{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "34a7a981-1718-4dcb-af8c-981e0fa84023",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "390c33fa-ab42-4d69-ac06-604beb2c69db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.optimize\n",
    "from impedance.models.circuits import CustomCircuit\n",
    "# from impedance.visualization import plot_nyquist # Kept if you want to switch plotting methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a055f3f-6b2e-4fa8-8395-acebacade488",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4d796ec7-48d9-4a23-bd98-c607067d330d",
   "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>E</th>\n",
       "      <th>i</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.325550</td>\n",
       "      <td>-7.687700e-08</td>\n",
       "      <td>2.99401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.325052</td>\n",
       "      <td>-6.444170e-08</td>\n",
       "      <td>5.98802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.324545</td>\n",
       "      <td>-5.771010e-08</td>\n",
       "      <td>8.98204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.324054</td>\n",
       "      <td>-5.352630e-08</td>\n",
       "      <td>11.97605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.323553</td>\n",
       "      <td>-5.153480e-08</td>\n",
       "      <td>14.97006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          E             i         T\n",
       "0 -0.325550 -7.687700e-08   2.99401\n",
       "1 -0.325052 -6.444170e-08   5.98802\n",
       "2 -0.324545 -5.771010e-08   8.98204\n",
       "3 -0.324054 -5.352630e-08  11.97605\n",
       "4 -0.323553 -5.153480e-08  14.97006"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Data Loading ---\n",
    "\n",
    "def lpr_cor_import(filename):\n",
    "    \"\"\" Import cor file as pandas dataframe.\"\"\"\n",
    "    return pd.read_csv(\n",
    "        filename,\n",
    "        skiprows=26,\n",
    "        sep='\\s+',\n",
    "        header=None,\n",
    "        names=[\"E\", \"i\", \"T\"],\n",
    "    ) #index_col=\"Freq\")\n",
    "    \n",
    "try:\n",
    "    LPR_CS_1_df = lpr_cor_import(\"Cast_Stellite1_Sample1_Actual/LPR.cor\")\n",
    "    LPR_CS_2_df = lpr_cor_import(\"Cast_Stellite1_Sample2_Actual/LPR.cor\")\n",
    "    LPR_CS_3_df = lpr_cor_import(\"Cast_Stellite1_Sample3_Actual/LPR.cor\")\n",
    "    LPR_HS_1_df = lpr_cor_import(\"HIPed_Stellite1_Sample1_Actual/LPR.cor\")    \n",
    "    LPR_HS_2_df = lpr_cor_import(\"HIPed_Stellite1_Sample1_Actual/LPR_2.cor\")    \n",
    "    \n",
    "except FileNotFoundError as e:\n",
    "    print(f\"Error: File was not found.\")\n",
    "    print(e.message)\n",
    "    print(e.args)\n",
    "    exit()\n",
    "except Exception as e:\n",
    "    print(f\"Error reading the CSV file: {e}\")\n",
    "    exit()\n",
    "\n",
    "LPR_HS_2_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1267470a-2c4c-4338-ad9e-3a72e2511d20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a0e44f37-75f5-4ca6-b815-c5ee2e527ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_concat = pd.concat((LPR_1_df, LPR_2_df, LPR_3_df))\n",
    "df_means = df_concat.groupby(df_concat.index).mean()\n",
    "df_err = df_concat.groupby(df_concat.index).std()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "07827e93-a83b-4020-856b-f8391fdf8c75",
   "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>E</th>\n",
       "      <th>i</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.224382</td>\n",
       "      <td>-1.450166e-08</td>\n",
       "      <td>2.99401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.223866</td>\n",
       "      <td>-4.205267e-09</td>\n",
       "      <td>5.98802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.223365</td>\n",
       "      <td>2.062133e-09</td>\n",
       "      <td>8.98204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.222874</td>\n",
       "      <td>5.875600e-09</td>\n",
       "      <td>11.97605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.222380</td>\n",
       "      <td>9.003733e-09</td>\n",
       "      <td>14.97006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          E             i         T\n",
       "0 -0.224382 -1.450166e-08   2.99401\n",
       "1 -0.223866 -4.205267e-09   5.98802\n",
       "2 -0.223365  2.062133e-09   8.98204\n",
       "3 -0.222874  5.875600e-09  11.97605\n",
       "4 -0.222380  9.003733e-09  14.97006"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4d717162-2cbf-4d0b-acf5-d7b2528539c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TT7N06VIyMzPZsGEDw4Y5ftnMnj3b2T6oLsxmM9dffz3bt29n6NChfPnll/WuTwghnM5Vav6RPApUzWP4j1KTlb9+lsI9C3bw/tqjDfqjtqn5RDBasmQJQJXHWwDjx4/H39+flStXYjQaG3QdhUJBz549gdr3Flq8eDGlpaX069ePLl26NOj6ouH69OkDwI4dO86/Yy0plUqGDBnCsmXLSEhIwGKxsHz58gsfWInNZmPq1KksX76c3r178/PPPxMQEOCR+oQQrVxupVH0Izt5r446sNrsPPDVTnal5QPw2rJDrDuSff6DfIhPPEqraBBdXYNajUZDjx49SElJ4dChQ/Tu3bCGZ6mpqUDtewt98cUXgA/eLfIPcTyWam78Qy68z3mMHz+e9957j4ULF/Laa6/VqWfa+QQGBtK/f3/S0tLIzq7b/8D33nsv3377LZ06deLXX38lNDTUIzUJIQR5lYJRWJLXyqgtu93Ocz/tY+UfrsFypw5MZPhFkec5yrd4PRgVFhaSn58PQHx8fLX7xMfHk5KSwqlTpxoUjDZs2MD27dvRaDSMHTv2gvufPn2atWvX4ufnx0033VTv6zYKhaJVzrA8duxYunfvzv79+3nppZfOO8BjYWEhaWlpzgHBLuTYsWMAxMXF1bqep556innz5pGYmMiKFSuIjo6u9bFCCHFBBZUGcwzz/XalH/6WyhdbXL23x3SNZs413X2nfW4teP1RmsFgcC4HBgZWu0/FdA+V962rwsJC7rjjDgAeeeQR2rS5cGOwBQsWYLPZuPLKK+v8gde9e/dqvyo+fEX9KBQKvvjiC/z9/ZkzZw5PPvkkxcXFbvvY7XZ++uknLr30UrZt2wY4Jo8dMGAA33//PWaz2W1/s9nM3Llz2bVrFwEBAbUKzeBo9/byyy8TGxvLypUrSUxM9MybFEKICpV7Hwf69uCOh84V8eqyg8713gmhvP2Xi/FTeT1q1IlH7hhNnjyZffv21emY+fPn079//1o1yGpooy2r1crNN9/MkSNH6N+/Py+88EKtjqt4jFYx/o3wDX369GHlypVcf/31vPLKK7z99tsMGjTIOYlsSkoKGRkZ+Pv7uw2vsHXrViZNmoRer+eSSy4hNjaW/Px8du3axdmzZ1GpVHzwwQe1esy6a9cuZs6cCUD79u156aWXqt1vxowZMpGsEKJ+LCawVGpbq9V7r5ZaSMstcS7r/f34+PZLCdR4/cFUnXmk4hMnTjjnW6qtkhLHP2BwcLDbNr2+6n/4in11Ol296rvrrrv45Zdf6Ny5M7/88gsajeaCx+zdu5e9e/ei1+u59tpr63zNmuZxqe1jHXF+Q4YM4ejRo8ybN4+ff/6ZPXv2kJeXh06no3Pnztx9993MmDHD+Xg2JCSETZs2sWzZMtauXUtqaiobN25EpVKRmJjI+PHjeeCBB+jVq3aTM+bn5zsD++bNm9m8eXO1+40cOVKCkRCifsqK3Nf9fTsYhetcn61+KiWRuuY5jptHglHFQHn1odfrCQkJoaCggPT0dLp161Zln4oZ0uvzqOLxxx/nk08+ISEhgRUrVriNXXQ+n3/+OQDXX3+99DDyUTqdjscee4zHHnvsgvsqFAoGDRrEoEGDPHLtkSNHNqvup0KIZujP7XLsNu/UUUsRQa5glFdiwmqzo1I2n7ZFFXziwV9Fg+rqumCbzWb27duHVqulc+fOdTrvyy+/zL/+9S+io6NZsWJFrUetttlsfPXVV4A8RhNCCOElmiD3dVNJ9fv5iIJSV/tNux0sNt8OcjXxiWA0frxjiPNFixZVeW3JkiUYjUZGjx7tnAS0Nv7zn//w1FNPERoayvLly+sUqtauXUt6ejoJCQmMGDGi1scJIYQQHqPSgELlWjf7djD6cZdrfMB+SWFo/VTn2dt3+UQwmjFjBnq9nh9//JHvvvvOuT0zM5NZs2YBVDtNQ5cuXejSpQunT592275o0SLuuecedDod//vf/5yDAtZWRaPrW265xW30bCGEEKLJKBSgrtRb24eDkdVm5+fdrmA0oU9bL1bTMD7RXDw8PJxPPvmEKVOmMHnyZEaMGEFkZCQrV64kPz+fBx98kNGjR1c5rqLBd+Xu15mZmdxyyy3YbDbat2/PvHnzmDdvXpVjJ06cyMSJE6tsNxqNLF68GPDBQR2FEEK0LuoAMJU3wjY3bPaHxrTmYCaZRY453fyUCsY1s/nRKvOJYASORs7r1q1j7ty5bNmyBZPJRNeuXbnvvvuYPn16rc9TUlKCyWQCXD3LqpOUlFRtMPrpp58oLCzk4osvlh5kQgghvEtdqQmJD98x+s+6VOfy6K7RhAdduPe3r/KZYASOLthLly6t9f7V9QpKSkpqUG+hKVOmMGXKlHofL4QQQnhM5UdppuKa9/Oinafy2Hoi17l+1/BkL1bTcNKARgghhPBVAeGu5dLcmvfzoo82uOZzu6RdGJe0Cz/P3r5PgpEQQgjhq4Iqjb1XnOW9OmpQUGJmxX7XhLF3Dmved4tAgpEQQgjhuyrPj1ac7b06avC/fWcxWR3jFUUEaRjTtflPpC3BSAghhPBV5yp1IPIP9VoZNfl+h2u4nGt6xzW7CWOr0/zfgRBCCNESFaTD6UpTbnW+ynu1VGN3Wr5bo+vrLm6+YxdVJsFICCGE8EV//OxaDk+GGN8aQuatlYedyz3bhtArPsSL1XiOBCMhhBDCFx1f51ruek3VSWW9aMepPNYccjUGf/TyTih8qL6GkGAkhBBC+KKzu13LiYO8V0c1Pl7v6qLfJyGUkZ2jvFiNZ0kwEs2OQqG44F8mc+bMQaFQMGfOnDptr/yl1WpJTk7mzjvv5OjRox5+F+6mTZuGQqFg7dq1tT5myZIlPPXUU4wZM4aQkBAUCgVjx45tvCKFEE3HkAWFleYBbdPbe7VUY3d6vnP5ruHJLeZuEfjYyNdCeFvv3r2dkw7n5+fz+++/89FHH/H111+zevVq+vXr590CK5k6dSoFBQXeLkMI0RjO7HAtB0VDsO/MPVZqsnI6v9S53iOuZbQtqiDBSIhKJk6c6HY3yWAwcNNNN/HLL79wzz33kJKSUvPBTez666+na9eu9OvXj6KiIq655hpvlySE8JRja1zLCf19qn1RaraBipm3NH5K2oYFeLcgD5NgJMR56HQ63n//fdq1a8f27dtJT08nPj7e22UB8PHHHzuX6/IITgjRDBxb5VrucJn36qjG0r3nnMsdo3SolL4T2jxB2hgJcQGJiYmEhzvm/klLS3NuNxgMvPDCC/Ts2ZPAwED0ej0jRozghx9+qPFcixcvpn///gQEBBATE8Ntt93GmTNnGvstCCGak4J0yHZ1hafjaO/V8idGs5UFv590rl/bJ86L1TQOuWMkxAXYbDaKix2zWmu1WgAyMjK47LLLOHDgAG3btuXyyy+npKSEzZs3c9111/Hyyy/zxBNPuJ3n3Xff5YEHHkClUjFixAgiIyNZuXIlAwcOpHdv32pYKYTwouwjrmV9WwhL8lopf/bDztPklZgBCFCr+Eu/RC9X5HkSjJopu91OkbnI22XUWbA6uNn1XlixYgVlZWWo1Wq6dOkCwPTp0zlw4ACzZs1i7ty5qNVqAFJTU7niiiv4+9//zrhx4+jVqxcAJ06cYObMmWi1WpYtW8bIkSMBKCkpYeLEiSxZssQr700I4YNMBtdygO/MVG+12fnP+lTn+uRL4gkJVHuxosYhwaiZKjIXMeSrId4uo842/mUjeo3eI+dq7ICVn5/PmjVruO+++wC4/fbbCQwMZNeuXSxdupTBgwfzyiuvuNWRnJzM66+/zsSJE/noo494++23Afjkk08oKyvjzjvvdIYigMDAQN555x26du2KvaI1oxCidSurFIy0Ou/V8Se/7D1Lapbj7rlSAdOHJHm3oEYiwUg0W7fffnuNr+3atYvdu3fX+HpNnn/+eZ5//vkq28eOHctbb70FOO4gAUyYMKHacDZ06FAAtm3b5ty2YcMGAKZMmVJl/86dO3PxxRezY8eOKq8JIVqhkhzXssY3gpHNZufd1a5HfFf3iiM5yjdq8zQJRqLZ+vTTT2t8bc6cOfUKRpXHMdJqtcTFxTF69Ghn2AHHYzGA2bNnM3v27BrPlZ2d7VyuaGCdmFj98/jExEQJRkIIh8pjGEV19l4dlfy0+wyHMxx3shQKuP+yjl6uqPFIMGqmgtXBbPzLRm+XUWfB6mBvl3Befx7HqDpWqxWAYcOGkZycXON+kZGRzuWKx2TNrX2VEMIL0l13m4n3/qCyZwtKee6n/c71q3rE0inGt3+XN4QEo2ZKoVB4rK2OqJuKcYwmT57Mgw8+WKtj4uLiOHz4MCdPnuSiiy6q8vqpU6c8WqMQopkyZEF+pd8HXg5GNpudmQt3U1Dq6IkWpFExe2wXr9bU2GQcIyHqaMyYMQDnHa/ozyoexS1cuLDKa4cPH2bXrl2eKE0I0dxZy9zXA0K9UkaFTzYeZ+NRV5un567tTruIIC9W1PgkGAlRRwMHDmT06NGsWbOGRx55BIPB4Pa6zWbj119/dTa4Bkf3fo1Gw/z581m/fr1ze2lpKQ899BA2m63J6hdC+LDgNqCs1AU+33t3k7edyOWVpQed61f1iOWGS3xj5P/GJMFIiHpYsGABvXr14q233qJdu3aMHj2am266iWHDhhEbG8uVV17pNq9acnIyr776KkajkVGjRjFmzBhuuukmOnbsyL59+7j66qvrXMOLL77IwIEDGThwIPfeey8AW7ZscW4bOHAgZ8+e9dh7FkI0AaUKQit10sg74ZUyMguN3LtgBxabo31krN6ff1zXs1W0k5Q2RkLUQ0xMDFu2bOHDDz/km2++Ydu2bZhMJtq0acPFF1/MhAkTqnTNf/jhh2nbti2vvvoqGzZsIDg4mCuuuILXXnuNp59+us41HDt2jN9//91tW0FBgdu2srKyPx8mhPB16kDXcmlek1/eZLFx74IdZBU5fn+oVQren9qXsCBNk9fiDQq7jCrXpLp37w7A/v37z7ufzWbj0KFDgGOcG6VSbu4Jz5OfMyF8TGkevNoeKP9ovmcTxHRv0hKe+WEfn29xzYc2d2IPpg5s16Q11KS2n6ENIb8FhRBCCF9xYiPOUBQYCVFdm/TyX2w56RaKJl8Szy0DWt58aOcjwUgIIYTwFSc3uZbbD4cmvIu7JTWHOZXGK+odH8LciT1aRbuiyiQYCSGEEL6i6IxrOaZbk102x1Dm1tg6OljLvFsvxV+tarIafIUEIyGEEMJXlOa7lgPCm+yyn206QW6xCQCNn5J5t15CbIh/k13fl0gwEkIIIXxF5V5oAWFNckmj2coXv7vGS3rwso5cnNg01/ZFEoyEEEIIX2ExupYrd9tvRD/sPO28W+SvVvpMDzRvkWAkhBBC+IzKDZ2bZjSdn3a72jVd3zee0MDWMV5RTSQYCSGEEL5CUeljuYmGGUzPK3Uuj+kW0yTX9GUSjIQQQghfUTkY2SyNfjm73U5mkevxXUxw62xwXZkEIyGEEMJXaINdyyZDzft5SFGZBaPZNYl1tF7b6Nf0dRKMhBBCCF/hH+JaNhY0+uW2n3T1ggvUqAhv5e2LQIKREEII4TuaOBitPJDhXB52USRKZesa5bo6EoyEEEIIX6Hycy1bzY16KZvNzso/XMFoTFdpeA0SjEQzpFAoLjh3z5w5c1AoFMyZM6dO2yt/abVakpOTufPOOzl69KiH34W7adOmoVAoWLt2ba32z8jI4OOPP+a6666jU6dOBAQEEBoayogRI/jss8+wN1FvFiGEh5VValekCWrUS208lk1GYRkASgVc1iW6Ua/XXPhdeBchWo/evXvTp08fAPLz8/n999/56KOP+Prrr1m9ejX9+vXzboHlHnvsMRYsWIBaraZfv35ccsklpKens2HDBtatW8eSJUv4+uuvUala3zxHonWw2W3klOY418P9w1EpW8DPu6nYtazRNeql/rMu1bk8vFMUETppeA0SjIRwM3HiRLe7SQaDgZtuuolffvmFe+65h5SUFO8VV0lERASvvvoqM2bMIDzcNZ/Stm3bGDNmDIsWLeLjjz/mrrvu8mKVQjQeo8XIZQsvc67/duNvhPs33dxijcZc4lrWNN7I13+cLWT9kWzn+l3DkhvtWs2NPEoT4jx0Oh3vv/8+ANu3byc9Pd3LFTn8+9//ZtasWW6hCKBfv3488cQTAHz11VfeKE0I0RCVpwTxa7wxhf6v0t2i7nF6BnWIaLRrNTcSjIS4gMTERGcASUtLc243GAy88MIL9OzZk8DAQPR6PSNGjOCHH36o8VyLFy+mf//+BAQEEBMTw2233caZM2dq3L8+evfuDeDx8wohmoDF5Fr2a5xHW+l5JW7TgNw5LPmC7TZbE3mUJsQF2Gw2iosdz/21WscvqoyMDC677DIOHDhA27ZtufzyyykpKWHz5s1cd911vPzyy847NxXeffddHnjgAVQqFSNGjCAyMpKVK1cycOBAZ5jxhNRUx1+CsbGxHjunEKKJWFzTc6BqnGD00frjWGyODhrxYQFc3atNo1ynuZJgJMQFrFixgrKyMtRqNV26dAFg+vTpHDhwgFmzZjF37lzUajXgCCVXXHEFf//73xk3bhy9evUC4MSJE8ycOROtVsuyZcsYOXIkACUlJUycOJElS5Z4pFaz2ex89DdhwgSPnFMI0YQMma7lIM8/3sorNvH1tlPO9buGJ+OnkodHlUkwaqbsdju2oiJvl1FnyuBgj92ybexbv/n5+axZs4b77rsPgNtvv53AwEB27drF0qVLGTx4MK+88opbHcnJybz++utMnDiRjz76iLfffhuATz75hLKyMu68805nKAIIDAzknXfeoWvXrh7pYv/MM8/wxx9/0L59e+6+++4Gn08IX2Wz29zWlS2hZYixAMoKXeshCR6/xIoDGc4pQMKDNNxwieev0dxJMGqmbEVFHO4/wNtl1Fmnrb+j0us9cq7bb7+9xtd27drF7t2763zO559/nueff77K9rFjx/LWW28BjjtI4LgjU104Gzp0KODoIVZhw4YNAEyZMqXK/p07d+biiy9mx44dda63sq+++orXXnsNf39/vvzySwIDG69HixDeZrVb3dZbRFf9gtOuZZUGAiM9fok1h1x3pMb3bEOApgX8u3mYBCPRbH366ac1vjZnzpx6BaPK4xhptVri4uIYPXq0M+yA47EYwOzZs5k9e3aN58rOdnWFrWgInZiYWO2+iYmJDQpGK1asYNq0aSiVSr766isGDhxY73MJ0RxUCUaKFvABX3kMI60elJ69C2a22ty66I/qEuXR87cUEoyEqOTP4xhVx2p1/EIeNmwYyck1j/0RGen6a6/iMVljPP77/fffue666zCbzXz88cdMnDjR49cQwtdYbBa3dT9lC/g4U6ldy40wHcjKAxkYyhz/blo/JYOSPX9HqiVoAT9JrZMyOJhOW3/3dhl1pgwO9nYJDRYfHw/A5MmTefDBB2t1TFxcHIcPH+bkyZNcdNFFVV4/depUNUdd2P79+xk3bhzFxcW88cYbTJ8+vV7nEaK5MVlNbutqpbqGPZuRyt3z//T+Gsput/POatfURmO6xshjtBpIMGqmFAqFx9rqiLoZM2YMzzzzDD/88EOtg9HQoUNZu3YtCxcuZMyYMW6vHT58mF27dtW5jhMnTnDFFVeQm5vLnDlzeOSRR+p8DiGaq8rBSKvStoxxeEpyLrxPPa36I5MDZ10Nu+8d1aHRrtXc+VQz/k2bNjFu3DjCw8PR6XT079+fzz77rM7n2b59O3PmzGHYsGHExcWh1WpJSEhg6tSp7Nmzp8bjrFYr77//PgMGDCA4OBiNRkO7du244447OHLkSEPemmhBBg4cyOjRo1mzZg2PPPIIBoPB7XWbzcavv/7qbHANju79Go2G+fPns379euf20tJSHnroIWw29x42F5KZmcnll1/OmTNneOyxx3juueca9qaEaGaMVtcI0RqlxouVeND+713LCf09dlqj2co/lx9yro/pGkP3uBCPnb+l8Zk7Rt9//z033HADNpuN4cOHExkZyapVq5g2bRq7d+/mjTfeqNV5LBYLl156KeBo49G/f38CAwPZuXMnCxYs4Ntvv+XLL79k8uTJbsfZ7XYmTZrETz/9RFBQEMOGDUOn07Fz507++9//snDhQtasWeM8t2jdFixYwBVXXMFbb73F/Pnz6dOnD1FRUZw+fZpDhw6RlZXFm2++6Wy0nZyczKuvvsojjzzCqFGjGDlyJJGRkaxfvx6lUsnVV19dp7GM/va3v3H06FECAwPJzs5m2rRpVfaJjIzkX//6l6feshA+Jd+Y71zWa1vA3XOr2T0Y9arag7W+nv1xH4cyXMO7PHBZR4+duyXyiWCUl5fH9OnTsVqtLF68mEmTJgGO0YWHDh3Km2++yTXXXMOoUaNqdb4BAwbwzDPPcNVVV6Esb9Vvs9l49tlneemll7jjjjucH0wVfv75Z3766Sfat2/Ppk2bnKMG22w2Zs6cyZtvvsljjz3Gb7/95uF3L5qjmJgYtmzZwocffsg333zDtm3bMJlMtGnThosvvpgJEyZU6Zr/8MMP07ZtW1599VU2bNhAcHAwV1xxBa+99hpPP/10na6fl5cHOAaIrOmuart27SQYiRYr2+jqXRUZ0AIaEaf+5nqUptJC12s8ctpvtp3i2xTXHI/TBifROyHUI+duqRR2T4wq10D//Oc/mTVrFhMmTKgyz9T333/PpEmTuPrqq/n5558bdB273U63bt04ePAgn376qds4ODNnzuT111/nlVdeqdIFOy8vj/DwcAICAigpKfnzaeuke/fugKPR7PnYbDYOHXLc+uzcubMz4AnhSfJzJpqr/+77L29sdzxJGJ04mrdGveXdghrqf7Ng6zzHcper4aYFDT7lrrR8pszbjMnieFTfNzGUr+8ahMav+f5/XtvP0IbwiX+dikcIf368BTB+/Hj8/f1ZuXIlRqOxyut1oVAo6NmzJ1B1gs2KObBqOg6oMpO5EEII78gvy3cuh2pDvVaHxxxd4Vq+6IoGn25vegG3ffy7MxSFB2l475a+zToUNRWf+BeqaBDdt2/fKq9pNBp69OiB0Wh0/mXbEDVNsHn55ZcD8J///Idz5845t9tsNudIyOcbaVkIIUTTKTa7BkPUqXVerMQDco5BbqprvePoBp1u3+kCpn78O4VGx5hFfkoF7/zlYtqEBDTovK2F14NRYWEh+fn5gGt8mD+r2F7fsV4qbNiwge3bt6PRaBg7dqzbayNHjuTRRx8lNTWVjh07ctVVVzFlyhQ6derEhx9+yEMPPVTtVBFCCCGaXuVgFKQJ8mIlHnDC1YOVqC4QUv1nYW3sP1PALR/9TkGpY4BIP6WC927py5COLaAdVhPxeuPryl2da5rbKSgoqMq+dVVYWMgdd9wBwCOPPEKbNm2q7PP6668THx/PrFmzWLZsmXN77969GTlyJH5+tf/nqngO+mfHjh2jQwcZP0IIIRqicjAK9Gvm8wKmb3Uttxtc79NkFZUx/b/b3ELRuzf35crusRc4UlTmkWA0efJk9u3bV6dj5s+fT//+/Ws1o3hD24dbrVZuvvlmjhw5Qv/+/XnhhReq7FNWVsZtt93G4sWLefrpp5k+fToRERGkpKTw4IMPct111/HOO+9w//33N6gWIYQQDVd5gMcAv2b+iCjNNeE08fUbv8hqs/PwNzvJLCoDQFX++GxsDwlFdeWRYHTixIk6t/+p6N0VXGmKiJKSEvTVjOZcsa9OV7/nyHfddRe//PILnTt35pdffkGjqToY2Msvv8y3337Lww8/7PbIbNSoUfzvf/+ja9euPPnkk9x88821aoRdU4v5mu4kCSGEqL0ya5lzuVlPB1JWBNmVPj/rObDjO6uPsPGoa+TsZ6/uxlU9qz4ZERfmkTZGKSkp2O32On2NHDkSAL1eT0iIYwTO9PT0as9fsb2mmcnP5/HHH+eTTz4hISGBFStWuI1dVNnnn38OVN8zLiEhgYEDB2IwGEhJSalzDUIIITzrz1OCNFtFrs4+KFQQ1r7Op9h6PJd/r3LNzjC+ZxtuG9TOE9W1Sl5vfA2ONjwAO3bsqPKa2Wxm3759aLVaOnfuXKfzvvzyy/zrX/8iOjqaFStWkJCQUOO+FeGrujtWlbfn5ubWqYb6qjzvT12nixCitir/bLWIuaZEq5Fd6hrgsVmPfG3IdC0HRUE9xhJ7Z/URKlqctIsI5OXre8r/zw3gE8Fo/PjxACxatKjKa0uWLMFoNDJ69Gj8/f1rfc7//Oc/PPXUU4SGhrJ8+fILhqqK7vvV3RGyWq3s3LkTgKSkpFrX0BAKhcL5yK+4uPgCewtRPxU/WxqNRn6RimbDbDNzrsR1pyVeV/9eXF5XXCkY6aLqfPixLAPrj7hC4suTeqL3b8aPFn2ATwSjGTNmoNfr+fHHH/nuu++c2zMzM5k1axYAjz76aJXjunTpQpcuXTh9+rTb9kWLFnHPPfeg0+n43//+R58+fS5Yw8SJEwF49tlnOXz4sHO71WrlySef5MSJE7Rr165J50qraH+VkZFBQUEBFosFm80mX/LV4C+LxUJBQQEZGRluP2tCNAfnDOew2R13OxUoiNPFebmiBji52bUcUvfmIp9vPulc7hwTzKDkCE9U1ap5vbs+OEaU/uSTT5gyZQqTJ09mxIgRREZGsnLlSvLz83nwwQcZPbrqgFcVDb7NZrNzW2ZmJrfccgs2m4327dszb9485s2bV+XYiRMnOsMQOALR8uXLOXToEL169WLw4MGEh4ezc+dOUlNTCQgI4JNPPqlTl/2GioiIoLi4GKPRWGWkbiE8xd/fn4gI+WUqmo8co6uRcZh/GBpV1Q41zYLNCgd+dK13HlvzvtXYm17Al1td4/vdNrid3Pn1AJ8IRgDXX38969atY+7cuWzZsgWTyUTXrl257777mD59eq3PU1JSgsnkaJS3d+9e9u7dW+1+SUlJbsEoIiKCbdu28frrr/P999+zdetW56Sgt99+O7Nnz6Zr164Neo91pVKpSExMJCcnh6KiIuf7EsITNBoNwcHBREREoFKpvF2OELVWOQiZbebz7OnjTm0BQ/kjQYXKMUdaLeUYyrj7i+3OKT8idRom9mnbGFW2Oj4TjACGDBnC0qVLa71/deMbJSUl1Xvco+DgYObMmcOcOXPqdXxjUKlUREdHEx0d7ezRJ0RDKRQK+ctSNFv+fq72pkZLw+bQ9KqTm1zL7YdBYO3m47RYbTz49U5O55cCoFTA23+5mCCtT32kN1vyr9iMyIeZEEKAweSaBcFis2C325vn78bKPdACwmp1iM1m5+8/7HMbs+jJq7oyuINM+eEpPtH4WgghhKithYcXOpd7RPZonqEIwD/EtWwsuODudrud53/ez9fb0pzbru7VhhnD6j72kaiZBCMhhBDNRk5pDr+k/uJcv7nrzV6spoE0lXqDXiAY2e12Xll6kM8q9UK7tF0Yr03u1XyDoY+SYCSEEKLZ+PHYj84G11EBUVzZ7kovV9QAZyoNahwUXeNudrud1389zLx1qc5tveND+O/0fgRqpEWMp8m/qBBCiGbjaN5R5/L45PGoVc10MEO7HQ7+z7XeqfqAZ7PZeWHJAT7ddMK5rVsbPfPvGECwDOTYKCQYCSGEaDYqTwXSVteMu6dn7IcC1xhEdL6qyi4Wq40nvtvLou2ueUQ7xej4/K/9CQmUUNRYJBgJIYRoNioP7hgR0IwHJs095lqO6AjBsW4vl1msPPz1Lpbuc0190is+hM+m9ycsqJkOaNlMSDASQgjRbFS+YxTh34yDka5SECpxn5w8LbeEexfsYO9pV4PsAe3D+ej2S+XxWROQYCSEEKJZsNgs5BnznOtRAXWfdNVnhFaaF600F8qKQBvM6oMZPPLNbgpKXSN6X9Ylmvdv6Yu/WkaobwoSjIQQQjQLecY87LhG/2/Wj9Iqj2EEWPLP8OYuO++tOea2ferARJ67pjtqlXQibyoSjIQQQjQL54pd7W0C/AIIVAd6sZoGOrjEuWhXB3L/LxksO1zk3BagVvHK9T2ZIPOfNTkJRkIIIZqFlIwU5/JFoRd5sRIP2P6Zc3FzwAi3UNQhKogPp17CRTHB1R0pGpkEIyGEEM3C5jObncsD4wZ6sZIGyj0OJzc4V/+Z5Xovl3eL4c0b+6CTCWG9Rv7lhRBC+Lwyaxk7Ml0jRQ9qM8iL1TRQxn7n4ilbFDvtHQEY0SmK927ui8ZP2hN5k/zrCyGE8HlnDWcps5YBoFQo6R3V28sVNUCpq3v+WSIABf2Swvhw6iUSinyA/BcQQgjh82x2m3NZrVQ336lAALPBNUhlrj2Yrm30fDytHwEa6Y7vCyQYCSGE8HmVg5FS0bw/uo4c2OlcziWEt27sg14GbvQZzfunSwghRKuQWZrpXFag8GIlDZOeW0zYWVfD64DkwXSOld5nvkSCkRBCCJ9mt9v5cPeHzvUekT28WE392Wx23vrqJ9ooXI/SxlzzFy9WJKojwUgIIYRPW5u2lp2ZrsdPd/e+23vFNMAnG4/jf+Z353peSDf0kXFerEhUR4KREEIIn2W323l759vO9eHxw+kX28+LFdXP4YwiXlt+iGBKndtC47t4sSJREwlGQgghfNbms5s5mn8UcLQteqjvQ16uqO4KSszc/cV2TBYbKqzO7QqlDCXoiyQYCSGE8Flf/vGlc3l4/HA6hXXyYjV1Z7LY+NsXKaRmFQOgVriCERKMfJIEIyGEED4prTCNdenrnOs3d73Zi9XUnd1u58nv9rIl1TWg46SIU64d/PVeqEpciAQjIYQQPmlbxjbs2AFI0ic1q2lAyixWnvtpP4t3pDu33dvVSEKBayJcuk1s+sLEBcl9PCGEED7JYDI4l5NCklAomsf4RUcyinjw6138cbbQua1/+3AeC1ng2qlNb0hsxhPhtmASjIQQQvikUourB1egX6AXK6kdu93Ogt9P8eKSA5RZXCN1d22j5/9u6Ijq/UWunQfcDc0k6LU2EoyEEEL4pCJTkXM5wC/Ai5VcWGaRkae/38eKAxlu228f1I4nx3XFf+cnUBH0AsKh+yQvVClqQ4KREEIIn7Qjc4dzOVGf6MVKamaz2fl6WxqvLP2DQqPFuT08SMM/J/didNcYsNth+2eug/rcDGp/L1QrakOCkRBCCJ+Tb8xnX/Y+5/qQuCFerKZ6RzKKePK7vaSczHPbPuyiSF6f0pvo4PLwc3Y3ZOx17dD3tiasUtSVBCMhhBA+Z/PZzc4eaZEBkT41fpHRbOX9NUf54LdjmK125/ZgrR+zrurCLf0TUSortR86ucm1HN8Pojo3YbWiriQYCSGE8DmnDaedyz0ievhMj7Tj2cXc88V2Dp4rcts+rmcsz13TnRh9NY/Isv5wLbe9tJErFA0lwUgIIYTPiQ2KdS5nlGScZ8+ms2zfWR5fuIeiMldborgQf16Y0IMx3WJqPjCzUjCKlvnRfJ0EIyGEED4nSZ/kXD5ReAK73e61u0Zmq43Xlh3k/9Yfd9t++6B2zBrbhSDtBT5Kc466lqMkGPk6CUZCCCF8TuU7RqWWUvLK8gj3D2/yOorLLNzx6TZ+P+6a1kOn9eO1yb0Y17PNhU9gtUBppcbZwbU4RniVBCMhhBA+Z3fWbueyTq0jWBPslTpe//WwWyjqHBPMB1P7khylq90JSt17rBHY9OFO1I0EIyGEED5n9anVzuVh8cNQK9VNXsP+MwV8usn1+OzqXm14bXIvAjV1+OgsyXEtK9WgqWWgEl4jk8gKIYTwKXnGPNakrXGuX5ZwWZPXUGax8swP+7CV98ZPigjkXzf0rlsoAlBXGrHbZgZzac37Cp8gd4yEEEL4DLvdzgubX3BOBxLgF8CQtk03uKPZamNhSjrvrj7CmQKjc/sLE3rgr1bV/YT6tqD0A1t5T7b8kxDd1UPVisYgwUgIIYTP+Dn1Z1aeWulcf+DiB5qkfZHFauP7nad5e/UR0nLd7+pc3asNwztF1e/EKj8ITYTcVMd63gkJRj5OgpEQQgifsPH0Rv7x+z+c6wNiB3BL11sa9Zo2m52f95zh3yuPkJpd7PaaRqXkL/0TeHJcA4KMqQRK813r8ijN50kwEkII4VVmm5l3d77LJ/s+cW7TqXW8OORFlIrGawq7/WQez/20j32nC922+ykV3HBpPPdfdhFtQwNqOLqWdn4OpeW92tSB0H5Ew84nGp0EIyGEEF5zxnCGWetmuXXP16q0vDT0JdroGmfMn6yiMl5ddpBF29PdtisVcN3F8Tw0+iISIwIbfiGrGTa941q/ZBoERTT8vKJRSTASQgjhFRtPb+TxdY87G1oDtA9pz79G/KtRJo21WG3M33ySN1ccdpvWA2B8rzY8MqYTHaM92J1+/etQkOZYVqph0P2eO7doNBKMhBBCNLmskiweWfsIpRZXm5sJHSbw1ICnCFR74G5NJWarjaX7zvHe6qMcynCf/LVLbDDPX9udAckevpNz6nf47VXX+sVTIaStZ68hGoUEIyGEEE1u3p55zlAU4BfAMwOf4ZoO13j0GllFZXy19RRfbDlJZlGZ22t6fz8eu6IztwxIxE/l4XZMxgL4bgbYbY710HZw+QuevYZoNBKMhBBCNKm0ojQWH17sXH+o70MeDUW70/L5bNMJluw5i8lqc3tNoYAbL03g8Ss7E6HTeuyabta8DPmnyi+ogus/An9941xLeJwEIyGEEE3q//b8Hxa7o41PXFAcN3S6ocHnNJqtLN9/jk83nWDnqfwqr2tUSq7pHccdQ5PoHhfS4Oud18FfXMvDH4eE/o17PeFREoyEEEI0qY1nNjqX7+59NxqVpl7nySoqY/XBDFb+kcn6I1kYzbYq+8Totdw6sB039U8ksrHuEFVWeAYKTrnW+97W+NcUHiXBSAghRJMpMZeQWZLpXO/fpvZ3U+x2O0cyDaw4kMHKPzLYlZaP3V79vpe2C2PakCSu7B6L2tNtiM4n7XfXckiCNLhuhiQYCSGEaDInCk84l7UqLW2Czj9WkdlqY9uJXFYeyGTlHxmcyi2pcV+d1o+xPWKZNjiJHm0b+XFZdcxG+O2frnV5hNYs+VQw2rRpE3PnzmXLli2YTCa6devGfffdx+23316n82zfvp2ff/6ZVatWcezYMXJycoiOjmbEiBHMmjWLXr16VXuc1Wrl/fff59NPP+XgwYP4+fnRp08fHn74Ya677jpPvEUhhGjVNp/Z7Fxup29X7cjWJSYL6w5nsXx/Bqv+yKDQaKmyT4W4EH/GdIthTNcYBiSHo/Wrx0SvnrL6Rcjc71qXx2jNks8Eo++//54bbrgBm83G8OHDiYyMZNWqVUybNo3du3fzxhtv1Oo8FouFSy+9FIDIyEj69+9PYGAgO3fuZMGCBXz77bd8+eWXTJ482e04q9XKxIkTWbJkCTqdjmHDhmGxWNi0aROTJk3i+eef59lnn/X4+xZCiNbCaDHy+YHPnevD44c7l/NLTKz6I5Pl+8+xrob2QhV6xYcwuksMY7pF062NHoVC0ah118qx1bD5Xdd6/7sgeaTXyhH1p7Dba3pC23Ty8vJo3749BQUFLF68mEmTJgGQkZHB0KFDOXr0KKtXr2bUqFEXPJfFYmHo0KE888wzXHXVVSiVjr9GbDYbzz77LC+99BLBwcGkpqYSGRnpPO71119n5syZtG/fnpUrV5KcnAzAgQMHGD16NOfOnWPLli0MGDCgQe+1e/fuAOzfv/8CewohRMvy9cGveen3lwDHY7QvrviRbcfMLN+fwebUHKy26j+ONH5KhnSIYEy3GEZ3iSE2xL8py76w4+vhyxvBXD4JbVQXuGstqBs4z5qooik+Q30iGP3zn/9k1qxZTJgwgR9++MHtte+//55JkyZx9dVX8/PPPzfoOna7nW7dunHw4EE+/fRTt0d0HTt25NixYyxYsICbb77Z7bj33nuP+++/n+uuu47vvvuuQTVIMBJCtFbX/nAtxwuOAxBiHkX60Str3DdY68dlXaO5snssIzpFEaT1mQcc7o6uhK9vAYvRsa7SwoyV0Kb6JhuiYZriM9QnftKWLFkCUOXxFsD48ePx9/dn5cqVGI1G/P3r/5eCQqGgZ8+eHDx4kDNnzji3FxQUcOzYMQBGjhxZ5biKbcuWLcNkMqHR1K9rqRBCtFYmi420wtPO9TPpParsE6nTckX3GK7sHsug5Ag0fk3Ym6w+Dv4CC6eB1eRY9wuAmxZIKGrmfCIY7dmzB4C+fftWeU2j0dCjRw9SUlI4dOgQvXv3btC1UlNTAYiNjXVuKy4udi6HhYVVOSY8PByA0tJSDh8+TI8eVf+HFkIIUZXFauO7nad5e9URzNHgbGttdzSSTggPYGz3WK7sHsvFiWGolD7QXqg20rfDt7eBrbxhuEYHN38DSUO9W5doMK8Ho8LCQvLz8wGIj4+vdp/4+HhSUlI4depUg4LRhg0b2L59OxqNhrFjxzq3h4eHo1KpsFqtnDx5ki5durgdd/LkSefyiRMnJBgJIcQFFJSYWXUwg7dXHeFEjqOLvS7KFXrG94rhnkHD6Nom2DcaT9eF3Q7LnnCFIv8QuGUxJPTzbl3CI7wejAwGg3M5MLD6GZWDgoKq7FtXhYWF3HHHHQA88sgjtGnjGjvD39+f/v37s3nzZj799FNeeeUVt2P/+9//OpeLitxnZq5JxXPQPzt27BgdOnSoa/lCCOGzsg1l7DtdwP4zhew7XcC+MwWk5Za676QsBaXZufrQZV3oGNZM5w878COkb3WtT5kvoagF8Ugwmjx5Mvv27avTMfPnz6d///7Upu13Q9uHW61Wbr75Zo4cOUL//v154YWqsxw/+eSTXHvttbz++utERUUxdepUrFYrH3/8MR999BF+fn5YLBZnLzchhGht7HY7GYVlzvCz77QjCJ0rNJ73OKUCBvbMZK/J0QU/3D+c9iHtm6Jkz7OYYOVzrvXO46RbfgvjkWB04sQJDh06VKdjSkoct1aDg4Pdtun1Vf+CqNhXp9PVq7677rqLX375hc6dO/PLL79U23j6mmuu4V//+hdPPPEEM2fOZObMmc7XbrnlFo4fP86mTZuqbYNUnZpazNd0J0kIIXyJ3W4nPa/ULQTtP1NAtsFU63OEBqq5rEs094/qyLv7n2VveauEkQkjUSm9OBBjQxxeCnknHMsKFVxe9Q9t0bx5JBilpKTU+1i9Xk9ISAgFBQWkp6fTrVu3Kvukp6cDkJiYWOfzP/7443zyySckJCSwYsUKt7GL/uyxxx5j4sSJLFq0iNTUVPR6PVdeeSVjxoxxNtaWYCOEaEmsNjvpeSWkZhVzLMvAsaxiUrMMHDxXREGp+cInKBep09KzrZ4ebUPoHhdCj7Z62oYGONsPbT3nevQ0Mn6kp99G0zm6yrXcfSJEXuS1UkTj8HobI4DevXuzbt06duzYUSUYmc1m9u3bh1arpXPnznU678svv8y//vUvoqOjWbFiBQkJCRc8pkOHDsyePdtt2969e8nIyKBjx460bSsTAgohmp/8EpMz9KRml3/PKuZkTgkma82jTFcnLsSf7m1D6FEegHq0DSFGf/6hVHRqHQVlBQCcKT5z3n19lt0Ox9a41jte7r1aRKPxiWA0fvx41q1bx6JFi5g6darba0uWLMFoNDJu3Lg6jWH0n//8h6eeeorQ0FCWL19e51BVWcV0JHfddVe9zyGEEI3NbLVxKtdx96ci+KRmO77nFNf+EVhlieGB9GirL78LFEL3OD2ROm2dz3NF0hX8d5+jI8vPx37mlq631Kser8o+DAWnXOsdLjwbg2h+fGLk69zcXNq3b09hYaHblCCZmZkMGTKEo0ePsnLlSkaPHu12XEW3+lWrVrndyVm0aBE33ngjgYGB/PrrrwwaNOiCNRQXF5OWlubWVd9ms/H6668za9YsOnfuzO7du9Fq6/4LoTIZ+VoI0VBlFiuHzxn442yh2+OvU7klWGqYVuN8FAqICwkgOSqIDlE6kqOC6Bilo3tcCCGBao/UfCTvCJN+muRcX3LdEtrp23nk3E1m6RPw+weO5ZiecM8G79bTCrWaka/Dw8P55JNPmDJlCpMnT2bEiBFERkaycuVK8vPzefDBB6uEIsDZ4Ntsdj0Hz8zM5JZbbsFms9G+fXvmzZvHvHnzqhw7ceJEJk6c6FzPysqia9eu9OjRg4suugiVSsXWrVs5deoUSUlJLF26tMGhSAgh6qrUZOXAWUfD532nHY2gD2cU1SsA6bR+JEcFkRwZRHJ5AEqO1NE+MogATeM2hu4Q2oEgdRDF5fOJZRRnNK9gVFYEO79wrV86zWuliMblE8EI4Prrr2fdunXMnTuXLVu2YDKZ6Nq1K/fddx/Tp0+v9XlKSkowmRy3jPfu3cvevXur3S8pKcktGIWHh3P33Xezbt06Vq5cidVqpX379jz33HPMnDmz3j3ihBCitgqNZg6UjwVUMSbQsSwDdclASgUkhAdWCT8dooKICtZ6bTDFI3lHnKFIqVDSObz+zRuaXN5JWP0imMrHsdOGQK+bvFuTaDQ+E4wAhgwZwtKlS2u9f3VPAZOSkuo17pFer+eDDz6o83FCCFEfucWm8rtAhew7U8D+0wXOEaJrQ+OnpEtsMBdFBzsfgXWICiIxIhCtn+91hd9ydotzuXtEd0K0IV6sppbO7oaNb8P+78FudW3veyto5Y/llsqngpEQQrREBSVm9p4uYM/pfPamF7AnvYDT+aUXPrBcoEZFtzZ6Z+PnHm1D6BitQ61qPgPOHsk74lzuHuHDw57Y7ZC6xhGIUtdUfT3iIhjycJOXJZqOBCMhhPCgIqOZfacL2Xs6nz3pBew9XcDJOtwJCvb3c+sG3z0uhPaRQc1nctUaJOpd49AdyqvbgMCNzmp23B06uRH2LoJze6ruE5YEg+6HPreApvrpq0TLIMFICCHqqbjMwv4zhexJz2fv6QL2pheQml1c6+PDgzTOO0A94kLo2TaEhPCA5jepai0MiRvCOzvfAWBP1h6KTEUEa4IvcFQjMZdCegqc3OQIQ+nbwFxDeG3TB4Y8BF2vBZV8ZLYG8l9ZCCGqYbfbyS8xk1lURlZRGZlFxvLvZWQUGjl0roijWQZq26QxNFBNz7Yh9IoPoWfbUHrGhxAX4t8iQ1B1AtQBzmWr3crOzJ0Mjx/eNBc3FkDaVkcIOrkZTm8H2wVG9e44xhGIkoY5xjMQrYYEIyFEq1JmsZJtMJFZ6Ao6lb9nlQegLEMZZmv9hnkL9vdzBiDH9xDiw1rmnaDa2J21m/tX3e+2Ldw/3PMXstuhJBcK0iDvuCsMndsL9guM7q3SQttLoN1g6H4dxPbwfH2iWZBgJIRo9ux2O4WlFrIMRjILHaHG9d3otp5fUvv5v2pDp/Wje5zeEYDiQ+nVNoR2EYGtNgT92W9pvzHzt5kYrUbntln9ZtEjsh7Bw1wKBaehMB0KKr7SKi2fBkstG7WrgyBxgCMItRsCcX1BXfvZFUTLJcFICOHTjGYrJ3KKSc8trfaxVsXdHZOlbvN91ZbGT0l0sJaoYK3ze5TOn3YRgfSMD6F9RBDKZt4wurFsPrOZh9Y8hLW8q7taqeYfQ//B2PZjq+5ss0FxZqWwc7pq8CnJrn8xAWGQOLg8CA2G2F7SZkhUS34qhBBeZ7HaSM8r5XhOMcezijme7fqqS7f2uggNVFcKPP7uwce57I/e30/u/tSDxWbh1a2vOkORTh3Evy+eSX+LH6T81xV2Ck+7gtCF2v3UljoIQuIhprvrjlBUF1A2n+ENhPdIMBJCNAm73U5mURmpzuBj4Hh2ManZxaTlltS7PU9lapWCKJ2WKL0/UTot0Xrtn747AlCkTuPZQRDtdkcbFpsVbBbHYIA2q2ubvXy7c9lWaZ+K1/68reJcNvdtzteq2Wa3/ek6la7nVtefanC+Vl1df34f56vBdZ2fVGaOBTpCkcJuZ97JY/Q6/NeG/1srlBAc5wg+IW3LvyeUfy//8g+VBtOi3iQYCdHa2e11+OA9zwdi+QepwVjGufxiMvKLycgvIbOghKzCYrILS7FYLKiwocSGChthChsDsDIYG0qVze01v0rLaqWd0AAlwRolOrUCnRoC1QqC1BDgpyBQDf4q0ChtKCp/iJdZwWiDzNoElrp/+Du3XahhbytTqlDwXnwbKj5irjEU06vMVLuD/UOqBh19peXgNvIITDQq+ekSzZvbh3oNf5HX+IF4vr/Ia/oLvqawcL6/yP/8F3wtPpQ9cqeglteh4XdqKtMBHcu/3CgBTQNObCr/Ej7v9fBQMv0cHy9qu5378gscLyjVoI+rGnyc621BG+zFyoWQYNQynPq9lh/KtbxN3pQfytXWVYfrePhDXYjGpwClChQqx3eln+PxUOVtCpWjPYzSr+o2t+MqXqt8vF812ypfr5ptCmWl4/50zhqvU/k1V13LcvfxzdEvne92asIVxI252RF8gqKlnY/weRKMWoL/XuU+waEQjUWhrPGDttQK+UYbVpTY7AqsKLGicqyjLF93X7baq9mGCisK92PsSrfzVT7G8V2BBZVzP/fzVd1mR4mfnxo/Pz/8/NSo1X6oy79r1GrUajUatQaN2g+tRoNW44dWrUGrVeOv0aDVqAnQavDXqAnQavHXOtZVKnX1IcYtsLTcti8nCk7w3I65zvWu4V25f+QroGrIrUIhmpYEo5ZA6QdWCUYXVOWv5Lr8VV5zILjwX96Vj/e7wAdnddep6Y7C+Wr481/6danhPHcbzvOhHgAUFBjJyC+huMxKicni+m6yUlJW/r3S9hKT1fmaY9lCSZkVk7UJ2uxYgbK6HmQu/6qev1pJkMaPQK3K8V2jIrD8e5D2T981foQHaegZH0KHKF2znwvNbrfz5PonKbE4ptbQqXW8PuJ1NBKKRDMjwagl0Mc5JkGs9Yd3QwJBY96mb+QaWvBf6r4iNsSf2JCGD5JnstgorQhK5UGqIjQVVwSqMkv5PrUPYY3NaLZhNJvIqf10aQAEalT0aBtC7/gQesWH0js+tNnNmbb13Fb25exzrr845EUS9AlerEiI+pFg1BI8tMvbFQjhURo/JRo/JSGBao+d02azU2p2D1ille5YVQlYVYKWK5CVmKzOZYut4e3cSkxWth7PZevxXOe2sEA1PeNDK4WlEKL1vjsy81cHv3IuD2k7hDHtxnixGiHqT4KREKJVUCoVBGn9CNL6gQc7Ppkstqp3qs4btFzB7GROCYcziqguW+WVmFl3OIt1h7Oc22L1/vSKD6F3gmMOtl5tQz0aHuvrrOEsa9LWONdv7nKzF6sRomEkGAkhRAM47m5pCA2s3/ElJgv7zxSyOy2fPekF7EnP50ROSbX7nis0cu6AkV8PZDi3xYcFkBylIzkyiPaVvuJCA5qs3dLH+z7GVj6WU0JwAkPbDm2S6wrRGCQYCSGEFwVq/OiXFE6/JNds8wUlZvacdgSlisB0rtBY7fHpeaWk55W63VkCR2BLiggsD0rlwSnKEZoigjQea7902nCaxUcWO9dv63YbSoV0yRfNlwQjIYTwMSGBaoZdFMWwi6Kc2zILjewuv6NU8T2/pOYeciaLjcMZBg5nGIAMt9eC/f0q3WHS0T4qiOTIIJIig9Bp6/ax8MGuD7DYLADEBcUx6aJJdTpeCF8jwUgIIZqBaL0/l3fz5/JuMYCje3xabilHs4oqzT/n+DpbUP3dpQpFRgu70wvYnV5Q5bUYvdYZmDpEBdExWsdFMcHEhfi73WU6azjLK1tfYXXaaue2v/X+m3TPF82eBCMhhGiGFAoFiRGBJEYEclkX99dKTBZOZJc4J+tNLQ9MqVnFFJSefwb7jMIyMgrL2JKa67Y9UKOiY7SO5Eh/igPWsKPwW0w2VwBrH9Keazpc47H3J4S3SDASQogWJlDjR7c4Pd3i9FVeyys2OYPS8WyDMzCdyCnGaK55YM0Sk5X9Obs4qv4BVZn7ozmNuSORxXfz7qrjXBSjo2O0jvaRQWj9VB5/b0I0NglGQgjRioQFabgkSMMl7cLctttsds4VGh1BKbuYY5kGjmUZOJpp4GxBMdro/6GJ2Oh+jCWIssxxFBX0ZTUWVnPE+ZpSAe0igugQpXOEpSgd3eL0dI4JRtnMR/kWLZsEIyGEECiVCuJCA4gLDWBIx0jn9uzSbB5dM5OdWdvd9teWDiYnbQx2a/XjFNjsONs8rfzDdYcpLFDNwOQIBneIYFCHCDpE6ZrVCN+i5ZNgJIQQolp7svbwyNpHyCzJdG7rGNqROYPn0DuqN0azldSsYo5mGTiaUcTRLANHMgycyCnGbK1+RPC8EjNL951j6b5zAEQFaxlUKSglhgdKUBJepbDb7Q0fz17UWvfu3QHYv3+/lysRQoiaLUldwrMbn8VsczXWvjr5ap4d9CwBfgHnPdZstXEyp4SjmQaOZhZxNNMxbMDBc4XVjvJdWdvQALc7SnGh57+WaF2a4jNUglETk2AkhPB1mSWZjF081hmKVAoVj/d7nJu73NyguzkFpWa2Hs9l87EcNqfm8MfZwgse07WNnsmXxDOhTxyROm29ry1ahqb4DJVHaUIIIdysOLnCGYpCtCG8NfItLo29tMHnDQlQc3m3GOdYTLnFJn5PzWHTsRw2HcvmWFZxlWP+OFvIi0sO8PL//mBUl2gmXxLPZV2iUatkdG3ROCQYCSGEcLPi5Arn8qSLJnkkFFUnPEjDVT3bcFXPNoBjdO/NqTnOO0onK80ZZ7HZWXEggxUHMogI0jChT1smXxJf7ZAEQjSEBCMhRLNgWLeOohUrUep0KIOCHF+6IFTO5crby5c1MgpzXeWU5rAjY4dz/Yp2VzTZtaP1/kzo05YJfdoCcDTTwOId6Xy3I52MwjJXjcUmPtl4nE82HqdbGz1X927D0I6RdI8LabKJc0XLJcFICNEslO7bR/7ChXU7SK12Bacq4alSqAqqtL38NWVQ+evlxyj8/VtFb6lcYy52HE1PFSjoENrBa7V0jNYxe2wXZl7RmQ1Hs1m0PZ3l+89hsrgGojxwtpADZwt5jUOEBKgZmBzO0I6RDO4YSXJkUKv4byY8S4KREKJZsBmqtj+5ILMZa34+1vz8hhegUlUKT4Gogqq5Q1VDqKoSvgIDUCh9s41MUkgSAX4BlFpKsWNnd9ZuBrYZ6NWaVEoFIzpFMaJTFAWlZpbsOcOi7ensPJXvtl9BqZnl+zNYvt8xblKs3p8hHSMZ0jGCIR0jidH7e6F60dxIMBJCNAtBAwegUCmxGgzYiouxFZdgcy4Xu5ZLSi58svqwWrEVFmIrvHBPqgtSKFAGBlYbrFS6oOrDVKXA5demDeqYmIbXUQ21Us0lMZew4fQGALae3er1YFRZSICaWwa045YB7TiaaeDn3WfYeDSbXWn5WP40FsC5QiOLd6SzeEc6AB2igpx3kwYmRxASoPbGWxA+TrrrNzHpri9E47LbbNhKSqoGpuLimkNVsWPZWlyMzVBcaXsx2GqeP8xrlEqiZ84k4o7pHj/1uvR1PLH+CYpMRQCMShjF25e97fHreJqhzMLW4zlsPJrDxqPZHDxXdN79FQro1kZP//bhDGgfTr+kcCJkOACfJ931hRCijhRKJSqdDpVO1+Bz2e127KWllYJVcZUw5R64XMHKmp+P6dQpz9xh+jObjaLlyz0ajKw2Kx/s/oB5e+a5bR+VMMpj12hMOq0fl3WJ4bIujjtp2YYyNh3LYeORbDYeyyY9r9Rtf7sd9p8pZP+ZQv678QQAF0Xr6N8+vDwsRRAbIo/eWiMJRkIIUQOFQoEiMBBlYCBERWG327EVFGDJznZ8ocBWUoq9zORoy5SdjSUrC0tWtmfaNdXAr00bIv72N4+dL9eYy5Prn2TTmU3ObQF+ATw/+Hmuan+Vx67TlCJ1Wq7tHce1veMAOJVTwoajjpC0+VgOucWmKsccyTRwJNPAgt9PAdAuIpD+Sa6glBAeII25WwF5lNbE5FGaEM2H3Wol+733MWzYgCU7C2tWNnaz+cIH1oefH36Rka6vqEj8oqJQObdF4RcdhV9EBMoAz02TseLkCuZumUuuMde5LUmfxFuj3vJqj7TGZLPZOZpl4PfjufyemsPW47lkFpVd8Lg2If70bx/OkA6RDL0oUqYr8QKZEqQFkmAkRPNRtGYN6ffc26BzKENCKoWdKFfoiawUfKKiUIWENGlPtYKyAv7x+z/43/H/uW2/vN3lvDD4BXSahj+KbC7sdjsnc0rYejyXLccdQenPj96qkxwZxNCLIhnSMZJBHSLQ+0tj7sYmbYyEEMKL1HFxoFbDBe4S+cXGoh8/Dk1Cgiv8REaiioxEqfW9Br3r0tcxZ9McskqznNsC/AJ4uO/D/KXLX1rd4yKFQkFSZBBJkUFM6ZcAwOn8UrYdz3XcVTqeQ2o105WkZheTml3M/M0nUSqgd0Iowzo6gtLFiWFo/HxzSAZxfnLHqInJHSMhmhfjoUMUfPcdRWvWYj51qsb9FFotQYMGoRs1ipCrx6MMCmrCKmtv4+mN3L3ybrdtF0dfzNwhc0nUJ3qpKt+XVVTmmAA3NZsNR7I5kXP+YSECNSoGtA9n6EVRDL8oko7RulYXOBuDPEprgSQYCdE82e12TMeOUbRmDYY1ayndtavGrvzaizrS/ocfUKhUTVtkLdyz8h7nGEUapYYHLn6AW7vdikrpe7X6srTcEjYezXY06D6aTV7J+e8qJoQHMLpLDKO7RjOgfYTcTaonCUYtkAQjIVoGS14eht9+w7BmLcUbNjjGPKqk847tjt5sPsRutzP8m+Hkl+UD8NaotxidONq7RbUANpudA2cL2XDUcTdp64lct2lL/kyn9WN4p0hGd4lhZOcoGT+pDiQYtUASjIRoeewmE2n33kfxBsedmMABA2j32afeLaoa6UXpXPWdq/v9hps2EKIN8WJFLZPRbCXlRB4bjmaz/kgW+8/UPJaVQgF9E8MY3TWaMV1juEgeuZ2XNL4WQohmwFZWRsn27c51Tbt2mNJPo24b51MfcruydjmXE4MTJRQ1En+1iqEXObr0P3FVF84VGFl9MJNVf2Sw4Wg2ZZXuJtntsP1kHttP5vHaskO0CfHn4sRQeseH0ichlB5tQwjSykd1U5J/bSGEaKCSbSnYS13du/O//Zb8b79FFRlJQO/erq8e3b3aKHtd2jrn8iUxl3itjtYmNsSfmwckcvOAREpNVjYdy2blH5msPphBRqH7+ElnC4yc3XuO/+09B4BSAZ1igumTEErvBEdg6hSjw08lbZQaiwQjIYRoIG2HZBQaDXaT+2jK1uxsDKtWYVi1yrFBqUTbqZMrKPXpjSYpqUnGLzLbzM5G1wAjEkY0+jVFVQEaFaO7xjC6awx2ew/2nS5k1cEMVv2Ryd7TBVX2t9nh4LkiDp4r4uttaY5zqFX0bBtC74QQeic47iy1DZVRuT1F2hg1MWljJETLZD5zhqI1ayjdvZvS3bsxn6y5a39lSr2egF69COjdm8AB/Qm8+GIUas8PFLj17Fb++utfAUdvtPU3rSdQ7VuNw1u7zEIjO07lszs9n12n8tmTnk+xyVqrYyN1GnrHh9K9bQidY4LpHKsjKSKoxd1ZksbXLZAEIyFaB0tuLqV79lC6ezfG3bsp3b2nSs+16iiDgwkaMgTdiBHohg/DLyLCI/XM3TKXbw59A8DQtkP5YMwHHjmvaDxWm53ULAM70/LZneYITAfPFmGx1e5jW6NS0iFaR5fYYDqVh6XOsXriQvyb7d0lCUYtkAQjIVonu9WKKTXVeUepdNduyo4edbS+rYlCgX/PnuhGDEc3YiT+3brW67GbxWZh9MLRzvnQXhj8AtdddF1934rwIqPZyv4zBexKK2B3Wj670vI5lXv+wSb/TKf1o1OMIyR1jtHRKTaYLrF6woM0jVS150gwaoEkGAkhKlgNBox791K6ezclW7dRsm3beSepVUVFohs2HN2IEQQNGYxKV7v5zCqPdu2n9GPtlLXSI60FyS02sTs9nz1pBRzKKOTguSJOZBdTyxtLTpE6reOuUoyezrE6OsU47jT5Uq+4VheMNm3axNy5c9myZQsmk4lu3bpx3333cfvtt9fpPOvWrePzzz9n+/btnD59mry8PHQ6Hb179+avf/0rU6dOrfHYP/74g2effZa1a9diMBjo2LEjd9xxBw899BBKDzSQlGAkhKiJraSE4i1bMKz9DcNvv2HJyKhxX4W/PxF3ziDir39F6e9f435H8o4w49cZzrtFI+JH8O7odz1eu/AtRrOVY1kGDp0r4lBGEYfPFXHoXBFnCox1Oo9KqWD/81fir/aNkdFbVTD6/vvvueGGG7DZbAwfPpzIyEhWrVpFfn4+jzzyCG+88UatzzVz5kxef/11OnXqRPv27QkLC+P06dNs2rQJq9XKrbfeyvz586sct2XLFkaPHk1JSQn9+/cnKSmJdevWce7cOa6//noWLlzY4OeyEoyEELVht9spO3QIw2/rMPz2W41TkKjj44l56kl0o0ZV+f10OO8wM5bPIK8sDwAFCj664iP6t+nfFG9B+KBCo5kjGUUcOmfg0LlCDmU4AlNNU5okRQSy9vFRTVxlzVpNMMrLy6N9+/YUFBSwePFiJk2aBEBGRgZDhw7l6NGjrF69mlGjavcf58CBA4SGhhIXF+e2/ejRo4wYMYIzZ86wdOlSxo4d63zNYrHQpUsXjh07xhtvvMEjjzwCgMFg4IorrmDz5s188sknTJ8+vUHvVYKREKI+LHl5FG/Y6JiGZN06bIXuoykHjRhO7FNPoWnXDnCEor8u/6tz+g+lQsncIXO5psM1TV268HF2u50sQxmHzxnKg1IhhzIMHMkoYthFkcy79VJvl+jUaoLRP//5T2bNmsWECRP44Ycf3F77/vvvmTRpEldffTU///xzg6/10ksv8fe//53HH3+c1157zbl94cKFTJkyhd69e7Nr1y63Y3bu3Enfvn3p0aMHe/fubdD1JRgJIRrKajCQ/e575H7+OVhd3bkVajWRDzxA5F13cvvS29mRuQNwhKJ/DP0H45PHe6tk0QzZbHYMJgt6f88PH1FfTfEZ6hMDHCxZsgSAyZMnV3lt/Pjx+Pv7s3LlSozGuj0brY6qfLZrjca99f35arj44otJTk5m3759nDhxosE1CCFEQ6h0OmKemE3yD98T2N/1WMxuNpP1xhuU7NjJnqw9zu3PD35eQpGoM6VS4VOhqKn4RDDas8fxP3Dfvn2rvKbRaOjRowdGo5FDhw416DppaWnMmzcPwO0xGsDu3btrrKHy9or9hBDC61Qq7GXuU0qgVmMO0mCxW5ybhrUd1sSFCdF8eb0PXmFhIfn5+QDEx8dXu098fDwpKSmcOnWK3r171/rcmzdvZt68eVitVs6cOcOGDRuwWCzMnTuXoUOHuu176tSpC9ZQeT8hhPAWu9VK7qefkfXvf7tNQ6KKiiRu7lwM8e6DQuq1+qYuUYhmy+vByGAwOJcDA6sfnj6ofNLFyvvWxrFjx/jss8+c60qlkueff56ZM2fWWIenaqh4DlpdTR06dKjVOYQQojqZb7xB7sefuG0LmXAtMU8+iSo0FEPxObfX0orSSA5JbsoShWi2PBKMJk+ezL59++p0zPz58+nfvz+1aftd3/bhU6dOZerUqZhMJk6cOMH8+fN58cUXWbJkCUuXLiUsLKzKMTV1x/eBNupCCAFA0bLlzmVVVCRtnn+e4Msuc26LCYyhU1gnDucdBmDhoYXM7j+7yesUojnySDA6ceJEndv/lJQ4hjAPDg5226bXV73lW7GvrpajvP6ZRqOhU6dOzJ07l4iICB599FGeffZZ3nnnHec+Op2OvLw8imuYy6iuNdTUYr6mO0lCCFEbVkMx5tOnnevtPpuPNrm92z4KhYIbO9/Ii1teBODHoz/ywMUPyKSxQtSCRxpfp6SkYLfb6/Q1cuRIAPR6PSEhjqHp09PTqz1/xfbExMQG11ox6vWPP/7otr3i3E1RgxBC1IXNZKJ0/37yFi7k3PPPO7crAwPRJLWr9pgrk65EgeMOeJG5iK3ntjZJrUI0d15vYwTQu3dv1q1bx44dO+jWrZvba2azmX379qHVauncuXODrxUeHo5SqSQrK6tKDbt372bHjh2MGzeuynE7djjGA+nVq1eDaxBCiJrYjEbKDh3CeOAAxgMHKN2/n7IjR6GaOdS0F11U46SyCw8vxI6jCYACBQnBCY1atxAthU8Eo/Hjx7Nu3ToWLVpUZR6zJUuWYDQaGTduHP7nmQ+ottavX4/NZqvSAHr8+PHMnz+fRYsW8fe//93ttZ07d5Kamkq3bt1o3979lrUQQtSX1VBM2aGDGPcfwLh/P8YDByhLTXUbtPF8QiZfX+32tKI0Ptz9oXN9SucpdAiVTh9C1IZPBKMZM2bw0ksv8eOPP/Ldd985pwTJzMxk1qxZADz66KNVjuvSpQsAq1atom3bts7tc+bM4e677yY2NtZt/5SUFO68806AKlN7XHfddbRv357du3fz5ptvOqcEKS4u5r777quxBiGEqC1bWRn5ixZRumMnxgMHMJ04AbXs2KFQq9F26oR/t274d+9G4CWXoL3oIrd9Ck2FfPnHl3zxxxeUWR3jG0X4R/Bg3wc9/VaEaLF8YkoQgMWLFzNlyhTsdjsjRowgMjKSlStXkp+fz4MPPsi///3vKsdU9CA7fvw4SUlJbtvVajV9+/YlKSkJk8nE8ePHnVN9TJkyhQULFuDn554LN23axJgxYygtLWXAgAG0a9eO9evXc/bsWSZOnMjixYtR1nDburZkShAhWq8zs2dT8ONPtdpXodUSNHQouhHDCejZE22HDij+NGJ/hXxjPp//8Tlf/vElBrP7kCKvDnuVcclVmwcI0Rw1xWeoT9wxArj++utZt24dc+fOZcuWLZhMJrp27cp9991X54lb33nnHdasWcOuXbvYt28fZrOZqKgoJkyYwLRp05g4cWK1xw0ePJht27bx3HPPsXbtWnbt2kWHDh147LHHePjhhxscioQQrZslN6/W+9rLyjCsWoVh3TrUbdqgbhuHJj4eddu25V/xFEUEsCDzF745/C0llhK34wP9Arm7991c1f4qT78NIVo0n7lj1FrIHSMhWi9TWho5//kPxsOHMZ8+gzU7u8HnNKsgSw+ZoQqyQqAwXEvnbsMY0X8K4R264hcRceGTCNFMNMVnqASjJibBSAhRwVZaivnsWczp6ZhPn8Z8+jSm06cxpzuWrbm5Db5GyMSJxL3ysgeqFcL7WtWjNCGEaG2UAQFok5PRJlc/XYetpATzmTOYnMHpDObTpzl1aBuqjFz0pRe+RsEPPxDz97+j0gV5uHohWiYJRkII4aOUgYFoO3ZE27Gjc9uOjB38bdkqwA//MjsPtLmJ6wIGkvXue5QdPFjlHPqrr0YZJCNeC1FbEoyEEKKZOFd8jsd/e9y5Hh9zEdd3nULGIzMpO3LEbV91XBzRsx4n+Mora5wDUghRlXSzEkKIZqDEXMIDqx8gszQTAJVCxfODnyfnH69WCUUA6sREylJTKdm8GavBUOV1IUT15I6REEL4OKvNyuPrHudgrutR2VMDnqJXVC/OtImt9piSLVso2bLFsaJQoO3YkYA+vQno3ZuAPn3QJCfXOJ2IEK2ZBCMhhPBhRouRZzc+y7r0dc5tt3W7jSmdpwDQ5rnnCLy0HyUp2yjdtRvTsWNVT2K3U3bkCGVHjpC/cBEASp2OgF69COjTB/3V42tsAC5EayPd9ZuYdNcXQtRWdmk2D61+iD3Ze5zbLku4jDdGvoFKqar2GGthIaW791C6e7fzy1ZYeP4LqdV0XLYUdaWplYTwRdJdXwghWqlDuYe4f/X9nCs+59w2oM0AXh72co2hCECl16MbNhTdsKEA2G02TCdOULprN4Y1qylasbLqQWYzlpwcCUZCIMFICCF8xlnDWbZnbmd7xnZ+Sf2FUotroKIpnabwxIAnUCvVdTqnQqlEGRiI8eAfGNZvqPK6MjCQyHvvwb9nzwbXL0RLIMFICCG8wG63c7LwJNsztju/zhSfqbKfUqFkVr9Z3Nzl5np1uy/83/84M/sJ7Gaz+3lDQgifOpXwW6eiCg2t79sQosWRYCSEEE3AZrdxJO8IKRkpbM/Yzo6MHeQYc857jE6t458j/snQtkPrfd2czz5zC0Wq8HDCp08j7C9/QaXT1fu8QrRUEoyEEKIRlFpK+SPnD3Zl7WJHxg52ZO6gyFR0weMSghO4JOYS+kb3ZWTCSML8wxpUh/7yyzHudjXeVun1hFx9tYQiIWogwUgIIRrIbreTVpTG7qzd7Mnaw57sPRzOPYzFbrngsR1DO3JJzCXOMBQTFOPR2sL/+lfMmZnkzf8cANOJE5y8ZSqJn32KJiHBo9cSoiWQYCSEEHVUZCpib/Ze9mbtZU/2HvZk7SG/LP+CxykVSrqGd3ULQqH+oR6pyVZaiunUKUwnT2Iu/2466fhuychw29d85gwZ/3iZhA/e98i1hWhJJBgJIcR5WG1WjhUcc9wJKv9KLUjFzoWHgAvwC6B7RHcujr6YS2MupXd0b4LU9Z/l3lZSUh5+TmE6ddIRgirCT2Zm3U4mQ9gJUS0JRkII8SeFpkI2nt7I2rS1rD+9vlZtgwDah7SnV2QvekX1ondUbzqEdsBPWbdfs3abjbLDhzGdcAQf06lK4Scrqx7vxkGhVqNOSECTmIi2S2fCb7+93ucSoiWTYCSEEEBaYRpr09fyW9pvbM/YfsH2QXqNnl5R5SEosjfdI7sTog1pUA2mtDTS73+AskOH6nW8Qq1GnZiIJjERTbt2aNo5vqsT26FuE4tCVfPAkEIIBwlGQohWyWqzsid7D2vTHGHoWEE1c4yVUylUdArr5AxCvSJ70U7frl7jCtWkdM8e0u6+B2tu7nn3U2g0qBMT0LRLKg9A5SEoMRG/WAk/QjSUBCMhRIthtVkxmA0UmYqc3yu+DGYDhaZCDCYDWaVZbDmzhbyyvBrPFe4fzvD44YyMH8mguEEEqgMbre6iVas4/dhM7Eajc5v2oo6o27VDk+gIPZqkSuFHqWy0WoRo7SQYCSF8gt1up9RSisFswGAqDzHVhJvzrZdYShpUQ8fQjoxMGMmI+BH0jOx53jnJzvc+7CUlWA0GbIWFWIsM2AxFWIuKsDmXDdiKHK9ZCwsoXrfe1Rjaz482c18kdOLEBr0XIUT9SDASQniExWbBYCoPKuby4GIyuC1XDjvVrddm3B9P8lP4cUnsJYxKGMXw+OHE6+Kxl5ZiLTJgOX4CU1GRK9gUFtUQcCq2FTnCkMEAVmu96lHqdMS/8zZBgwZ5+J0KIWpLgpEQogqz1cy2jG3kGfOqhJsqwad8vfKEp15ht6O2QGAZBJVBhMWfCGsAoRYNoRYNepMfOpMSXZmCwDI7oRYNYRYtFOdiK/qM0qJ3OWgwgKVpw1kFvzZtSJj3If6dOnnl+kIIBwlGQgg3VpuVW/53C3/k/uG1GtRKNcGaYII1wSTn+NH1mIlgk6o81IC/0YbWaEVTYsavtAxVcRmK4tI/hZri8q+alTXquwCFVosyOBiVTuf4HqxDGaxHGaxDpQt2fA8Oxi8yEt2IESiD6j/GkRDCMyQYCSHcWOyWBoUiBQp0ah3BmmB0Gsf3YLVrWafWodfo3V5z21cTjFalBcB4+DDHr53gqbdWt/ehVqMMrggvlcNMecDRBaPSB6OsFHCUuorw49hPqdF4pXYhRP1JMBJCuNGqtDzZ/0ne2P4GZdaq91SiA6K5pdstRAVEOYNM5bATpA5CqfBMrylLZv0HNKwNZXBw+Xg/5V/lPb/U7dqhCg31aHd8IUTzoLDbZVz4ptS9e3cA9u/f7+VKhDi/7NJsPtn3CQsPLcRoNbq9FugXSJh/GAF+Afir/AlQO777+/k7t/n71bCuCnAuV1kv368iWNmtVnI/m49h7VqseblYcvOw5uWBzdbo71/h748qPAy/8IhK38PxiwhHFVb+PTwcv3DHd2VAQKPXJERr1xSfoRKMmpgEI9HcZJdm8+m+T/nm0DdVAlJj0aq0zqAU4BfgFpoClFpCylSEligILrGjK7YRZLAQWGTG31CGttCIurAUv4JilAXFKAoNKJrg15wyMBBVZCR+ERH4RUagiojALyISVUQ4fhGR+EVG4BcRgSoyEmVQkNyNEqIeJBi1QBKMRHOVXZrNZ/s/45tD33i/B1odKGx2gkshpAT0JXb0xaAvgdASBWFGFWElCvSloC+2E1RsJbCkfl3t61STv7/jTlM1Qcq5XP6aUq+XECVEOQlGLZAEI9HcGUwGjuYfxWg1YrQ4vkotpe7r1lLncpV1q/sxpeZSTDaTt9+Wk8pqR1c5SJVASLFrWV8CIZWWdY18E82u9kMRFoo6MhJNZLQzMDkClPudKFVIiIyKLVq0pvgMlcbXQog60Wl09Inu49FzWm1WyqxlVQNWpfXzvVYRuEotpdWvly/b7Bdum2RVKSjQQYEO4MJ3alRWV3gKKbYTWuwIVc7l8u0hxaAvBWUd/xRVmC2QmY05MxszB8+7r12lxB6qRxURjiYyCm1UDOqoKPyio8u/Ysq/R0mPOSFqIMFICOF1KqWKQGVgo85HZrfbsdgsbnevqgtb9br7FVxKntXIWYux2p58FRQ2O/rSP4WoPy+X2Mu3gaquIcpqQ5GTjz0nn7LDqecdp8kSHIAtMgxlVCSamBgCY9sS2CYBTUyMK0RFhKPwk48J0brIT7wQolVQKBSoVWrUKjV6jb7RrmOz29xCU23CVamllCKLkazK68ZCyvKysebkQl4+wUVWZ3D6852o0GLwq2NHPb+iUigqheNnsAGG8i+396KAspAALOHB2CNCUUZHoYmJJTC2LcFxSejbtkMTE4sqLEzaQYkWQ4KREEJ4kFKhJFDt2btfdrudInMRecY88ox55BhzyDPmcdaYx35jLrmlOZTkZWHOzsaek4civ5Bgg9V5JyqsCMINdsKLHI/zav1e7BCQXwr5pZCaCRwGwALklX8BWFQKikM0GEMDMIcHQ2SYI0RFxxAQG0/wRZ2JSOxEqDYUP6V87AjfJj+hQgjh4xQKBXqNHr1GTzt9uwvuX1OQSjfmkV+URWnmWayZWdizc/HLKUCbV0xIkY0wA4QX2QkzOOabqy0/q52Q3DJCcssgNR9Ic3vdBmyLhh0dFRzsGkxBchShgeGEacMI8w8j3D+cMP/yZW2lZf9wNCppCyWalgQjIYRoYRoSpHKNueQaczmZn0HxuXRM585iycyE7Fz8corQ5pcQlG8krDxAaWs5525SJiRl2mFTIQWBhezokMqOjgo2tVdg1Nb8GC5IHeQMUGH+YYRp/xSk/N0DVoBfgDzWEw0i3fWbmHTXF0I0dxVBKrc0l/zs0xSeOUHJ2XTKMs9hzchCkZ2LX24R2rxigvPKCC2quQGURQn7ExVsv0jB9o4KskIbFmq0Kq1bgGof0p7Z/Wc36JzCd0h3fSGEED6n8h0pQpKgw5Dz7l+Wepy81SsoXLsGy849KKyuoORng94n7PQ+YeeOFXAmRs3nl6vZnlC/sa3KrGWcKz7HueJzgGNgUiHqQoKREEKIRqVNbk9s8l3EzrgLa2EhxRs2ULRmLYZ167AVFLjtG5dh5unVkSSuWOpsI5VnzCO3LNe1bCxfLnOtF5oKq712mH9YU7xF0YJIMBJCCNFkVHo9+nHj0I8bh91ioXTXLgxr11K0chWmEycAsBUZ0Kq0xAbFEhsUW6vzmm1mCsoKXKGpPDBFBkQ24rsRLZEEIyGEEF6h8PMj8NJLHV/9+5N2198A8AsPr/O51Eo1kQGREoREg8mkOkIIIbzOkpPrXFZFRHixEtHaSTASQgjhdQqV6+PIcu4c0mFaeIsEIyGEEF4XOHAglI8/ZD5zhrI//vByRaK1kmAkhBDC69TR0QT06eNcL1y23HvFiFZNgpEQQgifEDxmjHM557//xbBunRerEa2VBCMhhBA+IWTiBFShoY4Vs5n0Bx+iJCXFqzWJ1keCkRBCCJ/gFxFBwv/9H8qgIADsRiNpd9+D8cABL1cmWhMJRkIIIXxGQM8exL//PgqNBgCbwUDa3fdgycnxcmWitZBgJIQQwqcEDehP23+/BX6OMYgtmZmcnjkTu9Xq3cJEqyDBSAghhM8JHjWKmFmznOslm7eQ9e67XqxItBY+FYw2bdrEuHHjCA8PR6fT0b9/fz777LM6n2fdunXceeed9O3bl5iYGDQaDeHh4YwaNYovvvii2mOsVivffvstM2fOZNiwYQQFBaFQKLj77rsb+raEEELUQ9itUwm+aqxzPeeDDylas8aLFYnWwGfmSvv++++54YYbsNlsDB8+nMjISFatWsW0adPYvXs3b7zxRq3P9dNPP/HRRx/RqVMnLr74YsLCwjh9+jTr169n7dq1/Prrr8yfP9/tmKKiIm688UZPvy0hhBD1pFAoaPPiXMoOHsJ0/DgAZx6fRdK336JNbu/l6kRL5RN3jPLy8pg+fTpWq5VFixaxdu1aFi1axMGDB+nYsSNvvvkma+rwV8Idd9zB6dOnOXToEMuWLeOrr75i3bp1HDx4kLi4OD7//HOWLVvmdoxarebWW2/l7bffZvPmzXzwwQeefptCCCHqSKULIv7tf6MMDAQcjbHT778fq8Hg5cpES+UTweijjz6ioKCACRMmMGnSJOf2mJgYXnvtNYA63THq1q0bcXFxVbZ37NiRe++9F4DVq1e7vRYUFMT8+fN54IEHGDhwIP7+/vV5K0IIITxMe9FFtHnlZee6KTWVs08+6cWKREvmE8FoyZIlAEyePLnKa+PHj8ff35+VK1diNBobfC2VSgWAprwrqBBCCN+nv+IKIu5xtfksWrESo8ynJhqBTwSjPXv2ANC3b98qr2k0Gnr06IHRaOTQoUMNuk5aWhrz5s0DYOzYsRfYWwghhC+JeuABtF26ONdLduzwYjWipfJ6MCosLCQ/Px+A+Pj4avep2H7q1Kk6nXvz5s1MmzaNW2+9ldGjR9OxY0dOnTrF3LlzGTp0aIPqFkII0bQUSiWB/fo5143lf1QL4Ule75VmqNSALrC8cd2fBZUPD2+oY2O7Y8eOuXX3VyqVPP/888ycObMeldZN9+7da6ypQ4cOjX59IYRoiQJ69SKvfLlk1y7sdjsKhcKrNYmWxSPBaPLkyezbt69Ox8yfP5/+/ftjt9svuG9t9qnO1KlTmTp1KiaTiRMnTjB//nxefPFFlixZwtKlSwkLC6vXeYUQQnhHwMUXO5fNJ09h3LOHgN69vViRaGk8EoxOnDhR5/Y/JSUlAAQHB7tt0+v1Ne6r0+nqVZ9Go6FTp07MnTuXiIgIHn30UZ599lneeeedep2vNvbv31/t9pruJAkhhLgwTXxbAi+9lJKUFAByFyygrQQj4UEeaWOUkpKC3W6v09fIkSMB0Ov1hISEAJCenl7t+Su2JyYmNrjWqVOnAvDjjz82+FxCCCGaXtgtNzuXi5Yuw5yR4cVqREvj9cbXAL3L0/6OanoYmM1m9u3bh1arpXPnzg2+Vnh4OEqlkqysrAafSwghRNMLHjMGv6goAOxmM+n33IutuNjLVYmWwieC0fjx4wFYtGhRldeWLFmC0Whk9OjRHhl0cf369dhsNmkALYQQzZRCrSby/vud68YDB0h/5BHsFosXqxIthU8EoxkzZqDX6/nxxx/57rvvnNszMzOZVT678qOPPlrluC5dutClSxdOnz7ttn3OnDmcO3euyv4pKSnceeedAEyfPt2Tb0EIIUQTCrtxCuG33+ZcL163njNPPYW1sNCLVYmWQGGvb5cvD1u8eDFTpkzBbrczYsQIIiMjWblyJfn5+Tz44IP8+9//rnJMRRfN48ePk5SU5LZdrVbTt29fkpKSMJlMHD9+nF27dgEwZcoUFixYgJ+fe9vze++91/k4Lysri9TUVKKjo2nf3jVZ4ZYtWxr0PisaX9fUOFsIIUTt2G02Tj/8CEW//urcpgwJIeKvfyV86i3O+dVEy9EUn6E+E4wANm7cyNy5c9myZQsmk4muXbty33331Xh3p6Zg9O6777JmzRp27dpFRkYGZrOZqKgoLr30UqZNm8bEiROrPd/IkSP57bffzltjQ/+5JBgJIYTn2IxGTv11BqXbt7ttV0VGEnnXXYTedCNKmQKqxWh1wag1kGAkhBCeZSstJefjT8j973+rNML2a9OGyHvvIXTiRBRqtZcqFJ7SFJ+hPtHGSAghhKgvZUAAUfffR4eVK4iY8VcUlTrqWM6e5dwzz3L0iivJ/ewz6b0mLkiCkRBCiBbBLyyM6Jkz6fDrcsJuvhkq3SGynD1LxsuvcOSy0WS+9RaWnBwvVip8mTxKa2LyKE0IIZqGKf002R+8T8GPP8GfuvIrNBpCJl1HxPTpaNq181KFoq7kUZoQQghRT5r4tsS99BIdV/xK+PTpbr3U7CYT+V9/w7GxV3H2mWew22xerFT4EglGQgghWjR1mzbEzJ5Fx7VriHr0UVSRka4X7XbyFy7CsG6d9woUPkWCkRBCiFZBpdcTededdFy1ktgXX0AdF+d8rfCnn71YmfAlEoyEEEK0KkqtlrAbbiD6ySec24pWr8ZqkB5rQoKREEKIVko3YgRKvR4Au9FI+n33Yf7TFFOi9ZFgJIQQolVSajSETJjgXC/5/XdSr51A3sKFDZ7lQDRfEoyEEEK0WtGPPEzw5WOc67biYs498yxpf/sb5oxML1YmvEWCkRBCiFZLGRhI27ffJu6frzkfqwEUr1vP8euuw3z2rBerE94gwUgIIUSrplAoCLnmGpJ//omg4cOc2625uZRs3erFyoQ3SDASQgghAHVMDAnz5qFOTHRuU+p0XqxIeIMEIyGEEKKcQqHAVlDgXPerPBikaBUkGAkhhBDl7FYr1krBSBmsP8/eoiWSYCSEEEKUU6hU+MXGOtdLd+7wYjXCGyQYCSGEEJUEX3aZc7lo5SovViK8QYKREEIIUUnwmNHO5eKNG7GVlHixGtHUJBgJIYQQlQT264cyOBgAu8lEsXTZb1UkGAkhhBCVKNRqggYOdK4Xb9rkxWpEU5NgJIQQQvxJ0JDBzmUJRq2LBCMhhBDiT4IGu4KR6egx8r//wXvFiCYlwUgIIYT4E01iIoEDBjjXz73wAmVHj3qxItFUJBgJIYQQ1Yh75WVUISEA2EtLSX/4Yemh1gpIMBJCCCGqoW7ThjavvuJcNx09RtZ773mxItEUJBgJIYQQNQgeOZLw6dOd60XLf/ViNaIpSDASQgghziP89tucy+b0dMxnz3qxGtHYJBgJIYQQ56GOjUWdkOBcL0lJ8WI1orFJMBJCCCEuIPDSS53LuZ/Nx2YyebEa0ZgkGAkhhBAXoL9qrHPZuG8fGf/4hxerEY1JgpEQQghxAbrhwwm96Ubnev7X35D/ww/eK0g0GglGQgghRC3EPPUU/j16ONfPPf8C5nPnvFiRaAwSjIQQQohaUGo0xP/7LbdBH7PefsfLVQlPk2AkhBBC1JK6bVuiHn3UuV7www8YDx/2YkXC0yQYCSGEEHUQev0kNMnJjhWbjaw33/JqPcKzJBgJIYQQdaDw8yP60Uec64bffpPu+y2IBCMhhBCijoKGDXOt2GxYs7O9V4zwKAlGQgghRB0ptVqUer1z3SLBqMWQYCSEEELUg19kpHPZfFa67bcUEoyEEEKIetB27OhcLtku86e1FBKMhBBCiHoIGjzYuVy8cZMXKxGeJMFICCGEqIegIa5gZDp2jLJjx7xYjfAUCUZCCCFEPWgSElC3S3Sup9//ANb8fO8VJDxCgpEQQghRT9EPP+xcNh0/Tvr9D8iYRs2cBCMhhBCinvRXXUXkA/c710tSUjj71NPYzWYvViUaQoKREEII0QCR995LyMSJzvXCJUs4cdNfKDtyxHtFiXqTYCSEEEI0gEKhoM0LzxM4YIBzm3H/fo5Pup6cjz7CbrV6sTpRVxKMhBBCiAZSaDQkvP8eoTdMdm6zm81k/ut1Tt4ylbLjx71YnagLCUZCCCGEByiDgmjz4osk/GceflFRzu2lu3ZxYsqNEo6aCQlGQgghhAfphg8n+eef0F9zjXObraiIM088gd1i8WJlojYkGAkhhBAepgoNpe0/X6PNP/7h3GbcvYecjz72YlWiNnwqGG3atIlx48YRHh6OTqejf//+fPbZZ3U+z7p167jzzjvp27cvMTExaDQawsPDGTVqFF988UW1x5w8eZJ33nmHcePGkZycjFarJTIykrFjx/LTTz819K0JIYRohUInXUfIhAnO9az33sN46JAXKxIX4jPB6Pvvv2f48OEsW7aMXr16MXbsWI4cOcK0adN49NFH63Sun376iY8++oji4mIuvvhirr/+enr06MH69eu59dZbue2226occ8stt/Dggw+ydu1a2rVrx6RJk0hOTmb58uVMmDChzjUIIYQQADFPP4VfbKxjxWwmr4Y/0IVvUNjtdru3i8jLy6N9+/YUFBSwePFiJk2aBEBGRgZDhw7l6NGjrF69mlGjRtXqfAcOHCA0NJS4uDi37UePHmXEiBGcOXOGpUuXMnbsWOdrN998MyNGjGDq1KkEBQU5t//yyy9MnDgRi8XC8uXLueKKKxr0Xrt37w7A/v37G3QeIYQQzUfe199wbs4cAPx79aL9t994t6Bmqik+Q33ijtFHH31EQUEBEyZMcIYigJiYGF577TUA3njjjVqfr1u3blVCEUDHjh259957AVi9erXba19++SV/+9vf3EIRwPjx47njjjsA+Oqrr2pdgxBCCFHBv0tn57Lp2DF84J6EqIFPBKMlS5YAMHny5CqvjR8/Hn9/f1auXInRaGzwtVQqFQAajabWx/Tu3RuAM2fONPj6QgghWh9Nhw7OZVtxMZZz57xYjTgfnwhGe/bsAaBv375VXtNoNPTo0QOj0cihBjZYS0tLY968eQBuj9EuJDU1FYDYimfEQgghRB2ogoNRhYY61y2Zmd4rRpyXn7cLKCwsJD8/H4D4+Phq94mPjyclJYVTp045797UxubNm5k3bx5Wq5UzZ86wYcMGLBYLc+fOZejQobU6R35+PvPnzwdgQqWeBUIIIURdKAMDsZZ/3tlKG/4ERDQOrwcjg8HgXA4MDKx2n4p2P5X3rY1jx465dfdXKpU8//zzzJw5s9bnuOeee8jKymLgwIFcd911tT6uooFYdTV1qHRLVQghROugCAhwLtuMpV6sRJyPR4LR5MmT2bdvX52OmT9/Pv37969VA7T6NlKbOnUqU6dOxWQyceLECebPn8+LL77IkiVLWLp0KWFhYec9/pVXXuHrr78mPDycBQsWoFAo6lWHEEIIoaz0x7+9pMSLlYjz8UgwOnHiRJ3b/5SU/1AEBwe7bdPr9TXuq9Pp6lWfRqOhU6dOzJ07l4iICB599FGeffZZ3nnnnRqP+eyzz3jqqacICgril19+ITk5uU7XrKkrYU13koQQQrRsqkqfd9bCIi9WIs7HI42vU1JSsNvtdfoaOXIkAHq9npCQEADS09OrPX/F9sTExAbXOnXqVAB+/PHHGvf58ccf+etf/4parea7775j4MCBDb6uEEKI1k0Z4vrD31pQ4MVKxPn4RK+0igbVO3bsqPKa2Wxm3759aLVaOnfuXOX1ugoPD0epVJKVlVXt62vXruXGG28EYMGCBQ0e0FEIIYQAUOlDnMsSjHyXTwSj8ePHA7Bo0aIqry1ZsgSj0cjo0aPx9/dv8LXWr1+PzWartgH09u3bufbaazGZTHz00UfVjqskhBBC1IdK73qUZiuSR2m+yieC0YwZM9Dr9fz444989913zu2ZmZnMmjULoNq5yrp06UKXLl04ffq02/Y5c+ZwrprBs1JSUrjzzjsBmD59uttrhw4d4qqrrqKoqIh///vfTJs2raFvSwghhHBSVppZwVZc7MVKxPl4vbs+OB5vffLJJ0yZMoXJkyczYsQIIiMjWblyJfn5+Tz44IOMHj26ynEVDb7NZrPb9ueff55//OMf9O3bl6SkJEwmE8ePH2fXrl0ATJkyhYceesjtmJtuuomsrCyioqLYvn17tcGoS5cuPPHEE55500IIIVoVZZCrA5G1uG7Dz4im4xPBCOD6669n3bp1zJ07ly1btmAymejatSv33Xdflbs7F/LOO++wZs0adu3axb59+zCbzURFRTFhwgSmTZvGxIkTqxyTl5cHQFZWltvYR5WNGDFCgpEQQoh6UVVufJ2d48VKxPko7DKTXZNqipmBhRBC+J6SnTs5+ZebAVDqdHTatlXGx6ujpvgM9Yk2RkIIIURLp0lKci7bDAasubneK0bUSIKREEII0QT8wsJQhbi67Od9/bUXqxE1kWAkhBBCNJHgK690Lme/9z4l1YzfJ7xLgpEQQgjRRKIfn4k6Pt6xYrNxeuZMzBmZ3i1KuJFgJIQQQjQRVXAwbV//F/g5OoVbzpzl6OjRpD/wIIb167FbrV6uUEgwEkIIIZpQQO/eRD34oGuDxULRihWk3XkXRy+/nKx338N89qz3CmzlJBgJIYQQTSxixl+JefIJ/GJj3bZbzpwl+913OTp6DKf+9jcKV6zA/qdBjEXjknGMmpiMYySEEKKC3WrFsH49+QsXYVi7Fqp5lKbp0IG2b76Bf6dOTV+gj5FxjIQQQogWTKFSETxyJAnvvUvHNauJeuQR1AkJbvuYjh3jxJQbyf/+B+8U2cpIMBJCCCF8gDo6msi/3UWH5ctI/PS/6Mdd5XzNbjRy9sknOfvMM9iMRi9W2fJJMBJCCCF8iEKpJGjgQNq+8QYJ/5nnNihk/sJFnLjpLzL+USOSYCSEEEL4KN3w4bT//jv8e/Vybis7eJCTN99C2t33YDx0yIvVtUwSjIQQQggfpo6LI+mLzwmbOtVtu2HtWo5PvI7Tj8/ClJbmpepaHglGQgghhI9TaDTE/v1pEj/7DP/errtH2O0U/vwzx64ax7kXXsCSne29IlsICUZCCCFEMxE0oD9JX39N/HvvounYwfWCxULel19x/IYpWHJyvFdgCyDBSAghhGhGFAoFwaNHk/zjj7R55WXUbds6X7OcPcvZp/+ODFFYfxKMhBBCiGZIoVIROnEiyUv/R8Tdf3NuN6xdS/4333ixsuZNgpEQQgjRjCk1GqIeeoigYcOc2zJeeZXirVu9WFXzJcFICCGEaOYUCgVx/3gJVXg44BgQ8tTt08j817+wmUxerq55kWAkhBBCtAB+UVHEvfoK+Pk5Ntjt5Hz0MSdumILx0GHvFteMSDASQgghWgjdsGEkffUlmqQk57ayQ4c4MXky2f/5P5lOpBYkGAkhhBAtSEDPnrT//jvCbr7Zuc1uNpP1xhscHXM5OZ/8F1tJiRcr9G0SjIQQQogWRhkQQOyzz5Dwf/+HX1SUc7s1O5vM117j6OgxZM/7D1aDwYtV+iYJRkIIIUQLpRs2lPY//UjYrbei0Gqd2615eWS9+SZHR48h6733sBYWerFK36KwyyhQTap79+4A7N+/38uVCCGEaE0sWVnk/PdT8r76CntpqdtrCo2GwAED0I0aSfDIkajj4rxT5AU0xWeoBKMmJsFICCGEN1lyc8n99DPyFizAVlxc7T7aLl0cIWnUKPx79ECh9I0HTBKMWiAJRkIIIXyBNT+f3M+/IO+LL7AWFNS4nyoqEt2IEQSPGkXQoEEoAwObsEp3EoxaIAlGQgghfIndbKZkx04Ma9ZgWLMG08mTNe6r0GgIHDiA4FGjCL3hBhQVYyY1EQlGLZAEIyGEEL6sLPW4MySV7NgBNluVfdQJCXT4dTkKhaJJa2uKz9CmjXpCCCGE8Gna5PZok9sT8dc7sObnY1i/3hGU1m/AVlQEQPBlo5o8FDUVCUZCCCGEqJYqNJSQa64h5JprHI/ctm/HsGYN+quu8nZpjUaCkRBCCCEuSKFWEzRwIEEDB3q7lEblG/3vhBBCCCF8gAQjIYQQQohyEoyEEEIIIcpJMBJCCCGEKCfBSAghhBCinAQj8f/t3W9oVXUcx/HP2T8352owl07NBVtWszQm08wFM4IFLXQ9KBiBaBGaFsIIFf8slG0VJQwWYjCk6M+DWk+0gkFRS7LBcCVZPdiwO11KAx+4eVf79+3J2fC6uT/3nnvvudv7BffBPed3fuf3ww/y8dy7CQAAXBQjAAAAF8UIAADARTECAABwUYwAAABcFCMAAAAXxQgAAMBFMQIAAHBRjAAAAFwUIwAAABfFCAAAwEUxAgAAcDlmZvFexHySlZWloaEhFRQUxHspAAAklK6uLqWmpqqvry9q9+CJUYxlZmYqNTVVo6Oj6u3t1ejo6IQxXV1d6urqisPqwjPVXvx4n0jmmc21Mx07k3FTjSFL8bsPWfIvshTZWL9mKTU1VZmZmWFdO2OGuLh06ZJJskuXLk04V1RUZEVFRbFfVJim2osf7xPJPLO5dqZjZzJuqjFkKX73IUv+RZYiGzufs8QTIwAAABfFCAAAwEUxipPs7GzV1NQoOzs73kuJWKz24tV9IplnNtfOdOxMxk01hizF7z5kyb/IUmRj53OW+Kk0H1q9erUk6eLFi3FeCRIdWYJXyBK84vcs8cQIAADAxRMjAAAAF0+MAAAAXBQjAAAAF8UIAADARTECAABwUYwAAABcFCMAAAAXxQgAAMBFMUpwra2t2rJli/Lz8+U4jt588807jm1padG6deuUnp6u5cuX69ChQxoZGYndYpFwgsGgDh48qMLCQqWnp+vBBx/UBx98EO9lIQGNjo6qrq5Oq1atUkZGhpYvX64dO3bon3/+iffSkGDKysrkOM6EV1JSkid5SvFgjYij/v5+FRUVqaqqSnv37r3juI6ODlVUVGj37t36+OOP9fvvv+ull17S8PCw3nrrrdgtGAll586dam1t1YkTJ/TAAw/o7Nmz2rlzp1JTU7V9+/Z4Lw8J5N1331VdXZ2ampq0YcMGBQIB7dq1Sy+++KJaWlrivTwkkC+//FKDg4Mhx55++mnl5ubqnnvuifwGhhlrb2+3+vp6q6ystGXLlpkkW7BgwbTXDQwM2JEjR+z++++3BQsWWF5enm3fvt0uX77s6fry8/OtpqZm0nNVVVVWXFwccqyhocEyMjKsr6/P03UgPH7L18DAgKWkpNhHH30Ucvy1116z++67L6K5EV1+y5KZWUVFhT333HMhxxoaGmzRokURz43o8WOWbtfR0WGSrLm52ZP5KEazsGXLFpMU8pouIAMDA/b444+bJMvLy7Pnn3/e1q9fb5IsNzfXOjs7PVvfVMUoPz/fDh06FHKss7PTJNn333/v2RoQPr/l68aNG+Y4jn3++echx/ft22eSLBAIhD03ostvWTIze/vtty03N9fa29vNzKynp8dKS0vthRdeiGheRJcfs3S7V155xZYtW2ZDQ0OezMdHabOwceNGrV27ViUlJSopKdHSpUunvaaurk4//fSTNm7cqJaWFi1atEiSdPz4cVVXV2vHjh364Ycfxsdfv35d169fn3LOu+66a9aPC69evaq8vLyQY2Pr//vvv2c1F6LDb/nKyspSaWmpamtr9eijj6qgoEDnzp1TU1OTJKmnp0crV66MYMeIFr9lSZLeeOMNDQ8Pa8OGDXIcR8PDw3r22Wf14YcfRrBTRJsfs3SrGzdu6NNPP1V1dbVSUjyqNJ7Uq3lK0zTnwcFBy87ONkl2/vz5CefXrFljksb/BWVmVlNTM6Gd3/7atm3bpPeb6olRWlqavf/++yHHbt68aZLss88+m36ziDk/5Ku7u9vKy8vNcRxLTk62lStX2v79+02StbW1ebpfRI8fstTc3GxLliyxpqYmu3Dhgp05c8Yeeughq6qq8nSviC4/ZOlWjY2NlpycbFeuXIl4b2MoRhGYLiDfffedSbKCgoJJzx89etQkhZSZkZERGxoamvI1MjIy6Xx8lDa3+ClfAwMDdvnyZRsZGbHGxkaTZL29vRHvEbHhhyzde++9duzYsZB5W1tbTZL9+eefkW0QMeOHLN3q4YcftsrKyoj2dDt+XD+Kfv31V0lScXHxpOfHjo+Nk6SkpCSlpKRM+UpKmv0f26ZNm/TNN9+EHPv666+VkZGhdevWzXo+xF8s85Wenq4VK1bIcRx98sknKisr0+LFi6OwK8RDLLIUDAaVnJwcMu/Y+9HRUU/3g/iJ5d9LP/74o3777Tft2rXL0z3wHaMo6u7uliStWLFi0vNjx8fGhaO/v1+dnZ2SpMHBQV27dk2//PKL0tLSVFRUND6uurpajz32mKqrq/Xyyy/rjz/+0JEjR/T666+Pf/6LxBKLfH377bcKBoNavXq1rl69qnfeeUcXL17U2bNnw54T/hOLLFVWVuq9995TYWGhSkpKFAgEtHfvXj3yyCNatWpV2PPCX2KRpTEnTpxQYWGhnnrqqYjnuhXFKIr6+/slSQsXLpz0fGZmZsi4cLS3t2vz5s3j70+ePKmTJ08qPz9ff/311/jx4uJinT59WgcOHFBjY6NycnL06quv6ujRo2HfG/EVi3z19fVp3759CgQCysjI0ObNm3Xu3LmQ0o3EF4ssNTQ0aPHixdq/f796enqUk5OjJ598UrW1tROeJCFxxSJLktTb26vm5mbV1tbKcZyI5rodxSiKzEyS7viHNnY+EmVlZTOep7y8XOXl5RHfE/4Qi3xt3bpVW7dujXge+FsssrRw4ULV19ervr4+4rngX7HIkiTl5ubqv//+82Su2/EdoyjKysqSJN28eXPS88FgUJL4KAthIV/wClmCV+ZClihGUTT2O16uXLky6fmx4/wuGISDfMErZAlemQtZohhF0dq1ayVJ58+fn/T82PE1a9bEbE2YO8gXvEKW4JW5kCWKURRt2rRJd999t7q6utTR0THh/BdffCFJqqioiPXSMAeQL3iFLMErcyFLFKMoSktL0549eyRJe/bsCfnM9fjx47pw4YJKS0tVUlISryUigZEveIUswStzIUuOefUV8Xngq6++0rFjx8bft7W1yXEcrV+/fvzY4cOH9cwzz4y///fff1VWVqa2tjbl5eXpiSeeUCAQUFtbm3JycvTzzz+rsLAwpvuAP5EveIUswSvzMkue/h7tOe7UqVPT/n8up06dmnBdMBi0w4cPW0FBgaWlpdmSJUts27Zt1t3dHftNwLfIF7xCluCV+ZglnhgBAAC4+I4RAACAi2IEAADgohgBAAC4KEYAAAAuihEAAICLYgQAAOCiGAEAALgoRgAAAC6KEQAAgItiBAAA4KIYAQAAuChGAAAALooRAACAi2IEAADgohgBAAC4KEYAAAAuihEAAICLYgQAAOCiGAEAALj+B6nMe2gwguN6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4,4), sharex=True, dpi=150)\n",
    "\n",
    "ax.semilogx(np.abs(LPR_CS_1_df[\"i\"].to_numpy()), LPR_CS_1_df[\"E\"].to_numpy()-0.125, markersize=5, label=\"CS 1\")\n",
    "ax.semilogx(np.abs(LPR_CS_2_df[\"i\"].to_numpy()), LPR_CS_2_df[\"E\"].to_numpy(), markersize=5, label=\"CS 2\")\n",
    "#ax.semilogx(np.abs(LPR_CS_3_df[\"i\"].to_numpy()), LPR_CS_3_df[\"E\"].to_numpy(), markersize=5, label=\"CS 3\")\n",
    "ax.semilogx(np.abs(LPR_HS_1_df[\"i\"].to_numpy()), LPR_HS_1_df[\"E\"].to_numpy()+0.03+0.01+0.002, markersize=5, label=\"HIPed 1\")\n",
    "ax.semilogx(np.abs(LPR_HS_2_df[\"i\"].to_numpy()), LPR_HS_2_df[\"E\"].to_numpy(), markersize=5, label=\"HIPed 2\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fef0e0c4-1f42-46a0-b731-1ed4b82e673c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5159c47-93d9-4993-a073-f7b80cd4a81e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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