{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "western-flush",
   "metadata": {},
   "source": [
    "# P08: Plotting with Matplotlib\n",
    "\n",
    "## Graphs and visualization\n",
    "\n",
    "Data analysis starts with looking at data, and ends with communicating your results. Both of these are done most effectively with graphs.\n",
    "\n",
    "There are many skills associated with making graphs and visualizations:\n",
    "\n",
    "0. figuring out *what* to plot to answer a question.\n",
    "\n",
    "1. transforming data to expose the variables you want to plot\n",
    "\n",
    "2. choosing the right kind of plot for the data / question.\n",
    "\n",
    "3. instructing a computer to make the plot you want.\n",
    "\n",
    "4. making the plot interpretable, and appealing\n",
    "\n",
    "## Choosing plots\n",
    "\n",
    "What are you looking for, or trying to show?\n",
    "\n",
    "- the distribution of one variable.  -> histogram\n",
    "\n",
    "- the distribution of two variables: how two variables relate to one another  -> scatter\n",
    "\n",
    "- how one variable changes as a function of a categorical other variable -> barplot\n",
    "\n",
    "- how one variable changes as a function of another numerical variable -> line plot\n",
    "\n",
    "\n",
    "## data\n",
    "\n",
    "* Gapminder: dataset describing life expentency depending on factors like life expectancy, GDP, Region, etc.\n",
    "* [link](https://github.com/UCSD-CSS-001/ucsd-css-001.github.io)\n",
    "\n",
    "Standard plots:\n",
    "\n",
    "- histogram -> (density plot)  `hist`\n",
    "\n",
    "- scatter -> (bubble)  `scatter`\n",
    "\n",
    "- line chart -> (+ error bars)  `plot`\n",
    "\n",
    "- bar chart -> (+ error bars) `bar`\n",
    "\n",
    "- labeling axes.  `xlabel` and `ylabel`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flying-rough",
   "metadata": {},
   "source": [
    "## Our data\n",
    "\n",
    "[gapminder](https://www.gapminder.org/tools/#$chart-type=bubbles&url=v1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "altered-excellence",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "font = {'family' : 'Arial',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : '16'}\n",
    "\n",
    "plt.rc('font', **font)  # pass in the font dict as kwargs\n",
    "plt.rc('lines', linewidth = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d388c26",
   "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>Unnamed: 0</th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      country continent  year  lifeExp       pop   gdpPercap\n",
       "0           1  Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
       "1           2  Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "2           3  Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
       "3           4  Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
       "4           5  Afghanistan      Asia  1972   36.088  13079460  739.981106"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gm = pd.read_csv('gapminder.csv')\n",
    "gm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "perceived-receptor",
   "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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent  year  lifeExp       pop   gdpPercap\n",
       "0  Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
       "1  Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "2  Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
       "3  Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
       "4  Afghanistan      Asia  1972   36.088  13079460  739.981106"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop 'Unnamed: 0'\n",
    "gm = pd.read_csv('gapminder.csv').drop(columns = 'Unnamed: 0')\n",
    "gm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "029ecabb",
   "metadata": {},
   "source": [
    "## Plot population for each continent for a specific year (2007)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02035778",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Asia' 'Europe' 'Africa' 'Americas' 'Oceania']\n"
     ]
    }
   ],
   "source": [
    "# grab the names of the continents\n",
    "c_names = gm['continent'].unique()\n",
    "print(c_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6c1e9982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[115513752.33333333,\n",
       " 19536617.633333333,\n",
       " 17875763.307692308,\n",
       " 35954847.36,\n",
       " 12274973.5]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continents = []\n",
    "for c in c_names:\n",
    "    m = gm[(gm['year'] == 2007) & (gm['continent'] == c)]['pop'].mean()\n",
    "    continents.append(m)\n",
    "    \n",
    "continents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "suburban-manitoba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot! x-values (continent names), y-values (mean pop)\n",
    "plt.bar(c_names, continents)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f9d1dd",
   "metadata": {},
   "source": [
    "### Horizontal bar plots..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "previous-report",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEPCAYAAAAJYmAlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdM0lEQVR4nO3defxd073/8ddbkFaEIFQoQovkXkL9ooYS81ChuKgOfkEvaUsVP1qpUlNLrmpLr/4o6hpKUdWJxL1tFDXnm6LmqWIINSeGGILP/WOt88jOyTnffOezvsn7+Xicx/mevddeZ+1tO++stdc5WxGBmZlZKRZrdQPMzMyqHExmZlYUB5OZmRXFwWRmZkVxMJmZWVEWb3UDFgZDhw6N4cOHt7oZZmb9yrRp016OiBXrlzuYesDw4cNpa2trdTPMzPoVSU81Wu6hPDMzK4qDyczMiuJgMjOzojiYzMysKA4mMzMrioPJzMyK4mAyM7OiOJjMzKwo/oJtD7hvxiyGT7iu1c1g+sSxrW6CmVm3ucdkZmZFcTCZmVlRHExmZlYUB5OZmRXFwWRmZkVxMJmZWVEcTGZmVhQHk5mZFcXBZGZmRXEwmZlZURxMZmZWlB4LJkk7S7pe0iuSZkt6QNIJkob01Hv0JEk3Srq21e0wM7N59UgwSToNmAy8DhwE7ApcAnwdmCrpEz3xPj3sEOCoVjfCzMzm1e1fF5e0DzABODIizqysukHSr4DbgMslbR4RH3T3/XpKRDzY6jaYmdn8eqLHdCxwX10oARARTwPHAZ8GdgCQNErSZEmvS3pB0oWSlq9tI+mTkn4n6Q1JMyVdKmlotV5JX5Y0NQ8ZzpZ0m6QxlfUXSbpa0uGSnpL0tqS/SBpZKTPPUJ6kdSX9WtJLkt6TNF3S8ZLUA8fIzMw6qFvBJGlFYEPSMF4zvwUCGCtpDeAWYFlgHPBNYEfg8lzfx/L6NfL6rwGbAf8jaclcZm/gUuA6YBfgQGAIcGWtTLY9sD9wOLAfsDZwUZP9WBq4EVghbzMWuAE4mTQsaWZmfaS7Q3nD8/P0ZgUiYpakV0lhcwTwAbBzRLwOIOlt4AxJK+T1HwF2iIiX8/o7gceAL5CuW30S+FlEnFh7D0nvAdcA6wD358WDgbER8XwusypwlqQVIuKVumauCzwO7BsRL+XyU4A9ga2AP9bvl6TxwHiAAcus2PwImZlZp3Q3mGrDXO8voFxt/ebATbVQAoiIPwB/AJC0DXA7MFNSrW3PAA8C2wGXRMTEXHYIMIIUKp/LZQdW3vOpWihlz+bnQcA8wRQR04AtJS0h6V9IAfcpYIm6OqvbnAecBzBw2NqxgP03M7MO6m4wPZWfV29WQNIgYCgpYEYC97ZT3wrAJsCcBuv+metbGfgF8FngPeAB5vbYqteDZtdt/2F+bjh8KelY4NukYcanSJM25tTVaWZmvaxbwRQRL0iaCuwh6XsR0ajnsBswgHRNaBNgnnEvSQOBbUk9pVmk61Xfa1DPG/n5cuDjpGtP0yLifUm7AP/W1f2QNA74PmkK+a8iYlZe/mJX6zQzs67piVl5pwDrAcfUr8i9m4nA34D/IfVCtsqTDWq2BSYBK5EmPowgzfJri4g20jWjE4EtcvnNgCsi4s6IqA0R7lx7yy7uw2bAsxFxbiWUNiKFqHtMZmZ9qNvBFBF/BE4CTpN0laTdJW0l6QhgGmkI7Qs5RH5C7j1J2k3SF4GfA9dExKPAj0kz7CbnenYh9bS2zXUBTAUOkLS3pO0knQ0cmtct1cXdmAqsJul7ue1fy+8b3ajTzMy6oEd++SHPkNuB9CH+c9Jw3MHA+cBGEfFYLvckaZbbe8CVwI+A35GmaNe+97QF6frQL4Erchu3j4h78tsdCDwE/FdePxLYGniL1PPpiouA00nT0ycBhwE/JF3L2rSLdZqZWReo8WUh64yBw9aOYfuf2epmMH3i2FY3wcyswyRNi4jR9cv96+JmZlYUB5OZmRXFwWRmZkVxMJmZWVEcTGZmVhQHk5mZFcXBZGZmRXEwmZlZUbp9a3WD9VddljZ/udXMrEe4x2RmZkVxMJmZWVEcTGZmVhQHk5mZFcXBZGZmRXEwmZlZURxMZmZWFH+PqQfcN2MWwydc1+pmGL5ZotnCwD0mMzMrioPJzMyK4mAyM7OiOJjMzKwoDiYzMyuKg8nMzIriYDIzs6I4mMzMrCgOJjMzK4qDyczMiuJgMjOzonQ6mCTdIykkbdwbDepEO6ZLOruVbTAzs57XqR9xlbQ+MAp4EDgImNobjeqgPYHXWvj+ZmbWCzrbY9ofuBe4APiipEE936SOiYi7I2J6q97fzMx6R4eDSdIA4EvA9cCVwCDg85X1B0h6WdLOku6T9I6kuySNlLSnpEckvSnpWkkrVbZbXNLJkp7O27RJ2q6yfus8dPhVSc9LekbS8PqhPElrSLpK0quSXpH0G0mrV9Z/WtIkSTMlvZfb89W6ffyWpMdzO56QdLwkX4czM+tDnfnQ3QEYBlwWEc8BU0jDeVWDgXOA04B9gdWA64AfAMcC/w/YHjilss35wFHAWcAewMPAZEmb19V9PDAeOLa+pyRpGeAW0jDjIcABwIhcz4AcUH8B3gT2AXYHHgXOlTQq17FvbtePgZ1IvcKTgIM7fITMzKzbOnONaRxwd0Tcn19fAlwqaWREPJSXLQlMiIgrASRtCkwAtoqIm/OyLYFN8t8jSCFycERckOu4XtIw4PvAtpX3PzMi/tikbQcCKwPrRMSTue5ngN+SAmp14HbgyxExJ6+/E3gFGAP8PT9PB86JiABukjQHeK7RG0oaTwpKBiyzYjuHzczMOqNDPSZJg0m9jGskDZE0BLgBmM38vaa7Kn+/kJ/bKsteAYbkv7fOz5PykN7ikhYHJgFbSFqyst2D7TRxc+CBWigBRMQ9EbFmRDwQEZMjYntggKQNJO0NHJOLDszPtwHrAlMlTZC0XkSc0SwMI+K8iBgdEaMHLLVsO00zM7PO6OhQ3j7AUqShrtfyY0ZeNq4uQN6o3zgiZjepd4X8PAOYU3mcASwBDK2UfbGd9i3f3vo8nHdmbvffgFMr763cxstIvbcP8/r78tT4ke28r5mZ9bCODuWNI/WEjqlb/q/A2aTeVFfMAgL4DCmQ6r0MrNPBej5Rv1DSZ0lB9FXSsNs4YFJEvCVpKeDfq+Uj4mLg4jw5YzfgBOAawOFkZtZHFthjyhMHxgCXRsSN1QdwLvBP5h/O66hbSD2WwRHRVnuQJkgcCbzfwXpuA9aTtEal3SNJQ4IbAJsBbRHx64h4KxfZuVY0l79A0tUAEfFiRPwC+AXp+pSZmfWRjgzljSP1an5TvyIiPiBNHd8eWKN+/YJExD253l9KOkTSNpJOIs3iezoiPuxgVReSAvJaSXtJ2gO4itTLu4H0ReBNJX1D0laSjgYuyvu1VK7jJmAvSafmdowDvk7qMZmZWR/pyFDefsCtEfF8k/WXAYcDX+liG74MnAx8B1gJeIo0k++MjlYQETMljSFN9b4IeBeYDBwVEe9Lmkia6n4C8BHgMeAw0veyNst1XCppWeBQUm9tFnB1bouZmfURpZnR1h0Dh60dw/Y/s9XNMGD6xLGtboKZdZCkaRExun65f9XAzMyK4mAyM7OiOJjMzKwoDiYzMyuKg8nMzIriYDIzs6I4mMzMrCgOJjMzK0pn7sdkTay/6rK0+YudZmY9wj0mMzMrioPJzMyK4mAyM7OiOJjMzKwoDiYzMyuKg8nMzIriYDIzs6I4mMzMrCj+gm0PuG/GLIZPuK7VzegW3/nVzErhHpOZmRXFwWRmZkVxMJmZWVEcTGZmVhQHk5mZFcXBZGZmRXEwmZlZURxMZmZWFAeTmZkVxcFkZmZFKTaYJN0jKSRt3GDdfpKelfS2pG832f5ESW/2fkvNzKwnFRlMktYHRgEPAgc1KHIW8CiwE3B5k2ouALbplQaamVmvKTKYgP2Be0nh8kVJg+rWLw9MjoibI+LZRhVExLMRMbWX22lmZj2suGCSNAD4EnA9cCUwCPh8Xre1pMhFT6/9LWm6pImS7pA0U9IR9UN5kgZIOlbSE5Jm56HCPSrrl5F0lqSnJL0n6SVJF0sa0jd7bmZmUGAwATsAw4DLIuI5YApzh/P+BmyW//7Pyt8ARwGTgf3yc72fACcA/wXsBtwJXC1pi7z+cmB3YAKwI3AGKSCP75G9MjOzDinxfkzjgLsj4v78+hLgUkkjI+Ih4A5JAE9HxB2V7R6JiJNqL3KZ2t/LA4cAJ0XE9/PiKZLWBcZIagOWBL4WEdfn9TdK2hzYqlEjJY0HxgMMWGbFbu2wmZnNVVQwSRpM6rWcVhlCuwGYTeo1HdXO5g+2s24TYADwx+rCiNi68nLH3IbhwDrAesC/AO80qjAizgPOAxg4bO1oVMbMzDqvtKG8fYClgFOA1/JjRl42TtKS7Wz7Yjvrll9QGUmfk/QE8CRwGWlIcTagZtuYmVnPK6rHRBrGuws4pm75vwJnk3pTv+5CvbPy84rAc7WFkjYkBc+bud6Lga1qM/0kXUXqNZmZWR8pJpgkrQ6MAb4ZETfWrfsrcBxpOK8rwXQX8D6wK2kaes3PgSeA35OuMU2shNIgYAtSr83MzPpIMcFE6i0F8Jv6FRHxgaQrgcMkrdHZiiPiRUnnAsdJmgNMIw0bbggcCrwOfAD8h6RzgKHA0cDKwLtd2x0zM+uKkq4x7QfcGhHPN1l/Gam9X+li/UcAE4FvkCZBbATsEhFtEfEoKRhHAZOA04E20ky+1SWt0sX3NDOzTlKEJ5R118Bha8ew/c9sdTO6ZfrEsa1ugpktYiRNi4jR9ctL6jGZmZk5mMzMrCwOJjMzK4qDyczMiuJgMjOzojiYzMysKA4mMzMrioPJzMyK4mAyM7OilPRbef3W+qsuS5t/OcHMrEe4x2RmZkVxMJmZWVEcTGZmVhQHk5mZFcXBZGZmRXEwmZlZURxMZmZWFH+PqQfcN2MWwydc1+pm9Djf1dbMWsE9JjMzK4qDyczMiuJgMjOzojiYzMysKA4mMzMrioPJzMyK4mAyM7OiOJjMzKwoDiYzMyuKg8nMzIrS68Ek6UZJ0c5jQm+3wczM+o+++q28W4Gjm6x7uo/aYGZm/UBfBdPMiLijj97LzMz6sSKuMUk6IA/rDa0sG5KXHZBfnyipTdJPJM2UdGtevrSkH0p6UtLbku6StGOlnq1zPTtK+lsu0yZp27o2rCTpEkmvSnpT0h8krdlHh8DMzLK+CiZJWrzRo5P1bABsDOwFnCppMeB64EBgIvBvpKHBSZJ2qtv2V8Dv87YvAZMlrZcb91HgL8AWwGHA/wVWBm6WtFwX9tfMzLqor4bydgHmNFqRQ6GjFgeOiIi2vO1uwGeAnSPiv3OZyZJuB04F/ruy7fkRcVLe7s/AP4CjSKE2DlgXWC8iHs5lpgBPkYLq5AbtHg+MBxiwzIqd2AUzM2tPX/WYbiH1dBo93u1kXQ9V/h4DvFEJpZorgI0kDa5bBkBEvEfqaW2ZF20DPAY8XunJzQb+CmzXqBERcV5EjI6I0QOWWraTu2BmZs30VY9pVq2X04ikjtbzVkS8VXm9HPBCg3K1ZdVger6uzEvA8vnvFYARNO7VPdbRxpmZWfeVcmv1yM/VHtzSHdjuVeBjDZavXFlfswLzhthKwIv571nAvcBBDerqbI/OzMy6oYhZecDr+XmVyrItGxWscwswuMFEh32BaRHxTmXZrrU/JA0EPkua8FCrZ01gekS05d7dNODI6nZmZtb7+qrHNETSpk3WzSIFxDvAWZK+D6wOHM+CeyvXAXcCv5T0XdKMvAOBTYDd6sp+T9Ic4BHgcFKP7PS87kLgm8CfJJ1G6mmNJ83gq6/HzMx6UV8F02eA25usmxIR20v6PPAfwLXAA6Qp279tr9KI+EDSznm7HwCDgHuAXSLi+rriR5Nm2K1JCrMxEfFkrud1SWOAHwLnAgOB+4HdI2JSJ/fVzMy6QRGx4FL9mKStST2yjdubgNEdA4etHcP2P7M3qm6p6RPHtroJZrYQkzQtIkbXLy/lGpOZmRngYDIzs8KUMl2810TEjUCHvyhlZmat5R6TmZkVxcFkZmZFcTCZmVlRHExmZlYUB5OZmRXFwWRmZkVZ6KeL94X1V12WNv9KgplZj3CPyczMiuJgMjOzojiYzMysKA4mMzMrioPJzMyK4mAyM7OiOJjMzKwo/h5TD7hvxiyGT7iu1c0wM+tTvXWXa/eYzMysKA4mMzMrioPJzMyK4mAyM7OiOJjMzKwoDiYzMyuKg8nMzIriYDIzs6I4mMzMrCgOJjMzK0q/DyZJ90gKSRt3crsD8nZDe6ttZmbWef06mCStD4wCHgQO6uTm1wGbATN7uFlmZtYN/TqYgP2Be4ELgC9KGtTRDSPipYi4IyLe77XWmZlZp/XbYJI0APgScD1wJTAI+HxdmW9JelzSO5KekHS8pMXyunmG8iQtIekkSY9KelfSa5KukbRaH++amdkird8GE7ADMAy4LCKeA6ZQGc6TtC9wCvBjYCdSr+ok4OAm9f0EOAyYCOwIfBfYDjizd5pvZmaN9Of7MY0D7o6I+/PrS4BLJY2MiIeAMcB04JyICOAmSXOA55rUtyJwdERcmF/fJGld4MuNCksaD4wHGLDMij2xP2ZmRj8NJkmDgd2B0yQNyYtvAGaTek1HAbcBhwBTJV0NXBsRZzSrMyL2zXWvAowARgJbAAOblD8POA9g4LC1o/t7ZWZm0H+H8vYBliIN1b2WHzPysnGSloyIy4ADgA+BU4H78tTykY0qlLS5pHtzPdcAewNvA+rlfTEzs4r+GkzjgLuAbeoe3wCGknpTRMTFEfFpYGVST2p5UujMQ9KywLXAU8DaETEkIrYh9brMzKwP9buhPEmrk64ffTMibqxb91fgOOAgSTsBQyJi74h4EfiFpI8D325Q7QhgOeDMiHg817UYaYKFe0xmZn2o3wUTqbcUwG/qV0TEB5KuJM2u+wpwkaRTgT8BqwFfp0GPCXgYeAM4Pk9D/yhwKLABEJKUJ1CYmVkv649DefsBt0bE803WX0bar7VIAbUnMAk4HbiaFE7ziIhZwF6kXtMfgJ8BL5OuZS0GbNKzu2BmZs30ux5TRIxYwPqpzDv8dnaTchcBF1Ve/wnYsEFRD+WZmfWh/thjMjOzhZiDyczMiuJgMjOzojiYzMysKA4mMzMrioPJzMyK4mAyM7OiOJjMzKwoDiYzMytKv/vlhxKtv+qytE0c2+pmmJktFNxjMjOzojiYzMysKA4mMzMrioPJzMyK4mAyM7OiOJjMzKwoDiYzMyuKg8nMzIriYDIzs6IoIlrdhn5P0hvAI61uR2GGAi+3uhGF8TGZn4/J/BalY7JGRKxYv9A/SdQzHomI0a1uREkktfmYzMvHZH4+JvPzMfFQnpmZFcbBZGZmRXEw9YzzWt2AAvmYzM/HZH4+JvNb5I+JJz+YmVlR3GMyM7OiOJjMzKwoDqY6kg6W9JiktyXdLmmzBZRfT9IUSW9KelrSMZJUV2ZLSXdKmp3r/krv7kXP6sIx2VzSXyTNlPScpEskfayuzP2Sou7Rb7670YVjcm2D/Q1JS1fKLDLniaTpTY5HSDqhUq5fnyc1kj6Xv++4oHIL/edJh0SEH/kBjAM+AE4AdgEmA68DazYpvxLwT+DPufxxwPvA0ZUyI4G3gCuAnYEfAwHs3er97aVjMhJ4G/gD8FngS8ATwD3AErnMksB7wDHAppXH/2n1/vbGMcnbPA2cWbe/mwKLLaLnyacaHIurgDeAEQvDeVLZ183zsXhzAeUW+s+TDh+zVjeglAcgYDpwTmXZEsA/gJ822eYk0je0l6osOwV4pfIhfDHwAHmiSV52KfD3Vu9zLx2Tn+X1S1SWbZz/59klv94wvx7R6n3so2MyJO/vzu3Uu0idJw3qGA3MAQ6sLOu350lu/0Dg28C7wKsdCKaF+vOkMw8P5c31SWAN0r/0AYiIOcB1pH+ZNLI9MCUiZleW/Q5YnvRhXCtzbeQzqFJmfUmr9EjLe09XjskDwI9yuZrazzWtmZ9HAe8Aj/Voa/tGV47JqPz893bqXdTOk3o/BaYCF1WW9efzBNKIwXeAbwH/2YHyC/vnSYc5mOZaJz8/Xrf8H8AnJA1osk2j8gDrSBoErNJemS62ta90+phExP+PiJ/VLd4tPz+cn0eR/hV4paTXJc2SdIGkwT3V8F7UlfNkFOlfzd+X9Eq+NvBrSSsDLIrnSZWk3YHNgKPqPnD783kCKWjXjIifknp+C7Kwf550mINprmXyc/0FyjdIx2lQk20ala+ta6/O6nuWqivHZB6SVgPOANqAG/LiUcDKwL3AWNJY+l6kf/mVrivHZBRpWOcNYE/gENIH8Q2SBi6gzup7lqq758mRwC0RcXvd8v58nhARMyJiZic2Wdg/TzrMP+I6V23mS/2/bGrLP2yyTbN/CX3YxTpL0q3251CaQvpw+kLlX8PHAAMj4o78+q+SXgSukLRlRPy1+03vNV05Jj8GfhURf8mvb5b0EHAH8HnSMepsnSXp8nkiaV1gK2CfBqv783nSFQv750mHucc016z8XD9MsDTpP/hbTbapLz+4su71duqsvmepunJMgDTtFbiN9K+4HSLiidq6iLi78mFTc31+3qBbLe59nT4mEfFwJZRqy+4EZpL2d5E9T4DdgTeBa+tX9PPzpCsW9s+TDnMwzVW7wLpW3fK1SLe1aPQvmcealCdv8ybwfDtlHu1iW/tKV44JkjYBbiZNH94yIv5eWbe4pAMkfapus4/m59K/o9LpYyLpC5LG1C0TaXjv5UX1PMl2BiZHxDvVhQvBedIVC/vnSYc5mOZ6DHgG2KO2QNISpLHtKU22mQJsny9K1uxBumB7T6XMbnUXgPcA7o+IF3qg3b2p08dE0nDSd1heADaPiHlmVEXE+6RpsSfWbboXabpw/XWG0nTlPPk6cJak6v9vu5A+ZG/Orxep8ySXEWmaeH2vaGE4T7piYf886bhWz1cv6UG6KP0h8APSB8ckUvd5rbz+E8CmlfLDSMMxNwG7At9l/i/EbUD6kuA1pOmjPyKNEe/T6v3tpWPy+3wMvsj8X6AclsuMz8fgLNL01++QvpR7Rqv3t5eOyU65/OXADsChpO+1XL2onid52fC8j2Ob1Nmvz5O6fTmRuu8xLYqfJx0+Xq1uQGkP4CjSt/Rnk66RbFZZdxEQdeVHA7eSvm/xFHBMgzp3Iv2L5x1Sd/uAVu9nbxwT0pcq5+T/URo9qv+DHUD6Xs/bwJPAseRfQegPjy6cJ7sCd5GutzxHmqn40UXxPKks+3Q+Lz7TTp39+jyp7EejYFokP0868vBtL8zMrCi+xmRmZkVxMJmZWVEcTGZmVhQHk5mZFcXBZGZmRXEwmZlZuzp6B94G2y0n6fx8J+tXJf1eUv0vV8y/naeLm5lZM5I2J/1G4WIRsfSCytdtez3pbsXfIn2p/GRgOWD9SD+x1Hg7B5OZmdXLt2Q5nHQX3beAJTsTTJJWIv002b9HxIV52TqkG4fuExFXN9vWQ3lmZtZIu3fgzT+0e7KkpyW9I6lN0naVIh/Jz69Xlr2Sn5dv740dTGZm1siC7sB7PulnqM4i/ZDsw8DkPPRHRDxNup3JdyWNyD2on5KCalJ7b+yhPDMza5ekE0m/dbl0fj0CeAg4OCIuqJSbQsqVbfPrtYA/MffWHO8Au0ZE01+dB/eYzMys87bOz5PykN7ikhYn9YS2kLSkpFVJtyeZDewN7Aj8EfitpE3bq9y3Vjczs85aIT/PaLJ+KHAgaQbeRhExA0DSn0m/PH86MKbJtg4mMzPrtFnkW5aQbnVT72VgNeCZWihBuseHpFtJtzNpykN5ZmbWWbcAAgZHRFvtQbqh45GkGxw+Cqwu6eN1225CurdWUw4mMzPrlIi4B/gN8EtJh0jaRtJJpDsYPx0RHwIXAv8kzdTbR9JOki4j9bJOaa9+D+WZmVlXfJn0Sw7fAVYi3XF3AunuzETEa5K2yK/PJ/Ww7gW2jYgb26vY08XNzKwoHsozM7OiOJjMzKwoDiYzMyuKg8nMzIriYDIzs6I4mMzMrCgOJjMzK4qDyczMivK/84nxIw+oRT0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(c_names, continents)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hungry-bibliography",
   "metadata": {},
   "source": [
    "### Bar chart\n",
    "\n",
    "How does life expectancy differ by continent?\n",
    "\n",
    "{numerical variable} ~ {categorical}  -> bar plot \n",
    "\n",
    "category on the x axis, number on the y axis, and we get 1 number per category.\n",
    "\n",
    "Mean life expectancy, in 2007, by continent\n",
    "\n",
    "* Introduce color, edgecolor, fontsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c93987c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[70.72848484848484, 77.64859999999999, 54.80603846153845, 73.60812, 80.7195]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = []\n",
    "for c in c_names:\n",
    "    m = gm[(gm['year'] == 2007) & (gm['continent'] == c)]['lifeExp'].mean()\n",
    "    age.append(m)\n",
    "    \n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fitted-compatibility",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a bar plot\n",
    "plt.bar(c_names, age,\n",
    "           color = 'gold',\n",
    "           edgecolor = 'navy')\n",
    "\n",
    "# label stuff!\n",
    "plt.xlabel('continent')\n",
    "plt.ylabel('Mean Life Exp.')\n",
    "# plt.xlabel('continent', fontsize = 24)\n",
    "# plt.ylabel('Mean Life Exp.', fontsize = 24)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()\n",
    "\n",
    "# plt.rc - go over now..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f1c0092",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.bar?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corrected-wyoming",
   "metadata": {},
   "source": [
    "## Conventions\n",
    "\n",
    "> Learn the rules like a pro, so you can break them like an artist. \n",
    "> \n",
    "> -- *Pablo Picasso*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "taken-momentum",
   "metadata": {},
   "source": [
    "### Histogram\n",
    "\n",
    "Show the *distribution* of a single variable, which values are more or less common?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "smoking-remark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(gm['lifeExp'])\n",
    "plt.xlabel('life Exp.')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96b9015",
   "metadata": {},
   "source": [
    "### Make our hist look a little nicer...change number of bins, color, alpha (transparency), legend, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "binding-liberia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(gm[ gm['continent'] == 'Asia' ]['lifeExp'], \n",
    "             bins=20, \n",
    "             color='green', alpha = .35)\n",
    "\n",
    "# what happens if you put plt.show() here???\n",
    "# plt.show()\n",
    "\n",
    "plt.hist(gm[ gm['continent'] == 'Europe' ]['lifeExp'], \n",
    "             bins=20, \n",
    "             color='red', alpha = .35)\n",
    "\n",
    "# or can specify a range of bin edges\n",
    "# plt.hist(gm['lifeExp'], \n",
    "#              bins=range(20, 90, 5), \n",
    "#              color='navy')\n",
    "\n",
    "plt.xlabel('life expectancy (years)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Life expectancy distribution')\n",
    "plt.legend(['Asia', 'Europe'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acknowledged-pastor",
   "metadata": {},
   "source": [
    "## Line plot\n",
    "\n",
    "* How has average life expectancy changed from 1952 to 2007?\n",
    "\n",
    "* x = number, y = number, use line plot...\n",
    "\n",
    "* Mean life expectancy, by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "apart-meditation",
   "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>year</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1952</td>\n",
       "      <td>49.057620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1957</td>\n",
       "      <td>51.507401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>53.609249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1967</td>\n",
       "      <td>55.678290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972</td>\n",
       "      <td>57.647386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1977</td>\n",
       "      <td>59.570157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1982</td>\n",
       "      <td>61.533197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1987</td>\n",
       "      <td>63.212613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1992</td>\n",
       "      <td>64.160338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1997</td>\n",
       "      <td>65.014676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2002</td>\n",
       "      <td>65.694923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2007</td>\n",
       "      <td>67.007423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  life_expectancy\n",
       "0   1952        49.057620\n",
       "1   1957        51.507401\n",
       "2   1962        53.609249\n",
       "3   1967        55.678290\n",
       "4   1972        57.647386\n",
       "5   1977        59.570157\n",
       "6   1982        61.533197\n",
       "7   1987        63.212613\n",
       "8   1992        64.160338\n",
       "9   1997        65.014676\n",
       "10  2002        65.694923\n",
       "11  2007        67.007423"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## group by year\n",
    "## calculate mean life expectancy per group\n",
    "\n",
    "year_summary = (gm\n",
    "    .groupby('year')                            # group based on common year labels\n",
    "    .agg(life_expectancy = ('lifeExp', 'mean')) # aggregate lifeExp, compute mean, assign to new column 'life_expectancy'\n",
    "    .reset_index())  # reset index to default...\n",
    "\n",
    "# have a look...\n",
    "year_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5c5719c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## make a line plot\n",
    "plt.plot(year_summary['year'], \n",
    "             year_summary['life_expectancy'],\n",
    "            'bo-',            # blue, circle markers, solid line...\n",
    "            markersize = 5,   # size of circle markers...\n",
    "            linewidth = 1)\n",
    "\n",
    "## label stuff!\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('mean life expectancy (years)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expensive-carter",
   "metadata": {},
   "source": [
    "## Scatter plot\n",
    "\n",
    "How does the *distribution* of life expectancies across African countries vary by year?\n",
    "\n",
    "distribution of two number -> scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "remarkable-detection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0/lPsgH1XcVx+7J3ArRu0fZ9rkP/2l2KNk12scPhjYM8Gn29gVztrDASAneUaPYl1LrD59hg7MBXtnsyv+9edrc+btPP+IY/7fgbiRTY5J5u2WdwX67Zdg53JLmBVitezLsAQ6w32+Xz/yfxe+tH8/lJOx2E8n3VupGdq+yy/9fHY+Jhb8vt3DSscXgTEGxy/C6uqO4R13z2EtZ3cJz0MVkh8ceCe+eW8vY1cYO/TZzZ2835O/l1d4BfP8vmNrptghdr1+e9dxNp/vn+Dz/8QNl/XYn7s57DPYTDs+T3bn+QNebYpIqJY3/Bhko1dEOTqraPYVdkDWUl4PNseb5PwXIg8H6sW+YMt7ofH86DnQrdJeC4gROQtWHXddwD/pqpn8kX3eDz4lYTnwmIKm8Lkw8APbnFfPJ7zAm+T8Hg8Hs+mbCt1044dO/TAgQNb3Q2Px+M5r/jv//7vk6o6v9G+bSUkDhw4wPXXX3/2Az0ej8fTR0QObrbP2yQ8Ho/HsyleSHg8Ho9nU7yQ8Hg8Hs+meCHh8Xg8nk3xQsLj8Xg8m+KFhMfj8ZznGKMkmcGY0ce9bSsXWI/H47nQ6CQZR5c7GFUCEXZP1ajFw1RTHQ6/kvB4PJ7zFGOUo8sd4lAYq0bEoViBMcIVhRcSHo/HM2Jcqn8GyVQxqkShHcqjMMCoko0w3ZJXN3k8Hs8Ica3+GSQUIRAhzQxRGJBmhkCEUGRkbfiVhMfj8YyIMtQ/gwSBFUJJpjS7KUmm7J6qEQSjExJ+JeHxeLY9xlgVTCgy0gF0PafVP3blEIUB3TQlUyXATbu1OGTfbMPZ7/NCwuPxbGu2m/pnI4JAnAkhr27yeDzblu2o/ikbv5LweDzblu2o/ikbLyQ8Hs+2ZTuqfzYiTQ09Y6gEAVE0WgWRFxIej2dLKMOYXKh/ji536KZp3yZxvs/uB1lq9bjh8DKZUcJAuGbPFNONysi+3wsJj8dTOmUak7eb+meQNDXccHiZWhRQq0R0eik3HF7m8ZfOjWxF4Q3XHo+nVMo2JoNdUcRhUJqAKCviumcMmVFqFTvfr1UiMqP0jBlZG34l4fF4SmUrjMll0kkyjiy1STJDHAZcNF13tkqqBAFhILS7CXEUkqQZYSBUgtHN/72Q8Hg8pbJVxuQyMEY5eKrJYrNHEAjGKL3McOXOCSermCgKuGLnOJ+49WRfKD3hih0jNV57IeHxeEplOxuTk8xwfKXLRC3qC8DjK10OzI1RDUa/mjBG6SSGx1wy3be5dBKr5hrV+fRCwuPxAOWlroDyjcll/jYAo0qaGcwIs7FuRKZKO0lpdrO+E8BYNRyp6s4LCY/HU6q3UUFZsQRl/rY4DJiqR9xxsokCAly6Y4w4dOMjJAqn1nrUooBG7t10aq3HlfOja8N7N3k8Fzhb4W1UFsVvCwUqUUAoOP9t1ThkfqLKzokq8xNVqg6FrQrMjVVQgdV2cq/3o8KvJDyeC5zt7G1UhjpmfXtxGHDpjvG+equdZM7aK5wAemlGZhSTCkE19vUkPB7P6Bj0NgK2lbdRoY5BlUYlAlVOrfUQRwuJrTiX3SzjxEqPhWbCiZUe3Swb6feXKiRE5Ekiomf42y+WV4rIXSLSEpEPiMjVZfbT47mQ2I6ZSwvKUMcMEgTCdCPm4EKL246vcXChxXQjdnYuk8yw3ErZN9vgwNwY+2YbLLdSkuz8Dab7LPB167bVgPfk+w4BrwZeDrwMuBN4FfAhEXmYqi6X11WP58Jhu6auGJzZK5Bm6nRmb4yy1ErYP9cgEMGofT9ZcycowAqnKAxIGZ1wKChVSKjqCvCpwW0i8luAAs8BxoCXAq9V1bfk+z8OHAR+GHhTmf31eC4kys5cWppbqoAghKENbnP5Ewv7Tj06PbQ2u+7sO3EYsHOyaoP38jQgOyerI/Wm2lLDtYg8DHgR8BOqekJEvhUYB/6+OEZVF0XkY8C344WE5wKibN/+MikrdUVhSN4/N1a6IblYSbhcuQSBsH9ujEoY3Otcbqca1/8XuBn4w/z9VfnrbeuOux34n2V1yuPZarYibqEsykxdUQzaJhcWrg3JhU3ii3cv9Qfta/dOOxXytTi8lxAcdVtb5t0kIpcCTwN+Q1ULRdok0FXV3rrDV/N9G33PC0XkehG5/sSJE+467PGUxHaOW4DTqStqcUijElGLQ46vdEdqbC0o2yhvjHJsuUMcCI04Ig6EYyVcO5dZbrfSBfZHgEXgXQPbBGufWI/AxhYZVX2rql6nqtfNz48wzNDj2SJOxy3YxzMKA4xa1ZPn3KnFIXun6+yeqrHXYUZWyAXgapd6JWKiHlOvRBxfdSMAy2Ir1U1PB96nqt2BbctAVURiVU0Gto/n+zyebc9WZUktywYShwE7J6osNLsU88KdE6M1tg6ynVV3ZbAlKwkR2Qc8FPjbdbtuIU93sm77ZcBNJXTN49lytiJuoZNk3LXQ4tBCi7sWWnSS0QZkDRIEwq6pGolRWklKYpRdjn5f2aq7wtuok2S0eimdJBu5t1HZbNVK4nH566fXbf8k0MGuMn4VQERmgCcCryurcx7PVlNm3MLgQBqFIWlmOLrcYd9sw9nAvdRKuHTHmPNYgrJTjpThbVQ2WyUkrgFOquqpwY2quiYivw28QUQM1vPplcAK8Lbyu+nxbB1lxS2UPZCWGUuwFao7195GZbNVQmInsLTJvldgjdQvxdoiPgk8z0dbezxuKHsg7ccSJBkSCGrcxRIUqrt7lto0uylRGHBxCTP7sgMTXbIlQkJVf/wM+1JsWo6Xl9cjj+fCpexKcUUswQ2Hl8mMEgbCNXumnA7csu7VMzxbHUzn8ZxXbNco6LJtIGXlNyrsLVEgxFGE5u9d2Vu2I15IeDxD4l0pR0OZNomy60lsR85fvyyPp0S2IgraGCXJk7a5pkwX2DJrLpRdT2I74oWExzMEZUdBlzlol13is8w4kKJ+hARCq5cigTitJ7Ed8eomj2cIyvQAKjtuYStUMmXZQEIR6pWI8VrUt39khm1Rda8s/ErC4xmCMme/Za9aCpWMGkMlDFBjSlHJuExKN9jG7qkamYFuasgM26bqXln4lYTHMyRlzn7LjFtQgfFayKGFdt8l9ZLZ+rZRyWzXqnuDuPS680LC4zkHygiSKjtuQRTWOhm7JqpUopBemrHWyZyvJMp0J95OwW3rcV3AyQsJj+ccKGtgK3P2Wxh3W0lGLzOEYcBcLXS6kvDuxKOhjAJOXkh4PENS9sBW1uy3bONu2Yb57UxRwGmiFvVVk8dXuhyYG6MajObe9IZrj2cItnO1uLKNu76o0vmFX0l4zmvKUv+UnSm1bMpUb21VUaUyKb2AU6sLPQEZfQEnLyQ85y1lqn+2YmArO09UWeqtsg3zZVPmfVkUcDq22iHJMuIwGHkBJy8kPOclZeu1yx7YtsKwW6ZQKupO94yhEgRE0fbQfG9ZAae5sX7a9VEnS/RCwnNeshXqn7JUMlth2C1bKJXd3nZVS/aTJVbyoTwcfbLE7SG+PRccZSaJG6SMKOGyDbtlG+XLbm+7Ji8cbK+XZqSZoZdmI29vKCEhIn8lIk8VEb/y8DwoKNQ/vdSw3OrRS8220WuXPdCULZSK9gDavRTAWXtlC6Sy78uigNPBUy1uPb7GwVMtphujrcsx7KA/DbwXWBKRPwf+VFX/a2S98HjuJ7rudTtQtv1jcDZaxEm4nv2udRO+eKhpS8UpXLZzzEl7W+WVVtZ92S/gNNtwZpMYaiWhqk8B9gL/F/g64NMicpOIvEpEDoykJx7POVDMEKtRwFSjQjUKtk3cApy2f1wy22DfbMN50N50I+aOk02+enSFO042Rz4bHcQY5dRqjygUanFIFAqnVntOrl3Zq7KyVy6FEKzEIXEYUInDka/KhrZJqOoxVf0tVX0s8BDgHcDTgdtE5N9F5IdEpDaynnk8Z8AHZI0OY5Rjyx3iQGjEEXEgHHM4sPWMIQiEvbMN5scr7M0N8j1jRt5Wmdl7IU+73ks5vtrl8FKb46td2r3U2X1ZhhA8Z8O1iNSBxwBfgxUWy8BJ4NeAO0TkySPrncezCVtluC6LMo2tSWY4vtqlGofUKyHVOOT4apckG/2gDVAJAoxR7jrV5OhKl7tONTFGqQRu/GjKXJWJwqlmDzW2Ep4a5VTTXdr1MoTgUDaJ3GD9bcD/D3gaUAX+Dfhh4O9UtZuvIt4PvB24dGQ99JxXlOVquJ0DsgYrxUVhgObvXbrAdtOMtW6CiKCqI43YXU8QCOP1kNuOr2KwM9WL9ru9dmUFCqrA3HiF1U7KSrtHFAbMjbuthOfaNXtYw/UxrPH6i8CrgXer6vHBA1S1IyIfAX58pD30nDeU7fu+XesEFJXi1jppP8J7vBY5M7aGIoSB0O4pcSSkmVKL3a3KkszQ6SmPvGS6f+06PVvPe1RJ6baKUKwwyjKD5K8B5bhmuxKCwwqJPwbeqapfOstxbwZ+5YF1yXM+slWZPbdjnQBRW2N6rZMSRwFJahjvRlw5P+GkPRXYPVljrZqSZkoUCuPVyOnst5tlrHZMP711Jd5GIVsCkk9ajLHvz2eG9W56KTAhIi8vtonIo0TkXSLy6IHjllS146Cfngc53pA8OjJVjIEoFAT7agxOjZ/1SsSO8QrzExV2jFeoVyKnLrAh0o+VMKqEjmfbxtiVimvvtyxX1e2fG+Pi6Tr758aIw+C8fg6GDab7n8DHgKcMbFbgSuCTIvKkc2lURL5ZRD4tIm0ROSgirxORMN8nIvJKEblLRFoi8gERufpcvt9TPmVEfl5IVCKry56qRcyNV6g4zG0UBEItDvjsXUtcf3CRz961RC12F1WuYlORz09WGauEzE9W2T1Vc7Zy6SQZt59c45ajK9x+cq2UiGuTCwvXMSdlMKy66bXA21T1x4oNqvoF4GtF5P8BbwQeP8wXicgTgH8B/gz4BayX1OsBA7wOa/N4OfAy4E7gVcCHRORhqro8ZH89A5RhTC587W84vNyvk3zNnqltYycokzgMqMXCFw8tkSmEAtdeMu3MmJymhluPr7F3ukYYBmSZfb9jrOok8V5/IDVKlHs6uRpIjVFuPrrCLcdXbSGlAFrdlGv2TG+LRJBlMKyQuAp4ySb7/gp47jm0+Ubg31T1+fn7D4vIHPBkEXkT8FLgtar6FgAR+ThwEOtJ9aZzaMdDecbkMiI/HwyUIXCNUda6GfMTNcJAyPL3xqiTNnvG0ElSWioYtQNbIErPGCJX6d0EBCEI3ertu0nG5w8t5eU9wRhodjOu3DlBveomy9BWOFS4vC+HPUtHsCuFj2yw72uwcRJnRUTmgSdgg/D6qOrL8/3fCowDfz+wb1FEPgZ8O15InBNlGpPLyEa51ZQlcHvGxnzsmx0jVUMkAcudxNmgHSEst1PqUUCtEtHppbRTQ+TouhV6+0tmGiTGEAcB3cw4uVe6acahhRZz41XqlZB2z77vppkzIQHlOlS4vi+HPUtvB14tIgL8I3AcmAeeilUHvXHI73kEds7QFJF/AL4VWAF+D/gl7IoF4LZ1n7sd+J9DtuHJKTNvzXavNlZm7EIlsLrsQ4ut095NtchZsJmEwt7ZGjfcvUKSZsRRyDV7J5HQneE6yQzHltv9bbPjVSf3SiDCeC3CGKWb2NVYUct7O1Dcl4ISCAijvy+HFRJvBHZjbQavH9ieAf8PeMOQ3zOfv/4J1ibxJuCJWEHTxhrSu6raW/e5VWByoy8UkRcCLwTYt2/fkN24MChz4N6OuthBitiFZjfrz9jGqqETgRsEwtxYhZV2QifJEGBurOLsXIpCs2OYHasQBQGpMTQ7xlmUMNgZ/rGVbj/B31jNzay+UYl46O4J7l7soGJtIFfMjNGobI+E1pkqS+0uhxbafVvgJbN19mh9ZPflUGdKVQ3wYhF5DVbtNINNx/EZVT1xDu3F+eu/qurP5f9/RER2cHpFstGtKVjD9kZ9eyvwVoDrrrvu/PUzc0DZA/d2DW6DPN3CWo9aFNDIVTKn1npcOX/2z54rmSrjtZhH7Zum3U2pVyOy3AXWVeEaVaVWsQqmiADN3ZddDKVJZlhupeyba/Q9gZZbqZNguigKeNxlO6geXKSVpjSiiEfun9k2lfA0U2473qQeh0w2YprdlNuON3nYrikY0ak8p3tAVRexnkn3l7X89f3rtn8A+AlgCaiKSKyqycD+caxQ8pwjZQ/c2zG4DfJ0C2MV1nopq+2EKLKzfRdum0Uq7duPu0+lXVCJQmarYT8tx1rXnZto2dTikItn632B67oMbJmkKFO1iAxY66ZEoTAVRqQolRG1MWzupjHsTP87gTHuG1+hqnr5EF91a/66vv/FCiPBPhaXAjcP7L8MuGmYvnruy3YduMtkUHWnQJq5ddssUmkXasIilbYLIR+HATsnq7kHkHVN3TlZdeZyG4cBU/WIW0+skmRKHApXzE84ac8Y5eCpJqfWugCs9TKMwpU7J5xOmMrKYVYJAoIgYK3dIxAhSZXJemWk9qthVxK/Czwba7S+m01UP0PwZeAw8L3Auwa2fxdwD/AX2NQeTwd+FUBEZrB2i9fdzzYfdJRZcN4zQvJZvWBfXcndwVTaaWqIooDlljvvpiAQ9s+NUQkDkswQhwEXTded3pu9zHBiuUsvy6iEIftmx5y0k2SGuxfa9LLTBZXaPcOBuTFneaI6ScaRpfa9zqWr1UsQCJP1kNuPd0nUEEvA3tnRXrthhcT3AD+rqr/zQBpTVSMirwDeKSK/D7wH+BbgecCPqeqKiPw28AYRMdjVxCuxHlBveyBtP1goOwkeeKE0CgpPMQmk7wFUpB0Z9SqtEgRkRjl0qkUlCuilhrGqO+8msCqZS2Ya9IyhEgROdfbdJOPGe1ZopwYU2mq48Z4VHn7R1MjdUo1RFls9JusR1Sikm2YsttwUOCraO3iqea9VWS8zzlYuSWZYbqfsmq6jxiBBwHJ7tPadYa9ICnx1FA2q6p+ISAK8Avgh4BDwf3IDNPl2gw2qGwc+CTxvO0Rbb0USvK0QStsRUTiy1GGtk+QBbgmtbuYk6V4QCPMT1rupnWSgMD/hzrsJyp39dtOMuxdazI1VqMQhvcS+dxG7UGQCSDJDO8lQVadV95LMcHyly1g1tCuXQDm+0nW2cjFGWWj2mKrFVGsx3SRjoTlaITjsFflL4AXAB0fRqKr+OfDnm+xLsWk5Xr7R/vOZsuvtblVm1u1Ipko3TVlsn/aniCNx4gGUqTJWjXnsgVnnwWZwb719rlFzOvsNxOaKOtXqEWBnhLU4cBK7EIcBl8w27vXb5sbd2VsAeqlhpdPrqyRrkdugvdlGhZ5RKwSB2cZoJxTD9v4O4BUi8kXgM0Br3X5V1Z8aWa+2KWUHnG1VEfjtSJEqY9dElUoU0kszVrup07rMCNQrkfP7JMlMPwq5MLy0epmz2W8tCpkfr3FitdMPFpwfr1GLRt9W2faWUIQ0yzi+0u2rmy6ecnft4jBg70yDk80uaqw6dMfYaIXgsELix7HuqRPAN2+wXwEvJM5C2XEL2z0KukyCQJgdq9BNrY7ZYN+7TBJ3ZKlNs5s6H9iMUU6sdsnM6fskzBPvuUBC4dp9U9x5MqabZlSjkAM7Gs4ivGtxyP65sVLscpkqYRQwP1Hpe6OFUeAs5iQIhF1TNY4ut2n1UhqViF1bUb5UVS8dWYsXOGXGLWz3KGgozygfhwF7pxucXOv27Ts7HKstMlUr2Eu4XrbcgeTrS8Fl+YNQhOl6lUfuifvXToLA6eSlTDdwuxYTEEX759QNxiiHFpostBIyNXRS+36UiTVHItxE5HGq+plRfNeFQJk37HaOgi7TKF/M2I6tdGinGfUoHPmMrcAY5eDJJkvtng1u66ZkmXLlLkc2gkDYOVWl3U0xKA2EejXyaeXvB2Eeq3BipdNflV083XAmALtJxlePrlrDdVzpv79ifnRZbocNptsL/CY2XqHKaQ9xAWrY4DrvMvMgZTsG05WZcK9o79hKhygUJsMIBY6tdJykQk8yw/HVAQ8ZVY6vdjmww42NIA4Ddk1Uua2dkqoSibBrwt0qqZ9WfiAth+u08mWtOG2GW2HXdL1vuI5DNw4ORXuqIAFkRpHArgpHWQlv2H6/GWuLeAfwDdhkfJ/EVqp7BPCskfXI4xmCIuHecjuhl2ZUopCpeuzMKF+4NtZiW98hCsSpa2M3zayHTK68qDow6g5SCUN2jFX6AVmV0F17/bTyA14/LtPKd5KMe5bafdvcxQ7deyFPcTJWKSXFSS0KmazFHDrVutfKZZROAMMKiScDL1fV/yciPw48U1VfJiK/APwzNtjuvSPrlcdzFkTh4MkW9yzd++FwEbdQsNDscNuJFoWm+fL5hpN2QrGC6OhSGwkC1Bj2zrrL3ZSp0k5TTrV7pJkShcJ4I3I2aJfpUFGo7hZaXTvjFkhS40x1V3aKkyAQ9szUaPZSUmODLvfMjFYNOmzPG8CN+f9fAR4N/eywv4dVQ3mGpKyi7NuZJDMsNLtU4pBKFFCJQxaaXZLs/maMOTOaKYcW24goE9UIEftes9Ffw0y17xmT5Sk6CnWJC4pMonEQWGN8EHDb8aaT3wanHSqSTGl2U5JMnTlUJJnh7sU2S62E1U7KUivh7sW2s/ukcLmdH68yVg2ZH6+yf27MmYorUyUKA/bO1Ll4ssbemTpRGGyJuukgNsnex7GR19Micqmq3oGNmdgxsh5tc3wE9GgoHo6Zsbiv+11sJs4G0q4apmsxcRyQZjDViEkSQ1cNtRG3VaSSCIOAShiQqXGaSqLIJJpkynK7RxwETNVGm0l0PWU5VBTncqwa9tdELs9lgQRCrAHi2BhfpLCvhsJEvUKSZiNPYT+skPgL4DdyHds7ReRLwJtE5M3YNBo+Q+sQ+Ajo0VGLQqbrEYdOtVFRRIW9M24CsgDqYUi9Gt27xGdgqLvS3YsQCIQhaCZWT+KIfiW8pVY/KvnAbMNprigox6EiCIQ4gq/es4LBqk4u2+VuZj/4jFejCKNuHSpUYLwactdiiyRV4kjYN9MYaQr7YYXE64E5bJ3pdwI/BvwDtqToKvDM0XVp+3IhRECX5UUSBMLMeIWDp5p9N8qZcXf5jSqVkCdcsYNP3HqSlW6HOAx4whU7qFTcRAnvmqjSTU1fj16NAse5m5SVVtJXbXUm3KtCy7hXRKHdNUw14jznltLuuqu6l6nS7qWsddPT9p2qO/uOKCw0EzBQi2xiyIVmMtLfN2wwXQq8aOD9J0XkUuBq4KuqujK6Lm1ftnsEdJleJElm6PSUR++f7aub2j3jpLpZwe6pOt/9iItpZxn1MHQiIGAg1UJJgXudNKOTZjzikikKb6rlVkonzRh3lA22rISCKUotCji6kvaz9842Ks5UaaJwdKXDWie9V33yK3e6cajIVFGUMLS+r2EYoIy2quBQd4CIfFhErh7cpqoreQDdARH5/Ij6s60p02BXNoUXyYnVDkvthBOrHQ6ebG4743ylEjJVrzgTEHA6cC81SivJSI06C9wDO3kRAVGhFoWI2veuJi9FQsFjK/ZeObbS4eApN/dKYODYWoeZRoVLd44z06hwbK1D4MZuTaZKZpQggCwz9tWh0wHk0fKaz5TyuIlRsqmwEZGnDex/EvA0EXnYBod+CzBMVToP2zcCuggAm6hF/VWS6wCwWix8/uACaR638Mh9M07TZJSFMcqx5Q5RCHUJCAM4tuwmcA+gGodcvXuC2443aeWpya/ePUHV4SqwrHTaJoC9M3UWmynNbkYYCHtn6hiHt4lAHiNhVYUun/BQhDAQjNqsxElqVa+jFPBnWpF8E/Di/H8F3niGY8+0z7OO7RgBXaB5viF1mfyH3Gul2UOCkDhUUGGx6a7E52C7rgV84bY5WE2t02s7E7hBIFy5a5JGHNHJMmphyB7HzhS91LDWTfq/z1XwXiUImKpXma7FqE2nhErgzChvV2VCL8mvXabUotDZqkwFdk/WWKve2wZSluH657GpOAS4HXgG8Ll1x2TAsqqujq5LnvOROAyYbkTcdrzZL/N5+c4xp3r0pXbKvtl6v5LoYitxrkcvw+bSd9ushEhecMG122YtDrl0fryUFW6Ye25lRpHQGpODyI16K4psDMG/3nCEdpJRj0O+7ZqLnFXeU4HxWsjtJ7p00oxaFHLxTH2kg/YgoQj1SkQtDvoOHCKjTZa4qZBQ1R42PoLcSH0PcIWqfiXfNg88EvjwyHqzRfjynqOhEobsmqpSTNlcpnYIcweAoytdAgSDErrWo+eRu8XvcxW5a902ha8cXemrLC7fOe783ixrhasCu6drLLcTkiQjbsRM1WMnA2maGu442WTnZI0wCMiMfb93uuFEUGim3H6iSRQEzNQjksxw+4kmj9oz4yS7XRDYAk6fuHWh7wTwhCt2bEnRIQN8Fht5XdgfvgabkuNTIvI0VT05sl6VyIUQ3FaGEMxUiaOAAzvG+yqfdi9z5voXhwET1Zibj6/YUVSVq3ZOOlu5WBVQi3aS9e+VVjdzogIShVY3Y7oeEwUBqTG0upkzt82CsiZLhZefGu0nZ3Tl5ddJM06sdpkbyKV0YrXrbMXZM4Yss3aeQCAIQrLMbq84kBJparj1+Bp7p08LwVuPr7FjrDoyITiskCjUTs8oNqjq+0Xk4djSpr8OPH8kPSqRCyG4rSwhOOjeO/jqMt9QvRZy5a4JCv1WvRo6y7ZpjHJ4qc3CWhcJ7AA3O151ogJKUSZrMWlm+rPDyVrsNAK69MlSoSPsGwrcNBOKkBrD4SWbMThTiEJ3K85QhGocMF6LqIQBvcyw1kmdtdczhnYvo2mU1BiiwMbT9IwhGjrr0pkZ9nl6EvA8Vf3C4EZV/YqIvBp460h6UzLbPbitzHTaRY2ALx5aopukVOOIay+Zdipsq2HI7HSlHwDW7LnLtmmMcmKtS7OTEEchSZqR5dtHTYTQSTOmGzHVOKSb2DiGyNE9WfZkqXjuAhF6maES2YhvF89dHAZMVMpbcVbjkKt2jXP7iTWauY3gql3jzjzFIoRTzS7dxFCNg/7rKO+VYYWEYFVNGxFga0ycd2z34LYinfZaJ+3/xvGam+jPfoWsZo9UDVHPjLxC1iBxGLBzomoL86hVI+x0WAMhUyUOhJmxKgGKqUaouvF/l1C4ZLbODXcv9917r9k75ay8ZzFoI0KnlxI5HLThdMDZSrvXTwPS6mVOAs4yVRrViCt2jWMyCEJoVCKn5UT3zY31nShqUcg+hwn+VGCqFnG012GtbQhDmKpVtiQtx4eA14nIp1X1rmKjiFwCvAb44Oi6VB7bvbynKBxdXhf92Y2cpNMuKmJNViPiKCZJR18ha5AgEPbvGCNeCu7lbeTq2sVhwFQ9YqHZwyAEGGbHKk6Ekij0UuXhF03a+sipoZeqM5tEKMJaJ+ELh9YwCoHA5fPjTlWFy50edxxv9vMpXboLZwN3gUghktxRFFQ6MNfoa9RcF1QCG4xhxBBKwKgjM4a9Ji8FPgHcIiI3AseBeeAa4CjwkpH2qkRqccje6bo1LAWBM9e4rcCmnIYotHV2o1Awxs3DmKmSpBknU9PXNwtuI01rccjeqfrpNBkOdehxGDA7VuP4ShdQDDA7VnMiJFRgbrxCs2uN5GEUMlkNnblRGqMcWe5Ye0tu3G1UIh52kZuYkzQ1HF3qsHOq1leRHF3qkKZm5GqZUIRWL+WWo2t9W9JDdk+6rc1Rcu6mpXZCgM3c20kMS+2tyd10Vx5t/QLg64BZbOzEu4C3q+ry6LpULtvdu6kS2XTaRdBS01GVrEoQkBo4udalEkIvgx3jFaeZRJdavfvUSZ5uuDHtZqo0KiFX7ZroTygaFTeG8lCEehzRiMKBADB3atBOmnFqrUM1DjEGggBOrXWceQCpwEQtzlW99rmbqLlxgU0yw2o3YddknUDAKKx2E5LMOJkQFqq01XbSTyg4UY+d5W5KUeYnKiSZkmQZk/WYOJSROjkMfX/nAXNvzv/uNyIyB2zkLvs3qvossWvCVwA/iq1T8QngJ1X1qw+k3Y3YKu+mslwN76W3F7d6exWYH6+SGkOWKY2qMD9edTb7TVPDDYeXqQ2k7r7h8DKPv3TO2Wqwl2UstpO+i28ldldtbLoR86W7l+llGZUw5BF7p5xmSj3V7IGBOA5IEgMBztRbtSjk4qk6q92k77Y5UY2dpHnPVImCgF0Tp11gTzV7zla4mSqdJOVUs9u3J8WRuxrXlSAgDALWuj0CbNnbWjzaydnQ/c4D6l6JrXW9G3gC8Fzgy6r6tnNo85H567cBg9ljT+WvrwZeDrwMuBN4FfAhEXnYqFcsW+HdVObKpXS9fRQwOxaTZLYYfOxQddczhswotYq9hWuViGavO1LXv0FEYbmVAlCNI5IsY7mVOhlIi9xNQaBUCAgCdZq7KQiERiXg0EIbkQBVwyWz7u6TKAp49P4ZvnDXIq1eyngt5pH7ZpwI91oUMj9RvdfMfn6i6qzuiDHKiZUOSwNp10Nx4wUH9tqNV0NuO9YhNRAFsGfEz/hQQkJEHgV8FGt/+AfgJ4pdwB+ISFdV/3TINq8Fjqnqv23QzgTW/vFaVX1Lvu3j2MjvHwbeNGQbQ1G2d9NWrFxqcci+mYZzm0soQitJufVos6/7veri0aYHGMTOoIRWN6EShfRSm7zNlXorRZmoxXTXMprdhGoUMOEodqEI3OsZ7Xv/dHotZ7mbAGpxhQM7AgpLchy6NCFbFGzWUodtRFHAQy+a5D9uOUm3m1KNQh560aSz58AYZamTEgXSd19e6qTOhESSGVbaKbtmGqgBCWClnY40Zf6wZ+o3gf8EHoY1UguAqv4M8PvAz55Dm9cCX9xk3+OBceDviw2qugh8DFvwaKSUnbr79MrFnvYoPO1q6IpOknH3Upujyx3uXmrTSdzYJJLM0Oxk7JyssXuqzs7JGs1O5qyWcBQFXLFznNtPrnH9HSe5/eQaV+wcd/bwRwgrnQSjMF6NMAorncRJ7IIxykKrh6hdkYna9y5no7NjESAYEcC+d/UcpKnhcwcXWeumSBiy1k353MFF0nT094oxSicxfM2BaR5/6Sxfc2CaTuKuvrwKzI1VmWxExGHIZCNibsyd2rW4V6pBwFQ9phoEI79Xhp0ufC3wLFU1uc1gkPcAP3QObV4LdETkk8BjsPaJtwC/BlyVH3Pbus/cjq2CN3LKTN1drFx6SdafbZexcgkDiCVwWkoxU2s8np+pFzFLLDjU/Rqj3Hp8jduPN21A1mqPvTNr7JxwI+RVYKYRcWzFkOTXbaYx2mybBVaFEHFkpYMaRQJh96TbehLVKGKmbiisu9UocmooP7HWZbph65NXooATa25SZfS9jXopaapEkTBecedtVItCJqsxNx1roQiC8pBdU87UW1bAV+gkhqSTgNj3W5G7aQVrh9iIfdzbtrApIhJgVyNNrFrpLuA7gV8GakACdPPkgoOsApObfOcLgRcC7Nu3b5hu3IeyEpsVBsn1HjkuVy7tXkoryfrtNeLQyQNStu632U748FePIbmnTCtJ+fBXj/HIi6eYGHPj4TRWrXD1rgqJKrEIPUeFa4osqVqom4zaetcO003PjVdYbHXpdQ2VSJgbH21A1iCFW+o9Sy3CMCDLDNONipPfV8QKNXtpX6W8VnFXKQ6gEguT9dOG8krs1jllfqLKrcdWyTIlDIVLZidG6pwyrJD4a+CXReQO4OP5NhWRq7DBdO8b8nsEeCpwl6remm/7iIiMYw3V/5eNo10Eqy29D6r6VvK0INddd92DugxaEWhzyWy9H0vgMtCm8FqpRQGN3APoVLPHlQ7OUtm632aWstJO2D1VIwqFOqEdDLKUCQcZjuIwoF4RPntwsb/qfMx+N0WO7KosYOdktR/cFgaBMw8ZUTiy1GGpab3gWl3lyFKHh1805aC1wUI5QqhqX0dcKKcgUyXDrvwAAhGyEZf3HKRnDKEIl8w2+nm3QhltLqX7oPZ3aWBfR23kGfY8vQy7AvgQdhUANgPsPHB9vv+sqGrGxqnF3w/8n/y7qyISq2oysH8cOG9jMQrKnNnDvYOyWj0bUe5qhljofh976UxfABa6XxcCcCyKaMQBx5c7fTfKRhwwFrkxuBqjnFxNCEWIwxBjDCdXE2e/T6CfawgRp+vcJDMsNLtUKxFRIKRGWWh2ncUSpCgXTdaYbsT9XEP1KHSWwLAahszWy8nxFSEsd1LqsS1z2+ymLHdSZ3m3ksyw3E5t6o88Fmp5xIbrYYPpWsA3i8i3AU/GBtMtA/8B/IOqDrXwFpGLsSuJ96rqiYFd9fx1Eft8XArcPLD/MuCmYdq4P5QVt1DmzB5OB2XVgoBUlEgFCd14HBVG+XoclpIqvF6JeNieKT5583ESFWJRHnNgJ/WKGyHRSTNWOgn7dpx+GBebboochXmQ2YmVDkEQYIzh4qmG0yjhKAzulU7bZSxBJQjIDBxbbqMaIGK4ZGbciWdaESu00OqiqZW7LnN8SShcvnOMQwttFls9wsC+d5V3q9/uutdRck5PlKr+K/CvIjKuqmv3o70q8AfAGNZjquCZWKHwt/n+pwO/CiAiM8ATgdfdj/bOSplxC2XO7GGgIMmdC/1kY0+4crQFSQpCEZLUcGS51Y/a3TFWczqwTdWqXLtvhl5iqMQBU7WqMzWCLUsJaiCKA7pJhjiyE2SqhJEwN1YhVSWSiNBhQFbZ9iSArslYbJ5WFuyccjO7DwJh11SN4ysdOklKLY7Y5dCDMRRhul5lZk/ldDZ03DmnFBUhbzq22tdOPGTX1tgkEJFvAX4BG0QXi0gH+AjwS6r6mWG+Q1XvEJE/B14vIgb4CvC9WCHxdFVdE5HfBt6Q778ZG8C3ApxLwN5QlB230E+3EIel3EBpavjKkRVUDHEEKvb9KAuSDLLa7fGVwyv9pG2P2u8umM6qDVL2ztoSqUmet9+Va2M1Drl694R9GNt2hnj17glnKaDT1HBioGb3HocDdmFP+sQtJ1ntptQc25M6acZqO+Wi6Qaap0tcbadOVmXGKMdWOgQh1AgJQji24jYwcfdUbV2ZW7dJQztJxtJatx9M15ndLGH3/WPYYLrvBf4Ca394PXAC2Il1S/24iDxFVT82ZJs/DPwi8NPARVhB8UxVLWIjXoE1Ur8Ua4v4JLaWxchtEqfz2ttBJhTBqHGmIinbu6mTZtxydI0jq61+oM1FEymPPTA78oexm2TcebLNntlGfzZ658k2D78oc5YFdnosptPLyIxBVZkec5dpMwiE+YkaN92zSi/NqEcR847cbUXh+GqX5Vbaz4MVB11naTIKe9J1JdmTJI8xmarGVCsx3V7Gcme0SekKksxwaLFFLzHWJtFV2kmLA3PuAhPBrfpnkG6ScfBUm71zpydLB0+1uebi0T13w37La4F3qerz1m1/g4i8G/gVbCDcWVHVNlYQvGKT/Sk2LcfLh+zb/SYUIckMx1c6faPWzJgbVzw47d20f7bRj5Nw6d2UZYabj60QCjRqMa12ws2tFTIHAW6ZKt00o5dkqNgAMBWc6bULXfPtx9f6g9nOiYYzXXNRJvKSuXrfbXPUZSILesbYmJOJuK+zD3IPGRclMIvJUjUKSFM7++0m7iZLURRw8VSNW46t9fMbXbnLTSCkMcpSM6FRDQkAI7DUTJytOAvthM3ZFDkt9AWnr10lDAkDQRCMpiN97oYVEpcCP7PJvncC7x1Nd7aAvNA85K8OnWj7D2Nu3A2jgMShcTc1Si0WMhU6vYwgDIhFSR08IJXcwHpitddPEjc/4TYLbDUKc709RGLfu6JnDJ0kpaX2IQxECESduDaGYh92VSEMIDOCOAy6LOpJfOnuZl8NeumOMaftCfZ+Qe3AKrhxqAgCIQ4CvnrkdCjX5Tsm3MYmJWk/zXsgwljVnQdjYU8arBkzanvSsELiM9i0GPfJtwR8PfCFkfWoRDJV4ihg39xYKR45hXH3xGqn70UyXXe3cqlFIXPjVZbbKUEAxsBUPXJikFSBHeNVUlXSTBmvhuxwmAXWZtvMWGgnp+tANyruZr8Iy+2Uaig2V1SS0s3UiWtjHAbMjcccXuygQJYpuyZjZ6skY6w3U9F2mhlODdhDRk2SGda6CRfNjNnZPbDmKH23KLSTlOl63A+mayduEjMW7Z1a6/XvkyTNOLWWceW8m/aiKODR+2b40qElOknKWD3mEZdMj/Q8Diskfgd4q4jsAv4SOALMYd1ZfwR4pYg8ozhYVf92ZD10yGCCv8FXp+VLT7u+o8W0zRHVOOSK+Um+emSZDAhDuGJ+0pmxtRKFzDYqNk1GGFBxOLPXzKblqMch07k/+q3H13jorkkcaGSQUNg5WeFTt52ilyqVSHj85XNOXBtVYN/sGNONCr3U1oCedFRvAewqKc0z99r65CFp5maVBKddbneV4HKbokzWI3qZ6RuSJ+uRs5gMFRivhhw81aKbZlSjkP1zDWfXDmxqod1TNVpJSiOORu6dOayQ+Kv89dn533p+deB/xcljOnq2Ik1GHAbsnxvrx2W0E3crFxW4ZK5OgqHbzahWQy6Zqzu5YYtUCzcfW+tLwat3T7jz3ELtwIn17ojCgMkwcPfwZ8rdCx3mxqpEQUBqDHcvdND9OvK7PRShXoloVMO+IVnV3eQlQji61OLEWrdvb5kfrzoLACtDRVIQIbQTw2wjphKH9JKMdmKc/TZROLbS5eRqBwkCVtsJtTjkmoudNIcxysFTTU6sdlBVVjspBrhy5+hUaudik9h2lG1ILlYuJhcWrlcuorDWzbh4stYf2Na6mTMvksVmj0ooqASIKovNnrOo3UoQUK+ERAH935YanNlAesZ6+1SiEEWpBNau5MKYvBWTl26mrLST/hJ3sl5x5nRQqEi+eGiJdpoxVY+5dsQqkgIJhb0zdW48vNKf2T98z6Sz4LYkM5xa6xJHYe7lJ5xacxe9nmSG246vcWK10792K+10pN5bwwqJXWeKhRCRFxf1H84n+lHCRZRuCM2uu6JDhQ/1kaU2zW5KHAZc5LAIkAqM10IOLbT7g80ls25WEklmWOvZVOGFC+xiy+qZ62f/+DlTpAr/+C0n+iqZ/3HlvNN6GZnJONU8XUxmbix2IuCLycueyVo/Ut7l5CXJDK1uysxYFQlswGCrmzq7dmBVJHum63SyjFoYOgtgFYVWL2N2LCYIYoyx713ZJLK8PkYUCIoQBXYx6ErgpqnhroUmE7WYWhzSSTLuWmiOtF74sELiEyLyK9hiQGmxUUSuBt6OTSV+3gmJsosOFWSqti2HATaQryQ6GbsnqsS5EW2t4+YBicOA8WrE8ZVu30g+M1ZxamxdbiVcNF6jh6FCwHIrcZYqPBQBCfLgR8iw711FXC+3e9x1qkU3y6iGIfvmGmRad+ZQkaphtZ0SBiGZyZiou0sVXriJVqKAaiV06iaaqWKMtc8Vq3hjcFtOVIROmhEHAT1jqEWhsxWuCtSiiIVmt2/fmaiONovDsOfpF7FlRb9LRJ4H3IiNc3gltmrcyAsClUExsz+63KGbpv20HK4Gb2OUgyebnGx2+qkrsky5cpcbl7wiDchqJ6XdSwlzrxkXK4k4DNg1WSVJTV91t2vSXY6cJDPcvdSim9o02h3N6C65q96Wolw0VWd+wv7GOAqsmsuBDUQz5StHVvrJ75qdHs1e6swoHwTCZC1itZWQZBkBymTNXdGhst1EK1HAVP30UNdOHOV4x6q3Lp0f44bDK7R7tmripfPucjdVgoBaJUCJ+xUaa5Wg/BrXqvpGEXkv8IdYd9iDwF7gDcCvblD/4byhFofsna47L+8JdmC7/cQaR1fb/dzvKxOps4GtWCl1e6mNJcgMU3U3KhIV2Dc3xmQtpp1m1KOQ6TF3eamMURaaPcYqYT8J3kIzcxYkddoGEjq3gRT1u6MAktQQBZA5sn8UjFUr7J8PiURIVZ3GnBRuovdKdLnWc+ImGocBU42I2waCLi/fOe4uwZ9CkilX7x7rOx0kmbpzuQ2Fqy+a4K5TbdI0Y6JWZd9cfaRC6VxWXLcD/4KNi7gSm1fpw+ezgADrGXPvPCt1Z/rRNDV89egKK52kvzRcaiZ845XzztxSVzs9bjq62s+nVK+6aaevulODKKTqVnUXBEIlFL5ydLX/8F8xP+Zs9lvYQD5x68l+XMYTrtjhZFJhs8BmLDST/qps1pH9A8ij1Wt00jSvpabUIncrCVvis8JaN2W1nRCFNpmhMzdRJS8TjPW8cxgwqwJxBDfcvUaSZsRRyDV7J50WcJquV5m6OO57TEowWjXosLmbnoK1ORzArh7eDfwu8O8i8sfAy1R1YWS9KolC/bPU7vUH7SQ1ztQ/SWY4vtZhpZUQIBiUTuquDnQ3ybjjZIs9M6dTSdxxssXDL5p2kk9ppd3j5iOrZAqh4DSLqCgst1IwhjAIUGNYbrkLkiryGz3yoknaxlAPAmf5jWzK+gADVAR6QDDiB3+QOAzYO1vn2Eqb1BiiIGTXZN3ZbLtIyV/c90lmz6GL35dkNgtALT7tTnxitTfSeguDaKbcfrxFILY6XTc13H68xaP3zDpTFbr2hBt2pHg/NrnfY1X1S/m2p4jIDwG/jk30t3NkvSqJJDMcX+0yUYv6huvjq11n6h+D0uwkdrqRW3ebnQTjaGqTqZKkGScT049dCEWdeFoUicZ2Tdf7BsJRJxobpGcMig3K6mVKJQxQ3KlkMlWOrbS58Z6V/qrz4RdPsmdm9MbkoijPjokqSarEkRCLOIsBCQJhqh5zw93L/ZTyV+2adOtYMRBQ6jIdjjHKYqvHZD2iGoV004zFVs+ZWrJwla5VQgSoVUJM5u6+7LvxzzX6z92oPeGGfXp/HnjT+uJCqvrHIvIv2Ihsz1kIRBirRCy3EgygahirxP3SiqOmEgS0E8OdJ5vWI0fhwI4xJ3r0TJW1TsqR5SRXWAgT1diZ658oLLR6Vl0RCO2eYvLtLsgSw+cPLVKLQsbrMe0k4/OHFnnkxdMjn3FbY2TEmGh/hZupOPOQKZIX7p2pOU9eCKddz6MwIE2VKJRcHTR6w3WRLbjby0hSO3y5zBYcilCNQ+qVoH/t2j3jtK6KUaU+UJFx1G78wxquf734X0T2Y1N8fwkQVT0KPGskvSmZOLR1hBebPYLMzgB2OvTIiYOA+ak61Sjs65onxyrEjh7+TBVV6/2jIgRoPuCMfiSNENZ6Cb2kiKLNWBOcRbaCnYWafAA1qjiSRwB01YAKdy+0yVBChB0TVbpqqI24rSgK2DtT519vOEI7yajHId92zUXOnCp6xtDppbRFyExqs4mqu7QcgxUaJ+qx0wqNcRiwa6LKbcebpGqIJGCfw8p01Thk93SVT9x8gjSzAvAJV7mzOZbhxn8uRYeeCbwRuBybk+txwGtEZBX4oXU1qc8LgkDYPzdGJc/D7jq4LYoCHrprgnuW22QphBFcPFV39vAnmaGXKQ+9aLK/FF1wFOBmAphpxPz38WY/svVrDtQxrmq/C0zUIpZbPdpdK5gmapEzA2GswmKry3gtYqIes9pOWGx1iR00mKaGO0422TVZ6we33XGyyd7phpN7ZbAu81g1otlNaSeZMwE/6Jq92u7lrtnuDNeVMGTXZJWizFcldGcrM0ZpdVKm60VVQaGVF8NyXeSo2U37zjel2yRE5PuAPwf+GFudrsjl9F6sAfsObCzFeUctDtkzVaedZdTDkIojiQ+FgbBBM1/6xpF972zlEgZMNyokmSEQO+uebjgKcEuVQwst9s3V+wPNoYUWpIoLRXqEsNbJQJWxWkwvSVnruBvYiISrd09y8FSLxVaPSOx7otG310kzTqx22TFe7Uevn1jtOqncBuXXZS5mv0Vdk8xhEGuR6fnA/HgpmZ47acbh5Q7dLEMVMoHDyx1n165A1r2OkmFXEq8G3qyqLxGR/iiqqu/Ia1D/JOepkFhq9fj8oUW6qaEaBTzqkhmmGy7MgxZVG7IfRCFBgFMVSTUOuWrXOJ87uJR7rQQ8+mI3WWATUXZO1Gn2EtY6KVEo7JyokzgyEqQoM42YIFAyY9OPTNViUkcW0EoQsGOixq6Jal9VmOHGTlAMoq0k7a8AXboT990oL3LnRnkf1M6CC729K8N1P1+a0VKyKqhRji61marHjNVimp2Eo0tt9DwucjSskLgCW250Iz6HtVGcd6Sp4VO3neKepVb/Bur0DN/y0F3OknEtt1N2T9b6+tHldurMHQ9gohpz9a5xuiajGoRMVGMn7dTDkCgUuomtqtZNDFN1oe5oaV8YCCcltvoLsQFgzjKlRgHX7Jnii3cvkfQy4jDg2r1TTu6TahwyOxbzoS8fpZsq1Uj45oftdqbX3pJsyCXVcSk7X5oEwkVTDVa7CavtHop9L46j15fbCUmSEcchU/W4fMM1cBfwDcAHN9j3OODQSHpTMq1eyu0n15gdi4nDkCTLuP3kGq3eHJORm9XEYrPDLSeaZLl//5XzY07agbwwT2oL89gykYbJMTeFeYJAmBmLObrctu6FCjMOvUjiMGCsFnL4nlZ/Zn/Vxe4MknBaNek6KV2aGg4vtplpVAhCwWTK4cU2aWqoVM5PN8pByp7dQ3n50mp5/YjlTq8fCzVVqziLGRKFu042uXup3V+V7Z2uc+X8xMjaOJeiQ78uIgL8M3ZxuEdEHoPN3/T6kfWoZHqp4dhKG6O2HKWqu0FGM+W2Ey1WWz1qlYhWp8dtJwTNFBxM8IvCPNVQqFcjemnmrDBPzxjqcchDd0/QTAxjcUA9Dp0WrmlUIq6+eKI/G61VImeJ2/rL+lAYC63lw1VSumaSstJJ2D8/RiABRg3HV7o0k9SJkCjDjXKQMnOm3Sdgtps6zZcWRQGP3j/Dl+5eppdlVMKQRzhacYLVThxb7bLSSSnCTY6tjjY1+bAusG/JbQ8vA16FtY/8HZAAbxl0kT2fqEUhRpX/umOhH9Xz2ANzzqR+O8sQUXZN1QBhqh7RTQ3tLKPmYGhLUULg1hPNvvfWgdmGk6CsCOHIUrtf4/qevMa1SxfYahgyPRkXTitOE7dlqiy1u9x5skUvyajEIQd2NNjjIDNrJQgIAiFNlXpV6HWtEHQVJ7EV2ZBrcci+2UbfBuJqhl92wCzAdKPC1102V1o+uKVmQiWQvpPDUnO0HoxDj0yq+joR+S3g8djSpcvAp1X15Ij6UjrdNOPkWoeJatQPNju51qGbZk5mbJUgoBqHjFciKnFALzEgqbOHPzBw93KbzGTU44humnL3cpvAwVhq8+gLiFWXIKCIs2C6OLSZPe842SyyLXDpjjFn6ibNlC/dtcTJZo8oCkhTw2qrx8N2TY18VVavRjz2wAyfP7jEajclAB57YMZJ5DqUnw15sF0XK5UHA1EUOFlBr8c6GSgGIQ6ExCgS6NbESQCo6jLwryNrfYtpJintbsqO8QrkJdnb3ZRmkjLhwG+zXo24du8U/3HTCRIDcQDf8JB5Zw9/ijV6LqwZ1no9RGGyLk48gAqVRSjk9o/TUbSuqMa2DObge1d00ozFdkIc2Rm2RMJiO6HjYEIRBMI1e2aYqlX6dYsvmXOXvBDKzYZcJmUHzJZNkXjy2EoPNYZKLWbXZGWk18/N6HSeUI9CjMJyK6NWgU4vIwrtdldM1GIu3zWe5xsSJmpuvI3AGrUyA/vmGoRBQGYMncRN0aEIsfV1FcYqEe0kY7WTOlM3DdYLL1Rp3dQ406MXiNrVkqjDhEPYQfvynRPO1TEFnSTj6HKn7267e6rmzDBfJkXAbCTSdzjY48COtJ40NaUI3DgMuGzHBJO1bv/a7RgfrRDcUiEhIlXg81i11fPzbYItaPSjwA7gE8BPqupXR91+JQp56MWTfPXIGkmWEUcBV180TsWRkEgym6n0wOx4sXBhueXOBTaKAmbHK9x4aJkMqxV5+CVujGgmgL3TNe5caLPUTqjGIXuna84irkMRktRwYrXT9+qYrlec6dFrUcjMWIXjK92+anLnZNVpptuy1DGFUT4OhSgMSTPjzCg/SFkDKVjX1FgDZ66ogyy1evdxJ3YVexUEwv4dY8RRcK9yB1uRBdYVrwGuBj49sO3VwMuxRvI7sYbyD4nIw3J118gIAuHA3BiBQKuT0aiF7Jt1u6zvZhmLzY4tAiTQcLySMAYu2VFHjKCBLd3oaiXRzZSpWkQ0HpOmSjdTp4ZrBHpZZpPEReIm3LRoKhSu2jVBLzG0k4yJOOSqXRPOopLL5HTCPSvwojCgm7rzboLyBtJCAFajgLFq5FwApqnhhsPL1CKbpLHTS7nh8DKPv3TOmSCsxSEH5sacrTq3TEiIyKOBFwMnB7ZNAC/F1tJ+S77t49hKeD8MvGmUfQhF6GVq6xKIsNxK6U2N1uizvr2VdsJn71zsb3vMgRln7aUotTjgyGLarxa3Y7zmpuSmwFQtopNkpFmeftphLqVMlaVWj5uPNumlGZUopBK6K4FZVBy7Zs9kP1Oqy4pjZVK2d1OZA2khAIMg6MdJGIdqyaKqYK1ih9ZaJaLZ6zpzBS9wueocutciMiUirxKRj4jIV0Tk4SLyMhE55/rWIhIBbwd+DTg8sOvxwDjw98UGVV0EPoaDOtpJZmh2M+YmKuwYrzI3UaHZdVsE6PBii7mJKrunq8xNVDm82KKbZE7aCwzWR7yVYBCWWgkHTzadeDeBVd/tmKgyP15hx0TVmdoOTqfurgTCzskalUD4/KFFMkdusEU1tSi3Y0VR6LaaGnYWnOTGVpcU3k2dJOPUaodOkjn1btpoIC3Ks46aQi1558k17jrV4s6TaySpu9TdlSAgDIRWNyHNDK1uQujQfbkMhk3wdwD4d+wA/nHgG4EqcC3wBhF5mqr+yzm0+zJs2rdfBr5nYPtV+ett646/HVvYaKRkqnTTjLVOhlHr+hdI4Mwjp6eGzEAlgDSzr4mx213EXfeMyXM22YjkKBBSY/XAo05vHYrNw2Myqz7IMkVid7PRrlpd9lqSstTpEYUBtTBwkrob7O+rVyIalbDvcqu4+31lG5JtGd9OPwBsbrzqrL3BgbQShfTSzOlA2ssyji13+/E04468CaHcMreDGKNbrm56M3AE+Gagg62oiKo+R0RirB1hKCEhIldjo7S/WVV7cu+HbBLoblA3ezXfN1IihLWu9chpVCJavYy1rjuPnHoQkmQpB091CCUgU8PF0zXqjoJ6AATBGEOWCmGgiCMN4+n0zwmd1Aoml+mfqxLQM4ZGHDE+EbHWSemkKVVxFHNSYn6jQo8eBhBLgNHRJ20bpFD/1OOAmbGKcz16mQNpkhmWWin75xqn68o7dBYpytxet2+mSCnmrMxtgesJxbAjxjcBz1XVtcEssDl/ALxvmC8RkQD4I+CPVPU/NzqEjf0KBVvDYqPvfCHwQoB9+/YN040+JoDLdoxxYsWmYZ6px8xPVp3WQBivVlDtkGQGCfL3rvLoBwG9zHB4pdPXo19WjZzM2ERhtZOyc7zSz6W02nFXczqMAx61b5obD69waq1LFAqP2jdNGLu5eGXmN8pUafdSWknWF0iN2J29pWw9+lYMpCLSt7e4pJ/iJL9eoQjtxF1q8jI804YVEj3YNMp7FugO+T0/CewHnprbJQokf78MVEUkXlfEaDzfdx9U9a3AWwGuu+66cxqSKkFAFAhdYw1a3Vw142rZm2SGXppx2dwYqUCk0EszJ0WAwD78cRhxyUyjX1A4DiMn6iYViCO48fBqvyLXw/dMOhOAoQi7JhrsuLLan42GobsssGXmNxqs3NbIDbuuKrfBafVPp5f2Dcku1T/FuRyruj+XcRiwc6LKUrtHLzOoKjsdVqYLRWwqkJWONZIbZWbMnWt2GZ5pw56pf8LaHq4c2KYiMostQjRsFPb3AHuABWzepwR4JPCDA+8FuHTd5y4DbhqyjXPCqKDGkKQGNQbj0BIZilgDcjuh3ctYaluDssscOY1KwOW7JnnIrgku3zVJo+IubcXdCx3mx6scmBtjfrzK3Qsdm7zQAYX658hKl2OrPY6sdJluuK1dXHgAAU49gArVnQFW2j0MOFXdFWnQO6nh1FqXTmq4Zo+7pHRlnssijmB+osZ0PWZ+osb+HW7d3LFp4ID81aHfQRnnctiVxM8CHwVuBG7Jt/0RdjBfAH5uyO/5UWB9Dtt3AzcDr8tf3ww8HfhVgDyx4BPz/SOlZwwr7R53L7bpJoZqHDDTqDpbZgeBMFENWWopJjGoKBPV0NkN26hEXLZjjMNL7f62y3aM0ai4SSZYi4TFdkpmrEfHTD1y4m4Lp9U/l8zU+4Zkl+mty8xvFIp1Z8wyg+SvgePJxHSjwuMvLScpXdk1HlzHEQxSZq0MKOe+3HS0EJFIVVMAVT0hIl8DPA94EtZtdRl4J/B2VV0dpjFVvc9qQETawClVvT5//9vYVYvBCo1XAivA287hdw2FSQz/decCoDQqFVq9Hv915wLf+fCLnJTcBBirxhyYG0MQFKUWu/W0eOS+GZY7KZ0koxaHPHLfjLM6yZ1Uma5FfY+VTuoumK4otrLWSfv+/eO1yGkAWFmZSwGb+iNvwxicBgoWlJWUrkDXvbqkrOj1raiV4fq+PNMIdZeIPENVPyUirwbepqp/gDVUu+QVWCP1S7G2iE8Czxt1tDVAK8sIEDqpoZt2EaAWBbSyjPFRN0ZemKcRcc9SSponw5tpRM4Gm8JA+Nj9M/0byJWBUEJh72yNGw+v3Msm4SoiWdTWc1jrpMRRQJIaxrvRSIutbEQZg81gXqoyjJ9lU3YUdJlsx4y6ZxIS09iypZ/Cps/4F+CeUXdAVR+17n2KTcvx8lG3tZ5qGCABzE1UacQhrSSj1U2pOjRqCYGtdyugBgR3tYSL2fZqJyXLDGEYMOFoti0KSQoPv2iyn0wwSd2kAIHcYGcgCgXBvhqDs6JDBS790Qv6s9FcWJQxGy2TrUgDUialrjhL4EzP00eBd4rI72MXux/JVUAboao6NerOuaZWiXjEnmk+c+dJjhmIAnjcgR19V8BRk6liMBAIWZYRhCEG42xgE4WjSx2avbS/9G123My2C2PrWsf6oEdhwFzDXVoOgEoU2BKp+YDa7LqJXC8oK8Btq2ajZbEVRY7KZjvVyjjT2PRs4AewBYZeA/w5cHcZnSqLUIQdE1UeunuCXmaohAE7JqrOblZjlBMrPdY6CYjQTRJOrATO0i7YGRsoSpLazH5G3cy2iwc/yQyCdfd1+eAPujYWQVIuXRvLzpS63Wajg2x3Ibjd2HSsyG0AvwMgIk8CfsNFuu6tJFNFVanGMWFoiIIAzQvluKqTvNy2AqIahnSzjOV24jQ3Ty81nFzrWtVWALsnHNYIUAgD6Q/aLi2S/RTJS+5SJA9SdqI42F6z0fVsZyG43TiTd9MssKSqBnjmwLYNUdWF0XfPLcYoC82EQKAWRRg1LDTdDdoqtujQSqdHJ0mtS2wtdqaSEYXlTo+AgGo1oJsYljs9J3aCsl3/IB9oZhqluG2WXb/iQmA7C8HtxJkmzCeArwM+g03nfbah5bwsY5VkhoVWlwDBoMw2XKSHs1SCgFolQImpRAG91FCrBM4iW1OUHeMVkkxJsoypekwcipPYha1w/Su9mpoNWi+C10txS/WMhjIcDrYrZxISL+B0NtYXUI4785agKhhsVk+XSChcfdEEB0/a9ODjYxX272g4cxOtBAGhBDSTBAmEXmKoRbEToVTomQ8vtOhkPedlIsu2EWx3t9TtzHYtzVoWZ7JJvHPg/3eU0pstIBC14UOBIEYJHFaRCUWoxxE7xiv9G7YeR85m20EgTNRDbju+isHmYNkz685A2EkyDi216KaGahQwN+Eu3XTZbpTb3S11u7JVpVm3E2eySbzkHL5HVfU3R9Cf0gmDgJmxKkFgS32Grm8ctYO3qDjP65JkhnZPefT+mb6apNUzTtIkp6nhswcXaXZTKlHAajvlswcX+cYr553YCopBu5dm/cHb5aDtPXLOT7Z7TEYZnEnd9Ovn8D0KnHdCIgiEmbEKR1c6ZKkigeQCw7WHjFX9VGJbK8D1DRsGwUCaZDepkjtpxsm1LnPjFaIgIAqFk2s2Bfu4AyFRZn2HAu+Rc/5xIcRkuOZM6qbzt97ekIQiVKOQmfrpgKxq5C7dtCgcWe6w1rEJ8LKm0qxlXLnTTSqJMtMk28p0kGVKFNhXEZzGnCy1EvbPNvr1K1wm+CvwHjnnF34F+MBxmcHgQY8KzI7FrHQTOr2MSiVkdsydS2qmSi/NONXq9dUjlShwFpdRxBKEi0IvyajEIXtn3Ohiq3HI1bsnuO14k1aSgcLVuyeoOrZJ1Ivo+NBdTQLP+Y1fAT4wLmghIQoLzQRRGK/F1h02f++CIi4jCoQAm5LDZVxGQZhX5XK5xA4C4cpdkzTiiE6WOfdu8moEz7ngV4D3nwtaSGSqKEoYBijkr+4irgsEIQohydzetIVnR4DNc1RkTnWZSmL/3FipNQm8GsHjccsFLSQAUMGYjCyDMATUnf90EAi7Jmu0khRjlEZVaMTuUoVnqiy3e9x1qtVfau+ba5Bp3cmsqmx/dK9G8Hjcs+2N02ciFKHVS7n56Bq3n2py89E1Wr3UaVK6+ckKvTSjm2T00oz5yYqzpHSaKbceX7NV4hoVwkC49fiak5Kig/7oY9WIOBQrMByr0oJAiMPACwiPxxEX9EoiyQyr3YRdk3UCAaOw2k1sqmtHqhIBQgnQ0BCKWy1pijJZi1HsLD8KAybDwElaDu+P7vFsTy5oIZGpEgUB8+Nxv07yYishUzez3yQzLLVS9s81+knillqpk+A2sGk56pWQSij9kqK9TJ2k5fCGZI9ne3JBq5tqUchUPebQQosjyx0OLbSYqsfUIrd5XST3NhLHA2gUBVyzZ4pOajjV7NJJDdfsmXKySioMyUmmNLspSabekOzxbAMu6JVEEAgXT9dodVM7A65GXDztbmArM7itoBaH7Jmq991SvSHZ4/GcCxe0kMjUprW+eKbeDzaLwsCZHr0IbgtOQStJacQxl8yNOc+UWquEjIdxKcnNvD+6x7O9uKDVTaJwaq1HAEzUKwTY9w4TwdJJMo6sdDi51uPISodO4q4u82ljsr3MUXg6V5TH4/EMwwUtJFRgvBZydLXLnaeaHF3tMl4LnaXlSFPDDYeXqUUBc2NValHADYeXSVM3SfcGjcmANyZ7PJ5z5oJWN4nCWidjPs9cmhrDWidztpLoGUO7l9ECVBNEBMm3Rw7ktY9K9ng8D5QLWkiowHg15O6lNmmmRKGwd7rubCURIax0EupxyFg1otlNaScZkUMdvjcmezyeB8IFLSREYa2bsWu8ShyFJGnGWtfdSkJC4Yqd49x1qsViq0co9r2r8qUF3pjs8XjuL6XbJESkIiJvEJGDItIUkQ+LyGMG9ouIvFJE7hKRloh8QESudtEXFZgbryBBQC8zSBAwN15xtpIIRZiqV7h2zxSPumSaa/dMMVWveBuBx+N50LIVhuvfBF4MvBH4HqAFfERE9uf7Xw28ClsZ738BU8CHRGRq1B0pak7Pj1fYOVFlfrzivOb07qkaKoJRUG8j8Hg8D3JKVTflA/2PAC9X1d/Pt30cOAX8gIi8GXgp8FpVfcvA/oPADwNvGmV/ihKYX7priXaWUg8jHrFv2pfA9Hg8npyyVxJN4GuBPx7YlmBrZFeBxwPjwN8XO1V1EfgY8O2j7owxyqGFJidaXVY6KSdaXQ4tNH3mUo/H48kpVUioaqqqn1PVRREJRORS4O1YIfEu4Kr80NvWffT2gX0jo5tkfPXoKuOViPnxGuOViK8eXaXrMMDN4/F4zie2MpjuF7GD/w8Av6KqNwGTQFdVe+uOXc333QcReaGIXC8i1584ceKcOpCpogph7l0UhoIqPiLZ4/F4crbSBfa9wEeBJwOvFpEK0MauKtYjwIZhyar6VuCtANddd905je61KGTHeJVmN6USKb3UsGO86jwLrMfj8ZwvbJmQUNUv5v9+TEQmgJ8DXgZURSRW1WTg8HFgedR9iKKAx+yf4Yt3L5Fkhnol5tq9005rM3s8Hs/5RNneTbuB7wDeo6qrA7s+hzVcL2JXDZcCNw/svwy4yUWfphsVvv6yHfSMoRIEXkB4PB7PAGWPiNNYQ/Wz1m1/CnAceB/QAZ5e7BCRGeCJwIdcdSqKAhqVyAsIj8fjWUepKwlV/aqI/A3wG7kN4nbgGVjj9QtUdUVEfht4g4gY7GrilcAK8LYy++rxeDyerbFJ/CDwGuAXgIuALwPfq6rvyfe/AmukfinWFvFJ4HmqOnKbhMfj8XjOjOg2cve87rrr9Prrr9/qbng8Hs95hYj8t6pet9E+r4T3eDwez6Z4IeHxeDyeTfFCwuPxeDyb4oWEx+PxeDbFCwmPx+PxbIoXEh6Px+PZFC8kPB6Px7MpXkh4PB6PZ1O8kPB4PB7Ppngh4fF4PJ5N8ULC4/F4PJvihYTH4/F4NsULCY/H4/FsihcSHo9nSzBGSTKDMdsnE/V2ZMtqXHs8nguXTpJxdLmDUSUQYfdUjVocbnW3PBvgVxIej6dUjFGOLneIQ2GsGhGHYgWGX1E8KPFCwuPxlEqmilElCu3wE4UBRpVsGxVA2054IeHxeEolFCEQIc0MAGlmCEQIRba4Z56N8ELC4/GUShBYG0SSKc1uSpIpu6dqBIEXEg9GvOHa4/GUTi0O2TfbIFO1KwsvIB60eCHh8Xi2hCAQArxweLDj1U0ej8fj2RQvJDwej8ezKV5IeDwej2dTvJDweDwez6Z4IeHxeDyeTRHdRlGOInICOLjV/SiRHcDJre6E55zx1+38Zbteu/2qOr/Rjm0lJC40ROR6Vb1uq/vhOTf8dTt/uRCvnVc3eTwej2dTvJDweDwez6Z4IXF+89at7oDnfuGv2/nLBXftvE3C4/F4PJviVxIej8fj2RQvJDwej8ezKV5IPAgQkaeJyOq6bTtF5F0ispj/vUdEDmzw2Z8UkVtEpC0iXxKR71u3f0ZE3iEip/LveZuITDr+SRcM9/fa5dflD0XkHhFZEJG/E5HLNjjGX7sRIiKhiLxERL4iIk0R+bKIvEjEVjwSyytF5C4RaYnIB0Tk6nXfURWR3xSRoyKyml/fi9cds32unar6vy38A74eWAHWBrZVgC8BJ4AXAt8O/DNwGJgbOO7ngQR4OfDNWKOaAZ48cMyHgTuB7wWeBxwH/nGrf/d2+HuA1+79wDHgB4GnAp8F7gDG/bVzes1eC3SAV+bPzGuBFPj5fP9rgDbwYuBpwGfyazc18B1/DJwCng88C7gF+DwQbsdrt+UduFD/gGo+yHeBhXUDzTMBBb5t3fF3Ar+av58EmsBL133vx4Bfzv9/cv49Xzuw/5vzbY/Z6nNwvv6N4NrtzI95wcAxV+XbnuWvnbPrFuRC/fXrtv9uPohPAKvAywb2zeSfeUn+/nIgA75/4JgrsZOzZ2zHa+fVTVvHdwC/APwc8Nvr9l2FvRE/VGxQ1S7wX9iZKcBTgBrwtsEPquoTVfUX8rffAhxX1U8PHPIR7E3/7XjuLw/02tXy15WBz53KX2fzV3/tRs8U8CfA367bfhMwD3wTMA78fbFDVRexE6/inH9T/vqPA8fcAtw4cMy2unZeSGwd/wVcqqpvwc4wBjkEhMDF67ZfChzI/78WOAI8SkQ+KyJJbpt45sDxVwG3Dn6BqhrsrPaqUfyIC5QHdO1U9S7sIPNKEblaRHYCb8EOIv+cH++v3YhR1UVVfZGqfm7dru8G7gb25u9vW7f/dk6f86uAo6raPMsx2+baeSGxRajqYVVd2mT3+7FJxP5URB4qInMi8lrgGmAsP2YeO+v5C+xq4tuB64G/FpGvy4+ZxC6f17Oa7/PcD0Zw7QB+CnsNvoK1TTwDq664O9/vr10JiMj/xs78fxV7Xruq2lt32OA5H+a6bKtr54XEgxBVPQl8D7AP+DJ20Pka4A+BVn5YjF0+/7yq/p6qfgh4DtZo+ov5McJ9Z7rFduPsB1zADHPtRGQP8J/5+2dhVYf/ALxXRB6ff5W/do4RkecA/w94D/A7DHfOR3XMeYMXEg9SVPU/gMuwhrK9qvrd2JXDQn7IWv76/oHPGKxXxSPyTctYY9x6xvN9HgcMce1+CGsQ/XZV/RtV/QDw/Vi99q/mx/hr5xAR+RngT7Fqv+eotS4vA1URidcdPnjOh7ku2+raeSHxIEREdojI84ExVb1dVQ/nu67FutrBaZ1nZd3HY07PYm7BDlaD3x1gdeM3jbbXHhj62l0CHBrYRz5IfQJ4WL7JXztHiMj/B7wJKySeNaBeugU727903Ucu4/Q5vwXYLSL1sxyzba6dFxIPTipYX+ynFBtyO8NjsGoJgH/LX7934Jgo/8wn800fAi4SkccNfPeTsXrRD+FxwTDX7mZgn4jsXffZr8XGSoC/dk4QkZ/Ceqa9GXi+qqYDuz+JjaF4+sDxM8ATOX3OP4R1TPjugWOuBB6+7pjtc+222gfX//UDfNbWbfs77IDxPVhVxBHsTLQycMwfYQN/XgJ8G/A+rO/+tfl+AT6F9dx4NjZw67wN6nkw/t2fa4dVNR3C2o++N79278bqq5/mr52za3URVgh8EXj8Bn8RVt3XA16KDab7dH4Npga+56+waqMfYYNguu127ba8A/5v04FmFuvTfQobvfvHwPy6YyLgdfmA085vzG9cd8xO4C+xnhUnc8EyudW/ebv8PYBrtx/4a2ApH3D+HXiSv3ZOr9XzsarYzf525M/UG4GjWLvfvwFXr/ueMWx2g4X8+r0HuHi7XjufKtzj8Xg8m+JtEh6Px+PZFC8kPB6Px7MpXkh4PB6PZ1O8kPB4PB7Ppngh4fF4PJ5N8ULC4/F4PJvihYTH4/F4NsULCY/H4/FsihcSHo/H49kULyQ8niERkd8QkQURqazb/m8i8p78/xfnFQK7InKjiHz/umMvEpG3i8g9eTXBe0Tkt0Skmu8/ICIqIj8lIneKyFER+fryfqXHc298Wg6PZ0hE5FrgC9gkfP+Qb9uNTeT2TOBRwKuwuX8+Dnwn8GLg+1X1r/N00Z/D5gl6HTZn07cBPw+8WFV/W0QOYJMDrmITyFWBv1RbJ9vjKR0vJDyec0BEPg98RVWfnb//aWwlwIcCB4HfUNVXDRz/R9jEfZeLyCXYGgYvVtUvDhzzBeAWVX3WgJD4XVV9UTm/yuPZnGirO+DxnGf8CfB6ERlT1SbwXGyd8ccANeCf8roeBf8CvEBELlXVO4AniUiQ1yC4CngksAu4a107X3b9QzyeYfA2CY/n3Hg3trDQ00TkKmz96j8F5vL9nwSSgb+/zrdfBCAiPwzcgy089IfAY7Fp3mVdO8fd/QSPZ3j8SsLjOQdU9ZiI/Bu22MylwK2q+ikR2ZEf8j1YG8V6bhKRJ2IFw+uB31HVEwAi8pkSuu7x3C/8SsLjOXf+BGtwfiZ2FQG2glkC7FTV64s/4Brg1diVwuOxRus3DAiIi4FHcN+VhMfzoMALCY/n3Pk7rEB4DPAugHzQfwvwGyLyMhF5soj8DPB7QFNVV4D/wj5zvyUiTxKRHwQ+gvVgamzB7/B4zopXN3k854iqdkTko9iSpLcP7Pp5rC3hhcAvYWtb/xbW3RVV/bCIvAT4aeAFWLXUX2EFzk8XsRIez4MJ7wLr8ZwjIlIDDgMvU9W3bXV/PB6XeCHh8QyJiMxgg+OejI2LuFRVW1vbK4/HLV5IeDxDIiJ1bMBcB3iBqn5wi7vk8TjHCwmPx+PxbIr3bvJ4PB7Ppngh4fF4PJ5N8ULC4/F4PJvihYTH4/F4NsULCY/H4/Fsyv8fWdwFuJiWrdEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# filter out African countries\n",
    "africa = gm[ gm['continent'] == 'Africa' ]\n",
    "\n",
    "# plot scatter plot of lifeExp ~ year\n",
    "\n",
    "# alpha so more dots visible\n",
    "plt.scatter(africa['year'], \n",
    "            africa['lifeExp'],\n",
    "            alpha = 0.1,\n",
    "            s=20)\n",
    "\n",
    "# plt.scatter(africa['year'] + np.random.random(len(africa))*2-1, \n",
    "#             africa['lifeExp'],\n",
    "#             s=1)\n",
    "\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('life expectancy')\n",
    "plt.title('Life Expectancy for African Countries')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "specific-article",
   "metadata": {},
   "source": [
    "## Varying color and size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "stretch-grace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TdREze9HMfpN8EPQuRlDsbwqcCMwxswvM7EbgQOBVhF1QS1Nsse0ZqxECIj5KSgufvt6ezB3FjGn9Jf1HakE5Od4dx3EgpzAxsxOBV0maFZdJ2k3S5ZKmJdotN7PVeTuXtCNwLHCamS0F9gQmADcmrrkMuAt4T97rNotii+2mG4/MRpjl51Eqa2GW70ctF/p2SDblkX0dp7XIqzP5AGFBPyBRbMCbgHsk7VNh/18F/gh8L3q9ffT8eKrdE4m6lqXYYrsi5YdS6VFSIxb6VnfccwMBx2k98upM5gCXmtmn4wIzewB4m6TvAPMIu4rcSJoCHAIcY2bDUfFmwCtmtibVfGVUV+g6xwDHAEyePLmcIdScGdP6M3O1p3cO1SS7gvrEpUr30yrCI41H9nWc1iOvMNke+GJG3TXAv1TQ9yeBZcDlibIs/z0BwwXKMbNLgEsApk+f3vRgUnMO2SlXbKdqYkC18kLfCNxAwHFaj7wK+L+QvfPYg+BnUi4zgPlm9kqibAUwTlLaxnZCVNfy5D0iavWjpFbGDQQcp/XIuzP5ATBbkoCbCb4mk4CDCaa788rpVNJk4M0Ey60kjxF2IVMIupSY7Qg+LG1B3p1Dt+8wKsUj+zpO65FXmMwDXgucAZyZKF8HfAeYW2a/b42e702V3wOsJuxavg4gaSKwd9S34zRMb+Q4Tn7yBnocBo6TdDrhuGsi4djpvsikt1zeAvzdzJ5P9fOSpAuBuZKGCbuTU4EXgUsr6MfpUHxX5zitRVn5SSKfj5/XoN/XAMsz6k4hKNtPJOhK7gGObGXvd8dxnG4nlzCRtClBN/I+gpd6WnFvZvYPeTs1s8yQK2a2lhBOZVZWG8dxHKe1yLsz+RYhl8nNwJ/JMNN1HMdxupO8wuSDwAlmdlE9B+M4juO0J3mFyVrgkXoOpFWZv2jArYYcx3FKkNdp8WrgE/UcSCviMaAcx3HykXdn8iRwiqTfA/cBq1L1Zmafr+nIWoByYkD5DsZxnG4mrzD5DMGU91XA/gXqDeg4YZI3BlS8g4kFT7yDAVygOI7TFeR1WpxS74G0Inkj+3oUW8dxup28OpOiSHpr6VbtR97cIR7F1nGcbiev0+LrgW8QYmSNIwRjJHrehCCUxhZ+d/uSNwZUpblJHMdxOoW8OpNvEnQlPwLeCQwSwpwcAOwMfKgeg2sF8sSA8ii2juN0OzIrnU9K0gvAKWb2HUmfAQ4zs/0ljQF+BjxnZh+v81hLMn36dFu4cGFNr1nMSitZ1ze+B7OQnnfrvl72nTqJOx5Z6tZdjuO0PJLuN7Pp1Vwj785kPPBQ9PfDwDQI0YQlXQxcWM0gWo1YSAwsHxyR+jFppQWM2I0sWzVEb89YvnHEbqPq3LrLcZxOJ68weZqQoOpXBE/4PklTzOxJgs/JlnUaX8NJm/mm922xlVb8d7l1Lkwcx+lE8gqTq4D/kISZXSbpQeA8Sd8khIxvmyyIpShk5pvm2cgjPquu2Pscx3E6kbymwWcSBMp7otefBv4JWEDIAX9S7YfWHPIs+H3je9abs6XZuq/Xc5Q7jtN15HVaXAscm3h9j6QpwFTgETN7sU7jazhZZr4xvT1jMRt9/AXBTjq24HLrLsdxuolcOxNJCyRNTZaZ2Ytmdh+wraTF9RhcMyjkqBgzRkH3sXxwqGC9scGU+OxDd6a/rxcB/X29nH3ozq4vcRynY8ncmUg6JFG/D3CIpB0LNH0XkDvLYqsTL/hn3PQQy1aNFBrDJayo+xPHWJ6j3HGcbqLYMdd+wHHR3wbMK9K2WF1b8uLg2rLa+zGW4zjdTDFhchIhhIqAJ4BDgUWpNuuAFWa2sj7DazyxafC6HM6cED4cd0p0HKfbyRQmZraG4F9CpGx/FnijmT0clU0CdiVYdHUMeUyDY/r7erl71n51HpHjOE7rk9c0eBj4HXBzomwP4BfA/0rqGKfFvL4gfqzlOI6zgbzCJD7uOjQuMLNbgZ2ACcC5tR9ac8jjCyLgsD1cwe44jhOTV5jsA5xsZg8kC6Mjr9nA+2o8rqZRzDQ4xoAr732GKbNuYa95CzwnvOM4XU/ecCoiBHssxBhCjpOOIJ3DJEsNHyvoPYij4zhO/p3J7cAZkiYnCyW9ATgd+O9aD6yZzJjWz92z9uPJeQeN8B3JIhng0XEcpxvJuzM5EbgbeEzSQ8BzwCTgLcBfgS/WZ3iNJ52/ZN+pk7j+/oFcwR8dx3G6lVw7EzNbAuxI8D35Y/S+J4BZwC5m9nTdRthAYh+Tgeh4a2D5INffP8Bhe/SvD40yVoVDPHoQR8dxupm8OxMix8RvRo+OpJCPyeDQOu54ZOl6f5J0vhNwM2HHcZy8OhMkTZF0qaQnJQ1K2l3SeZL+rZ4DbCRZR1XJcg/i6DiOM5pcOxNJuwF3EvQjNwGfjauA70p6xcx+XI8BNpKs8PPpIywP4ug4jjOScpwWf03Qm3yRIEQws+OBbwMn1GV0DSbLx+TlV9a6L4njOE4R8gqTtwEXmtkwo/NCXQe8qaajahLxEdbE8T0jypcPDvGlGx50geI4jpNBXmHyIvDajLrJUX1uJO0v6d5I9/K0pDMkjY3qJOlUSUskrZL0y3RirnoyY1o/4zceffrnviSO4zjZ5BUm1wJnS9oX1qc/N0nbE5wW5+ftUNJewM+Bh4GDgIuAk4HToiazo7/PBf4Z2By4XdLmefuoljyKeMdxHGcDeU2DTyboS24HXo7KfkZwXFwY1edlHvALMzsqer1A0quBfSWdR3CQnGNmFwBI+hUhFP7RwHll9FMxWYr4vtTxl+M4jhPI67S4ysz2B94LfAu4FLgcOAx4h5nlOuaKcqDsBVySuv4sM9sH2JMQhfjGRN0y4C7gPXn6qAUzD9yBnrGjnROXrRpitzN+4boTx3GcFLmdFgHM7DbgNkkTzOylCvrbmXBM9rKkm4B3E/QtFwNfAbaP2j2eet8TwAcq6K8iZkzrZ86ND7F8cGhUXayMj9vlJR2mxTMzOo7TSZTjtPguSbdLWg2skPSypJslvbWM/iZFz/8JPELY6VxM0JHMBDYDXomyPCZZGdUVGtcxkhZKWrh06dIyhjKa+YsG2GveAraddUtBQRJTrjK+UJgWtw5zHKeTyCVMJB0O3EY4gjoT+DRwNrAV8CtJe+fsL1Y63GZmM83sDjObA3yHIFDGMNr0GMJuZrjQBc3sEjObbmbTJ02aVKhJLpILfh7KUcZnhWlx6zDHcTqFvMdcc4DLzezIVPlcSVcAXyPoO0oRH43dmir/JcGrfjkwTlKPmSW3BhOAFTnHWhHl5H6H8gI7unWY4zidTt5jrinAFRl1lxF0IXn4U/S8cao83rEMEXYhU1L12wF1vY0vZ2EvN7BjluDxSMOO43QKeYXJfWRbU70DeCCjLs0fgAHg8FT5QcCzwFXAamBGXCFpIrA3wSy5bpRa2MdKowI7xjqWUul7C4Vp8UjDjuN0EnmPuS4CLpG0FXA18Bfg1cDBwCeBUyUdGjc2sxsKXcTMhiWdAlwm6duEUCzvAo4EPm1mL0q6kHB8NkzInXIqweLr0kommJeZB+7AzGsfYGh4tMqmZ4w45/BdR1hfpUPRF0vfm04F7NZcjuN0GjLLynKeaBQW9ryYmY2Oljjyeh8BTiHE9HoGOMfMLonqNgLmAkcRdCX3AMeZ2SOlOp4+fbotXLiwjKGOZKfZt/LymtF6k4nje1g0+4ARZXvNW1BQWd/f17s+94njOE47IOl+M5tezTXy7kzSOoyqMLMrgSsz6tYSMjjOqmWfpZi/aKCgIAFYvmq0mbAr1R3HcTaQV5hsZWb3ZVVKOi4Of9KuFDPTLaRPyZv7xHEcpxvIq4C/W9Lc6AhqPZKmSrqHkO+krSm2oyikKHeluuM4zgbyCpMvE5Ji/VbSLpLGSvoysJigiG9Y3Kx6kbWj6OvtKago9/S9juM4G8h1zGVm8yT9FPgewUz4aeD1BEX51wuEP2k7Zh64wwjrLAg7jTmH7JT5Hk/f6ziOEygn0OMThDwk7yBYYf0RWNAJgiRmk54x64VJX28PB+/6Os657VGOv3qxm/Nm4AEsHceB/LG5DgAeJCTCmgvsACwB/kfS9yRtUb8h1p/YZ2RZwmrr5TVrufq+Zzw4YxE8gKXjODF5dSa3EhwH/9HM5pjZY2Z2AMFh8VBCBOC2pVBcrqF1NsqB0YMzjsQDWDqOE5NXmJwE7GlmDyYLzeyHwE7A/9R6YI2kHN8Q9yPZgPvaOI4TkzfT4rlmNgwgaRtJe0raNEqS9Vcz+1B9h1lfyvENcT+SDXgAS8dxYspJjnWYpMeAJ4H/JehNrpB0uaS2To5eyGekEO5HMhL3tXEcJyavAv7DwDWEXOwfTrzvp8AHgdl1GV2DiH1GiuF+JKNxXxvHcWLyBnr8P+AXZvZFSWMJeUemm9nvJB0PfM7MtqvzWEtSbaDHWgVvdHNZx3HaiVoEesx7zPVG4GcZdYuA11UziFahFsc2bi7rOE43kleYLAHemVH3VkIY+ban1LFNnmRYbi7rOE43Uk5yrHMlibBDMaBf0u6E5FVn1ml8DScrREreZFhuLus4TjeSNzbXBVH63JOB0wh52v+LoDu5wMzOrd8QG08hnUexHUdSmHhoesdxupHcpsFmdgZBN/I+4F+A9wP9ZjazTmNrCoV0HsdfvbiggIDROw43l3UcpxspJ9AjZrYCuK1OY2kJCu1Aitm7pXccnu/dcZxupCxh0g2Uo9vI2nF4aHrHcbqN3Mdc3UJe3YY76DmO42zAhUmKPKFVYidGFySO4zgBP+ZKkdR5DCwfRIzUmbgy3XEcZzS5hYmkzYHPAfsDrwU+BBwMPGBmt9ZneI0lbRJ8/hG7AbVVpnuoFcdxOpFcwkTStoScJROAXwH/BIwDdgHmSjrEzH5er0E2giynxLMP3bmsuFyV9AG4QHEcp63JqzP5JvAXYDJwGMFpETP7KCFycFtHDYbsMCgnXPNAzeJqeagVx3E6lbzCZD/gLDN7idFuF98F3lLTUTWBLJPgdWYcf/ViTpv/YMH6WvThoVYcx2l38gqTNUCWzewWwCu1GU7zKGYSbMAVv1lS9Q7FMxM6jtOp5BUmtxB0I29KlJmkLYAv0QFe8aVMgg2qPo7yUCuO43Qqea25TgDuBB4CHovKvg9MAV4A2j4+V6wAP+GaB1iXkTCs2uOodg614lZojuMUI1OYSNrIzNYCmNlSSXsARwL7AAPACuAy4AdmtrIBY607M6b1s/DpF7j8N0sK1tfiOKodQ624FZrjOKUodsy1RNKeAJJmA1uY2XfN7CNmdoCZHW5m3+wUQRJzxyNLC5YLuvY4yq3QHMcpRTFh0kdI1wtwOtAVt6BZR1lG996FuxWa4zilKCZM7gQuk7SScGN+h6QXMx4rGjLaBpB1lDVWKpqut5PJ+kzGSF33WTiOU5hiwuQjwOeBOIvilcB/ZDzOy9uhpFdLsgKP66J6STpV0hJJqyT9UtLUCuZWEVlWXevM1ifL+sLVi9ntjF90zUJa7DP50g0Pds3n4DhONpkK+CgR1kUAkvYB/sPMHqlBn7tGzwcCLybKn4+eZwOzCCmCnyKkCb5d0o7RmOpK2uJqjFTQumv54FDXKKGLWboVSl3sOE73Icswg418SJab2XD0d1HM7IVcHUpfAGaZ2WsL1L0KeBaYa2Zfi8omAk8Dc8ys6A5o+vTptnDhwjzDyM2UWbcUzbQYh6PvBrI+CwFPzjuo0cNxHKdGSLrfzKZXc41ix1xLgfjif49eF3vkZRfg9xl1exKCSd4YF5jZMuAu4D1l9FEzSpkDd5MS2j34HcfJopjT4ieAxxN/F7tBL4ddgNWS7gF2JwiqC4BzgO2jNo+n3vME8IEa9T+KYg55Mw/cYYSPRZpuWkgLfRbuwe84DhTXmVyW+PtHtehM0hhgR+Bl4ERgCfA+4GxgE2AIeMXM1qTeuhLYLOOaxwDHAEyePLnsMZVyyIuFyhk3PcSyVUMj3tsOC2ktPdfb2YPfcZz6Ukxn8sUyrmNm9o2SnUljgb2BJWb2p0T5t4GPA18Fvmxmvan3fRX4lJltWez6lehM9pq3gIECR1WFdCHtFlIkLSghCEDPXe84TpJa6EyKHXOdW6QujQElhYmZrQMWFKi6Ffh3wo5lnKQeM0tuAyYQwrfUnHIc8totFEoxz/V2mofjOK1PpgLezMaU8cgOt5tA0taSjpE0KVUV70SWEYyDpqTqtwPqErujk5XK7rnuOE6jyBuCvlaMIyTT+pdU+WHAH4EbgNXAjLgiMg3eG7i9HgPq5LDwnSwoHcdpLfKGoK8JZvakpCuBMyUNAw8DhxOEyQwze0nShYTcKcMEAXMqwbnx0nqMKalUHlg+yFhpVBDDdtKTJHHrK8dxGkVDhUnE0cCXgS8AryMIlMPMLPYtOQUYJlh7TQDuAY6sp/d7LBzSVl1fuHoxY6LBxGXt5PXu1leO4zSKTGuudqQaD/gsq65CdJPXu+M4nU+9PeC7inKU0q7AdhzHGYkLk4hylNKuwHYcxxmJC5OImQfugHK0cwW24zjOaFyYRMyY1s9H95w8SqD0jBV9vT2IoCtx73HHcZzRNMOaq2WZO2Nnpm+zRab1UxxO5firF7tllOM4ToKuFyaF4m0VstQqFRDScRynm+lq0+BCgRABxgiGLRxrxbuPcgJCOo7jtBP1DvTY8RQKhAhBkMDI3YfHuXIcx8mmqxXweQRBHFrF41w5juNk09XCJK8geHb5YEcHhHQcx6mWrhYmMw/cgZ4xpb1Ltu7rZca0fs4+dGf6+3rdTNhxHCdFV+tMZkzrL5iON0ly99FuybEcx3EaRVcLE4DlRQRJv/uSOI7j5KKrj7kgW28yVnJB4jiOk5OuFyaFFOsA68z40g0PMn/RQBNG5TiO0150vTCJFetjNVoRn8646DiO4xSm64UJBIEynBEJwJ0SHcdxSuPCJCJLdzJG8qMux3GcErgwiXDdieM4TuW4MGFD5OBCcbrAdSeO4zil6Ho/k6zIwWlcd+I4jpNN1+9Miu1IknhAR8dxnGy6Xpjk2XF4QEfHcZzidL0wKbXj6O0ZwyY9Yzj+6sXsNW9B1Yr4+YsG2GveAqbMuqUm13Mcx2kFul6YZFlxxQwODbNs1RDGhmRZlQqAWD8zsHywJtdzHMdpFbpemCRDy+ehGsuuQvoZtxRzHKcT6HphAkGglJPHvVLLLk/96zhOp+LCJEGh+FyFqNSyy1P/Oo7TqbgwSbAuIz5Xkmosuzz1r+M4nYoLkwSl9CZ9vT1Vper11L+O43QqXe8Bn2TmgTsU9IafOL6H09+/U00WfU/96zhOJ+LCJEG8yJ9z26M8u3yQrT1tr+M4Ti5cmKTwnYPjOE75NFVnImmcpIcl/ShRJkmnSloiaZWkX0qa2sRhOo7jOCVotgL+dCAtKGYDpwHnAv8MbA7cLmnzBo/NcRzHyUnThImkacBxwN8TZa8CTgTmmNkFZnYjcCDwKuDopgzUcRzHKUlThImkjYAfAOcAycBUewITgBvjAjNbBtwFvKeRY3Qcx3Hy0ywF/MnAxsDZwAcT5dtHz4+n2j8BfKAB46orcUZHtxRzHKfTaLgwiZTppwL7m9kajQxhshnwipmtSb1tZVRX6HrHAMcATJ48ufYDrhHpjI5xxGDABYrjOG1PQ4+5JI0Bvg9838x+XagJUCimiYDhQtc0s0vMbLqZTZ80aVLtBltjPGKw4zidTKN3Jp8DtgEOjvQmMYperwDGSeoxs6FE/YSorm3xiMGO43QyjVbAfxDoB14AhqLHrsDHE68FTEm9bzugrW/hPWKw4zidTKOFyaeAf0w9/gjcHP19FbAamBG/QdJEYG/g9gaPtaZ4xGDHcTqZhh5zmdmo3YWkQeB5M1sYvb4QmCtpmCBoTgVeBC5t5Fhrjcf9chynk2nF2FynEJTtJxJ0JfcAR5pZW+tMwON+OY7TuTRdmJjZbqnXa4FZ0cNxHMdpA5odm8txHMfpAFyYOI7jOFXjwsRxHMepGhcmjuM4TtXIrFD0kvZE0lLg6QreuiWJUPhdhM+7u/B5dxflzHsbM6sqHlVHCZNKkbTQzKY3exyNxufdXfi8u4tGz9uPuRzHcZyqcWHiOI7jVI0Lk8AlzR5Ak/B5dxc+7+6iofN2nYnjOI5TNb4zcRzHcarGhYnjOI5TNV0tTCR9UtJjkgYl/VrS25s9pjxIOkTSylSZJJ0qaYmkVZJ+KWlqqs04Sd+Q9FdJKyVdJ2nrVJuJkn4k6XlJyyRdKmmzVJs3SPqppBWS/ibp65I2rtNcx0r6oqSHJb0s6Q+SjpWkDp/3xpLmSno6mvcCSbsn6jty3gXG/7CkHyXKOnLekl4tyQo8rmubeZtZVz4I2R3XAacD7wN+TsibMqXZYysx7ndE43wpVX46MAgcBxwC3AcMAJsn2vwQeB44CvgQ8BiwGBibaLMAeAo4HDgSeA64OVE/DngY+F3Uz7HAy8BFdZrvHELCtFOB/aPXa4GTOnze34q+508DBxASyK0gOJd17LxTn8FZgAE/6oLf+X7RXA8A9kw83tQu867rj6FVH4TUwE8B306U9QBPABc0e3wZYx4HnAS8Qkh7/FKi7lXASuDkRNlEwmL0xej1PxCE5xGJNm8i5I45NHq9b/SDfluizf5R2e7R638lpFd+faLN0VHZVjWe85hoDmemyr8V/RN06rw3B9bEc4jKeoFVwGmdOu/UZzANeAlYSiRMOnnewBeAv2bUtcW8u/WY643ANsCNcYGZDQG3AO9p1qBK8F7gS8BM4MJU3Z6ERGLJ+SwD7mLDfPaLnm9OtHkMeCjR5l3Ac2Z2b+LadxB+tMk2vzOzPyfazCfkxtm/gnkVY3PgP4EbUuWPApMIc+rEeb8MvI1wpxkzRPinH0fnft8ASNoI+AFwDuHuO6aT570L8PuMuraYd7cKk+2j5z+lyp8A/kHSWFqP3xKO4C4gLCpJ4vk8nip/IlG3PeHO5+USbUZ8JmY2TNjFFWvzPOEHuT01xMyWmdmxZrYoVfV+4M/A66PXnTbvtWa2yMyWSRojaQphcTXgcjr0+05wMrAxcHaqvJPnvQswXtI9klZL+rOkkySJNpl30zMtNolY4bQyVb6SIGA3JXx4LYOZDRSp3gx4xczWpMpXsmGumzF6vnGbN+Rok+c6mxUorymS/o1w93Qc3THvLxP0RACzzexRSYfSofOOlMqnAvub2Zqwlq6nI79vSWOAHQk70hOBJQQ97tnAJoRdacvPu1uFSfwLTd/hx+XDDRxLLRCj5xKXD5fZptDcy71OXZD0UeA7wHXARYRjv06f90+BOwnn3bMjq5rBHGNpu3lHi+r3ge+b2a8z+uy4eUfXPBhYYmbxruAOSRMIu7Sv5hhL0+fdrcdcK6LnV6XKJxA+sPRWsdVZAYyT1JMqn8CGua5g9Hzr1abmSDoe+DHhTPijFjSDHT9vM/u9md1lZnOACwg6s5fpzHl/jqDLnC1po0h3AsEydiM69Ps2s3VmtiAhSGJuBcbTJt93twqTx6Ln7VLl2wGPRgtVO/EY4c5hSqp8O4KyOm7zWkm9JdqM+Eyiu8VtS7R5NWEL/Ch1QNJZwHkEYfKhxHa/I+ct6bWS/lVS+p96EUEBv4wOnDfwQaCfYK04FD12JZjxx687bt6StpZ0jKR0PpF4Du3xfdfaxK0dHtEXswS4OFEWmwZf2Ozx5Rj/HEaaBk8gHH2clCiLTQdPsA2mgwZ8ONEmNh08zEaaCb410SYu28M2mAmuYbTp4BrgtXWY6+ej/s8niiXX6fMGpkZ9/2uq/D+Bv0X/2J047x2A6anHo8BN0d+dOu8pUd/Hp8qvjebfFr/zui14rf4APhN90F8lKLt+Fn052zV7bDnGPofRTotfj77wEwnORvcSLJ42T7S5hrBV/SQFnJoIQvY30fs+QrgjTDs1jSdYf/wf4U7ys9TJmQt4HcFh8feMdOSKHxt14ryj/q4j3KF/Cng38G0SAqZT513gc1jMSKfFjpw38BOCX83nCY6L3yOsT4e0y7zr/mNo5QdwAmGHsgq4B3h7s8eUc9xzGC1MNgLmAX+NfpS/AKam2mxKCEv9ArCcsGBtnWrzGuBqgvXG3wkK0c1Sbd5IOM9dBfyF4A/QU4d5HkVYQLMeW3bivKO+xgNfi/6xXyEccX2ok7/vjM9hMSOFSUfOm3CkdRbwJOEGahHwwXaat4egdxzHcaqmWxXwjuM4Tg1xYeI4juNUjQsTx3Ecp2pcmDiO4zhV48LEcRzHqRoXJo7TAJSKWOg4nYYLE6diJN0p6ebSLbsLSftEKVenR693Am6v4fUPknRH9PdRUV9b1ur6zaKSuUi6XtLH6zkuJx/dGjXYcerJ74C3E9KfQkiR+tZaXDjK1/1t4NBaXK8DOBm4W9JtZva3Zg+mm/GdiePUGDN70cx+Y6MTFdWCLxCCkS6sw7XbDguRdu8ETmnyULoeFyYOkjaRdIGk5yS9KOlSSWdJeirRZoKk70l6QdLfJZ2cusa20RHF4ZLukjQo6Q+SPlyi7zslXSzp65KWS1oq6SJJm6TafUTSg1EWusclfS5Vb5JOkfSQpOclfSijv15J50SZ7F6KMtv9v0T96yT9QNKzkoai5/Mljcs7z+Qxl6Q5wOnAplHZUVGbHSRdG813jaSnJH25mG4lGsNngatKfKYflPRbSS9LekbSmcnw5QrZG0+PPoNV0VHR8ZKKhsOQNFPSnxLfwZejqLNx/TaSrol+I89H152cqH+rpJ9F3/MaSY9K+lSJPt8t6d7oc/6zpK9odCbUq4FPSJpY7FpOnWlEfB1/tPaDEGTuRcJCdTDhTm818FSizc2EWD6fAD5AiJm0hihIHCGMtRFiAn2TkFP6x4RgdQcU6fvO6D0Lo+seS4g9dEWizZHRtS8iBMGbDawFZibaWDSeTxOOlbbK6O+/CMHwjiNkbPxp1N8bCTdXD0Rz+yAhr/bXomt/Lu88gX2iNtMJqYUvJcQ52pOQu34CIebRAkKQ0XezIS3v+4t8Vu+L+tkiUXZU9L4to9fHRK8vjj6rkwgRZy9PvOes6LM6BXgvcH30fVuRvo+I2nwG2JuQmGwY+FRUvxnwDPAI8M+E1MoPRY+xwGRCwMBrovm+lxAN2IBdMuayf/Q9XxV9zp+PrvGt1Ng2iT7fjzX7f6mbH00fgD+a/AMIeZ0NOCpRNp4Q6vyp6PUuUZsjEm3iiL5pYXJV6vr3AXcU6f9OwmK+ZaLss9G1tiUs8APJxTBq82WCANw0em0kop9m9LVr1O5jibKNowXvSEJ60zvjxS3R5gHgurzzJCFMotdzGJkyYA/gV8CkRNkYQt6Kc4uM/xzgyVTZ+gU4usZzwJWpNp+KF21C4qPVwJxEvaI5WpG+v0UQFEqUnUgk/KKFfgiYkqjfjRC4cCeC8PhvEgEDgS2icR2bnkv0+tfA/6bG8TFgHbBtqvx+4AfN/n/q5ocfczl7R8/z4wIzWwXckmizV/T880SbvxD+2dOkj2BuBPZKHocU4L/N7O+J1/8VPb+TIOy2Bm5RlH1PIevezwkLY1Kx/YcifQC8I3q+KTGPNWa2k5ldZmbPmNk+wP9JepOC1dQpwFaEpFTVzjPu834z+3/Ackk7SppBOArrKdBPkm0Jd/9ZvJmw87k2VX5l9PxPhN3ROEZ+30bYnRTjHkK+kd9KmiXpLWZ2rpnFn+U7gIfM7MnEdReb2RQze8jMfm5m7wLGSto1OoaMj0pHzVnSeMJ3e3Pqe7+VIDT3Tb3lacLn4zQJFybOlsCQmS1PlSctYyZGbV5Mtflrgev9JfV6KWGRnFBkDIXeA+HO9dXR3z9hQ7a9IeC3UfnrEu97rkgf8fUKzXU9ko4GngX+SMgp8Y+EY6K0LqOSeSb7OSV6z0OExF9vYkMmwSw2JxznZBHrDEZYNUXf22rCUVRsdruUkRS1hDKzKwg7h2HCMdmDkhZLenPUZAuKfP6Sxko6n7D7+l10jfi7LTTniYT16WxGfu9xH69LtV9F+HycJuGmwc6zQI+kvtQim0wh+nxGm1cTzt5JlSV5DWEhW1lkDIXeA2HhiPNOf5ZwlJTmyQJlWawgzGNzM1ufz1rS2wmL3FYEAXImIRnQ0qi+UL+VzDPu7+PAXIL+4cp4LJJKCcPnKX73/UL0vFWqvz6CXuF5wvcN4fsdSDRLp4wdhZldBlwm6TUEncjpwA2EHdEKQra/EUh6L0F4fIqgz/k48DMzeznafRyd0V184zKXDTvVJM+mXk8kzM9pEr4zce4myugWF0jamKDwjLkzej400WYi4cgkzcGp1x8A7oyOUrLYN1pYYmZEY/ofwjn984Q0ogvjB2ExP5Py7kbvSY8xmus1hEVuT8KZ/dyEINka2JnRd8/lzHNd6vXbgT+b2XcSgmR3woJebGfyDEGhn8WjBCOJw1PlR0TPdxOMC1aS+L4j0q9HoGDhdx2AmT1nZt8nJFaKrbXuAd4iaZvEe95MyGC6K2HOC83sWttgMh3/xkbN2cxWEvQ4/5D63tcQditvSL2ln5DozmkSvjPpcszsT5KuAC6QtCnh7Pk4wjHC01GbRyVdDpyvYLK7hGDN01Pgkp+UtJSwuHycsJD8U4lhvBq4UdJ5BKuqs4CLzexZgMi89rzIavZ2Qs7sswlpSXPvTMzsdwoe+xcqOP/9Cfh3Qoa67xLurMdE87yWsFCeSjjTH5+6XDnzXA6Ml/QBwu7qt8C/S5oN3EW4sz+dIMjS/SS5HZgp6fVm9ucC81sn6Yxofi8Q7uh3Ac4ArjWz/wOIjptOkfQKQbh8jGAUUEzg3wX8p6SzgF8SFvNPE3YmEKzRjifoOOYQBOiZ0XwXEPRfsyQdCzxIOD6cXWLOs4H5klYQrO62JOxUhqNrEM1nAvAWwm/CaRbNtgDwR/MfhMX0O4RjkhcJC+tVwO8TbTYGziOcta+I/r6G0dZcJxHyTA8SFpL9SvR9J0GZfj7hjnmAsLCOTbX7BCEv9SuEI46LgYmJegNOzDHX8QST3jj96R3AtET98QQhOkgQVl8lWGMtJwiVkvNktDXXJIIAWRO9bwzB5PhZgqnrQ8AXCUdsDxcZ+8bRd/SJRNlRJCygEp/VH6LP6ingK4y0ohpLENh/JegargEuBF4s8dkdS/DqH4zeexEwIVE/hbDoxylhfwy8JvG5fy/6/awkHH0dCdwG3FpkLu+PPrvVhGPPK4A3pMb1gWgemzf7f6mbH562t8tRiIN0AHCThaOFuPwe4K9mlitsh6RtCbuEw83sujL6v5NgNps+NmpJKp1nDfufA7zLzN5Z4fs3Jhx7/cIS4Uck/YSQU3z3mgy0gUi6EXjCzL7Q7LF0M37M5QwS7vIPl/QdgpPY4QT9wbubOTCnIOcTjsjeZmb3lvtmM1sTHa99UtLXCTujdxEEzL/VdKQNQNJUgul6UU96p/64Ar7LsaAMPYBg0noVwV9iV4IzWs0i3Tq1wYI13aeAr1dxmYMIRg0/JCjIDyIcnf2w6gE2nrMJkRDSptpOg/FjLsdxHKdqfGfiOI7jVI0LE8dxHKdqXJg4juM4VePCxHEcx6kaFyaO4zhO1fx/w87vLoLgnOsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_2007 = gm[ gm['year']==2007 ]\n",
    "plt.scatter(year_2007['gdpPercap'], year_2007['lifeExp'])\n",
    "# plt.xscale('log')\n",
    "plt.xlabel('gdp per capita (log scale)')\n",
    "plt.ylabel('life expectancy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73541d78",
   "metadata": {},
   "source": [
    "### Same thing, but color code by continent and size by population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "international-edition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_2007 = gm[ gm['year']==2007 ]\n",
    "\n",
    "colors = {'Asia': 'red',\n",
    "          'Europe' : 'gold',\n",
    "           'Americas' : 'chartreuse',\n",
    "           'Africa' : 'teal',\n",
    "            'Oceania' : 'navy'}\n",
    "\n",
    "plt.scatter(year_2007['gdpPercap'], \n",
    "                year_2007['lifeExp'],\n",
    "                s = year_2007['pop']/1e6,\n",
    "                c = year_2007['continent'].map(colors),\n",
    "                alpha = 0.5)\n",
    "\n",
    "# plt.xscale('log')\n",
    "plt.xlabel('gdp per capita')\n",
    "plt.ylabel('life expectancy')\n",
    "plt.show()\n",
    "\n",
    "# color code by continent\n",
    "# sale size by population"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ebfb5c8",
   "metadata": {},
   "source": [
    "## Errorbar example\n",
    "* Errorbar plots allow you to show the variability associated with measurements, not just their mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38d80c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "## group by year\n",
    "## calculate mean life expectancy per group\n",
    "\n",
    "year_mean = (gm\n",
    "    .groupby('year')                            # group based on common year labels\n",
    "    .agg(life_expectancy = ('lifeExp', 'mean')) # aggregate lifeExp, compute mean, assign to new column 'life_expectancy'\n",
    "    .reset_index())  # reset index to default...\n",
    "\n",
    "year_std = (gm\n",
    "    .groupby('year')                            # group based on common year labels\n",
    "    .agg(life_expectancy = ('lifeExp', 'std')) # aggregate lifeExp, compute mean, assign to new column 'life_expectancy'\n",
    "    .reset_index())  # reset index to default..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f09b0e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the error bar plot showing mean +- standard deviation\n",
    "plt.errorbar(year_mean['year'], \n",
    "             year_mean['life_expectancy'], \n",
    "             yerr=year_std['life_expectancy'], c='black')\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Life Expectency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affected-great",
   "metadata": {},
   "source": [
    "### Conventions\n",
    "\n",
    "**Distribution of Number** -> histogram, with number on x, counts on y\n",
    "\n",
    "**Distribution of Category** -> histogram, with category on x, counts on y\n",
    "\n",
    "**Number as a function of Category** -> bar chart, category on x, mean number on y\n",
    "\n",
    "**Number as a function of Number** -> scatter plot (y~x) or line plot (mean(y) ~ x)\n",
    "\n",
    "**Number as a function of Number + Category** -> scatter plot or line plot with color varying by category.\n",
    "\n",
    "**Number as a function of Number + Number** -> if it doesnt matter much: bubble chart.  If it matters a lot, considering binning into categories.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}