{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate synthetic cointegrated data for GDP and Investment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>GDP</th>\n",
       "      <th>Investment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.496714</td>\n",
       "      <td>-0.459328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.358450</td>\n",
       "      <td>-0.031098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.006138</td>\n",
       "      <td>0.331712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.529168</td>\n",
       "      <td>0.863445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.295015</td>\n",
       "      <td>1.066865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        GDP  Investment\n",
       "0  0.496714   -0.459328\n",
       "1  0.358450   -0.031098\n",
       "2  1.006138    0.331712\n",
       "3  2.529168    0.863445\n",
       "4  2.295015    1.066865"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.vector_ar.vecm import VECM\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "\n",
    "# Generate synthetic cointegrated data for GDP and Investment\n",
    "np.random.seed(42)\n",
    "T = 100  # Number of time periods\n",
    "\n",
    "# Generate a cointegrating relationship\n",
    "gdp = np.cumsum(np.random.normal(0, 1, T))  # Random walk for GDP\n",
    "investment = 0.5 * gdp + np.random.normal(0, 0.5, T)  # Investment is cointegrated with GDP\n",
    "\n",
    "# Convert to DataFrame\n",
    "df = pd.DataFrame({'GDP': gdp, 'Investment': investment})\n",
    "\n",
    "# Display first few rows\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test for Nonstationarity - Perform Augmented Dickey-Fuller (ADF) test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Variable</th>\n",
       "      <th>ADF Statistic</th>\n",
       "      <th>p-value</th>\n",
       "      <th>Critical Values</th>\n",
       "      <th>Stationary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GDP</td>\n",
       "      <td>-1.358332</td>\n",
       "      <td>0.602081</td>\n",
       "      <td>{'1%': -3.498198082189098, '5%': -2.8912082118...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Investment</td>\n",
       "      <td>-1.642378</td>\n",
       "      <td>0.461018</td>\n",
       "      <td>{'1%': -3.5019123847798657, '5%': -2.892815255...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Variable  ADF Statistic   p-value  \\\n",
       "0         GDP      -1.358332  0.602081   \n",
       "1  Investment      -1.642378  0.461018   \n",
       "\n",
       "                                     Critical Values  Stationary  \n",
       "0  {'1%': -3.498198082189098, '5%': -2.8912082118...       False  \n",
       "1  {'1%': -3.5019123847798657, '5%': -2.892815255...       False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "from IPython.display import display\n",
    "\n",
    "# Perform Augmented Dickey-Fuller (ADF) test\n",
    "def adf_test(series, name):\n",
    "    result = adfuller(series)\n",
    "    return {\n",
    "        'Variable': name,\n",
    "        'ADF Statistic': result[0],\n",
    "        'p-value': result[1],\n",
    "        'Critical Values': result[4],\n",
    "        'Stationary': result[1] < 0.05\n",
    "    }\n",
    "\n",
    "# Apply ADF test to GDP and Investment\n",
    "adf_results = pd.DataFrame([\n",
    "    adf_test(df['GDP'], 'GDP'),\n",
    "    adf_test(df['Investment'], 'Investment')\n",
    "])\n",
    "\n",
    "# Display results\n",
    "display(adf_results)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test for Cointegration - Perform Johansen Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Trace Statistic</th>\n",
       "      <th>5% Critical Value</th>\n",
       "      <th>Cointegration (Trace &gt; Critical)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rank 0</th>\n",
       "      <td>52.020990</td>\n",
       "      <td>15.4943</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rank 1</th>\n",
       "      <td>1.936981</td>\n",
       "      <td>3.8415</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Trace Statistic  5% Critical Value  Cointegration (Trace > Critical)\n",
       "Rank 0        52.020990            15.4943                              True\n",
       "Rank 1         1.936981             3.8415                             False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
    "\n",
    "# Perform Johansen Cointegration Test\n",
    "johansen_test = coint_johansen(df, det_order=0, k_ar_diff=1)\n",
    "\n",
    "# Extract eigenvalues and trace test results\n",
    "trace_stat = johansen_test.lr1  # Trace statistic\n",
    "critical_values = johansen_test.cvt[:, 1]  # 5% critical values\n",
    "\n",
    "# Prepare results for display\n",
    "johansen_results = pd.DataFrame({\n",
    "    'Trace Statistic': trace_stat,\n",
    "    '5% Critical Value': critical_values,\n",
    "    'Cointegration (Trace > Critical)': trace_stat > critical_values\n",
    "}, index=[f'Rank {i}' for i in range(len(trace_stat))])\n",
    "\n",
    "# Display results\n",
    "display(johansen_results)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EStimate VECM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Cointegrating Relationship</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investment</th>\n",
       "      <td>-2.00135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cointegrating Relationship\n",
       "GDP                            1.00000\n",
       "Investment                    -2.00135"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the VECM Model\n",
    "vecm_model = VECM(df, k_ar_diff=1, coint_rank=1)  # One cointegrating relationship\n",
    "vecm_fit = vecm_model.fit()\n",
    "\n",
    "# Extract and display the estimated parameters\n",
    "vecm_params = pd.DataFrame(vecm_fit.beta, columns=[\"Cointegrating Relationship\"], index=df.columns)\n",
    "display(vecm_params)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IRFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute Impulse Response Functions (IRFs)\n",
    "irf = vecm_fit.irf(periods=10)\n",
    "\n",
    "# Plot IRFs\n",
    "irf.plot(orth=False)\n",
    "plt.suptitle(\"Impulse Response Functions (IRFs)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FEVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Shock to GDP</th>\n",
       "      <th>Shock to Investment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.988993</td>\n",
       "      <td>0.011007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.985025</td>\n",
       "      <td>0.014975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.983749</td>\n",
       "      <td>0.016251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.982927</td>\n",
       "      <td>0.017073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.982359</td>\n",
       "      <td>0.017641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.981959</td>\n",
       "      <td>0.018041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.981659</td>\n",
       "      <td>0.018341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.981426</td>\n",
       "      <td>0.018574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.981240</td>\n",
       "      <td>0.018760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Shock to GDP  Shock to Investment\n",
       "0      1.000000             0.000000\n",
       "1      0.988993             0.011007\n",
       "2      0.985025             0.014975\n",
       "3      0.983749             0.016251\n",
       "4      0.982927             0.017073\n",
       "5      0.982359             0.017641\n",
       "6      0.981959             0.018041\n",
       "7      0.981659             0.018341\n",
       "8      0.981426             0.018574\n",
       "9      0.981240             0.018760"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Shock to GDP</th>\n",
       "      <th>Shock to Investment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.403763</td>\n",
       "      <td>0.596237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.596612</td>\n",
       "      <td>0.403388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.697975</td>\n",
       "      <td>0.302025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.757460</td>\n",
       "      <td>0.242540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.795918</td>\n",
       "      <td>0.204082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.823000</td>\n",
       "      <td>0.177000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.843142</td>\n",
       "      <td>0.156858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.858692</td>\n",
       "      <td>0.141308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.871058</td>\n",
       "      <td>0.128942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.881129</td>\n",
       "      <td>0.118871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Shock to GDP  Shock to Investment\n",
       "0      0.403763             0.596237\n",
       "1      0.596612             0.403388\n",
       "2      0.697975             0.302025\n",
       "3      0.757460             0.242540\n",
       "4      0.795918             0.204082\n",
       "5      0.823000             0.177000\n",
       "6      0.843142             0.156858\n",
       "7      0.858692             0.141308\n",
       "8      0.871058             0.128942\n",
       "9      0.881129             0.118871"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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De/wdFCQN2vWSq9dvyobvvpBMmTJF2G4NydqZqB2H2uGoW7JkyWTRokVmPvaCBQukePHipsf7888/l2+++SZcp6TNsWPHJG/evA7zrLXHW9/DtulI4GfRXvm7d+/K9evXwx3Toejrf98hazdukxpVKzzzWq4qXud061CFEydOOCwJpkuBvfDCC+abHx0afv78efPLpbp27Wp+uXR4Q4cOHeS3336TZcuWmeIIcE0+9xdJYhQQ3w0AAABwcs0b1JQGNV+STTv/kO17DsovG7bIhJkLZPYnQ+Wdlo3s55UsUsD+OKVXCkmTOpVcuXbTPD984rRULlfCoVe4aoXSJpifu3hZLl25bnqwa75U8altadV9sOTImll+W/aFpEjhGe3PcvjwYVMELmXKlP9rR9WqJvjrPOwn615FRjNUo0aNZMeOHfL222+b0PwstnPC3oP/rt8kqQpUlUePH0tISKi0blJXRvTvGu3P5SriNXTv3r3brLlt069fP/PT19fXVNDT4RRh5zpouX0N2H379pWpU6dKjhw5ZPbs2SwXBsQRvuSIW9xvAACej6dncqld/UWzDe3bWTq995EMnzjLIXR7eDhGIs2WGmajQodVR0X9GlVl4Q+rZdueA1LjGQE9thQoUMAsr/zo0SNTZVylS5fObOfOnYtW4NcpujqPXC79+7pXq5Q3Q+2TJfOQbFkyUbX8GeL17rzyyitP/XZFg3dEr9Fy9wAAxCa+5Ihb3O+4w72OW9zvuHUgJE+0zk+Xv7zcWfO7w+sCQrI4PA+RJHI2NJPZlyFfGVn/y0rZH+xj7+ldttNPUqZKLTeyvCiP0j8UT88UMn/TKWnW6qVw73c+xN38rNm2j6QvVEleb99fPp+3VDq8GXk1bR1O/uT8aS1sptlI53bberu1orgu71WoUKEIr9OqVSuZNm2azJgxwyz7FRNXrlwxQ9t1HXN9L70nN0NTSXCKYAnK/bIEadG6f29aglYynt8/Uc3pBgAAAIBnuXXzhnRq2Uj++8NSOXb4kJwLPCO//vcnmTfrM3nltfpRvk6Ldh3l0oXzMnboB3L6xDHZsHa1zJw0Ttp27m5CaHJPT2nfvbdMHj1cVi5fImcDTsuBvbvkhyXfhLtW6/ZdpMf7Q6Rn+7dMD3RkfHx85MCBA2bY+LVr10xPtRYz8/T0NCOCDx06JBs2bDDrbLdt2zbSoeVa96p///5m0xHF+p5nzpwxVc3nzJljvkTQz2CjnaG6RLOONtbe7blz55oibrpG97hx46J8zxAe4wAAAAAAOBUvr5RSokw5WTh7ppw7c1oePXos3tmyS7NW7aRTj3+ntEZFlqzZZPr8ZTJp9DB5s041SZsuvTR5623p3Os9+zlder8v7u7uMmPiGLly+ZJkypxF3ny7fYTXe7tTNzN0vX79+maNbA21T+rcubNZJqx8+fKmBpYGbB3tu3btWtNjXaFCBfHy8pLmzZubwmhP8+mnn0rFihVl5syZJkQHBQWZkF69enXZtm2bw8pOd+7cMcuFaRjX/dqDriFf35MVoJ6PW2hUZs87Ef1l0m9rtFKfFb88PgMTZ1G3gHENJDHifsct7nfc4n7HLe533OJ+xx3uddxytvutFbO12LHWVtKe1oTmwDkzuDnRKZkjnSRGrni/7z/lv4GoZkuGlwMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAJdUKmd6+W3NKsuuf/5soHmPI38etOw9kPAlje8GAAAAAEiERqSN4/e7Ha3Tb1y/JjM+HSObfvtVrl+7KmnSppOCRYrLf/q8L2UqvCgJ1TvvvCO3bt2Sn3766bmuExAQIHny5JE//vhDSpcuLQnB0L7d5e6d2zJlzreSEMybN0/69Olj7reVCN0AAAAAnE7/Lu3k0aNHMmryTMmRK7dcv3pVdm7ZKLdv3ojvpsHFMLwcAAAAgFO5c/u27N25TfoMGiEVq1STbDlySYky5aRjj37yymv1Hc69dfO69On0tlQqkE0aVisn/r+udji+e9sWaf16TSmfL4vULFdYpowdIY8fP7YfDwkJka9nTpXXXyprzqlTqbh89dmnEbYrODhYhvXvIYULF5bAwMBwx0eMGCHz58+Xn3/+Wdzc3Mzm7+9vjh08eFBq1KghKVKkkAwZMkiXLl3k77//jvI90evo9fz8/KR8+fLi5eUlVapUkaNHj5rjx44dM8ePHDni8LrJkydLvnz57M+PH/lLurd9Q14slENeLVNQBvf+j9y8cd1+fN2qn6V5rSpSMX9WqV4ir3Rp1USCgu7JzEnjZMXyxbLh19VmyL1uu7Zttg/BX7vyR3mnWT3zutYNakjAqRNyaN9eaVX/VfNe+p46eiGsHxYvkCavVpIK+b2l8SsVZen82fZjtuuu/2WlvPrqq+bzlipVSrZt22a/H+3bt5fbt2/b77XefysQugEAAAA4Fa+UKcUrZSrZsHaVPHzw4Knnzpo8Xuq83kS++3WzvFSjtgzq9R+5ffOmOXb54gV517eFFC9VRr5bu0mGjJ4oPy1Z6BCqp44bKXOnT5Euvd+XH/22y9hpX0mGTJnDvY+2472u78jRvw7Kpk2bJFeuXOHOee+996RFixZSt25duXjxotk0GN+7d0/q1Kkj6dOnl127dsl3330n69evlx49ekT73gwZMkQmTpwou3fvlqRJk0qHDh3M/oIFC5ow/u23jkO/9Xnr1q3tX2Z0fquxFC5WUhav+k1mfLPcjCB4v1t7c/zq5UsysEcnadLybflxww6Zs2yl1Kz7ukhoqPj+p4e89npTqfpKTfHbc8RspctVtL+PhvLOvd6TJb/4i3vSpDKoR2eZPGa4fDByrHz9/Wo5G3DaTBewWfXjMpnx6Vjp8cGH8uNvO6TngKEy/dMxsuK7xQ7t/3zCx+a+7tu3z3zGVq1amS9N9L5OmTJF0qRJY7/Xep4VGF4OAAAAwKlomBw1abqM/KC3fLfwaylSoqSUq1RV6jZuZuZ1h9XozdZSr8kb5rEGt0Vzv5BD+/ZI1VdrybIFc8Q7W3YZ9PEnpic0T/6CcvXyRZkydqT8p88H8k/QPXP+oFETpNGbrcw1cvrkkbIVKzu8R9C9v6XHOy1N8J69dIVkypQpwnanSpXK9GQ/ePBAvL297fu19/v+/fuyYMECSZkypdn3+eefS8OGDWX8+PGSJUuWKN+b0aNHy8svv2weDxw4UBo0aGCu7enpKW3atDHXHTVqlL33e8+ePbJw4ULzfMm8r0zg7jVwmP16H02cJq9VLG56pv+5d88E2pr1XjejC1SBIsXs5+p7PHr4QDJmDt/edv/pYQK5at3hPya8f7nkZ/v8+yZvve0QqGdOHCf9h46SWvUamuc6heDUsaOy/Nuv7X8WtuvqZ1QjR46UYsWKyYkTJ8xog7Rp05o/17D32gr0dAMAAABwOrXqN5L1uw/L1LmLpMrLtWT39s3yVr1X5OdlixzOKxgmFHp5pZRUqVPbhzGfOnFMSpatYIKZTekKlUyIvnzxvJw6fswE6YpV/w2xkdEA+U9QkMz69gdJnSb6BegOHz5shkbbAreqWrWqGdpuGx4eVSVLlrQ/zpo1q/l55coV8/Ott94yBdi2b99u7+UuW7asCajq2OFDsmvbJjPc27Y1fqWSOXbuzGkpWLS4VHrpZXmj9kumV//7RfPlThSLlBUs/L8/B9tIgQKFi/5vX8bMcuPaVfNYh6ufPXNaRrzfy6EtX037VM6eCYj0uk9+3rhCTzcAAAAAp5Tc01MqV3/VbFq1XEPazEljpXGLf4dLq6RJPRxeowFbw2xUaM9tVOiw9VU/fCf79+6SSlWrS3zy8Pjf57V9mWD7vNrjq/PGFy1aJC+++KL52a1bN/v5+mXDy7XqmrnyT8qYJYu4u7vLF4t+lH27d8i23zfI4q+/lGkTPpaFK9abnuinSRpBu8L+2ei+0NB/26k96mrYhClSonR5h+skcXd/5nWj+ucbW+jpBgAAAOAS8hYoZHqco3x+/oJyYO8uCQ0Nte/bt2uHpEyVWrJkzS658uQTT88Upir607Ro21F6DxouvTu0NoXZniZZsmSm4FpYRYoUkf3795u53TZbtmyRJEmSSKFChSQ26RDzpUuXmoJjp06dMr3f9nYULyUnjx2RbDlzSa48eR02HSVgC7Y6JLx7/0GydM3v4uGRTH5b819zzEM/W4jjZ4sJ7QnPlCWrnDtzJlw7nhXun3WvrUDoBgAAAOBUbt28IZ1aNpL//rDUDIk+F3hGfv3vTzJv1mfhqpc/TYt2HeXShfMydugHcvrEMdmwdrUp+NW2c3cTeLUnvX333jJ59HBZuXyJKfalIf2HJd+Eu1br9l2kx/tDpGf7t2Tz5s2RvqePj48cOHDADBu/du2aWfZMg7D2qvv6+sqhQ4dkw4YN0rNnT2nbtm205nNHRbNmzeTu3bumh1urfmfLls1+rKVvJ7l966YZLq+VxfXzbvH3k6H93jXh9cAfu2X2tIny5/4/5OL5s+L3y0q5eeOa5C1Q0Lw+W46ccvzwnxJw8ripeK6fLaa69x8oc6dPlm/nfmHmk+t1f1r6rSz4cnqUr6H3WivAa0V3vddB0fhCJjoYXg4AAADAqWivqy4RtnD2TDPX+NGjx6YgWrNW7aRTj35Rvk6WrNlk+vxlMmn0MHmzTjVJmy69KeilVbZttGq5DqueMXGMXLl8STJlziJvvv1vNe8nvd2pmxnaXL9+fVmzZo2poP2kzp07m+WstJK4BkIN2K+88oqsXbtWevfuLRUqVDDLXzVv3lwmTZoksS116tSmQNuyZctk7ty5Dscye2eV+T+ukSljRkjXt5vJowcPJWuOnFL15ZrmS4hUqVLLnh3bZOGcWXLv77uSNXtOU+zspVdrm9c3a+0ru7ZtkVYNapih6rOXrbQXXIsu/bPUUQbzvpgmk0cPkxQpvMwc8DYd/zcc/ln0/nft2lVatmwp169fl+HDh1uybJhbaNixEi7gzp07pkqdrsem5eFjm8/AVZIYBYz7t6JfYsP9jlvc77jF/Y5b3O+4xf2OO9zruOVs91urWp8+fVry5MkT5fnLcenAuagV6UpoSuZIJ4mRK97v+0/5byCq2ZLh5QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAADgqXSZK8AVhcTC7z7rdAMAAACIULJkycz6yxcuXJBMmTKZ525ubpJQhD5+KImRLkOVGLnS/Q4NDZWHDx/K1atXzX8D+rsfU4RuAAAAABHSsKHrE1+8eNEE74Tmys1/JDFK9k8KSYxc8X57eXlJrly5zH8LMUXoBgAAABAp7eHT0PH48WMJDg6WhKTTD/6SGPn1f0USI1e73+7u7pI0adLnHt1B6AYAAADwVBo6PDw8zJaQnL+bsL4EiCpPT09JjLjfMUMhNQAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAACcNXRPnz5dfHx8xNPTUypVqiQ7d+586vlTpkyRQoUKSYoUKSRnzpzSt29fuX//fpy1FwAAAACARBG6ly5dKv369ZPhw4fL3r17pVSpUlKnTh25cuVKhOcvWrRIBg4caM4/fPiwzJkzx1xj8ODBcd52AAAAAAASdOieNGmSdO7cWdq3by9FixaVWbNmiZeXl8ydOzfC87du3SpVq1aV1q1bm97x1157TVq1avXM3nEAAAAAAFwqdD98+FD27NkjtWrV+l9jkiQxz7dt2xbha6pUqWJeYwvZp06dktWrV0v9+vXjrN0AAAAAAERVUokn165dk+DgYMmSJYvDfn1+5MiRCF+jPdz6updeeklCQ0Pl8ePH0rVr16cOL3/w4IHZbO7cuROLnwIAAAAAgFgO3SEhIXLixAkz91ofh1W9enWxir+/v4wZM0ZmzJhhiq5pG3r37i2jRo2SoUOHRviasWPHysiRIy1rEwAAAAAAsRa6t2/fbnqcz5w5Y3qbw3JzczO911GRMWNGcXd3l8uXLzvs1+fe3t4RvkaDddu2baVTp07meYkSJeTevXvSpUsXGTJkiBme/qRBgwaZYm1he7q16jkAAAAAAAluTrcO5y5fvrwcOnRIbty4ITdv3rRv+jyqkiVLJuXKlRM/Pz/7Pu011+eVK1eO8DVBQUHhgrUGd/XkFwA2yZMnlzRp0jhsAAAAAAAkyJ7u48ePy/LlyyV//vzP/ebaA+3r62tCfMWKFc0a3NpzrdXMVbt27SR79uxmiLhq2LChqXhepkwZ+/By7f3W/bbwDQAAAABAog3dtrAbG6G7ZcuWcvXqVRk2bJhcunRJSpcuLWvWrLEXVwsMDHTo2f7www/NEHb9ef78ecmUKZMJ3KNHj37utgAAAAAAEO+hu2fPntK/f38TknVOtYeHh8PxkiVLRut6PXr0MFtkhdMcGps0qQwfPtxsAAAAAAA4Xehu3ry5+dmhQwf7Pu191jnV0SmkBgAAAACAs4t26D59+rQ1LQEAAAAAwNVDd+7cua1pCQAAAAAArh661cmTJ02l8cOHD5vnRYsWld69e0u+fPliu30AAAAAALjOOt1r1641IXvnzp2maJpuO3bskGLFism6deusaSUAAAAAAK7Q0z1w4EDp27evjBs3Ltz+AQMGSO3atWOzfQAAAAAAuE5Ptw4p79ixY7j9Ws38r7/+iq12AQAAAADgeqE7U6ZMsm/fvnD7dV/mzJljq10AAAAAALje8PLOnTtLly5d5NSpU1KlShWzb8uWLTJ+/Hjp16+fFW0EAAAAAMA1QvfQoUMlderUMnHiRBk0aJDZly1bNhkxYoT06tXLijYCAAAAAOAaodvNzc0UUtPt7t27Zp+GcAAAAAAAEAvrdNsQtgEAAAAAeM7QXbZsWfHz85P06dNLmTJlTG93ZPbu3RuVSwIAAAAA4PSiFLobN24syZMntz9+WugGAAAAAADRCN3Dhw+3P9aCaQAAAAAAwIJ1uvPmzSvXr18Pt//WrVvmGAAAAAAAiGHoDggIkODg4HD7Hzx4IOfOnYvu5QAAAAAAcFpRrl6+YsUK++O1a9dK2rRp7c81hGuhtTx58sR+CwEAAAAAcPbQ3aRJE/NTi6j5+vo6HPPw8BAfHx+ZOHFi7LcQAAAAAABnD90hISHmp/Zm79q1SzJmzGhluwAAAAAAcJ3QbXP69GlrWgIAAAAAgCuG7s8++0y6dOkinp6e5vHT9OrVK7baBgAAAACA84fuyZMnS5s2bUzo1seR0fnehG4AAAAAAKIRusMOKWd4OQAAAAAAFq3T/SRdLmzfvn1y8+bN570UAAAAAACuHbr79Okjc+bMsQfu6tWrS9myZSVnzpzi7+9vRRsBAAAAAHCN0L18+XIpVaqUebxy5UoJCAiQI0eOSN++fWXIkCFWtBEAAAAAANcI3deuXRNvb2/zePXq1fLmm29KwYIFpUOHDnLw4EEr2ggAAAAAgGuE7ixZsshff/1lhpavWbNGateubfYHBQWJu7u7FW0EAAAAAMB5q5eH1b59e2nRooVkzZrVLBFWq1Yts3/Hjh1SuHBhK9oIAAAAAIBrhO4RI0ZI8eLF5ezZs2ZoefLkyc1+7eUeOHCgFW0EAAAAAMA1Qrd64403wu3z9fWNjfYAAAAAAODa63Rv3LhRGjZsKPnz5zdbo0aNZNOmTbHfOgAAAAAAXCl0L1y40Mzj9vLykl69epktRYoUUrNmTVm0aJE1rQQAAAAAwBWGl48ePVomTJhg1uW20eA9adIkGTVqlLRu3Tq22wgAAAAAQKIU7Z7uU6dOmaHlT9Ih5qdPn46tdgEAAAAA4HqhO2fOnOLn5xdu//r1680xAAAAAAAQw+Hl/fv3N8PJ9+3bJ1WqVDH7tmzZIvPmzZOpU6dG93IAAAAAADitaIfubt26ibe3t0ycOFGWLVtm9hUpUkSWLl0qjRs3tqKNAAAAAAC4zjrdTZs2NRsAAAAAAIjl0K12794thw8fNo+LFi0q5cqVi+mlAAAAAABwStEO3efOnZNWrVqZedzp0qUz+27dumXmdy9ZskRy5MhhRTsBAAAAAHD+6uWdOnWSR48emV7uGzdumE0fh4SEmGMAAAAAACCGPd0bN26UrVu3SqFChez79PG0adOkWrVq0b0cAAAAAABOK0brdGtP95OCg4MlW7ZssdUuAAAAAABcL3R/8skn0rNnT1NIzUYf9+7dWz799NPYbh8AAAAAAK4zvPydd96RoKAgqVSpkiRN+u/LHz9+bB536NDBbDY63xsAAAAAAFcV7dA9ZcoUa1oCAAAAAICrh25fX19rWgIAAAAAgKuHbpsrV66YTZcKC6tkyZKx0S4AAAAAAFyvkNqePXukePHikjVrVhOwS5cubd/KlCkT7QZMnz5dfHx8xNPT08wT37lz51PPv3Xrlrz77rvm/ZMnTy4FCxaU1atXR/t9AQAAAABIcD3dWihNg+6cOXMkS5Ys4ubmFuM3X7p0qfTr109mzZplArfOF69Tp44cPXpUMmfOHO78hw8fSu3atc2x5cuXS/bs2eXMmTOSLl26GLcBAAAAAIAEE7pPnTol33//veTPn/+533zSpEnSuXNnad++vXmu4XvVqlUyd+5cGThwYLjzdb9WRN+6dat4eHiYfdpLDgAAAACAUwwvr1mzpuzfv/+531h7rXWoeq1atf7XmCRJzPNt27ZF+JoVK1ZI5cqVzfBy7WXXYe5jxoyR4ODgSN/nwYMHcufOHYcNAAAAAIAE2dM9e/ZsU8H80KFDJvTaepxtGjVqFKXrXLt2zYRlDc9h6fMjR45E2sv+22+/SZs2bcw87hMnTkj37t3l0aNHMnz48AhfM3bsWBk5cmSUPx8AAAAAAPEWurUXesuWLfLLL7+EO6bzu5/W6/y8tFK6zuf+8ssvxd3dXcqVKyfnz5+XTz75JNLQPWjQIDNv3EZ7unPmzGlZGwEAAAAAiPHw8p49e8rbb78tFy9eNCE47BadwJ0xY0YTnC9fvuywX597e3tH+BqtWK5F3PR1NkWKFJFLly6Z4eoR0QrnadKkcdgAAAAAAEiQofv69evSt2/fcMPCoytZsmSmp9rPz8++T4O7Ptd52xGpWrWqGVIedm3wY8eOmTCu1wMAAAAAIFGH7mbNmsmGDRti5c112PdXX30l8+fPl8OHD0u3bt3k3r179mrm7dq1M8PDbfS4Vi/v3bu3Cdta6VwLqWlhNQAAAAAAEv2cbh3erUF48+bNUqJEiXCF1Hr16hXla7Vs2VKuXr0qw4YNM0PES5cuLWvWrLH3ogcGBpqK5jY6F3vt2rWmp71kyZJmnW4N4AMGDIjuxwAAAAAAIGFWL0+VKpVs3LjRbE8WUotO6FY9evQwW0T8/f3D7dOh59u3b49mqwEAAAAASASh+/Tp09a0BAAAAAAAV5/THVZoaKjZAAAAAABALIXuBQsWmPncKVKkMJvOr/7mm29icikAAAAAAJxWtIeXT5o0SYYOHWrmYesSXkqLqnXt2lWuXbtmipwBAAAAAIAYhO5p06bJzJkzzXJeNo0aNZJixYrJiBEjCN0AAAAAAMR0ePnFixelSpUq4fbrPj0GAAAAAABiGLrz588vy5YtC7d/6dKlUqBAgeheDgAAAAAApxXt4eUjR46Uli1byu+//26f071lyxbx8/OLMIwDAAAAAOCqot3T3bx5c9mxY4dkzJhRfvrpJ7Pp4507d0rTpk2taSUAAAAAAK7Q063KlSsnCxcujP3WAAAAAADgyj3dq1evlrVr14bbr/t++eWX2GoXAAAAAACuF7oHDhwowcHB4faHhoaaYwAAAAAAIIah+/jx41K0aNFw+wsXLiwnTpyI7uUAAAAAAHBa0Q7dadOmlVOnToXbr4E7ZcqUsdUuAAAAAABcL3Q3btxY+vTpIydPnnQI3P3795dGjRrFdvsAAAAAAHCd0D1hwgTTo63DyfPkyWO2IkWKSIYMGeTTTz+1ppUAAAAAALjCkmE6vHzr1q2ybt062b9/v6RIkUJKliwp1atXt6aFAAAAAAC40jrdbm5u8tprr5mgnTx5cvMcAAAAAAA85/DykJAQGTVqlGTPnl1SpUolp0+fNvuHDh0qc+bMie7lAAAAAABwWtEO3R9//LHMmzfPzO1OliyZfX/x4sVl9uzZsd0+AAAAAABcJ3QvWLBAvvzyS2nTpo24u7vb95cqVUqOHDkS2+0DAAAAAMB1Qvf58+clf/78EQ47f/ToUWy1CwAAAAAA1wvdRYsWlU2bNoXbv3z5cilTpkxstQsAAAAAANerXj5s2DDx9fU1Pd7au/3DDz/I0aNHzbDz//73v9a0EgAAAAAAV+jpbty4saxcuVLWr18vKVOmNCH88OHDZl/t2rWtaSUAAAAAAK6yTne1atVk3bp1sd8aAAAAAABcOXSHhobKnj17JCAgQNzc3CRv3rxSunRp8xgAAAAAAMQwdG/YsEE6duwoZ86cMeFbadjOkyePzJ07V6pXrx6dywEAAAAA4NSiPKf7xIkT8vrrr4uPj48pnqbzuP/66y/57rvvJEeOHFK/fn05deqUta0FAAAAAMAZe7qnTJkiL774ovj5+TnsL1y4sDRt2lRq1aolkydPlmnTplnRTgAAAAAAnLen29/fX/r06RPhMR1irsd0+DkAAAAAAIhm6A4MDJQSJUpEerx48eJmrjcAAAAAAIhm6P7777/Fy8sr0uN6LCgoKLbaBQAAAACAa1Uv18Jply5divDYtWvXYqtNAAAAAAC4XuiuWbOmfamwJ+d0637W6gYAAAAAIAah+/Tp01E9FQAAAAAARCd0586d29qWAAAAAADgqoXUAAAAAABA9BC6AQAAAACwCKEbAAAAAACLELoBAAAAAEhIofvx48eyfv16+eKLL+Tu3btm34ULF+Tvv/+O7fYBAAAAAOAa63SrM2fOSN26dSUwMFAePHggtWvXltSpU8v48ePN81mzZlnTUgAAAAAAnL2nu3fv3lK+fHm5efOmpEiRwr6/adOm4ufnF9vtAwAAAADAdXq6N23aJFu3bpVkyZI57Pfx8ZHz58/HZtsAAAAAAHCtnu6QkBAJDg4Ot//cuXNmmDkAAAAAAIhh6H7ttddkypQp9udubm6mgNrw4cOlfv360b0cAAAAAABOK9rDyydOnCh16tSRokWLyv3796V169Zy/PhxyZgxoyxevNiaVgIAAAAA4AqhO0eOHLJ//35ZunSp+am93B07dpQ2bdo4FFYDAAAAAMDVxWid7qRJk5qQPWHCBJkxY4Z06tTpuQL39OnTTSE2T09PqVSpkuzcuTNKr1uyZIkZ3t6kSZMYvzcAAAAAAAkmdI8dO1bmzp0bbr/u07W6o0t7zPv162fmhO/du1dKlSplhq9fuXLlqa8LCAiQ9957T6pVqxbt9wQAAAAAIEGG7i+++EIKFy4cbn+xYsVk1qxZ0W7ApEmTpHPnztK+fXszT1yv4eXlFWGwt9Hq6drTPnLkSMmbN2+03xMAAAAAgAQZui9duiRZs2YNtz9Tpkxy8eLFaF3r4cOHsmfPHqlVq9b/GpQkiXm+bdu2SF/30UcfSebMmc1ccgAAAAAAnKaQWs6cOWXLli2SJ08eh/26L1u2bNG61rVr10yvdZYsWRz26/MjR45E+JrNmzfLnDlzZN++fVF6jwcPHpjN5s6dO9FqIwAAAAAAcRa6dSh4nz595NGjR1KjRg2zz8/PTz744APp37+/WOnu3bvStm1b+eqrr8wSZVGdg67D0AEAAAAASPCh+/3335fr169L9+7dzfBwpVXHBwwYIIMGDYrWtTQ4u7u7y+XLlx3263Nvb+9w5588edIUUGvYsKF9X0hIyL8fJGlSOXr0qOTLl8/hNdomLdQWtqdbe+sBAAAAAEhwoVuX6NIq5UOHDpXDhw+bpcIKFCggyZMnj/abJ0uWTMqVK2d6ym3LfmmI1uc9evQId74WcDt48KDDvg8//ND0gE+dOjXCMK3tiknbAAAAAACI89BtkypVKqlQocJzN0B7oX19faV8+fJSsWJFmTJlity7d89UM1ft2rWT7Nmzm2Hi2qNevHhxh9enS5fO/HxyPwAAAAAAiS50ayAeN26c6Y3WtbRtw7ttTp06Fa3rtWzZUq5evSrDhg0zldFLly4ta9assRdXCwwMNBXNAQAAAABw+tDdqVMn2bhxoylopkuH6XDz56VDySMaTq78/f2f+tp58+Y99/sDAAAAAJAgQvcvv/wiq1atkqpVq1rSIAAAAAAAnEW0x22nT59eXnjhBWtaAwAAAACAK4fuUaNGmfnXQUFB1rQIAAAAAABXHV4+ceJEs162Fjrz8fERDw8Ph+N79+6NzfYBAAAAAOA6odu2njYAAAAAAIjl0D18+PDovgQAAAAAAJfEAtgAAAAAACSUnu7g4GCZPHmyLFu2TAIDA+Xhw4cOx2/cuBGb7QMAAAAAwHV6ukeOHCmTJk2Sli1byu3bt6Vfv37SrFkzSZIkiYwYMcKaVgIAAAAA4Aqh+9tvv5WvvvpK+vfvL0mTJpVWrVrJ7NmzzTJi27dvt6aVAAAAAAC4Qui+dOmSlChRwjxOlSqV6e1Wr7/+uqxatSr2WwgAAAAAgKuE7hw5csjFixfN43z58smvv/5qHu/atUuSJ08e+y0EAAAAAMBVQnfTpk3Fz8/PPO7Zs6cMHTpUChQoIO3atZMOHTpY0UYAAAAAAFyjevm4cePsj7WYWq5cuWTbtm0meDds2DC22wcAAAAAgOuE7idVrlzZbAAAAAAAIAahe8WKFVKvXj3x8PAwj5+mUaNGUbkkAAAAAABOL0qhu0mTJqZqeebMmc3jyLi5uUlwcHBstg8AAAAAAOcO3SEhIRE+BgAAAAAAsVS9/NGjR1KzZk05fvx4dF4GAAAAAIBLilbo1jndBw4csK41AAAAAAC48jrdb7/9tsyZM8ea1gAAAAAA4MpLhj1+/Fjmzp0r69evl3LlyknKlCkdjk+aNCk22wcAAAAAgOuE7kOHDknZsmXN42PHjoWrXg4AAAAAAGIYujds2BDdlwAAAAAA4JKiPacbAAAAAABY1NOtdu/eLcuWLZPAwEB5+PChw7EffvghJpcEAAAAAMDpRLune8mSJVKlShU5fPiw/Pjjj2bt7j///FN+++03SZs2rTWtBAAAAADAFUL3mDFjZPLkybJy5UpJliyZTJ06VY4cOSItWrSQXLlyWdNKAAAAAABcIXSfPHlSGjRoYB5r6L53756pWt63b1/58ssvrWgjAAAAAACuEbrTp08vd+/eNY+zZ89ulhBTt27dkqCgoNhvIQAAAAAArlJIrXr16rJu3TopUaKEvPnmm9K7d28zn1v31axZ05pWAgAAAADgzKFbe7SLFy8un3/+udy/f9/sGzJkiHh4eMjWrVulefPm8uGHH1rZVgAAAAAAnDN0lyxZUipUqCCdOnWSt956y+xLkiSJDBw40Mr2AQAAAADg/HO6N27cKMWKFZP+/ftL1qxZxdfXVzZt2mRt6wAAAAAAcIXQXa1aNZk7d65cvHhRpk2bJgEBAfLyyy9LwYIFZfz48XLp0iVrWwoAAAAAgLNXL0+ZMqW0b9/e9HwfO3bMFFObPn26WaO7UaNG1rQSAAAAAABXCN1h5c+fXwYPHmwKqKVOnVpWrVoVey0DAAAAAMDVlgyz+f33381w8++//94UVGvRooV07NgxdlsHAAAAAICrhO4LFy7IvHnzzHbixAmpUqWKfPbZZyZw67BzAAAAAAAQg9Bdr149Wb9+vWTMmFHatWsnHTp0kEKFCkX15QAAAAAAuJwoh24PDw9Zvny5vP766+Lu7m5tqwAAAAAAcKXQvWLFCmtbAgAAAACAk3mu6uUAAAAAACByhG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMCZQ/f06dPFx8dHPD09pVKlSrJz585Iz/3qq6+kWrVqkj59erPVqlXrqecDAAAAAOCyoXvp0qXSr18/GT58uOzdu1dKlSolderUkStXrkR4vr+/v7Rq1Uo2bNgg27Ztk5w5c8prr70m58+fj/O2AwAAAACQoEP3pEmTpHPnztK+fXspWrSozJo1S7y8vGTu3LkRnv/tt99K9+7dpXTp0lK4cGGZPXu2hISEiJ+fX5y3HQAAAACABBu6Hz58KHv27DFDxO0NSpLEPNde7KgICgqSR48eyQsvvGBhSwEAAAAAiL6kEo+uXbsmwcHBkiVLFof9+vzIkSNRusaAAQMkW7ZsDsE9rAcPHpjN5s6dO8/ZagAAAAAAEsnw8ucxbtw4WbJkifz444+mCFtExo4dK2nTprVvOgccAAAAAACnD90ZM2YUd3d3uXz5ssN+fe7t7f3U13766acmdP/6669SsmTJSM8bNGiQ3L59276dPXs21toPAAAAAECCDd3JkiWTcuXKORRBsxVFq1y5cqSvmzBhgowaNUrWrFkj5cuXf+p7JE+eXNKkSeOwAQAAAADg9HO6lS4X5uvra8JzxYoVZcqUKXLv3j1TzVy1a9dOsmfPboaJq/Hjx8uwYcNk0aJFZm3vS5cumf2pUqUyGwAAAAAACUW8h+6WLVvK1atXTZDWAK1LgWkPtq24WmBgoKlobjNz5kxT9fyNN95wuI6u8z1ixIg4bz8AAAAAAAk2dKsePXqYLSL+/v4OzwMCAuKoVQAAAAAAuHD1cgAAAAAAEjJCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAzhy6p0+fLj4+PuLp6SmVKlWSnTt3PvX87777TgoXLmzOL1GihKxevTrO2goAAAAAQKIJ3UuXLpV+/frJ8OHDZe/evVKqVCmpU6eOXLlyJcLzt27dKq1atZKOHTvKH3/8IU2aNDHboUOH4rztAAAAAAAk6NA9adIk6dy5s7Rv316KFi0qs2bNEi8vL5k7d26E50+dOlXq1q0r77//vhQpUkRGjRolZcuWlc8//zzO2w4AAAAAwNMklXj08OFD2bNnjwwaNMi+L0mSJFKrVi3Ztm1bhK/R/dozHpb2jP/0008Rnv/gwQOz2dy+fdv8vHPnjlgh5EGQJEZW3Q+rcb/jFvc7bnG/4xb3O25xv+MO9zpucb/jFvc7bnG/I75uaGioJNjQfe3aNQkODpYsWbI47NfnR44cifA1ly5divB83R+RsWPHysiRI8Ptz5kz53O13dmknRLfLXAt3O+4xf2OW9zvuMX9jlvc77jDvY5b3O+4xf12rvt99+5dSZs2bcIM3XFBe9HD9oyHhITIjRs3JEOGDOLm5iaJhX6Lol8UnD17VtKkSRPfzXF63O+4xf2OW9zvuMX9jlvc77jDvY5b3O+4xf2OW3cS6f3WHm4N3NmyZXvqefEaujNmzCju7u5y+fJlh/363NvbO8LX6P7onJ88eXKzhZUuXTpJrPSXMDH9IiZ23O+4xf2OW9zvuMX9jlvc77jDvY5b3O+4xf2OW2kS4f1+Wg93giiklixZMilXrpz4+fk59ETr88qVK0f4Gt0f9ny1bt26SM8HAAAAACC+xPvwch367evrK+XLl5eKFSvKlClT5N69e6aauWrXrp1kz57dzM1WvXv3lpdfflkmTpwoDRo0kCVLlsju3bvlyy+/jOdPAgAAAABAAgvdLVu2lKtXr8qwYcNMMbTSpUvLmjVr7MXSAgMDTUVzmypVqsiiRYvkww8/lMGDB0uBAgVM5fLixYuLM9Mh8rqW+ZND5WEN7nfc4n7HLe533OJ+xy3ud9zhXsct7nfc4n7HreROfr/dQp9V3xwAAAAAAMRIvM7pBgAAAADAmRG6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAtRrxQAANcW70uGIWLXrl2TuXPnyrZt28xSasrb29ssmfbOO+9IpkyZ4ruJAIAo0OVP9u/fL0WKFInvpgAAkCBcvHhRZs6cKZs3bzaPdYnovHnzSpMmTUzWcXd3F2fCkmEJ0K5du6ROnTri5eUltWrVsq9ZfvnyZfHz85OgoCBZu3atlC9fPr6b6jLOnj1r1g7UL0Lw/P755x/Zs2ePvPDCC1K0aFGHY/fv35dly5ZJu3bt4q19zubw4cOyfft2qVy5shQuXFiOHDkiU6dOlQcPHsjbb78tNWrUiO8mOoV+/fpFuF/vtd7nDBkymOeTJk2K45a5jnv37pm/P06cOCFZs2aVVq1a2e87ns/evXslffr0kidPHvP8m2++kVmzZklgYKDkzp1bevToIW+99VZ8N9Np9OzZU1q0aCHVqlWL76a4jM8//1x27twp9evXN7/L+js+duxYCQkJkWbNmslHH30kSZPSXxkbdu/ebTJO/vz5JUWKFKaTsXXr1vLw4UOTcfTfhmvWrJHUqVOL09DQjYSlUqVKoV26dAkNCQkJd0z36bEXX3wxXtrmqvbt2xeaJEmS+G6GUzh69Gho7ty5Q93c3Mw9rV69euiFCxfsxy9dusS9jkW//PJLaLJkyUJfeOGFUE9PT/M8U6ZMobVq1QqtUaNGqLu7e6ifn198N9Mp6O906dKlQ1955RWHTfdXqFDBPH711Vfju5lOpUiRIqHXr183jwMDA0N9fHxC06ZNa+63/s5nzpw59NSpU/HdTKdQsmTJ0HXr1pnHX331VWiKFClCe/XqFTpz5szQPn36hKZKlSp0zpw58d1Mp2H7f2SBAgVCx40bF3rx4sX4bpJTGzVqVGjq1KlDmzdvHurt7W3ueYYMGUI//vjj0DFjxpj/bw4bNiy+m+k0qlatGjpixAj782+++cbkH3Xjxg3z/1L9+8WZELoTIP2H8eHDhyM9rsf0HMSen3/++anb5MmTCYKxpEmTJqENGjQIvXr1aujx48fN4zx58oSeOXPGHCd0x67KlSuHDhkyxDxevHhxaPr06UMHDx5sPz5w4MDQ2rVrx2MLncfYsWPN7/KTX2IkTZo09M8//4y3djl7MLl8+bJ53KZNm9AqVaqE3rp1yzy/e/eu+XKpVatW8dxK56AhOyAgwDwuU6ZM6Jdffulw/Ntvvw0tWrRoPLXOOX+3169fH9q7d+/QjBkzhnp4eIQ2atQodOXKlaHBwcHx3Tynky9fvtDvv//e3tGiX0gvXLjQfvyHH34IzZ8/fzy20Pn+Pjl58qT9uf5O6++4/htQ/frrr6HZsmULdSaE7gRIv6mfP39+pMf1mPYUIva/UdafkW0EwdihPU8HDhxwGL3RtWvX0Fy5cpm/gAndsStNmjTmyw3b/9Q0AO7du9d+/ODBg6FZsmSJxxY6l507d4YWLFgwtH///qEPHz40+wjdcRO68+bNa/6hFtaWLVtCc+bMGU+tcy7a67d792773+MaTMI6ceKE+Yc0Yv93W/8uWbp0aWidOnVMGNQwol+e2v5ux/PT313bl/9KA+ChQ4fsz/ULJy8vr3hqnfPJnTt36ObNm+3PdcSj/s4HBQWZ56dPn3a6DkaqlydA7733nnTp0kV69+4tK1askB07dphNH+u+rl27ygcffBDfzXQqOvfvhx9+MPN2Itp0Lhtibz532DlRbm5uppBGw4YN5eWXX5Zjx47Fa/uckd5jpUVKPD09JW3atPZjOl/q9u3b8dg651KhQgVTr+Dq1aum7sahQ4fs9x/WsN1frQehf5eHlT17dvNngedXr14983e10r+rly9f7nBc59Lr/EzEPg8PDzO/W+e4njp1Sjp37izffvutFCpUKL6b5jS0WPFff/1lHh8/flyCg4Ptz9Wff/4pmTNnjscWOpcmTZqYPKO/0xs2bJA2bdqYv1d0frc6evSo+fvbmVANIAF69913JWPGjDJ58mSZMWOG+Q9faRW/cuXKybx588xfvog9el/1H8qNGzeO9B911ByMHVrISwtoPFnJWQuYqEaNGsVTy5yTj4+P+QdEvnz5zHMtVpIrVy77cS2C9GRQwfNJlSqVzJ8/X5YsWWIKxdj+Doc1atasab7Iu3PnjvmHWvHixe3Hzpw5QyG1WDJ+/HipWrWq+YexfqE0ceJE8ff3N3+X633XYo0//vhjfDfT6enf3yNGjDDFXdevXx/fzXEaGvq0gKv+O1CLFmvnlnaCXb9+3fwbcPTo0fLGG2/EdzOdxscff2wqlmuHi/4/Ugu9Lly40H5c77kWsXMmVC9P4B49emSWD1MaxPXbTsS+TZs2maq3devWjfC4HtOgqP/YwPPRv0T1fq9evTrC4927dzcVcXWEAZ6f3sucOXNKgwYNIjw+ePBguXLlisyePTvO2+YKzp07Z77Q0/CdMmXK+G6O0xk5cqTD8xdffNGs/mHz/vvvmz+DxYsXx0PrnM+tW7dk3LhxsnLlStPjqn9P65d2Gsb79u3LqiqxSKvE6787+NIobujvsv5u6xfTujzvwIEDZenSpSZ866pBGg61c4C/x2PX/fv35fHjx+bLamdH6AYAAAAAwCLM6QYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAB277zzjllD9XnoUlK65ItWuwYAwNURugEASMQBWcOtbsmSJZP8+fPLRx99ZJZgiampU6fKvHnzYrWdAAC4sqTx3QAAABBzdevWla+//loePHggq1evlnfffVc8PDxk0KBB0bpOcHCwCe9p06a1rK0AALgieroBAEjEkidPLt7e3pI7d27p1q2b1KpVS1asWGFC+HvvvSfZs2eXlClTSqVKlcywbxvtzU6XLp05t2jRouY6gYGB4YaX63V69eolmTNnFk9PT3nppZdk165dDm3QsF+wYEFJkSKFvPrqqxIQEOBw/MyZM9KwYUNJnz69aUuxYsXMawAAcAWEbgAAnIgG34cPH0qPHj1k27ZtsmTJEjlw4IC8+eabplf8+PHj9nODgoJk/PjxMnv2bPnzzz9NsH7SBx98IN9//73Mnz9f9u7da4aw16lTR27cuGGOnz17Vpo1a2ZC9b59+6RTp04ycOBAh2to77uG999//10OHjxo3jNVqlRxcDcAAIh/DC8HAMAJhIaGip+fn6xdu1ZatWplhpxrz3W2bNnMce31XrNmjdk/ZswYs+/Ro0cyY8YMKVWqVITXvHfvnsycOdP0iterV8/s++qrr2TdunUyZ84cef/9983xfPnyycSJE83xQoUK2YO1jbajefPmUqJECfM8b968lt8PAAASCkI3AACJ2H//+1/Ta6wBOiQkRFq3bi1vvPGGCco65Dss7W3OkCGD/bkWXytZsmSk1z558qS5btWqVe37dL54xYoV5fDhw+a5/tSh62FVrlzZ4bkOT9eh77/++qsZ/q4B/GnvCwCAMyF0AwCQiOkcau1t1gCtvdpJkyaVpUuXiru7u+zZs8f8DCvssG4diq7F06ymQ851SPqqVatM8B47dqzpGe/Zs6fl7w0AQHxjTjcAAImYFibTeda5cuUygVuVKVPGVCO/cuWKORZ206JrUaXDxjXMb9myxb5Pe761kJoWX1NFihSRnTt3Orxu+/bt4a6VM2dO6dq1q/zwww/Sv39/M0wdAABXQOgGAMDJ6LDyNm3aSLt27UzIPX36tAnG2sOsvc3RCfQ6LFznbut88L/++ks6d+5sCrB17NjRnKNBWouz6TlHjx6VRYsWhVvnu0+fPmauubZDi7Ft2LDBhHUAAFwBoRsAACekBdM0dGuvshY302XAtIdae8SjY9y4cWYOdtu2baVs2bJy4sQJE6B1+S+l19Pq5j/99JMpyDZr1ix7oTYb7XXXCuYatLWCun4poAXcAABwBW6hWu4UAAAAAADEOnq6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwCABOTvv/+WTp06ibe3t7i5uUmfPn3iu0kAAOA5ELoBAE5h3rx5JqRGtA0cONB+no+PT6Tn1a1b15xTsmRJyZUrl4SGhkb6flWrVpUsWbLI48ePJSAgwOE6Hh4ekjFjRqlSpYoMHjxYAgMDo/w5xowZYz5Lt27d5JtvvpG2bduKlfR+vP7665JYLFq0SKZMmSKJwYULF2TEiBGyb9+++G4KACAeJY3PNwcAILZ99NFHkidPHod9xYsXd3heunRp6d+/f7jXZsuWzfxs06aNCeqbNm2S6tWrhztPQ/a2bdukR48ekjTp//5X2qpVK6lfv76EhITIzZs3ZdeuXSYgTp06VebMmSNvvfXWM9v/22+/yYsvvijDhw+P1ud2FRq6Dx06lChGAGjoHjlypPliQ3/nAACuidANAHAq9erVk/Llyz/1nOzZs8vbb78d6fHWrVvLoEGDTMCLKHQvXrzY9IJrOA+rbNmy4a575swZee2118TX11eKFCkipUqVemrbrly5IkWLFpXYoj3x+iVAsmTJYu2aAAAg6hheDgDAE3LmzGnC9vLly+XRo0fhjmsYz5cvn1SqVOmZ18qdO7cZLv7w4UOZMGFCpOf5+/uboemnT5+WVatW2Yeqa6+6LYx37NjRDGn39PQ04X3+/PkO17ANc//0009ND7u2MXny5PLXX39F+bOHvcaXX35pv0aFChVMz72NHtfz9EuFJ+kXFhrytbffZseOHWb4ftq0acXLy0tefvll2bJli8Pr7t69a3qwtWdY3zNz5sxSu3Zt2bt3rzn+yiuvmHuj72m7P3pu2Pu3bNky07usX6ykTp1a3njjDbl9+7Y8ePDAXFuvmSpVKmnfvr3Z96SFCxdKuXLlJEWKFPLCCy+Y0Qlnz551OEfboaMn9L6++uqr5vPo+4X989X26D1T+l629urvAgDAtdDTDQBwKhqwrl275rBP51eHpUH6yXNUypQpTdhS2ovdpUsXWbt2rcOc54MHD5rhzcOGDYtymypXrmzC67p16yI9R3vBdQ533759JUeOHPbh75kyZZJ//vnHBL0TJ06YIe06fP67776Td955R27duiW9e/d2uNbXX38t9+/fN+3X8KrhMbr0iwUNwf/5z39MWNRA2axZMzl16pSZs96iRQv54IMPTMh9//33HV6r+7R3P3369PYh8zoCQcOsDptPkiSJaWONGjXMEP6KFSua87p27Wq+6NDPqL39169fl82bN8vhw4fNKIIhQ4aYP99z587J5MmTzWs0QIc1duxY82eo0wP0fk2bNs20V99TvwTQOdbbt2834VfvY9g/x9GjR8vQoUPNZ9NidlevXjWv1y9g/vjjD0mXLp39XL2Wfomg90TP13YPGDBASpQoYT6r/nnqVAe9vv45VKtWzbxO5/kDAFxMKAAATuDrr7/WqmcRbmHlzp070vPGjh1rP+/GjRuhyZMnD23VqpXD6wcOHGjOPXr0qH3f6dOnzb5PPvkk0vY1btzYnHP79u2nfg5tX4MGDRz2TZkyxbx24cKF9n0PHz4MrVy5cmiqVKlC79y549CONGnShF65cuWZ9yyi97NdI0OGDOYe2Pz8889m/8qVK+379P3LlSvncL2dO3ea8xYsWGCeh4SEhBYoUCC0Tp065rFNUFBQaJ48eUJr165t35c2bdrQd99996nt1bZqm5+0YcMG877Fixc398ZG//zc3NxC69Wr53C+tj3sdQICAkLd3d1DR48e7XDewYMHQ5MmTeqw/+WXX3b4jOrBgweh3t7eoc2bN7fv27VrlzlPfzcBAK6L4eUAAKcyffp006McdnuSDgt/8hzdtBCajfbSalG0FStWyL1798w+nce9ZMkSM2e8YMGC0WqXrUdWe4+ja/Xq1WYJsbDt097bXr16mSXGNm7c6HB+8+bNTQ/582jZsqW9p1rZemq1pzvsOXv27JGTJ0/a9y1dutT0rjdu3Ng818rdx48fN/PktedaRxjopve0Zs2a8vvvv5s550p7knUYuhYgi6l27dqZexP2z1r/3Dp06OBwnu7XYeM651398MMPph3aa21ro2563wsUKCAbNmwI9+cZdv6+DqfXHvuw9wcAAMXwcgCAU9Hg86xCajrcvFatWs+8lg4x//HHH+Xnn382oXHr1q1mzvOTw7mjQsOx0nnG0aVzmDX46RDpsHQIs+14WE9Wb48JXTItLFsADztP+80335R+/fqZoK1Lo2m41WHvOrw6TZo05hwN3EoLyUVGh4zr9XUIu56nc+p1KLp+6aEhOm/evDFut84hV3rNJ/dryNb3zpAhg2mntl/vc0TCBnmlUwB02H1Y+hkOHDgQ5bYCAFwDoRsAgEjoXG4NZzq/WUO3/nR3d4/S0l9P0nngWsTLFkatZJuX/jz0c0Yk7NrlusSa9oDrHG4N3TpXWtckHz9+vP0cWy/2J598EumyWbZRANrLrNfTLzp+/fVX8xq9lvZCa5B/nnY/6/NoOzVE//LLLxGe++Tc8ajcHwAAFKEbAIBI6DBprX69YMECuXz5sunF1eJfOuQ4OnRNbx2C/bRlyp5VAV17UDUYhu3tPnLkiP14fNEh5t27d5ejR4+aHm+t5N2wYUP7cS0gp/TLhqiMLsiaNau5nm5asV0LqGmBM1vofrJ3ObZoOzUw6yiB6E4diIxVbQUAJC7M6QYA4BlDzLXauVbx1mrWT67N/Sw69FurjOuc3yerfEeVDrO+dOmSCbU2OhdZK2trD6wuvxVfdP649vrq2uX6pYSODtAq8DY6TFwDrS4xZhtiH5beUxUcHGyGeoelIwO0Nz3s0l567SfPiw1ahVw/hy439mRvtT7X+ejRZbsPWmEeAOC66OkGALic8+fPm/WYn6QBtkmTJg77NNDq/F2d163DtjWcRUbXk9brao+0Bi1d1/r77783PZ66HFjJkiVj1F5dcuqLL74w4V0Ll+na1LpEla5zretxx2SeeGzRYKxrVU+aNMkUidOe77C0Z3727Nmmp7pYsWJmzWpd01r/DLQ4mfaAr1y50rxW77OOLNA1yPXPYv369eYeTpw40SHE65cPOpdc18HW88L2rMeUfjHw8ccfmzXGdd6+/h7ofdV103W4u/4ZvPfee9G+phaHmzVrlrmWhnAt4BYbc+4BAIkHoRsA4HK0onbbtm3D7ddh2k+Gbg2NWjVc5xdruHtawNXeXt2SJk1qwqQW5erTp49Zf/rJAl/RoWHf39/frD09f/58uXPnjhQqVMisda1BPL5p0NaArPdGe+WfpGuM6xD7UaNGyeeff256vHWIvgZQHUGgdFi6DinXudy2SuL58+eXGTNmSLdu3ezX0nP0z08/u67VrX9msRG6ld5fHVqu19Ueb1sBNl1zvFGjRtG+nhZf0z8vDfL6O6CjE7TdhG4AcC1uum5YfDcCAAAAAABnxJxuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIi63Treu+3nhwgWzlqibm1t8NwcAAAAAkAjp6tt3796VbNmySZIkkfdnu1zo1sCdM2fO+G4GAAAAAMAJnD17VnLkyBHpcZcL3drDbbsxadKkie/mAAAAAAASoTt37pgOXVvGjIzLhW7bkHIN3IRuAAAAAMDzeNa0ZQqpAQAAAABgEUI3AAAAAAAWIXQDAAAAAGARl5vTDQAAACD6goOD5dGjR/HdDCDOeHh4iLu7+3Nfh9ANAAAA4KlrEV+6dElu3boV300B4ly6dOnE29v7mcXSnobQDQAAACBStsCdOXNm8fLyeq7wASSmL5uCgoLkypUr5nnWrFljfC1CNwAAAIBIh5TbAneGDBniuzlAnEqRIoX5qcFb/xuI6VDzeC2k9vvvv0vDhg0lW7Zs5huzn3766Zmv8ff3l7Jly0ry5Mklf/78Mm/evDhpKwAAAOBqbHO4tYcbcEVe//+7/zz1DOI1dN+7d09KlSol06dPj9L5p0+flgYNGsirr74q+/btkz59+kinTp1k7dq1lrcVAAAAcFUMKYercouF3/14HV5er149s0XVrFmzJE+ePDJx4kTzvEiRIrJ582aZPHmy1KlTx8KWAgAAAHDGQPXjjz9KkyZNLLl+QECAyS9//PGHlC5d2pL3QMKXqNbp3rZtm9SqVcthn4Zt3Q8AAAAANlevXpVu3bpJrly5zNRUrUCt2WHLli2SkL3zzjux+iXA999/LzVq1JD06dObOcqFChWSDh06mC8CbHTKrn4BoZvOW9ZzK1WqJB999JHcvn07XPts5yZLlsxM+dXzHj9+HGttdjZJE1vlxCxZsjjs0+d37tyRf/75xz7RPawHDx6YzUbPBQAAAPB8fAauitP3CxjXIFrnN2/eXB4+fCjz58+XvHnzyuXLl8XPz0+uX78urmLAgAFmlHCvXr1k5MiRkjt3bvNlxC+//CKDBg2SNWvW2M9NkyaNHD161FTt1uJ5W7dulbFjx8rXX39tvqjQOlw2devWNfs1Z61evVreffdds6a1XhOJPHTHhP6i6C9YnBmRVhKlEY7fYCUa3O+4xf2OW9zvuMX9jlvc77jDvY5b3O+4deF/vbVh3bp9VzZt2iT+y7+SlwulE5EbkjuHh1T0rRvudddO7JWm9T6Ttf7bJLt3Jpk4vJ80eu1l+/GN2/bI+x9Pkf1/HZMX0qUV3zdfl48/6C5Jk/4bpUJCQuTTWQvky29/kLMXLkuWjBnkP283kyG9O4lcvvDvRa4cEbkQaqrBd35/lGzdd1R+/fVX0wsf1ogRI8yXBGHnEm/YsEFeeeUVOXjwoPTu3duM8tXiXvqlwqRJkyRVqlQR3oPt27fLhAkTZOrUqSZ02+h7litXzoTrsPT9dDSAbXksncqrRa+LFSsmH3zwgSxcuPDf+xZ0Q5KH3hfvkIsiHiLdGr8oPy6pJCuWL5ZBtvub0GQrE69vn6iGl+svgX5DFZY+129lIurlVvptiw6JsG1nz56No9YCAAAAiA+pUqaQVCm95Kc1G+TBg4dPPXfkpC+lRcPacmD9Eqlf8yVp02OI3Lj575cQ5y9ekfpte0qFUkVl/7olMnPsIJmz+Cf5eOps++sHjZ0m46bPk6G9O8tfG5bLoumjJUum8MuraTve/M8Hsu/PY+YLgScDt3rvvfekRYsWpif54sWLZqtSpYopQK1D43XY965du+S7776T9evXS48ePSL9XIsXLzaBvHv37jEuEKbLZLVp00ZWrFhhvjCITArP5PLwOap7O7tEFborV65shoSEtW7dOrM/Mjp/Q0N52A0AAACA89Je6HmTR8r85f+VdEVflqqN28vgsdPkwF/Hwp37TouG0qpJXcmfJ5eMGdhD/r4XJDv3HTLHZsxfJjmzecvnowdK4fx5pEndV2Vk/64y8YuFpof77t/3ZOqcxTJhSG/xbdFQ8vnklJcqlpFOrZs6vMffQUHSoF0vuXr9pmz47gvJlClThO3WkKydibY56LrpvOlFixbJ/fv3ZcGCBVK8eHEzR/vzzz+Xb775JlynpM2xY8fMsHpbj7yy9Yzbtifna0ekcOHCcvfu3QiH5Wtv+frfd8jajdukRtUKz7yWq4rX0P3333+bpb90sy0Jpo8DAwPtvdTt2rWzn9+1a1c5deqUGd5w5MgRmTFjhixbtkz69u0bb58BAAAAQMLTvEFNubBnraz4erLUfaWK+G/bI2XrtpF5S1c4nFeySAH745ReKSRN6lRy5dpN8/zwidNSuVwJh17hqhVKm2B+7uJlOXz8tOnBrvlSxae2pVX3wXIv6B/5ddEMSZsmdbQ/y+HDh81SyylTpvxfO6pWNcFf52FHlRZQ07z1xRdfmN7zJ4eYR8R2Tth78N/1myRVgarimfdFqde2p7Rs+JqM6N812p/LVcRr6N69e7eUKVPGbKpfv37m8bBhw8xzHU5hC+BKy+2vWrXK9G7rL50WBZg9ezbLhQEAAAAIx9MzudSu/qIM7dtZtq6YZ3q1h0+c5XCOh4djmSvNlhpmo0KHVUdF/RpV5cDh47JtzwGJKwUKFDAdlo/CDPtOly6dqTaePXv2aAV+HS2cIcP/hsy/WqW87Pt1sRzf/JP8c3KrzJ/6kfnCAgkwdGtBAP3m5MlNS9Yr/env7x/uNVreXivlnTx50pSsBwAAAIBnKVogr9wLuh/l84vkzyPb9hx06BHesmufpE6VUnJkzSIF8uSSFJ6e4rd551Ov063dmzJucC9p1L6vKcz2NDqc/Mn501rUbP/+/aZ32t6OLVskSZIkZgmwiLRq1cqMLNbRwTF15coVM7RdlzDT97LRgK3D8XNlz+owfB1OMKcbAAAAAJ7l+o1bUuPNLrLw+1VmHvfpwPPy3cp1MmHmfGlc53+VyZ+lu28LOXvhkvT8cLwcOXFafl7rb3rK+3VpY0Ko9qQPeNdXPhg9VRZ89185GXBWtu85YIqtPalnh7dM1fPXfXvL5s2bI31PHx8fOXDggBk2fu3aNdNTrcXMPD09xdfXVw4dOmQqmvfs2VPatm0bbkllG6171b9/f7PpiGJ9zzNnzpiq5nPmzDHDxcMGaf1iQZdo1tHG2rs9d+5cU8Qtbdq0Mm7cuCjfM4TH1xIAAAAAnIpWLq9UtoRM/upbOXnmnDx69NgUROvcuqkM7tkhytfJnjWzrP5mmlkyrFTtt8ySYR1bNZEPdTmw/ze0T2dJ6u4uwz6dKRcuX5WsmTNK17ZvRHi9Pp3bSEhIqNSvX9+ska2h9kmdO3c2o33Lly9veqptS4atXbvWLBlWoUIFhyXDnubTTz+VihUrysyZM02IDgoKMiG9evXqZumxsEWm79y5Y5YK0zCu+7UHXUO+vifFqJ+PW2hUZs87Ef1l0m9rtFKfJb88rM0Yt7jfcYv7Hbe433GL+x23uN9xh3sdt5zsfmvFbC12rLWVtKc1sazTneDF87rRMeaC9/v+U/4biGq2ZHg5AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAABwSW7Zy8pPazZYdv2AsxfMe+w7dNSy90DCR+gGAAAA4HSuXr8p3QaOkVwV6kvyPJXEu3RtqdO6u2zZtU8SsnfeeUeaNGny3NcJCAgQNzc32bcv4Xzed/oMlyYd+klCMW/ePEmXLp3l75PU8ncAAAAA4HxGpI3j97sdrdObd35PHj58JPOnjJS8ubPL5as3xG/zTrl+M3rXAZ4XPd0AAAAAnMqt23dl044/ZPyQ3vJq1QqSO0c2qVimuAzq2UEavfayw7nXbtySph37i1e+KlKgamNZ8etGh+Mbt+2Rig3amt7yrGVek4FjPpPHjx/bj4eEhMiEGfMkf9VG5hztWR89dXaE7QoODpYO/UZI4cKFJTAwMNzxESNGyPz58+Xnn382vdS6+fv7m2MHDx6UGjVqSIoUKSRDhgzSpUsX+fvvv6N8T/Q6ej0/Pz8pX768eHl5SZUqVeTo0X+Hvh87dswcP3LkiMPrJk+eLPny5bM/P3TkhNR7u4ekKlBVspSqJW17fijXbty0H1/+3/VSomYLSZGvsmQo9qrUatlV7gX9IyMmzpL5362Un9f6myH3uvlv3W0fgr9sxa9SrWkH87oK9d+WYyfPyK59f0r5em3Me+l76uiFsGYv+lGKvNxMPPO+KIWrN5MZ85bZj9mu+8NqP3n11VfN5y1VqpRs27bNfj/at28vt2/ftt9rvf9WIHQDAAAAcCqpUqaQVCm9zHztBw8ePvXckZO+lBYNa8uB9Uukfs2XpE2PIXLj/3vDz1+8IvXb9pQKpYrK/nVLZObYQTJn8U/ycZhQPWjsNBk3fZ4M7d1Z/tqwXBZNHy1ZMmUI9z7ajjf/84Hs+/OYbNq0SXLlyhXunPfee09atGghdevWlYsXL5pNg/G9e/ekTp06kj59etm1a5d89913sn79eunRo0e0782QIUNk4sSJsnv3bkmaNKl06NDB7C9YsKAJ499++63D+fq8devW9i8zarT4j5QpVkh2/7JQ1nz7uVy+dkNa/GeAOX7x8lVp9e5g6dCykRz2/178l38pzerVkNDQUHmvaztzn+u+WkUu/vGr2aqUL2V/n+ETZ8mHvTvJ3jXfSlJ3d2ndY7B88PFUmfrR+7Lpxzly4vRZGfbJzP+164fVMuzTmTJ6wLvmvcYMfFeGfjJT5i9b6fh5x08391WH2etnbNWqlfnSRO/rlClTJE2aNPZ7redZgeHlAAAAAJyKhsl5k0dK5w9GyayF30vZ4oXl5RfLyluN60jJogUdzn2nRUNp1aSueTxmYA/5bM5i2bnvkNR9tarMmL9Mcmbzls9HDzQ9oYXz55ELl67KgDGfybC+XUwP7tQ5i+XzjweIb4uG5hr5fHLKSxXLOLzH30FB0qBdL3nw8KFs+O4LSZspU4TtTpUqlenJfvDggXh7e9v3a+/3/fv3ZcGCBZIyZUqz7/PPP5eGDRvK+PHjJUuWLFG+N6NHj5aXX/63t3/gwIHSoEEDc21PT09p06aNue6oUaPsvd979uyRhQsX/vueXy+VMsULyZhBPe3XmztxuOSsUM/0TOvn1EDbrH4NM7pAlShSwH5uCk9PefDwkXhnzhiuXRrK67xSxTzu3am1tOo+SPyWzpKqFUqbfR1bNZF5YQK1hvSJw/pJs/o1zfM8ubLLX8dOyxcLv7f/Wdiuq59RjRw5UooVKyYnTpwwow3Spk1r/lzD3msr0NMNAAAAwOk0b1BTLuxZKyu+nix1X6ki/tv2SNm6bWTe0hUO55UMEwpTeqWQNKlTyZVr/w5jPnzitFQuV8IEMxsNgX/fC5JzFy/L4eOnTQ92zZcqPrUtrboPNgH910UzJG2a1NH+LIcPHzZDo22B27SjalUztN02PDyqSpYsaX+cNWtW8/PKlSvm51tvvWUKsG3fvt3ey122bFkTUNX+v47Jhq27zXBv21b45Wbm2Mkz56RU0YLmXpSo2VLe7PKBfPXtD3Lz1p2otSvMn0OWjC+EC+xZMr0gV67fMI/1Xp4MOCcd+3/k0JaPP5tt2hHZdZ/8vHGFnm4AAAAATsnTM7nUrv6i2Yb27Syd3vvI9JC+07KR/RwPD8dIpPlaw2xUpPBMHqXz6teoKgt/WC3b9hyQGs8I6Fbz8PCwP7Z9mWD7vNrjq/PGFy1aJC+++KL52a1bN/v52pPdsHZ1GT+4V7jrZs2SSdzd3WXdkpmydfd++XXjNpn29RIzvHvHfxeYnuintitp0nDtctgnbvZ26pce6qtPPpRKZYo7XEfb8KzrRvXPN7bQ0w0AAADAJRQtkFfuBd2P8vlF8ueRbXsOmjnJNrrkWOpUKSVH1ixSIE8uM2Raq6I/Tbd2b8q4wb2kUfu+pjDb0yRLlswUXHNoR5Eisn//fjO3296OLVskSZIkUqhQIYlNOsR86dKlpuDYqVOnTO+3jQ7T//PoKfHJmU3y58nlsOkoAVuw1dEAI9/rJn+sXSzJPDzkx1/+XQs9WbKk4T5bTOic+WzemeTUmfPh2vGscP+se20FQjcAAAAAp3L9xi2p8WYXWfj9Kjnw1zE5HXhevlu5TibMnC+N6zhWL3+a7r4t5OyFS9Lzw/Fy5MRpU3lbe8r7dWljAq/2pA9411c+GD1VFnz3XzkZcFa27zlgiq09qWeHt+TjD7rL6769ZfPmzZG+p4+Pjxw4cMAMG7927Zo8evTIBGGdc+3r6yuHDh2SDRs2SM+ePaVt27bRms8dFc2aNZO7d++aHm6t+p0t279zs9W777SUG7dum+HyWllcP+9a/63Svu9wE1537D0oYz6bI7v3/yWB5y/KD6t/k6s3bkqRAnn+/Ww5ssmBw8fl6IkAU/FcP1tMjezfVcZ+/rWZg6/zyQ8ePi5fL/1ZJn3x7/zzqNB7rRXgtaK73uugoH970GMbw8sBAAAAOBWtXF6pbAmZ/NW3Zo7vo0ePTUG0zq2byuCe/1brjorsWTPL6m+myfsfT5FStd+SF9KlNQW9tMq2zdA+nU21ba2kfeHyVcmaOaN0bftGhNfr07mNhISESv369WXNmjWmgvaTOnfubJaz0kriGgg1YL/yyiuydu1a6d27t1SoUMEsf9W8eXOZNGmSxLbUqVObAm3Lli2TuXPnOhzT3uUtP30tA8ZMlddad5cHDx5J7hzeZs68fgmRJnVK+X3HXpkye5Hc+fue5M6eVSYO6yv1alT997O1aWbm1pev/7YZIr7huy9Nr3lMdGrdVLxSeMonMxeYPx/taS9ROL/06fRvpfWo0PvftWtXadmypVy/fl2GDx9uybJhbqFhx0q4gDt37pgqdboem5aHj3Uj0kqiNOLfZRESHe533OJ+xy3ud9zifsct7nfc4V7HLSe731rV+vTp05InTx7T05rgXPhDEqVsjtXNEw0XvN/3n/LfQFSzJcPLAQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAMBTudiCR0Cs/u6zTjcAAACACHl4eJifQUFBkiJFivhuDuLZgZA8khiVfI7X6u9+2P8WYoLQDQAAACBC7u7uki5dOrly5Yp57uXlJW5ubpJgPE6kPfD370tiFPr4oSRG92Nwv7WHWwO3/u7rfwP630JMEboBAAAARMrb29v8tAXvBOXWVUmU7p2WxOjKzX8kMUr2T8xHaWjgtv03EFOEbgAAAACR0p7trFmzSubMmeXRo0eSkNSceEYSI7/+iXOYdqcf/CUx8uv/Soxep0PKn6eH24bQDQAAAOCZNHzERgCJTefvBkti5OnpKYkR9ztmqF4OAAAAAIBFCN0AAAAAAFiE4eUAAABALPG5v0gSo4D4bgDgxOjpBgAAAADAIoRuAAAAAAAsQugGAAAAAMAizOkGAABwYswxBoD4RU83AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFqF6OQAAiHNU1AYAuAp6ugEAAAAAsAihGwAAAAAAZw3d06dPFx8fH/H09JRKlSrJzp07n3r+lClTpFChQpIiRQrJmTOn9O3bV+7fvx9n7QUAAAAAIFGE7qVLl0q/fv1k+PDhsnfvXilVqpTUqVNHrly5EuH5ixYtkoEDB5rzDx8+LHPmzDHXGDx4cJy3HQAAAACABB26J02aJJ07d5b27dtL0aJFZdasWeLl5SVz586N8PytW7dK1apVpXXr1qZ3/LXXXpNWrVo9s3ccAAAAAACXCt0PHz6UPXv2SK1atf7XmCRJzPNt27ZF+JoqVaqY19hC9qlTp2T16tVSv379SN/nwYMHcufOHYcNAAAAAACnXjLs2rVrEhwcLFmyZHHYr8+PHDkS4Wu0h1tf99JLL0loaKg8fvxYunbt+tTh5WPHjpWRI0fGevsBAAAAALAkdIeEhMiJEyfM3Gt9HFb16tXFKv7+/jJmzBiZMWOGKbqmbejdu7eMGjVKhg4dGuFrBg0aZOaN22hPtxZgAwAAAAAgwYXu7du3mx7nM2fOmN7msNzc3EzvdVRkzJhR3N3d5fLlyw779bm3t3eEr9Fg3bZtW+nUqZN5XqJECbl375506dJFhgwZYoanPyl58uRmAwDgaXzuL5LEKCC+GwAAAGJ3TrcO5y5fvrwcOnRIbty4ITdv3rRv+jyqkiVLJuXKlRM/Pz/7Pu011+eVK1eO8DVBQUHhgrUGd/XkFwAAAAAAACS6nu7jx4/L8uXLJX/+/M/95jrs29fX14T4ihUrmjW4tedaq5mrdu3aSfbs2c28bNWwYUNT8bxMmTL24eXa+637beEbAAAAAIBEG7ptYTc2QnfLli3l6tWrMmzYMLl06ZKULl1a1qxZYy+uFhgY6NCz/eGHH5oh7Prz/PnzkilTJhO4R48e/dxtAQAAAAAg3kN3z549pX///iYk65xqDw8Ph+MlS5aM1vV69OhhtsgKpzk0NmlSGT58uNkAAAAAAHC60N28eXPzs0OHDvZ92vusc6qjU0gNAAAAAABnF+3Qffr0aWtaAgAAAACAq4fu3LlzW9MSAAAAAABcPXSrkydPmkrjhw8fNs+LFi0qvXv3lnz58sV2+wAAAAAAcJ11uteuXWtC9s6dO03RNN127NghxYoVk3Xr1lnTSgAAAAAAXKGne+DAgdK3b18ZN25cuP0DBgyQ2rVrx2b7AAAAAABwndCtQ8qXLVsWbr9WM9ch5wCA2OFzf5EkRgHx3QAAAIDEPLw8U6ZMsm/fvnD7dV/mzJljq10AAAAAALheT3fnzp2lS5cucurUKalSpYrZt2XLFhk/frz069fPijYCAAAAAOAaoXvo0KGSOnVqmThxogwaNMjsy5Ytm4wYMUJ69eplRRsBAAAAAHCN0O3m5mYKqel29+5ds09DOAAAAAAAiIV1um0I2wAAAAAAPGfoLlu2rPj5+Un69OmlTJkyprc7Mnv37o3KJQEAAAAAcHpRCt2NGzeW5MmT2x8/LXQDAAAAAIBohO7hw4fbH2vBNAAAAAAAYME63Xnz5pXr16+H23/r1i1zDAAAAAAAxDB0BwQESHBwcLj9Dx48kHPnzkX3cgAAAAAAOK0oVy9fsWKF/fHatWslbdq09ucawrXQWp48eWK/hQAAAAAAOHvobtKkifmpRdR8fX0djnl4eIiPj49MnDgx9lsIAAAAAICzh+6QkBDzU3uzd+3aJRkzZrSyXQAAAAAAuE7otjl9+rQ1LQEAAAAAwBVD92effSZdunQRT09P8/hpevXqFVttAwAAAADA+UP35MmTpU2bNiZ06+PI6HxvQjcAAAAAANEI3WGHlDO8HAAAAAAAi+Z0P0mXCzt48KDkzp1b0qdP/7yXA5CA+dxfJIlRQHw3AAAAAC4rSXRf0KdPH5kzZ449cFevXl3Kli0rOXPmFH9/fyvaCAAAAACAa4Tu5cuXS6lSpczjlStXSkBAgBw5ckT69u0rQ4YMsaKNAAAAAAC4Rui+du2aeHt7m8erV6+WN998UwoWLCgdOnQww8wBAAAAAEAMQ3eWLFnkr7/+MkPL16xZI7Vr1zb7g4KCxN3dPbqXAwAAAADAaUW7kFr79u2lRYsWkjVrVrNEWK1atcz+HTt2SOHCha1oIwAAAAAArhG6R4wYIcWLF5ezZ8+aoeXJkyc3+7WXe+DAgVa0EQAAAAAA11ky7I033gi3z9fXNzbaAwAAAACA687pVhs3bpSGDRtK/vz5zdaoUSPZtGlT7LcOAAAAAABXCt0LFy4087i9vLykV69eZkuRIoXUrFlTFi1aZE0rAQAAAABwheHlo0ePlgkTJph1uW00eE+aNElGjRolrVu3ju02AgAAAADgGj3dp06dMkPLn6RDzE+fPh1b7QIAAAAAwPVCd86cOcXPzy/c/vXr15tjAAAAAAAghsPL+/fvb4aT79u3T6pUqWL2bdmyRebNmydTp06N7uUAAAAAAHBa0Q7d3bp1E29vb5k4caIsW7bM7CtSpIgsXbpUGjdubEUbAQAAAABwnXW6mzZtajYAAAAAABDLoVvt3r1bDh8+bB4XLVpUypUrF9NLAQAAAADglKIdus+dOyetWrUy87jTpUtn9t26dcvM716yZInkyJHDinYCAAAAAOD81cs7deokjx49Mr3cN27cMJs+DgkJMccAAAAAAEAMe7o3btwoW7dulUKFCtn36eNp06ZJtWrVons5AAAAAACcVozW6dae7icFBwdLtmzZYqtdAAAAAAC4Xuj+5JNPpGfPnqaQmo0+7t27t3z66aex3T4AAAAAAFxnePk777wjQUFBUqlSJUma9N+XP3782Dzu0KGD2Wx0vjcAAAAAAK4q2qF7ypQp1rQEAAAAAABXD92+vr7WtAQAAAAAAFef021z5coVOXTokBw4cMBhi67p06eLj4+PeHp6miHrO3fufOr5uib4u+++K1mzZpXkyZNLwYIFZfXq1TH9GAAAAAAAJJye7j179pjebl2bOzQ01OGYm5ubqWIeVUuXLpV+/frJrFmzTODWoet16tSRo0ePSubMmcOd//DhQ6ldu7Y5tnz5csmePbucOXNG0qVLF92PAQAAAABAwgvdWihNe5fnzJkjWbJkMUE7piZNmiSdO3eW9u3bm+cavletWiVz586VgQMHhjtf92txNl0n3MPDw+zTXnIAAAAAAJwidJ86dUq+//57yZ8//3O9sfZaa6/5oEGD7PuSJEkitWrVkm3btkX4mhUrVkjlypXN8PKff/5ZMmXKJK1bt5YBAwaIu7v7c7UHAAAAAIB4D901a9aU/fv3P3fovnbtmhmKrr3lYenzI0eORBr4f/vtN2nTpo2Zx33ixAnp3r27PHr0SIYPHx7hax48eGA2mzt37jxXuwEAAAAAsCx0z54928zp1iJqxYsXtw/ztmnUqJFYJSQkxMzn/vLLL03Pdrly5eT8+fPyySefRBq6x44dKyNHjrSsTQAAAAAAxFro1qHfW7ZskV9++SXcsegUUsuYMaMJzpcvX3bYr8+9vb0jfI1WLNeQH3YoeZEiReTSpUtmuHqyZMnCvUaHr2uxtrA93Tlz5oxSG5Hw+dxfJIlRQHw3AAAAAEDCXDKsZ8+e8vbbb8vFixdNz3PYLTqVyzUga0+1n5+ffZ9eQ5/rvO2IVK1a1Qwp1/Nsjh07ZsJ4RIFb6bJiadKkcdgAAAAAAEiQofv69evSt2/fcHOxY0J7oL/66iuZP3++WYKsW7ducu/ePXs183bt2jkUWtPjWr28d+/eJmxrpfMxY8aYwmoAAAAAACT64eXNmjWTDRs2SL58+Z77zVu2bClXr16VYcOGmSHipUuXljVr1tgDfWBgoKlobqPDwteuXWtCf8mSJc063RrAtXo5AAAAAACJPnTrGt3a+7x582YpUaJEuEJqvXr1itb1evToYbaI+Pv7h9unQ8+3b98ezVYDAAAAAJBIqpenSpVKNm7caLYnC6lFN3QDAAAAAOCsoh26T58+bU1LAAAAAABw9UJqYYWGhpoNAAAAAADEUuhesGCBmc+dIkUKs2lRs2+++SYmlwIAAAAAwGlFe3j5pEmTZOjQoab4ma6brbSoWteuXeXatWumsjgAAAAAAIhB6J42bZrMnDnTrKFt06hRIylWrJiMGDGC0A0AAAAAQEyHl1+8eFGqVKkSbr/u02MAAAAAACCGoTt//vyybNmycPuXLl0qBQoUiO7lAAAAAABwWtEeXj5y5Ehp2bKl/P777/Y53Vu2bBE/P78IwzgAAAAAAK4q2j3dzZs3lx07dkjGjBnlp59+Mps+3rlzpzRt2tSaVgIAAAAA4Ao93apcuXKycOHC2G8NAAAAAACu3NO9evVqWbt2bbj9uu+XX36JrXYBAAAAAOB6oXvgwIESHBwcbn9oaKg5BgAAAAAAYhi6jx8/LkWLFg23v3DhwnLixInoXg4AAAAAAKcV7dCdNm1aOXXqVLj9GrhTpkwZW+0CAAAAAMD1Qnfjxo2lT58+cvLkSYfA3b9/f2nUqFFstw8AAAAAANcJ3RMmTDA92jqcPE+ePGYrUqSIZMiQQT799FNrWgkAAAAAgCssGabDy7du3Srr1q2T/fv3S4oUKaRkyZJSvXp1a1oIAAAAAIArrdPt5uYmr732mgnayZMnN88BAAAAAMBzDi8PCQmRUaNGSfbs2SVVqlRy+vRps3/o0KEyZ86c6F4OAAAAAACnFe3Q/fHHH8u8efPM3O5kyZLZ9xcvXlxmz54d2+0DAAAAAMB1QveCBQvkyy+/lDZt2oi7u7t9f6lSpeTIkSOx3T4AAAAAAFwndJ8/f17y588f4bDzR48exVa7AAAAAABwvdBdtGhR2bRpU7j9y5cvlzJlysRWuwAAAAAAcL3q5cOGDRNfX1/T46292z/88IMcPXrUDDv/73//a00rAQAAAABwhZ7uxo0by8qVK2X9+vWSMmVKE8IPHz5s9tWuXduaVgIAAAAA4CrrdFerVk3WrVsX+60BAAAAAMCVQ3doaKjs2bNHAgICxM3NTfLmzSulS5c2jwEAAAAAQAxD94YNG6Rjx45y5swZE76Vhu08efLI3LlzpXr16tG5HAAAAAAATi3Kc7pPnDghr7/+uvj4+JjiaTqP+6+//pLvvvtOcuTIIfXr15dTp05Z21oAAAAAAJyxp3vKlCny4osvip+fn8P+woULS9OmTaVWrVoyefJkmTZtmhXtBAAAAADAeXu6/f39pU+fPhEe0yHmekyHnwMAAAAAgGiG7sDAQClRokSkx4sXL27megMAAAAAgGiG7r///lu8vLwiPa7HgoKCono5AAAAAACcXrSql2vhtEuXLkV47Nq1a7HVJgAAAAAAXC9016xZ075U2JNzunU/a3UDAAAAABCD0H369OmongoAAAAAAKITunPnzm1tSwAAAAAAcNVCagAAAAAAIHoI3QAAAAAAWITQDQAAAACARQjdAAAAAAAkpND9+PFjWb9+vXzxxRdy9+5ds+/ChQvy999/x3b7AAAAAABwjXW61ZkzZ6Ru3boSGBgoDx48kNq1a0vq1Kll/Pjx5vmsWbOsaSkAAAAAAM7e0927d28pX7683Lx5U1KkSGHf37RpU/Hz84vt9gEAAAAA4Do93Zs2bZKtW7dKsmTJHPb7+PjI+fPnY7NtAAAAAAC4Vk93SEiIBAcHh9t/7tw5M8wcAAAAAADEMHS/9tprMmXKFPtzNzc3U0Bt+PDhUr9+/eheDgAAAAAApxXt4eUTJ06UOnXqSNGiReX+/fvSunVrOX78uGTMmFEWL15sTSsBAAAAAHCF0J0jRw7Zv3+/LF261PzUXu6OHTtKmzZtHAqrAQAAAADg6mK0TnfSpElNyJ4wYYLMmDFDOnXq9FyBe/r06aYQm6enp1SqVEl27twZpdctWbLEDG9v0qRJjN8bAAAAAIAEE7rHjh0rc+fODbdf9+la3dGlPeb9+vUzc8L37t0rpUqVMsPXr1y58tTXBQQEyHvvvSfVqlWL9nsCAAAAAJAgQ/cXX3whhQsXDre/WLFiMmvWrGg3YNKkSdK5c2dp3769mSeu1/Dy8oow2Nto9XTtaR85cqTkzZs32u8JAAAAAECCnNN96dIlyZo1a7j9mTJlkosXL0brWg8fPpQ9e/bIoEGD7PuSJEkitWrVkm3btkX6uo8++kgyZ85s5pLruuFP8+DBA7PZ3LlzR6zkc3+RJEYB8d0AAAAAAHBC0e7pzpkzp2zZsiXcft2XLVu2aF3r2rVrptc6S5YsDvv1uYb7iGzevFnmzJkjX331VZSHw6dNm9a+afsBAAAAAEiQPd06FLxPnz7y6NEjqVGjhtnn5+cnH3zwgfTv31+sdPfuXWnbtq0J3LpEWVRoL7rOGQ/b003wBgAAAAAkyND9/vvvy/Xr16V79+5meLjSquMDBgxwGCYeFRqc3d3d5fLlyw779bm3t3e480+ePGkKqDVs2NC+LyQk5N8PkjSpHD16VPLly+fwmuTJk5sNAAAAAIAEH7p1iS6tUj506FA5fPiwWSqsQIECMQq2yZIlk3LlypmectuyXxqi9XmPHj3Cna8F3A4ePOiw78MPPzQ94FOnTqUHGwAAAACQuEO3TapUqaRChQrP3QAd+u3r6yvly5eXihUrypQpU+TevXummrlq166dZM+e3czN1h714sWLO7w+Xbp05ueT+wEAAAAASHShWwPxuHHjTG+0rqVtG95tc+rUqWhdr2XLlnL16lUZNmyYKZ5WunRpWbNmjb24WmBgoKloDgAAAACA04fuTp06ycaNG01BM106TIebPy8dSh7RcHLl7+//1NfOmzfvud8fAAAAAIAEEbp/+eUXWbVqlVStWtWSBgEAAAAA4CyiPW47ffr08sILL1jTGgAAAAAAXDl0jxo1ysy/DgoKsqZFAAAAAAC46vDyiRMnmvWytdCZj4+PeHh4OBzfu3dvbLYPAAAAAADXCd229bQBAAAAAEAsh+7hw4dH9yUAAAAAALgkFsAGAAAAACCh9HQHBwfL5MmTZdmyZRIYGCgPHz50OH7jxo3YbB8AAAAAAK7T0z1y5EiZNGmStGzZUm7fvi39+vWTZs2aSZIkSWTEiBHWtBIAAAAAAFcI3d9++6189dVX0r9/f0maNKm0atVKZs+ebZYR2759uzWtBAAAAADAFUL3pUuXpESJEuZxqlSpTG+3ev3112XVqlWx30IAAAAAAFwldOfIkUMuXrxoHufLl09+/fVX83jXrl2SPHny2G8hAAAAAACuErqbNm0qfn5+5nHPnj1l6NChUqBAAWnXrp106NDBijYCAAAAAOAa1cvHjRtnf6zF1HLlyiXbtm0zwbthw4ax3T4AAAAAAFwndD+pcuXKZgMAAAAAADEI3StWrJB69eqJh4eHefw0jRo1isolAQAAAABwelEK3U2aNDFVyzNnzmweR8bNzU2Cg4Njs30AAAAAADh36A4JCYnwMQAAAAAAiKXq5Y8ePZKaNWvK8ePHo/MyAAAAAABcUrRCt87pPnDggHWtAQAAAADAldfpfvvtt2XOnDnWtAYAAAAAAFdeMuzx48cyd+5cWb9+vZQrV05SpkzpcHzSpEmx2T4AAAAAAFwndB86dEjKli1rHh87dixc9XIAAAAAABDD0L1hw4bovgQAAAAAAJcU7TndAAAAAADAop5utXv3blm2bJkEBgbKw4cPHY798MMPMbkkAAAAAABOJ9o93UuWLJEqVarI4cOH5ccffzRrd//555/y22+/Sdq0aa1pJQAAAAAArhC6x4wZI5MnT5aVK1dKsmTJZOrUqXLkyBFp0aKF5MqVy5pWAgAAAADgCqH75MmT0qBBA/NYQ/e9e/dM1fK+ffvKl19+aUUbAQAAAABwjdCdPn16uXv3rnmcPXt2s4SYunXrlgQFBcV+CwEAAAAAcJVCatWrV5d169ZJiRIl5M0335TevXub+dy6r2bNmta0EgAAAAAAZw7d2qNdvHhx+fzzz+X+/ftm35AhQ8TDw0O2bt0qzZs3lw8//NDKtgIAAAAA4Jyhu2TJklKhQgXp1KmTvPXWW2ZfkiRJZODAgVa2DwAAAAAA55/TvXHjRilWrJj0799fsmbNKr6+vrJp0yZrWwcAAAAAgCuE7mrVqsncuXPl4sWLMm3aNAkICJCXX35ZChYsKOPHj5dLly5Z21IAAAAAAJy9ennKlCmlffv2puf72LFjppja9OnTzRrdjRo1sqaVAAAAAAC4QugOK3/+/DJ48GBTQC116tSyatWq2GsZAAAAAACutmSYze+//26Gm3///femoFqLFi2kY8eOsds6AAAAAABcJXRfuHBB5s2bZ7YTJ05IlSpV5LPPPjOBW4edAwAAAACAGITuevXqyfr16yVjxozSrl076dChgxQqVCiqLwcAAAAAwOVEOXR7eHjI8uXL5fXXXxd3d3drWwUAAAAAgCuF7hUrVljbEgAAAAAAnMxzVS8HAAAAAACRI3QDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAADOHLqnT58uPj4+4unpKZUqVZKdO3dGeu5XX30l1apVk/Tp05utVq1aTz0fAAAAAACXDd1Lly6Vfv36yfDhw2Xv3r1SqlQpqVOnjly5ciXC8/39/aVVq1ayYcMG2bZtm+TMmVNee+01OX/+fJy3HQAAAACABB26J02aJJ07d5b27dtL0aJFZdasWeLl5SVz586N8Pxvv/1WunfvLqVLl5bChQvL7NmzJSQkRPz8/OK87QAAAAAAJNjQ/fDhQ9mzZ48ZIm5vUJIk5rn2YkdFUFCQPHr0SF544YUIjz948EDu3LnjsAEAAAAA4PSh+9q1axIcHCxZsmRx2K/PL126FKVrDBgwQLJly+YQ3MMaO3aspE2b1r7pcHQAAAAAAFxiePnzGDdunCxZskR+/PFHU4QtIoMGDZLbt2/bt7Nnz8Z5OwEAAAAArilpfL55xowZxd3dXS5fvuywX597e3s/9bWffvqpCd3r16+XkiVLRnpe8uTJzQYAAAAAgEv1dCdLlkzKlSvnUATNVhStcuXKkb5uwoQJMmrUKFmzZo2UL18+jloLAAAAAEAi6ulWulyYr6+vCc8VK1aUKVOmyL1790w1c9WuXTvJnj27mZutxo8fL8OGDZNFixaZtb1tc79TpUplNgAAAAAAEop4D90tW7aUq1evmiCtAVqXAtMebFtxtcDAQFPR3GbmzJmm6vkbb7zhcB1d53vEiBFx3n4AAAAAABJs6FY9evQwW0T8/f0dngcEBMRRqwAAAAAAcOHq5QAAAAAAJGSEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwJlD9/Tp08XH5//au/PYKqovgOOntEBZC5SlQkqhrK3se0FFsKYgARsWtaCIQQjKKlsADbsU/gAkIdCwCWGtCiIosjUQBcECFdmRvRjZCUuotNjOL+cmfb9WCr+f8t5MO/1+kpe+mTvv9XIcp3Pm3jlTQwIDA6V169aSnJz81O2//PJLqV+/vtm+YcOGsmXLFtv6CgAAAABAgUm6ExMTZeTIkTJp0iRJSUmRxo0bS0xMjFy/fj3P7X/66SeJi4uT/v37yy+//CKxsbHmdezYMdv7DgAAAABAvk6658yZIwMGDJD33ntPIiMjJSEhQUqWLCnLli3Lc/t58+ZJp06dZMyYMRIRESHTpk2TZs2ayfz5823vOwAAAAAA+TbpzsjIkEOHDkl0dPR/O1SkiFnet29fnp/R9Tm3Vzoy/qTtAQAAAABwSoBjv1lEbt68KZmZmVKlSpVc63X51KlTeX7m6tWreW6v6/OSnp5uXtnu3r1rft67d098ISs9TQoiX8XD14i3vYi3vYi3vYi3vYi3fYi1vYi3vYi3vYh33t9rWZbk26TbDvHx8TJlypTH1oeGhjrSn/wq6DOne1C4EG97EW97EW97EW97EW/7EGt7EW97EW93xfv+/fsSFBSUP5PuihUrir+/v1y7di3Xel0OCQnJ8zO6/p9sP378eFOoLVtWVpbcvn1bgoODxc/PTwoKvYqiFwouX74sZcuWdbo7rke87UW87UW87UW87UW87UOs7UW87UW87XWvgMZbR7g14a5atepTt3M06S5WrJg0b95ckpKSTAXy7KRYl4cMGZLnZ6Kiokz7iBEjPOt27Nhh1uelePHi5pVTuXLlpKDSnbAg7YgFHfG2F/G2F/G2F/G2F/G2D7G2F/G2F/G2V9kCGO+njXDnm+nlOgr97rvvSosWLaRVq1by2WefyYMHD0w1c9W3b1+pVq2amSauhg8fLu3bt5fZs2dLly5dZN26dXLw4EFZtGiRw/8SAAAAAADyWdL95ptvyo0bN2TixImmGFqTJk1k69atnmJpqamppqJ5trZt28qaNWvkk08+kQkTJkidOnVk48aN0qBBAwf/FQAAAAAA5MOkW+lU8idNJ9+9e/dj63r16mVehYlOkZ80adJjU+XhG8TbXsTbXsTbXsTbXsTbPsTaXsTbXsTbXsVdHm8/63/VNwcAAAAAAP/Kf+dtAwAAAAAAryLpBgAAAADAR0i6AQAAAADwEZJuAAB8iNIpAAAUbvmiejked/PmTVm2bJns27fPPEpNhYSEmEem9evXTypVquR0FwEA/wetxPrrr79KRESE010BACBfuHLliixcuFD27Nlj3usjosPDwyU2NtbkOv7+/uImVC/Phw4cOCAxMTFSsmRJiY6O9jyz/Nq1a5KUlCRpaWmybds2adGihdNdLTQuX75sHmOgF0Lw7P788085dOiQVKhQQSIjI3O1PXz4UL744gvp27evY/1zm5MnT8r+/fslKipK6tevL6dOnZJ58+ZJenq6vP3229KxY0enu+gKI0eOzHO9xlrjHBwcbJbnzJljc88KjwcPHpjjx9mzZ+W5556TuLg4T9zxbFJSUqR8+fJSs2ZNs7xy5UpJSEiQ1NRUCQsLM49+feutt5zupmsMHTpU3njjDXnxxRed7kqhMX/+fElOTpbXXnvN7Mu6j8fHx0tWVpZ0795dpk6dKgEBjFd6w8GDB02OU7t2bSlRooQZZOzdu7dkZGSYHEfPDbdu3SplypQR19CkG/lL69atrYEDB1pZWVmPtek6bWvTpo0jfSusDh8+bBUpUsTpbrjC6dOnrbCwMMvPz8/E9KWXXrL++OMPT/vVq1eJtRd9//33VrFixawKFSpYgYGBZrlSpUpWdHS01bFjR8vf399KSkpyupuuoPt0kyZNrJdffjnXS9e3bNnSvO/QoYPT3XSViIgI69atW+Z9amqqVaNGDSsoKMjEW/f5ypUrW+fPn3e6m67QqFEja8eOHeb94sWLrRIlSljDhg2zFi5caI0YMcIqXbq0tXTpUqe76RrZfyPr1KljzZw507py5YrTXXK1adOmWWXKlLF69OhhhYSEmJgHBwdb06dPt2bMmGH+bk6cONHpbrpGu3btrMmTJ3uWV65cafIfdfv2bfO3VI8vbkLSnQ/pifHJkyef2K5tug2855tvvnnqa+7cuSSCXhIbG2t16dLFunHjhnXmzBnzvmbNmtalS5dMO0m3d0VFRVkff/yxeb927VqrfPny1oQJEzzt48aNs1599VUHe+ge8fHxZl/++0WMgIAA6/jx4471y+2JybVr18z7Pn36WG3btrXu3Lljlu/fv28uLsXFxTncS3fQJPvixYvmfdOmTa1Fixblal+9erUVGRnpUO/cuW/v3LnTGj58uFWxYkWraNGiVrdu3azNmzdbmZmZTnfPdWrVqmWtX7/eM9CiF6RXrVrlad+wYYNVu3ZtB3vovuPJuXPnPMu6T+s+rueAavv27VbVqlUtNyHpzof0Sv2KFSue2K5tOlII719R1p9PepEIeoeOPB05ciTX7I1BgwZZ1atXNwdgkm7vKlu2rLm4kf1HTRPAlJQUT/vRo0etKlWqONhDd0lOTrbq1q1rjRo1ysrIyDDrSLrtSbrDw8PNiVpOe/futUJDQx3qnbvoqN/Bgwc9x3FNTHI6e/asOZGG9/dtPZYkJiZaMTExJhnUZEQvnmYf2/HsdN/NvvivNAE8duyYZ1kvOJUsWdKh3rlPWFiYtWfPHs+yznjUfT4tLc0sX7hwwXUDjFQvz4dGjx4tAwcOlOHDh8umTZvk559/Ni99r+sGDRokY8eOdbqbrqL3/m3YsMHct5PXS+9lg/fu5855T5Sfn58ppNG1a1dp3769/Pbbb472z400xkqLlAQGBkpQUJCnTe+Xunv3roO9c5eWLVuaegU3btwwdTeOHTvmiT98Izu+Wg9Cj+U5VatWzfy3wLPr3LmzOVYrPVZ/9dVXudr1Xnq9PxPeV7RoUXN/t97jev78eRkwYICsXr1a6tWr53TXXEOLFZ84ccK8P3PmjGRmZnqW1fHjx6Vy5coO9tBdYmNjTT6j+/SuXbukT58+5rii93er06dPm+O3m1ANIB8aPHiwVKxYUebOnSsLFiww/+MrreLXvHlzWb58uTn4wns0rnqi/Prrrz/xpI6ag96hhby0gMbfKzlrARPVrVs3h3rmTjVq1DAnELVq1TLLWqykevXqnnYtgvT3RAXPpnTp0rJixQpZt26dKRSTfQyHb7zyyivmQt69e/fMiVqDBg08bZcuXaKQmpfMmjVL2rVrZ06M9YLS7NmzZffu3eZYrnHXYo1ff/210910PT1+T5482RR33blzp9PdcQ1N+rSAq54HatFiHdzSQbBbt26Zc8BPP/1Uevbs6XQ3XWP69OmmYrkOuOjfSC30umrVKk+7xlyL2LkJ1cvzuUePHpnHhylNxPVqJ7zvxx9/NFVvO3XqlGe7tmmiqCcbeDZ6ENV4b9myJc/2Dz/80FTE1RkGeHYay9DQUOnSpUue7RMmTJDr16/LkiVLbO9bYfD777+bC3qafJcqVcrp7rjOlClTci23adPGPP0j25gxY8x/g7Vr1zrQO/e5c+eOzJw5UzZv3mxGXPU4rRftNBn/6KOPeKqKF2mVeD3v4KKRPXRf1n1bL0zr43nHjRsniYmJJvnWpwZpcqiDAxzHvevhw4fy119/mYvVbkfSDQAAAACAj3BPNwAAAAAAPkLSDQAAAACAj5B0AwAAAADgIyTdAAAAAAD4CEk3AADw6Nevn3mG6rPQR0npI1+02jUAAIUdSTcAAAU4QdbkVl/FihWT2rVry9SpU80jWP6tefPmyfLly73aTwAACrMApzsAAAD+vU6dOsnnn38u6enpsmXLFhk8eLAULVpUxo8f/4++JzMz0yTvQUFBPusrAACFESPdAAAUYMWLF5eQkBAJCwuTDz74QKKjo2XTpk0mCR89erRUq1ZNSpUqJa1btzbTvrPpaHa5cuXMtpGRkeZ7UlNTH5tert8zbNgwqVy5sgQGBsoLL7wgBw4cyNUHTfbr1q0rJUqUkA4dOsjFixdztV+6dEm6du0q5cuXN315/vnnzWcAACgMSLoBAHARTXwzMjJkyJAhsm/fPlm3bp0cOXJEevXqZUbFz5w549k2LS1NZs2aJUuWLJHjx4+bxPrvxo4dK+vXr5cVK1ZISkqKmcIeExMjt2/fNu2XL1+W7t27m6T68OHD8v7778u4ceNyfYeOvmvy/sMPP8jRo0fN7yxdurQN0QAAwHlMLwcAwAUsy5KkpCTZtm2bxMXFmSnnOnJdtWpV066j3lu3bjXrZ8yYYdY9evRIFixYII0bN87zOx88eCALFy40o+KdO3c26xYvXiw7duyQpUuXypgxY0x7rVq1ZPbs2aa9Xr16nsQ6m/ajR48e0rBhQ7McHh7u83gAAJBfkHQDAFCAffvtt2bUWBPorKws6d27t/Ts2dMkyjrlOycdbQ4ODvYsa/G1Ro0aPfG7z507Z763Xbt2nnV6v3irVq3k5MmTZll/6tT1nKKionIt6/R0nfq+fft2M/1dE/Cn/V4AANyEpBsAgAJM76HW0WZNoHVUOyAgQBITE8Xf318OHTpkfuaUc1q3TkXX4mm+plPOdUr6d999ZxLv+Ph4MzI+dOhQn/9uAACcxj3dAAAUYFqYTO+zrl69ukm4VdOmTU018uvXr5u2nC8tuvb/0mnjmszv3bvXs05HvrWQmhZfUxEREZKcnJzrc/v373/su0JDQ2XQoEGyYcMGGTVqlJmmDgBAYUDSDQCAy+i08j59+kjfvn1NknvhwgWTGOsIs442/5OEXqeF673bej/4iRMnZMCAAaYAW//+/c02mkhrcTbd5vTp07JmzZrHnvM9YsQIc6+59kOLse3atcsk6wAAFAYk3QAAuJAWTNOkW0eVtbiZPgZMR6h1RPyfmDlzprkH+5133pFmzZrJ2bNnTQKtj/9S+n1a3Xzjxo2mIFtCQoKnUFs2HXXXCuaaaGsFdb0ooAXcAAAoDPwsLXcKAAAAAAC8jpFuAAAAAAB8hKQbAAAAAAAfIekGAAAAAMBHSLoBAAAAAPARkm4AAAAAAHyEpBsAAAAAAB8h6QYAAAAAwEdIugEAAAAA8BGSbgAAAAAAfISkGwAAAAAAHyHpBgAAAADAR0i6AQAAAAAQ3/gP37uFQP1XpHUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming irf is already defined and contains the impulse response functions\n",
    "# Compute impulse response functions (IRFs)\n",
    "irf_values = irf.orth_irfs  # Orthogonalized IRFs\n",
    "\n",
    "# Initialize variance decomposition storage\n",
    "num_periods = 10\n",
    "num_vars = df.shape[1]\n",
    "fevd_results = np.zeros((num_periods, num_vars, num_vars))\n",
    "\n",
    "# Compute FEVD manually\n",
    "for i in range(num_periods):\n",
    "    irf_squared = irf_values[:i+1] ** 2  # Square IRFs up to time t\n",
    "    cumulative_variance = np.sum(irf_squared, axis=0)  # Cumulative variance\n",
    "    fevd_results[i] = cumulative_variance / np.sum(cumulative_variance, axis=1, keepdims=True)  # Normalize\n",
    "\n",
    "# Convert to DataFrames for better readability\n",
    "fevd_gdp_df = pd.DataFrame(fevd_results[:, 0, :], columns=['Shock to GDP', 'Shock to Investment'])\n",
    "fevd_investment_df = pd.DataFrame(fevd_results[:, 1, :], columns=['Shock to GDP', 'Shock to Investment'])\n",
    "\n",
    "# Display FEVD results\n",
    "display(fevd_gdp_df)\n",
    "display(fevd_investment_df)\n",
    "\n",
    "# Plot FEVD results as bar charts\n",
    "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
    "\n",
    "# Plot FEVD for GDP\n",
    "fevd_gdp_df.plot(kind='bar', stacked=True, ax=axes[0])\n",
    "axes[0].set_title('FEVD for GDP')\n",
    "axes[0].set_xlabel('Periods')\n",
    "axes[0].set_ylabel('Variance Decomposition')\n",
    "axes[0].legend(loc='upper right')\n",
    "\n",
    "# Plot FEVD for Investment\n",
    "fevd_investment_df.plot(kind='bar', stacked=True, ax=axes[1])\n",
    "axes[1].set_title('FEVD for Investment')\n",
    "axes[1].set_xlabel('Periods')\n",
    "axes[1].set_ylabel('Variance Decomposition')\n",
    "axes[1].legend(loc='upper right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}