0315TAcode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NCTS 0315 TA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 起手式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 畫一張圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0,10,1000)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 接力畫一張完整的圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sin and Cos')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8ITSU5PBeOXemLCwzodqix5A7gL1Sa2xxMjIMHP09PV77NWFlGUvNJsBcQ2u4\nxZmgHQhF8PrBLgDAZ1fw0DeIJ66uL8IssxYdNi8+Oiu+KNFEdGfWGx6tVjvzPR4AyK+hD0vIBxx/\nbdof33aaurOuXyBsUMFYGIbB5miQw0dnBwWWRiIpHHkFCLipa6t4gdDSjCKT0QKiAN3riYNtp/th\n9QRQV2TEsoq8JAo3PeQyBvdHQ7pfEWGQQSK6M+sNj1qtht+fYKXpZZ+n4+FXaDJenIQjBNvOUMNz\nzfzEWvHyzRVzqetl5znxzdSynnAQ2P8CPV79qLCyTMSiz9J9p+ZtcXUpZROv7105Ky32SMdy92Wz\noFXKsatpEOdFVjw0Ed0pGR4+DE/djYDeAgycomVH4uRwhx02TwAVZh3qioyJycAzyyvzkKNRoHXI\ngw5r8hvfSaSQU2/TXJn8WmDudUJLcykGCzDvBrr/dCR2x98uuxe7m4egUshw+9LyFAkYP3l6Fe5Y\nRiuCsAWAxYJkeBJAoVAgFEow3FGhoi19gWlVMmDdbNc0FKXdTE0hl2FdbQEAYFeTtOoRDYQAe6LV\nAdZ+ld/uonyy7At0PPp7IDJ5gMvrh7pACM1/EzqEejLY0Oo3D3fDLqL22Inozqw3PHK5HOEwD4mS\nyx8AGBlw5v8Az9CUbyeEYOspGs0mdNLoZFw1LxrddmrmvdUl0owLu4G+44C+kHYYTVeqNwI55YD9\nAi3pMwGEEK6g7Z3L02+1w1JjMWDDXAv8oQj+dEA8/a4S0Z2S4ZHLEYkxo4qb3Aqg9mraMjiOIIOW\nQQ8uWL3I0ymxvDJ9NkTHcm1DMZRyBntbrLC6k9dxVSKFsBv2yx8AlBphZYmFTA4sjhrGSZounut3\noWXQgzydEmtrktvoLVG+tK4KAPC/ey8gKJIUhUR0Z9YbHl5ZGg0yOPLqlEEGu5potNgVcy2C1Wab\nCpNOiRVVZkQI0Ng6syq0EmmEows49RZdmbPf1XRm8b10PP0OELy00+W7x2l9w80LiqGUp7cqu2KO\nBTUWPXodPrx/UvIgpPdfKwWEw2HIZDzdhrmbaT+TgdNA79GYb93fRvNj1qT5TI3d5/m4SQqrznj2\n/gqIBGkzwzxhWm9Mi4JaoGQJEHABzdvH/YoQgveihXVvXJgeidexkMkYrpX970QSZJCI7sx6wxMK\nhaBQKPg5mUI16h44NHn9tkAogt3n6T4Qq9jTFXafZ8fZfkREln2dVfico262dEoYnYr5t9Hx1Nvj\nXm7qd6Nl0INcnRKrqlPULTVB7lhWhhyNAoc7hnGkwy60OAmTiO7MesMTDAahVPIYDcO6ME6+CQQn\nrmN08IINnkAYc4sMKM9LbaeKtw9VAAAgAElEQVTR6TKv2IjyPC2G3AEc6ZRaYmcs596jK4eKNbQm\nWqbQEDU8TVvGudveP0lXO9c1pL+bjUWnUuDeVRUAgN/tuSCsMDyQiO7MjL9YEgkEAlCpVPydsKiB\nugf8DhrhNgGfNNPVzpV1hfxdN0kwDINrokVDt56WfNMZy8k36Mg2MMwUzLOBksW0ykLLh9zLbH3D\nzQvTMyJ0MthKBltO9sHpCwosTWIkojuz3vD4/X5oNDxH97DdFCdxt7HL7BVVmeEiYA0Pm3ckkWE4\nuoDz2wC5iu7vZBoNt9LxNJ3Iddm9ONvngkGtSPtotouZZdZhTXU+/KEIFxyRqSSiO7Pe8AQCAX5d\nbQCdVSo0QPvuS7opBsMRHOukvUaWVeTye90ksaLKDJNWidZBD9qGPEKLIzFdjv8ZAAHqbgD06b2n\nOCH1t9CxaQsQDmJ31GNweW0B1Io0TYCNAVvJ4K3Dmd3lNxHdmfWGh/c9HoA21Jp3Ez0+8vtxvzre\nNYyRYBjVFj3yDcKXb48HpVzG1W4TY5VdUUMIcPQP9HhxGieMxqJgDlAwF/ANAx17MeSiOWUb5qZJ\nK4dpsnlBMVQKGQ6029DnyNzW2NIeTwJ4PB7o9Xr+T8y52343rq/IznM0LHnDnMx6aDbOo/J+KBme\nzKLtY8B6HjCWALXXCC3NzJm7mY5NH6Ag2m9nw9wMXL0BMGqUuKquEIQAbx/N3FVPIrozqw1PJBKB\n0+lEbm4SXF5V62kRRncf7fIYZWziaCZxxdxCyBhgX5tVlK18RQtXqeCLgJyntAEhmHMtHZu3Y11N\nASrzdWkfERoLtsTPH/d3gEyjon26kKjuzGrDMzw8DEIIzOYkbPIzDLD8S/T4EG3ja/MEcKLbAZVc\nljG5ByxmvQrLKvIQDBN8IiWTZgZ+Fw2jBjLXzcZSsRpQGYHBs5glt+OmhSVCS5QQG+cVoihHjXar\nF/vaMq8qSKK6M+sND4DkrHgA+rAzchoG6h7EwQs2EAKsnG2GTpV5s89N9dHotjNSdFtGcO59IOil\nuTt5VUJLkxhyJVC5lh5f+CTt+ldNF7mMwd2X0W6pmdgkLlHdmdWGx26nYc15eUkq0qkvoIVDIyHg\n5Btc3/Ur6zLLzcbChlXvODMgmkKHoubMX+mYiSHUE1F9JR2bd2BecY6QkvDC51ZVQMYAH5zsg8Ob\nWTk9ierOrDY8DgcNazaZTMm7yOLP0vHo77F4Vi5kzGgZmkyjxqJHtUUPx0gQja1WocWRiEVwBGje\nQY/rbhBWFr6o2UjHto+hUWS+6ioxabGmJh+hCMm45OxEdWfm//USwOOhOSlJiWpjqbsR0OYBfcdR\n6jqJu5aXY3ZBEq+XRBiG4XzrmZ78JnqatgBBD1C6DMidJbQ0vODPmwPo8mnAjrVZaHF44foF9Hli\nC55mConqzqw2PG63GwBgMBiSdxGlZky7hFdw/5qqtOs2Oh1uWkwrAW851YeQ5G5LX06+SceFdwor\nB4+c7nWNuttadwooCX9sXlAMGQPsbh7CsDdzupMmqjuz2vAMDdEM6Pz8JJfdWHIfHU+9jfkWHuvC\nCcDcIiNqLHoMe4PYK7nb0pOAd7SNAJv1n+EQQmjJptlX0BfadgkrEE8UGNRYV1uAYJhw9ecygUR1\nZ1YbnsFBGhZcUJDcRLRwQR0tdOh3QNayI6nXSgU3LqKrHsndlqac30qj2couE42brc/pw44zA0Dl\nOvpCxz5hBeKRG6Pu60xqMZ+o7sxqw+P1eqHT6fhrBDcJTf2u0UKHbKRRBnP9AloRePuZfi5STyKN\nYL9j7HdOBBxqt6NpwAVfThWgNgGeAcApjonP1Q1FYBjg0+ahjEnOTlR3ZrXhsdlsycvhGcPbR7pH\n+4qcfXfCNr6ZxLxiIyrzdRhyB3DgQuYlv4macAho3kaP590orCw8cvCCHYQAVk8AKFtKX+zaL6xQ\nPFFgUHPJ2ZnS6TdR3ZnVhsdqtcJiSW5OjS8YxiuN7WgjxUDpUtqMq+mDpF4z2TAMg83z6aonk/zS\nWUHXfsDnoOWa8muEloY3Tvc4AYCusMtX0hc7xWF4gNEcue0Z0nokUd2Z1YZnYGAg6fs7e1ut8AbC\n2NMyBCyIRhid+EtSr5kKro/6pd8/2Su1xE4n2GZpmVwQ9CJC4QhO9kTzRnQqYFbU8HQfElAqftkU\nze3b2TSYEc9Torozqw3P4OAgCguTm8zJNk/zhyKj3R+btwOBzO5rs7jchFKTBv1Ov9QSO51o+YiO\nbLKlCDjb54I3EEZlvg4mrZLmJgFAz1EgnFkZ/5NRW2hAeZ4WNk8Ax7rS/3lKVHdmteFxuVwwGo1J\nO38kQrA1GqmyarYZyCmhkUYh36iCyFAYhsHmaPLb1gyKxhE1PifQcxiQKUbrmomApn4XAGB+abRM\njj4fyJ8DhEaAvuMCSsYfDMNgY1101XMu/fd5EtWdWW143G53Ug3PyR4HhtwBlOVq0VASfWjqrqdj\n0/tJu26quLqBPijbpaKh6UFHI0AidC9Rnbzvdao50U3dbPVj67OVX0bH7sMCSJQc2BqOOzMgwCBR\n3Zm1hicYDMLr9SY1qm0X2/RtrmW0WgFneD4AIpmd+c+2xG4Z9HCzUgkB6dhDRxGtdgDgWNSVu2jW\nmGe1bDkduw4KIFFyWFOTD5VchuNdw7B70reKAR+6M2sNT9IrU2N0JTCuGnVhA2CqADyD1C2SwSjl\nMi6n52/HegSWRgIXdtOx8nJh5eARfyiMk9GItiXlExmeAwJIlRx0KgWWV+aBEKR1VRA+dGfWGp5k\n9+LpGR7BsS4HdCr5+DbXDAPMvY4es026Mpibo7Xb3jjcnRHROKIl5Keb7WBo0zSRcKbXhUAoghqL\nHiadcvQXxQsBpR6wtQBu8bRjX1dLS9DsaRkSWJLJ4UN3Zq3hcTrpLConJzl9PdgN9/VzCqBVycf/\ncl60TP25LUm5dipZU52PUpMG3cMjONhuF1qc7KXnCBAJApZ5gCbze9WwHO2g36klsy6aXcuVQPEC\nejxwOsVSJY81NdTwNLamb2I2H7ozaw1Pslc870bLnN8wUYveynWAygAMnAKGO5Ny/VQhkzG4dWkZ\nAOD1Q5n9f8lo2j+lY+UaYeXgmePRwIIlsybo+5JfS0eRtEgAgEXludAq5WgecGPA5RNanAmRVjwJ\nkEzDM+Dy4WC7HSq5bOKmbwr1aHl3ERQN/cyycgDA+yf74AuGBZYmS2E32WeJx80GAEejgQULyyd4\nTovm07H3WAolSi5KuQyXVdHV3f629Fz1SIYnAdjlYjLCqT841Q9CaDSbUaOc+E01V9GRzTTPYGoL\nDVhYZoLLF5JCq4WAkFHDw4YZiwC7J4DWQQ/UCtloOsJYilhX29nUCpZkVs02AwD2pam7jQ/dmbWG\nJ5ltr9lqBdfNL5r8TWPa+GZ6WDUA3LqEBhm8L9VuSz32C7Rasy4fMFcLLQ1vsPk7C8pMUE3U6rp4\nIR37jtPiqCJh5Wy6z5OuBXj50J1Za3iSFVww5PZjT/MQ5DJmYjcbS95sGlY9Ygf6T/AqgxBcv7AE\nDEOLHDpGxFHGJGNgi2XOWkWjJkUCm7+zsGwSBacz02co5KPRbSJhUbkJKrkM5/pdcHjT71mSggsS\nwOVyQavVQi6XT/3mafDu8V6EIgRXzLUg36Ce/I0MA8zeQI9bM7+bYlmuFmuq8+EPRaQSOqmGzQdj\nc1tEAlsYdPFEgQUs7KpHRPs8GqUci2eZQAhwsD39Vj186M6sNjzJCKX+IKp0b148QTTbxcxeT0c2\n8S/D2RxNJs2EWlOiglW6pUuElYNnTnRFXW2lMQxPySI69mW+12AsK6P7POkYYMCH7sxqw8N3YIHT\nF8T+NhvkstGCfzGpimaYd+wFIpkfDXblXPp//uT8IELhzN+3yggIAYaa6LFlnrCy8IjdE0CPwwet\nUo4ai2HyN1rq6Dh4LjWCpYhV0X2efWlqeBLVnVlreEZGRqDRaHg955aTfQhFCFZU5SFXp5r6A6Zy\nILcS8DtFUWW3Il+H6gI9nL4QDnekf2l3UeDqBbxW2g46p0xoaXiDbQ0wvzQHMlmMfasiNsBAXCue\nxdHw8bN9zrRrL8+H7sxawxMIBKBSxWEcpgHbPfCmRaXxf6hyHR3b9/Iqi1BsjAZUfHhWPGVM0hq2\nGVrpElEFFrATl6UVU+SKmGcDCg3g6gFGxDPZMemUKMvVwheMoG3ILbQ44+BDd0qGhyf8oTB2N9P6\nSjGj2S6mYhUdRVLscLSniGR4UgJreMpXCCsHz7CJo0srpihEKZPTwruAKLwGY5lXTN1ZZ/vSq/K7\nZHgSIBQKQaFQ8Ha+xlYbvIEw5hUbUZqrjf+DrMIQieFZMTsPOpUcZ/tc6B4eEVoc8dMbVbYli4WV\ng0cIITgedbUtKo8jV4QNqug5mkSpUs/8aBg5G2SRLvChO7PW8BBCIJPx999/7zitzXbt/OLpfdAy\nD1AZAUcn4OzlTR6hUCtGq3F/KFUxSC6E0OKggKgMT4fNi2FvEAUGFcrimcSx//decRmeBdGOq6d7\nnQJLMh4+dGfWGh4+iUQIdkT3NNj+NHEjkwPl4uotcpW0z5ManN3AiI1WLMitEFoa3mDdbIvLc0cb\nKMaidBkdWbejSGiIGp5TPU4Qkl4BBomS1YaHrz/mkc5hDLn9KMvVcn7ZaVG+ko4iMTxXzqMrnj0t\nVowEMj9MPG2xtdIxv1ZUgQVHooEFS2bFWYSysAFQaGnpIG/6hR/PlLJcLXJ1Stg8AfQ506tSdaK6\nk79NjgQJh8P429/+hs7OTqxfvx6LF0/uOti/fz/cbjfC4TACgQAYhoHX68W6detQUhJH4iYAuVyO\nYJCfchQ7oi6laxqK4puhXQzro+4/yYs8QlNo1GBxuQnHuhz4tHkIVzfEqFknMXNYw5M3W1g5eOZI\ntAfPsso4O1zKFfQZ6thLi6XOvTaJ0qUOhmFQX5yDva1WnOl1osQ0jb3jJMKH7kwLw9Pa2oo777wT\nJ06cQH5+Pvr7+/Hoo4/iF7/4xYSK/P7778f58+fHvabRaPDKK6/gzjvvjOuaKpWKqzmUKLuaaKb+\nxulEs42FTfwTUZXdq+YV4ViXAx+eG5AMT7LojzZAK6wXVg4eGQmEcarHCRkDLI53xQPQckEde4Fu\n8RgeAJhbZMDeViuaB9y4al56PEd86E7BXW2RSASf/exnAQBnz55FX18f3n33Xbzwwgt48803J/xM\nWVkZ7rvvPjidTvh8Png8Hrjd7riNDgAolUpeVjyDLj9O9TihVsi4cubTJq+KNoZz9Yimje/GqLvt\n4yapfE7S6D9FR7Y9gAg42jmMUIRgXnEODOppzItnRdMSOsSRD8dSW0Rd90396ZPLw4fuFNzwNDY2\n4uDBg/jtb3+LmpoaAMANN9yAW265Bb/61a8m/IzVakVNTQ06Ojrw1ltvYfv27dP2OarVavj9/oTl\nZ1c7q6rzoVHOsGieTD7a1IpVJhnO/FITTFoluuwj6LB6hRZHnAxGV8hs2RgRwLYCWDndSVxFtAFe\n1yFRtUhoKKGG51RP+kS28aE7BTc827Ztw7x58y7Z01m9ejVOnZpYCff29uKll17CggUL8NBDD+HW\nW2/F4sWLMTQ0dMl7z5w5g7feegvvvPMOtmzZgsbGRgQCAWg0Gvh8iW/YsUVBr03UncQmwYlkn0cu\nY7A22j9+V5M4VnFphXsA8A4B6hxaekkksIZnRdU0DY+hkPYiCnpE0WaEZXYBrVPXafOmTWQbH7pT\ncMPT3t6OiopLQ0Hz8/MxMDBwyc0OBoMYGhqC1+vF9u3b4Xa70dbWBpvNhmeeeeaS8/z5z3/GHXfc\ngdtuuw3XX3891qxZg66uLiiVSoRCic2MwhGCfa1WAMCVdZaEzsUFGIiovPsVc+k9+bTZKrAkIoQt\nimmpE01EWzAcwaF2GliwYnacgQVjqVhDR5GUnwKAPJ0SJq0Sbn8IA67EPTR8wIfuFNzwGAwGeL2X\numI8Hg90Ot2EwQW33XYb3nvvPWzatAkMw6Cqqgp33XUXPvroo0veGw5fGs7r8/mg0WgwMpJYZv3J\nbgecvhDK87Qoz9MldC5uxTMongCDNdEVT2ObFZE0K3SY8VijwTX5c4SVg0dOdjvgDYRRXaBHoXEG\nRShZd1vHHn4FExCGYVAXTdFIl0RSPnSn4FFtRUVF6O29NGO/ra0NDQ0Nl7yuVCrx1ltvXfJ6Tk4O\nrNZLZ9b19fW49dZbEQ6H4ff7YbfbEQ6HYTAY4HYntmHHJkiyM/uEKKwHwAADZ4CgD1DyWzlbCCrM\nOpTlatE9PIKzfS4uIU6CB7gVz1xh5eARtgXAquoZBumMLbhLiGhWgg0lOdjfZsO5Pld87VaSDB+6\nU/AVz+WXX46Wlha0tIy2riWEYMuWLVi5cmXc52lsbERd3aWbrPfeey/efvtt/PWvf8XWrVtx4MAB\nLFy4EEajEX6/P6HojJ3RwIJN9Tx8GdRGanwiIdEUO2QYhov0298mudt4ha1LxrYFEAGNUbf16ur8\nmZ3AXA0YiujeF9ujSARU5lNvSoctPYJ0+NCdaWF4Kioq8OSTT3KrkW9/+9s4c+YM7r777kveb7PZ\nsGbNGhw9OlqX6bnnnsOOHTvwhS98Ie7rso2MZmq5h9x+HO8ahlLOcE2bEqYsWvqDrb8lAlZEDc+B\nqO9eggci4dHJSelSYWXhiVA4ggPsimemzxPDAJVr6XH7pzxJJjxsI7zz/elRpTpR3QmkgeGRy+X4\n4x//iBMnTqC8vBwWiwVPP/00nnrqKWzYsAEA8MwzzyAnJwcOhwM5OTnIycnBqlWrcOWVV6Kurg6P\nPvoovvzlL+P222+P+7oGA/1jzvTmfXR2AIQAa2oKoJ9OvkEs2NmrSEKqAeCyaPb5oQuS4eENawsQ\n9AI55YCep0mPwJzqccITCKMqX4diUwJu5oqo4elo5EewNKC+hLqoz/S60mKvNFHdCaTBHg8ArF27\nFqdOncIbb7wBr9eLa6+9FtXV1dzvtVotVCoVZDIZFAoFtmzZgv/7v//D9u3bsWrVKnz2s5/FsmXL\npnVN1mq7XDObRXxynoZub5pptYKJKIxWMBBRG98aiwFGjQJ9Th/6HL7ElIoEhQ25LxZP4igbRp2w\n96CSjWwTT4CBxaiGxajGoMuPTrsXlfl6QeVJVHcCaWJ4AECv10/qKnv44Yfx8MMPc/9mGAa33nor\nbr311hlfj715Myn9QAjBvuieBRu5xQuWaOmTgdOi2RyVyRgsmZWLT84P4WinHZtN8dXSk4iBtZmO\nBeIJLGhsjebvzLT6B0vhfECTS9uM2NuBvEoepBOehpIc7HIN4nSPM20MTyJlcwR3tQmFTkc37CYK\n5Z6KtiEP+p1+5OtVmFNo4E8oYxF9aPxOwCOeUjMLow2tjqdZQ6uMZSBao42t8ZfhhMIRLh9u9Uwj\n2lhkstF9ngufJChZ+sCGVJ8fEL50TiK6kyVrDY9eT2cNHo9n2p9lZ2erqs0zq0Ydi/xaOooon2dR\nOS32KBkenugTl6vtVI8TLn8IFWZd4vlwADCb7g2jTUSGJ1qz7VwatMFORHeySIZnRoYnwbDPWBSx\npXPEE2CwoGy0k2K6lP3IWIIjgK0FYORAgThqtO1upvul62p5ep6q1tOxbRd1WYuAagvVVxesM1f2\nfCEZngRgb950l4uEEOyNGp41STE80ci2PnHUbANoQyujWgGbJ4BBd3qU/chY+k8BJEL3d0SQZAwA\nn5ynbuXLa3lIxAZowV1DEeDqpQnZIqA6WrOtddAjeGTbTHXnWLLW8JjN1Jc8UbWDWLQNeTDo8qPA\noEItn/s7LAXREij2Nv7PLRBjy3409Qnvo85o2BwvtrZfhuPwBnHwgh1yGYPLawv4OSnDANVX0uO2\nXfycU2BMOiUKDCqMBMPocSRWriZRZqo7x5K1hsdoNMJgMKCnp2dan2PLeqyoSsL+DgDk09YQYtrj\nAUY3R8/2pUe9qYyl+zAdy5YLKwdPfHRuAKEIwcoqM0w6JX8nrr6Sjs3b+TunwLAT3SaBE0lnqjvH\nkrWGB6AVsG226fVon3G/kHgxzQJURsBrBdziiWzjVjxpkn2dsXQfomPp9PLW0pUd0XqHvJSdGkvt\nNQAYoO1jIJAepWYSZX4pjQ492S385G0munMsWW14zGbzhD18YsGWbb+sMkmGh2FGCz9az8d+bwYx\npzAalZNGnRQzDp+T1iCTKUUR0eYPhfHhmX4AwLUNxfye3GCh7shwQDRh1Wxawolu4aNDZ6I7x5LV\nhqe4uBh9fX1xv3/Q5Ue71QudSo76aGfApJBXRUf7heRdI8Ww9+tcnxPhNCj7kZH0HgVAqNFRqIWW\nJmH2t9ngCYQxr9iIinwewqgvZs51dDy/lf9zCwBb3f1MGrRHmK7uvJisNzzT8VMe7RwGACwqN0Eh\nT+KtYzPS2URBEZCrU6HUpIEvGEmbKrsZR9dBOopkf4dtK3Jlskr9126iY/MOUYRVVxfooVXK0WUf\ngVXg6NDp6s6LyWrDU1JSgoGBAUQikbjef7yLGp7Fs3KTKRZQFHWjiCiXBwCqLWxIqORumxFdB+hY\nvkJYOXiCNTzXNCTJ8JQtp5VA7G2iiBJVyGWYH131nOoRdtUzXd15MVlteIqLixGJRDAwMBDX+1nf\n6qKyJBuewmjNtkHx9BQBRqNy0qHsR8ZBCNC5nx6LwPC0DrrRbvXCqFFgcXmSnieZHJjNJpN+nJxr\npBjW3XayR9h9nunqzovJasNTUkILVsZ7805HZxnzk91JM7cSUGgAZxfgE34jkS9qCqUVz4yxtdIG\nZ3oLbXiW4ew4E41mm1eYXLf17Cvo2PJh8q6RQrgAA4HLT01Xd15MVhue/HxaeSCeRCibJ4ABlx86\nlRwV5iRshI5FrgDyo4mkQ+KJbJsdrarbNiR82Y+Mo2MvHWetEkXV8m3RaLaNfLYVmYg519CxdSdt\noJfhsHUPhV7xTEd3TkRWGx6Tic4e4invzRbnm1NkhEyWggef7c0jon2emkJqeJolV9v0ubCbjpXr\nhJWDBwacPhy4YINKIcNVyTY8eVX0x+eIRgVmNjUWPTRKGTptI7B7AoLJMR3dORFZbXjY8t7xFLtj\nQxjri5MYRj0WtuS9iHrHF+doYFArYPcGMSTVbIsfQkb3KNg9iwzmo3O0e++GOQUwanisVjAZbLXq\n1p3Jv1aSoQEGVOkfiwY7CcF0dOdEZLXhmU6xO3Zpm/T9HRaL+FY8DMNgThHbP15a9cSNrRVwdgO6\nfNroLMPZ00LdM+vn8FQUdCqqN9Kx5aPUXC/JLIlG1R7rFM7dlmihUMnwID6rfSpapmJ+dHMv6RRH\nq1T3i6dKNTC6z9Nhk/Z54obd36lYQxudZTCEkOS2FZmImo0AI6P30Sd88mWisOkcQq54Em2NkNnf\n4gSJd7noC4bRPOiGjAHqi1O04smtoDXbPIOAe2aRI+lIWZ4WANBpE7bCbkbButmqLhdWDh5oGXSj\n30mru/PavTcW2jxg1mogEgKatqTmmklkSTTA4GjnsGD9rSRXWwKo1WowDIORkdhK8Hy/G+EIQbXF\nAK1KnhrhGIb2FQGA3uOpuWYKmF1AZ0rtUvWC+CBkdG+i+koBBeGHj5tofa/LawtSE6TD0nArHZs+\nSN01k8QssxYFBhVsngAuWIV5juLVnZOR1YaHYRhotdop/ZStQ3Q/ImUzNBa254oIonFY2FB0KZcn\nToaaAHc/oC8c3ffLYNgmimtreOq9Ey81V9GxdScww2z7dIFhGCytyAMAHI4WLRZChnh052RkteEB\nqK9yquUim3dSFZ2tp4ySxXTsE8+Kh22PcH7ALRULjQd2hl59Zcbn74TCETRGAwvW8tXmOl4K5gCm\nCpqE27U/tddOAssrqeE5KJDhAeLTnZOR9YbHYDDA7Y49+2YNz+yUG57oioftOikCjBolLEY1AqEI\n+p0+ocVJf5q30XHudcLKwQPHuhxw+UOoLtCjPC/JSdgXwzDAvBvosQiaw62oom1Z2P5gQhCP7pyM\nrDc8Op0urj0eAKixpNjVVjAXUOqA4Q7AM/M2s+lGWS4NMLhglSLbYhLwAh2NAJhRV1EG82kz3d9Z\nx1eL6+lSfSUdRVC3bUFZDtQKGZoH3IIlksajOycj6w2PVquNefNC4QiXaT+3KMWGR64AihfRYxGt\neuax7jYplyc2HXtpI7OSRYAuSY0HU8ju89TwrK1JsZuNpXIdbaLXdSDjJ3JqhRzLovs87L5ZqplK\nd8Yi6w2PSqWC3z95Fn2vw4dAOIJCozo1WdYXUxZtcdxzOPXXThJsleqzfVIb7Jiw+zu11wgrBw+4\nfEEc6rBDLmOwVqgVjyYHqFoHkAjQmvnJpOui+2TsSjLVTKU7Y5H1hkcmk8XsKdEeDVdMeWABC9v0\ni20CJgLYAAMpsm0K2vfQUQRutv1tNoQjBIvLTTBpBZjAsdREm8Od3yacDDyxJhoZyFaCSDVT6c6Y\nn+VZloxDJpPFTMLqddClZKlJkyqRxsManu6DouiiCABV0eoFLYPSHs+kjNhp1QqZcnTVm8E0ChVG\nfTG1V9Ox9aOMf54Wl5uQo1GgbciDdgH2S6fSnTE/y7MsGQchBEyMMNVOOzU8bMZ9ysmrojkcXitg\nbRFGBp4py9VCq5RjyO3HsFe4CrtpTfseAIQ2fVMK9N3jkX1tNPpq5WyB96oK6wFDMc2NyvDW8gq5\njAvU2C2Au20q3RmLrDc8kUgk5s1rHqD7ELWpTh5lYRhg1kp63LlPGBl4RiZjUG1hVz2Su21C2Mir\n6iuElYMHHCNBnOh2QClncFlVnrDCMAyt3QaIomgoW2h117nBlF97Kt0Zi6w3PFNZbTaiLeWh1GOp\nWENH1ucvAqotbDdSyd02Ie2f0lEE/XcOt9tBCG1iplMphBZntCupCMKqr6ijhmd38xD8odQ2upNW\nPAkQDochl09cfy0Qiu+AUssAACAASURBVKB10AOGEXDFAwCVa+nIKiMRwCbjtkrdSC/FawP6ovs7\n5ZcJLU3CsG42wVc7LOwq8sJuIJTZrt6yXC3mFhngDYRxpCO11apj6c6pyHrD4/f7oVarJ/xdn8OH\nUISgJEcj7EyteBGgzgHsbYCjWzg5eITN5WEb7EmMoXk7AAJUrBbJ/k6K2yBMRU4pUFAHBD1A9yGh\npUmYDVF324dnU1vFPpbunIqsNzw+nw8azcQRaz3RiLZioSLaWOQKqoQA0bjb2IZ6p3okw3MJ596j\nY/0twsrBA/5QmOtlxSY8pgVsV9ILnwgrBw9sqi8CkHrDE0t3TkXWG55gMAilcuK8AjaUuizVdaUm\ngnW3ieBBAYBZeTroVXIMuvywSm2wR4lERvceRJC/c6rHiUA4gtpCg7D5OxdTFd07E4H7+rKqPORo\nFGgecONCCl3XsXTnVGS94QkEAlCpVBP+rtsucA7PWNgN0ZbMzz8AaGRbHedukyoYcPQepaHzOWVA\nfo3Q0iQMW7Z/WUWuwJJcREV0Itd5AAgHhZUlQZRyGdbPpe62T1IYVh1Ld05F1hueWFb7fDSijQ39\nFZSSJYCuAHB0AINnhZaGF+aX0jbip3qE6x2fdpzfSse5mzO+DQIAbsObLeOfNhiLAHMN3ecRQduR\nddHE3L0tqTM80oonAUZGRqDVTryBy7ZDEDSijUUmGy2NL4L2vQBQX0L3eU5LAQajtO6iowjcbABt\nzwyAa1yWVrD7pp2Z35+Hrdu2p8WKSIr6XMXSnVOR1YYnEonA6XQiN/dSNwAhBG3RHJPK/DRY8QBj\nuijuElYOnlhQRg3PiW5pxQOAtkHoOgCAGd2DyGCG3H50D49Ap5ILmwc3GeUr6Nh1QFg5eKDCrENZ\nrhbD3mBKJnKxdGc8ZLXhcbvdIITAZDJd8rs+pw8ufwhmvQoFhpmFDPIOu8/TsRcIZf6G/LziHKjk\nMrQOeuD0ZbafnRfa9wCRIO08q03DFcI0OdFFJxQLSk2Qy9LQbcjmSIkgpJphGKyfQ91tu5qSX8Ug\nlu6Mh6w2PMPD1A0w0c1jM+pr0mF/h8VgASz1QMgXbRCW2agUMjREw6qPdaY2+S0tOc+2QdgkrBw8\ncTxqeBaWz0w5JR1LPaDUA/YLgEeY1gJ8siEaYMD2PUomsXRnPGS14Rkaon+g/PxLE9vYGmIpb3c9\nFdw+zwfCysETS6PRTofbs9zwEDK6dzf3emFl4QnWhbooXQ2PXEFXlwDQnfn9rtgE3cMddviCyS2f\nE0t3xkNWGx67nYZ6TnTzmvppiO/cImNKZZoSdjYsgjpTwGi006EOu8CSCMxwO21xrs0bbYWR4ZyO\nRiuy0YtpiYgaLZr1KjSU5MAfinBtKJJFLN0ZD1lteFirbTZfWqq9KdqWeU66GZ7yFYBcRXu1jGS+\nsmYNz+F2O0LhmTWVEgVsRYqKNTSCMcMZ9gbQ4/BBq5Snn9dgLJzhEUdr+avrCwEAO84kt4pBLN0Z\nD5n/DU8A1k+Zlzd+I5cQgvPciifNonGU2uiMmIiifE6JSYsKsw5ufyi7W2GzK1gRVKMGRksh1RUb\n0zOwgKVkCR17jwkrB09cUUcNz8fnkxtgMJnujJesNjxeL21rrdePn5HZvUHYvUEY1AoU56RB1YKL\nqWb7iXworBw8saKKzpqS7R5IWwgZ/VuyvWIyHHYSwQaPpC15s2kBXlcv4OoXWpqEWTIrFyatEu1W\nb1K7kk6mO+Mlqw1Pf38/lEolcnLGPxzsaqfaop9xv4mkwubziMTwXD6H+ok/TkE0TloycIZ2xDQU\nA4UNQkvDC2ejuSR16eaqvhiZDCheSI9FUMFALmO4ZNJkdiWdTHfGS9YbnsLCQsgu8qmzboL64jSd\nrZUupbM0WytgbxdamoRh2/ceaLMhmI37PKybbfYGUZTJAYAzfdFnqCRNn6GxcIbnhLBy8MSaaHRb\nY6stadeYTHfGS1Ybnt7eXhQXF1/yOtsjZn5Zmj40csVoWfe2zK9iUGjUoNqix0gwnJ1VDDqjOVki\nqFYAAKFwhAvOmVeS5iseACiaT8f+U8LKwROrOcNjBUlSQeHJdGe8ZLXhGRgYQElJySWvsyUn0nq2\nVrWejhd2CysHT7APy96WLNvnIQTo2EePZ60SVhaeuGD1IhCKoCxXixxNGrVCmAyRGZ7aQgMKDCoM\nuvxoSVJr+cl0Z7xkteEZHBxEQUHBuNf8oTCa+l1gGKAhrQ3P5XS8sFsUbRLW1mSp4bFfAFw9gNZM\nu2KKgNZo8nVNOhTXjYeCuXS0tQDhkLCy8ADDMFgVncix3V/5ZiLdOR2y1vAQQjAwMIDCwsJxr5/t\ndSEYJqixGKBXC9jueioKGwBdPuDsBqwtQkuTMGujZd33X7BhJJDcrOu0gi19VLFaFPk7ANDMGp50\nKjcVC7URMFUA4QDdNxUBq2bTSNF9SdjnmUx3TgdxfNNngMPhQCAQuOTmsWXc07bMB4tMNhpWfe5d\nYWXhAbNehUXlJgRCkaRG46QdHWMSR0XC+ej+TtpHtI2loJaOtsyfxAFjPAhJ2OeZTHdOh7gNj9fr\nxd69e7F3717utVAohJ6enqRtYCWTgQGa2VtUVDTu9cPR0i1p2T/kYubdSMfm7cLKwRMbo8lvn2aT\n4emMluQXyf4OMJrDMyfdkq9jkT+HjoPnhJWDJ2osBhQY1Bh0+bmGlnwxme6cDnEbnn/5l3/B2rVr\n8cgjjwAA9uzZg7q6OpSVlWHDhg3weFLX65sPnE4aQHBxddWDF6jhWZ4Jhodtk9C5Hwj6hJWFB9hZ\n2p4UdlEUFJ+DdpOVKUeLVWY4oXAELVFFl3Z1DmNhie6vicTwMMxoPs8+nhOzJ9Od0yFuw7Ny5UoA\nwLZt2xCJRPDggw+ioKAAb775Jvbt24c//OEPMxZCCBwOGrY79ub1DI+ge3gERo0CdcUZ8NDo84HC\n+bRNggiaWS2tyINaIUNTvxtWd+b3G5qS7kMACDU6yjSskDED2m1eBMI0os2YCRFtLAXRFY9IXG0A\nsDK6z7OH54CdiXTndInb8Nx4441QqVR4++23cfz4cZw7dw5PP/00br/9dixYsAAnTmRW8hVrtY3G\nUQNz4ALdiLusMi+960uNRUT5PCqFDMuiK839bclLfksbuqINyNhOmCKgw0pLqaR1YdCJMM2io6Nb\nWDl4ZB0bsNNm43U7ZCLdOV3iNjxGoxE33HADfvCDH+DnP/85DAYDLruMdvDzer1QqVQzFkII2Js3\ntuTDnmY6M2BzSjKC6qi7TST5PGtqRnvHix62IjJbIVkEtA6x7eJ1AksyTYzRnBR3nyhCqgH6Nygw\nqGH1BHAhOiHgg4l053SZVlTbf/3Xf6G8vBwvv/wynnzySahUKjQ2NuLcuXO44oorZiyEELDLxbE9\nw9kVz6pMMjwVqwEw1G0THBFamoS5PNq+95MkV9dNC3qP0pGtkCwCeofpd3CWOcMMj1JDVz2REGBv\nE1oaXmAYhmu0eKidvxYqE+nO6TItwzN79mzs378fIyMj+NGPfgSAWr1//dd/xU033TRjIYSAvXms\n1R5y+9E65IFWKcf8dK+oOxZtHlC0gOYgdB0UWpqEWVRmglGtwAWrF502/mZpaYd7kOZgqQxAfq3Q\n0vBGW3TFMysvwwwPAFjm0XHwrLBy8MhlbKPFdv5c1xfrzpkwpeE5dOgQRkZGZ9IMw0CjGd0IbWho\nwD//8z+nZxXnGLjdbqhUKiiVdAP0YHS1s7QiF0p5hqU3Va6lowj68yjkMm7Vs/1M5pepnxR2tVO8\nUDSJowDQNJCBodQsbAWDoSZh5eCRFdEAAzZalw8u1p0zIeY3PhgM4rLLLsO//du/AQAeeeQR1NbW\nYunSpdiwYQM2btyIdevWYf369ejq6pqxEEIQDAbH3Ti2VMuq2RnkZmOZHa3b1rpTUDH4YvMCWnxw\n22kRGx62ErJIwqgBIBCKoNs+AobJwD0eYExItXgMz/zSHKjkMjQPuuHyBXk558W6cybErAmjVCrx\n4osv4uabbwYA3HHHHaioqIDT6YTL5UI4HIZKpYJer08owkEI/H7/uJXbJ9GkRXZzO6OouhwAQ0Oq\nA15AlYEP/RjWz7GAYegszeULZlZYbrz0n6QjW6BSBLRbPYgQYJZZC7VCLrQ40ye/ho7WZmHl4BG1\nQo76EiOOdTlwosuBtbUzr6/GcrHunAlTFiN76KGHuOPrrrsO1113XUIXTBc8Hg90Oqqgu+0jaB30\nwKhRcJtxGYU2j7ps+o5T41OdWYEeF2PWq7Ci0oz9F2z48OwAbl1SJrRI/NMXNTxsLxgRwEa0VRdk\noJsNGC3SOtREC+9m2PbBZCwqz8WxLgeOd/NjeMbqzpkSt3P5+PHjuOuuu9DdfWmc+9GjR2G1Zlb4\nq8/n46w2W8H18tqCzNvfYeHaJHwirBw8wbrbtpzsE1iSJBD0AdbzACMHLPVCS8MbzdGKBbWZUpX6\nYvQFtMGi3wl4M0ufxWJhGU30PMlTr6uxunOmxK1lCwsLcf78eWzcuBH9/dT3HolE8OMf/xjLly/H\nX//614QESTU+nw9arRbAaG2wdTzMBgSDbSLGVjvOcK6dT+tAfXJ+CCGxdSW1tQIkAuRWiKZiAQC0\nRnu/1Fgy1PAwDGCupsdD54WVhUfYvmJsDb1EGas7Z0rchqe4uBg7d+5ETk4OrrrqKpw9exY333wz\nvv/97+OZZ57BAw88kJAgqcbr9XI3b2cTzRm5Yq5FSJESo5yWNEL3YVEkwJXn6VCVr4PbH8KRaMVw\n0TB4ho6FDcLKwTMDLlovsMSUwca0MLoCHTgtrBw8UltogIwBLgx54Asm3nJkrO6cKdPyK+Xm5uL9\n99+Hz+dDfX09jh49il27duHxxx/PuHDqsZEZdk8Alfm6zEt6G4vBAuRVAUEPMCCOTopX19NVz3ax\nRbexPV/YzWyR0OughqcoJ4MNT9ECOvZlVgmw/8/eecfHUZ/5/7OzXbvSFq16tWRZknsvgAsdTDMl\nATsQOA4OEg4cWo7kF0oOLuR35O4I3M9HCOVCDRBwQgkYm+JesI17kSyrd2mrtH1nfn98d0aS1Va7\no53d2Xm/Xnp9R7uzM49GO88z3+f7lLHQquSYYtEhSDNcy4pY4COqbUKGh6ZpvPnmm2huboZGo4HJ\nZEJ5efLePFQ4f4JmgKXJGEZ9LmxPl8bdY++XJKwKt0kQXX8eazgz3lQirBw8QtMMl/BbZI7taVhQ\n2GAPNupQJLDuttOd/LjbqBhzzyL+dCgUwrp16/DLX/4Sv/vd73DmzBnQNI2VK1dyaz6x8OWXX+L8\n889HcXExbrvtNjQ0NIy5/zfffIPly5ejuLgYa9euRV3dxKrKnls0LynDqM+FNTxN4jA8C0pMUMpl\nONHuhMPDTw5CQsBmxrOZ8iKgxeaBL0gjO12d3OHvXPWCGlG0lGcpC6+7neGhNw8fBUcjNjwNDQ04\nfPgwtm/fjgceeAAFBQXYunUrKIrCpZdeir6+6P+gF154AZdffjmKiorws5/9DCdOnMCiRYvQ3t4+\n4v4vv/wyLrroIuTk5OBnP/sZ6urqsGjRIjQ1NUUtA5stn9QUhdd5RNAiASAugjmFRjAMcJDHWlOC\nwjADC9ciMjz1vSSwIOmqUp+LPhvQGACfA+gTj4u3Ktzm5VSHU2BJCBEbnvLycpw8eRKLFg2UcM/K\nysLmzZtx3nnngaajizzq6enBY489hmeeeQZ//vOf8dBDD2HXrl3Q6XR44YUXhu3vcDjwyCOP4Fe/\n+hX+8pe/4KGHHsLOnTthsVjwX//1XxGfVyaTcTJX5ZJufUmPpZLcNM5WwJFclSRGgy35sZvnZlaC\n4Wwl4bpaMwnfFQn13eTBsywryQ2PTDZQO49dixMBrOGp4SGybbDujJaYk1YKCgrw0ksvRV0w7ssv\nv4RcLsf69eu519RqNX74wx9i48aNw/b/6quvEAgE8PDDD3OvKZVK3HLLLSPuf/LkSWzcuBF/+9vf\n8MUXX2DPnj1gGAYURXEX78Kq6HuHJxQUNdDbpXmfsLLwBNuVlO8uioLRFY5oE1HFAkAEyaODMZWS\n0dYgpBS8UmxOg0pBoc3hhTPG0jmDdWfUx4j2g/39/di2bRseffRRVFZW4uuvv47qOLt27cKsWbOg\n1w/9wpaUlKCxsXGYP3HXrl2orq4eVpK7pKQELS0tCIWGhgu+//77uOGGG7BmzRpceeWVWLZsGYLB\n4JCLl1T9d8aDDasWQaVqAJhfTJryHWtzot+X/GHinJuNLUgpEprCgQXFyVij7Vwyw91IRVQsVCGn\nMDW8zhNrZBsfhmfckjkA4Pf7ceLECRw4cAD79u3D3r17cezYMYRCIRgMBtxyyy2YPz+6ZlYej2eY\n0QHIrCcYDA4L0x5r/1AoNMxQnWuIACAYDEKhUCAYJIpsQYk5KtkTkoIFZGwVh+HRqRWYWWDA4WY7\n9jVYcWFlks9OHc1kFFFEGwDUsa62ZF/jAQbaYIsoiRQAyrP1ONHuRENPPxaE2yVEw2DdGfUxxtvh\nrrvuwhtvvIFAIACFQoE5c+agra0NCxYswOuvv47KykrI5dEXBMzKysLhw4eHvd7b24usrOEJnVlZ\nWbBah/eW6O3thclkgkIx9E+qrq7Gddddh1AoBJ/PB5vNBpqmh1w8vToi+5scsN0s2w8DoQAgT+II\nozBLy8w43GzHfjEYHmcbGdPzhZWDR/p9QTRbPVDKZSgVg+HJGlSzTUSUhmejZ3tim/HwYXjGdbVd\nddVVUKlUMBqN+Oyzz7B//35ce+21sNlsmD59ekxGByDN5Wpra+H3+4e8vn///iGBDIP3P3v2LNxu\nd0T7r127Fn/961/xySef4Msvv8R3330HnU7Hy8VLSNLMgLkcCHqBTnEkki4oJk9nfPYUEQw2Uio9\nR1g5eIRt/laaqUveWoeD4dZ4GgE69kz/RGFaDgkwOB1jgEFcDM/111+Po0ePYs6cObj88stx5513\ncsbCbo+9lMnVV18Np9M5pNZbS0sL/v73v+OCCy4Ytv/q1avh8/mGBBJ0dHTg448/HnH/0VAqlQgE\nRJQbMhh21tN2UFg5eIJ1CxxpcSR/3TY2edRYLKwcPFITTkpM2uKg56JOJzPSkG/ANSoCBkKqYzM8\nfOjOiB5PpkyZgq+//hqvv/46PvnkEzz55JMASHfSWCkoKMBtt92GO+64A//2b/+Gl19+GUuWLIHB\nYOBaMjgcDrz11lsAiKvtH//xH/FP//RP+Nd//Ve88sorWLx4MdRqNe65556Iz6vRaOD1emOWPyHJ\nn0fGtkPCysETmXo1SjPT4AmEcLKdn8xrQfD3A30dgFwFGIqEloY32KTECrEYHmDgwUBEkW2lFh0o\nGdBq98AfjP4Bjg/dGfG8mKIo3HHHHaitrcWDDz4IiqJw44034uGHH465++irr76KJ598Ei+++CIe\neeQRrFy5Ejt27IDZTBb977nnHtx2221obiZPHxs2bMBvfvMbvPTSS3jwwQexdOlS7Ny5E9nZkfv/\n1Wo1fD5fTHInLGxXy44jwsrBI/PDs56DTUnsbmOVmLEYoJKwUdoonGwnSYmVudGlVCQklnAuj4gC\nDJRyCjkZGjAM0OmM3nDwoTsn7JA1Go147rnn8Oabb+L222/Hyy+/jLKyMuzatStqIZRKJR555BF0\ndHTA6XTinXfeQVlZGff+f//3f+PVV19FURF5SlQoFFi/fj3a2trgcrnw/vvvo6KiYkLnVKlUw9aV\nRAObI9J1Cogx7DFRmF8sAsPDKrHMiX1XE50TYcMzPV9MhkecAQZs5fBWuyfqY/ChO6NeCVy3bh1+\n//vfo6WlBf/7v/+L6dMnr8S7xWLBnXfeyesx09LS4PFEf/ETGq0J0OcAQQ9gbxBaGl6YW0Tyto60\n8NPMShDYGY+5bMzdkonePh86nT7oVHKUJHN193PJFN+MBwDyjKSAaywzHj50Z8whKAaDAevWrRuW\n0JnosBcv1kSohIUt7y6SyLZpOelQySnU9/TDFWPmtWCwodQZecLKwSPH28hspzovAxSVXK1RxiSb\nLRZ6Wlg5eCYnncx42BYW0cCH7hRB7GN0sD3DRRtgwDa0YishJzkqBYWqPBKVwyq7pMPeSEajeJJH\nj7WRGegMMbnZAPI/UmgAVxvgEU8jwkITmfG02qKfsfChO1PW8KSnEyXmciVxlNRYsJWP2dpgIoBV\nbieS1fA4W8mYUSCsHDzCPgTMyDcILAnPUPKBhzcR9ebJziDFkNlusdHAh+5MWcPDlt2JpZ1DQsM2\ntBJRJ8WqcNQUG0WVVDAMYG0g22yCogg41hqe8RSIbMYDDOpGKiLDE3a1dTqjj0rjQ3emrOHRaMg/\nQLQBBllVAGRAbx0pnSMC2BlPUrra+rsBvwtQG0h1CRFgd/vR2OuGWkFxWfGiInc2GUX08MZGtXXE\nsMbDh+4c0/C0tbUhPz8f999//6hN2ZIVrZb4OkVreJQakqTIhESTBFcZzrw+09WHQLJVMOC6jlaS\nni8i4HA4wnBmgUEcpXLOJY81PMNrSSYruQYNKBnQ6fLCF4yuHBAfunPMb0t2djbuv/9+vP322ygu\nLsYPfvADfPXVV6KIBBO94QGAzHIy9k6sLXiikq5RosishT9Ec9WQkwY2H0RE7RAON5NF9zmFyRXR\nGjE5MwEZRdZJA+IIQlLKKRSYtGAYoNkane6bdMOjUCjwi1/8AvX19fiP//gPHD9+HJdccgmmT5+O\nDRs2DCvUmUzodKSKbn9/v8CSTCKc4RFPLsKMPLKInXTrPD1nyMhmxIuAQ2HDM7dYpIZHrSeJpHRQ\nVAEGU8LN+s5G+fDGh+6MaH5sMBjwwAMP4Pjx4/j2228hk8lw3333oaSkBM8//3xSVgBgO6aKNqoN\nGJR9LR7DMy2Hn2ZWcccannWKJHmUYRjO8MwrEqnhAQbVPfxeWDl4hK2pV9sV3T3Eh+6ckGP2zJkz\n+OMf/4hTp07h2muvxTXXXIOHHnoI1dXV+PLLL6MWQghSYsbDKjmRuNoA0swKGChMmTSwazzsw0CS\n09jrhrXfD4texeWGiBK20rtIOvoCA1XEz3ZHp/viNuOx2+249957UVVVhW+++QavvfYa/vrXv+K1\n117DkSNHsGzZMhw/nlwZ8mxIoKgNT1Z4PUFErraKbBJgEO3TmiD4+wF7E0ApBtyfSc6+etKMcUGJ\naViXYFFRGO7x1bJPWDl4pCTGhnB86M5xW29++umnuOeee+ByufC73/0OP/nJT7hwOgCYOXMm17Ig\nmTAajaAoCl1dXUKLMnlkFAAyOWk+FvQDCpXQEsVMebYOCkqGht5+ePwhaFVJUOWZdXWay0XRERYA\n9tT3AgCWlmUKLMkkkzMTUOoA61mgrwvQJ3kHXMQ+4+FDd44542lqasI111yDuXPn4sSJE3jwwQeH\nGJ1kRqFQwGKxiNvwUHIgPVwXzBlb64pEQa2QY4pFB4aJvYVv3Og6QUY2Ez7JYRgGe8+SGc+iUnHk\nJI2KXDHgbmsWx6wnS6+GXq2AwxNAT9/EE0n50J1jGp7i4mLU1tbi008/RWFhYdQnSVT0er24gwuA\ngYZWdvF0UiwKV0FutiZJVGV7uC8SW00iyWmxedBq98CgVaI6T4QVC86leCkZm3YLKwdPyGQyVMQY\npBOr7hx3jWfq1Kmi9eHqdDpxr/EAgCFcF0xELXzLLGRxsy5KV0HcYSOi8uYKKwdP7KrrAQAsnmKG\nXEwVqUej5DwyNu4UVg4emZbNtsGOLi0hVt0pwnTjyNHpdEmdixQRbF0wEc14pmQRwxOtjzquhIID\nnWDZ0NwkZ8cZsr6zosIisCRxonAxSSRtP0ICRURAdbjS+6koW8nHqjtT2vCkp6eL39WmzyGjq01Y\nOXikLJwAV58MazzdJ4GAm5TZ1yX/QjzDMNiXKoEFLGo9aSfPhICmPUJLwwtVYRdptDOeWHVnShse\ng8EAhyOJO1pGgoG0C4ejVVg5eKQg3EUxlkKHcYPN/yhcKKwcPNFkdaPT6YNZp+Kio1KCKSvJWPe1\nsHLwRFW47mFNZx9ompnw52PVnSlteDIyMsRveDLyySiiNR620GGHM/pCh3GDjYQqXCysHDwxEM0m\n8vydcykLG57GXcLKwRPGNBVyMzTwBEJo6J24+zBW3ZnShsdkMsFuF093wRHJLAcgI3kIweQrbTQS\nKgWFkkwdaCYJ1nnYSKjiJcLKwRPbz5DAgpRxs7EULiYJwO2HAZ843POxtBmJVXemtOHR6/Vwu92i\nqLY9KiodcbfRQVHNesrDAQb1PQlseFwdgK0eUOmBnOQPpWYYBrvChmfFtCyBpYkzQ9Z59gotDS/M\nKCAFd9n25RMhVt2Z0oaHTYaNpXd4UsDm8tjqhZWDR4rNxPA09iZwVGJzWEEVLCCJiEnOqQ4Xevv9\nyMlQcyHtKUXZKjKe2SKkFLwxMzzjOdoyccMTq+5MacOTEvXaAMA8hYxW8RieKRaSRJrQkW2NYTcb\nmweS5OyuI9Fs50+1pNb6Dkv5xWQUSYDBnHBV8aMtjgkHGMSqO1Pa8GRmEj91d3e3wJJMMsYSMtqb\nhJWDR9gq1QmdRNqwg4wiMTzbasl9sizV1ndYChcB6gyg57Qo8uJyMjTIzdDA5Qvi7ARd1rHqTsnw\nALDZbAJLMsmwMx4RtUfINyR4SHV/L9B5FFBoRBHR5vIGsOtML2Qy4MKq5C+UGRUKFVB6Admu+0pY\nWXhiThFZ52F7K0VKrLozpQ0PO13s60tgdw0fWCrIaBWR4TFqIadkaHN44A0kYEg1W16lcBGgTP7C\nut+e7oY/RGNhiQkWvVpocYRj2hVkPPmJsHLwxLxiEwDgYNPEDEisujOlDU96OkmiEn31AtOgNR46\nAZV0FKgUFIq43vEJGGDAhlGXnC+sHDyxPexmu7g6R2BJBKbqKlI+5+xWUYRVLywhhue7cH+lSIlV\nd6a04TGbSUn3HnQ2JwAAIABJREFUnp4egSWZZDQZpDdPyEfyeURCQbjzZYvNI7AkI8Ct7ywTVg4e\nYBgGO8P12c4vT5H6bKOhsxDXKR0QRZDBrEIDVAoKtV19sLsjz/OLVXemtOHJyiK5CKIPLgBIQysA\n6DgqrBw8Up7F9o5PsCfP/l5yneVqoCj5E0ePtznRavfAoldjen4KtEEYj8qwu+3UZ8LKwQNqhRxz\nw9Fteycw64lVd6a04VGpVNDr9bBaJzbNTEpyw4an85iwcvBIBRvZ1pVgkW2NOwEwQNFiQKkVWpqY\n+fJ4BwDgipk5qdEGYTyqriZj7WZABMnnS6aQ2cv+hsj1YKy6M6UND0AWyUQfXAAMNCET0YynkG0I\nZ0uwNR62npdI1ne+OkU6TV6S6us7LJlTievaYyXVx5MctovsRGY8QGy6M+UNj0qlgt8vjhpmY8K6\n2jpPCCsHj0wNu9rOdCXYg8PZb8jIht4mMdZ+P060O6GSU6lXn200ZLKB/239dmFl4YEFJSbIKRmO\ntznR7wtG/LlYdGfKGx6NRiP+kjkAiWxTaABnC+ARR95SvlELtYJCl8sHpzcgtDgEexPQfQpQpYti\nfWdbTTcYBlhSZoZGKRdanMSBNTyNO4SVgwd0agVm5mcgRDM40Bi5bohFd0qGJ1UMj1wB5Mwg2+2H\nhZWFJ+SUjOsJkzCznrNbyVi2kiQcJjlsm+sLpqZ4NNu5sG7Uxl3iWOcJz2b3nO2N+DOS4YmBlHG1\nAQOtl9u+F1YOHikNF6usT5TSOWwY9ZQVwsrBAwzDYEctMTznpXoY9bmYy4CMQsDdK4r76bxyYni2\n1kQepSa52mJAoVAgGIzcr5nUcIbnkLBy8Eh52PCcTYRioQwzKH8n+QMLTra70ObwIitdzfVukQgj\nkwGVV5LtM5uFlYUHlpZlQquU43ibE+2OyPLiYtGdKW945HI5QiFxZPOPC2t4Wg8KKwePlCeSq816\nlqyhac1A9nShpYkZ9un3ospsUFIY9XDKLyJjzRfCysEDGqUcF1SQWe3X4SjG8YhFd0qGRy4XdyO4\nwWRVkUVvRxPgbBdaGl6YlkNKd5zuSIAk0trwk++UFQCV/LcW6+8/b6oUzTYi5RcCSh1xtYmg8vtF\n4eKv356OzN0Wi+5M/rtDInIoOVC4kGw37xFWFp4oy9JBTsnQZHULXyz01KdkrLpKWDl4wB+ksS+c\n17GsXDI8I6LUAlPDsx4RlM9hA0j21PUiGJrch/GUNzw0TadWUyu2N0zDTmHl4Am1Qo4ScxpoBqjr\nFtDd5rGRCCdKAVRcJpwcPHG01Q5PIITyLB2y05O/uvakUbqcjCK4n4rMaSjNTIPLF8TR1vG7ksai\nO1Pe8IRCIcjlKZSfwEZb1W8TVg4eqc4jC98n2pzCCVG7BWBCQPEyQGsUTg6e2HOWzHYWT5FmO2PC\n5vM07CDBJUkOO7uNpIpBLLpTMjypZnjy5wPKNNJF0dUptDS8UJ1H1nlOtgu4znNmCxnZSKckZ+cZ\nEkZ9vrS+MzZZ1UCaBXC1kcThJGdZOGx+y4nxdYNkeGKApmlQIlgIjhiFaiDUVwR+aQCYUUC6KB5t\nnVgXRd4IBQdCassuFEYGHvEGQlwGu5S/Mw4UBUy9hGzXfSOsLDxwcVU2NEoK+xttaLOPHVYdi+5M\nIY07MoFAAEqlUmgx4kt5WDk2JH+dKQCYW0hcW0dbHQhM8qLoiDRsI4mEmRVAdnX8z88z3zfZ4QvS\nqMpNh1mX/NUXJh3WfS2C+0mnVnDRbX8/Onbkayy6UzI8qWh4WL/02a2i8EubdCqUZqbBG6CFCas+\n8xUZq68hiYVJDlsmR5rtRAhXt22nKMrnXDEzDwDw1cmx83kkwxMDwWAw9QxPzixAayLJjrZ6oaXh\nBbZ3/PcT7B3PC7VfkrE8+d1sALD3rBRGPSGMxYChCPA6gK7jQksTMysqLKBkwHcNVrjGKL4bi+5M\necPj8Xig0aRYuChFDYSBnv1WUFH4Yn64d/xEquvygq0B6KkB1Aag+Lz4nnsS8AdpHG4ha2ULw9dU\nYhxkMhLNCABNyZ8fZ0xTYV6xCUGawe660YuGxqI7JcPj8UCrTf4ukROmbBUZ2WrKSc78YrLOcyDe\nMx72+k1ZTiqAJzkn2p3wBWmUZelgktZ3Iqc43AJDBIYHAJaHy+d8M0YVg1h0Z8obHr/fD5UqBW+w\nKSvJWL9NFH7pypx06FRyNFs96HLGsc0FO2Nkr2eSczA8Y1xQLM12JkTRUjI27xNWDp5gu81+dbIT\nND3yOnAsujOlDQ/DMOjv74derxdalPiTWT7Qvrcz+dthK+QU527bN4He8TERCgJ14cCCqRfH55yT\nzKFm4mabW5z8SbBxJbsaUGeE6yC2CS1NzMzIz0CeQYMul2/EKgax6s6UNjwejwehUAjp6elCixJ/\nZLKB/IOaTcLKwhNLppDe8fsm2Ds+atoOkgVlczkx5CLgSHh9Z06hZHgmxJA6iHuFlYUHZDIZVlWS\nsOod4WTiwcSqO1Pa8DidpMRKRkaK9hqZdjkZT/9dWDl4YlFpnA0PmzAokmg2lzeAhl43VHKKq/ot\nMQHYVufN3wkrB0+snEbWeTaPUMUgVt2Z0obHbidPd0Zjij7dlV8EqPSiKes+p8gIlYLCqQ4XrP1x\n6CrLlslh+7IkOcfDte6m5eqhUqS0aogOdsbTekBYOXhi5bRsqBUUDjXb0e3yDXkvVt2Z0t8uh4P4\nLg0Gg8CSCIRSO6A0T30mrCw8oFHKuUXxifSOjwpfH1EwMvlAaHqSwxZZnVWQovdDrOTPJ2P7YSA0\nev5LsqBVybG0jORyba8dGt0Wq+5MacPDThdT1vAAwPTryHjib8LKwRNs0uNY+Qe80PIdqUadNxvQ\niMNVy1Z9kNxsUZJmBkxTgKAH6DohtDS8wIZVb68dus4Tq+5MacPT398PANDpdAJLIiAVlwGUkiyI\n9kXW8jaROT/czGprTTeYySwH1BjuvyKCpFGWY23kKXZGfgo/iMVKQXjW03ZIWDl4gm2Hvbuud8j9\nFKvuTGnD09tLnopNphTOWdBkkMVxhh4o/ZLEzC0ywpimRJPVjcZe9+SdqHE3GUvEYXiCIRq1naSR\n3vR8cczgBCF/HhnbvhdWDp6Ylk0KxXY4vajv6edej1V3prTh6eoiT/g5OTkCSyIwXFj1F8LKwQNy\nSoZlYb/07sla5wn6gNb9ZJstlZLkNFrd8IdoFBi10KuTvwKDYOTNIWP7YWHl4AlqlPspVt2Z0obH\nbrdDrVanZsmcwVRdTcbazYBPwGZqPHEe624bo9xHTLQdAoJeIKsK0ImjkObA+k4KJlPzSd5cADKg\n8xh5QBEBi8P5cd8NSlOIVXemtOFxOp2pm8MzGEMByUEIekVRNJTtJ7K1phveQIj/EzSH63EVL+X/\n2AJxqp0sFlflSfdDTGgyAMs0IOQHOo4JLQ0vsPlx3zUM1EGMVXemtOHp6emB2WwWWozEoOJSMoqg\nK2mBUYsZ+RnwBEKTE1bN1uNiEwZFwOlOMuOplCLaYodd52kXR4BBZW460jUKtNo9XFfSWHVnShse\nq9WKzExxuEpipiycz3Nmiyiawy2vyAIwSWHVrQfJWLCQ/2MLRF03WTiemi252mImZzoZu08JKwdP\nyCkZFoTrIO4PF5GNVXemtOHp7+9P7VDqweTPBbRmUsGgt05oaWLmvHA+z0h1pmLC2Qa42khByMyp\n/B5bIAIhGo29xPBMsUj3Q8xkzyBjpzhyeYABd9v3YcMTq+5MacPT19eXmpWpR4KSA2Xh0v5nvxFW\nFh5YPMWMNJUcx9ucaLbyGFbN5mfkzyUN9URAXXcfAiEGxeY06KSIttjJqiRj90lReA+AgaaAJ8Jr\ngbHqTnHcOVHS29srrfEMpjxc2l8E+TwapRwXhqvrfnOax8TYznBr49zZ/B1TYI62kMTRmQVSYAEv\nGApJa3l3ryhaJADhOohyCr3hGoix6s6UNjx2u10yPIOpuIyM9duBQBybqU0SK6eRdZ5tNTy627rC\nhie7mr9jCgxbHHSmVKONH2SyAXdb10lhZeEJjVKO2YUGpIdnxLHqzpQ1PIFAAF6vNzV78YxGeg6Q\nM4vUmmraJbQ0McOW+9hzthf+IE9dVrtryCgiw8Pm8FTlSvcCb7ABBiJossiyoNQEnVrBi+5MWcOT\n8pWpR6MiXMWgdouwcvBAvlGLypx09PmC2FvPQ3QbwwDWs2RbJIEFDMNwfvtqKYeHP3LEF2CwsMSM\ndI2CF92ZUIZn//79eO+991BTUzPmfvX19Thy5AgOHz6MvXv3Yt++fdi+fTtXxiESpAKho8CWzzmz\nWVg5eOLi6vA6zykeqhg4W8lsMM0CaMTxwNLd54PDE4BBq0RuhkZoccRDNhtSLQ5XGwAsKDEhTSXn\nRXcmhOGx2WxYs2YNFi1ahFtvvRXV1dVYv349aHpk98jatWsxZ84czJ07F0uXLsWSJUuwYsUKvPvu\nuxGf0+slaxgajXSzDaFoCaBKB3pqAHuz0NLEDLvO821NV+zVqrnZjjjaXANAu53cBwVGLWQymcDS\niAhLBRl760QT2WbWqZBv1PKiOxPC8Nx99904cOAAtm3bBr/fjw8//BB/+MMf8NZbb424f0lJCVat\nWoW6ujo0NzejsbER3d3dWL9+fcTnlAzPKMiVwJRwY7P6bcLKwgPzS0wwpSlxtrsfNeHqy1FjaySj\nqTRmuRKFhnD+TpE5xesV8o3WRGbGATfgSP4HOJYpFp04DE9DQwM+/PBDbNiwAcuXL4dMJsOaNWuw\ndu1avPDCCyN+xmq1orKyEnq9HrW1taivr59whIW0xjMGpReQsWGHsHLwgFJO4aIqUkF3W02M7rae\n02Q0l8UoVeLAtkKQKhZMAmwASpc4KhgAQJlFx4vuFDxbbPPmzTCZTFi9evWQ15cvX4533nkHNE2D\nOidRr729HfX19XjttdcQCJAWs3PnzsWmTZuQnZ09ZN+TJ0/i1KlToCgKarUaRqMR5eXlMfcMFzVT\n2ETSb4mbIMldMMsrLPjwYAu21nTj7hUxGI2OcIRS7ix+BEsAznQRw1ORLUW08Y6pBGjYLqoZT65B\ngyYedKfgM566ujqUlJRALpcPed1iscDv98Nmsw37THt7O5qamrBhwwb09fXhxIkT6OnpwbPPPjts\n3/fffx833HAD1qxZgyuvvBLLli3DRx99JAUXjEXODCAtk5SGsdULLU3MrJyWBTklw+6zvXB6A9Ed\nhGEGqhawPVdEQG0XCaWWZjyTABv52DN2sFQykalXJ29wQSAQ4ITXarXw+/3D9vF4PNz757JgwQK8\n/fbbuOuuu6DT6VBdXY0777wTmzZtGrZvKDS8LL7RaOSmi9KMZwRksoEGZw07hZWFB0w6FeYXGxGi\nGew6E2VYtbMV8NpJPbuMAn4FFAhfMIT6nn5QMsnwTArszLj9iLBy8IhSTvGiO+PmaqNpGiUlJWhv\nb+eMwZYtW5CdnY2Ojg4wDDMkqqapqQlFRUVIS0sbdqwvvxxe0sVisYwYTl1dXY3rrrsOoVAIPp8P\nNpsNubm5aGpqAgApgXQ0pqwATn1K3G3zbxNamphZUZGF7xps2FbbjStm5k78AKybLWdG0rseWRp7\n3aAZoCQzDRqlfPwPSEyM3PDMuOMIQNOiqe3ncpFZciy6M26Gh6IoHD58GD6fDwqFAnq9HlqtFiaT\nCVarFYcOHcK8efO4/Tdv3ozFixdHfPyDBw+irGy4/37t2rVYu3btsNe3bNkCiqJGNGwSAMpWkbF+\nqyjWeVZWZuE/Ntfgm1NdoGkGFDXBv4dthZA/l3/hBKIuvL5TJlWknhz0WYA+F+jrIOs8phKhJeIF\np9MZs+6Mqwk2m83Iy8tDVlYW50KbN28eKisr8fjjj3OW9JVXXsHmzZuxZs2aYcfw+/1Ys2YNDh0a\naLK0ZcsWvPnmm1i3bl3EslitVhiNxmGBCxJhLNMAfQ7Q3w10nxZampiZmW9AnkGDdocX3zfbJ34A\n1k+fI57AgmNtxGUyTSqVM3mwOV89tcLKwSN86E7Bta5MJsPrr7+O77//HqWlpaiqqsLdd9+NH/3o\nR5wh2bRpE+dKUygUcLvduOCCC3DjjTdi1apVuPzyy7FixQr85Cc/ifi8brdbmu2MhUwGlJxPthuT\nP6yaomS4alYeAOCTw1FUDGarUmdN41EqYdkfbmW8oNgksCQihlvn+V5YOXiED90puOEBgGXLluHk\nyZN46qmnsG7dOuzcuRNvvfUWZ1G7u7vR19cHv98PiqLw+eef4+WXX4ZKpUJJSQk++ugjbN68GWq1\nOuJzBgIBKJXKyfqTxEEpa3h2CysHT1w9Jx8A8PmxdtD0BLLJfS6g9wxAKQeqDic5wRCNwy1k5sd2\nl5SYBPLnk7FVPIaHD90peB4PS0ZGBu6///4R37v11ltx6623cr/L5XKsW7duQq61c5EMTwSwkW1N\ne4SVgyfmFBpQaNKixebBdw1WLCmLsHVv1ykADGnwpVBNqozxoqazD94AjWJzGjL1kT+wSUyQ/PC6\ndZtkeAaTEDMeIQgGg1AoEsbuJiZZ1YDaADhbRNHQSiYbcLd9cbwj8g92HCZjjjhmOwBwrJWs78wq\nlCp3TCqZUwGVnuTE9fFQqDYB4EN3pqzhkWY8EUBRQEHYVdDynbCy8MSl00n5nC0nOyMvGsq6SfLn\njb1fEnG6kwTyTJdaIUwuFDVQqbpDHPk80ownBvx+P1QqcbhNJpWicEi7SAzPvGITMnUqNFs9XLmY\ncWkPz3hYf70IYJu/VUiJo5MPW+miSxy9efjQnSlreCRXW4TkhfNWRJJ9LadkWMG2xK6NoCU2TZPA\nAgDIrppEyeIHwzBcKPUMqd315MO2SBBBWgIgudpiIhQKDasPJzECebPJ2HFENH1FLphKWmLvqI3A\n595bS5q/ZRSIpvlbm8MLuztA+qsYpLYgkw7rahPJjIcP3ZmyhodhGCl5NBIyCkh9Mo8NcLQILQ0v\nXFBBDM/eeiv8wZGbDXK0HiCjiNZ3jobDqGfkZ0jN3+IBG5TSdZLMoJMcPnRnSmte6aaLAJls0Kzn\nqLCy8EROhgZTs/Vw+0M40jJOFYPmfWQsXDj5gsUJtnLDnEKpQG5cSDMDGYWkKVyPONxtserOlDY8\nMbdCThVyZpJRJFE5ALC0jDQO3FtvHXtHNqiiaOkkSxQ/DjaSigXzSyTDEzfYBxeRBOnEqjslwyMx\nPqybiXU7iYDFU0jy6L6xDE8oMLAgLJLmb95ACIebHZDJgAXFE+vaKxEDnOHZL6wcPCEZniiRy+Uj\n9uqRGAE2l4cNKxYBi0uJ0j3QaEMwNIrfvfs0QAcAUymgFkfY8ZEWB/whGtOy02FIk/LY4kbhIjKK\nYMbDh+5MWcOjUCgkwxMppimkgkFfJ+BsF1oaXsg1aFCamYY+XxDH25wj79S8l4wFC+In2CSzq46E\nkC8pk2Y7cSV/HiBXkQADTxTV0RMIPnRnyhoelUoFn88ntBjJgUwG5IeT4NoOCisLjyyeQpTvdw2j\nuNtYt0jRkjhJNPmwHVjPK7cILEmKoVCHE5CZpK99yIfuTFnDo9VqufbaEhHArvOIyN22KOxu23N2\nlHbYbBRfnjiav/X7gjjYZAMlA5aVR1ggVYI/Si8gY8N2YeWIET50Z8oaHp1Oh/7+fqHFSB5ywyHV\nIqlgAABLw9Wp9zfahi+W+lxA13GAUogmsGBvfS+CNIPZhUYYtNL6Ttzh2ozsElaOGOFDd6as4UlL\nS5NmPBNBhOXdC01aWPRq2N0BNPa6h77ZegBgaGJwVeJoGLithqzvLK+Q3GyCULgYkMmJ18A7yrpi\nEsCH7kxZw6NUKuH3+4UWI3ngAgw6AEer0NLwgkwmw5xwW4BD57bDZhNH2SKpImBrDSkRxNaqk4gz\naj0JVGFCQFPyNlfkQ3emrOFRqVSS4ZkIg1skiCjAYF4xSaIcZnhYd4hIAgsaevpR39OPDI0C84qk\nxFHBmLKCjPXbhJUjBvjQnSlveKQk0gmQH15kbzskrBw8MjNcnfnE4JDqoH9gxlNyvgBS8c+Wk50A\ngFWV2VDIU/a2Fx7O8GwVVo4Y4EN3pmxfALVaDYZhEAwGJ60hHMMwoGkaNE0jFApx2+zrwWBwyPuD\nfxiGGfFnJNi6STKZjPuhKGrINkVRkMvlQ0aNRgOtVht5wT8RrvPMChueo60OBEI0lHIKaN0PBPqB\nrCogPUdgCfmBNTyXTBfH35O0FC0B5GoSMdnfC+iSL7qQD92ZsoYnPT0dAOB0OpGZOfo/n2EYeDwe\n+P1+0OHKsoONx2DDMdiQsPucq/RZgyCXy7nXFAoFVCoVFAoF9/pgIzL4ByAGZrARYrfPNVLnyska\nv1AohEAgAJfLBZqmUVpaGpnxYRMpWw+QFgkiKLKaqVdjikWH+p5+nGx3YnahEaj7hrw5ZaWwwvGE\nyxvA/gYSRr2yQlrfERSlBiheSmY8Z78BZt0ktEQTJlLdORYpa3jYC2az2ca8eDabDVarFWlpaZxy\nZo2HUqkcMqNgDcdgAzNZFbAHHzfaczAMg/b2drS1taGgoGD842QUALpsoL8LsDeSUjIiYGGJCfU9\n/djfYCOG52zY8JRfKKxgPLG1phtBmsGiUpNUJicRmHoJMTy1m5PS8ESqO8ciZZ29JpMJAGC1jl4k\n0uv1oqenB0VFRcjPz0dubi5yc3ORnZ2NrKwsmM1mmEwmGAwGpKenQ6vVcjMX1vAkMjKZDLm5uXC7\n3ZGVwJDJBgIMkjz7ejALSsh3obbLRfoOtR4g+Ttswl+Ss+k4cbNdNj1XYEkkAAAVl5HxzGaATr6y\nXZHozvFIWcNjMBDfvsPhGHWfUCgEtVoNtVodL7HiDusGDAaDkX2g5DwyisjwzAlHeaWpFMDZrSR/\np2gpoE4XWLLY8QVD+OZUFwDg8hmS4UkIsiqJt8Ddm5RFQyPRneORsoZHp9MBwJgZuKlUwTri2Rlb\nPqbz2OQJE2cqc9KRrlGQQIO6r8iLUy8SViie2F7Tgz5fEFW56SjOFEcibNIjkwGVq8n26b8LK0sU\nRKI7xyNlDU8kVvvcRfxkoaGhAW1tbRHvzzDMBAzPbAAykn0dEEflB4qSYXpeBlZUWIAzrOG5RFih\neGLj9yTZ99q5+QJLIjGEyivJeOozEqiTREgznhhgF8V6enpG3SdZDc8DDzyAJ598MuL9g8EgFIoI\n40y0JtJDPuQXVVj1JdU5MPefAZytgD4HyEn++mx9viA2h8Oo18wtEFgaiSEUnwekZQK9Z4DO40JL\nMyEi0Z3jkbKGx2AwQKPRoL199P4yyWp4PB5PxGs2bOj1hAIhipeRsWFnFNIlJisrs4DaL8kvUy8h\nlRqSnK9OdsIfpLGo1IR8o1ZocSQGI1cA1deQ7RN/E1aWCRKJ7hyP5L+7okQmkyEvLw8dHR1j7pOM\nhmciiV1srtGEDA/bTVFEM54pmTri9gAGoo6SnI8PEXfrlTPzBJZEYkRYw3Psw6Ryt0WiO8cjZQ0P\nQMIC7fbRuwEmq+EJBAIRG55QKBR55QIWtn98896kumHGQtnfTiKMFFowIljf6e3z4duabigombS+\nk6hMWQWkWQBrXdK528bTneOR0oYnIyNj3OCCcwmE6IQ3RsFgEHK5PKJ9Q6FQxPtymMsArRlw9wD2\npigkTECOf0TGiktgDSR/kuUXxzsQohlcUGGBRS/edICkRq4YGmSQRIynO8cjZSsXAOTiNTY2jrnP\nuaVpKn/1OQBAo5QjQ6OEMU0JnVqBdI0CGoUcaWryeoZWiXS1AsY0JYxpKqSp5NAoKajkcigVMmiV\ncmiVcqgVcqgUFOSUDHJKBkoG0AwQohkEaRqBEFmDMaapIv67QqFQxMECNE1PfMYjk5F2ATVfkPLu\nppKJfT4ROfoBGWf9APvqrbhyVnK7pz45TNxsVyX53yF6pq8Bvn8TOPQ2sOLRpFlbjER3jkVKG57M\nzEx8993oCVznutqCNNmmGcDtD8HtD6HD6Z10OQGg4bdXRbyv3++HRqOJaN+oZjwAqdpc8wVQvx2Y\nc8vEP59I9NaR8HBVOnxll+CNN44kteHpdHqxt94KlZzC5TOlpNGEpvxCwFhMSlDVfQ1UJIebdzzd\nOR4pbXhyc3PR1dUVcVSXUk7h7LNXIRCi4QvSsLv9cHqC6PMF0ecLwBug0e8LwuEJwOUNwuUNwu72\nw+EJwO0PwRMIwR+kEQjR8ARC8AZC8AVo+EI0QjSDED1g5OSUDApKBpWcAkXJQNMMKCqyAACPxxOx\n4YlqxgOQG2YzgDNbkr9g6NG/kLHySuyo78Oeeius/X6YdZHPMhOJTw63gWGAVZVZyNAkv9tQ1FBy\nYP6Pga+fIbOeJDE8E9Wd55LShicnJwehUAi9vb2wWCJvB6yUU1DKKejVCsA0iQJGidvtjrjMT7Rf\nHOTMBNLzAVcb0HEEyJsz8WMkAgwDHHmPbM+5GSeanGAYYMeZHlw7JzkX5Vk323VS7k5yMGct8M1v\ngJMfA67OpGjFEa3uZEkOh+IkkZND/sHd3d0CS8IvXq83YsND03R0hkcmA6ZeTLbPbJn45xOF5n0k\nqkifC5RdCI2SuB231yTnd6Km04XDLQ7o1QpcVJUttDgSkWAoBKZdCdBBMutJAmLVnSltePR6PQCg\nr69PYEn4paSkBBUVFRHty+bxRAUbdlybxIbnWNjNNusmgJJj6RSSlb2vIfrKu0Ly+s4GAMCaefnQ\nqqJYu5MQhoV3kvHA60C471ciE6vuTGlXW0ZGBgDS0EhMHDhwIOJ9ow4uAMg6D6UAmveQdgLaBPQ7\njkXQP7C+M/uHAIDqvHToVHI09rrR6fQiJyOytbJEwOMPcW6225eVCiuMxMQovwgwFJH0hKb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P3gH/b1QCAwZB/WmLAGZPDv566vsEYBwBDDNXibNWrstkKhAEVRnHFhfx98rIRDYwBufovcMMc/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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.xkcd()#設定圖的大小是(6, 6)\n",
    "#設定一個風格\n",
    "\n",
    "plt.plot(x, y1,label='sin(x)')#y1的名字是\"sin\"\n",
    "plt.plot(x, y2, label='cos(x)')#y2的名字是\"cos\"\n",
    "plt.legend('lower left')#將圖標放在左下角\n",
    "plt.xlabel(\"X Axis\")\n",
    "plt.ylabel('Y Axis')\n",
    "#X軸的名字是\"X Axis\"\n",
    "#Y軸的名字是\"Y Axis\"\n",
    "\n",
    "\n",
    "plt.xlim(0,10)\n",
    "plt.ylim(-1, 1)\n",
    "#設定X軸的範圍\n",
    "#設定Y軸的範圍\n",
    "\n",
    "plt.xticks(np.arange(0,10,1))#X軸分成10個部分\n",
    "plt.yticks(np.arange(-1,1,0.5))#Y軸分成4個部分\n",
    "\n",
    "plt.title('Sin and Cos')#圖的標題是\"Sin and Cos\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ZIP and UNZIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keys = [\"K\", \"F\", \"C\"]\n",
    "values = [77, 88 ,99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('K', 77), ('F', 88), ('C', 99)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zipped = list(zip(keys, values))\n",
    "zipped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "values2 = [7, 8, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('K', 77, 7), ('F', 88, 8), ('C', 99, 9)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zipped2 = list(zip(keys, values, values2))\n",
    "zipped2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dic = dict(zip(keys, values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 99, 'F': 88, 'K': 77}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('K', 'F', 'C')\n",
      "(77, 88, 99)\n"
     ]
    }
   ],
   "source": [
    "u,v = zip(*zipped)\n",
    "print(u)\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "A= \n",
    " \\left[\n",
    " \\begin{matrix}\n",
    "   K & F & C \\\\\n",
    "   77 & 88 & 99\n",
    "  \\end{matrix}\n",
    "  \\right] \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$$\n",
    "A^T=\\\n",
    " \\left[\n",
    " \\begin{matrix}\n",
    "   K & 77 \\\\\n",
    "   F & 88 \\\\\n",
    "   C & 99\n",
    "  \\end{matrix}\n",
    "  \\right] \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "A = (A^T)^T=\\\n",
    " \\left[\n",
    " \\begin{matrix}\n",
    "   K & F & C \\\\\n",
    "   77 & 88 & 99\n",
    "  \\end{matrix}\n",
    "  \\right] \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable Length Function Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n"
     ]
    }
   ],
   "source": [
    "f(\"Hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "f() takes 1 positional argument but 2 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-dc5ed9511f9d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Hello\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"World\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: f() takes 1 positional argument but 2 were given"
     ]
    }
   ],
   "source": [
    "f(\"Hello\", \"World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ARGV(*argv):\n",
    "    for i in argv:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "World\n"
     ]
    }
   ],
   "source": [
    "ARGV(\"Hello\", \"World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "World\n",
      "I\n",
      "am\n",
      "hungry\n"
     ]
    }
   ],
   "source": [
    "ARGV(\"Hello\", \"World\", \"I\", \"am\", \"hungry\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ARGV1(arg1, *argv):\n",
    "    print(arg1)\n",
    "    \n",
    "    print(\"\")\n",
    "    for i in argv:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "\n",
      "World\n",
      "I\n",
      "am\n",
      "hungry\n"
     ]
    }
   ],
   "source": [
    "ARGV1(\"Hello\", \"World\", \"I\", \"am\", \"hungry\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ARGV2(*argv, arg1):\n",
    "    print(arg1)\n",
    "    \n",
    "    print(\"\")\n",
    "    for i in argv:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YA\n",
      "\n",
      "Hello\n",
      "World\n",
      "I\n",
      "am\n",
      "hungry\n"
     ]
    }
   ],
   "source": [
    "ARGV2(\"Hello\", \"World\", \"I\", \"am\", \"hungry\", arg1 = \"YA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable Length Keyword Argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def KWARG(**kwargs):\n",
    "    for key, value in kwargs.items():\n",
    "        print(f\"{key} == {value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first == N\n",
      "second == C\n",
      "third == T\n",
      "fourth == S\n"
     ]
    }
   ],
   "source": [
    "KWARG(first = \"N\", second = \"C\", third = \"T\", fourth = \"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Four(arg1, arg2, arg3, arg4):\n",
    "    print(\"arg1 is \", arg1)\n",
    "    print(\"arg2 is \", arg2)\n",
    "    print(\"arg3 is \", arg3)\n",
    "    print(\"arg4 is \", arg4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "args = (\"N\", \"T\", \"C\", \"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arg1 is  N\n",
      "arg2 is  T\n",
      "arg3 is  C\n",
      "arg4 is  S\n"
     ]
    }
   ],
   "source": [
    "Four(*args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kwargs = {\"arg2\" : \"T\", \"arg1\" : \"N\", \"arg4\" : \"S\" ,\"arg3\" : \"C\",}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arg1 is  N\n",
      "arg2 is  T\n",
      "arg3 is  C\n",
      "arg4 is  S\n"
     ]
    }
   ],
   "source": [
    "Four(**kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enumerate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mylist = [\"N\", \"C\", \"T\", \"S\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'N'), (1, 'C'), (2, 'T'), (3, 'S')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist2 = list(enumerate(mylist))\n",
    "mylist2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'N', 1: 'C', 2: 'T', 3: 'S'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist3 = dict(enumerate(mylist))\n",
    "mylist3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(7, 'N'), (8, 'C'), (9, 'T'), (10, 'S')]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist4 = list(enumerate(mylist, 7))\n",
    "mylist4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# map and lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def square(x):\n",
    "    return x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16, 25]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(square, [1,2,3,4,5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16, 25]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(lambda x : x**2, [1,2,3,4,5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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