historical_exploration_and_measurement/scripts/Shape Optimisation -- Failed Attempt 1.ipynb

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 "metadata": {
  "name": "",
  "signature": "sha256:7fe3e0923f3b9132243729c5efd588c71b29f44cbcb287e2c968e12984015131"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "import pandas as pd\n",
      "\n",
      "import csv\n",
      "\n",
      "from threshold_functions import *\n",
      "\n",
      "from equivalent_ellipse import *\n",
      "\n",
      "from scipy.interpolate import SmoothBivariateSpline, bisplrep"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "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]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_cutouts = len(x_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_array = [0,]*num_cutouts\n",
      "y_array = [0,]*num_cutouts\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')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cutout = [0,]*num_cutouts\n",
      "\n",
      "for i in range(num_cutouts):\n",
      "    cutout[i] = shapely_cutout(x_array[i],y_array[i])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cutout_dimensions = pd.DataFrame.from_csv('../data/cutout_dimensions.csv')\n",
      "#cutout_dimensions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boundBox = cutout_dimensions['box_bounds'].values\n",
      "#ref = boundBox < 11\n",
      "\n",
      "width = cutout_dimensions['width'].values\n",
      "length = cutout_dimensions['length'].values\n",
      "\n",
      "cutoutRef = arange(num_cutouts)\n",
      "\n",
      "ratio = width/length"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# ref = True\n",
      "\n",
      "# while any(ref):\n",
      "\n",
      "#     sortingArray = array([width,ratio,cutoutRef]).transpose()\n",
      "#     sortingTable = pd.DataFrame(sortingArray)\n",
      "\n",
      "#     sortingTable = sortingTable.sort([0, 1])\n",
      "\n",
      "#     width = sortingTable[0].values\n",
      "#     ratio = sortingTable[1].values\n",
      "#     cutoutRef = sortingTable[2].values\n",
      "    \n",
      "#     ref = (width[0:-1] == width[1:]) & (ratio[0:-1] == ratio[1:])\n",
      "        \n",
      "#     width = delete(width,find(ref))\n",
      "#     ratio = delete(ratio,find(ref))\n",
      "#     cutoutRef = delete(cutoutRef,find(ref))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(append(width,width),append(log2(ratio),log2(1/ratio)),'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fb87d324748>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TYiLiiNykvXekRRkbBdMQpkfMtm2mhr97d7Ta+qxZ5v/AgJm3J47249T6mu9///TH2ss8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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb87d987f60>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def give_calc(xGrid,log2yGrid,width,ratio,w):\n",
      "    \n",
      "    w = w/max(w)\n",
      "    \n",
      "    ref = (w == 0)\n",
      "    width = delete(width,find(ref))\n",
      "    ratio = delete(ratio,find(ref))\n",
      "    w = delete(w,find(ref))\n",
      "    \n",
      "    return fit_give(xGrid,log2yGrid,\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,log2yGrid,width,ratio):\n",
      "    \n",
      "    return angle_gap(xGrid,log2yGrid,append(width,width),\n",
      "                     append(log2(ratio),log2(1/ratio)),1,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 334
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resltn = 11 # Must be odd\n",
      "log2ratio = log2(ratio)\n",
      "mirr_log2ratio = append(log2ratio,-log2ratio)\n",
      "\n",
      "w = ones(len(width))\n",
      "\n",
      "xRange = [mean(width)-3.5*std(width),mean(width)+3.5*std(width)]\n",
      "yRange = [-3.5*std(mirr_log2ratio),0]\n",
      "\n",
      "xVec = linspace(xRange[0],xRange[1],resltn)\n",
      "yVec = linspace(yRange[0],0,(resltn//2+1))\n",
      "\n",
      "xMeshHlf, yMeshHlf = meshgrid(xVec, yVec)\n",
      "\n",
      "gapMeshHlf = gap_calc(xMeshHlf,yMeshHlf,width,ratio)\n",
      "giveMeshHlf = give_calc(xMeshHlf,yMeshHlf,width,ratio,w)\n",
      "\n",
      "xMesh = vstack((xMeshHlf,flipud(xMeshHlf)[1:,:]))\n",
      "yMesh = vstack((yMeshHlf,-flipud(yMeshHlf)[1:,:]))\n",
      "\n",
      "gapMesh = vstack((gapMeshHlf,flipud(gapMeshHlf)[1:,:]))\n",
      "giveMesh = vstack((giveMeshHlf,flipud(giveMeshHlf)[1:,:]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 313
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "contour(xMesh,yMesh,gapMesh, levels=[130], colors='r',alpha=0.7)\n",
      "contour(xMesh,yMesh,giveMesh, levels=[0.15], colors='g',alpha=0.7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 314,
       "text": [
        "<matplotlib.contour.QuadContourSet at 0x7fb87b8e09e8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Yd0nuViQrN4uolChGdBpR8ZNIZ6qwM9JyL54kdysScyGGDvU64O7sXvGTSHIX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\nesstvkoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb87b7e6cc0>"
       ]
      }
     ],
     "prompt_number": 314
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "\n",
      "cf = ax.contourf(xMesh,yMesh,giveMesh,60)\n",
      "\n",
      "give_contour = ax.contour(xMesh,yMesh,giveMesh,levels=[0.15],colors='k')\n",
      "ax.scatter(append(width,width),append(log2ratio,-log2ratio),color='black')\n",
      "\n",
      "\n",
      "colorbar(cf, label='Fit give')\n",
      "\n",
      "ax.set_xlabel('Width')\n",
      "ax.set_ylabel('log2(Ratio)')\n",
      "ax.set_title('Contour plot of the fit give')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 315,
       "text": [
        "<matplotlib.text.Text at 0x7fb87b766e48>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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y82cT3mlDJh3AMJR3hgViSz8N8kn2ZlnAuofsCd9BznEm/H8jRLZ+AtTNhKZn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\nCIIgCIIgCIIgCIIgCIIg5Mb/Bx6IeWP/3EeoAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb87b7b0da0>"
       ]
      }
     ],
     "prompt_number": 315
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def give_area(w):\n",
      "\n",
      "    giveMeshHlf = give_calc(xMeshHlf,yMeshHlf,width,ratio,w)\n",
      "    return sum(giveMeshHlf <= 0.15) / size(giveMeshHlf)\n",
      "\n",
      "\n",
      "def give_sigmoid_bias(w):\n",
      "\n",
      "    giveMeshHlf = give_calc(xMeshHlf,yMeshHlf,width,ratio,w)\n",
      "    return mean(200/(1+exp(-(giveMeshHlf-0.15)*100)) - 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 316
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "init_area = give_area(ones(len(width)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 317
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = rand(len(width))*0.2+0.8\n",
      "give_area(d)/give_area(ones(len(width)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 318,
       "text": [
        "0.94444444444444453"
       ]
      }
     ],
     "prompt_number": 318
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "give_sigmoid_bias(append(ones(len(width)-1),0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 330,
       "text": [
        "nan"
       ]
      }
     ],
     "prompt_number": 330
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w = append(ones(len(width)*2-1),0.0001)\n",
      "SmoothBivariateSpline(append(width,width),append(log2ratio,-log2ratio),zeros(len(width)*2),w=w).ev(1,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 333,
       "text": [
        "array(0.0)"
       ]
      }
     ],
     "prompt_number": 333
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "give_sigmoid_bias(d)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 320,
       "text": [
        "48.919278811184718"
       ]
      }
     ],
     "prompt_number": 320
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# rand_weights = zeros(100)\n",
      "\n",
      "# for i in range(100):\n",
      "#     rand_weights[i] = give_sigmoid_bias(rand(len(width)))\n",
      "    \n",
      "# mean(rand_weights)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 321,
       "text": [
        "65.355469460037824"
       ]
      }
     ],
     "prompt_number": 321
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def toMinimise(w):\n",
      "    \n",
      "    if (sum(w!=0) < 13):\n",
      "        bias = 100\n",
      "    else:   \n",
      "        t = (give_area(w) / init_area)\n",
      "        bias = exp((-(t-0.95)*100))/10\n",
      "    \n",
      "    return bias + mean(w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 336
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bounds = ([0,1],)*len(width)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 326
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minimizer_kwargs = {\"method\": 'L-BFGS-B', \"bounds\": bounds  }\n",
      "x0 = np.ones(len(width))\n",
      "\n",
      "output = basinhopping(toMinimise,x0,\n",
      "                      niter=1,minimizer_kwargs=minimizer_kwargs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "output"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 329,
       "text": [
        "                   nit: 1\n",
        "                     x: array([ 0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861944,  0.25861944,\n",
        "        0.25861944,  0.25861944,  0.25861944,  0.25861947,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041,  0.25862041,  0.25862041,  0.25862041,  0.25862041,\n",
        "        0.25862041])\n",
        "               message: ['requested number of basinhopping iterations completed successfully']\n",
        " minimization_failures: 0\n",
        "                   fun: 0.25929367093269146\n",
        "                  nfev: 1298"
       ]
      }
     ],
     "prompt_number": 329
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = linspace(0.9,1,1000)\n",
      "y = exp((-(t-0.95)*100))/10\n",
      "plot(t,y)\n",
      "ylim([0,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 307,
       "text": [
        "(0, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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65RP004BmYCOwF5gPzO5yzDPAm9n9RcDYItWnFJszJwzIXn997Eqk6pZP0I8BNnV6vjn7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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb87b89d518>"
       ]
      }
     ],
     "prompt_number": 307
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 206,
       "text": [
        "0.7189271775207503"
       ]
      }
     ],
     "prompt_number": 206
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum(giveMeshHlf <= 0.15) / size(giveMeshHlf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "0.28253968253968254"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum(giveMesh <= 0.15) / size(giveMesh)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "0.2742857142857143"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "width_scale = std(width)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log2ratio_scale = std(append(log2ratio,-log2ratio))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "give_xylist = give_contour.collections[0].get_paths()[0].vertices\n",
      "\n",
      "shapely_give = sh.Polygon([(i[0], i[1]) for i in \n",
      "            zip(give_xylist[:,0]/width_scale,\n",
      "                give_xylist[:,1]/log2ratio_scale)])\n",
      "\n",
      "shapely_give"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "svg": [
        "<svg\n",
        "            preserveAspectRatio=\"xMinYMin meet\"\n",
        "            viewBox=\"1.009342480531088 -3.8794415179995565 6.388600429729314 7.758883035999113\"\n",
        "            width=\"100.0\"\n",
        "            height=\"100.0\"\n",
        "            transform=\"translate(0, 100.0),scale(1, -1)\">\n",
        "            \n",
        "            <g fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" \n",
        "            stroke-width=\"0.15517766071998226\" opacity=\"0.6\">\n",
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       "text": [
        "<shapely.geometry.polygon.Polygon at 0x7fb87c739550>"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "\n",
      "cf = ax.contourf(xMesh,yMesh,gapMesh,60)\n",
      "\n",
      "gap_contour = ax.contour(xMesh,yMesh,gapMesh,levels=[130],colors='k')\n",
      "ax.scatter(append(width,width),append(log2ratio,-log2ratio),color='black')\n",
      "\n",
      "\n",
      "colorbar(cf, label='Angle gap')\n",
      "\n",
      "ax.set_xlabel('Width')\n",
      "ax.set_ylabel('log2(Ratio)')\n",
      "ax.set_title('Contour plot of the angle gap')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "<matplotlib.text.Text at 0x7fb87c46ad30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LpmefVNYsqW9Z7DspxceGeVspOe0Asq+/hNUmmaaF4FXLffEiuHUkNGveM2/f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       "text": [
        "<matplotlib.figure.Figure at 0x7fb87c639828>"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gap_xylist = gap_contour.collections[0].get_paths()[0].vertices\n",
      "\n",
      "shapely_gap = sh.Polygon([(i[0], i[1]) for i in \n",
      "            zip(gap_xylist[:,0]/width_scale,\n",
      "                gap_xylist[:,1]/log2ratio_scale)])\n",
      "\n",
      "shapely_gap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "svg": [
        "<svg\n",
        "            preserveAspectRatio=\"xMinYMin meet\"\n",
        "            viewBox=\"1.2163973673073962 -4.095656907740166 6.506808548514309 8.191313815480331\"\n",
        "            width=\"100.0\"\n",
        "            height=\"100.0\"\n",
        "            transform=\"translate(0, 100.0),scale(1, -1)\">\n",
        "            \n",
        "            <g fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" \n",
        "            stroke-width=\"0.16382627630960664\" opacity=\"0.6\">\n",
        "            <path d=\"M 3.7577232764851605,-3.096547215743501 L 3.753632480842845,-3.0882352941176467 L 3.5518409235439843,-2.895551790842453 L 3.541702945463524,-2.8823529411764706 L 3.470527680328336,-2.676470588235294 L 3.497431017895177,-2.4705882352941178 L 3.4286389558299595,-2.2647058823529407 L 3.3459585706028077,-2.183142750507435 L 3.2065940431265627,-2.0588235294117645 L 3.1400762176616315,-2.005120186947228 L 3.0487356489325896,-1.852941176470588 L 3.0732477014637634,-1.6470588235294115 L 3.0302480580068964,-1.441176470588235 L 2.934193864720455,-1.253327819230861 L 2.921603928984926,-1.2352941176470587 L 2.728311511779278,-1.0294676245993155 L 2.72824182210863,-1.0294117647058822 L 2.522429158838102,-0.9394266092451895 L 2.3165468058969254,-0.8612278134640032 L 2.294861745644136,-0.8235294117647055 L 2.215617488796809,-0.6176470588235293 L 2.266645767802903,-0.41176470588235253 L 2.3165468058969254,-0.27642283969675036 L 2.3725625665582437,-0.20588235294117627 L 2.4085848413942292,0.0 L 2.3725625665582437,0.20588235294117627 L 2.3165468058969254,0.27642283969675036 L 2.266645767802903,0.41176470588235253 L 2.215617488796809,0.6176470588235293 L 2.294861745644136,0.8235294117647055 L 2.3165468058969254,0.8612278134640032 L 2.522429158838102,0.9394266092451895 L 2.72824182210863,1.0294117647058822 L 2.728311511779278,1.0294676245993155 L 2.921603928984926,1.2352941176470587 L 2.934193864720455,1.253327819230861 L 3.0302480580068964,1.441176470588235 L 3.0732477014637634,1.6470588235294115 L 3.0487356489325896,1.852941176470588 L 3.1400762176616315,2.005120186947228 L 3.2065940431265627,2.0588235294117645 L 3.3459585706028077,2.183142750507435 L 3.4286389558299595,2.2647058823529407 L 3.497431017895177,2.4705882352941178 L 3.470527680328336,2.676470588235294 L 3.541702945463524,2.8823529411764706 L 3.5518409235439843,2.895551790842453 L 3.753632480842845,3.0882352941176467 L 3.7577232764851605,3.096547215743501 L 3.78190226349147,3.0882352941176467 L 3.963605629426337,2.9884548856490185 L 4.051730863894976,2.8823529411764706 L 4.169487982367514,2.761707630764594 L 4.26814010342276,2.676470588235294 L 4.3753703353086895,2.5966377158153477 L 4.581252688249866,2.47101540125074 L 4.582025178889069,2.4705882352941178 L 4.787135041191043,2.37675088282925 L 4.9930173941322185,2.3067650849779633 L 5.159141578565724,2.2647058823529407 L 5.198899747073397,2.2569712081050977 L 5.404782100014572,2.226244995171731 L 5.61066445295575,2.195101291296662 L 5.740822407522948,2.0588235294117645 L 5.7502280852356416,1.852941176470588 L 5.812173141955434,1.6470588235294115 L 5.816546805896925,1.6172496245265562 L 5.909879903950134,1.441176470588235 L 6.022429158838101,1.3075941348989508 L 6.093032814832102,1.2352941176470587 L 6.228311511779278,1.1404650435941603 L 6.4340224851242125,1.0294117647058822 L 6.434193864720454,1.029352284366392 L 6.6400762176616315,0.920826733024267 L 6.698441003014326,0.8235294117647055 L 6.6400762176616315,0.6650489501179189 L 6.620399291066215,0.6176470588235293 L 6.6400762176616315,0.5902516432377279 L 6.724266341167069,0.41176470588235253 L 6.6400762176616315,0.2963480805971358 L 6.510001807405734,0.20588235294117627 L 6.4692917161955865,0.0 L 6.510001807405734,-0.20588235294117627 L 6.6400762176616315,-0.2963480805971358 L 6.724266341167069,-0.41176470588235253 L 6.6400762176616315,-0.5902516432377279 L 6.620399291066215,-0.6176470588235293 L 6.6400762176616315,-0.6650489501179189 L 6.698441003014326,-0.8235294117647055 L 6.6400762176616315,-0.920826733024267 L 6.434193864720454,-1.029352284366392 L 6.4340224851242125,-1.0294117647058822 L 6.228311511779278,-1.1404650435941603 L 6.093032814832102,-1.2352941176470587 L 6.022429158838101,-1.3075941348989508 L 5.909879903950134,-1.441176470588235 L 5.816546805896925,-1.6172496245265562 L 5.812173141955434,-1.6470588235294115 L 5.7502280852356416,-1.852941176470588 L 5.740822407522948,-2.0588235294117645 L 5.61066445295575,-2.195101291296662 L 5.404782100014572,-2.226244995171731 L 5.198899747073397,-2.2569712081050977 L 5.159141578565724,-2.2647058823529407 L 4.9930173941322185,-2.3067650849779633 L 4.787135041191043,-2.37675088282925 L 4.582025178889069,-2.4705882352941178 L 4.581252688249866,-2.47101540125074 L 4.3753703353086895,-2.5966377158153477 L 4.26814010342276,-2.676470588235294 L 4.169487982367514,-2.761707630764594 L 4.051730863894976,-2.8823529411764706 L 3.963605629426337,-2.9884548856490185 L 3.78190226349147,-3.0882352941176467 L 3.7577232764851605,-3.096547215743501 z\" />\n",
        "            </g>\n",
        "            </svg>"
       ],
       "text": [
        "<shapely.geometry.polygon.Polygon at 0x7fb87c5ccfd0>"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shapely_valid = shapely_gap.intersection(shapely_give)\n",
      "shapely_valid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "svg": [
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        "            width=\"100.0\"\n",
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       "text": [
        "<shapely.geometry.polygon.Polygon at 0x7fb87c899d68>"
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     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shapely_valid.area"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "13.429001009871161"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# def valid_area()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = give_contour.collections[0].get_paths()[0]\n",
      "v = p.vertices\n",
      "x = v[:,0]\n",
      "y = v[:,1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# with open('../data/optimised_cutout_x.csv', 'w') as x_csvfile:\n",
      "#     with open('../data/optimised_cutout_y.csv', 'w') as y_csvfile:\n",
      "        \n",
      "#         x_writer = csv.writer(x_csvfile, delimiter=',', lineterminator='\\n')\n",
      "#         y_writer = csv.writer(y_csvfile, delimiter=',', lineterminator='\\n')\n",
      "        \n",
      "\n",
      "#         for i in range(len(x_list_optimised)):\n",
      "\n",
      "#             x_writer.writerow(x_list_optimised[i])\n",
      "#             y_writer.writerow(y_list_optimised[i])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 113
    }
   ],
   "metadata": {}
  }
 ]
}