535projectprac1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "459a7199",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "502bf9ea",
   "metadata": {},
   "source": [
    "## Load Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f753e232",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c583b89",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4fd5ae3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "281e24d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataDF = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
    "                     columns= iris['feature_names'] + ['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f7c75cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "        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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0     0.0  \n",
       "1     0.0  \n",
       "2     0.0  \n",
       "3     0.0  \n",
       "4     0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataDF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61eca958",
   "metadata": {},
   "source": [
    "# Data Privacy variables\n",
    "## &\n",
    "# Data Privacy Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7350fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DP = differnetial Privacy\n",
    "# DP = f(x) + L(0,b)\n",
    "\n",
    "# b = value used to generate random noise\n",
    "# b = Deltaf / epsilon\n",
    "\n",
    "# Deltaf = difference between highest and lowest observations\n",
    "# Deltaf = abs(max(f(x)) - min(f(x)))\n",
    "\n",
    "# epsilon is arbitrary value set by curator\n",
    "# epsilon is between 0 and 1\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc40aa1",
   "metadata": {},
   "source": [
    "#### Calculate $\\Delta$ f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "481ca0c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Δf sepal length: 3.6000000000000005\n",
      "Δf sepal width: 2.4000000000000004\n",
      "Δf petal length: 5.9\n",
      "Δf petal width: 2.4\n"
     ]
    }
   ],
   "source": [
    "deltaf_sepal_length = np.abs(dataDF['sepal length (cm)'].max() - dataDF['sepal length (cm)'].min())\n",
    "deltaf_sepal_width = np.abs(dataDF['sepal width (cm)'].max() - dataDF['sepal width (cm)'].min())\n",
    "deltaf_petal_length = np.abs(dataDF['petal length (cm)'].max() - dataDF['petal length (cm)'].min())\n",
    "deltaf_petal_width = np.abs(dataDF['petal width (cm)'].max() - dataDF['petal width (cm)'].min())\n",
    "\n",
    "\n",
    "print(\"\\u0394f sepal length: {}\".format(deltaf_sepal_length))\n",
    "print(\"\\u0394f sepal width: {}\".format(deltaf_sepal_width))\n",
    "print(\"\\u0394f petal length: {}\".format(deltaf_petal_length))\n",
    "print(\"\\u0394f petal width: {}\".format(deltaf_petal_width))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d109f86",
   "metadata": {},
   "source": [
    "### Choose arbitrary epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5d2e102",
   "metadata": {},
   "outputs": [],
   "source": [
    "epsilon = 0.15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45474f6d",
   "metadata": {},
   "source": [
    "### Calculate b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "07c54c53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b sepal length: 24.000000000000004\n",
      "b sepal width: 16.000000000000004\n",
      "b petal length: 39.333333333333336\n",
      "b petal width: 16.0\n"
     ]
    }
   ],
   "source": [
    "# b = value used to generate random noise\n",
    "# b = Deltaf / epsilon\n",
    "b_sepal_length = deltaf_sepal_length / epsilon\n",
    "b_sepal_width = deltaf_sepal_width / epsilon\n",
    "b_petal_length = deltaf_petal_length / epsilon\n",
    "b_petal_width = deltaf_petal_width / epsilon\n",
    "\n",
    "\n",
    "print(\"b sepal length: {}\".format(b_sepal_length))\n",
    "print(\"b sepal width: {}\".format(b_sepal_width))\n",
    "print(\"b petal length: {}\".format(b_petal_length))\n",
    "print(\"b petal width: {}\".format(b_petal_width))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e81d2583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.734572225960987"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.uniform(0, b_sepal_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a86f410",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "laplace_noise_sepal_length: 8.911376125931497\n",
      "laplace_noise_sepal_width: 8.00377983954368\n",
      "laplace_noise_petal_length: 19.864261947135216\n",
      "laplace_noise_petal_width: 9.064298367519044\n"
     ]
    }
   ],
   "source": [
    "laplace_noise_sepal_length = np.random.laplace(loc=(b_sepal_length/2.0), scale=1)\n",
    "laplace_noise_sepal_width = np.random.laplace(loc=(b_sepal_width/2.0), scale=1)\n",
    "laplace_noise_petal_length = np.random.laplace(loc=(b_petal_length/2.0), scale=1)\n",
    "laplace_noise_petal_width = np.random.laplace(loc=(b_petal_width/2.0), scale=1)\n",
    "\n",
    "print(\"laplace_noise_sepal_length: {}\".format(laplace_noise_sepal_length))\n",
    "print(\"laplace_noise_sepal_width: {}\".format(laplace_noise_sepal_width))\n",
    "print(\"laplace_noise_petal_length: {}\".format(laplace_noise_petal_length))\n",
    "print(\"laplace_noise_petal_width: {}\".format(laplace_noise_petal_width))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "06d44a9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sepal_length_array = dataDF['sepal length (cm)'].values.tolist()\n",
    "Private_SL_array = [x + laplace_noise_sepal_length for x in sepal_length_array]\n",
    "\n",
    "sepal_width_array = dataDF['sepal width (cm)'].values.tolist()\n",
    "Private_SW_array = [x + laplace_noise_sepal_width for x in sepal_width_array]\n",
    "\n",
    "petal_length_array = dataDF['petal length (cm)'].values.tolist()\n",
    "Private_PL_array = [x + laplace_noise_petal_length for x in petal_length_array]\n",
    "\n",
    "petal_width_array = dataDF['petal width (cm)'].values.tolist()\n",
    "Private_PW_array = [x + laplace_noise_petal_width for x in petal_width_array]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5326ff05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7603b8fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "private_list_all_data = list(zip(Private_SL_array,Private_SW_array,Private_PL_array,Private_PW_array,))\n",
    "private_column_names = ['Privatized Sepal Length', 'Privatized Sepal Width','Privatized Petal Length','Privatized Petal Width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "02aadf5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "privateDF = pd.DataFrame(private_list_all_data,\n",
    "                  columns = private_column_names) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6f26e25",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Privatized Sepal Length</th>\n",
       "      <th>Privatized Sepal Width</th>\n",
       "      <th>Privatized Petal Length</th>\n",
       "      <th>Privatized Petal Width</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <td>21.364262</td>\n",
       "      <td>9.264298</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.911376</td>\n",
       "      <td>11.60378</td>\n",
       "      <td>21.264262</td>\n",
       "      <td>9.264298</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   Privatized Sepal Length  Privatized Sepal Width  Privatized Petal Length  \\\n",
       "0                14.011376                11.50378                21.264262   \n",
       "1                13.811376                11.00378                21.264262   \n",
       "2                13.611376                11.20378                21.164262   \n",
       "3                13.511376                11.10378                21.364262   \n",
       "4                13.911376                11.60378                21.264262   \n",
       "\n",
       "   Privatized Petal Width  \n",
       "0                9.264298  \n",
       "1                9.264298  \n",
       "2                9.264298  \n",
       "3                9.264298  \n",
       "4                9.264298  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "privateDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee3a4212",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Privatized Sepal Length</th>\n",
       "      <th>Privatized Sepal Width</th>\n",
       "      <th>Privatized Petal Length</th>\n",
       "      <th>Privatized Petal Width</th>\n",
       "    </tr>\n",
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       "      <td>25.064262</td>\n",
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      "text/plain": [
       "     Privatized Sepal Length  Privatized Sepal Width  Privatized Petal Length  \\\n",
       "145                15.611376                11.00378                25.064262   \n",
       "146                15.211376                10.50378                24.864262   \n",
       "147                15.411376                11.00378                25.064262   \n",
       "148                15.111376                11.40378                25.264262   \n",
       "149                14.811376                11.00378                24.964262   \n",
       "\n",
       "     Privatized Petal Width  \n",
       "145               11.364298  \n",
       "146               10.964298  \n",
       "147               11.064298  \n",
       "148               11.364298  \n",
       "149               10.864298  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "privateDF.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c97e7b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given Sepal Length standard dev: 0.828066127977863\n",
      "Given Sepal Width standard dev: 0.4358662849366982\n",
      "Given Petal Length standard dev: 1.7652982332594662\n",
      "Given Petal Width standard dev: 0.7622376689603465\n"
     ]
    }
   ],
   "source": [
    "SLstddev = dataDF['sepal length (cm)'].std()\n",
    "SWstddev = dataDF['sepal width (cm)'].std()\n",
    "PLstddev = dataDF['petal length (cm)'].std()\n",
    "PWstddev = dataDF['petal width (cm)'].std()\n",
    "print(\"Given Sepal Length standard dev: {}\".format(SLstddev))\n",
    "print(\"Given Sepal Width standard dev: {}\".format(SWstddev))\n",
    "print(\"Given Petal Length standard dev: {}\".format(PLstddev))\n",
    "print(\"Given Petal Width standard dev: {}\".format(PWstddev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2881b712",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Privatized Sepal Length standard dev: 0.8280661279778629\n",
      "Privatized Sepal Width standard dev: 0.43586628493669827\n",
      "Privatized Petal Length standard dev: 1.7652982332594662\n",
      "Privatized Petal Width standard dev: 0.7622376689603467\n"
     ]
    }
   ],
   "source": [
    "pSLstddev = privateDF['Privatized Sepal Length'].std()\n",
    "pSWstddev = privateDF['Privatized Sepal Width'].std()\n",
    "pPLstddev = privateDF['Privatized Petal Length'].std()\n",
    "pPWstddev = privateDF['Privatized Petal Width'].std()\n",
    "print(\"Privatized Sepal Length standard dev: {}\".format(pSLstddev))\n",
    "print(\"Privatized Sepal Width standard dev: {}\".format(pSWstddev))\n",
    "print(\"Privatized Petal Length standard dev: {}\".format(pPLstddev))\n",
    "print(\"Privatized Petal Width standard dev: {}\".format(pPWstddev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2ee0ef6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given Sepal Length mean: 5.843333333333334\n",
      "Given Sepal Width mean: 3.0573333333333337\n",
      "Given Petal Length mean: 3.7580000000000005\n",
      "Given Petal Width mean: 1.1993333333333336\n"
     ]
    }
   ],
   "source": [
    "print(\"Given Sepal Length mean: {}\".format(dataDF['sepal length (cm)'].mean()))\n",
    "print(\"Given Sepal Width mean: {}\".format(dataDF['sepal width (cm)'].mean()))\n",
    "print(\"Given Petal Length mean: {}\".format(dataDF['petal length (cm)'].mean()))\n",
    "print(\"Given Petal Width mean: {}\".format(dataDF['petal width (cm)'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3ad056e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Privatized Sepal Length mean: 14.754709459264829\n",
      "Privatized Sepal Width mean: 11.061113172877015\n",
      "Privatized Petal Length mean: 23.622261947135218\n",
      "Privatized Petal Width mean: 10.263631700852377\n"
     ]
    }
   ],
   "source": [
    "print(\"Privatized Sepal Length mean: {}\".format(privateDF['Privatized Sepal Length'].mean()))\n",
    "print(\"Privatized Sepal Width mean: {}\".format(privateDF['Privatized Sepal Width'].mean()))\n",
    "print(\"Privatized Petal Length mean: {}\".format(privateDF['Privatized Petal Length'].mean()))\n",
    "print(\"Privatized Petal Width mean: {}\".format(privateDF['Privatized Petal Width'].mean()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac0ca7a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6137d2e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4a442e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de59059d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed508c0f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cea6a0d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80f5f899",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1efbc973",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10), dpi= 80)\n",
    "\n",
    "sns.kdeplot(data=privateDF['Privatized Sepal Length'])\n",
    "plt.title('Title!', fontsize=22)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2d7876a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=privateDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9ee41fcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GSqtuGy666CJ5/PjxTo+zZ88eec0115jdvrukVTdl+GtXXUUhhAOwAPi6wek3gf5AHJAB/MdUXynl21LKeCllfMOavQoFJRmgsQcXn5bbeIYC8uzqRKHoIp5//vl6F+POkJubyzPPPGMBiZpjzc3xNCCswftQoL1V0ucCe6SUWXUnGr4WQrwD/NQZIRV9kNIscA8ybIK3hKfR46roDHiFd41cik7RXdKqd5ZBgwY1q3HeEeo8z6yBNVccO4EYIUSUceWwCFjZzjGuAj5veEII0dAwfTGg6nwq2kdJpqG2eGt4Gp951Aa5QtEMq604pJQ6IcTdwK8Y3HHfl1IeFkIsNl5fJoQIwrAP4QHohRD3AUOllMVCCBdgNnB7k6FfEELEYTB7JZu4rlC0Tkkm+PZvvU1djEexUhwKRVOsGschDa6yq5qcW9bgdSYGE5apvuWAr4nz11pYTEVfozQTIie13sbRDZw81YpDoTCBylWl6FvUVBqixt2D2m7rGaZcchUKEyjFoehb1AX/uZmhODxC1Iqjh/Hhhx+Snt62D84NN9xgMsNtS+c7S29Lsa4Uh6Jv0Va6kYZ4hqo9jh6GuYqjq+ltKdaV4lD0LUrrFEcbXlVgcMmtKIDqMuvKpDBJcnIygwcP5vrrryc2NpbLLrusPuWGqdTq33zzDbt27eLqq68mLi6OiooKnn76acaOHcvw4cO57bbbWs3/1JTW0rc/+uijjBs3joEDB7J582YAysvLueKKK+pTrI8fP55du3b1yhTrKjuuom9Rt+Iw11QFUJwBfgOsJ1MP4N87/k1CvmWzrA72Gcyj4x5ttc2xY8d47733mDRpEjfddBNLly7l3nvvbTG1+uuvv85LL71EfHw8AHfffTf/+Mc/ALj22mv56aefuPDCC9uUra307Tqdjh07drBq1Sqeeuop1q5dy9KlS/H29ubAgQMcOnSoPhNub0yxrhSHom9RkgkaO0Pq9LZo6JLbxxWHrQgLC2PSJIMH3DXXXMOrr77KnDlzWk2t3pD169fzwgsvUF5eTn5+PsOGDTNLcRw7dqzVOS655BKgcXr0LVu2cO+99wIwfPjwXp1iXSkORd+iNMsQ/Kcxw0rrEWw4Fnc/m3lX09bKwFqYSpMupWw1tXodlZWV3HnnnezatYuwsDCWLFlSn7K8Ldqaoy6NuVarrU9j3h4zWE9Psa72OBR9i5IM81xx4eyKQ7nk2ozTp0/X37w///xzJk+e3GpqdXd3d0pKSgDqb7p+fn6Ulpa2y1uqrfTtppg8eTJfffUVAEeOHOHgwbMVE3tbinWlOBR9i5Is8/Y3AOydDCYtVdDJZgwZMoSPPvqI2NhY8vPzueOOO1pNrX7DDTewePFi4uLicHR05NZbb2XEiBFcdNFFjB071ux520rfboo777yTnJwcYmNj+fe//01sbCyenp5AL0yxbiplbm/7GTNmTNs5hRV9g+cjpfzxfvPbvzlZyk8vs5483Rhbp1U/deqUHDZsmE1laA86nU5WVFRIKaVMTEyUERERsqqqqlNjdmWK9fakVVd7HIq+g64KKvLNN1WBMQgw1XoyKXoN5eXlzJgxg5qaGqSUvPnmmzg4OHRqzLoU6zExMZ0ax9Ip1pXiUPQd6kvGtkdxBEPqn9aRR9EqkZGRHDrUc5Jfu7u7s2vXLouO2V1TrKs9DkXfoaQd6UbqqA8CbN9GpELRm1GKQ9F3qK81bkbUeB31sRzKJVehqEMpDkXfod5UZUaeqjpUXQ6FohlKcSj6DiUZILTg4md+HxUEqFA0QykORd+hpB1R43XUrzhULEdPoLNp1dti2bJlJtOcN0yVvm/fPlatOlu/bsmSJbz00kttji2l5Nxzz6W4uLjdcjVl1qxZFBQUdHqclrCq4hBCzBFCHBNCJAohHjNxfbAQ4g8hRJUQ4qEm15KFEAeFEPuEELsanPcRQqwRQpwwHr2t+RkUvYjSzPbtb8DZIEAVPd4jsHZa9cWLF3Pddde12qap4jCXVatWMXLkSDw8PDoqXj3XXnstS5cu7fQ4LWE1xSGE0AJvAHOBocBVQoihTZrlA/cALanjGVLKOCllfINzjwG/SyljgN+N7xWKtinJbN/+Rh0eIcpUZQO6Oq16dnZ2fTba/fv3I4Tg9OnTgCF5YHl5eaPVw+7duxk5ciTnnHMOb7zxBgDV1dX84x//4MsvvyQuLo4vv/wSMKQgmT59OtHR0S3WxVi+fDkLFy6sf//xxx8TGxvLyJEjufZaQ8XsG264gTvuuIMZM2YQHR3Nxo0buemmmxgyZAg33HBDfd8FCxbw+eefd+TXbhbWjOMYByRKKZMAhBBfAAuBI3UNpJTZQLYQYl47xl0ITDe+/gjYANgmA5uiZ1GSCaHmp52oRwUBkvncc1QdtWxadcchgwl6/PFW23RlWvWAgAAqKyspLi5m8+bN9WnIJ0+eTEBAAC4uLo3a33jjjbz22mtMmzaNhx9+GDCkKnn66afZtWsXr7/+OmAwVSUkJLB+/XpKSkoYNGgQd9xxB/b29o3G27p1K2+99RYAhw8f5tlnn2Xr1q34+fmRn59f366goIB169axcuVKLrzwQrZu3cq7777L2LFj2bdvH3FxcXh7e1NVVUVeXh6+vmZkgm4n1jRVhQAN/9vSjOfMRQK/CSF2CyFua3A+UEqZAWA8BnRaUkXvR1cN5bkdXHEEqz0OG9E0rfqWLVsapTyPi4vjX//6F2lppr3e1q9fz/jx4xkxYgTr1q1rM1HhxIkT2bp1K5s2beLxxx9n06ZNbN68mSlTpjRqV1RURGFhIdOmTQOoXxG0xLx583B0dMTPz4+AgACysrKatcnPz8fd3R0wFHG67LLL8PMzOHL4+PjUt7vwwgsRQjBixAgCAwMZMWIEGo2GYcOGNUrPHhAQYDWznTVXHMLEOfPzDsMkKWW6ECIAWCOESJBSbmqzV93kBmVzG0B4eHg7plX0SsqyDcf27nFA4yBAB5e22/dC2loZWIuuTqs+ZcoUNm/eTEpKCgsXLuTf//43Qgjmz5/fqJ2UsplsrdE0jXrDdOd12NnZodfr0Wg0rY7fMG1605Tq1kqj3hRrrjjSgLAG70MBs9WflDLdeMwGvsNg+gLIEkL0AzAes1vo/7aUMl5KGe/v798B8RW9ivZU/muKCgK0GV2dVn3q1Kl8+umnxMTEoNFo8PHxYdWqVfWrnjq8vLzw9PRky5YtgGF/oo6GMrSHQYMGkZSUBMDMmTP56quvyMvLA2hkqjIHKSWZmZlERka2Ww5zsKbi2AnECCGihBAOwCJgpTkdhRCuQgj3utfAeUBd0pqVwPXG19cDP1hUakXvpE5xtCdPVR3KJddmdHVa9bob7dSpUwFDjQ0vLy+8vZs7b37wwQfcddddnHPOOY2e7GfMmMGRI0cabY6bw7x589iwYQMAw4YN44knnmDatGmMHDmSBx54wOxxwLBxP2HChPp65RbHVMpcS/0AFwDHgZPAE8Zzi4HFxtdBGFYmxUCh8bUHEA3sN/4crutr7OOLwZvqhPHo05YcKq26Qm5/W8p/ekhZnNH+vrmJhr57l1term6MSqvetaSnp8tZs2ZZZKx77rlHrl27tl19uk1adSnlKmBVk3PLGrzOxGDCakoxMLKFMfOAmRYUU9EXKM0CoQHXDpgt1YpD0QX069ePW2+9leLi4k7HcgwfPpyZM613m1Rp1RV9g5JMcA0Ajbb9fVUQoE3oaWnVLcEVV1xhkXFuvfVWi4zTEirliKJvUJLZsf2NOlQQoEJRj1Icir5BqVIcCoWlUIpD0Tfo9IojWKVWVyiMKMWh6P3U1kBZbsdiOOpQlQAVinqU4lD0fkqzAdmxqPE6lGdVl1NYWGjVDK91fP/99xw5cqTthop6lOJQ9H5K64L/OpCnqg4vY9qawtOdl0dhFu1VHFJK9Hp9u+dRiqP9KMWh6P3UpxvpxIpDKY4u57HHHuPkyZPExcVx//33M3PmTEaPHs2IESP44QdDwojk5GSGDBnCnXfeyejRo0lNTeWZZ55h8ODBzJ49m6uuuqo+DfrJkyeZM2cOY8aMYcqUKSQkJLBt2zZWrlzJww8/TFxcHCdPnrTlR+4xqDgORe+nxAIrDvd+oLHvs4pj81fHyU0tteiYfmFuTLliYIvXn3/+eQ4dOsS+ffvQ6XSUl5fj4eFBbm4uEyZMYMGCBYAh9foHH3zA0qVL2bVrFytWrGDv3r3odDpGjx5dX2PjtttuY9myZcTExLB9+3buvPNO1q1bx4IFC5g/fz6XXXaZRT9fb0YpDkXvpyQTEB2LGq9DowXPUChMsZhYCvORUtanOddoNJw5c6Y+NXlERAQTJkwAYMuWLSxcuLA+d1Rd7Y3S0lK2bdvG5ZdfXj9mVVVVF3+K3oNSHIreT2kmuAWAtpNfd6/wPrviaG1l0BUsX76cnJwcdu/ejb29PZGRkfXZb11dXevbyRYq/On1ery8vNi3b19XiNvrUXscit5PSVbn9jfq6MOKwxY0TE9eVFREQEAA9vb2rF+/npQU0yu/yZMn8+OPP1JZWUlpaSk///wzAB4eHkRFRfH1118DBgWzf//+ZvMozEMpDkXvpySjc8F/dXhFGJIl1lR0fixFm/j6+jJp0iSGDx/Ovn372LVrF/Hx8SxfvpzBgweb7DN27FgWLFjAyJEjueSSS4iPj8fT0xMwrFree+89Ro4cybBhw+o32BctWsSLL77IqFGj1Oa4mShTlaL3U5oFwXGdH6fOs6ooDfxiOj+eok0+++yzNts0TYT40EMPsWTJEsrLy5k6dSoPPvggAFFRUaxevbpZ/0mTJil33HaiFIeid1OrMwQAdiZqvI56l9wUpTi6MbfddhtHjhyhsrKS66+/ntGjR9tapF6HUhyK3k1ZDoaocUsqDrXP0Z0xZ5Wi6Bxqj0PRuynJMBwtoTjcgwyxHAV9xyW3JS8lRe+ivX9npTgUvZt6xdGJ4L866mM5+saKw8nJiby8PKU8ejlSSvLy8nBycjK7j1VNVUKIOcArgBZ4V0r5fJPrg4EPgNEY6oq/ZDwfBnyMoSa5HnhbSvmK8doS4FYgxzjM48YStQpFc+pqaHgEW2Y874g+ozhCQ0NJS0sjJyen7caKHo2TkxOhoaaqeJvGaopDCKEF3gBmA2nATiHESillQ/eFfOAe4KIm3XXAg1LKPUIId2C3EGJNg77/q1MyCkWrlGSA0HYuarwhXuFwrLlnTm/E3t6eqKgoW4uh6IZY01Q1DkiUUiZJKauBL4CFDRtIKbOllDuBmibnM6SUe4yvS4CjQIgVZVX0VoozDMF/Hak1bgqvcCjLVrEcij6NNRVHCJDa4H0aHbj5CyEigVHA9gan7xZCHBBCvC+E8G6h321CiF1CiF1qqd2HKUkHDwvsb9ThFWE4Fqa23k6h6MVYU3EIE+fatcsmhHADVgD3SSmLjaffBPoDcUAG8B9TfaWUb0sp46WU8f7+FjJTKHoexRmW2Rivo84ltyDZcmMqFD0MayqONCCswftQIN3czkIIewxKY7mU8tu681LKLCllrZRSD7yDwSSmUJimJNNyG+MAPtGGY8Epy42pUPQwrKk4dgIxQogoIYQDsAhYaU5HIYQA3gOOSin/2+Raw8fHi4HG+QYUijqqy6CqyLIrDld/sHeF/CTLjalQ9DCs5lUlpdQJIe4GfsXgjvu+lPKwEGKx8foyIUQQsAvwAPRCiPuAoUAscC1wUAixzzhkndvtC0KIOAxmr2Tgdmt9BkUPp9gYw2HJFYcQhlVHvlpxKPouVo3jMN7oVzU5t6zB60wMJqymbMH0HglSymstKaOiF1NitIxacsUB4BMJ2QmWHVOh6EGoyHFF78UaKw4wrDgKU0Bfa9lxFYoeglmKQwixQggxTwihFI2i52CtFYd3FNRWn41KVyj6GOYqgjeBvwAnhBDPG1OFKBTdm+IMcPQARzfLjlvnWaU2yBV9FLMUh5RyrZTyagw5pZKBNUKIbUKIG41uswpF96Mk3fKrDQAfYxoO5ZKr6KOYbXoSQvgCNwC3AHsxJC8cDayximQKRWcpzrBs1HgdHiGG9OpqxaHoo5jlVSWE+BYYDHwCXCilNO468qUQYpe1hFMoOkVJBvhNs/y4Gi14RyqXXEWfxVx33Hebpi4XQjhKKauklPFWkEuh6Bz6WmPUuBVWHGAwVynFoeijmGuq+peJc39YUhCFwqKU5YCstc4eBxg2yAtOgSpypOiDtLriMEZ2hwDOQohRnA3K8wBcrCybQtFxLF3AqSneUVBdCmW54KaSaCr6Fm2Zqs7HsCEeCjTMGVUCPG4lmRSKzmPJkrGmaOiSqxSHoo/RquKQUn4EfCSEuFRKuaKLZFIoOk9dvQzPsNbbdZSGLrnh460zh0LRTWnLVHWNlPJTIFII8UDT600z1yoU3YaiVLBzAlc/64zvFQ4I5ZKr6JO0ZapyNR4tHHqrUFiZolTwDDVks7UGdo6G1YzyrFL0QdoyVb1lPD7VNeIoFBaiMNV6Zqo6fCLVikPRJzE3yeELQggPIYS9EOJ3IUSuEOIaawunUHSYojTwsrbiiFZpRxR9EnPjOM4z1vyej6Ek7EDgYatJpVB0hppKKMu2/orDOwrK86CyyLrzKBTdDHMVR10iwwuAz6WU+VaSR6HoPEVphqPVTVV1Lrlq1aHoW5irOH4UQiQA8cDvQgh/oLKtTkKIOUKIY0KIRCHEYyauDxZC/CGEqBJCPGROXyGEjxBijRDihPHobeZnUPQVioyuuFY3VRldctU+h6KPYW5a9ceAc4B4KWUNUAYsbK2PEEILvAHMxVBH/CohxNAmzfKBe4CX2tH3MeB3KWUM8LvxvUJxljrF4WmqKrEFUXU5FH2U9tQcH4IhnqNhn49baT8OSJRSJgEIIb7AoGyO1DWQUmYD2UKIee3ouxCYbmz3EbABeLQdn0PR2ylMBaExpD+3Jg6uhsh0pTgUfQxz06p/AvQH9gF1hZYlrSuOECC1wfs0wNwQ29b6BtaldZdSZgghAlqQ+TbgNoDw8HAzp1X0CorSDDd0bRfUGPMdAHmJ1p9HoehGmLviiAeGStmuVKCmIq/M7d+ZvobGUr4NvA0QHx+vUpj2JYq6IIajDp9oSPi5a+ZSKLoJ5m6OHwKC2jl2GtDwvzcUSLdA3ywhRD8A4zG7nXIpejuFp62/v1GH7wAoz4WKgq6ZT6HoBpirOPyAI0KIX4UQK+t+2uizE4gRQkQJIRyARUBbfczpuxK43vj6euAHM8dUWInqlBTOPPAgxydPIXH2eWS98CK1JSW2Eaa2xmCq8o7smvl8+xuOeWqfQ9F3MNdUtaS9A0spdUKIu4FfAS3wvpTysBBisfH6MmO9j10Y6nvohRD3YTCJFZvqaxz6eeArIcTNwGng8vbKprAcFYcOc/rGG0Gvx/2886gtLib/ww8p/vlnQl97FefY2K4VqCjVUMCpyxTHAMMx/ySEjumaORUKG2OW4pBSbhRCRAAxUsq1QggXDDf0tvqtAlY1ObeswetMDGYos/oaz+cBM82RW2FddAUFpN3zVzRubkR88gkOoQYvpooDBzjzwIOcvuFGwt5+C5f4LqwuXBeMVxdjYW28Iw0eXGqDXNGHMDdX1a3AN8BbxlMhwPdWkknRQ8j617PU5uYR+uqr9UoDwDk2lojly7ELCiL1rrupTk7uOqEKjHN5d5HiqMuSm3eya+ZTKLoB5u5x3AVMAooBpJQnAJNusIq+QcXhwxT//DM+N96I84jhza7bBwYQ9tYyhEZD6uI7qC3qonxOBadA62i9yn+m8O2vVhyKPoW5iqNKSlld98YYBKhcXPswOa+8gtbTE99bbm6xjUNYGKGvvUr1mTOkP/EE7fPm7iD5p8A7AjTmfrUtgO8AQxBgV3w+haIbYO5/10YhxOOAsxBiNvA18KP1xFJ0Z6pOnaJs02a8r78Orbt7q21d4uMJuP9+Stf+TuE331hfuIKUrjNT1eHTH6qKoSyna+dVKGyEuYrjMSAHOAjcjmHT+u/WEkphGn1FBbnvvEPOq6+hy7ddguLCr74GOzu8LrvMrPY+N1yPyzkTyHru/6g6ZcVMslIaTFVdtTFeR51nldrnUPQRzPWq0gshvge+l1KqxyorkptWwrHtWQREuDNgTADCWPpU6vWk3XU3Zdu2AVD8269EfvElWjfX1oYzG71e8se3iRzbnklQtCfnXjsEJ7fmKTv0VVUUffst7jNnYh9g3jaX0GgIfv55khYsJP2RR4n8/DOEXeOvXuapIk7uziZ4oDdRsR2sE16WC9WlXb/i8DUmO8xLhIhzunZuhcIGtLriEAaWCCFygQTgmBAiRwjxj64Rr2+RnVLMN//ezb41p/nt3cPs/iWl/lrR9z9Qtm0bQUv+Sfj771GdeJK8ZW9abO6dP59i39pU/MPcSTmcx+p3DpnckyjbvJnaoiKzVxt12AcG0u+pJVQePEj+hx82upaakM+3L+5h39pUVi09wKFNZzr2Ieqq8XVVDEcdnuGgsTfEcigUfYC2TFX3YfCmGiul9JVS+mBINjhJCHG/tYXrS0i9ZP2nCTi72XPD85OIGRvIjp9OkZ9RhtTryXvnHZyGDsXryitxnTgRjwsvJH/5ZxbxVirOrWDPrykMHBfI/L+OZMoVAzlzrIDE3c2zuRT/+htaT09cJ5ibr/IsHnPm4D57FjmvvV7voqurqWX9xwl4BThz04uTCR/qw9ZvTlBWWNX+D1LnitvVpiqtnWFO5Vml6CO0pTiuA66SUtYbpo2pzq8xXlNYiNSj+eSmljJhYTSuXo5MuSIGOwcNu39JpmLPHqpPncL7umvrTVe+N9+ErKig6Adzs7i0zP51qaCHcy7ujxCCoZOD8Q5yYc+vKY1WHfqqKkrXrcNt1kyEfccyzwY++STCwYGMJ/+B1OtJ+COTkvxKplw5EGd3B6ZeNYhanWT/76ltD9aU/CRAgFdEh2TrFD79VdoRRZ+hLcVhL6XMbXrSuM/RBTmr+w5Ht2Xg6GrHgDGBADi7OzDknH4k7s4mf9VvCHt73GfNqm/vNHgwjgMHUvzbr52at1an59ifmfQf7Y+btxMAGo1gxPRQclNLyTtTWt+2bOs29GVleMyZ0+H57AMCCHz0Ecp37qTw6284tDEN/3B3QgcbCjl6+jsTHefHkW3p1Nbq2zd47nHwCgd7pw7L12F8+xtMVfp2yqxQ9EDaUhzVHbymaAeVZTUk7c9h0LggtPZn/yRDJgWj1+kpXvM7LudMQOvm1qif+3nnUbF7D7qcjvsrpCUUUFWuI2Zc4+THA+IDEBrBiV1nzVWlmzaicXHBdXz7zVQN8bz0UlwmTCDrhRcoSTrDkIn96ldSAAPHBVFVpiP9WGH7Bs49AX4xnZKtw/j2B10llJibAFqh6Lm0pThGCiGKTfyUACO6QsC+QNK+HPQ6yeBzGkc7+4a4EuiYj8hJx33mrGb93M+bDVJSsnZth+dO3JWFg7Md4UN8Gp13dnMgdLA3ibuzkVIipaRsy1ZcJkxAODh0eD4AIQT9nn4KfVU1A5K+I3qUf6Pr4UN9sHPQkLSvHQpRrzfsMfgN7JRsHabeJVftcyh6P60qDimlVkrpYeLHXUqpTFUWIvVoPi4eDviFNV5RCCGI1hjs5vbjJzfr5xgTg0NEBCW/r+vQvLW1epL25xI10q/RSqeOAWMCKM6pIDe1lJqUFGrS0nCdPKlDczXFITycrCEXEJS1C47tb3TNzkFL+DBfkvbnIPVmRmOXpENN+dkbeFfjU5deXXlWKXo/XZiXQWEKqZekHS0gbKhPI3NNHR4FiZS5BHIms/k1IQSukyZSvmcPsqam3XNnJ5dQXaEjcoTpuImI4b6AQbGVbtkKgNskyyiO4twKErymI30CyXrmX0idrtH16JF+lBdVk5tW2sIITcg9bjjaasXh3g/sXQzmMoWil6MUh43JSS2hsqyGsCamIjAE/dUmHKTUdwBpx0xXmHMZNx5ZXk7FoUPtnjstIR8EhA7yNnnd1dMRn2BXUo/mU7ZlC/ZhYThEWMZjKe1YAXqtA173PkTViRMUfPZZo+vBAw0ypZ8oNG/AXKOJyFZ7HBqNQWnlHrPN/ApFF6IUh41JPWpIHWJKcVQnJaEvKkI7JJa0YwUmA/Jcxo0FoHz7jg7N7R/mbjJCvI6wwT5kHs+jbPt2i5mpwLAp7+zhQNDlF+A6cSI5byyltri4/rq7jxMefk6kJxaaN2DucXD0ALdAi8nYbvwHQ3aC7ebvRkgpKamsobiyhsqaWvTmmhwVPQJzKwAqrERmUjHeQS64eDTfcC7fswcA78njKF1XTlFOBV4BLo3a2Pn44BgTQ/mOHbD4drPnra7UkZVUTNzssFbbhQ7xJuXbdciKCtwmN99n6QhSSs4cKyB0kDcajYaAhx/i1MWXkPfOuwQ8+EB9u+ABXiQfykNKadKM14i8E4b9jbbaWZOAwXDgC6gsAidP28nRhdTqJadyyzicXsTh9GIOnSkiKaeM/LJqqhu4U2sEBLg7EeTpRD9PJ8J9Xejv50b/AFei/dzwdu2cw4Wia1GKw4ZIKck6VVS/l9CUij170fr4EDhlOKzbQVpCQTPFAeAyfjyFK1Yga2rMDszLSipGr5eEDDRtpqojOMYL78ITSCFwGTvWrLHboiCjnPLi6vrYDachQ/CYP5/8Tz7B+5prsA805MDqF+NFwp+ZFGSW49OvjZxcuScgcopF5Osw/oMNx5xjEDbOtrJYCCklJVU68kqryS+rIre0muySKo5lFnM4vZiEjBIqamoBcLDTMDjInUkD/PBzd8DHxQGtRlBdq6esSkdWcRVZxZUczyrh96PZjRSLj6sDI0I8GRflw4RoX0aFeaHR2PAhQNEqVlUcQog5wCsYysy+K6V8vsl1Ybx+AVAO3CCl3COEGAR82aBpNPAPKeXLQoglwK0YsvUCPG4sM9vjKM6tpKKkhsAo00+nlYcP4TxiBN5Brrh4OJBxspDhU0OatXOOi6Pg00+pOnECp6FDzZo7K9mQqiQwyqPVdg5OdvhVJVPtE4bWo/W25pJxshAwrCjq8L/3Hop//ZXcpUvp99SSRtczTxa1rjiqSqH4DPjZyKOqjnrFkdBjFUdeaRWrD2ey6XgOJ7JLSSuooFrXPKjR3dGOIcEeXDk2jKHBHowI8WRAgBv2WvOs37paPWkFFSTllpKUU0Zidil7Thfw4q+GPaIQL2euGhfG9RMjcXdSDpzdDaspDiGEFngDmA2kATuFECullEcaNJsLxBh/xgNvAuOllMeAuAbjnAG+a9Dvf1LKl6wle1eRmWS4eQdFN78h6ysrqUo6hdusWQghCIr2JDOpuFk7AOeRsYCh1re5iiPzlMFE5ujS+j+l1OlwzTtJZuB49HppkafArFPFOLra4RngXH/OISwM7yuuoOCLL/C54Xoco6LwDHDGwdmO7JRihk4ObnnAutgJW3lU1eEVDnbOhhVHD6OiupY3N57k3c1JlFfXEuLlzMgwT2YNCcTfzRFfNwd83RzxdXXAz82RQA/Hts2HrWCn1RDp50qknyvnDj57vqCsmg3Hs/l2zxle+u047205xd8uGMLlY0I7NZ/CslhzxTEOSDTmtkII8QWwEGioOBYCH0vDru+fQggvIUQ/KWVGgzYzgZNSyhR6GVmnirFz1Jp8mq46kQi1tTgNHgJAYLQHSftyKC+ubrYfYh8aitbbm4r9B/BetKjNeQ0msmIizUhfXplwDE11Jflu0eSnl+EX6tZmn7bISi4mMNKz2Y3A747FFK5YQd477xL83LMIIQiIcCc7paT1AetcYH1t5FFVh0Zr8OrKPmpbOdpJRlEFN36wk4TMEubF9uPuGQMYHORukxu1t6sDF48K5eJRoexPLeTZVUd55JsDbDiWzYuXjcTVUVnXuwPW9KoKARpmqksznmtvm0XA503O3S2EOCCEeF8IYdJIL4S4TQixSwixK6cTKTmsSc7pEvzD3NCYWN5XHTN45zgNMTyOBUUbzFlZp5pnwxVC4BwbS8WBA2bNW5xbQWVpDYGRbZueKvbsBqDIs3/9CqkzVFfoyM8oM7nKsvPzw+vSSylauZKaDMOzQ0CEO3lnSqmtaSUHVN4JEBrwie60fJ0mYEiPWnHklVZx5Vt/klZQwYc3juWNv4xmSD+PbvF0PzLMiy9uncBjcwfz6+Esrn53O4XlKtNRd8CaisPUN6+pT16rbYQQDsACDKVq63gT6I/BlJUB/MfU5FLKt6WU8VLKeH9/f1NNbIqUkvz0UnxDTD/BVx5NQOPign1oKAAB4e5oNKJFc5XTyFiqk5KoLWnj6RzqxzB1825K+e492AUHo/EPsIjiyEopBtny3orvTTcCkPfBBwD4h3ugr5XkpbcSCJh91FCDwxbJDZviPwiK06DS9N+pO6Gr1XPXZ3vIKq7kk5vHMX2QeYW5uhKNRrB4Wn+WXj2aI+nF/OWd7ZRW6druqLAq1lQcaUBDX89QoGkGuLbazAX2SCmz6k5IKbOklLVSSj3wDgaTWI+jtKCK6spafINNb/pWJiTgOHgwQmP4E9k5aPELc2vx5u0cOxKkpPLgwTbnzkpu2UTWECkl5Xt24xI/xrjHYgHFYVRaLa127ENC8Jw3j8Kvv0FXUEBAhKGmeavmquwjEGDe3o7VCRhmOGYdtq0cZvDib8f4Mymf/7tkBKPCW/euszXnDwvirevGcCyrhHs+30utiguxKdZUHDuBGCFElHHlsAhoWjxiJXCdsdLgBKCoyf7GVTQxUwkhGmYCvBhof8h0N6AuXbmPiRWH1OupSkjAafDgRueDoj3JTilGbyLduHOsIeekOeaqrKQiwwqmDQ+YmtOnqc3JxWW0QXEUZVdQUdI5U0FWctub8r633IysqKDw629w93XC0dWOnNMtKI6aCkMdjsBhnZLLYvQbaThmmmc2tBVHM4p5Z1MSV40L45LRobYWxyxmDApgyYJhrEvI5qXfeo45sDdiNcUhpdQBdwO/AkeBr6SUh4UQi4UQi43NVgFJQCKG1cOddf2FEC4YPLK+bTL0C0KIg0KIA8AMoEdWIsxPLwMw+dRfc+YM+rIyHIc0Vxy6aj15Z8qa9dF6eOAQFUXF/tZvWLqaWnLTSs02UwG4jBndYI+l4yaYuriVtvZWHGNicJkwgYIvPge9Ht9gt0Z1QRqRkwBS331WHO5B4OoPGfvbbmsjpJT844dDeDrb8+icwW136EZcOyGCRWPDWLbxJNuT8mwtTp/FqilHpJSrpJQDpZT9pZTPGs8tk1IuM76WUsq7jNdHSCl3NehbbixXW9RkzGuNbWOllAuarFB6DPnpZbh6OeLk2vzJu/KowSun6Yqjbl+gZXOVYYPcVGqSOnJTS9HXyhZjRxpSvmc3Gk9PHPr3xz/CHaERZCV3XHGU5BnjVqLbntv76r+gS8+gdMMGfEPdyE8vM50pt86DqbusOISAoFjI6L4rjp8OZLAzuYBH5gzGy6XnRWw/OX8oET4uPPDVfoor25/cU9F5VK4qG5GXXtri/kZVQgJoNDjGNHYvdfd1wtnDocWbt1PsCGrz8tBltKxL65ROW4F/ABW79+AyejRCo8HeQYtviKtJry5zyTT2Nceby33GDOz69aNg+XL8QtyoqaqlOK+yecOsw6B1BO8urjPeGv1iIeco6DpQN93K6PWS19adICbAjSvjW083011xdbTjf1fGkV5Uwf/WHLe1OH0SpThsgL5WT0FGucn9DYDK48dxiIxE49TYS0gIQWCkR4vmIucRdfscLW+QZ50qxs3HEVdPx1Zl1OXlUX3qFC5jRtefC4z0ICu5xPwaGU3nTirGzl6Db0gb6UMAYWeH96JFlG37A09pMEmYNFdlHzF4Mmm7kX9/v5Gg1xlk62asPZrF8axS7poxoEen9BgV7s3V48P5+I8UjmZ0fw+23oZSHDagKKeCWp2+xRVHdeJJHAeYTp8RGOVBYVY5lWXNl+iOgweDvT2Vh1pXHEFmmakM+xvOo8c0mru6Qkdhdnmb/U3OnVyMf0Tbm/J1eF58EWi1aP5cA6IlxXG0+5ip6ggyRPJ3N3OVlJI31icS7uPC/Nh+bXfo5jx03iA8ne355w+HWzXPKiyPUhw2oH5j3ITi0FdXU336NI4D+pvsW2diyjZhrtI4OOA0aFCLK46yoipK8ivNNlMJR0echp+9KQdGdnyDvLZGT05qiVlKqw77gADcJk+m9KeVePg6Nlcc5flQktF9Nsbr8I4CB/dut0G+K6WA/WlF3DY1GjszlXd3xsvFgYfOG8SO5HzWHs22tTh9ip7/7emB5J0pBWHao6o6ORn0ehyiW1AcER4gaHGfwzl2BJWHDyNra5tdq7vhm7Uxvns3ziNGoGlQX9w7yAUHJ22HFEdOWgl6nTRLaTXE8+KL0WVlEVp7qrk3WZ0pqLspDo0GguPgzG5bS9KI5X+m4O5oxyWjmyfK7KlcER9KtJ8rL/6aoGI7uhClOGxAfnoZnv7O2Dlom12rPmmoWd3SisPB2Q6ffq4tR5APH4G+rIzqU6eaXcs6VYxGK/APaz3flL68nMojR3AeM6bReaERBER6dMizqj7wrx0rDgC3c2eg9fTE99QWirLL0VU3UIhZRsUR2M0UBxiy42YehOrmrtO2IL+smlUHM7lkdAguDt1oP6iT2Gk1PHjeII5nlfLd3jO2FqfPoBSHDchLL8M32PTNuyrxJAiBQ2Rki/0DIz3ITi42adetDwQ82DwuMutUEX6hbiYVVkMqDhyA2lpc4sc0uxYY6UFeWmnjG7gZZCUX4+btiJt365vyTdE4OOAxfz72h/9AU1NBfkaDG3H2EXDyMtT77m6ETQBZ221WHd/sTqW6Vs9fxlum9G93Yu7wIIYFe/D6uhNq1dFFKMXRxehqainKLje5vwFQlXQS+7CwZh5VDQmM8qCyrIainIpm1xyiotC4uDRLPVJbqycrubg+kK81ynfvBiFwjoszObdeL1uO5G6BzKS2A/9awvPC+VBTjX/ugcbmqrpUI90gIV8zwoxFr05vt60cGDbFv9iRSnyEN4OC3G0tjsXRaAR3zxhAcl45qw72yLCuHodSHF1MQUY5UtJicsPqxJM49jdtpqqjztxjaq9BaLU4DR9ORRPFYVgl6Anq37biqNi9G8dBg9C6N7/J1M/dDnNVWWEVJXmVZs1tCqeRI7Hr14/A3D1nVxxSGj2quqGZCsDZG/yHQKrtFce+1EKScsu4PL5npBbpCOcPCyLa35WlG04qD6suQCmOLibfmOXV1IpD6nRUJSfj2L/19OA+wa7YOba8Se00YjhVCQnoq8/mlTpbNKr1m7esqaF87z5c4uNNXnfxcMDdx6ldG+QZJ41zd1BxCCHwmDsX7/yjFCZlGk4WpUJVcffbGG9I2DhI2wH6VlLCdwHf7z2Do52GuSO6oUnPQmg0gjum9edoRjEbjnXPMgq9CaU4upi89DI0dqJR9bs6qlNToaYGh/6tl0DVaAQB4e4tRnE7j4hF1tRQdexsIrjMk0W4eTvi7tN66vGKQ4eQFRWt1hcPjGo5CNEUmSeL0Npr8A/ruJnEY+5cNPpa2LPVOKhxRRU4vMNjWp3wCVBZZMinZSNqavX8eCCDWUMD8ejlJVgvGhVCiJczb6xPtLUovR6lOLqYvDNleAe5ojXhR9+WR1VDgqI9yE0rRVfTfJPaeYThZtrQXJWRVGTe/sZOQ7owl7GmVxxgUBwl+ZWUFZmXUiPjZCGBkR5o7Tr+dXMaPgy9bxCeydupqtAZg+tE9wv+a0jERMMxeYvNRNh4LIf8smoujus9LrgtYa/VcOuUKHalFLArOd/W4vRqlOLoYvJby1GVaFAcDlFtV7ILjPREXyvJOd08mtouOBitjw+VxkDA0oIqSvOrzFQcO3EY0B87H58W29SNk5HYdt6qmupaclNLO2ymqkMIgcOUmXgXHCPveLohbblfDDh2vpSt1fCOBK8ISNpgMxF+PJCOl4s90wZ1v2Jm1uCKsWF4ONnxwbZkW4vSq1GKowupqtBRWlDVqkeVXb9+aN3azuVUdyNOP1HQ7JoQAucRI6gwph6p399o4+YtdToqdu9ucX+jDv8Id+wcNKSfKGxTzuzkYvR6ST8zlFZbeM89Dw16Cn5bb1hx1KX26M5ET4fkzVDb9VXrKmtq+f1oNucPDcK+F0SKm4OLgx2LxoWz+lAm6YXNvQ4VlqFvfJu6CXWpRlqK4ahOPIljtHl1s108HPAJduXM8UKT151GjKD6ZBK1paVkJBZiZ6/BL7T1p/PKo0fRl5e3ur8BoNVqCIr2NKm0mtLZjfGG+EwaQ5WDB/qdaw3lWfv1BMUxzbCJn7Gvy6fefCKX0iodF/SCvFTt4doJEUgp+eTPFFuL0mtRiqMLOVv1z4RHlV5PVVKSWfsbdYQM9CYjsZBaXXOvHZfRo0BKKvbuJTWhgH4xXm3uMZTv2Gno24biMMztRd6ZMpPJFhuSfrwAn2BXk3VH2ovGTktZxGjc83YYTvSEFUfUNMMxaX2XT73qYAaezvZM7O/b5XPbkjAfF2YPDeTzHaepaGegqsI8lOLoQvLTy7B31Jr0bKpJz0BWVuLQRgxHQ0IGeaGr1ptMeOgcFwd2dhRu+pOCjDJCB7ddU7psx3YcIiKwDwhos23wQMN4rZmraqprST9RRNiQlvdL2ouMm4izuzE7b12Z1u6Mqx8EjoCkjV06bZWulrVHsjhvaGCfMVM15MZJURSW1/D9PpWGxBpY9RslhJgjhDgmhEgUQjxm4roQQrxqvH5ACDG6wbVkY4nYfUKIXQ3O+wgh1gghThiPbd8Ruwn56aX4BLsiTEQ6V580uBC2FfzXkJAYbxCQmtDcZKRxccF52DBK/jAEoLV189ZXVVG+YyeukyebNXdgpAf2TlpSDrVcvjPjhGE1FDbUcorD9ZxzcPSuRSfdwMVy41qV6GmGQMDqjqWj7whbTuRSUqXjgl4cu9Ea46N8GNLPgw+3JquAQCtgNcUhhNACbwBzgaHAVUKIptFac4EY489twJtNrs+QUsZJKRvu1j4G/C6ljAF+N77v9kgpyTtT1mLEeJ1HVXsUh5ObPUFRHiQfyDV53WVsPDLpGK7OevxamLeOit27kRUVuE4xT3Fo7TSED/Uh+WBui/+Yp4/ko7XTEBzjZdaY5uAT4Y29H1TkaJA2Dqwzm+jpUFsNp//osilXHczE3cmOSQP8umzO7oQQghsnRXIsq4Q/Tqra5JbGmmkyxwGJUsokACHEF8BCoGFZtIXAx9Jw5/lTCOElhOjXRh3xhcB04+uPgA3AoxaW3eKUF1dTWVbTskfVyZNo/f3Qenm1a9yokf788d1JSvIrm5nAHEfFI/TvEe2Zh2ij2lvp5i0Ie3tcx40ze+7IEX6c3JNDbmop/uGNg/uklKQcyqPfAE/s20iq2B58/cHFpYzcU27YHT5SH7PSrYmYCBp7g1vugJlWn66iuobVh9MZGq7jjf2vcLr4NMXVxUgkbvZuuDu44+/sz2DfwQzzGUaoe6jJVXBPZ8HIYJ7/JYEPtiUzsY8qUGthTcURAqQ2eJ8GjDejTQiQAUjgNyGEBN6SUr5tbBNYp1iklBlCCJMGeSHEbRhWMYSHh3fyo3Sesx5VLSmORBxbqMHRGlEj/fjju5MkH8hlxPTGuYjy3KLRCy39yo+10PssZVs24zI2Ho2Li9lzhw/zBQFJ+3OaKY6c0yUUZpUTN8uyda1dKxMRAioKHJHrfu8ZisPBFcLGWzWeo6a2hk1pm1iXuo5fj6RRVnUFhyo+JvFIIuHu4Xg5egFwpvQMJdUl5FTkoNMbXIR9nHyYGT6T2RGzGRs0FjtN70i77mSv5apxYby54SSp+eWE+Zj/3Va0jjW/IaYeYZraNFprM0lKmW5UDGuEEAlSyk3mTm5UNG8DxMfH29zIebbqX3OTkZSS6pNJeC5Y0O5xvYNc8e7nyrHtmc0Ux8nDJbj5DCTgUOuJ9qpOnaLqRCJel13WrrldPBwIHeRNwrYMxs6LalTD+vj2LDR2gv6j295obw8i01COtVAbg/umzXDvvRYd32pET4f1/4KyPHC1nJdTUVUR3xz/hs+OfkZ2RTYeDh64V99MqQOs/MsLRHmFmVQE1bXVJBYmciTvCNsztvNT0k98ffxrfJx8WDR4EYsGLcLbqcdsH7bINRMiWLYxiU/+TOHxC4bYWpxegzU3x9OAho+boUC6uW2klHXHbOA7DKYvgCwhRD8A47FH1IzMO1OKs7s9Lh4Oza7psrPRl5bi0A5X3IYMnxpM1qniRhlrK8tqSNqfi2b0OVQnJRnyYLVAya+/AuB+/vntnnvYlBBKC6o4ffisHblWp+fEriwih/tZxA23ERn7qdF4kO4UR+Xhw+hyTe/vdDuipxuOpyzjXVWpq2TZ/mWc9815vLznZaK8onhj5husuWwdBfmhzBkaTIxPVIurBwetA0N9h3LZwMt4cdqLbLpyEy9Pf5nhfsNZum8ps7+Zzb/+/BfZ5T3i36tF+nk6c/6wQL7cmapccy2INRXHTiBGCBElhHAAFgErm7RZCVxn9K6aABQZzU+uQgh3ACGEK3AecKhBn+uNr68HfrDiZ7AYeellJlcbAFWJdR5VrSc3bInBE/ph76jlwLqzyuHQxjR0VbVEXDMfgNINLd+win9ZjfOoUdgHBbV77qg4P5w9HDiwPq3+3LHtmZQXVzN0SnC7x2uTzANUegwly93gZ1G62XZ5oNpF8Chw9Oi0uUpKydqUtVz0w0W8se8NJoVM4usLv+bd895lauhUdicXU1he0+5MuE52TsyMmMkbM9/gh4U/MD96PitOrGD+d/NZum8p5TVd5xFmaW6YGEVRhXLNtSRWUxxSSh1wN/ArcBT4Skp5WAixWAix2NhsFZAEJALvAHcazwcCW4QQ+4EdwM9SytXGa88Ds4UQJ4DZxvfdGqmX5GeUtbi/0Z7khqZwcLZj2NQQju/MIi0hn4LMMnb/epqokX4EjR+CY8wAin/6yWTfqpMnqTp2DI+5czo0t1arYdTscFKP5HNiVxZlhVX8+f1JAiI9CLegGy4AtTWQfRQZFEupWyjC25eyzWZbL22L1g4ip3RKcRRUFnD/hvu5f8P9ONs589557/Hf6f9lsM/g+jarDmbi4qBl2sCO56aK9opmycQlrLxoJVNCpvDm/jeZ/9181qSs6fCYtmRspDdD+nnw0TblmmsprLoLJqVchUE5NDy3rMFrCdxlol8SYDK6S0qZB1jfNcWClORXoquqbdmjKvEkWi8vtK0kFmyLsfMiSTmYy4+v7UejEdg5apl8RQwAnpdeSvbz/6by2HGcBg1s1C//008R9vZ4XHBBh+eOnRFK0t5s1rx3uL4s7YxrBlveUycnAWqrcYweAxsEukFjKN26DanTIex6wIZu9HQ49jPknwKfqHZ13XZmG3/f+ncKqwp5YMwDXDv02mZmqFq9ZM2RTGYMDsDJvvOebGHuYfxn+n/Yl72PZ7c/ywMbHmB2xGweH/84fs49x0tJCMENEyN4dMVBtp/KZ0J034qktwZ9L6TUBtSlGmkxhuPkSRwG9O/UjdbByY6F949i2NQQYsYGcunDY/DwNdT88Fy4EGFvT+HXXzfqU1tYSNF33+Ox4ELs/Dp+I9DaaZj/1zjiZoUTHefPxQ+ObjMvVofI2A+AQ9QonFztKQmORV9URMWBg2107Cb0n2E4tmPVUauv5ZU9r3D72tvxcPDg83mfc+PwG03uXexMzie3tJq5w9tvcmyNuIA4Ppv3GfeOvpeNqRtZ+P1Cfkv+zaJzWJuFcSF4udjz4dZkW4vSK1CKowvIq/Oo6mciR5WUVCUmdnh/oyGuno5MvXIg5143BK/As66Hdt7euM+ZQ9F336HLO7uJnf/xx8jKSnyuv97UcO3C0dmOiZcOYNaNQ5u55lqMM3vAwR3hG4NPsCuZzgNAq6V0U9em8+gwvgPAI8RsxVFSXcI96+/h3YPvcmnMpXwx/wsG+Qxqsf3qQ5k42mmYMciynmwA9hp7bhlxC98s+IYIjwge3PggT/3xFBW6npGB1sley5Vjw/jtSCZnVNbcTqMURxeQf6YUdx8nHJybPyXW5uWhLypqV8R4R/C74w70VVVk/utfSCmpOHyYvHffw+OCC3AaOLDtAboD6XsgOA40GnyCXcnNA+e4kZRt2mxrycxDCIO56tTGNsvJphSncPWqq9l2ZhtPTniSJROX4GTXcvVGvV7yy6EMpg30x9XRema7KM8oPpr7ETcPv5kVx1ew6KdFJBUmWW0+S3LthAgAPlVZczuNUhxdQO6ZMnxbMN3Upxrp4Ma4uThGR+F/zz2U/LKaU5deyulrr0Pr50vg43+z6rwWQ1cFmYcM3kkYVm/VlbXYx0+k8sgRdDk9pM509HSoKDAUomqBgzkHuXbVtRRWFvL2eW9zxaAr2hx2b2ohWcVVzB1hWTOVKew19tw35j7emv0WhVWF/GXVX1h3ep3V5+0sod6GrLlf7DhNpYnKmQrz6QE7ij0bXU0thVnl9B9l2sulypjcsK0645bA99Zb0Li6UPTtd7hOmULgIw93eG+jqraKTWmb+DP9T47kHSGvMo9afS2eTp7EeMUwJnAMsyJm4eNkIc+qrMOgr4EQQx5MX2Nq+uqYMQCUbtmK18UXWWYua9IwzXpwXLPLm9M28+DGB/Fx8uGt2W8R4RFh1rCrD2VgrxWcOzjQgsK2zjnB5/Dl/C+5f/393Lv+Xu4YeQeLRy5GI7rv8+j1EyP59XAWK/elc8VYy2Y16EsoxWFlCjLKkXrZ4sZ49cmTaNzdsQuwfmlPIQQ+V1+Nz9VXd3iMoqoiPjnyCZ8lfEZJdQlu9m4M8x1GtFc0WqElvzKfXVm7WHVqFc9tf445UXO4dcSt9Pfq5IoqfY/hGGxQHD79DL/PQvt+uPn7U7ppY89QHO6B4D8ETm2Cyfc3uvRT0k/8fcvfGeg9kKWzlprtuSSlZNXBTCYP8MPT2cIBl20Q5BrEh3M/5Jk/nuHN/W9yNP8o/zf5/3Bz6J4lfc+J9mVwkDvvbTnF5fG9M0dXV6AUh5XJTTN4VLXkZVR1IhHH6Ohu/wWWUvLDyR/4z67/UFhVyKzwWVw+6HLGBY1r5uEjpeR4wXF+OPkD3xz/htWnVnPV4Ku4K+6ujt9QzuwFF1/wMuQdc3IzROHnZ5YROGUKJWvX9hy33KipsOdj0FWDnSGTwI8nf+SJLU8QHxTPqzNebdfv6dCZYs4UVnDvzBhrSdwqjlpHnpn0DEN9h/LCzhe4etXVLJ21lBC3EJvI0xpCCG6bGs0DX+1n/bHsLl2h9Sa675qyl5CXVoqdvQYPf+dm16SUVB47huPgwSZ6dh+Kqoq4b/19PLn1SaI8o/jmwm/434z/MTF4okm3UCEEg3wG8cjYR/j10l+5NOZSlh9dzmU/Xsb+nP0dEyJ9j2G10UDB+gS7kp9ehtvUKeiLi6k40PK+QbciehroKuCMocxMndIYGzSWN2a+0W7l+suhDLQaweyhtrsJCiH4y5C/8Pbst8mpyOHqn6/mcO5hm8nTGheODCbEy5llG3rGpn53RCkOK5N7xlC8SWMirbkuIwN9cTFOg1t2sbQ1KcUpLPppEZvObOLh+If5cM6HrbqENsXbyZsnz3mSj+Z+hJSSG365ge8Tv2+fENVlhuC/kNGNTvsEu5KfUYbLhHOMbrk9JIo8YhIIDZza1EhpvD7zdZztmj9gtIaUkl8OZTIh2gdv1+Z50Lqacf3G8encT3Gyc+LGX29kQ+oGW4vUDHuthpsnR7EjOZ/dKfm2FqdHohSHFZFSkpdW2qKZqjLBkO7ccVD3XHEcyj3Edb9cR2lNKR+c/wHXDbuuwxufowJG8fWCr4kPiufJrU/y2t7XzE//kHEApL7eo6oO3xA3dNV6SqvscR4V13MUh7MX9BtJ3pFv+fvWv3dYaQAczSjhVG4Zc4Z3n0p/0V7RfHrBp0R7RnPv+nv5IuELW4vUjEXjwvBysedNteroEEpxWJHyIkPxphZdcY8lAODYDeMotp7Zyk2/3oSznTOfzP2EuIC4To/p4eDB0llLuSTmEt4+8DZ/2/I3avQ1bXdssjFeh3+YIdAwJ7UEtylTqTpytMe45SZ6h+CRfYyJfnG8du5rHVIaACv3p2OnEczrZiVi/Zz9eP/895kaMpVntz/Lf3b9B73sPhUbXRzsuP6cSNYezSIhs7jtDopGKMVhRXLPtL4xXnk0AfvwcLRupnNY2Ypt6dv467q/Eu4ezidzPyHSM9JiY9tr7FlyzhLuGXUPPyf9zCMbH2lbeZzZY4i4dm9sw/cJdkVjJ8g5XYLbtKlAz8iW+1PST/wnbyf2wP/6X4mLfccKDOn1kh/3pzMlxg+fbmCmaoqLvQsvz3iZRYMW8eHhD3l448NU1VbZWqx6bpwUibuTHS/9etzWovQ4lOKwIrmpJYDp4k0AlccScOpmG+P7svdx3/r7iPSM5L3z38PfxfJuwkIIbo29lUfHPsra02t5dNOjrSuP9D3NzFRgyJHlG+xGzukSHAcNwi4ggNKN3Tv9yE9JP/HElieQ4ROQGnucTrdeZKs1dp8u4ExhBQvjup/3Uh1ajZbHxz/OQ/EP8VvKb9y+5naKqopsLRYAXi4O3D41mrVHs9idUmBrcXoUSnFYkezkEjz9nU0WM9KXlVFzOhXHbrQxnpCfwJ1r7yTAJYC3Z7+Np6OnVee7Zug1PDL2EdakrOGxTY/VlzJtRFke5CdByBiTY/hHuJNz2qCg3aZNo2zLFmR1tTXF7jB1SmNM4Bj+O3sZInRspwo7fb/3DE72Gpt6U5mDEILrh13PC1NfYH/Ofq7/5XoyyzJtLRYAN06Kws/NgRd/TVAp19uBUhxWJDulmIBID5PXKo8fBym7zYojvTSdxWsW4+rgyjuz3+mytNnXDr22/mn0n9v+2dwOnmp8Ig+fYLK/f5g7VeU6SvIqcZsxA31ZGWU7d1pZ6vbzc9LP9Urj9XNfN5inoqYaMv5WtP9pt1qn5+eDGcweGmTV3FSWZG7UXN6a9RZZ5Vlc/fPVHMs/ZmuRcHW04+4ZA/gzKZ/NJ3pINclugFIcVqKsqIrSgioCIkxniq1KMGyMOw2y/YqjrKaMu9fdTXVtNW/Neot+bl270Xr9sOu5K+4uVp5cyb93/Lvxk1/qn6CxN2mqAup/vzmnS3A9ZwLC0ZHS9Ru6QGrz+TnpZx7f8nhjpQEGxSH1kLKt3WNuScyhsLyGhSOtUGXRiozrN46P5n4EAm5YfQM7MnbYWiSuGh9OqLczz606iq62+2zgd2eU4rAS2cb63y2uOBKOofHwwC7Ytv/4tfpaHt30KEmFSbw0/SWivaJtIsftsbdz3dDr+CzhM5buX3r2QuoOQ04ne9NeR3UxMjmnS9A4O+M6cSKl69d3G7NDndIYHTC6sdIACI0HO2dD+pF28t3edDyd7ZnaiUp/tmKg90CWX7CcINcgbl97O7+c+sWm8jjaaXnigiEkZJbw8R8qc645WFVxCCHmCCGOCSEShRCPmbguhBCvGq8fEEKMNp4PE0KsF0IcFUIcFkLc26DPEiHEGSHEPuNPx0vXWZHslBKEOOsy2pTKgwdxGjrU5qlG/rf7f2xM28hj4x5jYvBEm8khhOCh+Ie4JOYSlu1fxkeHPzJkxD2zB8LGt9jPzl6Ld7Dr2X2OGdOpOXOGqhMnukjylmmoNN6Y+UZz7yk7R4MJrp2Ko7C8ml8PZ3JRXDAOdj3z2S/INYgP53zISP+RPLLpET46/JFNlf2c4UFMHejPf9ccJ7u40mZy9BSs9q0TQmiBN4C5wFDgKiHE0CbN5gIxxp/bgDeN53XAg1LKIcAE4K4mff8npYwz/jQqTdtdyE4uxifYDXvH5iU89ZWVVB4/jnNsrA0kO8u3J77loyMfcdXgq1g0eJFNZQGD8vjHhH8wO2I2L+16iY07XoXaqlYVB0BghDtZycVIvcRt+nQAStet7wKJW+aHxB9aVxp1RE+D7CNQmm322N/vPUO1Ts+VY8MtJK1t8HT05K3Zb3FexHm8tOslXtj5gs1iPYQQPLVgGNW1eh7/7lC3WbF2V6z5uDIOSJRSJkkpq4EvgIVN2iwEPpYG/gS8hBD9pJQZUso9AFLKEuAo0H19DpsgpSQrpZiAyBZWG0eOgk6Hc+yILpbsLDszd/LMH88wKXgSj4x9xGZyNEWr0fL8lOeZFDyJXbveMJxsYWO8jqD+XlSV6yjILMc+IACn4cMpXW87xfH18a95cuuTjAsax9JZS1uP04g2lpM9scassaWUfLEzldhQT4YGmzaD9iQctY68OO1FrhlyDZ8e/dSmsR5Rfq48cv4g1h7N4utdaTaRoadgTcURAqQ2eJ9G85t/m22EEJHAKKChw/vdRtPW+0IIb1OTCyFuE0LsEkLsyuniaOLi3EqqynQERLSwv3HQkIzPyUYrjpTiFO7fcD/hHuG8OO1Fk4kKbYmD1oH/Tv8v06Qjp+3s2FrUutmp3wCD23DGyULAYK6qOHAAXW7Xe8l8dvQznv7jaSaHTDYvjUi/keAeDMfNs/PvTysiIbOEK3tRLQmN0PDouEe7RazHTZOiOCfal6d+PExidqlNZOgJWFNxmDLeN13/tdpGCOEGrADuk1LW5QV4E+gPxAEZwH9MTS6lfFtKGS+ljPf379oNxEzjDSwo2rTiqDhwELugIOwDLF8bui2Kqoq4+/e7EQheP/d13B2sVB+8k7jYOTO6SscpD3/uW38fe7P3ttjW098ZZ3d7Mk4abjbus2aBlJSsXdtV4gLw0eGP+L8d/8e5Yefy8oyXcdQ6tt1JCBg0BxLXQU3btvUvd57G2V7Lgh7mTWUO3SHWQ6MR/OeKkTjZa7nt410UVZiREqcPYk3FkQY0fCwKBdLNbSOEsMegNJZLKb+tayClzJJS1kop9cA7GExi3Yr0E4U4utjh20LEeMWBAzbZ36jR1/DQxodIK03jf9P/R5hHN35qzT2BpjyXMWPvJsg1iLvW3kVCfoLJpkII+g3wIiOxEDDk/nKIiqJ49a9dIqqUktf3vs5Lu17i/MjzeWn6Szho25ECZOBcqCmD5NbTpZRV6Vi5L515sf1wd+ragk1dRdNYj6N5R7tchmAvZ5ZePZrT+eXc/dkeqnXKRbcp1lQcO4EYIUSUEMIBWASsbNJmJXCd0btqAlAkpcwQBlej94CjUsr/NuwghGgYZHAxcMh6H6FjpCcW0a+/J8JEKvWarCxqUlNxHmU6LsGavLDjBf7M+JN/nvNP4oPiu3z+dpG0AQC3QfN4e/bbuDq4cvua20kuSjbZvF9/T4pzKykrqkIIgcfcOZTv2GF1c5VOr2PJH0t468BbXDzgYp6f8jz2mnbe1KOmgr0LHGvdz+PbvWcoq67lqnHdWOFbgLpYD41Gw3W/XMevyV3zANCQ8dG+PHvxcDafyOWez/eq+I4mWE1xSCl1wN3Arxg2t7+SUh4WQiwWQiw2NlsFJAGJGFYPdxrPTwKuBc414Xb7ghDioBDiADADaFx/08aUF1dTmFVOvxgv09d3GKKaXcaO7UKp4POEz/ni2BfcOOxGLhpwUZfO3SFObQSvCPCJop9bP96Z/Q4At665lYzSjGbN+/X3AiAj0WiuOn8O6PWUrDFv07kjlNeUc8+6e/j2xLcsHrmYpyY+1bH9InsnGDATjq6EWhNpVzAkNPxg6ylGhnoyOtzktl6vYqD3QD6f9zmDfQbz0MaHeGPfG13ucXXl2HCenD+U1YczuXP5Hiqqa7t0/u6MVZ3ApZSrpJQDpZT9pZTPGs8tk1IuM76WUsq7jNdHSCl3Gc9vkVIKKWVsU7dbKeW1xraxUsoFUsrmdxEbkn6iEIDgAV4mr5fv3InGzQ2nIV2XamTbmW38e8e/mR42nXtH39t2B1tTq4NTmw2uqkYiPSN5a/ZblFWXcfNvN5NanNqoi1+4G3b2mgbmqhgcoqMp/mW1VUQ8U3qG6365jq3pW3lywpPcFXdX52JyYhdBWQ6cXGfy8sYTOSTllHHT5Cibx/50FX7Ofrx3/ntcNOAilu1fxoMbHqS8prxLZbh5chRLLhzKmqNZXPXOn2SpGA9ARY5bnNSj+dg7afFvIdVI+c6dOI8ZjdA2j++wBklFSTy08SH6e/Xn+SnPo9V0zbydImMfVBVB9PRGpwf7DGbZ7GUUVxdzzS/XcDjvbGlSrVZDvxgvUo8aKroJIfCYM4fynTupyTI/RsIctmdsZ9FPi0gvS+eNmW9wxaArOj9ozHng7A37Pzd5+f0tpwj0cGRuNyrY1BU4aB14euLTPBz/MOtS13HNL9e0aK60FjdMimLZNWM4nlXC3Fc2sz7Bst+nnohSHBZESsnpI3mEDvJGq23+q9Xl5FB96hSuXWSmyq3I5c61d2Kvtee1c1/D1b571f1okSRjDEbUtGaXYv1j+Xjuxzhpnbhp9U1sO3M2z1P4UB8KMsspyTc8FXouuBCkpOiHHywiVq2+lvcOvsfta27H18mXz+d9zuSQyRYZGzsHGH4ZJPwMFYWNLh1IK2TziVyunxjZYyPFO4MQguuGXcfSmUvJKc/hyp+uZHWydVaSLXH+sCBW3j2ZAHdHbvxwJ/d+sZecku5TW6Sr6XvfQitSmFVOaX4V4cN8TV4v3boVwFAj28qUVpdyx9o7yK/M542ZbxDs1oPcN4//ZohvcDWdoTfaM5pPLviEMPcw7vj9Dt4+8DZ6qSdsqA8AqUcMqw6HyEhc4uMpWrGi05HA6aXp3Pzbzby852XODT+X5fOWE+ER0akxmzHyKkOk/MGvG51+fV0ins72XDvBwvP1MCaFTOLrC78mxjuGhzc+zHPbn6O6tutS6A8IcOOHuydx78wYfjmYycz/bODTP1Oo1fe9KHOlOCzI6cOGG1a48QbWlNKNG9H6++E0dIhV5aiurea+DfeRWJDI/6b/j+F+w606n0UpyYS0HTB4fqvNAlwC+GjuR5wfeT6v7X2NxWsWI70qcfN25PThvPp2npdeSnVKChW7d3dInFp9LV8f/5pLV15KQn4Cz05+lv9M+491Vm8how11R/5cCnrDRuzRjGJ+O5JlrFbXO11w20OQaxAfzPmA64Zex+cJn3PdL9c12++yJo52Wu6fPZBV905haLAHf//+EAte38LulPwuk6E7oBSHBTl1IAfvIBc8/JpHC8uaGsq2bMVt6lSExnq/dp1ex982/43tGdt5etLTTAqZZLW5rELCz4ZjG4oDwNXelX9P+Tf/POef7Mnew6U/Xkr20COkHMlDZ/SA8Tj/PDSurhR+s6LdouzM3MmVP13J0388zWCfwaxYsIIF/RdYb3NaCDjnbkPhqoSfAHhl7QncHO24YWKkdebsgdhr7Hl47MO8PP1lThef5tIfL+XLhC+7NL/UgAA3Pr91Aq9dNYr8smouffMPHvhqH9klfWPzXCkOC1FeXE368UL6jzYdDV6+dy/6khLcpjW321uKOqXxW8pvPBz/MBf2v9Bqc1mNhJ/AJxoCzFuVCSG4bOBlfDbvM4Jdg/lY/ypfx/yHn7avRUqJxsUFjwsuoHj1amqL2k5joZd6tpzZwuK1i7np15sori7mxakv8v757xPi1gXp0oYsAN8YWP8cu07lsvpwJrdNjcbLpfvVFLc1MyNm8u3Cb4nzj+Nf2//F4rWLuzTaXAjBhSODWfvANO6c3p+f9mdw7ksbeXdzEjW9PO5DKQ4LkbQvBylpUXGU/v47wt4e14nWSV2u0+t4fPPjrE5ezYNjHuS6YddZZR6rUlFoSDE+eL7h6bsdDPQeyKcXfMqT4/9BmWMhTyY9xBU/XcFnRz9Dd/kcZGUlBV99ZbKvlJKU4hQ+PPQhC79fyB1r7yAhL4F7Rt3DyotWMidqTte5wGrtYOaTyOwEnvt6MwHujtwyJapr5u6BBLkG8dbst3hi/BPszd7LJT9cwo8nf+zS1Yerox2PzBnMr/dPJT7Sm3/9fJTL3txGUk7vzXUl+kL64Pj4eLlr1y6rzvH9//ZSWlDJ1U9NaHaTkTodJ6bPwGXUKEJfe9Xic1fXVvPElidYnbya+8fcz03Db7L4HF3C7g/hx3vhlnUQarrGuDms/mg/q07/TFrsbk4UHgcgpNyJiEw9Ay9YhKuTO1JKiquLSS1J5WjeUbIrDC6WsX6xXDXkKs6POB97rY32FKTkh9ce4N70WTw/N4RF0+JsI0cP43TxaZ7Y8gT7cvYxNmgsj497nAHeA7pUBiklqw5m8sT3B6msqeXv84Zy9fjwHht7I4TYLaVslmaie6VF7aEU5ZRz5lgB4xeYDs4q+3M7tbm5eFzYtt2+vRRXF3P/+vvZkbmDB8Y8wI3Db7T4HF3G3uXgP9iwSdwJRkwI5+QfE7hz9o3YTytnU9omdh5dQ2LlAXYmLKcGw/6Hi50LIe4hjAkaw+iA0UwJndI15qg2KKyo4ZnC84nVnOTy5HdgyrdgxX2x3kK4RzgfzvmQFSdW8MqeV7j8x8u5Zug1LB65uMtc0YUQzIvtR3ykNw99vZ+/f3+I3SkFPHfxCJwdekAMlZkoxWEBjmzJQAgYfI5pl9eilT+g8fCw+P5GZlkmd6y9g+SiZJ6b/FzP3NOoIzvB4E01+5l2m6maEjzQC88AZ45sTueSCWPo79WfG4bdQPIVV1Kbl0f4qpVoHZy6bTDk878kUFCp56OZHmg3r4e1/4Dz/mVrsXoEWo2WKwZdweyI2byy5xU+PPwhq5JWce+Ye5kXNa/L/uaBHk58dOM43lifyH/XHudoRjHLrhlDpF8PiaVqA/UY00l01bUc3ZZOxAg/3Lybp9HW5eRQ8stqPOfPR+NoRpptM9mZuZOrfr6KjLIMls5a2rOVBsD2N0HraIhl6CRCCIZNDiHjZBG5aaX15/zvuYea9HRKv/m+2yqNNUey+GJnKrdMiWLYudfA2Fth22uw/S1bi9aj8HbyZsnEJSy/YDn+Lv48seUJLvvxMtadXtdl+x8ajeCvM2P44IaxZBRVcuHrW/j9aFaXzG1tlOLoJEe2ZlBRUsPImaYzluZ/9hlSp8PnestsVuulnncOvMMtv92Cm70bH8/9mHOCrR9QaFVKc2Df5xB3FbhZpnbKkEn9sHfSsnt1cv0518mTcBk/npzXXkOX3/387s8UVvDQ1/sZHuLBA7MHGlZec56HQfPgl0dg6yu2FrHHEesfy2fzPuOlaS+h0+u4d/29LPp5EWtT1nZZ0sTpgwL46a+TifB14eaPdvHfNcfR9/CgQaU4OkFtjZ69v6XQb4AnIQO9ml8vKaHw8y9wm3kuDhGdj/pNK0lj8ZrFvLr3Vc6POJ8v5n/BQO+BnR7X5mz5L+hrDDEMFsLJ1Z4R00JJ3J1NfkYZYFh1BD35d/RlZWS/8KLF5rIE5dU67vx0N7V6yetXjcbRzrgi0trBFR/BsIthzT/gpwdA13XR0r0BjdBwfuT5fLfwO56a+BQl1SXcv+F+LvrhIlYcX9EliRPDfFz4ZvFELhsTyqu/n+Dmj3ZSVN5zi0QpxdEJ9q5JobSgirHzTW+K5765jNqiIvwW39GpeWr0Nbx/6H0u/uFi9ufs58kJT/Lvqf/uObmnWiP/FOx4B+KuBr8Yiw4dNysMB0ctW74+UW+ecBwwAN+bb6bo++8p/vU3i87XUXS1ev762V4Onini5SvjmtvBtfZw6Xsw8R7Y9R58cpFhlaZoF3YaOy6JuYSVF63khakv4KBxYMkfS5j59Uye2/4cJwpaL1HcWZzstbx4WSz/umg4WxJzufD1LRxJL267YzdEueN2kMKscr741w4iR/gx57bmKT2qkpJIWngRnhdeSPBzz3ZoDr3U8/vp31m6bymJhYnMDJ/JY+MeI8g1qLPidw+kNNwE03bB3TvBw/L5tPavS2XLVyeYffNQBo41/N5kdTXJf7ma6tOnifziCxyjbRcnUaWr5YEv9/PzwQz+ddFwrmkrH9WBr2Hl3YZMupe8A1FTukbQXoiUkr3Ze/nq+Ff8lvwbNfoaRgeM5oKoC5gZMRM/Z9O50izBntMF3PHpbooqanhy/lD+Mq57uuy25I6rFEcHqKmuZcW/d1NaUMmiJ8c32xTXV1SQfOUidNnZRP+4Ert21jzX6XWsT13Psv3LOF5wnEiPSO4fcz/nhp9rsc/QLfhjKfz6N5j3Xxh7s1Wm0Nfq+falPeRnlHH5Y/F4Bxme5qtTU0m+chEaFxciPltuk/rvheXV/PXzvWw+kcvf5w3hlinR5nXMOADf3Ah5J2HqQzDtMYNJS9FhCioL+CHxB1acWEFycTIaoWFM4BjOiziPmeEz8XexzN5bQ3JKqnjgq31sPpHL5AF+PH/pCEK9XSw+T2dQisNCikNXU8vqtw6RcjiPC+8e2SwTrqyp4cwDD1KyZg1h77yN2xTznwhTS1L57sR3fJ/4PTkVOUR6RHL7yNuZGzm323oBdZhjq+GLqwz1thct77QLbmuU5Ffy1XM7cXDSctEDo3H3cQIMtd9TbrgRO29vwt59B8eorlt57DiVz31f7CWntIpnLx7BFfHtLAdbVQq/PAr7PoWw8XDxW+CjIsw7i5SSE4Un+C35N35L+Y1TRacAGOA1gPH9xjOh3wTiA+Nxc3Cz2Hyf7TjNcz8fRS8NhaNunxbdbRJaKsVhAcVRWlDJb+8dJiOxiOlXD2LYlMbBYrqCAjIe+xulGzcS+LfH8Ln++lbHq9HXcDTvKJvSNrEhdQPHCo6hERomh0zmspjLmBI6pWOlSLs7+7+EH+6CwGFww8/gaJl/wtbIPFXEj6/ux8FJy/m3Dico2hOAioMHSb3tdmRVFQGPPYrXZZdZNQnl6bxy/rvmGN/vSyfcx4XX/zKK2FCvjg948Bv46X6orYFpj8DEvxr2RBSdRkpJYmEim89sZnvGdvZk7aGythKt0DLAawBDfYcyzHcYw/yGEeMdg6O24+72aQXlvLD6GCv3p+Pj6sD150Ry5dgwgjydLPiJ2o9NFIcQYg7wCqAF3pVSPt/kujBevwAoB26QUu5pra8Qwgf4EogEkoErpJQFrcnRWcVRUVLN4c1n2PvbafQSZlwzqN5eDqAvL6do5Upy31iKrrCQoCcex3vRosZj6CpILkrmZNFJThSc4EDOAQ7lHqKythKN0BDnH8f0sOnMjZrbe/YwmpJ3En5/Go58DxGTYNFn4OzVZdPnnC5h9dsHKcmrZMjEfsTNDsc7yJWajAzSH32M8h07cBw0CO+r/4LHBRegdbOMQiuurGHDsRxW7kvn94Qs7LUabpsSzR3T++PqaIEHg6IzsPpROPqjoU775PthxOVdopD7EtW11ezP2c/2jO0cyj3E4bzDFFYVAgbPrRC3ECI8Ioj0iCTKM4oQtxACXAIIdA3E3d7drD2Mg2lF/GfNMTYcy0GrEcwY5M+5gwOZOtDPJmasLlccQggtcByYDaQBO4GrpJRHGrS5APgrBsUxHnhFSjm+tb5CiBeAfCnl80KIxwBvKeWjrcnSUcWhl3oS92fyy3v7qJE6QoZ5MnJOCI6OlZQc3E9ZyilKk45RfPgA5aKGmphwNPNmUuHjQl5FHlnlWWSXZ5Ndnk1+5dm4ATthx2CfwcQFxDEyYCQTgibg5eTVbvm6JVJCTQVUFUNlMRScgqxDkLgOUraCvQtMuhemPGgTu3xVeQ07fjrFoY1n0NdKxsyNYMLC/ki9nuJVv5C77E2qE08i7O1xGhmL88iROERE4BAaitbXD62HOxp3D4SLMzo9VNTUUllTS0V1LRU1tRSUVZNeVElmUQXJeeXsTy0kMacUKcHf3ZFFY8O4ZkIEgR5WeJI8sQbWPwvpew2/5wEzIXwi+A80KBRnb3BwAztHq5oG+wpSSjLKMjicd5iE/ARSilNILkompTiFytrG6dWd7ZwJcAnA09ETTwdPw9HREw8HDzwcPHC2c8bJzgknrRNOdk4UlGjZcKSGTQnlZBfrAPB3t2dgoAsDAl0J8nAiyMOJAA8nvF0ccXGwx9XBHldHOxy0GrQaYZHNdlsojnOAJVLK843v/wYgpfy/Bm3eAjZIKT83vj8GTMewmjDZt66NlDJDCNHP2H9Qa7J0VHE888czfHXcdEbVtvB29CbQNZAAlwDDU4dLINGe0fT36k+4e7jtEuhZEynhuWAw5RfvPwSGXQTxN4Fb129EN6W8uJqEPzIIivYgOMa7/ryUksoDByj+ZTXlu3dTmZAANc397W+99lXSSlqPp/Bzc2BEiCdxYd5MHODL6HBvtBor37ClhNN/woEv4OQ6KDzdvI3QwuPpYG9bM0hvRS/1ZJdnk1GWQVZZFlnlhp+c8hyKqoooqi6iqKqI4qpiSmpKWh1LStBX+1NbNpDaihD0Vf3QV/ljXraoWtzCP+Wthfd2uC6PLZIchgANS3OlYVhVtNUmpI2+gVLKDACj8jB5FxJC3AbcZnxbalQ4PQk/INfWQliO7cafv7XVsGd87r+f12aTFKAddQe79nMvaV5szEb0jL+35Wnn517f4Ykm3/V1241axqR/uDUVh6lHq6bLm5bamNO3VaSUbwNvt6dPd0IIscuUpu/tqM/dt1Cfu2dizcjxNKChj2EokG5mm9b6ZhlNVBiP2RaUWaFQKBRtYE3FsROIEUJECSEcgEXAyiZtVgLXCQMTgCKjGaq1viuBOj/X64EfrPgZFAqFQtEEq5mqpJQ6IcTdwK8YXGrfl1IeFkIsNl5fBqzC4FGViMEd98bW+hqHfh74SghxM3AauNxan8HG9FgzWydRn7tvoT53D6RPBAAqFAqFwnKo7LgKhUKhaBdKcSgUCoWiXSjF0c0QQswRQhwTQiQaI+N7PUKIMCHEeiHEUSHEYSHEvbaWqSsRQmiFEHuFED/ZWpauRAjhJYT4RgiRYPzb9/BSluYhhLjf+D0/JIT4XAjR4yIxleLoRhhTrbwBzAWGAlcJIYbaVqouQQc8KKUcAkwA7uojn7uOe4GjthbCBrwCrJZSDgZG0gd+B0KIEOAeIF5KORyD88+i1nt1P5Ti6F6MAxKllElSymrgC2ChjWWyOlLKjLrkllLKEgw3kJDWe/UOhBChwDzgXVvL0pUIITyAqcB7AFLKailloU2F6jrsAGchhB3gQvP4tm6PUhzdi5ZSsPQZhBCRwCgM+Un6Ai8DjwB6G8vR1UQDOcAHRjPdu0KIXlALuXWklGeAlzCEEmRgiF3rHjWM24FSHN2LTqda6ckIIdyAFcB9UsqeWYy5HQgh5gPZUsp2pLTqNdgBo4E3pZSjgDKg1+/pCSG8MVgRooBgwFUIcY1tpWo/SnF0L8xJ09IrEULYY1Aay6WU39pani5iErBACJGMwSx5rhDiU9uK1GWkAWlSyrqV5TcYFElvZxZwSkqZI6WsAb4FJtpYpnajFEf3wpw0Lb0OY0Gv94CjUsr/2lqerkJK+TcpZaiUMhLD33qdlLLHPX12BCllJpAqhKgriTATONJKl97CaWCCEMLF+L2fSQ90CuiFdUl7Lm2kWunNTAKuBQ4KIfYZzz0upVxlO5EUXcBfgeXGh6QkjCmHejNSyu1CiG+APRi8CffSA9OPqJQjCoVCoWgXylSlUCgUinahFIdCoVAo2oVSHAqFQqFoF0pxKBQKhaJdKMWhUCgUinahFIdCoVAo2oVSHAqFQqFoF/8P/1amFyZM/acAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=dataDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8507159",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}