exercise-scaling-and-normalization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3399c965",
   "metadata": {
    "papermill": {
     "duration": 0.004995,
     "end_time": "2023-06-25T05:02:45.560088",
     "exception": false,
     "start_time": "2023-06-25T05:02:45.555093",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**This notebook is an exercise in the [Data Cleaning](https://www.kaggle.com/learn/data-cleaning) course.  You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/scaling-and-normalization).**\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "303d36d0",
   "metadata": {
    "papermill": {
     "duration": 0.004038,
     "end_time": "2023-06-25T05:02:45.568574",
     "exception": false,
     "start_time": "2023-06-25T05:02:45.564536",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In this exercise, you'll apply what you learned in the **Scaling and normalization** tutorial.\n",
    "\n",
    "# Setup\n",
    "\n",
    "The questions below will give you feedback on your work. Run the following cell to set up the feedback system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "726cbc0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:45.578983Z",
     "iopub.status.busy": "2023-06-25T05:02:45.578633Z",
     "iopub.status.idle": "2023-06-25T05:02:48.943233Z",
     "shell.execute_reply": "2023-06-25T05:02:48.942614Z"
    },
    "papermill": {
     "duration": 3.372269,
     "end_time": "2023-06-25T05:02:48.945220",
     "exception": false,
     "start_time": "2023-06-25T05:02:45.572951",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.data_cleaning.ex2 import *\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9efa538",
   "metadata": {
    "papermill": {
     "duration": 0.004229,
     "end_time": "2023-06-25T05:02:48.953880",
     "exception": false,
     "start_time": "2023-06-25T05:02:48.949651",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Get our environment set up\n",
    "\n",
    "To practice scaling and normalization, we're going to use a [dataset of Kickstarter campaigns](https://www.kaggle.com/kemical/kickstarter-projects). (Kickstarter is a website where people can ask people to invest in various projects and concept products.)\n",
    "\n",
    "The next code cell loads in the libraries and dataset we'll be using. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a47a66a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:48.964533Z",
     "iopub.status.busy": "2023-06-25T05:02:48.963586Z",
     "iopub.status.idle": "2023-06-25T05:02:50.010255Z",
     "shell.execute_reply": "2023-06-25T05:02:50.009144Z"
    },
    "papermill": {
     "duration": 1.054502,
     "end_time": "2023-06-25T05:02:50.012546",
     "exception": false,
     "start_time": "2023-06-25T05:02:48.958044",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# modules we'll use\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# for Box-Cox Transformation\n",
    "from scipy import stats\n",
    "\n",
    "# for min_max scaling\n",
    "from mlxtend.preprocessing import minmax_scaling\n",
    "\n",
    "# plotting modules\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# read in all our data\n",
    "kickstarters_2017 = pd.read_csv(\"../input/kickstarter-projects/ks-projects-201801.csv\")\n",
    "\n",
    "# set seed for reproducibility\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac693f0",
   "metadata": {
    "papermill": {
     "duration": 0.004092,
     "end_time": "2023-06-25T05:02:50.022467",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.018375",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's start by scaling the goals of each campaign, which is how much money they were asking for.  After scaling, all values lie between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b73a11df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:50.032710Z",
     "iopub.status.busy": "2023-06-25T05:02:50.032389Z",
     "iopub.status.idle": "2023-06-25T05:02:50.060419Z",
     "shell.execute_reply": "2023-06-25T05:02:50.059071Z"
    },
    "papermill": {
     "duration": 0.035511,
     "end_time": "2023-06-25T05:02:50.062237",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.026726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data\n",
      "Preview:\n",
      "    usd_goal_real\n",
      "0        1533.95\n",
      "1       30000.00\n",
      "2       45000.00\n",
      "3        5000.00\n",
      "4       19500.00\n",
      "Minimum value: 0.01 \n",
      "Maximum value: 166361390.71\n",
      "______________________________\n",
      "\n",
      "Scaled data\n",
      "Preview:\n",
      "    usd_goal_real\n",
      "0       0.000009\n",
      "1       0.000180\n",
      "2       0.000270\n",
      "3       0.000030\n",
      "4       0.000117\n",
      "Minimum value: 0.0 \n",
      "Maximum value: 1.0\n"
     ]
    }
   ],
   "source": [
    "# select the usd_goal_real column\n",
    "original_data = pd.DataFrame(kickstarters_2017.usd_goal_real)\n",
    "\n",
    "# scale the goals from 0 to 1\n",
    "scaled_data = minmax_scaling(original_data, columns=['usd_goal_real'])\n",
    "\n",
    "print('Original data\\nPreview:\\n', original_data.head())\n",
    "print('Minimum value:', float(original_data.min()),\n",
    "      '\\nMaximum value:', float(original_data.max()))\n",
    "print('_'*30)\n",
    "\n",
    "print('\\nScaled data\\nPreview:\\n', scaled_data.head())\n",
    "print('Minimum value:', float(scaled_data.min()),\n",
    "      '\\nMaximum value:', float(scaled_data.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a19a8b",
   "metadata": {
    "papermill": {
     "duration": 0.003971,
     "end_time": "2023-06-25T05:02:50.070584",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.066613",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1) Practice scaling\n",
    "\n",
    "We just scaled the \"usd_goal_real\" column. What about the \"goal\" column?\n",
    "\n",
    "Begin by running the code cell below to create a DataFrame `original_goal_data` containing the \"goal\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89080e00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:50.080563Z",
     "iopub.status.busy": "2023-06-25T05:02:50.080257Z",
     "iopub.status.idle": "2023-06-25T05:02:50.085694Z",
     "shell.execute_reply": "2023-06-25T05:02:50.084901Z"
    },
    "papermill": {
     "duration": 0.012896,
     "end_time": "2023-06-25T05:02:50.087836",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.074940",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# select the usd_goal_real column\n",
    "original_goal_data = pd.DataFrame(kickstarters_2017.goal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c378240e",
   "metadata": {
    "papermill": {
     "duration": 0.004357,
     "end_time": "2023-06-25T05:02:50.097030",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.092673",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Use `original_goal_data` to create a new DataFrame `scaled_goal_data` with values scaled between 0 and 1. You must use the `minmax_scaling()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0552f00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:50.107772Z",
     "iopub.status.busy": "2023-06-25T05:02:50.107234Z",
     "iopub.status.idle": "2023-06-25T05:02:50.124621Z",
     "shell.execute_reply": "2023-06-25T05:02:50.124046Z"
    },
    "papermill": {
     "duration": 0.024843,
     "end_time": "2023-06-25T05:02:50.126412",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.101569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 1, \"questionId\": \"1_TryScaling\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "scaled_goal_data = minmax_scaling(original_goal_data,columns=['goal'])\n",
    "\n",
    "# Check your answer\n",
    "q1.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84c1511c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:50.137755Z",
     "iopub.status.busy": "2023-06-25T05:02:50.137339Z",
     "iopub.status.idle": "2023-06-25T05:02:50.140677Z",
     "shell.execute_reply": "2023-06-25T05:02:50.139656Z"
    },
    "papermill": {
     "duration": 0.011376,
     "end_time": "2023-06-25T05:02:50.142823",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.131447",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q1.hint()\n",
    "#q1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e22d2c",
   "metadata": {
    "papermill": {
     "duration": 0.004412,
     "end_time": "2023-06-25T05:02:50.152064",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.147652",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 2) Practice normalization\n",
    "\n",
    "Now you'll practice normalization. We begin by normalizing the amount of money pledged to each campaign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af3d5aef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:50.163354Z",
     "iopub.status.busy": "2023-06-25T05:02:50.162512Z",
     "iopub.status.idle": "2023-06-25T05:02:50.303826Z",
     "shell.execute_reply": "2023-06-25T05:02:50.302571Z"
    },
    "papermill": {
     "duration": 0.149466,
     "end_time": "2023-06-25T05:02:50.306227",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.156761",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data\n",
      "Preview:\n",
      " 1     2421.0\n",
      "2      220.0\n",
      "3        1.0\n",
      "4     1283.0\n",
      "5    52375.0\n",
      "Name: usd_pledged_real, dtype: float64\n",
      "Minimum value: 0.45 \n",
      "Maximum value: 20338986.27\n",
      "______________________________\n",
      "\n",
      "Normalized data\n",
      "Preview:\n",
      " 1    10.165142\n",
      "2     6.468598\n",
      "3     0.000000\n",
      "4     9.129277\n",
      "5    15.836853\n",
      "Name: usd_pledged_real, dtype: float64\n",
      "Minimum value: -0.7779954122762203 \n",
      "Maximum value: 30.69054020451361\n"
     ]
    }
   ],
   "source": [
    "# get the index of all positive pledges (Box-Cox only takes positive values)\n",
    "index_of_positive_pledges = kickstarters_2017.usd_pledged_real > 0\n",
    "\n",
    "# get only positive pledges (using their indexes)\n",
    "positive_pledges = kickstarters_2017.usd_pledged_real.loc[index_of_positive_pledges]\n",
    "\n",
    "# normalize the pledges (w/ Box-Cox)\n",
    "normalized_pledges = pd.Series(stats.boxcox(positive_pledges)[0], \n",
    "                               name='usd_pledged_real', index=positive_pledges.index)\n",
    "\n",
    "print('Original data\\nPreview:\\n', positive_pledges.head())\n",
    "print('Minimum value:', float(positive_pledges.min()),\n",
    "      '\\nMaximum value:', float(positive_pledges.max()))\n",
    "print('_'*30)\n",
    "\n",
    "print('\\nNormalized data\\nPreview:\\n', normalized_pledges.head())\n",
    "print('Minimum value:', float(normalized_pledges.min()),\n",
    "      '\\nMaximum value:', float(normalized_pledges.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8190f6a6",
   "metadata": {
    "papermill": {
     "duration": 0.004528,
     "end_time": "2023-06-25T05:02:50.315819",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.311291",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The values have changed significantly with normalization!\n",
    "\n",
    "In the next code cell, you'll take a look at the distribution of the normalized data, where it should now resemble a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e29158e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:50.326150Z",
     "iopub.status.busy": "2023-06-25T05:02:50.325841Z",
     "iopub.status.idle": "2023-06-25T05:02:52.139539Z",
     "shell.execute_reply": "2023-06-25T05:02:52.138688Z"
    },
    "papermill": {
     "duration": 1.821074,
     "end_time": "2023-06-25T05:02:52.141310",
     "exception": false,
     "start_time": "2023-06-25T05:02:50.320236",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAHFCAYAAAAT5Oa6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABytklEQVR4nO3deXhU9d3//+cs2ZfJRhJCEhJ2ZHFBi8EqKItgEZX7rlbaFG8tal2Qr3rbUm2l/lqwti6tVKvUihUtvduKta2NgixqAVkEZZclEBKysCSTfSaZOb8/hhkICZCEJDPJvB7XNZfOOZ85530OJ5l3PqvJMAwDERERkSBm9ncAIiIiIv6mhEhERESCnhIiERERCXpKiERERCToKSESERGRoKeESERERIKeEiIREREJekqIREREJOgpIRIREZGgp4RIJEgtXrwYk8lEeHg4hw4darZ/3LhxDB8+3A+RdYw77riDrKysJtuysrK44447ujSOgwcPYjKZWLx4cbs+7/13OnjwYJs/+/777zNv3rx2nVck2CghEglyDoeDJ554wt9hdIlly5bx4x//2N9hdJn333+fn/70p/4OQ6RbUEIkEuQmT57M22+/zRdffNGp56mrq+vU47fGpZdeSv/+/f0dhogEICVEIkHuscceIzExkR/84AfnLVtfX8/cuXPJzs4mNDSUPn36cP/991NRUdGkXFZWFlOnTuWdd97h0ksvJTw8nJ/+9KesXr0ak8nE22+/zQ9+8AN69+5NdHQ0N954I6WlpVRVVXH33XeTlJREUlIS//M//0N1dXWTY//2t7/lmmuuITk5maioKEaMGMEzzzxDQ0PDeeM/s8ls3LhxmEymFl+nN3GVlJRwzz33kJ6eTmhoKNnZ2fz0pz+lsbGxyfGPHDnCrbfeSkxMDDabjdtuu42SkpLzxuW1fv16rrrqKsLDw0lLS2Pu3LktXtef//xnJk2aRO/evYmIiGDo0KH88Ic/pKamxlfmjjvu4Le//S1Ak+vyNr1dyH0U6Yms/g5ARPwrJiaGJ554goceeoiVK1dy3XXXtVjOMAxuvvlmPvroI+bOncvVV1/Nl19+yZNPPsm6detYt24dYWFhvvKff/45u3bt4oknniA7O5uoqCjfF/aPfvQjrr32WhYvXszBgwd59NFHuf3227FarVx88cX86U9/YsuWLfzoRz8iJiaG3/zmN77j7t+/nxkzZviSsi+++IKf//zn7N69mz/84Q9tuvaXXnqJysrKJtt+/OMfs2rVKgYPHgx4kqGvfe1rmM1mfvKTn9C/f3/WrVvHz372Mw4ePMjrr78OeGrAJkyYwJEjR1iwYAGDBg3iX//6F7fddlurYtm5cyfjx48nKyuLxYsXExkZyUsvvcTbb7/drOzevXu54YYbmDNnDlFRUezevZtf/OIXbNiwgZUrV/quo6amhr/+9a+sW7fO99nevXt3+H0U6REMEQlKr7/+ugEYGzduNBwOh9GvXz/j8ssvN9xut2EYhjF27Fhj2LBhvvJ5eXkGYDzzzDNNjvPnP//ZAIxXX33Vt61v376GxWIx9uzZ06TsqlWrDMC48cYbm2yfM2eOARizZ89usv3mm282EhISznoNLpfLaGhoMP74xz8aFovFOHHihG/fzJkzjb59+zYp37dvX2PmzJlnPd4vf/nLZtdyzz33GNHR0cahQ4ealP3Vr35lAMaOHTsMwzCMl19+2QCMv//9703KzZo1ywCM119//aznNQzDuO2224yIiAijpKTEt62xsdEYMmSIARj5+fktfs7tdhsNDQ3GmjVrDMD44osvfPvuv/9+ozW/5s91H0WChZrMRITQ0FB+9rOfsWnTJv7v//6vxTLemoczR2l985vfJCoqio8++qjJ9pEjRzJo0KAWjzV16tQm74cOHQrAN77xjWbbT5w40aTZbMuWLUybNo3ExEQsFgshISF897vfxeVy8dVXX53/Ys/iT3/6E4899hhPPPEEs2bN8m3/5z//ybXXXktaWhqNjY2+15QpUwBYs2YNAKtWrSImJoZp06Y1Oe6MGTNadf5Vq1Yxfvx4UlJSfNssFkuLNUwHDhxgxowZpKam+u7B2LFjAdi1a1erztdZ91Gku1KTmYgA8K1vfYtf/epXPP7440yfPr3Z/uPHj2O1WunVq1eT7SaTidTUVI4fP95ku7dppiUJCQlN3oeGhp5ze319PdHR0RQUFHD11VczePBgfv3rX5OVlUV4eDgbNmzg/vvvb3fH7VWrVnHHHXfw3e9+l//v//v/muwrLS3lH//4ByEhIS1+9tixY4Dn/pyezHilpqa2Kobjx4+3WPbMbdXV1Vx99dWEh4fzs5/9jEGDBhEZGcnhw4eZPn16q+5BZ91Hke5MCZGIAJ7E5he/+AUTJ07k1VdfbbY/MTGRxsZGjh492iQpMgyDkpISrrjiimbH62jvvvsuNTU1vPPOO/Tt29e3fevWre0+5pdffsnNN9/M2LFjWbRoUbP9SUlJjBw5kp///Octfj4tLQ3w3J8NGzY029/aTtWJiYktlj1z28qVKzly5AirV6/21QoBzTq2n0tn3EeR7k5NZiLiM2HCBCZOnMhTTz3VbHTX+PHjAViyZEmT7X/729+oqanx7e9M3iTr9M7bhmG0mMi0RkFBAVOmTKFfv3787W9/a7EWaOrUqWzfvp3+/ftz+eWXN3t5E6Jrr72Wqqoq3nvvvSafb6lTdEuuvfZaPvroI0pLS33bXC4Xf/7zn5uUa+keALzyyivNjuktc2aNT0ffR5GeQDVEItLEL37xC0aNGkVZWRnDhg3zbZ84cSLXX389P/jBD6isrOSqq67yjTK79NJLyc3N7fTYJk6cSGhoKLfffjuPPfYY9fX1vPzyy5SXl7freFOmTKGiooKFCxeyY8eOJvv69+9Pr169eOqpp1i+fDljxoxh9uzZDB48mPr6eg4ePMj777/P7373O9LT0/nud7/L888/z3e/+11+/vOfM3DgQN5//30++OCDVsXyxBNP8N5773Hdddfxk5/8hMjISH772982GUoPMGbMGOLj47n33nt58sknCQkJ4a233mpxHqkRI0YAnn/TKVOmYLFYGDlyZIffR5GeQDVEItLEpZdeyu23395su8lk4t133+Xhhx/m9ddf54YbbuBXv/oVubm5rFy5slmNRWcYMmQIf/vb3ygvL2f69Ok8+OCDXHLJJU2G5bfFzp07qa2tZfr06eTk5DR5/etf/wI8faE2bdrEpEmT+OUvf8nkyZPJzc3lD3/4A5dccgnx8fEAREZGsnLlSiZMmMAPf/hD/vu//5vCwkKWLl3aqliGDx/OihUriI2NZebMmdx9992MHDmy2czaiYmJ/Otf/yIyMpLvfOc73HnnnURHRzerSQJPh+7vfe97vPTSS+Tk5HDFFVdw5MiRDr+PIj2ByTAMw99BiIiIiPiTaohEREQk6CkhEhERkaCnhEhERESCnhIiERERCXpKiERERCToKSESERGRoKeJGVvJ7XZz5MgRYmJiOmVJAhEREel4hmFQVVVFWloaZvPZ64GUELXSkSNHyMjI8HcYIiIi0g6HDx8mPT39rPuVELVSTEwM4LmhsbGxfo5GREREWqOyspKMjAzf9/jZKCFqJW8zWWxsrBIiERGRbuZ83V3UqVpERESCnhIiERERCXpKiERERCToKSESERGRoOfXhCgrKwuTydTsdf/99wOeuQPmzZtHWloaERERjBs3jh07djQ5hsPh4MEHHyQpKYmoqCimTZtGYWFhkzLl5eXk5uZis9mw2Wzk5uZSUVHRVZcpIiIiAc6vCdHGjRspLi72vZYvXw7AN7/5TQCeeeYZnnvuORYuXMjGjRtJTU1l4sSJVFVV+Y4xZ84cli1bxtKlS/n000+prq5m6tSpuFwuX5kZM2awdetW8vLyyMvLY+vWreTm5nbtxYqIiEjgMgLIQw89ZPTv399wu92G2+02UlNTjaefftq3v76+3rDZbMbvfvc7wzAMo6KiwggJCTGWLl3qK1NUVGSYzWYjLy/PMAzD2LlzpwEY69ev95VZt26dARi7d+9udWx2u90ADLvdfqGXKSIiIl2ktd/fAdOHyOl0smTJEu68805MJhP5+fmUlJQwadIkX5mwsDDGjh3L2rVrAdi8eTMNDQ1NyqSlpTF8+HBfmXXr1mGz2Rg9erSvzJVXXonNZvOVaYnD4aCysrLJS0RERHqmgEmI3n33XSoqKrjjjjsAKCkpASAlJaVJuZSUFN++kpISQkNDiY+PP2eZ5OTkZudLTk72lWnJggULfH2ObDablu0QERHpwQImIXrttdeYMmUKaWlpTbafObOkYRjnnW3yzDItlT/fcebOnYvdbve9Dh8+3JrLEBERkW4oIBKiQ4cOsWLFCr73ve/5tqWmpgI0q8UpKyvz1RqlpqbidDopLy8/Z5nS0tJm5zx69Giz2qfThYWF+Zbp0HIdIiIiPVtAJESvv/46ycnJfOMb3/Bty87OJjU11TfyDDz9jNasWcOYMWMAGDVqFCEhIU3KFBcXs337dl+ZnJwc7HY7GzZs8JX57LPPsNvtvjIiIiIS3Py+uKvb7eb1119n5syZWK2nwjGZTMyZM4f58+czcOBABg4cyPz584mMjGTGjBkA2Gw27rrrLh555BESExNJSEjg0UcfZcSIEUyYMAGAoUOHMnnyZGbNmsUrr7wCwN13383UqVMZPHhw11+wiIiIBBy/J0QrVqygoKCAO++8s9m+xx57jLq6Ou677z7Ky8sZPXo0H374ITExMb4yzz//PFarlVtvvZW6ujrGjx/P4sWLsVgsvjJvvfUWs2fP9o1GmzZtGgsXLuz8ixMREZFuwWQYhuHvILqDyspKbDYbdrvdL/2J3G63bwbu9PR0zOaAaO0UEREJaK39/ta3ajdRWFjIs8vW8eyydc2WJhEREZEL4/cmM2k9W9LZR8WJiIhI+6mGSERERIKeEiIREREJekqIREREJOipD5GItItGPopIT6LfYCLSLhr5KCI9iWqIRKTdNPJRRHoK1RCJiIhI0FNCJCIiIkFPCZGIiIgEPSVEIiIiEvSUEImIiEjQU0IkIiIiQU8JkYiIiAQ9JUQiIiIS9JQQiYiISNBTQiQiIiJBTwmRiIiIBD0lRCIiIhL0lBCJiIhI0FNCJCIiIkFPCZGIiIgEPSVEIiIiEvSUEImIiEjQU0IkIiIiQU8JkYiIiAQ9JUQiIiIS9JQQiYiISNBTQiQiIiJBTwmRiIiIBD0lRCIiIhL0lBCJiIhI0FNCJCIiIkFPCZGIiIgEPSVEIiIiEvSUEImIiEjQU0IkIiIiQU8JkYiIiAQ9JUQiIiIS9PyeEBUVFfGd73yHxMREIiMjueSSS9i8ebNvv2EYzJs3j7S0NCIiIhg3bhw7duxocgyHw8GDDz5IUlISUVFRTJs2jcLCwiZlysvLyc3NxWazYbPZyM3NpaKioisuUURERAKcXxOi8vJyrrrqKkJCQvj3v//Nzp07efbZZ4mLi/OVeeaZZ3juuedYuHAhGzduJDU1lYkTJ1JVVeUrM2fOHJYtW8bSpUv59NNPqa6uZurUqbhcLl+ZGTNmsHXrVvLy8sjLy2Pr1q3k5uZ25eWKiIhIgLL68+S/+MUvyMjI4PXXX/dty8rK8v2/YRi88MILPP7440yfPh2AN954g5SUFN5++23uuece7HY7r732Gm+++SYTJkwAYMmSJWRkZLBixQquv/56du3aRV5eHuvXr2f06NEALFq0iJycHPbs2cPgwYO77qJFREQk4Pi1hui9997j8ssv55vf/CbJyclceumlLFq0yLc/Pz+fkpISJk2a5NsWFhbG2LFjWbt2LQCbN2+moaGhSZm0tDSGDx/uK7Nu3TpsNpsvGQK48sorsdlsvjJncjgcVFZWNnmJiIhIz+TXhOjAgQO8/PLLDBw4kA8++IB7772X2bNn88c//hGAkpISAFJSUpp8LiUlxbevpKSE0NBQ4uPjz1kmOTm52fmTk5N9Zc60YMECX38jm81GRkbGhV2siIiIBCy/JkRut5vLLruM+fPnc+mll3LPPfcwa9YsXn755SblTCZTk/eGYTTbdqYzy7RU/lzHmTt3Lna73fc6fPhway9LREREuhm/JkS9e/fmoosuarJt6NChFBQUAJCamgrQrBanrKzMV2uUmpqK0+mkvLz8nGVKS0ubnf/o0aPNap+8wsLCiI2NbfISERGRnsmvCdFVV13Fnj17mmz76quv6Nu3LwDZ2dmkpqayfPly336n08maNWsYM2YMAKNGjSIkJKRJmeLiYrZv3+4rk5OTg91uZ8OGDb4yn332GXa73VdGREREgpdfR5n9v//3/xgzZgzz58/n1ltvZcOGDbz66qu8+uqrgKeZa86cOcyfP5+BAwcycOBA5s+fT2RkJDNmzADAZrNx11138cgjj5CYmEhCQgKPPvooI0aM8I06Gzp0KJMnT2bWrFm88sorANx9991MnTpVI8xERETEvwnRFVdcwbJly5g7dy5PPfUU2dnZvPDCC3z729/2lXnssceoq6vjvvvuo7y8nNGjR/Phhx8SExPjK/P8889jtVq59dZbqaurY/z48SxevBiLxeIr89ZbbzF79mzfaLRp06axcOHCrrtYERERCVgmwzAMfwfRHVRWVmKz2bDb7X7pT1RQUMDvPzkAwPeu7kdmZmaXxyByOj2TItIdtPb72+9Ld4iIiIj4mxIiERERCXpKiERERCTo+bVTtYh0HrfbTWFhoe99eno6ZrP+BhIRaYkSIpEeqrCwkGeXrcOWlIL9WCmP3JKjjs8iImehhEikB7MlpRCfnObvMEREAp4SIpEgd3rTmprVRCRY6TefSJDzNq09u2xdkz5HIiLBRDVEIoItKQXD7aaoqMi3TbVFIhJMlBCJCACVJ47y+5XV9MlyqBO2iAQdJUQi4hObkKxO2CISlFQfLiIiIkFPCZGIiIgEPSVEIiIiEvSUEImIiEjQU0IkIiIiQU8JkYiIiAQ9JUQiIiIS9DQPkUiAO32tMejYGaSL7XX85YtjbC11km6CsA45qohI96OESCTAedcasyWldNgM0vbaBub8eQur9hz1bdtvieRryaC5qUUkGCkhEukGbEkpHTKDtNvtZte+Qzz8j3z2HK3DZIIRqZHsO1ZHTYOZNcUGtpQaYjsgZhGR7kR9iESCyO79h5jx5jb2HK3DFm7hnw9+nYW39OfGARGkhDXixsQne49hGIa/QxUR6VJKiKRHc7vdFBQUUFBQgNvt9nc4fvebT49gb7AQZoEXburHsDQbAGFWE5fF1RNiMjhe4+RQpcvPkYqIdC0lRNKjefvfPLtsXZOOycHGcLt5b3M+eXsqMAHX9g2nf2J4kzKhZuh/sq3sy1InbtUSiUgQUUIkPZ4tKQVbUoq/w/CrY8eO8psNdgD6xxokR1laLNc/1iDUaqbCYbB6v70rQxQR8SslRCJBYEdlGE7DQrTFzUW2s9f8hJrh0ow4AN76/OhZy4mI9DRKiER6uGPVDgrqPANKL4mrx3Ken/qLM+Iwm2DvsXqWb9qjvlciEhSUEIn0cJ/uOwaYSIs0SAw9f3ITEWKhd1gDAD9/f1dQ970SkeCheYhEerDiaheHjtdjwmBYnAEnW8sMt5uioiIAioqKmg2z7xvZSFF9CEX1IdQ2aMSZiPR8SohEeojTl/hIT0/HbRhsLnYCkBXZQHSIFbfnLZUnjvL7ldX0yXJQuHcHtpSm81MnhbqIshrUNJpYtc/OkP5deikiIl1OTWYiPcSZUwys+KqCE/VuQi1mBsc4m5WPTUgmPjmNmPikZvtMJsiK9tQa/WPniU6PXUTE35QQifQg3ikGHI1uFn1WCsDlWfGEteMnPTMazCbYWVrHJ3s14kxEejYlRCI90DvbjlNa3UBkiMk3jL6twi0wKMHTqv7zf+3C5dZEjSLScykhEulhahvcvLm5DIBLU0Kwnm+c/TmMTA4lOtTM7pIq3vlco81EpOdSQiTSjXhHh51tbTaX22BNgYNqp5vBvSLIjruwcRPhVhO5o5IB+NWHezh8ovaCjiciEqiUEIl0I57RYTvPujbbphInR2vdRIeamTcpA7PJdMHnnD4ikT5xEZRWOrj2V6v54d++ZPWeMirrGy/42CIigULD7kW6mdiEZGJiY4BTQ+3t9Y288NEB9hz3JClPTMigjy2sQ84XZjWz+H+u4Kf/2Mmn+46xdONhlm48DMBFSVYu790x5xER8SclRCLdkMPlWXx1/4ZSPtpeRFlDCM6T8ydekhLCmKzYDjmPt4muTx/4451XsOVwBW99VsCWggryj9Ww81gjfWP1a0REuj/9JhPpZGdOmGg2t7+l2uGCz0tNHK2vxdhZcHJrCACxVhcXJ5rITg690JB9vBM4RkcX8MgtOYzqm8movgkA3P/GWv61q5xNJc5mM12LiHQ3SohEOpl3wkSAR27JITMz8zyfaFmjy81n5RGUN3j6BWXFhzGidxRFJ6rok5wEZXuxhkV2WNxepzfRne7OK1LI213O0Vo3n+ZX0rdvh59aRKTL+LVT9bx58zCZTE1eqampvv2GYTBv3jzS0tKIiIhg3Lhx7Nixo8kxHA4HDz74IElJSURFRTFt2rRmnU3Ly8vJzc3FZrNhs9nIzc2loqKiKy5RzsLtdlNQUHDW0VI9jXfCxPYyDIMPdpZS3mAhxGxw44AI/nj7IP53XB+G9wqlX69ozBfef7pNekWHcFGSp3bqlfWlmqdIRLo1v48yGzZsGMXFxb7Xtm3bfPueeeYZnnvuORYuXMjGjRtJTU1l4sSJVFVV+crMmTOHZcuWsXTpUj799FOqq6uZOnUqLtepBSlnzJjB1q1bycvLIy8vj61bt5Kbm9ul1ylNnbnMhJzbnuON7CurxoTBlb0M4iP8/qMLwLBeIYSYoaDCwa7iSn+HIyLSbn5vMrNarU1qhbwMw+CFF17g8ccfZ/r06QC88cYbpKSk8Pbbb3PPPfdgt9t57bXXePPNN5kwYQIAS5YsISMjgxUrVnD99deza9cu8vLyWL9+PaNHjwZg0aJF5OTksGfPHgYPHtx1FytNXEiNSTBpdBvsONYAwLBYJ0nhIX6O6JRQi4lekRaOVLv4vKCc4X1s/g5JRKRd/P5n5t69e0lLSyM7O5tvfetbHDhwAID8/HxKSkqYNGmSr2xYWBhjx45l7dq1AGzevJmGhoYmZdLS0hg+fLivzLp167DZbL5kCODKK6/EZrP5yrTE4XBQWVnZ5CXiD//Jr6SmwSA8xExWZIO/w2mmV6Tn18jmQ+V+jkREpP38mhCNHj2aP/7xj3zwwQcsWrSIkpISxowZw/HjxykpKQEgJaVpLUJKSopvX0lJCaGhocTHx5+zTHJycrNzJycn+8q0ZMGCBb4+RzabjYyMjAu6VpH2+suXxwAY0ceGpYv7CbVGrygLoIRIRLo3vyZEU6ZM4b/+678YMWIEEyZM4F//+hfgaRrzMp0x065hGM22nenMMi2VP99x5s6di91u970OHz7cqmsS6Shut5vlm/bwZXEtJmBkepy/Q2pRUoQZswkKy+soraz3dzgiIu3i9yaz00VFRTFixAj27t3r61d0Zi1OWVmZr9YoNTUVp9NJeXn5OcuUlpY2O9fRo0eb1T6dLiwsjNjY2CYvka5UWFjIU//aBUB6lEF0mN+7/LUo1GKiX0I4AJ+rlkhEuqmASogcDge7du2id+/eZGdnk5qayvLly337nU4na9asYcyYMQCMGjWKkJCQJmWKi4vZvn27r0xOTg52u50NGzb4ynz22WfY7XZfGZFAVOt0caTe04G6X0xgD2kfnuqZ/0jNZiLSXfn1T85HH32UG2+8kczMTMrKyvjZz35GZWUlM2fOxGQyMWfOHObPn8/AgQMZOHAg8+fPJzIykhkzZgBgs9m46667eOSRR0hMTCQhIYFHH33U1wQHMHToUCZPnsysWbN45ZVXALj77ruZOnWqRphJQPv4QCUuA6IsbuI7bvLpTjE8NZJ3d5xgc4ESIhHpnvyaEBUWFnL77bdz7NgxevXqxZVXXsn69evpe3LK28cee4y6ujruu+8+ysvLGT16NB9++CExMadmzX3++eexWq3ceuut1NXVMX78eBYvXozFYvGVeeutt5g9e7ZvNNq0adNYuHBh116sSBt9+FUFABkRDZhMgTPUviXDU6MA2FFUSX2Di/AQy3k+ISISWPyaEC1duvSc+00mE/PmzWPevHlnLRMeHs6LL77Iiy++eNYyCQkJLFmypL1hSgDoyPXAuoPSyno2F1YDkB7RiHe9skBkuN24q8qIj7BSXtfItsIKrshO9HdYIiJt0rO/VaTHCLaZrf++tQgDzxw/Udbm/Ye8q9AXFRX5fWHVyhNHeW3VLl+cn+445Nd4RETaIzCHrYi0IFhmtjYMg3c+LwKgf5wVWsh3vKvQGw312FIySfDzrYlNSCaVWAqryjlU7vBvMCIi7aCESCTAbD5Uzu6SKkLMJvrarNRXtFwuNiEZt7O2S2M7l/goT8/vQxVKiESk+1FCJBIATu8j9esPPE1OkwbHEWZ10l2mOkyIPJkQqYZIRLoh9SESCQDePlLPrdjHJ/l2AG67OMnPUbVNfJSn4/eJ2kZ27M3H7Xb7OSIRkdZTQiQSIGxJKRyoDQVMjM6MJuvk7M/dRZjVQrjZkwQ9/e6moOj8LiI9hxIikS7iHRlWUFDQYu1JfaPBziOVAHzrkl5dHV6HiLZ6rqsxPM6/gYiItJH6EIl0Ee/IsKjIg9x+ZRZ9+vQBPPMqNbjcrCmop9FtkBBu5rI+UX6Otn1irG6OOaHSEdhLjYiInEkJkUgX8o4M+/3KnfTJcmA/VsrDN1/JL1cXUVrjJtRi5qqMMEwmk79DbZeYkzVEdof6D4lI96KESMQPYhOSiU9O41iti7nvH2LtoSpMwA0jUol12f0dXrtFn5ycUQmRiHQ3SohE/MDpgn9vL+ar0nqgHrMJvpYWSt/EKMrLAi8h8vZ/8kpPT2+xnLeGqNpp4GhUUiQi3YcSIpEudtxpZnOFiTpXNSZg0qA4ckcl8+H2ovN+1l+8/Z+8zXyP3JLTYrkws0GIyaDBMFFY4WBgF8cpItJeSohEulB1A6w9HoEbE7aIEK5Ks/DDCRkdeo7Ta3M6cp0zbzPfuZhMEB0C5U7NWC0i3YsSIpEu9GW5CTcmksIMvvm1TGpOlHT4Ofy9zlmMNyFqxYzVp8/QnZ6ejtmsmUBExD+UEIl0kZJ6C6V1JkwYXJJoEGo1U9NJ5/LnOmcxIQZgalVC5J2hG+CRW3LIzMzs5OhERFqmhEikC7jcBtsrwwDoH9VATMjZf/Q6q8mrq8R4VvBo9ZpmtqQursISEWmBEiKRLnCgopEal5kwi8GgaCfn+tE7s8mru/EmRIcrHLjcBhZz95xTSUSCixrsRbrAvvJGAAbEGIS04qcuNiGZmPjutbirV5QVzCZwugyKyuv8HY6ISKsoIRLpZIcrHBytdQMGGd1zRY42MZkgNsxTK7TvaJWfoxERaR0lRCKdLG9POQDJYS4igqSR2hbm+dWyr6zaz5GIiLSOEiKRTuRyG+TtrgAgM6LRv8F0ISVEItLdKCES6URr9x/jaE0DoWZIDVdCJCISqIKkAl/EP9753DN8PivOiqWHDLZqzbQAtpN9iPYfrcEwDEymHnLxItJjKSES6SR1Thcf7PDMRN0vzgotDLjqjnMOtWZagNgwMybAXtfAsWonvWLCujZIEZE2UkIk0klW7Cql1ukiNSaEXpFmKltIiPy9zEZ7nW8mbKvZRGpsCMWVDewrq1ZCJCIBT32IRDrJe18cAWDiwLhzNhl15zmHzqVvXDgA+46qH5GIBD4lRCKdwF7bwOo9ZQCMHxjn32D8pG+8p1ZovzpWi0g3oIRIpBPk7SimwWUwJDWGfonh/g7HL3wJkWqIRKQbUB8ikRa43W4KCwsBSE9Px2w2t7jtbN7d4mkum3ZJWucHG6C8CdFXpZqtWkQCn2qIRFpQWFjIs8vW8eyydb4kqKVtLfmqtIp1B45jMsG0i4M3Iep/smastNLBserWrXwvIuIvSohEzsKWlIItKeW828702if5AFx/USrp8ZGdFl+giwy10C/Js3jbjiOVfo5GROTclBCJdKCjVQ6WbfHMKzTrmmw/R+N/w/rYANheZPdzJCIi56aESKQDvbnuIE6Xm0sz4xjVN8Hf4fjd8LRYAHYcUUIkIoFNnapF2uj02aVP71xd7WjkzfWHAJh1dT+/xRdIhvtqiNRkJiKBTTVEIm3kmV16Z5PO1W63m//90wbKaxvomxDJpIu6yZTTnWzYyRqighO12Osa/ByNiMjZKSESaYfYhOQmnavfXL2Df+8uBwzuuMjCkaJC3G63/wIMEHGRoaTHRwCwUx2rRSSAKSESuUCHT9TyqzWeJrRB0Q18+sXe8w7NDybD0zzNZupHJCKBTAmRyAUosjv41qvrqXK4SIwwMzja2az2KNgN7+NpNtNIMxEJZOpULUGrLTNPt8Re7+bBdw9wrKaRjLhQRqdaaNB3fjO+ofdqMhORAKYaIglarZ15uiV2J3yQX8exmkYGpUTzm5v7ERmiH6eWeJvM9h+tZs/+gxQUFKh/lYgEnID5Db5gwQJMJhNz5szxbTMMg3nz5pGWlkZERATjxo1jx44dTT7ncDh48MEHSUpKIioqimnTpjX7cisvLyc3NxebzYbNZiM3N5eKioouuKqeye12U1BQ4Ht15y+31sw8faaKBjOflJqob4SBSeEsvTuHxMiQToqw++sVE0aaLRzDgHl/26T+VSISkAIiIdq4cSOvvvoqI0eObLL9mWee4bnnnmPhwoVs3LiR1NRUJk6cSFXVqcUi58yZw7Jly1i6dCmffvop1dXVTJ06FZfL5SszY8YMtm7dSl5eHnl5eWzdupXc3Nwuu76exluz8vtPDgTdl1uV083a4xE0uE0kRZh5YVo/EqJC/R1WwLtmUC8Ayk0x6l8lIgHJ7wlRdXU13/72t1m0aBHx8fG+7YZh8MILL/D4448zffp0hg8fzhtvvEFtbS1vv/02AHa7nddee41nn32WCRMmcOmll7JkyRK2bdvGihUrANi1axd5eXn8/ve/Jycnh5ycHBYtWsQ///lP9uzZ45dr7glsSSnEJ6cF1ZdbbYOLVQfraTBMxIUaTMgOJybc4u+wuoVrhyQDUFTlwjAMP0cjItKc3xOi+++/n2984xtMmDChyfb8/HxKSkqYNGmSb1tYWBhjx45l7dq1AGzevJmGhoYmZdLS0hg+fLivzLp167DZbIwePdpX5sorr8Rms/nKtMThcFBZWdnkJcHL7Tb4+YpCKhwGYWY3V/YyCLWY/B1WwPLO5u1tUr1qQBJWs4kqp0GVUwmRiAQevyZES5cu5fPPP2fBggXN9pWUlACQktK0BiIlJcW3r6SkhNDQ0CY1Sy2VSU5Obnb85ORkX5mWLFiwwNfnyGazkZGR0baLkx7l9bUH+SS/ErMJvhZfT4TGZ57TmbN5R4dZuSQtCoDCKtd5Pi0i0vX8lhAdPnyYhx56iCVLlhAeHn7WciZT07/CDcNotu1MZ5Zpqfz5jjN37lzsdrvvdfjw4XOeU3qu/cfr+UXebgAu7x1KQmj37UTelc6cj+nKvjEAFFY2Nit7Zo2SiEhX81tCtHnzZsrKyhg1ahRWqxWr1cqaNWv4zW9+g9Vq9dUMnVmLU1ZW5tuXmpqK0+mkvLz8nGVKS0ubnf/o0aPNap9OFxYWRmxsbJOXBB+X2+Cp5QU4G93k9I1hcELrqoZO/4IvKipSvxkg52RCVFbrptbZtJaopfXhRES6UrsSon79+nH8+PFm2ysqKujXr3WrfI8fP55t27axdetW3+vyyy/n29/+Nlu3bqVfv36kpqayfPly32ecTidr1qxhzJgxAIwaNYqQkJAmZYqLi9m+fbuvTE5ODna7nQ0bNvjKfPbZZ9jtdl8ZkbP5vMRJ/gkHSdGh/PDa9PPWTnp5v+B//8kBFn3wOdVV1Z0caWA6PTE01xwjJtSE24ANh5vfD83wLSL+1K6eEAcPHmwyrN3L4XBQVFTUqmPExMQwfPjwJtuioqJITEz0bZ8zZw7z589n4MCBDBw4kPnz5xMZGcmMGTMAsNls3HXXXTzyyCMkJiaSkJDAo48+yogRI3ydtIcOHcrkyZOZNWsWr7zyCgB33303U6dOZfDgwe25fAkSRVWN7Druad755X9fTHxkfZs+H5uQTHxyGvZjzWsog4UnMaymT5aDwr07SA3vS5XTxN+2Hee71/o7OhGRU9qUEL333nu+///ggw+w2Wy+9y6Xi48++oisrKwOC+6xxx6jrq6O++67j/LyckaPHs2HH35ITEyMr8zzzz+P1Wrl1ltvpa6ujvHjx7N48WIsllPDod966y1mz57tG402bdo0Fi5c2GFxSvficp+/+aqirpG1hU4AbhmewLVDkikoKOjs0Hqk0xPD/maD/VUmvjhSw+ZD5fTSQD0RCRBtSohuvvlmwNNJeebMmU32hYSEkJWVxbPPPtvuYFavXt3kvclkYt68ecybN++snwkPD+fFF1/kxRdfPGuZhIQElixZ0u64pPszDIPn1hSxtWQ/RRV1RIRYuH9Maotl6xtczPuwgLpGA1uYifvG9O7iaHuuCCtkx1nZX97I79bs58fjmo8AFRHxhzYlRN7RH9nZ2WzcuJGkpKROCUqkox2yN/Lx4RO+9w2uRhasLKRfnJWvpZ2aabrO6WLWHzfxeVENVjNcnRFGmLXlrnbe/jGAOk63wbCkEA6UN7J8Zym5IzVYQUQCQ7v6EOXn53d0HCKdxjAMthbXARayI5384r9GsKHU4LnlX3GgopHDlY2ERZaSuNfB+9uK2V1SRYTVzDWZoSREnH0m6jP7x9hSMrvuorqxuHAzX8+O5ZP8Sv609Ri9tAyciASAdk8v99FHH/HRRx9RVlbWbN6QP/zhDxccmEhHOVzporLRgtVk8LW+caTGhPLAsEwyIxv5yb/zqXAYvLGpDCgDIDrMytM3ZLJh3/k7Q6vjdPvMuLQXn+RXsvyrCm4aePZ5yEREukq7EqKf/vSnPPXUU1x++eX07t271UORRbqaYRh8WdYAQL8YCLOeelYvSYvixoERHKp0UeO2khwXw9cHJjFxaAr1FWVs2OevqHu+YamRjM5O4LP8E+w81sAgrY8rIn7WroTod7/7HYsXL9aK8RLwNhfWcKLejcVkMCC2eR8fk8lEls3K967uR2bmqSavgoouDDJIfX9cfz7LP8He8kayeoHqiUTEn9o1MaPT6dSkhtItrDvkWZQ3PbyRMC1MH1DGDurFgMRwGt2QX6OORCLiX+1KiL73ve/x9ttvd3QsIh1uU6FnRuReYZ4JFrWkRuAwmUzMuKwXAAdqQ9A/hYj4U7uazOrr63n11VdZsWIFI0eOJCSk6V93zz33XIcEJ3IhyqrqyT/hACApzDOzeksjw2JiY851GOlE4/rbeHrlYZwuMyccbjQIX0T8pV0J0Zdffskll1wCwPbt25vsUwdrCRRr93nW20sINxN2Wl1oW0aGaa6hzmU1m0iLtnDQ7qKkzkSWvwMSkaDVroRo1apVHR2HSIf7dN8xAHpHt7/zkOYa6nzpMdaTCZG/IxGRYNauPkQigc4wDP7jTYhiLqw3tbdGKSZeM7N3hrQYC2BQ2WCixuk+b3kRkc7Qrhqia6+99pxNYytXrmx3QCLt5Xa7KSwsBMAZFkexvZ5Qi4nkSDM1rVio/vTPq3ms64RbTcSHuClvsFBU5fJ3OCISpNqVEHn7D3k1NDSwdetWtm/f3mzRV5GuUlhYyLPL1gGQPWAQACN6R3KWpcjO+nlbUoqax7pYSlgj5Q0WCs+REJ2esAKkp6djNquSW0Q6RrsSoueff77F7fPmzaO6uvqCAhK5ELakFAA2nxxuP6pPNPW1rX8mbUkpWorDD1LDXeyuhpJqF47GlpvNTk9Y7cdKeeSWnCaTaYqIXIgO/fPqO9/5jtYxE78zDIMvjtQAcGmfaD9HI60Ra3UTbjFoNGBHae1Zy3kTVm/iKyLSUTo0IVq3bh3h4ZqAX/yrvN5NpcNFVKiFwb0i/B2OtILJBAlhnv/fU6bhZiLS9drVZDZ9+vQm7w3DoLi4mE2bNvHjH/+4QwITaa/SGk+Ty+VZCVgtmheru4gPNThSa2K3EiIR8YN2JUQ2m63Je7PZzODBg3nqqaeYNGlShwQm0l4lNZ6OuTn9E/0cibRF3MkV7/ccVUIkIl2vXQnR66+/3tFxiHQIwzAoPZkQXdkvEYxKP0ckreVNiI5UOqmodRIXGerfgEQkqLQrIfLavHkzu3btwmQycdFFF3HppZd2VFwi7XKi3o3TBZEhZoanxXKkSAlRdxFqgZhQE1VOg21Fdq4e2MvfIYlIEGlXQlRWVsa3vvUtVq9eTVxcHIZhYLfbufbaa1m6dCm9eukXmfiHt//QyN5RWC2ao6a7SYwwU+V08WWhEiIR6Vrt+sZ48MEHqaysZMeOHZw4cYLy8nK2b99OZWUls2fP7ugYRVqtpNrTXHZpnyg/RyLtkRjhWWZlW6Hdz5GISLBpVw1RXl4eK1asYOjQob5tF110Eb/97W/VqVr8ptrh8iVEl2n+oW4pMcLzN9q2IiVEItK12lVD5Ha7CQkJabY9JCQEt1uLM4p/LN9bQaMBtjATg3ppPqzuKCHCjAkoqqjjWLXD3+GISBBpV0J03XXX8dBDD3HkyBHftqKiIv7f//t/jB8/vsOCE2ktwzB4b8cJAAYlhJxz8WEJXKEWExlxnhkaVUskIl2pXQnRwoULqaqqIisri/79+zNgwACys7OpqqrixRdf7OgYRc5r6+EK9h+vx2yCfnEXNHhS/Mw7u7j6EYlIV2rXN0dGRgaff/45y5cvZ/fu3RiGwUUXXcSECRM6Oj6RVvnThgIAsmxWwqyqHerOBvYKZ/le2FWsKRNEpOu0KSFauXIlDzzwAOvXryc2NpaJEycyceJEAOx2O8OGDeN3v/sdV199dacEK+LldrspLCwEYGtRNf/4ohiAgQmqHeruBiR6aoiUEIlIV2rTt8cLL7zArFmziI2NbbbPZrNxzz338Nxzzykhkk5XWFjI039bx2F3PNuOOgETI1IjSY70d2RyoQYkeTrEHzpRS42jkagwJbki0vna1Ifoiy++YPLkyWfdP2nSJDZv3nzBQYmcy/5jdTz6j3z+XRrNtqMNgIkbhsTzqxuz1Zm6B4iLsNIrJgzDgN0lVf4OR0SCRJsSotLS0haH23tZrVaOHj16wUGJnI3TZfCD9w+x4XA1BpAQFcrVGWH88Lp0IkI0M3VPMbS3pxZ6d4mazUSka7TpG6RPnz5s27btrPu//PJLevfufcFBiZzN5yVOyqobSIsN5aaBEeRe2ZdsjSrrcYb2jgHUj0hEuk6bEqIbbriBn/zkJ9TX1zfbV1dXx5NPPsnUqVM7LDiR05VUu/jqRCMAj13bB1t4x9QIGW43RUVFFBUVYRhGhxxT2sf7b5Ec0gDAriNKiESka7TpT+snnniCd955h0GDBvHAAw8wePBgTCYTu3bt4re//S0ul4vHH3+8s2KVbur0EWHp6emYzW1PZAzD4LMjnpmLp12UwGV9ovn8QFmHxFd54ii/X1mN0VCPLSWThJQOOay0g/ffIio1G4CdxZW43QZms/qGiUjnalNClJKSwtq1a/n+97/P3LlzfX9Nm0wmrr/+el566SVSUvRtIk0VFhby7LJ1ADxySw6ZmZltPsb2klrsDoMQM9ybk9rRIRKbkIzbWdvhx5W2i01IJj29D+Z9+6hrcHO4vJa+iVqsV0Q6V5s7X/Tt25f333+f8vJy9u3bh2EYDBw4kPj4+M6IT3oIW9KFJcofflUBQGaslegwSwdEJIHMbDYRF2bmRL2bXcWVSohEpNO1uzdqfHw8V1xxRUfGItIiZ6Oblfs8yzhkx6sDdbCIj/AkRDuLq5g8XIM1RKRzaZyyBLzVe8qocriIsJpIjdIjGywSTnaa10gzEekK+nNbApa3M/bbaw8BkG2zYNbEi0EjIcKTEO3Qqvci0gWUEEnA8i7P8XFZNAApoU4gzL9BSZdJCDdjAo7Y6zlW7fB3OCLSw6n9QQLaUXMCbgNirC5sZ58kXboR71xDBQUF55z7KcRiIiPOkwBvUy2RiHQyvyZEL7/8MiNHjiQ2NpbY2FhycnL497//7dtvGAbz5s0jLS2NiIgIxo0bx44dO5ocw+Fw8OCDD5KUlERUVBTTpk3zzXnjVV5eTm5uLjabDZvNRm5uLhUVFV1xiXIBDMNgzwnPBH1ZkY2otaxn8Mw1tJPff3KARR98TnVV9VnLDkmOAGBboRIiEelcfk2I0tPTefrpp9m0aRObNm3iuuuu46abbvIlPc888wzPPfccCxcuZOPGjaSmpjJx4kSqqk4t+DhnzhyWLVvG0qVL+fTTT6murmbq1Km4XC5fmRkzZrB161by8vLIy8tj69at5Obmdvn1SnOn1xa43e4m+zYX1lDpMAi1mMmIaPBThNIZYhOSiU9OIyY+6ZzlBvfyJERfKiESkU7m1z5EN954Y5P3P//5z3n55ZdZv349F110ES+88AKPP/4406dPB+CNN94gJSWFt99+m3vuuQe73c5rr73Gm2++yYQJEwBYsmQJGRkZrFixguuvv55du3aRl5fH+vXrGT16NACLFi0iJyeHPXv2MHjw4K69aPGpcbr5194a/rSjhijrUW4YXsxT37yCEIsnT1+2/TgAQ3rHEOLSSKNg5KshKqoAkv0ai4j0bAHTh8jlcrF06VJqamrIyckhPz+fkpISJk2a5CsTFhbG2LFjWbt2LQCbN2+moaGhSZm0tDSGDx/uK7Nu3TpsNpsvGQK48sorsdlsvjItcTgcVFZWNnlJx9pf3ki920wjZuyNFv609Riz/7SFBpebA0er+c9Bzz2/OD3Ov4GK3wxIisBsgtJKB8dqVEsoIp3H7wnRtm3biI6OJiwsjHvvvZdly5Zx0UUXUVJSAtBsKZCUlBTfvpKSEkJDQ5vNkn1mmeTk5n9ZJicn+8q0ZMGCBb4+RzabjYyMjAu6TmnuoN2zUOtFcW5y+oQSYjbx7+0l3Pjip1z/wse4DUiNMpMQFernSMVfIkLMDEj2jDL86midn6MRkZ7M7wnR4MGD2bp1K+vXr+f73/8+M2fOZOfOnb79pjN60hqG0Wzbmc4s01L58x1n7ty52O123+vw4cOtvSRphfwT9VQ4DEwYZMfAwIQQ/r/JmYRYTOwuqaLBZTA8NZIr+2iYfbAb0ScOgN1lSohEpPP4PSEKDQ1lwIABXH755SxYsICLL76YX//616SmehbwPLMWp6yszFdrlJqaitPppLy8/JxlSktLm5336NGj51yINiwszDf6zfuSjrPq5FIcyWEuQk8+hWOyYnnjf77GHWOyePf+q3hpen9iw/z+iIqfjUy3AbBHNUQi0okC7tvGMAwcDgfZ2dmkpqayfPly3z6n08maNWsYM2YMAKNGjSIkJKRJmeLiYrZv3+4rk5OTg91uZ8OGDb4yn332GXa73VdGupZhGL61yfqENzbZN2ZAEvOmDeOSjDg/RCaBaHifkwlRWd1Z5ywSEblQfh1l9qMf/YgpU6aQkZFBVVUVS5cuZfXq1eTl5WEymZgzZw7z589n4MCBDBw4kPnz5xMZGcmMGTMAsNls3HXXXTzyyCMkJiaSkJDAo48+yogRI3yjzoYOHcrkyZOZNWsWr7zyCgB33303U6dO1QizFniXywDPtAhmc8fnzHtKqyiocGA2QWp4I6A+QnJ2w9JisZpNnKhrpKYhhAR/ByQiPZJfE6LS0lJyc3MpLi7GZrMxcuRI8vLymDhxIgCPPfYYdXV13HfffZSXlzN69Gg+/PBDYmJifMd4/vnnsVqt3HrrrdTV1TF+/HgWL16MxWLxlXnrrbeYPXu2bzTatGnTWLhwYddebDdRWFjIs8vWAfDILTlkZmZ2+Dk+2O5pwuwTYyEk4OooJVB456gCGN4nlq2H7ZTVuNHwBhHpDH5NiF577bVz7jeZTMybN4958+adtUx4eDgvvvgiL7744lnLJCQksGTJkvaGGXRsSWfvW9URvKuXp0RZzlOyfU7/Ij3X0hAS2DwzWlcTHV3A4OQsth6GslrX+T8oItIOWtxVutzeMs9M43FhJuiENTu9X6R9shwU7t2BLaXja7mka8QmJBMTG8Pg3pH8+Qsoq1FCJCKdQw0W0qUaXQaHjtcCYOvEEWStXRpCuocRvaMAqHAY1DcoKRKRjqeESLpUod1Bo9sgIsRMZIhWa5XWiYuwknly5fsjdg2/F5GOp4RIutTBck8bWd/4sPNOsClyuhG9IwE4UlHv50hEpCdSQiRd6tBpCZFIW4w82Wx2pEI1RCLS8ZQQSZc6VO756z4rPtzPkUh3M/JkDVFZpQOXWyMHRaRjKSGSLqUaImmvtNhQIqwmXIbBsTq3v8MRkR5GCZF0GcMwKKjwJERZCUqIpG1MJhPJUZ5fWaXVGmkmIh1LCZF0meoGA0ejQajVTO8YLdchbZd6cjLPEs1HJCIdTAmRdBl7vaeZo19SFBazRphJ2/WO9iREZbVuHI1qNhORjqOESLqM3eHpCDsgOdrPkUh3FRNqIirMgtuA7SW1/g5HRHoQJUTSZewOz1/050uI3G43BQUFWodMmjGZTGTEe0abbSmq9nM0ItKTaC0z6TJNE6LGs5YrLCzk2WXrqCo/hi0lk4TOXWtWupn0+Ah2l1TxeVGNv0MRkR5ECZF0maqTCVF2UhQ02s9Z1pakLEha5q0h2lVWS42jkagw/RoTkQunJjPpEg0ug/qTA4MyEyL9G4x0a7ERIUSHmHC5YePBE/4OR0R6CCVE0iWqnJ7aIVu4hZjwkHYfx3C7KSoqUv+iIJd6crTZuv3H/RyJiPQUqmuWLlHl9CQvfWwXNv9Q5Ymj/H5lNUZDvfoXBbHUKAv7yhtZd0AJkYh0DCVE0iW8NURpsRc+IWNsQjJup4ZcB7PUaE/l9vYiO/baBmyR7a91FBEBNZlJF6n21RBpyQ65cJEhZjLjwnAb8Fn+cd9UDd6X261JG0WkbVRDJF2iI2uIRAAu6xNFQYWDdQeOMyTGybPL1mFLSsF+rJRHbskhMzPT3yGKSDeiGiLpElUnZ6lWQiQd5dI+ngk+vR2rbUkpxCenacoGEWkXJUTS6RpdBjUNHdOpWsTr0j5RAOwuqaKi7uwTfYqItIYSIul0pdVODMBigsRItdJKx4iLsDIkNQaALZq1WkQukBIi6XRFdicA0aEmTCatci8dJ6d/IgCfa10zEblASoik0x2p9CREMaF63KRjeCfoHBjraYrdVKiESEQujNovpNOdSohUOyQdwztBZ1hkNBZzNEV2J1UOC/H+DkxEui39yS6d7lSTmR436TixCckkJacwItXTubqo2uXniESkO9M3lHQ61RBJZxqd6elYfaRKCZGItJ8SIulUhmFw5GQNUUyYHjfpeKMzPfMRlVS7aHRphmoRaR99Q0mnOlrloK7RjQmIDlENkXS8/onhJEZaaTTgiL3e3+GISDelhEg61VelntE/0aEmLGYlRNLxTCaTr9ns4HHNRyQi7aOESDrVV6VVAMSH61GTzvO1k81mh47X+jkSEemuNOxeOpU3IYrrxITIOycNePosSfC5Ij0GE3CixkmV0+LvcESkG1JCJJ1qz8mEyNaJHaq9c9IYDfXYUrTCeTCKCbeQHGWmtMbN4UqNNhORtlM7hnQawzDYe7IPUWc3mcUmJBMTn9Sp55DAlhnr+fuuwK6FXkWk7ZQQSac5Yq+n2tGI1WzSHETSbt4m0aKionM2iWbEeprKymrdnKht6KrwRKSHUJOZdJqvSjzNZRlxoRphJu12ZpNoQkrL5aJDzSTHhFFW5eA/B6u4ZEjXxiki3ZtqiKTTeDtUZyeE+zkS6e5a2yTaP9kz2uzjA/bODklEehglRNJp9ighki42oJcnIdpcWENlvZrNRKT1lBBJp/HWEPVLCPNzJBIsEqJCsYWZaHQbfLSr1Lfd7XZTUFBAQUEBbreW9xCR5vyaEC1YsIArrriCmJgYkpOTufnmm9mzZ0+TMoZhMG/ePNLS0oiIiGDcuHHs2LGjSRmHw8GDDz5IUlISUVFRTJs2jcLCwiZlysvLyc3NxWazYbPZyM3NpaKiorMvMWi53Ab7yjwjzLJUQyRdqK/N0zXy71uP+LYVFhby7LJ1PLtsXbPfDSIi4OeEaM2aNdx///2sX7+e5cuX09jYyKRJk6ipOTX9/jPPPMNzzz3HwoUL2bhxI6mpqUycOJGqqipfmTlz5rBs2TKWLl3Kp59+SnV1NVOnTsXlOjUfyYwZM9i6dSt5eXnk5eWxdetWcnNzu/R6g8nhE7XUN7gJs5pJiw31dzgSRPrFeRKiT/Ye41i1w7fdlpSCLeksPbJFJOj5dZRZXl5ek/evv/46ycnJbN68mWuuuQbDMHjhhRd4/PHHmT59OgBvvPEGKSkpvP3229xzzz3Y7XZee+013nzzTSZMmADAkiVLyMjIYMWKFVx//fXs2rWLvLw81q9fz+jRowFYtGgROTk57Nmzh8GDB3fthQeB7Uc8nVoHJEdrhJl0qdgwM0N6RbD7aB3//OIId1yV7e+QRKQbCKg+RHa750s0ISEBgPz8fEpKSpg0aZKvTFhYGGPHjmXt2rUAbN68mYaGhiZl0tLSGD58uK/MunXrsNlsvmQI4Morr8Rms/nKnMnhcFBZWdnkJa2Xt70EgJx+iX6ORILRxEFxALx7WrOZiMi5BExCZBgGDz/8MF//+tcZPnw4ACUlni/VlJSm1dwpKSm+fSUlJYSGhhIfH3/OMsnJyc3OmZyc7CtzpgULFvj6G9lsNjIyMi7sAoNIrbORj3aVAXDjxWl+jkZ6srNN2njdQBtmE2w9XMHBYzXnOIKIiEfAJEQPPPAAX375JX/605+a7TOZmja5GIbRbNuZzizTUvlzHWfu3LnY7Xbf6/Dhw625jIDhr1E1brebP3+yk7oGF2mxoQxPi+myc0vw8UzauJNFH3xOdZWnE7/hdlNfXsaodM8Q/L+rlkhEWiEgEqIHH3yQ9957j1WrVpGenu7bnpqaCtCsFqesrMxXa5SamorT6aS8vPycZUpLSznT0aNHm9U+eYWFhREbG9vk1Z34a1RNYWEhr3ycD0CUq9q3Cr1IZzlz0kZvktRYeQyAd7eee8kPERHwc0JkGAYPPPAA77zzDitXriQ7u2nnx+zsbFJTU1m+fLlvm9PpZM2aNYwZMwaAUaNGERIS0qRMcXEx27dv95XJycnBbrezYcMGX5nPPvsMu93uK9MT+WNUTbXDRZnT01d/SJqtS88t4hWbkMyQPgmEWU3kH6thd1mdv0MSkQDn11Fm999/P2+//TZ///vfiYmJ8dUE2Ww2IiIiMJlMzJkzh/nz5zNw4EAGDhzI/PnziYyMZMaMGb6yd911F4888giJiYkkJCTw6KOPMmLECN+os6FDhzJ58mRmzZrFK6+8AsDdd9/N1KlTNcKsg63ab8dteCbIiwvT6DLxnxCLia9nxfLRPjvL91YQ6e+ARCSg+TUhevnllwEYN25ck+2vv/46d9xxBwCPPfYYdXV13HfffZSXlzN69Gg+/PBDYmJO9U15/vnnsVqt3HrrrdTV1TF+/HgWL16MxWLxlXnrrbeYPXu2bzTatGnTWLhwYedeYJD504YCnv/Y019jcGoMJlO9nyOSYDdxUBwf7bOzcq+dG/qHYj5P30MRCV5+TYha065vMpmYN28e8+bNO2uZ8PBwXnzxRV588cWzlklISGDJkiXtCTOglNe7cboCa+kBwzCY//4uXv34AAB9bRYuy4ij6njLI/hEusrXMmKIjwzhRG0DJdUW0mL8+itPRAJYQHSqltYpqmrkH3vr+OXqwOqo/Jcvj/uSoe99LYVrMsKwWvRoif9ZLSamjvRM/XCgwnWe0iISzPSt1Y0cqfL8Ql+xt4KyqsBojjpc2chv/1MMwI9uGMJ3L08+75QIIl3p5ks9CVFBZSONbo02E5GWKSHqRo7VeZrKXG74yyb/L1BZ1+Dik8MODOD2r2Uy6+p+/g5JpJnLMuPpHRtCoxsOV6qWSERapoSom3C5DcrrTvUd+tOGAtx+/mv3wNFqGt2QFR/GUzcNU82QBCSTycTEgXEAHKho9G8wIhKwlBB1EwUVDhoNsJohJsxCYXkdH+896teY8k8uiTCuv42QVvQZ8i6zUFBQ0GypBZHO5F3b7EiVi4q6xiYzuXf1bO4iEpg05KKb8E4slxhh5ur+cfz1y+O8/VkB4wY3X6OtKzS6DQ4d98T09ezWzeLtmUG4mj5ZDgr37sCWktmZIYr49I0PJyHczIl6Nyv32YmL8MzkbktKwX6slEduySEzU8+jSDBTDVE3sefoqYToG0MTAFi95yguPzWbFVe7aHQbRIWYGJgU3urPxSYkE5+c1mSpBZGu0C/e8/ff8q8qAM9M7vHJaV0+m7uIBCYlRN3EnrJaABIjLGQnhBFqNeN0uTlS4Z8lCbydU9NjLeo7JN1Cls2CCdhRWkuR3eHvcEQkwCgh6gYaXW72HvMMs0+MMGM2mchM8CxEcOh4bZfH4zYMCk9OAZDRQRPdqX+RdLbIEDOp0Z5feSv2Vvg3GBEJOOpD1A3sLavG6TIIMUNMqKc2pm9CJPvKqjl0ooav07XNTztLa6lvNAi1mEmJ6picWv2LpKN5k2zAl2T3i7NSXO1k+Vd2xqZbznMEEQkmSoi6gW2FdsBTO+RtnuqbGAX4p4Zoc2HNyRgisZg7ribH27/Ifqy0w44pwaulJDsjNppQSwMFFQ6OJ4aT4O8gRSRgqMmsG/iyqALw9B/yykryNJkdPDn0vSvtLPUkYWlxEV1+bpG2OLMTf6jF5BsVma85iUTkNEqIuoHDJzwdp21hpzov+6uGyDAMX0KUGtv60WUigcI7J1G+3eX3yU1FJHAoIeoGyqo8I2IiQk4lRFmJJztVn6jp0l/qh47XYq93YTZBr5iwLjuvSEf5WkY0tnAL9Y0Gh8u7vslZRAKTEqJu4Kg3IbKeSojS4iKwmE3UN7h9CVNX+LygHPD0Z7KYNdxeup8Qi5lr+9sA2FNa5edoRCRQKCEKcI0uN8drvAnRqX+uEIuZ9HhPH55Dx7uuH9GWggoAekXq0ZHu67qTa5vtP1rjt8lNRSSw6FstwB2vcWIYYDFB2BljAv3Rj2jLYU8NUVKkhixL9zWydyQRVhPORjdHql3+DkdEAoASogDnbS6Li7BiPmNGaG8/ooNdVENU62xkV7GniaFXhB4d6b7MJhNZNk9Sr9FmIgJKiAJeWdXJGaqjmk8Z1dU1RNsK7bjcBr2irESF6tGR7i0rzvMzVVjlor5Bq92LBDt9qwW4skpPDVFiZEizfX0TuraGaMvhCgAuSonskvOJdKakCDOx4VYa3bDukDpXiwQ7JUQBzjuCLCGyeQ2Rd3LGQ8dru2Ttr00HTwBKiKRnMJlMDEyJAWDlvgr/BiMifqeEKMB5+xAltpAQpcdHYjJBtaOREzXOTo2jweVm3f7jAFyWHu3bfvqirG63mh0ksHmfV+/aZoNPJkTrDlVRVd/g5+hExJ+UEAW4U32ImjeZhYdYSLN5ht4f7OR+RFsKKqhxukiICmVg0qkZqj3rRe3k2WXrKCws7NQYRC6U93ld9MHnVFdVkxQdSmyoCafLYMUuraEnEsyUEAW4snPUEIFngVXo/LmIPtl7FICvD0hqNtotNiEZW1JKp55fpKPEJiT71jYzmUy+ztX//KLYn2GJiJ8pIQpwR8/RhwhOjTTr7Bqij/ceA+DqgUmdeh6RrpZl8/xsfbz3KBW1ndv0LCKBSwlRADMM47QaouZNZtA1NUQVtU62FVYAcPXAXp12HhF/iAs30z8xnAaXwQc7Svwdjoj4iRKiAFZZ14iz0dNR+Ww1RKcmZ+y8GqK1+4/jNmBgcjSpNq1wLz3PdQM8a5v9Q81mIkFLCVEA83aojg23EmZt+Z/K22RW0Ik1RN7+Q6odkp7KmxCt3X+Mz3ft14hJkSCkhCiAefsPJceevVbG22RWXtuAvbbjhw03uNys2n0yIRqk/kPSM/WxhTEsJRK3AT9+5wuNmBQJQkqIApi3/1ByTNhZy0SGWn37D53o+Fqi/9t0mJLKepKiQ8npl9jhxxfxN+/cRF87OVCyuCHCvwGJiF8oIQpg3iazXudIiOBULVFH9yNyNLp58aN9ANx/7QDCQ9q2wv2Zk+CJBCLv3ET79uzGhMGJejf5J+r9HZaIdDElRAHsaCtqiOC0RV6PdWwN0d93nKCksp7etnBu/1pmmz9/5iR4IoEqNiGZhIQEUk9WDn2wp8Kv8YhI11NCFMBONZmde2RXZ4w0a3AZvPW5p+/Q7PED21w75HX6JHgigS4jylOTufyrCtxu1WqKBBMlRAHMu9L9+ZvMTo4068A+RJ+XOCmvayQrMZL/HpXeYccVCWSpkRBqhqM1DXyy75i/wxGRLqSEKIAdrW5dk1lWB89WXVrjYs+JRgB+fssIQix6TCQ4WEzQL94z59eb6w75ORoR6Ur6pgtgZZWt61SdebLJ7GiVgxpH4wWd09HoZm2hJxGbOjSeqwaouUuCy+AEz6zwK3eXUljeuUviiEjgUEIUoByNLirrPcnN+RIiW0QI8SeX9jh0gbVE72w7TpXTIMJq4vtjel/QsUS6I1u4mVHpUbgNePuzAn+HIyJdRAlRgDpW7VlkMsRiwhbR8jpmp/ONNLuAGasNwyBvTzkAFyeHEBPWvo7UIt3dzcM8c279eeNhHI0uP0cjIl1BCVGAOnZyhFlSdBgmk+m85b0jzQ5cwND7HUcqyT/hwGyCrLiW1047G805JD3JVdmx9LaFc7zGyT+1vplIUFBCFKCOVZ9KiFpjUGoMADuLK9t9znc+LwIgI9ZCiMmgqKiIgoKCVq3rpDmHpKcw3G5Ki49w41DP+ma/WbmXBpfnZ8DtdlNQUOB7ac0zkZ7DrwnRxx9/zI033khaWhomk4l33323yX7DMJg3bx5paWlEREQwbtw4duzY0aSMw+HgwQcfJCkpiaioKKZNm9ZsHaLy8nJyc3Ox2WzYbDZyc3OpqKjo5Ku7MN5JGc/Xf8hrZJ84ALYX2dt1vgaXm/e+8CRE/eOsvgTn2WXrWr2uk+Yckp7A++xXVVYRanZz6Hgtf93s+RkoLCzk2WXr+P0nB9r0syEigc+vCVFNTQ0XX3wxCxcubHH/M888w3PPPcfChQvZuHEjqampTJw4kaqqKl+ZOXPmsGzZMpYuXcqnn35KdXU1U6dOxeU61e4/Y8YMtm7dSl5eHnl5eWzdupXc3NxOv74LcaqGKLRV5Uf08fw1e+h4bbsWef1k71GOVTuJi7CQFuPpOxSbkIwtKaXNxxLp7mITkknu3YeLUzyTov7mo73UN3h+p9iSUohPTtPPhkgP07aOIh1sypQpTJkypcV9hmHwwgsv8PjjjzN9+nQA3njjDVJSUnj77be55557sNvtvPbaa7z55ptMmDABgCVLlpCRkcGKFSu4/vrr2bVrF3l5eaxfv57Ro0cDsGjRInJyctizZw+DBw/umottI2+n6tY2mdkiQ8hMiKTgRC3biuxktu5jPu9uOQLAhIFxmA2t4yQCMCjByqEqg2J7PUvWH2JSX7/+yhSRThSwfYjy8/MpKSlh0qRJvm1hYWGMHTuWtWvXArB582YaGhqalElLS2P48OG+MuvWrcNms/mSIYArr7wSm83mK9MSh8NBZWVlk1dXamuTGcCIdE8t0bY2Npu53Qaf7PUs03Ftf1ubPivSk1nMJu64PBmAF1bspbTK6eeIRKSzBGxCVFJSAkBKStNq6ZSUFN++kpISQkNDiY+PP2eZ5OTkZsdPTk72lWnJggULfH2ObDYbGRkZF3Q9bXW0jZ2q4VSz2baiijada2dxJeW1DUSFWhiaHNmmz4r0dFOGxDOqbzzVjkZ+sUqjKEV6qoBNiLzOHHJuGMZ5h6GfWaal8uc7zty5c7Hb7b7X4cOH2xj5hWnrKDOAkX3aV0O0dr9nzabR/RKxWs4/xF8kmFjMJp7575GEWc1sKqxmX/mFzQYvIoEpYBOi1NRUgGa1OGVlZb5ao9TUVJxOJ+Xl5ecsU1pa2uz4R48ebVb7dLqwsDBiY2ObvLpSe5rMhp1MiA6fqMNe3/pf2v/ZdxxAy3SInEX/XtE8OsnT33BTsZOKWjWdifQ0AZsQZWdnk5qayvLly33bnE4na9asYcyYMQCMGjWKkJCQJmWKi4vZvn27r0xOTg52u50NGzb4ynz22WfY7XZfmUBT3+CiyrtsRxtqiGwRIb4JGr86Wteqzzgb3WzIPwHAVQMS2xipSM/mnXC0oKCAO8b0ZWTvSBrckLejBJfL1aa5ukQksPl1yER1dTX79u3zvc/Pz2fr1q0kJCSQmZnJnDlzmD9/PgMHDmTgwIHMnz+fyMhIZsyYAYDNZuOuu+7ikUceITExkYSEBB599FFGjBjhG3U2dOhQJk+ezKxZs3jllVcAuPvuu5k6dWoAjzDz1A6FWszERrTtn2h4HxsHj9eyp6x1CdGWgnLqGlwkRYcyOCWGw4cr2hquSI/lmZOomujoAh65JYcnJmTwnbf2UFrpYMMhJyWHD/r2ZWZm+jtcEbkAfk2INm3axLXXXut7//DDDwMwc+ZMFi9ezGOPPUZdXR333Xcf5eXljB49mg8//JCYmBjfZ55//nmsViu33nordXV1jB8/nsWLF2OxnFqH66233mL27Nm+0WjTpk0769xHgeDUkPvQVi3bcbqR6Tb++WUxu8rqyI4+f/n/7Pc0l43pn9Tmc4kEg9iEZGJiPb9zUmNCyUkPY02Bg701IaSmJGNLijnPEUSkO/BrQjRu3LhzjtgwmUzMmzePefPmnbVMeHg4L774Ii+++OJZyyQkJLBkyZILCbVL+dYxa0P/Ia/LsxIA+LyomsyB4VjM505y/rPP06FazWUirdPXZuWi3mHsLK7k8+OQkaRRZyI9gWYZC0DeIfdt6T/kdUl6HCmxYZRWOiiudpEe6/kndrvdTZYZSE9Pp8rh4ouTTWRj+qtDtcjZePsSgWeE6jWDkjhQWkFNo5kvSts+M7yIBB4lRAHo9JXu28psNjF5WCpvrDvEocpTCZF3DSZbUgr2Y6U8cksOW8utNLoNBiRHk5Gg+YdEzsbbl8hoqMeWkklCioWLbQ4+K49g57EGdpfVoi5EIt1bwI4yC2a+OYhiWreO2ekrcLvdbiYP7w3A4cpG3Kc1SZ65BtNHuzzTEUwYqjWZRM7nzMWLU8NdpEcaGMBzHx/B7VbTmUh3poQoALW1ycxb++Ndfftr2QnERVhwuqCkuuXhwI0ug1W7ywCYMLT5TN4icn4jEgxCzLC7rI6/bi48/wdEJGApIQpAx06ul9RSp+rT50U5fe4TW1KKr+bHYjZxdbZnksaCypYnaNxWUkNlfSMJUaFcmhnfYhkRObdwC1yc7KnJ/UXebux16k8k0l0pIQpA51q2w9OXYaevNuhsxvbzzKxdUNmI09W8lug/B6sAGDe413lHoonI2Q1OtJIZF8bxGie/XrHX3+GISDspIQpA51u2IzYh2VcbdDaX9YkmwmqivhHe23Gi2f61BysBmNiG/kPevkpFRVrgUsTLYjbx0NWefntvrDvIV6VVfo5IRNpDCVGAqW9wUeXwNHO1Z5SZl9Vi4uKUEAD+uLmMWqfLt+9EnYtCu5NQi5mrB/Vq9TG9fZUWffA51VXV7Y5NpKe5IiOGSRel4HIb/PQfO/QHg0g3pIQowPiW7bCaiQ2/sFkRBsRbiQk1UVHn4i9feiZgdLsN1hd5+ihNuCiZ6LBzn+P0PktFRUXEJjYdaSMS7Lw/I3deGkeo1cx/9h0nb3vJ+T8oIgFF8xAFmNLKesAzwuxCl9Iwm0xckhLKJ4cd/GnLMcb0CWFfbTnH6txEh5r5ydRh5z2Gd/6VPlkOCvfuwJaiyVZETuf9GYmKjGRqv2Te+aqeeX/fxtcHJBIT0bqpM0TE/1RDFGAKyz2LsqbHR3TI8bJsFoYkR1Db4GbFQQdrT65dNvvraaTawlt1jNiEZOKT01QzJHIWsQnJmMxmThw5SFSIidLqBp7462Z/hyUibaCEKMCcSog6ZuZok8nEM9/I4taLE7GcrHBKj7Fw/eC4Djm+iJwSn5jM9cPTAPj7jhOs+eqonyMSkdZSQhRgCstrAchI6JgaIoC4CCsPXJXG9MERXD8shaszL7w5TkRalpEQyZBET2+Ex/76BRW1Tj9HJCKtoYQowHR0DdHpIkLMDEmNJUTzDol0qstSQ8mIC6W00sH3l3xOQwtzgYlIYFFCFGA6ug+RiHQ9q9nEU9f3JSrUwroDx/nxu9s1FF8kwCkhCiBut0GREiKRHqF/Yji//tYlmE2wdONhfrZsU5PldkQksCghCiBHqx04XW4sZhOpsa0bASYigWtQtJOLYjxzi722oYxHF6/i0KFDSoxEApASogBy+ISnQ3VvWzhWi/5pRLor72SNRUVFXJoZz1UDEgF456t67vzj5xwqONykvHdZnDMXbRaRrqNv3QDi7T+U0QkdqkWk63gXYfYuc3N53wSGx3pqivbXhDL3/UNU1jf4ynuXxTnfos0i0nmUEAUQ75B7b/+h0/9q1IKqIt1LbELTZW76RzVweZIbiwnWF1Rx08L/8MXhCt9+W1LKeRdtFpHOo6U7AsiZQ+69fzXaklI6ZdkMt9vt+2s0PT0ds1n5sUhnyoiC5Ngw1h9pIP9YDdNfXsv91w7gpgFa4kPE3/QNGEBaGnJvS0rptGUzVE0v0vVC6o4z3FpMn/AGXG6D33y0l7v+bx+lNS5/hyYS1FRDFEDObDLrCqqiF+l6iYm9yIqNITs1gYVrSzlY7uBgOQyIt/LN+kZ/hycSlFRDFCDcboOiipM1RAnqVC0SDMYPjOOjR8Zy40XxAOwrb+T2JbtZtPwLXC7VGIl0JSVEAaKsykGDy8DayjmIvMN61eFapHuLiwzlf8elM7lfODFWF9VOg59/VMgdv1/L8WqHv8MTCRpKiALE4ZPNZWlxEVhasdaYd1jv7z854BvaKyLdV3KUhXFJdVwU58Zsgk/yK5nw7Gr+9PF2zU0k0gWUEAWI9vQfik1I7rQO1yLS9cwmGGyDaxJriTI3UF7XyNz3D/Hgm+updqhvkUhnUkIUIPaVeWp4Mv3Qf0jNbyJd71w/d7YQN+PTLVyWGQfAv3aVM+XXH7Mh/4SfohXp+TTKLEBsPlQOwCUZcV1+bk/zWzV9shydMt+RiDR3vp87iwmuHtCLJEs9Xx5zcfhEHbe9uo7vXtmXhyYMIiFKcxeJdCTVEAWABpebLw7bARjVN94vMaj5TaTrtebnLiXSxM+vjuaGIXEYBryx7hBjf7mKVz/ej6NRI9FEOopqiALA7uIq6hpcxIZb6d8r2t/hiEgAqTxxlLcLqjEa6rk0Ipb9DTYq62H++7t5/ZP93D06hWsHxpGZkaHZ5kUugH56AsDmQ55+AZf1jcdsNvnWMFN/HhGBU+uiZSXHMS6pjqHhdiKsJoqrGvjpikJuWvQFf/nPTv2+ELkASogCwOcFFQCMyvQ0l3mX1NBwehE5k8kEQ1Ji+J+v92NwtAOrycDeaOEH/zrEf/9uHWv3H/N3iCLdkprMAoC3Q/Xp/Ye0pIaInEuIxcyQmAb6x4VwsC6EveWNbD5UzoxFn3FFVjz3ju3PtYOTMbdiXjMRUULkdyX2eooq6jCb4OIuGmHmHe4LqIpdpJsLs8DA0AoSImo5Gp7GV8cb2HiwnI0HN5GdFMVtV2Qw/bI+JMecfwZ8kWCmhMjPPi/w1A4NSY0lKqxr/jm8w32NhnoNsRfpIXolJTFqQDZ9t21ld7nBkcYo8o/V8PS/d/NM3m6uyErg+mGp5PRPZFBKTKtmxBcJJkqI/Kyl5rKuEJuQjNtZ26XnFJHOF2ExGJUWyfD6Gg5UuCh1R1HeYOWz/BN8dnJix+gwC0N6RTA8NZKvX5TB0DQbabZwTCYlSRK8lBD5WWcnRGoeEwlOIWYYmhrDYGctRyvrcMb24eDxWqpdVqodLjYVVrOpsJrFm8oAiAo1MyQ1lkEpMSSHN9IvIZyrhvcjSU1tEiSUEPnZT268iE0HT3Blv0TcbjeFhYUdOtz+zOaxBPXVFgk6KUmJZA7oS5+dW6iqqqba6cIZ3ZtjdS7KHVDrDqHG6WZzQQWbT456BeDv+SREWumXEM7IvkkMSY1lcGoMA1OiiQzV14f0LHqi/eyyzHguOzncvqCggGeXraOq/FiH9u1R85iIeNkSk4lx1mIOhb5hDsyhkfTpN4Ad276krNpJdQM4Qm1UOg1qXWZO1DZyotZTm+RlAvrYQhmQGM6ApAiuHJLO0DQbqbHh6psk3VZQJUQvvfQSv/zlLykuLmbYsGG88MILXH311X6NyVsrBFBUVERsYnK7j3V685gmdRSR1rKYTcSGuIlLicXtrMUcauB21uK2RhKZ0pc9e/dyvNZFtctMjRGK07BQaHdSaHey+kAlv99QCkCoxURaXATJMeEkxYSSGBVKqLue+IgQBmak0Cs2nF7RYSRFhxERavHzVYs0FTQJ0Z///GfmzJnDSy+9xFVXXcUrr7zClClT2LlzJ5mZ/htp5Z2E0ZaUcsELq2qRVhHpSFYzpNrCqY9sJDsu8mSyZCIpM5vtO3ZS5Q6jot5FuRNq3VacLjh4vJaDx1uqkS5q8i4ixEx8hJWU6BD6944jIz6K9PgI+sRHkBQdSnxkKHGRoapxki4TNAnRc889x1133cX3vvc9AF544QU++OADXn75ZRYsWODX2GxJKcQnp2E/VnrBx/IuFtkRxxIRaUlkqJXkMBepoeCO8DS7NTpqOF5VS3RyJkWFBTSYw3E0uiHCRn2Di7oGNy5LGHUNbtyYqGtwU9fg5Eilky1Halo8jwmICbMQGWomIsRMTGQ4kSEWTO4Gwq1mwqwmQiwmQi1mEuJiCbWaCbGcfJlN1FTZsVpMJCclEh5iObXv5GdCrKfeh53+WYv55H4TIRYzVrNJI/CCQFAkRE6nk82bN/PDH/6wyfZJkyaxdu1aP0UlItJzmE2QkpREZr8MIuqPYQ6NPK35rR5zaCSZAwaQv2ML7pBI6urrcJojqHE4qahtwBwVz3F7FQ4jBKdhohELBlDpcFHpcHlOcsJxjgiOn2PfkQu+vhCzJ/myWkyEmE1Yve9P/tdsAhMmTCbP8irmkwmUZzsnt5uICD/Vz8rpqAfjVPnIyAhMpqbHqq+r83yW04/jKR8VFeU7Vl1tDSZMREdHYTGbz4jBdPK9JwbfPsOguroKE2Cz2XzlzmTizI0GlZV2AGJjbZ5jtnDPzjxWa5LKaRenkZEQed5ynSEoEqJjx47hcrlISWk6xColJYWSkpIWP+NwOHA4Tv3w2e2ef/zKysoOja2qqoqywnwctTUcLzmMyRqO0Vjf5L+hVnOb9rW1vM4TODHoWrvveQIhhu5wnhOlTfdFNtYTZQ0nLSGGooqDvn2GJZzGRicN5gjiU/tQUlhAbb0Dl8tFWEwCUfFJnDh2FMNswe1y4WhoxHC7wWwlNDKa2upqrOERuN0GDY0u3IYBZguW0DCcDgeGyYLbAMPkWdLTjRnMFtxuw7etyXfCyZd0rn42MzZrUoce0/u9fb5+tUGREHmdmZ0ahnHWjHXBggX89Kc/bbY9IyOjU2ITEREJdje80HnHrqqqwmaznXV/UCRESUlJWCyWZrVBZWVlzWqNvObOncvDDz/se+92uzlx4gSJiYl+a0uurKwkIyODw4cPExsb65cYApnuz7np/pyd7s256f6cm+7P2QXCvTEMg6qqKtLS0s5ZLigSotDQUEaNGsXy5cu55ZZbfNuXL1/OTTfd1OJnwsLCCAsLa7ItLi6uM8NstdjYWP3QnYPuz7np/pyd7s256f6cm+7P2fn73pyrZsgrKBIigIcffpjc3Fwuv/xycnJyePXVVykoKODee+/1d2giIiLiZ0GTEN12220cP36cp556iuLiYoYPH877779P3759/R2aiIiI+FnQJEQA9913H/fdd5+/w2i3sLAwnnzyyWZNeeKh+3Nuuj9np3tzbro/56b7c3bd6d6YDK3vICIiIkGu+WQLIiIiIkFGCZGIiIgEPSVEIiIiEvSUEImIiEjQU0LUjbz00ktkZ2cTHh7OqFGj+OSTT/wdUkCYN2/eyQULT71SU1P9HZZffPzxx9x4442kpaVhMpl49913m+w3DIN58+aRlpZGREQE48aNY8eOHf4J1g/Od3/uuOOOZs/SlVde6Z9gu9iCBQu44ooriImJITk5mZtvvpk9e/Y0KRPMz09r7k8wPz8vv/wyI0eO9E3AmJOTw7///W/f/u7w7Cgh6ib+/Oc/M2fOHB5//HG2bNnC1VdfzZQpUygoKPB3aAFh2LBhFBcX+17btm3zd0h+UVNTw8UXX8zChQtb3P/MM8/w3HPPsXDhQjZu3EhqaioTJ06kqqqqiyP1j/PdH4DJkyc3eZbef//9LozQf9asWcP999/P+vXrWb58OY2NjUyaNImamhpfmWB+flpzfyB4n5/09HSefvppNm3axKZNm7juuuu46aabfElPt3h2DOkWvva1rxn33ntvk21DhgwxfvjDH/oposDx5JNPGhdffLG/wwg4gLFs2TLfe7fbbaSmphpPP/20b1t9fb1hs9mM3/3ud36I0L/OvD+GYRgzZ840brrpJr/EE2jKysoMwFizZo1hGHp+znTm/TEMPT9nio+PN37/+993m2dHNUTdgNPpZPPmzUyaNKnJ9kmTJrF27Vo/RRVY9u7dS1paGtnZ2XzrW9/iwIED/g4p4OTn51NSUtLkOQoLC2Ps2LF6jk6zevVqkpOTGTRoELNmzaKsrMzfIfmF3W4HICEhAdDzc6Yz74+Xnh9wuVwsXbqUmpoacnJyus2zo4SoGzh27Bgul4uUlJQm21NSUigpKfFTVIFj9OjR/PGPf+SDDz5g0aJFlJSUMGbMGI4fP+7v0AKK91nRc3R2U6ZM4a233mLlypU8++yzbNy4keuuuw6Hw+Hv0LqUYRg8/PDDfP3rX2f48OGAnp/TtXR/QM/Ptm3biI6OJiwsjHvvvZdly5Zx0UUXdZtnJ6iW7ujuTCZTk/eGYTTbFoymTJni+/8RI0aQk5ND//79eeONN3j44Yf9GFlg0nN0drfddpvv/4cPH87ll19O3759+de//sX06dP9GFnXeuCBB/jyyy/59NNPm+3T83P2+xPsz8/gwYPZunUrFRUV/O1vf2PmzJmsWbPGtz/Qnx3VEHUDSUlJWCyWZpl0WVlZs4xbICoqihEjRrB3715/hxJQvCPv9By1Xu/evenbt29QPUsPPvgg7733HqtWrSI9Pd23Xc+Px9nuT0uC7fkJDQ1lwIABXH755SxYsICLL76YX//6193m2VFC1A2EhoYyatQoli9f3mT78uXLGTNmjJ+iClwOh4Ndu3bRu3dvf4cSULKzs0lNTW3yHDmdTtasWaPn6CyOHz/O4cOHg+JZMgyDBx54gHfeeYeVK1eSnZ3dZH+wPz/nuz8tCabnpyWGYeBwOLrPs+O37tzSJkuXLjVCQkKM1157zdi5c6cxZ84cIyoqyjh48KC/Q/O7Rx55xFi9erVx4MABY/369cbUqVONmJiYoLw3VVVVxpYtW4wtW7YYgPHcc88ZW7ZsMQ4dOmQYhmE8/fTThs1mM9555x1j27Ztxu2332707t3bqKys9HPkXeNc96eqqsp45JFHjLVr1xr5+fnGqlWrjJycHKNPnz5BcX++//3vGzabzVi9erVRXFzse9XW1vrKBPPzc777E+zPz9y5c42PP/7YyM/PN7788kvjRz/6kWE2m40PP/zQMIzu8ewoIepGfvvb3xp9+/Y1QkNDjcsuu6zJcM9gdttttxm9e/c2QkJCjLS0NGP69OnGjh07/B2WX6xatcoAmr1mzpxpGIZn6PSTTz5ppKamGmFhYcY111xjbNu2zb9Bd6Fz3Z/a2lpj0qRJRq9evYyQkBAjMzPTmDlzplFQUODvsLtES/cFMF5//XVfmWB+fs53f4L9+bnzzjt930+9evUyxo8f70uGDKN7PDsmwzCMrquPEhEREQk86kMkIiIiQU8JkYiIiAQ9JUQiIiIS9JQQiYiISNBTQiQiIiJBTwmRiIiIBD0lRCIiIhL0lBCJSJfIysrihRde6JBjHTx4EJPJxNatWy/oOOPGjWPOnDkdEtO5zJs3j0suuaTTz3M+JpOJd999199hiAQkJUQiIiIS9JQQiYgEMKfT6e8QRIKCEiIRaVFLTVyXXHIJ8+bNAzzNQJmZmYSFhZGWlsbs2bN95crKyrjxxhuJiIggOzubt956q03nNplMvPzyy0yZMsV3jL/85S/n/MzOnTu54YYbiI6OJiUlhdzcXI4dO+bbX1NTw3e/+12io6Pp3bs3zz77bLNjFBcX841vfMN3zrfffrvZfbDb7dx9990kJycTGxvLddddxxdffNHkOE8//TQpKSnExMRw1113UV9f3+prv+OOO7j55ptZsGABaWlpDBo0CICioiJuu+024uPjSUxM5KabbuLgwYO+z23cuJGJEyeSlJSEzWZj7NixfP75560+r0iwU0IkIm3217/+leeff55XXnmFvXv38u677zJixAjf/jvuuIODBw+ycuVK/vrXv/LSSy9RVlbWpnP8+Mc/5r/+67/44osv+M53vsPtt9/Orl27WixbXFzM2LFjueSSS9i0aRN5eXmUlpZy6623+sr87//+L6tWrWLZsmV8+OGHrF69ms2bNzc5zne/+12OHDnC6tWr+dvf/sarr77aJG7DMPjGN75BSUkJ77//Pps3b+ayyy5j/PjxnDhxAoD/+7//48knn+TnP/85mzZtonfv3rz00kttuvaPPvqIXbt2sXz5cv75z39SW1vLtddeS3R0NB9//DGffvop0dHRTJ482VeDVFVVxcyZM/nkk09Yv349AwcO5IYbbqCqqqpN5xYJWn5eXFZEAlTfvn2N559/vsm2iy++2HjyySeNZ5991hg0aJDhdDqbfW7Pnj0GYKxfv963bdeuXQbQ7HhnAxj33ntvk22jR482vv/97xuGYRj5+fkGYGzZssUwDMP48Y9/bEyaNKlJ+cOHDxuAsWfPHqOqqsoIDQ01li5d6tt//PhxIyIiwnjooYeaxLhx40Zfmb179zaJ+6OPPjJiY2ON+vr6Jufq37+/8corrxiGYRg5OTktxn7xxRe36tpnzpxppKSkGA6Hw7fttddeMwYPHmy43W7fNofDYURERBgffPBBi8dpbGw0YmJijH/84x++bYCxbNmyVsUhEmxUQyQibfbNb36Turo6+vXrx6xZs1i2bBmNjY0A7Nq1C6vVyuWXX+4rP2TIEOLi4tp0jpycnGbvz1ZDtHnzZlatWkV0dLTvNWTIEAD279/P/v37cTqdTY6ZkJDA4MGDfe/37NmD1Wrlsssu820bMGAA8fHxTc5TXV1NYmJik3Pl5+ezf/9+3/W3FHtbjBgxgtDQ0Cbn3bdvHzExMb5zJiQkUF9f7ztvWVkZ9957L4MGDcJms2Gz2aiurqagoKBN5xYJVlZ/ByAigclsNmMYRpNtDQ0NAGRkZLBnzx6WL1/OihUruO+++/jlL3/JmjVrfJ8xmUwdHtPZjul2u7nxxhv5xS9+0Wxf79692bt373mPfea1trTd7XbTu3dvVq9e3axcWxO+c4mKimry3u12M2rUqBb7YvXq1QvwNFMePXqUF154gb59+xIWFkZOTo46ZYu0kmqIRKRFvXr1ori42Pe+srKS/Px83/uIiAimTZvGb37zG1avXs26devYtm0bQ4cOpbGxkU2bNvnK7tmzh4qKijadf/369c3ee2t9znTZZZexY8cOsrKyGDBgQJNXVFQUAwYMICQkpMkxy8vL+eqrr3zvhwwZQmNjI1u2bPFt27dvX5O4L7vsMkpKSrBarc3Ok5SUBMDQoUNbjP1CXHbZZezdu5fk5ORm57XZbAB88sknzJ49mxtuuIFhw4YRFhbWpFO5iJybEiIRadF1113Hm2++ySeffML27duZOXMmFosFgMWLF/Paa6+xfft2Dhw4wJtvvklERAR9+/Zl8ODBTJ48mVmzZvHZZ5+xefNmvve97xEREdGm8//lL3/hD3/4A1999RVPPvkkGzZs4IEHHmix7P3338+JEye4/fbb2bBhAwcOHODDDz/kzjvvxOVyER0dzV133cX//u//8tFHH7F9+3buuOMOzOZTvwKHDBnChAkTuPvuu9mwYQNbtmzh7rvvJiIiwlczNWHCBHJycrj55pv54IMPOHjwIGvXruWJJ57wJYAPPfQQf/jDH5rEvmPHjvb8E/h8+9vfJikpiZtuuolPPvmE/Px81qxZw0MPPURhYSHgad5788032bVrF5999hnf/va323zPRYKZEiIRadHcuXO55pprmDp1KjfccAM333wz/fv3BzzNQ4sWLeKqq65i5MiRfPTRR/zjH/8gMTERgNdff52MjAzGjh3L9OnTfcPU2+KnP/0pS5cuZeTIkbzxxhu89dZbXHTRRS2WTUtL4z//+Q8ul4vrr7+e4cOH89BDD2Gz2XxJzy9/+UuuueYapk2bxoQJE/j617/OqFGjmhznj3/8IykpKVxzzTXccsstzJo1i5iYGMLDwwFPk93777/PNddcw5133smgQYP41re+xcGDB0lJSQHgtttu4yc/+Qk/+MEPGDVqFIcOHeL73/9+m679TJGRkXz88cdkZmYyffp0hg4dyp133kldXR2xsbEA/OEPf6C8vJxLL72U3NxcZs+e3eZ7LhLMTMbZGs5FRPzEZDKxbNkybr75Zr/GUVhYSEZGBitWrGD8+PF+jUVEOpc6VYuInLRy5Uqqq6sZMWIExcXFPPbYY2RlZXHNNdf4OzQR6WRqMhORLvXWW281GbJ++mvYsGF+ja2hoYEf/ehHDBs2jFtuuYVevXqxevVqQkJCOuwcZ7v26OhoPvnkkw47j4i0jZrMRKRLVVVVUVpa2uK+kJAQ+vbt28URda19+/addV+fPn3UEVrET5QQiYiISNBTk5mIiIgEPSVEIiIiEvSUEImIiEjQU0IkIiIiQU8JkYiIiAQ9JUQiIiIS9JQQiYiISNBTQiQiIiJB7/8HyaJDdpC3nkkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot normalized data\n",
    "ax = sns.histplot(normalized_pledges, kde=True)\n",
    "ax.set_title(\"Normalized data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de968121",
   "metadata": {
    "papermill": {
     "duration": 0.004828,
     "end_time": "2023-06-25T05:02:52.151511",
     "exception": false,
     "start_time": "2023-06-25T05:02:52.146683",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We used the \"usd_pledged_real\" column. Follow the same process to normalize the \"pledged\" column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f0df6b52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:52.162667Z",
     "iopub.status.busy": "2023-06-25T05:02:52.162374Z",
     "iopub.status.idle": "2023-06-25T05:02:54.038019Z",
     "shell.execute_reply": "2023-06-25T05:02:54.036725Z"
    },
    "papermill": {
     "duration": 1.883777,
     "end_time": "2023-06-25T05:02:54.040209",
     "exception": false,
     "start_time": "2023-06-25T05:02:52.156432",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data\n",
      "Preview:\n",
      " 1     2421.0\n",
      "2      220.0\n",
      "3        1.0\n",
      "4     1283.0\n",
      "5    52375.0\n",
      "Name: pledged, dtype: float64\n",
      "Minimum value: 1.0 \n",
      "Maximum value: 20338986.27\n",
      "______________________________\n",
      "\n",
      "Normalized data\n",
      "Preview:\n",
      " 1    10.013887\n",
      "2     6.403367\n",
      "3     0.000000\n",
      "4     9.005193\n",
      "5    15.499596\n",
      "Name: pledged, dtype: float64\n",
      "Minimum value: 0.0 \n",
      "Maximum value: 29.63030787418848\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index_of_positive_pledges = kickstarters_2017.pledged > 0\n",
    "positive_pledges = kickstarters_2017.pledged.loc[index_of_positive_pledges]\n",
    "normalized_pledges = pd.Series(stats.boxcox(positive_pledges)[0], \n",
    "                               name='pledged', index=positive_pledges.index)\n",
    "\n",
    "print('Original data\\nPreview:\\n', positive_pledges.head())\n",
    "print('Minimum value:', float(positive_pledges.min()),\n",
    "      '\\nMaximum value:', float(positive_pledges.max()))\n",
    "print('_'*30)\n",
    "\n",
    "print('\\nNormalized data\\nPreview:\\n', normalized_pledges.head())\n",
    "print('Minimum value:', float(normalized_pledges.min()),\n",
    "      '\\nMaximum value:', float(normalized_pledges.max()))\n",
    "ax = sns.histplot(normalized_pledges, kde=True)\n",
    "ax.set_title(\"Normalized data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e999b30",
   "metadata": {
    "papermill": {
     "duration": 0.005423,
     "end_time": "2023-06-25T05:02:54.051472",
     "exception": false,
     "start_time": "2023-06-25T05:02:54.046049",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "How does the normalized \"usd_pledged_real\" column look different from when we normalized the \"pledged\" column?  Or, do they look mostly the same?\n",
    "\n",
    "Once you have an answer, run the code cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb996ee7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:54.064270Z",
     "iopub.status.busy": "2023-06-25T05:02:54.063916Z",
     "iopub.status.idle": "2023-06-25T05:02:54.072300Z",
     "shell.execute_reply": "2023-06-25T05:02:54.071071Z"
    },
    "papermill": {
     "duration": 0.017329,
     "end_time": "2023-06-25T05:02:54.074291",
     "exception": false,
     "start_time": "2023-06-25T05:02:54.056962",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"2_TryNormalization\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> \n",
       "\n",
       "The distributions in the normalized data look mostly the same."
      ],
      "text/plain": [
       "Correct: \n",
       "\n",
       "The distributions in the normalized data look mostly the same."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "q2.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7d92036",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-25T05:02:54.087450Z",
     "iopub.status.busy": "2023-06-25T05:02:54.087128Z",
     "iopub.status.idle": "2023-06-25T05:02:54.091040Z",
     "shell.execute_reply": "2023-06-25T05:02:54.090230Z"
    },
    "papermill": {
     "duration": 0.012442,
     "end_time": "2023-06-25T05:02:54.092788",
     "exception": false,
     "start_time": "2023-06-25T05:02:54.080346",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Line below will give you a hint\n",
    "#q2.hint()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "645967fb",
   "metadata": {
    "papermill": {
     "duration": 0.005404,
     "end_time": "2023-06-25T05:02:54.104094",
     "exception": false,
     "start_time": "2023-06-25T05:02:54.098690",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# (Optional) More practice\n",
    "\n",
    "Try finding a new dataset and pretend you're preparing to perform a [regression analysis](https://www.kaggle.com/rtatman/the-5-day-regression-challenge). \n",
    "\n",
    "[These datasets are a good start!](https://www.kaggle.com/rtatman/datasets-for-regression-analysis)\n",
    "\n",
    "Pick three or four variables and decide if you need to normalize or scale any of them and, if you think you should, practice applying the correct technique.\n",
    "\n",
    "# Keep going\n",
    "\n",
    "In the next lesson, learn how to [**parse dates**](https://www.kaggle.com/alexisbcook/parsing-dates) in a dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e849fd8",
   "metadata": {
    "papermill": {
     "duration": 0.005312,
     "end_time": "2023-06-25T05:02:54.114926",
     "exception": false,
     "start_time": "2023-06-25T05:02:54.109614",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/data-cleaning/discussion) to chat with other learners.*"
   ]
  }
 ],
 "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": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 18.51256,
   "end_time": "2023-06-25T05:02:55.041749",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-06-25T05:02:36.529189",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}