{
 "metadata": {
  "name": "",
  "signature": "sha256:9075b7d06499720e96b460bd48268803855407101ba16dedceb957ce75c792db"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notebook to compare previous tides to tides over new topography"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# imports\n",
      "%matplotlib inline\n",
      "import matplotlib.pylab as plt\n",
      "import numpy as np\n",
      "import netCDF4 as NC\n",
      "from scipy.optimize import curve_fit\n",
      "from salishsea_tools import tidetools\n",
      "from salishsea_tools import viz_tools\n",
      "from salishsea_tools import bathy_tools\n",
      "import collections\n",
      "import pandas as pd\n",
      "import csv\n",
      "import math\n",
      "\n",
      "from __future__ import division"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pathname for data - all of the tide runs are stored in this directory\n",
      "path = '/data/nsoontie/MEOPAR/SalishSea/results/tides/'\n",
      "#path = '../../myResults/'\n",
      "\n",
      "#the run we want to analyze\n",
      "runname = 'corr15'\n",
      "\n",
      "#joining the two string together\n",
      "name = path +runname +'/'\n",
      "\n",
      "print name"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/data/nsoontie/MEOPAR/SalishSea/results/tides/corr15/\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pathname for data - \n",
      "path2 = '/ocean/sallen/allen/research/MEOPAR/SalishSea/topog/'\n",
      "#path = '../../myResults/'\n",
      "\n",
      "#the run we want to analyze\n",
      "runname2 = 'try3'\n",
      "\n",
      "#joining the two string together\n",
      "name2 = path2 +runname2 +'/'\n",
      "\n",
      "print name2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/ocean/sallen/allen/research/MEOPAR/SalishSea/topog/try3/\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " stations =  ['PortRenfrew','SheringhamPoint','PedderBay', 'Esquimalt',\n",
      "             'Victoria','CloverPoint','FinnertyCove', 'FulfordHarbour',\n",
      "            'TumboChannel','PatosIsland','WhalerBay', 'Tsawwassen',\n",
      "              'Sandheads', 'PointGrey','PointAtkinson','GibsonsLanding', 'WinchelseaIs',\n",
      "             'HalfmoonBay','IrvinesLanding','PowellRiver', 'LittleRiver', 'Lund',\n",
      "              'TwinIslets','CampbellRiver','MaudeIslandE', 'NympheCove',\n",
      "              'SeymourNarrows','BrownBay','ChathamPoint','KelseyBay','YorkeIsland']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1,figsize=(12,5))\n",
      "for stn in (4,4):\n",
      "    print stations[stn]\n",
      "    fT1 = NC.Dataset(name+stations[stn]+'.nc','r')\n",
      "    time = fT1.variables[\"time_counter\"][:]/3600.  # want hours not seconds\n",
      "    ssh = fT1.variables[\"sossheig\"][:,0,0]\n",
      "    ax.plot(time-4248,ssh,'+-')\n",
      "    fT2 = NC.Dataset(name2+'1h_'+stations[stn]+'.nc','r')\n",
      "    time2 = fT2.variables[\"time_counter\"][:]/3600.  # want hours not seconds\n",
      "    ssh2 = fT2.variables[\"sossheig\"][:,0,0]\n",
      "    ax.plot(time2,ssh2,'x-')\n",
      "    ax.set_xlim((15,25))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Victoria\n",
        "Victoria"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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Rdi9cx/anv6TFos/wrCllZ6+rifzPNURf0PGwsl/uWs2LO5PYaIbgV7OfMQFNeTZhMEGe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\nxpIkSRIA/wdVK4X+61MhxgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f162acbda50>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1,figsize=(12,5))\n",
      "for stn in (14,14):\n",
      "    print stations[stn]\n",
      "    fT1 = NC.Dataset(name+stations[stn]+'.nc','r')\n",
      "    time = fT1.variables[\"time_counter\"][:]/3600.  # want hours not seconds\n",
      "    ssh = fT1.variables[\"sossheig\"][:,0,0]\n",
      "    ax.plot(time-4248,ssh,'+-')\n",
      "    fT2 = NC.Dataset(name2+'1h_'+stations[stn]+'.nc','r')\n",
      "    time2 = fT2.variables[\"time_counter\"][:]/3600.  # want hours not seconds\n",
      "    ssh2 = fT2.variables[\"sossheig\"][:,0,0]\n",
      "    ax.plot(time2,ssh2,'x-')\n",
      "    ax.set_xlim((15,25))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "PointAtkinson\n",
        "PointAtkinson"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Hjl2V4mDKQRYPH0vrqa+wtsklNBw/iphesaVcSUREpOJV2KoU1toi4G5gCrAG\n+MJau9YYc7sx5vZDMT8DW4wxm4B3gLvKc00R8a7xW5Yc0VPcoUYsa3pfyHUhOSw8WEj1X7/llTUz\nAI5Zqi04KhjX5Efw3bqR4lp1CerTkVnt7iUlMdnbtyEiInJc2vlORE6Zx+PhscRfeCkllzo2k287\nldBe8Sepq1NYPfwF2i6fQGK322j38UNUbxzhxYxFRKSq0853IlIhHA4Hz7e/iOS+g4j1c9J1+Qou\nmv0R2YV5JcbXbBWFa+mr5C1IwJGZTnHTONwDniU7OftwjFazEBGRyqLCWETKLTIglDnn3cS01s1Y\nnFNAjRnf8dKaX0uNr9OtHn3Xvkv2lPn4blxDTt0mzLp8DHmZedokREREKo1aKUTktPJ4PDye+Asv\n7c2hNvv5ptMFdIlsUOaYDV8nsu+eJ4hJXc6uyA503vEtTj+ndxIWEZEqRa0UIuI1DoeD59pfxJ5+\ng2ng56Tb8kSGzP6w1PYKgJxdGeTGdWBL44F02/sDaQEx2iRERES8TjPGIlKhZuxZz9Ur5rLfBPF0\nvShGtuxfZvzMfqPwb9ucBm+NZGtMHxp99SK1u2izTBEROT00Yywileb82nEkD7yJB6Or8djODOpN\nHsfC1K2lxhtj6Dn2GqolraOwfhP8urXHfcEz5GbkejFrERGpilQYi0iFczgcPNtuCHv7DaGRv5Me\nCSu5sJT2ivChLuDQGsiznyJ39hL81yWQVqslCx76BuvRX5ZERKRiqJVCRLxuxp71XLNiLpkmmNEx\nkTzS6oLjjln+6kyCH7uPgwGRBL83hmbD2nohUxEROdeU1UqhwlhEKs0TK37mheSD1LL7+arT+fSo\n2ajM+KK8Iubf9B4tvohnTathtPn2KSKa1vBStiIici5Qj7GInJGePtRe0TTAh14Jqxg0awIHCkrv\nJfYJ8KHvZ3fis3EtGAfFcS2YNWwsRXlFXsxaRETOVSqMRaRSRQQEM9N1IzPaNGdFbhGRM7/n+dXT\nyhxTvXEE/RLHkvnNDEJnfM/W8PYse3H64e+1zJuIiJwKtVKIyBnlycRfeH5PFlH2AF+fQHuF9VgW\nPfY9tV/+B0k121Hn01fYEv8hLne8dxIWEZGzinqMReSsklmQw1/mf4m7MIIL/PbxdY+rCPMLLHNM\nXmYev131Km2nv8L2sLa0TpqKb5CvlzIWEZGzhXqMReSsEu4XxAzXjbjbNGdlbhGRM3/g2VVTyxyz\n7oPfoKBNyxUZAAAgAElEQVSAxPY30GG/m4yQesxqeYfaKkRE5IRpxlhEznjxiZN5ds8BatoDfNXR\nRa+oJmXGz+w3Cp/GDWjxwUhWue6h16RHNXssIiKAZoxF5CwX33YwqeddTIsAH/okrmXAcVavMMbQ\n5783UbhwOcErF7Apshsbvk70YsYiInI2UmEsImeFcL8gfnXdiLt1HKsOtVc8U0p7xR+759XuEkPn\nvT+T9te7ibiqP+7+T1OYU+jFrEVE5GyiVgoROSuNXjmZZ5J+b6/4skM/etdqWmZ80sKdJF10K8E5\nqTg/+oBmV7TxUqYiInIm0aoUInJOyizIYdiCr5hRUJ3z/fbxdY8rCfcLKjXeeixzb/4vLT58hFXn\n30evHx5W77GISBWjwlhEzmlz927kquWzSDVhPFGnGk+2GVRmvGaPRUSqLhXGIlIlPLNqKk/t3kcN\nm81XHfqW2V5hPZa5N71Pi48eZVX/++n1/UjNHouIVAFalUJEqoTHWw8k7bxLaR3oQ9/EtfR3f0Bm\nQU6JscZh6DNhBPnzlhKyfDabIruz8btVh7/X+sciIlXPKRfGxpgIY8w0Y8wGY8xUY0x4KXHbjDGJ\nxpjlxphFp56qiMjxhfkFMq3fDcxp15K1eUXUnDmJ+MTJpcbX7VGfTimTSbvyTqpfcR7uAc9SlFdE\n5kS395IWEZEzwim3UhhjXgTSrLUvGmMeBqpbax8pIW4r0Mlam3Gc86mVQkROu2dXTWX0ofaKL9r3\noW90s1Jjdy/YQfLFIwjMyyAttjN917ztxUxFRMQbKqqV4lJgwqHXE4ChZeVQjuuIiJyyxw61V7QN\n9MG1ch3nuT8gI+9gibGpC7dwoHUPskLr0HftO6wM6c7MHo+qrUJEpIooz4zxPmtt9UOvDZDxx/uj\n4rYA+4Fi4B1r7XulnE8zxiJSoRakbuHKpTPYa8J4tHYoT7W9sNRYd5eHcO5Pp8nmqWy77zW6vzwM\n49Dv+CIiZ7tTnjE+1EO8soTj0j/HHapoS6tqe1lrOwAXAn83xvQ5lZsQESmvHjUbsWvwCJ6Kqc4L\ne/ZTa/L7uJM3lBwcHEyfDf8l5fXPiHxzNEtqDWH7jM3eTVhERLyqPDPG6wCXtTbZGFMbmGmtbX6c\nMaOAbGvtKyV8Z0eNGnX4vcvlwuVynVJuIiLHc6AglysXfMm0gur09c3g2+5XEhEQfPj7hDFu2t/v\nAqAwp5B5w16jzeQXWXneffT4biT+Yf6VlLmIiJwMt9uN2+0+/H706NGnfx3jQw/fpVtr/2WMeQQI\nP/rhO2NMEOC01mYZY4KBqcBoa+3UEs6nVgoR8boFqVu4ctkM9lKNR6KDebrdkFJjd83bzu5h9xGV\nvpZ9z75Jx4f6ezFTERE5HSpkgw9jTATwJVAf2AZcZa3NNMbUAd6z1l5kjGkEfHtoiA/wibX2+VLO\np8JYRCrNC6un8+SuNKrbg3zRvg+uMlavWPjYD9T9171sj+lF0x9eIapttBczFRGR8tDOdyIiJ+BA\nQS5X/fYVU/PD6eOTznc9rjqiveLPDqYcZPGlT9N60fusHhZP74/vwOnn9HLGIiJyslQYi4ichIWp\nWxm29FeSTTVGRgfzdJvBOBwlP6u86fvVHBx+J75FuZh33qbF9Z2AI3uURUTkzKEtoUVETkK3mg3Z\nOXgEz9aL4OXkA0RPHc+MPetLjG1yWSva7ptFxjV/p8YNFzGr7T3s37FfO+eJiJyFVBiLiJRiZMv+\npJ8/lM5Bvlywaj2xk99jefr2I2K2Z6cxetUUeo+7EZ91qzGF+eQ0bIn/xlVYj/4KJiJyNlFhLCJS\nhhDfAH7uO5yFHdpSiKHj8tXctfhbPB4P27PTuHjBRG5q1BmAHT+txFOrDpsaD6ZH0jds92+Gu+MD\n2jlPROQsoR5jEZGT8OjySbyQDuFFKUQYDzN6DSU2JPKYOHefJ8DPjzYzX2fVVU/R5+M7cPhoLkJE\npLKpx1hE5DR5vsPFTG7RkMyARmxxRPFI4jSKPMXHBjqduH59gn0TZxPx08esrNGPLT+v837CIiJy\nwlQYi4ichO3ZaTy4ZgFzWtSlfnEKP+wvpMbUj5iStOaIuPChLgCaXNqSlulzyBxwFdUu7o174HMU\n5hRWQuYiInI8aqUQETlBf/QUT+rxe/vE9uw0hiyYSD0/P6YWhDPAbx/f9byaIN+St4veNW87e4be\nQVj2HorfGUfL4Z29fAciIqJWChGR02D8liWHi2KA2JBIfu4xlO7Vo5jdtgVLc4uJ/PUrPtm6uMTx\nMb1i6bz3Z9JufJCaN16Eu+tIctJyvHkLIiJSBs0Yi4icJh6PhzuWfMe4/U66ODL4pddfS905L3V1\nChuH3EfdpMVk/Os9OvzjPC9nKyJSNWnnOxERL1q1bzdDFv1CMqG8WL8W97dwlRq76IkfiXn+LjY1\nGUy7KS9RLTbce4mKiFRBaqUQEfGi1tXrsmPQCB6KDuX/dmbQYuo4dmRnlBjb9elLCN6yCpw+5DRq\nxcJHJx7+Tusfi4h4lwpjEZEK8my7IWzt6QKg0fyZPLbi5xLjqtWvRt/Vb5Ey5jOiXhnJgpgrSUlM\n1rbSIiJepsJYRKQC1Q+JYO3AEbxSvwYvJWdRf8o4Vu3bXWJsu3v6Ep28gvx6TTDt2xK4PkHbSouI\neJEKYxERL7ivuYvkfhdT28dB2yWLuW3RN3g8nmPi1n+4EPz9WRt3Od2Sv/99W+lO/1BbhYiIF+jh\nOxERL/tk62JuXb+eIAqY2KEPvWs1LTHO3ecJcDppNftN1t/yEr3eGY5xlPi8iIiInCA9fCcicga5\nrmEX0vpfSadAJ30T13LFvE8oKC46NtDpxOWOJ/2TKdT8+FUW176U5GVJ3k9YRKSKUGEsIlIJgnz9\nmdLvBn5q2ZjpWYXUmPYpP+xMPCLmj22lm1/TgYapi8mJ64Czc3vm3vGReo9FRCqAWilERCpZkaeY\n4Qu/4vPsIFy+GfzQ62pCfANKjF37yTKcI25kX7UGNJj8DrXa1/ZytiIiZze1UoiInMF8HE4+7XE1\nC9q3YXVeMZG/fsv4zQtKjG1xXUcapC4ht2k7HB3bMe/OjzV7LCJymmjGWETkDOLxeLhv2fe8uc/Q\n3qTzS68riQoMKzF27cdL8RlxIxnVG9FwyjtEtY32crYiImcfzRiLiJwlHA4HYztfzpqu3dlXbKk7\newovrvm1xNgW13eifsoSchu3wbRvx7y7PtHssYhIOZxyYWyMudIYs9oYU2yM6VhG3GBjzDpjzEZj\nzMOnej0Rkaokrlo0WwaN4Ik6Yfxz1z6aTR3H1qzUY+L8w/xxzX2G1A9+otZ/n2NhzF9IXbX38Pda\n/1hE5MSVZ8Z4JXA5MLu0AGOME3gDGAy0BK4xxrQoxzVFRKqUJ9sMYkfvCwgw0GTBXB5a9mOJcS2H\nd6ZeyjLyGrSAtm2Zf89nWI/VttIiIifhlAtja+06a+2G44R1BTZZa7dZawuBz4HLTvWaIiJVUZ2g\ncBIHjOCNBpH8OzWbOpPHkZCx85g4/zB/XPOfI+X9H6n17tMsrDcMsrIqIWMRkbNTRfcY1wX+/H/v\nXYc+ExGRk3Rnsz7sPe8yGvk76bh0GTf+9mWJ20oX7s9hZ5e/EJiVgmvZqyyr3h9378fVViEichxl\nFsbGmGnGmJUlHJec4Pn1FIiIyGkU7hfE3PNu4stm9fgqM5/IqROYsWf9ETHt73fhmvsM7Q7MYVbb\neygIrEaT3z4mZ3uKHs4TESmDT1lfWmsHlPP8u4F6f3pfj99njUsUHx9/+LXL5cLlcpXz8iIi56Zh\nsR25OKYNV87/nAtWreeiTYv4qufVBDh9j4iz1SPovuLfJIxxE/HPB1j5/r/xeWMMLYd3rqTMRUS8\ny+1243a7Tyi23OsYG2NmAg9aa5eW8J0PsB7oDyQBi4BrrLVrS4jVOsYiIqdgxp71XLFiPgX4MKFF\nK4bF/m+hoIQxbtrf7wKguKCY+bd9QNOPnmBTgwE0/uI5andWd5uIVC0Vso6xMeZyY8xOoDvwkzHm\nl0Of1zHG/ARgrS0C7gamAGuAL0oqikVE5NSdXzuO9IE3cGW4P1dt2EmfmeM5UJALcLgoBnD6Oenz\nwS0E71xPUa26+Hdti7v/0+Sk5VRS5iIiZxbtfCcicg5Zlr6DixdPJd0EM6ZhDHc261Nq7K6529hx\n7cPEJi1g2+0v0HPsNRhHiZMoIiLnjLJmjFUYi4icYzweDw8lTGJMWjGtTDqTew2jTlB4qfGJb87F\n56EHKHb4YF57jdYjunsxWxER79KW0CIiVYjD4eCVjpeyoUcv8izUnzudp1ZOKTW+7V29ab5/Ifuv\nuZMatw9jXoPrSFr4v5U2tcybiFQVKoxFRM5RjUOj2DBwBM/UDefppAM0mjKO9fuTS4x1+Djo/e5w\nwvaspzC2CQE92uPu+yTZydnaPU9Eqgy1UoiIVAEpuQe4cN5XJNgI7qoOr3e8DIej9LmR3Qt2sO2a\nR2m0cxbbo7rQdec3OHw0lyIiZz+1UoiIVHFRgWEsveAW3m0YzXtpudSeOp7FadtKjU9duIXCBk3Z\n1HgQ3ZMnste/HnPiRqitQkTOaZoxFhGpYrIL87h03ue4CyO4OiSHD7tdiY/DWWq8u98ofOIa0/j9\nf7KlvotGX75A7S4xXsxYROT00YyxiIgcFuIbwAzXjXwXF8uP+wuInPoRU5LWlD7AGHq/O5zQ3eso\nrNsAv27tcfd/mtyMXO8lLSLiBSqMRUSqqMvqtyN9wLX0D/XlwtWbGDz7Q3IK84+JCx/qAiAkOgTX\n3GfIcS/Gf30i6VHNmX/fF1iP/tonIucGtVKIiAhz925k6PI55ODHe3FxXNewy3HHJLw+i4BH7yff\nNwS/N1+nxXUdjztGRKSyaYMPERE5Lo/Hwx1LvmPcfiddnRn83POvRAQElzmmuKCYeSP+S9wnT7Kh\n8RDivn6WqLbRXspYROTkqcdYRESOy+Fw8G7XK0js3IWkQg/Rsybx+jp3mWOcfk76fngrAVvXUVwt\nAmf71riHvEj+gf+1ZGglCxE5W6gwFhGRI7SuXpcdg0bwUHQo/9iRQctp49iRnVHmmGr1q+Fa/BJZ\nUxYQuGwuyTVasfDRiViP1QYhInLWUCuFiIiUakd2BoPmf8tGqvNwrUCebTfkhMYtfWEa1eLv50BQ\nbbJqN6Xf6rcqOFMRkROjVgoRETkl9UMiWDtwBC/Xq8FLyVnUnzKO1ZlJxx3nDPBlV9crKHb60W/N\n26wM6cHMHo+qrUJEzmiaMRYRkROSkXeQC+d9wWJPBB1MGl93vYyGYTUPf789O43xW5YQ33bwEePc\nXR7CuT+dJpunsvXeV+nxypUYR4mTNSIiFU4zxiIiUm4RAcEs7H8zHzauw+qiQJr+Nouvty8Dfi+K\nL14wkZsadT52YHAwfTb8l9SxnxP59jMsqzmIrVM2eDl7EZHj04yxiIictJzCfAbN+ZS5tg7d2MX+\nYsvknkOJDYk8JjZhjJv297sAKMwpZN41Y2nz43Os7H0X3SY+SmBEoJezF5GqTOsYi4hIhXh5zQwe\nSnFgCjK5IjiH97tcTpjf8QvdPYt3se0v/6Bu8hL2Pv4GXUad2EN9IiLlpVYKERE57bZnpzFh9xbm\ntKhLXU8aMw7kEeH+iZsXfkVecWGZY2t3iaHHzi9JiX+Lms/ex291/sLuBTu8lLmISMlUGIuIyEn7\no6d4Uo+h9K7VlLm9h1LHx0N8dCjf7jtItenf8MDS7ynyFJd5ns6PDSI6ZSV5zdsT0Ksj7iEvUpBd\n4KW7EBE5kgpjERE5aeO3LGFSj//1FMeGRDKpx1CKsGQMHM7omEjeS91P6NTPeTLxFzweT6nnCggP\nwDXjSbKnLyR48Ux2RHYg4fVZ3roVEZHD1GMsIiIVwuPx8FjiL7yWvB9fChkdW59/tDivzDHWY1n4\nyHfUe/V+tsa6iPv+JWq2rnXEA3wiIuVRIT3GxpgrjTGrjTHFxpiOZcRtM8YkGmOWG2MWner1RETk\n7OJwOHi+/UUcGHAVwyPDeGRHChGTx/P+pgWljjEOQ/cX/0K1XWsoqhGNaduaWVe/yb5vZ3gxcxE5\nV639eGmZ35/yjLExpjngAd4B/s9au6yUuK1AJ2ttxnHOpxljEZFzWE5hPncu/Z5PDjiJ9GTyRosO\nDIstdV4FgI3frSLnxruonbWerY++R9enL9HmICJy0pIW7mTLNf+k6fbpRHuST/+MsbV2nbX2RFdo\n1//FRESquCBffyZ0v4o014X0DAngrxu2EztlHFOS1pQYnzDGze7Xvyaj/XlE2RQaPn8ryb4xuC96\nCevRRIqIHF9WUhbuXo8R0KM9RXUbELSz7NLVGw/fWWC6MWaJMeZWL1xPRETOYOF+QXzb+zp29z6f\n5gG+XLh6E82mjmNeyqYj4trf78Lljue8WaNx9xtFZMEett77GvWnv09i9X6sGDu7ku5ARM5kCWPc\nFOUVMfu6d8ip1wyf5F3kL1yBa87ThNYJLXNsmYWxMWaaMWZlCcclJ5FfL2ttB+BC4O/GmD4nMVZE\nRM5R0YHVmNLvBrb26EmUr5M+iWtpO20cift2lRjv8HHQ87WrqL9/FVl/HUH1f9zI0hoDWfX+Qi9n\nLiJnKuuxHBzzHtvC2xH68+dkfPgTvTdPoHaXmBMaX+5VKYwxMymjx/io2FFAtrX2lRK+s6NGjTr8\n3uVy4XK5ypWbiIicPVZnJjF8yS8sJ4qujlQ+6XIxjUOjAEpclaIwp5AFt42nyedPszOyA+GvP0Xc\nX9tXQuYiciZYNe43CkY+Ruy+FWx+fDxdRl+McRjcbjdut/tw3OjRoytuS+hDhfGD1tpjHvMzxgQB\nTmttljEmGJgKjLbWTi0hVg/fiYgIi9O2MXzZdNabKM7zzeCjrkOpExReanxeZh4Lb3mX5hOfZ0vt\n3tR8czRNLm3pxYxFpDKt/yKBglvupP7BNayL6ke3lB9x9/t9sjV8qOuYX6orarm2y40xO4HuwE/G\nmF8OfV7HGPPTobBoYI4xJgFYCEwqqSgWERH5Q5fIBqwdOILprePYml9EzDw3l8z5iIy8gyXGB4QH\n0O+bewnZs4n8tl2oNtTF3EZ/Y/uvR/YsJ4xxeyF7EfGWLT+vY379vxJ+7YVkDLoG/3176bb3B9z9\nRuFyx+Nyx5/0+uflWZXiO2ttPWttoLU22lp74aHPk6y1Fx16vcVa2/7Q0dpa+/ypXk9ERKqW82vH\nsWXQCL6Ja8DSg/nUnD2Faxd8TnZhXonxwVHBuH4eif+OTRQ1aErIgO7Mbn4ruxfsACBzotuL2YtI\nRdk5eytzm9xI2MV9KGjZgZA9m+j3zb0EhAeU+9zaElpERM5ol9dvT9LgEYxvUpep+3OoPuNHblv0\nDQXFRSXGh8WE4ZrxJI6NG/DUiCKwVwdmtb0Hsg54OXMROZ2SlyUxu/VdBLs6U1Q3Ft+tG3FNfoTg\nqOAj4sKHuk75GtoSWkREzipj183i8W1bycOfu6OC+Vf7i/BxOEuMTRjjJvOTn6ixbi5tsn9jbWAH\nUhr3pNotw7TFtMhZIm1tKqv+9gLtlo1nRadbaPPxw9SIizzl85XVY6zCWEREzjoej4fn1kzn+V17\nscAjdaN4vNUAHI7S/xDq7joSgoNpNes/bKrTl2rPPkzLG7p4L2kROa4/r0Czf3smy69/hbbz3mRl\nq6uJm/AY0R3rlPsaFfLwnYiISGVxOBw83nogWQOv4+5a4Ty3O4PwqR/x+jp36YOCgnDNHEVQ8lYK\nuvah2i1XsCyiP8tenK6d9ETOEJkT3WQnZ+Me9DyFDZvi3LubnNlL6bfyP6elKD4ezRiLiMhZr6C4\niHuXfs/7+4oJs9m82rQlNzTufkTM0WshF2QXsOiBz6j94b/I9wli/x2P0PX5y3H6ldyWISIVKzcj\nl9XNLqf+vgQ21XUR/c5oGl0Yd9qvo1YKERGpEnIK87l1yXd8nuVHLU8G/2nZmcvrl73ph6fIw+In\nfyRo7AuE5Kez8+qH6PbGcPzD/L2UtUjVNu/ezwkdP5bG2csJJpdZLW7HRkWXuAbx6aDCWEREqpSM\nvIPctPg7JuWFUN+Txri2velfu3mZY6zHkvifORQ8/QL10hNYN/gBOr5zO2ExYYdjStqBT0ROnvVY\nEl6bSf4rY2mWPJvEDjfQ8KW/s/Wpj3C54yv02uoxFhGRKiUiIJjv+1zPzl4uGgf4MmDVBlpMHcfC\n1K2ljjEOQ7t7+tIl5Wf2f/ozvquXU1i/Ee5ej5G6OgXQWsgi5XUw5SCzr32bTUFtCH3sHgpcg/BL\n2o5r6avEnt+4stNTYSwiIueuOkHhTO93Axu7dyfcx0GPFavoOP19VmcmlTku7q/t6bXtU7KnL8Sx\nfx++beKY3fouTEa6lzIXObdsn7EZd6d/kB9dH/+Zk8l+9t80zllF30/vICQ65HBcedYgPh3USiEi\nIlVG4r5dDF86mURq0cORysddLqFhaM0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TaCQs0Eg4fiLxk1haj0uJJMYDsV4v8V4vCWHh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\nAAVjEREREREA/h+53WHWM25/BwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f162ae5e590>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "0.5/12.4*360."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "14.516129032258064"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}