G2_data_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## G2 - Grado di soddisfazione della vita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# Librerie\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import folium\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "get_ipython().magic('pylab inline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Cartelle Input/Output\n",
    "dir_df = os.path.join(os.path.abspath(''),'stg')\n",
    "dir_out = os.path.join(os.path.abspath(''),'output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_g2_filename = r'df_g2.pkl'\n",
    "df_g2_fullpath = os.path.join(dir_df, df_g2_filename)\n",
    "df_g2 = pd.read_pickle(df_g2_fullpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Territorio</th>\n",
       "      <th>Popolazione residente</th>\n",
       "      <th>Reddito pro capite</th>\n",
       "      <th>Gradio di soddisfazione per la vita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nord</td>\n",
       "      <td>27799803</td>\n",
       "      <td>32964.21</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sud</td>\n",
       "      <td>14149806</td>\n",
       "      <td>17867.38</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Territorio  Popolazione residente  Reddito pro capite  \\\n",
       "1       Nord               27799803            32964.21   \n",
       "2        Sud               14149806            17867.38   \n",
       "\n",
       "   Gradio di soddisfazione per la vita  \n",
       "1                                  6.9  \n",
       "2                                  6.5  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_g2 = df_g2[df_g2['Territorio']!='Italia']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_g2['Popolazione residente'] = df_g2['Popolazione residente']/100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Zq8fMzIxVq1YRFBTEf//7XzZu3PjYM21jx9NxcHBg/fr1nDx5kq1bt7J//34m\nT55sMM3Zs2cJCgri559/xsbGhiFDhig/rKdPn2bfvn38+OOP+a6LPjb9qNKQNcbWypUrDc6YC7Ne\n+sE1/fz8GDFiRK7ps2/P/BR2O7du3ZoffviBuLg4goODH3tycfv2bdasWcOqVatwcHBgypQpueZb\nnGMjLzl/RL/55psSM2xFXtsOUBJUcU2ZMoXAwEBq1qzJjh07OHfuXKFjyamw++PTUOp79+nZ2Njw\n+eefs3btWjIzM2nWrBk7duxQBtzTXyVll5SURNmyZTE3N+fMmTNK+2twcDBr165l+vTpeY6g26BB\nAw4dOkRKSgopKSkcOnSIhg0b5hubvb09FStWZP/+/UDWFdz169dzTWdra1vgaJqJiYmUK1cOQLkf\nZoz69euzb98+AG7evMmNGzfynE7/g+Lk5ERycjL//e9/gawfcnt7ey5cuABgMCJrgwYNlL/DwsK4\ne/durnGMGjVqxL59+8jMzOT+/fu5Ru7NKTk5mcTERFq2bMno0aOV+47169fnzz//BLJOOPTboEGD\nBsrrf/zxR5GWGxsbi1arpW3btgwfPly5N5VdYmIiDg4O2NjYcOvWLf755x8A7ty5wzfffMM333xT\n5B+eZs2aGdxH1I+2mtPp06eJi4sjNTWVQ4cOUb9+fZo2bcp///tfZf+Oi4vjzp07hV52fts5J1tb\nW3x8fJg3bx4tW7bMM/npx3WCrOPL2toae3t7Hjx4oFxtZ1fYY8MY+u9UP+x79tG39ftFaGiosv/X\nq1ePc+fOERsbi0ajybfno52dnXKcenl5ERUVpVxx7d6926C1Jru9e/cCWVf69vb2eY4MXZjj59VX\nX+X3339XelHqm/uSk5NxdXVFo9EY7PcABw4cQKvVEhERQWRkZK555vztKez++DSU+iup7GrXro23\ntzd//PEHfn5+3Lp1i/79+wNZG2HatGlKMwhAx44dGT16NL169cLHx0c549q0aZMyAihkNdtl76FV\np04dOnfuTL9+/YCsjhO1a9cmKioq39imT5/OrFmzWL58OZmZmXTo0IGaNWsaTOPr68vYsWM5fPiw\n0o08uz59+jBlyhRWrFhhdDd6gJ49e/LVV1/Rs2dPqlSpQrVq1bC3t881nX702V69euHi4mLQmWHK\nlClMnToVlUpl0KzQs2dPZs+eTa9evTAzM1Nu2mbXpk0b/vrrL3r27Im7uzsvvfRSgfHqO7jof0z1\n91fGjBnJTsFDAAAgAElEQVTDlClTWLVqldJxArJu8k6aNIlffvnFoMmqMMuNjo5m6tSpyqjMn3zy\nSa5pmjdvzpYtW+jRowdeXl7Uq1cPyDpxiIuL47PPPgOyeloWtkfomDFjlO9Nq9XSsGFDJkyYkGu6\nunXrMmbMGKXjhH6bDBs2jI8//hitVouFhQVjx47Fw8OjUMsuaDvn9OabbzJu3Lh8r2TffvttRowY\ngaurK8uWLaN27dp0796d8uXL59tzrDDHhjEGDRpEYGAgvXv3RqfTUaFCBebPn0+PHj2YOnUqPXr0\noGrVqtSuXRsAV1dXBg8eTP/+/ZV7cHnx9/dn5syZSseJr776ivHjxysdJ/JrvbG0tOS9994jMzMz\n3x6fhTl+WrRowfXr1+nbty/m5ua89tprfPLJJwwbNowPP/wQZ2dn6tWrZ5B03N3d6du3L0lJSXzx\nxRe5rpBy/vYUdn98GqQK+gsoMzMTjUaDlZUVERERDB8+nF9//dWgt5soeQrTw02UTPn1Wn0apkyZ\nQqtWrWjbtu1TX7YpPFdXUqJwUlNTGTp0KBqNBp1Ox/jx4yVBCSFKJLmSEkIIUWI9Nx0noHDlUrKX\nNymMAQMGAFnPGWTvIHDt2jWOHTtW5Bizl2ApTJml7CVn8nu9sKVoirrs/BTmO8xeUiav5fv7+xMb\nG0t8fDybN29Wpsle1qmw8vuOsitM6SZjll1U2UvRPEk594mibu/8SgVNmjSJd955h169ejFt2jTl\nMQidTsfcuXOVe1vZO5xMmzaN9u3b5/r+ly5dSseOHQkICCAgIMCo40k8/56rJFUYPXr0wN/fv9DT\n//TTT0BWzy19jzHISlLHjx8vVizfffcdDg4OBU4zbNgwgwfv8np93bp1Re4aXphl56eo32FBEhMT\nDR4SNKasU37fUVGZsqRUfnx8fBgzZkyhp9d3US+qnPtEcbZ3dm+99RZbt25lw4YNpKamsm3bNiDr\nAdPw8HB+++03Jk6cyOzZs5XP+Pv759uBJCAggPXr17N+/fpidQYSz69Sf09qxYoV7Ny5k7Jly1K+\nfHmlp05+pVb05U30T1brPXjwgFmzZhEZGQnAF198Qf369WnZsiXHjh1j4cKF/PvvvwQEBPDWW2+x\nadMm0tLSuHDhAv3796dChQp5liwqqASLvsxScnJyvuWU8rvpqX/d2FI02Us85VXiB8i3hE9+36Ex\nFi5cSEREBAEBATRr1oyePXsqZZ127NjB4cOHSUlJITw8nD59+pCRkcHu3buxtLRkwYIFODo65vsd\n5Ve6KSoqismTJytXGmPHjqV+/fr5lpTKr3wWwPLly9m9ezfOzs7K/te3b18uX77MtGnTUKvVNGvW\njBMnTrBx40alFM38+fOJi4tj2rRpREZGYmNjw8SJE/H29s5VKqdZs2YcPXqUlJQUbt++TZs2bRg5\nciRAntt6/fr1ufaJ7Ns7u8LsK9llTyR169bl3r17QFYBWD8/P1QqFS+//DIJCQncv38fV1dXGjVq\nVGDPVyEKUqqTVHBwMHv37mXdunVkZmbywQcfKEmqqKVW5s6dS6NGjZg3bx6ZmZm5nlcaMWKE8uMC\n4OzsbNDTKr+SRVu2bMmzBEtO+ZVTehxjS9Fkl1eJHycnp3xL+JjSiBEjuHnzpvIwZ84fs9DQUNau\nXUt6ejrdunVjxIgRrFu3jsDAQHbt2pXnk/16+ZVucnZ25ocffsDKyorw8HAmTJjAmjVr8p1PfuWz\nrly5woEDB1i/fj0ajcZg/5s2bRoTJkygfv36LFy4MM/5Ll26lFq1ahEYGMhff/3F5MmTle8he6mc\nHTt2cO3aNdauXYulpSXdu3fn3Xffxd3dPc9tndc+kZ+i7it6Go2G3bt38/nnnwNZTaXu7u7K++XK\nlSM6OhpXV9cC57Np0yZ27dpF3bp1GTVqlEmu9sTzpVQnqfPnzxuUJ9KXmzGm1MqZM2eYNm0akFUJ\nIK8H7QqSX8mi/Eqw5JRXOSVjPa4UTU75lfjJr4RPYeT3RHtRK0E0adIEOzs77OzssLe3V+KpUaOG\nQTHhnOLj4/Mt3aTRaJgzZw7Xr19HrVY/tvRNfuWzLl68SOvWrbGyssLKykop1xQfH09SUpLyXNBb\nb72V5/2W7OWbXnnlFeLi4khMTAQMS+VA1pA0+n2yatWq3L17F3d39yJv65yM/fzs2bNp2LBhgQ+x\nP06PHj0YNGgQKpWKxYsXM3/+/FzVPYQo1UkqP6YutVIYxS1ZlFc5JWMUphRNdgWV+MmvhE9hODo6\nkpCQYPBaXFzcY8/sc8reNV6lUinfU3G+o3Xr1uHi4sL69evR6XS0aNGiwOkLKp/1pOSsWJH9e9Bv\ni6Ju65yM/fyyZct49OiRQQcQNzc3pWILZD0Ura+Okh99jUzIegj4cUVwxYupVHecaNiwIYcOHSI1\nNZWkpCSOHj0KGFdq5ZVXXlFu4GdmZub6gc1ZNiR7aRTIv2RRfiVYTKmopWiyy6/ET3HVrVuXCxcu\n8ODBAyCrV1tGRgbly5c3mM7W1laJ3ZQKKt2UmJiIi4sLarWanTt3PjbZ5Vc+q379+hw9epS0tDSS\nk5OV/c/BwQE7Ozv+/vtvAIMON9ll3zfOnj2Lk5NTnpU/Coorv22dfZ8w5vP52bZtG6dOnWLGjBkG\nJcN8fX3ZtWsXOp2OS5cuYW9v/9imvvv37yv/P3jwINWqVXvs8sWLp1RfSdWpU4cOHToQEBBA2bJl\nDZ7mLmqplc8//5wZM2awfft21Go148ePNyjj4u3tjUqlonfv3nTu3Bl/f39WrlxJQEAA/fv3z7dk\nUX4lWEzJmFI0evmV+CksfWLPWRHbxcWFMWPGMHLkSLRaLba2trl+2CCrXlz9+vXp1asXLVq0oGfP\nnkVafkEKKt00ZswYdu3aRYsWLR5bZy+/8ll169bF19eX3r174+LiQo0aNZQmuUmTJvH111+jVqtp\n3LgxdnZ2ueY7ZMgQpk2bRq9evbCxsVE6eRRWzZo1893WOfeJon4+P7NmzcLd3V0pN/bGG28wePBg\nWrZsyfHjx+nWrRvW1tZKiSqACRMmcPbsWWJjY+nYsSMfffQRXbt2ZcGCBcrJY8WKFZWyOzExMUyf\nPv2JDzIqSgd5mFeIYtAPX5CSksLgwYOZOHEiderUMRjWYOXKldy/f1/pZCCEKLxSfSUlxLM2Y8YM\nbt68SXp6Ov7+/tSpUweAY8eO8fPPP6PVanF3d2fKlCnPNlAhSim5khJCCFFileqOE0IIIZ5vkqSE\nEEKUWJKkhBBClFiSpIQQQpRYkqSEEEKUWE+1C/rKlSu5dOkSZcqUUR5cTEpKYunSpTx48AAXFxeG\nDh2a54OPQgghXjxP9UqqRYsW/N///Z/Ba3v27KFOnTrMmDGDOnXqGJSvEUII8WJ7qkmqZs2aua6S\nLly4oJSsad68uVJrTQghhHjm96Ti4+OVytiOjo5GD2kuhBDi+fPMk1R2KpWqyOMNCSGEeH498yTl\n4OBAbGwsALGxsYUabPCLL7540mE9MQUN1FfaPC/rIutRssh6lCzPej2eeZKqX78+J0+eBODkyZM0\naNDgGUckhBCipHiqXdCXLVvG9evXSUxMZMyYMXTp0oWOHTuydOlSjh07pnRBF0IIIeApJ6n8hlT/\n7LPPnmYYQgghSoln3twnhBBC5EeSlBBCiBJLkpQQQogSS5KUEEKIEkuSlBBCiBJLkpQQQogSS5KU\nEEKIEkuSlBBCiBJLkpQQQogSS5KUEEKIEkuSlBBCiBJLkpQQQogSS5KUEEKIEkuSlBBCiBJLkpQQ\nQogSS5KUEEKIEkuSlBBCiBJLkpQQQogSS5KUEEKIEsvc2A+Gh4dz9+5dANzd3fH09DRZUEIIIQQY\nkaT+/PNPQkNDKV++PK6urgAEBQWxc+dOqlevzptvvmnyIIUQQryYipyk6tatm28iioyMLHZAQggh\nhF6Rk1SlSpWIj4/n33//JSUlBRsbG6pWrYqDgwOVKlV6EjEKIYR4QRU5Sf3xxx+EhIRQq1YtbGxs\nSEhI4OjRo1SvXp2OHTs+iRiFEEK8oIqcpC5dusTYsWMNXmvfvj1z5syRJCWEEMKkipykXFxc2L17\nNz4+PlhbW5OamkpwcDBly5Z9EvEJIYR4gRU5SfXv358LFy5w5swZ5Z5UtWrV6NChw5OITwghxAus\nyElKrVbTqFEjGjVqZPC6RqMxWVBCCCEEmLDixMKFC001KyGEEAIw4kpqzpw5yv91Oh0qlQqdTkdU\nVJRJAxNCCCGKnKQSEhL46quvMDc3/Oh//vMfkwUlhBBCgBHNfb169SI9PT3X6927dzdJQEIIIYRe\nkZNUvXr1sLW1Vf6+fPkyAF5eXqaLSgghhMAEHSf27NljijiEEEKIXIqdpOQKSgghxJNS7CTVs2dP\nQJ6TEkIIYXrynJQQQogSq1jPSenJc1JCCCGehBLznNS+ffs4evQoKpWKihUr0r9/fywsLIo1TyGE\nEKVbiXhO6tGjRxw4cIBJkyYxdepUtFotQUFBRs9PCCHE86HIV1L16tXL8/Xi9vLTarVkZGRgZmZG\neno6Tk5OxZqfEEKI0k8VGxure9ZBAOzfv59t27ZhYWGBj48PgwcPznO6kJAQ/vOf//Dpp58+5QiF\nEEKYmre3d4HvF/lK6klISkriwoULzJo1CxsbG5YuXcqpU6do1qxZrmm9vb1xcnJ67IqVVCEhIaU2\n9pyel3WR9ShZZD1Klme9HkYnqdTUVP766y/i4uLQ6bIuxjp37mzUvIKDg3F1daVMmTIANGzYkNDQ\n0DyTlBBCiBeH0c9JLVmyBIDz58/j5OREbGys0UGULVuWmzdvkpaWhk6n4+rVq7i7uxs9PyGEEM8H\no6+kNBoNrVq14tSpU7Rq1Ypz584ZHUS1atVo3LgxX3/9NWq1Gk9PT3x9fY2enxBCiOeD0UmqTJky\nZGRkULFiRX7++WdSU1OLFUjXrl3p2rVrseYhhBDi+WJ0kho6dCgAvXv3JiIiQprnhBBCmFyRk9S2\nbdtQqVR5vidXQkIIIUypyEmqTp06TyIOIYQQIpciJ6latWop/4+Oji5Wrz4hhBCiIEbfk1q3bh0p\nKSncuHGD6tWrk56eTs2aNU0ZmxBCiBec0c9JRUZGMnDgQFxcXBg0aJApYxJCCCGAYiQptVqNVqvF\nxsaGEydOEB0dbcq4hBBCCOOT1KBBg9DpdHzwwQckJiYyYMAAU8YlhBBCGJ+kbty4gZmZGY6OjrRv\n316upIQQQpic0Unq0KFDyv9VKhWHDx82RTxCCBOrWLEia9eufdZhCGEUo5NUZmYmSUlJQNZQGxkZ\nGSYLSoiS7Mb9ZHZcjmbt+TvsvBLDrUcphf7ssGHDcHJyYs6cOQavHz16FCcnJx48eGDqcIUo1Yzu\ngt69e3cWLVqETqdDpVLRo0cPU8YlRImSnqllV3AMB288Ijw2lcT0TOU9ByszPJ2s6VDLhQ41XTFX\n512RRc/a2pqFCxcyYMAAXF1dTRNferpJ5iNESWN0kqpRowZjxowBID4+HgcHB5MFJURJEpOYxuS9\nNwm9n4w2j/fj0zL5514SwdFJ7Ln6gK/fqoGjdf6HVqtWrbh9+zZz5szJdUWld/z4cSZPnsw///yD\ng4MDPXr0YOrUqVhaWgLg5+dHrVq1sLW1Zf369Xh6erJs2TJu3rzJiBEjOHPmDJUrV+brr782xVcg\nxDNjdHPfsmXLgKxh31esWMGKFStMFpQQJcWjlAzG775BSD4JKrtMHQRHJzFu13WDK62c1Go1U6ZM\n4eeff+bff//N9X5UVBQ9e/bk5Zdf5siRIyxcuJCtW7cydepUg+k2bdqETqdjz549LFmyBK1Wywcf\nfIBWq2Xv3r18//33zJ49m7S0NGNWXYgSwegkFR8fD0BYWBijR48mJibGZEEJUVJM33eTsNiiDUNz\n40EKMw/cLHCaDh068OqrrzJ9+vRc761YsQJ3d3cCAwOpVasWb731Fl999RU//vgjycnJynSenp7M\nmDGDmjVrUqtWLYKCgrh69SrLli2jfv36NGvWjFmzZqHRaIoUvxAlidFJytLSkpUrV+Lp6anclxLi\neXItOombDwvfKSK76zHJRMUVnNymTp3Ktm3buHDhguFyr12jSZMmqNX/OzybN29Oeno6N2/+L/k1\naNDA4HP//vsvFSpUoHLlysprOecjRGlj9N47fPhw/Pz8aN++PZmZmfTr18+EYQnx7K2/cLfAZruC\nxKZqWH3uToHTNG7cmC5dujB58uRCzzf7yaCdnZ1RsQlRmhQ5SW3dupXbt29jbm6Om5sbAObm5mg0\nGn799VeTByjEs5Cp1Rl9FaV3/X7yY6eZPHkyJ0+e5MCBA8prtWrV4syZM2i1/7sLdvLkSSwtLala\ntWq+86patSpRUVFERkYqr509e9ZgPkKUNkXu3de2bVsOHTrExo0byczMOss0MzOjatWqtGnTxuQB\nCvEsJKZnkqop3o97aoaWTK0OswK6pFerVo1+/fqxZMkS5bWBAweyePFiPvvsMz766CNu3brF1KlT\nGTx4MLa2tvnOq2nTptSsWZOPPvqImTNnkpqayoQJEzA3N7oTrxDPXJH3XicnJ7p16/YkYhEvsObN\nm9OlSxe++OKLZx0KABqtDp1WV6x5aHU6NI9JUgBjx45l/fr1yt8VKlRg8+bNTJ48mVatWuHo6EiP\nHj0e2yyoVqtZs2YNI0eOpF27dlSqVImvv/6awYMHF2s9hHiW5BRLFMn9+/eZNWsWe/fu5e7duzg5\nOVGnTh1Gjx79XF1J21uaYW5evM5AFmYqrMwNW9QXL16cazo3NzeDJjqA1157zaAJMKddu3bl+XqN\nGjXYvXu3wWu3b98ubMhClDiSpESR9OnTh5SUFL7//ntUKhX29vYcP36chw8fPuvQTMrKXE15O0ti\nEo0v9+VexsqEEQnxYipWksrIyCAhIQGdLqtZxMXFxSRBiafr34fJbLx4j7hUDWYqFTVdben+cnns\nLM0MpouNjeXkyZNs27aN1q1bExISgre3N40aNVKmeemllxgyZAgjRoxQXvPz88PHx4e5c+cCEBMT\nw8iRIzl48CCurq6MGzfu6axoEbWv6UJwdBKZRrT6WZmp6Fq3nOmDEuIFY3SS2rNnD5cvX+bu3bu4\nublhYWHBp59+asrYxBMWm5LB1wducvNBCvFp/+tqfSo8jr0hD2hV1Zkhr1ZUuj3b29tjb2/P7t27\nadasmdHLHT58OBEREWzbtg0bGxsmTJhAeHh4sdfH1DrUdGHr39GEF/FhXoCKjtY093J8AlEJ8WIx\n+jmpCxcu8Pnnn1O+fHnGjRsnz2yUMglpGsbtDuFCVKJBgtK7m5DO9svRLDj2v+Rhbm7ODz/8wKZN\nm/Dy8mLAgAFMmjSJM2fOFHq5N27cYN++fcyfP59mzZpRv359Fi9eTEpK8bp7PwkWZmr6NvbAyaZo\n53IuthYMebUiannAXYhiMzpJ6bu1Wlpacv36de7cKfjBRVGyLDweTuiDghNDeqaOg6GPCI5OUl7r\n2rUrV69eZcOGDTRv3pygoCDatWtHYGBgoZZ77do11Go1jRs3Vl7z9PTEw8PDuBV5wl6vXpa+jTwo\nW8hE5WZnweBXK9KkslxFCWEKRiep3r17k5GRwbvvvsvZs2fp2bOnKeMST1BKRiZXsyWegiSlZ7Lh\nguEJiLW1NW3atGHw4MHs3buXPn36MHv2bNLT01Gr1co9Sr28aseVpjJaXeqWY3ybqtT3sMcpn+rm\nZW3MaVSxDF+2q0Y7b7k3K4SpGH1P6tSpU/Ts2RMPDw8CAgLYuXMndevWNWVs4gk5GRbHnfjCjz8U\n/qjgezK1atVCo9GQmpqKq6srd+/eVd5LTU3l+vXrvPzyywDUrFkTrVbL2bNnefXVVwGIiIgo8Vfi\njSo50KiSA5GxKay7cI8HSelkaHVYqFWUL2PF+408KG9v+azDFOK5U+Qkdf/+fWJiYggODiY4OBgA\nnU5HcHAw/v7+Jg9QmN6D5AyK0mEtPVOHTqfj0aNHfPjhh3zwwQfUrVuX+/fvc/nyZb777jtat26N\ng4MDvr6+rFmzho4dO+Lq6kpgYKBSmQTA29ubdu3aMXr0aObPn4+1tTUTJ07ExsbG9Cv6BFRysmHs\n61WedRhCvDCKnKQePHhASEgIycnJhISEAFllkd5++22TByeeDBdbiyJNb2mmQqVSYWdnxyuvvMKS\nJUu4efMmqampVKxYkR49eigDYI4ePZrw8HDef/997Ozs+Oyzz3JdJS1atIiRI0fSpUsXXFxcGDdu\nHPfv3zfZ+gkhnh9FTlL6sWs6deokQwCUUs29HKngYEVUfOEGw6vsZA2AlZUVkydPVsrz6J+Tys7B\nwSHXAJiDBg0y+LtcuXJs2LDB4LW+ffsWaR2EEC+GIiepTZs20atXL+bPn5/rvc8//9wkQYkny8bC\njJputoVKUnYWanrVd38KUQkhRG5FTlK9evUCYMiQITg4OJg8IPF0jHjNk7CHKfxbQKcISzMVvtWc\nqetu/xQjE0KI/zG6d9+6detIT0+nXr16NGrUCCcnJ1PGJZ4wR2tz5vjVZPr/rziRc3C/8vaWtKji\nxPDmlZ5RhEIIUYwk9dFHH5Gens4///zDli1bePTokXLzXJQOzrYW/KdzLa7HJLH50j0S0jSoVSqq\nlbWhVwN3ylhJ/WEhxLNVrF+h8PBwrl+/zqNHjwocMVSUbDXd7JjYttqzDkMIIXIxOknNnTuXGjVq\n0KJFCzw9PU0ZkxBCCAEUI0mZumkvOTmZX375haioKAD69etH9erVTboMIYQQpUuJuemwYcMG6tWr\nx7Bhw9BoNKSnF75sjxBCiOdTiXgaNzk5mevXr9OyZUsgq8K6ra3tM45KCCHEs6aKjY01YtzRrMKh\nf/31F3FxcUrV686dOxsVRHh4OKtXr8bDw4PIyEi8vLzo3bs3Vla5h98OCQnhP//5jwywKIQQz4Gc\nVWtyMrq5b8mSJTRu3Jjz58/z+uuvc+vWLWNnhVarJTw8nICAAKpVq8aGDRvYs2cP3bp1yzWtt7c3\nTk5Oj12xkiqvUkKl1fOyLrIeJYusR8nyrNfD6OY+jUZDq1atsLa2plWrVjx8+NDoIJydnXF2dqZa\ntaxu0I0aNSqRw4kLIYR4uoxOUmXKlCEjI4OKFSvy888/k5pa8JhDBXF0dMTZ2VkZh+jq1asldqRW\nIYQQT4/RzX1Dhw4FskbojYiIwN29eEVIAwICWL58ORqNBjc3N/r161es+QkhhCj9ipyktm3blu/Q\n3127djU6EE9PTyZNmmT054UQQjx/ipyk6tSp8yTiEEIIIXIpcpKqVavWk4hDCCGEyKVEPMwrhBBC\n5MWoJKXT6Th+/LipYxFCCCEMGJWkVCoVf//9t6ljEUIIIQwY3QU9MTGRKVOmUKlSJaW338CBA00W\nmBBCCGF0kurfv78p4xBCCCFyKVbHid27d/Pbb7/h7OzMlStXTBWTEEIIARQjSf3yyy+0a9eO2NhY\n1Go1QUFBpoxLCCGEMD5JabVag/p6+uE6hBBCCFMxOknVrl2b1atXExcXx4YNG6QShRBCCJMzuuOE\nv78/kZGR1KlTB3d3dypVqmTKuIQQQgjjk1RycjJ///03MTExxMTE4OzsjJ2dnSljE0II8YIzurlv\nyZIluLm50aFDB9zc3Fi8eLEp4xJCCCGMT1JmZmY0adIEd3d3mjRpgoWFhSnjEkIIIYxv7tNqtXz7\n7bd4enoSGRmJWq1m+/btQPHGlRJCCCH0jE5SnTp1Uv5fr149kwQjhBBCZGd0kpJxpYQQQjxpMp6U\nEEKIEqtYSSozM5PY2FhTxSKEEEIYMDpJnThxggULFrBgwQIyMzNZsmSJKeMSQgghjE9SR48e5dNP\nP8XOzg4zMzMSExNNGZcQQghRvOekUlNTAUhPT1cGPhRCCCFMxegk1b17d5YsWcLdu3dZsmQJ77zz\njinjEkIIIYzvgl61alVGjRplyliEEEIIA0YnqePHj3PixAmDZr7PP//cJEEJIYQQUIwkdfjwYcaO\nHYu5udGzEEIIIQpk9D0pb29v7t69i1arVf4JIYQQpmT0ZVBYWBhhYWEGr0lznxBCCFMyOklJQhJC\nCPGkGZ2kgoOD2b59O2ZmZmi1Wjp37oyPj48pYxNCCPGCMzpJbd++nVGjRmFtbU1KSgrz58+XJCWE\nEMKkjO44odPplO7nKpUKnU5nsqCEEEIIKMaVVNeuXZk/f76SoLp162bKuIQQQgjjk5SPj4807wkh\nhHiiipykNmzYQO/evfnmm29yFZUdO3asyQITQgghipykevfuDcC4ceNMHowQQgiRndEdJyZOnEhQ\nUJDy948//miSgIQQQgg9o5OUk5MTkZGRbNy4Ea1WS1xcXLGD0Wq1TJs2je+++67Y8xJCCFH6GZ2k\nVCoV77zzDt7e3nz//fckJSUVO5j9+/fj4eFR7PkIIYR4PhidpJo2bQpAo0aN6NmzJ1WqVClWIA8f\nPuTvv/+mZcuWxZqPEEKI54fRXdB9fX2Jjo4mNjYWgObNmxcrkI0bN9KjRw9lSPr8hISEEBsbS0hI\nSLGW9yyV5thzel7WRdajZJH1KFme5Hp4e3sX+L7RSWrdunWkpKRw48YNqlevTnp6OjVr1jRqXhcv\nXsTBwQEvLy+uXbtW4LTe3t44OTk9dsVKqpCQkFIbe07Py7rIepQssh4ly7NeD6Ob+yIjIxk4cCAu\nLi4MGjSoWEGEhoZy4cIFxo8fz7Jly7h27RrLly8v1jyFEEKUfkZfSanVarRaLTY2Npw4cYLo6Gij\ng3jnnXd45513ALh27Rp//vlnsROfEEKI0q/IV1L6e1CDBg1Cq9XywQcfkJiYyIABA0wenBBCiBdb\nkZPUsmXLAFizZg3m5uY4OjrSoUMHPD09TRJQrVq1GDlypEnmJYQQonQrcnNf+fLlmTNnDpGRkcyZ\nM6DmDMEAAB0nSURBVEcZokOlUkntPiGEECZV5CT14YcfArBt2zYZnkMIIcQTZXTvPjc3NwBu3brF\nd999x9mzZ00WlBBCCAHFSFKnTp0C4MCBA/Tt25c9e/aYLCghhBACipGk0tLSuH79OtbW1jg5OWFl\nZWXKuIQQQgjjk1SvXr0IDg7G39+fjIwMGjZsaMq4hBBCiKJ3nAgPD8fT01MpgxQVFQVAxYoVTR6c\nEEKIF1uRk1RkZCSenp65Cg6qVCrq1KljssCEEEKIIiepFi1aANClSxeTByOEEEJkV+QkNW/ePACS\nk5N59OgR5cuX5969e7i4uDBp0iSTByiEEOLFVeQk9fnnnwOwdOlSxo0bh5WVFWlpafzyyy8mD04I\nIcSLzejefdHR0SQnJwOQkpLCvXv3TBaUEEIIAcUYquODDz5g7dq1JCcnY2trywcffGDKuIQQQgjj\nk1TVqlX55JNPTBmLEEIIYcDojhOZmZnEx8dTpkwZEhIScHR0lCroQgghTMrojhMrV65k8ODBlC1b\nlocPH7Jr1y6TByeEEOLFZnTHicjISOzt7QGws7MjLCzMZEEJIYQQUIx7Up07d+bbb79FpVKh0+no\n3LmzKeMSQgghjE9S9evXp379+qaMRQghhDBgdJIKDg5m+/btmJmZodVq6dy5Mz4+PqaMTQghxAvO\n6CS1fft2Ro0ahbW1NSkpKcyfP1+SlBBCCJMyuuOETqdDpVIBKPelhBBCCFMy+kqqa9euzJ8/X0lQ\n3bp1M2VcQgghhPFJysfHR2nei4+Px8HBwWRBCSGEEFCM5r5ly5YBsH//flasWMGKFStMFpQQQggB\nxUhS8fHxAISFhTF69GhiYmJMFpQQQggBxUhSlpaWrFy5Ek9PT4NOFEIIIYSpGH1Pavjw4Tx69Ag3\nNzc0Gg39+vUzYVhCCCFEMa6kzM3NcXNzU/5fvnx5kwUlhBBCQDGSlBBCCPGkFbm5LzY2Ficnpzw7\nSuivrIQQQghTKHKSOnnyJB07dmTnzp0Gr6tUKrkvJYQQwqSKnKQ6duwIQP/+/U0ejBBCCJGdyYaP\nd3JyYsyYMSYPUAghxItLho8XQghRYsnw8UIIIUosGT5eCCFEiVUiho9/+PAhP/30k1IP0NfXl3bt\n2plk3kIIIUovo5OUKanVanr27ImXlxepqalMnz4dHx8fKlSo8KxDE0II8QyViIoTTk5OeHl5AWBt\nbY2HhwexsbHPOCohhBDPmtFXUlqtlnPnzhEdHY2bmxuNGzdGrS5+zrt//z4RERFUrVo1z/dDQkKI\njY0lJCSk2Mt6Vkpz7Dk9L+si61GyyHqULE9yPby9vQt83+gktWLFCipUqICXlxdhYWEsX76cIUOG\nGDs7AFJTU1m8eDG9evXCxsYmz2m8vb1xcnJ67IqVVCEhIaU29pyel3WR9ShZZD1Klme9HkZf+sTG\nxuLn50e9evXw8/MjLi6uWIFoNBoWL17Mq6++SqNGjYo1LyGEEM8Ho6+knJyc2LVrF56enoSFheHo\n6Gh0EDqdjl9++QUPDw86dOhg9HyEEEI8X4xOUgMHDuT8+fNERkbi4eGh1PQzxo0bNzh16hT/r717\nD46qPB84/t3d3EPCbmLuV4gSswQCQYUQiJFByh2hlmtKY4uCHe1UWy5TnfHntM5UsVBqxQkiiFyM\ntQoELFAsIhdlFJoAISZMIASyCea6uW1Yspf+sZPzIyZAEhbZhefzF7tnz3ueJ7u8z7m857xRUVG8\n+uqrAMyaNYshQ4b0uU0hhBDur9dF6uLFi8TGxlJSUoKfnx/x8fEAnD17lqSkpD4F8cADD/Duu+/2\naV0hhBB3r14XqYqKCmJjY7uM9lCpVH0uUkIIIUR3el2kRo8eDcD06dOdHowQQghxrT5P1WEymWho\naCAsLIzvv/+e4OBgXn75ZacHKIQQ4t7V56k6cnJyWL58Od7e3pjNZjZt2uT04IQQQtzb+nyfVHV1\nNSaTCYC2tja+//57pwUlhBBCwC0MQc/KymLr1q2YTCb8/PzIyspyZlxCCCFE34vUgAEDeO6555wZ\nixBCCNFJn4tUUVER+/bto6GhAbVaja+vL8uXL3dmbEIIIe5xfb4mtX37dpYsWUJAQADLli0jJibG\nmXEJIYQQfS9S3t7e+Pr6olKp8PT05MKFC04MSwghhLiFIpWWlkZ7ezvjxo1j5cqVDB061JlxCSGE\nEH27JmW322lubsbT05PU1FSZWkMIIcRt0acjKZVKhcFgoL293dnxCCGEEIo+j+6rqKhg2bJlhIeH\nA47CtWzZMqcFJoQQQvS5SL3yyivOjEMIIYTootdF6ty5c3h5eRETE8M///lPWlpaAMdAisTERKcH\nKIQQ4t7V62tSO3fuJDQ0FHAUrGnTpjF58mT27Nnj9OCEEELc23p9JGWz2fD29gYgMzOT4OBg5X0h\nhBDCmXp9JKVSqTCbzQCMHDkScDwFXYqUEEIIZ+v1kdS0adN46623GDVqFP3798doNHLs2DGZqVcI\nIYTT9fpIatCgQSxZsgSNRkNFRQUajYYlS5bIoAkhhBBO16ch6P369SMtLc3ZsQghhBCd9PnZfUII\nIcTtJkVKCCGEy5IiJYQQwmVJkRJCCOGypEgJIYRwWVKkhBBCuCwpUkIIIVyWFCkhhBAuS4qUEEII\nlyVFSgghhMuSIiWEEMJlSZESQgjhsqRICSGEcFlSpIQQQrgsKVJCCCFclhQpIYQQLqtPkx7eDoWF\nheTm5mKz2Rg7diyTJk260yEJIYS4w1yiSNlsNrZt28YLL7yATqfjtddeIyUlhcjIyC6fnTNnDsXF\nxZw/f/4ORHrrWltb8ff3v9NhOMXdkovk4VokD9dyu/P46KOPbrjcJYpUWVkZISEhhISEAPDwww9T\nUFDQbZG6WUJCCCHuHi5xTcpoNBIUFKS81ul0GI3GOxiREEIIV+ASRUoIIYTojksUKa1WS319vfK6\noaEBrVZ7ByMSQgjhClyiSMXHx1NdXU1NTQ0Wi4Vvv/2WlJSUOx2WEEKIO0xlNBrtdzoIgNOnT5Ob\nm4vdbic9PZ0pU6bc6ZCEEELcYS5TpG7Ele6hev/99zl16hQBAQG8+uqrgGOIZk5ODnV1dQQHB7N4\n8eJuh2x+9dVXfPbZZwBMmTKF0aNHA1BTU8O7775LS0sLcXFx/OpXv8LDwwO73U5ubi6nT5/Gy8uL\np556iri4OKfkUV9fz4YNG2hqagIgIyOD8ePHu10u7e3tvPHGG1gsFqxWKyNGjGDGjBlul0cHm83G\nn/70J7RaLb/5zW/cMo8VK1bg4+ODSqVCo9Hw8ssvu2UeJpOJTZs2UVlZCUB2djbh4eFulcfly5fJ\nyclRXtfW1jJjxgzS0tLcJg/NihUr/u+WWrjNbDYba9as4be//S2TJ08mNzeXQYMGERAQcEfi8fPz\nIz09nfz8fB577DEA8vLyiIyMZPHixRiNRoqKitDr9Z3Wa21tZf369bz00kuMHTuW9evXk5aWhpeX\nF5s3byY9PZ2FCxfy3Xff0djYSHx8PIWFhRQWFvKHP/yB2NhYPvzwQ8aOHeuUPMxmMwkJCcycOZO0\ntDQ2bdpEUlISX3zxhVvlolareeSRRxg/fjxjx45lx44dREZGcvjwYbfKo8P+/fuxWq1YLBZGjhzp\nlr+tzz//nOXLlzNhwgQyMjIA9/w/snnzZpKSksjOziYjIwM/Pz/27NnjVnn069ePzMxMMjMzycjI\n4MCBA8yePZv9+/e7TR4ucU3qRq69h8rDw0O5h+pOGTRoUJc9joKCAtLS0gBIS0vrNr7CwkL0ej3+\n/v74+/uj1+spLCzEbrdTUlLCiBEjABg9ejT5+flKu6NGjUKlUpGQkIDJZHLa0HytVqvs4fj4+BAR\nEYHRaHS7XFQqFT4+PgBYrVasVisqlcrt8gDH0e3p06cZM2aM8p475tEdd8vDZDJx9uxZ5bvw8PDA\nz8/P7fK41nfffUdISAjBwcFulYdL3Mx7I93dQ1VWVnYHI+qqqalJGY3Yv39/5RTatYxGIzqdTnnd\ncS9YS0sLvr6+aDSaTu+DY5Rjd/ePOXvkY21tLZcuXWLAgAFumYvNZuOPf/wjNTU1ZGZmMnDgQLfM\n46OPPuLJJ5/kypUrynvumAfAqlWrUKvVPProo2RkZLhdHrW1tQQEBLBx40YqKiqIi4tj7ty5bpfH\ntb799lseeeQRwL1+Vy5fpNyNSqVCpVLd6TB67MqVK7zzzjvMmTMHX1/fTsvcJRe1Ws0rr7yCyWRi\n7dq1GAyGTsvdIY+TJ08SGBhIXFwcJSUl3X7GHfIAWL58OTqdjqamJlavXk14eHin5e6Qh81m4+LF\ni8ybN4+BAweSm5vLnj17On3GHfLoYLFYOHnyJLNmzeqyzNXzcPnTfe5wD1VgYKCyJ2E0Gru9XqbV\namloaFBed+TRr18/2trasFqtnd4Hx17I7czdYrHwzjvvMHLkSFJTU906F3BcL0xMTKSwsNDt8jh3\n7hwFBQWsWLGCdevWUVJSwvr1690uj462wfFbGj58OGVlZW6Xh06nQ6fTMXDgQABSU1O5ePGi2+XR\nobCwkNjYWAIDAwH3+n/u8kXKHe6hSklJ4euvvwbg66+/ZtiwYYDjC/rLX/4CQHJyMmfOnKG1tZXW\n1lbOnDlDcnIyKpWKxMRETpw4AThG03Ssn5KSwrFjx7Db7Zw7dw5fX1+n/XDtdjubNm0iIiKCCRMm\nuG0uzc3NmEwmAK5evUpRURHh4eFul8esWbNYuXIlf/7zn3nmmWdITExk0aJFbpeH2WxWTleazWaK\nioqIiopyuzz69++PTqfj8uXLABQXFxMREeF2eXT45ptvlFN9Hdtzlzxc/nSfRqNh/vz5/PWvf1Xu\noYqKirpj8axbt46zZ8/S0tLC0qVLmT59OpMmTSInJ4cjR44owzkBGhsbUasd+wH+/v5MnTqV1157\nDYBp06YpAzB++tOfsm7dOnbs2EFsbKxysXbIkCGcPn2al156CS8vL7Kzs52WR2lpKceOHSMqKkoZ\nSj9r1iy3y6WxsZENGzZgs9mw2+089NBDpKSkkJCQ4FZ5XI+7fR9NTU2sXbsWcAxkGTlyJMnJycTH\nx7tVHgDz5s1j/fr1WCwWQkJCyM7Oxm63u10eHTsLWVlZynvu9Ltyi/uk3NWBAwcICgpS9jLc2d2S\ni+ThWiQP1+KKeUiREkII4bJc/pqUEEKIe5cUKSGEEC5LipQQQgiXJUVKCCGEy5IiJe4aJSUlLF++\nnDfffJNVq1bR0tLSo/VWrlx53feOHj1KeXk5tbW1fPfdd06N93bYtm0b4IjbZrP1aJ0TJ07w3//+\n16lxfPDBB5jNZqe2Ke5NUqTEXWXUqFH8/ve/Z/To0XzzzTe33F56ejpxcXHU1dVRXFx8y+3Z7Xbs\n9ts3oHb+/PmA4wbLnm7n2LFjfR5yfL1CmJKS4pS/vxAufzOvEH3R8RQKgPPnz/PJJ59gtVoZO3Ys\n6enpnDx5kry8PKKjo5XHu5SVlbFlyxZCQkKU9fPy8rj//vs5evQopaWlnD9/nt/97nd8+OGHXLp0\nCR8fHxYtWoSfn5+yvQ0bNqBWq6muriYxMZEZM2aQl5dHXV0dRqORRYsWsXv37uuuX1VVxZYtWwAY\nPnw4mZmZrFmzBqvVSkBAAIsXL6a+vp7169fTr18/mpqaePrppwkJCeH1119n9uzZXLp0iVWrVjFm\nzBgSEhLYunUrFouFpKQkpk6dqmyrtbUVjUaj3MC5a9cuiouLUalUZGdnU1dXR1FRETNnzuTo0aMA\nJCYm8v777+Pv78+QIUMICgpix44dADzxxBPo9XoSExN57733nDqNibg3SZESd5Vjx45x5swZrl69\nyooVKwDYuXMnzz33HD4+PqxevZqRI0eyZ88eli5dislk4s033wRg9+7d/PrXv8bf319Zt0NGRgb3\n3XcfM2fOpKysDLPZzLJlyzh27Bhffvlll4k4H3zwQbKzs/nb3/6mPP8sLCyMp5566qbrf/rpp2Rl\nZREREYHNZkOlUvH888/j5eXF9u3bKS4uJjQ0lNbWVpYuXUp5eTl79+7l5z//OQADBgwgJiaGF198\nEY1GQ05ODr/4xS8ICgpi3bp11NfXK0+qrq6uJjg4GICKigqMRiNLly6lqqqKPXv2dHqUzrWampp4\n8cUXUavVvP7667zwwgsArFmzBr1ej4+PD62trbf0XQoBUqTEXWbUqFHMnDmTDRs2UF9fj5+fHxUV\nFfz9738HoKWlhebmZmUeKh8fH+XhmiaTSemww8LCrruNmpoaZS6uuLg4zp492+UzsbGxAERHR1Nb\nW6t8tifrt7S0EBERATie8H7lyhU2b96M0WikqamJsLAwQkNDiYqKQqPREBMTQ01NzXXjvXz5Mu+9\n956S4w+nv+lQVVVFSUmJcj2uf//+120zJiZGOfoClCfou/LTtIV7kiIl7kqTJ08mLy+PZ555htjY\nWJYsWYK3tzcWi0WZ5tpsNtPa2kpzczPg6Gjr6+vx9/enurq6U3sajUa5xhMSEkJRUREA5eXlhISE\ndNn+pUuXiIyMxGAwkJmZCfx/B36z9QMCArh8+TLh4eHYbDbOnDlDWFgYTz/9NNu3b1fiqKysxGaz\nUVFR0aUNjUaDzWZDo9EQHh7OnDlz0Gq1Xa4hhYaGUldXBzgKs16vV65rWSwWKisraWxsBBxHWtHR\n0Z1yAcd1tra2NuXf4JgCprvpyIXoLSlS4q4UHh5OS0sLjY2NTJ8+nbfeegtwPDTz2WefZeLEibzx\nxhvExcUpRwxTp07l7bffJiwsrMuRRmRkJJ9++ik5OTksXryYr776itdffx0fHx+efvrpLts/e/Ys\nBw8eZNCgQV3aGjBgwA3XnzlzJps3bwYc16RSU1P517/+xYULF/D19SU0NBRwFLO3336b5uZmFi1a\n1KmNoUOHsnbtWsaMGcPMmTPZtGkT7e3taDQann32WWU2Y39/f6xWKzabjdjYWPr378/KlStRqVQ8\n8sgjjBkzhsbGRtasWXPdojNt2jRWr14NwPTp0wHHSMshQ4bc5FsS4ubk2X1CONmGDRuYOnWqUkxu\nh9raWnbs2NGlOPXF8ePHUavVypxizvDBBx8wZ84cvL29ndamuDfJkZQQ97iHHnrI6W0uXLjQ6W2K\ne5McSQkhhHBZcjOvEEIIlyVFSgghhMuSIiWEEMJlSZESQgjhsqRICSGEcFlSpIQQQrisTvdJff75\n58rDMIUQQogfg06nY/z48d0u61SkGhoaMBgMP0pQQgghxM3I6T4hhBAuS4qUEEIIlyVFSgghhMty\nqSI1cOBA5WGXo0aN6vapzIGBgWRlZd2wncDAQBITE29LjD+mvuYxY8YMvL298fb2ZujQobchMiHE\n7fTkk0+ycOFCFixYwIIFC7j//vv71I5er7+leb2io6OV6Ve609M+6lb6ZJcqUufPn+f48eO33M69\nXqR27tyJ2WyWIiWEG9u7dy9bt25l69atlJaW9qmNWy1SN/NjFKkbTtUxYsQIrFYrBQUFZGRkEBIS\nwieffEJ0dDTJycmcO3eOiIgIDh06xLBhwxg+fDgbN24kMDCQiRMn8uWXX/Lwww+ze/duBg4cyOTJ\nk1m7di0qlYqFCxeycePGTtvT6/WEhoZy8ODBTu+Hhoby+OOPA46ZTK9N/Cc/+Qmenp4AfPHFF1RV\nVZGenk5QUBALFiygqKiIU6dOMW7cOMLCwrDZbBw6dIiKigr0ej0JCQl4eHig0+k4ceIEarWapKQk\nrFYrO3bswGw2k5ycTHJyMhqNBqPRyL59+7BYLEyYMAGz2UxYWBj+/v4cPnxY+TE99NBDPPjgg9jt\ndi5cuMDRo0d73c4P8ygoKCA9PZ3o6Gg0Gg2nTp3i9OnTXb63X/7yl2zbto309HS0Wi0LFiygvLyc\nI0eO9P4XIoRwGcOHD2fw4MEAnDlzhvz8fAIDA3niiScwGAxERkbS0tJCXl4eAwYMIDQ0lIkTJ2K1\nWsnNzSU4OJiMjAw8PT25cuUK+/btw2Qy9WjbUVFRyizTdrudjz/+uEsfde7cuR71yfn5+T3O+YZF\nymAwkJqaSkFBAWFhYWg0GtRqNVFRURgMBgwGAyNGjFASaGtrw9/fX1leXV2tTGsdFRVFbW0tYWFh\nqNVqLl++3OMgH3/8cQ4ePIjBYGDMmDHK+yaTiU8//RSr1YpWq2XSpEl8+OGHHD16lNTUVPLy8gCU\n04ZbtmxBp9MpM5UCBAcHs3XrVjw8PMjOzubIkSNs27aNjIwM9Ho9+fn5lJaWUlhYCEBaWhqDBw/m\n5MmTgGNm03/84x8EBQUxffp0SktLiY+PZ+DAgeTm5mKxWJSJ33rbzg/zSE5O5urVq+Tm5qLRaJg9\nezbl5eU0NTV1+3c7evSokp8Qwr10FBeATz75hMDAQAYPHkxubi4qlYq5c+dSUVGB2WxGq9WyZ88e\n/vOf/zB58mQeeOABiouLGTZsGIcOHaK6uhq1Wk1mZia7du2ira2NQYMGkZ6ezv79+5VZlLvb6e0w\nYsQIDhw4QFVVFZ6enlgsli59lIeHR4/65N64YZGqrq4mLCwMLy8vrFar8joqKoqDBw9iMpnw8vLC\n09OTgIAASkpKiI6OJioqitLSUux2O0ajEZ1OR1hYGPn5+URHR6NSqXp8P1bHtZWOzxcXFxMfHw+A\nWq3mscceIyQkBLvdjlar7baNyMhICgoKAMe9YM3Nzeh0OgAuXbpEe3s77e3tmM1mzp8/DzhmPu0o\nsMHBwYwePRpvb288PT07Hc2dO3cOgPr6evz8/ACIiYmhqKgIi8UCgNls7lM7PxQXF8d9993HAw88\nAICXlxdarfa6RUoI4b727t1LdXW18vrBBx+ktLRU6VdKS0uJiori/PnzNDY2UlNTAzj67cDAwC7t\n6XQ6goODmTVrFgAqlYrW1lbgxsWpQ2VlJY8++ijFxcWUlpbS3t7e5TM97ZN744ZFymaz0djYiF6v\np7KyktraWqKjo9FqtdTX1yuBDx48mPr6egwGA4MHD1ZOAYLjaCw+Ph6bzcbFixeZMGECKpWKw4cP\n33LwqampmEwmtmzZgkql4vnnn+91Gx17Kt29VqlUAEyYMIFdu3ZRW1uLXq8nOjr6uuvfiDPaOXjw\nYKfiJoQQ1/YfNpsND4/uu/b6+no++uijPm3j+PHjlJWVMWDAAGbPns327du7fMYZffIP3XTgRGVl\nJampqRgMBiorKxkyZEin6n7t8pqaGmJiYrBarVy9elVZPnz4cKqqqmhra8PHxwedTkddXV2PAjSb\nzZjNZiIjIwHH3kQHLy8vZU8gKSkJtdqRztWrV/Hy8lI+ZzAYlPW0Wi0BAQG9evxTx3bUanWPLv5d\nvHgRvV6v/FA6Tvf1tp0f5lFeXs7QoUOVPLVa7XV/jN2tL4RwXwaDQbmG7uHhQUJCwk3PSF3bBzQ0\nNODr60tERATgOOoJCgrq8fb79+9PXV0dx48f5/vvvycoKKhLH9PTPrk3bngkBY4/zMMPP0xVVRUW\niwWr1drpD2MwGAgMDMRgMGC322lublaOsgCqqqrw8/NT1qmtre10Oqsn50L379/f7cCJU6dOMWXK\nFJKSkigvL1cKY21tLXa7vcvAiaysLGw2G//+9797dQT09ddfM3fuXNra2rh8+fJN/9jl5eWEhIQw\nb948bDYbZWVlfPXVV71u54d5dFwknT9/PgBtbW3s2rULgAULFnS59nTlyhUqKyvJysriwoULMnBC\nCDdWU1NDUVERc+fOBRwDJ2pqaro9tdehqKiIcePGKQMnPvvsMx599FG8vb1Rq9Xk5+dTX1/fo354\n+PDhREdHY7fbqa+v58KFC9jt9i59bU/65N4MnFAZjUZ7x4uPP/5Ynt0nhBDiRxUVFcXPfvazbpe5\n1H1SQgghxLWkSAkhhHBZUqSEEEK4LClSQgghXFan0X0dN7gKIYQQP5Yb1Z5Oo/uEEEIIVyKn+4QQ\nQris/wFrqa7a7WLpngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e5db7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style.use('fivethirtyeight')\n",
    "import matplotlib.ticker as mtick\n",
    "# Report G2\n",
    "tp = df_g2.plot(\n",
    "        x='Reddito pro capite', \n",
    "        y='Gradio di soddisfazione per la vita', \n",
    "        s=df_g2['Popolazione residente'],\n",
    "        kind='scatter',\n",
    "        xlim=(0,75000), \n",
    "        ylim=(0,10),\n",
    "        legend = False,\n",
    "        figsize = (6,4))\n",
    "\n",
    "for i, txt in enumerate(df_g2.Territorio):\n",
    "    tp.annotate(txt, (df_g2['Reddito pro capite'].iat[i]*1.070,df_g2['Gradio di soddisfazione per la vita'].iat[i]))\n",
    "    tp.plot()\n",
    "\n",
    "tp.tick_params(axis = 'both', which = 'major', labelsize = 10)\n",
    "\n",
    "# Generate a bolded horizontal line at y = 0 \n",
    "tp.axhline(y = 0, color = 'black', linewidth = 4, alpha = 0.7)\n",
    "tp.axvline(x = 500, color = 'black', linewidth = 0.8, alpha = 0.7)\n",
    "\n",
    "# Remove the label of the x-axis\n",
    "#tp.xaxis.label.set_visible(False)\n",
    "#tp.yaxis.label.set_visible(False)\n",
    "tp.set_ylabel('Gradio di soddisfazione per la vita (scala 1-10)',fontsize=8)\n",
    "tp.set_xlabel('Reddito pro capite (euro)',fontsize=8)\n",
    "\n",
    "fmt = '{x:,.0f}'\n",
    "tick = mtick.StrMethodFormatter(fmt)\n",
    "tp.xaxis.set_major_formatter(tick)\n",
    "\n",
    "text = tp.text(x = -7000, y = -2,\n",
    "    s = 'www.ildatomancante.it                                                                           Fonte: Istat',\n",
    "    fontsize = 10, color = '#f0f0f0', backgroundcolor = 'grey')\n",
    "\n",
    "text.set_url('http://www.ildatomancante.it/opendata/popolazione/799/la-grande-fuga-salute-ricchezza-e-origini-della-disuguaglianza-in-italia/')\n",
    "\n",
    "# Adding a title and a subtitle Reddito e aspettativa di vita\n",
    "tp.text(x = 1000, y = 11.7, s = \"Grado di soddsifazione per la vita in Italia\",\n",
    "               fontsize = 14, weight = 'bold', alpha = .75)\n",
    "tp.text(x = 1000, y = 10.5, \n",
    "               s = '''Relazione tra il grado di soddisfazine per la vita e il reddito pro capite \\ndei cittadini italiani. Ultimi dai aggiornati al 2015.''',\n",
    "              fontsize = 10, alpha = .85)\n",
    "\n",
    "fig_prj = tp.get_figure()\n",
    "fig_prj.savefig(os.path.join(dir_out,'G2_Reddito_e_Soddisfazione.png'), format='png', dpi=300,bbox_inches='tight')\n",
    "fig_prj.savefig(os.path.join(dir_out,'G2_Reddito_e_Soddisfazione.svg'), format='svg', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_g2.to_csv(os.path.join(dir_out,r'G2_Preston.csv'),header=True, index=False)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}