOzUXVxaLONFA/hHQiwWkKR32ShX56yYiMQXRVGTXnyuaioR6hM3Kei+uu6nOp4/GUbhhwXV6q72PiIiIqBuUZm0IBYjvMu/T5wdpF7mJraUWX8ojmoqg72SiI18/PhpFzXJDfTF8K36xNNYlGUg+Y0xDeaESikVEC1/zsp5G35np8JE0Z7A+4tVLBaTr2RIqNRcnw9qB5AdpZ79RgDGq7WnjQTStwimEd4TNXHIAsbsX18xRA7IqUbjJUEYiIiKiVivN2DBGNSjq3k7nk1N+AYnnbK3iOi6yXy9g+IkUlEhn1sRzE9vWrJz3/q5bQrR98TENsiphLfd+N+DCi3mkjxi7jpvplKFUvdbQQ5vYLs55G9hC3YEEAE6+BmNkby8KWiqCSj68VXtrqQKtT4USbf6fSboepM0cJCIiIqLWK83ae84/AoBoMgJ9UEXpNgtIrZJ7s4Sq6WLk6c51V6wXkEIyEtUMO+dAKIDe1z0h2sCd6BWzx3OQzMUK8ldNjL6zM+OdezGU6L0C0qX5AqIRgcNDyYbu33MFJGNEgxL1Kv27zT/yRVORUHcgWVkHxi7D5WKDUWgZFWtXWUAiIiIiaiW3JlGeqyAxsfcCEuCNsRUaHGGb/ttl3P6bXEu+bq9afGkNqqFg8ExnxteAjR1Ivd/N0iwr50Dvj3Yk3Hw7/jhqrwdpL7xYH197JljjawCQNlREIwLZHhphuzSXx9GRFLQGu1l7roAkFLHehRQf2VsBSUupcAq10IbPmUtO0xvYfEIIpI8a7EAiIiIiajFrqQJZlYi3oAMJ8DaxlWZsuLXtz3kLN0xc+u1ZXP2zxdCeH+/ErUksvVrA0DeldtXF3yqRmAItE2EH0ibsZQf6YHd1HwHeBXihip7vQJp/cQ3po8Z6kTNIhBAYTOjI9VAH0sW5Ak6ONTa+BvRgAQm4s850zx1I6QhcR6Jmhy8IWroSVm73HUiAl4NUmrXhlMM7BkhERNRLls8VcfY/3Q5FyGs3K9U3sLVihA3wNrHJqkR5bus3RW5N4vwnZyBdLyqChYnNrVwowSnUuqK7whjRYC7xebqXlXO6LkAb8BohjJFoT3cgledtFK5ZgQvP3mgwqSFX6o3naKVUwXzeajj/COjRApIfpL33ETavMu2EMAfJXq1CViViw3srIEEChWvNdSFd+t05vPGJW+snR0RERNQdFl/OY+Gra1hjh3FHlWa8Ny+JidZcwU8e2HkT262/yqJw3cKhvzcMAFh7m/8GNrP4Uh6KLjD4jsbyRNrJCNFa+EZJ6V0k77YAbV98TEe5h5+zha95gc2jzwQv/8g3mNR7JgPp0nwBAHCiwQ1sQI8WkMbf04+j3zuK+PheR9giAIBKCNdfWtn6estdjrABQPqIF6TdzEmmlXNw+29yWPxaHi/+5Nu4+NuzqKyFN4eKiIiom5TnvDc22dcKHT6ScCvN2tAyEUSTrRnDSUzqEApQ3CJIuzRn4+qnFzHyVBpH/uEIIrqCtcvllnztXiJdicVX8hh6NIWI3vm3WcaIBivr7DiaGCbVUg1uRSI20K0FJA3mfKVnR0QXXlxD5pgBYw/vMTttKKkh1yMZSJfmm9vABvRoASk2EMWh7x6GEHsLRoum6x1IIQzSturtrnsZYYsmVcTHtaZykOa+uAJI4Ml/exhT7x/AzN8u48v/4jKufWYxlKOERERE3aQ87111zX6DBaROKs/YiLcoQBsAIpqC+LiO4q37C0jSlbj4X2agRAUe+sFxCMXLuVx9mwWke61dLqOyUsXI093RXWGMaJCut3WMPFbOe18X28N7nHYyxjTUbLcnL6CX520Urgd7fA0AhuodSL1Q5Ls0V8BgQsNwsvHfJz1ZQGqVaL0DKYwjbOaS34G0txfX9BEDa1caO8GQUmL2i6voOxlH3/E4TvzQBN75iWMYOJPA1T9ZxFd+4jJmnl9h7gIREVEHuFUX5qIDNaGgcN2Ctcw3pZ1SmrVbln/kSxzQUbx9/1jGzBdWsHKhjOMfGVvv2ug7HkfxpsWLe/dYeCkPoQoMP9741fx2Mka956uXR6KaZdWLaXq3diDVI1h6MUh74cXgj68BXgeSXXVRtINf5Ls0n8eJ8VRTjTcsIG1Dq2cgVcLYgZR1oCYiUI3Inj5P5mgc9nK1oZPMtbfKKM9VMPnt/eu3JSZ0PPqvH8ATP3cI+kAUFz45g6/99FXk3uCVTyKiXuUvcnAdvjntJuaiA0hg8r0DANiF1CmVfBVOoYZECzuQACB5MAZzoYKadefnzso5uPyH8xh4OIGJDednmeMGZA3IN5lz2cuklFh8OY/BR5JQ43s7f24Vf8sVc5Du8LuxujFEG/A6kAD0ZJD2wotryBw3EBsK7vgaAAwmvNfeoI+x1VyJtxYKTeUfAUD37S/sImpcgVAAJ6QZSMYeu48AIH3Uy0HKXzERe2r7zzfzwioiuoKRTarS/ScTeOpjh7HwYh5X/mger33sJp7+hSNIHzb2fIxERNRdbv5/Obz9X+cBAFpGhT6oIjYQRWwoCn0githgFPpgFJmjBiIar4XtF/8NzchTaSx8ZQ25bxQxVS8mtVrVqgEuuuaNeDcpzbZ2A5sveUAHJFCctpA5GoeUEhd/axayJnHyn07edYU6cywOwBvZ6j+ZaOlxBFX+mgVrycGRfzDS6UNZFxuMQkQAczHYb3Rbyco5gAC0vu58G2wMRyGU3isglWZtFG5YOP59Y50+lD0bTHoFsGzRxoNDwX39u5krwXJcnBhrrmOyO39yuoRQBKKpCCr5EHYgLVX2vMUOAFIPxiAiwNqVMkae2rq6WbNcLLy4htF3pqHGNj9ZFEJg7F0ZJA/qePGjV1CatVlAIiLqQSsXStAHVUy+dwB2zoGVc2AuVLBysYRq6U53xIEPDuDED0508EjDxV/xHh/TMPR4CnNfWoXruFCirS/ivfFLtwABfNP/cajlnzvo/C21LS8gHaxvYrttI3M0joWvriH7WgHHPzK2Plbj09IqjDGNm9g2WHxpDUIBhr6pO8bXAO+9TGxI87oHCYBXQNIHVCiRvWXltouiKogNRXuua2zha2sAgNFngp1/BHgZSACQDXgHkr+B7eQ4O5BaKppSQ9eBJKWEmXXQ//De149GNAXJgzHkr25/grHw8hpqpouJ9/Rvez/gzmhh2J4XIqKwKFwzMfBwAke+5/4r+VWrBjtXxcXfmkHubLEDRxde5bkK1LiCaCqCoceTmP78MlYuljH4SGvXlZfnbSy/WYKaiEBKueelKL2mPGNDiYqWhwDHRzUomkDxloVKvopLvzOH9BEDBz80uOn9+44ZyL1Z4nOE+vjaS3n0n06sn6d2C2M0yg6kDexlp2s3sPmMMa3nOpAWXsyj76F4144ONsMvIOVK92fGBcnFuTwiisDRkeZ+h7PvewfRVCR0W9iqJRc1023JCBvg5SDlr5jbhl/PvbAKYzSKvpPxHT+fmqyHm4fseSEiCgN7xYG9UkVqiw5TNRZBYlLH0GMplGcrsFf5u2C/lOdtxMd1CCEwcDoJJSqQfa31OUizL6wC8NZt82LR/UqzFcQndAiltUUboQgkpnQUb1t46/fmUC3XcOqfTW75dTLH46isVmEtsbuldNtGea6Ckae7r7vCGNF6rptlL6ycA73LixjxMb2nCkilWRvFm8HfvuYbSNRH2ArBfo4uzhVweCiBWLS5UXEWkHagpVRUQnbyYmW9H4ZWXdlKHzVQNV2U5zb/ITMXK1g+V8LEt/U3dAVLiQiocQVOMVzPCxFRGPihvOkj248o95/ycgdWL5bafkzkKc9VEK8HvEZiCvpPJ1oepC1didkvrkBNeKeoft4P3VGasZGYaE8IbepgDCsXypj/8hoOfXgYqfpY22bWc5Debmzbbi9beCkPCGDkqe4ZX/MZIxqcQg1Vk+fNUkrYuWrXd8EYo5pXQC/2xgWShRfXvJ+PgG9f82mqgowRDXwHkreBrfnnhAWkHUTTETj5cL3gmvUrSa0qIGXqQdprVzc/wZj94ioggPH39DX8OaOpCAtIREQ9KH/VBISXobed1CEDEV3BygUWkPaD67iwsg7i43cKF0OPp1Ceq6A017qT6OVzJdi5Kg599zCAO3k/5KlVXJiLlZbnH/kSB2KQVYnEpI5DHx7e9r7JB2JQNIHVy8xBWnxpDX0n4tD7uq8wsb4WnjlI3pSF7XZ9ASneY5vYFl70fj66fXSwGYNJLdBb2PKWg+kVs+kAbYAFpB1pKRVOoQoptx6/6jV+K7Ix3JqrW4lJHZGYgvyV+08wpCsx98UVDDycgNHESsdoMnzZVEREYZC/ZiExqW+5UMGnqAKZhwyssANpX5iLDiC90Qrf8OPeiWeuhV1Is8+vQE1EcOCDg1CiAuXZ4J6gt4M5X/Geh4n2FJAGTiegJiI49c8mdwxHVyIC6SNG6DuQ7FUHxVs2hp/ozu6K2Ei9gMQxNljL3nscvcsLGb1UQCpOWyjesntmfM03lNSxVAzuBY7L6wHaLCC1XDQVgXSBatnd+c49wsxWoGgC0XRrVucKpX6CsUkBaeViCeai01B49kbRJDuQiIh6Uf6a2fCGzf5TCRRv2agwE6/t/A1sxtidiz3GiIbEpI6lFuUgOaUaFl/OY/xbMojoCuJjGkfY7uF/P9rVgZQ+bOA9v3MCfcd3zqQEgL7jcRSuW6hVwnOefC///LbR79l+i496xRIGaQN2rj5l0eUdSP4m7F4o+i18zRvvHH26OwusuzWU1JALcAHpYr2AdGKMI2wt5xdRwhTYbC05iA1FW7pRI3PEQOGGBde5+wRj9vlVqIaCkaea+8cbxnBzIqJeZy07qKxWmyogAcDqxXB3QOwH/0r4xhE2ABh6LImVC2VUrb1f1Fn46hpcR2Li272LSvEJHWUWkO7ij/QlxttTQALQ1Plf5ngcsiZRuG617Xi6Xf6KCaEAqUPbj912ipqIQI0rLCDBC9AGgNhgd23Ku1dEU6APqD3RgeSPr+n93V20a9ZgQkc2wCNsl+bySMdUjGeaf91iAWkH/irOSohykKys07LxNV/6qAFZlSjcvHOCUTVrWHhpDaPv8q40NiOajHCEjYioxzQaoO3LHDGgRAXH2PZBea4CNRG5b0X50OMpyKrE8tm9PwezL6wgeUBffyOemNBhLlTgVsPb3XKv0oyN2FAUkVh3nML7Qdqrl8NbxF27UkbygRgiWnc8J/cSQnATW52VcwABaF2YVXWv+JjmjawGWPG2hdJtG2M9Nr4GeCNsa6aDSkB/P12aL+DkeHpXDSPd+UrXRaKp8HUgmUuVlgVo+/wg7Y05SAsv5uHad640NiOaUlE1XbjV8GRTERH1usK1xgK0fUpUQeZ4nEHa+6A8b9/XfQQAfScSUA0F2T2OsRWnLay9bWLi2+9sZI1PapAuYC4w/NdXmrXbNr62G3qfCmMkGtocJOlK5K+aDRe9O8UY0RiiDW+ETe9Toaitm7JoF2NUC3wHUq9tX9toMOn9PlwpB+85cl2Jt+oFpN1gAWkHWtq70haWbpea7cLJ1xAbbm0BSR+MQutT78pBmn1hBfEJDZljzf/S9Qt71VI4nhciojDIXzWRnNKb6krtPxlH4YYFp8zfB+1Unq+sB7tupKgCA+9IIvuNwp4Wjsy9sAoRAcbffWcja6IeFM0cJI+UEuWZStsCtHcrcyyOtZB2IJXnK6iWXGSOdmf+kc8Y1WAuViDdcF94tZadll8kb5f4mI7KWrUl48GdsvC1PPpPJbpyO+FeDdULSEuF4P1+mlk1UbSru9rABrCAtCO/UBGWETZ/NriZjWiNEEIgc9Tw1jPDu5K5eqmMiff076p1LpqsPy8h6gwjIuplUkrkr1lINZh/5Os/lQAksHopnG9g90Ot4sLKOpsWkABvG5u9UkXx5u5ycNyaxOz/XMXQYylomTsjcn6hxM/9CTt7uYqa7SIx2dpztL3KHI/DXq7CygbvSvxe+Z31fqd9tzJGonAdCXs13OfNdq7a9RvYfP7rbVDH2Iq3LZSmbYy+s/e6jwBvhA0AcqXgPT8X5vIAgBPd1IEkhDgghHheCHFBCHFeCPET9dv/LyHEjBDi9fqfD214zM8KIa4IId4SQnxHO45rNyIxBUIVoRlhs5a8H4JWdyABXg5SacaGU65h9oVVQADj39q38wM3ofmjhdzERkTUE+zlKipr1aZHQTLH4hARgVWOsbWNuVhfHb9FcPPgo95VzN1uY8u9UURltXrfRtZoPAKtT2WQdt16gHbXdSB5P7Nrb9+/bbfXrV0pI6IrSEx113NyL3+rlxXiIG0pJays0/Ub2Hz+cxbUMTZ/+9rIU72XfwQAg34BKYCb2C7NFSAEcHw0uavHt6sDqQrgJ6WUpwA8A+DHhBCn6h/7ZSnlo/U/nwWA+sf+EYDTAD4I4NeFEK3ZIb9HQghoqQgqIRlhM5fq2wnaUEDy23vzb5cx+8UVDD6aRGyXVwHUZLhGC4mIep3fodroBjZfRFeQOWowSLuNynObb2Dz6X0q0keMXecgzb6wgmgqgqHH7z+ZTUxoKM0F8w1Uq/mjfN2UgQR4mWVKVIQySNvLP4pBKN2dqWOM1IsRIS4gVU0XNdsNTAeS4XcgBTT8vDRtIT6mQe/r7o13u+WPsGUDWEC6ni1iImMgru3uuWlLAUlKOSelfK3+9wKAiwAmt3nIdwH4EymlLaW8DuAKgKfacWy7EU2rcPIh6UDKOhAK2rJq0X9TcP0vsrBzVUx8W/Ph2b71DiQWkIiIekL+Wn0V9gPNr5TtOxlH/qoZ6KyIbuZfAY+PbV24GHosibW3TVSaPF+qFKpYerWA8Xf3QVHvPy2NT+goc4QNAFCesaEaCrQue0OmqArSR4zQBWm7VRf56xbSXZ5/BMDL/RHhDqS36zEdQclAisYjiKYjge1Aspargen22o2krkJTFeSKwXt+pldMHBjY/dht2zOQhBAPAngMwEv1m35cCHFWCPE7Qgi/gjAJ4PaGh01j+4LTvtJS4VkZby1VoA9GoURafyUlmowgPq5h5XwJaiKC4Sd2F9zlfy4AcIrhKOwREfW6/DUTiQOxpgK0ff2nEpAusHY5fCM0+6E8ZyOaiqz/7t3M0OMpQAK514tNfe75r6xBVrfeyJqY0OEUa00XpnpRadZGfFLfVXZku2WOxZG/ZsF1grnSejcKN23Iquz6/CMAiGgK9AHVG0cNKT/nNTbQXQXY7cQDvInNzjmB6fbaDSEEhhIalgLYgXR7pYwD/bsvfLe1gCSESAL4bwD+pZQyD+CTAI4AeBTAHID/2OTn+xEhxKtCiFeXlpZafrxbiaYioQlrNrPt3U7gZ1uMf0sGEW33//wihgIRYQYSEVEv8AK0TaQPNd99BAB9D8UhFGCFOUhtYW6xgW2j9GEDWkZtOgdp9vkVpA7Ftuw884O0mYPkZSB1W/6RL3PcgKxKFG7sLkg9iPJXvI6rdAAKSIA3xsYCkrcZOijiY1ogR9ikK2GvONADVKzbjaGUHrgOJLtaw0LexlQ3FpCEEFF4xaP/R0r5GQCQUi5IKWtSShfAb+HOmNoMgAMbHj5Vv+0uUsrflFI+IaV8Ynh4uF2Hfp9oSg1RB5IDY7h92z36HvL+sY6/Z/fja4BX9Y0mw/O8EBH1MivnwMnXmg7Q9qlGBKlDBgtIbVKe27mAJBSBoceSyL1RgFtrbFV44aaFwnXrvvDsjRIT3tctzQbrJL3VqlYN9nJ1/fvRbTLHvPO7MOUgrV0xoWXUwIzphL2AZOccQLQnpqNdjDEdVs5BrRKszr5KvgpZQ2B+NnZrMKEhVwrWxY2ZFa9Tu+tG2ITXW/spABellJ/YcPv4hrt9GMC5+t//EsA/EkLoQohDAI4BeLkdx7YbWjoCp1iDdBs7IQoqtyZhL7e3A2ny2X48+fOHkNnlm4SNoiEaLSQi6mWFa17XQrMB2hv1n0pg7YoZuBPtbleruLByDowtNrBtNPR4CtWS23AWzuwLKxARgbFv3npLjzGiQahiPUA6rPzsGmObHKpOig1EERuKhmoTW/6KifQRoytHCjdjjGiwl6uhfY20cg60PhWKGoznC/BG2CARuMKfvexN7vTyCBvgbWLLFoL13EzXC0jd2IH0zQD+MYBnhRCv1/98CMAvCiHeFEKcBfDtAP4VAEgpzwP4NIALAP4GwI9JKbumMhBNqYDs/XEpe9mBdNsbLqdEFfSdSLTkc0VTEWYgERH1gPxVEyICJHcRoO3rPxWHrMrQBfm2mz8+sVMHEgAMPJKEiKChbWxuVWLuS6sYfiIFLb31mINQBOLjWuhH2PznwV/t3Y0yxwyshaQDqVquoTRrByL/yGeMRgHpLcwJI3u5uuvtz50S1E1s63lTPd6BNJTUkSvZkDI4TSbTLehAastgopTyywA2K+9+dpvHfAzAx9pxPHsV3bDxa7uTnKCzlupXt4aD8cMeTUQC94JKRET3y18zkZiK7Skbr+9EAhDAyoUyBk7fvw6edqc8Vy8gNdCBFI1H0HcigexrBRz73rFt75v9RgFOvrZlePZGiXEdxenwZOtsxu9AiI927zla5ngcCy/mYS07gXuj3qz8NROQwck/AoD4SL0YsVjp2iytdrJyTuD+v/3CfdCCtP2Ndz2fgZTU4NQk8lYVGSMYr3m3V8qIRgRGUru/YNf2LWy9wC8a9XqQtpn1XpxibcxAaqVoKoJKj3eFERH1uvUA7T2ONkcTEaQeiGH1InOQWqk873X+NNKBBABDj6VQvGWvn1NsZfb5FWh9KgbfsXOxLz7hBcm61eBc5W01c6ECNa5ATWy9Ca/T/BykMHQBrl3xruIHqQMpNhLMbpZWsXNOoAK0Ae+9jmooMANWQLKWHYgIerrxAvA6kAAgG6BNbNMrJib6DESU3Y9y9vaz2iLrHUj53i5W+B1I7RxhayU/3FxKGZj5cyIiupuVdeAUakgf3v3VMF//qQSm/3YZbtWFovIaWSuU5yqIpiKINli4GHo8ibf/ELjyRwtIPbj5cypdrwPp4P8yBCWy8+/vxKQOWQtv5wQAlBcrMEa1rj7fSR+KQagCa5dNjD69da5VL8hfMWGMaYgmg/NWSu9ToUQFzMXwjbBVyzVUTRexweA8X4C3NMgY01AOWNHPXq5CH4hC7KFIEQSDSa8omytWcGT/9nvtye3lMg7sIf8IYAGpIVq9gNTrHUjWkgMtE9nTCMF+iiYjkFUJ15aIxHr7BYqIqFflr3pX8vcSoO3rP5XArc/mkL9itixvL+zK8xXExxvvTE5M6kgdimH+y2uY//LalvdTogKTzza2kdUvGpVmu3eNfbuZCxUkD+69yNpOSlRB+nAsHB1IV8voPxms1xihCG8TW8CKEa1gLfsjVcG4SL5RfExD/nqwRnjtZSeQ3+tmDSaC2YH0vpMje/ocLCA1IJryvk29vvHLzFYQGwrG+BpwpzOsUqzCiAXnuImI6I78tXqAdgveHPed8K6qrVwoNVRAyr5eAKQ3dkWbK8/bTWVKCSHw9L8/Arey/biZUNFwl1i8XjQqz9jAEw0fSs+QroS56GDkiXSnD2VHmWNxTH+ut7sArWUHdq4aqPE1nzESDdxGr1bwM3mCMmWxkTGqYfHlPNyabKhjsxtYOWfLDtReMpTyO5CCUUAyKzVkizYODOytA6k3X9lbLKIrUHQBJ9/jHUhZB7GABGgDgBaSwh4RUS/LXzORPLi3AG2fllaROKBj5eLOHRDLF0p4/eM38frHb2L5XHHPX7sX1WwXdq7aVAcS4HU6RGLKtn+aKS5EExFoGRWlkG5is1eqkFXZ1RvYfH3H43AdicLN3n2u1rsmj+7tTVgnGKNeB1KQKS/kFwAAIABJREFUtka1gpXz3sMFMdw9PuaN8AZle56UElZIOpAG4t5rcrYYjKLszKp3bjTVv7fiNwtIDdJSKio9XKiQUsLKOjACVJmPJu9sxyMiouCRUqJwzWrJ+Jqv/2QCq2+V4da2foNkLlZw9j/eQnxMR3xCx9lP3F4Pi6Y7/NyNRjawtVt8QkN5Nhgn6a3mjxwFoYCUOeb9LK9d7t0xtvwVr2syiB0WxoiGqumiWgrXubOVcwARzK1g/gIDMyC/o6plF64tEQtYYPluqBEF/fFoYEbYbq94xe+pPWYgsYDUoGgq0tOFCidfg1uRgdnABmwoIHETGxF1sem/Xcbtzy13+jC6krXkwCnWWltAOpVAzXJRuG5u+vGqWcPrH78J6QLv+KmDePSnDgIAXv/FW6iW+ftko/JccxvY2ikxoYe2A8kfOTJGuv8NWWxIgz6g9nQO0tqVcsu6JvebUd/EVl4IRjdLq9g5B1pGDeRYpVF//S0HZBObPy4Yhg4kwNvElgtIB9L0sve6fIAdSPvDKyD17ghb8bYXztZsm3onrW/H6+HnhYiCrWrWcPn353HpU7NYu7p5QSPM8tfqoyBHWllA8nOQ7n8DK12Jc786jdKMjUf+1QEkxnXEx3Q88tEDKM/aePNXpiHdcI12bKc8V+9A6oYC0qQOp1Dr+YUmmynPVwCBwORUZo7Fe7YDSboS+asmMgEcXwPudLGFLUjbWnYC2xGj96lQNBGYTWx+YHksgN1euzGY1JArBePixvSKCU1VMJTcW1cxC0gN0lIqKvnevTK5dqX1J/Httl5AYgcSEXWp+a+soWa7iMQUXPiNGbhVFic2yl81ISICyQOtG5HS+6KIj2tYuVC672NXP72IpVcLOP6RcQw+cicYeuDhJB76gXFkXyvgyh8vtOxYgs6cr0DLRKDGI50+lDtB2iEcYzMXK4gNRaGowQjQzRyPw1x0YK/2XrGvPF9BtewiHcAAbQCIj/rdLMF4w9sqVs4J5Pga4GXKxUc1mEHpQPI33gW0YNesoaQemAyk2ytlTPUbUJS9/S5hAalBvd6BlL9mwhiNrgdTB4GiekGcvTxaSETBNvN3K0gc0PHwj0+heNPCjb9c6vQhdZX8NRPJB3Qo0daejvSfTGD1UumubqL5r6zi+meWMPlsPw5858B9j5n6wAAm39ePG/89i7kvrbb0eIKqPG8jPtb5/CMASEx4b3zDOMZmLlTW3/gHQZ+fg9SDY2xrV7z/p0yALrhuFIkpiA1FUZoO18+RnQtuBxLgdY4FZYTNDyzX+4PznnIvvAJSMH6eplfMPecfASwgNSyaVr1QsB69epy/aiJ9JHjtuNFUhB1IRNSVCjct5K+YmHpvP0aeTGP0XRlc+7MlFKetTh9aV5BSIn/NbGn+ka//VALVsoviLe97vXbVxPlfn0HfyThO/PA4hLj/6psQAid+cAL9p+K48Bsz628Uw6w8V+ma0fbYsAahCpRngnGi3krmYmU9uyYIkg944dKlHnyu8ldMRHQFianuKKzuRmJS78nnZitVs4Zq2Q10ASk+Vt+eF4ARa3s5uHlTuzGY0FCwqrCc7n8/Or1i7jn/CGABqWHa+rhU73UhVfJVWEtOW07i2y2a7O3OMCIKrpkvLEOJCox/ax8A4MQPjEONKbjwX2YDcRLYbuZCBdWS26YCkp+DVIK94uCNX7oJrU/FO37y4LYntYoq8MhHD0LvV/H6L91az3IIo5rlwl6pdkX+EQAoEYH4mBa6DqSqVUNlrRaIDWw+1Yggmo4EZuSmGWtXTKSPxCD2OALSSYlJL5A+LL+H7OV6R0yAC0jGmA7XkbBXuv89j7XsQB8MR/cRAAylvGLycqm7X+9KdhXLpQo7kPZTtD7a5fRgDlK+HuwaxHludiAR/f/svXmUXNl93/e9b61Xe1XvG7YBMAPMDGYfkhluImlqjVZqs6xIjGMtli07R5GiOJGXc+JjJbalRIklRTJNSZZkxYsokjMkI3EoisMhKS6YITCDGexA71vt9fbl5o9Xr9DAAI2q7qqud997n3NwADS6ui769nvv3u/9/r6/hDDiWh7WXqpj8m15iFn/+SEVBJz8yWk0LmpJVzYAzWu+O2gYAlJqXEJqQsT2N9v45r9ahKN6ePwXD0HK339RK+UFPP5Lh+HqHr75LxfhWt7Ax8cCQUaKEpISNsDvxBYEe8cFfdMXMVkSkADfMcFK6G+veLaH1g0DeUYDtAMyczI8k8ZGIDcqQagzuwJSepqd7CqzYjP9ve6XsYw/N2HvxLZc8/f7C+XEgXRgSHnfgRTF7h+NqzpAgPzR1KiH0jdiVoCVZCAlJCSEjI2vNOCoHubeX7rt4zPvKmLs8Syu/NEG9K1wLzaGTfOaDiIQZA8NR6Aonc6g8kobjcs6Hvn788gd6X3RlD2UwiM/P4/mNR0XfmsFlMbjpH4nQd5GWErYACA9K0FbNyMbJ3A3gm5ZzAlIU1LkOn21bhqgDkWBwQPXnaTn/HtuXHKQugLSOLuixi0BKfzXlFl1IMdJQOp0NAt7DtJS1S/LTxxIB0jXgRRBsaJ5RUdmVoagjL7LSr/4JWzRm5N+8RwaSXEzIWHU2Jq7J5v/yos1KNMSSqczt32cEIJTf2cWAPDG767GUpgIaF3TkTucGlpOQvkR/3v/wA9NYvLZfN+vn3w6j+M/PIn1lxuovNoe9PBCT1dACkkJG+A7kKjrZwLFhUCESU+xtSFTpiQYFRueHR0HH8uO/Z1kAwEpJjlIZkdAYjnUWR4TQXgSelHWtTzYbZfpvKl+mWBEQFquBQJS4kA6MIKW8VbEStgopZ0AbTYfhmKOh7PHDV6UuPZfNvHy37sEfTvcD5aEBJawNRdf+geXcf7/WOrrdeqKifobGubeV7prWLMyIeH4j02h8mo7tt2+hhmgHTD9XBFP/soRHP2BiT1/jbkP+N3aolaK0wvamgmpKITqcCk96y/UtRjlIOmbFoQ0ByETnnnoBWVaAiigb0WnTKpxRYdUEJjfHIt5HmKWj42AZFRsSAV+4N0+DxKOJ1AmxdA7kMxOWaRcZles65exbKeELeQZSEs1HYrId0vu9gO7V9IB0w3RjpjLw6w6sBoOs6cpUo4HKGCr0RL2+qV2QYWje7F3NCQkDJLFT1VgNRxsfKWJja80en7dyudqIDww+97SPT9n4YNlFB9M49LvrcOsR+u50gv6hgVH85B/YHil05xAMPZo9q4iXq+IWR4ggNWI3xxp6+HpwBaQ6QhIcQrS1jcsKFPSvn6OR0F6qiP2hXzD2w/NKzryxxXm5uJOCCFIx6gTm1G1I1FSpUxLoQ+m75YLMi6y9kNGFqCIPLZb4b6elmsa5kuDuX8lAlKPcCIHXuEiVy4V2HELrDqQstEtLewV6lG0rhmQSgIqr7Sx/lLvG91+0TYsGInLKSEG2KqLxee3Mf5kDrmjKbz5kbWeunB6jofVv6ph4qk85OK9T+AIR3D6Z+bgGB4ufnR1kENngm4pSMi7fxKOQMrz8RSQ1iykQ5a7I2Z5SAUe6mp8nkPahgVlMlzz0AtBZlPYN7y9Ymsu1FWT+fyjgEyMBCSzYkdC0EhP+cH0YT4oNiudjncREOz6YSwrhd6BtFzTsVAeTAOAREDqAynHw2pGaxHZuKKB8ED2MHsB2sCt0sI4d2JTV0y4pofjPzKFwgkFF39/bSibHc+h+MY/vY4LvxO/zW5C/Fh8YRuO5uH4j0zi4Z+dg91ycOkP1u/7uq2vtWA3Xcx94N7uo4DMnIxjH5rAxpeb2PxacxDDZobmNQOcSJCZD/+zR8oLsROQHMOFVXeQnglPB7aA9KwcmxI26lHomzZzAdoAIBV48DIXmfLP1jUdoIiUgGQ33VjkZxoVJxoC0rQMV/dC3RHciGEJG+AHaYc9A2mpqg0k/whIBKS+EHNC5JwuzWs6sodS4CU2fxTEbDRLC/uh6yI7ofiOBs3Dmx9dG/j7bHypAaNio71kDPxrJySECbvtYvGFCiafzSN3REHuiILD3z2O1c/XUTm3e5jy8otVpMZFjJ3J9vReR757AtnDKbz5b1dha9F6vuxG85qO7OEUOCH8pSBSUYhc/uH90EPYgS0gMyvHpoTNrDmgDmXSgUQI8UtuIiIgNa50XJOMOvbvJNMJ0tYi7kJqLRpwVDeU97J+URjoxGZWbQgZDkKKrcy2/TKRlbDdDu+8NHQbTcPBwgA6sAGJgNQXYp6PlFLvB2gbTD8Muw6kiAl7/dC8qoNPccjMysgupHD0Byaw8aUGtr4+OEcDpRQ3PrkNwLenOkZ8v98J0efmC9twdA/HPnQrfPnYhyaRnpFw4f9ZuefPv75poXpOxez7SiBcb8IIJxA89OEZmDUH22dbAxl/2PHLbocboD1I4uhA0tbC14EtID3rOyd6KSllHVY7sAWkp6IjIDWv6EjPSN3oBNYJBKR2xAWkGx/bAp/iMPPu4qiHsm+C+7G2Ed45M6tO7MrXAGAsI6MSYgfSIDuwAYmA1BdSxBxI+oYFR3WZWcTfjW4GUoxL2BpXdeSPpbob1qPfO47sgow3BuhoqJ5X0b5pYPwJ31WhxSh/IiFe2G0HS5+65T4K4CUOp39mDsaWjat/snnX1658rgYQYO69/S1Uc0f8Mq6ge0nU0TctOLqH3NHwl68BgFSIo4DkL4TT0+ErYcvM+puoOOQg6Zv+/5HFEjagE/q7aUWiU27jisb0geudKBMiOJFE2oGkrZtY/1ID8x8sR0L4UyZEgIQ7V8yo2EjFUUDqZCB5Ib3XLdd8B+V84kA6eMQcH+q6037plj4xXM8tpDmAxFdA8hwPrRvGbSIgJ3A4/bNzMGsOLv/h/TNbeuHmJ7chFQQ88MNTAOLVASchXtx8vuK7j35w8i3/VjqVwfwHy1j8dAX1S9pt/+a5FKt/WcP441mkxvvb7AlpHnyKg1mNh0jRXvbvH7lD7AhIru7BtbxRD+XA0NYtSCUBfCp8y8Ru6U0MnkPahgUQIDXO5oYsPSXBsynz4rhRtWFWHabXy3dCOIL0bLSDtG98fBucQHD4O8dGPZSBwIkcUuNiqEvY/I537It1/TKeleF6FA09nPe6paq/Zl0oJw6kA0fK8XDN6CwiG1d1ZkJM7wXhCMQsHylnWD+0F01QhyJ//HZFuXA8jUPfOYaVz9ZQvaDu6z1aNw1UvtnGoW8vI7sgAyQeC/eE+GG3HSx+uoLJt+eRu0djgRN/cwqpsogLv70Cz771LKi80oJZczD3/vKe3lsuC8xvsnpFXfRz1DLz4XO33A2p4JdKR62Jxm5o61Yoy9cAIDUhgfAk0hvfAH3DQmpcBCewuVwPnFOsB2k3g/yj44M5vQ8LUe7EZlRtrH6+jtlvKUEusSnA3o10iHPFPIfCqkcjsLxfxrL+va6ihvN6Wq7pyMoCCspg5obNJ9KIEPPRahnfvKojd4SNENPdEHN8bEO0u62w72KrPv5DU1Cm/I3ufkTPm89vg5MJ5j9YBidyUCbFWJQOJMSPm5+swDU8PPCht7qPAoQ0j4f+zizUZRPXP7bV/fjyizVIRQHjT+b29N5ySYRRi8d9rL1kIjUhQlDYCNmUOs9+qx6P+QH8DKQwdmADAI4nSE9LsXDC6psW0oyWrwG3MlvCuuHtlaBjcVBuHBUyczL0LTsyB+M7ufnJbYBSHPnu8VEPZaAoU1I3oy5sWHUboIhlBtJE1n9ebrXCOTfLNR3zJQWEDGbPnwhIfXArsJn9RaQfYsp2gHaAmBNiW8LWuKpDzPJQJt96s+ZTHE7/9Bz0dQvX/tPdM1vuh1G1sf7FBua+pdStH8/EqIVyQnywmr77aOrteWTvU1o18WQO0+8s4PrHttFeNGBUbWyfbWH2vcU9C/KpGDmQ2ksGsgvsbMSkQkdAilAJ+244mgur4YTWgQQA6VkpFll8+obNZAe2AHlMBOHD3TWqF9qLJjKzMrMdi+9FZk4GaPRc5VbTwfJnq5h+Z5Hp6+dupKcl2G0Xthq+55HRKcOPo4A01hGQwutA0gaWfwQkAlJfSLnoLCLVFROu6SEfgXpuMcPDiogrrF+aV3XkH7i3olx+JIvZ95Vw85PbaF7T+/76S5+pgHoUh77z1glOutNCOQqhmAkJATef34Zreji2i/toJw/+5AyENIfXf3sFKy/WAArMva+05/eXyyLMqgNKo31deQ6FumL55bCM0BWQYhKkHWz2w9z2OjMnQ1u34LnRvV5cw4PVcJgN0AZ8t1hqQgp16G8vaGsm0nPs3LN6JcgTi1oZ2+KnK/AsiiPfGy33EYCuIBYE7IeJ4BAsziVs263wXUuU0q4DaVAkAlIfiPnoOJC6AdoPsF/PLeb4WLTzvRPX9KAu3d9FdvLHpyEWBLz+WyvwnN4X247uYvkvqph6W/42C31mVoZnURgxcUskRB+r6WDpM1VMvaPQszNGygt48MMzaF7Rcf1PN1F+NLOvjlVySQR1aWRKpO+Ftm6CupQtB1I+rgJSeDfMmVkZ1KWh3EQNCtY7sAWkpyWmM5A8h0LfsJAJ8fWwV9IzEkCiJSA5moulz1Qw+UweWYYzXu9FcD8IY1moUfH3BXEM0S6lJXAEqKjhm5e6ZqNtOlgoJw6kkdB1IEVggd+4ooNXuFCfMPaKLyCxPyf90rphgHp3zz/aiZjhcepvz6J908DlP17v2eGw+pc1OKqHw//17Sc46U4L5TiUDyTEg5ufDNxHE329bvq5AsafzIG6+3MfAbcWXFEvY1OX/I1K9hA7mzE+xYGXuRgJSP4chTl7Jz0b/U5sgehytxJ1lkhP+aG/rLor9U0L1Lu19okSvMRBmZQiJSAt/XkVjurhyPf19zxnha6AFELx3Kw64ES/uVHc4DmCckbCdjt887Jc800jiQNpRAidC8KOQCeW5jUd+WMKCMd2gDYASFkenkkjGQK4G7sFaN/J5LN5LHxrGYvPV3Dljzbuu5DzXIqbL1RQPJVG4cTtinXX8hzhhXtCfAjcR9P/VaHv00pCCE7/zByO/+gUJt9W2Nc4gsyAIEMgqrSXDIDcEgBYQSry8RGQ1izIZQG8HN4lYqazmVdXwrdYHxSBw4B1B5IyJcHRPGYP+oK1TpgdefshSp3YXMvD4gvbGHssi0IEMl7vhpjmIWZ56BvhO2wyKrafezagoGbWGMvI2G6H71paqmkAgIUkA2k0cDyBkGG/ZbzneGjdiEaANuCHaANgdnGyV5pXNUglAakew+oe/PAM5j9Yxo1PbOPyfUSkzb9uwtiycfi73lo/LhUECAoHLSILjjCQ5EmNjpuf2IZr9Z59dCdyUcDR75vYdzfLuDiQ2osm0tMSc2G0Ul6AFYHDo17Q1sPbgS1AzAoQ83ykHUj6pgVB4Zg/zVcY78QWuK0zjInevZKZk6GtWZFYh6x+rgar4UbWfRSgTEnhdCDVbKRiWL4WMJ6TUAmhgLTcEZDmy4kDaWRIefYDm9uLJqhDkT8WFQEpyKZie176pXFV7+uEhXAED/3tGSx8axk3P7GNy//+7uVslFLc+MQW0jMSJp56a0tyQkg3SDth/7iGhy/+/Uu4/mdb9//khIFiNRws/n8VTD9X6DrrRoVcFAECmLVoixTtZbY6sAVIBQFWIx7PGG3NDHUHtoDMrAx1LbrPIX3DgjIlMX+aH5RCstqJTVs1IeZ55oW8e5GZk+HZ7OeJeQ7FjU9so/BgGqVT7Oe77oYyKYYyV8ys2LHswBYwlpFDmYG0XNNRUETkU4Obm0RA6hMxxzMfot0N0I5ABzYA3Yc66/PSD7bmQlu1+hYBCSF48L+dwcK3lXHz+Qou/cFbRaTaGxpa1wwc/q7xe5Y4ZubkJANpQCz9RRXGlo3qa+1RDyV2LP1FFZ5F9+w+GiScQCDlhUg7kFzLg7ZmMZV/FCDlhViUsNltB3bTZSIfMTMrR9yBZEeiBXk3s4VRAUldMyMZoB0QlU5s61+sw9i2cfT7JpgXXe+HMiXB2AqXa4xSCqPq9FwVEUXGs3Iou7AtVbWB5h8BiYDUN2JOgNVk+xSycVWHmOORmojGRd4VkGJUwta61sk/2oMISAjBgx+ewcK3j2HxhQou/f7tItLNT2xDzPGYeU/xnl8jPSvBqNhwjXjlTg0a1/Bw4+O+86h90xjxaOJH5dUWCseV0JQmyOVoC0jaqglQIMNgZxypKMBuOqFasA+D+qXes/VGTXpWhtVwI/nsp57vCGE9/wjwg5rlkhBKx0QvaKtWJAO0A6IgIFGP4vqfbSF7OIXxJ7KjHs7QUaYkUPdW17MwYLdcUIdCHovG3nIvzBZTUC0XWyETkZZreiIgjRopIg6k/DElMgp9NwMpRiVs3QDtPZYhEkLw4E9O49B3jGHxUxVc/D1fRGovG9g+28LCt43tmlESbLijXD5wECz9eQV208XUO/KwGi7MengWA1HH1lw0r+goPxqexaZcEiNdwtZeZK8DW4CUF0C96B9U1N9UQXigcDz8JSDdIO0IupDMugPPppEQkAA/B4nFDCRbc2E1nNAcMgwDMctDKghMC0ibX21CW7Vi4T4CbpWFhumaCsSsVIwFpEfm/GYq51fqIx7JLSilWK7pAw3QBhIBqW/EvOCrrIy2I3VND+pSdAK0gR0ZSBFf2O+kcVWHMilCyu89rI4QgpM/4YtIS5+u4OJH13Dz+Qo4kWDhW8u7vjYTgxbKw8YxXNz4uN8tZP6D/ve7dSNxIR0UtQsqqAeUz4RIQCoLMCLsQGovGyA8QXqavc2YVPCfM1EP0q5f1JA7qoS6A1tA0MlPi+BBRrAxTE9FYzOWnpKYdCBpEe/AFpCZk5gVkCiluP4xP7dz6u35UQ/nQAhKW8N0TZkdAUmOcYj2I3MFEAKcW26MeihdKqoF3XYTB9KokXI8PJvCNdks3WndMEC9vZU+hRVe4sBJhHlnWD8ELrL90hWRvmsMS5+pYvVzNcy+t3RfYUqZlgDCtuV51Cx9pgq75eLYD04id9ify0RAOjiq59rgZILiifDcC+WyCLvpwrPZfL7cj/aiicyctO+OdaNAKvj3xCjnIHmOh+YVHcWHwu8+AgClU4avb0VPdA0EpCg5kKyaw9zaOch6jHIJGwBk5lJQV0wmD8cr32yjdd3Ake+ZuGduZ9SQx0QQPmQOpKr/bIxziHZWFnB8IhsqAWmp6ndgWygnDqSR0i2XYjQHqXnV/0GKSge2ADHHx8aBZDUdGFv2wERAQghO/vg0Dn/3OASFw6HvGrvva3iJgzIhQk2CtPeEo7u4+cltjD2eRfFkGmKWR2pcTHKQDpDqeRWlUxlwYngeg0H7W7MeTZGivWQwmX8E7BSQovucaV4z4NkUxQfZEJA4kYNUEmBsRe85pG9aAAFS49HYjIWx5KYX1FUTIGCiK+F+yMzJcFSPyfvb+hcbEPM8Zt5dGPVQDgyOJ0iNS9A3wyOem1UbhAPkYnwdSADw6HwB55YboRFjl2t+5Ml8UsI2WsR8x8bOaN5O44oOuSRELiVfzArMzkm/dPOPBliGSAjByb81jfd85KGeu42k5+RIlg4cBIH76IEfutX9K3s4lTiQDgijYkNdMUOVfwTcOrkzq9ETkBzDhbFlM5l/BMTDgVR/0z9gKj6YGfFIekcZF2Fsh2cTNSi0dQupcRGcEI1leuCkClPJTS9oayaUSSky83AvMnOdPLEV9tYg6oqJ3OFU5OfoTpQpMVSCrFm1IZWE2LjA7sVj80Vst02sNcJxLd0SkBgoYSOELBBC/pIQcoEQ8joh5B90Pl4mhPwFIeRy5/dS5+OEEPIbhJArhJBzhJAnhzGuQSB1A5vZXEQ2r+mRyj8KkGLkQGpc1QEC5I8Ofh77eQBnZmSoq2bkuxINmsB9NP5E9rag2tzhFNRVE67FlsWfRarn2wCAsUfDtVGWS4GAFL0NsbrcCdBeYNOBJGZ5gERcQLqoQpmWmDpBTk2E6xR+UOibVjfnJAoEDh5tPTwb3l5QV61uWHuUYbUTG6UU6qrZHX+cUCYl36kYEoyKHTlzwl54dN53woWljG2ppqGckZCRB/tcH5Zc6wD4BUrpaQBvB/BzhJDTAH4ZwIuU0hMAXuz8HQC+HcCJzq+fAvBbQxrXvukGNjNYwmZrLrRVK1L5RwFClv3ueL3SvKojMytDSPMjHUd6VoZn0khudofJ0qcrsNt+9tFOckdSAAXaS2wt4Fikcq4NqcAjeyhcYkYQPmnWondNdTuwLbC50CccgZQXIhuiTSlF/aKGEiP5RwHKhAijYkfuIEPfsCOTfwT4LnEhw4XKMXE/qEehrZndsPYoI4+J4FMc1BV25gcArLoDV/diMUd3kp6SYLdc2Fo49qNm1Yl1/lHA6Zk8BI7g3HI4OrEt1/SBu4+AIQlIlNI1SunZzp9bAN4AMAfgewD8fufTfh/A93b+/D0A/oD6fAVAkRAyM4yx7RepW8LG3iKydW1/rd/DjJiNhwOJUuoHaIfARXarhTJbC45R4mgubnyygvEnc29pk5074osZrRv6KIYWGyilqJ5XUX4kGzqrtZjlwYmkG0YZJdpLBjiJMO2qkAo8rIjmU2lrFuymiwIj+UcBqQkR1KGRyg1zDQ9Ww0F6MlqbMWVKYkpAMqs2PItGvgMb4McYZOZk5krY1E6XvEwMBaRAYDZC4kIyqjZSY9G6Z+2FlMjjwelcaBxIy1UNCwPOPwIOIAOJEHIEwBMA/hrAFKV0rfNP6wCmOn+eA7C042XLnY/d+bV+ihDydULI17e2toY25t0Q0r6N3WYwb2cY2TlhQcoJcNpuaELLhoVZdWDVnVDMYWAZDtrcJtyfxU9X4KhvdR8Bvh2ZT3FJkPaQUZdMWA0ndPlHgL+Il0tCJF19foC2HDrRrh+kQnQdSEH+UemhcJV13g9lorOJilAntqAsJUoOJMB3TLBUwhYcjsWhhA3w/5+slbAFjqliUma3AAAgAElEQVS4zNFOwpQr5mguXN3ruqjjzpn5As4t10e+J/U8iuU6Qw6kAEJIFsB/AfAPKaXNnf9G/e9qX99ZSunvUEqfppQ+PTExMcCR9g7hCMQcz+QisnFVhzIldnOcooSY40E9wNGinR8TJhFQKgrgFa57ApSwO7bm4ubz2xh/KofCXeaPcMQP0k4EpKFS6eQflc+Ec6Msl0WYNfaeL/dDXTKZzT8KkAoCk12KeqF+UYWY45lrV56a8E+89Qh1YtOiKiBNyzC2LXguGwd9weFYXMqjMnMyzIoDR2fnHqetmuBkEsvSKaXb2XD04rnROfSK4zzcjTPzRTQNBzcr2kjHsdU2YTke5ssMOZAIISJ88eiPKKV/2vnwRlCa1vl9s/PxFQALO14+3/lYKJFyArMOpPwDbNnTe0XMdrKpGCwt7IfmVR2Ev1XuNEoIIcjMyomA1COLn6rAUT08cBf3UUDucArtG8bITy2iTPW8ivSMBGU8nJuzKDqQ7LYDs+awLyDlhciGaNff1FA4mQYhbDnEgus4Sp3YgjIvlss974YyJYG67MyVumaBT3GQS9E7dL0bmXn//sxSLIG6ZiIzw7azda+IaR5ilg9FWWjQOTYpYfN5dK4TpL0y2jK25ZovYDHjQCL+CuQjAN6glP7ajn/6BICf6Pz5JwB8fMfH/5tON7a3A2jsKHULHWKevcBmq+nA2LIjmX8E7Ag3j3gOUuOqhuxCCrwUjnalmVkJGkOLjVFhqy4WX9jGxNO5Xa/B3OEUHN2LVDlGmPAcD7XX1VCWrwXIZRFG1Y6UiBgEw7MaoB0gFQS4hgfXjJbT1Wo60NYs5gK0AYBPcRBzfKi6Ee0XfcOCoHDddU1UUKYDxwQbc6WtmkjPSMyJqnslcB+ylIOkrcYj5PxeKFPh6MRmVBIH0k4enM5BFjicWxptkPZyza9aYSkD6TkAPw7gfYSQVzu/vgPArwL4G4SQywA+0Pk7AHwKwDUAVwD8LoC/O6RxDQQpJ8BizIHULX2KYAc2ABA7ZXksOsN6xQ/QNkJRvhaQnpVhbNtwjWhtqAZN4D66W/bRTm4FabOzgGOJxmUdrulh7Ey4BSTPpHD06FxT7SX/5zkKAhIAJkvYdyPIPyoyFqAdkJoQmXG19IK+6Xdgi5pwkQ4yWxjJQVJXzViFM6enZRAeUJfZcJW7lgd9045l/lGAMimGIgMpcE3Hxa13P0Sew+nZ/MgdSEvV4TmQhjLTlNIvArjXk+/9d/l8CuDnhjGWYSDmeNgX2RIqGld1gAD5o2yXENyLWyVsbM1LP+gbFhzVDZWAFCyutHUTuSPhGVeY6LqPnskhf3T371F2IQUQvxPb5LP5AxphfKiebwMEKJ0OZ/4RcGsBZlZtiOloOBDaiyYEhYPMuL1dKnS6sDacbnhzFKhf1MCJJFTPln5QxtkL/90NfcPqNqmIEnJJACcS6OvhnyvX8mBs20i/JzrX+f3gBIL0tMyMq1xftwAan4yqu6FMSdj8ahPUoyMt4zMqNsQcH5rqiDBwZq6A//SNZbgeBT+iuVmu6RjPykiJg19LJjO9B8ScX8LGUolB84qOzKwMQYnGhuROugJShEvYwhSgHZDuLHJZqpk/aJY+U4Gj7Z59FMCnOKSnpSRIe0hUzqvIP6B07xdhJNWxgAeZAlFAXTaQOZRi3lHRdSBFLAep/qaK/DEFnMjmkjA1KULfsphak90L6lHom1bkArQBv1GEMimFwjFxP+IqTqQZ6sQW5G/GySV2J8pkJ1esMloHpll1kvyjOzgzX4Rmubi61R7ZGJZqGhbKw9kzsrlaGDFSXmCq4xf1KOoXVWbt6b0gZnmA+GGtUaVxVQcnklAF0aanJYCAmQXHQUM9ipXP1lA+k+nZoZU7kkJ7DwKS53jwHPY3UMPC1lw0L2uhLl8D0G2DG5UgbUop2ksmsvPsL/KlfCAgReegwrU8NK8ZKDKYfxSgjEvwLBoJB7JZd+DZFMpUNDdjypTERAZSXMWJzJwMbd1kYi2hdrvkRU9s7ZVbndhGe02ZVTvJP7qDM/OdIO3l0ZWxLdd0zA8h/whIBKQ90Q1sZiRIu71owFG9UJdt7BfCEQhpPhILyHvRvKojdyQFTgjPKT4vcUiNi912twm3UznXhlGxMf/+cs+vyR1OQd+0YWv9/Sy/8qs38dpvLPU7xNhQu6CCekD50XDfB+WuAykaApLVcGC33FAJ33slihlIzSs6qEuZPmBKTfjXjB6B5gNR7cAWkJ6WoG+Ev0mAtubPQ3ommvNwLzLzMqg7ekGiF7RVC/KYACEVXkfxsAlyxUYdpG1U7O7hV4LPsYksMhKPc8ujCdJ2PYrVuo6FIeQfAYmAtCeCwGaryYZYUbvQCcg8ze4CsRfEHM9cuHmvUI+idS1cAdoBmVm5exKUcDsrn61BzPOYeCbX82uyHadSPy4ks2ajel5F5Vwb1Av3wnxUVM+1wUkExZPhvg/yEgchw8OISAlbe7HTge0Q+yf5vMyBT3Gw6tGYG8DPPwKAAsMCktIRkIyt8G9670dXQIpgCRvgd2JzTS/0ZaDqqgm5JEQ29uFeBI4rFlzlcQs5vxvymAjCA/rG6MRz1/Jgt9ykhO0OeI7gkbnCyBxIG00DtksTB1KYkPJsOZBqF1SkJkQo49FckARIOT6yJWzqignX9MIpIM35oYthP1E8aMy6g61vNDH7nhI4ofdbbbcTWx8C0tY3WgD1y2oTMe/uVM+rKJ3KMJHzkioLMGvsuykAP/8IQCQcSIAfpB0lB1L9TRWZORlSjt3T41Qn0DwSDqRNCyC3RLGo0XVMhLwTW1zbwwfh7cF9O6xQSqGtJAISxxOkxkebK2bW/OdhUsL2Vs7MF3BhrQnLOfjIm+Wan5ubZCCFCJZaxlNKUXtDjXT5WoCYjW4JWxgDtAPSs/6JYpRCfwfB6udroC4w975SX6+TSwLEHI/WjT4EpK81wSv+7bxxSe/r/eKAUbGhrpgoPxru/KMAuSxG5npqL5oQc3y3/It1pIIQevdEr1CPon5JYzr/CADEDA9B4SLjQEqNiX0dOrBE4KwKe5C2tmbFrnwNAASFhzwmhL4xitVw4OheLEW+O1GmxJGWsJmdAO/EgfRWzswXYTkeLm20Dvy9l6q+uzhxIIUIqZOBxEIJm7piwm65KJ2KiYAU0S5szas6+BQXytOWruU5cb50oZRi5XM1FE+l+27HTAhB7nDvQdqO7qJyXsXct5QgZHg0Lmt7GXKkqZ73u2CMnWHjPiiXhMhkILWXDGQPRcN9BPhB2lERkNQVE47qMZ1/FJCalCLhQNI27ciWrwGAMikCBNBC7ECymg7sthvK9dZBkJmVQ1/CFghcmRgHaAcok6MNpjc6a5UkA+mtjDJIe7mmgxBgtjic9VciIO0BXuFAeMJECVvtggoAKEU8/wjwnWFRFZAaV3Xkj6VAuPAEaAcEJ0BJkPYtaq+r0NctzL2/P/dRQPZICu1FA557/7LAyqttUIdi8tk8CicUNC4lAtKdVM+3IeZ5ZoQMuSzCrDvM51lRStFeNpFdiM5GzHcgReM5U3+zk4/IuAMJAFLjIoxt9gUkfcPyRZaIwgl+440whzRr3e5e0blv9UNmzheQwhxLoK3Ee452kp6SYLfcvhuvDIrgsCuVlLC9hUPlNIppcSRB2ks1DVO5FGRhODluiYC0BwghEPNsBDbXLmiQy0KkT7QCxCwPV/fgjaDWdJh4jofWDQP5B8K5yJdLAvgUF/oTq4Nk5XM1CBkOU28v7On1ucMpeDaFtnb/7+nm15oQczwKD6ZROJlGe9mEM6KFRBihlKJyXkX5kWwoBdi7IZcFgPo5WixjVGy4uofMPBvCXS9IBQFWk31xD/ADtKVCNNYHyoTIfAmba3qw6k43JyiqpKdG65i4H+pavN0tmXkZrhHuWAJ11QQnkaRsCrfKQo0RlbGZVQe8wkFIxytwvhcIIXh0REHayzUN80PqwAYkAtKekXI87JAHaXbzj05lQAgbG6f9IHZKC6PmQmpc0UEdisKJ8OUfAf4NMj0rddvexh277WDzr5uYeVcRvLS3W2zucCdI+z45SJ7jYftsCxNP5cDxBMUTCkD9n5kEH3XJhFV3mClfA26FUbJexhalDmwBUsEX96LwnKm/qaL4YDoS64PUhARH82Cr7M5LkGMSBUFvN5QpKdQlbNqqCcKTbjh73GChE5u2aiI9IzNzKDRMlMnR5ooZFTtxH+3CmfkCLm60YNgH+2xarulYKA/PeJAISHtEzAmhD2zW1i1YNScWAdoAW+Hm/bB9tgXCAeUz4Q0AzszKSQZSh7Uv1OHZFHPvL+/5a2TmZRCe3DcHqXZBg6N5mHg2DwDIn0gDBEkZ2w4qnfwjVgK0gVtW8KC7CauoS50ObBFzIAFgPgfJqNrQN+1IlK8Bt7qWsexCClw5UReQ0tN+yU1YnbLqqon0tASOj6c4kZkPOrGFd02nrpqxdYjdSXC/0DdGc+BkVu0k/2gXzswX4XoUF9aaB/aejuthrWEkDqQwIuV4WCHPQKq/EeQfxURAynYcSFETkF5po/hQGmKI7aGZWRnGlg3XjFb5YL9QSrH8Yg3540rXRbQXOIFDZl6+rwNp86tNcDLBWEdcFNM8MnNyEqS9g+p5FekZCQpDp8lyyV+MMe9AWjIhl4XuvTkKSPlOEw3GBaTGxejkHwF+BhIA6AznIAUOgsBREFXC3oktrh3YAqSCACHDhfZQ0LM96Jt23w1KooqY4SFm+ZF1YjOqdlJKuAvdIO2lg8tB+uZyHa5HEwEpjIj58DuQahc0SAUe6Zio9F0BKQKlBQHGtoX2TQPjT+ZGPZRd6QZp95DZE2Ual3WoSybm3re38Oyd5A6n0NrFgUQ9iq2vNzH+WO62UrnCyTQal/VQB2AeFJ5DUXtdZcp9BPgLeMIh1BkUvdBeMpBdiI77CACkYseBxEAX1t2oX9TASQS5I+Esje6XQHQxNtkVkPQNC7zCdcvxo0p6OnBMhE9Aoh6Ftm7FtgMb4McSBEHaYURbtwCaBGjvRBlRrhj1KKya0y27T3gr0/kUJnIyzq0cTA7Sal3Hz/7hWcwWUvjAqamhvU8iIO0RMee3jA9zkGbtgopiTPKPgJ0ZSGxvunay/YpffhN2ASk4CQpaq8aVlc9Wwcscpp/bW3j2TnJHUrDqzj2DlJvXdJhVBxPP3P6zUTihwG67SSYVgMYVDa7pofwoWy5MwhFIJQFmjd3NMPUo1GUTmQh1YAMAKd8RkBgPOK+9qaJwIg1OiMb6QMzx4CQCfZvd+562ZiE9LUV+zdZ1IIUwB0nftEAdGpuD13uRmZWhLu/ugB4VgbAVZ5HvTpRJcSQOJL9bLBIBaRcIIXhs/mCCtJuGjQ9/9GvQLRcf/fCzGMsO7xpJBKQ9IuXCHaSpb1kwtu3YlK8BOwSkkDvD+mHrbAupCTH0Vt30jASQW+1v44ijuVj/cgPTzxUgKPs/Qc4d8Z0b7Zt3D8Te+pqfjXWnuFg86ZekJDlIQPVcGyBA+WG2HEgAIJdEGAyXsGkbFjybRs6BJGZ5EA6wQt5EYzccw0X7hoHig9EoXwP8RboyIcHYYveaUZdNZOfD/awfBILCQ8zz0EMoIAUHL3F3t+SOKrAa7sjKonYjWGfGXeTbiTIlQd+0D9zUEJTZp5IMpF15dK6Iq1tttM3hrRts18PP/dFZXN1q47d//Ck8OD1c40EiIO0RMR9usaJ2oZN/dCo+AhIvcyACCe2c9Itreai+1sbEk7nQn0jyEofUuBjamvmDYP3lBjyTYu4D+y9fA4Bs0IntHmVsm19rongq44vZO8jMyRAUDo3LSSe2ynkV+QcUJjN4UmWR6RI2danTgS1iDiTCEYh5gekMpMZlHdSLTv5RQGpChM5oiLajuTAqNjIRCpzfjfSUFMoMpGANE3d3S+lhf+8Q7CXChLpqQS4LEFLsPdeHhTIpgbr0wA+djIr/fnKSgbQrZxYKoBR4bUhlbJRS/M8fO4+XLm/jX3z/o3ju+PhQ3mcniYC0R6SO2yWsQdq1CyrELB+5xftuEEIg5nhYIXWF9UvtggrPpBh7ItzlawFx78S2/GIV2cMp5B8YTKaIlBMgjwl3DdJWV02oyyYmn8m/5d8IR5A/oaAecweSo7toXtYwxlj+UYBcZruErd3pwJaJoKNCKrAtINUvagDx89KiRGpcZNaB1C3LieD1cjdGldlyP7RVC0Im+jlU9yM7L0PM8ai+Fj4BSVszYy/w3cmtTmwHe00Fh1xJiPbunJnrBGkvDydI+9/85RX8x68v4+fffwI/+PTCUN7jThIBaY90W8aHNEiz9oaG4kNpEC7czpVBI2V52CEV9fpl+2wLnERQfoQNF1l6Voa2asUyvLl5XUfrmoG595cG6hbLHVbu6kDa+rrfDvTO/KOAwok02osGHCOc96eDoHndAPWAAqNlOnJJhKN6zHY2bC+ZUCbFSJ4SS3mebQHpTRXZQ6lQd/bcC8qE3x7eNdi7ZtrL8RKQ0tMSjIoNzw7XXAXiRNhd38OGcASl0xnULqihWtNRSqGumLEvMbyT9KgEpIoNIpDYC673YywrY66oDCUH6c9eWcG/+vNL+P4n5vDff+DEwL/+vUgEpD0SlLCF0YFkVG3o61as8o8CgnBz1qGUYvuVFsqPZG/rsBVmMrMSXMODWQvfNTFsVl6sgRMJZt5ZHOjXzR1OQVsx4Vq3L7I3v9ZC7mjqnq3piyfTAAWaV+Jbxta65v/f88fY7DIldzIFWM1Bai8ZyEQs/yhAKgjMZiB5LkXjkh6p/KOA1IR/Cs5ikLa6bIITSXcjGHWUaQmggB4yx5i6aiI9k4gTAFB+OANj24Yeos6GVsOFo3mJA+kO5DERhAP0jQMuYavaSJWF2AuuvfDYwuCDtL9yrYJf+s/n8I5jY/jVHzhzoPPAxs40hAS5I2HM2+nmH8VRQMoKoZyTftFWLegbNsafZKf8JjgRiluQtmt4WH+pjqm35weetZM7kgL1/M1FgFm30bikYeIu5WsBhRO+aBLnIO3WdR1ySYBcZDPcMehqwqIg6zketFUzsiXUUkGAVWfzOdNeNOAaXuTyjwB0BXUWy9jUZQPpWTk2rvH0VGe9EKIgbcdwYVadJJy5QzcH6fXwlLElAdp3h+MJUhPigeeKmVU76cDWI4/OFbFY1VBTBzNHVzbb+Ol//w0cGkvjt//WU5CEg5V0EgFpj/AyB04isEN4Clm7oIJXuG4XpzghZqPhQNo62wIAjDOSfwSg2ykubjlIG19pwNE9zL2/PPCv3Q3S3pGDtPX1FkBx1/yjADErID0roR7jIO3mNQO5o2y6jwBALvnCl8mgA0lbs0BdRK4DW4BUEOCaHpOlUvWLvqgcRQGp60BiMEi7HZMObAGjymzZjaADW+Ju8cnMyxDzfKiCtJOQ83vjd2I72OvJqDiJgNQjj837OUjnBxCkvdUy8ZMf/SpEnuCjP/kMCumDn4NEQNoHUk6AFUK3Sz2m+UdAp4St5YaqZnsvVF5pIbMg37NEKYzIJQF8ioO6Ep4F4UGw8mIN6VkJxVOD35ClpyVwMrktB2nr6y0okyKyh3ZfQBVPptG4pDF/LewF1/CgrprIH2NXwEgFDiQGBaR2RDuwBUh5X9xjsYytcUmDXBagjLPzbOkVuSiA8IQ5B5JjuDC27NjkHwGAVODBy1yoHEi33C3xmYfdIISgfDqD6uvt0Kwj1FW/1DM1nogWd6JMHmwwPaUUZtVOArR75OEBBGnXVAu/+fkr+K7/6yVU2hY+8hPPYKE8msOgREDaB2KeD125lNVwoK6YKJ2KX/ka4AtI1KVMngwHOJqL2hsqJhhyHwH+YiM9I8WqhE1dNVG/qGHufYMNzw4gHEHuUArtm76TyNFdVM+3MfFM/r7vVziRht1yQ3XCe1C0bugABXKM5h8BgJDmwae4bpcTlmgvGiBcdDdiUqcsksUg7faS2XU2Rg3C+RvLsOXq3I/g0CVOAhIhBMp0uDqxqWsWQPyDmwSf0sMZmBUnNPOkrVqxKvXsh/SU30TA0Q5mX2ps2/Bs2s1rTNidgiLi2HhmTzlIVzZb+EcfO493/OqL+N8/cxEnp3L4w//uWTy2MNjc1X5IZn0f+G6XcC0ga2/EN/8I8Et3AD+bSlDY7ApQOdcGdYHxJ9kSkAC/jC0okYgDa1+oAwSYedfwbuLZwylsfKkBSikq32zDs+mu5WsBQYvuxiUd6en4bEwAvwMbwG6AdoBcEph0IKnLJpQpiZkGAP0iBU00GBOQqEehrZoYezS66wNlQoTBWAmbuuzfr7Lz0RT27kV6SoK6Ep4DJ23VRGpcjOx9ay90c5AuqKFYR6grJvJH43Wd9Eq3LHTTQu7I7msfz6GonGtDyvGQx0TfvbmLKEc9ivaSicZlDY1LGhqX9e61mwiuvXNmvoCvXKv29LmUUvzVpS38u5dv4AuXtiAJHL7/iTl8+LmjeHB69PvDREDaB3JRRGXJt3aGJYG+dkEFJxPmN057JWglabddKJMjHswe2T7bgpDhmGw/np6Vsf5yA67lRX4RRj2KtZfqGDuThVwanoU3dySFlc/WYGzb2PxqE2KO7+lnI7sgg09xaFzWMPPu0Z1SjILWdR1Sge/mCLGKXBZhMBiirW1Yke5kJBXYLGHTNy14No200yU1IWL71faoh9EX6rIJwvuOnDiRnpWw9Y0mbNWFmBn9gZ+6aibZOneQmZMhFQRUX1Mx977B5zz2g2d70DctTL+zMNJxhBVlMhCQ7PsKSDc/uY0r/2Gj+3fCAXJJhDwmIFUWIY+JSI2JsNuuLxpd0eHqfmWHmONROJnGzLsKKD6UGUp8Q1R5dL6IP3t1FZtNA5P5twqhrkdxs6LiS1cr+L0v3cCVzTYmcjJ+4W+cxN982yGMZcNzf2J7dT1iCscVrH2hDmPL7l64o6b2hobiyTQ4IRyC1kHTFZBC5gzrFepRbL/SxthjOXA8e3OYmZUB6odR5iJaJhFQf1ODsWXj+I9MDfV9goVA86qO7bMtTD6b7+lng3AEheMK6jHsxNa8piN3VAmNsL9XUmUBtTfZmj9KKfR1K9Iu2K6A1AhXCfv9CE6MMxF2uqQmJFg1B57tgRPZOMRoL5vIzEpMPvP3w+Qzedz4s21sfrWJuW8pjXQslFJoaxaKJ5PN8E4IISg9nEHtgjryw3JtwwJoEqB9LwIH0v06sTmaixuf2Eb5TAaHvmMcZsWG0fllVm20bhrYeqUFz6QgnO+Cn313EYUTaRROKlCmJObXVqMiCNI+t9zAYwsEF9dbeHO92fm9hcubLRi2L9Q9MpfHr//wY/jOR2cPvMNaLyQC0j4odnKGam+ooRCQ7LaD9qKBB36IUevNAAjaqLPaia11w4DVcDD+ZHbUQ9kTQSe29pIReQFp7aU6eJnrqZxsP2QPyQABFj9dgaN5mHi29/crnEjjxse34Boe+FT4HkDDwLU8qMsmJp4e7rwcBHJJhFlzRr5w7wer4cI1vUjb2nmJA69wzJWwqcsdAWkuuhswpROuq2/byDDiglOX2Q783yv54wqUKRHrL9dHLiBZdQeu7kU2t20/lE5nsPGlBrR1a6TXVLdkKpmjuyJmeAgZ/r55VYufqcBRXRz/0WkUHri7U4lSCkd1wYkceDkea8eD4PRsHhwB/u4fn4Xl3MrqHc9KeGg6jx9722E8OJ3Dw7N5nJ65f9bpKEkEpH2QXZAhZDjU39Aw+57RPvwA+CfVFLEN0AZuz0Bika2zLYAA44+Pvr51L2TmJBCeoH3TAN456tEMD9fysPHlBibflh+6MCOkeChTEupvaOBkgrEzvYuLhZMKqOc7cqLsCNlJ+6YB6gG5COQkyGUR1KGwW26381fYCRavwWloVJHyAnsC0ooJqSiEolxoWKQ6h3nGFhsCkmv6ZTlxKzMGfHfL9HNFXP/YFsy6A7k4untc0h7+3pQf6RyWv66O9JrSVjth8zPRfrbsB2VK3FVAsjUXNz9ZwfhTuXuKR4B/bQb7qYTBkZYE/Pz7T2ClpuOhmTwems7hwekcxkNUmtYryU/HPiAcQfHBDGpvqqMeCoBO/pFIkD8ez/wj4JYDyWLUgbR9toXCcYWZzeKdcAKH7CEZrRvG/T+ZYbbPtuBo3oEt+nNHUtDXLYw/lusrW6pwwrfj1y9psRGQmtf9jnVRyIELupuYVZuZe4K2Ho9gTanAM5eBpK6YyEY4/wi45UAyttkI0lZXTYAi8vNyL6afK+D6n25h48sNHPr2sZGNQ1vzf17SiTjxFtIzEqSigNoFFfMfGF0OkrpqQi4JENLRFcD3S3pSQuvmvdffS5/y3UcP/GB8K1VGzT/8wMlRD2EgJL60fVJ8KA1t1QrFSWTtgorCCSXy4cW7wQkEgsIx6UCyGg6aV3WMP8Gm+yggdziF1g0dlNJRD2VorH2hDqkkdE/mhk1QDjjxTH8/G1JegDItoXGZrRyd/dC6ZkDM8kiNDy/Y/KCQy53NcHX0z5de0Tf8VtjKBPvf/92QCgKsOjvzQin1s3YiXL4GdK4Z4gfJskC3rDCmAlJ2IYXsIRnrL9dHOg511QQnEaTGon3f2guEEJROZ1B9XR3puk5bNZPytfugTEvQN21Q763zZKsubr6wjYmnc5E4YEsYLfFVGgZEcKo/aheSo7loXTe6uUxxRszxTIZob7/aAigw/iTjAtIRBVbDZWpz1Q9Wy8H2K23MPFfYte3pIJl8Jo/yo5k95foUTyhoXIq2oLeT5nUduWOpUNeO98pOBxIraOsWUmMiMwHGe0UqCEw5kMyan/ESdaGCE3wRwNhm45rxO7DF2/ky/VwRjUs69M3Ruca0Vb9z5EE901mj/EgGVs3pOrUOGkpp0iWvB5RJCdSlMO6yZlj8VAWO6uFY4j5KGADRXuEdAPljKXASQbpwGnoAACAASURBVP2N0Z7w1y928o9iUqayG2JOYDJEe/tsC1JJYD67JRh/VMvYNr7UAHXpgWZWZA+l8NSvHN1TdknhZBpWw4GxxcaGaj94tof2oon80WicrslF/zTcrLEjVOgbVuTzj4BOBlLTvetJbxiJQ4B2QGpChL7FRglbe9lAeloGF8IuOwfF9HN+Z6L1LzVGNgZtzYy1iHc/uoflr4/msNxuunBUD5m5ZI52I3j23pmDZKsuFl/YxsQzucisjxJGS3yfWAOCEzgUTqRH7kCqXVBBeCQtSOHnILFWwuY5FJVvtjH+eJZ550S2U27V6mTRRI21L9SRPSQjd4SNh3Dh5K0cpKjTXjJBXYpcRBZInEAgFXi2HEgbFtJxEJCKAkDZadgQp1IpZVxkRjBXl81YzMluKJMSCicVrL88GgHJczzoG1bibtmF9IwEuSSgOiIBKQg5T0rYdifdFZBuv/8tvrANR/OS7KOEgZEISAOg+FAaresGHH10C8naGyryDyhJu0V0BCTGHEj1SxoczWO+fA0AxDQPZUqMpANJWzfRuKwz1TEneygFTiZoxEBAal4LArTZdvHtRC6LzAhIju7CbrpQIh6gDaAbah6G/MNeUFdMCBkeUoGNMPb9kJqQYFZteG643WGu5fmt0WMuIAF+GVv7poH20sGvG/QNG9QD0rPRv2/tFUIISg9nULswmhwkdSXpktcL8pgIwuG2clC77WLxhQomn80zc/CZEH4StWEAlE5lAIqRbdBcw0PzanzadN8PMcfDbrOxqA/YPtsC4ftr0R5mckeUXTtBsMraF+oAAabfyY6AxPEEhQcUNC5H0xG2k9Z1A0Kai1QJlVwSmSlhC2zzUe/ABvhd2AAwk4PUXjaQmZeZd7j2QmpCBPXCnx2mrVmx7sC2k6l3FACCkbiQgs6duUPROXgYBqWHM7DqDrTVgy8P1VZNcCKJRHOMYcLxxC/h3VHCdvOFbTh6kn2UMFgSAWkAFE4qIBxQG1EO0toX66Bukn8UIGZ5OKoX+tPHnWy/0kLpdBqCEo32pLkjKWhr1khdeYOGUoq1lxooP5JBqszWIqZwIo3WDR2u5Y16KEOleU1H7qgSqU2yXBbuGogZRrR1f9EaJQHvXgROHpYcSNkY5B8BgDLRKeMIeRmbuuwfsmTmE+FCLgooP5rB+sv1A3e4NK/o4CSCzEIyD7tRftjfY4yijE1dtZCekZKQ8x5QJqWuA8luO1j8VAWTb893u/kmJAyCREAaAEKKR+6ogvoIcpDWXqrjjd9dRel0+sBaiocdMecv7B2VDfFC37KgLpkYf4L98rWA3BH/QdWOkAupcUmHvmFh5l3suI8CCifToC7QuhZdF5LnULQXDeQZD6G/E7kswm668Ozwi3+BgBSLDCSGBCSr6cBuurEplUpN+AK/EfIg7fayCRAgk5ROAfDL2PQNG82rB/ucalzVkT+mgOMTcWI3lCkJcllA7fX2gb+3umom+Uc9okxJ0DoOpJvPV+DqHo59KHEfJQyWREAaEMWH0mhc1g90kb/6hRpe+7+XUTqdwRO/fCTWXTx2IuZ8Fw8r4abbZ1sAEIn8o4AgxDhKOUhrL9XBSQSTb8uPeih9UzgRBGlHV0BSlw14NkXuWLRq/OWSL1SY9fALFfqGBTHHQ0hHw0m5G2KGB+EAqxH+50w3PyQmDqTUWCAghd2BZCI9LYETk7UbAEw+mwcRCNa/eHBlbJ5D0bqmI388Ws+NYUAIQXkEOUie48HYTELOe0WZlGA3XehbFhY/VcHU2/NJeWbCwBnKU4sQ8u8IIZuEkNd2fOyfEkJWCCGvdn59x45/+58IIVcIIRcJId86jDENm9KpDDybHtjJyepf1fD6v1lB+eEMnvjlw+BTyQIkQMwGAlL4N1wAsP1KG8qUFKkWsnJJgJjj0YyIgOQ5Hja+1MDks3kmywzlogBlUkTjcnSDtJvX/Z+1fMQEpKBc0qyG/36mb1ixyD8CAMIRiHmBCQdSV0CKiQOJlzhIRQF6yB1ISQe22xEzPMafyGL9yw1Q72AEivaSf/BQOJ50MO6F0sMZWA23e085CLR1C9RLArR7JXgGv/mRNbhmkn2UMByGpTr8HoBvu8vHf51S+njn16cAgBByGsCPAHi485rfJIQwt0MrPuQ/fGpvDn+Dtvr5Gl7/zRWUH8ng8f/xcNJ57Q6CEjYWOrF5DkXtgoqxx7KRym0hhCB3JIXWjWg4XrbPtmG3XSbL1wIKJ9JoXNJG0kHlIGhd18GnuMgJGHIgINXC7aYAAG3DikX+UYBUEGCyICAtm+Bk0nXmxAFlQoSxHd5rxnM8aOsmskn+0W1MP1eEVXNQu3AwkRDNK53OnQ9E6+BhWJQe9hu91A4wBykI7U665PWGMul/n7bPtjD1jgKySbZXwhAYivJAKf0CgGqPn/49AP6EUmpSSq8DuALg2WGMa5hIeQGZORn1N4Z7U135yxpe/60VlB9NxKN70XUgMSAgNa/pcA0vkvlVuSMK1CUTnsO+YLH2Uh1SQUCZ4S55hZNpmDUHZiW8m6r94AdopyIXstktYQt5kLbneDC27cgJeLshFXjYDHRhU1dMZObkyF0bu5GakEIdoq2tWaBufFxhvTLxVA58ijuwMrbGFQ1ijocyGR9xdT8okyJSY+KBCkjqasdBmTiQeqJ7iEOAYx+aGO1gEiLLQasPf48Qcq5T4lbqfGwOwNKOz1nufOwtEEJ+ihDydULI17e2toY91r4pPpRG/aI2NOvtyudquPDbKxg7k8Xjv3QYvJSIR3cjyECyGMhAqr7mhxFGsYNe7mgKnk2hrR6c1XkY2G0XW99oYfq5AtMhm4UT/glr40o0XGE7oR5F64aB/NHonSKLOR6cSGCEvIRN37QBGo8ObAESIyVs7WUjNh3YApRx34F0UKVQ/aIux6ussFd4mcPkMzls/HUTnjP8TNHmFT//KEoO8GFCCEHp4QyqB5iDpK2akEpCLLL1BoGY4SGPCZh5ZyFxOCYMjYNUIH4LwAMAHgewBuBf9/sFKKW/Qyl9mlL69MRE+FTV4qk0HM1De3HwuS8rn6t2xaPHfvFQIh7tgqBwIDwbGUi111RkD6cg5YVRD2XgBJ3YmoyXsW18pQHqUMy8m93yNQC+jZkA7SW2Bb27oa6Y8CyK3LHoLZYIIZBLQugdSHHqwBYgFcNfwuboLsyKE5sA7YDUpAjq0NCGz3c7sMVsXnph6rkiHNVF5dXhdvtydBftZROFpHytL0oPZ2A33a4Iuh/UVRPbr7Z2rRhQV8zEfdQnb/sXD+DUT9/Vi5GQMBAObNdKKd0I/kwI+V0Az3f+ugJgYcenznc+xhylh3wXSe1NDbkjg3sgLX+2ijd+ZxVjj2fx2P+QiEf3gxACMcuHvoTNtTzUL2qY/9byqIcyFNIzMjiR+J3Y3j3q0eydtS/UkZmTkWO8PTwvc1AmRahL0Qg230nzeifHIoIOJACQSyLMWjg3wgF6p22wEqcStrwAz6RwDS+0jSxuBWizff/ql9S4/3NobNvdIPowoS4bUCalZD13F8bOZCHmeKy/3MDE08Pretq8bgAUyCcB2n1Rfriz13ld3Ve+TuV8G6/+bzfhWb6TKTMno3BCQeFkGoUTaWQXZIAA6qqFqXew1/12lMjF8N3zEqLFgQlIhJAZSula56/fByDo0PYJAH9MCPk1ALMATgD46kGNa5CkJkTIYwLqb6g49G1j+/56lFJc/9MtXP1/NzH+RBZnfiERj3pFzAqwQ17C1rikwbMpyo+wm6uzGxxPkD2cQpvhTmz6poX6mxqO/+hUJCzumfmUf/IdMVrXDHASQTqip5RyWfCF2BCjb1jgZQ5SIXpuynsR/F+tpgMlFU7hLK6lUsqEv4HSNy0UT4ZPIEg6sN0bTiCYfFseay/VhyrONq/4TW8Kx6N58DAslEkJqQkR1ddVLOxxr1M554tH6RkJJ35sGq0bBuqXNGx9o4XVz9cBAHyKQ/5YCo7qJg6khISQMZSVHiHkPwB4L4BxQsgygH8C4L2EkMcBUAA3APw0AFBKXyeE/EcAFwA4AH6OUhrunf89IISg9NCt2uD9bDg9l+LNf7uKlRdrmHl3Ead/ZhackIhHvSLmwu9Aqr6mgnBA6aHwLW4HRe5IChtfbu77ehgVay/5C5npdxVGPJLBkF2QUXm1Bc/xInU/aV7XkTuSYjqjajfksojts+1QX0faugVlSgzt+IZBV0BqON3ON2FDXTFBeBKrbCrAP9ADEMpObJ5Doa5aGH8yN+qhhJaZdxax8tkaNr/exMw7h1M+3riiQ5kUIxkhMGxKpzPYPtsC9Wjf4fw7xaOn/vFRSHkB40/41wKlFPqGhcYlHY3LGuqXNPApDsVT0csJTUhgmaHcNSmlP3qXD39kl8//5wD++TDGctAUT6Wx/nID+oaF9PTeFHPHcHH+15ew/UobR79/Ag/88GSsFuWDQMzyoe7AAvgB2vkHlEgHA+aOpLDy2RqMig1lnL0NTOVcG4UTCpNjvxvZ+RSo63cAikprV+pRtK4bmH0P2xlVuyGXRLimB0f3IIb0fqFvWLHLcwkEpDDnILWXTaRnpciKq/dCSPEQczyMEK4D9A0L1KWJA2kXig+lIY8JWH+5MTQBqXlFRyGE7jQWKD+cwdpf1dFeNpE71PtaYvvVFr75LxeRnpXx1K8ceYt4RwhBelpGelpmPncyISHKROcIOiQEOUj1N7U9vd6sO/jGP7uB7VfbOPVTszj+I9EonTlo0tMS1BUztC4kR3fRvKKj/Gg0y9cCgiywsJff3A1KKdQlE9nD0RBaACCz4G9YohSkra1bcA0PuWPRLUOQyx2hIqRB2tSj0Det2LlcpEKn42eIBSR1xYydsBeQGhehb1mjHsZbaC/7z8O45VL1A+EIpt9RQOXVNuz24K8vs+7A2LaT8rU9UurkIK18tgrX7K1bXiAeZeZkPPWP3yoeJSQksEMiIA2YzLwMIcOj9oba92vVVRNf+1+uor1s4PFfOoT5D0QzXPkgmHquCOpQbHylMeqh3JXaGxqoB5QfibYtN3fI7/zVus6egGQ1HNhtN1JtUDOznVDKZfbm4140rwUB2tGZpzsJQoDNajiFCrPmwLNp/ASkzgbIDqmA5Foe9A0L2Zg6XZQJKZQOpCCXKhtTYa9Xpt9ZBHUpNr7SHPjXbl71D3nziYC0J5QJCRNP57D0mSpe+tmLuPzH6zB2OeDYfqUjHs3LePJXjkDKJeJRQgLLJALSgCEcQfGhdN8OpPpFDV/7lWtwDA9P/5OjmHgq6TiwH/LHUsjMyd0Mm7BRfa0NTiSRt0/zKQ7pGQmtG/qoh9I33fDZhegs8v1ObFKkgrRb13RwIon0aX7YHUjauv/zlI5RBzYA4CUOgsLBbITT6aqtWQCNb6v41IQIfdsCpXTUQ7kNddlEakIMbee+sJA7mkL2cAo3n98G9QY7h40rOggH5AfYMTluPPaLh/D0PzuK0ukMbnx8G1/8uYs4/xtL3UOdgK2zLbzaEY+eSsSjhIRIkFzFQ6B0Ko3tb7Rg1u2eWilufrWJ8//nEuQxEU/+o8N7zk5KuAUhBDPvKuDKn2z6pRUhCzitvaai+GA6Fl31ckcUNC7vraRzlARlXtkICUiA//9RI1TC1rxuIHsoBU6Ibqmv3HUghVNA0jf8MqG4OZAAQCwIoS1hU1eCDmzRFVd3Q5kQ4ZkUdssNVblMe9mMrSusHwghOPYDEzj3a0t+FtK7BpeJ07yiI7OQSkS8fUAIQelUBqVTGWgbFpY+XcHK52pY/2IDxVNpHP7OcYADzv3aErILvngkZsNzHSYkJOyd5M45BIp95CAtf7aKb/7rReSOpPDs/3osEY8GyHRnsRE2F5LVdNC6YaAU8fK1gNyRFIwtO7R5VPeivWRAzPKRa0uemZehrZvwnN5yC8IMpRSt6zpyES5fAzpOlwwPsxZOoUJbt0B4P3MmbkgFAXYznPOiLhsAAdIz8RP2ACA14f+/w9RQg3oU2qoZW1GvXyafzSN7OIVr/3kTnjsYFxKlFI0r/397dx4f11Xfffzzm0Uz0mhfLNuyvNuJ7SxOcEIgJGQjLE1JoCwFCmEpS/s8XSjpU/rwQAuFQltaWqAFUqAsJSVAwr6GBAgJBJI4TmJns+NVsiVbuzQaaaSZ8/xx78iyo92zSfN9v15+eebOzL1npKM7Z373d34nofpHWVTRXMZZb1zBZZ8+i81vWM5I1xgPf/QwD//jYSpXR3jWe9cpeCSyhCiAlAPV66MEyozex2cOIB35SQ+P33yUxu2VE0tZSvaUN5VRt7WCY3f3FVUKe+9jXn2s+nOWdgHtjKq13kB58NDimsYWbxsl1hpZckXsK1v9ldiOFl9x2flKdCYZH05TvYQLaGdE6kMz1pgopERnkmhj6a30BVBWHSzaVdjibaOULysriUzXqZQ3eQHNkSIqpD3cmSQ9phXY5soCxoZXLmP4WJKOe7NzMTDRmWQ8nlL9oxwIVwRZc10jl358M+f9RSurX9LAs/7fOsKVxbl6qIgsTGmOKnIsEApQs6mCviemL6R95CfdPPHZozReWMX5N60mGNGvIheWX1bL8LEkA0/nJnixkMBUz+44wWigJL70wuJcic05x9CR0SVVQDsjMyVvKdRByhRnL4W/pWh9uGiLaA93Jkuu/lFGWZFPYSvlQEW0sfgykCYKaJfw72W+mi6qomptlP3fOJGVLKT+fd54sGbj0q5BWUiBoNF8SQ1nvXGFgkciS5CiFjlSt6WCwYMjjA8/c9rOkR9188Rnj9G0o4rz39VKIKxfQ640X1JDIGwcuzv709hSo2nuf98B9t3aOa/X9eweom5rbEnXbJksUhuirC60qFZiS/aNMx5PLakC2hkV/kpsQ0cWz+9jOgP7E1jQllydqqlE6kKMdBfPF+HJEp3Jkqx/BP4UtsFU1ov8nql0yhE/mizplb5CMa/IeTFlIE0szlDCv5f5MjPWv3IZiY4kHVkoSTCwL0GgzEo6uCoiciYUuciR2rNj4KDvqVOnsR3+UTdPfN4LHp33Fwoe5Vo4FqTpWVV0/Kqf9Hh2B/h7v9JB/5PDHPxWF4muuQ1QR3rGGD6apL5E6h9lVK2NMnho8QQsJgpoL8EBZrAsQMXysiVRSHvgQILK1ZGSOI/GWqMk+8aLLttlbGic8Xi6pDOQcDA2WFw13hKdSVyqtKdKmZm3EltRZSCNEG0IE6pQVsZ8NO2oompdlP23nXkWUv++YarXl5fklFsRkWxY+qPuAqnZXI4FoG9SHaTDP+jmyc8fo+kiP3gU0o8/H1ZcXsvYQIruhwezts+uXYMc+VEPzc+tAeDA7Sfm9Lqe3UMApRdAWhMl3jZCemxxFG6Ot3nBrqVa6DS2KsJQ2+IJ6E3FK6A9QtW6pT99DaBmg/c++3M0HXehhjtKdwU2gIhfZL/Y6iBNZLqUcAAJvGlsI13FE0AaaivtaYULNZGF1Jk8o4zy9Lj3uaH6RyIiC6cIRo6EokGq1pXT69dBOvT9Lp78wjGWXVzNee9creBRHjVsryRcFczaNLbk4Dh7PtVObFWEbX/cQsvVdRz9WR+JOaTJ9+6OE64KUrl6aQYmplO9rhyXWjx1d4aOjBKuClJWszSvEle2Rkl0JBdNQG8qI11jjA2mqF5fGn9LVeuiYOSsnttCZQJIFSUaQAr7AaRiywyLt/sBpJWlHawoby5j+NhoUawC6tKu5OtSnYmmZ1VRtT7KgduPLzijfOjwCOkxp/pHIiJnQFGMHKrbUsHAvgQHvnWCp77YwbJnV3Pun7eWTO2bYhEIBVj+3BpOPDDI2BQ1qebDOcfj/3mUsYEU5/zJKoJlAdbd0AgGB745cxaSc46e3XHqtsWwQGn1gcrMSmyLpA5S5irxUluBLSO2KoJLQ3wRr8Q2UUC7RDKQQuVBYi2RogsgJTpLOwOprNoLMhdjACnSECr5qVItV9aSHnMc/M7csoRzKXFijHSytKcVngkzb0W2ROfYgi8IniygXRqfGyIiuaAAUg7VbomRHnPsu6WTZZdUc+6fKXhUKCsu9waRx+8bOKP9HPtlH8fvG2DDq5dNfHGNNpbRclUdR3/WS+L49F/IE51JRrrGSm76GnjZCcFogMGDxfXldyrOOeJHRqhsXbqZLZn3tpgLaQ/sT2ABqFyzdH9Pp6veUM7A08MLWv0xVxKdScrqQiW7kmiktjgzkIbaRlSoGW8V0OWX1nD4B92M9BR2KltmavRSXN0zXxovrKJ6Q/mCs5AG9g0TrgoSbQrnoHUiIqWhNEd8eVJ7dgXB8gDNz6nm3D9V8KiQqjeWU7GijGNnsIJH4kSSJz93jNqzKlj70sZTHlv3siYwm7EWUs9ubzpj/TmVC27DYmUBo2rN4iikPdo7zvhwekkW0M6IrSzDAifrpCxGA/sTxFZFCJaVzsdYzYZykv0pRotoNbbhzmTJFtAGCFUEsWBxBZBc2jHcnlSgwrfh1c24FOz/xvGCtkN1qc7cRC2k42Mc/UXvvF/f/3SCmo3lSza7WEQkH0pn5F0AZVUhLvvUWZq2VgTMjBWX1dK7Jz7nFdMmc2nHnn9vwzk4509WPWMKWrQhzKpr6jj6i+mzkHp2x4nUhahYUZpftqrWRhk8OFJ0y12fLu5n5cSW8NLwgXCA8uVlizYDyTlH/95EydWxqC7CQtqJjmTJ1j8CLzgerg6RHCh8jZ2Mke4xUqNpZSD5KprLWPWCOo7e1Uv8aOGC5kNto0TqQoRjpT2t8Ew1XlBJ9cZyDtx+gvT43Ov4jSdSxNtGqS6xzw0RkWxTACnHwhVBXekoEssvqwWg45f9837toe910/vYMGe9aQXly6b+srT2hiYsYOy/7ZlXOV3a0bN7iPpzK0u2P1SujZJKpGec5lcMhvzl7ZfyFDbwplEs1gyk4WNJxuMpqjeVVh2LyjVRLFg8hbRTyTSjveMlW/8oo6w6RLKveDKQJgpoK4A0Yd3LlxEIB3j61s6CtSGuFdiyIlMLaeTEGEd/Pves8oH9CXCqfyQicqYUQJKSUdFcRu1ZFRy7u29eNUQGD42w76udNF1Uxcoraqd9XrQ+TMs19Rz7RR/DnacGSYbaRhkbSJVk/aOMqrXeoG3wYHFnvQy1jRKuDlJWHSp0U3Iq1hphuCNJKrn4VmLr3zcMQM2m0rqSHCwLULk6WjQBpFIvoJ0RqQ2RHCiiAJKmSj1DpDbE6usa6Pz1gBdIyDOXdn4AaWlfmMiXhu2V1GyaXxbSgF9AO5PJKSIiC6MAkpSUFZfXEm8fnfNqYKlkmt2fOEI4FmTr21tmzR5ad0MjFjQOnJaF1Lt7CIC6baUbQKpsjWCB4g8gLfUC2hmVrVFwMFzAKR0LNbA3QTAaWNJ1qqZTvaGcgf2JoiikPdzhBZBKuQYSeCuxFVMNpHjbKOGqpR8En6+1v9tIuCrI3ls68n7szLTCUjxn5UKmFtJI1xhHfza3LKT+pxOUN4f1dyEicoYUQJKS0vycGixkc14C9ulbjzN0eJStf9Qyp0FHpC7MqhfUc+zuPoY7Tn4x79kdp7y5jPKm0v2iFSwLEGuJFHUAyTnvKnEpDPIz73FoEU5j69+XoHpD+TNqkZWC6g3ljMfTE8GbQlIGkifWGmXkxBhDh4vj3BZv11SpqYQqgqx7WRM9j8TpfnQor8fuesg7XrWmT2VNw/kns5DGh2evQTawL0H1htLKWhURyQUFkKSkhCuDNF1YRce9/aRT01/BT42kOfT9Lg59r4uWa+pourBqzsdYe72XhbT/Nm9FtnTK0bsnTv25pZt9lOEV0i6O6TdTGe0eYzyRXtIFtDMqVpZhQYgfWVwBpFQyzeDBBDUlVv8oo8afflEM09iGO5KEYgHClaVdFLjl6jqCkQAHvjX9Kpz54pxjqG1U9Y+mseraeqINYfbd0pnXLL72O3uoXBOlau3Sz27NFzNj0+uWM9o3xs4PHWRshiDSaN8YI11jqn8kIpIFCiBJyVlxeS3J/nF6prgCOdI9xt6vdHD3Hz3JU1/soPbsCs56w4p57T9SF2bVtV4WUvzYKIMHEown0tSfU5mtt7BoVa0rZ7RnvKjqhUyWycYpheWvA6EAFcsji24ltsEDI7gUJbcCW0ZsVZRA2IoigJToTFLeXFayCwNklFWFWHVtPR339j+j/l2+JftTjMdTJZFFuRDBsgAbXr2MgacTHP/NQF6OObA/weCBEVZdXVfyfyvZVrc1xrl/3srA/gQPzRBEytQ/UgBJROTMKYAkJafxgkpCseAp09j69w3z6L8d4Z7//SQHv9NF/TkxLvrAOnb87TqC0fn/may9volA2Dhw2wl6dscBb6BT6jJXX4t1GltmBbZSyEACr8juYpvC1r83U0C7NL8IBEJG1briKKQ93JmkosSnr2Wsua4BCxoHv13YLKR4u3duVbHm6a24vJbYqgj7vto5YyZytrTf2UsgbBMrwUp2NT+7hvPeuZqB/Ql2fvAgY/FnBpH69yWwgHcRS0REzowCSFJyAuEAy59bw/HfDnDsnj7uf+9+fvt/93Ni5yCtL2rgeZ/YzPnvWk3t2bEFXy2M1IZovbaeY7/s49gv+qhsjRCpVeHGkwGkwn/5nUr8yAhlNUHKqkrjd1XZGiHRubhWYuvflyDaGCZSFy50UwqmekMFAwcSefnyO510yjFyIlny9Y8yInVhWq6s4+jP+xjpGStYOyZWYNMUtmlZwNj4mmaGjyY5+vPenB4rNZLm2D19ND+nhnCstKd65tKyi6s57y9WM3hgZMog0sDTCSpXRwlG9LVHRORM6UwqJWnF5bWkk47dH29jtHeMzTcu5/JPncVZN66gfFl2vhCtvb6JQJkRbx+l/lxNXwMIV4aINobnvApevg2V2DLLsVXeSmzx9sWThdS/d7jkpyFUb4iSHnUMF/D3qzXBKQAAIABJREFUNto9hktpBbbJ1ry0EdKOQ9/rKlgb4u2jBMsDROpLIwi+UE07qqjZVM7+rx/PaQC949f9pBJpWq6uy9kxxLPsomrOe1crgwf9INKQF0RyztG/b5jqDaX9uSEiki0KIElJqtlczqY/aOa8d7Vy6cc3s+Z3GglVZPfqYFlNiNYXNgBQt03T1zKq1kYZOFB8GUgTK7CVyPQ1YOK9LpZC2qN944ycGKN6U2nWP8qo8VcS6i/gNLbMKnDKQDqpormM5ZfW0HZHD8nBwtR5i/sFtFVrZ2ZmxsbXLWe0Z5wjP+rO2XHa7+ol1hKh9uzSPmfly7Id1Zx/UyuDh0Z48IMHGBtKMdyRZDye1gp4IiJZogCSlCQzY+1Lm2h+dk1OlwJf9/ImNr9hOY0XzH0Vt6WuZnMFw0eTJPuLq5D2SPcYqZF0SWUgVazwVmIbaivOjLDT9e8r7fpHGRUrygiVBwpaBykTQFINpFOtvaGJ9KjjyA9zF5SYyVC7VmCbq/qtMRouqOTAN7umrJtzpoaOjND/5DArr1Lx7HxqelY159+0mqHDozz4dwfo3uUtmFKqCy+IiGSbAkgiORSuCLLmukYCIQ0eMzLFxHsfixe4JafKZOGUUgZSIBSgYkVkonh4sRvY6xVCrS7xQqgWMKrWlxc0gJToTBIIG5H60q1FNZXK1ihNF1Vx+IfdjCeyH5SYyVg8RbJ3XCuwzcOm1zQzHk+x/7bjWd93+529WNBY+XwVz863pgurvCDSkVGe/MIxAhEjpr8LEZGsUABJRPKqen05wWiAnj3FFUAaKsEAEkDlqgjxRZSBVLlGhVABqjeUM3hohPR4YQqgJzqTlC8ry2kG52K17mVNjMfTtN3Rk9fjdv6qH4BYa+lkUZ6pqrXltFxTx+Hvd09kOGZDKpnm2N19LLu4irJq1aMqhKYLq9j+f1YTCBk1GysIBHWuEhHJBo3CRSSvAiGj9uyK4stAahuhrDZEuLK0Bvux1iiJ42OkRot7JTaXdvTvS1BT4vWPMmo2lOPGHYOHCpM9NtypFdimU7OxgvpzYxz6bnfeVjg8sXOQJz53lPpzYzScp0Ub5mPTHywnUhvisU+3Zy0ge/y3A4wNpWi5uj4r+5OFadxexSX/uJFtf9xS6KaIiCwZCiCJSN7VbY0RbxtltK946iANHSmtAtoZla2RRbESW7x9lFQiXfIrsGVkVhQaeDp7WRNz5Zwj0ZHUCmwzWPeyJpL94zlfJh6g76lhHvmXw1SujXL+Tas1ZXqewhVBtrx1JUOHRzn47eysoHf0rl7Kl4WpP0cLaBRarCVCeZPOVSIi2aIAkojkXWZQXSxZSC7trcBWSgW0MzJ1IYaOFPc0tv69Xr0fZSB5ok1hwlXBgtRBSvanSI2mlYE0g7ptMWo2lXPw212kx13OjjPUNsJDHz5EpD7MhX+9llB5dlcTLRVNO6ppfm4N+287ccaLCgx3jNKzO+4Vz9YUTxERWWIUQBKRvKtaV06wPEBvkdRBGunypnCVYgZSxfIIFrSJIuLFqn/fMKGKABUrFLQAbyXJ6g2FKaSd6PD6igJI0zMz1r28iZETY3T8qi8nxxjpSrLzQwcJhIwL37OWsprSmn6bbWe/aQWhaIDHPt2OSy886Nd+Vy8WgJVX1GWxdSIiIsVBASQRybtA0KgrojpIQ21+Ae0SXKUlEDJiK8smfgbFqn+vV/9IV/RPqt5QztCRUVIj+a1fNdyZBNAUtlk0XlhF5ZooB7/ZdUYBiamMDY2z8+8PMT6c5sL3rKFCwbwzVlYTYvMbl9P/VIIjP15YAfT0uOPoz/povLCKqFYoFBGRJUgBJBEpiLptMeLto4z2jRW6KcT96VulunpRrDVa1CuxjY+kGDo8QrXqH52iekM5OBg8mN8spERnEgzKm/QFeSZmxrobGom3j3LigcGs7Tc1muahfzjMcEeS7X+5mqq1+rvIlhWX1dKwvZJ9t3SSOJGc9+u7dg6S7B+n5WplH4mIyNKkAJKIFETdNm+loGKYxjZ0ZJRIXYhwrDTrh1SuingrseU5k2WuBvaPgFP9o9PV+IW0+/M8jW24I0m0MUwgrCHEbJqfU0P58jIOfPMEzp15FlI65XjkY0fof2qYc/90FfXnaMW1bDIztrx1JRg8fvPRef/O2u7sIVIXomF7VY5aKCIiUliaMC8iBVG1NkqoPEDPnjjLL60taFuG2kaIlWD9o4zMex9qH50IShSTgb3eSmNage1UkbowkfpQ3usgJTqTmjI1RxYw1l7fyOOfOcqeT7YRqpg+SG1BI1IfJtoQItIQJlrv/X4DIS9Q55zj8Zvb6do5yNl/uILmS2ry9TZKSnlTGRtf28yTnz/Gsbv7WPn8uWUTjXQl6d41xLqXNREIaqqtiIgsTQogiUhBBIJG7ZZYwTOQXNoRbx9l1dX1BW1HIVX6U/fiR0aKMoDUvzdBeXMZZdX6yDpd9fr8F9Ie7kyy7KLqvB5zMVt5eS1H7+qla9fQjM9Lj7kpswDLakJexlfI6HtymPWvaKL12oZcNVeA1mvr6by3n6e+2EHD+VVEamc/97T/zCuW3nKVpq+JiMjSpdG4iBRM3bYYXTsHGekZK1jB0cSJMdKjbmI5+1JU3lyGhaxoC2n37xumbmus0M0oStUbyznxwCBj8VRepmCOD6cYG0hpBbZ5CIQDXPyhDXN67vhwipGeMUa7xxjpHmOke9y77W9be0Mj61+5LMctFgsYW9/Rwq//ch9P/tdRznvn6hmf79KOo3f10nBeJeXL9LchIiJLV04CSGb2eeA64Lhz7hx/Wz1wK7AWOAi8yjnXa2YG/BvwEmAYeKNzbmcu2iUixaV+mxcU6H0szornFWYaW6aAdmWJFtAGLxss1hKZ+FkUk5HuMUZ7xqnZqPpHU8lkjA3sT9Bwbm7r4SS6krTf2QtoBbZcCVUEqawIUrmqdM9HxSLWEmH9K5p4+qvHOf68gRmz7rofHmKke4zNNy7PYwtFRETyL1cZSF8APgl8adK2dwN3Ouc+Ymbv9u//FfBiYJP/79nAp/z/RWSJq1obJVQRoHdP4QJImaybUs5AAq+Qdt9Tw4VuxjP0Z+ofbSq+qXXFoDoTQHo6NwGkxPEknfcNcPw3/fTvTUwcs/ZsBfRk6Vv70iY6fz3Ann9v48DK6T8jRrrHCFcHadqh4tkiIrK05SSA5Jy728zWnrb5euAK//YXgZ/jBZCuB77kvKUu7jOzWjNb4Zw7lou2iUjxsIBfB+mxwtVBih8ZIVJfuiuwZcRaI3Tc28/4SIpQtHh+Fv37EljIqFqrjIyphCtDlDeHs1oHyQsa9dP564GJ/Vatj7Lxtc00X1JNxfLSDrZK6QiEjHP/bBVPf/U4qeT0q1SGK4Msf17NRMFzERGRpSqfNZCaJwWFOoBm/3YLcGTS89r8bQogiZSA+m0xuh4sXB2kobbRkp6+lpGZMhNvGy2q6WL9e4epXhfVkvEzqN5QQf8cssfa7ujhwLdO4FLTL03uHCR7x/39lrPpdc0su6RGq65JyapcFeX8m2augSQiIlIqClJE2znnzGz6Eew0zOxtwNsAVq/Wh7nIUlCXqYO0J86Ky/I7jc2lHfG2UequVYHmzBS+oSPFE0BKpxwD+xO0XFW6K+TNRfWGcjp/1U+yf5yymmd+rLu0Y99XOzn4rS5qz6qgomXmDKLYygjNl1SrGLCIiIiInCKfAaTOzNQ0M1sBHPe3twOtk563yt/2DM65m4GbAXbs2DHvAJSIFJ+qNVFCsQA9BQggJY4nSY85Kls1JadieRmBsBVVIe34kRHSo071j2aRqYPU/3SCpgtPrcGSHk+z5z/a6binn5Zr6jj7LSsJBK0QzRQRERGRRS6fcwK+A9zo374R+Pak7W8wzyVAv+ofiZQOCxh1W2L07sl/HaShI5kC2prCZgGjYmVkoqh4McgUba7ZqADSTKrXRcF4Rh2kseEUD/39ITru6Wfja5rZ8lYFj0RERERk4XKSgWRm/4NXMLvRzNqAvwE+AnzNzN4CHAJe5T/9B8BLgH3AMPCmXLRJRIpX3bYYJx4YZKQrSbQxf9NmMtk2lSW+AltGZWuEnj1xnHOYFT7Q0L93mHBVkHLV35lRqDxIrCVySgBppHuMhz58kHj7KNv+dwsrL68rXANFREREZEnI1Spsr5nmoauneK4D/lcu2iEii0O9Xwep57E4Ky/PX7BgqG2UaEOYUEXxrDpWSHVbY3Tc00+8fXSiqHYh9e9LULOpoiiCWcWuen053Q8P4pwjfmSUnR8+yPhwmgv+ei0N51UWunkiIiIisgRoWRsRKbjK1VFCsWDep7HF20aJqf7RhMbtXqCh66GhArfEm34Vbx/V9LU5qt5YTrI/xbG7+7j/ffshDRe9f52CRyIiIiKSNQogiUjBWcCo21qR1wCSSzs/00YBpIxoYxmx1gjduwYL3RQG9iXAoQLac1TjF9Le8+/tROrDXPSh9VSt1c9ORERERLJHASQRKQr12ypJHB8jcSKZl+MNd3orsMVaCz9Vq5g0nl9J7+PDjI+kCtqO/n3DAFRvrChoOxaLyjVRwlVB6rZWcNEH1lOex1piIiIiIlIaFEASkaJQ59dBykcWUno8zd4vdwAnl0AXT8MFVbhxV5BV8Sbr35sg1hIhHFN9qrkIlgW49OObedb71hGu1M9MRERERLJPASQRKQqVrRHCVUF6H8tt4CI9lubhfz7CiQcGOevNK6harQykyerOriAYCRS0DpJzjv69w1Sr/tG8hGNBLKCC4yIiIiKSGwogiUhRsIBRtyVGTw4zXzLBo64HBzn7zStY/aKGnB1rsQqEA9SdE6N7l7eiVyGMnBhjbCCl+kciIiIiIkVEASQRKRp122KMnBgjcTz7dZAmgkc7Bzn7D1fQquDRtBq3e/Woho/lpx7V6Q5+pwvMq4slIiIiIiLFQQEkESka9Zk6SFmexpZKpnn4o4fp2jnIlreupPVaBY9m0rC9CoDuXfmfxtb7eJy2n/Sw+sUNxFq0Qp6IiIiISLFQAElEikZslVcHqWd39gJIE8Gjh4bY8raVrHpBfdb2vVRVNJdRsaKMrl2DeT1uKpnmsU+3E20Ks/H3m/N6bBERERERmZkCSCJSNCxg1G2L0ftYPCv1d1LJNA//02G6dw2x5e0rWXWNgkdz1XhBFb174qSS6bwdc/83jjN8LMnWt7cQjOrjSURERESkmGiELiJFpX5rjJGuMRLHx85oPxPBo0eG2PqOFlZdreDRfDRsryQ95nK+Kl7GwIEEh77Txcoramk4T7WPRERERESKTajQDRARmazuHK8O0sMfPUxla4RoQ5hIQ5hofZhoY5hIfZiyam+58lQyzWjPGCM944x2jzHSPTbx/9CRURLHk2x9RwstV9YV+F0tPnVbYwTCRveuIRr9mki5kk45Hvt0O+HqEJvfsCKnxxIRERERkYVRAElEikqsJcLq32lgYH+C/qeG6ewex6VOnc5mQSNUHmBsKPWM14diAaL1YSqWl7Hptc00P6cmX01fUoJlAeq2xejaNchZ5Daoc+i7XQweGOG8d7USrgzm9FgiIiIiIrIwCiCJSFExM8668WTAwqUdyYGUl2mUyTDqGWN8OE2kLuRlKE1kJ4UIRRWAyJbG7VU8+YVjJI4nKV9WlpNjxI+Osv/rx1l2cTXNz1awT0RERESkWCmAJCJFzQJGpDZEpDZE9fryQjenpDRs92oRde0apPXahqzv36Udj32mnUCZcfZbNHVNRERERKSYqYi2iIhMqWJFGeXNYboeGsrJ/tt+2kvf48Nsfv0KInXhnBxDRERERESyQwEkERGZkpnRcH4VvbvjpMfSWd33SPcYe7/SQf25MVZeWZvVfYuIiIiISPYpgCQiItNqvKCS1Gia3ieGs7ZP5xyP/+dRXNqx5W0tmFnW9i0iIiIiIrmhAJKIiEyrflslFjK6d2VvGlvHPf107Rxk46ubqWjOTXFuERERERHJLhXRFhGRaQWjAeq2VNC1a5DNr1++4P045+h5NM7h73fR9dAQNZvKWf2S7BfmFhERERGR3FAASUREZtSwvYq9X+5gpCtJtHF+GUOpZJqOe/o5/IMuhg6PUlYTZP2rlrH6RQ1YQFPXREREREQWCwWQRERkRo0XVLL3y9C1a4hV19TP6TWjfeO0/aSbtjt6SPanqFwTZdsft7D80hoCYc2eFhERERFZbBRAEhGRGcVaIkQbwnTPIYCUOJFk/9ePc+yefty4o/HCKtZc10DdtpiKZYuIiIiILGIKIImIyIzMjIYLKum8t5/0uCMQemYgyKUdbT/tYe9/d+LSjpar6lj9kgZiKyMFaLGIiIiIiGSbAkgiIjKrxu1VtP+0l/6nhqnbGjvlseHOJI99up3ePXHqz4ux9e0tlDdpdTURERERkaVEASQREZlV/TkxLAhduwYnAkgu7Tjy4x723tJBIGBsfcdKVl5Zp6lqIiIiIiJLkAJIIiIyq1BFkNqzKujeNcSm18Jwxyh7PtVO3+PDNGyvZOvbVs57hTYREREREVk8FEASEZE5adhexb5bOnn6a50c/E4XgZCx7Y9bWPH8WmUdiYiIiIgscVpLWURE5qRxeyUA+79xgvpzKnnOv2xi5RWasiYiIiIiUgqUgSQiInNSuSbKmt9tpGpNlOWX1ShwJCIiIiJSQhRAEhGROTEzNr9+eaGbISIiIiIiBaApbCIiIiIiIiIiMiMFkEREREREREREZEYKIImIiIiIiIiIyIwUQBIRERERERERkRkpgCQiIiIiIiIiIjNSAElERERERERERGakAJKIiIiIiIiIiMxIASQREREREREREZlRKN8HNLODwCCQAsadczvMrB64FVgLHARe5ZzrzXfbRERERERERETkmQqVgXSlc267c26Hf//dwJ3OuU3Anf59EREREREREREpAsUyhe164Iv+7S8CNxSwLSIiIiIiIiIiMkkhAkgO+ImZPWhmb/O3NTvnjvm3O4DmqV5oZm8zswfM7IETJ07ko60iIiIiIiIiIiUv7zWQgOc559rNbBlwh5k9MflB55wzMzfVC51zNwM3A+zYsWPK54iIiIiIiIiISHblPYDknGv3/z9uZt8ELgY6zWyFc+6Yma0Ajs+2nwcffHDIzJ7McXNFsqER6Cp0I0TmSP1VFhP1V1lM1F9lsVBflcVE/TU31ky1Ma8BJDOLAQHn3KB/+1rgA8B3gBuBj/j/f3sOu3tyUhFukaJlZg+or8piof4qi4n6qywm6q+yWKivymKi/ppf+c5Aaga+aWaZY9/inPuRmd0PfM3M3gIcAl6V53aJiIiIiIiIiMg08hpAcs7tB86fYns3cHU+2yIiIiIiIiIiInNTiFXYsuXmQjdAZI7UV2UxUX+VxUT9VRYT9VdZLNRXZTFRf80jc06LmYmIiIiIiIiIyPQWcwaSiIiIiIiIiIjkwawBJDMrN7NfmFnQzLab2a/NbI+ZPWJmr570vHVm9hsz22dmt5pZmb/9cjPbaWbjZvaK0/Z9o5nt9f/dOIe2fOH0fRQDM/uqmW0qdDuWuhz3xR+ZWZ+ZfW+ObTnLzH5uZrvM7HEzW3DqpJn934W+dop9vdHMPnmG+1B/LhK56vNmtsbfvsvf3zvm0JYvmNmwmVVN2vavZubMrHEB763MzO42s3wv5iALkOPzb8rvi7vM7DtzaMvfmtlN2X+XZ8bMPmpmVxW6HUtdjvviajP7if+5/piZrZ2lLRoLSM7luM//o7+vx83s42beSkcztEVjAZESN5cMpDcDtzvnUsAw8Abn3DbgRcC/mlmt/7x/AD7mnNsI9AJv8bcfBt4I3DJ5p2ZWD/wN8GzgYuBvzKzuzN5O7phZcIaHPwX8n3y1pYTlpC/6/gl4/Tza8nH/GNudc1uAT8z3zUyStUHjmfL7ufpz8chVnz8GPMc5tx3vHPxuM1s5h/bsA64HMLMAcBXQvpA35pxLAncCr57tuVIUcnn+Tfjn0u3OuZfm8k2cqVm+5HwCeHe+2lLCctkXvwT8k/+5fjFwfJa2aCwg+ZCr72LPBS4FzgPOAS4Cnj+H9mgsIFLC5hJAeh3wbQDn3FPOub3+7aN4H6xNfrT6KuAb/mu+CNzgP++gc+4RIH3afl8I3OGc63HO9QJ34J0I58TM3mdm95vZbjO7ORMx968E/YOZ/dbMnjKzy/ztp1yNMbPvmdkV/u1PmdkDfgT+/ZOec9Df1068L1g7Jz22adL9XwLXKHqec7nqizjn7gQG59GWFUDbpNc/Ct6gy8z+ye+bj5jZ2/3tV/hXWL5vZk+a2afNLGBmHwHK/auXX/Gf+wd+/91lZp/JBC/NbMjf9x4z+6mZXez39/1mNvlLV6u/fa+Z/U1m4yz7/Wczexh4DurPxSQnfd45l3TOjfp3I8x9OvNXOTnIuwK4FxjPPGhm3zKzB/0++jZ/25vN7F8nPeetZvYx/+63/PcoxS9n598z4fen+83sYTO7zcwq/O1f8K+m/8o/R77C336FTco0NbNPmtkb/dszjSv+1cweAN5jZgfMLOw/Vp2575w7BDSY2fJsvkd5hpz0RTPbCoScc3f4zxtyzg3P0haNBSQfcnX+dUAUKMMbC4SBzjm0R2MBkRI245cG81If1zvnDk7x2MV4J5yngQagzzmXOXm0AS2zHLsFODLp/lxeM9knnXMXOefOAcqB6yY9FnLOXQz8OV6W02ze45zbgReBf76ZnTfpsW7n3IXOuQ8B/Wa23d/+JuC/AJxzabxo/PnzaL/MQ4774kJ8DLjLzH5oZu+0k1d/3gL0O+cuwruS81YzW+c/djHwJ8BWYAPwcufcuzl59f11ZrYF70P5Uj87JMXJD9UYcJd/1WkQ+CDwAuBlwAcmte1i4Pfw+vMrzWzHHPb7G+fc+c65e9Sfi0Ou+7yZtZrZI3jn4X/wB6KzeQpvoFoHvAZvEDnZm51zzwJ2AH9qZg3A14DfzXzhxjt3ft6/vRvv70SKWB7Ov1HzLuLcZ2Y3zLN5t/tjgfOBxzl5xR28L/fPwxsffGQO+5ppXFHmnNvhnHs/8HPgd/ztv++3Ycy/vxPvir7kQI774magz8xuN7OH/CDNTNnnoLGA5Fgu+7xz7tfAz/Cyko8BP3bOPT6HZmksIFLCZrvq3Aj0nb7RzFYAXwbe5H/AFMKV5s3zfRQv4r5t0mO3+/8/CKydw75eZV420UP+frZOeuzWSbc/C7zJH1C8mlNTQY8Dc5kCIgtTVH3ROfdfwBbg63hXX+4zswhwLfAGM9sF/AbvAz1TQ+C3zrn9fgry/+B9sTnd1cCzgPv9fVwNrPcfSwI/8m8/CvzC/9LyKKf28zucc93OuQTe38LzZtlvCrjttHaoPxdeTvu8c+6Ic+48YCNwo5k1z/Glt+N9aX423hXqyf7Uv3p9H9AKbHLODQF3AdeZ2dlAOHOV3v9bSNqkWgpSlHJ9/l3jX8R5Ld50jA3zeO05ZvZLfyzwOk4dC3zLOZd2zj0GzKV/zzSueMZYwL89cTHJp3NnbuWyL4aAy4Cb8L7Mrseb9jMtjQUkD3LW581sI17/XYUXbLrK/Jkbc6CxgEiJmi0tNYGX2jjBzKqB7+Nl7dznb+4Gas0s5Ee+VzH7XNh2vA/bjFV4V/VmZWZR4D+AHc65I2b2t6e1MzM1I8XJ9zjOqQGzqL+vdfiDBedcr5l94bR9xSfdvg0vo+ku4EHnXPdp+0vMpf2yILnsiwviZ2x8Hvi8me3Gmz9uwJ845358WluvwEsVPmUXU+zWgC865/56isfGnHOZ16Tx+7lzLn1aivlUx5lpvyP+h/dk6s+Fl5c+75w76vffyziZ+j6TW/GC81/0+16mbVcA1+DVVho2s59Pav9n8ep7PMGpX7bBS5sfmWt7pSBy2hedc+3+//v9fnMB3hX1ufgCcINz7mHzpqJdMemx0Um3M4VhpxsLzDaumBgLOOfuNbO1fp8POud2n7Y/nTtzJ5d9sQ3Y5Zzb7+/3W8AlwOdmepHGApJjuezzLwPu84M7mNkPOTl9cTYaC4iUqBkzkJxXmyjoD6wyaZTfBL7knPvGpOc5vBTITGX/G/Hn6s7gx8C1Zlbnp0Be62/DzL7kp2VOJ3Mi6jKzyknHnclBYLt5c81b8VJ7AarxBob9/hX4F0+3A+fciN/GT/HME99mvBRMyYEc98VpmdmHzexlU2x/kZ2sgbEc7+piO17/+KNJj202s5j/sovNWyEjgJfBdo+/fWxSSu+dwCvMbJn/+nozWzPPZr/Af1053vz3exewX/XnAstlnzezVX7/wD//Pg940r8/4/nXeXVe3oP3ZXuyGqDXHzCejffFK/Oa3+BdhXwt3hX3TDsagK5J03+kCOW4L9b5GRuYt4LPpcBj/v0pz7+nqQKO+efQudTQOARsNbOIedONrva3z3dc8SW8LGSNBfIox2OB+/G+gDf5969ilr6osYDkWo77/GG80h0hv+89H28qsMYCIjKtuRRO/Qkn02tfBVwOvNFOLrmbqQn0V8BfmNk+vA/QzwGY2UVm1ga8EviMme0BcM71AH+H94F9P/ABfxt487WnqscRAkadc33Af+J9qP3Yf/1s7gUO4A0GPo5XpwDn3MN4U9eewBsM3jvLfr6Cd8XnJ5kNfuAp4ZzrmEM7ZOFy0hf9x36Jl4J+tZm1mdkL/YfOBab6vV4L7DYvRffHwF/6v//P4vWxnf6VyM9wMgvufuCTeB/OB/AGAAA3A4+Y2Vf8qRb/D/iJefVp7sCr4zEfv8XLlnsEuM0598B89qv+XFRy1ee3AL/x++8vgI9mUsmZ/vw7wTn3Gefc6RkiPwJCZvY4Xr2Z+057/GvAvf5gOONKvKuoUvxy2Rcf8Pviz4CP+OcrmP78G+JkdtF78aYI3Yv3OT4j59wRvL642///IX/7fMcVXwHqOPVazXPzAAAErklEQVRLUBhvSugDs7VDzkiuxqUpvIz0O82bxmh4fQI0FpDCytX59xt42Z6PAg8DDzvnvus/prGAiEzJTmbBTvMEswuBdzrn5rPE+cIb5KVlfs4598rTtgfwPnRfP2lwmXdmdhNQ45x776Rt7wQGnHMzpjnLmcl3X/SP+WPn3Atnf+as+7kCuMk5d91szy009efiUSzn3yzt+3t4ywvfOWnb7cC7nXNPZft4kl3FdP41s28C/+mc+0G+2jJFG14BXD/55+FnqFw4eXwg2VdMfXEB+7kCjQVknjQWEJFiMmsGknNuJ/Azm30liqxwzg1METxaiXdV8L4CB4++CbwB+LfTHurDWy5TcijffdE/5hkPGBch9eciUQzn3zNlZrVm9hTelezJA8YyvCLHGjAuAsVy/vUzQ07JAs43M/sE3pX1vzvtoRDwz/lvUWkplr5YAjQWKBIaC4hIMZk1A0lERERERERERErbXGogiYiIiIiIiIhICVMASUREREREREREZqQAkoiIiIiIiIiIzEgBJBERESkp5rnHzF48adsrzexHWT7OB83MmdnaSdtu8rdtn/6VM+7z5WZ29qT79yx0XyIiIiLzoQCSiIiIlBTnrSDyDuBfzCxqZpXA3wP/60z2a2ahKTY/Cvz+pPu/Bzx+Bod5OXD2rM8SERERyTIFkERERKTkOOd2A98F/gp4H/Al59zTZnajmf3WzHaZ2X+YWQDAzG42swfMbI+ZvS+zHzNrM7OPmNlDwMumONTtme1mthnoAnomvf4PzOxRM9ttZn/vbwuZWZ+/34fN7NdmtszMLgNeAnzMb99afze/77f5STN7bnZ/UiIiIiIeBZBERESkVL0feC3wYuAfzewcvGDPc51z24EQJ7OH3u2c2wGcD7zAzLZO2s9x59wFzrmvT3GMPqDDn3b2GuCrmQfMbBXwQeBK4ALgUjO7zn+4BviFc+584NfAm51zvwR+ALzTObfdOXcwsyvn3MXAX+IFw0RERESyTgEkERERKUnOuThwK/Bl59wocA1wEfCAme0Cng9s8J/+GjPbCewEtgCTA0i3znKoW/ECUS8Fvj1p+7OBu5xzXc65MeAW4HL/sYRz7of+7QeBtTPs//Y5Pk9ERERkwaaaqy8iIiJSKtL+PwADPu+ce+/kJ5jZJuDPgIudc31m9t9AdNJT4rMc4zt4dY9+5ZwbMrO5tCs56XaKmcdso3N8noiIiMiCKQNJRERExPNT4FVm1ghgZg1mthqoBgaBATNbAbxwuh2Y2Z+Z2Tsmb3PODeHVWvrwaU//DXClf5zMdLlfzNLGQaBqHu9JREREJCt0lUpEREQEcM49ambvB37qF88ew1ut7QHgMeAJ4BBw7wy72QLcOcW+b5liW5uZvRf4OV7203edc9+fZjW3jP8BPmNm7wJumNMbExEREckC81ayFREREZEzZWbfB653zo0Xui0iIiIi2aQAkoiIiIiIiIiIzEg1kEREREREREREZEYKIImIiIiIiIiIyIwUQBIRERERERERkRkpgCQiIiIiIiIiIjNSAElERERERERERGakAJKIiIiIiIiIiMxIASQREREREREREZnR/wepLOpq66PsWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index(['Year','Month'])['Cruise Arrivals'].plot(figsize= (20,6), color= 'mediumorchid', rot=45, grid=True)\n",
    "fit2.forecast(12).plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}