01_quick_introduction_to_python.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Quick Introduction to Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mostly sourced from http://scipy-lectures.org - please take a look!\n",
    "\n",
    "Python is an **interpreted** language with to the point concise syntax, making it easy to **write and read** code.\n",
    "\n",
    "It is multi-platform (platform-independent in a certain sense), free and open source.\n",
    "\n",
    "It is imperative, object-oriented in the original sense, can be used as a pure scripting language but also for full software projects.\n",
    "\n",
    "It was designed by Guido van Rossum, and first appeared in 1991.\n",
    "\n",
    "Current latest stable version is 3.5.0. The shift from python 2 to 3 was big enough to still have people using e.g. python 2.7 today.\n",
    "\n",
    "There is a philosophy around coding in python and there are very clear directives on how code is supposed to look like. All changes and directives are written into **\"Python Enhancement Proposals\" (PEP)**\n",
    "\n",
    "A poem/set of commandments outlining the philosophy is here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python interactive shells\n",
    "There are several ways to do interactive work with python. The simplest shell pops up by just calling `python` on the command line. The characteristic of this shell is the **>>>** prompt. It is the most basic of interactive shells, can execute any code, but does not cater to user/rapid development needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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YAOt0OsMWqGYoHIDTcwC5Z3fm/kEghDEyrJNJDTh3PzF3eglWhmcRFOKZbLrhD8smtLEB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\nAIA8JA8AACDP/wPV6+8cWqpdBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('python_interpreter.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The **IPython** shell can be called by typing `ipython` at the command line. IPython provides a very comfortable way of interacting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ac0d+/hvJy8+fv2CBu7vbseMnMMYDBwwAgC2/bT1z5oybu/vcjz6aNWOGRqOx\no1l606DK/VYlYGLhlW1rD13PyDdwAIQ6uFWHGOrO+cSULL3AuNXr+NzgrmFK218oX3jj4Jb9p2/n\nWQilT6OOAwe2DVQgecSgt2cgikAAEB3C3r+x5n5ykRDhTSnCegwKsx0i3Fe4dW3rvWwL9lHWlbxw\n7/jJLHXriQM7BjOAA2RZn684cuZhy77+tFh4dsMvB3RNx03r558dv3Xbj9dzTACsJqjt8JcjD6zY\nsMzJ843uvkxJKOReQSGhCgRQLyq2davzq77ctnFDSNCEps4498QvK/bdLuYAaNfQNs8O7hXtTEJp\nAKtGUrAvLxZd/X3d4RvpuTorAOXk37ht335tQ2zNOBgAcvYu/GAvAEDwqLkTYhQgFCcd2br35PUc\nE8i9otv0HdipnjMJ1dkRdVc2LFmTXv/FSYPryaWZDhL2kMnYoUOHrlu3jud5pVIZFh568NBBgRci\nI+sVFxcXFxfn5xW8887bCEG191Rc7medgY23tq07ZWnab8JAN5x+evPW9V+C76KXohUEiU10RK9x\n/bxY/Z1jm7atWCIP+nRoIFVX8oh7sOurJbtNrYa9OkqTdWjNti++kc9/t7snBQBYf2vbkiU773u0\njOs9fkiwp4oyF2WkXDi6Z/H0wx1efmtsKzcKADBvKjaAb7+po6JkvKnwYcrF+O3fzopv99qMF1q4\n/PX5wV+LUHDleCod9UbEX94wQ/n0nvpB78cy8fSc/ydjsVjmffyJh6e7rVZz/MRJW63mxMkTUdFR\ntlrNwkWL5388z0Gt5lFnDdjvrBHM2cn3dD5xwzsF0sZ7Z7bt233fo0ncs8O9Wf2NPb8fXL0/bMaA\nYAaw+d7OpWsTXToOeLWBxpB0aNOeX7a6vT0iUoEQiQADBgDRXKQXSFdvBVHuQKI57/bJhCw29JlA\nFgDjupHn8u+kmZiABt40BgxI5t/QB125e18n+CkfHN5zR93l9b6RpmNLfj3Ex/Z/aVAA3F6/7Fgm\n5znm+b43Fu7ce6v1uEbKskZnXHp/Jd0ax3U89tWBU9eKGrd1UYW27TOqq1qOi5OObD+45veAmaNi\nVKiqVgmEfXnBlJWUWuzVe+hgf9aan3J6794f0k2TpnT3Y2z6mrbjhrdwJQGxGhkWLWl7/vfrCWjU\nZ3ScN2Rd2LXv5++tr7/RK4AFx3YwxqIgiriqSxISj+jdu7coiuvWreM4juf4oKBALGKtTqvT6QoL\nil555ZXY2CY1dObikh+VrzWhIGEnEaNaAAAgAElEQVTVj78l3s/RcQCkJqJjXHPqxvEz19N1POvZ\nMG7sy/0iS/pR+fwL21ZvOnw120yoAps/M2ZUj1AFUkaP//RTgiIRADSJkKVc+jr5Zp4QFUApogaO\njrIdIsqfv5z4c8pDEw5Q15U8n7LnYIamy3sv9ApnAYfKHry7ZO/+Bx1HhtBC/vGlX+0sbDtpwfPB\n6XtW/7LxUqYRQOZSr9uEWY12Lv7xC2fvOYODmJKQKP3DI+upEEBM4zZdOhz7Zu4vy39uEPZGO1ec\ndeCbRZuvFXAAjFuDLsMnDI51pcoCCVXux4J9eaHw3Jplv128l6W1AlAuoS16Pje0Z6S6rEqTuXnW\nhM0AAJETv36vmQL4wis712w4eCnTCErf2B7DRvVtqKGgOjti8YWlc35MjZ360bgoZS2fz0JB4rF7\nbMyUMAVgq1037J1rR2UEAL7o2u61Gw4mpusxpQ7tMeWtoUEYAxSd/Hba0UI9j1R+TePGjokLVyI+\nfcvMD0/Evv/ZiGBaKEhY9ePWxLRsnRWgnAwACMXX967buP9imk4EUuHuV6/T+Ff6BdKVnRcKHITF\neOWbd7580H3e/IH+FADw6b+9P/tIvRmftKtc2MYO/ARwfC4cu13xz8qOjOX2D28tSuvz8dw+3hQA\nn7Pvw/d2BU1f9FLkE19UjGGZZ599Ztu23221mvCIsOMnTwq8UL9+pK1Wk5eb//prr0DpE7s8ZVe7\nrbPGURsrBgCZR3hEeAANwS75F5OOeDZqGRvOAgSQyYnL790pEIK9CP3Nw+e0IYMmdo91QgC+A4tv\nLNp7Id0cWa9sLRMuN2HTgWzfzhNj1CUPbaxP/H7RxrsigDJm2PBmLmSFoz+WvNVQYAa5s7JkdAyi\n1S4ySC00CJzuyi2DV6dWXpCx80yWuvWkZ1oF0lifJwcADIRTRHM/Yef1XK6h8lHl5dFRKE2QDwOp\nGYU8uMh8GkT5AAAE+CiyL35xPiWfj1HSYEfLhgN5W3gj6kcG0AD1Iv3Ql9+eOpjcdkyUAjAA0E6e\nXt7uZEn5bx4+XeDe462BHbwogPAQD2v6F8cP3m4/rqGyGjvqRiPmfGbXJQmJ8vTpEycI/MaNmzDG\nHM8RBGE0GouLtCNGDO/YsUON1w8uy0cq7zCm30gp9h84sW+YTJ+yf9W2deu92w8dPsmfLbq4deW2\n7zc1WPBCBAvYeHvdwm9Pu/cd804TV/3V31es+2KV14KXG6sQSZX2jgqmfK1Aefo5le8uFYxZVw8f\nTmejhofKEQDUjTyfk5RiYMKa+LEAAEgZ1jwQXbh9p1gIUd/Z8dt157gPn4827P34y+18u9HTJoTA\npWVf7k7j/Ka+OPLSjNW/Xe35RjN11RhRnm2H9tw1e8e+i/mtu7s71e86fFIfFyUuuLZn5bYfVoZ/\n9kYztePHPWFfHhvSriUV+g18fXwQa8m9dnjrhoX3DHNmDw5mbGru3SdN7OhOAZK5yQCbUzZ/9uV+\naDXsteH+8ODEpo1fLrK8P2doKAvV2cGirU5Ta4T8xGNpsoaDwsoeoRXdcHSuHZXRcue3RZ/v5ZsN\nmjA0TMUVaZXuJTdo2q/183GRzpB3afu6TV+RAQtfalj+sYuN6TdSivwHvt47WGbJurhj06av1gQs\nfKmhwnpny2eL91pi+48bFKERcxJW/3rmdiFnS0cqOO8wLPKQliFk4tVbRc/6u5Mg6u5cz6NCh/oy\nVQrryE+FxfG5cOB2+YTEkcxTbXbr2qUzFsWt237nXDie44ODg8pqNQX5hWNGj4qJiXGo/Kizprps\npOwLqXBTAac3WDEwCEiVixzS9BYRg1hwN08QdJs+nrPpkSqltWIsQwCArTlnVv+0My96xKSO3nSp\nTaSoP+y1VwsKMq4e37th6Tpq4vDGTrZK0uPLV+ozKSuFtSjbIPP2VhLWjFwdaILcqPKlxohRKAjO\nZBUAyu5f5WNiS+EwAOYKrh3YffjivVytlZBRHFBWHuNK4SpP7eRp7wYhilOpqcV8lKJKtw+fn5oj\nqBuEuZT0iZKu4aGqI7dSC7lyV2lVO3avTqHw0m/Lf0/MJd3qxbbr1Dras+jk+jPqQcPbeUjDYv7L\naLU6iqKcNc5FRUUAoNFoTEZzXl5+rVJZXO4/XHm73C+6cYNgGiLccs5c3enbqmPLGBlACHXt9Je3\nb+Tz4b6k9uq249r642YObqVBAMFjCy+899uxu+bGMWXjEbnMwys2ZwQ++34z5xKzWHtm0bRlSSKA\nutn4ie3dKw4XeCx5izbPCEo3BWXzH1HO7gq4mWsQLdqEy3r/Ph398YM1hzM03WaO7hTKYF2WzU2k\nadQ+iF9/KdPSLLKsdlLuKJRrRAALt+4W8thD4d+ouT8AQFiAKv3UB0ev53JN1YzDewiqRl7uFx3b\nMJgGaNQwCL0/f9+2G72mxKowAADj7O3v513SJa+7uu1wrveAeRN6+lAAMfW8LPc+3LXtWtwbzdTV\n2HFqPvnr5Q68sguff/HkA3nM0FBF2R2sghuOz7XdMtK6q9sOZPsM+OjVvr6P7mc8AIAyuFnL2GAa\nIFKTkzjvZEK6tWFouQCWFSommIbo+m55V+adSki3xvhc23Yw273PnNefDaQBRO/8HXCmVN6O83bD\nooxoE0KsOHND27WDC2F6cDkDBQ4PUUBOpcI68jPGpxbnoqLbUUGChbfVsEmWti/TMKhC2R0OH3hi\ndOvaVRCE7Tt2lq/VFBUWDxowoHWrVtW6UdpZ4ygbKZOyfRBFIhDEknoQSZNlzbIYg6zBcy939yv7\nEyQYJxUAYGzNOb1y2a786BETn4l2QuWOgmSuXoGuXoFhAYqcxRsOXytq2MaVgLqQRwpXGZiKjCX5\nPOb0hWaQaRQI52NABIEwKVOwkJ2nF0BZLvEQDLmFgiJcQdpJaQCAL7z7kANnPw3OOvLr2niixcCh\nAwPVODdh7ZobFWNYKZi8Y/nKwScQYCxiO6aq3qaqCti1UxVr+tGjmTHjZran7544sGvZku0AbHif\niS6E1ITyH2bjxo0HDh7w9fXR6XRWixVjrNPpvH28jx49QtPU8OEjqlevdJU6gFR7qsFarOMwyBBQ\nak8lpOjMGIDLS3nI88XL3x6//JE0XWjBIEcAgC3ph5cuXJ/T4rUZfQLKbjJI1Wj8h7PyslMv7Nny\n89wf6HmvttCUVFHqRN4eXEG6Xh4Q4ERyaZlacAvzrCiNGLWStBqsokMDpV26XM65bWt3nk3KKjQj\nBc0BHc5XG73aybN+Tesr999KzudjVVVN5Cal887Not1KnpSUZ0yk+vcrSbl8lWaZ8nbsxkPIP7Xi\nixWnskmvqPY9endr5pO/b/kBlzGTuntRfN75E+mKmKGhCgclcXiuqVx7ZeRybz0QnJpGu1dT86dc\n/F3AVGSq7tUElCbAJsPn3krn1Y0be9spWfXOlw+LJrpzBPHzyZvF7doqs66mcr6966sJyKnGgfJ+\n1v5clLptvPnjzM8v2VYGipzy1XC7Rav+6H8Ner2+Uq3GaDAVFBZWr2W7mKly3x01j1TNtSrsojSB\nbujMwxzQNKn0xyma7+76dVdW/eFTno12IuzfrGzGRV4UAaBO5ClNaID8QOrNh9aIIAawOf1KFnaK\nDVSRlMqFNecVmHCQT5MI2ZoTO8/XH9bUVW/gQdAX6otunziRpYoa5EFjECsVHIDPS9xzvJAKHxjt\nzD+4kw8Bg7q3jFAjEFlPBdyoLhsBa17t5IWC1PsG0jPQmcLAkywNVpOlLKOgXEM8yNOpKQV8oBcF\nAHz+nVQ96R3sQmEQHNuxBxM0YMpkjAgEDXuPie6szdchZ3c1g2p+mkj8S9my5bc9e/a4uLkUFhVa\nzBaL2QKARIx5nndxc9m//wDDsIMHD3JsoPTiwVBlnFLJxWlrW0Q0iUAQRGyr5NEkiCIWAQMWMcib\nTnhrUGkvAwBinVUAGGNr5qHv5q/Pbf7ae6ObacqPJUMKN/9QN//QehHqzDd/3JWQ36SnBwl1IU+o\nXBVgyDPy2FZh44rzjKBwV4AoiIAIhDEpU7OQnq3lQUmVHAJj4HQP83lVAyVVNhgPQzkH+Lxb963g\nGqzB6Tu//H4/0XHUq2NDNPDw8NJl50tCZEcLAHjH8uXDC4CBAABBLHcWyjrmbVsqakGVLVXt2MFy\nd8/2+03f+aobc+vQpg1fzFgNwNYfMceVBMzlnj+ZqWg8Ilj+qCwV3XBwrvn0HXbLCBhjQFDtdYUJ\nAgEWcaWCVJQhS2RELAhAksSjgJSJ8fadtxcW5BTTpT7549HLxc3CrlzXerZu6EZivnJhq/ezNuei\n1G06aNAb73TjMQCQ6kAGp9orGgYgbH9gZU35FWw+cbZv3xF//Lifn2/5Wo2Pr8+p06comrTNtakK\nLj0JFUZPVZUq+Vm1hl7uO1I16NzcaWX8LxugZ/NQV9JSkGvwbtIiUI4LL+05YwiIi9Vos9K1AACI\nVnt4qsX0U/F3ZH6+Ghk25t46sz8V+8Y10JAgFNSNPBPYvq3X5UPrd7j3a+SUe277FWvgs629aSC8\nogKoDWeuFjZqF/HsqA6rV+/69qNdACBTkuatSz4DJrDLqG6BTFnpDJn3UhSMYNbn3btx7tStXHXj\n54c2ciKwS6AzHD95+Kyisa8KFRWYK8bEkJGaLCvtwSQUPiFe1crrrh+K92ga4IJzE/ceyVM36x+h\nAADSLcidPHd+/yn/dj64UK+Ojg2s37WV6/KDK7fRcbHekHVh95F8t3ZDIhVQnR1Rd3Xj12vT64+f\nODCi/MwahFCJt0jm5C6zf/Yl/htYLJZDhw4RBDLo9VYrx1m5F8e/SJLkDz/+iEVR4AVEoEOHDvXr\n25dlHQyIe5TK4go3B3Bco6l4D6Fdw7xRfFoG8mjtU6FKg7Hx1qYv1mfGvjprTFMN6fAqRQgEnsd1\nJU+5RoYpdt5MzLBEhbGAjakXHmBN2zBnitZ4sOasXBMOD2rXULFk/6ZjjV7s4KHTccBrC7R51/fu\nz3COHe1LAYhVbptc9okNh3KpBi82deXvJGVByIv9OzR0RiDIfBRVwlLRc8tDx/Llvwj5t1IMlHeY\nGwkgUjIKLHpr2YORdo/wpY7eup7Ph/hQAMDnXr+to/zCPUgAwbEdezFkw0bO/wAjAkHzQW81jSvI\n0SIXTxcWARZyzp3OVDQaGSwvq0lVdsPBuTY4KCPtGu5DxN+8lseH+FYe1Gf3uqoayYoylEuwBxxL\nSirgQ7wqLHRR1flqwouROqpnM9lXh07eKD6f79GyqSdhp7COfPgD56LkJ1L51mvgW2qWT7dffELh\nroQL93Mtog9VvoL5l9zed+3ec/jIUVd3V1utxmwyl9VqXN1d4+OPMzTbt0+cHU1c8q9sKKt9h6s/\nuSUfpAjtN3GE6vdDZ7eujReBUHo1eiaqeaDMmncnGwv87pX/K7Pn3Ob1d3o6G7Xp548fzzWIADLX\noJhnXujV1o0EbK4b+d5+tGenF4dbN+7e++tlnnGL7DF6UBsXEjAow7q3dfnf7q2nQka1j+j+ygft\ni/N1glzjogR9XiGncHVRUrbHNCJZpRwyjqz/5QgA0GrvgHo9nh/Ztr4HiwDAp+vzz2i3H962OkEE\nAFrh6u8uR2WRyT26ccXRMocChsx+MbY6eYIyJB1ed0wv0pqwVkMH9QiVAwBGqgYDBsau23Vw7U0g\nncN6BsUEeQX0fnUku3X/kdVXzCDzjOo8dmDHQBaqt1PuPEr5hoRdGJb99NNPZ86cqdNpMYYRI0Z0\n7tIFAExm86pVq3hOcHJymj9/PsOyji4h9KiK+SdBzo2f7aBZvOeb74j+nSPdKFN+pt63XcdQpZh3\nalO8NnRQO7fiB/eLAQAQo/Hx1oj39+++KQ8McFeAPvPaga23xMBBTTwoEHLrRl4WFtfd9/T2n371\nHtpG8/DI2vOWsOe7+9NA+jYNI5cevpDbslvDka/0+fb71R+9sRoAFGrKuHrGdGBD+74xIPRR1qZ7\ncOumQsabi7LuXDx2KDHLuc2rL7RyIbFnqBsc2LfzuFOrQDVRkGOqGA5d2o3rstKJAIQqINK/Wvmi\nizu3e7cOd8eZp7duy9J0GhWtQqXZx4ltB0K7BUCB1rlJ27BGA7t4zN/27c/04PYBOO3E5t+zPXu9\n2LBsgRO7dsTiC99/uCy18ZS5YyvMrEFE6S+E3NW7dIgPn33udLYydnRI2TQGO27YP9eMgzIi58bP\ndnBZ9PtX/8MDOoRrKGOh0btZS+8/d6EBABCa2D7NNn+75fs1TP+WPij78skMgHp2na8mvABIEdGr\ng+vcHSuzePe+Lbwou4UNtu8DUtVwLv4klFfTZp7b96xeuWdIu2AVzrmvL9kh5J/89sNVOW3enjMi\nnAXgHmz/9ON9zKDZ7/TyJgEbrv4y9+trYa9++HIzpz/pgdVqPX7iBEkStlqN1WIdMXw4SZC/rl5t\nq9UQJHH8xIke3bsxDOPISFnriL0HFuXd7e0F3UolZA1e+vAT2w4MlGeXNxd0Kd0FlHNk9zGR3csr\nY5BFjJi7wN5B6/Ua/1avyhvrUB4D6VS/94T6vStuBKD9uo/on/Pz719/d79r13ax4f5uHjQhWnRa\n5OzhSqOyCCBlzNgP7A0CxgAAiPVtPezV1sMq7yKcmr++oLk9h+zLAwYAZb1nJjwTUHVWDuXefODk\n5gMrFqpe97H1KgW59KddO4QqZtjM+eXNSkhUxclJPX/+/FmzZnXq1DEurrftaunZs4fJZNy//8D8\n+Z84OamrvYQeDSJzkPo6ajMp3YXkUSOmT3Zav/Xo6q93CUCq/FsOa9Y+RGnNupGJeW7L4o/LdF16\nzv54qIu26O6pnXuz9AKA3KNes6HTn+vqQ2Iw1Y3880G0f5+pU6y/rNn83WmO9Wr0zLRxnTwpDKBs\nOKCrx7wtPx2KeLNngyEzvuhTmFvEKz3cVbg4J8+q9PRQUbYFWhChcJLD1d3ffbEbABiNf3jMM5Mm\ndWvizSLAENBn4vCilTvXfnlEAABa6RHupSDKopS1Z+lXe8ocCn1x8azWjuQBAAhad2XHsl1akXaL\n7P7y6OfqyxBgQE7NR4+8vnTj5qVXgHSJHhTaKtwnfPDbU2VrNu1autgICp/G/d8Y1TeUQdXaKWnq\nd1hjrQCflXAqR9VkXJDs0b3Ujhv2z7WjmCBZgxHTp6jXbT604pvtGFiP5iMjm3tXc11V2l5FBqma\njHtzNLt295bv4s20W6AnAiCQPeerCS8AAOXfLS78wOqUgLg2niTYjXmwIx/Y6s+FvaLV5s+KCnhm\n4ovGlVu2LzvPA1AKt8AGIeqSYYG4YgdOpXc54NqdYkcwDD1rxvT5Cz7T63UYw6CBA9q1bQMAZot5\n0+YtvJVXqdUz33uXYWi7BbG1JyFtcdG1K5datGorio7HX/2rwObMS8ePHk+8mWkoDYuq5etvPBtY\n7Ri2JwD/8NCSJYmRE6f2q3743F9lR+K/jdlslslktdlYCYIgziWcimkUm3Yv1T8wyGKxPDEf/w5w\nWfE/Llh1hY3tM6Bnq5gQTzVNCOaiIovS1Zl+zMrtH4bP+O39uScbz1wwPPjx7iF1Yod/sGX2hyfq\nT18wtt4TX+eizuAzts/+4Hj0zLmdEudVdr7GsFhSVs1akj1o3pttNYRdgf8Yer3+0wUL27Ru3a9f\n37KNe/ftj48/NnPGdLXazvx3AGBZNj3tfmBwaMlQ1v/UQlmsb2z3EbHdMW/Sa41WYOQqpYIpP+Tt\nrwKX/veYh64rOxL/aVgZW/UmYHdjJSoMGPn39wvS3h1fnx+SsGfHgQ2Ldi0vLaym03ufjgp12Az9\nhKirwQF1Yod7eCYhX92kfRDzN78GuIdnjtxCXl6uSsKQfn7n3hyXDi965Ntx3lFYsCX/foYeTCkH\nVp2Ud32vmYb4W5f3r0OlVM15/32WZctHrHfPnl06daq0sQKl26tdd+RfDiVXuZb0eT6VspM+Xd/8\ntOvjH72u7EhI/DlK7x4Yg4jxf6GdVebXcuCrLQdizqDV6i3AKlRqFUv+9SW3HRBj8TEPXRd2uPRT\nCQVOTVv70X/zK0A0ZCWf23UuvdAkAKH0jur80ov9A3K3fV/VeUdh4R4e+WnBnizCOazT+Mn9Aqm/\neYH/Umjazvm3u7EMEZfrrLl6+WKLVu2kiEpISPwJCII4l3CyYeOm9+/e8fULMJtNNetISEhIAACA\nTCbPzHgQFBJW+s6axx4bLyEh8d+k/Bi4/0jriISERF0hlo4yKLcMmpSNSEhI/ElKhu4/fr+BhITE\nfwpcsoRb6SLxosA/bZckJCT+kYgCjwGuXLoAAFpt8dN2R0JC4h8DTdMaF7egkDAobR0BgL96hpqE\nhMS/hhat2z9tFyQkJP5pYGy1WO7eTcnOyvT28StJRwiSFIW/xQt4JCQk/nFIdw8JCYk/AcOywSFh\nN69feZSO2Gjj3uhp+SQhISEhISHxn+J03hWWlXEcBwDSUnISEhISEhISTwlUMlZESkckJCQkJCQk\nnjJSOiIhISEhISHxlCGw9J4TiX8WhFPDV9+f9XZDec2iEhISEhL/ADDGBELSFN+/EUgZ0KRv94bu\n5NN2BJQ9fkwoWtHRqfYayKnz3O1FV9fNia6r13najwbh3Gj82CHdfSgHWhISEhIS/ywQQnXVWUM6\ntxg+a/eBeG1youHWkXNLX+3uUfoMYfwGTFt84mSCITnRcOPgqa/Hd3QlnpQWAOEcO3XJxvSkREPS\n0bNfj63w3mekaDDwvb1HzxiSE/Pjl33W118GAEC4d59/5fIFQ3KiIfl08vYlCwY3cKpNWEjf13Yk\nGlZ3/gMP7JphGrzw2caP+jze28IfE6SMeOaLVRvWdmHodkseXj1w4pOuXrVJjwh5YD0/WhYU5VFX\n7zX9O0RDQkJCQuIvoI5qmExg/0XTmmeu+2rk+Vy2Xq/3p7+ySZke/cLOTAFAEChl8YHvZs9LMznX\n7zH97clb2Hv1Xz2cj5+AFuk77rsf5je8vmTGGyeh6Rtz3tj5ja7JuN/uCwBABg5ceGRh/fjF73WJ\nz1bEjvhu0fc4Y+j0S2bGNShMcfeTCfOOYZ+m3Z+fsWBt7/ovd/70XNF/c61rZZMFKz96Qbdt2ksz\nz+eDxq9eQ/nd4tqsKiFkr3lp4PVg7tZ13RN3UkJCQkLiXwVR6V01bKOJ39+4nGhITri9fv73P2/P\nSb7w4Lfp/TxLaseUW7Mpi1el3Eg0JJ+/v+Ozt1q52HZY767q3m7wiC+37jh+YvNP81/fXMTEtA2z\ntdkLWZs/njd37f59J45vXL5w5nGrol60L/1EtGSRw95thffPfOf9LfG7tiwZNesUbj3hpXoMAICq\n2ewZ7fOXvzH+h6Nnb908uuHzORc8x77QUFVScF3ylSsnju39es7LHedfCx03e6pNCwifPgsuXzxv\nSE40JCckb/rwpWhlLZpOHGoRbu0+W7Pt3tVEQ3Ki4ebBk0te7OpRkhIqm3+UnnwmfrQ7eDwfbxNI\nPrWmrdK2l/HpOPv7LRnJiYako+eWvtbbp6TFoBqDNkiPLj8cOa89+nF/j1o1hbEB7bp6wJlvliw/\neiXx6pUjezd/vfWuuaby+g5dY0hO1Cb+fmzr7i091BX2OfSQ8Hp2uTZ578KGpZ076nbrryQmzWwg\nqykaAND+kx35yYmG5FPXVk571vdR+0k1gVq4ctPtCwmG5ERD8omrK94c4FdXrTgSEhISEo8HJiot\nD095NW4S9HDZoOdn7/eLG+1zcPyYeQd9n/9mUrQCACkbz1u3fG79lC+mvNh11MylWc0/+mXxC/62\nhATzXGljAqHw91VAxq2H1ooHI5UhbYdNbUGnHoi/W7KrbrUIzyYtA+DWhouqV1Yfz9481v3Svqvg\n07mxKwmgajToGc3dnzbeNjk3mvLl2jtXjvzamlaHhLhV7oawpmxbEc8HDOkdyAIAiIXXds57d2L3\nIWN6vvzlYU3/r5ZNaVHzGEqHWoQiuHPLoOK1M/uPfW3oB9sLWk/asemtDk4IAIzXlnTuPWLczmIo\n2jduwOBmcYObxQ1/O9EEAMi55aKNS96rn/L5tMmD31qVFPXylo3vdnZG1Ru0wfq36+VPkn5tewTU\n6unLF2fmALR4eXyfQFmthxWJOXvebhI3uOW4n5Kr7HPsoZh3dtdF8OrVwdfmmTy0Qwu5/uSRNHO1\n0bCRfuj7l16aOGT62vsNx6z9ZkgoBTUGqkvbcOu2+c+Nnzhsxsb0xqPXLB0eIXUDSUhISPwdQPY6\na3Bh0tkLp4Vrlhdcbp8+c8Jw/t3n6geo0DWXPtMm+p19pcu8dTkiAJxNwu1PffZSN+8VKzPKvYCP\niXjug6/a5S4dufXOo62EV//lyYubkACWy9/3+vqqvvIx60SLcvXXgOFKllUZG6BSsf5OlpuZJoj1\nd6Ih379pA3XxpYRczeAvv/806uik8Qtg0rJvPRgKAVep+MbMaznQKsKdhhQLgDntxOY0256r11D7\noT80b+dNJdyt4ZWD1Ws9vHDi4CkdwJnD1/G5bS982GtFz03Zgjk/5Y5eU8gBV3j3TuqtRy0ShH+f\nyRO8733cZ/YXyVaAE0eS6Oidr3wQ99Px9VnVGQQAAOOVpWM/1vVAp7+5XFMbBwAACFm7J3/SZdes\n0RsPjUg7vXXpz6t+ik8z1DT9itc9TNIBZco1ORCw72HumdVXYEnfDoE/3k3h6YC2rbytl7feNAIA\ndhiNEu4d3vXbUR1Awk11x+vv92nvuiE1B2oMVEbC4d3HdQAJV+Vtb8zp1cF9VfLD/2afnISEhMTf\nCgJjDHafNaKIAREIsMiLQBAIycLbhZNMy+UnbaM+E/VnP+tCgUeI66MaJlI2fuGr+I+jDk9/ddZZ\nXTmzYt6h2W2HvPz8nNUJQa8eXP1KbPkGhjrWAjDdeP+ZuAa9Fpw2lm2iPUJdoSC9QNV8Qkf5+a++\n+vVcSmphpTykvPGyb6Rnm0VnSzoAACAASURBVHE/bdjx4OoF/fVjlz5tzQCjpGvs9aitlin16MFc\nFN02sNoGF3lYm1DIPXcovaR1yJJ28kguNGhlR8uOQSE/fuWS91eczS43/oPy6vbV7ycMyefublv0\n4cDYEFfvru8u3zmxngwAsOnaismRHca99O2+7Mghny77PfGLPgF1N4ulgodC9q41F4V6z/T1p4B0\nbdMtiL+484z2D009F/JSM0zg5O9M/pFACfmpGUZQ+aie/hQmCQkJif84GDDGlMOJvlgUeLF8xREh\nAkHBrjHjf75ueSTFFWWWVFwJdetJ3+2a7LZ5yqjJe7IqdbkI+oxrlzOuXT53JFV9Y/XIqY1+fSHB\nWNdafEF6ESi9vBUEn5eVBkB4ePrKoTBdywGSqxjgzKLKy50w3sgwVDM0Eyl8YzwgOyWfA6CDnt+8\nYmroyWVvvXLsZhHybv/mhnfca4zsH9DCIALUarJ1RRmHCrUyyDaeMLnXrVn1xt6N7D9+7uxf3vkM\nwHRx1nP3S9sgsDnr8toll9cu/XbA3J/XDJkxb1P8uFOGGn0szSOqPXoFD8Xso2v3mxa/0C/wh7Vh\nz0Xhs7Mv5v/R1gqBE4Agy4pc20AJIiBCmuYuISEh8fRByG5nDQAAGI5Mbq8BAFCUbjGlnLyD45o0\np7J+u26sUoEl/frN2zrZY/PEMRP351bXkyFgDIgkn4SWmHPp7AMYNrSFZuOeAhEI9xa9GsLDpZcL\nBKAsBg5kasaUVgwyVxUFlbtoyqBDnxnbiUr/cu99C4A6pE1DIv3Lhcs33LYCQKr6oRlqTkdktdai\nfZp39IDk8+lleYDFaAW5S8VKu+nO6VSIa9EtgDmbZAUANrBdZw+4mZBmqvK4rWIQgHTrOHJkT+LM\nN6vKGkgsFz4ZFEOIgggZKz88vGaxf4Arn5WeZaqSCFgf7lm5+96Q8SG+CgIMNeYJ2GowA6jdFCRo\nHSV8lTwUC05/+Xv+/uHDutz3bgmJU4/llVO0G41q+AOBkpCQkJD4O/EHGuHFjN1f/e+Vn6asWu7y\n3bpdN/M5uUeEb/G2VUcf8ADymHdndEKHP/olTVO/vgYAALAh8+5drUB5tJ82IvDB9XtZRtAENhsz\nZYB77rZfrpsA6l7LfHvjooTnv/70sw/lv56G2Klz2qKEj5clWQFw5u1c6F7P17RjyzWY90Kf+se3\nV1xzRR3RMKaV1b1h52GzX2x4f9UrX922AoDl/oUkaDdm8ogrqy6kmZBbIy9Zpaio/KLrR5RNbOW1\nGUmZxhq1mk2YPEVxOoUPHDJ1SgPtwRF7s0qTKi7jcorplS5zp/RfeCKP8gpSX9qyJsWSvvubn17/\n4f0fP+Y/33WDrDfqzVcisjb33ZMtlJ5AxwZB0ejVX2cP8YABmit9Jl38P3vnHddE0sbxZzcJvUkR\nBEXs7SycvZ69YH8tYBeVAxuiYjkVxd4L6llRsQACtjtFsSMdFCK9hC5I74SQkGTfP0ClZDchgMA5\n349/SJKdfeY38zw7MzvleytFKPjeuOCXpiX9mJbDaD/9yGrdMO+Q6Cy2UEFvrOlsA2DdYkq06llY\nnBCQAZZma83y32Wr6MqGPHRO4InLctnHW45RJpb2+wEP2vm2xtiIaDUo7i9WKAQCgUC0SOj12CSe\nKAn5a/7q1O3r1649sEwRQFAc7+Pw3snzCx8YbfuN0ATl8Xvfjv/x+087J417mIvLq3cYNH+9eSct\nBgAni+l1fdFxe88iAqDxrxLy0x02WCgf2LX9uN1WKInysJtp8zhFAAAVaf4BmVun/a97/uZtx0ff\n+is4bDsAlAWU8Ajg5acmcqbsvnFnN3AzYgLv71py9GFU5eOXl+RoskH1jNXym/c2yQIQ3JKvUd7x\n1V/19LF+8/THXzzPDfpmviXirhIqGlodW6BN42cwH63bcPrf3O/PYCLf88Rax8NHl9q6rQJ+ftQN\na3fneK6wKMh6oVXeAautZ84pQ2mcp/2Cfdcq1RCXIHDT/F6lzTXB/F5/qfUqTDR0eWXN/vPPLl0n\nDwAgzIv1OmZ27HwC+Tyb6nBjT++0739sxemLs4SFMTesX7h8a45QWMhLfmzrsdp1KvHgpl9OjVaP\naDUo7k+IEwqBQCAQLQ+CILDiosIwZvDg4aOEAsFwzX7NbVLTIdNl5z8PdhYfH7j0foJARkNbS4GX\n/zWPI8n+Xo0IvcMSv3fW+evHTH3VOHuFNXqC31CcdO2dm8w2/ZVexQ1LSAILGT3W3g8y9hk79SxT\notU/CAQCgfgP4J8bhtNoH/19+hkO/HVO9OUlXtx5PW7Ajhdnl/6hyyjKzuPQZOhoQkFzgisb/Da4\nb9/JS/e6bVJ/cvhOGGqLIBAIxC9KfV7WtHKI0tArk42LL5zY+Pz9VgCAgicT/9jvT7ZRxq8O+/Wf\nQ9Wa9hZy/dacfGWsIyyIvm/75+Y3eT95pAqBQCAQLYPKlzUFYcyQX+BlzQ9wJa12OgqC/MzsfC7a\nAguBQCAQiGah2sua3+m/4ApIYWlOenxzG4FAIBAIBKIK7NeZO4JAIBAIBKKFgjf53BFcpa/Fnt3W\nfcWfO4f4zyBrMN12r/lEzV+ktYsr/7bE0WXfLMnOTJbqBn3Mdu7c/FvtXW8aBE1r3MzB7Rp/PxYx\navxidQOBqA9N5ZUtH4IgcIk2KJcMTLGD4fSJfTVr7qGJq/ZbtWL+xF9SYKlQmn4npPTeWJXmtqMh\nyHads23ZxN4Kv8IjB1PsvfIfF+vxZYlfmmxaNK42wGy18RRdkgOIMZWx+/8tDHfe20dWhHmivBIA\nZLvMOXbmWtDFed1EN3Kkq4fi1ahf3aBrTdlsFxwUxGYxi5kefheX9a6bRYnAdRc6Vh62xWYxPSYr\ni7CcRCgE4qchziv/w2BY3Zc1uMaEu54B3/zWJ8rloOVQDcmaEjK9TE+4HjAyaMZD22mqgxftfv76\nQzGLyY55//GyxUQt8dFFcdCBtG9x6vu/lEP9FcReCQCYXPf/HWOx3l0aWGP4B1cdsMnONS2OyY7z\nDDq/YoRaIzyYaTpzXscy2SzXTQa/QNtOqqKsgqRQKJC6vDCVQUeubjSMPDdp3T1mac2Z0bhij4mm\nF2/cjwsNzn+4UL+W+ZhC31VXs1jMMMsuUj5hf9xIXr+7HkOuY28tmTrfkXolN+7GjDUuBRP2uO8e\nodFI7UYqNaQB1556wHndwOz7+2cvXj1r85nLbyIoz5KgQJj9csfgmQtHrrlFMm+sMcMXpmn0lsWM\n3t5DDgBApo/VUzbLaXW7pmmdU3kKptBpyrHb/2axmGxWQKzL3tW9FcUbQel6YjyFxPVke6yPqBFg\nby1o0+B+sKh7SR3MSfMlbSAiTVBGb87mUz6+gWwWkx31xu/8qjHqPzRsCq9sPdBrv6vBGGqd9GQj\nz1pYeJXKqBlMMt169F4vlZmLD8VItKdn8yKjP/vk5kFfnc8t+ZQj233Knh3mboppfUyffaVcQsqJ\nvjbL+JFslXfgWn9subNW57VnGtX2nwAAdM1+U60s12/+QwegsMY3NN2Vf1890jfS7i8rX/jdaq/V\nswslhisfpTRoJSu90/TFg/P8vejD18ztdOUsS5x5rRvpipKqUCiQvrxkeq3ctUYjxGqRYwSnhidh\n8l3WnLp0zDDNxcltx63ElIzUrBpPZ5keS0+/tO4hqYXUCLIczeZGGlTERNZrKzxhzofTc/d1Dtq/\nf9+zuZsCSxv82pZUDWlh6A3oLJvnsf+ye0CDR574RWlRRUAv+b1M/G8bioxmp7YA+kbTetjFhhId\n589sD5DVQ4sBGY3vtRSegikNPOF4zCT9zvolHixat0Xb95y/I5syYc8byn2KqVyPylOoXI8mr6oA\nqWfW7HTJ4gMACMtS63dwdy1I7yVlMCfPl5SBiEIogYCuWPT6b5uDqRzVnpN2WG98KJvc0+JdXpUe\nje6VrQWCAJL2V1FyDDMiMsDH/ZD1kff8LoumG8gCrj3Lvpjlcbzvt76c8sj7Ycy4Xb3kqtqkAR+W\naYKWyYfwyjapn+MIxe8Jjjr8NI/FZLP8Im5vnlVtzFmm3RibKw/TWUx2nOfHy2untqv6CtcYefy2\nW2xwYOUgTbjDljl6dXt+teEl3Z04ct7is4+fevs8uHFk3YNCmd9GiO17CtlpISGf/YM/+wd/DkrV\nMFnaJ9tx59a34rbBkO2y8bjVRPaj5VaPMmp+I9fDePtQ4tWubXsefnB/aLd0tx8xbI1Zd/H2UyHT\nafHiTqxbZ7Y7JHeaN/f7LAJKofB2RsdCQz6xWUw2K5DlZmvWp1rfSEZnkvmhN++8S1lMNis4y/PK\n9p5VF0pVKJhSt9nn778sYDHZ0a9fW/WqZjqlGSRIV5QUhUKB9OUl33vdYoOsBxec02r22THFoVvP\nHdB0GjdxzbqLDx/7MEMS8rg/4gquPmrbI2vVS2v2PSuS2EoAw43XYiOYbFYQy8VmRQ8FDOD7O4hi\n5j9ej58/nFTjHYRYrwSoSHA7cihGc4XVRL2Gv6QgU4OqbgBdY6DlqbvxUUw261PK0xNbh7ahVTPe\ne0Vb0Jj3NqzS+EdbOlWOC5LWKNmeGyJZPg7DqjrDKhOvs1lulvpiRhMlEKp+MNQNlNO8/OUmGHeR\nke08Za5CyIt0xe7qDErjqQIsBRSeItd5/FStfOejl12CokP8/9134N8cVcNxemLGf6gSpPAUStfD\nFdWVBDnBn6MjYlgRMayIuHTSMzYlgfxeYoM5TWvc1fefij0Pza42t4kiX2IDUX0TBEHmg0MH9zu9\neunj7Wp/fJc3T6F7n5qvYRvVK1sT4uaOEAJuedXx7cLcIPcQ0J4yWrcyTst3Hj1YvtT3fWo5QFmE\n3dipi1c+K4LClyvnzBs4bd7AaYusmT96NGlvr5iZrZ+/wyml73KnC/M70wEAMNUhJ13tdvaMP715\n47ytd+N6//nQdftYVQwAcAWDcSO68p4cWbBqvfFfrmn9lzleXtRN/Dgqwa/41gnFFdrrKkB6TIbk\nwzqY0gjLHbOFL63sQsSfc8KNtTWaNGzT9eep5TV/i7c1HNIBYlxClMzveWc9WKH5+WU4tBvbX70h\nVUuui5FJO9aNF0kxz59Ea01c1L3KISiFEhZEPDu4ff3E+csn/3n2ndrsc9ctB1eOa9K0jc+6PLEe\nyf9w3WLDxlmrrTddcX+fwQdpC4WmOf6Ks62p+kdbK0vjv+74l1Vvb5CbQYVURUlaKBRIX15yncZO\n1Cjw+Ce2tObnuMao3YvaY6ozH/h+ZMf4xrja/tlX6bsidO0pl89MCN29+RizpF7vMzBO+N+7reZZ\nXfRqM/eSk+1MTRxAmP3C2nDavCErb7Dq/F6sVwIAVKQ624dgv88d3+C5pWRqUNQNTLH/QWf7/T3j\nz1iuHr901+XMQQdunTJtTxNt/JSNt6oaOtLVKFIkEqoeYPIaWgqln298oM+c1avf9IlyHx76FtPV\ntSrny5AZTxVgKSH1FF5uwhdCffy0Pmo4AMh2GtZPoyTS56vY86fIEqT0FCrXw+Q1tOR5ZXJtNVUZ\njTFXURI3Jwnmsu1HTmlPo+mNmNThR7eNMgKICUT1T/AbNMVOI4w3DWYkvv6QVCuyNZ5Xth4wDKPq\nNuDyml2N1lpOkc2+/jq1HAByAu6Fgd300frXkuL5jA4jhurwQh9HlwEAUZ4Xn1CqVlABFQVJCYkx\ndRwo+Z37I88SgMBo5TGRe4xGqbskZkN7o41rdJIPGdmcYfEAfN7HMfo8M9837Yb3/czKq9ID3z33\nLgEIDJcfEbV3ymjNu6wMCQO4TLcF+86NzLm85HGCxC+b6XpGBxdqhJ28/LpAoruQrEqiq7dXA3ZY\nJk9xQAclJdn2KtzorxwY0F6FAZnSdglkes6e2i721osMQQX2wSVpk8XsbjZhEd/jJZlQ5ak+D1Ir\nfxIegY1aeHXQSB16YBJf0dD8xGSlgAOzZ91Nq+kIuFSFgukbmc1VZe2ab2uXzAfw9iodY3b5x56u\nZGZImPd6FWX9l4pJX16yer304GtgnZFghe4ThsiUBLy8Zfc2Pl+m07xttmfvquRMtH6cKwSazqKj\nfw36sHvo8yy+rF6dJHF5JUVZGgYAhIBbWsqtbkCI/YXzr0oA4G0Urf8ry+1Tzz2/95VfkhFXAnRO\nTt2Hp1ivBAAAYX64fyK+fKyB3J2sBr3HIFGDRl43cF2jzev1gszHHXTOFgJAUBwxyu+E2QQdh9vp\nfErjG1ajaiOZUJJDU2unQucXf3Lzop1bt1co82pLRP5fuKq2Eh2yeeTGC8gDrGTU9hTB12fme4e5\nH7SPGur5LEV39h8VZ8wOvSyU3EFqJSjGU8hdj66qVv6VN+ja89c0EKQFue3dY+eaJHmHQQRi3Zws\nmJeFXV5xqGQS5n8h9HsxSxgBRAciqRLEtWfbs04Z0gC4oVemnA+v1YJvRK9sVeAAoop1hJ1nKSs4\n19/lzly+0571Np85AACCLHfHEEH3mdPb04GmPnxCR37Is4D6vQIU5Camc0ClvSoNQL7L8M6Q8/Ht\ntwciN9X3fQ70Gqpfp58jyEtMLwOldkqSjS9giv1Nz3041PvdDovdQSUS2yfTy2T5YJ7PicdfJDu+\nVhycqD0zp/Wacsy/4dVJtvPCqRoRD7zTBQD89GfPUnUmTe4p4s1FLaFobYevvOHy9Et4cGmk1+ej\nw2RARpGBA9B1fx+gCVEOHl/rDDdIVyhyBkP0IYfplynyeUBmhgRIWZRSUe/ywhiyMjhUlFXUsgtX\n1NZRgi9PnF+8C4v9/Mnj4O6b8Yp/mA1WwQBvO8H6aP+Q7Sd8c0W2eOUHnPf0Sv/0If3Th6/v/xpK\n0uOvyPjklQPdBrVvlNn3FcUZ+aCkq9rA+dFkalDUDbmuI7vSZIbY+wZXTjksDToxjg5andTFDYM2\noEb9DGgq2oq4gFcS+e8zGDKe7n0vqrScjyupK9AAqIxvSIAV6Sk0eb2OekpZvtcffs4VCgTyvRca\nj9KXcK4uhevV21Mq4u+Y9xg0QqXnyF4mh99qGt90/mucapPuv0kezAV5H27b7XEIyqrb1aDIF4Ua\n0iQozH1rM2L+nyZ77wV2tHhzz3xAHU9vJK9sVRB0DAMRLZLwE2vMvAo4RbmpWcW8aufYZ3k6veKc\nMp2hf9Wpy4LeRJBNSF59p88LKqpe/lRS810RaQ0lBELAcEkWJePKwzb87b5R44Hl0o0vMusx/1au\n24q5eiXvjrytd5Zqwc9PKwRFbR0FnJ+bmQqAa7XVlYeCtGKpWzmyHcfP1GN0tn1eZPv9s3EzDC4y\nY+vkr5pQjI4mDxw2dfa9vtXcK7oQ0xm1xWWbZtWvhAQAQdr1r3+hEEICMBwX9VMKM8QgdVHWD6nL\niygvyCuDzvqqNMiv/qwl+Dw+gIqOEg1ACAAVucnZgGu0VaJh2FjTCW1U4I5f8J3vP9/4IHvMod+M\nH34RAHDjTpqbO8liACDkZkWQTcDDAKvZQSS+f1F/cIa8DPALuQ08MohMDYq6gWE4Bvnuy1fdjPyR\nU6Ki8Cv18ARVjSKEQsBlaM271TRNUV0Rr+BVlEcfWrrYGUsLK8N0BZiCmhxNjDtIG2BJPEVp8JY7\na+TPGW05zuLB7bsnXXYG3d5z/Jm3yTtxbRzRCTY4sgnKUoMfb/urj5HL1BW9j79rhI4aCfUL5uLy\nVe9AJF4oQWl6RGh6ROjH94nKUfeWbOp3xzSwhhqN5JWtBwLDABcdwUrS4yNiE+Mzq7dFAACE+f5n\n/8nrtsh43MgZQ4B50yu3mloEt4wH8m0kHMIA4CT4J4Lm4AnfXrnJ6o8cqwXRgakNmERP05tx8PFG\nrQfrV64XWW9oGmOWWx5aOUS7jpFynSfNaFvu+Ti8uD5XiUKY/TnoC/RcOLhyaReuOXhKX8h4H5ov\nbc1i6I+fZJDhunBW5VvteQPn7npY1H7uWLJtKKqQ6zS8L5527bi9i19EWFS4X2jGtyjPz/wcngc9\nl09oVycF6QqFk+CXAJrDjDqLGLEhN4Ma6YuSMtW6V0lfXuXJQbGgP6mvas2OOcFOjkiDDhMNNSpv\nItehrz5wEpMK+UTRC+u5VYU4bd7A/+19XgbpTptHbnqVUXkzYWlccNB7v8D3foEfgpOLSWKpXMdR\n47WIKP8fW3sQPHY5gLKGQh0xxHulnN5vHSGLmdHQMUESNSjqBifeN4FQNxxEz4yNT4yp+peUkMul\nflhS1Ch+UWYByHfp2oasUyms4FYAKKjI1RlLqW/4ogBXVpfH+Tw+ISxOjmYmlQhAWFGByavKY+Lc\ngTLAkkHmKTT1rt3VK9Ijsis/ExZE+kcL5PT1xGaRLMHGiWyYyF5Lo1LPYE6dLykCUX2EEhAEYLS6\nz6NG8srWBFbvE33LPt5yjDKxtN8PeNDOmm3PivTQeI75uP2Ws4/75NK1Oyp/fugYT7HASpj2/MKN\ndVf3XDvEP+0eReu+dIt5t8wH019kCQCkHKKS/237X39g7w7cSlXr2bPy/TTB/pqU9G0at0I/izs2\n87VgjlqY0YaQ6nGApj1oWAdgHY5i102V5CpMTqtjFw2GvIG6LDA0Onb9jV1akJKSzhGWx7qeDDQ5\nf/SErfwdfxiwae8ILPDQ9Thpu/c0rQnTDMqYxz5EJ1a9YsTyn0QK5k0bqmufnE5+HTclOA5GLt+4\nOOxucCoH0+in/X1sv/Tz9X3eky4evPe05607vglZXLqqdjt68CPXRJ5UhSJMe37Rft1Va4fz9NMu\nnmlc9cFtAb697iE3gwopi5K0UCiukrq8+Jm+d8PwM6uNOj2/W/11cnnco8sRSw/v2b+j6JJHof4y\n28Xt0++v+FQKACXpyTE/8qhWWAHleV9Y6SWSzHrQHTxmUmm+vP5QM6sVXb+6bfT4MTwsLE4IyABL\ns7Vm+e+yVXRlQx46J1TaL9YrFfrPHqWe88bjS0OHn0jUoKgbwvTn5y6Z37C8a9/mb2f36LwKea1u\nukVP7np+oZSDokYJcoPuhxEnN+/bV3L3XSZfa5AWQI2ALixJCs2DlStXLSsMKFHToYc8dk2UQChM\ncdg2+6drtH32LV/onCbmEYHLqirg/KKKasGR4FcIGUqKshgUi3EHigBLArmnZAf7JTBMT9ku5t8K\n+kJojzW1nkBLOfExV0xlI0+Q0lPIXU+h98Z1w0siYlKKCVX9QSss/6eV9+/tqGodnHrJS30vACmC\nOVW+pApEFAnStUZtXqz/JTI5swzU9Acut5yjmfPkVmSt7l6jeWXrgSCI+j/2ecmPbT1Wu04lHtz0\ny6nhLES+54m1joePLrV1WwX8/Kgb1u7OVM0RIIqCrBda5R2w2nrmnDKUxnnaL9h3zVP8mhZSGG37\njdAE5fF7347/8eGnnZPGPax6U89N83uVNtcE83tdu5hl9fvpQv5rlqi1DiRXyfQyu+Jjql35x4zj\nd2YA223Z5JUBZcBPd9hgoXxg1/bjdluhJMrDbqbNY6k3HcHVDf/XE6LvJv4YyyNKw7xTYNuUkepu\nruQX8pIcTTaonrFafvPeJlkAglvyNco7ni0AAOCnO6wzzlq9ftsC8+tLFACAnRl+z+blg0SeUKpC\nIYqDti1Yn2FjaWF72ooGwC9K+vQiji0UYwY50hYleaFQXCV1eQm+upx9tvvWunP/e/s/12orFiqS\nL/65Tn6/9fqLDrsx3pcAp2XmFwIaMDItLEvx+pRmsvTQk5UAgrzQV3/PPuzgVX3EnRt7eqd9/2Mr\nTl+cJSyMuWH9wiWBJwQg88rvtZze3mjfPI2o8y6hDd9SlkQNirpBlIT8NX916vb1a9ceWKYIICiO\n93F47ySmOUJZsdPsN2xud8T6z+N2WwCAV5QY/JJVVs2pORFHdjv1Pmhy6bKJoCD6pvXLB4kSCEXT\nGDa2pwIGk+cZqrukZVE3EjC5NnJQkS2o7jOCCgEoqCrgkCvOHcgDrGgoPKU8+socS9rZzesePdwG\nICxIDLDbePSYuB2kqFyPylNIXY+moKbda9rmFRu05QC4uaFeN5Ycv/6+ekipl7yU9wIAaYI5eb6k\nDETkCeLy6h0GzV9v3kmLAcDJYnpdX3TcvlaAbUyvbE1gJcVFoSEfBw8fLRQIhmv2k+QaRo+194OM\nfcZOPcts6BR0RF2Upt/xcsG36C71FDHSiGhp0NSNjrq6zSk6v2LVbv+ihm9E+jPB5Lttuu14WPvh\n5OknfGvvtyRVPWzNalCCq/Qz/cdlQ0fX5b/tC2/ipQ6/YID9mfK2dCi98r+Hf24YTqN99Pfu//vg\neoyO4MoGvXsqY236Lzy1Sf3Jxjthv4qrIBDkCPJf7LOw1rx16tbfmdNM7ZJazbteXLn3hvNXD/eJ\nspl33q+xol6rVYMS2S5TllqvWjao4MXyvyOb7GH5ywbYnyNv66BJvLLVUI+5I3L91px8ZawjLIi+\nb/vn5jfiNi1FIH4NCE78ZYuF8dP1w1Nb09MXV9Q3VAnasXDv3zGNs6N7Ja1UDWoUDTrLBp8bb/H0\no2Q7EknFrxtgf4q8rYMm8srWAEEQWHFRYRjzU71e1iAQCAQCgUA0kO8va/oZDsIxDPvFGmEIBAKB\nQCBaCgQAhmF4/ffURiAQCAQCgWg0CIJoORsr/xrgKn0t9uy27tuA875aObIG0233mk/8hY6GQiAQ\nCIRYxJ3o24zQOywJint/aaCc4qD9aXEua1vgWcuYytj9/xaGO+/tI2I3UpHgqv1WrZg/sR3ZiiZM\nsYPh9Il9NevktdHVaC55ZbvO2bZsYm+Fb80RKg1J1aCghVQbEjNw3YWOleezsFlMj8nKtS+rf436\nyUgvL11ryma74KAgNotZzPTwu7isdwvNYiMhdVG2CKHIXa/FV1FE60TEib5K0+94uQ6v3UIR+G5q\nv9LrJ2+DQQh4FUI+p4IgaLwKIZ/Db3lvlXB5/e56DDluby0ZAKoN3yRGppfpCdepb/4YH15rd+hG\nV6OlyEulIakaFLSQ3Vk75AAAIABJREFUfJGYIcx+uWNwmLyM9rTb9qYiLmv8GtXISCsvrj31gPO6\nvoGX9+/wzuAranZQy8mR8vzdVoKURdlChCJ3vRZfRRGtltoLfcs+/LVgqDIOWJvpp67uxS5N3+qZ\nS4Cg5GudE5CbHAE7v5hbmlkq5NNyi7klOZyWtwZMkOVoNjfSoCImsqTJb9XYarQUeRtbwxaSLzIz\n+EVpUUVAL/ld9P4KP7FGSYe08jL0BnSWzfPYf9k94BfZaFLKomzxQrX4KoponYiYOyIsTU+IiGFF\nxCaklgGUZUbHsiJiWNHpbCHg2rPsi1kex/t+G6NTHnk/jBm3q5ccAK4x8oTjk+RwJpvFZEe/8bVb\nPV7rx8ALXWOg5am78VFMNutTytMTW4e2kWiEl5cbn5XByquoyE1Oy/qSW30XAxmdSeaH3rzzLmUx\n2azgLM8r23tWHfkm026MzZWH6SwmO87z4+W1U78dEkduIVW+APA2AxefvXw71N+nsHKMPfiWVRfG\n91H3YuY/Xo+fP5xUe9RdRmf07kuuX2KZbBaz5LPHR4ctk9R/aD3q8NM8FpPN8ou4vXnWt4PwFAcd\nSGMFfFimCVomHyrtZPk5jlAUr4Z0SCMvmRoAVKWMKXWbff7+ywIWkx39+rVVr2+3odKQUg1c5Tfj\nq24vclhMNotZEvYm4OCINt9H9KQSiqzaUGdZSnlFI6ZGUdR5MuVxjZHHb7vFBgeyWUw2yyfcYcsc\nPZnvt6PQkMph6y9vZVF6r2gLGvPehlUW5aMtnariQ6MqL9tv/ZWoUCabFRh7/8iVm/9ms4K/PNox\noy2tIfeqf/iiKkqKQmlsoajVwNsZHQsN+cRmMdmsQJabrVkfRbyaGaJcT1zQI4+95PUQgfgOXfK5\nI8LcIPcQ2DtltK5NeBIPQL7z6MHypV7vU8sB6AoGY4d0LLq5a82HAtn2Qyy2bnjqpjl11gnvYgJT\n7H/Q2d6C92S35dngEs1xa3YeuHWqaPKf9mnixt/LYw6bHywtAQJ81pkHZ3/fo5CmbXzW5eZkofe9\n6xYBiRkchpauUkIGHwAw1SEnXe3WCF7ZbD4bhfVYZr3hoav69BlHPIsInNRCqnwB0LSHG/85EW4f\nObyLlVssYKip02O+VgBA9gtrwxB5hraRo8PqWoZjKoNPuJw3w98f3H4hMBd0x2+/tnx4L6Xzr/Or\nupJpb6/s8kjlqP++Yc9qpwuZ/YydE/lQFmE3dqqz4YbLDqMCVq68Fs4FAEFR+rcuEpkaUlN/eSnU\noChlmub4K862s/Pd91i9TGAYTFy89reqOwkpNKRSQ77vYfud85IdLE0940pw1XYdOwlS2N8H+eov\nFEW1ociylPKSQqUGVZ0nVx5XMBg3oivvju0Czxxce9D63aaOl/MGzLvNqqDSUIzD1l9eEUUp5Gal\niXFYqZSna/c37Jhx/X+742ecO76q9Jbx8tR5Z/dd2PDi3d4wjlT3kip8URUlRaE0tlCKFGqUgbAg\n4tnB7Y/Tc8pw9T7Ld+04d50bNuFoIIfC9ajyRR17SeshAlEFhtXrRF9BTsC9MLCbPlr/WlI8n9Fh\nxFAdXujj6B+jzhnBPm/8SgAC3kUSH5+Y2k5xmOyWo2O0eb1ekPm4g87ZQgAIiiNG+Z0wm6DjcDtd\nzCtRgpuZlAEAAOyUpB9nMyoamp+YrBRwYPasu2k1D4PC2xttXKOTfMjI5gyLB+DzPo7R55n5vmk3\nvO9nkluYJTZfwIl/4PriXc3jIfklGXElQOfk1BlSxfWmblijm3Zy1s5j0TwAUJFfDsvVqv8i+Z37\nI88SgMBo5TGRe4xGqbskZguJ8rz4hFK1ggqoKEhKSIypFehJ1JCeesv7DRFq4LqkpZzZwchsripr\n13xbu2Q+gLdX6Rizy1VqkGsIVGow1NqrQAEr+K1/aJYAIJQpSb7Ioao2AtIsi6P+5UWhBkWdJ1e+\n6rDn9MB3z71LAALD5UdE7Z0yWvMuK0NIriFVgnyp8kVelE2iPFEQFxTsL4jgmraJ9Q/wYX/avqBn\nByUsQkOae4lTgwSKoqxEZKE0hVAkaoSVEVCe6vMgtfJX4RHYqIVXB43UoQcm8SlcjyLoiY29oush\nAlFFfRf6CrLcHUME3WdOb08HmvrwCR35Ic8CikU0ZziJnm9ysD4j9OVBruvIrjSZIfa+wZWrCUqD\nToyjg1YndQnGu0VC1/19gCZEOXh8rfOwlO8yvDPkfHz7LWJzU33f50Cvofp1l9VWs7Ae+ZIMuS4j\numB5gR7JYs+GFuQmpnNApb1qy1k0RCEvBRSlLGcwRB9ymH6ZjTcfrzhg54G3QpMLiX5OTrsWG3VV\naph89ag2zQRFoUjuX4K8xPQyUGpXqRapho3usBQ0ofJCIQEYjgEh5AsBxzFMuns1tRo1C4WUhgpV\nRw0AoLUdvvKGy9Mv4cGlkV6fjw6TARlFhtTL7yW3UMIsI35B6PVa5ivM8nR6xTllOkP/qlOXBb2J\nIJuQPJENXAKEABiGAWAYjkG++/JVNyN/zMImKgq/Sv22gRASAATpUGnNl0+k2fthIdQjXyKSqXsf\njMaggbBCojUHggoB4LSWtNhajLyioShlOiEkAMNx8iyK0pAabux9676vexnNnmEy18LN1DL4+qa5\npwMbdMiHhNWm6RGpBnmhUPlX7XhPCISA4VU5JdOw8R1WDE2jPCEU8IV1fLje92qQGhJV7BqFIsaW\nGn9JYMCPm9RRg9HR5IHDps6+17eae0UXYjqjtrhs05Q0NTITJI29EmcZ8QuBAeD1GwMQ5vuf/Sev\n2yLjcSNnDAHmTS/RCzAZ7QaN0QLWp7Ry4MT7JhDqhoPombHxiTFV/5IScrnSDj7wMz+H50HP5RPa\n1emgcBL8E0Fz8IQOVfOkZPVHjtWC6MDUukOm1SysR77qQvDY5QDKGgrVQj/3S1gGaP0+vG09jkv+\nnh63jAfybZqv50AhLwUUpcxJ8EsAzWFGnUl3KRClYdU3FGrw8qKf3DxpMmvy4OOJA812mupLoXaV\n8ZJXm8ZCWMGtAFBQkavbGxWlBlWdb4h/idKw0R2WgqZTnv1+4yi1RS9zfxgt3b0apAZ5xa4vDRSq\nrhog12l4Xzzt2nF7F7+IsKhwv9CMmg0sKtcTla+GWUjTGLPc8tDKIdpoxOTXhQCodwwv+3jLMcrE\n0n4/4EE739YcQxi4ZqOlgn88X3/+JstexW8We2TyQZj+/Nwl8xuWd+3b/O3sHp1XIa/VTbfoyV3P\nL9IO3pd+vr7Pe9LFg/ee9rx1xzchi0tX1W5HD37kmshLe37hxrqre64d4p92j6J1X7rFvFvmg+kv\nsgTfMirKQvH5okBYnBCQAZZma83y32Wr6MqGPHRO4CX96/B2w/5Dl3YJ/n6fQm8/xrgXQIZk6VWk\nh8ZzzMftt5x93CeXrt1R+fNDx3gJFvdjisO22T9do+2zb/lC57SGTBGjkJf8IopSFqY9v2i/7qq1\nw3n6aRfPNK764LYANZISqSGVGkqGNrvGFfoHMlMLuXI6Iw3bgiA1S/oFvUKKatNECEuSQvNg5cpV\nywoDStR06CGPv8srUg2KQpHSv0g1bHyHpZDhJyov3b0apAZ5xf45xlPBTQmOg5HLNy4OuxucysE0\n+mnL1fieKhCJzJfY2EuBQj+LOzbztWCOWpjRhpCmGoZDtHjq36XkJT+29VjtOpV4cNMvp+YjQKho\naHVsgTaNn8F8tG7D6X9zhQBAlIT8NX916vb1a9ceWKYIICiO93F479SA6MZPd1hnnLV6/bYF5teX\nKAAAOzP8ns3LB4k8YVGQ9UKrvANWW8+cU4bSOE/7BfuueRb96BSItFBsvqjgxp7ead//2IrTF2cJ\nC2NuWL9wSeDxM54tX6Z8dv/q4+fn4vzcqGQcgBBINGeYyPc8sdbx8NGltm6rgJ8fdcPa3TmeK94c\nmsawsT0VMJg8z1DdJS2rIVPEKOSlsJu8lInioG0L1mfYWFrYnraiAfCLkj69iGNXS0yUhkJyNTBZ\nOVxz2NZjy7RkAICXHRtweuMx10zp80yIqzaNDyfiyG6n3gdNLl02ERRE37SuJq9INSgKRSr/opFr\n2PgOS87PVF66ezVIDVFF+TONp4CX5GiyQfWM1fKb9zbJAhDckq9R3vHs780bykAksoo2wEJumt+r\ntLkmmN/rL1Lqg/hPgJUUF30O+Thk+GihQDBcs58k1zB6rL0fZOwzdupZ5reGLL3DEr931vnrx0x9\n1UL3xpHAQhH5agxonU2dw3cULBlm8aSw6Z5wuEo/039cNnR0Xf7bvnDRW2whEAgEAtGC8M8Nw2m0\nIH/vAb8PrsdCX1zZoHdPZaxN/4WnNqk/2Xgn7D8yqNYE+VLoabq4b0l8SiYbU+/2x6bN3Uo/7PAv\nabq2iGyXKUutVy0bVPBi+d+RqC2CQCAQiFYFQRD1eFkj12/NyVfGOsKC6Pu2f25+06B1DC2Jxs8X\nXa3L6Dnr5vZQkwEQlqT5/nN42sk3TTgTAUDRoLNs8LnxFk8/FqCl/AgEAoFodWDFRYWhzE/1elmD\nQCAQCAQC0UC+v6zpbzgIB/ixjhyBQCAQCATip0IAAEi9Cx8CgUAgEAhE44CaIwgEAoFAIJoZtFUv\nAoFAIBCI5gTDAJf8QF8EAoFAIBCIRocg0MsaBAKBQCAQzQ1qjiAQCAQCgWhmUHMEgUAgEAhEM4Oa\nIwgEAoFAIJoZ1BxBIBAIBALRzOBonS8CgUAgEIhmBAPA0TpfBAKBQCAQzQiBXtYgEAgEAoFodlBz\nBIFAIBAIRDODmiMIBAKBQCCaGdQcQSAQCAQC0cyg5ggCgUAgEIhmBjVHEAgEAoFANDOVzRG02heB\nQCAQCESzQAAaHUEgEAgEAtHsNGtzBFfpa7Fnt3Vf+eY0AtHEtMxSljWYbrvXfKImao+3PlpmjUIg\nEA0D/zlvajDFDobTJ/bVpNW8uWq/VSvmT2xH/wkWIJqLllnKsl3nbFs2sbdC0zVHRNf5nw65GZjK\n2P3/FoY77+0j2wx2SQSKGwjELwOBA9Q6tYbecYsHk82q9u/lqm4MAABcY8Jdz4Bvn/tEuRy0HKoh\nWUiQ6WV6wvWAkQGj0XMgMTJ6czaf8vENZLOY7Kg3fudXjVGX+FGEyXX/3zEW692lgZJ2yHDVAZvs\nXNPimOw4z6DzK0aoNVsvnKYz53Usk81y3WSAwnf9IXcHcbSAOk9tBi6v312PIdext5ZMk1rQuuMG\nAoH4OWB1owL/623zBa8U1KefuroXuzR9q2cOJyeFDwCAMdQ66clGnrWw8CqVUTOYZLr16L1eKjMX\nH4rh/XTD649AQFcsev23zcFUjmrPSTusNz6UTe5p8S5PzOAQXbPfVCvL9Zv/0AEolPReNN2Vf189\n0jfS7i8rX/jdaq/VswslhisfpQgamon6Q+80ffHgPH8v+vA1cztdOcvi/nwTWjXk7tDqEWQ5ms2N\nNKiIiSxp0vu07riBQCB+EiI67RV5KfERsQmpZQBlmdGxrMjkQl61Z3ZRcgwzIjLAx/2Q9ZH3/C6L\nphvIAq49y76Y5XG877dRX+WR98OYcbt6yQEoDjqQxgr4sEwTtEw+hFf2kPwcRyh+T3DU4ad5LCab\n5Rdxe/Ms3R/9IJl2Y2yuPExnMdlxnh8vr53aruorXGPk8dtuscGBlZ2tcIctc/Qk6N4JMh8cOrjf\n6dVLH29X++O7vHkK3fvoiu11yXbZeNxqIvvRcqtHGeLvUYVcD+PtQ4lXu7btefjB/aHd0t1+xLA1\nZt2btg8qGplOixd3Yt06s90hudO8ub/JVX0spYYAdI2Blqfuxkcx2axPKU9PbB3ahiZZgiSljLcz\nOhYa8onNYrJZgSw3W7M+irgkCcroTDI/9OaddymLyWYFZ3le2d5ThtpCAEyp2+zz918WsJjs6Nev\nrXpJpiCpO1BYSF3nyS3E2wxcfPby7VB/n8LKsYTgW1ZdGNRCUahBbgauu9CRzWIWM//xevz84STl\nH9nF2sy9GcQOsBn2fRxQptved8z8GxM0MGrjxdBa4wYCgfhJNGD8nhBwywWA0zAMhLlB7iGwd8po\nXZvwJB6AfOfRg+VLvd6nlgNgEXZjpzobbrjsMCpg5cpr4VwAEBSlc76nk/b2yi6PVI767xv2rHa6\nkNnP2DmRD5jqkJOudmsEr2w2n43Ceiyz3vDQVX36jCOeRQSuYDBuRFfeHdsFnjm49qD1u00dL+cN\nmHebVSGZ3TTFTkONNw1mJLp+SBLbPePG2hpN2kcQ8n23HZNUGLyt4ZAOEGMbomR+74mt3M3xVi/D\nYf/Y/uq06MyfPD4i18XIpB3r/IukGPqT6E1LFnW3Cw7jAoB0GmKK/Q8621vwnuy2PBtcojluzc4D\nt04VTf7TPk0gNkGRpQwgLIh4dnD74/ScMly9z/JdO85d54ZNOBrIobSQpm181uXmZKH3vesWAYkZ\nHIaWrlJCBp/aQprm+CvOtrPz3fdYvUxgGExcvPa3hmlLYWEZeZ2nsBCApj3c+M+JcPvI4V2s3GIB\nQ02dHvO1UkFSoSjUIDdDmP3C2jBEnqFt5OiwukauiCLfR0z+6JFzu8oGhHMBQEZ/3LwOFb6nmQUE\ntfGS0lrjBgKBaFqkbI7g8ppdjdZaTpHNvv46tRwAcgLuhYHd9NH615Li+YwOI4bq8EIfR5cBAFGe\nF59QqlZQARUFSQmJMeW100p+5/7IswQgMFp5TOQeo1HqLonZ0N5o4xqd5ENGNmdYPACf93GMPs/M\n90274X0/s/Kq9MB3z71LAALD5UdE7Z0yWvMuK0Mo1mzt2fasU4Y0AG7olSnnw0slyCpB1HeuL129\nvRqwwzJ5igM6KCnJtlfhRn/lwID2KgwQ2xzB5ZUUZWkYABACbmkpVyDRV2TI9Jw9tV3srRcZggrs\ng0vSJovZ3WzCIr5H9HpqiOsabV6vF2Q+7qBzthAAguKIUX4nzCboONxOF5ugqFIWAkB5qs+D1Mqf\nhEdgoxZeHTRShx6YxKdIUNHQ/MRkpYADs2fdTeNJamFmByOzuaqsXfNt7ZL5AN5epWPMLquJl1Ac\nIi0kr/NUGlblmRP/wPXFO3btG5EJRa4GlevxSzLiSoDOyeFALYS5gU98+EdmG3XeFx5dDjLdZszs\nyvHb41cgBFxPrPFiaKVxA4FA/ATqP8NyhJ1nKSs419/lzly+0571Np85AACCLHfHEEH3mdPb04Gm\nPnxCR37Is4Diej3IBbmJ6RxQaa9KA5DvMrwz5Hx8+y3AclN93+dAr6H6daaSCvIS08tAqZ2SJGPG\nwty3NiPm/2my915gR4s398wHNOlSQU7UnpnTek055l8m8SXyA857eqV/+pD+6cPX938NlZfsKzJk\nOy+cqhHxwDtdAMBPf/YsVWfS5J4iVlFIqKFc15FdaTJD7H2DK6cllgadGEcHrU7qdV55USRYvZQB\ngNZ2+MobLk+/hAeXRnp9PjpMBmQUGXUrZfUE6bq/D9CEKAePr3XGtigslDMYog85TL/MJpr30ega\n1oJMKAo1pESY63/Vm9th+rRecgCyXZbMbV/42vlDAdEA4wFad9xAIBA/AXq91/mGn1hj5lXAKcpN\nzSquNqdEmOXp9IpzynSG/lWnLgt6E0E2IXn17XUIKqoGcSvBaqz5wURcAAAAhEAIGI6Rfl/jDqXp\nEaHpEaEf3ycqR91bsqnfHdNAyRsLEsLPTysERW0dBZyfm5kKgGu11ZWHgrRi8aPC3LiT5uZOshgA\nCLlZEVzJviJBtuP4mXqMzrbPi2y/fzZuhsFFZmydJ5dEGmIYjkG++/JVNyN/3J2oKPxaDlA7qFMk\nWK2UGR1NHjhs6ux7fau5V3QhpjNqi8s2TdE3/5EgQQgJAELU4BCFhXRCSACG4xJVE6losIYUUAhF\nroYEJn+zquanRZ4ObwtvG63odZlFX7hEN9vpdmhxA4yvpFXHDQQC0dQQQAcMq1+DpCQ9PiK2qO41\nwnz/s//kvVpkPC5FZwgwN3nlVguRBLeMB/JtJO6KcBL8E2Ha4AkdZILieAAgqz9yrBZEB6ZyKOJL\nvRAQBGC06vbQNMYsWTIZD7hwNyhL8ugu4iph9uegL2C8cLCa64t8IeCag6f0hYzLofniUxWWxgUH\nxdX3K9Ew9MdPMshwXWjuklDZDpLrscvhyNyxusdik6Xaa4YT75tATDMcRM98FFnWKLvVyHUa3hdP\nO3vc3iWWBwCJyhnlQNIc+QE/83N4Hhgtn9DO7X56zRYehYUVCX4JMG2YUWfZwMifs7pIZJ2XUkNy\noSjUoDDj23c8djmAsoYCDYqre2tx8D2HdKPVqyeHy03ViLl0Naq8IcZX0grihnQRAIFANBQCADAR\nC32BodGxYzsFdX0FAEynV49umpwcVkqNxTUklH285RhlYmm/H/CgnW9r9HEq0kPjOebj9lvOPu6T\nS9fuqPz5oWM8xVNBmPb8wo11V/dcO8Q/7R5F6750i3m3zAfTX2QJpJ3uQtcatXmx/pfI5MwyUNMf\nuNxyjmbOk1uRP16dK/SzuGMzXwvmqIUZbQj53t/D5LQ6dtFgyBuoywJDo2PX39ilBSkp6RwhxVXl\nsa4nA03OHz1hK3/HHwZs2jsCCzx0Pe7nLmukaU2YZlDGPPYhOrFqigyW/yRSMG/aUF375HRpUhSm\nPz93yfyG5V37Nn87u0fnVchrddMtenLX84u0L0C4KcFxMHL5xsVhd4NTOZhGP2058RdB6efr+7wn\nXTx472nPW3d8E7K4dFXtdvTgR66JPHILhWnPL9qvu2rtcJ5+2sUzjas+uC2AJCUitTuIrvPSaUgh\nFIUaFGZUXissTgjIAEuztWb577JVdGVDHjon8AAAuHHX7cM37rM9DxwPy+eJVbY1fgUAgJYTN0gi\nAAKB+DnU9VG67oqrbge7VP6xzv3fdZB4YcCMm5LMP+clP7b1WO06lXhw0y+nxogrke95Yq3j4aNL\nbd1WAT8/6oa1uzNVWAGiKMh6oVXeAautZ84pQ2mcp/2Cfdc8RXSuJAWXV+8waP56805aDABOFtPr\n+qLj9tUT5Kb5vUqba4L5vf5S/Skl08vsio+pduUfM47fmQFst2WTVwaUUV3FT3fYYKF8YNf243Zb\noSTKw26mzeOfvOkIrm74v54QfTfxx7soojTMOwW2TRmp7uYqVZpESchf81enbl+/du2BZYoAguJ4\nH4f3TtI/jXhJjiYbVM9YLb95b5MsAMEt+RrlHc8WpxQ/3WGdcdbq9dsWmF9fogAA7MzwezYvHyTy\nhOQWEsVB2xasz7CxtLA9bUUD4BclfXoRxxb3YkB6dxBd5ykslFIoCjUozKhMlxt7eqd9/2MrTl+c\nJSyMuWH9wiWh8ipByr+X3awumZS6Hnr/o4HQ6BWgKnctI26QRAAEAvFzwIqLCj+HfBw6YoxQIBiu\n2a+ByTF6rL0fZOwzdupZJupdIBAIiUBxA4H4NfHPDcNptEA/rwG/D6Y3ylwMXNmgd09lrE3/hac2\nqT/ZeCcMxRQEAiEGFDcQCMQ3RM0dkQK5fmtOvjLWERZE37f9c/ObPDQPDIFAiAPFDQQC8R16o5zo\nW+a7Z5rinoang0Agfh1Q3EAgEN8gcEAL7xEIBAKBQDQjGNZs594jEAgEAoFAVIKaIwgAWYPptnvN\nJ2qi2gCA1PiPgqv0tdiz27pvkx4MgUAgpAZvlLkjiJ8AptjBcPrEvppNcMiGbNc525ZN7K3QwAcw\nuYWYytj9/xaGO+/tI+LInJZGI6nxE2k58tK1pmy2Cw4KYrOYxUwPv4vLejeDRaLrIa7ab9WK+RPb\nNc7s/R/Q2lu6M9n/LOjQWI7ZyBo2YdxAIBoTQsRCX9ley/ZdXT2qn54yA0ri3jtb7772NgfNeW8k\n6B23PHvybVstAACJt9WS6WV6wnXqmz/Gh+e20NIgtxCX1++ux5Dj9taSAfg5e7T/SrQUeXHtqQec\n1/UNvLx/h3cGX1Gzg1pODvUOaeTugGtMuP3w8P/0Kh/F7JSQ91fOnLsUmCfBjms/2VP4ZRUAAl5F\n4/Tr6q+hGFp+3EAgAED0Qt+K/MTIh+df7IovpHWbeebon077wrpv9G3AhqiIavC/3jZf8EpBffqp\nq3uxS9O3euZwclKa6JTZloMgy9FsbqRBRUxkSXOb8l+kpcjL0BvQWTbPY/9l9wCO+F8DULkDxlDr\npCcbedbCwqtURs1gkunWo/d6qcxcfCimhW2ZKuDksQlBRWFZfU/+E039NUQg/iPgQNRuZwizfB3t\nHnn7hIV/eHLzSjQotWur2HrGrX8aDL0JG02Gd6n3kH5FXkp8RGxCahlAWWZ0LCsyueoAFFxj5PHb\nbrHBgWwWk83yCXfYMkdPpvIaxUEH0lgBH5ZpgpbJh3Amm8Vks/wcRygCAADeZuDis5dvh/r7FLKY\nbBaTHXzLqkvVke8y7cbYXHmYzmKy4zw/Xl47td33o+AxpW6zz99/WcBisqNfv7bq9d0+2Z4bIlk+\nDsMUKv9UmXidzXKz1P/WbpXRmWR+6M0771IWk80KzvK8sr2nDKWFuO5CRzaLWcz8x+vx84eTlGvJ\nQWYhhRqU4O2MjoWGfGKzmGxWIMvN1qxPVeWlTJBUDWpEG4+1mXsziB1gM+z7LAWZbnvfMfNvTNDA\nAADoGgMtT92Nj2KyWZ9Snp7YOrTNt3F0kqKkSlCMvKLvpTj8fhjTe2Hb6nW3zVT7otCT4xSlyvK3\nCuC9oi1ozHsbVlkBHm3pJPbNCKk7VFKUHMOMiAzwcT9kfeQ9v8ui6QaygGvPsi9meRzv++0dhvLI\n+2HMuF295MR4CgDAqMNP81hMNssv4vbmWbrf3aEh9bAiN7MwJ62wZp8CV/nN+KrbixwWk81iloS9\nCTg4oo24NYzUGlJUG7I6T6EGpZtThRQKM6TIMgLxHYKgY+QLfWnaY1Zbdc+8vdYzE43y1QZT1Dc0\n3rHk2P4sbzdRgXdkAAAgAElEQVTni3cee8QVN3CMA1cwGDeiK++O7QLPHFx70Prdpo6X8wbMu82q\ngLIIu7FTnQ03XHYYFbBy5bVwLgAIitIre0807eHGf06E20cO72LlFgsYaur0mK8VAICpDjnpardG\n8Mpm89korMcy6w0PXdWnzzjiWUTQNMdfcbadne++x+plAsNg4uK1v0liIk3b+KzLzclC73vXLQIS\nMzgMLV2lhAw+AIWFwuwX1oYh8gxtI0eH1bVFJLeQQg1KhAURzw5uf5yeU4ar91m+a8e569ywCUcD\nOVTySqcGufFFvo+Y/NEj53aVDQjnAoCM/rh5HSp8TzMLCMAU+x90trfgPdlteTa4RHPcmp0Hbp0q\nmvynfZqAtCgJigSFOIW8ZPfKKUgoIProqjAgB9PQ06PlJmbz2+iqQv7nHEp5KcpLRAUQcrPSGm3c\njxBwywWA0zAMhLlB7iGwd8poXZvwJB6AfOfRg+VLvd6nlgNgVJ4CAJD29souj1SO+u8b9qx2upDZ\nz9g5kd/Aelge++DK0YqUGi/J5Psett85L9nB0tQzrgRXbdexkyCFLW6AmUJDympDWucp4wYF5CGF\nwgypsoxAfAMTeaJvJTSdibvfnBnstdl0k2dB4wxDtlZweSVFWRoGAISAW1rKFQAAEIX+p0YNvtF/\nwhzz5SZO7lYFYc+v3HK68TIyW4KzBilID3z33LsEIDBcfkTU3imjNe+yMoREeV58QqlaQQVUFCQl\nJMbU3UubE//A9cU7dg2z2xttXKOTfMjI5gyLB+DzPo7R55n5vmk3vO/n6BuZzVVl7Zpva5fMB/D2\nKh1jdllNrG2KhuYnJisFHJg9625arRFzCgv5JRlxJUDn5NSJghQWZlKoQW1kearPg9TK/4ZHYKMW\nXh00UocemMQnTxCTSg0q43MDn/jwj8w26rwvPLocZLrNmNmV47fHr0AIuJ7R5vV6QebjDjpnCwEg\nKI4Y5XfCbIKOw+30KhNFFKWQPEEQksurS3avu7lROcSqjm0YuMbMi//YK53+bc4jLYM2wsz4LKra\nS5VlgdgqKj24vGZXo7WWU2Szr79OLQeAnIB7YWA3fbT+taR4PqPDiKE6vNDH0WVAWQ8rSX7n/siz\nBCAwWnlM5B6jUeouidnQsHoozPZ3ta91G4ZaexUoYAW/9Q/NEgCEMiXJJ7nx5EV5O51PXufFxw0K\nRIUUKjOkyjICUQ2SVw245oSrZ2ZkHl9r6ZHZsMdr60d+wHlPr/RPH9I/ffj6/q+h1dcJ8gtCX95a\nt8So3Tjz48l995y99/nYIKXGuasgLzG9DJTaKUk9IV6+y/DOkPPx7beWAzfV930O9BqqLw9yBkP0\nIYfpl1mvzitd9/cBmhDl4PG1kd7eU1hYC8nVoLUdvvKGy9Mv4cGlkV6fjw6TARlFRt1aXj1B6dSg\nMl6Y63/Vm9th+rRecgCyXZbMbV/42vlDAQEg13VkV5rMEHvfYDaLyWYxS4NOjKODVid1BuXNyBOk\ngPxeQnZKQolSh3YqGgOX9MWh+9Sx2op6XZQLWOmU3VnJy6vRGGHnWcoKzvV3uTOX77Rnvc1nDgCA\nIMvdMUTQfeb09nSgqQ+f0JEf8iyguF49cUFuYjoHVNqr0pqkHhYH7DzwVmhyIdHPyWnXYqOu0rsx\nAIirNhLW+YZDaUYjZxnxC0IHghC11Lci3fm4beyzr796WwQAuHEnzc2dZDEAEHKzImotXKCp9B47\n23y58YoResUxry88TWm0biEhEAKGN3DT3JqXf/+DEBKA4bjItAmhEHAZmojvCCEBQEj34u5bHauT\nLImFda6XSA1GR5MHDps6+17fau4VXYjpjNrisk1TbIJUalBDZjxR5OnwtvC20Ypel1n0hUt0s51u\nhxZXXoBjkO++fNXNyB8Viago/Cqm2pAm+O37OiZQ3qsiJTRDOKzbb2O69o27ZUdfsHR8Tx8dSHmY\nIb72SlhejUT4iTVmXgWcotzUrOJqc0qEWZ5OrzinTGfoX3XqsqA3EWQTklffIVxBRdXLn0oatR4C\ncGPvW/d93cto9gyTuRZuppbB1zfNPR0o7ak8VNWmHnW+RkZI3Vw6Mxo7y4hfCwKAIOiiN4kXlqaF\nx9G5//kVH5IgLI0LDoqr87GMVv+FSxdZLJ5iqJYf9OT+mrkP/4nIb8rWG8Et44F8G4k7HZwE/0SY\nNnhCB5mgOB4AyOqPHKsF0YGpHOAk+CXAtGFGnWUDI2uvC+UXZRaAfJeubei+7Jrlz8/8HJ4HRssn\ntHO7ny4qp1QWEjx2OYCyhgINir8FKAoLpXzOyXUa3hdPO3vc3iWWBwCJyhnlIDY0U6lBdRW58QBE\ncfA9h3Sj1asnh8tN1Yi5dDWq8kHPifdNIKYZDqJnPoosq093nizBb1+LkpfiXvlxsUVaI8xWt4m+\nvv+qzO+mqxe10S58H19C+UynznKTUJIeHxErYlWfMN//7D95rxYZj0vRGQLMTV7VF7E2oqc0qL3F\ny4t+cjP6ya0LvVff/Lhjp6nbglNJ0oVUqqIUV+dFq0Hu5lKaUQlVlmkaY5YsmYwHXLgblIXaKIg6\nkM0doXddeS1oZ/cKv7+6m3pk/9ozR0jA5Hsv2j1T/fUVqzVu3jHFkmvE0OjYsZ2Cur4CAKbTq0c3\nTU4OK6XGagISKtJD4znm4/Zbzj7uk0vX7qj8+aFjPMXjU5j2/MKNdVf3XDvEP+0eReu+dIt5t8wH\n019kCYBIe37Rft1Va4fz9NMunmlc9cFtAapGqgW5QffDiJOb9+0rufsuk681SAugqu1R+vn6Pu9J\nFw/ee9rz1h3fhCwuXVW7HT34kWsiT6yFwuKEgAywNFtrlv8uW0VXNuShcwKP3EKQbrMqbkpwHIxc\nvnFx2N3gVA6m0U9bTvxFQgo1pJK30pS46/bhG/fZngeOh+XzxKqYLEx/fu6S+Q3Lu/Zt/nZ2j86r\nkNfqplv05K7nF7HPBNEJfrNGlLwU9+KkfGIxZhu191rkmfWF7hK878g4POjgF+pci8uyNEjtDmUf\nbzlGmVja7wc8aOfbGmMjjegpUtZDUDK02TWu0D+QmVrIldMZadgWBKlZHKkjKVW1EVfnRatB4ebS\nmSE2ywr9LO7YzNeCOWphRhtCGnNuEeK/Ap2os9AXAIRFyXFfhAYl0Zn16sH9ShBFXrt6jxfWWx66\n7oqrbt/2fVrn/u86ibdBI/I9T6x1PHx0qa3bKuDnR92wdneO51JEOKIoyHqhVd4Bq61nzilDaZyn\n/YJ91zyLCAAgioO2LVifYWNpYXvaigbAL0r69CKOLQQA4KfZb9jc7oj1n8fttgAArygx+CWrclMF\nfrrDOuOs1eu3LTC/vkQBANiZ4fdsXj5I5AnFWsiNPb3Tvv+xFacvzhIWxtywfuGSwBOSWygdvCRH\nkw2qZ6yW37y3SRaA4JZ8jfKOZ4t5XFKpIZW8AAAgSPn3spvVJZNS10PvfzwtiZKQv+avTt2+fu3a\nA8sUAQTF8T4O750kaI6QJFiFSHnJ7yUsivNJg67+d94VEELM59zLknG9AiJKxCgvLsv1R3p3AF7y\nY1uP1a5TiQc3/XJqyCG6Hv7MfNFk5XDNYVuPLdOSAQBedmzA6Y3HXDOl79hRVBtxdZ7EKyncXCoz\nxGaZm+b3Km2uCeb3WkyrF/FrQhAEVlJcxPwUNHTkGKFAMFyzX3ObhEAgEBLB6LH2fpCxz9ipZ5mo\ns41AtEr8c8NwGi3Q18tw0JDGPsABgUAgmhJc2aB3T2WsTf+FpzapP9l4Jwy1RRCI/wKoOYJAIFoT\ncv3WnHxlrCMsiL5v++fmN2jpBgLx34Bed5N4BAKBaLGU+e6Zprinua1AIBCNCkHgohf6IhAIBAKB\nQPwcMAydjYdAIBAIBKKZwUUu9G1+aBqjT965dXG4uANGEeQgDREIBALRGiAIoqGbkEsGpjJ2/7+F\n4c57+8iK/3HlFQr6fwwf0FW5sUdv6FpTNtsFBwWxWcxipoffxWW9JbWouWk5GlLQeuVFIBAIRLOB\nYUCvtQ2yTBfjg47L9ADPd9qy/XxMOdC0Fv7tYpu1fbjtJ+m3BcLl9bvrMeS4vbVkACTfh7vRwbWn\nHnBe1zfw8v4d3hl8Rc0Oajk5P30jfBmdMTsPWK0d10mFKIl9f3+37fUXGRLs+tRSNKSgRciLQCAQ\niNYHVmtXVvk+2zb08DBbfHekw/vNg2+b+8pP2Xmi59uVO4IbsvUiCLIczeZGGlTERJY01OIGwdAb\n0Fk2z2P/ZfeApjtmgxJMsf8BR7sNQved645F4L1M/7J64KgwceZpf8qjVAFajoYUNL+8CAQCgWiF\nEARR6xhTGXUDZXZyQXnelyJZHR11nfGnbXq777rg9a0xwmg7fLudU2IUk81iskNffbq9ZbIGDgCy\nPTdEsnwchilU/kxl4nU2y81Snw6A6y50ZLOYxcx/vB4/fzhJubYNMjqTzA+9eeddymKyWcFZnle2\n95Sp/RtcdfR216IY10ODVXAAALzNwMVnL98O9fcpZDHZLCY7+JZVl6oT2ukaAy1P3Y2PYrJZn1Ke\nntg6tE3l0VGKgw6ksQK8V7QFjXlvw5hsFpPNerSlk/T7rjD0Jmw0Gd5FoV6vQnCdyZZr9RMPWOy/\n+DrI8+Vtc/OrrA6L9k/Rov1sDamMbHR5ya6S7l64xsjjt91igwPZLCab5RPusGWOXrX8kqtBbgYC\ngUAgmhEMoNaJvryc2BLt0T3axg1tX/G1fNSunYM/7B0aUHVAHKYy6LjrJXPFoHP7N7/9wtMcbX1r\nzfCeiudfUZ3tLcx+YW0YIs/QNnJ0WF37S5q28VmXm5OF3veuWwQkZnAYWrpKCRk1x/dpGpN3XnFd\nXGG3cq3tx0pLaNrDjf+cCLePHN7Fyi0WMNTU6TFfKwAAU+x/0Nnegvdkt+XZ4BLNcWt2Hrh1qmjy\nn/ZpgrIIu7FTnQ03XHYYFbBy5bVwLoCQm5Um9bsETFHf0HjHkmP7s7zdnC/eeewRVyxBWoqGRr/R\nk+z/Ta16O1Oe+Mol2Xy7US/lR9nkAwpNoSEFjSwvxVXS3QtXMBg3oivvju0Czxxce9D63aaOl/MG\nzLvNqqBSg9IMBAKBQDQn3070/f6igBN16ri364kHLoV+5x+O3b/Cz2JGQEHV4wvXm2b5p176mbmb\nbCLKAUBFNkuwRuzp7cAvyYgrATonp+7jVtHQ/MRkpYADs2fdTRN5qhIm33X1+RPnhsTuXLDjUlTN\n9xmc+AeuL96xq3+E6xptXq8XZD7uoHO2EACC4ohRfifMJug43E7nl+fFJ5SqFVRARUFSQmJMPTaW\nxuWVFGVpGAAQAm5pKVcAAEAU+p8aNfhG/wlzzJebOLlbFYQ9v3LL6cbLyGyKeSD0Nj3aywgz4zO/\n/6YiN+KrUKZ9Zy36h1Ty65pKQwoaTV7Kq6S6V+Uv0gPfPfcuAfh/e/cZGFWxNnD8OWc3jYSeAAEE\nFPQKCkiRLoIozYK8SpMmAgJSBEVFEEFQBAQB8V5RQhGkF1FEKSKhhwhEOiShJ5ACpJO2u+f9EDrZ\n3SSETIL/3yfdcHZnnzM7Ozszz8yeQx6Njn7a6hnvhSEXbfajkYViAACUMOTuE30t53/7rOFvn+kl\nmvqt/WSn39LuS/9aUOzCynHvDfs9vkrjytql3387mVtHRJjL1n7KW45+sv6CvRMem0yY28Tl2Kg2\n7/33ZFYOgXSv0riKydXTb+c+v1sejXy4hIvcw1eOx1Pf+M95o6iIiMT+8kLTsbtudAosMQc2zHtn\nw/wPyj/dc9gnX037afCvfR97f2+ivafS3Qq7SVpy6i2/x63JyRZx9XTLYfpLrsfQgZyFN/evur2/\nZ718KvyqPOrrZRLR7Ufj/tQNAEAuMAzDnFmir17shRGjnt0z/eM6H4zb/V69ra9v/bzPwi3TNF0T\ne9uUGDab6K4mu1nD16+67R8YNkPEcDBWHrJmdewr//fZ1+/88+Y3/jFOj+fWNF2TK+t6vDX3yM3U\nEyM99sI9daBSg7/q12+xmyYittTIw3cktZiKVGvWrl+PTj0blYs/vmnm2rOOXsuWGp8ibp4et6xY\nMXt6miU1McWWT2LoQM7Cm/tX3bngw7DaRNM1TcSwH437UzcAALkgk0RfEdFLNBk6s8n+d9vtLznL\n5cyvoeHHAs97vezrmvJP4FlpVfdZX5fAU3dOSFjiImLEo3KV4uadSZn+1jTSklJECpcsZJL4698W\nloh/Dl2Wtj1a+K5YGp7pFEfE1hntZ//zv4Xjflvq+mqXKX9ecfxtmhy686TRplZdc8TqI1dzbXc3\nW2LwvsDgux529anZsVuX/m+0qlXsSuCapX3ar/rl8BUnCbuWmBPhaVrlx8u5bo7J+P3uWrp2eT0l\n5GSUJZ/E0IGchTcvr3IQjSw8oalk065dW+oBMxcGRrKeBADy0p2JviJ60Xqff94kYGynP6JTn4tM\nK/ZQUY9z5Yskno9Ks55b98OqIV+PnT3eOv2PEFOF5p1rmORCxlXWS4FLDxpfDRszJmHhXxEWn7o+\nIrd9HdjiTwZclCF9B/S98ldUkbJu+1ctOZmW+M/sMdtf+Hb8T2sfn7dg58nIVHPR0r7mfauXn7ox\n1m4kn1n7dsc0WTVx1dz457t9/3eig+8mW/jv0//Xb86QhX7F/7tk3bHL6R4+j5aNW7PQ/3zuD8dr\nHtW6jHq5xKZZQ/us2H7c6fLQa5KCfvsnfUrrjo/OOXwkRUQ8q77UqXz6rqlHE0Vs+SKGDuQsvHl5\nlTiIhtMnLFSj/4LRr/vIq8UOth20n0ETAMgzhmHckeiqFWk6/NM2B75ssPmyVWTnN/MufD3l9/Zy\n8Jvhe5PFluz/TpexMeMHjP66lasl+mDoLd9qljC/QcN8Jwx/e9KM90QkLe7Uvg0hV2/5kk49MXWE\nX82JPad++4ot9vic4X8sO5lms4TPf6dTZO+BH3ToN7trIRFJijj00+gNK0/dNvWffnHDO73KVvl1\nyMqxx+p8uPWS/a9+I2H/x6/3PvfhwAEDxnX3FLHGh+6Yv2Xx/eiOGHHbRlZ7zpbNL3ZbxMbpU04s\n+thvqvbNuiPmx3u+273c8VkdN1+2SX6JoaP3nKPw5uVVYj8aNmdPmBq2a2NY+87ark3n73GRDQAg\nu7SE+LigvXvqN37WZrU29K6RjUuLPD/74nfFPm7R5ZtzrAXMBr1o9T4fDBvRqVZp4+K2ZfM/nbLy\n77h7WdIBAEBBtPvSQd1k2rNza6269TnRN8/Z4g798MnALgsjJS1y67q/DtAXAQD82+XXE30fdMmB\nX/V/+1eP9xZuit614Mu6HLoLAPjXspfomzXxf/b1fDQ3i/OvYiSfWTSy84qvHq5fp+LVUI54AQD8\na2We6Iu8kxZzevufp1WXAgAApTQmawAAgEqGYdy1lFUvUr3/J6OGV/dQUSBkG/frVkQDAAomXbtj\n8YhetMZbPV9/3tds54LMn6Vsx0VJGQfEhwStb1k4V4uIDJrnQ7VefL669+17pOfofmWBqfyQdUFJ\nv3R46M4t2fO1B7r2Zl4BAKDg0zRNl1vO880xW9SGj55+uWPjPvNC771UDzS9ZIuF/gHXv/x2HF02\nfkj9kln78nSt2mvy8nFtK7nc5yJeY7maLmJJS3/w5/IKSu3N4woAAHkj42vmrk3ic8YSF3Y0TswJ\nta/mwpM9yDSXYg+XczsyrX//bYmuxSq90Ov9L3+qWuTlNz4/ns92ArUmX04yrOmxV/8Fu6JQewFA\nKcMw9Mzzapp8sfZySFBSyK7DPw57pezNn2PmknWGTFkYejQoKWTv2bWT369f3OnIsV6y8eRFa84c\nCkoKCUo69ufOGb2f88ntmQUVXMq1GNy5YeVCOdlILu7M8aDDRwJ2rPt8+IQtlspdXqzkJnrpV/zi\nQ9ZPqu527R8Vbrz0YFDwyKruIp51x4WFBGzt7i0+nbdmRDJk16JGN3crsXe/XH2bjp61KjwkKCnY\n/+/vBrT2vfYnvWTjST+uOLFvT8YgzaH5771azvX2MqZfioiNDou9fcNdvciTnb5f8Ud0SFBSSFDC\nwT8DxjcqnoXMrMyL4dlw6cGg7R1L3RrB4q394g581dwzKyW0Kx/UXnuBcnSXHYTXcQWw877cagyc\ndfRAUFLInhNLJ8ya+2tUyL7zqz96qRSTPQDyG03ELJluPBK2edbI9eeSS9Qe9EnvxTMjanRacsoi\nmmfN8Uv8+qetGTVk2r4E7+Z9RoybNyWu5dt+YY7OP9ULVWpWr2Lc3JF9tsa4la/X//1Ba1d4t35l\n8vb4Aj0NoHlWqNXpo64TP4vcvmLJtwt+Xh8cn4Ot8g1raopVdJOmie1S4Lr98mmrZ8qOPnQ6TcTj\nkWee9kjctuVcioh2eEaz1ktqDfpufpOAN9/84VCqiFjjwm/uVpL5/Spa76vlM/pYN44eNu2o9p/u\nwwetWl7ixZcm+McZeqFKzRtVSVswtoN/tF667sBRvRZ9d/mp134MuXlmX8qJlbO+TD+bemtxPap/\n4TfitTPzh/TyD07Qi/pWfNh6NsnZbbRbjJSYkzHGE2WLuEi0VrJcOdOlU1GW4mWLypV/otNFL+G0\nhHapr712A+XoLjsI71X7FcD++zKXrlmr4sXZ/zcq9KXpk95KnNepx7nXpo2ZOeiPvz49yDgQgHxF\nu/tE3wxn/lq32j9BZM+xwk2PfNK2SYllp6KkbNthA8sF9ms+fkmUTUQCg40muyb3bVFm/o/hTr+J\nL+7b8eeuBJGAv44Yf6/pNbbV/JYrCsop7rqHl6ebSRMRw5qamJhqFRExYndPafL0nJotXu3Xo/Pi\ndUNjDv4+a97iORuORGXh+/La03pXaTtgSCu3qNmbzqWISHTATwdlxovPVPjhdKjF5aFG9cukHfj5\n2FURMVIuh55MLBaTLukxp0+eOn7XcbOZ3q/ybQf3KXPm87ajvw5JE9mxJdjlid/6jWkzZ/vSiIyr\nwvf89fv2BJE9hzwaHf201TPeC0Mu3pibsUXtXu53x8u4FCtfRGJC9m3efSDSKnIgKCtv024xll86\nGm28VbG4i17y5W9/8fOa+uSrq30qFbdFhEZej6HDEtqlvvbaD5TV/l12cJX9CqDbf1+xImLEBAfu\n2209nNqr+IndATuS9n7Y4fGHvLSDVwv0bwEAD5rMEn1vZ710KjxZipQvahJxr9K4ism1nt/OfRkr\nMRMDJzc3i8/DJbK1tC75lP+f0doTjSoUmFxMj6e+8d8Wvndr+N6tF7Z8XP/WcltiDmyY907Xtr7N\n+006U/2TaT/9M7GuVxaestEM/8SQfZd2L1vQ3rL4k4Gj/0kWEbFGrlu03/rYyy+WN4upRMMWFS37\nfwvI3hjSrffLo3LDRyT6781h11alpJ7buSVaqta/O/LWy6fCr4qXr5ezYfz4gBHjNts6zzy1a/Hi\nkW+0reL0AnFUDFvS2ZMJXg/5FilZp2t1XR5r3ay0Z7nKhWNCwu8acclyCW+/SlntdRAoB3c5J+HN\n0vuy2QzRdE0Mm8Umup7znZgB4H5xukm8Nf3aZIJomq7JlXU93pp75OYIvpEee+Gu3+oOGWITuTO9\nOD9LDf6qX7/FbpqI2FIjD6fe/ldTkWrN2vXr0alno3LxxzfNXHs2K9E4NLlP320xyXGXzkXGp938\n6rVF+i/emDyl10sVvl9cuUM1I3D0/svZXUl6836JiNwxFWc36IbVJloWvqZSTywdXn1T1bbtXurc\nvv+KXkP2zX63/dQ9l50Oc2VejNSzBy7aGjz6ZNMq1YPnzTB36Pbc4zvKyNlVF1NE7uwnZ7WEt1NW\nex0EysFdzkF4Hbyvm2tcDJvVYvsXrEkGUFBlb5P45NCdJ402teqaI1YfyXys15aemi5SqIi7Lgn2\nGj8X37pNfSRkb1j2vgYUsiUG7wsMvuthV5+aHbt16f9Gq1rFrgSuWdqn/apfDl/J4kRNQnjo4RNx\nd8fQdmX3tF8ub+zSqfnZMvUk6N1tl275JjJSr6aJR/EsDxAkn9x9Sto83eIh18DgNBFxq9C4mY8c\n23Mu+d4OBki7fGzN3GNr5s2s1nvu3x+N6LWiw5TTDqY7HBTDZgSfiPNp1Ld38WOzP/vetXav3l2K\nl47dEppgu7s7cq8U1F57gXJ4lx2EN9MK4OB93eiOJG0Z3KSYiEihO4toKtm0a9eWesDMhYEFZeoU\nwIPJ3tqRzNjCf5/+v35zhiz0K/7fJeuOXU738Hm0bNyahf7nr38Z2RJOH7gsb775VvfYgIRiZcz7\nf15+6toQfZ0+g4cU2h1qqfD6u0Oqxv/5xvqIHCz8zE80j2pdRr1cYtOsoX1WbD8en1s/Pa/+PW/R\n0c5D/D4TPXDE5tvGRtLDD4Qm92v+2ZB2k3ZcMpeuWPifVYtCU+0+k9jCfp85553vP/nhc8vUdUdN\nj3V7r9+jEStf/CPSess3VfZ41Ro9snns7j1B52JT3cs0rlVKrOcikx2/d0fFSD67N8SlXdvy27r4\nR543L9s3ZkJzPXD8+fuR85y3tddJoOzcZSdXZV4BnL4vBwrV6L9g9Os+8mqxg20H7S8wPxAAPHAM\nwzBn43eykbD/49d7n/tw4IAB47p7iljjQ3fM37L4loYv+fCEUYurje/8v+86W2OOzR2+YeX1Bt3m\nWWvoxA6lTZaLQavfGTT110sFfejYiNs2stpztlxfEZh25uex63svb22snLsr+rYgGVf8Jw9Y9MWX\n3caueEssV47OGb5uiaPuiBhxgcM7Dr08buj7X08vLInB/n4dxvzgn8mgTFaZ3Nx17wbvT+zu4yoi\naVEnAqYOnrg8wsmtdFAMW1zwjjCpsnvBXzGGTdsxfUNC86oBhxPuyzLLvKy9TgOV6V12dlXmFcDm\n9H3Zlxq2a2NY+87ark33pQsIAFmkiWgJ8XH79+5p0PhZm9Xa0LtG7r+I+aGuu/4afmVg09YbE3L/\n2R9ALkOi4T8AABrnSURBVP8ZsDSw045mracF8XtVtftWe7nLALD70kHdZArYubV23fpmDvTNJ/TC\nlao9XlgrXrPjlHdLrBm84CDfUg8g7jIA3M0wcrqIALnOvUafrzZ2KmOLObZ07NvD/nSerYICiLsM\nAJm6/5M1AAAAd7l1ska/t6RPAACAe6TpuXOiLwAAQI5cP9E3ix0SvfATfUeMGPak+30tFB4wBbra\nFOjCF2hEHrdyq/Ti2E/7Pe+d2/sjIn8wRBPRMz/RN1N6saf69u7UqqzDUz4KVf9i3Z6YDR808rr3\nSSDN86FaLz5f3ZsT0UVyGA2tSLPPfo09tOTTJ9xueVQv23FRxhEnSSFB61sWzuWS3i5L1Sbbclo3\nTOWHrAtK+qXDQ1m7MH/V+WwWPn8p0JHPmZzdL7NPq2Ez9gUGJoUExQet3/Vt92puzi/KQ2qaZbcq\nr37Q/flqhbLSHSmoTeW/nJbrkzWae6knKrm6VqhS3uPeGwjXqr0mLx/XttKdLZJb1e4Ttvlviw0J\nSgrZFvTDgBY+BaV51r2fn3DwQMZpZ7tDfp0x8bWqRbLa3bcXDccv6FHhsXIu7hWr+bje8qgtasNH\nT7/csXGfeaHZebL8JEfREBGxXE0XsaSl5161z5M6nyH3C5+HCmjkHbQ2ThuiHLxlvXTrcUveqRO1\n9LN2b/R+ZdjX3/15ODp/7WCd449enqGpLIgMw8jtRF/jypZurbr+Rz8bFH3/9l1Nv3LqyKpv/hgZ\nGmt69OWvv3x78ZiDjw3eeQ/bjeYd1xIVKxc6/UWf8dsM39rPd/544uLWj7/d7Mu/Y+9TtKyRi/q2\nP1Ip/fiR23bxssSFHY0Tc0Ltq/fnZfMva/LlJMOaHns19wKeJ3VeRO5L4fNOQY28g9bGWUOUk7fs\nUu6pR9wur//su3UByffh3cAemkr1Mjsj1dW36ehZq8JDgpKC/f/+bkBr39s6mbUG/3DicFBSSGDI\nstE9/1PoxtGx3i/PTwoJSgrZF7ll0bbN29Y+53XjEnPJOkOmLAw9GpQUsvfs2snv1y9+80eEa5kX\n+n3+51/bEzOu9Z/14eOuIuJZd1xYSMDW7t7i03nroYyxsl2LGnmKiIgtcueiGau37zh4aOuaubOO\niZdvKc88n1J0KddicOeGlbM0eHiHhJCDB3dsW//Np283nXD4kTdHv/vYtf64vUA5jIbu23bigf17\nk0KCkkL2hKwY2/eJjGBcG2aMD/pl28+/r3oh68OMepEnO32/4o/okKCkkKCEg38GjG9U3PmPT3vF\nuMZOtbFb2dweH3QkZMf8BtdOfSvy/OykkBVDKpidRcOp9EsRsdFhsXf84Mz3dd5R4XPAQXgdVwBH\n78uJAhp5B62N04Yoe/croxjbe5aSkq9tPphRjNXvPWx2Fii9eJ03pn3344HdO2IzphX2zRta2fnI\ngJ1Amcq3m34xZMu8VhlzHXrpVpPCQtZ/06io7qwhsl8MJ42DHZrXo+2+WbohJiQo6dimTUOrZqHw\nBaKpRKYyO9FXK1rvq+Uz+lg3jh427aj2n+7DB61aXuLFlybcOOtESz7031Gzgi0VO7w79H+LC8e0\nGvHrJZuIEfPXiFptCukiZt+XF8/tcfMJPWuOX+LXP23NqCHT9iV4N+8zYty8KXEt3/YLs4qpdKdp\ny+a2tG3/aXb/gFMXk118ynqdvGgRkauHZzRrvaTWoO/mNwl4880fDqWKiDUu/I7fC6bSTXsPfSzi\nxwH+EXm9n5TmWaFWp4+6TvwscvuKJd8u+Hl9cHz2vyTSQtfM3/rhlNdbV5h4PDTNfqAcRsMWc/i3\n8R/+HB59VS/xRI+RH02fnXqwxZd7km1Rfwyvtd/DpXTbRfN7Z6NQHtW/8Bvx2pn5Q3r5ByfoRX0r\nPmw9m+R85MleMa7HK7Nq47SyZSprdcOelBMrZ32ZfvbWw34KTp3PpPC5z34FcPS+nCvQkReHrY29\nP2XvfmVSDFtqZJjFWaBMpRt2evt5+XHCFyNDLsVbXYqVMB+/4ORwcQeBCls7plvdZb9OnRgY/M7s\nlJb/m/TCZb83R+6OszkJlINiOGkcMmXyfm7WkrHtrqz7ZOiGky6Vnn9jwJNZKHxBaCphh2Y2bs+r\n0cu3HdynzJnP247+OiRNZMeWYJcnfus3ps2c7UsjMv7Ffr+Z32xMEJHNR001Nw75sPX033+6YBGx\nJkUEh4qImFMv3bLztV627bCB5QL7NR+/JMomIoHBRpNdk/u2KDP/x3C3Wv0mt/QKGNfulYVhdxzh\nZaRcDj2ZWCwmXdJjTp88dTyTvbRNZZ4f9efXT28b1utd/5j7OXqte3h5upk0ETGsqYmJqVYRESN2\n95QmT8+p2eLVfj06L143NObg77PmLZ6z4UiUk3bgNsbVC4ejpP6j3i5yytt+oCwOo5FybsfKcxn/\neeiw1qTj93UblzHvOW2xJFwMThBzcnT2Bn1dipUvIjEh+zbvPhBpFTkQlMXr7BUj46HMqk2Er7PK\nlqks1A0HbFG7l/vd9kgBqvN3F/4+sFsBHL2vLPTFC3TkHbQ2Dv6UvftlvxiOAnWt/5McunL5H38l\nZfGlHN5KW9xfE4ZNeOqnKbM/eyK2+Qvnv2v8zcFEI2uBslMMx41DZkwV2vZtXzRk5OtjZ5yxiGzf\nlti073fFnBe+IDSVyIwhGYm+t/Co3PARif578/UPbOq5nVuipWr9Ch53XZ1+ce+2aHm0bnmHqXju\nVRpXMbnW89uZsX4zKDFwcnOz+DxcwkXMZWs/5S1H56+/kIPjRHXvFt9//VLEpAFD1kdkpwOQfR5P\nfeO/LXzv1vC9Wy9s+bj+rYGwxBzYMO+drm19m/ebdKb6J9N++mdiXS+7T2THtTvgIFCOmUo1fHPO\nsrXnD+1LPLLtny8buIqrp8s9zF3FB4wYt9nWeeapXYsXj3yjbRWvrI3GZ7UYt1SbbFS2+6nA1Pk8\nYrcC5LiK2lNgIu+gtcmThijXPylObqWRfOyrwVMCy7fuVfPixPd/PHSvq1hy0Ea5V6pXQaKDdkXc\n3WUp6E0lMqWJmDNL9L39IbtzYZpoIs4yczRN1+TKuh5vzT1yc8jSSI+9kCJi2AwRI4fTLOnhSyaN\nPfGbs0HJe5ca/FW/fovdNBGxpUYevmPg1VSkWrN2/Xp06tmoXPzxTTPXns3Wb3WtUNknfSQy9HK6\nw0A54FKx88r57z6yc/b7/bYdi9XKNHlv2Qfet/6D67cn61OaqSeWDq++qWrbdi91bt9/Ra8h+2a/\n237qHsfHqzgtxk13VBt7lc2w2UR3NeXVTGyBqPO5yFF47VWAHFZRJwpE5B20NnnWEGUxUFl8Lie3\n0lyu/jPVTIZVKnX6v2ozJ/2TcA9zENloHG5h2AzR9Dt/L2ep8DksRt40lbBH08x3fMCTT+4+JW2e\nbvGQa2Bwmoi4VWjczEeO7TmXfNddcq/Y5Dkf4+ju8w67zsmhO08abWrVNUesPnL1jiod8c+hy9K2\nRwvfFUvDM/swG6lX08SjeKZ9Tlti2KFgc2oepMDZEoP3BQbf9bCrT82O3br0f6NVrWJXAtcs7dN+\n1S+Hr2SzSXJ55OWez5rDpq0/myoWB4ESEXvRcH+4YXU9bNokv2Un0kTkVOGLKXL7ZywtKUWkcMlC\nJom/42NiS09NFylUxF2XhDtmu9IuH1sz99iaeTOr9Z7790cjeq3oMMXRyKrzYtz8lzerjaPKJnER\nMeJRuUpx886kzF7YUd3IpgJT53ORxUl4M60Ajt5XjhSYyDtobfKkIXL4ScnREzq8lZpX9b5LP6tz\n8ItOg1Pe3z5+ylcBr7/jfyP5L9tVNOuNw60lPLnrpLRp0PYRtz1H7vgJ6LQe5pum0lSyadeuLfWA\nmQsDI+mjOGEYhunjjz+OuBBevkIlwzDmTP7OSDgfXvqVbkNfqpR+Oa14tZaffjmgUdLKt8etP50q\netEavXs2Kh59MtTq/WTj1ydO7F0ndmXf8evP3F5b9KI1evdsePW3+YtPp4kYCefOFmvbbXC3Rg/Z\n0jRP70cer92mqe/Fw2fibZIWFRJb87UBb7ZrXNKwuXiVKv9IrXoNq6cEH4nJuHU2i2e9Ae2fecI1\nOkrzrlrvmbppwYeuZPzJXOWt+fun9e5T48zcX0NVrB7SvJ4euuDtEjvnfdX33c+n/rbvaFRyFtav\naIWf+L8hLQodCTiW5vtEm24fzB1aO2rhB/1+PpdiOAqUiNiNhla6XfcXGpa8eu6ytVDJMo/VadGl\nsefuxcu3Xe+iG1bT4+1fa1eryKUIo0LtRk+lBh+Ouf4nm1vtjq+2ecwlPMpcuXaD6ikhR2Ks4lVr\n9Ng3ahbSXT2KlK5YrXnr1i0qRS7z++Xa7LHdd2a3GParjaPKZqQkeDTt0q3tY25RcVK8XM2GLV+t\nbdu8YNWeOJujaOREQanzDmmeDT5cEDRvYKNLm1cfjndaFR2F124FsDmrog9q5B20NrneEJnLPvtG\nryqn5v+488LNLo6jQImYvOt17FfnypI5G09n9feQo0BphetMWPhZkz2j2kwKPHM08GzVrp/2fOjA\n6i3ByYbDQNkvhrM2yk4Jw0u90nVQhxqeCVdNxcrWaNiyXS3r5gWr9sRZC0pTWeip9/78tmuLpk2K\nbF/2+8X8tXtMvtHnwwGaroedO+tbrrz5zrkaIy5weMehl8cNff/r6YUlMdjfr8OYHzIWutuunt22\nN6xzt8/XvClivXxg43/bfTF/W3zmH8EbjxoJ+z9+vfe5DwcOGDCuu6eINT50x/wti/3PW0Qs4fPf\n6RTZe+AHHfrN7lpIRJIiDv00esPKU2k2ERHjiv/kAYu++LLb2BVvieXK0TnD1y0JTbWJiNjizgSf\nt1VKOBaRKz/Tss+I2zay2nO27L542pVzp5JbjZqzYJSkXjy+Z+nIrl+uOprxu8NRoETsRSPt9KLO\ng4p+PbTH3J/edRMxUhMuHN0emnTL5zz1xNQRfjUn9pz67Su22ONzhv+x7GTatY9t8uEJoxZXG9/5\nf991tsYcmzt8w8pTaZqbu+7d4P2J3X1cRSQt6kTA1METl0c4+cJxUAwH1cZBZRNLmN+gYb4Thr89\nacZ7IpIWd2rfhpCbGzg4qBvZVkDqvEOmkg2aPV5Ik5av1SqxLCzS6QX2w2uyXwGcVdFsKyCRd9Da\n5FFD5OiTkrMntBcoq2e9IeMHeG3sMmbTRauIRK0eN6XXpjHT312xdcz+BENyUEWdt1GZljA+8IMO\nAy+OHtJ/7NShJhFL3Om9fwQnZaUe5pemMjVs18aw9p21XZvO59+lYvmGpomWkBC/PzCgQZNnbVZr\nQ+8aufCsbo+9s3ddt4M9X+i6K6vrvIECLR/Ueb1IjV6/LBtUcXmPJ8cc+vds2ZQPIg8gx3ZfOqib\nTAE7ttau1+DOtSM5ZfZ9/b0ulc4cPhZTqH73Lo+kHRgfwp6CeKDlozrvVrlVt+Fvda8b80eP/x55\n8Psi+SjyAHKFYRhmTSTLR/o64FKs4sMNB3bvXspdEsMDpw8es/p+b5gNqJWf6rxnpUfc9k1/rv/a\nv+/rFjz5RH6KPIBcYGhybbJmd4MmzXJtsgYAAMCZ65M1/rXrNcztE30BAACywzCMPD95DgAA4HaZ\nnegLAACQVzRN9HvdbRgAAOCeaLqwdgQAAChk3HWiLwAAQF7SRPTMTvQFAADIK5pGoi8AAFCJRF8A\nAKAeib4AAEAlTROduRoAAKCQYQiTNQAAQDESfQEAgEok+gIAANVI9AUAAGqR6AsAANTTNU0YHgEA\nAEoYIpom5n/JXE1QUoi9P9XyfDQvSwIAAG5Foi8AAFCPRF8AAKASib4AAEA1TTPnSaKv4frqhTLN\nrNf+73Cp837uzl/VLcXn8ygPl4z/McVPLRt7np4TAAAPGsMwzHc/6NIiwvfl9FsecYmdUCY+6p67\nAjFekTOLWiwi6fptfRFN82rl3nSqW7GLKb+3TYlNu/54mtvlceU0TTTfuNIDku/11QEAQH5lvmuu\nRrME+Fw8ZjM9eblUW4n9b8nkRN16JTeGJWy6Ld5ktdz2mHtN16pD3Ks2zmxFraHZEkwiIoVZbwsA\nwAMr80RfI8mcniS2srqIWCNc0xNu/HObe7vo4jXTzcVtmojtknvS2hKxB8w5nuzRirvUn1+onEeO\nLtZtHp0jfOoaV38sc+mAKadFAAAAimUz0Vezufwn1cVaKGZ2qeg5JRJj0wr3ulS4VM6Xnhgx6Ydm\npB4ekfjbEEu2n8Wc7lHNIrrV/XELK0oAACjQzNn+Lo/1uHrE3SaSHC7un14pVMUWH5XzwYkr85Ov\niLg8m/0r01xjfyxuqyrJW1z/HRu5AQDwYNJEzDlP9E0yW9LEtYhNE5OKDoFmCykca3erVQAAUEDc\nW6KvJoawrSsAAMg6/8GP3PHI9RN9C+Rsh2GqklDslQS3wgWy9AAAQORaJ+TuRF/RPC3mojZTCZuI\nmMqkuXjp1mizzXLX9bmhSNdCdZ7X9ZJmTUTKu9b/wZwWY/nn45SYFGdXuqYVeTOmsJd4ebiGL3Oj\nSwIAQAGVaaKvYW4QfWMbtGIDI4rl1jZomXGr6uLb8MYz6yUb6JJiHM/K0liLS8oxc+E6RsrxnGca\nAwAA5QxD7t6VVUvf7Htuc2b/3GZOmFThxi4kkup26aMK91iC6E/iFn+SoyttevKisucW3ePrAwAA\n9bKf6Jtjuk0vYjVZRNJ1a3IWXlYzdC+bponmZbv/hQMAAGrcW6JvdhVPLP1pokiWj9BzTS356c0j\n9O5r0QAAgDJ5daKvlram3Lk12bwo1T36g3udDLqhluejufVUAAAgF11P9AUAAFBHz7O5GgAAgLtp\nmuiGQX8EAAAoYxgakzUAAEAxnbERAACgkCai512iLwAAwN00TTcMo4CeoQcAAAo+g0RfAACgHom+\nAABApYxEX9WlAAAA/2KGIUzWAAAAxUj0BQAAKpHoCwAAVLue6AsAAKAGib4AAEA9En0BAIBKmiY6\nUzUAAEAhQ0j0BQAAqpHoCwAAVCLRFwAAqEaiLwAAUItEXwAAoB6JvgAAQCVO9AUAAIpxoi8AAFCP\nRF8AAKASib4AAEC1jERfVo8AAAAlDBJ9AQBAfkCiLwAAUIlEXwAAoBiJvgAAQD0SfQEAgEok+gIA\nANU0TRcWjwAAAIVI9AUAAMqR6AsAAFQi0RcAAChGoi8AAFBP10SEARIAAKCEIZqITlcEAAAoZAiT\nNQAAQDW6IwAAQDFdI9MXAACoo2mabpDpCwAA1DHYlRUAACjHrqwAAEAldmUFAACKsSsrAABQT2eu\nBgAAKKSJ6KJp7BIPAAAUMYREXwAAoBaJvgAAQD0SfQEAgEok+gIAAMVI9AUAAOqR6AsAAFS6kegL\nAACgCIm+AABALRJ9AQCAeiT6AgAAlTISfemPAAAAZQxDY7IGAAAoRqIvAABQiURfAACgGom+AABA\nLRJ9AQCAerrGZA0AAFBHY7IGAACoxWQNAABQj0RfAACgEom+AABANdaOAAAAtVg7AgAA1NM1TWN4\nBAAAKGGQ6AsAAJRjsgYAAKink1gDAAAU0jTRReiPAAAAhTTdENaOAAAAZQwx2JUVAACoxK6sAABA\nNRJ9AQCAWiT6AgAA9Uj0BQAAKl1P9GW6BgAAKGGICGtHAACAUoZBoi8AAFCKRF8AAKAaib4AAEAt\nEn0BAIB6usZkDQAAUEfTNEZHAACAYqwdAQAAKhmGkbErKz0SAACghHF9V1YAAABlSPQFAABKsSsr\nAABQjF1ZAQCAaiT6AgAA5Vg7AgAAVLqR6AsAAKAGib4AAEA5TRcmawAAgEIk+gIAALVI9AUAAKpp\n7MoKAACUMgyDfUcAAIBiusZkDQAAUEdjV1YAAKAca0cAAIBK7MoKAAAUY1dWAACgHIm+AABAKYNd\nWQEAgFrsygoAAFQj0RcAACjH2hEAAKASib4AAEAxEn0BAIByms5cDQAAUMgwhERfAACg0rVEX8ZH\nAACAEoaQ6AsAAPIBEn0BAIBKGYm+rB4BAADKaEzWAAAA5ZisAQAAKrErKwAAUIxdWQEAgHIZa0eY\nrgEAAEoYIiK6sHYEAAAoZBi6sHgEAAAoRKIvAABQjkRfAACgEruyAgAAxdiVFQAAqEd3BAAAKKaL\nGGw8AgAAFDFEDHZlBQAAarF2BAAAqEaiLwAAUIlEXwAAoBiJvgAAQD26IwAAQDFdNNaOAAAAdTQS\nfQEAgGKsHQEAAKrpQqIvAABQyDB0IdEXAAAoRKIvAABQju4IAABQjO4IAABQjO4IAABQjO4IAABQ\nTBch0RcAACjErqwAAEAxEn0BAIBqOmMjAABAIU1EZ+UIAABQyCCzBgAAKEd3BAAAKEZ3BAAAKEZ3\nBAAAKEZ3BAAAKKZrZPoCAAB1NE10g0xfAACgjmEwWQMAAFSjOwIAABTTNY70BQAAihgZm8SrLgYA\nAPi3ozsCAAAUozsCAAAUozsCAAAUozsCAAAUY1dWAACg0vVdWcn0BQAAShjsygoAAPIBuiMAAEAx\nXYTFIwAAQCGN0REAAKAY3REAAKCYrmkcogcAAFQxNE3TDYO+CAAAUMYwDCZrAACAYuzKCgAAVLq+\nKysAAIAi7MoKAADUozsCAAAUy0j0BQAAUINEXwAAoBiJvgAAQD0mawAAgEpM1gAAAMWYrAEAAOqx\nKysAAFBJ00QXoT8CAAAUYu0IAABQirUjAABAPRJ9AQCASiT6AgAAxZisAQAA6jFZAwAAVNI0jdER\nAACgGGtHAACASoZhsCsrAABQiV1ZAQCAcppuCJM1AABAGUNI9AUAAKqR6AsAAFQi0RcAAKhHoi8A\nAFApI9GXyRoAAKAMkzUAAEA9JmsAAIBKTNYAAADFrk3WMDwCAACUyOiEsHYEAAAoxtoRAACgEmtH\nAACAYiT6AgAA9XTD4ExfAACgiMFkDQAAUI3JGgAAoB7dEQAAoBjdEQAAoFhGd4S1rAAAQAlDGB0B\nAADKsSsrAABQiURfAACgGIm+AABAPSZrAACASkzWAAAAxW6ZrGGMBAAA5LHr3Q+ziLi4uqakpLi7\nu+++dFBpoQAAwL+FruspKcmurq4iYhZDfEqVDjl+9NHHq7m7uwtzNwAA4H4zjJSU5ODjR71LlRZD\ntKSkRE2M06dOXoqKTEtLU106AADwr+Dq6updqvTDj1Q2RNPi42JNZrNJNwnDIgAAIC8ZYrVZrRbL\n/wMcUdEeafanKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"IPython.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **IPython Notebook** contains all functionality of IPython, but is exposed in the browser as a sequence of command cells. This is the modality we will be working with"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some python syntax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting with the unavoidable first print command, we can write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"Hello, Plateau de Saclay\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some typical assignment of values to variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 3\n",
    "b = 2 * a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = 'hello'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hellohello'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hellohello'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hellohello'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The latter two operations are implemented in the string type. If multiplied by an integer, then the string is copied this many times. Internally, the `__mul__` and `__rmul__` functions of an object are invoked to take care of this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hellohello'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.__mul__(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hellohello'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.__rmul__(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical types\n",
    "Python has several numerical types built-in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = 1.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = 1. + 1j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "complex"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 > 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = 3 > 4\n",
    "type(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All number types can be verified as being a `\"Number\"` by the following test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True, True, True, False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numbers import Number\n",
    "(isinstance(a, Number),\n",
    " isinstance(c, Number), \n",
    " isinstance(d, Number),\n",
    " isinstance(e, Number),\n",
    " isinstance(b, Number))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 10  # Powers -- NOT 2 ^ 10 WATCH OUT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ^ 10  # 0010 ^ 1010 = 1000 what operation is this (maybe)? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 & 10 # 0010 & 1010 = 0010 what operation is this (maybe)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Containers\n",
    "Containers are structures for storing multiple objects of any type. Different ones cater to different needs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lists\n",
    "Python lists are dynamical array lists. This means that they consist of a memory-contiguous array of pointers to objects (instead of a non-contiguous linked list of object containers). In order to be able to quickly append to a list, it typically allocates an array of pointers larger than necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l = ['zero', 'one', 'two', 'three', 'four']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watch out, as for most general purpose programming languages, in Python, counting and indexing generally start at 0. (This is contrary to e.g. matlab and fortran, but in line with C, Java, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'two'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[2]  # the 3rd element of the list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can index the end of the list by using negative numbers (matlab correspondence `l[end]`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('four', 'three')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[-1], l[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List slicing\n",
    "We can construct sublists of lists by slicing them, using the syntax\n",
    "\n",
    "`l[start:stop:stride]`, where `start` and `stop` are the (inclusive and exclusive respectively) starting and stopping points of the slice and `stride` indicates the number of elements to ignore (jump over) at each step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'deux', 'trois', 'three', 'quatre']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['two', 'three']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['three', 'four']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zero', 'one', 'two']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zero', 'one', 'two', 'three', 'four']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[:3] + l[3:]  # no off-by-one-errors in slicing and stitching!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zero', 'two']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[0:4:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zero', 'two', 'four']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['four', 'three', 'two', 'one', 'zero']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['four', 'two', 'zero']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[::-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['three', 'one']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[-2::-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists can be modified using assignment operators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l[0] = \"nothing\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'two', 'three', 'four']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l[4] = \"quatre\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'two', 'three', 'quatre']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l[2:4] = ['deux', 'trois']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'deux', 'trois', 'quatre']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l.append(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'deux', 'trois', 'quatre', 5]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'deux', 'trois', 'quatre']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l.extend([5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'deux', 'trois', 'quatre', 5, 6]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l2 = list(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l2.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 5, 'quatre', 'trois', 'deux', 'one', 'nothing']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'deux', 'trois', 'quatre', 5, 6]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing',\n",
       " 'one',\n",
       " 'deux',\n",
       " 'trois',\n",
       " 'quatre',\n",
       " 5,\n",
       " 6,\n",
       " 6,\n",
       " 5,\n",
       " 'quatre',\n",
       " 'trois',\n",
       " 'deux',\n",
       " 'one',\n",
       " 'nothing']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l + l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 'deux', 'nothing', 'one', 'quatre', 'trois']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(l)  # not in place sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'deux', 'trois', 'quatre', 5, 6]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l2.sort()  # in place sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 'deux', 'nothing', 'one', 'quatre', 'trois']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionaries\n",
    "Dictionaries are efficient containers for **key/value pairs**, also known as *associative array*. The underlying storage method is the **hashmap**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "adjective_lookup = {'moon': 'lunar', 'sun': 'solar'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'moon': 'lunar', 'sun': 'solar'}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjective_lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "adjective_lookup = dict(moon='lunar', sun='solar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'moon': 'lunar', 'sun': 'solar'}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjective_lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adjective_lookup['sea'] = 'maritime'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'moon': 'lunar', 'sea': 'maritime', 'sun': 'solar'}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjective_lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sun', 'sea', 'moon']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjective_lookup.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['solar', 'maritime', 'lunar']"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjective_lookup.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('sun', 'solar'), ('sea', 'maritime'), ('moon', 'lunar')]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjective_lookup.items()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuples\n",
    "Tuples are very similar to lists, but they are **immutable**: You cannot change them once created. Further, you can also assign values to tuples, even if they are nested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = 12345, 'hello', 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12345, 'hello', 1.0)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12345"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-125-45f670ad85ec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'goodbye'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "t[0] = 'goodbye'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x, y = 1, 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x, (y, (z, w)) = 1, (2, (3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Strings\n",
    "Strings are **immutable** chains of characters with many possible high-level operations as built-in functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = 'Hello, how are you?'\n",
    "s = \"Hi, what's up?\"\n",
    "s = '''Hello,\n",
    "how are you?\n",
    "'''\n",
    "s = \"\"\"Hi, \n",
    "what's up?\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Hi,\\nwhat's up?\""
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-134-9e31b9e57a9b>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-134-9e31b9e57a9b>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    'Hi, what's up'\u001b[0m\n\u001b[1;37m              ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "'Hi, what's up'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'H'"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-136-855719672658>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'h'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "s[0] = 'h'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hi,\\nwhat'"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'up?'"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### String-specific operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hi,', \"\\nwhat's\", 'up?']"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.split(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Hello, \\nwhat's up?\""
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.replace(\"Hi\", \"Hello\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn to use **TAB-Complete** to explore python modules!\n",
    "\n",
    "`s.<TAB>`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getnewargs__',\n",
       " '__getslice__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '_formatter_field_name_split',\n",
       " '_formatter_parser',\n",
       " 'capitalize',\n",
       " 'center',\n",
       " 'count',\n",
       " 'decode',\n",
       " 'encode',\n",
       " 'endswith',\n",
       " 'expandtabs',\n",
       " 'find',\n",
       " 'format',\n",
       " 'index',\n",
       " 'isalnum',\n",
       " 'isalpha',\n",
       " 'isdigit',\n",
       " 'islower',\n",
       " 'isspace',\n",
       " 'istitle',\n",
       " 'isupper',\n",
       " 'join',\n",
       " 'ljust',\n",
       " 'lower',\n",
       " 'lstrip',\n",
       " 'partition',\n",
       " 'replace',\n",
       " 'rfind',\n",
       " 'rindex',\n",
       " 'rjust',\n",
       " 'rpartition',\n",
       " 'rsplit',\n",
       " 'rstrip',\n",
       " 'split',\n",
       " 'splitlines',\n",
       " 'startswith',\n",
       " 'strip',\n",
       " 'swapcase',\n",
       " 'title',\n",
       " 'translate',\n",
       " 'upper',\n",
       " 'zfill']"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterables\n",
    "\n",
    "All of the containers, and the strings are **iterables** - they obey an interface called `Iterable`. Any collection of items that can be enumerated in a certain order is *iterable*, at least theoretically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import Iterable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True, True, True, False)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(isinstance(l, Iterable),                 # our list from earlier\n",
    "isinstance(adjective_lookup, Iterable),   # our dictionary from earlier\n",
    "isinstance(t, Iterable),                  # our tuple from earlier\n",
    "isinstance(s, Iterable),                  # our string from earlier\n",
    "isinstance(5, Iterable))                  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coding in Python\n",
    "Some very basic programming concepts in python. Watch out for the **indentation**! It is purely by indentation that we define **blocks** (e.g. there are no brackets), so we have to be very rigorous about them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Control flows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A lot\n"
     ]
    }
   ],
   "source": [
    "if a == 1:\n",
    "    print(1)\n",
    "elif a == 2:\n",
    "    print(2)\n",
    "else:\n",
    "    print(\"A lot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The condition in the **if** statements is cast to **boolean** if it is not already boolean. Most non-boolean variables will evaluate to `True`, but some will evaluate to `False`, **beware the following**:\n",
    "(empty containers evaluate to `False`, `None` evaluates to False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, True, False, True, False, True, False, True, False)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(bool([]), bool(['hello']), \n",
    "bool({}), bool({1:'one'}), \n",
    "bool(tuple()), bool((1,)),\n",
    "bool(False,), bool((False,)),\n",
    "bool(None))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For loops\n",
    "For loops are generally over items in lists or other iterables. This is contrary to most other languages where an integer counter is the standard way of following the progress of the loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nothing', 'one', 'deux', 'trois', 'quatre', 5, 6]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, nothing\n",
      "Hello, one\n",
      "Hello, deux\n",
      "Hello, trois\n",
      "Hello, quatre\n",
      "Hello, 5\n",
      "Hello, 6\n"
     ]
    }
   ],
   "source": [
    "for a in l:\n",
    "    print(\"Hello, {}\".format(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you really need a counter, you can `enumerate` the iterator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0. Hello, nothing\n",
      "1. Hello, one\n",
      "2. Hello, deux\n",
      "3. Hello, trois\n",
      "4. Hello, quatre\n",
      "5. Hello, 5\n",
      "6. Hello, 6\n"
     ]
    }
   ],
   "source": [
    "for i, a in enumerate(l):\n",
    "    print \"{counter}. Hello, {item}\".format(counter=i, item=a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you really only want to count, then `range` is the appropriate iterable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(10): print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "For programming going beyond simple scripts it is essential to encapsulate functionality in functions. They are easy to define:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def double_x(x):\n",
    "    return 2 * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "double_x(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcabc'"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "double_x('abc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True, False, None, True, False, None]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "double_x([True, False, None])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python is **ducktyped** - *\"If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck.\"* - if the operation `2 * x` is possible, then `double_x` will apply it, no matter what the object is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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