Untitled.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "\n",
    "# ARIMA and Seasonal ARIMA\n",
    "\n",
    "\n",
    "## Autoregressive Integrated Moving Averages\n",
    "\n",
    "The general process for ARIMA models is the following:\n",
    "* Visualize the Time Series Data\n",
    "* Make the time series data stationary\n",
    "* Plot the Correlation and AutoCorrelation Charts\n",
    "* Construct the ARIMA Model or Seasonal ARIMA based on the data\n",
    "* Use the model to make predictions\n",
    "\n",
    "Let's go through these steps!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('perrin-freres-monthly-champagne-.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01</td>\n",
       "      <td>2815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1964-02</td>\n",
       "      <td>2672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1964-03</td>\n",
       "      <td>2755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964-04</td>\n",
       "      <td>2721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964-05</td>\n",
       "      <td>2946.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Perrin Freres monthly champagne sales millions ?64-?72\n",
       "0  1964-01                                             2815.0     \n",
       "1  1964-02                                             2672.0     \n",
       "2  1964-03                                             2755.0     \n",
       "3  1964-04                                             2721.0     \n",
       "4  1964-05                                             2946.0     "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1972-07</td>\n",
       "      <td>4298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1972-08</td>\n",
       "      <td>1413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1972-09</td>\n",
       "      <td>5877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Perrin Freres monthly champagne sales millions...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                                 Month  \\\n",
       "102                                            1972-07   \n",
       "103                                            1972-08   \n",
       "104                                            1972-09   \n",
       "105                                                NaN   \n",
       "106  Perrin Freres monthly champagne sales millions...   \n",
       "\n",
       "     Perrin Freres monthly champagne sales millions ?64-?72  \n",
       "102                                             4298.0       \n",
       "103                                             1413.0       \n",
       "104                                             5877.0       \n",
       "105                                                NaN       \n",
       "106                                                NaN       "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01</td>\n",
       "      <td>2815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1964-02</td>\n",
       "      <td>2672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1964-03</td>\n",
       "      <td>2755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964-04</td>\n",
       "      <td>2721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964-05</td>\n",
       "      <td>2946.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     Month   Sales\n",
       "0  1964-01  2815.0\n",
       "1  1964-02  2672.0\n",
       "2  1964-03  2755.0\n",
       "3  1964-04  2721.0\n",
       "4  1964-05  2946.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Cleaning up the data\n",
    "df.columns=[\"Month\",\"Sales\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Drop last 2 rows\n",
    "df.drop(106,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1972-06</td>\n",
       "      <td>5312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1972-07</td>\n",
       "      <td>4298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1972-08</td>\n",
       "      <td>1413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1972-09</td>\n",
       "      <td>5877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "       Month   Sales\n",
       "101  1972-06  5312.0\n",
       "102  1972-07  4298.0\n",
       "103  1972-08  1413.0\n",
       "104  1972-09  5877.0\n",
       "105      NaN     NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(105,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1972-05</td>\n",
       "      <td>4618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1972-06</td>\n",
       "      <td>5312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1972-07</td>\n",
       "      <td>4298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1972-08</td>\n",
       "      <td>1413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1972-09</td>\n",
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      "text/plain": [
       "       Month   Sales\n",
       "100  1972-05  4618.0\n",
       "101  1972-06  5312.0\n",
       "102  1972-07  4298.0\n",
       "103  1972-08  1413.0\n",
       "104  1972-09  5877.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Month into Datetime\n",
    "df['Month']=pd.to_datetime(df['Month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01-01</td>\n",
       "      <td>2815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1964-02-01</td>\n",
       "      <td>2672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1964-03-01</td>\n",
       "      <td>2755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964-04-01</td>\n",
       "      <td>2721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964-05-01</td>\n",
       "      <td>2946.0</td>\n",
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      "text/plain": [
       "       Month   Sales\n",
       "0 1964-01-01  2815.0\n",
       "1 1964-02-01  2672.0\n",
       "2 1964-03-01  2755.0\n",
       "3 1964-04-01  2721.0\n",
       "4 1964-05-01  2946.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('Month',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>2815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-02-01</th>\n",
       "      <td>2672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-03-01</th>\n",
       "      <td>2755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-04-01</th>\n",
       "      <td>2721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-05-01</th>\n",
       "      <td>2946.0</td>\n",
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      "text/plain": [
       "             Sales\n",
       "Month             \n",
       "1964-01-01  2815.0\n",
       "1964-02-01  2672.0\n",
       "1964-03-01  2755.0\n",
       "1964-04-01  2721.0\n",
       "1964-05-01  2946.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4761.152381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2553.502601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4217.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5221.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13916.000000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "              Sales\n",
       "count    105.000000\n",
       "mean    4761.152381\n",
       "std     2553.502601\n",
       "min     1413.000000\n",
       "25%     3113.000000\n",
       "50%     4217.000000\n",
       "75%     5221.000000\n",
       "max    13916.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Visualize the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2c881a2e8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Testing For Stationarity\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_result=adfuller(df['Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ho: It is non stationary\n",
    "#H1: It is stationary\n",
    "\n",
    "def adfuller_test(sales):\n",
    "    result=adfuller(sales)\n",
    "    labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
    "    for value,label in zip(result,labels):\n",
    "        print(label+' : '+str(value) )\n",
    "    if result[1] <= 0.05:\n",
    "        print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : -1.8335930563276297\n",
      "p-value : 0.3639157716602417\n",
      "#Lags Used : 11\n",
      "Number of Observations Used : 93\n",
      "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
     ]
    }
   ],
   "source": [
    "adfuller_test(df['Sales'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sales First Difference'] = df['Sales'] - df['Sales'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1964-01-01        NaN\n",
       "1964-02-01     2815.0\n",
       "1964-03-01     2672.0\n",
       "1964-04-01     2755.0\n",
       "1964-05-01     2721.0\n",
       "1964-06-01     2946.0\n",
       "1964-07-01     3036.0\n",
       "1964-08-01     2282.0\n",
       "1964-09-01     2212.0\n",
       "1964-10-01     2922.0\n",
       "1964-11-01     4301.0\n",
       "1964-12-01     5764.0\n",
       "1965-01-01     7312.0\n",
       "1965-02-01     2541.0\n",
       "1965-03-01     2475.0\n",
       "1965-04-01     3031.0\n",
       "1965-05-01     3266.0\n",
       "1965-06-01     3776.0\n",
       "1965-07-01     3230.0\n",
       "1965-08-01     3028.0\n",
       "1965-09-01     1759.0\n",
       "1965-10-01     3595.0\n",
       "1965-11-01     4474.0\n",
       "1965-12-01     6838.0\n",
       "1966-01-01     8357.0\n",
       "1966-02-01     3113.0\n",
       "1966-03-01     3006.0\n",
       "1966-04-01     4047.0\n",
       "1966-05-01     3523.0\n",
       "1966-06-01     3937.0\n",
       "               ...   \n",
       "1970-04-01     3370.0\n",
       "1970-05-01     3740.0\n",
       "1970-06-01     2927.0\n",
       "1970-07-01     3986.0\n",
       "1970-08-01     4217.0\n",
       "1970-09-01     1738.0\n",
       "1970-10-01     5221.0\n",
       "1970-11-01     6424.0\n",
       "1970-12-01     9842.0\n",
       "1971-01-01    13076.0\n",
       "1971-02-01     3934.0\n",
       "1971-03-01     3162.0\n",
       "1971-04-01     4286.0\n",
       "1971-05-01     4676.0\n",
       "1971-06-01     5010.0\n",
       "1971-07-01     4874.0\n",
       "1971-08-01     4633.0\n",
       "1971-09-01     1659.0\n",
       "1971-10-01     5951.0\n",
       "1971-11-01     6981.0\n",
       "1971-12-01     9851.0\n",
       "1972-01-01    12670.0\n",
       "1972-02-01     4348.0\n",
       "1972-03-01     3564.0\n",
       "1972-04-01     4577.0\n",
       "1972-05-01     4788.0\n",
       "1972-06-01     4618.0\n",
       "1972-07-01     5312.0\n",
       "1972-08-01     4298.0\n",
       "1972-09-01     1413.0\n",
       "Name: Sales, Length: 105, dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sales'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Seasonal First Difference']=df['Sales']-df['Sales'].shift(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Sales First Difference</th>\n",
       "      <th>forecast</th>\n",
       "      <th>Seasonal First Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>2815.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-02-01</th>\n",
       "      <td>2672.0</td>\n",
       "      <td>-143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-03-01</th>\n",
       "      <td>2755.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-04-01</th>\n",
       "      <td>2721.0</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-05-01</th>\n",
       "      <td>2946.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-06-01</th>\n",
       "      <td>3036.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-07-01</th>\n",
       "      <td>2282.0</td>\n",
       "      <td>-754.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-08-01</th>\n",
       "      <td>2212.0</td>\n",
       "      <td>-70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-09-01</th>\n",
       "      <td>2922.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-10-01</th>\n",
       "      <td>4301.0</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-11-01</th>\n",
       "      <td>5764.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-12-01</th>\n",
       "      <td>7312.0</td>\n",
       "      <td>1548.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-01-01</th>\n",
       "      <td>2541.0</td>\n",
       "      <td>-4771.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-02-01</th>\n",
       "      <td>2475.0</td>\n",
       "      <td>-66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-197.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sales  Sales First Difference  forecast  \\\n",
       "Month                                                  \n",
       "1964-01-01  2815.0                     NaN       NaN   \n",
       "1964-02-01  2672.0                  -143.0       NaN   \n",
       "1964-03-01  2755.0                    83.0       NaN   \n",
       "1964-04-01  2721.0                   -34.0       NaN   \n",
       "1964-05-01  2946.0                   225.0       NaN   \n",
       "1964-06-01  3036.0                    90.0       NaN   \n",
       "1964-07-01  2282.0                  -754.0       NaN   \n",
       "1964-08-01  2212.0                   -70.0       NaN   \n",
       "1964-09-01  2922.0                   710.0       NaN   \n",
       "1964-10-01  4301.0                  1379.0       NaN   \n",
       "1964-11-01  5764.0                  1463.0       NaN   \n",
       "1964-12-01  7312.0                  1548.0       NaN   \n",
       "1965-01-01  2541.0                 -4771.0       NaN   \n",
       "1965-02-01  2475.0                   -66.0       NaN   \n",
       "\n",
       "            Seasonal First Difference  \n",
       "Month                                  \n",
       "1964-01-01                        NaN  \n",
       "1964-02-01                        NaN  \n",
       "1964-03-01                        NaN  \n",
       "1964-04-01                        NaN  \n",
       "1964-05-01                        NaN  \n",
       "1964-06-01                        NaN  \n",
       "1964-07-01                        NaN  \n",
       "1964-08-01                        NaN  \n",
       "1964-09-01                        NaN  \n",
       "1964-10-01                        NaN  \n",
       "1964-11-01                        NaN  \n",
       "1964-12-01                        NaN  \n",
       "1965-01-01                     -274.0  \n",
       "1965-02-01                     -197.0  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : -7.626619157213163\n",
      "p-value : 2.060579696813685e-11\n",
      "#Lags Used : 0\n",
      "Number of Observations Used : 92\n",
      "strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "## Again test dickey fuller test\n",
    "adfuller_test(df['Seasonal First Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2da817e80>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gtn0tgCOG/Y5q2ypt70jyeYG5WBp9QTfOWBPCe188DgDYOxWt+riv/WY/3vufjy15246IZoEY/fxel6NqJfp8PI0nD59q+jWNV4qnYuYnjBMLSQx3e7VusuUnBfk+TbWxrXrp5DsjK8YCUWQdiH0tEON3rZYFIgWm9DMJ+1wQouCSZVpjOYLoZLJNVNle/gRENxDRDiLaMT299FfjVrCYzCCbF/rkuk2DQQDA3qnqnUb3nIwgmxdLflKKJLNwKqR34QXUNN50Nl/xau4/f3sQb/3aQ3rRWqPMGwRkzuSEIYsIh7u8cDvNJ+pJC2Q2lmqb6FZ1YYU8mI2mbXfFG0tn9Sp0YGVkYRndpfVYICGPEw6l+LTSxf2wLKWdAnJSc01B+y3HxR0FMGbYbxTA8SrbyxBC3CiE2C6E2D4wMGD5wpcC2WSvTys2C3ldGO7yYuJkdQtkn2ahtNMlY0ZUmwVibBYopxKaFfABwGw0jUxO4PkT9bXfLmUhkdEFy6wWRBYRDnf54HIo5llY2jYh2pdOW8sCyeaF7QK38VROr0IHjC4s+1ogUkC6fK6aQXSzzgBAoaW73T6PlUo7BeQ2ADKT6noAPzVsf5eWjXUJgAXNxXUngFcSUY8WPH+ltq0jmdVOZrJWAFCtkGourFgqq6ettjMobEYkmSmKfwBqEB2oPJVQxk12HV9o6jUXEhms7w8AMI+ByCLC4S6vKiBmFohhW7tEdzGRAREQ8jjL7tOr0W3kxhJCIFJaB6LYv5WJdGENd3mxmMyUNak8MhfHdx46iD/6r8dx584T6A24y56DLRBrKf/GNwERfR/AywD0E9FRqNlUnwNwKxG9D8BhAG/Rdr8DwGsATACIA3gPAAgh5ojo0wAe0/b7OyFEaWC+Y5iNFVsgALB5MITvP3oY+byAopR79PZNF8Rl6QUki5Cn+Iqu1lRC6WfeNbnY1GsuxDMY6/Vjz8mIqc9bFhEOd/ngqeLCCrgdiKVzbXvPpLvE7DMb0C4QpiNpbBosu3tZiKdzSGfz6DUU2TlXhAWirm24y4vnT0SwkMjoIpHO5vHKf74PiUwOI11evPqcYbztwrGy5+jy81RCK7FEQIQQ11W46wqTfQWAD1Z4nm8B+JYVa7I70gKRMRAA2DwURCKTw7H5BMZ6/WWPmZhaHgFZiGfw9NF5bBvtLtouXViVMrGkgOw83qSAJDI41+9Cj99tboHoAqJaIGZB9HQuj7FeP54/EWlbIH0xmdVPTKXYsZ2JfC97DFfoLj2N174WiOx6MNztA6AG0qWATC4kkMjk8Lev24LrLxsvcrUakUkg1fqrMfXDlejLxEw0DSKgx3Di2awF0icquLH2TUfhUAj9QfeSxkD+789341Q8gz+/8vSi7boFUsGFJf3Mz5+INFVfILup9gTcFSyQBDxOBd1+F1wOBbm8KHNrpLN5rOnywqFQ24oJzdqYSOzYzkS+l0UWiGY92TkLS1q6w2EvgOK42DHNnXn6UKiieADAYMgDt1PBoblYG1e6emABaYCJqSjueWGq9o51MBtLocfv1gu4gEImViUBmZiKYl2fH2t7/Etmgfx23wxueewI3v/S9di6tqvoPl8NAYkk1YZ96Wwe+6YbO2CN3VR7q1ggI90+EBHcWrC91AWTzubhdTo00W2fgJS2MZF0+1xwKGQrAalmgdi5DiSRVj/bNV2agBisiGOnVAEZ0ayTSjgdCjb0B2omqzD1wQLSAH9/x258+PtPWvJcs9E0+kqCfN1+N/qDnoqpvBNTUWwaCGIo5Gl7d1lAFYZP/PhZnNbrx59dcXrZ/Z6aLqwMLlinNhPY2WAg3Vic1xNwmdaBnNCKCIFCFlFpID2Ty8PtVDAU9rbNaqtmgSgKoS9gr3YmugUSKI+B2NmFJS9URgwuLMnxec2d2e2t+Tybh0I1662Y+mABqZN0No+H989iMZm1JAVwNpo2zRLZXCETK5PL49BsHJsGg+rJsI3dZSVfunsvDs7G8X/feA58hqIzSbUguhAC0VQW28a64XEq2NVgHEROjevyu9UYSIUgujxhSAukNJU3nVUFZDDkbZsFslhFQAD7FRPOaWJc7MKyfyGh7sLSLJD5IgFJYCDkgcdZ/j0tZfNgEEdOxfXGjEzzsIDUyROHT+lDeKS53AozsVRRCq9k81AQEyejZc0DD83GkM0LTUA8mI9nqvahmoulsfdkc/UXzx5dwPtvfgxfvXcf3nzBKF68qd90P5nGa9bSPZbOIS9UF86Za0INB9KLLRA3TpU0VDQWEQKA21HBhZXLw+VQMBT2YLpNQfRqFgigZtrZSUDm42k4FCrqa7YSWpnIE35f0AOnQsUurPlETfeVZPNgEEIUZzUyzcECUicP7C3M5T5aIiBCCLz7pkfxi+cqj14tZTaaLkrhlWweDCKSypa5W2RcZNNgEIOa26baCfGffvkCrvv6ww3NuI6msvjAt3fgdf/6AB49MIe/uPJ0fOaarRX3l1lYZu1MIvr8EBe2jHRh1+RiQ2sx9jLq9buRzQu9nQpQKCJc06WeNHQffrY8iO7RXFizsbRpsWErJDM5pLJ506I1yUDQoxeO2oG5WBrdPldR2vFKaKYoC1a9LgXdfndREP34fAKj9QrIUPVYo+TwbNz2M+KXGxaQOrl/77Re1HbsVHHDw9lYGve+MI1HDszW9VzpbB4LiUxRCq9k02AIQHlLExmE3jigurCA6qm8+6ajmImmG0pX/Nkzx3HXrpP40Ms34YGPvQJ/csVm3U1lRiELq/ykE0kWuvduGQljIZHRM2XqwdiOWwZ7S08YADCiWSAu6cLKFYtZOiddWFo9hsWWgHRnVhOQ/pAH09GUbU5Gp+LpogA6sDLauSczOSikWps9fpceAxFCaBZI7fgHAKzrC8CpUNW2QYdn47j8C/fg1h1HKu7DsIDUxXw8jWeOLeB120bgdSllJ8JDs+rJvd5JZ/KLb2qBaFdHe0uyRCamohju8iLgcWIorJ4Mq835PjKnrvHATP1murR6/vSKzRWzioxUS+ONGCYYnj0SBoCG4iBGF1ZvQF2LMRNrUqsBWVPiwkqbWCAuB9Ulus1QrQ+WpD/oRjqbL7KglpO5WLoo/gG0zwLZPbmIL961xxLxTGZy8LocICIttVt972djaaSyeayt0wJxORSs7w+UHWNGZJrvjfftt10fMzvBAlIHD07MQgjgd07vx0i3r0xADs6oFolZe4T901F88LtPFJ1kZ/Q2JuUC0hdwo8fvKgukT0xF9TTfNfrJ0PxqOpPLY3Ihob1+/emzU5EkevwuPSBdi2qFhIuGCYZnrgmBqLGCQmN/qR7tZGfMujkwo/5f6/pUq9DtLK+kzubyyAvA7XBgUBNdq2tBqrVylwyG1M9LVs4vN6diGfQEitfbriysnz0ziS/fvVf/PrRCQhMQQK2fkkF03RqtU0AALdZYxYUl3cP7pmO4d481qfudCAtIHTwwMY2Qx4lto91Y2+0rC6LrFohJdtZv983iZ89OFqWxzuqNFMtdWESEzYMhvWkioLZ+3zcdxcYBVUC6fOpJvtLJ8Ph8AvKiSZ5o62FqMYWBUPmaKlGtF5Z0YYW9TvjdTmzoDzQsICGv2k21V3dhFd5faZHJqXoyBmJM45UBYZnGC1jf1l0XOm/lpg7njqr1M48esEdnnrl4eQagS8vCylichSUHkVkh3MlMXq896vEXLJBmBGTTYAgHZ2NIVWjDI78n/UEPvn7fgVaW3dGwgNRACIH79szg0o19cDoUjPaYWCCzqgVi1l9HnmD2TRVO5LMx2cak3AIBgE1DQeyZiuhm/+RiEvF0TrdAiAhDYU9Fd4x0XwGNCch0NKVfLdeDohDcDsU0jdcYRAeALSNd2N1ATyxjZpMeAzFYIEaLDDAG0QsnQBkwdzsV9PrdcCrUBheWeoKsZoGs7w9guMuLh/bVFyNrJ0IInIqldatO0i4LRF5IWCHcyUxOrz2SQXQhhJ7UMtrTgAUyGEReVD4+piMp+N0OfOCl6/HQ/lk8d6y5hqCdDgtIDQ7OxnFsPoGXnq62jV/b7cNMNF2UQ16IgZQLiDSzjSmD1SwQQP1yz8czesNFYwaWZChUuTDuiBbkP3NNqGELZLABCwRQiwlTNYLoAHD2SBjH5hOmbdnNmI+n0a21eQl5nHAqpMdASi0ywFAHYrBAUlpA3e0gKAphIOSxvJiwHhcWEeGyjf347b6ZZfenR1JZZPOizALRW5lYHAORFxJWCHcyk9Ot3t6AC9m8Wmt0fD4Jv9tR9TMopVKsUTIVUa3xay86DQG3A998gK0QM1hAanD/XnVg1Uu1Woi12lWO0QqRFohZDES3QAyxiJloGi4HVXR7SKF4+si8+lgzAalSTHh4Lg6nQrh0Yx8OzsbqOmkJITAdacyFBajtTCoF0R0K6VPvZCD9o//9DH47UftEarRACkFTVUBKLTLAGEQ3t0AAYDBsfTFhtVkgRl68qQ+n4hnsPtFcY0mrkAJeaoEQEZwKWZ6FpbuwLLFA8nrcrVtb/3w8g+NaDUi1HlilrO8PQKHKE0CnI0kMhjzo8rnw1gvHcPvTx20Tw7ITLCA1uH/vDMZ6fVjXp3bHHe1Rf0sBmY+ntV5ITsTSubJCNpmOur/IAkmhL+Cp+IU/Z20Xuv0u/MF3HscnfvIsHj0why6fq8jlNRiu3M7kyFwca3t82DQYRDKTr5qtJVlMZJHO5RsWEG9FAcki6CkMoLpsYz9uuHwDHt4/i9//xiP4nX+8B/dW6Su2kMig21f4f439sMwsMrNeTsYYCIC2tIBZTGTgdzv016/EZRvVC5DldmPJ99CsC4LTQZZbIFJArLJAZEcEY2LFsflE3RlYEo/TgfG+ACYqpPJOGS6m3vvi9cgLgf96+FALq+9MWECqkM7m8dC+Wbxk04B+IpRfVBlIl9bHOVqgtHTWsrxCPTQXN4xXNS8ilHT73fjln1+O37/4NPxwxxH8YucJbBoMFgnOUNiLaCqrH6BGjpxKYKzHj/VahlI9bqwpzZppXECUinUgxkpnh0L4xGvOwqN//bv40rXnweVQ8LH/frZiNf1CyUQ5Yz8sMwExa6aoWyAO9aQzFPbq/6dV1KpCl6zp8mLDQAAPTszU3LedSCuutA4EUAPpVleiWxoDyRZcWLKL9VwsrVsgjbJpMFjRhTUdKcQDx3r9OGe0G09pHgGmAAtIFXYcmkM0lcXLziiMzR0Ke+FUCMfmVeGQ8Y9z1qqzMkrdWAvatLpcXuDwnPqY2WiqYvxDMhjy4u+u3oq7P/IyvP3i0/CuS9cV3T9UJS31yFwcY70+rB9QBWR/XQKS0l+3EbwuR8VK9JBJLYnX5cDV563FZ685BycWk/juI4fL9hFClJ2YewOFflgTU1F0+4stMrNmilJA5H2DIQ9OxTMVM2+aoV4BAYAXb+zHowfmlnVok1kfLInTQZb3wtIFxAILJJEupPFKF9aJhSRmY+mGAuiSzUNBHJiJlX0eyUwOkWS26GJqfZ8fB2e5BXwpLCBV+M0L03A5qKgXlEMhrOny6hbIIWmBaK3OSwPp8/EMTteqy2UgfSaaRn+FDKxSTuvz47NvOAdXn7e2aPuQrC0oOTBjqSzmYmmM9foxFPLC53LgYB0CIvPeZb1EvXid5i6sxRILpJRLN/bhxZv68NV7JxArsaISmRwyOaEH0QEtbVNzv+zTuhIbLTLTGIgMoksXlkzltdCNVa2VeymXbexDLJ3TY1vLgUzq6A6Ur9npUCzPwoqm1OPBqhiIzMKSLjg57bLeKnQjmwdDyOZF2fEhjwWjgKzrC+D4fMLSi49OgAWkCve8MIWL1vfqtQaStd0+PXXw4GwMw11e/cRrZoGcv061TvZNq00SZ2Opqi6sehjqMj8ZygyssR4/FIUw3h9oqwvL41KQNOkvFUlmq9ZGAMBHrjwDM9E0bn7oYNF2YxsTiZr3n0Y+LzAxXZzCC1RyYRXHQPRiQgvdWIvJbM0AuuTSjX0gUmuDlou5WBpOhUznt7sUstSFlcnldffmycVky9XoqWxOrwPp8rlABD29dqSrORcWUB5Il98PY0bieL8feVHeB2+1wwJSgWPzCew5GcXLTi8fZL3WUAtyaDaOdX25OAcpAAAgAElEQVR+/WRnLCaUQ5FGe/wYDHmwfzqGeDqHZCZf04VVi0qtOWQNiByJu6FOAZmOpOB1KaYnlmp4XQ7TbryVXFhGLljXg1ecOYiv/WZ/0ftmbKQo6Qm4kReqYM/F0mUC4jLpxivdWZ4SC8TKVN5ardyNdPvdOHskvKxxENkHyyyBw+lQLHVhRTX31dpuH5KZ1lu5GF1YDoUQ9rqwe1INgq9twoW1cSAIovJU3koWCIC6rPlW2D8dxV/96Glbz6Y3wgJSAZkh9PIzB8ruG+324eRiUpvREcN4X0A/iRgtEGOK58aBIPZNRws1IHW6sCoR9DgRcDvKToYyznKaJiDj/X4cnovX/EJOaUHDRlIhgepZWNVcWJKPXHk6FhIZfPP+Qp69mQUi+2E9dlCt5q4kIGZpvPI+eUVpZTuTRmIggBoHefLw/LLNojDrgyVRs7Css0BkgscGLRbX6vuezBbSeAE1kJ7QGizKi4NG8LkdGOvxlzVVNIsHjksBmS1upFrKF+58Hh++5Ul8+e69+Nkzkw1bu79+fgq37jiqx1btDgtIBe55fhqjPb6iYjXJaI9qzk5MqR1v1/UFdD+4saGiHIrU7XNh42AA+6aiejdYs1kgjWJWC3JkLo6A26FnqazvDyKXFzgyV/2L32gbE4nXWZ6FJYdJ1SMgW9d24VVnD+Hmhw7qtSFmtRUybfPRA6cAmAmIDKIb03iL60B6/G5tvKw1rdWzuTyiqSzCvvqttks39iGdy+tCuNSY9cGSqFlY1l35ygC6PIZaiT1lcnnk8kJ3YQGFQPpQ2FszjboSmwaDZeOWpyMpKFSc6tzjdyHkdVY9sefzAl/7zX78cudJfPGuPfjg957AVf9yf0PCKa2fpZg4agUsICaksjk8ODGDl50xYHpFLs1l6YoY7/PD61LgdiimFkiXz4UN/UEsJrP6kKdWYyCArAUp/nIePRXHWK9fX7dsQV8rg0RtY9KEgLgcZa1M4ukccnlR04UluXLLGszHM7ovWiYiGIPo8mB+7OAcfC5Hmc+bSG2rYp7Gq37NFYXQH3RbNlhKniAbsUAuHO8FANNAeiaXx+EaV7itYtYHS6JmYVlngcgq9I2aBdLKFE1p5RrHC8j/o9EaECMbBwLYPx0tKmydWlSHvTkM81KICON9gaoWyHwig2xe4KNXnYHdf3cVvv+BSxBPZ/GXP3qm7viP/G4uxcRRK2ABMeHRA3NIZHJ4+Rnl8Q+g8IWVwdB1fQEQEcI+Z5EvX7piuv0ubNSumGVDvVZjIABM53wfnovr8Q9AjYEAtbvyTi0mm7NAXEqZC6u0jUktLhxX56bLq/L5hGohlAbRAfX/2zgYKBqGJHE5qKoLC1D92lbNBKmnjUkpAY8TfQE3Jk2uSn/8xFG8/J/ubauf3awPlsTpsNYCkS4sKywQmSruKbJA1Pe9mRoQycaBIFLZfFFniemouTW+rs9f1QIpJKJ44XM7cOnGPnziNWfhN3um6y5ClN9Nq1vutAsWEBPufWEabqeCSzf2md4v53A/sl8KiHrCDvtcRRbIvB4MdutXYY9IAWkxBgJIASlktwghcGROLSKU9ATc6Pa7qgbSk5kcFpPZpiwQtZVJvugKq7SRYi1O6/WjP+jB44dU99RCQm2DYsx+M141bzJxKwKqqypT1AurOIgOqNMBrbJAmhEQQP3czNpi7J+OIZcXpkOMvnrvPvywxeFG+bzAqSoWiEtpTwxkqMtrGq9rBNlvzes0xkA0C6SJALpEXtgZe9VNaW1MSlnfH8DRU4mKImsWfH/nJetw+ekD+Owdu+saocsurA7gnhemcPH6Xvjd5lfQHqcDgyEPYukc+oMeBLQTXdjrKqoDMZ5gRrp8+jCqoMdZddJfvQyGPEhl83rcZTaWRiKTw1hv8QE13lc9E2u6ySJCoHBFmDJc+S82aIEQES4c79EtEBmYNroP/W6HHssojX9IXCVX0JmSXliAZoE0ICCLyQze8Y1HTJvpyatWs6ruagx3mQvIcW3bjx4/WtRS5NBsDF+483l868GDDb1OKYvJDPKivA+WxOpCQv174HFisMUuANLKla1MgEI1eqsWCFDcq65ST7h1fQHk8qJiKq+ZgBARvvDmc+F1OfDnP3iqpoXHLqwVzpG5OPZPxyq6ryTyqme8r3C13+UrEZB4GkTqiVRRCOv71S+rFfEPoDCNT37ZSjOwJLVSeadMvvj1YjaVUFogtepAjGwf78XRUwmcWEhiPl6e2UREevZQNQFJZcvTeI0C0h/0YCaaqqvBZDqbxx/91+N4YGIGX/vNPuRKHvOL506gx+/Si0jrZajLa9qf7MRCQp3zEknhPq2JJwB8/f79yAtg78lIxdYv9VCtDxYgBdhCC8QwVGywxT5k0oUlW5kABeFe20QRoaRXG+AmrYNcXmAmmja9mJLHeqV4opmAAKrF+XdXb8UzRxdwx7OTFdeSyeX1bgtWDz5rFywgJcjCpO2aX74SsqmizA8Hyl1YskpZ+uulG8sK9xVQmEz4hOb6kZlWYyUCsr4/gMmFZMXU0ekmiwgB86mEEcOJo162r1Pf7x2H5iqmxsoTRiUB8TiLT4CVYiDZvNDdi5UQQuBjP34GD07M4vfOGcZUJFU08z6RzuFXu0/iqq3DDWcADYe9mIuly8Tg+HwSV24ZQn/QjR88prqrpiMp3LrjKNZ2+5DNC+w5WXmOdy2q9cEC1JbultaBpFRXpNelWGCBaC4sg+W+rlftqLtpINTSOjcOBPWO16fiaeTyoqIFAgCHqswQ8bkcCLjLvQuvPWcY6/sDuKmKFTkXS0N6gq0efNYuWEBK2GvSqM8MGUgvtkCcRaM75xOZokyiDQPSAmk9gA4A28a6cf5p3fjU7buwe3Kx4mAd2ROr2vAcoPE2JoD5VMJGg+gAsGUkDJ/LgR0HT1UUkN6AC06FikTbiMuhlA2UIirMugAKIlnLjfXFu/bgx08cw0euPB3/+JZtCLgduP3p4/r9974whXg6h9eeO1z3/yhZY9JFIJcXOLmYxGm9frzx/FHcvXsK05EU/vO3B5DJ5fH3bzwHAPDcsebbwVfrgwVY38rE2JF5SJvF0mw1eiELq3DKevGmPjz08StwWp+/0sPqQq3RUo8N+ZmYxUD6g24E3I6KmVjT0RQGw+ZdthWFcP2l6/DUkXk8efiU+eO17+S6Pr8llftLAQtICXunohjt8VWMf0ikC2tdv8EC8aoWiPzg5+OZompqaYGYzUJvBpdDwX+84wKEfU7c8J0deOboPPqDnrK1nz6kXqG9cNL85DOl5b33BVpwYWXLXViNWCAuh4LzxrqrWiDr+wPYurar4hW/y0klI23zcDuUogN6IFhbQL73yGF85dcTuPbCMfzJKzbB53bgyi1DuOPZE7pV87/PTKI/6MbF63vr/h8lUkDk3HpAbbCZzQuMdHnx1u2jyOYFvvPwIXz7oUO46uw1uHxzP8JeJ5473vxkPGmBGC9qjLgcZG0WlqGYdDDsQSKTM+0eXQ9mabzqZM7m3VeSjYMBzERTWIhnqrb0IVIvXiplYk0tpvTvlxlv3j6GkMdZ0QqRGVhnj4SRzOQtmSPfblhASth7MoLNNawPANgyHIZDIX1QEqDGQHJ5gbjmKiptSS4Dds2cqCsxGPbiP95xAU4upHDnzpNlAXRAjYG4nQp2VZhJPh1RuwM7TFJja1HJhaUQTE35alw43oNdxxcxHUmZnuT+5rVb8N33X1zx8aVB9FQ2XxT/AAwWSNTcnfKrXSfxf/7nWbz8jAF8+pqtuvhcfd5aLCQyuG/PNOLpLO5+/iSu2roGziYK2Ia7yhthygD6mi4fNg2GcP5p3fjKr/ciksziD39nI4gIZ490YWcDo1X3T0fxwN5C25RTNWIgTkWxtg4kldUz6VptI5MwERCr0APpM9GaCSXj/X69gWopldJ/JUGPE2/ZPoY7np00nY8iX/vskS7tb/vHQVhADGRzeeyfiWHzUG2f6gXrevDk/39lUaV6uKSdyUIio1fLAuoXdbjLi61rw7CSF53Wg8+8YSsAFKXwSpwOBWcMhfS+QaWobUyaE7VKQXTjMKl6uWC8F3mhFiKaWSAep0PPeDPD7VBKuvHm9SJCSTUX1hOHT+FD338CW9d24V9///wiS+clm/vR43fhtqeP4+7dU0hm8njtuSMN/X8SeTI1ZmJNahldUlzeduEYhFA7+G4bU5txbl0bxu4TkbqshGeOzuOaf3sQ777pUT14PhdPw+1U9CmRpVjdykTth6Z+XvJ9bzYOoqfxuqw/ZekCMhWtmVAy3hfA4bm46eCteiZ6Xn/ZOuQqDKeS38kt2kXpSqgFYQExcORUAulsvmb8Q1Laxru0oaLqiimc8HxuBx76+BW4amvjfvNavHX7GL7w5nNxw+UbTO8/aziEXZOLpn7VqUhzRYSA0QIpjoE04r6SnH9aN6QR1GhtBVBeB5IxsUDUFGqlTEAOzcbw/pt3YCjsxbfefWGZULkcCl59zjDu2nUSP3z8KAZDHr2qvFFCXheCHicmjQKi3ZYpqa89dwQvP2MAf/mqM/R9tq7tQjqb1wdqVeLJw6fw9m88ArdTtSh+/pya+XNK64NVSditbmUSNbFAms3Eki5SXxsskNEeH9wOBfumY5iOpBDyOIvShY2M9wWQzQscny8WwlQ2h4VEpqoLC1AD8VecOYjvPXK4LIlCvvY6LQnG6vHL7YAFxIA8MOtxYZkhBWUhnkE+LzAfTxeNZW03b9k+hq0VUkq3DIcxF0ubZndMt2CBePQgenEdSCMBdEnI68IZa9Srr2YExOVQigdK5coFhIhMa0G+98hhRJNZ3Pyeiyr2Kbt62wgSmRzu2zON15wz3JTLT7Kmq3g+++RCAh6notc2BDxO3PSei/Ci0wrZgNK1sbOCKxIAHj80h3d+81H0+N346Ydego0DAT34PxfLVK1ZsbqVSdRwITHYogUiMwjb4cJyOhSM9/uxbzpa04pYVyGVV/ZXq+dC7D0vXo/ZWLospVe6wAal2K6ATCwWEAOyK2e9FkgpBQski2g6i7yoHLBcas4aVk/McgCPROa9N2+ByELCYhdWvUOWSpFtTZqyQBwKMtniNN5SFxagBtJLGyrum45hvN+P8X7zDC91bb26i6mZ7Csja8LeIgvk+EISw13VuyGv7w/A73boqeZm/H8/fAZ9QTd+8AeXYG23D6/bNoJHDszhxEJSq0Kv/L6WxpBaJZLMIqhdSAQ9TvhbqEY3S+O1Etktu5aAyO9HaSBd1m3Uk8l42cY+hL1OPFGSjTUdSaE/5EHQ40TQ42QLpBmI6CoieoGIJojoY0v52hMnoxju8jblfgGgd2VdSGT0Trz1DhtqN2dpftXSQPpcTM17b6YKHSgEZI2uiXpbuZsh3ULNFFu6nOXNFM0ytswskP0zUWzor37hoCiEd1yyDmcNh3H+adXrhGpRaoGcWEhiuMZQJIdC2DIcxs4KmVjJTA4HZmJ444tG9ed63bYRCAH87NnJqn2wAK0OxMoYSCqrz5chIrWYsMmr6mQ2B5eDWrL6qrFxIIjDs3Ecm09UFZDBkAdel1KWyqsXEQZrH0dEhA0DQeybKrViCuLVauHlUmErASEiB4B/A/BqAFsAXEdEW5bq9fdOlU+6awTjTBCzoUjLSdjrwmiPD7tLLJBC1klzFkhvwI2hsKfoeSOpTNMC8uqta/Dl616EF401foJ2OaisEr3UhQWUN1TMal1wZb1MNT748k34+YdfatrMsRHWhL2YiqT0YOzkfELvsVaNrWu7sPP4omklvcwOGu8vJFJsHAji7JEwbnv6eNVOvIC1A6VS2RzS2XzR92Aw7G36qjqZybXN+gDUVN5sXuDYfKLqxZTsyltqgcjvU72W/MaBIPbPlA+ykjGUwbDH0smZ7cJWAgLgIgATQoj9Qog0gFsAXL0UL5zPC0xMRbF5sPmq1pA+EyTTdKO9drJlOFzmwmp2lG215202iA6oJ7HXbxtp6gTtMbFAzASkP+jBXCyt73v0VALZvNBb3y8Fa7q8uvswlxc4GUnp7rFqnD0SRjydwwGTWgTply/9P163bQRPH5nHfDxT1QJR60CEJQVssZTq0jQ2xBwssfwaeZ22C4ghm7LWsbCuz1/RAqnXct4wEMDJxZReF5PM5BBJZvXXNuu0bUfsJiBrARhbjh7VtrWdY/MJJDI5bB5q3gJxaLOmFxIZQyv3pQui1+Ks4TAOzMQQTxcKlMymrzXKlpEw9k5FkczkIIRoyYXVCqU+fLM0XqBwgpDTIWWF/oYlFBBjLch0JIVcXtR0YQHQkyTM4iCyDXxppb4xXlPVAlHU96q051czyGLSoOFCYshggcxGU3jtVx7Al361t67nS2bybUnhlWwwCEgta3x9fxCHZmNFKePTkRR6A+6629rIouL9Wg+uggus4MJaCdXodhMQs8vOsneQiG4goh1EtGN6etrkIY3TagaWJOxzYTGZ0Wda2CWIDqgCIgTwwolCPUilBnCNsGW4C7m8wN6TUSQyjQ2TshJXaR1IBQuktBp9/4z5lXs7KdSCJHBcq0gfqcOFtWkwCLdTMc3EOjgbQ2/AXWb1jvb4cYHWa6xWFhYASzKxzNrZDIY8iKdzOD6fwLu+9Sh2Hl/Eb/ZM1fV8yUyuqJGi1QQ9Tr23XK1j4azhEDI5UdICvrFMRmnxyDk9pS6wobBX7bRt82p0uwnIUQBjhr9HARwv3UkIcaMQYrsQYvvAQPnM8maYqLMHVi3CWkdeO7qwZNW8saCwVt57PcjCp12TC031wbIKd0kzxUwNC0RWox+YiSLsdVa9Orca3QJZSGJSqylYE65tgbgcCs5aE6pggcSLerMZef02teixUh8s9blVAbEiE0u6ZkIGF5YUzbd/4xHsORnBuaNd2HMyWtdVdjKTa+k7Wg8bB9ULiFqZVGebJKTUU0Ro5LQ+PxQysUC059ALLyvEjLLaiN/lxm4C8hiAzUS0nojcAK4FcNtSvPDeqQgGQp6WXU5dPicWE1ksxDPwOJW2+m0bZbTHh5DHiV2T6sknnxd48vCpuoK31VjX60fA7cCu44uGPljL48JK5wrDrSpaICXV6AdmYlg/EGy4cr4VegNuuB0KJheTek+seiwQANgy0oXnji2UnXgPzsYqpiG/+YJR/OWrzsCF6ysnJ0gXlhWZWPJCIlhigQBqCuyXrn0R3rp9DNFUVm/jUo1Emy0QoGAV1CoGXN8fhNelFCWOGAPg9eBxOjDW69ebOJYKyFCNWpAfP3kMV/zTvcue6msrARFCZAF8CMCdAHYDuFUIsXMpXnvvVLRl9xVQaKhYqSHgckJEOGs4rFsg33n4EJ4+uoAbLt/Y0vMqivq8O48v6iZ3s3UgreDWr6ALAmLmk+4vcWEdmI5h4xK6rwCtEWCXR7VAFpLwuRx1f1/OHe3CYjKrz38B1EK7yYUkxit0Kg54nPjgyzfphZ9m6BaIBZlY0VR5Q83NQyEMhjz4/JvOxWvOGcaZa7QmnydqdxhOZvLwtDEGAgC/d84wrjlvpGqiAaDGOs8YCumJI0KImn2wzJC1J4D6XSQqxKgKvcPKBUIIgW/efwBel6Pp7EmrsJWAAIAQ4g4hxOlCiI1CiM8u0Wti4mRrKbySLhkDiWdsFf+QnDUcwvOTizgyF8fnf/E8Lj99AG86v/U8hS0jYeyeXNQHai2XCwsouGDSOWFqgXhdDoS9TkxHUkikczi+kFzS+IdkjTbadnJBTeGt1wLaNqr2xnrqyLy+TYpJtULIWsjGkFZYIHKYlDELayDkwaN//bt4y3bVSy17zr1wovao12Qm15Y2JkYu3tCHf7n2RXVlAG4ZCeutgRaTWaSz+YYFRA56y+cFZqIp9PoLQXgpDGaZWPftncELJyP4wEs3LKnVbIbtBGQ5OLmYQiSVtcYC0YZKzSeWto1JvWwZCSOWzuED394BAvD3b9hqyZdwy7D6vDK4u1xBdMAgINlc0Tx0I7IWRE99raMGxGrWdPlwYlG1QOpJ4ZWcPqS6UIwCIjPJ1lewQOpBzk2xQkDqGWvc5XNhuMtb15CsdqfxNsqW4TDm4xlMLiSbHsi2cTCIVDaPY/MJtQrd4AILaNXoZrUgX79vP4bCHrxuW3PNPK2EBQTGFiatTTYD1IMins5hNpq2TRW6EdnS5PkTEXz01WfqkxVbRfZpeni/OrVvuWIgQGESYaVCQqBQjX5gGTKwJMNdajuTyfnaVehGnA4F56ztwtMGAZFCuK6/+c9TF2BLXFhZOBWqKOCSM9aE8PyJegSkvWm8jbJFT0hZbHoktEwb3z8TM3WBDYbLq9F3HV/EAxMzuP6y8Yrf7aVk+VdgA2Q2RSs1IBI5B/zIqbgtXVinD4XgchAuHO/BOy5eZ9nzbh4KwqEQHtfG6y6LC0sKiG6B5HW/fikDIS+mIyk9C6ZS7KCdDIW9SGfzOLGYxEgDFggAnDfWjeeOL+rW1qHZGPoC7pZiT3oar0UurJC3dkv/M4ZC2DcVNW2PbiSZtZcFIpt+yvk1QOPdHDYY2sibZXHJWhAj33hgP/xuB95+kXXHbiusegF5cGIG//yrPTh3tMuSWeVdmmgkM3nbtDEx4nU58F/vuxj/8Y4LWm7HUfq8mwaCiKdzIAICNSY6toNCDEQglxfIC8DtMD/pyIaK+2diWBP2Vp0z0i6Mbqs1DVgggDrOOJ3N6zU9B2YqZ2DVi8zCsiKNN5LMFGVgVeL0oRDSuXzFMbGSpYiBNELQ48R4nx+7Jhcb6oNlpD/oRtjrxP4ZcwEZ0trdSE4sJHH708fx1u1j+nlmuVnVAvLA3hm89z8fw3hfADe9+0JLYgHGK0C7ZWFJLt7QZ9lcdiPSrA96nJaKU70YXVjSjVXJzO8PuRFNZbHr+OKyuK8AFI1jbTSVWgbSn9TcWGoNSGv/h8vCQkJ1Fkjt7/8ZeiZWZTeWEELLwrKPgACFQPp0NAW3Q9GbqdaLbKr49JEFpLL5sjRgWbkv07VvevAAcnmB971kvWX/Q6usWgG5f+803nfzY1jfH8B333+xZSdUo2jY0YXVTrZo8ZXlSOEFigvhagmIPFhfOBnBhmUIoAPFFshIgxbIaI8PfQE3nj4yj0Q6hxOLyYpFhPVSyMKywgKpr53NpsEgFFI/h0rIBpl2ioEA6vf90GwcB6ZjGAh5mroA3TAQ0Lsrm7mwUtk8FhNZ3PP8FL5+/35cfd5ajPVaE7e0Ant9IktEOpvHJ37yLDYMBPG9D1xi6dW4MXBuxyB6O5EVussR/wAKYpHO5ZHKqc383BVjIOpnLsTyBNDlGqShtqbBGAgRYdtYN54+Mo9Dc2oAvVUXlksprqNphUgyW1SFXgmvy4HxvgD2VLFA5OS+dhcSNoq0uB/aN9t0K6CNA0FIg688iK5+J379wkl88HtP4KzhMD5zzdbmF9wGludIX2bcTgU3v+cidPvdlrevKLZA7JfG205khteyCYjMIsrm9ZNgtSwsyXJZIC6Hgv6gB7FUVk++aIRto92454UpPHdMTQJpVQh1C8SiLKx6YiBA7UyshCYg7W5l0ijy+x5JZVsQkMJnVjoJUwbl//KHz2Aw5DEdtbzcrEoLBFAzINrR+6hIQFaZBdITcGNttw9dy1T/4jJYIDVdWIYDfn2NQVLtZLjLi+FuX1Puj21jXRACuE0bWbuuZReWhVlYqfo7Mp8+FMLB2VjZjHBJYRqhvU5Xa8JefQRxswKyoUobeRkj87kcuOk9FxXFzOyCveSsA/A4Fbi1nkx2DaK3ky++dduyFBECBgvEICCV2mv3BVT3kUKE0Z7G4g9W8vZL1hUNwWqE88bUQPqDEzPoD7pbft9dFmZhRZP1BdEB1QIRQm1oKtvVG7GrC4uIsGUkjAcnZhvqg2VkndZUUSEqu+Ac6/HhDS9ai7ddOKYnG9gNFhCLISKEfS7MRFOrLogOqBley0UhC0voJ0GzbryA2s+oN+BB2Ouse4ZDO3jr9rHaO1Wg2+/GuDbcyIo6FqvauSczOaRz+botEHlyfP5EpLqA2MyFBaiB9Acnmo+ByKaKqUy+LHPR6VDwz287z4pltg172YQdgkznW64r8dWK26kegOlcXr+qr1atu77frwdCVyrbNCuk1QA6YF07d72Ve50Csq7XD7dTqdjSJGFTCwQoBNJbmadz7mj3ssXhWoUtkDbQ5XMh7HXCsQy1EKsZWTSYqaMOBABufOd2OCpkaa0Uto1246dPHW85hRewrp27WSPFqq/rULBpIFixFiRl0xgIAFy+eQBXbhnC9nWV2+TX4vNvOgc2GO3RFCwgbSDsda26DCw74HIa6kBquLCA6tP5Vgrbx9UTl+xs2woFF1ZrFkikQQEBgDPXhPDbfbOm9+kuLJsVEgJAX9CDr79re0vP4V+Grg1WsXJXbmNev20EJ5Z50MtqxGXohZWpwwLpBM4d7caP//gynKdVprdCoZtxa5fDEZNZILXYOBjEj588plWwF5+W9DReGwrIaocFpA286YLR5V7CqkQvJMwaLJAOFxAAOP+05t0nRgrt3FuMgTQx1ljWQxyciZUF0gtpvCwgdqPzjy5m1eA2XEHrMZBlzLBaaRQKCWtbIEIIJNLmdRvNuLBkLY6c0Gek4MLiz9Ju8CfCdAxmzRSXM0V3peFy1N/K5OfPncD2z9ylZ1wZkdvqrUQH1HoIImC/NiPcSDJr3xjIaoePLqZjcCgEhYqD6LUGGjEFXA00U3zm6AJi6Rxmo+UjVxtN4wVUcVjb7dMHfBlJapYOf5b2gz8RpqNwO5W6uvEy5cgYSKYOF9aRU+r8DjMLZDGZgduhwNNg3caGgSD2z5i4sLLqNMLlnv/NlMNHF9NRuBwKUqssiG4VRASnQnVZIEfnNAFJmriwkvU3UjSyoT+AA9Mxff6FxG7z0JkCfHQxHYXboVogGSNxLKIAABMSSURBVI6BNIXTQXUF0Y+cSgAAYmnzGEgzHZk3DAQQS+eKpvABQCJtr2mETAE+upiOQndh5fIgKrhlmPpwKUrNViaxVBZzsTQAIJoqz8SKJMtrOephQ4VMLNWFxQJiR1hAmI7C5VD0NF63g/3mjeJ0UM1WJkc16wNQxaSUaLMCotWClAbSk5kcB9BtCn8qTEfhchDSWbWZIteANI7TodRsZXJEi38A5gISadKFtSbshdellKXycgzEvvARxnQUbqdDbWWSy3MAvQlcCtWsA5EZWIB5FlYkmWmqE7WiENb3B7G/1IWV4RiIXeEjjOko3A7S03hZQBrH6VBqZmEdmUvA53LA73aYZmFFks1ZIIDqxip3YeW5Ct2m8KfCdBQuh6L3wmIBaRyng2rWgRw5Fcdojw8Bj7MsC0sI0XQWFqCm8h45ldDreAB2YdkZPsKYjsJYSMgpvI3jUmpbIEdPJTDW60fQ4yzLwkpkcsjlRd3jbEvZMBBALi9weK5ghSRYQGwLH2FMR+FyKEjn1JG2HERvnFpZWEIIHJ2LY6zHh4DHURZEjzTRideITOU1BtJVFxYLiB3hI4zpKKQLK8UxkKZwOpSqLqyFRAaRVBZjvX4E3M6yIHqrArJeS+Xdr8VBpiJJLCYyTT8f0174CGM6Co/BhcUC0jiuGq1MjsypNSCjPX6EvM6yIHokKYdJNXfCD3td6A96cECzQD512y6AgLddONbU8zHthY8wpqNwySwsdmE1RS0XlkzhHes1D6IXLJDmYiCAGgfZPxPFXbtO4mfPTuLDV2zGxoFg08/HtA8+wpiOQrqwuA6kOVwOBZkqhYRHNQEZ7fGrAlLiwmqmlXspG/oD2HMyir/5n+dw5poQbrh8Q9PPxbSXlo4wInoLEe0kojwRbS+57+NENEFELxDRqwzbr9K2TRDRxwzb1xPRI0S0l4h+QETuVtbGrE6MWVhsgTSO2o23igUyl0DY60SXz6VlYZm7sJppZSLZMBDAQiKDk5EkPvemczmbzsa0+sk8B+CNAO4zbiSiLQCuBXA2gKsA/DsROYjIAeDfALwawBYA12n7AsDnAfyzEGIzgFMA3tfi2phViF4HwjGQpnA6qjdTPHIqjrFePwAg4HYimckXxUyscGFJd9W7LxvHeWPdTT8P035aOsKEELuFEC+Y3HU1gFuEECkhxAEAEwAu0n4mhBD7hRBpALcAuJrUjnevAPAj7fE3A7imlbUxqxO3U9FamQi+cm0CV4127kfm4hjr0QTEo6bWxgyz0ZuZh17KSzcP4NNXn42/etWZTT8HszS06whbC+CI4e+j2rZK2/sAzAshsiXbTSGiG4hoBxHtmJ6etnThzMpGDaILTuNtEmeVQkIhhFZE6ANQiHMY3ViRZBYBtwOOFtrou50K3nnpOHxurv2wOzUvE4joVwDWmNz110KIn1Z6mMk2AXPBElX2N0UIcSOAGwFg+/bttaffMKsGt8OBXF4gxS3Am8LpqNxMcTqaQiqbL7iwNCsjViQgzTVSZFYmNQVECPG7TTzvUQDGxO1RAMe122bbZwB0E5FTs0KM+zNM3bic6rVILJ2Fy8GzQBrFpRTauefyAn9x61N4xVlDeP22EUMNiGqBSAExWiCt9MFiVh7t+qRvA/A9IvoigBEAmwE8CtXS2ExE6wEcgxpo/30hhCCiewC8GWpc5HoAlawbhqmIzLzKC56H3gzGOpDdk4v4n6eO47anj8OlkD5nXsZAgqYWSHPz0JmVSatpvG8goqMALgXwMyK6EwCEEDsB3ApgF4BfAPigECKnWRcfAnAngN0AbtX2BYCPAvgIEU1AjYl8s5W1MasTo2i4HexDbxSXIQvr0QNzAIAz14Txp7c8iR89fhSAWgMCqFlYALuwVjMtXSoIIX4C4CcV7vssgM+abL8DwB0m2/dDzdJimKYxZl6xBdI4TqWQhfXogTmM9frw/Rsuwe9//WHcv3cG/UGPHtwO6i4sQxZWKotRLUbCdD58hDEdhVFAOAbSOOpAKQEhBB49OIeLxvvQ5XPh2++9CJsGgzhzTUjfV7qqolrxIKANk2ohhZdZWfAnzXQURquDs7Aax+UgZPJ57JuOYi6WxsXrewEAfUEP/vdPXgJhSNAyrwPhzrmrCf6kmY7CbbA62IXVOE5FgRDAQ/tmAQAXaQICoGwmh8fpgMtBehZWJpdHMpPnGMgqgo8wpqPgGEhrODUBfmBiBkNhD9b1VY9nGBsqRlucBcKsPPgIYzoKo2hwK5PGkXGjh/fP4aL1fVC7DFXGOFTKijYmzMqCjzCmoyiyQFhAGsapqO/ZQiJT5L6qRNBTGCoVSclhUuzCWi3wEcZ0FOzCag1j5trF9QiItzBUSlogYXZhrRr4CGM6CmPmFQtI4zg1Ae7xu7CpjimAAY9TrwPRXVgsIKsGPsKYjoJdWK3h1LroXjjeC6WOjrpBj6MQRGcX1qqDjzCmo3BxGm9LSAG+eENfXfsH3IUsrAhnYa06+AhjOgqOgbRGl1+1Hi7bWKeAeDgLazXDnzTTUXg4jbclLt88gF/++eU4fShUe2eo1kYslYUQApFkFm6HUlZwyHQufIQxHQXHQFrDoVDd4gGoFkheAIlMjtuYrEL4CGM6Chf3wlpSjEOleBbI6oOPMKajcHMMZEkJyoaKqRxPI1yF8BHGdBTGLCyOgbQf41CpSDKDkIdTeFcTfIQxHQUR6SLCFkj7CbILa1XDRxjTcbgdCogKRXFM+ygMlVIFhF1Yqwv+tJmOw+VU4M6Lmp1kmdaRQfRYWnVhhbkKfVXBAsJ0HC6HgpxD1N6RaRnpwooks4imslxEuMrgT5vpONwOBXknC8hSIC2Q6UgKecFtTFYb/GkzHYfbqSAvWECWAr9WdX5iIQmAGymuNlhAmI7D5SDk8pwfshQoCiHgdmByURUQzsJaXfCnzXQcqgWy3KtYPQS9TpxYSABgF9Zqgz9tpuNwORTk88u9itVDwOPUXVg8jXB1wXY+03G4HAoXES4hQY8Ti/osEI6BrCb4coHpOF5+xiAyOTZBlgrZzgTgWSCrDf60mY7jj162cbmXsKoIGESDYyCrC7bzGYZpCdmRl6jYGmE6HxYQhmFaQqbuBt1OKNx/bFXBAsIwTEtIFxa7r1YfLCAMw7RE0C0FhDOwVhssIAzDtIS0QLgKffXBAsIwTEsE2YW1amlJQIjoC0T0PBE9Q0Q/IaJuw30fJ6IJInqBiF5l2H6Vtm2CiD5m2L6eiB4hor1E9AMicreyNoZhloZCDIRdWKuNVi2QuwBsFUKcC2APgI8DABFtAXAtgLMBXAXg34nIQUQOAP8G4NUAtgC4TtsXAD4P4J+FEJsBnALwvhbXxjDMEqBnYXER4aqjJQERQvxSCJHV/nwYwKh2+2oAtwghUkKIAwAmAFyk/UwIIfYLIdIAbgFwNamj414B4Efa428GcE0ra2MYZmmQdSDcB2v1YWUM5L0Afq7dXgvgiOG+o9q2Stv7AMwbxEhuN4WIbiCiHUS0Y3p62qLlMwzTDJzGu3qp+YkT0a8ArDG566+FED/V9vlrAFkA35UPM9lfwFywRJX9TRFC3AjgRgDYvn07N+5mmGVEVp+zC2v1UfMTF0L8brX7ieh6AK8FcIUQ+hi4owDGDLuNAjiu3TbbPgOgm4icmhVi3J9hGBsz2uPDn75iE155ttl1JtPJtJqFdRWAjwJ4vRAibrjrNgDXEpGHiNYD2AzgUQCPAdisZVy5oQbab9OE5x4Ab9Yefz2An7ayNoZhlgYiwkdeeQZGun3LvRRmiWnV5vxXAB4Ad6lxcDwshPhDIcROIroVwC6orq0PCiFyAEBEHwJwJwAHgG8JIXZqz/VRALcQ0WcAPAngmy2ujWEYhmkjVPA6rUy2b98uduzYsdzLYBiGWVEQ0eNCiO2tPAdXojMMwzBNwQLCMAzDNAULCMMwDNMULCAMwzBMU7CAMAzDME3BAsIwDMM0xYpP4yWiaQCHSjZ3AVio4+GnAThcx371Pp/V+1m5PjuvrR378fqafy5+71rbb6Wsb50QYqCO/SsjhOi4HwA31rnftMXPZ/V+lq3Pzmvj9S3N69p5bbw++62vnp9OdWHdXud+8xY/n9X7Wbk+O6+tHfvx+pp/Ln7vWtuvU9ZXkxXvwmoFItohWqzEbCd2Xp+d1wbw+lrBzmsDeH2tYuX6OtUCqZcbl3sBNbDz+uy8NoDX1wp2XhvA62sVy9a3qi0QhmEYpnlWuwXCMAzDNElHCQgRfYuIpojoOcO2bUT0EBE9S0S3E1HYcN+52n07tfu92vZ7iegFInpK+xm02frcRHQjEe0houeJ6E12WR8RhQzv21NENENE/2KHtWnbr9P+foaIfkFE/a2uzeL1vU1b204i+gcr1tbo+ojo7SWfYZ6IztPuu0Dbf4KIvkzaHAebrO2zRHSEiKKtrsnq9RGRn4h+ph2vO4noc3Zan3bfL4joaW19/0FEjpovblU6lx1+AFwO4HwAzxm2PQbgd7Tb7wXwae22E8AzALZpf/cBcGi37wWw3cbr+xSAz2i3FQD9dlpfyXM+DuByO6xN2z4l3y8A/wDgb+3y3mm/DwMY0LbfDHXS55Kur+Rx5wDYb/j7UQCXQh1D/XMAr7bR2i4BMAwgasV7ZuX6APgBvFy77QZwvxXvncXvX1j7TQD+G8C1NV/byjfaDj8AxkveyEUUYj1jAHZpt18D4L8qPMe9aIOAWLi+IwACdl2f4bGbtbWSHdYGwAVgGsA67SD5DwA32OW9A3AhgF8Z/n4ngH9f6vWVPObvAXxWuz0M4HnDfdcB+Jod1lay3VIBsXp92n1fAvABO65PO05uB/C2Wq/bUS6sCjwH4PXa7begMJP9dACCiO4koieI6K9KHneTZuL9jRVmulXrI6Ju7f5Pa9t/SERDdllfCdcB+IHQvpXLvTYhRAbAHwF4FsBxAFvQ3smXjb53EwDOJKJxInICuMbwmKVcn5G3Afi+dnstgKOG+45q2+ywtqWm6fVpx/DrANzdttU1uT4iuhOqlR4B8KNaL7IaBOS9AD5IRI8DCAFIa9udAF4C4O3a7zcQ0RXafW8XQpwD4KXazztttD4ngFEADwohzgfwEIB/tNH6jFyL9h7gDa2NiFxQBeRFAEagupE+bpf1CSFOaev7AVQXx0GoI6GXen0AACK6GEBcCCF962YXUu26OGh0bUtNU+vTLgy+D+DLQoj9dlufEOJVUC1ND4BX1HqRVmei2x4hxPMAXgkARHQ6gN/T7joK4DdCiBntvjug+hHvFkIc0x4bIaLvAbgIwLdtsr5fA4gD+Im23w8BvK8da2tyfXdrf28D4BRCPG6jtS1qj9unbb8VwMdstL67hRC3Q6soJqIbAOSWYX2S0guAo1AvXiSjUC05O6xtSWlhfTcC2CuEaDmxpE3rgxAiSUS3AbgawF3VXqfjLRDSMqiISAHwf6D6vQHgTgDnatkRTgC/A2AXETlJy8zRrlhfC9UctMX6NHfQ7QBepu13BYBddlmf4aHXoc0HeBNrOwZgCxHJBnJXAthto/UZH9MD4I8BfGMZ1ie3vQXALXKbEGISQISILtHcuu8C8FM7rG2paWZ9RPQZqA0P/8xu6yOiIBENa7edUON0z9d8IauDTcv5A/WENQkgA/Vq6X0APgxgj/bzORgCugDeAWAnVIH4B21bAGrm0DPafV+CSXbRcq1P274OwH3aGu8GcJqd1qfdtx/AmXb6bLXtfwhVNJ6BKsR9Nlvf96GKyS7UkQXTxvW9DMDDJs+zXVvzPgD/anyMDdb2D9rj89rvv7XLewfVWhPad+8p7ef9NlrfENTMLXne+wpUD0LV1+ZKdIZhGKYpOt6FxTAMw7QHFhCGYRimKVhAGIZhmKZgAWEYhmGaggWEYRiGaQoWEIapAhEJIvqO4W8nEU0T0f82+XzdRPTHhr9f1uxzMcxywwLCMNWJAdhKRD7t7yuhFiQ2SzfUAkGGWfGwgDBMbX6OQiuIogp7Iuolov8hdYbHw0R0rrb9b0md03AvEe0noj/VHvI5ABu1Rp1f0LYFiehHpM6K+K5W5c0wtocFhGFqcwuAa0kd+nQugEcM930KwJNCiHMBfALFPdPOBPAqqL3UPqm1xvl/7d09SgNRFMXxc1dg4QYkpVWstcg2rFyFYBlciI07EGJpIdhYRlu7VKKNVin0WNwRZNBkuExCIv9f+d7jMa8Y7nzAuWeSnmwPbZ826w6U8Rb7kgaSDld5GKAvFBBgCdtTZb+FY0nXrekjSZfNuhtJuxGx08xNbM+doYnPyriI39zbntn+VEZc7PV7AmA1/n0aL9CTK2Vs/kjZOfDboojz+Y+xD/19v3VdB2wU3kCAbi4kndt+aI3fKvt6KCJGkl5svy3Y513ZnwHYejzpAB3YnimTmdvGyu6VU2WflpMl+7xGxF1EPCp/zk/6vlZgXUjjBQCU8AkLAFBCAQEAlFBAAAAlFBAAQAkFBABQQgEBAJRQQAAAJRQQAEDJF2p+Y/Ek8SWuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Seasonal First Difference'].plot()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto Regressive Model\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: 'pandas.tools.plotting.autocorrelation_plot' is deprecated, import 'pandas.plotting.autocorrelation_plot' instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import autocorrelation_plot\n",
    "autocorrelation_plot(df['Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Thoughts on Autocorrelation and Partial Autocorrelation\n",
    "\n",
    "* Identification of an AR model is often best done with the PACF.\n",
    "    * For an AR model, the theoretical PACF “shuts off” past the order of the model.  The phrase “shuts off” means that in theory the partial autocorrelations are equal to 0 beyond that point.  Put another way, the number of non-zero partial autocorrelations gives the order of the AR model.  By the “order of the model” we mean the most extreme lag of x that is used as a predictor.\n",
    "    \n",
    "    \n",
    "* Identification of an MA model is often best done with the ACF rather than the PACF.\n",
    "    * For an MA model, the theoretical PACF does not shut off, but instead tapers toward 0 in some manner.  A clearer pattern for an MA model is in the ACF.  The ACF will have non-zero autocorrelations only at lags involved in the model.\n",
    "    \n",
    "    p,d,q\n",
    "    p AR model lags\n",
    "    d differencing\n",
    "    q MA lags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For non-seasonal data\n",
    "#p=1, d=1, q=0 or 1\n",
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "model=ARIMA(df['Sales'],order=(1,1,1))\n",
    "model_fit=model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>D.Sales</td>     <th>  No. Observations:  </th>    <td>104</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-951.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th> <td>2227.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 18 Mar 2020</td> <th>  AIC                </th> <td>1910.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>13:40:32</td>     <th>  BIC                </th> <td>1920.829</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>02-01-1964</td>    <th>  HQIC               </th> <td>1914.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 09-01-1972</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>         <td>   22.7838</td> <td>   12.405</td> <td>    1.837</td> <td> 0.069</td> <td>   -1.530</td> <td>   47.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Sales</th> <td>    0.4343</td> <td>    0.089</td> <td>    4.866</td> <td> 0.000</td> <td>    0.259</td> <td>    0.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Sales</th> <td>   -1.0000</td> <td>    0.026</td> <td>  -38.503</td> <td> 0.000</td> <td>   -1.051</td> <td>   -0.949</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           2.3023</td> <td>          +0.0000j</td> <td>           2.3023</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           1.0000</td> <td>          +0.0000j</td> <td>           1.0000</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                D.Sales   No. Observations:                  104\n",
       "Model:                 ARIMA(1, 1, 1)   Log Likelihood                -951.126\n",
       "Method:                       css-mle   S.D. of innovations           2227.262\n",
       "Date:                Wed, 18 Mar 2020   AIC                           1910.251\n",
       "Time:                        13:40:32   BIC                           1920.829\n",
       "Sample:                    02-01-1964   HQIC                          1914.536\n",
       "                         - 09-01-1972                                         \n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "const            22.7838     12.405      1.837      0.069      -1.530      47.098\n",
       "ar.L1.D.Sales     0.4343      0.089      4.866      0.000       0.259       0.609\n",
       "ma.L1.D.Sales    -1.0000      0.026    -38.503      0.000      -1.051      -0.949\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            2.3023           +0.0000j            2.3023            0.0000\n",
       "MA.1            1.0000           +0.0000j            1.0000            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2d9f1fda0>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kX/GwFGWwGRFppVgLGkRWFwOBzeHPJioeAvDEY5xQYBNCCCETRr0hUaj5zSHHMIPtUUgCaA7xrcwnAdCpbaVVYPO4jA8KbEIIIWTCKIQVdFtbRBgRCVAd2Epg06ltkmuJiDCDPT4osAkhhJAJQzm0Wx1sRiECVAf2ykIQEaFT2yTTKrB54jE2KLAJIYSQCWObwLa4Kr0VdXxWFwIHmyceTTZHRHhcxgUFNiGEEDJhKAG5tM3BpiMJNDuwV0KB7dGpjchxAc9EQIFNCCGETBhbHewEHexNRBERVdNHIRmRKbFFZBKgwCaEEEImjK0C26aDvYl8xYdtCSzPBceHUYgmmbIHETxdeOIxRnoKbCHEnUKIF4UQT7Tc9mdCiB8LIR4TQvy/Qojllq99UAjxrBDiaSHETS233xze9qwQ4gMtt18qhHhICHFUCPG3QojkKH9BQgghZNrY7mBzk2MruYqHpRkHSYftKlvJVdzI2WdEZHzEcbDvAnDzltu+CeAVUsprADwD4IMAIIR4OYBfAXBV+D1/JYSwhRA2gP8B4A0AXg7gV8P7AsB/BfDnUsrLAGQAvHuo34gQQgiZcnIVD0nbilaBO+EmRy6aCchXgyU8aviTTm2TTNnDeYvccDluegpsKeX3AGxsue2fpJR++M8fADgY/v3NAL4kpaxJKZ8H8CyAnw7/PCulfE5K6QL4EoA3CyEEgNcDuDv8/s8BeMuQvxMhhBAy1agtjiK81m9HDjYFExAen5kEkuGJB539Jtmyi/OW6GCPm1FksH8dwNfCvx8AcLLla2vhbZ1uXwGQbRHr6va2CCHeI4Q4IoQ4cu7cuRE8dEIIIWTyyFU87Jl1on8n2IO9ieD4JJBweOLRipQS2VYHmyceY2MogS2E+GMAPoAvqpva3E0OcHtbpJR3SCmvk1Jet3///n4fLiGEEDIVKAGpUFEIRkQC8lUfS7MOEjYz2K0Uaz78hsR5izMAGBEZJ07vu7RHCPFOAP8BwA1SSvWKXwNwqOVuBwG8EP693e3rAJaFEE7oYrfenxBCCDGSfMWPlqgAgMOIyCZURCQRZbB54gEA2XCLIyMi42cgB1sIcTOAPwLwJillueVL9wL4FSFESghxKYDLAPxvAA8DuCxsDEkiGIS8NxTm3wXwH8PvfyeALw/2qxBCCCF6sNXBtiwBS7AHW6GGHBkR2UwksMOICBfwjI84NX1/A+BBAFcIIdaEEO8G8JcAFgF8UwjxqBDi0wAgpXwSwN8B+BGArwN4r5SyHrrTvwvgGwCeAvB34X2BQKi/XwjxLIJM9mdH+hsSQgghU8ZWgQ0ETSLMYAM1v46q1wgy2GrIkUISQHNN+spCCpZgu8o46RkRkVL+apubO4pgKeXHAXy8ze33Abivze3PIWgZIYQQQoyn0ZDIV9sIbEswa4wgPgMASzNOMzrDEw8ATYG9dy44+aDAHh/c5EgIIYRMEIWaDymBpXYCm0IyWpOuagyTtsWISEhzQVEyOC4+ny/jggKbEEIImSDylaaAbCVhW1yVju3Hx7EFIyIhmVJwbJbnEkg6Ftx6fcyPyFwosAkhhJAJYuuadIVtCQ45onl8lmbCNfJ0sCMyZReLqaC+MEEHe6xQYBNCCCETRCeBHQhJCqZ8Nchgq0U8CdtiBjskV/GwPB+eeDiCJx5jhAKbEEIImSA6CWzHFqgzItKMiIQOdpIRkYhM2cXybNCfnrQt1CiwxwYFNiGEEDJBdIuI0KltiYhEGWxGRBSZsofluZboDE88xgYFNiGEEDJBdIyIWBZr+hC0iCQdCzMJGwCQsAWjMyG5sou9c6GD7fDEY5xQYBNCCCETRL7iwbEE5pL2ptuDiAiFZL7iR/EQgEOOrbQ62En2YI8VCmxCCCFkglBbHIUQm253LDq1AMIlPM09eXRqA+rhgqLl0MFmi8h4ocAmhBBCJoh2a9IBtSqdQjJf8TZ1hPPEIyBf8SBlsMURABIOHexxQoFNCCGETBC5LQJS4bAHG0AosLdERCgkm2vSN0VEOOQ4NiiwCSGEkAki39HB5qp0IOjBbj0BYUQkIFNWWxzVkCN7sMcJBTYhhJCJwKs38LcPn0DDcBHZ2cFmiwigIjTNDHbCtujsA8hVAgd7b2sGm8+XsUGBTQghZCL49lMv4o/+4XE8upYd90MZK1sFpCJBBxtSym0RkSCDTSGZKYUO9iwjIpMABTYhhJCJ4Fi6BACouvUxP5LxIaVEvuq3jYjYzGCj4tXhN+Qmh5/DfAEqgx052I4F1/DnyzihwCaEEDIRHA8FtsnrnYs1H/WG7Ngi4hneIpKv+AA2L+FJMgoBILjyYQlgcSa4+sHjMl4osAkhhEwEx9bLAGD0Ze1OWxwBIGFx0Uy0Jn1TiwidfSBwsPfMJmBZQX86hz/HCwU2IYSQiSBysCmwO0REOMyXr4YCuyWj7tCpBRC0iKh4CBCceJh8sjpuKLAJIYSMnapXx+l8FYDZDraKQLRrEUnYHObLt3GwOcwXkGtZkw6E7SoNaXwrz7igwCaEEDJ21jJlyFAHmCyWujnYjs2IiHKwW49PcOJh9nEBgojIcouDnXQCiWd6bn9cUGATQggZOyp/DQCub26LSL6bwLYYhciVVURkq1Nr9nEBgOwWBztpBxLP5BPWcUKBTQghZOyoij4ARleudXWwLfZg56tBhEY1ZQAqgy0hpdnHJlt2t2SwQweb7v5YoMAmhBAydk5slDGXtAEANc9sgW1bAgup7YtmnDBTazL5ioe5pB2JRwBI2kFrhslC0vUbKLn1aMkM0BIRMfiEdZxQYBNCCBk7x9Jl/MT+BQB0sJdmHAghtn3NsYTxq9KDLZeb3f2mU2vuscmGS2aW57c72IyIjAcKbEIIIWPneLqES1bmkHTMboTIVby2DSJAMOTYkDC6FSJf3bwmHWgKSZMrDDNhNn3v3ObhT8DsE9ZxQoFNCCFkrHj1BtYyFRxemUfKsYzvwW6XvwZahKTJArvib+rABoKV4IDZQjJysGebDnaKEZGxQoFNCCFkrLyQraDekLhkZQ4pxzJaKHUT2Ha4oc/kxox8tU1ExFIZbHOPi3Kwt/ZgA4yIjAsKbEIIIWPlWDqo6LtkZR5J2zJ6yDHfLSJicZgvyKgzg70V5WDvbZPBNvm4jBMKbEIIIWNFrUg/rDLYBguCfNXfJiAVSjCZvGym3QlIwmEdXTasd2zXIuL65h6XcUKBTQghZKwcWy9jNmFj/2IqHHI0d9FMseZt6nhuJYqIGHoC0mhIFGo+lrYcn2ZNn5nHBQi2OCZtK6q6BFoiIgYfl3FCgU0IIWSsnNgIGkSEEEg5trGZUb/eQNVrYD7ZXmCrVgjPUAe7UPMhJbY52I7FKES2FGxxbK13VJscPUNfT+OGApsQQnaZekOi4prr0m7lWLqMS1bmAMDoiEipFjwnFjo42EpI1g2NQqg18p0jImY+bwAgW9m8xRHgoplxQ4FNCCG7zOcfPIaf+2/3j/thTAT1hsSJdBmHV+YBwOghx6IbrAFfSNltv+5EDraZxydfDQX2tiFHDn9myh72zLU/LqaesI4bCmxCCNllnjlbwJl81ehhNcWZfBVuvYFLlMA22sFWArtTi4jZC1XyleD4bK3pS7ItA9myu2nJDMCavnFDgU0IIbvMejGo1OIHH3B8vdkgAsDoTY6FaiAg53s42Kb2YOeiiMjmCI1DgY1s2dsWEUmxXWWsUGATQsgus16sAQBqBrdlKI5vBB3YF4cCO2WwwG462J0y2KpFxEzB1CsiYmodnZQS2bYREeVg831mHFBgE0LILpOmgx1xLF1C0rZw4Z5ZAIGDbeqq9KIS2J2GHKNV6WYen05DjknDj0vZrcOtN7Y52OwHHy8U2IQQsss0HWwzBUErx9fLOLRvNup4TlFgd67pM97B9iEEsLjF4Td9Y6FaMrM1g51kD/ZYocAmhJBdpOz6KIcVfaYKyVaOpUvRgCMQiAJTL2mriEjPRTOGDsfmKx4WUw4sS2y6PWpXMTQikikFV8T2zG5xsKPoDN9nxgEFNiGE7CIqHgIwgy2lxImNZgc2YHaLSDEacuweETHVqW23Jh2gU5stt3ewhRBI2MLY58u4ocAmhJBdRMVDADpL54o1lN161IENwOhNjkXXR9KxosjDVpQjaWq9Y77qbRtwBJoREVNXyGcrwUn73vnktq8lbYsCe0xQYBNCyC6yvsnBNvuD73g6aBDZ6mA3pJliqVj1t+WLW1EREVOH1nIVb1sHNsBhvkzoYC93ODamnrCOGwpsQgjZRdJ0sCOOhR3YmzLYoVgy8eSjVPM7xkOAFqfW0LaMfMXf1oENNOsLjY2IhBns5bntDnbCtuAaeuIxbiiwCSFkF2mNiJgoIls5sVGGbQkcWJ6NbksavH2uWKt37MAGmkKSEZHNsEXEw3zSjk5OW2FEZHxQYBNCyC7SGhExUUS2cixdxoHl2U3CQP3dRDeyWPN6CGyzoxD5DhER2xKwLWFsfWGm7LZ1rwGzN6OOGwpsQgjZRdaLtciJNL1F5Hi6tCl/DTTXO5soCkq1esc16UDLqnQDTz68egMlt962RQSA0W0Z2bKH5Tkel0mDApsQQnaR9WIN5y/NADBTRLZybH27wDY5g12s+VhoE4FQRALbwIhIIawwXOrQEZ6wzK13zJbdbVscFQlGRMYGBTYhhOwi6aIbZY5NFJGKbNlFvupvqugDmg62ie5+seZjoZuDbZlbR9dpTboi4ZgrJLNlD3s6ONhJgzejjpueAlsIcacQ4kUhxBMtt+0TQnxTCHE0/O/e8HYhhPikEOJZIcRjQohXtXzPO8P7HxVCvLPl9lcLIR4Pv+eTQojNK5oIIUQj1os1XLRMB/tYVNG3WWAnjY6I+N0z2AY72LlQYLfLYANBFMLkDPbWJTMKOtjjI46DfReAm7fc9gEA35ZSXgbg2+G/AeANAC4L/7wHwKeAQJAD+DCA6wH8NIAPK1Ee3uc9Ld+39X+LEEK0wK83kCl7uChysM1zaRXH06qib0tExA4cXNMEdr0hUXbr3Wv6lINtoMDOV3s42LaZEZFGQyJX8TpGRIIWEfOeL5NAT4EtpfwegI0tN78ZwOfCv38OwFtabv+8DPgBgGUhxIUAbgLwTSnlhpQyA+CbAG4Ov7YkpXxQSikBfL7lZxFCiFZshH21F+6hg31sPXCwL963ZcgxYWaLSMkNMsbdHGy1aMbMiIjKYHdzas0TkoWqj4bs7OyzRWR8DJrBPl9KeRoAwv+eF95+AMDJlvuthbd1u32tze1tEUK8RwhxRAhx5Ny5cwM+dEIIGQ+qom91IYWU4dnI4+kSLtwzg5nE5syxqT3YxWpvga1WpZsoJJWD3S0i4hn2nAGCeAiALkOObBEZF6MecmyXn5YD3N4WKeUdUsrrpJTX7d+/f8CHSAgh40EtmVldTBk/fHR8o7wtHgKY2yJSqgUCu1tERIig79nERTO5aMixQ4uIoVnjbHhc9s4zOjNpDCqwz4bxDoT/fTG8fQ3AoZb7HQTwQo/bD7a5nRBCtCNdCgX2QgopxzZORLZyPF3CJfvmt91u6pBjMRTYCx1q6BS2JeAZuCo9X/HgWAKzifYtKwnbgmfgiYdysPfMctHMpDGowL4XgGoCeSeAL7fc/o6wTeQ1AHJhhOQbAG4UQuwNhxtvBPCN8GsFIcRrwvaQd7T8LEII0Yr1QvBhuLKQRMrgD75C1cN60cUlq20cbFMjIrXeEREASBi6sTBf9bA0m0CnorGkbRkZEclGEZEOGWxDnf1JoPsrGYAQ4m8AvA7AqhBiDUEbyO0A/k4I8W4AJwD8Unj3+wC8EcCzAMoA3gUAUsoNIcRHATwc3u82KaUanPxPCJpKZgF8LfxDCCHasV6qIelYWEw5YQbbzBaRs/kqAER94K2oIceaYaIgiogku38sO7ZlZEQkX/E7LpkBggpD007KgKADG0DHVemmDn9OAj0FtpTyVzt86YY295UA3tvh59wJ4M42tx8B8Ipej4MQQqad9YKL1fkkhBBGX7rtJgpShtb0qU2Fiz0iIo5l5tCacrA7kbCt6CTFJDJlD0KwRWQS4SZHQgjZJdaLNawupgDA6BaRSGC3EQVJQzc5xhlyBAJ1C1MqAAAgAElEQVSn1siISMXrWNEHmOvU5soulmYSUYXjVjjkOD4osAkhZJdIl2pYmQ9cW5OdJdV8sNwmN2rqkGPJDU4o5rusSgeCdekmLpopVP2u7n7SMdPZz1a8tq8jRTKs6QsCBmQ3ocAmhJBdYr3gYnVBOdi2cS6tQg1mLbdpPrCtoIrONIFdqPpI2hZSTg+BbQv4BraI9BLYjmXmMF+m7LW9EqRIOhakNHP757ihwCaEkF1AShk42KHATjrmXrrNVYLcaCfBZGLDSqnm93SvgSCDbWREpMqISDtyZbfjgCMQHBcARp58jBsKbEII2QXyFR9eXWJ1IfgwTDkWap6ZH3rZsoc9swlYHXKjJp58lGp+zw5sIBBMpjnYfr2BslvHYheBzYhIeyKB7Zt38jFuKLAJIWQXWG9ZMgOYKSIV2UqPy9q2eQ52oeb3rOgDggiNaQ62aljptMURMHeTY6bk9oyIAECtbmYcbZxQYBNCyC6wXtgssE12sHMVD3u6XNY2cY18qdY9Y6xwDNxY2Kww7Cwkgwy2Wcel3pDIV/2uEZFkFBEx69hMAhTYhBCyC6RLzS2OgNkOdq7c23UzzcEu1vyeFX1AsMmxblhEJF8NWme6nYAkHGHc6ynfpY1HkXCCGJaJWy7HDQU2IYTsAuvFrQ62jZpn5mXbXrnRoGHFLEEQV2DbljDOjVQCu9uQY9K24BsmsDOqjadrTV+4uMmwYzMJUGATQsgusF50IQSwN/wwNNnBzsaoFjPt2JRqPhbjONgGCsk4Wy4TtoWGhFFr5Jt98t1aRAIH27QrQpMABTYhhOwC68Ua9s0l4YSZyJQTZEYbBgkCQOVGvY6rnQEgZVtwDesIL1bjOdiOLYwSkUAzCtHtOeOEQtKkQcdcl42oioTDmr5xQYFNCCG7QLpYi/LXQMvGQsM++ApVD1KCQ44tNBoSJbeOhTgC28CISBwHWw3zmfR6UhGRvV1eSynbzM2okwAFNiGE7ALrxeYWRwDRxj7TmkSyMVw304YcS24gIOMJbPN6sJXA7nZ8VN+zSRWG0Wup65AjW0TGBQU2IYTsAuvF2iaBbWo/bTZG84FpmxxLteA5EDciYtra63zVw3zSjuJV7TBxY2E22ogaY9GMQcdlUqDAJoTsGM+vl/D8emncD2MiSBfdTRGRlBLYxjnYMZoPDBtyLNZChzZOD7aRi2a8riISaGawTToxy5Zd7JlNwO6wERVoRmdMilxNChTYhJAd44/ufgz/5Z4nxv0wxk7Vq6NY87dERMzLjALBkhkA2DPbfTmGSUIpEtgpu+d9HQNbRPIVv+sWR6B1oYo5x6ZXGw8QrJAHzDoukwIFNiFkx3g+XcJGuGDFZJod2HSw4+RGTRtyLEUCu7tYAoLaNdMiIoVabwc7ymAbdGwyZbfrsDDAiMg4ocAmhOwIVa+Oc4VaNMBlMuvF4CSjXQbbNAdbCexulWumDTmqIb75GA62bRkosKs+lnrEZ0zse85VvKhXvxPR+4xBx2VSoMAmhOwIa5kKgKDf13TSoYO90rZFxLQhRxcLKSdy1tqRcmyjBEHTwY7XImKaG5mvxHewTTo2cSIiJh6XSYECmxCyI6xlygCAQo0Cu11ExFQHO1fuvmQGaA45SmmGU9tPTV/CwEUzharftQMbaBWS5hybTNntusURaB4X16DjMilQYBNCdgTlYLt+wyg3sh3tIiKmZrBzFa9r/howbwC0GRHpLbBtyzKqRUTKYPPnUk+nNoiImDIA6tcbKFT9+K8lw9+DxwEFNiFkR1ACG2heAjeV9WINCykHM4lmxtZUBzsbQ2CbVi1WqvlI2CISQ91I2AKeQYtman4DXl32drANez3lw5MyRkQmFwpsQsiOoCIiQLOGzFS2dmADLRls37AMdtnFcpeKPsC8waxizcd8yoEQnfuMFY5lQcpgvboJ5MNax6UeGeykYRGRTNQn3/21ZFsClqDAHgcU2ISQHaHVwTZdYG/d4giYJyIVuYqHPbysvYlizcd8snc8BGguVDHFxVZObS8HOzouhgjJOHWXCtMWN00KFNiEkB1hLVPBgeVZAIyIpIsuVua3OthmxSCAIE8bbzmGWQK7VOs9xKdwLJU1NsOpLVTjOdimRSFylXgONhAcG1NeS5MEBTYhZORUvTrWizW87IJFAGwSWS/WsLpIB7vk1uE3ZO8MtmF5WhURiYNj2EIV5WDH3+RoxnHJlEIHu8fJKhAcG1NOPCYJCmxCyMhR+euXXRgIbJO7sP16AxtlF6t0sJFVudFeGWzbrIaVYq0eq6IPaHWwzTg2ysFmD/ZmsmE2fW8MBzvpWPB8M048JgkKbELIyDkZ5q9fdsESALMjIpmyBymx3cE2rCkDaNniGNvBNmMAtFj14gtsVUdnioNdCR3sHgLbtAx2ruzCEr2z6UAYETHkuEwSFNiEkJGjBhyvVA62wQK7uWRms8AWQiDpWEa1iOQqvdekA60NK2aIglKtHmtNOgAkLLMiIk0Hm4tmWsmEC5ssq3fzTMIWFNhjgAKbEDJy1jJlJB0Ll6zMAzBbYKfDJTNbhxwBIGXY8FHc5gPT8umlmo+FVO8sLRDUrgEmRUR82JbAXLL7CUjSwIhInAFHAEg6NjxDXkuTBAU2IWTkrGUqOLg8i4RtYTZhG53BjhzsLRERAEglLGNcWgDIVuJlsE2q6ZNSouj6WIjpYDejEGY4tfmqh8WZ3h3hapOjKUIyW3Z7XglSJOlgjwUKbELIyFnLVHBgb1DRtzDjoORSYK/ObxfYSTrYbUkaNABaduuQMnidxEFFIerGRETiVRgqZ98YB7vsYW+MDmwgeM6YclwmCQpsQsjIOZUp4+DeOQDAYspBwWgH20XCFm1rxlIJ2wgRqchVPKQca9PK+Haoy/0mnHyo+FTcmj7ThGS+4vUccATCmQbbgmuIs5+tuH1ERNgiMg4osAkhI6Xi1rFedHEwdLDnU47hGewaVuZTbS9xBw62QUOOZS/W5rlUwpwebPXaiNsikjCsRSSugw0Ex8aUbHq25MWOiCRsCzVDjsskQYFNCBkpp7JBB7YS2Aspx+iavmDJTHunycQMdq/8NWCWg13qU2A7loqI6H9sgCCDHcfBBoCEY0YUwqs3UKj5sTqwgTAiYsBradKgwCaEjJSTG0FFn4qIzBseEUmXXKy0yV8DZmawe3VgA2a1iKgB4NibHC2zhhwDBzuewHYsMyIi+Uq8WQZF0hFGnHhMGhTYhJCRorY4Hgod7EXThxwLtW0d2ArTHOxcxYu32jkactQ/PtNvRCRalW6AkASaLSJxSNpmCMlMzGFhRZKLZsYCBTYhZKSsZSpIOlYkKudT5tb0SSmxXnKxutD+Uq6JDnYcUWBURMTtV2CrDLb+x6bRkCjWfCzFzRo7lhEZ7Jyqu2REZKKhwCaEjBTVga02jC2kEijV9Hci21Go+XD9RmcH27GNcGkVcZsPoi2XBoilQSMiJjjYRdeHlMBSHxWGJkRnorrLPk48TIjOTBoU2ISQkbKWKUcd2EAQEXHrDaOEpGK9EHRgr3RysB1zHOyqV0fVa8RuPjBly2UxPPmMG4NwolXp+h8bNbsRd8jRscxYqDJQRMTA999xQ4FNyJBIKfE7X3wE9z/94rgfykSwlqng0L656N/z4YpjE2Mi6VJwKbezg21OBjvX92CWKQLbg22JaHtlL0yq6VPDfLEz2Ia0iGTL/UVEguOi//Nl0qDAJmRI0iUX9z1+Bj94bmPcD2XslF0f6VKzAxsAFkL3ycSYCB3sJs3L2vFFgQknH6VaHQup3qvAFbZBERHlYMdtEUnYlhHHJVfxYIlgiVccEoYMf04aFNiEDMnxdNCaUfXME5BbOZXZXNEHAAupwMEu1LyxPKZxshE6TZ1q+oIMthkffE3XjQ52K8WaH3vAEWiuSjdBMCkHu90W1HYkbFMiIsEsg5pz6UXStuE3JBoGXPWYJCiwCRmSkxuBwK64FNhrkcBucbBT5jrY2R5ZSVNEJABkQ7EUO4NtyLEpVn3Mp7qvjm9FtYjUDRBL6qS8HwfbhBOPbDle3aUi4QTPGRNOPiYJCmxChkQ52BU62DiZ2bzFEUAkHooGOtjZsouZhIWZRHsBlXKCfloTnKVcv4NZjhndvSW3PwdbRUQ8A54zzSHHflpE9H/O5CrxFjYpkgZd9ZgkKLAJGZLjGyUAFNhA4GCnHAv7W4b61IBS0VAHu1vmONpYaMAHX7bP7l5TOsILVT92RR8AJFSLiAHPmeaQY1wHWxiRwc6U3dhr0gGzNqNOEhTYhAyJiogwg92s6Gsd2FIRERNbRLKV7otVUtHGQv0/+LLloC1Dtcr0Ihhy1P81Var5sVsyAMMiIlUfKceKBGIvEoZsLOw7IhI52Po/ZyYJCmxChiSKiDCDHSyZaRlwBMyOiOR6bC5MGeQsqTXpcdsyko5txHEp1XzMJ/sQ2JY5Yilf9WJvcQSCqx4mxCBy5f4iIiYNxk4SQwlsIcQfCCGeFEI8IYT4GyHEjBDiUiHEQ0KIo0KIvxVCJMP7psJ/Pxt+/XDLz/lgePvTQoibhvuVCNk9Km4dL4ZVbIyIKIE9u+k2JR5MjIhkym7XiEjKCU4+THBqs33mRk3pCC/U+ouIRKvSDRBL+Wr/7r7n633i4dUbKNT8gSIiJryeJomBBbYQ4gCA3wNwnZTyFQBsAL8C4L8C+HMp5WUAMgDeHX7LuwFkpJQvBfDn4f0ghHh5+H1XAbgZwF8JIeKPVBMyRtRQn2MJ4wV2qeZjY0sHNgBYYSyAEZHtmJSNzPV5WduEIUcpZf8REcusRTNxtzgCYQ+25hsu+13YBADJ8KSMDvbuMmxExAEwK4RwAMwBOA3g9QDuDr/+OQBvCf/+5vDfCL9+gwiuFb4ZwJeklDUp5fMAngXw00M+LkJ2BRUPecn+eVQNj4icym7vwFYszDgo1cwS2FLKMCLSzcE2x1nKVtzYA46AGavSK14dDYm+HGwhBGxLaC8kgSCD3c/JR8KA54zqk49bdwkwIjIuBhbYUspTAP4bgBMIhHUOwCMAslJK9Um6BuBA+PcDAE6G3+uH919pvb3N92xCCPEeIcQRIcSRc+fODfrQCRkZx9NBg8gVFyyhbLiDvdamok+xkHJQNExgV7w63HojloNthMAewMHW/bio10Q/AhsIXGwT2jIK/WawDVgJrrr12SIy+QwTEdmLwH2+FMBFAOYBvKHNXdWzvd1ki+xy+/YbpbxDSnmdlPK6/fv39/+gCRkxJzfKWEw5uGjPjPFDjmrJzKF2DnbKQcEwgd1cDd5tyDFIw5nwwdfvYJYJS3jU8qW4K68VQRRCbyEJBBnsuB3YQHDiobtL22t5VTuUg6175GrSGCYi8vMAnpdSnpNSegD+HwA/A2A5jIwAwEEAL4R/XwNwCADCr+8BsNF6e5vvIWSiOb5RxsUrc5hNBiuvTVgY0omTG2WkHAurC9udFRMjIpkYq8GbDrbeJ2dqMKvbwOdWTNjkqOYS+nWwbUsYMeRYqHqxO7CB5omHlPq+D0fvK328lljTNx6GEdgnALxGCDEXZqlvAPAjAN8F8B/D+7wTwJfDv98b/hvh178jg1fBvQB+JWwZuRTAZQD+9xCPi5Bd40S6jIv3zWE23NRX1VwodUM1iLSrYZtPOsYNOTY3F/bOYOsuJPODDGYZMOSoIiL9bHIEgoUqum9ydP0Gql6jLwdbnbDqLCSjIcf5/hp5AP3fZyaNYTLYDyEYVvwhgMfDn3UHgD8C8H4hxLMIMtafDb/lswBWwtvfD+AD4c95EsDfIRDnXwfwXimluSqFTA31hsRaphI52IDZXdjtOrAVCzPmZbCzMUSlKRnsOMdiK0nbRr0htXZqBxXYjmWhrrGIBAL3Goi/xREITjwAvYf51MKmfmJFHHIcD/29qrcgpfwwgA9vufk5tGkBkVJWAfxSh5/zcQAfH+axELLbnMlX4dYbuGTffNRNa3JV31qmjGsO7mn7NROHHJsZbDrY6lj003zQukbesfXciVaKhhz7a6a1LQFP8xaRQnjFa2l2kCU8+h6bTNnFnj4WNgFmnHhMInq+axGyC6gGkU0REUMFdrHmI1P2OjvYqSCDrXM2civMYDfJVdSxYPNBK5GD3UcMAggEk+4tInnlYKf6cLANiIj06tZvhylXyiYNCmxCBuTkRlBLd8lKU2BXXDPfwE5lVAf29oo+IBAQfkMa9Qafq3iYSViYSXR2J01pEYnTqLIVE9z9gSMitoW65hls5WD304NtwkKVfhc2AcEKeUDv4zKJUGATMiDH02U4lsCFe2aaGWxDHexuHdhAU0AUDBp0zPZYkw6Y4ywNUi1mwrEp1XxYAtEJelxMqKNTg7H99GCbkDXOlN2+OrCBluOi8WtpEqHAJmRAjm+UcWDvLBy76VKaK7A7b3EEmgLbpKq+bLn3pVxTNjlmKx6E6G9gLeXo391bqPqYTzl95WkBwLGF9j3YgzjYjgECO9tnnzyweZ6B7B4U2IQMyMmNoKIPQEtExFSB3bkDG2j2/Jo06Jgtez2H+tSlW90Fdr7iYWkmAduKLyTVsdE5IlKq+X3HQ4BgmE9nEQk0M9h9bXKMIiL6nnzkKl5fHdgAe7DHBQU2IQNyPF3GJSuhwE6aPeR4Jl/DBXtmOjpxiyYK7Irb08G2LIGkrf9ClWy597HYihEREXdQgS20z2Dnqz6EABaSrKNTuH4DxZqPvX2+llSLiO7vM5MGBTYhA5Are8hVvO0OtqECO12sYXUh1fHrkYNtVAbbi5WVTDqW9i0i2Ur/g1kmDICqiEi/OAa0iBSqHhaSDqw+rnroLrBzA/TJA4AQ4Ym8psdlUqHAJmQAToQNIhfvmwfAiEi66GJlvrOYVDVkJdcMgS2lRLYSLytpwkrwbNnr61I/YEZNX6nm95UxViRsS/se7HzF7/s540ROrZ4nH6ruck+fQ45AuP1T49fSJEKBTcgAHN8IOrBVRGQmGbyUjHWwSzWsdHGwFw1rEal4dbh+I1ZWMnCw9f7gy1W8vjqwgdbBLH1fU8Waj/k+IhAK24CISKHq9X3yoXL7vqYnH5mwjaffiAgQdITr6uxPKhTYhAzA8XTgYB8KIyJJ24IlzMxgNxoSGyW344AjYN6QYz+1dGY42O7A3b06H5tSrT5YRMSytB9YK1R9LPXROgPoHxGJsx22E4yI7D4U2IQMwMmNMlYXktGAkhACswnbyIhItuKhIdE1IjKXtCGEOTV92T6cJt0z2I2GDB1sDjlupThwRETA11ws5QdwsBPRSZmeJx9xtsN2ImFb2h6XSYUCm5ABOJ5uVvQpZpO2kRGRdLEGAF0jIkIILCQdYyIiWZWVjOE0pRxba5e2UPPRkOhZWbgV3TvCpZRBRCTV35IZwJSISP8Z7ITmmxxzAyxsUiQZEdl1KLBJX5jo0LbjxMZ2gT2TMFNgrxcDMbnSJSICBIOOpjnYcT4Idc9gN0VBf5e1dV+VXvMbqDckFlKDuZHaDzkO4WDrKiSzFRe2JQaqdjShDnTSoMAmsXn2xQJeces38NTp/Lgfylhx/QZeyFVw8cr8pttnE7aRGex0KXCwu9X0AcE2R2awt6N7Blu5+X1nsDUX2Oq1sDCAg+1Yetf0SSlRqPYfn0mEzxldj02mHNRd9rv5EwASjtD2xGNSocAmsXniVB71hsTJsKLOVNYyZUgJXNIuImKgw59WDnaXDDYQDDoaI7BDURm/B1vfD75+TjZa0X29s+qEH7gHW+OISMWro96QAww5hjV9mj5ncuX+ZxkUCQ457joU2CQ2qvvZxBhEK8dVB/YKIyJAkMG2RO8IwOKMOQI7V/aQcizMJHq7k/o72AMKbLVG3tPz2DQd7MFaRHQecsxXgmOz2KfAThoQEek3aqVgRGT3ocAmsVECu2ygS9uKcvC3OdgJGxVNxUA31ksu9s4lYffYuDafdIzZ5JjpYzV40rG1bhHJleMPfLbi2BZsS2jbgz2UwNZ8k2OhGpyULc32d2wczQV2puQN1IENcMhxHFBgk9hQYAccT5cxk7Cwf3Fz5ng2YaNq4LFJF2s9BxwB84Yc43bVau9ghxGRfltEAL1dN/VaWBigps+x9I6I5EOB3a+D3WwR0fPY5Cpe3yeqioStf3f6pEGBTWKjnNuKIeuuO6Eq+rYOmswmbZQ9845NsCa9+4AjEDh1BVMEdh+9z7pnsHMVD/NJO8pU90NS45MP5WAPlsG2tN1WCAD5qoqI9DnkaOntYGf7uDK2FZ1PVicVCmwSi6pXx5l8FQAd7JMbZVy8b37b7cGQo3lvYOmSG8/BTgUOtpT6uyj9DCNp72APsCZdkXT0HcwaJiKSsAS8utT2tZQPc/v9DjlaloBj6dmW4foNlNz6wBERrkrffSiwSSxOZStQ7+UmC2wpZdsObMDcmr71Yq1nRR8QXApvSDOGZDNlN3ZERHcHO1v2BoqHAIHrpuuQY2kIgW2HTq2uKRG1kGppkPiMLbSMQkTLqwY8WU3YQtuT1UmFApvE4kRLNZ+JVXSKc4UaKl4dl6y0F9gVr66tq9SOml9Hoer3rOgDmpfCdW8SkVL2FRFJOTbcekPb502uMvhl7VTCQk1TUVCs+hACmEsO0IOt+cbCSGAPcGIWZI31Oy7RwqYBT1Z1v1I2iVBgk1io/PViykHZAAeyEyc6VPQBQUSk3pBauied2CipLY69HexFJbA1bxKpeg24fiN2LEL3leDZIbp7dc6NFmt1LCSdwZaGhAJb10HHfNVDwhbRa6MfkpoK7EwosON067dD1xOPSYYCm8TiRNiccWjfHMqaO5DdOJ4OBXabiIjqPDYhAqFIx1yTDpjjYEebC/vIYAP6LsfIDtF8oLPrVqr5Aw04As2ISF3Tk/lC1cPizIAbC20Lnq/fcTm2XgIAXLQ8M9D3s0Vk96HAJrFQueP5lG10Bvv4RhlCAAf3zm772mwosE3KYadDB3s15pAjoL/AzpT6u5Sr2jV0zBpLKZEbJoOtscAu1nzMD7AmHWipo9O0SSRf8QfKXwMqg63fcXn8VA4LKQeHV7YP2MdB59fSpEKBTWKhBPZs0uyIyMmNMi7aM4uUs/2DcTYZvJxMyqinizUAiFXTpyq3dI+INIeR6GBnyh7cegPnL/V+frQjGADV8/VUrPlY6LMlQ+GEDrauy2aUgz0ISduCp2F05vFTOVx10RKsHgu9OqFWpes66zGJUGCTnkgpcXKjjEP75jCXsI3uwT6eLuHQvu3uNdB0sBkRaY+6HF7S/PmT6zMr2XSw9XvenM5VAAAX7hnssrYaANWRdKk2cOWaY6kMtp7HplD1+97iqAgiInodF6/ewFOn87j6wJ6Bf0ZS89z+JEKBTXqyUXJRcuu4eN8c5pJmR0TO5Ko4sLw9fw2YmcFeL9WQdKxYVWMLhgw5ZsMO335aRAA9HewzuaA7/4I97U9Ke6HzkOOpTKVt1CwOqkVEVwc7X/WwmBq071m/iMjRs0XU/AauPji4wE5ovkZ+EqHAJj2JmjP2zYXLVMwRkK1IKbFedLG62N6ZjDLYBh2fdNHF6nwy1jBSM4Ot9/HJlMMhx7g92La+GezTocAe1MHWNTdarPnIlD0c3Nv+ZL0XTvicoYO9HcfSbznRE6dyAIBXDONgqyiahq+nSYUCm/SkVWCb7GAXaj7cegOrHfLGs0nzHOx0sYZ9MeIhADCTsGBbAsWat8OParzkyh6SjoWZRLy311RC3wz2mVwVtiViLSJqh64C+1QmiM4M7GBbel/uz1eGy2Dr5uyrAcdLBxxwBJoOto7vM5MKBTbpierAPrg3GHKseHU0NH1j70avvLGRGeySG2vAEQCEEJhP2vpHRMoe9s7FrxhTERFdHezzF1OwBxzM0nXL5Vqm+Z46CJHA1kxIAkC9IVFy69FQdL/oGBEZdsARaF4pY1Xf7kGBTXpyYqOM8xZTmE3a0daxqqaT/d1YDxszOrlxUQbbIIc/XXRjDTgqFmcS2kdEspX4a9KBlku3df2Oy5l8BRcMGA8B9O3BXhvSwdY5T1uM1qQPmMHWbKGKP4IBR4ARkXFAgU16oir6gOZaXxNjIlElXScHO2lWD3aQSa/1dfl/IeVoHxHJlL3YFX1AyyZHTR3sCwcccARCB1sjsaRYywSLu1bmB1vAo4Yc6xpeScxXg/eHQR3sIIOtz3E5+uLwA46A3idlkwoFNunJyY1KJLBnDXRpFetFtVSlQwbbsIhIya2j5jf6EgnzKRslzR3sXNmLvWQGaHWw9frgk1LidLY68IAjAKTCFhHdunvXMhUcWJ4daFMhgChyo+PlfiWwlwZeTiTga/RaenwEA45AczkRHezdgwKbdMX1G3ghV8GhyMEOXAUzHexAYO/rICibEREz3sCajn4fDvZMAgXNNzlmK27sDmxAXwc7X/FR8epDRUTUyYduQnItUxk4fw003UgdW0TyleD9YeAMtmYRkSdGMOAI6HsiP8lQYJOunMpWICXaRET0FkntWC/WsDyXiD7ctmJbAknHMsbBVmvS+8lgL6RslHQX2GUvdgc20LJoRrMPvtN5tWRmuIgIAO22Oa5lygPnrwG9W0QKysEeKoOtz3F5bC2Hlw854Ai0DDnSwd41KLBJV6KKvpUwIpI0NyKSLtV6xiFmE7YxGWzl6HeqLWzHQsrRukWkEsZm+stgqxYRvZ43p6MlM8MMOYZLeDQSBcN2YAN6r0ovDD3kKLRxaUc14AgACU2vBk0yFNikK60d2IDZQ47rRbdnHGI2Yc4inl5Dn+2YTzkoauxgZyv9LZkBmhERXUSB4syQS2YAPS9rD9uBDbRuctTnuCiGHXJM2JY2x0UNOF4z5IAj0HSwdWwrmlQosElXTm6UkXIs7A+FZSSwNXPb4hA0ZvRwsJO2cRGRTpn0diymHJRcX9se9Wy5vzXpgL6bHE/nqrAEsH9xsPpaG+oAACAASURBVCUzQIso0MjBbnZgDy6w1cCajhGR0WSw9TguoxpwBFoWzfh6HJtpgAKbdOVEuoxD++ai/NdsOORYMTCDnS66PSvpZhLmCOz1Yg2LKSca7ozDwowDKfU9QRtEYFuW0OqytuJMroL9i6mOMwtx0LG791RWOdiDR0RsS98hx+MbJZy3mIrWwfdLwtZnVfqoBhyBoF0FYE3fbkKBTbrS2oENAHMJMyMirt9AruL13Fo4m7CMymD3Ew8BgogIAG0HHbPl/iMiQJA11tHBvmCIAUegdchRn2Ozlqkg5Vg9r4Z1w9G4pu/o2SKuuGBx4O9P2vpscnz81GgGHAEgaes3zzDpUGCTjkgpcXKLwJ41NIOdKcdrzJhNGpTBLtX6qugDgiFHoDnIpBvZSv8ONhAISd2ykWdyVVy4NHj+GmipMNRIFKgGkUE7sIHm5X7dFs3UGxJHXyzgivMHF9iObUHK6T82oxxwBIIV8gAd7N2EApt0JFv2UKj5UQc2EHzgWcK8FpFzhe5r0hWzBkVE0kW3r/w10BTY+jrYgwnslGNp6mAPJ7B1jIgM24ENNBfN6DLMpzi5UUbVa+DyIQS2LhsLnz1XRNVrjE5ga3JcpgkKbNKRrQ0iACCEwFzSMc7BVgN9vYccHWNOPtaLbt+XuZXA1rVJJFtxkXSsaKtnXAIHW58PvkLVQ7HmD9UgAujZsLKWqeDAEAOOQHPIUbeIyNNnCwCAy4eIiEQbC6f8OfPY2ugGHAE941aTDgU26Ug7gQ2opgw9BVIn4m4tnE2YsWim0ZDYKNV6ZtK3sjCjeUSkFKxJ7/fyv24OdlTRtzxkBluz3Gip5mOj5A7VIAIgGgCc9hjEVo6GAvuy8xYG/hnR9s8pf848cSqH+aSNl6wOP+AItCya0eykbJKhwCYdUQL70L7NHwZzSds8B7sYM4NtSEQkW/HQkP11YAMGREQqbt/xEEA/B/v0CDqwAf02OY6iQQRoGXLUrEXk6bNFHNo3Gw1DD0K0hGfKTz4eP5XDVQf2jGTAEWBEZBxQYJOOnNwoY3Uhhbnk5je72YR5Anu9WEPSsbDY441/xpAhx7iO/la0j4iUvb4bRICwRUQTEQk0HewLRjTkqIuDPYoObKBlVbpmbuQzZwq4/LzB4yFAS0Rkip8zox5wBILcvm2JqT4u0wYFNulIUNG3/YNgzhAR2cp60cXqfLLnpf/ZhI2a39B2kYpiPVqTPlhNn64CO1fxBnOwbUurDz7lYJ8/pMDWbchxbQRbHIGWIUeN3mdcv4F/P1ccKn8NtEREptipHfWAoyKhUYXhNDCUwBZCLAsh7hZC/FgI8ZQQ4v8QQuwTQnxTCHE0/O/e8L5CCPFJIcSzQojHhBCvavk57wzvf1QI8c5hfykyGrZ2YCvmUw7Khi2aiVtJp4bbqhq5ke3YKKnITH8OdsqxkLCFtgI7Ux4sIpJKWFoNH53JV7C6kIrEzqDotipddWDv7/N1sxUhBBxLaNUicixdgt+QQ1X0Aa1RiOk9+Xh8xAOOCp2W8EwDwzrY/x3A16WULwPwSgBPAfgAgG9LKS8D8O3w3wDwBgCXhX/eA+BTACCE2AfgwwCuB/DTAD6sRDkZH169gReylbYC29SISJy8seoJ193hT5dURKQ/B1sIgfmUo28Gu+xhea7/iIiODvaw+WtARwe7jANDdmArHFto5WA/oxpEhhTYzSU80/ucGfWAoyLl6PU+M+kMLLCFEEsAfhbAZwFASulKKbMA3gzgc+HdPgfgLeHf3wzg8zLgBwCWhRAXArgJwDellBtSygyAbwK4edDHRUbDC9kKGhKbOrAVpg459urABhCtDdd90HG96EIIYO8AYnIh5aCoYYtI1auj5jewZ3YQB9vWy8EeQQc20Gw+0OXYjKIDW5GwLK0y2M+cKcC2BF6yfzhRmdAgIvL4qRyuumh0A46KhG1N9XGZNoZxsF8C4ByA/yWE+FchxP8UQswDOF9KeRoAwv+eF97/AICTLd+/Ft7W6fZtCCHeI4Q4IoQ4cu7cuSEeOulFp4o+IOh6NklgSyljrwWPIiKaC+x0sYZ9c8koC9oPCykHBQ0dbLVkZpCTDjrY7dFtk2MgsIfLXytsW8DXqEXk6bMFXLIyF5kUgzLtdXR+vYEfnc7j6oOjjYcASmBP53GZRoYR2A6AVwH4lJTyJwGU0IyDtKPdJ7Hscvv2G6W8Q0p5nZTyuv379/f7eEkfRAJ7pb2DXTEog12o+XDrDazG6HxWArvi6vPB1464JxztWNA0IpKtBLn0wTPYepyUlV0fuYo3EgdbCKHNyUfZHU0HtsKx9BJLz5wtDp2/Bqa/ju6FbBVVrzGSY7GVJCMiu8owAnsNwJqU8qHw33cjENxnw+gHwv++2HL/Qy3ffxDAC11uJ2PkxEYZSdvC+YvbPyTnkjbKXh1S6vPm3o11tSZ9sY8Mtu4O9gBLZhQLM46WQ46ZUrgmfYCISNLWZ8hxVB3YCl1EwamwQeTAkMt3FAlboK6Jg1316jiWLg2dvwaCbDowvYOx6r1xaXbwLvBOTPuQ4z8/cw4/eiE/7ocRm4EFtpTyDICTQogrwptuAPAjAPcCUE0g7wTw5fDv9wJ4R9gm8hoAuTBC8g0ANwoh9obDjTeGt5ExcnKjjIP7ZttmwGaTNqTU57JtL9Sa9DiC0pQM9jAO9nxKT4GdCx3sPYa3iDQ7sEcjJIMlPNP/empW9I0mg21bQpsM9rMvFiElcMWQFX1AMyIyrcemFF4dHmbZTieSU17T95/v/jfc9S/Pj/thxGbY/wffB+CLQogkgOcAvAuBaP87IcS7AZwA8Evhfe8D8EYAzwIoh/eFlHJDCPFRAA+H97tNSrkx5OMiQ3Jio4xL2uSvAWAuFJFltz50Xm4aaC5ViZ/B1r1FZL1Yw0qfHdiKRU2HHIfJYKfCGISUciQNE+NEOdgXLY/IwdYkIqKWzBwaUUQkYVvwNGkRaTaIDL4iXTHtEREVn9u64G0UTPvVoLJb35HjslMM9UillI8CuK7Nl25oc18J4L0dfs6dAO4c5rGQ0SGlxPF0Ga++uH1bonqCl10f+wYUWdPEObVUJU4PdlL/IUfXbyBf9fvuwFboWtOXrYQRkYEc7OB549YbSDnTfdJ6Jhc4tcMumVHo4u6vZSpIOlas95E4OJY+EZGnzxaQtC1csjJ8LZ3a5DitAlsVCMynRv8+MM0tIlJKlN36jhyXnYKbHMk2chUPharftqIPMKfrWaEc7DgnE7MGRESaS2aGGHJ066hr4r4pMmUXSduKngP9oC5rT7O7pDidq2LffHJkV7f0cbArOLjcPnY3CLYltBlyfOZMAS/ZPx+5z8OQmPLXkjIf5nfAqQ0y2NP5nKn5DdQbcqocbApsso3j6eBSZic3YS7ZjIiYQLoYbOeL8+ZvQkRkXUVmBhxyXJwJ3iBLmjXR5Moe9swlBop4pBL61NGdyVVxwYjca2D6L2sr1JKZUZGwLW02OT5ztjiS/DXQFNjTuoQnEtg7kcGe4tdS5Own6WCTKeZYugQAONymog9oOtjGCOxS/LzxTDJ4SensYKuhz9UhhhwBaBcTyZY97B0gHgLo52CPqkEEUEOO039cRrlkBtBnk2Oh6uFUtjKSBhFg+iMipfBzdW4HhGRyiiMiO5lN3ykosMk2jqfLEKL9Fkeg+QSveHoJpE6sF9zYeeOkbcESemewm0OfA9b0hQJbt0HHbMXF8uxgJx1aOdj50WxxVOhQYVh2faRH2IENBBnsaW3KaOXoi0UAw69IV6hNjtN6slp2fdiWiJYsjZLEFLeIKNNqjhlsMs0cS5dw4dJMxwylaRGR9VIN+2OKSSEEZhO21hGRUWSwAWhX1ZcNIyKDkLTDIccpFQWKqlfHRskduYM97QL7VFTRN0qBbWmxyfGZM0GDyKgWqySs6d7kWKrVMZ+0d6RNaJojIjuZTd8pKLDJNo6ny12nuWcTZgnsfjufZ5O21hGR9WIwzLc4YEZwYUZfgT3IkhmgdSX4dD9vog7sPaMTkinHnlpRoBh1BzagT0Tk6bMFzCbskZ18qIjItObTSzV/R/LXwHS3iJR3MDqzU1Bgk20cT5dweLXzB8GcQS0irt9AruL1NdA3k9BbYKeLNawsJAd2WBZ0zWBXXOwdsLYyOeWXtRWj3uIIBCcf7pSfeIy6AxvQKCJytojLz18YabuKENObwQ66nndGRCamuJFnJ4c/dwoKbLKJQtXDetHt6mA3e7Cn+0MvDioOEWdNumI2YeudwS4NvsURaArsgkYZ7KpXR9VrYM/QDvZ0fvgpzuQDp3akGWwNhhzXMhUk7dF1YAOAM8VuZCtPny2MLH8NBDG9aa6jK7k752Cnpvi1FGWw6WCTaUVV9HVqEAGAmYQFIYCKZjVr7Rikkm42qXcGO12sDVzRB+iZwVZbHAdZMgPQwe6GDj3Ya5kKDuwdXQc2EEQhpr1LfqPk4lyhNlKBDQAJa3qH+cq1+o7ljIOIyHQ+Z0o1JbDpYJMppVcHNtAc5DPBwR6kkk73iMh6n5n0rehY05etBM+TgVtEwu2NOmSw98wmRvohqMOQ41qmPNIBRwCwLWvqM9jRivQRdWArEs70doQXa/6ObStM2BbqDTmVJ2bl0NBjiwiZWlQH9iVdHGwguExT1lhEKtYL/VfS6dwiIqVEulQb6lJ30rGQdCwUNBLYR88GVWMXLQ/m3CY1iYiMugMbUBns6T4up7KVkQvsaXZpFUfPjrZBRDHNEZGy6++YS6veZ6bxeRM52CPaELsbUGCTTRxPl3DeYqrnC1z3GIQiXQoEdj8O9pzGLSJlN8gax1kb343FlKNVD/a3nzqLffNJXHNweaDv1yaDnRttBzYw3dViQDAMvl50R9ogAgQtItPoRLby9NkClmYcnL80umw6MOULVdz6DjrYQURpGnPYZddHyrHgxNioPClMzyMlu8KxdBmHu8RDFHMJJ7pkozPpooukY0W54TjMahwRSRfDDuwhBfbyXCIaIJ12/HoD9z9zDq+7Yj/sATO2KY0y2KN2sJNOEIVoTKmYPJUNYnc7ERGZ1jyt4pkzRVx+/uLIO5+neaFKqebvWAY7crB3+X2m3pA4nasM9TPKbn2qGkQACmyyhePpUs94CBA42CZksNeLLlbn+6ukm0naqLjT+ebei/XI0R/Ocdq/mIoGSKedH57IIlv2cMPLzh/4ZzQz2NP7vKn5dawXa7hgabRCMhoAnVLBdHIHlswAgYic5kUzUsqgQWTE+WsgaFiZxgrDRkMGNX07JCST9nheS//r/3ser/uz+5ELh8EHoeT60Q6OaYECm0SUXR9n8zUcXo3hYBsSEVkv1rC62J+Y1LmmL3KwhxhyBAKBvl7Uw8H+9o/PwrEEfvby1YF/hg4tIi/mgxOmkTvYoSioedN5bHZiyQwQbHKsT6GIVLxYqCFX8UaevwZUBnv6ni/qyuf8DvZgA4Dn7+7z5mtPnEHNb+DxU7mBf0a5tnPRmZ2CAptEnNhQDSK9PwjmDHGw06Va33EIFRGRcno//DqRLvY/9NmO1YVUNEA67XznqRdx/Uv2YXFmsIo+oHXIcXpfU6ejLY4jHnIMXatafTqPzVqmjKRtYf8IO7CBIIPtTbGDHTWI7IDATk5pRKQUNWXsUE3fGK4GbZRc/PBEBgDw2KnswD+ntIPDnzsFBTaJOLauOrB7O9izSUfbnHErwZr0Ph3spI16Q059PrId51SrypAZ7P2LKRRq/tQ7/SfSZRx9sThUPAQIts85lphqB1tlLEfeImJPt7t/rlDD/sXUSDuwgenf5PhCNni+XBzD0OmXaV0JrpoyFnbIqU2O4bV0/9MvQspgzuTxtSEc7B0c/twpKLBJxPGwoi/OG95cwtaqx7gdUspQYPcnJmdCx03HE5BT2QpWF5LR7zgoqpVl2nPY3/7xWQDADVeeN/TPSk153/OZHXKwpz0+k6/4WJwZvfPm2MHw57ReKctXgs+PpR05NmIqDQ71mbpzNX3BSd5unnx8+8cvYv9iCj9/5fl4bEiBTQebTC3H0mWszCexFONStwk1ffmqD7fe6PvSrhrEmHZ3th2nshUcGEGWVA1JTnsO+9tPvYif2D/fdTFTXKa9ju50roqFlDNUVKYd0z7kWKh6WJod7TEBAgcbwNRW9RWqHoTAjjRmTKuDrWKXO7nJEdg9ge3VG/je0+fw+ivOwysP7cGpbCWKGfZL0A9OB5tMKXEbRIDmoplpdU/i0Mwb95nBTgYvKx1PQE5lKji4PHwbQiSwpziHXah6eOj5NH7+yuHiIYqUY091BvvMDlT0AdM/5Jiv+jvm0gKY2m2O+aqPhZQz8ugMML092CqDvZObHIHduxp05FgGhZqP1195Hq4+EOwIGHTQsVSjg02mmOMxO7CBYN11vSGn1lWKg1qTvjI/mIOtW0Sk0ZBYy1ZwYAR1Y6qZZZojIg8cXYdXl3j9y4aPhwAaONj50S+ZAYBUQgMHe8SuPgAkrOC4TKvALlT9HTkuQOhg73JTxihQEZGd6nve7atB3/nxWSRtC//nS1fxigNLAIAnBhTYZdffsXaVnYICmwAI4gwv5CqxL3VHIlJDl1bRXJPODDYQdGC7fgMHRuBgqyHJaRbY33rqReyZTeDVl+wdyc/7/9k77/g47jr9PzPbm7TSrpol2bKKux3XxCXFSUgltBQIcJQQCNwFjnbHFY7jd/c7jnpHcnAcPw4SIEdIIIEkcJCEuMRJnMQ9sR3JlousrlXbou3l+/tj5ru7knZXW2ZGGu33/XrxIpFW2slI2v3MM8/nedTuwR6fCpecj56J+VjMkhJvMCqLB5uWGsVUfOEhx3kB5E9YeaPPje/+6Sx8oeJznTMRoHXgMg2S+qRFRJmLjz1dQsKSRbSOtdZYivJhy50PLhdswGYAEKKkCAFanPlbRAAs6qi+MVHBLtqDvcjOzYCEhRlGnQY2o1a1Hux4gmD/GRd2r6yRrLpX7Qq2NxhFpQxeYyWWHPd1uZIJOVKSSBBMhWOyeLBp7bUal/kAQcGWa8CW0yISjMRx/6PH8OCebtz8wEs4cHZUsu+dtIjI7MFW4nXm4pgfF0b9uD7tDt+GxsqiLCKhmLz54HLBBmwGgFREX94KdhkM2NSDXVVoDrZ+cSrYA2KslhQWEUC4cBlVqYJ9os+NcX8E10vkvwbUrWAnEgS+sDwDUyojXJ5zM+wJ4Z6fHsYnHzkiuRrsj8SQIJAtRQRQ8ZJjOCr5QixFTovI9/Z2o38yiK++Yw1Meg0+/NAhfOmJN+AJlq5mJ5ccZbaIKOFP39vlAgBclxZhur7JjiFPCC5fqKDv5ZdZ2ZcLNmAzAAA9YkRfS95LjsILwKK2iEyFYTfrklf9+bJYPdi0kU4Kiwig7rKZvV0j0PAcrumokex7qlnB9kdiIASyeGoNMpfw0EHgWK8b3993TtLv7QsJiqQcgyS1iKhxmQ+QV8HWaeUpmuke8eFHBy7gjs1NuGfXcvz+M1fiz3e34Ymj/bjpuwewV4ztLJapcAw6DZcchKWG3vVQwoO9t2sEHbXWabG/G5oqARTuww5E5I0vlAs2YDMACAuOlSYd7Ob81NqURWTxZmGPT0WK8pQaF6k/fWAyiAqjdDFsTptetR7sPZ0ubF1WhUqzdIOTmlNEvOIgWWGS/g3QoBX+nuS6+NjbNYLmahPes6kR39t7Ltk6JwVe0aMry5Kj2lNEgvIsfwJCjbzUAzYhBF9+6hQsBi3+/tZVAITX+r+5eRWeun8XKk06fOynR/CPT58qOl0rEJa3rVCpfQZfKIrXL0zguhn9AGsaKsBzKNiHTRVsVjTDUCU9BUT0AWkWkUWm0qYzPhUpqrGQnpvFloM94A6iSYIMbIrTalClB7t/MoCuYZ9k8XwUvYotIt6gfIOknMkHoWgcL58bw/Wr6vBP71qL+gojPvfYCUxJVKKVUrBlsIjw1CKivt8ZQoi8HmwtL7k3/cljAzh0cQJ/d8uqWe2+G5rseOYzu/CxXcvx81cv4Xt7i7sT4o/EZfUZK2UReal7DLEEmdVwazFo0V5rLbjRkSnYDFVzaTxQUFkGVbAXm0qbzpi/uFSExWsRCUjmvwaEAdsTjKrOFrGPegslaG9Mx6Bii0hywJZjyVFG1e3g+TGEoglct6oWFUYdHrh7I/onA/inZ05L8v3lPC9aXr1LjqFoArEEkdGDLa1FxB2I4F//0InNS+1479bmjI8xaDX4ym2rcfvmRvz7n87i6RMDBT+PPxyTzX8NSFc0QwjB8d7JrHfc9nYJCUubl9pnfW5dYyXeHPAUpPKnvOlMwWaojEgsgf7JQN7+awAw64QXgcW85DjmCxcc0QekW0TUOSxlghCCgcmgZP5rIFU2M+5Xl03khU4XljstaKuxSvp950vB9oWiRWfTUpIWETkVbBnOzZ5OF8x6Da5orQYAbGupxl/sbsevj/bjjyeHSv7+sirY4rAUU+GATePtZPNgS1wj/81nu+AJRvG196zPWYzDcRy+fvt6XL68Gn/9xJs4emmioOfxyxxFJ0WKCCEE33n+DN7zg4P4wH+/Pit9J5Eg2NflwjUrMicsbWisxKgvjBFv/q/7VME26ZiCzVAZA+4gEiT/BBEgLSljkXqwI7EEvKFYUQq2hheWVBaTgu0JRuGPxCWJ6KM4xYuXMZ96bCKhaByvXhjHtSulVa8B6sFWbsAORGL4r/3ncdW39uEd338ZPWP+or8XVWrVlCJCCMHeLheu6nAmfd4A8Nm3dWBDUyX+9jcnMewpLO1gJnJ6sFNNjuq7kPfKeOEBpCu1pQ/YRy9N4JeH+vCxXS1Y3VAx5+MNWg3+359tQaPdhE/8/Ch6xwN5P1cgLG+ZSmrJsfjz8h97zuE/953HNStqcHrQg3d9/2WcHkxdoL/RTxOWMr9Grm8SVO03+915PyfzYDNUS6EJIsDiz8GeoC2ORSjYgGATWUwe7H4JM7ApamxzHPUJZTur6m2Sf2/BIiL/70woGsfDr1zE1d/aj28+24UVdTYQArx8bqzo70kVSbmsEDwnvYLdOeTDkCc0yyeq0/B44H0bEYkl8MVfn0CihCVCeT3Y6l1y9Ml44QGkZ4SX9jsTjSfw5d+eQkOlEZ9724q8v67KosdDH92GBCG456eH4AnkF+Hnj8hbB85xXEkZ4T/Yfw7ffeEs7tjchIc/ug1PfGonCIA7/+tVPHtKuOOzt8sFngOuWZE5YWlNQwU0PFdQHjbzYDNUyyVRuSpIwdYt7gGbDn2F1qRTTDrNovKnpyL6pFtypAU+asrC9sjoqZU7B5sQgscO9eLa7+zHP/3uLbTXWvDEp3bg8fu2o6HSiIPnix+w5VQkOU64IyT1kiONVNu9avYg0FpjxVduW4NXzo3jhc7io9e8wSj0Wj5pG5MSuuSoxpg+OS88AOm8xr947RK6hn346jvWFuyNXu604P/92Rb0TgTw5784mtex+MMxWGVWaXUarqiL1R+/dAHfevYM3rVxCb515wbwPId1jZV4+tO7sKrBhk/9zzE8+EI3Xuh0Yeuy6qyJZCa9Bh211oKSRPzMg81QKz3jAVj0muQt+3zgeQ5G3eKyQaRDB+xCzkk6Jr1mUZ0bqUtmgJQHW00KNrVCyNVYGIknJPONzuS508P429+cREOlEY9+/Ao8dt8ObG2pBsdx2NnmxKvnx4tWa73BKMx6TcGZ8fmi10i/ALqny4XLmipRazNm/PztmxsLVtpm4g3FUCHbECmotGosmpEzHxxI8xqXMGD7wzF8b+857Gh14Ka1xSUGXdHqwDdu34CD58fxH3u653x8IBKTvQ5cSFgp7Lz87GAP/uV/O3Hr+nr8212XJTPYAaDWZsQvP7Edt29qxHdfOIvOIe+cC+AbmoRGx3xf6wKRODgOMGrZgM1QGZfG/VjmsIDjsi9vZMKs1y7aHOxxMT6uGA82ICw6LqoBezIIs16DKglzn016DSx6jao82B4ZB2yDlgch8qVCPHqoD0sqjfj1p3ZiZ7tz2ud2tTswGYjirSFvUd/bG5Iv0xgA9BL708emwjjR557WMjcTo06DVqcFnUO+op/HJ+N5oUOOGpccvbIvOZaesPLQyxcx7o/gr29eWfB7Yzp3bGnC+sbKvBRbf1jemD5AbLksYMD+5aFefPWZ07hhTR0evHtTxsVFo06Df3vvZfi7W1ahvsKIW9c15Pye65vsmPBHMJjnjkMgHINJp8m5YLoQYQM2A5fGA2hxFn7r36TTLFqLCE22KN6DzS8yi0gAjXZTSW80mXDaDLIr2PEEwdhUGJ1D3oIremeSHLAlvNCgyJn33DcRwEvdo7hra/M09Ymys00YuF89P17U9/cGY7KUzFAE+4x0f0/7ulwgBFkXsSgr6204M1LcRQcgKNgL3QYxH8jp2QdS5yZW5LmZ9EfwowMXcMOaOmxeWlXy8djNOrjnqFKPJwiCUXk92IBwbiJ51sj7wzH849OncFWHE9//wKacd6g4jsMnr2nDa39//bT2xkxsaBQaHU/muegotzddLtR3xAxJicUT6JsM4KZ19QV/rVm/uHzG6YxNRaDX8rAWebvOrNcuLgXbHZTUHkIRymaKG7A9wSgeevkipsIxRGIJhGNx8f8T8EfiGPOFMToVxoQ/kryN3uIwY/9fX1v08cqrYAvKVTgaL/r3Lhu/PtoPALhra1PGz9dXGtFaY8Er58fwiatbC/7+3lBUttv9QOEZ4ZfG/YjEEuioy7yMurfLhboKA9YuyZ0KsbqhAr9/cwi+Iv/7fKGobEOkVuUWEZ6DbGptqRcfP3zxPKYiMfzVjSslOR67WY++idxpIvRusNR/+zMxFLDPcHrQi2ic4J5dLdOSdkplVYMNOg2HN/s9uHkOtRsQzo3a/NcAG7DLniFPCNE4KShBhGLWL14Fe2wqjBqroWjF1qjTJJNIyjG7wwAAIABJREFUFgMD7iA2Ns8uDSgVp1WPi0XGwz1xtB8P7umGRa+BQaeBXsPDoOOh1/Aw6zVoqDRiQ1MlnFYDamwGdA178ctDfcLFQpF53p5gFBqek2UwkEvBjicIfn2kD1d11ORs4tzV5sSTx/oRiSWSx5IvvlCs6H2FfNAXMGATQvCJnx9B70QAj923Y9bvbSSWwIGzo3jnxsY5/75pWszZER+2LKsu+Li9wSgaKjN7vEslWTSj0gHbatBKfkeMksp7LvzcDHtC+OnBHrxnYyNWSpQWZDfpkhfn2aDvpWbZlxx5RPP8W6JReutExVkqDFoNVtbb8t5v8IeZgs1QITSir5AEEYppESvY/RNB1FUU578GhHOzWGL6psIxuANRSWvSKU6rAYd7Jov62lfPjxWkSJ8a8OCXh/pwpGcCjRsbi3pOTzCKSpNOlsHAQPOeo9IO2Ae6RzHkCeEfb1uT83G72h145LVLeKPfjW0thQ2T3lAUrTWFv4bkSyEpIicHPDg7MgW9hse9Pz2M3/zFzmmvb4cuTsAfieP6VXNnmdMBq2u4uAHbF4rJ5sGmKSLF2iDmE7nveOi1xcf0/cfebiQIwedvyD+Wby4qxQE7kSBZfcT+sKBgW+S2iGjzb7k8OeBBQ6Ux6yJwKaxvtOMPJ4dACJnz9TQYlTcfXC6YB7vM6RFD8FuKGLDNei0C0cW35BiJJfBGvxubSvDemRZRwsrApPQJIhSn1YDJQKTgISEWT+D1CxPY0eac+8Eiq+ptsBq0OFLkQA+kBmw5kEvBfvxQHxwWPa5fnTsJYXurAxwHvFJEHrY3KPOSYwEpIk8e7Ydey+PxT25HnBB89OHD0+4m7ekagUHLY1f73L87jXYTbAYtuopcdBQGSXkGplTRjDoVbLnOC5B28VFgCU/PmB+/OtyH91++FM3V0gkKdrMOCQL4wtnfL1N14DKniGgKuFjt92C9xOo1ZUNTJTzBKPomgnM+1h+OJ8vt1AQbsMucS2N+GHU8am2Fq7WmRWoROTXoQTiWwLaWUgbsxZMiMuAWLsKkrEmnOG0GEIKC7TSnBr3whWPY2ebI+2u0Gh6bltpxuKew+uJ0PEH5PLUpD7Z0A/aoL4wXOkdwx5amOW0fdrMe65ZU4uC5whYdCSFCHJ2MS4751shHYgk888YgblxTh01Lq/DjD2/FgDuIj//sMELROAgh2NPpws42R15v2BzHYVWDDV3DhS86RmIJhKIJBRRsNQ7Y8l6QFWsR+fc/nYVOw+PT17VLejz0otybwyYylVSw5beI5HOx6g1FcWHMjw1N8gzYdHB/c2DuRcdAJCa7si8HbMAuc3rGA1hWbSkq/sa8yMpUKEfEAayYW8IU4yKyz1AFu1kGBbtG9O0WWjZDS1G2t+Y/YAPAtpZqnBnxzemHzIZXEQVbut+b3xzrRyxB8N6tzXk9fme7A8f7JguK3wxE4ogniKwDU75Ljnu7XJgMRHHHFmGZc2tLNR5430Yc73Pjc4+dQLdrCr0TAVw3h5qfzsp6G7qGfQXnk/tkjqJTc1W63Ap2MRaRtwa9eOaNQdyzq0VySwQtXXHnaHRMthUqkIOdz8XqKdEfTavNpWZFnQ16LY+TecYXyu1NlwM2YJc5QgZ2cbfCFuuS4+GeSSx3WlBThKpPMemE3N5SapYXCv2TQeg1fNGZ4LlIlc0UpmAfPDeOlXW2gn9GW1uqQAhwrLc4m4icFpFcHmxvKFpw5jwhBI8f7sO2liq011rz+pqdbU5E46QgX3wq01hOT21+A/YTR/tRYzPgqjT7x63rG/DlW1fj2dPD+PjPjgAArsvDf01ZVV8BXyiWd2YvhZapyBZFp2oFW94Bu5gUke88fwYVRi0+eXWb5MdDXzPcweyvc/6waBGRWcFuqjLjvGtqTlseHXzlsojotTxWN1TklQ/OFGyG6ognCC5NBNDiLG45ybQIi2YIITjSM4Ety0rLPqVV8iEJs3vni353EEvsRllC/umAPerLX8EOx+I43DOBne2FqdcAsLHZDi3P4fDF4mwiwoAtzws9VbDDGd74Pvjfr+Pqb+3H86eH8/5+h3smcWHMj/dtW5r312xrqYJOw+FgAT7s1CAppyKpmdM3Oj4Vxv4zLrxnU+OsMoyPX9WKe3a1oHcigFX1toLsTjRJpKvAEh65Lzw0Klaw5V5yTNXI53fx0Tnkxd4uFz61u02WjHu7+D3zUbDl9mDvanfAF47hzTkSPN4c8KCpyoRqi3zpQGsabDg7Mvd+QyDCFGyGyhh0BxGJJbC8yAHbrNcgGieqLDrIxvlRPyYD0ZL81wCS/s7FYBMZmJQnAxsQPNhAYXXpx3vdCMcSyXKUQjDrtVjbWFnUoiP1GiutYF8c8+PkgAfhWBz3PXIUn3vsONyBuRX/xw73wmbQ4tb1+Wfcm/VabFpahVfO5z9gU1/pfC85Pn1iELEEwR2bM2d9/8Pb1+D+a9vwhQLTIVakJYkUQvLCQy6LCF96W6HU5JPJTQgR0lVkvSArzCJC72i9Y8MSWY7HLr5m5LKmTSUVbHkH7B2irW6ui+iT/R7Z/NcUh0VYcs91pzcWF7oNzDqmYDNUBM0fbi1hwAawqGwi1H+9tcCYspkYRQV7MSw6DriDaLJLH9EHCLdDjToeYwUo2AfPj4PngMuXF/czurylCif63QU3A06FY4gniOwD9kyl9k9vCar17z9zJT57fQd+/+YQbvjuAezpHMn6vTzBKP5wcgjv3Lik4PzYXW1OnB705jXEAymlVi4rBEB9o7l/Xk8e68f6xsqs2cUansNf37QKN64trFSrwqhDU5Wp4AGbXnjIpdRSG8RCKZo50efGpn9+Hn84OZTzccGo4NmXU8Eu1CLSOeSFzahFk0xCQkUeA3ZAXHKUOy3DYTVgTUMFXsmxzOwORNA7EcD6Rnn81xSarjKV4054IErTVZiCzVARdMBeXmR+7WJSaSmHeyZRbdEXfdFBSVpEVD5gh6JxjPrCsinYHMcV3Ob46vkxrG+sLHrQ3dpSjUgskVziyRc5WxyB6U2O6Tx/egTrGiuwzGHB529Ygafu3wWHRY97f3YEX/zVGzgz7Js1fD5zYgChaAJ3F2APoexqd4CQ/GvTvUF5lVqAVqVnH5Y6h7w4PejFHZuLyzefi1X1toItInJbZ6hjayHkYPeM+fGxnx6GNxSb80KEnpeF5MHuHPJhdX2FbMU3Rp0gJOS6aPVH4tBr+IJLnophV7sDR3sns74/0QIYuRXspDfdn+vCQyzgYR5shpq4OOaH1aBFTZHLaykFe/H4sI9cmsDWZVUlv9DSATsYmf83v1IYdIsZ2DJE9FGEATs/tTQQieF4r7ug/OuZbBX99YUW3Mg9YGfKwR71hXG0dxI3rkmprusaK/HMp6/EZ65rx1MnBnDTAwew+ivP4trv7MfHf3YE3/hjFx4+2IO1Syqwvog3yMua7TDrNXnbRJRQsOdKEXnyaD90Gg7vLLJAaC5W1Vfgwpi/oLsecnuwOY6DTsPNe5Pj2FQYH3n4EAghMOk0mPDnvlj2KbAUSxNW8rHPJBIEZ4Z9WNUgTWtjNuwmfU4F2x9Wrg58Z7sTkVgiq1WOLh6uWyLvgF1F01VyLX8mvelMwWaoiPOjU1jutBQ9TJpET9RisYi4fCFcGg8U3GKXiaS6r3IFe8AtX8kMpcaWv4J96OIEYglSUP71TBxWA1prLEk7UL7QN0f5crBne7D3dI6AEOCGNdNj5fRaHl+8cSX2fXE3Hrx7Iz59bTtWN9jQO+HHT16+gAujfvzZ9mVFHYdOw+Py5dV552GnrBDy5mBH4omMUXmxeAJPnRjEtStrZVvIWtVgQzxBcM41lffXeEMxcBxgk3FpTcvz82oR8Ydj+NhPD2PEG8JPProNS+zGOTPtvQoo2PoCFOz+ySCmwjGsbqiQ7XgAwQ6Ra8nRH4kpptJe3lINLc/h5Sw+7JP9HrQ4zLIsfKaTz/InvUOuRgW75CPmOE4D4AiAAULIbRzHLQfwGIBqAMcAfIgQEuE4zgDg5wC2ABgH8D5CSI/4Pf4OwL0A4gD+khDyXKnHxZibi2N+bC6hrZBeUap9iKQcFa/mt5a44AgU58GOxRNw+cJYIqNaXCj9Yga2XN5EQFCwj+cZm/fq+XHoNFzJF0HbllXjubeGc1YXz8Q7Dwr282+NoLnalEyymMlShxlLZ8RsRuMJjPrCaKgsPst3V5sT+890YsgTRENl7p+9LxSDQcsnLS5yoNfwIERoLdRppv+8DnSPYmwqnMy+lgN6/s8M+7A2T1XPG4zCqtfKkr5D0fL5115LTTSewP2PHsOpAQ9+9KGt2Ly0Cg6LAeNz3I2Se/kTKMwi8pZo/ZF7wK4w6eDO6cGOK6bSWgxabFpqT/YJzOTkgAebS0zSyofkgD2Hsg+k7pirCSkU7M8C6Ez7928C+C4hpAPAJITBGeL/TxJC2gF8V3wcOI5bA+BuAGsB3AzgB+LQzpCRUDSOAXcQrUX6r4HFt+R4uGcSRh2f9xtoLlIWkfzPzS8P92H3t/ejbyJQ8vNLxcBkEBqeQ32FtMUL6dRY9ZjwR/JS4g6eH8empVUlLwJtbamCOxDFudH8FUnZLSKa6Qr2VDiGl8+N4cY19QXdZdJpeCyxm0qyOdEIxHxUbG9IvnZLCr34yKSOPnl0AFVmHa5dmX+2daG0OCzQa/mCFh2FpAx5z4tWw81LDjYhBF/+7UnsPzOKf3n3erxNvMNSbdFjfA4FWwmLSGrAnvvcdA55wXPAyjq5LSK6nE2OSirYALCr3YmTAx54ZqjHY1NhDLiD2CBT/nU6lSZawJP9dyaQVLDVNxaWNGBzHNcE4O0Afiz+OwfgOgBPiA/5GYB3i//8LvHfIX7+evHx7wLwGCEkTAi5COAcgMtLOS7G3PROBEAIio7oA1IWkeAi8WAfuTSBy5rskiyZ0AGwkCXHIz0TiMQT+PWRvqKeM54gODXgwSOvXUJ3Htmi+TDgDqK+wjgrV1hKnDYDEnnUpXsCUZwa9JRkD6FQBbyQ2nS5B2ythoeG55JNjgfOjiISS+DGNfm3DkrF6voKVFv0efmwvcGYrGokANRWCHsiO7+xF+/94av4z33n8JaYdPKnt0bwro2Nsi6HaTU8VtRZ0VnAoqOQ9SzvedFqeMTmwSLyH3vO4VdH+vGX17XjA1ekFmmrxYvlXNClWHmXHPOP6esc8qLFaZE9vWNOi0g4BqvMGdjp7Gp3CsvMF6ZfRJ9MNjjKP2DnYxHxK5QPLgelHvEDAL4EgF76OQC4CSF04uoHQLdOGgH0AQAhJMZxnEd8fCOA19K+Z/rXTIPjuPsA3AcAS5cWvh3PSHFhlEb05dfwlonFpGD7wzGcHvTiz6+RpsXLVIRFhKZa/OpIPz77thXQzHFrORpP4HivG4d7JnDo4gSOXZqET7yddlWHE4/ce0WRR59CzgxsSqrNMZyzmfG1i+MgBEXlX89kmcMMp9WAIz2T+OAV+XmVPcEoNDwn65ugQcsnFeznTw+j2qIvufSoGHiew45WB17LI0lECQX73Rsb0Vxlxv4zo9h3xoVvP3cG337uDCx6oYDmThntIZSVdRV4qXs078f7QlFZs8EBwSKidIpINJ7AD/afwy3r6vH5GZniTosekwHhblS21y8lFGyO4/K2z3QOe7FBpjrwdCpNupzLfIFIXJa23Gxc1iQsMx88P4ab16WWqE/2e8BxwNol8lpmAOFOg9WgzV3AE1avgl30OwXHcbcBcBFCjnIct5t+OMNDyRyfy/U10z9IyI8A/AgAtm7dujDCP1XKhTHh1niLs/h848U0YJ/ocyOeIJL4r4HCLSJT4RgujPmxdkkFTg968eJZF65blVu5/OQjR7G3ywUAWFFnxTs2LsHlLdV45dwYnn5jEMFIvGRVpn8ygO2tpSvGuUgfsHPx6vlxGHU8NjaX/mbIcRy2tVQVrGBXGLWyRXkBqWW+aDyBPV0u3Ly2Xta7B7lYUWfD/54cQiSWyKkOe4NR2M3ytb0Bws9ra0s1trZU469uWgmXN4T9Z0fx4plRmPQaRYaB1Q02PHmsH+NTYTjyGIS8wVhJPvh80Go4xRXsc64phGMJ3LxutnWp2qIHIcIt/2znyBeKgefkrwTXa/k535t8oSj6JoJFxVkWit2sRyiaQCgaT+7opOOPxBRVafVaYZn5lRmLjm/2e9DqtMh6AZTO3BceooKtwiXHUl65dwF4J8dxPRCWGq+DoGjbOY6jZ6IJwKD4z/0AmgFA/HwlgIn0j2f4GoZMXBz1o8ZmKOmPaDHlYB/umQDHQbLFDqNe+NPKV8HuHPKCEOAz13XAadXjsUO5bSKvXxjH3i4X/nx3G45/5QY8//lr8K/vWY93b2rEOzcuQSSWwGsX8kuByEY0nsCwN6SAgi0MZ3MN2AfPj2FbS7VkVoBtLdXonwxiyBPM6/GeoHwtjhSqYL9+YQK+UKzgUhQpoXcTxueIXfMq4DWeSW2FEe/d2oz//OBmfOeuy2S96KGsTFt0zAdfWH6LiI5X3iJyelCwyWTaVam20t+Z7AOTT6xJl/tntrLehhN97pyPoT/LbEvEUkJfO7L5sAPhuOIq7a42J86P+jHsCSU/dnLArYiiT7GbdbN84On4xflCbguPHBT9TkUI+TtCSBMhpAXCkuJeQsgHAewDcKf4sI8AeFr852fEf4f4+b1EyFx6BsDdHMcZxASSDgCHij0uRn5cHPOXXKZCFzIWg4J9pGcSq+orJLulq9fw4Ln8PdgnxdzRzUvtuGNLE/Z0ueDyhrI+/sE93XBaDfjs9R2omhFNtq2lGiadBvvPuIr/DwAw7AkhQeTNwAbS6tJ92d+UXb4Qzo5MYVd76fYQCvVh51ub7glGZR+wqYL9/FvDMOk0uKpDuv/eQqEXPqNztGz6FPAaLwRW1Qsqeb6Ljt6g/BcemnmwiJwa8MCs12Tc33GIr0W5kkR8oZgivy9XtTvxRp87mUeeiU6FEkSAtFKVLAP2lMIebADJ11OqYo94QxjxhrFegQVHSpVZsBVlIxCJQcNzyRhTNSHHEf8NgC9wHHcOgsf6J+LHfwLAIX78CwD+FgAIIacB/ArAWwCeBXA/IUT9E9sC5+KYv6QEEUB4cddreQSi6l5yjMUTONY7iW0S2UMA4Za2SafJW90/NeBBjc2A2goj7t62FPEEwRPH+jM+9nDPBA6eH8enrmnNeKvRqNNgR5sD+8/m7xfNRCqiT56adIrNoIVey+dUsGmroBQLjpTVDTaY9Zq887A9Qfm9xgatBqFoHH96awRXdTgz/nyVgirYuX4uhBBxyVFZBXs+qLEZ4LTq0TU896IjIUSRCw+ths8rKUNKTg96sLqhIqPH2iFelOVadPSGYorYD3a1O5GYo5H0rSEfKk062a08QO6Fvlg8gXAsoXjW86p627RlZir0yN3gmE6lOXd8oV9U9pW4SyU1kgzYhJD9hJDbxH++QAi5nBDSTgi5ixASFj8eEv+9Xfz8hbSv/xohpI0QspIQ8kcpjomRHU8ginF/pKQEEYpZn/8QWSy/f3MQPWKtuxx0DvkQiMSxVYKCmXRMek3eFpFTg56karDcacEVy6vx+OE+JDLc/n3whW44rfqcy3m7V9bg0nigpPOmRMkMIFyM1FgNGJ1jwLYZtZJEKFK0Gh6bl1bl3ejoVULB1vA43uvGkCc0r/YQIM0bn+POQjiWQCSekK0OfKGxst6Wl4Ltj8SRIJD9wkOn4RBPKKdgJxIEbw16sS6L550W/eRqc1TqjsempVUw6TSzPMbpdA55sbrBpsjwZhcj6TK1OQbE9wml2wp5nsOONgcOnhsHIQRvDnjAc8AaBXYaKHZTbotIIBJTpf8aYE2OZQldcFxeQoIIxazTyGoR6Rnz49OPHsfX/tA594OL5MglQcGUUsEGBCU5nwE7EInhnGtq2pvW+y9fikvjAbx2cbr6cqRnAi+fG8N9V7fm9KRds6IGAEqyiQyICrYS6o7Tqs9Zl37w/Di2tzrmTFYplK0tVegc9ua8jUxRwiJi0PEY9obAc8D1q+TLdc4HqmDnuvChftJyULABwSZydsQ3Z2a7EkkZgGgRkcCDfWbYhwN53PHqGffDH4ljbRYLAa2+zu3Blj/WERDsVle0VmdtK4yLFelK2EOAdAV79rmhSRnzEUW3q82JYW8IF8b8ONnvRketTVEl3S4q2JlaWgHBgmpWYU06wAbssuSiqGpKoWCbZFawf3moFwCwr8uVd512oRzpmUSj3TRnY12hmHSavDzYnUM+JAiwLu1N6+Z19agwamctOz64pxsOi37OGuxlDguWOy14sQSbyIA7gBqbQRGbgtNqwFgWr2/fRAC9EwFJ7SGUbS3VIAQ4dim3ik0IUcaDLSaGXL68epa3XmmMOg1sBm1ODza9MFF6yXG+WFVvQyiawKXx3HeGaNaz3Mq+judLbnKMxBL45CNH8Be/ODann/uUuOC4LsudJJ2GR6VJl9uDHY4qllBxZbsTF0b9GHTPXmTunQggGI0rNmDTv5FMCvbUPLYV7kqWSo3h5IBHkfzrdKrMesQTJBkxO5NARPnlT6lgA3YZcnHMDw3PYWl16d5as16bjNGRmnAsjl8f7ceahgrEEgRPHR+Q/DkIITjcMyFZPF86Zn1+6v6pDMH+Rp0Gt29uwrOnhjEpqkFHL03ipW5Bvc5HYbhmRQ1evTBeUNlNOv2TQVkr0tNxWg1ZL6Cef2sEALBbhqa+jc12aHhuzkVHfySOeIIooGALbyQ3rplfewilxpbbuuMJyl97vZDId9FRKQVbq+HyakDNxS9ev4Se8QCmwrHkAJ2N0wMe6DU8Ouqy3/10zFE24w0qs+QIAFeKS8KZVOzkgmO9MgO2zaAFz2WxiMxjFN3SajMa7SY8cbQfY1MRRf3XQGr5M5tNxB9WtuFSStiAXYZcGPOjucokWWOhXBaR506PYMIfwd/csgqXNVXiiaP9WW8jFUvfRBAuX1hy/zUgWkTyODcnBzxwWPSz6sjft60ZkXgCvxUvLB7c041qix4f2pFfMco1K2sQiiZw6GL+Wc/pDLiDsieIUJw2oWI5k+f8jyeHsKreJskdl5lYDFqsqrfhjf7ccV5ytzhSqIJ9wzy0N2Yi150FQLlBcqHQUWcFz809YCeVfZkHSQ3PlbTk6AlG8eCe7uRQNVe056lBD1bW25JV5JlwWPRZox0JIZgKKzdgr6yzwWnVZ/Rhdw55oeG5nBcLUsLznJD5nGGQ9NMylXmwQnAchyvbnXhDXHBUMkEEQDJDP1vZTCASlz0zXS7YgF2GXBj1SzasmAtY5CuUX7x2Cc3VJlzV7sSdW5vRNexLZrBKBS0akdp/DQgXH/mox6cGPFjXWDlr0WZ1QwUua7bj8cN9ON47iQNnR/GJq/JTrwFg+3IH9Foe+88UbhNJJAgG3fK3OFKcVgPiCTJrm3zEG8LR3kncur5BtuduqjJhJEckIpDyTco9YK+os+LKdieaJbi7JAVzKdjekKC8VZbJkqNRp0GL04KuOSrTfSFaBy73kiOPWAlLjj/Ydw6eYBRfv309OmqtORM3CCE4PejFusbcim+1JbuCHRDvBCnl2ec4DrvanXjl3NgscaZzyItWp0XRpB6hVCXTgC38vigd00fZKdpEtDynmGWGkvSmZymb8UdiMKuwJh1gA3bZkUgQ9Iz5JVlwBPK3QRTKOdcUXr84gfdfvhQ8z+GdG5ZAr+XxxNHM0XXFclLMdF1RK33RgCmPJcdQNI5u11RW1eDubc04M+LDF371BqrMOnw4T/UaEAb87a0OvHi28EVHly+MaJzIHtFHydbm+NzpYRAC3LJOPstEXYURI97c/n6lFOwv3bwKj9x7uazPUQhOqz6ngl1uS46AYCmYU8Gm50XmCw+hKr04BbtvIoCHX+nBHZubsHZJJba3OnC4ZyKrp3vAHYQ7EMWaOZJ8qi2GrB5spS480rmy3YmxqQjOjEz/mXUOKbfgSKk06zMuOfoj1IM9TwN2m2ClWVFnUzwatEocsCezKNhBpmAz1MKIL4RgNI7lJWZgU0w6rSxLjr881Astz+GuLULJZ6VZhxvX1OGpEwMIx6R7vkG34DPmJU6nAPIbsLuGhUSCbKrQOy5bArNeg4tjfnz8qtaCt8yvWVGD86N+9E0ECvq6Abfw+CalLCLJSLjpw9wfTw6jvdaKjjr5mtbqKozwBKM57zakBib5B4OFlPdaYzPAG4plPTfltuQICIuOvROBpOqYCarsy33hUUpV+reeOwOeB/7qxpUAgB1tDgQicbwpWgVmcmqALjjmHkqdVqE4JJPdK2UpUm6QpGUqL3enbCKeQBQD7qDyaq1Jl7HJkYpUSsf0UWpsBuxeWTMv1rRKGl+YpWyGebAZquHiqLD93iahRUTqJcdQNI4nj/XjprX1yagwALhzSxPcgSj2dpbWUJjOkCeEeonTQyhGvQbBSO7btyfFBcd1WRRsq0GL2zc3wmnV4yM7Wwo+ht0rxbi+AtNEBtyCZWKJQgN2jU1sDUxTsMemwnj94ris6jUA1Iq/Y64cKrZSCvZCwzlH9bU3GINew6uyZa1YVoi12udcU1kf4w1FFTkvWp4vqsnxeO8kfvfGIO67qhX1Ygzn9lbBJpDNh/3WoAeaPCwE1RY9EiRzY6E3qWArNzAtsZvQWmOZtuhIy4JWN8hfkZ6OPUupij88vwo2APz0nsvx+RtWKP68yYbLDAo2IYSliDDUwwUa0SeRgi2HReQPJ4fgDkTxwSuWTvv4VR01qKsw4NcS2kSGPEEskSnnOZ+YvlP9HtjNupzLhP9421rs+cLuovx5rU4LmqpMeLFAH/aIRxiwZy5eykXKIpIa5J4/PYIEAW5ZJ5//GhAUbEC4u5ON5IBtLq8BO5mFncUm4g1FUWHSLijVXW6aRdvUQIboNwrtINDIAAAgAElEQVStA5f7vBSjYBNC8LX/7YTTasB917QlP15t0WNVvS3rgH1q0Iv2GuucFoJcZTPztRR7ZbsTr1+YQCQmXIzQBJE1SltE5lhyVKsVohT0Wh4WvSbjhUcknkAsQeYlH1wK2IBdZlwY9cOk06DOJs3gZNJrEI4lSo6KSufR13ux3GnBjhm5xxqew+2bm/Di2VG45lhKy4dwLI6xqYjk+dcUahHJlXxCGxxzvRHrtXzRgx3Hcdi9sgYHz48VZK0Z9oZg1PGKNfRVmnTQabhpHuw/nhpCi8Msu8qUHLBz/E55glHwHGBV6a3KYslm3aH4FKq9XkjQxV9axJQJXyimiG2mGA/2c6eHceTSJL5444pZF+3bWx040jOZHETTOTXgwdo8Gv4cltkXyxRfaH5iHXe1OxGMxnG8V4jj7Bzyodqin3aHVAnsJh28oegs+0wgEoNBy0ObI51lMWM3C7aimdACHqZgM1TBxbEptDgtknmO6S++VEkiZ0d8OHJpEu+/vDnj0HnnlibEEyQZXVcKIx5haJCrqdCk1yCeIFljtMKxOM6O+LLaQ6TimhW1CETiOJpnJTggDNj1FUbFlEmO4+CwpCLhJv0RHDw/jpvXNch+DHUVwptsrkVHTzCKCpNOFq/+QmauNkdvMFo2GdiUCqMWVoM2p4LtDSpTB64tMEUkEkvgG3/swoo6K+7a0jTr89tbHQhG47NiK12+EFy+cNYGx3QcVqpgzx6YvPOkYO9oc4DnUnnYncPKVaSnU2nWg5DUhQbFH4mpVqWVArs5c116skJepcIGG7DLjItjfrRKmCdsEn/xAzkWfgrh0dd7odfwuFNcbpxJW40Vm5faJcnEHvSIVeB2eQZseis128XH2eEpROMkayuaVOxsc0Cn4QryYY94QkllVymcNn1Swf5T5wjiCYJb18tfuFJp0kGv5XPeFfEEY2XnvwZSw1I2BVuwiJTXeeE4Do12E/pzKthRRZJVdAVWpT9+uBc94wH83a2rM6ql21urwXGYFdd3ejC/BUdAyMEGMvv2ffPgwQaEZdPLmu14+dwYYvGEUJGuUMFMOkm/8YxIukBYvT5jKcjmTadzhUml54YN2GVEJJZA32QQrRL5rwHALA6RUviwgxFhufGW9fVJH18m7trajG7XVDIYv1iGRZ+xnBYRAFl92HTBUe5gf4tBi20t1QX5sIe9oeTyk1IIbY7CG8+zp4bRaDcpUnrAcRzqKgxzWkTKccA2aDWoNOnmULDL77w0VplyK9ghZcpUNDyft0UkkSD48csXsWmpHbtX1GR8jN2sx+r6ilk+7NPia9WaPAbsKurBzmgRiULDc/MyTF7Z7sQbfW6cHPAgHEsoniACCBYRYPZC31Q4Nm8Z2AsBuylbfOH8pquUChuwy4i+yQDiCSJpIx59oZRiwP7dm4PwhWL4wOVLcz7u7RsaYNTxeOJoX0nPl1SwZbOICH9e2WIMTw54UGHUorla/qSO3StrcGbEh8EcQwGFEAKXN6zYgiOF1qV7Q1G81D2KW9bVK3YLt85mxDAbsDPitOqz1th7QzHFfPoLiUa7CQOT2aMvFVOwNVzW3OqZ7O1y4dJ4APdeuTzn39WONgeOXpqctrNxasCLFoc5L2uHTsOjwqjN2Oao1PJnJna1O5EgwMOv9ADA/AzY4i7NzLp0NSdlSIHdnHn5M7AA0lVKgQ3YZcQFMaJPygHblPRgl2YRmQrH8KMDF9Bea8Xly3PXllcYdbh5bT2eOTGYV1NiNobcIVQYtbJ530xzWEROD2ZucJSDa1bUAgAO5GETmfBHEIknlLeIWIWCij2dI4jGCW6Rsb1xJnUVxpwxfd4yHrBrbIbsKSJlrGB7Q7FkKsZMvEFlFGythst7wfyhVy5iSaURN6/Nbbva3upAOJbA8d6UD/vUoCcv/zXFaTVktYgobQ+hbF5aBZNOg/89OQQtz6G9VpmK9HRSFpHpvzfMgy1YRGbaPpMKNhuwGQudi2NCbqu0CrbowS5BwY7FE7j/F8dwccyPr9y2Jq+B884tzfCGYvjxSxeKft4hT0jWnOdcHuxILIGuIZ8iFghAqOA26TQ4O5I9u5dCl/2Ut4joEYkn8NihPtRVGLCp2a7Yc9cyi0hW0q076YRjcYRjiXkbmOYTGquZySYSjScQjMYV8aZreB6xBJlzH6Vr2IuD58fx4Z0tcyZVXL68GnyaD9sTiKJ/MljQrki1RZ/VImIzzM/fkV7L44rWasQTBO21VujnIbudpkHNLFUpew+2SY94gmBqxi4X7dgwM4sIY6FzccyPaosednN2f3OhlGoRIYTgH546hRfPjuJf3r0O12TxBs5kZ5sDt66vx3eeP4sfvni+qOce8gRls4cAaR7sDOem2+VDJJ4oSBUqBY7jRN/o3I2OdNBUWsGmiRWvX5zALesaFE3sqKswwh+Jz3qBB4Tf0XIesLMp2MnItTI8L7mi+pRc5NOJfyNzLTo+/HIPjDoed2/LvDyeTqVJh7VLKpM+7NODtAwrf0tFtUWfJUVk/hRsQPBhA/NjDwFSCvZMi8hUuLwVbHrhMdMmQucKtV58sAG7jLgwKm2CCJBmESlywP7B/vN47HAfPn1tO94/h/c6HZ7n8ODdm3DbhgZ8449d+P7e7oKfW84WRyDdPjP73JxSaMExnUZ77sUsCvUiz8eSI0Xu9saZ1OfIwvZH4ognSNkO2E6rAVPh2Ky/8WR9fBlaRJpyKNjUNqLEeaFqdC6byPhUGL89MYA7NjflLa7saHPgeK8boWgcp8QBe20BCrbDqs/hwZ6/35crO4QBO588bzkwaDUw6zUZBsmYam0QUlAl/l7OPC8LoeGyFNiAXUZcHPNLag8BSlOwf3u8H99+7gzes6kRX7yx8IpWnYbHA+/biHdvXILvPH8WD7xwNu+vDUXjmPBHZGtxBFIK9tmRqVm3cE8OeGA1aLGs2izb88+ksSp3tBhl2BMCx6UqxJWCDthOqx5bW3L78KWmNpmFPXvALteadAq9szBz0dGbVLDV+eZXCk6rAXoNn1HB9gaVU7C1ooKda9Hx0dd7EYklcM+ulry/745WByLxBI5dmsSpAS+WVBpzJjvNxGExYDIwu1BFWP6cv9+XVfUV+NGHtuB9eSj5clFpmh1J54/EVWuDkAK6/DkrvpAp2Aw14AtF4fKFJatIp5h11INd2JLjwfNj+NITb2JHqwPfvGND0Yt+Wg2Pf3vvRtyxuQkPvNCNf3v+TF752MmIPhk92M3VZqyos+Kbz3bhrh++Oi366tSAF2uXVChqg2iqMsEdiCZVgWyMeENwWAzQKdwqRge5m9bWQ6NwoQu1w2RadKQFCGU7YFszl82Us4LN8xyW2I3oz6VgK9HkqBEtIlmi+iKxBH7+2iVcs6IG7bX5N6JubamChufw6oVxnC5wwREQLCLxBJllhVCqgCcXN66tn1cVvdKkm3ZeovEEIrFE2bXEppMtvtAfiUGv5RV/L5IKdR51GXFxzI+nTwyUXKrSMyZ4bxeCReTsiA+ffOQoljst+OGHtpS8bKLhOXz7zg1439ZmfG/vOXzz2bmHbLkj+gBhyfH3n7kK//LudeibDODuH72GD/3kdRy9NInOIa+i9hAg92JWOkIGtrLqNSC8Kf/re9bj09e1K/7cuerSmYItDti+mQq2coPkQqSxypQx9jLVVqhMkyOQ3YP9h5NDGPWF8bErlxf0fW1GHdY1VmJPpwsXxvwFl2HRgqL0JBFChCW2+RxuFwIzWwuTdeDMgz1L2Q9G4rCoVL0GgPL9iS5gCCE4eH4cD718EXvPuEAIsMRuwrYSbptfSCaISBtNJFxdcslK03z46tOnYdBq8PA9l0s2tPA8h6/fvh48z+GHL57HDWtqsWVZ9vOVKpmR12es1/L4s+3LcOeWJvzPa5fwg/3nccd/HQQA2SvSZ9KUtpi1oi67mjXsCSUfqzQfuCJ/H76UWA1aWPSajHXpdMAu10GSWndmWkTmq5VvodBoN2F/hvKmpHVGCQ92cslxtkWEEIKHXrmIthoLrha9x4Wwo9WRXCAv1LNM7STjU+FkHJ4/EkeClO/vC6XSpMPFMX/y3/3i3V81D5KlYjeJHuwZi7H+cFy1/muAKdgLilA0jscP9+LmB17CB3/8Ot7od+MvdreB54CXCqi5zsTFMT84DljmkN7za9Jp8law+yYCePXCOD66c1lSUZUKnufwl9cL6udbYrVvNoZkbnGciVGnwcevasWBL12LL9ywApc127Gz3aHIc1Ma7cLPPtNt7XRGvMrXpC8E6iqMGPHNVrC9Za5gUzVyloJdxhYRQPh7cvnC0wpZAGXPS3LAzmAROXppEm/2e3DPrtzFMtnY0ZZ6fSpUDHBYhIuy9CQRJa0zCxmhtTBNwU5G0al3kCwVvZaHRa+ZpWAHIjHV+q8BpmAvGN7sd+NjPz2MsakIVjdU4Nt3bsA7LlsCo06DV8+P40D3GL5w48qiv//FMT8a7aZkNrOUmPXavD3YTx0fAAC8e1Oj5McBCGkQVoMW51y5854H3UHYzbqkxUUprAYt/vL6Dvzl9R2KPi8gLC3qNBz6czTQhaJxTAaiirc4LgRqKwxw5bKImMtzMNBpeFSZdRmWHOev9nohsMQu/I0MuUNoSbPeUWXfqkRMXw6LyEOvXESlSYfbNxf3Wrt1WRW0PAe7WYe6isIsY5ksIuV+x4NiN0/3YE+JFhFrGS85AoDdrM/gwY6r+sJDvUe+iLgwOoWPPnwYZr0Gv/zEdmxvrZ6mOFzZUYPv7+2GOxApOsO6a8iH1hp5mqvMek1eKSKEEPzm+AB2tDrQVCVPegbHcWirtaJ7jgF72BNSTL1eKAiLWaaMyQcUuuRXp3BE30KgrsI4rb2O4glGwXMo6yWkTFnY3mAMFfNUe70QSGZhu4PTBmxvKAqrQavIoq4mqWBPt4j0TQTw7Klh3Hd1W9G32C0GLXa2O4uqNqexa5kU7HL3YFeadQjHEghF4zDqNKqvA5cKYflzZgFPTNXWGWYRmWeGPSF86CeHwAF45N4rsKPNMevF7OoOJxIEOHh+PPM3yeM5zoz4sLNNHkuCSZ+fReRY7yQujvlxx5YmWY6D0l5jnVvB9oRkjehbqMyVhT08TyUzC4G6CiNGvKFZC7KeYBQVJp2iiS8LjUxtjt5QtKxv9zeJlquZF6xK1oHrNDSmL/U7G4kl8LnHT8Co0+DDO5aV9P3/+8Nb8N33biz46/RaHjajFuNpdz28TMEGkFaXLqq1aq8Dl4oqiy5j0YyaLzzYgF0AI94QesfnbsLLF3cggg8/9Do8wSh+9rHLs2ZUX9Zsh82gxUvdY0U9z/4zLgDAtStriz7WXOSrYD9xdAAmnQY3y1wi0lFnhcsXnhURlc6QJ4gGe/kNkY1zKNjJkpkyHLBrbQaEY4lkjjGlnFscKU5rJgU7Wrb+a0AoYuK42TsNSp4XLT+7aOb//v4tHL00iW/duQFLStxzMWg1Rac8Oa2GjBaR+czBXgjQhT76/qT2OnCpsJv0mJxZIa9yDzYbsAvgn3/3Fm558AAeP9xbcmxeMBLHvT87gp6xAH70oS05l0h0Gh472hw4cHa0qOfd2+XCkkojVtTJYxEx6bVzpoiEonH8/s1B3LKuHlaZPVXtohUmm4odjMThDkTLziICCLe1My1mUUY85TtgJ6P6Ziw6sgFbsIhkShEpZzVSr+VRZzPOq4KtoQq2mCLy6yN9eOS1S7jv6lbctmGJIseQjZl16XT5s9wtIslSFXGYnBItInK/Ly50Kmd40wFB3beo+MKDDdgF8OW3r8ZlzXb8zZMn8YmfH531hpNOIBLDb4/348mj/Xiz3z2t3CMaT+D+R4/hWO8kHrx7I3a2zx2hdNWKGgy4g+gpUEEPx+J45dwYdq+qlc0radZpEJxjyfGFzhH4QjHZ7SGAoGADwDmXL+PnhxTIwF6oUO/7oHv2Mh8gKNhGHV+W7Xx0wKYRjhQ2YAtqZCASn/Y65g2Vt4INCBesA+7pr8lKWmd0ooIdixOc7Pfgy0+dws42B750U/EL8VIxc8BmS44CSYsIVbDD6m4rlAq7SbCIpIuIgXBM1RYR9R75PLDEbsL/3HsFHj7Yg28+24WbvnsA37hjA25YU5d8TOeQF4++3ounjg/AN6Mxr6nKhI5aK8KxBA6eH8fX3rMOt6xvyOu5aY7pS92jBdWdH+mZhD8Sl80eAuRnEXnyaD8aKo3Y3ip/NF1TlRl6LZ9VwVY6om8hkSybmQxm/D0a9oZQX2Esy8W1uix16d5gNLnQVq6k16VbRKXNG4yV5YVYOo12E473TU77mC8UQ3utMueFNjm6fCF8/Q9dqLEa8L33b0oW0MwnDot+2tKwT0ydMcmQZKUm6IBN1Vqag63mQVIKqsx6xBIE/kgcVoMWhBAEoqxopqzgeQ73XrkcV3U48bnHTuATPz+C921txtaWKvzyUC+O9bqh1/J4+/oGfOCKpai26NE9MoXuER+6XVPodk1hYDKAv7l5FT54Rf4LKMscFjRXm3Dg7Bg+vKMl76/b1+WCXsNjl4yZy3MtObp8IRzoHsMnr25VbLO+rSZ7ksiQQiUzC5Fk2Yw7852QEU95ZmADQK1NrEuf4TVmCjbgTMvCXuYQLsyYgi0o2H84OYR4giRf25Q8LzQH+/88cxreUAxPfGoHHFblW1gz4bAKntpEgoDnuaR1phwv3tOhFhHa5hiIxGHU8Yq8Ny5kaAzqpD8Cq0GLUDQBQtSdD67eI59nVtTZ8NT9u/DAC2fxwxfP4/EjfWitseAf3r4ad2xuQpUlFafXVmOVZLHvqo4aPH18ANF4Ipl/Ohf7zrhwRWu1rFfHcynYz5wYRDxBFLGHUNprrTjeO5nxc0PiUlJ9GQ7Y9ZVG8Nzs5APKsDeELcuqFD6qhYFJr0GFUTtNwSaEsAEb0xVsQLC5BSLxsk4RAQQFO5YgcPmE2E9CiKIebKpUj01F8K07N2BDk12R582HaosB8QSBNxSF3ayHj12QAUAywtEdTHmwy91/DQgWEUAQNJqRruwzBbss0Wt5fOnmVXjnxiXwhWLYuqxK1qvzqzucePT1XhzvdePy5XPXpveOB3B+1F+QUl4MJr0WwWg8qVTM5Imj/djYbEebTDncmeioteJ3bwyKW8jTf80HPSE4LHpZSncWOjoNj/oKY8Y2R0IIXN5wWS44UuorjdMG7EAkjliCsAFbVEVpkghLhBCg1qFBdxANlSYEInHEE0SxCw86mP3Z9qV479ZmRZ4zXxyWVNmMMGCX91IsheM4VJpSkXRq9xlLBe34SJ0X6k1X77mZf6PWImBVfQW2tVTLfutrR5sTPAe83J1fbfo+Gs+3Sj7/NZC6wgxlSKY4PehB17APdxTZJlYs7bXCMH9h1D/rc8OeYFmq15TGqsxRfZOBKCLxRNlaRACahZ2yiHjKvCadUm3Rg+OAUTELm5WGCDSJOw394t+T0ot8bTUWPPnnO/B/3rFWkecrhGo6YCd/Z9iATbGbdGke7LiqVVqpqKLpKqKyTxVsNXuw2YCtIipNOmxstuNAnnnY+8640OIwF7QUWQz0xWFmTi4A/ObYAPQaHu+4TNnIqI7a7FF9Q2XY4phOo92UHAjSoekZ5XzxUWszTqtLZwO2gFbDo9qsT/6N06zwsreIpLU5AoL/GoBiVgiO47BlWfWCWGqcCa1Ln/CLvzOhaNlfkFHSI+kCkVhycbicSXqwAzPzwdV7bhbeXyUjJ1d11ODNfncyQzMbwUgcr54fl129BoAl4rB63b+9iA/++DU88toluLwhROMJPH1iANevri264r1Yljks0PAcujNE9Q26g1hShiUzlMYqE4a9oVn1yiNl3OJIqaswwOULIyEWd7ABO0V6FnZqkFTvm58UmPVaVJl1yTtCKWW/vM8LADgsgq2Ils0wBTtFukVkKhxnAzbS0lXE2SaQbLhkCjZDIa5ekV9t+msXxhGOJWSN56O8bU0dnvn0Lnzy6lYMuUP4ylOncMXX9+DWB1/C2FQEt29WbrmRotfyaHGYZynY/nAM3lCsrFXaRrsZ8QTByIw7DskWxzI+N3UVRsQSBBPiizwbsFOktznS0pByV7ABIb41qWAzZT9JlUU4BxOiRYSlzqRIt4gEwjFVD5FSYdBqYNZrUhXyogfbpOJzwy6bVMZlTbQ2fRS35sjQ3nfGBZNOk9cypBRsaLJjQ5Mdf33TSpxzTeHZU8N49vQwVtbZsHtljSLHMJP22tlRfTSib0kZW0SSUX2TwWQuNiBYRDhOqAwvV9KzsJ1WAxuw06ixGdAzLuw0JBVsdl7QaDfh4tiM88KUWhi0GtgMWoz7hai+qTBTsCl2sz55FzoQiat6kU9Kqsz6VAFP0oOt3nOj3iMvU7TJ2vQxEEIyLlYSQrC3y4Vd7Q7FkzI4jkNHnQ0ddTZ85voORZ97Jh21NrzQ6UIkloBeK9ysKecWR0rjtCzs1AXYiDcEh8WQdwTkYqRWtMe4vGGsXcKU2nScVsGDTQhJKbVsYEJjlQkvnxNej73JdBX2+wIIPuxxfwT+SAyEMOsMpcKkgzcUQ1y88FBzHbiUCNYZuuQopoio+NyU7zupipmrNv386BT6J4PYrYA9ZCHTXmtFPEGSqhsADIkV4Uvs5atgU9W6f2L6ouOwN4T6yvJVr4GU/5z60T3BKDgOsDGPJGpsBoRjCUyFY/CFhPOiZnVJKhrtQjyfOxBl6SozEOrSw2npKuy8AKnMZ28wypYc07Cbp8cXAup+jWEDtgpJr03PxL4u4eNKLDguZNozJIlQi0htRfkOkkadBk6rPukbpQx7QmWdgQ2k8p5pVJ8nKPhGM+W7lxvOtCxsbygGm0HLzgvS21GD8AZj0Gk4GHXsrRUQymbGpyJpuelswAZSbY5jU2FE44R5sEXsZl2aRUT0YKu4r4K9CqiQZQ4LllabceBs5ri+fWdcWFlnm+avLUfaaqzgOKB7JH3ADsJpNcCgVe8frRQ0VplnDdgj3vKtSafotTwcFj1GfCkFm/mvBVJtjhF4g1FmmxFptJsBCFnYPjGKrtzrwCkOi2ARYekq06EDNn0NZh5sgUqTPqVgR2Iw6TSqvohnA7ZKuarDiRfPuvCFX53AvjMuRMXINV8oisM9E9i9an4WCxcSJr0GTVUmnBtNDdiDnlBZR/RRmuzTy2ZC0TgmA9GyV7ABwYft8rIBeyZUwR6bCrNEiDTSs7B9oRjzpafhsOox6Y8klz/ZgC1AX1MGRcsiq0oXqDILHmxCCPyRuOq96eynqlI++7YOhGMJPHd6GL85NgC7WYeb19ajxmZANE4UiedTA+01VnSPpLKwhz1BtDjkLd5RA41VJrzQOZJclHWJloi6Ml7+pNRVGKZZRNiALUAV7FFfGN5gDBUm9vYBCEOBSafBwGSQlanMoNqiRyxBkhfz7NwIVJqEXohBqmCrfJCUCrtZh1hCGK4XQ4W8uo++jKm1GfGduy7D196zDi+dHcPv3xzE794YhD8Sh82gxZZlVfN9iAuCjjobXjk/jniCQMNzGHKHsLPNOd+HNe802k0IxxIYnQqj1mZMZmCXu0UEAOpsRpwe9AIQBuxyjnRMp8qsB8+lFOyl1eb5PqQFAcdxaKwyYcAdEBRsduGRhLY50oV8pu4LUIsIHbDVvMgnJXbxwsMdiCyKCnn2U1U5Bq0Gb1tTh7etqUMoGsf+M6OoMGrLOmotnfYaKyKxBPomAnBY9fCFY2Ud0Ueh/vyByeC0AZtZRAQFe2wqjFg8wbzGaWh4Dg6xbEZo5WPnhdJoN2HQHUIoGkeN1Trfh7NgqBbbHHvEnHD2OyNA74qlPNjqHiSlgtaluwNRBCPqb7hU99EzpmHUaXDzuvr5PowFRXtdKkkkGhcUt3JuKqQ0Vad8o5uWVmHEwwZsSm2FEYQAo1NhZhGZgdMqXHwIFx7s7YPSWGXCyQEP9BqenZc0HBaqYPuh5Vm6CkWn4WHRazAo9jKofZCUiiozVbCj8Ediqvemq/voGYw5oFF93a4p6MSymXLOwKakK9iAkIFt1LHhAEjZZHrGAojGCRuw06ixGTDsDcEXjrElxzQa7SZM+CPQaTim0qZBLSJ9E0HYjFqWrpKG3azHsChssAFbgFpn3MEIAuG46luFi76c5DiumeO4fRzHdXIcd5rjuM+KH6/mOO5PHMd1i/9fJX6c4zjuPziOO8dx3Jscx21O+14fER/fzXHcR0r/z2IwBCqMOtRVGHDONYUhN2txpNiMOlQYtclblMNeIQObvQGmVPxul7AcywbsFE6rHj1jop+WnZckNAs7GifswiONalHBjsQT7MJjBhUmHaJxAgAsB1uEFvBQBVvt3vRS7tfEAHyRELIawHYA93MctwbA3wLYQwjpALBH/HcAuAVAh/i/+wD8FyAM5AC+CuAKAJcD+CodyhkMKWivteKcy4chTwgcxxb5KI1VZvSLCraLZWAnqRNLiM6OsAF7JjU2A6bCrCZ9Jul3xVgUXQqDVpO8zc/ujk3Hnva6YmYKNoCUB9sTjCIQias+XaXoAZsQMkQIOSb+sw9AJ4BGAO8C8DPxYT8D8G7xn98F4OdE4DUAdo7jGgDcBOBPhJAJQsgkgD8BuLnY42IwZtJRa8M51xQG3UHUWA1sAVSkMS0LW6hJZwM2ADisBvAccFYsKGIDdgradAkwBTud9FIvdl6mQ20iNgM7L+lQOwQAmFXcViglBq0GZr0Gk/6IUCFfxgp2Eo7jWgBsAvA6gDpCyBAgDOEAaCBzI4C+tC/rFz+W7eOZnuc+juOOcBx3ZHQ0c004gzGTtlor/JE4jve50cD810maqkwYcAdBCMGIN8wWHEU0PIcamyGZn84G7BQ1aZ5IptSmqKswQis2zrHzMh1qE2HnZTp0wDbr1d1WKDV2kw4T/ghC0QRMKrfOlDxgc9NJ7tUAABGDSURBVBxnBfAkgM8RQry5HprhYyTHx2d/kJAfEUK2EkK21tSwpkJGfnTUppJEljCVNklTlQlT4RgujQcQiSWYRSSNugojJsXKXjZgp3CmK9jMU5tEw3PJO0DsvEzHkRyw2XlJh97pUHuZitRUmvWpdBWVn5uSBmyO43QQhutfEEJ+I354RLR+QPx/l/jxfgDNaV/eBGAwx8cZDEmgSSIAi+hLh97WPnJpEgA7N+nU2lLngg3YKdIVbHZepkP/nphSOx2mYGeGlqqovQ5cauwmXbJCvmw92JwQN/ATAJ2EkH9P+9QzAGgSyEcAPJ328Q+LaSLbAXhEC8lzAG7kOK5KXG68UfwYgyEJDoseVeLtONbKl6JRTD44Kg7YTMFOQRcdOY4NBukwBTs79O+JnZfpOMTfGbYUOx1qEVG7Sis1VRYdhhaJgl3K0e8C8CEAJzmOOyF+7O8BfAPArziOuxdAL4C7xM/9AcCtAM4BCAC4BwAIIRMcx/1fAIfFx/0zIWSihONiMKbBcRzaa6043DOJBjsbIilNVULxzjGmYM+CXmzYDFrmj0zDbtJBy3OIJQisbGCaRpOoYLO0jOkwi0hm6B0gpmBPp9KkT8YXqr3hsuhXAkLIy8jsnwaA6zM8ngC4P8v3egjAQ8UeC4MxF+21NmHAZkNkkiqzDiadBmddPnAcVB/qLyVUwa40s6EgHZ7n4LDqEQjHoWEXHtO4a2sz7GY97GIbHUOAWUQyY2ce7IxMS1dR+blheWWMsmB1gw0cBzSLqi1DUPYbq0wgBHBYWHxhOrWigs18xrOpsRnYsJSB5mozPnbl8vk+jAVHNVOwM0Iv3pmCPZ3p+eDqPjfsVZJRFrx3azNWN1QkByeGQKPdhHOuKdRXMvU6nTobG7Cz0VBpApf15iWDMZ3VDRVY7rRgzZKK+T6UBQW906F2n7HUVKXdAVL7uVH30TMYeWLUabCtpXq+D2PBQRezWAb2dJIWETZgz+Ifb1uDcCw+34fBUAl1FUbs+6vd830YC46UB5uNYelUTrOIMAWbwWColCZxwGYJItOpMuuh03BswM5AczWzWTEYpWLRa+Cw6Nle0AzSLSJqv/hQ99EzGIySoNm9TMGeDs9z+PtbV+OyZvt8HwqDwViEcByH5z9/NfOmzyB9SZgp2AwGQ7UkFWymoszinl1sYY3BYMiHw8p2X2ZCOyt4DjBo1b14r+6jZzAYJbG+0Y77rm7F9atq5/tQGAwGg1Hm0Ap5i14Loc9QvTAFm8EoY/RaHn9/6+r5PgwGg8FgMGDUaWDSaVQf0QewAZvBYDAYDAaDsUCwiyVoaocN2AwGg8FgMBiMBYHdrMdi6D1jAzaDwWAwGAwGY0GwtNqEeGK+j6J02IDNYDAYDAaDwVgQfOeuy+b7ECSBDdgMBoPBYDAYjAXBYskGXwQuFwaDwWAwGAwGY+HABmwGg8FgMBgMBkNC2IDNYDAYDAaDwWBICBuwGQwGg8FgMBgMCWEDNoPBYDAYDAaDISFswGYwGAwGg8FgMCSEDdgMBoPBYDAYDIaEsAGbwWAwGAwGg8GQEDZgMxgMBoPBYDAYEsIGbAaDwWAwGAwGQ0LYgM1gMBgMBoPBYEjI/2/v7oPlqus7jr+/yU3kIeHBmAACIWpFykBQiIjjE5ahVPpAq9VAY+sMMkxrmbbTmXZopw8oOGWinRG1UyZjcUpUYsW2BkEZRRDLQKEgXAhEGlIqQdoEqyUhljzcb/8455bldm+S3fvbc/be+37NnNndc3Z/+z2fnL357tmze2ywJUmSpIJssCVJkqSCbLAlSZKkgmywJUmSpIJssCVJkqSCbLAlSZKkgmywJUmSpIJssCVJkqSCbLAlSZKkgmywJUmSpIJssCVJkqSCbLAlSZKkgmywJUmSBiET7vsMbLy57UrUMBtsSZKkQRjbA9/9HPz9pbB1Y9vVqEE22JIkSYMwdx6s/DzMOwTWXQQ/+VHbFakhNtiSJEmDcvixsHIt/Pgp+PIlMLa37YrUABtsSZKkQVp6Fpy/GjZ9E277SNvVqAEjbRcgSZI04624GJ4Zhbs+AUefCqf+atsVaYDcgy1JktSEd62GpW+Gr1wGzzzUdjUaIBtsSZKkJozMh/ddD4e8HNatgh1b265IA2KDLUmS1JQFS2Dl5+D5bfDpN8Jd18Dun7RdlQqzwZYkSWrSsafDJbfBcW+Eb/wZfOoMeGAt7N3TdmUqxAZbkiSpaUefAu+/ET7wVVh4NKy/DK59S3XWR3/Kb9obmgY7In4uIr4XEZsi4vK265EkSRq4V72t2pv9vuurMz+u+zW4+gRY+2749sfg374Du3a2XaV6NBQ/0xcRc4G/As4FtgD3RcT6zHy03cokSZIGLAJOvgBedz48th6e/Cf4/j1w+1XV8jkjcNQpcOQJsPCYao/3wmNgwVHVMd3zF8D8Q6szRs47uBpPrRqKBhs4E9iUmZsBImIdcAFggy1JkmaHufPglPdUE1SnVn/q3qrZfvp+2PoYPHE7vPDcPgaJqtkeOagab848mDtSXc4ZgTlzqynmdExzO5ryePF6RHX7/67T5XaX55+qJT8N53106uO0aFga7GOBpzpubwHe1FItkiRJ7Tv4SDjxvGrq9MIO2PGfsP0/4PmtsOv56jCSXTtg987q+u6d1SEnY3tg724Y210d2z22B3LsxWlsb3UJkAkkjI1Vl5n1E2bH8o7bE+Uk83u1a0eZcVo0LA12t7c7/+9fKSIuBS4FWLp06aBrkiRJGj4vW1BNi17TdiWaxLB8yXELcHzH7eOAH0y8U2auycwVmbli8eLFjRUnSZIkHahhabDvA14bEa+KiPnAhcD6lmuSJEmSejYUh4hk5p6IuAy4FZgLXJeZG1ouS5IkSerZUDTYAJl5C3BL23VIkiRJUzEsh4hIkiRJM4INtiRJklSQDbYkSZJUkA22JEmSVJANtiRJklSQDbYkSZJUkA22JEmSVJANtiRJklSQDbYkSZJUkA22JEmSVJANtiRJklSQDbYkSZJUkA22JEmSVJANtiRJklRQZGbbNfQlIrYB/97jww4H/nuKT70U+P4UxyhVS4kxSo1jLpMrkc0wrZO5DHYcX0vdmcvkZtpryVwGW4uvpe72lcsJmbm4p9Eyc9ZMwJoCY2wbolqmPIa5NDLOlLMZpnUyl4Hn62vJXBrPZpjWyVyGP5chXKeh+RszPs22Q0RuKjDGjwuMAWVqKTFGqXHMZXIlshmmdTKXwY7ja6k7c5ncTHstmcvgxgBfS5MplQswjQ8RaUtE/Etmrmi7jmFjLpMzm+7MpTtz6c5cJmc23ZlLd+bSXelcZtse7BLWtF3AkDKXyZlNd+bSnbl0Zy6TM5vuzKU7c+muaC7uwZYkSZIKcg+2JEmSVJANtiRJklSQDTYQEddFxNaIeKRj3mkRcXdEPBwRN0XEYR3LltfLNtTLD5ow3vrOsaarUrlExB0R8b2IeLCelrSxPqUUzGV+RKyJiMcjYmNEvKeN9SmlRC4RsbBjO3kwIp6NiE+0s0blFNxmLqpvj0bE1yPiFW2sTykFc1lZZ7IhIla3sS4l9ZJLRKya8JoZi4jX18vOqO+/KSI+GRHR1jqVUDCXj0bEUxGxo611KalELhFxSETcXP9ftCEirm5vjcopuM18PSIeqrO5NiLm7vfJS/7m33SdgLcDpwOPdMy7D3hHff1i4Mr6+ggwCpxW314EzO143LuBL3SONV2nUrkAdwAr2l6fIczlw8BV9fU5wCvaXrdhyGXCmPcDb2973YYhm3r+1vHtBFgNXNH2ug1BLouoTg6xuJ7/t8A5ba9bU7lMeNypwOaO2/cCbwYC+BrwrrbXbUhyOQs4BtjR9joNSy7AIcA76+vzge9M9+2l8DZzWH0ZwJeBC/f33O7BBjLzTuC/Jsx+HXBnff0bwPjexZ8FRjPzofqxP8zMvQARsQD4feCqgRfdgFK5zDQFc7kY+It6/lhmPjvQwges9PYSEa8FllD9oZ/WCmUT9XRovSfyMOAHg659kArl8mrg8czcVt/vmx2PmZZ6zKXTRcANABFxDFVTcHdWncH1wC8PpuJmlMilHueezHxmIEW2oEQumbkzM2+vr+8CHgCOG0jBDSq4zTxXXx2hegOy318IscGe3CPAL9XX3wscX18/EciIuDUiHoiIP+x4zJXAXwI7myuzcf3kAvDZ+iOXP53uH1NOoqdcIuKIevmV9fwvRcRRzZbciH63F6j+wH2xbg5mop6yyczdwG8BD1M11icDf9NsyY3odZvZBJwUEcsiYoSqiTyemWeyXDqt5MWm4FhgS8eyLfW8mabXXGaLvnOp/3/6ReC2gVXXrr6yiYhbqT5F3A7cuL8nscGe3MXAb0fE/cBCYFc9fwR4K7CqvvyViDinPk7npzLzH1qptjk95VIvW5WZpwJvq6dfb7bkRvSaywjV3oG7MvN04G7g441XPXj9bC/jLmRm/6fY69+YeVQN9huAV1IdLvFHjVc9eD3lkpk/osrli1SfdjwJ7Gm66AZMlgsAEfEmYGdmjh9r2m1Hxkx8s9prLrNFX7nUb1JvAD6ZmZubKrZhfWWTmedRHVr0MuBn9vckI8XKnWEycyPVR5JExInAz9eLtgDfHv84PyJuoTq+ZwdwRkQ8SZXrkoi4IzPPbrj0geojl9sy8+n6sdsj4gvAmVQfV84YfeTyLapPOsbfkH0J+GCTNTehn+2lvn0aMJKZ9zdedEP6yOa5+nFP1PP/Dri84bIHrs+/MTdRnyo5Ii4FZtzhafvIZdzEN6RbeOlH/McxzQ8p6qaPXGaFKeSyBvjXzJz2Xy6fzFS2mcz8n4hYD1xAdXjJpNyDPYmof+kiIuYAfwJcWy+6FVhef+N2BHgH8Ghm/nVmvjIzl1HtXXl8pjXX0HsuETES9S8d1HvgfoHq45kZpY/tJakagrPr+50DPNpo0Q3oNZeOh77k+LeZqI9sngZOjojF9f3OBR5rturB62eb6XjMkcCHgM80Xfeg7SOX8XnvBdaNz6uPMd4eEWfVh+X9BvCVRotuQK+5zBb95BIRVwGHA7/XXKXN6zWbiFhQf6dhfA//+cDG/T5RE9/iHPaJ6j/yZ4DdVO/6Pwj8LvB4PV1NfdbL+v7vBzZQNYqru4y3jJnxKyJTzgU4lOqXIEbrZdfQ5dciptNUansBTqD6osUo1Z7bpW2v2zDkUi/bDJzU9joNWzbAb1I11aNUb9AWtb1uQ5LLDVTN9qMcwLf7h33qI5ezgXu6jLOizuoJ4NOdj5mOU8FcVtePH6svr2h73drOheoTjqz/vjxYT5e0vW5Dks1RVL88Mt7HfIrqE9Z9PrenSpckSZIK8hARSZIkqSAbbEmSJKkgG2xJkiSpIBtsSZIkqSAbbEmSJKkgG2xJmmYiIiNibcftkYjYFhFf7XO8IyLiQx23z+53LEmSDbYkTUfPA6dExMH17XOpTkTTryOoTtAiSSrABluSpqev8eIpfl9y5suIeHlE/GNEjEbEPRGxvJ5/RURcFxF3RMTmiPid+iFXA6+JiAcj4mP1vAURcWNEbIyIz9dnA5QkHQAbbEmantYBF0bEQcBy4J87ln0Y+G5mLgf+GLi+Y9lJwHnAmcCfR8Q84HLgicx8fWb+QX2/N1CdMvlk4NXAWwa5MpI0k9hgS9I0lJmjwDKqvde3TFj8VmBtfb9vAYsi4vB62c2Z+UJmPgtspToNcDf3ZuaWzByjOm3ysrJrIEkz10jbBUiS+rYe+DhwNrCoY363wzmyvnyhY95eJv9/4EDvJ0mawD3YkjR9XQd8JDMfnjD/TmAVVL8IAjybmc/tY5ztwMKBVChJs5B7JCRpmsrMLcA1XRZdAXw2IkaBncAH9jPODyPiroh4hOrLkzeXrlWSZpPIzP3fS5IkSdIB8RARSZIkqSAbbEmSJKkgG2xJkiSpIBtsSZIkqSAbbEmSJKkgG2xJkiSpIBtsSZIkqaD/BZwa40eUs0mWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['forecast']=model_fit.predict(start=90,end=103,dynamic=True)\n",
    "df[['Sales','forecast']].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "model=sm.tsa.statespace.SARIMAX(df['Sales'],order=(1, 1, 1),seasonal_order=(1,1,1,12))\n",
    "results=model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2db70e278>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QQggxh5T6BHZOmxz3kK5PSzcdPRpxROQzj9VpW6OIyAyRwBZCCCHmkFI9iYgs5LoZbEVEeqQtInL1+0gc7KfDPBGBNjnOEAlsIYQQYg4pNyJyvqEQeN0MthzsHmlEpGPjoTyC7ibHS2GBEF81fTNEAlsIIYSYQ8rJmHRjTDeD3daGvi5pRh3Uhd0lEdgXmzkifDrt1owvyF0ksIUQQog5pNyIx6QDXQdblXQ90ogIaKNjl2YZjM+lukeIT0cO9syQwBZCCCHmkHIjZGUhB0DgK4O9mx0OtnLYMc0yLKyy3Yho49OJ5GDPCglsIYQQYg4pDXCwlcHukWawQc5+l0YJW1ihVA+J8LFqEZkZEthCCCHEHJJmsAFlsAdQbkSsJjcgGjaT0CzTya3QsRBaHxtJYM8KCWwhhBBiDhmUwVbWOKYVdWhGHU4u5QGtS5dmiSi3DEBEQEcO9syQwBZCCCHmkFhg73SwFRGJSeMhJxKBrQx2QrNEK1gCIMLXoJkZIoEthBBCzBntjqXS7HOwk02OEtgxaYPIyWIqsLUuADTLNLxYYIfKYM8UCWwhhBBizqj0jUmHXkREGeyYtEHkeFEO9g6aZepez8G2HdX0zQoJbCGEEGLOKCURiNXFXRERObVAb31OLsXrI2c/oVGiyiKAWkRmjAS2EEIIMWekDu1qNyKSOtgSktBz+E8uFQCI5GBD1IR2kwrF+FOrDPYskcAWQggh5ox0E1930Ew6yVECG+jdgJwoxuujDDbxkBmg1Ok52CgiMjMksIUQQog5o7Qng51OcpRTC3tbRNSDDTRLAGx1FgAICeRgzxAJbCGEEGLOSAXk7kEzymDHpC0iJ7TJsUfiYG9ESWxGDvZMkcAWQggh5ozybgdbGewdlBsRhcCjmPcBRUQAaMQO9tPRAnnfI8THSGDPjKEC2xjzUWPMU8aYh/qO/XtjzCPGmAeNMf+fMeZ439feYYx5zBjzTWPMq/qOvzo59pgx5u19x28yxnzNGPOoMeY/G2PyR/kDCiGEEFcbuzPYGjSzk1Iy5TKX9oNLYHcd7KfDPNcs52njQ0cRkVkxioN9N/DqXcc+B3yPtfYW4K+BdwAYY14I/CRwc/KYXzXG+MYYH/h/gNcALwR+KjkX4N8Bv2KtfR6wCfzsoX4iIYQQ4iqnlDi0+SD+M51msNWWERMP4cl1nX1lsOkK7KdaeU4tF4jkYM+UoQLbWvslYGPXsT+21qb/r/0ZcDb5+PXAb1trm9babwGPAd+f/PeYtfYJa20L+G3g9cYYA/wIcE/y+I8BbzjkzySEEEJc1ZQbYde9BvqEpJxaiNdnuRCQS248FBGhu8nxYiPPqeU8oZXAniVHkcH+GeDTycfXA+f6vraWHNvv+DXAVp9YT48PxBjzFmPMfcaY+y5dunQEly6EEELMH6VGxOpi0P28N8lRQhLiDPbKQtC98dAmR7oC+0Ij4JrEwfasIiKz4lAC2xjzy0AE/GZ6aMBpdoLjA7HW3mWtvdVae+vp06fHvVwhhBDiqiAWkD0HWxnsnVR2CWxFZ4BmGevlqLYDTi0XCAnkYM+QYPgpgzHGvAn4+8ArrbXpK34NuKHvtLPAheTjQcefBo4bY4LExe4/XwghhHCSciPsTnGE/h5sCWxIIyI58r4iIl0aJWxhFWqGU8v5OINt27O+KmeZyME2xrwa+NfA66y1tb4vfRL4SWNMwRhzE/A84H8AXweelzSG5Ik3Qn4yEeZfAP5B8vg3Ab8/2Y8ihBBCZINSPexW9AEkBrac2oReRCTZ/KlNjtAsE+WWAbotIp4c7JkxSk3fbwFfBZ5vjFkzxvws8CFgBficMeYBY8yHAay13wB+B/gr4DPAW6217cSd/nngs8DDwO8k50Is1H/JGPMYcSb7I0f6EwohhBBXGeVG1B0yA2CMIecbRUSATsdSaUWsLgS9EfJysKFZJgyWALhmqUCIj2clsGfF0IiItfanBhzeVwRba98PvH/A8U8Bnxpw/AnilhEhhBBC0HNo+/E9o4gIUG1FWAvL6sHeSbNE048d7OPFHB18PDrQ6YCnuYLTRisuhBBCzBFhu0M9bO/Y5AhxDlsOdm9M+spCDt8zGKMWEQCaJRpeEYBjizk6XvL80bCZmSCBLYQQQswRu8ekp8jBjtm9PjnPI1QGG5plaqYnsK2XPH/aEtizQAJbCCGEmCPSMemrexxsI6eW3vosFxKB7RtFRAAaJSrEAntloU9ga6PjTJDAFkIIIeaI/RzswJeDDf3rE9+ABL6ndhVroVmmbBdZKQT4nsEaCexZIoEthBBCzBGlxKFVBnswqcBOe8JzviF0fV2iJnRCSp0FVhfj540iIrNFAlsIIYSYI5TBPph0fZaT9Qk8jzBy3MFOxqRvthc4lgpsX5scZ4kEthBCCDFHlOr7Z7DlYEOludPhD9QPDs0yAJejnsDGyMGeJRLYQgghxBzRjUAs7nWwnc8aE6+PMbCU9wHI+542fyYO9uUo33ve+MpgzxIJbCGEEGKO6EYgCrs3OSqDDfH6LBcCjImnOAZqEYFGLLCfahV6EZFuD7YE9iyQwBZCCCHmiHIjpJj3Cfydf6IDZbCBvWPk482frjvYcUTkyWauK7BNKrAVEZkJEthCCCHEHDFoTDokEREJbMqNcMf65HxD6LqDPSiDrYjITJHAFkIIIeaIUiPcs8ERUgfbcaeWXkQkJVAGu5vBLtvFnoPty8GeJRLYQgghxBxxkIPtvFMLVJo71yfwlMFOBXaVxW4PNp5q+maJBLYQQggxR8QRiL0Ods73lMEmXp/lvvXJBx6h685+s0zHz9Mi1xXYJpCDPUsksIUQQswF1loeXNua9WXMHGWwD2b3+sjBBholomAFYG9EpNOe1VU5jQS2EEKIueC/PXaZ133ov/HwemnWlzJTSvs42Mpgx5R3R0SUwYZmmVawBPQEttfd5CgHexZIYAshhJgLHr9UAWCr5rYgKDWiPUNmIB0047ZT24zatKIOK4XdLSIS2E1vp8DWJsfZIoEthBBiLljbrAE4LZZSATmwRUQjwbtDeFb29GC7vS40S9QSgZ0+d7wgH39NNX0zQQJbCCHEXLC2WQegFbkrsHsCcq+DHXja5FgZsD4533Pe2adZpmaKLOZ88kEs7Tw52DNFAlsIIcRccE4O9hCBbZyfWDhojLwiIkCzRKWvAxv6WkSUwZ4JEthCCCHmgq6D7bBYKtVjMTQoIuJ7hrbjTm25Ea/PjoiIojPQKLG9S2D7iojMFAlsIYQQM6fcCLubGxURYXCLiIQk5eZehz/wHG8RsRaaZbbaCzsEdprB7kRysGeBBLYQQoiZc36r3v3YZQe759CqB3sQgyI0zkdEwhrYNpvtQm+KI72avnbUmtWVOY0EthBCiJmzttET2KEc7H03OUYuC0kGR0Sc3+TYLANwOVrYUe8Y5FIHWwJ7FkhgCyGEmDnpBkdw28EuDRCQKfGgGYeFJL0Wkf5NjoEf1/RZ6+jaJAL76bAwMIPdVkRkJkhgCyGEmDlrm3VyvgEgdNiNLDUijGHHIJUUXxlsys2IQuB1q+gAcl78vHF2bRrx5NOnw/xAgW0lsGeCBLYQQoiZs7ZZ41kniwA0nY6IhCznA7xENPYjBzten93ufuDHUsbZmEgzFthlW9whsIPAI7IebfVgzwQJbCGEEDNnbbPOs04Wnd+wVm5EA/PXAL7neBSCeH1Wd61P+s6Hs9GiJCJSYWdNX973iPDpSGDPBAlsIYQQM2dts87ZE0Vyvud4Td9ehzYljUK47GKXGxHLuwR2kEZEnBXYiYPN4o7+9FwisBURmQ0S2EIIIWZKqRGyXQ85e2KRfOB2p3GpHu1ogujH9x3PGpPegOxysJM8trPrkjjYZVvkWHHXAB452DNDAlsIIcRMSSv65GBDubm/gx3IwabSjFgp7FyfnBdLGWdvzBKBXWVhT0QkxMdKYM8ECWwhhBAzZS2p6Dt7YpG877mbpWV4BhscdmrZJyKSOvuubnJsbBN5C0QEOwR2HBEJJLBnhAS2EEKImbK2GTvYN5wsJhERR4UScc/z8oCKPlDWGAbfgKQtIi472M1gCWBni4hviKwc7FkhgS2EEGKmrG3WKeZ9ThRzsYMdtWd9STOj2jpAYPtuR0Q6HRtHRBZ2R0Qc709vlml4S+R9j0J/P3g3IhLN8OLcRQJbCCHETFnbrHH2xCLGGHKBcVYotTuWRtihmB/iYDsqsCutZIx8YXdNXxqdcdXBLlEzRVYXcxjT60/P+x5tfJCDPRMksIUQQsyUtKIPSBxsN4VSLRGQSwV/4NfTDLarDnY5GZO+NyIiB7tKkWOLe9clwoeOBPYskMAWQogp898ee5p3/Je/nPVlzA3nEgcbYjfS1U2OtVYcjZGDPZhKIrB3b3LsOtiOPm9olinbnUNmoBcRkYM9GySwhRBiynz+4Sf5rf/xHacn8qVs10PKjagrsPOBuw52tTnMwXZ7k2O5EQvFPaPSHb/xoFFi2y7sEdjpJEeUwZ4JEthCCDFlNqotwOHRzn2kFX039EVEXG2DGOZg5xwfNLN/RCSWMs6+npplNtt7BXYuMIQEiojMCAlsIYSYMqnAdjYz2kda0dfNYMvBZimvDPYgys39Njk63INtLTRLbEQFVhd3O/sekfXBysGeBRLYQggxZTZriYPtqJDspyewexls5x3sYT3YrgrsfSIiTmewWxXAshEVBkZE2ngYRURmggS2EEJMmY2KBHbK2maNpbzP8WIsDpx2sFvDHOy0B9vN9dkvIpI62KGLNx7JmPSSLe4bETFWEZFZIIEthBBTZkMOdpdzG3FFX9rfG7eIOCiUgFpzNAfb1WhRpRHhGSjuugEJPIcd7ERgV+zi4IgIPqYjB3sWSGALIcQUqbfaNMJYCDi7KauPtb6KPoBC4O4kx2EOdrqZz9kMdiNkuRDsGKYC/T3YDr6eGiUAygyq6TOEEtgzQwJbCCGmSOpegxxsay3nN+s7BHbOd3eS47AWEd/1DPaAMenQy2A7+bxpJgLbFlndtTbGGDomwJPAngkS2EIIMUXS/DXIwS7VI8rNiBtOFrvHXB40U21G5HxDPhj8pzlQBntP/hoc3+SYRkQGONgAbQKMWkRmggS2EEJMETnYPc4lHdj9DnY+8Gh3rJMxiFqrva97Df2DZtxbG4gjIoMEduByP3jiYFfsIseKewW29Xw8CeyZIIEthBBTZLPaE9hOZkb72N2BDf1v97u3NtVmtG/+GhwXkkBlv4iI53JEJHawB2WwgTgiIoE9EySwhRBiilyuysFOWRvgYBcCd6fyVVvRvg0i0NeW4ajALjcilgesT/fGw8HnDM0KAA2vOPDmTBns2SGBLYQQU6TfwW46L7DrLBeCHc5b6mC7ePNRbbZZOlBgK4M9MCLiOdwi0irTMgWWFgp72lUAOl6Ab91s5Zk1EthCCDFFdmSwXRQEfaQVff3CIN3g56JYqrUOjoi4nsGuNAZHRIwxBJ5xdtBMw9s7ZCbFegEecrBngQS2EEJMkc1qqxuDCB10aftZ26zvyF+DHOyDNjmmUQgXN4A2wjatdmeggw3x88bViEjdDM5fA1iTk4M9IySwhRBiilyutrj22ALgtoNtrU0E9uKO48472IXhDraLTu1+Y9JTAlf701sVqizsmeKYYj0fjw44GiuaJRLYQggxRTarLZ65mghsB13alO16SKUZ7RXYiUvrYj69OqSmL23LaDt481FpHiywc75H5KKIbFYodw5wsL3keCec4kUJGEFgG2M+aox5yhjzUN+xk8aYzxljHk3+PZEcN8aYDxpjHjPGPGiMeWnfY96UnP+oMeZNfce/1xjzl8ljPmgGpfSFECIjbNZaXCuBzbmNvRV90O9gu+dG1obU9PkO1/SVG7FAXC4MFpKBZwgj99aFZolSp3CAg53ckLQlsKfNKA723cCrdx17O/An1trnAX+SfA7wGuB5yX9vAX4NYkEOvAt4GfD9wLtSUZ6c85a+x+3+3xJCiEzQ6Vg2a6EiIgyu6AN3e7A7HUstbA+p6XM3gz0sIpLzPUIHHWzbqrDVLuzrYJMKbDnYU2eowLbWfgnY2HX49cDHko8/Bryh7/jHbcyfAceNMWfeTjMCAAAgAElEQVSAVwGfs9ZuWGs3gc8Br06+tmqt/aq11gIf7/teQgiRKUqNkHbH8oyVAuC2g50Omblht4Pt6CbHRtTGWkZrEZHA3kPON062q9hmhbJd2F9g+8nxtppEps2kGexnWmvXAZJ/n5Ecvx4413feWnLsoONrA44LIUTm2Eg6sE8tF8j5xnkHe6UQsLq4UzDlHB00U23GTQ8jDZpxUEimEZGV/SIizmawy1Q4qKYvzWBLYE+bo97kOCg/bSc4PvibG/MWY8x9xpj7Ll26NOElCiHEbNhMOrBPLOXJ+55zLm0/a5t1zp4s7hmO4aqDXWvFAmiYg22Mm4NmhraIeA62iLQjvKhO9QAH2ygiMjMmFdhPJvEOkn+fSo6vATf0nXcWuDDk+NkBxwdirb3LWnurtfbW06dPT3jpQggxGy5XYoF9zVKeXOA5lzPuZ1BFH7hb09d1sA9oEYFYSLoYEUlbRJbVg92jFY9JrzBKREQCe9pMKrA/CaRNIG8Cfr/v+BuTNpEfALaTCMlngduMMSeSzY23AZ9NvlY2xvxA0h7yxr7vJYQQmUIOdoy1lnPJFMfdOO9gH9CDDbGL7eYmx5CFnNfdBLsbJ3uwE4FdZZHVARMuAYyviMisOPhWGTDG/BbwCuCUMWaNuA3kTuB3jDE/C3wH+IfJ6Z8CXgs8BtSANwNYazeMMe8Fvp6cd4e1Nt04+S+Im0oWgU8n/wkhRObYqMYu0slinnzgrsDerIXUWu09FX3Qy2A752C3RnWwPScd7PI+Y9JTcp6D7wg1Ewfb7t+Dja+avlkxVGBba39qny+9csC5FnjrPt/no8BHBxy/D/ieYdchhBBXOxvVJgs5j8W8Tz7waLomCBKerjQBum0q/TjrYDdHd7Cdi0IA5WbEygEbQHOBoRE6ti7NMnBwRKTnYEtgTxtNchRCiCmxUQ25ZikWlXnfI3RMRKaU6vEf+0GioCuwHXu7P3Wwl4Y42Dnf0Qx2I9p3gyMkzr5rNx6tWGDXWNx3bYyfjz/otKd1VSJBAlsIIabEZq3FiaVYVOYDz7kqupRSUrk2aPpcusnRNQe7mjjYxQNaRMDdDHalGbF0kIPtYgY7iYh08st43uAh2EabHGeGBLYQQkyJjWqLE8XYUXJ5k2OpHovJ1QGuWy4ZB+5anrba3eSoDPYgKo2I5SEd4a49Z9JNjl5hZd9TPF81fbNCAlsIIabERrXFNUuJwHZ4k+NBDnbge3jGPQe71mzje4ZCcPCfZZcd7P0q+iBuEXHuxiPJYHsLq/ue4iUOdjuSwJ42EthCCDElNqstTiQCO+c76LglpBns/cdeu7c21VZEMe/vGbyzm3igiltrA7HAPmiTY97B50wqsPPFAwR2EP++aYetqVyS6CGBLYQQU6AVdSg3I04Wew520zGXNmW7HncaF4LBeeO8797a1JrtoRscIXZqXXOwrbWjOdiuZbBbFUICFhf39smneEHqYEtgTxsJbCGEmAL9Q2bA8U2O9WjfwRgQr41rbmS1FVEcUtEH4DuYwW6EHdody3Jh/+dM4HtEro2Qb5apscCx5KZ9EGlEJFJEZOpIYAshxBTYqPbGpAMUXN7k2Aj3H4xBHBFxbW1qrREdbAcz2OVmLA4PcrBznpstIhW7/xRH6EVEOnKwp44EthBCTIHN6k4H28WccUqpEQ7c4JjipIPdjIZW9EEyaMYxgV1pxA0rB2WwAwdfT51mmbLdf8gMgBfEa6aIyPSRwBZCiCmwkURETqpFJImIHNxp7Fp8ptZqD63og9jBdm2gSqU5vMIw53vOZbCjeokKiwferAbpJkdFRKaOBLYQQkyBNCJyoiiBPdzB9mlFbomltEVkGC7W0aUC+6Ae7JxvCB3LYHcaZapDHGy/GxGRwJ42EthCCDEFegJbkxxL9fDgTY4uOtijtoh4nnMZ7G5EZMiodGtxa22aZSossLq4/7p4ufh1pgz29JHAFkKIKbBZbXFsMUfgx7924wy2xVqHBAFx5VqpER0oCvKBR+iYuz96i4gc7EEEDk4ANa0KVbt4oIMdBAUAOu1oWpclEiSwhRBiClyutrr5a6A7sc81p7baatPu2AMd7JzvlrtvrR2zRcSdtYE+gT0ktw9uCWwvrMYZ7ANeS0FOLSKzQgJbCCGmwGZtp8DOJ062aznsdIrjQa6bay0izSjueR7ZwXZsM1+5MYKD7cWvJ2fWxlqCqEqFgzPYQdIi0mkrgz1tJLCFEGIKbFTD7gZHiEUkOCiwG/Ef+oM2ObrWg11rtQFGcrBzvnuDZirNiJxvuu/6DCKXfM2ZjY6tKgZL1R7cIuLn4oiI1SbHqSOBLYQQU2Cz2uLkUu8PYS5xsF0bjlGqx27ksEmOLkVEqkkEYtQebKc28hFvclwuBBhj9j0n58Vfc8bBblUAaHiLLOT2f97kA5/Ielg52FNHAlsIccX4429c5PN/9eSsL2PmWGvZqLa6Q2bAYQe7njrYB2xydGxoSLU1vOc5JfCMcyPBq81o6Nqkm4edEdjNWGC3cysHnpYLDBG+BPYMGP5qFkKICfm/P/8oxbzP33vhM2d9KTOl2mrTane6Y9KhT2C327O6rJnQjYgcWNPnVkSk2oyfAyM72K6IyIRyMzowfw29TY7OvPPRLMX/FpYOPC3wPEICrFpEpo4cbCHEFWN9u0615ZaAHMTmriEz0Nvk2HRISEK/g31ABjswTkVnauM42L4hdDAiclAHNvRtcnTF3U8iIqZwsIOd9z052DNCAlsIcUWot9ps1sKueHCZy9WdY9IB8kHiuDkmsLfrw4eG5H3fqXVJHWwNmhlMZQwH27WIiL8wSkTEg44E9rSRwBZCXBHWt+tAbwOXy2wOEth+HAdwyamFOCKylPe7mzwHkQvcmuTYc7BHrelzZ20gEdgHRIqgf9OwI2uTONj+4rEDTws8j4gAFBGZOhLYQogrwsXtBtBz51xmY6CD7e4mx4PiIQCFJIPtypTLNEZVHHnQjBvrklJuDHew00mOzlQYJhnsfHH1wNPyvkdkfVBEZOpIYAshrggXEoFdD9vOCYLdbNaSDLY2OVJqhAducISeG+mKWKo1x3CwfRdHpYcjZ7BdcbBtEhEpLB0ssHOBIcRXRGQGSGALIa4I61v17sf10C0RuZvL1RY537DS58J1Ww+cc7CjAyv6wD13v9pqYwwsBMMFdlzT547AjtodGmFnaD69NyrdjbVp1Up0rKE4RGDHEREfOoqITBsJbCHEFSF1sKHn0LnKZrXFiWJ+x6CMQtfBdkMQpIzjYLviRtaaEcWcj+ftP0glJd3k6Ex8JomYLQ9zsLs92G48Z8JaKR6T3tdMNIicb+IMtgT21JHAFkJcEdJNjhBvUnKZjWprR/4aepscXXFpU0qN4RlsFx3s4ggVfRA72IAzsatyM442rIzYIuKKgx3WtqmyOPRm1RhDGw+jiMjUkcAWQlwR1rcaXTFQc7wLeyNxsPtxTUSmbNdCjg0T2H7q7ruxNrVWxNIIQ2YgzmCDO/n09OZ8mIPdy+278ZzpNMtU7cLQ1xJA2wQYOdhTRwJbCHFFuLBd58ZT8ZQx16v6Nmp7HexeBtudm49Ox1JuRqwOE0uOdYRXm+2RGkTAPQe70kgE9rAWEc+tHmzbKFNhcei7QSCBPSsksIUQR06lGVFuRDz39DIgB3tzUEQkSHPGbggCgEorwtqDpziCex3htVY0UoMIgJ9OLHRkbcpjOtiuvOtBq0JlZAfbV0RkBkhgCyGOnItJ/vq5z4gFtssZ7HbHslUPd1T0QX9NnyOCgL4x6UM3OTrmYLdGd7C7EwsdiUKkDvawDHbg2CRHr1UZKYMN0CHAs+7+Dp4VEthCiCPnwlbcIJIKbJfHpW/VWlgLJ4s7/xCmOeOmIyIS4oo+YPSaPkduPmrNcRxstyIiVWWwB+JHVaosDO0HhyQiYt1+F3EWSGALIY6c9V0OtsvTHNMhMyeXCzuOG2PI+cYZlxbiBhEY7mB3Nzk6sja1MRzsbtbYEYFd6Q7hGSKwPbciV7moStNfGqna0Xo+njLYU0cCWwhx5FzYamAMPPt0vMnRZQd7oxqLypMD+mrzvudM1zP0RURGrOlzZW0qzTFaRBIh6YqDXU4iIsMGzfQiIg48Z6wl364RBUsjnd4xiojMAglsIcSRs75d5/RygWI+IOcbqg5vctyopmPS94rKfOA549IClBKxNGxjVs45BzsauwfbtZsPf4hTG7hUXxg1CYhoB8sjnS6BPRsksIUQR876doMzxxcBKOYDp2v6UoG9u0UE3BPY2yNucnTJwW5FHcK2HdnBToWkKw52pRENzV9DLyLixOupVYn/zY8osL0cvgT21JHAFkIcORe26lx3bAGI+2uVwWbPoBmInVpXNvJBLyKiyrUeaXxKGezBVJrR0A5sAM8zeMaRTY7NMgC2MJrAtnKwZ4IEthDiSLHWxg72sdTB9h3PYLdYyvss5PY6lPnAMYHdCFkpBEPf7i84NOUyjU+NIiLBwQx2M2J5hCo6iG/MnKjpSxxsf2FlpNOtF+CrRWTqSGALIY6UUj2i1mpzJnGwi4XA+Qz27g7slLzvVkSkVI9GmjyXOtguNELUkvhUccSaPtcc7GozGtqBnZLzPSeeM6mD7S+sjnS69QJ83DU5ZoUEthDiSFkvxRV9Z47HAnsp73dFhItsVFtcs4/ALjiWwS41wpF6e7s92A6MkU9vPoe1ZKT4nkNtGSQZ7FE3gPrGiYhIWC8BkCuOIbDlYE8dCWwhxJGyngyZSSMiS4XA6UmOm7X9Heyccw52OKKDnTZlZN+N7DrYY25ydMXBrjSjoR3YKYHnRu1lvbINQH7p2GgP8HJysGeABLYQ4ki5kAyZua7fwXY8IjKoAxtip9YFQZBSakRDK/rArUmOXQd7DBEJDmWwR3zXA+IbMxduyprVWGAvLo8msK0XEODu7+BZIYEthDhS1rca+J7hGSu9DLbrmxz3zWC7tsmxHg6t6AO3Ktd6LSLjjUp3wcG21o7cIgLpJsfsP2fGFdh4OTwsdCSyp4kEthDiSLmwXeeZK4WuEFjK+87W9DXCNrVWe2AHNri4yTFkdXG0yrWcb5y4+UhfG6M72GkPdvbXph626djhtY4pgW8IHbjxiJIM9tLKiA62n9zUalz6VJHAFkIcKetbvSEzEPf71sO2M29p95N2YO8nsHMObXJsdyzlZjSSgw1JI4QDazOpg+1CFKKSTP4c2cH23HCwo3qZmi2wWlwcfjJgvGT92uEVvCqxGwlsIY6A71yuUXc4Z9zP+na9W9EHvT+OLsZELlf2HzIDUHBo0EwqlkbZ5AjuxGdSB3vUQTNphaELN6zp5uhRM9iBb5zowbaNMlUWRtrPAICfrF9HAnuaSGALcUjCdofXfvDLfPyr3571pcycdMjMdf0OdtLv6+JGx2EOtkuj0kuNdEz6OJ3G2V+bWitiIecNHb6T4lIGOxXYo9f0uXFTZptlKnZh5JtV4yXntd0zOWaJBLYQh+TidoNKM+LpSnPWlzJzNmshzajDtas9Bzvt9606WNW3UU0F9uA/hK64tADbyZj0kR1s36PpwM1HtRWN3IENbmWw03c9Rs2n5zw3HGwvrFAziwOnww6km8GWgz1NJLCFOCTnt+JaukaY/T94w7iwtbOiD3rZUhcd7FRUHt8nIuJSD3bqYI/6tnZcYZh9sVRrtkee4gj9g2ayvzblMR3snO85MWjGCys0zNLI55tEYLej1pW6JDEACWwhDsnaZiqw3ROQu1nf3jlkBnp/HF0cNlNKBPZ+GVKXerBL9SSDPeImx7hhJfuvqUpzPAc7zWA7ERFpjJ/BduGmLIhqtPziyOenAjsK5WBPEwlsIQ7J+VRgO+JEHsT69s4x6RD3YIObmxy36yELOY9CMNihzPuxS9txQCyVuhGREd3IwA2xVGu1R24QAWWwD8IVB7sQVQmD0QW2F6QCWw72NJHAFuKQnN+qAXKwAS5sNcj5hlNLhe6xpUQ8uNiFXaofPLnQpYmF3U2OI0ZEXInPVFujjwKHvgy2A8+ZrsAe1cH2DGGU/RuPQqdGO7c88vk9B1sCe5pIYAtxSHoZbPcE5G7Wt+tce2wBr68RwWUHu9Q4eHJh3ndIYNdDjIHlEeMQeUcaIWrNMR1s3y0HO+/v/w7QbnK+R+iAg71ga3TGENheKrCVwZ4qEthCHJI0ItLUJsd4yMyxncMPXHawt+vhSA62CwNVSo2IlUKw4+brIFypMJy8RcQBgd2IRnavAXIu9GC3IxZoQWEMgZ1ERNpysKeKBLYQh6DTsVzYijf2NRzYkDWMC9t1rusbMgO9ARou1vSVGuGBkQinIiL1kGPFEQdjkObTs78utdZ4LSKB59Amx2Y0cv4a4h7srE9ytM0yAF5hZeTH9BxsbXKcJocS2MaYXzTGfMMY85Ax5reMMQvGmJuMMV8zxjxqjPnPxph8cm4h+fyx5Os39n2fdyTHv2mMedXhfiQhpselSrMrjlyPiHQ6lidLDa7d5WDnA4+871F1tKbvoMEq3YiIA07tsLjMbpzJYDcny2Bn3qkFyo3x1ibnG8KM33jUKtsAeIurIz/GC+KaUNX0TZeJBbYx5nrgF4BbrbXfA/jATwL/DvgVa+3zgE3gZ5OH/Cywaa19LvAryXkYY16YPO5m4NXArxpjRr+dF2KGpBV9xxZzzvdgP11tErbtjg7slGLBdzODPWSTYy5wSGDXo7EEtgsVhlG7QzPqjBUR8TyDMY4MmmmGrIx185F9B7tS3gQgtziGg51GRORgT5XDRkQCYNEYEwBFYB34EeCe5OsfA96QfPz65HOSr7/SGGOS479trW1aa78FPAZ8/yGvS4ipkG5wfM7pJecd7PWtvR3YKUv5wLkMdqdjKQ+LiDi0yXG7Ho5c0QduONi15HfGOJscIXaxnYmIjJHBdqEHu1aOHexc8djIj/ETgd1RBnuqTCywrbXngf8T+A6xsN4G7ge2rLWpVbUGXJ98fD1wLnlslJx/Tf/xAY/ZgTHmLcaY+4wx9126dGnSSxfiyEg3OD7n9LIEdtqBfWyvg71U8J3LYFdaER178OTCgksO9pgRkXiMfMbFUnLTOU4MAuIubGc2OY4VEcn+ux6NyhYAC0vjCOw0IiIHe5ocJiJygth9vgm4DlgCXjPg1PS3wKCt4/aA43sPWnuXtfZWa+2tp0+fHv+ihThi1jZrHC/mOLmcd37QTLrZ87rjex3sYj6g6lhEpDtY5aCaPpcEdv1gN383ed9kfpJj+poY18HOeW6Mka802+O3iGT8xqNZjR3sheXjIz+m62Argz1VDhMR+XvAt6y1l6y1IfBfgB8EjieREYCzwIXk4zXgBoDk68eAjf7jAx4jxFxzfqvO9ccXWQh8WlHHiYl8+7G+XacQeJwY0BSxVPCpObbJcbs+fLBKzpGISNTuUG21J8hgZ/v11HWwx8hgQ9yFrQz2XgLPo92xWJvd502rVgJgaWV0ge3lYge705aDPU0OI7C/A/yAMaaYZKlfCfwV8AXgHyTnvAn4/eTjTyafk3z9T238Kvgk8JNJy8hNwPOA/3GI6xJiapzfTAR2Lnagmg44kftxYbvBdccXiX8d7KSYD5yLiJTq8c97UO6424OdcYFdbsRrcWzMDHbW16XrYI9R0wduZLDDdodG2BkzImKSx2Z3bdr1WGAvjyGwgyQi0lFEZKocJoP9NeLNin8O/GXyve4C/jXwS8aYx4gz1h9JHvIR4Jrk+C8Bb0++zzeA3yEW558B3mqtdcvqElcl1trYwT6xyEIufim5nMNe36oPzF9DPGzGWQd7lEmOGb8xG3dMOsQ3H1HHZvpdobRZZ2wH24EMdnXMMekQ92BDtm9Y2424B3t5dQyBnZfAngXjvap3Ya19F/CuXYefYEALiLW2AfzDfb7P+4H3H+ZahJg2W7WQWqvN2RPFroPt8rCZi9sN/s5zTg382lLBQQc7EZWjTHLM+jsfXTd/zB5siOMzC142m1ur3U2O4zrYXuYd7PRdD3WE76JZpkVAPj/YzBhEkGawOxLY00STHIWYkLSiL46IpA52toXSfrQ7lifLzf0d7ILDmxxHqenLusCewMEuODDlstbd5Die1xX4Jvt9z8kN+TgZ7G7kKsv59FaFGns3kh9EGhGxcrCnigS2EBOytlkD4OyJeJMjuBsRearcoN2xnBkwZAbiloRG2Mn829r9lOohxhwsEHoZ7GyvS2/D53gZbIAwwzcf1Uk3OTqQwa5MEhFJx8hn+PXktyo0vOJYj0kjIlYtIlNFAluICUmnOPZvcnRVYHcr+gYMmYGegHBpmmOpEbFSCPC8QU2kMb2avmw/b0apLNxN3gEHO41NLU4waCbrN6tdgT1ORKS7yTG7zxk/qtIaV2AnLSK2487v33lAAluICTm/VaeY9zlezPUJ7Oz+Yj+I7pCZfRzsNEfp0kbH7RF6n10QkTBZRKTnYGdXSFZbbfK+130ejIrvQAa7kmSwV8bswQYyvTa5qEorWBrvMUEBAKuavqkigS3EhJzfrHP2RFxL181gZ9yJ3I+DxqRDbxNXxaGNjqV6eOAGR+gJgsxnsOsRvmdYGsOp7d18ZPc1VWtFY1f0gWsO9ug3ZWlEJMsOdr5TIxpXYOfjNZTAni4S2EJMSDpkBuj1YLsaEdmus5T3Wd3HbUo3caWDNVxglNHg3U2OGc6MQroWwcCO9P3Id28+srs21WZ77Pw1xFGILItI6DnY401yzL7AXuzU6OTGdbB9IuuBBPZUkcAWYkLWNuMObMD5iMjTlRanVwr7CqjUuXSpSWR7BAfbGEPe9xxwsMcbkw5uxGdqrWjsMenghoNdbkYYA8Xc6OvTjYhk9IY1bHcoUsfml8d6XOAZInxQBnuqSGALMQGVZsR2PeT64/FmkzQiUnfUwd6qtTixlN/368WCg5sc69FIrRn5wAGB3YjG2uAIbriR1Va7+9oYBydaRBoRS/mDNwnvJh00E2W0pq9UD1migSmsjvU4YwwRgRzsKSOBLcQEnE8bRFIH2/Gavo1qi5PF/QX2cjeD7c76jOJgQ+y6ZTlnDOmGz/GEpAsd4bVm1H1tjEPgeZl3sCvNcKwGEYCcl+1R6du1Jks08BZWxn5s7GBLYE8TCWwhJuD8Vq8DGxQR2awOcbC7GWw3HOxW1KEetkdybfOBl+mmDEgiIuM62A5ERKqt9thDZsANB7vabI+Vv4bsj0qvlEt4xhIsTiCwjQ+dbN/IzxsS2EJMQOpgn002OaZT55x1sGstTh4gsNONXFVHavrGqaXLB16mRSSMtuFzN0442K1orGaVlJwDkxzLzWhsBzvIeAa7Wt4CIFccLyICxBEROdhTRQJbiAlY26yT9z1OLcf9op5nyAeekzV99VabRtjheHF/AZVWkbniYKeDVUaJiLixyTHi2AHPj0H0plxmd22qzckz2JmPiDTCsTqwoXdTltXnTL2yDUCheGzsx3bwMdrkOFUksIWYgLWtOtcdX9ixAWch8Gg6GBHZrMXjdw/KYOeSYRruONjxH7JRcsc536OZYYHdi8uMmaeVg70vgQuDZg7jYGd0bRrVWGAvLI8vsNsmwMjBnioS2EJMwPm+ir6UhZzvZERkoxoL7IMy2BBX9VUdcbC3x3CwC4GXWccNoDzBFEfIvoPd6Vhqh8hgZ9/BnkBgZ3zQTFiLIyLFleNjP7Zt5GBPGwlsMRbnNmqzvoS54PxWnbNJRV+KqwK762APEdjFfOBMD3YaERl1k2OWXdqumz92TV+2p1ymlZ5LE05yzGoVXUq5GbE0bouIn+0WkbBWBiA/QUSkTSCBPWUksMXIfOPCNn/3A1/ggXNbs76UmdII21wqNwc42J6TLSKbtVhMnhiSsV0q+M5MckwdbG1y7LvZGLOmr+DHwjOrUy7Tm81JHOzAN5ndyAdgraXajMbOYHd7sDP6emo3YoFNYfwWkY4J8KwE9jSRwBYj8+iTFQCeKjVmfCWz5cJW0oF9fEBExMFNjptpROSADDY45mA3Ro+I5DK+yXF7DDe/n1yQbQc7vdmcxMH2M57BrodtOpbxe7BTBzuja2NTgT3mJEdIM9hu/P6dFySwxcicT4Slq9MKU9J12ONgB25GRDaqLYwZLiaXCwE1VzY51iPyvtetbzyIvJ/tDPY4Nxv9ZL0R4lAOdsYz2JUkVjRuD3bOy7aDTSt1sMcX2B1PDva0kcAWI5MKSxdFZD/dKY67HOyCoxGRrVqL1YVc9+3Z/Sg6tslxdTGHMcPHPGc+g11PG1XGE9i+ZzAmuw52NXWwJx00k1URSZy/hvEd7Kz3YJtWhQ4e5IrDT96FNb4E9pSRwBYjkwrLuiMu5H6c36rje4YzxxZ2HHd1k+NGLRy6wRFgqeBWRGTUzHE+yHZNX3fozpgREWNMpt39roM94SZHFxzscTPYabVjmNENoH5YpeEtwgg37rvpmABfAnuqSGCLkUmzx40Mi4FROL9Z59rVhT2O7ULOz7RQ2o/NamvoBkeIHWxXNjmW6uHIkYi8n/1NjjnfsJAb/89NltemdggHO/C9zOaMIe7ABlgujHdTFiRzCcIom2sTRFVCf2mix8YRETd+/84LEthiJKy1vQy24w722mZ9TzwE4kEzTjrY1YPHpKcsu+Rg10cfDZ7PeA92OiZ9lLjMbrIcn+llsOVg76bcmCwi4nvpoJnsPWestRQ6VaJgMoFtTU4O9pSRwBYjsVULuxvUXBSR/Zzf2jtkBtyNiGzVWhwf0iAC8WauRtjJdHY0Jc1gj0LWR6WX6tHY+euUXIYjIrXEpR236xl6g2aszabIrk6Ywe7FirK3LtVWmyVbJ8rJwb5akMAWI5G61+B2i0jU7nCx1BjsYDu6yXGjNpqDndaR1Rx4/pQaEcfGyGBnWWBv18Oxx6SnZHltqolhMamDDWTWxe5GRCZ43mRaQPkAACAASURBVMQd4dl7zpTqIUumgZ2gog/AegE+crCniQS2GIkdAtvhiMjFUoN2x3J2Pwc7amfWVRpEvdWmEXaGdmBDr44s6zlsa+1YEZGcH3cadzIqluINn5M62CaTbiRArRXhe2akKsfd+GlbRkafM5Wuuz/plMvsrct2PWSJBuTHHzIDgKdNjtNGAluMRNogcryYc9rB7lb07SOwrSWzm7IGsdEdkz5cQKV/LLOew6612kQdO/omx0RgZfV5UxojLrObfJDdjcPVZpulvD9RNr3b95xBIQlxBjsfeBSC8QV2LqMbY7frIcumjrcwmYONFxDg7t/uWSCBLUbiwladhZzHdccWncwZp6zt04ENdJ0ol2Ii6RTHUTLYS4442N1auhFFZSHrArsRjV3Rl5L3TXYz2K1oovw19DbztTPq7leaISsTrk1WIyLf2aixTJ3C0vGJHm89bXKcNhLYYiTOb9W57vgixbzvtoOdRGWuG5jBjt2WpkPrs9l1sEeIiCQOdiXjw2bGHQ3edbAz6NT24jLKYO+m2mpPlL+GvoEqGWzLgLgHe5L8NSSRqwzeeDxyocQSDVZWT0z2DeRgT53JnsHCOS5s9arpXJnGN4jzm3VOrxS6Yrqf9JhLDvZG4mCPksHuOtgZj4ikkwtHjYikwzGyKCQrzYhm1OGa5eHPj0HkMtywUmsegYOd0YhIpRmN3SCSkstoR/jjFy+TM22YNCLi5/AlsKeKHGwxEucTgb2Q86k7JCB3c35rcAc20B2k0Yjc+SW2VYvd2lEGzfQy2Nlen1LqYI/aIpJOn8vg29pPlZsAPHN1YciZg8kH2czTQhydmWTIDPRaRLKawT6MwA4yOEbeWsu59SfjTybe5JgjoJ3Zm7J5RAJbDKURtnm60uL644ssOtr1nHJh+wCBHaQOtjvrs1FtYcxobm3q1tUy/g5IGhEZe5NjBp3aJ0sNAE6vFCZ6fJYd7IvbDc4cm+zGI0g3OWYwCgGHFNgZ7MG+VG4SNcrxJ4UJBbYfkKOdyRv5eUUCWwwlHZF+/YlYYLtc07dRbe37dvdi3r2IyGatxbHF3J6x8YNIa/oy72A3JstgZ7Et41LiYD9jRQ52P+2O5clSg2snFdjKYO9LLoMbYx++WGaZpCq3MFlExHgBnrG0wvAIr0wchAS2GEr/xr5Fhzc5Ru0O2/Vw37xxNyLi0PpsVFucHCF/Db2BGlnP8KcO9sqIAiGNiGRRSD5VSgT26mQOdj6jkxyfrjSJOpYz+7wbNgxlsPcn7sHO1nPmkfV4gyMAEw6awY9/T0dh64iuSgxDAlsM5XxfNV2cwXZHQPazXQ+xdv/GjLSz1aX12aqFHB8hfw3x2/35wMt8D3apHouDUVx96DnYYQYd7CdLDRZz/sSVa1kdI5++K3jdxBGRbGewy4dsEclaROSRi2Wet1SLPymenOh7GD/+PS2BPT0ksMVQLmzV8Qxce2yBxZxPK+pk1jk5iLSS7sQ+ArvXIuKOwN6ojjYmPWUp72e+B3vc0eBZHjTzVLnJM1YLEw1TAcgF2ZzkeHE7diMnjYj4SQY7i7+HW1GHZtSZ+KYsrunL1mvp4fUSL13eiD85+eyJvofx4/VstSSwp4UEthjK2ladZ64ukPM9FvNpXjTbImkQG9X4rf/9IhFpRKTpWAZ7lIq+lKVCkH0He8zR4PkM1/Q9VW7wjAk3OEJ2NzleSAT2dccmi4ikGewsxmfSCNnkmxyzNSq9FXV4/FKF5+eehOVnTrzJ0Qvi39PtSBnsaSGBLYbS34G9mLi0Lm507HY+7zMWvOtgO3TzMb6DHWQ+gz3uaPAs92A/VWryjAkr+iC7mxzXk8m4o8ardhNkOIOdDqJannD6Z+Bl66bsiacrhG3Lde11uOa5E3+f1MFuh82jujQxBAlsMZR0iiNAIedezjglFdj7CUrXIiL1Vptm1BlpTHpKseBTy/jN2XY9HLmiDxyIiBzCwU4z2NZmS0iubze47tjixNEZP8MZ7K7ALkw25TKXMQf7kfW4nu9Y7W/gmudM/H28JIMdKoM9NSSwxYG0O5b1rQbXn9jpYLsiIvvpZrD3i4gEaYtI9oTSIDa6Y9JHF5MuONjlRjRyRR9AIaM92LVWRKUZTVzRB734TJYEE8D6dn3i/DX0erAz7WAXJnOws5bBfvhiiWv8OkHjMpw8hMBWRGTqSGCLA7lUjuuk9kZEsvMLbFQ2qi2KeX/gmHSIBxwEnnHm5mNzjDHpKUuOONijTnGE7DrY3Yq+w2SwM3rzsb7d4MyE+WvIuIPdSCMik2ews7Qx9pH1Mv/Lye34k0NERFIHux3JwZ4WEtjiQM5vxdVAXYGddzcisjlC3ngh5zvjYA9rVRnEUj7bmxyjdodKMxorIpLVDHY6xXHSMemQzTHyUbvDk6UG1x2ffF1y6aCZDK1LSvmQmxxznpepHuxHLpb4vtWkQeQwAjtIBLYGzUwNCWxxIGubvSmO0MsZuyiwN2qjCGzPmU2OGxM42MWCTzXDNX3p29vjRES6PdgZE0tPlQ83ZAay6WBfqjTp2Mkr+sANB3vUQU27CXyTmRHyG9UWT5aavCD3FGDgxI0Tfy8/lwyakYM9NSSwxYFc2ErqpNQiwmZ1eCVdIfCdi4ioRaRHOsVxrE2OGXWwuwL7EBGRQganXHZ/px4iIpLtDHb8GprYwfaz0zzzyMUSADfYdTh+A+QmvylLIyIdZbCnhgS2OJDzWzWOLea6v+zSiIgrIrKfUR1sV3qwN2ohxownJov5gGbUyeRb2xBPcQTGrOmL3cjMCexSg3zgjfX82E0uyN7arG/H7wqeOUREJMsO9nY9xDNQzE/WIhJ42XGw0waRE43vHCoeAuDnUoEtB3taSGCLA7mw1ejmr6HPwXZQYG9Ww6EOdpzBdmNttmotji3mun/sR2Epqd6qZXSNJnGwjTHkfY9mxm460oq+SavoAPJ+/HzJ0qa19cTBPrN6GAc77cHO1nMG4NuXa9xwsniI6Z/ZyWA/crHEqaUcua1vHapBBMDvtohIYE8LCWxxIOc3ex3Y4G5EpBm1qTSjoZV0CznfqQz2flMt92MpeSckq+PSS41YYI/TIgJxDjuMsiMi4fBTHKHn7mcpn76+3aCY98d+jvTTm+SYrecMwBOXqjz71NLEj895cYtIFrrTH7lY5vuf0YFm6fAOdiKwrSIiU0MCW+yLtZbzW3XOnugJ7IVkVLprDvZWLf6lNKwxYyHnOdUiMk6DCPTe9q1kNIedOtjjbHKEdGJhtl5TT5Wah+rAht4G0GbGIiJnji0cytnPaga707F86+kKzzm9PPH3CPxsrE27Y/nmxTIvO4IGEYAgiYi029n83TuPSGCLfSk14kER/XVSed/DM+5lsLtTHIdFRBza5LgxQmRmN0v5xMHOaFVfaYKICPQmFmaJJ0sNnnmIBhHIZk3fhe3GjncFJyGrGewL23UaYYdnH0pgZ8Pd//blKs2ow80LT8cHrnn2ob5f2iJiFRGZGhLYYl/OpxV9x4vdY8YYFnO+cxGR7lCVkXqw3VibrVqLE8XxhGQaEclqVV+pEeJ7ZuwNWrnAZEpgN8I2pUbEMw7RgQ19Q3gytDYXt+tce8h16WawM3TjAXE8BODZpw8TEUluyq7yHHa6wfG7uABeDo4961DfL0giIp22IiLTQgJb7Mv5rZ0d2CmLed+5iEhvLPiQmj5HIiLW2jiDPWZEpLvJMaMO9nY95Nhibuy3//O+d9U7bv1cSir6Th86g50tBztsd3iq3OTMYR1sP5sO9uOXKgCHioj0hvBc3WvzyMUSvmc42TgHJ28Cf/LMPvQiIlYCe2pIYIt9uZAI7N0TxxZy7gnsUceCL+R8mg5scqyHbZpRZ4IMdvxHIqsZ7FI9YnWCARn5wM9UzvgopjhC9hzsJ0sNrIXrDjFkBnoubdYE9hOXqqwsBJxaHu/3Sj9pBvtqrwJ9eL3Ms08t4W8+cej8NUCQi292tclxekhgi305v1UnH3icWtrpQi3kfGe6nlM2qvEvpeNDIhFxBjv7a7OZbvocOyKSOtjZvAnZrodjdWCnxJscs/O8OYohM9A3Rj4ja7O+Hd94HGaKI/Qy2Ff7Rr7dPH4p3uB4mA2g3eaZq3xtHrlY4gXXLsPlx+Hk4fLXAEEgB3vaSGCLfTm/Vee6Ywt4u3qOF110sGstVheC7h/8/YhbRLK/NqM6+rtJHeysTnMsNcKJBqvkfUMrQ+98PJU42IcV2IWMOdipwD7sJsc0g321xyB288Sl6qHy19BrWLmaHexSI2Rts86tJ2vQbh6Jg238ZJNjJ5u/e+cRCWyxL+c363vy14CTmxxHzRsv5Hyijr2qf7mPwsYEY9IBlvLZdrBL9XDsij5IHOyMiEiAJ8tNAs+MfQO2m14GOxtCcj2J3Z05pIPteQZjsjVoptKMuFhqHCp/Df0tIlfv2nzzYrzB8ZZug8jhBTa+HOxpcyiBbYw5boy5xxjziDHmYWPM3zHGnDTGfM4Y82jy74nkXGOM+aAx5jFjzIPGmJf2fZ83Jec/aox502F/KHE0nN+q75jimLLg4CbHUTufF3LxS6qRIbE0iM3aaK0quwl8j0LgUc3sJsdosohIxjY5xh3YhT3vfo1LL4Odjd8369sNVgoBKxPchO0m8EymMtjfShpEnnNIBzsLN2WPrJcAuMlbjw9cc7gpjgB4SbORBPbUOKyD/R+Az1hr/xbwYuBh4O3An1hrnwf8SfI5wGuA5yX/vQX4NQBjzEngXcDLgO8H3pWKcjE7mlGbS+XmwLcyFx2JQfQz6tTChWTSZdbXZ9KICMRVfVmOiEwyoS9rDvZT5QanD7nBEfonOV69Yqmf9e36ofPXKYHnZUpgP/F03CBymA5s6Ansqzk+8/DFMscWc6xW/wZyRVg5c/hv6iUOdkcCe1pMLLCNMavADwEfAbDWtqy1W8DrgY8lp30MeEPy8euBj9uYPwOOG2POAK8CPmet3bDWbgKfA1496XWJo2F9K84KDnKwncxgV0d0sAM3BPZGLcSY8QeqQDzNMYuj0hthm1bUmWhNcn62NjleKjcPnb+GPgc7I2uzvt04dEVfSuCZq1pE7ubxpyp4Br7rmuLwkw+gGxG5iuMzj6yX+FvXrmA2nojd60Ns+uySRETkYE+PwzjYzwYuAf+vMeYvjDH/yRizBDzTWrsOkPz7jOT864FzfY9fS47td3wPxpi3GPP/s/emAXKc9bX3r6qrt+ll9tGM9n3xIsmW5A0bAwZCjM0a9iTAJQRIQnJveLOQm/u+uSELCVmAEBIIkAVIgBCIDRgMxmAsr5JsybItjUb7jGaf6Znet6rn/VBdPSNN713Vo+rp88Wanl7KNdVV5zl1/udIhyVJOjw1NVXHprdQDvkM7EIE27UCPdjxyjzYbsMi0uRJIqFYmg6vM59mUA38bqUpLSLhGmvSofkU7Ilw0hSCbcTRNcu+GZ1L1h3RZ8DhkJrKg31mOsa6rjbcSnUlTVciH2Fo08WHlqtI3zUQhJnT0GWCPQRA1u+sSa0hx4ahHoKtADcC/yCEuAGIsWAHKYRCV2JR4vGlDwrxeSHEfiHE/t7e3mq3t4UqUKxkBlZeDnY8nSWZ0SqyQ3hXiEVktkJPeiG0uRxNOeQ4bxDsGhRsdxPF9KWzGqF4pu4MbNCH+RRZaop9k8qqTEdTJlpEmsuDfXYqxuae+vzXYP8hx/FwklhaZXuvG0IXzBlwBJAksjigRbAbhnoI9ggwIoR4KvfzN9EJ90TO+kHuv5OLnr9u0evXAqMlHm9hGXEplECSCue1eldQHTgsTswoT5xWigdbr0mvjWA3qwc7nNQJdm0xfc2jYE9FzcnANuBSZDJNsG8mw/p+Wd1ujkXEIUtNk4OtaYJz09G6E0RgsW/fnsdMJKmfG1drkyBU8wg25Ah2yyLSKNRMsIUQ48CwJEk7cg/dBbwI3A8YSSDvBu7L/ft+4JdzaSK3APM5C8mDwKslSerMDTe+OvdYC8uI0bkEvX53wdt1XqeDjCpsewKrFqGYUapSzZBjc++b2VimZoLd5nIQa0IPdjihXxhraXJ0NhHBNloc+4LmEOxm8acbzbgDHeYNOTbL8OfofIJkRqt7wBHsP+QYz9nnupIX9QfMSBDJQZUUW1tE3vTZx/jz759Y7s2oGPWV28OHga9KkuQCzgLvRSft35Ak6X3AReAtuec+ANwNnAbiuecihJiVJOljwKHc8/5YCDFb53a1UCcuzRXOwAbdgw26SluueKUZMBuvPPM5H9PX5Ap2KJbm+jXBml7rczWnB9uwiNSkYDeRRcRQavsC5hBJlyI3xWLeKJkZMEnBVprIg30mF9FXb8kMLCqasem+MeabggmDYJunYGs4bE2wz8/EdW+6TVAXwRZCHAX2F/jVXQWeK4BfL/I+XwK+VM+2tGAuLs0luH5Ne8HfGSptIqOakud6tSNURalKXsFuktzeQhBC6LngdVhEmtGDbVhEaq1KVzWBqomaBkevJkxFzFWwXQ6ZVBOo+wsE26QhxybyYJ+d0iP6zLWI2HPfGOfGQPQCeDuhrcu099YVbPtaRBJplTZXfUOwjUTzy48tVA1NE4zNJQsmiMCiQb60/S96laCa1sKFmL7m3TeJjEoqq9U+5Oh2NKUHez5eX4oI2Nc3uhgT4RSyBN0+Ez3YNiVLizE2nyDoUfC5671xrENpIg/22akYAY9Cj7++5k/Qy6zAvt+leO7upzdyzrwEkRxUyb4KtqYJEhkVr8uc708j0CLYLSzBdDRFWtXKWkRWSpJIKJ5GliojTivBIpJfcNSqYLsUUlmt6erkw8kMXqcjT5argStHCppBqZ2MJOnxu01T4p0OqSmaHEfnkgWLu2qFo4mKZs5M6QOOkgl5z0ruuLOrBzuRs8+5wudNtYeArmDLwp7fJeOusCHw2QEtgt3CEgyH4gCsLUawnSuLYM/GdDtEJbXP7hWQIpIf+qwjpg8g1mQ2kXAiW1OLIyyuBG8Ggp0yJaLPQDMp2GZF9IG+8GiWRerZqZgp/mtYdDfIph7seFrFQwolcsl0gq3ZeMjR8Ka3LCIt2BojIX3afV1n4UYto0xlpZTNhKrIfDYU7GZQIoshFDdq0mvz3xu3yONNNug4n8jUNOAICwq2XW9rL8Zk2JwWRwPNkrAyPp80bcARmseDHU1lGQ8nTfFfg/0V7HhaZaM0of/QvdnU99YkBVnY87xreNNbCnYLtoZBsItaRFbAIN9izMbSFdshXA4ZSWpyBdsg2DUq2AbBbraovnAyU5P/GppNwU6aNuAIuYxwmy88khmVmVjatBZHaB4P9rlcgsgWkxRsu3uwE2mVzfK4/oPJCraQHLYl2MY11dtSsFuwM4Zn43T7XLQVGSbIx/StFAU7lqGzgpIZAEmS8CjNXcRTvwdbP35aCvYCjLhLuxPJrKoxE0ubFtEHzRHTN24kiJjqwW4OBfvstJ4gYkYGNiykiNh138TTKtuVnIJt8pCjJjlx2JRgx1sWkRaaASOhBGu7CttDYAV6sOPpihJEDHicclOniIRiuaHPGsmksXCLNlmSSDiZqXmfNIuCPR1NI4R5EX3QHC2XZkf0gZ733AwK9pnJKLIEG7qLX3OqgZGDbdf2z0QmyxZ5HPz94DZn0WFAk1sWkUaiRbBbWIKRULzogCOsLIIthCAUqy7z2dPkVfKhuK7U1poS4XPnFOwms4jMxzM1tTjCIoJtc6U23+JoooLtdNhfwR6bz7U4mkmwm2TI8cx0jHVdbQVbg2tBPgfbpouPeFplgzRuuj0EQMiKbRXslkWkBdtD1QSX5hJFBxwBPEZM3wqwiERSWbKaqFLBdpC0qXpSCWarGPoshLwHu4ksIpomiKSyNVtE3I7mULAnI0aLo4kKttJMCrZ5FhGlSSwiZyajbO4xx38Nuk1Pke27+IinVdaLUVMr0g1oNo7pW7CItHKwW7ApJiNJMqqoSMFuZpXWgNHiWI2C7Vbkpt43oSqGPgvB5zJSRJpnH0XTWYSo3TbjbBKLyGSuxdHMmD5dwbY3kRydS9DR5jRVfXM0wZCjpgnOz8RMSxAxoDjsu/iQkvN0iHlLCLaQnTiwp7Bh3DFvWURasC2GZ/VbmaUIttMho8jSirCIVNPiaGAlWETqUbDbchaRZmpzzLc41hnTZ3uCHU4hSZjSyGfApdi/Kt3siD7QvcZ2JZEGRucTJDOaaQOOBpyyfW1FruS0/o/AavPfXHbgsKmCbRTwtCwiLdgWI7mSmXUlhhxBX0UmVkBVei2RdB6nTKrJhxxrzcAGaHMaBNueJ/pCCCdrr0mH5qlKn4wk6fa58lFpZsDlkGy/X0bnk6ZG9EFzKNhnTI7oM6DY+JiR0xH9H56gBW/uREG15XGTH3JsEewW7Ip8BnaZOCmPy7FCFGydOFVjidA92M25b4QQdXuwFYeMW5GbKqZvcFy/KJa681MKzTLkqJfMmEskm8ODnWCgw9z9YmcSaeDslLkRfQYUh2zbohk5oy86cAdMf28hKzjJ2vK4aVlEWrA9hmfj9AXceMocxN4mt0EYyHuwK8zBBpo6BzueVklntbo82AB+t9JUMX0Hh6bp8rm4ZqA21cmwiNjdCjEZSZka0Qf2TxFJpFXm4hkLLCLNoGBHCXgUUy1FoH+f7Orbd2T0RQcucxcd+pvrCrYdF/KJtIpbkWtOr1oOtAh2C5dhJJSoSIXTLSLNSSIXYzaexumQ8Lsrn1xu5hzshZr0+i6I7W1O5hIZMzZp2SGE4ODpaW7b0o1c48m/WXKwJ8JJUxNEQN83WU2g2ZRMWhHRB+BoAg/22Sl9wFGSzCVN+pCjPb9LrmyOYFugYCMrOCTNlhnh8bRqK3sItAh2C1dgOBRnbYmIPgMrxSJiZGBXcwFo5iHHUM4yU49FBHTLzWw0bcYmLTtOTUSZjKS4Y1tPze/hsnm9M+gRn9NR8y0idm+5tCKiD5pDwT47FWOzyf5ryEUY2lDBFkLgUg2LiAUebIcTJ9mGL8x+8PwYr/jrn9Z1XUxk1Pz8jl3QItgt5JFVNcbmk6zrqkTBllcEwZ6JVdfiCM1NsGfjRqpK7UOO+utd+YQWu+PRoSkAbt/WW/N7NIOCPRNLoQlYZbJFxG1zf/ronFUKtn2znkFvch0PJ02P6AP72opSWQ0f+vFidosjALKiW0QafJ7572dHOTsV49REpOb3SKTVfAeHXdAi2C3kMR5OomqiMgW7iUnkYlTb4gjgdspNWzRTSy54IXT7XXmybnc8dnqazT2+soPBpeBsgpi+ybBeMtNrkYJtx9vaoEf0AfSbTLCdNs56BjhnUYII2HcANJ5W8UtJVMkJirkLVQDJ4cSJ2tB9o2qCx8/o0YMnxsI1v08io9LWItgt2BVGBnapFkcDK8mDXbWCrThIZzXbekZLwSwPdpfPRSiWRgh776N0VuOpc7PcXoc9BBbqne2q0sJCyYwVQ45g330zHk7S2eYsOzheLezuwT43oxPsTT3mK7V2zQiPp7P4SZBxWqBeoxNsBbWh++b4pXnCSX2g/cRY7Qp2PJ2lzWmfFkdoEewWFsHIwK54yHGlKNhV2iGMC6ndEyEKYTKSwumQaq4EN9DZ5iKrCcIJeyeJPHMxRDyt8pKt9RFsSZL0ODqbkkhYULDNbHGERRnhWfsRJoBwMktHnQvSQrC7B3s+Ycxz1HcuKQSXTS0iibSKX0qgWkSw9RSRbEPvlD12WlevN3a31adgtywiLdgZI6EEkkRFea0el6NpkzIMqJpgLpGpOpLO49S/Vs1ooRmfT7Iq6Kk5LcNAdy6WayaWMmOzlg0Hh6ZxyBK3bumu+73cDnvnPU9GchYRv9kKtqHu2/P7FE1mqkohqhRG0Yxd7wJFc6pmwG0+wVYc9hxyjKdVAiTQLCLYssOJgtbQxcfBoWl2DQS5bWsPJ8bCNR+vrSHHFmyN4VCc/qAHt1L+IF4JOdjziQxCVFeTDgsKdjMq/KNzCVabkIbQ5dNJWMjmPuxHT0+zZ217zQ2Oi+G0eaHKRM4KYSjOZiE/5GhTBTuaylpCsJXcIteuKnYslUWWFgQJM6E4ZDI23C/xtIqPBJoVGdjoFhFZEmSzjblzmEirHLkQ4vat3ewaCBJOZhnNzSRUi1ZMXwu2RqUZ2LBgEbGrelIJZmPV16RDkyvY4aQpw1rGXYEZG0f1zcczHB+Zqys9ZDFcTaBgmx3RB/b3YEeSWfweCwh2br/Y0WsMCwsPszOwAZyyZMuh2EQmi19KWJOBDcgO/TjMZhpz3n36/CxpVeP2bb1cM6D/P50Yrc0mksy0CHYLNsbIbLyiAUcAr8uBqgnbtmVVglA+kq46gm1UuTabhUYIwdh80pS4sa6cRcTOUX1PnJ1GE9SVf70YLsWevlEDVrQ4wiIPtk33TTSVJWChgm1Xgh1JZgmYcOenEOxaNBNPq/hJIFlEsCVFP+9mM42x5j12ehqXQ+bAxk529Ou53ifHayPY8XTLItKCTZHOaoyHkxUr2M1sgzAwW2Mkndsg2Nnm2jeheIZ0VjNFwe72GR5s+xLsR4em8bkc7F3XYcr72X3IMRRL5/+uZsLuEYbRlDUKtlEZrdpU5Iilsvjc1hAmp0O21IM9Pp/kwRfGTb+DG0+rBKQEsseCkhnAoegLGjXTmBbdg0PT3LihgzaXgt+tsL6rraYkESEEiZaC3YJdMT6fRBNUlIENi1Xa5iKRi2FkPtcS0wfNt2/MrHz2OB20uRz5fWxHHDw9zS2bu/MEsF44bW4RiaasUSRdDSiaiaezltjdhBBEkxZ5sB2Ggm3PY8YqbzrkimYs2i9CCH7zP57lA18+wq9++Yip57BEWsVHEofHIgXbkSPYWevPuzPRFC+OhbljkYVu10CgpiSRVFZDCFoEuwV7YtiI6KugxRHA69IPnWbOwp6tMfPZ8GCnmswislCYCDPsrwAAIABJREFUYU7lc2ebfdsch2fjXJiJ151/vRguRbZttKMQgkgyY4lS67JYwU6kVW77+MP85YODpr93KquR1YS1CrZNLSLRVBafRQTbyqr0H744wdPnZ3nVNav46eAkd3/6UZ4+N2vKeyeSKdqkFEpbuynvdyVkwyKStV7BfuzMDMBlEaY7+4Ocm4kRT1c3ZBnP8YyWRaQFW8LIwK7Yg70CLCKhWBqv01H1qtnTpOq+Mf1tVuVzt99lW4vIwVy2q1n+a9Bj+uzqM05lNTKqsESRtNqD/fT5WebiGT73yBmOj8yb+t6RfBSd+fvFKef2i40JdsCChQfkUkQsINgZVePj3z/J1j4///CuG/nWh16CS5F5++ef4NM/Hqp7saMmdXVX8VplEdEJttoIgj00TcCjcP2adhAC4rPsGggiBJyaiFb1XgYhbynYLdgSw7MJHLJUMXlaGR7sTNX2EFhEsJvMgz0+n0CRJXpMyjnu8rlsG9N3cGia/qCHLb3mxWm5bBzTF03liKQFhClflW4RwTYGsbr9bn7/W8+RNfFzYrn90vJgL4VV1hnQs9OtOF7+/amLnJuO8Qd370RxyFy/tp3vfvh27t2zmr/50Sne9YUnmQjXFkMHoCV0gi1Z5sHW97dmsUVECMHB09PctqUbh1DhP98Df72DG7Tngeor0w2xyutqNTm2YEOM5DKwlQr9pHkPdhNbRELx6lscYXFMnz3JUjGM5UpmHHWWzBjo8rlsGdOnaoLHzkxz+7YeUyPGnA7JtkOO+dIQKywiirUWkUeHptm3oZM/ft21vDAa5osHz5n23sbCw29RmQrY14Mds9QiIpu6UAIIJzN88qFT3Lalm5fv6Ms/HvA4+eTb9vKJX9jNseF53vwPjzNXo3CgpXIDgBblYBsWEasJ9oWZOJfmEtyxtQu+/QF48b/BHaTv+7/CLvd01QTbsIh4WxaRFuyIajKwYeFWTXMr2Omq/dfQvEOO4/PmZGAb6LKpB/uF0Xnm4hlur7Me/UrYWcE2rBBWEMmFJkfzldrpaIoTY2Fu39bDa67r55W7VvG3D53i4kzclPdf2C8tD/ZiCCGIpq2JLwRwKpLp1pnP/uQMc4kMf3D3riULa0mSeMv+dXz1/TczEU7ykW8cQ6vl81M564RFMX2Nsog8enoaGY3Xn/9TeP6b8Mo/gvf9EAn4J+UTXLg0XtX7GbNebS2LSAt2xHAozrquyvzXsDIsIqF4uj6LSJMp2KYTbL+LREa13aDso0O6//olphNsh21z5SMp/YJtBZF0O/TvkxWLj8dyXvrbt+p3Iz72hmtRZJn//d/HTUkVWVCwWznYixFPqwiBZQq202QFeyQU50uPneONe9dw3ZriA4g3ru/kD197DT8+Ock/PHKm6s+R0jll122NRURx6gtgrU6CrWqC7x8fI5ws/D6Pn5rk074vERj8Jrz8D+H2/wXdW+BtX2FAHeVXJ/8YoVa+DfG8RaRFsFuwGVJZlYlwqjoF2yDYNiNH1aBWBduodm4mBTtfMhM0j2AbmcmzNvNhHxyaZmd/gN6AuaUqy9XkGElmeP5SfcN9VlpEnIpOJK3w1D52epp2rzNPmgbavfzua3bw6NA03372Ut3vHzUWHlY0OeaGHK3Me7YKUQu96aDbZzRhnrr/Vw8OIgEf+bkdZZ/7y7du4N49q/nrHw7y+Jnpqj5HSjdGwdaqILdXQgjBx777Ih/66jO89R+fYPIKz7mqqrzi7Me5R30YXvq7cOfvLPxy4+0cuu7/8BKOEb3/9yr+zETLItKCXTE6p39BKk0QgeZNyjCQUTUiyWxNCrYsS7gUuamGHMOJLImMaqqCbSxeZm3kw05mVI5cCJmaHmLApUgNjelTNcHXD13k5X/1CPd+5mBdtggrhxytiukTQnBwKDeItWiu4Bdv3sCN6zv42HdfZCZaX+Nd1EqLiI092FZaZ8DcwdjnRub476OjvO/2TazpKC9CSZLEx990PZt6fPzmfzybjzetBEqeYFvjwc4T7DoU7C8ePMe/PH6en7t2FRdn4/zCPz7BhZkYpOMQOk/om7/FW3iIoe3vh5f/wZLXOw+8h3/K3k3g2Bfh0Bcq+syWRaQF22J4NpeB3fJg52GkW3TW2EznUeSmysEezZfMmJOBDXpMH8BMrDG1vWZgJpYmrWps7TP/Aqgr2I35Pj11dobXfeYgv/dfx+n2uRACnjo3U/P7We01liTzFexz0zFG55NLrD6yLPHxN+8mmsryJ987UddnRCxceCg29mDHLLTOgHn2GSEEf/q9E3T7XHzoZVsqfp3PrfCPv7iPeFrlN/79mYqPXUc2pv/Dqqr0XNFMNfaMxXjg+Bh/+sAJXndNB//Y8RUO9f8FX41/gN6/2wx/NgCf2kPPiS/z+exr6bz3T6DAEPjO/gAfV9/Jua7b4YHfhTM/Kfu5eYtIS8FuwW4YCenkaW01HmzFKJppHhK5GKGYfgLqqsEiArrC30zqvqHCDHSY6MH26RYLOw06hhP6cWFVY6HVHuzh2Ti/9tUjvO3zTxKKpfnU2/fywG/dQbvXyeHzoZrf18pb/pIkWWKfKZVlvn1VgA/duYVvP3uJIxfq2C/JLIos5W1jZsJhYw+2ld50WFCw6/VhP3xykqfOzfI/X7mt6u/8tlUB/vxN13P4Qoi//MHJil7jVHME26IUEWR9fwu1+nPuofOz/M+vH+XG9Z389bbnkY78M742Hx3bbuV+xyv5JO/k7G1/wR93/yX/3fNBegKFrxU+t8K6Lj+f7vg96NkG3/2fZT87YdMcbHuFCrZgCYZDcRRZor8Kf63ikHE5mssGsRiGqlpLTB80H8EeM7lkBhYq6O1EsA2lNmgRwbYypu/URIR7/+4gsiTx26/azvvv2Jy/YO3f0MmhC7W30UWSWVwOGbdizQXQ5TB/3xwcmmZtp5f1RYSF992+mU8/fJpD52fZt6Gzps+IprL4PYqpcY4GDA+2HRVsqz3YRvJMPQtWIQR/9/Bp1nZ6eftN62t6j9fvXcPh8yH+6dFzHNjYxauv7S/5fFc2Skr24pYtIpIOg2BX16R4ZirK+//tMGs6vHzhF2/A+c+/DmsPwHu+SwC4Yy7BL33xKf7hZwk0IXjvS3pLvt+ugSDPjoXhhp+Hx/9OL6Ip8R0xhLyWgt2C7TASSrC6w1t1vrHHKTftkGNewa7VIuKUmypFZHw+gSxBr0klMwBBj4IiSzYj2IaCbU2hiqoJywjTNw4NIwT86Ldfym/ete0yNWj/xi7OTsVq9hxHUxnLWvnA/AjDrKrxxJkZ7iiRZd7e5qQ34Ob0ZHWtc4thZZmK4rBu+NNqWOlNB/J9DvX40584O8PR4Tk+cOeWvCJeC/7wnl2s6fDyn0dGyj7XrcVIOXw1f1ZZyIZFpPJz7lQkxXv++WkcksS/vPcAnZcehtmzcOuv55+zpsPLNz94Gzv7A2RUUTbCdGd/kAuzcVKuDtCyYOR/F0E8oy/gK+3puFpgr61twRKMhOKs66reW+t1NZdKuxhGskVdFpEmUvfH5pP0BSovIqoEkiTR6bNXFnbEpoUqqia4/9goL9vRy9oCw8w3bdIV2sM12iEiyaxlaiToiw8zieRzl+aJpLJloxa39vrrItiRlIUE28YebKstInkPdh0K9md/coYev5u37Ftb17a4FQdrOr15e1kxCCHwqAkyVhLsnAdbq1DBFkLwgS8fZiqS4ovvOcCGbh888ffQvh523nvZc7t8Lv79/bfwhV/eX3YIfNdAACFgLJ3jHfHS8x/JtGo7ewi0CHYL6DXpazsq918b8DodzTvkmCN9HbUSbKW5Fh/jYXMzsA10W0SwVU0wGU5yfGSeh16c4KtPXeBvfnSK7z03Vtf7GrmvQa8FFhEjLcMCRfKpszNMRlK8fu+agr+/bk07LkXm0LnabCJWKrVgvoL92NA0kgS3bSlDsPv8nJmM1pyJHU1mLVP2m8GDbVkOdp3fpWPDcxw8Pc2v3LEpn5hVD4IehXCyNKlNqxo+4mSd1ivYVDjkODaf5JmLc3zkVTvYu64DLj0DFx6DWz6Yt5sshs+t8MprVpW1RO0a0HO+z8dz15RE6fNOPK3aLkEEWh7sFY9kRmU6Wl0GtgGP09G0FpHZWJqAW8mritXC7ZTzamczYHQuwfZV5k+2d9bR5nh2KspHv3WcSDJLKquSzGikshqpjEo8oxZU9gJuhZ+/rh+5xrp3KxVst4UK9n1HR/G5HNy1q6/g792Kg71rOzhUq4JtoVILuqe2Gj/tT05OEk5mii4oHj09zbWrg2UtYFv7/ERSWSYjKVbVkAEfTWXp8de2SC8Hu3uwnQ5rhj9hUY18jQr2Z396mqBH4V031+a9vhJBj5MTidI2iERaxS8lyDpr8/tXhLwHuzKCbVSa37C+Q3/gib8HVwBu+KW6NmNtp5eAW2Ew4uRlAPHS5514RrWd/xpaBHvFYySkR/RV0+JowOtqYgU7nq45og/0xcdUxD7xc6VglMy8dHvpwZVa0OV3cWI0XNNrv/fcGE+fn+WunX24nQ7cij5k53HK+FwKq4Ju+oIeVgU99Ac9/GRwko9+6zhnp2M1x+yFkxlcijXDfPWqbsWQyqo88PwYP3ddf0k1bv/GTj7/s7PE01naXNVdGqLJLKsryAiuFU6HXHFGuKYJPvqt44yHk3idjiWDZbFUlmcvhnjf7ZvLvpdxnJyejNZMsDf2WKNI2lrBzt3xsGL4E+rLwR6aiPDgCxN8+BVbTUsLCnqdRVsPDcTTKn4SaM51pnxmQeRSRNCqI9g7B4IwPwIvfBtu+RB46mualCSJnQMBXpjNFVw1qUWkRbBXOIaNiL4aFGxvkyVlLMbwbLyqVJUr4XE6GloaYiUiqSzxtMpqEzOwDXT7XMzUqGAfvhBie1+AL7z7QEXPN5Igjg3P1U6wE1mCFt3yt8qD/dPBKSLJbFE118CBjV189qdnODo8V9Y6cSUiqQwBjzXZvaCr+5WSpWcuhhgPJwl6FH77G8e47zf8bOld+Hs/fW62okEsuJxgl/NrF0LEQuuMM6/S2u88E0tlLbOHwKJ9U8Pi4x8eOYPX6eC9L9lk2vbsThzixfQMmvbqonfP4mmVgJRAsyqiDxZZRCq7u3piLML6rjb9GH7kc4CAmz9gyqbsGgjy42dkkKjIImJHBbvlwV7hMDKwa1Kwm9SDnVE1XhgNs3tte83v4VHkpll8GBnYVniwu3wu5hOZqkmCqgmeuRBi/8bKb6du6fXjdyscG5mrdjPziCQzlkT0wQLBNjsV4v6jo3T7XLxkS3fJ5924oRNJoqY8bKs92M4qcrC/d3wMlyLzzQ/dhkuR+cCXj+Q9v6DnX7sUuaJjpy/gJuBWah50tDJdxc4KttWWooUa+eq+S8Ozce47Osrbb1pXc4JUIbzs4qd5r+P7RNPFiW0ireIjaVnJDJAfcpQqVbDHw+zsD+gpH0f+Fa55PXSYY5vZNRBkLOVGIEG8NMFOZOypYLcI9grHyGwcl0OuKX7N42pOD/bgeIRUVmPPuo6a36OZcrCtyMA2YFzEQvHqmsVOTUSIpLJVEWyHLHH9mnaODddDsK0bWrOiEjySzPDQiQnu2T1QNgGm3etkx6oAh85XN+gohMjnPVsFV4UKtqYJHjg+xp3be9m+KsBn3nkDZ6ei/M5/HssPKh4cmuamjV0VDa9JksSWvtqSRLKqRjKjWZiUYV8Pdsxqgp1TsKu1W/3To2eRJXj/HeXtQ9Ug6+6kS4qUTBKJpzL4SSBZSbCNohmtvIKdSKucn47pA4nPfhVS83Drb5i2KTv7A2jIZFztZS0iCZsOObYI9grHSCjBmk5vTUNfukXEfrcny8FQOPfWRbCbJwd7PFeTbpWCDdWXzRzOkcD9G7qqet2edR28OBYmVWOEYjiZsaTFEcCZU7DNtBb98IUJUlmN15Wxhxg4sLGLZy6EqlL+UlmNjCoszcF2Vlg0c+RiiIlwint2DwB6SshHf34X339+nH985CyTkSSDE5Gq7B5b+/ycnqqeYMdS+jFmFZG0s4Jt9YJsocmx8n0zFUnx9UPDvPGGNabPE2jeLjqJEE4UJ7bJZBynpCLV6W8uibyCXZ5gD05E0ATs6vfBk5+FdTfD2v2mbcqO/gCSBFFHsLxFJJNtWURasB+GQ/Ga/NeQK5ppEpV2MY4Nz9Hlc9W8X2AhB7vWeK+rCWPzSSQJ+opU39YDg2AbzZmV4tD5EKuC7qr/RnvXtZNRBSfGSk/0F0MkmSXotYYYuC1QsO87NsraTi83rq9ssbh/YyextMrJ8cr3Tz5Z5SqI6fvec7o95K5dq/KP/codm7hn9wCfePAkn/jBIFC4Hr0Ytvb5mYqkmC+TY3wlIin9+ZbnYNvQg90ISxFUVzTzpcfOkVY1PnjnFvM3qK2bTilSctAxE9cH/hSvlQq2TrATyfLnW2PAcV/8cZi7cFmxjBlocyls6vYR0vzlLSJpDW+Vg9dXA1oEe4VjJJQoWDxRCbxNGtN3bHie3Wvb65pw9zgdCGGuGrlcGJtL0uN31xxZWArdPt2aZDRnVoojF0Ls39hV9d/IsP0cvVhroUqGgLvxHuwvHTzH3//kdFXK8lQkxWOnp3n93tUV76cDG/U7AtXYRKyuvYbKqtINe8jLtvdeRt4kSeIvf2E32/oC/OeRETrbnFwzULlKuLV3YdCxGli9X5Q6BvmWG1HLPdjVVaWHkxm+8sQF7r5ugM295g8Zyr5uOokSjhcntpmETmgVb+2zP+U3RFeBE8kkY7k7k8VwYiyM363Q8/wXoGMD7LzH9M3ZORBgIttWAcFuKdgt2AyRZIbZWJr1NQw4wsKQYzOotAaiqSynJiPsWVu7PQQWMo1TTWATGQsnLfFfA3T6dLI6W4WCPTqX4NJcgv0bqs+L7Q966Au4OTYyX/VrQU8RscyDXSRFJJVV+cSDg3ziwUHe8U9Plr0wGnjg+BiqJsqmhyzG6g4vazq8VQ06LtReW7PwgMo82IcvhJiMpHhtzh6yGG0uhc/90j4CHoU7t/dWZYkzkkTOVEuwra4DNwb5WgR7Caq1iDx5ZoZIKssv3brBmu3x96BIGolI8e+VZhDsNgstIpKEkBQUshwq8x0/ORZhf5+GNPwk3PjLeXJuJgbavUxkfSUtIkII4pmWB7sFm2F4Vr9Q10qwPbkDvhlUWgPPX5pHiPr810B+gKoZ6tLH5xPWEew2wyJSuQfbqPM21NZqIEkSe9Z11DTomFE1EhnVkhZHKJ6Dffh8iERG5R03refF0TB3f+pRfnJysuz73Xf0Ejv7A1UXBO3f2Mmh87MVL5wNK4S1HmyprEXkgeNjuK+whyzGxh4fP/pfd/KxN1xX1Wev62rDpchV+7AjFivYjquwKv2JMzOXJbYUgqoJ4mnV0pg+Q92vNJFnKLd4una1NeTW1a53CGQiU0Wfo+YItttX37WnLBxOvA6Rn2MpBCEEJ8bD3BnMNd+a6L1ejHavkynVjyihYKeyGkLQShFpwV64OBsDYEN37Qo20FQ2kedyA471RPTBwr5phiSRsfkkAxZkYINOKtu9znw1fSU4fH6WNpdDj4+qAXvXdXB2OsZ8lcklUQtbHKG4gv3IqSlcDpk/fO0uvvPh2+lv9/LefznEnz9woiiBuDgT55mLc1Wp1wYObOxiMpLKL8DLIWKxUguGgl2cSKo5e8jLd/SV3I7+dk/VQ6oOWWJzj696i4jF3nTDBlFrW6HZ+Pqhi7zjn57kv46MlHxeLG3t9wjAKVcXeXl6MspADcdGpfAEdYKdjRZPyxDJHMFus9AiAkgOJ6sDCk+fK05qR0IJIskse5SL+gP9uy3ZlqBHYU74kbIJSMcLPsfgFy2LSAu2wsXZ2lscYRGJbAKV1sCx4XnWdXnpriG2cDHyCrbNLSLRVJZIMmtJgoiBrirLZg6fD3Hj+s6ysXPFYNh/nrtUnYq9UJNukQe7iIL9yOAUBzZ14nMrbO718+1fu4133byez/3sLG/93BP8dHCSyXDyMsX5/mOXALh3z1K7RDlU68O2euEB+kIsU0LBPnx+lslIirsL2EPMQC1RfVZ7sGVZQpJArWKQzyo8czHE//nvFwCYjCRLPtdq6wxU708fmoywrco7PdXA4deHakWsRBxdWj++ZK+FFhEAWWEgoDA4ESk6uGsMOG7MnIH2ddBW/d3CShD0Opklt9+L2ESMIIWWRaQFW+HCTJx2r5P2Gm95G7dsmknBPjo8V7f/GvSEFbC/gj1uYQa2gS6fq+KYvkgyw8nxcL6VsRbsXqcrRNXaRIwEAKuaHN0FFOyx+QSDExHuXFRT73E6+NM3Xs/fveMGhiaivOefD3HTn/2YfX/yEO/4/JP83++8wNcPD3NgY2dNA8zb+vwEPUrlBDvVGAU7VUKNzNtDdvZZ8vlbe/0Mh+JVfZ8bQSSdskxmmS0ik5EkH/rKkdzdAaVspn0sd7xY2+RYedGMpglOT0bZVmO7a0Vo00uepBJeYymdS+6xMgcbwOFklV8fwn/mQmEf9omxCJIEHfMnoP96yzal3eskJHL7vYhNJG4o2C2C3YKdcHE2XrM9BBZU2maJ6puKpLg0l6jbfw2LFWx77xtjoK6e2vhyqIZgP3txDk3U5r82EPQ42dLr4+hwdYOOBsG2LAe7QEzfz07pns07ty8ljvfuWc3jH30F//H+W/j/7r2GV1+zinhG5WtPDzM8m+At+9bVtB2yLLF/Y1f1BNviFJGMqhX0haua4IHnx3nFzj7LSNvWPj9CwJkqfNiGB9tnYbyYQ5aW1YOdzmr82leeIZzI8vlf3kdfwM1cvPR32WpvOixUpVeSIjISSpDMaA0h2I5k8e+UnFOwLSfYskKPV0aRpaLf8ZPjYXZ0ysgzpy2zh4CuYM+J3P9vkbIZO1tE6j7CJUlyAIeBS0KIeyRJ2gR8DegCngF+SQiRliTJDfwbsA+YAd4mhDife4+PAu8DVOA3hRAP1rtdLZTH8Gyca9fU7vdqJp8xLPiv62lwNJBXsG0+ALrQ4miNBxug2+eqWE0+fH4Whyyxt8Jc52LYs66Dn52aRghRcYRdpFEe7EWq2yOnpugPeti+qvDFP+hxcuuWbm5dVIOuaYKpaIq+QO02p/0bO3n45CQz0VRZu1Q4mcGlyLgV6y6ALoeMEPotf4M8GTh0fpapSIq7r7fGHgKwbdVCVN+1qys7ZxpZz7WUeFUKRZaW1YP9f7/zAocvhPjMO29gZ3+QzjZX2cjNWAPueChV5GAPTerK8bYi3zFT4PKTRcGVLp7c4chEUZFxKNaJGQDICgoq161pL5oWdGIszN1d0xAXMGAdwW6vyiKyMnOwfws4sejnvwD+VgixDQihE2dy/w0JIbYCf5t7HpIkXQO8HbgWeA3w2Rxpb8FCZFWNkVCCDTX6r2GxRcTeJNLAseE5HLJkyiS5QTaqWXw89OIEL/vET0qWETQahkVkVXt9nvRS6PS5CMXTFaVWHL4QYtdAoO6L8951HUxHU/kFRCUwao6DVnmwjRzs3KIsq2ocHJrmzu29VeV9y7LEqqCnrhx34w7BkSK3kBcjmsxaWjIDCy2XhYbWHjg+hscp8wqL7CEAm3p8yFJ1UX3RVMZSEgngcEjL5sH+j6cv8tWnLvLBO7dwz+7VAHS06d/lUmiIB7uKHGwjQWRrr4XKsSQRdbTjSRcXEpRMlLjUBnV8byuCwwlqhgMbOzk6Mrek1TaWynJhNs4Bd25Y1UoF2+NkrqxFRD9evC77GS7q2mJJktYCrwW+kPtZAl4BfDP3lH8F3pD79+tzP5P7/V25578e+JoQIiWEOAecBm6qZ7taKI+x+SRZTdQc0QeLUkSaRME+OjLP9lUBU1bKtVhEHjk1xfmZOD94fryuz84WuZVeC8bmk/T4XZaqk90+FxlVEE6WjvfKqBrPXpyruh69EAyf/dEqfNiGgm1Vk6OSG1ozFOxjI3OEk1nu3NFb5pXmY/fadlyKnI9ELAWra69h4Vzz4X9/lm8eGcnbEPT0EGvtIaAvmNd3tVUV1deI/aLI0rLkYB+5EOL/ve95Xrq9l9/5uR35xzvbnMyV8WBHGqBgG3arSlJEhiai9AXctLdZl+MOEFfa8WaLn2+c2RhJufbrccWQnaBlObCxi3RW4/gVnQAnxyMIAdvEWfB0QPtayzal3etkjtIE27iGep32U7Dr3eJPAr8LhsZPNzAnhDCulCOAkRO1BhgGEEJkJUmazz1/DfDkovdc/JrLIEnSrwK/CrB+/fo6N31lYziXIFIPwW4mD7YQgmPDc/z8df2mvJ9hEammaObkuD65ff/RUd66v3L/rKoJjl+a57HT0zx2eprDF0K8+9YN/O/XXlPdRhfA+HzC0gQRWKhLn42lSw7cnhgLk8io7N9Y+4CjgZ0DAVwOmWPDcxVbC6yOo5MkCadjoRL8kcEpHLLES7ZWXuttFtyKgz1r2yvyYUeT1pXvGLhn9wDnpmM8+MI4Pz45iUOWuGVzFzv7g0xHU7z2+tWWfj7oPuxqkkQiFteBg142sxwWkY984ygD7V4+/fa9+TxuWLgbVQqNsIg4ZAlZqizC8PRkxFp7SA5JZwf+VLjo711ajKTss3w7cCigZfOD4k+fn2X/opkWI0GkLzqo20MsVNQ9ThkcTpIOH54iFhFjyHFFpYhIknQPMCmEOLL44QJPFWV+V+o1lz8oxOeFEPuFEPt7exuv6jQTLhgEu44hR8MikmyCFJELM3HmExlT/NdQfdGMEIKT4xFcDpnHz0wzGS5vXTh0fpYPfPkwN/zxD3nD3z/GJx4cJBTPMNDu4cEXJurafgNj80n6g9b5r+Fygl0KRvOYGQq2W3Gwa3WwKgU7nMzQ5nLUHA9Y0XY55Hxx0yOnpti7rqPmlJ96sWdtBy+MhsveDWkEkez2u/mj113L47//Cu779ZewnhkMAAAgAElEQVTwgZduZmw+yRcPnqPN5eDlO62/Hmzp83NuOlZxXX00Zf3Cw7EMCvZMNMX5mTi/fOsGOnJFUQY62pykslrJZCnDImLlHQfQfdiZMvYZTRMMTUbZ1mfxYCGQcXcS1MJoRf5ebjVG2tEgBVvN0O13s6XXt8SHfXI8TIdHwjlz0lJ7COiiQtDjJO4INmWKSD1H+EuA10mSdDfgAYLoinaHJElKTsVeC4zmnj8CrANGJElSgHZgdtHjBha/pgWLcHE2jiJLdQ2vNZNF5Jgx4GhCRB9UbxEZnU8SSWZ5z20b+ZfHz/Od58Z43+2bij4/lVX5ta8+gxCCu68f4CVbe7h1Szc9fjdfPHiOj333RS7NJVjTUR85Hg8n60rsqASVEuwjF2ZZ2+k1TVHfu7ad/zwygqqJy1S4YogkM5b5rw0YleAz0RTPXZrnf71yu6WfVwp9QTfprJYjisX/vyOpbN3HWaUwmjj3rOvgd1+zk9OTEbKaaMgA1NZePxlVcHE2zube8opnLJW1NH0H9LznRnuwByf0ocAdBYqejGbW2XiaNa7Cx0Q0ncWlyPmZA6sQ9ChMR0qfU0bnE8TTakMUbNXTRY8UIZYu/H3yanEySneBV5oMWQFNt/HctKmL7z03hqaJ/DDuibEId3XPI82kYGCP5ZvT7nUSyQTpKpIikreI2JBg13yECyE+KoRYK4TYiD6k+LAQ4l3AT4BfyD3t3cB9uX/fn/uZ3O8fFro0cj/wdkmS3LkEkm3A07VuVwuV4eJsnLWd3oqIRTEYNohmINhHh+fwOOWiaQ3VwqMYOdiVXfwGc/aQ1+4e4Lo1Qe47eqnk879zbIypSIq/eetePv7m3dy7ZzU9ubSH23KJEk+cKVFqUAESaZW5eKaBFpFU0ecIITh0PmQq2d+7voN4Wq34tn+kAVYIl6JbRA6enkYILsu/bjS6fPrxVG7hE01lLN8vxbC1L8DOfouLOfKftZAkUgmiDVD2l0PBHhzPEewCxSydOR9zqWbWRgzFAuxd18mRC6UtTsaAYyMUbOHtooMo4Xjh85xHJMgqjbCIOEHV7yLs39BFOJnlVC5JRdMEJ8fC3ObPXX8szMA2EPA6mZMCRVNE4jaO6bNiCfl7wG9LknQa3WP9xdzjXwS6c4//NvD7AEKIF4BvAC8CPwB+XQhhf8Z2lePiTJz13fV9mT1K8xTNHBue4/o17abd/lcces5opQr2ibEFVegNe9fw3Mg8Z4sMVAkh+MKjZ9mxKsAd25b6c3esCtDZ5uTxM9O1/w+wkIFtZckMQHeeyBUfjhqeTTAVSdVVMHMljLsVlUYEhpPWE0mnQyatajwyOEWXz8X1dcRo1ovu3MKnXMtmIzzYVwO2GAS7wkHHSCprvQ1iGXKwB8cjdLY56S0QA2lYRkoNOjZi+BPg5k1dnJ+Jl7TbnZ4wCLb1CrbU1o1DEsTmCwsfPhEn67Se6F+pYAMcytWmD4fixNIq10rnQfFA9zbLN0cvmwmUtIg4HVJ+cNVOMGWLhRA/FULck/v3WSHETUKIrUKItwghUrnHk7mft+Z+f3bR6/9UCLFFCLFDCPF9M7aphdK4OBtnfVd9t3VlWcKtyLbPwc6oGi+Mhk2zhxjwOB1VKNgR1nR4CXqc3LN7NZIE9x0t7JR67PQMJ8cjvO+OTQWj2GRZ4pbN3Tx5ZqauNBEjos9qBdvrcuBxyiUVbGPYzkwFe2O3j6BH4ehIZQQ7kswStNgP7VJkUhmNnw1Ncce2HkszlMuh25+7sxAtTrCFEA3xYF8NCHqcrAq6K1KwhRAN8WArslxRFJ2ZODkeYUd/oOC5x7gbVWrQMZbKWlq+Y+BAjjw+XWJQd2gyQo/fRafPVfQ5ZkEJ6GJIYm5yye+yqoaPBMLVAAXb5YPYFAjB2k4vq4Lu/HyLMeC4JnUaVl2rD0RajKBHYUbzQaJwYlEyo9pSvYZWk+OKxHw8w3wiw4au+r/MXpfDcovI46crG/qrFYPjEVJZzbQBRwMep1zxkOPJ8TA7c57G/nYPt27u5v5jowUJ8hcOnqXH7+b1e4snJ9y2pZvR+SQXc8OstcDIiF5tYcmMgW6fu6RSevhCiKBHMVVpkmXdz1upgq1bRCwm2A6Zo8NzTEfTy2oPgcq88amsRlYTDVEkrwZs7fNXlIUdT6sIYW1SBjTeg61pgqGJSFFbTkfOIlKqzTGSbIyCfe3qIF6nI6/OFsLQZDRv/bEazoD+fU5Fppb8Lp5K45eSCFcDFOytd8HsWZh4HkmSOLCxi8O5RciJsQiyJPCHXmyIPQR0BXsq64NUGLJLj5t4OmtL/zW0CPaKhEG61tUR0WfA63RYahGZDCf5xS8+xZ89cKL8k2uEMeBoRkX6YrgVR0XqfiqrcnYqdtnQ0Ov3rubcdIznrsgoHZqI8NPBKd5964aS2dS3muDDHg83RsGG8nXph8/Psm9Dp+mK7p61HZwcj1R0DEcaYBFxKXJ+v9+xbXkJtmHdKbXwybdbrgAFG/RBxzNTsbJ3hhpRHw/mebDn45l8dGspXJpLEEurbC/gvwbo8BoKdnGLSCzdmDseTofMvg2dPFWEYAshOD0RLfr/YjY87fr3ORtZat1LxnTlWFhdkw5wzRt1m8hz3wD0u4Kj80lGQnFOjIW5uSuGlJy3PEHEQNDrZCyT4yIFVOxERrNliyO0CPaKxEUTMrANeJ3WKtj3HxtFE/CjFycss6IcG56jy+dibae5Sq3HKVeUg31mMkZWE+wcWFCFXnPdAC6HvMQm8sWD5/A4Zd51y4aS77ml109vwM3jdRDssfkEnW3OfCKKlejyuYoORs3F0wxNRi/LajULe9Z1oGqCF0bnyz43nGjAkGPOZ3jdmmBBj2sj4XU58DodzESLW3cMImm1sn+1YGufn2gqm18EFYPVmekGzPJgf+irR3jb554ou3A4OV48QQT0BaLfrZS0iDRi+NPAgY1dDE5EmE8sJfzj4SSRVLYh/muAtna9aVSLLT0nJ6K6yCN5GkCwfd2w9ZVw/JugLfQKHD4f4sR4mJe15yJeG5AgArr1akbL/Q0KDDom0tmGXIOsQItgr0BcmI0B9WVgG9B9xtYR7G89c4mARyGWVvnp4FLvmhk4NjzPnrXtdVVLF0Kl+2ZwQlcvdi66aLV7nbx8Zy/feW40fwGdiqT41rOXePONa/O374tBknQf9hNna/dhj80l6W+APQR0gl1MKX3stH5BunmTBQR7rT5EWC4PO5lRSataQ2L6YHnTQxaj3J2FSFInLivBgw2LBh3L2EQWFh5Xv4L95NkZHj8zk1MxEyWfa6QdFSPYoNtEroYhR9CH+ISgYJrIUG7AcWsDEkQAfJ06waZAHF06pi/wZXdjEnG4/i0QGYULj7GzP0jArfDwyUmGZxPcoFwESYa++ovKKkG710ko3+a4dN/E06otS2agRbBXJIZn43T7XKZcFL2uygf5qsXgeIQXx8L81l3b6Pa5+O5zY6Z/RjSlRxTtNnnAEXIEuwIP9skxvWBmU8/lnvjX713DVCSVt3l85ckLpLMa/6NEPvZi3Lalm6lIijNTseo3Ht2DbXWCiIFSRO7hk5O0e52mW3gA+oIeVrd7ODZSWsHO16Q3wCICcOf2Pks/p1J0+4svfGChNGQlebChAoKdV7CtXZA5HXLFxTfF8KmHhvKRq4fLxNoNTkRZ2+ktee3obCu9KIumGqdg37C+A6dDKmgTyUf0NSADG0Dx+EkJJ3IBlTYT1xcuDm+DCPaOu8Hlh+e+gUOWuHFDJ99/Xr++bsqe0dNDXA0ovQGCXkVPEYGCSSKJTItgt2AjXJyNm+K/BmstIt96dgRFlnjjDWt4zXX9/PjEJPF01tTPeP7SPEKY77+G3JBjBYuPk+MRtvT5l8QQvWJnHwG3wn1HL5HMqHz5yQu8clcfWyoouQC4dXPOh322NpvIeDjZEP816AQ7nlaXKP6aJnjk1CQv29FrWYPi5l4/I6HS/tNwTqm12grhVmQCboUb1pt/PNaCsgp2A2qvryb0+t0EPUoFCnZjlH1HnRaRJ8/O8MTZGf6fV+/A71Y4cqFwkoOBwfFwwfzrxdAV7MLHTFbVSGa0hh0vHqeD69e0Fxx0PD2pxw12NyBBBABJYl4KoKSW7uNsQl/gOxtFsF1tsOteePF+yCS5aVNXPo2mK3xSr0hvEPIxfVDEIqK2LCIt2AcXZuJsMMEeAvoJzIohR1UT3PfsKHdu76Xb7+ae3atJZFQePmmuTcRIkNi91vy8YU+FQ44nx8PsKnDL1eN08HPX9fOD58f52tMXmY2led/tmyv+/A3dbQy0e3iihjzsZEZlNpZmwOImOgPdRRIrnrs0z3Q0zSt2Wqfo9vhdTJfwGcMiBdtrLTF4/0s381dv3XPVZL6WI9jRvLK/MjzYkiSxtc9flmDnhz8tj+mrzyLyqYeG6A24+cVbNnDD+g6OXChulUpntSXD2IXQ2eYqOuQYS+nnw0YuyG7a1M3xS/NLrlNDE3pFutnWwFKIyO2400sJtprQFWzF18Dc++vfAql5GPoh+3P9Ahs8CRzR0YYNOIJ+7mhZRFpoCmRUjdG5hCkDjmBYRMwn2E+enWE8nOSNN64BdC9db8DNd4+ZaxM5Nx2jx++i22/+QFklHuxQLM1EOMXOgcIXrTfsXUMkleXPvn+S69YEuWVz5T5kSZK4dUs3T56dRavyIjyRG+IaaFAFdmcRgv3wiQlkyVpPcm/AzVQkVdKrHmmQgn3j+k5+7tp+Sz+jGvT43cyUyCfPe7BXiEUEqIhgRxuk7NejYBvq9Yfu3ILH6eDG9Z0Mjofzf9MrcWYqSlYTFRBsZ9Ehx0iDlP3FuGlTJxlV8OzwArEVQjA0GW2YPcRATGnHm1m6iNGS+vCou62BBHvTneDrg+PfYM+6DlwOmVd15wYcGxTRB7qCncRN1uFpWURasDcuhRJowpwEEQCvU7bEIvKtZy4RcCu8ctcqQL+Q3H1dPz8ZnMxfvMzAZCTFKotUWncFFpGFqfzCtwZv3dJNb8BNOqvxK7dvrlptuXVzN7OxdL4Kt1IYGdiN8mAXaw18eHCSG9d35hvirECP300yoxErcScmnGiMInm1ocvnIpnRilqzjO+iz23PC2At2NYXYCaWLlsHDjSgyVGuWcE21Ot33rwegH0bOtFE8YHfUxP6OaRcNX2nz0UkmS3oDW9UfOFi7NvQhSTBoXMLBHsqkmI+kWlYgoiBhNJBmxpe8riW1B9zN1LBdihw3Zvh1IN4smE+/IqtvHkgt48alCAC5Mu7Ukp74Zi+lkWkBbvAzIg+sMaDnUir/OD5MX7++v7Lvlj37FlNKqvx0IsTpn3WRDhpGcH2OB2kygw5GlP5hSwioC8s3nHTejb3+Lj7+oGqt6HWPGxDwV4VbExUXL4BbhFpmQwnef5SmJdbaA8B8nF4U5HySu1KsUIYMP4uM0XaHCOpLC5FLpnJ3mzY3KsPI5+bKT48HE1lcStyfmjVKigOqaYhxyvVa9AHAiWJoj7sk+MRFFlaMox9JTqNuvQC0XixVGMWHovR7nWysz+Yb4OFxQOOjUkQMZB2dRDQlhJsKaUvXjz+BhJsgN1vATUNL97Ph+/axi7OQXAttJmf2FQMhmgRV9qXWESEEC0FuwX7IE+wzfJgu8z3YP/wxXFiaZU33rD2ssf3re+kP+gxNU1kIpyyjER6lPL75uS4PmhTKvP4t1+1nR9/5M6aLtZrO9tY39VWdR62QTZ7A40bcoTLFeyf5GIZ79plLcHuydmDSvmwG+WpvdpQ7M6CgWgya3myytWGgVx05cR88SzsSANq0qH2mL5PPnSKvkXqNej2px2rAkUJ9uB4hC29/rLnoVJtjo3KB78SN23s5MiFEJncYmQop8Y3WsHOeroIEAX1ijtC6SgJ4aLN05jzbR6rb4SuLXD8P/Wfx483dMAR9CQcn8tBRA4usYikVQ1VE62q9BbsgYuzcVyKzCqTiJPX6SCV1ar2+JbCt5+9xJoO75LcY1mWeO3uAX52aqpgcUC1yKgaM7EUfRaRSL0qvbxFZGd/sKz1o55BnFs3d/PU2ZmqvJpTkRQuRW4YeQp6nDhkidlFft+HT06yut1TNrWgXhiLm+kyCrYkgc+mjWK1YqEuvfC+iTSwNORqgWGbGitBsBtVplJL0cwTZ2Z48uwsH3rZliW33vdt6OTZi3MF33NwPFLWfw0LCnahQcdG5YNfiZs2dZPIqLwwqqvHQ5NRgh6l4WVOmqcTGYG4wgohZ6LE8FqWlFQUkgS73wrnD8L0aZgZaqj/2kDQ6yQsBZakiBgCldem590WwV5huDgTZ12n17TKaeMEXUnecyWYiqR4dGia1+9dXXAb79k9QFrV+JEJNpHpaAohoM8qBdvpQNVEXjW5EpomODVR2UWrHty6pZtwMsuJsaW3JothMpKiL+Bu2IS9LEuX5eemsiqPDk3z8p19lm+DoWBPlVCwwznCZHZV+9WOfF16EYtII0tDrhZ0tDkvq7QvhFgq2xAbhKMGD/anfqyr1++4af2S3+3b0Kl3A0xcPrMRSWa4NJeoimAXSp9ZDosIwIFNekqGEdenDzg2NkEEgDbdshefvzwNS8lEiUmNGShfguvfAgj48R+B0BqaIGKg3etkjsASi4hhP21ZRFqwBS7Oxk3zXwP5Wzdm2UTuP6Y3F74plx5yJfau62BNh5fvPjda8PfVYDKsEyqz1PwrYZQ3FEsSGQ7FiadVdhVJEDELtfiwpyKphqs73Ysi4Z4+N0s8rVoaz2egy+dClkor2OFkZsX5rwG6/MXJEjS29vpqgSRJDLR7SirYkQaVqTgd1SnYT54trl4D7N+g3zW80iZiEO5K7iZdjRaRvoCHTT2+fOHM6clow+0hALJPPxcn5pYS7ITUmGKXJejeAmv2w4nv6D832CIC+h3Mac0PiTnQFq6XcUPBbllEWrjaIYTg4mycDd2lh1SqQZ5gmzTo+O1nR7h+TXvR+lpJkrhn9wAHh6ZLTvFXgoVBPuuGHIGiSSInxkoniJiFVUEPm3t9PF5FHvZUJEWvBdGFpdDpc+aJ3MMnJ3ErMrdt6bH8cx2yRJfPXVLBjiQb46m92uBzOXArclGCrXuNV97Coz/oYXy+eK14tEHHi0OWit4hK4R/eew83T5XQfUaYF2Xlx6/m2euINgLaUcVKNi+4hYRIwfbtwyK5IGNnRy+MMtUJMVsLN3wAUcAZ1CPG02GLyfYrmyMpLxMBBt0mwiApwPa1zX844NeJ9OqDxCQXGjVXbCItAh2C1c5QvEM0VTWtBZH0IccobhKWw2GJiI8fynMG28orF4buGf3arKa4MEXxuv6vImcYmnZkKOz9L4ZHI8gSbC9AVmst27u5tD5UMWJA1PR5VCw3czE0gghePjkJLdt6W7YibXH72IqUnzBFk6sTAVbkiS6fcXr0iPJDIEVpmADZRXsRtWBV+PBnoqkeOjEBG/et7Zo7JkkSezb0MHhKwj24HgEv1thbWd5G4PP5cDpkApmYUdTGbxOR+O9xsCBjV3MxTP5SvDlULBdAZ1gZ8KX3010qjGSsnnCV9W49k0gOXT/daNtM+gFXhPZHC9ZZBMxhLuWgt3CVY8LuVipDZZYRKqPiroS33r2Eg5Z4nV7V5d83nVrgmzobuN7x+tLE5kMJ5ElLCmZgQWCXSyq7+R4mA1dbbQ1YIDj1i3dRFNZjl+aL/vcdFZjNpZuOME2WgPPTse4MBNviD3EQG+gvIJtdYvj1Youv4uZIvtmJXqwAfrbvUyGU0WHuxu1X6rxYH/zyAhZTfC2A6UVyv0burg4G2cysrCAGByPsH2VvyLPsiRJdLS5mIsVHnJcruPl5k26PeOrT14EaHjJDIC3QyfY2ejldxPdWpyMYxkVbH8vvPpP4LbfXJaPD3qcjKVzC4xFSSKGRaTlwW7hqofZEX1gnkXk/HSMrzx5gZdt780PnRWDJEm89voBHj8zU/TCXwkmwkl6/G4cFg2ueRTDg1148TGYSxBpBG7ZrF9cFmfBFoPR3GdVukoxdPpczCcy+Zxzq/OvF6PX7y6dIpLKrEgrBECXz13QIiKEWJEebNAV7LSqMVuksVDfL9YfL5Uq2EIIvn7oIjdt7GJLb2lieWOuNvuZXG26EILBKoexu9pcRRRsddmOl3VdXlYF3QxO6Gp8v0XWwFII+IPEhRtxxTCfV4uRVhpP+C/Drb8G21+9LB/d7nUyms7dHVmUJNKyiLRgGwznCPa6ThMJtks/hOoh2PF0lg9+5QiyJPFHr7u2otfcu2c1qib4qx+eKllxXQpWtjhCaYtIIq1ybiZmeYKIgR6/m3avk5FQcd+ogYUM7MYPOQqht3huX+VnrYnHaTkYCnaxYymcWJkebKCoRSSV1chqYkUuPIzzxngBm0gqq5JWtYYcL0puyLHcOfDJs7Ocn4nz9pvK+2uvWxPEpcg8c1G3iUxGUszFM1XFZXa0OZkrFNOXzCwbwZYkiZtyKvbWvsrUeLMR9DoJ4Ue+kmCLOFnnMlpElhnGfgGusIjoQ7Eti0gLVz0uzMTpC7hNXQ166kwREULw+/91nMGJCJ9+xw0V+8N3DQT5wJ2b+Y+nL/LJh4Zq+mwrS2ag9JDj0GQEIbA8QWQxyvlGDSwXwTYylwcnIrxi56qGfnaPX6+jj6SWVoILIYimsivSgw0L1p0rEc61W65Ei0ipLOxoA5MylNzdt3I2ka8dukjAo/Dz15Vvg3UrDnavaedw7m7XwoBj5XfbOoso2LGUis+9fGTppo26Or8c/mvQ879DIoAjtcjjnk3hIouqNH7o8mpBu9dJSOT+/wtaROx5jmkR7BUEsyP6YGFlWa4SvBj++bHz3H9slI+8ajt3bu+t6rW//5qdvGXfWj714yH+9fHzVX/2ZDhJn6UKdvGYvpMNShBZjP52T0HF7UpMLqOCbaCR/msoXZceT6uomlixCnaXz0U8rS45jg0iuVKHHIGCSSJGmUojCLZD1s8xpWwic/E0339+nDfesKZicWXfhk6evxQmmVE5VUWCiIFOn7NgiogeX7h8C1VDwW7UncMr4XTIzEtBnIsJdkqvbddcK1jB9ihE8SIkpbBFpKVgt3C1Y9gKgu2qXcF+6uwMf/rACV51zSp+7WVbq369JEn8+Zuu55W7VvFH33mB+49Vno2dzmrMxNKWZWDDgoIdSS290Jwcj+B1Okz/e5SCrmBXbhHp8bvKPNNcGPFe7V4nN67vaOhn5+vSCxBsQ6ldiVYIKF6X3kgiebWh2+9GkaWCCnY+67kRFpEKFOxvP3uJdFbj7QcKR/MVwo0bOkmrGi+MznNyPEJvwJ2/w1QJOtpczMXTS6wr0VRmWReqO/oDfOadN/DWMoOeViLqaMeTmVt4IJUrAHOtbAUbJDLujsstIi0Pdgt2QCqrMhZOmjrgCLUPOY7PJ/n1f3+WDV1t/PVb99TckKc4ZD7zzhs4sLGLj3zjKD87NVXR64zECKtaHEH3aQY9Ch/91nH+9kenLluEnBwPs32V37IBy0LoD3qZjqbL3m2YiqToaHPiVhp7UjOI3Eu39zY8xiuvYBcYmjUI00pNETFSdmavaHPMK9grUNl3yBKrgp6CbY75OvCGKNj6+UNVCxNsIQRfe3qY3WvbuWZ15XfL9uUGHQ+fDzE4EWZnlYpvZ5uTrCaWWK6W2yICeszrctq9Eko73uxCmpOa1Am2cK9cgh306n+PlKvzMotIIqOiyBIuxZ5U1Z5b3ULVGAklEALTFVNPDQQ7lVX50FePEE9n+dwv7av7ZOdxOvjCu/eztS/AB79yhGcvhsq+ZjJfMmMdwW73Onngt+7grl2r+NSPh7jrr3/K/cdGEUJwsoEJIgYGOnS13miwLIblKJkBXUW+d89q3nPbhmX4bJ3cF1KwIytcwTaUy+nY5fsm3ECl9mpEMctVtIH7xekwFOzCSUVHh+cYnIhUpV6D/l3c2N3GofOzDE1EqxpwBF3BBpZE9TUqXeVqRtLZgU+Lgqrvm1RcJ9iSp0WwE0oQEgvX73hata09BFoE+6rGcyNz/Oq/HebGj/0oTwhrhRHRt8FkBdutyEgSJKuwiHzyoSGevTjHJ35hj2ltWkGPk3/9Hwfo8bt5778cuizDtRAmwo2Jolvb2cbfv/NGvv6rt9DR5uI3/+NZ3vDZx5mNpRvuAyw1mLUYk5Fkw/3XALIs8XfvuIF9ubrmRqKzzYVDlpiOFhrmW7lKLSzcWViiYOeV2pVJmPQ2x+IKdiM92MUsIl97ehiv08G9e8oPN16JGzd08sipKVJZrepzVWeb0ea4cMwY6Sr+ZVawlxtpt353wCCS6ZhuF5E9jRVcria05wh2zBG8XMFOq7a1h0CLYF+VOHJhlnd/6Wle95nHOHh6mtlYmkeHKq+5LoSLM7mIPpMVbEmS8DodFSvYWVXjG4eGec21/bx2d/Un/VLoC3j427ftYS6e4fD50iq2QcCtjOlbjJs3d/OdD9/On7/pekZyi51qbtmagQWCXdqHPRVN0bcMBHs5Ict6Y2GhIcdwQleagiuUYHfl1P0rk0SiKzhFBHQFe2w+ucRnbNgiltuDHU1l+c5zo9y7Z6Cmuy/7NnSSyVlPqiXYXT798xYTbKMmfSV69hdD8+QIds5rnInrdhFlBRNsn8uBQ5YIS8ElTY52LZkBWNlH+lWGJ87M8OkfD/HE2Rm6fC5+9zU7eNfNG/7/9u49StK7rvP4+1v36kv1fS6ZzGRmciGJuZAQMpEESIiAgi63g9xUdgE9LnjUdY8siLugoLKsclZkDy4iHmQRBRQNF4mIgpDAhASTyQ1y60kyk8lM39Jdfamqrqrf/vE8T3VVT3X3dM9Tt67P65w+6amqrv7VL09Vf5/v8/19f7zgQ//KHePTvOY55275uZ+YXiQdjzbk0n9qEwH2HePTTC0UNiX9dBIAACAASURBVNytcauCsovxyYV1H3dyLkfUD6qaJRox3nDtPl5+xW6+Pz7NoQPNzdTuGvAa+a/XScQ555WIdFmADd5l8cn1arC7tESkPxkjHjUtclxl90CKpeUSc0tFBnpWjo2V7iqNP17Wq8H+0j1PsVgo8fprN1ceErjGv5JkBhfu2GKJSFUnkZXSme58HwVc2utkEgSSRb9EJNbTvQG2mZFJxZil3+si4hyYsVgoVcpQO1F3fjK2oc/d+STv/MIRxvqT/PbLL+GNh/ZVej8+d/8wh8enNniG9T0+5XUQaURz/XQ8esZbpX/l3hOk41FuelZj2rD1JmPs6E9ydMMA26sz3uriyrORScW5+ZLm9nkGLxDqT8bWLRGZzxfJLZe7MsBea7v0bKVEpDsDAzPze2HXzk02VyQZi3TsAqSztSu4IjS3VBtg55eJ2EqbzkaKrVOD/dd3PMFFO/u4au/WOvJcuKOP/mSM0S3snVCvRGTlhKxzA6YwWK934uIWJjGgtOQF2PGegRaOqvUy6TjTrg/KRchnIZUh1+EZ7O78ZGwzdx6d5j1fvJcbLhjl2++8ibc9/2BNY/XrDg5zdGqRk2dRh/2jk3McGG1Mn81UPFK31/NqxVKZW+9/mhddsqOhdVUHRns3zGB7uzh2XxC5US/sVvXAbgeja2yXPpdbJhaxpgRM7aredunZfPfubgnVvbBr308L/nbgzdgpMLZGH+x7j81yz7FZXv/cfVseRyRivPHQPl757D2b/tmBdBwzanphrwTY3XmiGoj2efs9FLJe0qycy1J2RrKLM9jgHTOT5drdHBcLRdVgy9Ydf2aJX/5/d7FnMM1H33hV3cshh/zm+N97bGtZ7CenF3lyeolDBxtTkpBOnFmJyB3j00zOF3j55eHWXq92YLSXo1MbBNgN3mSmXe0aSHFinRO1oAa50Ys/29FYf5LJ+dN792Zzy2TS8ZZsrdwuRvtO3y7d6wjRvQH2WiVX2VyxaVc7ghKR5aoSkXyxxH/72yMM9yZ49dWbD46rvftll/BrP3HhlsaVScWZWaiuwfYC7Fa36Wu1RP8oAPms11LW5eeYJ0VPF7+XwLuye6roJwH9zWa8LiKdOy8KsDdhPl9kqs4l5K1aLBT5xU/dSX65zCfefE2lbm21S8/J0J+McXh8uu79G7n9UW+B5PUXjG55rOvxSkQ2DrAbXR4S2D/ay+R8obJBSD0n53JdmcHePZCqu/tcoFXbpLeD0b4EhVKZuaXa3r1ewNS5H/JhGO5NMFWni0i3ls0A7OhPYnZ6V575/HLTTjyCRY7VGew/vPVHPHBijv/5mivW/JvSDEM98ZoSkWDxZ7e/l/p6+5h3KYpZv3FBPss86Y7O1IYhk45xYtk7aWXRa1KgEpEu8j/+/j5++k++ww/OoM/yRpxz/Obnj/Dg03N85A1XccE6i0iiEeOa/UMc3mIG+7ZHphjrT3Lhjr6tDnddZ7LIsVR2XnnIxY0tDwEqpTBr1WHniyVmFpcbuotju9o1kOZUNs9yqX7NfCXAbkEf7FZb2WymNmCaW2rt7nPtwKvBXlUikmteINmO4tEIo33J0zLY8/li0zqrRFfVYH/n4Un+7NvjvOnQPl58afPXeVTzdnOss8ixy0tEMukYM66f8oIXYFt+nnmX7uhAMgwD6ThPFfwMdqVERH2wu8ZbbjhALGq87v9+l0/dfvS0S8mrzS4tV+rOVvuTf3mEr9x7gnf/1MXcdPHGGd1DB0d4dGKhbhux9TjnuP3RKZ53/kjDLnGn49ENa7APj0955SEht+arJwiw16rDDjZaaeQuju1q90AK51jzODqVzROPWqUvaTcJTiomsqsDyWLXdhAJjPQmmM8Xa3YBzeaaF0i2q911Sq6aWTpTncGeWSjwG5+7m/PHevntl1/alN+/ntUZbJWIeDKpODP0VYLIyPI8C6Tp6eBSiDBkUnGO5fykl18i0ul9sLv7/+gmXbZngC//yvP5jc/dzXtvuZ+7Hp/hD159Ob2rPkwfPDHHJ749zi33HGe55Ng33MMlu/u5ZHeGS3ZnmF1c5sNff4hXX72HX3z+wTP63UFLtzvGpzcVpD58ap7J+TzPO3/kzF/oJp1JDfZXm1QeAvjdUtYJsIM64y6twQbvsvY5g+nT7p/I5hltUXeVVltru/Rsrsj+0XD7x3ea4V5/u/SFArv92uP5fLEp24G3s12Z1GnrPbL5IueGvN/AWoJFjsslx7v+7ggziwU++R+f2xZByVBPgodOzlf+HZSI9Ca6+5jJpOM84fo51w8io8vzZJ1KRDLpOBPFNC5mmL/ZzNKyAuyuMtAT589+4Ro+9q1H+aN/+hEPnpjjYz/3HM4f6+VbD03w598Z59sPT5KOR3nDtfvY0Z/kwRNZHjwxxz89cJIg6X3VvkF+/1WXn3FW+bI9A/Qkohwen9pUgH3bI95lqOed35j6a9i4BrtUdnztvuaUh4BXsnLOQHrNEpHKNuldWCJyjh8ceZvNDJ12fzduMhMY9TPYqzuJzOWWu7rWGFa2S5+aXxVgK4N92uLz+VzzTjyCNn2fOfw4t95/kt962cVctqc92r0N9SZOy2D3JqJdefJeLZOKMU0/8fxRAOLFeRZtrGvbXQYy6ThlIrjUELY4RaFYplh29HRwiUh3fzpuUSRivOOmC3j23kF+9bP/zis++h3OGUzz8Kl5dmaSvPMnn8Ubr9132gKTxUKRHz2d5YnpRW68aMemGqjHoxGec94Qd2xyoeNtj0yxb7gn9B0cq21Ugx2Uh7yswd1Dqh0Y7WXc371ytaDdYTcucty1RmuxwEQ2z57B7jvxAK8GMB61uhnsbg8kR1bt5uicU4kI3pqGuVzRCx79oHo+37wSkaCLyJePnOB554/wthvO7IpoMwz1xFkslMgXSyRjUa90psuPF/D66c+4fhIFb4v0eHGBpch5LR5V6wVlicXUIIml6UpM0ckZ7O4+ZTpL118wypd/9QauPm+IdCLKH732Sr79zhfx9hsvqLt6uycR46p9Q7zi2XtqNiY4U4cODPPDp7M1rY/WUyyVOfzYVEPLQ8B7A+SX195oplIecvFYQ8dRbf9oD+MT83Xr5E/6dcZDLVxh3yqZVIyeRHTNzWYmsrmu7CACwXbptb2wS2XHfF412EEGOwiwc8tlSmXX9QvWKr2w/ZP2UtmxWCg1LZAMarAH0nE+/LPPbqvs8OrdHJt54tHOErEI2UiGZGkBigUSpQUKke4uQQPvbxNAIT4Ii9OVq+KdHGDraD9LuwfSfPqth5ryuw4d9ALlO45O89If27Xh4+97ao5svsjzGtSeL5CORymUyhRLZWLR2nM2rzzkJC+6eEfN5jmNdmC0j7lckZnF5UpwEDg5l2vZLo6tZmZrbjZTLJWZWih0ZQeRwGh/oma79Hm1FgNg1K/BDuYmm/eCpm7PSO7MrFwROn+sr+nbx+8aSDHal+T3X3VZ5epUuwgSGNMLBXZmUgqwqyzFB6EELE6RLC+Sjzemw1cnyfgZ7Fx8kL7FCRYL3nupk7urKIPdQa44d4BkLMLhx86sTGSl/rrBGWy/1CVXPD2L7W0uk29qeQjAAX9RWr2FjhPZfFcucAzsHkj5Ndi1phcKOAdjXTw3Y32126XPLXmBZLdnsDPpGLGIVTLYQcu1TJcH2LurFg1D80/IdvSn+P57buYlZ5BwabYh/yptUIfdzPaF7W454W9fP/skERzLscbsstxJghKRxVgGqktEOri7igLsDpKMRbl63xCHx8+sH/btj05y8a7+yuKtRkn5Z5jT86eXrnzl3qdIxSNNLQ8B2D+ydqu+bt1kJrArk66bwT7VxT2wA9526VWbYwSBZLpzP+TDYGYMVfXCzuaam6ltVytrGrwT1lb0em7XHUZXl4gsKINdsZz0F5jPHAWgGFMGO0hiZCOZbVMiogC7w1x7YJgHTswxu7T2LoXg7YB059EZfrzB2WuAc/w/Mj/x4W/xy5++i6/dd4J8sVQpD7n54p1NLQ8B2DvcQzRidTuJnJzLVy7tdqPdAylOZvM1u79Bd+/iGPC2S89T9ucm6+8G2u1dRMDrhR1sl97sUoh2lYpHGeqJV2qw51U6UzHUW5vBzuaKp7W07VbltP932Q+wS3FlsIMkxiwZKC6RW/RaPHZyiYiO9g5z6OAw7htw59Fpbr5k7Z26fvDEDPlimesb2J4vcPMlO7nlV67ni/9+nC/dc4Kv3f80/akYhw4Mt6Q8BLyuK3uH0oyv6lGbWy4xu7Tc1QH2roEUpbJjcr72RCMIsLu1TR94Gexi2TG7tMxQb4K5nGqwA8P1MtiaF3YNrFwRUmZ/xVCdRY7d3je9oicIsB8HoJxQBjsZi5KKR3gGby5KC96Veu3kKE1z9b4hEtEIhzdo13f7I1NEI8ahg8NNGdcV5w7y3p/5Mb737hfxqbdcy4sv2cntj06RScWaXh4S2D/ay/hEbYAd7OLYzVna1XWjgaD2uJvnZvVmM0EGu9trsKE2wA4y2JqXYE1DbQ22Amwvu5+KR5hZKOCcq2ll2O2ivf7f5We8ANslMy0cTfsYSMeZLHkBdjkIsJXBlmZJxaNcuXdg4wD70UmuOHeg6Ze2Y9EIL7xojBdeNMYHCkUWC6Wml4cEDoz2csf4NM65Sp3iyWzQA7u7M9jg143uHazcPpHN05+Kbao/+3ZTvdnMRTv7KxlJZbD9EpFVJx4KJL330z1Pej2N55XZrzHck2BmcZm8v2mI5sXT29ND1qXpmxnHAEv2t3pIbSGTijNR9spl3OIU0NfRJSLKYHegQwdGuO/4bCVbslo2t8w9x2Yb3j1kIz2JWMMXWK7nwGgvi4VSpfQBVjLY3bzIcXdlN8faDPapLu6BHVidwQ66iKgGG0b6kszlihSK5UogqYykt1361EKB3HJJGexVBnsSPLNYWOmuonkBvJZ0064fZo8DYEmViIA3LyeLfk/wxRlAJSLSZIcODlMqO+56fKbu/XeMT1Mqu6bUX7ezoJPIY1ULHU928TbpgaGeOMlY5LROIhPZfFd3EIGVDirBSVk2XyQVj3T9NsawstnMjB8wJWOaF1i5InRqLq8Ae5Wh3rh3vOiErEYmFWeGPgxvMXUk3R7b27faQDrOUwUvwI7kvKv0nVwiok/HDvSc84aIRYzDj9Vv13f7o1MkYhGuPm+oySNrLwdGvQC7upPIyWyORDTC4BZ20twuzKymbjTQ7f3BwVvJnohGamqwlb32jPgB9tR8gWy+qHnxVe/mOJ8r0pOIVrYw73aDfomITjxqZdIxZtxKWUg0rRps8PrqH895V1hjuWkiBolo54apnTvyLtaTiHHZnrXrsG97ZJJrzhvq6lpagHMG0ySikZpe2Kfm8uzIJNu2d2yz7Kqz2Ywy2N7Jx2hfotILe26pqPprX/V26dmc5iWwsmh4SbsVrjLUE69c8QAF2IFMKs40XoCddzFSqXSLR9QeBtJxpnMOkhnihWfoScQ6+m+1AuwO9fwLR7nr8Rle+X9u4zOHH2fOX3Q0OZ/nh09nub7B26N3gmjE2DfSUxNgn5zLdXUbusDugXRNBnshX2ShUOr6GmyA0f6V3RznlMGuGOnzM9gLeeZzywqWfLv8NQ1Pz+bIarfCGkM9CWaXliv7NmhuPJl0vJLBXiDV0XXGYcqk42Rzy7j0EInCMx1dHgLqItKx3nHTBWRScT5/15O854v38btfeoCfvGxXZQFbqxc4tosDo70creqFfSqb58IdWlCyayDFybkc5bIjEjFtMlNlrC9ZOfnI5opdvx14YLjXOzamFwrK1FbpS8boT8Y4MeuViGgh34rBngTOwYlnvKtlOmY8mVTMW+QIzLt0yzpttZuBdJyyg3J6mOTcbMefeOj/aodKxaP84gsO8rbnH+DIsVm+cNcx/uHu48z5H/CX79GiCfAC7G89NFEJJE/O5bhB2X12D6RYLjmmFgqMVWVsld33WvXde3wW8Gqw9wzq8i3AYDpOxFZKRPYN97R6SG1j50CKp2dz3omHTsgqhvy1Lk/OKMCulknHmfFLRObp6ehWdGEK+urnM/tZnJvu+HnR0d7hzIwr9w5y5d5B3vPyS/jnB0/Sn4oT6+CFAWE6MNpLoVjmqdklhnsTZHNFdnRxi77ArkzQC9trzacM9oqx/iRTCwXKZeedsCpgAiASMYZ6EkzOewG2AskVuwdSnJjLkSuUGO3TiUcg2M3x2MwioBKRQH9VBjtLuuNLIcISbJd+9IUf4Y//8UHSa7Qi7hQ62reRVDzKT19xTquH0VaCVn1HJxcplryWSN3coi+w0gt7icvPHeCU375QATaM9iUolR0ziwWyuWUyadVgB0b6Ekwv5LXt9Sq7MikePjlJNGL0JXW8BIb8hbFPTi8Rsc7uaRymZCzKYtTrHDLv0owpwAaofNbOLi2zWCh1/PGiT0jZ1oJWfeOT88Sj3mpkZbCrdnP0A+uJ+TxRP0PZ7cb6V7aSzy2XFUhWCbZLVylErd0DKU5lc6TjUV3xqLJSIrJIb7KzO0KEbTk5BEVvkeN5CrCBlRKRuZwXYA91eDvdLdcRmNleM/tXM3vQzO43s1/zbx82s6+b2cP+f4f8283MPmJmj5jZETO7uuq53uw//mEze/PZvywRz85MknQ8yvjkIqeywS6OymCP9CaIR62ymG8im2ekN6H+vXgZbIBHJ+YBbZNebaQ3yfGZJUplp+4qVXYNpCk7WCiUVGdcZdA/Yc9q8edpiqlhwMtgp7XIEfAWOYKXwc4tlzq+1fDZFOoWgf/qnLsEuA54h5ldCrwL+IZz7kLgG/6/AX4KuND/+iXgY+AF5MB7gUPAtcB7g6Bc5GyZGfv9TiLaxXFFJGLszKQquzl6m8wosw8rZTJBe0eViKwY7k1wwn8fKZBcEfTCBtUZV8ukYpWTdu3iuEraC3OypOnp8EAyLMFn7dzSMouFYscvctxygO2cO+Gc+4H/fRZ4ENgDvAL4lP+wTwGv9L9/BfCXzvM9YNDMdgMvBb7unJt2zs0AXwd+cqvjElntwKjXC/tUNk8yFqkspOh25wykK5vNnNImMxWjfoD92IQXYCtTu2K412u7BsrsV9tVHWArkKwwMwb9oEknHrV602nevfxWPle6UYscff3JGGYwlyuyVCh1fPvCUFpNmNl+4CrgMLDTOXcCvCAc2OE/bA/wZNWPHfNvW+v2er/nl8zsTjO7c2JiIoyhSxc4MNrLk9OLHH9miZ2ZlOoAfbsGajPYWuDo6U/GSMYiPDapEpHVgs1mQIFktV0ZBdhrGfTraDUvtTLpOJ8t3cw4e0jG1PULvCur/ckYc0vLLHV5iQgAZtYH/C3w6865ufUeWuc2t87tp9/o3Medc9c4564ZGxvb/GClK+0f6aVYdvzg8Rn1ea6yeyDFidkcpfJKP2wJtktPMl7JYCswCATbpYMy+9UGe+KVIEmBZK1g4bTmpVawgVWnbwcetkw6ztRCgeWS694SEQAzi+MF159xzv2df/NJv/QD/7+n/NuPAXurfvxc4Kl1bhcJRdBJ5MRsTgscq+waSJEvlhmfnKdUduxQbXrFaH+ShUIJWFnZLt4ix4ACphVmVqnDVilErUEF2HUF9cYqD6k1kI5z0r+y2ult+s6mi4gBfw486Jz7cNVdtwBBJ5A3A/9Qdfsv+N1ErgNm/RKSW4GXmNmQv7jxJf5tIqEIAmxQi75qQUBwz5PeroXKYK+orkdXgL2iukREmf1aQR22Aslaw73e+0eLHGsFnyudnqUNWyYVr7SP7fSTj7M54q8Hfh6418zu9m/7LeCDwOfM7K3AE8Br/fu+CrwMeARYBP4TgHNu2szeD3zff9zvOuemz2JcIjWGexP0p2Jkc0VlsKvs8jebOXLsGUABdrWx/qpaYwWSFdUlIgokawWbN+nEo1ZQIqJ5qRUstu/0LG3YBtJx7npiBuj8k48tH/HOue9Qv34a4OY6j3fAO9Z4rk8Cn9zqWETWY2YcGO3lyLFZdiqDXRFksI8c9zPY6iJSEcxFXzKm3uBVhnoSmIFzOvFYTRns+oISEWWwaymDXV8mHaNQLAOdf/KhpavSFYIyEdUZrxjtSxKNGA885a1NVgZ7RdCqT1m3WtGI13YtFY8Qj+rPR7UXXjTGzRfvqASU4hlSF5G6ghrsTm9FF7aBqn0HurlERKRj7B/xAmxlsFdEI8bO/iRPzeboTUSVYaoSZLAVYJ9uuDfB7FKx1cNoO9cdHOG6gyOtHkbbGVSJSF1BF5FODyLDVr3mpdMz2DripSu8+NKd3P/UHHuHe1o9lLayayDFU7M5Za9XCTLYWuB4upHeJOW6jVRFTjfqL4zVe6nWSga7s4PIsFXvnNvp2f3OHr3IGbpszwCfePM1rR5G29k9mIYnnlGAvYoy2Gv78fNHOJXNtXoY0iGu3jfEh15zBTdcONrqobQV1WDXpxIREdkWdvtdVVSbXmulBltZt9X+y4svavUQpINEIsbPPnfvxg/sMsHJezquMKxa0F0FOj/A1ioVkS4WdD5QBrtWbyJKbyJa2eZZRCRMqXiUq/YNcvm5mVYPpa1UZ7B7VIMtIp0q6N2rALuWmfHRN13N+aN9rR6KiGxTX3z79a0eQtupWeTY4RlsBdgiXaySwVYP7NPc9KwdrR6CiEhXCTLYZpCMdXaRRWePXkTOyo+dk+GNh/Zx47PGWj0UERHpcpXuKvEoZp29yZcy2CJdLBWP8vuvurzVwxARESEZi5CIRjq+PAQUYIuIiIhIGzAzMuk46UTnF1gowBYRERGRtpBJx4hHFGCLiIiIiIRiIB3fFrvFKsAWERERkbbwumv2UtwGEbYCbBERERFpC6+/dl+rhxCKzi9yERERERFpIwqwRURERERCpABbRERERCRECrBFREREREKkAFtEREREJEQKsEVEREREQqQAW0REREQkRAqwRURERERCpABbRERERCRECrBFREREREKkAFtEREREJEQKsEVEREREQqQAW0REREQkRAqwRURERERCpABbRERERCRECrBFREREREKkAFtEREREJEQKsEVEREREQmTOuVaPYUvMbAJ4fJM/NgDMnuWv3gc8cZbPEdZYwniOsJ5H87K2MOamnV6T5qWxz6P3Un2al7Vtt/eS5qWxY9F7qb715uU859zYpp7NOdc1X8DHQ3iOiTYay1k/h+alKc9z1nPTTq9J89Lw+dV7SfPS9Llpp9ekeWn/eWnD19Q2nzHBV7eViHwphOd4JoTngHDGEsZzhPU8mpe1hTE37fSaNC+NfR69l+rTvKxtu72XNC+New7Qe2ktYc0L0MElIq1iZnc6565p9TjajeZlbZqb+jQv9Wle6tO8rE1zU5/mpT7NS31hz0u3ZbDD8PFWD6BNaV7WprmpT/NSn+alPs3L2jQ39Wle6tO81BfqvCiDLSIiIiISImWwRURERERCpABbRERERCRECrABM/ukmZ0ys/uqbrvSzL5rZvea2ZfMLFN13xX+fff796dWPd8t1c/VqcKaFzP7ppn9yMzu9r92tOL1hCXEeUmY2cfN7CEz+6GZvaYVrycsYcyLmfVXHSd3m9mkmf3v1ryi8IR4zLzB//cRM/uamY224vWEJcR5eZ0/J/eb2Yda8VrCtJl5MbM3rXrPlM3s2f59z/Ef/4iZfcTMrFWvKQwhzsvvmdmTZjbfqtcSpjDmxcx6zOwr/t+i+83sg617ReEJ8Zj5mpnd48/Nn5pZdMNfHmbPv079Al4AXA3cV3Xb94EX+t+/BXi//30MOAJc6f97BIhW/dyrgb+qfq5O/QprXoBvAte0+vW04bz8DvAB//sIMNrq19YO87LqOe8CXtDq19YOc+Pffio4ToAPAe9r9Wtrg3kZwdscYsy//VPAza1+bc2al1U/dznwWNW/7wB+HDDgH4GfavVra5N5uQ7YDcy3+jW1y7wAPcBN/vcJ4NudfryEfMxk/P8a8LfA6zf63cpgA865fwOmV938LODf/O+/DgTZxZcAR5xz9/g/O+WcKwGYWR/wG8AHGj7oJghrXrabEOflLcAf+LeXnXOTDR14g4V9vJjZhcAOvA/6jhbS3Jj/1etnIjPAU40eeyOFNC8HgYeccxP+4/656mc60ibnpdobgM8CmNluvKDgu86LDP4SeGVjRtwcYcyL/zzfc86daMggWyCMeXHOLTrn/tX/vgD8ADi3IQNuohCPmTn/2xjeCciGHUIUYK/tPuA/+N+/Ftjrf38R4MzsVjP7gZm9s+pn3g/8EbDYvGE23VbmBeAv/Esu/73TL1OuYVPzYmaD/v3v92//vJntbO6Qm2Krxwt4H3B/4wcH29Gm5sY5twz8Z+BevMD6UuDPmzvkptjsMfMIcLGZ7TezGF4QuZftZ615qfY6VoKCPcCxqvuO+bdtN5udl26x5Xnx/z79DPCNho2utbY0N2Z2K95VxCzwhY1+iQLstb0FeIeZ3QX0AwX/9hhwA/Am/7+vMrOb/TqdC5xzX2zJaJtnU/Pi3/cm59zlwPP9r59v7pCbYrPzEsPLDtzmnLsa+C7wh00fdeNt5XgJvJ7t/Udxs58xcbwA+yrgHLxyiXc3fdSNt6l5cc7N4M3L3+Bd7TgKFJs96CZYa14AMLNDwKJzLqg1rZfI2I4nq5udl26xpXnxT1I/C3zEOfdYswbbZFuaG+fcS/FKi5LAizb6JbHQhrvNOOd+iHdJEjO7CHi5f9cx4FvB5Xwz+ypefc888BwzO4o3rzvM7JvOuRubPPSG2sK8fMM5d9z/2ayZ/RVwLd7lym1jC/PyL3hXOoITss8Db23mmJthK8eL/+8rgZhz7q6mD7pJtjA3c/7PPerf/jngXU0edsNt8TPmS/hbJZvZLwHbrjxtnXkJrD4hPUbtJf5z6fCSonq2MC9d4Szm5ePAw865jl9cvpazOWacczkzuwV4oHP83AAAA4JJREFUBV55yZqUwV6D+Z0uzCwC/Dbwp/5dtwJX+CtuY8ALgQeccx9zzp3jnNuPl115aLsF17D5eTGzmPmdDvwM3E/jXZ7ZVrZwvDi8gOBG/3E3Aw80ddBNsNl5qfrRmvq37WgLc3McuNTMxvzHvRh4sLmjbrytHDNVPzMEvB34RLPH3WjrzEtw22uBvw5u82uMs2Z2nV+W9wvAPzR10E2w2XnpFluZFzP7ADAA/HrzRtp8m50bM+vz1zQEGf6XAT/c8Bc1YxVnu3/h/SE/ASzjnfW/Ffg14CH/64P4u176j/854H68QPFDdZ5vP9uji8hZzwvQi9cJ4oh/3x9Tp1tEJ32FdbwA5+EttDiCl7nd1+rX1g7z4t/3GHBxq19Tu80N8Mt4QfURvBO0kVa/tjaZl8/iBdsPcAar+9v9awvzciPwvTrPc40/V48CH63+mU78CnFePuT/fNn/7/ta/dpaPS94Vzic//lyt//1tla/tjaZm514nUeCOOZP8K6wrvu7tVW6iIiIiEiIVCIiIiIiIhIiBdgiIiIiIiFSgC0iIiIiEiIF2CIiIiIiIVKALSIiIiISIgXYIiIdxsycmX266t8xM5swsy9v8fkGzeztVf++cavPJSIiCrBFRDrRAnCZmaX9f78YbyOarRrE26BFRERCoABbRKQz/SMrW/zW7HxpZsNm9vdmdsTMvmdmV/i3v8/MPmlm3zSzx8zsV/0f+SBwvpndbWb/y7+tz8y+YGY/NLPP+LsBiojIGVCALSLSmf4aeL2ZpYArgMNV9/0O8O/OuSuA3wL+suq+i4GXAtcC7zWzOPAu4FHn3LOdc7/pP+4qvC2TLwUOAtc38sWIiGwnCrBFRDqQc+4IsB8ve/3VVXffAHzaf9y/ACNmNuDf9xXnXN45NwmcwtsGuJ47nHPHnHNlvG2T94f7CkREtq9YqwcgIiJbdgvwh8CNwEjV7fXKOZz/33zVbSXW/jtwpo8TEZFVlMEWEelcnwR+1zl376rb/w14E3gdQYBJ59zcOs+TBfobMkIRkS6kjISISIdyzh0D/rjOXe8D/sLMjgCLwJs3eJ4pM7vNzO7DWzz5lbDHKiLSTcw5t/GjRERERETkjKhEREREREQkRAqwRURERERCpABbRERERCRECrBFREREREKkAFtEREREJEQKsEVEREREQqQAW0REREQkRP8fymUbzmzMBxgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['forecast']=results.predict(start=90,end=103,dynamic=True)\n",
    "df[['Sales','forecast']].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import DateOffset\n",
    "future_dates=[df.index[-1]+ DateOffset(months=x)for x in range(0,24)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_datest_df=pd.DataFrame(index=future_dates[1:],columns=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Sales First Difference</th>\n",
       "      <th>forecast</th>\n",
       "      <th>Seasonal First Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-05-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sales Sales First Difference forecast Seasonal First Difference\n",
       "1974-04-01   NaN                    NaN      NaN                       NaN\n",
       "1974-05-01   NaN                    NaN      NaN                       NaN\n",
       "1974-06-01   NaN                    NaN      NaN                       NaN\n",
       "1974-07-01   NaN                    NaN      NaN                       NaN\n",
       "1974-08-01   NaN                    NaN      NaN                       NaN"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_datest_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_df=pd.concat([df,future_datest_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2daee5048>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "future_df['forecast'] = results.predict(start = 104, end = 120, dynamic= True)  \n",
    "future_df[['Sales', 'forecast']].plot(figsize=(12, 8)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}