no-show-medical-appointments-brazil.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Project: Investigating no-show medical appointments in Brazil\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#structure\">Data Structure</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "This dataset collects information from 100k medical appointments in Vitoria, Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row.\n",
    "\n",
    "In this analysis, I'll try to evaluate:\n",
    "\n",
    "- What are the proportions of each attribute in the sample;\n",
    "- What are the waiting times, the biggest and lowest waiting times by neighbourhood;\n",
    "- The neighbourhood with the most and the littlest appointments;\n",
    "- Verify correlation between waiting times and total appointments;\n",
    "- The distribution of appointments by weekday;\n",
    "- Attendance rates: what are the general attendance rate, and how each attribute affects the attendance rate;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='structure'></a>\n",
    "## Data Structure: ##\n",
    "\n",
    "The received CSV has the following structure:\n",
    "\n",
    "- PatientId: Identification of a patient \n",
    "- AppointmentID: Identification of each appointment \n",
    "- Gender: M or F\n",
    "- ScheduledDay: The date someone requested the appointment.\n",
    "- AppointmentDay: Actual Appointment Date - **Does not contain appointment time.**\n",
    "- Age: How old is the patient. \n",
    "- Neighbourhood: Location where the appointment takes place. \n",
    "- Scholarship: 0 (False) or 1 (True) - Indicates if the patient's family is enrolled in Brazilian welfare program [Bolsa Familia](https://en.wikipedia.org/wiki/Bolsa_Fam%C3%ADlia)\n",
    "- Hipertension: 0 (False) or 1 (True)\n",
    "- Diabetes: 0 (False) or 1 (True)\n",
    "- Alcoholism: 0 (False) or 1 (True)\n",
    "- Handcap:0 (False) or 1 (True)\n",
    "- SMS_received: 1 or more messages sent to the patient. \n",
    "- No-show: Yes or No. (Yes if patient **did not**  attend, no if patient **did** attend to consultation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "file='./noshowappointments-kagglev2-may-2016.csv'\n",
    "no_show_appointments = pd.read_csv(file)\n",
    "\n",
    "no_show_appointments.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         float64\n",
       "AppointmentID       int64\n",
       "Gender             object\n",
       "ScheduledDay       object\n",
       "AppointmentDay     object\n",
       "Age                 int64\n",
       "Neighbourhood      object\n",
       "Scholarship         int64\n",
       "Hipertension        int64\n",
       "Diabetes            int64\n",
       "Alcoholism          int64\n",
       "Handcap             int64\n",
       "SMS_received        int64\n",
       "No-show            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_appointments.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         0\n",
       "AppointmentID     0\n",
       "Gender            0\n",
       "ScheduledDay      0\n",
       "AppointmentDay    0\n",
       "Age               0\n",
       "Neighbourhood     0\n",
       "Scholarship       0\n",
       "Hipertension      0\n",
       "Diabetes          0\n",
       "Alcoholism        0\n",
       "Handcap           0\n",
       "SMS_received      0\n",
       "No-show           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are any NaN values\n",
    "no_show_appointments.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, let's start by correcting the column name 'Handcap' to 'Handicap'\n",
    "# Also, change the column names 'SMS_received', 'AppointmentID', and 'No-show' to CamelCase for consistency:\n",
    "\n",
    "no_show_appointments.rename({'Handcap': 'Handicap', 'SMS_received': 'SMSReceived', 'No-show':'NoShow', 'AppointmentID': 'AppointmentId'}, axis='columns', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentId</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SMSReceived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5.675305e+06</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>7.129575e+04</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172614e+12</td>\n",
       "      <td>5.640286e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680573e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.439172e+13</td>\n",
       "      <td>5.725524e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentId            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism       Handicap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "         SMSReceived  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_appointments.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Something Is not quite right in the handicap column: we can see in the describe above that the maximum value found is 4 - but the column is supposed to be binary(\\*) (0/1). Let's take a better look.\n",
    "\n",
    "(\\*): as defined on [Medical Appointments No Shows](https://www.kaggle.com/joniarroba/noshowappointments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Handicap\n",
       "0    108286\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: AppointmentId, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "no_show_appointments.groupby('Handicap').AppointmentId.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can se above, there are other values different than 0 or 1 in the handicap column - It may indicate the handicap type. As we don't have more information about that, I'll infer that:\n",
    "- 0: No handicap;\n",
    "- \\>0: Some sort of handicap;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Handicap\n",
       "0    108286\n",
       "1      2241\n",
       "Name: AppointmentId, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_appointments.Handicap = pd.Series((no_show_appointments.Handicap != 0).map(int))\n",
    "\n",
    "no_show_appointments.groupby('Handicap').AppointmentId.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, there's a problem with the `Age` column: at least one record has a recorded age of -1. Let's check it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "-1       1\n",
       " 0    3539\n",
       " 1    2273\n",
       " 2    1618\n",
       " 3    1513\n",
       "Name: AppointmentId, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_appointments.groupby('Age').AppointmentId.count().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there is a single record, we can safely remove it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_show_appointments = no_show_appointments[no_show_appointments.Age >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "0    3539\n",
       "1    2273\n",
       "2    1618\n",
       "3    1513\n",
       "4    1299\n",
       "Name: AppointmentId, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_appointments.groupby('Age').AppointmentId.count().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting data and creating new column\n",
    "\n",
    "The `ScheduledDay` and `AppointmentDay` columns are currently strings - let's convert them to datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentId</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SMSReceived</th>\n",
       "      <th>NoShow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentId Gender        ScheduledDay AppointmentDay  Age  \\\n",
       "0  2.987250e+13        5642903      F 2016-04-29 18:38:08     2016-04-29   62   \n",
       "1  5.589978e+14        5642503      M 2016-04-29 16:08:27     2016-04-29   56   \n",
       "2  4.262962e+12        5642549      F 2016-04-29 16:19:04     2016-04-29   62   \n",
       "3  8.679512e+11        5642828      F 2016-04-29 17:29:31     2016-04-29    8   \n",
       "4  8.841186e+12        5642494      F 2016-04-29 16:07:23     2016-04-29   56   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "0    JARDIM DA PENHA            0             1         0           0   \n",
       "1    JARDIM DA PENHA            0             0         0           0   \n",
       "2      MATA DA PRAIA            0             0         0           0   \n",
       "3  PONTAL DE CAMBURI            0             0         0           0   \n",
       "4    JARDIM DA PENHA            0             1         1           0   \n",
       "\n",
       "   Handicap  SMSReceived NoShow  \n",
       "0         0            0     No  \n",
       "1         0            0     No  \n",
       "2         0            0     No  \n",
       "3         0            0     No  \n",
       "4         0            0     No  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the dates on ScheduledDay and AppointmentDay to datetimes (currently strings)\n",
    "\n",
    "no_show_appointments.ScheduledDay = pd.to_datetime(no_show_appointments.ScheduledDay)\n",
    "no_show_appointments.AppointmentDay = pd.to_datetime(no_show_appointments.AppointmentDay)\n",
    "\n",
    "no_show_appointments.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that unlike `ScheduledDay`, `AppointmentDay` does not include time, just the day - so, let's remove the time information from ScheduledDay to be consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_show_appointments.ScheduledDay = no_show_appointments.ScheduledDay.map(lambda sched: sched.floor(freq='D'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     110526\n",
       "mean     10 days 04:24:39.789370\n",
       "std      15 days 06:07:14.979140\n",
       "min            -6 days +00:00:00\n",
       "25%              0 days 00:00:00\n",
       "50%              4 days 00:00:00\n",
       "75%             15 days 00:00:00\n",
       "max            179 days 00:00:00\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate statistics for the difference between AppointmentDay and ScheculedDay\n",
    "# The mapping is removing the time part from the ScheduledDay, because AppointmentDay does not have this information.\n",
    "\n",
    "(no_show_appointments.AppointmentDay - no_show_appointments.ScheduledDay).describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the statistics that we have some inconsistency: there's at least one record of `ScheduledDay` happening after the effective `AppointmentDay`. Let's take a look on how many records are affected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentId</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SMSReceived</th>\n",
       "      <th>NoShow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27033</th>\n",
       "      <td>7.839273e+12</td>\n",
       "      <td>5679978</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-10</td>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>38</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55226</th>\n",
       "      <td>7.896294e+12</td>\n",
       "      <td>5715660</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-18</td>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>19</td>\n",
       "      <td>SANTO ANTÔNIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64175</th>\n",
       "      <td>2.425226e+13</td>\n",
       "      <td>5664962</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>22</td>\n",
       "      <td>CONSOLAÇÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71533</th>\n",
       "      <td>9.982316e+14</td>\n",
       "      <td>5686628</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>81</td>\n",
       "      <td>SANTO ANTÔNIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72362</th>\n",
       "      <td>3.787482e+12</td>\n",
       "      <td>5655637</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>7</td>\n",
       "      <td>TABUAZEIRO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentId Gender ScheduledDay AppointmentDay  Age  \\\n",
       "27033  7.839273e+12        5679978      M   2016-05-10     2016-05-09   38   \n",
       "55226  7.896294e+12        5715660      F   2016-05-18     2016-05-17   19   \n",
       "64175  2.425226e+13        5664962      F   2016-05-05     2016-05-04   22   \n",
       "71533  9.982316e+14        5686628      F   2016-05-11     2016-05-05   81   \n",
       "72362  3.787482e+12        5655637      M   2016-05-04     2016-05-03    7   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "27033    RESISTÊNCIA            0             0         0           0   \n",
       "55226  SANTO ANTÔNIO            0             0         0           0   \n",
       "64175     CONSOLAÇÃO            0             0         0           0   \n",
       "71533  SANTO ANTÔNIO            0             0         0           0   \n",
       "72362     TABUAZEIRO            0             0         0           0   \n",
       "\n",
       "       Handicap  SMSReceived NoShow  \n",
       "27033         1            0    Yes  \n",
       "55226         1            0    Yes  \n",
       "64175         0            0    Yes  \n",
       "71533         0            0    Yes  \n",
       "72362         0            0    Yes  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_appointments[no_show_appointments.ScheduledDay > no_show_appointments.AppointmentDay]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there are no sufficient information about why these records have this discrepancy, and considering how small is the sample (5 occurrences in a 110000 population), it seems to be safe to remove them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_show_appointments = no_show_appointments[no_show_appointments.ScheduledDay <= no_show_appointments.AppointmentDay]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     110521\n",
       "mean     10 days 04:25:27.412889\n",
       "std      15 days 06:07:25.262183\n",
       "min              0 days 00:00:00\n",
       "25%              0 days 00:00:00\n",
       "50%              4 days 00:00:00\n",
       "75%             15 days 00:00:00\n",
       "max            179 days 00:00:00\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(no_show_appointments.AppointmentDay - no_show_appointments.ScheduledDay).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To speed up the analysis, let's add a column `intApptSched` that stores the number of days (as an integer) between the Appointment and the Scheduling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_show_appointments['intApptSched'] = (no_show_appointments.AppointmentDay - no_show_appointments.ScheduledDay).map(lambda delta: delta // pd.Timedelta(1, 'D'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the column 'NoShow': currently, it says 'No' when the patient actually shows for the consultation. This 'double negative' is confusing. \n",
    "\n",
    "So, let's change the column name to 'Attended' and store a 0/1 value to False/True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentId</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SMSReceived</th>\n",
       "      <th>intApptSched</th>\n",
       "      <th>Attended</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentId Gender ScheduledDay AppointmentDay  Age  \\\n",
       "0  2.987250e+13        5642903      F   2016-04-29     2016-04-29   62   \n",
       "1  5.589978e+14        5642503      M   2016-04-29     2016-04-29   56   \n",
       "2  4.262962e+12        5642549      F   2016-04-29     2016-04-29   62   \n",
       "3  8.679512e+11        5642828      F   2016-04-29     2016-04-29    8   \n",
       "4  8.841186e+12        5642494      F   2016-04-29     2016-04-29   56   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "0    JARDIM DA PENHA            0             1         0           0   \n",
       "1    JARDIM DA PENHA            0             0         0           0   \n",
       "2      MATA DA PRAIA            0             0         0           0   \n",
       "3  PONTAL DE CAMBURI            0             0         0           0   \n",
       "4    JARDIM DA PENHA            0             1         1           0   \n",
       "\n",
       "   Handicap  SMSReceived  intApptSched  Attended  \n",
       "0         0            0             0         1  \n",
       "1         0            0             0         1  \n",
       "2         0            0             0         1  \n",
       "3         0            0             0         1  \n",
       "4         0            0             0         1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Avoiding the double negative in the no-show column \n",
    "# (the 'no no-show' to indicate that the patient actually attended to the consultation)\n",
    "def convert_noshow(appt):\n",
    "    \"\"\"\n",
    "        Transforms the column 'no_show' to a column named 'attended',\n",
    "        that contains 0/1 for False/True \n",
    "    \"\"\"\n",
    "    appt['Attended'] = 1 if appt['NoShow'] == 'No' else 0\n",
    "    del appt['NoShow']\n",
    "    return appt\n",
    "\n",
    "attended_appointments = no_show_appointments.apply(convert_noshow, axis=1)\n",
    "\n",
    "attended_appointments.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attributes Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f6e44a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.hist(\n",
    "attended_appointments\n",
    "        .Age,\n",
    "        bins=20)\n",
    "\n",
    "plt.title('Age distribution', fontsize=16)\n",
    "plt.grid()\n",
    "plt.xlabel('Age in years', fontsize=14)\n",
    "sns.despine(bottom = True, left=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Percentages of attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2023e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attended_appointments.groupby(['Gender']).AppointmentId.count().rename({'F':'Female', 'M':'Male'}).plot.pie(figsize=(5,5), autopct='%2.2f%%')\n",
    "\n",
    "plt.title('Proportion between genders', fontsize=16)\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Attributes distributions on data')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b268710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, axarr = plt.subplots(3, 3, figsize=(16, 12))\n",
    "proportions = pd.concat([\n",
    "    pd.DataFrame(attended_appointments.groupby(['Scholarship']).AppointmentId.count().apply(lambda x: round(100 * x / attended_appointments.AppointmentId.count(),2))).transpose().rename({'AppointmentId': 'Scholarship'}).rename({0: 'False', 1:'True'}, axis =1),\n",
    "    pd.DataFrame(attended_appointments.groupby(['Hipertension']).AppointmentId.count().apply(lambda x: round(100 * x / attended_appointments.AppointmentId.count(),2))).transpose().rename({'AppointmentId': 'Hipertension'}).rename({0: 'False', 1:'True'}, axis =1),\n",
    "    pd.DataFrame(attended_appointments.groupby(['Diabetes']).AppointmentId.count().apply(lambda x: round(100 * x / attended_appointments.AppointmentId.count(),2))).transpose().rename({'AppointmentId': 'Diabetes'}).rename({0: 'False', 1:'True'}, axis =1),\n",
    "    pd.DataFrame(attended_appointments.groupby(['Alcoholism']).AppointmentId.count().apply(lambda x: round(100 * x / attended_appointments.AppointmentId.count(),2))).transpose().rename({'AppointmentId': 'Alcoholism'}).rename({0: 'False', 1:'True'}, axis =1),\n",
    "    pd.DataFrame(attended_appointments.groupby(['Handicap']).AppointmentId.count().apply(lambda x: round(100 * x / attended_appointments.AppointmentId.count(),2))).transpose().rename({'AppointmentId': 'Handicap'}).rename({0: 'False', 1:'True'}, axis =1),\n",
    "    pd.DataFrame(attended_appointments.groupby(['SMSReceived']).AppointmentId.count().apply(lambda x: round(100 * x / attended_appointments.AppointmentId.count(),2))).transpose().rename({'AppointmentId': 'SMS Received'}).rename({0: 'False', 1:'True'}, axis =1),\n",
    "    \n",
    "]).plot.bar( stacked=True, figsize=(16, 5), table=True)\n",
    "\n",
    "proportions.xaxis.set_visible(False)\n",
    "proportions.set_ylabel('Percentage', fontsize=14)\n",
    "proportions.set_title('Attributes distributions on data', fontsize=16)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Waiting days distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a228e0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "waiting_days = plt.hist(\n",
    "attended_appointments\n",
    "        .intApptSched,\n",
    "        bins=20)\n",
    "\n",
    "plt.title('Waiting days distribution', fontsize=16)\n",
    "plt.xlabel('# of days', fontsize=14)\n",
    "plt.grid()\n",
    "sns.despine(bottom = True, left=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the graph is clearly right-skewed, which seems to indicate that the mean is not a good choice for average times - let's take a look on how it compares to the median time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean      10.184345\n",
       "median     4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({\"median\": attended_appointments.intApptSched.median(),\"mean\": attended_appointments.intApptSched.mean()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on that, we will be using the median instead of mean for the average of days.\n",
    "\n",
    "Let's see what neighbourhoods have the highest and the lowest median waits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Neighbourhood')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m7j2oBrCY1URl5uWU0QL/g9KYvp5lo9/9o48436ecSdyB0tXpdMqIe1PXYfyVcnTvBOASyujF78rM/6vzvwZ8hzJAwLV02FAOaR9KsfN+yob7BMrIiJfX/JdSGvOnsexzOHCGmO+mvM/vUrqy/p2yAzIKJ1Ma7sXdpmXmtZSzjbtSCrX/4c4jAy+nntX9H+A9lAa6/Wbt/dib0hgtpgwocgxwDV2+O5l5LGUgr/dQzppvDnyobbGXUAb8OJNSyH6W0uC3Wzy1/sxsPWJ/E+Vo+FcoDfGimtv7en1jkjRiT6Rs81p/Dqm9gXaltH2LKdv6q4Bd28Zx6LVdSODfKYXwpymjxn6ZMmjf1HWWt1C2wedQCt/7ULqQTnkjpT3/fc1zZDLzXEr7O59ykPYcymCUV7cs9ibKe/tG/X0+pZ2dLmbfbWGfFlP22X9c13cZpe1p7SkFg+0XHEI5WHsh5Tsw0PW1mflzyuBgr6VczrUrpc3r1h7fTil4V6AcGDm6vodbWpb5FqVA/UiNuzPw39OEPJLSpfrItuk3AG+mfDZnUbpfPz0zb+rnPWpuiuW3U9LkRcTrKGda16gbOqknUW6dcyVl5MevzcL6VqXcTuI1mTmqAwmSJDVeRHyYMiL25rO0vrcA+2bmQ2ZjfZobvGZWExcR+wO/pBwR3IYyQMRRFrKaSUTsSDmifx5lRMX3UAZi+t6Y17sC5Zqw11Nu1fSVca5PkqS5LiLeTLmF3o3AkymXSx00C+tdjXLd8uso+wG6G7GY1VzwYMrG7t8o3WYOp5yZlWZyD0qXpI0oXXtPA7bLzL+Peb3zgN9Rvq/7tN2WSZKku6OtKV20V6e0kf9FGadi3D5OuTzseOBTs7A+zSF2M5YkSZIkNY4DQEmSJEmSGqdx3YzXWmutnD9//qTTkCTdRZx55pnXZebak86jyWybJUmj1Gvb3Lhidv78+ZxxxhmTTkOSdBcREZdPOoems22WJI1Sr23zrHQzjohVIuL0iDgnIi6IiHfU6Q+KiNMi4pKIODYiVp6NfCRJkiRJzTZb18zeAuyYmVtQblT8tIjYhnIz5Q9n5ibAn4F9ZykfSZIkSVKDzUoxm8WN9ek96k8COwJfrdMXAbvORj6SJEmSpGabtdGMI2LFiDgbuIZyQ+XfAn/JzH/VRa4A1pvmtfvVrsjXLl26dHYSliRJ07JtliRN2qwVs5l5W2ZuCawPPAZY0GmxaV57RGZukplrz5s3b5xpSpKkHtg2S5ImbdbvM5uZfwEWA9sA94uIqRGV1weunO18JEmSJEnNM1ujGa8dEferj1cFngwsAU4GdquL7QUcNxv5SJIkSZKabbbuM7susCgiVqQU0F/OzBMi4kLgSxHxbuBXwJGzlI8kSZIkqcFmpZjNzHOBrTpMv5Ry/awkSZIkST2b9WtmJUmSJEkalsWsJEmSJKlxLGYlSZIkSY1jMStJkiRJahyLWUmSJElS48zWrXnGav6B3+5pucsOfsaYM5EkSZIkzQbPzEqSJEmSGsdiVpIkSZLUOBazkiRJkqTGsZiVJEmSJDWOxawkSZIkqXEsZiVJkiRJjWMxK0mSJElqHItZSZIkSVLjWMxKkiRJkhrHYlaSJEmS1DgWs5IkSZKkxrGYlSRJkiQ1jsWsJEmSJKlxLGYlSZIkSY1jMStJkiRJahyLWUmSJElS41jMSpIkSZIax2JWkiRJktQ4FrOSJEmSpMaxmJUkSZIkNY7FrCRJkiSpcSxmJUmSJEmNYzErSZIkSWoci1lJkiRJUuNYzEqSJEmSGsdiVpIkSZLUOBazkiRJkqTGsZiVJEmSJDWOxawkSZIkqXEsZiVJkiRJjWMxK0mSJElqHItZSZIkSVLjWMxKkiRJkhrHYlaSJEmS1DgWs5IkSZKkxrGYlSRJkiQ1jsWsJEmSJKlxLGYlSZIkSY1jMStJkiRJapxZKWYjYoOIODkilkTEBRHxujp9YUT8ISLOrj//Phv5SJIkSZKabaVZWs+/gDdm5lkRcR/gzIj4YZ334cw8ZJbykCRJkiTdBcxKMZuZfwT+WB/fEBFLgPVmY92SJEmSpLueWb9mNiLmA1sBp9VJr46IcyPisxGxxjSv2S8iLomIa5cuXTpLmUqSpOnYNkuSJm1Wi9mIWA34GnBAZv4N+CSwMbAl5cztBzu9LjOPyMxNMnPtefPmzVq+kiSpM9tmSdKkzVoxGxH3oBSyx2Tm1wEy8+rMvC0zbwc+DTxmtvKRJEmSJDXXbI1mHMCRwJLM/FDL9HVbFns2cP5s5CNJkiRJarbZGs14W+BFwHkRcXaddhCwR0RsCSRwGfDyWcpHkiRJktRgszWa8alAdJj1ndlYvyRJkiTprmXWRzOWJEmSJGlYFrOSJEmSpMaxmJUkSZIkNY7FrCRJkiSpcSxmJUmSJEmNYzErSZIkSWoci1lJkiRJUuNYzEqSJEmSGsdiVpIkSZLUOBazkiRJkqTGsZiVJEmSJDWOxawkSZIkqXEsZiVJkiRJjWMxK0mSJElqHItZSZIkSVLjWMxKkiRJkhrHYlaSJEmS1DgWs5IkSZKkxrGYlSRJkiQ1jsWsJEmSJKlxLGYlSZIkSY1jMStJkiRJahyLWUmSJElS41jMSpIkSZIax2JWkiRJktQ4FrOSJEmSpMaxmJUkSZIkNY7FrCRJkiSpcSxmJUmSJEmNYzErSZIkSWoci1lJkiRJUuNYzEqSJEmSGsdiVpIkSZLUOBazkiRJkqTGsZiVJEmSJDWOxawkSZIkqXEsZiVJkiRJjWMxK0mSJElqHItZSZIkSVLjWMxKkiRJkhrHYlaSJEmS1DgWs5IkSZKkxrGYlSRJkiQ1jsWsJEmSJKlxZqWYjYgNIuLkiFgSERdExOvq9DUj4ocRcUn9vcZs5CNJkiRJarbZOjP7L+CNmbkA2AbYPyI2Aw4ETszMTYAT63NJkiRJkrqalWI2M/+YmWfVxzcAS4D1gGcBi+pii4BdZyMfSZIkSVKzzfo1sxExH9gKOA24f2b+EUrBC6wzzWv2q12Rr126dOlspSpJkqZh2yxJmrRZLWYjYjXga8ABmfm3Xl+XmUdk5iaZufa8efPGl6AkSeqJbbMkadJmrZiNiHtQCtljMvPrdfLVEbFunb8ucM1s5SNJkiRJaq7ZGs04gCOBJZn5oZZZxwN71cd7AcfNRj6SJEmSpGZbaZbWsy3wIuC8iDi7TjsIOBj4ckTsCywFnjtL+UiSJEmSGmxWitnMPBWIaWbvNBs5SJIkSZLuOmZ9NGNJkiRJkoZlMStJkiRJahyLWUmSJElS41jMSpIkSZIax2JWkiRJktQ4FrOSJEmSpMaxmJUkSZIkNY7FrCRJkiSpcSxmJUmSJEmNYzErSZIkSWoci1lJkiRJUuNYzEqSJEmSGsdiVpIkSZLUOBazkiRJkqTGsZiVJEmSJDWOxawkSZIkqXEsZiVJkiRJjWMxK0mSJElqnJ6K2YjYIyIW1McPjYhTIuKkiNh0vOlJkiRJknRnvZ6ZfTdwfX18CHA6cArwiXEkJUmSJElSNyv1uNzamXl1RKwCPAHYDfgncN3YMpMkSZIkaRq9FrPXRsSDgc2BX2bmLRFxLyDGl5okSZIkSZ31Wsy+CzgTuA3YvU7bCThnHElJkiRJktRNT8VsZh4VEV+uj2+qk08Dnj+uxCRJkiRJmk6voxl/CHhISyFLZl6TmVeNLTNJkiRJkqbR62jG9wC+HxHnR8RbImL9cSYlSZIkSVI3PRWzmfka4IHAgcCWwJKI+FFEvDgiVhtngpIkSZIktev1zCyZeVtmnpCZewDbAGsDRwFXRcRnImK9MeUoSZIkSdJyei5mI+K+EbFvRJwMnEIZAOqJwALgRuC740lRkiRJkqTl9TSacUR8FXgqpYg9HPhmZt7SMv8NwF/HkqEkSZIkSW16vc/sL4BXTzd6cWbeHhH3H11akiRJkiRNr9f7zB7SwzI3zbSMJEmSJEmj0Gs34/sCC4EnAWsBMTUvM+eNJTNJkiRJkqbR6wBQnwAeCbwTWBN4DbAU+PCY8pIkSZIkaVq9XjP7FGBBZv4pIm7LzOMi4gzgW1jQSpIkSZJmWa9nZldg2WjFN0bE/YA/Ag8eS1aSJEmSJHXR65nZcyjXy54I/AQ4jHJv2YvHlJckSZIkSdPq9czsy4DL6uPXAjcD9wNePIacJEmSJEnqqtdb81za8vha4KVjy0iSJEmSpBnMWMxGxKrAPsATKSMZXw+cAhyVmTePNz1JkiRJku6sazfjen/Z04G3AbcCZwH/BN4OnF7nS5IkSZI0q2Y6M3sgcC3wuMy8cWpiRKwGfKPOP2h86UmSJEmSdGczDQC1C/Dm1kIWoD4/EHjmuBKTJEmSJGk6MxWzGwLnTTPvPGD+SLORJEmSJKkHM96aJzNv7TL99pFnJEmSJEnSDGa6ZnaViHhnl/n37GUlEfFZSpflazLz4XXaQsr9a6+tix2Umd/pJZ4kSZIk6e5tpmL2C8AGXeZ/scf1HAV8HDi6bfqHM/OQHmNIkiRJkgTMUMxm5j6jWElmnhIR80cRS5IkSZKkGa+ZHbNXR8S5EfHZiFhjuoUiYr+IuCQirl26dOls5idJkjqwbZYkTdoki9lPAhsDWwJ/BD443YKZeURmbpKZa8+bN2+28pMkSdOwbZYkTdrEitnMvDozb8vM24FPA4+ZVC6SJEmSpGaZtpiNiA+0PN5x1CuOiHVbnj4bOH/U65AkSZIk3TV1OzO7X8vjbw6zkoj4IvBz4KERcUVE7Au8PyLOi4hzgR2A1w+zDkmSJEnS3Ue30YzPiYivAhcC95zufrOZ+d8zrSQz9+gw+cjeUpQkSZIkaXnditndKGdnNwSCzvebzXEkJUmSJElSN9MWs5l5DfBugIhYaVT3nJUkSZIkaVjdzszeITP3qfeBfSawHvAH4ITMvH6cyUmSJEmS1ElPt+aJiMcBvwVeATwCeDnwmzpdkiRJkqRZ1dOZWeAjwKsy80tTEyJid+BQ4NHjSEySJEmSpOn0dGYWeAjw5bZpXwUePNp0JEmSJEmaWa/F7CXA89umPZfS9ViSJEmSpFnVazfjA4ATIuK1wOXAfGATYJcx5SVJkiRJ0rR6Hc34ZxGxMfAM4IHAt4DvOJqxJEmSJGkSej0zS2b+Gfi/MeYiSZIkSVJPer1mVpIkSZKkOcNiVpIkSZLUOBazkiRJkqTG6bmYjYgNx5mIJEmSJEm96ufM7K8A6u15JEmSJEmamK6jGUfEmcCZlEJ2xTp5IXDoeNOSJEmSJGl6M52Z3Q34AbAhcK+IOAu4Z0TsEBGrjz07SZIkSZI6mKmYXSEzv5qZBwI3AM8CAngNcHZEXDLuBCVJkiRJate1mzHwhYiYB1wIrAKsAfwjM58DEBFrjjk/SZIkSZLupGsxm5mPjYiVgM2BU4GPA/eJiE8CZ9Wf68eepSRJkiRJLWYczTgz/5WZvwJuzcztgL8Di4FNgPeNNz1JkiRJku5spm7GrV5ff2dmHgscO4Z8JEmSJEmaUc/3mc3Mo+rDjcaTiiRJkiRJvem5mJ2SmX8eRyKSJEmSJPWq72JWkiRJkqRJs5iVJEmSJDWOxawkSZIkqXEsZiVJkiRJjWMxK0mSJElqHItZSZIkSVLjWMxKkiRJkhrHYlaSJEmS1DgWs5IkSZKkxrGYlSRJkiQ1jsWsJEmSJKlxLGYlSZIkSY1jMStJkiRJahyLWUmSJElS41jMSpIkSZIax2JWkiRJktQ4FrOSJEmSpMaxmJUkSZIkNc5Kk05gTlq4eo/L/XW8eUiSJEmSOvLMrCRJkiSpcWalmI2Iz0bENRFxfsu0NSPihxFxSf29xmzkIkmSJElqvtk6M3sU8LS2aQcCJ2bmJsCJ9bkkSZIkSTOalWI2M08Brm+b/CxgUX28CNh1NnKRJEmSJDXfJK+ZvX9m/hGg/l5nugUjYr/aHfnapUuXzlqCkiSpM9tmSdKkNWIAqMw8IjM3ycy1582bN+l0JEm627NtliRN2iSL2asjYl2A+vuaCeYiSZIkSWqQSRazxwN71cd7AcdNMBdJkiRJUoPM1q15vgj8HHhoRFwREfsCBwM7R8QlwM71uSRJkiRJM1ppNlaSmXtMM2utbqoaAAAgAElEQVSn2Vi/JEmSJOmupREDQEmSJEmS1MpiVpIkSZLUOBazkiRJkqTGsZiVJEmSJDWOxawkSZIkqXFmZTRjweaLNu9pufP2Om/MmUiSJElS83lmVpIkSZLUOBazkiRJkqTGsZiVJEmSJDWOxawkSZIkqXEsZiVJkiRJjWMxK0mSJElqHItZSZIkSVLjWMxKkiRJkhrHYlaSJEmS1DgWs5IkSZKkxllp0gloMEs2XdDTcgsuWjLmTCRJkiRp9nlmVpIkSZLUOBazkiRJkqTGsZiVJEmSJDWOxawkSZIkqXEsZiVJkiRJjWMxK0mSJElqHItZSZIkSVLjWMxKkiRJkhrHYlaSJEmS1DgrTToBzQ2HveKknpbb//Ade1rug7vv0vO633jsCT0td8WBP+lpufUPfmJPyy1cuHCky5140sY9LbfTjr/taTmAB5x8dk/LXbXDlj0tN//Ab/e03GUHP6On5SRJkqRJ8cysJEmSJKlxLGYlSZIkSY1jMStJkiRJahyLWUmSJElS41jMSpIkSZIax2JWkiRJktQ4FrOSJEmSpMaxmJUkSZIkNY7FrCRJkiSpcSxmJUmSJEmNs9KkE5DUXPMP/HZPy1128DN6D7pw9R6X+2tPi22+aPOeljtvr/N6Wm7Jpgt6Wm7BRUt6Wu6wV5zU03IA+x++Y0/LfXD3XXpa7o3HntDTclcc+JOellv/4Cf2tNzChQtHuhzAiSdt3NNyO+34255jSpKkuc0zs5IkSZKkxrGYlSRJkiQ1jsWsJEmSJKlxLGYlSZIkSY1jMStJkiRJahyLWUmSJElS41jMSpIkSZIaZ+L3mY2Iy4AbgNuAf2Xm1pPNSJIkSZI01028mK12yMzrJp2EJEmSJKkZ7GYsSZIkSWqcuVDMJvCDiDgzIvbrtEBE7BcRl0TEtUuXLp3l9CRJUjvbZknSpM2FYnbbzHwk8HRg/4jYrn2BzDwiMzfJzLXnzZs3+xlKkqTl2DZLkiZt4sVsZl5Zf18DfAN4zGQzkiRJkiTNdRMtZiPi3hFxn6nHwFOA8yeZkyRJkiRp7pv0aMb3B74REVO5fCEzvzfZlCRJkiRJc91Ei9nMvBTYYpI5SJIkSZKaZ+LXzEqSJEmS1C+LWUmSJElS41jMSpIkSZIax2JWkiRJktQ4FrOSJEmSpMaxmJUkSZIkNY7FrCRJkiSpcSxmJUmSJEmNYzErSZIkSWoci1lJkiRJUuNYzEqSJEmSGsdiVpIkSZLUOBazkiRJkqTGsZiVJEmSJDWOxawkSZIkqXFWmnQCkiRJkjSoJZsu6Gm5BRct6Wm5w15xUs/r3v/wHXta7oO779LTcm889oSelrviwJ/0tNz6Bz+xp+UWLlw40uUATjxp456W22nH3/Ycs51nZiVJkiRJjWMxK0mSJElqHItZSZIkSVLjWMxKkiRJkhrHYlaSJEmS1DgWs5IkSZKkxrGYlSRJkiQ1jsWsJEmSJKlxLGYlSZIkSY1jMStJkiRJapyVJp2AJEmS7uywV5zU03L7H75jT8t9cPddel73G489oaflrjjwJz0tt/7BT+xpuYULF450uRNP2rin5Xba8bc9LQfwgJPP7mm5q3bYsqfl5h/47Z6Wu+zgZ0wkHgtX7205gIV/7WmxzRdt3tNy5+11Xu/r1t2SZ2YlSZIkSY1jMStJkiRJahyLWUmSJElS41jMSpIkSZIax2JWkiRJktQ4FrOSJEmSpMaxmJUkSZIkNY7FrCRJkiSpcSxmJUmSJEmNYzErSZIkSWqclSadgCRJ0iRsvmjznpY7b6/zelpuyaYLelpuwUVLelpOktSdZ2YlSZIkSY1jMStJkiRJahyLWUmSJElS41jMSpIkSZIax2JWkiRJktQ4FrOSJEmSpMaZeDEbEU+LiF9HxG8i4sBJ5yNJkiRJmvsmWsxGxIrAYcDTgc2APSJis0nmJEmSJEma+yZ9ZvYxwG8y89LMvBX4EvCsCeckSZIkSZrjIjMnt/KI3YCnZeZL6/MXAY/NzFe3Lbcf8GbgfsBqwAU9hF8LuG6E6Y463jhizvV444h5d4s3jphzPd44Yt7d4o0j5lyP10/MDTNz7RGv+y7PtvkuE28cMe9u8cYRc67HG0fMu1u8ccSc6/H6idlT27zS8PkMJTpMu1N1nZlHAEf0FTjijMzcetDExh1vHDHnerxxxLy7xRtHzLkebxwx727xxhFzrscbV0wtY9t814g3jph3t3jjiDnX440j5t0t3jhizvV444g56W7GVwAbtDxfH7hyQrlIkiRJkhpi0sXsL4FNIuJBEbEy8Hzg+AnnJEmSJEma4ybazTgz/xURrwa+D6wIfDYze7nmphd9dX2aQLxxxJzr8cYR8+4Wbxwx53q8ccS8u8UbR8y5Hm9cMTWcu+P3Zq7HG0fMu1u8ccSc6/HGEfPuFm8cMed6vJHHnOgAUJIkSZIkDWLS3YwlSZIkSeqbxawkSZIkqXEsZiVJUmNFxAMnnYMkaTIsZnsUERtExJsnncdMImLDEcVZIyI63Qd4IiJix5bHD2qb95whY68WEfceJsZsiYiNI+JtEXH+XIxXY64SEc8dVbwacyTf6xpr4Pwi4r5d5s0bPKs7xWrE9mbUIuLRI4rzbxHx7Ih41CjiabQiYtSDT/5ixPGIiPuPON49Rhhr24g4bFTx1J+IWLPbz4jXNZJtYo01dLtS28+HR8TDImKVUeU2TsP+70XEDhHx6ojYPyJ2GFVec824v9fj3NdufDEbER9pefy6tnlHDRl7rYh4ZUScAiwGBm7c6j//0RFxRkT8MiIWRcQjhoj3uIjYLSLWqc8fERFfAE4dINZ/R8Sm9fE9I+Jk4LfA1RHx5CFy3DAiVm95vkNEfDQi3lBvxdSPQ1oef61t3tsGzO9VEbEUuBz4fURcHhGvGiTWOEXEuhFxQEScDlxAGfl7j7kSr8ZcMSKeHhFHUz7P3QeMM7Lv9Tjyo2wHpmKe2DbvmwPGnIo3yu3Npi2P79k2b5s+Y90QEX9r+bmh9fegObbE3ywi3hkRlwCfHDDGCRHx8Pp4XeB84CXA5yPigGFzVP8i4tSWx59vm336qFc3kiARq0fESyLiR8BZI4gXEbFjRHwGuGLIWFtGxPsj4jLg3cBFI8hvrDvpgxYREbFORLwjIr4aEV+pj0d6cKFlXYPkeCZwRv3d/nPGCHIaepvYEmsk7UpErBQR76d8jxcB/0fZb3r/sMVih3UNfbBmFP97EbFeRJwGLAQ2Ah4MLIyI0yNivSHzWzki9omIQyLiA/XxPWd+5XIx3hAR+3aY/poB273W7/WVLP8dH/h7PSv72pnZ6B/grE6POz3vMd59gBcD3wMuBT4IXDFkjs8CLqHsXD0C2KI+vgR41gDxPgAsAb5IuVfv/wBXA68DVhkg3gUsG9l6P+BkSoGzADh9iPd9GvDA+nhL4DrgjZQN4Wf6jPWrTo87Pe8x3tuA7wAbtUzbCPgW8LYB3+95wLkdfs4Dzh0g3suAk4CLKTsvjwB+N8TfY6TxasztgMOB31MOMlwF3GvAWCP9Xo86vzF9D0e+valxR7ZdpBTpvwD+E5g3bG415obAgcA5lIbyOmD+EPEuaHl8EHB0y+fb9/+ePyP5G7f+r7R/B/v+X5lhXUuHeO2qlINbx9XtxF+A7YEVhoj5WOCjwFLgRmAvYI0B4jwE+O+6XTwVeA1w+Qg+r/Uo7fOPgQ8BH66PTwfWGzJ2ADsCnwGuHuD121J2et8B/Adl/+kdwGXAtiP6vgyV4zh+RrlNHEe7Ur8jnwHu0zLtvpRbrHx0BO9/S+D99e98MvCaAeOM5H+vxvoGsHeH6S8GjhvivW4G/IayL/xayj7Oojptsz7inA+s3GH6PYdt90a1jWYM+9od1zOqQJP6ofvO5SDF7M11o/5ElhV4lw6Z4zmdNkrAfOCcAeJdSN25B9aoOW8yos/wa8DLh/kMW157bsvjQ4D318cr9PuPxugPWvyaDgUSZcfm4gHf74b1Zz7lAMGGrT8DxLu1fhe3bpk28HdxDPGuAH4GvIjawDFcsT3q7/VI8xvT93Dk25saY9RF9+rAPpR7gv8YeBWw5oC5/az+f7x96u87gr/L2S2PTwSe32meP7P3M4b/lY8Bh3b4+RjwtwFzPIZSwB4J7Ew5iPu7Id7zeygHqU8EXgr825Dxbq//bw9umTaK7cPId9IZXQH/C2CrDtO3BE4b8n2PstBZuW4TD6EciN0HuOeAsUa6TRxHu1K/19Fh+orAJQPGHNnBmlH/79WYvx5kXg9xTwR27jD9ycDJfcQ5b5B5PcYeeN+//XNixPvanX5Gfd3KJKwQEWtQCqSpx1NdjlYcIN5BwPMpXTu+EBHHjiDHe2TmZe0TM/OyAbtn3JyZ/6gx/hwRv87MS4bI75baRe9qYAfgTS3z7jVE3NauXzsC/wWQmbdH/5fjbhQRx9eYU4+n1vGg6V82vanPsG3azRFx+4DxLp96HBG3tD4f0AOB5wIfql2svgwM051n1PG+BuxKOatxW0QcB+QQ8Ub9vR51fgDrRMQbKN+7qcfU52sPEG8c2xtY/n22v+e+P4PM/CvwuYhYRPk8PwasQjmr069rgfUp3d3WpuyADPt3+X1EvIZyAOORlDMSRMSqDPcd1+DuFxHPprTN94tlYxsE5eBIv7p1cxu0C9zDgT9TdqYvyszbImKY7+J+lJ23TwInZOY/hoz3/yjbh5Mj4nvAlxhNl+rNMvPZ7RMz8+iIeGs/gSLiPcDzKAXiF4F3Amdk5qIBc7tvZv6qQ25nR8R9Bgk46hwjYjPgeOCnlLOoQTmb/9aIeFZmXtBnyFFvE8fRrmTWKqRt4jD/MxcBPwGemZm/AYiI1w8Ya9T/ezBNDRERK0w3r0frZeYP2ydm5o8i4mP9BIqI+2fm1e3Thsht5Ea9r91JdPhuNkq9fuR2Om/gMzM3GjDuRpTrCJ8PbELp8viNzLx4gFjnUP5Zl7ZN3xD4Vmb2de1sRPwFOKVl0natzzPzP/qMtw1wFGUj+pHMfFed/u/AizJzoOspI+KjwLrAHyndhR6Smf+s17R9KzO37iPWk7rNz8wf95nbicB7M/PEtuk7Am/PzKGuH4qIszLzkcPEaIu3PuW7uAflAMM3MvOgSceLclRihxrn3yndjvYFvpOZN/YZa6Tf61HnV+P9T7f5mfmOfmPWuCPb3tR417Bsx3f3+pj6/HmZ2VdjFxGPr/k9kXIE/djM/MkgudV4q1N21PegXId0P+CpmTnQtZRRrrF+J2V7c1hm/qBO3wF4VGYe0u31Gr2I+Fy3+Zm5z4jWswqlff3KgK/fFNiT8n9yDbApsHlmXjVArBWBp1C+1ztSuks+GdggM/81SH417r0pB+am4i6ibB9+MGC832TmgztMX4FytuRO87rEupZSRHyEZUXEpUPsey0BHp+Zf26bvibws8zctPMrZzXHE4GD2wuSKGOMvHWQ/YdRbxNrzFHux34T+HpmHt02/YWUNmWQ9vnZNbfHUw5AfolyCVrfJyjG8b8XER8GVgMOyMy/12n3pnS5/kdmvnbAuBdTtjG3tE1fhXJGdZMe47yY0k35jSy7xv9RlO7ah/V7sKbl4DzAG2g7WJ2ZfR+8Hve+9h3xml7MzoaI2JzS2D0vMzce4PW7Ur5c76UcxUvg0ZTrI96SmX0NHDPqwm5caiGxO2UH88uZ+Yc6fStgncz8/oBx1wbIzGuHyO1hlGukTmX5v8m2lOuY+z2ySkS0Fq/HUL4zdxxkycyhBxSp63kIpRvlO0cU76HA7sPGq70MnkZpTJ6SmWv1+fqxfq+Hza8t1lqZed0w+XSJPdT2psbYq9v8fhq5esDwL5QdjZOA5XYMhv1e16PIu1N2ajbIzA2Gidch/krDFBIaTEQ8JzO/PqbYrTuuTwV+kpm7jSDu1pT/vd0o1xg+fohYqwC71ByfAJyYmXuOIMc1KT1sds/MHWdafpoYI9tJH3URERH7UcZ3eBPL76C/D/hsZn5qgJijzvGi6YrqiFiSmQv6jdkWYx3KNnEPRrRNrO3KHpTvzSD7sesBX6d0YW7dZ1oVePbU/t2AuY36YM1I/vfqPsP/AntTruNOymVji4CDMvPWAfN7G7AN8OqpXpsRMZ9y2cQZ/eyLRcTTKbXEw2t+F1AOtHx3gLxGfrB+HPvaHddzVyhmowzz/3TKEVUo1959fy7twETEFpSjJw+jFDgXAIdk5jkjXMcGlCLnA32+7iOZeUB9/LrM/GjLvKMyc+8h83oQ5X0nsCQzLx0gRlCurXgN5fNbgbJT/bFBi7C6wduT5f8mx3TqEtFjvJO7zM5Bdzw6rGdn4D8zc+c+X/dF4J2ZuaTDvM9n5otGkV+Nt11mnjLzkpMxaH4RsQvwOeCflB4hz8vMn406v1GoB302BH6TmX8ZIs5ipu/yNrLvdV3XhjlA9/yIODUzn1AfL/ddHnUvCfVmHJ97RGxH2WY/gzJY0baUgUVuGvF6AthuVAeGo3SPfc4AZ0q63g4jM68fMJ9x7aSPqojYhTLg3NR+w4XABzLzW4PkNeocR3VmreV1752uZ9Sg28S2GP9G6em0NDPPHDLWjrTsM7WfcRvWKA7WtMUb6H+vLcaqlLPlQWlPb4qIe2TmP4eI+WrKd3zqUr6/U2qCvroZd4l/76kDVf3klJkfH8X62+KOdF+74zqaXsxGuVn6yZSurL+ifFBbAQ8AdsjMK/uMdwOdd9yCsuM27X0mJyEi1qL84+9BGaHwG5n5pu6vulOMO3Y62ndAhtkhiXJPzs9QjqqeQ/kMt6Acndk3M3u+rUeU6yj+HdgvM39Xp21EuT7ie5n54UFynGZdP83MbQd43eMy8+cjzGNHyki8D6SMKPte4GjK5/iefs961K5WN1EG4jqsbV7ff+d6tPt5lO/d9zLz/LoTchCwamZu1We887jztZ7XUf6/D+l3wzfq/GrMcykF7EUR8VjKZ9n1jPIM8cayvYmIl1K+L78FHkT5vzm++6tmR5Tup92K4zvdaqCHmL+a+nt22Ib9apC/tYYz6mI2Iq6gXPP4SeCbmXlDRPwuB+iS2BLzY3S5NrGfM5Q13ou7zc+2Lpo9xPsdJb+g9HC6kmW9fTIH7CbbEn/onfTpzsDX9v/ZAxTwY9mhnmZdgx5kGNmZtfraUf+vnAAcWNu8dSlnuM+gjCL76cz8SNcAnWOO/MDKKGPG8l1kO8UaZHyH9nVMXba0J+XShqGvTa3fQTLzhvr8TtfAzvD69SjbhnMz89Z6Vv8AygBvD+wzl1k78Fv3z56fmceMIt5dYQCo9wKfbP/njIjXUo48du1u1y4zBxpgoJuI+BbdG8x+r3G9D/Bsyj/UQygjE26UmesPmuI0j4d1KOWI6vMz83a4Y2PwduDjlJETe/Viyuhvd3TtzMxLo1yv8QNK96hRmTfg6w6jDD4zKh+kDGrwc0rPg19QrjH4aNdXTe+KGmdR7Zqyd8vnOcjf/UhgA8oZkkMj4nLgcZRGdJB7ru7SYdqalP/hj1G6nk0yP4B/ZeZFAJl5Wgw4IMmUcWxvqgOAh2XmtfWgzzGUAUv6FssG7umo34MqwAkdps2j5DzooBrdjso2+4htc21aD/60mzpQ0+991scxoFvrwFHvoFxTOIxHd5gWwDMpB9X6KmZbC/VxHJTJzJuB86Z20iNiz5prPzvpb6N0P22P/TfKmd5+vYSyfzAytdD5a2Ye2TZrbwbY5mTmu+uZtVMi4l6Uv/GNDH5mbcVYfvDS9vX1Wyg+KDPPr4/3AX6YmS+u7dVPKdcO92uqi2iw/P/d1PNBDqy0xrzTwZo+Y46sLY2IjbKlB2E9cL0nZb97TWB/4M2jWFc9KLd6RLykrmMBZVvRS54HAG+l3NLnnlHGqfkQZTvzqFHkN6x6UGt/yns6DvgRyz6/syn7JsOv5y5wZrbbtQu/zsyHjnBdSzOz70InRj940c2UHfS3AadmZsZwgxmcQ72vHuWauO1ZtkE5OTO3GDDuJdN1t+k2b5rlz8/Mh/c7bxBD/J1HurPR4QzTb3PAayjb49XG/bXAKzPzu4PkHhHnA4/IMjr1KpSzqA/OAQZO6WFdcyK/enao9QjvcoMkjOLob13P/YD9M/M9A75+lD0sug3kk5n5kkHi1tgbUc6Ub0c5IHVkDtDFMSIupVzGsQLlNhlTvVOCcvZ84P8bDSYiLqD0pukoB+tOPtIB3dpij3r7HcALgLdQDuq+JzM7Ffe9xhv6rEmPO+nHZ9vgS+POa5zxaszzgUe2b1si4p7ALwc4sNIaY7kzawPGuAX4A52L2b7PwEfE2Zm5ZX18IuVs7Jfa580l4+xBE312uY2IT2Tmq+LOo2B/g3LmfaDeIBHx6Mz8ZX28KmVg1D0pJ0HuQzlYd8rUyZ8e4l0IPCEzr4+IeZSidrvM/MWA+f2L0nvvTrMYsKdYPej4Z8pJmZ0ot11cGXhdZp49SJ6d3BXOzN7cZd5Ir6NhwLOW3YrViOi7OyujH3Z9dZYNLw/LBl2A4Y58j/Isb7cd3EF2fqc72xSUAQ0G8aBYdsugO+n3DDzL384Cyv7RHc8HOCPWmsuHaiP3f1FGrV55gDC3Tm10s4wOefE4CtlqhQFeM478Ps3yR4Dbn/clynXub2dZV/IvAO+i9ET4wuBpsn5EHDrd8366T+aIRp1tFRELKEeUt6IUn6/I4cY4+DFlx2Dq8TNb5s3Za7fv4m4dpGDtJsvR95OAk6Jc9/l0Slv4CWDgAd2mwg/5egCijOGxN+XgymnAbpn561HEHoE3AZ120oe5Vc2oz8A/IiI6XYI0zKUX2ekgWWbeUg869K2epLg+M8+LiOdFuZ77t8Ansu1a2h5cOOJCbuS3KqvF0rSy7W4dAxj6/69bl1tKG9urqdvsvZxy+6BR3e5nX+CXEfEFyp0BfkDphXASpZv/4j7j/WPqrH1mLq37OAMVstV5YzigsFFmbg4QEZ+hnFSYN8zBn07uCsXs6tMUJkE5ajtKA32JY4Zr9yg7dL0nUa4P/XAsG3b9m8ADI+ItDDDsembO72f5Pvw0Iv4beFe2dAGIiLdTusz2Y4suDdwqA+T2zC7zOnWD7MW1lK7Bo9K+U976POnQtWsGyzXamXlORDyakvMgoy+27sQEsHF9PtBOTCw/GvSUNYAXMlhBMtL8YPBb73RxNOXv+jXKSMu/oAyOMNCtQVq0d4EadtCPJwF/zsxzI+J5lDOpA+24RcRXgK2BQ4DXA7cB953apxygS91YCm4N7afjDJ7lus7jgePrTvrERcT+wOuAE4GnDVvMx/LXAa7T9nyQniDj2En/Hd3b036NY4e647WIMeD9OCPiMOARwCoR8WvKyNDfo9xi5rOUM/KTtC/lAMWTKQMpTQ0AuA1lAMNBfJtlXYKnJOU7tQ7D3Xd1aCPucjs1ivL9WTYK9keiDPK5agw+Qv7UAbeHMZr7W7cftF5n0IPWY3TH9ff1ff5u1IUs3DW6GY/0XnbtjUXrLMr9w7pesD5NzKNYdu3eYymjBw577V77OgYedr0W26tOddOKct/ZqTN1vxr0ixelr/yRlCODZ1M2fFtRBup6aQ4xwupcNM5uMqMQEStM130lItbNzD/2GW/DbvP73ZGLO48GncCfgMXAEdnnyIGjzq/GPLTb/H4bj4g4J1u68UfE1ZSjlv0e2Z96/TqZec0gr+0S844dN8q9Glt33FbMzL523KLc6meq4Zn6PfSgNlFuMbUfy0a1X0L53gx0r14NJyKeSTlDcnl9/t+U+2heTuli9rs+47UPELecQQ5OxfIDsN2LZb25BjoLGBG3U+5Ve21broMe4BvprTIi4mWZ+ekY4a1qxtA9e+TtaIz+fpwXZuZmUS5f+QPlVoO31bO8506dieoj3t6ZeVTbtDWAv7SeCBjGGOLNp3ShfzJwaA5wrXCM8L6mo+5y2yH+KEbB3nVqnz9GcH/rGOEt+Gq8gzLzvf28poeYt1FGap5q41elbGdHOqhu48/MditWBzzq1q3L4KAD72zNmK8tzMzzKAM5bD/Ay99H+Wd6f33+ReB8ys7rWZQN1iA5/Q14bkRsDGxG+fK+JTN/O0i8drHs3mR7ZuYz+nztOG5H1NfO2Uy6HFgBet/Q167PLwf2mKFHVV9H+acrBqN0nd+Tcv1VP/FGcvPslngjza8a6gxnJ7H8wB9XAfeq3+1BzlIeHRHvoPT6GNWgcztMs+P2KaDvawDH0RMkIh5H6alwRP2ZGtV+cZTRVkeyQ6O+vIdyJojaE+mF/5+9c4+3baz3//uz5ZYIFUokpAsRRUlXKpVKEfam1KmoUIjuLqWLRKdQnNQR5ZaOQnWUQiRJLttlozghOpXy63SThM/vj+8z9xprrDnnWuOZz1hrrrXn5/Varz0ue3zHs9Yc8xnP9/b5EAvBTQmW9m0b2utGEDcoVm0aJJsE2czK3VC6EsT2l9K/DwLnAedVFukPB34jqekivXQG/huF7WH7qwo2/8MIPU6INc6hztDjBO5Ldu+TdEf6e2LbknKep7UlPcXBkr8sESzcBHhA0q62f9jEWAocnVnKXs32k4gM6LOJqq53D/AdKtayQ/mS23FwqCn8F/BfKVHzugwbZ1e2bybkJg9R6FsvAK6QNGV9617OavpO51RLLO6llnSE7fdX9s+3/bKmBm1PS8Z+1juzdUh6JBH9bcoKtpzt+1ooI4Tp7S3MYTTehvEsjP9n+9Upyvjj3IHUykY7gtqP7By3ffXEqya1uQxB/LErUZZ5FrEwaooXVLbfxPhARS4ZxOKJU9JOtr9R2e+pI9cHpdj59iaCCW0x5yLpGcRnsjPh1Gf180raiCiRreoLHpWCNTM+PuDrwIq2/1CzvxowZampCur96jCWOchhiHwlsaA+KmMsvVB04SbpDbZPSdtb2f5J5VyuLMchwAKP7zk6W9KFBE6NJV4AACAASURBVEPtKzJsjjAY7DH91x0Icq+rgKsk7ZVhrFdwaimibzanpPdnFGSgz6n26IfklPS5nT826D1qi/QVic+qCb6vihbqoBl44LH9KmCaVr9UrjuPcOBLoFPyLcaXf4uxUu4m2IXgSoAxBY7HEGoVJxMMsDNpr/Nu/jDxbv40Ia/4YFM7VRReb7dScpt+7/cRa6jqmiRbt7YO21cCV0o6kPFr0ybjrFZbbEus3ZsGhtavbL+U8YmsnOe6Or6nM1Y1daPtRYPYq2NOOLPqwwrWwMy5kvYF3tnvP2V+IYr37hXGvFpp0fth8WL1EQPYrfaPPpOQQajSrk9ZEFvSSxn7kl4EfA3Yol9mfjKTPbYHwXzGstsfZPxE8nIiWzZllJrobd8J3En0cRWDpA2I33kBUQ78daJ1ISvDKml7wgk7nHh2RDw335R0oO1zZnJ8CccQUe66M/xSovSo7/xRR+ksZQqa/U/6KYXSC7f3AKek7WMZ70zkynKs5y7kGbYvlnRChr0RBofS++NeImB6XOVcY54DjZd4OBf4AbAPQWqUK/FQkqSwXrZcv09OSV03BtYViJ7IRzHmsDSCykrVlM7AXzn5f2kGFdYTZnwWsZ5R/HJDWxAJj874tgXOSI7iTQpCsZm2B3AtsY74LrAFsEW10itnbVw4WFOUJwImrEmOYsA1ScXup4Ff2a4nYvYD1iB4NKZq6wWE77Md0cq4FfDESiCxFHI5gx5JSPKsTTxDAp4u6dfA9qmCc2DMemdW0qlEJGNQVrAdgCfTQhkheeQ6PaHyTLzLSFrRqTfW9vnpPo8kj1yJZGex06Dog5my89oF3yciTc/rRHoVDf65mJfKO+dVtjszc25ZRD8HufGiSdKZtndO2wOXfHSJdpsoeb/I9qVNx0cQiPyYEA+/Nd1j/ww7HRxGaAnfXjl2bcqunZN+ZnJ8EM/fnvWDtk+V1DTz3hWpLH8+kWnMkpzSxP7CxZ81EVW+r4G50gu3ot+ThH59/VOWZBihKD5HOJl/AW5K2QckbQo06s9P+BpjEg9vIxavyxALolyJh8f0a+dwc4KlomXLthcHhFPWdF9CN/QMBiMbfAvdM9InEGyrFwDLOcmITD7Mohn4YhmvCoo6yC1U8P0zZQB/T0hPHVg59/AhsAfxzJRGsWBN9blJQTS7gRxPD5Rek3TwKsbK3as4GrhO0sXAMp6EU0chFfhrgsjtvQ7N2tsGcGQfnubneQTR1abEO3kQlY+PEd+/rTsVqpLmAZ8iAmHvyrQ7DrPemSUeiIFZwRzkR1cptPFKlhEuLj2S9ETGyidvckXvrSFKM/F+Cfi6pHc40asryHOOT+dKYFDSgWcSC/wfKjQlz2Aw9rw25IjqzkOvc1NFVYe3RMlHt0DNqsCRkr5uu6mQ+o7EZ3KRpO8Rn8kgmY6lay8NAGzfrpDhaIrS42OS63Pkg8Ko9FiiNGxXosz9cCK7kYtu/YWrEiVnxwJ7TNVQCwu30t8TgLV6lCaKKbaajFAWtk+U9H2C6fTayqnfEQ5ZU7Qh8bAUQWhWKkNbtGwZQNKqRDXDbkSJ6GZuoAPbA3ZvqZqViffN3sBUyCSLZuCTwTcRjvuT06GbCJKhr+bYa8lBLon9iFLvxwCfrQTsX0kQZs60vVacxdLBGknvJKriVkj7fwOOsH1c3wt7o/SapGJiIhmng1dnZWL8hxNKJf1wFlGFugvwoELTdZC19m8Z4075HeN5VHJbI19C4gzqHEi/54eAgdrHxsH2rP8h6rAPI5g2f0wwCa6RaesEYIcux3cDjs+0uRJwJvArojzxW2n7G8BKM/33S2N8BxHhuYdYJNwBvLOg/asL2tqKyML/luiB2XOm/35pXA8SAY+/Ag+k7c7+vwb5m9X/foX/nssTrNW516+Qvh/fIRY0xwMvy7BzLbE4rR9/AsEQOaPjS7YuJsrb68c3J8TOm9rbg6gm+SXwccKRva3kc9nlno0+a6K0uvpzNKGN+7zM+99LEEddX9nu7P890+ab+v20+fcc/fT8TN5Q2d6qdm6fDHvF58CS82iylz2P9rB3JNEy8H7gEQXtXg+s3uX46oQ0DkQFx1RsvYVgjr2akB7sHN+UYHxtOrbdCYfrxUTQeWWiJekqYPfM3/d51WsJR+/C9LN1yc9sLv8QbTSddeI9xDpxrwFtrprefbcBHwFWybRzEPDfRNCrc2xd4NvAQZk221qT/Bx4UpfjTyL0ngHeM0VbSt+PLxHcNH8luEGKzRcDfr4Lc841/Zn10jx1KHQzFwCvB6bMCla5/kbbT+txbpHtDRvYepjtBxTSPLcDh3kszS5iQbi+7d2bjLFNpIibXEAHqtanMp+Iti2GB9TASqUKLyXkiBqXwKTekQdtW9JaBDvfrc4vWSsKSTcTz/I8osdwV8ZKPk6xXax8XYXkEFIWYSfiM2lUVi7ptUTP8SeJhYsJJ/EDBAv2wDJWg4wvXb8FEZg6ibFM97OIBdh82z9raO9+omzyAI+VYf7KmfI0U7znODmgKfz/bvT/qxIvzMYZfbUgmTTZ/UrbHGFySLra9mb17W77U7TXkXiAsbK3gSQeSs17FXt30YcV3g3LlhVSP/8kgqPdpH6yZC1UXqpmTVIGvrLGeSyR2fp1Q1uXE3Pp7bXj6xC9n89pYi9dewHwLts3pv3rif7gFYAP2X55U5sl0a/UHbKem6JqDQp2/f2IRMI+TlWFktYlgps/s/3xJjbT9UcSpeknEM/d3zJsfJYgaLoB2MS1FhoFp861tjfIsN3KmkTSK4gKqY8zfh3xQWA/2/+daXdpgp9lARGwf/Qkl0zV7kuB99l+aca1nXVst5aiYuvYOefMdpCcxRfYnnIjdbrupl5/3H7nevz/84nengtsP6nH/7ml17nphIIJbRXbf0z7yxCT/f65D1uPRfBiNH1h9rjHk4EDbU+5bDJdtwchSfQ3oqb/vcRLfVPgRNtHDDq2yr1WBva2/YmG19V1V8fBBaRskkP/RqIaoZjwvaRf214747pNiAXWhsRkt4jIEFzb98Lutuqa0KaAzp5C8msvxnpeFgGfd4a+q6RHE871AiIrcibwZttrDTjGbo7CKgQ5y99sD9ynkhYJlxV2Bn5ie6vMa7ckSoovsX23pI2JRcfzB/17jtAcVUex7jTmOJGSlnZZGR0kreou8lcDzNm/JSo/upYtux21hCykBfUHiHnMxDz2KTeUqukx1yyGG6oWTJJQ6HluEps/t715Zf+btndI21lzjrqz3H7GdmO5MpXXEy4WSFKQFV1KVAmUdhYHDtak5/hRRPb1KT3+z829zk3BfrE1Sc1uR7mhuo440gMqN1TsL2/7Hw2v2ZogbXscUeL8SeCrxO/9CduNVSAk/Yj+5GtlJBlLpHdn+ocoR/km8TAsIkpIXpRpq1gZIfBYIv1/S5//0/PcNP795gN/Bv43/f4vBu4iyqE3m+nxpTFuTJB83UBEs1Yn+gXuIhzupvYWEYv7tYlo/6PT8YcDizLHuBYRYfwOEcR4ONH38Qfg6CH4G/6VsdLnzs/vCQfqcYXv9esh+H1vI8r5b6v8/JGQJXjCTI+vy3gfTxB1XEX0iH1yAFsX1X4uJNoa9iayJaXGXLqs8s7M645Mf7PTiRKuQ9OzvS9BZDPjn++S9kPhNomca6Zgs9ecfXfOnF16jFRKYIEn1s5NaIcqdM/NG/7/+lwzbt7JuP9VOecmsdlvDXZrhr3tgVuIEuuNCQ3Xt6Rj22fYe1bhz/Cabttpv9EzCvworZV+0ef/3Fxy/Jm/8wXANl2Ob02QXM7o+Fr4fQ9tweY1wIuAZYle3L8Q8loz/vtO9jPrCaAkbUf0Tx4GfJSIIGwGnKjQLGyarn8vcGYqDZ5QRtjEkO3fAr+VdJmCgvxjTk9MGvvBVLRJm0BBSLU34/U4j7P9+wxzBwHPtH1rirL+lCjz+VbO2Cpj/Aq9IzK2/dYG5r5ERLx/SpRRXA2cBuzmZsysHdzvINH4k6RbnTLStu9NpZ85+CoRDDgrjfFywml+ugvqCueWfNjuqTMr6XEDD2xApCzl3gSh24mEg/J8omfsACdG4qnC9hN73GcH4IvEZ9R0jBfR/5nepqnNysV3EfT/RylkhbIJoFwq2tkDlYz+XYVN52bNtwM2dWjhrkIE5ja2fUu5oY3QEB1JuqocHWk/p4y+qIxOQq85e+PMObv0GI9ijFDqLMaTSx1Evl72OEh6GmMyZn8m1jxTQgtzzVMrz0oVuc8NwM2StrP93XEGQ0roFxn2SrPcfim1eJ1OlFLfmDGmKkqqNewObAncJWkb2+Mk/lI2L4edvFv11Di4S9VEH7wbOEfSpYwvCd6KCD7kjO/b9H4n/ZNYm3zBIX/YxG6JtfHvJZ1me9cm954E9pgSzNmS/uBKiXoOJL3P9qfT9k62v1E590nbZVQgKr7VrERKYe/rWso/lZgda/uFGTY7juLAZYTJ3krAfxIvooXEQ7wpEQV5q+0/N7S3FeHIncQYI+9mBNnJbrZ/0tBevQwluySjZnfHLofXJnovlrL9+Aa2Ftp+RmX/TmAdZ4p2q4V+VNV6ESX9niAP+GfmGIuXfPS5V+Oy4D59PgI+bLvvi6qLvfMJCvcVCVbMkwg9yecTz/WLmtib5F6Ne/bSdc/scvg5RLnZ3a6Usk3RXi+ZLQAG+YxLtQ6ou3bmPwgnYD/b/9twXP2kxf7DdmOmbklX2X5mZX/cfDHC9EOFe6NVuB812Sw9Z68N/NapHDq1wbwSuCPnu1y6VLtm+wnEO3ABUeb5BCJDeHtDO/Xvs4kKmIXO4N4o/dwkm+sT+qiXMb5H+LnAq2z/sqG9Nkqhn0wEFHYB7mfMsc35fW8HHqJ7cMXO4GSQtCHhpHd1Fm0vyrB5W7JTZJySliPWctWS4FMzEx5I6uc/PCzdZ4HtLRvaLbI2lrSu81VRutn7FeNlnI6q7mfOYUW5E3ph1mdmCdbiCbXrtq9T9LY1gqSVktM6oYdB0tpuSGaQxvIXYCeFfuTTiC/Z+23/T1NbCZ8BXmu7SrF+jqRvERmnZze0t1rNMXlEdT9nkZCuO6uzrSAK+BChCfwpwrlvguU0pnkF0eu6sRSq3W7Yl0M7FOTUIqC/I3S7VkhjbBJlhPic9ySy0a8gsgYHDxop64GcjELPTC9BCtEUq9v+UPpM7+hE84io+t4Z9roiRcCzZHQc+okdOy8kSNyWBd7hhr1mCf9FBLg6pGPVz8FkZl4kzSfmgr9LuoVgifwaUYK7WxNb/TL6mSgtLQaR+Tu3sr9Odd/2azLtjpCJXovwFGSZTzChNkFpGZ3OeErO2acQGpm3JAfqp8CpwKskbW77gw3tucd2t/0pQ9JlBFPwGcDrbd+i0Ke8PcNct+/zqsT7+a22L2xob3nbN6dxLlsNLEh6Ds2fG1LV2cbE3Nch8byEmLdzHJ1/dVsPJkf8gQx72P4FUV34UUWf5nzgQkm/c8OeXtvr9DqnIOvKGd8iRZ9n1Vm8BHh7rrPYq3oqZ5ySzrf9MqKqqwg8OefOBem5amq3yNq4pCObcDHjv8/V/dz1SBu68hMwF5zZfjpXORpYPyKV8ki6oFY2eDYZGnIpWgvwLyp6e53jGQ7ySjVHlmRnoUKrqym+xHjHpL6fDUlPBT5MZKKPJF4eOZN9N4ezs2+iL2LKaKkMs65dC2NRYNO8RKp4yUe/ezW+oDyZyYPJriX9sXZugibbZOiROV4FeA3RmpAFSdsSTux9RIa8L1HXJNiRiMRvTES9T3fDcuoeaKV1oAplkq/ZztEYnQz1MrLGGoUjlEWqSNqbIOU6F/gBsA8R6V9IOHlN8FvbhxUdZPk5exWPlba/ifg+vytVRVxFsJU2wbopKKPKNmm/pyMwBfyB6NFfndAivYVM57jX9zk5dmfSPLh+GmPrrJ8yfs11HJk6vskpLuXoHEpo3ndluR3EsEKlYTXis1mB+KxK4qdEFrDpuIo7i5Og6TgbV/SUgO235VxXcG3csfeYNJ7s56XfuzknOdgx22O723425oIzW4/Id1CiL6deJpkbRfguE0spTHz5VqN5D4MkreKacLqi/6BxxqkFpwQASd8gem+OAvYnnJWVUjK1UdS7ZIlpW+gXDc3EyrUSLlX3m5Z8aLxU0rhThJZfIyhYDn9l+z9qx/cnKiaavtRLL9zqARkTQZA3OJMxUNLPie/tkcTLFlXYPJtWCCTn8lspE7Q98BlJjyLKtBsxsddwf8cptn11yrpkObIp8nwUY+XuxxKLymeTJ2xfVDYCphRBH2H68TWi//2nBLnSe4FliJLEHPmz4j2zLczZ1fl1a2KewPb9CubWpqgGaY6qnavvTxm2t5f0SCKY9tGURV5Z0ha2r8i1W7vHHQqpkKYonsnp0SrRsWc3lDiyfXYqkT0AeBdjJa07d6sUnOIYn0+UfL+WILo8g2gJadSGNpVbZV433c5i03E+skvJ+2LklMi2hVJr41TFdigRJBTRH/0A0WI5cOCvMkfsCjyVCEw2xSaS/pLGt3zaJu0vN+gYO5gLPbN9e2KbLnKmo75boZf2fuAlwDG2j214/Z7AHkSEu9r/cQQhK/PFjDG9gogaV2nmj3Cm3lWyeTtjL5AJznyTfghVGsUlvdT2D3LHNZ1IpeXzib6KjSb7/7Vrv9LntN1QW1eFpZIk3Qhs5KQrWDk+jxAUb/r7Fv0u12yvlGz8ZbL/O4mdH9GfuKGxdm2yuxRBQDOf6NX/gO3vZw0y7NX7C99T3W/SOiDpZ4wnX3sfkT05OKe8rI05VqEbWY8A/5FgVD0qtwxuhHxIut7209P2UsTnsbYzNcxVuB+1z30GmbNPIQJmvyGydE90kAquDFzsBvrOFZubAusRLPs3Nb1+ivdYjagQWQCs5QJSVunzOcnN+wmnpcdumKDgAPk14cCe6Twiz6neK1c2r95POQ6lncWm45R0D1Hd1Kv/ttF6qYv9RyQ7ORWfdVu3M35tDGPjnvLaOCUOXgnsafu2dGxd4n39PdufzRjb8kT12q5EFcSKRIDlkvpab5gw653Z0qgsAkVETDqLPhFkJ9mTvKQnEWUFnYzGyc7UzVOw8L2Psf6PjkbVtzNs7QG8Pdm7Mh1+FlG//2XbJ+SMsST6veCGDQqx+F2IyWBj4HDgm7nZwGGFpEW2N2x6LuM+axElskdmXLsv8VwvR3yH/wgcYvsMSWu5IQthaUh6MbGA3IKQDDrD9pX9r5qS3Qk9/xW4SdRW5cnX+pHa5Dqz3UhjViVKPVdoWgo9wuAo7YhIuoQgTOz0o15BlCo/DbjCzftRq7aLzNlpIbgvIct3YidLJ+m5wHq2v9bQ3iGENvRVxLrhcNtfamKjKSSdZbsbQU2v/9+N8XVV4m/wBts/bXj/uwmnTsRnckbnFJH5zC137HavLD3hdO2bCAbdDlnmTURy4qsZtp7gDKKnPvb6VWG9qWkmOtks7iyWHGdb60JJexGBqRXSuP5KJHqOK32vppB0DcGq/cfa8ccA57u5lvepRO/u+cT37kJCuiq7pUFByvUOYH3gOmJezC6n7oVZX2as8lIZ1X7Reu/olxvaAkDRNP9hwvH8NPFCzloIdmD7O+STpdSxP/C8WmnDhSlbeymhw9cYKiymXhKS3mD7lLS9lSsM0ApJp8Y9lSkosIDoRTqTKK07xwOUcasQI21LuFfSk1yTP0lBm0Zi3XUoZHp2Iv6eaxKax01tfIRwEp/vRJSQopZHJ+dnD2KCnYqt/yTKyfrK+WREpy8gJvhLCSKp3SXtXrH37ob2Otf1fOYkNWJcpjz5WknZCNIYui0E7wCuSS/8EaYf1fIyGCsxyyrtpHw/ahtz9tK2P1U/aPuyFChvil2AZ6Ts7qOA7xHrkjbRdH6olzsbuIfQds2RuXtvZbse2MsK9KWA6MGMtUqcBnyMkJ05LcPe7gTz7HuI6riOosSRkmjq0JZ0ZBP6/Z1yg6V3DJrd7IKS4yzehiDpIILx+kVd1hCr2v54pt1S68+l644sRN+s8kr8NyJaQ24itIMflDRoxvNkgi/ox0QWeUMi4FcUsz4zq8JSGW1A0oPAnUTv7AQnNmfBmjI6+zA+Kvh5jxEGNbF1Uy/HqN+5KdjtR4xjNyjJ7JMx7xhrxLjcUqnj/UQp5gGd7JqkX021ZKSLvcWMtARBx0cYY6T92EwGA4BOafqxwMcZr8n8QaKKoVGJuoK87HVEdmQDwoHdxQ0knGr2biE0fu+rHV+eINXY1Xa3fvtutp5FkFH0Y+JtHJ1W4dLvPvcZpyNpe8o6kiW/x8ne7RSWjZjkfuPkV0aYnZB0ne2N0/ZPiEqks9N+1mfcwpxdfa+MI5DMea9oouTUuP02IOnOnAq0FMTsrBWuduhmDwXSHHYxY60S2xDVbPs7Q09Y0uVEtdDttePrENU1zxlwyK0gZcle7YrWZ4NrB5KCyrjfw5pk8CRt6Ax5oEls/gLYpMca4lrbG2TaLbL+7Pd/B1jHPoVYg+0C3E34GE/P+Z4ke9V2k4cRVTTFM+izPjPrwlIZKk9qA1A0miVpO4KN9bD004kKnpiiOk37XP8iaRNP1OrdhCipyILLMgb3y5jnoA268McR2cR/VzC/nQnkRMc6aJ2RtgOFbMTPm1xj+zxJryUi6e9Kh28AdnReSfXdROngQcClti3pdRl2Onio/hICsP0PSb+ZqiObrrmSiBQX7Qsq5ax2gwrpSBb+HrdButOrCmQVokTzktL3G2FytFBedp2ko4h+1PWJUrhOqWguSs/ZpQkkqwSXqu1nS04pyCK7nqLhONPf/ySiPHthuv6zkn5IfP7b2v5eA3tfoX+13VubjC9hVdsfSdvfV+gJb+5MPWFCUeL2LoO7XYmfoRRy3s2165cCXka8B7YlMmSNnVliLi0KSZfafl7a/prtN1ZOX0Ez5urLe2QRcytBIC7stYYYpH+01PqzU/3SzX4WuZJDFusQ4JAUxF8AXCHpLtvPzTC5uJXS9gOpoKs4Zr0zC6CyUhmvIlLtdRxNvJAbO7PVBavKNJG/l9CZrTqfCyVdSWTKmjqzBwDnppdIlWb+TQw4gaXSqF0Zn0E+zQ31+wYo++ppssd2t/2pGYxyj+OB4yU9nsiE3S3pJuBbTgRWDVCMkbYb6tk6IqvaCLZvIJ6TEvhQGs/xwGmSvj6gvbskbWP7gupBSVsTC+IsaLhLvwFQWR3JoujheC5GZsVBnVW5U+r4IzLbJEYYGKXLy/ZI168DvMz2ven408hk9m1hzi79XqlLTmUzGNfQec93W1k2LQ0+lnBid3AiiFGsWA8Cvg08GXhSA3vd2qfWJsp6s9oQ0phK6gn3a6MZqMUGyrybJb2AWHttRziGW0EQkmUO6zs1Z1FUSIxsr5dhc4XKdp1jo5HX4/J66NB/DfHbAewWmSdsZ38fpmj/SuBKSQcSgZAcVB3uKqPxQEGGOuZCmfEEqYwqmi6M1BKpjaR3EuWXnS/v38hsIpd0s+2nND03ic01gL0YE8NeBHwht7Qg2Xwq0UD+feCaZHdT4KXA1ikCNCOQdC9waxrTemmbtL+u7RV6XZtxrycT5bKNqNJVkJG2YrNItq5NaIxNdD6xCDqUWFj+sqGdDQnCiksZH6TZCniN7RszxjbUpd8dSDqH+K6dSwSPLhukfLIkamXLz2SsRB0GYIQeYbgwXeVlbUDSBgSbcaMgqlokkBxWSLrFdldnVUHmtJVrvAoNbK9LBDlfAHwW+E9n9OGWbm2orB8mnCJz/VDy3Zyew18TgZqzbf81BTMHIfJ5VO3QPGBnkqqGG5CGVWwWa/eS9B2i//nsARz2us1+a4jtc8uap3P92XBcvwU+ZHuCkkZu2fJ0YS5kZv9OOIavTz9VmNB6a4LipDYq30TeL6ublfFNTushOdf2wceAfW2fWT0oaUfgE4R+1UyheAZN0unAYa7JJ9j+RXp+mqJeTj1QefUwZ+uqsP0/xPPxCUlPJ17u5xGTfhM7ixTka7syFqS5BHh7t9KhKWKoS7878DToSOaiWras6MMqUsacPuv3Ep91R17sqMyS9xEGR9HyMrUgv6Q+upRAznNTnECyLaSKkt0Y/305LaP0tt8H++ccRzYFwj9MBOSOJNrGskvUW2htKLp+aOHdfBYhp7IL8GAKbg6UubJ9TxrrPOCNxFy7ENguJzCcsHJqJZqXtjvfRxF/jyb4EhEAP1rShcDpwH/nBD86aGkNAS2sPwvhPuDNilbGt3q8xnE79cGFMOszs6WhwqQ2yWbRJnJJ/0f3PjARrMSrNLTXkQz6f0Qk+UvA84H/Ad6Wu6CW9AvbT256bjog6XzbLyts8w/AvcCnbX+hdm7Go1rTma3LccTUX9j+n0T08sP1kp/pRJfocVYlRB/72URNk9hdnVjYzKeAjmTKni8gHPlGWpw1O6W0u7cnSjAPJ3qbRWR9PwgcaPucQe8xQjMoiA87wVUByxPzY1Z5mVqQX1L0vS1MP51xdmAXZG+VtJ/tz5WyNwjSPHMu8BNindPh3WiccZJ0MrFW+JgrC8oUxN/A9u49L+5u7xvEmusoood5HGlmRklwt9YGA3/0DEuzddDGuzmVenek314JrAS8lXDw/pZhb2mC/2V/IlN5eAo8Z0PR2tYTtv8tw2ZHJ3U+sCXRdne67R9kDbL7PbYiCCT3HtDOyoyV4P+y5jxOOyRdTbw3P0x81m9xIpUdhjVsP8x6Z3aSyGqWkHMlwt9ZpN3AABH+SZy6xothBdFVT9i+uKG9S4GvEpPd/kRvyrcJh/bjtp/dxF7FblGmtfS5vI/okepEkj9j+7qMsRVn5lNIgLyCsV6xN3usr7Lx/SSdaXvntH2EK+Rjuc54JVu3gCBRWZkg6Bg4W9eWI5ZsL0V8H0+dqvPUxTmuZnLe34k0NxzHrC/9VqamoVrQTy7ozF5LLMJvrx1fh5Bajm4dWQAAIABJREFUGbEZz2HkzucpK7QLMReeQyx6u5WPDgxJv7a9diFb2ay06foLgE/VF/iSXkIEDKdcLaEgPPpPwhleSMyzmxKtRW+x3Y2gpp+926n0YnYOd/ZzHDx1Z2RfFViGKCdf2OX8tKLld/PSBIvzAqLf/NEZNu4i3k+fI0qYxyFnrd02JG1MrMc29oD9pZKeQfz9dgFuI959x2baWobgcnhtsiXivf8togohO5uc7Gc527Wy7y0Iv+BsoiJtqNtE5oIz2y+yUzSymov04vhkPaOkaCI/uFSZXS4kLbT9jLR9q+31u53LsFtf+C8+RcP+odKZF0m/Ino9uiIzCFKdCN5DCKq/08H6m+PMLr6mS0ZwYGdc0mrExLyAzGzdDDhib7f9xQGuX4Uga3qu7Z0yrj+033k377GrlpedUSkvy+5tSnb7MjW7AQuqJmpxnkk4iFljlHQsY4vU+cTvXh1bjlTZjbaf1vTcCO1B0n8De7U1F9TuNZD8koIIaHtiPnwU4dA1CgpP4R5ZkjeV6yew0tqut1ZN1VY/3o0sOb5UrfE04t28aNCs3XRAwdb677ZfMNNjqWLQd7OCsOfr3TLPkpa33bhlTtJJ9GeZzlprqzChYqpC2pl4tzyWYG4+vUnAQtJHbH9E0TvfCdDfA3ydWG92qxJpMsbDiLapd9j+azq2IvAFQs/34AybzyACzTuT6WzX15UKwtpjgE2ARw/ye0t6ImMtDTc5tVyWwqzvmc0pQ5gBvBs4J2VAJzSRz+TAEqoU4/Uo6iD04/16PJv2Dx0GvLS2MLpW0RtxTvppgkcSzNVdCSEYUILF9r+nIMYpkl5JRIAbm8k81xWSPukKO6ftu4mS+mN7lPBNZm/ae3AHcWTT9X8iZCPeOOl/7n79RwEkPdpdxMoz8AfCSVydILK7hQF7mxK2JLStTwd+RvfnfKr4AtEbvKvHtDgHGeOVle2rev6vZviXpLVtj8sYpOd6EDmYEfJxEnB+KkP9tO1/TfL/+6JLqSiUk1+6j2CN/QvBnJslazEJsr4zKs9KCzBP0rKu9cemjG+jdaGkTrb5X8C19eP17+QU7L3B9ilpeyvbP6mc28f255vY6wfbV6YFexFIWotovThyEDu1d/NZGSbWBC6TdBvxDvhG532V48im697c61xyIBtDFUJFhS78RxgjVNytoa1O0PXJxPrtfdVnpyE6/Bw3E2zsr+5UbChkOgfFDsAW1e+wg6RrL+ByQp1lUvRwtjVAguya6o6jHP0tkl5PtF42Rqrc+DLROtCR7tpE0lVEX26jyo1emPXOLJSP7JSG22siL4WnSLoOFuvYdcp2BWT3bPTLUknavKG5pbs5SQ5dtxxdwDtayNqPcxhsX5t+z8+Q1/D/cEmbEuQIy6ftjg7g8hn2Xk4wQ05ATtkp7TlirSI9L1lzn6RXAV8hnKeHgJ1tX5Y7FrdH1LQGwRq+gJh3vktEp3PYF4tqcbodbd1DgR9K+iTjA4YfIENObYTBYftMSd8liAWvlPQ1KsFRNy/JLy6/JKnTU7gF8EPg6E7AJtNev77/h2fYq7LSvtdjrLSDsrV+FTgrOYe3p3utQ2RhvtbQ1neZKPNj4p2wGs3ldN4DnJK2j2W81uhbgGLObJrPBnpnSXo0MT8uIJzI0mSATddK2N4/VYe9gHB2Dla0YpxOKAP8ddBBVd5buxLrmzUzzJQkVHwu8Cngh04SUQOgE3jbkfj7XSTpe0TgvgQR0kPdvsO2/9YwUFzU2e6zJv4O+b/3MURL4HyPl+46mPguN+qp74W5UGY8bVIZGlC8um0os49msqxcpqPT7T7ZPZVpIn51j8zLt21v3HAsbfTMzus1iUp6rO1GumSSfkSfF23T6Fv6G76IHpOS84g1WuvzGRTq3k+/ClG+dakbSiUlm9cRDuzNkp5NZJz69rA3tD9w6XcXm8sme0cSbNtZfT7JVkeLcwGxOM/R4mwFkjYhNLM7AcMbiJ76a/teOEJrSIHlDxAL3q8z3pktrR3eGCkgdR1BaGNq821OyXtJSDqa6Ku7npAdOQe43gVI+yTtQ3BQPJz4vvyN4AbJnh+S3XWIANJLgGMGKXXsUvaY2xtdbW3oYFXCAdrX9rcb2lsReB3xXG9AOLC72H5807FN4V4DlacnG0sRn8engCfbbhxYSXY65Eq7EkGGFYnn85IcB1ItEyrmQtJy1USTog3htcR7b2uiD/dbts/PtN9vLXaRp9gyoej5n088xx1n+8sesE0p2S7S1qD+0l09zzW+zxxwZm8AXlsostPN/sCkNpNEa+0BRINLPXBtQYV6KiW9Fvg00DXzYvvshvY2zMxSNbnHuMil7ZzIZcnx/BP4DT1KqwddILXhiA04nno//eJMju3vZtocSAuvi71xpd+1c08YJJCUnNjtiM9jHYIp80Tbv8m1WbP/ZOBztl9Rwl5bkHSU7Z798SO0A0kvJzgTziWCKANrP6Znbk+gs+C9CTjBDTWoK/be1O98iSqCykJ4V9vbZVxflJW2i/0VIcocB7TTUUV4NpFFPzmntFwFtUcr19U/54eId8HPbf8hw94/iJLvg4jAqDUA+7CkVXudIhQvsp1khbzdfOLdfA9RodOYVVvSqUSm93zCaboQuHUQx0ktECq2jfRZvZ7wM7I00VVe97iYs63ubQ3r5s7fqvHw1M6NnNkO2ojslHLAetgukhEs/cC1ARUmt+mSeVlERJIbZ14UvSTVh1+VfdtupGlasVsscll1dCS91ANSy5fORrfpiJWACvdXJZtFX76DOsN97J5MsD+fR3z3bih9j3SfYgytyV7x6pfSYxxhapD0Y6KVJleDsm5vS6IX7gTgamLO3hTYA9jB9uUl7lMCKSP9SuI98HJC9/ObTTOAXewuTTDmzyeTlTbZeTVwXWeOlnQIEXi9g8hU3tbA1kaEE7shEXA+3faD/a/qa+9eQoZNRO9ih11axBpnhQybrwXWdJLMk3QFUQZtorfyvxra25/4DFYgMuZfB34wgDPbWY/0qppqtGZKgYVOEuZBYg12ugcg3UnZRBEl6l+3fecgDnyyWZRQsTQkPRz4Vycok4JpryTa1IaOvRkWO9s7Ec520+q9alvD2R5raxgkYNFLuutgQrori79kwn3mgDNbenHZCrtoxf7Ai9c2Hrg2oGnUNW0KSY+qHZpHsMAdCFxte8cMm0Ujl/0i1Jn2Sjuzw6071sL4Sr98Jyk3yir9TnYfYkzjc0LQZpBqkNp9SpTAtSbplOwPPMYRmkMh7fAo2+fVjr8G+I3tRuRfks4DjnDSPawcfyHwgWGoEJDU6VPflpAA+zpwrO11Cth+DEAnk6hMVtp07XXAc2zfq+AB+Pc07k2BnWxv28DWgwTZ3HepacKm8TYq1VYLbU+SfkIs7u9M+wuBbQhn9Cu2t2lqM9lZl6S3TeiFHkpkxLIqBUpBodZwOrGGzZZO62L3KUSAZhfgbqJC4um2f5dpr1jAWdLWti9M20+sBmQk7ZDjfEq6hCApukXBZ3EFcCrB2n2F7Q9mjnXaSM4ajqt4W4P6S3e9zfb/DTpumBvObOnFZasOWCGnpLU+mtJQwZ7KVCr0bsaXmB1j+6sDjG8e8EZCV3ghIaGUlUkoHblswZl9s+2TBrFRs9eKI1YKw+5sA6jl0u+2kZv1LF390maZ3gh5UPT8v7n+maZF4QluWKIn6Ze2N+hxrqeW+3QiBZF+TPzet6Vjg7wDRDhI+xABVxHfl2Od0fNfsbtYykjSicAvbB+R9hvNm5qeUu1HA/dUMzsNr/+57c0r+5+3vU/avtz2cwqM8ekkaRQ3rOyaLudGmfqjPWw9i5i/dwLusv3cDBvF3tH91ku595F0ve2np+2PAava3jtVXlzVOTdTY9X4FsbFWswEweUythsTXaY5p3hbg1qW7poLbMb3lIxiuAV2UY0nolm5tt9Y09T2vpL2Y+yBOxJYSdLOZDxwCkHxfvphWVHLdPGfgROBEzXWU/k5SY16KiXtDuxHZN47JWabAUdKoqlDqyjXeguwP0H+sf2gXy7bm1Qilz+UdDewoqQ1MiOXqykYCVXZrt6vaT/JCxXl6d1g229taO8pRP9yL3mjmXbENpbUjfa9aHZyQNxYMlveBiR9m949//UKh6nYa0PSqdNH3+1ZHEiAfoRsPKrbZ+rgt2j83AD9ejr/3udcFpRX8v5MIkv3w5QdO4PmbL5V7Ee0EG1ecY7XBY6XtL/tz2balUKS5l4iQ3lc5VwjWaKqs5ps2nb25yHpOQRR0f8DPkYQej6akBPa3fb3MsyuUhvzPpXdx+SOtWbzekL3Pidb1xqDs7roj+baqsLB+n2lQtN2GHR61WO72/5UUX3vbU2stbF9fwpc5aLIWG2Pk75U9L/vBbydTFbtFDC6ELhQ49sajiO+h1lI6+v/SeNcT9JBRLXERrk2q5gLzmxRqnYo54BV8OrK9sW1/SxN0y4P3MsJxzbngetGjvIcgunw7qZj6wWP105bp+HlewGvqy2OLpS0I7FgaJqdvY2IcH+OKNneRNGT2xlr1oRv+2ZCiuKQFLncFbhCUk7ksqrT20+zd6r4TpdjaxMLppwF17A7YtcP+fhmC47KPNcLxSWdPGQtFiMA/eXDGvc9AmtJOqbLcZEnCzLRUK3kndBGnDJsX0OUz70/ZcEWAMsoSqS/ZbuphNDuhL76Yk1r27+S9AainSXXmf0cUYn0F+Amj+lHbwo0Yt1P172TcOJWSPt/I0rCj+t7YXd8npCQeySxxnmF7ctToPh0grW1KX4maQ/bX6qN++1E6WgjqDypZ1FHTOX1R3sirUUvzry8ZMDZPba77U8V10k6iqieWp/4ziFp5Ux73cYz8FjTePYj5ovTiODXPfnDSwOJXuFzgXMVfDBNxrSK7T9V9h9L+FK7AhsDhxPPZxHMhTLjoqWEGnJSmzSOxwCPqZfDKogY/uKGIuU1Gy8k9J+WJUpuz5vkksnsbUksNC6xfbekjQkG4uc3zMzeaPtpTc/1sXcS/bPRxTRoU9nGC2znTvjFkaL7HyIiqp8F/tN2owyWWpA3KolhHx+UL/2eLdAQSzqNUAaS/oNYSB9ULQ+V9FHgsbb3bGivlXLW0iXvXezPIzSf59v+t4bX3tArc9Hv3BRtr0nowF7rMf3HxxKa7lNeQ6QMy3OBfZwIhtL75WjgZ7Y/3nBcC20/I23fZPuplXO50jyrAWcD/yQquyCy6MsSahi/b2pz0DHVbBQtkdVYuftbPaY/OhR8JVWUfEdL+j/gEsIRfn7aJu0/z/Yqva7tY3N5YF/gsYQSwLXp+HOB9Ww31WTu2C1CcqYovz+AcBJPJNoP/pwzpmSvWJWmpGNtv0vSHsTc+nhCo/5M4JzSAei54Mw+QJTKTDhFRoSstHPcBiSdARxfd44kbQu8yfauGTa3JZzY+4BP2L6owDiPBF5FRIDXJzKDexHyOl90RcdrCrausv3MpuemEwpGyJ7wAD1OpSDpqQTz5KZEycwpth/ItDXUjpikD9n+ZAt2NyKqFp5GTPw3Enqm12XY+gr9Xx5NS7+LQ9L19Nc7bqTx3MX+UEk6jVAGCrmILwNbEO8AgE2AKwnij4FlZQaFyjPur0YECdcnOC0Ot90t8zRVez3XI4OsVST17XNv6Mz+Atik/j5PjsC17tHn3Mde8d7HyvVbE6zLEH17F+baKjWmZKMog7Na1B8ticLObF+99yFLJhQhOZP0d6LS6St0acNwcwLcbuvoxVWarvSdT8HWGbbnS7qfkEw9oFIBUjywMhec2dIMrUNNagMgaZHtDXuc+yVRKy/b75uivZ8T5X5HEg/dONi+esJFU7N7I7CZ7fskrQL8L7Cx7VsybHUm+wmnyKfr34ggftqQMafkKGey/0k6oMvhFYjm+UfZfkSO3VKQ9A2ibO4oIjo2jnmy6bM97I6YpGOZWM7zR0KU/NJMm9sTf7/DiUW5iAj/B4EDbZ/T0F431uzFpd8eAuKiUi/eir2hr34ZoRxSlq7qQGTJg7Qx36gw4aOk7xH925cQgdwVbb85x1ay9yDd+4EFLGd76Uy7nQBVdZ1jYh2wmu0pt52oD/mWMqQSK7+ziFL1TrJioN+5LRRyZoszOCe7xfRHk71igdxkr62A8zjm79kCSUsRFRynTvH/f4T+c+IgJHEDVWlK+rDtT6Ts8U7EM7g6sfZ8c+mg9ciZnWhv6NlF1Z/V8deEw/IjT7H0VsE62e8LkSsMPS5jWi0fyrBVekFd1CnpYn9FojzlrcSX9zOOnuEZg0Koe7GWbudwZ7/psz3sjliPssRVCSKMrztPOP5agizs9trxdYjSmU26XDZV2wOXfrcBSZ8nLfQL2WtDMqm4LMMIw4W25huVZdwf946bDZVesHj+ej/wEkIh4NgG115ALHYvqB3fhigxL96nOdPQeBLPo6jxjpSYbzQgg3MXex390V1y1nRtrJlSCeqPUkWEiFLZHYHbCYdnyomUdP0hwLvS2OZRgPm7DSjkavYmWvDOBX5AMJYfCCy0vX2Be2RptrdRpVmx/XjGerkfTgRWuga2G9ueA85s0chOaee4Ync14uGtZgGPc0avhqTvAl+w/d+1468A3m37FZJeONNlFRrrYejgBdV926+Z9kEltOWUpBfGe4DdiCjo0a40wWfYKxoJbQvD6oh1Qyp/uyzne67CvdvpumKl321A0r7EC+ixBJHI6bYX9r+qr73i1S9tliaOMHxoa74ZtOS9y7N9UXV/GCq7qpD0JGLueTbwGeBkB+lLExsbEvKAlzLGKr45wcK8ve1FRQc9BEhVAr1gN+TcUB8GZ6Axg3M1gKcaEU8u2lgzSboB2NT2vyTtSvR/vox4Fx5q+/kNbO1PSMns6RrzN/A95zN/97rfw3Lf06ki5E9EJeQ2BNv2MsC+A75bB9Jsb6tKs8e9NgAWuKF8ak97s92ZLY02nFkFq+FpwEmMSZlsBrwJ2M0VTbEp2tuA6D+9LNmDyMZuCbzKGYLdPZztLwySTdQQ9zC05JQcCewAnED87QbqCWs7e5zusR5p8nMGmciwO2K9kPs9Ty/0V7vWU5YqB77thv2jpUu/20T6Heenn+UIZtEzms43bVS/VD/P+mfbVoByhOlH6fmmdMl7qn55iCGu7ILFQdIPE+/7TxMBqgf7X9XT1vrAGsAGyZ6ARQRL+W9cWE9y2CFpR9tnNbzmSsYYnE+gxuDcdP7qF9zLRUtrpirZ12kEYdjRab+p3vE11Ji/0/HHAOdnvu8vtf28tP0122+snBukZ72qX7sU0f60tu1+8mO9bBUjsKtVaU5oQ8jM6G8O3OkkT6mQ2dwRuAP4SKk1zsiZrUEtkNpIuhx4p4O2v3r8GQQR0rMzbC5LUFx3HJBFRCnglEmVKraKOtsVu2vXF/3DgtJOSbr2IYIt8QHGl23nkpG1lT3uRpH+TTfsFZ5NjlgHkh4GvBHYwfarJ/v/Xa5/LbH4+yTjsxAfAN5v++yG9m6nYOn3dEEh4XEi0QPfSNappYDhKDM7pJD0YipB0tyytTbmmyX12VD0pd4JfJfa3xHA9rsb2PoO8KF6tZBCmu7QnHl2NkPSr233Jdjqck1RBud+wb1ctLRmuhrYjshS3gFs3cnk1/8OU7BVnPm79nesv1ey/66l3lEqTGDXBtJn/BLb/0/SC4ixvgt4BvBU268vcZ+5oDNbGi9Mf/BusPNIbVaqO7LJ2EJFb2Vj2P4nwWBWAp8h6OmrYzxH0reALxIlSDk4myT+Leks2916ngaCpLWIhvkjG156KCFs39UpyRmL7Xk51/XB0t2ia7ZvV2gLN4ImUqS/jXCKc8s8Nif+bgcS5UFQccSAGXXE1F0L8B+EJt7bc2zaPlvSbcTv2+nNWQTs7ETb39DeOjnjmAloTM96PlEadTFQpESoANaVdC7xeXS2SftD82JfkqCQfvkm0XvVCZLuLOkIQjP8Nw1N1uebOnlRznyzlIKccGgJH1tCMek5YJ26Iwtg+0o115NfDElPZCwIcpMzicNmAI11YYlsfgf/qJ3LyTgtnwKO84Dl0vbicWWWixZfMxE9rlcSOvfnVhzZFwJNP+9+bQa5LQj9/vaDZAI30Zi+rojP6y80T3wU12xvAUtV5tFdgBNS5cJZkrJLquuY9c6spM/Z3i9t79spUUj7J7k5k+B3uhxbTDKRP8yJfQuK/srGDlBaTPcjbFqvocniznZCdVIv5thoPDvamgR7cyOUdkpawr+6ZbdTJDSntO4LRB/Erh6jSM+e+IbdEbM9yLPbz+61hDh5Kxi09LuF8byU+K5tB1xBRFb3tN2NZXUqOHry/9IYVcKMo2rn6vsjTA8+T0jInVQ9mMrMjmP8ZzYpWppvnsKYoz3hlsxwQK4tuKLJK+kRcSj7+7xcn3PLNzWmIMf5MpGFX0h8NptIuorQTc2WOpom5LxTN6k4M8vXHJ1+f99e+C3QkWX5XWW7M77G5aI91kw3MMCayfZ30npmxdr6+ErC8WmCqoNYRe7fEGBlhczRvLTdIf4SkRHNQtNqpj52ttcYgd1HU8n/ypK28PBoti+lsf7ibYCqvngxH3TWlxm3WV6mQiQTkvYE9iAiylXB7iMIIeYvNrT3qNqheQRD64HA1U0zoJJuAp7bw9m+zA2p9SvXF+vbSE7164jS2A0IB3YXD4F0SVtooaS1dYr0YXLEFKx8K9r+r9rx3QjNtB9k2n0T8G5iIQxwE8EA+tUBxlqk9LsNKITUTwPOKpGpUsuSTpqlsgxzDeov2dLzXB97fd8fOdmmNkreZwskvZPgX+jI2v0NOML2cQ3tnA5caPtLteNvBV5mu5FTIukkgs32MNsPpWMiGFbXt91aIHGqUG/tbQEb2F52mofUOtSnv3wAm++z/em0vZPtb7R5v6ZQf6IvbP/bdI1lKpC0OrGOmM+QaLZL+jBBzPVHIjG4mW0nx/tk21sVuc8ccGb7EX/k1qEXJ7WR9CqClXax3h5wpO1vD2BzHtH/914igvlJ2zdm2OnnbH/F9n9kjq+fXlyTUgok/YPICh0EXJq+DIPoAZ7b77xnkGm5CkmbEJHQDrHGDQSb8UDZYxWkSB9WR0zRq/7qulMjaQ3i990yw+buwP4EY/XVsLi//EiCubqRQ9ul9PtMovR7zpbGqgWJlbTYnRWyDEsKJN1qe/0ux+cBv+x2bhJ7/Xpt7TxyktadWYXW5+uIAN92bd5rqpB0EPBcYJ9O+W4K3h9NkPB8vIGt1Yng8v2MJ6Rchign/13Dsd1i+0lNz00n1JIu7DBj0ITEZDZLJ6NKQLNI1q1eYaE8Ars32D4lbW/lCl+OpH1sfz5zbM8h1BDOr4xvA+ARmSXvE+8xB5zZDhX+POBCarT4bkiSo1lAaqPoXXsLsai+FDjcAzIG1pztDpvxQM52SSho1+cTUeTTCImQHwzgzP6BIMA4HfgZtTIzz7CsEUxfZFKZFOnD7ohJus49SCn6nZvE5uVEj/bttePrEAQMz2lo736i9PuASul3dpBmtqFg9cu0yjKMMDkkfRZ4BLBfZQGzAvE53+cGJENtQS0QPia7yxDP465En/lZRIBvWN6nvwA2cY0wUiFbdq176NhPYvPFVAgpnXSfM+x0DYKkc0PhzC6JUDuSasVY6DXGkVHvpX8YsIztxiWtw+BQTwZJexHVeo9Ih/5KRoVFsjXUwYV+mPU9s0TderXnperl53jqxUltJB3bbywZL/XbiKzD54BfE70Ci532nEiS7e/QpV9Y0n62P9fUXmmkxehn0wJ1AUEu9ThJ7yeybE3liNYAOr2AuxKsjqd7uDTxXk4s9ItAk1CkZ5gs2oPbApZTFy24FAxq3MuVsFLdkYXFpFyN2KoTHkeUfv97ynCcCTQm95pt6FL98o4Bq192pybLYPtXkt4AnE84UCNML95HVGncIamTIVib0N+e0fLBCooSPmqsv3xbQmP2a8AWw1aOCFB3ZNOxfyhY+XPsXUT8zoPiJ5IOAT7mSrZF0sHA5QXsDwx1JxeEjKqzWYQ2+svdY7vbfn9DNY4MRWvaXgTZY2NeldmASoXFi+oVFpJWbVJh0THZY7vb/lBh1mdmZwMUPXY94QoZwxTtnUT/nrNiTIXKoJmfLkh6OuGI7uzmpFdVO8sSC5AjiT6dYwsNcSCUjoSqMEW6pqEHdxBI+hQxrn1qmaFjgD/abszAKOkq289sem6KtouVfg8z2qh+UQuyDCOUQcr2rU/MY7favneSS6YNpUvekyP4Y2Ie7FQIDF2lhaQLiLakC2rHtwEOsv3imRkZpKDgfxLtGwuJtc6mwDXA22z/30yNbTYhVQfsxvhqu9McShg59tqQVOvXirac7RzVhpWJ7+/uRBXfZ23fkzm+e4Fbu50i1tqNq7tKonSFxWzOzM4JZ1ahHfkKxghZbgS+P2Ckv2p/aEhtJoOk1W3/vqC9O4fFOSmN5MRuRzgP6wDnEoRcTSUjWoGkfwK/oUcktOkCSdK1nbJ7SV8A/mD7I2l/sc5d5liHzhFL88LHCQmiO4i/41rEQulg2//KsNnv5bau7RW6nGuM3NLv2QC1oK3b70U77C/huQqNMX92RU4FUZsoUfKukECZD7yekBY5AzjEdt8ey+mGpA2Bc4g2pSq54FaEtvmMVyilddfTiLlh0aCtVEsSJD2NWM/8hLFs6mbE5/sa53GrDDVZWgquH0Dwd5xI8CX8eUCbi4h2ga7wDPdGqz/J3s1uSN5aWd8IWI+xtU7R9U0bmPXOrKTHEaUtvyUidyKieGsAL7b9v5l2i5HaqH32zg41965Ehm3NQezVbA9FZrZ0WY+kk4n+nvOIXscbBh9lWZR+eUi6AXiG7Qck3Uz0F17SOVcqUDNsjlglMwSRGarr+DWxVZT4Y7LS75ws5ZKISoR/wikyI/wjDAb1ZwJtXEGk9shJihM+dsZIBPfumc3vAAAgAElEQVR2JDKM37J9wqB2S0DBJLoGoQzQIRdcROhU/mbYHMfk2C4g+AqGOqEwDEiZ90+5xtgv6SXAh3My72qpv7wUJP2d0F39CtE3Og62/33CRZPbHHYHvleFxdZEwL7R51x6fTOdmAvO7EnAQtf6OiW9G3im7b4lvl3sFSe1KV3KlGwuD7yGcGA3A1YEXgtc4kRn38BWr8Z5Acs7o3G+TZSYYFI5WGfxW/0SDE3PSwvObFGK9GF3xLpkhkz87gttT3jZzQRKl37PVsym6pcRZgZtlMC1UfLe5R7zCH6G+R6S3llJ3wE+ZPu62vFnAYfafvUMjGkVV+QBSyYUljT0y8pJusn2UzNstpqUGRSSPkL/8TVmtZf0edv7DDSwFjFJhcVrc5M0qVS7Q7T2y0Ez3NOBueDM9vvS5mjZtcouWqiU6dR0/fnE4vdCIuM0FCyybWNJKRtsIxKqghTpw+6I9cgMrUosjN7qTLbNkmiz9HvYMVqszn1IWgpYxYmYK/XxvRnYv+mCWgWZTyvX3U7BkndJNwKnEtU+Q5XdrKJfJY6k620/fQbGdKztd7WRUFjSIOmXwNNd64+VtBxwvTMYodtIykwXJG1u++eZ1xabw9pA+kx3pVKSD5xa/+ynaGsZ4AQiMXZbsvcEgkDrHU19lenEUGXcMtGvZDCHaKIVdtEupUyDsHduBPwJuAm42faDKsAiq6DW75AFLLL9o0FtjjAQijJtposmsEG6ORN0B0tVMhe7ACfYPgs4S9LCTJvF0CsLkkppzgSePb0j6oqlNMa4vA2wZ+XcXJifJ6DLYvVtxGJ1KMrSRygDSfOBLwJ/l3QLwZj+NeDnBDFNUxRjPl18kb1OznV9sICoMDhf0h8J6bczc9udWsRyfc7lMr0Pisekf4edJX824KvEe3gfJ/Z9hXzcMcR3sDHSu51kq5qU+RTBQzFUSH3DHR6PPxMVGE1t7EI4d6XmsKLoUVUJ8DkF58r/EGXlF0y4uDsOIvydtTrVawpW6C8AB6efocRcWCw9sks5IcSH27hUNEVfjgeO1xipzd2SbiKT1KZWyrQ/Ucq0kqTOPRuVMtneRNJTiGjMDyXdDawoaQ03FChP41sT+CZwH2NkATunUubXeQgIkWqf8cr1z9xDRiZSCBOkkqhEQqd5LN0wKx0x23co5HmKQdJaRBnhkQ0vPR24OC18/0EwoXZ62oa+tCcTo8XqkoGDiFafWyVtRnzm823nymQ8RdJ1xPtpvbRN2i/GFjxIybvta4FrgQ+mKphdgMsl3UpIv32p1DgHxM8l7VEfj6S3EmuAmUCnImOJlCsrCdsfl7QPcImkhxPfkb8BR3kAtYbCSZniSIHqBennASKr+Cx3kdObIg6m7BxWFK7JEVWRMsobEZUiU53HdiCkxBYnAm3/VaFlezlD7MzOhTLjfiQTPbMzGffJJrXpUso0rjd10BLm1OeygHgB3GX7uQ2v/xaRGTmpdnx3YEfb2w8yvhKY5HO2C8oRDSNKlKe3MKaiPbjTBUlPBk6yveWAdqrSRGsSwa4DM+wUK/2eDdCQSzqNUAZd+lobs2vW7LVGTtJmybukFxFz9tNsLzuovRJITuK3gPsZc16fBSxDBLAbB8XbgIaQJX+2IWXW8IA8EZqG/vJBIOky4JFEu9MZtm+RdNsg5eml57CZgKS32/7iFP/vde4hNzRT7QdTxax3ZktDQ05q0w+KVO8rbX+34XX96L0b9x1PNyTtWC2BmUvoEgktwrRZCsPsiEn6NhPLD1clxvtG25dl2FwReB2x6N2AWBDuMsz9QsOM0WJ17kLSXUCVQfQ91X1nsIuWRlv9mWkd0WEyvp1YYH+j03c3LEitRZ2szaJh4BHohUESCksiUtB2T8YkK28iWoGy2opK95eXhqRziHXSuYSe7mUakO9mNsxhJSHpWuBFTCxbBrjIid9jGDFyZmtQS6Q2Ki9gvSaxKL/O9v2SViPKT99s+3ENbd1qe/0ux+cRTGYTzg0TNCTyQaUx7JHQYYekFzKxz+4e4BZnaMwmm/8AriBKKC9NmehiBHFLMkaL1bkFSYf2OW03ZBfVRHm2Djv5RcD7bd+TMcaihI+SPklkeP/EWIborhxbSypmc0JhWCBpS6J17ATgalgsWbkHsEM37oy5AI3JVC4g5PhWBra1fUWmvX5zGHPtXZUCFg/R3Zmd8YBFP4yc2RrUAruo+gtYNxYol7Qfka27FVgWOJqIFn0V+LTt3za091ngEcB+lezaCkRp1H22393E3nRD0p1zsURx2COhw44e5Aidv2MOOQKS9icyiSsApwFfB34w+iymjtFidQQNwC5as7MKwSz6XNs7ZVxftOQ9LX5Pz81+jdBeQmFJgqTzgCNcI/FMAd4P2H5FofsMraRaSvDsQny315qLa8QRxjByZmuQdAPwDNsPSLoZ2NP2JZ1zTb6wiRRmI+BACgpYK+j/n5cm+7UJp/YFudE2BRnO4cSioNN3tDZwMqFFN7R03DB3M7MjtIcqOULOSzj1MHeYS58EHEqUyDZaxEp6iu2b0/ay1UoNSc+ZixH00WJ1yYRq7KK2G7OL9rE9sFxbqZL3tIjem/FVWMfZ/v0g41tS0EZCYUmDpF/a3qDHuYFax9rsL28LktZxJgmUpFcAHySkbzrf5yNs/3e5EY4wKObN9AAGhaTNJa1R2d9d0jmSjpG0aobJDrvoOQzOLnoyUcK0Zt2RBbD9Q2CNCVdNjvs62QvbvyZKgbMXvbb/5SCuWYtwaP8NeILtA4fFkZV0vaTruvxcT0TU5xwkvaGyvVXt3NAKec8G2H7QwTyaxexo+1e2P5EIETYnypnOyzB1WmX7p7Vzx+WMbRagq6ST7YOJ0rARZjFSS01n+wmSPpB6sb4G7AW8tLAjuzQF2NNt32X7KNvPBLYnqjeajmUrQrYDolLqlLT9s/ocPkJPLCWp83luA1T7eIeWJX/I0I/s6e85BiXtIelC4GLg0YSk2m9tf3RYHFlJW0p6fQooIWljSaeR1vEZ9vYAPkZI8qwLrAd8FPiIpD37XDrCNGPWZ2bbiPKrEKmNpM8AlxDEPcUErBVSPGdUDs2v7jctC54NZX9qkclyWFHNNtQzDyUyESPMPCRdY3vT+na3/bmCktUvIwwfJP0AeAfhvBZjF1V3Cb5ViIDIpU17cJPNou8+SZcD77R9Te34M4Av2h4GbeuhhmYpS/4wocsacfEpYGfbjRMApfvLS0PSkcCrgIVEUPQ7RPDsk8R3774Mm4urIGvHH0XMOU8deOAjFMFciHJ1jfITgtELcwx2y3Lm9MDYPiD1npYWsH5vbX9QXbgvAi8BSAGBTzEWEDgBmPGyv7norE4B6rHdbX+EaYAmktAsPkX0MTfVtq4T2vQ6N5ewJGrrLklYQDCo/oFgCV4deAxwC4M906+u7XcI3Y52Qwb/Ckq/+1aqO7IAthcqSaSM0B+2PyHpAsYSCp1nZh7x2YwwOeprxCquzLQ57Pq/2wGb2r4v9dL/L7Cx7VsGsKluAS3b90hzbwmm0CT+lxNBpoIR+5XAHba/OaODmwRzwpmV9DCHXMk2BBV5BzP++6XMbl3AGqLUI0vA2vbJJcdICwGB0mjBiZgNWBIdnaGGKyLlhTKnj5d0DPEcd7ZJ+2sOaHsoMVqszm045GcuBS7VGLvoR1OwYmVJWziDXdSFNONrKP3uk6RVbP+pdnBV5kBb13ShVEJhSUW/NaKkozJt/hE4Hji+0l9+t6SbGA5JtX90sq+2/5R6gwdxZAH+ImmT1JK0GJI2oX8p92zF94C3Arek+fqnwKnAqxSkfR+c0dH1wVwoMx76khRJO3SiGiogYK2J+pmLJQpsn9L9qr72RmV/QwhJ9xLkXiJ6NW7tnALWtb3CTI1thGKkM2/qd76FwNUII8wIUjZnF2IRnMUuKml74H1Ap7zvSuAw25dKeqTtRpn90u++1Ee3B0H62GlJeiZwBHCi7S82sTfCCKWhwoSZGhJJNUn/R7T1dfCC6r7t12TYfB7hzH2FqIA0wZHxJuANti8dZMzDBknXJx4QJH0MWNX23okH4arOuWHEjGcuB8UkUf5hIck5iND8GsiJraBbZG1V4A2SNrL9gYb2RmV/w4lRP8Ycx8hZHWFJgYPN9xjgmMk4ELpB0l7AWwhntlMq+Szg05KOBj4EbNLQbNF3n+0TJP0vQRqzYTq8CPi47W83tTfCCC0gqz52sv7yYqPLx/a1/c8MajAFybYg2MnfTPztFgHP6fwd5hiqSbKtCb4fbN8v6aGZGdLUMOszs92Q+lRfR0SLthuC8UwLWY9CbuQq52nhFiG9GqFdKHQR7/Fc/OLOAtRIaI4iMjCLkdNXkrKz+wIduYSbgGNsfzV3nCOMMNOQdG6/800zJamccaseZCx3Ae+xfXzGOEfvvhHmFNRbyUPAtbYfn2FziZNUk7S2QzFkiYCkU4DfAb8BPgA80fa9klYGLnaSzBpGzBlnNqXBX0loX70cOIvQvprxaGilXHTCKaLfc+OC9xrpsM0RpEXWp4D/R0T6v0ZQ4s8Ddrf9vRkc3hIJSV/pc9q239LQ3u7A/sB7iLJEAZsREdGjRw7tCLMVkv4A3ElkP39GLSNk++KG9m7qxR4q6WbbT8kdaylIOqTPadv+2LQNZoQlFpJuI7JsXbOwzmAU15Dr/6YWhMfb/kLa/xlBPAfwPtv/lWGzqihxlu0diw14CCFpeSKw/liiLeLadPy5wHq2cwhrpwWzvsxY0ksJ9sRtgYuIBf8WLZFF5OI2JjIxZqNH1G0VYHeiBGKEuYHPE6VzjyS09l5h+3JJTyEWiCNndprRb16RlPOi2wt4nccLul+YbJ1BMKGPMMJsxBpA5/28K/Bd4HTbue+ofmQsw9IO003DcwWCVOVRRFByhBFaRY6zOgUMNdkq0X4wv7K/LNHfugLR89rYmWV8MGAoJIjahO1/EAmU+vHLgMumf0RTxzA8gIPi+0Sfy/Ns3waQ+meGCfe7rLRMpxG980XrSBRcBLyz4H1GmFk8zPb5AJIO6zA82r55LtLCzwF8lqgIaYKVao4sALZvlzQXGbpHWEJg+0Ei4PY9ScsSTu2P0lzWmMUfOAA4N1VHTCBjKTTsgWB7cZ9eInvcF/g3IjA1cA/fCCPkQtJ6hLO3IJPUc9i5VZaxfWdl/1Lb9wD3pNbDHPRTlJhzkHQ9PchlCfWVxlq904W54Mw+k/iC/lDSr4iXxlIzO6QJ+ElJYy1F3UYYPlQb7v9ROzfnJ9ZZiJwIQ/1zneq5EUYYeiQndjvCkV2HIIDK0itMZCzPJqoZ3syQkrGkyqn3ALsBJxMKC3/qf9UII5SHpMcSDOK7AhsDhxPfxcaYhGx1GCTVVqnu2K4SwD6GPGwi6S/EXLN82oa5Kwn5qi7HViUChscSTO1DiTnTMwsgaSvii7ojsJDQvjphZke1mOCl5x+6aV9cytis3tHQkrQTsHw6/f3EGjnCLIekB4myNRGf772dU8BytodJsHyJR47kwST99CP5pRFmLSSdDGwEnAecYfuGFu+1le2iQePMcRwJ7ACcAHzB9t9meEgjLIGQtAexFn48cGb6OWcuJ0IknQr8yPaXasffDrzIdpYTP0JA0jW2N53pcfTCnHJmO5A0D3gJML8pIUtL4+lWUiWij3ZN240y5JJOAC6zfVLav5VYMCwPPGD7HYONeIQRRuiGLmU4i08BG9hetqG9vhIlhdsTRhhh2pCkHDo9pNXvTFZWI7H17wysCZxne5GkVxG8AssPw0Ir/c7/BB6gwO88wgg5kHQ/8FPgANtXpmO/sj1n+z4lrQacTXz/qhrPywKvHSV5BkOVAGwYMeudWUnbAivWmcok7QbcbfsHMzOy7lA0O+4GvB+4EfiE7esa2riGKF1yZ7/zIpd0qe3nFR72CCOMwMj5HGGEmYKkk4C1gCuAZxP6llsCH7B99gwObYQRhgpJwm8nIju7OpGZfbPttWZ0YNMASVtT0Xi2feFMjmc2QVI3CdFVCE6Cv9kehnLyrpgLzuzlwKtt/6F2fA2izHjLmRnZeEh6GNHncwAhU3C47V9k2rre9tMr+xt1Srgk3ZDZ3D/CCCOMMMIIQwlJNwAb235I0nIEMcn6w9QvO8IIwwZJjycRPwEPJ9bFH5rZUY0wjJB0Ue1Qh1z2R8AJtv817YOaIuYCAdTD644sgO3fDcBgVhSS9iZYDS8AXl4ge/OQpDU6L/GKI7sm40mDRhhhhIKQ9Fd6lxmPyghHGKE93G/7IQDb90n65ciRHWGEiZC0dMfxsH0XcBRwlKQnM16+ZoQRFsP2i2d6DLmYC5nZXwJPS9pX1eNLAzfaftLMjGzcWB4C7gb+QPc+mo0b2nsD4RwfAFyTDm9GTFjHDLOw8QgjjDA5JK1F9PwfOdNjGWGEYUCNLE3Aemk/6z06wghzFZLuBs4h5HQu8mxf6I8wLZD0auC6TsJN0iEEoe4dwL4d+dNhxFxwZj9F9ATsY///9u49WNK6vvP4+zNcBRRwRTZcBhwQRbyAwdIUuwsicb0giCA3lQLBVcOiZYIGFIW4orCixQZLYxRQA8hQXFQwIuwKKobFEgS5RNlhmGHiQAApdDKICHz3j+c52tP0nJwznNPP6Z73q+pUPf37ne7n0wzQ8+3frVa2bRvTHAHwYFX9dZf52jwzvs4uyetoNr7YhaZAvh04raq+s0YhJXWqb53T1jTTwY7vNpU0N7heXZqaJP8BOIhmFPb5wMXA16vqhk6DaU5L8jOao84eaTfX+yzN30d2A95aVf+104CTGIdidl3gE8AxNN8ehGaTiLOBj87lOd7tUUKHV9WxXWeRNHxJngkcQHMO4E7AZcAhVbVNp8GkEeHnqLR6Sbai+ZL0UOC5NMdkfaTbVJqLencsTnIO8IuqOr19fFNVDdogak6Y13WAp6uqHq+qE2gK2CNpDvedX1UnzMVCNsmuSf5nkiU0RfjP1+A1Luq5Pr2v76qnHVLSsNwPHA2cCuxQVX8FPNZtJGlum4nPUWltUFXLaQZ3vgCsoBn4kQZJkk3a401fQ7PPz4QNO8o0JSO/AVSStwxofn5zAg5U1aXDTfRUSXbij7vJ/QpYSDMqvqaLrXvXAf85zTE/E7ZYw9eUNHwfpvl/wxeAC5Is7DiPNCfNwueoNLbaHb/fRPPfyx7AlcCJgAMeWp0zgZuB3wD/3HNG8W7AvV0G+/eMwzTjcyfprqp659DCrEa7AdQPgaOralHbtsYHWPcO9/cP/c/1qQCSnirJApq/dEyscTqZZs3snZ0Gk+aImf4clcZVkguAfYAfABcCV1TVo92m0ihoT0V5LnDLxO7xSf4EWK+q7uk03CRGfmS2qo7qOsMUHEjzl9RrklxJ8z+XPI3X26j9pmQe8Iz2Ou3PM55uWEnDVVWLaaYan5rkJTRraL9Ds2OrpJn/HJXG1XeBd1fViq6DaHQkeXtVnQf8st2L4EcAVXVvkv8OfK7TgJMYh5HZIybprrl0TE27y/KbaUZg9ga+SjP6Mq1pH0muZfBZl8BonxUlSdLqzNTnqDTOkrwY+CB/PPHiDuAzVfWzToNpzhrlWZ/jUMyeNaiZZq3A1lU1J0efkzybZoe5Q6pq767zSBq+JCsY/MXUxNmZzxpyJGlk+DkqPVWS/YEzgE8BP6H5PPlTmjWzx1fVNzuMpzkqyU+rarf+60GP55qRL2Z7pdn16W00GyLdAZw6jt9CJXkFsKyq7msfH8EfDzY+paoe6jKfpOmb6x8WUpfaDW3eA+wI3AqcXVWPd5tKmnuS3ALsX1VL+tq3B745cfyK1GuUR2ZH/mgeaM6aTXIMTQG7D3BQVR0yjoVs64u0x3ck+S/AacDXgF8Df99hLklrbny+WZRm3leB3WkK2dcDn+k2jjRnrddfyAK0besNPY1Gxc5JfpbkVuCF7fXE4xd0HW4yc3IK7nQkORZ4P815SK+rqqUdRxqGdXpGXw8B/r6qLgEuSXJzh7kkSZoNL6qqlwAkORv4ccd5pLnq90nm9+8+m2Q7wNkMWp0XMqJfqo98MQucBdwP/Cfg8onzZfnjmrOXdhVsFq2TZN12itVrgP/W0zcOf6bSWqHvnOzN+s/NngvnZEtzxO8nLqrq8Z7PekmrOhn430k+CdxIU6C8AjiBZhmeNMhtrL6Y/V2Su4CPVNX/GWKmKRn5NbPtN02rNY4jtUk+ArwBeBCYD7y8qirJjsBXq2qPTgNKmpJROCdbmguSPAGsnHhIcwzdI7hZmvQUSV4G/BXNbsYBbgfOqKpbOg2mkZRkHeDFwPlV9eKu8/Qb+WJ2ddozkg6vqmO7zjIbkrwK+BPgqqpa2bbtBGxSVTd1Gk7S05bkwHb5gCRJUqeSvLuqvth1jn5jVcwm2RU4HDgYuBu4tKoGHd0jSXNaknuqan7XOSRJoyPJ5Uyy9rGq9htiHGnWjez6ynZkcmuanQ0PpTlA/VfAQpoi/dUdxpOkp8tFgZKk6Tqj6wDSMI3syGySfwL+M82mED8Ejq6qRW3f4qpa0GU+SXo6HJmVJM2kJHtU1Y+6ziHNpJEdmQW+DBwEvIVmVPaaJFcCF+KIhqQR0J7fNugbxQBbDjmOJGnEtZv1HEwze/HKqrotyb7Ah2k2Ttuty3zSTBvZkdl+STYG3kxT2O5Nc8D6ZVV1VafBJGk11sbd2CVJsyfJV4Btac5ifiWwFPgz4ISq+kaH0aRZMTbFbK8kzwbeChxSVXt3nUeSJEmabUluA15aVU8m2ZDmGMcdq+q+jqNJs2KUpxkDkGTvqvpee/28qrq7qh4CvpjkwY7jSdJqJVnB6qcZe3amJGm6HquqJwGq6tEkd1rIapyN/Mhskpuq6uX914MeS5IkSeMqySPAoomHwA7t44kvSV/aVTZpNoz8yCyrbvbUv/GTG0FJkiRpbbFz1wGkYRqHYrZWcz3osSRJkjSWVrdxYJI9gMOBY4ebSJpd41DMLkjyLZpR2Ilr2sfP6y6WJEmS1I0ku9IUsAcDdwOXdptImnnjsGZ2z8n6q+r7w8oiSZIkdSXJTsChNEdV/gpYCBxfVZMeBSeNqnEoZrcAtqiqO/radwHur6oHukkmSZIkDU+SJ4EfAkdX1aK2bXFVLeg2mTQ75nUdYAacBWwxoH0b4H8NOYskSZLUlQOB+4BrknwpyWtwQ1SNsXEYmb29qnZZTd9tVfXiYWeSJEmSupJkY+DNNNON9wa+ClxWVVd1GkyaYeMwMrveGvZJkiRJY6eqVlbV+VW1L81sxZuBEzqOJc24cShm/1+SN/Q3Jnk9sLiDPJIkSdKcUFUPVdUXq2rvrrNIM20cphnvBFwB/BNwY9u8O/BnwL5VdWdX2SRJkiRJs2Pki1mAJBvQnKM1sT72duCCqnq0u1SSJEmSpNkyDtOMqarfAdcC1wDfA661kJUkSdIgSf4uyUen+LtfSfKJSforyY4zl27SLKckOW8I99m+fV/rzva9pKdj5P8FTfIs4MvAn9Isbp8HvCzJjTRnbP2my3ySJEmaeUmWAM8AFlTVyrbtGODtVbXXZM+tqvfMekBJs24cRmb/FrgDeH5VHVhVBwA7ALcCn+s0mSRJkmbTusD7uw4xDI6SSk81DsXsHlV1SlU9OdFQjY/TbAIlSZKk8fRp4Pgkm/V3JHlhkquTPJTkF0kO7ulbZepwkg8luTfJ8iTHDJg6vHmSbydZkeSGJDv03e4NSRYneTDJp5PMa193XpKTkixNcn+SryXZtO3bK8m/9GVekmSf9vqUJBcnOS/Jb4Aj219bv32dFUluT7J7z/N3TnJtkofbvv16+jZtn/dAm+eknpzrJDmjzb8YeOM0/gykzoxDMZuuA0iSJKkTP6HZN+X43sYkGwNXAxcAzwUOAz6fZJf+F0jyOuAvgX2AHYE9B9znMOBvgM2BRcCpff0H0Jym8XJgf+CdbfuR7c+rgQXAJkxv5uD+wMXAZsD5bdt+wIVt27cmXi/JesDlwFXtez4OOD/JC9rnnQVs2ubYEzgCOKrtexewL7Bb+z4OmkZGqTPjUMz+KMnHkqxS1LaL+v9vR5kkSZI0HB8DjkuyRU/bvsCSqjq3qh6vqpuASxhcpB0MnFtVt1fVIzRFa79Lq+rHVfU4TVG5a1//6e15rvcAZ9IUvwBvAz5bVYur6t+AE4FDpzFl+Pqq+kZVPVlVv23brquqf6yqJ4B/AF7Wtr+Kplg+raoeq6rv0RxfeViSdYBDgBOrakVVLQE+A7yj55/BmVW1rKoeAj41xXxSp8Zh7v1xwNnAoiQ3A0XzrdJPgaO7DCZJkqTZVVW3JbkCOAH457Z5O+CVSR7u+dV1aYq/flvRjPBOWDbgd+7ruX6Epmjs1fucpe1rTrz20r6+dYEtB9xjkKlk2bAtjrcClvUuvWvvtzXwHGD9AVm27snZ/x6kOW/ki9l2t+K3tmsXXkQz7fivq+qubpNJkiRpSE4GbqIZbYSmMPt+Vf35FJ57L7BNz+Nt1+D+2wK3t9fzgeXt9XKawpqevseBf6UpIDea6GhHT3tHl6EZpJmq5cC2Seb1FLTzgTuBB4Hft1nu6On7ZXt9L6u+7/nTuK/UmXGYZgxAVd1VVZdX1bcsZCVJktYeVbUIWAi8r226AtgpyTuSrNf+vCLJzgOefhFwVLt50kY005an64NJNk+yLc3uygvb9q8DH0jyvCSbAJ8EFrbTle+kGVV9Y7ve9SRggzW494QbgJXAh9r3uxfwJuDCdkryRcCpSZ6ZZDuadcITZ9ZeBLwvyTZJNqcZ5ZbmvLEpZiVJkrRW+ziwMUBVrQBeCxxKM2J5H3A6A4rFqvoOzVGP19Bs7nR92/W7adz7m8CNwM3At2mWwAGcQzO1+QfA3cCjNEvkqKpfA38BfJlmhHQlsMruxtNRVY/RbA71epqR2M8DR1TVz9tfOa69x2LgOprNsRTCtAwAAAciSURBVM5p+74EfBe4hWaE+9I1zSENU6qmM3tBkiRJGl/t6O1twAbtCKqkOWrkR2aTbNROzZh4/IIkH0jyli5zSZIkaTQkOSDJ+u0U29OByy1kpblv5ItZ4Epge4D2cOvrac7POjaJ24pLkiTp3/Nu4AHgLuAJ4L3dxpE0FSM/zTjJrVX1kvb6fwDPrqpjk6wP3DjRJ0mSJEkaH+MwMttbje8NXA1/WAT/5MBnSJIkSZJG2sifMwv8LMkZNLvA7QhcBZBks05TSZIkSZJmzTiMzL6LZvvx7YHXVtUjbfuLgDO6CiVJkiRJmj0jPzJbVb8FThvQtQzYY8hxJEmSJElDMA4js3+Q5DlJ3pvkB8C1wJYdR5IkSZIkzYKRH5lN8kzgAOBwYCfgMmBBVW3TaTBJkiRJ0qwZh6N5fgv8GDgJuK6qKsniqlrQcTRJkiRJ0iwZh2nGHwY2BL4AnJhkh47zSJIkSZJm2ciPzE5IsgA4DDgUeD5wMnBZVd3ZaTBJkiRJ0owbm2K2V5KX0KyhPbiqHKmVJEmSpDEzlsWsJEmSJGm8jcNuxiuAQRV5gKqqZw05kiRJkiRplo3VyGySn1bVbl3nkCRJkiTNrnHYzbjX+FTmkiRJkqTVGrdiVpIkSZK0FhiHNbNv6Xm4Wd9jqurSIUeSJEmSJM2ykV8zm+TcSbqrqt45tDCSJEmSpKEY+WJ2MkkOrKpLus4hSZIkSZpZ417M3lNV87vOIUmSJEmaWeO+AVS6DiBJkiRJmnnjXsyO77CzJEmSJK3FxmE341sZXLQG2HLIcSRJkiRJQzDya2aTbDdZf1UtHVYWSZIkSdJwjHwxK0mSJEla+4zDNOMVrH6acVXVs4YcSZIkSZI0yxyZlSRJkiSNnHHfzViSJEmSNIYsZiVJkiRJI8diVpIkSZI0cixmpVmW5O+SfHSKv/uVJJ+YpL+S7Dhz6SbNckqS84Zwn+3b9zXyG9JJkiRpePzLozQFSZYAzwAWVNXKtu0Y4O1Vtddkz62q98x6QEmSJGkt48isNHXrAu/vOsQwOEoqSZKkuc5iVpq6TwPHJ9msvyPJC5NcneShJL9IcnBP3ypTh5N8KMm9SZYnOWbA1OHNk3w7yYokNyTZoe92b0iyOMmDST6dZF77uvOSnJRkaZL7k3wtyaZt315J/qUv85Ik+7TXpyS5OMl5SX4DHNn+2vrt66xIcnuS3Xuev3OSa5M83Pbt19O3afu8B9o8J/XkXCfJGW3+xcAbp/FnIEmSJAEWs9J0/AS4Fji+tzHJxsDVwAXAc4HDgM8n2aX/BZK8DvhLYB9gR2DPAfc5DPgbYHNgEXBqX/8BwO7Ay4H9gXe27Ue2P68GFgCbAJ+bxvvbH7gY2Aw4v23bD7iwbfvWxOslWQ+4HLiqfc/HAecneUH7vLOATdscewJHAEe1fe8C9gV2a9/HQdPIKEmSJAEWs9J0fQw4LskWPW37Akuq6tyqeryqbgIuYXCRdjBwblXdXlWP0BSt/S6tqh9X1eM0ReWuff2nV9VDVXUPcCZN8QvwNuCzVbW4qv4NOBE4dBpThq+vqm9U1ZNV9du27bqq+seqegL4B+BlbfuraIrl06rqsar6HnAFcFiSdYBDgBOrakVVLQE+A7yj55/BmVW1rKoeAj41xXySJEnSH1jMStNQVbfRFG0n9DRvB7yynW77cJKHaQrL/zjgJbYClvU8Xjbgd+7ruX6Epmjs1fucpe1rTrz20r6+dYEtB7+bp5hKlg3b4ngrYFlVPdl3v62B5wDrD8iydU/O/vcgSZIkTYvFrDR9J9NMlZ0ozpYB36+qzXp+Nqmq9w547r3ANj2Pt12D+/c+Zz6wvL1eTlNY9/Y9DvwrsBLYaKKjHT3tHV0GqGlkWA5sO7EOtud+vwQeBH4/IMsv2+t7B7wHSZIkaVosZqVpqqpFwELgfW3TFcBOSd6RZL325xVJdh7w9IuAo9rNkzaimbY8XR9MsnmSbWl2V17Ytn8d+ECS5yXZBPgksLCdrnwnzajqG9v1ricBG6zBvSfcQFMgf6h9v3sBbwIubKckXwScmuSZSbajWSc8cWbtRcD7kmyTZHNWHeWWJEmSpsRiVlozHwc2BqiqFcBrgUNpRizvA05nQLFYVd8B/ha4hmZzp+vbrt9N497fBG4Ebga+DZzdtp9Ds671B8DdwKM0GzNRVb8G/gL4Ms0I6Upgld2Np6OqHqPZHOr1NCOxnweOqKqft79yXHuPxcB1NJtjndP2fQn4LnALcBNw6ZrmkCRJ0torVdOZWShpJrWjt7cBG7QjqJIkSZKmwJFZaciSHJBk/XaK7enA5RaykiRJ0vRYzErD927gAeAu4Alg0EZRkiRJkibhNGNJkiRJ0shxZFaSJEmSNHIsZiVJkiRJI8diVpIkSZI0cixmJUmSJEkjx2JWkiRJkjRy/j96yufDHzHqEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e513c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(1, 2, sharey='row', figsize=(16, 6))\n",
    "\n",
    "\n",
    "(attended_appointments\n",
    "     .groupby(['Neighbourhood'])\n",
    "     .intApptSched\n",
    "     .median()\n",
    "     .sort_values(ascending=False)\n",
    "     .head(20)\n",
    "     .plot.bar(ax=axarr[0])\n",
    ")\n",
    "axarr[0].set_title('Highest median waiting days', fontsize=14)\n",
    "axarr[0].set_ylabel('# of Days', fontsize=12)\n",
    "axarr[0].set_xlabel('Neighbourhood', fontsize=12)\n",
    "\n",
    "(attended_appointments\n",
    "     .groupby(['Neighbourhood'])\n",
    "     .intApptSched\n",
    "     .median()\n",
    "     .sort_values()\n",
    "     .head(20)\n",
    "     .plot.bar(ax=axarr[1])\n",
    ")\n",
    "axarr[1].set_title('Lowest median waiting days', fontsize=14)\n",
    "axarr[1].set_xlabel('Neighbourhood', fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Neighbourhood')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1de0d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(1, 2, sharey='row', figsize=(16, 6))\n",
    "\n",
    "\n",
    "(attended_appointments\n",
    "    .groupby('Neighbourhood')\n",
    "    .AppointmentId.count()\n",
    "    .sort_values(ascending=False)\n",
    "    .head(20)\n",
    "    .plot\n",
    "    .bar(ax=axarr[0])\n",
    ")\n",
    "axarr[0].set_title('Highest total appointments', fontsize=14)\n",
    "axarr[0].set_ylabel('# of Appointments', fontsize=12)\n",
    "axarr[0].set_xlabel('Neighbourhood', fontsize=12)\n",
    "\n",
    "(attended_appointments\n",
    "    .groupby('Neighbourhood')\n",
    "    .AppointmentId.count()\n",
    "    .sort_values()\n",
    "    .head(20)\n",
    "    .plot\n",
    "    .bar(ax=axarr[1])\n",
    ")\n",
    "axarr[1].set_title('Lowest total appointments', fontsize=14)\n",
    "axarr[1].set_ylabel('# of Appointments', fontsize=12)\n",
    "axarr[1].set_xlabel('Neighbourhood', fontsize=12)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Correlation between Total Appointments and Waiting Times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalAppointments</th>\n",
       "      <th>MedianWaitTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TotalAppointments</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.189272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MedianWaitTime</th>\n",
       "      <td>-0.189272</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   TotalAppointments  MedianWaitTime\n",
       "TotalAppointments           1.000000       -0.189272\n",
       "MedianWaitTime             -0.189272        1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(attended_appointments\n",
    "    .groupby('Neighbourhood')['AppointmentId', 'intApptSched']\n",
    "    .agg({'AppointmentId': pd.Series.count, 'intApptSched': np.median})\n",
    "    .rename({'AppointmentId':'TotalAppointments', 'intApptSched': 'MedianWaitTime'}, axis=1)\n",
    ").corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation matrix above seems to show a lower negative correlation between the wait times and the total appoitments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_weekday = attended_appointments.groupby(lambda x: attended_appointments.loc[x].AppointmentDay.weekday())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mon    22713\n",
       "Tue    25638\n",
       "Wed    25866\n",
       "Thu    17246\n",
       "Fri    19019\n",
       "Sat       39\n",
       "Name: AppointmentId, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_weekday.AppointmentId.count().rename({0: 'Mon', 1:'Tue', 2:'Wed', 3:'Thu', 4:'Fri', 5:'Sat', 6:'Sun'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# of appointments')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1047419e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_fig = by_weekday.AppointmentId.count().rename({0: 'Mon', 1:'Tue', 2:'Wed', 3:'Thu', 4:'Fri', 5:'Sat', 6:'Sun'}).rename('Total Appointments').plot.bar(table=True, figsize=(16,6))\n",
    "weekday_fig.tables[0].set_fontsize(16)\n",
    "weekday_fig.tables[0].scale(xscale=1, yscale=1.75)\n",
    "weekday_fig.xaxis.set_visible(False)\n",
    "weekday_fig.set_title('Appointments by weekday', fontsize=16)\n",
    "weekday_fig.set_ylabel('# of appointments', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data indicates that there are rare occurrences of appointments on Saturdays, and none on Sundays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attendance Rates\n",
    "\n",
    "Now let's take a look on what are the attendance rates on several attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# of schedules')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fbc2c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attended_appointments.groupby(['Age', 'Attended']).AppointmentId.count().unstack().rename({0: True, 1: False}, axis=1).plot.area(figsize=(16,5))\n",
    "plt.title('Attendance by Age', fontsize=16)\n",
    "plt.ylabel('# of schedules')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a24703f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(2, 1, figsize=(16, 12))\n",
    "plt.suptitle('Attendance percentages by attribute', fontsize=16)\n",
    "ax1 = pd.concat([\n",
    "    pd.DataFrame(attended_appointments.groupby(['Attended'])['AppointmentId'].count().apply(lambda x: round(100 * x / attended_appointments.AppointmentId.count(),2))).rename({'AppointmentId':'General'}, axis=1).transpose(),\n",
    "    attended_appointments.groupby(['Attended', 'Gender'])['AppointmentId'].count().unstack().apply(lambda x: round(100 * x / x.sum(),2)).rename({'F': 'Female', 'M':'Male'}, axis=1).transpose(), \n",
    "    attended_appointments.groupby(['Attended', 'Diabetes'])['AppointmentId'].count().unstack().apply(lambda x: round(100 * x / x.sum(),2)).rename({0:'Not Diabetic', 1:'Diabetic'}, axis=1).transpose(),\n",
    "    attended_appointments.groupby(['Attended', 'Handicap'])['AppointmentId'].count().unstack().apply(lambda x: round(100 * x / x.sum(),2)).rename({0:'Not Handicapped', 1:'Handicapped'}, axis=1).transpose(),\n",
    "], axis=0).rename({0:'Not Attended', 1:'Attended'}, axis=1).plot.bar(ax=axarr[0], table=True, stacked=True)\n",
    "# axarr[0].legend(ncol=2)\n",
    "ax1.xaxis.set_visible(False) \n",
    "ax1.tables[0].scale(xscale=1, yscale=1.75)\n",
    "ax1.tables[0].set_fontsize(16)\n",
    "ax1.set_ylabel('Percentages', fontsize=14)\n",
    "\n",
    "ax2 = pd.concat([\n",
    "    attended_appointments.groupby(['Attended', 'Scholarship'])['AppointmentId'].count().unstack().apply(lambda x: round(100 * x / x.sum(),2)).rename({0:'No Scholarship', 1:'Scholarship'}, axis=1).transpose(),\n",
    "    attended_appointments.groupby(['Attended', 'Hipertension'])['AppointmentId'].count().unstack().apply(lambda x: round(100 * x / x.sum(),2)).rename({0:'No Hipertension', 1:'Hipertension'}, axis=1).transpose(),\n",
    "    attended_appointments.groupby(['Attended', 'SMSReceived'])['AppointmentId'].count().unstack().apply(lambda x: round(100 * x / x.sum(),2)).rename({0:'SMS Not Received', 1:'SMS Received'}, axis=1).transpose(),\n",
    "    attended_appointments.groupby(['Attended', 'Alcoholism'])['AppointmentId'].count().unstack().apply(lambda x: round(100 * x / x.sum(),2)).rename({0:'No Alcoholism', 1:'Alcoholism'}, axis=1).transpose(),\n",
    "    \n",
    "], axis=0).rename({0:'Not Attended', 1:'Attended'}, axis=1).plot.bar(ax=axarr[1], table=True,  stacked=True)\n",
    "# axarr[1].legend(ncol=2)\n",
    "ax2.xaxis.set_visible(False) \n",
    "ax2.tables[0].scale(xscale=1, yscale=1.75)\n",
    "ax2.tables[0].set_fontsize(16)\n",
    "ax2.set_ylabel('Percentages', fontsize=14)\n",
    "\n",
    "fig.tight_layout(h_pad = 10, pad=3.4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "The data sample consists of more than 100,000 records of scheduled appointments in the health public system for Vitoria, Brazil. By analyzing the data, I was able to reach the following conclusions:\n",
    "\n",
    "- The median days between scheduling and appointment is 4 days. This does not take in consideration that there are almost no scheduling for appointments on weekends, which can increase the waiting time;\n",
    "- There is no apparent correlation between the number of appointments from a neighbourhood and the waiting times for that region;\n",
    "- There are no clear busier days on the week - but apparently there are no appointments on weekends, or they are made only for Saturday in exceptional circumstances;\n",
    "- The general attendance rate is around 79%;\n",
    "- There is a clear difference on appointment rates between genders: Female patients are responsible for 64% of the appointments, while male patients only correspond to 36%;\n",
    "- Besides the differences between the appointment rates among genders, the attendance rate is similar: around 80%, which is consistent with the general attendance rate;\n",
    "- The attendance rate for the various attributes is consistent with the general rate, with the exception of the SMS message: The appointments for patients that received a message have a higher no-show rate than the ones where no message was sent.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limitations\n",
    "\n",
    "There are some limitations presented by the data presented, like:\n",
    "\n",
    "- Handicap data: the data presented has more distinct values than what was specified. I made some assumptions, but the ideal would be to recheck those data or the data specification to be sure of the valid values;\n",
    "- Besides not having appointments scheduled on Sundays - and very few on Saturday, it is impossible to attest that the waiting time would be smaller if those days were available for assignment;\n",
    "- There is no information about the reasons for the interval between the scheduling and the appointment days. I named this interval as 'waiting time,' but this does not adequately cover when the patient requests this timespan."
   ]
  }
 ],
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