workbench demo used at RAND Corporation

rand_demo.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import logging\n",
      "import multiprocessing\n",
      "import datetime\n",
      "import seaborn as sns\n",
      "\n",
      "from expWorkbench import ema_logging, load_results, save_results,\\\n",
      "                         ModelEnsemble, Outcome, util, UNION,\\\n",
      "                         ParameterUncertainty, CategoricalUncertainty,\\\n",
      "                         INTERSECTION\n",
      "\n",
      "from connectors.vensim import VensimModelStructureInterface\n",
      "from connectors import vensimDLLwrapper\n",
      "            \n",
      "ema_logging.log_to_stderr(ema_logging.INFO);\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Top Down model\n",
      "\n",
      "in this model, demand is modelled in a top down way. It is thus a function of GDP development\n",
      "\n",
      "```python\n",
      "class TopDown(VensimModelStructureInterface):    \n",
      "    model_file = r'\\Copper model comparison Top Down 20140312.vpm'\n",
      "\n",
      "    #outcomes, or Key Performance Indicators (KPIs)\n",
      "    outcomes = [\n",
      "                Outcome('Per capita copper stocks', time=True),\n",
      "                Outcome('Conventional copper ore grade', time=True),\n",
      "                Outcome('Unconventional copper ore grade', time=True),\n",
      "                Outcome('Part potential copper demand substituted', time=True),\n",
      "                Outcome('Recycling Input Rate', time=True),\n",
      "               ]\n",
      "    #Uncertainties\n",
      "    # floats\n",
      "    uncertainties = [ParameterUncertainty((7,15), \"Average delay time mining capacity development\"),\n",
      "                     ParameterUncertainty((15,40), \"Average mine lifetime\"),\n",
      "                     ParameterUncertainty((3000000000,50000000000), \"Bimodal copper resource base\"),\n",
      "                     ParameterUncertainty((0.2,0.5), \"Copper collection rate\"),\n",
      "                     ParameterUncertainty((0.002,0.0035), \"Copper grade in EOL goods\"),\n",
      "                     CategoricalUncertainty(range(12,18), \"Global copper resource base exponent\", default=15),\n",
      "                     ParameterUncertainty((1,2), \"Global copper resource base radix\"),\n",
      "                     ParameterUncertainty((40,60), \"Lifetime of copper products\"),\n",
      "                     ParameterUncertainty((5,15), \"Long term adjustment period substitution\"),\n",
      "                     ParameterUncertainty((0.03,0.07), \"Long term substitution strength\"),\n",
      "                     ParameterUncertainty((0.2,0.4), \"Losses during production\"),\n",
      "                     ParameterUncertainty((8000,12000), \"Normalization factor for GDP per capita\"),\n",
      "                     ParameterUncertainty((2,5), \"Power for relative attractiveness\"),\n",
      "                     ParameterUncertainty((15,30), \"Proposed mine lifetime\"),\n",
      "                     ParameterUncertainty((0,0.05), \"Relative economic growth\"),\n",
      "                     ParameterUncertainty((0,0.02), \"Short term substitution strength\"),\n",
      "                     ParameterUncertainty((1.5,2), \"Threshold value aluminium price\"),\n",
      "                     ParameterUncertainty((550000000,750000000), \"Initial cumulative conventional copper extracted\"),\n",
      "                     ParameterUncertainty((0.1,0.2), \"Initial mothballed capacity\"),\n",
      "                     ParameterUncertainty((0.045,0.055), \"Initial per capita copper stocks\"),\n",
      "                     CategoricalUncertainty((1,3,10,100), \"Delay order capacity development\", default=3),\n",
      "                     CategoricalUncertainty(range(1,5), \"Switch copper demand\", default=4),\n",
      "                     CategoricalUncertainty(range(1,3), \"Switch lognormal bimodal distribution\", default=1),\n",
      "                     CategoricalUncertainty(range(1,5), \"Switch population scenarios\", default=3),\n",
      "                    ]\n",
      "\n",
      "    def model_init(self, policy, kwargs):\n",
      "        policy.pop('name')\n",
      "        self.policy = policy\n",
      "        super(TopDown, self).model_init(policy, kwargs)\n",
      "\n",
      "    def run_model(self, case):\n",
      "        for key, value in self.policy.items():\n",
      "            case[key] = value\n",
      "        super(TopDown, self).run_model(case)\n",
      "        \n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Bottom Up model\n",
      "\n",
      "In this model demand is a function of usages. \n",
      "\n",
      "```python\n",
      "class BottomUp(VensimModelStructureInterface):\n",
      "\n",
      "    #model\n",
      "    model_file = r'\\Copper model comparison Bottom Up 20140312.vpm'\n",
      "\n",
      "    #outcomes, or key performance indicators\n",
      "    outcomes = [\n",
      "                Outcome('Per capita copper stocks', time=True),\n",
      "                Outcome('Conventional copper ore grade', time=True),\n",
      "                Outcome('Unconventional copper ore grade', time=True),\n",
      "                Outcome('Part potential copper demand substituted', time=True),\n",
      "                Outcome('Recycling Input Rate', time=True),\n",
      "               ]\n",
      "    #Uncertainties\n",
      "    # floats\n",
      "    uncertainties = [\n",
      "                     ParameterUncertainty((7,15), \"Average delay time mining capacity development\"),\n",
      "                     ParameterUncertainty((60,100), \"Average lifetime architecture\"),\n",
      "                     ParameterUncertainty((7,13), \"Average lifetime automotive\"),\n",
      "                     ParameterUncertainty((30,70), \"Average lifetime energy infrastructure\"),\n",
      "                     ParameterUncertainty((5,15), \"Average lifetime other uses\"),\n",
      "                     ParameterUncertainty((4,12), \"Average lifetime stationary electric engines\"),\n",
      "                     ParameterUncertainty((60,100), \"Average lifetime water treatment\"),\n",
      "                     ParameterUncertainty((15,40), \"Average mine lifetime\"),\n",
      "                     ParameterUncertainty((3000000000,50000000000), \"Bimodal copper resource base\"),\n",
      "                     ParameterUncertainty((0.6,0.95), \"Copper collection rate Architecture\"),\n",
      "                     ParameterUncertainty((0.6,0.95), \"Copper collection rate automotive\"),\n",
      "                     ParameterUncertainty((0.6,0.95), \"Copper collection rate energy infrastructure\"),\n",
      "                     ParameterUncertainty((0.5,0.8), \"Copper collection rate other uses\"),\n",
      "                     ParameterUncertainty((0.6,0.95), \"Copper collection rate stationary electromotors\"),\n",
      "                     ParameterUncertainty((0.3,0.8), \"Copper collection rate water treatment\"),\n",
      "                     ParameterUncertainty((0.0008,0.0012), \"Copper grade in EOL architecture\"),\n",
      "                     ParameterUncertainty((0.0008,0.0012), \"Copper grade in EOL automotive\"),\n",
      "                     ParameterUncertainty((0.005,0.015), \"Copper grade in EOL energy infrastructure\"),\n",
      "                     ParameterUncertainty((0.0015,0.0025), \"Copper grade in EOL other uses\"),\n",
      "                     ParameterUncertainty((0.0025,0.0035), \"Copper grade in EOL stationary electric engines\"),\n",
      "                     ParameterUncertainty((0.0025,0.0035), \"Copper grade in EOL water treatment\"),\n",
      "                     CategoricalUncertainty(range(12,18), \"Global copper resource base exponent\", default=15),\n",
      "                     ParameterUncertainty((1,2), \"Global copper resource base radix\"),\n",
      "                     ParameterUncertainty((5,15), \"Long term adjustment period substitution\"),\n",
      "                     ParameterUncertainty((0.03,0.07), \"Long term substitution strength\"),\n",
      "                     ParameterUncertainty((0.2,0.4), \"Losses during production\"),\n",
      "                     ParameterUncertainty((2,5), \"Power for relative attractiveness\"),\n",
      "                     ParameterUncertainty((15,30), \"Proposed mine lifetime\"),\n",
      "                     ParameterUncertainty((0,0.05), \"Relative economic growth\"),\n",
      "                     ParameterUncertainty((0,0.02), \"Short term substitution strength\"),\n",
      "                     ParameterUncertainty((0.4,0.6), \"Substitution threshold architecture\"),\n",
      "                     ParameterUncertainty((1.2,1.6), \"Substitution threshold automotive\"),\n",
      "                     ParameterUncertainty((1.1,1.3), \"Substitution threshold energy infrastructure\"),\n",
      "                     ParameterUncertainty((1.3,2.5), \"Substitution threshold other uses\"),\n",
      "                     ParameterUncertainty((1.5,2.5), \"Substitution threshold stationary electric engines\"),\n",
      "                     ParameterUncertainty((0.3,0.5), \"Substitution threshold water treatment\"),\n",
      "                     ParameterUncertainty((0.005,0.015), \"Yearly relative growth architecture\"),\n",
      "                     ParameterUncertainty((0.05,0.15), \"Yearly relative growth energy infrastructure\"),\n",
      "                     ParameterUncertainty((0.02,0.025), \"Yearly relative growth other uses\"),\n",
      "                     ParameterUncertainty((0.03,0.046), \"Yearly relative growth stationary electric engines\"),\n",
      "                     ParameterUncertainty((0.02,0.04), \"Yearly relative growth water treatment\"),\n",
      "                     ParameterUncertainty((550000000,750000000), \"Initial cumulative conventional copper extracted\"),\n",
      "                     ParameterUncertainty((0.1,0.2), \"Initial mothballed capacity\"),\n",
      "                     ParameterUncertainty((0.045,0.055), \"Initial per capita copper stocks\"),\n",
      "                     CategoricalUncertainty((1,3,10,100), \"Delay order capacity development\", default=3),\n",
      "                     CategoricalUncertainty(range(1,3), \"Switch car types scenarios\", default=2),\n",
      "                     CategoricalUncertainty(range(1,3), \"Switch lognormal bimodal distribution\", default=2),\n",
      "                     CategoricalUncertainty(range(1,5), \"Switch population scenarios\", default=3),\n",
      "                    ]\n",
      "                                            \n",
      "    def model_init(self, policy, kwargs):\n",
      "        policy.pop('name')\n",
      "        self.policy = policy\n",
      "        super(BottomUp, self).model_init(policy, kwargs)\n",
      "\n",
      "    def run_model(self, case):\n",
      "        for key, value in self.policy.items():\n",
      "            case[key] = value\n",
      "        super(BottomUp, self).run_model(case)\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Generation of data\n",
      "\n",
      "The workbench has been designed for generating computational experiments, executing these experiments and storing their results for subsequent visualization and analysis. The workbench supports full factorial sampling, Monte Carlo sampling and Latin Hypercube sampling. By default, it uses LHS. To speed up the execution of the experiments, the workbench can utilize multiple cores on which the experiments are executed in parallel. Work is about to start on also including support for running on MPI supported clusters. For visualization and analysis, the workbench comes with a variety of usefull defaults. For analysis, this includes CART, PRIM, random forests, feature scoring techniques, and hierarchical time series clustering. For visualization, some usefull defaults like line plots, scatter plots etc. are supported. As well as a set of utility functions that can be used when developing your own plots using any of the available python scientific plotting libraries. \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rand_demo.copper_model_comparison import TopDown, BottomUp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "\n",
      "fn = r'./rand_demo/data/1000 experiments copper model comparison.tar.gz'\n",
      "\n",
      "try:\n",
      "    results = load_results(fn)\n",
      "except FileNotFoundError as e:\n",
      "    vensimDLLwrapper.use_double_precision()\n",
      "    \n",
      "    # apparently we have not yet generated the data, so let's do that now\n",
      "    model_bottomup = BottomUp(r'./rand_demo/models', \n",
      "                              'BottomUp')\n",
      "    model_topdown = TopDown(r'./rand_demo/models', \n",
      "                            'TopDown')\n",
      "    models = [model_topdown, model_bottomup]\n",
      "    ema_logging.info(\"model instantiated\")\n",
      "    \n",
      "    ensemble = ModelEnsemble()\n",
      "    ensemble.add_model_structures(models)\n",
      "    ensemble.parallel = True\n",
      "    ema_logging.info('ensemble instantiated and configured')\n",
      "    \n",
      "    n_experiments = 1000\n",
      "    results = ensemble.perform_experiments(n_experiments,\n",
      "                                           which_outcomes=UNION,\n",
      "                                           which_uncertainties=INTERSECTION,\n",
      "                                           reporting_interval=100)\n",
      "    util.save_results(results, fn)\n",
      "    \n",
      "    # this triggers cleanup of temporary files and directories\n",
      "    del ensemble\n",
      "  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO/MainProcess] results loaded succesfully from ./rand_demo/data/1000 experiments copper model comparison.tar.gz\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Analysis"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from analysis import plotting\n",
      "from analysis import plotting_util\n",
      "\n",
      "oois = list(results[1].keys())\n",
      "\n",
      "for ooi in oois:\n",
      "    if ooi==plotting.TIME: continue\n",
      "\n",
      "    plotting.envelopes(results, group_by='model', outcomes_to_show=ooi, fill=True,\n",
      "                       density=plotting_util.KDE)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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p6YimmTCLAy4XnHmmzoYNKj4fOJvuujUJJWqJXFEUpk+ffth93bt3r/1+woQJ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\nFPXAjkUSeeJSFOXHi4DD/zR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CdnnXyH7iQpygU1p0o7O7Cyv2fd3k+8mPxmaDyZODjB4d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\nIYQQKez/AVbVvXNFC/ysAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x68ff950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NG94x9u7du/POO+/w/PPP06ZNG0aOHHnP1+KGG++F+vXr88QTTxAWFkaHDh0YNWoU8fHx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\n1CbS0O7dzezdm42imGP6Ogdbt3DYsA2jVUJdxJemBsnM7KK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GyTHmUZFTQVlVuYzOhTiG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hAAAE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8bed670>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4gP79+/PYY49RWlrK/fffz+rVqwkLO/UMWPHxsadc3pwES9ZgyQnBkzVYckLT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\nuTiQl8Y3B9eRWZ+JM8qFMUy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RIlUMUQZMUuCFEP/DZrVTVeXEYlWw2w2gxWD4sQ++JXbF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\n0RuGwYwZM9i4cSMWi4WHH36Ybt26NRxfunQpc+fORdd1hg8fzsiRI0k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3b545a9Yss7Ky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\nrZgkeiGEEKIV+/+3lW7CdpN5pQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8e5e490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QSUlshREQqvNc9RgHISIi/aK+fPlyLFiwAF6vF0JUtr569uyJt99+O+rhqPkL\n1dJSB1jUiYiMoFvU//nPf+LNN9/EggUL8Ne//hVffvkl9u3bF4tsZAK1zf8OAF6OkyMiijndY+pt\n2rRBly5dkJGRgT179uCqq67Cli1bYpGNTIBFnYio+dAt6jabDZ9//jl69+6NTZs24fjx4ygpKYlF\nNjKBIGrvfg8GLQiFQjFOQ0TUsukW9ZkzZ2Ljxo0YOnQoysrKkJWVhalTp8YiG5lAXS11SeK56kRE\nsaZ7TP3tt9/GAw88AABYtGhR1AORuQhZQygUgKpaq61X1USUlpYjObmVQcmIiFoe3Zb6xo0boWla\nLLKQCQkV8AdqtshlWeYIeCKiGNNtqaempiIrKwt9+/ZFYmJiZD0noCEAUFQVvkA5kuwpNbb5az/c\nTkREUaJb1MePHx+5LUkShBCQJCmqocg8ZFlBIFh7k5xFnYgotnSL+oUXXhgp5kBlt2pCQkLUg5F5\n1DUBDU9rIyKKLd2iftttt2H37t3o06cPAGDv3r1o27YtFEXBY489hsGDB0c9JDVvdY2AZ0udiCi2\ndAfKtW/fHq+99hry8vKQl5eHNWvWoF+/fli+fDmeeuqpWGSkZi5YZ1GXIz08REQUfbpFvaCgAP36\n9Yssn7qeeseOHesdFa9pGmbNmoXs7Gzk5OTUeQ32mTNn8suBydXV/a5pifBxCDwRUczoFvUuXbpg\n/vz52Lt3L77//nvMnz8fZ599NrZu3QpZrvvhGzZsQDAYxMqVK3HPPfdg7ty5Ne6zcuVK7N27lwPv\nTC6E2q/Upig2lJfzwDoRUaw06HrqoVAId999N2bMmAEhBJ544gkUFBTg0UcfrfNxW7duRWZmJgCg\nf//+yM/Pr7H922+/xeTJk9lFa3K1XVMdAFTVArebU8USEcWK7kC5Vq1aYfr06TXWjx07tt7HuVwu\nOByOyLKiKNA0DbIs4/jx43j22Wfx7LPP4p133mlw2CS7eUbdmyVrU+QMaQG0apVYa8+N3e5AenrT\nzCrXVPuJNrPkBMyT1Sw5AXNljYaW/vqNplvUz5TD4YDb7Y4snyroAPDee++htLQUN954I0pKSuDz\n+dCzZ0+MGzeu3n26PeYYTp1kTzBF1qbKGfT7cOJEKaxWW41tR4640aFDxS9+jvT0Vigu/uX7iTaz\n5ATMk9UsOQHzZY0Gs7x+s2js3ylqRX3AgAHYtGkTsrKysH379sgpcQCQk5ODnJwcAEBeXh727dun\nW9Cp+ZKt62ivAAAgAElEQVQtCnwBV61FnePkiIhip0FF3e124/Dhw+jduzd8Ph/sdrvuY0aOHInN\nmzcjOzsbQOW0suvWrYPH48GkSZOq3ZcD5cxNVlT4Qi4kI73GNp6rTkQUO7pF/bPPPsOsWbMQDofx\nr3/9C2PHjsX8+fMjg+DqIklSjYF03bt3r3G/qtPQkjlJkoSgVnv19vn4hY2IKFZ0R78/9dRTeOWV\nV5CcnIz27dtjxYoV+Nvf/haLbGQidU1AEwyqCIU4Ap6IKBZ0i7qmaWjXrl1kuVevXuwupxrCdZzW\nJkl2uFyeGKchImqZdLvfO3TogI0bNwIAysvL8corr6Bjx45RD0bmUldL3WJJRFmZE6mpMQ5ERNQC\n6bbUZ8+ejbfeegtFRUUYMWIEdu3ahdmzZ8ciG5lIXVPFSpIEDxvqREQxodtSX758ORYsWBCLLGRi\nQdQ9zJ2ntRERxYZuS33jxo31XriFCKi8/Gpd0/3yuupERLGh21JPTU1FVlYW+vbti8TExMj6OXPm\nRDUYmYtQwggG/bBaE2tsY0udiCg2dIv6qfPIT414F0Jw9DvVpJ6aVa5mUfd6Zf5/Q0QUA7rd71dd\ndRXOO+88uFwuOJ1OnHvuuZwwhmpQLRb4guW1bhPCBi/74ImIok63qK9duxa33norCgoKUFhYiFtv\nvRWvv/56LLKRydR9CVY7SktZ1ImIok23+/3ll1/G66+/jtatWwMAbrnlFuTk5GDixIlRD0fmUte5\n6rKswO0OxzgNEVHLo9tSF0JECjoAtGnTptbrZhPVda46wDngiYhiQbel3rt3b+Tm5mLChAkQQmD1\n6tXIyMiIRTYymYBW9zB3HlInIoo+3Sb3448/DovFggceeAAPPPAALBYLHn744VhkI5MJofbud4Cn\ntRERxYJuS91ms+G+++6LRRYyuaCou3J7POx+JyKKNh4cpyYTloLQtNoHxIVCFgSDwRgnIiJqWXRb\n6kQNJRQBv98Dm61VjW2y7MDWrcdgsVjOaN+tW3tQWuqusV6SAIsFUJTTy6oqoChAamoCHA77GT8n\nEZHZ6Bb1GTNmcEpYahDFYoEnUF5rUVdVK8rKupzxvgMBO5zOxl3uLRDwQQg3kpKCSE0VOOssBWed\n1YYz2xFR3NIt6rt374bL5YLD4YhFHjIxWVEQ8Def66xWTlmbiFAIKCkBjh0LID+/BD17Aj17phsd\nj4ioyekWdVmWMXz4cHTv3h0JCQkAKueBX7ZsWdTDkfnUNatcc6AoVgjRAXv2BFBcfAS//nU6u+aJ\nKK7oFvV7770XQGUhP3VpTXZfUl3qm4CmuZBlK5zOLvj440IMHdoWqsqhJUQUH3RHv1944YVQFAU/\n/vgjLrjgAsiyjN/85jexyEYm1Jxb6j8XDHbCJ58UIxzmFLZEFB90i/rSpUuxcOFCLF26FG63GzNn\nzsQ//vGPWGQjEzJDS70qn68Tvv76uNExiIiahG5Rz8vLw0svvQSbzYY2bdpg9erVWLNmTSyykQmZ\nqaUOVB5KKilJQ2HhSaOjEBH9YrpFXVEUWK3WyHJiYiKPQVKdAsIHTdOMjtEoimJDfn4YoVDI6ChE\nRL+IblEfNGgQ5s6dC4/Hgw0bNuCWW27BhRdeGItsZEYWgWDQfBO9C9EeO3aUGB2DiOgX0S3q9913\nH7p164aMjAysXbsWw4YNw/Tp02ORjUxItqjw+J1Gx2g0SZJQVJQIv7/ui9IQETV3uv3oiqKgf//+\n8Hq9UBQFF198MbvfqU6yosDnrwBwltFRGk1R0rB79xGcf357o6MQEZ0R3Zb6Sy+9hDvvvBPHjx9H\nQUEBbr75ZqxevToW2cikAiYbLFdVYaGFx9aJyLR0m9wrV67EG2+8gVatKufzvu2225CdnY0JEyZE\nPRyZU1B4jY5wxiSpLb7/vgj9+rG1TkTmo9tSb926dbWpNO12O5KSkqIaiswtoJm5qEsoLJQjsycS\nEZmJbku9W7duuOaaa3DllVdCURSsX78eKSkpePHFFyFJEm688cZY5CQTCcK83e8AEA63QVHRSXTs\nmGZ0FCKiRtEt6l27dkXXrl1RUVEBoHLaWEmSEAhwlDDVLiDMd0pbVYpiRUFBGB07Gp2EiKhxdIv6\n7bffjpKSEnz99ddQFAWDBg1CSkpKLLKRSWlyCMGgHxZLgtFRztjx4xYEg0FexY2ITEX3mPqbb76J\nK6+8EuvWrUNeXh7GjBmDDz/8MAbRyKwkiwKPv9zoGL+IqqZh3z5OHUtE5qLbUn/uuefwxhtvoH37\nytHAhYWFuPnmm3HppZdGOxuZlGqxwBcoRwrSjY5yxiRJwtGjMvr0MToJEVHD6bbUHQ4H0tNPfzh3\n6tSJXZKky8wj4E+pqLDB7Tb/6yCilkO3pd63b1/ccsstmDhxIhRFwbp169C+fXu88847AIDRo0dH\nPSSZj9kHywGAxZKCgweL0LevzegoREQNolvUg8EgWrdujf/85z8AAIvFgtTUVHz88ccAWNSpdsE4\nKOoAUFwsGR2BiKjBdIv63LlzEQgEsH//foTDYfTq1Yvd76TLbNdVr0t5eSI8Hi/sdrbWiaj50y3q\nO3bswF/+8hekpKRACIGSkhIsXrwYF1xwQSzykUkFNI/REZqE1ZqKgweLcO65LOpE1PzpFvXc3Fws\nWLAA/fv3BwBs374djz/+OC/qQvUKKUGEQgGoqtXoKL9YSQm74InIHHRHv3s8nkhBB4ALLrgAfn98\ndK1S9EgWBW6f+a6rXpuyMiv/nyciU9At6ikpKdiwYUNk+YMPPkBqaqrujjVNw6xZs5CdnY2cnBwc\nOnSo2vb33nsPEyZMwMSJE7Fs2bIziE7NmWqxwBuIj6JusbTG4cPx8VqIKL7pdr/Pnj0b9957Lx58\n8EEIIdClSxfMmzdPd8cbNmxAMBjEypUr8c0332Du3Ll47rnnAADhcBhPP/001qxZA7vdjtGjR2Ps\n2LEN+rJA5uE38SVYq5IkCSUlEs45x+gkRET10y3q3bt3x5IlS2Cz2aBpGk6cOIGzzz5bd8dbt25F\nZmYmAKB///7Iz8+PbFMUBe+++y5kWUZJSQk0TeOI+jgUL4PlAODkSRmapkGWdTu3iIgMo1vUly1b\nhjfeeANr165FQUEBbr75Zlx33XXIzs6u93EulwsOhyOyrChKtQ9FWZbx/vvvY/bs2Rg+fDhsNv3R\nxUl281wgxCxZo5nTAg0pKfYm219T7quxQqFOCASc6NKlre5909NbxSBR0zBLVrPkBMyVNRpa+us3\nmm5RX7VqFV5//XUAQOfOnZGXl4eJEyfqFnWHwwG32x1Zrq2V87vf/Q4jR47E9OnTsXbtWlx11VX1\n7tPtMcdgpSR7gimyRjun2+2H09k0rfWUFHuT7etM7dzpRGJi/V+C0tNbobi4IkaJfhmzZDVLTsB8\nWaPBLK/fLBr7d9LtSwyFQtW6xi0WCyRJ/xSfAQMG4KOPPgJQeRpcnypXxnC5XJg6dSoCgQAkSYLN\nZmO3ZhwKwY9wOGR0jCZz4gRPbSOi5k23pT5ixAhMmzYNo0ePhhAC77//Pi677DLdHY8cORKbN2+O\ntOjnzJmDdevWwePxYNKkSRg7diymTp0KVVWRkZGBK6+88pe/GmpeLIDXVw5HUhujkzQJv78VnM4K\npKSwe5GImidJCCH07vTuu+9iy5YtUFUVgwYNwogRI2KRrZqnl61BgVc3arPA7vfTOgUy0L51j1+8\nn+bQ/Q4AXboUoW/fdnVuN1v3qxmymiUnYL6s0WCW128Wjf076bbUASArKwtZWVlnFIhaNn8cjYAH\n2AVPRM0bD2RTVAVEfBV1p1NFKBQ/4wSIKL6wqFNUxdO56gCgKK1RWFhmdAwiolrV2f3+5Zdf1jvK\nfdCgQVEJRPElgPi4rvopsqyguFhDt25GJyEiqqnOor5o0aJ6H7h8+fImD0PxJyh80LQwZFkxOkqT\nOXmSx9WJqHmqs6izaFNTEFbA63MhyZ5idJQmEwgko6ysHKmpyUZHISKqRnf0+5YtW/CPf/wDXq8X\nmqZB0zQUFRVh48aNschHJqdarXD5S+KqqFssdhQWOlnUiajZ0R0o9+CDD2LEiBEIh8OYOnUqunXr\nhmnTpsUiG8UJX5wNlgPYBU9EzZNuUU9MTMSECRMwaNAgJCcn4/HHH8d7770Xi2wUJ/yay+gITc7p\ntCAYDBodg4iomgYV9bKyMnTv3h3ffPMNJEnCyZMnY5GN4oQ/zs5VBypPbSso4KltRNS86Bb16667\nDnfeeScuu+wy5OXlYcyYMTjvvPNikY3ihF+40YDZiE1FlmWUlMTXayIi89MdKJeVlYXLL78ckiQh\nLy8PBw4cQEZGRiyyUZzQ1DD8fjcSEx1GR2lSPK5ORM2NblEvKCjAK6+8grKy6l2Nc+bMiVooii9K\nghXlvuK4K+qhUCpOnHAiLS1+RvYTkbnpFvU777wTgwYNqjaDXEOup050iiRJ8IXdRsdocqqaiKKi\nUqSlGZ2EiKiSblEPh8O4//77Y5GF4li8XdjlFF61jYiaE92Bcr/+9a/xn//8B4FAIBZ5KE754vC0\nNgAoL0+E1xtf89sTkXnpttTXr1+PFStWVFsnSRJ27doVtVAUf+LxtDYAsFhSUFBwFL16JRodhYhI\nv6h/8sknschBcS6sBBAIeGG12oyO0qQkSUJJiYRevYxOQkTUgKLu8XiwePFifP755wiFQrjoootw\n5513wm63xyIfxQk5wYJyTzHaWrsaHaXJnTwpQ9M0yLLu0SwioqjS/RR67LHH4PP58MQTT+DJJ59E\nMBjEww8/HItsFEdkWYEnXG50jKiQpDYoKio1OgYRkX5LPT8/H2+99VZk+eGHH0ZWVlZUQ1F88mkV\nRkeICkVRcfRoGJ06GZ2EiFq6BvUXOp3OardVVfe7AFENPhGfI+ABoKSEp7YRkfF0q/N1112HiRMn\n4rLLLoMQAhs3bsRNN90Ui2wUZ3xwQdPCkGXF6ChNLhRKwYkTTqSntzI6ChG1YLpF/eqrr0a/fv2w\nZcsWaJqGxYsXo0+fPrHIRnFGSpBR4TmJFEe60VGanKracORIGXhZBCIyUp3d7xs3bgQA5OXlYdeu\nXbDb7XA4HNi5cyfWrl0bs4AUPxTVggp/sdExoqa4mF3wRGSsOlvq+fn5uOyyy/DFF1/UOtf7uHHj\nohqM4pNPxN8c8Kd4PEmoqIjf10dEzV+dRf2OO+4AAFxxxRUYMmRItW3vvfdedFNR3PJq8XlaGwBY\nLK1w8GA52rdPMjoKEbVQdRb1t99+G4FAAIsWLYoUeAAIBoN44YUXMGrUqJgEpPjiE24IIeL2Sn/H\njgHt2xudgohaqjqLusvlwrZt2+B2u/HFF19E1iuKgr/+9a8xCUdxyKLB66uA3ZZsdJKoKCurvMCL\nzca54Iko9uos6pMnT8bkyZPx6aefYvDgwbHMRHFMSbCi3Hs8bou61ZqKAwd+xLnnsqgTUezpntKW\nkpKCO+64A2VlZRBCAKi8iMWyZcuiHo7ik0dz6t/JxDgRDREZRbeo33///cjOzsY555wTOQ4ar8dD\nKTbivag7neyCJyJj6BZ1m82GqVOnxiILtRBeURHXVzWzWFJx8GARMjJY1IkotnQ/VYcMGYJly5Zh\n//79OHLkSOSH6IwlSHB5ThidIqo4EQ0RGUG3pf7mm28CAJYuXVpt/akZ54gaS7GocPqOIzkOp4s9\npawsER6PF3a7zegoRNSC6BZ1Fm+KhniehAY4NQq+CH37sqgTUezodr+XlZXhoYceQk5ODk6ePIkZ\nM2ZUuxQr0ZnwxHlRB4Djx9kFT0SxpVvUZ86ciX79+qGsrAxJSUlo164d7r333lhkozgWkD0IhQJG\nx4gqt9uB8vL4vYY8ETU/ukW9oKAA2dnZUBQFCQkJuOuuu1BUVBSLbBTH5EQVpa74/v9IVR04eNBj\ndAwiakF0i7qqqqioqIgsHzhwAIqiRDUUxT9ZVuAOlxodI+qOHmUXPBHFju5Audtvvx05OTkoKirC\nLbfcgu3bt+OJJ56IRTaKc64WUNQDgdY4evQkOnRoY3QUImoBdIv6JZdcgn79+uGbb75BOBzG7Nmz\nkZ4ev6ciUex4hROaFoYsx2/Pj6om4PDhEDp0MDoJEbUEut3vl156KRYvXozWrVtjxIgRLOjUZCSb\ngtKK+D6uDgDFxRaEQiGjYxBRC6Bb1N966y1kZGTg6aefxqhRo7Bo0SIcPHhQd8eapmHWrFnIzs5G\nTk4ODh06VG37unXrMGnSJEyZMgUPP/xw5GIx1HLIioKKUInRMaJOltOwf/9Jo2MQUQugW9RTU1Mx\nadIkLFu2DPPmzcPGjRuRlZWlu+MNGzYgGAxi5cqVuOeeezB37tzINp/Ph4ULF2L58uX417/+BZfL\nhU2bNv2yV0Km1BKOq0uShKIiDpgjoujTPaZ+4sQJrF+/Hm+//TacTid+//vfY/Hixbo73rp1KzIz\nMwEA/fv3R35+fmRbQkICVq1ahYSEBABAKBRCYiIvftESeUV53B9XB4CKimQcP16Gdu1SjY5CRHFM\nt6iPGzcOl19+OR544AH069evwTt2uVxwOByRZUVRIlfmkiQJbdpUjgZevnw5vF4vBg8efAbxyewk\nm4zSiiKkpXQ2OkpUqaode/aUoV07o5MQUTzTLeqbNm2CqurerQaHwwG32x1Z/vmlNjVNw7x583Dw\n4EEsWrSoQftMsic0OodRzJK1OeQUfhdSUuy692vIfZqDunKGQp2gaX60b998Wuvp6a2MjtAgZskJ\nmCtrNLT012803Wr973//G08++WS1+d4lScKuXbvqfdyAAQOwadMmZGVlYfv27ejTp0+17bNmzUJC\nQgKeffZZSFLDjje6Pf4G3c9oSfYEU2RtLjkD7kK0Tcyo9z4pKXY4nc1/dja9nJs3lyAzs3kcakhP\nb4Xi4gr9OxrMLDkB82WNBrO8frNo7N9Jt6gvXrwYy5cvR69evRpcfAFg5MiR2Lx5M7KzswEAc+bM\nwbp16+DxeNCvXz+sWbMGAwcOxB/+8AcAwLRp0zBixIhGhaf44JMqEAz6YbEY32sQbRUVrXlsnYii\nRreod+jQAb179270jiVJwqOPPlptXffu3SO39Vr61HLINgtKKg7hrDa9jI4SdYpiw48/8tg6EUWH\nblE/77zzcMcdd+CSSy6B1WoFUFmwx40bF/Vw1DJIkoSKcAnOQvwXdQA4edIBp9OFlBSH/p2JiBpB\nt6hXVFTAbrdj+/bt1dazqFNTqtBOQAjRqEM8ZqWqrbB37xEMHMiiTkRNS7eoz507F4FAAPv370c4\nHEavXr1gsVhikY1aEC1BQ7m7GCmOltEvffRoIrxeH2w2zs9ARE1Ht6jv2LEDf/nLX5CSkgIhBEpK\nSrB48WJccMEFschHLYRiUVHqO9JiirrF0gZ79hxB//4s6kTUdHSLem5uLhYsWID+/fsDALZv347H\nH38cq1evjno4alnKw/E/D3xVR46o6NcvDEVpHqe4EZH56c797vF4IgUdAC644AL4/caf20zxx6+4\n4PE69e8YJyQpHXv3njA6BhHFEd2inpKSgg0bNkSWP/jgA6Sm8hxbanpKohUlnkP6d4wTkiShoEDi\nFQqJqMnodr/Pnj0b9957Lx588EEIIdClSxfMmzcvFtmoBXKGjwH4H6NjxEww2BaHDp1At25tjY5C\nRHFAt6h3794dS5Ysgc1mg6ZpOHHiBM4+++wYRKOWyCtXwOtzwZbYMk73kmUVBw5o6NbN6CREFA90\nu9+XLVuGG264AUlJSXA6nbj55puxcuXKWGSjFki1JaDEfcDoGDHlcqXi6NH4v648EUWfblFftWoV\nXn31VQBA586dkZeXhxUrVkQ9GLVcZeFjRkeIKVW1Yd++oNExiCgO6Bb1UChUbbIZi8XSImb9IuP4\nlIoWNQoeAEpLK6eOJSL6JXSPqY8YMQLTpk3D6NGjIYTA+++/j8suuywW2aiFUhKtOObZh+62Xxkd\nJWZUtRV27y7Eb37TMsYSEFF06Bb1e+65B+vXr8eWLVugqiovkUoxURoqwtnighbVK3T8eBIqKjxo\n1cpudBQiMindoi5JEnr27Im2bdtGzqf96quvMGjQoKiHo5ZLs4VxsrwQaSmdjY4SMxZLKnbvLsTA\ngSzqRHRmdIv6o48+ik2bNqFLly7V1i9fvjxqoYhkRUGJ+xDS0HKKOgAcO2aH2+1FUpLN6ChEZEK6\nRX3z5s1Yv349EhN54QmKrQoUIxQKQFWtRkeJGVVtjd27CzFgAIs6ETWe7uj3Ll26QNO0WGQhqkZO\nsuBI2W6jY8RcURFHwhPRmdFtqScnJ2PMmDH41a9+hYSEhMj6OXPmRDUYEQCcDBeii+hndIyYUtUU\n7NhRiCFDOBKeiBpHt6hnZmYiMzOz2rqWNCKZjBWyBVHiPIjU1L5GR4kppzMNBQUn0LlzmtFRiMhE\ndIv6VVddFYscRLWSFQXHvQdwDlpWUVfVROzaVYazzuL11omo4eos6hkZGdWWJUlCcnIyLrnkEsya\nNYuXX6WY8ahlKHefANCyBo+Fw+3x3XdHcP75HYyOQkQmUedAue+//77az86dO/H222+jZ8+emD17\ndiwzUgunJFpx2LnT6BgxJ0kSDh92oLS0wugoRGQSuqPfT5EkCW3btsWtt96K77//PpqZiGpwysVw\ne8uMjhFzqpqC7dvdkYmfiIjq0+CiXlXVC7wQxYKaaEFhxS6jYxjC5+uAXbuKjY5BRCbQ6KL+/vvv\no3Xr1tHIQlSvcuU4vL5yo2PEnCwrOHDAjrIynrtORPWrc6BcbVdic7lc6NatG+bNmxfVUES1kRMt\nOOTMR5/EwUZHiTlFScG2bUcwbJgdsnxGHWxE1ALUWdSXLVtWbVmWZSQnJ8Ph4IQYZJwKSzHK3cVI\nTko3OkrM+Xwd8O23R3DBBWcZHYWImqk6i3rnzi3rQhpkDnKCBYcq8tEvabjRUWJOlmUUFrZGevpJ\ndOrUxug4RNQMsR+PTMeXWIFi50GjYxhCVZOQnw94vX6joxBRM8SiTqYjW1Qc8u+Az99SB46l44sv\nTvI0NyKqgUWdTElKkrGn9LMWewVBr7cjtm8/anQMImpmWNTJtIKOAHYVf9RCT3OTUVSUhv37S4yO\nQkTNCIs6mZYkSfA7PMh3bcKPxVtwvGw/gsGWc6xZUWzYtSsBJSUt70sNEdVO9yptRM2dYrPAieMo\n047igHM7rGEb7HIKbHIyWlnbIjmpLWQ5Pq90piip+PrrYmRm+mC3Jxodh4gMxqJOcUOSZai2BGjQ\n4EIpXCjF0dAPQDGQKCXBJqfAJrVCqq097LZUSJJkdOQmko7PPjuCoUMVTuFM1MKxqFNcU1QL4ACC\nCCCIYpSjGIX+7yFXKEiUHLDLybDLKUi1d0BionknVgqFOuLTTw8jM7MDZ5wjasFY1KnFUa1WwAoE\n4EUAXpSKozjk3gHVmQCb3Ao2ORkOpTVSHR2gqlaj4zaY19sZn35agIsvbg9Fic/DDURUPxZ1avEk\nSYKamAAA8KICXlSgRDsErfRrWDU7kpRU2KRWaG07q1l320uSBJerM/773yO48MIUJCXZjI5ERDHG\nok5UC1lWINsVaAijAidQgRMo8u+FXKHALicjSU5FsrU9kpPSm1V3tyRJCAY74cMPTyIpyYWkJAG7\nXYLDAaSnJ8FutxsdkYiiiEWdqIEUqwWwAl644IULx0L7IZVIsCEZDqUNUqzt0apVN6NjAgBUtQ38\nfsDvB06eBDRNQyjkhqqWICFBwGIB0tM9qKhwQ1UBVQVkGT/dFpBlICFB+elHhapW/iiK0mx7KoiI\nRZ3ojCmqBVABPzzww4Pi0AEUHfkG8CXCIaehja0TkuypRscEUDlZjdXaCkArBINAMAiUldnhdHrq\nfEw4HEI4HIIQIUhSCIAPkhSGogjIsoCiVH4JkCREbitK5fKpLwmnlhUFUBQRWbZaFaiqDItFhsWi\nQFEUyLIc+d2cej+IzIRFnaiJyKoK2a7ALVe25I/69sJSkQiHkoYUNR1tWnWGopjnn5yiqPXmDYcr\nf86EpoV/+tGgaWFIkgZJCgDQAFQuy3LlF4A2bTwoL3dDlhH5kSTUugzUvv5U58LpdaLatlPbFUWC\nLEtVfstQFAmSJEV+y7IMSZJq/QmHK1/TqeWW5rvvgHbtjE7RskX9E0bTNDzyyCPYs2cPLBYLcnNz\n0bVr12r38Xq9uP766/HEE0+gR48e0Y5EFBNKghVagoZyFFdOjHNiO5LQGq2Utmib1A02E59C90vJ\nstLgCYGEsEPTPIjFNP9CCAih/fRbABA/XV9ARNZJkgZAgySJyPpTt5OTQygv90SWT31hqFrfK5dF\nlds1f6pu+7m67qO3rurv1FQgPb3VL36/fm7UKGDbtibfLTVC1Iv6hg0bEAwGsXLlSnzzzTeYO3cu\nnnvuucj2HTt24OGHH8bx48db5DdbahlkWQGSFPjghg9uFLn2wOq0/zTzXStYpAQkqEmwWZNhsSSY\nqkUfTypb2Gd+OmBCgh1Wa92HNH5OiMqfWNO06FwMqLCw8tAO50AyTtQ/ObZu3YrMzEwAQP/+/ZGf\nn19tezAYxHPPPYd777032lGImo3Kme/CcOEkXDgJANDCYYTdQUhhCbJQoUpWWGCFIllhkaxQpASo\nkgoVVljkBCRYkmC12KCq1ridBpfMp7RUQrt2vCywUaJe1F0uFxyO092MiqJA07TIQJgBAwZEOwKR\nKciKArnKpDEawvDDC8Bb476aFoYWDEN4NUiaBEkoUCULFFigQoUsWX5arrytSAoUqFAkFaqSCKuS\niIQEgWAwAEVR+aWAmsyJEyzqRop6UXc4HHC73ZHlqgW9sZLsCU0VK+rMktUsOQHzZDU+ZxhAGGH4\nUNs4Nk0LQwuFccCpAWEJEBIkAciSChkKFCiQJQUyFEiSAhlyZFmWZEiQIUky5J9+S5W3KruuK28B\nUpFCY4MAABoCSURBVOXvyDa58lQ4BQogVY5ulyFX3k+STt+3yiA0oPJwXCDgg80mV1t36lBd1XWn\nlqtvi72UlOY/F0BqalJ0dtzxK2jaIKSnR2f3pC/qRX3AgAHYtGkTsrKysH37dvTp0+eM9+X2mOOy\nmkn2BFNkNUtOwDxZzZITAJLsiZVZJfFTXQz+9FOF+NnvMyQ0DQIClf9VHkiuPJZ8+qBy5aKo8ZxJ\n9kR4PD5EinfVLD/d/VT5lkTlrVPHqU/Xdanab+lnv/HT14qqd5WqfllowOMrsybA4wlUe0b8bEmq\ntkWq5dbP1bVF/0tLXV9szkrRMGRIlu7jG23CFPz44w6cd16o6ffdQjV2QGPUi/rIkSOxefNmZGdn\nAwDmzJmDdevWwePxYNKkSdF+eiJqBiRZbkAJqp3FlgDVgN5cUeXbg2jgt5qQXUJQav5f6tzROpXA\n4kZhIQc8GynqRV2SJDz66KPV1nXv3r3G/ZYvXx7tKEREFE2Shr17OXGQkfjuExFR00h0YuMm+Ywn\nJaJfjkWdiIiahurHEWcJXn+d8ywYhUWdiIiajKXLNvztbwnwN/+hBXGJRZ2IiJrM+WM2o6BAxv/7\nf5xWzggs6kRE1CQUSUGg63okJQn8/e9WeGvOm0RRxqJORERN4rc9fosdJ7/GlX/6CsePy1i+nK31\nWGNRJyKiJnH3xXcDAPadcx/sSWEsWmSFz2dwqBaGRZ2IiJrEyB4j8btul+Pz4//F+bc+jmPHZLz4\notXoWC0KizoRETUJSZKw8LLn0TGpE75Mmo3k/v/BvHlWfP89S02s8J0mIqImk2ZLw/+NWlp58Z8J\n18BvOYpbb01EIGB0spaBRZ2IiJrUoA4X4uGLH4MzfBzt/pyNHd8JPPUUu+FjgUWdiIia3E3n/xlj\neozFcftHcPx+JpYsscLpNDpV/DNPUQ8HEC4/gZCnGEKUNehH09jfQ0RkBEmSsHD4szg7uTtcv5oL\nb+d3sG2bYnSsuGeaCXr/ev0UFBdXYMf+b3BEOtKgx3z//9u7//go6juP46/Z3WyW3fwOgQgcPxIo\ngVSRiIo+aPEi1fTR9GoBgRBSbL1TRFp7Ukv4IUZPhfYe1bMHnj+g9QxYak/F4rWlUFQQK4Tjh4Qf\nCkGCgJAQkpDdbLI7M9/7Y/MTAiZK3J3weT4eeZCdnZl9M5udz3dmZ77fg9XU1yd3czIhhBAdiYuO\nZ2VOMbf9/laMiQWcNd8H+oc7Vo9mnSP1JrGueMxOjgU8YEA0piFdGgkhRLhc3fsaxp59GnpV8/SJ\nfPy67JO7k+WKekpcCkF/506rx8S4iYmt7+ZEQgghLmU0P4Rdd/FR3U7u33gPQSMY7kg9luWKusfj\nIcrs/FWUcrQuhBDhlT9dx7nhv9DKv8lbR94kb90UqvxV4Y7VI1muqAPERMV2ft4YN73c0k+hEEKE\nS3q64tVXTJL/8hZ8/B02n/wbo164kUW/e5O6OhXueD2KNYu609Ol+WNj5I9GCCHC6eabDbZvhZ8N\neJWkHb8koNXyQnUBQ/99PLc/8Fd+/3u73PJ2GVizqEfFdGn++AQHhi63twkhRDjFxMDPf2ZwYOUs\nim/aRkZwCqrvHnYNn8yPS8eR8b1NTJ3mYtWqKM6c0cId15IsWdR7x6YQbOx8kY6L82CzywVzQggR\nCTQNbr8+jc0PrGBL3jYmpE6G1N0YU+7g7aFjefC//kbm191MnNiL4uIoqqvDndg6LFnU4+MSIND5\nVpymacR45BS8EEJEmuFJGbwy8TdszvuA76VPROu3C/Jzcf/4Zt47sZm5c118/esx3Huvi337LFmy\nvlKW3EI2mw2P3d2lZWJiOndvuxBCiK9eRtIIXrz9Jd6Z+ne+k/ZP+BK3wcxbyXzkTgZnfsYbb0Rx\n661uli+PCnfUiGbJog4Q4+z8FfAAycludF1OwQshRCQbkTyS3+asYsPkdxnT9wb2af+DN/96lvxm\nO337Kh591EVpqWVLV7ez7JaJc8Z3aX5Xr2iczsZuSiOEEOJyGtVnNG9N/CuLxhbxme8kS07dRmbu\n3wCorZWL6C7GskV9UJ/BBLxdu6I9IV5OwQshhFXYNBs/yXqQZye8iF+vZ2NqLp4bX+GGG4xwR4tY\nli3qLpeLBHtCl5ZJTXVj6L5uSiSEEKI7TP7aVJ4e8xoEXfi+nc/SHY9IH/IXYdmiDpDi7tul+V29\noomJkVPwQghhNd8efguOl7diPzeE/9z1NOPXjGXTsY3hjhVxLF3UB/ft+in4lBQbpql3UyIhhBDd\nITYW5kwZhrHsQ1KP/Cuf1h1j2lsTueevd3HK91m440UMSxd1l8tFgiOxS8sk947FZqvrpkRCCCG6\nS2FhgO99O5pTLz9F1o5tjE65nrWHX+fmV8aw4sP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       "text": [
        "<matplotlib.figure.Figure at 0x8ee4750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Aa/gJeLII+rLweGrvWg6JdSv8wP6zi1aOWnx/nkVluXDdcgxjT5Vy4fXyvT8T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\nSqqsV7Zfw5XpctsrVXH66pyvp1TV+VV3WXGG1WbDai2pNL/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       "text": [
        "<matplotlib.figure.Figure at 0x8f3fc30>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# a usefull function used below"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.axes_grid1 import AxesGrid\n",
      "import matplotlib as mpl    \n",
      "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
      "\n",
      "import mpld3\n",
      "from mpld3 import plugins\n",
      "    \n",
      "sns.set_style('whitegrid')\n",
      "\n",
      "def show_tradeoff(prim_box):\n",
      "    '''Visualise the trade off between coverage and density. Color is used\n",
      "    to denote the number of restricted dimensions.'''\n",
      "    \n",
      "    # Define some CSS to control our custom labels\n",
      "    css = \"\"\"\n",
      "    table\n",
      "    {\n",
      "      border-collapse: collapse;\n",
      "    }\n",
      "    th\n",
      "    {\n",
      "      background-color:  rgba(255,255,255,0.6);;;\n",
      "    }\n",
      "    td\n",
      "    {\n",
      "      background-color: rgba(255,255,255,0.6);;\n",
      "    }\n",
      "    table, th, td\n",
      "    {\n",
      "      font-family:Tahoma, Tahoma, sans-serif;\n",
      "      font-size: 16px;\n",
      "      border: 1px solid black;\n",
      "      text-align: right;\n",
      "    }\n",
      "    \"\"\"    \n",
      "    \n",
      "    fig = plt.figure()\n",
      "    ax = fig.add_subplot(111)\n",
      "    \n",
      "    nr_colors = int(prim_box.res_dim)+1\n",
      "    cp = sns.cubehelix_palette(n_colors=nr_colors,light=0.05, dark=0.95, reverse=True, gamma=0.5)\n",
      "    res_dim = prim_box.peeling_trajectory['res dim']\n",
      "    \n",
      "    colors = []\n",
      "    for entry in res_dim:\n",
      "        entry = int(entry)\n",
      "        colors.append(cp[entry])\n",
      "    \n",
      "    p = ax.scatter(prim_box.peeling_trajectory['coverage'], \n",
      "                   prim_box.peeling_trajectory['density'], \n",
      "                   c=colors, s=40)\n",
      "    ax.set_ylabel('density', fontsize=16, labelpad=30)\n",
      "    ax.set_xlabel('coverage', fontsize=16, labelpad=30)\n",
      "    ax.set_ylim(ymin=0, ymax=1.2)\n",
      "    ax.set_xlim(xmin=0, xmax=1.2)\n",
      "    \n",
      "    # make the tooltip tables\n",
      "    labels = []\n",
      "    columns_to_include = ['coverage','density', 'mass', 'res dim']\n",
      "    frmt = lambda x: '{:.2f}'.format( x )\n",
      "    for i in range(len(prim_box.peeling_trajectory['coverage'])):\n",
      "        label = prim_box.peeling_trajectory.ix[[i], columns_to_include].T\n",
      "        label.columns = ['box {0}'.format(i)]\n",
      "        # .to_html() is unicode; so make leading 'u' go away with str()\n",
      "        labels.append(str(label.to_html(float_format=frmt)))        \n",
      "    \n",
      "    tooltip = plugins.PointHTMLTooltip(p, labels, voffset=10, \n",
      "                                       hoffset=10, css=css)  \n",
      "    plugins.connect(fig, tooltip)\n",
      "    \n",
      "    # make the colorbar\n",
      "    divider = make_axes_locatable(ax)\n",
      "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
      "    \n",
      "    ticklabels = np.arange(0, \n",
      "                         nr_colors, \n",
      "                         step=1)\n",
      "    ticklabels = list(ticklabels)\n",
      "    norm = mpl.colors.Normalize(vmin=0, vmax=nr_colors)\n",
      "    cmap = mpl.colors.ListedColormap([entry for entry in cp]) \n",
      "    cbl = mpl.colorbar.ColorbarBase(cax, cmap=cmap,norm=norm, \n",
      "                                    drawedges=True, spacing='uniform',\n",
      "                                    ticks=ticklabels)\n",
      "    \n",
      "    ticklocs = cax.yaxis.get_ticklocs()\n",
      "    ticklocs = ticklocs + (ticklocs[1]/2)\n",
      "    cax.yaxis.set_ticks(ticklocs)\n",
      "    \n",
      "    cbl.set_label(\"nr. of restricted dimensions\", fontsize=14, labelpad=20)\n",
      "\n",
      "    return fig, ticklabels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Scenario discovery\n",
      "\n",
      "The ema workbench has full support for scenario discovery. It includes support for PCA-Prim, calculates the quasi-p values, and allows for interactive exploration and adaptation of the identified boxes. In the example below, we use the following function for specifying our experiments of interest\n",
      "\n",
      "$\n",
      "    \\begin{aligned}\n",
      "    f(x) = \n",
      "        \\begin{cases}\n",
      "            &1 &&\\text{x>0.7}\\\\\n",
      "            &0 &&\\text{otherwise}\n",
      "        \\end{cases}\n",
      "    \\end{aligned}\n",
      "$\n",
      "\n",
      "Where $x$ is the the part of potential copper demand that is being substituted"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from analysis import prim\n",
      "\n",
      "def classify(outcomes):\n",
      "    '''classify runs with a terminal value for substituted demand above\n",
      "    0.7 as being of interest'''\n",
      "    \n",
      "    outcome = outcomes['Part potential copper demand substituted'][:,-1]\n",
      "    classes = np.zeros(outcome.shape)\n",
      "    classes[outcome>0.7]=1\n",
      "    return classes\n",
      "\n",
      "\n",
      "prim_obj = prim.Prim(results, classify, threshold=0.8)\n",
      "box1 = prim_obj.find_box()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO/MainProcess] 2000 points remaining, containing 382 cases of interest\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO/MainProcess] mean: 1.0, mass: 0.056, coverage: 0.2931937172774869, density: 1.0 restricted_dimensions: 9.0\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with sns.axes_style({'figure.figsize':(6,6)}):\n",
      "    fig, ticklabels = show_tradeoff(box1)\n",
      "mpld3.display()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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\"el660157064144\", \"height\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"type\": \"zoom\", \"enabled\": false}, {\"button\": true, \"type\": \"boxzoom\", \"enabled\": false}, {\"voffset\": 10, \"labels\": [\"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 0</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>0.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 1</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.34</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.50</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>1.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 2</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.36</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 3</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.38</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 4</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.40</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.43</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 5</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.42</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.41</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 6</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.39</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 7</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.37</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 8</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.49</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.35</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 9</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.51</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.33</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 10</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.53</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.31</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 11</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.55</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.30</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 12</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.58</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 13</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.84</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.60</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 14</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.83</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.62</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.25</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 15</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.82</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.65</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.24</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 16</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.81</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.67</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.23</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 17</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.79</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.69</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.22</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 18</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.77</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.71</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.21</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 19</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.75</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.73</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.20</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 20</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.73</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.75</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 21</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.71</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.77</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.18</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 22</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.68</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.78</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.17</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 23</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.67</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.81</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.16</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 24</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.65</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.82</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.15</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 25</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.63</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.84</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 26</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.61</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 27</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.59</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.13</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>4.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 28</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.57</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>5.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 29</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.54</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>5.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 30</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.52</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.91</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.11</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>5.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 31</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.50</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.92</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>6.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 32</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.48</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.93</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>6.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 33</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.46</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.94</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.09</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>7.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 34</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.40</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.96</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.08</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 35</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.38</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.97</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.07</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 36</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.36</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.97</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.07</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 37</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.34</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.98</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.07</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 38</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.32</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.98</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 39</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 40</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 41</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 42</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 43</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 44</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 45</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 46</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 47</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 48</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\"], \"id\": \"el660160368528\", \"type\": \"htmltooltip\", \"hoffset\": 10}], \"width\": 480.0, \"axes\": [{\"xscale\": \"linear\", \"xlim\": [0.0, 1.2], \"ylim\": [0.0, 1.2], \"paths\": [], \"axes\": [{\"nticks\": 7, \"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": true, \"alpha\": 1.0, \"color\": \"#CCCCCC\", \"dasharray\": \"none\"}, \"position\": \"bottom\", \"fontsize\": 10.0, \"tickvalues\": null, \"visible\": true}, {\"nticks\": 7, \"scale\": \"linear\", 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        "   }(mpld3);\n",
        "}else if(typeof define === \"function\" && define.amd){\n",
        "   // require.js is available: use it to load d3/mpld3\n",
        "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
        "   require([\"d3\"], function(d3){\n",
        "      window.d3 = d3;\n",
        "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
        "         \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
        "                                                hoffset:0,\n",
        "                                                voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                  tooltip\n",
        "                    .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                    .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
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\"el660157064144\", \"height\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"type\": \"zoom\", \"enabled\": false}, {\"button\": true, \"type\": \"boxzoom\", \"enabled\": false}, {\"voffset\": 10, \"labels\": [\"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 0</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>0.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 1</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.34</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.50</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>1.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 2</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.36</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 3</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.38</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 4</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.40</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.43</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 5</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.42</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.41</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 6</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.39</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 7</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.37</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 8</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.49</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.35</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 9</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.51</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.33</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 10</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.53</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.31</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 11</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.55</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.30</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 12</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.58</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 13</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.84</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.60</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 14</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.83</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.62</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.25</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 15</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.82</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.65</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.24</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 16</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.81</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.67</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.23</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 17</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.79</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.69</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.22</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 18</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.77</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.71</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.21</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 19</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.75</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.73</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.20</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 20</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.73</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.75</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 21</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.71</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.77</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.18</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 22</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.68</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.78</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.17</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 23</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.67</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.81</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.16</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 24</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.65</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.82</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.15</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 25</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.63</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.84</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 26</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.61</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 27</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.59</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.13</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>4.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 28</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.57</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>5.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 29</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.54</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>5.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 30</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.52</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.91</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.11</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>5.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 31</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.50</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.92</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>6.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 32</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.48</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.93</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>6.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 33</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.46</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.94</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.09</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>7.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 34</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.40</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.96</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.08</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 35</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.38</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.97</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.07</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 36</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.36</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.97</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.07</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 37</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.34</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.98</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.07</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 38</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.32</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.98</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 39</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 40</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 41</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 42</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 43</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 44</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 45</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 46</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 47</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  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        "      });\n",
        "    });\n",
        "}else{\n",
        "    // require.js not available: dynamically load d3 & mpld3\n",
        "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
        "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
        "                 \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
        "                                                hoffset:0,\n",
        "                                                voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                  tooltip\n",
        "                    .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                    .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "                 mpld3.draw_figure(\"fig_el6601570641445082544649\", {\"data\": {\"data01\": [[1.0, 0.191], [0.900523560209424, 0.344], [0.900523560209424, 0.36210526315789476], [0.900523560209424, 0.38137472283813745], [0.900523560209424, 0.40186915887850466], [0.900523560209424, 0.4231242312423124], [0.900523560209424, 0.44559585492227977], [0.8979057591623036, 0.4679399727148704], [0.8952879581151832, 0.49137931034482757], [0.8900523560209425, 0.5143721633888049], [0.8769633507853403, 0.5342902711323764], [0.8638743455497382, 0.5546218487394958], [0.856020942408377, 0.5787610619469027], [0.837696335078534, 0.5970149253731343], [0.8272251308900523, 0.6208251473477406], [0.824607329842932, 0.6521739130434783], [0.806282722513089, 0.6724890829694323], [0.7905759162303665, 0.6942528735632184], [0.7696335078534031, 0.711864406779661], [0.7513089005235603, 0.7321428571428571], [0.7329842931937173, 0.7526881720430108], [0.7094240837696335, 0.7677053824362606], [0.6832460732984293, 0.7791044776119403], [0.6701570680628273, 0.8050314465408805], [0.6465968586387435, 0.8178807947019867], [0.6308900523560209, 0.8426573426573427], [0.612565445026178, 0.8634686346863468], [0.5890052356020943, 0.8754863813229572], [0.5654450261780105, 0.8852459016393442], [0.5418848167539267, 0.8961038961038961], [0.5235602094240838, 0.91324200913242], [0.5026178010471204, 0.9230769230769231], [0.4816753926701571, 0.934010152284264], [0.4607329842931937, 0.9411764705882353], [0.39528795811518325, 0.9617834394904459], [0.3769633507853403, 0.9664429530201343], [0.3586387434554974, 0.9716312056737588], [0.3403141361256545, 0.9774436090225563], [0.32460732984293195, 0.9841269841269841], [0.2670157068062827, 1.0], [0.27225130890052357, 1.0], [0.27486910994764396, 1.0], [0.2774869109947644, 1.0], [0.2801047120418848, 1.0], [0.28272251308900526, 1.0], [0.28534031413612565, 1.0], [0.2879581151832461, 1.0], [0.2905759162303665, 1.0], [0.2931937172774869, 1.0]], \"data02\": [[0.0, 0.0]]}, \"id\": \"el660157064144\", \"height\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"type\": \"zoom\", \"enabled\": false}, {\"button\": true, \"type\": \"boxzoom\", \"enabled\": false}, {\"voffset\": 10, \"labels\": [\"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 0</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>0.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 1</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.34</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.50</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>1.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 2</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.36</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 3</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.38</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 4</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.40</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.43</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 5</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.42</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.41</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 6</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.39</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 7</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.37</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 8</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.49</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.35</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 9</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.51</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.33</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 10</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.53</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.31</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 11</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.55</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.30</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 12</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.58</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 13</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.84</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.60</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 14</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.83</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.62</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.25</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 15</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.82</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.65</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.24</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 16</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.81</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.67</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.23</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 17</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.79</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.69</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.22</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 18</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.77</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.71</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.21</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 19</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.75</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.73</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.20</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 20</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.73</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.75</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 21</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.71</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.77</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.18</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 22</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.68</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.78</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.17</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 23</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.67</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.81</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.16</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 24</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.65</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.82</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.15</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 25</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.63</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.84</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 26</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.61</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 27</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.59</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.13</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>4.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 28</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.57</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>5.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 29</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.54</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>5.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 30</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.52</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.91</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.11</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>5.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 31</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.50</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.92</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>6.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 32</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.48</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.93</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>6.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 33</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.46</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.94</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.09</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>7.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 34</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.40</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.96</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.08</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 35</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.38</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.97</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.07</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 36</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.36</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.97</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.07</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 37</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.34</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.98</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.07</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 38</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.32</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.98</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>8.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 39</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 40</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 41</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 42</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 43</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 44</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 45</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.05</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 46</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 47</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.29</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.06</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>9.00</td>\\n    </tr>\\n  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        "            })\n",
        "         });\n",
        "}\n",
        "</script>"
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       "metadata": {},
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       "prompt_number": 18,
       "text": [
        "<IPython.core.display.HTML at 0x90d6a30>"
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     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "box1.inspect(27)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "coverage    0.589005\n",
        "density     0.875486\n",
        "mass        0.128500\n",
        "mean        0.875486\n",
        "res dim     4.000000\n",
        "Name: 27, dtype: float64\n",
        "\n",
        "                                                   box 27                   \\\n",
        "                                                      min              max   \n",
        "Long term substitution strength                0.05329323       0.06996288   \n",
        "model                                     set([BottomUp])  set([BottomUp])   \n",
        "Short term substitution strength               0.00775853       0.01998587   \n",
        "Long term adjustment period substitution          5.00333         14.57858   \n",
        "\n",
        "                                                        \n",
        "                                             qp values  \n",
        "Long term substitution strength           6.206736e-41  \n",
        "model                                     2.846896e-35  \n",
        "Short term substitution strength          1.197496e-11  \n",
        "Long term adjustment period substitution     0.3253413  \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "this looks like a fine box to use, with one expection. The last dimension that is being restricted is not significant according to the qp values. So let's select this box and drop the last restriction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "box1.select(27)\n",
      "box1.drop_restriction('Long term adjustment period substitution')\n",
      "box1.inspect()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "coverage    0.612565\n",
        "density     0.863469\n",
        "mass        0.135500\n",
        "mean        0.863469\n",
        "res dim     3.000000\n",
        "Name: 49, dtype: float64\n",
        "\n",
        "                                           box 49                   \\\n",
        "                                              min              max   \n",
        "Long term substitution strength        0.05329323       0.06996288   \n",
        "model                             set([BottomUp])  set([BottomUp])   \n",
        "Short term substitution strength       0.00775853       0.01998587   \n",
        "\n",
        "                                                \n",
        "                                     qp values  \n",
        "Long term substitution strength   1.013646e-41  \n",
        "model                             3.738499e-36  \n",
        "Short term substitution strength  3.553472e-11  \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The box resulting from box 27 with the restriction on `Long term adjustment period substitution` dropped explains 61% of our cases of interest. Let's see if it is possible to find an explanation for the remaining 40%. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "box2 = prim_obj.find_box()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO/MainProcess] 1729 points remaining, containing 148 cases of interest\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO/MainProcess] box does not meet threshold criteria, value is 0.5042016806722689, returning dump box\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "PRIM is not able to find a good box that explains the remaining 40%. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# PCA PRIM\n",
      "\n",
      "Do demonstrate the ease of doing PCA prim, look at the example below. It is identical to the previous, with one additional line of code"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prim_obj = prim.Prim(results, classify, threshold=0.8)\n",
      "\n",
      "# we add this one line for doing PCA preprocessing.\n",
      "prim_obj.perform_pca()\n",
      "\n",
      "box1 = prim_obj.find_box()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO/MainProcess] 2000 points remaining, containing 382 cases of interest\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO/MainProcess] mean: 1.0, mass: 0.0935, coverage: 0.4895287958115183, density: 1.0 restricted_dimensions: 4.0\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with sns.axes_style({'figure.figsize':(6,6)}):\n",
      "    show_tradeoff(box1)\n",
      "mpld3.display()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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<th>density</th>\\n      <td>0.36</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 3</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.38</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 4</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.40</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.43</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 5</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.42</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.41</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 6</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.39</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 7</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.37</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 8</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.49</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.35</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 9</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.52</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.33</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 10</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.55</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.31</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 11</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.58</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.30</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 12</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.61</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 13</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.64</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 14</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.67</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.25</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 15</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.70</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.24</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 16</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.74</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.23</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 17</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.77</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.22</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 18</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.80</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.21</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 19</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.85</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.82</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.20</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 20</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.83</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.85</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 21</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.81</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.18</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 22</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.79</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.17</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 23</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.76</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.92</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.16</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 24</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.74</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.93</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.15</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 25</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.71</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.94</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 26</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.68</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.96</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 27</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.65</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.96</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.13</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 28</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.62</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.97</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 29</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.59</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.98</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 30</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.57</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.99</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.11</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 31</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.54</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.99</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 32</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.51</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.99</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  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0}], \"id\": \"el660151887792\", \"images\": [], \"bbox\": [0.8634920634920635, 0.125, 0.03650793650793649, 0.775], \"texts\": [{\"alpha\": 1, \"coordinates\": \"axes\", \"v_baseline\": \"hanging\", \"rotation\": -90.0, \"zorder\": 3, \"color\": \"#262626\", \"h_anchor\": \"middle\", \"id\": \"el660146566992\", \"position\": [3.026706861413043, 0.5], \"text\": \"nr. of restricted dimensions\", \"fontsize\": 14.0}]}]});\n",
        "   }(mpld3);\n",
        "}else if(typeof define === \"function\" && define.amd){\n",
        "   // require.js is available: use it to load d3/mpld3\n",
        "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
        "   require([\"d3\"], function(d3){\n",
        "      window.d3 = d3;\n",
        "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
        "         \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
        "                                                hoffset:0,\n",
        "                                                voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                  tooltip\n",
        "                    .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                    .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "         mpld3.draw_figure(\"fig_el6601538079206816895449\", {\"data\": {\"data01\": [[1.0, 0.191], [0.900523560209424, 0.344], [0.900523560209424, 0.36210526315789476], [0.900523560209424, 0.38137472283813745], [0.900523560209424, 0.40186915887850466], [0.900523560209424, 0.4231242312423124], [0.900523560209424, 0.44559585492227977], [0.900523560209424, 0.4693042291950887], [0.900523560209424, 0.4942528735632184], [0.900523560209424, 0.5204236006051437], [0.900523560209424, 0.5486443381180224], [0.8979057591623036, 0.5764705882352941], [0.8979057591623036, 0.6070796460176991], [0.8952879581151832, 0.6380597014925373], [0.8900523560209425, 0.6679764243614931], [0.887434554973822, 0.7018633540372671], [0.8821989528795812, 0.7358078602620087], [0.8769633507853403, 0.7701149425287356], [0.8638743455497382, 0.7990314769975787], [0.8455497382198953, 0.8239795918367347], [0.8272251308900523, 0.8494623655913979], [0.8115183246073299, 0.8781869688385269], [0.7879581151832461, 0.8985074626865671], [0.7643979057591623, 0.9182389937106918], [0.7382198952879581, 0.9337748344370861], [0.7068062827225131, 0.9440559440559441], [0.6780104712041884, 0.955719557195572], [0.6492146596858639, 0.9649805447470817], [0.6204188481675392, 0.9713114754098361], [0.5916230366492147, 0.9783549783549783], [0.5654450261780105, 0.9863013698630136], [0.5392670157068062, 0.9903846153846154], [0.5130890052356021, 0.9949238578680203], [0.4895287958115183, 1.0]], \"data02\": [[0.0, 0.0]]}, \"id\": \"el660153807920\", \"height\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"type\": \"zoom\", \"enabled\": false}, {\"button\": true, \"type\": \"boxzoom\", \"enabled\": false}, {\"voffset\": 10, \"labels\": [\"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 0</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>0.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 1</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.34</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.50</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>1.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 2</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.36</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 3</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.38</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 4</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.40</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.43</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 5</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.42</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.41</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 6</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.39</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 7</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.37</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 8</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.49</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.35</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 9</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.52</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.33</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 10</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.55</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.31</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 11</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.58</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.30</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 12</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.61</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 13</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.64</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 14</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.67</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.25</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 15</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.70</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.24</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 16</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.74</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.23</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 17</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.77</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.22</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 18</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.80</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.21</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 19</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.85</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.82</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.20</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 20</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.83</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.85</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 21</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.81</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.18</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 22</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.79</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.17</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 23</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.76</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.92</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.16</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 24</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.74</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.93</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.15</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 25</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.71</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.94</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 26</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.68</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.96</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 27</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.65</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.96</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.13</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 28</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.62</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.97</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 29</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.59</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.98</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 30</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.57</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.99</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.11</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 31</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.54</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.99</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 32</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.51</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.99</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 33</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.49</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.09</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>4.00</td>\\n    </tr>\\n  </tbody>\\n</table>\"], \"id\": \"el660146655280\", \"type\": \"htmltooltip\", \"hoffset\": 10}], \"width\": 480.0, \"axes\": [{\"xscale\": \"linear\", \"xlim\": [0.0, 1.2], \"ylim\": [0.0, 1.2], \"paths\": [], \"axes\": [{\"nticks\": 7, \"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": true, \"alpha\": 1.0, \"color\": \"#CCCCCC\", \"dasharray\": \"none\"}, \"position\": \"bottom\", \"fontsize\": 10.0, \"tickvalues\": null, \"visible\": true}, {\"nticks\": 7, \"scale\": \"linear\", 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0}], \"id\": \"el660151887792\", \"images\": [], \"bbox\": [0.8634920634920635, 0.125, 0.03650793650793649, 0.775], \"texts\": [{\"alpha\": 1, \"coordinates\": \"axes\", \"v_baseline\": \"hanging\", \"rotation\": -90.0, \"zorder\": 3, \"color\": \"#262626\", \"h_anchor\": \"middle\", \"id\": \"el660146566992\", \"position\": [3.026706861413043, 0.5], \"text\": \"nr. of restricted dimensions\", \"fontsize\": 14.0}]}]});\n",
        "      });\n",
        "    });\n",
        "}else{\n",
        "    // require.js not available: dynamically load d3 & mpld3\n",
        "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
        "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
        "                 \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
        "                                                hoffset:0,\n",
        "                                                voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                  tooltip\n",
        "                    .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                    .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "                 mpld3.draw_figure(\"fig_el6601538079206816895449\", {\"data\": {\"data01\": [[1.0, 0.191], [0.900523560209424, 0.344], [0.900523560209424, 0.36210526315789476], [0.900523560209424, 0.38137472283813745], [0.900523560209424, 0.40186915887850466], [0.900523560209424, 0.4231242312423124], [0.900523560209424, 0.44559585492227977], [0.900523560209424, 0.4693042291950887], [0.900523560209424, 0.4942528735632184], [0.900523560209424, 0.5204236006051437], [0.900523560209424, 0.5486443381180224], [0.8979057591623036, 0.5764705882352941], [0.8979057591623036, 0.6070796460176991], [0.8952879581151832, 0.6380597014925373], [0.8900523560209425, 0.6679764243614931], [0.887434554973822, 0.7018633540372671], [0.8821989528795812, 0.7358078602620087], [0.8769633507853403, 0.7701149425287356], [0.8638743455497382, 0.7990314769975787], [0.8455497382198953, 0.8239795918367347], [0.8272251308900523, 0.8494623655913979], [0.8115183246073299, 0.8781869688385269], [0.7879581151832461, 0.8985074626865671], [0.7643979057591623, 0.9182389937106918], [0.7382198952879581, 0.9337748344370861], [0.7068062827225131, 0.9440559440559441], [0.6780104712041884, 0.955719557195572], [0.6492146596858639, 0.9649805447470817], [0.6204188481675392, 0.9713114754098361], [0.5916230366492147, 0.9783549783549783], [0.5654450261780105, 0.9863013698630136], [0.5392670157068062, 0.9903846153846154], [0.5130890052356021, 0.9949238578680203], [0.4895287958115183, 1.0]], \"data02\": [[0.0, 0.0]]}, \"id\": \"el660153807920\", \"height\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"type\": \"zoom\", \"enabled\": false}, {\"button\": true, \"type\": \"boxzoom\", \"enabled\": false}, {\"voffset\": 10, \"labels\": [\"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 0</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>1.00</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>0.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 1</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.34</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.50</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>1.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 2</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.36</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 3</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.38</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 4</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.40</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.43</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 5</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.42</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.41</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 6</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.45</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.39</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 7</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.47</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.37</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 8</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.49</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.35</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 9</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.52</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.33</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 10</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.55</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.31</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 11</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.58</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.30</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 12</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.61</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.28</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>2.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 13</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.64</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.27</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 14</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.67</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.25</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 15</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.89</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.70</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.24</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 16</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.74</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.23</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 17</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.77</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.22</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 18</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.86</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.80</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.21</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 19</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.85</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.82</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.20</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 20</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.83</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.85</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.19</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 21</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.81</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.88</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.18</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 22</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.79</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.90</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.17</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 23</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.76</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.92</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.16</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 24</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.74</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.93</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.15</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 25</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.71</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.94</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 26</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.68</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.96</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.14</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 27</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.65</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.96</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.13</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 28</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.62</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.97</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 29</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.59</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.98</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.12</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 30</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.57</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.99</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.11</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 31</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.54</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.99</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  </tbody>\\n</table>\", \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n  <thead>\\n    <tr style=\\\"text-align: right;\\\">\\n      <th></th>\\n      <th>box 32</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>coverage</th>\\n      <td>0.51</td>\\n    </tr>\\n    <tr>\\n      <th>density</th>\\n      <td>0.99</td>\\n    </tr>\\n    <tr>\\n      <th>mass</th>\\n      <td>0.10</td>\\n    </tr>\\n    <tr>\\n      <th>res dim</th>\\n      <td>3.00</td>\\n    </tr>\\n  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        "            })\n",
        "         });\n",
        "}\n",
        "</script>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<IPython.core.display.HTML at 0x92c9dd0>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "box1.inspect(18)\n",
      "box1.inspect(22)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "coverage    0.863874\n",
        "density     0.799031\n",
        "mass        0.206500\n",
        "mean        0.799031\n",
        "res dim     3.000000\n",
        "Name: 18, dtype: float64\n",
        "\n",
        "                box 18                               \n",
        "                   min              max     qp values\n",
        "r_13         -2.249619       -0.1912861  6.707717e-75\n",
        "model  set([BottomUp])  set([BottomUp])  4.467653e-49\n",
        "r_12         -2.662946         1.543905    0.06265836\n",
        "\n",
        "coverage    0.787958\n",
        "density     0.898507\n",
        "mass        0.167500\n",
        "mean        0.898507\n",
        "res dim     3.000000\n",
        "Name: 22, dtype: float64\n",
        "\n",
        "                box 22                               \n",
        "                   min              max     qp values\n",
        "r_13         -2.249619       -0.4943165  1.260468e-94\n",
        "model  set([BottomUp])  set([BottomUp])  1.624482e-53\n",
        "r_12         -2.662946         1.543905    0.04083153\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is a more difficult choice. It appears that box 22 has increased the significance of r_12 by peeling further on r_13"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "rot = pd.DataFrame(prim_obj.rotation_matrix, \n",
      "             index=prim_obj.row_names, \n",
      "             columns=prim_obj.column_names)\n",
      "\n",
      "rot.ix[:,'r_12':'r_13']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>r_12</th>\n",
        "      <th>r_13</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Proposed mine lifetime</th>\n",
        "      <td> 0.210050</td>\n",
        "      <td> 0.005602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Short term substitution strength</th>\n",
        "      <td>-0.656882</td>\n",
        "      <td>-0.338562</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Power for relative attractiveness</th>\n",
        "      <td> 0.235121</td>\n",
        "      <td> 0.013441</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Long term substitution strength</th>\n",
        "      <td> 0.310015</td>\n",
        "      <td>-0.904694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Long term adjustment period substitution</th>\n",
        "      <td> 0.356769</td>\n",
        "      <td> 0.220035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Initial per capita copper stocks</th>\n",
        "      <td> 0.350598</td>\n",
        "      <td>-0.016469</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Global copper resource base radix</th>\n",
        "      <td> 0.060321</td>\n",
        "      <td>-0.035378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average mine lifetime</th>\n",
        "      <td> 0.182914</td>\n",
        "      <td>-0.034291</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Initial cumulative conventional copper extracted</th>\n",
        "      <td> 0.099462</td>\n",
        "      <td> 0.084184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Initial mothballed capacity</th>\n",
        "      <td>-0.110734</td>\n",
        "      <td> 0.047296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Losses during production</th>\n",
        "      <td> 0.158939</td>\n",
        "      <td>-0.061626</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average delay time mining capacity development</th>\n",
        "      <td> 0.175541</td>\n",
        "      <td>-0.005378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Relative economic growth</th>\n",
        "      <td> 0.004247</td>\n",
        "      <td> 0.033417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bimodal copper resource base</th>\n",
        "      <td> 0.086283</td>\n",
        "      <td>-0.036204</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "                                                      r_12      r_13\n",
        "Proposed mine lifetime                            0.210050  0.005602\n",
        "Short term substitution strength                 -0.656882 -0.338562\n",
        "Power for relative attractiveness                 0.235121  0.013441\n",
        "Long term substitution strength                   0.310015 -0.904694\n",
        "Long term adjustment period substitution          0.356769  0.220035\n",
        "Initial per capita copper stocks                  0.350598 -0.016469\n",
        "Global copper resource base radix                 0.060321 -0.035378\n",
        "Average mine lifetime                             0.182914 -0.034291\n",
        "Initial cumulative conventional copper extracted  0.099462  0.084184\n",
        "Initial mothballed capacity                      -0.110734  0.047296\n",
        "Losses during production                          0.158939 -0.061626\n",
        "Average delay time mining capacity development    0.175541 -0.005378\n",
        "Relative economic growth                          0.004247  0.033417\n",
        "Bimodal copper resource base                      0.086283 -0.036204"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# PRIM regression mode\n",
      "\n",
      "Prim uses a lenient hill climbing algorithm aimed at maximizing the mean of the points inside the box. We can use this directly rather then first classifying our results as being of interest or not"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import imp\n",
      "imp.reload(prim)\n",
      "experiments, outcomes = results\n",
      "\n",
      "outcomes['fraction demand substituted'] = outcomes['Part potential copper demand substituted'][:,-1]\n",
      "results = experiments, outcomes\n",
      "\n",
      "prim_obj = prim.Prim(results, 'fraction demand substituted', threshold=0.7)\n",
      "box1 = prim_obj.find_box()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO/MainProcess] 2000 points remaining, containing 382 cases of interest\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO/MainProcess] mean: 0.7649459954604362, mass: 0.0515, coverage: 0.2643979057591623, density: 0.9805825242718447 restricted_dimensions: 7.0\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "box1.show_ppt()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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g4jGiEo/SrXGE2pFEDZRntrL9YDxrd57nYnI2Br2WZ0dGcF/PZtIizgGm+HgA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\nEUhBFUIIIRqBFFQhhBCiEfw/6ujn2KGAxRMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x90de550>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "box1.inspect()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "coverage    0.264398\n",
        "density     0.980583\n",
        "mass        0.051500\n",
        "mean        0.764946\n",
        "res dim     7.000000\n",
        "Name: 41, dtype: float64\n",
        "\n",
        "                                                             box 41  \\\n",
        "                                                                min   \n",
        "Long term substitution strength                          0.05618792   \n",
        "model                                               set([BottomUp])   \n",
        "Short term substitution strength                        0.007668726   \n",
        "Long term adjustment period substitution                    5.00333   \n",
        "Global copper resource base exponent      set([17, 12, 13, 14, 15])   \n",
        "Power for relative attractiveness                          2.231102   \n",
        "Losses during production                                  0.2001518   \n",
        "\n",
        "                                                                     \\\n",
        "                                                                max   \n",
        "Long term substitution strength                          0.06996288   \n",
        "model                                               set([BottomUp])   \n",
        "Short term substitution strength                         0.01998587   \n",
        "Long term adjustment period substitution                   11.84578   \n",
        "Global copper resource base exponent      set([17, 12, 13, 14, 15])   \n",
        "Power for relative attractiveness                          4.998865   \n",
        "Losses during production                                  0.3855156   \n",
        "\n",
        "                                                        \n",
        "                                             qp values  \n",
        "Long term substitution strength           2.031334e-35  \n",
        "model                                     1.422542e-18  \n",
        "Short term substitution strength          1.583007e-06  \n",
        "Long term adjustment period substitution  0.0001225164  \n",
        "Global copper resource base exponent         0.1832714  \n",
        "Power for relative attractiveness            0.6222829  \n",
        "Losses during production                     0.6575431  \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    }
   ],
   "metadata": {}
  }
 ]
}