{
 "metadata": {
  "name": "",
  "signature": "sha256:f2f317d9f52a6130ff40722ad18f46dc658f03783885e62fc00b630f381399c0"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "import pandas as pd\n",
      "import csv\n",
      "\n",
      "from scipy.interpolate import SmoothBivariateSpline, bisplrep\n",
      "\n",
      "from threshold_functions import *\n",
      "from equivalent_ellipse import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_list = list()\n",
      "y_list = list()\n",
      "\n",
      "with open('../data/cutout_x.csv', 'r') as x_csvfile:\n",
      "    with open('../data/cutout_y.csv', 'r') as y_csvfile:\n",
      "        \n",
      "        x_reader = csv.reader(x_csvfile, delimiter=',', lineterminator='\\n')\n",
      "        y_reader = csv.reader(y_csvfile, delimiter=',', lineterminator='\\n')\n",
      "        \n",
      "        for row in x_reader:\n",
      "            x_list += [row]\n",
      "  \n",
      "        for row in y_reader:\n",
      "            y_list += [row]\n",
      "\n",
      "            \n",
      "num_cutouts = len(x_list)\n",
      "\n",
      "\n",
      "x_array = [0,]*num_cutouts\n",
      "y_array = [0,]*num_cutouts\n",
      "\n",
      "\n",
      "for i in range(num_cutouts):\n",
      "\n",
      "    x_array[i] = array(x_list[i], dtype='float')\n",
      "    y_array[i] = array(y_list[i], dtype='float')\n",
      "\n",
      "    \n",
      "cutout = [0,]*num_cutouts\n",
      "\n",
      "for i in range(num_cutouts):\n",
      "    cutout[i] = shapely_cutout(x_array[i],y_array[i])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cutout_dimensions = pd.DataFrame.from_csv('../data/cutout_dimensions.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ellipse_def = transpose(array([cutout_dimensions['a'].values,\n",
      "                               cutout_dimensions['b'].values,\n",
      "                               cutout_dimensions['width'].values,\n",
      "                               cutout_dimensions['length'].values,\n",
      "                               cutout_dimensions['theta'].values]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "width_init = cutout_dimensions['width'].values\n",
      "\n",
      "cutoutRef_init = arange(len(width_init))\n",
      "\n",
      "sorted_ref = cutoutRef_init[argsort(width_init)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "similar_list = diff(width_init[sorted_ref]) < 0.0001\n",
      "duplicates_removed_ref = sorted_ref[~similar_list]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boundBox = cutout_dimensions['box_bounds'].values\n",
      "too_big = boundBox[duplicates_removed_ref] > 11\n",
      "\n",
      "too_big_removed_ref = duplicates_removed_ref[~too_big]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "final_ref = too_big_removed_ref"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "width = cutout_dimensions['width'].values[final_ref]\n",
      "length = cutout_dimensions['length'].values[final_ref]\n",
      "\n",
      "cutoutRef = final_ref\n",
      "\n",
      "ratio = width/length"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot(append(width,width),append(log2(ratio),log2(1/ratio)),'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def give_calc(xGrid,yGrid,width,ratio,w):\n",
      "    \n",
      "    w = w/max(w)\n",
      "    \n",
      "    ref = (w < 0.01)\n",
      "    width = delete(width,find(ref))\n",
      "    ratio = delete(ratio,find(ref))\n",
      "    w = delete(w,find(ref))\n",
      "    \n",
      "    return fit_give(xGrid,log2(yGrid),\n",
      "                    append(width,width),\n",
      "                    append(log2(ratio),log2(1/ratio)),\n",
      "                    zeros(len(width)*2),append(w,w))\n",
      "\n",
      "def gap_calc(xGrid,yGrid,width,ratio):\n",
      "    \n",
      "    return angle_gap(xGrid,log2(yGrid),append(width,width),\n",
      "                     append(log2(ratio),log2(1/ratio)),1,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# w = ones(len(width))\n",
      "# valid = (give_calc(width,ratio,width,ratio,w) < 0.15) & (gap_calc(width,ratio,width,ratio) < 130)\n",
      "\n",
      "# curr_give = give_calc(width[valid],ratio[valid],width,ratio,w)\n",
      "# curr_gap = gap_calc(width[valid],ratio[valid],width,ratio)\n",
      "# curr_gap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# curr_give = give_calc(width[valid],ratio[valid],width,ratio,w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# curr_give[2] = 0.2\n",
      "# sum((curr_give/0.15)**15)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# curr_gap[2] = 100\n",
      "# sum((curr_gap/130)**30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# sum(w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# sort(rand(100))[0:-5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "global keeping_track\n",
      "\n",
      "def toMinimise(w):\n",
      "        \n",
      "    if (sum(w) > 13):\n",
      "        curr_give = give_calc(width,ratio,width,ratio,w)\n",
      "    else:\n",
      "        curr_give = ones(len(w))\n",
      "    \n",
      "    weights_bias = sum((2*exp(-((w-0.5)*2)**2) + 3*w - 1)/3)\n",
      "    too_few_bias = exp(-(sum(w)-15))\n",
      "    give_bias_init = 4/(1 + exp(-(curr_give-0.15)*200))\n",
      "    give_bias = sum(sort(give_bias_init)[0:-5])\n",
      "    \n",
      "    output = weights_bias + give_bias + too_few_bias\n",
      "        \n",
      "    return output"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# t = linspace(0,20,1000)\n",
      "# y = exp(-(t-13))\n",
      "# plot(t,y)\n",
      "# ylim([0,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# t = linspace(0,1,1000)\n",
      "# y = (2*exp(-((t-0.5)*2)**2) + 3*t - 1)/3\n",
      "# plot(t,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# t = linspace(0,1,1000)\n",
      "# y = 4/(1 + exp(-(t-0.15)*200))\n",
      "\n",
      "# t5 = 0.15\n",
      "# y5 = 4/(1 + exp(-(t5-0.15)*200))\n",
      "# print(y5)\n",
      "# plot(t,y)\n",
      "# scatter(t5,y5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_fun(w, f, accepted):\n",
      "\n",
      "    global successCount_basinhopping\n",
      "    global minVals_basinhopping\n",
      "    global nSuccess_basinhopping\n",
      "\n",
      "    print(\"Output %.4f\" % (f))\n",
      "    w_show = reshape(w,[3,23])\n",
      "    imshow(w_show, interpolation='nearest', cmap=cm.gray, vmin=0, vmax=1)\n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bounds = ([0,1],)*len(width)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# x0 = ones(len(width))\n",
      "x0 = loadtxt('x0')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "keeping_track = 0\n",
      "\n",
      "global nSuccess_basinhopping\n",
      "nSuccess_basinhopping = 5\n",
      "\n",
      "global minVals_basinhopping\n",
      "minVals_basinhopping = np.empty(nSuccess_basinhopping)\n",
      "minVals_basinhopping[:] = np.nan\n",
      "\n",
      "global successCount_basinhopping\n",
      "successCount_basinhopping = 0\n",
      "\n",
      "\n",
      "minimizer_kwargs = {\"tol\":0.01,\"method\": 'SLSQP', \"bounds\": bounds}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# curr_give = give_calc(width[valid],ratio[valid],width,ratio,ones(len(width)))\n",
      "# curr_give"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# give_calc(width[valid],ratio[valid],width,ratio,x0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# (give_calc(width[valid],ratio[valid],width,ratio,ones(len(width))) - \n",
      "#  give_calc(width[valid],ratio[valid],width,ratio,x0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resltn = 25 # Must be odd\n",
      "\n",
      "xRange = [min(width),max(width)]\n",
      "yRange = [min(ratio),max(ratio)]\n",
      "\n",
      "xVec = linspace(xRange[0],xRange[1],resltn)\n",
      "yVec = linspace(yRange[0],yRange[1],resltn)\n",
      "\n",
      "xMesh, yMesh = meshgrid(xVec, yVec)\n",
      "\n",
      "giveMesh = give_calc(xMesh,yMesh,width,ratio,ones(len(width)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "while 1:\n",
      "    output = basinhopping(toMinimise,x0,\n",
      "                          niter=20,minimizer_kwargs=minimizer_kwargs,\n",
      "                          callback=print_fun)\n",
      "\n",
      "\n",
      "    print(\"Final %.4f\" % (output.fun))\n",
      "    w_show = reshape(output.x,[3,23])\n",
      "    imshow(w_show, interpolation='nearest', cmap=cm.gray, vmin=0, vmax=1)\n",
      "    show()\n",
      "    \n",
      "    w = output.x/max(output.x)\n",
      "    \n",
      "#     give_pic = give_calc(width,ratio,width,ratio,w)\n",
      "#     print(around(give_pic,4))   \n",
      "      \n",
      "    saveStuffs = w > 0.5\n",
      "\n",
      "    widthSave = width[saveStuffs]\n",
      "    ratioSave = ratio[saveStuffs]\n",
      "\n",
      "    plot(width[~saveStuffs],ratio[~saveStuffs],'rx')\n",
      "    scatter(widthSave,ratioSave)\n",
      "    \n",
      "    print(\"Currently %d cutouts require measurement:\" % (sum(saveStuffs)))\n",
      "    \n",
      "    \n",
      "    newGiveMesh = give_calc(xMesh,yMesh,width,ratio,w)\n",
      "    contour(xMesh,yMesh,giveMesh, levels=[0.05,0.1,0.15], colors='g',alpha=0.7)\n",
      "    contour(xMesh,yMesh,newGiveMesh, levels=[0.05,0.1,0.15], colors='r',alpha=0.7)\n",
      "    \n",
      "    title('Optimisation of where to measure')\n",
      "    xlabel('Width (cm)')\n",
      "    ylabel('Ratio')\n",
      "    \n",
      "    x0 = w\n",
      "    \n",
      "#     print(output)\n",
      "    \n",
      "    savetxt('cutouts_to_be_measured',cutoutRef[saveStuffs])\n",
      "    \n",
      "    savetxt('x0',x0)\n",
      "    \n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 71.5533\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACBdJREFUeJzt3X2MHGUdwPHvWSsRSjQg6RU4PYzvL2mtSX2pjZeASDFR\nSQA1kTSQEDW+ECGhippr/2tIjJiozRkh8SUpGolNVRQhYQ0JiXByPVB6xYZrIlIpsRoRgxR6/vHM\nsXt7szt798zMzux+P8nkZnemN8/NPX32uef5Pb8BSZIkSZIkSZIkSZIkSZKk2hqJ+LdnAT8FXgcc\nBa4E/pVy3lHg38CLwElgS8Q1JUkFuxm4MdnfCezpcN484YNAklQDc8D6ZH80eZ1mHji7lBJJkqL9\ns2V/pO11q8eBGWAauLboQkmS4OUZx+8m9Mrbfa3t9UKypdkKHAPOSb7fHHDfCsooSSrRHM2GfwOd\nh2VaTQI3pB3YuHHj4geEm5ubm1sPW9JupnpZpwM9OADsSPZ3APtTzjkdODPZPwO4GHgk7ZvNzs6y\nsLCQuk1OTnY8FrPFKKI8MVtR92hQtiLvz7DUoUH6/zIoP+fs7GzHssQ07nuADwFPAN8AriBEzZwL\n/Do5Z5QwBPM0cAJ4fbIvSSpQTON+Avgw8BzwduAtwKeAVwEfSc55HLgJeBA4Dfg4sDfimpKkHsQ0\n7hAWJB0hLFQ6CdwOfKztnI8CP0z2/wC8mmYIZU8mJiZiyjgUvEfdeX+yeY8GS2zjfh7w15bXTyTv\nZZ1z/kouYqXL5j3qzvuTzXs0WGIb915nH9rTHMTNWkiSusqKc8/yN2Cs5fUYoWfe7Zzzk/eW2bVr\n10v7ExMT9iQkqUWj0aDRaPR0bkziMAgfDoeBC4EngQcIk6qHWs65FPhC8vW9wC3J13YLsWFIKzUy\nsvofv+yyqrpWW4/qVoeG5f9LnX7OpKypBY7tub9AaLjvAtYAtxIa9s8kx6eAOwkN+xHgWeDqyGtK\nkjLENu6wdMXUqeS9qZbjE8BVhLBICA39QzlcV5LUQWzjvgb4DnARYRz9QcLK1UNt5/2eEBIpSSpB\nGXHuED+2L0lagTLi3BeA9wOzhPH3t0VeU5KUIXZYppep4YcIoZD/BbYTEoy9KfK6uYiZ2a5ThESd\nZv/raLX3aJh+LzE/a9n60S4UoYw492da9n8DfI/w2L0T7d/MOHdJykcZce7rgeOEXv4W4GfAeMr3\nKj3OPYY9d8Wq2++lSr3SItWw515InPv3Cb3ww4Sx97Q499sJY+6ngMeAT0ZeU5KUIfZjZhvwH+BH\nwDtTjreuTn0P8G3SV6eCPffC1K2HOCzq9nux556tSj332GiZ++j8YGzIId2vJGnlYhv3LNHpfiVJ\nK1d04w6m+5Wk0uWRW6abntP9gqGQkpSXPEb/x4Ffkj2h2i3dLzihWpi6TdwNi7r9XpxQzTZIE6r7\ngEeBdwDPA9cQwiAXQyHvBP4HvAjcS5hQ/XrkNSVJGfL4mMkKh5wAric7K6Q994LUrYc4LOr2e7Hn\nnm2Qeu6QHQ7Z8eKSpGKUES1jVkhJKlnR0TJQ4ayQkjSoymjce84KWadx7H6YmZnpdxFKUbex6NWq\n29jupk2bVv1v61R3q1z/Go0GjUbjpde7d+/ueG4ZjXt7VsgRUhp2SVJ37et/im7c9wEfBF5DSDUw\nCaxNjk0BlwOfA14gDM2YFVKSChbbuI8Bo4RomROEFMC3tZ3zXeDNhPH2EULcuySpQLGN+0ngy8BB\nYB3wR+Bulj6s41LgDcAbCWl/99J5laokKQexoZB/JzTsEBYyHQLObTvHtL+SVLI849zHgXcRGvBW\npv2VpJLl1bivA34OXEfowbcz7a8klSiPaJm1wB3ATwgLlNqtKO2vJClde5x7N7ErIUYI4+n/IEys\npuk17e+qe/N1SqIUU9bVLgTZvHnzqq9Zp3sL9VrEFMNFTMWpU/1Lyppa4Nie+2XAVcBzwGcJjfy1\nwGuT41OE2PYLCSGQp1geKilJylnsmPv9hEnUVwLnAM8CRwmN+lTLeXcBpyXnfT7ymkoxPT3d7yJU\nWq9/ykqDooxQSDDlb+Fs3LuzcdewKSMU0pS/klSyvBKHdQuFNOWvJJUsj+GStcCvCOl8b+nh/Hng\n3SzPDHkQ2JhDeSRpWMwCqw9j6mKE8OzUb3U5Zz3ND5EthAlXSVKBYodltgKfBh4GFgNZb2JpKKQp\nfyVJkqRBdwkwB/wF2NnnslTVUZp/OT3Q36JUxm3AU8AjLe+dRUhH/RjwO0J20mGWdo92ERL7zSTb\nJeUXqzLGgHuBPwN/Ar6UvG89ysEa4AghxHItYcL1rf0sUEXNEyqcmrYRwnJbG66bgRuT/Z3AnrIL\nVTFp92gSuL4/xamcUZoTleuAw4T2x3qUg/cBv215/ZVk01LzwNn9LkQFjbO04Zqj+RyB0eT1sBtn\neeN+Q3+KUnn7gYuoUT3KcxFT3tLywJ/Xp7JU2QJwDzBNyOujdOsJwxAkX31gTLovEsLrbsUhh0Xj\nNBdo1qYeVblxH470fvG2EiredkLenm39LU4tLGD9SrMXuIAwHHEM+GZ/i1MJ6wgpza8Dnmk7Vul6\nVOXGvT0P/Bih966ljiVfnwZ+QVhLoOWeIvwZDbABON7HslTVcZoN1g+wLi0+q+LHNJ9VUZt6VOXG\nfZrwUO1x4BXAJ4AD/SxQBZ0OnJnsnwFczNIxVDUdAHYk+ztIf7DMsNvQsn8Zw12XRghDU4+ydOW9\n9Sgn2wmz1EeAr/a5LFV0ASGK6CAhXMt7FOwDngSeJ8zbXE2IKLoHQ9gWtd+jawirzR8mjLnvp8Lj\nySX4AOH5EwdZGhpqPZIkSZIkSZIkSZIkSZIkSZIkSZIkgP8D6j6XT0Y7JGkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x92530b8>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 74.4339\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJEkSwP8AlQ8U6wfcQNYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x20f64e0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 72.9055\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJID/A0kabRoR4V0GAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4566f98>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 71.1501\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJEmSJEmSJID/AXtkxmBJJ+8NAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8f307b8>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 70.4769\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8f47940>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 74.4066\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSQD/A8b3Im4TqmYtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x48ee908>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 76.1726\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiQB/B/7wwpgjS7JrQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8f30748>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 71.1335\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAVB2oQY78aTyAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x900fda0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 69.6944\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x77fa58>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 67.4352\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x922f828>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 77.5026\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkgTwf26YormyyrkWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7241f60>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 74.9814\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZIA/geBluub0DxR9wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9506f98>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 78.1372\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x902c0f0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 73.4267\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACD1JREFUeJzt3X2MHHUZwPHvWVoilGhA0haoHsb3l1TTBNTaeAmIFBOR\nBN8SSAMJUaNIxIQqatr+ZUNixERtaoTElwQ0GpoqKNKEMyQkwl3bA6UtNrSJSGkbqxExtIWufzxT\ndm9v9mZ787Izt99PMtmZ3bmdXye/Pjf3+z3zDEiSJEmSJEmSJEmSJEmSJDXWSI6fPRf4JfAm4ADw\naeDfKfsdAP4DvAKcAC7JcUxJUsnuAG5L1tcBm3rst5/4RSBJaoA9wJJkfWmynWY/cF4lLZIk5fav\njvWRru1OzwA7gQngprIbJUmCMzI+f4i4Ku/2za7tVrKkWQUcBM5Pvm8P8MhptFGSdJqygvtHZ/ns\nEBH4nweWAYd77HcweT0C3EdMqM4I7itWrGhNTU1lNEeS1CU1MeY1Ob5wG7A2WV8LbE3Z5yzgnGT9\nbOAK4Mm0L5uamqLVaqUu69ev7/lZniWPMtqTZynrHM2Xpczzc+zYsTktgz4nVZ6j+bLUMRb1kie4\nbyKu7J8Fvg18isiauQC4P9lnKXGVfgQ4Crw5WZcklShPcD8KfAx4CXg38A7gc8DrgI8n+zwD3A48\nDpwJfBLYnOOYkqQ+5AnuEOPn+4gblU4A9wJXd+3zCeCnyfqfgdfTTqHsy9jYWJ42DgXP0ew8P9k8\nR9madI7y3KEKcC1x9X4qxfE64FLg5o59fgt8B3g02d5ODN9Mdn1Xq6yxp15GRub+z6+6raqv48eP\nz+nnFi1aVHBL1FR5YhElTKhC7/THrIMbGSWpRFmpkFn+ASzv2F5OTLDOts9FyXszbNiw4dX1sbGx\nRv0JJEl1kndY5gxgL3AZ8BzwGDGpurtjn6uALyevHwDuTF67OSyjRnJYRnmVMSyT98r9ZSJwPwgs\nAO4iAvvnk8+3AA8QgX0f8CJwQ85jSpIy5A3u0C490AJOJu9t6fh8DLieSIuECPQ7CjiuJKmHvMF9\nAfAD4HJiHP1x4s7V3V37/YlIiZQkVaCKPHfIP7YvSToNeYP7hcDfO7afTd7r1AI+BEwR4+/vynlM\nSVKGvMMy/aSM7CBSIf8HrCEKjL0t53EHbq6z203LsvHfmW2u/9amHTOPpvWH+aCKPPcXOtZ/D/yI\neOze0e4vM89dkopRRZ77EqLWe4sYo/8VMJryXY3Kc5+rpl3BeOWezSv3bE3rD1WrY577j4mr8L3E\n2Htanvu9xJj7SeBp4LM5jylJypD31/hq4L/Az4D3pnzeeXfqpcD3Sb87FbxyryWv3LN55Z6taf2h\nanUsHPYIvR+MDQWU+5Uknb68wT1LWqrkRSUfU5KGXtnBHSz3K0mVK6K2zGz6LvcLpkJKUlGKmF0Z\nJZ62lDWhOlu5X3BCtZacUM3mhGq2pvWHqtVxQvUe4CngPcBx4EYiDfJUKuQDwDHgFeBhYkL1WzmP\nKUnKUMSv8ax0yDHgVrKrQnrlXkNeuWfzyj1b0/pD1ep45Q7Z6ZA9Dy5JKkcV2TJWhZSkipWdLQPz\ntCqkJNVZFcG976qQVY8HNm0ccMeOuT2dcOXKlXM+5iDO0bA8uDxPW50LydakuZB+f258fJzx8fFX\ntzdu3Nhz36Ki6Si90yH7rgpZUFv61rSObnDP1qT/0HkY3LMNQ19I2lpKVUiIdMiPAG8gSg2sBxYm\nn20BrgW+CLxMDM1YFVKSSpY3uC8HlhLZMkeJEsB3d+3zQ+DtxHj7CJH3LkkqUd7gfgL4KrALWAxM\nAg8x/WEdVwFvAd5KlP3dTO+7VCVJBcibCvk8EdghbmTaDVzQtY9lfyWpYkXmuY8C7ycCeCfL/kpS\nxYoK7ouBXwO3EFfw3Sz7K0kVKiJbZiHwG+AXxA1K3U6r7K8kKV13nvts8ua5jxDj6f8kJlbT9Fv2\n1zz3DOa5ZxuG3GYwz70fw9AXysxzvwa4HngJ+AIR5G8C3ph8voXIbb+MSIE8ycxUSUlSwfKOuT9K\nTKK+FjgfeBE4QAT1LR37PQicmez3pZzHVIqJiYlBN6HW+v1Tdph5juaXKlIhwZK/pZucnBx0E2rN\nwJXNczS/VJEKaclfSapYUVUhZ0uFtOSvJFWsiOGShcDviHK+d/ax/35gJTNL/u4CVhTQHkkaFlPA\n+8r44hHi2anfm2WfJbR/iVxCTLhKkkqUd1hmFXAd8ASwM3nvdqanQlryV5IkSZrvrgT2AH8D1g24\nLXV1gPZfTo8Ntim1cTdwCHiy471ziXLUTwN/JKqTDrO0c7SBKOy3M1murL5ZtbEceBj4K/AX4CvJ\n+/ajAiwA9hEplguJCdd3DrJBNbWf6HBqW02k5XYGrjuA25L1dcCmqhtVM2nnaD1w62CaUztLaU9U\nLgb2EvHHflSADwJ/6Nj+erJouv3AeYNuRA2NMj1w7aH9HIGlyfawG2VmcP/aYJpSe1uBy2lQPyry\nJqaipdWBv3BAbamzFrAdmCDq+ijdEmIYguTVB8aku5lIr7sLhxxOGaV9g2Zj+lGdg3uzytcNziqi\n460h6vasHmxzGqGF/SvNZuBiYjjiIPDdwTanFhYTJc1vAV7o+qzW/ajOwb27Dvxy4upd0x1MXo8A\n9xH3EmimQ8Sf0QDLgMMDbEtdHaYdsH6CfenUsyp+TvtZFY3pR3UO7hPEQ7VHgUXAZ4Btg2xQDZ0F\nnJOsnw1cwfQxVLVtA9Ym62tJf7DMsFvWsX4Nw92XRoihqaeYfue9/agga4hZ6n3ANwbcljq6mMgi\n2kWka3mOwj3Ac8BxYt7mBiKjaDumsJ3SfY5uJO42f4IYc99KjceTK/Bh4vkTu5ieGmo/kiRJkiRJ\nkiRJkiRJkiRJkiRJkiSA/wMdwNU3MDaRGQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x900fba8>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 73.0739\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACB1JREFUeJzt3W2IHVcZwPH/GmOxTVFaSzZtV7fi+wtRhBiNwQuttWlB\nLfgKltBCUfGl2EKjVdn9GApiBTWs2IIv0CqKIWq1ttCVgsQ2NtlWm00N3YC1sSlGsVZq02b98Mzm\n3r07d+funnvnzuT+fzDs3DuzOycnZ889e84zz4AkSZIkSZIkSZIkSZIkSVJtjSR87znAj4FXAUeA\njwL/yjnvCPBv4AXgBLAp4ZqSpD67Gbgx298B7Oxw3hzxQSBJqoFZYH22P5q9zjMHnFtKiSRJyf7Z\nsj/S9rrVY8B+YB9wbb8LJUmCFxccv5sYlbf7Stvr+WzLswU4CpyX/bxZ4L4VlFGSVKJZmh3/BjpP\ny7SaAG7IO7Bx48aFDwg3Nzc3ty62rN/M9aJOB7qwB9ie7W8HdueccyZwdrZ/FnAp8HDeD5uZmWF+\nfj53m5iY6HgsZUvRj/KkbP2qo9NlK6qfQRh0ndiGet8Wyi7rzMxMx7KkdO47gfcBjwNfAz5CRM2c\nD/wqO2eUmIJ5CjgOvDrblyT1UUrnfhx4P/As8GbgDcAngJcBV2TnPAbcBDwAnAF8CNiVcE1JUhdS\nOneIG5IOEzcqnQDuAD7Yds4HgO9n+38AXk4zhLIrjUYjpYxDwTpanvVTzDo6vaR27hcAf215/Xj2\nXtE5F67kIja6YtbR8qyfYtbR6SW1c+929aE9zcFgVrAkaUgUxbkX+Rsw1vJ6jBiZL3fOhdl7S0xO\nTp7abzQajiQkqcX09DTT09NdnZuSOAziw+EQcDHwBHA/sah6sOWcy4HPZV83A7dkX9vNp4YhrdTI\nyOr/+WWXVf2V0hZWyzZUTXXqF7Ky5hY4deT+PNFx3wWsAW4lOvZPZcengDuJjv0w8AxwdeI1JUkF\nUjt3WHzH1MnsvamW4w3gKiIsEqKjf7AH15UkdZDaua8BvgVcQsyjP0DcuXqw7bzfESGRkqQSlBHn\nDulz+5KkFSgjzn0eeDcwQ8y/vynxmpKkAqnTMt0sDT9IhEL+F9hGJBh7Xd6Jq12lHkTUwbBEV9Tp\n/yRFneoWVl/eQbRbGEx5V3vNlLZQpd+XMuLcn27Z/zXwHeKxe8cTry1JQ6Vqce7rgWPEKH8T8BNg\nPOdnrfqjq26jmNWq0+iybiP3QXDkXqxOMedQ/u9LP+Pcv0uMwg8Rc+95ce53EHPuJ4FHgY8nXlOS\nVCD1Y3wr8B/gB8Bbc4633p36TuCb5N+dCo7cCw3DSGSYOHIv5si9q+vlXjQ1WuY+Oj8YG3qQ7leS\ntHKpnXuR5HS/kqSV63fnDqb7laTS9SK3zHK6TvcrSVpemaGQEGGNv6B4QXW5dL/ggmqhYVggGiYu\nqBZzQbWr6/VlQfV24BHgLcBzwDVEGORCKOSdwP+AF4B7iQXVryZeU5JUoBcf40XhkA3geoqzQjpy\nLzAMI5Fh4si9mCP3rq7Xl5E7FIdDdry4JKk/yoiWMSukJJWs39EysIKskJKk3iijc+86K+TExMSp\n/UajQaPR6OoCdZoXTplDHJY0w8Oibqll9+7du+rvXa26tb9+l7fsUEhYPhyy66yQZS+61K1zH4S6\n/XINi7p17ps3d4qAVop+ZoWECId8L/AKItXABLA2OzYFfBj4DPA8MTVjVkhJ6rPUzn0MGCWiZY4T\nKYBvazvn28Drifn2ESLuXZLUR6md+wngi8ABYB3wR+BuFj+s43LgNcBribS/u+h8l6okqQdSQyH/\nTnTsEDcyHQTObzvHtL+SVLJexrmPA28nOvBWpv2VpJL1qnNfB/wUuI4Ywbcz7a8klagX0TJrgZ8B\nPyJuUGrXddrfycnJU/sriXOXpGFQZpz7CDGf/g9iYTVPt2l/jXOvIOPcq8k4d0F/49yvBK4CngU+\nTXTy1wKvzI5PEbHtFxMhkCdZGiopSeqx1Dn33xOLqC8FzgOeAY4QnfpUy3l3AWdk5312pRfp9s8Q\nqRPbUDHrqFid6qiMUEhInP6pU4WqmmxDxayjYnWqozJCIU35K0kl61VWyOVCIU35K0kl60Xoxlrg\nl0Q631u6OH8OeAdLU/4eADb2oDySNCxmgLf14wePEM9O/cYy56yn+SGyiVhwlST1Ueq0zBbgk8BD\nwP7svZtYHAppyl9JkiTpdHcZMAv8Bdgx4LJU1RGafzndP9iiVMZtwJPAwy3vnUOko34U+C2RnXSY\n5dXRJJHYb3+2XVZ+sSpjDLgX+DPwJ+AL2fu2ox5YAxwmQizXEguubxxkgSpqjmhwatpKhOW2dlw3\nAzdm+zuAnWUXqmLy6mgCuH4wxamcUZoLleuAQ0T/YzvqgXcBv2l5/aVs02JzwLmDLkQFjbO445ql\n+RyB0ez1sBtnaed+w2CKUnm7gUuoUTvq5U1MvZaXB/6CAZWlyuaBe4B9RF4f5VtPTEOQffWBMfk+\nT4TX3YpTDgvGad6gWZt2VOXO3XSE3dlCNLxtRN6erYMtTi3MY/vKswu4iJiOOAp8fbDFqYR1RErz\n64Cn245Vuh1VuXNvzwM/RozetdjR7OtTwM+Jewm01JPEn9EAG4BjAyxLVR2j2WF9D9vSwrMqfkjz\nWRW1aUdV7tz3EQ/VHgdeAnwM2DPIAlXQmcDZ2f5ZwKUsnkNV0x5ge7a/nfwHywy7DS37VzLcbWmE\nmJp6hMV33tuOemQbsUp9GPjygMtSRRcRUUQHiHAt6yjcDjwBPEes21xNRBTdgyFsC9rr6BribvOH\niDn33VR4PrkE7yGeP3GAxaGhtiNJkiRJkiRJkiRJkiRJkiRJkiRJAvg/L2bQO7blbrQAAAAASUVO\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8f1b358>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 86.0535\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8f47940>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 69.8285\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8f30710>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 86.1100\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJEmSJEkA/wPpvjju0A9riAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x72318d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 70.5087\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACARJREFUeJzt3XuIXFcdwPHvGmOxTVFaS5K2q1vx/SCKUB8xuNBamwpq\nwWewhBaKio9ihUarkuS/UBArqGHFFnyEVKk0RK3WFrpSEGxjk221SWroBqxNk2AUa6U2bcY/fjed\n2dm7e2f3zNx7Z+/3A8PemXt27pnL3TNnz/md3wVJkiRJkiRJkiRJkiRJkqShNZLwu+cAPwNeBRwG\nPg78K6fcYeDfwPPASeDihGNKkgbsJuCGbHsTsG2OctPEF4EkaQgcAFZm26uy53mmgXNLqZEkKdk/\nO7ZHup53egzYC+wBrh10pSRJ8OKC/XcTvfJuX+963soeedYCR4Dzsvc7ANy3gDpKkhaoqHF//zz7\njhIN/5PAauDYHOWOZD+PA3cQE6qzGvc1a9a0pqamCqojSeqSGxjzooQ33A1szLY3ArtyypwJnJ1t\nnwVcBjyc92ZTU1O0Wq3cx+bNm+fcl/JIMYj6pDwGdY6WymOQ56cp11AVfy8ec/FSGvdtRM/+ceCb\nwMeIqJnzgV9nZVYRvfTjwAng1dm2JGmAUhr3E8AHgGeANwNvAD4FvAz4YFbmMeBG4AHgDOAjwPaE\nY0qSepDSuEOMnx8iFiqdBG4DPtxV5kPAj7LtPwIvpx1C2ZPx8fGUOjaC52h+np9inqOlJbVxvwD4\nW8fzx7PXispcuJCDeNEV8xzNz/NTzHO0tKQ27r3OBHTP5g5mBkGSBBSHQhb5OzDa8XyU6JnPV+bC\n7LVZtmzZ8sL2+Pi4PQlJWqSUxGEQXw4HgUuAJ4D7iUnV/R1lrgC+kP18F3Bz9rNba1AhQXMZGVn8\nxy+7rqqvxV5Hw3YNVfH34jF7+/W8F1N77s8RDfddwDLgFqJh/0y2fwK4k2jYDwFPA1cnHlOSVCC1\ncYd26oEWcCp7baJj/zhwFREWCdHQP9iH40qS5pDauC8DvgtcSoyjP0CsXN3fVe73REikJKkEZcS5\nQ/rYviRpAcqIc28B7wGmiPH3NyUeU5JUIHVYppep4QeJUMj/AuuJBGOvSzxu5RJntxdl2KIrFmvY\nopiGKSojxbAds4oopirahbmUEef+VMf2b4DvE7fdO9H9Zsa5S1J/lBHnvpLI9d4ixuh/DozlvNdQ\nxblXwZ57sWE6R035nFVpUM99IHHuPyB64QeJsfe8OPfbiDH3U8CjwCcTjylJKpD6NbMO+A/wY+Ct\nOfs7V6e+E/gO+atTwZ57oab01prSo23K56xK03vuqdEy9zH3jbGhD+l+JUkLl9q4F0lO9ytJWrhB\nN+5gul9JKl0/csvMp+d0v2AopCT1Sz9G/8eAX1I8oTpful9wQrVQUybRmjLR2JTPWRUnVNPsBB4B\n3gI8C1xDhEGeDoW8E/gf8DxwLzGh+o3EY0qSCvTja6YoHHIcuJ7irJD23As0pbfWlB5tUz5nVey5\npysKh5zz4JKkwSgjWsaskJJUskFHy8ASzQopSXVWRuPec1bIsserhm1szTTDg1PF+PewXX/Ddi1U\nkYp5x44di/q9DRs29FRucnKSycnJF55v3bp1zrJlNO7dWSFHyGnYJUnz617/M+jGfSfwPuAVRKqB\nzcDybN8E8FHgc8BzxNCMWSElacBSG/dRYBURLXOCSAF8a1eZ7wGvJ8bbR4i4d0nSAKU27ieBLwP7\ngBXAn4C7mXmzjiuA1wCvJdL+bmfuVaqSpD5IDYV8kmjYIRYy7QfO7ypj2l9JKlk/49zHgLcTDXgn\n0/5KUsn61bivAG4HriN68N1M+ytJJepHtMxy4BfAT4kFSt0WlPZXkpSvO859PqkrIUaI8fR/EBOr\neXpN+1t6b37YFpFUoYqFK1UsKBq2JF4uYhqcOi9i6pbVNbfCqT33K4GrgGeAzxKN/LXAK7P9E0Rs\n+yVECOQpZodKSpL6LHXM/Q/EJOpLgfOAp4HDRKM+0VHuLuCMrNznE48pLViv/8pKS0UZoZBgyl9V\nzMZdTVNGKKQpfyWpZP1KHDZfKKQpfyWpZP0YLlkO/IpI53tzD+WngXcwOzPkPmBNH+ojSU0xBbxt\nEG88Qtw79dvzlFlJ+0vkYmLCVZI0QKnDMmuBTwMPAXuz125kZiikKX8lSZKkpe5y4ADwV2BTxXWp\nq8O0/3O6v9qq1MatwFHg4Y7XziHSUT8K/I7ITtpkeedoC5HYb2/2uLz8atXGKHAv8Bfgz8CXste9\njvpgGXCICLFcTky4vrHKCtXUNHHBqW0dEZbb2XDdBNyQbW8CtpVdqZrJO0ebgeurqU7trKI9UbkC\nOEi0P15HffBu4Lcdz7+aPTTTNHBu1ZWooTFmNlwHaN9HYFX2vOnGmN24f6WaqtTeLuBShug66uci\npn7LywN/QUV1qbMWcA+wh8jro3wriWEIsp/eMCbfF4nwultwyOG0MdoLNIfmOqpz496MFHTp1hIX\n3noib8+6aqszFFp4feXZDlxEDEccAb5VbXVqYQWR0vw64KmufbW+jurcuHfngR8leu+a6Uj28zhw\nB7GWQLMdJf6NBlgNHKuwLnV1jHaD9UO8lk7fq+IntO9VMTTXUZ0b9z3ETbXHgJcAnwB2V1mhGjoT\nODvbPgu4jJljqGrbDWzMtjeSf2OZplvdsX0lzb6WRoihqUeYufLe66hP1hOz1IeAr1Vclzq6iIgi\n2keEa3mOwk7gCeBZYt7maiKi6B4MYTut+xxdQ6w2f4gYc99FjceTS/Be4v4T+5gZGup1JEmSJEmS\nJEmSJEmSJEmSJEmSJEkA/wePffRucqHUzQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8f1b358>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 76.8883\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x8f307b8>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final 62.6919\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x924bf98>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Currently 37 cutouts require measurement:\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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puYI//viDX37Zik4XR3b2aS5dGsGwYc8WNzAYrlr8oQGhiv32qsV/jRCXHkez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\nEMb3fzpbpOdZ1/jSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x902c240>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 69.6023\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x953c198>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 74.6371\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACElJREFUeJzt3W2MXFUZwPH/WioRSjQgaQtUt8b3l1RjAmpt3AZEiolK\ngm8JpIGEqFEkYkIVtS+fbEiMmKhNjZD4khSJhqYqijTZNSQkQqFdUNpiw24iUtrGakQMUuj44bnt\nzM7e2dndM/fOvTv/XzKZe2fOzj175/T07DnPfS5IkiRJkiRJkiRJkiRJkiTV1lDCz54L/AJ4PTAJ\nfAr4V065SeDfwMvACeDihGNKkgp2G3BLtr0B2Nqh3ATxH4EkqQYOAEuz7WXZfp4J4LxSaiRJSvbP\nlu2htv1WTwF7gT3ADUVXSpIEZ3R5/35iVN7uG237jeyRZzVwGDg/+7wDwANzqKMkaY66de4fnuG9\nI0TH/yywHDjaodzh7PkYcA+xoDqtc1+1alVjfHy8S3UkSW1yA2NekfCBu4D12fZ6YGdOmbOAc7Lt\ns4HLgcfzPmx8fJxGo5H72LRpU8f3Uh4piqhPyqOoc7RQHkWen0FpQwvp30vV2lERUjr3rcTI/mng\nW8AniaiZC4DfZmWWEaP0Y8Bx4A3ZtiSpQCmd+3HgI8ALwDuAtwKfBV4NfDQr8xRwK/AwcCbwCWBb\nwjElSbOQ0rlDzJ8fIi5UOgHcBXy8rczHgJ9k238CXkMzhHJWRkZGUuo4EDxHM/P8dOc56q5O5yjl\nClWAq4nR+6kQx2uAS4AbW8r8Gvg28GC2v5uYvnmk7bMaRc09dTI0NP9fv+y6qrrm247q1ob891Kc\nlHNLAQuq0Dn8sdvB/aYlqUDdQiG7+TuwomV/BbHAOlOZi7LXptm8efPp7ZGRkVr9CSRJVZI6LXMG\ncBC4FHgGeIhYVN3fUuZK4EvZ8/uA27Pndk7LqJaclumubr9r2YqYlkkdub9EdNz3AYuAO4iO/XPZ\n+9uBe4mO/RDwPHBd4jElSV2kdu7QTD3QAE5mr21veX8EuJYIi4To6B/twXElSR2kdu6LgO8DlxHz\n6A8TV67ubyv3RyIkUpJUgjLi3CF9bl+SNAepnfuFwN9a9p/OXmvVAD4AjBPz729PPKYkqYvUaZnZ\nLIE/SoRC/hdYRyQYe3PicWurHxEHiSvx8zJI0RH9+F7qdMx+GZQopk7KiHN/rmX7d8APidvuHW//\nMOPcJak3yohzX0rkem8Qc/R3A8M5nzUQce51O+Z8LZTRT5Hq1hbq9p3WaeRexTj3HxGj8IPE3Hte\nnPtdxJz7SeBJ4DOJx5QkdZE6pFsD/Af4KfCunPdbr069BPge+VengiP3Sh5zvuo2yuuHurWFun2n\ngz5yT42WeYDON8aGHqT7lSTNXWrn3k1eqORFBR9TkgZe0Z07mO5XkkrXi9wyM5l1ul8wFFKSeqUX\nK23DxN2Wui2ozpTuF1xQreQx56tui2/9ULe2ULfv1AXVNDuAJ4B3Ai8C1xNhkKdCIe8F/ge8DIwS\nC6rfTDymJKmLXgzpuoVDjgA30z0rpCP3Ch5zvuo2yuuHurWFun2njtzTdQuH7HhwSVIxyoiWMSuk\nJJWs6GgZMCukJJWujM591lkh165de3p7eHiYlStXzuoAGzduTKvhPPRjPq9Oc4gpBmUuuh9tYZAs\nxLYwNjbG2NjY6f0tW7Z0LFtG596eFXKInI4dpnbukqSp2q//Kbpz3wF8CHgtkWpgE7A4e287cDXw\nBeAlYmrGrJCSVLDUzn0FsIyIljlOpAC+s63MD4C3EPPtQ0TcuySpQKmd+wngK8A+YAnwCHA/U2/W\ncSXwRuBNRNrfbXS+SlWS1AOpoZDPEh07xIVM+4EL2sqY9leSStbLOPdh4D1EB97KtL+SVLJede5L\ngF8CNxEj+Ham/ZWkEvUiWmYx8Cvg58QFSu1mnfZ3dHT09PZc4twlaRC0x7nPJPVKiCFiPv0fxMJq\nntmm/W3MFLM5k/lexFS3hFpexNRdnS5cSVG3tlsndWoLWV1zK5w6cr8KuBZ4Afg80cnfALwue387\nEdt+KRECeZLpoZKSpB5LnXN/kFhEfRVwPvA8MEl06ttbyt0HnJmV++JcDzIxMZFYTQ262f4pKy0U\nZYRCQuL0z+TkZMqPS3buGjhlhEKa8leSStarxGEzhUKa8leSStaLJffFwG+IdL63z6L8BPBepmeG\n3Aes6kF9JGlQjAPvLuKDh4h7p353hjJLaf4ncjGx4CpJKlDqtMxq4BrgMWBv9tqtTA2FNOWvJEmS\ntNBdARwA/gps6HNdqmqS5l9OD/W3KpVxJ3AEeLzltXOJdNRPAn8gspMOsrxztJlI7Lc3e1xRfrUq\nYwUwCvwF+DPw5ex121EPLAIOESGWi4kF17f1s0IVNUE0ODWtIcJyWzuu24Bbsu0NwNayK1Uxeedo\nE3Bzf6pTOctoLlQuAQ4S/Y/tqAfeD/y+Zf9r2UNTTQDn9bsSFTTM1I7rAM37CCzL9gfdMNM796/2\npyqVtxO4jBq1o15exNRreXngL+xTXaqsAewG9hB5fZRvKTENQfbsDWPy3UiE192BUw6nDNO8QLM2\n7ajKnftgpKBLt5poeOuIvD1r+ludWmhg+8qzDVhJTEccBr7T3+pUwhIipflNwHNt71W6HVW5c2/P\nA7+CGL1rqsPZ8zHgHuJaAk13hPgzGmA5cLSPdamqozQ7rB9jWzp1r4qf0bxXRW3aUZU79z3ETbWH\ngVcCnwZ29bNCFXQWcE62fTZwOVPnUNW0C1ifba8n/8Yyg255y/ZVDHZbGiKmpp5g6pX3tqMeWUes\nUh8Cvt7nulTRSiKKaB8RruU5CjuAZ4AXiXWb64iIot0YwnZK+zm6nrja/DFizn0nFZ5PLsEHiftP\n7GNqaKjtSJIkSZIkSZIkSZIkSZIkSZIkSZIA/g97b3pnNXkeIgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x48ee908>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 78.0186\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACCpJREFUeJzt3X+IHGcZwPHveYnFNkVJLUnaRq+inr9CFKFVk+BCa00i\nxBbUWrCElhQVNUWFRquSy3+hILb4I0TSij8gEZSGw1ZrCz1pEG1jk2u0SWpoAsamSfAs1mrtXbP+\n8cxl9za7t3v77s7O5b4fGHZ2992dN8Pk2bn3feYZkCRJkiRJkiRJkiRJkiRJmrX6Ej67EPg58Gbg\nGPAp4IU67Y4B/wJeBcaBqxK2KUnqsruAO7L1TcDWBu2OEj8EkqRZ4BCwKFtfnD2v5yhwSS49kiQl\n+2fVel/N82rPAvuAvcBt3e6UJAnmNXn/YeKsvNY3ap6Xs6WeFcAJ4NLs+w4Bj82gj5KkGWoW3D8y\nzXsnicD/PLAEONWg3Yns8TRwPzGhek5wX758eXl0dLRJdyRJNeomxrwm4QuHgfXZ+npgd502FwIX\nZ+sXAdcBB+p92ejoKOVyue6yefPmhu+lLP39/W0v3ehPytKtfXS+LO6f9H2Uohf/nl4oUl9TgvtW\n4sz+OPAt4JNE1sxlwANZm8XEWfppYAx4S7YuSeqilOA+BnwUeBl4N/AO4Cbg9cDHsjbPAncCTwAX\nANcD2xK2KUlqQUpwhxg/P0JcqDQO7AI+XtNmHfDjbP2PwBuopFC2pFQqpfRxTnAfTc/905z76PyS\nGtwvB/5W9fx49lqzNlfMZCMedM25j6bn/mnOfXR+SQ3urc4E1M7m9ma2Q5LmiGapkM38HVha9Xwp\ncWY+XZsrstfOMTQ0dHa9VCp5JiFJbUopHAbx43AYuAZ4DnicmFQ9WNVmLfDF7PEDwN3ZY61y3ulL\n8+a1/9s2MTHRwZ5IxdfX13646EVqYkp/29XuvzOxr3U/nHrmPkEE7oeAfuBeIrB/Nnt/O/AgEdiP\nAC8BtyRuU5LURGpwh0rpgTJwJntte9X7JeBmIi0SItA/2YHtSpIaSA3u/cD3gGuJcfQniCtXD9a0\n+x2REilJykEeee6QPrYvSZqBPPLcy8CHgFFi/P1diduUJDWROizTytTwk0Qq5H+ANUSBsbfXa7hj\nx462OrFhw4a2PpeS8dLu7HavChpJqVavXp37NmdTxkvR5JHn/mLV+q+BHxC33Rur/bLh4eGz64OD\ngwwODiZ2T5LmptTgvhd4GzBA5LnfSOS5V1tE1HovE2P0fdQJ7ADr1jnnKkmdkBrcf0ichR8mxt7r\n5bnvIsbczwDPAJ9O3KYkqYnU4P4j4LvAT4BlVa9P5rmvBf5LlPu9GrgH+EPiNiVJTaRmyzxG4xtj\nQwfK/UqSZi41uDeTXO5XkjRz3Q7uYLlfScpdJ2rLTKflcr9gKqQkdUq3g/swUTVyF1Hm9wXgZKPG\npkJKUmekDsvsBJ4G3gO8AtxKpEFOpkI+CPwPeBV4lJhQ/WbiNiVJTaSeud8ErAL+TaRD3lenzT1E\n9UhPyyUpJ52YUG2WDglWhZSkXOWRLWNVSEnKWbcnVGEGVSElSZ2RR3BvuSrk8eOVgpKlUolSqdTt\nvrWtF2VBFy5c2Nbnxsbq1mlryZ49e9r63MqVK9ve5oEDB9r+7LJly5o3Og/0ohTuxo0bc9+mN9ae\namRkhJGRkbPPt2zZ0rBtHsG95aqQQ0NDOXRHkman2pPebgf3ncCHgTcSpQY2A/Oz97YDnwA+D0wQ\nQzNWhZSkLksN7kuBxUS2zBhRArg2HfL7wCAx3t5H5L1LkrooNbiPA18G9gMLgD8BDxM13SetBd5K\n3NTjamAbcbWqJKlLUlMhnycCO8SFTAeBy2raWPZXknLWyTz3AeB9RACvZtlfScpZp4L7AuAXwO3E\nGXwty/5KUo46kS0zH/gl8DPiAqVaLZf9rU6FLHqeuyTlrTbPfTqpwb2PuCn208DdDdq0XPbXPHdJ\naizPPPcbgJuBl4HPAf8AbgPelL2/nchtv4ZIgTxD/cqRkqQOSh1z/z0xifo64FLgJeAYEdS3V7V7\nCLgga/eFmW6k1T9D5rLx8fFed6HQPIY01+SRCgmJJX/9j9ncxMREr7tQaB5DmmvySIW05K8k5axT\nhcOmS4W05K8k5awTtS3nA78iyvk2ypipdhR4P+dWhtwPLO9AfyRprhgF3tuNL+4j7p36nWnaLKLy\nI3IVMeEqSeqi1GGZFcBngKeAfdlrdzI1FdKSv5IkSdL5bjVwCPgrsKnHfSmqY1T+cnq8t10pjPuI\nq6Cr79e3kChH/QzwW6I66VxWbx8NEYX99mXL6vy7VRhLgUeBvwB/BibvMehx1AH9wBEixXI+MeH6\nzl52qKCOEgecKlYRabnVgesu4I5sfROwNe9OFUy9fbQZ+EpvulM4i6lMVC4ADhPxx+OoAz4I/Kbq\n+deyRVMdBS7pdScKaICpgesQlfsILM6ez3UDnBvcv9qbrhTebuBaZtFx1MmLmDqtXh34y3vUlyIr\nA48Ae4m6PqpvEZWCdSfxhjGNfIlIr7sXhxwmDVC5QHPWHEdFDu7WfG/NCuLAW0PU7VnV2+7MCmU8\nvurZBlxJDEecAL7d2+4UwgKipPntwIs17xX6OCpycK+tA7+UOHvXVCeyx9PA/cS1BDrXSeLPaIAl\nwKke9qWoTlEJWDvwWJq8V8VPqdyrYtYcR0UO7nuJm2oPAK8FbiRqw6viQuDibP0i4DqmjqGqYhhY\nn62vp/6NZea6JVXrNzC3j6VG96rwOOqQNcQs9RHg6z3uSxFdSWQR7SfStdxHYSfwHPAKMW9zC5FR\n9AimsE2q3Ue3ElebP0WMue+mwOPJOVhJ3H9iP1NTQz2OJEmSJEmSJEmSJEmSJEmSJEmSJAng//Vh\n2/e3M7qIAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x924bfd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 75.0176\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nq7v72gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x902c240>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 73.5341\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x46c4cc0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 67.1599\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x48ee908>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 71.1460\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJEmSJEmSJEmSJEkA/wdqfzJPw1GgBQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7241f60>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Output 75.8123\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABNCAYAAABdViSBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x72318d0>"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# x0 = output.x\n",
      "# oldF = 200"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# savetxt('oldF',oldF)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# newGiveMesh = give_calc(xMesh,yMesh,width,ratio,x0)\n",
      "# contour(xMesh,yMesh,giveMesh, levels=[0.05,0.1,0.15], colors='g',alpha=0.7)\n",
      "# contour(xMesh,yMesh,newGiveMesh, levels=[0.05,0.1,0.15], colors='r',alpha=0.7)\n",
      "# len(width) - sum(w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# saveStuffs = x0 > 0.5\n",
      "\n",
      "# widthSave = width[saveStuffs]\n",
      "# ratioSave = ratio[saveStuffs]\n",
      "\n",
      "# plot(width[~saveStuffs],ratio[~saveStuffs],'rx')\n",
      "# scatter(widthSave,ratioSave)\n",
      "\n",
      "# sum(saveStuffs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# oldF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# output.fun"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}