CosPls.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MathFunc( x ):\n",
    "    return np.cos( x ) - x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4.141592653589793\n"
     ]
    }
   ],
   "source": [
    "print( MathFunc( np.pi ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeDomain( from__, to__, step ):\n",
    "    return np.linspace( from__, to__, step )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot( x, y ):\n",
    "    figure, xAxis = plt.subplots()\n",
    "    line, = xAxis.plot( x, y( x ), label='Super Duper Plot!' )\n",
    "    plt.show()\n",
    "    #line.set_dashes( [2, 2, 10, 2] )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W6ZpAxJ/seH8Sm9lcoN0RvnQP8B/A+c65A2b2JZDrnNtzvJPm5ua6/Pz8k4groWLrvjLueW0N89YVclpWKr++tDf9s1K9jiUS0MxsmXMu97ivO9mxTjPrC7wPlDU8lQkUAHnOuZ3H+l4VuwA453h9ZQH3vbWWwpJKLh/QkbtG9aRdSqzX0UQCUrMX+xFO+CW6YpeTUFpZw58+2MDUjzYTacZPz+rKdWd2Jj5ad2kSOdyJFrvmsYvnEmN83DWqJ+/fcRbn9MzgD3PXMeL/f8DU+Zu0clXkJPjtir0xdMUux7Jsy34emruO+ev3kJEUw41nd+WqvGxioyK9jibiqRYfimkMFbuciMWb9vLge+tYvHkfrROiuXZYDtcMzSE9McbraCKeULFLyFi4cS9T52/i/c93E+2L4PIBHZkwvDM92iV5HU2kRZ1osevdKQl4w7q2ZljX1mzYXcqMBZv5y7JtzF66ldOyUvlJbhaXnNaepNgor2OKBAxdsUvQ2XewileWb+Ol/K2s21VKbFQE3+/Tnov6teeM7unE+DQWL6FJQzES8pxzrNp2gJfyt/LmqgJKKmpIjPFxbs82XNinHWd0T9eVvIQUFbuElaqaOj7euId31uzk3U93sr+smsgIY0BWKmd0T2dE93T6ZaYSFakZvhK8VOwStmpq61j65X7mry/kow17WL39AM5BbFQEfTumMCA7jf5ZqfTLTKFjahxm5nVkkROiYhdpsP9gFR9v3MuyLftZsXU/n24vpqq2DoCE6Ei6tU2ie5tETmmbSOf0RDqkxpKZGk9ynE+lLwFFxS5yFJU1tazdUcKnBQdYv6uUdbtKWL+7lMKSym+8LiE6kg6pcbRJjiE1PppW8dGkxUeRlhBNanwUcVE+4qIjifVFEBcdSVxUJLFRkURGGBFmRERAhBmRVv/YIvjX58a/XmfoF4icEE13FDmKGF8k/bNSv7Ob5P6DVWzZV0ZBUTkFReVsLypn+/5y9h6sYm1BMfvLqigqr6a5roUijIai/2bx29fPQ1xUJHHRkSTE+IiPjiQh2kd8jI/UuChaJ0bTOjGG9IT6/2YkxdAhNVazhMKQil2kQVpCNGkJ0cfcPri2zlFcXk1ReTXlVbWUV9dSUV379efl1bU456itgzrn6j/qHHXuX48Pfc25w5+n4XHDc3WHfe4cztWfu6K6lrKqWg5W1VBWVcuukgoO7qmlqKyK/WXV38lrBu2SY8lKiyezVRydWyfQs30yPdslkZmm9xdClYpdpBEiI+zrXwCBpqa2jn1lVewtrWLfwSp2FVewdV85X+0rY+v+MhZu3Msry7d//fqkGB892iXRp2MKuZ3SyM1ppS2TQ4SKXSRE+CIjaJMUS5uko5fzwcoavthVwuc7Svh8ZzFrdxTz4tKtPP3xlwBkpsWR16kVZ/XIYET3DFoF4C8wOT4Vu0gYSYjxMTA7jYHZaV8/V11bx2cFxeRv2U/+l/v4cF0hr6zYjhmclpnK2T0y+H7f9pzSVnvzBAvNihGRb6itc6zefoAPv9jNh18UsmpbEc5Bj7ZJXHJaey7u14FO6QlexwxLmu4oIn5RWFLJ22t28OaqApZ+uR+AgdmpjBmSw0X92muf/BakYhcRvysoKufNVQW8uHQrm/YcJCUuissHdmTMkBy6tUn0Ol7IU7GLSLNxzrFo0z5eWPIV76zZQXWt43untuWGs7swKKeV1/FClopdRFrEntJKnl24hZkLv6SorJrBndK44eyunNOjjebJ+5mKXURaVFlVDS8u3crU+ZvZXlRO/6xU7hrVg9O7pnsdLWSo2EXEE9W1dcxZto2H31/PjgMVjOiezr9f0IN+mUdf0Ssn5kSLXZtTi4hfRUVGcGVeNh/ceTa/vOhU1mw/wKWPLuD22SvYVVzhdbywoGIXkWYRGxXJ5BFdmHfXOdx8TjfeWr2Tcx/4kCf+uZGqmjqv44U0FbuINKuk2CjuvKAH791xJsO6tub+tz9n1MPzWLBhj9fRQpaKXURaRE7rBKaOG8yM8YOpq3OMmbqYu+d8QnHFd3ellKZRsYtIizqnZxveuf1Mrj+rCy/lb+X8B+fx/tpdXscKKSp2EWlxsVGR/OLCU3n1xuGkxEUxaWY+P3txpa7e/UTFLiKeOS0rlTdvOYPbRnbnjVUFfP/h+Szbss/rWEFPxS4inor2RfCz807h5Z8Owwx+/OeFPDR3HTW1mjlzslTsIhIQBman8datIxjdvyMPzV3PlU8uoqCo3OtYQUnFLiIBIyk2igd/0p+HftKftTuKufiRjzQt8iSo2EUk4Iwe0JE3bjmD1gnRXDttMY99sIG6upbf/iRYqdhFJCB1zUjktZuGc1G/DvzPu18w5dllmjVzglTsIhKwEmJ8/PHK/tx7SS8+/GI3l//pY7bsPeh1rIDX5GI3s1vM7Asz+9TMfu+PUCIih5gZE4Z35rnJQ9hTWsnoxxaweNNer2MFtCYVu5mdA1wG9HPO9QYe8EsqEZFvGdqlNa/dOJxWCdFcM20xL+Vv9TpSwGrqFfsNwO+cc5UAzrndTY8kInJkndITeOXG4Qzt0pq7/vIJ//3WWmr1pup3NLXYTwFGmNliM/unmQ32RygRkaNJiYtixvjBXDs0hyfnbeKWWcupqK71OlZA8R3vBWY2F2h3hC/d0/D9acBQYDDwkpl1cUe4LZOZTQGmAGRnZzcls4iEOV9kBL8Z3Yec1vH89m9r2XdwCU+OzSU5NsrraAGhSbfGM7N3qB+K+bDh8UZgqHOu8Fjfp1vjiYi/vL5yO3e+vIquGYnMnJhH2+RYryM1m5a6Nd5rwLkNJzwFiAa0TExEWsxl/Tsyffxgtu4r4/I/fczGwlKvI3muqcU+HehiZmuA2cC4Iw3DiIg0pxHdM5g9ZRgV1bX86PGP+WRbkdeRPNWkYnfOVTnnrnHO9XHODXTO/cNfwUREGqNvZgpzbjidhBgfY55aTP6X4bv9r1aeikjI6JSewEvXDyMjKYZrpy3h4zDdQEzFLiIhpUNqHLOvH0pWqzgmPL2UD74Iv+U1KnYRCTltkmKZPWUY3dsmMuWZfN5Zs9PrSC1KxS4iIalVQjTPTx5K344p3PTCcl5fud3rSC1GxS4iISslLopnJw0hNyeNn724kjdXFXgdqUWo2EUkpCXE+JgxYTC5Oa24/cWVvL16h9eRmp2KXURCXny0j+kTBtM/K5VbZq3g75+G9pi7il1EwkJijI+nJwymT8OY+/trd3kdqdmo2EUkbCTFRvHMpDxObZ/MDc8t58MQnQqpYheRsJIcG8WzE4fUT4V8dhnz1x9zz8KgpGIXkbCTEh/Fc5OG0DUjkeueyQ+57QdU7CISltISonl2Uh4dUupXqH5acMDrSH6jYheRsJWeGMOzk4eQFONj7LQlbAqRLX9V7CIS1jqmxvHc5CEAXDN1MduLyj1O1HQqdhEJe10yEnlmUh4llTVcO3Uxe0orvY7UJCp2ERGgd4cUZowfTMGBcsZNX0JxRbXXkU6ail1EpEFup1b8+ZpBrNtVwqSnl1JeVet1pJOiYhcROczZPdrw0E8GsGzLfm58fhnVtXVeR2o0FbuIyLdc1K89vx3dlw++KOQ/XllNsN3K2ed1ABGRQHT1kGx2FVfw8PvraZMcw79f0NPrSCdMxS4ichS3f687u0sqeeyDjbRJimXc6Z28jnRCVOwiIkdhZvzmst7sKa3kV29+SkZSDN/v297rWMelMXYRkWPwRUbwyFUDGJidxu2zV7Jw416vIx2Xil1E5DhioyKZNi6X7NbxTHkmn7U7ir2OdEwqdhGRE5AaH83MiXkkxPgYN30J2/aXeR3pqFTsIiInqGNqHDMn5lFeXcvY6UvYd7DK60hHpGIXEWmEHu2SmDo2l237y5kYoKtTVewiIo00pEtr/nhlf1ZtK+KWWSuorQusBUwqdhGRkzCqT3t+dUlv5q7dxa/e+DSgVqdqHruIyEkad3onCorKeWLeJjqmxfHTs7p6HQlQsYuINMnPR/Wk4EAFv3v7c9qnxHJZ/45eR1Kxi4g0RUSE8cCP+7G7uII7X15FRlIMp3dN9zaTp2cXEQkBMb5Inrw2l06tE7j+mWV8sbPE0zwqdhERP0iJj+LpiXnERUcyfsYSdh6o8CxLk4rdzPqb2SIzW2lm+WaW569gIiLBpmNqHDMmDKa4vJrxM7y7vV5Tr9h/D/zaOdcf+M+GxyIiYat3hxQev2YQG3aXcsNzy6iqafk7MDW12B2Q3PB5ClDQxOOJiAS9M0/J4Hc/7MeCDXu5e84nLT7HvamzYm4H3jWzB6j/JXF60yOJiAS/Hw3KZEdROf/73jo6pMZx5wU9Wuzcxy12M5sLtDvCl+4BRgI/c87NMbMrgGnA945ynCnAFIDs7OyTDiwiEixuPrcb24vKefSDDbRPjWXMkJwWOa815U8EMzsApDrnnJkZcMA5l3y878vNzXX5+fknfV4RkWBRU1vHdc/k8891hTw1NpeRp7Y96WOZ2TLnXO7xXtfUMfYC4KyGz88F1jfxeCIiIcUXGcGjVw+kd4cUbn5hBau2FjX/OZv4/dcBD5uZD6igYahFRET+JSHGx/Txg7njpZUkx0U1+/maVOzOuY+AQX7KIiISsjKSYnh20pAWOZdWnoqIhBgVu4hIiFGxi4iEGBW7iEiIUbGLiIQYFbuISIhRsYuIhBgVu4hIiGnSXjEnfVKzQmDLSX57OrDHj3H8RbkaR7kaR7kaJ1BzQdOy5TjnMo73Ik+KvSnMLP9ENsFpacrVOMrVOMrVOIGaC1omm4ZiRERCjIpdRCTEBGOxP+l1gKNQrsZRrsZRrsYJ1FzQAtmCboxdRESOLRiv2EVE5BiCqtjNbJSZfWFmG8zsbq/zAJjZdDPbbWZrvM5yODPLMrMPzGytmX1qZrd5nQnAzGLNbImZrWrI9WuvMx3OzCLNbIWZ/dXrLIeY2ZdmttrMVppZwNxT0sxSzewvZvZ5w8/ZsADI1KPh3+nQR7GZ3e51LgAz+1nDz/waM5tlZrHNdq5gGYoxs0hgHXAesA1YClzlnPvM41xnAqXAM865Pl5mOZyZtQfaO+eWm1kSsAwYHQD/XgYkOOdKzSwK+Ai4zTm3yMtch5jZHUAukOycu9jrPFBf7ECucy6g5mWb2UxgvnNuqplFA/HOuea/79sJauiM7cAQ59zJrpvxV5aO1P+s93LOlZvZS8Bbzrmnm+N8wXTFngdscM5tcs5VAbOByzzOhHNuHrDP6xzf5pzb4Zxb3vB5CbAW6OhtKnD1ShseRjV8BMTVhZllAhcBU73OEujMLBk4E5gG4JyrCqRSbzAS2Oh1qR/GB8Q13Eo0nvp7RjeLYCr2jsDWwx5vIwCKKhiYWSdgALDY2yT1GoY7VgK7gfeccwGRC3gIuAuo8zrItzjg72a2zMwC5b7CXYBCYEbD0NVUM0vwOtS3XAnM8joEgHNuO/AA8BWwAzjgnPt7c50vmIrdjvBcQFzpBTIzSwTmALc754q9zgPgnKt1zvUHMoE8M/N8CMvMLgZ2O+eWeZ3lCIY75wYCFwI3NQz/ec0HDAQed84NAA4CAfG+F0DD0NClwMteZwEwszTqRxg6Ax2ABDO7prnOF0zFvg3IOuxxJs34p0woaBjDngM875x7xes839bwp/uHwCiPowAMBy5tGM+eDZxrZs95G6mec66g4b+7gVepH5b02jZg22F/bf2F+qIPFBcCy51zu7wO0uB7wGbnXKFzrhp4BTi9uU4WTMW+FOhuZp0bfhtfCbzhcaaA1fAm5TRgrXPuQa/zHGJmGWaW2vB5HPU/8J97mwqcc79wzmU65zpR/7P1D+dcs11RnSgzS2h485uGoY7zAc9nYDnndgJbzaxHw1MjAU/fmP+WqwiQYZgGXwFDzSy+4f/NkdS/79UsfM11YH9zztWY2c3Au0AkMN0596nHsTCzWcDZQLqZbQPudc5N8zYVUH8Fei2wumE8G+A/nHNveZgJoD0ws2HGQgTwknMuYKYWBqC2wKv1XYAPeME59463kb52C/B8w4XWJmCCx3kAMLN46mfPXe91lkOcc4vN7C/AcqAGWEEzrkANmumOIiJyYoJpKEZERE6Ail1EJMSo2EVynVYvAAAAJElEQVREQoyKXUQkxKjYRURCjIpdRCTEqNhFREKMil1EJMT8HyO60A9VDQm2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot( MakeDomain( 0, 8, 500 ), MathFunc )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = np.linspace( .001, 20, 500 )\n",
    "q = np.cos( p ) - p\n",
    "fig, ax = plt.subplots()\n",
    "line1, = ax.plot(p, q, label='plswork')\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FindPoint( begin, end, of, maxExecutions, currentExecutions ):\n",
    "    midPoint = ( begin + end ) / np.float( 2.0 )\n",
    "    if currentExecutions >= maxExecutions:\n",
    "        return midPoint, MathFunc( midPoint )\n",
    "    if ( of( midPoint ) * of( begin ) ) > 0:\n",
    "        return FindPoint( midPoint, end, of, maxExecutions, currentExecutions + 1 )\n",
    "    else:\n",
    "        return FindPoint( begin, midPoint, of, maxExecutions, currentExecutions + 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.738983154296875, 0.0001706693011150895)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FindPoint( np.float( 0 ), np.float( 10 ), MathFunc, 15, 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mathDemo = MathFunc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Radial( r ):\n",
    "    return ( np.float( 1.0 ) / r ) - ( np.float( 1.0 ) / ( r * r ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "MathFunc = Radial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mathDemo( 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = Radial( p )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "line1, = ax.plot(p, q, label='plswork')\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qfOfccufcynoGjQNedM6VOefWA2uA3KZMS+RoDMtszwvXj2RfaSUXPPyZdtmUqBaqdfhdgU21fs8PPiYSdsd1S+GNySeSHB/gsimzeGdxgdeRRDxx2MI3s/fMbEk9t3GHelo9j9V7ohMzm2RmeWaWV1hY2NjcIkekZ1oir39vFAO6tGXytPn85b3VVFfr3DsSXQ57ARTn3JijeN18oHut37sBWxp4/SnAFICcnBx9AyVkOiTFMe2G47nj9cXc/94q8jbu4oFLh9AhKc7raCJhEapVOtOB8WYWZ2Y9gWxgToimJdJo8TF+/nzJYH5/4SBmr9/F2L98wsylW72OJRIWTd0t8wIzywdOAN42s5kAzrmlwMvAMuCfwM3OOZ2wXCKCmXFZbiZv3nwi6Ulx3PjcPG6ZNp9tOkhLWjmLpHOI5+TkuLy8PK9jSBSpqKrmsY/W8tf31+D3GZNG92LS6F4kxulyz9JymNk851zOYcdT4YvAlztLuHfmCt5eVEBKQgxXn5DFNSf00Pp9aRFU+CJH4Ysvd/Pwh2t5d9k2YgM+xhybwYVDuzG6TzqxAZ2JRCKTCl+kCdZs38/zszYyfeEWdhWXkxDr54ReHRjVO41BXdtxbOdkkuNjvI4pAqjwRZpFRVU1n6wu5IMVhXy8upCNO/97yuWuKW3omtKGTu3iyUiOIzEuQGKcnzaxAeIDPmIDPmL8B29GrN9HTKDO78HHkmIDtG0TwKy+Q1hEDq2xha8tUyKHEOP3cXq/jpzeryMA24pKWbaliGUFRazeto+CvaUszN/D9qIyDlQ0bUe05LgA3VIT6JmWwMCu7TiuawrDeqSQEKuvqTQPfZJEjkDHtvF0bBvPaf0yvjGsutpxoKKK4vJKyiqqqaiqpqLKUVFVTXlVNRWVdX4/eKt0FJVWsGlXCZt2H2DJ5iJmLK45NiDW72Nkr1TO6JfBeYO7aCOyNIlW6YhEoD0l5SzM38snqwr5cFUha7bvJ+AzTu+XwYQTszihVwet/pGvaB2+SCuyYmsRr83L5/X5m9lZXM7g7in84PTenN4vQ8UvKnyR1qi0oopX5+Uz5eN1fLmrhBN7d+BX5/bn2M5tvY4mHmps4WvHYpEWJD7Gz5XH9+D9H5/CXef1Z8nmIs796yf8/p3llDZxo7G0fip8kRYoxu9jwok9+egnp3JJTnce+2gd337wUxbl7/E6mkQwFb5IC5aSEMs9Fx3H1GtzKS6r5LuPfM5zszYSSatqJXKo8EVagVP6pDPjByczqncH/ucfS/jRSwu0ike+QYUv0kq0T4zlqWtG8ONv9eHNhVu44onZ7Cou9zqWRBAVvkgr4vMZ3z8jm79dPozFm/dy0SOfsXFnsdexJEKo8EVaoXMGdWba9SPZXVLOBQ9/xtIte72OJBFAhS/SSuVkpfL690YRF/BxxROzWbJZpR/tVPgirViv9CRemnQCibEBLn98FovzVfrRTIUv0spldkjgxUnH07ZNDJc/MUtL+lFMhS8SBbqn1pR+clyACU/PYf0ObciNRip8kSjRrX0Cz10/kmoHVz4xm617S72OJGGmwheJIsekJzF1Yi57Ssq5+qnZ7C2p8DqShJEKXyTKDOrWjsevyWHDjhJufD6P8spqryNJmKjwRaLQqGPSuPe7g5i1bhe/eGOxzr0TJXSJQ5EodcHQbmzcWcID762mZ1oiN5/W2+tIEmIqfJEo9sMzstm4s4T7Zq4kMzWB8wZ38TqShJBW6YhEMTPjnosGkZuVyu2vLNSBWa2cCl8kysUF/Dxy5TDSkuKY9FwehfvKvI4kIaLCFxE6JMXx2FXD2V1Szs0vzNeeO62UCl9EABjYtR33XnQcczbs4jdvLfU6joSANtqKyFfGDenKsoIiHvtoHQO6tOOy3EyvI0kz0hK+iHzNT8/qx+g+6dz55hLmbdzldRxpRip8Efkav894cPxQuqS04cbn5uucO62ICl9EvqFdQgyPX53DgfJKbnx+ni6I3kqo8EWkXn06JvOnS4awcNMefvWPJTr9QiugwheRBo0d2IkfnJHNq/PyefbzjV7HkSZS4YvIId16RjZjjs3gN28t4/O1O72OI02gwheRQ/L5jD9fOoQeHRK4edp8Nu854HUkOUoqfBE5rLbxNRtxKyqrmfRsHgfKtRG3JWpS4ZvZxWa21MyqzSyn1uNZZnbAzBYEb482PaqIeOmY9CQeGD+EZQVF3PH6Im3EbYGauoS/BLgQ+LieYWudc0OCt5uaOB0RiQBnHNuR28b04R8LtvDkp+u9jiNHqEmnVnDOLYeaU6yKSHS4+bTeLN1SxN0zltOvU1tOyk7zOpI0UijX4fc0sy/M7CMzOzmE0xGRMPL5jD9eMpjeGUnc8vf5bNxZ7HUkaaTDFr6ZvWdmS+q5jTvE0wqATOfcUOA2YJqZtW3g9SeZWZ6Z5RUWFh7dv0JEwiopLsCUq2o22018ei57Sso9TiSNcdjCd86Ncc4NrOf25iGeU+ac2xm8Pw9YC/RpYNwpzrkc51xOenr60f47RCTMstISmXJVDvm7DzDp2XmUVWrPnUgXklU6ZpZuZv7g/V5ANrAuFNMSEe/k9kzlvotrzqH/k1cWUV2tPXciWVN3y7zAzPKBE4C3zWxmcNBoYJGZLQReBW5yzuk8qyKt0LghXfnJWX2ZvnALf3p3pddx5BCaupfOG8Ab9Tz+GvBaU15bRFqOyaceQ/7uEv72wVq6pLThipE9vI4k9dAVr0SkycyM34wbSMHeUn71jyUkx8dw/uAuXseSOnRqBRFpFjF+H49cMZwRWanc9tIC3lu2zetIUocKX0SaTZtYP09ek8OALm2ZPG0+n67e4XUkqUWFLyLNKjk+hqnX5tIrLZFrp87l3yu0pB8pVPgi0uxSEmL5+w3H07djMpOencfbiwq8jiSo8EUkRNonxvLCDSMZmpnC9/8+n5fmful1pKinwheRkGkbXL1zYu80fvbaYn4/YzlVOjjLMyp8EQmphNgAT00YwZXHZ/LYx+u48bl5FJdVeh0rKqnwRSTkYvw+fjtuIHed159/r9jGd/72H1Zu3ed1rKijwheRsDAzJpzYk6nX5rK7pJzzH/qU52dt1JWzwkiFLyJhdXJ2Ou/8cDS5PVP51T+WcMUTs1m/Q+fUDwcVvoiEXXpyHFMn5vK7CwayePNeznrgY/44cyVFpRVeR2vVVPgi4gmfz7hiZA/ev+0Uxg7oxEMfrGH0Hz7g0Y/WsveAij8ULJLWn+Xk5Li8vDyvY4iIB5Zs3st9M1fy0apCEmL9XDisK5fkdGdQ13a6bvZhmNk851zOYcdT4YtIJFmyeS/PfLaB6Qu2UF5VTWZqAmMHdmLUMR3IyUolKU4n+a1LhS8iLdqeknJmLt3KW4sKmLVuJxVVDr/PyOqQQJ+OyWRnJNElpQ1pSXGkJceRFOcnLuAnLuAjLuAnJmD4fUbA58NntOq/ElT4ItJqlJRXMn/jHuas38mKrftYvX0/G3cWcyQH7QZ8B/8DCP70+2gT46dDUixpSXF0ahdPdkYS2RnJ9O2UTHpyXOj+Qc2ssYWvv41EJOIlxAY4KTuNk7LTvnqsrLKKHfvLKdxXxo59ZZRUVFFWUUVZZTWlFVVUVjuqgrea+9U1P6scVa7m8eKyKnbsL2NbUSnzNu7+2sbiXmmJjOzVgTHHZnBSdhpxAb8X//RmpcIXkRYpLuCna0obuqa0aZbXc86xY385q7fvY3H+Xmav38X/LdzC3+d8SXJcgLMHdeLqE7IY2LVds0zPC1qlIyLSgPLKav6zZgdvLSpgxuICDlRUMbxHe75/em9O6ZMeMdsFtA5fRKQZ7T1Qwavz8nn6P+vJ332AEVnt+fnZ/RjeI9XraCp8EZFQKK+s5qW8TTz4/mq27yvjstzu/GxsP1ISYj3L1NjC15G2IiJHIDbg46rje/DB7acyaXQvXs7LZ8yfP+L95ZF/KUcVvojIUUiMC/CLc45l+i0nkp4cz3VT87hr+lJKK6q8jtYgFb6ISBMM6NKONyaPYsKoLJ75bAOXPPY5W/eWeh2rXip8EZEmio/xc9f5A5hy1XDWbt/PeQ99yhdf7vY61jeo8EVEmsmZAzrx+uQTiY/xcemUWfxr6VavI32NCl9EpBn17ZTMmzefxLGd2/K9F+bz2rx8ryN9RYUvItLMUhNjeeH6kRzfK5Ufv7KQJz9d73UkQIUvIhISSXEBnpowgrEDOvHbt5bxxCfrvI6kwhcRCZW4gJ+HLh/KuYM6879vL+fZzzd4mkcnTxMRCaGA38cD44dQXlXNnW8uJdbvY3xupidZtIQvIhJiMX4fD10+lFP6pHPHG4t5e1GBJzlU+CIiYRAX8PPYVcMZntmeH728gDnrd4U9gwpfRCRM4mP8PH51Dt3at+H6qXNZvW1fWKevwhcRCaP2ibFMnZhLXIyfCU/PZVtR+E7DoMIXEQmz7qkJPD1hBHtKypn49FyKyyrDMl0VvoiIBwZ2bcdDVwxjxdYibn1pAdVHckX2o6TCFxHxyGl9M7jz2/15d9k27p25IuTTa1Lhm9l9ZrbCzBaZ2RtmllJr2B1mtsbMVprZWU2PKiLS+lwzKosJo7LokZoY8mk1dQn/XWCgc+44YBVwB4CZ9QfGAwOAscDDZuZv4rRERFodM+Ou8wdw+cjQH4zVpMJ3zv3LOXdwa8MsoFvw/jjgRedcmXNuPbAGyG3KtEREpGmacx3+tcA7wftdgU21huUHHxMREY8c9lw6ZvYe0KmeQb90zr0ZHOeXQCXwwsGn1TN+vZugzWwSMAkgM9Ob80uIiESDwxa+c27MoYab2TXAt4EznHMHSz0f6F5rtG7AlgZefwowBSAnJyf0+yWJiESppu6lMxb4GXC+c66k1qDpwHgzizOznkA2MKcp0xIRkaZp6umRHwLigHfNDGCWc+4m59xSM3sZWEbNqp6bnXNVTZyWiIg0QZMK3znX+xDDfgf8rimvLyIizUdH2oqIRAn773ZW75lZIbCxCS+RBuxopjjNSbmOjHIdGeU6Mq0xVw/nXPrhRoqowm8qM8tzzuV4naMu5ToyynVklOvIRHMurdIREYkSKnwRkSjR2gp/itcBGqBcR0a5joxyHZmozdWq1uGLiEjDWtsSvoiINKDFFb6ZjQ1eVGWNmf28nuFxZvZScPhsM8sKQ6buZvaBmS03s6Vm9sN6xjnVzPaa2YLg7c5Q5wpOd4OZLQ5OM6+e4WZmfw3Or0VmNiwMmfrWmg8LzKzIzG6tM07Y5peZPWVm281sSa3HUs3sXTNbHfzZvoHnXhMcZ3XwvFKhztXgRYfqPPeQ73sIct1lZptrvV/nNPDcQ35/Q5DrpVqZNpjZggaeG8r5VW8/ePIZc861mBvgB9YCvYBYYCHQv844k4FHg/fHAy+FIVdnYFjwfjI1F4Opm+tU4C0P5tkGIO0Qw8+h5rTWBhwPzPbgPd1KzX7EnswvYDQwDFhS67E/AD8P3v85cG89z0sF1gV/tg/ebx/iXGcCgeD9e+vL1Zj3PQS57gJub8R7fcjvb3PnqjP8T8CdHsyvevvBi89YS1vCzwXWOOfWOefKgRepudhKbeOAqcH7rwJnWPBEP6HinCtwzs0P3t8HLKflnP9/HPCsqzELSDGzzmGc/hnAWudcUw64axLn3MfArjoP1/4cTQW+U89TzwLedc7tcs7tpuYKcGNDmcs1fNGhsGlgfjVGY76/IckV7IBLgL831/Qa6xD9EPbPWEsr/MZcWOWrcYJfjL1Ah7CkA4KrkIYCs+sZfIKZLTSzd8xsQJgiOeBfZjbPaq49UJfXF6sZT8NfQi/m10EdnXMFUPOFBTLqGcfreVf7okN1He59D4Vbgquanmpg9YSX8+tkYJtzbnUDw8Myv+r0Q9g/Yy2t8BtzYZVGX3yluZlZEvAacKtzrqjO4PnUrLYYDDwI/CMcmYATnXPDgLOBm81sdJ3hXs6vWOB84JV6Bns1v46El/Ou7kWH6jrc+97cHgGOAYYABdSsPqnLs/kFXMahl+5DPr8O0w8NPq2ex456nrW0wm/MhVW+GsfMAkA7ju7PzyNiZjHUvJkvOOderzvcOVfknNsfvD8DiDGztFDncs5tCf7cDrzBN6/9bTTZAAAB40lEQVQt3OiL1YTA2cB859y2ugO8ml+1bDu4aiv4c3s943gy7+y/Fx26wgVX9NbViPe9WTnntjnnqpxz1cDjDUzPq/kVAC4EXmponFDPrwb6IeyfsZZW+HOBbDPrGVw6HE/NxVZqmw4c3JL9XeDfDX0pmktw/eCTwHLn3J8bGKfTwW0JZpZLzbzfGeJciWaWfPA+NRv8ltQZbTpwtdU4Hth78M/MMGhwqcuL+VVH7c/RNcCb9YwzEzjTzNoHV2GcGXwsZKzhiw7VHqcx73tz56q93eeCBqbXmO9vKIwBVjjn8usbGOr5dYh+CP9nLBRbpUN5o2avklXUbO3/ZfCx31DzBQCIp2YVwRpqrrLVKwyZTqLmz6xFwILg7RzgJuCm4Di3AEup2TNhFjAqDLl6Bae3MDjtg/Ordi4D/hacn4uBnDC9jwnUFHi7Wo95Mr+o+U+nAKigZonqOmq2+7wPrA7+TA2OmwM8Ueu51wY/a2uAiWHItYaadboHP2cH90jrAsw41Pse4lzPBT8/i6gpss51cwV//8b3N5S5go8/c/BzVWvccM6vhvoh7J8xHWkrIhIlWtoqHREROUoqfBGRKKHCFxGJEip8EZEoocIXEYkSKnwRkSihwhcRiRIqfBGRKPH/AZXLceJ18bZpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = np.linspace( .001, 20, 500 )\n",
    "n = np.cos( v ) - v\n",
    "fig, ax = plt.subplots()\n",
    "line, = ax.plot( v, n, label='Super Duper Plot!' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}