decisionstats/timeseries.ipynb

## timeseries.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KOGENTIX\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm  \n",
    "from statsmodels.tsa.stattools import acf  \n",
    "from statsmodels.tsa.stattools import pacf\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas.core import datetools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Source data https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/AirPassengers.html\n",
    "df=pd.read_csv(\"http://vincentarelbundock.github.io/Rdatasets/csv/datasets/AirPassengers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>time</th>\n",
       "      <th>AirPassengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1949.083333</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1949.166667</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1949.250000</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1949.333333</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         time  AirPassengers\n",
       "0           1  1949.000000            112\n",
       "1           2  1949.083333            118\n",
       "2           3  1949.166667            132\n",
       "3           4  1949.250000            129\n",
       "4           5  1949.333333            121"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>time</th>\n",
       "      <th>AirPassengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1949.000000</td>\n",
       "      <td>112</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1949.083333</td>\n",
       "      <td>118</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1949.166667</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1949.250000</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1949.333333</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1949.416667</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1949.500000</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1949.583333</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1949.666667</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1949.750000</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1949.833333</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1949.916667</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0         time  AirPassengers\n",
       "0            1  1949.000000            112\n",
       "1            2  1949.083333            118\n",
       "2            3  1949.166667            132\n",
       "3            4  1949.250000            129\n",
       "4            5  1949.333333            121\n",
       "5            6  1949.416667            135\n",
       "6            7  1949.500000            148\n",
       "7            8  1949.583333            148\n",
       "8            9  1949.666667            136\n",
       "9           10  1949.750000            119\n",
       "10          11  1949.833333            104\n",
       "11          12  1949.916667            118\n",
       "12          13  1950.000000            115"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>time</th>\n",
       "      <th>AirPassengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>1959.916667</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>133</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>134</td>\n",
       "      <td>1960.083333</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>135</td>\n",
       "      <td>1960.166667</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>136</td>\n",
       "      <td>1960.250000</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>137</td>\n",
       "      <td>1960.333333</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>138</td>\n",
       "      <td>1960.416667</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>1960.500000</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>1960.583333</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>1960.666667</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>1960.750000</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>1960.833333</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>1960.916667</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0         time  AirPassengers\n",
       "131         132  1959.916667            405\n",
       "132         133  1960.000000            417\n",
       "133         134  1960.083333            391\n",
       "134         135  1960.166667            419\n",
       "135         136  1960.250000            461\n",
       "136         137  1960.333333            472\n",
       "137         138  1960.416667            535\n",
       "138         139  1960.500000            622\n",
       "139         140  1960.583333            606\n",
       "140         141  1960.666667            508\n",
       "141         142  1960.750000            461\n",
       "142         143  1960.833333            390\n",
       "143         144  1960.916667            432"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = datetime.datetime.strptime(\"1949-01-01\", \"%Y-%m-%d\")\n",
    "type(start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1949-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_list = [start + relativedelta(months=x) for x in range(0,df.shape[0])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[datetime.datetime(1949, 1, 1, 0, 0), datetime.datetime(1949, 2, 1, 0, 0), datetime.datetime(1949, 3, 1, 0, 0), datetime.datetime(1949, 4, 1, 0, 0)]\n"
     ]
    }
   ],
   "source": [
    "print(date_list[0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "for x in range(0,144):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_list = [start + relativedelta(months=x) for x in range(0,len(df))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(1960, 9, 1, 0, 0),\n",
       " datetime.datetime(1960, 10, 1, 0, 0),\n",
       " datetime.datetime(1960, 11, 1, 0, 0),\n",
       " datetime.datetime(1960, 12, 1, 0, 0)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_list[140:144]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['index'] =date_list\n",
    "df.set_index(['index'], inplace=True)\n",
    "df.index.name=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AirPassengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AirPassengers\n",
       "1949-01-01            112\n",
       "1949-02-01            118\n",
       "1949-03-01            132\n",
       "1949-04-01            129\n",
       "1949-05-01            121"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['Unnamed: 0']\n",
    "del df['time']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AirPassengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AirPassengers\n",
       "1960-08-01            606\n",
       "1960-09-01            508\n",
       "1960-10-01            461\n",
       "1960-11-01            390\n",
       "1960-12-01            432"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['AirPassengers'] = df.AirPassengers.apply(lambda x: int(x)*1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AirPassengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AirPassengers\n",
       "1949-01-01         112000\n",
       "1949-02-01         118000\n",
       "1949-03-01         132000\n",
       "1949-04-01         129000\n",
       "1949-05-01         121000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hdDrR1NSEffv2pfX23r59O5qamgAAs2fPxo4dO+S1UCiEvXv3oqmpCXq9HrNm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\nD1WkBI2x8Kn1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24046fe90f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.AirPassengers.plot(figsize=(12,8), title= 'Monthly Passengers', fontsize=14)\n",
    "plt.savefig('month_ridership.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x240490cdf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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r3L1gAl5uAys512rU3HRZNHddE4evhxPLLhvHzZdH263SxtpSY/0Z5etCoLfz\nebd3G1BkfPzxxyxYsIAvv/ySJ598kpSUlAsa6COPPMILL7zA0qVLMRgMzJ07F39/f2699VZuuukm\nbr/9dn7961/j6OjIsmXLKCoqYtmyZbz77rundll/6qmneOihh1i8eDHjx48nKSmJ+Ph4UlNTWbp0\nKb/4xS944oknLmh8QgghhBA/Bu9tLyarpJH4cB+WnmNX4G95ujmy7LJx9BhM/Pfz/BHd0susKHyy\n9xhqlYqrpllnFtvVyYGFMyPo7jWxYUepVa5hS4qi8EV6BRq16pztnwZDpVJx59VxeLrq+PCbUsqq\n26x2rXNpbu9h++ET+Ho4kZY4ym7jGCxHnYafL5iAVqPitU+OnvYmkVlReO2TozS29XBtWjjjL6Bj\nwPT4Ufz13ulcnmqZfRmGKo1azRO3T+bJO6ecd3s3lTKAO+rChQvZuHHjBQ9wqBpKpSNiZJPyTmFL\nEm/CliTehp+dmVW8sTmfUb4u/P7WVFycBj6jqSgK/3g/i6ySRu6YF8vMpGArjvR0toy1Q4X1vPhh\nNtMmBPGz+eOtdh2T2cyK19OpbuhgxR2Th/Uu1FkljTy/PpNpEwL52fwJVr9eblkTz717hABvZ1b8\nZLLFZ+YHEm9vbylk26FKfjIvllk2fC1Yy5b0CtZtKyI+3IcHliShVqnYvP8467eXMH6sN79Zkjxi\nZ7ltYdDl635+fhw8eJDe3pFRWiOEEEII8WPU0NrFmi8LcXXS8qvFieeVkEPfLOVtc2NwdtSw7qti\nmtt7rDRS+1EUhY/3HEMFXDPdumu9NWo1N14ShQKs21Y0rKsPvi0lnzvIXu4DNSHchyunhlHX3MU7\nXxba5Jrf1dTWzTeZJ/DzdGJ6fJDNr28Nl6eGkBDhS05ZE1+mV1BY0cIHX5fi6abj7vkTJCG3ogEl\n5Tk5Odxyyy0kJiYSFxdHbGwscXFx1h6bEEIIIcR5M5nNHC6qp6PbYO+hDDnvfVWMwWjmpsuiCfC+\nsNZmPh5OLJkTRVePkbdGYBl7TlkTx2vaSY0NOGeLKEuIj/AlMdKX/PIWDhc1WP161nC8pp28483E\njfG26Wz/olkRjAlyZ3dODfuO1tjsugCf7D2O0aSwYEb4kF0rfr5UKhU/vToOD1cd739dwr825qCg\ncM+CCXi46uw9vBFtQG+P7tu3z9rjEEIIIYSwiA+/KWXz/nKcHbXMmxrGZakhOOlGTuudC5V/vJmD\nBfVEjvZ+eWMSAAAgAElEQVTgogmBgzrXrKRgDuTVkVnSyL6jtUybMDJmChVF4ePdxwC4ZvpYm113\n6SVR5JQ28d5XxSRE+J62YZitbdhRyo7MKqbFB3FJymj8PJ3Peqy+y8D2wyfYdrACgCun2maW/Fta\njZp7FkzgyTfSWf1FARHBngR4nX28A3WkuIHmzGqmxvjhcoa1wQ0tXezMrCLA25lp8YN7LQ01Hq46\n7romjr+/m0lrRy/XXxxBTJi3vYc14mmefPLJJ891UG9vL//+979Zv34906ZN49VXXyU5ORmNZnj3\nVOzslHJ8YRuuro4Sb8JmJN6ELQ21eCupauWNzfl4ufXN6mSWNLIzswqNRk1YgDuaH2n5pcls5sUP\nc2jr7OX+RQl4uw9uN3GVSkV0qBc7Mqs4WtbEjIRROOms+3fh+cZa3vFm3tycz4n6DgwmMx4uDmft\nwWwwmjhW3c7OrGr25taQHOXHFZNttymVu4sOfZeB7LImXBy1RIVYtr/3QB0pbmD1FwX0GEwUn2hl\na0YllXV6PF11+Ho4oTq5m3Rjazcf7SrjP58cJaesCbVazfzpY0hLHHXqGFtxc3bAy82R9Pw6ymva\nmZ5w4WPo7jXy1hcFrN9eQlZxA98cqcJBq2ZMoPtppdvvbS/mWE07N10WPaz3ATibAG8XPF11BPu7\nsiAt3OY/05HK1fXsu9YP6G3jP/7xj/j4+JCbm4tGo6G8vJzf//73/PWvf7XYIIUQQgghBsNgNPH6\np3koCvx8wQTCAt3Zkl7BFwfKWbu1iC8OlLNgRjgzEoLQqEdGuelA7ThSRWW9nrSEUYSP8rDIOf29\nnFl8cV/P6Le3FHDvdQkWOa8l9Bj6YqGxrZu84818fqAcFRAa6EZMqDfRoV70GkyUVrVRWt1Kea0e\nk7mvDN9Bq2ZB2libj3lBWjh7c2v4eE8Z0xOC8HCxbblwU1s3r31yFK1GzaM3p1Dd2MHWg5VkFNaT\nUVhPiL8bFycHU1rVyv6jdZgVBW93RxbODGdWUrDVWqANxIyEII4UN3CosJ6vMiq57AJ2+S6pauXf\nm45S19JFWKAbaUmj2fBNMWu3FrEto5LFF0cyKcaf+pYudmXVEOTjwtTxI2uW/LtmT+y/TaKwrAG9\nenJzc9mwYQM7duzA2dmZlStXMn/+fGuPTQghhBBiwDbtPkZ1YyeXpIw+VW55bVo4l6SMZvO+crYd\nquTNzfl8ffgED9048bw3ORuu9F0GNuwsw0mn4fqLIyx67ksmhXAgv46DBfUczK8jNTbAoue/UJv3\nHaexrZvLU0NJivKloLyFgooWSqv6EvAvT5ZbA2g1KsIC3YkI9iAi2IOYUC+r9CU/FzdnB65NC+ed\nrUW8u63Yqru+f5/JbObVTbl0dBu55YroU9+L6fFBfTPmByvJKKjn7ZMbqgX7uTJvahhTxwcOifXU\nKpWKW6+IpqC8mfe/KSExym/AZewms5lP9x5n065jKIrCvKlhXDcrglFBnkyN9WfT7mN8ffgEqzbm\nEDXaEydHDWZF4dq0cNn4TFjMgH4bqVQqent7T5UuNDc3SxmDEEIIIYaMsuo2Ptt3HD9PJxbPjjzt\nc+4uOpZcEsXlk0N596siDuTV8c/3M/n10mQcHYb3UryB+GhXGfouAzfMicTT7ezlkxdCrVJx51Vx\nrHj9AGu2FBA7xhs35/Prz2tp9S1dbN5fjqebjoUzw3F21J7qrWww9s2OF1W24qjTEBHsQViAu13X\ncH/X7Imj2Z1Tw97cGmLCvM67zZbBaKaprZuG1m4a27ppaO1CrVJxxeTQM66N/tamXccorGxlUow/\nc74zQ6pSqRgX4sW4EC+a2rpJz68jyMeFhEhf1EMsF/B0c+Tmy6N59eOjvPlZHg8tm3jOMda3dPHv\nj49SfKIVb3dH7rpmPHFj/rd+2t1Fx82XR3PppBDe/7qEQ4X1AIz2c2XyEHkDSowMA0rKb7vtNu64\n4w7q6+t5+umn2bp1K/fdd5+1xyaEEEIIcU4Go/lU2fod82LPuqmbt7sjd5/sn3wgr45/bczh/kUJ\nQ2Kmz1pO1OvZfugEgd7OXH4BJb0DEeTjwsKZ4azfXsLarYU26VHdn3XbijAYzSyZE/WDkmoHrYaY\nMO8hu3GVVqPmvoXxPPVmOmu2FDIm0J0xQf2vWW7r7OW/m/MprW6jVX/mNfc7s6r5ybxYEiJ8f/C5\nvGNNfLLnGH6eTtwxL/asE28+Hk42a3d2oaaOD+RAXh1Hihv4+vAJLkkJOeuxR4obeHVTLt29JibH\nBnDblTG4nuWNiyAfF+5flEBhRQtfHarkskmhMksuLGpAG73FxsaSlJTEqFGjcHd355577mHWrFk2\nGJ51DaWNacTINtQ2QhIjm8SbsKWhEG8f7Solo6Ce2RNHn3MtqUqlInmcH2U1bWSXNlHX0kVKtP+I\nrABUFIVXP+5bI/vTa8YT7Ge99l4RwR7klDaSXdrEmCB3gnwurN1afwYSazlljWzYUca4EE9uvHTc\nsPy5ujg5EOLvxt6cGnLKmpgWH4TuLBUdze09/HXtYYoqW3Fz0jIm0J2YMC9Sov2ZET+Ky1ND8fd2\nJrukkT05NTS39xAT5nWqMqCto5fn3j2CwWTmgSVJBF5gm7yhQqVSERPmxc7ManLKmrhofOAPKgQU\nReGLAxW88VkeKpWKO+bFct2siB98j88Ub76eTqTGBthleYMY/vrb6G1ASXlLSwvHjh3j6quvZu/e\nvXz22WeMGzcOHx8fS47T5uz9R4T48RgKf7SKHw+JN2FL9o634zXtvPZJHj4ejty/KGFAZchqtYqU\ncf4UlLeQXdpIe5eBxAjfYZnA9edwUQOf7j1OfIQPC628g7JapSJytCc7MqvIP97MzMRRZ93p/EKd\nK9aMJjP/fD+bjm4Dv7g+ES8Ll+rbUqCPC4qicLiogRMNHUwZH/iDn19DSxfPrj1EbXMXV0wO5ddL\nkkhLDCYl2p/YsL5+4X5ezsSEeZMc5UfxiTaySxvZf7SW0AA3fD2dWLUhh4o6PYtnRzIlbmRsWuak\n0+LlriM9v47K+g6mxwed+t4ZjGbe3JzP5wfK8XLT8eDSZBIj/c742rD3vU2MPINOyu+77z68vLzo\n6Ohg9erVLFy4kJdeeonrr7/ekuO0OXmhCVuRG7uwJYk3YUv2jDejyczz6/t66d57XcJ5zQRrNWpS\nYvzJLmkkq6QRReG0taTDXVtnL//amENPr4lfXp+Iuw128vZw7bvGkeJG9F0Gksf5W/T854q1LekV\n7M+rZU7K6PNeiz0URYd6UVLVRk5pExqNmphQr1Ofq2nq5Nm1h2ls7WHBjLFcf3Fkv2+6eLo5MjNp\nFAoK2SVN7M6uJu94M/nlLSRE+HLzFdEj6k2pEH83jte0k1PWhJebI2NHedDW0cvz6zPJLGkkfJQ7\nDy9LYZTv2e8Z8rtUWNqgW6K1trZyyy238Kc//YmFCxeycOFC3nrrLYsNUAghhBDifH2y5xiV9R1c\nnBzMhLHnX73n6uTAb5Ym839rDvHxnmO4Omm5Ygiumd2bW8Obm/Nxc3bA19MJP08nfD1O/uvphMFo\npqapk+rGTmqaOqlp7ETfZQDgismh/SYelnbVRWPIKKhnR2Y1k2MDmRA++KrKhpYuco410dFjYnyY\n1xlburXoe/hodxluzg5cN9OyO8zbi1qt4u7543nqzXQ27iglYpQHE8J9qKzT87d3j9DW0csNsyOZ\nd9GYAZ1Pq1GzaFYkyVH+vPbpUYoqW/F00/HTa+KG3KZtg6VSqbjtylj+8J/9vLu9GC93R97eUkBj\nWw9T4gK486q4sy4JEMIeBpSUm81mcnJy2Lp1K2vWrCEvLw+TyWTtsQkhhBBCnFFxZSuf7DmOj4cj\nS+ZEXfB5vNwcefDGZP6yJoN1XxXj7qpj2oQgC450cEqqWnnjs3w0ahVqFZScaKW4svWsx6tUff3D\nI4M9GBPkzrypA0vYLEWrUXPnVXH86b8HeXNzPn/86ZTz7l/d02siv7yZnLImcsqaqG3qPPW5D4DI\n0R5cnhpKSrT/qU361m8vpqfXxNIro+y++7slubvouHdhAv/3dgavbMrlJ/NieeOzvFOty/rbyOxs\nIoI9WPGTyezMqiYmzMvm/dBtxdvdkWWXjeO1T/P45/tZACycGc786WNHVFWAGBkGdJd8+OGHefbZ\nZ7nzzjsJDQ1lyZIl/O53v7P22IQQQgghfqCz28Arm3JRUPjZNePPO+n7Pn8vZx5cmswzaw7xxmd5\n+Ho4Ef2dUmF7aW7v4cUPszGZzfzy+iTiI3wxmsy0tPecbHfVTWNrNxqNiiAfV4J8XQjwcrZ7e68x\nQe5cNS2MT/YcZ922In7Sz47e39VrMPH6Z3kcKqzHaFIAcHTQkBzlx4RwH8aM9uKTnSVklTRSciIX\nLzcdc1JCCPFzZW9uLWMC3ZmVOPzL1r8vItiDZZdFs/qLAl78MBuVCn56dRwzEkZd8Dl1DhounXT+\nCf1wMz0+iMNFDeSWNfHTq+NIlTZmYohSKYqiDOTA3t5eysrK0Gq1jB07Fo1m+Jd81Ne323sI4kfC\n399d4k3YjMTbyFFY0cILH2Th6+lEfLgv8eE+RIV4DqkWXraON0VReGVTLgfy6pg/fSzXzbJcqXLu\nsSaefy8TZ0ctv79tkl13ojYYzTz7ziFKqtpYMieKK6cOvbL6/hiMZv781kEq6vQsmDGWhecoKTea\nzKzakMOR4gaC/VxJjvL7Qbx/G2u1TZ1sy6hkV3Y13b3/q9x87NZJRI32tOrzshdFUXhjcz77cmu4\n65rxI2ZTNlswKwoGoxnH8yxXl9+lwtL8/c/e3nBASfmBAwf47W9/i4+PD4qi0NHRwXPPPUdCQoJF\nB2pr8kITtiI3dmFLEm8jQ21zJ0+/lUFntxG1mtNmDmPDvIiP8GXiOD+7t+axdbztzKrijc/yiRrt\nySM3T0SjtuwbFDsyq3hzcz6BPi78/tZJdimFVhSFNz7LZ1d2NdMmBHLXNeOHZbltc3sPz6zJoKG1\nm2WXjTtrn3SzovDaJ0fZm1vLhLHe/HJx0hln+78fa109RnZnV7Mjs5r4cB+WXHLhyxiGA0VR6DGY\ncNINrjJEDIz8LhWWNuikfNGiRTzzzDPExMQAkJ2dzVNPPcX7779vuVHagbzQhK3IjV3YksTb8Kfv\nMvD06gxqmzr5ybxYpsYFkl/eTO7JNbY1J9fYOjpo+M3SJMaF2K/U2pbxVtPUyVNvpKNWq3jqjsn4\neTlb5TrrtxezeX85MaFePHhjss0rE7YerOCdrUWMDXLn0ZtThvWGVHXNnTyz5hCtHb3cdU0c0+NP\nL7lWFIV3thaxLaOSyGAPHrwx+axJp9zbhC1JvAlL6y8pH/BvmW8TcoCEhATZ6E0IIYSwAqPJzEsf\nZlPb1Mm8qWHMSgrGUachKcqPmy6P5i93X8Szy6dx4yVRGIx9LcGO14z8PxwNRjOvfJRLj8HEbXNj\nrJaQA1w/O5JJMf4UVLTw3835DHCl3zl1dBvIP97MzqwqSk60YjCaf3BM3rEm1m0rxsNVx/2LEoZ1\nQg4Q4O3Cg0uTcXHU8vqn+Rwuqj/t85t2H2NbRiWj/V351Q1JMgsshPhR6vfOl56eDkB4eDhPPPEE\nixcvRqvV8vHHHw/70nUhhBBiqFEUhTc351NQ0cKkGH+unx15xuP8PJ25YkoYnm6OvLopl+fePcJv\nb5pIiL+bjUdsOx/uKOF4bTszEoKYOt6662nVKhV3XTOeprbD7M6pIcDHhfnTx57XOfRdfQl4eZ2e\nyjo9FXXtNLb1nHaMRq0iLNCNiGBPIoI98PVwYtXGHFQquO+6eLsvTbCUkAA3HliSxN/WHeZfG3P5\nzZIkYsd48+XBCj7aVYa/lxMPLk0eUbumCyHE+ei3fP3WW2/tO+h765gURUGlUg37XuVSkiJsRUqg\nhC1JvA1fH+8uY8POMsJHefDbmyYOaGOib9dYe7jq+N3NKQT62HZzMlvEW05pI39/L5NAb2dW3DHZ\nZrOprfoe/vzWQRrbevj5ggkDfjOgub2HP/43nVZ976nHPN10hAa4ERrghr+nMyfqOyitbqW8Vo/J\nfPqfYrdfGcPFyaMt+lyGgpyyRv6xPgsHrZrLUkP5ZM8xPN10/O6WSQQMoPJB7m3CliTehKUNak35\ngQMHWLVqFdnZ2QAkJiZy7733MnnyZMuO0g7khSZsRW7swpaGerwpikJDazcF5S0UVDRTUN6CwWTm\noaXJjB7BM73nsu9oDa9uOoqvhyOP35aKp5vjgL92W0Ylb39ZiI+HI4/enIKfp/VKu7/vfOPNZDbT\n2W2ks8d46t+uk//29JowmMwYjN/5MJk5VFBHR7eR3982ibFBHlZ8Nj9UWa/nmTUZGIxmHrpx4jlb\npRlNZla+c4iSE21cmhJC8jg/QgPc8HA9cy9og9HE8Vo9pVVtlFW3ERrgxlUX2ba3uC2l59fx8sYc\nFMDFUcujN6cQEjCw1/1Qv7eJkUXiTVjaBSfle/fu5ZFHHmH58uWkpqZiMBg4fPgwL7/8Mn/729+Y\nOnWqVQZsK/JCE7YiN3ZhS0Mx3sxmhf15teSUNlJQ0ULTd8p4nR21dPUY8XLT8dgtk6y6VnioKqps\n4a9rj+CgVfG7WyZdUBn6Z/uO8/7XJQR4OfPIzSl4uw88qR+M84m3jII6/vNpHj2957cvjQq4sZ/d\nu60tt6yJ59dn4qTT8LtbJhHs53rWY1d/UcD2wye4aHwgP5s/PHdNt7ZdWdVsSS/ntitjz6uF2VC8\nt4mRS+JNWNoFJ+W33HILv//974mLizvt8ZycHJ555hnefvtty43SDuSFJmxFbuzCloZavJ2o1/PG\n5nxKq9oAcHN2ICbUi+gwL2JCvQgJcOPL9Are/aqYAG9nfndzynnNEg93eceb+ecHWRgMZh5Ykkh8\nuO8Fn2vDjlI+3nOMUb4uPHJzCh4uZ56dtaSBxltWSQMvfJCNVqsmfqwPzk5aXBz7Pr79byedBget\nGgeNGgdt339rtWpcnbR42TkmdmVV8/pnefh6OPH4bZPOGKPfLiUI8Xfj97dNOu++yKJ/Q+3eJkY2\niTdhaf0l5f0uytLr9T9IyAHi4+NpbW0d/MiEEEKMWEaTmU/3HueTPccwmRWmjg/k6mljCPZzRf29\n2cO5U8Lo6DbwyZ7jPPduJo/ePBEXp5G/6dOhwnpe/igHgHuunTCohBxg4cxwegwmtqRX8H9rDvHg\n0mR8Pe2/WVje8WZe2pCDRq3igcWJxIR523tI5y0tcRRNbd1s3FXG8+uzeOTmiaetbS+rbmP1F4W4\nOmm5//oESciFEEIMWL8t0To7OzEajT943Gg0nvFxIYQQAqDkRCtPvZHOR7vK8HDV8cvFifx8wQRC\n/N1+kJB/67qZEcyZOJrKej3Pv59Fj2Fkt97cmVXFSxuy0ajV/OqGJFJjAwZ9TpVKxdJLorhyahg1\nTZ38ZU0G1Y0dFhjthSs+0co/389CURTuX5QwLBPyb82fMZaZiaM4XtvOyx/lYjL3tTRr6+jlpQ3Z\nmExm7l4wYUCblgkhhBDf6jcpT0tL429/+9tpj5lMJp555hlmz55tzXEJIcSwZKl+xsNVV4+RtVuL\n+MvqDE40dDB74mj+fNdUkqP8zvm1KpWKm6+IZkpcAMWVrazakIPR9MM+ziPB5/vLeeOzfFwctTy8\nbCITxvpY7NwqlYolc6K4YXYkze09PLPmEGXVbRY7//k4XtPO/3svE4PRzD3XxhMfMbhKAHtTqVTc\nOjeG+HAfskoaWf1FIUaTmZc/yqGprYfrZkWQMMyfoxBCCNvrd015Z2cn99xzD9XV1cTHx2MymcjJ\nySEqKooXX3wRnc76a9WsSdaJCFuRdUkjm77LQHp+HXuyq6lp6uTe6xKIG2O/2UB7xFtdSxdfZVSy\nM6uKrh4Tgd7O/GRe7AXNihpNZl74IJvs0kamxAVw9/wJqNUjY7MsRVF4/5sSNu8rx9vdkd8sTWZ0\nP5uGDdaOzCr++3k+OgcNv1iUwHgLJP9mRSH/eDPfHKkis7iBkEB3YkI9iQ/3ZVyIJ1pN3/v9J+r1\nrHznMB1dBn62YDwXjQ8a9LWHiq4eIyvfOUR5rZ6wQDfKa/WkRPtz33XxsrGbFcnvUmFLEm/C0gbV\nEg362qJlZ2ejUqlITEwkNTXVogO0F3mhCVuRG/vIYzSZySltYk9ONUeKGzCaFFQqUKtUaDQqfn1D\nkt3KdM8n3jq7Dbz+WT4NrV2nbbCl1ahw0KpxcXJgtJ8rYYFuhPi74ez4vzW0iqKQd7yZrQcrySxu\nQAE8XXVckjKauVPC0A1iTW2PwcRz7x6huLKVKXEB3HFV3JBao9tjMLFpdxnuzjpiwrwIC3RDoz57\n8ZnRZKaiTs9XhyrZnV1DoI8LDy5NsknrsoyCOl7ZlAvAzxdMYFLMhZXJt3X0sju7mm8yq6hr7gLA\n18OJ1o7eUxUNjg4aYsO8iAnz5osD5bR29PKTebHMSgq2zJMZQlr0PTx9sof5KF8XHr8t9bTXh7A8\n+V0qbEniTVjaoJPykUpeaMJW5MY+MiiKQnmtnt051ew/Wkt7pwGA0X6uTE8I4qLxQRyvaeelDdlo\nNWp+szSJcSH99zS2hoHGm9ms8Pz7meSUNqFzUGMyKZjM/f9K8PdyIizAnUAfFzKLGzjR0LdeOXyU\nB5enhpAaG3BqpnSwOrsNPL8+i+ITrYQFuHHfogT8h8ha3be3FLLtUOWp/3fSaRgX4kXMyR3lPVx1\nlFW3UVrV93G8th2DsS9xHRPozq+XJJ21b7U15B1r4p8fZtNrMHHb3BguTh494K89VtPGZ/vKOVxY\nj8ms4KBVMyU2gIuTRxM52gMPTxd2H64gp7SJnLImapo6T33tMju2MbOF6sYOvjhQzryLxhDo7WLv\n4Yx48rtU2JLEm7A0ScrPQl5owlbkxj68teh72Jdby+6cak7U9yWhbs4OXDQ+kBkJowgLdDutZPVQ\nYT3/2piDVqvmwaXJ59WH1xIGGm/rtxezeX85CRG+/GpxImq1CrNZwWAyYzSZMRjNtHX0UlGnP+1D\n39X3ZoRGrWJybACXpoYQGWyd52g0mXnny0K+PlKFq5OWexbGW3T99YXILWviuXePMMrXhWumj6Wg\nvIWCihZqv5OMfpdapSLE35WIYA8igj2ZHBuAo872s/5l1W38v/cy0XcZuCw1hCVzos75BsrXh0/w\n9peFmMwKo/1cuTg5mGnxQbh+Z2f878dbQ0sXuceacHFyYLIFNq8T4lvyu1TYksSbsDRJys9CXmjC\nVuTGPvwYTWYOFtSxJ6eG3LImFKUvCU2O8mN6QhAJEb79JjQH8+t4+aNcdA5qHrwx2WpJ65kMJN72\n5dbw6sdHCfRx4Q+3TRpw+zFFUWjR91LV2EGwryve7rbpHf3NkROs2VKIWVG4YXYUc6eE2mXtbke3\ngSdeO0BbRy+P35bKmKD//YJt0fdQWNFCQXkLHd0GxgZ5EBHswZgg9yFTel/b1MkLH2ZT1dBBbJgX\n9yyMP2Mvc4PRzNtfFrIjswo3ZwfuumY8CRE+Z/yey/1N2IrEmrAliTdhaZKUn4W80IStyI19eDGa\nzLz0YTaZJY0ARAR7MD0+iClxgbg5D7x39oG8Wl7ZlIuTTstDNyYTPsrDWkM+zbni7VhNG8+sOYRW\no+Lx21IZ5Wu9jcYsqfhEKy9tyKZV38vU8YH8ZF6szZPdVzblsv9oLdfNDGf+jHCbXttSunqMvPZp\nHocK6/HxcOT+RQmMDfpfbDa397BqQzYlVW2EBbpx/6KEfte+y/1N2IrEmrAliTdhaXZJyg0GA489\n9hgnTpygt7eX5cuXExUVxaOPPopKpWLcuHGsWLECtVrNe++9x7p169BqtSxfvpw5c+bQ3d3Nww8/\nTGNjI66urqxcuRIfHx+OHDnC008/jUajIS0tjfvvvx+AF198ka+//hqtVstjjz1GYmLiOccoLzRh\nK3JjHz7MisJ/PjnKvtxa4sZ4c8sV0YNKWvcdreHfHx/FWaflgSVJNill7y/eWjt6+eOb6bS09/DL\nxYkkDaBV2VDSou9h1YacU+vMf70kCU8328zWH8ir5eWPcokM9uDRW1L63dhtqDMrCp/uPc7GHaVo\ntWpuvzKG6fGjKK48+cZHRy8XTQjk9ivP/caH3N+ErUisCVuSeBOW1l9SrnnyySeftMZFN27ciF6v\n5x//+AdXXHEF9957L/n5+Sxfvpxf/epXbN++HZPJhIeHB3/6059Yv349V199NQ899BCLFy/mnXfe\nwd3dnWeffRaNRsPHH3/MrFmz+PnPf87zzz/PnXfeyX/+8x+ioqKoq6vjvffe45133mHGjBk88sgj\nLF269Jxj7OzstcZTF+IHXF0dJd6GAUVRWPtlETuzqokM9uCBJUl4uzsN6pwh/m4EeDlzIK+WXVnV\n1Ld0MTbI3aq7NJ8t3owmM8+vz6SqoYPrL45g5jDcEdtJp2VafBCtHT1klTZxpLiRlHF+5/X9VBSF\n7l4TjW3dVDV0UFrdRlFFC06O2rNWQjS39/CP9ZmoVPDg0mTcz1DyPZyoVCpiQr0YG+TO4aIGDuTV\ncbymnQ93lNJjMLP0kihuGMCac5D7m7AdiTVhSxJvwtJcXc8+iWC1vwqvvPJK5s6dC/T9AaTRaMjN\nzWXKlCkAzJo1i927d6NWq5k4cSI6nQ6dTkdYWBj5+flkZGRw1113nTp21apV6PV6ent7CQsLAyAt\nLY09e/ag0+lIS0tDpVIRHByMyWSiqakJHx/7bgYkxFCXW9Z0avfs79M5qJkU7T/sk4/z8dGuMrYd\nqmS0vyu/uiEJJ51lbpHT4oPwcndk3bYi9uTUkJ5fxxWTQ7nqojE2a6GkKAprthSeajF21UVjbHJd\na9Bq1Nx+ZSyuzg5s3lfOM2sO8fCyZAL62f26p9fExl2lZBTU09bRS+/JndC/S6WCqeMDuWbaWIK/\n01XujooAACAASURBVDtcURTe+CyPjm4jt14RTaDPyNllOynKjyduT+WFD7M5UtyAm7MDy6+dQJyd\nN9MTQgghfkys9tegq2vfHzR6vZ5f/vKXPPDAA6xcufLUJjGurq60t7ej1+txd3c/7ev0ev1pj3/3\nWDc3t9OOraiowNHRES8vr9Meb29vP2dS3l8JgRCWNpTiramtm5c/zGJvdnW/x63bVswVU8JYODtq\nWCQidU2dOOo0F1TOvGlnCZt2HyPI14W/3JuGj8fgZsi/z9/fnRkpoXydUcHqzXl8uvc4u7KrWXZF\nLHMvGnNebcTqmjrZk12FvtNAR5cBfXffvx1dBjq7jZjMP0w4zWaFE/UdRIz25OHbJlvsDQd7Wr44\nGV9vF9ZszufZtYf508+nExb0w3X7RwrreGF9JnVNnbi76AgLcsfL3Qlvd0e8Tn44aNR8tucY+3Jr\n2X+0lrSk0Sz9/+zdeXRc1Z0v+u+p6dR0apKqNM+2PCKPYGOLqUljCIEmSXeInZDbHXjk8mJnQYcs\nE0LssBo6y89NQgLxy83L7dwbbpopJmkSQrKCCXbbJiYYDJ6xZdmSNZZqnqdz3h9VLqtsSZaDVCVZ\n389atU7p1KnyrvJPpfrW3mfvT7SiocqC1/Z04lCnF0vnuvAPt8wtyQRzk8nplPD9hxzY8ZdurFhY\nOeaXG2M9BlExsNaomFhvVCyT+qmsr68PX/3qV7Fu3Trccccd2Lp1a/62SCQCi8UCs9mMSCRSsF+S\npIL9Yx1rsVig1WpHfIxL4XkiVCxT5bwkRVGw+8M+vPjmSUQTabTWWvGJ5XVQqS4OGW5/DG+8243f\n7unE7/aextXzXLj1mvqC2aanglQ6g3ePubHzQA8+OhuAShBwVbMDq6+qwqJZZdBqLj0R2N5Dffjp\nb4/CatLhwX9YhEwiBbc7NSntbWu044n7VuCPf+nGa38+gx+/8iF+/dZJfP7m2WhrKRvzvoqi4K0D\nvXjpzZNIpDIX3S4AMIgaaLUqyCOsN95YKeG/3zkfoUAMpa/GifE3i6qRTmbwwo4T2Pjsbnz97sX5\nGo3EU3hxx0nsPtgHlSDgkysbcOfqRuhGOUd66awyfHBiCK/uOY3/OtCD/zrQgyWzy3G40wuTXoMv\n3DwbQ0PhYj69olo51wmkM5f9XjVV3t/oysdao2JivdFEG+tLnkkL5UNDQ/jyl7+MTZs24dprrwUA\nzJ8/H/v27cOKFSuwa9curFy5Em1tbXj66aeRSCSQTCbR0dGB1tZWLF26FDt37kRbWxt27dqFZcuW\nwWw2Q6vVoqurC3V1ddi9ezfWr18PtVqNrVu34t5770V/fz9kWebQdaILDPpj+N+vH8PRMz7odWrc\ns2YOblhcDdUYvX43L6vFX44N4vU/d2HfkWwP4oJGO+5sb8LsWtuo9yuGnqEIdh3oxd5DfYjE0wCA\neQ12RONpfNDhwQcdHhhFDa6ZX4FVCyvRUm2BIAhQFAXJtIxYIo1oPI3OviB+9rtjMIoafP3uxXDZ\nRp9leqKIWjU+taoR1y2qxqu7O/HWgR48/fIHWNRShs/fPHvEUQneYBw/e/0YDnd6YRQ1uOeWVtQ4\nzTCIGhhFDYx6DUSdGipBmHEfJG65ug46rQrP/f44/p/n38dDn1sEfyiB//PHjxCMJFFfYcY/3Tbv\nkl8oqQQBS1qdWDy7HB92ePDqnk68f2IIAPDl2+cVbfk3IiIimlkmbfb1J554Aq+//jqam5vz+771\nrW/hiSeeQCqVQnNzM5544gmo1Wq89NJLePHFF6EoCr7yla9gzZo1iMVi2LhxI9xuN7RaLZ566ik4\nnU4cOHAA//qv/4pMJoP29nY89NBDAIBnnnkGu3btgizL+OY3v4nly5dfso0z6UMrldblhqRAOAEA\nEzKrdDCSxNuH+/GrXaeQTMtoaynDl9bMuazh2Yqi4HCnF6/v68LRMz4IAD6xvA6fvaF51F7HiaYo\nCvq9URw65cW7xwdx4mwAAGAxatHeVo3rF1Xlh92edYex91A/3j7cj0A4O0mLxaSDoii54d2Fb3s6\nrQoPf35JUWZGH8nZwTD+442PcKzLD7VKwC3X1OFT1zbCIGqgKAr2HOzH8zs+QiyRwVXNZfjH2+aO\nGRBnWig/5+3D/fifvz0KQQAysgKNWoW7rmvCLVfXXdbpAecoioLDp70Ix1JYOb9yElp8ZZip9UbF\nx1qjYmK90UTjOuWj4C8aFcvlvLEf6vTg//31ISRTMlbMr8CtK+pR6zRf8n6ynA2t3YNhdA+G0TUY\nQvdgOB9KzQYt1v3tbKyYV/Gxzok9cdaPf//dMQx4o6hwGHHf7fPQMklhNhpP4chpHw51enG40wNP\nMJG/bUGjHTcsrsHi2eWjBq6MLOPoaR/2HOrHibN+iFo1jKIGBn2udzl3/eq5roJ1mktBURTsP+7G\ni2+egCeYgNWsw9+1N+GDE0P4oMMDvU6Nz988G9e1VV3y/28mf5DYf9yNn/zmMJoqJfzjJ+ehchrM\nhTDdzeR6o+JirVExsd5oojGUj4K/aFeGjCzjzfd60NkbHPF2tUrAgmYHlsx2XnK93cky3jf2XR/0\n4ue/Pw6VSkC5VY9+bxQA0NZShttW1KO1zlYQyIYCsWxgPeXFkTM+xBLpgsezSyLqXWY0VEq4eVnt\nhM2knkhl8MrOU3jj3W5AAG5b0YC/a2+CVvPXrducSGUw4I2if9ilbyj7BYOce4sy6TWY1+jAwqbs\nZaInYpsqEqkMfr+vC7/78xmkcjOEz2uw458+ORfl1vENrZ/pHySSqQy0GtUVNyHbVDXT642Kh7VG\nxcR6o4nGUD4K/qJNf10DIfzsd8dwZuDS/5cGUY3lc1xYfVUVZtdai/qB/VJv7LKi4JWdp/C7P5+B\n2aDFhs9ehZYaKz486cHr+87kh2o3V1vQ3laFXncEhzq9+dAOAOVWPebU2VBXIaHOZUadyzzqmssT\n5XiXD//ztaMYCsRR4zThvtvnj3siuHRGxo79Z7Fj/1l4AnFc+EakUavQWClhYZMDC5ocaKqyjDgh\n3ZVqKBDD794+g7oK6ZLn/l+IHySomFhvVCysNSom1htNNIbyUfAXbfpKpTN4dc9pvP7nLsiKgtUL\nK3HH6sYRZ9oOx1J45+gA3j7cD29u+HO5VY9VCytxXVs1yqyT3+M61ht7MpXBT187inePDaLCbsCD\nn1uEiguWJDp5NoDX953JTzoFZCcLm1tvw8LmMixscsBlN5SkZzCeTOOlP3Xgrfd7oBIErFpYidtX\nNVz0HIY72RPAz39/HGfdYRhENRoqJFSWmVDpMGYvZUaUW/QzKoRPJH6QoGJivVGxsNaomFhvNNEY\nykfBX7Tp6aNuP372evac5nKrHl+6dQ4WNo29lBSQ7Y0+fsaHvYf68e5xNxKpDEStGv/ttjmTPonT\naG/swWgSz2z/EB09QbTWWrH+s21j9m73eSI42OFBfYWEWbXWv2ryqslyqNOD5984gT5PFCpBwMoF\nFbj92gZUlZnyx4RjKfzyrQ7s+qAXAHD9oir8/Y2zJr1Hf6bhBwkqJtYbFQtrjYqJ9UYTjaF8FFPx\nFy0YTaLXHRnxNpVKQEOFBFFXmvOiSy2eTOPltzrwp/d6IAC4eXktPnN9M/S6y1/ZL55M489HBvDS\nmycRT2bwN0trcPffzP6rz4m+lHNv7LFEOnvOtCeKPm8Ufz7cj6FAHCvnV+CfPjlv0v79YpFlBe8e\nH8Rv9p5GjzsCQQBWzKvA7asacboviJf+dBKhaAo1ThO+tGZOyZdVu1LxgwQVE+uNioW1RsXEeqOJ\nxlA+iqnyiyYrCo6d8WHngV6895H7ouWahhN1aiyf48SqhVWYU2+7rPNMp7OugRB+/J+H0e+Norrc\nhH+8be6ELF/V54lg268OoWcogqYqC/7vuxZ+7OHssqLAG4ijb1j49oYS6OoPwp+bCf0cAcCnVjXi\nruuarqhJqWRFwXvH3Xh1z2mcdYfz+3VaFe5qb8YnltdOqV7+Kw0/SFAxsd6oWFhrVEysN5poDOWj\nKPUvWjCSxJ6Dfdj5QS8GfTEAQE25CYtnl0M9wrm08WQG733kxlAgDgAos4i4dmElVi2smpLL/pzq\nDeIP73TBE4yj1mlCnSs7AVmt0wyjfny924qi4K33e/D8jpNIZ2TccnUdPntDy4T2KCeSGfz8D8fx\n9uF+mA1a3H/HfCxsvvRw+OH6PBH8Zs9pnHVHMOCL5mfNHq7MIubPm64qy547XVNumpC1yKcqWVHw\nwYkh/PHdblhMOvzDjbOKcg7/TMcPElRMrDcqFtYaFRPrjSYaQ/koLucXTVEUZGTlY/fuKYqCE2cD\n2LH/bL5XXKtR4Zq5LtywuAYtNZYxe0xlRcGJbj/2HOrHu8cGEU9mAACtdTbc1d6EuQ32j9W+j0tR\nFBw85cHrf+7C8W4/AEAlCPllrc4pt+pRXyFhQaMdi2c7YZcuDqbReAo/e/0Y9h93w6TX4N5Pzcfi\nWeWT1u6dB3rxH298hExGwR2rG3Hn6qZxTTT2/gk3/r/fHEE8mT1H/dxEZVW5baXDiAWtLoQCsUlp\nO9GF+EGCion1RsXCWqNiYr3RRGMoH8V4ftESyQz2Hu7HG+92o88ThUHUwGLSwTr8YtZlJ96qscIg\njtwDnEpnsO/IIN7Y342ugexw3ppyE25YXI1rF1bCpL/8ia4SqQze/8iNPQf7cPi0DwCwoNGOz9zQ\ngqYqy2U/3seRzsjYd2QAv3+nCz25c+IXNjtw24oGzK61ot+TXXO6ezCMrsEQugfDCEVT+fs3VkpY\nPLscS2Y7Ues04VRfEP/jPw9jKBBHa60V99+5oCjrUnf2BbHtV4fgCcZR7zJj3d+2orVu5POeZUXB\nq7s78eqe09BpVPhvt83FyvkVI36pwjd2KibWGxUT642KhbVGxcR6o4nGUD6KsX7R3P4Y3nzvLP7r\ngz5EE2moVQJaqi2IJtIIRJIIR1MXrausEgQ0VJoxp86O1nobWmutSKRk/On9Huw80INQNAVBAJa2\nOvGJZbVorbNN2HnEnX1BvLLrFA53egFk/41PX9eEGqd5Qh5/JIqi4MxACO8cHcS+IwPwhRJQCQKu\nme/CrdfUo75i9MJTFAWeQBwfdHjw/gk3jnf58+fSl1n08IcTkGUFn1rViDvbG6FWFe/843AshRd3\nnMCeQ/0AgBXzK/APN7YUfCkQjafx098ewYGTQyiz6LHhs1eN+Xz5xk7FxHqjYmK9UbGw1qiYWG80\n0RjKR3HhL1o0nsap3gD+9H4PDpwcgqIAFpMONy6uxk1LagrO/c3IMkLRFALhJHyhBDp6Azje5Udn\nXzAfLgUAQm7otkmvwfWLqnHT0hqUWw2T9pyOnfHhlV2ncLInAAHAygUV+NSqxoJlqcYiKwrO9Ieg\nUatgMekgGbQFQ7gVRUH3YBh/OTaIvxwdxKA/OyRbr1Ojva0Kt1xd91c9v2g8hQ9PeXDgxBAOnvJA\nr9PgvtvnYV6j47Ifa6J09AbwH3/8CJ19Iei0Ktx+bSNuvaYOQ4E4ntl+EP3eKOY12PHf/24BJKNu\nzMfiGzsVE+uNion1RsXCWqNiYr3RRGMoH0G/J4IDRwfQnRtK3T0Yzk+gBmSHU//t8josn+u6rEnF\nEqkMOnqyAf14tx+ptIzrFlXh2gWVELXFWcpMURR82OHBK7tOoXswDAHAklYnbltRj5ZRZiyPJdLY\nfbAPO/afzU86BwCCAEjG80P13YE4BrxRAICoVWPx7HJcPdeFq5od0Gom5vllZBkqQZgSs5HLioI9\nB/uw/a0OBKMplFv1CMdSiCczWHNNHf7+xpZx9eLzjZ2KifVGxcR6o2JhrVExsd5oojGUj+COr/9n\nwc+SUYs6lxl1LjOWzXGhpXrsCdemg3PLUr2+7ww6+7JvKq21Vty6sgFtLWVQCQIGvFG8sf8sdh/s\nQyKZgUatwtVznTCIGgQiSQQiSQRz20QyA51GhbaWMlwzrwJXtZQV7YuGUovG0/jN3k688e5ZqFUC\n/vG2uVi5oHLc9+cbOxUT642KifVGxcJao2JivdFEYygfwfeffw8Osy4fxK0m3bQP4aNRFAUfdfvx\nuz934eApD4DsJHN2i4hDp7LnoNslETctqcH1i6thGWUodiKZgUqFCesRn46GAjFAAcptlzdEn2/s\nVEysNyom1hsVC2uNion1RhNtrFA+vsWir0APrV06Y37RBEHAnHo75tTb0T0Yxu/3deGdowPoGYpg\nVo0Vn1hei6Wtzksu9ybqZm4YP2cy5wMgIiIiIqKZZ8aG8pmqzmXG/3XHfPz9jS2IJ9PjngCOiIiI\niIiIJh5D+Qxll0QA4iWPIyIiIiIioslTvMWfiYiIiIiIiKgAQzkRERERERFRiTCUExEREREREZXI\njF0SjYiIiIiIiKjU2FNOREREREREVCIM5UREREREREQlwlBOREREREREVCIM5UREREREREQlwlBO\nREREREREVCIM5UREREREREQlwlBOREREREREVCIM5UREREREREQlwlBOREREREREVCIM5URERERE\nREQlwlBOREREREREVCIM5UREREREREQlwlBOREREREREVCIM5UREREREREQlwlBOREREREREVCIM\n5UREREREREQlwlBOREREREREVCIM5UREREREREQlwlBOREREREREVCKaUjegVNLpDHy+aKmbQTOE\n3W5kvVHRsN6omFhvVCysNSom1htNNKdTGvW2GdtTrtGoS90EmkFYb1RMrDcqJtYbFQtrjYqJ9UbF\nNGNDOREREREREVGpMZQTERERERHRtCUrCgKRJNz+GBRFKXVzLtuMPaeciIiIiIiIpodgJIkedxhn\nhyJw+2LwhRPwhxPwhxLwh5PIyNkw/s+fW4SFzWUlbu3lYSgnIiIiIiKiKSGWSKN3KIKeoQjOusPo\ncUfQ4w4jGE1ddKxaJcBq1qGxUoJNEuGyGdBYZSlBqz8ehnIiIiIiIiIqqnRGRr8nmg3eQxH0uLMh\nfCgQv+jYcqsei2dZUeM0ocZpQqXDCLtZhGTSQSUIJWj9xGIoJyIiIiIiokmhKArcgTh6BsMFAbzf\nG80POT/HYtRiXoMdNU4Tap3mbAgvN0Gvu7Jj65X97IiIiIiIiKho0hkZZ/pDOHE2gJM9AZw8679o\n6LmoU6OxUkJNLnjXlptQ4zTDYtKVqNWlxVBOREREREREf5VUOoOTPUEcPePFR11+dPaHkErL+dvt\nkojlc11oqDCjxmlGbbkJDqv+ihh2PlEYyomIiIiIiGhcZFnB6f4Qjp7x4ugZH06cDeRDuCAAtU4z\nZtVaMbvGilm1VpRZ9BAYwMfEUE5EREREREQjisRT6OwN4mRPAKd6g+joDSKWSOdvr3WaMa/BjnmN\ndrTW2mDUM2JeLr5iREREREREBFlW0OuJoKMngI6eIDp6A+jzRAuOqbAbcPVcF+Y32jG33j5jzwOf\nSAzlREREREREM1A4lsKp3gBO9gRxqjfbEx5PZvK3izo15jXY0VJjQUu1FS01VpgN2hK2+MrEUE5E\nRERERHSFSqQyGPLH0O+NYcAXRb8nin5fFAPeKEIXzIpe6TBmA3iNFS3VVtSUm6BS8XzwycZQTkRE\nRERENI35Qgl09gXh9sfgCcbhDSbgCcbhCcQRjqUuOl4lCCi36dFUZUFDhYSWGiuaqy3sBS8RhnIi\nIiIiIqJpIp2RcWYglD3nuyeAjt4AvMHERcdpNSo4LHo0VJhRZjWg0mFEhSO7ddoM0KhVJWg9jYSh\nnIiIiIiIaIryhRL58N3RE8Tp/hDSmfPrgEtGLRbPKkdLjQWVDiMcFj3KrHpIBi2XIpsmGMqJiIiI\niIimgHRGRvdgGCd7ArkZ0APwDOsFVwkCal0mtNRYMavaipYaC5w2A8P3NMdQTkREREREVAKBcAIn\nc0uPdfQEcLo/hFT6fC+42aDFopYyzKrNTrzWWCVBr2OEu9Lwf5SIiIiIiGgSKYoCTzCOroEwugZC\n2e1gqOBccEEAap3m3MznFsyqscJlZy/4TMBQTkRERERENIF8oQRO9QbR2Ze9dA2EEImnC46xmnRo\naynLDUW3oLHKAoPIeDYT8X+diIiIiIjorxSNp9DZH0JnLoSf6gsiEE4WHOOyGzCv0YGGCjPqKyTU\nu8ywmsUStZimGoZyIiIiIiKicUilM+gaDON0XyjfE97vjRYcY5dELJldjuZqC5qqLGistMCoZ+yi\n0U16dXg8HnzmM5/Bv//7v0Oj0eCRRx6BIAiYPXs2Nm/eDJVKhZdeegkvvPACNBoNHnjgAdx0002I\nx+P4xje+AY/HA5PJhC1btsDhcODAgQN48sknoVar0d7ejvXr1wMAnn32Wbz11lvQaDR49NFH0dbW\nNtlPjYiIiIiIrlCyoqDfE833fnf2BtE9GEZGVvLHGEQ15jXY8wG8qcoCu8QecLo8kxrKU6kUNm3a\nBL1eDwD47ne/iwcffBArVqzApk2bsGPHDixevBjPPfcctm/fjkQigXXr1mH16tV4/vnn0draig0b\nNuC1117Dtm3b8Nhjj2Hz5s145plnUFdXh/vvvx9HjhyBoih455138PLLL6Ovrw8bNmzA9u3bJ/Op\nERERERHRFSKVltHniaB7MIwedwS93ig+6vIhnszkj9GoBdRXSGiqktBUZUFztQUVDiNUnIiNPqZJ\nDeVbtmzB5z//efzkJz8BABw+fBjXXHMNAOD666/Hnj17oFKpsGTJEuh0Ouh0OtTX1+PYsWPYv38/\n7rvvvvyx27ZtQzgcRjKZRH19PQCgvb0de/fuhU6nQ3t7OwRBQHV1NTKZDLxeLxwOx2Q+PSIiIiIi\nmkYURYEvlED3YBhn3WGcdUdwdjCMPk8UsqIUHFtVZsz3fjdXW1DrNEOrUZWo5XQlm7RQ/sorr8Dh\ncOC6667Lh3JFUfJT+ptMJoRCIYTDYUiSlL+fyWRCOBwu2D/8WLPZXHBsd3c3RFGEzWYr2B8KhS4Z\nyp1OaczbiSYS642KifVGxcR6o2JhrdHlSKUz2cnXegM4nTv/+3RfEJFYquA4g6hGa70NjdVWNFZZ\n8heTQVuiltNMM2mhfPv27RAEAW+//TaOHj2KjRs3wuv15m+PRCKwWCwwm82IRCIF+yVJKtg/1rEW\niwVarXbEx7gUtzs0EU+V6JKcTon1RkXDeqNiYr1RsbDWaCyptIyz7jBO94dwpj+I0/0h9LgjBed/\nCwBcDiPm1dtQ6zKjzmlGrcuMMqv+oiHoJoOW9UYTaqwvFSctlP/iF7/IX7/nnnvwne98B1u3bsW+\nffuwYsUK7Nq1CytXrkRbWxuefvppJBIJJJNJdHR0oLW1FUuXLsXOnTvR1taGXbt2YdmyZTCbzdBq\ntejq6kJdXR12796N9evXQ61WY+vWrbj33nvR398PWZY5dJ2IiIiI6AojKwo8gXh+6HlPbjvgjRYE\ncI1ahfoKCY1VEhoqJNS5zKguN0HUqkvYeqKRFXVu/o0bN+Lb3/42vve976G5uRlr1qyBWq3GPffc\ng3Xr1kFRFDz00EMQRRFr167Fxo0bsXbtWmi1Wjz11FMAgMcffxwPP/wwMpkM2tvbsWjRIgDA8uXL\ncffdd0OWZWzatKmYT4uIiIiIiCZIMpWBJxiH2x/HUCAGtz+GIX8c7kAMA74YEsMmXwMAvU6Nxiop\nG8IrJDRUSqguN0Gj5vnfND0IinLBjAYzCIekULFwyB0VE+uNion1RsXCWpsaFEVBOqMgnZGRyshI\np2WkM/II+xSkMjIy+X3nb8/kbkskMwhFkwhFUwhFkwhGkwhGUxeF7nN0GhWcdgNqyk2odZpzFxPK\nrPr8vFUThfVGE60kw9eJiIiIiGjqUBQF0UQankAcnmAcoWgK4VgKkVh2G46lEImnEUukkcqH7fMB\nO52WC4aITyS1SoBk1MJlM0AyauGw6OG06uG0GVBuM8Bp1cNi0k14+CaaChjKiYiIiIimKVlWkEhl\nEImnEIml8+H6XNgORJP5EO4JxAvW3R6JAEDUqaHVqKBRq6DTqGHUq6BRC9CqVVCrVdCqBWjUKmg0\nKmjVqvx1jUrIbs8dk7+ugjp3/+H302lVsBh1kIxaGEQNAzfNWAzlREREREQTQJYVhOMphHPDscOx\nFGKJDBKp3CWZ3SZTGSTTMmRZgawoyMgKZFmBoiB7Xcn+LA+/rijIZBQk0nL2/rnHTGfG13NtENUo\nt+pRZtGjzJq9WIw6mA1amAxamHMXo6iBSsVwTFRMDOVERERERJcQS6ThDcbhDSWy22AC3lB26w8n\nEIpme6YnenC3IAAqQYBKJUCtEiBq1dBpVTDqReg0aohaFXRaNYx6Dcx6bWHINmohGbQot+ph1HPN\nbaKpiqGciIiIiGY0RVEQiCQx6MvO9O0ZFrp9uW0sMfqwb7NBC8moRXWZEZJRlw3DRi3MBh0Mohqi\ndthFl93qNKp80BZUAlRC9no2gAOq3HUO6Sa68jGUExEREdGMkEhm0OuJoMcdwYAvigFvFAO+GAZ9\nMSRSI4dug6iBw6KHQ9KjzCLCbtHDIYnZfRYRDkmEVsO1r4nor8dQTkRERERXFEVRMOiL4XR/CD1D\nYfS4s0Hc7Y9dNLxcp1XBZTOiwmGAy26Ay2ZAmUWfD98GkR+XiWhy8V2GiIiIiKa1cCyFU71BnOoN\n4FRfEJ29QUTi6YJjzAYt5tTbUFNuRrXThOoyI1x2I2xmLrNFRKXFUE5ERERE00YqLaNrMIRTvdnw\nfao3iEF/rOAYp02Phc1laKqUUOsyo8ZphsWoZfgmoimJoZyIiIiIpqR0RkbvUARdA2EMBjtxuMOD\n7sFQwTJgRlGDBU0ONFdZ0FxtQVO1BRajroStJiK6PAzlRERERFRSiqIgGE1hwBtF92AYXQMhdA2E\n0TMULgjgapWAWpcZzdWWfAivcBihYg84EU1jDOVEREREVBTReAoDvhj6vednPu/3RjHoi160oAUx\nxAAAIABJREFU5JhGLaDGaUZDhRn1FRIWzamApMuuyU1EdCUZM5T39vaOeefq6uoJbQwRERERTW+J\nVAaDvlgudEdzATyGAV8UoWjqouM1ahUq7AZUNBhRYTegutyEhgoJlWVGaNSq/HFOpwS3O1TMp0JE\nVBRjhvIvfvGLEAQBiUQCHo8HdXV1UKlU6OrqQl1dHf7whz8Uq51ERERENAXIsoJAJAlvKA5fMIGh\nQByDvvO93r5Q4qL7qAQB5TY9mqoscNkNqHQYUeHIhnCHRc/h50Q0o40Zyt98800AwEMPPYQvfOEL\nWL58OQDgww8/xE9/+tPJbx0RERERFY2sKAhFkvCGEvAGE/ng7Q3F4Q0l4AvG4Q8nkZEvXO07y2ER\nMa/BjgqHEZV2QzZ4O4wot+oLer2JiOi8cZ1T3tHRkQ/kANDW1obOzs5JaxQRERERTbxYIg1PMA5v\nMA5vMJG7nsj+HIrDF0oUTKw2nCAANrOIxioJDkkPh0XMbyvsRjjtBog835uI6LKNK5RXVlbiBz/4\nAT75yU9ClmW8+uqraGxsnOSmEREREdF4KYqCcCyFQX8Mbn8MQ/5s+Pac6+kOxi+aTG04q0mHOpcZ\nDkkP+7DA7bDo4ZBEWM06qFXs7SYimmjjCuVbt27FD3/4Q/zzP/8zAGDVqlX47ne/O6kNIyIiIqJC\n6YwMbzCeC95xuH3ZAO72xzDojyGeHDl0G0R1LlzrUWYRYbdktw5JD4dVD7tZhFbDwE1EVArjCuVW\nqxXf/va3J7stRERERIRsr/egP4bTfSGc7g+iezCMQV8MnmAcygijy3VaFZw2A5xWA1x2A5w2A8qt\nepRZs0HcqOcquEREU9W43qFfeeUVbNmyBcFgEED2D4UgCDh69OikNo6IiIjoSqcoCtyBOE73BXGm\nP4TTuUsskS44zmrWoaXGCpctG7qdNj1cNiOcNj0sJh0EzmBORDQtjSuU/+hHP8Jzzz2H1tbWyW4P\nERER0RVLURR4AvF88D7dnw3ikXhhAK9wGLGopQwNlRIaKyXUV0gwiOztJiK6Eo3r3b2iooKBnIiI\niOgyKIoCbzBREL5P94cQjqUKjnPZDVjQ5EBjpSUfwDncnIho5hjXO/6CBQvwta99DatXr4Yoivn9\nd91116j3yWQyeOyxx9DZ2QlBEPD4449DFEU88sgjEAQBs2fPxubNm6FSqfDSSy/hhRdegEajwQMP\nPICbbroJ8Xgc3/jGN+DxeGAymbBlyxY4HA4cOHAATz75JNRqNdrb27F+/XoAwLPPPou33noLGo0G\njz76KNra2j7mS0NEREQ0PoqiwB9O4nRfEJ39oVwADyIULQzgTpsecxvsaMr1gDdUSjDqtSVqNRER\nTQXjCuXhcBgmkwkHDhwo2D9WKP/Tn/4EAHjhhRewb98+fP/734eiKHjwwQexYsUKbNq0CTt27MDi\nxYvx3HPPYfv27UgkEli3bh1Wr16N559/Hq2trdiwYQNee+01bNu2DY899hg2b96MZ555BnV1dbj/\n/vtx5MgRKIqCd955By+//DL6+vqwYcMGbN++/WO8LEREREQjk+XsJGxnB8M46w7nh6IHI8mC48qt\nerTOsaGxUkJjlQUNFRLMBgZwIiIqNK5Qfm75s0AgAKvVOq4H/sQnPoEbb7wRANDb2wuLxYK9e/fi\nmmuuAQBcf/312LNnD1QqFZYsWQKdTgedTof6+nocO3YM+/fvx3333Zc/dtu2bQiHw0gmk6ivrwcA\ntLe3Y+/evdDpdGhvb4cgCKiurkYmk4HX64XD4bisF4OIiIjonHO93z3uMM66I/ltryeCVFouONZh\nEbG01ZkN4LkecMmoK1HLiYhoOhlXKD927BgefPBBxONxvPjii/jiF7+Ip59+GgsWLBj7wTUabNy4\nEX/84x/xwx/+EHv27MnPDGoymRAKhRAOhyFJUv4+JpMJ4XC4YP/wY81mc8Gx3d3dEEURNputYH8o\nFLpkKHc6pTFvJ5pIrDcqJtYbFdOVUG/hWApd/UGc6QvmZ0E/0xe86PxvrUaFuopcz3elhIYqC1pq\nbLBJ4iiPTBPpSqg1mj5Yb1Qs4wrl//Iv/4If/ehH+PrXv46Kigp85zvfwebNm/HLX/7ykvfdsmUL\nHn74YXzuc59DIpHI749EIrBYLDCbzYhEIgX7JUkq2D/WsRaLBVqtdsTHuBS3OzSep0/0sTmdEuuN\niob1RsU03eotlZbR54mgxx3B2XM94ENheIOJguMEAXDZjZhTZ0ON04Rapxk1ThNcdgPUKlXhY8aT\ncMcLh67TxJtutUbTG+uNJtpYX/KMK5THYjG0tLTkf169ejW2bNky5n1+/etfY2BgAF/5yldgMBgg\nCAIWLlyIffv2YcWKFdi1axdWrlyJtrY2PP3000gkEkgmk+jo6EBrayuWLl2KnTt3oq2tDbt27cKy\nZctgNpuh1WrR1dWFuro67N69G+vXr4darcbWrVtx7733or+/H7Isc+g6ERHRDCYrCtz+GM4OZkP3\nueHnA94YZEUpONZm1mFBkwO1w8J3dZkJOq26RK0nIqKZZFyh3Gaz4dixY/mh56+++uolzy2/5ZZb\n8M1vfhNf+MIXkE6n8eijj6KlpQXf/va38b3vfQ/Nzc1Ys2YN1Go17rnnHqxbtw6KouChhx6CKIpY\nu3YtNm7ciLVr10Kr1eKpp54CADz++ON4+OGHkclk0N7ejkWLFgEAli9fjrvvvhuyLGPTpk0f5zUh\nIiKiaUJRFAQjSZzN9Xyf6wHv9USQTBWe920Q1WiusaC23IQapxm1zuyWk68REVEpCYpywdfFI+jq\n6sLGjRtx8OBB6PV6NDQ04N/+7d/Q1NRUjDZOGg5JoWLhECgqJtYbFVOx6k2WFQwFYuj3RtHniea3\nvUORi8771qgFVJWZ8qH7XA+4XRLzHQw0/fC9jYqJ9UYT7WMPX9+zZw+ef/55RKNRyLJcMNkaERER\n0USJJdK5wB05H8A9UQz4YkhnCnu+BQBOmwGza635Yee1TjNcdgM0atXI/wAREdEUM65Q/otf/AJr\n166F0Wic7PYQERHRDJDOyOj3RtE9EEb3YBhdgyH0DEUQCF88YZpep0ady4RKhxGVZSZUOYyoLDOi\nwm6AVsPzvomIaHobVyivrKzEl770JSxatAiieH7Jj/Xr109aw4iIiOjKkJFl9LgjONUbRGdfEF2D\n2XO/L+z5LrPosbDZgUqHEVVlptzWCKtJx2HnRER0xRozlP/qV7/Cpz/9aSxevLhY7SEiIqJpTFEU\neIJxdPaFcKo3gFO92TW/k+nzAVyjVqHGaUK9y4z6Cgl1LjPqXGYYxHH1FRAREV1Rxvzr9/Of/xyf\n/vSn2SNOREREI4rEUjh82pvtBe8N4lRfEMHI+SHoggDUlJvQXG1Bc7UVjZUSqstNPOebiIgoh19J\nExER0bikM+eGoWd7wE/1BdHniRYcY5dELJvjRHOVBc3VFjRUStDr+HGDiIhoNGP+lTxx4gRuvvnm\ni/YrigJBELBjx45JaxgRERGVTiKVQb8nip6hMLoGwtlh6AMhpIYNQxd1arTNKkdtrie8qcoCuySO\n8ahERER0oTFDeUNDA37yk58Uqy1ERERURIqiIBxLYcAXw4A3u+Z371AEvZ4IhvxxKMOOVQkCap3n\nw3dztQVVZSZUVFi4li8REdHHMGYo12q1qKmpKVZbiIiIaBJE4+eD9/ltFAPeGKKJ9EXHW4xazKm3\nobrchOry7NrfDZUSRC2XHyMiIppoY4bypUuXFqsdRERE9DGk0hkMeGPo90bR540WBO9wLHXR8WqV\nAJfdgNY6G1x2AyocRlSXGVFdboJk1JXgGRAREc1MY4byTZs2FasdREREdAmKosAXSqDfG81ePNH8\ndU+gcLg5kB1yXm7To6nKggqHARV2Y35bZtFDpeLa30RERKXG6VCJiIimkIwswxdKwBOIYygQh9sf\nw4Avhj5PBAPeGBKpzEX3sZp0aK2zobLMiErH+UuZVc+lx4iIiKY4hnIiIqJJpigKYokMQrEkQtEU\nQpEkQrEUQtEkgpEUQrEk/KEEhgJx+EIJZOQL+7wBrUaFCrsxH7yrHNnrFXYjjHr+OSciIpqu+Fec\niIjoMo0nZF+4P525OGhfyGbWoanKgnKbHuVWPcqtBpRZ9aiwG+Cw6KESONyciIjoSsNQTkRElJOR\nZfhDSXhDcXiDCXhDcQTCSQSjf13IFnVqSAYt6iskSAYtJKMOkkkLyaCDZNTCYspuJYMOFpMOWg2H\nmhMREc00DOVERHTFUxQF6YyMcCwNbzA7RNwbjMN7wTYQSUIZI2uLWjUkoxZ1LgkWYy5kX7C1DAvd\nOi4hRkRERJfAUE5ERAVkWUEwmoQvlEAgnEQsmUY8mUE8kUYsv00jlZaRSstIZ7KBN5WWkcrI+evp\njIx0bl8qnT1mLIKQXaZLJQhQDd+qBKhytwm5fRcfh/PXBQHJtIx4Mo1EKoN4IoNEKjPiedrnqFUC\n7JKI2TVWOCx62C0iHJIeDkmETRJhMTJkExER0eRgKCcimkHSGRmBcDZwe0Nx+EMJeEMJ+PKXOPzh\n5JgBdjSCkJ2MTKtWQZPbGvVaaDUqaNQqaNXZUD0SBYCsKJDl3OXcdQXIyAoUWUEmd0ml5WG3K5Dl\n3DGKkm+3Ri1Ar9NAr1PDYREh6tTQ6zQw6TVwSHrYJREOiwiHJRu8JZOO52sTERFRSTCUExFdIVJp\nGb5wAgPBBDq7fbngfT5se0MJBMPJi9ayPkclCLBJOjRWSbDneomtJh0MogZ6MRtqDTp19medGlqN\nelgIF6BWTY3zoWVFYcAmIiKiaYOhnIhoGlAUBaFYCm5/DG5fLL901rkeb18ogVA0Ner9Nerc8Ow6\nGxySCHv+oofDIsJmzgZwlWr6h1kGciIiIppOGMqJiKYQWVYw4IuiezCMroEwBrxRuP0xDPpjiCcz\nI95Hp1XBLulR6zTDLomoqZCgVwuw54Zp2y0iJIN21KHjRERERFQ6kxbKU6kUHn30UfT09CCZTOKB\nBx7ArFmz8Mgjj0AQBMyePRubN2+GSqXCSy+9hBdeeAEajQYPPPAAbrrpJsTjcXzjG9+Ax+OByWTC\nli1b4HA4cODAATz55JNQq9Vob2/H+vXrAQDPPvss3nrrLWg0Gjz66KNoa2ubrKdGRDQhEqkMzrrD\n6B4Io2swjO6BELrdYSRThROi6bQqOG0GuGwGOPMXffbcaIsIo6gpCNxOpwS3O1Tsp0NEREREf4VJ\nC+WvvvoqbDYbtm7dCr/fj7vuugtz587Fgw8+iBUrVmDTpk3YsWMHFi9ejOeeew7bt29HIpHAunXr\nsHr1ajz//PNobW3Fhg0b8Nprr2Hbtm147LHHsHnzZjzzzDOoq6vD/fffjyNHjkBRFLzzzjt4+eWX\n0dfXhw0bNmD79u2T9dSIiC6LLCsIRJLoGcr2fncNhNA9GEa/N1qw/JZaJaCqzIT6CjPqXWbUucyo\nKjfBatKxl5uIiIjoCjVpofzWW2/FmjVrAGTPhVSr1Th8+DCuueYaAMD111+PPXv2QKVSYcmSJdDp\ndNDpdKivr8exY8ewf/9+3Hffffljt23bhnA4jGQyifr6egBAe3s79u7dC51Oh/b2dgiCgOrqamQy\nGXi9Xjgcjsl6ekRESKVlhGOp7CWaRCiWQjCSLFj72hcceTZzvU6N2TVW1FVIuRAuobrcBK1makyW\nRkRERETFMWmh3GQyAQDC4TC+9rWv4cEHH8SWLVvyvT0mkwmhUAjhcBiSJBXcLxwOF+wffqzZbC44\ntru7G6IowmazFewPhUKXDOVOpzTm7UQTifU2fcSTaXiDcXgCcXgD8fPXg7lLIA5/OIFYIj3m46gE\nwGHRY1adDeU2A2pdZjRXW9FcY4XLbpzUSdVYb1RMrDcqFtYaFRPrjYplUid66+vrw1e/+lWsW7cO\nd9xxB7Zu3Zq/LRKJwGKxwGw2IxKJFOyXJKlg/1jHWiwWaLXaER/jUnjOJRULz/GdGtIZGcFIEr5w\nAv5QAv5wEv5wdgZzfzj7sy80dtgWBMBi0sFp1cNs1MJs0EIy6M5fN2rhyM1objXrRl4mTJbh8YQn\n7Xmy3qiYWG9ULKw1KibWG020sb7kmbRQPjQ0hC9/+cvYtGkTrr32WgDA/PnzsW/fPqxYsQK7du3C\nypUr0dbWhqeffhqJRALJZBIdHR1obW3F0qVLsXPnTrS1tWHXrl1YtmwZzGYztFoturq6UFdXh927\nd2P9+vVQq9XYunUr7r33XvT390OWZQ5dJ5ph4sk0hvxxeEPxfLj258K3Lxe4Q5HR1+gGAJNek1se\nzAK7WYRN0mW3ZhE2Kbu1mLRTZj1uIiIiIpr+Ji2U//jHP0YwGMS2bduwbds2AMC3vvUtPPHEE/je\n976H5uZmrFmzBmq1Gvfccw/WrVsHRVHw0EMPQRRFrF27Fhs3bsTatWuh1Wrx1FNPAQAef/xxPPzw\nw8hkMmhvb8eiRYsAAMuXL8fdd98NWZaxadOmyXpaRFQiiqIgGEli0B/LLhHmi+WXCnP74whGkqPe\nV6dRwSaJqHLYcuE6F7alYYHbpINOqy7iMyIiIiIiAgRFUcbqOLqicUgKFQuHQI1POiNjKBAvCN3n\ng3fsoqXCAEAlCCizivnlwsqs+oKebbtZhEFUz6jZy1lvVEysNyoW1hoVE+uNJlpJhq8TEQ137nzu\nQCR7Hncgdz63P5yA2x/HoC8GbyiOkb4m1OvUqLQbs+tz24et1203oMwicjg5EREREU1bDOVENC6K\noiCaSCMYSSISTyOWyF6iifPXY/FM/ueC/Yk0IvGxZyq3SyJm11jhtGcDtysXup02AySDdkb1dBMR\nERHRzMFQTjTDybKCQCQ7MZovFM9tsz3YwWgKoUgSwWgSoWjqorW2L8UgqmEQNbBJIupcZljNIqwm\nHWy5rdWcvV5u1fN8biIiIiKakRjKia5gqXQGvnASvmAubIcT8AUT56/nwvdYM0uIOjUsRi0aKyVI\nRh2k3NJfBlEDg6iBUdTAoM9tRQ0MohpGUQO9TjOp63ATEREREV0JGMqJpqlYIp3v1faG4tmlv0IJ\neEPZZcC8oQTCsdSo91erBNglEbNqrLDnJkVzSNkJ0uxSdoI0yaSDyB5sIiIiIqJJw1BONMUoioJw\nLJUP2L78JT7segLxZGbUxxC1athzQ8bPBW2HJMIu6bOBWxJhNmqh4nnaREREREQlxVBOVEQZWUYw\nkoI3FM8OIw8nCoJ2Nngnkc5cvPTXOSa9BuVWAxyW873bdkmE3ZLt3bZL+hm3BBgRERER0XTFUE40\nQcY6fzscT2PQFx3z/G0BgMWsQ53LlAvb+mzQzg0lPxe6OSEaEREREdGVg6Gc6DKk0hkM+mIY9MUw\n4IthwBfFgDeKAV8MvlBi1Ptp1AJs5vPnb9svGErukERYTDpo1Fxvm4iIiIhoJmEoJ7pAIpWBJxDP\nBe9s4B7wRjHoi8IbTGCkjm67JGJuvQ0Oi35Y6M72dtskEc31Dng84aI/FyIiIiIimtoYymlGURQF\nkXgankAcnmC8YDsUjMMbjCMUHXnGcptZh9Y6GyocBlTYjXDZjahwGOC0GS45QzmXBiMiIiIiopEw\nlNMVQ1YUhKMp+MOJ3CWZ3YYS8AQT+fCdSI08a7lWo4LDokedy4wyix4uezZ8VziMcNkMEHU8l5uI\niIiIiCYWQzlNWbKsIJ5MI5bIIJZMI57MIBpPIxDJBu7A8OAdTiAQTiIjjzKLGrKzlrvsBpRZ9Ciz\n6lFm0aPcqocj97PFqOWM5UREREREVFQM5TRpFEVBPJlBJJZCKJbKb8OxFMLRFMLxFOKJbOiOJ9OI\nJTOIJ7LhO5ZMI5kafVmw4dSq7CRqjVUSbGYRNpMIm6TLXjeLsJl1cFj0MIgsdyIiIiIimlqYUmhc\nFEVBLJFBOJ4L1LEUwrEkwrH0+W00mdt/PoSnM6P3XF9Ip1XBoNNAr1PDJokw6NTQ6zQwiNmtXlTD\noNPAatLBJp0P3CaDFir2cBMRERER0TTEUD6NyYqCTEZGKq0gnZGRzshIZWSk0zLSGWXY9dz+jIJ0\n+tx1edj17P1T545Ny4jG0wW925FYasyh4cMZRA0kgxYOlx6SUQuTXpvdGrSQDFqYh10MYjZ0izo1\n1CouB0ZERERERDMLQ/kU895Hbrz53tlhAbkwcGeGhe3xhuSPw6TXwGTQwmnVnw/VxsJgnb8YdTDp\nNVxrm4iIiIiIaJwYyqeYzr4gjpz2QQCg0aigUaugVQvQaFQQNWpo9Cpo1AK06uxtGo0KWrUK6gv2\njXSMJvc4+f1qFbQaYdj1c/sFmAxamPQa9l4TERERERFNIobyKeazN7TgruuaoBIEzgRORERERER0\nhWMon4LYO01ERERERDQzMP0RERERERERlQhDOREREREREVGJCIqiTP4U3kRERERERER0EfaUExER\nEREREZUIQzkRERERERFRiTCUExEREREREZUIQzkRERERERFRiTCUExEREREREZUIQzkRERERERFR\niTCUExEREREREZUIQzkRERERERFRiTCUExEREREREZUIQzkRERERERFRiTCUExEREREREZUIQzkR\nERERERFRiTCUExEREREREZUIQzkRERERERFRiTCUExEREREREZUIQzkRERERERFRiTCUExERERER\nEZUIQzkRERERERFRiTCUExEREREREZWIptQNKJV0OgOfL1rqZtAMYbcbWW9UNKw3KibWGxULa42K\nifVGE83plEa9bcb2lGs06lI3gWYQ1hsVE+uNion1RsXCWqNiYr1RMc3YUE5ERERERERUagzlRERE\nRERERCUyY88pn6r6PBEc7/KjzKqHy2ZAmVUPjZrfnVzKmf4QOvuDcNoMcNkMcFhEqFV83S7lo24/\n+r3R/Otmt4hQCUKpmzXlHTzlQSCchMtugNNmgM2sg8DX7ZLePTaIVFqG056tN8mo5et2CYqi4O3D\n/VCpBLhsRrjsBpj0Gr5ul5DOyNh7qB8GUQOXLft7atTzI8+lxBJp7Ds6AMmgzf5dsBug1/F1u5Rg\nJIn3PnLDatbl602n5dDnSxnyx3Cw0wuHJMJlN6DcaoBWw89ul9IzFMGJbj/KrXo47QaUWZgVxqOz\nL4iugdCwrKCHSjV1/pbynXaK+dnvjuFkTyD/syAAZRY9nDYDKh1G3LaiHuU2QwlbOPUoioIf/eog\nhgLx/D61Ssh/sVFZZsSnVjXCYtSVsJVTTzoj4/svf4BEMpPfp1ELKLdmP4jVlJtw5+omiDp+sBgu\nEEni6Zc/gKKc36fTqODMfRCrrzDjztVNU+qNfiroHgxj268PFewTdWo4c/U2u9aKW66uY9i8wKFO\nL37626MF+/JB027AVU0OXLeoukStm7r2HurH/3r9WME+87CguazVieVzXSVq3dT1x3e78ev/6izY\nZzFq81+krbqqCgsaHSVq3dT1692deOv9noJ9tnMB3W7ATUtq0VxtKVHrpq7/eOMEDpwcyv8sALBb\nRLhsBrjsRqy5pg5VZabSNXCK+ulvj+BMfyj/s0oQ4LBkv9iocBhx+8oGOCz6ErZw6lEUBc9s/xD+\ncDK/T60S8l9sVJeZ8KlVjTAbtCVrI0P5FBJLpHGqN4iqMiNWzK/AoC+GQX8Mbl8MR8/4cPSMD1qN\nCp+/eXapmzqlDPpjGArE0VJtwYImR/41G/THcKjTi0OdXjgkPW5dUV/qpk4pHT0BJJIZLGxyoKFS\ngtsfw6AvBrc/hn5vFB92eFBfIWHF/IpSN3VKOXrGC0UBls91wWnT52vN7Y+hZyiCAyeHMK/Bjjn1\n9lI3dUo53OkFAFzXVgWjXpOvtUF/FGfdYbz3kRuLZ5ejwm4scUunlnOv2y1X10FWlHy99QxFcGYg\nhP3HBnH1PBd7My9w5HT2dbtzdSMisTQG/dnXrWsghM6+IA52eLBsjpNfAl3gSKcXAoDP3tiCoUAc\nbl8Ug/4YOntD6OgJ4sxAGE/ct6LUzZxyjnR6YRA1uP3ahvPvbb4YTpwN4KOzAfhCCTz8+SWlbuaU\nks7IONrlQ5lFxPWLawr+lh7r8uNYlx+ZjIx7PzW/1E2dUsKxFLr6Q6h1mrB8jiv73pZ77Y6c9uHI\naR+MogafvaGl1E2dUnqHIvCHk2ittWJOvT33+SP72g2c8uLQKS+cNgNuXlZbsjbyr/gUcrzbD1lR\nsGyOC3eubiq4LRBJ4qFndqPXEylR66auo2d8AIBVCytx09LCX6Yz/SE8/r/+wtdtBOdet5uW1mDJ\nbGfBbR+cHMIPfvkheof4ul3oWO51u21FPZqqzvd8KIqCXR/04n///jh6PVGG8gsc68q+bndd1wy7\nJOb3K4qCV/ecxn/u7kTvUISh/ALHzvigUavw2RuaoR02E7CsKHjuD8ex80Av+r1RNFayF+4cRVFw\n9IwPNrMOf9feVBC8ZVnBs68cxIGTQwhEkrCZxTEeaWZJJDPo6A2ioVLCJ1c2FNyWzsj47v/Zj66B\nMNIZmUNlhxkKZD/cL5ldftHrlkrLeOR/vM2/pSM43R9CIpnBqgWVuGNVY8FtiWQGG36wi5/dRnDs\njA8KgKvnunDHBVnBG4zj4W17WW8jOPeZd3VbFa5rKxxd1tETwJPP7S/568Z31Snk3If9eQ0Xf5i3\nmnSwmnX4/9l78/DGzvLu/6PNkrV5kWVLtsf2jGfNZJlsk4QshCRsaRKSNATSsrWhbfhBX8oLXJC+\nLIWyU/r2pUDbAIUSskATAiUkBBIgC8lMMplMJpmxZ7HH+yLZlrXvOr8/jo+syXhs2T46RzrS97ry\nR8ay/OjWc57nXr73956cqc1LfC0ku21fwm4dbht6nY7J2sF+CvqHA+h0sG1D4yk/62oT5yjW7HYq\n+oYD1JuNdLedPGtSp9Mt2q12IZ6ETDbHkdF5PM3WkwJyEO22odUOwORs7XwrRCSeZsQX4hfEAAAg\nAElEQVQXYUtnw0kBOYh0xbzdavfCSRifiRKOpdnR3XRKJVyv19HZKtJha8/pyTg2Pk82JyzpgxgN\nejpa7GRzAv75uAqrK1/0D88DS/sgJqOe9hYb85EUsURG6aWVNfqW8XnNdQbamq1MzsYQCnvFaqBv\nRLLbqW0kTQ4zNouRidpdegqW22+deR+kFpTXsIDDQyI9fXPH0hUPb7OV2VCCZDq75M+rEbmCioin\n+dQKm9Ggx91Uz1TtYD8JUkWkx+PAajm1f6bRXoelzsDkXO1gL8TMfBz/fILtXY1L9oxLe7Bmt5Mx\nNClWRJa6DAG8rgW71ZJAJ2G5hCOIdwLA5FzNboXoG1rJbgtBee05PQmS3VZ+Tmt2K0TfsNgqcVq7\nLTynU7X9dhL6FlpMtnWdWhgA0W6JVPakHuAaxOfUXGegx+s45Wc6nQ6vy4Y/ECeTzamwuvJELidw\nZGQed6OFloZTdbnMJgMup0X1s60WlJcJQrEUY/4ImztOrYhI8LaIjsRU7ULMY8IvVUSaT9sb2O6y\nEk1kCMXSCq+ufHFsTKqILC3YIx7sVqbnYmRztYNdwnKZVhAFuJoc5lpw+Rqs5LS6G+sx6HWqX4jl\nBmm/nXE6Z79FqvjW7FaIlZ5Tb8tCcFmz20noGw5g0OvY0nmaIGlBcKt2vi1CapVwWk10tCwtSJZ/\nTmt2yyOVznJ8PERXqx3HaUR4pf1Wo7AvIhBOMjUXY9uGxtO2kHhdVnKCwHSgxmiRMDwdJpbMnNbn\nBfFeCEZTxBLqxQq1oLxMcGREpD+dzokAaK9diKdgJecLChyJGlUxj2LtlskKzMwnTvuaasMibWw5\nu1mZCyVJpGpURQl9K1R8jQY9rU31TM5Ga4yWAvQNB7CcpiICYltTvdlYc1oLkM3lODIaoLWxfsmK\nCCwyWmp2W0Q0kWZ4Okxvu/O0EzekZMZELZmRx9RcjPlIiu1LtEpIaHfV9ttrMTAeJJPNnfZOgMLk\nWc1uEvLsqWU0a2o+76lYrj1YQns+CaTe+VYLyssEEo1nuQ3jyR/stQtRQnHBZY0a+1ocHg5gNOjY\n3Nlw2td4a47ESRAEgb6hAE5bHe2nqYhAATW29pwCJ1dElhs10u6yEU/WqIoSpIrI1g2NGPRLX9U6\nnY52lxVfjaqYx/BUhHgyu6yzb6kz0uysMVoKcWRkHkE4feIMoKXBgtFQ02gpxOoKA7U7QcLhYuxW\nu0tPweEVWGdQ83mXwuEVCgNQYDcVkxm1oLxMsFJFBBazOFO1Bw0oqIg01eNqOP08xkXKXe1gB7Ei\nMjIVpre9AbPp9DPIva5au0QhJmdjBKOpJcWjCiFl92t2E3F8oSKyo2d5NXop6Vg730RIlP/TUdcl\neFzWmvhWAfJ2W2G/eV2i+FY8WWO0QEGrxDIzyA16vSi+NVfTaJFQTFDusJqwWYw1DYMC9A8H0Ot0\nbF1CaFaCx2VFRy24lCAIAv3DAWwWIxva7Kd9Xb5dorbfAFFo9tjoPB0tNhpsS7dKQEGsoKLdakF5\nGWAulGA6EGfbMhURKBDfqjn7gDhOI548vXiUhFrW8GT0D88jsLwTAbVK+WtRjPMFtT6416JYu5UD\ndaycsJJYmYT2WtLxJEg0xZVGEtZEy05G33CAOqOeTe3Lj9bzumwkU1kC4aRCKytf5BaCJJfTgrtx\n6VYJqIlvvRbxZIYTk2E2tjuoN59+MrPZZMDVoL74VrnANx9nNpRke3cT+mUKAy1OCyajvsbMWMDA\neJBUJld8rKBipVyVOeU33XQTdruY5ens7OSOO+7gk5/8JDqdji1btvDZz34WvV7PT3/6U+6//36M\nRiMf+MAHeMMb3kAikeDjH/84s7Oz2Gw2vvrVr9Lc3MyBAwf44he/iMFg4LLLLuNDH/qQGh9tTSjW\naZUO9pHpMNlcbtkAvhpQTI8ILIpv1Zx9ESspOkuoiW+djJX6oiW015z9k7CSeJSEfP9gLZkhtkqM\nBLDXm/KjWk6Hk8W33AqsrnyRzuQ4Nhakw718RQRO1mhZKRDVOoKRJBMzUXZubF5x/njh+dbsPD1D\nrRowOh0hmshw7hb3suwpEB3+4+NBpudidLiXf6a1jqOj8+SEpUfvvRZel41XBmeJJdJLToqpJhQb\nK+j1OjzNVibnouQEYdkAvhpQrN0c1jrs9SZVfTfFo7pkMokgCNx9993cfffdfPnLX+bLX/4yf/d3\nf8e9996LIAg88cQT+P1+7r77bu6//36+//3v88///M+kUinuu+8+tm7dyr333suNN97Id77zHQA+\n+9nP8o1vfIP77ruPl19+mcOHDyv90daM/IZZhjYmwbtAVayJbxUESStUREC0WyCcrFEVEcXKzCbD\nio5oTXxrEeI4jQAtDRZal6mIADgXxLdqwSXEEhlOTIbY6HUuWxGBgnFytWQGvvk4c0VURKAmvlWI\nwYniKiJQq5QXQhKwXKlVAhbbTGrnW/HOPtTa6AqxaLfifF6o2Q2KL0SBaLdUOkcgVGO09A8H0OlO\nP3qvEO0uK/5gnHRGndHTigfl/f39xONx/vIv/5L3vOc9HDhwgEOHDrF7924ArrjiCp599lkOHjzI\nueeeS11dHQ6Hg66uLvr7+3nxxRe5/PLL86997rnniEQipFIpurq60Ol0XHbZZTz77LNKf7Q1QRqn\nYa830eE+vXiUhBqlWEQ6k+XYWJBOtw3nChURWBQMqfY5ofMLFZEtGxpWrIjAovhWMFrd4lsjvjDR\nRGbFKjnUxLcKcXR0ZfEoCTXxrUWsNC+6EJL41lRtVvkag6Sa3YptlYBau0QhimVPQa2NrhB9wwFM\nRj2bO1ZmqNR8XhG5hVih0V6XT2Avh9r5JiKZyjIwEaLH4yiKaeFx2RAEmJ5TR6NFcfq6xWLh9ttv\n5+1vfztDQ0P81V/9FYIg5Kk/NpuNcDhMJBLB4VgUPbPZbEQikZP+vfC1Eh1e+vfR0dEV1+J2n15U\nTSmM+yMEwkkuO6edttaVD6jtm1rgyUFCiWxZrF8tvHJ8hnQmx3k72oqyw5aeZp7YP0YklVPNbuXw\nfR0aDQJw4RneotbT29XEi0f9xDICW8pg/Wrh6VenALjorPai7Laxo5GBiRAZnR5vFe+3oWeHAHjd\nOR1Frafb4+Slo36sdgu2ZZTatY7BqTAAl53bibsIqmuH287UXJyWFvuKNNpSoRz22/GJEHodXHru\nhhX3T0uLgMNqwjcfL4u1q4mjY0Fs9SbOP7Mdg375/eNstKLTwUwoWdV3aSab49jYPJ2tdrZualnx\n9WcutBvORdJlsX61EIwkGfVFOGdLC+3elSuXO3rdwBGCsUxV77ehyRDhWJo3nN9JaxGxwraNLnjm\nBKFkdccK+/t9ZHMC520vMlbobuaplyeIpNWJFRQPyjdu3Eh3dzc6nY6NGzfS2NjIoUOH8j+PRqM4\nnU7sdjvRaPSkf3c4HCf9+3KvdTpX3rR+f1jGT7Y2/PGlcQA2eRxFrcdmEg/248NzZbF+tfDcy6Ld\netz2ouzgMIsq40eHZjmre+WLQG643cV9v6XG869MANDVYi1qPQ314hHRNzBDe2P19g++cFgMyjua\nLEXZrckuBgSHjvmxqCD9UC77bX+fD5NRj8tmLGo9LqcZgFeOTtPbfvpxfVpGThB4+ZifJocZo5Ar\nym7uxnqGp8IcHZxRpc+3HPZbIpXhyHCAbo+TWCRBLLJyi1dbs5XB8RCTU8GimENahH8+zvRcjHO3\ntDA3Gynqd1xOC8NTIVW+83LYawDHx4IkUlm2dDYUtR5dTsBk1HNiYr4s1q8WXuj3AdDrdRZlB6tR\nTBINjAaqer89+9IYABvbiluPfSFWODY8h9/fWtK1lTP2HBRjhe5WW1F2c1rEWOHIiVm2F8HkWAuW\nC/YVv4UeeOABvvKVrwAwPT1NJBLh0ksvZe/evQA89dRTXHDBBZx99tm8+OKLJJNJwuEwAwMDbN26\nlfPOO48nn3wy/9rzzz8fu92OyWRiZGQEQRB45plnuOCCC5T+aGtCMfPJC+FutIjiW1VOw+5b6BFZ\nbpxGIWriWyL6pHEaK4hHSahR7sSKyNHRebwuK412c1G/s9guUb12C0VTjPkjbO5owGQ8/ei9QrTX\nxvAx7o8SjqVXHL1XiNr5BsfGgmRzxYlHSfA2W8kJAtOB6h0ntxrKvwSvy0YomiKaSJdqWWWPw0WO\nLJQgiW9NzcXIVbFGy2r3m73eJIpv1XxeoHi7tTXXo9NV950A4nzyYoRmJajt8ypeKb/lllu48847\nue2229DpdHzpS1+iqamJT3/60/zzP/8zmzZt4s1vfjMGg4F3v/vd/Nmf/RmCIPCRj3wEs9nMbbfd\nxic+8Qluu+02TCYT3/jGNwD43Oc+x8c+9jGy2SyXXXYZ55xzjtIfbdXICQL9I/M0O820Ni0vHiUh\nPyd0QXxLLaqimhDHaYjiUVZLcVu4Jr4lVkRmggnO3+pGvwJFUYIUXFbzwT44ESKVznFGEaI0Emri\nW9AviUetMC+6ELX+wbUFSYXiWzs3Fr9PtYRFwdTVBZcgjsDpaFlZ00WL6F+F0KwEr8vKK4OzTM7G\n2NxRnYyW/uEAOlYevVcIr8vKqC/CXChBS0NxPp/W0DccwFJnoMdbPDW43WXl2HiQdCZbdIJXS8jm\nchwZDdDaVI+roTgmlMlowN1QX9U+bzSRZmQqzJYNjZhNxe2bZqeFOpNeNZ9X8aC8rq4uH0gX4sc/\n/vEp/3brrbdy6623nvRv9fX1fPOb3zzltbt27eKnP/2pfAtVAGO+CJF4mkt7PasKrr0uKxMzUeYj\nKZocxVXutIS1VEQk8a2hqTCZbK4qqYqrEaWRYK4z4Kpy8a1iR8gVwt1Qj9Ggq9mN1dltMUiq4mTG\nGoLymviWKFZmNOhWFSS2V/kYPklo1mmry7MtikF7y2IyoxqD8lQ6y/HxIBva7NhXoX1RqMBejUH5\nXCjB9FyMc3pdqxrp622xcXQsyPRcfMURkVrE8FSEeDLL7h3F3wkgxgovD8wSiadXtU+1giMj8wis\n7i7V6xbGyc3GyOWEogtYcqH6IpMywlqCJFCfXqE2+hZoY6u3m41sTsBXpVTFtVTgQFSjnI+kiCWq\nc5zc4XxFpHgtAr1eJzJa5mJVO07u8HCAerOBHk/xFRGH1YTNUr2MFqki0tZUv6recE+zFR3VeydE\n4mlGpsP0tjcUXREB8WyD6k1mTMzGCEZTq2qVgNr4wmPjQTJZYVXsKSjw3Waq8zldqw/iba5uBpXk\n867abgvn20S17rdVTDEpRLvLRjqTYyak/OjpWlCuItZ8QFW5I9E3LFZEtqwyQ+9tqV5HQqqINNjr\n8o5Bscg7ElXYH51MZxkYD9Llcaw60+x12UimsgTC1TcndDaYwBeIs21D06oqIjqdDm+LDf98gnSm\n+sbJDU2FiSezq6ISA9SZDLgaLExU4dkGBRWRVVDXAVqcFkxG9aiKamMtrAwoqJRXaZC0FhYQFDBa\nqrQ/ei2tEiBWyqF6tUbyBbxVtErAou9WraOA+0YC1Jn0bGpfnWBb3m4qnG+1oFwlZLI5jozO09Zs\nXbVabnsVzx+MxNOMToviUXWrqIhAYX909dltYjZGaA0VEahu8a3ja2iVkFDN4ltSP/lqnVZYFN/y\nBarQbmsMkqC6xbfWWkmSxLcm56JVKb611sKAvd6Ew2qqyrMNRLuJ4lGrKwzkxbeqsHIpCAKHhwPY\n6010uFen31DNWiPpTI5jY0E63TactrpV/a6UzKjGSnkwkmRiJsrWzsZVt6suMgyUP99qQblKGJoK\nk0xl1+R8VTN1rH84gMAanf0q7h/Mq/yvMtMK1X0hHl6jsw8FB3s12m2NtDGobiaQZLfVtEpI8FZx\nEqhvOIDZZGCjd/UjbLwuK6l0jjkVqIpqIpcT6B8O0NJgwd24+v5mr8uGPxgnncmWYHXli1hiUWi2\n3rw6WSZJfKsaGS2+QJxAOMn27ib0qywMqC2+pSYGxoOkM7m1+bxVfifAWn0Q9WKFWlCuEqQNU+w4\njUJUs/hWn6TovMpeLpDEt6rzYF+LMrGEahbf6l+oiGwtcpxGIRYpUNVlN0EQ6B8J4LCuviIC1Su+\nlc4siEe12nFaV1cRgZPFt6oJ85Ekk7Mxtm5YfUUEFs+3antOR3xhYsnMmpxWEM83QYDpuerSaDk6\nOo8grM3ZB9FukXiacCwl88rKG+vxeSXxrWocJ7dot9X7vDaLCaetruruUlifz9vWbEWvU2f0dC0o\nVwkSTXEtFREQHYlqFN/qX6iIrGachgSRqlhfdeJbuZzAkZF53I2WNSm+Vqv4ViyRZmgqzKZ2J+a6\n1Y9hqVbxrWmpItK1+ooIVG+lfGA8RDqTW5ezD9Vnt/VURKCQCVSz22pQrUygddutpTrPt8PrtJsk\nvjUbrC5GS99IAJ0Otm5YW6zQ7rIyG0yQSlcXo6VvOIDVbKSrdfWxgtGgx91Uz+RMVPFYoRaUq4BU\nOsuxMbEi4lhDRQQKHNcqEt8KhNdXEQFRbbfaxLeGp9dXEalW8a0jI+uriFSr+Fa+VWKNdnMtiG9V\nm7N/eI3iURK8Vao1st4gqVo1WiRl4rXut2rVzOgbnsNk1NPbsfpWCVhUEq+m/ZYTxFaJJoeZ1qa1\njYKrxqlDiVSGExMhejxOrJa1TbD2umwIVJfYm38+zkwwwbauxjWPNPM2W4kmMoRjymq01IJyFSDN\nyl6tkmIh8v3RVUQpPjY2D8D27rVlDGHRkagmh//Y6ILd1rHf2l3VJ751dEwGu7VUn/jW0bEgsHZn\nXxLfmpqtLqrisdF5dDrYtsaKSLWKbx0bncdqNrKhbW3zi6tRfCsnCBwbD+J1WWm0m9f0HtWYBIom\n0oz5xdnsJuPq2VNQnZXyqdkYkXia7V2rF5qVoKb4lloYmAiRzQnr8nmrURPoqOTzrtEHAfU0qGpB\nuQoYmQ4DrImCLSGfba2iSvnIdASAHs/aMtRQndTYEd+C3dYggiTB01yFdlvYb92rmLP9WlSjKOPI\ndJh6s4G2NVZEYEF8K1M94luCIDDii+Bptq5aPKoQ1Sa+FU9m8AXidHsca2qVgEXxrWoaU+UPxEmm\nsvSs42xrcpqrTnxrTLpL12G3amQYjPhk8Hnz472qx+cdXfBBNsrg81aTZsaob/12a1cpVqgF5SpA\nCpI2tK4tsw8F2dYqyhpKB/u67FaNF+J0hDqTntY1KOxKkMS3qiXbKggCo74I7kbLuoKkahPfSqWz\nTM3F6HTb11wRAfUuRLUwG0wQT2bWdbaB6PALAkxVifjWuD+KwPruBBDvhXAsTSReHYyWRR9k7UGS\nXqfD22wTxbdy1cFokRK169lvVouJhioT35KCy6512E0S36qmdjA5fd6qsttC4XMtQrMS1NLMqAXl\nKmDUF8Fo0OWraGuB01qHvd5UXQe7L0KTw4y93rTm98iLb1VJkJTJ5picjdLptq+5twaqL9s6H0kR\niafX5bRC9SWBxmeiCML6gySPZLcqeU5HZUjUgqiZAdVDKR6VwWmF6pvnK5/drKQzOWaqhNEi13Pq\nXRDfSlaJ+JZkt8512E1N8S21MOqLYDYZcK+DddbkMGOuM1TNnSAVVFob69dVUFGL5VgLyhVGNpdj\n3B+lo8W+ZrEyCR6XFd98vCrEt0KxFMFIal2ZVlgU36qWA2piJko2J6zbbtUmviU5reu1W7UpFEvO\nV1fb+pIZ7Xm7VUcyY1SGyiVUHzVWtiCpysS3RmWo+EKB3aommRHBaNDnk4ZrRV58q4qeU5fTgs2y\n9oIKqCe+pQbSmRxTszE6W21rbs2BBaHeZivTczGyOe3HCoFwkmgis2aNEQlWi5FGu/KMllpQrjCm\n5uJksrl1X4awSFWsBvGtvPO1zgcNFsS3qoSqKJfTWm3iW3LZTRLfqhrnSyZnv63Zik4HU1USJMnR\n0gTVJ7414otg0OvybSJrRbWJb436IzTY63Da1jb9RUI1abRksjnGZyJ0uG0Y9OtznfMMqirojw5G\nUwSjKVl8XrXEt9SAVFBZb6IWxOc0kxWYqYJxcnLdpSDabS6UJJFSbvR0LShXGKPT8tDGoLouxEVn\nX44DakEwpBrsJlMFDgrEt6rhYJcpuITqEt8a8YXR6aBjnUGSyajH3VhfRZXyMPZ6E4329QVJTU4z\nZpOhKhSKczmBMX8Er8u2btZZNTEMIvE0c6GkTEFS9SSBpuZiZLKCvHargudUrlYJqC6tETn6ySW0\nV9G0JrkKKrC435QcJ7fsTTYxMbHsfzWsHnJumGqixsp5sFeX3cT9th7BCwn5C7EKVIpHfRHqzUZc\nDZZ1v1e1iG8JghgkeZqt1JnWNi6oEN5mK5F4mnAsJcPqyhfxZAb/fIINresTxwNRfMvTbGU6oH3x\nLd98nFRaHtZZNYlvyemDtDXVo9fpqiJIktV3q6J2iZrPuzaUwm61/bY6LGrbKHe+LdsF/653vQud\nTrekqIJOp+OJJ54o2cK0Cjlp2NVW8TWbDOtSEJdQLXbLC140rU/wQkKh+NZZm1zrfr9yRTKdZToQ\nY0tn47qDJDhZfEuOi6JcMRNMEE9mOWuTPJ/R22Lj5YFZJmdjOKzrqyCXM8b8Uh++XHazMjwdZiaU\nkOW8LFcs6hfIZDeXlSMj8yTTWcwyJJXKFXm7ycCeyotvzYriW3Kcl+WKRbutf781OcxY6gxVk+AG\neZ5TSXxL674biOP3dECnDAWVahKcHfVFsJqNuJzyFFRA2TaTZT313/3ud0qto2ogCl6Y1y14AeBq\nsFBXBeJb6UyOydkYPR7HuhTEJVRLtjUQThKJp9nW1SjL+1WL+Na4Xx4FcQnVQo2VM0MNhY5ElK0b\n5NnD5Qg5WyWgoCoyE9V0UD4iYysYiHbrH5lnei62bqHCcoacrDMQz7eX5mKEY+l196iXM+RsPdTp\ndHhdVkZ9EbK53Lp71MsZo9MRzHUGWmQ4i9QS31IahQUVS936CyruxnoMep3m7ZZMZfHNxdi6QZ6C\nihptJkV924ODg9x7773EYjEEQSCXyzE2NsY999xT6vVpCpLgxa7NLbK8n0RVlMS31qPQWM5YFLyQ\nx4mw15twWrU/Tk7uIKmtuR6dTvsUKDl7uaB6qGOLQZI8AU219A/KqfsAJyeBztksy1uWJUqVBJqY\njWo7KJ+OYDLqaWuWJ2Hjddl46dgMk7NRbQflCwriVhkKKiDa7cRkmJn5BG3rGI9bzkhnskzOxtjU\n7pTNP/W6bPQNB0ikMrIErOWIuZCoIL6ju0mW9zMa9LQ2iRotWma0jPkjCMh3JzTY6qg3GxUt4BWV\nnvvIRz6C0+mkr6+PHTt2MDs7y5YtW0q9Ns1BylCvZ1bja+GpAvGtRcq/fI6Sx2VjZj5BSsNzQuV2\nWk1GA+6G+ioKkuSxW5PTTJ1Jr3nxrVLQiUH7jJbRBQVx7zrHLEnwVAkTaNQXodFeJ1trwyLDQLvP\naSabY2I2SqcMCuISFp9T7dotGEkSiqVlO9ugOs63iRmxYCRn21a+/VDD1H+5fRAQz7d4MkMwql2N\nFjnbg2GR0eILiFOzlEBRp3Iul+N//a//xeWXX84ZZ5zBd77zHQ4ePFjqtWkOcvYkSagG8a1SHFDt\nLisCMB3QrvhWaQ527YtvjfoisiiIS9DrdHibbZoX3xr1RXBYRcEsOSCJb2m5fzCXExj3R2hvWb+C\nuARJfEvLdovE0wTCSdnYBVA4pkq7dpualU9BXEI1MIFKFSSBtvuj5WadQXVMHVpsMSnB+aZpu8mn\nlyHB67KSzQn455WJFYryAurr60mlUvT09HDo0CHq6upIJpOlXpvmIHcWBwp7HrR8IYZlE7yQUA2O\nxIiMghcStD7PNycIjPnEMUtyKIhL8LZYSWdyzIS0yWiJJTLMBOVREC+E12VlNpggqVFGy3QgRiqT\nkzVRK1EVJfEtLULq75WzcpkX39LwnSB3qwRUh7Mv5+xjCdVQKc+PspXxOW13aV+5Xm7WGRS2g2nX\nbiO+MHqdLj8CTg4o3UZXVFB+ww03cMcdd3DllVfy4x//mPe///20tbWVem2agyR44ZZRfEfr1DFJ\n8MItk+CFBO/CQzuh0WSGJHhRiiAJtOtIzMzHSaSysqukF4pvaRGSgrjsdmuxIaDdalIpKnAgPqfR\nRIZQLC3r+5YLSmE3iao4PRcjm1OGqqg0SlG5rDcbaXKYNe3sl2K/LYpvafNsgwXWGdDZUopClHbt\nNuKLYLOIz5Vc8Gp8VrlYUInidVkxGWUsqCiczCgqKH/Xu97FN7/5TZqbm7n77rt5xzvewbe//e1S\nr01TkAQvOt02WQXZ2pqsmhbfCoRFwQs5K0kA3mZtV3zHZuQVvJCg9b7LkgVJzdquJpWCNgban+db\nuqBc20mgUtnN02wjkxWYmdcmo0WyW6dbbrtZmQslSaQysr5vuWDUF8Eik4K4BK0zWvIK4s1WzHXy\nBUmi+JZBs4WBRCqDPxCXvaAijZPTqt3883GS6aysrAwoKEQp5PMWVXoMhUL88pe/ZH5+Pn94HDly\nhA996EMlXZyWsCh4Ia/TajLqcTdqV3yrFLQxgGanGbNJu1TFUZnHLEnQOnWsZEFSi7bFt+QesyRh\n0W4aPd+m5RexhJPHyW2XScG3nDDii1Bn1NPWJK9qtUR7nJiNak4RWwqSWhosWC3yqla3LyhiT87G\n2Oh1yvreaiOVzjI5G6W3o0H2CTftLhuTszHmIylZq6LlgNlQglgywxkbm2V9X5HRYmN4Kkwmm5NN\ni6NcMOaPIiCvIDSApc5Is1O7jJZS+bwtjRaMBuXGyRW1mz/84Q+zd+9echqldCkBiTYmd8UXREGq\nSDzNfER7ff6l6IED8WBvbxEvRKVUFZXEYk+SvHazWkw0Ocx5urLWUAoxRhDFt4wGHeMatpvRoMMj\nk4K4hM6FoFy7dgvT5DBjr5dnzJIEqRI65teeA5bJ5piYidLhtqPXyxskdbRo101TlIsAACAASURB\nVG7BaIpwLC270wrQsaD3osV7YXwmiiDI7+zDot20eL6V6i4F0efN5gRNKrCXinUG4vk2H0kRiWuv\nralUBTyDXo/XZWNiJqqIUG9R6dKZmRl+8IMflHotmkapsjgA3R4HLx2bYWgqzK7N2sq2jk6XpgIH\not1OTIYY90fp9mhrLu2oLyK74IWE7jYHB47PEIwkabBra7+NTEdwWk2yfy6jQU+H286oL6q57H42\nl2PMH6XdJZ+CuIQGu5kGex1DU2FZ37ccEI6lmI+kOLvXJft7t7fYMOh1mrTb5GyMbE5eBXEJ0j0w\nrEG7jZTYBwHRbpefLfvbq4pSsadAvEsBhqbCnLlJ/nNATZTUbh4HTx+cZHgqLHsrhtoorc9r55XB\nWYanwuyUmcGgNsZKVMAD8Tkd9UWYnBWTwaVEUR7Ujh076O/vL+lCtI684EUJvtAeDTsSowuCF81O\n+YM/yW5DUyHZ31tN5ASBUX9EdsELCd2eRUdCS4gl0syGEiW5DEHcb1KVT0uYnouTzuRk7+WS0NPm\nIBBOEtLYfNVSOq0mo55Ot51RX0RzTKBStUoANNrrcNrqGNbYnQCLdpObPQUszD3XadYHgdJULrWc\nBCp1UA7a80FAtJtBLzI55UZ3m9haojWfF8TzrcFWJ9tI1kIoud+KCsqPHTvGTTfdxGWXXcbVV1/N\nVVddxdVXX13qtWkGecGLpnpZBS8kSNlWrR3syVQWXwkELyRo1W7++TjJEiiIS9CqI1GqVgkJhVUR\nLaHkdtOoA1bKyiWIVREtJoFKaTedTkd3m4PZUJJwrJYEKhYmo4H2FhujvojmlOtHp8PodItUcznR\n5DDjsJo0d7aByA6VW0Fcwga3Hb1Oe0mgnCAw5o/icVkxGeVn03V7xGdfa3aLxNPMhpKa8HmLoq9/\n61vfKvU6NI25ULIkghcSGuxmGu11mst+jfkjJRG8kNDhtmE06Bie1tYBVYrZoIXIJzO0Zjdfie1W\neLCfU5I/oQpK6exDgd2mwyWhequFUuk+SOj2OOHlSYanwyX7G2pAif0mUTy1RCke9UWoNxtoabCU\n5P27PRLFM6YZSrEgCIz6o7Q1WTGb5C+o6HQ6uj0OXh2cIxJPy64toRbiyQy++Tg7uptKUlCpMxlo\nb7Ey4guTywmya0uoBX9AVBAvRR8+gMtpwV5v0pzvNlbiO0EsDCrj8xaVimlvb+fJJ5/kq1/9Kl/8\n4hd54okn8Hq9pV6bZlCK2aCvRY/HyXwkRVBDYm8jJaSNwal9vlpBqZ3WJoeZBpv2+nxLJRQiodNt\nFymeGrsQS32+9XhEyp3Wsvujvgh1Jj2tMo5ZKoQW25ok1pm70UK9WV4FcQk9Hu0lHVPpLFNzMTa4\nS8M6A23ut9lggngyQ1eJErWgTbuNLwglltLn7fY4SKVzTGpI7G2kxKwzKQnkn08QTWhH7K3UBRWz\nSWQCjUxHSi72VlRQ/rWvfY1nnnmGt73tbdx8883s2bOHr3zlKyVdmJZQ6iAJTq4maQVK2E2Lfb6l\nphODuN+01uebVxAv0Sgkk1FPh1t7FM9RX4Rmp/wK4hK02OebyeaYnI3SWQIFcQla7POVlINLebb1\naLBdYlFBvLR3AmjLbor4bhrs8y2l7oOExWRtzW6rgRaTQEr4vD1tDpILyc1Soqig/I9//CPf+ta3\nuPrqq7nmmmv45je/ydNPP13ShWkJpRwNIUGL/aqjvnDJBC8kaNVuzhIJXkjQmuOazeUY90dpb5Ff\nQbwQ3W0O0pkcEzPayO6HYimCkRQbSkhX1WKf78RMtGQK4hK02OerhNMqjajTpNNawoqvFvt8S82e\nAm32+SpiNy36biXWGQFtaimN+MIYDXo8zaVhnQF0KZTMKMr7zGazZDKZk/7fYJC/v0arkBTESyF4\nIUFr4ls5QWDMVzrBCwlaYxhEE6LgRSkTQKC9vvKpuTiZbK5krRIStKb4r4SzD9p7TpVI1MICxTOT\nY3JWG0kgJeym0+no8TiYCSY0M89XCWf/tX2+WoASFTgt9vmWUkFcwoa2hT5fjfi8AKP+CA0LzLBS\nQWt3qcR07XDbMOhLFyso1dZU1Ce4/vrrec973sPdd9/N3XffzXvf+16uu+66ki5MK4gnM/hLqCAu\nQerz1cqD5p8vreCFhHyfr0YO9lILXkjQWhJIiQocLIhvASNTkZL+HaUgOftKJTO0s99K7+yDlu1W\nSwKtBqO+BQXxEgZJoL0+31FfGHu9iUZ76YIkrfX55nICY/4IXldpWWdmk4F210Kfr1D5SaBIPM1c\nCRXEJbQ0WLBZjJphGEzNxchkhZLHCl2tDnS60jMzinpi7rjjDj7wgQ8wMTHB+Pg4H/jAB7jjjjtK\nujCtYNwfRaD0zheIF+JcKElIAxTPxcx+ae2mtT5fJWhjICaBnFaTZvq5lKgkie9vQ6/TMTStEbsp\nlMzQWruEFFyWYsxSIbTW5ysqiBtxlUhBXIKWkhmigngET7OVuhIoiBdCS32+8WQG/3yi5AUV0NZ+\n883HSaVzJb8TQDzfkuks0xpIAimVcJSSQL5AnFgis/IvlDmUspu5zoDXZWNkOlzSJFBRQXkqlaKt\nrY1PfOITnHHGGezZswefz1eyRWkJSjmtoK1eEaWCS9BWn69SwaV4sDs10+erFA073+c7rY0k0Kgv\ngtlkwN1Uul4u0FafryAIjEyHaW2sL5mCuAQt9fkm8writpIHSVrqV50JJogns4r6IFqwm1LOPmjM\nd5tW3uet7bfVQUvth0r5vADdbXYSqdImgYoKyj/+8Y/z2GOPcfDgQb797W9jt9v55Cc/WbJFrQW5\nXI7PfOYzvOMd7+Dd7343w8PDai8JKJxFW/oNo6VqklI0bNBWllpUENfjcZVGQbwQeYEaLRzsvggu\npxmbpfRzYns00uebXvgMnW6x+l9KaKnPNxBOEk1kSp4AAm31+Y77FxTEFZi57lqgeGqh4quksy/1\n+Y5o5C4FhZx9DbVLKOnzaqmNTirglboVDLRpN2We0wUmUAmf06KC8rGxMT784Q/z61//mj/90z/l\ngx/8IMFgsGSLWgsef/xxUqkUP/nJT/joRz9aNiPbJMELr6u0NEVYfNC0cSGGSy54IaFbI3OQM9kc\n4woIXkiQRrlUut2C0RTBaEqRFhPQzoU4ObugIK5AkATacVyVdPZBO32+SjpfUhJIC32+i0FS6Z9T\nqc932Ff5fb5K2k1Lfb5Knm9dbXZ0VP5dCqLdTEY9bSVUEJeglGhZqSEIAiO+CC0NFqwKFVSgtPut\naPX1ubk5nnjiCa688kr8fj+JRKJki1oLXnzxRS6//HIAdu3axauvvqryikTBi1F/BG+JFcQlSH2+\nlX6wR+KigrhSTqtW+nxFwQtlerlAO8wMydnvVNhule5IjChIGwMN2U3hoFwrfb5K221RlLHC95uC\ndGJY6PMtMcVTCUgjWb0KsM601Oc76ovQaK/DYS19QcVSZ8TjsjJc4j7fUiOvIN6iTEHFvdA6Vem+\nWzCaIhxLKxgrlD4JVFRD2+23386tt97KVVddxdatW3nzm9/Mhz/84ZItai2IRCLY7YtfjMFgIJPJ\nYDSe/iO63cVnQBPJDKFYitam4g/ocX+EVDrH5q6mVf2t9WBzVxP7+32YrWZFqswrIRJPk0pnaXYW\nL8wzdXwGgG3dzYrZrcvjYNQXpdklqrGXAqv5LMFIEp1Ot6rv8NDIPAA7NrkUsVtLix2nrY4xf1Sx\n72klzAbj1JuNq8qaBl6dAuDMzW5FPoejoR69DsZnYyX9e6t5b99cjAaHGfMqhKBmIkMAnL21VRG7\nnWswwEOvMhWIl81+m5yJ4m6qX5XKsC8oJrR3bffgbi69w3/Otjbu+e1RfKFkWew3QRCYmInicdlW\nddZOzcXR6+CcHZ5V7dO14qytbh7ZM8xMJFUW+y2XE5icjdLesrqe+onZGA32Ojb3uEreiw+ws7eF\nZ1+dYi6W4eztpbHbar6PbDbHdCBGe0vxjns2JzA+E6PL48DraVjLEleNHRtdHB4KEEpm6d7QpMjf\nXA6pdJa5UALPKlieoWiKQDjJ+duVuRNA9BP/sH+MjE5Ph7s0wdlqPks8mSEaT9PSWHzFe2gyRCYr\nsEXBWGHLhkYOHp/B5lCmyrwSIrEU6WyOJkfxscLIQgvgth5lfF6AdredEV8El8uOvgSxQlFB+fXX\nX8/111+f//9HHnmEdLq8KF12u51oNJr//1wut2xADuD3F5/tuOt/DvF8n49br9rMGy/oLOpye7lf\nFMNrdVpW9bfWg/bmevYDLx6a4MyNLkX+5nL42r37GZwI8d63bOeSMz1F/c4rR0W7tTjMitmts8XG\n0GSIV/qnSnKwu92OVX2WO//jOcKxNH99w07O7i3uezw0ICYzmq0mxezW1Wrn1RNznBiZw16v7sGe\nyeb4yL8+g8mo54M3nUVvR3HOVN+JWQAa6g2K2c3bYmNgfJ7p6VBJDvbV7LdQNMXHvvNHXA31fOjm\ns4oem3R0aA4dYDPpFLGbThCwWYwcHQ4o9j0th/GZKJ/+3l56PA4+eNNZRSuCHx+dx2o2QiajyOdw\nmPXodNA/OFuyv7ea/XZ4aI5/uv8AO3ua+OsbdhZVUcsJAicmgnhcNkLzylRgm6zieXZoYIbLi7y7\nSolnX53kew/3sXtHK+9763YsdSu7b/Fkhum5GDt7mpiZUWYMo2thfNgrR33s3CB/QLvau/RXzw3x\n4JODXHVeB++8ektRCbTJ2SipdJb2ZqtiZ43baQbgQP80ngazIn9zOdzz26P87sUxbrhsI9df2lOU\nbkjf0BwAnqZ6xezmaRTP3f2HJ6k7Q/7ndLX77dsPvcKBYzPcds0W3nBuR1GxwsH+aQBaG5SMFawc\nBPYfmmRbl/pJoC/+aB9j/ih/ce12du9oK+p3Xj3mB8QzRym7bXDbGPdHOHzcR9sqirSFWC6BUFR6\n/7HHHuOGG27gmmuu4eqrr+aaa67hyiuvXNNiSoXzzjuPp556CoADBw6wdetW2d5bEAT6hgPkBIH7\nnzjGXb88TDKVXfH38j1wCgheSCinPt90JsuxsSCpTI7vPnyYe357lEx2ZcVppXsuobxGB82FEkwH\n4sSSGf7ff7/M/zxzoihqlpp2K4fepJHpCNFEhvlIiq/cs5/f7x9DKNJu5joD7lVktteLnjaxz3eq\nDCiex8eDZLIC03MxvvBf+3ihf+XJGoIgMOqL0NpUX1RwIAekPl/ffJxYGfT59g8HAPHM+NwPX+Dw\ngkO6HJKpLL65mCJjliSUW59v34LdDg0F+PwP9zFUBK1+JpggkVJGQVyCOy/2pv7ZBot2e77Pxxd/\n9GJR9PDFO0G5Sn+59flKz+nv9o/ztXtfIhBOrvg7atyl5dbn2z8cQAB+8cwJvvnAwaK0FdT0QUam\nlEk6LQdBEOgfDpDNCfz4N0f5/q/6SKWLiRVU9N3K4DlNpDIMToZIprP8+y8Ocf8Tx4qaTqPU1JxC\nlNpuRQXlX//61/n7v/97ent7+ad/+iduvvlmrr322pIsaK144xvfSF1dHe985zv58pe/zJ133inb\ne8+FkgSjKbZtaGRzRwN7D0/zhbv3nfZSDEaS/PT3x/ntC2PoUOlgL4MHbWQ6QjYnsGtzC+0tNp54\ncYyv3fcS85GlL0X/fJwfPXaEPYenMJsMigheSCgnuw1OiE7qxTvbaHaa+fkzJ/jXBw6eNhAZ90f4\n7i8PcXhoDnejslSkchrlMjghik9ecU479WYjd//mKP+5zKV4YjLEvz54kHF/lK5We8kVxAtRThei\ntN+uPr8TgH/7+av89HfHl7wUBUGgb6HSGU1k8p9DKZSTKKNkt7fs7iKezPCNnxzg0T3DSyaCcoLA\nS0f9fO2+/Qiggt3Kp89XsttbL+piLpTgS3fv5+mXJ5Z8bTaX47lDU3zzgYPA4nmjBHQ6HV1tDjFB\nWgZ9voMTIcx1Bq4+r5PxmSif/68XeGmhUvRapDNZfv/SON97+DAAXR7lfJBy6vPNCQKDk2FaGizs\n3tHK8fEgn/vhCxwdnV/y9fFkhkf3DnPv48cAZZ/TcurzjSczTMxE6fY42LmxmYMDs/zjD/flg6DX\nIhJP8/OnB/mfPw4ByojjSejKj0VTXzPDF4gTTWTY0d3ERq+TZ1+d4kt3v4h/Pr7k6wPhJPc/cYzf\nvTSGXqejs0T0+6WQ1wQqgyTQ0GQYQYDzt7rxNFv5zQujfOP+A4SiS4/bnQ7E+OGj/ezr92E1G2kp\nkqUmB0qtpVRUicPpdHLxxRezf/9+wuEwf/u3f8vNN99ckgWtFXq9ns9//vMlee+BBWf/7M0u3njB\nBvEh2j/O5/9rH391/Rns2twCwEwwzqN7R3j65Uky2RwN9jre9aatOBUQvJDQ7BTn+ZbDwT6w4Hzt\n3tHKri0t/OCRfl7o9/G5H7zA/3fTmWzpbARgYibKI3uG2XNompwg4G608M6rtygieCGhs1Uc5VIO\nWWrJab3i7HZuu3oL//E/h3h5YJbP/9c+PnTTWXlBshOTIX713DD7j4qOWYfbxrveKB9DpBiUYzLj\nLRd1cf3revj2Q6/wx1enGPVH+NBNZ9HSWI8gCBwZmefh54Y4PCRWUHrbnfy5wnYrZGYU29ZRKgxO\nBNEBN12+iSvP7eBbP3uFXz8/wtBUiDtuPBOntQ5BEHj5+Cy/em4o/1zv7Gni5tf3KrrWRWZGhB09\nzYr+7ddicCJIvdnILW/o5bxtbr7z0Cv89x8GGJwM8ZfX7qDebCSby/FCn49f7Rlm3C+2V5231c21\nF3crutbuNgfPvjrF8FRYkUkgp0MuJ3BiMoTXZeXtb9jMtq4m7vqfQ/zg0X5OTIa47ZqtmIx60pkc\nf3x1kkf3DOOfT6DX6XjdmR7ecG6Houvt9jjoGw4wMh1me7d6FM9YIs3kbIwd3U38+Zu2sqndyX/9\nup9/ffAVrntdDzdethG9XkcileHJAxP8+vkRgpEURoOeq8/v5MLtrYqut9vjYHI2hj8Qp00B3YTT\nYWo2RjyZYdfmFt5/3Q42tTfw098d5+v3vcStV23mmvPFVsRIPM0TL47x+L5RookMljoDN1zaw9YN\njYqtVafT0d1mp39knngyQ71ZGQbSUjgxGUIAzuhp4k+v6OWhpwf51XPDfPFH+3jfW7dz8U7xzpqP\nJHns+RH+8NIEyXQWe72JP7tmCx4Fv/N6s5G2ZivD0xEEQVCMgbQUpFhh15YWrtzVwb2PH+XJAxN8\n/ocv8Dc37OTMTWIrom8+zq/3DPPMK5NksgJNDjO3XNmL1aLcd+5uqsdSZygL302y20VntLFzYzPf\n/1Uf+4/6+dwPxViht11sgxnzR3jkuWH29k0jCNDWVM9t12xRtKAisY5KZbeidoDFYuHEiRP09vby\n/PPPc/HFFxMOq/9FKgXJ2d/kdWI06HnXm7ax0evkR48d4ZsPHOStF3URiqXYc2iabE6gpcHCtRd3\nc+lZHkzG0gvSFEJS8Tx0Yo5IPK1qn69UudzU7sRSZ+SOt+1kU7uT//79AF+79yWuv7SHUV+E/Uf8\nCEBHi41rL+lm945WRQNyWKB4ttgYmY6Qywkl6fMtFoMTQXQ66PE6sNQZ+d+37uJnTw3yyJ5hvnD3\nPm68bBOHhuY4dEKky270Ornudd2cs7lF0cMJCuf5qn8eDE6EsFmMtDXVo9PpuPNd53HPb4/y1MuT\nfO6HL3DDpRt5od/H8XFxX+7obuK61/WwvatR8Yu8q9UhJoFUzu6LQVIYb4sNq8WI1WLk0++5gO//\n6jAvHZvhcz94gbdc1MXTL08y5herJOduaeFPLulhU7tT8fUuJjPUtVsknmY6EGdnTxN6nY7NHQ18\n9i92828/f5UXj/iZmIny+l0d/O7FMXzzcfQ6HZfsbOPai7tLJka0HAqTQJJDrQYmZ6MkUlk2ecW9\nc3avi8+87wK+/dCr/OHABCO+COdvc/P4vjEC4SRGg543nNvBWy7qUrS9REJhVUTNoPzEpHi+Ss/c\nJWd66HDb+PZDr/Dws0MMTYXY3N7A4y+OEYmnMdcZeMtFXbz5wg002JXvT+5pc7Dn0DRDU2FVg/K8\n79buRKfT8aYLN9DdZufffv4q9z1+jBMTIRodZn7/0jjJVBabxciNl2/k6vM7sakgftXjcdI/Ms/I\ndFjVPt9Fn7cBvV7Hn76+l41eJ997+DB3/fIwx8eDCAI8fVAsQDXa67jp8o28flcH5jplfV4Qn9O9\nh6fxzcfX3OcrBwr3m8mo571v2c5Gr5Mf/+Yo//enL3PtJd3MhRLsPewjJwi0NtZz7SXdXLLTo8iE\npkLodTq62xwcHZ0nkcoo1oa2FArtVm828sGbzuTRvSM8+OQAX71nPzdcupETkyFeOibqJnW67Vz3\num4u2NaquK9uXfAxR6bDJUkCFfUt/N3f/R3/8i//wte//nXuuusufvKTn3DLLbfIupByxuBECL1O\nlx8tA3DpWV463Xa+/dArPLp3BACvy8p1l/Sw+wzlg8pC9CwE5cPTYXaqWE0anAhhrzflnSmdTseb\nd3fR3ebg337xKj9/+kR+vde9r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tFEmv1H/WSyudO+Ju+0VnAFDuRNAs1Hkjx/eGrZqrt0\njlb8+baw/kEZAiX/fJxDJ+aWvZclu1VyMgPE/ZZMZRmToT96YibKocHZZV8zMBHEXGegvaXyhI0l\n6HU6NnU48QXihKKpdb/f8FR42RFrOUFgsMI1u0AUhu5osTM0GVr2LF8NtN0Yvgzu/M4f+eg7d7Gz\np/mUn4kPdJRN7c6KVHQuRG9HAy8dm+H4eJDda6wmjs9EeeipQfYf9WPQ6/js+y6kc4lkxUwwQSiW\n5oLtretdturobW/gxGSYoakwWzob1/QegxMhHnxygL7hAPVmI/94++4lK7rD02GyOaHiL0MQqW/7\njviZCSZwN65NLObw0BwPPjnIickQjfY6vvD+i7FaTj2q8iJIFW43k1FPt8fBiYkwyVR2TX2QgiCw\n/+gMDz09yMRMlG6Pg//z7vOXPL+0kKEGaLSbcTktDEyEEARhTdXrXE7guUNT/OKZE8wEE2zvauTj\nt5275Hst7rfKdvY73XbqjPp1BUnpTI4nD4zz8HPDhKIpDg0HeM+bti75Wq08p1KQtJ5kRiKV4fF9\nYzy6d4R4MsNbL+ri7W/YvORrByeCoqJzhYqVSZDo0MfHg1xxTvua3iMST/Po3mGe2DdGKpPjtqu3\n8MYLNyz52sHJEPVmIx5X5Sk6FyKfdBwPcXZvy5reIxBO8vCzQzz18gTZnMAHbjyTC5fwz3I5gROT\nITzNVmyWyg2SQGTsPXNwkoHxIF1ta9Ow8AVi/PyZE+w9NI0AfPLPz2PrhlP9wFgizeRsjO1djej1\nlcueAtjc3sCrg3MMTAQ5d4t7Te8x5ovws6cGOXB8BqNBx+f+cjde16nJiuk5URB412bXepetOjZ3\nOBnzRxj1RWRR36/siHMd0Ot1/OjX/SSXGAEwNBUiJ1TubNBCrIdS7J+P872HD/OZ7+9l/1E/nW4b\n2ZzADx7tX5IatNhPrgG7da7dbuP+CP/64EG+8KN99A0H6HTbiScz/Pg3R5fMVGslsw+F+231juvA\nRJCv3/cS/3T/AU5Mhuhw25iPpHjgD8eXfr1GKnAg2i0nCGuixh4amuMLP9rHtx96hcnZKO0tNoan\nwjyyZ3jJ1+dpihUsxihhc2dDXsV1NRAEgReP+Pj09/fy/V/1MR9J0tZspX9knqcPTi75O4PjwQVF\n58oUK5NgNOjZ6HUy7o+uelJCLifwzMFJ/v6uPdz7+DGS6SwtDRb+sH+MgwNLV5QGx0PYLJU59qYQ\nTlsdrU31DEyEVt0fnc7keHzfKJ/89+f42VOD6HXQaK/jsedHl1SKzuUETkyF8bbYlkxIVhIkwbW1\n3AmJVIZfPjvEJ/79OR7dM4Kt3oS93sSDTw0ws4QwZiSeZnouxiavo2IVnSWsh5kRiaf5798f587/\neI7fvzSOq8GCyajnnt8cWZIOLwpmZiu6n1xCYTJjtQiEk/zosSP8n+/uZc+hadpbbOh08MNH+5dk\nTUoCX5XcTy6hdx0+ry8Q465fHuKz//k8B47P0Om2kckK/PDR/iXPSulvVHqCG+RvB6vs034duPGK\nXn72h+P8/OlB3nHVlpN+pgXhBglr6Y8ORpL88tkhnjwgZlc73TZuvqKXcza7+O7Dh9lzaJrH943y\npt1dJ/1efjaoBpx9KUBezYPmn4/z86dPsOfQFAKiHW55fS9bNzTy/x58hQPHZ3ih38fuHW0n/V5+\nv2noQhwYD3HxTk9RvzPmj/DQU4O8dExsKTlzYzM3v34TnW47n/vhC/zhwAQXndHGtq6mk35vcDKE\nXqer2LE3hRD32ygDE0G2dzet+HoQ9+bPnhqkbzgAwAXbW7np8o002Mx85j+f5+Fnh7hgW+sptLrB\nCVHRuXWNTIZyQm+7k72Hpzk+Hiwq6BMEgcNDAR58coChqTA6HVx+tpcbLt2ITgef+t5efvK745zd\n66LRvtiCI4296WixV+zYm0L0djRwZHSewcnQkmyx10JMYvh56OlBJmdjGA163nThBq69pJtgJMXn\nf/gCP3qsn3+8/aKT7BOOpfDNxzlzU3PFB0kgPqfPHZpicjZGRxF01Wwux3OvTvOLZ04wG0pgrjNw\nw6U9vHl3FycmQ/zT/Qf4wSN9fOq9F5zEapmYiZJMZTWRcNTrdWzyOukbDhBNpIuqxKYzOf5wYJxf\nPTtEKJbGXm/iHVdt5g3ndnBkIsz/vW8///XYEf73reecxGo5oRE2C5zaH11MJTaRyvDbF0b59fMj\nxJNZmhxmbru0h0vP8vKbF0Z54A8D/OR3x7j9T8446fcGNdIqAeBdQ390JJ7mkT3DPPHiGOlMjram\nem66YhMXbG/lF88O88unB/nls0PcfEXvSb+nhT58CZu8TnSszucNhJP88o8n+P/Zu/P4NqtrX/i/\nR7OsyZYlz/OcxLEzOKEQJ5BAgLRAw5BAGFqghUKhA6dwgV4OXE4PTfu29H0vU3t6L4dCyhSgUCgQ\nhgQSEocMTjzG8zzItiwPGmxrfN4/5EeRE9uSbMeypPX9fPi0H0Vytp88kvbae621v6nSwelikRYn\nxw2XZmNllhovfVCD8gY9vj7dgy1rUqa87myMFfrXzbMR1WvEFQvw80J/djFHu67Kx+GKHnx+ogvr\nl8VPSTsIh8YNHJGQj9S4s/XRQsHsqbH9w2N4+pUTmLA5ERctxfaNmVi/PN4zodp1eS5qWofwj29a\nsTpPOyVFmevonD7HlKGlRK0UI1ou8tRH+0qNbe014nevl8PhZJGileOGS7NQnB3red0DO4rx4B++\nwutfNGJ5hnpKHU1r7yiUUULEhuixN964+mh/Uzwrmgfx/LtVYOH+cLvx0qwpwfed2wrw29fK8bdP\n6/H03es9jcncHZ1NSNHKQvbYG2/ZAWa0HKzowav7GgAAhVlq3Lgpe8pxUz+9sQj/+cpx/O3Tejx2\n+xrP+3fUYsPg6ASKvO7NUOa9Sr1hZaLP5390pB0fHG4DAKwriMP2jZlT0ut2XJaNPZ834vXPG/HA\nDSs9j3cPWGBzhH6zMo5nF6571K+g/LXPGnCwohc8hsGmYvciBleKo4wS4aYtuXj7y0b842ArbvNK\nYw+n7CnAnap4tLYPLT2jfgXlL7xXjcoWw5RFDGWUCACwPEON0qJEHK7S4bPjnfjexRme14X60Xvn\nyk5Woa5jGC09RhRlz56y6mJZ/Pbv5ejoM01ZxOAWezavTcEX37ajpm0IZTV9U9734ZQ9Bbivm65K\nh2692WcqttXmxFP/fRz6kYnJRYxMbFmT7JnzXbU+Fcfr+nGkug/fWZ6AFZln3/ctYfQ+5THuJnk1\nbUMwjtk877eZjJqtePK/j8M0ZkeMQozvl2biksIEzyLZHduWoayyF59+24l1BfFT+kyF+tGi3qRi\nAZK1MrRN1kf7Kt3tHbTgN6+ehNXuRLw6CtdvzERJQZxnrnH71jzUtQ/jna9bsCpHM6V0k+vZlaIN\n3Z5dnLjJunh/e9s0do1Aq535vRyx6esSkQA/3FYAlgVe+aR+SpF+q84IlVwEtTJ0m5V5y0lWweli\n/Tq78VhtPyZsTlx7SQb+856L8J0VCVN2OBRRItx6RS5sdhde3VfvSce2O1zo7DchJcQ7OnO4Jnmj\nFhsMo76b5B2u6oXDyeK2rXn4X3evw6oczZSgJ0kjx/aNmTCN2fHW/ibP4+4mfNaQPhvUG1cf3TVg\nhtXmu0neoYpesAAeuL4Qj9++5rzd8OwkFa4oSUX/8Dg+PNLuebxbb4bd4QqLlX0AiFG466P9bZL3\n9ele8HkMHr11Nf5t56rzzn++qDAR6wri0Nwziq9O9XgeD6eVfQDuDvICnl+piizL4quKHsgkAjx1\n5zrcv73wvHq3S1cnIy9FhfJGPcobBjyPh1M2C+BVH+3HbpLV7kRZTR80Kgl+8+P1uHPbsvN6Y9y8\nNQ+JsVE4cKp7yuTEc8xSmLxPz6bG+r5uQ8YJVLYYkB6vwO9+8h3ccnnueQHCzVtyoJSJ8M/D7egb\nOtvUMhyOyPSWE0AqdrvOhI4+E5ZnxOD/ue9ibN+YNSX7gmEY/ODqfIiFfLy1v2lKY6pw6V/A8d6F\n86W2fQj6kQmsXxaH3993Ma5anzZlE4bP4+GubcvAYxi8uq9+yvdza68RIgEPKXGh26zMWyBldCcb\n9DCN2XH52hT87iffwabipCkBqVQswA+vzneXbn5SB6fLHSuwk83KNCoJVLLZA/9QkZOsgs3h8qtJ\n3tHaPljtTly/MRP/+eP1WL8sfkqsoJKLcfOWHFhtTrz2WYNnXsP17EpPUIR8zy5gMlZIUsJgnMCI\n2erz+d9U9c7656F/ReZhWXoMNhUnoltvxqfHOgF4HXuTGNrH3ngLZBeussUAPo/BVetTZ3zDXLQ8\nHiuzYnGmfRhHqvsAAJ0DJjic4VGHz/F34sqyLCpbDJBJBLhsddKMaZpXrktFeoICZTV9qJns6Bku\nHZ29ZSe566Nn674JuLvOn+kYQpJGhrX5cTO+327YlAWNSoJ9xzo99ZctYZTNwslOVvpVHz1ssqKj\n34T8tOjzFjG83bo1DzKJAO8ebPEsLIXb/Sbg85CRoEDPoNlnfXRnvxmjZhuKczTnLWJweAyDH24r\ngIDPw98/b/R0Y28Nsx04pUyEuGgpWnt810fXdQzD7nBh/bL4aZv2AIBQwMed29yntLzyaR3sDvfE\nlVsEWogGOEtBIPXRXI19aVHijEc2yiRC3L41Dw6na0r9ZWuvEWIh36/d+FDA7ST6k1JcNXkyzubV\nyVDMsMupUUlxw6VZsEw48MaXjQDc38NtvUbERUtnfF2o4RYB/bvf3Ndta0nqjCU26QkKXHVRKgZH\nJ/D+N60A3CnvPYPmyVLH8AgJApnzcu/Tq89ZxPBWmBWLi1ckoL3PhC9OdANwlyuax+1h850ABH7d\nBHwetq5LnfG+KS1KxLL0GFS1GHCsrh+AV8+uMPlOAPyvK3exLKpn6L3CCY934Dzs3JwDlVyEj460\nQWewhN3KPuB/w5BRiw1tOiNyU1SImqXui2EY/OCqfIhFfLx9oAmjFpvXyn74vNH8bfbWNWDGsMmK\nlVmxs36puVeqCyZXqhswYXOgVTdZhx9O183P3aT6zhHY7C6f6YxiER8/uDofLtbdOMTpcnm9T8Pn\nuvm7C1c9uaDjqyOvSibCzVtyYbU5sefzBs/KPhBmixkpKrCs7/OjK5vdk1Zf91tirAzXbcjAqMWG\nd75yNxl0d3TmIzFMgiTAfb+NWR3o83F+dJWf1y03JRqb1yRDZxjDx0fb3cfe6EyIV0eF9LE33rj6\naJ1hbNbj8wD/77e1+VqsztWgsWsEhyp6MW51oHfQgsxERch3dObIpZP10b2+z4+ubHZvDCz3UVZx\n+ZoUZCcpcbxuABVNg+gfHodlwhEWDSw5iRoZpGKBz7kbtzEglwp9LoB9f0Mm4mKk+OJkF1p7jWjX\nmcCy4VFPzslK8q8+2mpzTjbjlfksH9x1RS4UUUJ88E0rBobHvBa4w+e6+RtcDhkn0DVgRkFaNCSi\nmaugGYbBD6/Oh0jAwxtfNME0Zgurnl0cfxtqt+tMMI7N/r0R8UF5lESI27fmezoFchPicJq0xiol\nUMlFaO6dPTWWW8Hx5/iNWJUEN12a7V6p/qIxLD+g0v2sj+ZWWov8ON4hLV6Bqy9Kg8E4gfcPtaG1\nxwgGQEYY3W/+rrZyK/vFPiatAFCYGYtLChPQ0W/C5ye60No7Cqk49Ds6e/M3VZGb7Bf7cb9tWJmA\nFRnuleqjtX1o1RndjXBC/Ngbbzl+HlXFZQEVZvquob76ojSkaOU4VKlDecMAdIYxZCQow6JZGSfH\nj/PKvbOA/FkAu/HSbMQoxPj4aAfKG/QYtzrC6rsUOLsQ2DrL+9Rmd0/2kzQyn0dDMgyD26/Mh1Qs\nwDtfN+NUox4sQv+86HNlJ6kwYXOiZ9Ay43O8s4B8NVTk8Rjcua0AfB6DPZ83oLZtCEB4zd14k6mx\n/cPjMI7NfH40lwVUlB3rcyFHJOTjzqvdpZt/+7QOjV0jAMLruknFAiRpZWjrM3rSzadT1zEMh9OF\n4hzfc165VIhdV+TC5nDh1X0NXh3Ew+e6ceeG+zvn9ee6xcVEYfvGLJjH7Xhrf3NY9eziZCa65wa+\nr9ugz58V8UE54F6pXpunRVP3KL4+3eM+9iYx9JuVcRiGQU6yCqNmGwzGmeujK1v8n+wDwOY1ychO\nVuJE/QBON+rdx97EhH5HZ45QwEN6vALdA+Zpj87jVLYMgmHcgaM/rtuQgfgYKb486e62naSVhUVH\nZ467Plo8a300y7KobDYgSizwZCT4csvl3Ep1G/qHx5GVFF5BElcfPdsqtd3hwpn2YcSroxAf43tB\nwl1/WQCRkIfX9jWETUdnb/4sAvmbBcQR8Hm467sFYBjg/3x0ZvLvCdfrNvP95m8WEEcqFuAHV7nr\nL1/+V3heN3/qVes7h2Fz+M4C4sQoxNi5ORvjVif2fOZu4Bgu9eQcfzL2/M0C4iRr5fjexekYNlk9\nWS3htAMHnP19Wmf5fPM3K4NTkB6DTcVJ6NZbPEdnhtt1y0lWwWZ3oXtg5kUgbs7r73W7aFk8irJj\nUdcxjG+qeicbG4d+szIOVx89ODqB0VnqowO937auS0F6ggJHa/tQ1WoIq55dgDubMyVOhvY+05T+\nZOfisoBmQ0H5pNuuzEOUWACbw4VkjXzWlIxQlO1jN8nhdKG2bQhx0VIk+Ln7yGMY3LltGQR8ZrIz\ncXg0K/OWzTXJmyE11jRmQ2uPEbnJKr9TNEVCd/0lC7jr8MNoxZCTnTx5fvQ0Z8kC7s6dBuMECrPU\nftexyaVC3LY1z1OvGm7XjauP7tbPXB/d0DUMq93pV3YBRxstxfUbs2DjrluYTfaVMhG00RK09o7O\nWB8dSBYQJzNRiSvXpZ69bonhdd2StTKIhfxZMzMCyQLiFOdocNHyeK/7Lbzep1l+ZGZUcjtJAbxP\nNxYnIT81Omyvmz+LQIFkAXG+d3EGkjQy2BzujtHe3bHDgWcxY5Z6/ECygDg7N2dDJRfB5nAhRiFG\njCJ8giTA95yXZVlUcVlAfn4ncqWbEhEfNocLafFynycahRpf57wHkgXE4Uo3+TzG3aA3jHp2cbKT\nVe5Tgfqnb6jtnQU0GwrKJ0XLxdi5JQcAkJsaXpMvwHfNQ2PXCCZszoCPSkrWyHDN5FEu/u54hhJf\nKcXVrQawAIr8SOPxlp8Wg8tWJQFw12KGG18TsLOT1sCu27qCOKyavNbh+D7NTp69PrqyOfDJPuBu\n/pM5mf2TG2Y7IoD7fWqZcKB/aPr66ECzgDjbN2ZBGy3xHLMTTvg8HrKSlOgdtGBshvroQLOAOLuu\nyIVcKnTvIITBsTfe5FIhEtQz10ezLIuq5sGAsoAAbpG7AEIBD9poCaLl4RUkJWlkkIr5aJ7hu9Tu\ncAaUBcQRCni4c1sBGABZieHR0dlbVqJq1vroQLOAOFESIe64Mh8AkBuGczdfmRmeLCA/Uv69qZUS\n3HSZ+7zysJ67zbAIFGgWEIcr3QTC87r5irG4LCBfc97w2g6ep41FiVDKRGE3+QKA9AQ5+Dxm5iCp\n2f8akXN975J0JGtlPhuzhCLPquEMZxDONUgC3BPXZRlqrM4N/JovdTleq62XFJ5/fnRl8+RkPyuw\ne4ZhGNxz7XLUtg35db5yqPFezDj3/eRO+R+EVMxHbmpgX2o8HoNf3FSMVp0RKWG2kwS4r9vR2n40\nd4+e1yF8LllAHLGQj0duWQ396ASUYXLsjbfsZKX7/OheI1ZmTf0MM3JZQCn+ZwFxlFEiPHrbGkzY\nHGEXJAHu63akug+9g5bz3k89egsMRivWL4sLuJt1vDoKj922BsIwvGbuhS0VatuGYBqzndchvaFz\nJOAsIE5Osgr/49bViA6z3V4AiJK466Nbde766HPvqSpPCnbg84g1eVr8283FSNaE33dCgjoKMolg\nxp1yT1bGHK7b5tXJiFGIwzK4zExUgGFmzjCYz5x3+8ZMpMcrsHIOr13qvBv1Xrku9bw/96T8+9gY\nCL9P/nlgGAarcjTnnSUaDoQCPjK486OnqY+uahmEWMhHXoCTfcC947I2Py6s6qI5MQox1EoxWqZp\nkudwulDTNoRYpQRJc+jKLBTwsa4gLiwnralxcghnqI82j9vR3DOK7CTVnI6ukYoFKCmY+Qi1UJY9\nS2aGzjCGwdEJrMhQz+meUcpEniyDcJM9y5FLc80C4miipViWPvPRc6HMc92meZ/WzDELiJOskYVd\nXTTHs+g4zf3mycqYw2QfcJdNhOPCGeB1xNc0n29zSfn3lp8WE9AOeyjJTpq5Pvps0625XbfCzNiw\nS10HJuujk1Uz1kdXtRjAY5iANwa4n706Vxs2p0p4k4gESI2To113fn20O+U/8CwgDp/HQ0lBHMTC\n8Er5BwCtSgJllHDa71IuCyjBjyyg8IsGyIxmqo/uGxpD//A4VmSL91JtAAAgAElEQVSqIRTQLXGu\n7CQVTGN26M+pj27uHsW41YHinLlN9sPZbPXRNa0GsOzcJxHhTDVZH93Sc3599NkU7PAMrOcjJW6y\nPnqa1LH5ZAGFu9nKTOazIxLuPNdtmgyqyhbDnLKAIsFMTfLmkwUUCbJnOClhPllAkWCm+mjjmPsY\n35wUFWRhdBLJQuHqozv7zVMe57KAAukFFCm4RaBhkxVD5zTU5rKA/En5p6saQWbahfP3HNpINVN3\n56o5NI+KJFx99LmLQHTdZpc9Q310VbMBDHBemjFxr8BnJiqmrY+eTxZQuJNLhYhXR6FVN7U+er5Z\nQOFupvpo87gdLfPIAgp3XGnguUH5fLOAwl3ODHW+880CCnc5SdM3yatucWcB0YLj9HJmyKCabxZQ\nuJspxqoM4Ag5+vSLINkzfCFWeoIk+oCaTo5XrYi3ypZBiIQ8LEunyf50prtuTpcL1a0GqJVipGhp\nsj+d6brGWibsaOoeRWaSMixrmxdCdrIKLKaeH01ZQL7lJCsxbnWi1+v8aMoCmh2PYZCVqET/0BjM\n42cXgaopC2hWURIhkjVn66M5lAU0u3iuPvqczAzKAppdZpISDDPLnJeu27RmysygLKDZ5UzTg2pK\nFpAfKf80S4kgaqXEXR/tdX70uNWBxq4RpCcowq7b60JxH3sxtT56YHgMOsMYlqerw+5IjIUy3aph\nS48RlgkHirI1NNmfwXRdPGvbhuBiWVo4m0X2NItAlAXkW/Y09dGUzeLbdKn/dN18y05WnlcfTVlA\ns+N510dbbJ7HKQtodhKRAKla+ZTzo90p/wZoVBIkxVLK/3S00VJ3fbTXdwJlAfmWkaBwN9T2um6e\nLKDMWL+ygCgojzDZSSoYx+zQj7prHmrbhuB0sZTGMwsBn4f0BAW69GZM2Nz10ZVzOL830qhkImhU\nkimLQGfTn+i6zcRTH+31wT6fTrGRYromUpQF5Nt0qYqUBeTbuSnFTpcL1S2UBeTLuU0ZKQvIP+dm\nOlIWkH+yk1WwO1zoGnDXRzd1j2Lc6kQxbQzMiKuPHjJaMWxyN8mjLCDfREI+UuPk6Ogzwe5wN9QO\ndM5L7+QIc26DGkob809OEnd+tAmA144IrezPijs/um+yPrqqxQChgIeCMO1mvRA89dF6C8YmHHC5\nWFS3DiFaLkJafHh2ZV4IiigR4mOkaO11N8mjLCD/JGlkkIjONsmjLCD/cPXRXKpiS48RY1bKAvLl\n3AwDygLyz7nXjbKA/ONJxZ58n3qOkKPgclbn3W+UBeQXrqF2R597ESjQLCAKyiOM5wNqcuJa3WKA\nUiZCeoIiyCNb2rxrbCZsDjR0DiMtTg61UhLkkS1t3inFg6Pj6NFbsCw9JiyPxFhInvpo3Shae40w\nj9tpsu+H7GSVpz6asoD8w+MxyEpSom+yPpqygPwTJREiSSNDm84Ep8vllc1C1202CbFRiBKfPT+a\nsoD8k5monHJ+NGUB+Sf7nIyWymYDREIeCtIoC2g2XGZGc88oZQEFwDtWmEsW0KIeLM2yLDZt2oSM\njAwAwKpVq/CrX/0KFRUVeOaZZ8Dn81FaWooHH3wQAPDCCy/g66+/hkAgwK9//WsUFRVhaGgIDz/8\nMCYmJhAXF4fdu3dDKpXiwIEDePHFFyEQCHDjjTdi586di/mrhYz0eAUEfHd9dLvOBOOYHaVFieDR\nZH9W3quGyRoZHE6WmoT4wbs+2u5w13RRVoZv3h3/uVo4ShvzLSdZhbKaPrT0jHomr3S/+ZaTrMKZ\n9mG09IyePfeYgiSfcpKV6B20oEdvQVWLASIBL2zPtF8oXH10dasBI2YrZQH5SSoWIGWyPto8bkdj\n1wgyKAvIp7hoKRST50f3D4+hb2gMq3M1lAXkQ0ai0l0f3TPqyQK6aHk8bQz44H3so1ophosNbGNg\nUYPyzs5OrFixAn/5y1+mPP7UU0/h+eefR2pqKu69916cOXMGLMvi+PHjeOedd6DT6fCzn/0M7733\nHl566SVcc801uOGGG/DXv/4Vb7/9Nm677Tbs3r0b7777LqRSKXbt2oUtW7ZAo6FJxbkEfB4yEhVo\n7THieF0/AFrZ90e0XAyNSoLWXqNnxYuum28pcTKIhDy09I56apMo5d837/rBEbMNAj4Py9Op46kv\n2V7dT6tbKQvIX9x1q20f8mQBxShosu9LdpIKhyp1OHamHz2DFhRlx0JEWUA+ZScrUd1qwBcnu2Ae\nt2NTcRJN9v2QnaxC14AZnx7rgNNFKf/+YBgG2UkqVDQP4lBFLwDKLvCHmKuP7jfhZMMAALpu/ohV\nSqCSi9DcOwqR0J2MHkjK/6Kmr9fW1qK/vx933HEH7rnnHrS2tsJsNsNmsyEtLQ0Mw6C0tBRlZWUo\nLy9HaWkpGIZBUlISnE4nhoaGUF5ejo0bNwIANm3ahLKyMrS0tCAtLQ0qlQoikQhr167FiRMnFvNX\nCyk5SSq4WBZfn+4Bn8dgeQZN9v2RnayCedyO42f6oYgSIjNRGewhLXl8Hg+ZCUr06i2o6xhGilaO\nWBWl/PvC1Uc3do2gW29GQXo0xCKa7PuSPFkffaJ+AMYxO4qyYykLyA9cffShil7KAgoAt5ixv7wb\nAGVl+Mtz3U5y140m+/7gFmvPXje63/zBpRRz71Oqi/ZPdrIKDieLQxW9lAXkJ4ZhkJOkwqjZhvIG\nfcBZQBdsp/ydd97Bq6++OuWxJ598Evfeey+2bduGkydP4pFHHsGLL74IufzsgGUyGbq6uiAWixEd\nHT3lcZPJBLPZDIVCMeNj3ONms9nnGLXayNxBWb0sHvuOd8LmcGFVrhZpKfRG80dxXhyOnemHzeFC\n6apkxMcHFpRH6v22MleLhq4ROJwuXFyUGLHXIVArsjU4cLILALChODng6xap1zk/PQaVTe461Y2r\nUyL2OgRCCyA1Xo6ufvf35qUlqXS/+SE2Vg6ZVAjL5Fnll61LgzaGjlnyZb1CAubtCtgcLggFPGxa\nmwaJ2P/paCTeawCwfiWDlz+ug83hQrRCjJLCJPB4tOjoy9oViXjvYCtsDheyklXIywosKI/U+211\nQTz2l3fD5nChZFk8kpOoDt8fRXlxKG/Uw+ZwYXNhIuLi/I8VLlhQvmPHDuzYsWPKY+Pj4+Dz3bs9\nJSUlGBgYgEwmg8Vy9rxKi8UCpVIJoVB43uMKhQJyuRwWiwUSicTzXO6xc5/ri15vmu+vGZK0irMN\nB5alRUfsdQhUQvTZdM78FFVA102rVUTsdU6KkXr+f26iMmKvQ6BSvM5QzYqX0/3mpzStHJVNg+Dz\nGKSopRF7HQKVEa9AV78ZiighYiQCut/8lJXoTsVO0crBOJwRex0ClayRo1tvRn5aNEzGcfh71SL5\nXhOwLORSIczjdhRmqmEw+N58IkCMVAAew8DFsliRHkOfbX6KU1KsMBeJ0WezQfOSz5/zzrbIs6jp\n6y+88IJn97y+vh6JiYlQKBQQCoXo7OwEy7I4fPgwSkpKsGbNGhw+fBgulwu9vb1wuVxQq9VYs2YN\nDh48CAA4dOgQ1q5di+zsbHR0dGBkZAQ2mw0nT57E6tWrF/NXCylcfTRAHXYDkaKVQyTggc9jsIJS\n/v2WNZk6JpcKPWmyxDcuxTNJI4M2Wurj2YTDXbf8tGhIA9h9i3TcdVuZFUu7bwHgUmMpBTswOdx1\no1RivzEM42kkRT1t/CcW8pE6mUJMc17/cfXRAPUCCkR6ghx8HjOnXkCLOmO599578cgjj+DgwYPg\n8/nYvXs3AODpp5/Gww8/DKfTidLSUhQXFwNw76bffPPNcLlcePLJJwEA999/Px599FHs3bsXMTEx\nePbZZyEUCvHYY4/hRz/6EViWxY033oj4+PjF/NVCzvaNmdAZxhBPqXZ+E/B5uPHSbDhcLkRJaLLv\nL2WUCNdtyECsUkKT/QAka2W4fG0KCtKovCQQy9KjUboyERtWJgR7KCFlbb4WZ9qHcPX6tGAPJaSU\nrkxEt96CzauTgz2UkLJlTQrGbU58ZwXN1QJx1fpUyKOE1HQrQNdekoH6jmHqBRQAhmFww8YsDI5O\nUC+gAAgFfNx4aTZ4PCbgXkAMy7LsBRrXkkepGGSxRHIKFFl8dL+RxUT3G1ksdK+RxUT3G1loSyZ9\nnRBCCCGEEEIIIWdRUE4IIYQQQgghhAQJBeWEEEIIIYQQQkiQRHRNOSGEEEIIIYQQEky0U04IIYQQ\nQgghhAQJBeWEEEIIIYQQQkiQUFBOCCGEEEIIIYQECQXlhBBCCCGEEEJIkFBQTgghhBBCCCGEBAkF\n5YQQQgghhBBCSJBQUE4IIYQQQgghhAQJBeWEEEIIIYQQQkiQUFBOCCGEEEIIIYQECQXlhBBCCCGE\nEEJIkFBQTgghhBBCCCGEBAkF5YQQQgghhBBCSJBQUE4IIYQQQgghhAQJBeWEEEIIIYQQQkiQUFBO\nCCGEEEIIIYQECQXlhBBCCCGEEEJIkFBQTgghhBBCCCGEBAkF5YQQQgghhBBCSJBQUE4IIYQQQggh\nhASJINgDCBaHw4nh4bFgD4NEiJiYKLrfyKKh+40sJrrfyGKhe40sJrrfyELTahUz/lnE7pQLBPxg\nD4FEELrfyGKi+40sJrrfyGKhe40sJrrfyGKK2KCcEEIIIYQQQggJNgrKCSGEEEIIIYSQIKGgnBBC\nCCGEEEJC2IjZilc+qUPPoCXYQyFzQEE5IYQQQgghhISoCZsD//udKnxTpcORal2wh0PmgIJyQggh\nhBBCyJIxarbC4XQFexghwely4S//rEVHvwkAYBidCPKIyFxQUE4IIYQQQghZEjr7TXjkz2X4V1l7\nsIey5LEsize+bEJViwErMtUQ8BkMjo4He1hkDigoJ4QQQgghhCwJ/zzcBoeTRWe/OdhDWfI+P9GF\nr071IEUrx0+3FyJWKcEg7ZSHJArKCSGEEEIIIUHX0WfC6aZBAMCQkYLL2ZysH8DeA82Ilovwyx1F\nkIoF0KgkMI3ZYbU5gz08EiAKygkhhBBCCCFB9+GRNgCAgM9gyGQN8miWruaeUfyff52BSMTHL3cU\nQ62UAABiVVIAwCAtaIQcCsoJIYQQQgghQcXtkmcnK5GXGg3zuB1WO+34nmtgeAzPvVsFp5PF/d8v\nRFq8wvNn2mh3cD44QnXloYaCckIIIYQQQkhQcbvk3y/NhFrhDi4phf18b3zZBPO4HbdflYei7Ngp\nfxarmgzKqa485AiC8Zdef/31kMvlAICUlBTcd999eOyxx8AwDHJzc/HUU0+Bx+Nh7969eOuttyAQ\nCHD//fdj8+bNmJiYwCOPPAKDwQCZTIbf//73UKvVqKiowDPPPAM+n4/S0lI8+OCDwfjVCCGEEEII\nIQHw3iVfkaFGc/coAGDIZEVirCzIo1taOvpNiFVKcNmq5PP+TDOZvk7HooWeRd8pt1qtYFkWe/bs\nwZ49e7B7927s3r0bv/zlL/HGG2+AZVns378fer0ee/bswVtvvYWXX34Zf/rTn2Cz2fDmm28iLy8P\nb7zxBrZv346XXnoJAPDUU0/h2WefxZtvvonKykqcOXNmsX81QgghhBBCSIC8d8kZhvHUSNNO+VRj\nEw6Mmm1I1ERN++daz045pa+HmkUPyuvr6zE+Po67774bP/jBD1BRUYHa2lqsX78eALBp0yaUlZWh\nqqoKq1evhkgkgkKhQFpaGurr61FeXo6NGzd6nnv06FGYzWbYbDakpaWBYRiUlpairKxssX81Qggh\nhBBCSADO3SUHALVSDAAYNlKzN299Q2MAgET19NkDSpkIQgEPetopDzmLnr4ukUjwox/9CDt27EB7\nezvuuecesCwLhmEAADKZDCaTCWazGQrF2cYFMpkMZrN5yuPez+XS4bnHu7q6fI5Fq1X4fA4hC4Xu\nN7KY6H4ji4nuN7JY6F4LP3/9lzu79QffW4G4OCUAIMfl/jOLzRnUf/Oldr9VdwwDAHIz1DOOLS4m\nCkNG65IbO5ndogflmZmZSE9PB8MwyMzMRHR0NGpraz1/brFYoFQqIZfLYbFYpjyuUCimPD7bc5VK\npc+x6PWmBfzNCJmZVqug+40sGrrfyGKi+40sFrrXwk9Hnwnf1vQhO1mJlBiJ59+Xcbi7rvfqzUH7\nN1+K91tj+xAAQCHizTi2GLkIPXozOruHIRUHpX0YmcFsCyWLnr7+7rvv4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QUQC/kRWVOu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+mTfffNPYyzK4+IwKOjTymD8l77Jqth+JWZXsPZvPw+unGG0dre0a/vl1ChqtzL03RIzaTE1D\n6wrKC8oaDBaUH0soor6pnbXz/bG2NH76riRJ3L0mgsKKRvadu8IkLztmR4y9ZoIFZQ1czqsm3M9h\nzDQTHKowPwe+P5tPar7+u7nKsswnhzKRZdi8PHhYp51mKiWxM3xYNNWTIxeL+OZ0Ht+cyuNgXAEr\nZ/qyeo7fgCnoV8oa+CG+EA8na2Jn+KBSKrhxQSALpnjy6eFMzl4u4/n3z+PtYkNRRSNdlXtWFiqm\nh7hQVt3M+dQysopqCfIy3kZkh0bLobgCvjyRS1NrB24OVqydH0BBeQMnk0r47kw+353JJ8zXgcXR\nXpRU6nptaGWZZdO92bQs6KqO9o5qC66f48/1c/zJL63n+KViDl8o5M+fJrBoqiebl4f0+f5QXd/K\nf45mcfJSCZIkcefqsD7HAvYmJbeKyrpWFk/z7LPL/uwIXVB+7nJZr0F5ZW0Lr+68SE1DG3Fp5aye\n48ctiyeN6FR939l8ymtbmBPhTpC33YhOW7VamYq6FpzUFkNe09fdteQBw/75/VFbmzMv0oOjCUXE\nZ5QzI2zs3H/Iskxzq2ZYn03JudVIYLAAeWaYLig/n1Zm+kF5ia7Jmz7GyDmpLcgtqUerlU1+A+iH\neN3EmqXTh7bJPFx21uZM8rYjs6CWhuZ2o5QaDUddYxs7D2VyKrkECVge480tiyeNuYyzkRr0u1RQ\nUBCvvvoqN910EzY2NrS3t+tzXUMWFxfHokWLAIiOjiYpKcnIKzKOc6m61PXZJpJ6He7ngL+7mrj0\ncsqqm3Bz1E8tzUA++yGL4somVs70NemulF3B3ZWyBoPUTnZotHx3Jh9zM8WonvaOlJWFip+vn8Lz\nH5zn3W9T8Xa1xXuMdd4+ENd5Sm7C6Z7ddeUGaPZ2KbuS5JwqJgc4Mi1oZNe2uZmSlbN8WRztxQ/x\nhXx7Oo+vTuaSklfFE5uj+wzsZFnmo/3pyDLctiLkquDIyc6Sn62LYtn0aj7cn0FxZSOhvg5MDnBk\ncqATAR5qlAoF6VdqePnDC+w6lMmvt8YYvCmiLMtcCnX2TQAAIABJREFUSC9n1+EsymqasbZQsWV5\nMMs7NxhAN4del45cSGp+TXffAGc7C+5eEzHge6Sfu5rb3dUsnOLJO99c5lhiMcm5VWy/PpyoHqeO\nrW0a9p7N59szebS1a/FxtaGmoY33vkulqq6FdQsDB/X6HEnoavDW943tJC87nO0suJBRwZ0d2qvS\n6hua2/nzrgRqGtpYMdOHxMxKvj+TT2peNQ/eFHlVtsBgZRXW8smhTEDXmNDVwZJ5kR7MjfQYdM1o\ndX0rSTmVJGXrMi0aWzpQKRX4utng72GHv7stAR52eLva9BmoXylrID6jgiBvO70Geitn+XI0oYh9\n566MqaB879kr7Dqcya3Lg1k1e/Angy1tHWQV1uLvoTZY4BLq54CtlRkX0srZuiLUpAPOnOI6VEoJ\nHz00G/ZwtiarqI7ErEqiQ0x3hFxNQysXMyrwdbNl0ihnFPRnWpALWYV1JGVXMjfSOD2ABkuWZY4k\nFPHZ4SyaWjvw91Bz56qwUc/AGCsGFZS7uLjw/PPPk5SUxB//+EdefvllvLwGv4ttCNfWuSuVSjo6\nOlCp+n6Krq7ja8ZhQ1MbyTlVTPK2Jyps7J0M9mXTilD+9GEcx5NKefCWqQb/+RfSyjgYV4Cvu5oH\nN07TW22sIa63qZIEJFFe12qQn7f/TB7V9a3ctHgSk/zHVgMlV1c1j2+Zzh8+OM/fv0jm1ccXj5ld\n1dqGVk4nl+LhbE3s3ECUerj5MtT7W5C3PTnF9ajtrfQ2C7xDo+Wzf51FIcFDG6Nxcxu9D+RtXg5s\nXBHG33YlcCS+gLe/usz/u3dOrylxxy4WknalhjmRHiyfE9Dr3+fqqmbhDD80Gi3KXgIlV1c1cy4W\ncSa5hJyyRuZEeY7acxnIldJ6/v6fSyRmVqBUSNy0aBKbV4ZhZ/PTpndenvasXRJMUUUDB87mo1BI\n3LI0uM/fod6uN1dXNVPDPdh1MJ1PD6Tz6s4EVs31Z/vaSM4mF/PBt5eprG3BQW3Bg+sjiJ3lR0ll\nI8++fUp3gt+m5eebpvV7Mlxd38LFjAoCPO2YPdWr3yB+0XQf9hzJoqC6mdmTdTejbe0a/rTzJEUV\njaxbHMR966Joamnnrc8vcej8FZ577xw/u2Uqy2f6DnoDRZZl/vBRPAD3rYsis6CGU5eK+fJELl+e\nyCXUz4GF07xRW5vT9Vfq/lv3f/KK67iQVkZuZwowgKujFdPD3CiubCSvuI6cznFTACqlhL+nHcE+\nDgT5OBDsY0+Apx1mKiXvfJcKwLbrJ4/q7821XF3VxIS7cSG1jJqWDkJ89bcBMNj3No1Gy4G4AmRg\n56FM2rSw/YbJgwp2z6WUoNHKzIr0MOi94oJpXuw9nUdFYzuRJtqUsL1DQ2F5A5O87fH0GP1soNtX\nR3AquZTdR7NZNsdf7z0i9PXvfyihCI1W5sZFk/T6u3mtZbP9+c/RbFILarlx6dgt7wOITyvjg+/T\nsLZU8eD6KVw/Xz/3S2PFoO6gXnnlFQ4cOMBdd92FtbU1vr6+PPLII/pe25DY2trS2PjjWBatVttv\nQA5QXl7f75+bmmOJul/w6cHOJvXcQr3UONtZsO9sHtfN9DFoOk1DczuvfhSHUiFxz/Xh1NU06eXn\nuLqqDfJvopRlLMyUZF6p1vvP02pldu5PQ6mQWBzlMSavuTAvO66b5cu+c1f4nw/O8dC6qDFx+vD1\nyVzaOrQsi/amqnL0u+Ub6noDCPLSpcKdTSgkQk9ZJgfjCigoa2BptBc2Kkkvz23rimBq61u4mFHO\nC++c5uH1UVc1W2pt1/DPLy6hUkqsXxgwojXcNN+fcymlvPNlEv6u1gM2dSqtasLV0arPBnIDaWvX\n8PWpPL47nYdGKzMtyJnNsSF4OFnT2tRKeVPf9ZlmwPWd2RyN9S009jKKaKDrbWWMN6FedrzzTQp7\nT+dx8NwVOjS60+q18wO4vrNsoKqyAXPg6a0xvLYrgQPn8impaOChm6N6LSuQZZmvT+me0/xIdyoq\n+v9divJ3ZA9w4HQega42aLUyb+5JIiWnilnhbtw4z6/7eWxbEUKwp5od+9L4yyfxnEwo5M5V4YNK\ngz6dXEJafjUzw1yZH+HG/Ag3bl0yifj0Ck4ll5CcW0X6AKOvujolRwU6ETXJGU9n6+5NgQ6NlsLy\nRnJL6sgrqSevtJ684nqyCmqBPACUCglvFxuulDUQ4KHGz9lK7+8JS6d5ciG1jE/3pfHATZF6+RlD\neW+LTy+nqq6FGWGuFFU08vkPmZSU13P3mogBA7mTCbq04gA3W4N+tkX6O7D3dB4HTufiph7dOdWG\nkl1UR4dGxsfFRi+vnZVSYkm0F4cvFPLpvlS9Zunp67NUq5X57kQuFmZKIv0cDHqNWSt1WU/nU0op\nKa0d000FL6aWAnDvDRFMD3HVy/2SofW3yTOooNzW1haFQsHu3bv52c9+ho2NzZjrvh4TE8Phw4dZ\ns2YNFy9eJDRUv+OAxqIfu66bzik56G4+Vs705ZNDmRyOLxzV7rD9kWWZD/amUdvQxoYlk/Te9dIQ\nFJKEj6sNuSX1tF+TojnazqeVUVrdzOJpXnqZqTlaNi4NIre4jri0cn777jm2rgwhzM949XodGi2H\nLhRgaa5k4VTDnZLqS5ivI3vPXiHtSo1egvLGlnb2HMvGykLJzYsmjfrf30WlVPDQzZH8ZVci8RkV\nvPttKvfcENEdCH93Oo+qulZumOc/4jIbT2cbFk/z5IeLRRxLKO63nvCrEzl8fiyH2Bgftl439M+1\npJxK/r03nbKaZhzVFmxdGWqUETL+Hmqe2T6Lr07ksvdsPnMj3dm4JKjX9w57G3N+fXsMb36hG7f1\nh48u8PimadjbmFNW3UzalRpS86tJy6+hur4VlVIxqDTMQE81LvaWxGeU096hYeehTOLSywn3c+C+\ntZN/sukxN9KDIG973v4qmbOXyygob+Q322L6zbhpbdfw2ZEsVEqJTcuCu79uaa5iXpQH86I8qG1o\nJSWvmg6NVveHMt29B2RZxsnOklBfhz6ztlRKBf4e6qs+szo0WooqGskrqSe3tJ68knrdJA4YcHTg\naIkMcMLbxYZzqWVsWhY84okSI/XDRV1Zw43zA3Cys+S1XQmcSi6lvqmdh9dH9ZvZk5JbjbmZgmAD\nN6AN93PExlJFXHo5W1aEDHsjzpj01eStp3ULAzmdXMqXx3OYF+lhMrXRXZJyKqmsa2FJtJfBR2lK\nksTUYBcOXygks6DWqPdDAymp0h2UeY2x8kN9GdSV8Kc//YmSkhKSk5O5//772b17N6mpqTz99NP6\nXt+grVy5khMnTrBlyxZkWebFF1809pIMqqG5nZTcagI81Lg56Heepj4smubFFydyORhXwOrZvpip\n9N9N8XRyKedTywjxsef6OeOnU7+Pmy1ZRXUUVzbi566fjQZZlvnmVB6SBNfPHdsdPFVKBY9smMrO\nQxmcuFTCHz6KZ2a4G7cuC8LF3vC/K+fTyrprV8fDXOtQX3skIHWAk7/h+vJ4Lo0tHWxaGtRrmvVo\nMlPpRjy9svMiJ5NKsDJXcfvKECpqW/j2dD4OtubcMG903ivWLQzkVHIpe47nMDfSvdcA4fsz+Xx+\nLAeAQ/EFLIn2+skM7r7UNLTyycEMzl4uQyFJXDfLl5sXBeqtxGAwVEoF6xdPYt2iwAGDDQtzJY9u\nmMKOvekcTSjiuffOIQE1DW3dj1FbmzEzzJUl070HdVMuSRKzwt347kw+r//nEknZVfi42vDILVP6\n3MB0dbDi6a0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wlw1LJtGhkfnyRI5e15nbdT9qpHurVbP9UCkVnE0xzFjdnroaul2bRWht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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24049049748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposition = seasonal_decompose(df.AirPassengers, freq=12)  \n",
    "fig = plt.figure()  \n",
    "fig = decomposition.plot()  \n",
    "fig.set_size_inches(15, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range(0, 2)\n"
     ]
    }
   ],
   "source": [
    "# Define the p, d and q parameters to take any value between 0 and 2\n",
    "p = d = q = range(0, 2)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]\n"
     ]
    }
   ],
   "source": [
    "# Generate all different combinations of p, q and q triplets\n",
    "pdq = list(itertools.product(p, d, q))\n",
    "print(pdq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples of parameter combinations for Seasonal ARIMA...\n",
      "SARIMAX: (0, 0, 1) x (0, 0, 1, 12)\n",
      "SARIMAX: (0, 0, 1) x (0, 1, 0, 12)\n",
      "SARIMAX: (0, 1, 0) x (0, 1, 1, 12)\n",
      "SARIMAX: (0, 1, 0) x (1, 0, 0, 12)\n"
     ]
    }
   ],
   "source": [
    "# Generate all different combinations of seasonal p, q and q triplets\n",
    "seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]\n",
    "\n",
    "print('Examples of parameter combinations for Seasonal ARIMA...')\n",
    "print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[1]))\n",
    "print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[2]))\n",
    "print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[3]))\n",
    "print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y=df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0)x(0, 0, 1, 12)12 - AIC:3618.0303990983703\n",
      "ARIMA(0, 0, 0)x(0, 1, 1, 12)12 - AIC:2824.743996368423\n",
      "ARIMA(0, 0, 0)x(1, 0, 0, 12)12 - AIC:2942.2733127230185\n",
      "ARIMA(0, 0, 0)x(1, 0, 1, 12)12 - AIC:2922.17815113314\n",
      "ARIMA(0, 0, 0)x(1, 1, 0, 12)12 - AIC:2767.105066400224\n",
      "ARIMA(0, 0, 0)x(1, 1, 1, 12)12 - AIC:2691.2333986436743\n",
      "ARIMA(0, 0, 1)x(0, 0, 0, 12)12 - AIC:3890.8167777778344\n",
      "ARIMA(0, 0, 1)x(0, 0, 1, 12)12 - AIC:3541.1171284719767\n",
      "ARIMA(0, 0, 1)x(0, 1, 0, 12)12 - AIC:3028.8377323188806\n",
      "ARIMA(0, 0, 1)x(0, 1, 1, 12)12 - AIC:2746.7797312913585\n",
      "ARIMA(0, 0, 1)x(1, 0, 0, 12)12 - AIC:3583.5236406200975\n",
      "ARIMA(0, 0, 1)x(1, 0, 1, 12)12 - AIC:3531.2937768979236\n",
      "ARIMA(0, 0, 1)x(1, 1, 0, 12)12 - AIC:2781.198675746604\n",
      "ARIMA(0, 0, 1)x(1, 1, 1, 12)12 - AIC:2720.702308820595\n",
      "ARIMA(0, 1, 0)x(0, 0, 1, 12)12 - AIC:3029.089945668332\n",
      "ARIMA(0, 1, 0)x(0, 1, 1, 12)12 - AIC:2568.2832251220993\n",
      "ARIMA(0, 1, 0)x(1, 0, 0, 12)12 - AIC:2841.3157814595106\n",
      "ARIMA(0, 1, 0)x(1, 0, 1, 12)12 - AIC:2815.4011044132576\n",
      "ARIMA(0, 1, 0)x(1, 1, 0, 12)12 - AIC:2588.533386513587\n",
      "ARIMA(0, 1, 0)x(1, 1, 1, 12)12 - AIC:2569.945327248374\n",
      "ARIMA(0, 1, 1)x(0, 0, 0, 12)12 - AIC:3327.5177587522303\n",
      "ARIMA(0, 1, 1)x(0, 0, 1, 12)12 - AIC:2984.7167061123273\n",
      "ARIMA(0, 1, 1)x(0, 1, 0, 12)12 - AIC:2789.1285421540424\n",
      "ARIMA(0, 1, 1)x(0, 1, 1, 12)12 - AIC:2537.0293659293957\n",
      "ARIMA(0, 1, 1)x(1, 0, 0, 12)12 - AIC:2984.4555708516423\n",
      "ARIMA(0, 1, 1)x(1, 0, 1, 12)12 - AIC:2939.460958374484\n",
      "ARIMA(0, 1, 1)x(1, 1, 0, 12)12 - AIC:2578.786235277444\n",
      "ARIMA(0, 1, 1)x(1, 1, 1, 12)12 - AIC:2537.771484229266\n",
      "ARIMA(1, 0, 0)x(0, 0, 0, 12)12 - AIC:3391.5248913820797\n",
      "ARIMA(1, 0, 0)x(0, 0, 1, 12)12 - AIC:3038.1420742813116\n",
      "ARIMA(1, 0, 0)x(0, 1, 0, 12)12 - AIC:2839.809192263449\n",
      "ARIMA(1, 0, 0)x(0, 1, 1, 12)12 - AIC:2588.503671751839\n",
      "ARIMA(1, 0, 0)x(1, 0, 0, 12)12 - AIC:2993.4630440139713\n",
      "ARIMA(1, 0, 0)x(1, 0, 1, 12)12 - AIC:2995.049216326953\n",
      "ARIMA(1, 0, 0)x(1, 1, 0, 12)12 - AIC:2588.246328431529\n",
      "ARIMA(1, 0, 0)x(1, 1, 1, 12)12 - AIC:2592.80110502724\n",
      "ARIMA(1, 0, 1)x(0, 0, 0, 12)12 - AIC:3352.035013362148\n",
      "ARIMA(1, 0, 1)x(0, 0, 1, 12)12 - AIC:3006.549336661891\n",
      "ARIMA(1, 0, 1)x(0, 1, 0, 12)12 - AIC:2810.642372489451\n",
      "ARIMA(1, 0, 1)x(0, 1, 1, 12)12 - AIC:2559.5840319488525\n",
      "ARIMA(1, 0, 1)x(1, 0, 0, 12)12 - AIC:2981.2250436795084\n",
      "ARIMA(1, 0, 1)x(1, 0, 1, 12)12 - AIC:2959.3142304724843\n",
      "ARIMA(1, 0, 1)x(1, 1, 0, 12)12 - AIC:2579.8245645892216\n",
      "ARIMA(1, 0, 1)x(1, 1, 1, 12)12 - AIC:2563.139225892575\n",
      "ARIMA(1, 1, 0)x(0, 0, 0, 12)12 - AIC:3354.7462930846423\n",
      "ARIMA(1, 1, 0)x(0, 0, 1, 12)12 - AIC:3006.7029976360013\n",
      "ARIMA(1, 1, 0)x(0, 1, 0, 12)12 - AIC:2809.3844175191666\n",
      "ARIMA(1, 1, 0)x(0, 1, 1, 12)12 - AIC:2558.484602766444\n",
      "ARIMA(1, 1, 0)x(1, 0, 0, 12)12 - AIC:2959.885810636959\n",
      "ARIMA(1, 1, 0)x(1, 0, 1, 12)12 - AIC:2960.7127097642824\n",
      "ARIMA(1, 1, 0)x(1, 1, 0, 12)12 - AIC:2557.9459070926982\n",
      "ARIMA(1, 1, 0)x(1, 1, 1, 12)12 - AIC:2559.2741664585046\n",
      "ARIMA(1, 1, 1)x(0, 0, 0, 12)12 - AIC:3326.328551170047\n",
      "ARIMA(1, 1, 1)x(0, 0, 1, 12)12 - AIC:2985.8685312192615\n",
      "ARIMA(1, 1, 1)x(0, 1, 0, 12)12 - AIC:2790.767714996711\n",
      "ARIMA(1, 1, 1)x(0, 1, 1, 12)12 - AIC:2538.8206355415427\n",
      "ARIMA(1, 1, 1)x(1, 0, 0, 12)12 - AIC:2963.2789505804367\n",
      "ARIMA(1, 1, 1)x(1, 0, 1, 12)12 - AIC:2941.243698474832\n",
      "ARIMA(1, 1, 1)x(1, 1, 0, 12)12 - AIC:2559.8258191422447\n",
      "ARIMA(1, 1, 1)x(1, 1, 1, 12)12 - AIC:2539.712354465302\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n",
    "\n",
    "for param in pdq:\n",
    "    for param_seasonal in seasonal_pdq:\n",
    "        try:\n",
    "            mod = sm.tsa.statespace.SARIMAX(y,\n",
    "                                            order=param,\n",
    "                                            seasonal_order=param_seasonal,\n",
    "                                            enforce_stationarity=False,\n",
    "                                            enforce_invertibility=False)\n",
    "\n",
    "            results = mod.fit()\n",
    "\n",
    "            print('ARIMA{}x{}12 - AIC:{}'.format(param, param_seasonal, results.aic))\n",
    "        except:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n",
    "c3=[]\n",
    "for param in pdq:\n",
    "    for param_seasonal in seasonal_pdq:\n",
    "        try:\n",
    "            mod = sm.tsa.statespace.SARIMAX(y,\n",
    "                                            order=param,\n",
    "                                            seasonal_order=param_seasonal,\n",
    "                                            enforce_stationarity=False,\n",
    "                                            enforce_invertibility=False)\n",
    "\n",
    "            results = mod.fit()\n",
    "\n",
    "            c3.append( results.aic)\n",
    "        except:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3618.0303990983703,\n",
       " 2824.7439963684228,\n",
       " 2942.2733127230185,\n",
       " 2922.1781511331401,\n",
       " 2767.1050664002241,\n",
       " 2691.2333986436743,\n",
       " 3890.8167777778344,\n",
       " 3541.1171284719767,\n",
       " 3028.8377323188806,\n",
       " 2746.7797312913585,\n",
       " 3583.5236406200975,\n",
       " 3531.2937768979236,\n",
       " 2781.1986757466038,\n",
       " 2720.7023088205951,\n",
       " 3029.0899456683319,\n",
       " 2568.2832251220993,\n",
       " 2841.3157814595106,\n",
       " 2815.4011044132576,\n",
       " 2588.5333865135872,\n",
       " 2569.9453272483738,\n",
       " 3327.5177587522303,\n",
       " 2984.7167061123273,\n",
       " 2789.1285421540424,\n",
       " 2537.0293659293957,\n",
       " 2984.4555708516423,\n",
       " 2939.4609583744841,\n",
       " 2578.7862352774441,\n",
       " 2537.7714842292662,\n",
       " 3391.5248913820797,\n",
       " 3038.1420742813116,\n",
       " 2839.8091922634489,\n",
       " 2588.5036717518392,\n",
       " 2993.4630440139713,\n",
       " 2995.0492163269528,\n",
       " 2588.246328431529,\n",
       " 2592.8011050272398,\n",
       " 3352.0350133621482,\n",
       " 3006.5493366618912,\n",
       " 2810.6423724894512,\n",
       " 2559.5840319488525,\n",
       " 2981.2250436795084,\n",
       " 2959.3142304724843,\n",
       " 2579.8245645892216,\n",
       " 2563.1392258925748,\n",
       " 3354.7462930846423,\n",
       " 3006.7029976360013,\n",
       " 2809.3844175191666,\n",
       " 2558.4846027664439,\n",
       " 2959.8858106369589,\n",
       " 2960.7127097642824,\n",
       " 2557.9459070926982,\n",
       " 2559.2741664585046,\n",
       " 3326.3285511700469,\n",
       " 2985.8685312192615,\n",
       " 2790.7677149967112,\n",
       " 2538.8206355415427,\n",
       " 2963.2789505804367,\n",
       " 2941.2436984748319,\n",
       " 2559.8258191422447,\n",
       " 2539.7123544653018]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "index_min = np.argmin(c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2537.0293659293957"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2537.0293659293957"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c3[index_min]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0)x(0, 0, 1, 12)12 - AIC:3618.0303990983703\n",
      "ARIMA(0, 0, 0)x(0, 1, 1, 12)12 - AIC:2824.743996368423\n",
      "ARIMA(0, 0, 0)x(1, 0, 0, 12)12 - AIC:2942.2733127230185\n",
      "ARIMA(0, 0, 0)x(1, 0, 1, 12)12 - AIC:2922.17815113314\n",
      "ARIMA(0, 0, 0)x(1, 1, 0, 12)12 - AIC:2767.105066400224\n",
      "ARIMA(0, 0, 0)x(1, 1, 1, 12)12 - AIC:2691.2333986436743\n",
      "ARIMA(0, 0, 1)x(0, 0, 0, 12)12 - AIC:3890.8167777778344\n",
      "ARIMA(0, 0, 1)x(0, 0, 1, 12)12 - AIC:3541.1171284719767\n",
      "ARIMA(0, 0, 1)x(0, 1, 0, 12)12 - AIC:3028.8377323188806\n",
      "ARIMA(0, 0, 1)x(0, 1, 1, 12)12 - AIC:2746.7797312913585\n",
      "ARIMA(0, 0, 1)x(1, 0, 0, 12)12 - AIC:3583.5236406200975\n",
      "ARIMA(0, 0, 1)x(1, 0, 1, 12)12 - AIC:3531.2937768979236\n",
      "ARIMA(0, 0, 1)x(1, 1, 0, 12)12 - AIC:2781.198675746604\n",
      "ARIMA(0, 0, 1)x(1, 1, 1, 12)12 - AIC:2720.702308820595\n",
      "ARIMA(0, 1, 0)x(0, 0, 1, 12)12 - AIC:3029.089945668332\n",
      "ARIMA(0, 1, 0)x(0, 1, 1, 12)12 - AIC:2568.2832251220993\n",
      "ARIMA(0, 1, 0)x(1, 0, 0, 12)12 - AIC:2841.3157814595106\n",
      "ARIMA(0, 1, 0)x(1, 0, 1, 12)12 - AIC:2815.4011044132576\n",
      "ARIMA(0, 1, 0)x(1, 1, 0, 12)12 - AIC:2588.533386513587\n",
      "ARIMA(0, 1, 0)x(1, 1, 1, 12)12 - AIC:2569.945327248374\n",
      "ARIMA(0, 1, 1)x(0, 0, 0, 12)12 - AIC:3327.5177587522303\n",
      "ARIMA(0, 1, 1)x(0, 0, 1, 12)12 - AIC:2984.7167061123273\n",
      "ARIMA(0, 1, 1)x(0, 1, 0, 12)12 - AIC:2789.1285421540424\n",
      "ARIMA(0, 1, 1)x(0, 1, 1, 12)12 - AIC:2537.0293659293957\n",
      "ARIMA(0, 1, 1)x(1, 0, 0, 12)12 - AIC:2984.4555708516423\n",
      "ARIMA(0, 1, 1)x(1, 0, 1, 12)12 - AIC:2939.460958374484\n",
      "ARIMA(0, 1, 1)x(1, 1, 0, 12)12 - AIC:2578.786235277444\n",
      "ARIMA(0, 1, 1)x(1, 1, 1, 12)12 - AIC:2537.771484229266\n",
      "ARIMA(1, 0, 0)x(0, 0, 0, 12)12 - AIC:3391.5248913820797\n",
      "ARIMA(1, 0, 0)x(0, 0, 1, 12)12 - AIC:3038.1420742813116\n",
      "ARIMA(1, 0, 0)x(0, 1, 0, 12)12 - AIC:2839.809192263449\n",
      "ARIMA(1, 0, 0)x(0, 1, 1, 12)12 - AIC:2588.503671751839\n",
      "ARIMA(1, 0, 0)x(1, 0, 0, 12)12 - AIC:2993.4630440139713\n",
      "ARIMA(1, 0, 0)x(1, 0, 1, 12)12 - AIC:2995.049216326953\n",
      "ARIMA(1, 0, 0)x(1, 1, 0, 12)12 - AIC:2588.246328431529\n",
      "ARIMA(1, 0, 0)x(1, 1, 1, 12)12 - AIC:2592.80110502724\n",
      "ARIMA(1, 0, 1)x(0, 0, 0, 12)12 - AIC:3352.035013362148\n",
      "ARIMA(1, 0, 1)x(0, 0, 1, 12)12 - AIC:3006.549336661891\n",
      "ARIMA(1, 0, 1)x(0, 1, 0, 12)12 - AIC:2810.642372489451\n",
      "ARIMA(1, 0, 1)x(0, 1, 1, 12)12 - AIC:2559.5840319488525\n",
      "ARIMA(1, 0, 1)x(1, 0, 0, 12)12 - AIC:2981.2250436795084\n",
      "ARIMA(1, 0, 1)x(1, 0, 1, 12)12 - AIC:2959.3142304724843\n",
      "ARIMA(1, 0, 1)x(1, 1, 0, 12)12 - AIC:2579.8245645892216\n",
      "ARIMA(1, 0, 1)x(1, 1, 1, 12)12 - AIC:2563.139225892575\n",
      "ARIMA(1, 1, 0)x(0, 0, 0, 12)12 - AIC:3354.7462930846423\n",
      "ARIMA(1, 1, 0)x(0, 0, 1, 12)12 - AIC:3006.7029976360013\n",
      "ARIMA(1, 1, 0)x(0, 1, 0, 12)12 - AIC:2809.3844175191666\n",
      "ARIMA(1, 1, 0)x(0, 1, 1, 12)12 - AIC:2558.484602766444\n",
      "ARIMA(1, 1, 0)x(1, 0, 0, 12)12 - AIC:2959.885810636959\n",
      "ARIMA(1, 1, 0)x(1, 0, 1, 12)12 - AIC:2960.7127097642824\n",
      "ARIMA(1, 1, 0)x(1, 1, 0, 12)12 - AIC:2557.9459070926982\n",
      "ARIMA(1, 1, 0)x(1, 1, 1, 12)12 - AIC:2559.2741664585046\n",
      "ARIMA(1, 1, 1)x(0, 0, 0, 12)12 - AIC:3326.328551170047\n",
      "ARIMA(1, 1, 1)x(0, 0, 1, 12)12 - AIC:2985.8685312192615\n",
      "ARIMA(1, 1, 1)x(0, 1, 0, 12)12 - AIC:2790.767714996711\n",
      "ARIMA(1, 1, 1)x(0, 1, 1, 12)12 - AIC:2538.8206355415427\n",
      "ARIMA(1, 1, 1)x(1, 0, 0, 12)12 - AIC:2963.2789505804367\n",
      "ARIMA(1, 1, 1)x(1, 0, 1, 12)12 - AIC:2941.243698474832\n",
      "ARIMA(1, 1, 1)x(1, 1, 0, 12)12 - AIC:2559.8258191422447\n",
      "ARIMA(1, 1, 1)x(1, 1, 1, 12)12 - AIC:2539.712354465302\n"
     ]
    }
   ],
   "source": [
    "\n",
    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n",
    "c4=[]\n",
    "for param in pdq:\n",
    "    for param_seasonal in seasonal_pdq:\n",
    "        try:\n",
    "            mod = sm.tsa.statespace.SARIMAX(y,\n",
    "                                            order=param,\n",
    "                                            seasonal_order=param_seasonal,\n",
    "                                            enforce_stationarity=False,\n",
    "                                            enforce_invertibility=False)\n",
    "\n",
    "            results = mod.fit()\n",
    "\n",
    "            print('ARIMA{}x{}12 - AIC:{}'.format(param, param_seasonal, results.aic))\n",
    "            c4.append('ARIMA{}x{}12 - AIC:{}'.format(param, param_seasonal, results.aic))\n",
    "        except:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ARIMA(0, 1, 1)x(0, 1, 1, 12)12 - AIC:2537.0293659293957'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c4[index_min]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0, 1, 1'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "order1=c4[index_min][6:13]\n",
    "order1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 1]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1=[int(s) for s in order1.split(',')]\n",
    "order1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0, 1, 1, 12'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "seasonal_order1=c4[index_min][16:27]\n",
    "seasonal_order1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 1, 12]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasonal_order1=[int(s) for s in seasonal_order1.split(',')]\n",
    "seasonal_order1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.tsa.x13 import x13_arima_select_order\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(df.AirPassengers, trend='n', order=order1, seasonal_order=seasonal_order1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 Statespace Model Results                                 \n",
      "==========================================================================================\n",
      "Dep. Variable:                      AirPassengers   No. Observations:                  144\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 12)   Log Likelihood               -1413.963\n",
      "Date:                            Wed, 13 Dec 2017   AIC                           2833.926\n",
      "Time:                                    14:05:20   BIC                           2842.835\n",
      "Sample:                                01-01-1949   HQIC                          2837.546\n",
      "                                     - 12-01-1960                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1         -0.2174      0.054     -4.011      0.000      -0.324      -0.111\n",
      "ma.S.L12      -0.1069      0.094     -1.138      0.255      -0.291       0.077\n",
      "sigma2      1.424e+08   1.64e-10   8.67e+17      0.000    1.42e+08    1.42e+08\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       54.51   Jarque-Bera (JB):                10.78\n",
      "Prob(Q):                              0.06   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               2.38   Skew:                             0.16\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         4.37\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 6.17e+33. Standard errors may be unstable.\n"
     ]
    }
   ],
   "source": [
    "results = mod.fit()\n",
    "print (results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AirPassengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>342000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>406000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>396000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-01</th>\n",
       "      <td>472000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>548000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-08-01</th>\n",
       "      <td>559000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-01</th>\n",
       "      <td>463000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>407000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-11-01</th>\n",
       "      <td>362000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>405000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>417000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>391000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>419000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>461000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>472000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>535000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>622000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>606000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>508000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>461000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>432000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AirPassengers\n",
       "1959-02-01         342000\n",
       "1959-03-01         406000\n",
       "1959-04-01         396000\n",
       "1959-05-01         420000\n",
       "1959-06-01         472000\n",
       "1959-07-01         548000\n",
       "1959-08-01         559000\n",
       "1959-09-01         463000\n",
       "1959-10-01         407000\n",
       "1959-11-01         362000\n",
       "1959-12-01         405000\n",
       "1960-01-01         417000\n",
       "1960-02-01         391000\n",
       "1960-03-01         419000\n",
       "1960-04-01         461000\n",
       "1960-05-01         472000\n",
       "1960-06-01         535000\n",
       "1960-07-01         622000\n",
       "1960-08-01         606000\n",
       "1960-09-01         508000\n",
       "1960-10-01         461000\n",
       "1960-11-01         390000\n",
       "1960-12-01         432000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[121:144]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1959-01-01    342813.657496\n",
       "1959-02-01    335196.848862\n",
       "1959-03-01    385520.267936\n",
       "1959-04-01    388242.084324\n",
       "1959-05-01    408431.187144\n",
       "1959-06-01    488809.426481\n",
       "1959-07-01    530228.286422\n",
       "1959-08-01    556730.003691\n",
       "1959-09-01    461737.123888\n",
       "1959-10-01    416548.365777\n",
       "1959-11-01    360902.880717\n",
       "1959-12-01    389240.307597\n",
       "1960-01-01    422597.141371\n",
       "1960-02-01    399888.534634\n",
       "1960-03-01    454900.145012\n",
       "1960-04-01    416449.467049\n",
       "1960-05-01    474259.995734\n",
       "1960-06-01    526557.563687\n",
       "1960-07-01    606873.955872\n",
       "1960-08-01    629882.608817\n",
       "1960-09-01    515108.700640\n",
       "1960-10-01    454595.480488\n",
       "1960-11-01    414268.710945\n",
       "1960-12-01    436615.191512\n",
       "1961-01-01    445967.930512\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.predict(start=120,end=144)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# hands on exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts2=pd.read_csv('C:\\\\Users\\\\KOGENTIX\\\\Desktop\\\\trainingWeek2\\\\co2-ppm-mauna-loa-19651980.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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