{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data APIs and pandas operations\n",
    "\n",
    "\n",
    "Several of the notebooks we've already explored loaded datasets into a python pandas dataframe for analysis. Local copies of some of these datasets had been previously saved to disk in a few cases we read in the data directly from an online sources via a data API. This section explains how that is done in a bit more depth.  Some of the possible advantages of reading in the data this way is that it allows would-be users to modify and extend the analysis, perhaps focusing on different time-periods or adding in other variables of interest.  \n",
    "\n",
    "Easy to use python wrappers for data APIs have been written for the World Bank and several other online data providers (including FRED, Eurostat, and many many others). The [pandas-datareader](http://pandas-datareader.readthedocs.org/) library allows access to several databases from the [World Bank's datasets](http://data.worldbank.org/) and other sources.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you haven't already installed the pandas-datareader library you can do so directly from a jupyter notebook code cell:\n",
    "\n",
    "```!pip install pandas-datareader```\n",
    "\n",
    "Once the library is in installed we can load it as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as smf\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas_datareader import wb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data on urban bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our earlier analysis of the Harris_Todaro migration model suggested that policies designed to favor certain sectors or labor groups \n",
    "\n",
    "Let's search for indicators (and their identification codes) relating to GDP per capita and urban population share.  We could look these up in a book or from the website http://data.worldbank.org/ but we can also search for keywords directly.\n",
    "\n",
    "First lets search for series having to do with gdp per capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>6.0.GDPpc_constant</td>\n",
       "      <td>GDP per capita, PPP (constant 2011 internation...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7588</th>\n",
       "      <td>NY.GDP.PCAP.KD</td>\n",
       "      <td>GDP per capita (constant 2010 US$)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7590</th>\n",
       "      <td>NY.GDP.PCAP.KN</td>\n",
       "      <td>GDP per capita (constant LCU)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7592</th>\n",
       "      <td>NY.GDP.PCAP.PP.KD</td>\n",
       "      <td>GDP per capita, PPP (constant 2011 internation...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id                                               name\n",
       "685   6.0.GDPpc_constant  GDP per capita, PPP (constant 2011 internation...\n",
       "7588      NY.GDP.PCAP.KD                 GDP per capita (constant 2010 US$)\n",
       "7590      NY.GDP.PCAP.KN                      GDP per capita (constant LCU)\n",
       "7592   NY.GDP.PCAP.PP.KD  GDP per capita, PPP (constant 2011 internation..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.search('gdp.*capita.*const')[['id','name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use NY.GDP.PCAP.KD for GDP per capita (constant 2010 US$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also first browse and search for data series from the World Bank's DataBank page at http://databank.worldbank.org/data/.  Then find the 'id' for the series that you are interested in in the 'metadata' section from the webpage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look for data on urban population share:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>SP.URB.GROW</td>\n",
       "      <td>Urban population growth (annual %)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>SP.URB.TOTL</td>\n",
       "      <td>Urban population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001</th>\n",
       "      <td>SP.URB.TOTL.FE.ZS</td>\n",
       "      <td>Urban population, female (% of total)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10002</th>\n",
       "      <td>SP.URB.TOTL.IN.ZS</td>\n",
       "      <td>Urban population (% of total)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10003</th>\n",
       "      <td>SP.URB.TOTL.MA.ZS</td>\n",
       "      <td>Urban population, male (% of total)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id                                   name\n",
       "9999         SP.URB.GROW     Urban population growth (annual %)\n",
       "10000        SP.URB.TOTL                       Urban population\n",
       "10001  SP.URB.TOTL.FE.ZS  Urban population, female (% of total)\n",
       "10002  SP.URB.TOTL.IN.ZS          Urban population (% of total)\n",
       "10003  SP.URB.TOTL.MA.ZS    Urban population, male (% of total)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.search('Urban Population')[['id','name']].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the ones we like but use a python dictionary to rename these to shorter variable names when we load the data into a python dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "indicators = ['NY.GDP.PCAP.KD', 'SP.URB.TOTL.IN.ZS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are interested in exploring the extent of 'urban bias' in some countries, let's load data from 1980 which was toward the end of the era of import-substituting industrialization when urban-biased policies were claimed to be most pronounced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dat = wb.download(indicator=indicators, country = 'all', start=1980, end=1980)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NY.GDP.PCAP.KD', 'SP.URB.TOTL.IN.ZS'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's rename the columns to something shorter and then plot and regress log gdp per capita against urban extent we get a pretty tight fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dat.columns = [['gdppc', 'urbpct']]\n",
    "dat['lngpc']  = np.log(dat.gdppc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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EENUiN9ecVR55C5Ajj4S3EEKIKsvOhpMnJbhri6zzFkIIUSWZmXDiRM0E9+HD\nsj68JBLeQgghKi0jA9LSai5gBw6MotCeNeIXEt5CCCEq5dQphfT0mu0ZZ2fLcrOSyDVvIYQQFWIY\n5vXt7Ozaez1RlIS3EEKIcgvWKS+0xXitvKYoSobNhRBClIuum8VXKhrcmgaLFtlrplH1lIS3EEKI\nMmkapKRUfIORnByYMcPJsmWVD2/peRcnw+ZCCCFK5febPe6Kzvo+cUJh+nQn+/erVXp9qdZWnIS3\nEEKI86psnfIffrDw4INOTp82B3jbtdOAyoW4LBUrTobNhRBClMjjMYfKKxrcn36qMnGiKz+4+/QJ\nsGRJXqXboWlSqOVc0vMWQghRTGXqlBsGvPmmjX/8w5F/29Chfu6/34u1Cmkju5MVJ+EthBCiiMrU\nKff74ZlnHKxfbwNAUQzuu8/Hbbf5UarYcZbwLk7CWwghRL7MzIqXO83MhFmznGzfbkaKy2Xw+OMe\n+vevntSVa97F1fg17507dzJy5EgAfvjhB0aMGMHIkSMZM2YMp06dAmDVqlUMHTqUYcOGkZSUVNNN\nEkIIUYLK1Ck/ckRh3Dh3fnAnJuq89FJetQU3SM+7JDXa8166dCnr1q3D5XIB8OSTTzJ79my6dOnC\nypUrWbp0KWPHjmX58uWsXr0ar9fLiBEj6NevH3a7LOgXQojacuqUwtmzFXvMjh0WHnrIRWamGfid\nO2s884yHxMTqXZhtTliTxd6F1WjPu3Xr1ixYsCD/+3nz5tGlSxcANE3D4XCwa9cuevXqhd1uJyYm\nhtatW7N3796abJYQQohfGIa5Hruiwf3BB1buvbcguAcMCLBoUV61BzfIsHlJarTnPWTIEI4ePZr/\nfePGjQH45ptveOONN3jzzTf54osviImJyb9PVFQU2WVUu4+Pd2O1Vm3Rf1UkJsaUfad6Ro5JcXJM\nipLjUVyoj4lhwLFjYLdDQkL5H/PCC7BwYcFtY8bA9OlWLJboGmlnTEwUiYk18tQRq9YnrL3//vss\nWrSIJUuWkJCQQHR0NDk5Ofk/z8nJKRLmJcnIqMWK+OdITIwhLS0rZK8fjuSYFCfHpCg5HsWF+pjo\nOqSmVqzcqdcLTz7p4H//M2eUq6rBjBlebropwJkz5XmGyoX7sWM5tGxZ/8qslXZyV6tFWtauXcsb\nb7zB8uXLadWqFQDdu3dn+/bteL1esrKyOHDgAJ06darNZgkhRL0SCFS8Tnl6usI997jygzs62uC5\n5zzcdFOcdpbbAAAgAElEQVTNj2l7PFKk5Vy11vPWNI0nn3ySZs2ace+99wJw6aWXct999zFy5EhG\njBiBYRjcf//9OByOMp5NCCFEZVSmTvnPP1uYPt1JaqrZ32vRQufZZ/No27Z2JpFVdDOU+qDGw7tl\ny5asWrUKgK1bt5Z4n2HDhjFs2LCabooQQtRrXq8Z3BXZ6GPLFpVHHnGSk2P2frt313jqqTzi42uo\nkSWQnndxUqRFCCHqgbw88xp3RaqmvfOOlXnzHPm1xYcM8fPww15qe3C0ovuH1wcS3kIIUcdVtNyp\npsGLL9pZubKg3sbYsV5Gj65cqVNFoUpLyKTnXZyEtxBC1GEVLXeamwuPPebkiy/MeLDbDR55xMu1\n11ZuYprVCk2bGlXqredVfkOyOkvCWwgh6qiMDHOWeHmdPKnw4INOfvzRrKMRH6/z1FMeunev3DIt\ntxsaNzZQq1iWI1gIRhSQ8BZCiDooLU0hM7P899+3z5xRfuqUOaO8bVtzRnmLFpUb7k5IMKptUtuZ\nMxLe56rVdd5CCCFqVrDcaUWC+/PPVe6+25Uf3JdeGmDJktxKBbfFAs2aVV9wA2RkSHifS3reQghR\nR+g6HD+ulPsasWHAihU2XnzRjmGYAXnzzX6mT/dirUQ6OJ3QpIlRqceWRnrexUl4CyFEHaBp5lIw\nr7d89w8E4NlnHaxda1ZMUxSDe+/1cfvtlZtRHhcHDRsalXpsaex2g7NnJbzPJeEthBARzu83g9vv\nL9/9s7Jg1iwnX39tRoDTafD44x4GDKj4xtmKYk5Ki66ZPUlo0MCQYfMSSHgLIUQE8/nMqmlaOXM3\nJUVh2jQXycnm9e1GjXSefdZD584Vn1Fut5vD5HZ72fetrPh4o0JL3eoLmbAmhBARyuMxw7i8wb1z\np4WxY935wd2pk8Yrr+RVKrijo6Fly5oNbjCH4jMyFHy+mn2dSCPhLYQQESg3t2J1yj/80Mq997ry\nJ3/17x9g0aI8Gjeu2IxyRYFGjQyaNKn+69sladbMwDAUTpyQ3ndhMmwuhBARpiLlTg0DXn3Vxssv\nF5Q4Gz7cx+TJvgoXT1FVs1qa01nBBldB8+bm2cmxYxZatar4Nfm6SsJbCCEiSEXKnXq9MHeugw8/\nNGeUq6rBtGlebrml4qVOq6taWkU1b26eoaSmSs+7MAlvIYSIEBUpd5qRAQ895OK778y0jYoy+Otf\nPfTuXfHea3y8QUJChR9WLZo1M8M7JUXCuzAJbyGEiAAVKXd66JA5o/zYMXNaU/PmZqnTdu0qdn3b\nYjF721FRFW1t9WnVyhw2P3xYpmgVJuEthBBhzDDM69vZ2eW7/7ZtKrNmOcnONnuq3bpp/O1vHhIS\nKhbcdrt5fdtmq2iLq1e7dmZ4Hzgg4V2YhLcQQoQpwzDLnebmlu/+a9daeeYZB5pmBvc11/h55BFv\nhbfjjI42e9y1MZu8PG1p2lTn558lvAuT8BZCiDCkaWZwezxl31fXYeFCO2++WbDoevRoH2PH+ioU\nwMFlYLGxlWhwDerQQefLL63k5YHLFerWhAc5lRFCiDATCJhruMsT3Hl5ZqnTYHDbbAZz5ngYN65i\nwa2q5szucAtugPbtzaHzgwclsoLkSAghRBjx+cyZ1eWpKJaWpjBxoovPPjMHUePiDBYsyOM3v6nY\nUjCn06yWVpvrtysiWAFuzx6JrCA5EkIIESaC5U4D5cjeffssjBnjYt8+cylY69Y6S5fm0qNHxUqd\nxsaaPe7q3sazOnXvbr6n4LI3IeEthBBhoSLlTr/4QmXiRBdpaeZH+MUXB1i6NJdWrco/ozy4G1hi\nYnhMTCvNhReaa9O//14iKyiMz7WEEKJ+KG+5U8OAf//bxgsv2DEMM3FvvNHPjBneCvWcQ1HmtCpi\nYswlY999p2IYhP3JRm2Q0xghhAihzEw4caLs4A4E4NlnHTz/vAPDUFAUg8mTvTz8cMWC2+WCVq0i\nJ7iDLrpI48wZhSNHJLlBwlsIIUImI6N8dcqzs2H6dCfvvGNWTHE4zFKnf/yjv0K90Lg4s9xobdcn\nrw6//rU5dL51awQ2vgZIeAshRAikpZWvTnlqqsL48S62bDG7140a6SxalMfAgeWvUa4o0KSJQaNG\n4X99+3wuu8x8v5s3S3iDXPMWQohaFSx3areXfd/vv7cwY4aTjAyzn9Wxo8Yzz3ho0qT8E9OsVvP6\ndkWrrIWbiy7ScbsNtmyR8AbpeQshRK0JljstT53yjz+2MnmyKz+4L788wKJFeRUKbpfLXL8d6cEN\n5knIJZdo7Nuncvp0hA4fVCMJbyGEqAWaZi4FK6tOuWHAP/9pY/ZsJz6fGVLDhvl4+mlPhXb3atDA\nXL8dide3zyc4dL5pUx16U5Uk4S2EEDWsvOVOfT74858dLFlidpVV1WD6dA/33+8rdwgHr283bFix\nXcQiQf/+ZvWapCQJb7nmLYQQNcjnMyedlVU17exZmDnTxY4dZjC53QZPPOHJ722Wh81mXt8uz/X0\nSHTxxTrx8QaffGLFMLwRO/muOkjPWwghakh5y50mJyuMGePOD+6mTXWWLMmrUHC73eb17boa3GAW\nlxk0KEBKioW9e+t3fNXvdy+EEDWkvOVOt29XGTfOTUqK+XF84YUar7ySR4cO5a9RHh9v0KyZgaUe\nfKJfdZV5JvTxx/V74LjG/1Pv3LmTkSNHApCcnMzw4cMZMWIEc+bMQf/lt3rVqlUMHTqUYcOGkZSU\nVNNNEkKUQ1KSyvjxToYMcTN+vDPirzPW5vvJzjZnlZdVNW39eitTpjjJyjLHf6+6ys+LL+aRkFC+\n69WKYg6TJyRUtcWRY9AgDUUx+N//Ivv3sapq9NRl6dKlrFu3Dtcvu6fPnTuXqVOn0qdPHx599FE2\nbNhAz549Wb58OatXr8br9TJixAj69euHvS6P/QgR5pKSVP7614L1RQcPWn753sugQeUfyg0Xtfl+\nMjPLrpqm6/Dss7B0aUGN0jvv9DFunK/cvee6fn37fBo1MujTR2PLFpUTJ5QKLZ2rS2q05926dWsW\nLFiQ//3u3bvp3bs3AAMGDOCrr75i165d9OrVC7vdTkxMDK1bt2bv3r012SwhRBlWrLCVePvKlSXf\nHu5q6/2kp5cd3B4PPPKIk6VLze+tVoM//cnDhAnlD+6oqLp/fbs0N90UwDAU/vOf+jt0XqPvfMiQ\nIRw9ejT/e8MwUH6ZHhgVFUVWVhbZ2dnExMTk3ycqKorsMioYxMe7sVpDN2SSmBhT9p3qGTkmxUXy\nMUlJocTNLlJSVBITKxd4oTweNfF+znXypDmMXdoQ9smTcO+98P335vcNGsCCBQq9e5d/l5BGjUp/\njbrm7bejUNWiZzWqah7rV15xkpBQcOzGj6/t1oVOrZ62WAqdVubk5BAbG0t0dDQ5OTlFbi8c5iXJ\nyCijykENSkyMIS0tK2SvH47kmBQX6cekRQsnBw8W7wa2bq2TllbGYuUShPp4VPf7KSxY7rSsqmn7\n91uYPt3JiRNmO9q2haefzqFVK4P09LJfx2Ix99/WNLMueiSqzAlcbq6v2G2qCu3a2TlwQOHoUS9x\ncebtaWn+qjYxrJR2vGp1bmLXrl3ZsmULAJ9//jmXXHIJ3bt3Z/v27Xi9XrKysjhw4ACdOnWqzWYJ\nIc4xfHjJH4K33x6ZH4419X7KW+70yy9VJkxw5Qd3r14a//63uTVnedjt5jB5RSqs1XU9emgYhsLO\nnfVz4lqt9rwfeughZs+ezbx582jfvj1DhgxBVVVGjhzJiBEjMAyD+++/H0ddKMQrRAQzJ3F5WbnS\nRnKyhTZtdG6/3R+Rk9WgZt6PppnFV7ze0u/31ls25s+3o+vmJcPrr/fz0ENeGjSILlePOzra7HHX\n54IkJenRQ2PtWivbtqn076/Vu+OjGEZZixnCTyiH30I9/BeO5JgUJ8ekqLp2PILlTv2ldNwDAZg/\n387q1QWzyiZO9DJypP+Xa+PRpKefv8tu3segQYPqbHloVWbY/O9/P/9ljddes/H99ypTp3pp2dJg\n1KjIHBk6n7AZNhdCiEjn85lV00oL7pwcePBBZ35w2+0GTz6Zx6hR/nL1EFUVmjWrW8FdEy691Bw5\n+frr+jd0Xn/n2QshQi4pSWXFioKh7OHDw3to3uMxh8pLq5qWmqowfbqTn382AyUhQefppz1ceGH5\nKqY5nebGIiXNjhdFXXCBTkyMwTffqNxwQxk1aOsY6XkLIUIiWDjl4EELul5QOCVcK7mVp9zp7t0W\nxo515Qd3hw4aL7+cV+7gjo01t/GU4C4fVYVf/1ojN1fhu+/qV5zVr3crhAgbkVQIpjzlTj/5RGXS\nJBfp6ebHat++ARYvzqNZs7KnFSmKOSktMVEmplVU377mSM1XX9WvM5769W6FEGEjObnkvsP5bg+V\nssqdGgYsW2bjpZcKVsn8/vc+pkzxlasHbbWaZU5lkU3lJCYadO6ssW+fynffWbjoovJv6BLJwuuv\nRAhRb7RpU/KH7PluD4WMjNKD2++HJ5905Ae3xWLwwANepk0rX3C73eZabwnuqunXz+x9//Of4Tdq\nU1MkvIUQIRHuhWDS0hTS088f3GfPwpQpLt57zwwMt9vgmWc8/N//la/9DRtSb7bxrGkXXKCTkKCz\nerWNjIxQt6Z2yK+NECIkBg3SmDXLS/v2OqoK7dvrzJoV+l3LDANOnFDIzDz/fY4cURg3zs2335oT\n05o00Vm8OI/LLy+77cFtPBs2rK4WC4sFLr9cIy9POe9cirpGrnkLIUJm0CCtwmFdk8vLguVOc0vZ\nPuHbby3MnOkiM9PslXfpovH00x4aNSp7Yprdbga3rX7kS63q3Vvj44+t/POfdiZM8KOG56KFaiM9\nbyFExKjJ5WWaZhZfKS2433vPyn33FQT3oEEBFi7MK1dwR0eb9ckluGuG2w233uonOdnCxx/X8eRG\nwluIeispSWX8eCdDhrgZP94ZtuurC6up5WWBgBnc56tTruuweLGdJ55wEgiYwT1ypI8nnvDgLGM3\nT0WBRo0MmjSRZWA1bexYc77Biy/W/Y3OZdhciHoo2IMNCvZgIfTXnEtTE8vLfD6z+Ip2nrft8cAT\nTzjYsME8QVBVg4ce8nLjjWVX9FJVs1qay1Xp5okKuPBCnauvDvDxx1Y2b1bz14DXRdLzFqIeiqQC\nKYVV9/Iyj8fscZ8vuNPTFe65x5Uf3DExBs8/7ylXcDud5jC5BHftuu8+c//vut77lvAWoh44d4j8\nfKUkw61Ayrmqc3lZWeVODxywMGaMi927zcsJLVroLF2ay8UXl92bkzKnodO3r0bv3gE++sjKnj3h\n/ftcFXX3nQkhgJIneZ0+rZBVwg6d4VQgpSTVtbysrHKnmzerjB/v4vhx8yOyZ0+Nl1/OpU2b0iem\nSZnT8BDsfS9YUHd733JeKEQdV9IQeUKCQXq6QkxM0TAKlwIppanM8rLCyip3+s47VubNc6Bp5n1+\n+1s/M2d6sZeRA1LmNHxcfbVGly4a775rZeZMpcyTrkgkPW8h6riShsJjYswZ0FXtwVZlxnooZrun\np58/uDUNnnvOzjPPOPODe/x4L7Nnlx3cUuY0vFgscM89PjRNYdGiutn7lp63EHVcmzY6Bw8WD/CL\nLtJZvNhT6eetyoz1UMx2T0s7f9W0nBx49FFn/s5UdrvB7Nlerr667Ilp8fEGCQnV2VJRHW65JcDf\n/qbzr3/ZmDbNR2Ji3ep9S89biDqupmqIV2XGem3Odi+r3OmJEwoTJ7rygzs+Xucf/8grM7gtFnOY\nXII7PFmtMHGiD49H4eWXw3sVRWVIeAtRx9VUDfGqrLmure1AdR1SUxWys0v++Q8/mDPKf/rJHLJv\n317jlVfy6Nat9Il7dru5DCwqqlqbK6rZ8OF+GjXSeeUVe4kTNCOZhLcQ9cCgQRqLF3v44INcFi/2\nVMvQdFXWXNfGdqCaZi4Fy8sr+eeffqoycaKL06fNj8E+fQIsXpxHs2alD69KmdPI4XbDuHF+MjMV\nXn+9bv0Hk/AWQlRKWcPxhSek3X47RSak1fR2oKWVOzUMeOMNGw8/7MLrNSemDR3q59lnPURHn/85\npcxpZBo92kd0tMFLL9nxVH6KR9hRH3vsscdC3YiKys31hey1o6IcIX39cCTHpLhQHJOkJJW5cx28\n+KKdL75QiY42aNeu5ibptGtn0KqVTkqKhcxMhXbtdCZO9DFokJY/Ie3MGXMt9ZkzFj77zEKrVjrt\n2hnFHut2G9jt8Omn1iq33eczgztQwiVrvx/+9jcHb7xhzkBWFIOpU32MG+crdRcqVTX33i4t3Cuq\nPv7dREVVfDr+pk1lTxoM6tGj+MiN0wlnzih8+qmVli2NEu8Trko7XjLbXIg6IFS1ys+35rq0CWnB\n+wcfW51tz8szi6+UVDUtMxNmzXKyfbv5sedyGTz+uIf+/Ut/DZfLrE9e17eYrAuWLSv59y4hQUdV\nDebOtaNp5mTD8xk1KvxrHYAMmwtRJ4RbrfKKTEirrrbn5JiT00oK7qNHFcaNc+cHd2Kizksv5ZUZ\n3A0amGVOJbgjW1wcXHKJxqlTFnbtqhuxVzfehRD1XG3N3i6vikxIq462Z2WZS75KKne6Y4eFsWPd\nHD5sPl/nzuaM8k6dzj98qihmb7thw7q1Nrg+GzhQQ1EMkpKs5y2LG0kkvIWoA2pj9nZFFJ6QlpUF\nP/8MP/5o4fhxpVgltbLaXlYltowMOHmy5OD+4AMr993n4uxZc4bZgAEBFi3KK7Vgh81mziavzuvb\nIvQSEw26dzfnWfz4Y+RHX+S/AyFEjc/erqjg2vKoKCN/c49mzXRycxX++lfHeWeeZ2VBcrKSH/Tz\n59uKbapS+PGnTimkpxef+m0YsGSJnccfd+L3mz//wx98zJ3rKXWLzqgoM7jLKocqItOgQebkt08+\nifzrIBLeQtQB5xZiiYoyiIoyeOopR63VDS+pTY0bG3TsqNO+vVlPPajw9ezSgn7hwpKLa6xYYePE\nCYWzZ4v/zOuFOXMc/POfZgKrqsHDD3u45x5fqROVEhIMmjY1Sr2PiGwtWxp06qRx4IBKcnJkr/eT\nX1Mh6ohgIZaHHvKSk6OQk6OU2FutTeW9nl046Nu0MfKD3ust3rM2DNi/31Ji1bT0dIV77nHxv/+Z\nJwfR0QbPPefhppvOv9woWOY0Pr4Cb0xErMGDzUmKSUmRvdhKwluIOiacZp5XdeKaw2Hg8xWEt2GY\na7WbNi3++IMHFcaNc/H99+ZJSosWOkuX5nLppeefUS5lTuufDh10WrfW+f57lRMnIrf3LeEtRJip\n6laZ4TTzvCLX4ksK9IQEA7vdnFwWDG7DgOuvL9qT3rpVZdw4N8eOme+xe3eNl1/OpW3b809MkzKn\n9ZOiwODB5u9PKEajqouEtxBhJFiw5HwTtMojnGaeB69nd+xImZuilBT0MTEwaZKPtm11NM3sTU+Y\n4KNv34LHr1lj5YEHnOTkmL2oIUP8LFiQR4MGJbepusuchmJfclE1XbvqNG6s8803KhkZoW5N5UT2\noL8QdUx5KpOVZfhwf5GKZUGhnHk+bBikpeWWeT/wsnKljeRkC23a6Nx+u5/LLtNITQ3kF1/ZvFll\n9mwHR49ayM2Fw4cLwnLsWC+jR/vPG8qqaq7fLm3GeUWEqrKdqBqLxfx9+/e/bXz+uZWbby5/CdZw\nUavh7ff7mTlzJikpKVgsFv7yl79gtVqZOXMmiqLQsWNH5syZg0Wme4p6qjqGvM8XgpEQJueWW83J\nMXcGC67h3rxZZfFiO7oOx45ZyM42U1pVDR591Mu1157/Q9jpNCemVWe1tOo42RKh0auXxocfWtm8\nWeXqqwMRN++hVsP7s88+IxAIsHLlSr788kvmz5+P3+9n6tSp9OnTh0cffZQNGzZwzTXX1GazhAgb\nbdroHDxYPKgrOuR9vprjlZGUpLJiRcGJwPDhtXMikJUFaWlFi6+8954Vvx+OHrXk7wimqga9emml\nBndcHDRsWP27gYXT/AJRMVYr9O8fYP16G5s2qVx9dWSdbNXqb1i7du3QNA1d18nOzsZqtbJ79256\n9+4NwIABA/jqq69qs0lChJVwK7ZSHdfgK+PMmZKrpu3fbyE5uSC47XaDNm3MNeElCZY5bdSoZrbx\nDKf5BaLi+vTRcDoNvvzSPCmMJLXa83a73aSkpPDb3/6WjIwMXnrpJbZt24byy19VVFQUWSVVZDhH\nfLwbqzV0k0ISE2PKvlM9I8ekuMock2HDzM0w/vlPs6Ro+/Zw111w7bXuGmhh2d55x+yhnGvNGjfD\nhpX9+I8+gldfDb6XGEaPhmuvLf0xaWnmjPKEhKK3f/wxHDkC2i8dpKgoaNVKQVVV2rSBhISi9Uxt\nNmjeHBwV34Wy3CZOhFmzit9+990qiYllT2OXv5uyud12VLVm+pkxMTBggPl7umePkyuugMREZ428\nVnWr1fB+7bXXuOKKK5g2bRqpqanccccd+Aud7uTk5BAbG1vm82RklD7xpSYlJsaQllb2CUZ9Isek\nuKock169zH+FpaVVQ6Mq4ccf3SXu0vXjj2VPQCs8mctqVdm7V2PGDDhzpuTJXIZh9rbPLb5iGOa1\n5RdftGMY5ol+gwY6TZqY3XJNg2uu8ZGeXvCcbjfExRlkZlbk3VZcr14wY4ZabH5Br15amf/N6uPf\nTWVOVmp6z/M+feDjjx18+KHBRRf5SEsLny54acerVsM7NjYW2y+LKuPi4ggEAnTt2pUtW7bQp08f\nPv/8c/r27VubTRJClKIq1+ArMpnLMMx9uHPPOR8IBODZZx2sXWs+l6IY3HRTgOxsSE210Ly5zvXX\nB4osHYuPN4r12mtSdc4vELUvLg569dLZvl1l797ImatQq+F95513MmvWLEaMGIHf7+f++++nW7du\nzJ49m3nz5tG+fXuGDBlSm00SQpSiKsvOyjuZS9PMfbi93qL3y8qCRx5xsm2b+THldBo8/riHAQNK\nDkqLBRo3lmppouKuvDLA9u0qn35qBbxl3j8c1Gp4R0VF8fzzzxe7/Y033qjNZgghyqkqy87K02sP\nBMylYOdOFkpJUZg+3cWhQ+bjGzXSefZZD507l9zjt9vNZWBSLU1URvPmBp07a+zbp7Jjh4WePcN/\nwqEUaRFClKqyw8Ln67V36aIxfryTgwctNG6sc911RYe9d+2y8NBDLs6cMa9vd+yo8eyzHho3LrnU\naXS02eOuidnkov7o398M79dft9GzZ/j3viNngF8IEVHO3aa0fXud66/38957Ng4csOD1wpEjFhYv\ntrN5s7l65KOPrEyeXBDcjRrpjBnjKzG4q7vMqajfOnXSiY/XWbPGVuJWs+FGet5CiBoT7LUnJtpI\nS/MwfrwTTTOHywv7z3+s7N5t4eWXC3rq8fE6DRsavPaaHYejaD1zVTWHyZ2RsapHRACLBfr21fjv\nf228/baNMWPCZ9Z5SaTnLYSoNT//bCkW3LoOX3+tFgpugyZN9CI96vfeK+hnOJ3QqpUEt6h+vXtr\nWK0Gy5bZihUICjcS3kKIWnHmDDRuXHQiUCBgDp2fPRv8KDJwuQxstqKfnMGtPuPizMlF1VmfXIig\nmBhzu9kfflD5+uvwjsfwbp0Qok44dQpOn1aK7MPt9ZrLxvLygheszVAOBBTS0ixFirW0aKHXaJlT\nIYKCyyCDtQXClYS3EKLaBfe4vvZaN6NGOVm3zry9b1+NCRN8uN0Ghw5Z8PsLgttqNa87+v3mUPrZ\ns+bPFAXuvNNPdHTJryVEderfXyMuzmD9emuJ1QXDhUxYE0JUWkk7jgH89a8ODMMcFj90yMK8eTB2\nrErfvhonTyrs2KHmlzoFc4g8EDDrkdtsZsU1v1+hbVuNO+7wc9VVUsFM1A67Ha67LsCKFTa2bVPp\n0yc8f/ckvIUQlVK4djkU7DgWFWX8Er7kT/rJzjYDXdMgPb3wgF/Ra9uaZoa3osDgwQFee81TC+9E\niKJuusnPihU21q+3hm14y7C5EKJSzle7fPduS5HgPnVK4fBhc2/uguA2ODe4oeAxDofBHXeE91Id\nUXf176/RoIHBf/5jDdtZ59LzFkJUSkm1y3XdDODCPe7Tp4P7chcdJi+Jopg1zCdP9slmHyIkli0z\nT0rbttXZsUPl73+3FysSNGpU6E8specthKiUc3cW03VzqLxDh4Lb09OVc3ou5w9uiwX69NFYssTD\n1KkV/3AMTpIbMsTN+PFOkpJkPZmovI4dzd/jH38Mz5gMz1YJIcJecHIamNeqg5uLjBrlZ8IEHw6H\nQW6uwvl63IpiBnbw68yZXtauzatUjzt4/f3gQQu6XnD9XQJcVFbwJDS4OU64Cc9WCSHCXrB2eatW\nOroOLVvqTJjgo08fjUOHLOzapVIQ3MU5nQZ2O8TGGvTtq1Wqtx1U2t7hQlRGw4YGbrfB4cPhWVhA\nrnkLISqtVy+N1q3NHsrmzSrr11v5858dZGQU9AssFgNdL/oBqKrmpiINGhhYLDBliq9K7Sjv3uFC\nlJeiQKtWOvv2qeTkEHb7xMtvthCiUk6dUjh92gzlzZtVFi60s2WLtVBwG9jt5o5ill9usljAaoWo\nKIOsLIVf/Upn1ixvlSennXv9vazbhSiPZs3MSz0nT4Zf71vCW4hqFJw01bs3dXbSlGHAiRNKkW0T\n337bSnKyhZycguvbimKWOvX7zZ62w2Gu4Xa7Ddq1M2jf3mDxYk+1zCovfP29sGCpSyEqIzjL/OTJ\n8ItKGTYXopoULlpitRZMmoKq9yzDhWHAO+9YWb3aSkqKhRYtdC68UGPzZiuaFixnahSqkmb2tjWt\nYHJao0bmB2J19orN4+tl5cqCam+33+6vM8ddhEbDhubvaHp6+PW8JbyFqCalTZqqCyGiaWZwL1hg\nz79tzx6VDRus+aVOLRazRrmimP9cLjOoPR4Fp9OgYUODmBjzsdXdKw7uHS5EdYmLM79mZoa2HSWR\n8CSKoUQAACAASURBVBaimtTlSVOBABw7prBmjfmRYRhm8ZVTpwreW3y8jt9Poc1GzBm7sbGQmAgJ\nCRrffWfh1CkzyIMnOxK4IlzFxJgnn5mZ4dfzjvxPFSHCRKgnTdVUkRKvF44eNa9dp6SY66hTUwsH\nt0HTpjqPPuqlY0cdRQG73aBxY50GDczr3LNmmT3tqChz2Dw6WtZii/Bn+2UwrfAJabiQ8BaimoRy\n0lRNFSnJy4OUFAXtl85xYqLOkSMWMjPNjw6LxaBlS51u3TT69tVYuNDD3LkeLrtMo2FDs0rVrFle\nrr226GWFrCxITlb48UcLDzxQNyf2ichnsYCqGvm//+FEhs2FqCaFJ02lpKi0bq3TpYvGihU2nnrK\nkb9lZk0ME5d0vT0rCx54wEnjxkalXjs721wiEyxvmpyssGePSl6e2QuxWg1atdJxOOD66wP5jxs4\nUOP22wOo5+Rx8PJBVhakppr/X9Pg+HGFO+900auXxpQpUtNchBebraB6YDiR8BaiGgUnTSUm2li1\nyl/ilpk1Mfu8cDCmpyvk5SkEAuas90aNjAq/dmamuQtY0Btv2Fi82E4gYN7mdps97tatda6/PkDf\nvuZzxsWZ17mVEkYZ27TROXjQkj9zN1hSNbgGfOdOCxMmOGnY0OCii2ruREeIilBVc85HuJHwFqKG\nnNsbDgbr3Xc7ufJKrVrCKSlJZcUKGwcPKvmTxYIfNrpufs3KIn+Gd3lmvqenQ0ZGQfo+/7z9lzKj\n5m0xMTrNmhlMnOjLD21FMdfERkef/3mHDzdPZrzegvAG88NR04Iz0uH06bq5zE5EpuDvZ7iRa95C\n1JDCs8yDQ8Ver4LXq1TLNemkJJWHH3bw6acqubkKublmgGuaGdxgfvAUXqNa1sz3tDQlP7h1HRYu\ntLNypZ1gcDdsqNO8uVnS9L33zHN/qxVatCg9uKGgFnpCQkHP3GYr+HAM9sB9voL2Sm1yEWp+P9jt\nZd+vtkl4C1FDCs8yLxygdnvB7lpVCafHH3eQnGzh7FlzQllwH+3gcHkwGAuH4flmvgerpgXXs3o8\n8Kc/OVm+PPipZdCsmU5iYkHwHjtmweWCli0NHI4Sn7aYQYM05s3z0LGjTkyMkX9dXNfJ//+Fj09d\nWGYnIpvPJ+EtRL1SePZ5cKgYICGh6uGUlKSyb58lv4cd/BosjtKqlV5iGJY08z249Cs72/z+1CmF\nSZNcJCWZPWubzaB1a524uKJberZta/bCz52YVpZgD7xrV3NZmcNh7t4UfJ7Cx0dqk4tQCgRA05Qi\nf0PhQq55C1FDCs8+P3zYnLWdkFBQYQwqH07nq+YWnBluvoZOerpCo0YG7duXXC5U08ziK75fNvX6\n6ScLDz7o5MQJ86SiVSudkSN9vPNO0dezWuGOOyo/Bdec2JdHUpLKypW2X4q3FD8+UptchFLw76K8\nI0u1ScJbiBrw0UewaJEzv872xIk+Vq60kZ6ukJqq4HAYJCQYlQ6n5GQLTqdBbm5Bjz44nB0fb/Zi\ne/Qovb63328Gd3Am7Zdfqjz6qDP/OXv10pg7N4+4OEhMNHjvPSupqRY6dND5wx+qZyZ44ZKmwSCX\n2uQiXATDOxyHzSW8hahmSUkqTz8NgYDZez140MKuXRZyc82fl7SMqqLatNE5e9bC0aMF17sVBZxO\n+Mc/yt6py+Mxh8qDw+1vvWVj/nx7/r7b113nZ+ZMb36Fqb59zZBt0qTiw+TlJbXJRbgJzheRYXMh\n6oGShrSDE9batDGAohPWKhNYw4f7OXjQQcuW5tC4z2del5s0qewiJ7m5ZmGU4OS255+38/bbBV2L\niRO9jBzpL3KS0aCBuX5biPrE6zW/Ss9biHogOdmSv+wpyOtVfglDo9h9K6P4FphauYaZs7LM5WCG\nATk5MHu2k02bzI8Bu91gzhwPgwcXPEd51m8LUVfJsHkhixcv5pNPPsHv9zN8+HB69+7NzJkzURSF\njh07MmfOHCznfvIJEUFcLoMffgCPx5J/bdvhKLnXWpXZ1BUdZs7IKBgBOH5cYfp0JwcOmGPgCQk6\nTz/t4cILC9pjs0HTpkZYfnAJURvCedi8VlNyy5YtfPvtt6xYsYLly5dz/Phx5s6dy9SpU/nXv/6F\nYRhs2LChNpskRLVKSlI5flzJH27zehVSU83JZYWXQAXV1mzqtDQlP7h377YwZowrP7jbt9d4+eW8\nIsEdFWWu364rwV1TO66Jui2ce961Gt4bN26kU6dOTJ48mbvvvpuBAweye/duevfuDcCAAQP46quv\narNJQlSrFStsxMRAixbm+uXgOub27Q3mzvXSvr25/rp9e3O3rZqeoHVu8ZVPPlGZNMlFerr5p3/Z\nZQGWLMmjWbOCE4uEBIOmTY1iQ/+RqqZ2XBN1XziHd60Om2dkZHDs2DFeeukljh49ysSJEzEMA+WX\nmTFRUVFkZWWV+Tzx8W6s1tD94SUmxpR9p3pGjokpJcVcAx0bC7GxwfRTCAQsDBtmZdiw4D1VoGZL\nf5pruM0Pnvh4WLIE5s0r+Pkf/gCzZlmxWs0L2qoKTZuave6PPoJXX4Wff4b27WH0aLj22qq1J1S/\nI++8Y/43OdeaNe5C/z1CQ/5uyuZ221HV0JxJBldWxMbaiIkp+HtNTHSGpD2F1Wp4N2jQgPbt2/9/\ne/cdHlWV/gH8e++dlkkhCSQkBAnwAxUVVwRBWRBjw7IsFhYQhUUXQWWVYqhSFEFAgguyIii6KiAq\nC66KK2shiIpiAQuIIAhBekghZfq95/fH4U6fZJJMz/t5Hh5lMpk5GSbz3nPOe94XOp0OHTt2hF6v\nx8mTJ51fr62tRVpaWr2PU1FhCucw65SVlYrS0vovMJoTek1c8vIMOHRIhEYjweFwzarbtVNQWmqJ\n2Djsdn4UjDcrARYu1OP99/mHjygyjBtnw+DBdueMXKsFcnMZTCbg/fclj25ov/wCTJ4MVFY2fqUg\nmu+R/fuNziNxnrcDpaX0WRJJjblYMZlsYRhJcKxWfpFtNttQXe16E5WWRma7q67XK6KXM927d8dn\nn30GxhhOnToFs9mMq666Cjt27AAAbNu2DT169IjkkAgJKfeSqO4iWSnMYgGOHuWB++xZYNy4JGfg\nNhoZnn7agsGDXeNR97fVM92BqrfFa5OQQEmBVHqV1EedeffqJWPECLvzTyyI6My7oKAA33zzDQYN\nGgTGGGbNmoW2bdti5syZeOaZZ9CxY0f0798/kkMiJKTUI1xvv23E/v2IeKUw9zPcv/8u4NFHk/D7\n7/wavXVrBUVFFnTq5ApaGRkMmZmejxHo+Fq8NQlR26X+9JOIsjKBSq+SBtNoeC6I3R6CykohFvGj\nYpMnT/a5bc2aNZEeBiFhU1AgY/DgwEuyalBRy4CGoq83ANTUAKdP88C9c6eIadOSUFXFP3S6dJHx\n9NMWtGrFP4zqOr+dn6/g0CHfQB1PM1U1SQ0AUlIAxhjKywWIIkPXrlR6lQTH1aY2uuPwh4q0EBJB\n7kEFcGU+A03LPK+sBMrKeKB+/30NFizQw+Hgfy8ocGDWLAsM53Js6ju/fddddo8xquJppuq99J+a\nCqSm8gYtK1dGLveAxDc10VE9+hlLgloHW7lypc9tz7inrRJCghKO/eSyMgFlZbxO+cqVOsyda3AG\n7uHDbZg71xW4jcb6z2+rLTsjfawtlBJl6Z9EV3IyX6lSL4xjSZ0z76KiIpSVlWHLli04fPiw83aH\nw4Eff/wREydODPf4CEkooQwqjPFl8poanqT25JN6bNnCLwIkiWHqVCv+9CeH8/7+9rcDCaZ6W7iW\n/0MhEZb+SfSph5/UFrmxpM7gfeONN+LgwYP46quv0KtXL7BzzYIlScLYsWMjMkBCEkmogoqi8OIr\nJhMveTp5sgF79vDU2NRUhvnzLejenQdSQQBat2ZITq7/cYMNyOFa/g+VRFj6J9GXlsYgCAy//x57\nM+86LycuvfRS3H777diwYQNSU1Nx++23o1+/frDZbMjPz4/UGAlJGKE4SsaLr/DAffAgL3WqBu68\nPAUvvmhyBm6tli+TBxu4g61EFuvHyRJh6Z9En1YLnHcew4EDcTbzVi1atAiKouC6664DwGuU//jj\nj5gzZ05YB0dIovHtBtawzGebjRdfcTiAL7+UMGOGASYTnxVcdpmM+fPNSE/n9zUa+Yw72DKndQVk\n7/HFw54y9QcnodCpk4ItWzSoqOCVCmNFUMF79+7deO+99wAAmZmZWLRoEQYMGBDWgRGSqBobVMxm\nfoZbUYANGzR45hk9FIUH7ptusmPaNKszEa0x/bcbEpBpT5k0F3/4g4wtWzT44QcJ11wTOxeDQV0m\nK4qC06dPO/9eVlZGbTsJiaCaGle503/8Q4eiIoMzcI8ebcWsWTxwq/vbDQ3cQMMqkcVCJTlCIuGy\ny/j7f+fO2GpkE9TM+4EHHsDtt9+O7t27gzGGH3/8EY899li4x0ZIQmpolvbZs8CZMwJqa4FZswzY\nvp3/2up0DDNnWnH99TyjvKn9txuS5NXU5X9C4kWPHvw9vX27hFg6YBVU8B4wYAB69uyJ77//Hlqt\nFjNnzkR2dna4x0ZIwmlolvaZMwLOnuWZ5YWFBhw4wK/+MzIULFxoQdeufFaQnMwrpjVlQayhAZn2\nlElzkJXFcNFFMr7+WoLFAmfNhGgLKnhXVVVh1apV+Oqrr6DRaHD11VfjwQcfhCFWfgpC4kSwSWHu\nZ7j37hUxaZIBZWU8MnfoIKOoyII2bfjSeGYmC1kiDQVkQnxdfbWMn3+W8OWXUsz8fgR1nT5p0iRo\nNBoUFRVh/vz5MJlMtGxOmrXiYgmjRxvQv78Ro0cb/B6n8ieYpDBF4UfBamqArVslPPhgkjNw9+rl\nwAsvmNGmDZ9l5+aGLnATQvzr359vTW3eHDsVxYMaybFjxzxKpD722GP405/+FLZBERJtde1LN6VA\nib8s7epqwGoV0L+/Eeedp+CGGxzo3l3G2rVaLF+uA2M8Me322+2YONEKjQbQ63limjY2jlUTktB6\n9ZKRkcHwwQcazJ9vbdL2VKgEFbzz8/Px7bffOntt//LLL1SkhSQEf0EaQJ3BuSHnob15J4VVVwMn\nTojIzVXgcAD794vYt0+HjAyGr77iv56CwPDwwzYMHWqHIPAuWdnZDELsFX0iJCFpNMAtt9ixdq0O\nX3whoW/f6C+dBxW8jxw5gnvuuQcdOnSAJEk4dOgQWrRogWuvvRaCIOCTTz4J9zgJCblAM2ij0f8x\nKzU4N6VAiXdS2NmzAkQROH5chFbLkJrKUFkpYt8+HpkliSE3V8HPP4vYsUPCLbc4aJmckCgYMsSB\ntWt1ePNNbfwE7xUrVoR7HIREXKAZ9C+/iGjXzjeA//STiNGjDTh0iAfWzEyG1FTX14MtUKImhRUX\nSxg5MgmM8QQ1q1VATY3gXCbXaBjatlVgMABHj4p46SUdSksF/PyzFJPNQAhJZL16ycjPV7BpkwYL\nFvAVsGgKauU+Ly8P+/btw+rVq7Fu3TocOXIEeXl5zj+ExKOGlPKsrubHtg4dEpGZyWC1CjhxQkR1\ntes+DS1Qsm6dFlotA2M8Sc1mg0fgzs9XPHpwm0zA8uW6oGqPE0JCSxCAwYPtMJkEbNoU/cS1oD69\nFi9ejFWrViEvLw/Z2dlYsmSJ3x7fhMSTQDPlLl18by8vF5CZyWfjqalAbq4CvZ6hokJodNOLAwdE\npKUxyDJgtwMAD9yiyNC6tQKtln9g6HSAKPIx2Gyuje7qaqCkRMADDxgalPFOCGmcwYP5BXqgVbtI\nCuryYevWrdi4cSO051JbhwwZgjvvvBNjxowJ6+AICadAFcXGjbMBgEexkpoaEYzxYGm1CtDrGTIz\nGdLTgZUrLQ16XrWdZ+vWCk6ckOBwuAKyTscLQrRqxXDihAiN22+o+ryAK9EN4AG+IRnvsdyHm5BY\nlp/P0LevA599psHPP4u46KLo1fIPKni3aNECtbW1SD/XrshutyMl2gv+hDRRfRXF3APabbclYdcu\n18yWL5sLyMlpWNCTZV6jvKqKz6TV89sAQ04OQ3o6w8iRdrRowbBkieeFhXrBAPDvVel0rv35+jLe\nY70PNyGxbtQoOz77TIOXXtJi8WJr1MZRZ/CeNm0aAN6YZODAgbj22mshSRK2bduGjh07RmSAhIRT\nsBXFmFf+mizzP99+KyE/PwWKArRqxfDXv9owfrz/ve/Fi7V45RUdKisFyDKcM26NhqFNGwXnn6/g\nz3924LbbHDAYAKPR88Li1ltlvP8+X/2yWl3BWw3oQP37+E055kYIAW680YF27RRs2KDFzJlWZwve\nSKszePfs2dPjv6qLL744fCMiJAZZLAJycxWUlwswm3nwBXiSmXreurRUwD/+wWe18+Z5fn9RkRZL\nl+qhKJ7722lpDCtXmtC+PYNezxuLqEvl/i4s/vAHBW+8ocWRIwIYa3jGezz04SYklkkSMHKkDXPm\nGPD661o89FB0OunVGbxvv/12AMB9992Hl19+OSIDIiQWqZXRUlMZSkr4zNfitdUty/wX+7XXdB7B\n22QCXn1V5xO4BYFBr2do354FXXjF/ZhZsB3A/P0c/m4nhATn7rvtWLRIj5df1mHMGDukKOSKBnW5\nbbFYcOLEiXCPhZCIakh9cvf+1eqSNWPwCLbKufjnvh9dUwOcPCmgrEzwySjXaoHqagEtWzK0bt2w\nimkFBTKmT7eiY0cFkoSgM96pDzchTZeRAdx5px1Hjoj4+OPonPIIKmGtoqIC1157LVq2bAm93nW1\nT5XVSLxqaOKWe3KbumRtswnOgA3AWe+Y70ELqKzkncGee07nkVEuSQySxAO/mrGujqkhWeCN6QBG\nfbgJCY2//c2ONWt0WLVKh/79zRF//qCC9+jRo8M9DkIiqjGJW95L1qdO8SNfKnXpbMQIG06fNuD3\n3wU8/rgBn32m/prx/Wz1foLA986AyGaBU9tPQpru4osVXHWVA59+qsGvv4ro3DmyW09BBe+vv/7a\n+f92ux3fffcdevTo4dwTJySWBDODDUV98qVLdaislGC18kCclsZw//02DBniwL59wIMPJmH/fh6p\n09MZ+vWz46uvNDh7li+Vu2emqxcT1dV82V090710qQ4FBZG/qieEcK+9FrggS6dOCr78Epg0SY87\n7nA4bx8xIvzbUEEF7/nz53v8vbKyEhMmTAjLgAhpimBnsKFI3KqtFXDBBa77Mwa0bcuwa5eIyZOB\n06d54G7fXsHdd9uwY4eErCyGq66SfS4oSkpEj8IrAN9b//57CcXFEs2UCYlBl1yioEULhm+/5U2D\n1HLGkdCo8yFGoxHHjh0L9VgIabK6lsPdNTVxy/t51NrkK1dq8cADSTh9mt/eo4cDo0dbsWGDFidP\nipAk/zXJ8/MVZ6KbLPPHslh4dvrSpbqgxhQKDUniI6S5kySgd28HbDYB330X2d+VoGbew4cPhyCo\nGbYMR48eRb9+/cI6MEIaI9jl8IYmbnkvxf/0k+jsKqTWJq+oEHD6tAg1o3zgQDsKC62YM0cPrdaV\n0KZy31+/6y47/vc/jVudc04QELHZN1VfI6ThevaU8eGHGmzfLqF3b7lBp0aaIqjg/fDDDzv/XxAE\nZGRkoFOnTmEbFCGN1ZDl8GATt/wFtbIyAYwxGI082J46JaCyUq01zjBlioDsbAeefFKPzz/XOEub\nuhdUcb+gKCiQcdllMr79VoIg8KAtSfyPTsciUgGNqq8R0nCpqcCllyrYtUvCwYMiOnWKTOJaUMHb\nu8IaIbEqULORppxjdg9qakKZ2SzgyBEB2dkKKitFmEz8clunY5gzx4KsrCQsXqyDRsNrkqu10AHF\nGcC9LyjGj7dh5Mgkj2YkAD9OFokKaFR9jZDG6d3bgV27JGzfLkUseNNvJUko3sVLkpMZjEaGBQv0\njd7DVYOXmlBmsQjOpbGTJ12Bu0ULhhdeMKNfPxnvvQdnS0/32uPuBVy8LygKCmR06yZDr+cFW/R6\nhtxcHuwjUQEt0HNQ9TVC6ta+Pf9d3b1bxNmzkXlOCt4k4RQUyFi50oIpU6yorRVgMvFiKv4SxYKh\nBq/ycl6chTFXYxJF4cH4/PNlvPaaCRdeqCAnh8E9n9O9/7fNVnf/73HjbMjPZ+jcWUF+vmuZPRIV\n0Kj6GiGNIwhA794yFEXAjh2RSVyLSvAuKytDv379cPDgQZSUlOCuu+7CsGHDMHv2bCgKXeWT0Ag2\n87w+alCzWARn4LbbAcZ44E5JYXj+eTPy8hjatmVITga8m+7x2TND//4OrFxpqbMQTGPKnoZCNJ+b\nkHh3+eV81eyrrzQeSafhEvHgbbfbMWvWLBjOHYibP38+xo8fj9dffx2MMSq5SkKiuFjC1q0S9u8X\nUVIioLra9bWG7uEWFMh49FEr0tKYWytPHrgzMhT06uVAq1b8jLfu3Kmu++7z/1jBzGLVlYPNm011\nBvpwcF+1YAxN2m4gpDnR64Hu3WVUVQnYsiX8vy8RD94LFy7E0KFDkZ2dDQDYs2ePMyHu6quvxvbt\n2yM9JJJgvLPDebKY6AzgDd3Drari33PeeYpbjXKG1q0VtG7NMHSoHbm5zOMo2I03Imyz2HCfxVZf\nv0OHxCZtNxDS3PTsyX+/A636hVJQ2eahsnHjRmRmZqJv37544YUXAPBz4+oZ8uTkZFS7T5ECyMgw\nQqOJ3gdJVlZq/XdqZmLpNdm4EdBogKwseOw9V1RIyMgAHnhAQlZWcL9cZ84ApaXAhAnAzp38No0G\naNtWwMUXCxg1CrjtNqPf7x082IjBg9W/SQCa/gv94YfA00/z/xdF4PffJTz9tBbp6fyCIRTU18/b\n22+7/zwNF0vvkVhBr0n9jEYdJCk+0rMuvBDIywM+/FALQIusrPA9V0SD94YNGyAIAr788kvs3bsX\nU6ZMQXl5ufPrtbW1SEtLq/dxKipM4RxmnbKyUlFaWv8FRnMSa6/J/v1GKApgNAI5OTzRzGYTADBM\nnmxBt24ySkvrfgzGeEew3bsFFBYm4dgx/uGRm6ugqMiCzp15YprBAL+PFY7XpLhYwsSJBpSXCz7n\nxlesUNCtm6XuBwiS+vr53g6Uljbudy/W3iOxoDm+Jo25WDGZbGEYSfh07y7h3Xe1WLnSgjFjmrb5\nXdfrFdHLmbVr12LNmjVYvXo1unTpgoULF+Lqq6/Gjh07AADbtm1Djx49IjkkkoDcl8XVRLHOnRVc\nc41vURZ/S9CyDBw7Jpz7mtEZuC+5RMaqVWZ06aKgbVvmU8fY/bGGDkVIl5nVpWz1qJn3VkAoz2LT\nkTFCGu/yy2VoNCzsS+dRX4uYMmUKli1bhiFDhsBut6N///7RHhKJc8EeefK3tzt3rh7//rcG69dr\nMGGCAdXVPFhef70dy5aZ0a4dQ14ew2efeQb9JUu0Ho914ABCuk+sfhDo9czjdjWYhzKw0pExQhov\nJQW4/noHfv5Zwr594QuxEV02d7d69Wrn/69ZsyZawyAJKNi65d5XxuoRsKIiHUpKXEG3QwcZN9/s\nQE4O0LIl81sudetWyaf8KRC60qLqzDozk52r1Map2wGhDKwNrftOCPE0cKADmzdr8Z//aDBlSniW\n/aMWvAkJp2DqlrsvNTscPHAfPy6ipoYHR0FgyMlh0OuBf/1Lh7Zteba4d9CvrgaqqwVUVwtITeV7\n0RkZvs/RFGrNdn5xoDj38TMzWVjOYgdb950Q4qt/fwcMBoZ33tFg8mRbWJqVUPAmccG7q5d3P+zG\nyM9X8NtvIhwOwGoFjh3jpU8BQJIY8vIUGI1wdgRbulSHdesYPvxQA52OOcueqj24FQXOGuaSxBPm\nGrOc7e9nda/ZnpoKpKYyAOEJ3ISQpklJAW64wYH33tNizx4Rl1wS+nyRqO95E1KfUJ079k5Ou/BC\nGXY7YDLxGbIauDUahvx8BcnJrsBdXc1bcx46JEKnY86EsVOn1GDv2fLzzBn+34YuZwf6WYHwnRsn\nhITebbc5AADvvhueOTLNvEnMC0WrSu996oMHRXz3nQi7nTcXUSumdeokIy9PwenTorOxCMATw3Q6\nPtN233e2WATodDx4Z2YqsFj4crYgoFHB1V8HM6tVwMSJBjzzjAUrV4bmOBghJLyuu84BvZ6v1E2f\nHvp9bwreJOaFolWle1CUZaCyki93yzKgBu4WLRSMGWNDWhrDc8/pPfaprFYBubl86ct739nzzDVf\nzu7SRWrUrNi7g5mqokI4d/FBs21C4oHRyJuVFBdrcPy4gDZtWP3f1AC0bE7CrqnlPENx7lgNimpi\n2qlTImTZVaM8K4sXXdm6VYPbb5fx2GNWJCczHDkioKREgNHo+Yunnh/v1Uv26P6luvfe4H8+fz+T\ne+tQAM5Zf0ObqhBCouf66/nS+SefhH6eTMGbhFUo9qtDce64XTsFdjtgswFHj4qw2101yvPyZLRs\nyaDVes52a2sFZGTwoFldLaCkxLXHrRo3zuZ3L7qxpUrVn9Vq9XweNTkulMVYCCHhdd11avAOfTlv\nWjYnYRWK/eqmnjuWZeDGGx346Scdjh4Vz52NBgCG7GwFqamuxDR15rtunda5dK327pZl4NQpAaII\n9Oole4zBfSzFxRKGDuVlRhuaGa/+rBMnGlBRITiz2tWZPVU5IyR+dOzI0LGjgk8/5W1CtSFcOKPg\nTcIqFPvVQOPPHVutwIkTPAiePu0K3AYDQ3q6gowMeCSmqbP5khIR5eUCZJnP1lWMAWfPIuDFg7rS\noNHAK1s8+L3qggIZzzxj8UiwU1GVM0Liyx//6MDq1Tr88ouIrl1Dd/FNa3AkrCJdJ9t9f/3eew3Y\nsEGD//1Pg7Fjk5zFV3r3dmDTplrMmmXDBRco0Gh8j17l5yuwWgU4HL7PYTYLWLpU5/f561ppaIiC\nApmOhhGSALp14591u3aFdumcZt4krNyLi7gLxwzS/TiYwwEcOCBixw49zpxxXaMOHmzDI4/Ybiyw\nawAAIABJREFUkJEBXHqpA4MGOXweY906LX76iRdv8e6uJQj8z969DVtRaMxeNVU5IyT+devGf4d3\n7RIxYkToHpeCNwmrUNbJrq/K2rp1WjDGA7fDAZw8KaCqigdNUWSYMMGGQYPsaNWKoUUL38d+/HE9\n9u/n9zcYgJQUhooKV+KYurQu1XEBrZYx9Xc7IaT5ueACBUYjw86dNPMmcSYUM0h/zUC895IPH+ZF\nVxwOnlFuNvNoK4oMRUUW9O4to3VrhuRk38eeNk2Pw4dF50y7tpZnmwsC3+cWRR68JYn/6dLFfzCO\n5EoDIST2aTTApZfK+PprCSYTP/8dkscNzcMQElgo6pLXl7VusfCz2gcOiB5HwTQahh49HOjTR0Zu\nLm8y4u+xT50SfZbIVYIAJCXxIK5mf48b579ikrrS8PbbRuzfD+rIRUgz9Nprvp9XiiJg2TIdcnPr\nLtYyYkRwF/oUvElYBTNjDkZde8k1NcDp0wLOP1/BJ59ooCiujPK2bRXcc48DbdsyaAK823ldc/9f\nYwxITuZdwrKzWVDBuKBAxuDBQGmpKeifjxCSuFq25AG7vFyoN3gHi4I3CatQnPMGAu8l5+QoOHVK\nwKZNGqxYoXMG7rQ0Bd27yxg82IHbb3d4NA3x99g//hj4Dq1bM6SnA5s3UzAmhDRcejoP2JWVoesN\nSsGbhJV7rW61yYZez1Bb27Dsa++9ZDUx7brrHHj+eR1ee811dGvkSBvuv9+GFi34bDmYx/7oIw1M\nJv647gwGtRQqJZwRQhpH3ec2m0P3mBS8SVips9qjR3mlMkUBbDYBdjtfUveefQfaH3fPWj98WER2\ntoJrr3Vg0yYtiov521ijYZg61Ypbb3UgM5MvdQejoEDGuHFWLF2qg9ksQFF4gppGA7Rpw4M2JZwR\nQhorKYnPCkwmmnmTOMFntUmwu8U+RQHsdl7opKDAdSla3/54QYGMq66ScfKkgNOnBUyaZMDevfz4\nRWoqw4gRNnz1lYR339WiY8eGJcaNH2/HH/6g4I03+Blvi0WAwcDQtWvwCWfuFx7nnw/ccUfjOosR\nQhJLXdt2jUXBm4QVD14MoshntOpZabsd+OYbyWP27b0/ri61jxljwDXXyBg40I4uXRT8+quIwkID\nTp3ivxHnnafgnntsePttrbNGeWPLkjY22HpfeBw4AGrhSQgB4LsdFwpUHpWEnVYL6HT8v4y53siK\nAo8OY+4Z5WpTEKuV98z+9VcRCxfq8dJLWowZk+QM3N26yXjxRRO+/VaCTud7hauWJW1qW9L6uF94\nVFcDv/0G7N8vYuLE0D8XISS+qP0RQtmYhII3CTu1oInsNQE1GDx7VLsnhan9rB0O/sY/cEDEoUMC\nXnxR59w3uuUWO5YuNSMnBygtFZ2zenclJWJI2pLWxz0xj1908NsrKoSQPxchJL5UV/MPp9TU0E3B\nKXiTsBs/3obcXB6YBYHPjrVafgQLcAU+977dalMQda/cbgesVhEA/yV48EErZsywYu9eEbNn63H4\nsICSEgHV1Z7PnZ+vhKxZSF3UCw/1okOl03leoBBCmp+qKgreJA4VFMiYP9+KnBx27ugVL57i3aPa\nvZOWXs/AGJytNdXz2wBD164OjBhhx88/i1i+nJc1zcxksFoFnDghegTwoUPtIW0WEoh64WG1egbv\nzEzPCxR/wr2kTwiJrpMn+eeCOmEJBQreJCLUHtWdOyvIz2fOwA0AXbrIzuC1bp0Wt95qx+zZVogi\nzh0vcwXurCwFsiwgO5th0ybXbDY1FcjN5UG/okLwaKEZibak6oVHZiaDIAB6PR+P9wWKt0gs6RNC\nouv4cRGSxJCVRcGbxCF/PapvvdWO99/X4tAhfg58/34RRUV6HDnCs9MZczUXyclR0LIl8H//x4Oi\n92yWF1Nh6NCBYeVKizPL23053l2oz267X6B07AiPC5RAzxWJJX1CSPSYzcDx4wLatmV1diRsKDoq\nluBC0RQklLyPY40ebQDAM9Dtdv7fqipg6VK9c8adnMzQpg0P+FotcM89PBAG234zlG1Jg/n5GtKY\nJBJL+oSQ6Pn1VxGKIuCCCxwhfVwK3gksVE1BAj12KAqSlJSIOHsWKCsTzu0XM9hsrsDVp48DNTV8\n7ILg2YqzIe03Q9GWNFgNaUxC/b8JSWy7d/Pp9gUXhPZ3mi7vE1i4lmS992nVgiSN2afVaJjzPDc/\nFqa+JRkmTrTizjvtsNkE5Ocz5OczmEyuo1eBluHXrdPGTfJXpJb0CSGRZzYDP/4oolUrBe3ahbZS\nC828E1i4lmTVi4JTpwSUlQmQZUCSRDzxhB4FBcF13mKMt/F0ONyXzF2JaR07KvjLX+yYM0fvt7CB\n2pXMfUa9ZIkWy5e76pPv2SNi61YJDz1kw/jxdjzyiB7vvKOF1coTygYOtOPZZ61Nei2aKpJL+oSQ\nyNq1S4LDIeCKKxx+61A0BQXvBBauJdmSEhGnTgk4dYq/GwWBF1PZu1fEkiVajB9f96zRbudHJ2w2\nfv7RPTFNEHhGucHAe+CePBncBUhxsYTly3kBF/c66iaTgOXLdfjiCwnbtrne7hYL8Oab/KrAO4BH\nOk8gkkv6hJDIkGVg61YJGg3DFVeE/vebls0TWLiWZPPzFZSV+V5GiiI8WnO6U88yX3+9ESNHGrBt\nm4Rdu0QcOSLC4eCPZTAw/N//KdDreWWyIUOMOH3at/CKOgZ369ZpYbUKPlXcZJl3Mfv8c//Xqe+8\n4zmtp6NbhJBQ2LlTRHm5iJ49ZaSlhf7xaeadwMK1JHvXXXa8+67vW0eSfCuMATwgTpumx5kzPCnt\n0CEB334roapKgCzz+2s0DIoCHDsmwuHgzUYUBdDr+Z44oNR59KqkRIRez2CxeD6/ovAqZyaT4HfZ\nyuq1al5XngDNjgkhwbDZgA8/1EKSGAoKQptlrqLgneDCsSRbUCAjI4OhstLVKUyj4cFbrSjmbskS\nHY4fF51NSUwmV9AGgJQUPot2OPiSt3tzER6wFdhsAiSJBbwAyc9XcPasiOpqPiaVKPIxed+u0nsl\nq9PRLUJIUxUXa1BRIaCgwIGMjPA8R0SDt91ux/Tp03Hs2DHYbDY8+OCD6NSpE6ZOnQpBENC5c2fM\nnj0bYjian5KQGjPGhn/8g0c+QXB1ChsxwuZxP5sN2L3bFbgdDs+KaV26yGCMz4q1WoZff+X/9nwG\nz1BezmfrBgPDlCmBj7jddZcdhw7pkZXFUFoqgDE+rqwsPmPv08fhseetGjjQcwZPR7cIIU1RWiqg\nuFhCWhrD9deHZ9YNRDh4v/vuu0hPT8eiRYtQWVmJ2267DRdeeCHGjx+PXr16YdasWfjkk09www03\nRHJYJAj+krgmTLDitdd0qKgQkJnJMGKEzSNZraaGZ5QD/jPKjUYGQA3cOFdWlNcoN5sFnDjhmp0z\nVnd/bPctgp9+EmGx8IDftatrph5MtnlDzo4TQog7WeZbbA6HgIEDbT4re6EkMBaONuH+1dbWgjGG\nlJQUVFRUYNCgQbDZbNi2bRsEQcDHH3+ML774ArNnz67zcUpL/WQwRUhWVmpUnz8avIu9qKZP54Fv\n40Yj9u+XPTKzy8t50hkAjBxpwL59EtSOYAB/y6WnM3TqxJfE1f1otaWmw8GX4lVqnfDkZIbsbBbW\nTPDiYqnJeQLN8X1SF3o9fDXH1yQrK7X+O3lZvNgShpGEx8cfS9i8WYtu3WTcfXfjLvhHjHB9X12v\nV0Rn3snJyQCAmpoaPPLIIxg/fjwWLlwIQRCcX6/2l1rsJSPDCI0metm/jXkDxrONGz0Dqer5543O\nTHBRlLBnj4QxY7TIyAC6dAHuvJPPmEtKPL9PEARIEs8C/+knEVotkJsLpKUBGRl87/zkSX4Vq9cD\nrVoBaWkSqqqAX3/ljy2KwO+/S3j6aS3S04Ebbwzdzzt4MP/DSQAaV9Smub1P6kOvhy96TepnNOog\nSbG/lbp/P/Dhh0B6OjB8uITk5MbFqKwsQ1D3i3jC2okTJzB27FgMGzYMAwYMwKJFi5xfq62tRVoQ\nOfUVFcEVAgmH5ni1vH+/0W+y1w8/CGjXjkGjkVBRITuT0mSZ4bffGKZPF1BaKjj3uA0G3uZTFBkc\nDuHc//MAX1rKwJhrmXvdOq3H3rPDwfeSdDrA4fBcLFqxQkG3bg2/Og/nee7m+D6pC70evprja9KY\nixWTyVb/naLs7Flg5Uq+Onn33TYoCvN7xDUYpaXBzbwjejlz5swZ3HfffZg0aRIGDRoEALjooouw\nY8cOAMC2bdvQo0ePSA6JBCFQspb7hotaac1uB6qrBRw8KOLUKdEZuEePtqJ9ewUdOijQ6Txn8ozx\nbmBduyrObmD+zqhbrYLfbPbGZILTeW5CSCjY7cCrr+pQUyNgwAAHOnSIzE50RIP3ihUrUFVVheXL\nl2P48OEYPnw4xo8fj2XLlmHIkCGw2+3o379/JIdEghCo2MtFF6lHvOCsbKbO0O121/72k09acO+9\ndrRtq0Cj4cvl7nQ6/mZ3D8L+6pZ36yZ7nPVWNSYTnFpxEkKaSlH4Z8mRIyIuv1xGnz6RqwUR0WXz\nGTNmYMaMGT63r1mzJpLDIA0UqNgLY8CcOXqPo2KcK3BnZPDjEqII/PWvdhQV6Z0Z5Sp1Nu2vlaf7\nMnagxLnGZIIHmq3/9JOI0aMNMdNClRASPu7JYY0xd64OP/4o4corHVi/3hzW7HJvVKSFBMU7kDoc\nvD75/ffb8NFHSdi3T/2Kq0a5VgukprJzCWkMHTrI0OmsWLpUh++/l6DTMWRmMudsur4gHMqKcf7O\nc1dX8/Pl6u2hbKFKCEks//ynFs8+q0eHDgpeeSWygRug4E0awWzmHcVkGbjyShlaLbB5M6AGblHk\nZ7jT0xm6dFGQl8cgndtK5hcB5gYfx/JOLqurYEsw/J3nLi/3v6dOpVEJIe5eeUWLOXMMaNNGwfr1\nJmRmRn4MFLxJg1RVAWfOCM6KaWvXarF8uav4Snq6gtatGQSBZ5Lfe6/dGbjdNaRsq/dyeShmxP5m\n8TU1IlJSfO9LpVEJIar16zWYMkWPVq0U/PvfppD36Q4WBW8SlC1bJLzyCg90eXkKbrqJlxt9912e\n4CUIDAMH2lFdLeDECRHt2yv4619Ds18crmYh3hcQo0cbPJbS1WV0QeBfo/1vQpq399/X4JFHDEhL\nA956y4xOnaITuAEK3iQIH38s4ckn9c6ktJISEbNmGWAy8dm20Qg8/rgFffvywJaZyUJajD9SzULc\nl9LVSm8Ar+5G+9+ENG9btvC2xno9sG6dCZdcEt1+B7QeSOpkNgP/+pfWGbhtNh401cCdlaXg9deB\nvn1lCAKQnR3awA0EPgoW6mYh7sfTKioE6PXMWZZVRUfJCGl+PvpIwogRSZAkYM0aM3r0iH6jIpp5\nJ6BQVQ5T97ePHuXXeCYT77ettvM0GBheesmMCy5IRkUF0Lo1w7kKuCEdS0OahTT1+dSl9P79/VeV\no/1vQpqXDz7QYNQoAzQaYPVqc0TPcteFgneCCUVyFy9XKjjL++XlKdizR8LJk4IzMS0lheGKKxzI\nyuKZ5G3aMBi8SvKGKtEs2CNioUxso9aghJD33tNgzBgDdDpg7Voz/vjH2AjcAC2bJ5ymVg5zOIBj\nx1yBW60/fuKE6AzcmZkK8vIU/PnPDkgScN558AncoRiLu4ICGStXWrB5s8lZQjWczxeoqhy1BiWk\neXjnHY1zj/uNN2IrcAM08044TUnuMpn4+W11udhqBebN0+Ojj1wZ5Tk5DBdfLOPWWx3o21dGmzYM\nOl3ox9IYoXy+UBaEIYTElw0bNBg71gCjEXjzTROuuCL2VtwoeCeYxi73VlTwY1Gq8nIBU6YYsHs3\nP6SdksIwb54FPXvy4GUwADk5zO8Zbn9jUY9dqc1FioulkAfCUC91N+QsOiEkMbz5pgbjxhmQksID\nd/fusRe4AVo2TzgNXe5VFD7bdg/chw8LuP/+JGfgbtNGwYsvmpyB22jke9x1BW73sajHrtR65no9\nC0sHL1rqrltxMT/q0r+/EaNHG6iDGiFeXn/ddY57w4bYDdwAzbwTTkOWe+12Xp/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KAAAM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\nJoQQQuLM/wNIxR6/A0aNsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24da2bbba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(\"lngpc\", \"urbpct\", data=dat, kind=\"reg\",\n",
    "                  color =\"b\", size=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is a pretty tight fit: urbanization rises with income per-capita, but there are several middle income country outliersthat have considerably higher urbanization than would be predicted.  Let's look at the regression line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mod = smf.ols(\"urbpct ~ lngpc\", dat).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 urbpct   R-squared:                       0.744\n",
      "Model:                            OLS   Adj. R-squared:                  0.743\n",
      "Method:                 Least Squares   F-statistic:                     494.3\n",
      "Date:                Wed, 03 May 2017   Prob (F-statistic):           3.43e-52\n",
      "Time:                        15:26:52   Log-Likelihood:                -670.94\n",
      "No. Observations:                 172   AIC:                             1346.\n",
      "Df Residuals:                     170   BIC:                             1352.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    -68.0209      5.185    -13.120      0.000     -78.255     -57.787\n",
      "lngpc         13.9583      0.628     22.233      0.000      12.719      15.198\n",
      "==============================================================================\n",
      "Omnibus:                        8.555   Durbin-Watson:                   2.204\n",
      "Prob(Omnibus):                  0.014   Jarque-Bera (JB):               17.062\n",
      "Skew:                          -0.027   Prob(JB):                     0.000197\n",
      "Kurtosis:                       4.542   Cond. No.                         47.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(mod.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's just look at a list of countries sorted by the size of their residuals in this regression line.  Countries with the largest residuals had urbanization in excess of what the model predicts from their 1980 level of income per capita.\n",
    "\n",
    "Here is the sorted list of top 15 outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country               year\n",
       "Singapore             1980    35.469714\n",
       "Chile                 1980    30.563014\n",
       "Malta                 1980    30.448467\n",
       "Hong Kong SAR, China  1980    29.957702\n",
       "Uruguay               1980    29.143223\n",
       "Argentina             1980    25.369855\n",
       "Cuba                  1980    24.148529\n",
       "Belgium               1980    20.731859\n",
       "Israel                1980    20.456625\n",
       "Iraq                  1980    20.262678\n",
       "Peru                  1980    17.810693\n",
       "Myanmar               1980    17.719561\n",
       "Bulgaria              1980    17.360914\n",
       "Jordan                1980    15.706878\n",
       "Colombia              1980    15.258744\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod.resid.sort_values(ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is of course only suggestive but (leaving aside the island states like Singapore and Hong-Kong) the list is dominated by southern cone countries such as Chile, Argentina and Peru which in addition to having legacies of heavy political centralization also pursued ISI policies in the 60s and 70s that many would associate with urban biased policies. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel data\n",
    "\n",
    "Very often we want data on several indicators and a whole group of countries over a number of years.  we could also have used datetime format dates: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = ['CHL', 'USA', 'ARG']\n",
    "start, end = dt.datetime(1950, 1, 1), dt.datetime(2016, 1, 1)\n",
    "dat = wb.download(\n",
    "    indicator=indicators, \n",
    "    country = countries, \n",
    "    start=start, \n",
    "    end=end).dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets use shorter column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NY.GDP.PCAP.KD', 'SP.URB.TOTL.IN.ZS'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat.columns = [['gdppc', 'urb']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>gdppc</th>\n",
       "      <th>urb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Argentina</th>\n",
       "      <th>2015</th>\n",
       "      <td>10501.660269</td>\n",
       "      <td>91.751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>10334.780146</td>\n",
       "      <td>91.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>10711.229530</td>\n",
       "      <td>91.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>10558.265365</td>\n",
       "      <td>91.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>10780.342508</td>\n",
       "      <td>91.133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       gdppc     urb\n",
       "country   year                      \n",
       "Argentina 2015  10501.660269  91.751\n",
       "          2014  10334.780146  91.604\n",
       "          2013  10711.229530  91.452\n",
       "          2012  10558.265365  91.295\n",
       "          2011  10780.342508  91.133"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice this has a two-level multi-index.  The outer level is named 'country' and the inner level is 'year'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pull out group data for a single country like this using the `.xs` or cross section method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdppc</th>\n",
       "      <th>urb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>14660.505335</td>\n",
       "      <td>89.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>14479.763258</td>\n",
       "      <td>89.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>14364.140970</td>\n",
       "      <td>89.175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gdppc     urb\n",
       "year                      \n",
       "2015  14660.505335  89.530\n",
       "2014  14479.763258  89.356\n",
       "2013  14364.140970  89.175"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.xs('Chile',level='country').head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Note we could have also used `dat.loc['Chile'].head()`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can pull a 'year' level cross section like this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdppc</th>\n",
       "      <th>urb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>9830.759871</td>\n",
       "      <td>90.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>12223.484611</td>\n",
       "      <td>87.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>49979.533843</td>\n",
       "      <td>80.269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      gdppc     urb\n",
       "country                            \n",
       "Argentina       9830.759871  90.445\n",
       "Chile          12223.484611  87.926\n",
       "United States  49979.533843  80.269"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.xs('2007', level='year').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that what was returned was a dataframe with the data just for our selected country.  We can in turn further specify what column(s) from this we want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2015    14660.505335\n",
       "2014    14479.763258\n",
       "2013    14364.140970\n",
       "2012    13963.665402\n",
       "2011    13385.131216\n",
       "Name: gdppc, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.loc['Chile']['gdppc'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unstack data\n",
    "\n",
    "The unstack method turns index values into column names while stack method converts column names to index values. Here we apply unstack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">gdppc</th>\n",
       "      <th colspan=\"3\" halign=\"left\">urb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Chile</th>\n",
       "      <th>United States</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Chile</th>\n",
       "      <th>United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>5605.191722</td>\n",
       "      <td>3630.391789</td>\n",
       "      <td>17036.885170</td>\n",
       "      <td>73.611</td>\n",
       "      <td>67.836</td>\n",
       "      <td>69.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>5815.233002</td>\n",
       "      <td>3692.112435</td>\n",
       "      <td>17142.193767</td>\n",
       "      <td>74.217</td>\n",
       "      <td>68.660</td>\n",
       "      <td>70.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>5675.060043</td>\n",
       "      <td>3796.728684</td>\n",
       "      <td>17910.278790</td>\n",
       "      <td>74.767</td>\n",
       "      <td>69.435</td>\n",
       "      <td>70.757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>5290.764999</td>\n",
       "      <td>3939.263166</td>\n",
       "      <td>18431.158404</td>\n",
       "      <td>75.309</td>\n",
       "      <td>70.200</td>\n",
       "      <td>71.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>5738.598474</td>\n",
       "      <td>3955.026738</td>\n",
       "      <td>19231.171859</td>\n",
       "      <td>75.844</td>\n",
       "      <td>70.955</td>\n",
       "      <td>71.508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               gdppc                                  urb          \\\n",
       "country    Argentina        Chile United States Argentina   Chile   \n",
       "year                                                                \n",
       "1960     5605.191722  3630.391789  17036.885170    73.611  67.836   \n",
       "1961     5815.233002  3692.112435  17142.193767    74.217  68.660   \n",
       "1962     5675.060043  3796.728684  17910.278790    74.767  69.435   \n",
       "1963     5290.764999  3939.263166  18431.158404    75.309  70.200   \n",
       "1964     5738.598474  3955.026738  19231.171859    75.844  70.955   \n",
       "\n",
       "                       \n",
       "country United States  \n",
       "year                   \n",
       "1960           69.996  \n",
       "1961           70.377  \n",
       "1962           70.757  \n",
       "1963           71.134  \n",
       "1964           71.508  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datyr = dat.unstack(level='country')\n",
    "datyr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now easily index a 2015 cross-section of GDP per capita like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Argentina         5675.060043\n",
       "Chile             3796.728684\n",
       "United States    17910.278790\n",
       "Name: 1962, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datyr.xs('1962')['gdppc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'd get same result from `datyr.loc['2015']['gdppc']`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also easily plot all countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XXVpbW0hISDzrc4GBgej1emw2G7/5zX+jKArLl1/J3Xff+5Wv3717Fw0NdTz77PNYrVbu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\nwldERMTFFL4iIiIu9v8BzYrWIQKw3uIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24da11e45c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datyr['urb'].plot(kind='line');"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}