{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a2d6511-d449-4265-9c5f-0886e07e94d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Dear Readers,\n",
    "\n",
    "This ipython notebook was used to create the meta-analysis results, meta-regresion, and the funnel plot for publishing bias. \n",
    "I hope this notebook demonstartes the steps clearly, please feel free to contact thl44@cam.ac.uk if anything is unclear.\n",
    "You have full permission to adopt the code for any use.\n",
    "\n",
    "Jason\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "08c0afeb-5762-46e2-a5b1-229d38167d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6016a699-5e28-44ee-88aa-682e8eee6774",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mixed_effect_model(df, speech_condition='Noise', intervention_type='Site_selection'):\n",
    "    '''\n",
    "    ASSERT\n",
    "    \n",
    "    df.columns\n",
    "    Index(['Study', 'Data Source', 'SD Calculation', 'Correlation', 'Changed',\n",
    "           'ENI_Measure', 'Intervention', 'Study Design', 'Adaptation Period',\n",
    "           'Intervention_Group', 'Speech_Stimuli', 'RoB', 'CleanMA', 'Noise_ES',\n",
    "           'Noise_Diff', 'Noise_SD', 'Noise_N', 'Quiet_ES', 'Quiet_Diff',\n",
    "           'Quiet_SD', 'Quiet_N', 'ExtractionNote'],\n",
    "          dtype='object')\n",
    "\n",
    "    params:\n",
    "        speech_condition: str ['Noise' or 'Quiet']\n",
    "        intervention_type: str ['Site Selection' or 'Frequency Allocation'] or None to include all\n",
    "\n",
    "    return:\n",
    "        combined_effect_size, upper_bound, lower_bound, standard_deviation, es_p_value (ES !=, two tailed, Z), Q, q_p_value (Cochran's Q), table\n",
    "\n",
    "    df means dataframe btw\n",
    "    '''\n",
    "    # Identify the Group, select data\n",
    "    if intervention_type:\n",
    "        df = df.loc[df.Intervention_Group == intervention_type, :].reset_index(drop=True)\n",
    "    selected_columns = ['Study', 'Noise_ES', 'Noise_N'] if speech_condition == 'Noise' else ['Study', 'Quiet_ES', 'Quiet_N']\n",
    "    df = df.loc[:, selected_columns]\n",
    "    df = df.dropna().reset_index(drop=True)\n",
    "    df.columns = ['Study', 'ES', 'N']\n",
    "    df['Var_d'] = 2 / df.N + df.ES**2 / (4 * df.N)\n",
    "    # Fixed Effect first to get naive Weights and combined Cohen's d\n",
    "    k = len(df) # number of studies\n",
    "    V_within = df.Var_d.to_numpy()\n",
    "    W_fixed = 1 / V_within\n",
    "    Y = df.ES.to_numpy()\n",
    "    Y_hat = np.sum(Y * W_fixed) / W_fixed.sum()\n",
    "\n",
    "    # Random Effect, calculate tau^2 by DerSimonian & Laird's Method\n",
    "    Q = np.sum(W_fixed * (Y - Y_hat)**2)\n",
    "    degree_of_freedom = k - 1\n",
    "    tau_squared = (Q - degree_of_freedom) / (W_fixed.sum() - np.sum(W_fixed**2) / W_fixed.sum())\n",
    "    V_within_and_between = V_within + tau_squared\n",
    "\n",
    "    W_mixed = 1 / V_within_and_between\n",
    "    combined_effect_size = np.sum(W_mixed * Y) / W_mixed.sum()\n",
    "    standard_deviation = np.sqrt(1 / W_mixed.sum())\n",
    "    upper_bound = combined_effect_size + 1.96 * standard_deviation\n",
    "    lower_bound = combined_effect_size - 1.96 * standard_deviation\n",
    "    Z = combined_effect_size / standard_deviation\n",
    "    es_p_value = 2 * (1 - stats.norm.cdf(abs(Z))) # Two-tailed could perform better or worse\n",
    "    q_p_value = 1 - stats.chi2.cdf(Q, k-1)\n",
    "\n",
    "    print(combined_effect_size, standard_deviation, k)\n",
    "    df.loc[k, :] = [\"Combined Cohen's d\", combined_effect_size, k, standard_deviation**2]\n",
    "    table = df.copy()\n",
    "    table['SE(d)'] = np.sqrt(table.Var_d)\n",
    "    return combined_effect_size, upper_bound, lower_bound, standard_deviation, es_p_value, Q, q_p_value, table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69910c08-7385-466c-9499-a0763d68146d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forest_plot(df, title, row_height = 1.2):\n",
    "    # Create the forest plot\n",
    "    plt.figure(figsize=(8, len(df)*row_height), dpi=500)  # Adjust the figure size according to the number of studies\n",
    "    plt.errorbar(df['Cohen_d'], range(len(df)), xerr=[df['Cohen_d'] - df['CI_low'], df['CI_high'] - df['Cohen_d']],\n",
    "                 fmt='o', color='#1a80dc', ecolor='black', capsize=5, capthick=1, markersize=5)\n",
    "    # Add study labels\n",
    "    plt.yticks(range(len(df)), df['Study'])\n",
    "    \n",
    "    # Add vertical line for no effect (Cohen's d = 0)\n",
    "    plt.axvline(x=0, color='grey', linestyle='--')\n",
    "    \n",
    "    # Highlight the combined result row (last row)\n",
    "    plt.errorbar(df['Cohen_d'].iloc[-1], len(df) - 1, \n",
    "                 xerr=[[df['Cohen_d'].iloc[-1] - df['CI_low'].iloc[-1]], \n",
    "                       [df['CI_high'].iloc[-1] - df['Cohen_d'].iloc[-1]]],\n",
    "                 fmt='D', color='#ff00b4', ecolor='black', capsize=7, capthick=2, markersize=10)\n",
    "\n",
    "    # Add Cohen's d and CIs as text labels\n",
    "    for i in range(len(df)):\n",
    "        d = df['Cohen_d'].iloc[i]\n",
    "        ci_low = df['CI_low'].iloc[i]\n",
    "        ci_high = df['CI_high'].iloc[i]\n",
    "        plt.text(d, i-0.15, f'{d:.2f} [{ci_low:.2f} - {ci_high:.2f}]', \n",
    "                 ha='left', va='center', fontsize=9, color='black', \n",
    "                 bbox=dict(facecolor='white', alpha=0, edgecolor='none', boxstyle='round,pad=0.5'))\n",
    "    \n",
    "    # Titles and labels\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Cohen\\'s d (Effect Size)')\n",
    "    plt.gca().invert_yaxis()  # Invert y-axis to have the combined result at the bottom\n",
    "\n",
    "    # Add grid lines with sub-gridlines\n",
    "    plt.grid(True, which='major', linestyle='--', linewidth=0.5, color='grey')\n",
    "    plt.minorticks_on()  # Enable minor ticks`\n",
    "    plt.grid(True, which='minor', linestyle=':', linewidth=0.5, color='lightgrey')\n",
    "    \n",
    "    # Set the minor and major ticks\n",
    "    plt.gca().xaxis.set_major_locator(plt.MultipleLocator(1))  # Major gridlines every 1 unit\n",
    "    plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(0.5))  # Minor gridlines every 0.5 units\n",
    "    # Display the plot\n",
    "    plt.show()\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d452b235-2c7f-42bb-b8b6-60980cd1b895",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Meta-Analysis & Forest Plot\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6fc0d68-7d96-4ec7-a93b-4474994ddbb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "df = pd.read_excel('C:/Users/Jason Lien/Desktop/BestSystematicReview/SRrawData.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c476605-6051-4d65-8aae-e6ea7ab0450c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Study', 'Data Source', 'SD Calculation', 'Correlation', 'Changed',\n",
       "       'ENI_Measure', 'Intervention', 'Study Design', 'Adaptation Period',\n",
       "       'Intervention_Group', 'Speech_Stimuli', 'RoB', 'CleanMA', 'Noise_ES',\n",
       "       'Noise_Diff', 'Noise_SD', 'Noise_N', 'Quiet_ES', 'Quiet_Diff',\n",
       "       'Quiet_SD', 'Quiet_N', 'ExtractionNote'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66e4ba78-d2ae-4391-995a-bde3b0c13798",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df.CleanMA == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8d3c987-8faf-46fd-a1a8-9a81c3ea7dd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6481822692428025 0.33766366360445377 4\n"
     ]
    }
   ],
   "source": [
    "combined_effect_size, upper_bound, lower_bound, standard_deviation, es_p_value, Q, q_p_value, table = mixed_effect_model(df, 'Quiet', 'Frequency Allocation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ed18b1ff-3f89-4f5f-b35d-59671836c281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.310003049907532,\n",
       " -0.013638511421926847,\n",
       " 0.054907283314417965,\n",
       " 9.293754760963914,\n",
       " 0.025629778701742212)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_bound, lower_bound, es_p_value, Q, q_p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5410cc0-946f-4c95-a771-dffb7fff3d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "table['CI_low'] = table.ES - 1.96 * table['SE(d)']\n",
    "table['CI_high'] = table.ES + 1.96 * table['SE(d)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16fa526a-b691-4381-8bd8-018b0a3dab02",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = table.rename({'ES': 'Cohen_d'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f3ef55c5-f46c-4de0-84fe-958c8098588a",
   "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>Study</th>\n",
       "      <th>Cohen_d</th>\n",
       "      <th>N</th>\n",
       "      <th>Var_d</th>\n",
       "      <th>SE(d)</th>\n",
       "      <th>CI_low</th>\n",
       "      <th>CI_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jiam et al. (2019)</td>\n",
       "      <td>0.22</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dillon et al. (2023)</td>\n",
       "      <td>0.18</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.47</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fan et al. (2023)</td>\n",
       "      <td>1.51</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kurz et al. (2023)</td>\n",
       "      <td>0.55</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Combined Cohen's d</td>\n",
       "      <td>0.65</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Study  Cohen_d     N  Var_d  SE(d)  CI_low  CI_high\n",
       "0    Jiam et al. (2019)     0.22  16.0   0.13   0.35   -0.47     0.92\n",
       "1  Dillon et al. (2023)     0.18   9.0   0.22   0.47   -0.74     1.11\n",
       "2     Fan et al. (2023)     1.51  24.0   0.11   0.33    0.87     2.15\n",
       "3    Kurz et al. (2023)     0.55  13.0   0.16   0.40   -0.24     1.33\n",
       "4    Combined Cohen's d     0.65   4.0   0.11   0.34   -0.01     1.31"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d89965a2-9c0d-4dfb-8dd7-a8cea4603694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum N except last (last N is number of studies)\n",
    "table.N.sum() - table.at[len(table)-1, 'N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d0e78688-d288-4f74-b04c-f66975f84e23",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 4000x4000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_plot(table, 'FTPM Reduction, Speech in Quiet', 1.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39eff69d-dcd9-49fe-8d17-1b3c281b09ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Relationship Between Noisy & Quiet Conditions\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4fddfd68-9287-45ef-8cdd-99cdb0a81489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3385493288387479 0.2888189039623953\n",
      "without 0.00011576687887354712 0.8877805327615818\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fcbcff9dc0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fJjQ0lDFjxlCtWjUsLS11lq9Dhw4sWLAAPz8/Xr9+zfTp0+M9Vtd7/jVxbN68Wf0bkSNHDjQajV6HoH6LJJkL8uTJQ9euXZk0aRKFCxdm+/btTJ06lTx58lC4cGFmzpwZo9d2tHHjxnHx4kWyZ89Os2bNaNu2bYz9o0ePZvLkyZiZmTFr1qxY569fvx5vb28KFixImzZtmDBhAo6OjjrjDQ0Nxc3Njdy5c5M/f35evHjB1KlTE1X2Vq1asXHjRnLkyMHq1avZunUrxsbGnz3e1tYWGxsbmjVrRq9evQBo06YNo0aNolOnTmTLlo2yZcvGGHo0fvx4unXrhpmZmdqL2d7ensDAQLXW+9/Xuq6bNWtW9u3bx4YNGyhYsCD58+dn1KhRhIaGJuq92LJlC/Xq1aNx48ZkzJiRGjVq0LhxYzw8PNRjJk2axP3798mRIwfjxo2L8zlxtD///JOwsDBKly5Njhw5aN++vdrc7ODgQJkyZcifP3+8PdTXrFlDvnz5qFChAoULF2bt2rVcuHABRVHo2bNngspUq1YtqlatGmPbwoULyZIlC8WKFcPOzo7OnTvHeb13797Rp08fcuTIQZEiRciVK5faggUfPxsfHx/atGlDlixZ4o2hZs2aHDp0iEOHDlGsWDFy5sxJ3759adq0KQD169dn0qRJtGvXjgIFCnD//v0E9y3o06cPjRo1okKFClSqVCnW/8FP6XrPvyaOc+fOUa1aNUxNTWnZsiXz58+naNGiCTpXJA2NoiRwRggh0pjx48dz7969eIcwfeu6devG48eP2bVrFxkyZNB3OKlS8eLFWbp0aYKe4QuRnKRmLoSI0/Lly3F0dOTixYv6DiVV2rJlCxqNBgcHB32HIgTp9R1AZGQklStXplChQuzcuVPf4Qgh/p+xsXGiOxamdXXr1uXmzZusXr2adOmkTiT0T+/N7HPmzOH8+fO8e/dOkrkQQgiRCHr9Sunn58e///5L79699RmGEEIIYdD0msxdXV2ZMWOGNFMJIYQQX0Fvz8x37txJ3rx5sbW1/ez60h4eHurQmBs3bmBhYZFCEaa8qKioNP3FJi2XLy2XDaR8hk7KZ7hevXqVoJk19fbMfPTo0axevZr06dMTEhLCu3fvaNu27WeHCWm1Wry8vFIwypTl6empl7W2U0paLl9aLhtI+QydlM9wVa5cOcbKj/HR21eZadOm4efnh7e3Nxs2bMDBwUHG+wohhBCJkDbbJYQQQohviN7HmcPHMZtptYlECCGESG5SMxdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsDpLZmHhIRQtWpVKlSoQJkyZRg3bpy+QhFCCCEMWnp93djExIRDhw5hampKeHg4dnZ2NGnShOrVq+srJCGEEMIg6a1mrtFoMDU1BSA8PJzw8HA0Go2+whFCCCEMlkZRFEVfN4+MjMTW1pZ79+7h7OzML7/8EusYDw8PPDw8APD19WXTpk0pHWaKCQoKUr/gpEVpuXxpuWwg5TN0Uj7DNWLECM6fP6/7QCUVeP36tVK3bl3l2rVrnz3OysoqhSLSj8OHD+s7hGSVlsuXlsumKFI+QyflM1y2trYJOi5V9GY3MzOjbt267NmzR9+hCCGEEAZHb8nc39+fN2/eAPDhwwcOHDhAqVKl9BWOEEIIYbD01pv96dOndOvWjcjISKKioujQoQPNmzfXVzhCCCGEwdJbMi9fvjyXLl3S1+2FEEKINCNVPDMXQgghROJJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMXHp9ByCEEEKImIKDg5k2bVqCj5eauRBCCJFKKIrC1q1bsba2ZvLkyQk+T5K5EEIIkQrcuXOHxo0b065dO7Jnz86RI0cSfK4kcyGEEEKP3r9/z+jRoylbtiynT59m3rx5XLx4kTp16iT4GvLMXAghhNADRVH466+/GDZsGH5+fnTt2pVffvmF/Pnzf/G1pGYuhBBCpLBbt27h6OhIhw4dyJUrF8ePH+ePP/5IVCIHSeZCCCFEigkMDGTkyJGUL1+e8+fPs3DhQs6fP0+tWrW+6rrSzC6EEEIkM0VR2LBhAyNGjODJkyf07NmTadOmkTdv3iS5vtTMhRBCiGR048YNHBwc6Ny5M/nz5+fUqVOsWLEiyRI5SDIXQgghksW7d+8YNmwYFSpU4MqVK/z222+cPXuW6tWrJ/m9pJldCCGESEKKorB27VpGjhzJ8+fP6d27N1OnTiV37tzJdk9J5kIIIUQSuXr1Ki4uLhw7dowqVaqwfft2qlatmuz3lWZ2IYQQ4iu9efOGIUOGUKlSJW7evImHhwenT59OkUQOUjMXQgghEi0qKorVq1fz448/4u/vT79+/Zg8eTK5cuVK0TgkmQshhBCJcPnyZZydnTl58iTVqlVj165d2Nra6iUWaWYXQgghvsDr169xcXHB1taWO3fusGLFCk6ePKm3RA5SMxdCCCESJCoqilWrVjFq1CgCAgIYMGAAkyZNIkeOHPoOTWrmQgghhC4XLlygZs2a9OrVCysrKy5cuMCiRYtSRSIHSeZCCCFEvF69ekX//v2pUqUKDx8+5I8//uD48ePY2NjoO7QYJJkLIYQQ/xEZGYmHhwdarZbly5czePBgvLy86Nq1KxqNRt/hxSLPzIUQQohPnD17FmdnZ86fP0/t2rVZtGgR5cuX13dYn6W3mvmjR4+oV68e1tbWlClThvnz5+srFCGEEIKXL1/Sp08fqlevjp+fH2vWrOHIkSOpPpGDHmvm6dOnZ/bs2VSqVInAwEBsbW1xdHSkdOnS+gpJCCHENyi6Sd3d3Z13794xdOhQxo0bR7Zs2fQdWoLprWZeoEABKlWqBEDWrFmxtrbm8ePH+gpHCCHEN+jUqVNUrVqVgQMHqqubzZ4926ASOaSSDnDe3t5cunSJatWq6TsUIYQQ34AXL17Qs2dPatasybNnz1i/fj2HDh2iTJky+g4tUTSKoij6DCAoKAh7e3vc3d1p27ZtrP0eHh54eHgA4Ovry6ZNm1I6xBQTFBSEqampvsNINmm5fGm5bCDlM3RSvv+JjIxkx44drFy5kg8fPtC+fXu6du1K5syZkznKxBkxYgTnz5/XfaCiR2FhYUrDhg2V2bNnJ+h4KyurZI5Ivw4fPqzvEJJVWi5fWi6bokj5DJ2U76Pjx48rFSpUUAClfv36ys2bN5M3sCRga2uboOP01syuKAq9evXC2tqaYcOG6SsMIYQQadyzZ8/o1q0bdnZ2vHr1ik2bNrF//36sra31HVqS0VsyP3HiBKtXr+bQoUPY2NhgY2PDrl279BWOEEKINCYiIoJ58+ah1WpZv349bm5u3L59m++++y5VTvzyNXQOTXvx4gUnTpzgyZMnZMqUibJly1K5cmXSpfu67wF2dnYo+n1cL4QQIo06evQozs7OXL9+nYYNG7JgwQK0Wq2+w0o28Sbzw4cPM336dAICAqhYsSJ58+YlJCSEv//+m/v379O+fXuGDx9ucN33hRBCpF1Pnjxh5MiRrFu3DgsLC7Zu3Urr1q3TXE38v+JN5rt27WLZsmVYWFjE2hcREcHOnTvZv38/7dq1S9YAhRBCCF3Cw8NZsGAB48ePJywsjJ9++onRo0en2l7qSS3eZD5z5sz4T0qfntatWydHPEIIIcQXuXTpEs7Ozty8eZOmTZsyf/58SpQooe+wUpTOB9/Pnz+nV69eNG7cGICbN2+yYsWKZA9MCCGE+Bw/Pz86derEsGHDCA4OZvv27ezcufObS+SQgGTevXt3GjVqxNOnTwGwsrJi3rx5yR2XEEIIEaewsDBmzJhBqVKl+Pvvv+nWrRs3b96kZcuWaf7ZeHx0JvOXL1/SoUMHtfd6+vTpMTIySvbAhBBCiP86cOAAFSpUYNSoUTg4OHDz5k26d+9OpkyZ9B2aXulM5lmyZOHVq1fqt53Tp0+TPXv2ZA9MCCGEiPbo0SO+++47HB0dCQsLY+fOnezYsYNixYrpO7RUQec489mzZ9OyZUvu379PrVq18Pf3Z/PmzSkRmxBCiG9caGgoc+bMYfLkyURFRTFx4kRGjhxJxowZ9R1aqqIzmdva2nLkyBG8vLxQFAWtVouxsXFKxCaEEOIbtmfPHgYPHszdu3dp3bo1c+fOxdLSUt9hpUo6m9mLFy/O8uXLKVOmDGXLlsXY2JjmzZunRGxCCCG+Qd7e3rRp04YmTZoAsHv3brZt2yaJ/DN0JnNjY2MOHz5Mjx49CAsLA+Dx48fJHpgQQohvS0hICJMmTcLa2pp9+/YxdepUrl27pg6NFvHTmcwzZ87Mxo0bsba2pnbt2vj4+HyzXf+FEEIkj3///ZeyZcsyduxYmjdvzq1btxg9ejQmJib6Ds0g6HxmHr0Yyo8//oitrS2NGjUiICAg2QMTQgiR9j148ABXV1f++ecftFot+/btw9HRUd9hGRydyXzixInq7/Xr12fv3r388ccfyRqUEEKItO3Dhw/88ssvTJ8+nfTp0/PLL7/g6upKhgwZ9B2aQYo3md++fZtSpUpRqFAhLl68GGOfdIATQgiRGIqi8M8//+Dq6srDhw/p2LEjs2bNwtzcXN+hGbR4k/mcOXPw8PBg+PDhsfZpNBoOHTqUrIEJIYRIW+7du8eQIUPYtWsXpUuX5tChQ9SrV0/fYaUJ8SZzDw8P4OO65kIIIURiBQcHM23aNGbMmIGJiQmzZ89m0KBBMmdJEoq3N/u5c+d49uyZ+vrPP/+kVatWDB48WDrACSGE0ElRFLZt20bp0qWZPHky3333HV5eXgwbNkwSeRKLN5n369dP7Yhw9OhR3Nzc6Nq1K9mzZ6dv374pFqAQQoiks3btWiwtLUmXLh2WlpasXbs2We5z584dGjduTNu2bcmWLRtHjhxhzZo1FChQIFnu962Lt5k9MjKSnDlzArBx40b69u1Lu3btaNeuHTY2NikVnxBCiCSydu1a+vbtS3BwMAA+Pj5q5czJySlJ7vH+/XsmT57M7NmzyZQpE/PmzcPZ2Zn06XUOnhJfId6aeWRkJBEREQAcPHgQBwcHdV/0diGEEIbD3d1dTeTRgoODcXd3/+prK4rC5s2bKVWqFNOnT+f777/Hy8uLIUOGSCJPAfG+w99//z329vbkzp2bTJkyUbt2beBjb0RZAlUIIQyPr6/vF21PqFu3bjF48GB1rfENGzZQq1atr7qm+DLxJnN3d3fq16/P06dPadiwoTqFa1RUFAsXLkyxAIUQQiQNCwsLfHx84tyeGIGBgUyaNIm5c+eSJUsWFi5cSP/+/aUmrgeffcerV68ea5uVlVWyBSOEECL5TJkyJcYzc/i4/saUKVO+6DqKorBx40aGDx/OkydP6NGjB9OnTydv3rxJHbJIIJ0LrQghhEgbnJyc8PDwoEiRImg0GooUKYKHh8cXdX67ceMG9evX5/vvvyd//vycPHmSlStXSiLXM2kLEUKIb4iTk1Oieq6/e/eOCRMmsGDBArJmzcqvv/5K3759MTIySoYoxZfSWTMfNWpUgrYJIYRIexRFYe3atWi1WubOnUuPHj24c+cOAwYMkESeiuhM5vv374+1bffu3ckSjBBCiNTj2rVr1K1blx9++AFzc3NOnz6Nh4cHuXPn1ndo4j/ibWb/7bff+PXXX7l//z7ly5dXtwcGBlKzZs0UCU4IIUTKe/v2LePGjWPRokWYmZnh4eFBz549pSaeisWbzDt37kyTJk0YPXo006dPV7dnzZpVnRlOCCFE2hEVFcXq1av58ccf8ff3p1+/fkyePJlcuXLpOzShQ7zN7NmzZ8fS0pIhQ4aQM2dOihQpQpEiRTA2NubMmTMpGaMwUP+dA1oW6BEi9bp8+TK1a9eme/fuFC1alHPnzvHbb79JIjcQOp+ZDxgwAFNTU/V1lixZGDBgQLIGJQxf9BzQPj4+KIqCj48PPj4+ybaogxAicV6/fo2Liwu2trbcuXOHFStWcPLkSWxtbfUdmvgCOpO5oijq7G8A6dKlk7nZhU5xzQEdFRWVJHNACyG+XlRUFCtXrkSr1fLbb78xYMAA7ty5Q8+ePUmXTqYgMTQ6P7FixYqxYMECwsPDCQ8PZ/78+RQrViwlYhMGLLnmgBZCfL0LFy5Qq1YtevXqRcmSJblw4QKLFi0iR44c+g5NJJLOZL5kyRJOnjxJoUKFMDc358yZM3h4eKREbMKAxTfXc2LngBZCfL2AgAAGDBhAlSpVePDgAatWreLYsWOyrHUaoHMGuLx587Jhw4aUiEWkIXHNAZ0uXbovngNaCPH1oqKiWLZsGaNHj+b169cMGjSICRMmYGZmpu/QRBLRWTO/c+cO9evXp2zZsgBcvXqVyZMnJ3tgwrDFNQd0kSJFEjWNpBAi8c6dO4ezszN9+/aldOnSXLp0ifnz50siT2N0JvM+ffowbdo0jI2NAShfvrzU1EWCODk54e3tTVRUFN7e3jI/gRAp6OXLl/Tt25dq1arx4sUL1qxZw5EjR2JMAibSDp3N7MHBwVStWjXmSbJWrRBCpEqRkZEsW7aMMWPG8O7dO4YOHYqDgwPNmjXTd2giGemsmefOnZv79++rw9P++usvChQokOyBCSGE+DKnT5+matWqDBgwgAoVKnDlyhVmz55NlixZ9B2aSGY6q9iLFy+mb9++3L59m0KFClG0aFGZ+EMIIVKRFy9e4Obmxu+//07BggVZv349HTt2jDFHiEjb4k3m8+fPZ8iQITx9+pQDBw7w/v17oqKiyJo1a0rGJ4QQIh4REREsWbKEn3/+maCgIEaOHMnPP/8sf6e/QfE2s//+++8ADBo0CPg4jav8AxFCiNThxIkTVK5cmUGDBmFra8vVq1eZMWOG/J3+RsWbzK2trbG0tMTLy4vy5curP+XKlZPekF/B29ubBg0axNhWokSJJLn2qlWrEjxs8NM4xo8fz5o1a2Id8/r1axwdHalVqxb9+/eP91oajSbGfSdOnPjZ5j1PT0969+4NwNOnT7G1teX48eMJijsukZGRtGrVioiICPbu3Uv16tWxt7enadOmvHr1CoCQkBCcnJyoXbs2Tk5OhISEAB/fBwcHB2rVqsXUqVPVa/bs2ZMCBQqocQLcvHkTV1fXRMcpRFJ4/vw53bp1w87OjlevXrFp0yb279+PtbW1vkMTehRvMl+/fj2nT5+mRIkS/PPPP+rPzp07+eeff1IyRqEna9eupWnTppw4cYKJEyfGe5ylpSWHDh1SXx8+fBhLS0ud13/69CktWrRg3rx52NnZJTrOv//+G3t7e9KnT4+1tTVHjhzhyJEjNG/enHnz5gEfv+iUKlWKY8eOodVqWbVqFQBubm5MmDCBEydOcOjQIW7fvg3ApEmTWL9+fYz7lC5dmvv37+Pv75/oWIX472qCCe2DFBERwfz587GysmL9+vW4ublx69YtvvvuO3k2LuJP5vXr1yd//vw0atRInfDj05+k0LNnT/LmzatOSPOtCwoKokOHDtSvXx8HBwfu3bsHQN26dXFzc6Nhw4bUr1+f0NBQnj17Rp06dahXrx5169bl3bt3AFy7do127dpRtmxZjh07BsCcOXNwcHCgSpUqjBs3LsHxmJiYcPDgQRRFIW/evPEeZ2RkRLly5bh48SIXLlygQoUKGBkZARAWFkbPnj2pXbs2Q4cO5cqVKwA8e/aMFi1aMGfOHGrXrg3AunXrqFevHjVq1KB3794oisJvv/3G3LlzgY+L/lSqVIn379/HuP+mTZto2rQp8HG6WBMTEwAyZMigDqP09PSkefPmALRo0YKjR48C/1v2EaBZs2bq9kKFCsVZ1gYNGrB9+/YEv4dCfCqu1QT79u2rM6EfPXqUSpUq4erqSvXq1bl27RrTpk2LsaKl+LbFm8yfPn3KkSNH+Oeff7h06RIXL16M8ZMUunfvzp49e5LkWobkwoUL1K1bV/2Jtm7dOtq2bcvBgweZO3cubm5u6r7q1auzb98+ihcvzv79+zl58iR2dnYcPnyYw4cPq8/JQkND2bJlCx4eHsyfPx+Afv36cejQIc6cOcP+/fsTvNhJ5syZCQkJYdiwYTqP7dy5M+vWrWPdunV07txZ3b58+XLy58/PsWPHmDt3rvrF7cSJE+TJk0dNpACtWrXi8OHDnDp1isDAQI4dO4aTk5M6SdGRI0eoWrVqrGE2N27coHjx4jG2PX/+nIULF6rL9QYEBKiLSJiZmanN71FRUeo5n26Pj5WVFdeuXdP5fggRl7hWEwwODo53NcGnT5/yww8/YG9vz9u3b9myZQt79uxBq9WmRLhCT0JCQrh+/ToPHjxI8Dnx9mafOHEi06dPx8/PL9Yfc41GE6NZNbHq1KmDt7f3V1/H0Nja2nLgwAH1dfQz8wcPHjB//nyWLFkCxJycJ3ptYQsLC169ekWnTp24cuUKP/zwA4ULF2bChAlxHgewZcsWli9fjkaj4cGDBzx69Cjemmc0Pz8/NmzYwIEDBxgwYAAzZszAysqKW7duMXr06FjHV6tWjR9//BGA2bNnq9uvX79OmzZt1NfRNfZ27dqRKVMmnJ2d+fXXX4GPtY+ZM2cSGRmJj48PLVu2pE6dOpQtW5bTp0+zcuVKBg8eHOveiqLEeP3u3Tvat2+Ph4eH2qKQM2dO3rx5A8Dbt2/V2eg+Xerx0+3x+e+SwEJ8iYSuJhgeHs7ChQsZP348oaGhuLu7M2bMGDJnzpwSYYoUoCgKwcHBZMmShaioKIYPH87t27fx8vLC29sbRVHUDugJEW8yb9++Pe3bt2fSpEn8/PPPSRK8+DxLS0s6duyoJr+wsDB136cJRFEUIiMj1QTeu3dv9u7dG+dxAD///DO3b9/GxMSEWrVqxUp+cXn//j2PHz8mLCyMX3/9lS5durBhwwa1GTouPXv2jLWtbNmyeHp64ujoCMSsCS9YsIAuXbrg7u7OlClTcHNzY8+ePRQoUICOHTuqcfbt25fZs2fz+PFjKleuHOc97t+/T6lSpfjw4QNt2rRhzJgxVKtWTT3G3t6eXbt2YWNjw65du7C3twegQoUKnDx5kpo1a7J79271GXt87t69S5kyZT57jBDxsbCwwMfHJ87t0Tw9PXF2dubmzZs0adKE+fPnU7JkyZQMUySD7du3c/nyZby8vPDy8uLOnTs4OjqydetW0qVLx86dOzE1NaVq1ap06dIFrVZL5cqVOXnyZIKur1Hi+cu+Zs0afvjhB+Bjk2itWrXUfYsWLcLFxSUJivexN3Hz5s25fv16nPs9PDzUJVd9fX3ZtGlTktxXX549e8bMmTNj1F6dnJxYu3Ytz58/x8PDg9evX6MoCjVq1KBDhw64urri7u5Onjx5WL16NXny5MHMzIy1a9diZGSEsbExY8eO5cSJE/j7+9OlSxf8/f2ZMmUK8+bNY/HixVy5coXChQvz9u1bunXrRp48edQ4Vq1aRaFChdSEG23Tpk0cPHiQrFmzkjdvXoyNjcmfPz/ff/99jOOi449rW3h4OHPmzMHPz4906dIxaNAggoKC2L9/PyNHjiQyMpKxY8dSvnx5NBoNu3fvVv+w2dnZqTH17t2bZs2axajlRzty5AjPnz+nQ4cObNy4kXXr1lG0aFEAKleuzA8//EBoaCgzZszA39+fPHnyMGrUKDJkyMCTJ0+YOXMm4eHhVKtWjS5dugCwYsUKzpw5w+vXr7GwsGDy5MlkypSJ0aNH8+OPP8Za9zkoKChNP7+U8iWNgIAAfHx8YnypTZcuHUWKFCEyMpIlS5Zw6NAh8ufPj4uLCzVr1kySliD5/JKfv78/Dx8+5NGjR+pPxowZ1ZUiBwwYgJeXF/ny5aNw4cIULlyYMmXK4ODgAMTf6jdixAjOnz+vOwAlHhUrVozz97hef42HDx8qZcqUSdCxVlZWSXbf1Ojw4cP6DiFZfU35atasqbx+/TrOfREREUqLFi2U8PDwRF8/IW7cuKEMHjw4zn3y2Rm2lCzfmjVrlCJFiigajUYpUqSIsmrVKuWXX35RsmTJopiYmCjjxo1TgoODk/Se8vkljTdv3ihnzpxR/vzzT8Xd3V3p1auXuq9t27YKoABKtmzZlCpVqigDBgxQ9/v5+SXqc7W1tU3QcfE2syufVNiV/1Te//taiOTy5MkTunTpQvPmzeNdstHIyIgdO3YkeyylS5dWOxUKkVhOTk7qUsAHDhxg0KBB3L59Wx2mWaxYMT1H+G2LiIjg4cOHanO4i4sLJiYmjBkzhmnTpqnHGRkZUaxYMUJCQsiYMSNubm4MHjwYrVZLvnz5YtWydfVT+lrxJvNPA/lvUEnVAej777/H09OTly9fYm5uzoQJE+jVq1eSXFukDQULFuTgwYP6DkOIJPXo0SOGDRvGX3/9RbFixdi5c6esapbCXr58iZeXF+XKlSNbtmxs27aNMWPGcP/+fcLDw9XjmjZtirW1NY6OjpiZmaHVatFqtRQrVowMGTKox1WpUkUfxVDFm8xv375N+fLlURSF+/fvq7O+KYryRd3lP+e/k3IIIURaFhoaypw5c5g8eTJRUVFMnDiRkSNHkjFjRn2HliaFhoYSFRVFpkyZuHXrFjNmzFBr3AEBAQDs3buXhg0bkiNHDkqVKkWrVq3UhK3VasmVKxcA9erVo169evoszmfFm8xv3bqVknEIIUSatnfvXgYNGsTdu3dp3bo1c+fOTdBMiSJh3r59y4YNG9Rk7eXlxcOHD/Hw8KBXr16Ehoayd+9etFot3333nZqso0fI/HfeD0MTbzK3sLDQ2ZyuyJhbIYT4LB8fH4YOHcq2bdsoUaIEu3fvpnHjxvoOy+BERUVx7dq1GMnay8uLjh07UrlyZcLCwujfvz+ZMmXCysqKSpUq8f3331OxYkUAbGxsePLkiZ5LkXziTeb16tWjXbt2tGrVKsYYyLCwMI4fP84ff/xBvXr16N69e0rEKYQQBiUkJIRZs2apC/hMmTKF4cOHq9MNi9iioqLw8/OLkayLFy+uLnBUvXp1dZEkCwsLtFotefLkASB37tz4+Phgbm4eYzKob0W8yXzPnj2sXLmS77//nocPH2JmZkZISAiRkZE0bNiQoUOHYmNjk4KhCiGEYdi1axeDBw/m/v37tGvXjjlz5sSoFH3rAgMD1WQdGRlJ165dgY+TOH0654ipqSmdOnUCPo7H37ZtGwUKFKBkyZIxZsPz9PREo9F80+9xvMk8Y8aMDBw4kIEDBxIeHs7Lly/JlClTvMODhBDiW/fw4UNcXV3ZsWMHWq2Wffv2xZqM6VsRGRmJt7c3fn5+6oyLw4YNY8OGDTx9+lQ9rnjx4moyHzRoEIqiqM+z8+fPH+NRrjyeiF+8yfxTxsbGFChQILljEUIIg/ThwwdmzJjB9OnTMTIy4pdffsHV1TXG0KW06vXr15iZmaHRaNi8eTPr16/Hy8uLe/fuERYWhomJCe/fv8fIyIgCBQrQsGHDGL3Fo9emgI9TN4vESVAyF0IIEbd//vmHIUOG8PDhQzp27MisWbMwNzfXd1jJ4ubNm+zcuTPGM+2XL1/y7Nkz8uXLx4MHD7h58yZarZZmzZqpCTvayJEj9Rh92ibJXAghEuHevXu4urry77//Ym1tzcGDB9V5tg1VYGAgly5ditVjfOPGjVSsWJGzZ88yatQo8ubNi1arpXXr1mi1WoyNjQEYNWoUo0aN0nMpvk06k/moUaP45ZdfdG4TQohvQXBwMNOmTWPGjBlkyJCBWbNmMXjwYDWhpXYhISHcvXs3RrLu3bs3derU4ezZszRo0AD42G+qZMmSVKhQQe0d3r59e1q3bi19p1Ihncl8//79sRL37t27JZkLIVKttWvX4u7ujq+vLxYWFkyZMkWdDz2xFEXh77//ZujQofj4+NC5c2dmzpxJwYIFkyjqpKMoCk+ePFGTdXh4OHXr1uX+/fuULFkyxvoahQoVokmTJsDHVQb37NmDVqvFwsIi1hAvfa9MJuIXbzL/7bff+PXXX3nw4IE6lSt8bIb5dDlUIYRITdauXUvfvn0JDg4GPk7aEt2xKrEJ/c6dOwwePJi9e/dStmxZPD091R7a+hQUFMSdO3cwMjKiQoUKhIeHo9Vq8fb2jpGw27Vrx+DBg7GwsGDs2LHqs2wrK6sYCTp79uw0atRIH0URXyneZN65c2eaNGnC6NGjmT59uro9a9as5MyZM0WCE0KIL+Xu7q4m8mjBwcG4u7t/cTJ///49U6ZMYfbs2WTMmJG5c+fi7Oycok3qUVFRvH79Wp0j3M3NjXPnzuHl5cXjx48BaN26Ndu2bWPTpk34+vrGSOQZM2bk+++/Bz6OTBo/fnyKxS5STrzJPHv27GTPnp3169cTGRnJ8+fPiYiIICgoiKCgoG96cL4QIvXy9fX9ou1xURSFLVu2MGzYMB49ekSXLl2YMWMG+fPnT6ow4/Xvv/9y6tQptYn87t27lC1blnPnzgFw+fJlgoKCcHBwUGvY0a2n7u7uREZGxrheSEhImp7GVHyk85n5okWLGD9+PPny5VOfn2g0Gq5evZrswQkhxJeysLDAx8cnzu0Jcfv2bQYNGsSBAweoUKEC69atw87OLsnie/ToEZcvX47RAe3Nmzfq39RVq1axbds2ihcvjlarpVGjRlSoUEE9f8+ePfFeO74vLGFhYUkWv0iddCbzefPm4eXlpTbxCCFEajZlypQYz8wBMmfOzJQpUz57XlBQEJMmTWLu3LlkzpyZhQsX0r9/f9Kn//IRvNFrZUf/3Llzhw0bNmBiYsKcOXOYN28e8HE+ca1WS5UqVYiIiCB9+vQsXbqUdevWJaopP74vMt/C5DXfOp3/SgsXLkz27NlTIhYhhPhq0c/FE9qbXVEUDh06xA8//MDjx4/p0aMH06dPJ2/evJ+9T2hoKPfu3VMTds+ePcmXLx8LFy5k8ODB6nEZMmSgZMmS+Pv7Y25uTv/+/enQoQNarTbO/kdf0ycpvi8yhQoVSvQ1hWHQmcyLFStG3bp1adasWYzVfoYNG5asgQkhRGI5OTklqLPbjRs3GDRoEIcPH6ZixYps3ryZGjVqqPsVReHZs2d4eXlhZWVFwYIFOXbsGN27d8fb25uoqCj12KpVq5IvXz7s7e2ZO3eu+jy7SJEiGBkZqcd9OiNaUovvi4x0Wk77dCZzCwsLLCwsCAsLk+cuQgiDt3btWkaPHs2jR48AyJIlC87OzkycOJGcOXPy+PFjRo0apda4AwMDAVi2bBm9e/cmb968VK5cGScnpxhDvLJlywZA+fLlYwznTWlxfZHx9PTUTzAixehM5uPGjQM+DtHIkiVLsgckhBDJZc2aNfTs2ZPw8HB12/v371m8eDG5c+dm/PjxZMyYkePHj6PVaunevbuasCtWrAh8rFlv3LhRX0UQIk46k/mpU6fo1asXQUFB+Pr6cuXKFZYuXcqvv/6aEvEJIYQqoTO7Xbx4kVu3bsXohFa6dGm2bt0aI5FHa9q0qTpZSq5cufD29k7uogiRpHQmc1dXV/bu3UvLli2Bj4vHHz16NNkDE0KIT8U1s1uvXr3YtWsXpqamZM+enRkzZgDQqVMn7t69S7p06ShSpAiKorBx48YYz7g/5eDgEONZuRCGJp3uQz72aP/Up505hBAiubx69YqTJ0/y+++/M2jQoFgzu4WGhrJu3Tr++usvHj58qG7/888/uXHjBsuWLSM4OFid0jW+pUll6JYwdAkamnby5Ek0Gg1hYWEsWLAAa2vrlIhNCPENCAsL4/79+9y9e5cWLVqg0WiYPHky8+fP5+XLlwm6xqtXr2K8zpQpE3379uXEiRNUq1aNnTt3UrlyZezs7GTolkiTdNbMlyxZwuLFi3n8+DHm5uZcvnyZxYsXp0RsQog0InqIV/SImN27d9O8eXNKlixJ5syZKV26NK1ateL58+cAFC1alLZt2zJ79mx27tzJ3bt3453BrUiRIurvb968YdCgQVSqVAkvLy9WrFjByZMnqVy5MvCxp7eHhwdFihRBo9FQpEgRPDw8ZOiWMHg6a+a5c+dm7dq1KRGLECKNuH//Phs2bIjRAe3t27ecPXuWKlWq8PbtW3x9fbGxsaFjx45qj/HopBrX8KqpU6fGO7NbVFQUf/zxB6NGjeLVq1cMGDCASZMmkSNHjlixydAtkRbpTOYPHz5k4cKFeHt7ExERoW7fsWNHsgYmhEi93r9/H2MxkOif+fPn06pVK3x8fPjpp58wNzdHq9WqY7Kjm7M7depEp06dvuie8U2IYm1tTa1atTh9+jQ1a9Zk79696jAyIb4VOpN569at6dWrFy1atIi1UL0QIu0KCgpSk/TevXtZsmQJbdq0oWPHjjx+/BhHR0cATE1NsbKyombNmuoaDnZ2dgQGBsZYKzspfFqrDggI4KeffqJLly7kyZOHVatW0aVLF/k7Jb5JOpN5xowZY8wzLIRIOyIjI/H19VWTdpEiRWjdujUfPnwgW7Zs6rrYGo0GS0tLateuDXyc5vngwYNotVoKFiyIRqOJcd0MGTIkWw/xqKgoVqxYwejRo3n9+jWDBg1iwoQJmJmZJcv9hDAEOpP5kCFDmDBhAg0bNowxN3ulSpWSNTAhRNJ5/fo1Xl5ehIWFUadOHQDq1q3L6dOnCQ0NVY/r1KkTrVu3JlOmTMydO1dtJn/y5AkNGzZUj0ufPj0ODg4pXo5z587h7OzMuXPnsLOzY/HixXqdOlWI1EJnMr927RqrV6/m0KFDMdYzP3ToULIHJ4RIuPDwcJ4/f66OpZ40aRL79u3Dy8sLf39/4OOX8AsXLgAfFwapUqWK2vlMq9WSJ08e9XpDhgxRf0/oELHk8vLlS8aMGcPy5cvJly8fq1evxsnJKVaLgBDfKp3JfNu2bTx48EAmVRAildm/fz/79+9Xm8jv379Pzpw51eFdjx8/BqBly5Zqsv50jojo2dJSs8jISJYtW4a7uztv377F1dWV8ePHq4uaCCE+0pnMK1SowJs3b3Su7SuESFp+fn6cOXMmRm/xe/fu4efnR8aMGdmzZw+LFi2iZMmSlClThrZt26LValEUBY1Gw5IlS/RdhASJb77106dP4+LiwoULF7C3t2fRokWULVtW3+EKkSrpTObPnz+nVKlSVKlSJcYzcxmaJsTXURSFp0+fxhretWTJEiwsLNi8eTPDhg0DoGDBgmi1Wr777juCg4PJmDEjEydOZMaMGQY9vXJc86336dOHZcuWceTIEQoWLMj69evp2LGjNKkL8Rk6k/mECRNSIg4h0qz3799z584dNVl/99136gpe7du3V4/LnDkzVlZWBAQEYGFhQYcOHahTpw5WVlZkzZo11nXTwpLE7u7useZb//DhA0eOHGHkyJH8/PPPcZZdCBGTzmRub2+Pj48Pd+/epUGDBgQHBxMZGZkSsQlhMKKiovDx8VGHd2m1Wm7dukXDhg3x8/NTj9NoNBQvXpzSpUtTrVo1Fi1apD7PLlSoUIwx0oUKFTLoOcMTslypr69vvOcbwjN9IVILncl82bJleHh4EBAQwP3793n8+DH9+/fn4MGDKRGfEKnK27dvCQkJIV++fAQFBdGzZ0+1xh09xGvs2LFMmDCB/PnzU7du3Ri9xUuWLEmmTJkAMDc3x9nZWZ/FSTZxNZ/37dsXIEZCL1SoUIwvO9E+nW9dCKGbzmS+ePFizp49S7Vq1QAoWbIkL168SPbAhNCX6A5kAAsWLOD69etqwn7+/Dk9evRg5cqVZMmSBS8vLwoXLoxWq6VBgwZotVq1k1aOHDlYvXq1PouiN3E1nwcHB+Pu7o6TkxMRERH8+uuvsVY7g//Nty6ESDidydzExCTGsLSIiAjpiCLSjAsXLnD16tUYHdCKFSvGzp07gY9fZl+9eoVWq6Vp06ZotVpq1qwJfGwyv3LlCvBxoY66devqqxhfLCFN4F8jvuZzX19fjh49iouLC9euXaNhw4Y0aNCAxYsXJ1ssQnwLEvTMfOrUqXz48IH9+/fz66+/0qJFi5SITYivFhYWxr1792Ik69DQUNatWwfAjz/+yKFDhzA2NqZEiRIxkjV8TPZJPb+4viW0CfxrWFhY4OPjE2t75syZsbe3x8LCgi1bttCmTRs0Gg0jR45MkvsK8a3SmcynT5/OihUrKFeuHEuXLqVp06b07t07JWITIkEUReH58+cxJk+ZPn06Go2Gfv36sWrVKvXY/PnzU65cObUpfcGCBZiYmGBpaUn69LH/O6S1RA66m8CTwpQpU2ItVwoQGhqKu7s7Y8aMIXPmzElyLyFEApL59u3b6dq1K3369EmJeISIV3BwMHfv3sXLy4umTZtiamrKb7/9hpubG+/evVOPy5QpEyNGjCBPnjz06NGD+vXro9VqsbKyInv27DGuWaZMmZQuht59rgk8qUR/KRg+fLg6I12FChXYvHkzJUuWTLL7CCE+0pnMd+zYgaurK3Xq1KFTp040atQozhqMEEkhKiqKx48fY2ZmRtasWTlx4gQTJkzAy8srRrI5c+YMVatWxcrKii5duqi9xUuVKoW5ubk6xCt6URHxP/E1gVtYWCTZPR4/fszOnTt5/vw5lpaWzJs3j5YtW0p/GyGSic6s/PvvvxMeHs7u3btZt24dAwcOxNHRkeXLl6dEfCKNim7mfvLkCUuXLlWbyO/cuUNwcDBbtmyhbdu2wMd1q+3s7GIM8YqeY7x+/frUr19fn0UxOHE1gX+uB/natWsJCAjAwcFBZwe1sLAw5s+fz8SJEwkPD2fs2LG4ubmpw/GEEMkjQVVsY2NjmjRpgkaj4cOHD2zfvl2SuUiQ0NBQDh48iJeXF4cPH1Zr2W5ubgwePJjg4GAmT56MpaUlWq1WHZcdvcRurVq1OH/+vJ5LkbZEJ+KE9GaP7iw3ceJEFEX5bGe5gwcP4uLiwu3bt2nRogVz586lePHiyV8gIYTuZL5nzx42bNjA4cOHqVu3Lr1792bTpk0pEZswENFrZX/6Y2dnx9ChQ4mMjKRZs2YAZM2alTJlytCwYUO0Wi0AxYoVIzg4OMa8/yL5OTk5JaizW0I6yz169Ijhw4ezefNmdVhf9GcuhEgZOpP5qlWr6NSpE0uXLpU/uN+w8PBw7t+/rybr7Nmz069fP+DjRELRk3+kT5+eYsWKqTXrzJkzc/r0aYoVK8b169epV69ejOumS5dO/l2lYp/rLBcaGsrcuXOZNGkSUVFRTJw4kZEjR5IxY8YUjlIIoTOZb9iwgefPn7N//34AqlatmmTLoe7Zs4chQ4YQGRlJ7969cXNzS5LrisRRFAV/f3+8vLx4/fo1LVu2BKB169bs3Lkzxpz8Dg4OajKfP38+WbNmRavVUqxYMYyNjWNcN3r2QOn8ZHji6yyXJ08eypcvz507d2jdujVz587F0tIy5QMUQgAJSOabN29mxIgR1K1bF0VRGDRoEDNnzoyx2lNiREZG4uzszP79+zE3N6dKlSq0bNmS0qVLf9V1hW4hISF4e3tTqlQpAObNm8eGDRvw8vLizZs3AOTOnRt/f3/g48RBZcqUidEBzczMTL2ezNaVdkV3lvuUkZERL168IFu2bOzevZvGjRvrKTohRDSdyXzy5MmcO3dOrY37+/vToEGDr07mZ8+epUSJEhQrVgyATp06sX37dknmSURRFOBjbfjo0aNs3bpVbSL38fEhKiqK9+/fkzlzZoKDg8mcOTOdOnWKkbCje5wPHTpUz6UR+hL9RS16rLhGo8HIyIiJEycyfPhweUQiRCqhM5lHRUXFaFbPlSsXUVFRX33jx48fU7hwYfW1ubk5Z86ciXWch4cHHh4ewMeOVp6enl9979QqKCgoUeV79eoVV65c4dGjR/j6+vLo0SP8/PxYtmwZhQoV4q+//mLFihUULlyYokWLUqdOHQoXLsyxY8cwMTGhZs2aMaYwhY/PRJNyEhFIfPkMQVou26NHj1iwYAEAtWvXZuDAgeTLl49Tp0590XUCAgJ4/PgxYWFhZMiQgUKFCpEzZ87kCPmLpeXPD6R83wKdybxx48Y0atSI77//HoCNGzfStGnTr75xdM3xU3E9U+3bt6/azBc9dCmtim+xjqioKHx9fWP1GJ88eTLVq1fnr7/+YtKkSWg0GiwsLNBqtTRu3Bg7OzuKFClCzZo1Wbhwod6fWRvaYiRfIi2W7eHDh7i6urJjxw4KFy7Mvn37cHR0TNS1/jsfPHzsHOnh4ZEqHtOkxc/vU1K+tO+zyVxRFAYPHsy5c+c4fvw4iqLQt29f2rRp89U3Njc359GjR+prPz8/ChYs+NXXNWRBQUGcPXtWTdYNGzakTp06nDt3jurVq6vHZc+eHa1Wq/5hbNCgAVeuXImxVvanPl31TghdPnz4wIwZM5g+fTpGRkZMnz6dSpUqJTqRQ8rMBy/Et+yzyVyj0dC6dWsuXLigzsaVVKpUqcLdu3d5+PAhhQoVYsOGDepKVmlZREQEDx8+xMvLi7x581K1alVevXpFmTJl1OeS8LGTUY4cOahTpw6lS5fGw8NDfZadN2/eGLVsMzOzGB3ShEisf/75hyFDhvDw4UM6dOjA7NmzMTc3/+omzJSYD16Ib5nOZvbq1atz7tw5qlSpkrQ3Tp+eRYsW0ahRIyIjI+nZs2eaWvTi5cuXBAYGUrRoURRFoWPHjly7do379+8THh4OQLdu3ahatSo5c+akVatWADRp0gStVkvx4sXVGnXWrFlloRuRrO7fv8+QIUP4999/sba25sCBA0k6TW5KzAcvxLdMZzI/fPgwS5YswdLSkixZsqg9nK9evfrVN2/atGmSPH/Xl6ioKHVBj6VLl3L69Gm1iTwgIID69etz4MABdRpca2trWrduHWNREPjYArJ06VJ57iNSXHBwMNOnT2fGjBkYGxsza9YsBg8eHGuugK/1pfPBCyG+jM5kvnv37pSII9W7dOlSjOfZXl5emJiYcOPGDQC2bNnC9evX0Wq1fPfdd1hZWVGxYkX1/H/++UdfoQsRi6IobN++HVdXV3x8fOjcuTMzZ85Mtn4rXzIfvBDiy+lM5kWKFOHixYscP34cjUZDrVq11Kk605JP18r+dDz20aNH0Wg0LFiwgFWrVpEpUyasrKywtbWlbNmy6vn//vtvktdmhEgOd+/eZfDgwezZs4eyZcvi6emJvb19st83ofPBCyG+nM5kPnHiRDZv3qx2gOvRowffffcdP/30U7IHl9SioqLw8/OLkbAnTZqEmZkZ06ZNY/Lkyeqx0UO8AgMDyZYtG+PHj2fChAkx1sr+lCRykdq9f/+eqVOnMmvWLDJmzMjcuXNxdnaWf7tCpAE6k/n69eu5dOmSuniCm5sblSpVStXJ/N27d9y5cwcvLy8cHBwoUKAAmzdvplu3bnz48EE9LmvWrPTt2xczMzM6dOhAuXLl0Gq1lCxZksyZM8e4ZpEiRVK6GEIkCUVR2Lp1K0OHDuXRo0d06dKFGTNmkD9/fn2HJoRIIjqTuaWlJSEhIWoyDw0NTRVrFEdGRuLt7U22bNnIkycPN27cwMXFBS8vL54+faoet3nzZtq3b49Wq6V///4xpivNnz+/OsSrXLlylCtXTl/FESJZ3L59m8GDB7N//37Kly/PunXrsLOz03dYQogkpjOZm5iYUKZMGRwdHdFoNOzfvx87OzsGDx4MoE7zmBJevnxJ27Zt8fLy4t69e4SFhTFnzhyGDh1KlixZCA0NpVGjRjESdokSJQAoX748c+bMSbFYhdCnoKAgJk2axNy5c8mcOTMLFixgwIABpE+v87+8EMIA6fyf3aZNmxgzvulz6NTr16+5desWWq2W5s2bo9VqqV27NvCxBeHkyZN6i02I1EBRFDZt2sTw4cN5/Pgx3bt3Z/r06eTLl0/foQkhkpHOZN6tW7eUiCNBSpYsya1bt/QdhhCp0s2bNxk0aBCHDh2iYsWKbN68mRo1aug7LCFECojdLfs/du7cScWKFcmZMyfZsmUja9asZMuWLSViE0IkQGBgICNGjKBChQpcvHiRxYsXc+7cOUnkQnxDdNbMXV1d2bp1K+XKldP7qltCiP9RFIX169czYsQInj59Su/evZk6dSp58uTRd2hCiBSmM5kXLlyYsmXLSiIXIhW5fv06Li4uHDlyhMqVK/P3339TtWpVfYclhNATncl8xowZNG3aFHt7e0xMTNTtw4YNS9bAhBCxvX37lvHjx7Nw4UKyZ8/O0qVL6dWrF0ZGRvoOTQihRzqTubu7O6ampoSEhBAWFpYSMQkh/kNRFNasWcPIkSN58eIFffv2ZcqUKeTKlUvfoQkhUgGdyTwgIIB9+/alRCxCiDhcuXIFZ2dnTpw4QbVq1di5cyeVK1fWd1hCiFREZ2/2Bg0aSDIXQg/evHnD4MGDqVSpEl5eXixfvpyTJ09KIhdCxKIzmS9evJjGjRuTKVOmVDs0LSoqin79+lGrVi1q166trsy0Z88eVq9eDXwcvtOrVy/s7e2xs7NjxowZKIqS6HuGhITg5OSk3i8kJCTWMT///DNFihShQYMGMbZfuHCBhg0bUq9ePX788UfgYwvI5cuX8fT0JCAgAIAlS5ZQrVo17O3tqVWrFgEBATx79ozhw4cDMH78eAoWLKjON79q1aoYi8UkR5kAwsPDKVmyZIx7NWrUiDx58nzV/cVHUVFR/P7771hZWbF48WIGDBiAl5cXvXr1inORHyGE0PmXITAwkKioKD58+MC7d+8IDAzk3bt3KRFbgu3Zs4eIiAhOnDjBsWPHWLhwIQCNGzemS5cuwMchdra2thw5coQjR45w6dIl1qxZk+h7rlq1ilKlSnHs2DG0Wi2rVq2KdczAgQM5fPhwjG1hYWG4ubmxZcsWDh8+zIwZMwDYunUr+/btY/Xq1Wzbto2goCAWLFjAiRMnOHLkCHv27MHU1JT8+fMze/Zs9Xp58+Zl6dKlCY45rji/pEwAS5cupVSpUjG2rVixgpkzZyYoDhG/ixcvYmdnR8+ePSlRogTnz59n0aJF5MyZU9+hCSFSsXiT+cWLFz/7k5qYmppy/fp1dXa46D980TXVqKgoDh8+zIABAwAwMjJi/Pjx/Pnnn4m+p6enJ82bNwegRYsWHD16NNYxBQoUiFWTOnXqFKampnTu3BkHBweOHTsGEGvon5GREW/fvuXkyZOEh4eTNWtWMmTIgLe3d4ya/oABA/j9999jrAaXnGUKCgpi9+7d6pK40czNzb/6/t+ygIAABg4cSOXKlbl//z6rVq3i+PHjVKxYUd+hCSEMQLwd4KKbcuOi0Wg4dOhQsgSUGHXq1KFfv34MHDgQb29vhgwZgqurq7rf39+fvHnzxkiYRYoU4dGjR7Gu1a9fP7y8vGJss7CwiJX4AwICyJEjBwBmZma8evUqQbE+efKEK1eucPnyZQIDA6lfvz63bt2iTZs2BAYGYmNjQ/ny5cmUKRMbNmxg7ty5/PDDDzRs2JDFixfHul6WLFlwcnJiyZIlajyJlZAyzZw5E1dXVx4/fvxV9xIfRUVFsXLlSkaPHk1AQACDBg1iwoQJmJmZ6Ts0IYQBiTeZ/7d5OLXr2bMnPXv25N27d9SpU4eWLVuq+/LkycOLFy9QFEVN6L6+vhQsWDDWdRLaZJ0zZ07evHkDfBz7m9Bm0Jw5c1KzZk2yZctGtmzZyJ07t/plw8bGJsZCNrVr16Z27dpERUXRt29f1q1bR7169WJd09nZmZo1a9K/f/8479mjRw8ePnzIs2fPgI8tFkWLFuX333//ojK9ePGCS5cuMWHChM8214uEOX/+PM7Ozpw9exY7OzsWL15M+fLl9R2WEMIApYn1EJ88eYKpqanaQc/U1DRG57Z06dJhb2+Ph4cH/fr1IyoqiokTJ9K9e/dY10pozdze3p5du3ZhY2PDrl27sLe3T1Cs1apV4+effyYiIoIPHz7w4sWLOMcKh4SE8OzZMywtLUmXLh158+YlKioqzmtmyZKFH374gaVLl9K+fftY+6OTdnQCjqvcCSnT1atX8ff3p3Hjxjx+/JjQ0FAqVKhAixYtElR28dGrV68YM2YMy5YtI1++fKxevRonJyeZZVEIkWhpIpn7+fkxdOhQ0qVLR0REBC1atKB48eLq82iA+fPn4+rqyrp163jw4AHdunWja9eusa6V0Jp59+7d6dmzJ7Vr18bc3FxNmNOnT6dZs2aUK1eORYsWsWHDBm7dukWDBg1YunQpxYsXZ9CgQdStW5fw8HB++eWXOGfvCg8Pp0+fPgQHB2NkZIS5uTljx45Va9f/5ezszKxZsxIUe2LL1KBBA/V5/apVq/Dz81MTeZ8+fTh58iShoaGcP3+ev//++6tiSYsiIyNZvnw5Y8aM4e3bt7i6ujJ+/PhUNzpECGGAFANiZWWVJNfZtWuXUrNmTSUwMDBJrpdUDh8+rO8QklVaLp+usp0+fVqxtbVVAMXe3l65du1aygSWRNLyZ6coUj5Dl5bLZ2trm6DjdA5Nq1+/foK2GZImTZpw4sQJTE1N9R2KSOP8/f3p3bs31atX5+nTp6xbt47Dhw9TtmxZfYcmhEhD4m1mDwkJITg4mJcvX/L69Wv1GfS7d+948uRJigUohCGKjIxkyZIl/PTTTwQFBTFixAjGjh1L1qxZ9R2aECINijeZL126lHnz5vHkyRMqVaqkbs+WLRvOzs4pEpwQhujkyZM4Oztz+fJlHBwcWLhwIaVLl9Z3WEKINCzeZvYhQ4bw8OFDZs2axcOHD9WfK1eu4OLikpIx6tXUqVOpVasWDg4OeHt7x9o/Z84c6tSpQ61atejatSvh4eHqvrimPU0uuqZi/fDhA46OjtjZ2VG9enV2796t7hs3bhw1a9akbt26XL16NdljTaueP39O9+7dqVWrFv7+/mzatIkDBw5IIhdCJDudz8x79uzJ5MmT6du3LwB3795l586dyR5YanD79m0OHTrEiRMnGD9+PG5ubrGOcXFx4ejRo5w4cQIgxqI0cU17qktkZGSiYtU1FWv69OlZtmwZx48fZ+fOneqkOpcvX+bs2bOcPHmS1atXM2TIkETd/1sWERHBli1b0Gq1rFu3Djc3N27fvs13330nw82EECkiQck8Q4YMnDx5Evg4bedPP/2U7IEllqenJ40aNaJdu3bY2NiwefPmr7pWs2bNgI+zzF25ciXWMRkyZAA+rjcdFRVFiRIlgPinPY3L+PHj6d69O+7u7mzatCnRsX5uKlZjY2MsLS0ByJgxozrN7J07d7C1tQWgcOHCPHz4kNDQ0ETF8C06duwYtra2LFq0iGrVqnHt2jWmTZsmnSuFEClKZzK/f/8+P/74I8bGxgBkypTpq1YbSwmPHz9mw4YNHD16FHd391iTrfz222/UrVs31k/0amXRPp3eFOKvNU+ZMgUrKysCAgIoXLgw8L9pTxNaMzMxMWHKlCl8//33Mbb369cvVpxxjY//kullhwwZoq7WVrZsWTw9PQkLC+PKlSv4+fnx+vXrBMX8LXv69CldunShTp06vHnzhgkTJrBnzx60Wm2Kx7J27Vp1ciFLS0vWrl2b4jEIIfRL56QxGTJk4MOHD2pSun//PiYmJske2NeoWLEixsbGGBsbkzdvXvz9/cmXL5+6f8CAAeqiK5/z6fSmQJyTuwC4u7szZswYXFxcWLVqFe3bt//iaU9r1qwZ5/aknl520qRJ5MiRgx49egBQunRpOnfujKOjI8WLF6dMmTLkyZMnQff8FoWHh7No0SLGjRtHaGio+tmfPXtWL03qa9eupW/fvgQHBwPg4+OjPhKLXgpYCJH26UzmEyZMoHHjxjx69AgnJydOnDiR6uflvnz5sjpd6vPnz8mdO3eM/b/99hsbN26Mdd7WrVtjJEF7e3tcXV1xdXXl5MmTVKhQIdY5ISEhZMyYEY1GQ/bs2cmcOfNnpz319fXFwsIi1nXi+6KQlNPLLlq0iLt37/LHH3/E2D5w4EAGDhzI9evXmT59eryxfOs8PT1xcXHhxo0bNGnShPnz51OyZEm9xuTu7q4m8mjBwcG4u7tLMhfiG6IzmTs6OlKpUiVOnz6NoijMnz8/VnJMbQoWLMh3333Hw4cPmTx5cqzklNCaubW1NXZ2dtSqVYsMGTKwYsUK4OP66f7+/nTp0oXhw4dz48YN9Xn5hAkTMDY2jnPa0/DwcJo3b/5FPcaTanrZfPnyMWTIEGrUqKEu1nLw4EGMjIxo2LAhERER5MqVK86V2b51jx8/ZsSIEWzYsAFLS0v+/vtvWrZsmSo6t/n6+n7RdiFE2hRvMr99+zalSpVS1y4vUKAA8PGPhK+vb4yx56lN4cKFWb58eZJc6+eff+bnn3+Osa1x48bq77qS36eLmpw9e5Y+ffrEOmb8+PHAx5pfYmXKlIn169fH2v5pD/z4nvl/2gNf/E9YWBjz589n4sSJhIeHM3bsWNzc3MiUKZO+Q1NZWFjg4+MT53YhxLcj3mQ+Z84cPDw84lzXPLWtZ24oatWqRa1atfQdhkiAgwcP4uLiwu3bt2nevDnz5s2jePHi+g4rlilTpsR4Zg6QOXNmpkyZoseohBApLd5k7uHhARjeuubRPb6FSIxHjx4xfPhwNm/eTLFixfjnn3/UIX+pUfRzcXd3d7U/xpQpU+R5uRDfGJ3PzP/b0SpaXMOjhDBUYWFhzJkzh0mTJhEVFcWECRP48ccfyZgxo75D08nJyUmStxDfOJ3J/Ny5c+rvISEhHDx4kEqVKkkyF2nGvn37GDRoEHfu3KF169bMmTOHokWL6jssIYRIMJ3JfOHChTFev337li5duiRbQEKkFF9fX4YOHcrWrVspUaIEu3fvjtG5UQghDIXOGeD+K3PmzNy9ezc5YhEiRYSGhjJlyhRKlSrF7t27mTJlCtevX5dELoQwWDpr5i1atFDH00ZFRXHz5k06dOiQ7IEJkRx2797N4MGDuXfvHu3atWPOnDkyjEsIYfB0JvMRI0b87+D06SlSpAjm5ubJGpQQSe3hw4cMHTqU7du3o9Vq2bt3Lw0bNtR3WEIIkSR0NrMXLFiQt2/f8vbtW0nkwuB8+PCBCRMmULp0aQ4cOMD06dO5evWqJHIhRJoSb838zZs3dO/enatXr2JjY4OiKAwdOpQGDRqwZMkS9u7dK88YRar2zz//4OrqyoMHD+jQoQOzZ8+WL6NCiDQp3mQ+aNAgbGxs2Lp1q7r2taIoTJ48mRYtWuDl5SUd4USqdP/+fYYMGcK///6LtbU1Bw4coH79+voOSwghkk28zeynT59m/PjxaiKHj9O4/vzzz5w9e5Zdu3Yl+qabN2+mTJkypEuXjvPnzyf6OkJ8Kjg4mLFjx1KmTBmOHDnCzJkzuXz5siRyIUSaF28yVxQl3pOyZ8/+VUs/li1blq1bt1KnTp1EX0OIaIqi8Pfff1O6dGkmTZpE27Zt8fLyYsSIEWTIkEHf4QkhRLKLN5nXqlWLiRMnxkrqkydPpkaNGl91U2tra7Ra7VddQwiAu3fv0rRpU9q0aYOpqSmHDx9m3bp1FCxYUN+hCSFEion3mfnChQvp1asXJUqUwMbGBo1Gw6VLl6hYsSIrV65MyRiFiOX9+/dMnTqVWbNmYWJiwty5c3F2dsbY2FjfoQkhRIrTKJ9rT+djZ6KbN2+iKAplypRJ8DKQDRo04NmzZ7G2T5kyhVatWgEfVzibNWsWlStXjvc6Hh4e6gpuvr6+bNq0KUH3N0RBQUGYmprqO4xkkxTlUxSFo0eP8uuvv/LixQscHR3p378/OXPmTKIoE0c+O8Mm5TNsabl8I0aMSFjfMkWP7O3tlXPnziX4eCsrq2SMRv8OHz6s7xCS1deW7/bt24qjo6MCKOXLl1eOHTuWNIElAfnsDJuUz7Cl5fLZ2tom6LgvnptdiJQWFBSEm5sb5cqV4+zZsyxYsIALFy5gZ2en79CEECJV0Esy37ZtG+bm5pw6dYpmzZrRqFEjfYQhUjlFUdi0aROlSpXil19+wcnJCS8vLwYNGkT69DpnIhZCiG9Ggv4iRkZG8vz5cyIiItRtX7M4RZs2bWjTpk2izxdp382bNxk0aBCHDh2iYsWKbNq0iZo1a+o7LCGESJUStJ75hAkTyJcvnzqBjEaj4erVq8kenPj2BAYGMmHCBObPn4+pqSmLFy+mX79+GBkZ6Ts0IYRItXQm8/nz5+Pl5UWuXLlSIh7xjVIUhfXr1zNixAiePn1Kr169mDZtGnny5NF3aEIIkerpTOaFCxcme/bsKRGL+EZdv34dFxcXjhw5gq2tLdu2baNatWr6DksIIQyGzmRerFgx6tatS7NmzTAxMVG3Dxs2LFkDE2nf27dvGT9+PAsXLiR79uwsWbKE3r17S5O6EEJ8IZ3J3MLCAgsLC8LCwggLC0uJmEQapygKa9asYeTIkbx48YK+ffsyZcoUeZQjhBCJpDOZjxs3LiXiEN+IK1eu4OLiwvHjx6latSo7d+787AyAQgghdIs3mbu6ujJv3jxatGiBRqOJtX/Hjh3JGphIW968ecOCBQvYvn07OXPmZPny5fTo0SPGErtCCCESJ95k3qVLF+DjvLBCJFZUVBR//vkno0aN4uXLl/Tv359JkybpfS51IYRIS+JN5ra2tgDY29unWDAibbl48SIuLi6cOnWKGjVqMHnyZPr06aPvsIQQIs2RNk6R5AICAnB2dqZKlSrcu3eP33//nePHj1OyZEl9hyaEEGmSJHORZKKioli+fDlarZYlS5bg4uLCnTt36N69uzwbF0KIZCR/YUWSOH/+PDVq1KBPnz6UKlWKixcvMn/+fMzMzPQdmhBCpHk6h6bduXOHmTNn4uPjE2OhlUOHDiVrYMIwvHr1ijFjxrBs2TLy5cvH6tWrcXJyinMEhBBCiOShM5l/99139O/fnz59+sjMXEIVGRnJ8uXLGTNmDG/fvsXV1ZXx48eTLVs2fYcmhBDfHJ3JPH369AwYMCAlYhEG4syZMzg7O3PhwgXs7e1ZtGgRZcuW1XdYQgjxzYr3mXlAQAABAQG0aNGCX3/9ladPn6rbAgICUjJGkUr4+/vTu3dvqlevzpMnT1i3bh2HDx+WRC6EEHr22XHmGo0GRVEAmDlzprpPo9Hw4MGD5I9OpAqRkZEsXboUd3d3goKCGDFiBGPHjiVr1qz6Dk0IIQSfSeYPHz5MyThEKnXq1CmcnZ25dOkSDg4OLFy4kNKlS+s7LCGEEJ/QOTRt8eLFvHnzRn39+vVrfv311+SMSaQCL168oEePHtSsWZMXL16wceNGDhw4IIlcCCFSIZ3JfNmyZTHGCufIkYNly5YlZ0xCjyIiIli4cCFWVlasXbuWUaNGcfv2bTp06CDDzYQQIpXS2Zs9KioKRVHUP+SRkZGyrnkadezYMVxcXLh69SqOjo4sXLgQrVar77CEEELooLNm3qhRIzp06MDBgwc5dOgQ33//PY0bN06J2EQKefr0KV26dKFOnTq8fv2aLVu2sHfvXknkQghhIHTWzH/55ReWLl3Kb7/9hqIoNGzYkN69e6dEbCKZhYeHs2jRIsaNG0doaCju7u6MHj2aLFmy6Ds0IYQQX0BnMk+XLh3du3fHwcFBamppyJEjR3BxceH69es0adKE+fPny6pmQghhoHQ2s+/YsQMbGxu1af3y5cu0bNky2QMTyePJkyd07tyZunXrEhQUxN9//82///4riVwIIQyYzmQ+YcIEzp49q/Zot7GxwdvbO5nDEkktPDycWbNmodVq2bp1K2PHjuXmzZu0atVKeqkLIYSBS9Dc7NmzZ0+JWEQyOXToEC4uLty6dYvmzZszb948ihcvru+whBBCJBGdNfOyZcuybt06IiMjuXv3LoMGDaJmzZopEZv4Sn5+fnTs2JH69esTGhrKP//8wz///COJXAgh0hidyXzhwoXcuHEDExMTOnfuTPbs2Zk3b14KhCYSKywsjF9++YVSpUqxY8cOJkyYwI0bN2jevLm+QxNCCJEMdDazZ86cmSlTpjBmzBgZsmQA9u3bx6BBg7hz5w6tWrVi7ty5FC1aVN9hCSGESEY6a+YnT56kdOnSWFtbA3DlyhUGDhyY7IGJL+Pr60u7du1o1KgRUVFR7Nq1i7///lsSuRBCfAN0JvOhQ4eyd+9ecuXKBUCFChU4evRosgcmEiY0NJQpU6ZQqlQpdu/ezeTJk9Wx40IIIb4NOpvZAQoXLhzjtZGRUbIEI77M7t27GTx4MPfu3aNdu3bMnj2bIkWK6DssIYQQKUxnzbxw4cKcPHkSjUZDWFgYs2bNUpvchX54e3vTunVrmjZtSrp06di7dy9//fWXJHIhhPhG6UzmS5YsYfHixTx+/JhChQpx+fJlFi9enBKxif8ICQlh4sSJWFtbc+DAAaZPn861a9do2LChvkMTQgihRzqb2XPnzs3atWtTIhbxGTt37mTIkCE8ePCADh06MHv2bMzNzfUdlhBCiFRAZ838wYMHtGjRgjx58pA3b15atWrFgwcPUiI2wf/e/xYtWmBiYsKBAwfYuHGjJHIhhBAqncm8c+fOdOjQgadPn/LkyRO+++47vv/++5SI7Zv24cMHxo0bR+nSpfH09GTmzJlcvnyZ+vXr6zs0IYQQqYzOZK4oCl26dCF9+vSkT5+eH374QRbmSEaKorB9+3ZKly7NxIkTadu2Lbdv32bEiBFkyJBB3+EJIYRIhXQ+M69Xrx7Tp0+nU6dOaDQaNm7cSLNmzQgICAAgZ86cyR7kt8LPz49mzZqxe/duypQpw+HDh6lbt66+wxJCCJHK6UzmGzduBGDp0qUxtq9cuRKNRiPPz5PA+/fvmTp1KjNnziRjxozMmTMHFxcXjI2N9R2aEEIIA6AzmT98+DAl4vgmKYrC1q1bGTZsGL6+vjg6OvLHH39QoEABfYcmhBDCgMT7zPzcuXM8e/ZMff3nn3/SqlUrBg8erDaxi8Tz8vKiUaNGtG/fHjMzM44ePcqYMWMkkQshhPhi8Sbzfv36qR2ujh49ipubG127diV79uz07ds3xQJMa4KCgnBzc6NcuXKcOXOGBQsWcOHCBWrXrq3v0IQQQhioeJvZIyMj1c5tGzdupG/fvrRr14527dphY2OTUvGlGYqisHnzZoYPH46fnx/du3dn+vTp5MuXT9+hCSGEMHDx1swjIyOJiIgA4ODBgzg4OKj7orcn1siRIylVqhTly5enTZs2vHnz5quul9rdunULR0dHOnbsSO7cuTlx4gS///67JHIhhBBJIt5k/v3332Nvb0+rVq3IlCmT2gx87949smfP/lU3dXR05Pr161y9ehUrKyumTZv2VddLrQIDAxk5ciTly5fnwoULLF68mPPnz1OzZk19hyaEECINibeZ3d3dnfr16/P06VMaNmyoThQTFRXFwoULv+qmny4MUr16df7666+vul5qoygKGzZsYMSIETx58oRevXoxbdo08uTJo+/QhBBCpEGfHZpWvXr1WNusrKySNICVK1fSsWPHJL2mPl2/fh0XFxeOHDmCra0tW7dupVq1avoOSwghRBqmURRFSY4LN2jQIMbQtmhTpkyhVatW6u/nz59n69at8U4R6+HhgYeHBwC+vr5s2rQpOcL9akFBQfzxxx9s3boVU1NTevXqRbNmzTAyMvqia5iamiZjlPqVlsuXlssGUj5DJ+UzXCNGjOD8+fO6D1T0ZNWqVUr16tWV9+/fJ/gcKyurZIwocaKiopTVq1cr+fLlUzQajdK3b1/l5cuXibrW4cOHkza4VCYtly8tl01RpHyGTspnuGxtbRN0nM4Z4JLDnj17+OWXXzhy5AiZM2fWRwhJ4sqVK7i4uHD8+HGqVq3Kzp07qVy5sr7DEkII8Y3RuWpacnBxcSEwMBBHR0dsbGzo37+/PsJItDdv3jB48GAqVarErVu3WLZsGadOnZJELoQQQi/0UjO/d++ePm771aKiovjzzz8ZNWoU/v7+DBgwgEmTJsnKcUIIIfRKL8ncEF26dAlnZ2dOnTpFjRo12L17N5UqVdJ3WEIIIYR+mtkNyevXr3F2dqZy5crcu3eP33//nePHj0siF0IIkWpIMo9HVFQUK1aswMrKiiVLluDs7MydO3fo3r076dLJ2yaEECL1kKwUh+gpV3v37k2pUqW4ePEiCxYswMzMTN+hCSGEELFIMv/Eq1ev6N+/P1WrVsXb25s///yTo0ePUqFCBX2HJoQQQsRLkjkfV4jz8PDAysqK5cuXM2TIELy8vOjSpUu8M9MJIYQQqcU335v97NmzODs7c/78eerUqcOiRYsoV66cvsMSQgghEuybrZn7+/vTp08fqlevzuPHj1m7di2enp6SyIUQQhicby6ZR0ZG8uuvv6LValm1ahXDhg3j9u3bdO7cWZrUhRBCGKRvqpn91KlTODs7c+nSJRwcHFi4cCGlS5fWd1hCCCHEV/kmauYvXrygR48e1KxZkxcvXrBx40YOHDggiVwIIUSakKaTeUREBAsXLsTKyoq1a9cyatQobt++TYcOHaRJXQghRJqRZpvZjx8/jrOzM1evXsXR0ZGFCxei1Wr1HZYQQgiR5NJczfzZs2d07dqV2rVr8/r1a/766y/27t0riVwIIUSalWaSeUREBPPmzUOr1bJx40bGjBnDrVu3aNeunTSpCyGESNPSRDP7kSNHcHFx4fr16zRu3JgFCxZQsmRJfYclhBBCpAiDrpk/efIEJycn6tatS2BgIH///Te7du2SRC6EEOKbYpDJPDw8nNmzZ6PVatmyZQtjx47l5s2btGrVSprUhRBCfHMMrpn90KFDuLi4cOvWLZo3b868efMoXry4vsMSQggh9MagkvnTp0+pX78+xYoV459//qF58+b6DkkIIYTQO4NqZg8KCmLChAncuHFDErkQQgjx/wyqZm5pacnYsWP1HYYQQgiRqhhUzdzY2FjfIQghhBCpjkElcyGEEELEJslcCCGEMHCSzIUQQggDJ8lcCCGEMHBpIplHRUXRr18/atWqRe3atXFycgJgz549rF69GoDAwEB69eqFvb09dnZ2zJgxA0VREn3PkJAQnJyc1PuFhITEOubly5d07NgRBwcHGjZsqG5v1KgRefLkYfLkyeq2gIAALl++jKenJwEBAQAsWbKEatWqYW9vT61atQgICODZs2cMHz4cgPHjx1OwYEE+fPgAwKpVq2Jc82tNnTqVWrVq4eDggLe3d6z9AQEBNG/enNq1azNo0CAUReHDhw84OjpiZ2dH9erV2b17d5LFI4QQIm5pIpnv2bOHiIgITpw4wbFjx1i4cCEAjRs3pkuXLgC4urpia2vLkSNHOHLkCJcuXWLNmjWJvueqVasoVaoUx44dQ6vVsmrVqljHuLq6MnbsWA4dOsS+ffvU7StWrGDmzJkxjt26dSv79u1j9erVbNu2jaCgIBYsWMCJEyc4cuQIe/bswdTUlPz58zN79mz1vLx587J06dIExxxXnHG5ffs2hw4d4sSJE4wfPx43N7dYx8yYMYOOHTty7Ngx3r9/z969e0mfPj3Lli3j+PHj7Ny5E1dX1wTdTwghROKliWRuamrK9evXuXXrFgA5c+YE/ldTjYqK4vDhwwwYMAAAIyMjxo8fz59//pnoe3p6eqoT17Ro0YKjR4/G2B8ZGcn169eZPXs29vb2/Prrr+o+c3PzWNf775zyRkZGvH37lpMnTxIeHk7WrFnJkCED3t7eNGjQQD1uwIAB/P7772rtPKl4enrSrFkzAOrUqcOVK1fiPOa/74GxsTGWlpYAZMyYkXTp0sQ/MSGESNUMatKY+NSpU4d+/foxcOBAvL29GTJkSIwaob+/P3nz5o2RMIsUKcKjR49iXatfv354eXnF2GZhYREr8QcEBJAjRw4AzMzMePXqVYz9L1684Nq1a/zxxx9YW1vj4OBAvXr1sLa2jrMMbdq0ITAwEBsbG8qXL0+mTJnYsGEDc+fO5YcffqBhw4YsXrw41nlZsmTBycmJJUuWqPEkhYCAAAoWLKi+joyMjHXM69evMTMzA+J+D4YMGcKPP/6YZDEJIYSIW5pI5gA9e/akZ8+evHv3jjp16tCyZUt1X548eXjx4gWKoqgJ3dfXN0ayipbQJuucOXPy5s0bAN6+fau2Bny6v2DBglSoUAGAunXrcu3atXiTec6cObGxsaFu3brqttq1a1O7dm2ioqLo27cv69ato169erHOdXZ2pmbNmvTv3z/Oa/fo0YOHDx/y7Nkz4GOLRdGiRfn9999jHNe8eXOCgoJwcXGJUT742FLwXzly5ODt27eYmZnFeg8mTZpEjhw56NGjR5wxCSGESDppIpk/efIEU1NTsmXLRtasWTE1NY3RuS1dunTY29vj4eFBv379iIqKYuLEiXTv3j3WtRJaM7e3t2fXrl3Y2Niwa9cu7O3tY+w3MTGhWLFiPHr0iMKFC3PhwgXatm2b4DKFhITw7NkzLC0tSZcuHXnz5iUqKirOY7NkycIPP/zA0qVLad++faz90Uk7+nl5XOUG2Llzp/r7rVu3cHV1xdXVlZMnT6pfSj4V/R507tyZXbt2qeVbtGgRd+/e5Y8//khweYUQQiRemkjmfn5+DB06lHTp0hEREUGLFi0oXrw4x44dU4+ZP38+rq6urFu3jgcPHtCtWze6du0a61oJrZl3796dnj17Urt2bczNzdWEOX36dJo1a0a5cuWYP38+P/zwA+Hh4Tg4OFCpUiUA+vTpw8mTJwkNDeX8+fP8/fffsa4fHh5Onz59CA4OxsjICHNzc8aOHavWrv/L2dmZWbNmJSj2hLC2tsbOzo5atWqRIUMGVqxYAXzsbOjv70+XLl348ccf6dq1K7/99hvly5enYcOGvHjxgiFDhlCjRg21FeHgwYNx1uyFEEIkEcWAWFlZJcl1du3apdSsWVMJDAxMkusllcOHD+s7hGSVlsuXlsumKFI+QyflM1y2trYJOu6b7GrcpEkTTpw4gampqb5DEUIIIb7aN5nMhRBCiLREkrkOq1atombNmtSqVYuLFy/G2h/fTHDe3t44ODhQq1Ytpk6dqh7fs2dPChQoQO/evVMk/rhmafuvadOmUaVKFapWrcqcOXMAUBSFPn36UKdOHRo1ahTnMD4hhBCpgyTz/xcaGkp4eHiMba9fv2bBggV4enqyZs0aBg8eHOu8+GaCc3NzY8KECZw4cYJDhw5x+/Zt4OOQrfXr139xfHGN806IuGZp+1RgYCArV67k9OnTnDx5kiVLlvD+/Xu2b9+OkZERR48eZeLEiXHOACeEECJ1SHPJ3NPTk0aNGtGuXTtsbGzYvHlzvMcqisKxY8fo168fDRo04O3btzH2nzlzhtq1a5MhQwaKFi1KUFAQoaGhse4X10xwly9fpnbt2gA0a9ZM3V6oUKEEl6V79+7079+f5s2bx+iZ/yV0zVSXKVMmdX73Dx8+kClTJoyNjblz5w6VK1cGoEqVKhw5ciRR9xdCCJH80sTQtP96/Pgxly5d4sOHD1SuXJl27drFmFbUx8dHnT+8WrVqDBw4MM5x1J/O8gaQPXt2AgICKFCgQJzHfDoL2qdjws3MzOIdUqZLkSJFWLJkSaztrVq1ivXlo2rVqsyYMSPGNl2ztKVPn56mTZui1WqJiorip59+IkOGDJQrV46VK1fSq1cvdu/ezcuXLxMVvxBCiOSXJpN5xYoVMTY2xtjYmLx58+Lv70++fPnU/efPn2fTpk1069YNJycndS7x//rvLGjxzfQW10xwn355iOu8hKpZs2ac27dv356g8z83SxvAnTt32LJlCw8ePCAqKgp7e3tat25NkyZNOH36NHXr1qVSpUqUKVMmUfELIYRIfmmumR0+NnFHREQQGBjI8+fPyZ07d4z97dq149atW1SqVAl3d3ccHR1ZtGhRrCb0atWqcfz4ccLDw/H19cXU1BQTE5MYx0TPggbEmAmuQoUKnDx5EoDdu3dTp06deOONiIjgyZMnce6Lb7KVVq1aUbdu3Rg/cc2DHl980RRFIWvWrJiYmJApUyZMTEwICgoCYMKECRw5coQmTZpQv379eOMXQgihX2myZl6wYEG+++47Hj58yOTJk+NMiEZGRjRp0oQmTZoQFBTEli1beP/+fYxknSNHDgYOHIi9vT0ajYb58+cDH78s7N+/n5EjR8Y7E9y0adPo1asXYWFhNGnSRJ2T/aeffmL37t08e/aMBg0asH37dp48ecLw4cMZNmxYgsuY0Jp5XLO0wcflWd3d3dFqtVStWpXq1aujKAr16tVDq9Xy+vVr2rRpg5GREUWKFFGXlRVCCJH6aJS4xiqlUlqtNta86f8V3fN8+fLlKRTV11u7dq06r/ynC62kNZ6enmm2fGm5bCDlM3RSPsNVuXJlzp8/r/O4NFkzNzROTk7Ax3+QQgghxJfSSzL/+eef2b59u7oa2KpVq+JcjjQxop8fCyGEEN8KvXSAGzlyJFevXuXy5cs0b96ciRMn6iMMIYQQIk3QSzLPli2b+vv79+/RaDT6CEMIIYRIE/TWAc7d3Z0///yT7Nmzc/jwYfLkyRPncR4eHnh4eADg6+vLpk2bUjLMFBUUFJSmV3JLy+VLy2UDKZ+hk/IZrhEjRiSoA1yyJfMGDRrEOevZlClTaNWqlfp62rRphISEMGHCBJ3XTEhvdkOWlntkQtouX1ouG0j5DJ2Uz3DpvTf7gQMHEnRc586dadasWYKSuRBCCCFi08sz87t376q/79ixg1KlSukjDCGEECJN0MvQNDc3N7y8vEiXLl28C4kIIYQQImH0ksy3bNmij9sKIYQQaVKaXGhFCCGE+JZIMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsBJMhdCCCEMnCRzIYQQwsDpZW52IYQQiRceHo6fnx8hISEJOj579uzcunUrmaPSn7RQvowZM2Jubo6xsXGizpdkLoQQBsbPz4+sWbNiaWmJRqPReXxgYCBZs2ZNgcj0w9DLpygKr169ws/Pj6JFiybqGtLMLoQQBiYkJIRcuXIlKJGL1E+j0ZArV64Et7TERZK5EEIYIEnkacvXfp6SzIUQQiTKtm3b0Gg03L59W93m7e1N2bJlAfD09KR58+axzgsODsbJyYly5cpRtmxZ7OzsCAoK4s2bN/z6669fHMexY8fU++zYsYPp06cnskSGS5K5EEKIRFm/fj12dnZs2LDhi86bP38++fLl49q1a1y/fp0VK1ZgbGyc6GT+qZYtW+Lm5vZV1zBEksyFEEJ8saCgIE6cOMGKFSu+OJk/ffqUQoUKqa+1Wi0mJia4ublx//59bGxsGDlyZKyavYuLC6tWrQJgz549lCpVCjs7O3bs2KEes2rVKlxcXADw9/enXbt2VKlShSpVqnDixAkAjhw5go2NDTY2NlSsWJHAwMDEvg2phvRmF0IIA1e3bt1Y2zp06MDAgQMJDg6madOmGBkZxdjfvXt3unfvzsuXL2nfvn2MfZ6enjrv+ffff9O4cWOsrKzImTMnFy9epFKlSgmKt2fPnjRs2JC//vqL+vXr061bN0qWLMn06dO5fv06ly9f/mwcISEh9OnTh0OHDlGiRAnatWsX53FDhgxh6NCh2NnZ4evrS6NGjbh16xazZs1i8eLF1KpVi6CgIDJmzJiguFMzqZkLIYT4YuvXr6dTp04AdOrUifXr1yf4XBsbGx48eMDIkSMJCAigSpUqXzRO/Pbt2xQtWpSSJUui0Wjo2LFjnMcdOHAAFxcXbGxsaNmyJe/evSMwMJBatWoxbNgwFixYwJs3b0if3vDrtYZfAiGE+MZ9riadOXNmdu3aFe847Ny5cyeoJv6pV69ecejQIa5fv45GoyEyMhKNRsOMGTMSfA1TU1Patm1L27ZtSZcuHbt27YpVw06fPj1RUVHq60+HbiWk93dUVBSnTp0iU6ZMMba7ubnRrFkzdu3aRfXq1Tlw4AClSpVKcOypkdTMhRBCfJG//vqLrl274uPjg7e3N48ePaJo0aIcP348QeefOHGC169fAxAWFsbNmzcpUqQIWbNmjfH8ukiRIty8eZPQ0FDevn3LwYMHAShVqhQPHz7k/v37AGzevDnO+zRs2JBFixapr6Ob7+/fv0+5cuUYNWoUlStXjtEb31BJMhdCCPFF1q9fT5s2bWJsa9euHevWrUvQ+ffv38fe3p5y5cpRsWJFKleuTLt27ciVKxe1atWibNmyjBw5ksKFC9OhQwfKly+Pk5MTFStWBD5Oferh4UGzZs2ws7PDwsIizvssWLCA8+fPU758eUqXLs2SJUsAmDdvHmXLlqVChQpkypSJJk2afMW7kTpoFEVR9B1EQmm1Wry8vPQdRrLx9PSMsyNLWpGWy5eWywZSvtTm1q1bWFtbJ/h4Q5/uVJe0Ur64PtfKlStz/vx5nedKzVwIIYQwcJLMhRBCCAMnyVwIIYQwcJLMhRBCCAMnyVwIIYQwcJLMhRBCCAMnyVwIIcQX8/Pzo1WrVpQsWZLixYszZMgQwsLCdJ43derUGK9NTU0BePLkSaw54r+UpaUl5cqVUxdRGTx48Bdf49OFWhKqe/fu/PXXXwk+/tNlYpOKJHMhhBBfRFEU2rZtS+vWrbl79y537twhKCgId3d3nef+N5lHK1iw4BclxMjIyDi3Hz58mMuXL3P58mUWLFiQ4OsZOknmQgghvsihQ4fImDEjPXr0AMDIyIi5c+eycuVKgoODY9VumzdvjqenJ25ubnz48AEbGxucnJxiXPPT2mpkZCQjR46kSpUqlC9fnqVLlwIfJ/epV68enTt3ply5cgmKNSIigipVqqjzz48ePVr90rFnzx4qVapEhQoVqF+/fqxz/1vjjm5FUBQFFxcXSpcuTbNmzXjx4oV6zIULF7C3t8fW1pZGjRrx9OlTdXuFChWoUaMGixcvTlDsX0IWWhFCCAPm6uqqzjken8jIyFhLoH6OjY0N8+bNi3f/jRs3sLW1jbEtW7ZsWFhYcO/evXjPmz59OosWLdIZ74oVK8iePTvnzp0jNDSUWrVq0bBhQwDOnj3L9evXKVq0aJzn1qtXTy1rt27dGDp0KKtWraJ9+/YsWLCAPXv2cObMGfz9/enTpw9Hjx6laNGiBAQEfDamT23btg0vLy+uXbvG8+fPKV26ND179iQ8PJxBgwaxfft28uTJw8aNG3F3d2flypX06NGDhQsXYm9vz8iRIxN8r4SSZC6EEOKLKIoS56pl8W3/Uvv27ePq1atqrfjt27fcvXuXDBkyULVq1XgTOXxsZs+dO3eMbWXKlKFLly60aNGCU6dOkSFDBk6fPk2dOnXUa+XMmTPB8R09epTvv/8eIyMjChYsiIODAwBeXl5cv34dR0dH4OOXqAIFCvD27VvevHmDvb09AF26dGH37t0Jf0MSQJK5EEIYsM/VoKMl9dzlZcqUYcuWLTG2vXv3jkePHlG8eHGuXLkS79KlCaEoCgsXLqRRo0Yxtnt6epIlS5ZExXzt2jXMzMx4/vy5eg9dXzw+XYJVUZQYHfzi+zJTpkwZTp06FWP7mzdvkuRLzufIM3MhhBBfpH79+gQHB/Pnn38CH2ugw4cPp3v37mTOnBlLS0suX75MVFQUjx494uzZs+q5xsbGhIeHf/b6jRo14rffflOPu3PnDu/fv090vFu3buXVq1ccPXqUwYMH8+bNG2rUqMGRI0d4+PAhQJzN7JaWlly4cAGA7du3q/HUqVOHDRs2EBkZydOnTzl8+DDwcTEwf39/NZmHh4dz48YNzMzMyJ49u7pE7Nq1axNdlvhIMhdCCPFFNBoN27ZtY/PmzZQsWRIrKysyZsyo9lSvVasWRYsWpVy5cowYMYJKlSqp5/bt21dd0jQ+vXv3pnTp0lSqVImyZcvSr18/IiIiEhRbvXr11KFpXbt25eXLl7i5ubFixQqsrKxwcXFhyJAh5MmTBw8PD9q2bUuFChXo2LFjrGv16dOHI0eOULVqVc6cOaO2CrRp04aSJUtSrlw5BgwYoDafZ8iQgb/++otRo0ZRoUIFbGxsOHnyJAC///47zs7O1KhRg0yZMiXsjf4CsgRqKmJoyzB+qbRcvrRcNpDypTayBGpMaaV8sgSqEEII8Q2TZC6EEEIYOEnmQgghhIGTZC6EEAbIgLo7iQT42s9TkrkQQhiYjBkz8urVK0noaYSiKLx69YqMGTMm+hoyaYwQQhgYc3Nz/Pz88Pf3T9DxISEhX5UoUru0UL6MGTNibm6e6PP1msxnzZrFyJEj8ff3jzX9nhBCiLgZGxt/dkrT//L09KRixYrJGJF+pfXyJYTemtkfPXrE/v37sbCw0FcIQgghRJqgt2Q+dOhQZsyYkezz1QohhBBpnV6S+Y4dOyhUqBAVKlTQx+2FEEKINCXZnpk3aNCAZ8+exdo+ZcoUpk6dyr59+xJ0HQ8PDzw8PADw8fGhcuXKSRpnauLv70+ePHn0HUayScvlS8tlAymfoZPyGa7bt28n6LgUn5v92rVr1K9fn8yZMwPg5+dHwYIFOXv2LPnz5//suQmdo9ZQSfkMV1ouG0j5DJ2Uz3AltGwp3pu9XLlyvHjxQn1taWnJ+fPnpTe7EEIIkUgyaYwQQghh4PQ+aYy3t3eCj+3bt2/yBZIKSPkMV1ouG0j5DJ2Uz3AltGwGtZ65EEIIIWKTZnYhhBDCwBlsMp81axYajYaXL1/qO5Qk8/PPP1O+fHlsbGxo2LAhT5480XdISWrkyJGUKlWK8uXL06ZNG968eaPvkJLU5s2bKVOmDOnSpUtTPWv37NmDVqulRIkSTJ8+Xd/hJKmePXuSN29eypYtq+9QktyjR4+oV68e1tbWlClThvnz5+s7pCQVEhJC1apVqVChAmXKlGHcuHH6DilZREZGUrFiRZo3b/7Z4wwymafVqWBHjhzJ1atXuXz5Ms2bN2fixIn6DilJOTo6cv36da5evYqVlRXTpk3Td0hJqmzZsmzdupU6deroO5QkExkZibOzM7t37+bmzZusX7+emzdv6jusJNO9e3f27Nmj7zCSRfr06Zk9eza3bt3i9OnTLF68OE19diYmJhw6dIgrV65w+fJl9uzZw+nTp/UdVpKbP38+1tbWOo8zyGSeVqeCzZYtm/r7+/fv01z5GjZsSPr0H/tcVq9eHT8/Pz1HlLSsra3RarX6DiNJnT17lhIlSlCsWDEyZMhAp06d2L59u77DSjJ16tQhZ86c+g4jWRQoUIBKlSoBkDVrVqytrXn8+LGeo0o6Go0GU1NTAMLDwwkPD09zfzP9/Pz4999/6d27t85jDS6Zp/WpYN3d3SlcuDBr165NczXzT61cuZImTZroOwyhw+PHjylcuLD62tzcPE0lhG+Ft7c3ly5dolq1avoOJUlFRkZiY2ND3rx5cXR0THPlc3V1ZcaMGaRLpztV631oWlySairY1OhzZWvVqhVTpkxhypQpTJs2jUWLFjFhwgQ9RJl4usoX/Xv69OlxcnJK6fC+WkLKl5bENdglrdV+0rqgoCDatWvHvHnzYrT+pQVGRkZcvnyZN2/e0KZNG65fv55m+j/s3LmTvHnzYmtri6enp87jU2UyP3DgQJzbr127xsOHD9VauZ+fH5UqVUrQVLCpRXxl+6/OnTvTrFkzg0vmusr3xx9/sHPnTg4ePGiQSSGhn19aYW5uzqNHj9TX0dMvC8MQHh5Ou3btcHJyom3btvoOJ9mYmZlRt25d9uzZk2aS+YkTJ9ixYwe7du0iJCSEd+/e8cMPP7BmzZo4jzeoZvboqWC9vb3x9vbG3NycixcvGkwi1+Xu3bvq7zt27KBUqVJ6jCbp7dmzh19++YUdO3aoc/OL1K1KlSrcvXuXhw8fEhYWxoYNG2jZsqW+wxIJoCgKvXr1wtrammHDhuk7nCTn7++vjoj58OEDBw4cSFN/M6dNm4afnx/e3t5s2LABBweHeBM5GFgyT+vc3NwoW7Ys5cuXZ9++fWluKImLiwuBgYE4OjpiY2ND//799R1Sktq2bRvm5uacOnWKZs2a0ahRI32H9NXSp0/PokWLaNSoEdbW1nTo0IEyZcroO6wk8/3331OjRg28vLwwNzdnxYoV+g4pyZw4cYLVq1dz6NAhbGxssLGxYdeuXfoOK8k8ffqUevXqUb58eapUqYKjo6PO4VtpmcwAJ4QQQhg4qZkLIYQQBk6SuRBCCGHgJJkLIYQQBk6SuRBCCGHgJJkLIYQQBk6SudC7KVOmUKZMGXXFuDNnzqTIfS0tLXWuunf+/HkGDx78xddt166d+vqvv/6ie/funz1nx44dSbIiWfRc1d+ay5cvf3bY1aVLl9T5rVetWkWePHnU4Vo2NjbqAiQjR46kTJkyjBw5En9/f6pVq0bFihU5duxYksUTHByMk5MT5cqVo2zZstjZ2REUFARAzZo1v+g+0Ro0aMDr168Tda5IG1LlDHDi23Hq1Cl27tzJxYsXMTEx4eXLl4SFhek7LFXlypWpXLnyF593/vx5bty4keAx2S1btky1k7FERESoC+SkVpcvX+b8+fM0bdo0zv1Tp07lp59+Ul937NiRRYsWxTpu6dKl+Pv7Y2JiwoYNGyhVqhR//PFHksYzf/588uXLx7Vr1wDw8vLC2NgYgJMnT37xvQC6dOnCr7/+iru7e6LOF4ZPauZCr54+fUru3LkxMTEBIHfu3Op0oZaWlowaNYqqVatStWpV7t27B3yc+aldu3ZUqVKFKlWqcOLECeDjSnM9e/akyv+1d64xUR1tHP+7BJWoMVEgUYzghWt396zgAluiS0t2kXiJEoUQ06xUEyX1EoxYm0bcqhGbtNFCL/hFJGorES8xygeqQoIGWyESYjeKhWg1oOJtC8q66/LvB7IT13WBt+/rS6HzSzZwzs6Z+c/MCQ/zzHPOo9djzpw5IruX2+3Gli1boNFooNVqUVJSItovKSlBfHw8NBoNbty44aOvtrZWvIjCarXi448/RmpqKmbOnIni4mK//dqyZQv27Nnjc/7JkydYunQptFotkpOT0dzcDKBvtbh+/XoAfXnR1Wo1FEUR6VTdbjcKCgqg1+uh1Wpx4MCBfse1trYWRqMRWVlZiIqKwrZt23D06FEkJiZCo9GgtbUVQF8K0HXr1mHevHmIiorC2bNnhZ4VK1Zg8eLFMJvNb9Xd29uLiIgIr7z0s2fPxoMHD/zOkdVqhcVigdlsRkREBE6ePImtW7dCo9FgwYIFcLlcAIDGxkYYjUYkJCQgPT0dHR0dAIDU1FRxT0RFRaGurg5OpxOFhYWoqKiATqdDRUWF11h0dXWhubl5wORMS5YswfPnz5GUlIQvv/wSW7duRVVVFXQ6HXp6elBdXQ2DwYD4+HisWLFCrKavXr2K999/H4qiIDExEXa7vV89HR0dCAsLE8fR0dHi/vd4VgoLC4XXICwsDLm5uQCAI0eOIDExETqdDmvXroXb7Rbaf/rpp377JxnhUCIZQrq6uqgoCiMjI5mXl8fa2lrxXXh4OHfv3k2SLC8v58KFC0mSOTk5rKurI0neuXOHMTExJMnPPvuMhw8fJkk+ffqUkZGR7O7u5vfff8/MzEy6XC6S5OPHj0X9xcXFJMnvvvuOq1ev9tFXU1Mj2t2xYwcNBgMdDgc7Ozs5adIkOp1On2vCw8N5//59xsTE8NatWzx+/DgtFgtJcv369bRarSTJCxcuUFEUkmRZWRk/+eQTkqRarea9e/dEP0jywIED3LVrF0nS4XAwISGBbW1tPm2PGzdO6J44cSLb29vpcDg4depUFhYWkiT379/PTZs2kSQtFgvT09PpdrvZ0tLCsLAw9vT0sKysjGFhYWKs/OneuHEjDx48SJK8cuUK09LS+p2jHTt2MCUlhU6nk01NTQwKCmJVVRVJcunSpTx16hSdTicNBgMfPnxIkjx27Bhzc3NJkkajkZs3byZJnjt3TrT3+vi9ycWLF5mZmSmOy8rKGBwcTEVRxOfFixde4/dmnZ2dnZw3bx67u7tJknv37uUXX3zBly9fcsaMGfz1119Jkna7nS6Xq189165dY0hICJOTk/n555+zpaXFZ/48PHv2jBqNhg0NDbTZbFy0aJG45/Ly8lheXi7Kzp49m48ePXprm5KRzz/bdyYZ8YwfPx6NjY2oq6tDTU0NsrOzsXfvXrHHnJOTI37m5+cD6Et24tnjBIA///wTXV1dqK6uxpkzZ/DVV18BABwOB/744w+cP38e69atE67i1/NXe5JPJCQk4OTJkwPqXbhwIcaMGYMxY8YgNDQUDx48wLRp03zKBQQEoKCgAEVFRV6pXi9duoQTJ04AAD788EM8fvwYdrvd69qUlBSsWrUKWVlZQl91dTWam5tRWVkJALDb7bh16xZmzJjhV6ter8eUKVMAALNmzYLZbAbQl+OgpqZGlMvKyoJKpUJkZCRmzpwpPBQmk0mMlT/d2dnZ2LlzJ3Jzc3Hs2DFkZ2cD8D9HAJCRkYHAwEBoNBq43W4sWLBA6Lp9+zZu3ryJ69evw2QyAejzSnj6AXjP2e3bt/3230NHRwdCQkK8zvlzs/vjypUrsNlsSElJAQA4nU7xGtgpU6ZAr9cDwKCykul0OrS1taG6uhrnz5+HXq9HfX09YmNjvcqRxMqVK5Gfn4+EhAR8++23aGxsFG319PQgNDRUlA8NDUV7ezsmT5486H5JRg7SmEuGnICAAKSmpiI1NRUajQbl5eXCmL+eWc3ze29vL+rr6xEUFORVD0mcOHEC0dHRPuf9ZWjzuDcDAgLw6tWrAbV6yg/mmo8++ghFRUVe++YcRErR0tJS/PLLLzh37hx0Oh2amppAEiUlJf/R+95f16pSqcSxSqXy0v1m+57jcePGDajbYDDg999/R2dnJ06fPi32pf3N0eu6VCoVAgMDRXseXSTx3nvvob6+vt9+DXbOgoKC4HA4BizXHyRhMpl8XNnNzc1/K/vf+PHjkZmZiczMTKhUKlRVVfkYc6vVimnTpgkXO0lYLBYUFRW9tU6Hw/HW8Zb8O5B75pIh5ebNm17Z4pqamhAeHi6OPfuNFRUVMBgMAACz2ey1qmpqagIApKeno6SkRBiea9euifKlpaXiD/+TJ0/eXYdeIzAwEPn5+di/f784N3/+fBw9ehRA3752cHCwz2qutbUVSUlJ2LlzJ4KDg3H37l2kp6fjhx9+EHvKLS0teP78+f9E5/Hjx9Hb24vW1la0tbX5/DPUn+5Ro0Zh2bJl2Lx5M2JjY8Wq0N8cDYbo6Gh0dnYKY+5yufDbb7/1e82ECRPEyv9NYmNjRbzF3yU5ORmXL18W9bx48QItLS2IiYlBe3s7rl69CqBvf/7Vq1f96rl8+bKIPHc6nbDZbF73PNCXy/rnn3/2istIS0tDZWUlHj58CKDvPr5z5w6APkN///59RERE/Ff9lAxfpDGXDCnd3d2wWCyIi4uDVquFzWaD1WoV3798+RJJSUn45ptvsG/fPgBAcXExGhoaoNVqERcXh9LSUgDA9u3b4XK5oNVqoVarsX37dgDAmjVrMH36dGi1WiiKgh9//PH/1r/Vq1d7rR6tVqvQvm3btrdGShcUFIjHlubPnw9FUbBmzRrExcUhPj4earUaa9euHdSqdDBER0fDaDQiIyMDpaWlGDt2rE+Z/nRnZ2fjyJEjwsUO+J+jwTB69GhUVlbi008/haIo0Ol0A0Z5f/DBB7DZbG8NOIuJiYHdbvcyrp7gNM9noPpDQkJw6NAh5OTkiCDAGzduYPTo0aioqMCGDRugKApMJhMcDke/elpbW2E0GqHRaDBnzhzMnTvX61FGAPj666/R3t4ugt0KCwsRFxeH3bt3w2w2Q6vVwmQyicDAxsZGJCcn/+OfOpC8O2TWNMk/loiICDQ0NCA4OHiopYxYVq1ahUWLFmH58uVDLeWdsm/fPkyYMEE8az7S2LRpE5YsWYK0tLShliIZIuTKXCKRjHjy8vK8YghGGmq1WhryfzlyZS6RSCQSyTBHrswlEolEIhnmSGMukUgkEskwRxpziUQikUiGOdKYSyQSiUQyzJHGXCKRSCSSYY405hKJRCKRDHP+ApSofwk+YQhEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1: Filter the DataFrame for 'Site Selection' intervention group\n",
    "sdf = df.copy()\n",
    "\n",
    "# Step 2: Further filter for rows where both 'Noise_ES' and 'Quiet_ES' are not NaN\n",
    "filtered_df = sdf.dropna(subset=['Noise_ES', 'Quiet_ES'])\n",
    "noh = filtered_df[~filtered_df['Study'].str.startswith('Hen')]\n",
    "\n",
    "# Step 3: Extract the necessary columns\n",
    "Xs = filtered_df['Noise_ES']\n",
    "Ys = filtered_df['Quiet_ES']\n",
    "names = filtered_df['Study']\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(Xs, Ys)\n",
    "print(p_value, r_value)\n",
    "\n",
    "# Without\n",
    "X2s = noh['Noise_ES']\n",
    "Y2s = noh['Quiet_ES']\n",
    "slope2, intercept2, r_value2, p_value2, std_err2 = stats.linregress(X2s, Y2s)\n",
    "print('without', p_value2, r_value2)\n",
    "\n",
    "\n",
    "# Step 4: Create the scatter plot\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.scatter(Xs, Ys, c='black')\n",
    "plt.plot([-4,4], [slope*-4+intercept, slope*4+intercept], color='black', ls='--', label='All Studies')\n",
    "plt.plot([-4, 4], [slope2*-4+intercept2, slope2*4+intercept2], color='black', label = 'Outlier Excluded')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Speech in Noise Improvement (Effect Size)')\n",
    "plt.ylabel('Speech in Quiet Improvement (Effect Size)')\n",
    "plt.title('Relationship between Quiet & Noisy Conditions')\n",
    "# Optionally, add labels for each point\n",
    "for i, name in enumerate(names):\n",
    "    if name[:3] in ['Hen']:\n",
    "        plt.text(Xs.iloc[i] - 1.1, Ys.iloc[i] - 0.3, name, fontsize=9)\n",
    "        \n",
    "plt.text(-3.9, -0.7, f\"SiQ = {slope:.2f}*SiN + {intercept:.2f}\\n p = {p_value:.2f}, r = {r_value:.2f}\", fontsize=9)\n",
    "plt.text(-3.9, -2.9, f\"SiQ = {slope2:.2f}*SiN + {intercept2:.2f}\\n p < 0.001, r = {r_value2:.2f}\", fontsize=9)\n",
    "plt.xlim(-4,4)\n",
    "plt.ylim(-4,4)\n",
    "plt.grid()\n",
    "plt.legend(loc = 'lower right')\n",
    "#plt.savefig('F7_QuietvsNoise.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "625283c4-c8af-4ba2-b1d9-3c0302ca0326",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Meta-Regression to find best ENI measure for site selection\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "18dc84c5-846d-464a-816a-d21ecc1ec69c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Study', 'Data Source', 'SD Calculation', 'Correlation', 'Changed',\n",
       "       'ENI_Measure', 'Intervention', 'Study Design', 'Adaptation Period',\n",
       "       'Intervention_Group', 'Speech_Stimuli', 'RoB', 'CleanMA', 'Noise_ES',\n",
       "       'Noise_Diff', 'Noise_SD', 'Noise_N', 'Quiet_ES', 'Quiet_Diff',\n",
       "       'Quiet_SD', 'Quiet_N', 'ExtractionNote'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4c8e15f8-5d58-42fc-a2d7-6e2e333bacaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "sites = df.loc[(df.Intervention_Group == 'Site Selection'), ['Study', 'Noise_ES', 'ENI_Measure', 'Noise_N']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2fc9c843-fda0-4044-8373-797088924fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "sites['Var(d)'] = 2 / sites.Noise_N + sites.Noise_ES**2 / (4 * sites.Noise_N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "238f7543-7931-4976-a0e8-182383a37b9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            WLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               Noise_ES   R-squared:                       0.795\n",
      "Model:                            WLS   Adj. R-squared:                  0.557\n",
      "Method:                 Least Squares   F-statistic:                     3.332\n",
      "Date:                Sun, 19 Jan 2025   Prob (F-statistic):             0.0819\n",
      "Time:                        16:20:55   Log-Likelihood:                -5.8281\n",
      "No. Observations:                  14   AIC:                             27.66\n",
      "Df Residuals:                       6   BIC:                             32.77\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================================================\n",
      "                                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "C(ENI_Measure)[CT_Image]                           0.0891      0.171      0.522      0.620      -0.328       0.507\n",
      "C(ENI_Measure)[ECAP IPG Effect]                    0.0664      0.416      0.160      0.878      -0.951       1.084\n",
      "C(ENI_Measure)[Electrode_Discrimination]           0.9690      0.312      3.105      0.021       0.205       1.733\n",
      "C(ENI_Measure)[Low-Rate Threshold]                 1.6591      0.497      3.341      0.016       0.444       2.874\n",
      "C(ENI_Measure)[Modulation Detection Threshold]     1.3726      0.476      2.886      0.028       0.209       2.536\n",
      "C(ENI_Measure)[Multidimensional Pitch Scaling]    -2.0295      1.253     -1.619      0.157      -5.097       1.038\n",
      "C(ENI_Measure)[Partial Tripolar Threshold]         0.3300      0.592      0.557      0.597      -1.119       1.779\n",
      "C(ENI_Measure)[Polarity_Effect]                   -0.4158      0.630     -0.660      0.534      -1.958       1.127\n",
      "==============================================================================\n",
      "Omnibus:                        3.724   Durbin-Watson:                   2.423\n",
      "Prob(Omnibus):                  0.155   Jarque-Bera (JB):                1.735\n",
      "Skew:                           0.844   Prob(JB):                        0.420\n",
      "Kurtosis:                       3.352   Cond. No.                         7.35\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\PythonIDEs\\lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=14\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n"
     ]
    }
   ],
   "source": [
    "#WLS\n",
    "import statsmodels.formula.api as smf\n",
    "formula = 'Noise_ES ~ C(ENI_Measure) - 1'\n",
    "model = smf.wls(formula=formula, data=sites, weights=1/sites['Var(d)'])\n",
    "results = model.fit()\n",
    "\n",
    "# Print the summary of the regression\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b61b5b0-4751-4cd7-9613-3b8df2a042df",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Funnel PLot for assessing publication bias (Speech in Noise performance)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "eab65a05-7db3-4269-b810-e97fd2c6ea8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4829106683872356 0.19611032671689335 21\n"
     ]
    }
   ],
   "source": [
    "combined_effect_size, upper_bound, lower_bound, standard_deviation, es_p_value, Q, q_p_value, table = mixed_effect_model(df, 'Noise', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c93d7f9-f068-4dc4-8d56-d494465c23f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Study</th>\n",
       "      <th>ES</th>\n",
       "      <th>N</th>\n",
       "      <th>Var_d</th>\n",
       "      <th>SE(d)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Goehring et al. (2019)</td>\n",
       "      <td>-0.415816</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.255403</td>\n",
       "      <td>0.505374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jiam et al. (2019)</td>\n",
       "      <td>0.262357</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.126075</td>\n",
       "      <td>0.355071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Noble et al. (2014)</td>\n",
       "      <td>0.016145</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.027779</td>\n",
       "      <td>0.166669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saleh et al. (2013)</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.084020</td>\n",
       "      <td>0.289861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DeVries &amp; Arenberg (2018)</td>\n",
       "      <td>0.450253</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.227854</td>\n",
       "      <td>0.477340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Zhou (2017)</td>\n",
       "      <td>1.833152</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.355014</td>\n",
       "      <td>0.595830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Zhou &amp; Pfingst (2014)</td>\n",
       "      <td>1.339651</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.272074</td>\n",
       "      <td>0.521607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Noble et al. (2013)</td>\n",
       "      <td>0.826870</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.217093</td>\n",
       "      <td>0.465932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Zhou (2019)</td>\n",
       "      <td>1.518760</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.286295</td>\n",
       "      <td>0.535066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Grasmeder et al. (2014)</td>\n",
       "      <td>-0.976306</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.223829</td>\n",
       "      <td>0.473106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Zhou &amp; Pfingst (2012)</td>\n",
       "      <td>1.410427</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.312166</td>\n",
       "      <td>0.558718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bierer &amp; Litvak (2016)</td>\n",
       "      <td>0.329984</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.225247</td>\n",
       "      <td>0.474602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Schvartz-Leyzac et al. (2021)</td>\n",
       "      <td>0.066439</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.111172</td>\n",
       "      <td>0.333425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Henshall &amp; McKay (2001)</td>\n",
       "      <td>-2.029496</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.009904</td>\n",
       "      <td>1.004940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tabibi et al. (2020)</td>\n",
       "      <td>-1.070457</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.207861</td>\n",
       "      <td>0.455918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Di Maro et al. (2022)</td>\n",
       "      <td>0.942774</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.222221</td>\n",
       "      <td>0.471403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Fan et al. (2023)</td>\n",
       "      <td>2.100694</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.129301</td>\n",
       "      <td>0.359585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dirks et al. (2022)</td>\n",
       "      <td>-0.603497</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.298722</td>\n",
       "      <td>0.546554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Warren &amp; Atcherson (2023)</td>\n",
       "      <td>1.949151</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.245816</td>\n",
       "      <td>0.495799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kurz et al. (2023)</td>\n",
       "      <td>0.952118</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.222663</td>\n",
       "      <td>0.471872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>McRackan et al. (2017)</td>\n",
       "      <td>-0.189553</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.118175</td>\n",
       "      <td>0.343767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Combined Cohen's d</td>\n",
       "      <td>0.482911</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.038459</td>\n",
       "      <td>0.196110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Study        ES     N     Var_d     SE(d)\n",
       "0          Goehring et al. (2019) -0.415816   8.0  0.255403  0.505374\n",
       "1              Jiam et al. (2019)  0.262357  16.0  0.126075  0.355071\n",
       "2             Noble et al. (2014)  0.016145  72.0  0.027779  0.166669\n",
       "3             Saleh et al. (2013)  0.634000  25.0  0.084020  0.289861\n",
       "4       DeVries & Arenberg (2018)  0.450253   9.0  0.227854  0.477340\n",
       "5                     Zhou (2017)  1.833152   8.0  0.355014  0.595830\n",
       "6           Zhou & Pfingst (2014)  1.339651   9.0  0.272074  0.521607\n",
       "7             Noble et al. (2013)  0.826870  10.0  0.217093  0.465932\n",
       "8                     Zhou (2019)  1.518760   9.0  0.286295  0.535066\n",
       "9         Grasmeder et al. (2014) -0.976306  10.0  0.223829  0.473106\n",
       "10          Zhou & Pfingst (2012)  1.410427   8.0  0.312166  0.558718\n",
       "11         Bierer & Litvak (2016)  0.329984   9.0  0.225247  0.474602\n",
       "12  Schvartz-Leyzac et al. (2021)  0.066439  18.0  0.111172  0.333425\n",
       "13        Henshall & McKay (2001) -2.029496   3.0  1.009904  1.004940\n",
       "14           Tabibi et al. (2020) -1.070457  11.0  0.207861  0.455918\n",
       "15          Di Maro et al. (2022)  0.942774  10.0  0.222221  0.471403\n",
       "16              Fan et al. (2023)  2.100694  24.0  0.129301  0.359585\n",
       "17            Dirks et al. (2022) -0.603497   7.0  0.298722  0.546554\n",
       "18      Warren & Atcherson (2023)  1.949151  12.0  0.245816  0.495799\n",
       "19             Kurz et al. (2023)  0.952118  10.0  0.222663  0.471872\n",
       "20         McRackan et al. (2017) -0.189553  17.0  0.118175  0.343767\n",
       "21             Combined Cohen's d  0.482911  21.0  0.038459  0.196110"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "200dd3c4-22d6-4994-bab6-6897ee485dd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exclude the combined result, we want each dot representing one article\n",
    "table = table.loc[:len(table)-2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e0deb054-23d4-44f8-90a4-c57aa15f392d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Goehring et al. (2019)</td>\n",
       "      <td>-0.415816</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.255403</td>\n",
       "      <td>0.505374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jiam et al. (2019)</td>\n",
       "      <td>0.262357</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.126075</td>\n",
       "      <td>0.355071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Noble et al. (2014)</td>\n",
       "      <td>0.016145</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.027779</td>\n",
       "      <td>0.166669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saleh et al. (2013)</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.084020</td>\n",
       "      <td>0.289861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DeVries &amp; Arenberg (2018)</td>\n",
       "      <td>0.450253</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.227854</td>\n",
       "      <td>0.477340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Zhou (2017)</td>\n",
       "      <td>1.833152</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.355014</td>\n",
       "      <td>0.595830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Zhou &amp; Pfingst (2014)</td>\n",
       "      <td>1.339651</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.272074</td>\n",
       "      <td>0.521607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Noble et al. (2013)</td>\n",
       "      <td>0.826870</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.217093</td>\n",
       "      <td>0.465932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Zhou (2019)</td>\n",
       "      <td>1.518760</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.286295</td>\n",
       "      <td>0.535066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Grasmeder et al. (2014)</td>\n",
       "      <td>-0.976306</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.223829</td>\n",
       "      <td>0.473106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Zhou &amp; Pfingst (2012)</td>\n",
       "      <td>1.410427</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.312166</td>\n",
       "      <td>0.558718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bierer &amp; Litvak (2016)</td>\n",
       "      <td>0.329984</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.225247</td>\n",
       "      <td>0.474602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Schvartz-Leyzac et al. (2021)</td>\n",
       "      <td>0.066439</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.111172</td>\n",
       "      <td>0.333425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Henshall &amp; McKay (2001)</td>\n",
       "      <td>-2.029496</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.009904</td>\n",
       "      <td>1.004940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tabibi et al. (2020)</td>\n",
       "      <td>-1.070457</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.207861</td>\n",
       "      <td>0.455918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Di Maro et al. (2022)</td>\n",
       "      <td>0.942774</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.222221</td>\n",
       "      <td>0.471403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Fan et al. (2023)</td>\n",
       "      <td>2.100694</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.129301</td>\n",
       "      <td>0.359585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dirks et al. (2022)</td>\n",
       "      <td>-0.603497</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.298722</td>\n",
       "      <td>0.546554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Warren &amp; Atcherson (2023)</td>\n",
       "      <td>1.949151</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.245816</td>\n",
       "      <td>0.495799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kurz et al. (2023)</td>\n",
       "      <td>0.952118</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.222663</td>\n",
       "      <td>0.471872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>McRackan et al. (2017)</td>\n",
       "      <td>-0.189553</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.118175</td>\n",
       "      <td>0.343767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Study        ES     N     Var_d     SE(d)\n",
       "0          Goehring et al. (2019) -0.415816   8.0  0.255403  0.505374\n",
       "1              Jiam et al. (2019)  0.262357  16.0  0.126075  0.355071\n",
       "2             Noble et al. (2014)  0.016145  72.0  0.027779  0.166669\n",
       "3             Saleh et al. (2013)  0.634000  25.0  0.084020  0.289861\n",
       "4       DeVries & Arenberg (2018)  0.450253   9.0  0.227854  0.477340\n",
       "5                     Zhou (2017)  1.833152   8.0  0.355014  0.595830\n",
       "6           Zhou & Pfingst (2014)  1.339651   9.0  0.272074  0.521607\n",
       "7             Noble et al. (2013)  0.826870  10.0  0.217093  0.465932\n",
       "8                     Zhou (2019)  1.518760   9.0  0.286295  0.535066\n",
       "9         Grasmeder et al. (2014) -0.976306  10.0  0.223829  0.473106\n",
       "10          Zhou & Pfingst (2012)  1.410427   8.0  0.312166  0.558718\n",
       "11         Bierer & Litvak (2016)  0.329984   9.0  0.225247  0.474602\n",
       "12  Schvartz-Leyzac et al. (2021)  0.066439  18.0  0.111172  0.333425\n",
       "13        Henshall & McKay (2001) -2.029496   3.0  1.009904  1.004940\n",
       "14           Tabibi et al. (2020) -1.070457  11.0  0.207861  0.455918\n",
       "15          Di Maro et al. (2022)  0.942774  10.0  0.222221  0.471403\n",
       "16              Fan et al. (2023)  2.100694  24.0  0.129301  0.359585\n",
       "17            Dirks et al. (2022) -0.603497   7.0  0.298722  0.546554\n",
       "18      Warren & Atcherson (2023)  1.949151  12.0  0.245816  0.495799\n",
       "19             Kurz et al. (2023)  0.952118  10.0  0.222663  0.471872\n",
       "20         McRackan et al. (2017) -0.189553  17.0  0.118175  0.343767"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ee06e711-985a-4110-8da1-03cb76497c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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27MnzoWMMDAwwbNgwTJ48GQ8fPkRGRgZOnDiB9+/fK+3rR/RfHMJIJ5mZmeHUqVNo3LgxTE1N0aRJE9SqVSvrOEWtWrVCzZo1UbZsWVhZWQEAXF1dYWxsjDJlymDw4MFwcnLKWp6VlRU2bdqEKVOmwNLSEjdv3kSzZs1yXfe+ffuwfv16lC9fHmXLloVcLs/6BNjq1athb28Pc3NzREZGYs2aNXl+Xi4uLti9ezcuXbqEoKAgODg4oEmTJjA3N0ebNm1w/fr1rPuWLVsWJUuWRPny5eHk5ITIyEhUq1YNADB//nyYmpqiUqVKaN68OX744QcMGzYs1/Xm9PX61Po7duwIFxcXtGrVCg4ODmjVqlWuyzY2Nsa9e/fQpk0bmJubo1atWihcuDBiYmLy9Fw+1dGwYUMsXboU48ePR8mSJeHg4JC1XENDQ+zYsQO3bt2Cra0trK2tsWHDhjy/Fqoyffp0vHjxIuty1apVsWbNGkyYMAFWVlbYsWMHduzYAWNj448eu3fvXtSsWRPFihXDpEmTsH79ehQpUgQ2NjbYvn07Zs6ciVKlSsHGxgYhISHZjk2WF8pYzrRp09CwYUPUqVMHtWvXRv369TFt2rR8deTGzMwM8+bNQ58+fVCyZEmsXbsWXbt2zfPjQ0NDUbt2bTRq1AgWFhaQy+XIzMxU2teP6L9kUl620xKRVjly5AgGDBigE/9S16XnQkT0b9wSRkRERCSASoewvXv3omrVqnBwcMDs2bM/ul2SJEycOBEODg6oU6cOzp8/r8ocIiIiIo2hsrcjMzIyUKVKFezfvx/W1tZo1KgR1q1bl/VpLOCfI4fPnz8fu3fvxqlTpzBp0iScOnVKFTlEREREGkVlW8JOnz4NBwcHVKpUCcbGxujXrx+2b9+e7T7bt2/HoEGDIJPJso6HxOOuEBERkT5Q2cFxHjx4kO2gfdbW1h9t5crpPg8ePEC5cuWy3S8qKirrAIZXr15VeDBAbZaZmQkDA+6qp634+mkvdbx2GRkZuH37NoB/jun1OcfEouz4vae9dP21e/78ebYDBv+byoawnN7l/O8PnLzcBwCcnZ3h7OwM4J+PnCs6Ga42O3LkCBwdHUVnUAHx9dNe6njtpkyZgqCgIAD/HG5k7NixKl2fPuH3nvbS9deuYcOGud6mstHT2to62xGzExISsh0VOa/3ISLSBY8fP8b8+fNRu3ZtWFtbIzQ0VOGpjIhIt6lsCGvUqBFu3ryJu3fvIjU1FevXr//ooHldu3bFqlWrIEkSTp48ieLFi3/0ViQRkS6YPXs23r17h82bN2PevHm4e/cuNm/eLDqLiARS2duRRkZGWLBgAdq3b4+MjAwMGzYMNWvWRGRkJABg9OjR6NSpE3bv3g0HBwcULVoU0dHRqsohIhLmwYMHWLx4MQYNGoQqVarAwcEBVapUQXBwMPr27ct9w4j0lErPWtupUyd06tQp23WjR4/O+rNMJsPChQtVmUBEJNzMmTORkZEBHx8fdOzYEQDg4eGBkSNH4sCBA2jbtq3gQiISQXc/jkBEpAHi4uKwdOlSDB8+HBUrVkRKSgpSUlIwcOBAlCtXLmtHfSLSPxzCiIhUKDAwEAYGBh+dpLpw4cJwcXHBwYMHce7cOUF1RCQShzAiIhW5desWYmJiMGrUKFhbW390+6hRo2Bubo7g4GABdUQkGocwIiIV8ff3h7GxMX788cccby9evDhGjx6NzZs3Zx3ElYj0B4cwIiIVuHr1KmJjYzF+/HiULVs26/rOnTujc+fOWZddXFxgZGSE0NBQEZlEJBCHMCIiFfDz84OpqSk8PT2zXe/u7g53d/esy+XKlcOgQYMQHR2Nx48fqzuTiATiEEZEpGQXL17Epk2b4OLiAisrK4X39/DwQGpqKubNm6eGOiLSFBzCiIiUzMfHB8WLF8fkyZM/us3R0fGj8+RVqVIF3bt3x6JFi5CYmKimSiISjUMYEZESnTlzBj///DPc3NxQsmTJPD/O09MTr169wtKlS1VYR0SahEMYEZES+fj4wNLSEpMmTcrX4xo3boyWLVsiPDwcqampKqojIk3CIYyISEmOHTuGvXv3wtPTE+bm5vl+vFwux4MHD7B27VoV1BGRpuEQRkSkJN7e3ihTpgzGjRtXoMd36NABtWvXRkhICDIzM5VcR0SahkMYEZESHDp0CIcPH8aPP/4IU1PTXO/Xp08f9OnTJ8fbZDIZPD09cfXqVezatUtVqUSkITiEERF9JkmS4O3tjQoVKmDUqFGfvO/YsWMxduzYXG/v27cvbG1teWJvIj3AIYyI6DP98ssvOH78OKZNm4YiRYp88r7JyclITk7O9fZChQrBzc0Nx44dw7Fjx5SdSkQahEMYEdFn+LAVzN7eHsOGDVN4/06dOqFTp06fvM/w4cNhaWnJrWFEOo5DGBHRZ/j5559x9uxZ+Pj4wNjYWCnLNDU1xfjx47Fjxw5cvXpVKcskIs3DIYyIqIAyMzPh4+ODypUrY+DAgUpd9vjx42FiYoKQkBClLpeINAeHMCKiAtq8eTMuXboEX19fGBkZKXXZVlZWGD58OGJjY5GQkKDUZRORZuAQRkRUABkZGfD19UWNGjXQr18/lazDzc0NmZmZmDNnjkqWT0RiKfefbkREemLt2rW4du0aNm3aBENDwzw/bsiQIXm+r729Pfr27YuoqChMmzYtX+eiJCLNxy1hRET5lJaWBn9/f9StWxc9evTI12OHDBmSr0HMw8MDSUlJWLx4cT4riUjTcQgjIsqnVatW4fbt2wgMDISBQf5+jD579gzPnj3L8/3r1auH9u3bY+7cuUhJSclvKhFpMA5hRET58P79ewQEBOCrr75C586d8/34Xr16oVevXvl6jFwux5MnT7By5cp8r4+INBeHMCKifFi+fDni4+MRGBgImUymlnU6OjqiUaNGCA0NRUZGhlrWSUSqxyGMiCiPUlJSMH36dDRv3hxt27ZV23plMhnkcjlu376NrVu3qm29RKRaHMKIiPIoMjISjx49UutWsA++//57VK5cGUFBQZAkSa3rJiLV4BBGRJQHSUlJmD17Nlq3bg1HR0e1r9/Q0BDu7u44d+4cDh06pPb1E5HycQgjIsqDBQsW4MmTJwgMDPys5YwZMwZjxowp0GMHDRqEMmXK8MTeRDqCQxgRkQKvX79GcHAwOnXqhK+//vqzltW3b1/07du3QI8tUqQIXFxcsH//fvzxxx+f1UFE4nEIIyJSICIiAi9fvkRAQMBnL+v+/fu4f/9+gR8/evRomJmZITg4+LNbiEgsDmFERJ/w4sULhIeH4/vvv0eDBg0+e3kDBw7EwIEDC/z4EiVKYNSoUdi4cSPu3Lnz2T1EJA6HMCKiTwgLC0NiYqJStoIpi4uLCwwNDREWFiY6hYg+A4cwIqJcPH36FHPnzkWfPn1Qu3Zt0TlZKlSogIEDB2LFihV48uSJ6BwiKiAOYUREuQgKCkJKSgr8/PxEp3zEw8MD7969w4IFC0SnEFEBcQgjIsrBo0ePsHDhQgwYMADVqlUTnfORatWqoVu3bliwYAGSkpJE5xBRAXAIIyLKwcyZM5GWlgYfHx+lLtfNzQ1ubm5KWZZcLsfLly+xbNkypSyPiNSLQxgR0X/Ex8cjKioKw4YNwxdffKHUZXfp0gVdunRRyrK+/vprfPPNNwgPD0daWppSlklE6sMhjIjoP6ZPnw4AmDZtmtKXff36dVy/fl1py5PL5bh//z7Wr1+vtGUSkXpwCCMi+pc7d+4gOjoazs7OsLW1VfryR40ahVGjRilteR07dkTNmjURHBzME3sTaRkOYURE/xIQEAAjIyNMnTpVdEqeGBgYwNPTE5cvX8bu3btF5xBRPnAIIyL6n2vXrmH16tUYO3YsypUrJzonz/r37w8bGxue2JtIy3AIIyL6H39/f5iYmEAul4tOyZdChQph8uTJOHr0KE6cOCE6h4jyiEMYERH+2Rds/fr1mDhxIkqXLi06J99GjBiBkiVL8sTeRFrESHQAEZEmiImJgbm5Odzd3VW6HlV84hIAihUrhnHjxmHGjBm4du2aRh5gloiy45YwItJ7586dw9GjRzF58mRYWFiodF1t2rRBmzZtVLLsCRMmoHDhwggJCVHJ8olIuTiEEZHe8/HxgZmZGVxcXFS+rgsXLuDChQsqWXbp0qUxbNgwrF69Gg8ePFDJOohIeTiEEZFeO3HiBHbv3o1+/fqhePHiKl+fi4uLSoc9Nzc3ZGRkYO7cuSpbBxEpB4cwItJr3t7eKFWqFLp37y46RSkqVaqE3r17IzIyEq9evRKdQ0SfwCGMiPTWr7/+ioMHD2LKlCkwMTERnaM0crkciYmJiIyMFJ1CRJ/AIYyI9JIkSfD29ka5cuUwZswY0TlK9eWXX6Jt27aIiIjAu3fvROcQUS44hBGRXtq/fz+OHj0KLy8vndoK9oFcLsfjx4+xatUq0SlElAseJ4yI9M6HrWC2trYYMWKEWtc9c+ZMtaynVatWaNCgAUJDQzF8+HAYGhqqZb1ElHfcEkZEemfXrl04ffo0vL29UbhwYbWuu2nTpmjatKnK1yOTyeDp6YmbN2/ip59+Uvn6iCj/OIQRkV7JzMyEt7c3KlWqhMGDB6t9/cePH8fx48fVsq6ePXviiy++QFBQECRJUss6iSjvOIQRkV7Ztm0bLly4AD8/PxQqVEjt6586dSqmTp2qlnUZGhrC3d0dZ86cwZEjR9SyTiLKOw5hRKQ3MjIy4OPjg2rVquGHH34QnaMWgwcPRunSpXlibyINxCGMiPTGhg0bcPXqVfj7++vNjuomJiaYOHEi9u7di4sXL4rOIaJ/4RBGRHohPT0dfn5+qFOnDnr16iU6R63Gjh2LYsWKcWsYkYbhEEZEemH16tW4efMm/P39YWCgXz/6SpYsCWdnZ2zYsAH37t0TnUNE/6NfP4mISC+lpqYiICAADRo0QLdu3YS2REREICIiQu3rdXV1hYGBAcLCwtS+biLKGYcwItJ5K1aswL179xAYGAiZTCa0pV69eqhXr57a12ttbQ0nJycsX74cz549U/v6iehjHMKISKe9e/cO06dPR9OmTdGhQwfROThw4AAOHDggZN0eHh5ISUnBggULhKyfiLLjEEZEOi0qKgoPHjzQiK1gADB9+nRMnz5dyLpr1KiBLl26YP78+Xj79q2QBiL6fxzCiEhnJScnY+bMmXB0dESrVq1E52gEuVyOFy9eYPny5aJTiPQehzAi0lkLFy7E48ePERgYKDpFYzRr1gzNmjVDWFgY0tLSROcQ6TUOYUSkkxITExEUFIT27dujefPmonM0iqenJ+Lj47Fx40bRKUR6jUMYEemkuXPn4vnz5wgICBCdonE6d+6MGjVqIDg4mCf2JhKIQxgR6ZyXL18iNDQUXbt2xVdffSU6J5slS5ZgyZIlQhsMDAzg4eGBS5cuYe/evUJbiPQZhzAi0jnh4eF4/fq1Rm4Fq1q1KqpWrSo6Az/88AOsra0RFBQkOoVIb3EIIyKd8uzZM0RERKB3796oW7eu6JyP7NixAzt27BCdAWNjY7i6uuLXX3/FqVOnROcQ6SUOYUSkU4KDg/H27Vv4+fmJTslRWFiYxpw6aOTIkShRogRP7E0kCIcwItIZf//9NxYsWIAffvgBNWrUEJ2j8czMzDB27Fhs27YN169fF51DpHc4hBGRzpg9ezZSU1Ph6+srOkVrTJw4EcbGxggNDRWdQqR3OIQRkU5ISEjA4sWLMXjwYFSuXFl0jtYoU6YMhg4dilWrVuHRo0eic4j0CocwItIJM2bMgCRJ8Pb2Fp2iddzc3JCeno65c+eKTiHSKxzCiEjr3b17F8uXL8eIESNgb28vOueTVq9ejdWrV4vOyMbBwQE9e/bE4sWL8fr1a9E5RHqDQxgRab3AwEAYGBjAy8urQI+PjY3Fn3/+CQMDA9jb2yM2NlbJhf/PxsYGNjY2Klt+Qcnlcrx580b4gWSJ9AmHMCLSajdu3MCqVaswZswYVKhQId+Pj42NhbOzM1JTUyFJEuLi4uDs7KyyQWzDhg3YsGGDSpb9ORo0aIDWrVsjIiIC79+/F51DpBc4hBGRVvP390fhwoUxZcqUAj3ey8sLycnJ2a5LTk4u8FY1RRYvXozFixerZNmfSy6X49GjR1izZo3oFCK9wCGMiLTWlStXsG7dOkyYMAFlypQp0DLi4+Pzdb0ua9OmDb788kuEhIQgMzNTdA6RzuMQRkRay8/PD8WKFYOHh0eBl2Fra5uv63WZTCaDp6cnrl+/ju3bt4vOIdJ5HMKISCtduHABmzdvhouLCywtLQu8nBkzZqBo0aLZritatChmzJjxuYlaqVevXqhYsSKCgoIgSZLoHCKdxiGMiLSSj48PSpQogcmTJ3/WcpycnBAVFQVjY2PIZDLY2dkhKioKTk5OSirVLkZGRnB3d8epU6dw9OhR0TlEOs1IdAARUX6dOnUKO3bswPTp01GiRInPXp6TkxOOHDmilv2gNm/erPJ1fK4hQ4bA19cXQUFBaNGihegcIp3FLWFEpHV8fHxgZWWFiRMnik7JNysrK1hZWYnO+KSiRYti4sSJ2L17N/7880/ROUQ6i0MYEWmVo0ePYt++fZDL5TAzMxOdk28xMTGIiYkRnaHQuHHjYGpqiuDgYNEpRDqLQxgRaY0P54YsW7Ysxo4dKzqnQLRlCLOwsMDIkSOxbt06xMXFic4h0kkcwohIaxw6dAi//vorpk6d+tEnGkn5XF1dIZPJMGfOHNEpRDqJQxgRaQVJkjBt2jRYW1tj5MiRonP0gq2tLfr374+lS5fi+fPnonOIdA6HMCLSCnv27MHJkycxbdo0FClSRHSO3vD09ERycjIWLlwoOoVI53AIIyKN92FfsIoVK2Lo0KGic/RKrVq18N1332H+/PkfnWOTiD4PhzAi0ng//fQTzp8/Dx8fHxgbG4vO+Sy7d+/G7t27RWfki1wux7NnzxAdHS06hUincAgjIo2WmZkJHx8fVKlSBQMGDBCd89mKFi2qdR8qaN68OZo0aYLQ0FCkp6eLziHSGRzCiEijbdq0CZcvX4afnx+MjLT/JB+LFi3CokWLRGfki0wmg1wux71797Bp0ybROUQ6Q6VD2N69e1G1alU4ODhg9uzZH90eGxuLOnXqoE6dOmjatCkuXryoyhwi0jLp6enw9fVFzZo10bdvX9E5SrFx40Zs3LhRdEa+de3aFdWqVeOJvYmUSGVDWEZGBsaNG4c9e/bg6tWrWLduHa5evZrtPhUrVsSvv/6KS5cuwdvbG87OzqrKISIttHbtWly/fh0BAQEwMOCGe5EMDAzg4eGBixcvYt++faJziHSCyn6qnT59Gg4ODqhUqRKMjY3Rr18/bN++Pdt9mjZtipIlSwIAmjRpgoSEBFXlEJGWSUtLg7+/P7788kt0795ddA7hnxOdly9fnqcyIlISle1g8eDBA9jY2GRdtra2xqlTp3K9//Lly9GxY8ccb4uKikJUVBQAICEhAUeOHFFqqyZJSkrS6een6/j6Kc/OnTtx584dzJw5E7/++qvK16eu1+7Vq1cAoLV/T7p06YIlS5ZgyZIlqFq1quicLPze0176/NqpbAjLaZ8BmUyW430PHz6M5cuX4/fff8/xdmdn56y3Khs2bAhHR0eldWqaI0eO6PTz03V8/ZTj/fv3GDRoEBo3bowpU6bk+rNDmdT12pUoUQIAtPbvSf369bF+/XocOHAAo0aNEp2Thd972kufXzuVDWHW1ta4f/9+1uWEhASUL1/+o/tdunQJI0aMwJ49e2BpaamqHCLSIkuXLsX9+/exfPlytQxg6qTt/+I3NzfHmDFjEBQUhJs3b6Jy5cqik4i0lsr2CWvUqBFu3ryJu3fvIjU1FevXr0fXrl2z3Sc+Ph49evTA6tWrUaVKFVWlEJEWSU5OxowZM9CiRQu0adNGdA7lYNKkSShUqBDCwsJEpxBpNZUNYUZGRliwYAHat2+P6tWro0+fPqhZsyYiIyMRGRkJAAgICMDz588xduxY1KtXDw0bNlRVDhFpicWLF+Pvv/9GYGCgzm0FA4DQ0FCEhoaKzvgsZcuWxeDBgxETE4O///5bdA6R1lLpkQ87deqETp06Zbtu9OjRWX9etmwZli1bpsoEItIiSUlJmD17Ntq2bYsWLVqIzlGJnTt3AgDc3d0Fl3wed3d3LFu2DPPmzcPMmTNF5xBpJR54h4g0xvz58/Hs2TMEBgaKTiEFqlSpgh49emDRokV48+aN6BwircQhjIg0wuvXrxESEoLvvvsOjRs3Fp1DeSCXy/H69eusQwgRUf5wCCMijTBnzhy8fPkSAQEBolMojxo1aoRvv/0Wc+bMQWpqqugcIq3DIYyIhHv+/DnCw8PRo0cP1K9fX3SOSpmYmMDExER0htJ4enri4cOHiI2NFZ1CpHU4hBGRcKGhoUhKSoK/v7/oFJXbs2cP9uzZIzpDadq3b4+6desiODgYmZmZonOItAqHMCIS6smTJ5g3bx769euHWrVqic6hfJLJZPD09MS1a9ewY8cO0TlEWoVDGBEJNXv2bLx79w6+vr6iU9QiMDBQ5z792adPH9jZ2fHE3kT5xCGMiIR5+PAhFi9ejEGDBmnUyaBV6eDBgzh48KDoDKUyMjKCm5sbjh8/nus5gInoYxzCiEiYmTNnIj09HT4+PqJT6DMNGzYMlpaWCAoKEp1CpDU4hBGREHFxcYiKisKwYcNQsWJF0Tn0mUxNTTFhwgTs3LkTly9fFp1DpBU4hBGRENOnT4dMJsO0adNEp5CSjB8/HkWLFkVISIjoFCKtwCGMiNTu1q1biI6OxqhRo2BjYyM6R60sLS1haWkpOkMlLC0tMWLECKxduxb3798XnUOk8TiEEZHaBQQEwNjYGD/++KPoFLXbsmULtmzZIjpDZVxdXSFJEubMmSM6hUjjcQgjIrX666+/EBsbi3HjxqFcuXKic0jJ7O3t0a9fP0RFReHFixeic4g0GocwIlIrPz8/mJiYwNPTU3SKED/++KPObwH09PTE27dvsWjRItEpRBqNQxgRqc2lS5ewceNGuLi4oFSpUqJzhDhx4gROnDghOkOl6tSpg44dO2LevHlISUkRnUOksTiEEZHa+Pj4oHjx4nBzcxOdQirm6emJp0+fIiYmRnQKkcbiEEZEanH27Fls374dbm5uKFmypOgcUrGWLVviq6++QmhoKNLT00XnEGkkDmFEpBY+Pj6wsLDApEmTRKeQGshkMsjlcty5c0enPw1K9Dk4hBGRyh0/fhx79uyBp6cnzM3NRecIZW1tDWtra9EZatGtWzdUqVIFQUFBkCRJdA6RxuEQRkQq5+3tjdKlS2P8+PGiU4Rbs2YN1qxZIzpDLQwNDeHh4YE//vhD505aTqQMHMKISKUOHz6MQ4cO4ccff4SpqanoHFKzAQMGoGzZsjyxN1EOOIQRkcpIkgRvb29UqFABo0ePFp2jEVxcXODi4iI6Q22KFCkCFxcXHDhwAOfOnROdQ6RROIQRkcrs27cPx44dg5eXF4oUKSI6RyNcuHABFy5cEJ2hVqNHj4a5uTmCg4NFpxBpFA5hRKQSkiRh2rRpsLOzw/Dhw0XnkEDFixfH6NGjsXnzZty+fVt0DpHG4BBGRCqxY8cOnD17Fj4+PjA2NhadQ4JNmjQJRkZGCAsLE51CpDE4hBGR0mVmZsLHxwcODg4YNGiQ6BzSAOXLl8fAgQMRHR2NJ0+eiM4h0ggcwohI6bZs2YKLFy/Cz88PRkZGonM0SpUqVVClShXRGUJ4eHjg/fv3mDdvnugUIo3AIYyIlCojIwO+vr6oUaMG+vXrJzpH40RFRSEqKkp0hhBVq1bF999/j4ULFyIxMVF0DpFwHMKISKnWrVuHv/76C35+fjA0NBSdQxpGLpfj1atXWLZsmegUIuE4hBGR0qSnp8Pf3x9169ZFz549RedoJGdnZzg7O4vOEKZx48Zo0aIFwsPDkZqaKjqHSCgOYUSkNKtWrcKtW7cQEBAAAwP+eMnJjRs3cOPGDdEZQsnlciQkJGDdunWiU4iE4k9JIlKK1NRUBAQEoFGjRujSpYvoHNJgHTt2RO3atREcHIzMzEzROUTCcAgjIqVYvnw54uLiEBgYCJlMJjqHNJhMJoOnpyeuXr2K3bt3i84hEoZDGBF9tpSUFEyfPh3NmjVDu3btROeQFujbty9sbW15Ym/SaxzCiOizLVmyBA8fPsT06dO5FUyBevXqoV69eqIzhCtUqBAmT56M33//HcePHxedQyQEhzAi+ixv377FrFmz0KpVKzg6OorO0XgRERGIiIgQnaERRowYAQsLC24NI73FIYyIPsuCBQvw5MkTBAYGik7RCbGxsbC3t4eBgQHs7e0RGxsrOkllTE1NMX78ePz888+4evWq6BwiteMQRkQF9ubNGwQHB6Njx45o2rSp6BytMGDAAAwYMCDH22JjY+Hs7Iy4uDhIkoS4uDg4Ozvr9CA2fvx4mJiYIDQ0VHQKkdpxCCOiAouIiMCLFy8QEBAgOkVrJCQkICEhIcfbvLy8kJycnO265ORkeHl5qSNNiFKlSmHYsGFYs2ZNrl8XIl3FIYyICuTly5cIDw/H999/j4YNG4rO0Qnx8fH5ul5XuLm5ITMzk/vKkd7hEEZEBRIWFobXr1/D399fdIrOsLW1zdf1uqJixYro06cPlixZgpcvX4rOIVIbDmFElG9Pnz5FREQE+vTpgzp16ojO0RkzZsxA0aJFs11XtGhRzJgxQ1CR+nh6eiIpKQmRkZGiU4jUhkMYEeVbcHAwUlJS4OfnJzpF63z99df4+uuvc7zNyckJUVFRsLOzg0wmg52dHaKiouDk5KTmSvWrV68e2rVrh7lz5+Ldu3eic4jUwkh0ABFpl0ePHmHBggVwcnJC9erVRedonVmzZn3ydicnJ70YunIil8vRunVrrFy5EqNGjRKdQ6RyCoews2fP4ujRo3j48CFMTExQq1YttGnTBhYWFuroIyINM2vWLKSlpcHX11d0CumYb7/9Fg0bNkRoaChGjBgBQ0ND0UlEKpXr25ExMTGoX78+Zs2ahZSUFFStWhWlS5fG77//jrZt22Lw4ME6/4kdIsru/v37WLJkCYYOHYovvvhCdI5W6tmzJ3r27Ck6QyPJZDLI5XLcunULW7duFZ1DpHK5bgl7+/Ytjh07BhMTkxxvv3DhAm7evKnzn9ohov83ffp0SJKEadOmiU7RWs+fPxedoNG6d+8OBwcHBAUFoVevXjwXKem0XLeEjRs3LtcBDPhnJ8rWrVurJIqINM+dO3ewYsUKODs7w87OTnQO6ShDQ0O4u7vj3LlzOHz4sOgcIpXKdUvYxIkTP/nAefPmKT2GiDRXYGAgjIyMMHXqVNEppOMGDx4MX19fBAUFoVWrVqJziFQm1y1hDRo0QIMGDfDu3TucP38elStXRuXKlXHhwgXuLEmkZ65fv45Vq1Zh7NixKF++vOgc0nFFihTBpEmTsG/fPvzxxx+ic4hUJtctYYMHDwbwzw76hw8fRqFChQAAo0ePRrt27dRTR0Qawd/fHyYmJpDL5aJTtB5348ibMWPGYObMmQgJCcHatWtF5xCphMKDtT58+BCJiYlZl5OSkvDw4UOVRhGR5rh8+TLWr1+PCRMmoHTp0qJztJ63tze8vb1FZ2i8EiVKYNSoUdiwYQPu3r0rOodIJRQOYVOmTMGXX36JIUOGYMiQIahfvz73CSHSI76+vjAzM4OHh4foFNIzrq6uMDQ0RFhYmOgUIpVQOIQNHToUp06dQvfu3dG9e3ecOHEi661KItJt58+fx9atW+Hq6soDNCtJx44d0bFjR9EZWqFChQoYMGAAVqxYgadPn4rOIVK6PJ07smzZsujWrRu6deuGsmXLqrqJiDSEj48PSpYsCVdXV9EpOiMlJQUpKSmiM7SGh4cHUlJSsGDBAtEpRErHE3gTUY5OnjyJXbt2wcPDA8WLFxedQ3qqevXq6NatGxYsWIC3b9+KziFSKg5hRJQjb29vlCpVChMmTBCdQnrO09MTL168wLJly0SnECmVwiHs9u3beP/+PQDgyJEjmDdvHl69eqXqLiIS6LfffsOBAwcwZcoUFCtWTHQO6bmmTZuiefPmCA8PR1pamugcIqVROIT17NkThoaGuHXrFoYPH467d+/ihx9+UEcbEQnw4dyQ5cqVw5gxY0Tn6JzOnTujc+fOojO0jlwuR3x8PNavXy86hUhpFA5hBgYGMDIywrZt2+Di4oI5c+bg0aNH6mgjIgEOHDiAo0ePYurUqZ88f6yuio2Nhb29PQwMDGBvb4/Y2FilLt/d3R3u7u5KXaY+6NSpE2rWrIng4GBIkiQ6h0gpFA5hhQoVwrp167By5cqsf71xczCRbpIkCd7e3rCxscHIkSNF56hdbGwsnJ2dERcXB0mSEBcXB2dnZ6UPYpR/BgYG8PDwwOXLl7Fnzx7ROURKoXAIi46OxokTJ+Dl5YWKFSvi7t27GDBggDraiEjNdu3ahVOnTsHb2xuFCxcWnaN2Xl5eSE5OznZdcnIyvLy8lLYOR0dHODo6Km15+qR///6wtrZGUFCQ6BQipVA4hNWoUQPz5s1D//79AQAVK1bElClTVB5GROolSRJ8fHxQqVIlDBkyRHSOEPHx8fm6ntTL2NgYkydPxm+//YaTJ0+KziH6bLkOYd9++y1atWqFXr16qbOHiATZtm0b/vjjD/j6+qJQoUKic4SwtbXN1/WkfiNHjkTJkiURHBwsOoXos+U6hMXExCA6OhoRERFqzCEiETIyMuDj44OqVavCyclJdI5SFGQH+xkzZqBo0aLZritatChmzJihqkzKp2LFimHcuHH46aefcP36ddE5RJ8l1yHMzs4OdnZ2sLa2VmcPEQmwceNGXLlyBf7+/jA0NBSd89kKuoO9k5MToqKiYGdnB5lMBjs7O0RFRenMYKorJkyYgMKFCyMkJER0CtFnUbhP2NatW1G5cmUUL14c5ubmMDMzg7m5uTraiEgN0tPT4efnh9q1a6N3796ic5Tic3awd3Jywr1795CZmYl79+4pfQDr06cP+vTpo9Rl6pvSpUtj6NChWL16NR4+fCg6h6jAFA5hnp6e+Pnnn/H69Wu8efMGiYmJePPmjTraiEgN1qxZgxs3biAgIAAGBrpxJjNN3sF+7NixGDt2rOgMrefu7o709HTuMkNaTeFP3DJlyqB69erqaCEiNUtNTUVAQAAaNGiAbt26ic5RGk3ewT45OfmjrXSUf5UqVULv3r0RGRmJ169fi84hKhCFQ1jDhg3Rt29frFu3Dlu3bs36j4i0X3R0NO7evYuAgADIZDLROUqjyTvYd+rUCZ06dRKdoRM8PT2RmJiIyMhI0SlEBaJwCHvz5g2KFi2Kffv2YceOHdixYwd27typjjYiUqF3795h+vTp+Prrr9GxY0fROUqlrh3sVX2KI/q0+vXro02bNoiIiEBqaqroHKJ8M1J0h+joaHV0EJGaLV26FAkJCYiJidGprWAfODk5qfRTjR8+gfnhrcUPn8D8sG5SD7lcjrZt22Lfvn1o166d6ByifFG4JezGjRto3bo1atWqBQC4dOkSpk+frvIwIlKd5ORkzJgxA46OjmjVqpXoHK2kjlMckWKtW7dG/fr1sXHjRmRkZADgFkrSHgqHsJEjR2LWrFlZR9CuU6cO1q9fr/IwIlKdRYsW4fHjxwgMDNTJrWDqoMmfwNQnMpkMcrkc9+/fx/bt23kSdtIqCt+OTE5OxldffZX9QUYKH0ZEGioxMRGzZ89Gu3bt0Lx5c9E5WsvW1hZxcXE5Xv8p+npeTlXq0aMHypcvj6CgIPz999+5bqHk28SkaRRuCbOyssLt27ez/rW8efNmlCtXTuVhRKQa8+bNw/PnzxEYGCg6RasV9BOYQ4YM4SCmZEZGRujTpw9Onz7NLZSkVRQOYQsXLsSoUaNw7do1VKhQAREREVi8eLE62ohIyV69eoXQ0FB06dLloy3clD8F/QTms2fP8OzZMzVV6o8OHTqgdOnSKFKkSI63a8Ix4oj+S+H7ipUqVcKBAwfw9u1bZGZmwszMTB1dRKQC4eHhePXqFQICAkSn6ISCfAKzV69eAIAjR46ooEh/FS5cGBMnTsS0adNQpEgRvHv3Lus2TTlGHNF/KRzC3r9/jy1btuDevXtIT0/Put7Hx0elYUSkXM+ePUNERAR69eqFevXqic4hUroxY8Zg1qxZqFu3Lh48eID4+HjY2tpixowZ3B+MNJLCIaxbt24oXrw4GjRogMKFC6ujiYhUICQkBElJSfDz8xOdQqQSFhYWcHZ2xrx583Dr1i3Y29uLTiL6JIVDWEJCAvbu3auOFiJSkcePH2PBggX44YcfULNmTdE5RCrj6uqK+fPnIzw8HPPmzROdQ/RJCnfMb9q0Kf788091tBCRisyePRvv37+Hr6+v6BQilbKxsYGTkxOWLVvGD0CQxst1S1jt2rUhk8mQnp6O6OhoVKpUCYULF4YkSZDJZLh06ZI6O4mogBISErB48WIMGjQIlStXFp2j98aMGSM6Qed5eHhg5cqVWLhwIf/hQRot1yGMJ+km0g0zZ85EZmYmP0yjIfr27Ss6QefVrFkTnTt3xvz58+Hu7g5TU1PRSUQ5yvXtSDs7u6z/Xr16hR07dmDHjh149eoV7Ozs1NlIRAV07949LFu2DMOHD+dOyhri/v37uH//vugMnSeXy/H8+XOsWLFCdApRrhTuEzZ37lw4OTnhyZMnePLkCQYMGID58+ero42IPlNgYCAMDAx4UmkNMnDgQAwcOFB0hs5r3rw5mjZtirCwMKSlpYnOIcqRwiFs+fLlOHXqFAICAhAQEICTJ09i6dKl6mgjos9w8+ZNrFy5EqNHj4a1tbXoHCK1k8vliIuLw6ZNm0SnEOVI4RAmSRIMDQ2zLhsaGkKSJJVGEdHn8/f3h7GxMaZMmSI6hUiIzp07o3r16ggODubvLdJICo8TNnToUDRu3Bjdu3cHAPz0008YPny4ysOIqOCuXr2KtWvXwsPDA2XLlhWdQySEgYEBPDw8MGzYMPzyyy/o0KGD6CSibBRuCZs8eTKio6NhYWGBkiVLIjo6Gi4uLmpII6KC8vPzg6mpKTw8PESnEAnl5OSEChUqICgoSHQK0Udy3RJ25swZPHv2DB07dkT9+vVRv359AMDPP/+MzMxMNGjQQG2RRJR3Fy5cwKZNm+Dt7Q0rKyvROfQfbm5uohP0irGxMVxdXeHu7o7Tp0/jq6++Ep1ElCXXLWEeHh6oXr36R9fXqFGD/7om0mC+vr4oUaIEJk+eLDqFctClSxd06dJFdIZecXZ2RvHixREcHCw6hSibXIew58+f53hcIQcHBzx//lyVTURUQKdPn8bPP/8MNzc3lChRQnQO5eD69eu4fv266Ay9YmZmhrFjx2Lr1q24ceOG6ByiLLkOYSkpKbk+6O3bt3la+N69e1G1alU4ODhg9uzZud7vzJkzMDQ0xObNm/O0XCLKmY+PDywtLTFp0iTRKZSLUaNGYdSoUaIz9M6kSZNgbGyM0NBQ0SlEWXIdwtq0aQMvL6+PPtbr6+uLVq1aKVxwRkYGxo0bhz179uDq1atYt24drl69muP95HI52rdvX4B8Ivrg999/xy+//AK5XA4zMzPROUQapUyZMhgyZAhWrlyJR48eic4hAvCJISwsLAx37tyBg4MDevbsiZ49e8LBwQHXr19HeHi4wgWfPn0aDg4OqFSpEoyNjdGvXz9s3779o/vNnz8fPXv2ROnSpT/vmRDpOW9vb5QpUwbjxo0TnUKkkdzd3ZGWloZ58+aJTiEC8IlPR5qammLdunW4c+cOrly5AuCfk6JWqlQpTwt+8OABbGxssi5bW1vj1KlTH91n27ZtOHToEM6cOZPrsqKiohAVFQUASEhIwJEjR/LUoI2SkpJ0+vnpOlGv3/nz53HkyBGMHz8ep0+fVvv6dYG6XrtXr14BAL/PlSyvr1+LFi0wf/58NG/enCf21hD6/HtP4cFaK1WqlOfB699yOjqxTCbLdtnFxQVBQUHZjsifE2dnZzg7OwMAGjZsCEdHx3z3aIsjR47o9PPTdSJeP0mSMHXqVFhbWyMkJARFihRR6/p1hbpeuw8fmOD3uXLl9fULDQ1Fo0aNcPXqVX7SX0Po8+89hUNYQVlbW+P+/ftZlxMSElC+fPls9zl79iz69esHAHj27Bl2794NIyMjfP/996rKItI5e/fuxYkTJxAZGckBTAtMmzZNdIJea9iwIVq1aoU5c+Zg4sSJKFy4sOgk0mO57hN29+7dz1pwo0aNcPPmTdy9exepqalYv349unbt+tE67t27h3v37qFXr15YtGgRBzCifJAkCd7e3rC3t8fQoUNF51AetGnTBm3atBGdodfkcjkePXqE2NhY0Smk53Idwnr16gUAaN26dYEWbGRkhAULFqB9+/aoXr06+vTpg5o1ayIyMhKRkZEFqyWibLZv345z587B19cXxsbGonMoDy5cuIALFy6IztBrbdu2Rb169RAcHIzMzEzROaTHcn07MjMzE/7+/rhx40aOn4bMy9G4O3XqhE6dOmW7bvTo0TneNyYmRuHyiOj/ZWZmwsfHB1WqVMGAAQNE51AefTj3rr7uiKwJZDIZPD098cMPP+Dnn3/mOzAkTK5bwtavX48iRYogPT0diYmJH/1HRGJt2rQJf/75J3x9fWFkpLLdO4l0Uu/evVGxYkUEBQXl+EEyInXI9Sd31apVIZfLUadOHXTs2FGdTUSkQEZGBvz8/FCzZk307dtXdA6R1jEyMoKbmxvGjx+Po0ePokWLFqKTSA/luiXsg6ZNm2Ly5Mlo2LAhGjZsCDc3N7x+/VodbUSUi7Vr1+LatWvw9/dXeIgXIsrZ0KFDYWVlxRN7kzAKh7Bhw4bBzMwMGzduxMaNG2Fubs5PYREJlJaWBj8/P9SrVw/du3cXnUOktYoWLYoJEyZg165duHz5sugc0kMKdyS5ffs2tmzZknXZ19cX9erVU2UTEX3CypUrcefOHezYsQMGBgr/HUUaZubMmaIT6F/GjRuHoKAgBAcHY9WqVaJzSM8o/AluYmKC33//PevysWPHYGJiotIoIsrZ+/fvERgYiMaNG+O7774TnUMF0LRpUzRt2lR0Bv2PpaUlRo4ciXXr1iE+Pl50DukZhUNYZGQkxo0bB3t7e9jb22P8+PFYsmSJOtqI6D+WLVuG+Ph4BAYGfnQaMNIOx48fx/Hjx0Vn0L98OOTSnDlzBJeQvlH4dmTdunVx8eJFvHnzBgBgbm6u8igi+lhKSgpmzJiBb775hkdc12JTp04FwOOEaRJbW1v0798fS5cuhbe3NywsLEQnkZ7I8w4l5ubmHMCIBFq8eDEePXrErWBEKuDh4YG3b99i4cKFolNIj3CvXiItkJSUhNmzZ6NNmzZo2bKl6BwinVO7dm106tQJ8+bNQ3Jysugc0hOfHMIyMzO57wKRBliwYAGePn2KwMBA0SlEOksul+PZs2eIjo4WnUJ64pNDmIGBAdzc3NTVQkQ5eP36NYKDg/Hdd9+hSZMmonOIdNY333yDJk2aICwsDOnp6aJzSA8ofDuyXbt22LJlC8+tRSRIREQEXr58iYCAANEppAQRERGIiIgQnUE5+HBi77t372Lz5s2ic0gPKPx0ZHh4ON6+fQtDQ0OYmJhAkiTIZLKsT0sSkeq8ePEC4eHh6N69O+rXry86h5SAB7vWbN26dUPVqlURFBSEvn378kMwpFIKt4QlJiYiMzMTaWlpePPmDRITEzmAEalJaGgoEhMT4e/vLzqFlOTAgQM4cOCA6AzKhYGBATw8PHDhwgXs379fdA7puDx9OvLnn3+Gu7s73N3dsXPnTlU3ERGAJ0+eYN68eejbty9q164tOoeUZPr06Zg+fbroDPqEAQMGoHz58jyxN6mcwiFsypQpmDt3LmrUqIEaNWpg7ty5mDJlijraiPRaUFAQUlJS4OfnJzqFSK8ULlwYLi4uOHjwIM6dOyc6h3SYwiFs9+7d2L9/P4YNG4Zhw4Zh79692L17tzraiPTWw4cPsWjRIgwcOBBVq1YVnUOkd5ydnWFubo6goCDRKaTD8vR25KtXr7L+/Pr1a1W1ENH/zJw5E+np6fDx8RGdQqSXihcvjjFjxmDLli24deuW6BzSUQqHsKlTp+LLL7/EkCFDMHjwYDRo0CDr3GdEpHzx8fFYunQphg0bhkqVKonOIdJbkyZNgpGREUJDQ0WnkI5SeMR8AwMDnDx5Ej169ECPHj1w4sQJ9OvXT119RHrnw07b06ZNE7L+2NhY2Nvbw8DAAPb29oiNjRXSoauWLFmCJUuWiM6gPChXrhwGDx6MmJgYPH78WHQO6SCFR8xfsGABypUrh65du6Jbt24oW7asutqI9M7t27exYsUKjBo1CjY2Nmpff2xsLJydnREXFwdJkhAXFwdnZ2cOYkpUtWpV7uenRdzd3ZGamop58+aJTiEdpPDtyLZt2yI0NBT379/Hixcvsv4jIuULCAhAoUKF8OOPPwpZv5eX10cnL05OToaXl5eQHl20Y8cO7NixQ3QG5VGVKlXQvXt3LFq0CImJiaJzSMcoPGL+ihUrAAALFy7Muk4mk+HOnTuqqyLSQ9euXcOaNWvg6uqKcuXKCWmIj4/P1/WUf2FhYQCALl26CC6hvJLL5di6dSuioqJ4PmVSKoX7hM2ePRt3797N9h8HMCLl8/Pzg4mJCeRyubAGW1vbfF1PpA+++uorODo6Ys6cOUhNTRWdQzpE4T5h/94CRkSqcenSJWzYsAGTJk1CqVKlhHXMmDEDRYsWzXZd0aJFMWPGDEFFlBt+gEK95HI5Hjx4gLVr14pOIR3CfcKINICvry/Mzc2Fv9Xh5OSEqKgo2NnZQSaTwc7ODlFRUXBychLaRdnxAxTq1759e9SpUwfBwcHIzMwUnUM6gvuEEQl27tw5/PTTT/D394eFhYXoHDg5OXHo0nCf+gAFXzvVkMlk8PT0xIABA7Bz50507dpVdBLpAIVbwv67Pxj3CSNSLh8fH1hYWMDFxUV0CqnB6tWrsXr16s9ahqIPUIwdOxZGRkaQyWQwMjLC2LFjP2t99I++ffvCzs6OpzIipcl1CPv32eM3bdqU7TYeMZ9IOU6cOIHdu3fD09MT5ubmonM+ifsgKYeNjc1nHwPuUx+gGDt2LBYvXoyMjAwAQEZGBhYvXsxBTAmMjIzg5uaG48eP49ixY6JzSAfkOoStX78+68+zZs3KdtvevXtVV0SkR7y9vVG6dGmMHz9edMoncR+kvMnLoLphwwZs2LDhs9bzqQ9QREVF5fiY3K6n/Bk2bBgsLS25NYyUItchTJKkHP+c02Uiyr8jR47g4MGDmDJlCkxNTUXnfBIP4qrYpwbVfw9ngwcPhp+f32et61MfoPiwBey/crue8sfU1BTjx4/Hjh07cOXKFdE5pOVyHcJkMlmOf87pMhHljyRJ8Pb2Rvny5TF69GjROQrxIK6K5TaoTpo0Kdtw9v79e9y4ceOztyI6OTnh3r17yMzMxL1797J2yDc0NMzx/rldT/k3fvx4mJiYICQkRHQKablch7CLFy/C3NwcZmZmuHTpEszNzbMu//nnn+psJNI5+/fvx++//w4vLy+YmJiIzlGIB3FVLLeB9Pnz5x8NZ5mZmSrbiujs7Jyv6yn/rKysMGLECMTGxiIhIUF0DmmxXIewjIwMvHnzBomJiUhPT8ebN2+yLqelpamzkUinSJKEadOmwc7ODsOHDxedkyc8iKti+R1IVbUVcdGiRRgzZkzWli9DQ0OMGTMGixYtUsn69NXkyZMhSRLmzJkjOoW0mMJDVBCRcu3cuRNnzpyBt7c3ChcuLDonT3gQV8VyG1QtLS1zvL8qtyIuWrQI6enpkCQJ6enpHMBUwN7eHn379kVUVBRevnwpOoe0FIcwIjXKzMyEt7c3vvjiCwwaNEh0Tr7ktg8S/SO3QXXu3LkfDWcmJibciqgDPD09kZSUxCGXCkzhEfOJSHm2bt2KixcvYvXq1ShUqJDoHFKyT51twMvLC/Hx8bC1tcWMGTM4xOqAunXrokOHDpg7dy4mT56sFft3kmbhljAiNcnIyICvry+qV6+O/v37i84hNfr3VkQ/Pz/uV6tD5HI5nj59ipUrV4pOIS2U65YwMzOzTx6K4s2bNyoJItJV69evx9WrV7Fx40YeLkCPxcTEAACGDBkitIOUo2XLlmjUqBFCQ0MxcuRIfm9TvuQ6hCUmJgL457x2ZcuWxcCBAyFJEmJjY7NuI6K8SU9Ph7+/P+rUqYOePXuKziEiJZHJZJDL5ejVqxe2bNmCPn36iE4iLaLw7chffvkFY8eOhZmZGczNzTFmzBhs2bJFHW1EOmP16tW4efMmAgICYGDAvQCIPoemncf0+++/R+XKlREUFMQzylC+KPxtYGhoiNjYWGRkZCAzMxOxsbHc3EqUD6mpqfD390fDhg3RtWtX0TlEWi2300O9ePFCWJOhoSE8PDxw/vx5HDp0SFgHaR+FQ9jatWuxceNGlClTBmXKlMGmTZuwdu1adbQR6YQVK1YgLi4OgYGBPOUX0WfK7fRQDx48EFT0j4EDB6Js2bI8sTflyycPUZGRkYGFCxdi+/bt6uoh0inv3r3D9OnT0axZM7Rv3150DmmA3bt3i07QarmdaSA1NVXNJdkVKVIEkyZNwo8//ojz58+jfv36QntIO3xyS5ihoSHOnTunrhYinbNkyRI8ePCAW8EoS9GiRT86eCvlXW5nGjA2NlZzycdGjx4NMzMzBAcHi04hLaHw7cgvv/wSXbt2xerVq7F169as/4jo096+fYtZs2bh22+/xbfffis6R6tp2o7Yn2PRokU8wvpnyO30UBUqVBBU9P9KlCiB0aNHY9OmTbh9+7boHNICCoewFy9ewNLSEocOHcKOHTuwY8cO7Ny5Ux1tRFpt4cKFePz4MQIDA0WnaLXcdsTW1kFs48aN2Lhxo+gMrZXb6aEsLCxEpwEAXFxcYGhoiPDwcNEppAUUnrYoOjpaHR1EOuXNmzcIDg5Ghw4d0KxZM9E5Wi23HbG9vLx46h89ldPpoY4cOSIm5j/Kly+PgQMHYsWKFfD19UXp0qVFJ5EGUziEvXv3DsuXL8eVK1fw7t27rOtXrFih0jAibTZ37lw8f/4cAQEBolO0Xm47Yud2PZFoHh4eiI6Oxvz587klnD5J4duRAwcOxN9//41ffvkFLVu2REJCAszMzNTRRqSVXr58ibCwMHTr1g2NGjUSnaP1ctsRO7friUSrVq0aunXrhoULFyIpKUl0DmkwhUPYrVu3EBgYCFNTUwwePBi7du3Cn3/+qY42Iq0UHh6O169fcyuYkuS2I/aMGTMEFREpJpfL8fLlSyxbtkx0CmkwhUNYoUKFAPzzqY/Lly/j9evXuHfvnqq7iLTS69evERERgT59+qBOnTqic3RCbjtia+v+YEeOHNGY/ZdIdZo0aYIWLVogPDwcaWlponNIQykcwpydnfHy5UsEBgaia9euqFGjBjw9PdXRRqR11q1bh+TkZPj5+YlO0SlOTk64d+8eMjMzce/ePa0dwEi/eHp64v79+1i3bp3oFNJQCoewESNGoGTJkmjZsiXu3LmDJ0+eYPTo0epoI9Iqf//9N3766Sf88MMPqF69uugc0lChoaEIDQ0VnVEgunS8NnXo1KkTatWqheDgYGRmZorOIQ2U66cjFR3jZPLkyUqPIdJms2bNQlpaGnx9fUWnkAb7cJxFd3d3wSX58+F4bR8OF/LheG0AuGUyFzKZDJ6enhg0aBB2796Nzp07i04iDZPrlrDExEQkJibi7NmzWLx4MR48eIAHDx4gMjISV69eVWcjkca7f/8+IiMj0aFDBzg4OIjOIVK6Tx2vjXLXr18/2NjY8FRGlKNct4R9+Nd8u3btcP78+azDUvj5+aF3797qqSPSEjNmzIAkSRg4cKDoFCKV4PHaCqZQoUKYPHkyXF1dceLECXz99deik0iDKNwnLD4+PtuJUY2NjfnpSKJ/uXv3LpYvX46RI0eibNmyonOIVILHayu4D/tWBwUFiU4hDZOng7V+9dVX8PPzg7+/Pxo3boxBgwapo41IKwQGBsLQ0BBTp04VnUJawMTEBCYmJqIz8o3Hayu4YsWKYfz48di+fTv++usv0TmkQRQOYV5eXlixYgVKliyJEiVKIDo6mr9siP7nxo0bWLlyJcaOHYsKFSqIziEtsGfPHuzZs0d0Rr7p2vHa1G3ChAkwMTHR2k/GkmooHMIAoF69eujduze6d+8OS0tL7gNA9D/+/v4oUqQIpkyZIjqFSOXyerw2HsriY6VKlcKwYcOwevVqPHjwQHQOaQiFQ9j8+fNRpkwZtG3bFp07d8Z3333Hj9kSAbh8+TLWrVuHiRMnonTp0qJzSEsEBgbq9EmdPxzKIi4uDpIkZR3KgoPYP4d2ysjIQEREhOgU0hAKh7C5c+fi+vXruHLlCi5duoQ///wTly5dUkcbkUbz8/NDsWLFtO54TyTWwYMHcfDgQdEZKsNDWeSuUqVK6NOnD5YsWYJXr16JziENoHAIs7GxQfHixdXRQqQ1/vjjD2zZsgWurq6wtLQUnUOkMXgoi0/z9PREYmIiFi9eLDqFNECuxwn7oFKlSnB0dMR3332HwoULZ13PI+aTPvPx8UHJkiXh6uoqOoVIo9ja2iIuLi7H6wn48ssv0a5dO8ydOxeurq4oUqSI6CQSSOGWMFtbW7Rt2xapqalZR9FPTExURxuRRjp16hR27twJd3d3lChRQnQOkUbhoSwU8/T0xOPHj7Fq1SrRKSSYwi1hPA8eUXbe3t6wsrLCxIkTRadovdjYWHh5eSE+Ph62traYMWOGzh/yQNffvv7w+unb65ofrVq1QoMGDRASEoLhw4fD0NBQdBIJonAIe/r0KYKDg3HlyhW8e/cu6/pDhw6pNIxIEx09ehT79+9HaGgoihUrJjpHq+nrCaG3bNkiOkHlnJycdPo1/FwymQxyuRx9+vTBtm3b0KtXL9FJJIjCtyOdnJxQrVo13L17F76+vrC3t0ejRo3U0UakUSRJwrRp01C2bFmMGTNGdI7W46foSJ/16NEDDg4OCA4OhiRJonNIEIVD2PPnzzF8+HAUKlQILVu2xIoVK3Dy5El1tBFplIMHD+K3336Dl5fXR/u8UP7p66fofvzxR/z444+iM0gwQ0NDuLu748yZMzhy5IjoHBJE4RBWqFAhAEC5cuWwa9cu/PHHH0hISFB5GJEmkSQJ3t7esLGxwciRI0Xn6AR9PSH0iRMncOLECdEZpAEGDRqE0qVL88TeekzhEDZt2jS8fv0aYWFhCA0NxYgRI3i0X9I7u3fvxsmTJzFt2rRsh2qhguOn6EjfmZiYYNKkSfjll19w4cIF0TkkgMIhrGTJkihevDhq1aqFw4cP49y5c7CwsFBHG5FGkCQJPj4+qFSpEoYOHSo6R2fwhNDaj+eI/HxjxoxBsWLFEBwcLDqFBFA4hE2YMCFP1xHpqp9++gnnz5+Hj49P1tvzpBx5PSE0aR6eI1I5SpYsiVGjRmHjxo24e/eu6BxSs1wPUXHixAkcP34cT58+RXh4eNb1b968QUZGhlriiETLzMyEj48PqlatygGBlMLa2lp0glJ86tOt/F7JHxcXF8ybNw/h4eGYP3++6BxSo1yHsNTUVCQlJSE9PT3bEfLNzc2xefNmtcQRibZx40ZcvnwZ69atg5GRwsPqESm0Zs0a0QlKoa+fblUFa2trODk5Yfny5fDx8UGpUqVEJ5Ga5PpbpWXLlmjZsiWGDBkCOzs7AMDLly9RokQJyGQytQUSiZKeng5fX1/UqlULffr0EZ1DpFF4jkjl8vT0RExMDBYsWAB/f3/ROaQmue4TFhAQgGvXrsHOzg7v379Hq1at8MUXX6BMmTI4cOCAOhuJhIiNjcWNGzcQEBAAAwOFu08S5YmLiwtcXFxEZ3w2frpVuapXr46uXbtiwYIFePv2regcUpNcf7Ns2LABVatWBQCsXLkSmZmZePr0KX799VdMnTpVbYFEIqSlpcHf3x/169fH999/LzqHdMiFCxd04nAE/HSr8snlcrx48QLLly8XnUJqkusQZmxsnPW24y+//IL+/fvD0NAQ1atXR3p6utoCiUSIjo7G3bt3ERgYyLffiXLBT7cqV9OmTdGsWTOEhYUhLS1NdA6pQa5DWOHChXH58mU8ffoUhw8fRrt27bJu++8nYoh0yfv37zF9+nQ0adIEHTt2FJ1DRHpELpcjPj4eGzZsEJ1CapDrEBYREYFevXqhWrVqcHV1RcWKFQH8c+TwL7/8Um2BROq2dOlS3L9/n1vBiEjtvvvuO9SoUYMn9tYTuX46skmTJrh27dpH13fq1AmdOnVSaRSRKMnJyZgxYwZatmyJ1q1bi84hHVSlShXRCaTBDAwM4OnpiSFDhmDv3r3cGq/jct0StmbNmk9O4bdv38bvv/+ukigiURYvXoy///6bW8FIZaKiohAVFSU6gzRY//79YW1tzRN764Fct4Q9f/4c9erVQ4MGDdCgQQOUKlUK7969w61bt/Drr7/CysoKs2fPVmcrkUolJiZi9uzZaNeuHb755hvROUSkp4yNjeHq6go3NzecOnUKjRs3Fp1EKpLrlrBJkybh/Pnz6N+/P54+fYqDBw/i/PnzqFChAlavXo0tW7agcuXK6mwlUqn58+fj2bNnCAwMFJ1COszZ2RnOzs6iM0jDjRw5EiVKlODWMB33yfOwGBoaom3btmjbtq26eoiEePXqFUJCQtC5c2d89dVXonNIh924cUN0AmkBMzMzjBs3DjNnzsT169ezjttJuoWHAScCMGfOHLx69QoBAQGiU4iIAAATJ06EsbExQkNDRaeQinAII733/PlzzJkzBz179uThV4hIY5QuXRpDhw7FqlWr8OjRI9E5pAIKh7CMjAx1dBAJExISgqSkJJ40l4g0jru7O9LT0xERESE6hVRA4RDm4OAADw8PXL16VR09RGr1+PFjzJ8/H/3790fNmjVF55AeqFevHurVqyc6g7TEF198gV69eiEyMhKvX78WnUNKpnAIu3TpEqpUqYIRI0agSZMmiIqKwps3b9TRRqRyQUFBePfuHXx9fUWnkJ6IiIjgVg3KF09PT7x58wZLliwRnUJKpnAIMzMzw8iRI3H8+HEEBwfD398f5cqVw+DBg3Hr1i11NBKpxIMHD7Bo0SIMHjyYRzEn0jCxsbGwt7eHgYEB7O3tERsbKzpJmAYNGqB169aIiIjA+/fvReeQEuVpn7Cff/4Z3bt3x6RJk+Dm5oY7d+6gS5cuPH0RabWZM2ciIyMD3t7eolNIjwwYMAADBgwQnaHRYmNj4ezsjLi4OEiShLi4ODg7O+v1ICaXy/Ho0SOsXr1adAopkcIhrHLlyti+fTs8PDzwxx9/YPLkyShTpgx69eqFDh06fPKxe/fuRdWqVeHg4JDr0fWPHDmCevXqoWbNmmjZsmXBngVRPsXFxWHp0qUYPnx41snpidQhISEBCQkJojM0mpeXF5KTk7Ndl5ycDC8vL0FF4rVp0wZffvklQkJC+IE5HfLJg7UC/+wTVqxYsRxvmzdvXq6Py8jIwLhx47B//35YW1ujUaNG6Nq1K2rUqJF1n1evXmHs2LHYu3cvbG1t8eTJkwI8BaL8CwwMhIGBAaZNmyY6hYj+Iz4+Pl/X6wOZTAa5XI5+/fplvTtF2i/XIWzChAmfPIHxpwYwADh9+jQcHBxQqVIlAEC/fv2wffv2bEPY2rVr0aNHD9ja2gL455goRKp269YtxMTEYNy4cbC2thadQ0T/YWtri7i4uByv12c9e/ZEpUqVEBQUhO+///6Tv6NJO+Q6hDVs2BAAcOzYMVy9ehV9+/YFAGzatAkNGjRQuOAHDx7AxsYm67K1tTVOnTqV7T43btxAWloaHB0dkZiYiEmTJmHQoEEfLSsqKgpRUVEA/tmUf+TIEcXPTEslJSXp9PPTBDNnzoSRkRFatmyp9K81Xz/tpa7X7tWrVwDAvyefEB4ejri4OGRmZmZdZ2BgADs7u1y/bvryvdelSxfMnTsXc+fO1ZlDnejLa5cjSQFHR0cpNTU163Jqaqrk6Oio6GHSxo0bpeHDh2ddXrVqlTR+/Phs9xk3bpzUuHFjKSkpSXr69Knk4OAgXb9+/ZPLbdCggcJ1a7PDhw+LTtBpV69elWQymeTh4aGS5fP1017qeu2mTJkiTZkyRS3r0mZr1qyR7OzsJJlMJtnZ2Ulr1qz55P315XsvOTlZKlWqlNSxY0fRKUqj66/dp+YWhfuEPXz4EImJibCwsADwz8T68OFDhcOdtbU17t+/n3U5ISEB5cuX/+g+VlZWMDU1hampKVq0aIGLFy/ycAGkMn5+fjA1NYWnp6foFNJTs2bNEp2gFZycnODk5CQ6Q+OYmJhg4sSJ8Pb2xqVLl1CnTh3RSfQZFH46csqUKfjyyy8xZMgQDBkyBPXr18fUqVMVLrhRo0a4efMm7t69i9TUVKxfvx5du3bNdp9u3brh6NGjSE9PR3JyMk6dOoXq1asX/NkQfcLFixexceNGuLi4wMrKSnQOEVGBjB07FqampggJCRGdQp9J4ZawoUOHomPHjln7c82ePRtly5ZVvGAjIyxYsADt27dHRkYGhg0bhpo1ayIyMhIAMHr0aFSvXh0dOnRAnTp1YGBggBEjRqBWrVqf+ZSIcubr64vixYtj8uTJolNIj/Xs2RMAsGXLFsElpK0sLCwwcuRIzJ8/H9OnT4ednZ3oJCoghUPYb7/9BgAoWbIkgH92pr9x4wZatGihcOGdOnX66ICuo0ePznbZw8MDHh4eeQ4mKogzZ85g+/btCAwMzPq7TCTC8+fPRSeQDpg8eTIWLFiA8PBwzJ07V3QOFZDCIezfmzvfvXuH06dPo0GDBjh06JBKw4iUycfHB5aWlpg0aZLoFCKiz2ZjY4MffvgBy5Yty/r5RtpH4T5hO3bsyPpv//79uHz5MsqUKaOONiKlOHbsGPbu3QtPT0+YmZmJziEiUgpPT08kJydj4cKFolOogBQOYf9lbW2Ny5cvq6KFSCW8vb1RpkwZjBs3TnQKEZHS1KxZE507d8b8+fM/Os0TaQeFb0f++8j5mZmZuHDhAurWravyMCJlOHToEA4fPoyIiAiYmpqKziFC69atRSeQDvH09ESLFi2wYsUKjB8/XnQO5ZPCIezDkfOBfz7x2L9/fzRr1kylUUTKIEkSvL29UaFCBYwaNUp0DhGAf7bMEilL8+bN8fXXXyMsLAyjR4+GkZHCX+ukQRS+Hfnq1SsMHjwYgwcPhpOTE5o1a8ZPYpBW+OWXX3D8+HFMmzYNRYoUEZ1DRKR0H07sfe/ePWzatEl0DuWTwiFs5cqVH10XExOjihYipfmwFcze3h7Dhg0TnUOUpWPHjujYsaPoDNIhXbp0QbVq1RAUFARJkkTnUD7kut1y3bp1WLt2Le7evZvtSPdv3rzhR2FJ4/388884e/YsVqxYAWNjY9E5RFlSUlJEJ5COMTAwgIeHB4YPH459+/ahffv2opMoj3Idwpo2bYpy5crh2bNncHNzy7rezMyM56oijZaZmQkfHx9UrlwZAwcOFJ1DRKRyTk5O8Pb2RlBQEIcwLZLr25F2dnZwdHTEiRMn0LJlS9SqVQsvXryAJEnc8Y802ubNm3Hp0iX4+fnx7yoR6YXChQvD1dUVhw8fxpkzZ0TnUB7lOoR17tw563hgjx49Qq1atbBixQoMHDgQERER6uojypeMjAz4+fmhRo0a6Nu3r+gcIiK1cXZ2RvHixREcHCw6hfIo1yHs7t27WSfTjo6ORtu2bbFjxw6cOnUKK1asUFsgUX6sW7cOf/31F/z9/WFoaCg6h+gjnTt3RufOnUVnkA4yNzfH2LFjsWXLFty8eVN0DuVBrkNYoUKFsv588ODBrBNxm5mZwcAg3wfaJ1K5tLQ0+Pn5oV69eujRo4foHKIcubu7w93dXXQG6aiJEyfC2NgYoaGholMoD3KdpmxsbDB//nxs27YN58+fR4cOHQD888metLQ0tQUS5dWqVatw+/ZtBAQE8B8KRKSXypYti8GDB2PlypX4+++/ReeQArn+plq+fDmuXLmCmJgYbNiwASVKlAAAnDx5EkOHDlVXH1GevH//HgEBAfjqq6/4Vg9pNEdHRzg6OorOIB3m7u6O1NRUzJs3T3QKKZDrR8dKly6NyMjIj67/9ttv8e2336o0iii/li9fjvj4eCxdujTrXKdERPqocuXK6NmzJxYtWoQpU6bA3NxcdBLlgu/ZkNZLSUnBjBkz0Lx5c7Rt21Z0DhGRcJ6ennj9+jWioqJEp9AncAgjrRcZGYmHDx9i+vTp3ApGRASgUaNG+PbbbzFnzhy8f/9edA7lItchTC6XAwBPCEoa7e3bt5g9ezZat26Nli1bis4hItIYcrkcDx8+RGxsrOgUykWuQ9ju3buRlpaGWbNmqbOHKF8WLFiAJ0+eIDAwUHQKUZ706dMHffr0EZ1BeqBdu3aoV68eQkJCkJmZKTqHcpDrjvkdOnSAlZUV3r59C3Nzc0iSBJlMlvX/N2/eqLOT6CNv3rxBcHAwOnXqhK+//lp0DlGejB07VnQC6QmZTAZPT0/88MMP2LFjB7p16yY6if4j1y1hISEheP36Nb777ju8efMGiYmJ2f5PJFpERARevHiBgIAA0SlEeZacnIzk5GTRGaQnevfuDXt7ewQFBUGSJNE59B8Kd8zfvn07Hj9+jJ07d2Lnzp14+vSpOrqIPunFixcICwtD9+7d0aBBA9E5RHnWqVOnrDOQEKmakZER3NzccOLECfz++++ic+g/FA5hmzZtwldffYVNmzZh48aN+Oqrr7B582Z1tBHlKiwsDImJifD39xedQkSk0YYNGwYrKyue2FsD5bpP2AfTp0/HmTNnULp0aQDA06dP0aZNG/Tq1UvlcUQ5efr0KebOnYs+ffqgdu3aonOIiDRa0aJFMWHCBPj6+uLy5cuoVauW6CT6H4VbwjIzM7MGMACwtLTkpyxIqKCgIKSkpMDPz090ChGRVhg3bhyKFi2KkJAQ0Sn0LwqHsA4dOqB9+/aIiYlBTEwMvvvuO+7PQMI8evQICxcuxIABA1CtWjXROUREWsHS0hIjRozA2rVrER8fLzqH/kfhEBYSEoJRo0bh0qVLuHjxIpydnREUFKSONqKPzJw5E+np6fDx8RGdQlQgQ4YMwZAhQ0RnkB6aPHkyJEnCnDlzRKfQ/yjcJwwAevTogR49eqi6heiT4uPjERUVhaFDh+KLL74QnUNUIBzASBQ7Ozv0798fS5cuhbe3NywsLEQn6T2eO5K0xowZMwAA06ZNE1xCVHDPnj3Ds2fPRGeQnvL09MTbt2+xaNEi0SkEDmGkJe7cuYMVK1bA2dkZtra2onOICqxXr178dDkJU7t2bXTs2BHz5s1DSkqK6By9xyGMtEJAQACMjIwwdepU0SlERFpNLpfj6dOniI6OFp2i9xQOYceOHUPbtm1RpUoVVKpUCRUrVkSlSpXU0UYEALh27RpWr16NcePGoVy5cqJziIi0WosWLdC4cWOEhYUhPT1ddI5eU7hj/vDhwzFnzhw0aNAAhoaG6mgiysbf3x8mJiaQy+WiU4iItJ5MJoNcLkePHj2wZcsW9O3bV3SS3lK4Jax48eLo2LEjSpcuDUtLy6z/iNThzz//xIYNGzBx4kSUKlVKdA4RkU7o2rUrqlSpwhN7C6ZwS9i3334LDw8P9OjRA4ULF866vn79+ioNIwIAX19fmJmZwd3dXXQKkVKMGTNGdAIRDA0N4eHhgZEjR+LAgQNo27at6CS9pHAIO3XqFADg7NmzWdfJZDIcOnRIdVVEAM6fP49t27bBz8+Px7MhncG3fkhTDBw4ED4+PggKCuIQJojCIezw4cPq6CD6iI+PD0qWLAkXFxfRKURKc//+fQCAjY2N4BLSd4ULF4aLiwvkcjnOnTuHBg0aiE7SOwr3CXv9+jUmT56Mhg0bomHDhnBzc8Pr16/V0UZ67MSJE9i1axc8PT1RvHhx0TlESjNw4EAMHDhQdAYRAGDUqFEwNzdHcHCw6BS9pHAIGzZsGMzMzLBx40Zs3LgR5ubmGDp0qDraSI/5+PigVKlSGD9+vOgUIiKdVbx4cYwePRqbN2/G7du3RefoHYVD2O3bt+Hv749KlSqhUqVK8PX1xZ07d9TRRnrq119/xYEDB/Djjz+iWLFionOIiHSai4sLjIyMEBoaKjpF7ygcwkxMTPD7779nXT527BhMTExUGkX6S5IkeHt7o3z58hg9erToHCIinVeuXDkMGjQI0dHRePz4segcvaJwCFu8eDHGjRsHe3t72NnZYfz48YiMjFRHG+mhAwcO4OjRo5g6dSqHfSIiNXF3d0dqairmz58vOkWvKPx0ZL169XDx4kW8efMGAGBubq7yKNJPkiRh2rRpsLW1xYgRI0TnEKmEm5ub6ASij1StWhXff/89Fi5cCLlcDjMzM9FJeiHXIWzNmjUYMGAAwsPDc7x98uTJKosi/bRr1y6cPn0aS5cuzXZgYCJd0qVLF9EJRDmSy+XYtm0bli5dyt/xapLr25Fv374FACQmJub4H5EyZWZmwtvbG1988QUGDx4sOodIZa5fv47r16+LziD6SOPGjdGyZUuEh4cjNTVVdI5eyHVL2KhRowD8c9oYIlXbtm0bLly4gFWrVqFQoUKic4hU5sPP1iNHjogNIcqBXC5Hp06dsG7dOv6DWA0U7pjv6emJN2/eIC0tDa1bt4aVlRXWrFmjjjbSExkZGfD19UW1atXwww8/iM4hItJbHTp0QO3atREcHIzMzEzROTpP4RC2b98+mJubY+fOnbC2tsaNGzcQEhKijjbSExs2bMCVK1fg7+8PQ0ND0TlERHpLJpPB09MTV69exa5du0Tn6DyFQ1haWhoAYPfu3ejfvz9PpExKlZ6eDj8/P9SpUwe9evUSnUNEpPf69u0LW1tbBAUFiU7ReQqHsC5duqBatWo4e/YsWrdujadPn6JIkSLqaCM9sHr1aty8eRMBAQEwMFD415GIiFSsUKFCcHNzw7Fjx3Ds2DHROTpN4W+92bNn48SJEzh79iwKFSoEU1NTbN++XR1tpONSU1MREBCAhg0bomvXrqJziNRi2rRpmDZtmugMok8aPnw4LCwseGJvFcv105GHDh1Cq1atsHXr1hxv79Gjh8qiSD9ER0fj3r17WLRoEWQymegcIrVo06aN6AQihUxNTTF+/HgEBATg6tWrqFGjhugknZTrEPbrr7+iVatW2LFjx0e3yWQyDmH0Wd69e4fAwEA0bdoUHTp0EJ1DpDYXLlwA8M/ZSIg02YQJExASEoKQkBBER0eLztFJuQ5h/v7+AMAvPKlEVFQUHjx4gFWrVnErGOkVFxcXADxOGGk+KysrDB8+HEuWLEFgYCCsra1FJ+kchfuETZ06Fa9evcq6/PLlS+7PQJ8lOTkZM2fOxLfffotWrVqJziEioly4ubkhMzMTERERolN0ksIhbM+ePShRokTW5ZIlS2L37t2qbCIdt3DhQjx+/BiBgYGiU4iI6BPs7e3Rt29fLFmyBC9fvhSdo3MUDmEZGRl4//591uWUlJRsl4nyIzExEUFBQWjfvj2aNWsmOoeIiBTw8PBAUlISFi9eLDpF5ygcwgYMGIDWrVtj+fLlWLFiBdq2bcvzSVGBzZ07F8+fP+dWMCIiLVGvXj20b98ec+fORUpKiugcnZLrjvkfeHp6ok6dOjhw4AAkSYK3tzfat2+vjjbSMa9evUJYWBi6du2KRo0aic4hEmLmzJmiE4jyTS6Xo1WrVli1alXWSejp8ykcwgCgevXqMDIyQps2bZCcnIzExESYmZmpuo10THh4OF69eoWAgADRKUTCNG3aVHQCUb45OjqiUaNGCA0NxYgRI3ieXyVR+Hbk0qVL0atXr6zJ98GDB/j+++9V3UU65tmzZ5gzZw569+6NunXris4hEub48eM4fvy46AyifPlwYu9bt27lehB3yj+FQ9jChQtx7NgxmJubAwAqV66MJ0+eqDyMdEtISAjevn0LPz8/0SlEQk2dOhVTp04VnUGUb927d0flypURFBQESZJE5+gEhUNY4cKFYWxsnHU5PT2dB9ekfPn7778xf/58ODk58dQXRERaytDQEO7u7jh37hwOHTokOkcnKBzCWrZsiZkzZyIlJQX79+9H79690aVLF3W0kY6YPXs2UlNT4evrKzqFiIg+w6BBg1CmTBme2FtJFA5hQUFBKFWqFGrXro0lS5agU6dOmD59ujraSAckJCQgMjISgwcPhoODg+gcIiL6DEWKFIGLiwv27duHP/74Q3SO1vvkpyMzMzNRp04dXL58GSNHjlRXE+mQGTNmIDMzE97e3qJTiIhICUaPHo2ZM2ciODgY69atE52j1T65JczAwAB169ZFfHy8unpIh9y9exfLly/HiBEjYG9vLzqHSCNERETwPHyk1UqUKIFRo0Zh48aNuHPnjugcrabw7chHjx6hZs2aaN26Nbp27Zr1H5EigYGBMDAwgJeXl+gUIo1Rr1491KtXT3QG0WdxcXGBoaEhwsPDRadoNYUHa+XO1FQQN2/exKpVqzBhwgRUqFBBdA6Rxjhw4AAAoE2bNoJLiAquQoUKGDhwIFasWAFfX1+UKlVKdJJWynUIe/fuHSIjI3Hr1i3Url0bw4cPh5FRng6wTwR/f38ULlwYU6ZMEZ1CpFE+fLCJQxhpO3d3d6xYsQLz58/nmVAKKNe3IwcPHoyzZ8+idu3a2LNnD9zc3NTZRVrsypUrWLt2LSZMmIAyZcqIziEiIhWoXr06unXrhgULFiApKUl0jlbKdQi7evUq1qxZg1GjRmHz5s04evSoOrtIi/n5+aFYsWLw8PAQnUJERCokl8vx8uVLLFu2THSKVsp1CCtUqFDWn/k2JOXVhQsXsHnzZri6usLS0lJ0DhERqdDXX3+Nb775BuHh4UhLSxOdo3VyHcIuXrwIc3NzmJubw8zMDJcuXcr684fzSBL9l4+PD0qUKAFXV1fRKUREpAZyuRz379/H+vXrRadonVw3cWVkZKizg3TA6dOnsWPHDkyfPh0lSpQQnUOkkZYsWSI6gUipOnbsiJo1ayI4OBgDBgzg+aXzQeFxwojyytvbG1ZWVpg4caLoFCKNVbVqVVStWlV0BpHSGBgYwNPTE5cvX8bu3btF52gVDmGkFL///jv27dsHuVwOMzMz0TlEGmvHjh3YsWOH6Awiperfvz9sbGx4Yu984hBGn02SJEybNg1ly5bF2LFjRecQabSwsDCEhYWJziBSqkKFCmHy5Mn47bffcPLkSdE5WoNDGH22Q4cO4ddff8XUqVNRtGhR0TlERCTAiBEjULJkSQQFBYlO0RocwuizSJIEb29vWFtbY+TIkaJziIhIkGLFimHcuHHYvn07rl27JjpHK3AIo8+yZ88enDhxAt7e3ihSpIjoHCIiEmjChAkoXLgwQkJCRKdoBQ5hVGCSJMHHxwcVK1bE0KFDRecQEZFgpUuXxrBhw7B69Wo8fPhQdI7G4xBGBbZ9+3acO3cOvr6+2c6wQES5W716NVavXi06g0hl3NzckJGRgYiICNEpGo9DGBVIZmYmvL29UaVKFTg5OYnOIdIaNjY2sLGxEZ1BpDKVKlVC7969ERkZiVevXonO0WgcwqhANm3ahMuXL8PPz4/nFiXKhw0bNmDDhg2iM4hUSi6XIzExEZGRkaJTNBqHMMq39PR0+Pr6olatWujbt6/oHCKtsnjxYixevFh0BpFKffnll2jbti3mzp2Ld+/eic7RWBzCKN/Wrl2L69evw9/fHwYG/CtEREQfk8vl+Pvvv7kP5CfwNyjlS1paGvz9/fHll1+ie/fuonOIiEhDtWrVCvXr10dISAgyMjJE52gkDmGULzExMbhz5w4CAwMhk8lE5xARkYaSyWSQy+W4efMmfvrpJ9E5GolDGOXZ+/fvERgYiMaNG6NTp06ic4iISMP17NkTX3zxBYKCgiBJkugcjcMhjPJs2bJluH//PqZPn86tYEQFtHnzZmzevFl0BpFaGBoawt3dHWfOnMGvv/4qOkfjcAijPElJScGMGTPQokULtG7dWnQOkdaysrKClZWV6AwitRk8eDBKly7NE3vngEMY5cnixYvx6NEj7gtG9JliYmIQExMjOoNIbUxMTDBx4kTs3bsXFy9eFJ2jUVQ6hO3duxdVq1aFg4MDZs+e/dHtr1+/RpcuXVC3bl3UrFkT0dHRqsyhAkpKSsKsWbPQtm1btGjRQnQOkVbjEEb6aOzYsShWrBiCg4NFp2gUlQ1hGRkZGDduHPbs2YOrV69i3bp1uHr1arb7LFy4EDVq1MDFixdx5MgRuLm5ITU1VVVJVEDz58/Hs2fPEBgYKDqFiIi0UMmSJeHs7IwNGzbg3r17onM0hsqGsNOnT8PBwQGVKlWCsbEx+vXrh+3bt2e7j0wmQ2JiIiRJQlJSEiwsLHgKHA3z+vVrhISEoHPnzmjcuLHoHCIi0lKurq6QyWQIDw8XnaIxVDbxPHjwINtJaq2trXHq1Kls9xk/fjy6du2K8uXLIzExERs2bMjxCOxRUVGIiooCACQkJODIkSOqyhYuKSlJo55fTEwMXr58ia5du2pUl6bStNeP8k5dr92HExrz74ly8XtPO7Ru3RpRUVFo3bo1ihcvDkC/XzuVDWE5HQ/kvzt0//LLL6hXrx4OHTqE27dvo23btvjmm29gbm6e7X7Ozs5wdnYGADRs2BCOjo6qyhbuyJEjGvP8nj9/jm3btqFHjx4YOXKk6BytoEmvH+WPul67EiVKAAD/nigZv/e0Q+nSpVGzZk388ccf8PPzA6Dfr53K3o60trbG/fv3sy4nJCSgfPny2e4THR2NHj16QCaTwcHBARUrVsS1a9dUlUT5FBoaisTERPj7+4tOIdIZu3fvxu7du0VnEAlRo0YNdOnSBfPnz8fbt29F5winsiGsUaNGuHnzJu7evYvU1FSsX78eXbt2zXYfW1tbHDx4EADw+PFjXL9+HZUqVVJVEuXDkydPMG/ePPTr1w+1atUSnUOkM4oWLYqiRYuKziASRi6X48WLF1ixYoXoFOFUNoQZGRlhwYIFaN++PapXr44+ffqgZs2aiIyMRGRkJADA29sbx48fR+3atdG6dWsEBQXxIIYaIigoCO/evcvaXExEyrFo0SIsWrRIdAaRMM2aNUOzZs0QFhaGtLQ00TlCqfSjiJ06dfroHIOjR4/O+nP58uWxb98+VSZQATx8+BCLFi3CoEGDUKVKFdE5RDpl48aNAP45bhKRvvL09ES3bt2wceNGVKhQQXSOMDxiPn1k5syZSE9Ph4+Pj+gUIiLSQZ07d0aNGjUQHBys1yf25hBG2cTFxSEqKgrDhw9HxYoVRecQEZEOMjAwgIeHBy5duoQzZ86IzhGGQxhlM336dMhkMnh5eYlOISIiHfbDDz+gQoUKWLdunegUYTiEUZZbt24hOjoao0ePznagXSIiImUzNjaGq6srLly4gNOnT4vOEYJDGGUJCAiAsbExfvzxR9EpRDrryJEjent0cKL/cnZ2RrFixRAUFCQ6RQgOYQQA+OuvvxAbG4tx48ahbNmyonOIiEgPmJmZoVu3bti2bRuuX78uOkftOIQRAMDPzw9FixaFp6en6BQinRYaGorQ0FDRGUQao0ePHjA2NkZYWJjoFLXjEEa4dOkSNm7ciEmTJqFUqVKic4h02s6dO7Fz507RGUQaw8LCAkOHDsXKlSvx6NEj0TlqxSGM4Ovri+LFi8PNzU10ChER6SE3Nzekp6dj7ty5olPUikOYnjt79ix++uknuLm5oWTJkqJziIhIDzk4OKBnz55YvHgxXr9+LTpHbTiE6TkfHx9YWFhg0qRJolOIiEiPyeVyvHnzBlFRUaJT1IZDmB47fvw49uzZA7lcDnNzc9E5RHrBxMQEJiYmojOINE6DBg3QunVrzJkzB+/fvxedoxYcwvSYt7c3SpcujXHjxolOIdIbe/bswZ49e0RnEGkkT09PPHr0CGvWrBGdohYcwvTU4cOHcejQIfz4448wNTUVnUNERIS2bdviyy+/REhICDIzM0XnqByHMD0kSRK8vb1RoUIFjB49WnQOkV4JDAxEYGCg6AwijSSTyeDp6Ynr169j+/btonNUjkOYHtq3bx+OHTsGLy8vFClSRHQOkV45ePAgDh48KDqDSGP16tULFStWRFBQECRJEp2jUhzC9MyHrWB2dnYYPny46BwiIqJsjIyM4O7ujlOnTuHo0aOic1SKQ5ie2bFjB86cOQMfHx8YGxuLziEiIvrIkCFDYGVlpfMn9uYQpkcyMzPh4+MDBwcHDBo0SHQOERFRjooWLYqJEydi9+7d+PPPP0XnqAyHMD2yZcsWXLx4EX5+fjAyMhKdQ6SXLC0tYWlpKTqDSOONGzcOpqamCAkJEZ2iMhzC9ERGRgZ8fX1Ro0YN9OvXT3QOkd7asmULtmzZIjqDSONZWFhg5MiRWLduHeLj40XnqASHMD2xfv16/PXXX/D394ehoaHoHCIiIoVcXV0BAOHh4YJLVINDmB5IT0+Hn58f6tatix49eojOIdJrP/74I3788UfRGURawdbWFv3798fSpUvx/Plz0TlKxyFMD6xatQq3bt1CQEAADAz4khOJdOLECZw4cUJ0BpHW8PT0RHJyMhYuXCg6Ren4G1nHpaamIiAgAI0aNUKXLl1E5xAREeVLrVq18N1332H+/PlITk4WnaNUHMJ03PLlyxEXF4fAwEDIZDLROURERPkml8vx7NkzREdHi05RKg5hOiwlJQXTp09H8+bN0a5dO9E5REREBdK8eXM0adIEoaGhSE9PF52jNBzCdNiSJUvw8OFDbgUj0iDW1tawtrYWnUGkVWQyGeRyOe7du4dNmzaJzlEaHrFTR719+xazZs1Cq1at4OjoKDqHiP5nzZo1ohOItFLXrl1RrVo1BAcHo1+/fjqxcYFbwnTUwoUL8eTJEwQGBopOISIi+mwGBgbw8PDAhQsXsH//ftE5SsEhTAe9efMGQUFB6NixI5o2bSo6h4j+xcXFBS4uLqIziLSSk5MTypcvrzMn9uYQpoPmzp2LFy9eICAgQHQKEf3HhQsXcOHCBdEZRFqpcOHCcHFxwaFDh3D27FnROZ+NQ5iOefnyJcLCwvD999+jYcOGonOIiIiUatSoUShevLhObA3jEKZjwsLC8Pr1a/j7+4tOISIiUjpzc3OMGTMGW7Zswa1bt0TnfBYOYTrk6dOnmDt3Lvr27Ys6deqIziEiIlKJSZMmoVChQggNDRWd8lk4hOmQ4OBgJCcnw8/PT3QKEeWiSpUqqFKliugMIq1WtmxZDB48GDExMfj7779F5xQYhzAd8ejRIyxcuBBOTk6oVq2a6BwiykVUVBSioqJEZxBpPXd3d6SmpmLevHmiUwqMQ5iOmDVrFlJTU+Hr6ys6hYiISOWqVKmCHj16YNGiRUhMTBSdUyAcwnTA/fv3sWTJEgwdOhRffPGF6Bwi+gRnZ2c4OzuLziDSCXK5HK9fv9barcscwnTAjBkzAADe3t6CS4hIkRs3buDGjRuiM4h0QqNGjeDo6Ig5c+YgNTVVdE6+cQjTcnfu3MHy5csxcuRI2Nrais4hIiJSK7lcjgcPHiA2NlZ0Sr5xCNNygYGBMDIywtSpU0WnEBERqV379u1Rt25dBAcHIzMzU3ROvnAI02LXr1/HqlWrMHbsWJQvX150DhERkdrJZDJ4enri2rVr2Llzp+icfOEQpsX8/f1hYmICuVwuOoWI8qhevXqoV6+e6AwindKnTx/Y2dlp3amMOIRpqcuXL2P9+vWYOHEiSpcuLTqHiPIoIiICERERojOIdIqRkRHc3Nxw/Phx/P7776Jz8oxDmJby9fWFmZkZ3N3dRacQEREJN2zYMFhaWmrV1jAOYVrojz/+wNatW+Hq6goLCwvROUSUDwMGDMCAAQNEZxDpHFNTU0yYMAE7d+7ElStXROfkCYcwLeTj44OSJUvC1dVVdAoR5VNCQgISEhJEZxDppPHjx6No0aIICQkRnZInHMK0zMmTJ7Fz5054eHigePHionOIiIg0hqWlJYYPH47Y2Fjcv39fdI5CHMK0jI+PD0qVKoUJEyaITiEiItI4kydPhiRJmDNnjugUhTiEaZHffvsN+/fvx5QpU1CsWDHROURERBrH3t4e/fr1Q1RUFF68eCE655M4hGkJSZLg7e2NcuXKYcyYMaJziKiAvv76a3z99deiM4h0mqenJ96+fYvFixeLTvkkI9EBlDcHDx7Eb7/9hgULFsDExER0DhEV0KxZs0QnEOm8OnXqoGPHjpg7dy4mT56ssb83uSVMC0iShGnTpsHGxgYjRowQnUNERKTxPD098fTpU8TExIhOyRWHMC2we/dunDp1Ct7e3ihcuLDoHCL6DD179kTPnj1FZxDpvJYtW+Krr75CaGgo0tPTRefkiEOYhvuwL1ilSpUwZMgQ0TlE9JmeP3+O58+fi84g0nkymQxyuRx37tzB1q1bRefkiEOYhtu2bRv++OMP+Pr6olChQqJziIiItEa3bt1QpUoVBAUFQZIk0Tkf4RCmwTIyMuDj44Nq1arByclJdA4REZFWMTQ0hLu7O86fP4+DBw+KzvkIhzANtnHjRly5cgV+fn4wNDQUnUNERKR1Bg4ciLJly2rkib05hGmo9PR0+Pn5oXbt2ujdu7foHCJSktatW6N169aiM4j0RpEiReDi4oIDBw7g3LlzonOy4RCmodasWYMbN24gICAABgZ8mYh0hbe3N7y9vUVnEOmV0aNHw9zcXONO7M3f7hooLS0NAQEBaNCgAbp16yY6h4iISKsVL14co0ePxqZNm3D79m3ROVk4hGmg6Oho3L17F4GBgZDJZKJziEiJOnbsiI4dO4rOINI7kyZNgpGREcLCwkSnZOEQpmFSU1MRGBiIr7/+Gh06dBCdQ0RKlpKSgpSUFNEZRHqnfPnyGDhwIKKjo/HkyRPROQA4hGmcnTt3IiEhgVvBiIiIlMzDwwPv37/H/PnzRacA4BCmUZKTk7FmzRo4OjqiVatWonOIiIh0StWqVfH9999j4cKFSEpKEp3DIUyTLFq0CC9fvuRWMCIiIhXx9PTEy5cvsXTpUtEpHMI0RWJiIoKCgtCoUSM0b95cdA4RqUjnzp3RuXNn0RlEeqtJkyZo0aIFwsPDkZqaKrSFQ5iGmDdvHp49e4ahQ4eKTiEiFXJ3d4e7u7voDCK9JpfLkZCQgPXr1wvt4BCmAV69eoXQ0FB06dIF1atXF51DRESk0zp27IjatWsjODgYmZmZwjo4hGmAOXPm4NWrVwgICBCdQkQq5ujoCEdHR9EZRHpNJpPB09MTV65cwe7du4V1cAgT7Pnz55gzZw569eqFevXqic4hIiLSC3379oWtra3QE3tzCBMsJCQESUlJ8Pf3F51CRESkNwoVKoTJkyfj999/x/Hjx4U0cAgT6PHjx5g/fz5++OEH1KhRQ3QOERGRXhkxYgQsLCwQHBwsZP0cwgSaPXs23r9/D19fX9EpREREesfU1BTjx4/H9u3b8ddff6l9/RzCBHnw4AEWL16MwYMHo3LlyqJziEhN+vTpgz59+ojOIKL/GT9+PExMTBASEqL2dXMIE2TGjBnIzMyEt7e36BQiUqOxY8di7NixojOI6H9KlSqFYcOGYc2aNUhISFDrujmECXDv3j0sW7YMw4cPh729vegcIlKj5ORkJCcni84gon9xc3NDZmYm5s6dq9b1cggTIDAwEAYGBvDy8hKdQkRq1qlTJ3Tq1El0BhH9S8WKFdGnTx8sWbIEr169Utt6OYSp2a1bt7By5UqMHj0a1tbWonOIiIgI/5zYOzExEYsXL1bbOjmEqZm/vz8KFy6MKVOmiE4hIiKi/6lXrx7atWuHuXPn4t27d2pZJ4cwNbp69SpiY2Mxfvx4lC1bVnQOERER/YtcLsfjx4+xcuVKtayPQ5ga+fn5wdTUFB4eHqJTiIiI6D++/fZbNGzYEKGhocjIyFD5+jiEqcmFCxewadMmuLq6wsrKSnQOEQkyZMgQDBkyRHQGEeVAJpNBLpfj1q1b2LZtm8rXZ6TyNRAAwNfXFyVKlMDkyZNFpxCRQBzAiDRb9+7d4eDggKCgIPTs2RMymUxl6+KWMDU4c+YMfv75Z7i7u6NEiRKic4hIoGfPnuHZs2eiM4goF4aGhnB3d8fZs2dx+PBhla6LQ5gaeHt7w9LSEhMnThSdQkSC9erVC7169RKdQUSfMHjwYJQpU0blJ/bmEKZix44dwy+//AK5XA4zMzPROURERKRAkSJFMGnSJPzyyy+4cOGCytbDIUzFvL29UaZMGYwbN050ChEREeXRmDFjUKxYMZVuDeMQpkKHDh3C4cOHMXXqVBQtWlR0DhEREeVRiRIlMGrUKGzYsAF3795VyTo4hKmIJEnw9vaGtbU1nJ2dRecQERFRPrm6usLQ0BBhYWEqWT6HMBXZu3cvjh8/jmnTpqFIkSKic4hIQ4wZMwZjxowRnUFEeVChQgUMGDAAK1aswNOnT5W+fA5hKiBJEnx8fGBvb4+hQ4eKziEiDdK3b1/07dtXdAYR5ZGHhwdSUlKwYMECpS+bQ5gK/Pzzzzh79ix8fX1hbGwsOoeINMj9+/dx//590RlElEfVq1dH165dsWDBArx9+1apy+YQpmSZmZnw9vZGlSpVMGDAANE5RKRhBg4ciIEDB4rOIKJ8kMvlePHiBZYtW6bU5apsCBs2bBhKly6NWrVq5Xi7JEmYOHEiHBwcUKdOHZw/f15VKWq1efNm/Pnnn/Dz84OREc8KRUREpO2aNm2K5s2bIzw8HGlpaUpbrsqGsCFDhmDv3r253r5nzx7cvHkTN2/eRFRUlE7sqJqRkQFfX1/UrFmT+3wQERHpELlcjvj4eGzYsEFpy1TZENaiRQtYWFjkevv27dsxaNAgyGQyNGnSBK9evcKjR49UlaMWa9euxbVr1+Dv7w8DA77Tqw9iY2Nhb28PAwMD2Nvb48WLF6KTiIhIBTp16oSaNWsiODgYkiQpZZnC3i978OABbGxssi5bW1vjwYMHKFeu3Ef3jYqKQlRUFAAgISEBR44cUVdmnqWnp2PKlCmoXLkySpYsWeDGpKQkjXx+9LEXL17gyZMnmDBhQtZ16enp2Lp16yf/AUKaSV3fe69evQIAfp8rGX92ai9teu06d+6MoKAgBAUFoUmTJp+9PGFDWE5TpEwmy/G+zs7OWQc8bdiwIRwdHVWZViDLli3Dw4cPsWPHDrRq1arAyzly5IhGPj/6mL29PeLi4rJdFxoaivnz5+PevXtioqjA1PW9FxgYCAD8Plcy/uzUXtr02jVt2hSxsbHYs2cPpkyZ8tnLE/aembW1dbaPaSckJKB8+fKicj7L+/fvERgYiMaNG+O7774TnUNqEh8fn6/riQCgS5cu6NKli+gMIioAY2NjTJ48Gb/99htOnjz52csTNoR17doVq1atgiRJOHnyJIoXL57jW5HaYNmyZYiPj0dgYGCuW/NI99ja2ubreiIAuH79Oq5fvy46g4gKaOTIkShZsqRSTuytsiGsf//++Prrr3H9+nVYW1tj+fLliIyMRGRkJIB/dnCrVKkSHBwcMHLkSCxatEhVKSqVkpKCGTNm4JtvvkGbNm1E55AazZgx46MTsxsYGGDGjBmCikgbjBo1CqNGjRKdQUQFVKxYMYwdOxY//fTTZ/+DSmX7hK1bt+6Tt8tkMixcuFBVq1ebyMhIPHr0COvXr+dWMD3j5OQEAPDy8kJ8fDxsbW1hZ2eHHj16CC4jIiJVmjhxIsLCwhASEvJZB3DlcRQ+Q1JSEmbNmoU2/9fe3cY0dT1gAH86K44Z0RFg8mYMGl5WhFoUTBF8KzMq2YLM6dItusU4zXxBAnQ6omgUpVYtCsmiRmMkKokZwSEjAzbQwQxKQEVEErJmoGLqG0Kmhtb+Pyw28a9Ai+DhyvP7BL3n3D7lfODJbXuPRoPY2FjRcUgArVYLk8mE58+fw2Qy8VuRRETDgJeXF7755hucOHECt2/f7vd5WMLeQE5ODsxms/3bTkRERDQ8pKSkwGKxIDs7u9/nYAnrp46ODuj1eixatGhA7hVCRERE0hEQEIAlS5bgp59+QkdHR7/OwRLWT0ajEQ8fPsT27dtFRyEiCUlPT0d6erroGEQ0ANLS0vD48WP7lw6dxRLWDw8ePMC+ffuwePFiqFQq0XGISEI0Gg2/SU30jlCpVNBoNDAajXj69KnT81nC+sFgMKCzsxPbtm0THYWIJKa+vh719fWiYxDRANHpdGhvb0deXp7Tc1nCnGQ2m3HgwAEsXboUoaGhouMQkcQkJSUhKSlJdAwiGiDz5s2DSqXCnj17YLVanZrLEuakrKwsPHnyBBkZGaKjEBERkWAymQxpaWlobm5GYWGhU3NZwpxw+/Zt5Obm4uuvv0ZQUJDoOERERDQEJCYmIiAgAFlZWbDZbA7PYwlzwq5du2CxWLBlyxbRUYiIiGiIkMvlSElJQU1NDc6fP+/wPJYwB/3zzz84dOgQvv32WwQEBIiOQ0REREPIihUr4OXlhaysLIfnDNreke+aHTt2AADv70NEbyQzM1N0BCIaBK6urli/fj3S09Nx9epVhIWF9TmHV8Ic0NLSgmPHjuG7776Dv7+/6DhEJGFqtRpqtVp0DCIaBGvWrMHo0aOh1+sdGs8S5oDt27dDLpdj06ZNoqMQkcRVV1ejurpadAwiGgTu7u5YtWoVTp8+DZPJ1Od4lrA+NDU1IS8vD2vXroW3t7foOEQkcZs3b8bmzZtFxyCiQbJx40bIZDLs37+/z7EsYX3IyMiAq6sr0tLSREchIiKiIc7f3x9arRZHjhzB/fv3ex3LEtaLa9euIT8/Hxs2bICnp6foOERERCQBqamp+Pfff5GTk9PrOJawXmzduhVjx45FSkqK6ChEREQkEQqFAvHx8Th48GCv41jCelBbW4uCggIkJyfjww8/FB2HiIiIJESn0/X5diTvE9aDLVu2wN3dnRvtEtGAMhqNoiMQ0Vswc+ZMqNVqPHv2rMcxMpszmxwNAR4eHpg4caLoGIPGbDbz82cSxvWTLq6dtHH9pOtdXzuTyYR79+699pjkSti7btq0abh8+bLoGNRPXD/p4tpJG9dPuobz2vEzYUREREQCsIQRERERCcASNsSsWrVKdAR6A1w/6eLaSRvXT7qG89rxM2FEREREAvBKGBEREZEALGFEREREArCEDWEGgwEymazH+4vQ0JOamorg4GCEhYUhISEBjx49Eh2JHFBSUoKgoCBMnjwZu3fvFh2HHNTa2oo5c+YgJCQECoUC2dnZoiNRP1itVkydOhXx8fGio7x1LGFDVGtrK0pLSzFhwgTRUcgJcXFxaGhowNWrVxEYGIhdu3aJjkR9sFqt+P777/Hrr7+isbERp06dQmNjo+hY5AC5XI69e/fixo0buHjxInJzc7l2EpSdnY2QkBDRMYRgCRuiNm7cCL1eD5lMJjoKOeGTTz6BXP7fbmAzZsxAW1ub4ETUl5qaGkyePBkBAQFwcXHBsmXLUFhYKDoWOcDb2xsqlQoAMGbMGISEhODWrVuCU5Ez2tracO7cOaxcuVJ0FCFYwoags2fPwtfXF+Hh4aKj0Bs4evQoFixYIDoG9eHWrVvw9/e3/+7n58d/5BJkMplQV1eHqKgo0VHICUlJSdDr9XjvveFZR7iBtyAajQbt7e2vPL5z505kZmbit99+E5CKHNHb2n322Wf2n+VyObRa7duOR0563V16eAVaWrq6upCYmAij0Qg3NzfRcchBRUVF8PLyQkREBCoqKkTHEYIlTJCysrLXPn7t2jX8/fff9qtgbW1tUKlUqKmpwfjx499mROpBT2v3wvHjx1FUVITy8nL+M5cAPz8/tLa22n9va2uDj4+PwETkjO7ubiQmJkKr1WLx4sWi45ATqqqqcPbsWRQXF+Pp06d4/PgxvvrqK+Tl5YmO9tbwZq1D3MSJE3H58mV4eHiIjkIOKCkpQXJyMiorK+Hp6Sk6DjnAYrEgMDAQ5eXl8PX1xfTp03Hy5EkoFArR0agPNpsNy5cvh7u7O4xGo+g49AYqKipgMBhQVFQkOspbNTzfhCUaJGvXrkVnZyfi4uKgVCqxevVq0ZGoD3K5HDk5OZg/fz5CQkLwxRdfsIBJRFVVFU6cOIHff/8dSqUSSqUSxcXFomMROYxXwoiIiIgE4JUwIiIiIgFYwoiIiIgEYAkjIiIiEoAljIiIiEgAljAiIiIiAVjCiEgy2tvbsWzZMkyaNAkff/wxFi5ciObm5h7HV1RUID4+fkCeu6KiAitWrOjX3IyMDBgMhgHJQUTvDpYwIpIEm82GhIQEzJ49Gy0tLWhsbERmZibu3r0rOhoRUb+whBGRJPzxxx8YOXLkSzfAVSqViImJgc1mQ2pqKkJDQzFlyhTk5+fbx3R1deHzzz9HcHAwtFqtfa/I2tpazJo1CxEREZg/fz7u3LkDAJg9ezZ0Oh0iIyMRGBiICxcuAABcXFwwduxYAEBlZaX95qBTp05FZ2fnK3l37tyJoKAgaDQa3Lx5c9D+LkQkXdw7kogkoaGhAREREa899vPPP6O+vh5XrlzBvXv3MH36dMTGxgIA6urqcP36dfj4+CA6OhpVVVWIiorCunXrUFhYCE9PT+Tn5+PHH3/E0aNHAfy3lVFNTQ2Ki4uxbds2lJWVQa1WQ61WAwAMBgNyc3MRHR2Nrq4uvP/++y/lqa2txenTp1FXVweLxQKVStVjdiIavljCiEjy/vzzT3z55ZcYMWIEPvroI8yaNQuXLl2Cm5sbIiMj4efnB+C/K2cmkwnjxo1DQ0MD4uLiAABWqxXe3t72873YCDoiIgImk+mV54uOjkZycrJ90+gX53/hwoULSEhIwAcffAAA+PTTTwfjZRORxLGEEZEkKBQKnDlz5rXHett9bdSoUfafR4wYAYvFApvNBoVCgb/++qvXOS/G/78ffvgBixYtQnFxMWbMmIGysjIEBwe/NEYmk/X5mohoeONnwohIEubOnYtnz57h8OHD9scuXbqEyspKxMbGIj8/H1arFWazGefPn0dkZGSP5woKCoLZbLaXsO7ubly/ft3hLC0tLZgyZQp0Oh2mTZuGpqaml47HxsaioKAAT548QWdnJ3755RcnXy0RDQcsYUQkCTKZDAUFBSgtLcWkSZOgUCiQkZEBHx8fJCQkICwsDOHh4Zg7dy70ej3Gjx/f47lcXFxw5swZ6HQ6hIeHQ6lUorq62uEsRqMRoaGhCA8Ph6urKxYsWPDScZVKhaVLl0KpVCIxMRExMTH9ft1E9O6S2Xq7jk9EREREg4JXwoiIiIgEYAkjIiIiEoAljIiIiEgAljAiIiIiAVjCiIiIiARgCSMiIiISgCWMiIiISID/Aep4Mx3FpGLtAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(table.ES, table['SE(d)'], c='black')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.grid()\n",
    "plt.ylim(1.05, 0)\n",
    "plt.xlim(-5,5)\n",
    "plt.vlines([0.48], 1.2, 0, ls='--', color='black')\n",
    "plt.plot([0.48, 5], [0, 1.05], color='black')\n",
    "plt.plot([0.48, -4.52], [0, 1.05], color='black')\n",
    "plt.ylabel(\"Precision of Study (Standard Error of Cohen's d)\")\n",
    "plt.xlabel(\"Cohen's d\")\n",
    "plt.title(\"Studies Reported Speech in Noise Performance\")\n",
    "plt.savefig('F5_funnelPlot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "098fad15-0898-49c6-8191-ae1f0f5023be",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Eggers test\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3d012cc2-9c05-477c-9422-bf168fc33f8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "bead646c-06da-41c0-81d0-c9b222e4c523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.012802286729409279, intercept=2.371716940631416, rvalue=0.014056200953738187, pvalue=0.9517796931807688, stderr=0.20892955746203187, intercept_stderr=0.24004283290610928)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linregress(table.ES, 1/table['SE(d)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "aa49976c-80a5-4b16-915d-393d087012cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.015432929243648247, intercept=0.4084357586406137, rvalue=0.014056200953738187, pvalue=0.9517796931807688, stderr=0.2518608702780599, intercept_stderr=0.6461960856999622)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linregress(1/table['SE(d)'], table.ES)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Spyder)",
   "language": "python3",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}