notebooks/1-Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to Jupyter Notebooks\n",
    "\n",
    "Notebooks are a handy way to combine code with text/explanations.  What's more, we can use notebooks to break up a program or analysis into several parts, running just one piece of code at a time.  For our purposes what this will do is allow us to look at a topic, execute some sample code to see how it works and then try writing something similar all in the same environment.\n",
    "\n",
    "So for a quick background.  Notebooks are broken up into 'cells'.  Each cell can be interpretted by the browser in many different ways, but for our purposes each cell is either a documentation (Markdown) cell or a code (Python) cell.  Each cell is 'run' or 'executed' by either pressing a key combination or selecting one of the buttons at the top of the page.  For instance to run a cell, select the cell by clicking you mouse on in the box, then press CTRL+Enter.  \n",
    "<br>\n",
    "Try that with the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print('Welcome to Jupyter.  You have successfully run a code cell.')"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have done this correctly, you will see the results of running the cell immediately below the cell like such.\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAEqCAYAAABa9iSdAAAgAElEQVR4Ae296ZcVx5n/eXk3f8E0fjU/z+kXP5/TZ7p9bp2x6Tmen0xPe8b2SMgI5DZuTbdabbdkmsKyhJCFJSFEy4uwyyVrsS0ko91oo5CN0GIESGKXKEoIKFFQILEUpSqW2ijQ9sz5RsSTGRG53Lz7Us89JyuzbmbG8s2I55PPE5F5cyQfUUAUEAVEAVGgARTINUAZpAiigCggCogCogAJkKQRiAKigCggCjSEAgKkhrgMUghRQBQQBUQBAZK0AVFAFBAFRIGGUKDlgfTZZ58Rlk8++YQuXryothtCeSmEKCAKiAKigKNAywNpbGyM7r33Xvre975HW7ZsESA5l1/+EQVEAVGgcRRoeSCdOnWKrrzySpozZw69+eabLQWkrctylMvlKPflO2nrWIFGtW05TZuWo9zcJ+jkZwWOzbL77H564vp7aXcF0tq6bJqqx+wnTmbJucbHDNATV6J8s+nxD8qobN9K+kYuR41ZxxpLKtmJAgkKtDyQBgYGaO7cuXTFFVe0LpByOZqxbCudT7jI6uuKAmk3Lf8cjPRy2lKGjebitjyQjqyj/5zRyNDlKyFrUaC+CuRGRkboo48+ingOCHWdO3eOsMYYTNwH4zKjo6OENC5cuOAcgnPGx8fVvomJCZUGjj9//jwNDQ3RiRMnCLA4ffq0Gtv59NNPnfPxP/JG2jjf388HIx+MDeE4lIXHiVAefHfo0CHlHX3rW9+iV199lc6ePRsci/L4H6SHc3Hchx9+SCdPnqTh4WFVFhwfpwWXFeWFlvgf5cB5SMPXxs+z1P8DDwleUm4G3bk1BUkVBdJWWj5NgJTlup3dtYK+Md14suIhZZFMjpnCCuQWLFhA3d3djqGF0b3tttto/vz5dMsttygg+BrhmMOHD9ONN95ICxcuVMbePgaG+de//jX94Ac/oAceeEAZ6F27dtGPf/xj+u53v0uXX365Wq6++mr6+c9/ruBkG3tA6Kc//Sn953/+J910000KInb6vA1IrF27llAPlGP37t1qAsPLL7+svvv+979Ps2bNUgvyQnmwXH/99QpgnA7WyH9wcJB++ctfEs6DZzV79mxV3htuuIFeeumlWHgDPnfccQf98Ic/pL1799KZM2eoo6OD/vVf/5W+853vKH3tfCq1zUCaPn26Dt393ZLk0J0AqUTZSwzZDe2mJ66fSZ9TNwvTabqBkoTsSrwMctqUUCAHY+0P9sMwsyG/5pprlKfjqwEv4MCBA8rgIhz2/PPPO4fASP/Xf/2Xgs7SpUtp3bp19E//9E/KW/mP//gPam9vp3nz5ilQAE7XXnstHTlyJAAjgPbb3/6W4NkAClu3bnXS538ALkAOafzLv/yL8koAqT/96U/qf8CPgYT8r7rqKrX8+7//uwM51Lmnp4f++Z//WaWF/YAyQAMoohyo57PPPqvgyvljDQ8I9eFxqhUrVqgyI1+MXwH41fgwkGY/8hda+fUcTcvl6Iu3bqXzcQ5tASCd7VtH9143m2b8tbmb/+sZNPu6e2mDN6wz8OQVGn7K0PKd/2x64phbw4HX7qX5c2fQ581xn//ybJp/zwYa+Ng9Dv85Ibsj62j51Xze52nG1cvpub1noyfxNwNb6Yll19DMvzFQnv4FmlnonI8HaOuTy+mar36BphtgfOGr19Dy5/dTNKdkIE12s/czg+a/0M8lIiI+J0e5v55NK94469bROlI2RQFRIFQgd9lll6mxFTskxkDCvn/7t39LBNL+/fsVZACM5557LkyVSHkSd999t4IBoPDtb39beSz79u1T4TCE1xDOgjeC8wGUm2++OcgLZYAHBhDAsANOfogNxyD8x2B75JFH6OOPP1ZQQ2gQIbPe3l4FEqSzfv16lSe+R6gQx/IH4UV4hIAOPDbsn5ycVAvCd7/73e9UGQEYeHrImz8A0o9+9CO1H54R0gDI//KXv9CxY8eCOvHxlVoHQMJkgL6V9E0VRvsiLXk9JnSXCKRJ2v2rbxjDnKPpfzOTZv7jzBBMuc/TNU/3E9d24IUbaeY/WsD4Rxx/A60b4Fr107oFMwJoff7LOr0vcNjqy/Ppz/2cmj6HgTTzqmtopjru8zQD6TJkctNp3pO2wdfn9b8wn/4eEzUAlb+eocptnzPzrphxtSPraP6XGaQmnwBMOZr+1eW0ZZTrgjXDxZvUcOQJmqfK6sNIn/PcTfPp3hf7A8BxHWvnIW2l5bnojYJds5bb3rZcTT7xb46y13OAnpibo9lPBo05+6kNfOTAk7Np2rL4G/pGK3ZVgQTjDKgBKJh2jfEcH3wAEzwpHAND/vbbbwfHABjwUgCr6667LhI6BKBeeeUVdS5CYzD+NiggdpZJDTjnnnvuUfnDM0LYzi4ntgE4hOQANkDUhiOAhNAl12Hx4sUKfDjGL08lG4ADJCLq//03tXGefgNt8GfdJQBp8pUbNIymz6QlL7odsf95Nvgz6M6dNuSSx5B2/8rA6MtLLEgR0ccDtOGub+q8vv4g2XhhYw2wfP7qlbTvTKjSwItLaIbyYmbQCtvRNLPWcgDmw/vJOoUwbvPNzwE602neatvF61eeJOez33aHzu6mFZd9Tuk3/apnKVQiBkgBjDSsw9Imb3EdawUktI1WM6zJ6po9AqQEiXQbXr4tYXepX4/300u/voueOjBeagqR86oKJBh5AAlAgbdgG3EuCQz28ePHlQcFg75s2bIgJIZ9b7zxhgIFYPX666/zaWoNDwaQwHmAAMJ3/qcQkJAHPDWM98BTW7NmTSxEAKUXXnhBAQnQAqD4w0BCXZEGQnRxdeXjK7X2gUTUr+7wELqbvmiDG7qLBVJooJOMVwC5q58N7vaJEoB0dh3NV/CYTU8ciaulvgOdlptOS16bDLwuNta5v7uTtk5Gz+v//Tc0KH68gfTuSdpwi4bHF+/YYr5zz5t85UYNv7+7m3abXZOvLTHfLY/Nh8Y20A3K6/miBT8PSEeeo2tUWDPea3NLEf7HdawJkJRhXk7NcU8calT2lgApWUJoU6lHPpDLeD89d+s8Nc4+d95N9GiFoFRVIHV2dipYIGQHryPpg3AZJi4ALDgWs/vwASxwHmCBfYsWLXLCbAjHYeIBvJZnnnkmFgJZgISJCAgpYsHx8MywACq8YExr27ZtCo4I2wGi/MF4GcqGMgJWAFQtPlEgEdGRJ+gKFbqbTje8HEKT4oB05AmarQByA22IAYGqQwCZ+bQu8CjigQRvS4XPrltnwctVYuDpeep5qOl37Y4AaebvDrsH839czunLDVy20k9UHWfSgwfd8B+fQrSbVvwdvKQQLltv1aG6mb+3/bPwDGzt/tUX9Vjcr7h8FpDe2UrLVbivOBgh3VoCSbULJ0SD8F2O3Dtkc3OwbIsRwByzdYs6Vl1HtA0nHSI6xm2Gw57Rqf9c1yCNuY8HHqcq29wngv9ZfS5zcDX9fO7gcvIZRGjTQR4ITz6ZLWTnl+/OrZwrh+y2qhu7IO0YQ+6nkbPqyGHe2U9G0wk9b10P1Bs3kCqvuY/TE/jfSYvIH7d1r6O+JrrPJ18TdROJNhDU1dKx2E0bRnPnKhs87/7ddLHYdGKOrwmQMEECM8+SPjDoy5cvVwbdN/aAEkJkgA4mHBw9elSBCt9jggG8L0xWwFgSvvM/WYAEzwseGICCBXn5C49zYT8mL7z77rtBVgwklOXWW291oBkcVIUN1YljphL3P2omHkyfT+uGTMZxQOIOHWMgwuJGDXuSh9T/+5m6Y/F4jhpf0mNIGJdSy5c/bzpf+IAud+4lr0evny6HNpZ4OFWNDxxj6EaNYVjus7TuOt3Z578Akmpjg44f6dDhSXT2hfn6AeLr1tEZVRwGUjhTDmnM+BX7XdbJKZtcx+p7SHHwifsuHkiBxqiLaR+BXgYSwf8KtK4BVQbWMajm5mXZVn0DotL0x7a88pnrC69dtwhzDWwwJJTNKX/kepg62+Uzeek6xbQRU2f7uhWsYzDuaLU1Tscan1JtwqoTtxEbbmr8Z5qll58OXxMLNNHyaSHU98ENSEQc54vxQ7tp9/GYUFwcjH6wkvbGHOokmPGfTEDC8zX+ByGsQpMa2EMqBCSeIg6DDiC9//77TnZ4ZgneC6CBqdfIG+cgvAdw+GM69smFgIS0MAMQwEFayB+AS1swU++DDz4IsrGBhPGwWoTrkDkaGIyj3Vl0oazQHRvWkoFkOrFjyOM9JC6Putvju76kdUxHDO9UA2nNRilA0vrg7lOHI2OMjZ8N/o/oxECC1tNp3q1L1BsXcrlv0Mq+uATiv2NjE71W8ceX/G0Wg68SN9c1MFBaY7d8RjP2kmLTtktqwGIZR+x1Da/JxzLMGnxhiDHWoDqG11wTLpcpAox3KpBUGrPpicQ3bnj1Nem65clSx/jyqf7BN38KhBZoVF467RBIRivvDSa6nkYvc02yvEVEwY3zN3WLW43vXknfU57P92hlj0WaBBi9E8VDXLKZvksEEqZm8yw7PGDqex/4vxCQMIYEj6IQkGDQ77rrLnUsQIAxHfsD+GDqOICFsSL8D48IYABAduzYESkfn58FSK+99pqCHTwwfggWEE5aMFZlQ6fxgKRDd99Vb1OYTvPXnokxtOEdMGLLfiiB9QtDXzMtAxwPJIS7ACOE44r5sLEu7CHNo+dQ0CI9pBteQTwyG5ACDykYg2MgTad5j+iQYjBx4+srnckZaXXmOroGP+2M0vY5xipIwhhRZ1A7HkhuSMc30MZgGsgn+bM6W24jJozk3IDoV1jpNhefR1QnfdzsJzFJRadte2oqzwLA1KGvNK+a83B7gwukQNSgHMENWFBH3Wb8cVkbSAoO00IIc6pOXkmwYbCqRy2KuCbqZiuaJ+et1h9tp04ThsNwyNy536OVe8bcMSOzf14FPSMuQyyQsBPTmAETPNuDcZw4IO3Zs0cBAd5F3LRv9pCQBgCS9IHh51lqACEmK9gf5P3nP/85CKMhbIfJB/CY8LArpmj75ePzfSDBI7I/OA8z+wA2hOL6+5PHF+zz7O2GBBJm3T35XTODbh4992TMu+zMXWculzKGNPQcXaO8nMJjSDDmqnOmAs5WTm8HxtoY/MgRfStpJspgjSHpVxcVOYa0TD+rlDqG9IuUMSS+s57cbcaScjT70WztJaijd7cbqWuZX1QXSCicAYjl+dqerTKovM+ExlTdA2PNN0Jh+BXjmCFcQgMbGHpOj6MBTpjNEswx1Nb3ZlMByS5H5JBsQCpcx0oCyQDd0kDrYntXfNMUHmtfk6CaCkj2ecEeZ2PwxSU0z4PS/B+YCQwMo0WPUm8FPSMuQCKQfvaznwXjM3gA1jf48BC6urpUqAtA8h+MhRcDIPEsu+3btztTqbkASBfhL3hGAOBjjz3meB98HMJ2mNoNL+nxxx9XD6IixIY3eftl43OwBgj5jQuY6RcHJKQNaHL4zz/GTi9uu1GBpGbdXTVdDZoGb3NwOmQRs+zsuHvSLLsAXknhrEnauuyL9Lm/mUnf+JUZU+CQDjqcNx2ctWaPZHoQoililt30O4PZZtln2U23DCR3dvc5pMk3ltAXUebp8xJmFHLp9bp1gGTXSxvwadbYnoJLasgO52vowIOIAtTsSwV3HT0kcxPnepJ+WLKSQHLbna1+/LZ3TeyDsnhI5nhA6bsOlPTkBdjSeYsepfesSJ6dRbnbiUDasGGD8kBgpB9++GEHEjDYeKYIRhwQAZDw+h77YwMJUELYzh534WMRIluyZIlKBzPs8HbuOMBg1tt9992njsNMNng0eCA2Lk1OG2vM4MNxqMfKlSsjQMIxqA9eU4RjAEa8FSKuDHa69nbjAskO3UVDJ6hDec8hzabHnYdcJ2n3Xfwc0nx6wnnDwiT1P/+fsc8UsbHGnd+MH6+jkx+F6uJZKPUc0vR59Jz9Noh6PoekijdJGxZpj2v6Vc+lhDx1XbiO0VBUWNeKbMWGrUIAhGE244kEs9f0/66hNd5QcCMQV0ILDipvPyRmQnfOjZAZ/5y7nJbP9WfyGYMajG1xnnb5zE2CVy4NtxQPICkExlkY7y8u1BbMfMtUx8JA0mFnv6zmmvDNXwL8/DG3oPjBhnVNgu8wW282TSsiejH44k88T0nDqBqeERczEUgYJ8HrfAAcUBFvKkDoDgu8IUzFZjAASAih2R8bSDD0OAbhuM2bN6uxGaSPtzZgujT2MfhwXtwH0MCDrygLyoQFkxqSjuc0AAsOPwJiGC/CmxdOnjzpQBbjZHjHHTwweGL333+/giOeN8KC8iJEiYd9AU3709BAskN3uKP3DANRcW9q0PVmr4HfkHANPREM8PfTc1ebmXR40NW8qSF4JREeZF3tTu9mYz3/jjsNfL7gvXVhBi1/I5hzHkhf1zc1oBRD62i+em5pOumZfEHRIhtcx6oDyXgfYQgMRTFgsa49lydXDJBiYKeMHI+FGANq1zEMb3mgUmnpmyS3rOEYIcDAAFXltcqfNAMwdVKD0SGAi7pK2njrvLROqUDKVMcMQOLIgFWn6DUxELFn2VnepSp+DGSda2K1RHUtIqC3DojZtD2lanpGnLUCEp6v8T0CAABvx4YRZy8IYyz4H1DAy0zxCh2M4QAmTz31FKep1gwknIu3NGCKNiCGNDAZAa8kwhgQzkWaAB6Mvl8OO1GMNeElp0gT5+FVQGnH41zUA+NbPK0ba5Qf6QAk/MFxmMqNl64yIHEcoIsFkELZscYkCvvT6ECCQXrOhO6iQNI1Obv3OVrhvcvumkUraYM7vhtW+8g6uuFSfhdcjvTEAd49Sf677PBqH/VuvJj0uCMi7n127xN0w2Um3elfoG/gfXox53BONLCBVi5y32X3jetWpL//Dm+NePgG9112l86nFUW+yw5lwHNV6n149hT7oHDhBtfRNtbh3spuwfBEnh8yhozHZVAO10DZHgiXJ8ZDskDCYxnODC+epchjHsu20kkz+805LihPwiC7Mfxc3nDmGZeNx6J43KT055DCa5IBSMi+YB2zAQlJhcDOEW4O3Gui6+o/h+QDM1Ke2N/uiru+lpYpm4Mv30Xzlz5VtTCdnbUCEu78YZD9D8JkGPAHfAARzEIDgPCOOnhK2I/3z8GT+uMf/+icbgMJnhHGctjgI9SH9LAGGGDgkVYhuOCB0zvvvFMBCeE9hPuyfACMJ598UkEQ+WLBC1ltICEdaAAorl69WtWJy4njUW+8QPXpp59WkyjsfFFXzBJEWBJhxUL1sM+VbVGg4gooaCQY+opnJglWTgEGov26qwqljjZheWMVSrXiyai3fdtv2fZzgHEFCDDwj5AZHnC1DS4mN2DxgeYDiR+MxbGAGcZ+ML0bx2X5IE+EynjcCiFCQCzrB+ejDMgXdbFf/ROXBmCF8qHOmKmHMF9afkk6xKUt34kC1VYAd9qRO+lqZyrpZ1fAeFlOuDIm/JY9wbQjtcfm5JV2eB335eBppE3J5rLBoNsg4u+T1klAwvGcVjHp4SWsmPmHcB08LgZcUv5x3xebr318MWWNy1u+EwVqqwBCNP6geW1LILmlK+CH4tLHv9LTSturxpS8CSBpx9dzXw7vkMNMtEp/0oBUbF7wVh588MFgbAeTKtK8lWLTl+NFAVFAFBAF6q9ALukt3OUWrVwgwSNBGKyvr0+9GggTCjDZAONXeHBWPJZyr5CcLwqIAqJAYymQg9GvhnGHB/Ob3/xGTaPGbxlhDKaYD8qEt3njHXaYUYfZcZgA4Y9hFZOmHCsKiAKigCjQuArkqlU0gA7TrfHQK15+iinbxX7gCfFUcbzBgX+Woth05HhRQBQQBUSBxlegakBC1THzDmDCuhQvDOdgSjimd5eaRuNfAimhKCAKiAKiABSoKpAqIbGAqBIqShqigCggCjS+Ag0PpMaXUEooCogCooAoUAkFBEiVUFHSEAVEAVFAFChbAQFS2RJKAqKAKCAKiAKVUECAVAkVJQ1RQBQQBUSBshUQIJUtoSQgCogCooAoUAkFBEiVUFHSEAVEAVFAFChbAQFS2RJKAqKAKCAKiAKVUECAVAkVJQ1RQBQQBUSBshUQIJUtoSQgCogCooAoUAkFBEiVUFHSEAVEAVFAFChbAQFS2RJKAqKAKCAKiAKVUECAVAkVJQ1RQBQQBUSBshUQIJUtoSQgCogCooAoUAkFBEiVUFHSEAVEAVFAFChbAQFS2RJKAqKAKCAKiAKVUCA3MDBAsogG0gakDUgbkDZQ7zaQu3DhAskiGiS1gXo30EbIP0kb+V76jbSByrYBAZIAOfWGpBGAUO8yiNGprNERPUXPpDYgQBIgCZAKhK2TOo98L4ZV2kBl24AASYAkQBIgpbYBMbqVNbqiZ7KeAiQBUqoxqne4rBHyFwOSbEBEG9Gmkm1AgCRAEiCJh5TaBippcCQtAVhaGxAgCZBSjVEjeCj1LkNaB5J9YmClDVSuDQiQBEgCJPGQUtuAGNzKGVzRMl1LAZIAKdUY1ds7aYT8xYikGxHRR/SpVBsQIAmQBEjiIaW2gUoZG0lHwFWoDQiQBEipxqgRPJR6l6FQJ5L9YmilDVSmDQiQBEgCJPGQUtuAGNvKGFvRsbCOAiQBUqoxqrd30gj5iyEpbEhEI9GoEm3AA9IxeuXuxbR48SrqTjHU3Y/yMd20avFiWtU91S4G13sy1ZhX4gJlTuP4K7Ti0e6Kl6dqQNj3NN00dy7NtZZ7Np4s/s3zG++huXPvoc0n8abmd+npRXPpptXvFp9OipeU+Rqk9Bknje5VtHjxCnrleLTf6L61mBZX4Vo6ZSiqrOn2oKR0s+Zf8Dhjs2L0OvbyClq8eDGtePlYpF8one9+hY5d0OfHHRPWK8xjsmB5otc0TCdmX5X6bWqela7DhQsU6plQx4yc8IB0gS6ozhJ/EVUlIeDiBuowVRC38MVsNCCFHaZw2WMaTIqG1QDS5s4rae7cm+jpd63X/TOgOjfTyRQ4RMrTQkBKM6CVvq6Z01P2oJGBlGQMTZ+ArVLgsdu97r/pELKPD/tXZYEUppv5eqT01XqmUT0gXdAXK3oR9QXSnSb+7q6egtQ2bwFSBAxZIbLxHroSMNpnwYjPVXCZS0V5Si0CpIaEEYxfEwBJl9G3SeijK+iVbtxAe/vMTfWq7qwRjhAcAiQb1OF2FYF0gZKhYy5McMfBhjks2AX2oHBn4nlSOl33biv6nc4j+92LlXfN7h643tyguczdJuRp6h7oxGU0+rE2yo3lNMwxKfoBukqvR18J83n0TypsqrRW6a6i7knOr/x1yeBhyDhrHVKb27k5IaS2mTZv9EG1me6xwnpzFz1N79pptgCQdB9Ijkrwfr7GriHVbWrVyyZywaERBomJeMSfi/ah2zLvj9yIcjo161ultFnuj9a5Qbmj+wKbo/oJ9107rOf1U+5vj3ZTCKTkY+ybY2Wog/5ug9HTHcMkVnmC6xFnIxrwWpQCpDhtoiE7VNYYxQgUgjsLvvDexY6cZy4aG2a1374ofFHt77w0G1B87sShYeB6WONpSVrYsW7Vaay6J53D+jGQInFxk7+ddoV0qyyQNFyye0AGRhbANndi3InHjAZooNmBZIAR6Wvm+mnjmaGN+F6ASdcGjDYA9g2hMYqWoQ2O4ZuawLBzn2/EdbT9K91Mf1B18usY/K/PDfU3aVl9LjCc3jkY59OAMud4N+CBltwXzTUJx9z9cvv/s4dqXX9Oq8HWqq6WZjaUmSdhvU2YNYAw13MxxQMpYZAquLMIxHDhEVsoY2S18dbHBxffAGrF3ZYhb4oOwPVm7yamIUU09OoeaBh28HT99HHRa4Dv4/N3GkVMfln2VxRIZpwoK5DeXX2Tho/tEQ1oSN20eq/2spoZSC9jcgO86SSDE99mgjZg31EHxtG0J9WPvHSDvpjWlrw8m6I/mshBYBB1f2A7o/RK2Md9h49NDv/pcXMFoDhNjLbBZBRPa+5rbh/3+62nfYl9lvOq5VrVK/AETYTI+z8AUoo2iUDiyQ1BIuzaO3fhWkB9TJKYbuNQBec0cGEf7dYzNEyHcvY37AXhertAChq1Kbfb+PiuIG6QFQYim35u5zLGpyWBlBzeU14Se01NCyTdaVe8zGFe23NJgQralurQDBu3fwVGKMVo6v7qG0NuS6ad2n20wKzbIM969le7vo4+nl7+Pm+WXaH+BSD5/VrX39UzuGnwNbHLGdNvVdow5AFAw+vSEDr79TH/x2tiyu4BKE2bZCD5YikhuROwSGyY8b/etmOf9nZgrK0LgkqozoHv1AXQFzUMhXE+jbbmehcHJDQofTGsOwju+Bn1K9RhKt1oK+ohGe8mm4dkgGSPH9nbLQCkoE+YDhvcXXOnV33FaivOHSf3xTKB5KRp5cXt0uqvlW5blU2P+6QJ/zgG3dIoUh9rXyJsQkgXByRLT0dnvvnQefvXPdlGNJodDMtTPJDitUkBEhtPLV58hlYjSLzDDwutGmBwh4KLYToVfxc3I4Y7Z0Otud7FA8nuhNzwtGHSaQZGKqG+zQ2kZK9Hg8/eb2/7Ex2s/5vWQ2Kg6P7htgXTZ5TxdI+z24/edg1qsD9ieNlT4PB4vDEMzuf2F5cO72uodaiDslVeCJO/UzozbFX5w/NQ90L9qzggMXg8GxjoVvgaxLaL4PykdGv/fTwfTDmyekgXLiSPIemGyYaX135F7e+TxOVjPOP96CpaEdzF6GNW3L2iSVzVhDp5D+ClXiTTqLijTPoeadDoOC+tfaEOEzEoQTr+tcv2f2U9JD0JIeu0bxWa82fVBQ+/tsAYkvdgrGoL9nhS0JG575hrxpCwxpAiNzJ8jH39g/R0OvHt0zXQTTHt29RR941VtMoek+b6Q4+79T5Xq7j6+jcBug/Ck8k8hqT099PxgZdkM92+GdoI9/tK9/Vy0otvS6a8XruLH6fT2qR6SCig7iRwr+Jo7xpLnk1hX/C4gjL1bVeV8yriK40AACAASURBVLHPLUeg6p7L9WZD4TVq0wncuutznPr5F8r8bx/jpuE3aG6g2Rp2KZpUHEgDA5T9wdjoLLtgooN6M0MLzLJjg6nWxvBZfU33Fduw+e0ovu3FgsRvbxwmZkPLHkJk9lNc3+e210BrU7/YSSKJ+3z9TF8KbpYtGxjoFPY3BSi+mfRmv2qbZmkX0T9MR/dN/9r6Xm0Dae2026RxNVPeSL1ZU57qHtazIJDiIBMaNi1gOPEhTDgYP7IubHCeunvg0IEudLTjNa74PF4WjnX5jVqX3YcJnxdo4zVgpU/QcUyM1dNP3wXilSeePkbT2M7oH1vE/9UAkkpTPSCb5dVBBkrB+NE9tNmeddciIbugbwTXPzRkwQ2cGYewb1gis8T42qr2EKZhty2nvzKUgjEO75y4dDiPhlsboHt9RmtrjL8NW1X+uL7Lx5o+mOE5JJ6c4l4bNrzheImrfTjdOey3pg7B9Uh+Pi1oMw1wHaK2zrJPMUBC2TWwXW0KA6kBKttIwk+1slQNSDZUGnx7ql1zqa9lTDPbP74556hJKWnIOQKkzA1uajYWAdJA5MWcYrCnZl/g6x4XzdHfWSEosSsl9RsBkjSc1IYjQBIgsSGWdQhiP9wUP8YeHi/aZdNCgCRAKhtIJ0+epFZeJicnSRbRoNnbQDNAUYAkQCoIpFaGTZa6NbshkvILTLO0gUYAlgBJgJQIJDTiLAa71Y/J0pnlGDH6rdYG6gEoAZIAKQIku2MlwebEiRM0VZbz58+TLKJBq7UBu5+nbdcSTAIkAVIApLhGyUCaKvCJq2erGSKpj8A1rg3E9X/+rlZQEiAJkBIH7NFo4ww0vjt+/PiUWSYmJkgW0aDV2kAclPg7BpG9rgWUBEgCpAiQuFHaQEoD0LFjx6iVl/HxcZJFNGiFNpAGVbvf87YNJN6uJpgESFMYSNzA7DU3RF77IPLB88EHH1CrL2NjYySLaNAqbcAHqw8p7vu8tu0Db1cLSgKkKQokblj2mhsg1txIASQbQq0On7j6jY6OkiyiQau0AR+sNqC432Nt2wNs27YC29WAkgBpCgLJb1h+w+NGiYa6Z88e6u7uDpbdu3cTL2+//TbZy1tvvUWyiAbSBhqrDdh9lLe5D2Nt92/0d/R7tgE+mHzbUWkoCZCmOJBsGNmNEI0Sd1JorPIRBUSBqaEA+jv6fVYoVRxI8q4y69dHG/yt05W4VjyNm9c8i47Hijg8x6GrTZs2TY2eKLUUBUQBQn/nvs+2gG0D2wqs2X5gXQm7xGnk5BpMLQU+++wz4uXTTz8lXj755BP6+OOP1fLRRx/RxYsX1bJ3796pJZDUVhSYwgqgv3Pfhx1gmwD7gIXtBdsQrCv5ESBVUs0GT8tuRNywsObGhsZnwwju+DvvvNPgtZLiiQKiQKUUQH9Hv/ehxDbCthu2PalU/gKkSinZBOnYDYgbFje0OBhhALOnp6cJaiZFFAVEgUoogP7OExcYTOwpsa1g22HbE2xX4iNAqoSKTZCG3Xi4QWGNRsYwYu8IDRGNEpMcMOtGPqKAKDA1FEB/R79nKBXylGy7UgmFBEiVULEJ0rAbDgOJ73gYSGh8Noww00Zm2TXBxZUiigIVUgD9nWfYAUrFeEmVKIIAqRIqNngaNoywbQMpDkaYCo67JEz/xHMK8hEFRIGpoQD6O/o9+j8/DFtLKOVKfYgNscZSz63GeX19fXTkyBHlaiY1HRAfxxw8eLBhyo5ZLdXQw05z165dxMvOnTvJXnbs2EHbt2+n/fv303vvvUdnzpxRjRF3SXgyHQ/SyUcUEAWmhgKPP/44vf766/TGG2/Qm2++SVu2bKGtW7fStm3blJ2AvbDtB7bZtmBt251Stkv2kJBZo30wlx3ASfpgH45ppE8tdLQ9pDTv6OjRo3TgwAHlsuMuaWRkRDW2RtJLyiIKiALVU2Djxo3qOSQ8e8TPG506dYqwDA4O0ocffkhDQ0M0PDxMp0+fVgtuYnk5e/YslbO0FJBwmeD9JH3gRTXap5GABBcdni9gBO/o3LlzAqRGazBSHlGgigrAIwJ0GDiAC+wA7AHsAo8v8RteePIDwnoc2uOJEKWsWw5IaQY+bV8Vr3Fq0tUuU5J3ZM+uQ8NBw0Ijg6uOxgfvCI0RLrl8RAFRYGoogJAcvB0bQjyexMDhaeAYf+aJURx5wdq2OcVuC5Dq3M4aAUhoaAASGh4DCQ0SDRMNVD6igCgwNRTADagNJPaIYB98IAFGDCIbPOUoJUAqR70KnFsvIPmz6+AdAUgY0GTvCA0TEx7kIwqIAlNDAfaQEB2BHbBn3AmQSmgDaQY+bV8JWVXklGqXyb5z4bsZP1yHhgYg4W7IBhIGLQVIFbnMkogo0BQKoL+j39tAgl2AfYCXhPA+h+ya10M6sYF+/oN5NO+qwsuiJ3rpQhmXLs3Ap+0rI8uyTq12mbIAicePGEgcrsPAJqZ7ykcUEAWmhgLo7+j3ftiupYA0vLad2vJ5yl/VQeu376SdMcubD5pj8h20p4zXIqUZ+LR99WpuZZfpk2Ha+cx9dN8D91HXO6ORamQFEsJ1dQdSdwfl0U5il3bqGoxUr+pfBG03KFMHlfJ2v+G1Cym/oIuGVNsepq72NmpfO0xlNPWq110ymHoKJAEJ9iHOQ2IvybYz5ahWkzGkoFO3ApBGd9J98/6eLlm8mg6PlyO9PrdcII2+ukTDHgbzK/c5xtJuJNhOCtmhodlAgrsOtx3PG+ChuJp9FJDaac2pqJnu6QSoSoNBaeXvoY62NgsiOhW0ZQCzo8jfLRQglXYV5KzaKoD+jn7PYTvMuMWNKgMJ4X07ZMdAsmfXlVPi3OjZUQqWiexJFWNIAyAFd5lJd8Ha6DSshzS6kzquCMs+4/rV1F+EZnHqFqNj3Pmutq7BtoFkw8geQ0IDYyBhAHPz5s0qftxoQCLqoY58XnkVcTpU+jsAsG3BGhqOSbgUOAqQYoSUrxpOAR9I9sQG2Ame2BA35ZvtTTmVygXjOt/5Gs3w7rDTEi7GkLpGMzTouNO85AprXAllyM+hZa8cT8s6dV9audL2pSaKnYDRnLZIOAlQKsdTKqtMRORqWxyQMEBZCEh4UK5mnxQPiWiYuhbkKd/JATP9f/vaIad4PZ22V8OhsR59Lt8QLeiKBU2Q0GAXtcML2h311NQx3T2OJ6q+M+cE4cZO9y3phYCkIRf2DYTz5CMK1FoB9HfbQ6o5kIIKD3bRwjbXoAX7YjaKMaSu0Qw7XST0oTr1DJrxlTm07NUTMbkW/iqtXGn7UlP2PKPA6BgDV46nVHKZTIFdbd3rx3csfriOPaRCQMIT240DJN9Dyg4kp50ZcKQafAXGIsYyrTQ1wjQIwzEj3DgkjyEpGNmQNOkVGxZMbcOyUxTIoAC/qYFDdgKkfAftfL+L2gGlEjylNAOfti/xWhWAEcOpVE+pqDJN9NB935lB+a+0U9cHusTxQLpIPQ/Mo7/PX0KL/nycPvXGj5oRSNEwWRFACrwqrVkEAN7FV/AoYnJNbHoMFeNlJQPJ9/y8wsi/okANFUgDEmbaccgO40iwI1h4OIBvgMspbjipoWE8pA7qwW3mB1208P8s3lNKM/Bp+2JFzAgjG0rFjikVU6aeB2aEIUMDpSiQLlJP59zwuHw7rfvw06DRcCNCg2pMD8n1oFnb6ISG7EDyvaFYgFgNoDgg+Z4bJ+SWLxlIOuyq6+l6uJySrEWBWikw8//7Fb3+1PU0a9Ysuvzyy+nyy39DO0bDn6OwgcTjSC0OpEtojnleac4/wDjNocf6EmL5MVcpzcCn7Ysm1U+PWRMY8vkZ1P78DlqNcQwei+h8g3o654Sz3PJ5mnHHBhqJJpb4TTFlutjdQXM4b6y/0k4dv7w2LE9+GXV0zrH+z9OM69fQsY+SZ9gljSHhWYRGCNkBuNA7Gr5yDT4LHD+G5I7HFAISFRWy00AK2oR9fTAJo0uPcaUBSZVd5Wm1LTuEx5WTtShQZQVmzpxJ1z+13zyL9B49f8Pl9K17d6hZdr6H1PpAoot0ott9TumxxZhZ5Q5cp12TNAOfti+a5jCtv549EsDoBH3GA+tsdNTAteuRXPLLnUU92FtcmYhGd3lQ4rLErGdc/0c6PP6Zetkh38VwuM72kNDQMK3TnmXXKEDCdVEAyfvPIVURSF64LdI2eL+a+p3kIblnFQSSfTjDyQs12ofItihQDQVmzvwxvW7e9o2HYw89eyN968Y1dNT8YN/U8pBiFIYxqg+QiGiilx678Vr6+Us8wUIbweBuOJhJdZF6H1lE1/50A534JKYSKV8VCyQklQVKGNM6NKZh5E9qSHqPHQMJDbGRgMRTvjFJIPR1zLUIrgGU8ScT8Cy78CwcVdBDMsdkm/bN5eDZf3yxNah4pl5RQDKzJ936crqyFgWqpwBCdvsFSMkC1xVIkWIlASlyYOYvSgESEk+DEs/640HG5gcSkQ6juc8hoW20WQ/LBuNpMW9EsC9IFiApCGZ9MNZ4TPabF9zQYdosuzigZfO67DrJtihQCQUESAWeeBcgJTezOCjZs/1aCkjs2TjjScYj4nBlZw8NYcypIkDSumvoWWM7FgCdK2OgFHjP3kO16R6Sd6OD+ki4zpFX/qmNAlMCSKObl9FX2+xOHW5HB6td4QVIrh7+f4DSXKMte0Z8TNMBiQsua1FAFKiLAlMCSPHK6rvC5gLSRdr5y0vMrLoZdO3zpb9RgjUpNWTH52M9vGs13ffI+shrjARItkqyLQqIAoUUqDuQavHqoHgRRmnDLd6rg2J+ngJTv9vX1WOWXXyp8e1FvP9v9GLyAUXsqQSQkrITICUpI9+LAqJAnAL8YCz/BIX9poaaTPsOXqwKI1vEi0IrYkgvWi92tV/y6m1fLGLmWlq50vbFXZxafFfNMgmQanEFJQ9RoHUUqDuQSpWymoa01DLhvLRype0rJ89yzq1mmQRI5VwZOVcUmHoKCJAqfM3TDHzavgoXI3Ny1SyTACnzZZADRQFRgEi9TBlvaKlbyK7Uq1BNQ1pqmXBeWrnS9pWTZznnVrNMAqRyroycKwpMPQXEQ6rwNe/r60tM8eDBg4n76rWj0YDU3d1NdXtTQ70uguQrCogCSgEBUgUbwsDAAB05ciQxRezDMY30aSQgHTp0iPbu3StAaqQGImURBWqoQN2BBINYytLT01PSeaXkleUceD8ADn5mN+mDfTgGXlSWNGtxDABQrXx27dpFvOzcuZN42bFjB23fvp22bdtG+MliNEJcz3379tHg4KAAKakByfeiQIsr8NBDD9Grr75KGzZsoNdee402b95Mr7/+Or355pvKTsBewG7AfsCOsE3Bmm1NOfYs/D2kFhe6EaqHefy9vb1VA5DfELiBYG03HBtI77zzDuHXIflt33juwA7ZoVHKRxQQBaaGAk3rIfnGb6r9D7AAMMV8cA48kFp9skxqOHbsGO3ZsycRSE899VStiiv5iAKiQJ0VqDuQzp49S7IUpwE8CBhyAKaYD6Bdy08WIOH3TdAIkzwkuPDyEQVEgamhQN2BhJ+xlqU4DTAWNTo6qkJvxTRTAVIxasmxooAoUGsF6g4k+y5atsMftCukBSBeLGCKPb7cxmjXIe0XY8VDKldpOV8UaA0F6g6k1pCx9rXAr64WC5hijy+3VgKkchWU80WBqaWAAKlJr/cnn3wiQGrSayfFFgVEgXgFBEjxujT8t1UH0kQvrb71Wgp+HiTmpzni9l370/XUb34ZQzykhm9GUkBRoKEUECA11OXIXphqA0n95PW/dtCL23fSzsxLF/380jx17P5MVaTpgNTdQcFPgPPPkgfrduqq3Yz5oCEMr203P8jIv3LcQT3B3uwb6T9hnj2dmhzZ3UFtbRn0jrtenT2kW18xJe2hDuen6Ys5N/lYaJ76A6D+z84HbS1P7WuHkxNO2KPayoIuKv7MhATr8LUAqQ6iVyLLmgCp6M7Nv8LbzEBqpzWnoiYNP2Wfz5cGg9Kudw91tLVRfkEXDVnFgdEBNFMNXUyGrQYkDWofWrr95ResKdIoVwFIg120sK3AdTJA4hu44LKZ74uFkgBJTwoLdCxhQ97UUIJoOEWAVKJwaaepO+54IBFpo1WskUjLLm0fANiWYFhLgWNrAUmDp31tzC85ZwFBRPgGAxIRqWvcWZwvLEASIEWadq2+qAmQ7thAQ96v5zq/8BvZ10OPXYW7wtbzkIjM3XdgJOKNYk+n7dUMU1d7G7Wv7aGuBRxyyyuvJzWsknTnzI2ruycatvPDP517+Gi1LgQkDbmwjJnAGwmZuR6L8uYWdFGP8ep0ONQ9RhVOheg473bqWovQacxxQY3MzUFXDJCCY9wNBfggJOanHQOkAnqq1A38gjAvh8t8XbxrEZQs5TrHAsnPz4tgxAFJXYOg3nYoULfNvJMGt3Gr7ag8fb2CGlR8oylDdoMvLqG5t7xEtQ3p76W9WW9YelbSlXPn0sqsx5dwWWsCJKshB50uw3ccgmjOMaSsHlJ2IDkhNmOEUg2+MmgdtMcK1aU2EStNfYoxNla4Lw1IyvixMUVGbCi7U3KNGedRMM6H5Q6MoQ3xdhvYRGSMN7cZzjsdSNqDaENbtOqYVFq/XEGeQf08IBnDj2uUpGeQhhkvRd6OjiaN1NAq62yloeqgvvfCw3yNAwgbeFhetA8kVR4b7JyGGZ9y20R43Z2Qp7rOXlmShK7A900JpArUu8gk9tJKACarhcgIpKeffprGx8cTy4J9OCbuUxMgLVhNxz8murirgy7NX0oduy4SfXKCVi/I04yfvkmjnxANv7SE/j7fTqs/IKLRndTR9JMa4oGkO7fdMYsAUmCQ9ZV0DFfMxVWGwjLsMYc4X8Wm5xk71/iw5waDy3fFxd09Ic82/85fwSXUTwPJ1owBxMfE3aUTKcNqG1KntuE/GjTsWel1BAAJYHA1c4Gk6mYZepWjp6fK2/EuwnLx8VnHkOJv9lyvROXnw9ermwMkLm8AXV0+55pErtdCyi9op3ZrrDT2OntVreS/AqRMalYeSADN3LlzadGiRbFQAoywD8fEQakmQLrjTboAfVTj5g5ykd68I0/znujXyu17iC5r66Cdn+Dfflrd9CE718CFxsIzrMaQ++MYruEIDb/dzFxjaO/R28UByYSvIrOyXGAmA0kDQNfTr2O0bHHfuGBg2Jh0bc8LJztGUE/ciEAkxvuKyzf8TmsQd6003GLqpcrB39tAMnoGngjnYuupyx16UHyMtfZgYe0JNxkavodkxivh/enQblJ+YfuCJ+cAyegcmRnKeSpQmXqb/HEdO7rta+LuDwteva26AwkGd+7cJfTSh1zJvbTySoS7NAT0/rm05MUwQOeE7D58iZbg/BdXKuMdpDfI8Q4DkxdxHPLSSyScZrwa3o/8dAq6PPz93Af3qu9VGaz07PJRBg8JwLnxxhsDKI2NjbEAClAMIxwT50XVBEj5GfS178yjOf9gjPQ/zKF53/kazVBhu0tozlXhvhlfn0fzrrhEzwAzDbwVQnb6jjJutpRtoIJLR5UAkjbaYegrTD1uSxuN0Bi7QG03hjUNSCpVZcCscwNjGJenBktbGx9vblYc2GQAUpLRVt/zDVBC/klfm/EohoUGEpfTXycDKV1P22gnFCSpbvbhDIcIkEJwa6Do/BLLZEKLUSBx/axMOU/jOYUeEPLQx6MNK/3UsR3Uw6bUSqZam3UHEio2+OJP1JiQnm3LAFhJe7nWPYBNCK0okACZ8HjsvzL4n8EWng9gRNKbu5LeCYTX5yx58ZQpgYEah+zU+WF+xFBkqGYAEhL2oYT/sRSCEc6tCZC+cjXdt/5NWn3HHAWaOXespjdfuo+u/kqe8lcso9Wb19N918ygfH4GXf3AenrzmWU0B1OSWwhI0Fp5NJEQUhWBxEYjzlChQLzfusuFAUn7FASSfTLDyQs1hofouuezhOx8sDnQSjDsRXtIYcnU5BOMU5lwmgZSjGG2TzEeifbUkjwk+wRdboaevSfYrgKQUvMrykMKYR9ADG3KXCtuK3swGcUPEwYVrM5GQwDJrZoGkuNxkAGCCXPHAckd3xmkl5Zcqbyqz8y5IVx0bnsfnEvwdogG6aVbYiYgKKgwdFwgKYAGwHNLr/7LCCQc60MpC4xwXk2AxDHys+tpUX4RrT+r6woDfe3zJ/Q/H6ymfzN3VjwTrfVm2RkvxDGucUbZjIkEnTgMqWixQv3CcIy9J9xWd67+OIbZrQHJRpbL4Y8BueEWNjL6maawXME9WJi12oKhSi6jmzafqkN3xYTsjF7czkxCGiKh0eT0g7UDteBbPtvMbDQTEhLg5tbP1EcBnvW0ZpqplO06x5fbKUlFgGRdYwuyYT4u0AO44ADnpiU8Q9XbGiPCcQvb2qmjs52C8LPRt32BnpWX1EbCVCu3VXcgKU8lCK+hYhyysytZCEhL6CUnDaK9D16pgMNAcoEFr4xn6um0g5CcFYYLvS4XSKqM1nEuPHFLXdwsOxtKKEdSmM5WpCZAunQJrXm3n3oebqcZ+RnU/nAP9e/roiWXwkP6Oa3f10vrfwHvyeo4C1p02rfxGmxPREEjqLuJ4Tszv0LDb187BRQHbvZe3tbGxr9D1QbFCyEa42PfQbuhQ5QNA9b8kG1YrvhJDdr42nXlUum1Mdo2ME2oDGEl9pBdo29S8GFidOVz2JCmz7Iz+VvaB+VT5eD2qL/VoLRCoBFjbQNJG3NMSEjTM26WnWPsywJSjP58ja2xLf+mBfm3We1K37hYYOc0HG/awNW6bvzMXfo1CBSv6EbdgQTvRMOAoVJjIKlwW4yH5MjsAyncqcBm4BSAqUggITWGUhYY4fiaACmfpxlfn0NfQ4gOhvYrX6M5X0eITv9/yRVz6BK1zQZAG4rW85D09dYd3IZB2JmVJp09NOSEOULDH7YYb3qwvSNmWxkdo7fWnbX2DjbGhq+NM3VXhXOSgIR02MBbYyyJ4TrO1wAzKFsH7TnVRe35PIXjVjFelg8kJGfBTBnBgs8h6TIoyAb5m7J73lZQWswKtI51J1J4QMJJBfRU6RroBJp7gNQgxNT0hDdH+HlY5Yu9GfDzC24wWA8XSPiWb2C4jG699XkRYHN7SCq3Pq0qf+sOJF0rHTbTYbVSgORPyS4mZKdhA5gku6bJQOKrEnpcxXtInAaghCXLpyZAWryeRiDKkdV0df5qWn0EJRul9YvzdOkDOkR0ccvP6e9bJWSXRXg5RhQQBaqmQGMAiScFqLBbaUAKw2s6HFf8pAb20LTWaowpePjWBqaZhOGMIfH+cCZg1a6YSbhWQBr62APSJ8MxQFpGb07Abetv7jc1VPuiSfqigCiQqkDdgcRjN+E07FKA5E/7js6YW+lM+7Zm3Bl54OHg7QpcHrwJwn7HZrDffK+AZR+vJkiYxEoI2aVepZid1QbS6GvL1PTubCE7Hc77mpoePoce69O+ZtNN+47RWb4SBUSB2ilQdyCVXVXHu4pLrXC4Le6sRv+u2kBC/S9G3lU3Sunvshul0VHzY0hEJEBq9FYk5RMFGksBAVJjXY/MpakFkDIXJuFAAVKCMPK1KCAKxCogQIqVpfG/FCA1/jWSEooCokBxCjQ/kIqrb8scLUBqmUspFREFRAGjgACpSZuCAKlJL5wUWxQQBRIVECAlStPYOwRIjX19pHSigChQvAICpOI1a4gzTp06Rb29vUWVBccPDtbuWakskxqOHTtGe/bsoYmJCcIbz0dGRujMmTM0PDxMH374IT311FNF1VEOFgVEgeZVoO5Aeuutt0iW4jUAXM6fP19Uy8PxOK9Weu/atYt42blzJ/GyY8cO2r59O23bto3eeecdOn36dCKQXnvttaLqKAeLAqJA8ypQdyA1r3RS8kIKZPGQLly4oMCa5CGhgcpHFBAFpoYCAqSpcZ3rUksBUl1kl0xFgaZVQIDUtJeu8QsuQGr8ayQlFAUaSQEBUiNdjRYriwCpxS6oVEcUqLICAqQqCzyVkxcgTeWrL3UXBYpXQIBUvGZyRkYFBEgZhZLDRAFRQClQdyDhmRNZWlMDTOfmBc8VYRkaGlLPF+F5KDxLNTAwQCdPnqQTJ04Qnkl6//33qb+/nw4dOkQHDx6kl156SbqqKCAKTBEF0N/R79H/YQdgD2AXYB9gJ2AvYDdgP/CcIuwJ2xa2NeXwJFfOyXJuY4OMGwjW3GgESFPEskg1RYESFBAgiYdWNQ+16YDU3UH5fD5haaeu2r3kIujKw2vbqc0pUwft0b9/GByTZWN47ULKL+iiIXXuMHW1t1H72mEqIaks2ZV3THcHtbWl693Tmae2zj3l5VPRs3uoY0EXDaek2dPZltC2Oqgn5bz4XT3Ukc9Tx+6GvILxRc7wrQBJgCRA4o6igNROa+yfCjb7YADz+VIMByde7LqHOtraLIjo8xlQHd3FpSdAKk6vYo9W7SMLkIKbgjAHDapibzQESBKyE3gVBa/m9JDigUSkDQC8ilp8lAewYE3sHTcbsGLuqgVI1b1q5QCJBruoPd9OXTE3QsmlFiAJkARIUxhIw9S1IE/5TsaA/r997ZBjMxQsgrtgDo316HM59FbgTlobqJRwTHdPNMSjjJoVbvTCWYWApD3A8PxM4I2EON0wG7w5hAl7sOa6w/D6oU8VouO826lrLUKnMcdZSitgW3VUnqPJKwxx+mmYaxiUJU9RT1Mb+qC8/k0B6rygi9YojzlP+QWP0+NoF0Gafp5hod22EX6fBCT3mvjpxgHJeNVBWSyP3nj/jvYxEQFfV6uUNdmse8jOvouW7XBWWitowRMZeHZd2oSG48eP0wcffEBHjx6lw4cPU19fH7333nu0fv36mnQElUlMBw0z9z2k7ECCsQoMnwFHqsFX5SgihGOlqUcUNAjDMSOiNCApw2dD0qQXlDkUIdyKGedhz43HuBSQYBxtoP7ePwAAIABJREFUiLd7YUhVVwu+AVh9AxxmjS3fcHIoE3nFa2A0CcpCROZ6h0baGPQgDeSDcR/rWpjy5i0Ycnmgd5r/nAQkfO+P52kY+UCxdDIeezCGxLpZ5XLTMACzQr26bnaa8W3aVb66/6G/o9+j/8MOwB7ALsA+pM20i5ttV4oNzZVykpzTHOBqJSC5nRudMr7zukYnxggaY5pmvBQ8bCNYwAaosvnGkKFiBr2TgWS8BttQF8gPu5FnZFKBMfA8BqeBZBlVnOgcE+pjD81ruJQCpLS8NGzSbgR0vl4aHKrtMp6wU/5QqNhrEO5WWwyA0JuyPCsLgoGHbMEDCSjNA4/N9ZDcdscZ2zdR/nWGHu3UviBPgZev2kyxoUPOqzJrAZL1rIyArrKga04gWUYiCH3gO99QZQeSbwQLGa/igGQbHdsouOVLBhK8Jw6p+XW000vedg1tOAan0vVB6Rh0DYmIJxbjffm5+0BUMEnNi70dhNniPBk22NGZe05eqvyWx2QKVuia4rB4aBjv1Zoxp69HzLVQuvD3NpDiwa7zDOvrXI/BLlrIIU6jh9tGfMVr878ASYAUPLxaaSA3J5BCg4ouqI2DFXIL+qVr8Plr1+hoQ1EskLQXETV6nIe71oYp9q47n6d2c2fvGpuwXIFnogytBeNYo23l7I/7YFzIgY3Rzk/HPgZGsS1GW/V9CR5SWl6m6Hw9A70Cz9AAybkJsfTgUFgVgMTeNocBI2V0ypQMJD/shyo7oGQPaFBfG+UZKa11mmi7cWlYV73qmwIkAZIAibuZbSz5O+7UkUH2KgLJC7dZRdGbvF+FdJI8JPesgkCyD1c62GM/9k5sJ3gTnn7OHTkn4RxTWw+Ji8BrNvz6hiGhTnwwr1X5ozcLjuHnY721e7Ni73Tz1uVi8NjH2dtFeEgBdM053RpUHd24HeFrwGs7j9pvC5AESAIk7neOseQvsTZeiHMH7hoRfbQJnURm2blD3dmMV57agvECuywcemKDxeXg2X98rG2wTFgoplyBh8SnmXUsTIJj3LT5a2Vw86GHGZuGo3F8qAnntUVuADgXvYaG9hiWOse5PlGPzU0hTIcnXag0I5prfYNxlioCyc0j6iG6dXSvQTzs9DG2h67aXmcHdSAErS6+8ZYXIGzLbSpOqdp8V3cg2WEd2dbve2sVHTDzhRc8M4DFf4cd3k+FGTT8HrsjR44E77HDbJsXX3yxNj0BuTjG0stW7cMAcAgXZcCsTqwNqY7Z+29EsFNTRsE3nvYBalvfsdoz5fA15+GMuxiPyQ63+AYq2UOKA1rUkLnFM+fYxtsK4fHML5Q1Ml7ja2x05XN4QL/Uad/h1fGvp9bTvn6cV6il0dyaYKB0syeYqPJW1kOK5BF45RYg2CsO3szgAonrwmE/XC/Vzqz2qa6h0dtuVzp/eMTR8TP3ulf/P/R39Ht+nx3sAb/PDnYC9iLunXb8TJI9264UO5pjgyXr0Hi3ihYMIQaRDyNM4+SXqvKUb7xQkad89/b20rp166rfCzgH31jy92atO7g95mHu8DnG39lDQ8YIlw8knamGnjWWYRtHu3zGYAVjIzYsFMjSXh1kAMP1wDoI89iZ2NvGeAfndNCeU3jA0x63ygAkJGnBTIGojOeQkoGEjLQRDzRCWa0bDF07/xgLCqaszjRwfRIFQLCn+PM+s9YepH0tedvLIzie9+t1CE4c4AFJneNdk7ibnpibF30jZk//NgWowwr9Hf2ep37DHvDUb37Jatz0b37Zqg2mUuyoAMnyIkoRsJHPaTog1aEDSpaigCgQKlB3IIFssrSmBnhNPC94bbz9UxNwv+1QHe6C+GcncHeEu6T9+/fTn//857C1ypYoIAq0tALo7+j37CXxz1DAPtihO9gO21Pin6Xgn6YolSk5+y5atvU4S6voYDcKG0yIAaMxMZQQrkNjQ7wYT2cjfgwoHThwQIDU0uZHKicKuAoASOj3PI4Ee8DjSPzGBh9GbFvY3pRjP3OcmKxDb6JVtGCviD0jG0SIB2PhsSOGEY8foVHu27eP/vSnP7ktVv4TBUSBllUA/R39nqHErxCCfbDHkmA7bDDxRAfbUyrFjuZsoyXbOqzVKjqgkfACj4i9IgaR7RlxuA7eEYfr3n33XXrhhRdatvNJxUQBUcBVAP0d/Z7DdjzbDvbB9pRgO2wocfjOBlMpdjTHhkrW2mC3kg4cksMajYdBhPAcFtzxsGfEP1luw+idd96hrq4ut8XKf6KAKNCyCqC/o9/HQYnDdzyeBBvCYGI4sddUqh3N2UZLtvVAf6vowBBiELFHhAbFMGLPyA/V7d27l3p6egRILWt6pGKiQFQBAAn9Hv3fD93xBAcO3zGYYFfiwFSKHc1xYrLWoraSDuwJsTdkQwggwh0Pe0Y8iQGuOsOou7ubnn/++WirlW9EAVGgJRVAf0e/ZyjBHvB4EofvYDdgPziMxze4bG/KsaE5TkzW2mtoJR1wJ8MLNyCGEEAEr4jHjHgSA1x1NEY0yt27d9Nzzz3Xkh1PKiUKiAJRBdDf0e8ZSrAH7CkhnA97AbsB+4HFh5PtPZViS3NssGQdGu9W0YIhhDUaThyMbM8IDQ/e0Z49e1SjfPvtt+nZZ5+Ntlr5RhQQBVpSAfR39HtACXaAQ3e+p+RDKQ5MpdjRnG20ZFu7oa2iA0PIBxF7RoART2IAjHA3hAFN3B2hUb711lv0zDPPtGTHk0qJAqJAVAH0d/R79H/YAZ7gAPvAM+/4GSXYkTQwlWJHc9EiyTetosBnn31GvHz66aeE5ZNPPqGPP/6YPvroI7p48SJduHCBzp8/TxMTEzQ2NkYjIyN05swZwosR8YDbli1bWkUOqYcoIAoUUAD9Hf0e/R92APYAdgH2AXYC9gJ2A/YDdgT2hG0L25oCWaTuFiClytPcO7mBYM2NRoDU3NdUSi8KVFMBAVI11Z3iaQuQpngDkOqLAkUqIEAqUjA5PLsCAqTsWsmRooAoQCpELyE7aQlVUUCAVBVZJVFRoGUVEA+pZS9t/SsmQKr/NZASiALNpIAAqZmuVpOVVYDUZBdMiisK1FkBAVKdL0ArZ990QFI/Ye7+bHT4c9ft1DVY66vFP6edkLcqL//8tf4Z8va1Q7UupOQnClRMAQFSxaSUhHwFmhNI7bTm1Gd+VainE6Bi4x/ZXZ0vAJwFHdSxIE/5zh6KlEqAVB3dJdW6KSBAqpv0rZ9xKwGJSHsr7WuHa3ThhqmrvU2BaGhtu4LhHp9IAqQaXQvJplYKCJBqpfQUzKe1gKRDYvBU9Cc+RNbT2Ub5BV00pOChodK+toe64OXkzbKgiwpibbCLFrblqaObiAa7qD2fp47dHpEESFOwV7V2lQVIrX1961q71gKS7yFlBxJApMCCq2HgUsjTGl7bTm1BiJBhuMe9ngIkVw/5r+kVECA1/SVs3Aq0EpCiY0hFACnwqvS1UmmleklRAGlAeZMbBEiN2/ilZCUpIEAqSTY5KYsCzQkkK7TGITa19ic0ZAeS7w0VBJICjT+5ooc62toIaQWBOwFSlmYoxzSRAgKkJrpYzVbU5gSSCwJ4Jk7ILbgI1QOSGodyYGhBMhifIiIBUnA1ZKM1FBAgtcZ1bMhatAKQIKwO13nhMqoWkGI8Ib66vuckQGJlZN0iCgiQWuRCNmI1WgVIPOUbs+fC2XHRcR4CpDBVO/BieJZdeBauU2rIrruD2tp8+PHVNQ/KdprJDQIkFkbWLaKAAKlFLmQjVqN1gMThsbwaw2GtAZZwJhyRnniQLwNIBnIL1ljg49z02slTgOSKI/81vQICpKa/hI1bgZYCEns29hRu9oh4vKezh9RDrKV6SDwlvCvl9T/KgzLTyAVIjdv4pWQlKSBAKkk2OSmLAk0HpCyVkmNEAVGgagoIkKomrSQsQJI2IAqIAsUoIEAqRi05tigFBEhFySUHiwJTXgEB0pRvAtUTQIBUPW0lZVGgFRUQILXiVW2QOgmQGuRCSDFEgSZRQIDUJBeqGYspQGrGqyZlFgXqp4AAqX7at3zOAqSWv8RSQVGgogo0BJDOnz9PBw8epO7ubllaSIPdu3cTL2+//Tbx8tZbb9GuXbto586dtGPHDtq+fTtt27aN0BjffPNN2rx5M23atIlee+01WrVqVUUbvCQmCogCjasA+jv6Pfo/7ADsAewC7APsBOwF7AbsB+wI2xSs2daUw5EcpAGMTp48SZOTk7K0kAa40eBlYmKCsIyPj9PY2BiNjo7SyMgInTt3js6cOUOnT5+moaEhGhwcpIGBATpx4gQdO3aMNm7c2Li9R0omCogCFVUA/R39Hv0fdgD2AHYB9gF2AvYCdgP2A3YE9oRtC9uacjiigASilZOInNuYIOcGgjU3GgFSRfuvJCYKtJQCAqQW8kgaDcwCpJayFVIZUaDqCgiQBEhV804FSFXvv5KBKNBSCgiQBEgCpJbq0lIZUaB5FRAgCZAESM3bf6XkokBLKdDUQDr66kGadvMByqmlj9aPhIP72Ke/x/6D9NSJcF/qWMvIMfphkKZO+9uvnk4x2sO0agWXwV675fHz3P50r1W+A5S76wgdqCgcY8r16ImUemTUp4gySsiupWyFVEYUqLoCJQNp7DRte6aPnvrgfFk2roxZdhO0adWBEEi/OGoZdL0vANLSftqW2ZCepTX322A5QLmVx+hs0vlDH9C1HsB0vr30s31JRn6IVv3CzeOvnqw0LARIVe89koEoIApUVIGSgHT4CLXfuY9yN/fSH+oHpEF64K4QSK5B1/sCIK08RmeSgBLz/YEuz3tZ2k/bY46D13N2W5/r6VhwmvXiEJ2POy/GC1u4bbQssvse2OSkAKmiPUUSq60CHx8n+uQE0eRmd6ltKSS3GitQFJBGhujtrgP0+cX7KIelrkDyPJNrN58NDfrQB3RdFjDEwQLf7eunv7XOz938Hq06FuftTNCmR11PJ4Agzr//AxqIy6PnME330v9DbPpxeWb9ToBU474k2UGBTz4kurCTaOI5orGVRCO/IBr5ubt8copo5G6ikV8SjXQQnfsp0dnbiE5fR/ThV4lO/k9EJ3Pxy/n1onMLK1AUkN46SLnF72oY1R1IPYfprwKj3kv391mG2jH4aaEz6xwbHCPHaGGQtgYOPJiot3OKfrY0BUg3H6KN49E8Drz4XhhqRD53HaH9dv4V2RYgFey3n10g+vgDogu7iCb+RDS+mmjsYaLRe6PLZ+fNd/cRjWK5n2j0AWv5LdEolt8Rjf5eL2MPaqMMwzz2kE4b6QfLH4jGVpnlEaKxR83yGNH449byBNE4lieJxp+yFvzPCx9jn2e2xx4jChbOA/nxwmVAeXixy8nbqAMW1McsAMvZW4iGryQa+F/jIZIEl1K+FyAVbNbNfEDTAglGPfRGXMPvGvz0yQXRUBcAoseRwgkTB+hvnz4VBVLfEbrEBtctByxIAlS99NPIOFI07b969EQ07bKhJEAKO+Zn+m783FKiMz8iGrqC6NTfRI1nd47y+eSlqxQDWuY5HaY8sXmb8vaYPLoW5Kh9bYJnYZVjeG2O2pBuZ44+s75nr6SnM0f5BTkajtnHx9RtLUAKm3ULbjUHkI4dpVm24S9p+zBtKsLIYxzJBlLuF0cjXszRl20oYvJDP/3sLtdjmvXycBhKVPmfoKW3uMfEe1+TNDlxlg5s6adrV/TSf+dzbjlAl6zoo5+9fIKOjkW9rxCwxQJpnE71HaMHVh6kS5ZZ5ftJL11yzyF6YMsgnUrNzyrL2DBt33iEFnYeoC/9ZD/lbt6v3Or/fud++pffH6JnDw6lv8tu826atnAb5dq3UO6qJ8vvdribz2JcjYFfE3OsMtL5HLHxz5ReTDpFnYfyLMhRx4IEeJQIJICrA9DJ5yixrgKk8tudpFC0Arl/eYr+j6U76LZn3qWeY/Hvsutb9y7lbtpLuZsQrrNDdsbWGD4s7bZsUmD7x+lo91Faek8vfeknoZ370l0HaeELxyjbLLs6AGnSCfuh4H30/JBdwehsPIxjRaZz3/8BnQrEmKTJ9324JoxPHXmfFi4PBQu9Qeu7n7xHHXvPecDjMhYBpLOD9MA9VrpJwF/WR388Mp6QH/IdpwMb++irDE+AiBceeEQDuuld+l86+mjnibPxL1dtQCABJPBWsnggRUEnBVpd7RpEQ2s1PPb4x5YCJAu6qj5dUY9KPKSi7aicUCEFcAOqbkRxQ/qjnfTDV45EXq5aMpCGTlJH7CM6lu1DPQq+XLUeQIqZCXd7Dxt7eC/HabFjuHvpgb6YWXe3HKZNE+F5pzZ7s/Linj96/yh9OzDqllhOfvx9L93eHQeljEA6e4KW2h5RbB6c1wHK3XKQ/hg7AeMcbX/6PS9kmQwkdYdz+wF64f0z0bd9NyiQ4FkgzMXAiQuRsTEfMvAAVAAxdS6HAzN6HwvbctTRrfNrz+eoY3eYtypDCUCyy4fttpiy8DFdxotSYcwFOeI6cf2xRrmCMKedlilbJNRovrfhymFJzic1VCghuwqZ/sZMxgHSD3dQ7vpddMUf++mk9bbvkoCU1c5lApLtYWB71yFr/Og9WvV+aPD9fX+w9/nppP5/mv74a8sQ33yALukaDL0DfyYeTw33Zv9hHAmg4lDa9ifdNDF+xPvUeuIU/cwDxF/94jCtf99MCx8ZojWrel3Dv/QwbbIeCtbpZQHSKG161Jvifst7tHTHEJ0FRCdG6eiWQ/QlD1J/FfNcFqa/uzMHD9CXHuinjUdH1E9QTIycpX3b+uj//rH2kBSQFr1DueXvUfew9/MTDQok30PKCiQYWgYLG/FCnhaP83CI0IdhqUDqaNOAVGNH3Tlqa8uRDw0FJDPGxPBR+XvAwbmoB49DKY/OApevF9JSaVtjVyiPPZbFedvA4jKotQCpMUlSoVK9/MKf6Dd3b6UcPCQDpNyP3qb5GwfotP/zE5ln2Z2JPlu6rI9W7Tujbe/YGdr+wkH6b7BzpQDJeU7olsPOM0L+vm3nQxg4hj8VRvocJy0U1gq/+ftCsGgQ2ONPGEfSM/S856NuPkDOdPXJSTq10X7DxAHKLTtMm876dThH61eGz2AhnBfmwcdmANLg+3S1A5teWrzL97Ym6ECXNyvQnwY/cTIy2/BvVx2nk+Mxv4fUf5i+fbOJAQNIi3ro/33huPt7SA0IJDaUDAgYx8xAsryqwCjbxt0PxZm0bW+MAeXAo1gPKQZADATb6MfVFfttjy0WkMZjYk9OpePVE/kxnLlOtqbIJymUqMooQKqQ6W/MZPSkhkP0wv3baZoFpNySd+mlU97vIWUF0t5++jvbzt1ykP7w/oTrCExqO1cCkLyxGwsSPDsuGG9Jeg4oA4wUvJyp5QhXcfgtauztB1sxjmQDCW96UA/mes9H4fkm9/kjr26+V2aXu9t7lmnFUTpi78/wYCwmZTjlTJp+/v5R+qdl79G1q/pp1ZZTdOC0+xDv2R2HXI/t5oP0x8HJ4Mf53N9DGqW3ntmnByUNkHJL9tMm+wf66gykIARlh6NiJjRkBZLvDcUZahsIZEDjTzhwvBtArEggxUEEUEB9bSio8nkQRfk4/AiPKAkaTh6mfAxRH0DOsRaUkX9bTP4CpMaESCVLFcyyO/wOXW0D6YZu+vdXBtwf6MsIpER77NjLSZo8drQUD8mdpeaE0Saj+6LPDrEHkWEdeR7JhN8iYbk+WmNPePBhccth/TyS/33klUYnaKlN8psPUPxMES3e5c6xffTiObtOUWjmnPCg9+olpLXqeEnTz/0wJHuSie+y6z1E/wOzZBhIN/bQ73utX4ytM5BsELDB5rt6GxzVApICggfDAJJWSKxYIDnjPV76dv2yAok9IV8T27Ozw3Y+gNT/XjmCegqQKmnnmyatAEgnjtIz9+gxJITscjd0U+73h0oAUlrEyraX2D5RApC8WWqLd1mu1/tHyTbS2FcWkCb1OJLtRSDEdsZ/XdCKo3TUpm1kQoR+cBdhPjutyPNHJ96nbzuQccebAs8v9pj3yB0vKwSk6IUCsIrXKzmfRCAN9NP3HSB105JtjQkkGFsGBN/pswGuFpAinhB7D77nVISHxGCNG5vxQZEEJLtcaR6S7RGqtEzYzp+Y4efLuqauJWTXNHAppaAhkE7Qq3/YqSY1BED6aS8dtH/CPJOH5Dop6TY06xhSPWbZGcD4EIEXsdF7XZDrpYG00dDbt189pQbWbCABbg4Ayqzn7c68+2RQ2JMe7PLUDEhj79OtTQQkGEgYYP9h0TiD6g/sc5jLNrK2kba/V9sx4zz2Maoc7D0UASRVLmsygZ0mw4qBm1S+YseQuD7wevi5JxuIyCdull8c6IPyCpBKsfNNc44NpJ7ndlUGSLE38Ak3+1CqIad9s8fjP4+09D36f5zXBcW/msif9JD7bZ/3VvCY54+mCpCazEOyDat/9483HvD4C4+RAFw8RbpYICnIeRMBAmNsvLUgz6xAMpCzw3J2mtiG98J1U0ACgBl8PMnCLpdJE+ckzbLjPBRETXp8bLDPm2XHcLTBxceqtQCpaeBSSkFtIFXMQ2opIEXCbx5Zg4kOXjzSnxbui8LTxBl8al3EGJJznpe32lfIQ4oZQ+LJFwXTdvMregzpnT763xwPqXHHkGxjyIbaNuzK8+BxkM4cqYdYywCSAkPMw6pBOWy4ZASS8kQscAZpcSjQA46qZ2cYqlTjOgnelTMuZQHMziNON3t/ACyjIwPePibYFiCVYueb5pwQSIfpsRW1HkOaLHIMyZtk4Py8g7cv/Uf1XIOaPh08+jySE4d0ZvlZ6U6kxy7DaeLWOWbMyk4/+uoh+/i07UJAmqTILDtMsrAe4g11GaQHftFLs+4/RB0vH6PtR87p55QMuKI/wVHkLLs7DlB3I8yyswx0YADlu+BB4Lpr8uElRHjJrXxaVoEASDGz7OZvGixhUsMk4YbZHpqIjN1bN+DFTfv2vI6l9psTnH1xLzVNM97p+yLhN8vbSQaGHkeyhbBB4z9/xMY/23NI6eW1x4jsPN1ZdpM0GXkO6QAt3OY/hzRJk/48fvPcE5d5cuQ4LfbeLFHMc0hXvXSyMZ5DEvhUBz6D/zvR6e/rn6DAG9DxJnP1pnG8WZzfJG7W5/+i32A+sYYI3tCF7UQfHyOiz1rWCEvFQgU0kOKeQ9pHm4ar+RyStqlFAcn9WXJ3qrW/z50CncWApxzTbf/UhR2yc9/CEBhoQ9yIBxKAzJ8RZ+U9coKWOmNUB0i9qeHIiHmQa5zOHjlOS39xgL60oo8WP3uU1uwbolMj1mxDlX9hD2lycpS2P+m/qaGXFm8ZorPqRarjdGrfUbrWe3NE7pY+Wu89rAv9S3lTw/+84mDjvKkBQBr4HNGH/xfR6e8RnbuTaORX+mclgp+J8I2o/ZMM+LmJB8Ofn1A/RcE/S2H/VAV+uoJ/xgI/dfEbs9xDNNqpl5FfE6mlQ/9mEH43yF8+PRv9Ljjv12Fao0iXF87L/okNLgvKxYtdXt7muuAnNqyf2cDPbaif2jA/tzH+DNH5V4k+2k/02XhobWRLFCigQPybGnbTkm0D6hVj5+xZdkcO0WXey1UXvom3L0zQ2YFBOjDIdjXGFqo3NZw2kR4cf4pW/ba3mJCd97PkzjvgvH0xb+b2YVHU/144MPA6YseBWIRJmuw7Ql8NIGSBLPL8kXUOfoW2+3DkdT1BnnHp3XyAvvToCe9XcWMugvMckskT73gq9BJXL89r3zCv3LBc3cnJc9HXEPGLVc3bvtWvOpqXq+p32fXSq6fOVf9ddugE6nd8GB4PEU1uIhp/muj8Or39US8RDLx8RAFRoG4KxL7LruuoiqCc8V8dNPI+3YZXkfGLmwN7o22t/0hQpveDouYFZ9kpw+e9dmfVcevVD+4+/Jy5M53aMZyu8c8GJm3c/fBb/DiQnX7CD/gVfAB1gk719NMs6/XoaUCa9cfjdCoy9pMRSNAm69u+b+mlhZuHLN3tumL7HHW/nO1t3//jgUO0c3CkNm/7rlv3koxFAVGgGAUcIN24i+7YdDTytu+RkRH18zVjYyP07voDND0BSJHhlCPv0w8L3XyjsJmA5L0dwZm04L2SJ/J8T9lAmqTI80g3Y7zFfYVOFG6j6p1zPkwyl29smDa9fFj9HpL92x05/EYRwnUvHKPuwaSfgygCSEqflN9D4t9fioQFfSCZ/8fOUPeWuN9DOkD/8fhh2nD4TG1/D6mYHiHHigKiQN0UUL+HdNdOWvFiL72X8HtIIZDGaHx8hPq3HaL/uHs//Tf2kIx9/Nmu09GbZ/Ubc0do4T29dMltVtQKvzN318FiQnYJxq8CsImCRPKqhCaJb2oYG0sH0sAAnThxgo4dO0YY5JSPKCAKTA0Fgll2J07QwED8D/S5QBqniYkJtbC9Kcd2FTWpoZyM5NzaQ5YbCNbcaMbHx2lMgDQ1rIvUUhQoUgEBknhYUbe2QpoIkIrsjXK4KDDFFRAgVcj4igcW9cAESFPcukj1RYEiFRAgCZDEQyqy08jhooAoUB0FBEgCJAFSdfqWpCoKiAJFKiBAEiAJkIrsNHK4KCAKVEcBAZIASYBUnb4lqYoCokCRCgiQBEgCpCI7jRwuCogC1VFAgCRAEiBVp29JqqKAKFCkAgIkAZIAqchOI4eLAqJAdRQQIAmQBEjV6VuSqiggChSpgABJgCRA4k7T3UH5fD5haaeuQT6wVuse6lDlSchblbeDelRxhqlrQZ7a1w4lF667g9ra4uvX0Z18WvyeYepqb6P2tcPy03nxAsm3JSggQBIgCZC44ygD305rTkV/nbSnE4acjT+fUOU1yrOggzoW5Cnf2RM1/CUBKQZuKp08FQclAVKVr/6UTF6AJEASIHHXTwESkfZW4BHU5qMNPkA0tLZdwXCPz8lKAYnYuyqmbgKk2rSDqZWLAEmAJEDiPp8KJG3BUiWyAAALr0lEQVS0AQj9YSPuhsh6Otsov6CLhhQ82Gj3qHBaEA5c0EUFTf9gFy1sM17LYBe15/PUsdsjUpWBNKxAGIb43Py5bm7IDp5kmxX2DL0uA/QuW68e6mjzwn6mThH48jWSdUsrIEASIAmQuIunAsn3kLIDCSAKDLOBSyFPCzBoC0KEDMM9XFK9rhSQkI4HSQ0jK7xnABmOGflA0v8jrBnARJUvrLuCVadVBx7TssKRw2sXWkB3qyv/tb4CAiQBkgCJ+3kKkKJjSEUAKfCqdEYqLQ8AXAS9jgJIA8oCBA4sCUihxxN4bMqjscfHfPiaUgEWAXA8ICm4eOUjIruuLmT1vvYF7RaAvDRdUeS/KaCAAEmAJEDijm7u6F1DzQbcNtg4ITuQfG/INtKctbOOBWNyeKu4WXZRaJDx2oJwpMk/MqtQHddOXWrShwsPHzZBfey0lJfF+UO/durqRjjSpOnsD1KQjSmkgABJgCRA4g4fAwIdugrDTnxoNYGkxqGscRgHkMH4VKkeEgMhrAm2dD0NdJUODGJ/zbMQY4AU5/XZQALEeao44KbqosHe0f2Z8vja4tJwiyr/tbACAiQBkgCJO3gMkLBLh+t8Q14tDynGE0oqnyove27x5eFT1TohrIZ9USD59XVS0h4iw8WcH455Wcc6ZYSWbXoKO743Y0fQt61zj9IZa/lMXQUESAIkARL3/wQg8ZRvd+BfAyDvGFDtAURn2blz6lJDdinQCMrBeTrGvgJAYu/EhPCCiRiOPjxpwfJ2sD+h3JG6mjJ3dIZepwr3LWiPn0nIect6SiggQBIgCZC4qycCicNjeBNCCBd1Zx/MhNNehpryHITVQqPNWWAdMdLBTgO5BWsSp4U7eVYKSDEA0h6T7SX5nltYNz0Z3cA4mPQQauaCTU+YyGPciN98YWbwhRMmAkFkY4opIEASIAmQuNOnAYlBYk/hNmMiwRgPP8RaKpAMGNqdZ3W4cGatPBHjXZQEJH9MSP/vQkPnpaEUHu+WyweSPkcDk8+xoBNUIw66GnYcwgsOlY0pp4AASYAkQJpy3V4qLAo0pgICJAGSAKkx+6aUShSYcgoIkARIAqQp1+2lwqJAYyogQBIgCZAas29KqUSBKaeAAEmAJECact1eKiwKNKYCAqQKAOnoI7Mol1tKmyqQ1mTWNDYvpWnTZtGqQ5NVA0rmsiSU+fz588TLxMQEYRkfH6exsTEaHR2lkZEROnfuHJ05c4ZOnz5NQ0NDNDg4SAMDA3TixAk6duwYoYHKRxQQBaaGAg6Q/ryYcrlv0gM9Hyr7ADsBewG7AfsBOwJ7wraFbU05disHmbu7u+tjVA+tolm5HC3dXJ5RFyDF68cNBGtuNAKkqWFYpJaiQCkKNA2QtNHPUW72Kjpq35EzVDadLx5qfK4AqXjt7GuQsC1AKqVLyjmiwNRVoMmANItmzfY8GoaKAKkqUCnH/RUgTV3DIjUXBUpRoOmAtOqRpZS7bVNofBOAtOm2aZTL5YJl1qojwTmbbgu/t4/h7dt9uG1eGqSjjrltY5AWDHYYsttES+08HzlK533vwZSX88rlbo8de/LLOMtPK2YM6egjl5tyumkG3iWX7bZNdP58fJitIIB8LXJLaaOT1iZaOm0aobwTm253dLv9tXFvDKmPVl5uX4tv0gN7ZAyplI4s54gCraDAV9hGBeuYMaS/3OrYldytG9SQAN8AF7Rhk0dp1RXTFEd8O5h5DEkbVQziw+hbg/kRIBkozP5DGNo7tIoun6YL4BSWz00J2SFfd/KANriAIldGlW32LHc8yhhuG246LdfD0+CcRX/o45Ajl98KTTLEbBB6QOK0fXCpstl6GYAu3cz5FQEmlMO+GcCFnZ2jaQ5UjT6qQd1OG83Ehv5Vl9G0aZfRw+/xpIa/0K04ZsmrwaSGfb/7phrEvO9tmdTQCsZF6iAKFKvANfdtVJOZMKlpIGZSw8GHLqVp0y6lh3p5UsNrdDts+62v0cSEnkTl2HjfITD/K7s7exUdcW6mJ6kEIGmPBIZXZcxQMV6N9hL8u3acA+/BAhkKxucmAknDwfauVJ4KNiFEtNF3QTPJxjoAY0JakwZABjY6reiMPQ0cq/w2kAz8IuWcnCTlafnjbgkXKcuFjByjymHX3QBp9irqNw1ETWo4+AeaNW0a3bZxnEbHxujQw5dSLncrverMsttHD1yao9xNf6LjMsuu2L4sx4sCTa9AesjuZfpJLkeXrjzozrLbiEjMZfTwwQk1qzdio2LsneJBAKQwqlMSkCaVETdGm6GigGRcsSAj6+6fj7PhE/edXXgPPEFF+bwAgvHTvgGDwHuI8Zg4PXWcApf2OCITNww84eUFMwIZSI9g+ndOhcgi4cEgnBgzGcSuZznbxvsMyoVrY7xRvmNRQHKmfR+ihxCqW/JqZNr3usU5yl16H+0+LtO+m966SAVEgSIVSAXSK0sol7uUHtx/zgXSwT/QZbkc3b4xO5AmlT02jgtH0GavKs1DgiGHEVfhKQcOybFB9oYCz8oY+bRp3+GYjD3OEW5zOC7dqzHgTIKbqYsGlwGSExYzUDWiBSE545nwWFQIBAvCDBoDQz7WDjcyFLOulccVxHdDLcL8swBpgw7XxaSjyihAKrIby+GiQGsokAak3gcRVQltjr9dFJAUN2bRqr7zag7A5Y+soqW5paUDSQEGnlCfeZbI9pCs8Z3A0DK4KuEhsaE36yQghZ7PJGkih2G+oFwMJNtDSgFSYPjZQzoEOOsJHAxIO21/OwCKPR7l1cc/h//X51phQ5xXYQ9JHoxtDcMitRAFSlEgDUhnkjwkE33JPqnBGq7ZdJ423QabdkSNh5cYsoMXAG9iFq3abAOJx4qSxpD8sRkzrsPjUb5hZoiZ0BwbZn8dDyR3bEiHGXMUHedxx5bi09LjZtOmWeW3gBSMV/ljZH59zP8OKBOO8evI5XdmOOLckoA0Rn/5SY5yl6+kg84YkrypoZROLOeIAq2iQAikbrrvMtxoW7Ps3v09XZrL0a2veiG7UoBkxu6XbtqoPKNN57WNLQNI2uuYxrPbAmj4IAiNphOuU4bYhMhSXvujjHcBQx8HEe21uGDEce6MPfZu7OO4/NGp7Q7MHCAB0CZU5kzDRv0siKk6c/ru1PUogPzQX4xWVigw8Ny4HLdtCma9RMeQxmi09yHVuOxZdvLqoFYxK1IPUaA0Be7dqGfZvbAoR9Mu/QZ9M/dN+q316qBXl2Bc3p5lV+qrg7Q9m4YQIEfUDpUxhqQNKBvhHLnhqvB7jjO6+21jawy0FZv0j40dS7InThhPivNS62B2nZ0Xe3BWHDT2uGiZQoNv0osAKXRD3dmE0bSCcajM3hHXwUsLs/dK9JD0u+xeVbNmHN1yOVr8Z5n2XVp3lrNEgeZWgG3B1+/rppNv3xcBEt5l1/fQZdGxpG/9IZjVW/jmWtsz7WzYM4SPZh9DypqJHMfwqP+aY7qxHpK8XLW5LYeUXhSoggJhyO6EeskyxpQROcHLl1v/5apFewj1N/LNBFwBUhV6rCQpCrSwAgIkgZLzGqRKAq9SQMJT27KIBtIGWr8NCJAESA0PpBa+IZSqiQKigKWAAEmAJECyOoRsigKiQP0UECAJkARI9et/krMoIApYCgiQBEgCJKtDyKYoIArUTwEBkgBJgFS//ic5iwKigKWAAEmAJECyOoRsigKiQP0UECAJkARI9et/krMoIApYCgiQBEgCJKtDyKYoIArUTwEBkgBJgFS//ic5iwKigKVA0wFJ/yaP9XLSEn7XB28jUOngd4e8F6O6LzE1b4RFHuYlovzyv+gLSr0Xjya9Idx6Qzanpde30yYFR52OejO5d6z/0le/7O5LVcM66reR67fa2ue4dc34WiRPr5zzdnGkoV9si/JPbMJPC4fX6vbXxml8fJzGxsbULz6OjIxEfjFWfg/J6p2yKQpMMQW+YtmLXG4xraviu+z0ry/g1xDMD7vmcvT/A3r16gHGFRfPAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number on the left side of the cell just tells us the order in which the cell was run, so yours maybe different than the picture.\n",
    "\n",
    "You can also run the cell by using the 'Cell' menu item at the top of screen.  This is useful if you want to run a series of cells, such as the entire notebook at once or all the cells up to a certain point.  For instance, say you are working your way through this notebook at get pulled away and decide to come back later.  You can scroll the browser down to the cell where you left off and simply select Cell-> Run All Above and this will ensure that all the prior cells are run before picking up where you left off.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's talk Python\n",
    "\n",
    "Okay, there are [many](https://medium.com/velotio-perspectives/the-ultimate-beginners-guide-to-jupyter-notebooks-6b00846ed2af), [many](https://www.kdnuggets.com/2018/05/jupyter-notebook-beginners-tutorial.html) valuable resources to learn the ins and outs of Jupyter notebooks, so I'll leave you to explore the many ways they can be used.  _If you are already a Python whiz_ try some of the [Coding Challenges](Coding_Challenges.ipynb) or explore [Jupyter Notebook Tips,Tricks](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/). \n",
    "\n",
    "Now, one more thing before we get too far. You will see in many places, where there is non-computer code that gives other humans an idea of what is going on in the code. We call these pieces of text comments. Comments in Python start with a # symbol and tell the computer that everything after this on the current line is to be ignored. Comments are a valuable part of coding as they can help you to capture what you are trying to do before writing any code and can be used once the code is in place to remember or clarify what exactly a bit of code is supposed to do. Here are a few examples of comments.\n",
    "\n",
    "```python\n",
    "# this is the first comment\n",
    "spam = 1  # and this is the second comment\n",
    "          # ... and now a third!\n",
    "text = \"# This is not a comment because it's inside quotes.\"\n",
    "```\n",
    "You will see comments sprinkled in among the examples, these don't need to be typed to make your code work, but it's a good idea to get in the habit early on so that you have good practice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Strings, numbers, and lists\n",
    "\n",
    "As you will recall we can do pretty much everything we can do with the interpreter (iPython) right here in a notebook cell.  So let's start again by doing some simple calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2+2\n",
    "53*285"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With Python, math works exactly as you expect.  Equations are evaluated left to right, and the order of precidence can be changed using parathensis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "5+5*5 # This equals 30 because 5*5 happens first (order of precidence)\n",
    "(5+5)*5 # This evaluates to 50 because we have told Python to add first due to parenthesis\n",
    "5*(5+5) # Same thing, this will be 50 because of parens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to go to the previous cell and try a few more calculations.  Or if you want to keep the original, just 'insert' a cell above this one and try a few others.\n",
    "\n",
    "### Operators\n",
    "[Operators](https://www.tutorialspoint.com/python/python_basic_operators.htm) are the constructs that execute on operands.  For instance, in the equation 4+5,the operator is '+' and the operands are 4 and 5.  For more details on all the Python Operators check the [on-line docs](https://docs.python.org/3/library/operator.html#mapping-operators-to-functions).\n",
    "\n",
    "A few of the most valuable ones to us are '+', '-', '>', '<', '=', and '=='.\n",
    "\n",
    "#### About = and ==\n",
    "Python uses a single = sign to specify assignment, whereas a double = like == is a check for equality.  So,\n",
    "```python\n",
    "a = 5 # Set the value of a variable named 'a' to be the value 5\n",
    "a == 5 # Returns True if the value of the variable a is 5 or False otherwise\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you are no doubt already aware, variables are text used to store values in our programs.  The computer doesn't care what you call your variables, but other humans do, so name your variables something useful such as\n",
    "```python\n",
    "width = 5\n",
    "height = 12\n",
    "area = width * height\n",
    "```\n",
    "\n",
    "You will notice when we use assignment (=) that the value is not displayed on the output of the cell, also if we try to use a variable before it has been defined we'll get an error.  Try executing the next cell.  \n",
    "\n",
    "You should get an error like\n",
    "    \n",
    "    NameError: name 'n' is not defined\n",
    "    \n",
    "Try removing the n on the last line and replace that line with the variable 'area'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "width = 5\n",
    "height = 12\n",
    "area = width * height\n",
    "area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strings\n",
    "Besides numbers, Python can also manipulate strings.  Strings are one of the most basic concepts in many languages and have tremendous value, as we most often use text to communicate with users.  Strings are used to provide real time feedback (think text boxes and labels, usernames and passwords), error messages, status logs, and communication with other programs.  Getting good with manipulating strings is extremely important to be successful.  Read about all the things you can do with strings in the Python [tutorial](https://docs.python.org/3/tutorial/introduction.html#strings).\n",
    "\n",
    "Strings in Python can expressed in several ways.  They can be enclosed in single quotes('...') or double quotes(\"...\"), both are equally acceptable.  If you need to use one or the other in your string definition, you can use the '\\' as an escape character or just define the string using the opposite type of quote.  Run the next cell to see the results (note that the output for the interactive cell will always quote strings in single quotes.  If you want a cleaner version of the output try using ```print()``` instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "'spam eggs'  # single quotes\n",
    "'doesn\\'t'  # use \\' to escape the single quote...\n",
    "\"doesn't\"  # ...or use double quotes instead\n",
    "'\"Yes,\" they said.'\n",
    "\"\\\"Yes,\\\" they said.\"\n",
    "'\"Isn\\'t,\" they said.'\n",
    "print('spam eggs')\n",
    "print('\"Yes,\" they said.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Joining strings together\n",
    "Strings have some other handy features as well.  Say we want to put two strings together, that's easy just use the '+' operator.  What's more, all Python objects can be represented as strings (some more cleanly than others) so we can use this handy trick to show the result of a function for instance, we just have to tell Python to convert the thing to a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'Hello '+'World!' # Two strings concatenated together\n",
    "'4 + 3 = ' + str(7) # We need to tell Python this is supposed to be a string\n",
    "seven = '7' # Assigning the value of 7 to the variable named seven\n",
    "'4 + 3 = ' + seven # Since 'seven' is already a string (see the quotes above) no need to tell Python to convert.\n",
    "'Hello ' 'World!' # Word's that are enclosed in quotes right next to each other are automatically concatenated too!\n",
    "\n",
    "('Line 1 is on the same' # This is a great trick for multi-lines to make reading the code cleaner too!\n",
    " ' line as Line 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### String formatting\n",
    "This can be a little clunky when dealing with a string that has some variable in the middle of it like:\n",
    "\"Hi, my name is Michael.  What's your name?\"\n",
    "\n",
    "In order to change the name `Michael` to a variable the result would be something like\n",
    "```python\n",
    "my_name = 'Michael'\n",
    "'Hi, my name is '+my_name+'. What\\'s your name?'\n",
    "```\n",
    "That gets ugly real quick.  Fortunately, Python has helped us out by giving us 'f-strings' (f stands for format).  So what we can do instead is to put an expression in the middle of the string and have it figured out when we need the string.  So the last example looks like:\n",
    "```python\n",
    "my_name = 'Michael'\n",
    "f'Hi, my name is {my_name}. What\\'s your name?'\n",
    "```\n",
    "In this way, everything in the `{}` are figured out at the last possible minute.  Here's a few more examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Try it!\n",
    "my_name = 'Joe'\n",
    "f'Hi, my name is {my_name}. What\\'s your name?'\n",
    "f'Calculating stuff is easy too, see 4+8 = {4+8}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your turn- Hello, World!\n",
    "For this exercise, write the classic Hello World! with a twist.  Your output should be a string that contains an introduction to yourself, by stating your name and your age (not your real age if you don't wish).  You can use string concatenation or f'strings, it's up to you.  Two caveats, we want to make this easy for someone else to try and your age never stays they same, therefore the name must be stored in a variable `my_name` and the age should be calculated based on the current year something like `age = 2020 - 1985`.\n",
    "\n",
    "The output should look like<br>\n",
    "```\n",
    "Hello World!  My name is Michael and I am currently 40 years old\n",
    "```\n",
    "\n",
    "**BONUS**: _Can you calcuate your age in months instead of years?  How about days? (built in date functions is cheating)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display a string introducing yourself to the world with your name and your age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lists\n",
    "Python knows about a number of different _compound_ data types that is ways to store multiple variables together.  Two of the most common are _list_ and _dictionary_.  We'll start first with a list and then give you a chance to work with a list in a simple challenge.\n",
    "\n",
    "Lists are written as a set of values between square brackets that are separated by commas.  While lists, can have different types of 'elements' most often they are the same type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "squares = [1,4,9,16,25,36] # This is a list of squares\n",
    "people = ['Alice', 'Bob', 'Charlie'] # this is a list of people's names\n",
    "squares\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is super handy about lists is that they can be indexed.  That is we can ask for a particular item in the list.  The first item starts at index 0 the last item in the list is the length of the list-1.  So a list with 5 elements can be indexed on any value between 0 and 4.  And just like other built-in [sequence types](https://docs.python.org/3/glossary.html#term-sequence) (including strings), we can use handy 'slicing' notation to get parts of the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "squares[0] # Returns the first item in the list of integers called squares\n",
    "squares[-1] # Returns the last item in the list\n",
    "squares[0:2] # Slicing returns a new list [starting position : ending position : step]\n",
    "squares[0:6:2] # Starting with the first item, give me every 2nd item between index 0 and 4\n",
    "squares[0::2] # leaving out the end assumes the last item in the list\n",
    "squares[:2] # Not specifying the the step assumes 1, and not specifying the initial index assumes 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists support concatenation just like strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "squares + [49,64,81]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists are changable too, so we can do something like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f'Names before the change: {people}'\n",
    "people[1] = 'Bonita'\n",
    "f'Names after the change: {people}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another handy feature of sequence types is that support several built-in functions.  Probably one of the most useful is the `len()` function, which returns an integer that shows the length of the sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(people)\n",
    "len(squares)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, lists can contain other lists.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = [1,2,3]\n",
    "a = ['a','b','c']\n",
    "x = [a,n]\n",
    "x\n",
    "x[0] # This syntax says, I want to see the first item in the list x (which is a list)\n",
    "x[0][1] # This syntax says, I want the see the 2nd item (index 1) in first item of the list of x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your Turn - Hello Worl?\n",
    "For this challenge, use what you have learned about strings, integers and indexing to fix the message stored in `message`.  It seems that when I typed up this example I left out a letter and also used a question mark instead of a `!`.  Can you fix this by using indexing and replacement and then print the parts into a single line?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Don't change this declaration directly, instead just manipulate the output by putting the correct\n",
    "# in place of the ellipsis `...`\n",
    "\n",
    "message = ['Hello', 'Worl','?']\n",
    "\n",
    "message[1] = ...\n",
    "message[2] = ...\n",
    "f'{...} {...}{...}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "This is a good start and a very basic introduction to types in Python, there are many, many more things that can be done with built-in functions to manipualte strings, numbers, dates and lists.  In order to do anything interesting our code needs to be able to make decisions and execute different actions based on conditions - often many times over.  In the next notebook we'll look at how to get Python to make decisions and act on the results. \n",
    "\n",
    "Feel free to play around here by adding cells and experimenting with different language elements.  When you are ready to move on, head over to the next section.\n",
    "\n",
    "[next](2\\-Flow_Control.ipynb)|[toc](toc.ipynb)\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.6 64-bit ('base': conda)",
   "language": "python",
   "name": "python_defaultSpec_1594132474756"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}