9-25-2020_eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('data/v2 Census by county 2010.2016.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "      <th>POPESTIMATE2012</th>\n",
       "      <th>POPESTIMATE2013</th>\n",
       "      <th>POPESTIMATE2014</th>\n",
       "      <th>POPESTIMATE2015</th>\n",
       "      <th>...</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>RDOMESTICMIG2016</th>\n",
       "      <th>RDOMESTICMIG2017</th>\n",
       "      <th>RNETMIG2011</th>\n",
       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
       "      <th>RNETMIG2016</th>\n",
       "      <th>RNETMIG2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>54571</td>\n",
       "      <td>54571</td>\n",
       "      <td>54750</td>\n",
       "      <td>55199</td>\n",
       "      <td>54927</td>\n",
       "      <td>54695</td>\n",
       "      <td>54864</td>\n",
       "      <td>54838</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.950739</td>\n",
       "      <td>4.831269</td>\n",
       "      <td>1.047102</td>\n",
       "      <td>5.911832</td>\n",
       "      <td>-6.102101</td>\n",
       "      <td>-4.050282</td>\n",
       "      <td>2.099325</td>\n",
       "      <td>-1.659040</td>\n",
       "      <td>5.103709</td>\n",
       "      <td>1.317904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>182265</td>\n",
       "      <td>182265</td>\n",
       "      <td>183110</td>\n",
       "      <td>186534</td>\n",
       "      <td>190048</td>\n",
       "      <td>194736</td>\n",
       "      <td>199064</td>\n",
       "      <td>202863</td>\n",
       "      <td>...</td>\n",
       "      <td>17.047872</td>\n",
       "      <td>20.493601</td>\n",
       "      <td>22.383175</td>\n",
       "      <td>16.285940</td>\n",
       "      <td>17.196786</td>\n",
       "      <td>22.615285</td>\n",
       "      <td>20.380904</td>\n",
       "      <td>17.903749</td>\n",
       "      <td>21.317244</td>\n",
       "      <td>23.163873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>27457</td>\n",
       "      <td>27457</td>\n",
       "      <td>27332</td>\n",
       "      <td>27351</td>\n",
       "      <td>27175</td>\n",
       "      <td>26947</td>\n",
       "      <td>26749</td>\n",
       "      <td>26264</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.222436</td>\n",
       "      <td>-18.755525</td>\n",
       "      <td>-19.042395</td>\n",
       "      <td>0.256021</td>\n",
       "      <td>-6.822433</td>\n",
       "      <td>-8.018920</td>\n",
       "      <td>-5.549762</td>\n",
       "      <td>-16.411069</td>\n",
       "      <td>-18.947692</td>\n",
       "      <td>-19.159940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>22915</td>\n",
       "      <td>22919</td>\n",
       "      <td>22872</td>\n",
       "      <td>22745</td>\n",
       "      <td>22658</td>\n",
       "      <td>22503</td>\n",
       "      <td>22533</td>\n",
       "      <td>22561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.931388</td>\n",
       "      <td>-1.416117</td>\n",
       "      <td>-0.882983</td>\n",
       "      <td>-5.041980</td>\n",
       "      <td>-4.096646</td>\n",
       "      <td>-5.890038</td>\n",
       "      <td>1.243450</td>\n",
       "      <td>1.818424</td>\n",
       "      <td>-0.531044</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>57322</td>\n",
       "      <td>57324</td>\n",
       "      <td>57381</td>\n",
       "      <td>57562</td>\n",
       "      <td>57595</td>\n",
       "      <td>57623</td>\n",
       "      <td>57546</td>\n",
       "      <td>57590</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.563369</td>\n",
       "      <td>-1.736835</td>\n",
       "      <td>6.212416</td>\n",
       "      <td>0.243599</td>\n",
       "      <td>-1.354672</td>\n",
       "      <td>-0.486035</td>\n",
       "      <td>-1.771310</td>\n",
       "      <td>-0.538494</td>\n",
       "      <td>-0.659997</td>\n",
       "      <td>7.285313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 127 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    STNAME         CTYNAME  CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010  \\\n",
       "0  Alabama  Autauga County          54571              54571            54750   \n",
       "1  Alabama  Baldwin County         182265             182265           183110   \n",
       "2  Alabama  Barbour County          27457              27457            27332   \n",
       "3  Alabama     Bibb County          22915              22919            22872   \n",
       "4  Alabama   Blount County          57322              57324            57381   \n",
       "\n",
       "   POPESTIMATE2011  POPESTIMATE2012  POPESTIMATE2013  POPESTIMATE2014  \\\n",
       "0            55199            54927            54695            54864   \n",
       "1           186534           190048           194736           199064   \n",
       "2            27351            27175            26947            26749   \n",
       "3            22745            22658            22503            22533   \n",
       "4            57562            57595            57623            57546   \n",
       "\n",
       "   POPESTIMATE2015  ...  RDOMESTICMIG2015  RDOMESTICMIG2016  RDOMESTICMIG2017  \\\n",
       "0            54838  ...         -1.950739          4.831269          1.047102   \n",
       "1           202863  ...         17.047872         20.493601         22.383175   \n",
       "2            26264  ...        -16.222436        -18.755525        -19.042395   \n",
       "3            22561  ...          0.931388         -1.416117         -0.882983   \n",
       "4            57590  ...         -1.563369         -1.736835          6.212416   \n",
       "\n",
       "   RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  RNETMIG2015  \\\n",
       "0     5.911832    -6.102101    -4.050282     2.099325    -1.659040   \n",
       "1    16.285940    17.196786    22.615285    20.380904    17.903749   \n",
       "2     0.256021    -6.822433    -8.018920    -5.549762   -16.411069   \n",
       "3    -5.041980    -4.096646    -5.890038     1.243450     1.818424   \n",
       "4     0.243599    -1.354672    -0.486035    -1.771310    -0.538494   \n",
       "\n",
       "   RNETMIG2016  RNETMIG2017  \n",
       "0     5.103709     1.317904  \n",
       "1    21.317244    23.163873  \n",
       "2   -18.947692   -19.159940  \n",
       "3    -0.531044     0.000000  \n",
       "4    -0.659997     7.285313  \n",
       "\n",
       "[5 rows x 127 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['STNAME', 'CTYNAME', 'CENSUS2010POP', 'ESTIMATESBASE2010',\n",
       "       'POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012',\n",
       "       'POPESTIMATE2013', 'POPESTIMATE2014', 'POPESTIMATE2015',\n",
       "       ...\n",
       "       'RDOMESTICMIG2015', 'RDOMESTICMIG2016', 'RDOMESTICMIG2017',\n",
       "       'RNETMIG2011', 'RNETMIG2012', 'RNETMIG2013', 'RNETMIG2014',\n",
       "       'RNETMIG2015', 'RNETMIG2016', 'RNETMIG2017'],\n",
       "      dtype='object', length=127)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find counties with flight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_growth = df[['STNAME', 'CTYNAME', 'RNATURALINC2011', 'RNATURALINC2012', 'RNATURALINC2013', 'RNATURALINC2014', 'RNATURALINC2015', 'RINTERNATIONALMIG2011', 'RINTERNATIONALMIG2012',\n",
    "       'RINTERNATIONALMIG2013', 'RINTERNATIONALMIG2014', 'RINTERNATIONALMIG2015', 'RDOMESTICMIG2011', 'RDOMESTICMIG2012', 'RDOMESTICMIG2013',\n",
    "       'RDOMESTICMIG2014', 'RDOMESTICMIG2015']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3143"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pop_growth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>RNATURALINC2011</th>\n",
       "      <th>RNATURALINC2012</th>\n",
       "      <th>RNATURALINC2013</th>\n",
       "      <th>RNATURALINC2014</th>\n",
       "      <th>RNATURALINC2015</th>\n",
       "      <th>RINTERNATIONALMIG2011</th>\n",
       "      <th>RINTERNATIONALMIG2012</th>\n",
       "      <th>RINTERNATIONALMIG2013</th>\n",
       "      <th>RINTERNATIONALMIG2014</th>\n",
       "      <th>RINTERNATIONALMIG2015</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2.346542</td>\n",
       "      <td>0.980695</td>\n",
       "      <td>-0.164201</td>\n",
       "      <td>1.150065</td>\n",
       "      <td>1.221491</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.272415</td>\n",
       "      <td>0.036489</td>\n",
       "      <td>0.237315</td>\n",
       "      <td>0.291699</td>\n",
       "      <td>5.911832</td>\n",
       "      <td>-5.829686</td>\n",
       "      <td>-4.086771</td>\n",
       "      <td>1.862010</td>\n",
       "      <td>-1.950739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>1.964052</td>\n",
       "      <td>1.131228</td>\n",
       "      <td>1.335814</td>\n",
       "      <td>1.152869</td>\n",
       "      <td>0.826021</td>\n",
       "      <td>0.936036</td>\n",
       "      <td>0.786017</td>\n",
       "      <td>0.800449</td>\n",
       "      <td>0.716100</td>\n",
       "      <td>0.855877</td>\n",
       "      <td>15.349904</td>\n",
       "      <td>16.410768</td>\n",
       "      <td>21.814836</td>\n",
       "      <td>19.664804</td>\n",
       "      <td>17.047872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>0.585191</td>\n",
       "      <td>0.293438</td>\n",
       "      <td>-0.406489</td>\n",
       "      <td>-1.825089</td>\n",
       "      <td>-2.150416</td>\n",
       "      <td>-0.146298</td>\n",
       "      <td>-0.403477</td>\n",
       "      <td>-0.369535</td>\n",
       "      <td>-0.335221</td>\n",
       "      <td>-0.188633</td>\n",
       "      <td>0.402319</td>\n",
       "      <td>-6.418956</td>\n",
       "      <td>-7.649385</td>\n",
       "      <td>-5.214541</td>\n",
       "      <td>-16.222436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>-0.482276</td>\n",
       "      <td>0.264300</td>\n",
       "      <td>-1.062864</td>\n",
       "      <td>0.266454</td>\n",
       "      <td>-0.443518</td>\n",
       "      <td>0.438433</td>\n",
       "      <td>0.616699</td>\n",
       "      <td>0.620004</td>\n",
       "      <td>0.754952</td>\n",
       "      <td>0.887036</td>\n",
       "      <td>-5.480413</td>\n",
       "      <td>-4.713345</td>\n",
       "      <td>-6.510042</td>\n",
       "      <td>0.488498</td>\n",
       "      <td>0.931388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>3.044988</td>\n",
       "      <td>2.066744</td>\n",
       "      <td>1.058862</td>\n",
       "      <td>0.555705</td>\n",
       "      <td>1.407032</td>\n",
       "      <td>-0.226199</td>\n",
       "      <td>0.364719</td>\n",
       "      <td>0.624902</td>\n",
       "      <td>0.955118</td>\n",
       "      <td>1.024875</td>\n",
       "      <td>0.469798</td>\n",
       "      <td>-1.719392</td>\n",
       "      <td>-1.110938</td>\n",
       "      <td>-2.726428</td>\n",
       "      <td>-1.563369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    STNAME         CTYNAME  RNATURALINC2011  RNATURALINC2012  RNATURALINC2013  \\\n",
       "0  Alabama  Autauga County         2.346542         0.980695        -0.164201   \n",
       "1  Alabama  Baldwin County         1.964052         1.131228         1.335814   \n",
       "2  Alabama  Barbour County         0.585191         0.293438        -0.406489   \n",
       "3  Alabama     Bibb County        -0.482276         0.264300        -1.062864   \n",
       "4  Alabama   Blount County         3.044988         2.066744         1.058862   \n",
       "\n",
       "   RNATURALINC2014  RNATURALINC2015  RINTERNATIONALMIG2011  \\\n",
       "0         1.150065         1.221491               0.000000   \n",
       "1         1.152869         0.826021               0.936036   \n",
       "2        -1.825089        -2.150416              -0.146298   \n",
       "3         0.266454        -0.443518               0.438433   \n",
       "4         0.555705         1.407032              -0.226199   \n",
       "\n",
       "   RINTERNATIONALMIG2012  RINTERNATIONALMIG2013  RINTERNATIONALMIG2014  \\\n",
       "0              -0.272415               0.036489               0.237315   \n",
       "1               0.786017               0.800449               0.716100   \n",
       "2              -0.403477              -0.369535              -0.335221   \n",
       "3               0.616699               0.620004               0.754952   \n",
       "4               0.364719               0.624902               0.955118   \n",
       "\n",
       "   RINTERNATIONALMIG2015  RDOMESTICMIG2011  RDOMESTICMIG2012  \\\n",
       "0               0.291699          5.911832         -5.829686   \n",
       "1               0.855877         15.349904         16.410768   \n",
       "2              -0.188633          0.402319         -6.418956   \n",
       "3               0.887036         -5.480413         -4.713345   \n",
       "4               1.024875          0.469798         -1.719392   \n",
       "\n",
       "   RDOMESTICMIG2013  RDOMESTICMIG2014  RDOMESTICMIG2015  \n",
       "0         -4.086771          1.862010         -1.950739  \n",
       "1         21.814836         19.664804         17.047872  \n",
       "2         -7.649385         -5.214541        -16.222436  \n",
       "3         -6.510042          0.488498          0.931388  \n",
       "4         -1.110938         -2.726428         -1.563369  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_growth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_growth = pop_growth.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# goal: predict result in 2016 elections\n",
    "\n",
    "# %age change in rate of natural increase\n",
    "means_rni = pop_growth.iloc[2:7,].pct_change().replace([np.inf, -np.inf], np.nan).mean()\n",
    "means_rni.name = 'Avg %Change in Natural Inc'\n",
    "pop_growth = pop_growth.append(means_rni, ignore_index=False)\n",
    "\n",
    "# %age change in int'l migration\n",
    "means_imig = pop_growth.iloc[7:12,].pct_change().replace([np.inf, -np.inf], np.nan).mean()\n",
    "means_imig.name = 'Avg %Change in Intl Migration'\n",
    "pop_growth = pop_growth.append(means_imig, ignore_index=False)\n",
    "\n",
    "# %age change in domestic migration\n",
    "means_domig = pop_growth.iloc[12:-1,].pct_change().replace([np.inf, -np.inf], np.nan).mean()\n",
    "means_domig.name = 'Avg %Change in Domestic Migration'\n",
    "pop_growth = pop_growth.append(means_domig, ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_growth = pop_growth.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find counties with domestic flight\n",
    "pop_growth['dom_flight'] = np.where(pop_growth['Avg %Change in Domestic Migration'] <= 0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 50)\n",
    "pd.set_option('display.max_columns', 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# domestic flight w intl flight\n",
    "pop_growth['dom & intl'] = np.where((pop_growth['dom_flight'] == 1) & (pop_growth['Avg %Change in Intl Migration'] <= 0), 1, 0)\n",
    "\n",
    "# domestic flight w intl replacement\n",
    "pop_growth['dom only'] = np.where((pop_growth['dom_flight'] == 1) & (pop_growth['Avg %Change in Intl Migration'] > 0), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>RNATURALINC2011</th>\n",
       "      <th>RNATURALINC2012</th>\n",
       "      <th>RNATURALINC2013</th>\n",
       "      <th>RNATURALINC2014</th>\n",
       "      <th>RNATURALINC2015</th>\n",
       "      <th>RINTERNATIONALMIG2011</th>\n",
       "      <th>RINTERNATIONALMIG2012</th>\n",
       "      <th>RINTERNATIONALMIG2013</th>\n",
       "      <th>RINTERNATIONALMIG2014</th>\n",
       "      <th>RINTERNATIONALMIG2015</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>Avg %Change in Natural Inc</th>\n",
       "      <th>Avg %Change in Intl Migration</th>\n",
       "      <th>Avg %Change in Domestic Migration</th>\n",
       "      <th>dom_flight</th>\n",
       "      <th>dom &amp; intl</th>\n",
       "      <th>dom only</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2.34654</td>\n",
       "      <td>0.980695</td>\n",
       "      <td>-0.164201</td>\n",
       "      <td>1.15007</td>\n",
       "      <td>1.22149</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.272415</td>\n",
       "      <td>0.036489</td>\n",
       "      <td>0.237315</td>\n",
       "      <td>0.291699</td>\n",
       "      <td>5.91183</td>\n",
       "      <td>-5.82969</td>\n",
       "      <td>-4.08677</td>\n",
       "      <td>1.86201</td>\n",
       "      <td>-1.95074</td>\n",
       "      <td>-2.42286</td>\n",
       "      <td>1.53299</td>\n",
       "      <td>-1.10927</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>1.96405</td>\n",
       "      <td>1.13123</td>\n",
       "      <td>1.33581</td>\n",
       "      <td>1.15287</td>\n",
       "      <td>0.826021</td>\n",
       "      <td>0.936036</td>\n",
       "      <td>0.786017</td>\n",
       "      <td>0.800449</td>\n",
       "      <td>0.7161</td>\n",
       "      <td>0.855877</td>\n",
       "      <td>15.3499</td>\n",
       "      <td>16.4108</td>\n",
       "      <td>21.8148</td>\n",
       "      <td>19.6648</td>\n",
       "      <td>17.0479</td>\n",
       "      <td>-0.165911</td>\n",
       "      <td>-0.0130237</td>\n",
       "      <td>-0.168591</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>0.585191</td>\n",
       "      <td>0.293438</td>\n",
       "      <td>-0.406489</td>\n",
       "      <td>-1.82509</td>\n",
       "      <td>-2.15042</td>\n",
       "      <td>-0.146298</td>\n",
       "      <td>-0.403477</td>\n",
       "      <td>-0.369535</td>\n",
       "      <td>-0.335221</td>\n",
       "      <td>-0.188633</td>\n",
       "      <td>0.402319</td>\n",
       "      <td>-6.41896</td>\n",
       "      <td>-7.64938</td>\n",
       "      <td>-5.21454</td>\n",
       "      <td>-16.2224</td>\n",
       "      <td>0.196079</td>\n",
       "      <td>0.285912</td>\n",
       "      <td>-3.19652</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>-0.482276</td>\n",
       "      <td>0.2643</td>\n",
       "      <td>-1.06286</td>\n",
       "      <td>0.266454</td>\n",
       "      <td>-0.443518</td>\n",
       "      <td>0.438433</td>\n",
       "      <td>0.616699</td>\n",
       "      <td>0.620004</td>\n",
       "      <td>0.754952</td>\n",
       "      <td>0.887036</td>\n",
       "      <td>-5.48041</td>\n",
       "      <td>-4.71334</td>\n",
       "      <td>-6.51004</td>\n",
       "      <td>0.488498</td>\n",
       "      <td>0.931388</td>\n",
       "      <td>-2.62117</td>\n",
       "      <td>0.201143</td>\n",
       "      <td>-0.748287</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>3.04499</td>\n",
       "      <td>2.06674</td>\n",
       "      <td>1.05886</td>\n",
       "      <td>0.555705</td>\n",
       "      <td>1.40703</td>\n",
       "      <td>-0.226199</td>\n",
       "      <td>0.364719</td>\n",
       "      <td>0.624902</td>\n",
       "      <td>0.955118</td>\n",
       "      <td>1.02487</td>\n",
       "      <td>0.469798</td>\n",
       "      <td>-1.71939</td>\n",
       "      <td>-1.11094</td>\n",
       "      <td>-2.72643</td>\n",
       "      <td>-1.56337</td>\n",
       "      <td>0.0619647</td>\n",
       "      <td>-0.324386</td>\n",
       "      <td>-1.00516</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    STNAME         CTYNAME RNATURALINC2011 RNATURALINC2012 RNATURALINC2013  \\\n",
       "0  Alabama  Autauga County         2.34654        0.980695       -0.164201   \n",
       "1  Alabama  Baldwin County         1.96405         1.13123         1.33581   \n",
       "2  Alabama  Barbour County        0.585191        0.293438       -0.406489   \n",
       "3  Alabama     Bibb County       -0.482276          0.2643        -1.06286   \n",
       "4  Alabama   Blount County         3.04499         2.06674         1.05886   \n",
       "\n",
       "  RNATURALINC2014 RNATURALINC2015 RINTERNATIONALMIG2011 RINTERNATIONALMIG2012  \\\n",
       "0         1.15007         1.22149                     0             -0.272415   \n",
       "1         1.15287        0.826021              0.936036              0.786017   \n",
       "2        -1.82509        -2.15042             -0.146298             -0.403477   \n",
       "3        0.266454       -0.443518              0.438433              0.616699   \n",
       "4        0.555705         1.40703             -0.226199              0.364719   \n",
       "\n",
       "  RINTERNATIONALMIG2013 RINTERNATIONALMIG2014 RINTERNATIONALMIG2015  \\\n",
       "0              0.036489              0.237315              0.291699   \n",
       "1              0.800449                0.7161              0.855877   \n",
       "2             -0.369535             -0.335221             -0.188633   \n",
       "3              0.620004              0.754952              0.887036   \n",
       "4              0.624902              0.955118               1.02487   \n",
       "\n",
       "  RDOMESTICMIG2011 RDOMESTICMIG2012 RDOMESTICMIG2013 RDOMESTICMIG2014  \\\n",
       "0          5.91183         -5.82969         -4.08677          1.86201   \n",
       "1          15.3499          16.4108          21.8148          19.6648   \n",
       "2         0.402319         -6.41896         -7.64938         -5.21454   \n",
       "3         -5.48041         -4.71334         -6.51004         0.488498   \n",
       "4         0.469798         -1.71939         -1.11094         -2.72643   \n",
       "\n",
       "  RDOMESTICMIG2015 Avg %Change in Natural Inc Avg %Change in Intl Migration  \\\n",
       "0         -1.95074                   -2.42286                       1.53299   \n",
       "1          17.0479                  -0.165911                    -0.0130237   \n",
       "2         -16.2224                   0.196079                      0.285912   \n",
       "3         0.931388                   -2.62117                      0.201143   \n",
       "4         -1.56337                  0.0619647                     -0.324386   \n",
       "\n",
       "  Avg %Change in Domestic Migration  dom_flight  dom & intl  dom only  \n",
       "0                          -1.10927           1           0         1  \n",
       "1                         -0.168591           1           1         0  \n",
       "2                          -3.19652           1           0         1  \n",
       "3                         -0.748287           1           0         1  \n",
       "4                          -1.00516           1           1         0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_growth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Votes in 2016 elections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "votes = pd.read_csv('data/2016_US_County_Level_Presidential_Results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>votes_dem</th>\n",
       "      <th>votes_gop</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>per_dem</th>\n",
       "      <th>per_gop</th>\n",
       "      <th>diff</th>\n",
       "      <th>per_point_diff</th>\n",
       "      <th>state_abbr</th>\n",
       "      <th>county_name</th>\n",
       "      <th>combined_fips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93003</td>\n",
       "      <td>130413</td>\n",
       "      <td>246588</td>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93003</td>\n",
       "      <td>130413</td>\n",
       "      <td>246588</td>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93003</td>\n",
       "      <td>130413</td>\n",
       "      <td>246588</td>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93003</td>\n",
       "      <td>130413</td>\n",
       "      <td>246588</td>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93003</td>\n",
       "      <td>130413</td>\n",
       "      <td>246588</td>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   votes_dem  votes_gop  total_votes   per_dem  per_gop    diff  \\\n",
       "0      93003     130413       246588  0.377159  0.52887  37,410   \n",
       "1      93003     130413       246588  0.377159  0.52887  37,410   \n",
       "2      93003     130413       246588  0.377159  0.52887  37,410   \n",
       "3      93003     130413       246588  0.377159  0.52887  37,410   \n",
       "4      93003     130413       246588  0.377159  0.52887  37,410   \n",
       "\n",
       "  per_point_diff state_abbr county_name  combined_fips  \n",
       "0         15.17%         AK      Alaska           2013  \n",
       "1         15.17%         AK      Alaska           2016  \n",
       "2         15.17%         AK      Alaska           2020  \n",
       "3         15.17%         AK      Alaska           2050  \n",
       "4         15.17%         AK      Alaska           2060  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_lookup = pd.read_csv('data/state_lookup.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "votes = votes.merge(state_lookup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "votes.drop(columns=['state_abbr', 'votes_dem', 'votes_gop', 'total_votes', 'combined_fips'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>per_dem</th>\n",
       "      <th>per_gop</th>\n",
       "      <th>diff</th>\n",
       "      <th>per_point_diff</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.377159</td>\n",
       "      <td>0.52887</td>\n",
       "      <td>37,410</td>\n",
       "      <td>15.17%</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    per_dem  per_gop    diff per_point_diff county_name   state\n",
       "0  0.377159  0.52887  37,410         15.17%      Alaska  Alaska\n",
       "1  0.377159  0.52887  37,410         15.17%      Alaska  Alaska\n",
       "2  0.377159  0.52887  37,410         15.17%      Alaska  Alaska\n",
       "3  0.377159  0.52887  37,410         15.17%      Alaska  Alaska\n",
       "4  0.377159  0.52887  37,410         15.17%      Alaska  Alaska"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3143"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pop_growth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3141"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(votes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = pop_growth.merge(votes, right_on=['state', 'county_name'], left_on=['STNAME', 'CTYNAME'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3143"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alaska          29\n",
       "New Mexico       1\n",
       "Hawaii           1\n",
       "Louisiana        1\n",
       "South Dakota     1\n",
       "Name: STNAME, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ignore\n",
    "data[data['diff'].isna()]['STNAME'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[~data['diff'].isna()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>RNATURALINC2011</th>\n",
       "      <th>RNATURALINC2012</th>\n",
       "      <th>RNATURALINC2013</th>\n",
       "      <th>RNATURALINC2014</th>\n",
       "      <th>RNATURALINC2015</th>\n",
       "      <th>RINTERNATIONALMIG2011</th>\n",
       "      <th>RINTERNATIONALMIG2012</th>\n",
       "      <th>RINTERNATIONALMIG2013</th>\n",
       "      <th>RINTERNATIONALMIG2014</th>\n",
       "      <th>RINTERNATIONALMIG2015</th>\n",
       "      <th>...</th>\n",
       "      <th>Avg %Change in Natural Inc</th>\n",
       "      <th>Avg %Change in Intl Migration</th>\n",
       "      <th>Avg %Change in Domestic Migration</th>\n",
       "      <th>dom_flight</th>\n",
       "      <th>dom &amp; intl</th>\n",
       "      <th>dom only</th>\n",
       "      <th>per_dem</th>\n",
       "      <th>per_gop</th>\n",
       "      <th>diff</th>\n",
       "      <th>per_point_diff</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2.34654</td>\n",
       "      <td>0.980695</td>\n",
       "      <td>-0.164201</td>\n",
       "      <td>1.15007</td>\n",
       "      <td>1.22149</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.272415</td>\n",
       "      <td>0.036489</td>\n",
       "      <td>0.237315</td>\n",
       "      <td>0.291699</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.42286</td>\n",
       "      <td>1.53299</td>\n",
       "      <td>-1.10927</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.239569</td>\n",
       "      <td>0.734358</td>\n",
       "      <td>12,202</td>\n",
       "      <td>49.48%</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>1.96405</td>\n",
       "      <td>1.13123</td>\n",
       "      <td>1.33581</td>\n",
       "      <td>1.15287</td>\n",
       "      <td>0.826021</td>\n",
       "      <td>0.936036</td>\n",
       "      <td>0.786017</td>\n",
       "      <td>0.800449</td>\n",
       "      <td>0.7161</td>\n",
       "      <td>0.855877</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.165911</td>\n",
       "      <td>-0.0130237</td>\n",
       "      <td>-0.168591</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.195653</td>\n",
       "      <td>0.773515</td>\n",
       "      <td>54,371</td>\n",
       "      <td>57.79%</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>0.585191</td>\n",
       "      <td>0.293438</td>\n",
       "      <td>-0.406489</td>\n",
       "      <td>-1.82509</td>\n",
       "      <td>-2.15042</td>\n",
       "      <td>-0.146298</td>\n",
       "      <td>-0.403477</td>\n",
       "      <td>-0.369535</td>\n",
       "      <td>-0.335221</td>\n",
       "      <td>-0.188633</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196079</td>\n",
       "      <td>0.285912</td>\n",
       "      <td>-3.19652</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.466603</td>\n",
       "      <td>0.522714</td>\n",
       "      <td>583</td>\n",
       "      <td>5.61%</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>-0.482276</td>\n",
       "      <td>0.2643</td>\n",
       "      <td>-1.06286</td>\n",
       "      <td>0.266454</td>\n",
       "      <td>-0.443518</td>\n",
       "      <td>0.438433</td>\n",
       "      <td>0.616699</td>\n",
       "      <td>0.620004</td>\n",
       "      <td>0.754952</td>\n",
       "      <td>0.887036</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.62117</td>\n",
       "      <td>0.201143</td>\n",
       "      <td>-0.748287</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.214220</td>\n",
       "      <td>0.769662</td>\n",
       "      <td>4,859</td>\n",
       "      <td>55.54%</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>3.04499</td>\n",
       "      <td>2.06674</td>\n",
       "      <td>1.05886</td>\n",
       "      <td>0.555705</td>\n",
       "      <td>1.40703</td>\n",
       "      <td>-0.226199</td>\n",
       "      <td>0.364719</td>\n",
       "      <td>0.624902</td>\n",
       "      <td>0.955118</td>\n",
       "      <td>1.02487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0619647</td>\n",
       "      <td>-0.324386</td>\n",
       "      <td>-1.00516</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.084699</td>\n",
       "      <td>0.898519</td>\n",
       "      <td>20,658</td>\n",
       "      <td>81.38%</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    STNAME         CTYNAME RNATURALINC2011 RNATURALINC2012 RNATURALINC2013  \\\n",
       "0  Alabama  Autauga County         2.34654        0.980695       -0.164201   \n",
       "1  Alabama  Baldwin County         1.96405         1.13123         1.33581   \n",
       "2  Alabama  Barbour County        0.585191        0.293438       -0.406489   \n",
       "3  Alabama     Bibb County       -0.482276          0.2643        -1.06286   \n",
       "4  Alabama   Blount County         3.04499         2.06674         1.05886   \n",
       "\n",
       "  RNATURALINC2014 RNATURALINC2015 RINTERNATIONALMIG2011 RINTERNATIONALMIG2012  \\\n",
       "0         1.15007         1.22149                     0             -0.272415   \n",
       "1         1.15287        0.826021              0.936036              0.786017   \n",
       "2        -1.82509        -2.15042             -0.146298             -0.403477   \n",
       "3        0.266454       -0.443518              0.438433              0.616699   \n",
       "4        0.555705         1.40703             -0.226199              0.364719   \n",
       "\n",
       "  RINTERNATIONALMIG2013 RINTERNATIONALMIG2014 RINTERNATIONALMIG2015  ...  \\\n",
       "0              0.036489              0.237315              0.291699  ...   \n",
       "1              0.800449                0.7161              0.855877  ...   \n",
       "2             -0.369535             -0.335221             -0.188633  ...   \n",
       "3              0.620004              0.754952              0.887036  ...   \n",
       "4              0.624902              0.955118               1.02487  ...   \n",
       "\n",
       "  Avg %Change in Natural Inc Avg %Change in Intl Migration  \\\n",
       "0                   -2.42286                       1.53299   \n",
       "1                  -0.165911                    -0.0130237   \n",
       "2                   0.196079                      0.285912   \n",
       "3                   -2.62117                      0.201143   \n",
       "4                  0.0619647                     -0.324386   \n",
       "\n",
       "  Avg %Change in Domestic Migration dom_flight dom & intl dom only   per_dem  \\\n",
       "0                          -1.10927          1          0        1  0.239569   \n",
       "1                         -0.168591          1          1        0  0.195653   \n",
       "2                          -3.19652          1          0        1  0.466603   \n",
       "3                         -0.748287          1          0        1  0.214220   \n",
       "4                          -1.00516          1          1        0  0.084699   \n",
       "\n",
       "    per_gop    diff  per_point_diff     county_name    state  \n",
       "0  0.734358  12,202          49.48%  Autauga County  Alabama  \n",
       "1  0.773515  54,371          57.79%  Baldwin County  Alabama  \n",
       "2  0.522714     583           5.61%  Barbour County  Alabama  \n",
       "3  0.769662   4,859          55.54%     Bibb County  Alabama  \n",
       "4  0.898519  20,658          81.38%   Blount County  Alabama  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['diff'] = data['diff'].str.replace(',', '').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['per_point_diff'] = data['per_point_diff'].str.replace('%', '').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['trump_vote'] = np.where(data['per_gop'] > data['per_dem'], 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e5125c0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['trump_vote'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['per_gap'] = data['per_gop'] - data['per_dem']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e5b3e48>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['per_gap'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3110.000000\n",
       "mean        0.319025\n",
       "std         0.308816\n",
       "min        -0.887245\n",
       "25%         0.148912\n",
       "50%         0.382174\n",
       "75%         0.546839\n",
       "max         0.916364\n",
       "Name: per_gap, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['per_gap'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counties with flight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plt.gca()\n",
    "axes.set_ylim([0,1])\n",
    "sns.boxplot(x='dom & intl', y='per_gap', data=data)\n",
    "plt.ylabel('Trump\\'s Victory Margin in \\n Percentage of Votes')\n",
    "plt.xticks([0,1], ['Counties \\n without Flight', 'Counties \\n with Flight'])\n",
    "plt.xlabel('')\n",
    "plt.title('Trump\\'s Victory Margin in Percentage of Votes \\n at County Level')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dom & intl\n",
       "0    2021\n",
       "1     603\n",
       "Name: trump_vote, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('dom & intl')['trump_vote'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2447\n",
       "1     663\n",
       "Name: dom & intl, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['dom & intl'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8228136882129278"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1082/1315"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9095022624434389"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "603/663"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['migration_score'] = data['Avg %Change in Intl Migration'] + data['Avg %Change in Domestic Migration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_flight = data[data['dom & intl'] == 1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    603\n",
       "0     60\n",
       "Name: trump_vote, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_flight['trump_vote'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9095022624434389"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "603/(603+60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "663"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_flight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U/RaRgVavrt5FR6RNuUh55orIEOv3/pZ8g6zjw0VkfmxZ4snW3P0zGIX0X+NN9A7sc2IviDZnMgdtjToLuA5t/6zeEOZL6N3u2WgLAbE+dN4HtkPbTpuHtcteaX9JL6KtfacopQZHRlJKrUVbqxgScfp04HWllF9ETkRbhzgZbZTzS7RJmmYRbbViJ7RFDEQPV84GFqANTB6GNhFzVCNJbFSZLG5CWxTfFdgFGIi2Cl9PJyDdkuM84FHR+9JAW0e/SCmVasn/vxbGi6UT2jhmV7RJpCdFpKXDWXuh3arkoBsyz4hIPLtm+6BdpDS1T6e5umiO09DLrjPR9/JOAKVUvWmqXaz7ELVUvAX3+2HgYaVUGtp6SAMbbxZz0VbjAQ5EW504KOJ4bmyEJmRrzf37z2EU0n8LhTZfM76+hRrBGcB7Sqn3lHYe9jHwEzBItM2tPdGuFXyWqaAo8zxKqWeVUlVKqTq0wtrFaj23hJewvO5aH73hbDCxcxHaXcef1ubhu4Bdm+klFYuIB63oHkOb+McqQ65S6narHMvQ5m3iDmNuYplGALcrpQqVUkXoD2qkEvdb1/1KqffQhix3iLi2o4ikKaXKlFLzWhgvHuOUUnVKqbnoBkejjg5jWKmUesqyX/c82kp0fpxw2UBxMxu7m6uL5nhTKfWDlceLRPR4m6G5++0H+oi2+VetlGrMptxcohXQ3RHHBxFHITVBa+/ffwqjkP5jWC/BP2jnaZH0BE6VaFP1+6M/RF3Qxl1rI8KHTfKL3tE+UbQXy0q0LSxouen614F9RHuRPRCtOL+MkOvhCJlK0RsX45rQj8g3Bd3LOxhtLLI+rS4xZbyROB/aNihTPNcLkW4iGnOJAbq3OAhYaQ0X7dPCeLGUxRj7bcxVRTzCbhAi7nu8fEqAHGnaZXZzddFiWWjGPUoMzd3v89COKReJ9pDamGfab9HGfvPRynA60F20Z+WBbPDa3BJac//+cxiF9N/kZvQwSqRbhFXACzGm6t1KqYloI4tZos261BNpkv909AT24ejhiF7W+Ra5yLAMR36Ebr2fjjbwWR9nFXr4KlKuJKXUN82kGVRK3Y92WnZpRFrLY9JKVUrF8xW0SWUivuuFBm4iGpH9R6XUCeihwrdofCipOTIl2rJzi2VoBd+i6/jEJsI0VRdR7jlEZFPdc0TS5P1WSv2tlDoNXc/3AK9LHEvYlkL+GW0Y9neljdR+g3bXsFRtnKFiQxyMQvoPYi1N/o1oVwszgMEicpTVO0i0Joi7Ke3v6Se0m4YEq8UeOW+SirZWXoL+uNwVk+V6GnePUc9LaOdwQ4i2iP04cINsWFCQLiKntqK4E9GLDxLRc2CVIjJGRJKscu4kInvGibepZXoZuFlEcq2W9HjiOI+LxarfESKSrrTV7Uq0eamN5TYrzQPQVt9f24S0GqCUqkCX7VEROVG0Gw+naKeJk6xgTdXFAqC/aNckieih0dbQ1H1o8n6LyBkikqu0O4xyK05jdT0XbRm+fnju85jj1spmiINRSP9dbkYvXgBAKbUK3SO4EW1PbBXaD0z9MzICPYFdgl748AobXGZMRw/DrAH+oKF/l2fQcyLlIvIW8XkHvYBgvVJqQYRcs9Ct15nW0NnvQNwVX40wB216/wJrPmQwethlOdr3y9PoHlAsm1qmO9BK/Fe08p9nnWsJZwIrrPJeTOMuA5qjAF32tei5l4uVUos2Mq1GUUo9gO4t3MyGZ2cUG+buGq0LpdRf6FV69a4PolbctYBbgeet+xA1P9aC+300sFBEqtELHIYrpbyN5DMX3Uj5opHjVslmiI+xZWfYKETkFbSHzlvaWxZDQ0Qv2Z+hlIr1MGswdFhMD8nQIkR7ndxWtKfYo9G9qcZ6OwaDwdBqmloZYzBE0gm9jykbWI32FPtL+4pkMBj+TZghO4PBYDB0CMyQncFgMBg6BEYhGf7VSDu6I4iQod4K+b9yiFy2kOsK2Ujr2RJtG6/FLjokjkV3w+bFKCRDi7Be6hrRhiJLRORTiTAY2VGI/Wi1pzuCliIRRllbEDZsBLU9kDg+o9Qmuq5oC6x68coG1yPVMRYugE1z0REnT+Neoo0xCsnQGnZR2q3DDsA0YIqImGXfWxEiYm9vGTYjoyxDpvV/37a3QIbWYRSSodUopYqVUi8Al6CtKGRD2NT/OyJSKiJLROSC+jhWy/o1EZkh2o3AbyKyvYjcINoU/yoRiXRuli4iz4g20b9GtDNBu3Wtj2gbbxWiXSu8Yp1viTuC7iLypmh3BCUiMiVeGUW7JvjW2tS4TkSmiEhCxHUlIheLyN+iXTQ8KqKtYVsWAe6zZFsGHNvSupUmXD+IyJ3AAeiGQHW97CLSV0Q+tup9ceQmTGvYaaqIvCciNcAh0oRLByvOw9b9qBSRn0VbecBa7n8jMMzKf4F1PtJ1hU1EbhaRldZ9nS6WQVrZMHR5toj8Y9XPTS2t87ZAYobhROR6K6+1InJ+nF5PZrx6ivestaWc/1mUUubP/DX7h7bd1ifmnBMIAMdYx3PR1rUT0bvji4DDrGu3om2eHYXebjAdvXv+JiudC9B2x+rTfgt4AnCjbY39gLZpB9oUzU3oBlUisH9jcqKNq662ftvRpmoetNKNihtTtj3QLhMcaDt2fwJXxeTzLpCBts1WBBxtXbsYWIS295cFfGaFdzSS1wrgcOv3OWiL0BdY8l6CtrRQvyL2c+D8iLhutGWEcy1Zd0dbJOhvXZ8GVAD7RdTXNLSR2oFWnBeBmRFpnoFe3u8ArkVbfEiMuI8zYuQPywSMRLuI2AZtNPRNtI1ErHpUaIvbSWhXFHVAv1bUeZ9G6jCqXhp7dq2y32H9PtoqW3+0eagX4oRtqp4alcf8bdyf6SEZNhqlba0Vow2vdkdbBx+jlPIqpeajzbREuhn4Uin1odLWjl9D+zeaaKUzE+glIhmirSofg/4Y1SilCtFKJNJtQE+gi5VXS83NDERbmR5tpdtoXKXUz0qp75RSAaXUCrRyPCgm2ESlVLlS6h+00ql3izAUeEgptUopVYp2V9AaWur6AbR9uhVKqecsWecBb6C95dbztlLqa6XditSbxmnUpYNSaoZSqsRK737ARctdJIwAHlBKLVNKVQM3AMMlekHHbUopj9ImohagFVNL67wpJssGy97zmg/OUOA5pdRCpQ2o3hYnzMa6vjBsBEYhGTYa0R5dc9GtyHoXFVURQVYS7SZifcRvD9qPTjDiGHSruie617RONrgNeALdUwK4Hm11+wcRWSgiI1socnf0x74p3z31ZdteRN4V7TG2Em1cNdb1RGNuEboQ4Z6DaNcLLaGlrh9A19VeEu1iYQR6I3M9q+LEa9Slg2iPpn9aQ6LlaNtvm+J2w0G0Qo2bdwvrvCmuUBuseu/eQlkj66ZV9WRoe4xCMmwKJ6CH7H5ADytliUhqxPUeaOOkrWUVeignJ+IDk6aU6g+glCpQSl2glOqCduD3mLRstdMqoIe0bPn1VPSw23ZKexS9kQ2uJ5pjHdHuOXq0MF5LiN3JvgqYq6JdLKQopS5pIk6jWPNFY9C9h0ylVAZ6yG9T3G4EiG6MNMam1PnGsA6ItPXXvbGAhi2DUUiGViMiWSIyAngUuMca3lmF9hFzt2jXFf+HdoD2YmvTV0qtQ/tHul9E0qyJ8m1F5CAr/1NFpP5DUob+SNb3tJpzR7AOmCgibkvO/RoJm4p2/VAtIn3Rczkt5VXgChHpJto99dhWxG2O2PK9i3Yed6Zotw9O0XYH+21k+qloBVIEOERkPJAWk38v0e7B4/EycLWI9BaRFHQv55WW9ErZtDrfGF4FzhWRfqJ9fY1vZXzjXqKNMQrJ0BoWiDbVvwQ4H7haKRX5Ep+GnoxeC8wCblHaFfrGcBaQgHb9UIb2KtvZurYn8L0lyzvAlUqp5da1W2neHUEftNfc1UBjq6OuQzvpq0JPwr/SCtmfAj5Ez4/MQ0/stxUPA6eIXoE32RoiPRI9v7YWPcR0D3reZ2P4EHgf+As93OYleiir3p9SSSPzNM+iFwd8gV604gUub2Hem1LnrUYp9T4wGT3/twTtbBA2uFVpjlsx7iXaFGPLzmAwGACrV/k74Gphj87QxpgeksFg+M8iIieJ9qibie5ZzjbKqP0wCslgMPyXuQg9X7YUPQ+5ueetDE1ghuwMBoPB0CEwPSSDwWAwdAiMQjIYDAZDh8AoJIPBYDB0CIxCMhgMBkOHwCgkg8FgMHQIjEIyGAwGQ4fAKCSDwWAwdAiMQjIYDAZDh8AoJIPBYDB0CIxCMhgMBkOHwCgkg8FgMHQIjEIyGAwGQ4fAKCSDwWAwdAi2GoUkIs+KSKGI/N7IdRGRySKyRER+FZHdI64dLSKLrWtt6U7aYDAYDG3EVqOQgGnA0U1cPwbYzvq7EJgKICJ24FHr+o7AaSKy42aV1GAwGAytZqtRSEqpL4DSJoKcAExXmu+ADBHpDAwEliillimlfMBMK6zBYDAYOhCO9hagDekKrIo4Xm2di3d+r8YSEZEL0T0s3G73Hn379m17SQ0Gg+Ffys8//1yslMrdmLj/JoUkcc6pJs7HRSn1JPAkwIABA9RPP/3UNtIZDAbDfwARWbmxcf9NCmk10D3iuBuwFkho5LzBYDAYOhBbzRxSC3gHOMtabbc3UKGUWgf8CGwnIr1FJAEYboU1GAwGQwdiq+khicjLwMFAjoisBm4BnABKqceB94BBwBKgFjjXuhYQkVHAh4AdeFYptXCLF8BgMBgMTbLVKCSl1GnNXFfAZY1cew+tsAwGg8HQQfk3DdkZDAaDYSvGKCSDwWAwdAiMQjIYDAZDh8AoJIPBYDB0CIxCMhgMBkOHwCgkg8FgMHQIjEIyGAwGQ4dgq9mHZDAYDP82gmWlBFYsx79sCYkD98aWlYPN7W59OqUlhKoqEbsDcadgz8zcDNJufoxCMhgMhnYgWFFO2V234f34Q31ChOxHniBxvwMQiWcTupF0SoopvuQ8/H8tBsC134FkTbgbe1Z2i9MIVVagQgp7RkarytDWmCE7g8FgaAdUdfUGZQSgFBX33U2orCm3bzFphELUvD0rrIwA6r7+At/vv7Uofqi2lrpf5lFyzeWUXHkJnq/mEqqqanH+bY1RSAaDwdAOKF9dg3OhqioIhVqeRsCP/8/fG5z3L/6zRfGDhespOu8M6n76Ad+CXygZdRH+5UtbnH9bYxSSwbCFCXm9BIsKCawvIFRV2d7iGNoJW1o6jp69o86lDDsdW3rLh81sCS6Sj2voADvx4MMajaMCAQKFBQRLiql9/90GCrDm1ZcJeb1R50K1tYQ8tS2Wa2Mxc0gGwxYkWFFB7dtvUPnYI6g6L4mHHUnmjeNbNd5v+Hdgz84h96lpVM14Hv9fi0g+9ngSDzgIcTqjwoV8PkSkwfl6EnbdnfRrx1I1/VnE5SL9imuwd+oUN2yoqpJgaQkV995NqLqa5BNObihXbh6qzguJiYRqawisWEHlU1MRh4O0Cy/F3r0HtsTETa+AOBiFZDBsQYKF66l4YFL42PvJh9T035nUs85F7PZ2lGzLEKqpRnk8SLIbW3Jye4vT7tjz8km//GpUXR3idkctZgjV1hJYtZKq559F3G7Szj4Pe6dOiMOp67HWgyQnYU/PIOW0M0g+5lgQwZaZhdjiD34FyyvwfPg+3q++ABGy7pqEY5ttCSzTw3T2Tp1JOuxI8Pl0+HXrKDzj1HAvyvPZJ3Sa9R627j02S30YhWQwbEF8v/7S4Fzdt1/hPmUo9tS0dpBoyxEoKKDigYn4FswnYcBAMq66DntuXnuL1e6I0xm39xNYtZLC4SeDUgB45swm/+0PIBSk4v6J1P0yD9fue5B+zRgc+Z2w5+TGTV+FQqjqKnC5CK5bg+9Pyx2cUnh//J7MW+8kVFSICgSwZ+fgnfcT7kHH4V+xnOqZM6KH9AIBama/RfqlV7R5PYBRSAbDFiVh510anHPttQ+2pH93byFYWkLJlZeEJ9s9c94htL6ArPsmt/tS45aiAgGw26N7MZUVBEtK8C/6A2e//khKKgT8oBSSlNxs2YLlZag6H9gEbHao8yKJiUhKClXTngkrIwBVW0Nw/TrKJ07Ab62i81pJmbQAACAASURBVHz4PsG1a8mePBV7Zlbc9D2ffETtnHdwbtuHlAsuxTVgIN7PPgWg4t67yb7vYUI+H/7fFuAasBfJhx1BwYmDSNx7X+zdujVI0xYnn7bCKCSDYQtiz+9M2qirqHzyMfD5SDzgYNwnnYI4/t2vovJ6G6z8qvvpBz1X0UpCnlpCFRUEli/D0b0HtvR0bJuxdxmqqsK/Yhk1r76Mo0dP3CcOwZ6bR8jjoebtWVTcPzEcNv26sfj//ovat98k8YCDyLztrkbnBwPrCygdczW++b9gy8sj4/qb8Hz0PoGVK8l+7GkkzgZZW3JyWBnV4/ttAaouesVeyOtFVVUSKFiH59OP8P3yM75ffsb77dfkPvU8/r8WUztnNoQUvj//wH3qaSQfeQyhmmqKLxqJqq7C+9Vccp+eTu3bs8JL0e15+SQffuSmVmmj/LvfAoOhg2FPTydlxFm4jz8JFQrpVnR6enuLtflxOJFkN6q2JnzKlpmJ2Fo3b6aCQep+/J6Sq0dBMAhoJeA+eehmm5Oqm/8zJZdfHD6umfU6eS+8AsEQFVMejApbOfURsh+YQu3bb+L9ci7eLz7HfeKQBmmGqqoov/t2fPP1EG6osJCym8eQ88RzFJ19GpVTHiJt5AV45sxGWavb7PmdkGQ3kpqGilidKWnpUfOPwbJSqp55kppZr2FLzyDt0itw9tqG6pdfILhmNaGaGtIuu5K0i0dZc06Z4A8QLC6CQAD3yadS8chqVGUFZRMnkDPlCfz/rAQE154DUUBgzWpwOrGlpLZpvW9Vy75F5GgRWSwiS0RkbJzro0VkvvX3u4gERSTLurZCRH6zrv205aU3GDS2pGTsefk4OnX+bygjwJaeTsaN46F+st3hIPPWO7Fltm64LlRWStnt48PKCKDi4fv1HMlmIFheRtWTU6PPrV1DYMUKVCgIMT0TVVsbNR9U9+P3qDj7ipTXS93PPzY4pzy14HBS993XSHIy+bPmkDF2HJkTJpL30uvYs7LJHD8BR6/epF1xDZkTJpIz5QlsGdpUkAoGqX1/DtUzpqFqagiuXUPZuLEkHT0ISUwCwJaUhCMvH0fnLjg6dUZ5PFROfYSCQYdRcOzheL/4jJwHpiBpaaSeNZJgSTG2tHSc2+1AqLSEkkvOo+DYwyk49nBq3ny1TbcubDU9JBGxA48CRwCrgR9F5B2l1B/1YZRS9wL3WuEHA1crpSK3PR+ilCregmIbDAbA5nKRdMjhuObsSXB9AfZOncFuJ1Rd07o5JKUIlZZEn/P7UX5/2wpcjwjEG051OpDEJFx77UPd99+GTyfufxC+334NHycddUzcFW/iSiCh/87UffdNRJq6F0nAT8KAgbr3nJ2M+5ShhMrKCKz6B1tWNgm770H2fQ9TPukuAuvWkjxoMI7uPbBnZhGqqMDz0fvRom63PeJykf/GbAIF65CYRlBgxXKqX3w+fOz97huSTx5K7rSX8f/+K5XPPQ11XhIPOYxgQQH+v//SAf1+Ku6bSNLBh7XZkOlWo5CAgcASpdQyABGZCZwA/NFI+NOAl7eQbAaDoRlsbrc2HOpwUD5xAnXffoNz+x3IvPVOHD16NrpUORJJTCTxkMPwfvpx+Jyjz/ZIUtJmkdmenkH65VdTdP5Z4dVmjm374OjaDXtGBll33UvVjOfx/fQDrr33JXnQYIovuwBJTCLlzHNI+L9d46ZrS0snc9xtFF92IYEVy5GUFDKuHUvNrNdx7bkXGVdcgzidBAoLCZWWUHTeGagaPdzpHnY6jm36UPfDdwBUPfEo4nbjPuY4vD//hLPPdvjmzwPA2X8nMq4eQ+lNYwgs/Rtn3x3JnvQgRCgQ3y8/bxDM5SLnocfw/vg9lZPvx9GjJ9kTJuL7ZwUJvbeh5JrLo8rhHn4Gyuul6vlncfTqTcLO/7dJ9S0qYhVHR0ZETgGOVkqdbx2fCeyllBoVJ2wyuhfVp76HJCLLgTJAAU8opZ5sLs8BAwaon34yo3sGbQgTnx9JSsSWktre4mx1qFCIUGkpKhCg5t23qHpscvgDb+/UmbwZrza6bDmWYEkxVc8+hffrL3Du9H+kX341jvz4G0HbglBtLcH1BdR+MAdHt+4k7rs/9uycDfJUlBOqqEDV1up5MYcTVAhJTcPmcjVbFuX1gtOpV9kFA4grEZSi+pUXsWVm4f34A+p++iEqXu5zL1J04Tlg9Qyz7nmA6hen41/6N7mPP0vp+BsILF9G9oNTKL/3boJr14TjOvpsT/YDj2DPyMSWloZv4e8UjjgFAPeQYUhCAtUvvxAOb8vKJmvi/Xj+9wm2rGxsiYnYc3IIrFuLo0dPSsdcE76XCbsPIP+5F39WSg3YmLremnpI8czfNqZNBwNfxwzX7aeUWisiecDHIrJIKfVFg0xELgQuBOjRY/Ns/jJsPSilCK76h9Lbbsa/+E9cA/cmY+w4HHn57S3aVkOwuoq6776lcvL9KK8X9ynDyLz1TsrG36CvF6xDeTwtTs+enUPaFVeTOvICJCl5s2+wtSUnY+u9DemXXN7gWrC8jPJJd+F5bzYAkphE7vSZJGy/Q6PpKWvYUdXVgcOBo2v00moVClHzxqtUPT6FzDsnEYhQJvWEysuRpGSUvwIAR7fu+H6dD0DpTdeTftV12DKzsGXnRCkjgMCSvwgVFuKbP4/kY4/H1qkz+W++S6iiAklNo+z2cdF5lZYgiYm4TzgJW1o6tZ99QsUjD5I5/g4qHnkgap+Sb96mNeC3pkUNq4HuEcfdgLWNhB1OzHCdUmqt9b8QmIUeAmyAUupJpdQApdSA3NyGLbZQTQ2B9QX4Fv5GsLCQ0EYsWzVsPYRKiim6eCS+n3/U1pn/9wll42/QPSZDiwgVFFB63RUE/llJsHA9lY9NBqVw7rgToIfhaKYnEYvNlYg9O6fdrT2EysrCyghAeT0EVq8isGY13u++IbBmNcGIBRcqGMS/+E8qHp+ir61dQ2DNanwrlxMoKLCWa1dRa6VZ9+P3JB16eFSekpqGvWs3VKVWRpKSgqSlhee6Av+s1Na7r7oMQqEG+4bsXboSLC+jcuoj2p7dm69SNuEWat56g8qnppJ15ySSjjk2Ok+Hg8IzhlJw4jGoqircpwwnuG4NytvyhkRL2JoU0o/AdiLSW0QS0ErnndhAIpIOHAS8HXHOLSKp9b+BI4GGJnKbIeT14vn0IwqOOZTCEaey7rjD8S2Yj4pY8WP4d6E8ngYtzLrvvgmbVjE0j2fu/xqc8345F9euu4HDSca427GldYzVhiqglz8H/llJsKiQYE1Nk+FDMQ2TtGvGoKqqKDjuCIovHknB4CPxfvE5Iet5CZWVUv3qyyTuviclV19G0TmnU3j2aeDxUHLDtfj/XAguF85t+wAgyW5STjuL1PMuwt6tO6699iHv2RnYMjLIfeYFsu6fTN60l1G1HlLPPi9KlvRrx6BEkXnrHYhVv7bMLDJvupXqGc8hCQmomhqcO/TTZoeUInGvvQkVF+EeMozUCy4BwH3KMOp++lGvbPT7qXriUVy77Ibn269xDxkWlae9a8ONtK1hqxmyU0oFRGQU8CFgB55VSi0UkYut649bQU8CPlJKRT5J+cAsa4e1A3hJKfVBq2WorKD8rts2dFF9PsrGjSXvxddaPP5t2LqQxEQkMVGP81vYu3YD2Zracu2Lc7uGw1fOHfqRdNxgUs45H1tq6mYz1tkalFL4/1pM8WXnEyorQ1JSyLz9bhzduuHo3DXuSjJH125IWnq4t5J08KEUnj5kg4WFUIjyuyfg2n0Atk6dUV4vycccR8nVl6Gqq3WQ4iJKbxxN9oOPEiwrI+SpJfX8i7F36w7BEAVDjyf58KNIv/JanH131Eu8A34qn3+WtIsvo/KpqQTXF5B66ZXkHXUM/sWLSNhxJ2o/+YCaObNJv+Z68l+ZhaqtJVhaQtWTj+Gb/wsZN99KyOuh9oN38bz3LgC178zCPWQoySecTPJxJ5B8/EkEC9dTPOqiqHL7Vywj8Pdiko8+luwpT1Lz5ms4t9mWlKGnwSbM5201CglAKfUe8F7MucdjjqcB02LOLQMa2mxpbf5+f9SHCSC4vqBV/ksMWxeSmkbm+AmUjr8RAn4kKZmsO+/Flr1lrHOHfHXgrdPDMi1YhdYRSdhpZ1z7HUDd118CelLdfdKQTWrEBcvLIRjAlpHZZkZpQyUllFx/FaGyMkA70Cu75Say732IuvXrSTrg4AZxbJlZ5L0wk4oH7yW4ZjWIhBVNPaqqMvyNEJcLcblQ3uj9S4FlSxGbDf+iP7CnpaKA5MOPpOA4bRWh9u03Caz6h8zb79Jzbl4PmdffSN2ff5B87PEor4fg0r+x774HCbvuTuWjk6mb9wO5T03XVhe+/ZqEvv1wdOpMwi674T5lGKHqauxp6Xg+eC9aljWrkaRkyu++neDaNSQecRQ590+m+MpLwosonNttT6i6GmePnji32x7XgIGIw7HJFke2KoXU3khiIo6evQmsXB4+59pnP+gArTvD5sFmLTPu9N4nqMpKbaYmPaNVLqY3lmDheiqnPU3gr8UkHTWIpCOOwm5tgNyasGdlk3XHPajqalQggC0tHftGKvSQ14v/78VU3D+RUEUFKcPPIOnoQdhb4UOoMVQwQHD1quhzVZWIw0nlE4+S8H+7NshHHA6cPXuTdcck6hb8AkqRcu4F+H6dryf4lSJhtz3A4UAFAuGOU+6zM/DN+5GKKQ9BIEDCLrvh+/1XEvr1p2zCLaSPvgH/qn8iMhIyb7mDqqlTqJ2jZyrsXbqS+8wLlI69Ft8CbfFBEhPJnT4Te6dO5E57Ge+nH0eZNsoYezPJgwZTct0VBMvLSdxnP73XKoK0iy6jaOQZYWsQ1c88CcEQyYMG4537P9JH3wgOB9mTHqTy+WdIv+KaNlvkYxRSK7Bn55Az9WnK77od3x+/49p7XzKuHo29g4x/GzYPtqRkbfx0C66sC5YUU3ThOQRW6MZP3U8/ECxYR+pFl2JLaN0CgI6APTML2sAoZ6islKJzz9AGTIHyu2/Hlp5O8tHHNhOzeSQhAWf/nfAv3DC9bO/UmVBNNZKY1KSZI1tKit7oW1GO2GwkHXoE6VeNxvPRe6SMOBtHXj7+JX9TeO6I8IfePXwE6aOuwvO/T0i/ZgylN40m6ZDD9abhYFA773M49UbZPfYkVFYaVkYAyufDv3xZWBmBtvZQOeVh0sfejKqqpPLRh6LkrJj8IPmvH0LCzruQdtEoVMBP8vEnUTvrdV2O9AxtMSLG+oLno/fJmfIkyUcNQoVCeP73CdXPPAGAs/e2uE8Z1iZWR4xCaiWOLl3JuvteQt46vRw0jgFEg2FTCVVXh5VRPTWvv0LK8DMgzurPlhAsKSawbq3+2HXrHrWXZmuh7qcfwsqonpo3XiVxvwM22VqAPTOL7EkPUXrDdfh+nY9zu+3JGHMzFY88SMaYm7ClNr3/zJaYSNHZp23YQNuzNzlPPocjvxOBoiLKJ06I+tDXzHyR/FnvISmplN40muDqVQSWLcG5Qz/E4SSwYjnZD0ym/M7bsGXnEKqpxtGrd/i5sCW7w0ZPIwmVl+H/8w+cffo0mGKotyWYcdMtejXf/z7GPfhEXHvsiW/hbyTud6BWiDE4unTF++XnVDx4L4iQ+/xMqqc/C34/wbVr8P38PYkHHmqG7NoDW2oaNrM30rAZkYSEBuds6enaTcFGECwppuiikQSWaLMv9m7dyXvuxQ7pjyjk9RJcX0DN6zOxpaaRfMLJ2HPzEJutwZ4dAEfPXhCnvhqk6/PpxQdNOLFzdO1G9uSpKK+XUEUFvsV/knnHPXHnu1QggAoFsSW4CFVWUvnow1HzyYGVy/H/sZCQx4OqqSEQMxyow6ygYvID4UURrv0PxNmrN+JygQjO7XYgd/pMVF0ddd99oxc7ZGZRevMYAqtWktB/pwbGVvWquO9xdO8Rx7TRgbr8CS5CSQGCpSXYc/Pw/bEQSU7Gt/A3nNv2wT1kGDVvvAKAuN2kXnipXtAFYfNN9qxsgusLSDr0cGrfn41rr/2MQjIY/o2I262HUt6ZZZ0Q0kffiG0jXZ17v/oirIwAgqtXUTP7LdJGXtgW4rYpwTWrWT/sRAgEAKh+aTr5r72DPTcPR69tcO27P3XffAWALTeP1PMuQhxOgmWlevVjKIQ9K3p4MFheRs0rL1E9cwaS7Cb92rEkDtxbD7XFYM/IJFBQQO2H7xNcvw7f77+SfuGlYJknUsEgwcL1VM+YRrCkhJQzztZGSuNs/1BeD6F1a/B8+zWJBx9GzcwZ4WuSkoKj9zY4unYjoBTuU4aReMDB1LzyItUvTAPA0bcfmTfdGjVMmbDzLmTedhe++fNQdT5ypjxB9cwXCRUXkXzcCSTsPgDntttBKEjm+Al4vpqLZ/bbJOy6G8nHnaCVUF4+tmQ3aWeNpPLJx0j4v11x9u2H57NPWH/6EDLHjiNlxFkE1qzGnt+Jiofu3dBjF8HRrTspIy/A5k6hbv48XHvv1yajRUYhGdqUkM+nPWBugUn/fzP2tHTSrx6Ne+hpBJYt1cuGM7M2ul4D/6xseG7Fcu0CowOt3gv56qh67qmwMgJtlcD79ZfaD1FWFll3TiJUUkKopgZH164ou4Oq55/B98vPuAYMxNn//wiVleLo1Tu8As/7zVdUTn1EJ1hWRuk1o8h/6/2wQgqWFONfsRxV58XZZ3vKH5iI96MNO0OC//xD1qQHsKelEyotoXD4yeE9SJ4P5pD3yizSLryU4otHhuPY8zthz80nVF2F58P3yZowUXtp/ewT7N26kzHmZkI11WTcMA57fids6emESkvxfPpxeO4o+YhjqHzkwahhSt9vC7ClpeE+/SwKBh2qe5HHHIuzV288X31Bwi67UXbbTfj/WowtO4esOyeRMPpGQl4PofJyKp99gqwJ9+DI74TYBPfxJ4HTQeGpJ4DDQdqFl2Lv1AUSEghVVeDo2i089CcpKaRffg2ezz8l6bAjKX/gHpzb7aB7Xm2AUUiGNiFYXkbd99/i+eRDXHvsSdKRxzTqmMzQMuyZWdgzs3DttGkGKwGSBw2m6tknozyQuk8d3qGUUZh4SjfiXH29gH7uyq67MmzrzfvlXJJPOgV7py6kDDkVe04uoZqaKGsK9Xi//hLl92NLS6P4kvMILFsKgC0nl5yHHqXui7lhSwR1332tP8pp6dTNn9dgQ2zlIw+SeftE8l56g+pXXsSenUPSIYdRets4Evr2I2XoaZRccTHJx51A+rVjcPbrT9ltN+OzXFA4++5I9sOPobwe0i65HEfPXtS+9y7O7XfA8+lHDWQPrluHLTsXW3oGoaJCqmdoa93pY2+mbMJ4/H8tBrSlkZJrLifv+ZepuPcufL//StIxx4FS+Bb9QcV99xAsK8F90qlk3XkvJCQQXLOasgnjCSz5C9c++5Ow4864Tx5K+uVXg8uFOJ34F/2J8tSSOU4vKrG52malcQd8Gg1bGyGPh6pnn6R0zDV4Pv6Q8ol3UHrT9XqviKFDYO/UiZwnntNDM/13Ivvhx3D26t3eYjXAluAi9dwLdA+h/lxmFon77h83vPJ4GhgerX33bVy770GoVju2E5cLZ98dG8S15+ZS9fgUPJ9+HFZGoDeq1n4wh8SDD90gQ1Z2WHmLK5GkQcdpx4DDRiCpqdogqs1Gwo79Sb9mDKG6OgpHnkFgyV94vviM5GOPJ/vhqVoBZmRSM+v1sDICcA8ZSu3st1l/8nGUjRtL6U3Xk3L6mfj/WYF7yNDoOsrKxt65M6qinIxrx4RNBonbTeLe+0WtugO9kCHk9eBb9Ae2vDy9MCYQoPCs4dT99D2B5cvw/7kQ5479cXTrTqi6itSzziV78uP4fl9AYPUq6ub9SNmE8ajKSorOHE7ZuLEUnn4KFQ/dR6iiQhuJbQND3aaHZNhkVHUV1a+8FHWu7tuvtbOx1vi6MWw2bO4UEgfujXPyVFBgz9y4/UxKKULl5eB0YN9MVs/tXbrS6c13qZ71GrbUdNzHHY8tOwcVDBIqK0V5PNqCRnq6dviXlETSwYdhz8/HN38e/qVLIBDAZjmkE4eDlOEj8Hz2KYGlfwOQeOgRehOr10OotKGLtGBJCbY0a9We3U7mLROwZWQSLCnG2bcf9l9+onbObJzb70DOY0/rugkFCZaWECwtIeWkIaScMhT/sqUk7LwL9uwcHJ064xq4N8rnCy8YAG35I3HvfSk4aVD4XPplV1I6+kr8ixeRcYN20Of56H3s+Z1wn3AyvuXL8M+fh6Slk//6bELlZbq3VFlBws67RLmU0AZo3XSa/ZHuLX73Dc68vLD5q8xb7wS7Hd8vP1N26006ks2Ga5/9yLjuBjzvvUvaZVfg+20BlU9NjbJf53lvNiknD6Vs4h1k3nI7CXEUf2swCsmw6YggTqe2XhxxLu7Qi6FdacnG2mB5OarOi9js2LKywvMwoYpyPF9/Sc3LM7BlZJJ+5bU4evaK8pDaFtgSE7H16EnGlddFnff9/RfFl5xHqLgISXaTNfF+Enbbg/wZr1Lz1psEli0j+dgTSNhxJ5SvDolYpm3PySX3yecIVVcjdhveb76ibPyNYLeRev7FVE17Jmreyn3CydjSM0jc/0Cc2/fFlpaO8vnw/vAddd9+HV5s4v9zIf4/fid97Djw+ym67MLw4pGE/9uVrPsmY0tLI1RVpZdHL/yNhIF74z7lNDzWHJX7+JMIVVZE5W/P74R/8SIAyu+egLP/TiTucwDuIadqG3trVlPz5mukjbqK2k8+wNGlG2XnjsDetRtZd06i7PZxBJYuwZaZScYN46l8/BFsqWmknHs+9uQU7JZ5n8ieY9WzT2LLzCJj9A3YO3fR9yI7B2w2yu65k7TzLiRYuL7B/QrVVBEqKaL4opF0evv9Btdbg1FIhk1G0tJIu3gUFfdt2BGefMJJ2JLNHq2thWCZ9lVEIKD34cyfhy03j6w77iFh192wuRLxfv8dZTeODsep+/E7Or39QfjjtlnlKymh9IbrCBUXAXoYqnTsteTPmkPJ6KvCQ27eLz4j7cprSRl6OrYYp3327Bzs2TkEy0qpeWcWylOLvXsPJDWNnCeeo+rpx1E+HykjzsY3fx6VTz+OLTeP/OkzsSUnEygqwpHfibKY+Sj/33/h6NqN2tlvRa1k9P06H+83X2Lv0pW6776h+tkNLthynp1B5h2TqJ75AvZevQmWl+HYZtsNQ4d2u+79WcvI/Qt/J7h+PcknnkztR++jSkvIeeYFJMGF8noou+MWUIrg6lWU3T6O7AemECoqQtV5qZ45A++Xc7ULiZOHEvh7Ea6BA0k5eyS4EvF+9QVJRxyF8nrJmvQglY8+HHbwl7DrbmTdfT9lt9xIdYqb5KOOoerpJ8LlsKVn4Nx2u/B+qKBldmljMQrJsMnYElwkDz4R16674/nqC1y77oZzh34bhjwMHZrAurWUjL6SpAMOxvfnwvDHKFRUSPEVF9P53Y8JuXzUvBo9LKu8XurmzyP5qEHxkm1bQqGojz1oSwWqqipq/geg+uUXcA8+ERpZhmzPzCL7/keoeGAS7pNPpfTaK8Bu09YeHA5EhLp5P0EgYO0P0j19W6KLQDCILTOLUFFhRIJ2cCYQWLmiQV6BZUtJ3Gc/Si49P+p8yeUXkfvUNJIHDUbsdoJFRWTeeifVL72A/69FBIuLSDnzXKqff0ZHECH9iqupeOg+nH37kXzKcCofm0xgzSqSjz2BjKuvp+TayyEYJLB0CaqygqILzopaxGLLyiZUXYVr/wMhEMCe34mE3QZQ9/03eD77hLRRV+H/a1H4/gP45v+C95uvyHv9HVSdD3tmJuJOwfP+u9i7dCX1vIvwr1qJvWs3ggXrNnnpt1FIhjbBnp6BPT2DhDZYEfZvRCkFSnW4VW3BinLKxo3F//tvpJ13EVUvTo8OUFdHsLQUR4+e2OKYTtpiG2udThJ2HxDlAM6WkRme0I/EluRudrjYkd+JzPG341+2LGybsvKxyTqrHfriHjKMuh++I/WCS7SvIfSGeEfP3qRfcY12Lmh97FPPGkmoqoKkI4+mdvZbUfkk7r0vqqZaG1SNGJJTXi+qzk/FA/dCKEj2Y0/jX7YU5447kXzkMTh26Is9L5+kw4/SCw769tObZQNBEvbYk6JzTgsbga1Y+Dtpo64iefCJJB1xtLZA7rCTO30mxReN1NYZbDbSR12lLZMXF7L+pEFafoeDvBdfw/PRh9hPGY7vgzkN6sq/8Dd8v/yM+6RTKL7hOpx9tsN98lCClRVgt6NqPUhWFjk33xZtf28jMArJYNjMBAvXU/PW6wTWrSNl6Gk4uveMuyFzs8tRWgKhEOJybTCz4/fr3gB6X1JC/52o+/brDZEcTmyZmdiSkki/5HK8X84NWwVI2H0Aji20Us+ekUHWnZMovXE0vl9+xrHNtmTefjf+5UtJPOAgvF/O1QFFSL9mdIs2ENvcKYgzzifQ7sDRqze502fi6NUbVV1F7ddfEly/jqT9D8LZf2c6vf8ZoZoqxJVIcPU/hNYX6Hm1seOoeekFcNhJPWskdQt+wVHei6TDjgzPO7kG7EX66LEor5fcZ6bjWzAfe3Y2tXPeJqHfTvgW/UH5fXeTecsd+IuLcPToSfGF56K8HhL22BN7125hZVRP7ey3yXnyOcrG3xC2zJB4+FHkv/lu2NJ3qLYW/D4qHn5gQ88pEKDk8ovJeXo6iJB8zLHUvBrl2xTXvgdQO/st/MuXEVih/zyffAjoDdZpV11H9nb3UjXt6bimjFqDUUgdkGBVFcGCdXg//xRn334k9N/Z7OnZSgkWF1F4xtDwZHDtrNfJeep5Evfca6PTVKEQoapKrVgSk5oPHwziX7qEsnFj8C9dQuJ+B5B5023Y8/LAbse5Y3/8v/9G9SsvkXXPg5QVrCOwfBnidpN522dYJwAAIABJREFU651hG271q998i/7ElpGBo2u3LfpcOjp3IfvBR8HvQylF1TNPUDvnHbJuv5vkk04huHoVifseoHs0wQDKZidUUoJ/yWIkJQ1H164N5LXn5ePcoW94AQFOJxk330rC9n0Rh4NgSQnFl54fvl5x/yTyXn6Dup9/oPLh+wlVVpI8+ASSjhqkXYS4XKRecDEEg9R+MIe6778ld/pMXHvsiT03j0DhelJPO4OiC84JmwtyDzsdVzCIZ/bbeGaH/YpS9fwzZI6fQM07b5H9wCN45n6Go2cvvdAghsQDD8L7+f+izAR5P/kQ/3HHk3jQIdrUT24eBIOommj3GMHC9QTXF2DPy0eFFBk33Rres5Zy5jkEC9cTqihHRXi+rSdUVYWqrqJwxKmoqobXW4tRSB0MFQxS9+1XlF5/dfic64CDyJowcat0PfBfx/fXogYrk6qeeIyEvv1aZAw05PNpPzPWUF+wvBzPJx9S++5bOHr0Iu3iUdg7d2nSgkOotJTiC8/Wy7UB79zPKBMha8I92DOzyLrzXoovPZ/gmtWU3zeR7PsfQVwJSIILSU8PWxcXux17bh5J7Wj/zm5tIwj56gDte8jzv09wHXgw3u++ofIp7R4t9/FnseXlUXjq8dgys0gefCKE9kT5fBAMIklJ2LOysWdlk/PY03i/+YpgWRnJRxxF7ey3qJnxPO7TzwSfb4OyApJPGoLYbJTddH34XM1rM7F36ox/2TJSzzmPqqem4vnkQ2zp6WTeOQl7fmfw+wisWon7xFOpePj+sDICqHnlJVJOHa7t8fl8eqjusCNxbLMtwdJSat96Hd+CedjS0gkWrUdESDz0CLz/+1gn4HLhHnZ6eMgxkrp5P+Nf+jeObj1I6L8zNXPewX3iECofnxIO4+jRE3t2DpKYSPH/s3fe4VEVbxu+55ztfVNpgiId7IAiSFOxUywoTQRRUIoCAjZAkaaAIvizNxQUlGLFrgh2BbFgRbHQ0jbJZns58/1xwiabBIyAjS/3deWCnT1zzuzJZt+dmfd9niuGYul+Ku5rr0PYHWA0UjRymB6oJ05BWG16OUc5jkGXEv/tl4MSjKAWAUkIcRIwHWhSfrwApJSyxUEZQR1paMXFlN69IK0tuuFdZDAIdQHpP4dQa/gTM+75M9o7yZISYl9sIvTyCxjbHq3X4rg9BFc/i3/RAlAUFKeb0NtvYDv7PAz7mKlowUAqGO0h8t4GZDQCTieGxk3IeWI5MhJBmEy66d1BTuU+2OwpoI2se0vf2xh9ZVp9jO/GSXim3oq5Uxfs5/UlWVpCZP06ypbo6d3Glq3IuudB1Owc1Mws/ZiiQvIvvUQ32gNdw258RVahkpWN4/z+RN57t9p4oh9+gOf6m5HRKJ4bpuGZdIMuYurx6MrdO7YTfvN1LKd0J1HDPkuypATjkc0xNG6C46IBBFatQNvyNZaTu+CZfBOJHb9jPvpYcLmJfPgerpFX4xw2guTOHZjatgOXB2vXHinn1z2Yj29PyR2zSO7cgef6qcS/+wbbmefiuXEakXffwdCsOY7+AwmtfRHF7cF6bl8S339LyewZaKUl5KxYg3PUGIIrnqLsmafJXvIUZUseRQbKsPe9gOTO7XoCk8eDWq9BjRJVf4bazJAeAyYDG4Hq6oF1HGRkej3PntZKG6KHElJKZDCoFzoeoFLwv5E9HzKpP1RVxXX1uH1aGchYjOBzK/EvnA9A+PVXCb+2lsy77iG4cjkYjWQuWEz8xx+IvP2mvo4//ErUvfg1KTYbGI0pt08AY7PmenYYIIT4T1pRGOrVI2fZs2h+f1owAl1pW7HZcAwYTOHYUWQtWETxDRV1TYZGjUls344sLxIWRj1o7AlGoOv/qTm5elZdsQ/LKd0Ivvw8lo6dqo+lZSvKnnyMyPp1eGfOxXxCx5QtuxaLEvvma+z9LiK66TOsPU9LSf2ArrBgaHSYrjJeUkLewAsgHidzwSJ8N03Ws94UhYzZ85BbfyT86lpiX2zGednlCLeHRJGP+PvvYe3WA+dlIwg88zQYDDiHDiexcwfJnTsACDzzFM4hw/DdMBHTcSdgPr49pg4d8d06jdgnH5C5+AFcHU4k+vlGjEccgXB7iH/1Jckd28m8czHC7qDsycdQXW5ETi6li+/C2KwF5hNPJvOeB4l/s0V/X53QYf9/p7U4xi+lrC4EVcdfguL24Lx0GKV33pFqM7Zs9YdeLP9FkiXFhN9+k/Abr2JqdxSOiwcdkK01QNLnI7FzO1phAcY27VC9Gf/ot301K4vsR5cRWf8Oid27sJ1z3l4Dxx40fymBJY+mtcW/+RoZiaBkZmLp0pXoxx8QKM+Ii236jOhHH5D9yJM1BhbhdOKddhvFM/TiTcXr1ZeAD4Jh3j/NnveLmpObtjRq7ngSwmojuu4tVK+XeKWUbPc1E8FkwjdlPDIexzlsBLbe5yMMRqynn4G5U2e0khJCz6+h7JmnyVn2LIGlS1AaNiS5fTtasQ9bn/P1JAUpMbZoif28vhRcNhAZCVM0fgz1X36zwklak4TfegNLp84o3gzUBg1BUQm/tha1YSO8N0xFzcpCGIyUrlwBmoZrwmSMLdvgnToDLRQi/NbrJHbuwL/oTv2cmzcR/eh9Mu+8h2RRAdGP3ie25Stco0an9rPK7r8nzdBP2Gwo2dnY+pxPZN3bxD7fSE6vszDkZKOeeQ5aSTF5Y0emjndefiW28y/SHW/LiX36Mcndu/QHBiMZ8xcRWLaEwBOPHZTfZ20C0ttCiDnAaiD11V1K+eVBGcGfQAhxJnA3oAIPSynnVnm+O/A8sMfZbLWUckZt+v5bEEYjtt7n6+KKLz2PsU077L37HXJJDVokQtmjD6beyNEP3yeyYT1Z9z603681WezDd+N1RD/6ANAlU3KWPYOxabODNu79Qc3Kwn7+RX+ih9BTfKu2Gg14Jt2IVlqC78bJac8lftmGFgjUGJAUqw3raWdgObETWjiEYnfoKdOHCEpGJln3P4pv2vXEv/sO84knkTFtJtjtGI44ksTOHRibtQAhdAuL5i0oGlPxwVt65x0Yj2yOsXVbjM1bEli6BDUrG8+N0xBOF4YGDXGPn4RMJtCKisgbeCHOocPJXvI0SImSkUnh6CsqZmmxGEmfL5USr1gsOIddoWfcuVy695DTSfbjT4HZAlqS8LvrMB7ZHMXlwnPjdBK//8bu3r1A03Th1bvuoei6a9Net1ZcTLKwgPC6d7B06UbJbdOIvvsOlp6nYe/dTzdjTN0kBffYCUS/+gJhMJC5+H6974Z12C8ZjOLxUDA4/T1atuRRLKd0p+i6a/DefCuGFi3JXLCYyEcfIMv8WHqcimKxpL4YHQxqE5C6VPkXQAIHR2+8lgghVOB/wOnAduBTIcQLUspvqhy6QUp57n72/VegejxYu/XEfFJn3crhX1a7cjCQgTKCzyxPa4t/9w0yFIL9DUi7dqWCEYAMhyhduICM2fP+kTTr/UXxenGNm5CmimDp2h1hs2NskYXmK0Jxu0lWsZmuydQvdU6rFaxW9m7C/d9FKArGpkeStfgBPWGhUlq76ehjMLVqQ/jVl/FOn0l008a098gegi8/j92gphIDEj//ROzLL8gtl8IRRqP+k5tL7oo1hN96nfgP32E5pTtljz9CsvLeSXmqfGUUjwfFbCby3nos3Xpg6XgSsS8+RwsFMTZrTtmjDxD//jtylq9BhoKU3DYt1Tf+3TeULXkE6+lnEN+SPg9QnE5imzfiHjceW98LCL38ApH172Jo3ATvjdOI//oLie2/Y+3ag8DyZQRXLAMg9PILZC99hsCTS7B26YpWWIBWNTGhfJtAKyygaMp4ch5dikwkMDZtqtuWGAz63mQNPlD7yx8GJCnlKQftagdGR2CrlPJnACHEcqAPUJugciB9/zGUGr4lHzoIhMNRbe2/pkLH2pL0FVVvKypAxmP7fc5/AqGqer3LM88ReedNjG3a6an/5bMaUa8+nik3UTTuqlQ9ia3vhYiDYJD2X0Xzl4JQULPSlyHVjEwyF/6P5M6dSFXB3LmrroZdaQ8HwNTuaMJvvZnWJiNhXRaokjSSMBgx1KuPc9DQVJtzyDBiGz8h/sP3CKcL762z0zIoE/l5+CaOI/a1HkzKHnmAzAWLwGSkZMrN+gxv0X3kDxtEYPmTmI9rX+31xb/ZguPiQfjvvTslimru3BWZ1HBcMojIJx/iHDka15VXkyws0CWSioowHNkMGY8TWP1MKhjpry1C+PVXcV5+JcLuIPr5Jqynn0m4UmGsqd3RFQkY0aguzPruOzguvJjYV19SeN01eMaOx3xKd2w9T9OdezUNDqCkYa9//UKIAVLKp4UQ42p6XkpZPcfwr6UhUNkDeDtQ0yvvJIT4AtgJXCel3PIn+iKEuBK4EqBx48YHYdh11ITi8eC57np8109Mtdn6XaRvwO8nppat9CAXqKizsPcfiOL+7ymOKy4XJpcLU4tW1Z4TioL5+A7Ue/F1fQO6aTPUhg1RXe5/YKT/LFo4RPzHH/EvuhMZi+IcfiXmE9qnBYQ96d17EMefgLlLV6LvrQd0EVRbr7Monj2j2vnVnH2nuGuxKBhUvLfO0RNzrFY9hbrSvqVWVJgKRgBIif/RB8mYoe8aaL4iwhvexTnsCiydT9HHLkSa7I+544kIu43sh58g/t03qNm5ICWGJodjaNyE+K+/ENv0ma4gkUymEl9UqwVj02bEv/u2+uCFIPj0k8R+/J6su/6HqXkLDA0bEf3sE4yt22I7+1x8k8qXCY1GDI2bICMRCsdcial1WzJvvxP/Q/fjvf5mXf/wy82IWtTF7Yt9fR3dM+c8sF3mg0dNebJVDTg2AU2klAEhxNnAc0DzWvbVG6V8EHgQoH379gdu8FFHjQiDAUvnruSuWUv0008wtmqlKxgcwIeq4s0gZ9mzlC5eiJafp1tCd+1xSC55KnY7Snl21v9ntMICCoYNTC0bFV17NdmPLsV8fPVZxh7UjEwyZt6ul1JoGsLuQM3IwH3NRKKffZKqEbKc2kv/4N8HiW3byB98EcTjmI46Bs+N0wm//SYk4rpJZVZWjRmyMhJFahVLXTIaxX5Bf/Iv6oNr7LV4Z82jdMFctNJSbGeeje3s3siyALGvvyT0+mskd/6Olp9P9uNP6cW9jZuQP2B8xfJZPE7xrTfjnT4T3/UTyX5oCcGVy1Nf1hSvF+sp3fQC2HicyIfvE930GWpuPTy3zkKxWCmaMIZk3m49KWb6LEJvvEao3DYjvGM78Z+34r11Nv4H/kfsy83lr6vKisefZK8BSUp5b/m/Uw/oCgeP7UDlv75G6LOgFFJKf6X/rxVC3CuEyKpN3zr+fhSnE8XpxHhE04NyPmEwYGxyBBm3zkbGYyhuT52V+h+QLCnRCx0VBcVmq1Wx7sFCC4eQZWVIKfWZxR98GdHCYV0tQOqp0ordTuitN6rtYQSWL8PY7qhUQW9NqB5vtbo+Q8NG1Fv1EvHffkH1eFEyM/dZjK4FyvAvvlNPpzcY8Uy5icKrL0/J+vjv/x+5K1/AUK+BLj5aKaXcednloChYTulO5KP3cfQfQPjtN5GRMKXz5uAaN5Hsx5albFxCa1+i7L5FWLp0xTPmWgpGXqYX0iqKvqzYuIkeYCuPr6gQxW5HhoKU3j2fnCdWEHlvPUitvD5pdqoUQMbjeKbcjDAYSOTtpuSehTiHDMfQrBmK1YYWCuGbdE3a+RM//4Rid6TP/g6Q/9JXx0+B5kKII4QQJuAS4IXKBwgh6onyTyAhREf011dUm751HDoodjuqx1sXjP6ApK8I302T2H1WT3af2YOSBbeTPEAtslpfu7SEwNIl7Dr3dHaf0Z3iaTfUuAcIoEUjJHbvIrn9d0Kvv0LegH6U3DWPZHExhgYNqx2vNjqs5oLkP0AYDKjZ2VhO6IDxyGZ/qIwiE0m0cjkdc4eORN7fkKYxJ0NBAk8vRcnMJPuxZTivvBprrzPJuOMuNH8phcMGY7+wP7mrXyb48gsIW8Vyl3/RAsqeflJ//fl5mFq3xnnl1aCqRDZ+gqXn6Xim3IxWWkr4vfUIiwVDs3StAtPx7cFoxNa7H+YTOhDe8C7GDh2wnNqL/MuH6LMaoxHrOb2xnXE2geVLKZowFjUrG7VhI3w3TEQrKqJo0rUkd+9Eya6yWGYyIVxuzAewZ1SVgx6QhBB2IYRS/v8WQojeQogDLgSRUiaAMcBrwLfAM1LKLUKIUUKIUeWHXQh8Xb6HtAi4ROrU2PdAx1RH7ZCJhF68eIgW9/4XkVISfv1Vou9v2NNA6LlVaTI5fyXJXTvx/69igz6y7m3do6jKbEfz+wk9v5q8888lr38foh99SOb8RYReWE30o/cxn9gJY4uWqeOVnBwcFw/SLR2KCkn8/huJ/Dx9NraX91/quLzdaIFAjcfUhOrx4Lx0OKAHM1mp8HgPMhrR93pycnEMHoqxZWv8D95H6R2z9fT9aTcgzGYsx52AucNJeqq5zY79gosxt2xFfv++FAwfTOm8uVh7nIrp+PZYu5+Ke8JkTMcci+JwYD/7XGLffoN36gwsXXugZGRiPbUX7vGTiH72iW6z7vZgOPxwCIWR4Qg5jy3Ddm4fcp5YgaFhI3zTrod4HM9N0wksfQzHxYOwdO2B4bDGJLb9TGL773im3JyWdOS+ZiKyzI/tvH6YO3XW78MBzrDFwfBBTzuhEBuBU9D3oD4CPgNCUspBB/VCfwPt27eXn3322R8fWMdeSfp8BJ9fReS99Zjbd8DRf+B/UhXgUEOLxSi+aRLhN15La3eOGoN71Ji/5pplfrTSUhK7dqJmZeN/6N40qRvzSSeTOX9RWop+/Jdt5PU9K+08jkuHoxUWIDWNjJm3o5WWkNj+OzIaxdj0SNSsbBI7d1A4ariukGEy4Rk/Wc9ClBJL1+6pouDE7t0UXjWcxLafdRWNkaOx9x8A8TjJ0hIUpwvF4UCx11w2kPSXEt/yFYGVz+AaNYaCIRdX7KMYjOSufAFjuSJ6siCfXaenV8t4bphKsqQktZdjPeNs3KOvQUpJXr9zIFER5MwnnYzlpJMJrn6WrAceo+CqEWQtvJfErz8jY3GCq57BddVYknm7iP/4A8GVK9CKfWQuup/IBxsQZjP28/ohHA4S235G8Xope/yRtMw6Y7ujcF42AhkM6gWxJhP+BXMxHXUMWiiEtfMpJHZsx9C0GfFtP6O6XPhumoxz+BW6U28kgrXzKRullHvfxNsHtdGyMwF9gcMrHy+lnL23LlLKkBDicmCxlPIOIcTn+zO4Ov7baGVllNwxK/WGj238lNjnm8i4Y2FKJPNQJVnsQ/MVoQWDGBo0RMnM+lctISomE5ZTe1ULSNYuf015oRYIEFjxFP57FuoNRiOZcxeQ3L075XFkPrkLoorLa/ybr6udK7Z5E5buPVG8GfoyW7kTbOpaZWWUzL2tQq4pFqNk3mxyljxN/mUDsZ7aC+/Nt4DRSOk9d+nBCCCZxH/vIqyn9qLgquFo+fmgqnim3IztnN7VzOeSviKin31KsrAA9zUTUdwecle+QOCpJ9CiMZxDLkOtV7+ig8GA6ahjiH31BaDP5tQGDSmZc1vqkPArL2FqexTmjielBSOA2JavcVw8kMRvv5LcvRNr9574H7oXa7ceCJsN0zHHEVz9DKHnV6f1i2xYh+b3E35trV4cnJVN6fy5ZD/yJOE3Xk2/319/hfHwIyhb9gSGxk2IfvE57vGT0WJRwq+uJf+ygSjeDDJunU3pgtvJumsxmq8ozS36QKjNkt0a4GL0YJSs9LM3hBCiEzAI2BN6Dz2Rsjr+EBkOEX79lbS26CcfpakFH4roezOTybvgPAouvYS8ARdUyK38i7CceDKOwZfpewEOB+6JU1AP+2tKHbRgAP99iysa4nFK716A4+KBUK5ebT/zHISaXrprbNWm2rlMRx2NsNmw7CV4ymiE2HdVSgw1jWRBvu52+saraKEwMhwm/m31UsTYt1sQxvIi42SSkjtmpfaK9pAs9uGbMgHf5GspvWMWeb3PIPrBBtT6DXBPvB7vDVMxHn5ESs9OJhO6svqc+RhbtgZ0X6T4jz9Wu370k48QRgNij/RQOeYT2hP/4Xv9fLE4jv6X4Ligvx7ktv6Ivd8FmGvQkTO1aZdyszUf34Gyh3VFdC0cqi5JZjCCwYjllO4kCwsQHi9SShLbfsbSrQe2s85FKykhWeYnc+58tFAotVwH6LbrB0BtAkUTKWW7P3HOa4EbgDXlezxNgXf2a3R1/LcRAmGzpdUFYTCmRD0PVRLbthH94L3UY60gn7JHHsA96QYUs2UfPf9eVK8X1+hrcA4dDgiE27XPzLQDQUaj1bLhErt2YmzTjuxHniS6eRNlTz2Be8z4NJFdNSsL17iJlD34P2Qkgvmkk3EMuozYd1v2mhEobHYsJ3VOGeIBYDKhZGXr5oKKAgKEw4mla3cCP1UKCqqKocnhJPMqWYYkEshIJO0aWpmf6Kcfp7WV/u9uzB1PSputJQsLCb3yEvFvt2Drcz6G5i3ImH+3XodiNpPcVT3Z19TuKCIbPyNz4b0UT7+RZN5uzO074h59LQUjh2Fs0RI1tx55F/XRhYltdjIXLML/0H3YzjxHL3AtN9CznnkOisdLvGqABkLPr8Z19TWUzL411eYcPJTgmmdR69XH0uN0jMEAgScfI/HrL9jO6Y1z5NXYB12KMBgJv/Mm4ddfIXPe3SR2bCfxy89YunSDA8iarU1A+kgI0aa2MjtSyneBd4UQ9vLHPwM1FtfWcWgj3G7c4yamveFdI0aiOA49odjKJHZur97226/6Bv4/GJBkIkGyqJDQq2tBatjOPAc1Kxvlb/A3Uuz2aqnP1tPOIPT8KsoefkB/fNY5SC2JqPSxpLjcmI49jqx7HgJVIf7Dd+QPvkjfT+lc8wxJsdlwj5uAVm45odZvgHvCFAJPPQlSYr/gYl3Pz2TCWW5AF35tLWpmFp6bbyG25eu05TIlIzO1XKeFQshAGUJR8dw4Df+jD+pBoEs3JCArLcsmfUUUjq4w9wutfRH3hMnEvvyC8JuvYWh8OFkPL8E5/ArKnngckgks3XpgOu4EhMFA2fJleKbchOLyEN/6A1o8hmvcBCwndKRw8rWpNG8ZCuK78ToyZs2jaNxVuEaNxjF4KKL8y0XBsIGpMcV/2orjsivwL5xH+I3XMDRtRu7ql4l+8hGGI5oS/+F7Su+6A8NhTbCc3IXCMVemgmb0w/dxjR2PuX1HErt3E3n3HbxTb8M3+VpkNIaak0OkkkHg/vCHSQ1CiK+AFsBWdHHVPX5Ix+/l+E7AI4BDStlYCHEMMFJKefUBjfQf4K9MakgWFRH7+gsSv2zD0v1U/YPhEJR+0fx+koUFxL7cjLFNW9Tceqj/QeWEP0Ni5w529z4z7UPNO2c+9rPO3Uevv57E7t3kXXReykxN2O3krnoRQ70Gf8/1d+6g5M47SJRrwFlP60XhuFGp8eQsW6l7+1QhWZBP3sX90PakhRsM5K54DuORumiu1DQ0nw+QCIezwvahzI+MRPTni4sJvfEqlg4dMbZsg1pJa04LBpDxBDISRthskEhSMm82kffexXhkM7zTZmJocjhaMEBw5Qrd3C4Ww9zhRLzTZ1L2xOME1zyDMJpwjbwaW98LUN0e4tt+Jq/f2WmvRW3QEPe4CSmFEte112HrdxGE9CLd6OcbCT67HNt5fSmZOT2tr6VnL8wdOmBq3oqCEUOq3aecZSvJH3RhxbVy65G58F5kKEh042cYW7Yqnx3qQTPx+6+Y25+I4nAQevM1Ak88hlaQD+hq6a5RYykYnp6Lph7WGNfI0ZiOOobwe+uRJcWUPXRf2jGHffH9X5fUgJ7Q8GdYCJxBeZ2PlPILIcTfKsT6byfp81E0cQyxzXquR+ndC8i6/1EsHU/6h0d28FFcLhSXC2PTI//poaShRaNo+XkEVj2jZx/1uxA1O6faHsb+oGRkkv3Y0lSlveOSwVg6dfnjjn8xoRfXpDl7ymCQ4KpncY++Rv9QDod1U7mMzL8kAcPQoCEZt8xCRiMIq41kfh6WU7ojQ0Gcw0fqack1oGRlk7t8NcGXX0QrLcF+/kUpCw8tGCT62ceU3D4LraQE+/kX4rx8JKo3Q1/S27Osl1sPU6vWNZ5fJhJENqzTa4bcHtzjr8Nz43SIRMBoSNUjafn5FfYPQPTTjwmsWoEMlkE8jozHKb1rnm5X7vbUuJ8iDIa09PPEjz+gmM0kdmyn9K7bcY4cg2PQpfrvotpAk5hP6IiwWLAPHorxsMZoxcUEX1iDMJsRLhe28/oS+fB9jM2a4xo5muKZ00hu346xdRusffohfT4iH3+I+aROqOEQhaNHoJWUYDv7PDJum0vhmCsBcI4amwpcab8LpwsZCpLcvQtbrzMpXXB7jfd0f9mXlp1dShkECv7sSaWUv1d5Q9cZ+1UiWVSYCkYAaBqld8/HeM9Dad/c6vjrSObtJu/C81J1MIGnniB35YtpQpr7i2KxYD7qGDIX3gvJpO7A+i/YN6uxTkaTJIuKKFkwl/Drr6Dm1sN7yyxMRx2TmmkcTBSHA8rTupXDj8A77TbQtH1qGAohUHNycQ0bUe05rdhH0bWjU7pvgaVLUBs0TNUi1Yboxx9RPPX61OP8TZ9Sb81ahNGUZgMS+6566WLs803VkisiH7yHelgT0JKYTuhAbOOnqeccQy9PS7O2X3gxssxPwRWXIoNBop9eium4E/DeMhMlI7NiVqgoOAZeikzEISYw5NQjuHIFar36ZMxZgPB6keEQtt79cF55NZEN6/DdNDllzofJRPzrL/FNHIewWDEfexy+yeNT9y303KqUSaChUWMQIENhzJ1PqahVU1Vcl19J2WMP4511uy5rdF6ftNdzoOxrhrQSOAvYgq77VjnCSGBv6TjX7UNlAAAgAElEQVS/CyFOBmR5yvg49GLUOvYQq64+LSMR0Ori9t+BTCQILH087fcgy8oIv/k6zkGXHrTr/FGl/9+Nve8FBJ54tGKD3mTC0f8Syh57iPBa3YMzuWM7hVeNoP7aSgZz+0GyqBDNX4owWxB2+16XafcV9LRoRJ/RSYlwOFCs1YNWdPOmNBFSgPAbr2E/pw/C/ce6iJrfT/CZp6ucNEp4wzrCr6zF0Kw57qvHomZkYmp7dLX+5o4nEv/xh7Q209HHUjh2JMnffiFj7gIS5/QmuWM71tPPBAHBZ5djaNYC1xWjMB7ZjMSOHWmyP7HPN1I8ZyY5y54luGYlWmEB1jPPIfzma2glJRhbttKLijWN+I8/EN28iax7HybxyzYCy5fiGDIMxelOy+x0XXE1xTfpdiaGJk2IfbOl2n2LrF+HofHh5Pfvi7l7T5wDBuMYeCnOIcOIf/8tpmOPJ/TCGizdexJ+dS3++xbjmToD95SbCDz5uL5kOXocnJG+TPln2JeW3Vnl//5Z9cZR6EZ4DdE15F4H/nP7R38lam696tpWQy9HOQQcPP/T/IvqhP4K1Owccle+SODppUip4RwwBIRC+N230w9MxIn/+ssfOtvujUTebgouH0Jyuy6wbzuvL+4JU2o9+5fJBFo4TPDZFZQ9dB8yEcdx8UB9Ka5KkN+zj5TW1roNWGsZTE0mlBpmxWpGJsm8XcQ2b0Rxu3GPGqMnPdw4ndK75yNDISzdT8V+4SUVKuGKgr3fRQirlXi52GjhqMsxtm2H57obURs1QnU4ybrvYdAkSkYGQgiUUKiaurdIxBE2G+YTTyL0/Bp8N05CKyzAO3c+itNN9oOPg8FA/KcfKb3zDpK7dhL5YAOWk08h8csvWHudSXa5vbvicoHUtQBBXx3YU6ybdt9atCT68QfISBhbj1Px3TyF5O+/YenSFecIXQzHOWIUgVXPEHhET0QpmTkd63l9yVr8AMnSktrd833wh0njQoija/hpskceqAZaSikHSSlzpZQ5UsrBQM2Lt/9PUbOyyHn8KZxXXIX11F5k/e8hLN16HpKq1P9GhMGAY8gwXZxyT5vThfXUXv/gqP56hNGIodFhuCdOwXPdDRgOa4xit9VY62Oov3+JDlo0QtnDD6SCEUDoxedI7tqx1z7J0hLiv/1KYsd2As+twnfz9UTWr8N4+BHlS1QxAk8+Xi3NGkCt3wD7RQMqxn1kM1zDrqh1+rpiseC+amyaRYme5WZMZZdF3n4DzV+K4nJh6XkqWfc+TPajSzEddTQFY0fhvWEq9V55m3qvvI1z9DgCy5emXSO+5Wuin3xI4KmlJIuLUb0ZqJkV+3SK3YF7wmS9HEIIlNx6eG6+BdXtQSssJPTCGrTCAjAaMbVsQ+ld8yi4fAgFQwcQeectMm67XV8WttsxtmqN+aijif/4PUJK4l99gVbsQ83IwDFAT4TQSnRlC/slg1L7XMbmLbD3OZ/QK/rym5qZnfodRt5bT8FlAykYOgAZjRJ4+P6K4Ckl4RfWoJWWUHr7LBTbgSVm1Sap4RHgWPSlO4EeXL4G3EKIK6WUb1U5fjFQNQOvprb/16jZObhGjUHG43/JWn0d+0bNyaXe6rUE1zyLMJux9T4fNev/h6RR5S8+it2BZ/wkCr7don8AqSquq8fttw2IjESI//h9tfbEL9swtUnPoJPxOMliH2UP34+h4WHEvtxM+K3XAV2xwH5Bf5yDL9NldYDwm69j7X5qmteQ6vHiHjse5+VXQDyOsDlQM/+c47Bavz65q14kvvVHhN1OMi+P4ltvSj1vbN4CYbGSLC4msW0b8e++xXTU0WiFBSS//5bQqy/jGnq5/jrzdmM77QzCr1TaVzEYsXTrScGwQVi7dq82U1ScTuznX4St19nIRBwtFESxO5GahvmEDig5OWj5+Vh7nErohTUktlYsEUY2vIut9/nIYBDX0BFEtnxF+O23MLVsRf6UCanjbH0vwHX1NRgaNiS09kWShfk4h16Oc8iw8mDrxnfzFGRIXzqMff0FlpO7ENmzf4SeISjMZoyt2xL/tmI/zXTcCcR/2oqxeQuUP3nvq1KbgPQjcLmU8ksAIcRRwHhgNvo+07Hl7Z2Ak4FsIcSESv1dcEg6Jx8wQlX/FZvd/x9RzGaURo1wjx3/Tw/lH8fQoCE5jz+tK2iYzCgO+1612/4IxenCdta5xDZvqmhUVUzHpn8fTRYXE1y1HHPHkwk+8zTZjz9F6cJ5accEn19D9v2PpgKSueOJacEodc3yTM79RaiqrnCdlU2yzE943dtopbonklq/Ae5rJ0Eygf+ehQTL/YAQAu+MOZg7dUnbi4x/9y3xbT/jnXUHwVXPICwWnEMvR8ZjyHCI5O6dUFO2X1IjsHK5rqIgZXmm5jKMTQ4nd9lKQuvextSmbbUUa0Cf7fS7EMXpxObxYDnmOILPryb74SeRUiOy7i0CK57COfxKhMWK4fAjSPz6K7v7nIn11F44+g8g9OnHuK64isCKp7B0646amY31nD6Uzp9L5MP3MLVohefGaUgpyZy3kGRJMTIcIvLxR1i7dqf03sV6gsoBLnvXJiC13hOMAKSUXwkhjpdSbq2SSWcCHOXnrFz56EdX4a6jjjr+pextdigTcbTiEr1Gx2JB8XoRhr2L9wtFwXrGWSQL8gmuegbF68Uz+SaUSrMCmUgQfH4VZUsew9isZaXO6fsoqErKxM7StTvWHqcd2IusBarTheuyETguGoCMRvSC3qxsErt36cHIYMR6Wi9MrdsS/2Ub9v6XYKq05Gk4oilF11yF6ehjsXTvqZvfffIR1p6nYzz6GEyt29Z4Xa2sNC3YaL4iSm6fSebtd6J4vFg6nEjotZex9jyNyLr0PT9r1+6QTOjWF0YjKCpaQT4Fo4aDInBcPAjv9JmQTFI8c3qq1gj0mah7zLX475qHa9wEHBcPpHThPLTSUuwXDcA1ehyu0eOQmoaUENvwLiXz5kAijpKZRfYDjyLNFrxTbiT63jpMbY86oPtfm4D0kxBiMbC8/PHFwFYhhBlIJdRXUmh4XEr5qxDCqTfL2uu513HQ0PylYDAekCV4HfsmVbujKGkW2YcKMpkk9u03FI0diVZSguL2kHn3vZiOOmafM3vV48V55VU4LhkEQqm2hKaVlhJ66QVkmb7hLlxuoh9/iO28vmnCoM6hwzE0OYJ6L72hJx/8TVmLe4wj04jFdImeu+8l+uH7hN54BVOLVphatUHNqvAJUr1e3BOnUHrPQmJfbsbU7mjck2/Ef8+dZNx2O2IvryFZUL26JvHLNmQ0ihYMkn9JP2QkQsbseThHjtZnX1Yr7muuI/bj9xTffD2K10vGjDkkS4oJrnomdZ7Ak4+RsWAxqGo1Ez8AGYshzBbMx7enYPhgSCYxtmiJsWVLZCSi/45UFWJxSu6YlZKA0ooKKZ45Hfekm3TX3IPgHFGbgHQpMBa4Hn0P6T10rboEcGoNxzvL1b0zAIQQhcBQKWV12d46DjpJfymxjZ8SWPYEijcD1+hrMDRqtM9vtXX8eZKFhZTMn0P4nTcxHNaEjFtnY2zRssYlpf8qWrEP33XXoJXo2VNaaQlF111D7tOrUP9AbkgxmSEru8bnhNmMoVEjElt/oPR/d5N11z34lzyC44L+WLv1IPrVl1i6dEX1eMjr3xtZWopavwFZ9z5ca3fhpK9IX/o6SDVgwu7AddVYQs+tIvSy7u0Z//orYlu+Iuu+R1JfSBSnC/PJp5Ddph0IQeL33/BNHq8vKzqdKOaaky0MDRshLNY0C3Brz9PB7iC8+llkJIKw29FCYaxnnI3tnN4Io4noN19TXK76oBXkkyz2EX1vfbXzx774HGPrNmTedQ/FM6amMnyNrdogk0ksPU/XayOTSZSsbDw33Yrvhol6HZOi4Bg2AnufC6rpEcZ/2oridJK58F6Czz6tO9IeAPsMSEIIFbhPSjkUqKkkt7SGtgeBCVLKd8rP0b287eQDGmkdtSK26TOKxlf42UTeW0+951/Z7xTeOqqjhUKULr4zVRCY2PoDBVcM1e/z36AL93ch43GSebvT2rTCghoLbP8MisOBe/xkYp9vIrbxU3zTbsA9fhLGlq1RM7Ow9jydRH4eeb3PTH1AJ3ftxHfTZLL+92DKy6gmtECA6Ocb8S9agIzFcQ4bgaXHaai1qEnaF2pmJtbTz6D07vlp7fHvv6umqqC63eRfdTnJ/AqBVtfoa/Y5w1M8XrIfeYLimbeQ2LEda68zcV52uZ5kUL41kjHzDoKrn6Vk5jQAjK3b4p06QzfNSySwXzIYoagY2x0F5XVlezC2bkPR2FEoDgdZ9z2CjITR/H4MTQ4n8ORjOIYMRZa73dr7XkDZkkdSRbWK00Xi558QRiNKdjZa+WxO8XqxnnUesa+/pPTOO/BMnKKXrnyx/+oN+wxIUsqkEKK+EMIopaztu9C+JxiVn2PdHqHVOv5aNL+fwLIn0tpkOET0803YzjhrL73q+LPIYIDI+nQBexkKkiwqOqQCkjCZMRzRtMIvCDA0bpKmXrC/GBodRu7KF0js2oni9qC4PSmPLC0UJJmflzZbAPTMruS+i8eTebspGjsy9bj4lpvIqt8A9cROBzxmYTCgeLx6CvYeVDXNRRVAzcom+8kVBJYtIfHrL9gvvATT0cfus6wjWVxM7NtvcF01BrVeA7RQkOgXmxFWK6Y2bTGfdDJaoIzIhnWpPvFvtxDZ8A6WU7oReectbGefS8HwwWTdfR/W088g/ObrIAS28/oiDAYS5armxdOux3b2eYReeZmMOfNxDBpK0YSxejnAtBmYjz0BLRDA0uFEEjt2YO3Rk/C77xBZv46cx5+m5K752HqdgeLxIMNhDI2bYGzeEt/UG8h5/CmY+RcFpHJ+BjYIIZ4HUguQUspFezteCDEVeLL88WBg236PsI7aYzSg1LBMcqCpmHVUwWTC0LRZmiQMioJyiJkOqpmZZN59L77JE4h/9w3Glq3IuP0ulIOwXyZUFTU7p8YALsNhPYXb5Ub6KxZhzCd0BLNFV4Eo8+sqEDZ72uxnj+1CZYJrVmI+vv0BL6cq3gw8N0zFd901qf0Sx2UjasxINOTWwz1uIjIeq1FhojJJnw/fhDHEvk7ljuEcMQoJ2M46l9L5c8mYdQeBZ5dX6xv/4QdM7Y4m8s5bEItDIkHRpGtxXTEKx6ChqJmZhNa+hO+myak+sc2f4x4zgdjnGwm99Dy2c/uQMXcBQjUQ/fgj8i7uC/E45k5d8N40nd3nnwuxqP66Gjch68HHCax4msCyx1N7a1mL7sf36zZk+XH7S20qMQuANwAbkF3pZ28ML39+dflPFjDsgEZZjhDiTCHE90KIrUKI62t4fpAQ4svynw/Klcb3PPeLEOIrIcRmIcQh6UuuWG24Ro3RrZrLMbY96l8nbPpfR3V78E69tSL4qyruCVPSrLcPFYyNDyfr3oep/8Z6su57BOPhR/zlzrfC4SS29QcyZ89DbaQLxZiOOQ7vjNnIYID8oQPI63s2u8/qiX/RnSRLilN9DU1rUG5o3uKgeHAJVcVy0snUe+kNMuYtJHfVSzgvHbbX37swGP4wGMlkEhmN4B4/iewlT2PrewGgywtZOpyI4rDjGHIZRZN024eqWLp2x9S2HdmPLUNt0ECftYSClN69gILLBqKVlVH2xKNpqemGI5uR2L0Te/8BKG4PeeefS8HQASR+/5WyB+6B8iXZ6IfvEXxuFdbuPVN9E7/9SvyH71Gzs8h55EksXboiQ0HKljyiz8SqOP7+Wf5whiSlnFqbEwkhLgReklIW8xf4H5XvZ/0POB1dkuhTIcQLVXyatgHdpJTFQoiz0PeuTqz0fA8pZeHBHtu/CUPDRuSuWUvsy80o3gyMRzQ9JDPA/mkMjQ8nd/lqtGAQYbGiOByHpH0IgJrx90paKWYztp69KFvxFO5xE1FzclAcToTLRemC29NUIIKrVmC/eEBKUsh8fHtMxx6XEi82HH4E9j7nHxQVFC0WRYb19Hfrqb0OyjnjP22lcORlesq2yYTnuhsAiLy/ATU7B0NufYjFiW36lNjGT/HcOI2yRx9CRiM4Bg1FmEwUjtKLcs3tTyTznocomXULiW0/Ye7SDTUnl4zb78Q3ZQIyGNQTFibfROnC+XhumErB8MEYGh2G4YimxLZ8VW18sS8+x3R8upOEVlpC4Oml+BfdRdYDjxL7+kuSRYU4O3dJJXzsL38YkIQQWcBEoC2QkhSQUlbVWRkE3CuEeBV4GnhdSnkw1UI7AlvLDf8QQiwH+gCpgCSl/KDS8R8BjQ7i9f8TCIMBQ04uhtPO+KeHckgjFCVVTFnHwUfNzMR16bDytHqB4s1A85eSqCJkCpD49RdMLVoBIGw2MubeBckEslxFvLKD6/6SLC4muGIZgeVLEXYH7onXY+l40gHNipO+IopvnqwHI4BYjJIFc8m+/1FMxxyHNBhI7NqpS1wZjPjvW4ylWw/cE6cgbDYMjRqTd/45qfNZTz2dxC/bcAwcgpqTqzvvmkwIi5Wc5WuQZX6E1Ubx7bNI/PYLUpNkP7KUxLafEB4vhtzcNHsNAPNJndMClZqTi6FBQ5K//wboy6HWnqdjaNEKjGaMzVtyINRmD2kpsAboB4wGhgK7qx4kpewnhHCVHzcOeKR83+lpKeWB5QLqNAR+r/R4O+mzn6pcDrxSeYjA60IICTwgpXywpk5CiCuBKwEaN96boHkdddTxV1PZqgL0bC9rr7PS9lpQ1VQxZrKkmLLHHiKwfBlCNeC8YhT28y864HHIZJJk3m7URodhOuoYIhvexTdhDLnPvXJgy7SaRnzrj+lt0ajuORQOk9/7DFAUsp9cgWvMNfgXzify7jtE3luPd8YcqN8QYbMhy8pQD2uMmptL0YSxaafLevBxQq++hKFRY2QkgrFNW9wjRoJqQM3MJH9wf7RyUVTP9Jm4J16P/77FyEgYa8/TsXbrjql1GxSnEzWnHtau3SieUbFoJqNRrOf1QZaWUjDwAhxDh+///aB2ASlbSvmAEGK0lPItIcTbwDs1HSil9ANLgCVCiEx0hYbFQoiM/VANr0pNC9c1VmIJIXqgB6TKrmidpZQ7hRA5wBtCiO9qCpTlgepB0B1jD3DMddRRx0FCqCq28/qQLCokuGYlakYGnhumpVQgop99SmDJowBIovjvXoD5uPaoxx6339dM+kuJvPMW/ofuQygKjiHDMB17PP7Fd+lGeDWoZmuRiF7P8wdLuMJkwtypc4XfELq5Y7KkmNI7ZpWfTKPo2tHkPKXPRBI/bUXJziH04hqSPh+5zzxP6PVXMLU7mvDrr1S7RvjVtZiP70DxjKl4b52DsdFh5F3YG+eVo0kW5KeCEUDJrTeTvWwlWQ8tQXW70QJlBF96EXP7Dth690PNyiZ/6ADknj07RcExYAjBZ58m9NLzICWBpUv24y5XUJuAtCfde7cQ4gxgJ7DP4CKE8ALno6s6ZACrDmSQ5Wyvct1G5WOpeu2jgYeBs6SURXvapZQ7y//NF0KsQV8CPBgztzrqqOPvIpnEfNwJWLv1QDidKPUaoFhtyEQiJcxamfCGdZgPICAlftpK8fQbU49LZt1C5j0PoNarj6lF+vKUTCRI7tqJ/8H70Ep8OAZfhqlNW925Fl0RIVlSTOLnn1BzclEyM/FOn0nJjGlEPnwPY7PmeGfMoeSO2alzmtufiGvMNSS2/YyamYXpuBMgmcR1xVUIuwOkhq3n6cS+3YKpVRuq6jAYmjbF1KYthnr1Kb5hIq7R1+KdMUe3yPi2ulZB7MvNyJISAs88Rc7qF7GecaZu9PfzT5g7dSbn4SUEVq5ABgI4Bg4huGYVoRefq3TDEtXO+WeoTUCaLYRwA9ehJxW4gElVDyqXCuoLDEBX9n4BmAm8I+VB0JSAT4HmQogjgB3AJcDAKmNojJ7ZN0RK+UOldjugSCnLyv/fC5hxEMZURx11/E0kfUUUXj2C+A8VauKua6/DOWgowmjE3OFEwq+8lNbHckL7qqepNVJKgpWkjPYQ2bAex5BhGKpkr2q+IvIu6ZeS54lseJes+x7B0qkzAPGtP5A/bBBE9dRo+4WX4Bo3gYw585DRKCiqrt9X5gdArVcf19VjKRw7KtVmOfV0vDfPQPXqiUrRLV9TMKQ/aBpZDzyGuX1Hop99AoDp+PYYW7am+I7ZeG+bi7BadT1CjxeEgjAYiWx4t+IFmExYTuxE8exbETYbQpMYDmtC9n2PkNy1U9+XslhR69VHrd8Q4XSihdJDoLlTZ/iiutp7bREHJ1akJIJeQ9e8e/VPFNL+mWucDSxEVw9/VEo5SwgxCkBKeb8Q4mHgAuDX8i4JKWV7IURT9H0w0IPwU1LKWX90vfbt28vPPjskM8TrqOM/R/zXbeT1SS/wVrKyyV2+Wlfq9vkovuVGIuvXgRBYz+uLZ/ykfSo7/BGB5csomXtbWpt3xhxd/aGK3l3o1bX4rp+Q1mbu1JnMeXcjE3GKxoxM3/8C6r30BoZG6QtOyfw8Sv93N8bmLYlu/ESvMapEzvOvolgsRD54D4FeG1R8681o/lLcE6/HdPSxaP5StLIyPekmmSS+czum5i1RM7NSS4mJgnyiH3+oi+A6nDiHjQCLhYKBF5IxfxHWnqdVyyRMFhVSMHwIiV+3Iex2sp9YQWT9O0Q/+RhT23ZYTu6C5YQOG6WU+/VNYK8zJCHEnXt7DkBKOaFKU2MpZWh/BlFbpJRrgbVV2u6v9P8RwIga+v0MHFO1vY466vjvINTqH1fCYklJ66gZGXhnzNVtNIRAsdtTy2X7i/X0Mwg+t4r4d3oyr+mY43Sdvariq4BSgzyR4nbrNVCRMIn8arlgaIGyam1qTi6e66eilZboezPlCJsdzy0zEVKS1++clHeRelhjMm6dTcHwwRRPvZ6cpc+iZucg/X6Kb7kJGQljv6A/0aIi1IYNsZx8in4+gxFLpy6YO5wImoZMJol8+D65a9aiZmXXmNauuD3YevfFv/guZDCILC0htmkjpjZtSWz7mYLHH67lna2ZfS3ZjQW+Ap4F8qg5qSDFXx2M/hEOcD20jjrqOHgIhwNLt55EKlmuu6+9TtdPK0f1eOAgKmaomVlk3fuQvvkvFBS3e68zLmOLlhibtyBenpouLFZco8aiWK1oqoLt3L4EHq1I7lW8GaiZNZcNKFYrwmTC3u8CSubowdA7dQbJoiLKHnsoFYwAkr//RnzrjxhbtUHz60K0WsBP4ZgrU4oSpfPmkDF7HsHVz2Js0YrYps8ILF2C4nbjGjcRQ5PDUcxmjBddUuN4kv5SfalRKNj6D8DStQcyGEDNyib29VckfvtV1+77C/eQGgL9y3+CwApgdXkm3f8LpNT+6SHUUUcd5ageL97pM4l/9w2x77/F2rUHSm7uQSlQrYwWDiMMhpTUkJqRWavicjUzi6z7HyX27Ra04mLMHU+qUAE3mXEOuQzFaiX06ssYW7TCPX4SwlV9prUHoapYe52NjOsJG4rXS+ybr5GB6o4+MhrBdkF/bN16oGZmEnxxTTU7iPBbb2Dp1IXYV1/iq+QmG/l8I/VWv4wmpR5QzGYUhxOlXHUhWZCPb9oNRD98H7VBQ7wz5hB8biXhl17AO3cB2Q89RvTLzRgPb6rbzI8aw/5Sqz0kIUQT9CSCccBkKeWyGo55Uko5RAhxjZTy7v0e0b+I9sceKz/bvPmfHkYdddTxN5D0lxL/ZguBFcswNGiIY8gw1Nx6B0UqSWoaWrFPXxozmSAYJLDsCZK+QhyDhmI8/AgUR83BSSaTaH4/sc2bKF04H/eEyRRdc1XqeWG1kbvmZRS3m+SuXYTfeRO1YaOULcUeHEOGYTuvL6UL55HY9jNamR8ZCOC9ZRbRLV8RWrkCpMTYshXe224HIVBzciiZdQvh11+tuJ7FStb9jxBYvgzzccdTMqdij8193Q24hlx28PeQUhfX06gHAGcCbwJf7OXQE8oD13AhxBNUWeKTUvr2Z4D/KHX24nXU8f+G6Ccf47uuQvUs9OpaclesOWA1Di1QRuSTjym96w5kOIxj4KW6H9Rvv2Du1FlPwgDM7Y6usb9QVVSvF2ObdiTzdhHbvInMu+8j+NxKPRnhiqtQM7OI//iDbpSnaWTd+zCmY44j9oUuoaQ2bIS9/yUoTjfu8ZN0PbrMLJK+IhS3m1Al4db4998RfG4VxmbNMZ90MpGPPkwbj4yEkaEQ9r7nUzRpfNpz/nvuOqB7ta+khqlAb+An9My56VLK2N6OB+4HXgWaAhtJD0iyvP2/xV8sIllHHXX8O0iWFBOosiGvFRUS/2nrAQekZH4+vgkVy1j+xXeSs3wN5hNPJvTSC6hZ2Vi6dCMZCKDuQ/lBzcgg+4kVlM6fQ+ynrTgHD8XYoiWqx4sWCuF/6D7Q9G0G302T8Nw4HcNkvYZKzclFzc4hvvUHCi4doCd+ANZeZ+EYclm1ayV++A5z+w4Qj2Pr3Y/g0scrnjQYEA4HJBLIKkkZMhLZz7uks6/F11sBN9ASmA58JITYtOen6sFSykVSytbo6dhNpZRHVPr57wWjOuqo4/8NQlHTVPJT7bZ9q3XXhsh776Y9tnTpSvTTjymdP4f4N18TWf8OhVcNRwar7w2ljcVoxNSiJZnz7yZj+kzMJ3RIicoCUGkvTSsuxjfpWhIF+ZjaHoWanYPmL6Xk9lmpYAQQfv0VFKsNx4iRlS+F+aSTCT23mvyBF+AcMBi1yeH6GCwWvNNuI/bFZpI+H5Yu3dL6mY7Z/yJk2PeSXfP9OaGU8qpy24dTypvWSym/3FefOuqoo45/EsXlwn3NRPIvvSSVKWZs3RZDwwPXZzZWUXQwd+pM6KV0VWwZDBL/8QcMufVqPEeysNwDymJB2O0objcyHk/Zsys2G66RV+sZiIqCvd9FWLv3xHBEU6SUCLcvylcAACAASURBVCF0B+D8/Grnjm/7GWOTI7APHEJo9bPYzu2DqXVb/PcuAk0jvH4dGXMXoNgdKBYLwuVG8/lIlhbjuf5myg5rTGzjJ5iOPR7n8JHwRHXfptqy14Akpfxpf04ohBiHLk66p8R5mRDiQSnl4v05Xx111FHH/iKlRCsqQgsFdZUC+95tQgxNm1Hv+VcJr1+HoUEDTO2OPijWLcYWraukq+vJAvFv0o9TqxhpaqEQKALN76dg2CCSO7YDYOt3Efbz+hJYtgT7hRdjbNMO1e3GcFgTcle9hIyECb74HEWTr0XxePFcfzPm49rrNUR9zse/aEHqGsLpQs3KonD0leSuehH7uX0JrX2BwgljUst/wmRCKyzE1LJ1KqNRMxnRCgsoHD4E65lnY+tzPsnCQlAObJujNtJBf5YRwIlSyiCAEOJ24EOgLiDVUUcdfyvJ7b9TcMVQkrt3gcGAe/xk7H361ZjRplgsKA0b4Rww+KCOQc3IwHvrLGQggEwkUNweLJ06E/3045TMkLlzV9R69QE9ECW2/Yz/4fsRJhOOQZdiOuoYwuUBKbTmWWyn9SK66VPCb72O97a52M7prdcuNTqMwOpnCZaLnP5fe+cdHld17e13nZlRl2xJlm0Z27gb25hiGzAYCMUEbDqB0BJaAmkkNzfflxsCNwkp34WQm04SQg0hhNACMRBCMR1iAzbGBfcC7kUu6m1mfX/sI2kkjUZjzUga2et9nnnmnH32Pmedo9Gs2Xuv/VvhigrKvv5lBj/zIsFDhpJ74cVIwKPqmX8QLB1C/jVfoPx3v0Grq1yqj2CAqicebZY3yjhhBlkzTqLx4w3UL1tC8JChBIqK0fp6Kh/5C1pTTfVTTzTfa+aRRyX1rLrDIQkQnQcpTCeLag3DMFJNpLycPf/zQ+eMABob2fe/t5F92swOQ6y7i0D/Qoia7/Hy8xn87EvUL1tKoLjY6cP5C27Dmze6aDl/SU7N3Jcoue8hat98rdmBNW7eiFdcQmTPHir+dB9ZM04CESK7dxMaOowBd93P3p/dRuPa1RCJUL94kXMmhYXkXHgJgSGH0Lh2Dbu/dxPhLZvJOOIoJDcPLzuLQU8+Q/WL/8IrLCbr2OPYceUlRMpcXtPQ4Ucw4Fe/R0Kh2Ou/JLk1YYmEfZ8FvLAfAqkPAPN9RW1wgqv3ddE+wzCMLqH1dc2qCS2FSrisjGDpkJRdJ1y2y83nhEJ4RcVx1y1pYwPhnTupfftNvLw8MqZMIzhwUMvxcJiKvz7UelFrYwO1r80lc+qx1L7xKgQChCZMIuxLEUl2FtrQwJ5bb6Hu328DECgdQvEdv2Tnl65Dq6sIjnBxZeHdZdR/tIzQmLFOCy8YJOe8Cym48ZsEBw5CGxqI1NeTe+ElEPAov/NXzc4IoGHpYuqXLSFrxknkX/8Vat95q3loL1A6hIyJhyf1LBPpIV0D3CkijwEPqOrqeJVV9Rci8houF5EA16rqB0lZaRiGsZ9Ibi5ZJ32q1ZCSZGURHDQoTqv9o2H9Osq+daNLDzFsOMU//w2h0WObgw3a0rhlCzsuu6hFh+6QoQz4/b14eXkus61IKymkJrziAbBhHcFRo+n/7ZsBYcAv7qRx8yYChwwjvGtXszMCCG/dQtUzT5N9xplIMESgtJTwnj3svvnb1M17B8nPJ/fcCyj66S8IDB9BIDeXSGUFNa+9wt47/getrKD/D35CeOvWdraEt25GgkFCY8cx6PE5VD39JIFBg8g562wCA5LLzpuoUkN/XIrya4EaXC/o0aZ5ogMVU/s2jPZoOEx41y5qX5sLoSDZJ52CN6AkJYoGyRCpqUErytFIxKVJ6N+fcNku9t7+E2pefZngIcMo/OH/IzRhEl5mZtLXC5ftYucXr6Jx/brmssCgwQx8+PGYa5ci9fXsve1HrRwkQP/vfo/aBe9T+J1bCBQPoHHbNnZcdgGRvS55XmBwKSUPPeoi5err2fuz26h99WUAsk49nf633Ertq3OpfPRhsk8+lYYN66h94zWyTjyZ/t/9fnMPBmDH5Rc1nxcgMOQQBv75UQIDBtDwycfsuPJicmadQ2BQKeHdu8g8elqrxcIEgwx66jlCww7t8LmISPcpNQCo6l4R+Suux/NtnHLDzSLyC1X9fVcubBhG3yS8YzvbLzmvWVOtvKjYpYAYmLqex37btG8fVU89QfkffgN1dWTOOImiH91GoHgAhT/4Cf2/c4uTwilO7hd8NNrQ0MoZAYS3b0PrYusHaFVlu4WkAJGqKiK7dtK4aSOB4gEEBg5k0ONzXF6jUIjMo6YQGFBCpLaWfbf/uNkZAdS+OpfwdTeQdcppBIcOo37ZUvKvvYF+X/8WiFB+1++ofvoJUCVz+gkU//y37Lz+6hYn1dhIU+Ltxo2fUPL7e6l88jFq33yVrBknExw9hsIf3U7l3/6Cl5NLwVe/jlfQXtU8VSQyhzQb1zOaADwMTFfVrX6iu48Ac0iGEQMNh4ns2e3mFzIyOp1f6AtoJELlow+3EviM7C6j+uUXyb/i871mV2TnDsp/9bPm/bq336TysUcouP4reHl5EEcBoatIKERg2HDCGz9pLvMGlCCZGe3qhnftZPett5B36RXUvPRCyzmyc8g65jjKf/9b1F//JJ5HoGQgObPOabm/mmoi5eXknHMBWlXZ6hyNGzdS8+rcFiXxO39J4W0/J1BURPVTjzfXq5v3DnVTjyVrxknNifnyrv2iS9gHhEaMYtdXrqPxE5dOrn7RB0TKy8mcfgI555yPl59PcNRoAv1Sp6belkR6SFcCf1DVV6ILVbVKRK6P1cDXtBurqi+LSDYQVNX2Pw0M4wBFGxup/2gpZf/nG0R27iA4/FCKf/0HQiP7vmiJVrXPNKPVvZt9pv6j9um4696dh15xFRIjT1Eq8IqKKf75byj7+pcJb9+GN6CE4l/+rt0ckIbDVD31BHVvvUFo9BiKf/tHqp9+EsnLI+/Kqyi/604CJSWEfDUEcAthG9atAY0QGjWG8nv/SNVTj+Pl5lJww9cIjhxNxd2/h2CIzCOPYs8Pvtv63t96neChI9vZXL98GXlXXIXkuTmk0KTDkaBzA1pf1+yMmqie8xSZxxzHvjvcUGfWiSen6OnFJq5DEpEAMKCtM2pCVdslsfed1A1AETAaGIrTuTs9aWsNo48Q2bOHsv/4CpE9ewBo/ORjyr79TUrufiAliy17C/E88q74PFVPPd6S+yYzk5zZ58Rv2M1kHD65XVnW9Bkx5YBShYgQGjOOgQ8/jtbVI5kZeIVF7QIatLGRhpUrAKh68jFyZp5F9tnn0rB+HdX/fIZ+X/9PvIJ+zcOJ4V072XHtlYQ3fkLWiSeTMe1Yqh51CRYi9fXs/elPKLnvL9QvXkTBV7/hQq0bWiforluzitzLroTft068kD3z02ROP4HMY45rdkTN95OV1e4evf6FRMr3Abj0GdFSRd1AXIekqmERqReRgv3Ig/Q14Fhgvn+O1SIyMEk7DaNPoXW1zc6oicY1qw6IpI+BwaUMevRpKv58PwQC5F/zxaQFSJO2acBA+v3XzZT/9ldobQ1ZnzqN3Isvbfelm2rE8zq890hFOVpXh+QXkHPuBdS8/AL5n7uGioceoObF55vr1b07nwF3tiTuq5n7UvMwYGjCJOrmvdPu3A1rVlH0s18RyC8gvHs3oQmTaFi9itD48fT76n8gftRev2/fTPldv0Xr6sj77OVkHT/DDRvHeC6Sm0vOBRe7OScAz6Pft28iMHAwA/78KKFDR3T7kHMif61K4EMReRGXqA+ImcK8iTpVrW8yXESCNM2aJYm/JurXQAC4V1Vvb3Nc/OOzgWrgGlVdmEhbw0glkpWFV1jkcuD4hMaOi/lF0NfwsrPxRo+h/y23IiLNiex61aaCAnIv+iw5Z5yFqjrVhW6cfI+HRiKEN29k7x230bBuDdkzzyTvsisp+M9vk3nEUU6VO4qGj5aitTXN+41bN7dsb9pIaOz4dk4pNPFwAn569kBRESV33YvW1SAo4d172P3DH9Cw4iP6fee/GfTEM048Ni+vOeleLAIF/ej3H98i77OX07B+LaGRo6j6++PUvPIyhT++DUlBZGJnJLKs9mXgJ8C7wLKoV0e8LiI3A9kicgYuBfozyRrqDx/+DpgFTAQuF5GJbarNwonCjsUNG/5hP9oaRsrw+hdS/Ju7CPhimcFDR1L0s1/36eG6tngZGWnhjJrwsrIIlAwkOHBQrzkjcEEeO679HLVvvkZ48yYqH7yP8vvvxivo1/xqRTDYqieXe95Fzalvaua+SPbMM8loypUkQu6lVxAcNqy5vtZWw5I34Pn70Ofvx1vyMsX/czte/0L23f5jwju2EygpieuM3HmqkE3L8RrLqV+5gp3XfY6qJx4lsruMsm9+rXnorjvp9Oeaqt7n93LG+EVrVDXeuMNNwBeAJcCXgH8C98apnyjH+tdeByAifwPOx0X6NXE+8GdfVWKeiPQXkVJgRAJtDSNlSDBIxsRJbn6hoQHJyGwnnmkcmETKy4ns2tmqrOaF58m//PNUPPIQBTd+k70/+UGzGkPB9V9BcluiAAODSym5/2EXwh6JoBqh+Jd3ovX1EAjg5eTiFRS0nLy6HF0blVChugLZ8CG5F15Exf330rh+HRnjx0M4jO7bCbXVSMlQyMptnu+K1FbD+mXowldg2tnUPPN069xGDQ3unro5tD+RsO+TgIeAzbh1SINF5POq+nas+qoaAe7xX6nkEGBj1P4m4LgE6hySYFsAROQGXO+K4cOHJ2excVAjgUCvz60YPY/k5LgeTpToQLB0CBLy6HfDV1Fg8JwXqF+xnNCYMXjFA1opkHs5OWQePYXin/8GVDvt7WnF3vaFVXsJDhkCgQAZEyegy+ZBVi76rktFrsEMvHO+CP2K0ZpK+HgFut4NfEnVHjImH+lkippvIOjUImJdXxUa6iHYgb7dfpBI618Cs1V1hqqeAJyNm4uJiYgsEZHFbV5visgvRSSZn4ixZtPazk11VCeRtq5Q9W5Vnaaq00pK7MvEMIz9w8vNJe+6G5r3JSuL/t/+DrL4VbycLIIlAwkOG07OGWcSGjkaLzsbrdxLZM2H6I6NaI2bqvfyCxIaepTiwa2S8wEweDQN6zcw4Fd3IttWoeuXIgNKW4431qOLXkcb6qG6AsrLkDy3vkgaa+n/Xze7OU9A8vIo+p+fte6V+WhtFbpqIZHXn0CXvO2cWxIkMsOaoarNQ1uqulxE2q/8auF5nML3X/39y/z3cuBPwLldsBNcr2ZY1P5QYEuCdTISaGsYhpE0Xn4B+VdeRe6ZZxLZuYPAkCHI2gWwazOEY8x2lG0l8qITVFWA4ePxjj8HyUosW63u3o53yiVEFr0OtVXIqCNg9BHkFQ7Ga6hCisaggw+FzBzksGnoCieHprVViEbA89B1S/FOvxQVgdIRyMLnKf7B9yAzD/IK8AqL8dqEhWuD79RWLnD7W9ahW7qURq+ZRBzSQhH5I27YDtxC2XhiqTNUdUbU/hIReVtVZ4hIMolG3gPGishI3PDhZcAVberMAW7054iOA/b5qhI7E2hrGIaREry8PGTZm3hb1sGaN11hQTGEWv+W19oqIu+92Frd+5OVMOV0SNAh0VhP5KP5yLijISMbtqyFle/hFQ2C+loiz//JXUsEOf5sGDYeNq5EJhyLZGSh4TDkFxKZ/y+8ky8i8s/7oa4GWeCG98jJR87+AtBmnVJDHbp6UeuyHRtJhkQc0peBbwD/hRv6egP4TZz6eSJynKrOBxCRY4GmGbsuL8JQ1UYRuRF4ARe6fb+qLhORL/vH78IFUMwG1uDCvq+N17arthiGYcRDMjJh2hmw7B100xqkaBAy9XQku42EkSrU1bQ/QdgtdNXaKnTrBtj+MRw6ASkc1K7nJKUjXU9lnr+2qXAg3mmXQSRC5B9RIeaq6IJX8E65GB1zJDLICaRKdi7eaZeiOz6Bxvr29lRXtHaYLVeGYAgaFBl5OAwd4+aS+H6ij6kdiTikL6rqHcAdzWa4L/c7O6oP3C8iec5iyoEv+tp3t3XZUkBV/4lzOtFld0VtK25hbkJtDcMwugvJzoWjTkUmHQ/BDCQUY6YjM9sNoy2Y21KW2w+y89C6aiL/fs71mABWLoCjT4FJ05FAS7i9ZOfhzboG3bUF9QJ4ef2I/Ps5vCNPgki49fXqqiG/PxLMgH07iXy8HEqGIoMOxTt0gpsDKiiC8pb1cxQNhljpNLKykakzkcwstGwb+v5LroeWBIk4pOto73y+EKMMAFV9D5gsIv1w6S2iQ0Ae65KVhmEYHaA1VaBhkIBzAmmEBIMQ7FjYVbwAjDnKRcCtXYz0G4BMnoFk56GV+1qckY8ueQcZczTktF7/Jdl5yLBxRKor0Y0rkVCmi74rGgy7t7VUHDoWgiF0/TJ0fpNaxHz0kDF4J57vnNvplxN562nYtQUGDnPlWe2fq3gBdMQEdMNH6JK3XGF1cpKlHTokEbkUN9cyUkT+HnWoAIgRZ9iq7dnAJCCrSbFBVX+UlKWGYRhRqCqUlxF5/e+wZ7sbqvrUZ6Cgb6mqS1YOjD4CGT4eAkEk0Fk/IbbwjVbuhYWvoPvKkKFjkJx8ZPosdMX7aNlWpHQkcsSJEI6gH77e2oaMLBd5Vx2G7Dy8Uy5xKSo04oblYl2voQ5pqIfiUjjpAnThq1CV3OLZeHf+LlCGi0j7XVR5BXGCGkTkLiAHOBW3IPZi/1yGYRipo7aKyCuPQXmZ29+zg8grj+KddTW0natJc0QEMtoEDYQyYPj4Vr0kOfx4yGwvgqo1lUSefxCqneSo7t4G9XVOeLVwIN6UUyErBwmE3LBc1JyQHH0qBAJEnrkHGhuQsUchY44i8tx9rkK/AXif/hySk+/OXVuFblkHW9dD6ShkwBAIBPE+dRFaVU63zCGp6npgvYisBrarai2An05iMK0XmkZzgqoeISKLVfWHIvJz4O8d1DUMw+ga4XCLM2qifLcrPwCQzGy86WejIybBtg3NQQ3U1aIN+yCUCRlZSDDkAhGqW+tf6/pleCec46LkclvWM2kwAzn8BDdvlZmNDBhC5KWHW46vXAC5/WHIKNiyDvbtQpf9G6acBo0NROY9Dx8vd5XXfAgTp0MkTGTFezB4RFL3nMgc0pPACVH7Eb/s2A7qN4VoVIvIEFwvq31iDsMwjGQIBCAnv/W8RU5+7An4Popk5yIjJ8HISWg4jG5bj776uFvPFAy56LhBw2MPq+XkuTmkNr1Fqa9FM3OQky+Eir3o7u3tmuq2DUhRqesJiaBeEKmrhoi2OKOmuivfxzv9MnTFe85xJkEiSg1BVW3OyauqdUA82ddnRaQ/8DNgIbAB+FsyRhqGYbQjMwfvlIshMyf2/oFGXTX65tMti2sbG4i88RTUVkMoExk3taWu5+EdNwvJ69c+P1PZVvSdZ9DFb0FdNdK/vSSQFA1CK3a785z6WSQzm8irj0N9jBD1FM7XJdJDKhOR2X7YNCJyDrA7Tv07fKf1pIg8i1tNVRunvmEYRlw03OiGpTyvOeJLPA8tLsU77wZobHC9hKycpPXU0pZIuP0aodoqiESQnGw4+hQXQl65Fyka1KFjlpx8Fxaxdye6dycUDkLGTUVXL3RzSwOHIeOmoC8/gow9Gt25uTmKTreuh0MntOolyWHHoE37nQZkxEc05oKnqAoi43AyQE06dDuBz6nqqg7qL1TVKZ2V9QWmTZum77//fm+bYRgHNVpbjS77N7rmQ8jJx5s+232JHgC5pfYHrakk8uJfYG+UknjRYLwzrogZlg2gddUuWk6kuY7WVhF5+xnYtNpVys7Fm32dC4BoiqrLzHHOLhwm8vwDLcOiwRDeyRe5HwjbPnZBF5nZ6DvPQmY23rSZeMWlC1R1WlfusVOH1FzRDcPRZl1R9PHBOFXtv+BkeZr6cQXAXap6WFcM7E3MIRlG76KRCLr0HfSDKOVpL4B30Y1IbnuxzwMdrdjjnMmuzVAyFG/Guc2iqO3qVleiuzYjKLp3F3LoBCgoQkRcDqWaSpfUr6AIzchGGupc2HlGy4yM1lYReemvrdcyAd7F/9H8/FXVLbgVD8nMRkS67JDirUO6XFUfEZFvtClvMqKtfNCZwDW4MPFfRJVXADd3xTjDMA5y6mvQDW1UviJhdO+Og9IhSX4h3qmXuHmkQBDJjK2MEKmpQsq2oIteQ2sqnbRPdTlkZEJ2nlv7lJWD4JwOH80jsn4p5PXHm3YG5Bci/vCod9xZRF74s+tpATJqcquhORGBDnpo+0u8Pm+h/55QDgZVfRB4UEQ+o6pPJm2ZYRhGIOhESffsaFUcHcZ8sNGRE2pVJ9zgghDUORFd/q5b51RQ1CoXj4Yb3cLZD99wBXt3Etm5Ce+8L7mIRXDDghfdiO7aguQVQm5Bwkrk+0s8h/QOgKp+bz/P+ayIXIHL0tp8flNqMIyeRRvqAUVC8YJi0xsJZeJNPZ3Ijo3g59qRcVMhzSSC0g0t29bsjJrLNq5yenVeZYvIa12tm5uLpq4Gqva59UoZmW6dU7Bfj/wIiOeQHvJTlz8C/K2jIIYY/APYBywA6pK0zzCM/UQb66F8t8uPE4k4kc3+JX3XMeX1xzvnejfJnpHpQpwT6CUczEh+Yfvspf1L0J2bkeLSlrVJnge5Be0lf1SJzHsOb+rMmEOjzVGPcYYNu0I8pYbJIjIRuBzX6ynHOadHVXVTnHMOVdWzUmahYSSJRsJQW+3mHbJy3cr1FI15pyXVlUSeva/5F3Jk8xoXGl04qJcN6xoi4hZ55vQtOaBeJSffhW6vWuj2cwuQSdOJvPgwMuua5mqSlYN37FlE/vUnFzqPmyPS3dtg/TIi+8rwZl7RSrRWq8rdjwNV1xMbNMzpB6Yg3D5u3KSfKfZ7wPdEZCpObPVNEflEVT/VQbN3RGSyqi5J2jrDSAUVe50uV0Od+9V4yBi8E887YJ2SrlvSfrhm+XswffaBu0bHaIVk5cCU05BJ06Gq3OVGemsODBwKbXs0/UvwLvwaumeHW9tVvht99wV3bPe25txM4ARcIy/+BSr2uFDyCceikUYnzto055QECQXyiwutKwD64Ra6lsepfiJwjYisxw3ZCS5V0RFJ2moY+43W1xFZOBcaokaPN69x/6QHqEOKeV/ZeSldUX+wo7VVblHp3l3IkFGQldsqXDodkMxsNBAEL4Bu+xhv+iwX1NA2wV+TBJOI++FWFfX1Hsps/txoQz264BXnjMD1kD6aj3fmVVBd2f0OSUSOxw3ZfQZYiRuyu0lV4yk1zEraKsNIFZHG1v9gPlpTyYH69SzDD0OXvtMyL5CVi4yb0qdSMqQzWltN5J1nYaObVldAZl6BHDK6dw2LgQRDkNcfGRN7rVIrMrKRY89CX3vC9bBFkKkz0bVLYfTh4AXRvTvaNdPKvUiSoqpNxFuHtAHYjtOhO0ZVtyRyQlX9WEROBMaq6gMiUkJLCnPD6Fkyc9xY+r+faykLhpy0ygGK5OThzb4OLdsCkTBSMrTPpWNIa+prm51RE/r+S2jR4LRLEJgoGg4369rJRTeiVfvc8N3aJejK99FPljunO2yc6xk2IZ4LkoiVDbcLxOshna6qa/f3hCLyA2AaMB54AAjh1BtmdMlCd84i4FFcKPkG4LOquqdNnWHAn3GpMSLA3ar6a//YrcD1ONkjgJubtPmMAxsRgeGHueGFVQtd8rFpMyGzb35xJIrk5CE543rbjAOTSGP7svq6VjmG+hIaCcPOTURefcw528xsvBPPJ7L037D9Y1epbCsSboAJx7kAoZ2bQARv2hloVi5eiiLt4kXZ7bcz8rkQOBqn9I2qbhGRZAcXbwLmqurtInKTv/+dNnUagf+jqgv96y0QkZf8wAyAX6rq/yZph9EHkawcGHs0MvwwCATcBKxhdJXMHCgocrmXfGTCMZCVmi9lVe3Z4dXaaiKvP+mcEUBdDZF/P4d33CwiTQ4pK9dp3QVDLuvs7u1IQRFk5eGl6L4hwaCG/aReVVVEFEBEUvFT9HzgFH/7QeA12jgkVd0KbPW3K0RkOU5b7yOMgx7xPFtMaaQEyc7DO/Mq9KP5binBqCOQIaMQL7k8TFpbjW7/BD5ZgQ4ZhRwyumciQSNhF8YdTXVFSwbbQBA58Xw0Mwe2f4y+/Fc34gDI5Blw+Akp+5HXHQ7pMRH5I9BfRK4HrgPuSfKcg3yHg6puFZGB8SqLyAhcL21+VPGNInIV8D6uJ7UnRlNE5AbgBoDhw4cnabZhGAcikpMPU05Fwo0pWXCsDfXo4jedxA/AuiXoiEl402d1/yLgQLBdj4/+JZDbD+/sL7gfcpk5UO96TtFDk7r0HWT8tPbp17tIp4sSROQ2ESkQkaCIvCAi231poJj4w2JP4LLKjge+r6q/TeA6L4vI0hiv8/fnhkQkz7/2N1W1KbzqD8Bo4ChcL+rncey/W1Wnqeq0kpKEZPwMwzgIES+QOvWLhjqXOjyaDcuaF6t2K1m5eKdd5rLLAgwYgnf82URe+RuR5x8g8vyf3HCe0jo7LzjnFI4xp9ZFEukhzVLV74rIBcAOYBIwF5cjqR0iMhJ4U1Vf8vezRWSEqm6IdxFVndnRMd8Jlvq9o1Lfjlj1Qjhn9LCq/j3q3Nuj6twDPBvPFsMwjB6n7bxRCueRtLbKOTfxIJTRaohNRNCCImTmFc7B7N3pUlyUl7kKVeXoxtXIqMOdisOaRS0nLihKWYQdJOaQmurMBh5R1V1N80Md8DhwQtR+2C87pmsmAjAHuBq43X//R9sK/uLd+4DlqvqLNsdKm4b8cEEXS5OwxTAMI7VkZCKTjkcXv9lcJKOPTMmXvVZXEnn1Udi1xa0tmngcHD6jeYGs1lah65ehWzcgQ0YiA4agbbXt6qqRUAZMOQ1y8tGNK5GiwcjRp7QItaaABOhlMwAAF71JREFURBzS8yKyFOdYviYiA4gvmhpU1fqmHVWtF5Fkn+rtuLmpLwCfAJcAiMgQ4F5VnY0LK/88sEREmlx4U3j3HSJyFK7TuQH4UpL2GIZhpAwJZsCEY2HwCJfJtXQkMqA06WABjYTR5fOdMwIXjLBsHjJyMmTloHXVRN6eA5vWuOP9imHQcLxZ17jou0WvuYi6kYc7O7Nz4ciTkMOOcRF3KewdQQIOSVW/LSI/A3araqOI1AIXxWmyU0TOU9U5AP4c0K5kjFTVMuD0GOVbcD03VPUtiL34XlU/n8z1DcPoPrS2Gt2xCTavhqFjkZKh3ZZvJ52RrBykdASUjkjdSRvq3ZqhNujubUjxYGiob3ZGcuTJTuX7mXvc0F1OPt6pn4WMjFYRquIFui1iNZ5SQ9OwW72qNufxVtVKoDLOOb8MPCwid/r7m3A9F8MwjFZofZ3Lato0ob9qIYyf5oRB00wbrk8SykSGjXfh5FHIwGFoXY1zSCKAIIeMJvLPB1oqVVcQ+eBVvJMvchJEPUC8HtJX/Pc9uFDpThERD5iqqtP9aDdR1YrO2hmGcZDSUNeSIsFHVy10iy/NISWNeB6Mngz7dqFrP4RQFnLMGa6HU1eDbvgIGXs0uuEjqKlqf4K9O1MaRdcZ8ZQa9rtXo6oREbkReMzvSRmGYXSBvinDk45IVi4c82nkqE8BApnZSCCA7t2JLn0bOeFcvCGjXMRcMNQq1FyGjYu7xkhrq52SfiQCGVlJa/l1OockIoXA93FBAwq8Bfyko4WlwEsi8n9x2nPNLrcThXDDMA5GQhnI2CnoqpY1ODLuaAha76gtGglDTRX68XKnnjBsHGTnJSQzJKGM9hF7ef1daoq3/oHm9YdRk/FOv4zI/H9BxR7k0AnIkZ9qNVznkl3WgIBKAH3nmRah2cKBeGdcmdQ9JhJl9zdgHtB0pStwzubTHdS/zn//WlSZAqO6YqBhGAcukpEFR58Ch4x2k+tDxyADh9r8USyqK4jM+aOb9wF00et4517f9TxEmTl4Z11DZN5zULkXqa+F/oPwPv15F9QQCrVa+Ku11ejqD5yaRCgDmXI6UlDU0pfdswNd9UFSt5iIQxqgqj+I2v+hiCzoqLKqjkzKIsMwDiokKwcZPh6Gj+9tU1BVqKlA1y+HcAMyarLrhQSS06lL2q5IxDmChvqWwlrXW5IJx3bpnBIIQPFgvNMvaxlyixO8oFvXowtfcTs1oK897jT9Vi9qFmbVsq0dtk+ERPIZvy4iFzffhMhFwPMdVRaRHBH5bxG5298fKyLnJGWlYRhGT1BTSWTOPej7L6IfvErkH3+A6ngJsnuQWDJCKZAWkqxcJCc/rjOK1Fajaxe3K9ftH0NUbjEZNTkpWxJxSNfiFqXWiUgdTqfuayKyR0RizQs9ANTTotawCfhJUlYahmH0APrxcqirbilobHCq3pFI7xmFi5aTicc56Z8mgqHmBavdjyL9itsXF5cC4rISTzkNGXxoUldJaMhuP885WlUvFZHLAVS1Rix3smEYfYFYjicS7nk7YpFbgHfeDeiyeRAMIhOn91gmYIlEYMREdPNa2OfrHAwdixQOQk6+0O1nZiedgiMRpYawiEzEZWsNRpXP6aBJvYhk48dtisho4ksNGYZhpAUyYiK66HVo9Odq/J6JeIkMJnUvEsxwaSGOnw1Iz9oUykS3fYI35TS3PswLoOFGt/A2hQEoiYR934NLSf4RLjU4OGfTkUO6FfgXMExEHsaFi1+TrKGGYRjdTlYu3vlfQle8B42NLmAgt6C3rWpFsr2QLl0zlAFjj0L37oDN62DISNc7SnE0pGgneeD9zKsTtbOKrdsUA9Nx2nLzVDUpLbveYtq0afr++wmJVBiGcQChqqCaFj2jvoaILFDVaV1pm8gc0nxgHLCyEyMGAjcDY4AlwG1RCfIMwzD6DCKS0nxEfQGtrYa6GjdcmZOf0rQSiZKIQ7oPmC8im3FzQQKoqk5pU+/PwALgt8A5wG+woTrDMIy0R2urXXryT1a4gpx8vFnXInn9etSORBzS/Tj1hSW0zCHFYrCq3uJvvyAiC+PUNQzDMNKFit0tzgigusIlCzz2zB5T+obEHNLG6HTgcRBf966pnxuI3jctO8MwjPREK9pLk2p5GRJuhGDIrcOqqUQ3rgQ8ZNhYp2CR4jm2RBzSRyLyZ+AZosK3Y4R998MN2UUPvDb1kkzLzjAMI02RgcNR8UBbBsFk9BEtSt81FUTm3N0iEbQoG+/cG1IegZiIQ2oaRDwvqqxd2LeqjkiRTYZhGEZPkpWDd9ZVRN59AepqkHFTkWHjm5XEddUHzc4IcLmU1i1BJs9IqRmJLIzt9WyvIlKEUxgfAWwAPhsr/YWIbAAqgDDQ2BR6mGh7wzCMgxEJhmDgMLyZlzul77aqC4317Rs1xChLkkQWxt4dq1xVb0i5NR1zEzBXVW8XkZv8/e90UPfUGOue9qe9YRhGn0ZVXY8mGEQCiQclSFbsBHsyfppbLKyKjJ+KDD8M8ovQhnq3aDZFJDJkNzdqOwu4ENiYMgsS43zgFH/7QeA19s+hJNveMAyjT6C11ejHy92QWuFAmHwikuxcT04B3nlfQqsrYMt6InP/5pzThGPg8BlIVk5KbE9kyO7R6H0ReQh4KSVXT5xBqrrVt2ervwg3Fgq8KCIK/FFV797P9ojIDcANAMOHD0/ZDRiGYXQ3Gm5El7/rQrYB3bER3bwWb/a1SS10lWAQ+g1AqyvRpW+3XG/ZPBg43OWzSgGJ9JDaMhJITmM8BiLyMjA4xqFbYpR1xAxV3eI7nJdEZIWqvrE/dvhO7G5w0kH709YwDKNXqa9FV7VZAlq51ykwpEJ5YdPq9mUfr0CHjUsolXpnJDKHtAeas9R6wG7cHExKUdWZcWzYLiKlfu+mFNjRwTm2+O87ROQp4FjgDSCh9oZhGH0bgawcqK1qXbwf80hxKT0UPprXumzIyJQ4I+gkQZ+fx+hIoMR/FarqKFV9LCVXT5w5wNX+9tXAP9pWEJFcEclv2gY+DSxNtL1hGEZfR7Jz8Y49s5UOn4ya7FJGpOL8Aw5BxhzZUnDoBOSQ0Sk5NySm9r1AVaem7IpdwFcPfwwYDnwCXKKqu0VkCHCvqs4WkVHAU36TIPBXVf1/8dp3dl1T+zb6MtpQD4GgKVYfZGhDPdRVo9s3uiyvef07DTrQcAMgSKDzWRxtEmAFCGYgmdmtjne32ve7IjJFVXtNm05Vy4DTY5RvAWb72+twvbmE2xvGgYjWVqPb1qPrliJFpTB+Sq8oNxu9g4QyIJSB5PXvtK421kPFXj9QQWDyDMgrdEEMHZ0/MxvaOKFU0eFVRSSoqo3AicD1IrIWqKJjtW/DMHoZDTeiqxagH7zm9jeuQj9ZgXfGlUh27DUmxkFMVQWRZ+5plgzSDcvwLvgq5Bf2ijnxekjvAlOAC3rIFsMwkqW+Fl3+buuyPduhoQ7MIRltcItdo5I4RCLo6kXIlFN7xZ54DqlJpXttD9liGEYqCGYA1a3LbB7JiEWsobcmQdVeIJ5DKhGRb3V0UFV/0Q32GIaRDJk5yNTT0defbCkbfhikUN7FOHCQsUe7XlJdjSvIykVGHd5r9sRzSAEgj9bpJAzDSGPE82DIKOT8L6Ob1kDxYKRwIJKZGmkX4wAjJ99JAm1ZCwgyZFRqFtB2kXgOaauq/qjHLDEMIyVIRhZkZCH9S3rbFCPNERHIyUfGHNXbpgAJzCEZhmEYRkdobXWrdUnJEM8h2bodwzAMo0O0porIO8/CplWuYFByMqcdht4komRgGIZhHLzojo0tzghg+8dJna8rat+GYRjGQYTWVkPlXrRsKzJouJt3ysiCXZtTeh1zSIZhGEaHaH0tuvgtdPl8tw/IjHNh1GRk+GHo0ndSdi1bLWcYhmF0TEM9uqK1+oe+/7Jbu1RQhBw3y6W8yMhCppyW1KWsh2QYhmF0jEagbVaIhjqXwjwzG8YdjQw/zJVnJqfyYD0kwzAMo2OCGVAytFWRjDkKQi7HkngBJCfPvRJIXxH3Ukm1NgzDMA5oJCsH79RL0BXvozs2IsMPQ0ZOdGkuUow5JMMwDCMukp0HR56ENDa4pHzdJNZrDskwDMPoFPECkBHo1mvYHJJhGIaRFvQJhyQiRSLykois9t/bpTMUkfEisijqVS4i3/SP3Soim6OOze75uzAMwzDi0SccEnATMFdVxwJz/f1WqOpKVT1KVY8CpuIylD0VVeWXTcdV9Z89YrVhGIaRMH3FIZ0PPOhvP0jnadVPB9aqanLCSoZhGEaP0Vcc0iBV3Qrgvw/spP5lwCNtym4UkcUicn+sIT/DMAyjd0kbhyQiL4vI0hiv8/fzPBnAecDjUcV/AEYDRwFbgZ/HaX+DiLwvIu/v3LmzC3diGIZhdIW0CftW1ZkdHROR7SJSqqpbRaQU2BHnVLOAhaq6Perczdsicg/wbBw77gbuBpg2bZp2VM8wDMNILWnTQ+qEOcDV/vbVwD/i1L2cNsN1vhNr4kJgaUqtMwzDMJKmrzik24EzRGQ1cIa/j4gMEZHmiDkRyfGP/71N+ztEZImILAZOBf6zZ8w2DMMwEiVthuzioaplxEiprqpbgNlR+9VAcYx6n+9WAw3DMIyk6Ss9JMMwDOMAxxySYRiGkRaYQzIMwzDSAnNIhmEYRlpgDskwDMNIC8whGYZhGGmBOSTDMAwjLTCHZBiGYaQF5pAMwzCMtMAckmEYhpEWmEMyDMMw0gJzSIZhGEZaYA7JMAzDSAvMIRmGYRhpgTkkwzAMIy0wh2QYhmGkBeaQDMMwjLTAHJJhGIaRFphDMgzDMNKCPuGQROQSEVkmIhERmRan3lkislJE1ojITVHlRSLykois9t8Le8ZywzAMI1H6hEMClgIXAW90VEFEAsDvgFnAROByEZnoH74JmKuqY4G5/r5hGIaRRvQJh6Sqy1V1ZSfVjgXWqOo6Va0H/gac7x87H3jQ334QuKB7LDUMwzC6SrC3DUghhwAbo/Y3Acf524NUdSuAqm4VkYEdnUREbgBu8HfrRGRpdxibQgYAu3rbiAQwO1OL2ZlazM7UMb6rDdPGIYnIy8DgGIduUdV/JHKKGGW6v3ao6t3A3b5N76tqh3NW6UBfsBHMzlRjdqYWszN1iMj7XW2bNg5JVWcmeYpNwLCo/aHAFn97u4iU+r2jUmBHktcyDMMwUkyfmENKkPeAsSIyUkQygMuAOf6xOcDV/vbVQCI9LsMwDKMH6RMOSUQuFJFNwPHAcyLygl8+RET+CaCqjcCNwAvAcuAxVV3mn+J24AwRWQ2c4e8nwt0pvI3uoi/YCGZnqjE7U4vZmTq6bKOo7vc0i2EYhmGknD7RQzIMwzAOfMwhGYZhGGnBQe2QRORnIrJCRBaLyFMi0j/q2Hd9CaKVInJmB+17RJKoI+kkEblSRBZFvSIiclSM9reKyOaoerN72M4RIlITdf27Omjf28/zDBFZICJL/PfTOmjfq8/TP5Y2n88213w06rlsEJFFHdTb4D/nRcmECSdhZ0J/w47kyHrIxg6/n9rU65Vn2dmzEcdv/OOLRWRKpydV1YP2BXwaCPrbPwV+6m9PBD4EMoGRwFogEKP9HcBN/vZNTe27wc4JuMVmrwHTOqgzGVjXwbFbgf/bA88zpp3ACGBpAu179XkCRwND/O3Dgc1p+jzT6vMZx/6fA9/v4NgGYEBP2rO/f0Mg4D/bUUCG/8wn9qCNMb+f0uFZJvJsgNnA87g1otOB+Z2d96DuIanqi+qi8wDm4dYugZMa+puq1qnqemANTpqoLT0iSaSJSSddDjzSHddPlATtjEevPk9V/UBVm9auLQOyRCSzO2xIhDjPM60+n7EQEQE+Sy9/JpMknhxZtxPn+ykdSOTZnA/8WR3zgP7+OtAOOagdUhuuw3lziC1DdEiMNq0kiYAOJYl6gEuJ/89/o99tvr8nhm5iMFJEPhCR10XkpA7qpNPz/AzwgarWdXC8N59nX/h8ngRsV9XVHRxX4EV/aPSGDup0N539DRN9zj1B9PdTW3rjWSbybPb7+aWNUkN3IQlIEonILUAj8HBTsxj1uzU+PhE747Q9DqhW1Y509/4A/Bh3Dz/GDaVc14N2bgWGq2qZiEwFnhaRSapa3hUbutHOpraTcEMkn+6gSm8/zx7/fLa6eGI2d9Zjn6GqW8TpSr4kIitUtUM1/1TbSWJ/w25/zl38fmpLtz/LGCTybPb7+R3wDkk7kSQSkauBc4DT1R/4JL4MUTQpkyTqzM5OuIw4//yqur1pW0TuAZ7t6oW6Yqffy6jztxeIyFpgHNB2ArbXn6eIDAWeAq5S1bUdnLtXnye98PmMJoH/qSAuXczUOOfY4r/vEJGncENAKf0STfTZxvkbJvqcu0wXv5/anqPbn2UMEnk2+/38DuohOxE5C/gOcJ6qVkcdmgNcJiKZIjISGAu8G+MUvS5JJCIecAluDLejOtHjthfi8kv1GCJSIi5fFSIyCvc818Wo2qvP049ieg74rqq+Haderz5P0v/zORNYoaqbYh0UkVwRyW/axvVEe/ozmcjfMJ4cWbcT5/spuk5vPctEns0c4Co/2m46sK9pCLlDejIyI91euMngjcAi/3VX1LFbcFEkK4FZUeX34kc8AcW4hH+r/feibrLzQtyvjTpgO/BC1LFTgHkx2kTb+RCwBFjsf0hKe9JO3HzMMlwkzkLg3HR8nsB/A1VRn4dFwMB0e57p9vmMYfefgC+3KRsC/NPfHuV/Fj70Pxe39IRdbeyJ+TeMttPfnw2s8p91j9pJB99P6fIsYz0b4MtNf3vckN3v/ONL6CBCOPpl0kGGYRhGWnBQD9kZhmEY6YM5JMMwDCMtMIdkGIZhpAXmkAzDMIy0wBySYRiGkRaYQzKMOIiIishDUftBEdkpIs/6++elUgVaRG5us/9Ois47XUTm+4rQy0Xk1lSc1zBSiYV9G0YcRKQSt47nBFWtEZFZwG3AJlU9pwvnC6hqON71VDWv6xZ3eN6VwGdV9UN/kfJ4Vf0oyXPGvRfD2F+sh2QYnfM8cLa/3UqjTUSuEZE7/e3RIjJPRN4TkR/5zgwROUVEXhWRv+IWCCIiT/timMuaBDFF5HYg2+/FPOyXNZ1DxOXHWSou982lUed+TUSeEJc752ERiaUhNhCnKYiqhpuckYjkicgD/jkXi8hn/PLL/bKlIvLTqPut9O9tPnC8iEwVJ5i7QERekE7UnA0jLj29Qtpe9upLL6ASOAJ4AsjCrZg/BXjWP34NcKe//Sxwub/9ZaDS3z4FpwAxMuq8Rf57Nk7qpbjpem2v779/BngJl4dmEPAJUOqfex9OJ8wD/g2cGOM+vg/swen0fQnI8st/Cvwqql4hTgngE6AEp3f5CnCBf1xxPS2AEPAOUOLvXwrc39t/M3v13Zf1kAyjE1R1MS7J4OXAP+NUPR543N/+a5tj76rLXdTEN0TkQ1yem2E4Pbp4nAg8oq53sx14HTgm6tybVDWCc5gjYtzDj4BpwIvAFcC//EMzcfIuTfX2+Od9TVV3qsvH8zBwsl8lDDzpb4/HJTJ8SVxm2P8mvXL2GH2MA17t2zBSxBzgf3E9kuIutK9q2hCRU3CO4HhVrRaR13C9r3jEGoZrIjpnU5gO/q/VqZf/wVe33ikixf55E0kb0ESttswbCbBMVY+Pa7lhJIj1kAwjMe4HfqSqS+LUmYcbWgOnftwR/YA9vjM6DJfeuYkGEQnFaPMGcKmIBESkBNdjiaXwHRMROTtqbmksznHtxfWYboyqVwjMBz4lIgP8AIjLcT2ytqwESkTkeL9tSFwuKcPoEuaQDCMB/CGxX3dS7ZvAt0TkXdz8zr4O6v0LCIrIYlxyuHlRx+4GFjcFNUTxFE6Z+kPcnM5/qeq2/biFzwMr/aG1h4Ar/Z7OT4BCP3jhQ+BUdSkCvgu86l9vocZIFqgudfXFwE/9touAE/bDJsNohYV9G0aKEJEcoEZVVUQuwwU4nN/bdhlGX8HmkAwjdUwF7vSHxvbSxbTmhnGwYj0kwzAMIy2wOSTDMAwjLTCHZBiGYaQF5pAMwzCMtMAckmEYhpEWmEMyDMMw0oL/DzDmxnJp87NKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plt.gca()\n",
    "axes.set_xlim([-20, 0])\n",
    "axes.set_ylim([-1, 1])\n",
    "sns.scatterplot(x='migration_score', y='per_gap', hue='trump_vote', data=all_flight, \n",
    "                legend=False, palette='Reds')\n",
    "plt.xlabel('Migration Score')\n",
    "plt.ylabel('Trump\\'s Victory Margin in \\n Percentage of Votes')\n",
    "plt.title('Migration Score and Trump\\'s Victory Margin Share a \\n Negative Relationship in Counties with \\n Domestic and International Flight \\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacement by Foreigners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['repl'] = np.where((data['Avg %Change in Domestic Migration'] < 0) & (data['Avg %Change in Intl Migration'] > 0),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "foreign_repl = data[data['repl'] == 1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1012\n",
       "0     228\n",
       "Name: trump_vote, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foreign_repl['trump_vote'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8161290322580645"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1012/(1012+228)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1240"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(foreign_repl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plt.gca()\n",
    "axes.set_xlim([-20, 0])\n",
    "axes.set_ylim([-1, 1])\n",
    "sns.scatterplot(x='migration_score', y='per_gap', hue='trump_vote', data=foreign_repl, \n",
    "                legend=False, palette='Reds')\n",
    "plt.xlabel('Migration Score')\n",
    "plt.ylabel('Trump\\'s Victory Margin in \\n Percentage of Votes')\n",
    "plt.title('Migration Score and Trump\\'s Victory Margin Share a \\n Negative Relationship in Counties \\n with Foreign Replacement \\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Avg %Change in Domestic Migration</th>\n",
       "      <th>dom_flight</th>\n",
       "      <th>dom &amp; intl</th>\n",
       "      <th>dom only</th>\n",
       "      <th>per_dem</th>\n",
       "      <th>per_gop</th>\n",
       "      <th>diff</th>\n",
       "      <th>per_point_diff</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state</th>\n",
       "      <th>trump_vote</th>\n",
       "      <th>per_gap</th>\n",
       "      <th>migration_score</th>\n",
       "      <th>repl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.10927</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.239569</td>\n",
       "      <td>0.734358</td>\n",
       "      <td>12202</td>\n",
       "      <td>49.48</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>0.494789</td>\n",
       "      <td>0.42372</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.168591</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.195653</td>\n",
       "      <td>0.773515</td>\n",
       "      <td>54371</td>\n",
       "      <td>57.79</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>0.577862</td>\n",
       "      <td>-0.181615</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.19652</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.466603</td>\n",
       "      <td>0.522714</td>\n",
       "      <td>583</td>\n",
       "      <td>5.61</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>0.056112</td>\n",
       "      <td>-2.91061</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.748287</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.214220</td>\n",
       "      <td>0.769662</td>\n",
       "      <td>4859</td>\n",
       "      <td>55.54</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>0.555441</td>\n",
       "      <td>-0.547145</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.00516</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.084699</td>\n",
       "      <td>0.898519</td>\n",
       "      <td>20658</td>\n",
       "      <td>81.38</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>0.813820</td>\n",
       "      <td>-1.32954</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Avg %Change in Domestic Migration  dom_flight  dom & intl  dom only  \\\n",
       "0                          -1.10927           1           0         1   \n",
       "1                         -0.168591           1           1         0   \n",
       "2                          -3.19652           1           0         1   \n",
       "3                         -0.748287           1           0         1   \n",
       "4                          -1.00516           1           1         0   \n",
       "\n",
       "    per_dem   per_gop   diff  per_point_diff     county_name    state  \\\n",
       "0  0.239569  0.734358  12202           49.48  Autauga County  Alabama   \n",
       "1  0.195653  0.773515  54371           57.79  Baldwin County  Alabama   \n",
       "2  0.466603  0.522714    583            5.61  Barbour County  Alabama   \n",
       "3  0.214220  0.769662   4859           55.54     Bibb County  Alabama   \n",
       "4  0.084699  0.898519  20658           81.38   Blount County  Alabama   \n",
       "\n",
       "   trump_vote   per_gap migration_score  repl  \n",
       "0           1  0.494789         0.42372     1  \n",
       "1           1  0.577862       -0.181615     0  \n",
       "2           1  0.056112        -2.91061     1  \n",
       "3           1  0.555441       -0.547145     1  \n",
       "4           1  0.813820        -1.32954     0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:5,19:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3110"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['Avg %Change in Intl Migration'] = np.where(data['Avg %Change in Intl Migration'].isnull(),0,data['Avg %Change in Intl Migration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['Avg %Change in Intl Migration'] = data['Avg %Change in Intl Migration'].astype(float)\n",
    "data['Avg %Change in Domestic Migration'] = data['Avg %Change in Domestic Migration'].astype(float)\n",
    "data['Avg %Change in Natural Inc'] = data['Avg %Change in Natural Inc'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "dom_flight = data[data['dom_flight'] == 1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Texas             153\n",
       "Georgia           108\n",
       "Virginia           94\n",
       "Missouri           72\n",
       "Kansas             70\n",
       "Kentucky           69\n",
       "Indiana            68\n",
       "Nebraska           68\n",
       "Tennessee          67\n",
       "North Carolina     57\n",
       "Name: STNAME, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dom_flight['STNAME'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Avg %Change in Natural Inc</th>\n",
       "      <th>Avg %Change in Intl Migration</th>\n",
       "      <th>Avg %Change in Domestic Migration</th>\n",
       "      <th>dom_flight</th>\n",
       "      <th>dom &amp; intl</th>\n",
       "      <th>dom only</th>\n",
       "      <th>per_dem</th>\n",
       "      <th>per_gop</th>\n",
       "      <th>diff</th>\n",
       "      <th>per_point_diff</th>\n",
       "      <th>trump_vote</th>\n",
       "      <th>per_gap</th>\n",
       "      <th>repl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg %Change in Natural Inc</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004327</td>\n",
       "      <td>0.045257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.017890</td>\n",
       "      <td>0.020021</td>\n",
       "      <td>0.049244</td>\n",
       "      <td>-0.052668</td>\n",
       "      <td>0.005986</td>\n",
       "      <td>-0.040607</td>\n",
       "      <td>-0.046492</td>\n",
       "      <td>-0.051197</td>\n",
       "      <td>0.020021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg %Change in Intl Migration</th>\n",
       "      <td>-0.004327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.556679</td>\n",
       "      <td>0.545226</td>\n",
       "      <td>0.061754</td>\n",
       "      <td>-0.062066</td>\n",
       "      <td>0.010105</td>\n",
       "      <td>-0.074775</td>\n",
       "      <td>-0.033159</td>\n",
       "      <td>-0.062183</td>\n",
       "      <td>0.545226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg %Change in Domestic Migration</th>\n",
       "      <td>0.045257</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049483</td>\n",
       "      <td>0.040313</td>\n",
       "      <td>0.011468</td>\n",
       "      <td>-0.017952</td>\n",
       "      <td>0.026625</td>\n",
       "      <td>-0.011059</td>\n",
       "      <td>-0.007767</td>\n",
       "      <td>-0.014809</td>\n",
       "      <td>0.040313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dom_flight</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dom &amp; intl</th>\n",
       "      <td>-0.017890</td>\n",
       "      <td>-0.556679</td>\n",
       "      <td>-0.049483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.920400</td>\n",
       "      <td>-0.165185</td>\n",
       "      <td>0.175156</td>\n",
       "      <td>-0.099716</td>\n",
       "      <td>0.150178</td>\n",
       "      <td>0.115199</td>\n",
       "      <td>0.170970</td>\n",
       "      <td>-0.920400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dom only</th>\n",
       "      <td>0.020021</td>\n",
       "      <td>0.545226</td>\n",
       "      <td>0.040313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.920400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.219369</td>\n",
       "      <td>-0.231064</td>\n",
       "      <td>0.115100</td>\n",
       "      <td>-0.218592</td>\n",
       "      <td>-0.130421</td>\n",
       "      <td>-0.226266</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>per_dem</th>\n",
       "      <td>0.049244</td>\n",
       "      <td>0.061754</td>\n",
       "      <td>0.011468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.165185</td>\n",
       "      <td>0.219369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.982556</td>\n",
       "      <td>0.285671</td>\n",
       "      <td>-0.688309</td>\n",
       "      <td>-0.766876</td>\n",
       "      <td>-0.995537</td>\n",
       "      <td>0.219369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>per_gop</th>\n",
       "      <td>-0.052668</td>\n",
       "      <td>-0.062066</td>\n",
       "      <td>-0.017952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175156</td>\n",
       "      <td>-0.231064</td>\n",
       "      <td>-0.982556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.279222</td>\n",
       "      <td>0.710852</td>\n",
       "      <td>0.755770</td>\n",
       "      <td>0.995721</td>\n",
       "      <td>-0.231064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diff</th>\n",
       "      <td>0.005986</td>\n",
       "      <td>0.010105</td>\n",
       "      <td>0.026625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.099716</td>\n",
       "      <td>0.115100</td>\n",
       "      <td>0.285671</td>\n",
       "      <td>-0.279222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016333</td>\n",
       "      <td>-0.257426</td>\n",
       "      <td>-0.283652</td>\n",
       "      <td>0.115100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>per_point_diff</th>\n",
       "      <td>-0.040607</td>\n",
       "      <td>-0.074775</td>\n",
       "      <td>-0.011059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.150178</td>\n",
       "      <td>-0.218592</td>\n",
       "      <td>-0.688309</td>\n",
       "      <td>0.710852</td>\n",
       "      <td>0.016333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.336070</td>\n",
       "      <td>0.702770</td>\n",
       "      <td>-0.218592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trump_vote</th>\n",
       "      <td>-0.046492</td>\n",
       "      <td>-0.033159</td>\n",
       "      <td>-0.007767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.115199</td>\n",
       "      <td>-0.130421</td>\n",
       "      <td>-0.766876</td>\n",
       "      <td>0.755770</td>\n",
       "      <td>-0.257426</td>\n",
       "      <td>0.336070</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.764606</td>\n",
       "      <td>-0.130421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>per_gap</th>\n",
       "      <td>-0.051197</td>\n",
       "      <td>-0.062183</td>\n",
       "      <td>-0.014809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.170970</td>\n",
       "      <td>-0.226266</td>\n",
       "      <td>-0.995537</td>\n",
       "      <td>0.995721</td>\n",
       "      <td>-0.283652</td>\n",
       "      <td>0.702770</td>\n",
       "      <td>0.764606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.226266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repl</th>\n",
       "      <td>0.020021</td>\n",
       "      <td>0.545226</td>\n",
       "      <td>0.040313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.920400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.219369</td>\n",
       "      <td>-0.231064</td>\n",
       "      <td>0.115100</td>\n",
       "      <td>-0.218592</td>\n",
       "      <td>-0.130421</td>\n",
       "      <td>-0.226266</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Avg %Change in Natural Inc  \\\n",
       "Avg %Change in Natural Inc                           1.000000   \n",
       "Avg %Change in Intl Migration                       -0.004327   \n",
       "Avg %Change in Domestic Migration                    0.045257   \n",
       "dom_flight                                                NaN   \n",
       "dom & intl                                          -0.017890   \n",
       "dom only                                             0.020021   \n",
       "per_dem                                              0.049244   \n",
       "per_gop                                             -0.052668   \n",
       "diff                                                 0.005986   \n",
       "per_point_diff                                      -0.040607   \n",
       "trump_vote                                          -0.046492   \n",
       "per_gap                                             -0.051197   \n",
       "repl                                                 0.020021   \n",
       "\n",
       "                                   Avg %Change in Intl Migration  \\\n",
       "Avg %Change in Natural Inc                             -0.004327   \n",
       "Avg %Change in Intl Migration                           1.000000   \n",
       "Avg %Change in Domestic Migration                       0.016047   \n",
       "dom_flight                                                   NaN   \n",
       "dom & intl                                             -0.556679   \n",
       "dom only                                                0.545226   \n",
       "per_dem                                                 0.061754   \n",
       "per_gop                                                -0.062066   \n",
       "diff                                                    0.010105   \n",
       "per_point_diff                                         -0.074775   \n",
       "trump_vote                                             -0.033159   \n",
       "per_gap                                                -0.062183   \n",
       "repl                                                    0.545226   \n",
       "\n",
       "                                   Avg %Change in Domestic Migration  \\\n",
       "Avg %Change in Natural Inc                                  0.045257   \n",
       "Avg %Change in Intl Migration                               0.016047   \n",
       "Avg %Change in Domestic Migration                           1.000000   \n",
       "dom_flight                                                       NaN   \n",
       "dom & intl                                                 -0.049483   \n",
       "dom only                                                    0.040313   \n",
       "per_dem                                                     0.011468   \n",
       "per_gop                                                    -0.017952   \n",
       "diff                                                        0.026625   \n",
       "per_point_diff                                             -0.011059   \n",
       "trump_vote                                                 -0.007767   \n",
       "per_gap                                                    -0.014809   \n",
       "repl                                                        0.040313   \n",
       "\n",
       "                                   dom_flight  dom & intl  dom only   per_dem  \\\n",
       "Avg %Change in Natural Inc                NaN   -0.017890  0.020021  0.049244   \n",
       "Avg %Change in Intl Migration             NaN   -0.556679  0.545226  0.061754   \n",
       "Avg %Change in Domestic Migration         NaN   -0.049483  0.040313  0.011468   \n",
       "dom_flight                                NaN         NaN       NaN       NaN   \n",
       "dom & intl                                NaN    1.000000 -0.920400 -0.165185   \n",
       "dom only                                  NaN   -0.920400  1.000000  0.219369   \n",
       "per_dem                                   NaN   -0.165185  0.219369  1.000000   \n",
       "per_gop                                   NaN    0.175156 -0.231064 -0.982556   \n",
       "diff                                      NaN   -0.099716  0.115100  0.285671   \n",
       "per_point_diff                            NaN    0.150178 -0.218592 -0.688309   \n",
       "trump_vote                                NaN    0.115199 -0.130421 -0.766876   \n",
       "per_gap                                   NaN    0.170970 -0.226266 -0.995537   \n",
       "repl                                      NaN   -0.920400  1.000000  0.219369   \n",
       "\n",
       "                                    per_gop      diff  per_point_diff  \\\n",
       "Avg %Change in Natural Inc        -0.052668  0.005986       -0.040607   \n",
       "Avg %Change in Intl Migration     -0.062066  0.010105       -0.074775   \n",
       "Avg %Change in Domestic Migration -0.017952  0.026625       -0.011059   \n",
       "dom_flight                              NaN       NaN             NaN   \n",
       "dom & intl                         0.175156 -0.099716        0.150178   \n",
       "dom only                          -0.231064  0.115100       -0.218592   \n",
       "per_dem                           -0.982556  0.285671       -0.688309   \n",
       "per_gop                            1.000000 -0.279222        0.710852   \n",
       "diff                              -0.279222  1.000000        0.016333   \n",
       "per_point_diff                     0.710852  0.016333        1.000000   \n",
       "trump_vote                         0.755770 -0.257426        0.336070   \n",
       "per_gap                            0.995721 -0.283652        0.702770   \n",
       "repl                              -0.231064  0.115100       -0.218592   \n",
       "\n",
       "                                   trump_vote   per_gap      repl  \n",
       "Avg %Change in Natural Inc          -0.046492 -0.051197  0.020021  \n",
       "Avg %Change in Intl Migration       -0.033159 -0.062183  0.545226  \n",
       "Avg %Change in Domestic Migration   -0.007767 -0.014809  0.040313  \n",
       "dom_flight                                NaN       NaN       NaN  \n",
       "dom & intl                           0.115199  0.170970 -0.920400  \n",
       "dom only                            -0.130421 -0.226266  1.000000  \n",
       "per_dem                             -0.766876 -0.995537  0.219369  \n",
       "per_gop                              0.755770  0.995721 -0.231064  \n",
       "diff                                -0.257426 -0.283652  0.115100  \n",
       "per_point_diff                       0.336070  0.702770 -0.218592  \n",
       "trump_vote                           1.000000  0.764606 -0.130421  \n",
       "per_gap                              0.764606  1.000000 -0.226266  \n",
       "repl                                -0.130421 -0.226266  1.000000  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dom_flight.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dom_flight[['Avg %Change in Natural Inc', 'Avg %Change in Intl Migration', 'Avg %Change in Domestic Migration', 'dom & intl', 'migration_score']].copy()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.rename(columns={\n",
    "        'Avg %Change in Natural Inc': 'rni',\n",
    "        'Avg %Change in Intl Migration': 'foreign_migration',\n",
    "        'Avg %Change in Domestic Migration': 'domestic_migration',\n",
    "        'dom & intl': 'flight'\n",
    "    }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>-0.165911</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.196079</td>\n",
       "      <td>0.285912</td>\n",
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       "      <td>-2.91061</td>\n",
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       "      <td>1</td>\n",
       "      <td>-1.32954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rni  foreign_migration  domestic_migration  flight migration_score\n",
       "0 -2.422855           1.532985           -1.109266       0         0.42372\n",
       "1 -0.165911          -0.013024           -0.168591       1       -0.181615\n",
       "2  0.196079           0.285912           -3.196521       0        -2.91061\n",
       "3 -2.621169           0.201143           -0.748287       0       -0.547145\n",
       "4  0.061965          -0.324386           -1.005157       1        -1.32954"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X[['rni', 'foreign_migration', 'domestic_migration']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.42285513,  1.53298541, -1.10926589],\n",
       "       [-0.16591071, -0.01302366, -0.16859101],\n",
       "       [ 0.19607851,  0.28591212, -3.19652103],\n",
       "       ...,\n",
       "       [-1.76268169,  0.80979836, -5.93486452],\n",
       "       [-0.00847951,  0.06345621, -0.30976379],\n",
       "       [-0.00847951,  0.06345621, -0.30976379]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dom_flight['trump_vote'].values\n",
    "# y = data['per_gap_multi'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1978, 1978)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X), len(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline: Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=786)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg = logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.07907921, -0.17092285, -0.00136818]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8518518518518519"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, logreg_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, logreg_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, logreg_y_pred)\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC curve for Logistic Model')\n",
    "plt.legend(['AUC = 0.50'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbors = KNN(n_neighbors=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=15, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbors.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_y_preds = neighbors.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8636363636363636"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, knn_y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5467836257309941"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, knn_y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, knn_y_preds)\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC curve for KNN Model')\n",
    "plt.legend(['AUC = 0.57'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84375"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# when it says it's a 1, is it a 1?\n",
    "precision_score(y_test, knn_y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9662027833001988"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many of the 1's does it capture\n",
    "recall_score(y_test, knn_y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []\n",
    "\n",
    "for n in range(1,201):\n",
    "    neighbors = KNN(n_neighbors=n)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=5001)\n",
    "    neighbors.fit(X_train, y_train)\n",
    "    score = neighbors.score(X_test, y_test)\n",
    "    \n",
    "    scores.append([n, score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "scores = list(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = pd.Series([l[1] for l in scores])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores.index = scores.index + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22447ac8>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plt.gca()\n",
    "# axes.set_ylim([.6, .9])\n",
    "# axes.set_xlim([0, 50])\n",
    "scores.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41    0.845118\n",
       "42    0.846801\n",
       "43    0.846801\n",
       "44    0.846801\n",
       "45    0.846801\n",
       "46    0.846801\n",
       "47    0.846801\n",
       "48    0.846801\n",
       "49    0.846801\n",
       "50    0.846801\n",
       "51    0.846801\n",
       "52    0.846801\n",
       "53    0.846801\n",
       "54    0.846801\n",
       "55    0.846801\n",
       "56    0.846801\n",
       "57    0.846801\n",
       "58    0.846801\n",
       "59    0.846801\n",
       "60    0.846801\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores[40:60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}