bacen.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "977df17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from bcb import sgs\n",
    "from bcb import currency\n",
    "from bcb import Expectativas\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ccafe6",
   "metadata": {},
   "source": [
    "#### Buscando SELIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f264af5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "selic = sgs.get({'selic' : 432}, start = '2023-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "474c4ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "selic = selic.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "dedb565a",
   "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>Date</th>\n",
       "      <th>selic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2023-03-05</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2023-03-07</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2023-03-08</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2023-03-09</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2023-03-10</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  selic\n",
       "63 2023-03-05  13.75\n",
       "64 2023-03-06  13.75\n",
       "65 2023-03-07  13.75\n",
       "66 2023-03-08  13.75\n",
       "67 2023-03-09  13.75\n",
       "68 2023-03-10  13.75"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selic.tail(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cc4ce0a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12, 6))\n",
    "\n",
    "sns.lineplot(data = selic, x = 'Date', y = 'selic', color = 'green')\n",
    "ax.set_title('Selic', fontsize = 18)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "fig.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fc36803f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tjlp = sgs.get({'tjlp' : 256}, start = '2023-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9cd6df7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tjlp = tjlp.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "364b429a",
   "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>Date</th>\n",
       "      <th>tjlp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  tjlp\n",
       "0 2023-01-01  7.37\n",
       "1 2023-02-01  7.37\n",
       "2 2023-03-01  7.37"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tjlp.tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d8164c",
   "metadata": {},
   "source": [
    "#### Inflacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "baab3ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "inflacao = sgs.get({'ipca' : 433, 'igp-m' : 189, 'inpc' : 188}, start = '2010-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2c6487b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "inflacao = inflacao.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ec3bc17e",
   "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>Date</th>\n",
       "      <th>ipca</th>\n",
       "      <th>igp-m</th>\n",
       "      <th>inpc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>0.21</td>\n",
       "      <td>-0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>0.59</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  ipca  igp-m  inpc\n",
       "145 2022-02-01  1.01   1.83  1.00\n",
       "146 2022-03-01  1.62   1.74  1.71\n",
       "147 2022-04-01  1.06   1.41  1.04\n",
       "148 2022-05-01  0.47   0.52  0.45\n",
       "149 2022-06-01  0.67   0.59  0.62\n",
       "150 2022-07-01 -0.68   0.21 -0.60\n",
       "151 2022-08-01 -0.36  -0.70 -0.31\n",
       "152 2022-09-01 -0.29  -0.95 -0.32\n",
       "153 2022-10-01  0.59  -0.97  0.47\n",
       "154 2022-11-01  0.41  -0.56  0.38\n",
       "155 2022-12-01  0.62   0.45  0.69\n",
       "156 2023-01-01  0.53   0.21  0.46\n",
       "157 2023-02-01  0.84  -0.06   NaN"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflacao.tail(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "993f5480",
   "metadata": {},
   "outputs": [],
   "source": [
    "def inflacao_ipca_anual(df_inflacao):\n",
    "    inflacao_df = df_inflacao.copy()\n",
    "    inflacao_df = inflacao_df.reset_index()\n",
    "    inflacao_df = inflacao_df.tail(13)\n",
    "    \n",
    "    # acumulando IPCA\n",
    "    inflacao_ipca = inflacao_df[['Date', 'ipca']].dropna()\n",
    "    inflacao_ipca = inflacao_ipca.assign(ipca_indice = False)\n",
    "    for i in inflacao_ipca.loc[1:, 'ipca_indice']:\n",
    "        inflacao_ipca['ipca_indice'] = (inflacao_ipca['ipca'] / 100) + 1\n",
    "    \n",
    "    inflacao_ipca.iloc[0, -1] = 1\n",
    "    inflacao_ipca['ipca_acumulado'] = np.cumprod(inflacao_ipca['ipca_indice'])\n",
    "    \n",
    "    inflacao_12meses_ipca = inflacao_ipca['ipca_acumulado'].tail(1).item()\n",
    "    inflacao_12meses_ipca = round((inflacao_12meses_ipca - 1) * 100, 2)\n",
    "    \n",
    "    # acumulando IGP-M\n",
    "    inflacao_igpm = inflacao_df[['Date', 'igp-m']].dropna()\n",
    "    inflacao_igpm = inflacao_igpm.assign(igpm_indice = False)\n",
    "    for i in inflacao_igpm.loc[1:, 'igpm_indice']:\n",
    "        inflacao_igpm['igpm_indice'] = (inflacao_igpm['igp-m'] / 100) + 1\n",
    "    \n",
    "    inflacao_igpm.iloc[0, -1] = 1\n",
    "    inflacao_igpm['igpm_acumulado'] = np.cumprod(inflacao_igpm['igpm_indice'])\n",
    "    \n",
    "    inflacao_12meses_igpm = inflacao_igpm['igpm_acumulado'].tail(1).item()\n",
    "    inflacao_12meses_igpm = round((inflacao_12meses_igpm - 1) * 100, 2)\n",
    "    \n",
    "    # acumulando inpc\n",
    "    inflacao_inpc = inflacao_df[['Date', 'inpc']].dropna()\n",
    "    inflacao_inpc = inflacao_inpc.assign(inpc_indice = False)\n",
    "    for i in inflacao_inpc.loc[1:, 'inpc_indice']:\n",
    "        inflacao_inpc['inpc_indice'] = (inflacao_inpc['inpc'] / 100) + 1\n",
    "    \n",
    "    inflacao_inpc.iloc[0, -1] = 1\n",
    "    inflacao_inpc['inpc_acumulado'] = np.cumprod(inflacao_inpc['inpc_indice'])\n",
    "    \n",
    "    inflacao_12meses_inpc = inflacao_inpc['inpc_acumulado'].tail(1).item()\n",
    "    inflacao_12meses_inpc = round((inflacao_12meses_inpc - 1) * 100, 2)\n",
    "    \n",
    "    \n",
    "    return inflacao_12meses_ipca, inflacao_12meses_igpm, inflacao_12meses_inpc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ac3b9314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.6, 1.86, 4.66)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflacao_ipca_anual(inflacao)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6cd986e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAGoCAYAAABbkkSYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydd3gc5fW271HvxerFluVe5G5sDKaY3lsInUAgQGgpQHrypZdfIKEl1CQQCDV0CMU0FzC4G/deJNnqVu/SzvfH2dGupN3VStoi2ee+Ll9r7c7Ovltm5n3e5xTDNE0URVEURVEURVGUwRMS7AEoiqIoiqIoiqIcKajAUhRFURRFURRF8REqsBRFURRFURRFUXyECixFURRFURRFURQfoQJLURRFURRFURTFR6jAUhRFURRFURRF8REqsBRFUZRhg2EYlxqG8ZVhGM2GYZiGYZxsGMb11v8D8PrjDMPYYRhGmWEYtxqGMdL+2on+fm1FURRleKACS1EURQkIdjFkGoZxzwCfPwF4AagF7gCuBbb5cIjecBNQBzwE/Ao4ADxvmmZtgMehKIqiDFHCgj0ARVEURfGSk5Hr1vdM01xn3WkYRiDH8Bfg/0zTPGwYxp+BVNM0SwI5AEVRFGVoowJLURRFGS5k2m8PB2sApmmWO/2/HVBxpSiKonRDQwQVRVGUoGAYxmh7yOCvDMM4zzCM1YZhtBiGUWIYxr2GYYQ5bWsCv7b/uc/+vP0e9h1vGMbvDMNYaRhGpWEYrYZh7DYM40+GYcS42N4wDOMm+/YN9n+bDMP4zSD2GWsYxh8Nw9hj37bUMIxnDMPIG9QHpyiKogxp1MFSFEVRgs05wG3AY8C/gAuBe4Bq4A/2ba4FLgEuBr4PVAINHvaZA3wLeBV4HugATgJ+CMwCzuyx/bPA1cBK4PdADTAJuBT4f/3dp10cfgAcD7yChBaOB24FzjAMY65pmsV9fjKKoijKsEMFlqIoihJspgJTTdPcD2AYxmPAJuBO7ALLNM3/GIYxDhFYb1jbemAvMNIexmfxd8Mwfgv83DCMeaZprrK/3mWIuPoPcJ1pmjbrCYZhhAxkn8A3EXF1r2maP3Ta30fAO8AfEdGoKIqiHGFoiKCiKIoSbLoJJtM0TeBTINMwjLiB7NA0zTZLCBmGEWYYRrJhGKnAR/ZN5jttfrX99h5ncWXfj83p//3Z58WADRFSzvv7H7ABuLCHeFMURVGOEPTkriiKogSbvS7uq7Lfpgx0p4Zh3GYYxkagFSmMUQEssT+c7LTpeKDENM0yH+4zHzhkmma1i91sAeKBVK/fjKIoijJs0BBBRVEUJdh0enhsQDXYDcO4C8l7Woz0rDoEtCF5VE/TfYHRAEw/7FNRFEU5ClGBpSiKohyJXAvsB87ukVN1lottdyAhexl9uFj92ece4CzDMJJM06zp8dgUpFlxpRfvQ1EURRlmaIigoiiKciTSibhSXU6SvbLfj11s+5z99s8986KM7l2M+7PPN5BrbLfHDMM4G6k4+FbPfC9FURTlyEAdLEVRFOVI5BWkwMR7hmG8BiQAVwHtPTc0TfO/hmG8BHwDGG8YxltIifgJSOn1gv7uEwkZvA74kWEYo4FlwDikHH0Z8FOfvEtFURRlyKECS1EURTkSuRdxmm4EHgRKgZeAp4CtLra/Clhu396q/LcJ+O9A9mmaZrthGGcCPwcuR3p41dj393PTNIsG+wYVRVGUoYkh1XAVRVEURQEwDCMZEUwLvOi3pSiKoijd0BwsRVEURXHCXlr9SyRkUFEURVH6hYYIKoqiKApgGEYU8CMklG8BrvtzKYqiKIpHNERQURRFUegSWNuAbGAzcJlpmnuCOypFURRluKECS1EURVEURVEUxUcMqxDBs846y3z//feDPQxFURRFURRFURTD1Z3DqshFZaU2vVcURVEURVEUZegyrASWoiiKoiiKoijKUEYFlqIoiqIoiqIoio9QgaUoiqIoiqIoiuIjhlWRC0VRFEVRFEVRAkt7ezvFxcW0tLQEeyhBISoqitzcXMLDw73aXgWWoiiKoiiKoihuKS4uJj4+ntGjR2MYLgvnHbGYpklVVRXFxcXk5+d79RwNEVQURVEURVEUxS0tLS2kpKQcdeIKwDAMUlJS+uXeqcBSFEVRFEVRFMUjR6O4sujve1eBpSiKoiiKoiiK4iNUYCmKoiiKoiiKMqSJi4sDYP/+/URHRzNz5kymTJnCt7/9bWw2GwA7d+7knHPOYdy4cUyePJnLLruMsrKyrn1897vfJScnp2t7f6ECS1EURVEURVGUYcPYsWPZsGEDGzduZOvWrbzxxhu0tLRw7rnncuutt7J79262bdvGrbfeSkVFBQA2m43XX3+dkSNHsmzZMr+OTwWWoiiKoiiKoijDjrCwMI477jh2797N888/z4IFCzj//PO7Hl+0aBEFBQUAfPrppxQUFHDrrbfywgsv+Hdcft27oiiKoiiKoihHDN97/3tsKN3g033OzJzJA2c90O/nNTU18fHHH/Ob3/yGDz/8kDlz5rjd9oUXXuDKK6/kwgsv5Kc//Snt7e1e97XqL+pgKYqiKIqiKIoybNizZw8zZ87k+OOP59xzz+Xss8/2uH1bWxvvvvsuF110EQkJCcyfP5/Fixf7bXzqYCmKoiiKoiiK4hUDcZp8jZWD5czUqVNZunSpy+3ff/99amtrmTZtGiDOV0xMDOeee65fxqcOlqIoiqIoihJcNm2CkSOhtDTYI1GGKVdddRUrVqzgf//7X9d977//Pps2beKFF17gH//4B/v372f//v3s27ePxYsX09TU5JexqMBSFEVRFEVRgsumTVBcDDt2BHskyjAlOjqad955h4cffpjx48czZcoUnn76aRISEvjggw+6uVWxsbEsXLiQt99+2y9j0RBBRVEURVEUJbjU18vt4cPBHYcyZGloaABg9OjRbN682eU2kyZN4v333+91/2EXv6vXXnvNtwN0Qh0sRVEURVEUJbjYJ89UVwd3HIriA1RgKYqiKIqiKMFFHSzlCEIFlqIoiqIoihJcVGApRxAqsBRFURRFUZTgoiGCyhGECixFURRFURQluKiDpRxBqMBSFEVRFEVRgosKLOUIQgWWoiiKoiiKElw0RFDpg7i4uK7/79q1i/POO4+xY8cyZ84cFi1axLJlywB4+umnSUtLY+bMmUyZMoUnn3zS5f4Mw+Daa6/t+rujo4O0tDTOO++8QY9VBZaiKIqiKIoSXNTBUrykpaWFc889l5tvvpk9e/awdu1aHn74Yfbu3du1zeWXX86GDRtYsmQJP/3pTykrK+u1n9jYWDZv3kxzczMAH374ITk5OT4ZowosRVEURVEUJbiowFK85LnnnmPBggVccMEFXfcVFBRw/fXX99o2PT2dsWPHcuDAAZf7Ovvss/nf//4HwAsvvMCVV17pkzGG+WQviqIoiqIoijJQrBDB2lro7ITQ0OCOR3HP974HGzb4dp8zZ8IDD3i16ZYtW5g9e7ZX2+7du5e9e/cybtw4l49fccUV/OY3v+G8885j48aN3HDDDSxfvtzLQbtHBZaiKIqiKIoSXOrrISwMOjqgpgZSUoI9ImWYcPHFF7Nr1y4mTJjAa6+9BsBLL73EZ599RmRkJI8//jgjRoxw+dzp06ezf/9+XnjhBc455xyfjUkFlqIoiqIoihI8TFMcrLw82L9fwgSHgsDq7IR774Xbb4f4+GCPZujgpdPkL6ZOndpV0ALg9ddfZ82aNdxzzz1d911++eX87W9/6/q7qKiI888/H4Bvf/vbfPvb3+567IILLuCee+5hyZIlVFVV+WSMKrAURVEURVGU4NHUJCJr1CiHwBoKbN4MP/kJjBsHl14a7NEodq666ir++Mc/8tZbb3XlYTU1NXl8zsiRI9ngJqzxhhtuIDExkWnTprFkyRKfjFEFlqIoiqIoihI8rAIXo0bJ7VAp1W6vLkdra3DHoXQjOjqad955h7vuuovvfe97ZGRkEB8fz89//vMB7S83N5fvfve7Ph2jCixFURRFURQleFgCKy9PboeKg9XSIrft7cEdhwJAg1UIBZg0aRLvvvuuy+2uv/56lxUFPe3P4uSTT+bkk08e6BC7CHqZdsMwQg3DWG8YxjvBHouiKIqiKIoSYKyJruVgDRWBZTlXbW3BHYcy7Ai6wAK+C2wL9iAURVEURVGUIGA5WCNHyu1QCRG0HCwVWEo/CarAMgwjFzgX+Ecwx6EoiqIoiqIECUtgjRgh1frUwVKGOcF2sB4AfgjY3G1gGMbNhmGsMQxjTUVFRcAGpiiKoiiKogQAK0QwPh6Sk4eOwNIcLGWABE1gGYZxHlBumuZaT9uZpvmEaZpzTdOcm5aWFqDRKYqiKIqiKAHBcrDi4sTF0hBBZZgTTAfreOACwzD2Ay8CpxiG8Z8gjkdRFEVRFEUJNJbAio8XgTVUHCwNEVQGSNAElmmaPzFNM9c0zdHAFcAnpmleE6zxKIqiKIqiKEHAChGMixuaIYIqsIYEcXFxAOzfvx/DMHj44Ye7Hrvjjjt4+umnASnTnp+fz8yZM5k9ezZffPFF13b33XcfkyZNoqCggBkzZvDMM8/4ZazBzsFSFEVRFEVRjmbq6yEyEsLDh1aIoDpYQ5b09HQefPBB2tx8N/feey8bNmzgT3/6E7fccgsAjz32GB9++CGrVq1i8+bNLFu2DNM0/TK+ISGwTNNcYprmecEeh6IoiqIoihJg6uslPBAcIYJ+mvj2Cy1yMWRJS0vj1FNP5d///rfH7U488UR2794NwB/+8AceeeQREhISAEhMTOS6667zy/jC/LJXRVEURVEURfGGhgaHwEpOFseoqQliY4M7LnWwXPK978GGDb7d58yZ8MAD/XvOj3/8Y84++2xuuOEGt9u8/fbbTJs2jfr6eurr6xk7duygxuktQ8LBUhRFURRFUY5S6usl/wrEwYKhESaoOVhDmvz8fObNm8fzzz/f67Ef/OAHzJw5kyeeeIJ//vOfmKaJYRgBG5s6WIqiKIqiKErw6BkiCBImmJsbvDGBCiw39Ndp8ic//elPufTSSznxxBO73X/vvfdy6aWXdrsvNjaWvXv3MmbMGL+PSx0sRVEURVEUJXj0DBGEoVFJUEMEhzyTJk1iypQpvPPOO31u+5Of/ITbb7+duro6AOrq6njiiSf8Mi51sBRFURRFUZTgUV8PI0fK/4diiKAWuRjS/OxnP2PWrFl9bnfrrbfS0NDAMcccQ3h4OOHh4dx9991+GZMKLEVRFEVRFCV4uAsRDDbqYA0pGuz90kaPHs3mzZu77p8xYwY2m63rb6sfVk8Mw+CHP/whP/zhD/06TtAQQUVRFEVRFCWYDNUQQc3BUgaICixFURRFURQlOJhmdwcrLg7CwoZGiKA6WMoAUYGlKIqiKIqiBIeWFujsdJRpNwxHs+Fgow5WN8yh0Pw5SPT3vavAUhRFURRFUYKDPa+my8ECCRMcCgLLcrC0yAVRUVFUVVUdlSLLNE2qqqqIiory+jla5EJRFEVRFEUJDvX1cusssEaMGBohgupgdZGbm0txcTEVFRXBHkpQiIqKIrcffdlUYCmKoiiKoijBwRJYVoggiMAqKQnOeJxRgdVFeHg4+fn5wR7GsEFDBBVFURRFUZTgMBxCBFVgKf1EBZaiKIqiKIoSHDREUDkCUYGlKIqiKIqiBAd3IYK1tdDREZwxWWiRC2WAqMBSFEVRFEVRgoO7EEGAmpqAD6cb6mApA0QFlqIoiqIoihIc3IUIQnDDBDs6wGaT/6vAUvqJCixFURRFURQlOLgLEYTgFrqw3KvISBVYSr9RgaUoiqIoiqIEh4YGCA8XIWNhhQgOBYGVkCBOVmdn8MaiDDtUYCmKoiiKoijBob6+e3ggDI0QQavAhTU2LXSh9AMVWIqiKIqiKEpwqK/vHh4IQytE0BJYGiao9AMVWIqiKIqiKEpwaGjo7WAlJcltMAVWTwdLBZbSD1RgKYqiKIqiKMHBVYhgeLjcF8wQQeccLFCBpfQLFViKoiiKoihKcHAVIggSJqgOljJMUYGlKIqiKIqiBAdXIYIglQSHQg6W5WBpkQulH6jAUhRFURRFUYKDqxBBEAdrKIQIqoOlDAAVWIqiKIqiKEpw0BBB5QhEBZaiKIqiKIoSHIZLiKAKLKUfqMBSFEVRFEVRAk9bm/zzFCJomoEfF6iDpQwKFViKoiiKoihK4Kmvl1t3IYJtbdDUFNgxWfTMwdIiF0o/UIGlKIqiKIqiBJ6GBrl1FyIIwQsTVAdLGQQqsBRFURRFUZTAYzlY7kIEIXiVBDUHSxkEKrAURVEURVGUwNNXiCAEz8HSMu3KIFCBpSiKoiiKogSeoR4iGBoKMTHyt+ZgKf1ABZaiKIqiKIoSeIZ6iGBUFISHy9/qYCn9QAWWoiiKoiiKEniGcohgaytERkJEhPytAkvpByqwFEVRFEVRlMDjKUQwNhbCwnoLrPXroabG70PrcrBUYCkDQAWWoiiKoiiKEng8hQgahqPZMEBJCXz96zB7Ntx3n//Hpg6WMghUYCmKoiiKoiiBp74eQkLEKXLFiBFQWQmPPQaTJsHbb7t2tfxBTwdLi1wo/UAFlqIoiqIoihJ4GhrEvTIM148nJ8Nrr8Gtt8LcubBpE2RlQXOz/8emRS6UQaACS1EURVEURQk89fWuwwMtJk8WF+vpp+Gjj2D8eBE9Vo8qf2KFCIaGigBUgaX0g7BgD0BRFEVRFEU5CulLYD36qPyzwvQAoqMD62AZhry+CiylH6iDpSiKoiiKogSehgbXJdotIiK6iysIvINljUMFltIPVGApiqIoiqIogacvB8sVgXawQASWFrlQ+oEKLEVRFEVRFCXwDERgBdLBsgRWeLg6WEq/UIGlKIqiKIqiBJ76es8hgq6Ijg6MwGpp0RBBZcCowFIURVEURVECj1WmvT9ERQUnRFAFltIPVGApiqIoiqIogWeohwiqg6UMEBVYiqIoiqIoSmDp6BChNJAQwUA7WOHhWuRC6RcqsBRFURRFUZTA0tAgt0PRwTJNdbCUQaECS1EURVEURQks9fVyOxTLtLe3i8jSHCxlgARNYBmGEWUYxirDML4yDGOLYRi/DtZYFEVRFEVRlABiCaz+hghGRUFnp4QY+ovWVsdrgQospd8E08FqBU4xTXMGMBM4yzCMY4M4HkVRFEVRFCUQDDREMDpabv3pYlkhiBoiqAyQoAksU7AfXYTb/5nBGo+iKIqiKIoSIAYaImi5Sv7Mw7L2rUUulAES1BwswzBCDcPYAJQDH5qmudLFNjcbhrHGMIw1FRUVAR+joiiKoiiK4mMGGiIYCAfLChFUB0sZIEEVWKZpdpqmORPIBeYZhlHgYpsnTNOca5rm3LS0tICPUVEURVEURfExg6kiCIF1sFRgKf1kSFQRNE2zBlgCnBXckSiKoiiKoih+ZzBVBEEdLGVIE8wqgmmGYSTZ/x8NnAZsD9Z4FEVRFEVRlAAxmCqCMHgH68UX4fzzXT+mDpYySMKC+NpZwL8NwwhFhN7Lpmm+E8TxKIqiKIqiKIGgoQEMA2Jj+/c8y8EarMB69VV45x0p+R4a2v2xnmXatciF0k+CJrBM09wIzArW6yuKoiiKoihBor5e3CvD6N/zLNEz2BDBTZvktqEBEhO7P6Zl2pVBMiRysBRFURRFUZSjCEtg9RdfhAi2tMCuXfL/ujrXjzu/lgospZ+owFIURVEURVECS0ND/wtcgG+KXGzbBjab/N+VwNIiF8ogUYGlKIqiKIqiBJb6+oEJLF84WFZ4IEBtbe/HXTlYnZ0OUaYofaACS1EURVEURQksAw0R9IWDtXmz4//eOFjh4XKrhS4UL1GBpSiKoiiKogSWgYYI+srBsoSatzlYoGGCiteowFIURVEURVECy0BDBH3hYG3aBPPmyf89OVgqsJQBogJLURRFURRFCSwDDREMD5fS7gN1sKqr4eBBOP54+duTg+Vc5AJUYCleowJLURRFURRFCSwDDRE0DHGxBupgWflXCxbIrTuBFR4OIfZpsgospZ+owFIURVEURVECh80GjY0DE1ggoXsDdbCsCoIzZsjru6oi2NrqcK9Ai1wo/UYFlqIoiqIoihI4GhrkdiAhgjB4BysxEXJzISHBvYNl5V+BOlhKv1GBpSiKoiiKogSO/fvlduTIgT1/sA7WtGkSauhOYPV0sFRgKf1EBZaiKIqiKIoSOLZvl9tJkwb2/OjogQks0xSBVVAgf6uDpfgJFViKoiiKoihK4Ni+XRyk8eMH9vyoqIGFCB48KDlX06bJ354cLGeBpTlYSj9RgaUoiqIoiqIEjh07IC8PYmIG9vyBhghaBS6cBZarIhctLRoiqAwKFViKoiiKoihK4Ni+HSZOHPjzB1rkwhJYVohgYqKGCCp+QQWWoiiKoiiKEhhsNhFYA82/gsE5WDk5kJwsf2uRC8VPqMBSFEVRFEVRAsPBg9DUNDiBNVAHa/NmR3ggiMCqrxfR54w6WMogUYGlKIqiKIqiBIYdO+Q20A5WRwds2+YIDwQRWODoy2WhRS6UQaICS1EURVEURQkMVol2f+Zg1dbCrl3d79u1S4RTTwcLeocJapELZZCowFIURVEURVECw/btImwyMwe+j74crN/9DiZMgJtvhupquW/zZrl1JbB6VhLs6WCpwFL6iQosRVEURVEUJTBYBS4MY+D76MvBKikRUfSvf8HkyfDyy1LgIiSke2hiYqLcqoOl+BgVWIqiKIqiKEpgGGwFQRB3qb0dOjtdP15fL8Jq9WrIzYXLL4f77pPGxtHRju08hQiqg6UMAhVYiqIoiqIoiv+pr5cqgoPJvwKHSGptdf14QwPEx8OsWbByJTzwgLhXxx/ffTt3AqtnmXYtcqH0k7BgD0BRFEVRFEU5Cti5U2594WCBhAnGxPR+vL4eUlPl/6Gh8N3vwg03QFiPaa8rgWWamoOlDBp1sBRFURRFURT/Y1UQHKzAshwsd4Uu6ushLq77ffHx3cMDwXWRC0tEOQssS5ipwFK8RAWWoiiKoiiK4n+2bxdHaezYwe3H2cFyhRUi2BfWNs4OliXanEMEDUNcLBVYipeowFIURVEURVH8z44dkJ/fXbwMBEtg9cfBckVoqGznLLCsvC5nBwtUYCn9QgWWoiiKoiiK4n98UUEQHKF+rhws0/TewQIJE+zLwQIpdKFFLhQvUYGlKIqiKIqi+JfOTily4QuB5cnBammR1/LGwQL3AksdLGUQqMBSFEVRFEVR/MuBAxJ+528Hq6FBbgfqYFkhgj0dLBVYSj9QgaUoiqIoiqL4lx075HawPbDAs4NVXy+3/XGwnKsIqoOl+AAVWIqiKIqiKIp/8VWJdvCtg5WY6F2Ri/BwFViK16jAUhRFURRFUfzL9u2QkuJoADwYfO1geVPkIiJCi1woXqMCS1EURVEURfEvvqogCIHJwdIQQWUQqMBSFEVRFEVR/MuOHb7JvwLfO1j19WCzdd+nFrlQBoEKLEVRFEVRFMV/VFdDWZnvHSxXAmsgDpbVO8t5n+pgKYNABZaiKIqiKIriP6wKgr4SWJa75CpEcCAOFjjCBN2VadciF0o/UIGlKIqiKIqi+A9fVhAEMAxxmHzhYCUmyq0lsDw5WFrkQvESFViKoiiKoiiK/9i+XRyg/Hzf7TMqyr2DFRYmgsgb3DlYGiKoDAIVWIqiKIqiKIr/2LEDxo0T4eMrPDlY8fHicnlDT4GlRS4UH6ACS1EURVEURfEfO3fChAm+3Wd0tHsHy9v8K/A+B0sFltIPVGApiqIoiqIo/sE0obAQRo/27X77crC8xRJYtbVy29IiYqqnA6ZFLpR+oAJLURRFURRF8Q+1tSJ6Ro707X595WC5KnLRM/8KtMiF0i9UYCmKoiiKoij+obBQbn0tsHzlYFnbOocIuhNY6mApXqICS1EURVEURfEPRUVyO2qUb/frKwcrNBRiY7s7WD3zr0AFltIvVGApiqIoiqIo/sESWEPVwQLJw1IHS/EhKrAURVEURVEU/1BYKOXZMzN9u19fOVggAsu5yIUrBys8HDo6pGiHovSBCixFURRFURTFPxQVQXa2hOL5kmA4WKCFLhSvUIGlKIqiKIpypPLBB3D55cF7/aIi3+dfgThYPQVWR4e4Wv11sBITvcvBAg0TVLxCBZaiKIqiKMqRyiefwMsvB08YFBX5Pv8KxGXqGSLY2Ci3g3GwPJVpBxVYileowFIURVEURTlSsURHfX3gX9tm85/AcuVgWe/RnyGCKrAULwiawDIMY6RhGJ8ahrHNMIwthmF8N1hjURRFURRFOSKxBFZDQ+Bfu7xccpb8ESJo5WA5F52wBNZAilz0FSIYHi63moOleEEwHawO4G7TNCcDxwK3G4YxJYjjURRFURRFObIIpoPlrxLt4HCZWlsd91kicqAOls2mDpbiE4ImsEzTLDFNc539//XANiAnWONRFEVRFEU54jhSBVZ0tNw652EN1MFKTBQnrLFRi1woPmFI5GAZhjEamAWsdPHYzYZhrDEMY01FRUXAx6YoiqIoijJsCWaIYGGh3PrTwXLOwxqMgwXiYqmDpfiAoAsswzDigFeB75mmWdfzcdM0nzBNc65pmnPT0tICP0BFURRFUZThSrAdrOhoSEnx/b596WA5C6y+crBUYCleEFSBZRhGOCKunjNN87VgjkVRFEVRFOWII5gOllVB0DB8v29/OVh9lWnXIheKFwSziqAB/BPYZprmX4M1DkVRFEVRlCOWYDpYhYX+CQ8E/zhY1dUioDREUBkkwXSwjgeuBU4xDGOD/d85QRyPoiiKoijKkUWwHSx/lGgHzw5WbGz/9mUJLCvXX4tcKIMkLFgvbJrmZ4AfPGNFURRFURQFCJ6D1d4OJSWBd7BiYyGkn/5BYqLcWgJLHSxlkAS9yIWiKIoyTOnshAsugGXLgj0SRVFc0dnpcHgCLbAOHZLS5/4SWO4crP7mX4HDwSovl1stcqEMEhVYiqIoysAoLYW334bnngv2SJQjic8/h8cfD/Yojgyamhz/D3SIoFWi3V8hgpaD5Syw6uv7n38FDlHmjYOlRS4UL1CBpSiKogwMazKyslcLQ0UZOH/9K/zkJ8EexZGBFR4IgXew/NlkGBwiyDlEcKAOVlgYxMR4drA0RFDpByqwFEVRlIFhCaxNm7pP5BRlMGzbJuWyTTPYIxn+OB+XgXaw/C2wfOlggYQJWgJrOOVgmaZEE6xaFZxCJopLVGApiqIoA8MSWDYbrF0b3LEoRwbt7bBrl+QOOTsTysAItoOVlDRwwdMXvnSwYHgJrOXL4ZxzYPJkcd6ysmD+fJg7VxYnlKCjAktRFEUZGJbAAlk9VZTBsns3dHTI/2trgzuWIwFLYEVEBF5gFRb6L/8KXBe5GIyDlZg4fIpc/Otf8OmnMGUK3HYbPPwwPPKIHD/XXSeLXkpQCVqZdkVRFGWYU1Eh5ZBHjtQ8LMU3bN3q+H9dnazMKwPHElgZGcEJEfRXeCD4x8GyioIM9SIXFRXiXr36avf7W1vh+9+HP/4Rfvaz4IxNAdTBUhRFUQZKZSWkpsKxx6rAUnzDtm2O/2uo0+CxBFZmZnBCBP0psEJDxVXyZQ6WxVAvclFeDmlpve//7nfh6qvhF7+A994L/LiULlRgKYqiKAOjokIu8vPny2SqpCTYI1KGO84CS0MEB0+wHKymJqiq8m+IIEihC8vBMs3BO1gWrhysoRQiWFEB6em97zcMeOIJmDEDrrpKQgaVoKACS1EURRkYzgILNA9LGTxbt0J2tvxfHazB4+xgNTVJ8ZBA4O8KghZRUQ4Hq7lZco/85WAZhoisoSKwXDlYIEUvXntNwre//nWtxhkkvBJYhmGMNwzjFcMwthqGsdf65+/BKYqiKEOYigoJEZw1S/rIaJigMhhsNtixQ0JOQR0sX2DlFGVmym2gXKxACSxnB8t6bwN1sBITHf935WDB0BBYTU0inN0JLID8fPj1r2HDBtizJ2BDUxx462A9BTwKdACLgGeAZ/01KEVRFGUYYK2iRkfD9OkqsJTBceCATJYtgaUO1uBxDhGEwAssf4cIOjtYVo6ZLxwsdwIrIiL4RS6s6q2uQgSdeKLyEqawBXPZ8gAMSumJtwIr2jTNjwHDNM0Dpmn+CjjFf8NSFEVRhjSdnXD4sGMVdf58WL06cCFInnj+eamkpQwvrAqC8+bJrTpYg6exUYpBpKTI34EqdFFYKCF1OTn+fZ3oaIfAGqyD1VeIIIjACraDZQksTw4WsLIwi21M4fCH2qMwGHgrsFoMwwgBdhmGcYdhGBcDnqWzoihKsPnnP+GLL4I9iiOTqiqJ7XcWWPX1EuIVbF57DZ7VIIthh1XgYto0ySNRB2vwNDZCbKxDdATSwcrIcFTe8xdRUY4QwUA5WMEWWFavrj4E1qESA4DCzwr9PSLFBd4KrO8BMcB3gDnANcB1fhqToiiKb/jRj+Bvfwv2KI5Meq6iWoUuhkKYYFlZ4EtSK4Nn61aZlI8YIZNddbAGjyWwLNERqOPC3yXaLfzlYFkVA3syFHKwvAwRPHRIbguLDccfSsDwSmCZprnaNM0G0zSLTdP8pmmaXzNN80t/D05RFGXAmKasgFurfYpv6SmwJkyQJPGhIrDa2oI/EVL6x7Zt0jwV5LekDtbg6elgBVJg+Tv/CvzjYEVFSXijK4aCg+VliGCXwGIULNc8rEDjbRXBDw3DSHL6O9kwjA/8NipFUZTB0toqychlZcEeyZFJZaXcWhf5kBA45pihI7BAXazhhGmKwJoyRf5OSFCB5QuCESJompKDNdwcLKuKoLvwQBgaRS7Ky2UcHt5nW5vjFH0gbJwKrCDgbYhgqmmaNdYfpmlWozlYiqIMZazwIhVY/sFaRU1Nddw3fz5s2uQoDR0MmpsdE3MVWMOHkhI5Zp0dLA0RHDzBCBGsqZHXDYTAcuVgDTZE0F2BCxg6DlZ6unuXDSgtdfz/wIgZsGxZAAamOOOtwLIZhtHl9RqGkQdo5zJFUYYu1iS7snJoVLY70nAnsDo7Yd264IwJugtqFVjDB6vAhSWw1MHyDcFwsAJVoh26l2m33psvQgTdMVQElpfhgRgd7A3Ng82bobra/2NTuvBWYP0M+MwwjGcNw3gWWAb8xH/DUhRFGSTW5Mxmc8RKKL6jogKSkrongw+FQhcqsIYnVol2K0RQHSzfYAms2Fj5OxDHRKG9al2gQgSdHayIiIFXLrREqCcHaygUuSgv97rABZlfcaA5VcI2P//c/2NTuvC2yMX7wGzgJfu/OaZpag6WoihDF+fVby104XtcraKmp8Po0cEVWM6xMSqwhg/btomoysyUv9XB8g2WwAoJkdtAOFjFxXKbm+v/1+rZaHig7hWIeIqOPrIcrNwvqaqJpzU8TsMEA4y3DhbAiUhz4UXACf4ZjqIoio9wnpxpHpbvcXeRnzdPHSyl/1gVBK28ksRE+f5stuCOa7hjCSwQ8RGIY+LwYbl1Dh/2F5aDZZoiHgeaf2WRmNh3Dlawi1x4IbD2FbZCSDtkSZPh4mlna6GLAONtFcFHgG8Dm4DNwC2GYfzdnwNTFEUZFM7hRSqwfI+7i/z8+RIi5OwkBRIVWMOTrVsd4YEgDpY1afaWHTvg3nvleYrgLLDi4wNX5CI62rNQ8RVRUfJ9t7cP3sEC+d0NZQerqUm+0z5CBPcWNUFcKSTtB6Bw0hmwZo08VwkI3jpYJwFnmqb5lGmaTwHnACf7bVSKohyZ7N4duAIIR4GDdeKJ8Ic/BOnF3QmsggK53bXL49PnzYMHHvD9sCgthdBQ+b8KrOHB4cMSxmsVuABHwYH+hAk+8wz88IcOB+VoxzR7C6xAhAjW1Eh+ZiCIjpbb5mbfOFgjRngWacEWWF72wCos7oD4Q4waJY5wYeY86OgYGm00jhK8FVg7AOdyMCOBjb4fjqIoRzQ/+QlcdVVgXsuamIWGDsscrJISR+62K6qqJOLjy2C0fDdNKRziKgTIuq+qyu3T6+th9WpYutQPYysrkzww64WUoU/PCoLg6EnUn0IXJSVye+CAb8Y13GlpkWM10CGCNTWO78/fWG5TS4tvHKzHHoP77nP/eLCLXFjXsj4EVmlJCMQf4rSZkwDYHZ4v4bcaJhgwvBVYKcA2wzCWGIaxBNgKpBmG8ZZhGG/5bXSKohxZVFbCvn2Byauoq5PVxqysYelgzZ0Lv/iF+8fXSmh915wyoNTWymqoq4t8SorcehBY+/bJ7e7dfhhbWZmUhw4PV4E1XOhZQRAG5mCpwOqOFQ52NDhYLS2+cbBmzOj+O+zJUHGw+ggRrC6PxogvYdH4BRBXwtaiDpg5UwtdBJAwL7f7f34dhRJYqqpg/Xo47bRgj0Q52qitlYtTWZkIH39SVyerqOnpw05gNTVJFagPPnC/mLp6tdwGRWB5ClPpp8Cy2aTAmc8oLRV1Gqh8E2XwbNsmE+W8PMd9g3Gw9u/32dCGNT0FVlwc7Nnj/9etrZVQu0AQFcUKFnDxnBy2xoSSMlgHqy+CXeTCixDBlhZoro8hMbWJiSnHQGIhe/bnwwknwJNPyjV4oKXsFa/xtkz7Uk///D1Ixcfcdx+cdZbn+CPF/3R0OGaaRwvWZMnqk+JP6upkFTwjY9gJLGu4mze7b+G1Zo3clpYGoY+yp4t8bKxcvL0QWC0tTuWEfUVZmXznKrCGD1u3wsSJXUq7qQnK25Plsf44WFZhFXWwBFcOVqBCBAPoYC3lJMqrwthblzp4B6svgu1geREiaB0G6ZkdjBsxDhILOVQcJkm7zc3BbQR/FOFRYBmG8Zn9tt4wjDqnf/WGYWiDiuHK+vUyIwvK0rcCyLL9lVfKpOJoSsi2BFYgJkC1tcNWYDkX4HMX0bFmjcxHOzuD0EfZk8AyDHGxvBBY4OMwwaYmmUBmZqrAGk5s29YtLOuXv4QTbxgrf3jrYHV0OCafKrCEoyFEMCqKHUwEoLopcvA5WH0RbIFVUSHVGT0ISWvRKjcnhOToZKJSKqgujcM8fqE8oHlYAcGjwDJNc6H9Nt40zQSnf/GmaSYEZoiKz9lor0/i86VjxWvuuQdeeUVCDTZtCvZoAoNpBlZgOTtY5eXDqnSzsx50VQiitFR6eR53nPwd8LWSvsJU+hBYe/c65l8+FVjWB6cO1vChoUEcbacCF3v2wP5iewaDtw6W8zGuAktwFSLY0ODfc6FpBtzB2skEAKrbY/3vYAW7yIVVvdXqF+eC4mIJaRgzSgqAZOS00NEWQWVoBkyYAJ9/HpChHu34MvJdGQ5UVDhmYyqwgsMDD8D998MVV8jfmzcHdTgBo7lZVpkh8CGC7e1y0R8mWA7WlCmwZEnvx63wwPPPl9uAH8qDFFj79sHxx8tisAqso5zt2+XWSWBVVkJrq0Ez0d47WNZBk5WlAsvCElgxMXIbHy/RE/5MD2hpEQESwCqCXQ4WyYFxsNrbg7dgV17eZwXB7fvlvDcpX3yQPKtUeyEwbpyszil+RwXW0YazW6ICK/C8+ircdRdccgn85z+yyne0CCzniVKgHCyryAUMqzDB0lJZoPz61+WQ7RlFumaNPH7uufJ3UBys2FhHBa+eeBBYpikCa/x4GDOmz3ZZ/UMF1vBj5065nTSp6y4r5LUmLtd7B8s6CI49Vg6YQITCDXVcOVjg3+PCWsgKkINV1RLLYaSwTjXJgcnBAsdiYaCpqOizguCu/Y0Q0sbkUbLdpLEisPfsa5fnDsO2JcMRFVhHG1Z4YEiICqxA8/nncPXVsGCBiKvQUJg2TQWWv3B2sGBYCayyMtEop54qgqRnyPyaNbLgP26c/B0UgeWqB5aFB4FVUSGpUvn5Mn6fOliWi6E5WMOHroSR3K67LIO0OnaAAgvUxQLXOVhwRAmsHSWObJXD9NEk2BdYAitYYYLuGrw7UXiwDeJLyEuS9rUzJ0pFx/XbDzsE1jAKmR+uqMA62ti4UQ6wvDwtchFIqqrgggvkc3/zTcfKf0GBCKyj4WRnCaz8fP+HCFr5XsNUYJWWikaYN0/6aDqHCZqmlGg/5hjJdR4xIkghgp4u8pbAcvG7tgpcOAssn/38re84PV0F1nChtFR+5Pa+V52dDse2OibH+xBB63o2f77caql29w6WP9096/sKkMDaeVDem4EtsA5WsASWFyGCJYcMiD/EqEQRWLPGjILwRrbtaZRzY2urnhsDgAqso42NG2H6dMjOVgcrkKxfL7OGv/2t+8p/QYGs+B0N34V14Z0+Xd5zf8ov95fWVomTdxZYwygswqo0HhkphqdzoYviYnkrc+fC31b9jaS0xsCvlVRW9i2wOjpcXsT37pVbS2A1NXWvmjgoyspEcYaHOwTW0bB4MZyxVhPsSfs1NY4+5DWRGd6fJ0pL5bufIAUP1MHi6HCwCqMJp42siF2BycEKD5fbYAispib510eIYFV5JOGJFSREyqLF+BQp1b7vQOewDJkfrqjAOpro6IAtW1RgBYOiIrkdO7b7/QUFcns0hAlaAmvaNLn15wTImpQlJMhkPyRkWF1QrDknwEknwYYNUF0tf1sFLmpGfMSd791JQ+Tu4IQI9iWwwGWYYE8HC3yYh+X8wQUioV8ZPKWl3ZqOO7ccqI5I75+DlZUlKxMRESqwwCGwrIgJS2D508EKsMDauT+csewhxagIrIMVjGbDXjQZBqiviichrbHr7xHRIwhLPkRpcbhDYA2jBcfhigqso4ndu6XCz/TpciFSgRU4LIGVk9P9/qlT5fZoEljTp8utP8MELYGVmCi5bqmpw0ZgmaYM1dIJJ58s9332mfy9Zg2EhZncu+caADpiioZmiCC4FVjp6bKoPn683Ld7NzLRPvvswSVlWdYfBGa1Xhk8JSWOHzuOOSRATWhq/3KwMjNlMWXUKBVYIAIrJqargfORWORix54wJrCTFFt14KoIQnAcLC+aDDc3Q3tjHKnpDgFoGAZJGfXUlCWqwAogKrCOJqwCF5aDVVfnWOFS/EtRkZzYIiO735+SImL3aBRYgXKwYFg1G25okCgQSyfMny8/GytMcNUqk8jsXdjCGjlz7Jk0Re2mtDSAkXCNjXIVH4TAys+HR1c/SnnYGsLC7JrqN7+B999331nZG1RgDT+cXUd6OFghKf13sEByXVVgybFqhQdCYEMEA1CmvbNTzh3j2UG6JbCO5Bwsa/XBQ4igFc2Qk929T1ZWbhttdck0x6vAChQqsI4mNm6U1fzJk0VggRa6CBRFRTBypOvHCgqOjmbDtbWSZzFunP9DeKxJmbPAGiYXFOdCeCD5//PnS6EL04TPV7bQmLqUR855hFPyT6Elai/t7R7bTvkWb8JUPAisvXshZ1Qrt797O39d+Wfy82H3hgZ48knZYDAJWaWlKrCGE62tkpvqTmCR5J2DZZrdQw0DJbC+8x247DL/v85A6SmwAlHkoqZGzu9RUf57DTuFhdIvbXToTjI7A+Rg+TgHa3P5ZtYeWuvdxl6ce3cfaAJg9MiIbvfnjw4FYG9zktwxTK6HwxkVWEcTGzfCxIly4rMEloYJBoa+BNbWrbIcdyRTWyuT3tBQ+SwCESI4DB0sa5jP7f0rl7x0CQ+vfJgpx5Szfr3Jsx9sork+mmOOMbhm+jWMSR4D8bJIErBD2brI91WmHXoJrI4O+dqN5P2YmKw8uFIqCX5ZKROX6OiBC6ymJpk4OudggQqsoYw1yXOVgxVTSXVngnyvffUcqqkRseYssEpKJCTen7zzjlSF9ffrDJSmpsE5WO+9B7fc0r/XrK2V8EDD6HPTwWK1UMsL20Ey1dSRSGdkjH9f1IcO1vbK7Zzw1Alc+t9LMb0JQfAiRHDzHinBOXF0dydvylgRnmt2V0NysgqsAKAC62jCqiAIKrACTXGxZ4HV3OzI/j9Sqa11hI34e4W5p8BKTx82AsvSFx+UPs3iPYv5zvvf4bGKK7DZDG7++VYA7r3ucgzDID8pH+LlGA6YGe2Ng5WcLBOsHgKruFjWEaqj1gFQWFtIbnwRu6pTMO/8juTODFRgOTcZBhVYwwHrR9sjByskohniDlHVYRcHfblYPfeTlye3Vu6rP6ipkXN2Wxus9dKBCDR2B2v7dvj735FFjMhI7x2sd96BJ57onz1eUxO4/KsdcpsTsYtkpApQTZ2fp7U+KnJR2VTJuc+fS01LDftr9rO3em/fT6qokO/PQxjkzn3y3U4bl9Lt/jmTZUGsWy8sxa+owDpaqK2VCe2MGfK3tdKnAsv/1NXJP08CC478PKxgCCzr9TIyHA7HEKdLB8aV8uKlL7Lvu/t47NvfJCSsg9b1lxAeYWPBHLnA5ifnQ5xMLoeUwAoLk0lWj4mZtYawl49JjZELft76l2ggnvIbfiwT5IG+EUuYqcAaPvSMh0UcrJDYwxBdTVWLPcysL4Fl7ce6ro0eLbf+PMdYOc3gqEAz1LALrCeegDvuEJOPuDjvjwkr1Lo/IewBFFg7d0JYdCMjomq6BJZVbdVvDMLB6uiAV16BisOtXPTiRRysO8i/L/o3AB/t/ajvHVjFhTy4g/uKWiG0hamjsrvdf+yUkYCNHXuaVWAFCBVYvsRmG9iK2cMPwx/+4PvxOGOdIC0HKzFRwnFUYPkf6zfhTmBNmSK3R5PAGjVKJtL+ShR2FSIIw8LFKi0FI8QGMVUUpBcwOmk0tyy4luOODQNbOLNmhnRd45OjkolPlUI1AQ8R7KNUcFezYScsgbWfT7ht7m2cUBTC7F0fA7C7MkkmyIN1sDREcPjgRmDZosshqobDLfaiQH0VurBEuXOIIPhXYG3YILepqfD55/57ncFgF1jWJaiqCjkuvF1osj53ZzHZFwF2sIzUXYTExAwLgfXCC/D1r8PI0W18/uKxPHnWf7h2+rXkJuTy0T4vBJYXTYYPlZgQX0J2Qla3+3OS0jASSjlQaKrAChAqsHzJf/8LY8bAnj39e94jj8B99zm6K/oD5wqCICsg2gsrMPQlsOLipKza0SCwrAtvXp4kphcX+++1IiIcVRuHUbPhsjKITqwnLiqGUYmjuu4/6SS5nTvXsa1hGIxNzyYspkHmmG1tUo3PZ517XVBpz5eyxKs7XAisvXvt4jGxiHPGnc39y2JIjZNcvN27kYm2hggePfR0HYGy8k5s0WUQVU11s/349TZE0BJYOTlSmtyfAmv9epmonn++CCx/Xr8HSg+BVVnJwBysr77y/jVragJSQRBg+w4b7UmbCYuJDZzAGkSRi08/hai4ZlozPoMP7+PHF17KY48ZLBp5Jp/s+wSb2cdvqKKizybDlWXhRCRVERYS1u1+wzCISamk/FCkCqwAoQLLl6xfLx7wW295/5zmZvG5q6th27bBj2H1atd9ZDZulLwI5z5M2dlaRTAQWFe33Fz32xQUHB0CyzlEEPw3Aaqr6y4AhlH3+tJSCImrYGraVEIMxyn65JPl9phjum+fn5RPSEKZHMqvvAK//CW8/rr/BuhFmAogAsu5JBziYMWlHiY+Opq5m6qYs6uB504uJDTUdAishoaBhXJa3631XQei548yOEpL5XcS4ah4Vl5hg5hKiK6mrtEeIuiNgxUd7RDV4eFyrfO3gzVzJpxwglRCtBKChhJ2gXWgUCbuXQ6Wt8eEJWyHoIPV1ATFRSGQuoPI2ERGIMUdhrKD9cHHLbTkvs91977EkiUmY8bAbbfBlkd+yeHmw2wo3eB5B331HwTqKuOIT3X9/Y7IaqCuPFnOkVVVfRePUQaFCixfsmuX3L79tvfP2bLFsfI12DAD04SLLpIVtZ4HjlXgwnlSpA5WYCgqcjiG7igokAt0MHprBIqeIYLgv0qCPQXWMAsRbI0uZFr6tG73n3IKPPssXHFF9+3zk/LpiCnk0CHTnsmOfxdOvLjIA25DBDsSd3Ni3omE/vo31Oek8cjMBrJHtsvp03IgBuJilZbCiBGOFeawMJl0q8AauvRoMgxQVWWIwIqqobklig5CvcvBysrqfn3Ly4P9+30/ZpDz9JYtMGsWHH+83DcU87AaG2mPiu86nErLO2Thob8hgps3e1/lNkACq2sdOWUn0Qkjuhyssko/X0MHWOSiqAgOFUYRMvoz/nbO3zjpJINlyyQ3btNnudAWw8d7P/a8Ey9CBJurR5CS3urysZzcDtqrM2kfkSLzxYD19jg6UYHlSyyBtXy5o9leX1jWe2Tk4E/QBw+KYNq+Hf71L8f9Nlv3CoIWWVkqsAJBUZF81tbEzxUFBSKKrbqzRxqm2V1gWeGS/nSwnMNUhpGDVVLaSXt0EQXpBd3uDwmBa67p3V4mPzkfW1wxB/e3wIoVcqc/j+uKCs8l2i1cCKw9eztpjtvCabknwurVtFx+Ke1hkJhd4XCwYGACq6ys12S9X6v1SuDp0WS4tRWaGsIgtgKiZMJcS6J3IYJZ3XNO/FpIZ9s2mWDPnAnjx8ukd4gKrENmFpgy1SssaezfMVFbKwtVLS2uI2N60toq2wZAYFmGYWTGAaLjk7sEVkl5s39feIAOltUofvaCOuIixF03DDjnHGhvNxhVf4XnPKzGRrHtPIQI1tV3YrbE9zoULMaOjoCOKLab9lL2GiboV1Rg+QrTlBPQvHkyUV682LvnffWVrCidffbgT9CrVsltVpaECVmrVPv2ycHZU2BlZ8s2OgHxL55KtFtMs7sVR2qYYEuLTEgs0RMZKb/TQDlYERESIjvEBZZpQlmZAXGlvQSWO8Ykj4G4EsrKQjGjomHsWP8LLA+rqOvWiWG40xwv5xf7RKSpCcrLQiFpH6eHTwLTJGXqHBIiEzCTd7F7N5gZ9sn2QBy4srJuuTyACqyhTg+B5dwDKzxWirfUkORdiKArgVVc7J8wKKvAxcyZMkteuHDoFbpob4f2dorbHcdEUWmz90UuTFPOo5ZD502YoPU9BUBgWWuRkyaGYkTHEEUr4UYzpYFysPopsN77qAmiqrn4pHHd7j/+eGkNmVFxGcsPLKe1w7X75E1xoU175ADKy3W9mFswQa6Ja+vsuY0qsPyKCixfceiQ5FNdc42s3L7zjnfP++ormVyfcIIIocFMjFavlrCY55+XC9df/yr39yxwYaG9sAKDpybDFhMnynd3pAos68Lr7CqNGuW/FWZr5dWZjIwhf0GprYX2tpB+Caz8pHyiokpotUVQfelNMHlyUAXW22/Lx/zPrQvkDruLZUVrxaSXM7lJVlBDRudzTPYx1MSsobYWqiLt56SBhgiqwBo+mKYjtM+OQ2BVkZMuPbCqQ1K9c7B6upd5eRLW5o9jYf16iIkR9wpklrxnz9DKaW4UgVrU6jhWS8ravC9y0dgon9+CBaIAvBFYVuROgBys0MRDTMsd02XrR4dVU1E1uP5UfTLAIhcff9oBo5Zz1oTTu92fkACzZ0Pj7rk0dzTzRfEXrndgCSwPDtbG3XIATRgd6/LxY6bIc9fXhModQ/x6ONxRgeUrrPDASZPE83333b5jlk1TTlozZsgKGAxuFWzVKhFRJ58MX/sa/PnPsqq7caOssk2d2n17S2ANpYvCkYZpeiewIiJgwoSjS2D5M4Snp4MFw6LZsKUr4kY0kR7ruVqUxeik0SyqtDcbvuhW/xavaW2Vz9aDwLJOYc+tnUgnIV0CyyrRPnfqCEIK7YVf8vKYnzOfkohlAOyqGiGTOQ0RPPKpqxNn24WDlZjcTkaqOAXVsbmeHazmZnm8p4Plz15YGzbItTbUPlH1xfXb19gF1oGGZPk7upKyCpv3x4QlatPTZQGwPwIrAFUEt23voHPENqamTZVcSyAyvJqqatO/LzwAB+vQISgrTCB2wmpmZs7s9fhJJ8HujSMI6Yh13w/LCwdr+z75zqaOGeHy8VkTpfnwlkp7hUEVWH5FBZavsATW+PFw3nkyqfjyS8/PKSqSE9KMGZIsGx098BO0zQZr1kiIIsAf/yiToV//Wk6M48ZBbI9VDXWw/E91tcRG9SWwQPKw+tPQcTjhTmAVFooI9TWuBFZGxpAXWNbwxo6MxeirSp+d6NBIrj0gF8qSxElyXJeX9zsJ2yusGbCbi3xHB3zxhZiTBw/H8CmLugTWmi1ye8ac8TLpNQwYOZL5ufOxJUtCxe69IfI99VcgNjZK2JM6WMMH6zt2IbDS0w3SRohTUBOV6dnB6tlk2MJflUpN01FB0GLWLHFRhqDA2lUTDZG1kLxXDsW4ODk39CUQnM/Z06cPKQfLNO05WCk7RGDZHayIiGpqAlVFsB/n1yVL5Bq38ARbt8qwFiefDG1tBpNarufjfW4KXVhiyIPA2lvUAsCs8RkuH09ONgiJbGRvWbhEzKjA8itBFViGYfzLMIxywzCG/7L9rl1y4I0cCWeeKT/evsIErQIX06eL7Tx//sDzsHbtkouQVcN5/Hj49rfhiScku9IpPLCzU+phqMAKAN6UaLcoKJBGQfYL4xGFuxDB1lb/nOR7FrmAYSGwSkrkQjxldIr3T/roI+bWSj+xQ4dwHNf+6IXVxyrqxo2ic371K0iI6+RZru0SWCs2lUBYExfMmS+T3uxsiIhgfs58SNqPEWKTPPqBNBvu2QPLQgXW0MVFk2Hr55WVEU52urgS1ZGZnh0sF0INcFQq9XUlwQMHZDzOAisiYnDXb39gv47sOxwNCUUQU0VtdZj3/eGszzwhQeYP+/f3nQsXIIFVWQn1dWGQspMpaVO6HKywiGrqrPA3fzEAB+u19w9DZC2XnTrB5eMLF0oRo5TyS1h1cBW1LS4+Zy9CBA8etEFYM6MyXTuIhgGxaVVUlMTJOVwFll8JtoP1NHBWkMfgG3btkibDoaEysTvxxL7LtVsCyypwsHChrIwNpAeMVeDCuUnOL34hceJVVd0E1je/KZGMzWHx4mqpwBoc69bBo4+6fqyvJsPOFNhzbrZu9c24hhLuHCzw/Qpza6tc/Fw5WLW18vgQZfv+GgDmjM/xvKEzjzxCTLz0gCkpwb8LJ304WNYC/mmnwWXnNfMqX6PxkHz323a2EDqiiIL0qTJZs3//GXEZ5KVkEZNSOfBmwyqwhh8uBJb188rNiCHXnoNVFZYyMAcrOlomo74+vzgXuHBm4ULJzRoqC2RWDlZ1LCQWQUwl9dVRDoHV1zyjp4MFfYewB0hgWQUuIjL2k5+c3+VghUXV0lgX4eGZPsBNDtbLL8P777t+yrKlwKjlnDn+NJePJybKz6lux2xspo2lB5b23qiiQopDWf39XFBRFkZ4UqXHFoWpWQ00VqRharNhvxNUgWWa5jKwd4cb7uza5Uh4BQkT3LLFkXjgio0bpeKXdcI7/nixl1au7P/rr14tYmnKFMd96enwox/J/2fMAOA//5FeOg0N9r7G/emF1dIiuWWKA9MUp/D2211PAgYisI7EPCxPAsvXlQSdV16dsSbfQ/iisnV/FYS0MW/c+L43Bvns3n6b3edNgsg6Dh7qdEw0/SGwrFVUN2XaP/sMMnJamftSBiFzX6aROF5fnoppmpQWR5Oe2yChjwcOOL5/YF7OPDqTdzgEVn9DBC2BpTlYwwcXwqiiwoToKrIS0sgekQwhbVQZSd45WK5qU/sjz3P9erEbrIVRi8Fcv/2BXWCVVSdAQhFJIzppqY/xvgG3K4HVV5hggKoIWiXax08wJeTO7mBFxDbSVB/l4Zk+ICREFtJ7CKzvfx+uvLJ3a6nSUqgoTCFj6g5yEtwvnJ18MmzfkEiUmeQ6D8vqgeVBPdVUxBCX4tllHJnXgXk4j7aUxCF9LTwSCLaD1SeGYdxsGMYawzDWVFgX96GGzSYVhJwF1vnny+3//uf+eV991b2y34IFcvAMJMxg9WopRRPawx6/5x745z/hnHPYu1e6hk+wu9SbNtE/gfWXv8C55x6ZDstAWb5cPnvTlNueFBdLuGjPiZ8rxtirIR0tAssK4fH1BMgSuq6KXMDQCBN0c8ztLWqC2HKmZU51+XgvHnsMgNKrL4S4EvYcaO5VvObdd8W19kmqm4cQQdOUU1d43koONx/mibpvkcc+/rliNDurdtFelcvEcZEyCS0q6iaw5ufMpyVhEzt32WSiXF7udWNT04RD2+3fuSsHq7HR0cxdGTqUloobkJzsuKuiA2IqyYzLJD02DaKrqTT76INVUiLXPVei3x8Ca8MGKfoQE9P9/sFcv/1BYyOtRFBXn0hcWg3JI2x0NMfQGmk/L/YlsKzPPDFRQtyTkvoWWDU1cr3r+dn4mJ07gdA2Zky0X0/sDlZUfDPtTTFe90QeMBER3QRWZaWc0mtqJO3dmQ8/lu1OWeQ5dPGkk6C11WBax42uBVZFhcfwwE6bjYbSbNKzPEdojB8bDq1JFEanDo1r4RHMkBdYpmk+YZrmXNM056b10cE6aBw8KO6Os8AaN05Owu7CBBsbxfWyO0uAY6Wov4mybW2yqmYVuHAmMhJuuIF2wrnqKll8ee89OR9t3Ij3FcdME556Sv6/bl3/xnck85e/yATBMCS7vydFRfIZ9xS+rggNFQfySBVYhuFwa0Eu2AkJgRNY1uQ72BeVJUtkwuLiey4ptRGeeJikqCTv9vXii3D22WRMnQfxhyg82C7iJzS0S8T97W/w9NM+WlivqJDvcUTvKlUHDshLHkx+ibsX3M1rV7zKRZHPsbR4Clc++jtoTeTYgnQ533R0dBdYufNhxG5qqkM4HJ8ngqirZrdnXn4Z8n52NcXk9J6AeBsOpQQeq7S604r8odJ2iKkkIzaDtNg0iKqmyhbXt8BKT3d9jvVHIZ2eBS4skpIkCmGoFLpobOQg4pikZ7UyIkUWGapsdkHbnxBBw/Cu0EVNjWN7P7J5azsk72Zapj1ix+5gRSe1dQ3Dr0REdCtyYWV7FBSYPPIIbN/u2PTld8sgop6rz5jscZcnnCAfW1LpRWyr3Mah+h6LcH20x1j+VRHU5TBnfovH15kxSc6JG21Z6mD5mSEvsIYFzhUEnTnvPJlMuVop2rxZTvrOAgskzOCLL/rXHHHzZskrcc6/6sGvfy0TrCeeEKOkoMBJYB061PcF6LPPxKUDEXOKxCm89Rbceaf0HnJVNdKbEu3OjB8vhS6ONGprZbIb0uOUY02AfInzyqszQ0Vgbdkix9vS3nH21ZURJKV4vkB2YbPJZzdjBvlJ+dJsuNQevpKZCYcO0doqpyCQ8OBBU1Ehff5cTGathXtz5OfcMucWLp58MTfnfYFJKOtfOgeAY6amOQS1VUYbmJ01m5AU+d3vto2RO73Mw/roI+iwhbIxfqEjP8LC24R+JfD0aDIMUFFpQkwFmXGZpMWkQVQNhzviPIcI9uil1Y3Ro2Xx01cTycOH5ZhzJbBA8rC++MJr99WvNDZShFx7Ro40SE+Tc29Vh/286E2IoGE4QgqnT5ewF09ucE1NQHpgbdnWDqk7pMAFdI0xOkXmMdU+rCRYWirrYVaaO9DLwbIEVub13yc21uQHP3Bs+sVn4Rh5K1g09gSPr5OcLNPBmh0yJ/x4b49qglaIoBveWCyxiWefGu92G4BjporTu7E9Qxb6h0rO4BGICixf4ElgtbXBBx/0fo51RPYUWAsXyspSf8p1W6FpbgTWkiXwhz/ADTfAZZfJfdOm2V8iK0vKiPdYIWxqklZaXdGATz0lJ7GpUx1Jvkc7998vDuHtt0t4yJdf9haq/RVYaWler9wPK2prXfdG8Uez4b5CBIO9amfl5XW7YkOHrYPmmgSvokkBETudnZCdTW5CLkZCGdUV0fITtC+cfP65tAnKzhaza9CV28vK3IapLF3eiRFVx7kL8yTxHJiS08z8uC0YWy8HYMwYw1HVzcnBigmPYcJ4EW27mu0VN73Mw7KM4+0xs3s/qAJr6OJCGB2uCukKEUyzhwgebo+T66i74jQlJe4FlvUb81UlQeu67Ulg1dcPjXYbTgJr3OhIMtKk99GhJnuOkjcOlvOi2PTp8t48na/9LLCamiTcef/uGMhZKSXaAS64AJ57jpB8e+VJLwVWe2c7pQ2eF3LWr5cgpW7BSOHhPQSWDeJK+ajhQSZd/BrvvCMLP+XlUFWYyZiZRcSE9x02edJJsHltHKkRObyzq0cV6j5CBD9fHgJR1Zy3cLTH15g8Qb7/bU0pjv0qfiHYZdpfAL4AJhqGUWwYxo3BHM+A2bVLYu56luJeuFAm1w8/3Ps5GzfKyctpktH1HOhfHPeqVbKqnJ/f66HaWrjmGtF+Dz7ouH/6dJkrlcXaV4t75IQsXw6vvSbhNzQ0yH8uvxyOO04Elj96Fw0nKirg3/+Gb3xDTnrHHivZrbt3O7YxTcnB8qZEu0VamlykfNDD6JFHvGtdEhDcCSwvcyQ+2vsRb2x/w/vXAkhI4Kn1T/Flsd1ZjI2Vf8F2sCyB1SNmb1flHmjIYHSul0na1jGbnU1oSCjJac10tEaIvrQLrMWLZS7w17/Kz9PVWk+/KCx05M714INP6jFzP+eO+bc67kxN5Ruxr2KaEjKUn4/j++6xn4UzssGwsbPankvjhYNVUyOGIMD2kCm9N1CBNXTp4WCZJtRXR0iIYFwG8RHxhETXUdtm79/ozsXyRmD5ahHHit5wJ7COP15uly/3zesNBieBNWVcPCMzRXwcqLVfu71xsJzP2Va+uCUyXeFHgbVjh1TC//e/Yf5Vi4k66W+MThotD0ZHw1VXkZYqDvbhw33PT+pa61j070UUPFKA6WE+Y9Up6xag0sPBWr2+DTI2MCNjBqtyryYps4a77oLX3hWld+apkV69x5NPhuZmg4Wh3+ftHW/T0GYXwY2Noi49OFg71mcQNWYtSdEJbrcB+UrDYurY3WAXWMFecDyCCXYVwStN08wyTTPcNM1c0zT/GczxDJhdu6QaYM/wp7AwuOsuWLasd36OVeCi53NGjpR//RFYq1eLe+Ui7vmDD2T15bHHulf3tAogbWp0LbCsl1+3Dnj1VTnAr79eLiyHD4twOJp55BEJPbnrLvn72GPl1vksXFEhq679dbBg0C7W+vVirP31r4Paje/wJLCqq/u82P/4ox9z53t3evdadgerJtzGTW/fxI8++pHjsaHQC8sSWDt2dEsW+HLXTrCFMzEv2fXzeuIksACy7XPMkhJkwmkXWMcdB5dcIjUAnn12kGMvLOy9KIR8hUW7kxgxYRtnjD3D8UBKCpd3PEd4uKRtJSYik93U1F6Nz4/Lnw0JhayzIka9EFjW4RZltLC9Y2zvDVRgDU06OuT86CSw6uuhoz0UI/YwKdEpGIZBdHwL9W32C5erPKzOTpkgurN9fS2wNmyQ482dkzBqlJxjhkKecmMjhSF5EF3F+Iwc8rLleDtQaw/x66/AmjpV5hieVu1qa/0isF58EebOlVPCe+9B4tl/YXL6BEJDuocqZ6RKifaySs89qmpaajjj2TP4vOhzqpqrKGt0f02wIvZXrnSK/HQSWO3tsGt7OGR+xRPnP8G1cy6j5oSb2LQJfvbjCAhv4PpzprneeQ9OsEcRplZ8jeaOZt7c/qbc0Uf/wdJSqD+URd70/V69TkJWJQdr7ftSgeU3NERwgJgmrFhhd9l37ZKiFq741rdkZvF//9f9yRs39g4PtFi4UBSONy5RY6Ms4boJD1y7Vs4Fxx3X/X5rMWpTVfeKYxaWwFq7FsmQHzdOVueslbujOUywuVkqB5x7rjQUA8nBio/vLrD6U6LdwqqENUiB9cADcrt27aB24zs8hQiCxzys1o5WNpZtpLiuuHfiryvsE7F3yz+j0+xk+YHllDfaLyJDRWBZK+5OlSfX7JTfy4yxGa6e1ZseAisvN9Jxd3Y25VUhrF8PZ5whLtYVV0jKYF+9Qt3S1CS/SxcO1nP/k2Xey87OkbLJFikppNTs4YorTEcNnh4l2i2OyTkGUnaydbdNwju9CBH84gtZo7ow5B12NLpwilVg+YYPPhC3vj+5wZ4oL5frm4seWAnJLV0T57iEDppa4jDB9Q+3okJygtw5WImJ8s+XAsudewUiQKZOtfdACTKNjRSFjISEIvKS8hiTLeffg1WdctD0FSLYs1l7XJwsJHsSWD5ysExTpjUPPQTnnCPlz2fMgM9XNlGa8W9WHVzF1PTelVat5tTF5e7zig43H+b0Z09nXck6bplzCwD7qt2307EEVn29U/EKpyIX27fLwgAZGylIL+AfF/yDU86thlGfcbgslvDRa5iTO931znuQkiKL3/u/yiM3IZcXNr8gD/TRZHjZMlF+s49t8up1MnKaqK6zl4xXgeU3VGANkA0bRG8887SLEu3OxMXBHXfAm286Trr798vJa7qbg27hQpkleXNRWLdOLjCuKggCa9bIARvZw6FOS5Nr28bCJLnDycFqb5fVmrg4ubt0yTZxr6xKQoZxdBe6ePZZmQ3cc4/jvtBQiV9wdiotl28gDtYg4qJLS+GFF6RS7tatMi8OOp4cLPD4W99cvpl2m1zMVhZ7UQqvrg4iInh9/3tEh0VjYvLWjrfksWALLJtNLOULL5S/nfKwNu2T73xkjpeNMi0BYi/eMWG0rPTvK2qB7Gw+5HRABBZIqHBLi4T+DghLBLsQWP98czuEtPOzK0/v/kBKCnR28tT9tY4WegcOdCtwYTEhZQJGym4OHYjFzPCu2fCKFTBjWidzO7+kvCmewz27KqrA8g1PPSXnPVfh7gPBRQ8sS2CNSHUsLCYk2bCZYTQS69rB8tQDyyIjwzd5Jp2dcg13d922mDJFTrzBDqNvbKSQkZBYxKjEUeQkp0FEnZTC96Y/XG1t7zzWGTP6FliuzvNeUlkJ114rX2dBAXz3u7Bzp8k3v1fI+O/fzJyXMrj+zetJjUnljmPu6PX8nHTJcyqtcJ2vV9lUyanPnMrGso28fvnr3DlPoiL21XgWWFZrm67Lu5ODZX0ceRNriQmPISI0gtcuf5WxV/4NsDH12JLui059cPLJsOJzg8smXc0Hez6gqqmqTwfrnY/qIbyRRQu8++zzRpu0143GhqECy4+owBogM2eKpnngr53Y2trdCyyQKnPR0XDvvfK3dUS6c7CsOG5vyr16KHBhmuJgzJ3r+qnTpsHGbeFyEnUSWOvXi0nzzW/K3+uYI6uXIKpr/Pij18Gy2aQ0++zZkpHqzLHHyndrVeUZiIPlgxDBRx6Bjg6TuDPuxWYbInlYgxBYaw6t6fr/yoPeCSwzIYH3dr3HN2Z8g7zEPF7f/ro8lpER3AtKWZmsYBQUiPvplIe1u1AmPF4XuTh0SH4vESLICsZKTP22fdWQnc1iziAlsZ3Z9toP8+aJET3gaoJuBFZtSy0b18STOraI3JQe5dtTZEyhNVUSwWyabh2siNAIUkfW0NIQTWXKxD4FVmenGMYLpjUwCVlathqQdqECyzdY15n/9/9kgWCwWN+tCwcrPdUxLRmRLGHv1SQPXGAlJg7CtnWiokIcvL7O51OmyO/NF5/TYGhs5KAth/DkMpKikqQqY0ylzNXj4rwrctHznD19uuQZu6o8194u9w/CwXrnHTk/nXiitO/cvx/OePB2nkrK4787nufrU77OsuuXsfOOndLaoQdZyUkQ2kJZlesQwWteu4btldt564q3yGs7l7u+MRGe/IJ91ftdbm+aIrDOPFOq/HUFqDgVufjqKyC0jdkFjpDnxKhElvz0Pub9/lv8+Wf9yMFGphVNTTCt45t02Dp4ZesrjkVBNw7WZ58BuV8yI8dzKXiLyRMioTOSougxKrD8iAqsAWIY0rl7175w3uUczwIrNRVuvFHOHMXFckQahkyyXFFQIPaDN/Fdq1fLCb9ng03EWKut7S2wbKaNqqYqpk+XhbaOzNxuAsvSdV+7Vu5bO/ay7heVmTOPXoG1YYN0Obzzzt45b8ceK7M+63srKpLJb3/6t1khggNccW1pgYf+3kbopPcoz38IGCJhgu4EVmamFIjxUJp+bclakqOSmZM1h1UHV7ndzvm1mqLDaGxv5KJJF3HJ5Ev4aO9H1LXWyQWqstJ3oU79xVl0z58vDpZp0tLR0jXndHEou+bQIUdTYWBq7igIb2T3/ibMLBFYpxeUdqV5Goa4WJ9+OsAUSktg9RBH/1z9H2xFczlzUWzv59gFFlVSQpiKClm9cSGwAMaMle9lV+zMPkMEt2yROeJxY8qYiCgr5/4zgAosX1BVJcfnLbfIcWPlnQ4GFwLLOuXlZDoc3NQREipYQ5JrkeRiP71I7KNRsbdYk9y+DtDJ9kluVwne4NBU18FhWwojMiSEISkqCWKrqKkO8d7BciWwrPg9V9vDoATW9u2iXZ5/XqoeJ2fW8dSGp7ii4ApK7ynlXxf+ixPyTsBw02crJToFoqupqnJdJv/L4i+5Iu97vPrnM5kxAxZ/EAYHj2Xr3p7Wt1BVJR9TWk4txx7rJLCcHKx16zsgbTMzc7qHLOYm5LLyp//i9Imey7P35MQT5XbfmglMTJkoYYIefnu1tbB/eyLkLXOUre+DmZPke90WO3lYCazLX7mcuz+4O9jD8BoVWIPga1+D3KQGHuB7ngUWwN13i/tx//0isMaO7V51wpnQUJmAeTMLWrXKY3ggwJw5jvtaOlo457lzyH8wn/yJ9bS0wO6kud0E1mefQW5eG799ZCQT2MEbIRPYUu50Qp05U0rr+L2b3xDECo20qj06YxW6sOIIioqkgmDPQiaesCakAxRYd/7pS2oPR5B7xn+547SLIaacZV8EuclqS4tcjFwJrJAQaczmXH2xB2sOrWFu9lyOzT2W1YdW02nro8dMXR1VYe3ER8RTuORUYrfdTFtbJ+/tek8uUKYZvFL4zgJr3jy5cBYWsqNyB2ZDGuERnd5H2PQQWGOS86XZ8KEONteOpJQszsjrbulcc428/eefH8DYCwvl+3J6TdM0efCN5dAZxSVnuph49hRYllPpRmBNmyw5FDtCJvTpYK1YIbfHZe0jn32Eh9l6O1ixsaIsVWANHOtCctll8LOfSUXZxYsHt88e4a0AFRUSUjcqy1HOOiNVYtv7dLA8CayEBN84WNYkty+LeYp9khvkPKziw/I5ZmZLeLVhGETG1VNXHSECa6AOFrgOi7DmA4MUWOPHS30wgDe3v0lLRwt3zruTuAg38yUnRkSPgKhql2Xaq5urqf3yIl645dc89ZSskb78sjy2bZtrwWZVEPzlV9eRPH47W7faf0pOAmv9VzbI2Mj0DO/yrPoiLU3Su++91+D0xNtZdmAZDUV7HFVwe/D552CaBhlTdnn1GYGjF9a28OHjYLV2tPL2jrfpsAVpcXQAqMAaBOHhcEfBEj7mNDZWZnveePRoyTJ/4glZBnEXHmiRm9u3wLJWFj0UuIiMlJxbEHF18UsX88GeD6hvq+dApPRZ2BjhEFimKQIrbtwGrl9vMiN0HevLcih4tIB5T87jwS8f5KtMORnZNhyFeVgbNsjFacyY3o+lpMjVwVrmsgRWfwgLk6Io/RQApmnyx+V/4h+PxBE7cjfr/ng/t8+7DbLX8vkqLxvX+gtrcuNOOYwb52hi3YOWjhY2lW9iTtYc5uXMo6GtgW2VnicuZl0dB406ThpxNbfeEs7vvj+J0Ee3c/+T5XSmBrnZcE8HC2DVKjaVb4KGTNIyOl0VA3VND4GVGpNKSEI55aWhLF6dBMDpSau7PWXsWGnZNqAwwQMH5PWcmvluLt9M4SZxt63I5m5YAsv6PfchsOYXpIPRwabmTBFFHppgrlgh8/PRLdsJo5PxY229HSyrUaoKrIFjhQfOmQM/+IGc426/XRZOBkppqZwPoqO77jpY1gIhbYxMS+q6LytNWhZUk+xaJJWUSOxWlIfWBr4KEfTWYk5Lk9+9JwerpMQhXP1EUa3kT40e5ai0F5PYRGNtTN/HRGur60Wx0aPluX4SWDt2OOpGAbyw+QXyEvNYkLvAq+ePiB4B0dXU1PSe2u6t3guf/J6c0Y1s2SKFoKyqfUV7XAsTK7DCTNpNdcp7mKY9bdZe5KKsDA5XREDmV0xL965SoDc88YQcGkvvvwmzM5TiXWvd/u6WLwdC2pk1102fOBeMyQ8Fw8Z2Y9SwEVirDq6iuaOZRfmLgj0Ur1GBNUhuinmOGKOZBx4Kob2z3XOVsx/+UFaNDh3yjcCyTtAeHKzp0+Vc0NrRytde/hrv736fJ857grnZc3mv9q+Ehpps6pwsJ3zTZM8eOd4ORzzH17eFMP+YcMy6UfzmmEdp6Wjhex98j7O2/ASAH9x7OlMfmcoNb97AM189Q1FtkefxHgmsXy/fnTtXyoojMM3+Nxm2SE3tt4P1m6W/4adPfgQVBdz/yzySo5OYmDKR5DH7ObQ3aVBzoUHTl8AaO1YElouk8E1lm+iwdTA3ey7zc0SQ9FXoorGqhIqwdqI23orNBo8+Colxkax86E6m/vBsXuAKzLIgXVSKimQyaJWLioyElSvZXL4ZoyGLnMww7/bT2Ski0Sn3xDAM4kbUU1MRzeIPQ5gStpPcxp6WDlz49Xo2bYIf9Ldmu4sS7Yv3LIbChYwe0+76+t9PB6sgayIk72Nzrf15HlysFSvs1VG3b8OWlMSkqaG9BRZ4Fw6luGf1apg4UY7fyEj4+9/FcbZyigeCiybDRSWtEFNJVrzDIcpNl4nv4bBU9w6Wp/wrCHyIoGE4Cl2444c/hNNOk6gWP3GgTs63E8Y4RGx8chut9XF9HxNOvQS7ERIiKQybN7t/zgAFVnu7/KwsgVXRWMHiPYu5ouAKtyGBPYkOjyYkpo6Gut7n0W2HCqE+h7POa+kqWpGRAVHxTRwuzHQZGdEVuZ68j60RT2MY9vVTu4Nl6cyonJ3kh6X6pH8lyDrW44/DpvVRZK3/O/VFu93+7pYuMzFy1jI9100laxdERkJkciW7OnKGjcD6ZN8nGBiclHdS3xsPEVRgDZIRB9Zz5cgPeeY/HWT/diZ5D+S5L/k5fbrUHLX+74lce15Up4dwqFWr5GTuHANox2ZzFLho7Wjl0v9eyru73uXx8x7npjk3ccPMG9h8eA15Y1vYWJ8vq5E1NV3l2e/d8CFhIWHMvkXcsWNCvs1X3/6K/d/dz39u+4imEQlc0T6JscljeXPHm1z3xnWMemAU4x4ax90f3E17p29ONEMKm03COz2V6V2wQCYP+/ZJkvNABFZaWr8EVn1rPfd9cR+Zm/+P9HSTb1wjDoNhGJwwPxqzM4wv1gQxTNAbB6upyeVk2ipwMTd7LuNTxpMUldRnoYuWw+XURYbyxVsFnHEGfPvb8O/3N8HXL6XVaOUqXuBfL3sXSuFzLNFtGHKRnj0bVq1ifel6IltHkZXl5Sm5vFx+j9ndnfOUjFaaqkawbBmckbq2W+ivaZo8veFp/lQzG0Lauf/xCkf5em9w0WT4gz2LCS0+kZNPDHf9nKQkmZRZAmv/fpm0uZmETUqdBCN2sfuwvViGG4FVXi6a/LjjoGTNp6yMryFrdD179riY46jAGhxWn0WL00+XpvO//71b57lPejQZBigt74DYCjLiHBPJvEyZ4FdFpLvPweorZC8hQb5/T9dSbygrE1vBKbT/ww+lTWQvJk+WPCV3lQQ/+0zez0A/Py8oapJ+etPGO/rqJY3oxNYSS2t0kucQQU/n7AkTJA+5J5aDNcAqgnv3SorfxIny9ytbX6HT7OTKgiv7tZ+ouCaa6no39l23Rd7T3ALH+AwDcsfWYiubzMH63kVJ9u6FqKRaiGjiQOtGxk1oE4FlL3Jh9VwumNpByPELJYHKR/m9l14qtcXK3vsWu0on05DcOzywuRnWrAZz5DKXZes9kZxZw4HWXEergyHOp/s/ZVbWLJKjvewTOQRQgTVA6lrreOzLv9O+eydJKT+gsz2MxI0/psPWwXu733P/xN/8Ri5WrnJ4nMnNdaxSu2PLFsjP773KhKwE1dfDrNmdXPbKZbyz8x0eOecRbp5zMwBXTruSqLAoQjK2sLHMfkE7dIjPPoPYsGquKdmO8Y9/MPtiWWleu1Ym7HlJeZw65lRijlnAMRXhvHXlW1T8oIINt2zg/jPvZ+yIsfz1y7/ywZ4PPL+/4cjevXJR8iSwrDysN9/0ruKUK9LS+hUi+Pym52k4lE3p+jncdpvRrST/1WdKwvXzi11cEAfD9u0eC1N0wxuBBS7zsNYcWkNKdAqjEkcRYoQwL2den4UuQurq2WVcxcHiEG6Wnzunjz2V+FmLOf9332cW67j/f+ODU0W5p6s5bx7m2rV8vncpIY3ZjkXK5mYRX4884no/PXpgWWRnGZjt0bS0wBn5u7u221W1i1OfOZVvvvlNCkZnMm9RJZ0brubhFY97N26bTcbuJLCa25tZurKGzsbkrsTsXoSESAiXs4OVl+eyKTpIIn5M5iGKK9Kl95EbgWWlOc4/tpOwXXvYlgIhaTvp6HDxs1SBNXAOHhSXqGcY+l//KgsE/+//DWy/LoRRZaUJMZVkxjnuz88cAdioHKyDBYP/DVhjtv92TRNu+nYb11xr6326njJFum+7cgdKSmShAfza7qSoJY34sDLGpTnON6mWoRye6Z2D5U5gHTzYO3x3kCGClvtsOVgvbH6BKWlT+p3bFB3fSktDTK/7t+yQnKkZU7s/NmFSB1RMdVlJcO9eCE8pJDtezrMZk/ZKgEp4hF1gmRgJJVxbFyKu3pdfyrHhIx56CLKybfy89j+sN3uHoaxcCe3tBuQtY2pa/wRW1shWylvzZJ4yxPPpm9ub+aL4C04ZfUqwh9IvVGANkPLGcv7vxTsI7zQ588yZnHZWK/WfX8Oo2AkSNuOG1oI5FL26yhE64w4rd8dTmOC+fa5zgXBEDzanfcZbO97ir2f8lVuPubXr8aSoJC6ZfAnFMe+xvyKOOuLh0CGWvlPFoo7PWPv14wi5+hoSE2X+26sx/cyZIvDa2ggxQpiROYPvHfs93rziTSJDI/l036ee399wxLoYehJY06ZJBcj//lf+9nOIoGmaPL72cVI2/prISJNbb+3++MXHzsGIOcySL3wQIuPMdddJVTFv8CZEEFwKrLUla5mbPbcrRGR+znw2lW+isc11bs72yu3ENneytPrbpKfD+efL/ZFhkZw74VxePPQud4Q8wpbSVJYs8W74PsWFwDKamhhzsJ2W2njHnPOvf5Xfm7tiAm4E1mh7s+GICJMTp1XTcbCIO9+9k2mPTmNdyTqeOO8Jll6/lF/elQWNGTz4zH6a25v7HndpqVhDTgLrs8LPaNsqfa8sY94lKSm9BZYHskc30t4WTSnue2GtWCGLyJXmy6TXdbI9FZoS5CTlspKgCqyB4a4NSHY2nHLKwHtAlJT0EljVVaG9BFZGXBpE1lEZmtRbYJlm/wTWYMMEy8q6hWlt2wYH9kbQ0hzCAw/1cC08Fbpw7pXY68LqOw52ZJIQKT2wLDLSJR+r1EgZnIMFvc/XgxRYVoGaiROhqLaI5YXLubLgSq/DAy3iEtvpaIrtZVju2yNhgz3rkc2aFgXNKWzc27tq6d690JawnXPGnUN2fDYtWUs5fBh2tYyE9nbWbujATF/PBYsL5fd8wQWy6OAyVrn/JCbCc0/DfnMsP9l8I2aPVcHly8EwTBi1gslp3pVotxgzxqChJYdWIoZ8mOCKohW0dbYNq/wrUIE1YMaNGMfS4/8BwOln3caP7o6kvNwgv/gnfLLvk64QubY2qfLyu99JyHVysswv+qzg6q3Ays93+dDatZLqsSv0TaLCorjtmNt6bXPjrBtpGSHhVpspoOKVpewuSyE7+TNGPv5C13azZ7so9T1zpky4eryR4v1RZK//Gx/t9KKH13BjwwYpQjHVw0pRWJhMRqyL6GAcLC8sljWH1rC+cDuNay/mssuMXm0ywkPDyBx/kH1bk31bfWffPtelel3Rl8DKy5PPrUe4THN7M5vLNzM3ey6ffCKhOPNy5mEzbawtcV17/u2Nr1DVmcVnxfP55je7WkQBcPGki6loruSSlPdJiaz3Wc9Ur+nokAmh82/CXuhi7oEsbDZD5pwlJfDHP8rj7iqRuRFYE/OlLHnmlN38u/xlwmrqeGbl41xRcAXbbt/GTXNuIsQI4cwzISO7hfrPr+SZr57pe+wuSrQv3rMYY/d5zJnb6TktpZ8Ca+J4uSztDJnktlT7F19IZPTixX8GoC4/i5KoJYAKLJ+yerUcm64WlUaOdPwu+kNDg7gfPYRRXXUUIbFVJEc5hbRFJUnZbVdl2mtrJbS9rxBB67wz2EIXPQTWa6/bz6e5K3jgoY7uho4lsFxd6FeskCSYKVP852DZbBTbcomPOkhWvONzzk6XBZiDHXESlu0ubNISo67O2ZZC6RkmWFMjjrW76sh9sH27/CQSE+GlLS8BcEXBFf3eT2KSvKeeX3dZYQKRiTVdnRssFsyW97jmq+4LTe3tUFRk0hq/nQkpEzg1/1R2x0h1oC+rJ9LWarJrRyhJiV8x+outEov++ONS6e+GGwYfkmrnpMkV3MN9fF50PVd+q9wqXgiIwEoYVciY7GRiwnu7dp4omBgNhFDI0C908en+Twk1QjlhVP9K3gcbFViDYFS53bIdP55TTxXzYucbX6P+zd9w8pl1TJ4s1/aFC+EXv5A583XXybz5g74i6PoSWI2N4nK4EVhr1sg1cVnxxywctZDIsN4xySePPpnc8TUAbGIaK57YJC/5LRuZyY5J4Jw5Mi+y5kgAzJolt079sP77XxFj+177FhvfPkE6kB9JbNggsfWeKlaBI0wQBi6wOjqgtpb168VBdBnnDzy25jEidl5FS2MkN97oepv5x4TTWTqZJbu/cL1Bf2ltld9eSYl3k5a+BFZYmEy6e6yIflX2FZ1mJ9NT5nL11RKTXvy5xKK5K3Tx8Vev8xTfpNMM5Vvf6v7Y2ePOJjI0ksb4Nm4a+QFvvumxv7HvOXRIQu2cfxNjxlAdG8IxlbL6mJEB/PznsjJz5ZUiOltdVIc6dEhClXoom5njpedaYeo/KLFPJPZf9gVPX/R0t4lWaCjccWsk7DuNP731Kjazjxh8F02G3/1qNWbxPC44P9TNk+xYAqumRn4LfQisOdNk4DvjZrp0sNraZN4/ZnopzZs2ABBdMIstdSvIynLTbFgF1sBYvVqKGjhV++ti1CiZiPfXGXLRu6qjA1oaoohNaunmWBiGQVhsA4fNhN6vY+2nLwfLCqH3hcByGvOLr7RA9ipCz/oRjbVRPPmk04JYdrb87twJrLlzZXFl/XqvFtL6TVMTRYwkOaGcEMMxzbNK4Be32yfj7lwsT+dsK6TblcBKTOxfWxIntm935F+9sPkFjsk+hnEjvC/cYJFs1+fOpdptpo36kgxSR/au3z5zmqzC7djW/TxWVASdnQYk72VCygROG3MaNXGfExffyZeV49jWPJqO9hDOr9mIGR4uER2ZmfDgg7IC9NBD/R67S8rK+D0/Y+LEh3jpXxmccIJctzo65KcUkvd5v8MDAeZMliiqvQz9Uu2f7PuEY3KOIT4yvu+NhxAqsAbDrl2yWpGVhWHAj34EJQfiYf232LO/jalTpRnxa6+JuNqwQSqajRsnzT49kpIiE3l3AsuK4R49utdDNptEHkyZ3szGso0sGu3aVg0xQvjWojMgspavIuayPOQEjJBWbrnu9G7bWTU0ukUzjBsnoXAbNtDaCt/5jrRJmTIFps2thc9/yPtbjzAXa8MGz+GBFpbAio6Wkuv9xd5seOsXtZxxhsyxf/az3nmotS21vLjlRVK238PYMTZOnOV6EnnxojywRfDvD31UFtjZVeg1m3WBdbHuuXTojItS7WsPiUtVvuYESktlLeE7tySQWXGly0IXRbVF7Ny3nie5iVOmlHTNAyziI+M5bcxp7A2v59txUkHv0Uf7Hr7PcC7RbmdP9V6+yLKReki+88y6nXQ1aTn/fFkF3bWr975KSqRpstUwxs65C8Zxw6+W8/HDl/Kbq8RhT65xXULyxhsNQkNt8Mrp7L3pUjjzTPdJ9z0EVkl9CVtXjAIzhHPP7eN9WwLLUrMuzlnOHDs1G0Jb2RYx2aXA2rBBjIvChJeYXh2BGR5O5rQFHKg9wNjx7epg+QrTlJU6N21Aun7H1u/aW1wIrOpqwAwhaURvlz0ytpnDNhel1q3wRTdh8l34IkSwo0MWlewLGocOwZYNcTDpTX5+9akwahl/vLfVUWDFXSXBlhYJBznuOFmkrKjoVohmQFhOnhP1ZU3UkkRKcndBkZ8t5+CSVrtg7ktgucjvJi4OcnJ6n5dqawccHmiaIrAmTYIdlTtYV7Ku38UtLKzm1IcPO4RrSX0JZtU4RuX3PhdmZ0NodD2FPUq1OyoI7u1ysAgxyZpYzBdl+XzVKslidxRtxPj61x2/56uvhvPOk4u2h/6OXlNWRjgd3HjDJ4RfeSXbttuYNUuCHBoboTbzbQrSC/q925mT5bjYR37QBdaePXDJJa5Twepb61l9aLXbeexQRgXWYNi1SyaG9hW3q66SH8ixfz+DUT+6kFdegT/9CS6+uHvK1aJFsHRpH8VmDMNzqXarA54LB2vnTjlvRo2SUqqefpjXz7wO0jexfMSxvJR+GhGjNnHu5FO7bTN7ttx2CxMMDYXp09n/ZSknnAAPPyxictkyeOTBGGhK429/8/D+hhvl5XIhtJw7T1gCKzfXbTK/R9LS2MU4Tr02i7AwCS/dsUPqZjjzn43/oaksk5LNk/hm7MsYF17gcncLj5WL6QfLq3rFcA+Ig07VlryJNa+tlUluqAenY+xYOZ6cxremZA1pMWk8/88RjB8vc73x46HqqSf5bE1Nr138cskvCd97FgcYzc3nuD5uLp50MYURLWRWr+Oii+DJJ6WeREBwIbDe3/0+q3KgqURWUTMe/42I8l/8QtxScB0m2KMHlkVIiME/f3kCp0w6BiMnx7FtT955h6zLT+RC25s0bLuezKfelXyvN95wPfYDB2SyZZ+sfrj3Q9h5HqkZ7X0fEj0FVh8OVkHGZEjeyzYz32WIoBV9+wV/4Yz2kRjjxjEtV05SI0ZWsH17D1NABdbA2LNHlE9fAqu/YYIumgNbRSLsa0vdiElopbbThYP1yCOSD+SmTUkXvggRtEK27QLrrbfk7oy5X/Kj439EwqmPUn4oihdecHrOlCm9j91168SCtQQWDD5M8OST5eLrRNFucb0z05q63T8uV+ydQ812geXuuPAksMB1JUHLwRoAlZXyU5s0SdwrA4PLCy4f0L7SU6SiaUmFQ0xtLjoADVlMnND7GmQYkDyqlMMHukcDWALLGLGfMcljyEnIYVLqJGw5n7OxMpsv2ucSFtLC7LYdsiDmvMPHHpP49BtvHHyFPvuCxAUn3kT7xBe57cl/Mnq0o76MbeSSATlYOTkGRlgbe8nvVUztYN1B7ltxn2/mC17w3HPw+uuOxs/OfFb4GR22Dk7JH14FLkAF1uDYtatbxqRhyPnlrHFnsubQGrchcqecIteKPs+rAxRYVoGLyqT3iYuIY272XLcvkZeUR+74arZUj6a4fArHLGgjNKT7SSg5WV6mZx7WofEnMXfV39m50+S11xyFpRYeF07qzJWs/u9JPunv2I0NG7yvXufr1wXvHKzMTFml72Mi6Y4DHTmcyse0t5l89JE4o/n58H//55g4WsUtMnf/hJAQuK7tHxKQ7aIxa34+RMe3ULF7JDuqvHCc+mIgAquvC++4cbLd4cNdd605tIbxrVfwxRcGt98uuuO99yA6xqTs8X+xbofjorCuZB1Pb3ia2ML/RyoVXHSa6yIYF066kMo4A8rLuPNOeblukyJ/4kJgvbf7PYomZ1GOJM9lrnwDfv1rWQmeOFFOKq7CjNwIrG5Yj/cUWDYbfOtbUFjILZdWUUkaI86/mLasdPcnpR49sN7f8THG3rO44NywviOCUlIk38NyO/s4LnIScghN3cvO1jyXDtaKFZCQcRgz/iATK02YNIkZGdJXMCx9N9XVPWrExMdLmKWPetQcNbgrcGFhhYv6wMGyvq+MtN4/pvjEduo77ALLOgGuWycV2267re+QNF+ECPbogfXGGxCWuo8T5qQQHR7NnVePg/SN/O6PrY759JQp8l6dzmmsWCG3CxZIP0XDGJzAstkkF7ZHSMz+vSIucrO75wHlJKdBRB2lzV44WDEx3ZqKd2P8eNcCa5AVBCdMMHlh8wucNPqkrsp9/SUrXUL4C0sd723VZpmLzZrqOj8sd2wdraXjaOt0JDjt3QshYe3k5YZ3pVicln8axYmvYDNDeMF2GRNDNlMyIc3RNN4iJwfuv19WnK2CVwPF/tubOPVEThh1Av8t/ROffW7jO9+B4885APGl/S7RDnLYxKRVsDOsd4jgr5b8ih98+AOXpev9wdKlcvvii70f+3T/p4SHhHPcyOMCMhZfogJroHR0iMjpWZIGOGPsGZiYfLT3I5dPPflkuf3kkz5ew5PA2r9fQtB6VjVAhFB0NHzV+RInjDqB8FA3J0lrvMdnYrbGgy2CGy+c6HKbOXN6C6wf7riRBjOWFS8f5OKLuz92xR1b6WxK5Hd/9mH/pbY26cFyxhkBtB3s2AVW2ajx5MxbxSuL+1i1ffZZUUT95NAhOPWOSdSRwIfff4+pUyUK7Ac/kJKsy5bJdl8Uf8Gm0i20rr2CM8+E3LK1Ek62snfonGHYF0tLZvPWjrf6PaZeWAIrM9O3Agu6QtSa2pvYWrGVhs++SWwsXH+9PDxqFDz47G5oSeZrF0bxv//B3/9uctGNOwl/9Q02bZ3H9TxNZJrrldfUmFSSR04gsrmdhXMbmDZN3NeALNQVFclk3/5ZtHS08Mm+T0g78RxKySSGRuImj3JUZ4yOFnXszsHqK/dkxAhZ8egpsFavlov2H/7AaS9+i9H5Nmwbb2Njdpj7qmZOPbBspo33Pq3DbEng/PO9cGgtW2L9egl7dnHOcibECCElt5qiplHYSsu7rQCbJnz2uY3mjE+4fMIlhO8rhMmTyY7PJiU6hcYEOUl1i1yNj6eTEK7/hi1wlSNNU1zIv/89QC/oB1avlu/LXVGfrCyZpXkSWI88At/7Ht0y80tLxc12sqvKK+Q7zsnsnSucmGjS2JEo57emJsd+Y2IkqbkvfOFgOYnCujr45BOTjgmvcNzIBQDcPu82Qk/4K7u2R/Luu/bnuHKgV6wQtz4jQ84F48YNTmBVVMjCwY4d3eKrtu0Sty8/r3sIcXxEPMRWUdFszyP25GB5OmdPmCCutLN49IHAsqVsZWfVTq4quGpA+wHISZf8spIKxxxh0zYRnMdOc2GRAhMndUJjOhv2OATF3r0QPuIQE9McseanjjmV1kxRA7UkMa9jI8XfuMh1pMo118itq55hzvz73/CXv7h/3Kn/2m3H3Mbe6r0sKX6fBx+EU+/5FyFGiPQPHACp2XXsJh/TSWDVt9bzwmZZdaxt8fUKeW/a2iQqITYWlizpHbTwyb5PWDByQb+LeAwFVGANFCvLsGeiB3BMzjEkRSW5LdeemSmLW33mYeXmymTWlcW8b5+4JC4O7DVrYOr0NnZUb/HKVr36VEefifNPc10+fs4ceUkrcXTZMnhu1Xh+yJ+Z0tC7L9G1Z02FSa/x94ciup2DB8Xbb0sswZ49ssofSNavh1Gj+PeKMg6tnsd1l4/oMhFdsnChI7ayH9x4I5RWhvEeZzMr2iFerr9e5qWWZnt87eNEF15IdVkcN1zV4ri4fu467+34+VEY5TN4Y8u7Lh/vFwcPygl//nzfCawepdo3lG7A1jCCbZ9O57rruj/98lMnEnLFZRTujuO88+COOwyKFl9EUu0JnDezmLv4q/vQFmDyFOkE/+WGt7jzTtHObj4239KjRPvyA8tp7mjmxLmXUBI3nkxK5ULrnFc1eXJvgdXeLiuOfTlYhiHb9BRYb70lE9yzzyYkBL59Swid+07g3yGJmDt2uHRBnQXWxrKN1Hx1PGHhnZx2mhfv24qPXrtW9uFF2OzosW2026I4aMvs1hNu+XI4dDCE9jFv86PMS+UcPGkShmEwM3MmhyLlpNrtZxkfz+tczL9fjOTJJ70Yry947DGJ7f3HPwL0gn5g9Wo5h7lzMcLCZKXeU4jg009L0v+55zpC/EpLRWA4OU+FJfKby8vq3Ux1RHII7Z2xtBEu55Lqanj+eZnAejOhj4mR3/tgcrCcHKz33rP3Hpr4JgvsAisrPosrLjcwkgr53R/ssf89Kwmapgis45xW4mfNGpzAcha3Tiugu/a3YmBjwtju513DMAiPq6Wq0T5ZdSew6ur6FljQPQ9rkAIrKgp2dsii9LkT+krsdM/IDHGpSisdon73bjnnTJkU4fI5s6eLsP9snSPqaN8+k47EnUxImdB138mjTyYktoacRFn4HhP+FQnfuMn1QMLDRTX01WPq2Wc9F8SwqlcaBpdMvoSM2AweWS39EbdUbGFs8liiwvoovOWGnFHtFNny6ShxCMsXNr9AY2USrLiL2hYft3dxwerVsl5+4jVLMc3uYYI1LTWsL10/LPOvQAXWwMnIkInK6af3eigsJIxT809l8d7FbmNYFy2SZu7OC3u9yM2VDVw1nXVTor2zUxahR4yV2b83P8w5M+XkMmFSh9v2XJZWWLdO5jR33AF5o2z8OP4Rl77u7KzZxJzxZ5obInzXd+/pp2XCeN11cN993SoY+p0NG2DWLD5fKys6LS0yZ/Blf74tW+D99+EnPzFYEP1Vt+89Ohpuu6Od996Dv721nJe3vEzWrp+RkgLnz3S6yH72mct9z5kDZkcEX6yro6LRET+1Zo3EPm/Y0I/3cvCgTKwmT5YLbF+hVx4EVlOTRF3+v/9MwMTocrDWHFoDa2+ivS2UO+5A7t8sOYXR4dHMOr6CY353A0uWtTP6t8cz+YG5HNwXz+s3vEMWpR4F1vQCyTH88MvnuPpqCYENSMn2HgLrvd3vERkaSciBU3il+Vzmj6mAs8/u/pwpU2R12rnkb1mZTNb6ElggLoMrgXXiiV3ltr75TQgPN3m39iYMm613b6OGBlmptof2Ld6zGHady/EndHhXkdk6qezc6XXY7FT7RGgX47uFCT7+uI2Q6Drmn1HEjBq722HvTDojYwY7Oz4iKsrsJrDMuHju4x5AFob87lauWAHf/a5M6nfvDpA96mM6OuRk7y480GLkSM8O1v798v0sWQInnCC/RRdNhg8cEmdqTG7v4zZ1hCw41JAkE/+nnpIZ2W29W4+4xDDkfOCjEME334ToxAYiRq9jVqYjAfGuhXdiLriXlV+EyYJNXp6cuK0Fkv37ZT89Bdb+/d1L3vUH5wgXK6QTOHAohExKyc7qfbxFJzRS22gXsp5CBD2cQ7sElrM7MwiBtWOHRERvKF9HZlzmgMMDAbKTkyG0hYoqR5L7of0xRCRWuj1fLZwjxajWbXTkbe3ZY9KZuIvxIxxRSklRSczNnkt2skSKHJi8iQnZ09wPJtFFcZae1NbK9+juOurUHiAiNIKb59zMu7veZV/1PrZUbBlQeKDFhHFh1NlSOFziENpPrnuSkI//Aov/wp4DLirY+hgrquC9hK+RkLeHF150nC+XHViGzbQNy/wrUIE1cOLipMqXmzLcZ449k+K6YrZVuu5jc8opslDsdE7sjadS7W4E1o4dMmltTfucpKgkZmbO7OONyDlg9my44Lwwt9tYlQTXrpXojE2b4P4HQoi57XqpId6jWk5YSBinHJtG3Kx3efBB1xqxX5SWSgLON74hyV6pqXDTTX1UCvERjY3ywc6cydYtBsRUYLv8fHbtMrnsMt+ldjz4oKzi3XILmGmplO7bxC8++QUnPnUi2X/J5leN6RBRz50/L8RoSqNw1SyuuQYiK+y/j4kTxWt30X+jy0wrmcXjax8HZJ49f75U75k1S+bbyckyp/LY4soSWJMmyeffV06cB4G1ciV89RX89o9hXB/zEm07ZGFgdfF6QtbezmmnmRJp881vOiZoSMPhrebrrA59kP2dK/jrWfcRFhLWd3I2EJktx+zGTR9jhjVy3XUiMq3oI7/hQmDNjbyKqy6LYvzEUB5Ze2zv50yeLPlDznapFUPhjcDKzu4ec7F3rwjVCxwFUdLT5TdwoPB6monqvaLeo4LgGys2QeVkLrmwdziXS5xXbfqoIGgxb5qIP2eBdfgw/PcVE1vBM/zgpNsdNpW9tvOMzBm02prJG9vaTWCtKBrJSo5l+rhGiov9XJq/pAS+9jX5rH75S5nADvESyC7Ztk0OCG8EljsHy2ol8o1vwDvvyG/v2GPl4tFDYBWXtkJEHSNH9A4fzUyV31k1yTKJf+QROP54yWHyFm8mup4oLYWYGNoi4vjf/yB+2ifMzZ3Vrf3J7KzZHH/hTozIep79j00cusmTHQ6WlX/VU2DBwBcLLXE7YgSsckSSlFREM5IiMjJ6V1iMS2qhscmuNAYaIpifL+/PElidnbKvQThYEydK5dg5WXMGtA+LEdEjILqaw4cdkT/Vh1JJznE/CTlmchZE1LN9q+Sf19RAdXVIVwVBZ07LPw0j61USqGXXhQ2eUzCSkvpeuaytlSgldwsVPfqv3TznZkKMEB748gF2Ve0aUIELi2kT5XdQVC7VJdeXrGfN5irMTZcCUF7iOpe5Pzy94Wl+u/S3bh9futQkPGs7Gelh1I1/gpVfGuzdKyLrk32fEBUWxfyc+W6fP5RRgeUnzhh7BkCvMMEt5Vv4w/I/MP/4FgyjjzBBdwLL6ifjYrJiFbjYE/MSJ+Wd1KtghTtWrvScMpSSIgty778v1WvOPBMuughZqQ0PF0epB4tGL6LhuLtpajL585+9GoZ7/vMfOYlff71cTB56SN6sC2t93z6JtOpRuXbgbNokq9AzZ3JwzwgisnZC/hK+9f9W8+GHUkBosIvUlZUSKXDaxQe56r0zWN9RzLqNi/nDZ3+gtbOVs8edzW/PuZuzrygidOtVfKNuOx3tIXzzmzh+H1dcIRe5TZt67X/sWNEc+c2X8n+f/x//+/gwV1whwuvLL+GVV+QrvOYaebrHyCZnBwv6DhP0UF1qxQr54C68cRvPNH2dc9+5lbo6WPJeErbaHO6805CJ3pdfyn5uvRVMk3k586hvq+dnn/yMM8eeyVnjzpId1tXJ7zHSw+TfngOUUNfK69tf57TTRCSv6h3p2m8+K/yMlza/1PuB1laZaNsF1v6a/WwvLGfHQ38hLEzmny7nJlaYkXOYoJsmwy7pGSL49ttye/753Ta7+WaDzpZkXoy8oHcelpPAampvYtUS6bXVZ3l2C2eB5aWDdfzU0RihzSKw7ALxP/+B9rZQsk9+l4smXSS/u+zsLjFtFbpIHlnWLQfrL2+PJ5nDPHa7OKDLl3s57v7S1gZf/7r8Bl9/3SFOXJXZH+r0VeDCYtQoOf+4OgE6l+U/80yxD6V7a6/8wbLyToipJCO2d8dqK6emmmQ5Ue3ZA7ff3r/3k5g4+BDBjAyWLjOoq4PDeU+zIHdBr83uOelWzPSvWLbaHmrmXKp9xQrJu3LOaRtsJcHiYsmzPOOMbqu15dWJjKSIyMTebUISkttpbbSfjz05WJ4EVkSEiCxLYHlqTNwHLS1yzR4zvo1tldt8I7CiqqmpkbDA1o5W2srzyB7tfgUtPDSMyMw9FO8RodG1nuVCYJ065lRWnfECTT9JI3fmBDzijbC3BJi7nIMeAis3IZcLJ13II2seodPsHJTAOmaq5KQVNWVBWxtPrnuS6W/+ANOUeWPrhsEXuXjmq2f41dJfsa+69/trb4fln9loH/kh955+L7deL9eK2/4sE+NP93/qto/rcEAFlp/IS8pjYspEPtgjHYVbOlr4xSe/YNbjs/jZJz/jud0PMX16H4UuLIHVc2XDQwXBtWshJtZGcfhH/YpbDQvruxjTnDkiCJuaRNcYBnKhvO46Cd/rUfFr0ehFkLadWScVuawO4zWmKWEhCxY4OhF+/esySfzFL3qdmH71K7jnHkmDstqF9eSVra8w6v5RvLjZi4HZVxdbCqbQfHAMBQUGSVFJtE5/jJ/8RJq333//gN8dICkbLS3wZc5VbK3YSnTWKBZEj+fwDw+z8lsr+eeF/+TnJ/6cJ/8whZAQg8f/FsOcOfZFXEtgXW4va+siTDAkRMRUfNXJNJWM5NJLIsjJgf/9T1ysr30N7r4bHnzIxuyFFbz2mulaNJqmQ2BZ30VfAsvDxfrdT6ohbQtvjpzCTaNvZEntTGbPb6D4vatIyqyWSfwXX8iZ+KyzxHZ74QXm58qKVqetk7+c4ZQgbOUOeMrzSROBMNGWzLMbn+1aUPZFHtaPPvoR175+Lftr9nd/wPqO7ALr7a2L4cXXqatI5M033fYL7wp/61ZJ0BJMfRW5ABEgtbWOvKq33pIJnpXzZufEEyE8qpU3Ik/E5kFgLTuwjM7tZzFybEPPXbhnAAJrQuo4jBG72ckEKC3FNOHBR5ogZyU/vORMWTiyGufYmZw2mfCQcEJSd7FvnxxPu3bBG8tTuJVHmZ9TTFKSo1CMz7nrLvkR/fOf0nXeys/1RS+cQLN6tRxHLnKMuzFypKPxeE969mqcNUuO5eOPlxAOJ6qqgJhKMuO6O1sAufacmhqS5ESZni4nrP4wWAfLPsl94w2Iiu6kY/R7LquanT/hfKKy97B3R7ScPydPlut3fb0IrGOP7d6uIj1dzqWDEVg5OXISLy6GkhJME6rq0xhJkeQA9SAlxcTWGi85bQN1sEDCBK3FA0skDMDB2r1bDJyozP3YTBuzs/qfu+xMZFgkITG11NVKRM7W4mJozGDcOM+roMl5JRwukt+fFZQRllrEqMRR3bY7buRxRIVH0RHZzvSM6T13052+HCzTdPwuXU1WOjtl9bVHQ/nbj7mdDptE7wykB5bF1AmyeLGPfJqK9pH5+7fYeeB6ZsSKcO7cNtjQIyhvLMdm2nh4Ve84/DVroKU5lIixX3Lx5Iv5+1U/IHXCLj54M5n7v7jfYx/X4YAKLD9yxtgzWLp/Ke/vfp/pj07nd8t/xxUFV3D6mNP5w/I/sOCEZlas8OC0pKXJanxPB6uPEu25EyogxPdxq1aY2d13O0KwAVEzbW0S4+bEjMwZJEclEzb2M4qK+l/Nt4s1a2SCef311LfWy4nFMKRCV0gIfPvbXSuo7e2ySD93rpy458yR9j4WbZ1tfPe97/L1/36d8sZybnr7JvYcdtNc1WLDBkhK4uPDbdAWz5yZEZw25jQW71nMb39rcuGF8NOfuq4N4A1tbfJWJh17gMq4ZTx3yXNMnnwCyfXtJEZ1v8jl5MC118r/b7jBfufBg3IinzxZRLmbPKzZs2HHlmhiX1pOS2cjjzy/t1tBN9M0uePdO/gi7gcUFhquC8pVVcmEKidHLsBZWZ4FVkuLvEEXF2ubDdaujiR01CrevOJNFh2/hv9xLnv2mnDoGC7+RonMRZYule/5hRdkgnLnnUzoSGRU4ijumHdH9xj0ujrPuQMgq8iRkZwQNYmP9n5Ec9ghCgrcfmxe09jWyKqDq2i3tfcOiXAq0W6a8Ocfj4PCE3nqXwYLei+EO0hMFJHU08EKCemzGh/gcLlKSiTPY+nSbuGBFmFhMHl2Das6ToDNm7onhx44IJPC7Gze2bQUDpzE17wNDwRxE62JnpcCKzIskujsQ+w0JETwyy9h744Youb/hxtm3dC9M6mdiNAIJqdNpiFhDTabHP8PPCCn0Dv4GyGN9Sxc6CcH69VX5SC++25xkq33auVhDTdWr5aTaF+rblapdldhgtZ1yjnSYvRoOdCu6l4lruZwGCFxVSRE9j528zPl3FFNspxkb7pJHJT+MNgcrNJSOtOzePNNGD9/L4S3uHSwQkNCGTW+ntaGOFlvtBzo1aslt/G43qJsUIUurLBjy2lcvZqaGmjriHUrsNJS5TutjB458CIX4CjVbpqDEliW29yUKBecOdmDc7AAIuKaaKyT0L3Pv5L8uelToj0+Z+TYetprUzl82CGwxuQbvaKAosKiWDhqoexzsALLuj6CawerslIulD1CaheNXsSk1EmEGqG9HLb+kJwMkRF17COf+q+dR9sX36aVSH77nMwtzb2Dj5sva5TP/x/r/kFda3cX+aNPJb/igtOTiIuIwzAMfvztfCidxV3PSSqDCizFJWeMPYPmjmbOfu5sOs1OPrz2Q565+BkeOOsB6tvqOZTyHK2tjsaZvQgJkYlsT4HVc2XQie3bgbQtpMakDir50RVXXAE33ywNyrsxfrysKD76aLcwjBAjhJNHn8yBeCn56fZ99sVTT0F0NG2XXszcJ+dywlMnSL+KkSOlk/PixbLS+tvfsuy/ZVRXQ955z3HfK0vIyu7krLPg97+HfYcPcMJTJ/DQqof43vzvseW2LYSFhHHVa1fR3ukhkWrDBpg5kw+/FIdu0bw0zhhzBgfrD7Lj8DZuv100x0BXxl96Scy/8mk/4cS8Ezlp9Ekirl2tCiOpHTfe6BBaFBc73M6FC2UC48J+mjNHxtnZmEz0dV/nkb13dz1mmiZ3vncnj655lMTpyyGkg1dedVG90irRbjWxnTTJs8CyJjUuLtZbt3fQWh/LrHktXDDxAq686BecwYf83y//zaSzPuX3P7BPxpcuFXWYlAT/+hc0NhJy+x3svH0Hfz2zRwUVbwSWYUBaGtNDs7CZNp7f9DzHHy8LzC7S17zmi+Iv6LB1MCNjBv/+6t/sPuw0sXYSWE/9u53i5adwzNVvc9VVXpQ5dw4zAhFYGRndqw26w7kX1vvvyxt0IbAATj0pgtKmAmrb47q/XmGh/L5CQ/nfBy3QGcmF53tu/dALy8XqR2+4rFH17DPH0HmwlPv/1gAR9dz8jQTiI+PFUait7SawAHslQQkL+PxzOXVc/bUWsijlsy3vM25mCTt2+CEt6t13JS/0T39y3BceLotgw01gtbaKGOgrPBAcOYWuVs/27xdxndE77K8n9dVRRCc0YbhwnvOy5NxRQ5JcE602Bv3BByGC77SezsGDEDvrbfIS88iKd+0gT58m06qvNnY6BNbTT8tE2Z3AsnLe+ot17p81S8T86tXs2iXn/jxjv0shmp0hx25ZdI7rEMGODhGyfZ1HJ0yQ7UpKBiWwrMvHwYhPSY9NJyc+p9/76El0fDPN9SKoNmyRlc/503uHSzozabKc/NdvbGXfPgiNrWFyrusw7LPGnkWoEdoVluyWvpxT58dcCawe/dcsDMPgwbMe5Ncn/3pQ4XOGAckp5ewjn5ivSvlr+B1cdJHBguPlN2IUD+KCCHTYOqhqquKc8edQ31bPv9b/q9vjr71XBWlbuPnEi7ruu/LyMAzDJG3fnSRGJnrs4zrUUYHlRxaNXsTCUQv58fE/ZtOtmzhtjNQ0npI2hRtn3ci77T8mJMTsOw/LlYOVkNBVBcyirk4SwcsivpRyooZvv96xYyUczmUlnh/9SE4Wjz/e7e5T8k+hNO59oqJtAxNYLS3iXFxyCc/sf5OdVTv5svhL7lksVcG47TYp2Wtvbf7a1a8QaTTRvPkmbvp8EdsvGkHKvMX8/OcwJjWXVbd8TshvO3n4vPuZmTeWb0X9j1UHV/HLJb90/fqdnTLZmDmTNRvEajxzQS6nj5XqkYv3LGbhQilO4eyUeYtpSnhhZn4Vh7Nf4Jcn2ceRmioXLxf9vkaNkhyp+Hj7HT0F1sGDLleUFy2SyKXXXg3hp5efwRvb32D5geWYpsl33/8uf1/9d+5ZcA/PXH0/5C3l2ZdcXHzdCSx3SWgeBNZTb4krc9159ipN9pizH07MYtt7i8hKjZX3/+WXjuZxkydLDOhrrxH5+lu9f+N9Vb+ySE8nsbaF+TnzeearZ1i4UI4fj8U9+mDJ/iWEGqH89+v/JSI0gt8s/Y3jQfsEtDV9JD/6aStkre29UOGOyZO7f8YlJd7lX4EjjPDQIQkPTE+HefNcbnrBGUlACJ9zfPcVdXuJ9urmavavKiAqroXjj/dy7BYpKV0umLeMHWfSRiQbt4Xy+qsRGNNe4O6T7ZNra1bWQ2DNyJhBZbTEev7yl/Lzufse+Y28u+4l/nH4G4AfXKy9e2XC2VP0jhs3/ATWmjUSCtCzeaor+hJYo0f37YIBLXVxxCe7rlg2Ilmef9gYARde6LawlEcGEyLY0QGVlTyw/SxGjYLCrAe7yrO7YuHcJACWrzkMY8aIyPnvf2U26+oznTVLxJeL3FmP2Gxy7h85UkrRFxTAqlWs+kqE5PjI3S5DpXMyRHiUh2e6drC8zadyLtU+SIE1ciRsPPwFs7NmuxTZ/SU2oZ22Bgl/27FTFgoXTPfs+M+aJqXOP193mD17TGxJu926Q3fOv5O1N691K7K7sBysvq6P4DpE0I3AAlnA/9mJ3l5E3JM8qomtoWM4ueAmmtuT+NGPja6v3lYdPqieo1VNVZiYnDPuHI4feTwPrXyITpuIto4O2LQmgZjxa7pFW2Vnw0knGSTt+Taf37Cizz6uQxkVWH4kNiKW5d9czh9P+2OvJmm/OvlXRMQ2kzRmz8AEVn5+r5OntQBSG72eU0YHuKzl3Llw6qmiFlodF8pFoxdBaAejppR1FVHqF2++CTU1dFx7Db9f/nuOyT6G783/Hg+veljypwwDrrwSPv6Yw5vX8XzkxZwc+gHvvNbKp5e8zQ9Pvp38G3+BcenVZJz1FLd9r56f/DiEH/9Yrn3/+s1xXJX/ff702Z/4dJ+LL2LnTjnBzJrF7u2RRIwoZURyKKOTRjMhZQKL9ywmOlpyWAYisJYvl7lsy9z/Y2HeQocdbs8T8qr8opUTBXTNfF0kFGVliVY880y4a8FdZMdn84MPf8BdH9zFw6se5q5j7+LPp/+Zc8efy4jZSzm4N6F3j1tXAqumxr0lYF1AXFx43/74MEZ0NTeedoLcYSX17HEK2Vy5UkIoTjrJcd8998jv7fbbe7t83jhYIJ9veTnXTr+WTeWbGDFB3uhgwgSXHljKqP2/4Mn/G89tc2/nuU3Psb3SLgSKimDECH70UAmVJXFMvuI/nD3hTO92PHmyTISsz/7QIe+FirXdgQNShfP8891OeOfPNzBC2/k49KTuhS4KCyEvj6X7l8PO8zh+UZ3b1khuSUmRWZQ3rpudGVPknPmbzV+jozWC079+wJEP4UFgEdFEWlYLZWWStjd1VgQdoQbptihG5O+H8CZefd/HFtbevXJC6cm4cTIBHU6l2q26ySec0Pe2qamyuuQqRNASWH3Q3AydrdEkp7iuCBsZCUZ4Mx/OmiQxnx7otHV2a0PRhRUiOJDvobKSDeZ0lhzI59qbajjUVMhxuS6cKDvHTRwPMeWsXN8ov/cJE2ShsKDAtWgZaKELq8mwtbh2zDGwZg2fr6sihHbGxJW5fNrobHufqLB01w6Wh0WxbjiXah+kwJowsZOtFVsHXeDCIiGxg87meDo7oWhfFGGJpcTFep7uzp2cDuGNrNvYys49HZhJe9wKrIjQCGZkelHFMjFRviN3eSDW55aR0S8Hy5fkjoN9Rj7rCu/i+BPbmD9fzPfwsEbqzEQp8ztArPDA9Nh0vn/s99lXs4+3drwFwMefV9PZEsOpJ4f1CsO84grYtSOU9kNTBv7GhgAqsIJEdnw2dy+4m8MZr/DlSpv7/B1LYDlfGNyUaHeufLMoPwhxqz/6kayuP/ts111T0qaQHptOWN4q1q0bwGLIU0/ByJE8k1rM/pr9/PKkX/Ln0//McSOP41tvfYttFTIxbulo4dQXH6OmNZuTL7dh2GycHJLPH079A6tuWknD809S8r8b+ftfkvnd76QH6IsviknU+MafmZAygWtfv5aqpqrur28vcGHOmEHl/kwy8h0X7zPGnMGS/Uto7Wjl9NMlqsqaA3vL/fdDbGIzNRMe5pcn/dKxemcJLDdhgl20t8tJ2LrITpsm1lYfSiEmPIbfLvotKw+u5IGVD/Dd+d/lvjPuwzAk5vy2a2Vi/uizPdqqHzwootZyRqwJrrswQTcX65aOFvZsTCdnSiHREfYQh4QEed/OK/5Ll8rrLVzouC8sTH4XtbWSiObciNub3AEQJ6eigssLLic8JJyPDv+D7OyBF7poam9i1cFV1H9yC/feC3kHfkF0WDS/WvIr2aCoiMr00Tx0XzzR41ey7Lc/JyLUyzySng1LDx3yrsAFyIQnKkriUGtr3YYHgrTsyZ5UzIehCx2FLjo75fwzahQvfbAPGjP4xmXJbvfhlttuk0TFfrBwpkwq3uBiRkav4zdXX+h4cPt2yS/J6R5OZE16knMlnPfuu2HX4d3UhZscm1jAkhsXE5m3gZffLeGr0oFPHLrR2ioC2lXVj3Hj5Dc56D4VPmLPHikU4YmlS+U84q4pojOG4b4XlpcCy/po0lLdbxMe28j2uCRHzpcbHlvzGOn3pXPiUyfyzFfP0NRuD7tLTJQl84GUli0t5X6+T2xUB2NPlUU4Tw7WlLQpkL6ZHVvtiwnW8esqPBAkbDY5uf8CyynsGBBn+vBhtq9tIjNyN9EJvfOvAMblJgFQGpriuv+WtwJr5EhRvzt3ev+cHpim5GCNGFlBp9npM4GVmCTzpdpaqCxOIj7btdh0ZmxKPqRtZduWMIoLQyF5b7ceWAPCEpzu8rCsz23mTJk79ZwgWYXD/CiwJo2PhI5oqMvl5z9xXJNCY+slLNcqTT0AyhvLYf+JbP6kgAsnXUheYh73fykVwR59RcJFvn9F76ImX/uaXOYHVRxtCKACK4j84LgfkDhpPR3tIXz2mZuVtdxcuSgcPix/m6bbC5eVmJmR28zElIl+GbNHTjtNVuP+/OcutWcYBotGL6I06XU6Ovp5rB48CB9+SOe11/D7z//I7IxjiCo6h4NF4bx86cvEhMdw6X8vpb61nuveuI4NS0YTEmrj5mvtF2Gnhjcx4TG9Qg+saLM3Xw/jhoj3KG8s51tvf6t7c+j16yEigv0Z8XSWT2DiZMcqq5Vj93nR55whVfn58EPv3lpnpywUv/mmSegxT7Igfxan5p/q2CDVPtvoa2JWUiK/CWuiGRoq1Ra9sGKum3EdF0y8gB8f/2PuP/P+bp/P98+4DGPkSl54uUfYzsGDIk4sC2OAAuvlNR9iK5/MaSf1mAT0DKlaulR+Uz1XRgsKpB/aO+/Ab50KSnjrYKWnQ3k5qTGpnDP+HJ7+6ikmz64csIP1ZfGXtNUmUnkgk4gI+Nk9CVyX90te2vISm8o20X5gHz88fD5mQzrP/i2X1FgvJq8WVjn8bdvEzauo8N7BMgzZdv16EVqnneZx87nHtrCtfS4N6+3NjUtKZGI6ahSffhCPEdrB+ecOIGTj4oulOEE/OG7KGIiQEKYftP6D+ZFOi0pW45weblxqTCrZ8dkkzVrCJZfYTfUv76c+EmbE5JOfnM8tl0yms6SARY9fxMayHk2VB8KBA3IMunOwYOiECd5/v7Q6cHaJnWlvl1UGKyTXG1z1wmpokHOXFwKrrFxChjLS3bubUXHNXUULPLG2ZC1xEXGUNpRy3RvXkf2XbG7/3+3UWmkqAwgTLN1WzQtcyTfPLWdj7TKiw6I95t7ERsSSMLKY0n0psi5qCSx31WwMQybY/RVYVmSLs4MFlOyOIz9yJ6Fx8S6fNj5XcpGKIzNd/w68DREMCZHft7OD5c2514mSEjHnjVSpWjfYCoIWKSPsYaWHTZrKcsgc1Xf+XWZcJiHpO9jzVQYd7SGQtG9QBSQAx2fo7nfnLLCgd5O+sjIRsf38XPvD8dPlWjJ+ShNnOgVVhMU3UxY2YvACa9nP+M13JvPSC2F8Z/53WF64nLWH1rJkKURm7GPRtMly3nEK5UpNhdNPF4E1nMz/nqjACiLxkfH8v2tPhZB2nnzVzQWvZy+sigpJhnXjYBlRdZw8eYZP4pj7jWFI4sOuXTLZGDMGbrqJG3fE0h77DkD/wgT/8x+w2Xh0VD57/3sjB361lNNOMzj7bEiLyuH5rz3PtoptTHt0Gi9veZm0wptZdHIII6bZxYa7Gu1OWNFm9/08nx/P+sv/b++8w6Mqsz/+uem9hxRISCCB0EOTIqBYKHYsa1vb2lh7Wcuuqz91LWtfy9p1ddfeRRBEVFCk994CSQik917v7493LplMZiaTZJJJwvk8D8+QmZvJO3Pb+33POd/DN3u+Yd0Rs4ZIW7bAyJEs2ZwBjd5MGtcsCGYmzsTTzZOlaUsZNUotMtlLE1y+Po9nn2vknHPU4vDMmeDjX0fZ6H+2jF6B4xEsy5ssqGjPjh1tNjh0d3Pn20u+5cnTnmx1vIT5hjHxlCwK0hLYutfsfczTEY2/6+fXboH15gIVPbj8DIvjePDg5pu+4QBjnh5ozs03qyamDz+shBa0L0WwuhoqK3ny1CcJ8Qnhl6bHycyEzMz2X9FXpK9Ay1Bpuf/9r3pu82t3EOgZwv0/3U9WWj4fF97JjFnFXDCrnUXckZGq99vu3c0rmu2oZTq27emnq31lh7NnBdOke7KherQSBaaJc0V0GLmbJpIwOtOy9LPLCPcLwyPyEJpHJVc2fdiyvtPCQdCcMVFjqE59ni+/hMLqAv6z5T94BIXgW6MWR86ZFQq6O25Z0zj1v6dytPyo1fdxGON4tSawkk0r4D1FYBnN3pYssf76hg3q/mJxzi3ev5hrv72WJt2K8U18fOsIlnkPrDY4eERNfgdE+9jcxi+ojppy+y5wAGnFaYyNHsveW/ay/KrlnD30bN7a9BafZH6vNuiAwHr10zAa8OC2+fWsylrFhNgJbdaFJA6tpKHGV50+RpHuTDtZJWPHqhqsButpklaxaP3AiBHUewdQUNKfoV6HrDoIAoQG+IN3GZme4UrhWJp/tCcaZVi1l5So6667u81NH3lE9ac0nzAbt43ywHWE+4a3skTvKJFhSqyv31mAXhlB4uC2zRrcNDfC4nNorFf71qdfttW2Ae2irQiW8byRJmo5ZzF6YHXhfG7qhEC8vOCf//Br8Wd8Amo56hWmev90kLzKPChX959rroGk0hsI8Arg1u/voHTfKMZONtUAfvedat2wYsWx3731VvWv3o7/WE9HBJaLuXXaNfgM3MaPv9jInbMUWHYs2vceqEUPPshEV7qunHuumgi+/LJq0vTFF5z+93f59rtCogaWtEtgbfshm+m+67l1/vXw+31MGu/Dgw+qi/LLL8Npg07jkZMfIaM0g8ui/kF+Zjjz5qEsTb28Wq8GWcHINispgZ3/vQF3zZ1v937bvMHWrTBmDL+uVxHE2VOaJ8cBXgFMjZvK0rSlaJrq9fjjjy0z1gw+WHSQmZPCuecv7vy07ghDZ2zl0ZcPEH7vVCYNiz/WmPoYnRVYut4J20bFgzeq/hoPvWp2gbUUWG5uKpLQDoFVVlvGmtVuaG5NTJ5kcUNOSlKTtdpaNRmsqbEtsDRNpTuNHas6JO/cqX7P0QgWQF4ewyKHsenGTcyYri6HFz73PCU1JW2/hxkrMlYQnnsRgYEqveGVV2D1755MyviM5Tu/57Xqv1DbGMi/n+uAOtG0ZifB9jQZNjC2tZMeaHDBrBigiV+ZoeqwTAJrQUEt5I3ijLM75yrVXlLO/p6IC54gaNZkZYNeW6sEQEaGTYGVGp3K7oLd1DbU8tr616hpqCG0X/yxgv7Jk9V5f7bP0xRUFbB4/+LODdJIHbBIEVy8GH48kKjOkZ7QbLim5ljKM4ubP/NH2z9iTdYa9YNRfzVjxrHXG5sauX3J7by75V0+3v5x6/eNi1MTdfOZkDWLdhvs2KfqgAYNsC4IAAKDGmmoCrDv9gocKDrA4LDBaJrGSQkn8b95/2Nq3FTSmkyZAO10EqyuhteWJXMWC2kYnMem7E2cGNe2w0vqKDXB37ytToVRi4vtpzeOHav2T1s9Bc05fFjd54xsB09P9qWcQaPuyWg7AgvAw7+Eo4SoH4xmwQbGNduR6+iQIWrxoLDQbv3V6tXN/Sn/z8xPyvi4GZ5LGR873mkLw9GRKtXt+xWq1nL4UMei7nGDm00/Bg/SOj8eR1MEx5giopZ1WBZNhruCuDg1vPPPb/m8X2Adee7B6r7TwR40uRW5UBHDJZfoDBkCV1wcwDnBD7B6fRXUBvPHs02LA8Z97bPPjv3u3Lkqxbu9HRl6EiKwXIynuydJKTWUHY21vjpoKbDsWLTvPVAPoQcZGzO2S8bqMCkpcMst8PXXUFCAfvnljMzXCBi0jVWrHA/5PrrxDLbVD4HT7uXt5T+waJHGo4/CGWeo1bDsbHhgxgOsv349wwpVfcd556EmNAMHOhTBApVt9uCD8NXn3gwr+GuzwCouVuYNw4axbXsTaE1MGNPypjVr8Cw252wmrzKPWbNUVowxhzHnmacBnxIufOtuBj54OuvGp/JQYTJZTZt46KSHWl/IQ0LUamBbKYKWphOgcvHd3Tvd2OmsKUPxH5DGD4sCjjn/tBJYYN+q3biBBDanq3yz5xsaM04geXhVa0fKpCR1gBw61Fx/Za/Y3tcXvvpKzZjPOks952gEC44J2BCfEJbe/SzefnVsWOPL+DfHO5w+VtNQw5qsNTQenM6MGWooV1yhhNaK/5zGxKxbeZlbufzEdEZ2tCfksGFq4SLbVBPXHoFl7C/j+7FDSIhGQFwaK7TpKmXJtEjx3ioV+br5cuesMDvKD09fycY35qPddZeK3n32WfOE0E4Eq6Gpgc05m3ll/SuckXwGfqH9jgksf3/VsmD/5mhCfEKaxUVHSUtTx6FZrxpdV60ULr/ak5q45J4RwdqyRYmghATV4b6mhoq6Cq759hr+8PkfqK6vVufciBHN5weqKfv+ov0Eewfz0PKHVIsMc+Lj1arSUbNIoJ37lCXfL/CF0DRSh1uzp1WEhOpQHUphdaHNbarqqzhafpSk0JbNkQOKprOjzJR+2M4I1kcfQUGlH3d4v8bFS64l1CeU2yff3ubvnTRRiZ5f1poWyHxsR+eAjhldGE2GzdJkV4erxdVUtzS7Ass7qJycBtN10mhEZdCeCFZysjqmtm61KbB0He65R50eV12lMrofeaT5TwcE6Oyt+4Vx0c5JDwSIiVTRzvXr1c8TRjlWGzZsuGkO5tbA8MHWUyzbhSMpgm5u6nv09naJwAJ1+bIkIKieYj1YndvWJjUOkF1SBNXhDBum8f33yoH658f/AtsvB+C8OaYFx0LTef3VV53rldLDEIHVAxie4glV4WxMs+IiEx2tJsttRLB0HbIPe0PoIcZGu1hgmePujjZiBJGVOhUhCykocHCu0dTElvLBDAn9idEX/MA10+cce+lf/1KLffffr8L6E2In8O03bpxwgtncvx0CC9R7paZCxof3sysrU/UwMiZyQ4eSuT8Q/6icVhciI/K07OCyY+UtlmmC+/bBtt8S8D/xv3x27bPsunkXOXfn8MkFn/D6ma8zN2lu6wG5uak8QkciWL6+LS37/f1V36iOOjaYccY5tdSmTeSDVT+qL72w0LrAysiw3seltFSJK7PUkY+2fYp2dDKnzbAyATCiAAcOqMne6NEqPc4eCQkqWduoA3HU5AJauB96empMn+pFUsVVVNZVctvi29p+H2Bt1lpqi8Mpzup3LAvICK6Fh2v8+r8XacCDR27qhHPd8OFKbBuOTo6aXADcdpuyiY52LN0leXwOq5lCzfqt6jsNDWXNqjj8+h9k2JCO91zpCLGBscQFx6nw8LBhqobIsLa0JbBMRhf3/ngveZV53D3lbnUMmllST58O69drTOw3gzVHOimwDAdBs0WS7duVFs7Ph4/9r+sZAmvtWvX4wAMqNPPbb/x08CfqGus4XHaYf/32jLpmmEWMdV3niZVPkBKRwgfnf8DB4oO8s+mdlu9rzao9PV2JijYmh3l5sHlVGIz8hITQgTa3Cw91g5oQ6w6BJg4Wq0hiUlizwNq4EZbc/3+sWWXq+dcOgWW00BgdksGokFVsz9vOe+e951Da2OSkFAjMYuNWB001hg5VKzOtbFvtYDQZNuN3TX2HY4/8Zldg+QVVU1wTqO4znRFYhpPgzp02Bda336rD6tFHVRvDq69W0ax//MNk0T6oika9wSkNhg3iopRYT98ZBTQxPdWxBamRyYHgUQVBmaREWTGtaS+OpAgaqZXW5izdJLCsERjURHW96RjoYJpgVraKOMfEqHWYRYugoswDVt9FfGJd81TCWEjOyXHKvKWnIAKrBzDZ1ABv2QYrKW3u7uroNBdYERGtmlHl5kJ9rSdhsWWE+nZTkYSjmGoTArwWAY7VYZXuzSFNH4xb6HoemvFQi35Hyclw112q1mXVKnWf2bBB1dEfIyHBoRRBA09PeOklKC/yh93z+HbPt8duPFWJcVRkJRCX3PrmPDZ6LOG+4SxNW0p0tIr0WwqsF14A3BqYfuG2Y5GqqIAoLh55MTdOuNF2GoKdZsPHMHpgWb7HiSc2W5x3gvuuGwK48eQ7O5tXqK0JLF1vnWoC6mZtdqPOr8znx9XZ6LUBTJ1q5XMbpgC7d7ea7NnltNPgySfV/81W321iIwVz2jRI2+PPOQOvYGP2RutRZQtWZKyAQ6r+yrzMIiJCpZ/qusaNvMGgSQ6MyxaG0cVPP6lrgiOf0SAhAS680OHNZ0zTqNX92LCxETIyKIgdRvm+sYw92YpTXHehaXDHHWqF/623mld9rZAcloyvhy+/Zf5GanSqan1gIbBmzFCnRv/SeezM20lZbSea0FqxaP/wK5VS7BaWwT+PnE/T/h4isPr3h8suU6vlixezaP8ifPdexXT3O/nh8yeVOYXZObdo/yK25W7j/hPv58zkM5kWP41Hf3202Z0PbAushIQ2a0c++ayRpkY3Js05xICgATa3iwz3hJoQcitsXw+Nxt6Dw9TEuLhYHfaN9R5UlZuuK+1IEVy2TOmG68Lf4YBnGbedcBtnJJ/h0O8mhSWhRe3mwB4HFyQ8PNSCWnvcJs37H5pYX96PARwmSC+zK7ACQ+uoqQhQC7XWBJaXV9tRN2gWWE1NVgVZfb0yFx42TNXguLmpHo5XXgkPPaQCqYGx6r7iLAdBgPgoFX2qLY5AC8kiJsSxOdHgiASI3gqRuztvcAGORbCMbRITW0awmprU/clFAis4REevC6YhKrrDRhdHj6p0JWM9MDUVvvxSHe5zTjfL/SssVBv5+MAXX3Ry5D0HEVg9gFMmqBvUmq020h/Me2EdOmQ17cI4L4cm9cCEVVO0bRi78Q2oc0hgbVumVvuDkrOYN2xeq9f//neVJXXbbSqqDBYCa+BApTrb4Qs/bZr6agP23ajSBPfuBXd3NnhUQlEyo0e2Pl3c3dw5bdBpLE1biq7rzJqlMvOMlOWCAnjvPR1G/5eTR1hfcbdJRIRjKYKWgsf4MDU1LfsZdYBxqR6Ex5aw97cR/LDC1IXdmsAC62mCFgLr812f05Spmm1adS0OD1crep98ovadowILVB7KmjVtOuUBViNYoL62piYIzDuNiroK9he2XTuzPH05YbkXEBranEpvMGcObL/pNV7gzlaToXZhCKw1a5qj2l3EH85Qd8PV5WNh9Wq+4FRo8uDSC2xP2LqFK65Qx8cvv6hrio0JoLubOyP7qVzMv0z5i1rAsBBYRru4powT0dFbGtu0B11XAsus/iq9JJ0XP9yLe/QO4s9+n32lg/ilZCwZB9vpEmfi999VgMOao3a7WLtWNbr184OTTkJfvJiFexdT99Xr5H/yJJMPmBZjTOecrus8/tvjJIQkcNmoy9A0jSdPfZKcihxeWvtS8/saAsvcSdBBi/bX3i2Gftv56/ln290uJsIHcCMzt8TmNmlFymxkcOhgmprUJP7IEUgaUUZduSk61o4I1ksvQURkI9MrnqU6PIinTn/K4d/1dPckYmA2+RmRjmc8hYc3p0q1hXmTYTMO5kaQ7GYSTHYEVmhYI42VoehDh7YWWI62ugB1HTXSv61EsN5+W627PfZEPWd/Opdbv78Vd3cVyfrjH5WnR2P4TkJ9QkkISXDsbzpAbFgouCsHXP+oow7XUiWGJMJFf4BzrnOOwPLzU2rCXg2W8b0lJLQUWIWFKl3OwcwDZxMaooHuTv6oiR0WWAV5qvbNPOFi1iw1LfnnP803LFAhrrlzlQKzVsjeCxGB1QMYNsQbtEZ277VxJTYXWOnpVg0udu1TK4oThreRTuUKTOMdVeVLWPIehwTWr78ot7RZ56S0iF4ZBATAM8+oyPWDD6r551BzZ3rj5t6OKJamqcXdyj1TWLl7P7W7t8OgQfywKQd0d6ZPtL4KNnvwbLIrstmWu43TT1erdoYZzmuvQU2NBlOeZ/KAyQ6PBWhfBMsSYwb5n/90yoZH0+CGq4MgbQ5/fcEdHVoLrORktWEbAqukpoSnfn+KkPwziY7Wrc+/NE1FsQxhaFZs79BgJ01yrJmtv79KrbQQWJMmKe1SfiAVgE3Z9gVqbUMtq7NW05A2nZNOsq57RtZtwrNfmIoadJS4OHXQNzS0r/6qA0xOScQveJ8yuigq4ouSqRCQw1VnuLjpo68vzJ+v/m8jPdDgpIEnkRSWxB9G/EE9YQgsUwFoWJhq9XR4ewJAx+uw8vLUaoopgpVRksFJb5xN7cEJ/PGCCHa8+gAhAWW8wJ1c+fQUnvn9GRqa2uEUB3z4oZqk7tzZsSECagJz8KA6wAHmzkXbswdtWwCNtT7s2enNqKx57I6ALbqq81uevpw1WWu4d+q9x1zzpsVP44zkM3jq96corjYpvsBANUm0FsGyQ0YG7NkcQcjExZw1xH59YP8oVQN4OK/c5jYHig4Q5htGqG8oTz+tjEWffx5mz61Hr4ilDk+HBVZeHixerBMw6QtiK6pJHTMHHw8HIjpmDB5aQ1O9t9X+sVZpj8CybDIM1NTXUp0Tz4Bw01zBjsCKiNCgNoiKRJPNuvmEtrTUcVtwTWuOYlkIrPJylQo4Ywb87HkXSw4s4ZX1r7Bg7wLc3eG999T9sW7Ma4yLGedU5+NwvzDwUcdnRJzjojohJAGCsyAwp/M9sEB9PyEh9lMEzSNYhYXNC0Hd0GTYHuFh6maWNWikurdba0ptB13XKclXNRWWGe2jRrWsaqCwUB3/F16oMmU6adDVUxCB1QPw8oKAfgVkHbJhoTxggLp5NTWpu5IVgbV6u5oozhzb+jWXExEB/v5MbYihMnoZO3e2fZ9buaWBfuRy+YVX2Nzm0ktVLUV5eWsHnI4ILIDLLwe9yQ19x4VU7tgMQ4eyZpO6sMw8wXpa1pwkVR+25MCSY468S5eqANIrr0DSpL24Re1lQnvdHdsSWEZhubUIVnS0ysl4803lQ7+ugyv0wMP/58b5F1eyefsjzOd1qiP6tdzA11d933YElq7r3LjwRo6WHyUg9zSmTNFsZw8Z0YCRI5sdsroCU7NhcwICVBrD/i2ReLt7tymw1h1ZR01+FGW54Zxyio2NrNRKtBtNaxYV7am/6gBumhuhI/ewkmnU4sWqvClEjV1HgLd9i/du4aab1AVz1Ci7m/3ztH+y/c/bm+20AwPVarBZo9np02H9Wk9SQkd2XGCZWbQfLj3MzPdnUrBjNDR5cvWF0fj7uXP7lbUs4iymVszl3mX3ctmXlx1LPf3pp7Z75xkpx+28lLXEOP/NBBZAynZlLODhobPowMWsHezNXT/cdSx6FR0QzTVjr2nxVo+f8jglNSU8s+qZ5ifj4yk/sIsnfnuCtIwtasLUhsB64S0l5OZfFYK7m/2I7IBIJRYyc2wLrLTiNJLCkli+XJWZXXyx6uQwckgg4MZBr0EOC6xPP4XGRo3M/o8SWaURltDO7ANg/BglyNZuctCBrT0Cy9KiHfhl216oDSJhiCkSaUdgRZt6jqVHJKhMAeP9oFXWQZvYEFjPPquE6unzl/LvDa9w2wm3MSZqDPMXzqe4uhh3d/jT9XXsqfnFqemBoCKIbn5qX8cn1raxdTMRfhH4e/oT4RfhvFKL4GDHUwShuQ7LxQIrMkxdOzNjB6uFqXb2aauoq6C+NBxN048ljNiksFDd6886Sy1Efv55B0fdsxCB1UOITaikOrc/RdVFrV8cMECtkhqNRq0IrB17KyEgm8kJthsgugxNg0GDGF7hS0nkInS9ud7aGo1NjezP7k+K13Ziw2xPTDVNOTePGKGyh1ow0JQS0g6jC1BeAmPG6Hjtuhr/9KMwdCh7dnmgudczZIh1RRATGMOYqDEsPrAYX1+1Yrd0qWrjlZcHQTPfZnTUaPy92pliFRGhGkzbyjGxsorZgnffVU6OhYXKm/q221qkSTmKlxd88bE/80d9ypvcyOjzClq7ttpyEjTdQN7f+j6f7fyMe0Y/S1aGj/X0QAOjDqs96YEdwdRs2BJVvubGyPCxbMy2X9y7ImMFpLeuv2qBMwQWNKcJdnEEC2DklApKCOVVbqK6PpgZs9rnvtZlxMaq6OZf/2p3M3c395YRByONycLooqICkmovZk3WmpYNxh3FZNGeGxXAye+fTGF1IbN5gcDA5hTYP98biBe1lO37G0+f9jSf7/qcO5fcyfff68yerRZ1bLU/Sktr1nB70xxPd27F2rWqAGa8aSI7ZAhHw72JODIFHx+Yf34eXzSci8/JN/JL+i/8/ee/89Ohn7h7yt2tIjep0alcOvJSXlz7InsL9vLmxjf5nUwObP2FB35+gHe/MflwtyGw/vdRA1rcWu45q+36wPBwJcB2Z+XY3OZA0QFiGc8ll6ig+ltvqXtE8iCVNr/Xe5DDNVjv/68Rt5htXDIyCk3XO5SmNfMENTFevs7Buqr2CCwjWmh27V+6Vj03erpprHYEVv8oFU0/YBh2mKcJOkFgZWcrgTXn3DL+eeh8Tow7kWdnPcu7575LXmUed/5wJwA78nZQ31TvVIMLA09/tTCaMsTxaa6maQwKHeSc9EADexEsyxRBaE4TdLHA6heuzptD4aYF3HamCeZV5kFFNIGhNW0nlRQUNJcHzJ7dZ9IERWD1EFKGuEPhELbmWLGGNi6ihu22lRtXRrqGZ3gWMYFdu7rdYRITicmvgf7r0Nya7KYJfr9nGZk1wxkVlWV7IxOjRqmeui3SA0FNxDw82i2wAC67TKMuYzxH6gdSO2gguQcjCIvPw9NOK405SXP4/fDvlNWWMWuW0sKPPAKpqTr7At9gyoAp7R4HkZFq5ajIiugG6z2wLDnvPNXH4uabVTht+PBmJ7p2oGnw2rCveCr4Tg6sHcL4qWUttUlKirpJW14US0sp8da55ftbODnhZCY03gLYqL8y6C6BZSNCOG2aWtSNrz6HTdmb7E68V2SsICT7fCIjldC3irME1nBTil43CKzZM1W06jH+jpt7FVec1/V/02FGjHA8hcnAisCaNUtdIuq2n01hdeExk4R2kZaGrmlcsflB8irz+OHypWz8tR+nntrcvyVqoA+X+S/gvU2juXbYPdw5+U5e+noV511QT3CwOgR/+8362x8zzHGrZ8seG9cBR1i7Vn1vJnOkvKp8vk2sJadgFGNGN3FrwkLq8WK//gjDIobxxMonCPUJZf6E+Vbf7tGZj1LXWEfKv1O4ceGNZAVppFT7ce7Qczm0+We1kR2BtXpzCUWH4pg0+yBhvm2ntRtz0H1HrDtx1jXWkVGaQe5Pl1JYqOZnxi431tr2ucc7FMHauxc2rnenadT7/HXIterJDkxyJyYOg5BDbNnuoNGQIbAcEfpWIlgbtqlVrymXTlb72aIvmznx0er83ucRop7ojMAyzGbMBNbHHytT2f2pF+Pn6cenF36Kp7sn42LGcf+0+3l/6/ss3r+YjUfVAta4GOdZtBt4B6qyibEj2me3/u8z/s0Ls19w3kDsRbAsUwShx0SwYiJUet/RJh+VJdNOJ0ElsGKI6NdGiUJNjQogGNkqF12kjm97q/C9BBFYPYRJY0KhLpCVu6wU1RsTaOMubCWCVZgdTET/9uXIdiuJifgcziY0yIPQ+Cy7AuuVRT9RjzdTR3RgRdnA3V0VTXYgr+bSS9XjR1zGAvbSmDuMwUPtrx7PTZpLQ1MDPx/6mVmmnsFZWXDJ9UeoqC9vf/0VtN1s2FoPLGsEBanOzMaXPnduh4QnR45wd+pGYq+/hX27Pbn2BrPUi5QUdaE0L3QH9NJSvs1ejreHN/+b9z/WrHHH01O5yNvkjDNUE6G5VuzrnYmdCBaAR9bJlNaWcqjEehFFXWMdKzN+pz5tGjNn2jBMW7hQTeotXOY6RDdGsGZPGEqAz2GKCMczcRmnDOnA8duTsCKwwsLUYum2n4dBk9axNMGDB6nsF8KPR3/j+VnPE1wxiczM1ofuHSN+pKrRm7ffhvmDnsX702XU+2TxwP++wc/PdkbMp98WQcgh6Ledgxkd7A+j6ypF0EgPBBbvX8yiJI0teirjoo8yZNc3nO63kjc/CuHpU54H4PZJtxPgZb03VVJYEs/Pep5bJt7CuuvW8Ye5f8G3tIqrk/9AeJ7pPmTlPmXw4Eu7QGvk8dscy7gw6jUKixoprGod5ckoyaBJb+Lo1uFMndpysSMuDtCaSNMHOCSw3v9fA2hNnHjGYUbqpklfBya5cUFxeETv5eBeB1Nrw8NVhoojTV0tmwwD+/a64e5bQewoU+rzGbYdDxP7qwWKQ7Ue6twwF1jtMbmA5nRds9TlPXt0vAPLOaj9wMcXfEz/oOZ71IMzHmRE5Aiu/+56fkn/hWDvYAaHOsES3QL/wFrQGpk8qq38tJZMHzidE/qf4LyB2Ipg6XrL79pUSnEsgpWTo/axnQbOXUn/fuq4LShqUGUGHYlglccQHdPGPM6I2oaHq8ezzzalzfR+N0ERWD2E8SPVBW/VVivpBJYCy1iSM1FeXUN9UbRT5nBdRmIiWmUlZ4VNpqH/b6xZYz3zrbi6mI3L1Qk3brxj3ddt0lYvrF9/tWpjHhcH0wdn8SGX80jmYigdyIRU+wXOU+OmEugVyOL9ixk5UmWUDBgAIePVEnSHBJZx87TlJOhIBMucyZNhyRIVnpkzx/F0FIMjR3AfEM93D18PI77g59/MrJqNyb95c+PaWrTaWvY25PL22W9TVzCA119XJn92HYCjo5X9VKsuxE7GiGBZrBjHxqq54eFNKmJkrLJasuHoBmry+lNZGGI9PXDZMlW0O2EC/OlPnR/v1KmqQMxu+M85DI0YQm2yKjTuf+KO9qe39jSsCCyASy6BnCNe+OaealVg6brOGR+ewU2Lbmputm1G3f49bPUr46SBJ3HtuGtZskQ9P3t2y+3GjHVjpsdvvPQSzJ3jRqBXEJPuf5j7Nl7EhJNyrPbXrK+H33/1wWvIctxCD5OT1UGH2AMHlAWhmcBauH8h2/tPooxgxlX9Dr/+ys0nbiUrC+r3zGHVn1bxt+l/M30H8Le/qcvpvfc2t2q6ddKtvHzGy0zsPxEtXjWgPt0rhcGl7tR5e9hsJdDQ2MiKhbGEDNvMKaMcM045VhBfE8KWnC2tP2LRAagMJ3NPWCsTUW9vCAgr51DTAPQ2UgR1Hd56rwoSf+Khs65Tk1zokMDSNI2oxAKKs/o55jNkTDAduS5bNBluaGqgICOCyPgCtdDj42PXIj9pQAgA2fkNKv1j716a9CZ+OfQLemlJ+yLEo0crAW920K/eVkBt8A4enfkopw46tcXm3h7e/Ofc/5Bdkc3HOz52usGFwYDU/TD8c4ZE2e6v1i3YElgVFSrjwxBQmtbSSTA3Vy0CdsF34wgDTL3Eiot1lVq8d2+72hzkVuZCRQwD+reRH2gc78Z8JzhYpRd88YVj0dwejAisHoKRxrxrj5V0gpgYdZJlZjb3CjDjp837QHdn9FAndB7vKkzqb477UMr6Laa83Lor1sc7PsY7fSR+VJI8pZMGBwkJtgXW3r0qBe2NN6y+/Me439jDMHZuV+EoWwYXBp7unpw26DSWpC0BdD74QBVKb8hdTahPaMccidqKYGVlqRynNitIzRgxQnV+TE9XK0XWmgNbQ9ePGWqMixnH5PE+VBWFkn7E9PuTJqkb7Z13qgR8YM2OHwAYNeREzkqax2WXqfnAa685PtwupV8/qK21Wpd23XWwZkUw7odm2zS6WJq2FA6pyUMrg4uVK+Hcc9WJ/cMP7U9ps0ZkpCo0NsRsF+Lu5k7i5D3gVc6ZZ/bumxxgU2Cdc466nIan3czqrNbOVT8f+pnFBxbz2obXuO6761r1RavYs40DoTpvnv0mbpobixer3TPQck6XlMSdDU9z5Iiasy9cqPHD7S8zPHI4G0P/Rm5uy7UJgG+WHaWh2o+5sz0IjS6jJDfY5nxD13XuWHIHL619qbUQNFJtTlCr8nWNdSxNW8pg7WoAxq96GcrKOPOPocTFqbrWKXFTjhmEPPywajEXGqp6+g0frtZq3njDLCBkSlXzzy1iYn0kGSEato6af321koaCBC65xMYGVggIAD8/HYqSrAqstOI0SJ+JrmtWuzRExFZxpDGexmL7aZa/r2qi4EgQ8TN+5fRBpzenaXXQKnvIsDr0Rk/27XPgHGqPwLJIO95bsJem/CEkDXXMoXJAtEr/ys9vQh86hKodm0l9PZVT3ztFidD2RLAAJk48JvYAMg56ooWncf+0+61v3n8if5nyF6Br0gMBzrwoj5E3P46fp4vNeWylCBqiy/y7Nu+F5cImwwD9wtQ8s7gEtUgI7TK6yCnLh4ooEga04Z5rGcECtTCZmQnr1zs+4B6ICKweQnw8uHs2kHXIn/pGi+UuL6/mE81K2sWKrSota9roNlLFXIlp3FPqoyFOpapZa9j93pb38CycxGi24T40qXN/MyFBTfZrrbgIGelyy5ZZ/dUL9C/w1Org1wcBGJ/atsX2nKQ5ZJZmsrtgN6eeqgINa46sYfKAyR1boXMkRTAmpv09kWbMUN7Pa9aofEhbFfbmFBSYOrOqY+yCmWrfvLPYlDbg5aUS7ysr4eqroamJD1b+G4DzJ13DI4+oed6bb1qZfLoK4/u1kiZ4111qTcDzh1fZkGW9Zu3zXZ8TmfcHYmMtet6uX6/Sc+LilEVcWA9sneAAs88rhL9Ec8ZY5xegdzs2BFZQEJx5JhRvOI2tR3dSWdcyPevldS8T4RfBA9Mf4L0t73H9guuPiayFmz8jrLiGgWNnMiR8CJWVqj2D1czWpCTOZBF3XZbDggVqPSLYJ5gv//AletIi3Lxq+PzzluLt2Q+2gdbIY9eeQuyAehprfW2WY+ZW5vLi2he5fcntTH5ncksRsnatSj0y5c2tzFxJWW0ZIcWn4OneyIgq5TDoccoM5s9XzoaGX81TT8GjjypD0k2b1JrOc8+p03z+fLV+8PHHoMepCBaZmQwt92J/YD3b87ZbHetL7+SDey2P3pRq/cNYQdNg9mwNt33z2Jzd+nw8UHQAj/Q5BAbqTJzY+vfj4hrJaxhIU6n9ZmKPvZIOHlU8PH+Mumbn5qpeRh2Mpk8aqyK/v613oIlZeyNYZpkLv+/fARWxTBjt2Di9vEDzLudAVgmvlv2MX3YBbtU1zIk+ETcdqvw6nj1SVQXl+SEEx+bh4WY7evHIzEe4ftz1XDHatlNwZ/j7jL+z4fqO9W9yKiEhKlpleZ81RJelwEpPVwuaLhZYHh7qGCkvNTPHaUeaYGZ2FegexLUVwTIydMwF1jnngKdnr3cTFIHVQ3B3h6i4SpoKBrG3cG/rDYyLqRWBtWmXunhPGeWahnQOYSp4HljUSFB0Ef4RRXz/fctNdubtZP2R9RQVjyJV2+ZQo0q7GDN5i7ogoNm2eMUKq7mK4Yc2MCtmG1TE4uFT45AoMLdrByirLWNn3s6OpQdC8wXHXopgR5vXXnCB6qS5YIHKk2or9G9R7/XHWSrvfsEKs+92+HC1xL10KRVPP86G3T8BsProMJ54QmXJ/eEPHRtul2BE/qwIWB8f+Ne/oCZnEKu+GN/K6GJX/i525e2iZv9kTjnFLItj+3aVKhMRocS7C2+QneX84fOYmDCCafHTXD2UzmNDYIE6/CtLAmhKn8a2bT+qUFJTE4eKD7Fg7wJuHH8jj53yGA/NeIh3t7zLjd/dSElNCc99rAxbps28CoDly9UaxJw5Vv5+cjJu6Dx39nJONcuYSgpL4tV5T9M0eBH//aTymEdMcXUxG34LJSL5ECMH9mdwoprwph2yvhiyK38XoOqmMkszmfDmBO798V6q6quUwJow4dhCzKJ9i/By96LkUCKjhtbhRb0yRBgwgOuuU/Oa115TZZv336++n7feUgGKqCi1+LBtm2pVk5Cgegee8eeBHCIRDh8mLLecjBD4Zs83rcb57Y6lHP79REZMzSQy3IF+dWbMmwdNZdGsWtM63y6tOA239NM5+WTNqmPZ4EGeFNbHo5farm+qrdVZ9l04/qOXccXE89STOTmdOodnTowBrZGVGx0wKHFUYDU1qeuxuUX7BpU1MGO8/UwLc7wCy8gvaGJvuLq2bZz5Cc+c8HcAVpV1vOma4XoZO9B+LZmPhw9vnv0mY6K7xvnY3c0db49O9B50FoaAsrzHGgLLvMYqMVFtV1zscoEF4O5bTkWZKUsmLq5dRheHj6rztM3gr2WKIKhw+Wmn9fo0QRFYPYihQzSTk6CVFXM7Amt/WiOaewNxca7J1XWIgACIjMQtPYOTEmbgOfprfvihpUHee1vew71sMOV1gaRGZDnWMNYehkCzlia4dq2aSZSWtg57V1VBZiZXnKgMMoYN182zH2wSHxzP8MjhxwTW+iPr0dE7LrC8vdUSu70UwY4KLIBbblHL0d98oyZg9twFLQRWVKQHARFF7NzuqSZxBjfcAOedh++DjzDtYCNFhPLHf40nKQlefLHjQ+0SDIFlJYIFqiXH8KkZVP14Dxv3H2nx2he7voD8EZQX+7Wsv3roITWR/emnzu2bHsDJCSez7vp1No0OehV2BNaZZ4J/QBNDf7mEsTNNzfXGjePnV+7GDe2Yk97DJz/MA9Mf4O3NbzPm9TEEZqnz0iNJ5XcvWaKCHdOnW/n7RoHs/tYmRleOuZKps49SXhjIW9+oie2/lr9PU9YEzj9Lffcjk1WK6aY91hdbDIF134n3sfvm3VyTeg3PrHqGsS8Oo3HzJqrGNfcNW7h/IScPnMmWzR6Mn+qjFkbOUo1++/VTJl5vvKG6Opx7Lvz3v62D5Jqm0gRXrVLrNCtXuTGCnTz71SCaistoShjYSmDVNNTwp/u3Q0UMzz3U/jD2WWeBm3sj6WvGUl3f0nRo1/4q6vLjW4hXc0YmB9Cke5JfE2YzYv/M+ztorAzmqivcmyMvubkdTg8EmBA/EsIOsHW7fctpXdd5YIsyF2lTYOXnKyVvdn3ZvF31dxs5wvFshkH9g4gsnEdy9Dp2k4LbvgOM8FGibWHubzQ16RQUqLl+ezhgMuMcnNT7bbadgiGgLOuwrKUIGnOWgwfVfakTx54z8PCvpLLcVPs5aFDLfmltYKoUaLtto7UIFigH5PR05YLcSxGB1YMYO9wfipPYkm3Hqt0iqtPQ1EBulh9B/Uo6rUe6HFN+8UkDT6Ik6d/U1ys7XYD6xnr+t+1/jHdTtrhjBzteTGkTW82Gq6rUEqzRPOuXX1q+brpDnH2WTlAQTJ3o6/CfnDN4DisyVlBZV3msaL5TjkSRkdYjWLquRE9bDoJtcddd6vNXVqoZ09tvW18xsuJYOGpMI41HR7J4/+Lm7TQN3n6bggA3nlwG1/MWecWefPxx13tWtJs2UjA1DR5+sgQafLj3vpaThQ+WbSbwh0/QNIv6q23b1BN2HNQEFxAYqBZsnnhCHfPbm9PXfNN3M8/3R/IOX8CuuFB49VWaysu49qGv2flRKMufKmf9emVa8I+Z/+Cv0/5KZmkmVwealJRJPC1ZonqhWTVw8fVV13Bj9mmGpml8+rcrwaOWe19cR0FVAS9/uhN0d648X02wThiuVrK37LE+292dvxvfbbdQkRNNmG8Yb53zFiuuXsG0okDcGxq5KvtV5n06jzc2vMG+wn1MCbiE4mIYN15Tq9LPPnvsvW65RWVVz5ql6kjttadwd4dbb1VzoNND1nPPtiu4kxcYMOpENudsJrO0OcL99wWvUPTjDUw9LY/Zp7XfsCM0FEZOKkDffS478pojLI1NjWRsVinL1uqvAIYlqTqcDAbajNa/8k4Jmn8BT95gdkJ3MooQ5huGT2wamQfsX/yWHVzG03veAWDbruX239TCol3XddIPeOPmUd+uy84tNwTirQVw2z/jGc5uYq+by8W3RXEhn/PuN4sIDG4kMlLVFLanJdHuvUrAjkjpoClLX8MQUJZ1WLZSBEHl4zY0uDyC5e1fRU2FKQpow3XXFoV56sLRpsAqLFTXZy+L48VwwVy0yOG/2dMQgdWDSElxhwYf1u4+2vpFGxGsPQV7aCqKZ0C8IzZFLmbQICWwEk6CmM0ExWbz8Ct7mP3BbIa+MpTcylwSq+fhRiMjx7Szrsga/furGYBlBGvTJpUWeN55yl78559bvm6yrPUbncTatfD4447/ybnJc6lrrGN5+nLWHFnDsIhhhPiEdPwzRERYFwClpUoUOSNKMn26iuJNmwbXXw9XXaUs1805ckQpDrMVtVOmhEPhUD7a/HWLTbfUH+aSc+v5Rr+Qr7iAJ+4pOZbC3aOwU4NlcNaUIWhT/8Uv38SzerX6Wv58VyH7n/iUpoLBfPih2ZpHZaVaebTZEEtwGZ6eymxkxgzVD270aBW1/fOfYcwYLql+l2LCOHXyqejz5/PuR/dw41mwLOcqrnhiGLNmVJOWpsTQ46c8zppr13Cu12g1MYiI4MABpZ3sdhZISrIqsAAGRAYz/ZQKyraczqQ3p1C8fSJ+AfWGLwUTkhPAo4q9aTVWf39rRgbVX73Mvfc2ZzHMGDiDd6JvAGDMWdez+vBq5i9S0biocpXHOG4cShGarc5NmaIukQsWqCC6I8TFwTenvMStvMQr3Ep9wPUAfLvnW0DVSL3wRBhuTb6892r7bLPNuWCeGxQOZfHq9GPPHSk/QuOBkwkKrzrWKs6ShAT1vdgSWL/t3U7uxolMmn2IID+zBbVOpggCxA4upuxoVKtLqjkvr3uZsKB+VPi6s3zjF3y641PbG1s0GT5UcojanEFEx5e3a5H1pptU9vz+/fBW2H2cGrGNtTv82MFIvAPziD5xKZdeqjSmjcPWKlt2VoJ/Lin9e1DvPFdiK4JlLUXQuJkYxjQuFlg+AbXUVvg1j8UwfXGA4nx1HjkUwbKMXoE6vseMEYElOAejUH77LitX4tRUdSO0uINsyt4EJYmkJNu3Ee8RJCZCRgapkaOIDoymLPkNjm4fQk62GxP7T+S5Wc9RlZbAUPbiN8wJTggeHuoktRRY5q5ap5yi7O/NfXSNniDJyaSkWD/3bTE9fjp+nn4sPrCYNVlrOp4eaGCjGa7DPbAcpV8/tQT/8MPwv//BP//Z+u9FR7dYzh431g2aPFj0e3qLNMH3trzHqiQvvhj2V6K0XO76aw/Ig7eGr68Kq9lKwQR8PX1JOf8LvEMLuO46NS9//YVwGPUxqzeXHOuZBjT7V4vA6pmccooKmR85ogrsGhrg9dfhD3/g9N0v4xdUQ8mG2WSUZvDSptdYNugGbq98lpPDtqHV1jDv3CYqK5XImjRgEu6H0tWikabx4YfqT1itvzKwI7AAbrwyHMoGcHB7JJ7pZzLrNI9jp1t0QBRuIVnH5taW7Nqlos7ffdecmgOoa11sLH+//HUO33mYBZcs4D/n/ocj+6Jxd1fHszXGjnVcXBloA+N5kr+SwCEeeHUaQ4NS+WbvN+i6zpWvPUfTpqu59saaloYw7eRPl6qL8aJvmwe3r+AAHDyVidPKbDpaGzW0GQy06uj22Bu7oNGHx+4w61jf0KBW1zs5yR01pg50d775xnoY6GDxQRbuW8gN427ALyqOFC2Sy7+6nM932ijwt4hgbcreBPnDSElp/9g0TR2W143fzAcRd5D+4gL2MIz7HvyUjKnncNWf1eJTO8zj2Lu/CcIOkBCS0P4B9UXakyIYEqL+rTG1jHCxwPIPrKOhytSio18/NWYrrW0saWhqoLo4GG//GnzbSgAqLLQ9yTrzTOWG1t481R6CCKwehGHVXnIkkpyKnJYvzp6tDkSLnNy1B3dCZRRjh7XTVtUVJCZCQwMeR3PYd8s+Nrz8F9DduMZzMZ9e+Cl3TbmLrVs0UtlCp+7C5gwc2DpFcN069XxUlMrpqaxs6Y6zd68SZv7t7/3j7eHNKYmn8MG2DyioKnCOwLKWItjeHliO4O4O//d/cP75yqzCvEDOSjriGFNtcm1WyrE0wbrGOj7c/iHnJJ/Hz/njmHVJGG6BPbiHkgNpDyckDsdn7kPs2qWyJwfdfgNTb3uTUYkWS3NG3wERWD2byEi4/XbYskVFMz74AK8B/Zh1VgXsPZfHf3qB7TsayH73JYYN0/j2g3I+1i9hxy6N6683y6BNS4PBg3n6abUucfbZyivCJsnJ6lizkaJ21lng5aUzaMfb1Bf1Z9asZrWgaRoB/QrJP9r6XCqsKqT4sLovNDbCe++Zvbh27bH+V57unpw99GyuTr2ajRvVYWq3H117iYvDnyre8r6VfWkehK57gRXpK3h70zusfudC/INqeeofncsTHtDfjcDBO9j5W/P94bf1xVDVj7mzbOcy+vuDv18Z6SRYFVib1wThGVzAKdPM2ikYPfI6WQcz7xxP6LeNO++ptRrFenX9q7i7uTN/wnzcIiI4JTiV8cFzuOTRz7nk1l2tywYtmgyvz9gGJYM4IbUTrSBMvbCM7+bCydfSpDexsuYNPD1VRNNRMg96Qdh+Bob0FLtYF2MvRdDTs/VJmJjYvFjnaoEV1EBjtal+1Y4plCUFVQVQEU1IZHWb21JY2NLgwpwzz1QXtR9+cHDEPQsRWD2ImBjw8WuwbXTh17qfw9qdKmQ7eFAv2JVGeuOhQwR6BzJ+VABjx8JHH6mnCwshM9ebsWxWy2rOwFovLLNJByefrB7N0wT37VM3nA4yZ/AcSmvVxbTTAstIEbSsi+oKgWXwyCPKDMCsLsOawEpMhMBAHZ/CKXy+S622Ltq3iIKqAqZ730pBAcw6o5PNorsaWxFCM8bFjKM0+TW+WVrAV78c4GDoW1w0/KLWG+7cqSY+zjp2ha7HML8A5l8VAnWBvP1uPW6ffE+gvxcLF0LQnKnMHnmUx2L+zccfq+AXTU00HUznnsxbue8+uPhiZXhlF+O4sBHFCg5WVuQH16kshVmzWr4eEVtFRX5ry//dBbshfzjePo3MmKEc/5qaUAszaWktGgyDupRs3GhKD3QmpmbDpw0+xLXXwvrPT6LxyGhufG4hHDqVx//h1dwwuBOMmpFGZUYKh9JVROi35ap244IzQ+z+XmRkMRkMpLqwZZpTfWM9BWnxDBia2zIC1okmw+ZcNOp8Yi96jpwsX555vuXqf2VdJe9sfofzh53P/k39uS3vAcb++irr7lhI02ef8ekrw7n7EQtjAYsmw6u25UGTByOHd6IIe+hQdc03ZW8kJIxhdtJs3t3+OiNG6g5HsCoroSTfHy08jQFBvdvkx2nYSxEMCWndSDgxsfl+72KBFRTchF4TRGOj3qYplDl5lXlQHkNEpAOlK7ZSBEFdu8LDe22aYC+YlR8/aBokJwFFyWzNtePoZqJJb2LXfrVC0Ctq6g0nLaORHqoN0/r1as5hmNg5xaLdICFBiQMjrJ2bqyJaxqQjIkLlyRhGF7qubjKdEVgmu3Z/T39GRHYymhEZqSrOKypaPm+kCMZ2QZ77yJFqxvjSS83iw7ipm+HmBmPGaIQUn8R3+76jqr6K/2z5DzEBMVTsngLYLjrvMTgQwRoXMw40cB+4hoWHlJA8f9j5rTfcuVPV9PV4txnBGqed6oFnUCF8/ypuFf35boGmUss0DW6+mb8evZV5JxVxzz3w4yeF/KnuNZ7dcDK33KIWiSxrtFvRhsAC1V8T1KXSMho2MB6aKiIoLmvZ129X/i7IH07SkAZuvFFdXn/+mWYjD4sCyKNH1Wnt9LpIwzY8IYFnn1WnlsfC/6H/8DQDk6q4ab4T6mqBs85Vk7Z3PlJue7vWxeLV7xAJA+2/f0xsHRkMpDD7YIvn16XvQM9PYdw4i0Uso96kk5NcP08/Pr7nWhjyHY891tSijOXD7R9SUlPC6MKHmTkT3jkyhxhyeOIJWPpLBV4pP/KfNwKpqDAbW1bWse86tyKX9VtViKtT/ceN+926derC7u/Pnyf8maPlRwkflMGmTY65ZRuHdviAIrs9sI4rjCbzlhGskhLrDZ2NuY+HB05ZkegEIcGA7kZecXXzeeBAHVZuRS5UxLRdfwX2I1ju7qqwdfFiq+10ejoisHoYKUM98Cge7pDAOlh8kOo8ddD3CoEVF6cu3gebb3AXX6weP/lEZewApMYVOjBbcZCBA9WdwYj4GPVX5qu6M2eqPN/aWjXZLi3tlMAaHDaYlIgUpsZNxd2tk5MKw4jBMk0wK0vNYJz1PVnyf/8H1dWq02h1tcqBtlLvlZoKpZkJVNVW896W9/h+//dcMfoKlv3ozpgxLneZbRsHBFZqdCoaGpuyN/HF7i+Y1H8S8cHxrTfcuVPSA3sx7u6QeprqsPvSmyXHDCYAuPxytMBA3ou6j+RkmP3HCN7nav5x1X5eegmH2jgweLCaNP3nP9abn6P6a3p7WzfLGDJIpRKt3tkyorErfxdawXBGj/Ti/PNVX+u33kIpKWjRLwmaW9k4PYJlJrBCQuC11zQajo6AoiH8+19+dt0I28OcEwZD5A6+/lqnrg7ydw0jenTbVs4Jg9zJYCAluS37In67Ih10d2ZNs+ghZUwknXARmzFwBpffvYW6WnduuEtFxnRd5+V1LzOk5jIevzOFKVMg/7q/sdT7bP76Vzj95ABuurOUhopg7ntmT/ObHT58LHPh0RWPUperlHgnblnNv7xpkxIEmsaZyWcSHxzPYb8FFBY65tBtCKz4QdaP7+MSd3cVKbcWwbImsIzJXL9+Dl5Yuo6wMBVdy8qtaFcEK7dCRbAGtNVkuL5efQ/2Ct3PPFOJMKN3aS9CBFYPY8gQaCyKZ8uRthv9bc7eDCWJ+Po1HpuH92g8PdVN2CyCFR+vTOw+/lgV0vb3zCVyaOs0mA5j2Qtr7Vp1wRs7tnmbU05R9nBr1qj0QGguiOsgiy5bxH/O/U+n3gNoXtmxTGPrbA+stkhJgcsvh3//u7k+zYbAqq70IKxmIvf8eA+NeiN/SL6GlStbpzj1SIwUQTvLswFeAQyNGMqXu79kU/Ym6+mB5eUqMioCq1ez8N1RfLJsN3++yuKCGhgIV11F0Df/5et3ihgRW8Ib3MDfH3SzaazQCn9/1QxuyRJV52ilICckRM0jHnus9a+PTVHXxXW7Wq4gbzt8EL1kICOGa/j4wJVXwtdfQ/7+ErWBhUDYtMmIPjs4bkeJilIp16YT/9xzVS+tq69udlx2BsMjh6MN/5bdmyJYuFCnqdaPUZPbXlUfmhJINX4czmqZDfDrWvXznOkW7oZOimAZvH71nQRN+4AFH0eyflMNy9OXs2NfKblvv0VUlMY334BfdJCaiJt6df3zT2fjnbiet/8dTG1dU4smw3sL9vLGxjdIajqL+PgOlQw3M2CAMv2prj426Xd3c+eK0Vewz+sTwDGjC6PN29AhzolW9hlCQmynCFpiCKwe0KQ+PEztx6P5Ve0SWJl5JdDgR2JcG0WeRp23PYE1e7aas/XCNEGXCixN0+ZomrZX07QDmqbd78qx9BSSk0FvcmfP/lpqGmz7um7J2cLbm99GKxnMoETN8Zu8qzH1wjLn0ktVH5XvvtNJ1Tc7z+ACWgusdetUSqB5PduMGWrG8csvzQ6CnVoOhEGhg+gf5ASHP1u9mpzRA6stHnpIrTDdcYf62YbAApjgdi1V9VVM6j+J3F0p1Nf3EoHVr5+azFje/CwYFzOObbmqP90Fwy9ovYHRDFEEVq+mX3AQF59qI9fqppugro6UX99k+zXPc4PbO8fqjhzmpptUF9/vv1cKpLp1Efjo0dbnXVNGqnTgnftbuh7s3K1SZwyD2euvV6ft+7/Eq0mzkaJkYtMmtX7SqQm5NYxr6LnnHnvqxRdVwM6Z9ydvD2+STtyG3uTGX+5tBJqYNt1682BzRg1XAjUzq+W0Z892P7yCSoiLsxhkTo66TzipgV+AVwDv/2sweJdy0fWZPL/8bdw/WYxe58uiRab5qzHRNLmmeXt4M//2MuoKY7nnhfUtmgz/7ee/4ePuh5YzrkMOgi1wc2teVDSLqkyNmwpRW9E03SGji737GiEgm+Qen7rQzQQHO54i2IMEVr8wlSFzNL9aLTJ5ezuUInjosJq7Doprw0LQaKptK0UQVJrk1KkisNqDpmnuwL+BucBw4FJN02x0sTh+MK5xTQWD2ZnXMopVWFXIS2tfYuwbYxn7xliWpy8novYEBvUGgwsDUy8scy68UC1QlJZqpDZscK5JwIAB6u6ekaFW/9ata1X0TUiIimgZAsvbu/0Tp67CEFiWRh1dHcECtR+uvrrZQsqKwBoxQu27qAqlpv409k8sXaqMkaZN69rhOYU2mg0bjI9RBSsTYydatx8WB8G+z7BhKtr9+utqqT4+3n4XXlvccAO8+y78+KOyDqysdOjXhg8OBq2RA4eaxURZbRl5h9Sk3BBYw4er+chbm8ejR8e0UjddYnDRzUye4IN76GEOpXlA7EbGJMa1+TuDBqnV+KzcZsFUWlNK6cHBxA8taC0CO9lk2BrnjZ3B1CuWkbFpCAvv/ytNeSl88YXW3H3FEFjGxBN45uaT8Y7ZzxsvhVCfkQ7Abt9Kvtr9FWN3LmHfHg8ushJUbzfGoqLZpH9i7ETwqiIyvsihCNbOPXXiIGgNWxEsawLL6CnQEwRWuBJYuYW16joSFeVQBCvrqLpGxca2sbJilD601QvnzDNVDYlRe95LcOXM/ATggK7rB3VdrwM+Ac5t43f6PMeCN0XJPLPqGW5adBNzP5xLyispRD8Xze1LbsdNc+PluS9z9K5sqvOje0f9lUFiomrUYrZyGxkJp5+u/p/KFucKLC8vJQzS05V4KitrLbBATZxWr1YncXKyUg09gf79VRTu9tvhnnvUZKy6WoXWuzqCBfDgg82TSCt/z8dHrYYXpyfw69W/cu3Ya1m6FE46yckW0F2Fg2kP42LUjPTC4Rda32DnTvWBDSMXoW9y001qseabb9rwZG+Da66B//4Xli9XzY4dwMMDvELzyc5qFnW783dDwTA8PJtaDOeGG2BfeSy/+rcs5srJUaVZvV1gjYsZS+NQk23joGUMDmt7Xxjz1tziZuOAVYc2Qf4Ixo+3MhXqAoEF8O1zc/CIPAh5I3ni+ZJj9z7AqsDy9HDnxtvKqDs6lKffVAV0z2R8RNDOu1j58VRuugmuvdYJA7MisCL9I0kIScAnbrdDEay0A27SA8sa1iJYtlIE/f3Vwsupp3bL0OwRE6kiUAWFpkUdB2qWAXJylLBq0+TCkQgWKIEFKvLfi3ClwOoPmLdNzDI91wJN027QNG2Dpmkb8h3w3+/thIdDaKiOb2kqn+78lE92fEJ+ZT6jokZx34n3sXX+VjbesJFbTrgFvSqMiopeNqcz1KBFROa668DLo5FJrHW+zbVh1W7eYNiSmTNVXs1PP3W6/sqpeHurJedrrlG26SNHqpwb6PoIFqhZyc03q9o5i1Qjg9RUpUunD5zO0SPu7N7dS9IDweEI1vT46bw450XmT5hvfYOdO1WEo6cIc6FrOPdctdBQW9v5C+8f/6hcLdrRZCgkqpTinGZrecNBMHFwfQvzyosugmC3Mt4q/UOL3zcugU53EOxmUqNTYeTH4NaAlrLQoQl9SAgEaOXkVTSnry1cmQ5NnsyZbqWIuaCArihujggM4ssvde56dj33326xcm9FYAE8c9c4vMJyePMbVTj31Z4IKr56htmzVRqmU1IwDYFlcZ2fGDuR8rBfycqyf5ksL4eifG8I3y8CyxLLCFZDg3IGthbBAtUx/IorumNkdhnQT0V7C4vbJ7AK8tQiUJuZoo5GsEaMUBkDvSxN0JUCy9oloVWlua7rb+q6PkHX9QmRvcLJoXNoGgwZonGC7+UU31dM0X1FbLhhA59f9DmPnfIYo6NGH9vWMOPrdREsaJUmeMEFkP+Xp4nTjjj/AxnNhteuVTcPawnr06apybGud7r+yukYtmArVijBdfPN6vnuEFgAzz3XXGNkhdRUlbFYUKCynqAXCSwHI1jubu7cNuk2grxtNPMUB8HjAw8PuPFG9f/ORLAMIiJaTabtETOgnrrCGCrqlDHD7oLdaPkjGDOyZaqinx9c7vEZXxydwrx56hwNCYHzzjO5JaZ2fuiuZEzUGBiwHu4NI2FkNl7ubbupahrEeh+hoLo/9Y3K6v33tSqT4qQpga1/obCw7YlfBzln+mCeu3ti6xdsCCwvL43rbiojs/BE3nS7mvKFXzMsRePTT53YFcJKBAvghP4nUByyDLBvdHGs+0DYAemBZYmlwDKajdsSWD2E2AglsIpLTE9ERTlUg1Va4IebZ53VAF0LjOO8rfNM01QUa9kymw6sPRFXCqwswDxxegBw1EVj6VEkJ8PBAx6E+ITY3c7QKH1BYAEEHd6pVimcnVuWkKAUwKpVMHGidevTwMDmyFZPE1gGM2aoZmEPP6wq4Z1uA2YDNze7hd7GZG3rVli6VKUF9Bqt4WAEyy6lper46jUfWugUN9wAo0Y1NynvDOHhKt3XkSZDQOJAdygbwN58NZvdfmQ/enEiI0ZYXNOqq7ml7jkiA6rZv1+txVxxhQqC//yzzWB0ryHUN5SBwQPBp9yh9ECDAX555NcP5Ej5EXRdZ9/OQLz8K623XSwqUotb3YkNgQXw1H1DCHIv4sam/xDk583ChZpz5+fGfc9iVjwxdiJEbwEcE1j94ksdErzHFUaKoHGeG+mCPVxghfoFgXdpszY0Ilh2rle6rlNZFERAWEXbkdWCAmXEY246Zoszz1QlEitWODp8l+NKgbUeSNY0LVHTNC/gEmCBC8fTYxgyRLW6MDeY0nUVsdq3r/mf0c+kVwms6GgloKwILPbvd356ICiB1dgI27ZZr78ymDlTPfakFEFLvL1Vj6qtW9vOW+4mDJ23caOKYM2a5VzXsC7Fy0vd5BxIe7CJOAgeX0RFtX0tcZSwMOUKV1Xl0OYjkwNA92DdbtWUaPvuWtDdWzeZzclhGHs4/MKX7NgBCxfCyy/D3XerdZq+wNgY1WojKdTxe0Z8cDE5jQPJKMkgqyyL6swUEoYVtb5e1dSofdJFESybBAaqkJQVgRUQAPfEfIivWxXff+dlXRR29m9/9JFaQDBjXMw4NL8SQqKL7Qosw6J9cJJjiwXHFSEhag5iGNoYiqXNEI9rcXdzR/MtpbzUlPrer19z7yobVNRV0FTWj9DI1g6prWhPlHjmTDV37EVpgi4TWLquNwC3AD8Au4HPdF1vu/nTcYBhdGGsCP30E0yZojJShg5t/vfMMypa4CQX2e5B05QiNGs2fIwDB7pGYBnVzWC9/srgT3+Cq67q/RXg3UxkpCpLee89tejba9IDDRzMK7fJjh3qUQSW0F6MCImDaYLjh6mI66Y9hVTVV3EkTa2AD7f0383OVo992C47NSoVoF0RrITIKsr0UHZnHeX3Q+shdzQnTLCSY+do6pKz0TT1N20cDw80PEn+5XczZUoXrWBdeimWyi3QO5DhkcPxHmDf6GL/fnAPzmFQP9e73/U4jEiVIUx6SQQLwN23gopy0zlipNTbSRPMrcyFimgio9punUBhoeMLxX5+6vjsReF3Z2Xvdghd178HepctSDdgBFA++US1IPr5Z+Ux8MILzce3Qasba2/ASi8siorUv66KYBnYW3UePFipBKHdjBnTbPBz2mmuHUu7MZoNd5SdO9XF3+lLykKfxxBYRUUOtYYYmqTSp/ekVbO3YC/kD8PNTWfIECs9nMABG6/eS2p0KgBJYY7fM5JjmgDYvreUivpqaPRm1jQrEzyjAWp3pwiCbYFVW4uWk41/Uvfv04n9J/JZ2K/kbphKWZn1Oe6+/TqNoXvF4MIaRqSqpEStRvYigeXpX0l1uSmFz3DVzMuzWUqRV5kH5SnExJRbfb0FBQXtW8R4913Ht+0BuFRgCdYxIlhPPKEE1Ysvqqh9r7C9doTERPj995bPpaWpR2c2GTaIM5X6xcf36RVdV5KaqgRWamrrRYAeT79+ZhXaHcBwELRW2ycI9jAmF8aEvg0MDZaZqR9zEIxPrMPb27vlhkYEqw8LrLnJc3lh9gvMTZrb9sYmBg9QAit9Xw0H8usAmDTRSi8zV0WwjL9pTWAdNpkuu6BH48TYibwXsQi4n61bYfr01tvs3dcEcftEYFnDXGCZP/bwFEEAb/9qaopNCw0OmEJlFRVATRgDYmvafvPCwp7Tc7QLkBlBDyQwEO67TwmstDS47bY+JK5A2RuXlh7rVk9lJTz1lPp/p1vSW8HHR6UJnnii899bAJqNLnpdeiA4J4Il6YFCRzCPYDmAvz94B1aQe8RXCayCYYwaYUUgZGcrwd9D6jS7Ai93L+6YfAfeHt5tb2xiYLyqD8pOa+LgrlA8fautJ030xAhWZqZ6NE957yZO6H8CRKsCLGt1WGVlUJjvLj2wbNGLUwR9A2qprTRFsBwQWPszVORqUJxv22/e3ghWL0MiWD2Uf/7T1SPoQgxXjoMHVa7B+eerSeqTT3adg9+SJa65WR4nTJ+utPGll7p6JB0gJkbdMOrqlOlFeyguVpPZkSO7ZmxC36adNVgAEbGVHCmM4rdD/4PChxlp6SAIKkUwKkr6slkQFeeFD9UcPqjTkD+KocPLcHOzMhF0ZQQrLMy64M7IUI8uEFijo0bjGVKIZ2g5mze3trQ3DC4I28/A4Ou6d3C9AcsIVi8SWP7BdeRW+asfDNddOzVY6VnKRj15YBvmAI2N6v7ZhwWWRLCE7scQWK+/DhMmqAnqkiVw//1d9zdTUnph7lrvIToadu/upf11EhKUTaexQtwedpp8eSSCJXSEdkawAOLim6A0npVbc6DJ03odbnZ2n04P7ChacBDxZNJYlAg5Y5g8wcaCSk9IEbS0ws7IUCYY3dX/0Awvdy9So8fgM2CXVaOLYxnW4fuJD+67KV8dxjKCVVKi6nY9rUSfexgBgQ00VQfR2IhyuAwPtxvBOnJEpeHGD2jjs5WUqGO8D0fZRWAJ3Y8hsN5+W9VcbdzYS3PLhD7BoEHq0VrrgLYQB0GhM/j6qn/tEFjJid5QMhA9T6VTi8BqB8HBDCSD8qOnQIMfp54YYn27oiLVEsPXgTQnZxMerpqpWlr3Z2SofdreKLuTOKH/CVSE/8quXTo1FuU1RgQrOr6qXSmbxw3WIli9IHoFEByihL7RG7kt190cU3CrzctPQYF6lAiWIDiR4GCYOxfmz4eVK12S8iAIx7DT/LpNdu5UfRL6cKGu0MXYSgmzwaghQVAfAJnT0DTdetlqdrYY+ljDJLAa6kMAGD/eht250Z/HFQ39bDUbzsx06b1yYuxE6iLX0tCgHVtXMti/H7zD8kiMFIt2q/j4KGFsXoPVSwSWoQ0LixrVf/r1s5siWJjnBVrjsWxC2xuajm+JYAmCk/n+e3jttT7m3iH0Svr3V6kaHRVYw4f3os7KQo8jLKxdNViDE02l0/vnEjOgDj8/iw0aG9UKs0SwWhMUxEBULZOnT53tkt+iItfV7NoSWBkZrhVY/SdCjHWji/37QQsXgwu7hIS0dBHsBQ6CAGEhSiYczTdFVKOi7EawSgv88Akpbbv8UyJYgiAIfRx3dxWB6qjAkvRAoTOEh7crgnUsWFqYwqiRVnyq8vOhqUkEljWCg0kgHYCRoxttTwKNCJYrsCawmpqUTbsLI+VDw4cSEJWPl38VX38Nu3Y1l4nt369TG7xTBJY9zAVWL4pghYepk+SYwGojRbCqOJiAsIq231giWIIgCMcBgwYpV8v2UFCgbjQisITO0M4UQfMgxqgRVhTCcdADq8MEBjIQZWZz4gl26qt6msDKzVUupy6MYLm7uTOh/3gCRy1n8WJ12YuNhUsugYICDT10HwODJd3fJsHBvTJFsF+4qvnLKTAV3vXrp4RiXV2rbesb66kvCSc0woEeWBLBEgRBOA5ITGx/BMtwEBSLdqEztDNFMCKi2XvBqsFFTo56lBqs1ri5MTTgCD7udZx8sp3telqKoAst2s2ZGDuR8jPnsWd/HW+/DTNnwvLlphdjNkoEyx69NEUwOkKVceQWKvt1okx1dlaiWAVVBVARQ2RUY9tvXFioUvMD2rBz78WIwBIEQUhMVCtqFQ6kNhhs364eJYIldAYjgmVpy20DTWueZw8bZmUDiWDZpV9IHdkX38n559vYQNddG8Gy1hvNEFguNtM5of8J1DXWUe63jWuvhY8+UofbKz9/Dom/iMCyRy+NYMVEqNWcgqIG9YSdZsPZZXlQ2Y/YWAdqkgsL1WpRH65fFoElCILQESfBVatUjkz//l0zJuH4IDxcpdtY2nLbwZhn2xVYEsGyTnAwITU5tud1lZVQX++6CJaXFwQG9tgIFsC6I+uOPadpUOy+FzSkB5Y9jAhWTY2y4e8lAis2MgBoosjcRRCsCqzvt64H3Z34/lZqQy0pKOjT6YEADnwLgiAIfRyjF9bBgzBqlGO/s3IlTJvWp1fghG7AvNmwv79DvzJ2LGRl2ZijZWdDaKg4tNrCPJJgDVc2GTYwmg0bZGaqCXpQkMuGBEpA9fPvx48Hf+TPE/6MZrr2pZekE+Ufha+nC/qG9RYMgWUce70kRTDENwi8yyguMT1hEliNOfnMvx6qqyEhQWdP4xK+3LEeuI7JwxxYCDAiWH0YiWAJgiC0N4KVmalcvaZN67oxCccH1lLC2uAf/4C1a228mJMj0St7BAWZdU21Qk8UWBkZLk8PBNA0jWtSr+GbPd8wf+F8GptUVCOjNEPSA9siOFipEcPcoZdEsIK9g8GnhNJS00KiqQZr40Z4+21YulTniSeb+PKfc2HhGwAMSvBs+40lgiUIgnAcEB6uim0dFVi//64eTzyx68YkHB8Yk4x2OAl6eqp/VsnOlvorewQH23cMNfaDq1IEwbrASkhw2XDMefLUJ3HT3Hhy5ZMUVBfw4fkfkl6SzriYca4eWs/GiFgZ6Z69RGD5efqBbwnlZd7qiYAA8PFh6eZINE1nwuNXsfjwR9ww5FEu7n8/1VVujHPkUHBlnWM3IQJLEARB09rnJLhypbrRjB7dteMS+j7mKYLOIDsbpk51znv1RXpLimBaWvPPGRlw0kmuG48ZmqbxxKlP0M+/H3f+cCdnfHgGmaWZnJ9iyzVEAJoFlSGwekmKoKZpePiVU1kaYDwB/fqxdH8C/vF7WXL0A14680VunXSr429qGMn08RRBEViCIAigBJb5pMYev/8OU6aAh1xChU7iTIGl65Ii2BaOpgj2lAhWaakar4sNLiy5Y/IdRPpFcvW3V9PQ1CApgm3RSyNYAJ7+VVQZESygLGIQqzcn0XDiM/xr9gvtE1egjunGxj4fwZIaLEEQBFBGF4cOtW2XXVoK27ZJeqDgHDpQg2WTsjJV5yEpgrYxamHq662/bghdV0ewSkqgoaHHWLRb4/LRl/Pdpd+REJLApAGTXD2cnk0vFlg+ATXUVjSb5ix3m0mD7gGDl3Lh8Avb/4bGtU4iWIIgCMcBiYnKKjs/v9mK1hqrVysRJgYXgjPw9VX/nBHBkh5YbWNMbEtLrU/wCgtV+q+XV/eOyxxDdBcX9xiLdlvMSZrDodvb2aT9eKSXpggC+AbUUlbpd+znpRUn4kclMaPz6B/UgTYlhtGHRLAEQRCOAxx1Evz9d3B3h0myYis4CaPZcGcRgdU2xkTXVppgUZFr0wOhpfFJZqb6fw8VWIKDGIIqM1PVMQUGunQ47cE/sJ7GGn8aTL2Gf8wdzQyWc/LgyR17w55Q59gNiMASBEEAxwXWypWQmqpWuQXBGYSFOSdFMCdHPUoNlm2MXlK2jC56gruZ8fcLC1XEw8vLflRd6PkYwv7oUSWu3HrP9DswWNnxl5VBejrsK45kDks5PWxix97QiGD18RTB3rOHBUEQuhJDYNmzcK6vVw2IJD1QcCYSweo+zFMErdETBVZ8fK+akAtWCAxUkStd71XpgQDBwaouuaQEfvxRPTeLpUz3HdqxN5QIliAIwnGEv79aJbYXwdq8WRXIi8GF4EzCw50nsHx8elUBfbfTm1IEDYEl6YG9Hze35uhpLzs/DT1YUgJLl0JQYC4p7CG2uoM2DoWFKs2+l30P7UUEliAIgkFbvbCkwbDQFTgzghUTo1bKBesYk9ySEuuv97QIVmZmj3QQFDqAoVR6mbAID3MHIDuvjmXLdHzjl6IB5OZ27A0LCtQ1r49HZfv2pxMEQWgPbQmslSuVnXtsbPeNSej7GDVYbbUIaAvpgdU2RvpkVlbr15qalHOfqyNYQUGqx152tvonEay+gSGwelmKYESoilQtWVpPSYlGTcJC9UJeXsfe8DhoMgwisARBEJoZNEil5Bh2SebouhJYEr0SnE14ONTVqTYBncGIYAm2sZcKXFqqRJarI1iapkTeli3qZxFYfQMjctXLIlj9wlXLgm++9ELTdMpTflIvdFRgFRS4/hzrBkRgCYIgGCQmqg7z1la309LUDUUMLgRnY0RMOpsmKALLMYym4pb0pOL78HDYtEn9XwRW36CXpghGR6omw1mZnoQMSmPAAH91fHYmgtUTzrEuRgSWIAiCgT2r9pUr1aNEsARn4wyBVVur0ttEYLWNrVRg4/t3dYogqAlocbH6v9Rg9Q0MYdXLUgSjwvxAU1btNfELOTnhZIiK6ngNlqQICoIgHGfYE1i//w6hoTBsWPeOSej7GBP6zvTCkh5YjpOYaD0VuKdFsEClC8bFuXYsgnPopRGsUN9g8FZtDarjv+GkgSepNNuORLAqKlSk/ThYNBCBJQiCYBAXp5yNrPXCMuqv+rjzkeACjMl0ZyJY0gPLcWylAve0CBao/enl5dqxCM6hlwqsIO8g8CnBzbsK4lZ3TmBt3arqmceNc/5AexgyUxAEQTDw9FQra5YRrNxc2LNH0gOFrsEZKYIisBxn0CD1aHme98QI1nGw0n/c0EtTBIN9giH0IE2DFzIgrB+DQgd1PEXQqCscO9a5g+yBdLBLmCAIQh/FWn3GY4+pyNU557hmTELfxpkpgiKw2sY8FXjmzObnje8/NLT7x2SJIbDE4KLv0JsjWJeeC1oTJw28AE3TVASrtFTVfnp7O/5mmzer3z0OWp1IBEsQBMEcS4G1bRu8+ir8+c8wfLjrxiX0XXx9wcen8xEsNzeIjHTeuPoqcXHg7t46FbioSE2C3d1dMqwWiMDqe/TSPlhe7l74+DWBZ41KDwQlkgDy89v3Zps2qejVcdAMXQSWIAiCOYmJKhpQVaVyxW+9Va1oP/qoq0cm9GXCwx0XWI2NrQ0asrPVpKcniIOejoeHElnWUgR7QnogNEc1JUWw7zBnDjz1FEyc6OqRtJsg7yAA5SAIzQKrPXVYtbWwc+dxUX8FIrAEQRBaYtRnpKfDp5/Cr7/CE0/0jMJ3oe8SFua4wLr/fhWpevddtQgAalFA0gMdx1ovrMLCnnOeGylUSUmuHYfgPPz94d57e+UiSLB3MDEBMSSFmY7HqCj12J46rB071MLQcVB/BSKwBEEQWmLUZ+zYAX/5i1ptu/Za145J6PuEhTlWg9XUBB9+CNXV6ricPVstBmRni0V7e7BWa1lU1HMiWJMmwdKlcPrprh6JIDA9fjpXjrlS1V9BxyJYmzerx+MkgiUmF4IgCOYYAuuee+DIEfjss1654ij0MsLDYe/etrfbuFGJqffeUz1l7rsPRo5UwmvMmC4fZp/BPBXYz089V1gIQ4e6dlwGmibiSugxvHPuOy2fiI5WNZ/79jn+Jps2QVBQ8z22jyMRLEEQBHOiopTpQGYmXHEFTJ3q6hEJxwOOpgguWKAmNmedBTffrGoaTjxRRbTEEMFxjEleenrzc0VFPSdFUBB6Mv7+MG2auh45yubNKj3wOOkleXx8SkEQBEfRNDX5CgxUBcmC0B0YAsuoqbLFggVqYmPuMrdkCSxfDrff3uXD7DNY9sJqaFC20z0lRVAQejrz5qlU+gMH2t62sVE1GT5O6q9ABJYgCEJrnnpKpQaKaYDQXYSFKZetqirb26Snq7YBlv3YNA1OOqnX9ddxKea9sKA5eigRLEFwjPPOU49ff932tnv3qij7cVJ/BSKwBEEQWnPWWcpSVxC6CyNyYi9N8Lvv1KM0vO48/fqp2iujF5bxvUsESxAcIyFBRaQcEVibNqlHiWAJgiAIgtBtGJETewJrwQJISYHk5O4ZU19G09QE0YhgGQ6OIrAEwXHmzYPVq5Xxjj02b1bN1FNSumdcPQARWIIgCILgatoSWKWlqs5KolfOw7wXlqQICkL7mTdPPX77rf3tNm+G0aNVk+/jBBFYgiAIguBqjIm9rV5YS5YoIwYRWM7D6IWl6xLBEoSOMGKEaoZtL01Q15sdBI8jRGAJgiAIgqtpqwZrwQKIiIDJk7tvTH2dxEQoK1PfuSGwJIIlCI6jaXD++fDzz1BSYn2b9HT12nFkcAEisARBEATB9dhLEayvh++/V+Yr0vTaeZg7CRYVqe9WnBgFoX3Mm6ei6wsXWn/9ODS4ABFYgiAIguB6fH1VEbi1FMGVK9UKsKQHOhfzXliFhUrkapprxyQIvY0TTlAtTWylCW7erBYvRo3q3nG5GBFYgiAIgtATCA+3HsFasAC8veH007t/TH0ZywiWpAcKQvtxc1M9sZYsUb2uLNm0CYYPVwtIxxEisARBEAShJxAW1lpg6brqf3XqqRAQ4Jpx9VUCA5WoPXhQRbDE4EIQOsa8eapJ+tKlrV/bvPm4q78CEViCIAiC0DOwJrB274a0NEkP7CoMJ0ERWILQcU4+GUJCWqcJZmdDTs5xV38FIrAEQRAEoWcQFta6Buvdd1X9wtlnu2ZMfR2jF5akCApCx/H0VCY8330HxcXNz2/erB4lgtU9aJp2kaZpOzVNa9I0bYIrxiAIgiAIPQrLGqzSUnjzTbjoIoiNdd24+jKJiZCRAQUFEsEShM5w2WXq+hURARMnwv33w8cfq9fGjHHt2FyAq1oq7wDOB95w0d8XBEEQhJ6FkSKo68rN7u23obwc7r7b1SPruyQmQl2d+r9EsASh48ydC6tWKbOLn36C555T9u1Dh0JQkKtH1+24RGDpur4bQBM7VEEQBEFQhIVBba1y4vL0hBdfhJNOggmS6NFlGE6CIBEsQegsU6aof488AhUVqsXEcRp9d1UEy2E0TbsBuAEgPj7exaMRBEEQhC7CiKAUFqqJyeHD8O9/u3ZMfR2jFxaIwBIEZxIQAHPmuHoULqPLarA0TVumadoOK//Obc/76Lr+pq7rE3RdnxAZGdlVwxUEQRAE12JM8AsL4dlnVWrNmWe6dkx9nfj45ubCkiIoCIKT6LIIlq7rp3XVewuCIAhCn8OY4H/9tWrO+cYbqomn0HV4ecGAASpaKBEsQRCchFy5BUEQBKEnYAis55+HyEi44grXjud4wajDkgiWIAhOwlU27fM0TcsCpgCLNE37wRXjEARBEIQegxFBqaiAm28GX1/Xjud4bsDUfwAACOJJREFUwajDkgiWIAhOwlUugl8DX7e5oSAIgiAcLxgRFB8fuOkm147leGLmTNi+Hfz8XD0SQRD6CJIiKAiCIAg9AV9fiIqCa69VKYJC93DllbBhQ7PZhSAIQifp8TbtgiAIgnDcsHWr1AIJgiD0ckRgCYIgCEJPISrK1SMQBEEQOomkCAqCIAiCIAiCIDgJEViCIAiCIAiCIAhOQgSWIAiCIAiCIAiCkxCBJQiCIAiCIAiC4CREYAmCIAiCIAiCIDgJEViCIAiCIAiCIAhOQgSWIAiCIAiCIAiCkxCBJQiCIAiCIAiC4CREYAmCIAiCIAiCIDgJEViCIAiCIAiCIAhOQgSWIAiCIAiCIAiCkxCBJQiCIAiCIAiC4CREYAmCIAiCIAiCIDgJEViCIAiCIAiCIAhOQtN13dVjcBhN0/KBDFePw4IIoMDVgxA6jOy/3o3sv96N7L/ejey/3o3sv96N7L+eQYGu63Msn+xVAqsnomnaBl3XJ7h6HELHkP3Xu5H917uR/de7kf3Xu5H917uR/dezkRRBQRAEQRAEQRAEJyECSxAEQRAEQRAEwUmIwOo8b7p6AEKnkP3Xu5H917uR/de7kf3Xu5H917uR/deDkRosQRAEQRAEQRAEJyERLEEQBEEQBEEQBCchAksQBEEQBEEQBMFJiMCyQNO0OE3TftE0bbemaTs1Tbvd9HyYpmk/apq23/QYano+3LR9haZpr1i813hN07ZrmnZA07SXNE3TXPGZjiectf80TfPTNG2Rpml7TO/zT1d9puMJZ55/Zu+5QNO0Hd35OY5XnHz99NI07U1N0/aZzsMLXPGZjiecvP8uNd3/tmmatkTTtAhXfKbjiQ7sv9M1Tdto2k8bNU07xey9ZP7SzThr/8n8pWcgAqs1DcDduq4PAyYDN2uaNhy4H/hJ1/Vk4CfTzwA1wIPAX6y812vADUCy6V+rRmSC03Hm/ntW1/UUYCxwoqZpc7t89IIz9x+app0PVHT5qAUDZ+6/B4A8XdeHAMOBFV09eME5+0/TNA/gRWCmruujgW3ALd3zEY5r2rv/CoCzdV0fBVwF/M/svWT+0v04c//J/MXFiMCyQNf1bF3XN5n+Xw7sBvoD5wLvmzZ7HzjPtE2lrusrUTeaY2iaFgME6bq+WldOIv81fkfoOpy1/3Rdr9J1/RfT/+uATcCA7vgMxzPO2n8AmqYFAHcBj3X9yAVw7v4D/gQ8adquSdf1gq4dveDE/aeZ/vmbIh9BwNEu/wDHOR3Yf5t1XTf2y07AR9M0b5m/uAZn7T+Zv/QMRGDZQdO0BJT6XwtE6bqeDeokAPq18ev9gSyzn7NMzwndRCf3n/n7hABno1aOhG7CCfvvH8BzQFVXjVGwTWf2n+mcA/iHpmmbNE37XNO0qC4crmBBZ/afruv1wJ+B7ShhNRx4pyvHK7SkA/vvAmCzruu1yPzF5XRy/5m/Twgyf3EJIrBsYFr9/hK4Q9f1so68hZXnxBO/m3DC/jPexwP4GHhJ1/WDzhqfYJ/O7j9N01KBJF3Xv3b22IS2ccL554Facf1d1/VxwGrgWScOUbCDE84/T5TAGgvEolIE/+rUQQo2ae/+0zRtBPAUcKPxlJXNZP7STThh/xnPy/zFhYjAsoLp5vAl8KGu61+Zns41hc2N9L+8Nt4mi5Yh2QFIikS34KT9Z/AmsF/X9X85faCCVZy0/6YA4zVNSwdWAkM0TVveNSMWzHHS/itERR4Ngfw5MK4LhitY4KT9lwqg63qaKcXsM2Bq14xYMKe9+0/TtAGo8+xKXdfTTE/L/MVFOGn/Gcj8xYWIwLLAlC/+DrBb1/XnzV5agCoixPT4rb33MYVxyzVNm2x6zyvb+h2h8zhr/5ne6zEgGLjDycMUbODE8+81XddjdV1PAKYB+3RdP9n5IxbMceL+04HvgJNNT50K7HLqYIVWOPH6eQQYrmlapOnn01H1JEIX0t79Z0ofWwT8Vdf1342NZf7iGpy1/0yvyfzFxWjqPiYYaJo2DfgNlTveZHr6b6g82M+AeCATuEjX9SLT76Sjini9gBJglq7ruzRNmwC8B/gCi4FbdfnCuxRn7T+gDDgM7AGMnOZXdF1/uzs+x/GKM88/s/dMABbquj6yWz7EcYyTr58DUa5YIUA+cI2u65nd9VmOR5y8/+YDtwP1QAZwta7rhd32YY5D2rv/NE37Oyp1c7/Z28zSdT1P5i/dj7P2H+pclPmLixGBJQiCIAiCIAiC4CQkRVAQBEEQBEEQBMFJiMASBEEQBEEQBEFwEiKwBEEQBEEQBEEQnIQILEEQBEEQBEEQBCchAksQBEEQBEEQBMFJiMASBEEQej2apjVqmrZF07SdmqZt1TTtLk3T7N7jNE1L0DTtsu4aoyAIgnB8IAJLEARB6AtU67qequv6CFRj2zOA/2vjdxIAEViCIAiCU5E+WIIgCEKvR9O0Cl3XA8x+HgSsByIAo2mxv+nlW3RdX6Vp2hpgGHAIeB94CfgncDLgDfxb1/U3uu1DCIIgCH0CEViCIAhCr8dSYJmeKwZSgHKgSdf1Gk3TkoGPdV2foGnaycBfdF0/y7T9DUA/Xdcf0zTNG/gduEjX9UPd+VkEQRCE3o2HqwcgCIIgCF2EZnr0BF7RNC0VaASG2Nh+FjBa07QLTT8HA8moCJcgCIIgOIQILEEQBKHPYUoRbATyULVYucAYVO1xja1fA27Vdf2HbhmkIAiC0CcRkwtBEAShT6FpWiTwOvCKrvLgg4FsXdebgCsAd9Om5UCg2a/+APxZ0zRP0/sM0TTNH0EQBEFoBxLBEgRBEPoCvpqmbUGlAzagTC2eN732KvClpmkXAb8AlabntwENmqZtBd4DXkQ5C27SNE0D8oHzumf4giAIQl9BTC4EQRAEQRAEQRCchKQICoIgCIIgCIIgOAkRWIIgCIIgCIIgCE5CBJYgCIIgCIIgCIKTEIElCIIgCIIgCILgJERgCYIgCIIgCIIgOAkRWIIgCIIgCIIgCE5CBJYgCIIgCIIgCIKT+H/PYxBUBd9BCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12, 6))\n",
    "\n",
    "sns.lineplot(data = inflacao, x = 'Date', y = 'ipca', color = 'green', label = 'IPCA')\n",
    "sns.lineplot(data = inflacao, x = 'Date', y = 'igp-m', color = 'red', label = 'IGP-M')\n",
    "sns.lineplot(data = inflacao, x = 'Date', y = 'inpc', color = 'blue', label = 'INPC')\n",
    "ax.set_title('Inflação', fontsize = 18)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "fig.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3bef1d0",
   "metadata": {},
   "source": [
    "#### PIB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "214833dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pib = sgs.get({'ibcbr' : 24363}, start = '2022-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5695ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pib = pib.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0057526",
   "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>Date</th>\n",
       "      <th>ibcbr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>149.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>149.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>144.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>142.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>141.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>143.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   ibcbr\n",
       "6  2022-07-01  149.41\n",
       "7  2022-08-01  149.46\n",
       "8  2022-09-01  144.64\n",
       "9  2022-10-01  142.79\n",
       "10 2022-11-01  141.55\n",
       "11 2022-12-01  143.62"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pib.tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6dffc3d",
   "metadata": {},
   "source": [
    "#### Taxa de Desocupação (Desemprego) - PNADC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01cd96b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "desemprego = sgs.get({'desoc_pnadc' : 24369}, start = '2022-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1f58e216",
   "metadata": {},
   "outputs": [],
   "source": [
    "desemprego = desemprego.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "97c3690c",
   "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>Date</th>\n",
       "      <th>desoc_pnadc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  desoc_pnadc\n",
       "5  2022-06-01          9.3\n",
       "6  2022-07-01          9.1\n",
       "7  2022-08-01          8.9\n",
       "8  2022-09-01          8.7\n",
       "9  2022-10-01          8.3\n",
       "10 2022-11-01          8.1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desemprego.tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6a2341",
   "metadata": {},
   "source": [
    "#### Moedas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "5b772ffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Um dia na frente por causa do python fazendo o range + 1\n",
    "hoje = datetime.date.today()\n",
    "inicio = hoje - datetime.timedelta(days = 7)\n",
    "fim = hoje + datetime.timedelta(days = 1)\n",
    "moedas_busca = 'USDBRL=X EURBRL=X GBPBRL=X'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "56c14270",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pelo yfinance é mais recente\n",
    "def obtendo_cambio(moedas, data_inicio, data_final):\n",
    "    df = yf.download(moedas, start = data_inicio, end = data_final, progress = False)\n",
    "    df = df[['Adj Close']].reset_index()\n",
    "    df.columns = df.columns.droplevel(0)\n",
    "    df = df.rename(columns = {'' :'Data'})\n",
    "    dolar = round(df['USDBRL=X'].tail(1).item(), 2)\n",
    "    euro = round(df['EURBRL=X'].tail(1).item(), 2)\n",
    "    libra = round(df['GBPBRL=X'].tail(1).item(), 2)\n",
    "    return dolar, euro, libra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "dedc4628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.15"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obtendo_cambio(moedas_busca, inicio, fim)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "00e1f2a6",
   "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>code</th>\n",
       "      <th>name</th>\n",
       "      <th>symbol</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>type</th>\n",
       "      <th>exclusion_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>AFEGANE AFEGANIST</td>\n",
       "      <td>AFN</td>\n",
       "      <td>132</td>\n",
       "      <td>AFEGANISTAO                                   ...</td>\n",
       "      <td>A</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>785</td>\n",
       "      <td>RANDE/AFRICA SUL</td>\n",
       "      <td>ZAR</td>\n",
       "      <td>7560</td>\n",
       "      <td>AFRICA DO SUL                                 ...</td>\n",
       "      <td>A</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>490</td>\n",
       "      <td>LEK ALBANIA REP</td>\n",
       "      <td>ALL</td>\n",
       "      <td>175</td>\n",
       "      <td>ALBANIA, REPUBLICA DA                         ...</td>\n",
       "      <td>A</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>610</td>\n",
       "      <td>MARCO ALEMAO</td>\n",
       "      <td>DEM</td>\n",
       "      <td>230</td>\n",
       "      <td>ALEMANHA                                      ...</td>\n",
       "      <td>A</td>\n",
       "      <td>2002-01-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   code               name symbol  country_code  \\\n",
       "0     5  AFEGANE AFEGANIST    AFN           132   \n",
       "1   785  RANDE/AFRICA SUL     ZAR          7560   \n",
       "2   490    LEK ALBANIA REP    ALL           175   \n",
       "3   610  MARCO ALEMAO         DEM           230   \n",
       "\n",
       "                                        country_name type exclusion_date  \n",
       "0  AFEGANISTAO                                   ...    A            NaT  \n",
       "1  AFRICA DO SUL                                 ...    A            NaT  \n",
       "2  ALBANIA, REPUBLICA DA                         ...    A            NaT  \n",
       "3  ALEMANHA                                      ...    A     2002-01-02  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_moedas = currency.get_currency_list()\n",
    "df_moedas.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "64aa32ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2022, 11, 9)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hoje = datetime.date.today() + datetime.timedelta(days = 1)\n",
    "hoje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cd6f2de",
   "metadata": {},
   "outputs": [],
   "source": [
    "hoje = hoje + datetime.timedelta(days = 1)\n",
    "hoje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "b5e00793",
   "metadata": {},
   "outputs": [],
   "source": [
    "moedas = currency.get(['USD', 'EUR', 'GBP'], start = '2022-01-01', end = '2022-11-09')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "0810e9c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "moedas = moedas.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "746cc2c3",
   "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>Date</th>\n",
       "      <th>EUR</th>\n",
       "      <th>GBP</th>\n",
       "      <th>USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-03</td>\n",
       "      <td>6.3595</td>\n",
       "      <td>7.5724</td>\n",
       "      <td>5.6309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>6.4219</td>\n",
       "      <td>7.6954</td>\n",
       "      <td>5.6776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>6.4210</td>\n",
       "      <td>7.6816</td>\n",
       "      <td>5.6628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-06</td>\n",
       "      <td>6.4435</td>\n",
       "      <td>7.7178</td>\n",
       "      <td>5.7042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-07</td>\n",
       "      <td>6.4415</td>\n",
       "      <td>7.7037</td>\n",
       "      <td>5.6753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>2022-10-31</td>\n",
       "      <td>5.1976</td>\n",
       "      <td>6.0534</td>\n",
       "      <td>5.2570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>5.0851</td>\n",
       "      <td>5.9029</td>\n",
       "      <td>5.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>2022-11-03</td>\n",
       "      <td>5.0122</td>\n",
       "      <td>5.7434</td>\n",
       "      <td>5.1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>4.9841</td>\n",
       "      <td>5.6801</td>\n",
       "      <td>5.0360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>2022-11-07</td>\n",
       "      <td>5.0901</td>\n",
       "      <td>5.8356</td>\n",
       "      <td>5.0886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date     EUR     GBP     USD\n",
       "0   2022-01-03  6.3595  7.5724  5.6309\n",
       "1   2022-01-04  6.4219  7.6954  5.6776\n",
       "2   2022-01-05  6.4210  7.6816  5.6628\n",
       "3   2022-01-06  6.4435  7.7178  5.7042\n",
       "4   2022-01-07  6.4415  7.7037  5.6753\n",
       "..         ...     ...     ...     ...\n",
       "208 2022-10-31  5.1976  6.0534  5.2570\n",
       "209 2022-11-01  5.0851  5.9029  5.1500\n",
       "210 2022-11-03  5.0122  5.7434  5.1349\n",
       "211 2022-11-04  4.9841  5.6801  5.0360\n",
       "212 2022-11-07  5.0901  5.8356  5.0886\n",
       "\n",
       "[213 rows x 4 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moedas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "b631c21d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.09"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(moedas['USD'].tail(1), 2).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "370df5d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12, 6))\n",
    "\n",
    "sns.lineplot(data = moedas, x = 'Date', y = 'USD', color = 'green', label = 'Dólar')\n",
    "sns.lineplot(data = moedas, x = 'Date', y = 'EUR', color = 'red', label = 'Euro')\n",
    "sns.lineplot(data = moedas, x = 'Date', y = 'GBP', color = 'cyan', label = 'Libra Esterlina')\n",
    "ax.set_title('Câmbio', fontsize = 18)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "fig.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1662315d",
   "metadata": {},
   "source": [
    "#### Expectativas\n",
    "\n",
    "Expec Describe\n",
    "EntitySets:\n",
    "  - ExpectativasMercadoTop5Anuais\n",
    "  - ExpectativasMercadoSelic\n",
    "  - ExpectativasMercadoTop5Selic\n",
    "  - ExpectativasMercadoTrimestrais\n",
    "  - ExpectativasMercadoAnuais\n",
    "\n",
    "ExpectativasMercadoTrimestrais:\n",
    "- PIB Total\n",
    "- PIB Agropecuária\n",
    "- PIB Indústria\n",
    "- PIB Serviços\n",
    "- Taxa de desocupação\n",
    "- IPCA\n",
    "- Câmbio\n",
    "\n",
    "ExpectativasMercadoTop5Mensais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "16614820",
   "metadata": {},
   "outputs": [],
   "source": [
    "expec = Expectativas()\n",
    "#expec.describe()\n",
    "\n",
    "# Utilizarei\n",
    "# ExpectativasMercadoTrimestrais\n",
    "# ExpectativasMercadoAnuais\n",
    "# ExpectativasMercadoTop5Anuais TALVEZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "94611ca6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EntitySets:\n",
      "  ExpectativasMercadoTop5Anuais\n",
      "  ExpectativaMercadoMensais\n",
      "  ExpectativasMercadoInflacao12Meses\n",
      "  ExpectativasMercadoSelic\n",
      "  ExpectativasMercadoTop5Selic\n",
      "  ExpectativasMercadoTop5Mensais\n",
      "  ExpectativasMercadoTrimestrais\n",
      "  ExpectativasMercadoAnuais\n"
     ]
    }
   ],
   "source": [
    "expec.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7d7be97",
   "metadata": {},
   "outputs": [],
   "source": [
    "ep = expec.get_endpoint('ExpectativasMercadoAnuais')\n",
    "df = ep.query().filter(\n",
    "                       ep.Data > '2022-08-01',\n",
    "                       ep.numeroRespondentes >= 20).select(ep.Data, ep.DataReferencia,\n",
    "                                                           ep.Media, ep.Mediana, ep.Indicador).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5982d9b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IPCA                                               1516\n",
       "Selic                                              1510\n",
       "Câmbio                                             1466\n",
       "PIB Total                                          1424\n",
       "IGP-M                                              1251\n",
       "IPCA Administrados                                 1203\n",
       "IPCA Livres                                        1178\n",
       "IPCA Serviços                                      1151\n",
       "IPCA Alimentação no domicílio                      1142\n",
       "IPCA Bens industrializados                         1136\n",
       "Taxa de desocupação                                1084\n",
       "Balança comercial                                   869\n",
       "Dívida bruta do governo geral                       737\n",
       "Resultado primário                                  602\n",
       "PIB Agropecuária                                    574\n",
       "PIB Serviços                                        530\n",
       "PIB Indústria                                       529\n",
       "Conta corrente                                      362\n",
       "Investimento direto no país                         349\n",
       "PIB Formação Bruta de Capital Fixo                  318\n",
       "PIB Exportação de bens e serviços                   318\n",
       "PIB Importação de bens e serviços                   318\n",
       "PIB Despesa de consumo da administração pública     318\n",
       "PIB Despesa de consumo das famílias                 318\n",
       "Dívida líquida do setor público                     245\n",
       "Resultado nominal                                   194\n",
       "Name: Indicador, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Indicador.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13f4e5e",
   "metadata": {},
   "source": [
    "##### Funcoes auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "206f3b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtendo_expectativas(indicador, entidade, respondentes):\n",
    "    \n",
    "    # Filtrando para previsoes dos ultimos 3 meses\n",
    "    tres_meses = datetime.date.today() - datetime.timedelta(days = 90)\n",
    "    tres_meses = tres_meses.strftime('%Y-%m-%d')\n",
    "    \n",
    "    # Obtendo dados\n",
    "    ep = expec.get_endpoint(entidade)\n",
    "    df_ep = ep.query().filter(ep.Indicador == indicador,\n",
    "                              ep.Data >= tres_meses,\n",
    "                              ep.numeroRespondentes >= respondentes).select(ep.Data, ep.DataReferencia,\n",
    "                                                           ep.Media, ep.Mediana).collect()\n",
    "\n",
    "    return df_ep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d0481c95",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unindo_prev(x):\n",
    "    \n",
    "    # Selecionando trimestres\n",
    "    trimestres = ['1/2023', '2/2023', '3/2023', '4/2023', '1/2024', '2/2024']\n",
    "    \n",
    "    # Calculando as metricas\n",
    "    dicio_metricas = {}\n",
    "    df_aux = x.copy()\n",
    "    for trimestre in trimestres:\n",
    "        df_aux = x[(x['DataReferencia'] == trimestre)]\n",
    "        dicio_metricas['media_{}'.format(trimestre)] = df_aux['Media'].mean()\n",
    "        dicio_metricas['mediana_{}'.format(trimestre)] = df_aux['Mediana'].mean()\n",
    "    \n",
    "    # Colocando em um dataframe\n",
    "    df = pd.DataFrame(list(dicio_metricas.items()))\n",
    "    df.columns = ['metrica_trimestre', 'valor']\n",
    "    df[['metrica', 'trimestres']] = df['metrica_trimestre'].str.split('_', n = 1, expand = True)\n",
    "    df = df.drop(columns = 'metrica_trimestre')\n",
    "    df = df[['trimestres', 'metrica', 'valor']]\n",
    "    df['metrica'] = np.where(df['metrica'] == 'media', 'Média', 'Mediana')\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a01b68",
   "metadata": {},
   "source": [
    "### Obtendo Expectativas do PIB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75ba423",
   "metadata": {},
   "source": [
    "#### Trimestrais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7d5210ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "pib_total = obtendo_expectativas('PIB Total', 'ExpectativasMercadoTrimestrais', 50)\n",
    "df_previsoes_pib = unindo_prev(pib_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aae535a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (14, 6))\n",
    "\n",
    "sns.barplot(data = df_previsoes_pib, x = 'trimestres', y = 'valor', hue = 'metrica', palette = 'cividis')\n",
    "ax.set_title('Expectativa para o PIB', fontsize = 16)\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.set_xlabel('Trimestres')\n",
    "ax.set_ylabel('%')\n",
    "ax.legend(title = 'Medidas', handles = handles, bbox_to_anchor = [1.1, 0.7, 0, 0])\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "for container in ax.containers:\n",
    "    ax.bar_label(container, fmt = '%.2f%%', padding = 4)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a187af7",
   "metadata": {},
   "source": [
    "#### Anual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "e07236e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pib_total = obtendo_expectativas('PIB Total', 'ExpectativasMercadoAnuais', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "c5f3cbc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.49543625"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pib_total[pib_total['DataReferencia'] == '2022']['Media'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ba9bd4",
   "metadata": {},
   "source": [
    "#### Setores - Agropecuária, Industria e Serviços"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "95cb6cef",
   "metadata": {},
   "outputs": [],
   "source": [
    "pib_servicos = obtendo_expectativas('PIB Agropecuária', 'ExpectativasMercadoAnuais', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dce09e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6543108695652172"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pib_servicos[pib_servicos['DataReferencia'] == '2022']['Media'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "696eaf68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2023    102\n",
       "2022     92\n",
       "2024     85\n",
       "2025     75\n",
       "2026     74\n",
       "Name: DataReferencia, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pib_servicos['DataReferencia'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a442d014",
   "metadata": {},
   "source": [
    "### Obtendo dados de expectativa para o IPCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fc26361",
   "metadata": {},
   "outputs": [],
   "source": [
    "ep = expec.get_endpoint('ExpectativasMercadoTrimestrais')\n",
    "df = ep.query().filter(ep.Data > '2022-08-01', ep.numeroRespondentes >= 30).select().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "8fd30eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IPCA Administrados               475\n",
       "IPCA Livres                      457\n",
       "IPCA Serviços                    451\n",
       "IPCA Bens industrializados       450\n",
       "IPCA Alimentação no domicílio    450\n",
       "Name: Indicador, dtype: int64"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Indicador.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "697caf78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtendo_expectativas_varios(entidade, respondentes):\n",
    "    \n",
    "    # Filtrando para previsoes dos ultimos 3 meses\n",
    "    tres_meses = datetime.date.today() - datetime.timedelta(days = 90)\n",
    "    tres_meses = tres_meses.strftime('%Y-%m-%d')\n",
    "    \n",
    "    # Obtendo dados\n",
    "    ep = expec.get_endpoint(entidade)\n",
    "    df_ep = ep.query().filter(ep.Data >= tres_meses,\n",
    "                              ep.numeroRespondentes >= respondentes).select().collect()\n",
    "\n",
    "    return df_ep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd86b3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ipca_grupos_func(x):\n",
    "    \n",
    "    indicadores = ['IPCA Administrados', 'IPCA Livres', 'IPCA Alimentação no domicílio',\n",
    "                   'IPCA Serviços', 'IPCA Bens industrializados']\n",
    "\n",
    "    trimestres = ['4/2022', '1/2023', '2/2023', '3/2023', '4/2023']\n",
    "    \n",
    "    x = x[x['Indicador'].isin(indicadores)]\n",
    "    x = x[x['DataReferencia'].isin(trimestres)]\n",
    "    x_media = x.groupby(['Indicador', 'DataReferencia'], as_index = False)['Media'].mean()\n",
    "    x_media['Indicador'] = np.where(x_media['Indicador'] == 'IPCA Bens industrializados', 'IPCA Bens Industrializados',\n",
    "                                   np.where(x_media['Indicador'] == 'IPCA Alimentação no domicílio','IPCA Alimentação: Domicílio',\n",
    "                                           x_media['Indicador']))\n",
    "    return x_media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "33af5971",
   "metadata": {},
   "outputs": [],
   "source": [
    "entidade_trimestrais = 'ExpectativasMercadoTrimestrais'\n",
    "ipca_grupos = obtendo_expectativas_varios(entidade_trimestrais, 30)\n",
    "df_ipca_grupos = ipca_grupos_func(ipca_grupos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95f80b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def inflacao_junto(x):\n",
    "    \n",
    "    ep = expec.get_endpoint('ExpectativasMercadoTrimestrais')\n",
    "    df = ep.query().filter(ep.Data > '2022-08-01', ep.numeroRespondentes >= 30).select().collect()\n",
    "    \n",
    "    # Calculando as metricas\n",
    "    dicio_metricas = {}\n",
    "    df_aux = x.copy()\n",
    "    for trimestre in trimestres:\n",
    "        df_aux = x[(x['DataReferencia'] == trimestre)]\n",
    "        dicio_metricas['media_{}'.format(trimestre)] = df_aux['Media'].mean()\n",
    "        dicio_metricas['mediana_{}'.format(trimestre)] = df_aux['Mediana'].mean()\n",
    "    \n",
    "    # Colocando em um dataframe\n",
    "    df = pd.DataFrame(list(dicio_metricas.items()))\n",
    "    df.columns = ['metrica_trimestre', 'valor']\n",
    "    df[['metrica', 'trimestres']] = df['metrica_trimestre'].str.split('_', n = 1, expand = True)\n",
    "    df = df.drop(columns = 'metrica_trimestre')\n",
    "    df = df[['trimestres', 'metrica', 'valor']]\n",
    "    df['metrica'] = np.where(df['metrica'] == 'media', 'Média', 'Mediana')\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3891fd82",
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtendo_expectativas_varios(entidade, respondentes):\n",
    "    \n",
    "    # Filtrando para previsoes dos ultimos 3 meses\n",
    "    tres_meses = datetime.date.today() - datetime.timedelta(days = 90)\n",
    "    tres_meses = tres_meses.strftime('%Y-%m-%d')\n",
    "    \n",
    "    # Obtendo dados\n",
    "    ep = expec.get_endpoint(entidade)\n",
    "    df_ep = ep.query().filter(ep.Data >= tres_meses,\n",
    "                              ep.numeroRespondentes >= respondentes).select().collect()\n",
    "\n",
    "    return df_ep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d79ba4ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ipca_gp_ano_func(x):\n",
    "    \n",
    "    indicadores = ['IPCA Administrados', 'IPCA Livres', 'IPCA Alimentação no domicílio',\n",
    "                   'IPCA Serviços', 'IPCA Bens industrializados']\n",
    "    \n",
    "    anos = ['2022', '2023']\n",
    "\n",
    "    x = x[x['Indicador'].isin(indicadores)]\n",
    "    x = x[x['DataReferencia'].isin(anos)]\n",
    "    x_media = x.groupby(['Indicador', 'DataReferencia'], as_index = False)['Media'].mean()\n",
    "    x_media['Indicador'] = np.where(x_media['Indicador'] == 'IPCA Bens industrializados', 'IPCA Bens Industrializados',\n",
    "                                   np.where(x_media['Indicador'] == 'IPCA Alimentação no domicílio','IPCA Alimentação: Domicílio',\n",
    "                                           x_media['Indicador']))\n",
    "    return x_media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "ce9c4a65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (14, 6))\n",
    "\n",
    "sns.barplot(data = df_previsoes_ipca, x = 'trimestres', y = 'valor', hue = 'metrica', palette = 'cividis')\n",
    "ax.set_title('Expectativa para o IPCA', fontsize = 16)\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "labels = ['Média', 'Mediana']\n",
    "ax.set_xlabel('Trimestres')\n",
    "ax.set_ylabel('%')\n",
    "ax.legend(title = 'Medidas', handles = handles, labels = labels, bbox_to_anchor = [1.1, 0.7, 0, 0])\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "for container in ax.containers:\n",
    "    ax.bar_label(container, fmt = '%.2f%%', padding = 4)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36c140b",
   "metadata": {},
   "source": [
    "### Obtendo expectativas para os Juros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2307b29c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtendo_expectativas(indicador, entidade, respondentes):\n",
    "    \n",
    "    # Filtrando para previsoes dos ultimos 3 meses\n",
    "    tres_meses = datetime.date.today() - datetime.timedelta(days = 90)\n",
    "    tres_meses = tres_meses.strftime('%Y-%m-%d')\n",
    "    \n",
    "    # Obtendo dados\n",
    "    ep = expec.get_endpoint(entidade)\n",
    "    df_ep = ep.query().filter(ep.Indicador == indicador,\n",
    "                              ep.Data >= tres_meses,\n",
    "                              ep.numeroRespondentes >= respondentes).select(ep.Data, ep.DataReferencia,\n",
    "                                                           ep.Media, ep.Mediana, ep.Minimo, ep.Maximo,\n",
    "                                                           ep.Indicador, ep.IndicadorDetalhe).collect()\n",
    "\n",
    "    return df_ep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "cfaad604",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ExpectativasMercadoAnuais tem SELIC\n",
    "entidade = 'ExpectativasMercadoAnuais'\n",
    "df = obtendo_expectativas('Taxa de desocupação', entidade, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f813f993",
   "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>Data</th>\n",
       "      <th>DataReferencia</th>\n",
       "      <th>Media</th>\n",
       "      <th>Mediana</th>\n",
       "      <th>Minimo</th>\n",
       "      <th>Maximo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>2022</td>\n",
       "      <td>8.9550</td>\n",
       "      <td>9.000</td>\n",
       "      <td>6.20</td>\n",
       "      <td>10.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>2023</td>\n",
       "      <td>9.4392</td>\n",
       "      <td>9.590</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>2024</td>\n",
       "      <td>9.2886</td>\n",
       "      <td>9.500</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>2025</td>\n",
       "      <td>8.9268</td>\n",
       "      <td>9.000</td>\n",
       "      <td>6.50</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>2026</td>\n",
       "      <td>8.7249</td>\n",
       "      <td>9.000</td>\n",
       "      <td>6.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>2022</td>\n",
       "      <td>8.4365</td>\n",
       "      <td>8.400</td>\n",
       "      <td>7.50</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>2023</td>\n",
       "      <td>8.9988</td>\n",
       "      <td>8.920</td>\n",
       "      <td>7.60</td>\n",
       "      <td>11.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>2024</td>\n",
       "      <td>8.9451</td>\n",
       "      <td>8.900</td>\n",
       "      <td>7.50</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>2025</td>\n",
       "      <td>8.7025</td>\n",
       "      <td>8.600</td>\n",
       "      <td>7.41</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>2026</td>\n",
       "      <td>8.5720</td>\n",
       "      <td>8.461</td>\n",
       "      <td>7.00</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>334 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Data DataReferencia   Media  Mediana  Minimo  Maximo\n",
       "0    2022-08-12           2022  8.9550    9.000    6.20   10.70\n",
       "1    2022-08-12           2023  9.4392    9.590    7.00   11.60\n",
       "2    2022-08-12           2024  9.2886    9.500    7.00   11.50\n",
       "3    2022-08-12           2025  8.9268    9.000    6.50   11.00\n",
       "4    2022-08-12           2026  8.7249    9.000    6.00   11.00\n",
       "..          ...            ...     ...      ...     ...     ...\n",
       "329  2022-11-04           2022  8.4365    8.400    7.50    9.10\n",
       "330  2022-11-04           2023  8.9988    8.920    7.60   11.23\n",
       "331  2022-11-04           2024  8.9451    8.900    7.50   11.50\n",
       "332  2022-11-04           2025  8.7025    8.600    7.41   10.50\n",
       "333  2022-11-04           2026  8.5720    8.461    7.00   10.50\n",
       "\n",
       "[334 rows x 6 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "6d23d74f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unindo_selic(x):\n",
    "    \n",
    "    # Selecionando trimestres\n",
    "    anos = ['2022', '2023']\n",
    "    \n",
    "    x = x[x['DataReferencia'].isin(anos)]\n",
    "    \n",
    "    # Calculando as metricas\n",
    "    dicio_metricas = {}\n",
    "    df_aux = x.copy()\n",
    "    for ano in df_aux['DataReferencia']:\n",
    "        dicio_metricas['media_{}'.format(ano)] = df_aux['Media'].mean()\n",
    "        dicio_metricas['mediana_{}'.format(ano)] = df_aux['Mediana'].mean()\n",
    "        dicio_metricas['maximo_{}'.format(ano)] = df_aux['Maximo'].mean()\n",
    "        dicio_metricas['minimo_{}'.format(ano)] = df_aux['Minimo'].mean()\n",
    "    \n",
    "    # Colocando em um dataframe\n",
    "    df = pd.DataFrame(list(dicio_metricas.items()))\n",
    "    df.columns = ['metrica_trimestre', 'valor']\n",
    "    df[['metrica', 'anos']] = df['metrica_trimestre'].str.split('_', n = 1, expand = True)\n",
    "    df = df.drop(columns = 'metrica_trimestre')\n",
    "    df = df[['anos', 'metrica', 'valor']]\n",
    "    \n",
    "    df['metrica'] = np.where(df['metrica'] == 'maximo', 'Máximo',\n",
    "                              np.where(df['metrica'] == 'media', 'Média',\n",
    "                                       np.where(df['metrica'] == 'minimo', 'Mínimo', 'Mediana')))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "59c310c9",
   "metadata": {
    "scrolled": true
   },
   "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>anos</th>\n",
       "      <th>metrica</th>\n",
       "      <th>valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>Média</td>\n",
       "      <td>8.914928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>Mediana</td>\n",
       "      <td>8.961242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>Máximo</td>\n",
       "      <td>10.815489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>Mínimo</td>\n",
       "      <td>7.027517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>Média</td>\n",
       "      <td>8.914928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023</td>\n",
       "      <td>Mediana</td>\n",
       "      <td>8.961242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023</td>\n",
       "      <td>Máximo</td>\n",
       "      <td>10.815489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023</td>\n",
       "      <td>Mínimo</td>\n",
       "      <td>7.027517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anos  metrica      valor\n",
       "0  2022    Média   8.914928\n",
       "1  2022  Mediana   8.961242\n",
       "2  2022   Máximo  10.815489\n",
       "3  2022   Mínimo   7.027517\n",
       "4  2023    Média   8.914928\n",
       "5  2023  Mediana   8.961242\n",
       "6  2023   Máximo  10.815489\n",
       "7  2023   Mínimo   7.027517"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unindo_selic(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94182cfe",
   "metadata": {},
   "source": [
    "#### Trimestral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "a7d2b234",
   "metadata": {},
   "outputs": [],
   "source": [
    "entidade = 'ExpectativasMercadoTrimestrais'\n",
    "df = obtendo_expectativas('Taxa de desocupação', entidade, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "22eadef8",
   "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>Data</th>\n",
       "      <th>DataReferencia</th>\n",
       "      <th>Media</th>\n",
       "      <th>Mediana</th>\n",
       "      <th>Minimo</th>\n",
       "      <th>Maximo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>3/2022</td>\n",
       "      <td>9.1017</td>\n",
       "      <td>9.1000</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>4/2022</td>\n",
       "      <td>8.9485</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>10.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>1/2023</td>\n",
       "      <td>9.8013</td>\n",
       "      <td>9.8781</td>\n",
       "      <td>7.1</td>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>2/2023</td>\n",
       "      <td>9.7386</td>\n",
       "      <td>9.9000</td>\n",
       "      <td>7.3</td>\n",
       "      <td>11.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-08-12</td>\n",
       "      <td>3/2023</td>\n",
       "      <td>9.6337</td>\n",
       "      <td>9.8950</td>\n",
       "      <td>5.7</td>\n",
       "      <td>11.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>4/2022</td>\n",
       "      <td>8.4261</td>\n",
       "      <td>8.4000</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>1/2023</td>\n",
       "      <td>9.2302</td>\n",
       "      <td>9.1000</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>2/2023</td>\n",
       "      <td>9.1343</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>7.7</td>\n",
       "      <td>11.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>3/2023</td>\n",
       "      <td>9.0991</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>7.7</td>\n",
       "      <td>11.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>2022-11-04</td>\n",
       "      <td>4/2023</td>\n",
       "      <td>9.0027</td>\n",
       "      <td>8.9050</td>\n",
       "      <td>7.6</td>\n",
       "      <td>11.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>425 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Data DataReferencia   Media  Mediana  Minimo  Maximo\n",
       "0    2022-08-12         3/2022  9.1017   9.1000     7.7   10.10\n",
       "1    2022-08-12         4/2022  8.9485   9.0000     6.2   10.70\n",
       "2    2022-08-12         1/2023  9.8013   9.8781     7.1   11.67\n",
       "3    2022-08-12         2/2023  9.7386   9.9000     7.3   11.76\n",
       "4    2022-08-12         3/2023  9.6337   9.8950     5.7   11.72\n",
       "..          ...            ...     ...      ...     ...     ...\n",
       "420  2022-11-04         4/2022  8.4261   8.4000     7.5    9.10\n",
       "421  2022-11-04         1/2023  9.2302   9.1000     7.5   11.67\n",
       "422  2022-11-04         2/2023  9.1343   9.0000     7.7   11.76\n",
       "423  2022-11-04         3/2023  9.0991   9.0000     7.7   11.72\n",
       "424  2022-11-04         4/2023  9.0027   8.9050     7.6   11.23\n",
       "\n",
       "[425 rows x 6 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "b73890fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unindo_prev(x):\n",
    "    \n",
    "    # Selecionando trimestres\n",
    "    trimestres = ['4/2022', '1/2023', '2/2023', '3/2023', '4/2023']\n",
    "    \n",
    "    # Calculando as metricas\n",
    "    dicio_metricas = {}\n",
    "    df_aux = x.copy()\n",
    "    for trimestre in trimestres:\n",
    "        df_aux = x[(x['DataReferencia'] == trimestre)]\n",
    "        dicio_metricas['media_{}'.format(trimestre)] = df_aux['Media'].mean()\n",
    "        dicio_metricas['mediana_{}'.format(trimestre)] = df_aux['Mediana'].mean()\n",
    "    \n",
    "    # Colocando em um dataframe\n",
    "    df = pd.DataFrame(list(dicio_metricas.items()))\n",
    "    df.columns = ['metrica_trimestre', 'valor']\n",
    "    df[['metrica', 'trimestres']] = df['metrica_trimestre'].str.split('_', n = 1, expand = True)\n",
    "    df = df.drop(columns = 'metrica_trimestre')\n",
    "    df = df[['trimestres', 'metrica', 'valor']]\n",
    "    df['metrica'] = np.where(df['metrica'] == 'media', 'Média', 'Mediana')\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "d2556718",
   "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>trimestres</th>\n",
       "      <th>metrica</th>\n",
       "      <th>valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4/2022</td>\n",
       "      <td>Média</td>\n",
       "      <td>8.613451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/2022</td>\n",
       "      <td>Mediana</td>\n",
       "      <td>8.639792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/2023</td>\n",
       "      <td>Média</td>\n",
       "      <td>9.407821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/2023</td>\n",
       "      <td>Mediana</td>\n",
       "      <td>9.407993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/2023</td>\n",
       "      <td>Média</td>\n",
       "      <td>9.370833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2/2023</td>\n",
       "      <td>Mediana</td>\n",
       "      <td>9.391372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3/2023</td>\n",
       "      <td>Média</td>\n",
       "      <td>9.325289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3/2023</td>\n",
       "      <td>Mediana</td>\n",
       "      <td>9.413403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4/2023</td>\n",
       "      <td>Média</td>\n",
       "      <td>9.184332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4/2023</td>\n",
       "      <td>Mediana</td>\n",
       "      <td>9.312500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trimestres  metrica     valor\n",
       "0     4/2022    Média  8.613451\n",
       "1     4/2022  Mediana  8.639792\n",
       "2     1/2023    Média  9.407821\n",
       "3     1/2023  Mediana  9.407993\n",
       "4     2/2023    Média  9.370833\n",
       "5     2/2023  Mediana  9.391372\n",
       "6     3/2023    Média  9.325289\n",
       "7     3/2023  Mediana  9.413403\n",
       "8     4/2023    Média  9.184332\n",
       "9     4/2023  Mediana  9.312500"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unindo_prev(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9e40e27",
   "metadata": {},
   "source": [
    "#### Obtendo expectativas para o cambio: Taxa de Desocupação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2443ecb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thari\\AppData\\Local\\Temp\\ipykernel_1204\\3837323876.py:21: FutureWarning: In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.\n",
      "  df[['metrica', 'trimestres']] = df['metrica_trimestre'].str.split('_', 1, expand = True)\n"
     ]
    }
   ],
   "source": [
    "previsao_desocupacao = obtendo_expectativas('Taxa de desocupação')\n",
    "df_previsoes_desocupacao = unindo_prev(previsao_desocupacao)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43f66a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (14, 6))\n",
    "\n",
    "sns.barplot(data = df_previsoes_desocupacao, x = 'trimestres', y = 'valor', hue = 'metrica', palette = 'cividis')\n",
    "ax.set_title('Expectativa para o Desemprego', fontsize = 16)\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "labels = ['Média', 'Mediana']\n",
    "ax.set_xlabel('Trimestres')\n",
    "ax.set_ylabel('%')\n",
    "ax.legend(title = 'Medidas', handles = handles, labels = labels, bbox_to_anchor = [1.1, 0.7, 0, 0])\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "for container in ax.containers:\n",
    "    ax.bar_label(container, fmt = '%.2f%%', padding = 4)\n",
    "    \n",
    "fig.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d5f2b4",
   "metadata": {},
   "source": [
    "#### Obtendo expectativas para o cambio: Dolar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "e6592cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "entidade = 'ExpectativasMercadoAnuais'\n",
    "df = obtendo_expectativas('Balança comercial', entidade, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "ecb6f853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Saldo          365\n",
       "Exportações    362\n",
       "Importações    362\n",
       "Name: IndicadorDetalhe, dtype: int64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.IndicadorDetalhe.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8e7ea814",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unindo_gov(x):\n",
    "    \n",
    "    # Selecionando trimestres\n",
    "    anos = ['2022', '2023']\n",
    "    \n",
    "    x = x[x['DataReferencia'].isin(anos)]\n",
    "    \n",
    "    # Calculando as metricas\n",
    "    dicio_metricas = {}\n",
    "    df_aux = x.copy()\n",
    "    for ano in anos:\n",
    "        dicio_metricas['media_{}'.format(ano)] = df_aux['Media'].mean()\n",
    "        dicio_metricas['mediana_{}'.format(ano)] = df_aux['Mediana'].mean()\n",
    "        dicio_metricas['maximo_{}'.format(ano)] = df_aux['Maximo'].mean()\n",
    "        dicio_metricas['minimo_{}'.format(ano)] = df_aux['Minimo'].mean()\n",
    "    \n",
    "    # Colocando em um dataframe\n",
    "    df = pd.DataFrame(list(dicio_metricas.items()))\n",
    "    df.columns = ['metrica_ano', 'valor']\n",
    "    df[['metrica', 'anos']] = df['metrica_ano'].str.split('_', n = 1, expand = True)\n",
    "    df = df.drop(columns = 'metrica_ano')\n",
    "    df = df[['anos', 'metrica', 'valor']]\n",
    "    \n",
    "    df['metrica'] = np.where(df['metrica'] == 'maximo', 'Máximo',\n",
    "                              np.where(df['metrica'] == 'media', 'Média',\n",
    "                                       np.where(df['metrica'] == 'minimo', 'Mínimo', 'Mediana')))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2c3c2b08",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'unindo_bc' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12528\\4095127369.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0munindo_bc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Saldo'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'unindo_bc' is not defined"
     ]
    }
   ],
   "source": [
    "unindo_bc(df, 'Saldo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b59382bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dab8af3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74cf1957",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thari\\AppData\\Local\\Temp\\ipykernel_1204\\3837323876.py:21: FutureWarning: In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.\n",
      "  df[['metrica', 'trimestres']] = df['metrica_trimestre'].str.split('_', 1, expand = True)\n"
     ]
    }
   ],
   "source": [
    "previsao_cambio = obtendo_expectativas('Câmbio')\n",
    "df_previsoes_cambio = unindo_prev(previsao_cambio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c2f954c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (14, 6))\n",
    "\n",
    "sns.barplot(data = df_previsoes_cambio, x = 'trimestres', y = 'valor', hue = 'metrica', palette = 'cividis')\n",
    "ax.set_title('Expectativa para o Dólar', fontsize = 16)\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "labels = ['Média', 'Mediana']\n",
    "ax.set_xlabel('Trimestres')\n",
    "ax.set_ylabel('Reais')\n",
    "ax.legend(title = 'Medidas', handles = handles, labels = labels, bbox_to_anchor = [1.1, 0.7, 0, 0])\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "for container in ax.containers:\n",
    "    ax.bar_label(container, fmt = '%.4f', padding = 4)\n",
    "    \n",
    "fig.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b249634",
   "metadata": {},
   "source": [
    "### Expectativas Governo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ed24aef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "expec = Expectativas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c09f02c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "entidade = 'ExpectativasMercadoAnuais'\n",
    "df = obtendo_expectativas('Investimento direto no país', entidade, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "762db502",
   "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>Indicador</th>\n",
       "      <th>IndicadorDetalhe</th>\n",
       "      <th>Data</th>\n",
       "      <th>DataReferencia</th>\n",
       "      <th>Media</th>\n",
       "      <th>Mediana</th>\n",
       "      <th>Minimo</th>\n",
       "      <th>Maximo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Investimento direto no país</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-08-15</td>\n",
       "      <td>2022</td>\n",
       "      <td>61.8532</td>\n",
       "      <td>58.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Investimento direto no país</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-08-15</td>\n",
       "      <td>2023</td>\n",
       "      <td>66.2800</td>\n",
       "      <td>64.50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Investimento direto no país</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-08-15</td>\n",
       "      <td>2024</td>\n",
       "      <td>70.6642</td>\n",
       "      <td>70.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Investimento direto no país</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-08-15</td>\n",
       "      <td>2025</td>\n",
       "      <td>74.5426</td>\n",
       "      <td>73.41</td>\n",
       "      <td>55.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Indicador IndicadorDetalhe        Data DataReferencia  \\\n",
       "0  Investimento direto no país             None  2022-08-15           2022   \n",
       "1  Investimento direto no país             None  2022-08-15           2023   \n",
       "2  Investimento direto no país             None  2022-08-15           2024   \n",
       "3  Investimento direto no país             None  2022-08-15           2025   \n",
       "\n",
       "     Media  Mediana  Minimo  Maximo  \n",
       "0  61.8532    58.00    45.0    88.0  \n",
       "1  66.2800    64.50    50.0    90.0  \n",
       "2  70.6642    70.00    50.0    90.0  \n",
       "3  74.5426    73.41    55.0    98.0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fbc0ba36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.61625108695652"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['DataReferencia'] == '2023']['Media'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2924bb64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.39609019607842"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['DataReferencia'] == '2022']['Media'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "03053a49",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unindo_gov(x):\n",
    "    \n",
    "    # Selecionando trimestres\n",
    "    anos = ['2022', '2023']\n",
    "    \n",
    "    x = x[x['DataReferencia'].isin(anos)]\n",
    "    \n",
    "    # Calculando as metricas\n",
    "    dicio_metricas = {}\n",
    "    df_aux = x.copy()\n",
    "    for ano in anos:\n",
    "        df_aux = x[(x['DataReferencia'] == ano)]\n",
    "        dicio_metricas['media_{}'.format(ano)] = df_aux['Media'].mean()\n",
    "        dicio_metricas['mediana_{}'.format(ano)] = df_aux['Mediana'].mean()\n",
    "        dicio_metricas['maximo_{}'.format(ano)] = df_aux['Maximo'].mean()\n",
    "        dicio_metricas['minimo_{}'.format(ano)] = df_aux['Minimo'].mean()\n",
    "    \n",
    "    # Colocando em um dataframe\n",
    "    df = pd.DataFrame(list(dicio_metricas.items()))\n",
    "    df.columns = ['metrica_ano', 'valor']\n",
    "    df[['metrica', 'anos']] = df['metrica_ano'].str.split('_', n = 1, expand = True)\n",
    "    df = df.drop(columns = 'metrica_ano')\n",
    "    df = df[['anos', 'metrica', 'valor']]\n",
    "    \n",
    "    df['metrica'] = np.where(df['metrica'] == 'maximo', 'Máximo',\n",
    "                              np.where(df['metrica'] == 'media', 'Média',\n",
    "                                       np.where(df['metrica'] == 'minimo', 'Mínimo', 'Mediana')))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "52ad27df",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = unindo_gov(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "da89c131",
   "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>anos</th>\n",
       "      <th>metrica</th>\n",
       "      <th>valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>Média</td>\n",
       "      <td>66.396090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>Mediana</td>\n",
       "      <td>63.826446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>Máximo</td>\n",
       "      <td>88.104975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>Mínimo</td>\n",
       "      <td>51.868627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>Média</td>\n",
       "      <td>69.616251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023</td>\n",
       "      <td>Mediana</td>\n",
       "      <td>67.563586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023</td>\n",
       "      <td>Máximo</td>\n",
       "      <td>92.108989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023</td>\n",
       "      <td>Mínimo</td>\n",
       "      <td>51.476428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anos  metrica      valor\n",
       "0  2022    Média  66.396090\n",
       "1  2022  Mediana  63.826446\n",
       "2  2022   Máximo  88.104975\n",
       "3  2022   Mínimo  51.868627\n",
       "4  2023    Média  69.616251\n",
       "5  2023  Mediana  67.563586\n",
       "6  2023   Máximo  92.108989\n",
       "7  2023   Mínimo  51.476428"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56d1bb2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf9ede01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ecc7a83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Balança comercial                                  1074\n",
       "Dívida bruta do governo geral                       775\n",
       "Resultado primário                                  739\n",
       "Resultado nominal                                   590\n",
       "Dívida líquida do setor público                     587\n",
       "Câmbio                                              570\n",
       "IGP-M                                               570\n",
       "IPCA                                                570\n",
       "PIB Total                                           570\n",
       "Selic                                               570\n",
       "Taxa de desocupação                                 554\n",
       "IPCA Livres                                         549\n",
       "IPCA Administrados                                  548\n",
       "IPCA Alimentação no domicílio                       519\n",
       "IPCA Bens industrializados                          510\n",
       "IPCA Serviços                                       510\n",
       "PIB Serviços                                        429\n",
       "PIB Indústria                                       426\n",
       "PIB Agropecuária                                    425\n",
       "Investimento direto no país                         389\n",
       "Conta corrente                                      381\n",
       "PIB Importação de bens e serviços                   377\n",
       "PIB Exportação de bens e serviços                   377\n",
       "PIB Formação Bruta de Capital Fixo                  377\n",
       "PIB Despesa de consumo da administração pública     377\n",
       "PIB Despesa de consumo das famílias                 375\n",
       "Name: Indicador, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Indicador.value_counts()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}