{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0fb30d10",
   "metadata": {},
   "source": [
    "# Numerical Data\n",
    "\n",
    "Numerical data consists of *discrete* and *continuous* number values. Discrete data values are countable values, such as the number of marbles in a jar or shoe sizes. Continuous data  values can be thought of as values having decimal values, such as height recordings or temperature collections. In this section, we will practice making histograms, scatter plots, and line graphs to represent numerical data.\n",
    "\n",
    "Let's load the necessary libraries and read in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3e479dc7-ce8e-4ca7-ae35-79038cf4bf99",
   "metadata": {
    "tags": []
   },
   "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>CAN-PercentGDP</th>\n",
       "      <th>MEX-PercentGDP</th>\n",
       "      <th>USA-PercentGDP</th>\n",
       "      <th>CAN-USD</th>\n",
       "      <th>MEX-USD</th>\n",
       "      <th>USA-USD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>4.185257</td>\n",
       "      <td>0.673509</td>\n",
       "      <td>8.993125</td>\n",
       "      <td>1.702443</td>\n",
       "      <td>0.084000</td>\n",
       "      <td>47.346553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>4.128312</td>\n",
       "      <td>0.651780</td>\n",
       "      <td>9.156031</td>\n",
       "      <td>1.677821</td>\n",
       "      <td>0.086400</td>\n",
       "      <td>49.879771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>3.999216</td>\n",
       "      <td>0.689655</td>\n",
       "      <td>9.331673</td>\n",
       "      <td>1.671314</td>\n",
       "      <td>0.099200</td>\n",
       "      <td>54.650943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>3.620650</td>\n",
       "      <td>0.718686</td>\n",
       "      <td>8.831891</td>\n",
       "      <td>1.610092</td>\n",
       "      <td>0.112000</td>\n",
       "      <td>54.561216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>3.402063</td>\n",
       "      <td>0.677507</td>\n",
       "      <td>8.051281</td>\n",
       "      <td>1.657457</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>53.432327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1.164162</td>\n",
       "      <td>0.495064</td>\n",
       "      <td>3.418942</td>\n",
       "      <td>17.782776</td>\n",
       "      <td>5.336876</td>\n",
       "      <td>639.856443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.351602</td>\n",
       "      <td>0.436510</td>\n",
       "      <td>3.313381</td>\n",
       "      <td>22.269696</td>\n",
       "      <td>5.062077</td>\n",
       "      <td>646.752927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.324681</td>\n",
       "      <td>0.477517</td>\n",
       "      <td>3.316249</td>\n",
       "      <td>22.729328</td>\n",
       "      <td>5.839521</td>\n",
       "      <td>682.491400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.278941</td>\n",
       "      <td>0.523482</td>\n",
       "      <td>3.427080</td>\n",
       "      <td>22.204408</td>\n",
       "      <td>6.650808</td>\n",
       "      <td>734.344100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.415056</td>\n",
       "      <td>0.573652</td>\n",
       "      <td>3.741160</td>\n",
       "      <td>22.754847</td>\n",
       "      <td>6.116377</td>\n",
       "      <td>778.232200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CAN-PercentGDP  MEX-PercentGDP  USA-PercentGDP    CAN-USD   MEX-USD  \\\n",
       "Year                                                                        \n",
       "1960        4.185257        0.673509        8.993125   1.702443  0.084000   \n",
       "1961        4.128312        0.651780        9.156031   1.677821  0.086400   \n",
       "1962        3.999216        0.689655        9.331673   1.671314  0.099200   \n",
       "1963        3.620650        0.718686        8.831891   1.610092  0.112000   \n",
       "1964        3.402063        0.677507        8.051281   1.657457  0.120000   \n",
       "...              ...             ...             ...        ...       ...   \n",
       "2016        1.164162        0.495064        3.418942  17.782776  5.336876   \n",
       "2017        1.351602        0.436510        3.313381  22.269696  5.062077   \n",
       "2018        1.324681        0.477517        3.316249  22.729328  5.839521   \n",
       "2019        1.278941        0.523482        3.427080  22.204408  6.650808   \n",
       "2020        1.415056        0.573652        3.741160  22.754847  6.116377   \n",
       "\n",
       "         USA-USD  \n",
       "Year              \n",
       "1960   47.346553  \n",
       "1961   49.879771  \n",
       "1962   54.650943  \n",
       "1963   54.561216  \n",
       "1964   53.432327  \n",
       "...          ...  \n",
       "2016  639.856443  \n",
       "2017  646.752927  \n",
       "2018  682.491400  \n",
       "2019  734.344100  \n",
       "2020  778.232200  \n",
       "\n",
       "[61 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "military = pd.read_csv(\"../../data/NorthAmerica_Military_USD-PercentGDP_Combined.csv\", index_col='Year')\n",
    "\n",
    "military"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af7e1d3",
   "metadata": {},
   "source": [
    "## Scatter plots\n",
    "\n",
    "Scatter plots can be used to visualize the relationship between two numerical variables. They are most commonly used to visualize two continuous numerical variables against each other (in other words, the data takes on values that are between whole number integers). These plots can also be used when data takes on a large number of different discrete integers. We will use a scatter plot to visualize the percentage of the GDP (<u>G</u>ross <u>D</u>omestic <u>P</u>roduct) of Mexico spent on the military versus the absolute dollar amount (in USD) over 1960-2020.\n",
    "\n",
    "We'll simply extract the columns for this data and assign them to `mex_gdp` and `mex_usd`, respectively. Then, we can plot this data using the `plt.scatter()` function and use `plt.show()` to display the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "deb40aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mex_gdp = military[['MEX-USD']]\n",
    "\n",
    "mex_usd = military[['MEX-PercentGDP']]\n",
    "\n",
    "plt.scatter(mex_gdp, mex_usd)  # mex_gdp on the x-axis, mex_usd on the y-axis\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fba0f1f",
   "metadata": {},
   "source": [
    "Looking at this scatter plot out of context, it would be hard to understand what the data means. Let's add some important details to make it clear.\n",
    "\n",
    "Firstly, we can add a title using the `plt.title()` function. This function accepts a string argument to be used as the text for the title. It also has an optional `pad` parameter, which dictates the space between the title and the plotting area.\n",
    "\n",
    "We can also use `plt.ylabel()` and `plt.xlabel()` to label the y- and x-axes, respectively, and `plt.figure()` to set the figure size. We can pass (6,3) into the `figsize` to make a 6in x 3in figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eccd1b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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580bgdTgPRoL6Z44ePZpnn4YmJSXFbYWomCgmJSUhJSUFnTp1Evj74cQdFRUl0nU4iXXlBJ9TbktLS57rA4CSkhIsLCzw7ds33L17t9prVNuc7erqyv0SHTFiBCIiIpCbm8u9Ody7dw/+/v44duxYjUcC3r9/H0VFRdDW1kbHjh1rdA5RGBgYQFtbG6mpqXj48CGGDh1a7TH79+/nHltd/4Dp06fj8OHDOHnyJGxtbXleCw8PR3p6OqZNm8atZQ0KCkJ5eTn+/vtvdO/enWf/7OxsvqaumsjMzERYWBh0dXW55Z0+fTpiYmJw5MgRbNu2TeBxaWlp0NfXR0xMDPcJx8vLCyNGjMD+/fthb28PCwsLAN+/COTk5HDr1i2oq6vznKdy7e3SpUuRmpoKLy8vnmR04cKFGDVqFJYuXYrhw4dDW1u71mXn0NPTw8qVK+Hj44PWrVsL7PKQl5cHV1dXyMrK4vr16zAxMeG+lpCQgJEjR8LT0xOPHj2qthbcxsYGO3fuRGBgIPLz8zFhwgT06NEDenp61T78xMTEYNGiRdi4cSN325w5czBmzBgsWbIEI0eO5L5/Tp8+jUOHDsHa2hoHDx5EixYtuMf4+vri999/x5YtWwQ+3CUlJSEuLg5t2rQB8L3v0ODBg3Hq1CmsXbuW2+/s7du3WLduHZSVlREVFcW9sa1duxZubm64cOFCleUR5O7du7h9+zb3QbS0tBQ2NjaIjo7GvXv30Lt3bwBAfn4+li5dCmlpaVy5cgXm5ubcc6xbtw47duwQ+ZpsNhvz5s1DXl4e/P394ezszH3txo0bsLOzw9y5c/H48WO0bNmS59irV6/Cz88P06dP5247fPgwfvrpJ+zbt09gbXtVWrVqhcmTJyMoKAjv37/n/h6OHj0KRUVF2Nvbi5Q0iMva2hp5eXm4fPkyBg8eLPBz0LlzZ7x48YKvv11qaipGjhyJVatWwcrKiu+49PR0dOnSBf/88w/f78/U1BSXLl1CVlYWz3k/fvyIq1evwtTUFAMGDKh1+UR9T4tqxowZ8PHxQVBQEPc9+eDBAzx58gReXl58X74ct2/fxvr169G3b1+EhITwfC+eOnUK8+bNg5eXF44dOwbg+0Pn4cOHMW7cOLi6unITBDc3N+6DuCjN+HPnzsX79+/x66+/Yvny5Tyv5ebmcr9PavNZqAuc94CgFoRPnz5xH3wTExO5Tc33798H8L0lqWfPnjz9LlksFmbPng0fHx9usvT161d8+PABioqK0NTU5LsOJ88QNhinvp0/fx75+fno27cvz/uD81AqLA/ibBe1goLzHqucSItynYiICLx69QrDhg2r8hrV1kRaW1vD398fpaWlOHToENhsNvbv349x48ahuLgYnp6esLOzw/jx43Hz5k0MHjwYqqqqMDMz4yZg1eE8mbRv317g6ydPnoS3tzfPvwcPHoh07so41xDUNP3kyRPu+Tk3y+3bt0NaWlqkWtY+ffrA2NgY169fx6dPn3he++uvvwCA5wPLUfHLn0OUDq2iOHHiBEpKSjB16lRuAjNp0iQoKioiODgY3759E3rsmjVreBImVVVV7sjuyjWGMjIyAvuoVvzS+PDhA65fvw5tbW2+EeJmZmZwc3NDUVERTp8+LX5Ba+nUqVPIzs7GihUreBJIADA2NsasWbOQlpaGmzdvVnuuHj164MCBA1BXV0dwcDBcXFxgbm4OPT09TJkyBWfPnkV5ebnAY5WVlfm+APr06QM7Ozvk5OTg8uXL3O3+/v6QlpbG7t27+d5DS5cuhaqqKreGv7L169dzv2yB74mNo6MjysvLeT5bISEhKCkpwZw5c3iejKWkpLBu3TqRm7AqWr58OU9LhoyMDDdBq1gTc/nyZeTl5cHBwYEngQSAn3/+Waz+cfHx8Xjx4gV69erF9xkcPnw4rK2t8enTJ57fL0f//v15Ekjge4IhIyNT45kbXFxcUF5ejuPHjwP4nqRdv34dDg4OQrvyNITWrVsLHLChra0NW1tbJCYm8nRlqmjjxo0Ckw43NzcUFxfz3TOOHj2K0tJS/PDDD3USu6jvaVFpaWlh1KhROHfuHHeEa1BQEKSkpATWmnPs27cPbDYbf/75J1/FytSpU9G9e3dcvnyZp5WgT58+WLNmDd69e4eFCxfC09MTKSkpWLduHXr16lVtrA8fPsSdO3dgamrKbcKsSEVFhfu+qs1noS6MHTsWALBnzx6e7+Hy8nJs2LCB+3PFVg7O9+mqVaswZMgQ3LlzBykpKbh48SIMDAwQEBDA0+eP09LI6YZXGWe7sC409enNmzdYvnw5ZGRksHnzZp7XOHELu7eJE/elS5dw5MgRaGtrY/HixfV2HZGqupydnQUmP1u3bkVWVhZ8fHyQmpoKJycnWFhYcEcDrVixAlpaWrCxsRHlMkKdOnWK78tbQ0ND6CjrqnCapwXVCD19+pTbxCsrKws1NTXY2dnB09OTZzRUVaZNm4Z169YhODiY2zE4NzcX//zzD/T09DB48GDuvo6OjggNDcXIkSNhZ2eHIUOGoG/fvnXW0ZczoIbFYvH8/ThN6idOnMC5c+cE3hBlZGS4NY0VDRo0CADw+PFjnnKsXr0aFhYWsLOzw8CBA2FhYcH3BPjo0SMAgIWFBeTk5PjOPXz4cPj5+XH3a0ic/l7Pnj0T2NeI88SakJAgsCamMjs7O1hbWyM6OhqxsbF4/Pgx4uLicO3aNVy7dg1//fUXTp48yfd76NGjh8CBaYMGDcKZM2e4ffO+ffuGx48fo02bNti3b5/AGOTk5PDx40dkZ2fzPZRUTsqA711XAN6bN+dvwfm7V6Svrw9tbW2xm5VFvTbnPSaolkpRURHdunUT2teuMk45hLU+DB8+HKGhoXj06BEmT55cbbyysrJQV1evUXM+55zdu3fHiRMnsHz5chw7dgxlZWX11pQtjri4OOzbtw///fcfMjMz+foUfvz4kW/QjoKCArp27SrwfE5OTli/fj2OHDmCRYsWgcVioaysDMeOHUOrVq3g5ORUJ3GL+r4Sh4uLC65evYozZ85g8uTJOHv2LEaNGsU9ryDx8fGQkZFBaGgot/9fRcXFxSgrK8Pr1695Yl64cCFu3brFHawzduxYbh/W6vz3338ABDdPVlabz0JdsLe3R0hICK5evYp+/fph/PjxUFRUxO3bt5GQkIDOnTvj5cuXPOXgPHR37twZhw8f5j68Dhs2DEFBQRg2bBj8/f2xbNkygd8ttfH27VtuJVBFgmryq5Oamgp7e3tkZWVhx44dfCOz68rNmzcxd+5cKCkp4fjx4/U6X2yN20sfPXqEHTt2ICAgAKqqqtiwYQOKiorg7+8PbW1tDB8+HP/++y/8/PyqTSI5TaBpaWkCX6/YF8zb2xs+Pj41DZt7jYodejmmTZuGvXv31vjcwPcb5saNG3Hy5EluEnnu3DkUFRXx1AYCgK2tLYKDg+Hn54eTJ09yO3GbmppixYoV3L6TNXXz5k0kJSVh0KBBfP0rpk+fjhMnTgjti6aqqiqwlklNTQ0AePqULly4EGpqaggMDERAQAC3Brpv375Ys2YNhgwZwnNM5SZvDk5zExNPh5zOzZzqf2FE6WjMISsrC0tLS1haWgL43i/20qVLWLhwIcLDw3Ho0CG+gT3CfjeVf++5ublgs9nIzs6u9vPw5csXviRS0E2F8/eu2BeXcz3O9StTV1cXO4kU9PRb02uLqjbvPWFP69LS0rXqt+zi4oJly5bh6tWrOH78OLp27SpSrVN9Cg0NhYuLCxQUFDB8+HAYGBigZcuWkJKSwq1bt3D79m0UFRXxHdeuXTuhXTWUlJQwZcoUHDp0CDdv3sTw4cNx7do1pKSkYObMmXU24lrU97Q4xowZw+2zKyUlhfz8/GpntcjOzkZpaalIn8uKWCwWJk6ciKtXrwIA372hKpz3rbDWvIqYvg9LSUnhxIkT2L9/P06dOoWQkBDIyMigb9++CAsLg6+vL16+fMnzuee8R8aOHcv3vdStWzfo6ekhKSkJCQkJ6NatG7cmTdjYh+pq4ip69+6dwL+luEnk+/fvYWtri7dv32L79u0Ca+CrqwEUJe7IyEg4OztDXl4eZ8+eFVjZVhfX4ahREllSUgJPT0+MGzcOkyZNAvC9hkZVVZWnL1u3bt2qHFXK0atXL8jLyyMlJQWvX7+ut36Rr1+/RmpqKmRkZAQ+tdaF9u3bw9LSEv/++y+ePn2Krl274q+//gKLxcK0adP49reysoKVlRUKCgpw//59XL9+HQEBAfjhhx8QGhrKU3MpLk6n9du3bwt9Evnvv//w7NkzvtGgWVlZKCsr4/vAZmZmAuBvJnBycoKTkxM+f/6Mu3fv4p9//kFQUBAcHR1x69YtdOrUiXsMp/tCZZxRi8KaICqSkpISOiltTW5+nGveuHGj3t4b0tLSsLOzw7Nnz7Bt2zbcvHmT74tC2O+m8u+d819TU1O+KSLqEuc6nOtXJizeusCpka2La9fle6+uODo64rfffsMvv/yC1NTUGg0AqWubN2+GnJwcIiMjYWxszPPakiVLhM6gUF1f39mzZ+PQoUM4fPgwhg8fzh1ow+lbL6mkpaUxffp0bNu2DRkZGdDU1OQ2xwqjrKyMkpISoc3+wiQnJ8PLywvKysr49u0bli5dihs3bog0ZR7ny17YjAmV4wOY/SzIyMjA09NTYE3rs2fPIC0tzXMf7tSpE+7duyc0qeF8vxUWFgL43tqmpaWFDx8+IC0tja9VjNOnUNBUf5UNGTKkxjXZHElJSbC1tUVqaip27dqFmTNnCtzPyMiIJ77KONuF5UhXr17FrFmz0KpVK5w7d07od5mo1xHl91OjZQ//+OMPpKam8nQoZ7FYfM0egp5YBWnRogWmTJkCALWqZawO59yc6vP6wmk6/uuvv5CYmIi7d+9i4MCBAkdbcbRo0QKDBg3C2rVrsXHjRrDZbISFhdU4hoyMDFy+fJk7N6Sgf5waQkEjJ0tLSwVO6cH5Eqk8EIhDWVkZlpaW8PX1xcKFC1FYWIh///2X55j4+HiB025wuiyIksSpqKggIyNDYCIprP+TlJSU0L6InGaFhpi/kfOlIGiqpEePHgkcUV35966oqAhTU1MkJiZWOfVUbXHmfBOUPCQnJ/OMMq1rnLIK+pt8+fIFT548EflcnHIIG40pznuvrigrK8POzg6pqalo2bIlHB0d6/2a1dXMvXnzBsbGxnwJZHl5OeLi4mp8Xc7gmcuXL+P+/fv4999/YW5uXqMuSQ1t5syZYLFYSE1NxfTp06sd8Ni3b1/k5+eL9f4sLi6Gq6srPn/+jL1792L16tV4/fq1yKuLce5fERERQu9xHJL4Wah47ZSUFIwdO5YneeZMifb8+XO+Y4qKirhJD2eWAQDc77fr16/zHcP5TuLsU58SEhIwfvx4fPjwAfv37xeaQALfB/B26NABr169EriiDSduQV0RLl68iBkzZkBZWRmhoaFV/v045Rb0fsnPz0d8fDxatmwpUjc+sZPI58+fY/v27diyZQtPdbiJiQk+f/7MvdHk5+cjNjYWXbp0Eem8q1evhra2NoKDg7Fy5UqhzYY1qWXizHcYHByMNm3aYN26dWKfQxzjx4+HiooKQkJCuM2jgvqU3rp1S+CKB5wnwYqd1L99+4aXL19WuVxaRZwBNZyRnoL+HTp0CDIyMggODha4ssXGjRt5HgSysrLwxx9/AADPQIObN28KTIgql0NbWxsjR45Eamoqdu7cybPv//73PwQGBkJeXp77QFGVPn36oLS0lG8et/DwcKFLCrZt2xafPn0SWNYZM2ZARUUFvr6+uHPnDt/rbDYbsbGxIs05d+bMGURGRgq8maenp3NjFtTP8PPnz3yTwt69exfnz5+HiooKz4T3np6eKCkpwYIFCwSuEpGfny/SFA1VcXR0hKysLA4ePMhzUysvL8f69evrbBoqQcaPHw9lZWWcO3eOb57Obdu2iXUvsLCwgLGxMe7du8c3cOvmzZsIDQ2Fqqpqgy8o8Ouvv+L48eM4c+ZMg0ykzenWkJKSIvB1XV1dvHnzhqdGi81mw9vbGy9evKjVtd3d3VFSUoIZM2agvLxc4mshOfT09HD+/HkcP35cpCZmTu3akiVLBD5kFRYW8j0YrVmzBg8ePICHhwcmTJiAxYsXY+TIkQgJCeGbCkoQc3NzWFhY4Pnz5wJn3MjLy+M2n0vCZ0HQZzcpKQk//vgjFBQU8Ntvv/G8Zmtri/bt2+P8+fN8g9l8fX3x+fNnDBkyhGcEPuf9tX37dp6axLdv3yIgIADy8vJ8A+bq2tOnTzFhwgR8+vQJgYGB1T4oslgsbtxr167l+Q4JCwtDbGwsTExM+FooT58+DTc3N7Rr1w5hYWFC55nlMDAwgKWlJd69e8c3J6e3tze+fv0KJycnkVa1E6s5u6ysjDtvWOXO0G5ubvDz88PMmTPh4OCAW7duITc3Fz/++KNI59bQ0MDFixcxc+ZM7N27F3/99Rd3vUkpKSlkZGTgzp07ePXqFVRVVflG0HJwBkWUl5dzlz2MjY1FQUEBjI2NsX//fhgaGopTbLHJy8tj8uTJCAgIwN69e3nmhqzIy8sLqamp6N+/P3R1daGgoIBnz54hPDwcbdu25elkf+/ePdjY2EBHR6faJ1w2m8298VQ18lFdXZ27MsP58+d5El1NTU0UFBRg4MCBGDduHIqKinDp0iWkp6fDw8ODZ9DNzJkz0apVK/Tp0we6urpgsVi4d+8eYmNjYWBgwO3yAHyvxR47dix+//13REVFoW/fvkhLS8OFCxdQWFiIHTt2VNlpncPDwwMnTpzAL7/8gqioKOjp6SEhIQERERGwsbEROKfiiBEjEBISAgcHBwwcOBDy8vLo2rUrxo0bhzZt2uDo0aOYMWMGrKysMHToUJiYmEBWVhapqam4e/cuUlJSkJycXG3H7bt372Lfvn3cCdo5A6Xevn2La9euoaCgAP369eOb5BX4PogkKCgI9+7dQ//+/fHhwwecP38ebDYbO3fu5KlBnz59Oh49eoQDBw7A3NwcI0eOhK6uLvLy8vDu3TvExMRgxIgRAjuFi0pPTw9r167F6tWrMXToUNjZ2aFNmzYIDw9Hbm4uzMzM8OzZsxqfvyrKysrYtm0bPDw8uF1n2rdvjzt37uDJkycYNGgQbt++Xe1AAuD7zXnv3r2YNGkS5s2bh/Pnz8PMzAxJSUm4dOkS5OTksG/fvnqZ0qQq2tradTqdVXX69evHbeqSlZWFjo4OWCwWnJycoKuriwULFuCnn37C0KFDYWtrCxkZGcTHxyMhIQFjx46t1ZrDtra2UFdXx4cPH6CsrNyolv2svDhAVYYOHYqNGzdi7dq16N27N0aPHg19fX0UFhbi/fv3iImJga6uLndQWFhYGPbt2wdzc3Pu1F4sFgv79u3DkCFDsGLFCvTt27faCpn9+/fD2toamzdvRlhYGLe2iTOx9NWrV9G9e/c6/yzExsZyv284lT9JSUk8CfemTZt4Rv3b29tDTk4OZmZmUFZWxuvXr3H16lXuZNuVv99btWoFf39/ODk5Ydy4cbCxsUH79u253zNqamp8U35ZWFhg4cKF2LNnDwYNGgRbW1uUlJTg3LlzyMnJwdatW+tttRrge+WVjY0NcnJyMGDAADx//lxgTaqzszNPHJ6enrh27RouXryIkSNHYtiwYUhJScGFCxfQsmVL7Nmzh+eeFxUVhfnz56O8vBxDhgwRWolSuQ/n9u3bYWVlhRUrVuDmzZswNjbG3bt3ER0djU6dOvEl8sKIlUTu2rULr1+/FjghtJaWFoKDg7F69WocOnQImpqa2L59O8aNGyfy+Tt16oSoqCicOXMGFy9exL1793Dt2jWwWCy0a9cOZmZmWLhwIezt7YX21+A0WcvKykJRURFaWlqYOHEiJkyYgLFjxwqchqY+TJ8+HQEBASgpKcHkyZMFNp8vW7YMYWFhePDgAbdpQUtLC/Pnz8eCBQtESqYEuXHjBpKSkmBqalptdbSLiwvCwsIQFBTEk0TKysriwoUL2LBhA86cOYPs7GwYGBhg2bJlfMnPunXrEBERgSdPniA8PBwyMjLo0KEDVqxYAQ8PD54aFj09Pdy4cQPbtm3DlStXEBcXh1atWmHQoEH48ccfRW5e6Ny5My5duoSNGzfi+vXrkJKSQs+ePXHp0iUkJSUJTCK9vb0hJSWFGzduIC4ujrvGNec9OnToUNy+fRt79uxBeHg47ty5AxkZGWhoaKBfv37c+RKrs2jRIhgZGSEyMhLPnz9HZGQkvn37hjZt2qBfv36YNGkSZsyYIfC9qK+vjz///BPr1q3DoUOHUFxcjF69emHFihUCv8i2bt0KKysrHDp0CLdu3UJOTg5at24NLS0tuLu718noyoULF0JTUxO7du3CqVOnoKioiJEjR2L9+vVwd3ev9fmrMmXKFLRp0wa+vr64cOEC5OTkMHDgQPz777/cm5yoy6z26tULN27cgK+vL27cuIHw8HC0bt0aEyZMwLJly4R20WhKVFRUcPz4cfj4+OD8+fPc2inOg6yrqyvk5OSwd+9enDx5EgoKChgwYAD8/Pxw6dKlWiWRsrKycHJywu7du0Wu5WisFi1ahP79+2Pfvn2IjY3FlStXoKioiPbt22PKlCmws7MD8H2whaenJ5SUlBAYGMjzgKqmpoYDBw5g0qRJcHV1RURERJWJnb6+PqKiorB79278/fffOHjwIOTl5dGhQwfMmTOHp6m3Lj8Lb968wcmTJ3m2ZWVl8Wzz8vLiSSInTZqEs2fPIiQkBAUFBdDQ0MCUKVOwZMkSoZU8I0aMQHh4ODfmz58/Q0NDA25ubvjll18EDiratGkTzMzMcPDgQe7gqO7du+PHH3+stm9rbeXl5XFbiGJjY4V2lRo8eDBPEikvL4/z58/jzz//xNmzZ+Hv7w8lJSVMmDABK1eu5Euw379/z62xFDalG8CfRBoYGODGjRvYvHkzrl+/jn///RcaGhrceUxFHdHNys3NrZu19QghNRYdHQ0bG5s6mSGgOSgrK0OPHj1QUlKChIQEpsMhIrK3t0dERARiYmJgamrKdDiEkFqq0cAaQghpCHl5eXwT4rPZbPj6+iIlJQXW1tYMRUbE9ejRI0RERGDw4MGUQBLSRNR+XT1CCKknDx48wKxZszBixAjo6uri69ev+O+///DkyRN06NCBbx13InkOHTqEjx8/cqc6+/XXX5kOiRBSRyiJJIRILENDQ4wdOxZxcXH4999/UVpaCi0tLXh4eGDZsmVCJyInkmPHjh1ITU2Fvr4+/Pz8MHDgQKZDIoTUEeoTSQghhBBCxEZ9IgkhhBBCiNgoiSSEEEIIIWKjJJIQQgghhIiNkkhCCCGEECI2SiIJIYQQQojYKIkkhBBCCCFio3kiGfTlyxeEh4cjPj4eCQkJyMrKAovFgqqqKjp37gwLCwtYWlqKvDYwIYQQQkhDoXkiGfDs2TPs3r0bf//9N75+/YoWLVpAW1sbKioqYLPZyM3NRWpqKgoLC9GyZUtYW1tj0aJF6Nq1K9OhE0IIIYQAoCSywbm6uuLixYvo2bMn7OzsMGLECJiYmEBaWppnv7KyMrx48QIRERG4ePEiHjx4gEmTJuHQoUMMRU4IIYQQ8n+oObuBsdlsREZGokePHlXuJy0tDTMzM5iZmWHRokV4+PAhduzY0TBBEkIIIYRUg2oiCSGEEEKI2Gh0NiGEEEIIERslkQyLjY3FwYMHebadPXsWffr0gZGREby8vFBeXs5QdIQQQgghglESybDff/8dMTEx3J9fvXqF+fPnQ0pKCubm5jhw4AD27dvHYISEEEIIIfwoiWTYixcv0Lt3b+7Pp06dgoKCAq5fv46QkBA4OTnh+PHjDEZICCGEEMKPkkiG5efnQ0VFhftzeHg4RowYAWVlZQDAgAED8O7dO4aiI4QQQggRjJJIhmlqaiIhIQEA8PHjRzx+/BiWlpbc1z9//gwZGZqJiRBCCCGShbIThtnY2ODgwYMoKirCvXv3oKCggPHjx3Nff/r0KfT09BiMkBBCCCGEHyWRDFu5ciUyMjIQHBwMZWVl+Pv7Q01NDcD3WsjQ0FDMmTOH4SgJIYQQQnjRZOMSrLy8HPn5+WjZsiVkZWWZDocQQgghhIuSSEIIIYQQIjZqzpYAubm5OHPmDJKTk5Gbmws2mzevZ7FY2LNnD0PREUIIIYTwoySSYeHh4XBxccHXr1+hpKTEM90PB4vFavjACCGEEEKqQM3ZDBswYACKiopw7NgxmJmZMR0OIYQQQohIaJ5Ihr158wYeHh6UQBJCCCGkUaEkkmEdO3bEly9fmA6DEEIIIUQslEQybNWqVQgMDERycjLTodSrxMREpkOoM1QWyURlkUxNqSxA0ypPUyoLYQYNrGFYREQE2rRpAwsLCwwdOhTa2tqQlpbm2YfFYmHbtm0MRUgIIYQQwo+SSIYFBgZy///69esC96EkkhBCCCGShpJIhuXk5DAdQoN7m1+CTffz8fFbGdq3lMbqXkrQU6IVeQghhJDGhJJI0qDe5pdg0tUsJOWXcbfdzSzGhTGqlEgSQgghjQglkRLixYsXuHbtGt69ewcA0NXVhZWVFUxMTBiOrG5tup/Pk0ACQFJ+GTbdz8fBYW0ZiooQQggh4qIkkmFsNhs///wzDh8+DDabDSmp7wPmy8vLsW7dOri5ucHX17fJrFrz8VuZwO1pQrYTQgghRDLRFD8M27lzJwIDAzFt2jTExMQgPT0d6enpiImJgbOzMwIDA7Fr1y6mw6wz7VtKC9yuKWQ7IYQQQiQTJZEMO3bsGGxtbeHn54cuXbpARkYGMjIy6NKlC/bs2QNra2scPXqU6TDrzOpeSjBQ4k0YDZS+D64hhBBCSONBSSTDUlJSMGzYMKGvDxs2DCkpKQ0YUf3SU5LFhTGqcDRsgSGacnA0bEGDagghhJBGiJJIhqmpqeHRo0dCX3/06BHU1NTEPm9AQAC6d+8ODQ0NDBs2DDExMUL3nT9/PlRUVPj+aWlpiX1dUegpyeLgsLYIHaeGg8PaUgJJCCGENEKURDLMzs4Ox44dg6+vLz5//szdnp+fj23btuHEiROwt7cX65znzp2Dl5cXli1bhqioKPTr1w+Ojo54//69wP23bNmChIQEnn/6+vqYNGlSbYpGCCGEkCaMRmcz7Ndff8XTp0+xefNm+Pj4QF1dHQCQkZGBsrIyjBgxAitXrhTrnH5+fnB2doaLiwsAwNfXF+Hh4QgMDMTatWv59m/dujVat27N/TkuLg7JycnYv39/LUpGCCGEkKaMkkiGtWjRAufPn8fly5fx77//cmsLx4wZgzFjxmDs2LFina+4uBgPHz7EokWLeLZbWloiPj5epHMEBQWhS5cusLCwEOvahBBCCGk+KImUEOPHj8f48eNrfZ6srCyUlZXx9aNUU1NDRkZGtcfn5eXhwoULWLNmTbX7JiYmihWbuPtLMiqLZKKySKamVBagaZWnYlmMjIwYjIQ0RpREEh7BwcEoLy/H1KlTq91XnBtOYmIid//GvnZ2xbI0dlQWyURlkVxNqTxNqSyEGZRENjBra2tISUnh3LlzkJGRgY2NTbXHsFgsXLp0SaTzq6qqQlpaGpmZmTzbMzMzuf0tqxIUFARbW1u0adNGpOuJi9bOJoQQQpoGGp3dwNhsNsrLy7k/l5eXg81mV/mv4v7VkZOTg7m5OSIjI3m2R0ZGVtvH8d69e3j69ClmzZolXqHEUNXa2YQQQghpPKgmsoGFhYVV+XNd8PT0hIeHB3r37g0LCwsEBgYiLS0Nrq6uAAAPDw8A4Bt9feTIEXTs2BFDhgyp85g4aO1sQgghpGmgJLIJsre3R3Z2Nnx9fZGeno4uXbogODgYurq6ACBwBZz8/HycO3cOy5cvr9fYhK2drSjDqtfrEkIIIaRuURLZRLm7u8Pd3V3ga4JqP5WUlJCamlrfYWF1LyXEphUi5RubZ/uTnBK8zS9pUv0iG/sAIkIIIaQqlEQ2sDZt2oDFEq/WjcViISsrq54ianhlbBYA3iQy5Ws5Nt3Px8FhbZkJqo7RACJCCCFNHSWRDWz58uViJ5FNBSex+lggeKBQU+oXWdUAoqaSKBNCCGneKIlsYOIuYdiUCEqsKtIU0l+yMaIBRIQQQpo6muKHNBhhiRUAGCh97zPYVAgbQNSUEmVCCCHNG9VENrDbt2/X6LhBgwbVcSQNT1hipaso3eT6Cq7upYS7mcU8Na9NLVEmhBDSvFES2cCsra3F6hPJZrPBYrGQnZ1dj1E1DGGJVVNJICuPxt4zqDWOvCxA2rcyaNLobEIIIU0MJZENLDQ0lOkQGKOnJIsLY1Sx6X5+k0usaDQ2IYSQ5oaSyAY2ePBgpkNglJ6SbJMcnUyjsQkhhDQ3lESSBtVUJ+Cm0diEEEKaG0oiG5iPjw9YLBZ+/vlnSElJwcfHp9pjWCxWvS9H2BCacpMvjcYmhBDS3FAS2cC2bNkCFouFJUuWQE5ODlu2bKn2mKaSRDblJl8ajU0IIaS5oSSygeXk5FT5c1PWlJt8m/KgIUIIIUQQSiJJg2nqTb5NddAQIYQQIgitWCNhSktLkZCQgPv37+PLly9Mh1OnVvdSgoESb8JITb6EEEJI40RJJEMuXLiA6dOnw8XFBVeuXAEAXLx4EV27dsWAAQMwatQodOrUCZs2bWI40rrDafJ1NGyBIZpycDRs0SQG1RBCCCHNETVnMyAsLAyurq5QUlJCq1at8Pfff+PPP//E0qVLYWpqCnt7e5SUlCAyMhJ//PEHdHR04OLiwnTYdUISm3yb6rRDhBBCSH2iJJIBfn5+6N69Oy5fvoxWrVrBy8sLy5cvx8iRI3Hq1CnusoilpaWwsrJCYGBgk0kiJU1TnnaIEEIIqU/UnM2Aly9fYsqUKWjVqhUAwM3NDUVFRXB0dORZV1tGRgYODg5ITExkKtQmr6pphwghhBAiHCWRDMjKyoKamhr353bt2gEAzzYONTU1FBYWNlhszU1TnnaIEEIIqU+URDKkYo0jYU5Tn3aIEEIIqS/UJ5IhycnJuHfvHgDg8+fPAIDExEQoKiry7JeUlNTgsTUntNIMIYQQUjOURDLE29sb3t7ePNsELW3IZrOp1rIe0UozhBBCSM1QEskAPz8/pkMgFUjitEOEEEKIpKMkkgHOzs5Mh0AIIYQQUis0sIYQQgghhIiNksgGdvz4cZSWlop9XFlZGY4fP14PERFCCCGEiI+SyAa2ceNGmJubw8fHBwkJCdXun5CQgC1btqBHjx5Nah1tQgghhDRu1CeygT148AB79+7Fvn374OPjA01NTZibm0NfXx8qKipgs9nIzc3F27dv8fDhQ6SlpaFdu3aYP38+5s2bx3T4hBBCCCEAKIlscC1btsSyZcuwePFi/PPPP7h8+TLu3LmDK1eugM1mA/g+EXnHjh1haWmJ8ePHY8yYMZCWFm/y64CAAOzatQvp6ekwMTGBt7c3Bg4cKHT/4uJi+Pr64vTp00hLS4O6ujoWLlxIiSshhBBCBKIkkiEyMjKwsbGBjY0NgO99HnNycgAAbdu2hZRUzXsanDt3Dl5eXti+fTv69++PgIAAODo6Ii4uDjo6OgKPcXNzw4cPH7Bz504YGhoiMzMTBQUFNY5BHG/zS7Dpfj4+fitD+1rM01hX5yGEEEJI9SiJlBDS0tLcNbRry8/PD87OznBxcQEA+Pr6Ijw8HIGBgVi7di3f/hEREYiKisKDBw+gqqoKANDT06uTWKrzNr8Ek65m8awYczezGBfGqIqVANbVeQghhBAiGhpY08QUFxfj4cOHsLS05NluaWmJ+Ph4gceEhYWhZ8+e8PPzg6mpKXr16oXly5fjy5cv9R7vpvv5PIkfACTll2HT/XxGzkMIIYQQ0VBNZBOTlZWFsrIyqKmp8WxXU1NDRkaGwGOSk5MRFxcHeXl5HD16FHl5eVi+fDnS0tJw9OhRoddKTEwUK7aK+6cWsLDvnQxuZcsA4F/W8U3WFyQmZol87jef5AHw9xsV9zyiErfskozKIpmoLJKrKZWnYlmMjIwYjIQ0RpREEpSXl4PFYuHgwYNo3bo1gO9N4Pb29sjIyIC6urrA48S54SQmJnL3f5tfgp8qNT1XZqiqCCMjXZHPb/ghG/c+8/fhFPc8oqhYlsaOyiKZqCySqymVpymVhTCDmrObGFVVVUhLSyMzM5Nne2ZmptBkUENDA+3bt+cmkADQuXNnAEBKSkqdxyio6bkiA6Xvg2LEsbqXEgyUeGsia3IeQgghhIiGksgmRk5ODubm5oiMjOTZHhkZCQsLC4HH9O/fH2lpaTx9IF+/fg0AQkdz18bHb4ITyNayLDgatqjRYBg9JVlcGKMKR8MWGKIpV+PzEEIIIUQ01JzdBHl6esLDwwO9e/eGhYUFAgMDkZaWBldXVwCAh4cHAGD//v0AgMmTJ8PX1xeenp7w8vJCXl4evLy8MHHiRL6+lXWhfUvBc15a6Sjg4LC2NT6vnpJsjY6nqYEIIYQQ8VESyaCMjAwcOHAAkZGRSEpKwpcvX6CoqAhDQ0OMHDkS7u7uNUri7O3tkZ2dDV9fX6Snp6NLly4IDg6Gru73voGVm6gVFRVx4cIFLF++HJaWllBRUcGECRMETgdUF1b3UsLdzGKeJm2mmp5paiBCCCGkZiiJZEhUVBRcXFyQm5uLFi1aoGPHjlBUVMSXL1/w/Plz3Lt3DwEBATh+/DgGDBgg9vnd3d3h7u4u8LWwsDC+bUZGRjh//rzY16kJPSVZ7BnUGvNv5SGvqByt5aWwZ1BrRpK2qqYGqk2tKCGEENLUURLJgKysLLi6ukJWVhYBAQGYOHEiZGT+709RWlqKCxcuYOXKlZg1axbi4+PRtm3TSWje5pdg4e08vPvyPXnLKynDwtt5uDBGpsFXqhHWPzNNyHZCCKmIusOQ5oySSAYEBQUhPz8fkZGRMDMz43tdRkYGkydPhrGxMSwtLXH06FEsWbKk4QOtJ3VZ+1fb5mhh/TM1hWwnhBAO6g5Dmjsanc2AiIgIjB07VmACWVG3bt0wbtw4XL9+vYEiaxh1WftX25VqaGogQkhN0UpZpLmjJJIBL1++RL9+/UTa18LCAi9fvqzniBpWbWr/3uaXYM7NbFj/k4k5N7ORlF8qcD9RE1KaGogQUlPUHYY0d9SczYC8vDy0a9dOpH3btm2LvLy8eo6oYdV0dLagpqNWMvxLJgLiNUfXdGogQkjzRt1hSHNHNZEMKC4uhrS0aDcZaWlplJSU1HNEDaumtX+Cmo6+lrLRqtKjEDVHE0IaAnWHIc0d1UQy5O7duzwjsoX577//GiCahleT2j9hTUddVGRgoCyLtG9l0KTRkYSQBsJ5IN50P5/uP6RZoiSSIQcOHMCBAwdE2pfFEtxk29wIazoyUKbmaEIIM6g7DGnOKIlkQGhoKNMhNEqStNINIYQQ0txREsmAwYMHMx1Co0RNR4QQQojkoCRSAqWmpiItLQ0dO3aEiooK0+HUm5qs9EBNR4QQQohkoCSSAXfv3kV4eDjc3d2hqqrK3Z6eno7Zs2cjJiYGwPeR2QsWLMD69euZCrXe3P5YgCnXs/G1wjSPlVd6oOXECCGEEMlFU/ww4NChQwgJCeFJIAHA09MTt2/fxoABA+Dp6QkTExPs3r0bJ0+eZCjS+vE2vwRTrufwJJAA70oPnDkhQ94U4FZaMULeFGDS1Sy8zW9a0x0RQgghjRXVRDLg7t27GDVqFM+2pKQkhIeHY+TIkThz5gwAoKSkBCNGjMCxY8cwbdo0JkKtF5vu5+NrKVvga5yVHupyfW1CCCGE1D2qiWRAeno6OnXqxLPt6tWrYLFYcHNz426TlZXF5MmT8fz584YOsV4Jm+8RABRlWJhzMxtX3xcKfJ2WEyOEEEIkA9VESog7d+4A+L5WdkVqamooKChgIqR6I2y+RwUp4ElOCVK+lgs9lrOcWEP3l6x4PcUyWfhollD/TEIIIc0a1UQywNDQEPHx8dyfS0tLER0djY4dO/L1k8zKyhJ5ne3GQtBSYa1kWOivIVdlAsmZE7Kh+0tWvt6VTFnqn0kIIaTZoySSAdOnT8f58+exdetWREVFYenSpcjKyoKTkxPfvrdv3+Zr+m7sBK2dHTNJDUK6SaK1LItnfe2q+kvWh4a+HiGEENIYUHM2A1xdXREREQFvb2+wWCyw2WwMGTIECxcu5Nnv/fv3iIiIwJo1axiKtP4Imu9RWDO3lY4Cz77C+lTWV3/Jhr4eIYQQ0hhQEskAGRkZnDx5Eg8ePEBSUhJ0dXXRp08fvv1KSkoQEBCAQYMGMRBlwxN1WUNhyaamkO211dDXI4QQQWjuXCJpKIlkUM+ePdGzZ0+hrxsaGsLQ0LABI2KWqMsa1tUa2qLekGnNbkII0zh9syvehyov0EBIQ6MkkgGZmZlCX2OxWJCXl4eSUvNMUERZ1rAu1tAW54Zc+Xqtyr7CZ5g63bhrgWpUCBEPzZ1LJBElkQzo3LkzWCxWlfu0bt0aVlZW+PXXX6Gnp9dAkTUetV1DW9wbcsXrJSbmCkx4KDESDdWoECI+6ptNJBElkQxYvnx5lUlkQUEBXr58iUuXLiE8PBzh4eGUSNaxur4hV5UYAaDksgKqUSFEfML6ZivJfl+gge4vhAmURDJg5cqVIu338uVLjBo1Cj4+PvD396/nqJqXuh4sIywxWhmfh//lllKtWwVUo0KI+AT1ze7QSgqPs4qR8u3/5kdr7vcX0rBonkgJ1rlzZ/zwww+4ceMG06E0OYImPK/NYBlhidF/lW76AM0xSaPdCRGfoPl1u7WR5UkgAbq/kIZFNZESrmPHjvj06RPTYTQ5dTE4pyJhiRHAAsA/i3pzrnWj0e6E8BOlT3XlvuDW/wgepNmc7y+kYVESKeE+fPgAZWVlpsOQeNXdgIW9Xld98IQlRiatZfBPShHf/s251q2uE3hCGruaDjajWn3CNEoiJVhubi5OnDgBCwsLsY8NCAjArl27kJ6eDhMTE3h7e2PgwIEC942OjoaNjQ3f9jt37qBz585iX7uhVXcDbojRwMISIwB4UenaVOtW+9H1pPZoNgHJUdPBZlSrT5hGSSQDzp8/X+XrBQUFePXqFUJCQvDp0yf8/PPPYp3/3Llz8PLywvbt29G/f38EBATA0dERcXFx0NHREXpcXFwc2rRpw/25Xbt2Yl2XKdXdgBtqNLCwxIhq3YikoWmWJEtNB5tRrT5hGiWRDHBzc+OumV2Vbt26wd/fv8pVbQTx8/ODs7MzXFxcAAC+vr4IDw9HYGAg1q5dK/Q4NTU1qKqqinWtulDbGpHqbsCi3qDrq2aGat2IpKFpliRLbZql6f5CmERJJANCQ0OrfF1BQQE6OjrQ0NAQ+9zFxcV4+PAhFi1axLPd0tIS8fHxVR47fPhwFBcXw9jYGD///DOGDh0q9vXFVRc1ItXdgEW5QVPNDGlOGtM0S82h2Z2apUljxcrNza26Oow0Kh8/fkSXLl0QFhaGQYMGcbf7+PggJCQEd+/e5TsmMTER0dHR6NWrF4qLi3H69GkEBgYiLCxMaD9KznG19VuCLK5k8n8hjFUrwUbjEpHOkVrAwsJn8kgp/L8ZqzoolGOPWRG0W7Crfb2u4iCksWgs73dRPrtNRWoBC/veySCzWApqcuWYp1va4GU0MjJq0OuRxo9qIgmMjIx4bh79+vXDu3fvsGvXriqTSHFuOImJiQL3//IqE0Ax3/av0q1gZKQm0rmNAIQZlAjtF1Td6+LGIawsjRGVRTLVd1l8NEuQIGDAV32sCV+bsmy9mY2UwgKebSmFUjiR0xYHuzPThFtffxsjAMO71/lpq9SUPjOEGZRENjGqqqqQlpZGZibv/GGZmZlQV1cX+Ty9e/fGuXPn6jo8Pkoygpd/VBSyXZjq+gVV9zpNlUGak8YyIKMxNbsT0hxREtnEyMnJwdzcHJGRkZg0aRJ3e2RkJGxtbUU+z5MnT2rUJ1NcwpYQr2Jp8XpBfZJIc9MYBmTQwx0hko2SyCbI09MTHh4e6N27NywsLBAYGIi0tDS4uroCADw8PAAA+/fvBwD4+/tDV1cXXbp0QXFxMYKDgxEWFoajR4/We6yfSwT3+ckXsr2+NJaaGUKaE3q4I0SyURLZBNnb2yM7Oxu+vr5IT09Hly5dEBwcDF1dXQBASkoKz/4lJSVYs2YNPnz4AAUFBe7+VlZW9R6rJNU0NIaaGUKak4Z6uGsOI8AJqQ+URDZR7u7ucHd3F/haWFgYz8+LFy/G4sWLGyIsPlTTUL/oy5E0dnX5cCfo8wCApvcipIYoiZQAZWVlCA8PR3JyMnJzc/kmIWexWFi+fDlD0dUvakauPzT3JSH/R9jnoYuKDE28TkgNURLJsAcPHmDmzJn48OGD0BVsmnISCVAzcn2hVUkI+T/CPg9fSsoF7k8jwAmpHiWRDFu2bBkKCwtx4sQJDBgwACoqKkyHRBo5TpPd1feFAl+nL0fSHAmbLghgAeB/gKcR4IRUj5JIhj179gyrV6/GuHHjmA6FNAGCmuwqoy9H0hRwHpaS8kuRUVAONQUWDJVlhXaHETaIr087WbzIK6V+2YTUACWRDNPS0mI6BNKECGqyq4i+HElTIOhh6d0X4N6nUqH9foUN4tvSvzUAUL9sQmqAkkiG/fTTT9i1axdcXFygrKzMdDikkRPWZNdalgUrHQWJ+nKkkeOkpqp6WBLW77e6QXzUT5gQ8VESybCcnBy0bNkSvXr1wsSJE6GtrQ1pad5mFxaLhR9//JGhCEljIqzJzkpHQewvyfpM8mjkOKkN4f0bvxPW75cG8RFStyiJZNi6deu4/x8YGChwH0oiiajqat7N+k7yaOQ4qQ1hD0sc1O+XkIZBSSTDHj16xHQIpAmpq3k36zvJE1aTRCPHJZ8kdEMQ9LDEQf1+CWk4lEQyjLMUISGV1fTLui6a7IQleTc+FMH6n8xaJw+StNwlEZ2kdEOo+LCUnF+K9IJyqCuwYFDF6GxCSN2jJJIQCcT0l7WwJC+zsByZacW1joeWu2ycJKkbAvVvJIR5lEQ2MGtra0hJSeHcuXOQkZGBjY1NtcewWCxcunSpAaIjkoLpL+uqmgvrIp76WO4ytYCFrTezabR3PaJuCISQiiiJbGBsNhvl5f+3zFZ5eTlYLFa1x5Dmhakv64pN6CatZdBFRQb5JWy8yC1BZiH/+7A28dRlTdLb/BIsfCaPlMIC7jZhNaWS0KevsaJuCISQiiiJbGBhYWFV/kwIwMyXtaAmdAMlaW6NYcibAr5jJCV52HQ/HymFUjzbBNWUMt1NoLGjbgiEkIqkqt+FENLQVvdSgoESb4JW31/WVTWhMxGPOEStua2qjKR6nG4IjoYtMERTDo6GLUROwN/ml2DOzWxY/5OJOTez8Ta/pAEiJoTUJ6qJJEQCCGpires+g9WpKhGrjz6MdUnUmlvq01d7NemGQDXAhDRNlEQSUsfE7XNX1RdsQ44+rS4Rk+TRsKt7KSH2w1eeJm1BNaXUp48ZTA8UI4TUD0oiCalDNalxkZQv2Mbc301PSRZ7zIpwIqdtlTWljbmMjRnVABPSNFESSUgdqklCKClfsJLeZF0d7RZsHOxeddLd2MvYWFENMCFNEyWRhNShmiSEkvQFK8lN1nWlOZRR0lANMCFNEyWRhNShmiSE9AVb/2huSGZRDTAhTRMlkQyrbsUaFosFBQUFaGlpYciQIZg4cSJkZOjPJqlqkhDSF2z9opHBkoFqgAlpeigbYVh5eTk+fvyIpKQkqKioQFdXFwDw7t075ObmwtDQEMrKyrh79y6CgoLw559/4uLFi1BVVWU48qajLmupapoQ0hds/ZGUgUuEENLU0GTjDFu1ahVycnKwd+9evHr1Cjdv3sTNmzfx6tUr+Pn5IScnB1u3bsXr16+xZ88evHjxAuvXr2c67CaDU0sV8qYAt9KKEfKmAJOuZtVqImROQhg6Tg0Hh7Wl2i6GScrAJUIIaWooiWTYmjVrMHPmTEydOhXS0v/Xb05aWhrOzs6YPn06fv31V7BYLEyfPh0zZszAtWvXGIy4aaEVTJo+SRq4RAghTQklkQx79uwZdHR0hL6uo6ODp0+fcn82NzdHTk5OQ4TWLFAtVdMn6Us2EkJIY0VJJMM0NDRw4cIFlJXxJy1lZWU4f/481NXVuduys7PRpk2bhgyxSaNaqsar8lrMqQUsgfvVZr1nQgghwtHAGoZ5enpi+fLlGDVqFFxcXGBgYAAAePPmDYKCgvD48WP4+Phw979w4QJ69erFVLhNDk2v0zgJGnEdqyCPMIMSgckhDVwihJC6R0kkw+bMmQMpKSls3rwZP/30E1is77UpbDYbbdu2hY+PD+bMmQMAKCoqwubNm7kjuKsSEBCAXbt2IT09HSYmJvD29sbAgQOrPS42NhbW1tbo3LkzYmNja1e4RoCm12mcBPVlTSmUanYjrmn+S0IIkyiJlACzZ8/GrFmz8ODBA7x//x7A976QPXv2hKzs/30hyMvLY/DgwdWe79y5c/Dy8sL27dvRv39/BAQEwNHREXFxcVX2v8zNzcW8efMwbNgwfPz4sfYFaySolqrxob6sNP8lIYR51CdSQsjKyqJfv35wcHCAg4MD+vXrx5NAisPPzw/Ozs5wcXGBsbExfH19oaGhgcDAwCqPW7hwIaZNm4a+ffvW6LqENBTqy0ozCxBCmEc1kRLixYsXSE5ORm5uLthsNt/r06ZNE+k8xcXFePjwIRYtWsSz3dLSEvHx8UKPCwgIQGZmJn755Rds3bpVvOAJaWCC+rJ2UChvVn1ZqTaWEMI0SiIZlpSUhLlz5+LevXsCk0fg+9KHoiaRWVlZKCsrg5qaGs92NTU1ZGRkCDzm2bNn8PHxwb///sszV2V1EhMTRd63JvtLMioL8/7szMK+dzLILJaCmlw55umWojgtGYlpTEdWN6r7uyiWyQLgb61gFX3F1L+/IrNICmry338v2i0E31saSmN9jwnTlMpTsSxGRkYMRkIaI0oiGbZkyRI8f/4c3t7eGDBgAFRUVBr0+kVFRXBzc8PGjRuhr68v1rHi3HASExObzA2KyiIZjAAM7/5/PzfmslQmSll8NEuQUKlPZIeWLLwplkdKTvn/3yKNhEIFRvtJNqW/C9C0ytOUykKYQUkkw+Lj47F06VJ4eHjUyflUVVUhLS2NzMxMnu2ZmZk8801ypKWlISEhAZ6envD09ATwfT1vNpsNVVVVhISEwNLSsk5iI4TUHUEzC3wtKcfl90U8+9E64YSQ+kJJJMNUVVWhrKxcZ+eTk5ODubk5IiMjMWnSJO72yMhI2Nra8u2vpaWFmJgYnm2HDh1CZGQkjh8/LtJ0QoQQZlSeWcD6n0yB+1E/SUJIfaAkkmFubm4IDg7GnDlzxOqPWBVPT094eHigd+/esLCwQGBgINLS0uDq6goA3FrP/fv3Q1ZWFqampjzHt2vXDvLy8nzbCSGSjUatE0IaEiWRDNPX10dpaSkGDRqEadOmQVtbW2AyaWdnJ/I57e3tkZ2dDV9fX6Snp6NLly4IDg7m1iqmpKTUWfyEEMlBKzARQhoSKzc3l9lhe82cKOtgs1gsZGdnN0A09acpdeCmskgmKst3nFVsJGUFpqb0dwGaVnmaUlkIM6gmkmGhoaFMh0AIaUJoBSZCSEOhJJJhoixjSAghhBAiaWjZQ0IIIYQQIjaqiWxgnp6eYLFY2LlzJ6SlpblzM1aFxWJhz549DRAdIZKJ08/v47cytJeAfn6EEEIoiWxwUVFRkJKSQnl5OaSlpREVFQUWi1XlMdW9TkhT9ja/BJMqrcxyN7OY0VVYCCGEUBLZ4J48eVLlz4QQXpvu5/MkkACtwkIIIZKA+kQSQiTaRyGrrdAqLIQQwiyqiSSESLSarsJC/SgJIaR+URLZwNq0aVOjPo6NfbJxQmqqJquwUD9KQgipf5RENrDly5fzJZF///03EhISYGlpiU6dOgEAXr16hYiICJiYmGDChAlMhEqIRNBTksWFMapircJC/SgJIaT+URLZwFauXMnz85EjR5CdnY34+HgYGhryvPbq1SvY2Nigffv2DRkiIRJH3FVYqB8lIYTUPxpYw7Bdu3bB3d2dL4EEgE6dOsHd3R07d+5kIDJCGq+a9qMkhBAiOkoiGfbhwwfIyAivEJaWlsaHDx8aMCJCGr/VvZRgoMSbMFbXj5IQQoh4KIlkWJcuXRAQECAwUUxNTcWhQ4dgamrKQGSENF6cfpSOhi0wRFMOjoYtaFANIYTUMeoTybDNmzfDwcEBvXv3xrhx47jN2m/evMGVK1fAZrNx4MABhqMkpPERtx8lIYQQ8VASybABAwbg+vXr+P3333HlyhUUFBQAAFq0aAFLS0usXLkSZmZmDEdJCCGEEMKLkkgJYGpqihMnTqC8vByfPn0CALRr1w5SUtTbgBBCCCGSiZJICSIlJQV1dXWmwyCEEEIIqRYlkRIgNzcXZ86cQXJyMnJzc8Fms3leZ7FY2LNnD0PREUIIIYTwoySSYeHh4XBxccHXr1+hpKQEFRUVvn1qskwiIYQQQkh9oiSSYatXr4a6ujqOHTtGA2gIIYQQ0mjQyA2GvXnzBh4eHpRAEkIIIaRRoSSSYR07dsSXL1+YDoMQQgghRCyURDJs1apVCAwMRHJyMtOhEEIIIYSIjPpEMiwiIgJt2rSBhYUFhg4dCm1tbUhL8675y2KxsG3bNoYiJKRxSC1gYevNbHz8Vob2Lb+vk03LHBJCSP2hJJJhgYGB3P+/fv26wH0oiSSkam/zS7DwmTxSCgu42+5mFtN62YQQUo8oiWRYTk4O0yEQ0uhtup+PlELe3jlJ+WXYdD+f1s8mhJB6Qn0iCSGN3sdvZQK3pwnZTgghpPYoiZQQN27cwMaNG/Hjjz/i5cuXAIAvX77g9u3byM3NFft8AQEB6N69OzQ0NDBs2DDExMQI3ffWrVuwsrKCgYEBNDU10bdvX+zevbumRSGkwbVvKS1wu6aQ7YQQQmqPmrMZVlBQgBkzZiAyMpK7zcHBAZ07d4acnBxcXFwwZ84crFixQuRznjt3Dl5eXti+fTv69++PgIAAODo6Ii4uDjo6Onz7KyoqwsPDA6ampmjRogXi4+Px008/oUWLFnB3d6+TchJSn1b3UkLsh688TdoGSt8H1xBCCKkfVBPJsI0bN+LWrVs4cOAAnjx5wrNutpycHCZNmoQrV66IdU4/Pz84OzvDxcUFxsbG8PX1hYaGBs8gnorMzc3h4OCALl26QF9fH05OTrC0tERsbGytykZIQ9FTksUesyI4GrbAEE05OBq2oEE1hBBSzyiJZNiFCxfg7u6OyZMno0WLFnyvGxkZiTWHZHFxMR4+fAhLS0ue7ZaWloiPjxfpHI8ePcKdO3cwaNAgka9LCNO0W7BxcFhbhI5Tw8FhbSmBJISQekbN2QzLysqCsbGx0NdZLBYKCwvFOl9ZWRnU1NR4tqupqSEjI6PKY01NTfHp0yeUlpZixYoVcHNzq3L/xMREkeOqyf6SjMoimagskqkplQVoWuWpWBYjIyMGIyGNESWRDOvQoQMSEhKEvh4XFwdDQ8MGieXy5cv4+vUr7t69i7Vr10JPTw9Tp04Vur84N5zExMQmc4OiskgmKotkakplAZpWeZpSWQgzqDmbYY6OjggKCuLpf8hisQAAhw4dwoULFzBt2jSRz6eqqgppaWlkZmbybM/MzIS6unqVx+rr68PMzAwuLi7w9PTEli1bxChJ1ZrSjYrKIpmoLJKpKZUFaFrlaUplIcygmkiGLV26FPfu3YO1tTU6deoEFosFLy8vZGdnIz09HWPHjsWCBQtEPp+cnBzMzc0RGRmJSZMmcbdHRkbC1tZW5POUl5ejuLhYnKIQQgghpBmhJJJhcnJyCAkJQUhICC5cuAAWi4XS0lL06NEDdnZ2cHJy4tZMisrT0xMeHh7o3bs3LCwsEBgYiLS0NLi6ugIAPDw8AAD79+/n/ldPT4/7VHr79m3s2bMHs2fPrsOSEkIIIaQpoSRSQjg6OsLR0bFOzmVvb4/s7Gz4+voiPT0dXbp0QXBwMHR1dQEAKSkpPPuXlZVh3bp1ePfuHWRkZKCvr4+1a9dWO7CGEEIIIc0XKzc3l139bqQhPH36FO/evQMA6OrqomvXrgxHRAghhBAiGNVESoALFy7gt99+Q2pqKneycRaLBS0tLWzYsAH29vYMR0gIIYQQwouSSIadOnUK8+fPh5GREdavX49OnToBAF69eoWjR4/C3d0dxcXFVU61QwghhBDS0GiKH4Zt27YNvXv3RlRUFBYtWoRx48Zh3LhxWLRoEaKjo2Fubo5t27YxHWaNBQQEoHv37tDQ0MCwYcMQExPDdEg1cvv2bUydOhVdunSBiooKTpw4wXRINfLHH39gxIgR0NHRQceOHeHk5ITnz58zHVaNHTx4EAMHDoSOjg50dHQwevRoXL16lemwau2PP/6AiooKfvnlF6ZDqRFvb2+oqKjw/OvcuTPTYdVYWloa5s2bh44dO0JDQwMWFha4desW02GJrVu3bnx/FxUVFUyZMoXp0EgjRUkkw1JSUuDo6AgFBQW+1xQUFODk5ITU1FQGIqu9c+fOwcvLC8uWLUNUVBT69esHR0dHvH//nunQxPb161eYmppiy5YtApenbCxu3bqF2bNn4+rVq7h06RJkZGQwadIk5OTkMB1ajWhpaWH9+vW4efMmIiMjMXToUEyfPh1Pnz5lOrQa+++//3DkyBGYmZkxHUqtGBkZISEhgfuvsT5A5ubmYsyYMWCz2QgODkZ8fDy2bt3KtypYYxAZGcnzN7l58yZYLBbPdHCEiIOasxlmYmKCjx8/Cn39w4cPVS6LKMn8/Pzg7OwMFxcXAICvry/Cw8MRGBiItWvXMhydeKysrGBlZQUAYs3bKWnOnTvH8/P+/fuhq6uLuLg4jBs3jqGoam7ChAk8P//22284dOgQ/vvvv0Y5MC0vLw9z5szBnj174OPjw3Q4tSIjIwMNDQ2mw6i1Xbt2QVNTkzslGvB9YYbGqF27djw/Hzt2DEpKSrCzs2MoItLYUU0kwzZs2ICgoCCcP3+e77WzZ8/i6NGj2LhxIwOR1U5xcTEePnwIS0tLnu2WlpaIj49nKCpS2ZcvX1BeXg4VFRWmQ6m1srIynD17Fl+/fkW/fv2YDqdGlixZgokTJ2Lo0KFMh1JrycnJMDExQffu3eHm5obk5GSmQ6qRsLAw9O7dG66urujUqRMGDx6MAwcOcAdBNlZsNhvHjh2Dk5NTo25dIcyimkiG7d69G6qqqpg9eza8vLxgYGAAAEhKSkJmZiY6duyIXbt2YdeuXdxjWCwWgoODmQpZJFlZWSgrK+Nr8lFTU0NGRgZDUZHKvLy80K1bt0abdAHAs2fPYGVlhcLCQrRq1QrHjx9vlE3BQUFBePPmDQ4cOMB0KLXWp08f+Pv7w8jICJ8+fYKvry+srKwQFxeHtm3bMh2eWJKTk3Ho0CEsWLAAS5YswZMnT7BixQoAwNy5cxmOruYiIyPx9u1bzJo1i+lQSCNGSSTDXrx4ARaLhQ4dOgD43nwNAPLy8ujQoQOKioqQkJDAc4y4K9gQIsivv/6KuLg4XLlyBdLS0kyHU2NGRkaIjo7G58+fcfHiRcyfPx9///03TE1NmQ5NZImJidiwYQOuXLkCWVlZpsOptdGjR/P83KdPH5ibm+Ovv/7CwoULGYqqZsrLy9GzZ09uF5wePXrgzZs3CAgIaNRJZFBQEHr16oVu3boxHQppxCiJZNiTJ0+YDqFeqKqqQlpaGpmZmTzbMzMzoa6uzlBUhGPlypU4d+4cQkNDG23/Lg45OTkYGhoCAMzNzXH//n34+/tjz549DEcmujt37iArKwv9+/fnbisrK0NMTAwCAwPx4cMHyMvLMxhh7SgqKsLExARv3rxhOhSxaWho8PVL79y5M9/KX41JZmYmLl++3Khn/iCSgfpEknohJycHc3NzREZG8myPjIyEhYUFQ1ERAFixYgXOnj2LS5cuNeppV4QpLy9HcXEx02GIZcKECYiJiUF0dDT3X8+ePeHg4IDo6GjIyckxHWKtFBYWIjExsVEOtOnfvz9evXrFs+3Vq1fQ0dFhKKLa++uvvyAvLw8HBwemQyGNHNVESpjo6GgEBwcjLS0NnTt3xrx58xrtzcrT0xMeHh7o3bs3LCwsEBgYiLS0NLi6ujIdmti+fPnCrUUpLy9HSkoKHj9+jDZt2jSqv8/PP/+M06dP4/jx41BRUUF6ejoAoFWrVlBUVGQ4OvGtW7cOVlZW0NbWxpcvX3DmzBncunVL4vsMV8aZr6+ili1bok2bNo2qWZ5j9erVGDt2LDp06MDtE/nt2zdMmzaN6dDEtmDBAlhZWWHbtm2wt7fH48ePceDAAfz2229Mh1YjbDYbR48ehb29faP8zBPJQmtnM2DLli3YuXMnnjx5wjPlwokTJ7Bo0SKeUX/t2rVDeHg4dHV1mQi11gICArBz506kp6ejS5cu2Lx5MwYNGsR0WGKLjo6GjY0N3/Zp06Zh7969DERUM8JGYa9YsQIrV65s2GDqwPz58xEdHY2MjAwoKyvDzMwMP/74I0aOHMl0aLU2YcIEmJqawtfXl+lQxObm5oaYmBhkZWWhXbt26NOnD1atWgUTExOmQ6uRq1evYsOGDXj16hU6dOiAOXPmwMPDo1H2T4+KioKtrS3Cw8PRu3dvpsMhjRwlkQyYMGEC36onRUVFMDIygpSUFI4ePYrevXvj2rVrWLBgAZycnLBjxw7mAiaEEEIIqYT6RDLgzZs3MDc359l28+ZN5Ofn48cff8TQoUPRqlUr2NnZYcqUKbhx4wYjcRJCCCGECENJJANycnKgqanJsy06OhosFgtjxozh2W5ubo60tLSGDI8QQgghpFqURDJAQ0ODb6nD2NhYtGzZkq/PkJSUVKMfmUkIIYSQpoeSSAb07t0bJ0+eRG5uLgDg6dOnePDgAYYNG8Y36XNCQgK0tbUZiJIQQgghRDia4ocBK1aswLBhw9C7d28YGxvjyZMnYLFYWLJkCc9+bDYbf//9N9/604QQQgghTKOaSAYYGxvj0qVL6N27Nz59+gQLCwucO3cOffv25dkvOjoaioqKsLW1ZShSQgghhBDBaIofQgiRYN++fcOqVatw+fJlpKenY968ediyZQvTYRFCCNVEEtJcnDhxgrsyioqKClRVVWFqaooFCxbgw4cPTIdXax8/foS3tzceP37MdCh1ys/PD0eOHMGsWbOwf/9+TJ06tdpj4uPj4ebmBjMzM6irq0NHRwcjRozA77//zve35sxbq6Kiwl2BqU+fPpg7dy7fsqUc3bp143kvaWtrY+TIkTh58mSdlJkQ0jhQn0hCmhkvLy8YGBigqKgIcXFxOHXqFG7fvs2dIaCxSktLg4+PD3R1ddG9e3emw6kzt27dQteuXbFq1SqR9vfx8YG3tzf09fXh5OQEfX19FBUVcZfrO3LkCBITE3mO0dTUxPr16wF8r/l88+YNQkNDERwcDDs7Oxw4cACysrI8x3BWBwK+/+6PHDmC+fPno6CgAG5ubnVQckKIpKMkkpBmZuTIkdz+t7NmzUKbNm3g5+eHy5cvY/LkybU697dv3xp1IiqJMjMz0aZNG5H2vXDhAry9vWFnZ4f9+/fzTQ+2efNm7Ny5k+84JSUlODk58Wxbt24dVqxYgYCAAOjq6nKTTA5NTU2eY6ZOnYpevXrB39+fkkhCmglqziakmRs6dCgA4O3bt9xtISEhGDFiBDQ1NaGnpwcXFxckJyfzHDdhwgT07dsXjx8/hrW1NbS0tLBs2TIA32cWOHjwIAYPHgxNTU0YGhpi0qRJiImJ4TmHONd58eIFbGxs0L59e3Tp0oUnGYqOjsaIESMAAJ6entxmVm9vbwDfp9FasGABzM3NoaGhAUNDQ7i5ueH9+/d8v4+nT59i/Pjx0NTU5K5dfezYMaioqPD8jgAgIiIC48ePh7a2NrS1teHg4CByc3pWVhYWL16Mzp07Q0NDAwMGDEBQUBBPmVRUVPD8+XPcvn2bW6bKMVS0efNmqKqqYvfu3QLnl1VSUsLq1atFik9aWho+Pj4wMTHBwYMHkZeXV+X+Ghoa6Ny5c5XxEUKaFqqJJKSZS0pKAgC0bdsWAPDnn39iw4YNmDhxIqZPn47c3FwcPHgQY8eOxa1bt9CuXTvusXl5eXBwcICtrS0mT56M1q1bAwAWL16Mo0ePYuTIkXB2dgabzcadO3cQExODgQMHin2dz58/Y/LkybC2tsakSZNw8eJFrF27Fqamphg9ejSMjY3x66+/YvPmzfjhhx8wYMAAAN+bXAEgMjISr169wtSpU9G+fXskJSUhMDAQ9+7d42nG//DhA2xsbAAAS5YsQatWrXDs2DG+plzgewI8d+5cjBgxAmvWrEFxcTGOHDmC8ePHIyIiAp07dxb6Oy8sLISNjQ1evnwJd3d3GBgYICwsDIsXL0Z2djZ++uknGBsbY//+/diwYQNatWrFTdAr/l4qev36NV6+fIlZs2ZBUVGxuj+7SKSlpeHg4IDff/8dcXFxfCtqVVRSUoLU1FTu+4gQ0vRREklIM/P582dkZWWhsLAQ8fHx2Lp1K1q0aIExY8bg/fv3+P333+Hl5YUVK1Zwj3FwcED//v3h7++PNWvWcLdnZGRgy5YtmDdvHndbdHQ0jh49Cnd3d2zbto273dPTE2z298kgxL1Oeno69u7di2nTpgEAZs6ciW7duuHYsWMYPXo01NXVMXr0aGzevBl9+/bla5qdPXs2Fi1axLNt3LhxGDNmDEJDQ7n779ixAzk5OYiMjETPnj0BADNmzECvXr14jv369St++eUXODs7w8/Pj7t95syZ6NOnD7Zu3YqAgAChf4MjR47g+fPn8Pf3h7OzMwDA3d0d9vb22LJlC1xcXKCurg4nJyfs2LEDbdu25StTZQkJCQCALl268L2WlZXF87OysrLAxFgQzvk4DxscJSUl3PN+/PgRf/75JzIyMjB37lyRzksIafwoiSSkmXFwcOD52cTEBD4+PtDS0oK/vz9KS0thb2/Pk3goKyvD1NQU0dHRPMfKyMjghx9+4Nl26dIlAMDKlSv5rs1isQAAoaGhYl2nRYsWPEmUnJwcevXqxdf0LUzFfppfvnxBcXExOnXqhNatW+Phw4fcc4eHh6N3797cBBIA2rRpA0dHRxw4cIC7LTIyErm5uXB0dORL0AYMGMAXf2XXrl1Du3bteMokLS2N+fPn4+bNm7hx4wbs7e1FKhtHfn4+AKBVq1Y82wsLC9GxY0eebaGhoRgyZIhI5+XUan758oVne1RUFM95ZWVlMXv2bL6+k4SQpouSSEKaGR8fHxgbG0NeXh4dOnRAhw4duMnd69evAYBv4nsOfX19np81NTWhoKDAsy0pKQnq6upQVVUVGoO412nfvj2kpHi7cKuoqODZs2dCr1FRbm4u1q1bh4sXLyInJ4fntc+fP3P///3793y1jgBgaGgoMP5JkyYJvF7lWCt7//49DA0N+ZY5NTY2BgC8e/euyuMFEZbsycnJ4cKFCwCAmJgYbN26Vazzcs5XuYm8Z8+eWLt2LVgsFlRVVaGvr19nzeiEkMaBkkhCmplevXoJTd7Ky8sBAGfOnIGMDP/toXLC2KJFixrFIO51KidbHJzm8er88MMPiI+Ph6enJ7p37w4lJSWwWCy4ublxYxEH5xh/f39oaWmJfXx94CSg//vf/3i2S0lJYfjw4QBQ7eAYQTjnq5xIt23blnteQkjzREkkIYTLwMAAANChQweYmJjU+BzXr1/Hp0+fhA4CqYvrVMapTa0sNzcXN27cgJeXF7y8vLjbCwsLkZuby7Ovjo4O3rx5w3eOyts48bdr165GiZSOjg4eP36MsrIyngT55cuXAABdXV2xz9mpUyd06tQJYWFh8Pb2rpNawbKyMpw5cwYtW7ZE//79a30+QkjTQlP8EEK4bG1tIS0tja1btwqs5avc/0/YOQAIXJqPc866uE5lnH6PlRNDTtNy5ev4+/vz1UJaWlri3r17ePDgAXdbTk4OQkJC+PZr3bo1/vjjDxQXF/PF8unTpypjHTNmDD59+sRz3vLycuzbtw/y8vI1ruFbuXIlsrOzsWjRIpSUlPC9LmrNLfA9gVyxYgUSEhLg4eEBZWXlGsVECGm6qCaSEMKlr6+PdevW4bfffsP79+8xYcIEtG7dGm/fvsXly5dhZ2cncMBMRUOGDIGzszMCAgKQlJSEUaNGAQD+++8/mJmZYdmyZXVyncoMDAygoqKCwMBAKCoqQlFREV26dIGpqSkGDx6MXbt2oaSkBDo6OoiNjUVMTAzfdDSLFy9GcHAwHBwc4OHhgZYtW+LYsWPo0KEDcnJyuLWdysrK+PPPPzFnzhwMHToUDg4OUFdXx/v37xEeHg4TExPs3btXaKwuLi4ICgrCokWL8PjxY+jr6yMsLAw3b97E2rVrazxNjoODA168eAFfX1/cv38fkydPhp6eHgoKCpCQkMCtVazcXzU/Px+nT58GABQUFHBXrElKSoKDg4PIq+UQQpoXSiIJITwWLVoEQ0ND+Pn5Ydu2bSgvL4eWlhaGDh0qdCBJZXv27IGZmRmOHTuGtWvXQlFRET169MCgQYPq9DoVycrKYv/+/Vi/fj1+/vlnlJSUYMWKFTA1NUVAQAC8vLxw+PBhlJaWYuDAgbh06RImTpzIc44OHTogNDQUK1aswB9//IF27dph9uzZaNmyJby8vHj6atrb20NTUxN//PEH9uzZg6KiImhqasLCwgKurq5VxqqgoIDQ0FCsX78eISEhyMvLg6GhIXbu3AkXFxexy17RqlWrMGLECBw8eBAnT55EZmYmFBQU0LFjR8ydOxeurq7Q1tbmOSYtLQ0eHh4Avg+g0dDQQN++ffHHH39wJ3EnhJDKWLm5uaK3bxBCSDPk5eWFI0eOIDU1VeggH0IIaW6oTyQhhFRQUFDA83N2djZOnz6N/v37UwJJCCEVUHM2IYRUMHr0aAwePBjGxsbIyMjAsWPHkJ+fj19++YXp0AghRKJQEkkIIRVYWVnh4sWLCAoKAovFQo8ePbBnzx6e/pyEEEKoTyQhhBBCCKkB6hNJCCGEEELERkkkIYQQQggRGyWRhBBCCCFEbJREEkIIIYQQsVESSQghhBBCxPb/AOxJOuXUns3QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3)) \n",
    "\n",
    "plt.scatter(mex_gdp, mex_usd)\n",
    "\n",
    "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=30)\n",
    "\n",
    "plt.ylabel('Spending in USD (Billions)')\n",
    "plt.xlabel('Percentage of GDP')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd5d171b",
   "metadata": {},
   "source": [
    "Now we have a better understanding of the data. \n",
    "\n",
    "In addition to this information, we can add a color scheme that will color each data point based on the year of collection. This adds another dimension of analysis, using year as a feature; the context of the spending relationship can be examined over time.\n",
    "\n",
    "The `plt.scatter()` function minimally needs two arguments - *x* and *y* - which are array-like variables. Other optional arguments include `c`, which determines how to color the data points; `alpha`, which sets the opacity of the data points; and `cmap` which sets the Colormap used to color the data points. \n",
    "\n",
    "The `plt.colorbar()` function displays a scale of the Colormap based on the feature used to color the data, which in our case is the year of collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "23a45386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "\n",
    "mex_years = mex_gdp.index\n",
    "\n",
    "plt.scatter(mex_gdp, mex_usd, c=mex_years, alpha=0.4, cmap='winter')\n",
    "\n",
    "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=30)\n",
    "\n",
    "plt.ylabel('Spending in USD (Billions)')\n",
    "plt.xlabel('Percentage of GDP')\n",
    "\n",
    "plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20146a6e",
   "metadata": {},
   "source": [
    "We used the years of the dataset (which we defined as the index earlier in this chapter) as our `c` argument to color the data points based on the year of collection. We used the *winter* Colormap as our `cmap` argument, but many other Colormaps are available for your choosing. A list of other possible Colormaps to explore can be found <a target=\"_blank\" href=\"https://matplotlib.org/stable/tutorials/colors/colormaps.html\">here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ba35e7",
   "metadata": {},
   "source": [
    "## Line graphs\n",
    "\n",
    "Next, we'll examine the use of a line graph as another visualization tool for numerical data. Line graphs are used to visualize sequential numerical data. By using line graphs, we can easily see trends within data over time.\n",
    "\n",
    "Let's examine the spending (in USD) on the military in Canada in the 21st century (2000-2020). We can extract this data and call it `can_usd`.\n",
    "\n",
    "In Python, visualizations can be made using DataFrame methods or by directly calling functions from the `pyplot` library in `matplotlib`. We can quickly create a line graph using `plot()` method on the `can_usd` DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b26501e3-def6-48f3-80e5-d238e8220f9f",
   "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>CAN-USD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>8.299385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>8.375571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>8.495399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>9.958246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>11.336490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CAN-USD\n",
       "Year           \n",
       "2000   8.299385\n",
       "2001   8.375571\n",
       "2002   8.495399\n",
       "2003   9.958246\n",
       "2004  11.336490"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "can_usd = military[['CAN-USD']].loc[2000:2020]\n",
    "\n",
    "can_usd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e9499c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "can_usd.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2ccd60",
   "metadata": {},
   "source": [
    "The same plot can be made using the `pyplot` function `plt.plot()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d5f4f198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3)) # Set figure dimensions\n",
    "plt.plot(can_usd)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e199c81c",
   "metadata": {},
   "source": [
    "Notice how the `plot()` method automatically uses the `Year` column to label the x-axis, while the `plt.plot()` function does not. This can simply be remedied using the `plt.xlabel()` function. We can add a y-label as well using `plt.ylabel()`.\n",
    "\n",
    "Also notice the increments of the x-axis for the second plot is listed as floats. To change these increments to integers, we can create an array consisting of years of the desired increments and then use it as an argument for the `plt.xticks()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab3bb392-3509-4adc-b1ae-4460db515862",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2000, 2005, 2010, 2015, 2020])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = np.arange(2000, 2021, 5)\n",
    "\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "88bcffa6-027e-4208-aeab-9c61649b913c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3)) \n",
    "plt.plot(can_usd)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('USD (Billions)')\n",
    "plt.xticks(years)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90f500d",
   "metadata": {},
   "source": [
    "We can see from the graph that Canada's spending on the military has increased overall since 2000. The country had a period of time (around 2011 to 2017) where military spending was decreasing consistently.\n",
    "\n",
    "### Visualizing multiple trends using line graphs\n",
    "\n",
    "We can view trends for multiple variables at once. Let's add the data for Mexico as well to see the country's spending in the 21<sup>st</sup> century."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3eff4b2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ygtz9JkqcGsdtTo7mO/kp38mxfBc/5Ts5WuDkF1vDhRqLSSE53H0KqkO4vuyxnvbhBtqF6jHolBZTjzaHymPb8nntYGGlbXoFZvYJZ2afcIy6wPzybah6vO27HN4/enaAv2A9bJwYT8eIpt+JOxA0xuex2pqrT/BoTqFFiEBzyOpuZfnxTOVWlnizjmeHWRiX5D1OSLBBIdViJLWK0c9KnBrpNnegOZrv5Gi+i6MF7uW6hpryVpMOZYGkPKS0D3M/Lg9SAsKMOhYNtTA+KZi71lv5tcLUAC4NFu4s4POf3X1fukfJ39yqfPNriVdoAZjdN0JCS4Crde1lZmZy6tQp+vTp41l3+PBhXnzxRfLy8rj22mu56qqr/FJIIcT5uVSNF/fZ+PuO/Co7uU7uaObJwZF1nq8n2KDQ1WKkaxWhptSlkV4WYo4WuDwtNjaHSmJoxXDi3Woiam9U22A2TGzFg1srz3i9K9vBpasymdwxhKHxJobGm+gUYZBTILgvn79vo9VrXc9oI3f1lGH9A12tg8vMmTPJyspi9erVAOTm5nLllVeSl5eH2Wxm5cqVvPPOO4wdO9ZvhRVCVO2nPCd/Xp/L5kx7pW0xQToWD7NwTbLvR2MN0it0sRjpIuPU+5UlSMeLI6KY0D6YezdaK1waDnYV3jlS5Ak1scE6BrUyMbSVicHxJvrGmDDpW16Qmb+jgBO2swleAZ4bZgnYU2virFoHlx9++IHp06d7lt977z2sVivff/89KSkpXH311Tz33HMSXIRoQKqm8eqBQh7blk9xFZcMTUgK5plhFuKayoQ9ol6uTDIzuJWJmZvy+PB41RMznilR+exECZ+dcA9eF6yHfrHu1pjBrYIY3MrU7E/J7Txj58X9Nq91t3cPpX9cYHdoFm61Di45OTme0WwBPv/8c4YNG+a5NPm6665j3rx5vi+hEKJK6QVO7lqfy7rTlVtZLCaFp4ZYmNzRLKcNmpmYYD3/GhXNVUeLmLUljzPnucS8xAWbMuxsyrAD7i/zbhYDQ8qCzJB4E+3D9M3m/4lT1fjLBu9h/duF6nm4f0TjFUr4VK2DS1RUlGeQuKKiIrZs2cLs2bM92xVFobS01PclFEJ40TSNNw8X8fDWPGxVDNU/pl0Qzw6PonWItLI0Z9d2DGFckpmtmaVsyrCzJdPOD5n2Kv9PnOuA1ckBq5N/HXKfXkow6xgcb2JIWZDpFW2sd18kTdNwalDi0rC7NEpclN1rlLo0ThYqmAqcRJh0hBkVn53CWbrfxu5zLv9/eqiFMBl5uNmodXAZMmQIr7/+Ol26dOGbb76htLSUcePGebanpaXRunVrvxRSCOH2a6GLv2zI5ZtfK/9IiDAqzBscye87hzSbX8+iZmaDwsg2wZ7pDpyqxr5cB1sy7GzOtLM5o5STRecf9O90scrK4yWsPO4+vRRqUBgQZ6KbxYBLc3fCLnVplKreAcRzr+K97HIv1xyhzLAj4+ySXiHcpBBuVAg36tz3Jvd9hFFHuEkhrHx9he0R5csmBWupyvwdBV6vcm0HM2MSZVj/5qTWweX//u//mDRpEjfeeCMAd911F127dgXA5XKxatUqrrjiCv+UUogWTtM03jlSxNyteeTbK38djGoTxHPDLSSGyWWeLZlBp9AnxkSfGBO3dXf/v/m50OUVZPbnOs97CXuhU+P7U6V8f6rhWtGLXRrFxRqZxQB1nPuhGpEmhQWDZVj/5qbWf+U6dOjAtm3bOHjwIOHh4bRv396zraioiKeeeoqePXv6pZBCtGSni1zcu9HK5z9Xnik41KDwxEWR3NJVWllEZYqikBRmICnMwG87hQBgLVXZlmVnc4adzZmlbM9yVNmxuzl44qJIWknH9GanTj/PDAZDleEkPDyc8ePH+6xQQgj3r+UVx4r562YruaWVv1iGJ5h44eIoksOllUXUniVIx+Xtgj2zIttdGntyHGzKKGVLpjvQ+GpOKb0CwXoFk77sXqeULSsUlpRiV4zkO1QK7Oc7rVR3FyeY+ENKiI+PKpqCOv/FO3jwIMePH8dqtVY5W/S0adN8UjAhWrISp8bdG3L539HKl7wG6+H/BkRye/dQdNLKIurJpHf3ZxkQZ+Iu3IH5WIGLLZl2zhS7MOnPho1gPe7wYagYQtyhJKjs5g4o7jF+aurg6x4qPhHKXrPQqVHg0Ciwq9gcGgUOlfyy5QLH2W0FDg1b+bay0FPgKN9Hxe6Ci+JMvHJJtLRCNlO1Di7Hjh3jtttuY/v27VUGFnA3S0pwEaJ+8u0qv/8mu8rLnC+KM7J0RBSdI2XAN+EfiqLQMcLQoMPiK4pCmFEhzEi9r4ZzqRp6GWSuWav1/8x7772X/fv3M3/+fIYOHYrFYvFjsYRomTKLXUz+MrvS5ZwmHTzUP4K7eoTJH2UhaiCfj+av1sFly5Yt3H///dx+++3+LI8QLdbxAifXfnGGowXeV1R0iTTw5qhouslEekIIUfvgEhMTQ0SEjDwohD/sy3Fw3ZdnOF3s3SlyYJyR9y+PqfPEiEII0VzVeijBP/7xj7z//vu4XL65vl4I4bYpo5QrV2dVCi2XtQniozGxElqEEKKCWre4JCcn43Q6GT58ONOmTaNt27bo9ZX/oE6aNMmnBRSiOVt9ophbvs2h5JzfA9d1MLN0RFSLnNVXCCFqUuvgUnFm6Mcee6zKfRRFkeAiRC29k1bI3RusnDv215+6hbJwcKRc6iyEEFWodXD5+OOP/VkOIVqU5/cU8Mi2/ErrH+oXzsw+4TL+hBBCVKPWweXiiy/2ZzmEaBE0TeP/tuXz3F6b13oFWDzUwi2poY1TMCGECBB1HmHI5XKxa9cuTpw4AUBSUhJ9+/ZFp5Mpw4WoiVPVuGejlbfTirzWm3Tw6shorkk2N1LJhBAicNQpuHzwwQc89NBDZGRkeEbPVRSF+Ph45s2bJ/1bhKhGsVPjj9/msPqciRLDDApv/yaGkW2CGqlkQggRWGodXD799FOmT59Oly5duP/+++nSpQsAhw8fZtmyZUyfPp2goCCuvPJKvxVWiEBkLVWZ9k02mzK8h/CPDdax/IoY+saaGqlkQggReGodXJ5++mn69u3LZ599RnBwsGf9yJEjufHGGxk7diyLFi2S4CJEBaeLXFz35Rn25Tq91ieG6flwdIzMOSSEEHVU644pBw4cYMqUKV6hpVxQUBBTp07lwIEDPi2cEIHsaL6TMZ9mVQot3S0GvhwfJ6FFCCEuQK1bXMxmM9nZ2dVuP3PmDGazdC4UAmBXtp3JX2aTVeI9Gu6QVibevTwGS5B0ZhdCiAtR67+eI0eO5OWXX2bjxo2Vtm3evJlXXnmFSy+91JdlEyIgrTtVyoTVZyqFljHtgvhgjIQWIYSoj1q3uDz++ONs2rSJCRMm0KdPH1JSUgBIS0tj165dxMfHVzuirhAtxcfpxUz/LofSc4bwv76TmecvjsKok4HlhBCiPmr90y8pKYn169dzxx13YLPZWLVqFatWrcJms3HnnXeybt06kpKS6vTiGzZs4Prrr6dbt25YLBbefvttr+0zZszAYrF43S6//PI6vYYQDeXfhwu5aW3l0HJXjzBeHCGhRQghfKFO47jExsYyb9485s2b55MXLywspHv37kybNo077rijyn0uvfRSXn75Zc+yySSXjoqmRdM0ntlj42/bKw/h/7eBEfylV3gjlEoIIZqnOo+c60ujR49m9OjRANx5551V7hMUFER8fHxDFkuIWlM1jYe25rF0f6HXep0Czw23cEOKDOEvhBC+VG1wWbhwIYqiMHPmTHQ6HQsXLjzvwRRFYdasWT4t4KZNm+jcuTORkZEMHz6cRx55hLi4uGr3T0tL8+nr+/u4omH5sh4zShUWHDGxPlfvtd6kaMxLtTOYk8h/G/+Qz2PzIPXYPPijHsv70VZFsVqtWlUboqKiUBSF06dPYzKZiIqKOu8LKYpCTk7OBRWybdu2PPnkk/z+97/3rFuxYgVms5n27dtz4sQJ/v73v6OqKt9++y1BQQ03RHpaWlqN/4giMPiqHlVNY9nBQh7fnk+Bw/vjE2FU+O/lMQxPkCH8/UU+j82D1GPz0Bj1WG2LS25ubo3LDeG6667zPO7Rowd9+/alV69efPHFF1x99dUNXh4hDlod3LPBypZMe6Vtrcw6VoyOpVe0DCwnhBD+0qh9XOqqdevWtGnThqNHjzZ2UUQLU+rSeGZ3AU/vLsChVt4+KM7EKyOjSA4PqI+UEEIEnID6K5udnc2pU6eks65oUFsySrlno5WDVmelbWEGhccGRvDH1FB0ilzuLIQQ/lZtcLnqqqvqfDBFUVi1alWt97fZbJ7WE1VV+eWXX9i9ezdRUVFERUWxYMECrr76auLj4zlx4gR/+9vfiIuLY8KECXUumxB1lW9XeWJ7Pq8dLKSqjmBjE4NZNCSSdmEBlf+FECKgVfsXV1VVlDr+gtS0Kvv5VmvHjh1eAWn+/PnMnz+fadOmsXjxYvbv38+7775LXl4e8fHxjBgxgn/961+Eh8u4GMK/Vp8o5oFNVk4WVT4vFBes46khFq5JDq7zZ0QIIUT9VBtcPv30U7+/+IgRI7BardVu/+CDD/xeBiEqyihyMWdLHh8eL65y+w0pITxxUSRRMt+QEEI0CmnjFgJ3a+F/0op4+Ic88uyVWw47hOt5dlgUI9vIZc5CCNGYJLiIFu9ovpN7N1r5/lRppW16Be7uGcbsvhGYDXJaSAghGlu1waV8ALq6UBSF7OzsehdKiIbgUDVe2Gtjwc58SlyVt/eNMbJkuIU+MTI/lhBCNBXVBpdZs2ZJx0PRbO08Y+fuDVb25DgqbTPrFR7sH86M7mEYZEZnIYRoUqoNLnPnzm3IcgjRIAodKvN3FPDifhtqFRfBjWoTxDPDLDKQnBBCNFHy11m0GFtydTy1M5N0W+XzQlFBCvMGWbi+k1laGoUQogmrNrhs2LABgOHDh3stn0/5/kI0FVnFLh75IY93fwoGKoeW33Y0M29QJHFmfeUnCyGEaFKqDS4TJkzwmh26fLk6mqbVa3ZoIXzN7tJ45YCNJ3cWkO+ofF6oXaiexUMtjE4MboTSCSGEuBDVBpePP/4YAJPJ5LUsRCD46pcSHtyaR1pe5fmFFOD27qE83D+CMKMMJCeEEIGk2uBy8cUX17gsRFN0JM/Bg1vz+PKXymOyAHS3GHju4igGxsklzkIIEYikc65oFvLsKk/tLOCl/TacVVwtFGFUuLVdKXNHtMGkl863QggRqM7bTp6ens7SpUt59dVXOXXqFABHjhzhxhtvJCUlhTZt2jBmzBjWrFnj98IKcS6XqvHvw4UMXJHBP/dVDi0KcFOXELZfF8/v2joltAghRICrscVl7969jB07lsLCQgD+8Y9/sGLFCq6//npsNhspKSk4nU527NjBlClT+PDDDxkxYkSDFFyITRmlzNmSx67syoPIAQyNN7FgcKRn5FtrA5ZNCCGEf9QYXJ566inCwsJ47733aNWqFX/961+54YYbiI2NZd26dSQkJABw7Ngxxo8fzzPPPCPBRfjdLzYnj23PZ/nRqmdwbheq528DI5jUQcZkEUKI5qbGU0Xbtm3jlltuYfjw4aSkpPDwww9z+vRpZsyY4QktAB06dODmm29m+/btfi+waLmKnRoLd+Zz0QeZVYaWYD3M7hvO1mtbcW3HEAktQgjRDNXY4nL69Gnat2/vWS5/nJSUVGnfpKQkCgoKfFw8IdxjBK08XsLDP+TxS2EVsyEC13Yw8/jACBLDpL+5EEI0ZzX+lVdVFb3+7Gii5Y+r+iWr08l4GML39uQ4mLPFyobT9iq394o2snBwJMMSghq4ZEIIIRrDeX+eVhVSpAle+NuZEhf/+DGfNw8XVTkZYkyQjkcHRHBDSgh6mcFZCCFajPMGlz//+c/cfffdXut++9vfVmphUVXVtyUTLZJD1XjtQCELduaTZ6+cWAwK3NY9lFl9IrAESSufEEK0NDUGl2nTpjVUOUQLp2ka3/xaykNb8zhUxTD9AJe3DWLeoEi6WIwNXDohhBBNRY3B5cUXX2yocogWStM0vvilhEW7CtiWVfV4LJ0i9MwbZGF0uyA5TSmEEC2cXIIhGoVL1fg4vYRFuwvYm1N1YAk3KszqG87t3cJkxFshhBBADcHl2LFjdOjQ4YIOWp/niubNqWosP1rM4t0FHK7mlJAC3JASwiMDImhl1le5jxBCiJap2t6NF110EdOnT2fDhg21Ptj69ev54x//yKBBg3xSONF8lLo03jxUyMAPMrhjXW61oWVsYjBrr4rj+YujJLQIIYSopNoWl6+++oonnniCCRMmEB8fzyWXXELfvn1JTk7GYrGgaRpWq5X09HR27tzJ999/T2ZmJqNGjeKrr75qyPcgmrBip3sSxOf22Pi1qOrB4xRgYrKZ+/uE0ytaOt4KIYSoXrXBpV+/fnzwwQfs2bOH//znP3z22Wf873//A86O46Jp7stV27dvz7XXXssNN9xA9+7dG6DYoqkrcKgsO1jIP/fayCqp+lJ5vQK/7Wjm/t7hcqWQEEKIWjlv59xevXqxcOFCFi5cyKlTpzh8+DC5ubkAREdH07VrV+Lj4/1eUBEYrKUqLx+wsXSfDWsV47AAGHXuPiz39AonOVz6hwshhKi9On1rtG7dmtatW/urLCKAZRW7eHGfjdcOFlLgqDqwBOvh5q6h3N0znLah0n9FCCFE3cnPXVEvJwtdPL+3gDcOFVHsqjqwhBkUpncL5c4eYdLhVgghRL006pjpGzZs4Prrr6dbt25YLBbefvttr+2apjF//nxSU1NJSEhg/PjxHDhwoJFKKypKL3By/0YrfZefZun+wipDS6RJYU7fcPZMSeCxgZESWoQQQtRbowaXwsJCunfvzoIFCzCbzZW2L1myhBdeeIGFCxeyZs0a4uLimDRpEgUFBY1QWgFwJM/Bnety6b8ig2WHCrFX0e82NljHYwMi2PPbBOb0iyBK5hQSQgjhI416qmj06NGMHj0agDvvvNNrm6ZpLF26lHvvvZdrrrkGgKVLl5KSksLy5cu55ZZbGry8LdnxAidP7izg3Z+qnq0ZoE2Ijrt7hnNT1xBCDBJWhBBC+F6T7eOSnp5ORkYGl112mWed2Wxm2LBhbNmypdrgkpaW5pfy+Ou4Td3pUoV//WxgZYYBl1b1sPttglRuSnQwoZULk87Gr8cauJB10FLrsbmRemwepB6bB3/UY0pKSrXbmmxwycjIACAuLs5rfVxcHKdOnar2eTW92QuVlpbml+M2ZaeLXCzeXcAb1ZwOAkiJNHB/73AmdzRj1DX9uYRaYj02R1KPzYPUY/PQGPVY6+Dy3XffsWbNGo4dO4bNZiMsLIyOHTty2WWXcckll/izjKIBnSlxsWSPjdcOVN3hFqBLpIE5fcO5JtmMPgACixBCiObjvMHl5MmT3HzzzWzbts0zUm5Fzz33HIMHD+Zf//oXCQkJPitY+aB2WVlZJCYmetZnZWXRqlUrn72OcLOWqvxzr42X9tuwOasOLB3C9czpF8HkDhJYhBBCNI4ae1Da7XamTJnC9u3buemmm1i9ejXHjx/nzJkzHD9+nNWrV3PjjTeydetWpk6disPh8FnB2rdvT3x8PGvXrvWsKykpYdOmTQwePNhnr9PS5dtVntyZT+/lp1m0u6DK0NIuVM9zwy1svTaeqZ1CJLQIIYRoNDW2uPzvf/9j3759vPrqq0yePNlrW2RkJEOGDGHIkCEMHz6c2267jeXLlzNt2rRav7jNZuPo0aMAqKrKL7/8wu7du4mKiiIxMZEZM2awePFiUlJS6Ny5M4sWLSI0NLRSWUTdFTpUXj1QyJK9BeSWVt3C0jpExwO9w/lDl1CC9BJWhBBCND7FarVWc3ErXH/99eTl5bF69erzHmjcuHFYLBb++9//1vrF161bx1VXXVVp/bRp01i6dCmaprFgwQLeeOMNrFYrAwYMYNGiRQ0+kWNz6kRW4tRYdqiQZ3YXVDv5YVywjnt7h/PHrqGYDc0nsDSnemzJpB6bB6nH5qHJdc7du3cvN910U60OdNlll/Hmm2/W6cVHjBiB1WqtdruiKMydO5e5c+fW6biiMrtL4620QhbtKuBUUdWBJSpI4Z6e4fypWyihRhmHRQghRNNTY3DJycmp9aSKrVu3JicnxyeFEr7jVDX+e6SIJ3cV8LPNVeU+EUaFP/cMY0b3MCJMEliEEEI0XTUGl+LiYoKCgmp1IJPJRElJiU8KJerPpWosP1bMwh35HC2oOrCEGhTu6B7KXT3DZVh+IYQQAeG8l0MXFBSQlZV13gPl5+f7pEACHKpGkbPs5tAodKoUly0Xlt0XV3hc5FTP7u/UKHRoHM5z8FN+1YElWA/TU8O4t3cYscEy8aEQQojAcd7gMnPmTGbOnHneA2mahqI0n46cFR0vUsjPslPq0rCrGqUusKsadpdWtg73vUujVK1pm4a97LnudVDscgePikHEUc1ItfVl0sFNXUN5oHc4CSESWIQQQgSeGoPL7NmzG6ocTdpDh4I4/OP5W52aKoMCN6SE8ECfcBLDmuwsD0IIIcR51fgtNmfOnIYqR5NmVKq9YrxJ0ykwtVMIs/uGkxwugUUIIUTgk2+zWmjoC20U3B1nQ4wKIQb3LdSgYDboPI9Dzr0ZdZ715rJ9ekQZiZdTQkIIIZqRGoNLRkYGR44coU+fPoSFhXnWO51OnnrqKd5//31Onz5Nly5dmDt3LmPHjvV7gRtDe7OGZjQSpFcw6XDf6xWCdApGPQTplLJ17scmvXvZqKu4TSFIBya9gkmnEKR3Pw7We4eTEIOOID3Ntr+QEEIIUR81BpdnnnmG5cuXs3//fq/1jz76KC+99BLh4eGkpqZy6NAhbrjhBj755BOGDBni1wI3hodS7KSktG/sYgghhBAtXo0nQTZt2sSYMWMwmUyedTk5Obzyyit07tyZXbt2sXbtWjZv3kxsbCz//Oc//V5gIYQQQrRcNQaXX375hdTUVK91X375JS6Xi7vvvhuLxQJAUlISv/vd7/jhhx/8VlAhhBBCiBqDS3FxMZGRkV7rNm/ejKIojBw50mt9hw4dyM3N9X0JhRBCCCHK1BhcEhMTOXjwoNe69evXExsbS1JSktf6qkKOEEIIIYQv1RhcrrjiCt5++202btyIqqq89dZb/PTTT0yYMKHSvjt37iQxMdFvBRVCCCGEqPGqogceeICVK1cyYcIEFEVBVVViY2N54IEHvPaz2Wx88skn3HrrrX4trBBCCCFathqDS1RUFOvWrePf//43x48fJykpiT/84Q/ExMR47Xfw4EGmTp3KlClT/FpYIYQQQrRs5x0512Kx8Je//KXGfQYOHMjAgQN9VighhBB+5nKi5OWiWLNRrGdQrNnorNkouWdQ8nJQcs+AORTnwBE4LrkSgkMau8RCADLkvxBCNC9egcQdSnSex+XBJBsl34qinX8eNv2hXZg+fAPHqKtxXHEtWlRsA7wJUR9Kfi76PT+g370F/cGdKA4HarsOuJI6oyZ1Rm3fGbVNezCazn+wJqjG4DJ48OBqtymKQlBQEImJiYwZM4Zp06ZhMEgOEkIIv9M0lFMnMOzbju7no54WE8V6ptaBpC6UIhumT9/B+Pn7OIdejmPcFNR2HX36GqIeVBe6owcx7N6CfvcWdMcPV/o/oD+0C/2hXZ5lTa9HbZPsDjJlYcaV2AnCIhq69HVWY9KIjY2tcc6c4uJiNmzYwKeffspbb73FRx99REiINCcKIYSvKdZs9Pu2o9+/Hf2+7ehyzzR8GVxOjOs/x7j+c5y9BuEYNxVX9/4gc6s1OCUvB/2ereh3b8GwdxtKYUHdnu9yof/5J/Q//wQbvvCsV2PiPWHGVRZotNiEJlXHNQaXTz/99LwHUFWVd999l7vuuovFixfz8MMP+6xwQgjRYhUXoj+4yx1W9m1Hf/K4z19CDbegWWK8bmpULFpkDFqEBcPeHzB+/RFKYX6l5xr2bMWwZyuupM44xk7BOfgykFZ3/3E50f10oKxVZSv69MN+eRlddga67AzYscGzTgsJRU3s3GRONSlWq9UnbYozZsxg27ZtzXLY/7S0NFJSUhq7GKKepB6bh2Zbj06H+4upLKjoju5HUdULOpQWHolqia0cSiyxaFFly5HRYDCe/2ClxRjWf4Hp8/fRZZ6sdjc1Og7HFdfhuHQChISd97DNth59SLFmu0PK7i0Y9m1DKbLV+rmu9l1w9R6Es/dgtMhodD8fRX/iCLryW3ZGvcpWfqpp35S76di7b72OVVc+i8cDBw7kww8/9NXhhBCiedM0dL8cO3v65+BOlNKS2j/dFIyra29c3fqhxrdDs0SjRcXWPpDUVpAZ528m4hx1FfofN2Ja/R76I3sr7abLySLovZcwrfw3jksn4Bg9GS2mle/K0RK4nOiO7Pf0VdGfOFLrp2qh4Th7XoSr9yBcPS9Cs3gPW+KKb4tr4IizK2z56H/+CV16hTBz8jiKy1Wr11NcLnTZGbga4WoznwUXm83mNYu0EEIIb0p2xtlTP/t/RJdf+/ndNEWH2jEVV48BOHsMQO3UvWGb6nV6XANHUDxwBLq0vZg+fx/99nWVOoEqJUWYPn8f41crcA4ahWPcVNT20rJSHSU7E/3eHzDs2Yp+3zaUosJaP9fVoSuu3oNx9h6M2jEVdPrav3BYBK5u/XB163d2ncOO7mS6J8yUt9AoxVWXyZXUuVH6vvgkuKiqyqeffkqPHj18cTghhAg8qguKbCgFee6bLR/F5r7XZZ50B5WMX+p2yNZJOHsMwNVjAK7UvrU6BdMQ1JSelKT0RMn4BeMXyzGuW41iL/XaR3G5MG76GuOmr3F2749j7FRcvQc1qU6ejaKkCP3Bnej3bsew9wd0p07U+qlaWATOXoNw9RqEq9dFaBFRvi2b0YTaPsU7aGoaypnT6NKPVDrVpCZ19u3r11KNwSUrK6vGJxcXF3PkyBFef/11tm3bxmuvvebTwgkhRKNQXVBYUBZAykJIpccV1+W596/nZciqJQZX9wG4evTH1b0/WnTTPtWixbfDfuO92CfdjHHNKoxff1hlK5Jh/48Y9v+Iq20yjrFTcQ79TSOUtpGoLnTpaej3bnMHlbR9KC5nrZ6qKQpqh1RPXxW1Q9e6tar4gqKgxbXGFde60qkmxemArJyGLQ/nCS5dunSp8XLockajkYceeohrr73WZwUTQgi/Ky5Cn7bHffXOT/tQcrNRbPlQVP8QUhtacAiu1L6e0z9am/aB2SIRbsFxzY04xk3FsPErd0feKloS9L8eR//6QtTlr9I2tT/Gg8kQZEYLCoZgM1qQGS3Y7F5Xfl+2DX3gXLFUfvpHv3cbhv3b3f+nakkNt+DqdZH7FFCPgRBh8V9B6yMsAg2aXnCZNWtWjcElODiYxMRERo4cSWysjKYohGjiiovQH97jbqo/tBPdsUMXfOXOhdD0etROPTynf9QOqc3rEmJTEM5LJ+C85Er0uze7O/Ie3FVpN11eDq22fA1ban9ozWj0BBotyOwJOlQVdsIjUS3RaJHR7ku7LTEQFOzDN3qO+pz+0etRO3V3d6ztNQg1uQvodP4razNQ4ydm7ty5DVUOIYTwveLCs0Hl4C50x/0bVLSQULSwSPctvOw+LML9RZrYCVdqn5Yx549Oh6vvMIr7DkN37CDG1e9j+OHbev3bKw4HOBx1ar2oSAsOKbssPBq1QqDRIqO91hMWef5Wr3qc/gFQ49vh7DkQV8+LcHXrC+bQC3pPLVWTjvrz589n4cKFXutatWrF4cP+GXhHCBHgimwVgspOdMfTULQLHAslJKwsfERUG0a0sEh3k3l4JFpoRPNqPfERtUMqpXc+ij3rTxi/XIHxu0/qdNm3ryglRSini+D0z9TUS0TTG8paaqIrBJto1LLLi/UHdmDYV7fTP1pIGK7u/d2tKj0HosW1rue7adma/KcsJSWFTz75xLOs1zdwxyQhRNNVWODuo3KgLKikH6lzUFFbJ7r7maT2RW3XES3CghYaHlB9KgKBFtca++/vwj7xJgw7NpCTdpDY0BCwl6CUFENJMUppsftxabE73JSto6T4ggNoXSkuJ0pOJuRkXvAxNJ0OtXMPT1BplE61zViT/2QaDAbi4+MbuxhCiMamqigFViIO78K07Sv0B3aiO5FW5060auskd1Dp1hdX1z6VBuoSfhYajvPisWTEdyKitiPnaho47GfDTWmxd6gpKfYEIKW4CCU/ByUvB8Wa4558Mi+nTqdyLoSc/mk4Phvy3x/mz5/Pc889R2RkJCaTiYEDB/Loo4+SnJxc7XPS0tIaroBCiHpTXE4MtjyMtnyMtjwMNqvnsddyYT6KWrtRPSsqjm2NrX1XbO27YEvqgjMs0g/vQjRpmoa+uLDC/yn3vbEwD2NBxeV89KXFtTqkMziEguRUCjr2oKBDN+xRcX5+Ey1LTdNBNOng8tVXX2Gz2UhJSeHMmTM89dRTpKWlsXnzZqKjoxusHDKnRvMg9diANA1Kijy/dnXWbPev37yK99no8nIuuLNldVxtk8+e+una2z0EvmhymuznsbTkbCtNXjY6a3nrTTZKSRGuxE5lV4R1ldOJNE49Nul/9SuuuMJreeDAgfTt25d33nmHu+66q5FKJUQLoaru8FFkQykscE/wVmRzL5fdKDxnufxxQR6KvWE6YLradSgLKn1Qu/bx/WiiomUJCkaLb4sW3xaAurfxCX9r0sHlXGFhYaSmpnL06NHGLooQTY/T6T7Pby91/2q0l3otYy91h4nSsvuSYpTiwgohpMA7hBQXNsggbHWhmUMpjojC0HsQrq59cHXt03QH6BJC+EVABZeSkhLS0tIYMWLE+XcWIpCpKvr9P6Lftdnd2mF3Bw+vQFJaIZjYS2o9q2tToymK+0qeyIpjargfuwcRO7ueoOCme4pBCNEgmnRwefjhhxk7dizt2rXz9HEpKipi2rRpjV00IfyjsADj+s8xfrOyzhPyNTWa0Xh2kC9LTNmgX9HnDPoVgxYeKX0FhBC11qT/Wpw8eZLp06eTnZ1NbGwsAwcO5KuvviIpKamxiyaET+nS0zB+8xGGTV9XmmW3MWnBZvdAbCFhEBKGFhLuXg4tXy6/hUNoxWX39oCcd0cI0aQ16eCybNmyxi6CEP7jsGP44TuM33yE/si+eh9OU3TujoVBQWAKRjO57wkKQjMFg6nsvuJyedA4N4iEhrvHoZCWECFEEyN/lYRoYEp2Bsa1H2P49hN0BdZq99P0BpwXjcTVY4A7kJSHjaBgdzCpGFCCgt0hQ1o4hBDNnAQXIRpCWWdb4zcfot+xqcbhy9XoOByjrsZ5yZUyqqsQQpxDgosQ/lTe2XbNKnSnf65xV2ePATgum4ir31A5RSOEENWQv45C+IG7s+3Kss621Q/EpplDcVw8FsdlV6O1ad+AJRRCiMAkwcVfVNU9gJctH6WoAMVWgFKY7x7ky5bvHuTLlg8up/vXtd6AZjCAXu9ZRm9A0+vBYCxb1pftYzi7XP7YUL5/+WM9mjkULSzCfXWHzEzqfw47hm3fY/z6I/RH9ta4qyuxE47fTMQ59DcQHNJABRRCiMAnwaUWDLZ8lF+PuwcCK3QHEK8gcs46pdAGRQVNZtRRTVEgJBwtLKLCLbLSMucum4Iau+i1p7pQ8q1l84uUzQqbd/amy8uhS76V4PAINKMJym7ljzWDsdI6r+2mc9YZKjx2OTFu+hrDd5+iy8+ttoia3oBz4CU4Lp+ImtJLOtIKIcQFkOBSC13+NY+gvOzGLsYFUzQNCvPdQSuj9s/TTEHeQSc04my4CTK7L6s1BpVdZlt26a3JdPaSW6P7ahfNaLqwq17OnaivUijJPhtO8vNq7PAKEApwqvYv7ytqdByOS6/COXK8dLYVQoh6kuBSCy5zKARwcLlQir0UJScLcrJ8cjz3OCPlgabsUt6yx551KCj5uWcDSRMajK2unN374/jNJOlsK4QQPiR/TWvBFRx6Qc/TgkPQwsLRQiPcA3qFnn2shZWtMxjB5QKXE8Xp8DzG5URxOc8uO8vWld1Ttk1xVVh2Os8uO50oRYUohXkoRYU+/he5MIqmuif2KymGgsYujX9IZ1shhPAvCS61UGqJJbR1onu487LAoYVGuIc4PyeIeLaFhLk7yTYFTqf7NJEt390x2JZXdl95GVu+O+zY8gNu0j4tLOLsfDjnzosTGc2JrGwSWyegOOxQdlPOua/u8fm2azEJOC4ejXPo5dLZVggh/KiJfLM2bT9PuJHgQJ6N1mBAi4yGyGhq3V24vH9JFUEHW777FI6jFKW0xP0FXuqevbh8pmLsdvdlwI7Ss7MYqzX3QamyGEaTV/jQIqPd4eScdVqExd15tgZFaWmogVyPQgghJLiIaigKmEPdl1THtfbNMZ1Od4Cxl1YIOeWP3WEH1YUWYfEEEsyhcvWNEEIIDwkuouEYDGAomzkYat/6I4QQQpTRNXYBhBBCCCFqS4KLEEIIIQKGBBchhBBCBAzFarVKVwMhhBBCBARpcRFCCCFEwJDgIoQQQoiAIcFFCCGEEAFDgosQQgghAoYEFyGEEEIEDAkuQgghhAgYLSK4LF68mFGjRpGYmEinTp2YOnUq+/fv99pH0zTmz59PamoqCQkJjB8/ngMHDnjtY7Vaue2220hKSiIpKYnbbrsNq9Xqtc++ffu48sorSUhIoFu3bixcuBBNkyvO66uh6jA9PR2LxVLp9vXXXzfE22z2fFWPixYtYsyYMbRp0waLxVLla/38889MnTqVNm3a0LFjR2bNmoXdbvfXW2tRGrIeq/o8Llu2zF9vrUXxRT2mp6dz11130adPHxISEujTpw+PP/44xcXFXsfx5eexRQSX9evXc+utt/LFF1+watUqDAYDEydOJDc317PPkiVLeOGFF1i4cCFr1qwhLi6OSZMmUVBQ4Nln+vTp7N69m+XLl7N8+XJ2797N7bff7tmen5/PpEmTaNWqFWvWrGHBggU8//zz/POf/2zQ99scNVQdlluxYgWHDh3y3C655JIGeZ/Nna/qsbS0lAkTJjBjxowqX8flcjF16lRsNhufffYZr7/+OqtWreKhhx7y+3tsCRqqHss999xzXp/HadOm+e29tSS+qMe0tDRcLheLFy9m8+bNPPnkk7z77rvMmTPHcwxffx5b5AB0NpuNpKQk3n77bcaNG4emaaSmpvKnP/2JmTNnAlBcXExKSgpPPPEEt9xyC4cOHWLw4MF8/vnnDBkyBIBNmzYxbtw4fvjhB1JSUnj99dd57LHHOHz4MGazGYCnnnqKZcuWsX//fhSZ5dhn/FWH6enp9OnTh7Vr19KvX7/GfIstwoXUY0UrV67kpptuqtTy+dVXXzFlyhT27NlDu3btAHjvvff4y1/+QlpaGhEREQ3y/loKf9UjuFtc3nzzTa655pqGeCstWn3rsdxrr73GP/7xD44dOwb4/vPYIlpczmWz2VBV1dM0mZ6eTkZGBpdddplnH7PZzLBhw9iyZQsAW7duJSwsjMGDB3v2GTJkCKGhoV77DB061BNaAH7zm99w6tQp0tPTG+CdtRz+qsNyf/jDH+jcuTNjxoxh5cqV/n9DLdSF1GNtbN26la5du3r+SIL7s1haWsrOnTt9VXxRxl/1WG7OnDl07NiRUaNGsWzZMlRV9VXRRQW+qseCggKvU3++/jwa6vyMZmDOnDn06tWLQYMGAZCRkQFAXFyc135xcXGcOnUKgMzMTGJiYrxaTRRFITY2lszMTM8+bdq0qXSM8m3Jycl+eT8tkb/qMCwsjCeeeIIhQ4ZgMBj47LPPuOWWW1i6dClTp05tiLfWolxIPdZGZmZmpWPExMSg1+s9dS18x1/1CPDggw8yYsQIQkND+e6773j44YfJzs7mr3/9q28KLzx8UY8nTpzg+eef5/777/es8/XnscUFlwcffJDNmzfz+eefo9frG7s44gL4sw5jYmK4++67Pcv9+vUjJyeHJUuWSHDxMfksNg/+rsdZs2Z5Hvfu3RtVVXn66acluPiYL+oxMzOTyZMnM2rUKP785z/7uIRntahTRXPnzmXFihWsWrXKq/UjPj4egKysLK/9s7KyaNWqFQCtWrUiOzvb6wohTdM4c+aM1z5VHaN8m6g/f9dhVQYMGMDRo0d9+C5EfeqxNqr6LGZnZ+NyueSz6EP+rseqDBgwgPz8fGk58yFf1GNGRgZXXXUV3bp14+WXX/Zq2fb157HFBJfZs2d7KqZLly5e29q3b098fDxr1671rCspKWHTpk2e/hCDBg3CZrOxdetWzz5bt26lsLDQa59NmzZRUlLi2Wft2rW0bt2a9u3b+/PttQgNUYdV2bNnj+cDLOqvvvVYG4MGDeLQoUP8+uuvnnVr164lKCiIvn371vs9iIapx6rs2bOH4OBgIiMj63Uc4eaLejx9+jQTJkygS5cuvP766xgM3idzfP15bBGnimbOnMl7773Hf/7zHywWi+e8XWhoKGFhYSiKwowZM1i8eDEpKSl07tyZRYsWERoayuTJkwHo2rUrl19+Offddx/PPvssAPfddx9jxowhJSUFgMmTJ7Nw4ULuvPNOZs6cyZEjR3j22WeZNWuWXFFUTw1Vh++88w5Go5HevXuj0+n4/PPPee2113jsscca4203O76oR3CPCZGbm8uJEycA2L17NwAdO3YkLCyMyy67jG7dunHHHXfw97//ndzcXB599FFuvPFGuaLIBxqqHlevXk1mZiYXXXQRZrOZdevWMX/+fG666SaCgoIa/o03M76ox1OnTjFhwgQSEhKYP38+2dnZnuPHxsai1+t9/nlsEZdDVzew0ezZs5k7dy7gPmWwYMEC3njjDaxWKwMGDGDRokV0797ds7/VamXWrFmsXr0agHHjxvHkk096HX/fvn3MnDmTH3/8EYvFwi233MLs2bMluNRTQ9XhO++8w5IlS/j555/R6/V06tSJGTNmSP8WH/FVPc6YMYP//ve/lY7z8ccfM2LECMD9pThz5ky+//57goOD+e1vf8sTTzwhX3g+0FD1+PXXX/P4449z7NgxVFUlOTmZP/zhD/zpT3+q9Kte1J0v6vHtt9+utj/Lrl27PGcbfPl5bBHBRQghhBDNQ4vp4yKEEEKIwCfBRQghhBABQ4KLEEIIIQKGBBchhBBCBAwJLkIIIYQIGBJchBBCCBEwJLgIIYQQImBIcBFCNJirrrqKDh06cObMmUrbbDYbPXv2ZPjw4TidzkYonRAiEEhwEUI0mGeffZbi4mIefPDBStvmzZvHyZMnee6552RUVCFEtSS4CCEaTKdOnZg5cybvv/++18Rtu3bt4uWXX2b69OkMGDDAr2VwuVyUlpb69TWEEP4jwUUI0aDuueceunfvzn333UdxcTGqqnL//feTkJDAI488wpEjR7j55pvp0KED8fHxjBgxgpUrV3odIzc3l0ceeYRhw4bRrl072rZty/jx49m4caPXfunp6VgsFp555hleeeUV+vfvT6tWrbxmCBdCBBZpjxVCNCij0cizzz7L2LFjefLJJ2nbti3bt2/nnXfe4eTJk4wePZr4+HjuueceQkND+eSTT7jpppt4+eWXPZNdHj9+nJUrVzJp0iSSk5PJy8vjrbfeYuLEiaxZs4aePXt6veZ7771HYWEhN998M2FhYSQkJDTGWxdC+IBMsiiEaBQzZ87kjTfewGw2M2rUKP79738zadIkTp06xdq1azGbzZ59J02axKFDh9i3bx+KolBaWorRaESnO9tobLVaueiiixg7dizPP/884G5x6dOnD6GhoWzfvl0CixDNgJwqEkI0ikcffZSYmBg0TePJJ58kNzeXb7/9lokTJ1JUVER2drbn9pvf/IaTJ09y5MgRAIKCgjyhpaSkhJycHFwuF/3792fnzp2VXmv8+PESWoRoJuRUkRCiUURERNC5c2cyMzNJSEhg+/btaJrGggULWLBgQZXPycrKIiUlBVVVWbJkCW+88Qbp6ele+7Rv377S85KTk/3xFoQQjUCCixCiSVBVFYA777yT0aNHV7lP9+7dAVi8eDF///vfmTZtGg8//DDR0dHo9XoWL17MsWPHKj2v4mknIURgk+AihGgSyltFDAYDl156aY37fvTRR1x88cUsXbrUa/38+fP9VDohRFMhfVyEEE1CXFwcI0aM4M033+TkyZOVtlccbVev16Np3tcVbNmyRS5zFqIFkBYXIUSTsXjxYsaMGcPw4cO56aab6NChA1lZWWzbto1Dhw6xY8cOAMaNG8eCBQu4/fbbGTZsGD/99BNvvPEGqamp2Gy2Rn4XQgh/kuAihGgyUlJSWLt2LQsXLuTdd98lOzub2NhYevbsyUMPPeTZ7/7776e4uJj//e9/rFy5km7durFs2TJWrFjB+vXrG/EdCCH8TcZxEUIIIUTAkD4uQgghhAgYElyEEEIIETAkuAghhBAiYEhwEUIIIUTAkOAihBBCiIAhwUUIIYQQAUOCixBCCCEChgQXIYQQQgQMCS5CCCGECBj/D0ZHa/ZlVoY2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3)) \n",
    "\n",
    "mex_usd = military[['MEX-USD']].loc[2000:2020]\n",
    "\n",
    "plt.plot(can_usd)\n",
    "plt.plot(mex_usd)\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('USD (Billions)')\n",
    "plt.xticks(years)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e3e39f",
   "metadata": {},
   "source": [
    "We can now see that the military spending for both Mexico and Canada is vastly different. However, just looking at this graph out of context, we wouldn't be able to tell which line corresponds to which country. Let's add some descriptive details, such as a legend and a title:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "50c364ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MzxBAGT16tMW633//3fz+unf5uHHjFEDp0qWLEh0dbV5+5MgRxcfHRwGUAQMGZCm/wcHBCqD88ccfWX7fZdyTx48ft1qe8ZrpdDqLeyLjOgFK7dq1lQsXLpiXX716VSlfvryiVquV06dPW+xz8OBBqzSio6OV/v37K4Dy119/Waxr3bq1AigTJ060WP7zzz+bX+fZs2dbrDt06JASExNjlc6IESMUQJk/f3627pf7X/uM+8LDw0PZsWOHxbqnnnpKAZSFCxdmKY2M+/v+c7h/fXbu/+x+7j7KY/78+Qqg1K9f/6Hb3vt9bevRvXt3BVC2bdtmte78+fM2X/v7P7tz675Yu3atolKplPr161t97sydO1cBlO7du1ssz4gXypQpYxGX3Lp1y/y98Pfff1u9Rpl9V2T2Prz/NTl8+LDVPoDSr18/5datWxb73LhxQ/Hx8VH8/f0tvtP0er2yadMmBVA6duz40Ncn26H6oEGDMJlM5mLMq1evsmnTJnr37o27u3t2D2c3Xl5eNks9SpcuTY8ePQgLC7NZpQIwceJEXF1drZZnlBTcX8f+ww8/YDAYeP7557OUt5dffpm3336btLQ0vvzyS5588kkqVapE9erVeeWVVzKtsgVo0aIFHTt2tFg2dOhQypQpw+bNm83VATExMfz000/UqlWLN99802J7Jycnc1RvqxTH1dWVTz75BI1GY15WtWpVGjZsyOnTp0lISDAvz3gtxo0bZ9FgX6fTMWLEiCy9HvebMmWKxbH8/Pzo0qULcXFxhIWFmZdnFM+/9dZbFsXyjo6ONjvC5ETv3r1p2bKlxbKMXz/79+9/pGM+99xzxMXFsWLFCiC908PJkycZOHCgzapygD///JOzZ8/y4osvWpX0BgYGMnz4cJKSkiwa7To6OrJo0SI8PT157bXXmDNnDt9++y2PPfYYH330UZby+s033wAwffp0qyoQtVpt8av5hx9+AOCTTz6xqJbx8vIyv+++//77LKVbqlQpmyUlbdq0oWrVqmzevNnmfmXLluXdd9+1WNa2bVvKlCljdb2WLFmCSqUylz5lCAoK4pVXXslSPjNkVNE8rBThfk5OTjZLCby8vHjmmWfQ6/UcOHDA5r4TJkwwl9oDuLm50adPH0wmEwcPHrTY1lb7RLVazauvvgpg8XpevXqVLVu2UKZMGavqoE6dOtG8eXOb+QkODrb5/s1oxpLZNcuuV155xarJzXPPPQc8+j2Zmaze/zn53M2q8+fPM3LkSLRaLZ9++ukjH+d+tjpcPahZRnY86n0xd+5cFEVhxowZVp87/fv3p1atWqxbt85mj/uRI0da1BxotVqeeeYZwP7vj8w4Ojry8ccfW5UoOzs78+yzz3Lz5k3Wrl1rsS47tXXZLqeuU6cOtWrVYsmSJYwcOZIff/wRo9H48KK8PLB7927mzp3Lvn37iIyMtGrTcf36dauqIGdnZ2rUqGHzeP369WPChAksWrSI4cOHo1KpMBqN/Pjjj7i5udGvX78s5UulUvHBBx/wv//9j82bN7Nv3z4OHz7M/v37WbZsGcuWLWP06NFW1X8ATZs2tVqm1Wpp2LAhV65c4ciRI5QqVcpcJahWq20OfZLR+/DMmTNW6ypUqGCzZ1ZGr069Xm8Oxg8fPoxKpaJJkyZZyuvDeHp6UqFChQemneHIkSMA5qq4e9WvX99u1aGQ/j7PSp6yo0uXLvj5+fH999/zzDPPsGjRIosPFVsygvkrV67YvK7nz58HsGpbExwczJdffsmgQYMYM2YMnp6eLFy4MEvtXBITEzlx4gQ+Pj48/vjjD9w2Pj6e8+fP4+/vT7Vq1azWZ3zp3d92LzOKovDLL7+Yq2L0er1FW6vM8l+zZk2LHxwZypQpYzFkTkZ+AwMDCQkJsdr+Ud7Dj+rkyZPMmjWLnTt3EhERQXJyssX669ev29wvO+/N6OhoZs2axZ9//kl4eDiJiYmZppFxfzVq1MjmvdS0aVObwzskJiYyd+5c1qxZw7lz54iPj7dot5XZeWRXbtyTOU0rJ5+7WXH16lWefPJJoqKi+OKLL6x6iD6KPn36sHr1atq2bUuvXr1o3rw59evXp1y5cjk+dk7t2bMHrVbL6tWrzdWY90pNTcVoNHLu3Dmra5SX74/MBAUF4efnZ3PdCy+8wFdffcXChQvp2bMnkN4cacWKFZQpU8Zmk637PdI33KBBg3jnnXfYuHEjixcvpkaNGg/9YM9tq1evZtCgQTg7O9OqVSvKly+Pq6srarWaf//9lx07dthsFO/r65tp6YaHhwd9+/Zl/vz5bN26lVatWvHnn39y5coVBg4caNXu4WF0Oh1PPvkkTz75JJD+QTdjxgymTZvG5MmT6dKli1UbN39/f5vHynhTxMXFAZgboh46dIhDhw5lmod7S8syZHYeGV+A935hxsXF4enpabMUJLO8Pkh20wZs3hAajcZuvw4zy1fGl1hmjbUfxsHBgaeffpqZM2eyZ88e/vjjDzp06EBgYGCm+2Rc11WrVj1w+I37v4ghPVjy9vYmJiaGzp07ExwcnKV8ZjTSLVmy5EO3zbgmmV17V1dXPD09LRr+PsjYsWOZM2cOgYGBtG3blpIlS+Ls7AzA0qVLMy0lf9D7yGQyWeU3sw/V7L6HAwICuHjxIteuXctWD8d9+/bRo0cPDAYDLVu2pHPnznh4eKBWqzl69Cjr1q3LtBOPrUbftu4XvV5P69atCQ8Pp27duvTv3x9vb280Gg2xsbHMnTvXIo1HeW3S0tLo0aMH+/fvJzQ0lF69euHr62u+V6ZMmZLpeWSXrWts67xzKy1b939OPncf5vLly/To0YPw8HA+//zzLNfoPEyPHj345ZdfmD17Nj/99JO59Ds0NJRRo0bxxBNP5DiNR70voqOjMRgM5ra3mcnq91huvT8y86DPj+DgYNq2bcumTZs4d+4cFStW5Oeff+b27du89dZbWWqb+EgBW58+fRg3bhzvvvsuV69etSoKzg+ffvopjo6ObNmyxapHyptvvplpY+LMgrUML774IvPnz2fhwoW0atXK3JjyhRdeyHGe3dzceP/999mxYwe7du1i27ZtVgFbZmPqREZGAphLxjL+DhkyhKlTp+Y4b5nx9PREr9eTkpJiFbTl9vg/Hh4eQPq5339zGo1GoqOjsxRk5KdBgwYxa9YsXnjhBW7fvv3QD+GM6/rDDz/Qo0ePbKU1dOhQYmJiKFGiBL/88gtPPfUU7du3f+h+Ga9tVkpGMvKX2bW/ffs2cXFxWQqmIyMjmTdvHqGhoWzcuNF8vTP8/vvvDz1GVvObcf/cL7vv4UaNGpnHoGzVqlWW95s2bRpJSUmsXr3aqqpx+vTprFu3Llv5sOXHH38kPDycUaNGMWbMGIt1e/fuZe7cuRbLHuW1WbduHfv37+fpp5+26vBw48aNh37xFna59bl74cIFevTowdWrVy06FdlLhw4d6NChA0lJSRw4cIBNmzbx3Xff8fzzz7N69WqbHe2y41HvC09PT9LS0jL9YZYXMgKnzIK8B/34zEo88ddff7Fo0SImTpxormXJ6vV9pO4mnp6e9OrVi6tXr+Lq6kqfPn0e5TDZ8rBI+fz581SpUsXmWC+7d+9+5HRDQ0Np3Lgx69at48CBA/z111/UqVOHxx577JGPeb+M6kZb3b9tBZoGg8FcVZYR4NWrVw+1Wm013IK91a5dG0VRrMYCyiyv9pRxrrbOcd++ffky6Gx2VahQgWbNmnH16lXz0BcPklEFkt3rOnv2bNavX0+3bt34888/cXd359VXX81SEObm5kZoaCjR0dGZtqPK4OHhQYUKFbh58yanTp2yWr9t2zbAdnXF/S5evIjJZKJ169ZWwdrVq1cz7RKfHRn5jYiI4OzZs1brs/sezgi4Fy1axI0bNx647b0lTefPn8fb29tmuzB73UcZ1eW2An1baWTcX3v27LF5L9naJyONe0cNeND2+c3eJS658bl7+vRpunTpwrVr15g3b57dg7V7ubi40LRpUz788EMmTpyIoigWbawyXq97S6mz4lHvi/r16xMfH8/Ro0ezlV52qdXqTM8powT7ypUrVusMBoO56cCj6NChA0FBQSxdupStW7dy8uRJunTp8sBaFot8P2rCY8eOZfHixfz222/Zrhp8FBm/0G29iJBed3z+/HmLLyRFUZg0aZLNL5LseOmll0hLS+PZZ5/FZDJlu3Rt1qxZnDx50ua6Xbt2mduF2Go/s23bNqsxkObMmcOVK1do3bq1uVGnr68v/fr14+jRo0yaNMnmB+7Vq1cfuS1Fhoz2VhMnTrQYY0mv1zNt2rQcHfth+vfvD8CMGTMs2iSkpqZmuTF9QTBjxgwWL17M4sWLH1oM3qVLFypUqMDChQszLXU5fPiwxdhMBw4cYPz48ZQtW5avvvqKihUrMn36dKKionjppZey9GWV0fj+7bfftmr/oSiKxX2W8YXy/vvvW0wzExcXZ74uGY3DHyRj2IDdu3db5DEhIYE33njDbgH5M888g6IofPDBBxYf2pcuXWLevHnZOlajRo0YNGgQMTEx9O7d26KTTAZFUVi1apXFF29QUBAxMTEcO3bMYtsffviBv//+O5tnZFvG63n/WJKHDx9mxowZVtuXLl2a1q1bc/nyZathKDZs2GCz/VpmaVy8eNHuHYHs4WHfI9ll78/dY8eO0bVrV27dusWCBQtypTDk33//tZnPjI4C93bAy3i9slvi9aj3RUZHlTfffNPmEGHJycl2CY59fHy4deuWzXECPTw8qFKlCnv27LEYqkpRFCZPnpyj945arWbw4MFERUWZO/ZkJ5545FbapUuXttnLKbc0aNAANzc3li9fjoODA2XLlkWlUtGvXz+CgoIYNmwYb731Fi1atKBHjx5otVr27NnD6dOn6dSpU6aDUWZFjx49zGNQeXp60rt372zt/8svv/DBBx9QuXJl6tWrR2BgIImJiZw6dYpt27ahKApDhw612Q6wY8eOPPPMM/To0YPg4GCOHDnCpk2b8PHxsQqQpk6dyvnz55kyZQrLli0zDyCYUZqwb98+PvnkkxyNJv7UU0+xfPly1q9fT+PGjenatStpaWmsXr2aOnXqcO7cuUc+9sM0a9aM559/nkWLFtG4cWO6d++Ok5MT69evx9PTk5IlS+Z4jKK8UKlSJSpVqpSlbR0cHFi8eDFPPvkkTz/9NPXq1aN27dq4ublx9epVjhw5QlhYGNu2bcPHx4fY2FgGDx6MoijmwRghvRnDtm3b+PHHH5k8eTLvvffeA9N97rnn2LVrFz///DOPP/44Xbt2xc/Pjxs3brBjxw46d+7M5MmTgfQP2U2bNrFp0yaaNGlCx44dze+Ja9eu0b9/f3Mj2wcJCAigd+/e/P777zRv3pzWrVsTFxfHli1bcHZ2pmbNmnb55f3666+zdu1a1q1bR/PmzWnXrp25927jxo2tBgZ/mGnTpqHRaFiwYAGNGjWiadOm1KhRA2dnZ27cuMH27du5fPkyrVu3Nu8zdOhQ/v77bzp37kzPnj3x9PTk4MGD7N69myeeeMIuU/X079+fWbNmMWbMGLZv307FihU5d+4cGzdupHv37ixfvtzmubRv355x48axdetW8zhsq1evtvk52qlTJypUqMDs2bM5ceIEtWrV4sqVK2zcuJEOHTrYLTCylxYtWqBWq5k7dy4xMTHmNkdDhgx55IIHe33u6vV6unfvTkxMDI0bN+bEiROcOHHCarunn37aooPAmTNnrALwuLg4i56+b731ljn90aNHc/XqVRo1akRQUBDOzs4cP36cv//+Gx8fH4sOhK1bt2bWrFl89NFHnDx50vx5cn+PbFse5b5o0aIFEydO5MMPP6Ru3bq0b9+e4OBgkpOTuXz5Mjt37iQoKCjTAe2zqnXr1vz666/07t2bJk2a4OTkRI0aNejcuTMAb7zxBsOGDTPfn66uruzZs4erV6/SrFmzHKU/cOBAJk+ezLVr16hYsaJVb+QHsV+3ulym0+lYvHgxU6ZMYcWKFeZGhxlvusGDB+Po6MicOXP46aefcHZ2pnHjxsyePZtVq1blKGBzcHCgX79+fPnll/Tr1w83N7ds7f/111/z119/sW3bNnbs2MHNmzcxmUz4+/vTvXt3Bg4cmGnboh49ejBo0CCmT5/Ohg0bcHBw4IknnuDDDz+06l3p4eHBmjVr+PHHH/n1119Zs2YNycnJ+Pn5Ua5cOT788EN69er1yK8DpNfRf//998yYMYOlS5fy7bffEhAQwNNPP83IkSMJCAjI0fEfZvr06YSEhLBo0SIWLVqEj48P3bp1Y9y4cVSvXj3LDesLk9DQUHbs2MGcOXNYt24dP/30E4qiEBAQQNWqVRk+fLi5x+Pw4cMJDw9nwoQJVj3KpkyZwr59+/j8889p1qzZAz8oVCoVc+fOpW3btixatIiVK1eSkpKCn58f9erVs3gfOTo6snz5cubMmcMvv/zCd999h1qtplq1aowePTpbVTpffvklwcHBLF++nO+++w5fX186d+7M2LFj7VY15OTkxB9//MHkyZNZsWIFc+fOJSgoiHfeeYfu3btnO2BzcHBg+vTpPPvssyxatIidO3eyf/9+UlNT8fX1pXbt2rz//vsWP/TatWvHzz//zLRp01ixYoV5cNvVq1dz8eJFuwRsJUuWZP369YwfP57du3ezefNmQkJC+Pzzz2nZsqXNgK1ixYps2rSJ8ePH888//7Bz506qV6/OkiVLuHXrltXnqJubG6tWrWLChAn8+++/7Nq1i+DgYN59911ee+01m2nkp8qVK/PNN9/w5ZdfsnjxYnMJS9++fR85YLPX525sbCwxMTFAes1LZiVJzZo1swjYIiIizEMeZUhOTrZY9vTTT5sDtnfeeYe1a9dy8OBBc6lpqVKlGDp0KMOGDbOY87l169ZMnjyZRYsW8d1335mrL7MSsD3KfQHpn2GNGjVi7ty57Nq1iw0bNuDu7k7JkiXp27dvjr/DIH1GG7VazT///MPu3bsxmUwMGDDAHLBlDH781Vdf8fPPP+Pu7k6bNm348ccf+eSTT3KUtq+vL506dWLlypU8//zzD233di/VnUHkxEM8+eSTbN68mZ07dxIaGprr6U2aNIkpU6Ywe/bsBw77INKdO3eOunXr0rt3b+bPn5/f2RFCCCGsKIpCgwYNuHTpEidPnszW6AYFv/6oADh8+DCbN2+mWbNmeRKsicxllE7e6/bt2+ZecN26dcuPbAkhhBAPtXbtWsLCwujdu3e2h6IqNFWi+WH+/Plcv36dpUuXolKpGDt2bH5nqdj75ptv+Pnnn2nWrBmBgYFERESwbds2rl69Svv27bPUVkoIIYTISzNmzCAmJoYffvgBZ2fnR5oZSAK2B/jiiy+4evUqwcHBzJ492+bo/iJvtWjRgsOHD7NlyxZiYmLQarVUrFiRV155haFDh2arPYAQQgiRFyZMmIBWq6Vy5cqMHz/e5gw/DyNt2IQQQgghCjhpwyaEEEIIUcBJwCaEEEIIUcBJwCaEEEIIUcBJwCaEEEIIUcBJwCaEEEIIUcBJwCaEEEIIUcBJwGZHYWFh+Z0FkUNyDYsGuY5Fg1zHokGuo31IwCaEEEIIUcBJwCaEEEIIUcBJwCaEEEIIUcDJXKJCCCFEAZWYZiIs1sApvYHrt42UdNVQw8eByl5aHDUFc+7k2FQTR6PTOBKVRmKaiZ5u+Z2jokECtvsYDAYSExMfaV9nZ2diY2PtnCMB4ObmhlYrb1chRNEUnWzkdKyBM3oDp2PTOK03cFpv4Eqi0eb2DmqoonOghreWGj4O1PRxpKaPFh9nTZ7mOzLJyJHoNA5HpQdoh6NSuRB/N8/uWhU9GuZploos+Qa8h8FgID4+Hp1Oh0qV/V8uTk5OODs750LOijdFUdDr9Xh4eEjQJoQotBRF4fptE2di0zilvxucndEbiEw2ZetYaSY4Fp3Gseg0OJdkXl7KVU0NH4f0h3f634qeWjTqnJXGKYrC5UTjnaAsjSPRaRyJSuX67QfnO8GgcCVZRZUcpS5AAjYLiYmJjxysidyjUqnQ6XTExcXh5eWV39kRQogHMpoULiUYOaVP40xseknZmTuBWVyakqtpX7tt4trtFP68kmJe5qJREXqnJC7jUd3bAU9H283YjSaFc3GGO0FZRoCWSkzKo+X9dIKato+0p7iXBGz3kWCtYJLrIoQo6NaGJzHtSDwnYtJIsV2TmW1qFZT30FDZy4Gy7houxhs4Fp3GtYeUbN0ryaiw/1Ya+2+lWSwv564xB3CBLhpO6NMDtGPRaSQaHj2wVKugspeWWj4O1CrhQFVTxCMfS9wlAZsQQgiRQ4ejUnluSzTGR4xzHNVQyUtLFS8Hqui0VNFpqeyVXp3prLX+wRqVbORYtIGj0anpVaMxBk7r00jLRs1qeIKR8AQjay8lP1qm7+Q71Ds9MKvl40DtEo5U99Hiqr1behcWduORjy/ukoBNCCGEyAGDSeGNHfosBWvuWlV6MKZzoIqXlso6LVV1DpRz12SrnVkJZw0tS2loWcrJvCzVqHAm1mBu23b0zt+olOy1j8uMm1ZFzTulZrVKpAdnVQpwb9WiRgI2IYQQIgfmnUzkUJRldaOvs5rKXto7pWUZwZkDpVzVudbEw1GjMldxZlAUhRtJJnMQl/EIizNgekCA6eOkTg/KfDKCMwcqeGpRS/OUfCMBm8h1jRs3pkePHowZMya/syKEEHYVHm/gkwNxFst6BbuwsLVPPuXIkkqloqSrhpKuGtqXuTuKwW2DiVMxBo7FpJfExaSYqOCpNQdoZdw00na4gJGArYi4efMmn3/+ORs3buTatWuUKFGC6tWrM2TIEDp06JDf2RNCiCJHURTe2aXn9j0N9L0cVUxuWPB7s7tq1Tzu58jjfo75nRWRRRKwFQHh4eF06tQJd3d3PvzwQ2rUqIHJZGLr1q28/fbbHDt2LL+zKIQQRc7vF5LYdDXFYtnE+l4EuObt4LWieJCA7SF0C6/maXr6waWzvc+IESMA2LJlC+7u7ublVapUoW/fvgB89dVXLF26lIsXL+Ll5UW7du2YOHEiOp0OgCVLljBy5EiWLl3K6NGjCQ8P5/HHH+err74iODgYgAsXLjB27Fj2799PQkIClSpVYuzYsXTq1MmcZmRkJP/73//YsmULvr6+jBo1yiq/D8uLEAXRiZg0Jh2MIzZVoWVJJ3oGu1DRSz5Ci6voZCOj91jObNM00JGBIa75lCNR1Mnk74VcTEwMmzZt4qWXXrII1jJkBEFqtZpJkyaxa9cuvv32W/bv38/IkSMttk1JSWH69Ol89dVX/Pnnn8TGxvL222+b1yckJNC+fXtWrFjBv//+S48ePRg4cCBnzpwxbzNs2DAuXLjAH3/8wZIlS/j555+5dOmSRTpZyYsQBYWiKMw/lUCb1TdZHZ7MtuspTDwQR93lETT9I4LPDsURFpv28AOJImXcf3Hcumd2AicNzGwiA6+L3CM/Dwu58+fPoygKlStXfuB2w4YNM/9frlw5PvroI55++mnmzp2LWp0etxsMBqZNm0ZISAgAw4cP5/XXX0dRFFQqFTVr1qRmzZrm44wYMYINGzawcuVK3n33Xc6ePctff/3Fhg0baNSoEQBz5syhTp062c6LEAVBTIqJ4f/GsCaTcaqOxxg4HhPPJwfjCdVpeaK8C08Eu1BV52Bze1E0bL2WwpKw2xbLRtTyoJKXXHeReyRgK+QUJWujNG7dupUZM2Zw5swZ4uLiMBqNpKamEhERQcmSJYH0uVAzgjWAwMBAUlNT0ev1eHt7k5iYyJQpU9i4cSM3btzAYDCQnJxM9erVATh9+jRqtZq6deuajxEUFGQ+fnbyIkR+23kjhSHbYjKdfPt+J/QGThyMZ9LBeKrqtPQIdqFnsAvVdFopdSlCkgwKb+6MsVhWTafljZoe+ZQjUVxIwPYQ2WlTlpycnOeTv1esWBGVSmVRLXm/S5cu0a9fP5577jnGjh2Lj48Phw8f5sUXXyQ1NdW83f0Tq2d8yZhM6cX+48aNY9OmTUycOJGKFSvi6urKq6++anGMe/fLSV6EyC9Gk8K0I/FMORRvNU6Vu1bFK6Fu7ItM498bKZmOY3VKb+DUoXimHoqnstfd4K26twRvhd1nh+O4EH83iFcBM5vqZPBYkeskYCvkvL29adu2Ld9++y2vvPKKVTs2vV7PwYMHSU1NZdKkSWg06b2XNmzYkO20du/eTf/+/XniiSeA9AD1woULVKxYEYDKlStjMpnYv38/DRs2BODy5ctcv37dfAx75UWI3HA10cjLW6PZGWH946FOCQcWtPKhgmf6x2ZkkpE14cmsDE9i+/WUTEe5PxNrYNrheKYdjqeip4aewS70CHahlo+DBG+FzLHoNGYdTbBY9lJVNxr4O2WyhxD2Iw2GioBp06ahKAqtW7fmjz/+ICwsjDNnzjB//nyaNWtGxYoVMZlMfP3111y8eJHffvuNuXPnZjudihUrsmbNGg4dOsTx48cZMmQIKSl3u7SHhITQrl073nrrLfbu3cuRI0cYNmwYLi4uFsewR16EsLe14Uk0WxlhM1gbXsOdP7v6mYM1AD8XDYOruvFHR1/O9A9kVlMdbUo58aCClnNxRj4/kkDLVZE8/nsE4/+L5dCt1Cw3bRD5x2hS+N+OGO6dE72Uq5pxdT3zL1OiWJGArQgIDg5m69attGrVig8//JCmTZvSo0cP1q9fzxdffEGNGjWYPHkyX3/9NY0aNeKHH35g4sSJ2U7nk08+wc/Pjy5dutCnTx/q169P48aNLbb5+uuvCQoKokePHgwYMIA+ffoQFBRkXm+vvAhhL8kGhXd363lmczQxKZaBk5+zmt/al2Bifa8HVnmVcNbwXGU3lnf0Jax/IF821dG+tBM25uw2uxBv5IujCbRaHUmd3yL4YF8sR6KkWUBB9e2pRA7csuwN/FkjHZ6O8jUq8oZKr9fLT7s7YmNj8fJ69BGq86MNW3GS0+uTFWFhYRYdL0ThlNXreFqfxuB/ojkRY7Ba17qUE3Obe+doEFR9iom1l5JYdTGJzddSSMvCHNxPV3JlUkMvvCQQKDD34+UEA41W3CTxnuK1HuWc+aFNiXzMVeFRUK5jYSdt2IQQxY6iKPwYdptRu2NJuq/xmVYF4+p6MryGe44nutY5qXkmxI1nQtzQp5jYcDmZPy4msflqMqmZBG9Lz95m2/UUvmqmo1Up+QGY3xRFYcQuvUWw5umoYkojXf5lShRLBf4n3PTp02ndujVly5alYsWK9OvXjxMnTlhsoygKkyZNomrVqgQGBtK1a1dOnjyZTzkWQhRk+hQTL/wTw/926K2CtXLuGjZ09eONmh45Dtbup3NS07+SKz+3K8HZASX5poU3XYKccbJRgHcl0UjPjVGM2KUnISvFciLX/HExiY1XLKefmlDXi5Iy/ZTIYwU+YPv333958cUX2bhxI6tWrUKr1dKzZ09iYu6OgzNz5kxmz57NlClT2Lx5M35+fvTq1Yv4+Ph8zLkQoqDZezOFFqtusuJiktW6pyq4sO0Jf+rlwWTYno5q+lZ0ZWnb9ODtu5beVNNZV3h8dyqR5itvsisixcZRRG7Tp5gYdd/0U40DHBlURaafEnmvwAdsy5cv59lnnyU0NJTq1aszb948bt26xe7du4H00rU5c+bw5ptv8sQTTxAaGsqcOXNISEjgt99+y+fcCyEKApOiMP1IPJ3X3eJSguVAuK5aFbOb6fi2hXe+tBvzcFDzVAVXtnT3540a7txfrnch3kiXdbd4f28syQZpcpyXPvgvlptJd0s4HdXwRROd3UtfhciKQtfp4MaNG1StWpX169fTuHFjLl68SJ06ddi8eTOPP/64ebu+ffvi4+OT6ZARYWFhVsucnZ3x8/PLtbyLnImMjCQ52fYUQUJkJjJFxYdnHNkXa12FVdnNxCdVUgh2LTgfg4fj1Ew448jlZOvgsbyLifGVUwn1kGrS3LY/Vs2rRy3bEA4JSuXlIOsOKiJ/FLeODIWu08Ho0aOpWbMmDRo0ACAiIgLAKtDy8/OzGLD1frYudGxsbI56eUov0dzl6elJ2bJlczUN6c1UNGRcxz8vJzN0XwxRKdYBzquhbkyo54VTARuhPgToXMvE+P1xfHsy0WLdhSQ1Lxxx5u1aHrxb26PIj66fX/djskFhwMqbwN3grIqXlomtShW490thIJ+r9lHgq0TvNXbsWHbv3s2PP/5oHiVfCCHul2qCsXv19N0UZRWs+Tip+bmdD5Mb6grsl6+bg5rPGulY2bEEZdwsP+uMCnx2OJ62ayI5Hp2WyRFETkw7Es/ZOMuStC+aFtz3iygeCk3ANmbMGH7//XdWrVpFcHCweXlAQACQXl12r8jISPz9/fMyi0IUKfoUE0vDEvnt/G0ORKait1FKVdAYTAqHo1J58bAzXx9PtFrfLNCRf5/wp1NZFxt7FzwtSzmzs6c/A0OsG7kfjU6j1eqbzDgSjyGzSU1Ftp2ISeOLI5Yd1gZXcaVxgEw/JfKX3atEo6Ki2LNnD6dPnyYqKgqVSkWJEiWoXLkyDRs2pESJ7A80OGrUKFasWMHq1aupXLmyxbpy5coREBDAli1bzG3YkpOT2bVrFx999JFdzkmI4mbVxSTe3qXnVrJ16VQFTw0VPLVU8NBS0VNLBc/0vzqnvPn9p08xcTHewMV4452/Bi4mpP9/OcF4Z05Py7xoVDDmMU/equmORl24Skk8HdV82cybruWceWOHnoh7GsGnmWDC/jjWXUpiTnNvKnk55GNOCz+TovDGfdNPBbqo+bBu7g7YLURW2CVgS01N5ZdffmHJkiXs2bMn03nxVCoVDRo04JlnnqFv3744OT38F8uIESNYtmwZixcvRqfTmdusubm54e7ujkqlYujQoUyfPp2QkBAqVarEtGnTcHNz46mnnrLH6Yn71KxZkyFDhjB8+PD8zoqws5gUEyN36/n1vPWwFwDRKSaiI038F2ldFeftpEoP4Dy0lPe0DOa8sxHMpZkUriamB2AX4u4EZQl3A7TY1OyVJpV11/BdC28aFvISkk5lXdjV05F3d8fy+wXL67MvMo3mKyP5oK4nr4S6SS/GRzT/VCL77ntvT2mky7MfI0I8SI57iS5YsIBp06YRFRVF69atad26NXXq1CE4OBidToeiKOj1esLDwzl48CD//PMPW7ZsoUSJErz77rsMHjz4gcfX6XQ2l48aNYoxY8YA6UN7TJ48mUWLFqHX66lbty7Tpk0jNDQ0W+dSWKemGjp0KD/99BPPPvssX331lcW6Dz/8kJkzZ9KxY0eWLVtml/Ru3bqFq6srrq55OxaRTE2VuzZeTuaNHTHcSLJ/1afOUWUO4DKCudJuGiKTjPeUlKX/vZKYUUqWc08EOzOziXeR+8L940J6CWi0jWrqZoGOfNXMm2CPQtenzEpe3o9XE400WhFBfNrdN1+XIGeWtPFBJQFwjhTnz1V7ynHAVr16dYYNG8azzz6b5S/T2NhYfvzxR+bOncuxY8dykrxdFeaAbfv27ej1ek6fPo2bmxsABoOB6tWr4+DgQPXq1e0WsOUXCdhyR2yqibF7Y1kSdttqnYMaWpR04mqikQvxBlKMNg5QwPi7qCntkMbQOr70qeBSZL9sbyYZeWOHnvWXrYe6cdeq+KSBF89Vdi3U559X96OiKDyzOZp1l+6+lh4OKnb3CqC0m3Rwy6ni+LmaG3L8E+zQoUM4OGSv3YSXlxevv/46r7zySk6Tz3Xug1plfVs7pJfw/T+PtF/16tW5fv06K1as4NlnnwVg48aNODk50aRJE4uZIRYvXsyXX37JxYsXKVOmDC+88AJDhw5FrVYzdepUvvvuO3bs2GEeKuXFF18kLCyMTZs24ejoaFUlGhsby4QJE1i7di16vZ5y5coxevRonnzySQBWrVrF5MmTOXv2LH5+fgwePJh33nmnUH+RFBVbriYzfIeeK4nWkVh1by1zW/hQ0yf9/jaaFK7dNnI+zsD5OCPn4gycjzdwPs6Qp8GckwbKuWsp76GhnIeWYA8twR4agj20lHPX4OagTv+CqFi0R6P3d9GwtK0PP59LYtRuPXH3lAwlGBTe2KlnTXgSM5t6U0qCjgdaFZ5sEawBfFDXU4I1UaDkOGDLbrBmr32FtYEDB7JkyRJzwLZ48WKeeeYZLl68aN7m+++/59NPP2Xq1KnUrl2bkydP8sYbb+Dg4MCQIUN455132LJlC6+//jrLli3jp59+Yt26dWzduhVHR+spexRFoW/fvuj1embPnk2lSpUICwszD3B76NAhnn/+eUaMGEHfvn05cOAAb731Fh4eHoUiYC+qEtJMfLAvjgWnrXtSalTwVi0PRt43zpdGraKsu5ay7lpalrLcx6Sktzs7H3cnoIs3cC7OwIU7wVxyNoO5ABd1egB2JxALdr/z10NLoKta2mjdoVKpGFDJleaBjgzfoWfLNcsprP66mkLjPyL4rJGuSJc25oQ+xcSo3XqLZQ38HHmxqlv+ZEiITNi9kcPNmze5fv06tWvXNi87c+YMX3/9NbGxsTz55JN0797d3skKoE+fPowbN45z587h7u7O33//zdSpU/n000/N23z22WdMmDCBJ554AoDg4GAuXLjA/PnzGTJkCBqNhnnz5tGsWTM++OADFi5cyMcff2zVOzfDP//8w969e9m9ezdVqlQxHzPD7Nmzadq0KWPHjgWgUqVKnDt3jpkzZ0rAlk/+vZHCa9tjCE+wjqKqeGmZ09ybx7M5n6ZadW8wZ9m436QoXEs0cj4+o3QuPZi7ftuIn7M601IykXVl3LUs71CChadvM25fLIn3dHOMTVUYsi2GxWG3aVfaiUYBjtQu4Shjit0xYX+sRbtNB3X6mGvyo0AUNHYP2EaMGEFkZCTr168HICYmhi5duhAbG4uLiwsrV65k6dKldOrUyd5JF3s6nY5u3bqxePFivLy8aNasmcXMALdu3eLKlSu89dZbvPPOO+blBoPBomdvUFAQkydPZtiwYXTs2JEXX3wx0zSPHDlCYGCgOVi73+nTp+nQoYPFssaNGzNlyhTi4uLw9PR81NMV2XTbYOKj/XHMPWFdqqYChtdwZ+xjnjhr7ftFpVapKOOupYy7lhYlC3dPzYJMpVLxQlU3WpdyYti/MeyKSLVYv+16Ctuup5fAOWngcV9HGvk70ijAiYb+jkWuY0ZW7LyRwsLTlm0336jpQai31P6IgsfuAdu+fft46aWXzM+XLVuGXq9n27ZthISE0KNHD2bNmlVoArbstCkrCFNTPfvsswwdOhQ3NzdzqVYGkyn9V+T06dNp2LDhA4+zc+dONBoNV65cISUlJUtDsGSXVM/knb03Uxi6PYZzcdalahU9NXzdrPAPeyHSlffUsqaTL3NOJDDxQJzNtoUpRtgVkZoe1B1NAKCaTkvDOwFcowBHyrlrivQ9mmJUeHOn3mJZJU8tI2p55E+GhHgIu/+kio6ONs8+ALBhwwaaNGlCaGgoDg4O9O7dm1OnTtk7WXFHy5YtcXBwICoqiq5du1qs8/f3p2TJkly4cIEKFSpYPTKsWrWKX3/9lVWrVhEXF8eECRMyTa9WrVrcuHGD06dP21xfpUoV9uzZY7Fs165dlC5dGg8P+WDMbckGhQ/3xdJp3S2bwdor1dzY/oS/BGtFjEat4vUaHmzr4U99v6yVFp3UG1h05javbo+hzm8RVF12g0Fbovj6eAIHb6UWudkUph+J50ys9fRT9i5hFsJe7F7C5u3tbR7c9vbt2+zZs4dRo0aZ16tUKlJSUjLbXeSQSqVix44dKIpis1RszJgxjBw5Ei8vLzp06EBaWhqHDx/m+vXrvP3221y7do033niDcePG0bRpU+bNm0ePHj3o0KEDrVq1sjpey5YtqVevHs899xyffvopFStW5MKFCyQmJtKtWzdee+012rRpw6RJk+jTpw8HDhxg9uzZjBs3Lg9ejeLt4K1Uhm6P4ZTeYLUuyF3D7GbeNJcqyiKtis6BP7v6cTQ6jd0Rqey5mcruiFSu3n54L5CIJBMrLyaz8mJ6ByJXrYp6fo409HekcYAj9fwc8XTM+W9+o0kh2aiQaoJko0LKPY/wBBWaOAMeDio8HNQ4aexTMn9an8b0+6afeq6yK80C5X4QBZfdA7ZGjRoxf/58KleuzN9//01KSgqdO3c2rw8LC6NkyZL2Tlbc40ElV8899xyurq7MmjWLjz76CGdnZ6pVq8bLL7+MoigMGzaMWrVq8dprrwHQpEkT3nzzTYYOHcqOHTvw8fGxOJ5arebXX3/lgw8+YMiQISQkJBAcHMzo0aMBqFOnDosWLWLy5MlMnz4df39/3nzzTYYMGZJ7L0Axl2pUmHo4nhlH4m0OQDu4iisf1ffCQxr2FwsqlYpaJRypVcKRIXfGEr+cYDAHcLsiUjgRY+Bh5We3DYpFOzi1Cqp7O1DPzwEHteqeQAtSTJaBV4oxvQry7vL058lG5SGDJLvAoQjzMwc1eDio0wM4xzt/7wRzlsvUeDim//W8b7m7g4o3d+pJu2fMYX8XNR/Vk+mnRMGW44Fz73fhwgV69epFeHg4AK+//joTJ04EwGg0UrNmTdq3b8/MmTPtmaxdFNaBc4sLGTj34Y5GpzF0ewzHoq2njirtquHLZjralC7679HCfh3zWmyqiX03U9l9M5XdESnsj0wjyV7TTRQCC1t506t80R63Lz/J/Wgfdi9hK1++PP/99x+nTp3Cw8ODcuXKmdfdvn2bzz77jBo1atg7WSGKNYNJYcaReKYejrcoOcjwdCVXPm3gVSx7AoqH83JU066MM+3KpAfzaSaFI1Fp5gBuz81UbubClGUFQccyTvQMdsnvbAjxULky2ZxWq7UZlHl4eFg1hBdC5MwpfXqp2sFb1qVqAS5qZjbV0amsfCGJrHNQq6jr50hdP0deq+6OoihciDeyOyKF3TdT2RORyulY67aRj0JF+jAjThqV+eGsUeGohtSUVFLVDsSnKcSnmWz+GMkJN62KaY11Rbo3rCg6cm124FOnTnHx4kX0er3FGF8ZBgwYkFtJC1Fs/HAmkXd3620O3dCnggtTGnrh4yzT64icUalUVPDUUsFTy9Mh6TMARCcb2XMzlbNxBrSqjGCL+4IuFc73BWMW26lVOKgz70iQXpV2dyzJFGN64BafqhCXZkoP5FJN5oAuIU3JdF18mkLCnX0TDAolXdXMbOJNWfdc+xoUwq7s/k69cOECQ4YMYf/+/TYDNbgznYoEbEI8MkVR+PxIAh8fiLNa5+us5vPGOp6Qah6Ri3ycNXQOytv3WHqwp8E3h80wjSYFjVpK1UThYveA7c033+TEiRNMmjSJxo0bo9Pp7J2EEMWaSVEYsyeWeSetZyzoUc6Zzxvr8HORUjUhMiPBmiiM7B6w7dmzh7fffrvQzhOpKIq0ZyiAMiutLW5SjQrD/o3ht/NJFsudNTCrqbdM8C2EEEWU3QO2EiVKFNr5Id3c3NDr9eh00gi1IFEUBb1eX+xnRkhMM/Hclmj+vmo58LSno4pl7UrQWGYrEEKIIsvuAdsLL7zAL7/8wssvv4xGU7iqZbRaLR4eHsTFWbcLygqZzDz3eHh4oNUW38bB0clG+m6K4r9Iy56gAS5qfu/gSw0fmaxaCCGKMrt/AwYHB2MwGGjatCkDBgygdOnSNgO3Xr162Ttpu9BqtY88OOvNmzcpW7bswzcUIhuuJBjo/WeU1TAKFTw0LO/oS7BH8Q1khRCiuLD7J/1LL71k/n/8+PE2t1GpVAU2YBOiIDmjT+PJP6O4kmg5bkctHwd+61ACf+lcIIQQxYLdA7bVq1fb+5BCFEv7I1Pp81cU0SmWo4U2D3RkSdsSdpl4WwghROFg94CtWbNm9j6kEMXO5qvJDNwcTaLBsnds93LOfNvCB2etdIoRQojiJNcavxiNRg4fPsylS5cACAoKok6dOqjVUiogxIP8fv42r26PsZqGZ1BlV6Y31skYUkIIUQzlSsC2fPly3nvvPSIiIszjZ6lUKgICAvj000+l/ZoQmfjmRAKj9sRy/6hzI2p58N7jHjLcjBBCFFN2D9jWrl3LSy+9ROXKlXn77bepXLkyAGfOnGHBggW89NJLODk50aVLF3snLUShpSgKkw7FM/VQvNW6SQ28GFrdPR9yJYQQoqCwe8D2+eefU6dOHdatW4ez890J31q2bMlzzz1Hp06dmDZtmgRsQtxhNCm8uzuWBactp5rSquDr5t70reiaTzkTQghRUNi9QdnJkyfp27evRbCWwcnJiX79+nHy5El7JytEoZRiVHhha7RVsOaqVfFzuxISrAkhhAByoYTNxcWFqKioTNffunULFxcXeycrRKETn2bi2b+j2XrdcqopbycVv7Tzpb6/Yz7lTAghREFj9xK2li1bMm/ePHbu3Gm1bvfu3XzzzTe0atXK3skKUajcSjbSff0tq2CtlKua9V38JFgTQghhwe4lbBMmTGDXrl1069aN2rVrExISAkBYWBiHDx8mICAg0xkQhCgOwuPTp5o6G2c51VSIl5blHUpQ1l2mmhJCCGHJ7iVsQUFB/Pvvv7z66qskJCSwatUqVq1aRUJCAsOGDWP79u0EBQVl+Xg7duygf//+VKtWDZ1Ox5IlSyzWDx06FJ1OZ/Fo166dvU9LCLs4EZNGp3WRVsHa474ObOjiK8GaEEIIm3Ll28HX15dPP/2UTz/9NMfHSkxMJDQ0lAEDBvDqq6/a3KZVq1bMmzfP/NzRUaqTRMGzOyKFfpuiiE21HGWtdSknfmzjg7uDDCothBDCtgL/c75Dhw506NABgGHDhtncxsnJiYCAgLzMlhDZsvFyMs9viSbJaBmsPVnehTnNvXHSyIC4QgghMpfjgG3KlCmoVCpGjBiBWq1mypQpD91HpVIxcuTInCZttmvXLipVqoSXlxdNmzZl3Lhx+Pn5PXCfsLAwu6WfF8cVecee1zDNBIuuaJl/yQEjlkHZUyXTGFHqNpfOZ96rWjw6uReLBrmORUNuXMeMNvLFhUqv198/C062eHt7o1KpuHHjBo6Ojnh7ez88UZWK6OjobKdVunRppk6dyjPPPGNe9vvvv+Pi4kK5cuW4dOkSH3/8MSaTiX/++QcnJ6dsp5ETYWFhxe4NVNTY8xruvZnCGzv0nNQbrNaNruPBqDoy1VRukXuxaJDrWDTIdbSPHJewxcTEPPB5buvdu7f5/+rVq1OnTh1q1qzJxo0b6dGjR57mRQhIH1/to/1xfHcy0WpOUBXwWSMvXqomU00JIYTIugLfhi27SpYsSalSpTh//nx+Z0UUQxsuJ/HOzliu3jZarfNzVvNFEx1dy8nA0UIIIbKnyAVsUVFRXL9+XTohiDx1M8nI6D2xLL+QZHP9MyGufFzfC28n6QkqhBAi+3IcsHXv3j3b+6hUKlatWpWlbRMSEsylZSaTiStXrnDkyBG8vb3x9vZm8uTJ9OjRg4CAAC5dusRHH32En58f3bp1y3a+hMguRVFYcvY27++NRZ9q3Rw02EPDzCY6WpaynltXCCGEyKocB2wmkynbDacVJev9HA4ePGgRFE6aNIlJkyYxYMAApk+fzokTJ/j555+JjY0lICCA5s2bs3DhQjw8PLKVJyGy60KcgTd36q2mlwLQqOD16u6MeswDV62UqgkhhMiZHAdsa9eutUc+MtW8eXP0en2m65cvX56r6QtxP4NJYfbxBCYdjCPZuqkatXwcmNVURx1fGcBZCCGEfRS5NmxC5KZDt1L53w49R6LTrNa5aFSMfcyDodXd0apluA4hhBD2IwGbEFlw22Bi0sF4vj6egNFGjX6rUk7MaKyjvKfcUkIIIewvx98uGQPnZodKpSIqSkZ3F4XDP9eSeXOnnovx1vWfOkcVnzbwYkAlVxkEVwghRK7JccA2cuRI+aISRVJ0spH39sXx09nbNtc/VcGFSQ288HPR5HHOhBBCFDc5DtjGjBljj3wIUWAoisLyC0mM2hPLrWST1foybho+b6yjY1kZqkMIIUTekAY3QtzjRrKK9zdFsfGK9VAdKmBINTfer+uJh4MM1SGEECLv5Dhg27FjBwBNmza1eP4wGdsLURAYTArfnUrko4PO3DZaB2vVdFpmNfWmvr8M1SGEECLv5Thg69atGyqVihs3buDo6Gh+nhlFUVCpVERHR+c0aSHsYuu1ZEbvieWk3kB6Odpdjmp4t7YHb9T0wFEjbTWFEELkjxwHbKtXrwbA0dHR4rkQBd3FeAPv741lzaVkm+sbBzgys4mOyjqHPM6ZEEIIYSnHAVuzZs0e+FyIgiYhzcT0I/F8dSyBVOs+BXg6qJhQz4tBVVxRSw9oIYQQBYB0OhDFhklR+OVcEuP/i+VGko1IDejqb+Dz1mUIdJWhOoQQQhQcduvqFh4ezpw5c/j222+5fv06AGfPnuW5554jJCSEUqVK0bFjRzZv3myvJIXIsv2RqXRYG8mr22NsBmv1/Bz4u5sf4yunSrAmhBCiwLFLCduxY8fo1KkTiYmJAHzyySf8/vvv9O/fn4SEBEJCQjAYDBw8eJC+ffuyYsUKmjdvbo+khXigG7eNTNif+eC3gS5qxtfzom9FF9QqFWH6vM2fEEIIkRV2Cdg+++wz3N3dWbZsGf7+/rz77rs8++yz+Pr6sn37dgIDAwG4cOECXbt2ZcaMGRKwiVyVYlSYczyBaYfjSTBYT/7pqIbhNdx5q5YH7jKmmhBCiALOLgHbf//9x+DBg81jq73//vu0b9+eMWPGmIM1gPLly/P8888ze/ZseyQrhBVFUVh3KZn398VywcbcnwDdgpz5uIEXwR7ShFMIIUThYJdvrBs3blCuXDnz84z/g4KCrLYNCgoiPj7eHskKYeGUPo0xe2LZcs164FuAUJ2WSQ29aFlKppQSQghRuNglYDOZTGg0dxtqZ/xvawBdtVqqn4R96VNMTDoYx3enEjFa136ic1Tx3uOeDK7ihlYtw3QIIYQofOxWJ2QrOHvQjAdC5JTBpPD9mUQ+ORBPdIp1z0+1Cl6s4saYxzzwcZaen0IIIQovuwVsr732GsOHD7dY1qdPH6sSNZPJ9vhXQmTHtuspjNmj53iMweb6FiWdmNTAi+o+MkuBEEKIws8uAduAAQPscRghHupivIEP9sWyKtz2dFLl3DV83MCLbkHOUsIrhBCiyLBLwPb111/b4zBCZOqMPo3pR+L59XySzXZqbloVb9fy4LXq7jhrJVATQghRtMi4BqJAOxqdxvTD8fxxMQkbcRoA/Sq68GFdL0q5STs1IYQQRVOOA7YLFy5Qvnz5PN9XFG37I1P57HA8Gy7brvoEeNzXgckNvWjg75SHORNCCCHyXo7H2Khfvz4vvfQSO3bsyPI+//77Ly+88AINGjTIafKiiNlxI4VeG2/Rdk1kpsFaZS8t81p4s6mbnwRrQgghioUcl7D99ddfTJw4kW7duhEQEECLFi2oU6cOwcHB6HQ6FEVBr9cTHh7OoUOH2LZtGzdv3qR169b89ddf9jgHUcgpisLmaylMOxzProjUTLer4ePAiFoedC/njEbGUxNCCFGM5Dhge+yxx1i+fDlHjx5l8eLFrFu3jl9//RW4Ow6boqS3PipXrhxPPvkkzz77LKGhoTlNWhRyJkVh/aVkph2J5+CttEy3q+fnwIjaHnQsIz0/hRBCFE9263RQs2ZNpkyZwpQpU7h+/TpnzpwhJiYGAB8fH6pUqUJAQIC9khOFmNGksPJiEtOOxHMik3HUAJoGOvJubQ9alnSSQE0IIUSxliu9REuWLEnJkiVz49CiEEszKfx67jbTjyRwNi7zQK1daSfeqe1B4wBpnyaEEEKADOsh8kCyQWHp2dt8cTSeSwnGTLfrGuTMiNoePObrmIe5E0IIIQq+Aj8T+44dO+jfvz/VqlVDp9OxZMkSi/WKojBp0iSqVq1KYGAgXbt25eTJk/mUW3GvxDQTXx9P4LHfb/D2Lr3NYE2tgt7lXdjxhD9L2paQYE0IIYSwocAHbImJiYSGhjJ58mRcXFys1s+cOZPZs2czZcoUNm/ejJ+fH7169SI+Pj4fcisA4tNMzDgST+3fIhi7N5brt63nj9Wq4JkQV/b28md+Kx+Z81MIIYR4gAJfJdqhQwc6dOgAwLBhwyzWKYrCnDlzePPNN3niiScAmDNnDiEhIfz2228MHjw4z/NbnCWmmfjuVCJfHI0nJsX2vASOahhY2Y3/1XCnnEeBf/sJIYQQBUKh/sYMDw8nIiKCNm3amJe5uLjQpEkT9uzZ88CALSwsLFfylFvHLchSTLD8upZFVxyITrPdm9NZrfBkoIFnSxvwc7pN6o1Iwm7kcUazqDhew6JIrmPRINexaMiN6xgSEmL3YxZkhTpgi4iIAMDPz89iuZ+fH9evX3/gvrlxocPCworVGyjVqLA47DbTDsdxzUa1J4CHg4oh1dwYWt0dX+eCP9dncbuGRZVcx6JBrmPRINfRPuwesG3dupXNmzdz4cIFEhIScHd3p0KFCrRp04YWLVrYOzmRDwwmhZ/O3uazw5n3+vR0UDG0ujtDQ93RORX4ppJCCCFEgWa3gO3atWs8//zz/Pfff+aZDe41a9YsGjZsyMKFCwkMDLRLmhkD8UZGRlK2bFnz8sjISPz9/e2ShrjLaFL4/UISUw7FcS7OdqDmplXxSqgbw2t44C2BmhBCCGEXdvlGTU1NpW/fvuzfv59Bgwaxfv16Ll68yK1bt7h48SLr16/nueeeY+/evfTr14+0tMynIcqOcuXKERAQwJYtW8zLkpOT2bVrFw0bNrRLGiJ9CqmVF5NouvImQ7bF2AzWnDXwenV3Dj0VwAd1vSRYE0IIIezILiVsv/76K8ePH+fbb7/lqaeesljn5eVFo0aNaNSoEU2bNmXIkCH89ttvDBgwIEvHTkhI4Pz58wCYTCauXLnCkSNH8Pb2pmzZsgwdOpTp06cTEhJCpUqVmDZtGm5ublb5ENmnKAobLifzycF4jkXbDrId1PB8FTferuVBSdeC30ZNCCGEKIxUer3e9vgL2dC/f39iY2NZv379Q7ft3LkzOp2On376KUvH3r59O927d7daPmDAAObMmYOiKEyePJlFixah1+upW7cu06ZNy5fJ5YtKw0pFUdh8LYVPD8SxP5NJ2TUqeDbElRG1PSjrXqj7rlgoKtewuJPrWDTIdSwa5Drah12+aY8dO8agQYOytG2bNm34/vvvs3zs5s2bo9frM12vUqkYM2YMY8aMyfIxRea2X0/h04Nx7IpItblerYK+FVwYVceT8p5FJ1ATQgghCjK7fONGR0dnebL3kiVLEh0dbY9khR3tvZnCJwfi2Xo9JdNtnizvwug6HlTWyawEQgghRF6yS8CWlJSEk5NTlrZ1dHQkOTnZHskKOzh0K5VPD8bx55XMA7VuQc6MecxTpo8SQggh8ond6rTi4+OJjIx86HZxcXH2SrJYMykKtw3Wj8Q0hdsGE0kGhUQb628bTCQaFJIMCreSTZlWfQJ0KOPE2Mc8qSMTsgshhBD5ym4B24gRIxgxYsRDt1MUBZXK9vRFhVnEbSMn4tVERaSQYoQ0k0KKUSHVpJBi5M7f9EeaCct1RoUUk0KqUSE1Y51RIcWUvi7ZqNwXgJlItj0Mml20LOnEe4970MA/a6WmQgghhMhddgnYRo0aZY/DFGoLTycy+bAzHL6V31l5ZI0DHBn7mCfNS0qgJoQQQhQkdgnYRo8ebY/DFGpOmsJbaljX14H3HvekdSmnIln6KYQQQhR2Mi6DnTjmQ8DmolHhqlXh6qDCVXPnr1aFm1aFq1aNi/n/u8tdMv53UOOqVVHaTUM1nVYCNSGEEKIAs0vAFhERwdmzZ6lduzbu7u7m5QaDgc8++4xffvmFGzduULlyZcaMGUOnTp3skWyBEuiipoqbCU9XJxw1KpzUKhw1KhzV6aVvd5fx4HUaFQ5qFU73bOeksR10qSXIEkIIIYoFuwRsM2bM4LfffuPEiRMWyz/44APmzp2Lh4cHVatW5fTp0zz77LOsWbOGRo0a2SPpAqN3BVdqGa8SElL24RsLIYQQQmSDXWbo3rVrFx07dsTR8e7wD9HR0XzzzTdUqlSJw4cPs2XLFnbv3o2vry9fffWVPZIVQgghhCgW7BKwXblyhapVq1os+/PPPzEajQwfPhydTgdAUFAQTz/9NPv27bNHskIIIYQQxYJdArakpCS8vLwslu3evRuVSkXLli0tlpcvX56YmBh7JCuEEEIIUSzYJWArW7Ysp06dslj277//4uvrS1BQkMVyW8GdEEIIIYTInF0Ctvbt27NkyRJ27tyJyWTixx9/5Ny5c3Tr1s1q20OHDlG2rDTMF0IIIYTIKrv0En3nnXdYuXIl3bp1Q6VSYTKZ8PX15Z133rHYLiEhgTVr1vDiiy/aI1khhBBCiGLBLgGbt7c327dv54cffuDixYsEBQUxcOBASpQoYbHdqVOn6NevH3379rVHskIIIYQQxYLdZjrQ6XT873//e+A29erVo169evZKUgghRG5SFEiMQx0ThUp/C5U+yvxQ66NQxdwCgwFj5ZqkdeiN4l8qv3MsRJElU1MJIURxYxGIZQRj0aj0t9IDsXuXGdIeejhN+BkcNq3AWK85qZ37YaoYmgcnIXIkNQXN6SNojuxBe2wfquhITP4lMQVVMj+MQZXAzSO/cyrusEvA1rBhw0zXqVQqnJycKFu2LB07dmTAgAFotRInCiFErovToz15APXZ46ijI7MdiGWHSjGh3bcV7b6tGCvXJLVzP4x1moDaLn3bhB2obl5De2QPmiN70Jw8hCo12WK95tI5NJfOARvNy0y+ARYBnCmoEopvIMjUiHnOLpGTr6/vAycPT0pKYseOHaxdu5Yff/yRP/74A1dXV3skLYQQIkNKMpozR9Ac35/+uHQ2X7KhOXMUlzNHMQWUIbVTHwzNOoGjU77kpVhLTUFz6hCaI3vRHtmDOuJKtg+hvhWB+lYEHNhhXqa4ulkEcKagSphKB4PWwY6ZF/ezS8C2du3ah25jMpn4+eefef3115k+fTrvv/++PZIWQojiy2hAffHM3QDt7HG7l5wpzq4ouhKYdCVQMh7eviheJTB5l0B96wYO639Bc+W81b7qiCs4fz8DZfkC0tr2JLVtL/DU2TV/wpIq4graI3vTS9FOHUKVmmL/NG4nojl1GM2pw+ZlikaLqXQ5qVLNRSq9Xq/kZYJDhw7lv//+K5LTU4WFhRESEpLf2RA5INewaCiy11FRUF2/hPbEgfQA7dRBVLcTH+1Qzi4oOl8UnQ8mne/dYEzni8n7zv9eJcAlC7UhioLm2H84rF+G9vh/mW/m4IihWUdSO/ZBKRmU6XYZiux1tKeU5DulaHvSS9FuXsvyriZvX4y1GmKo1RBT+SqoI66ivnQWdfjZ9L/XLqIymXKUPZNvABdbP4V/tz45Oo7Ih04H9erVY8WKFXmdrBBCFEoqfVR6cHYivRRNHXMry/sqKhWmoBCM1etiCqqYXlJ2p3QsS4FYljOpwlizPsaa9VFfOovDhl/R7t6Eymi03CwtFYctq9H+swbjY03SOyiE1JT2UNmhKHdK0e60RTt1GFVaatZ21WgxVq6JsVZDjDUbYCpT3uK1N5YIwBj6+N0dUlNQXwtPD94unUVzJ5BTJd/OcnbVtyIwOrlkeXuRuTwP2BISEnB0dMzrZIUQonBIuo3m9KG71ZxXL2Zrd5N/KYzV62KoXhdjtcfAPW+nAjQFVSJlyBhSn3oRh79W4LBlFaoky1JAlaKgPbAD7YEdGCtUS++gULcZaKRDmk1Jt9NL0Y7eaYsWeT3Lu5p8/O+UojXAGFo3e4G6oxOm4MqYgivfc0ATqls30gO4e0vjom9mnv2AMllPU2QqT+8Ok8nE2rVrqV69el4mK4QQBYOiQHISqoTY9Ef8nUdiXHpJ2pmjqM+ftCqZeuAhPbwwhD6OMbQuxup1UfxK5uIJZJ3i409qv1dI7fEsDlvX4fDnb6ijIqy205w/icvs8Zj8SpLWsQ9pzTuBczHvlGYyor5wBs2xfWiP70d99liW3xOKRouxSi1zVadSqpx9SzDVahT/Uhj9S2Gs1+Lu8oRYNJfOWVWpKj5+GF3c7Jd+MWaXgC0yMvKB65OSkjh79izz58/nv//+47vvvrNHskIIkX8UBZJv3w26EuIsA7GE9GXcuy4+FpXRkLNkHZ3Sv5DvBGimshUL9tAZLm6kdepDWvteaPdtxWHdMjThZ6w2U0dex2nxLBxXLCStzROkteuVD5nNP6pbN9Ac+w/Nsf/QntiPKjE+y/uafAMw1kwP0Iyhj+VPwOvuhTH0casqVZU+CmIfrZ2lsGSXgK1y5coPHNYjg4ODA++99x5PPvmkPZIVQojcZzSkV/+cOozm9BFUkdfuBmg5DL6yQlGpMVWoYg7QjJWqg0MhbFai0WJo1BZDwzZoTh1K76BweLfVZqrEeBxXL8Zh/TLKVa2LQ0g1cHZBcXK589cZnFxQnF3MfzP+R+tQeNrDZVRzHtuH9th/qG9czvKuitYBY5Xa5qpOpWRQwTxvR6f02S9iw/I7J0WCXQK2kSNHPjBgc3Z2pmzZsrRs2RJfX197JCmEELnDaEAdfjb9y/TUITRnjlq1wcptppJB6W3QqtfFWKV20RoaQaXCWO0xjNUeQ3UtHMcNv6Dd8afVcCQqQxo+x3bDMeugLjOKWn1fcHf3r9UyV3cULx8UnU/6ECW6Eumvc24FPjmo5gQwlSyLoXq99M4d1R5LD1BFsWKXgG3MmDH2OIwQQuQ9owH1xTDLAC0bveCyS3FwRPHQobh7onh4obh7obh7gocXJr9SGEMfQ/Hxz7X0CxKlVDlSXniX1CdfwOHvP3D4eyWqxLhHPp7KZILbiY8+1InW4Z4g7m4gl76sxN3lnj6QhRl7clLNqbh5YAiti7FGPYw16qXPLiCKtULfJWfSpElMmTLFYpm/vz9nzli3kRBCCAwG1OFn0oOzk4fQhB1FlZz0SIdSHJ2tAq+M/8n437zuTmDm5GznEyr8FF0JUnu/SGq3p3HYvgGHDb+ijsz6eGL2ojKkoYqKABudI+6lqFTp1/P+QE5XAsXVA/WFU2iP/4f6ejaqOTUaTJVqYKhRD2P1epjKVwa1JqenJIqQQh+wAYSEhLBmzRrzc41G3uRCiDsMBtQXT98tQQs7lu0ATfHwSm8zVLUOxoqh6V/Q7l4y3ZK9ObmQ1q4XaW16oDn6H9GH9uDn7pbeszYlCVKS069dShKqjGXJSahSktOX2XmWh8yoFAVVvB7i9WBjhoesMgWWTQ/QatTHWLWOfcfGE0VOkQjYtFotAQEB+Z0NIURBkHQb1yvncDi1906AdjT9Cz0bTB46TFXvBGhV62AqVa5g98QsatQajLUbctPVB6/szHRgSLMM6u4EdhkBHvcGeQmxqPTRqGOjUemjUMVGPXJJa1ZJNafIiTyfmsreJk2axKxZs/Dy8sLR0ZF69erxwQcfEBwc/MD9wsKk14oQhYZiQns7AYeEWLQJsTjceWgT4nBI0Fs816Rlf+7ENDcPEoIqk1CuCgnlqpDsW7Jg9roTuUqdmnzPeyrO6v3mkBCLNjEWbWICKh7+1amoNSSWrkBchVDiK1TndkkJ/O2puE1bVugDtr/++ouEhARCQkK4desWn332GWFhYezevRsfH588zYvMe1f4yTXMY2mpqMwlHNF3Sjyi7nkehUofjSouOsdzGt7L5OWNsUp66ZmxWp2COyxCMVdg70eDAVVcTPp7NPbOezQ2GrU+ClW8HpOuBMbq9aSa844Cex0LmUJfJdq+fXuL5/Xq1aNOnTosXbqU119/PZ9yJUQxoSjpg2PeTkjvAXc7If3/Ow/z88R4y+cZy7LRay4nTF4+GO+p4pQATeSIVovi44fi45ffORHFSKEP2O7n7u5O1apVOX/+0RuCClFkmYzpAVZqCqQk3/k/GVLu/E1NSW/vlXrP89uJd4OtO4GXRfCVB4PHZoeidSDV3Qt1lVoYq90J0ALLSoAmhCjUilzAlpycTFhYGM2bN8/vrAiR61Q3rqDdtSl94uWU5PRALDUZVUrG3+T0vxnL0/KmF11uUFzdrMfF8ro7nELGctw8CDt7VqpghBBFSqEP2N5//306depEmTJlzG3Ybt++zYABA/I7a0LkDpMRzaHdOPz9B9pj+/I7NzmiqNQonro7Y1nZDsAylssQGkKI4qzQB2zXrl3jpZdeIioqCl9fX+rVq8dff/1FUFBQfmdNCLtSxcWg3boOhy2rUD9kYM+8pGgdUNzcwdU9fbofV3cUV4+7z93S/3JnecYyXN3TB5KVwUGFEOKhCn3AtmDBgvzOghC5R1FQnzuBw6YVaPdttcvAoIqjMzg5pf91dLJ+7nTPckdHFJd7A677AzB3KfkSQog8UOgDNiGKpJRktLs24bB5JZrwB48ZaKxYDUPj9unTIjk6gaMzitOdv45O4ORsXo6Do4wDJYQQhZAEbEIUIKobV3DYvBKH7evTe2JmQnFwxNC4HWltnsBUvkoe5lAIIUR+kIBNiPyWjU4EJv9SpLV5grTmncHdM48yKIQQIr9JwCZEfonT47B17UM7ESgqFcbajUhr2xNjjfpSpSmEEMWQBGz5SVHSx8ZKiEeVGJc+6ntiPKqEuDujwMehSkyAlKT0nnQaDWi0KFotaO48tFoUjda8Ds09z7UOd7a/u44765SM/52cUNy90nvraR3y+xUp+jI6Efz9B9q9/zywE4Hi7klay66kte6B4lcyDzMphBCioJGAzV6SbuMYfRP1OYM50FIlxt0XgN0bmN3Zxg69/uxFcXZJb7julh7AZTxwt3yu3PMcF7fCM4K8oqSPzh8bjfrOvJXpcwFGm+etrBwdibObO4qDY3oDfQdHm/9bLnN4yHpH0DqgOXEAh7//yFIngrS2vTDUbyk9MIUQQgASsNmN4/IFVP/zt/zORo6okpNQJSfBrayP8aVoNChunmAroHN2Te+h6OAETk7g4GTZe9HR6b6/d3oxZjcATE25ZyLmeyZhjo22fjxkpP/8uiGkE4EQQogHkYDNTpRi2gBcZTSiiouBuBi7HVNxvD+4c7QI8tA6QGL83aDsAb0pCzrpRCCEECIrJGCzFzePR9pNcXBIr4J0cwc3TxR3DxQ3TxQ3jzsPT3B2SZ+022gEoyF9sm2D4e5zQ5r5f/N6o/HONvdun/H8nm2TkyAhLr3aVjHZ+UV5NOnzXqagSszvnOQO6UQghBAiuyRgsxOTrgQpOl+0Op+7gZabR3rV4J0peMzL3T3Sp+hx9yw4bZRMJkhKRJUQmx68mR93n3Pfc1VCHKrU5PzOebYojs4ounvmq8yYu/LO45I+jrKlSqUHwWmpkJaK6s7fe/+3WJb6gPWGtLv/OzljeKyJdCIQQgiRbRKw2YmxXgtOeZUkJCQkv7PyaNTq9ADTzQMlIBv7pabcDd4S4+DegC45KX19RolZajKY/793WWr637SUh7Yxs0VRqVG8vC0mDs/43xyQ3QnScHZ94LFuh4VhKqzXUAghRJElAZvIGUcnFB8/FB8/+xzPZLwT1KWaAzzV/UFeWkp6qWVGcCYTiAshhCjiJGATBYtak14K5uyKcmeR8sAdhBBCiKJPWjsLIYQQQhRwErAJIYQQQhRwErAJIYQQQhRwKr1eL02EhBBCCCEKMClhE0IIIYQo4CRgE0IIIYQo4CRgE0IIIYQo4CRgE0IIIYQo4CRgE0IIIYQo4CRgE0IIIYQo4CRgu2P69Om0bt2asmXLUrFiRfr168eJEycstlEUhUmTJlG1alUCAwPp2rUrJ0+etNhGr9czZMgQgoKCCAoKYsiQIej1eottjh8/TpcuXQgMDKRatWpMmTIFRZHRVewhr65jeHg4Op3O6rFp06a8OM0iz17Xcdq0aXTs2JFSpUqh0+lspnX58mX69etHqVKlqFChAiNHjiQ1NTW3Tq1YycvraOt+XLBgQW6dWrFhj2sYHh7O66+/Tu3atQkMDKR27dpMmDCBpKQki+PIvfhgErDd8e+///Liiy+yceNGVq1ahVarpWfPnsTExJi3mTlzJrNnz2bKlCls3rwZPz8/evXqRXx8vHmbl156iSNHjvDbb7/x22+/ceTIEV555RXz+ri4OHr16oW/vz+bN29m8uTJfPnll3z11Vd5er5FVV5dxwy///47p0+fNj9atGiRJ+dZ1NnrOqakpNCtWzeGDh1qMx2j0Ui/fv1ISEhg3bp1zJ8/n1WrVvHee+/l+jkWB3l1HTPMmjXL4n4cMGBArp1bcWGPaxgWFobRaGT69Ons3r2bqVOn8vPPPzN69GjzMeRefDgZODcTCQkJBAUFsWTJEjp37oyiKFStWpWXX36ZESNGAJCUlERISAgTJ05k8ODBnD59moYNG7JhwwYaNWoEwK5du+jcuTP79u0jJCSE+fPnM378eM6cOYOLiwsAn332GQsWLODEiROoVKp8O+eiKLeuY3h4OLVr12bLli089thj+XmKxcKjXMd7rVy5kkGDBlmVdv/111/07duXo0ePUqZMGQCWLVvG//73P8LCwvD09MyT8ysucus6QnoJ2/fff88TTzyRF6dSbOX0Gmb47rvv+OSTT7hw4QIg92JWSAlbJhISEjCZTObi9/DwcCIiImjTpo15GxcXF5o0acKePXsA2Lt3L+7u7jRs2NC8TaNGjXBzc7PYpnHjxuZgDaBt27Zcv36d8PDwPDiz4iW3rmOGgQMHUqlSJTp27MjKlStz/4SKqUe5jlmxd+9eqlSpYv6CgPT7MSUlhUOHDtkr++KO3LqOGUaPHk2FChVo3bo1CxYswGQy2Svr4g57XcP4+HiL6m25Fx9Om98ZKKhGjx5NzZo1adCgAQAREREA+Pn5WWzn5+fH9evXAbh58yYlSpSwKCVTqVT4+vpy8+ZN8zalSpWyOkbGuuDg4Fw5n+Iqt66ju7s7EydOpFGjRmi1WtatW8fgwYOZM2cO/fr1y4tTK1Ye5Tpmxc2bN62OUaJECTQajflaC/vJresIMHbsWJo3b46bmxtbt27l/fffJyoqinfffdc+mReAfa7hpUuX+PLLL3n77bfNy+RefDgJ2GwYO3Ysu3fvZsOGDWg0mvzOjnhEuXkdS5QowfDhw83PH3vsMaKjo5k5c6YEbHYm92PRkNvXceTIkeb/a9Wqhclk4vPPP5eAzY7scQ1v3rzJU089RevWrXnttdfsnMOiTapE7zNmzBh+//13Vq1aZVHaFRAQAEBkZKTF9pGRkfj7+wPg7+9PVFSURY9PRVG4deuWxTa2jpGxTthHbl9HW+rWrcv58+fteBYiJ9cxK2zdj1FRURiNRrkf7Si3r6MtdevWJS4uTkpn7MQe1zAiIoLu3btTrVo15s2bZ1GLIffiw0nAdo9Ro0aZ35CVK1e2WFeuXDkCAgLYsmWLeVlycjK7du0yt3Vq0KABCQkJ7N2717zN3r17SUxMtNhm165dJCcnm7fZsmULJUuWpFy5crl5esVGXlxHW44ePWr+8BI5l9PrmBUNGjTg9OnTXL161bxsy5YtODk5UadOnRyfg8ib62jL0aNHcXZ2xsvLK0fHEfa5hjdu3KBbt25UrlyZ+fPno9VaVvDJvfhwUiV6x4gRI1i2bBmLFy9Gp9OZ6+Xd3Nxwd3dHpVIxdOhQpk+fTkhICJUqVWLatGm4ubnx1FNPAVClShXatWvHW2+9xRdffAHAW2+9RceOHQkJCQHgqaeeYsqUKQwbNowRI0Zw9uxZvvjiC0aOHCk9RO0gr67j0qVLcXBwoFatWqjVajZs2MB3333H+PHj8+O0ixx7XEdIH9cpJiaGS5cuAXDkyBEAKlSogLu7O23atKFatWq8+uqrfPzxx8TExPDBBx/w3HPPSa80O8ir67h+/Xpu3rxJ/fr1cXFxYfv27UyaNIlBgwbh5OSU9ydehNjjGl6/fp1u3boRGBjIpEmTiIqKMh/f19cXjUYj92IWyLAed2Q2GOOoUaMYM2YMkF4tNnnyZBYtWoRer6du3bpMmzaN0NBQ8/Z6vZ6RI0eyfv16ADp37szUqVMtjn/8+HFGjBjBgQMH0Ol0DB48mFGjRknAZgd5dR2XLl3KzJkzuXz5MhqNhooVKzJ06FBpv2Yn9rqOQ4cO5aeffrI6zurVq2nevDmQHgyMGDGCbdu24ezsTJ8+fZg4caJ80dtBXl3HTZs2MWHCBC5cuIDJZCI4OJiBAwfy8ssvW5XkiOyxxzVcsmRJpu3VDh8+bK5dknvxwSRgE0IIIYQo4KQNmxBCCCFEAScBmxBCCCFEAScBmxBCCCFEAScBmxBCCCFEAScBmxBCCCFEAScBmxBCCCFEAScBmxBCCCFEAScBmxCiwOnevTvly5fn1q1bVusSEhKoUaMGTZs2xWAw5EPuhBAi70nAJoQocL744guSkpIYO3as1bpPP/2Ua9euMWvWLBnFXghRbEjAJoQocCpWrMiIESP45ZdfLCaVPnz4MPPmzeOll16ibt26uZoHo9FISkpKrqYhhBBZJQGbEKJAeuONNwgNDeWtt94iKSkJk8nE22+/TWBgIOPGjePs2bM8//zzlC9fnoCAAJo3b87KlSstjhETE8O4ceNo0qQJZcqUoXTp0nTt2pWdO3dabBceHo5Op2PGjBl88803PP744/j7+7N37968PGUhhMiU1CcIIQokBwcHvvjiCzp16sTUqVMpXbo0+/fvZ+nSpVy7do0OHToQEBDAG2+8gZubG2vWrGHQoEHMmzePfv36AXDx4kVWrlxJr169CA4OJjY2lh9//JGePXuyefNmatSoYZHmsmXLSExM5Pnnn8fd3Z3AwMD8OHUhhLAik78LIQq0ESNGsGjRIlxcXGjdujU//PADvXr14vr162zZsgUXFxfztr169eL06dMcP34clUpFSkoKDg4OqNV3KxP0ej3169enU6dOfPnll0B6CVvt2rVxc3Nj//79EqgJIQocqRIVQhRoH3zwASVKlEBRFKZOnUpMTAz//PMPPXv25Pbt20RFRZkfbdu25dq1a5w9exYAJycnc7CWnJxMdHQ0RqORxx9/nEOHDlml1bVrVwnWhBAFklSJCiEKNE9PTypVqsTNmzcJDAxk//79KIrC5MmTmTx5ss19IiMjCQkJwWQyMXPmTBYtWkR4eLjFNuXKlbPaLzg4ODdOQQghckwCNiFEoWIymQAYNmwYHTp0sLlNaGgoANOnT+fjjz9mwIABvP/++/j4+KDRaJg+fToXLlyw2u/e6lUhhChIJGATQhQqGaVgWq2WVq1aPXDbP/74g2bNmjFnzhyL5ZMmTcql3AkhRO6QNmxCiELFz8+P5s2b8/3333Pt2jWr9ffOjqDRaFAUy35Ve/bskeE6hBCFjpSwCSEKnenTp9OxY0eaNm3KoEGDKF++PJGRkfz333+cPn2agwcPAtC5c2cmT57MK6+8QpMmTTh37hyLFi2iatWqJCQk5PNZCCFE1knAJoQodEJCQtiyZQtTpkzh559/JioqCl9fX2rUqMF7771n3u7tt98mKSmJX3/9lZUrV1KtWjUWLFjA77//zr///puPZyCEENkj47AJIYQQQhRw0oZNCCGEEKKAk4BNCCGEEKKAk4BNCCGEEKKAk4BNCCGEEKKAk4BNCCGEEKKAk4BNCCGEEKKAk4BNCCGEEKKAk4BNCCGEEKKAk4BNCCGEEKKA+z8GkhnKXkfZzQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3)) \n",
    "\n",
    "plt.plot(can_usd, label='Canada')\n",
    "plt.plot(mex_usd, label='Mexico')\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('USD (Billions)')\n",
    "plt.xticks(years)\n",
    "\n",
    "plt.legend(loc=\"best\")       # Adds a legend to the figure\n",
    "plt.title(\"Military Spending in Mexico and Canada in the 21st Century\", pad=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab35d3c",
   "metadata": {},
   "source": [
    "We can see that the overall trend of military spending in Mexico also increased from 2000 to 2020. However, this increase was a lot less drastic than observed in Canada. Mexico's military spending was a steady rise from about \\$3 billion to \\$6 billion over the course of 20 years, while Canada's spending rose from \\$8 billion to about \\$23 billion over the same period of time.\n",
    "\n",
    "Let's add data from the United States to see the trends in all North American countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "18c12059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3)) \n",
    "\n",
    "usa_usd = military[['USA-USD']].loc[2000:2020]\n",
    "\n",
    "plt.plot(can_usd, label='Canada')\n",
    "plt.plot(mex_usd, label='Mexico')\n",
    "plt.plot(usa_usd, label='United States')\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('USD (Billions)')\n",
    "plt.xticks(years)\n",
    "\n",
    "plt.legend(loc=\"best\")\n",
    "plt.title(\"Military Spending in North America in the 21st Century\", pad=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae241382",
   "metadata": {},
   "source": [
    "With the addition of the data from the United States, it's difficult to discern the data from Canada and Mexico. Because the spending on the military in the United States was a lot higher, plotting all three datasets on the same graph with the same axis does not allow us to clearly see trends in the other countries.\n",
    "\n",
    "To address this, we can graph the data for each country separately with axis limits that are tailored to each country. If we graph this data side by side, we can see the trends in each country while acknowledging that the axis intervals for each country provides a numerical context for cross-comparisons.\n",
    "\n",
    "To do this, we use the `plt.subplots()` function. This function creates a `figure` object and `axis` objects, which we will name `fig` and `ax`, respectively. More information on the workings of `plt.subplots()` is linked at the end of this section.\n",
    "\n",
    "By using `plt.subplots()`, we can add data for Canada, Mexico and the United States to the same figure by specifying the data assigned to each `ax` object. Here, we will define three `ax` objects: `ax1`, `ax2`, and `ax3`. This will allow us to create <u>three</u> separate plotting areas, bounded by <u>three</u> different axes, that are contained within <u>one</u> figure.\n",
    "\n",
    "Once the figure and axes are defined, we can create a title for the entire figure using `fig.suptitle()`. Using the `.plot()` method for each `ax` object, we can specify which data to plot in each axis. We can also specify other plotting features about each axis by using various methods on each axis object, such as the `set_title()`, `set_xlabel()`, `set_ylim()`, and `set_xticks()` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af08cad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defines a figure object and three axis objects\n",
    "# Sets the dimensions of the figure (1 x 3 axes)\n",
    "# Sets the figure size (15in x 3in)\n",
    "(fig, (ax1, ax2, ax3)) = plt.subplots(1, 3, figsize=(15, 3))\n",
    "\n",
    "\n",
    "# Sets title for entire figure\n",
    "fig.suptitle('Military Spending in North America in the 21st Century', y=1.1, fontsize=15)\n",
    "\n",
    "\n",
    "# Makes plots for each axis\n",
    "ax1.plot(can_usd, color='blue')              # Plots Canada data to ax1 \n",
    "ax1.set_title('Canada')                      # Sets title for ax1\n",
    "ax1.set_ylim([8, 24])                        # Sets y-axis limits for ax1\n",
    "ax1.set_xlabel('Years')                      # Sets x-axis labels for ax1\n",
    "ax1.set_ylabel('USD (Billions)')             # Sets y-axis labels for ax1\n",
    "ax1.set_xticks(years)                        # Sets x-ticks for ax1\n",
    "\n",
    "ax2.plot(mex_usd, color='red')               # Plots Mexico data to ax2\n",
    "ax2.set_title('Mexico')                      # Sets title for ax2\n",
    "ax2.set_ylim([2.5, 7])                       # Sets y-axis limits for ax2\n",
    "ax2.set_xlabel('Years')                      # Sets x-axis labels for ax2\n",
    "ax2.set_ylabel('USD (Billions)')             # Sets y-axis labels for ax2\n",
    "ax2.set_xticks(years)                        # Sets x-ticks for ax2\n",
    "\n",
    "ax3.plot(usa_usd, color='orange')            # Plots U.S. data to ax3\n",
    "ax3.set_title('United States')               # Sets title for ax3\n",
    "ax3.set_ylim([300, 800])                     # Sets y-axis limits for ax3\n",
    "ax3.set_xlabel('Years')                      # Sets x-axis labels for ax3\n",
    "ax3.set_ylabel('USD (Billions)')             # Sets y-axis labels for ax3\n",
    "ax3.set_xticks(years)                        # Sets x-ticks for ax3\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efee0c99",
   "metadata": {},
   "source": [
    "Now that we've created separate subplots, we can see the trends for all three countries over the last 20 years. All three countries seem to have decreased spending around 2011 and 2018. By observing the difference in scale, we can also see that while the trends are similar, the magnitude of spending was very different between Canada, Mexico, and the United States."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5190be46",
   "metadata": {},
   "source": [
    "## Histograms\n",
    "\n",
    "Histograms are a great way to view a **distribution** of numerical data. A distribution of a dataset is a visual display of all the values within the dataset when plotted on a graph, showing the frequency of occurence of said values.\n",
    "\n",
    "\n",
    "In histogram plots, a numerical component of data is divided into what are called **bins**. As data points are assigned to their respective bins, the total number of data points in each bin is quantified and plotted, visualizing a distribution of frequencies. In the upcoming exercise, we will explore how to visualize distributions of values in our dataset.\n",
    "\n",
    "\n",
    "Let's examine military spending in the United States from 1960 to 2020. We can look at multiple ranges of dollar amounts spent on the military as our independent variable and organize them into bins. After, we can determine how many fiscal years fall into each of these bins and visualize the distribution.\n",
    "\n",
    "First, we will need to extract the data pertaining to the military spending in the United States. We will call it `hist_data`. Then, we will need to determine the minimum and maximum values of this subset of data so that we can determine the range of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a45eb875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min: 47.34655267\n",
      "max: 778.2322\n"
     ]
    }
   ],
   "source": [
    "hist_data = military[\"USA-USD\"]\n",
    "\n",
    "print('min:', hist_data.min())\n",
    "print('max:', hist_data.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21d2e3e",
   "metadata": {},
   "source": [
    "We see that the minimum amount the United States spent on the military between the years of 1960 and 2020 was about \\$47 billion, while the maximum amount was about \\$780 billion.\n",
    "\n",
    "With this information, we will create a range for our bins, named `binnum`, with integers between 0 and 801, so that it is inclusive of all the data values. We make the interval of the range 100, giving us eight evenly spaced bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ccfb3ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 100, 200, 300, 400, 500, 600, 700, 800]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binnum = np.arange(0, 801, 100)\n",
    "\n",
    "list(binnum)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e62707",
   "metadata": {},
   "source": [
    "To graph the distribution of military spending, a histogram can be made by using the `hist()` DataFrame method. We can specify the bins so that they are evenly distributed on the x-axis. We can do this by inputting `binnum` as our `bins` argument. If we do not specify the `bin` argument, the data will be divided into 10 bins by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90028e2d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_data.hist(bins=binnum)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e31df5",
   "metadata": {},
   "source": [
    "We can also use the `plt.hist()` function to make the same graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "daae9915",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(hist_data, bins=binnum)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d71458",
   "metadata": {},
   "source": [
    "When determining the bins for a histogram, the bin size controls the number of bins that will show. A smaller bin size will result in more bins, which will show more granularity of the data, but could make it difficult to see patterns in the data. A larger bin size decreases the visible detail of the data, but could also make it hard to discern useful take aways from the data. While exploring data, it's important to try different bins sizes out to see which display provides the most useful information for your analysis needs.\n",
    "\n",
    "Consider the different bin sizes below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a2713731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(hist_data, bins=range(0, 801, 200))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5844a254",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(hist_data, bins=range(0, 801, 50))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ece7f2",
   "metadata": {},
   "source": [
    "Both histograms show the same data in different ways. The top histogram has a larger bin size and from it, we can see that it shows most of the values in the data fall within the range of 0-200. The second one has a smaller bin size, and we can see that most of the values of the data fall within the range of 50-100. The latter gives us a more specific range of where most of the data lie, which can be useful down the line.\n",
    "\n",
    "For now, let's stick with the bin size in the latter graph. Now that we have our plot, let's add additional details to make it more informative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "942043f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(hist_data, bins=range(0, 801, 50))\n",
    "plt.title(\"Distribution of Military Spending in the United States from 1960 to 2020\")\n",
    "plt.ylabel('Counts of Fiscal Years')\n",
    "plt.xlabel(\"Dollar Amount (USD)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e52c962",
   "metadata": {},
   "source": [
    "Awesome! From this plot, we can see that the United States had the highest frequency of fiscal years where \\$50 - \\$100 billion was spent on the military, while the \\$400 - \\$550 billion and \\$150 - \\$200 bins had the lowest frequencies with only 1 year spending those ranges of money."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778960f9",
   "metadata": {},
   "source": [
    "### Visualizing multiple distributions using histograms\n",
    "\n",
    "We can also view multiple distributions on one plot on using multiple plots. Let's look at the distributions of the percentage of GDP spent on the military in Canada and the United States from 1960 to 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea5e0162",
   "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>CAN-PercentGDP</th>\n",
       "      <th>USA-PercentGDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>4.185257</td>\n",
       "      <td>8.993125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>4.128312</td>\n",
       "      <td>9.156031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>3.999216</td>\n",
       "      <td>9.331673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>3.620650</td>\n",
       "      <td>8.831891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>3.402063</td>\n",
       "      <td>8.051281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1.164162</td>\n",
       "      <td>3.418942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.351602</td>\n",
       "      <td>3.313381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.324681</td>\n",
       "      <td>3.316249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.278941</td>\n",
       "      <td>3.427080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.415056</td>\n",
       "      <td>3.741160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CAN-PercentGDP  USA-PercentGDP\n",
       "Year                                \n",
       "1960        4.185257        8.993125\n",
       "1961        4.128312        9.156031\n",
       "1962        3.999216        9.331673\n",
       "1963        3.620650        8.831891\n",
       "1964        3.402063        8.051281\n",
       "...              ...             ...\n",
       "2016        1.164162        3.418942\n",
       "2017        1.351602        3.313381\n",
       "2018        1.324681        3.316249\n",
       "2019        1.278941        3.427080\n",
       "2020        1.415056        3.741160\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perc_gdp = military[['CAN-PercentGDP', 'USA-PercentGDP']]\n",
    "perc_gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fb06b325",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CAN-PercentGDP    0.989925\n",
      "USA-PercentGDP    3.085677\n",
      "dtype: float64\n",
      "CAN-PercentGDP    4.185257\n",
      "USA-PercentGDP    9.417796\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(perc_gdp.min())\n",
    "print(perc_gdp.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aab7d8f",
   "metadata": {},
   "source": [
    "We see that the minimum values between these two countries is about 0.98%, while the maximum value is about 9.4%. To plot both of these distributions on a single plot, we can create another array called `binnum2` that can include all of the values.\n",
    "\n",
    "\n",
    "We can then make histograms for each country, specifying the labeling and colors for each. We'll also add a legend to show which color corresponds to which country, as well as proper titles and labeling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "64136357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binnum2 = np.arange(0,11, step = 0.5)\n",
    "\n",
    "# plotting histograms\n",
    "\n",
    "plt.hist(perc_gdp['CAN-PercentGDP'], label='Canada', alpha=0.6, color = 'blue', bins=binnum2)\n",
    "plt.hist(perc_gdp['USA-PercentGDP'], label='United States', alpha=0.6, color = 'orange', bins=binnum2)\n",
    "\n",
    "# labeling\n",
    "plt.legend(bbox_to_anchor=(1, 1))\n",
    "plt.title('Distribution of the Percentage of Military Spending from 1960 to 2020')\n",
    "plt.xlabel('% of GDP')\n",
    "plt.ylabel('Counts of Fiscal Years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b901d2d",
   "metadata": {},
   "source": [
    "Additionally, we can use the `plt.subplots()` function to create two separate plots in one figure. By specifying `ax1` and `ax2`, we can use the `hist()` method to create a histogram and set titles and axis labels for each axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3b1fef90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(fig, (ax1, ax2)) = plt.subplots(1, 2, figsize=(12, 3))\n",
    "\n",
    "plt.suptitle('Distribution of the Percentage of Military Spending from 1960 to 2020', y=1.1)\n",
    "\n",
    "ax1.hist(perc_gdp['CAN-PercentGDP'], color = 'blue')\n",
    "ax1.set_title('Canada')\n",
    "ax1.set_xlabel('Dollar Amount (USD)')\n",
    "ax1.set_ylabel('Counts of Fiscal Years')\n",
    "\n",
    "ax2.hist(perc_gdp['USA-PercentGDP'], color = 'orange')\n",
    "ax2.set_title('United States')\n",
    "ax2.set_xlabel('Dollar Amount (USD)')\n",
    "ax2.set_ylabel('Counts of Fiscal Years')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e35bfce",
   "metadata": {},
   "source": [
    "Notice with the above use of the `hist()` method, we did not specify the bins for each axis. Thus, each subplot created 10 bins by default to fit the range of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "886787a1",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "In this section, we learned functions and methods to create histograms, scatter plots, and line graphs as a means of visualizing numerical data.\n",
    "\n",
    "The `plt.scatter()` and `plt.plot()` functions require numerical arrays that serve as `x` and `y` arguments. The `plt.hist()` function requires one numerical array of values for plotting distributions of data.\n",
    "\n",
    "The `hist()` and `plot()` methods can also be used directly on DataFrames to create histograms and line plots, respectively.\n",
    "\n",
    "We can also create subplots within a figure using `plt.subplots()`.\n",
    "\n",
    "Lastly, we learned about a number of other functions that can be used to enhance and annotate our plots. Documentation for the functions used in this section, and related functions, are listed below:\n",
    "\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html\">plt.hist( )</a>\n",
    "\n",
    "- <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html\">DataFrame hist method</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html\">plt.scatter( )</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html\">plt.plot( )</a>\n",
    "- <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html\">DataFrame plot method</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html\">plt.subplots( )</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.suptitle.html\">fig.suptitle( )</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html\">ax.set_title( )</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html\">ax.set_ylim( )</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html\">ax.set_xlim( )</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html\">ax.set_ylabel( )</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html\">ax.set_xlabel( )</a>\n",
    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.colorbar.html\">plt.colorbar( )</a>"
   ]
  }
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