Country-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sayan Bandyopadhyay\n",
    "SBU ID 110946522"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mlp\n",
    "import scipy\n",
    "import numpy as np\n",
    "import sympy\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib.patches import Polygon\n",
    "from matplotlib.collections import PatchCollection\n",
    "from sklearn.preprocessing import scale\n",
    "from scipy.stats.stats import pearsonr \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import cross_validation\n",
    "from sklearn import metrics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Population Data Map\n",
    "The below Data map shows the population of countries. The interesting thing to notice here is the fact that most of the entire population is located in a circle containing China, India, Bangladesh, Indonesia, Pakistan etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country Name\n",
      "Country Code\n",
      "Government Type\n",
      "Capital City\n",
      "Date of Founding/Independence\n",
      "Latitude of Capital\n",
      "Longitude of Capital\n",
      "Population\n",
      "Life Expectancy\n",
      "GDP (PPP) in US $\n",
      "Area (sq km)\n",
      "Land Boundaries (km)\n",
      "Coastline (km)\n",
      "Literacy Rate (%)\n",
      "Health Expenditure/GDP\n",
      "Military Expenditure/GDP\n",
      "Renewable Water Resources (cu km)\n",
      "Net Migration Rate/1000 Population\n",
      "Labor Force\n",
      "Internet Users (circa 2009)\n",
      "Commercial Prime Lending Rate\n",
      "warning: width and height keywords ignored for Cylindrical Equidistant projection"
     ]
    },
    {
     "data": {
      "image/png": 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32B90jHcXFay1Dju8Cx+v+pX6Pqa+/QFfrlmf/9rU1JSM8LOYGmF4PysrG6uG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0mzLyeJWq2W8r8/+tRxoZOCMKYd6kpqn5a5ln0d6pqNqVmevnj4eIuVlZWkRl9+oEFtWK+uvNiw\ngUhcjPy7ZoVotVppUKe2FHN0EK1GI16eHuJgby9DBvaT4sWKiSHhlkjibXEv4SafDB8m7/TuYXxz\nD/B76sf8KOKYni4Nu3UVlSrDOaZKFWH8eGHZMvEd+p6Uq15NqrzYWOrs3pVl+tBaly7ePt5ZRqiv\nvdI+kzFdsWCuWFpYiD49TQ4cOCDfffedaLVaadGihWi1WrGyshKVSiWVK1eWL774Qs6fPy9RUVES\nGBgoarVaVCqVVKtaVWZMmyK//TRLalWvJoqiSOWKFcXCQiuWlpby64ypEnn6sFQo5ysO9nZiZWUl\nlpaWMqB3T5n9w7ciCbdkSP9+AkitJYuf+nl/XNGmpYmHZ0njy42bMZh/tTYtxd3bS4KqVxWJizFG\nAXu5zUPbctu2NdP/7f6Zh9FbN2dbx6KQfdbItZiNab5Irk6Q2Zg+u1L9XIhYW1uJo4O9pMVceaAx\nnfX1FNFo1CJxMXL7ygVRFEWqV6ksX3z8oeguHJZp4z4Rp+LF5fj+3aIoivyzcpno429Kx/YvS6UA\nf3mjcydRqVSiVqvEMS31qR/7o4pXUpJ4XL2a63ruSYlSb/3fUmHXDgm6ei+ryf3GVOJipZxvGXGw\ntxe1Wi1qtVp8fHzkLpcvX5aIiAjJicTERLHIcH5yKlZMqletLFqNRk7u2y5yO0rm//S9ONjbi1aj\nkSYv1BeNRiMvNmooknDLJGM+GiGANBw54qmf6ycVuzu3JbBGVQGkTmCAAOKc4US2afUy8SxZUqq9\n9FKO9aucC5HARo3EwsJCvD095JtJE8TCwkKaNzBOI1YfP+apH+NTkwIwpsEd60lav1a5luCO9czG\n1CxPURLuLQf5e8n/cjSikWePy/L5c6RZ44YCSPrNSJG4GLG2No6aDBcPi+FssIwfMUxKuLmJIeGW\nVKtSxTRFdv/b/ewpEwSQlwb0ElUhXELzJOIZWMk0RWttZSVqtVpsbGykvK+vALJk8SIBxDFj6vh+\nmT17tmSLwSDOTsWlbq0gU0ze5k0aS92aQVn/l3eixcJCazKit6+GycsvtTIa0j69Cp3DWE6iToiX\nYsUcxc7GWkb2eD3bb9ilbt/KVMd/3u9SsnRpKVOhvFhZWsrb3btIRMhxcXN1kY8G95erG5cY61Uo\nK4iIVXKSkJRkqu8celFKrlgmiEi1nm+K1spSPEIvPvVzkadSEMa0Uz1J69861xLcqfAaU3PQhkKO\n/+UwnCtUACCoWlVeekDM3kZtXqFzj77sDj4AQJO2r+AXVI+0tHRmTfnctEB+ZK9uxCfEo3Fw5lpk\nJH7lyzF57CdEH9vLpmULOPT3Unq0M/az7uffsbsRm89H+QwhwtWTp9BqNej1erZv/AeNRkNqaipB\nNapTtXIg9eobM0Fll6i8b9++eHt7Z9m+ad1a7sTFMbhvbwBiYmOJun4dOzvbHBQxRorZvXcfpStW\nZf2mzbzwzdfs/GX2Y0aeefbQ29qh7NpFMWdnZv+5jmXTxmUp471wgelv5cYNQnr0IjIsjNBz59Fq\nNSxauoKyVWsRFxfPxAFv4u5izPkcfu4i5d8fSoq1DdjYoNFqKVG6FDfKlCXy1c74JCVwdO4C0lNS\nUbd7OUu/Zh6C8pihBAvxtWsOJ1jIcZw4iZBr1+jU/mU2b9uBVQkftFotb3TuxC/fZo4veiXiKtZW\nViQc28HWI6fo2PdddDodrRrWJS4mCjHoURQFKzXEH99FQrpgrzaA8Y0TgGZVygPw2/I1ANhaW1Fl\n0gRSevTkYNUaoCra72dOCfHcBNLTdQzu35eg6tW4fS0cB3dvPpswgQoBFZk0aZKp/JUrV/Dy8gIg\nMTEROzs73u7RI0u7Q4YO462ur1PCzRUrN29Tbtbvp07KUvYux0+eoslL7XD39sLh0EF2lS2Xtwf7\nDHCrUiBeE8YT0asvFhYa4navwaFBewDs7Gy50aOnqWzxG7HcBLq90ob9R09Rwbc0f347iajbd/B2\nLgYpSVy4fNVU/vyM70x/63Q6rodfBqD1kP6st7Q27bty8ozxD4OBgH83kGRrR3jtuqDV5t+BF3Ye\nM9B9YTamRfvJV8SpcOsmwb/OxtLSkhWL5nP7WhjRoWdpUKc2v85bQGDdxhgywg8CuLm50qheHRAD\nPk72xMXHk5SczD/bdjNy6veo/evyw8JlACh6HfYqfSZDeper16PpO9r4kE9MTmHXtz9wsEYtYx5I\nRcF92pRs6xUF3AcOBKBTh3bMnP4VKpUKS0tLFEVhz9496PV6Pv3UGFKwY8eOJkMKYGtri4gwaeLE\nzI3q9URcvUZaahpvDRiM7r4cpUv/XJOtHmIw8EKL1ji7uxN75CiXi6Ahvcuprt0pF1iRjsPGcvTs\nRc6vMSZr16WlY7FjK2SES7xd3jhDs3jVPxyY9y1/f/Up2vRkvG0tICWJtPR0Bn9pNKCVVi7Ltq86\nc35m43c/gVqNKiPoerEPh1Nv22a0VpacadWG8BcaYePoQInIDMMsAleuFNlr/rHIp0D3zzJmY1qI\ncdixHYBhgwfe/V6Bo4MDZ8+fp/EL9Ym8fp3Wr3YjNPwKiYlJTJswhuDDRwBQKSoWffM5W+bPInzb\nGvq+3gGAdyd8xdhvfzb1sePAEd6b+DUqvzom8W7U7oF6RY0chV/rlpQ5fbLIPWDcq1YB4KMPhgGw\nc/delv+5Co+S7hw9cgRNRno7b29vVqxY8UhtXjwXQnJKCguXreB6dAx6/b0XoD9WrGLn7n3Gr0gZ\nbPh3M+k6HSkpqSTt2E5KbpJvF0IMVtaEHjiIq6sLvcZNo4ynO4u+HI1BDBxr0944QtyyBYOiYO9R\nEoCF/2QN7j917hL+3XcYv1o1KH7sSJb9NjbWHOz2JoaM0ZFPbDS127bk9sB3cNizk/R0o9F2Le5I\nUnIKlbZvoeKubai1WvDxoZizExWGvoftyRNZ2s6OYmLAqojdH881ufmobHZAerbEc3+wyRFj54a1\n8s0Xn4u9nZ2U9vEW/e3rciPsrGi1WlEUxbSu0bd0KfFwc828zKNVUzGcDZbEY9uzdfJ4FFn941di\nOBssi6dPzLTd1cVZvMqWEZ9yvlL7y8lCIQxPeL9YpaeLi6uruLm5St+MCD93JfzShXuevrmgbetW\n2Z7TLh07yOjhw0Sr1Up53zJy89IZuXzS5Dwi1bZkv7SjqErtpUbHrjnjPxT94U1iZ2Od+ZylpAgG\ngzjY24lrcUcJKOMjA197WT7q2UXOrporXiWMy2xKhF6UEiVcs5xvTYYjkspgkJ4hx2XgiQNib2cr\ngJQs7SP1Jo3PUked4dXeq0NraVijsmgylt6U9PSUKp9/JjZXco7v23fKF9KsSWPpcTO2YM9lQTgg\nvd5Q0oe8nGsJfr1hoXVAytUJMhvTZ0vKXbz38P7w3UFiYaGVb7743BSAITEq/IEG8MM+bz7USNpY\nW8l7b3cx/b66c63sXPSL1KtWWXYu+lkAad+skRjOBovhbLDoQ/YJGMO+zZk8Rto0ri8NalSRyhXK\nCiAe3p7iGnH5qZ+7J5EKIWfExsZGFEURJ6fiIumpIrp0iY2NFUVRcgwTmBOVAgJM5/fTEe9LjapV\npFnjhpIWfUXkVpToYq+Kj5enVKkYIN1f6yQWFlqpXdQ8TB9RytcOErVKJX9O/0xit62UGSMGCSDF\natU0eTG/OPSdbI2lSQwGsc0wkvdL41VGL97WYeeNRve+JAeAKapTppfF4o5yY/ufoj+8SfSHN0nS\nvrViaWkpFf39TAFOylatKl7hYVmOpUvIGXEqbuxj9Pn8T0ZgkoIwpl0aSvq77XItwV0KrzFVMk7A\nA1EUpQZwiEOHoEaNh5Y3UzBUb96MIxmppDQaDYvm/ETnjh0ylfnrn/X0HfI+0bH3PG5njf+Ifq93\nQK1WE341kjJNM+eGHPduP959szOpaemUdHMBYMveg1QLKI9TsXtTiiLCl7/Mo2/nDrg6FTdtv3Un\nDkd7O1T/cUbadegYL/V5D42FBRV6vE1qs6Ycb98xb05GARN4/hznKlfBoNejzshlajAYaNeuHStX\nrsxVWyXd3Ym6fp0X6tZh57rV2Za5efMm/nUaEhN7g5f69GLd7N/y4jAKHUpCAn4N6hJy/BSfD+pF\ng+qVaNrvQ1yPHCKmWuZnk//6tdypWg3HlSsIGTKUKjWqYjnzOw7Ua4Tf19M4++HITOXrVa/M3sPH\ncdDreONoMAmJSey5Fo3W24tif/zBaStbfFs05fhL7Uy+CFaWFrRpUJshXV+hUVAVFEXB4YUOHNi5\nBQ93d+YuWMzYiZNJ1+nwOHmCiDK+WCH479sHwfsIfn84AD26d+PirB84Yu9IYn478R0+DEFBAEEi\ncjgvm75rK4K7NqKG28OTtGdRLfo2df7YkS+65TdmY1qIadG7J5t+n8efC+ez8q+/mf/LD1nK2Lr7\n8EKd2qz66SsuXQrD09UJR3u7TGX0ej2HT52lZuWAx/PAywUXwq/QfsBwLl65SrpOR7XtmznaKEs+\n30JB0Px5HOrREwsLLWVKlUKt1tCwQT3GjBmDZ6kyj9zO6FEfMXnKVD4aOoQvx2fNh3qX8V9OY8LU\nb3C/eYPIYsVzLFfU0aanE9DtdY6vuJd3t26XV0lt0YITHTujc3LCSpdOilpj8g5ViwG9cs9INfjf\nb+x+uw8A9atWYs+xUwzt3olvFz7Cd26DgcGzvibu/EUOR8YQtnYdiUnJtKxfk3Uzv8ChYQf2bd1E\nYMWKAISGheMbWA0LCwtqv9qJXYv/yNJkm9gYrrdoQbMmjfj56+ncUfLRoBaEMe3e5PGN6aJt+aJb\nfmN2QCrExPv5ATD5m+n8/J9lMAB7gw+QlprG+tkzsMJARV+fLIYUQK1WU6tKxXw3pADlSnlzev1S\nko7vwMrSAu0vv+Z7n/lFaJeuAKSlpWNnZ4ulpQXLVq7ixWbGNbii13E0eDenjx+F+7yq/0vJkh4A\nTPl2JvHxCTmWuxxxFUVRcDx/Pg+PovCRrtVyfNlKynTtbNq2b8kKjvQdiE0pH+o2a4xiZ49D8WLU\n6PkG6PWZDClAcMfXqTt8KLU+HsGx/YcYcu4Y2355tNG+q17HD++OpIIulVNLV5AcFwfAxj0HuRIV\njYKCXnfv/126lA9L5/9OWloauxb/keU+q7A/mH+cXeg/eSJTp3/HWx+PQnXq1OOeHjNPCbMxLcQc\nGDmKtgvnc/DwUUaOmZBlf/P2rzKoXy/Q656Cdg9GrVYTULY0Nw4cfNqqPDY3LS2p07cvGo2GdSuW\ncnjXVs4eDiY0/DJrli+hQf361KjXkEpVq+Pg6JijQR34zjumB+zcbEYtd/nq83EYDAaUH7LOQDx3\nKAqhi5fi0b2raZO9rQ1xCYns27IDrUZNgI8nh+ctoky5MjTo/jr+SfEAFDt8GJ/wS5z4YipRAwaS\notGwOg2O3bgXZEMrgrNBn23XsRotL65axj/jjcvDrFNSsMhYc1q6zRuICDdv32LE6DEodsVR2Tvx\n17r1lPIxBuuoMG8uZS9ewDfuDohwrlZtAD5u9RIzli/hnJMThsBAaN0avvnmgS9izy4ZARhyK5iX\nxph5CugVhbXd36J5j7eY+ctszp6/kGm/p0dJjh4/AfLs3YxJySmERkRi65M1GlBhQjduDDqdjsXL\njdODLi7OjBg6hG49enH+4iW2rluDxN8gJTUVgy4tawMidGj3Mmq1GoBho8Zw5PjJbPtKTk4xVsmY\nPjQD1xYuxvvUCaoPHYJHRX8C6tTC2sqKuMQkgk8Ygy2Ehl1h9+JlVGrflhIe7twOCuJSYFWsPUpy\npXRZ9GoNVwKrUv2j4bw0/StcXF1JV6m4odbA5ctZ+hRFYWuH19jrapxR8JzzK2np99YGJyUl0bVH\nH776dqZp28Ilywi/fAVHR0fOvt0DfWgol+wdMrV7U1Ex7NXX2ThylHFJ2eefw/Dh0KULvPVWfpy+\n/EPh8Yxp4bWlZmNaFDj6w49YWloy+avpmbYHb1nPjj3BfPTDPEb/8DsfTLl3c9RD39EAACAASURB\nVOt0OmYvXcVXcxb8t7kCYfBnU4lLSMBl7Jin0n9e4XI9GgD3EiVM2yaPH0Pi9Qhiws7RuEF90/b4\njOnA+xF9Ohs2b+H6pRAG9etNubJlqdW0FUpxd/xqNcg07etR0h0ba2ucDh7IxyMqfFypGMiRGd9z\ndv8hzuzbT3J8PN779xG06H9U+3IiXvVq41e1MjazZuKasQ4VIDb2ZqZ20s+eZd0HI4jNcNYrX6c2\n3Bd0IyfO9exDyx3/0nrdGhquXgGKQnRMDAABVSqDCJUunMfSwgLJGGWGNW9B6fbt4D4jnIVatWDr\nVti5ExYsgMI0vf9YhrRwhxM0OyAVEer17sWB/y0gNvQsjo733niXr/6LXu+8S0pyCr6lS3Fi/TK+\nnzOf0d/Mwt7WhjvxCaSe2lOgus5ftZaeH03gpffeYd2MHwr1DeRo0ENxJ6pXrczWf7KPVgQQEFSP\noOpVeH/oUKrXCEJlYWU8boMea1tbkmMjTWVv375NcS9fAL4c/ykfDR1i2vdylzdYu3EzFX/+mdP9\n++ffgRVRVCKgS6f0vLlc6jcAgCZjP+bMByO4XswYt7dqq+ac/2UOST4+mSuL4HM5nGifUqQ84Jot\nERmJ5+pVpG7YwJllyzFkBPKosWkjR1u/lCkqWdkRI7g4dWrOCkdFwejRxghLVlawatWTh+wsCAek\nt5tTo0TuneQOX79Fnfn/5otu+Y15ZFpEuDRhAnq9nrGTvsy0/bUO7Yi/FsYP30zlYlg4luWDGP/d\nLyz6cQbR508iGL15C4rwq5H0Gz2ZgBfqsW76zEJtSAHuqNRU69aFHbv38qAX03K+pVm8dAU16zdE\nbWVDxQB/Fs37nYXz5mY5/zqd3jTt++nnX9CgVTuqN2yGdclSbNy6AwCL7dserFhIiPFBbCYTBkXB\noLXgUt/+2CbEE7RyKSc/HsN1x+LUXTiX6udOc2zdxqyGFLAaOZLLpcuQolIR2LsXdTaso9bYMdTb\ntjVTueslS3Lmg+GcWrWahv9uNG0/3KIlxa5dJWjLZpqMHIGPvx9W7w97sMLu7jBnDvxkDHFIcnJe\nnIYCwPzN1Ewh5bqXN7X79OL7n2dz+OixLPv793ob3a0oHB0dGDNqBJ26dAONBeVKeeNUqzmvvDOC\n23eyTkPmJQtWr6NM01ewsrLi4qq/ikRQfKu0NMI3b8Xdze2B3tB/Lv4fidERODs58fXkiSiKiv6D\nh9CjX3/S03VoHFyIjjZODcbciDUZZi+Pktjb2eJR0p3ju7aREh2BlZUV8oAXIOfjxyEgANcaNUyZ\ngMxkJdHWjkMdOxNrZQxqv697D46UD8j+BU9RSPnqnsf8yd/nEty6DQc+n8jeF5tSNvSSaV/VUydI\nzjB6V0aOytTMzRLuHHqxKdumTOXymRBOZXhyPxRfX/jzT7DNKYvQ84OiKIMVRQlVFCVZUZR9iqLU\nekh5R0VRflAU5VpGnRBFUVo/SZvZUfifZmZMnJ75A46ODnTvMzDHMo4ODsTeMH4rUjRaTh85wOih\ng7gUcQ23eq2p/WpPlqzdlC/6vT1yPABefy4nzdk5X/ooaAK6dSH84kV++X7GA8s5eZelmFcZbt+5\nQ51aQZw6uIeE6xFEXTyLpaUleoOB2Bs3AOj/7vuUzhgZJaWksn7VctauWEL5CuVRqdWoFIX4i5ey\n7yghAV3btgDEREZSdfWfeXewzwtxcXhNnkTQrJkoKSk0XHLPw3rxb79w6fghTuzbyWeffGTafsXz\n3rdV5z3GzyavdmjHpRMn8HqeUhTeJZ8ckBRF6QJ8DYwDqgPHgA2KorjkUF4L/Av4AJ0AP6AfcPW+\nMrlqMyfMxrQIEW9pRUCrlpw9f4GY2Oxv4Ju3btG2VQvTb0Wl5qNPPuH4oX2c3P0vri5OvDlibKbv\nOnmFlaUlDXq9xZnmrfK87aeBdWIix1f/xbDBA2nbusUDy6brdEScPYXuTgwN6tUxbXdxcSbq4hnW\nr1rGmXPnqd+0Fbv27CM0PByA6JgYzp7L7HhStkxpJC012358hr7HnYgIXF2csbWxQfXPP092kM8Z\nJa5HgaMjEaM/5dDgd1FsbdnZtRsAMaFn6fpaJ8qULkVgxQCsrCwBaLBsKWkWFqY24sLDUavVfPzB\nUAAiXFwJ2Lghz3XV6HS8FHEFi0fweylw8s8B6X3gZxGZLyIhwEAgCeidQ/k+QDHgFRHZJyKXRWSn\niNyfjSC3bWaL2ZgWMaKmfYW1tTUBNRvw9/qNmb7j6fV6kpNTqFcn6wyGoqjwC6zK2tV/otcbuJYx\n5ZiXpKWno3LJ1cveM025v/9Gr9czsHfPJ2qnWLFiHDl+gtfe6IGjgwMHtv3L7JkzUBQF9xJujBr/\nuamsiJCu05F8+77E4yI4njmNe/VqXP7tdwDWr1xKYlIS4av/eiLdnie80lK57n7P27dypYqZXiqd\nnZwylV/zzwbUajX2ipLJoFk4OaPX6/ErX45BfXvj5urK9W7d81zfQSFnWOftQ4+buRz5FoTxVake\nX3IgY5QZBJhSAonxAfcvUC+Hau2AvcAsRVGiFEU5oSjKx4pijOLxmG1mf8i5KWzm2SfU24fiBw9g\n7VScdq+/QeU6Ddm+azdgXA4DcOvWnZwbUFTY2VhTq1NPfJq0J/xqZM5lc0FKaioGgwFtsdyHGHtW\nsVi9CkdHByqUf3guUZWisGNXVq9pg8GAX7VafDZ5KiqVim07d/HZlGl06fQKKpXCrdu3KeVtnEKM\nj4/n/VGfEnLuPB7TppjaqNa8KXcqViIq41v50nlzOHUmBIBiLkVjOj2/8Rs/jghLK9PvG2Hnmf+z\nMThGp/Yvc/7I/izfxJf/7zcqBfix/rXOuPmWIeB/80Gn48qc3/AtUwo7Ozt++GYqvd/szq1bt7CL\ne8B99xicLFeWShUDMIwZ+0jlXcTApONHaPHRiDzVI3vyxQHJBVAD1/+z/TrgnkMdX6AzRlv3EvA5\nMBz45AnazBazMS2CXKtYiYiz56i3eBExt27TpM0rvNi2I1t37kJRFGxsrHOsq6hUHNu2gXEfvkd0\n7A3+2rIzT3S6HWdcL3m7XPk8ae9ZwDYhjjt34rBwcsfLL5DaTVpQs2FTqjdoQtV6jfjtfwtNZb+d\n+gWvv92Lhi3b0KbT68TGxqLX63HxKUdKSgqx4edJvx3N0vm/s2fffjq+0QO93kBqahojh73HeyNG\n4VrGj+9++oWavXpyuHMXAEpevMDRLdv48L3BRF04zY2w83Tu2IEDh44CkLoh76cXixq+GzZw9rN7\nEcQmjvkEJ6fiVKtSGYmLZcWCuZQr65ulnnuJEhzasYXl//sdS0XhzNs98A0K4srp03wweJCpXNfX\nOqIoCgGffJKljSdhi6U1g1Yux6F1y4eW9UxOpnLXLsxo1oKTp87kqR7ZUrBBGxQyZfzNhAqjYewv\nIkdEZCkwCXjnCdrMFk1uCpspRKhU7O3aDfXrXej6w/csHfYBO3YbR0b29vYPrOpbMZB3Kgai1WgZ\n9PE4hrz1+hOrM33uYgCu+hed6D0GT2/UajWRF0MYMXoMKSmpaNQaNFoNeoOe9z4cxZAPRpKckmKq\ns2vPPgBcS1dAo9FQuVJFDu/eZtrfrm1rKvpXQEHh52+/ZsDQ4bzQog3hERFUe7UTN6dN42Cp0qby\nzlFRRAKvd+xACTc30/YTZ85QwtOTqz6l8vs0FHp89+zirjvXF+PHMCrjW+ejoNFoeLVDO17t0I6f\n5vzOmIlfUL6sL/17vW0qU7VyIAEVKnB77768VVxRGOxXEfwefk/5ff8tW5YuB6DhiuVEvvpa3ury\nGPxxMpQlp8IybbuT+oAgFhAL6IES/9nuRtaR5V0igTTJvG7tDOCuKIrmMdvMFrMxLeLoVSr+GPIe\nnRRY+e4wbG1tGDX2M76cMC7bj/0r1/zF0hWrSEhMolvHdg9c7pEbVv+7Hf9qVQipUjVP2nsWuPTR\nKPh1NgPeHcbyRf/Lsn/eLz8BYOXkxoypX/JGl8788MtsBvTqgWeFSowf/REjh72Xpd6Zs+eZPPYT\nypQqRdkyZbgYGkr1BfM5/EbWkHJXatRAURQ2bd1OrSBjQBW9Xk/wwUNUeK1T7p4GzxM6HY4nT+K7\nbStbJk6mWZNGbFq94omu94F9ejGwT69s99WrU4s58xdQs2sXzs2cSZyL62P38zicHziIBpGRWHh6\nscurAEJ4PoIzUdfKvnStnHnEfzjyBnVmZ+80JyLpiqIcApoBa4zdKErG7+9y6GY30O0/2/yASBHR\nZbSR2zazxTzN+zygKJyt1wCAVs2asnTlqmyLder2Ft179uPCxVBu3b7NmwOGYGdr88Tdnzh7gfPh\nV3C6L7ReUeBa6dJUGf4BK9f8zZmQsw8oqfB2967Y29szavj7FHdyYuXC+YweP5F+Q7Iu2tdqNJwK\nOUfLV17jYmgoJX7/nSPZGFKAeLUGW1tb9h++Fyxm8/YdJCcnc2Pgw2ayni+U5GQq/TaHBqM+olgJ\nN+5Ur86R9z+gRtUq/LVkYZ69OGbHd1MnM/zdwRxdsZIUTy9qb/738RqKijIaqW7/tQ8P5oqDA7un\nf8vWD0eQrsn/MZSiUj22PIRvgP6KorytKIo/8BNgA8wFUBRlvqIok+8r/yPgrCjKt4qilFcUpS3w\nMTDzUdt8VMzG9DkhKWNdp6JAalo2AdeB9Zs2s/vf9RzctZXd/65n5LD3sLLK+fvqo2AwGGg/cDhO\nzk4cnPr1E7X1LHJu/GfY2dnRa8CgB0ZA+i+tW7Xg2L6d/P6/hWzZvt20/fKVCGJv3uTTER/g6VES\nFzdXYt5+O8d2mr8/lISEBEa+965p2/SZP+JQvBgR9YrWy8tjk5ZGhX/WUrxUKU716cvuKVPx8y3D\nhj+XEn8tjAPb/8Xa+smu84dhbW3NtInjuXr2BLY2NiSPGPF4XrV3PYr/+AM8PSExMU/1zDvyJwJS\nxjfP4cAE4AhQBWglIneXH3hxn+OQiEQALYFaGNePzgCmA1Ny0eYjYTamzwmaSKNX7rBB7xAdE8vO\nPXuxdi5JMY9SVK/fGPsS3qSkphJUo7qpjqurC7E3blDllR7MWJK7JRYiQuiVa4yZ8TPh16KoOOID\n0myefJT7rJFobU29b2cQfPAQS1eszLLfp0Il0tPTUWXzxl0xIICPPhhKy/av4VDShxeat6ZC1Zq0\nat4UV1cXfp7xNbHRMdT4O4dzL8KuufN4veMr1K9rTOOl0+nYunM3vq91LvShGvMCtcEAlpaca/sy\nxe1sObp7GzfCzrNvy0ZaNmuKnV3W/L75iZurK2NGDufEkaOgUqHNWE/8yFhYwJkMB6Jr18DOrmCW\nuuSWfHRAEpFZIlJaRKxFpJ6IHLxvX1MR6f2f8sEiUl9EbESkvIhM+c831Ae2+aiYjelzgmb9BjQa\nDXVr16RsmdI0avUyQwb04evJEzh6/AS/zfoOK0tLmr/8iqnOB+8OZvh7QwiqUZ2RE74Ey4cbw7iE\nBDoNHonavy5lm3fki5/nUqFWdU70L7pTjht79qJESXfGfD45y77rMTFEh13Aysoqyz5dehqr1vyN\nWq3mxYYvYGNtg06vZ81i4/fXNhnBNWxO55AoWlEwGATb+6biN23ZRmpqKrf69cuDIyv8eF40piVU\nqVScP3qAqpUDcXLKfQD2vOTN+5KalxnwGMkK/P2N2Wb69jUGrDe/ND0TmI3pc0L6oUO4Ojuj0WgY\nP3okarWaSZ9+TJ83uyG3o+jcoS03QkM4fPQoDu7eDP7gQ1QqFZM/G8vcn2dhodVSvEoDNh7M/sF+\nPuwyNTu+TbGgZqzZspM6LZvh2+xF6o8fQ8yWbdwu9nQfYPmKouDm5UVSUtYg5GqVKlMWHxNioHKt\n+uj0OhIiLrJ64Vw2rvwDeztb5i9aAmDMRQuUyMYQ38W3UUPWbrgXTP2HX+dQrHgxwo1ZQZ57PDYa\nz01s6Ll8/SaaG1xdXNDduk5Ffz/ObdhI4JQvH17pv2g08OuvcDDXA6iCIf8iID2zmI3pc4K9AgYR\nToeE8EbvgXw1cTwWFtpMZaytrbgZdpZfZ05n2cpVWLuUZOKX0wA4eWAvGo2a4GMnsrT99W8L8WvV\nmXNhV2g67hPKHD9K8IZ/ufTvFvaMm8Atu2yMSRGjeMWK3LiZOT8mIqSkprJw8WIQ4fjRY2zevBkM\nBvyr1SIqOoYTu7eh1d77PwQG+LNw2XJe6vQ6NRo2xcXNld2vdSYnXDq/RnRMLD0HDqF9lzdZt2kz\n5V97rUgkEcgL9NHGfLOpOYRffFqo1WoO7dhMx3ZtOfXxJ9TfsO5pq5S35EMEpGcdcz7T54TAOrWI\nj7zO+tXLCHqhKYnXcgiUfh/Tvv+JTydMIvWW8YGk2BXHpXgxovfdCwSg0+mwqGT0FPaOusaVEiWz\nbauo0+iXn9kxYCC3roZR7G6UJzGgdnDB0sKCTatW0Lhteyy0WvR6PdZWVty+nDnm7jc//srwT+4l\nS6/82XjOjPoY3X1xX/+LZWoq/l06c/qf9Tg4OODdrBlXZ80ipogkEnhSNG5u6GJiKOHmRtSF009b\nnSykp6dTuW5Dom/e4lYO8bTznALIZ7p/UEdqeOY+dOjhq7HUnvVnvuiW3xTe1wAzj47BwMWTp2nS\n8AWC9x9El/7AhdEAhIVfZtS4CVhbW1O/aUuatW2PlaUlt+PiM5VLS9ehUik0GNj3uTWkACGNGgMw\ne+78TNstLS1ITUujcdv2TBg1gqTIMJKjwrMYUoCp336P9X1TuifGjnugIQVItbTk2Ko12KYkkxQT\nw9ElS8yG9D7sMkY6lpYPPo9PC61Wy8utW5L8zHrlPibP4TSvOWjDc4Bd8D4SkpJo26o5b/QdyPuD\nc07RdpcyVY3eoXfi4ti7/wCKoqAAb3V4KVM5G2srbKysSM3nXKjPOtH+/tRo2IBPxn/O9agopk6e\niKKAQW8g4uRh3NxcTQm/s/PsBVi3bDEd3+xJ+OUrtJz6JRuzLZU9t1XqPDiKokfisWPg7k71ypWf\ntio5YmdrS1paGgHR1znj9t9APIWUu968j1OvkGIemT4H+C1ejFar5evvZ1GjahWmjB/9wPL3f/u7\ncek09YOqYWNlxdkNy/l9yrhMZd+fPJ2EpGQM7drli+6FieSVf4IIX30/C9dSZQmoXhsUhRIl3EyG\n9L+80e8d7L3LMud/i6jkV55qgcbQcOdealOQqhdZ0kuU4MURI1j9zzpOni6AmLSPwTt9e2EwGLBf\nueJpq2LmCTAb0+cAdboxW8zxU6f5efpXDy3/9UxjGLx5P37Hiy934sipM8QGb6BsqXsJkP9Yu5EK\nLV/l23l/UH9Abw53yfsUU4WNMy6uBH1jPL/tW7ekdlANokKO5TgS/XTiF/z59zrKli7FJ59P5q0B\nQ1izbiP1pn9NWOCzO5IqbOz9/HM0Gg1zFy1+2qpkS9R1o09CtO/Dsw8VGp5DB6TCq7mZR+b63j24\nubqgS0/HwvLhM/t/rDCGG/Tx9uLEmbOk6/TU7jaQc5E3AGM6te4fjOFCeAQAe2b9Wqhvgrxk3+B3\n8fYtw9pNm/llxrR7zkjZ8GLDBqSnpxMafplRw95j6ao11Jownr3DPihAjYs+KZaWOJd0Z8u2x8+A\nlJSUxMVLoZnym+YVK1b/jaIohNUvShGr8icC0rOM+Qn4HJCemIhapcbe3p6ACg9OgfbrvAWEhl8G\nYP4fywioUIFj+3bh6upCYLN2DJz8HS++dS8Ag/fBYLMhvR+VCutly4iOiWX6rJ8fWLRZ40akxUQw\n4eORfDhmPGWrV2f/xw+egjfzeHh9+ilHjp9gT/D+h5YNC7/MrF/nMOj9D6lWvzH2JUth6+5DuWq1\ncPIpR7de/di3/976ThEhISHhsfRKSkrip99+p7S/nzGaUVHhOXRAMj8FnwPUtrbY2dmR8Ageg2s3\nGANwH96xic3bd1C7VhAVA/zZvHYN544fZu7iZQQfMwZu8NyziytBtfNV98KIxtWYEWT/4aMPLJeQ\nkEj7bm8z7JOxVGvbhtDduyGHb6tmnoyIjJRjIeeyelHf5VpkJLVfbEGZyjUYPPwj/rdkGQmKQtnu\n3an66y/UXrmCsvXrsnz1X9Rr3poPPzX6D0yf+SP2HqWJvXEjVzqJCD0GDCEm9gZu72XNHlSoUSmP\nOc1beI2p2Zv3OUBu38HZ04OwcDXLVv1F51dydhb67OMRrP5nPZGR17l85SrR0fdiPft4e5mmufx2\nbDVlojGTmTMaYxCGvfsPEh9vHLEsWLqCtZv+xVKrxc3VhX0HD3E65BwGMfDi11+x9f0PCvVb+bOO\nzQsvAFArI/Z0cnIyk6ZNZ+/+A6Slp2NtZcXxU6e5ExdHwy8mcWzwEFLs7Ln43/9Jx06o4uIoUTGA\nr7/7AS+PkgwfPRaAYydO0axJo0fWafrMH1m+eg31Bw5gX7/HCCv4AOyjo0l0csJQABlisuVxR5mF\n+B4wG9MiTpVLFzkeHs4HA/ty8PARNA+5uapWroTcjjK9wf+zcRNupcrh7eXJ1chIdDodzX+axb8N\nmxSA9oUTKVkS3zq1uRS8H3f/yiQnpyAiFHdxISU5meTEREp4elJ9YH9OD36XrX5+T1vlIo9NRvzi\nsRO/xK9COX7+bR534uLwLF0KNBokLR0rp+L4Ll3CzsZNHtiWwcGB22dCwMGB90d9ato+5MOPOHNw\n7yPpYzAYGDvpS8qUL8f+WT9iyEMjYn3xIvHljM5MpQ8fIqy6OdBOQWCe5i3iWI0cgZWVJd5enqhU\nKjq+/NLDKwH+FcozbtRwAGJiY7mp0+FRMYAqf/7JvwOKbtD6vOLStu28fOgA/o0aUmnCZ9iFXuJW\nTAzJCQmQns71iAiCv5tJvNmQFghn/1lHpSGD2bR9B9O+nYlzKR/qHz9KxKVQIs6d52pYGOFnQtj7\nEEN6l1Q7Ozz/k1M05Nx5eg4c/Ej1l6z4k8SkJJzffBNdHo/GlFKlTH+H1QgCvT5P239ELZ47ByRz\nOMEijOfVCKLLlKHP22+xdv1GSri5cmDr+ty1EVCNa5FR2N26SUJRDlZvxsxjYhUTg9fyZVwYZDSk\nulvXc1xXfJf2Xd5g78HDxEZF5YsDn/+5s4T4+Zt+l4uK5EKJjDSfBRFOcHh3ani75br+4SvR1P56\nUb7olt+YR6ZFGKVFS9LTdXw8fBhR0dHs35I7Q5qWlkbxYo5YW1uToH02w7GZMfO0SXF15cI7g3Da\nsgWAoSM/ITrG6GuQmppKj4GD2bpzFzdu3ERxcMHJpyzr/91McR/vfPOED6ngh10pH9Pv9MZNKOXv\nh0+lihAf/4CaeYTZAclMUcLOxRn3Em54eXqgKLD/8BHqBFXPtqxOp6P/0A/RarWkpadx4lQIp0JC\nSElJpfSK5YTZ2haw9mbMFC5uNmlCjSGD+WHmD/zw6xzcXF2IT0gkOTnZlFYP4NbtO9Tt0J4bo/Nx\nGZQItefPZ0vjJgT4+RFx7RrxGUbUCkjJv56NPIcOSOaRaRGmRK2aXI+OYcSnY7GwsKRaYKUcy8bF\nx/P7wj/4Ze7/WLZ6LTdSUwl4ozueJ48T1unVAtTajJlCiqJw+PuZFL96lWpz5uBUowbupUvh17Mn\nVjbWANQ6dRJE2LdqNedr5d+yMrukJLZkfP+dP/snnIsbP9EEvdSKFHv7fOvXhHmdqZkigwhn/1hC\nRX8/Vq7+m0F9emabOWPO/IVM/XYmm7cbo8M0nDGdxPh4wk6f4cjs37hayRzWzoyZ3HDLw4OjvXsT\nsn4DoYcOI4kJ1B01imYH9mO1aCEtV/2Zr/1X/HEWCXZ21GnZAkVRaN62A2GXL9Pvpx+4sObvfO37\necY8zVtEKRYfR9S1SKKuRaIoEOBXIdtywz4ZS0KCMZiDVqvBokXzglTTjJkijUdYKOeWLefcsuUw\n1hjkQa1Wg06XP/2dPs3pDEeo4I2bAGPmpwqBgczv/w6pBTbyU0B5nLGaeWRq5hkjyNoaa2vj1JII\nWN2XJ/N+7hpSAE1CIpsrBhaIfmbMPA9cu8+jduKYTwDQ6/XGmzKPsTEYsHztNRwdHBjUvw8Abh4e\nNOjWhYStWwvQkJLhgPSYUkgxG9Miilv0dZKTk02/a1WvmqVMSso9NwRnLy+SH5KI2owZM7lEpaLc\nlC8B+PTzyQAU8/LK02+D1jdu0HDlclzK+hJ65gx34uKY9cscKlasSEJYGLsX/cE1F5c86++RUFSP\nL4WUwqu5mQeijU+gor8xIIBGo6FBq6whBNf/uxWAOosWcOPKlQLVz4yZ54ULIz+i3e9zcHZ3p96k\nidzety/P2vbbvBm8vdn5amfSU1Mz7bvRowdJWm2e9ZUrzA5IZooEIkRPnszpkLMAWFlZcicuzrQ7\nPj4B/5ov0PHNXlhaWBDS+fWnpakZM88Ff/XszY3ISPZ+Mho8PfOkzbJbt3C2eXM0Gg0n9u/h1IF9\nqNVqmvXtjSYxkesjR+ZJP88aiqIMVhQlVFGUZEVR9imKUusR63VVFMWgKMrK/2z/PWP7/fJPbvUy\nG9MiiO9bb7L+fwtMv0u4utL+pVam31O/ncnZCxeoN3ECZcJDuaN5Sm+vZsyYeWzKrzcGYYmPj2fM\nhEn4V6+JiHBy1CfobGyernLKYwZteMjIVFGULsDXwDigOnAM2KAoygPnsRVFKQVMA3bkUGQdUAJw\nz5BuOZTLEbMxLYJcWrjI9HdFfz8uR1xlSP/epm193uqOna0tez8dizJg4NNQ0YwZM0/Imfffp+H4\nsdRu24atu3bjXK4cXgf2c71s2aetWn5O874P/Cwi80UkBBgIJAG9c6qgKIoKWACMBUJzKJYqIjEi\nEp0hd3J7yGZjWgQZGrzH9PfpkLM42tvTsF4d07ZSPt58NGwIAPqo6wWun5lCSkb6PTPPBuHuJdk5\n7jP2/72WO7ducWbPXi7XCHraahnJBwckRVG0QBCw+e42MQaX/xeo9wBtJMhy6QAAIABJREFUxgHR\nIvL7A8o0URTluqIoIYqizFIUxSl3B2w2pkUSTUaM0Lu0aNrE9HdMbCwjx05gzKQp+NerS9LypQWr\nnJnCyYED4OoKLVtCRMTT1sbMs47CY45MH9iqC6AG/jsCuI5xajarGorSAOgF9H1Au+uAt4GmwEig\nMfCPouTOG8octKEo4uCQ6efCX38w/e1WzriOtMoL9Tm+Y1eh9p4zU4AYDMZMIw4OWa4vM2ayoKge\nL4j/4y2NUYAsC3cVRbED/gf0E5FbOVUWkftHFKcURTkBXASaAFsfVQmzMS2CODRpjFarJT09HQBH\n7/LERVxg4dIVpjLhq9bkbEiPHYPr1+HMGejaFUqUKAi1zTzL1KkDGzc+bS3MFCH+2HecP/adyLTt\nTtIDQ/DHAnqMjkL340bW0SpAWaAU8Nd9o0wVgKIoaYCfiGT5hioioYqixALlMBvT55urHl7UqVmD\nXXuDAYhPSKCYT3nuxN1LvXSneA65SefOhV69jH+rVBAZCV9+mc8amzFjpkjxCM5EXf/f3p1HR1nk\nCx//Vjp7p7N2FiAbS9i3yUAQyHUUcBkWBQQRdPTV2VDP1XHE6zKiMzrgDB4VnevCHb0v4jLRq+e+\ngkRZZBuRbZLIFhKSGAwhJIGsHbJ2d71/PEkIS4AknZCQ3+ecPkk//TzVlU7Sv66qX1VNHMNdE89d\nTCb1WAEJz7990fO11g1KqRRgKrDWeBqlGu+/cZFLjgDnLy6+DPADHgEuOrleKRUJhAAnL/kDnEeC\n6TXo44Ag5iUkNAdT4JxACtDnZAEn+0VeePGdd0J0NFitxq1v386urhDiWqPauTbv5YedXgXebwyq\nezGye32B1cblag2Qr7V+RmtdD6SfW7wqx8hbOtJ434yRoPQ5UIjRGv0rcBTY0JaqSzC9BlUpRfFL\nLzFx92527dl30XNKBw5i0M3TcNx8C7kPPgQmk/GAry9MmdKFtRVCXHNUO9fZvUww1Vp/2jin9AWM\n7t7vgVu01qcaT4kE2rKLgAMYjZGAFAgUYATR57TWDW2pugTTa1Syj5nI73bxwvp1PHfbnAser6ur\nI3vdeli3nrCICIrnzb8KtRRCXJPau87uFVyjtX4LeKuVxy7ZEtBa33/e/Vrg1jbUsFUSTK9h+W4m\nnps1m4H5xxm7di21e/fy4969VFdXMyRuEO5WK4OuS2DV3LlXu6pCiGtJe9fZ7cGzCySY9gI5/SLJ\nefAhePAhLFoTqp18pS6/dJcQQogrI8G0l7EphU2ZrnY1hBDXss5LQOq2JJgKIYRwrU5KQOrOJJgK\nIYRwLRkzFUIIITpIunmFEEKIDnJrZzdve67pJmTXGNG6ggJYvx4KC692TYQQoluTlqk4y+mEf/wD\nPvgAtmyBhhYLgNjtZ1dJEkKIS2rnog09uH3Xc2suXGfXLpgzB/z84J57YMOGs4F02DBISpJAKrrW\ngQPw7rvGzkW6cXet6mqoqICyMvjkEzh9+urVz+GA9PTLn9dbtWsv03YmLXUT0jLtzU6fhocfhk+N\n7fw8PT354JNP+PDjj1m/aRPOHTuMPSxFz+R0wsGDkJFhbO6dkQHBwTBiBEybBvHxnfvmVVQEjz5q\nPNc99xgf0HJzjduxY8YHtZtuurAOdjtu//7vxt8f4G61Yk9IwLRzJ46KirPnKYXb9dfjvOMO48Ng\nZOPGDWVl8PHHsHs3Ki8PHRUFzz8PXl7GdoJeXmfL2L4dtWQJ+o03IC4OkpOND5VhYca2cx4eRlmp\nqRAQABaLcdu92wj206bBRx8ZG6f34EDgcpKAJHqF0lJYuLB5f8qEhAS8vb3ZsWMH+/fv58t169Br\n10og7WkaGuDECeP7X/wCvv22+SFPLy8Cg4MpKy2loa7OOBgWBps2wfDh4N4JbwUffGC0ID/5BPXw\nw+j6+uaHlFJorVHjx2MymSA7G0dlJSaLBe104igr48033yQuLo4dO3bw7a5dxMyeTWZmJvX19dx5\n553s2rWLf377LeXffovzkUcwjRyJIyQEtXMnOBz4BwTgb7FQsGsXjo8+Mp7X2xt9663Qv7/xf7Bv\nHzo9HSZNMgJni6EN5eeHnjjReI0AD09PHHY7TqfTuO/hgX3LFnR4OLi7YwoMRAcH4/T3h8BAI/gG\nBxvfnzxpfH399fZtmt3T9MIEJAmmvdGSJecE0hkzZvC73/0Of39/Xn31VeMchwNsNuNTuOieTp+G\nw4fh0CHYtw/19dfoogv3SM7KymLQoEEAOJ1O0tLSGDduHBQXw5jG/STd3HDz8kJ5eaG1RjudKF9f\nMJtRfn44fXzQAQGwaBHcd1/rddLaaLWlpRnj7oDZbCYmJgYvLy/MZjM+Pj4sWrSII0eOsGbNGtzd\n3fENDsYjPByHw4HT6SRk6FCSk5Nxc3PD6XRi9vZGa01SUhIxMTHNT1dVVcU777zDjh07yMzMpDIr\ni6kLFrBkyRLGjh0LQFFREdu3bycvL4+UlBS+2riR+k2b8PL0xNPTk+hx47j55pupqKhg+vTp1NfX\n43A4SEpKYv1XX1Hv7s5dixbxwerVKKVwOBycOXMGi8XCwYMHWblyJSdPnqSuro7q6mqqa2ooz86m\nsrKSqspKHPYWm5js2GG0xl980fgfs9uNr06nseVhD26Z9XZKN41HXOokpeKBFFJSjK4h0XPY7fDf\n/22M74SEwJNPwowZsHkzAImJiSxdupQbb7zR+KRttzNs5EiyMzNhwQJjvLQ32L7deKO77bazAaat\namvB27t912ptjF2bzUaL5p57UCdOoBctgt//3vjdgfHGu3IlppdewlFSAhitPEtAAMOHDmXChAmU\nlJTQt29fli5diru7O96t1Km0tJTU1FR+/PFHDh06xPfff091dTVRUVFERERQUFBAaWkp5eXl1NbW\nUlRURHl5Odx1F4waBXPnwtChUF5udJ2WlcHjjzf/zQQGB3P7rFmsWLGCsLCw9r0uPZzWmpSUFO6/\n/36qq6v54YcfWj3XzWrFOX8+TJ5s9BqEh0O/fmd/966SmtrU6/RTrXWqK4tuihV7X3uG+EHRba9a\ndh4Jjy3vlLp1Ngmm15rjx41gYLOhbrwR3dhFdb5bb72VN998Ex8fHz788EOSk5NZsWIF8fHx3HTL\nLWz95hvjn+4nP+niH6ALOZ1GAMjNhfHjAXAPC8P+X/9lBDR//7M3k8no7quuNvZ8NZmMrlGTCbKy\ncPvf/8W5a5exF2xAAFRVGW+IAwcaASc2FmJijGN2O+TlwQ8/NN9Myck4Dh8+p3pjxo7lwP79aEBZ\nLEYZNhs6N5exY8cyZ84cpk2bxvjx4/Hw8OjUl6quro6nn36agoICco8fJ/3wYc7YbDBiBPrwYeO1\nxAjszz//PE8//TSenp6dWqeeqKysjHXr1lFeXk5paSklJSWcOnWKuro6ysrK2Pndd9gbzt1GU914\nI3rxYpg9G1zxmnZFMF35h/YH098t65S6dTbp5r3WKGUECEBv2kTfvn2xWq388Y9/pLy8nAceeAD/\ngADWr1+PW+PYzRNPPMFvfvMbZs+eze23324E0vXre34gtdmMFnhyMh5bt+IMDcXp7Y2puBhOn8ZR\nUoJ2OAAwubtz7y9+wbrkZE7Pnt2mp1FKERoWhj0oiNo9ezCbzXh5emLbtw/b+d18JpPRujx7Md4+\nPgT4+3PDggUEBgZSV1fHqFGjeOyxxzh16hRJSUns2bOHdcnJ+Pj48NGmTUybNs0Vr9AVOXbsGMOG\nDWPOnDm89957mM1mampquOOOOzh8+DBjZszA3d2dsWPHsnDhQuLi4rqsbj1NUFAQ99577yXPqaqq\nIiMjg6NHj/L111+z7ssvKV+wAOXlhZufnzG26+kJ7u44w8PRM2caORD9+3fRT3EF3FT7xoZlzFRc\nUmEhfPGFkYTg42O8oWoN9fUQFQV33+26qSeRkZCQAHv3AlBQUMCgQYOYNm0aFouFadOmMX/+fJYu\nXcqyZcuaLwsICGD16tUsXLjQOODqrqWuUFNjjCHW1EBKCuqpp9B1dZj9/IiKjCTcy4vDhw7h6+uL\nn9VK5NixVFdX43Q6CQwMxGQycdf8+WRkZGC32/Hw8CA8PJzo6GiCg4OZN28eYWFhOBwO/vWvf7Fx\n40YSEhK4+eab8fX1paGh4YIWotaa8vJyUlJSSE5Oxul04u/vT1RUFNHR0YwYMYJ+/fqhWhkrCwsL\n45FHHgGguroaDw+PTm+Fni8iIoL+/ftjsVgwm80cP36cX/7yl0yfPp3k5OQurUtv4Ofnx7hx4xg3\nbhyLFi0CYPPmzbz77ruUl5djt9upq6vD4XBwsrCQH599Fv2HP5wtIDMTBg++SrVvJGvz9mK1tcan\nPRdn2qnp09FffXXpk+67D+691+hCrK7GVF2NqqmB0lLsWVkwcSL8x3+c/UNr+bXp+507cfv2W0xl\nZTSc1124Y8cOVq1aRVZWFjk5OSxYsIDPPvvsgmrExMQQGRmJJTAQ29q1xtSA7iw7G557zpgiUVCA\n/u67czJGY2JjmTlzJitWrKCwsJANGzYQFxfH1KlTOXbsGFVVVURGRhIYGAhAamoqXl5eDB8+vNXg\n1uSGG27ghhtuOOfYxYKcUoqgoCCmTZvW4dakr69vh65vL601dXV1nDhxglmzZgHwzjvvMGDAgKtS\nn97oUn8/lZWVLFmyhL///e/GgY0br34w7YWLNvTOYOpwGC2YPXvgzTeN+Xd1deDujkdUFHaHAx0a\naoxRmc3Qpw+MHm2MCzmdRovS4TCC3LBhRrdLK/R5b8q33HILNTU1OJ1Ohg4dyrvvvgtA3y1bcNjt\nKCAwMBClFLV2O7lwdlGFy3ACM2+7jcrwcA6mp1PSYhnATz/9lICAALy9vUlLS+O11167aBn33HMP\nW7ZuxW3LFpyXfcarpLQUvvwS06OP0icggNjYWPwtFmoTE6mtrSUoKIiEhAQeeOABIhvnHvbv35/F\nixc3FxEbG3tBsfGSD3BRPj4+5OTkGFm+WjcPD4ir58SJExQWFvL6G2+weds2KkpKjLH6L7+ExMSr\nXb1e6doKpoWFxkRqb29jvtihQ5CfbwTAykqoqMCtvh5dUtLcggkICsInKIiSkhIaGhq4YdAgUlNT\nKcnLg5SUyz+nuztukZE4J06EmBjcP/8cx7Fj6KYkgtWrjS7XxtVannnmGa6//noKCgrYuHEj48eP\nJzw8nAEDBhAbG4uPjw9Hjx7F09OT/v3743A4sNlsaK3Jz88nPT2dqqoqCgsLycrKYvz48YwdO5a3\n336bUaNG4XA42LVrF9dPnEhcXBz9+vUjKCiI+fPnt5rVCbBv3z42bNjA8pdeok4pnH/6U0d/G65T\nWws7d8Lmzbhv3ow9JQW05tZZs/hwzRoCAwPZu3cvEyZMYOvWrRe0GIVrKKUu22IXnau8vJyZM2ey\nc+dOAJSPDzoiwliI47nnuk8gVbSzm9flNekyPS+b9+uvYdkyo0vWx8cYhywuNlqLJSW4NX56bvq5\nfMxm/IOCCLNa6RMWhpeXFyEhIYwZM4ZJkybhcDgoKyvDZrPh4eGBUgq73Y7D4cBkMhEcHMzYsWPx\n9fVtHkfz8PDAZrPx2muvkZGRYQS5jAyqbDbCw8IICQnhwIEDF1Q9ICCAyZMns379+q5+1S5QW1vL\n8ePHSUlJYf369VgsFo4cOcK2bdtQnp6YRo7E/pOfGElI8fFGy9xs7tpKFhbi9sADsHUrztpagsPC\nuHXaNKZOnUpiYiI2m43du3eza9cuSktLufXWW/ntb3+LV8sVboS4RuzcuZPElsEyPh7WrYO+fdtW\nUFdk8771J+LjYtt8fWrWMRIeer5T6tbZrn7LtKDA6DK1WIxlvJpWYlmzpnlyuMfIkeiGBnRVFY6m\nFV6AIKuVoOBgwmNjsQYFMXr0aB5//HHMZjNFRUVYrVZ8fHw6pdqBgYH86SKtN4fDgdaazMxMKioq\niIuLo6ysjJKSEiwWS/Pk+c5SWlqKw+Ggrq6OM2fOYDKZcHd3p76+ng0bNrB161bq6+vx8vIiOjqa\nMWPG8Oc//5mYmBhqa2tJSUkhLS2N1NRU9qakkLlmjZGqrxQeQ4bQ0BRgm26uTFTS2uhy37LFeJPY\nuhUcDqZOmUKfPn3w8fGhuLiYzz//nHXr1jFy5EgmTJjAggULsFqtrquHEN3Qy6+8cvbOzJnG/0h3\nJQlIXaC21mhJFhfDtm3wxBPnPu7mZqzCUlPTfCi0tBRvb28iYmKwjByJp6cnf/nLXxg+fHirTxMV\nFdVJP8ClmRqzckeMGNF8LDQ0tEue22azEdIY3Nzc3Jg3bx79+vXDbrfj6enJ+PHj+fjjj1vt7vX2\n9mby5MlMnjy5+Vh9fT2HDx9uDrD70tI4sHYttWfOAOARHX1hgA0NhawsY5HyM2eMpC4vL2M5taAg\n4xYYaMy1fP993L/5BsrLcZaV4aytBaWI6NOHsVOmMH36dCZNmoTFYsHX15ewsDCZvyh6pd179qD8\n/dGTJsGrr8J//ifqqacwmc0os9mY/+zri7Pxpn19ISLCyPCPijK+RkYa+SGdrp1r8/bgft42dfO6\nDxmCfeBA442x5aT1ll8vdkxrTMnJOI8eRZ/3i4yMjOS6665jwIABWK1WlFJkZ2dz/Phxhg0b1vxm\neqnxPmH48ssv2bhxIxkZGeTk5PDDDz9QVFTk8tVnHA4H2dnZpKWlsXPnTj79n/+htLT0gsnmV8LT\ny4s+EREEBgYyYMAAfv7zn3PXXXdhkWUMhTjHwYMHWf7SSyQlJZ3dSQd44YUXqKmpobq6uvlrdXU1\nlVVV5BcWcuL4cWyNc8/P03ndvO/8mfjBbZ/3mno0l4TFz3ZK3Tpbm1qmt40YgcPhoL6ujga7HbvD\ngd1uP+d7R+PXlvcddjsRYWF839BA4r/9G/369WPPnj3MmTOHZcuWsXbtWjZu3MiBAweIiorC39+f\nxMREgoKCyMvLo6ysjFGjRjFo0CDJJLyEBx98kOXLl/Pkk09is9kYMGBAu1pxTePFrTGZTAwZMoTs\n7GyOHDnCurVrGT9+PPn5+aSlpZGdnc22bdtYtGgRCQkJBAYG4unpSVlZWfPtk08+oby8nF//+tfY\n7XZKSkooLy+nurqa3bt3c9NNN3XkpRDimpKamsqHH35ISHAwt82aRUpKCjabjcGDB7N06dLLXn/m\nzBlOnDhBfn4+u3fv5g8t56X2MEqph4ElQASwH/h3rfW+Vs6dAzwDDAI8gCzgFa31h+ed9wLwKyAQ\n2Ak8qLXOblO92tIyTUlJ6dD0AZvNxmeffcauXbvw8fEhPj6e+1osml1TU0N5eTmVlZWUlJRQVlZG\nbW0tlZWV7N+/n6NHj+Lu7s4XX3whWYUXkZqayooVKzCbzVx33XVYrVbCw8MZPHjwRccU33rrLerq\n6ggICMDHxwc/Pz82bNhAamoqEydO5JWWYzQY/5CvvvoqOTk5FBQUMG7cOJYuXdo8Ll1fX09OTg5H\njhxh8+bNhISE8OKLL160riUlJaxevRqtNZ6enlitVgIDA6mvr2fhwoUsW7aM3//+965/kYToYQ4e\nPMjjjz/OypUrMZvNBAYGEhAQ0O7yUlNT+WlnJyCtWt7+lulvn2m1bkqpBcD7wG+AvcBjwHxgsNb6\ngg1ulVLXA0FABlAPzAJeAaZrrTc1nvMk8CRwH5AL/BkYBQzTWtefX2ZrunTM1GKxcP/993P//fef\nczw9PZ34+Hh+9rOfMWnSJCoqKigrKyMmJoa4uDh+/PFH8vPz0VrLXMBLiI+PJykpiby8PNLS0igp\nKSE9PZ1Vq1ZRXFyMr68vI0eOZNiwYYSGhnLs2DFefvnlc8oIDQ1l2LBhjB49+oLy8/LyePvtt4mN\njSUqKor8/HwWLVrUvNOHp6cnAwcOZOjQofzqV79i1KhR1NXVceDAAU6fPo3T6aS+vp6TJ0+Sn59P\nXl4eFRUVzdtxgbEwweLFiwkKCuqS10yI7qy4uJjRo0ezcuVKhg0b1nMaEZ23BdtjwCqt9RoApdRi\nYAbwALDi/JO11jvOO/SGUuo+IBFoWrj8UeBFrfW6xjLvBYqA2cCnV1r1q5/NizGB/q9//SsbNmzg\nb3/7GzExMbz44ouEhISQk5PDjBkzeOKJJzB39dSMHio6Opro6AsXma6uriY9PZ0jR45w6NAhzGYz\nDz30EAUFBTQ0NKCUYuDAgc1Zsi3ZG9eXff3118nOziY3N5e8vDxMJhMmkwk3NzcaGhrIzMwkIyOD\n5ORkgoKCyM3NJTExkT59+uDm5oa7uzv9+/cnMTGR6OhoAgICes4bhBBdLCwsjIKCAt5//31mzJjB\n7bffzpQpUxg0aFA3/79xfQKSUsoD+CmwvOmY1lorpTYDE6+odKWmAoOB7Y33+2N0F3/TosxKpdSe\nxjJ7VjD19fXl0Ucf5dFHHwXg8ccfp7S0lOnTp1/wpi7az9fXt3nNz4tpSiw6ePAgr732GlVVVVit\nVrKysnB3d2fw4MGMHDmSxMRE7r77biIjI1sdw66rq+PkyZNER0fLOLcQHdCnTx+eeuopHn74YTZt\n2sQbb7xBTk4OXl5ejBgxgoEDBzJq1KhW/6+vis6ZGmMFTBitxpaKgCGtF6n8gROAF2AHHtJab2l8\nOALQrZQZccX1po3BdMuWLfj6+pKVlUVpaSlTpkwhNzeX7du3U1paSkVFBU6nk4kTJzJ58mSGDx/e\n5jdSp9NJUlISR48e5bPPPmteSDw4OJiQkBD8/f27+SeynqspsWjIkCHMmzeP9PR0lFIMHTq0za+5\nl5fXRZfsE0K0j8ViYe7cucydOxcwFl5pytx/++23KSwsJDQ0FKvVyrhx4xg5ciRFRUUUFRVhsVgY\nOHBg162nrC6/Nm/Spn+StPnbc45VVFW369kwAmJrbMAYwA+YCrymlPrhIl3AbSnzwgvakoD07LPP\ncubMGWJjYwkNDWXz5s3ExsZy0003YbVaCQgIQGvNd999x86dO8nMzGwuw8vLi/79+zNw4MDmZJOA\ngAAsFgsVFRWcPHmS4uJiPDw88PPzw8/PD29vb/bv309RURFnzpyhuLiYzMxM8vPzOXXqFACLFi3i\no48+asvPLIQQ1xyHw0F5eTmnT59m27Zt5ObmEhERQXh4OJWVlc3T5XJzc0lNTYXOTEB672Xih7Q9\ncKdm/kDCL5+4aN0au3mrgTu01mtbHF8NBGitL7+AuXH+34FIrfXPG7t5c4CxWusDLc7ZBqRprR+7\n0rq3qWU6Z86ccxKAmrfrOs/s2bOZfd6ekDU1NeTm5pKTk0NJSQnHjx+noqKCyspKAgMDiYiIICws\nDLvd3hw4bTYbxcXFHDt2jLq6Otzd3ZkwYQJz586lb9++9OnTR7qBhRACo2cpJCSEkJAQhgxptdeT\n5cuXNwXTTmP08ra9B/FSl2itG5RSKRity7XG+Uo13n+jDU/jhtHli9Y6VylV2FjGgcYy/YEJwJtt\nqXuXjZn6+PgwfPjwS65aJIQQ4hpwBd28rV53aa8C7zcG1aapMb7AagCl1BogX2v9TOP9p4B/YbQ+\nvTAyf+8BFrcocyXwrFIqGzgGvAjkA1+0perdIgFJCCHENaSTgqnW+lOllBV4AQgHvgdu0Vqfajwl\nEiPJqIkZo4UZCdRgzDe9W2v9WYsyVyilfIFVGIs2/BP4eVvmmIIEUyGEEK6maN880yu4RGv9FvBW\nK49NOe/+UuCyS0Rprf8I/PEKatiqKw2m3gDvvfceX3/9dUeeTwghxFW0bdu2pm9lwXMXutJgGgvG\n8nNCCCGuCbHAd51ScueNmXZbVxpMNwB3YwzO1nZabYQQQnQ2b4xAuqHTnkH2M704rXUJ8HEn10UI\nIUTX6JwWaRPVzuUEr/VgKoQQQly5drZMe/Dm4BJMhRBCuFYvHDPtuTUXQgghuglpmQohhHCtztvP\ntNuSYCqEEMK1JAFJCCGE6ChJQBJCCCE6phcmIEkwFUII4VqKdi7a4PKadJme+zFACCGE6CakZSqE\nEMK1pJtXCCGE6CDlBm4STIUQQoh2U0qh2jFm2p5rugsJpkIIIVxL5pkKIYQQHdQLt2DruR3UQggh\nRDchLVMhhBCuJdm8QgghhAv04C7b9pBgKoQQwrXc2jk1pj3XdBMSTIUQQriWJCAJIYQQ3ZdS6mGl\nVK5SqkYptVspNf4S5w5XSn3WeL5TKfXIRc55vvGxlrf0ttZLgqkQQgjXappn2ubbpVumSqkFwCvA\n88BPgP3ABqWUtZVLfIEc4Eng5CWKPgSEAxGNt8Q2/bxIMBVCCOFy6mxXb1tul9825jFgldZ6jdY6\nA1gMVAMPXOxkrfW/tNZPaq0/BeovUa5da31Ka13ceCtt608swVQIIYSLqQ7cWilRKQ/gp8A3Tce0\n1hrYDEzsYIXjlFInlFI5SqkPlVJRbS1AgqkQQgjXak+r9PJJS1bABBSdd7wIo2u2vXYD/we4BaOl\n2x/YoZQyt6UQyeYVQgjhWl2bzasA3Z4LAbTWG1rcPaSU2gv8CNwJ/N8rLUeCqRBCiC73j/+3nqQv\nks85Vm6zXeqS04ADI1GopTAubK22m9a6Qil1FBjUluskmAohhOgEl25lLpw9k4WzZ55zLPXgYcZN\nn3fR87XWDUqpFGAqsBZAGXu2TQXecEGFaSzTDxgIrGnLdRJMhRBCuFbndfO+CrzfGFT3YmT3+gKr\njcvVGiBfa/1M430PYDhGZPcE+imlxgBVWuucxnNeBtZhdO32A/4E2IF/tKXqEkyFEEK41hXNcmnl\nukvQWn/aOKf0BYzu3u+BW7TWpxpPicQIhE36AmmcHVNd0njbDkxpcc3HQAhwCvgWuE5rXdKWqksw\nFUII4WKdFE0BrfVbwFutPDblvPs/cplZK1rrhW2oYKskmAohhHCxdnbztisAdw8yz1QIIYToIGmZ\nCiGEcK1euGuMBFMhhBCdoOcGxvaQYCqEEMK1pGUqhBBCdFTnZfNR2aKZAAABWUlEQVR2VxJMhRBC\nuFYvbJlKNq8QQgjRQdIyFUII4Vq9sGUqwVQIIUQn6LmBsT0kmAohhHAphUK1o5WpenAAlmAqhBDC\ntXphN68kIAkhhBAdJC1TIYQQLibzTIUQQoiO6YXdvBJMhRBCuJaincHU5TXpMhJMhRBCuJh08woh\nhBAd0wu7eSWbVwghhOggaZkKIYRwvZ7byGwXCaZCCCFcTMZMhRBCiI7phWOmEkyFEEK4lkyNEUII\nITqq93XzSjavEEII0UHSMhVCCOFaMmYqhBBCdJAEUyGEEKKjet+YqQRTIYQQLnUk82iXXtcdKK31\n1a6DEEKIa4BSKho4Avh2oJhqYJjWOs81teoaEkyFEEK4TGNAtXagiNM9LZCCBFMhhBCiw2SeqRBC\nCNFBEkyFEEKIDpJgKoQQQnSQBFMhhBCigySYCiGEEB0kwVQIIYToIAmmQgghRAf9fyXQU9nDyC1l\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c423cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_name = []\n",
    "country_data = pd.read_csv(\"./country-data.csv\", na_values='unknown')\n",
    "\n",
    "for name in country_data:\n",
    "    print name\n",
    "\n",
    "for name in country_data:\n",
    "    col_name.append(name)\n",
    "\n",
    "col_name = col_name[7:]\n",
    "\n",
    "country_data = country_data.replace(\"1.78%\",1.78)\n",
    "\n",
    "for i in col_name:\n",
    "    country_data[i] = (country_data[i].fillna(country_data[i].astype(float).mean())).astype(float)\n",
    "####################\n",
    "#create Datamap\n",
    "####################\n",
    "map = Basemap(width=24000000,height=18000000,projection='cyl',\n",
    "        resolution='c',lat_1=45,lat_2=54,lat_0=50,lon_0=0)\n",
    "# draw coastlines.\n",
    "map.drawcoastlines()\n",
    "map.drawmapboundary(fill_color='aqua')\n",
    "map.fillcontinents(color='white',lake_color='cyan')\n",
    "map.drawcountries()\n",
    "\n",
    "# load the shapefile, use the name 'name'\n",
    "\n",
    "\n",
    "map.readshapefile('ne_110m_admin_0_countries', name='country', drawbounds=True)\n",
    "\n",
    "# collect the  names from the shapefile attributes so we can\n",
    "# look up the shape obect for a country by it's name\n",
    "\n",
    "country_names = []\n",
    "colors = []\n",
    "patches = []\n",
    "colormap = plt.get_cmap('Reds')\n",
    "for shape_dict in map.country_info:\n",
    "    country_names.append(shape_dict['name'])\n",
    "\n",
    "\n",
    "for i in range (0, len(country_data)):\n",
    "    color = colormap(country_data.at[i,'Population']/float(country_data['Population'].max()))\n",
    "    colors.append(color)\n",
    "    ax = plt.gca() # get current axes instance\n",
    "\n",
    "    try:\n",
    "        find_name = country_data.at[i,'Country Name']\n",
    "        indices = [j for j, x in enumerate(country_names) if x == find_name]\n",
    "        for k in indices:\n",
    "            seg = map.country[k]\n",
    "            poly = Polygon(seg, facecolor=color,edgecolor=color)\n",
    "            patches.append(poly)\n",
    "            ax.add_patch(poly)\n",
    "    except ValueError:\n",
    "        pass\n",
    "\n",
    "\n",
    "p = PatchCollection(patches, cmap=colormap)\n",
    "p.set_array(np.array(country_data['Population']))\n",
    "cb = plt.colorbar(p)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter plot - Health exp/GDP vs GDP/Capita\n",
    "The below plot reveals that there might be a correlation between the 2 entities. Countries with higher GDP definitely spend more on Health"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RERG/GdRtHWutGwgfplpEREREfBpz8u/A/zDG+H1FYxERERFfAsZsYAmw1BizB2i9cKO1\n9iF/FCYiIiIjky/hpBFY6e9CRERuVN3d3ZSXl9Pc3ExISAg5OTkkJiYGuiyRoOXL0zrfHI5CRERu\nNF6vlw0bNrB1w0e0ninH4enAi4GIJMZNncftS+8kLS0t0GWKBB2NGxERGQZer5fXfrOCsk9XMSsr\nhNm3Z5AcF0m3x8u+o7UU73qb5w/v52vf/gFZWVmBLlckqPiyKvFRY0xFf1/DUaSIyPWmuLiYsk2/\nY/m8VO6+KZ+U+CiMMYSGOJmWn8ZTd00mvbuc37z0HG63O9DligQVX3pOfnrJ61B61tW5E/g/Q65I\nROQ65/F42LpxLTMyHOSP7ns9VFeokwcWjONf1xxi7969TJ8+/RpXKRK8fBlz8i99tRtj/gCYNeSK\nRESuc0ePHqXl9GFmLcq44n4JMRHkxXezq2S7wonIBXyZ56Q/q4Ev+vF8IiLXpZaWFujuICU+8qr7\npsZF0NJYfw2qErl++HNA7JeABj+eT0TkuhQaGgrGSafbQ6Tzyr8DdnR1Exobdo0q61FXV8eOHTuo\nrq7G43YTExdHYWEh48ePx+l0XtNaRPoy6HBijCkF7IVNQBqQAnzfT3WJiFy3cnJycESnsPdoDXMm\nZva7n7vbw4HqTqbNnHRN6mpububNlSvZvWkT7ro64h0OnA4Hrd3drI+IIKOggHsefJCioqJrUo9I\nf3zpOXmbi8OJF6gFPrHWHvRLVSIi17GYmBgmzLiZTZtfZcrYVCLCQvvcb+uBU7S7Upk1a/iH6zU2\nNvLsz37G6ZISitLSyJ08Gafj816dhnPn2LNvHy+cOMEjTz7JTTfdNOw1ifTHlwGxfzcMdYiI3FCW\n3H4Hvyjby0sf7ueBeWMZlRj92bYut4ctB06y9nAXC+5bTlJS30/0+IvX6+XlF16guqSEZRMmEBl2\n+W2kxOhobpk4kR0VFbz+/POkpqYyduzYYa1LpD++3NbxAOnW2ppL2pOAGmutbliKyIiXlJTEN779\nA1a89Dw/X7uX7OgORsWF0en2UFbbTWfYKBbc/xhLliwZ9lrKy8s5vGMHN+fm9hlMzjPGMHPsWFbv\n2UPxxo0KJxIwvtzWMf20hwFdQ6hFROSGMmrUKH7wx3/GwYMH2VVawonGOkJCw5g1r4BZs2YRHx9/\nTerYsnkzYa2tpAwgbBhjyE9JYfemTZy97z4SEhKuQYUiFxtwODHG/LD3Wws8aYw5d8FmJ3ALoDEn\nIiIXcDqdFBYWUlhYGLAayvbsITs+HmP6+93yYrmpqZQePEhlZaXCiQTEYHpOnu79rwG+C3gu2NYF\nHOttFxGRINLZ0YErZOD/3Ic6neD10tWlznAJjAH/abXWjgEwxnwMPGStPTtsVYmIiN9EREbS3tw8\n4P073W6M00nYFcaniAynQc8Qa61drGAiInL9mDR9Oieam7HWXn1noKKmhujUVHJzc4e3MJF+DKjn\nxBjzz8D/tNa29n7fL2vtH/ulMhER8Ys5c+fy6erVVJ09S2Zi4hX39Xi9HKmvZ84jjxAbG3uNKhS5\n2EBv60ynZ/Xh89/3Z2CxXERErpmcnBwK581jy/vvsyQ8nLjIvtf88VrLprIyQkeP5uaFC69xlSKf\nG1A4sdYu7ut7EREJfsYYvvLVr/JcaysfbtxIQXw8+enphIf2/M5preVUQwP7Tp3Ck57OV598kszM\n/qfdFxlu/lz4T0REglRUVBTf/t73eH/MGLZ+8gmHysqIthYH0O71Qnw8ebfeyl333kteXl6gy5UR\nbqBjTn470BNaax/yvRwRERkuERERPPjggyxdupRdu3ZRU1ODx+MhKiqKwsJCRo8ePeC5UESG00B7\nTpou+N4AD/a2be9tmwnEAwMOMSIiEhhRUVHMnz8/0GWI9GugY06+ef57Y8xPgNeA71prPb1tTuA/\ngIE/SC8iIiLSh0HPcwJ8C/i/54MJQO/3/9y7TURERMRnvoSTEGBCH+0TfDyfiIiIyGd8CRO/BH5h\njPljY8zNxpgFxpg/AZ7r3TZoxpiFxphVxphTxhivMeb+Pvb5e2NMlTGmzRjzgTFmnC/XEhERkeDm\ny6PEfwpUA38CpPe2nQb+D/D/fKwjCtgJPA+svHSjMeZ/AH8IfAM4CvwDsMYYM9Faq5WpREREbiCD\nDifWWi/wT8A/GWNie9uGNBDWWvs+8D6A6fs5th8BP7bW/q53n68DZ4AH6BmcKyIiIjeIIY0RsdY2\nDzWYXI0xZgyQBnx04XWBLcC84by2iIiIXHuDDifGmFHGmJd7x390G2M8F34NQ41p9KzZc+aS9jO9\n20REROQG4suYkxeAbODH9Iw1CdRifyaA1xYREZFh4ks4uRlYaK3d6e9i+lFNTxAZxcW9J6lA6ZUO\nfPrpp4mLi7uobfny5SxfvtzfNYqIiFx3VqxYwYoVKy5qa2pq6mfva8dYO7jOB2PMfuAxa+0Vg4HP\nBRnjBR6w1q66oK0K+D/W2md6X8fSE1S+bq19vY9zzAB27NixgxkzZgxHmSJyHfB6vZSVlVFfX4/L\n5SIzM5OMjIxAl+UTay0VFRVs3bqVk0eO0NHWRqjLRVxyMtNmzWLatGlEREQEuky5AZSUlDBz5kyA\nmdbakkDU4EvPyR8B/2iM+Y619pg/ijDGRAHj6OkhARhrjJkKNFhrTwA/Bf7aGHMEOEbPLaWTwNv+\nuL6I3HjWrFnDsy/+krKmerxYkqMiyB+bx8yxE7jvzrvJzs4OdIkDtnPnTj7+/e85tW8fkS0tZEVH\nExYSgsfr5ezBg7yxYQPvZ2Ux45ZbWLp0qUKKXPd8CSevApFAuTGmDXBfuNFam+jDOWcBH9MzhsTy\n+XwpLwLfstb+kzEmEvhPehYY3ADcpTlORKQva9as4c/+9f/RPmcq6UseIdThpOnTbVQ1NmC9NRx/\n6Tm+99g3GTNmTKBLvSJrLR988AEfvPIKaW1tLM3IID0397KVg9u6uth/+jTFL77IscOH+caTTxIf\nHx+gqkWGzteeE7+y1q7jKk8OWWv/Dvg7f19bRG4sHR0d/Otz/0nHzEKm/cX3MY6ef1qix+Vy5ucv\nMHvaRGrLjvPKm6/z5z/6E5xOZ4Ar7t+6detY8+KLzIyIoGjSpM/arbWcaWnhREMDbo+H6LAwJqWn\nMy4lhdXFxbwIPPX97xMZGRm44kWGwJdJ2F4cjkJEAs1ay5kzZzhx4gTV1dV0dHTgcDhITEwkPT2d\nMWPGEBYWFugy5Sp27dpFZXsL6bfO/SyYAIQlxmPTRtFUXcvEpfPZ8+ybHDp0iEkX/NAPJidOnGD1\nK69Q5HJRNHr0Z+21LS18fGQ/9dQQmeAmPNLQ0mzZtCuKgtgclubl8e6nn/L+2LE89NBDAXwHIr7z\npecEY0we8E0gD/iRtbbGGHMXUGmt3efPAkWGm7WWPXv2sG7DJnaWnaapwwERKRAaBV4PtqOCMNtK\nRqKLRTcVsXDhwsueApPgcaT8CBFJMXQ3n7uo3evxYBubCBubRlxaCjY1hvLy8qANJ1u3biWktpYZ\nRUWftdW2tPDWwS0kj2vmwdnx5GRGYoyho9PD/sPNbNq0n+aKNgrjkihZt45ly5YRFRUVwHch4ptB\nhxNjzK3AaqAYuAX4K6AGmAo8AXzJnwWKDKempiZWvvk2a7dV0BmdT9rkr5CZNhaH8/O/GtZaOs41\ncuboLp5/bzsbtuzh0QeXMX369Mvu/UvgdXW7yRw7muo9+2kak01sYQHezk7OfLiRmPY2sqZNBCAk\nPIzu7u4AV9u31tZWStevZ2Ji4md/xqy1fHR4L8kFzXzx7nRcoZ/3CoWHOZkxOYFRyeGs/O1x0rwJ\ntJ86RWlpKTfffHOg3oaIz3zpOflH4K+ttf9sjGm5oH0t8AP/lCUy/Orq6viP/3qR0lOGrOlfJTGj\n74WujTFExCSQW7SI7gk3UV6yhn/55Soeq6vjjjvuUEAJMskJScTWx5GalsTBN9/l7HsfYbq7ibXd\nzL9/MbEpiXS73XTWNJIwNiHQ5fZp7969tFdVMb6g4LO2001NNJpaHpqdcFEwuVBmWgQFhc0c2V5F\nVmQiOzZvVjiR65Iv4WQK8JU+2muApKGVI3JttLa28vPnXqK0OoKJt30dV0T0gI4LcYUz/qYvcPJg\nGi+9vZrIyEj94x9kpk+fzns7NpJ153jGL5xJ3dETOF0uMiblERHT8/+5cucB4joN06ZNC3C1fWtu\nbiYSiAgN/azteEMD0cndZKVf+THhieOiObDjLNkhqdTU1AxzpSLDw5eF/xqB9D7apwOnhlaOyLXx\nzjvvUXq8mwm3fG3AweRCoyfMxZFxM6+t+pgzZy5d9kkCKTMzkzl5kyhbtR7r9VKwcDZ5c6cSERON\ntZbThyqoXLOFW6fODtrHbd1uN5c+Q+T2eIiIMFftqQsPc2KMF2MMXZ2dw1ekyDDypefkN8BPjDEP\n0zMnicMYswD4v8BL/ixOZDhUVFSwpngfaVO+RFhkjM/nyS1axJ4Pynj7d+/x7Se/6ccKZSiMMSx/\n+FHcr/yKrc+/gyMnmeRxWXi9lrpDx3BUNXHH+Gncf+99gS61X+Hh4bgvmb070uWiuclLl9vb720d\ngIbGLmx3CMYYwvUosVynfOk5+UvgIHACiAb2A+uBT4F/8F9pIsNj8+atNJpRpOQUDuk8DmcIGZMW\nsW3vcfWeBJnw8HCe+MY3efrhx5nrSCVk01HCth5nUXQO/99jT/GVLy8nJMSnhxWvidTUVDpcLhpa\nWz9ry09NpfNsBGUVLf0eZ61l9/5zZIalU9/eTubYsdeiXBG/82Weky7gKWPMj4HJ9ASUUmvtYX8X\nJ+JvHR0dbCo9RFLuXX4ZyJqUOZ69u6PYvXs3d9xxhx8qFH9xOp0UFhZSWDi0EBoIEyZMIGncOPaV\nl7MwPx+AuIgIciOz2FhcxqiUcFISL55zx1rL1l0NVB0JZUFqCjva2rh/3rxAlC8yZD7/6mCtrTTG\nnOj9fnCrB4oEyOnTp2lst6SNyvXL+RxOJ87YbCpPVPnlfCIAISEhzL31Vt7ft4853d2E9fbyLC6Y\nyKo97bz2xkkmTgllwtgYwsKc1J/tZPf+Fk4cCmFOUhGNbW0kjRvHxIkTA/xORHzj6yRsTwBPA/m9\nrw8DP7XWPufH2sRP2traaGxsxFpLbGwsMTG+j7O43tXW1tLe7SQyNtlv54xKSKPixBa/nU8EYPbs\n2WzMz+eDgwe5a9IknA4HYSEhPFA0k50nUtlffJzdm5vAYbHuENJCs7kzOwevtezq6OChpUuDemp+\nkSvxZRK2vwf+GPgZsKm3eR7wjDEm21r7N36sT4bg+PHjbNm6mf0Hiun2tgAWQwQF4+YyZ/Y8CgoK\nRtwcHV1dXeBwXTSt+VCFhIbR2ey++o4igxAbG8vXvv1tnv+Xf+H9Awe4Y/x4XCEhhDqdzM7NZUZ2\nNg2trXR7vUS6XMRFRHCwuppNDQ3Mf/hhFixYEOi3IOIzX3pOvgc8Za1dcUHbKmPMbnoCi8JJgFlr\n+fjjj/l4/W+ITz7LLctSyMpOwxjDmeoWSre/z8srPmF60b088IUHR9RvVyEhIVivG2ut34KZp7uL\n0NDgHVwp16+xY8fyrR/9iJf/8z959cAB8iIjKczIIC4iAqfDQUpMDN1eL+U1Naw9coTmmBhueewx\n7r333hH3i4fcWHz5FzUU2N5H+w4fzyd+VlxczNr1L3LrHRHMnX/xFOuj0mIompbB/r3VvPvmSpzO\nEB74wgMBrPbaSklJIcLZTXtLA5GxA5sz0N3RhruzjbCoOJwhoZdtb22qITc3xd+ligA9AeWHf/7n\nbN26la3r1nGoooLo7m5CAQ/Qai0mOZnC++5j7ryR2SMqNx5fwsTL9PSe/PEl7d8Gfj3kimRIWltb\n+ejjlcxe4OSmBTn97jdpchpdXR5+v+pd5syeQ0ZGxjWsMnDS09OJDYemmuNXDSdd7eco2/4u1Wd2\n4TXdhDmiyR67kNwpt352W8h6vXQ3VpKTFZyLx8mNISEhgWXLlnHbbbexb98+Tp8+TUdHB6GhoURH\nRzNlyhSSkjRBt9w4fO3peMIYsxTY3Pv6JiALeMkY88/nd7LWXhpgZJiVlpbiNVXctODqPyyLpqXz\n6bqdbN1jA+WzAAAgAElEQVS2dcT0nkRGRjK7KI93dpWQPm5Gv/t5PR52fvICjWEnGXXPNMITE2k6\ndoxDm9/B4+li3IxlADScLife2czkyZOv1VuQESw0NJRp06YF7bT7Iv7iy6jAyUAJUAvk9X7V9rZN\npmca++mA/vYEwO69OyiYFEpkpOuq+zocDopmxLNn76eMpKfB5980h6iukzRUHel3n/pTh2joOMqY\nB5aRUjSFmNGZjL55AckLJ1N5tBh3ZzvW66XqwDqmj08nMzPzGr4DEZEbmy+TsC0ejkLEP1pbz5Kb\neOWFwS4UnxBBl7sVt9uNy3X1QHMjKCgoYPHsPFZtXUVs8vcJcYVftk9T7QlCkiKIGpV6UXtifj71\n6/Zx7mw1LQ1VpHCKL9z3Td3jFxHxo0H3nBhj+h35Z4wpGlo5MlShIaG43Z4B79/d7cXgCOqpvP3N\nGMNDD9zPpFQ3Bza8Qrf78sXRQsMi8LR34nVf/IhwZ0sLxuug6cxx2so/5KE755GdnX2tShcRGRF8\nua2zxxhzz6WNxpg/BTQTVYClp+dRcfjcgG/TVBw+y6hReTj8OO/H9SAuLo7vPfEYBdE17PvoF7Q0\nnL5oe2rOFJzNoZz6dDNeT0/Yc7e1cbp4C90NHXRWfsKXlkzh9ttvD0T5IiI3NF9+Ij0DrDTG/NwY\nE2GMyTTGrAX+DPiKf8uTwZo9aw71NVFUHm+86r5NTR0cOehlzqyROVnT6NGjefoPvsXiAgeniv+b\nQ1tW0VJfhbWWiJgEJhY9QHPxMQ78YgWHXn+T0n/+OfXrdjIlPZzvfnkxX/zigyMu1ImIXAu+jDn5\niTFmDfArYDeQSE+PSZG1ttrP9ckgjRkzhoy0aaxetZGvfiuS6OiwPvfr6upm1cqDREdOoKho5N6N\nS01N5Q++9xQzN29m7YYtlG0u4RhRmKh0nK4okmLH0XBkF12tFYxJjOK+rz7APffcxahRowJWs9vt\nZt26dRw/fpKUlERuu+02oqOjA1aPiIi/+TrQoBzYC3yx9/WrCibBwRjD8ke/zi+eb+Ll53Zz6+2Z\njJ+YitPZOy+HtZQfqWfD2hM01+fyja89SVhY3wFmpHA6nSxYsIB58+ZRXl7OyZMnqaqq5lzbWUKc\noaQsuYOMjAzy8/OJi4sLdLm88cZKfvvbQ0Ae3d37OHKkkh/84DuEhl4+QZyIyPXIl7V1FtDTa1IP\nFAELgJ/1jkP5jrX2rH9LlMFKSEjgqSd/xFtvr+SdN7bwYdQuMkY7MMZQc8ZDy9kYMtMX8sQ3v0R6\nenqgyw0aDoeD/Px88nuXqA9G7e3trF+/n4SEO8nImEFz8ylKS1/k5MmTjBkzJtDliYj4hS89J2vp\nGXfyP621buCAMeZjemaO3QOM9mN94qO4uDi+8fVvUVt7HyUlJdTV12G9XiblJTBt2jQyMzP1+Ot1\nyOFw4HD0LmAIeDxuHA409kVEbii+hJOl1tp1FzZYa8uNMTcDf+WfssRfUlJSWLZsWaDLED8JCwtj\n6dKZvPrqWvbuPYgxtSxZks3o0fqdQERuHL4MiF3XT7sX+PGQKxKRK7rvvvtIS0ujqqqK+PjxLFiw\nYEStLC0iN74BhxNjzHvAcmttU+/rPweetdY29r5OAjZYa7UCmsgwcjgczJ07N9BliIgMm8HcqF4G\nXPhYx1/S8xjxeSHAeH8UJSIiIiPXYMLJpaMnNZpSRERE/E5D/EVERCSoDCac2N6vS9tERERE/GYw\nT+sY4AVjzPklXMOBZ40xrb2vR/Y0o9ep7u5udu3axa7d++jscjNubDZz5swJiplQRURkZBpMOHnx\nkte/6mOfl4ZQi1xjXV1dPP/Ll/mk5CTdEXk4QuP5YPsO1m7YwR9+52tkZmYGukQATp48yY4dO+jq\n6iIvL4/p06drAjm5brS1tdHV1UVsbKwmyxMZoAGHE2vtN4ezELn2iouLWVtSzehZTxCb1DOJV3dX\nB/s3vsxvXnuLP/6j7wc8BBw9epRnnnuZEzYBR0Q0EZtW8fW6OpYuXRrQukSupq6ujtXvv8uhI9sA\nN/GxWdxy81Jmz54d8L9XIsHO14X/5AZQvHknIUlTPwsmACGucLImLWHv/peoqqoKeO/J+x+s5aRr\nNFPu/QYOp5PK0o28tfZD5s+fr5V4JWi1tLTw/Av/jte1n8X3ZRATG8OhfRW8/d6zuN1uFixYEOgS\nRYKawskI1tDUSmR88mXtkbHJ1LqhtbW1j6N6eL1eDh8+TGnpLurPNuP1WhLiopgypZDCwkJCQvzz\nR6uh+RxRqRNx9M6AGpeeQ+O+ntoUTiRYbd26lVb3AZ743jSionqG4+XlpxDqOsC6De8yZ84crSIt\ncgUKJyNYXvYoPjlSzuiCmy5qb6g+QlykISUlpc/jdu7cyburP+bA0SbaTAauyFEYY+hqb+DdT94k\nb/Qa7rx9PgsWLBhy9/XY0elsL91L6/hphEXFcnJXMeNiwkhISBjSeUWG05GKg4wdH/5ZMDmvaEYm\ne7Yep6qqipycnABVJxL8FE5GsFsW3sS2fSs5uudjsibMxxniouH0YWrLPuBLiyf0GQDWrVvHC69+\nTGvoZLKmPEpMQsZF29ta6jlavoX/eOFDzpyp5aGHHhhSQLn3nrs4efoFSn73H3gsZMeG8vhjX8Ll\ncvl8zuFkraWiooKKigo6OztxuVzk5uYybtw4DYYcQZzOELrdl8+04O7yAA6thSRyFQonI9jkyZP5\nxpfqeOOd9Rz6qBgvIcS6Orhrbi4PPnD/ZfuXlpbywqsfQ+JtTBy/sM/QERmTRP60uzlzIos3Vv+W\n+PhYlixZ4nONMTEx/OgPvkN5eTlut5usrKyg7DWx1rJz5042fPABdfv2EtPWRoQxdFgvGyMiSZgw\nkflLljBnzhwNhhwBJk+ayjsfrOdMdTOj0mKBnluhW4uPkxg3noyMjKucQWRk8ymcGGMKgEVAKpdM\n5Gat/fuhlyXXgjGGxYsXM2PGDA4ePEhXVxc5OTlkZWVd9gPU6/Xyznsf0xY6hQn9BJMLjcqaQvu5\nelat3sC8efOIjIz0uU6Xy8XEiRN9Pn64WWv54IMP2PjKr5nQ2cGdmRlkxY357DM61dzMjn27eWf/\nPqoffoT7779fAeUGN2PGDEp33cSKX3zKhKJIYmLDOby/gabaZL7yyBfViyZyFYMOJ8aYp4CfA3VA\nNRfPEmsBhZPrTFxc3FVXuT106BAHjzWTNXXg40gyx86hrHgjpaWlN/TTCdu2bWPjr3/F7eEu5owb\nc9n2zNhYMmNjSas4yks/fYZt27cxJm8sEWHhpKakMnPmzKDsDRLfuVwuvvX4tykuLmTXni2c6Gwl\na/RNPHzPrWRnZwe6PJGg50vPyV8Df2Wt/Ym/i5HgVVq6iw7HaKLj0wZ8TGhYJM7YQjZt2XnDhhOP\nx8O6d9+lqNvNnKyxfe5TX1fH8ROV1DU1kHquiQ07N1EzKQVsM5079rGqeC2zxk3klgULyc3NvbZv\nQIZNWFgYt912G7fddlugSxG57vgSThKA1/1diAS3mvomwqIHP+dJVFwaZ+oODENFweHAgQM0HznM\nnKzRl2+0lmPHjrHveDneuAhiJ+WyKDqKExUnSc3PJXdWEd1uN1V7y/hk6262v/jffO3uB5g9e/YV\nr1lTU0NDQwOdnZ2EhoYSGxtLZmambhWJyA3Dl3DyOrAUeNbPtUgQ83q8GDP4++TGGLzeG3d9yLKy\nMtI6O0iNjrps2/Hjx9lz/AgRYzNIzMrgfHbI9XioPlhB7qwiQkJDyZ5eyOipE9n/+w388t2VhISE\nMH369IvO5Xa72bdvH1uKizlWWorn3DnweMDpxBEeTtqkScxduJBp06YRERHh8/ux1lJZWUlmZqbf\n5qoRERmsAf3rY4z54QUvjwA/NsbcBOwB3Bfua639V/+VJ8EiPi6KzsNnB31ce+tZchJ8Hwwb7Npa\nW4npY3BjU2Mj+44dIWJMOknZFz+ZERUaQnfLuYvaHA4HhctuYU93Ny//7rdkZ2eTlJQEQEVFBb95\n4QUay8rI6O7mjrQ0MlJTcYWE4PZ4qGttZU9JCW9v28bvc3K4d/lyZs6c6dP7OXjwIJX1lVRVVTFv\n3jyfziEiMlQD/dXo6UtenwNu7f26kAUUTm5ARVMmsWbju3S0NREeObAViz3dbtrrdzP3julX3/k6\n5QoLo4XLe4ZOnjqJOzKUtOzLb4V1ezw4wi5fxNsYQ+Gdt/Lp/hfYtm0bd955J/v27eM3zz5L8unT\n3DN2LAmXPPUU4nCQFR9PVnw8rV1dbDp2jNf/7d849/jj3HrrpX89ry4nJ4eqqiomzJgw6GNFRPxl\nQOHEWnv5IwgyohQVFZGb9gFVFdsYO/n2AR1Tc3IPKdHtzJo1a5irC5zMzEw+xNDW5SbS1TMdeVdn\nJyfraogZ9/mtnPPc3R6OeSxx2el9ns8ZEkLSjAls3L6NvLw8Xv2v/yKztpZlkybhvMrjp1EuF0vy\n84mprGT1Cy8QHR096B6UyMjIIc1LIyLiD4MeRGCM+RtjzGX99MaYCGPM3/inLAk2LpeLZUvm0lVb\nTN3pQ1fdv6XxNHXl73PbgkISExOvQYWBMW3aNEjPZOfp05+11dTU0G68xI66fN2iipp6mlOTyZ1V\n1O85c2ZO4WR7M7989lniTpxgWUHBVYPJecYY5mRnU9DZyTsrVtDR0TH4NyUiEmC+zAT0t0BfK65F\n9m6TG9SiRYv4wu0TqD34KpVlxXS7Oy/bx+vp5vSxUo6VvMBts1N48MEvBKDSofN6vRw8eJC33nqL\nFStW8MYbb7Bp0yba2tou2i8qKoqpixfzaWMT1b3jSDo7O3FEuHCGXDxFeVNbO5vONJBw8ywi42P7\nvXZUQhzNba1U79/PTdnZAw4m5xljmJebS9vx4+zcuXNQx4qIBANfhuMb6OMmO0wFGoZWjgQzh8PB\no48+TFzcGt778CMOblhHaPwUouNGYYyDtnP1tNXtIiW6jS8tncQXH3rgmq+B4/V6qa2tpaqqitOn\nT1NdXUNnZxcejweXK5SkpAQyMjLIyMggLS2NsD7GfpSUlPDuh59wqLaZ9rg0iIwF9zkcWz5k9OqP\nWDxrKnfeueyz93b33Xdz5uRJfv3Jx9wxKoUQtxtzQaDwei3H6xpYd6qWtvmzmPfgsqu+j4aqanJa\nWxkdN7DxPZeKcrnIdTjYvGEDc+fO1WPGInJdGXA4McacpSeUWKDMGHNhQHHS05uix4tvcA6Hg7vv\nvoubb15ASUkJm7bs4kzdTqy1pMdGMu+2ImbNmkVy8uW3NIZTU1MTO3bsYP36zVRW1nLuXBdebzjG\nRON0hvY+0uyhu/sw0EpUlJPExEgWLJjJ3LlziI2NZe/evWzZsoVN5VU4J81n9BfmE5P6+diQrrZW\nqveX8lLxek5Vn+GJx7+Oy+XC5XLx+FNP8duEBN5Zt46WylO4Opo4kxBLZ7eH8tYO6hPjibl3CTc9\nei+uiPArvhd3Zyft5ccpHDV6SKGiKD2d3+3fT1VVFZmZg5+jRkQkUAbTc/JH9PSaPE/P7ZumC7Z1\nAcestZv8WJsEsdjYWBYtWsSiRYuwtienBuK386amJlavXs369Ts4c6aD8PBMUlLmkpGRSEhI3702\n1nppbW3k7NkqVqz4lFdeWUUr9YQmR1Be1YYpXMKU7IlEp148G64rMorsWTfTkjWWD995kYQ33+bL\njz4M9MwGuvyxx6hdupSVK1fyym9/Q3lSEhHJicRPymfO7CLi0lIG9J6q9pYR1uUms/dRYl8lRUbi\nra6mpaVlSOcREbnWBhxOrLUvAhhjjgKfWmvdVzlERohAhBJrLSUlJbz22irKyhpJSZnEpEljcTpD\nr3qsMQ6ioxOJjk4kPX08H3363zgmx+DuaCN01nzSFt/G0cOVJCclk5qSetnxMaMySL5pGR9vWcXS\nO5ZcNOA3JSWFp556ippzjdRNTKHonsFPXX5s224SwqJI8PGWznmhTid4vXR1dQ3pPCIi19qARtoZ\nY2LPfwGlQMSFbZdsFxlWnZ2dvPTSr3jmmReprAylsPAeMjLGDyiYXKq5uQZHvJPcKTNobImgMzWX\nc+4OPOGGurq6fo9LLZhCrYlg27Ztl21zOp0snDmXpl1H6DjXOqh6Wmrr8RyrYVRaGm6PZ9Dv50Kd\n3d0Yp5Pw8CvfRhIRCTYDfQygETh7la/z+4gMm/b2dv77v5/n7be3kJAwh/Hjb+739s1AGGMw1tB8\nphYblULc2CmcO+emob6B9vb2fo9zhobiyprAwfJjfW6fPXs2ea44Sla8g7vz8qea+tJxrpWdr7zD\ntPRc0seO5VRT09UPuoKq5mYc0dFa8VhErjsDva2zeFirkKBkraWlpYWamhrcbjehoaGkpqYSExMT\nkFs5XV1dvPDCS6xde4AxYxYTEzO0MRkAcXGjCO+K4fTuQ3jj83A4HHjOeWit6eSUp5r8/GZiY/ru\nEAyNiKT1bN/BIzo6mqce+wb/9sJzbP3lSoq+uIyYlP7rPXuqmr2vv0++J5JvP/EtiouL2fqLXzDX\n6x30o8Tn7T5zhrw77iAlZWBjXUREgsVAZ4hdN9yFSPBoampi+/btlJZupLnlFNhWwAM4wUQRF5vF\n9OkLmDlzJnFDHBcxGKtW/Y6PPtrLmDG3+iWYADgcTibnL2b95pdpqj1O145DGLchLTaP9vYOSkt3\nMX/+TYSGXH7LyN3eRlT45Y8in5eens6PnvgOz7/yMnv+/TVCxo5i9KwpJGal43SF0t3ZRd3Rk5zc\ntgdzsp4Zo7J5/CtfIy4ujlmzZrHxrbcor6+nwIdwUd/aypnQUO6YP3/Qx4qIBJrPy472zhKbDVzU\np26t3T3UoiQwvF4vmzdv5qOP3sA4jjOlKJwxYxJIS0skLCyEjo5uzpw5R0XFQT7dtJPi4hxuv/1h\n5s6di8PH3+4H6tChQ7z33kaSk6cSE+Pfx5Tj49O4ec5XWFP6Ds5mQ3xWIS5XON3dXVRXH+HIkSNM\nnDDxomM8bjddJw4yYdG0K547NTWVP/3DH7F//34+3baFPa99zDE8eLA4MUTiZGHeBG768hcoKCj4\n7HMcNWoU+XPm8Ol775EVH09E6MDH03i8Xj45epTEyZOZOHHi1Q8QEQkygw4nxpgU4JfAXf3s4uyn\nXYJYd3c3r766grKy1cye62TRokm4XBf/8YiICCUhIYIJE1JYsqSbTz45yvvv/4yjR8t55JEvExLi\nc9a9ovb2dlasWElLSzSTJuUNyzUSEjIYG5/J8ZpKQsfOACAkxEVUVBplZcdJTUn9bJVggJqyPaTY\ndmbPnn3Vc4eEhFBUVERRURG1tbWcPXuWrq4uwsLCSE5O7ndMyINf+hLPnjzJ23v2cP+ECUQOYEK7\nbq+XNYcOcTYjg289/viw/T8RERlOvvy6+1MgHpgLtAN3At8ADgP3+680uVastbz++quUV6zi0eXJ\nLF067rJgcqmwsBCWLcvnkS8nc+TIW7zxxuufzXfib8XFxezde5px4+YN21gXYwxjsgoJO3mQpiOf\nP4ETFZVAZ6eLg4fKsL0TI7ecqaJu8xoWTx/8ukEpKSkUFBQwefJk8vPzrzhYNSEhgce/+108U6bw\n2oEDHKqpweP19rmvtZbKs2dZuXcv1RkZLP/e98jNzR1UbSIiwcKXX6tuA75grd1ujPECx621Hxhj\nmoG/AN71a4Uy7EpKSjhwcDUPP5LGuHGDG8uRn5/EAw95eeP1dyktHc+MGTP8Wlt3dzeffLKJ8PAs\nwsIuW2/Sr1JTxzC1rYmdOz+itrmO2LxZhMWnEhubSk3NCWqqTtJRdYyWXRu4PS+Vh67BukHp6el8\n94/+iDffeIO1W7eyYe9eJsbEkBEbiyskBLfHQ31rK3sbGmiNi2P0woU8+sUvkp2dPey1iYgMF1/C\nSRRQ0/v9WSAFKAP2AP79ySTDrqOjgzVrXmPqNC/jx/s2lmPChBSKpjbw/vuvMWnSJL/Oq7F//37K\ny8+QlXWH3855Jbm50wgLi6Ts+G7qj+6hMTEdomJprqukZMdvmT8+h4dunsayZUuv2bpBCQkJfOup\np6j5whfYvn07O9atY3dDA3g84HQSEh1N0bJlzJk7l+zsbK2jIyLXPV/CySFgPHAM2AV8xxhzDPgu\ncLr/wyQY7dy5k66uChYtyh/SeRYtymHv7nJ2797NnDlz/FQdbNu2A7c7lsjIa/dUUHp6AWlp46iv\nP0ldXSXdjQ00njtHnLONv/rhd6/5ukHnpaamcvfdd7N06VLa2tro6OjA5XIRFRVF6CAGzIqIBDtf\nwslPgfOrof0v4H3gMXrW13ncP2XJtbJ79w4KxjuIien/kdiBiI0NZ1w+7Nq13W/hxOv1cuBAOXFx\nGX4532AY4yA5OZvk5J7bI+3tLZw8+XvOnj0bsHByXkhICLGxscTGakJmEbkxDXpArLX219baF3q/\n3wHkALOBLGvtq/4tT4aTx+Ohuvow2dn++SGXkxNLdfURvP0M2rySvgbT1tfXc/ZsK9HR/pnTZCjC\nw6Pp6DBUVVUFuhQRkRveUOY5cQFjgHJrbYn/SpJrpbGxkW5PCympMX45X0pqFO7uZhobG6/6FIu1\nllOnTrFt61bKSrbTfq6ZkFAXqbljmTVvPpMnT6aqqoqWli7S0wMfTowxGBPNqVMKJyIiw82XeU4i\ngZ/R8/gwQAFQYYz5GXDKWvuPfqzv/DX/FvjbS5oPWmsn+ftaI0l3dzfgJTTUPxOohYY6AS+eqyxY\n19bWxmuv/Jpj2zeR0HaWuSlxxES56PZ2UL5rHb/btpHfZ41l9PhJeL0hQ1o7x59crhjOnOl/MUAR\nEfEPX3pO/jcwFVhEz3iT8z4E/g7wezjptRdYApx/FKF7mK4zIjQ3N1NeXk5t7VkqKzuIjTVDXjOn\nrc0NhBAW1v/4lfb2dn757M9pKy1meX4GBaOyLrrm3DGjOdvazpoDR/ho315aWoNn0TqnM4TOzq5A\nlyEicsPzJZw8ADxqrd1sjLlwoMA+YHim7+zRba2tHcbzjwjHjh1jy9bN7Dv4KW5PHUcry9i6rYOO\nrgRiolPJzMhm1KhROJ2Dn+i3urqFqMhMYmL6v0309m9/S2tpMU9MH0dSdN/zliRERfDIjIk0/H49\nuytP0j6thYgI/9x6GgprrR7TFRG5Bnzpz0/h83lOLhQFDM8UoT3yjTGnjDHlxphfGWOyhvFaNxxr\nLR9++CHPvfC/qax9m4V3Ovj+/yjiwS9PxRUZxYSpUTjDT3Pw8GZKd+6gq2tgPQRer5dz51pobGxk\nZ+lpIiJSqK+v73OAa0NDAwc3beCOnJR+g8l5DodhaUE2GZ4mqqoO+fSe/c3j6SY8PDhuMYmI3Mh8\n6TnZDtxDz7gT+DyQPAls8kdRfdhMz2PKh+h5jPnvgPXGmMnW2tZhuuYNZe3atazd+DILl8Yye96s\nz3oAps7K5s0VZbS3OZg4OY1z5zo5sKeSXbu9TJ8267K1Way1NDQ0UFdXR1NLIy2tzXTbbmprO1m7\nsY2UrO0c+7djRLuiyU7PJTszm6KiItLS0ti+fTsRTbVMLhzYUKG4mGgKo0PYfmw7Y8ZMx+EI7LJN\nXV3NjB49PqA1iIiMBL6Ek78EVhtjJvUe/yNjTCEwD7jVn8WdZ61dc8HLvcaYrcBx4BF6FiHs09NP\nP01c3MWTdy1fvpzly5cPR5lBq7Kyko83vMbCpbHMmT/mom15BSkkpWSy5r1jfOOJdKKjwyicmsKe\nkpNUVCRSUFAAgNvt5vTp05ysOkFzRxPOKENEbATxGTGERYSz9ddV5N1cxB1fv5OOcx3UVdVz+vRR\n9u4uZc2n7zE+axInj1QwNcpJ6ABvGcXExDA2IYrtdadobT3r99WIB8NaL3CO9PT0q+4rInK9WLFi\nBStWrLiorampKUDVfG7Q4cRau9EYMw34c3qmrF8KlADzrLV7/FxffzU0GWPKgHFX2u+ZZ57x+1ov\n16MtWzcTm9zM7Hn/f3t3Hh/VdR58/PeMdmm07wsCCYEEiEUSe8BgDMaOY8cr2HEcp07SLG7TLG3e\npm3epG775k2atnHTuM3SNHYcO3ac2PFOvGDMagzCrAIJJCS0gBa07zNz+scdwWjQikEaMc/385kP\nzNxz7z33zJ3RM2ddfMk2m83GR+8o4MmfNbH1zSbWbYgnPDyY1GlhnK2qJDs7m+bm85SUltDpbMee\nFMH09AwiIq1mGWMM2/9YR31rEjd97kbikmIhCdKyrT/iTqeTM6U1lL5/kr2n9tLc3M2qnEySo+yj\n5jskJITYyDBctV04HP1XtlDGqaurlfBwG2lpEz8hnFJKXS1D/WAvLi6mqKhoknJkuax5Towxp4DP\nXeG8jJmI2LE63z4xWXmYKjo6OjhasosVG5KG7cyZmh7NupsX89aru3E6G1m3IZ7k1CiqTp1l1+5d\ndDk7CUsMZvasmQQFX5wm3eFwseONOt7fF0ThLetJSLt0PpKAgABmzMlkxpxMAmIdnHxxNz/cu4+b\nM7NYlZOJzTZ8B1MRITouml5Hy6Q36TQ0VJKUFKU1J0opNQHGHJyIyJimETXGtF1+doY99z8DL2E1\n5aRjTZvvAJ4eaT8FZ86coc/ZSF7+vBHTFS2bjtiEra/t53RFHavX2GluPQ+hDnKLZhGTGH0huHG5\nDJUn29n+ZiPnWhJZfOs68hbPHjUvmXlZBPc0EdbQy3O7T3G2vYO7CuYQYBu+X3ZbQAiO8FCczskb\nOW6Mi7a209x557oRh0krpZS6MsZTc9LCyKNxxL39avzEzQCeAuKBBmAHsNwY03QVznVN6enpweAk\nPHzkheEcDifh4cHMK5jNkQ+q+KdHThAW3cuCpQGExjmoq22mr9dF47luKk/3c77VTuy0Qm659yPE\nJsWMKS9paWnUltqZPjeKxORItr98Asd+F/cW5Q9Zg+J0uSjrgdmFBZw7d5K4uMlpUmlsPENcnO2K\nLnQgpGIAACAASURBVGiolFJqeOMJTq73+L8Ar2KN0Km5ojkagjHGv3qwXkFBQUEINvr7XYSEDF1D\n0dHRyy//q5iDxQH09oRytq6bqGlRxObN4nitnTP1NlxOB7aAYKKSppMwO5UlBTNJSItHRHA4nFSW\nVNFUe56+3n6CggOJTogiK38GIR5DbyMiIohJTedUZRmFGfGsuC2P3S8cJ/JIKbcuGDwKxhjDH4+d\nois+nU0bb+Hxx1+hvb2JyMiJncre5XJSV3eYjRvnkJKSMqHnVkopfzXm4MQYs83zuYg4gT3GmPIr\nnit1xSQmJmLDTlVFE7PykodMs/OdU+zdFcOMnNs5V+eky3kAu7xH/LQIFi1dS1Z2Ni6XC5vNNqjf\nSndnD0d3l3Bwfzm1XS6csbFIaAj0d8F7lSRuOciihZnMWzGH6HirVTBv3jyKO9o5UHuWuUkxLNyQ\nzdaXy8k7l8CsZCvw6Ol38ObxcvY5wrjloQcpLCyktPQU27btJj//5gntf1JZeZC0tAA+/vFbJ+yc\nSinl7y574T81NSQnJ5OZsYgD7+8aNjgpOdxKWMQKnM4wqmqqyVywkPNnT9NUfZ7U29MQkUtmjG1p\naOHlJ7dR1iWELVpEWtFcwhIuTjXf19ZBw4HjbCk+wuFDW7j1vtWkZaUQEhJCwdJlHD5wgOL6OiLt\nQv/0YH669wD3LZxLdXM7h9v7cSVlcNuffOrCaKvNm++mrOzfqKw8RFZWwZiv3+ov0ojD0UtAQBBR\nUYljDm7a2xvp6SnnoYfu0VoTpZSaQBqc+IFlS1byzPO7qK5qJiPz0rVqomMD6e0+z4nj5wiMCiI6\nKZjaihYSklIJCwu7JH1HaycvPL6VitBYZnzhFkKiL51aPjjKTvqaxTiXL6DiuS288Ovt3P3ptSRl\nJBIaGsri5ctpamykprqaaEcw71UcpPZgNUXLV7B69RoKCwux2y8ON05JSeHuu2/mpz/9HfX1MSQl\nZV1yTk9Op4Pq6qNUNxynM7AdY61JSHh/OOlxuWRmzh9xQcGeng7Ky7ezbt1cVq1aNeK5lFJKXVkf\nNji5mtPVqyskPz+f/cWr+cNv3ubOT+aSmjZ4YroVq6fx7lsHqDzdSNrcbI7ufpPY1FaWrP3YkMfb\n9sIeym12Zn7yNoJGmYY+ICSY7M03c+rJl3j92Z188iu3XWgeSkhMJCExkfkLFhAbNY2mPR188Wt/\nOWRABLBmzRqams7z7LNvAwwboDgcfRw8+gaNoeeIXj6d7NylBEeE09/dQ9PJCsoOHabhUBUF+TcR\nHHzpubq72yktfZtly6bx6U8/cFnrDCmllLp84xlK/Huvl0KB/xKRQdPHG2PuvBIZU1dOQEAA9937\nAE886eCZ/9nBoqWRLFo8jZjYcIzLRXyijfikasIbSuh2xpIwy0b+mtmcqDhE/fmzZKRlkJCYiM1m\no6WhhZLyBhJvv2XUwGSALTCQ9JtWU/Wzp6g6Uc2MOZmDtwcEkL9iLi/veYPDhw8POypGRLjjjtsB\neO65rXR1tZKZOf+SZprjpTtptDeQc9s6IhIvdqANDg8nIj6OxLxZnHzxbY6UbKVgwc2D+tGcP1/D\nmTN7Wb48k89//jMjLmKolFLq6hhPzYn3fLZPXsmMqKsrLCyMhz79ObZuzWHf/nco3nWcEHszbR3n\nON/SxgclHSy8cy55K7LJmJFISFgQ3V19NNTWcbCkhrCTUWTPmEXVoVraw6NImTNys4q38JQEHBkZ\nHNtbeklwAhAeGU5iXhx79u0ecciuzWbjzjvvIDY2hueee40jR2rIzl6B3R4HQHd3G3Vd5aRvKBoU\nmAwqi+goMq9fSuVvd9DW1kB0dBIORx/l5fsQqeNjHytk8+Z7BjUrKaWUmjjjGa3zJ1czI+rqCwoK\n4sYbb2Tt2rX84he/4PW3nyc6LQ57VDrpuQ3c9MmVRMZdrA0Jt4cSnxRFd2cvdWeaOVZ2gEM76wlb\ncR22wPG3CMYunMOJV15nfW8fwSGX9veYMS+TQ789QWdnJxEREcMeR0RYt24dubm5PPPMc7z33lZs\nthTS0vJoaDiNiQ8kNmv6iHmJSk8lIC2cqjOHiWpJpKXlFLNmRbFp04MUFRUNO5uuUkqpq087xPqh\nPXv2cOpsMfd+ZQkFK3LYvuUwjhMMCkw8hUWEkJ2XwrnIZt54+hzhAZf3hzs42k4HNnq7hw5O4lPj\n6KOXuro6cnJGXDYJgPT0dL785YfZs2cP77yzk+PHt1FaepqAlUn0O3oRm1zS5GOMob+/h97ebpxR\ncLp4F9etXMZtt63l+uuvJyZmbBPKKaWUuno0OPEzFRUVvPr2syy8Pp7ClbMAOFfXTHTa6E0Yyemx\nRMQG09beTFd3F+FhY+tzcoGx+k8PVysRGWtHQg21tbVjCk4AAgMDWbVqFStXrqS0tJRHH/0RRwNb\n6e2tpq3NiXWLD0w+ZwAHQUFCSEggcbGBzFu/lO/83beG7YSrlFJq4mlw4md27t5JREoPS667uEJx\nU1M7cdmXDjEeSkqKnZaudtraWscdnPScbyNYDCFhQw/hFREiEsNparq4KoHL5aK3t3fU4MFms5GX\nl8fmzffwxM6XWLp6KT19vXR1deF0ujDGYLPZCA0NwW6PJCIinG3H61m94iMamCillI/R4MSPtLS0\ncKxsH0tunTao9qK/30Fg8NiGy+bkx3Ni+2k62lroj08gKHDkNXsGGGNoOXCUVbkpg1Y29hYQbKO/\nv//C81//+hk+OFjOw1+6j+zs7FHPU1hYyIvv/pGG0ipmLV84bLozR8oIbXOyePHiYdMopZSaHMMv\nB6uuOfv378cV0sqs/IzLPkbO4nTi6KTz5Gna28a+AHXHmbOE1p9j7tKRVy/2bvLp6enFZQLo6+sb\n03mioqJYU7Cc028UU1dWOWSapuqzHH95N8tmL9CZX5VSygdpzYkfKSs/zrTcSIKDB7/tgYEBOPtd\nYzpGREwYc+dEsXXvB7ROn05c3OgL8Tl6eql9dRtzEsNJy04dMa2z30lg+MX8PfjgJ2hrayMhIWFM\n+QO45eaP0tHRzrtPvUNFTiLTC/MIi7bT29FF5Qcn6Dxex/KMOWy6654xH1MppdTE0eDEj3R1dxBr\nD7nk9bjYSNoaO4fYY2jLbsuj4sQOyp95nZSHUwhLihs2bV97J6efeY2MjkZu+sz6EYfoGmPobOgm\ndsbF/i/BwcHjCkzA6iR73+b7mHtwLjv37qb0mV04cBGAkJ2YwUdu2kxhYSGBlzEcWiml1NWn385+\nxOVyYrNd2pKXkhZLcXnFmI8TEh7Mqrvy6Hu8lOqfPU1A3iwSivKJnJ56IfjoOttI/b6j9B4uISvM\ncOun1xGTOPIw3c62LlxdhrS0tPFd2BBsNhsFBQUsWrSI1tZWuru7CQkJITY2VucwUUopH6fBiR8J\nC42gt6fhktcTU2Pofr+H/l4HQSFjuyWCwgJZ9fFC4oKTObS3jNO/Ok5dUCi20GBMXz9Bvd1kRAWx\n8LqZ5C6eTVhE6KjHbKxtIpiQKxKcDBARYmJidP4SpZSaQjQ48SMpSekcKj+EMWZQ7UHa9HgCjY3a\n0kamzx9DB1ED7ed7iItJZW5+LnOWzOZsZT2NtU309/YTFBxIdEI0GbPShqypGc6ZE9WkxKbpejZK\nKeXnNDjxI4uLlvDeobeormhgWnbShddj4uzkZKVy6v2aMQUnHW3d9HXaSJuZDli1E6kzkkmdkXwh\njTGGjtZOmurO09naictlCA4NJj4llpikGAIDBw9d7unq4eyRBjZfv/EKXa1SSqmpSoMTP5KZmcm0\npFwO7S0bFJwALFiSzYlnt9N8tp3YlJFrLs7VtGAPiyUu9tKJ23q6eigtPknZrqN015wloKebEONA\nROhzgTMkFGO3k7FkHnlLc0melghA2YFT2ImmoKDgyl2wUkqpKUmDEz8iIixf8hGeffUwZyoamJaV\neGFbVm4qidFRHH7rFKs/sXDYTqMdrd20NfSTm50JHmmMMZQeOEXx8+8SdK6OefEB5MyOIjkuCnuY\nNelav8NFY0sP1fUdHHprK2++8z6pHymg4IZFlO4qZ03+esLDxzklvlJKqWuOTsLmZwoLC8nPXs0b\nzxzhXG3zhdcDAmzccEshrWVtVHxQN+S+XR29nDxST3xUBunp6RdedzicvP2bd9j/89+zyFHPF9al\ns3FZBjPTLwYmAEGBNlITwlkyN4mHNszgjpxAOrdu56df/jEBzSFsWL/h6l24UkqpKUODEz8TEBDA\nvZvuIyt+GS//8iClR6pxuawJ2LJmp1C4KIfDr5+is7Xnwj6OPienPqhhx/PH6KqxkRydgstp7eN0\nOnn76a20vrOLu+dGsHFZBuGho1fIiQh502O5Iz+SmU21uJqaaW1tvToXrZRSakoR414p9loiIoXA\n/v3791NYWDjZ2fFJfX19/P7533GgZDshsT3MWZzG3IJMbDbhV//5Jq2BPcwoTOX4jnKajtUS2OMg\nIjCY6KhoTFAIrtAw4mZl4UDo3lvM5qI4Mkfpq+Kts7WT0wfPkBqdyf4maE1ZxMNf+wahoaMPO1ZK\nKXV1FBcXU1RUBFBkjCmejDxonxM/FRwczOZN97KqZjV739/LB1t3U/zWbkLtAbTV97Pz9WJOPNXD\n0rwo1ucmkjUtnsQ4O4jQ1tHPuaYuDh3dw4691WTE2wkKTBz9pB46WzupPHSGlMg0FuTPI7unn8e2\nHeT1117j9jvuuEpXrZRSairQ4MSPiQgZGRlkZGRw08abKCkpYe/evdSefoW756UzK6aHuJRAMnNT\nsMfYL+wXGhJIUnwY4V0t5PVAZVs3z712hDUrZrIgZ5S1doyhoaaJ+vJGUqPSmZ8/n4CAAKIjAlg/\nK45Xtr3O6uuuIz5+9DV7lFJKXZs0OFEAF0bJnCvdz6ZFMWwoKqKvr5djJceo+qCGqHQ7adkp2AKs\n+Ul6u3rpPNvI/IwI1kaH8lZJB+/sKsNmE/Kzh15rp7e7lzMnanC0uMhJm83MmTMJCLg430lBdipb\nTx5h3759bNyo850opZS/0g6xCoCGhgZee+5JFid0cePimdhsQmhoKAWLCpifs5DeOgcn9pyktryO\nvp4+WhpbCervJT46FJtNWD/XTlGig217Kmhu6714YGOsJpySKk7urSCsx87ShcuYPXv2oMAEIDDA\nxoLkcI7uf2+Cr14ppZQv0ZoThTGGl174PTE9p9m4Nn/QHCcDTT/x8fHU1NRQXXuGsqoK2rtaSejr\nob8nlMDgoAsBSuWOFl7fcYqNhal0tXfR1dKNs9NFVGgM+Vk5pKenXxKUeMpMjGbPqRo6Ojqw2+3D\nplNKKXXt0uBEUVNTQ9XR9/hEQQZBgUMHDmFhYeTk5JCVlcW5c+fYu2cnoU6ho74LF9awYoOhINLJ\nbw+fYbpLmJEYT3pkIikzU8a8GnBKjB16a6ivr9fgRCml/JQGJ4ri4mJiTDM5adNGTRsQEEBaWhrJ\nCYmkmECmxUfS19eHMQZjDKnxcPB8CxGJOaxYNmfceQkJCgCXg76+vsu5FKWUUtcADU4UZ06dICcp\nDJtt9JqNAWKz4XIYAgMDCQwcfBvNTemhuqnlsvLidBkQ24hNP0oppa5t2iHWz/X399NYV0Vq3Pia\nUMIiIunqcwy5LTUqhIbmFpzuWWTHo6G1E4LDiYsbesSPUkqpa58GJ36ur68P4+gjIjR4XPtFRkfT\n3mc15XiLCLHhcjnpczjHnZ/a5nZCouI0OFFKKT+mwYmfs9lsIILTNb5ajtjYWPpswbR29V6ybaDC\nZDzNRGCNGjpc00r2vIIxdZ5VSil1bdLgxM+FhoYSFhVHY2vXuPaLiYkhPDaZmvPtl2xr6OgjPDSc\n4GFG/gynsqGVeiJZsmzZuPZTSil1bdHgxM+JCKnTc6hu7MDlclFfX09FRQWVlZV0dHSMuN+0GVk0\n9AVwvr170Lbqlj7SEuPGVfvhcLp49WAVqXMWk5WVddnXo5RSaurT0TqKvLn5/Pbt3xL99nZ6xOAK\nDMG4nISdqmBaXCz58+YSFBR0yX4pKSnUp2dxovoEhaFBhAQF0trtoLzFxi3zk8aVhzcPltMUkcGf\n3rNJm3SUUsrPac2JIioqin1n2tl9rp/o+UtJKlpBUtFKArLnUtrSwYGDh3AN0SdFRMibMxeiUzlY\n1Uhvv4Nd5a0Eh8ewYHrymM5tjGHr4Qr2NAdx4133k5w8tv2UUkpduzQ4UWzbsQvX/FWckAyaO/sB\nax6TiIQkYvIWUNXaTkNDw5D7hoSEsLBoCc6oVF4+fJY3TvawdmEewUGj9zdp6+rlqe3H2NYYzPrN\nn2GZ9jVRSimFBid+r7m5mf0VVcy9436cc9ay5b3TtLZd7BwbYo/EZY+h7uy5YY8RHh7O9LyFvHs+\nlgpHAiV1LRw704BjiHlOjDE0tHby2v4yfrytjLMJ8/nEw3/FqlWrrsr1KaWUmnq0z4mf6+jooMcF\nyYnJzLnzAY78upM/vLuTtQVpZKYnABAQFk53b9uwxzhe1ciL+2uZvfY+PrVqDQeL9/HskX0EHC4h\nKcRFXKgNm03o6nNR123oklAi0maydNMaVq5cSVhY2ERdrlJKqSlAgxM/Z7fbCbVBZ1MDSQlJzL//\ni5x4NZHnD7zB/Ip65s5OQ7o6CQsbPEmbMYaq+lZ2H6vheFsYs5ffwe133kV4eDhFRUXU199BZWUl\ntbW1tJ5vwuVyEhpuZ2lqKmlpaWRnZ18y7b1SSikFGpz4vdjYWIqyMtlSvIfEWXMICg9n3l0PcG7O\nQg7s2ML+d98noqaMdfMyqOstA6Cls4/aNkM7ESRkLePOTTcyf/78QaNskpKSSEoa34gdpZRSCjQ4\nUcCNN1zP4f/5FSUvPcuMVesJj08gKS8fCQig/Px5kuMzCFs4n4rW84gI9rR4FmVMIzs7mxkzZujQ\nX6WUUleUBieKrKws/vz+zTz1wouUPvkf9IdHQX8fMc4e7i2cx+Z7rOYapZRSaiJocKIAyM3N5Vtf\n/yrHjx+nvr6ewMBAcnNzdd4RpZRSE06DE3VBYGAg+fn5k50NpZRSfk7nOVFKKaWUT9HgRCmllFI+\nRYMTpZRSSvkUDU6UUkop5VM0OFFKKaWUT9HgRCmllFI+RYMTpZRSSvkUDU6UUkop5VM0OFFKKaWU\nT9HgRCmllFI+RYMTpZRSSvkUDU6UUkop5VM0OFFKKaWUT9HgRCmllFI+RYMTpZRSSvkUDU6UUkop\n5VM0OFFKKaWUT9HgRCmllFI+RYMTpZRSSvmUKRWciMjDIlIhIt0iskdElkx2nqaCp59+erKz4BO0\nHCxaDhdpWVi0HC7SsvANUyY4EZHNwL8A3wYKgIPAFhFJmNSMTQH6YbNoOVi0HC7SsrBoOVykZeEb\npkxwAnwV+Ikx5gljzHHgC0AX8NDkZksppZRSV9KUCE5EJAgoAt4aeM0YY4A3gRWTlS+llFJKXXlT\nIjgBEoAA4JzX6+eAlInPjlJKKaWulsDJzsCHJIAZ4vVQgJKSkonNjY9qbW2luLh4srMx6bQcLFoO\nF2lZWLQcLtKyGPS3M3Sy8iBW64hvczfrdAF3GWNe9Hj9l0C0MeYOr/SfAH49oZlUSimlri33G2Oe\nmowTT4maE2NMv4jsB24AXgQQEXE///chdtkC3A+cBnomKJtKKaXUtSAUmIH1t3RSTImaEwAR2QQ8\nDnwe2Is1euduIM8Y0zCZeVNKKaXUlTMlak4AjDHPuuc0eQRIBj4ANmpgopRSSl1bpkzNiVJKKaX8\nw1QZSqyUUkopP6HBiVJKKaV8ik8HJyLyNyKyU0Q6ReT8MGmmicgr7jRnReT7ImLzSrNWRPaLSI+I\nlIrIg0McZ8RFBUUkRER+LCKNItIuIs+JSNKVveIra6ovlCgiq0XkRRGpERGXiNw2RJpHRKRWRLpE\n5A0RyfHaHisivxaRVhFpFpGfi0iEV5oFIvKuu5wqReSvhjjPPSJS4k5zUERuvvJXPDQR+aaI7BWR\nNhE5JyLPi8hsrzSj3p8T9Vm5WkTkC+6yb3U/donITR7br/kyGIr7/nCJyL96vOYXZSEi33Zfu+fj\nmMd2vygH9/nTRORX7mvtcn9WCr3STJ3vS2OMzz6wFvn7C+AHwPkhttuAw1jDneYDG4F64B890swA\nOoDvA7nAw0A/sMEjzWasIcefAvKAnwDngQSPNP+JNTR5DdbCg7uA7ZNdRiOU3ajX5OsP4CasDtC3\nA07gNq/t/8d9TbcC+cALwCkg2CPNa0AxsBhYCZQCT3psjwTqsEaCzQE2AZ3AZz3SrHDfM19z30N/\nD/QCcyeoHF4FHnDnbz7wsvteDBvr/TmRn5WrWA63uO+JHPfjH93vwxx/KYMhymQJUA4cAP7Vn+4H\n9/m/DRwCEoEk9yPOD8shBqgAfo611Mt0YD2Q5ZFmSn1fTugH6UMU/IMMHZzc7C4EzyDi80AzEOh+\n/j3gkNd+TwOvejzfAzzq8VyAauAb7udR7sK9wyNNLuAClk52+QxTZiNe01R7uMvaOzipBb7q8TwK\n6AY2uZ/Pce9X4JFmI+AAUtzPvwg0Dtwv7te+CxzzeP4b4EWvc+8GHpukskhwX9cqj+se8f6cqM/K\nJJRFE/An/lgGgB04AawDtuIOTvypLLCCk+JhtvlTOfx/YNsoaabU96VPN+uMwXLgsDGm0eO1LUA0\nMM8jzZte+23BvWCgjG1RwcVYw64905wAqvDBhQfHeE1TmohkYa2r5HmNbcB7XLzG5UCzMeaAx65v\nYi15sMwjzbvGGIdHmi1ArohEu5+vYIR7aBLEYF3DQFNnEaPfnxP1WZkQImITkXuBcKwvPr8rA+DH\nwEvGmLe9Xh/L99W1VBazxGr6PSUiT4rINPfr/nRP3ArsE5FnxWr6LRaRzw5snIrfl1M9OElh6MUA\nB7aNlCZKREIY26KCyUCf+80cLo0v8YeFElOwPjQjXWMKVhXtBcYYJ9Yf9dHuD8aQZsLLUkQE+CGw\nwxgz0Laewuj350R9Vq4qEckXkXasX8SPYf0qPo4flQGAOzBbBHxziM1j+b66VspiD/BprF/4XwCy\ngHfd/ST86Z7IxqrVOAHcCPwX8O8i8kn39in3fTnhk7CJyHex2r6GY7DakEs/5KnMSNkYY5qRto81\njS+Zavm9HFfifZMxppmMsnwMmAusGkPaseZxIj4rV8pxYCFW7dFdwBMict0I6a+5MhCRDKwAdYMx\npn88u3KNlYUxxnN69SMisheoxOoLMdzSJddcOWBVNOw1xnzL/fygiMzDClieHGE/n/2+nIyakx9g\ndRga7jEHq4PXWJzF+pXgKdlj23BpkoA2Y0wfVvuZc5g0A9HfWSBYRKJGSONLxnJNU91ZrBt+tPfN\nu2d+ABDL6PeH56+M4dJMaFmKyH8AHwXWGmNqPTaN5f6cqM/KVWWMcRhjyo0xxcaYvwUOYnWa95sy\nwGpCSAT2i0i/iPRjdfj8CxHpc+cjxE/KYhBjTCtWJ84c/OueqANKvF4rATLd/59y35cTHpwYY5qM\nMaWjPByjHwmw2prnizWt/YAbgVYuvlG7sRYIxCvNbnd++oH9nmncVec3YPXsxr3d4ZVmNtYbv3uM\neZ0wY7ymKc0YU4H1IfC8xiisttGBa9wNxIhIgceuN2B9SPd6pLnO/SEccCNwwv1FN5DG+x7awAS+\n9+7A5OPA9caYKq/NI92fnmUxEZ+ViWYDQvCvMngTa2TJIqxapIXAPqxfyAP/78c/ymIQEbEDM7E6\nf/rTPbETq7Ovp1ysWqSp+X15NXsQf9gHMA3rw/Z/sW6WgQ9ihHu7DeuX02vAAqx2x3PAP3gcYwbW\nMLDvYb1ZXwL6gPUeaTZh9Vr2HAbWBCR6pHkMa6jWWqxfLjvx7aHEo16Trz+ACPf7vQirF/lX3M+n\nubd/w31Nt2J9Wb8AlDF4aNyrWF/WS4CPYLXJ/spjexTWF9njWM0lm933y2c80qxw3zMDQ+O+g1Vl\nPFFDiR/DGj2wGusXycAjdKz350R+Vq5iOfwTVnPWdKyhkN/F+uOzzl/KYISyuTBax5/KAvhn4Dr3\nPbESeMN9HfF+Vg6LsfphfRMrOPsE0A7c65FmSn1fTsoHaRwF/j9Y1WXej+s80kzDmvehw31TfQ+w\neR1nDVZk2+1+Mx4Y4lxfwhoP340V4S322h4C/AirCq8d+C2QNNllNEr5jXhNvv5wv2+uId7/X3ik\n+Y77w9KF1SM8x+sYMVi/KFux/sD/DAj3SjMf2OY+RhXwl0Pk5S6s/g7dWPMqbJzAchiqDJzAp8Zz\nf07UZ+UqlsPPsZp8u7F+Bf4Rd2DiL2UwQtm8zeDgxC/KAmtIb7X73FXAUwye28MvysF9/o9ifTd1\nAUeBh4ZI8x2myPelLvynlFJKKZ8y1YcSK6WUUuoao8GJUkoppXyKBidKKaWU8ikanCillFLKp2hw\nopRSSimfosGJUkoppXyKBidKKaWU8ikanCillFLKp2hwopRSSimfosGJUsrviUiFiHx5svOhlLJo\ncKLUFCIiySLyqIiUiUi3iJwVkXdF5PMiEuaR7rSIuNyPLvcf32dE5Hqv4033SOcSkUYR2SIiiyb+\n6ibVYuCnA0/cZXHbJOZHKb+mwYlSU4SIZAEfAOuBv8ZarfkG4IfAxxi8TLkB/g5IAWYDDwAtwJsi\n8k2vQxtgnTvtjYAdeNW9pPqEEZHAiTyfJ2NMkzGmZ7LOr5QaTIMTpaaO/8RairzIGPM7Y8wJY8xR\nY8zvjTG3GmNe9krfYYypN8ZUG2N2GGM+D/wD8IiIzPJIJ8B5d9pi4C+BZGDZUJkQkW+LyAER+VMR\nqRKRTnetTKRXus+KyDF3Dc8xEfmix7aBGptNIvKOiHRhLfM+1PmiReQn7lqibhE5JCIfdW+LE5Gn\nROSMOx+HRORer/23isiP3I8WEWkQkUe80lxo1hGRCqyA7QV3Hsvdr2eLyAvufLSLyF4R8QwIxIq6\njAAABEhJREFUlVJXiAYnSk0BIhIHbAD+40P+wn8U63P/8RHS9GAFLMEjpMkB7gFuATYCBcBjHvm9\nH2t59m8CecDfYAVFD3gd57vAvwFzsJZwH0REBHgdWIEVvMzBqjVyupOEAvuwloufB/wEeEJElngd\n6lNAP7AE+DLwNRH5zDDXtsR9/Q9i1SYNHMsOvIJVy7QIeA14UUQyhjmOUuoyTVo1qlJqXHKw/mCW\ner4oIg1Yf6DBCly8m2wGMcY0i0g9MGOo7SISA3wLaAf2jnCoEOBTxpg6935/DrwiIl83xtRjBSZf\nN8b8wZ2+UkTmAV8AfuVxnH/zSDOUDVj9QfKMMafcr532uJ5a4F890v9YRG7CCpze93j9jDHma+7/\nl4nIAuCrwH97n9AY02jFRLS6r2Xg9UPAIY+k3xaRO4Hb8AjMlFIfngYnSk0txuv5EqyakKewAoax\nkCGOs0tEDBABnAI2GWMaRjhG1UBg4rbbnY9cEekAZgL/LSI/90gTgNXvxdP+UfK6EKj2CEwGX4iI\nDfhbrGAkHau2Jxjo9Eq6x+v5bqzaEzHGeJfFkEQkAvh7rFqaVKzvz1Agcyz7K6XGToMTpaaGk1gB\nRR7w4sCLxpjTACLSPZaDuJuHEoEKr02bgBKgyRjTdhn5Mx7/2t3//yyX1r44vZ57BxHeRruubwB/\nDvwFcMR9vEcZuUnqcv0LVqfjr2MFcN3A767SuZTyaxqcKDUFGGPOi8gbwJ+JyI+MMWMKRobwFawA\n4QXPw2PVTngHLCPJFJEUY8xZ9/OV7uOeMMY0iEgNMNMY85sRjjGWGotDQIaI5BhjTg6xfSXwB2PM\n03Chj8os4JhXuuVez1cAZSPUmvRj1fR4n+uXxpgX3eeyM0zzmFLqw9HgRKmp40vADmCfiPw91h9u\nF7AUq0Zln1f6SBFJBoKALKzhxA8Bf22MKfdIJ5eRl17gcRH5KyAaq7biGY+moO8Aj4pIG1aH1hCs\nviMxxpgfjvW8xph3RWQ78DsR+TpWDVIe4DLG/BEoA+4SkRVYTUZfxerE6h2cTBORH2DNZVIE/Jk7\n7XBOAzeIyC6g1xjT4j7XnSIyMCrqkbFcg1Jq/DQ4UWqKMMaUi0gB1siX/wdkYAUJx4DvYw019vSI\n+9EHnMXqd7HOGPOu96EvIztlwO+BV4FY4CXgYY+8/reIdGI1u3wfq7nlMNacLOM9753AD7D61URg\nBSh/7d72j1iB1+tAF1bw8TxWwOTpCSAMq5nJgdUR17M/jHdevo7VjPM5oAbIBr6G1YF2J9AIfA+I\nRCl1xckY+4IppRRgzXMCfNwYUzjZeRkLEdkKHPAYraOU8nE6z4lSSimlfIoGJ0qpa51WDys1xWiz\njlJKKaV8itacKKWUUsqnaHCilFJKKZ+iwYlSSimlfIoGJ0oppZTyKRqcKKWUUsqnaHCilFJKKZ+i\nwYlSSimlfIoGJ0oppZTyKf8LA8MCdghystMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d10a990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################\n",
    "#create Scatter plots\n",
    "####################\n",
    "## Health exp/GDP  vs  GDP/Capita\n",
    "#insert some new data based on pop\n",
    "\n",
    "gdp_pc = np.float32(country_data['GDP (PPP) in US $']) /  np.float32(country_data['Population'])\n",
    "country_data.insert(1,'GDP per capita', gdp_pc )\n",
    "\n",
    "\n",
    "normed_pop = np.array((country_data['Population'] - country_data['Population'].mean()) /\n",
    "                                        country_data['Population'].std(ddof=0))\n",
    "#create scatter plot for each country\n",
    "x = np.float32(country_data['GDP per capita'])\n",
    "y = np.float32(country_data['Health Expenditure/GDP'])\n",
    "\n",
    "colors = np.random.rand(len(country_data))\n",
    "area = np.pi * (np.log10(normed_pop*100)*2)**3  # 0 to 15 point radiuses\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel(\"Health Expenditure / GDP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter plot - Literacy Rate vs Labor Force\n",
    "In the below plot, it can be seen that the countries are distributed towards the X-axis. This might be because Labor force measures do not reveal the income of the work force in question. Countries with bigger population (shown by the size of the dots) tend to have the biggest work forces. It can be seen that the 2 countries with the biggest populations, India and China, have obviously the biggest Labor force."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O8biTjns6PgDuCr4cERER6c+CDR6DCKxaer46YHDw5YiIiEh/Fmzw2A/c0sHx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\nVgP3AyucK9R+N9Dl6Wq1DMC+gd6Vxx/GmJeMMW85++dNbFK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c423710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################\n",
    "#create Scatter plots\n",
    "####################\n",
    "## Literacy  vs  Labor Force\n",
    "\n",
    "normed_lit = np.array((country_data['Literacy Rate (%)'] - country_data['Literacy Rate (%)'].mean()) /\n",
    "                  country_data['Literacy Rate (%)'].std(ddof=0))\n",
    "\n",
    "normed_lab = np.array((country_data['Labor Force'] - country_data['Labor Force'].mean()) /\n",
    "                  country_data['Labor Force'].std(ddof=0))\n",
    "#create scatter plot for each country\n",
    "x = normed_lit\n",
    "y = normed_lab\n",
    "\n",
    "colors = np.random.rand(len(country_data))\n",
    "area = np.pi * (np.log10(normed_pop*100)*2)**3  # 0 to 15 point radiuses\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "\n",
    "plt.xlabel(\"Literacy Rate (normalised)\")\n",
    "plt.ylabel(\"Labor Force (normalised)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram - Military exp/GDP\n",
    "\n",
    "The below histogram reveals that most countries spend equivalently on military. Looks like very few countries spend more than their financial capabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pWvFi0bTiIiKNYyKnFf8bkgm5BrsPaEnxfiIiIhJQmibjCWDaMMtfAGwZXzkiIiISRZom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\nqMkQERGRulCTISIiInXx/wEulzdSV7MjugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d38d890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################\n",
    "#create Histograms\n",
    "####################\n",
    "## Military Expenditure/GDP\n",
    "\n",
    "normed_mil = np.array((country_data['Military Expenditure/GDP'] - country_data['Military Expenditure/GDP'].mean()) /\n",
    "                      country_data['Military Expenditure/GDP'].std(ddof=0))\n",
    "\n",
    "fig1 = plt.figure()\n",
    "ax1 = fig1.add_subplot(111)\n",
    "ax1.hist(normed_mil, 50, normed=1, facecolor='green', alpha=0.75)\n",
    "ax1.set_xlabel('Military Expenditure/GDP')\n",
    "ax1.set_ylabel('Number of countries')\n",
    "ax1.axvline(normed_mil.mean(), color='b', linestyle='dashed', linewidth=2)\n",
    "ax1.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram - Internet Users\n",
    "\n",
    "The below histogram reveals that most countries are on the lower end of internet users spectrum. The mean is low. However, a graph showing internet users/ total pop would reveal more interesting results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M7K1mdnT0vJJzM4bC7cDL+lto2rRpFAqFTo+JEyfS2traabl169ZRKHS/uGbO\nnDmsWrWq07RisUihUGDXrl2dpi9atIglS5Z0mrZ161YKhUK3Lnr58uXdLtvq6OigUCh0u0lRS0sL\ns2bN6lbbpk23s3dvW6dpu3evY9Omsu246SZW3HADc846K7PbMXPmzJp8P1asWFEX2wGD/36sWLGi\nLrYDBvf9WLFiRV1sR4n+fwSD2Y7Sz8Rw346SgWxHS0vLs5+NjY2NFAoF5s1L7u96C7deqWAFs78G\nrgXeTHRlibv/0cy+DfzF3S8aVEFmh4B/cPe1Fa63Dnjc3d/dy/wmoK2trY2mpqbBlJhZU6bMoL19\nOo2N53Wbt2PHGhoaVrNhQ0WxioiIUCwWaW5uBmh29+JgXquaPR5fAZ4hDAfRUTb9GuBt1RRhZseY\n2avKztE4KXo+Npr/JTO7qmz5T5hZwcxeamanmdlXCY3QimreX0RERGqjmqtapgJvdff2LkdXfg+8\nqMo6XgP8krAHxYHLo+lXAbMJ43SMLVv+iGiZFxCan7uAye7+6yrfX0RERGqgmj0ex9B5T0fJ8cD+\naopw91+5+wh3P6zLY3Y0f1b5iavu/m/ufrK7H+Puz3N3NR0V6HrcMK+UQ0xZBMohpiwC5ZC8ahqP\n3wDvL3vuZjaCcLHELxOpSoZUR0dPfWP+KIeYsgiUQ0xZBMohedWcXHo6cBNhLI9JwFrgNMIejzPd\n/Q9JF5kEnVyqk0tFRKQ6qZ5c6u7/Rxg4bCPwE8Khlx8Br85q0yEiIiLZUM3Jpbj7HuCLCdcitbBs\nGTz+OIweDfNrNbq9iIhIUNUAYmY2xsw+aWarzOxbZnaRmR2fdHEyBJYtY9ell4YGJOe6DtiTZ8oi\nUA4xZREoh+RV3HiY2d8CDwD/DIwhnNvxz8CfonmScbPTLiAjZs9WEiXKIlAOMWURKIfkVXOoZSVh\nsLCPuftBADM7DLgimveK5MqTobA47QIyYvHixWmXkBnKIlAOMWURKIfkVXOo5WXA5aWmAyD6ehkD\nuFeKpK8+r+mpXL1e3VQNZREoh5iyCJRD8qppPIrAKT1MPwW4c3DliIiISD0b0KEWM3tl2dOvA18z\ns5cBt0XTXg/MAT6VbHkiIiJSTwa6x+MO4HfRvy2E+6YsBX4dPZYS7tOyeghqlISt6n+RXOh6e+o8\nUxaBcogpi0A5JG+gjcdLgJOif/t6nDQENUqSxo+nOGYMjB+fdiWpKxYHNfheXVEWgXKIKYtAOSSv\n4iHThyv0uBMSAAAb6UlEQVQNma4h00VEpDpJDple1cilZvYC4I1AA132mrj71wdTkIiIiNSvihsP\nM/sA8A3gaeDPQPkuEyecfCoiIiLSTTV7PC4DvgB8yd0PJVyPiIiI1LFqxvEYBaxR0zF8FQqFtEvI\nBOUQUxaBcogpi0A5JK+axmMV8J6kC5HamTt3btolZIJyiCmLQDnElEWgHJJXzaGWTwP/bWZvAzYB\nz5TPdHfdaz3jpk6dmnYJmaAcYsoiUA4xZREoh+RV23i8Fbgvet715FIRERGRHlXTeFwEzHb37yZc\ni9TCpEmwYweccAKsX592NSIikjPVnOOxH7g56UKkRrZsofWee2DLlrQrSV1ra2vaJWSGsgiUQ0xZ\nBMohedU0Hl8DLjQzS7oYqY2WtAvIiJYWJVGiLALlEFMWgXJIXjWHWv4GmAS8w8zupvvJpTOSKEyG\nzjVpF5AR11yjJEqURaAcYsoiUA7Jq6bxeAz4UdKFiIiISP2ruPFw91lDUYiIiIjUv2rO8RARERGp\nSsWNh5n9ycz+2NtjKIqUZGmXVTBrlpIoURaBcogpi0A5JK+aczy+2uX54cCrgbcB/zboimRozZ/P\n1JtvhjPPTLuS1GlEwpiyCJRDTFkEyiF55p7MYKNmNgd4TVbPATGzJqCtra2NpqamtMsZElOmzKC9\nfTqNjed1m7djxxoaGlazYcPaFCoTEZHhrFgs0tzcDNDs7sXBvFaS53hcB7wrwdcTERGROpNk4/Fu\nYHeCryciIiJ1ppqTS39nZsWyx+/M7GHgX6OHZNzGjRvTLiETlENMWQTKIaYsAuWQvGr2eLQCPyl7\n/Ai4FDjd3a+spggze5OZrTWz7WZ2yMwKA1jn78yszcz2mdkWMzu/mvfOo6VLl6ZdQiYoh5iyCJRD\nTFkEyiF51QwgdukQ1HEMcAfwbeCH/S1sZi8G/hu4Angf8BbgW2b2kLvfMAT11ZU1a9akXUImKIeY\nsgiUQ0xZBMohedVcTps4d/8F8AuAAd587mPAH919YfT8PjN7IzAPUOPRj1GjRqVdQiYoh5iyCJRD\nTFkEyiF5A248zOwQ0N+1t+7utWhmXg/c2GXa9cBXavDew9t998GBAzByJEyYkHY1IiKSM5U0CdP7\nmPcG4EJgIHsrktAI7OgybQcw2syOdPf9Napj+Jk8GbZvhxNPhPb2tKsREZGcGfDJpe7+k64P4D7g\nA8BFwA+ANP+ELjU9yYyIVscWpF1ARixYoCRKlEWgHGLKIlAOyatqHA8ze4GZfRO4i7DX5Ax3P9/d\ntyZaXe8eAU7oMq0BeNzdn+5rxWnTplEoFDo9Jk6cSGtra6fl1q1bR6HQ/eKaOXPmsGrVqk7TisUi\nhUKBXbt2dZq+aNEilixZ0mna1q1bKRQKbN68udP05cuXd/sB7+jooFAodLucq6Wlpcf7B2zadDt7\n97Z1mrZ79zo2beq8HeOAOY89ltntmDlzZk2+H+PGjauL7YDBfz/GjRtXF9sBg/t+jBs3ri62o0T/\nP4LBbEfpZ2K4b0fJQLajpaXl2c/GxsZGCoUC8+bN67ZOtSoaMt3MjgUuIRxWuQO42N1/k1g1PHsu\nyT+4e69je5vZl4G3u/uryqatBo5z92m9rKMh0xtWs+H+og61iIhIRVIZMt3MFgJ/BN4BnOPub0iq\n6TCzY8zsVWZ2RjTppOj52Gj+l8zsqrJV/h/wUjNbYmYTzOzjhJFTlyVRj4iIiAyNSk4u/TLwFHA/\ncH5vA3a5+4wq6ngN8EvC+RkOXB5NvwqYTTiZdGzZezxgZn9PaDT+GWgHPujuXa90ERERkQyp5ByP\nq4FrCfdj2dPHo2Lu/it3H+Huh3V5zI7mz3L3ST2s0+zuR7v7ye7+n9W8dx5t7n+RXOh6nDTPlEWg\nHGLKIlAOyavkqpYPRA1An4+hLFaSsbD/RXJh4UIlUaIsAuUQUxaBckheJkYulRq66SZWbNsGY8f2\nv2ydW7FiRdolZIayCJRDTFkEyiF5ajzyZsIExmnEUiC+TE6URYlyiCmLQDkkr6pxPERERESqocZD\nREREakaNRw51HQ0vr5RDTFkEyiGmLALlkDw1HjnU0dGRdgmZoBxiyiJQDjFlESiH5FU0ZPpwpiHT\noyHTN/Q6Er2IiEiPUhkyXURERGSwdDlt3ixbBo8/DqNHw/z5aVcjIiI5oz0eebNsGbsuvTQ0IDnX\n9TbUeaYsAuUQUxaBckieGo8cmp12ARkxe7aSKFEWgXKIKYtAOSRPjUcOLU67gIxYvHhx2iVkhrII\nlENMWQTKIXlqPHKoPq/pqVy9Xt1UDWURKIeYsgiUQ/LUeIiIiEjNqPEQERGRmlHjkUOr0i4gI1at\nUhIlyiJQDjFlESiH5KnxyJvx4ymOGQPjx6ddSeqKxUENvldXlEWgHGLKIlAOydOQ6XVEQ6aLiMhQ\n0JDpIiIiMiyp8RAREZGaUeMhIiIiNaPGI4cKhULaJWSCcogpi0A5xJRFoBySp8Yjh+bOnZt2CZmg\nHGLKIlAOMWURKIfkqfHIoalTp6ZdQiYoh5iyCJRDTFkEyiF5ajxERESkZtR45M2kSXDaaeFfERGR\nGlPjkTdbttB6zz2wZUvalaSutbU17RIyQ1kEyiGmLALlkDw1HjnUknYBGdHSoiRKlEWgHGLKIlAO\nyVPjkUPXpF1ARlxzjZIoURaBcogpi0A5JE+Nh4iIiNSMGg8RERGpGTUeIiIiUjNqPHJoVtoFZMSs\nWUqiRFkEyiGmLALlkLzMNB5mNsfM/mRmT5nZbWb22j6WPd/MDpnZwejfQ2bWUct6h63585k6YwbM\nn592JanTiIQxZREoh5iyCJRD8kamXQCAmc0ELgc+AtwOzAOuN7Px7r6rl9X2AOMBi577kBdaD+bP\n5xw1HQCcc845aZeQGcoiUA4xZREoh+RlZY/HPOAb7n61u28GLgA6gNl9rOPu/qi774wej9akUhER\nEala6o2HmR0ONAM3laa5uwM3AhP7WPU5ZvaAmW01s1YzO3WISxUREZFBSr3xAJ4LHAbs6DJ9B9DY\nyzr3EfaGFIBzCdtxi5mdOFRF1pONGzemXUImKIeYsgiUQ0xZBMoheVloPHpj9HLehrvf5u7fc/e7\n3P03wAzgUcI5In2aNm0ahUKh02PixIndxuNft24dhUKh2/pz5sxh1apVnaYVi0UKhQK7dnU+HWXR\nokUsWbKk07StW7dSKBTYvHlzp+nLly9nwYIFnaZ1dHRQKBS6/eC3tLT0eKb1pk23s3dvW6dpu3ev\nY9OmztuxdOnSTG/HzJkza/L9WLp0aV1sBwz++7F06dK62A4Y3Pdj6dKldbEdJfr/EQxmO0o/E8N9\nO0oGsh0tLS3PfjY2NjZSKBSYN29et3WqZeGoRnqiQy0dwLvcfW3Z9O8Cx7r79AG+zrXAM+5+bi/z\nm4C2trY2mpqaBl94Bk2ZMoP29uk0Np7Xbd6OHWtoaFjNhg1r6ejoYNSoUSlUmC3KIaYsAuUQUxaB\ncgiKxSLNzc0Aze5eHMxrpb7Hw92fAdqAyaVpZmbR81sG8hpmNgI4HXh4KGqsN/pPFCiHmLIIlENM\nWQTKIXmZuJwWWAZcZWZtxJfTjgK+C2BmVwPt7n5J9PxzwG3A/cBxwELgRcC3al75MPL00/t54Prr\nsYMH8cMO45mTTuo0/9hjj6WhoSGl6kREJA8y0Xi4+7Vm9lzgC8AJwB3AW8sukX0hcKBslTHAlYST\nT/9C2GMyMboUV3pw4MAe7r57M0e9o0Djgad5ZOQRvP1lb+i0zPHHH8mPf3y1mg8RERkyqR9qKXH3\nK9z9xe5+tLtPdPffls2b5O6zy57Pd/eXRMu+wN3f6e53pVP58HDoUAfPPHMEZqNZAIywMYwefeWz\njyOO+Ay7d+9nz549aZdaM11PxsozZREoh5iyCJRD8jKxx0NqxziMcYDZSEaNOrnTvKefTqemtIwb\nNy7tEjJDWQTKIaYsAuWQvMzs8ZDauTDtAjLiwguVRImyCJRDTFkEyiF5ajxERESkZtR4iIiISM2o\n8cghXfoTdB39L8+URaAcYsoiUA7JU+ORQwvTLiAjFi5UEiXKIlAOMWURKIfk6aqWnJnz8qs40g7j\noqPGpl1K6lasWJF2CZmhLALlEFMWgXJInhqPnNl69EndLqPNK10mF1MWgXKIKYtAOSRPh1pERESk\nZtR4iIiISM2o8cihrVuXpF1CJixZohxKlEWgHGLKIlAOyVPjkUMHD3akXUImdHQohxJlESiHmLII\nlEPy1Hjk0EtecmnaJWTCpZcqhxJlESiHmLIIlEPy1HiIiIhIzehy2pw55+Fvc9yII+kYOZofjJ2f\ndjkiIpIz2uORM+975Du848FLec+2ZWmXkrpdu3alXUJmKItAOcSURaAckqfGI4dmp11ARsyerSRK\nlEWgHGLKIlAOyVPjkUOL0y4gIxYvXpx2CZmhLALlEFMWgXJInhqPHGpKu4CMaGpSEiXKIlAOMWUR\nKIfkqfEQERGRmlHjISIiIjWjxiOHVvUy/cCBp3nggQf4/e9/3+2xc+fOmtZYC6tW9ZZE/iiLQDnE\nlEWgHJKnxiNnth71EjaMHMO2UeM7TX/mmT/T3v4gF1ywmELhI90e06e/v+6aj2KxmHYJmaEsAuUQ\nUxaBckieBhDLmTmnXM2oUSdzUZfpBw/u5dChIzniiE8zevSETvP27XuQ3bu/yJ49e2hoaKhdsUNs\n5cqVaZeQGcoiUA4xZREoh+Sp8ZBOjjxyHKNGndxt+tNPp1CMiIjUHTUeMiCl8z96c+yxx9bV3hAR\nERkaajykX+XnfxxxxBE9LnP88Ufy4x9freZDRET6pJNLc2jTpkJFy3c+/+PKbo8jjvgMu3fvZ8+e\nPUNU8dAoFCrLoZ4pi0A5xJRFoBySpz0eOXTiiXOrWq+38z9geJ4DMndudTnUI2URKIeYsgiUQ/LU\neOTQ8cdPren77dy5s9e9IWmeGzJ1am1zyDJlESiHmLIIlEPy1HjIkNq5cyfTp7+f3bv39zhf54aI\niOSLGo+cWXnv+3nuwcfZfcQJXHTG+iF/vz179rB7936OOOIzHHXUizrNq9fxQUREpHc6uTRnxu37\nE3d03MPYji01fd+jjnoRo0ad3OnRtRGptdbW1lTfP0uURaAcYsoiUA7Jy0zjYWZzzOxPZvaUmd1m\nZq/tZ/n3mNm90fJ3mtnba1XrcLck7QIyYskSJVGiLALlEFMWgXJIXiYaDzObCVwOLAJeDdwJXG9m\nz+1l+YnAauCbwBlAK9BqZqfWpuLh7XlpF5ARz3uekihRFoFyiCmLQDkkLyvneMwDvuHuVwOY2QXA\n3wOzgaU9LP8J4Dp3XxY9X2RmU4G5wMdrUK8kpL8RUZ9++uleBy3ra15/84899tiK6izX11U6pdfW\nOSvSVVav7pJ05fHnIvXGw8wOB5qBfy1Nc3c3sxuBib2sNpGwh6Tc9cDZQ1KkDIn+RkQ9cOBpduzY\nxgknvJiRIw8b8LyBzD/++CN5znO84pr7u0qn9Nq6UkfK6eou6Ulefy5SbzyA5wKHATu6TN8BTOi+\nOACNvSzfmGxpw88zz/yZjo7f9zB9dwrV9K2vO+IC7NlzM/v3X8bIkQu7ze9rXn/zS1fTHHVU5Uca\n+7pKp/y1daWOlNPVXdKTvP5cZKHx6I0BlfxJ2t/yRwHce++9g6kp044++jAOHvweO3d+r9u8gwf3\nMWLEAe70/dwO3HnoKR59ND5b+4kn7uDQof3s2bOeAwf+2GndvuYB7N//MPv3/4UbbriBu+++u9O8\nhx56iCef3MOBAzfy5JOd55Ved9+++znssAPdXnffvgd6nd/XvP7ml+r93e+2VnzGerw9v+fgwSe6\nze8riyy79dZbdfY+Q5dDXz83Wf2Z0c9EMJQ59PdzceDAXu6++2727t07JO9fibLPzqMG+1rmXvnu\n5iRFh1o6gHe5+9qy6d8FjnX36T2s8yBwubt/vWzaYuBsd391L+/zPuD7yVYvIiKSK+e6++rBvEDq\nezzc/RkzawMmA2sBzMyi51/vZbVbe5g/JZrem+uBc4EHgH2Dq1pERCRXjgJeTPgsHZTU93gAmNk/\nAlcBHwVuJ1zl8m7g5e7+qJldDbS7+yXR8hOBXwGfAn4GnBN93eTu96SwCSIiIjIAqe/xAHD3a6Mx\nO74AnADcAbzV3R+NFnkhcKBs+VvN7Bzgi9Hj94TDLGo6REREMiwTezxEREQkHzIxcqmIiIjkgxoP\nERERqZncNR5m9iIz+5aZ/dHMOszs92a2OLqst65VeiO+emRmnzaz283scTPbYWY/NrPxadeVtiiX\nQ2a2rP+l64+ZvcDM/tPMdkW/F+40s6a066olMxthZpeV/W6838w+m3ZdtWBmbzKztWa2Pfp/UOhh\nmS+Y2UNRNjeY2cvSqHUo9ZWDmY00syVmdpeZPREtc5WZPb/S98ld4wG8nDDY2IeBUwlX0FxAOEm1\nblV6I7469iZgOfA64C3A4cA6Mzs61apSFDWgHyb8TOSOmR0H3AzsB94KnAJcBPwlzbpS8CnClYUf\nJ/yeXAgsNLO5qVZVG8cQLmqYQw8DUZrZxYR7gX0U+BvgScLvz95vFjU89ZXDKMJNWS8lfIZMJ4wu\n/pNK30QnlwJm9kngAnevuw62xMxuA/7H3T8RPTdgG/B1d+/pRny5EDVeO4G/dfeNaddTa2b2HKAN\n+BjwOeB37j4/3apqy8y+DEx097PSriVNZvZT4BF3/3DZtP8COtz9/elVVltmdgj4hy4DWj4E/Ju7\nfyV6Pppwm47z3f3adCodWj3l0MMyrwH+B3iRu7cP9LXzuMejJ8cB2buZSULKbsR3U2mah46zrxvx\n5cVxhM6+br///VgJ/NTd16ddSIreCfzWzK6NDr8VzexDaReVgluAyWZ2MoCZvQo4E/h5qlWlzMxe\nQrgPWPnvz8cJH7j6/Rl+fz5WyUqZGMcjTdFxurlAPf+VV82N+OpetNfnq8DGPI4BY2bvJew6fU3a\ntaTsJMIen8sJh1xfB3zdzPa5e/cbH9WvLwOjgc1mdpDwh+ln3H1NumWlrpHw4aobk5YxsyMJPzOr\n3b37jav6UDeNh5l9Cbi4j0UcOMXdt5StcyJwHXCNu397iEvMokpvxFdvriCc53Nm2oXUmpm9kNB0\nTXH3Z9KuJ2UjgNvd/XPR8zvN7DRCM5KnxmMm8D7gvcA9hKb0a2b2kLv/Z6qVZVNuf3+a2UjgB4Tt\n/3il69dN4wH8O/CdfpZ59taqZvYCYD3hr92PDmVhGbALOEgYFbZcA927+FwwsxXANOBN7v5w2vWk\noBl4HtAW7fmBsFfsb6OTCY/0/JwA9jDQ9bbV9wIzUqglTUuBf3X3H0TP7zazFwOfBvLceDxCaDJO\noPPvywbgd6lUlKKypmMsMKnSvR1QR42Hu/8Z+PNAlo32dKwH/heYPZR1ZUGVN+KrW1HTcTZwlrtv\nTbuelNwIvKLLtO8SPnC/nKOmA8IVLV0POU4AHkyhljSNovtf8IfI+bmA7v4nM3uE8PvyLnj25NLX\nEc6Ryo2ypuMk4M3uXtWVX3XTeAxUdM3xBsJdahcCDaU/+Ny9nv/6XwZcFTUgpRvxjSJ82OSGmV1B\nuKlgAXjSzEp7gfa4e27uWuzuTxJ2pz/LzJ4E/uzuXf/6r3dfAW42s08D1xI+UD5EuMQ4T34KfMbM\ntgF3A02E3xPfSrWqGjCzY4CXEfZsAJwUnVy72923EQ5LftbM7id8dlwGtFPFpaRZ1lcOwEPADwmH\n4N4BHF72+3N3JYdsc3c5rZmdD3Q9n8MIF3oclkJJNWNmHyc0W6Ub8V3o7r9Nt6raii4R6+mHfpa7\nX13rerLEzNYDd+TtcloAM5tGOFHuZcCfgMvzdt5X9KFzGWF8hgbCB81q4DJ3P9DXusOdmZ0F/JLu\nvxuucvfZ0TKLgY8QruT4DTDH3e+vZZ1Dra8cCON3/KnLvNJ5Lm92918P+H3y1niIiIhIenJ97E5E\nRERqS42HiIiI1IwaDxEREakZNR4iIiJSM2o8REREpGbUeIiIiEjNqPEQERGRmlHjISIiIjWjxkNE\nRERqRo2HiIiI1IwaD5E6YWbfMbMfVbjOITMrDFVN1RhITWb2omi5V/Yw75dmtmzoKhSRwVDjISKD\nFt0uu9ZqfqMpMxthpdtZi0hV1HiI1KnoL/+vmdkSM/uzmT1sZovK5pfuNNka7T34Y9m8s82szcye\nMrP7zezzZnZY2fxDZnaBmf3EzPYCl5jZWdH0SWb2v2b2pJndbGYnd6mrp9ce0V9NPW3iAHNYbGYP\nmtk+M2s3s6+WzTvCzP49mv6Emd0a3aGzNP98M/uLmb3TzO4G9gFjzezvzOx/onX+Yma/MbOxA6lH\nJO/UeIjUt/cDTwB/AywEPm9mk6N5ryV8eJ8PNEbPMbM3Em6D/RXg5cBHo2Uu6fLai4AfAa8Aym8h\n//8B84Bm4ED5vD5e+zN91VQtM3s38C/Ahwm3vP8HYFPZIiuB1wH/GG3HD4DrzOylZcuMImT3QeA0\n4C/Ajwm3Dz8deD1wJSnsgREZjtLYPSoitXOXu18Wff0HM5sLTAZucvdd0VGDPe6+s2ydRcCX3P17\n0fMHzezzwFLgsrLlvu/uV5WeRB/WDlzi7hujaV8G/tvMjnD3p/t77T5qqtZY4OFoew8C7cBvo9rG\nAh8Axrr7I9Hyy8zs7cAs4LPRtJHAx9z9/6L1xgCjgZ+5+wPRMvclUKtILqjxEKlvd3V5/jDQ0M86\nrwLeYGafLZt2GHCEmR3l7vuiaW29rF++R+Hh6N8Gwof+QF87KT8g7PH4k5n9Avg58NOoCXlF9N5b\nupy3cQSwq+z506WmA8Dd/2JmVwHrzOwG4Ebg2rLmRUT6oMZDpL490+W50/8h1ucAnyccRum8cufG\n4MkBvGfp8EPpPQf62v3ZE/17bA/zjivNd/d2MxsPTAHeAlwBfDI6j+M5hENBTcChLq/xRNnXT/VQ\n62wz+xrwNmAmcJmZTXH32yvYBpFcUuMhkm/PEP7qL1cEJrh7Xyd2Vmsgr91TTZ24+2NmtotwHslv\nStPNbDThXI4tZcvuB/6bcMjnCmAzYW/H76L3OcHdb650Q9z9TuBOYImZ3QK8D1DjIdIPNR4i+fYA\nMDn64Nzv7o8BXwB+ambbgP8i7A14FXC6u3+un9fr6UqT8mkDee2eaurJMsLVNDuB24DnAp8DdhLt\nUTGz8wnNxf8AHcB50b8PRodMVgNXm9knCY1IAzAJuNPdr+txA81eDHwEWAs8RDhJ9mTgu72FIiIx\nXdUiUr8GcpXFRYTDEFsJeyNw93XAO6LptwO3Es6TeGAAr93T9GenDfC1u9XUiyXApYQrTu4knM+x\nF3hztJcD4DHCFS0bo2UmAe9w979E8z8AXA38O2FPyI+B10Tv3ZsOQrPxX4STSv8fsNzdr+xjHRGJ\nmLuuABMREZHa0B4PERERqRk1HiIiIlIzajxERESkZtR4iIiISM2o8RAREZGaUeMhIiIiNaPGQ0RE\nRGpGjYeIiIjUjBoPERERqRk1HiIiIlIzajxERESkZv5/o7H/b0WC5MIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d622d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Internet Users\n",
    "\n",
    "normed_internet = np.array((country_data['Internet Users (circa 2009)'] - country_data['Internet Users (circa 2009)'].mean()) /\n",
    "                      country_data['Internet Users (circa 2009)'].std(ddof=0))\n",
    "\n",
    "fig2 = plt.figure()\n",
    "ax2 = fig2.add_subplot(111)\n",
    "ax2.hist(normed_internet, 50, normed=1, facecolor='blue', alpha=0.75)\n",
    "ax2.set_xlabel('Internet Users')\n",
    "ax2.set_ylabel('Number of countries')\n",
    "ax2.axvline(normed_internet.mean(), color='r', linestyle='dashed', linewidth=2)\n",
    "ax2.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2 - Pearson Coefficient and Permutation test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most correlated metrics are Population and Labor Force with factor 0.980172630031\n",
      "max value in permutation test = 0.980172630031\n",
      "max p_value in permutation test = 4.75192220857e-139\n",
      "Fractions producing correlation higher = 0\n",
      "\n",
      "\n",
      "Least correlated metrics are Life Expectancy and Commercial Prime Lending Rate with factor -0.441828547965\n",
      "min value in permutation test = -0.441828547965\n",
      "min p_value in permutation test = 8.06070133067e-11\n",
      "Fractions producing correlation Lower = 0\n"
     ]
    }
   ],
   "source": [
    "######## Q2 ############\n",
    "max_p_val = 0\n",
    "min_p_val=0\n",
    "\n",
    "max_k_val = 0\n",
    "min_k_val=0\n",
    "\n",
    "corr_matrix = np.zeros(shape=(len(col_name),len(col_name)))\n",
    "for i in range(0,len(col_name)):\n",
    "    for j in range(0,len(col_name)):\n",
    "        if (i != j):\n",
    "            [k,p]=pearsonr(country_data[col_name[i]].astype(float), country_data[col_name[j]].astype(float))\n",
    "            corr_matrix[i][j] = float(k)\n",
    "            if(max_k_val < k):\n",
    "                max_k_val = k\n",
    "                max_p_val = p\n",
    "            if(min_k_val > k):\n",
    "                min_k_val = k\n",
    "                min_p_val = p\n",
    "\n",
    "i_max,j_max = np.unravel_index(corr_matrix.argmax(), corr_matrix.shape)\n",
    "i_min,j_min = np.unravel_index(corr_matrix.argmin(), corr_matrix.shape)\n",
    "\n",
    "print \"Most correlated metrics are \" + col_name[i_max] + \" and \" + col_name[j_max] + \" with factor\", corr_matrix[i_max][j_max]\n",
    "\n",
    "\n",
    "### Permutation test\n",
    "permute = []\n",
    "count_max_p_val=0\n",
    "count_min_p_val=0\n",
    "permutation_test = 10000\n",
    "\n",
    "\n",
    "for i in range(0, permutation_test):\n",
    "    [k,p]=pearsonr(country_data[col_name[i_max]].astype(float),\n",
    "                   np.random.permutation(country_data[col_name[j_max]].astype(float)))\n",
    "    permute.append(k)\n",
    "    #now check the number of tests for which the correlation is higher than the actual value\n",
    "    if( k > corr_matrix[i_max][j_max]):\n",
    "        count_max_p_val +=1\n",
    "\n",
    "\n",
    "print \"max value in permutation test =\", max_k_val\n",
    "print \"max p_value in permutation test =\", max_p_val\n",
    "print \"Fractions producing correlation higher =\",(count_max_p_val/permutation_test)\n",
    "\n",
    "permute = []\n",
    "for i in range(0, permutation_test):\n",
    "    [k,p]=pearsonr(country_data[col_name[i_min]].astype(float),\n",
    "                   np.random.permutation(country_data[col_name[j_min]].astype(float)))\n",
    "    permute.append(k)\n",
    "    #now check the number of tests for which the correlation is higher than the actual value\n",
    "    if( k < corr_matrix[i_min][j_min]):\n",
    "        count_min_p_val +=1\n",
    "\n",
    "\n",
    "        \n",
    "print \"\\n\\nLeast correlated metrics are \" + col_name[i_min] + \" and \" + col_name[j_min] + \" with factor\", corr_matrix[i_min][j_min]\n",
    "print \"min value in permutation test =\", min_k_val\n",
    "print \"min p_value in permutation test =\", min_p_val\n",
    "\n",
    "print \"Fractions producing correlation Lower =\",(count_min_p_val/permutation_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 - Linear Regression\n",
    "\n",
    "In the below test result, GDP per capita is showing least error with Life expectancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population  vs GDP per capita Error ->  1.38105859948\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/scipy/linalg/basic.py:884: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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sBH4YY7wixvgA8AlgHfDRDI5dtdatKw0BU6Yk3QKDgSRppMoaDkIIWwPTgf/u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\nUkX0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10722f050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Life Expectancy  vs GDP per capita Error ->  0.798271294795\n"
     ]
    },
    {
     "data": {
      "image/png": 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M+xblayrfnNKVw57+NCJ+DnRHxHNTSnePd9+GDRtYtGjRiLb29nba29snKEWq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\nfEz7sTZvHphOD8RFwJcmuea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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daf4190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDP (PPP) in US $  vs GDP per capita Error ->  1.15931587033\n"
     ]
    },
    {
     "data": {
      "image/png": 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oDgSagHsm7L8HOHQH5xw6zeM1wyLg2GNL2z/8oV0JSdLO5fWURwDT+RG1y+NX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\nSZLKZqCQJEllM1BIkqSy/f8vU1BKrisTugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc2ca50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area (sq km)  vs GDP per capita Error ->  1.25648418878\n"
     ]
    },
    {
     "data": {
      "image/png": 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B0BoKEZGA/OlPftFlb5hYvNivn1CYkGKkDoWISI699prfR+Ldd/11QwM0NflwIVKsAu1Q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\nEBERkaz9f/FtqvbXxiqpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc2cc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Land Boundaries (km)  vs GDP per capita Error ->  1.29014131103\n"
     ]
    },
    {
     "data": {
      "image/png": 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OQEiSKm6+voYTT4QDB5yaaAb+EUqSKmKh9Rp++EN40YuqW4/y5RSGJGnJnnwSjjtu7vtQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d2551d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coastline (km)  vs GDP per capita Error ->  1.13715270497\n"
     ]
    },
    {
     "data": {
      "image/png": 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/MyJ+KyI2RMTTIuIq4BbgwGxhYqWoVIZ48Yufz6l7eRwHzqVxoubo6CFXzWwy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\n/wF1mxMsZWjHzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d137f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Literacy Rate (%)  vs GDP per capita Error ->  0.966438811411\n"
     ]
    },
    {
     "data": {
      "image/png": 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jKv0KtBx7IEwgpJZw113VE4fqjwfmq/nXUjdvt9r6xMRE2rVrV0usA7FYkUnO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\nU9pczGcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d34b410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Health Expenditure/GDP  vs GDP per capita Error ->  1.23564889154\n"
     ]
    },
    {
     "data": {
      "image/png": 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rGRoktSJ7INQyFu81yFZtvPHGG5c8vNHd3U1f3wAdHReQBZI7gCGOOOLj5SOu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\nlJsBQpIk5fb/AwmJUGG8hL5pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d34b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Military Expenditure/GDP  vs GDP per capita Error ->  1.26180894065\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10de5fd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Renewable Water Resources (cu km)  vs GDP per capita Error ->  1.22286636492\n"
     ]
    },
    {
     "data": {
      "image/png": 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SZWWlgoRIxN5+O+wImh4vffzxcM010dYkElcKFCIi/fjlL0OASHvuOdh114GvFyl142GW\nh4hI0XR1hZUu02Hixz8O4ycUJkQGp0AhIpJyzjkwZUp4vPXW8MYbcOaZ0dYkMl6oy0NESt5TT8Fe\ne2WOb74ZPvvZ6OoRGY/UQiEiJcsd6uszYWLmzLAnh8KEyMiphUJESlJrKxyRtWTen/8MH/tYdPWI\njHdqoRCRkvL3v8MOO2TCxOLFoaVCYUIkP2qhGAeSySSdnZ1a5EokT//+73DKKZnjdetgxx2jq0dk\nIlELRYx1dXVRXz+P6upqGhoaqKqqor5+Ht3d3VGXJjKuvPRSmAqaDhNXXhlaJRQmRMaOAkWMzZ+/\niNbW1UAT8BzQRGvrahobF0ZcmeQrmUxyyy230N7eHnUpE94pp8DOO4fHO+8cujxOOinamkQmInV5\nxFQymaSlZTkhTCxInV1AT4/T0rKI9vZ2dX+MQ11dXcyfvyj13zaoq2sgkWiivLw8wsomnocfhgMO\nyByvWAGHHRZdPSITnVooYqozvXkAs/o8MxuAjo6OotYjY0OtToXX0xM27kqHic9+Fnp7FSZECk2B\nIqamTZuWerSqzzMrAaioqChqPZK/dKtTT89lhFanXQmtTpfS0rJc3R9j4A9/gC22gAceCMfJZFik\nyizaukRKgQJFTFVVVVFX10BZ2WLCt9m1QBNlZUuoq2tQd8c4pFanwnn99RAkjj46HH/722HQpf6Z\niBSPAkWMJRJN1NbOABYBuwGLqK2dQSLRFHFlMhpqdSqMH/4QttsudHVA2NzrgguirUmkFGlQZoyV\nl5fT3LyM9vZ2Ojo6tA7FOJdudWptXUxPjxNaJlZSVraE2lq1Oo3Us8/C7rtnjhMJOO64yMoRKXkK\nFONAZWWlPmwmiESiicbGhbS0LHr3XG1tg1qdRsAdFiwIAQJg773DjI4tt4y2LpFSp0AhUkRqdcrP\nn/4En/pU5nj1ajjooOjqEZEMBQqRCKjVaWTeeSfstZFMhuNFi+Daa6OtSURyKVCISKz96lewMGuZ\njmefhd12i64eEemfZnmISCx1dYX1I9Jh4sILw/gJhQmReFKgEJHY+da3YMqU8HirrcI6E2edFW1N\nIjI4dXmISGwkk1BdnTn+wx/gyCOjq0dEhk8tFCISOXdoaMiEiYMPhk2bFCZExhO1UIhIpG6/HWpr\nM8cPPwz77RddPSIyOmqhEJFIvPpqGHSZDhOnnhpaKhQmRMYntVCISNHV1EBbW+b4pZdgp52iq0dE\n8qcWChEpmkcfDa0S6TCx//6hVUJhQmT8UwuFiBSFWe5xdzdsv300tYjI2FMLhYgUVFNTbpg455zQ\nKqEwITKxqIVCRApi06bNdwDt6YFJ+hojMiHpn7aIjLmvfjU3TNx8c2iVUJgQmbgK+s/bzGaa2U1m\n9oKZ9ZrZkMvUmNmhZrbGzN4ys6SZnVDIGkVk7KxbF7o3rrwyc84dPvvZ6GoSkeIo9PeFbYGHgVMB\nH+piM9sd+CNwO7AfcClwtZkdUbgSRWQs7LwzTJ2aOe7oCGFCREpDQcdQuHsz0Axg1neMd7/+GXja\n3c9OHf+PmX0aWArcVpgqRSQf994LhxySOZ47F5Yvj64eEYlG3AZlzgBa+5xrAS6JoJZYSSaTdHZ2\nUlFRQWVlZdTliACbTwXduBG22SaaWkQkWnEbIjUVWNfn3DpgOzPbKoJ6ItfV1UV9/Tyqq6tpaGig\nqqqK+vp5dHd3R12alLCf/jQ3TFx0UejeUJgQKV1xa6HoT/rH1qC9sUuXLmXy5Mk55xobG2lsbCxU\nXUUxf/4iWltXA03ALGAVra2LaWxcSHPzsoirk1Lz979vHhp6ezdvqRCR+EkkEiQSiZxzGzZsGLPX\nNy/SqCkz6wWOdvebBrlmJbDG3c/IOvcl4BJ3Lx/gnunAmjVr1jB9+vQxrjpayWSS6upqQphYkPVM\nE7CIZDKp7g8pmqOOgpuy/vWuWgUzZ0ZXj4jkr62tjZqaGoAad28b6vrBxK2F4k/A3D7nPpM6X3I6\nOztTj2b1eWY2AB0dHQoUUnDPPAN77pk5Li+Hrq7o6hGReCr0OhTbmtl+ZrZ/6tSeqeNdU8//wMyu\nybrlP4BpZvZDM6s2s1OAY4GLC1lnXE2bNi31aFWfZ1YCUFFRUdR6pPSY5YaJF19UmBCR/hV6UOYn\ngIeANYQxEBcBbcB3U89PBXZNX+zufwHmAbWE9SuWAv/b3fvO/CgJVVVV1NU1UFa2mNDNsRZooqxs\nCXV1DWqdkIK55ZbccRFf+lIYdLnzzpGVJCIxV+h1KFYySGhx9y8PcE9NIesaTxKJJhobF9LSsujd\nc7W1DSQSTRFWJRNVf8tjv/325ntyiIj0Fbdpo9JHeXk5zc3LSCaTLF++nGQySXPzMsrL+x2jKjJq\n556bGyZ+8YsQMBQmRGQ44jYoU1L6LmSV/iUy1l57DfrMuNaS2SIyYmqhiBktZCXFdNBBuWHioYcU\nJkRkdBQoYiZ3IavngCZaW1fT2Lgw4spkInn88TDo8v77w/HHPhaCxP77D36fiMhA1OURI8lkkpaW\n5eQuZLWAnh6npWUR7e3t6vaQvPVd1bKrK6wtISKSD7VQxMhwFrISGa1EIjdMnH12aJVQmBCRsaAW\nihjJXcgqe6ltLWQlo7dp0+YzNXp6Np8eKiKSD/1IiZGqqirmzDkCs6+TvZCV2WnMmXOEujtkxE45\nJTdM3Hhj/2tNiIjkSy0UMeT+d2BR1nFJ7twueXj5Zdhpp9xzmr0hIoWk7ykxkkwmWbHiNuDnQBJY\nnvr9alasuI329vZI65PxYdddc8NEMqkwISKFp0ARI7mDMisJG69WokGZMhyrV4dBl88/H44/85kQ\nJNRTJiLFoC6PGNGgTBmtvlNB33gDtt02mlpEpDSphSJGtLuojNQVV+SGiQsvDK0SChMiUmxqoYgZ\n7S4qw/HWW7D11rnnens3b6kQESkWBYqYSe8u2t7eTkdHx7ubg4mkHXss3HBD5viOO+DQQyMrR0QE\nUKCILe0uKn09+yzsvnvm+H3vg9dfj6wcEZEcChQi40Dfroznn4cPfziaWkRE+qNBmSIxduutuWFi\n4cIw6FJhQkTiRi0UIjHU3/LY//gHvOc90dQjIjIUtVCIxMz55+eGiauvDgFDYUJE4kwtFCIx8frr\nsN12uee0ZLaIjBdqoRCJgUMOyQ0TDz6oMCEi44taKEQi9MQTsM8+mePqanjqqejqEREZLQUKkYj0\nnQq6fj1MmRJNLSIi+VKXh0iRXX99bpg444zQvaEwISLjmVooxqlkMklnZ6eW5h5Henpgiz7/4jZt\ngrKyaOoRERlLaqEYJ5LJJLfccgsPPPAA9fXzqK6upqGhgaqqKurr59Hd3R11iTKIiorcMHHDDaFV\nQmFCRCYKtVDEXFdXF/PnL6KlZXnqzCTMtiNsbz4LWEVr62IaGxfS3LwsukKlX3333wDN3hCRiUkt\nFDE3f/4iWltXEwLEnUAv7pcDC4BdgQX09FxKS8ty2tvbI6xU+jLLDRP33KMwISITlwJFjCWTSVpa\nltPTcxkhQLyZemZWnytnA9DR0VHE6mQgv//95jM43OFTn4qmHhGRYlCXR4x1dnamHqUDxLTU76sI\nASNtJQAVFRXFKUwG1DdIvPIKfOAD0dQiIlJMaqGIsWnTsgMEQBXQAJxG6AJZCzRRVraEuroGzfaI\n0Ekn5YaJhobQKqEwISKlQi0UMVZVVUVdXQOtrYvp6XFC18aRwO3Aonevq61tIJFoiqjK0vbmm7Dt\ntrnnens3b6kQEZnoitJCYWanmtkzZvZ3M1ttZgcOcu0JZtZrZj2p33vN7M2Brp/oEokmamtnEALE\nbsDXqKs7nAceeIDly5eTTCZpbl5GeXl5xJWWni22yA0TV10VWiUUJkSkFBW8hcLMvghcBJwM3A8s\nBVrMrMrd1w9w2wZC+376R3PJjo0vLy+nuXkZ7e3tdHR0aCGrGHjsMdh339xzmr0hIqWuGF0eS4Gf\nufu1AGb2NWAecCJw4QD3uLv/rQi1jRuVlZUKEjHQt/XhscdyN/cSESlVBe3yMLMtgRpCpz8QkgLQ\nChw8yK3vM7O/mNlzZnajme1dyDpFhnLllblh4j3vCa0SChMiIkGhWyh2AMqAdX3OrwOqB7jnfwit\nF48Ck4GzgHvNbB93f6FQhYr0xx0m9YndGzfCNttEU4+ISFxFNW3UGGBchLuvdvcmd3/U3e8CjgH+\nRhiDIVI0c+fmhomTTw4BQ2FCRGRzhW6hWA/0ADv1Ob8jm7da9MvdN5nZQ8CgqzYtXbqUyZMn55xr\nbGyksbFx+NWKEBaj2mGH3HMadCki410ikSCRSOSc27Bhw5i9vnmBf1Ka2WrgPndfkjo24DngMnf/\n0TDunwQ8Bix392/08/x0YM2aNWuYPn362BYvJafvoMsbb4SjjoqmFhGRQmtra6Ompgagxt3b8nmt\nYszyuBi4xszWkJk2ug3wXwBmdi3wvLv/S+r4O8BqoAPYHjgb+AhwdRFqlRJ1990wc2buObVKiIgM\nX8EDhbtfb2Y7AOcTuj4eBuqypoXuAmzKuqUcuBKYCnQDa4CD3f2pQtcqpalvq8Szz8Juu0VTi4jI\neFWUQZnufoW77+7uW7v7we7+YNZzc9z9xKzjM9x9j9S1H3L3z7n7o8WoU0rL976XGyaqqkKrhMKE\niMjIaS8PKTmbNsGWW+aee+edsJS2iIiMjnYblZKy9965YeLcc0OrhMKEiEh+9GNUSsLatZt3ZWjQ\npYjI2FELhUx4ZrlhYtUqhQkRkbGmQCET1k03bT6Dw33z6aEiIpI/dXnIhNQ3SKxfD1OmRFOLiEgp\nUAuFTCgzZuSGiSOOCK0SChMiIoWlFgqZEN54A97//txzvb2bt1SIiEhhqIVCxj2z3DBx1lmhVUJh\nQkSkeNRCEXPJZJLOzk4qKiqorKyMupxYue++0MWRTbM3RESioRaKmOrq6qK+fh7V1dU0NDRQVVVF\nff08uru7oy4tFsxyw8RddylMiIhESYEipubPX0Rr62qgibDbexOtratpbFwYcWXR+r//t/+poJ/+\ndDT1iIhIoC6PGEomk7S0LCeEiQWpswvo6XFaWhbR3t5ect0f7jCpT/x99VWYPDmaekREJJdaKGKo\ns7Mz9WhWn2dmA9DR0VHUeqI2ZUpumPj4x0PAUJgQEYkPBYoYmjZtWurRqj7PrASgoqKiqPVE5eWX\nQ/dGV1fmnDs88kh0NYmISP8UKGKoqqqKuroGysoWE7o91gJNlJUtoa6uoSS6O8xgp50yx5deqkGX\nIiJxpkARU4lEE7W1M4BFwG7AImprZ5BINEVcWWH98Y/9D7pcvDiaekREZHg0KDOmysvLaW5eRnt7\nOx0dHSWxDkXfIPH447D33tHUIiIiI6NAEXOVlZUTPkiccAJce23uOXVviIiMLwoUEplNm2DLLXPP\nvfUWbLVVNPWIiMjoaQyFRMIsN0wcfXRolVCYEBEZn9RCIUXV2Ql9Z72qe0NEZPxTC4UUjVlumLj+\neoUJEZGJQoFCCu7nP+9/KugXvhBNPSIiMvbU5SEF1TdIPP88fPjD0dQiIiKFoxYKKYhZs/pvlVCY\nEBGZmNRCIWNq40Z43/tyz/X0bL5TqIiITCz6MS9jxiw3TJx+ev/bjouIyMSjFgrJ24MPwoEH5p7T\n7A0RkdKi746SF7PcMHHHHQoTIiKlSIFCRuWCC/ofdHnooZGUIyIiEVOXh4xIf2Miurth++2jqUdE\nROJBLRQybDvvnBsmqqtDwFCYEBGRogQKMzvVzJ4xs7+b2WozO3CI679gZk+mrn/EzOYWo07p3/r1\noXvjpZcy59zhqaeiq0lEROKl4IHCzL4IXAScCxwAPAK0mNkOA1x/MHAdcBWwP3AjcKOZ7V3oWmVz\nZvDBD2aOL7pIgy5FRGRzxWihWAr8zN2vdfengK8BbwInDnD9EuAWd7/Y3f/H3c8F2oCvF6FWSbnu\nuv4HXZ5xRjT1iIhIvBU0UJjZlkANcHv6nLs70AocPMBtB6eez9YyyPUyxsxgwYLM8aOPqlVCREQG\nV+gWih2AMmBdn/PrgKkD3DN1hNfLGJk5s/9WiX33jaYeEREZP6KaNmrASL7zjvR6GYFNm2DLLXPP\nbdwI22xQdoSRAAAT3UlEQVQTTT0iIjL+FDpQrAd6gJ36nN+RzVsh0l4a4fUALF26lMmTJ+eca2xs\npLGxcdjFlqK+LRK77AJr10ZTi4iIFE4ikSCRSOSc27Bhw5i9vnmBO8fNbDVwn7svSR0b8Bxwmbv/\nqJ/rfw1s7e5HZZ27B3jE3U/p5/rpwJo1a9Ywffr0Qv01IpFMJuns7KSiooLKysoxfe0nnoB99sk9\np3ESIiKlpa2tjZqaGoAad2/L57WKMcvjYuBkMzvezD4K/AewDfBfAGZ2rZl9P+v6S4G5ZnaGmVWb\n2XmEgZ2XF6HWWOjq6qK+fh7V1dU0NDRQVVVFff08uru7x+T1zXLDxCWXKEyIiEh+Ch4o3P164Ezg\nfOAh4ONAnbv/LXXJLmQNuHT3PwGNwMnAw8AxwFHu/kSha42L+fMX0dq6GmgiNOY00dq6msbGhXm9\n7g9/2P+gy9NPz+tlRUREijMo092vAK4Y4Lk5/Zy7Abih0HXFUTKZpKVlOSFMpOduLqCnx2lpWUR7\ne/uouj/6BolkEsa4F0VEREqY9vKImc7OztSjWX2emQ1AR0fHiF7PrP9WCYUJEREZSwoUMTNt2rTU\no1V9nlkJQEVFxbBe57XXNg8S77yjsRIiIlIYChQxU1VVRV1dA2VliwndHmuBJsrKllBX1zCs7g4z\nyJ5Bu8ceIUhsoc3qRUSkQBQoYiiRaKK2dgawCNgNWERt7QwSiaZ3r0kmk9xyyy20t7e/e+7KK1/o\nt3vj6aeLUraIiJQwfWeNofLycpqbl9He3k5HR0fOOhRdXV3Mn78oNXAzOOywWu644zbgw++e22ef\nS7nrruOB8iJXLyIipUiBIsYqKys36+LInVI6C9iVO+7oe+eveOKJ8zj22GXcfvutxSlWRERKmgLF\nONL/lNL+LMDdWbFi9NNMRURERkJjKMaRlStXph71Fyb6bsAxu889IiIihaNAMQ6kl+I++eSL2XzT\n1asIm7H2P81URESkGNTlEVPZG4OddtrptLQs6+eqw4F6Qi48jRA2ZhPCxGJgErNnzy5azSIiUroU\nKGJm81kc/wH0FyaagBXACsrLP0h392uEaaZpWzFnzuEaPyEiIkWhLo+YyZ3F4cBXB7gytDxcddVV\ndHb+D3V1h+c8W1d3OL/73W8KWeq41t86HiIiMnpqoYiRwWdxGJAE0i0OYYzE7NmzB123QnL1t45H\nXV0DiUQT5eVas0NEZLTUQhEj/W8M1klmBseVDLYUd2VlJXPnzlWYGEShtoYXESl1aqGIkczGYEcC\nD2c9s3/q9x+nfkFtbUPOUtwytEJtDS8iImqhiJWqqiqmTNkJeIbsb9DwDFOm7EQymWT58uUkk0ma\nm5epiX6ExnpreBERyVALRYwkk0leeWUdfb9Bg/PKK2EGx9y5cyOqbvzL3Ro+e4zKyLaGFxGRzamF\nIkYK8Q1asxkyxmJreBER6Z8CRYzkfoPONvJv0OnVNaurq2loaKCqqor6+nl0d3ePTbHj1HC2hhcR\nkZFToIiRsfwGrdkM/UtPsdV4FBGRsaUxFDGTSDTR2LiQlpbMqpcjndGh2QxD629reBERGT0FipgZ\ni0WqhjMWQx+mIiIylhQoYiqfb9CazSAiIsWmMRQTkGYziIhIsSlQxFg+Uz41m0FERIpJXR4xNBYb\nWGnDMBERKSa1UMTQWE751IZhIiJSDGqhiBlN+RQRkfFILRQxM9SUz+OOm1/yq12KiEj8KFDEzFDL\nbz/8cHvJr3YpIiLxo0ARMwNN+YQlQAO9vZfT0rJcm32JiEisKFDEUCLRxH77TSN7yifMIASLgXce\n1c6iIiISFQWKGCovL89aL+IsIAksA8rpb7VL7SwqIiJRU6CIqUzXx8+B+xhstUvtLCoiIlEraKAw\ns3Iz+5WZbTCzbjO72sy2HeKeO82sN+tXj5ldUcg64+qCC85jv/32ZLDVLtPTTHt6LiNMM92VMM30\nUo21EBGRoil0C8V1wF7A4cA8wlzInw1xjwNXAjsBU4GdgbMLWGPspLswPvnJT9LW9iAA06cfyAMP\nPEBz87Kc1TKHs7OoiIhIoRUsUJjZR4E64H+7+4Pufi9wGnCcmU0d4vY33f1v7v5y6tcbhaozjvrr\nwnjkkU6+/e1zN7t2qGmm2llURESKoZAtFAcD3e7+UNa5VkILxEFD3LvAzP5mZn82s++b2dYFqzJm\nRteFMYmQ1bKnmS5GQ2QCzX4RESm8Qi69PRV4OfuEu/eYWVfquYH8CngWeBH4OHAhUAUcW6A6Y2U4\nXRiVlZUkk0k6Ozt54YUXgF7gAMJYi7Q5wIp3ry9FY7HJmoiIDM+IA4WZ/QD45iCXOGHcxIAvkbqm\n/5vdr846fNzMXgJazWwPd39moPuWLl3K5MmTc841NjbS2Ng4SCnxk9uFsSDrmdCFscMOO1BfPy/n\nQzI4kfCfZTWhcWgdsKKkuzxyu45mAatobV1MY+NCmpuXRVydiEhxJRIJEolEzrkNGzaM2eub+4Cf\n7f3fYDYFmDLEZU8Tvi7/2N3fvdbMyoC3gGPd/Q/D/PO2Ad4A6tz9tn6enw6sWbNmDdOnTx/m3yLe\n6uvn0dq6mp6eSwktEyspK1tCbe0MgNRzl5H+kAxhwoB/ZL3KVsyZM4vbb781r1rSLSHjbfvzZDJJ\ndXU1uZuskTpeRDKZHFd/HxGRQmhra6Ompgagxt3b8nmtEXeyu/sr7p4c4tcm4E/A9mZ2QNbthxM+\n+e4bwR95AKFF468jrXW8SiSaUuEhd7roBRec1+/4CqgG3kv2IM58h52M98WyNPtFRKS4CjZqz92f\nAlqAq8zsQDM7BPgpkHD3lwDM7ENm9qSZfSJ1vKeZfdvMppvZR8zsSOAaYKW7P1aoWuOmvLyc5uZl\nJJNJli9fTjKZpLl5GevXr09dkf0hmQT+DPwb2SHD/aesWHHbqAcibj7T5Efcdtsqjjzyn0b99yom\nzX4RESmuQk8DmA88RZjd8UfCT/evZj2/JWHA5Tap47eBWkIQeRL4EfBb4MgC1xk7yWSSjo6OnK6G\n/j8kx/6beO5Mk7nA14Cz6O19g7vvXsmsWYfGvqVioE3W+ltpVERE8lfQQOHur7r7Qnef7O7l7n6S\nu7+Z9fyz7l7m7qtSx8+7+6Hu/kF338bdq939nFJah2Kwrob+PyTTDTdj9008t7tgEWGgZ6Y75Z57\nHh0Xy3oP1HWUvdKoiIiMjUJOG5VRyHQ1/AjYEfgbra3ff3dmQiLRRGPjQlpaMlNEp0zZiVdfXUxP\nj5M7iHN038QzLSG/AZaTO7BxAb29TkvLItrb22P9TT/dddTe3r5Za4+IiIwtBYoYSXc1wP6EXUaD\nnp79313UqrKycrMPyR122GGzkFFb2zDqb+LplpDbbvsuvb0w1JoYcVdZWTku6hQRGc8UKGIkdDVM\nIt21kJkWGla9zP4A7/shOdbfxBOJJo488mjuvnsVA62JoYGNIiKSpkARI5MmTSKsepmeFpoEPgCc\nA5zFFlsM/p9roG/io1lLory8nLvuCgMw77nnNHp7x6Y7RUREJiZt9hAjvaF/gbDi+DzC+hINhO6P\nSXzjG2ePaHbFWKwl8Yc//J4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e14fcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net Migration Rate/1000 Population  vs GDP per capita Error ->  1.48288231626\n"
     ]
    },
    {
     "data": {
      "image/png": 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VHsOBVCZihBEj4I030vY778CQIdnWJKkyGQ6kMrBpU/4IhK1b84cuSlJfskOi\nVOJWrcoFg9NPT3cQDAaSCslwIJWwRYvgkENS+/rrYcGCbOuRVB0MB1KJmjULTj01tevr4dZbs61H\nUvWwz4FUgqZNgzvvTO3GRqipybYeSdXFcCCVmJoa+M1vUvv11+Ggg7KtR1L1MRxIJWLrVhgwILf9\n3nswcGB29UiqXvY5kEpAS0suGBx5ZBqRYDCQlBXDgZSxJUtg+PDUvvJKWL4823okyXAgZeihh+C4\n41J75ky4555s65EkMBxImbn5ZrjwwtSePx+uuirTciRpOzskShmYNCmtpgjwyitw+OGZliNJeQwH\nUhHFCHvtBVu2pO1334XBg7OtSZJ2VJTHCiGEgSGE50II20IIxxfjmlKpWb8e+vVLwWDvvWHbNoOB\npNJUrD4HtwF/BGKRrieVlBUrYN99U3vyZNi4EULItCRJ2qmCh4MQwnnAucCXAH8cqurMnw9HHJHa\nN98MDz+caTmStFsF7XMQQjgI+D5wAbCxkNeSStHMmXD11an90ENw/vnZ1iNJ3VHoDok/BGbGGH8T\nQvhwga8llZQrroAf/Si1lyyBsWMzLUeSuq3H4SCE8C/A9bs4JAJjgInAfsDXO17a4+qkMhRjeozw\n6qtpu6UFhg3LtiZJ6ok9uXNwO+mOwK68AnwcOBV4L+T3vHo2hHBfjPGKnb14+vTpDBkyJG9fXV0d\ndXV1e1CuVDybN6ehih22bIEPOGBYUh+rr6+nvr4+b19ra2ufnT/EWJgBBCGEQ4H9O+06GGgA/hx4\nOsa4qovX1ACNjY2N1LiAvcrM6tUwYkRq19bCs89mW4+k6tLU1ERtbS1AbYyxqTfnKthohRjjH2OM\nz3d8AS+RHi283FUwkMpZY2MuGEybZjCQVN6KvbaC8xyo4tTXw0knpfasWTBjRrb1SFJvFe1paIzx\nVaB/sa4nFcN118E3vpHaixbBKadkW48k9QW7Skl7aNw4eOqp1F61CkaOzLYeSeorhgOph9ra8kcg\nbNyY1kqQpEpR7D4HUllrbc0Fg5Ej0+JJBgNJlcZwIHXT0qUwdGhqT5mSHiW4eJKkSmQ4kLph7lw4\n9tjUnjED7rsv23okqZAMB9Ju3HYbTJqU2vPmpXkMJKmS2SFR2oWLLoIHH0ztZcvgqKOyrUeSisFw\nIHUhxtS/YO3atL12Ley3X7Y1SVKxGA6kHWzcCPvsk9tua4N+PoCTVEX8kSd1snJlLhiMH5/uIBgM\nJFUbf+xKavBTAAAMeElEQVRJ7RYuhMMOS+0bboDHHsu2HknKio8VJODuu+Gzn03tBx6Aiy/Oth5J\nypLhQFVv6lT43vdSe/FiOP74bOuRpKwZDlTVxoyBF19M7TffhAMOyLYeSSoFhgNVpS1bYODA3Pbm\nzTBgQHb1SFIpsUOiqs6aNblgMGZMGpFgMJCkHMOBqspvfwsHHpjaU6fC889nW48klSLDgarGAw/A\nRz6S2nffDXfdlW09klSq7HOgqnDDDXDLLam9cCGcdlq29UhSKTMcqOKNHw+PP57aK1fCoYdmW48k\nlTrDgSrWtm3Qv39ue8MGGDQou3okqVzY50AVad26XDAYOjQFBYOBJHWP4UAVZ/ly2H//1L74Ynj7\nbQgh25okqZwYDlRR5s2Do49O7dtuSyMUJEk9Y58DVYwZM2D69NSeOxcmTsy2HkkqV4YDVYS6OvjJ\nT1J76VIYPTrbeiSpnBkOVNZihBEj4I030vY778CQIdnWJEnlznCgsrVpU/4IhK1b84cuSpL2jB0S\nVZZWrcoFg9NPT3cQDAaS1DcMByo7Tz8NhxyS2tdfDwsWZFuPJFUaw4HKyqxZ8NGPpnZ9Pdx6a7b1\nSFIlss+Bysa0aXDnnand2Ag1NdnWI0mVynCgslBTA7/5TWq//jocdFC29UhSJTMcqKRt3QoDBuS2\n33sPBg7Mrh5JqgYF7XMQQpgcQngqhLAhhNASQvjfhbyeKktLSy4YHHlkGpFgMJCkwitYOAgh/Dkw\nC7gH+FPgNODHhbqeKsvzz8Pw4al95ZVpMSVJUnEU5LFCCKE/MAP4XzHGH3X61ouFuJ4qy0MPwYUX\npvbMmXDVVdnWI0nVplB3DmqAgwFCCE0hhFUhhDkhhLEFup4qxC235ILB/PkGA0nKQqE6JB4JBOCr\nwHTgVeBLwBMhhFExxncKdF2VscmTYc6c1H7lFTj88EzLkaSq1aNwEEL4F+D6XRwSgTHk7kjcEmN8\nsP21VwB/BP4C+MGurjN9+nSG7LB6Tl1dHXV1dT0pV2UiRthrL9iyJW2/+y4MHpxtTZJUyurr66mv\nr8/b19ra2mfnDzHG7h8cwnBg+G4Oexk4A/glcEaM8dedXv8U8GiM8YadnL8GaGxsbKTGGW6qwvr1\nsO++qb333rBhA4SQbU2SVI6ampqora0FqI0xNvXmXD26cxBjfAt4a3fHhRAagfeAY4Bft+8bABxO\nesQgsWIFHHFEak+eDA8/nGk5kqR2BemQGGNcB3wXuDGEcG4IYTRwF+mxw38V4poqL088kQsGN99s\nMJCkUlLIGRK/BGwhzXUwCFgEjI8x9t1DEZWlmTPh6qtT+6GH4Pzzs61HkpSvYOEgxtgGXNf+JQFw\nxRXwox+l9pIlMNbBrZJUclxbQUURY3qM8Gp7j5OWFhg2LNuaJEldMxyo4DZvTkMVO2zZAh/wkydJ\nJaugCy9Jq1fngkFNTbqDYDCQpNJmOFDBNDXBiBGpPW0aNDZmW48kqXsMByqI+npIc3HArFkwY0a2\n9UiSus8bvOpz118Pt92W2osWwSmnZFuPJKlnDAfqU6edBk8+mdqrVsHIkdnWI0nqOcOB+kRbW35H\nw40b01oJkqTyY58D9Vpray4YjBwJ27YZDCSpnBkO1CtLl8LQoak9ZUp6lOCqipJU3gwH2mNz58Kx\nx6b2jBlw333Z1iNJ6huGA+2Rb3wDJk1K7Xnz0jwGkqTKYIdE9djFF8PPfpbay5bBUUdlW48kqW8Z\nDtRtMab+BWvXpu21a2G//bKtSZLU9wwH6paNG2GffXLbbW3Qz4dSklSR/PGu3Vq5MhcMxo9PdxAM\nBpJUufwRr11auBAOOyy1b7gBHnss23okSYXnYwXt1D33wGc+k9oPPJA6IkqSKp/hQF266ir47ndT\ne/FiOP74bOuRJBWP4UDvM3YsvPBCar/5JhxwQLb1SJKKy3Cg7bZsgYEDc9ubN8OAAdnVI0nKhh0S\nBcCaNblgMGZMGpFgMJCk6mQ4EKtWwYEHpvbUqfD889nWI0nKlo8VxLJl6c+774a/+Ztsa5EkZc9w\nID72sfQYQZIk8LGCJEnageFAkiTlMRxIkqQ8hgNJkpTHcCBJkvIYDiRJUh7DgSRJymM4EPX19VmX\nUHV8z4vP97z4fM/LV8HCQQhhVAjhwRDCmyGE1hDC/w0hnFWo62nP+Q+4+HzPi8/3vPh8z8tXIe8c\n/ALoD/wZUAMsBn4RQvhgAa8pSZJ6qSDhIIQwHDgauDXGuCTGuBz4e2Af4LhCXFOSJPWNgoSDGONb\nwIvAp0MI+4QQPgBMBVYDjYW4piRJ6huFXHjpXOBBYB2wjRQMJsYYW3fxmr0BXnjhhQKWpR21trbS\n1NSUdRlVxfe8+HzPi8/3vLg6/e7cu7fnCrEHy/GFEP4FuH4Xh0RgTIyxOYTwc1Kfg1uATcBngAuB\nk2KMq3dy/inAfd0uSJIk7eiSGOOPe3OCnoaD4cDw3Rz2MnAW8AgwNMa4vtPrm4G7Y4y37eL8E4AV\npEAhSZK6Z2/gcKCh/fH+HuvRY4X2i+32giGEQR0v2eFb29hFP4f28/cq7UiSVMV+3RcnKdRQxieB\nt4F7QwjHt8958A1SovlFga4pSZL6QCFHK0wE9gUeA54BTgMuiDH+rhDXlCRJfaNHfQ4kSVLlc20F\nSZKUx3AgSZLylEw4CCH8QwhhYQhhfQihZSfHbNvhqy2E8JfFrrVSdPM9/1AI4Rftx7weQrgthFAy\nn5tyF0JY0cVn+rqs66o0IYSrQwivhBA2hhCeCiGcnHVNlSqE8NUuflY/n3VdlSSEcGYI4aEQwmvt\n7+8FXRxzUwhhVQhhQwjh0RDC0T25Rin9kB8A/Cdw126O+2vgIGAEMJI0C6P2zC7f8/YQMIc05PVU\n0nt/OXBTkeqrBhH4Cvmf6W9lWlGFCSF8CvhX4KvAiaRF4BpCCAdkWlhl+z25z/QI4Ixsy6k4g4Hn\ngKt5/5QBhBCuB74AfA44BVhP+swP7O4FCjl9co/EGG8ECCH89W4ObY0xvlmEkipeN97zCcCxwMdj\njGuA34UQbgBuDSF8Lca4tUilVrp3/UwX1HTgezHGWQAhhKnAZOBKoMsJ2dRrW/1MF06M8RHSRIOE\nEEIXh0wDbo4x/nf7MZ8mLWHwSdL/EO5WKd056K7vhBDeDCEsCiFckXUxFe5U4HftwaBDAzAE+JNs\nSqpIfx9CWBNCaAohfCmE0D/rgipFCGEAUEsaUg1ATEO05gHjsqqrCoxqv+W9PIQwO4TwoawLqhYh\nhCNId2s6f+bXAovowWe+ZO4cdNMNwC+BDcAngJkhhMExxm9nW1bFGkFKm52t7vS9xcUtpyLdATQB\nLaS5QG4lvbdfyrKoCnIAaY2Xrj7HxxS/nKrwFOnx41LSY7KvAb8KIRzXeTp9FcwI0qOGrj7zI7p7\nkoKGg54s1NSd88UY/6nT5uIQwr7AtYDhoF1fv+e7OY+60JP/BjHGGZ32/z6EsAX4bgjhyzHGLQUt\ntLoF/AwXRIyxodPm70MITwOvAn8J/DCbqkQPP/OFvnNwO7v/MLzci/MvAr4SQhgYY9zci/NUkr58\nz18HduzVfVD7n12urCmgd/8NFpH+XR4OvNSHNVWrNUAbuc9thw/iZ7goYoyt7Yvu9ai3vPbY66Qg\ncBD5n/EPAr/p7kkKGg66u1BTL5wIvG0wyOnj9/xJ4B9CCAd06nfwCaAVcGjSTvTyv8GJpAXK3ui7\niqpXjHFLCKEROBt4CLZ34DobuDPL2qpF+x3eo4BZWddSDWKMr4QQXid9xn8LEELYH/go8J3unqdk\n+hy0d1j5H8CHgf4hhI+0f2tZjHF9COF/kpLPU8B7pF9SX8bexntsd+858H9IIeA/2ofGjARuBr7t\nLe/eCyGcSvoH+ziwjtTn4N+A/4gxtmZZW4X5N9IicI3A06TRC/sAP8qyqErVvsjef5MeJRwC3Ahs\nBeqzrKuShBAGk+7EdIxUOLL953dLjHElMIN0V30ZsIL0c/uPwM+7fZEYY0l8kW7DtnXx9bH2708g\nddxqBda2tz+Tdd3l/LW797z9mA8BDwPvkm5RfR3ol3XtlfBFukvwJKkz4nrS2PDrgAFZ11ZpX8Dn\n239Ibmx/z0/KuqZK/SKFgD+2v9d/AH4MHJF1XZX0BZxFusO448/uf+90zNeAVaQO/A3A0T25hgsv\nSZKkPOU4z4EkSSogw4EkScpjOJAkSXkMB5IkKY/hQJIk5TEcSJKkPIYDSZKUx3AgSZLyGA4kSVIe\nw4EkScpjOJAkSXn+P6Hr6lUztkS6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e14fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labor Force  vs GDP per capita Error ->  1.38761060008\n"
     ]
    },
    {
     "data": {
      "image/png": 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7d52dnTz66KNrvYzxkUdgp53KH4sxo+IkSVqFvDZBCkDNfuytz3HNIZQHg8WL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dff7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Internet Users (circa 2009)  vs GDP per capita Error ->  1.21979628174\n"
     ]
    },
    {
     "data": {
      "image/png": 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kVn6AtuMOT0MDHHIILIhPddl7b3j2WcjLC7cuEZEgqYciw2k77vTy8MM+ODSG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\nK++PVERXVnwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e45fbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Commercial Prime Lending Rate  vs GDP per capita Error ->  1.18031070677\n"
     ]
    },
    {
     "data": {
      "image/png": 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1Bq5398oBPxXuThAAUPTcpT/+MTWePl2aNy++eoCx4NoEADBK//APIRRMTR78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\nIwwAAFDmCAMAAJS5/w+HsUbXXn6DUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e502e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############## Q3 ############\n",
    "\n",
    "normed_gdp_pc = np.array((country_data['GDP per capita'] - country_data['GDP per capita'].mean()) /\n",
    "                  country_data['GDP per capita'].std(ddof=0))\n",
    "\n",
    "country_data = country_data.replace(\"1.78%\",1.78)\n",
    "\n",
    "for i in col_name:\n",
    "    country_data[i] = (country_data[i].fillna(country_data[i].astype(float).mean())).astype(float)\n",
    "\n",
    "for name in col_name:\n",
    "\n",
    "    normed_value = np.array((country_data[name] - country_data[name].mean()) /\n",
    "                  country_data[name].std(ddof=0))\n",
    "\n",
    "\n",
    "    X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(normed_value.reshape(-1,1),\n",
    "                                            normed_gdp_pc.reshape(-1,1), test_size = 0.8, random_state = 0)\n",
    "    linReg = LinearRegression()\n",
    "    linReg.fit(X_train, Y_train)\n",
    "    error = metrics.mean_squared_error(Y_test, linReg.predict(X_test))\n",
    "    print name ,\" vs GDP per capita Error -> \",error\n",
    "    plt.scatter(X_train, Y_train)\n",
    "\n",
    "    plt.plot(X_test, linReg.predict(X_test))\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Question 4 - Calculating Social Welfare\n",
    "\n",
    "Some Economists, like Amartya Sen, have proposed ways to measure social welfare of an economic state. However, most of the measure, apart from GDP being of the highest importance, take wealth inequality as a huge measure in order to calculate social welfare. In lieu of this information not being available, Social welfare of a country has been calculated based upon GDP per Capita, and other HDI indicators. Some would argue that Literacy rate and miltary strength should be taken into account because security and education should count in social welfare.\n",
    "\n",
    "Monaco made to the top of the list. Many deserving nations are present in the top 10 including Norway and Switzerland. \n",
    "Points noticed are as follows ->\n",
    "    \n",
    "    1. When only considering GDP per capita, countries like Brunei made to the top of the list. This is because Brunei is a very rich nation due to a lot of Oil and their population is relatively low. However their wealth distribution is not very good and they have a relatively low HDI. Same goes for some gulf countries as well.\n",
    "    2. Bottom of the list is dominated by developing nations as is expected. But there are skewed results like Zimbabwe which is nearly a failed state is above many relatively doing well nations like Pakistan, Nigeria and Myanmar(Burma).\n",
    "    \n",
    "Overall, this measure lacks a lot of accuracy mostly because a good data regarding wealth distribution wasn't present.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Social Welfare Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>6.416227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.957332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>2.649434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>2.564085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>2.108095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1.917304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1.899192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1.854401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1.834477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>1.713598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1.657090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.602980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1.589009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>France</td>\n",
       "      <td>1.585694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1.576655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>1.562824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>1.498191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Japan</td>\n",
       "      <td>1.463415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>1.461759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>1.417286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1.335154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1.245536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1.245382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>1.224886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Italy</td>\n",
       "      <td>1.148198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1.137322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1.112788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>1.105084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>1.076438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Israel</td>\n",
       "      <td>1.065740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>-0.930116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>-0.953176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>-0.963740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>-1.003152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>-1.019776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>-1.030974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Gambia, The</td>\n",
       "      <td>-1.031814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Mali</td>\n",
       "      <td>-1.050201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>-1.051061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>-1.075453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>-1.081854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>-1.086732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Benin</td>\n",
       "      <td>-1.124623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>-1.129456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>-1.135537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>-1.147455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>-1.156666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>-1.157278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>-1.164132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>-1.230405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Laos</td>\n",
       "      <td>-1.238795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Niger</td>\n",
       "      <td>-1.271027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>-1.280193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>-1.287708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>-1.295231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Burma</td>\n",
       "      <td>-1.299586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>-1.341644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Congo, Republic of the</td>\n",
       "      <td>-1.405138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Chad</td>\n",
       "      <td>-1.530380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>-1.593145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country Name  Social Welfare Index\n",
       "116                    Monaco              6.416227\n",
       "187             United States              2.957332\n",
       "100             Liechtenstein              2.649434\n",
       "102                Luxembourg              2.564085\n",
       "141                     Qatar              2.108095\n",
       "130                    Norway              1.917304\n",
       "169               Switzerland              1.899192\n",
       "125               Netherlands              1.854401\n",
       "31                     Canada              1.834477\n",
       "9                     Austria              1.713598\n",
       "63                    Germany              1.657090\n",
       "156                 Singapore              1.602980\n",
       "16                    Belgium              1.589009\n",
       "59                     France              1.585694\n",
       "8                   Australia              1.576655\n",
       "46                    Denmark              1.562824\n",
       "168                    Sweden              1.498191\n",
       "84                      Japan              1.463415\n",
       "75                    Iceland              1.461759\n",
       "80                    Ireland              1.417286\n",
       "186            United Kingdom              1.335154\n",
       "126               New Zealand              1.245536\n",
       "58                    Finland              1.245382\n",
       "149                San Marino              1.224886\n",
       "82                      Italy              1.148198\n",
       "163                     Spain              1.137322\n",
       "3                     Andorra              1.112788\n",
       "92                     Kuwait              1.105084\n",
       "171                    Taiwan              1.076438\n",
       "81                     Israel              1.065740\n",
       "..                        ...                   ...\n",
       "167                 Swaziland             -0.930116\n",
       "111                Mauritania             -0.953176\n",
       "37                    Comoros             -0.963740\n",
       "69                     Guinea             -1.003152\n",
       "196                  Zimbabwe             -1.019776\n",
       "162               South Sudan             -1.030974\n",
       "61                Gambia, The             -1.031814\n",
       "108                      Mali             -1.050201\n",
       "87                      Kenya             -1.051061\n",
       "104                Madagascar             -1.075453\n",
       "60                      Gabon             -1.081854\n",
       "26               Burkina Faso             -1.086732\n",
       "18                      Benin             -1.124623\n",
       "122                   Namibia             -1.129456\n",
       "56                   Ethiopia             -1.135537\n",
       "30                   Cameroon             -1.147455\n",
       "120                Mozambique             -1.156666\n",
       "160                   Somalia             -1.157278\n",
       "132                  Pakistan             -1.164132\n",
       "195                    Zambia             -1.230405\n",
       "94                       Laos             -1.238795\n",
       "128                     Niger             -1.271027\n",
       "129                   Nigeria             -1.280193\n",
       "70              Guinea-Bissau             -1.287708\n",
       "4                      Angola             -1.295231\n",
       "121                     Burma             -1.299586\n",
       "54                    Eritrea             -1.341644\n",
       "39     Congo, Republic of the             -1.405138\n",
       "33                       Chad             -1.530380\n",
       "32   Central African Republic             -1.593145\n",
       "\n",
       "[197 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "############## Q4 ############\n",
    "\n",
    "country_names = country_data[['Country Name']]\n",
    "\n",
    "#Normalise the required data -> gdp/pop, internet_users/pop,\n",
    "\n",
    "normed_life = np.array(list((country_data['Life Expectancy'] - country_data['Life Expectancy'].mean()) /\n",
    "                  country_data['Life Expectancy'].std(ddof=0)))\n",
    "\n",
    "normed_gdp = np.array(list((country_data['GDP per capita'] - country_data['GDP per capita'].mean()) /\n",
    "                  country_data['GDP per capita'].std(ddof=0)))\n",
    "normed_health = np.array(list((country_data['Health Expenditure/GDP'] - country_data['Health Expenditure/GDP'].mean()) /\n",
    "                  country_data['Health Expenditure/GDP'].std(ddof=0)))\n",
    "\n",
    "#Fill in the nan values with definite values\n",
    "normed_health = np.nan_to_num(normed_health)\n",
    "\n",
    "### Scoring function\n",
    "\n",
    "normed_data = normed_life * 0.3 + normed_gdp * 0.7 + normed_health*0.4\n",
    "\n",
    "country_names.insert(1,'Social Welfare Index', normed_data)\n",
    "\n",
    "result = country_names.sort_values(by=['Social Welfare Index'], ascending=[0])\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Question 5 - Calculating Neighboring States\n",
    "\n",
    "The definition of a country being \"similar\" to another is very vague. One usually thinks of cultural similarity when bombarded with this question. If you ask for Japan, one would usually reply with China, South Korea, Taiwan. Similarly for Denmark, it will be Sweden, Norway and Finland.\n",
    "\n",
    "However, to find out what other countries might be similar to one, we can take other factors like Life expectancy, GDP per capita, internet users and other such metrics into consideration. A statistical distance can be calculated and then the countries can be clustered based upon this distance.\n",
    "\n",
    "Total of 10 neighboring countries have been printed along with closest and farthest neighbors\n",
    "\n",
    "Observations ->\n",
    "1. Strange pairs like (India Syria), (Canada\tSwitzerland), (Brazil\tMontenegro), (Greenland\tBahamas, The) (China\tTonga) and many others have been observed.\n",
    "2. Interesting pairs like (Brunei\tUnited Arab Emirates) -> Both are Oil based extremely rich small countries.\n",
    "                     (Singapore\tKuwait) -> Both are Trading focal points and major seaports.\n",
    "                     (Iceland\tSweden) -> Nordic countries\n",
    "                     \n",
    "3. Goof up in farthest neighbor being nearly same for all.\n",
    "    \n",
    "Even though the most neighboring countries might be a little skewed, the top 10 set shows better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Afghanistan = \n",
      "Uganda Swaziland South Africa Congo, Democratic Republic of the Somalia South Sudan Mozambique Zimbabwe Burundi Rwanda Mali\n",
      "Albania = \n",
      "Lebanon Tunisia Morocco Macedonia Dominica Dominican Republic Colombia Saint Lucia Ecuador Mexico Tonga\n",
      "Algeria = \n",
      "Sri Lanka Libya Thailand Armenia Vanuatu Saint Vincent and the Grenadines Philippines Egypt China Tonga Fiji\n",
      "Andorra = \n",
      "San Marino Israel Taiwan Korea, South Finland Czech Republic Iceland Malta Australia Japan United Kingdom\n",
      "Angola = \n",
      "Congo, Republic of the Central African Republic Gabon Cameroon Niger Botswana Nigeria Namibia Chad Ethiopia Benin\n",
      "Antigua and Barbuda = \n",
      "Mauritius Dominica Mexico Romania Colombia Poland Grenada Venezuela Lebanon Estonia Lithuania\n",
      "Argentina = \n",
      "Uruguay Panama Croatia Chile Cyprus Hungary Saint Lucia Lithuania Bulgaria Barbados Slovakia\n",
      "Armenia = \n",
      "Thailand Vanuatu Egypt Philippines Algeria Fiji Peru Libya Jamaica Cabo Verde China\n",
      "Australia = \n",
      "Iceland Sweden Ireland United Kingdom Finland Japan Norway Austria Belgium Israel Switzerland\n",
      "Austria = \n",
      "Belgium Germany Canada Switzerland Denmark Ireland Sweden Netherlands United Kingdom Iceland Australia\n",
      "Azerbaijan = \n",
      "Suriname Jamaica Peru Belarus Egypt Iran Venezuela Cabo Verde Uzbekistan Saint Vincent and the Grenadines China\n",
      "Bahamas, The = \n",
      "Greenland Barbados Estonia Czech Republic Hungary Slovenia Slovakia Latvia Korea, South Poland Lithuania\n",
      "Bahrain = \n",
      "Seychelles Saudi Arabia Saint Kitts and Nevis Malaysia Oman Libya Saint Vincent and the Grenadines Antigua and Barbuda Venezuela Estonia Thailand\n",
      "Bangladesh = \n",
      "Fiji Philippines Vanuatu Syria Bhutan India Cabo Verde Indonesia Papua New Guinea Armenia Bolivia\n",
      "Barbados = \n",
      "Hungary Croatia Lithuania Czech Republic Poland Uruguay Argentina Bulgaria Chile Cyprus Slovakia\n",
      "Belarus = \n",
      "Suriname Azerbaijan Venezuela Peru Iran Trinidad and Tobago Saint Vincent and the Grenadines Jamaica Romania Grenada Russia\n",
      "Belgium = \n",
      "Austria Germany Denmark Canada Switzerland Ireland Sweden United Kingdom France New Zealand Netherlands\n",
      "Belize = \n",
      "Guyana Mongolia Bolivia Iran Tajikistan Kyrgyzstan Nepal Suriname Azerbaijan Russia Kosovo\n",
      "Benin = \n",
      "Ethiopia Kenya Mauritania Comoros Gambia, The Cameroon Senegal Cambodia Madagascar Guinea Yemen\n",
      "Bhutan = \n",
      "Syria India Bolivia Papua New Guinea Bangladesh Kazakhstan Fiji Cabo Verde Mongolia Philippines Vanuatu\n",
      "Bolivia = \n",
      "Mongolia Bhutan Nepal Papua New Guinea Belize Ghana Cabo Verde India Tajikistan Guyana Syria\n",
      "Bosnia and Herzegovina = \n",
      "Paraguay Serbia Cuba Georgia Costa Rica Montenegro Nicaragua Solomon Islands Brazil Maldives Jordan\n",
      "Botswana = \n",
      "Namibia Nigeria Gabon Angola Niger Cameroon Zambia Burkina Faso Chad Mozambique Central African Republic\n",
      "Brazil = \n",
      "Montenegro Maldives Iraq Jordan Georgia Honduras Solomon Islands Bulgaria Nicaragua Paraguay Serbia\n",
      "Brunei = \n",
      "United Arab Emirates Kuwait Saudi Arabia Oman Singapore Bahrain Seychelles Taiwan Malaysia Saint Kitts and Nevis Estonia\n",
      "Bulgaria = \n",
      "Hungary Croatia Uruguay Turkey Lithuania Ecuador Latvia Saint Lucia Argentina Mexico Macedonia\n",
      "Burkina Faso = \n",
      "Mali South Sudan Zimbabwe Mozambique Zambia Cote d'Ivoire Somalia Niger Nigeria Cameroon Guinea-Bissau\n",
      "Burundi = \n",
      "Malawi Congo, Democratic Republic of the Djibouti Sudan Haiti Tanzania Togo Sao Tome and Principe Uganda Cote d'Ivoire Western Sahara\n",
      "Cabo Verde = \n",
      "Egypt Philippines Vanuatu Uzbekistan Jamaica Azerbaijan Mongolia Fiji Bolivia Armenia Peru\n",
      "Cambodia = \n",
      "Yemen Comoros Mauritania Senegal Tajikistan Ghana Nepal Kenya Gambia, The Benin Ethiopia\n",
      "Cameroon = \n",
      "Niger Guinea Ethiopia Benin Senegal Burkina Faso Nigeria Cote d'Ivoire Mauritania Mali Zimbabwe\n",
      "Canada = \n",
      "Switzerland Germany Austria Netherlands Belgium Denmark France Sweden Ireland New Zealand United Kingdom\n",
      "Central African Republic = \n",
      "Chad Nigeria Angola Namibia Niger Zambia Cameroon Gabon Guinea-Bissau Botswana Congo, Republic of the\n",
      "Chad = \n",
      "Central African Republic Nigeria Namibia Zambia Guinea-Bissau Niger Angola Botswana Mozambique Gabon Cameroon\n",
      "Chile = \n",
      "Cyprus Argentina Panama Croatia Uruguay Saint Lucia Lithuania Hungary Poland Czech Republic Barbados\n",
      "China = \n",
      "Tonga Saint Vincent and the Grenadines Jamaica Venezuela Peru Egypt Romania Libya Dominican Republic Colombia Dominica\n",
      "Colombia = \n",
      "Tunisia Romania Macedonia Grenada Dominica Mexico Mauritius Lebanon China Morocco El Salvador\n",
      "Comoros = \n",
      "Mauritania Cambodia Yemen Kenya Gambia, The Ghana Benin Ethiopia Senegal Nepal Tajikistan\n",
      "Congo, Democratic Republic of the = \n",
      "Burundi Malawi Uganda Zimbabwe South Sudan Cote d'Ivoire Mali Tanzania Djibouti Burkina Faso Swaziland\n",
      "Congo, Republic of the = \n",
      "Angola Laos Eritrea Benin Ethiopia Burma Kenya Gambia, The Cameroon Central African Republic Madagascar\n",
      "Costa Rica = \n",
      "Cuba Bosnia and Herzegovina Serbia Portugal Paraguay Georgia Montenegro Nicaragua Slovakia Brazil Greece\n",
      "Cote d'Ivoire = \n",
      "Zimbabwe Guinea Mali Burkina Faso South Sudan Tanzania Senegal Western Sahara Cameroon Malawi Congo, Democratic Republic of the\n",
      "Croatia = \n",
      "Uruguay Hungary Argentina Panama Cyprus Chile Bulgaria Lithuania Saint Lucia Barbados Ecuador\n",
      "Cuba = \n",
      "Bosnia and Herzegovina Paraguay Costa Rica Serbia Georgia Montenegro Portugal Maldives Solomon Islands Brazil Panama\n",
      "Cyprus = \n",
      "Chile Argentina Croatia Saint Lucia Uruguay Panama Lithuania Poland Hungary Czech Republic Ecuador\n",
      "Czech Republic = \n",
      "Chile Korea, South Cyprus Poland Barbados Malta Lithuania Hungary Argentina Croatia Israel\n",
      "Denmark = \n",
      "Germany Belgium France Austria Netherlands Canada New Zealand Switzerland Ireland United Kingdom Sweden\n",
      "Djibouti = \n",
      "Haiti Sudan Togo Sao Tome and Principe Tanzania Malawi Western Sahara Burundi Cote d'Ivoire Senegal Congo, Democratic Republic of the\n",
      "Dominica = \n",
      "Mauritius Antigua and Barbuda Colombia Lebanon Romania Mexico Tunisia Dominican Republic Macedonia Albania Grenada\n",
      "Dominican Republic = \n",
      "Tonga Dominica Albania China Lebanon Morocco Tunisia Colombia Saint Vincent and the Grenadines Libya Romania\n",
      "Ecuador = \n",
      "Saint Lucia Macedonia El Salvador Bulgaria Lebanon Tunisia Albania Croatia Uruguay Mexico Samoa\n",
      "Egypt = \n",
      "Jamaica Uzbekistan Armenia Cabo Verde Peru China Azerbaijan Vanuatu Philippines Tonga Thailand\n",
      "El Salvador = \n",
      "Samoa Vietnam Macedonia Tunisia Guatemala Colombia Ecuador Turkey Grenada Morocco Romania\n",
      "Equatorial Guinea = \n",
      "Timor-Leste Kazakhstan Turkmenistan Bhutan Russia Syria Trinidad and Tobago Belize Ghana Guyana India\n",
      "Eritrea = \n",
      "Laos Burma Pakistan Madagascar Congo, Republic of the Gambia, The Kenya India Papua New Guinea Syria Benin\n",
      "Estonia = \n",
      "Trinidad and Tobago Latvia Antigua and Barbuda Poland Mauritius Mexico Lithuania Belarus Romania Grenada Turkey\n",
      "Ethiopia = \n",
      "Benin Mauritania Kenya Comoros Gambia, The Cameroon Senegal Guinea Cambodia Yemen Madagascar\n",
      "Fiji = \n",
      "Philippines Vanuatu Bangladesh Armenia Thailand Cabo Verde Indonesia Bhutan Egypt Syria Kazakhstan\n",
      "Finland = \n",
      "United Kingdom Ireland Iceland Sweden Australia Slovenia Israel Malta Spain Italy New Zealand\n",
      "France = \n",
      "Germany Denmark Netherlands Canada Belgium New Zealand Austria Switzerland Spain Italy Portugal\n",
      "Gabon = \n",
      "Angola Botswana Central African Republic Namibia Chad Congo, Republic of the Nigeria Niger Cameroon Zambia Guinea-Bissau\n",
      "Gambia, The = \n",
      "Kenya Madagascar Ghana Papua New Guinea Comoros Yemen Benin Mauritania Ethiopia India Cambodia\n",
      "Georgia = \n",
      "Paraguay Solomon Islands Montenegro Bosnia and Herzegovina Maldives Jordan Nicaragua Cuba Serbia Brazil Honduras\n",
      "Germany = \n",
      "Denmark Belgium Canada Austria Netherlands France Switzerland New Zealand Ireland Sweden United Kingdom\n",
      "Ghana = \n",
      "Papua New Guinea Gambia, The Yemen Madagascar Nepal Kenya Comoros Bolivia Tajikistan Cambodia India\n",
      "Greece = \n",
      "Malta Spain Italy Slovenia Slovakia New Zealand Panama Portugal Argentina Finland Czech Republic\n",
      "Greenland = \n",
      "Bahamas, The Barbados Estonia Czech Republic Trinidad and Tobago Hungary Korea, South Taiwan Latvia Slovenia Poland\n",
      "Grenada = \n",
      "Romania Turkey Colombia Mauritius Mexico Tunisia Latvia Macedonia El Salvador Dominica Iran\n",
      "Guatemala = \n",
      "Vietnam Samoa Kosovo Kyrgyzstan El Salvador Korea, North Ukraine Iran Turkey Grenada Uzbekistan\n",
      "Guinea = \n",
      "Senegal Cote d'Ivoire Mauritania Cameroon Ethiopia Tanzania Western Sahara Cambodia Comoros Benin Zimbabwe\n",
      "Guinea-Bissau = \n",
      "Zambia Somalia Mozambique Nigeria Namibia Burkina Faso South Sudan Mali Swaziland Niger Chad\n",
      "Guyana = \n",
      "Belize Mongolia Tajikistan Iran Bolivia Nepal Kyrgyzstan Yemen Kosovo Ukraine Russia\n",
      "Haiti = \n",
      "Togo Djibouti Sao Tome and Principe Sudan Tanzania Western Sahara Malawi Burundi Senegal Cote d'Ivoire Cambodia\n",
      "Honduras = \n",
      "Iraq Nicaragua Jordan Brazil Solomon Islands Montenegro Maldives Georgia Ukraine Paraguay Kiribati\n",
      "Hungary = \n",
      "Croatia Barbados Uruguay Lithuania Bulgaria Argentina Chile Cyprus Poland Panama Saint Lucia\n",
      "Iceland = \n",
      "Sweden Ireland Australia United Kingdom Finland Japan Belgium Austria Israel Italy New Zealand\n",
      "India = \n",
      "Syria Papua New Guinea Bhutan Madagascar Bangladesh Bolivia Ghana Gambia, The Pakistan Fiji Mongolia\n",
      "Indonesia = \n",
      "Fiji Turkmenistan Bangladesh Philippines Vanuatu Sri Lanka Syria Thailand Armenia Pakistan Bhutan\n",
      "Iran = \n",
      "Suriname Russia Azerbaijan Belarus Belize Grenada Turkey Guyana Guatemala Kosovo Romania\n",
      "Iraq = \n",
      "Honduras Jordan Brazil Ukraine Maldives Kosovo Solomon Islands Samoa Montenegro Bulgaria Guatemala\n",
      "Ireland = \n",
      "United Kingdom Iceland Sweden Finland Australia Belgium Japan Austria New Zealand Spain Italy\n",
      "Israel = \n",
      "Korea, South Andorra San Marino Taiwan Finland Malta Czech Republic Iceland United Kingdom Ireland Australia\n",
      "Italy = \n",
      "Spain New Zealand Greece Malta United Kingdom Japan Finland Slovenia Ireland Sweden Portugal\n",
      "Jamaica = \n",
      "Egypt Peru China Azerbaijan Uzbekistan Tonga Suriname Saint Vincent and the Grenadines Venezuela Armenia Cabo Verde\n",
      "Japan = \n",
      "Sweden Iceland Australia Ireland United Kingdom Italy Spain Finland New Zealand Israel Belgium\n",
      "Jordan = \n",
      "Maldives Solomon Islands Iraq Brazil Georgia Honduras Montenegro Samoa Ecuador Paraguay Bulgaria\n",
      "Kazakhstan = \n",
      "Bhutan Syria Turkmenistan Fiji Peru Thailand Azerbaijan Suriname Vanuatu Philippines India\n",
      "Kenya = \n",
      "Gambia, The Madagascar Comoros Benin Ghana Ethiopia Mauritania Yemen Papua New Guinea Cambodia Nepal\n",
      "Kiribati = \n",
      "Sudan Moldova Honduras Togo Rwanda Nicaragua Haiti Djibouti Sao Tome and Principe Burundi Malawi\n",
      "Korea, North = \n",
      "Kyrgyzstan Kosovo Guatemala Ukraine Vietnam Samoa Tajikistan Guyana Iraq El Salvador Belize\n",
      "Korea, South = \n",
      "Israel Taiwan Andorra Czech Republic San Marino Malta Finland Slovenia Chile Poland Barbados\n",
      "Kosovo = \n",
      "Ukraine Guatemala Korea, North Samoa Vietnam Kyrgyzstan Iran El Salvador Iraq Turkey Guyana\n",
      "Kuwait = \n",
      "Brunei United Arab Emirates Singapore Saudi Arabia Oman Qatar Bahrain Seychelles Taiwan San Marino Liechtenstein\n",
      "Kyrgyzstan = \n",
      "Korea, North Guatemala Mongolia Kosovo Tajikistan Vietnam Belize Guyana Nepal Ukraine Uzbekistan\n",
      "Laos = \n",
      "Eritrea Burma Pakistan Madagascar Gambia, The Congo, Republic of the Kenya India Papua New Guinea Syria Benin\n",
      "Latvia = \n",
      "Turkey Mexico Grenada Lithuania Bulgaria Mauritius Estonia Romania Poland Antigua and Barbuda Russia\n",
      "Lebanon = \n",
      "Albania Tunisia Macedonia Dominica Colombia Morocco Mexico Dominican Republic Saint Lucia Romania Mauritius\n",
      "Lesotho = \n",
      "Rwanda Afghanistan Uganda Congo, Democratic Republic of the South Africa Burundi Swaziland Malawi Kiribati Sudan Somalia\n",
      "Liberia = \n",
      "Sierra Leone Marshall Islands Lesotho Micronesia, Federated States of Rwanda Moldova Kiribati Uganda Afghanistan United States Nicaragua\n",
      "Libya = \n",
      "Saint Vincent and the Grenadines Thailand Algeria China Armenia Saint Kitts and Nevis Peru Venezuela Tonga Dominican Republic Malaysia\n",
      "Liechtenstein = \n",
      "Luxembourg Singapore Norway Qatar Kuwait Australia San Marino Taiwan Iceland United Arab Emirates Brunei\n",
      "Lithuania = \n",
      "Poland Hungary Mexico Croatia Cyprus Latvia Saint Lucia Chile Bulgaria Barbados Uruguay\n",
      "Luxembourg = \n",
      "Liechtenstein Norway Singapore Australia Iceland Kuwait Taiwan San Marino Switzerland Austria Sweden\n",
      "Macedonia = \n",
      "Tunisia Colombia Lebanon Mexico El Salvador Ecuador Albania Grenada Dominica Romania Saint Lucia\n",
      "Madagascar = \n",
      "Gambia, The Papua New Guinea Kenya Ghana India Comoros Syria Yemen Laos Nepal Benin\n",
      "Malawi = \n",
      "Burundi Djibouti Sudan Congo, Democratic Republic of the Haiti Tanzania Togo Sao Tome and Principe Western Sahara Cote d'Ivoire Zimbabwe\n",
      "Malaysia = \n",
      "Saint Kitts and Nevis Thailand Libya Seychelles Saint Vincent and the Grenadines Peru Kazakhstan Algeria Armenia Venezuela Sri Lanka\n",
      "Maldives = \n",
      "Jordan Solomon Islands Brazil Montenegro Georgia Iraq Ecuador Paraguay Bulgaria Uruguay Croatia\n",
      "Mali = \n",
      "South Sudan Burkina Faso Zimbabwe Mozambique Cote d'Ivoire Somalia Zambia Niger Nigeria Guinea Guinea-Bissau\n",
      "Malta = \n",
      "Greece Slovenia Spain Italy Czech Republic Slovakia Israel Finland Panama Chile Argentina\n",
      "Marshall Islands = \n",
      "Micronesia, Federated States of Moldova Sierra Leone Liberia Costa Rica Serbia Nicaragua Bosnia and Herzegovina Cuba Portugal Kiribati\n",
      "Mauritania = \n",
      "Comoros Cambodia Senegal Yemen Ethiopia Benin Kenya Guinea Gambia, The Ghana Madagascar\n",
      "Mauritius = \n",
      "Romania Antigua and Barbuda Mexico Dominica Colombia Grenada Venezuela Tunisia Macedonia Latvia Lebanon\n",
      "Mexico = \n",
      "Mauritius Romania Colombia Antigua and Barbuda Grenada Dominica Macedonia Tunisia Turkey Latvia Lithuania\n",
      "Micronesia, Federated States of = \n",
      "Moldova Marshall Islands Serbia Costa Rica Nicaragua Bosnia and Herzegovina Cuba Paraguay Kiribati Georgia Montenegro\n",
      "Moldova = \n",
      "Nicaragua Kiribati Serbia Micronesia, Federated States of Honduras Bosnia and Herzegovina Georgia Montenegro Paraguay Brazil Costa Rica\n",
      "Monaco = \n",
      "Qatar Liechtenstein Luxembourg Singapore Kuwait Norway Brunei United Arab Emirates Australia Switzerland San Marino\n",
      "Mongolia = \n",
      "Bolivia Belize Guyana Nepal Tajikistan Cabo Verde Kyrgyzstan Azerbaijan Bhutan Ghana Papua New Guinea\n",
      "Montenegro = \n",
      "Brazil Georgia Maldives Paraguay Serbia Bosnia and Herzegovina Solomon Islands Jordan Nicaragua Cuba Iraq\n",
      "Morocco = \n",
      "Tunisia Albania Tonga Lebanon Colombia Dominican Republic Macedonia China El Salvador Dominica Romania\n",
      "Mozambique = \n",
      "Somalia Zambia Burkina Faso South Sudan Mali Guinea-Bissau Zimbabwe Nigeria Niger Swaziland Namibia\n",
      "Burma = \n",
      "Pakistan Eritrea Laos Turkmenistan Syria India Madagascar Indonesia Bangladesh Papua New Guinea Bhutan\n",
      "Namibia = \n",
      "Nigeria Zambia Botswana Niger Chad Guinea-Bissau Mozambique Burkina Faso Central African Republic Somalia Cameroon\n",
      "Nauru = \n",
      "Tuvalu Palau Western Sahara Kiribati Kosovo Honduras Sudan Moldova Togo Nicaragua Sao Tome and Principe\n",
      "Nepal = \n",
      "Tajikistan Yemen Ghana Mongolia Bolivia Papua New Guinea Guyana Cambodia Belize Comoros Kyrgyzstan\n",
      "Netherlands = \n",
      "Canada Germany France Denmark Switzerland Belgium Austria New Zealand Sweden Ireland United Kingdom\n",
      "New Zealand = \n",
      "Spain Italy United Kingdom Portugal Ireland Greece Belgium Finland Slovenia Sweden France\n",
      "Nicaragua = \n",
      "Honduras Serbia Georgia Bosnia and Herzegovina Montenegro Paraguay Solomon Islands Brazil Moldova Jordan Maldives\n",
      "Niger = \n",
      "Nigeria Cameroon Burkina Faso Zambia Namibia Mozambique Mali South Sudan Zimbabwe Guinea Cote d'Ivoire\n",
      "Nigeria = \n",
      "Niger Namibia Zambia Mozambique Guinea-Bissau Burkina Faso Chad Cameroon Central African Republic Mali Somalia\n",
      "Norway = \n",
      "Australia Iceland Sweden Austria Switzerland Ireland United Kingdom Finland Belgium Canada Japan\n",
      "Oman = \n",
      "Saudi Arabia Seychelles Bahrain Malaysia Saint Kitts and Nevis United Arab Emirates Kazakhstan Turkmenistan Libya Thailand Brunei\n",
      "Pakistan = \n",
      "Burma Laos Eritrea Syria Turkmenistan India Bangladesh Madagascar Indonesia Papua New Guinea Bhutan\n",
      "Palau = \n",
      "Serbia Nicaragua Bosnia and Herzegovina Montenegro Moldova Brazil Georgia Paraguay Costa Rica Honduras Cuba\n",
      "Panama = \n",
      "Argentina Uruguay Croatia Chile Cyprus Hungary Saint Lucia Slovakia Lithuania Ecuador Bulgaria\n",
      "Papua New Guinea = \n",
      "India Ghana Madagascar Gambia, The Syria Bolivia Bhutan Nepal Kenya Mongolia Yemen\n",
      "Paraguay = \n",
      "Georgia Bosnia and Herzegovina Cuba Montenegro Serbia Solomon Islands Maldives Nicaragua Costa Rica Brazil Jordan\n",
      "Peru = \n",
      "Jamaica Saint Vincent and the Grenadines Azerbaijan Venezuela Egypt Suriname China Thailand Belarus Armenia Libya\n",
      "Philippines = \n",
      "Vanuatu Fiji Armenia Cabo Verde Bangladesh Egypt Thailand Bhutan Peru Jamaica Uzbekistan\n",
      "Poland = \n",
      "Lithuania Antigua and Barbuda Mexico Cyprus Chile Hungary Latvia Estonia Czech Republic Saint Lucia Croatia\n",
      "Portugal = \n",
      "New Zealand Costa Rica Spain Greece Slovakia Italy Cuba Slovenia Malta Serbia Bosnia and Herzegovina\n",
      "Qatar = \n",
      "Kuwait Liechtenstein Singapore Brunei Luxembourg United Arab Emirates Saudi Arabia Oman Norway Taiwan San Marino\n",
      "Romania = \n",
      "Mauritius Colombia Grenada Mexico Dominica Tunisia Venezuela Antigua and Barbuda China Macedonia Turkey\n",
      "Russia = \n",
      "Iran Trinidad and Tobago Belarus Latvia Turkey Suriname Grenada Belize Guyana Timor-Leste Azerbaijan\n",
      "Rwanda = \n",
      "Uganda Burundi Kiribati Malawi Congo, Democratic Republic of the Sudan Lesotho Djibouti Afghanistan Haiti Togo\n",
      "Saint Kitts and Nevis = \n",
      "Malaysia Saint Vincent and the Grenadines Libya Venezuela Peru Thailand Seychelles Belarus China Antigua and Barbuda Mauritius\n",
      "Saint Lucia = \n",
      "Ecuador Cyprus Croatia Macedonia Chile Lithuania Uruguay Lebanon Mexico Argentina Albania\n",
      "Saint Vincent and the Grenadines = \n",
      "Venezuela China Peru Libya Jamaica Tonga Saint Kitts and Nevis Thailand Romania Egypt Belarus\n",
      "Samoa = \n",
      "Vietnam El Salvador Guatemala Kosovo Macedonia Turkey Ecuador Korea, North Tunisia Grenada Colombia\n",
      "San Marino = \n",
      "Andorra Israel Taiwan Korea, South Australia Iceland Finland Japan Sweden United Kingdom Ireland\n",
      "Sao Tome and Principe = \n",
      "Togo Haiti Djibouti Sudan Tanzania Western Sahara Malawi Ukraine Burundi Korea, North Cambodia\n",
      "Saudi Arabia = \n",
      "Seychelles Oman Bahrain United Arab Emirates Saint Kitts and Nevis Malaysia Estonia Trinidad and Tobago Brunei Belarus Venezuela\n",
      "Senegal = \n",
      "Guinea Mauritania Cambodia Comoros Western Sahara Cote d'Ivoire Tanzania Ethiopia Yemen Benin Cameroon\n",
      "Serbia = \n",
      "Bosnia and Herzegovina Paraguay Montenegro Cuba Costa Rica Nicaragua Georgia Brazil Solomon Islands Maldives Portugal\n",
      "Seychelles = \n",
      "Saudi Arabia Malaysia Saint Kitts and Nevis Bahrain Oman Trinidad and Tobago Saint Vincent and the Grenadines Libya Venezuela Kazakhstan Belarus\n",
      "Sierra Leone = \n",
      "Liberia Marshall Islands Lesotho Micronesia, Federated States of Rwanda Moldova Kiribati Uganda Afghanistan Nicaragua Burundi\n",
      "Singapore = \n",
      "Kuwait United Arab Emirates Brunei San Marino Taiwan Andorra Liechtenstein Luxembourg Norway Korea, South Israel\n",
      "Slovakia = \n",
      "Slovenia Greece Malta Argentina Panama Barbados Uruguay Hungary Croatia Portugal Czech Republic\n",
      "Slovenia = \n",
      "Slovakia Malta Greece Finland Spain Italy United Kingdom New Zealand Czech Republic Portugal Barbados\n",
      "Solomon Islands = \n",
      "Jordan Maldives Georgia Paraguay Montenegro Brazil Honduras Iraq Nicaragua Bosnia and Herzegovina Ecuador\n",
      "Somalia = \n",
      "Mozambique Guinea-Bissau Zambia South Sudan Mali Burkina Faso Swaziland Zimbabwe Nigeria Namibia Niger\n",
      "South Africa = \n",
      "Swaziland Afghanistan Somalia Uganda Mozambique Guinea-Bissau South Sudan Congo, Democratic Republic of the Zambia Mali Zimbabwe\n",
      "South Sudan = \n",
      "Mali Zimbabwe Burkina Faso Mozambique Somalia Cote d'Ivoire Zambia Congo, Democratic Republic of the Niger Guinea-Bissau Swaziland\n",
      "Spain = \n",
      "Italy New Zealand Greece Malta United Kingdom Slovenia Finland Portugal Ireland Japan Sweden\n",
      "Sri Lanka = \n",
      "Algeria Thailand Armenia Libya Vanuatu Fiji Philippines Indonesia Malaysia Egypt Saint Vincent and the Grenadines\n",
      "Sudan = \n",
      "Togo Haiti Djibouti Sao Tome and Principe Malawi Tanzania Burundi Western Sahara Kiribati Congo, Democratic Republic of the Ukraine\n",
      "Suriname = \n",
      "Azerbaijan Belarus Peru Iran Jamaica Venezuela Egypt Saint Vincent and the Grenadines Grenada Romania China\n",
      "Swaziland = \n",
      "Somalia South Africa Mozambique Afghanistan South Sudan Guinea-Bissau Mali Zambia Uganda Zimbabwe Burkina Faso\n",
      "Sweden = \n",
      "Iceland Ireland United Kingdom Australia Japan Finland Belgium Austria New Zealand Italy Spain\n",
      "Switzerland = \n",
      "Canada Austria Germany Belgium Netherlands Denmark Sweden France Ireland Australia Iceland\n",
      "Syria = \n",
      "India Bhutan Papua New Guinea Bangladesh Bolivia Kazakhstan Turkmenistan Fiji Madagascar Pakistan Philippines\n",
      "Taiwan = \n",
      "Korea, South Andorra Israel San Marino Czech Republic Finland Iceland Australia Malta United Kingdom Ireland\n",
      "Tajikistan = \n",
      "Nepal Yemen Mongolia Guyana Belize Kyrgyzstan Cambodia Ghana Bolivia Comoros Papua New Guinea\n",
      "Tanzania = \n",
      "Western Sahara Djibouti Haiti Sao Tome and Principe Togo Malawi Cote d'Ivoire Senegal Sudan Guinea Burundi\n",
      "Thailand = \n",
      "Armenia Libya Peru Algeria Vanuatu Saint Vincent and the Grenadines Egypt Philippines Fiji Sri Lanka Jamaica\n",
      "Timor-Leste = \n",
      "Russia Trinidad and Tobago Belarus Belize Kazakhstan Guyana Suriname Equatorial Guinea Iran Azerbaijan Bolivia\n",
      "Togo = \n",
      "Haiti Sao Tome and Principe Sudan Djibouti Tanzania Western Sahara Malawi Burundi Ukraine Korea, North Senegal\n",
      "Tonga = \n",
      "China Dominican Republic Jamaica Morocco Saint Vincent and the Grenadines Egypt Tunisia Colombia Venezuela Uzbekistan Romania\n",
      "Trinidad and Tobago = \n",
      "Estonia Belarus Russia Latvia Mauritius Venezuela Suriname Grenada Antigua and Barbuda Romania Iran\n",
      "Tunisia = \n",
      "Colombia Macedonia Lebanon Romania Dominica Morocco Albania Grenada Mexico El Salvador Mauritius\n",
      "Turkey = \n",
      "Latvia Grenada Mexico Romania El Salvador Bulgaria Macedonia Colombia Mauritius Iran Tunisia\n",
      "Turkmenistan = \n",
      "Indonesia Syria Kazakhstan Pakistan Bhutan India Bangladesh Fiji Burma Philippines Vanuatu\n",
      "Tuvalu = \n",
      "Nauru Palau Marshall Islands Sierra Leone Liberia Micronesia, Federated States of Moldova Lesotho Rwanda Kiribati Nicaragua\n",
      "Uganda = \n",
      "Congo, Democratic Republic of the Afghanistan Burundi Swaziland Rwanda Malawi South Africa South Sudan Zimbabwe Somalia Mali\n",
      "Ukraine = \n",
      "Kosovo Korea, North Guatemala Iraq Kyrgyzstan Samoa Vietnam Guyana Iran Belize El Salvador\n",
      "United Arab Emirates = \n",
      "Brunei Kuwait Saudi Arabia Oman Bahrain Seychelles Singapore Taiwan Saint Kitts and Nevis Malaysia Estonia\n",
      "United Kingdom = \n",
      "Ireland Finland Iceland Sweden Australia New Zealand Spain Italy Japan Belgium Slovenia\n",
      "United States = \n",
      "Netherlands France Canada Marshall Islands Denmark Germany Switzerland Austria Belgium Micronesia, Federated States of New Zealand\n",
      "Uruguay = \n",
      "Croatia Argentina Panama Hungary Chile Cyprus Saint Lucia Bulgaria Lithuania Ecuador Barbados\n",
      "Uzbekistan = \n",
      "Egypt Jamaica Cabo Verde Tonga China Azerbaijan Armenia Peru Morocco Suriname Vanuatu\n",
      "Vanuatu = \n",
      "Philippines Fiji Armenia Cabo Verde Egypt Thailand Bangladesh Peru Jamaica Bhutan Algeria\n",
      "Venezuela = \n",
      "Saint Vincent and the Grenadines China Peru Romania Mauritius Belarus Jamaica Suriname Azerbaijan Tonga Dominica\n",
      "Vietnam = \n",
      "Samoa Guatemala El Salvador Kosovo Korea, North Kyrgyzstan Macedonia Tunisia Uzbekistan Ukraine Morocco\n",
      "Western Sahara = \n",
      "Tanzania Djibouti Haiti Sao Tome and Principe Senegal Togo Guinea Cambodia Cote d'Ivoire Sudan Mauritania\n",
      "Yemen = \n",
      "Cambodia Comoros Ghana Tajikistan Nepal Mauritania Kenya Gambia, The Papua New Guinea Senegal Guyana\n",
      "Zambia = \n",
      "Mozambique Guinea-Bissau Nigeria Somalia Burkina Faso Namibia Mali Niger South Sudan Zimbabwe Chad\n",
      "Zimbabwe = \n",
      "Mali South Sudan Burkina Faso Cote d'Ivoire Mozambique Somalia Congo, Democratic Republic of the Zambia Guinea Niger Tanzania\n",
      "                    Country                            Closest Farthest\n",
      "0               Afghanistan                             Uganda   Monaco\n",
      "1                   Albania                            Lebanon    Nauru\n",
      "2                   Algeria                          Sri Lanka    Nauru\n",
      "3                   Andorra                         San Marino    Nauru\n",
      "4                    Angola             Congo, Republic of the    Nauru\n",
      "5       Antigua and Barbuda                          Mauritius    Nauru\n",
      "6                 Argentina                            Uruguay    Nauru\n",
      "7                   Armenia                           Thailand    Nauru\n",
      "8                 Australia                            Iceland    Nauru\n",
      "9                   Austria                            Belgium    Nauru\n",
      "10               Azerbaijan                           Suriname    Nauru\n",
      "11             Bahamas, The                          Greenland    Nauru\n",
      "12                  Bahrain                         Seychelles    Nauru\n",
      "13               Bangladesh                               Fiji    Nauru\n",
      "14                 Barbados                            Hungary    Nauru\n",
      "15                  Belarus                           Suriname    Nauru\n",
      "16                  Belgium                            Austria    Nauru\n",
      "17                   Belize                             Guyana    Nauru\n",
      "18                    Benin                           Ethiopia    Nauru\n",
      "19                   Bhutan                              Syria    Nauru\n",
      "20                  Bolivia                           Mongolia    Nauru\n",
      "21   Bosnia and Herzegovina                           Paraguay    Nauru\n",
      "22                 Botswana                            Namibia    Nauru\n",
      "23                   Brazil                         Montenegro    Nauru\n",
      "24                   Brunei               United Arab Emirates    Nauru\n",
      "25                 Bulgaria                            Hungary    Nauru\n",
      "26             Burkina Faso                               Mali   Monaco\n",
      "27                  Burundi                             Malawi   Monaco\n",
      "28               Cabo Verde                              Egypt    Nauru\n",
      "29                 Cambodia                              Yemen    Nauru\n",
      "..                      ...                                ...      ...\n",
      "167               Swaziland                            Somalia   Monaco\n",
      "168                  Sweden                            Iceland    Nauru\n",
      "169             Switzerland                             Canada    Nauru\n",
      "170                   Syria                              India    Nauru\n",
      "171                  Taiwan                       Korea, South    Nauru\n",
      "172              Tajikistan                              Nepal    Nauru\n",
      "173                Tanzania                     Western Sahara    Nauru\n",
      "174                Thailand                            Armenia    Nauru\n",
      "175             Timor-Leste                             Russia    Nauru\n",
      "176                    Togo                              Haiti    Nauru\n",
      "177                   Tonga                              China    Nauru\n",
      "178     Trinidad and Tobago                            Estonia    Nauru\n",
      "179                 Tunisia                           Colombia    Nauru\n",
      "180                  Turkey                             Latvia    Nauru\n",
      "181            Turkmenistan                          Indonesia    Nauru\n",
      "182                  Tuvalu                              Nauru   Monaco\n",
      "183                  Uganda  Congo, Democratic Republic of the   Monaco\n",
      "184                 Ukraine                             Kosovo    Nauru\n",
      "185    United Arab Emirates                             Brunei    Nauru\n",
      "186          United Kingdom                            Ireland    Nauru\n",
      "187           United States                        Netherlands    Nauru\n",
      "188                 Uruguay                            Croatia    Nauru\n",
      "189              Uzbekistan                              Egypt    Nauru\n",
      "190                 Vanuatu                        Philippines    Nauru\n",
      "191               Venezuela   Saint Vincent and the Grenadines    Nauru\n",
      "192                 Vietnam                              Samoa    Nauru\n",
      "193          Western Sahara                           Tanzania   Monaco\n",
      "194                   Yemen                           Cambodia    Nauru\n",
      "195                  Zambia                         Mozambique   Monaco\n",
      "196                Zimbabwe                               Mali   Monaco\n",
      "\n",
      "[197 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "############## Q5 ############\n",
    "\n",
    "internet_users_pc = np.float32(country_data['Internet Users (circa 2009)']) /  np.float32(country_data['Population'])\n",
    "country_data.insert(1,'Internet users per capita', internet_users_pc )\n",
    "\n",
    "internet_users = country_data[['Country Name','Internet Users (circa 2009)']]\n",
    "\n",
    "# Normalise the required data -> gdp/pop, internet_users/pop,\n",
    "\n",
    "\n",
    "normed_internet = np.array(list((country_data['Internet users per capita'] - country_data['Internet users per capita'].mean()) /\n",
    "                  country_data['Internet users per capita'].std(ddof=0)))\n",
    "\n",
    "normed_life = np.array(list((country_data['Life Expectancy'] - country_data['Life Expectancy'].mean()) /\n",
    "                  country_data['Life Expectancy'].std(ddof=0)))\n",
    "\n",
    "normed_gdp = np.array(list((country_data['GDP per capita'] - country_data['GDP per capita'].mean()) /\n",
    "                  country_data['GDP per capita'].std(ddof=0)))\n",
    "normed_health = np.array(list((country_data['Health Expenditure/GDP'] - country_data['Health Expenditure/GDP'].mean()) /\n",
    "                  country_data['Health Expenditure/GDP'].std(ddof=0)))\n",
    "\n",
    "arr = np.array([normed_gdp, normed_internet, normed_life, normed_health])\n",
    "\n",
    "distance_matrix = np.zeros(shape=(197,197))\n",
    "\n",
    "dataframe_final = pd.DataFrame(columns=('Country','Closest','Farthest'))\n",
    "\n",
    "for i in range (0, len(country_data)):\n",
    "\n",
    "    #calculate distance for ith country wrt every other country\n",
    "\n",
    "    for j in range (0, len(country_data)):\n",
    "\n",
    "        # calculate distance between the 2 countries and keep in array\n",
    "        if ( i==j):\n",
    "            distance_matrix[i][j] = 0\n",
    "        else:\n",
    "            distance_matrix[i][j] = scipy.spatial.distance.euclidean(arr[:,i],arr[:,j])\n",
    "\n",
    "    #sort the list\n",
    "\n",
    "    print country_data.iat[i,0] + \" = \"\n",
    "    temp_array = distance_matrix[i].argsort()\n",
    "\n",
    "    dataframe_final.loc[i] = [country_data.iat[i,0], country_data.iat[temp_array[1],0], country_data.iat[temp_array[len(country_data)-1],0]]\n",
    "    print (\" \".join(country_data.iat[k,0] for k in distance_matrix[i].argsort()[1:12]))\n",
    "    \n",
    "print dataframe_final\n",
    "dataframe_final.to_csv(\"./final.csv\")"
   ]
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