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prim_blog.ipynb
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"# Performing Scenario Discovery in Python\n",
"\n",
"The purpose of this blog post is to demonstrate how one can do scenario discovery in python. This blogpost will use the exploratory modeling workbench available on github. I will demonstrate how we can perform both PRIM in an interactive way, as well as briefly show how to use CART, which is also available in the exploratory modeling workbench. There is ample literature on both CART and PRIM and their relative merits for use in scenario discovery. So I won't be discussing that here in any detail.\n",
"\n",
"The workbench is mend as a one stop shop for doing exploratory modeling, scenario discovery, and (multi-objective) robust decision making. To support this, the workbench is split into several packages. The most important packages are the expWorkbench that contains the support for setting up and executing computational experiments or (multi-objective) optimization with models; The connectors package, which contains connectors to vensim (system dynamics modeling package), netlogo (agent based modeling environment), and excel; and the analysis package that contains a wide range of techniques for visualization and analysis of the results from series of computational experiments. Here, we will focus on the analysis package. It some future blog post, I plan to demonstrate the use of the workbench for performing computational experimentation and multi-objective (robust) optimization. \n",
"\n",
"The workbench can be found on github and downloaded from there. At present, the workbench is only available for python 2.7. There is a seperate branch where I am working on making a version of the workbench that works under both python 2.7 and 3. The workbench is depended on various scientific python libraries. If you have a standard scientific python distribution, like anaconda, installed, the main dependencies will be met. In addition to the standard scientific python libraries, the workbench is also dependend on `deap` for genetic algorithms. There are also some optional dependencies. These include `seaborn` and `mpld3` for nicer and interactive visualizations, and `jpype` for controlling models implemented in Java, like netlogo, from within the workbench. \n",
"\n",
"In order to demonstrate the use of the exploratory modeling workbench for scenario discovery, I am using a published example. I am using the data used in the original article by Ben Bryant and Rob Lempert where they first introduced 2010. Ben Bryant kindly made this data available for my use. The data comes as a csv file. We can import the data easily using pandas. columns 2 up to and including 10 contain the experimental design, while the classification is presented in column 15\n"
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"import pandas as pd\n",
"\n",
"data = pd.DataFrame.from_csv('./data/bryant et al 2010 data.csv', index_col=False)\n",
"x = data.ix[:, 2:11]\n",
"y = data.ix[:, 15]"
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"The exploratory modeling workbench is built on top of numpy rather than pandas. This is partly a path dependecy issue. The earliest version of prim in the workbench is from 2012, when pandas was still under heavy development. Another problem is that the pandas does not contain explicit information on the datatypes of the columns. The implementation of prim in the exploratory workbench is however datatype aware, in contrast to the scenario discovery toolkit in R. That is, it will handle categorical data differently than continuous data. Internally, prim uses a numpy structured array for x, and a numpy arrea for y. We can easily transform the pandas dataframe to either. "
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"x = x.to_records()\n",
"y = y.values"
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"the exploratory modeling workbench comes with a seperate analysis package. This analysis package contains prim. So let's import prim. The workbench also has its own logging functionality. We can turn this on to get some more insight into prim while it is running."
]
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{
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"source": [
"from analysis import prim\n",
"from expWorkbench import ema_logging\n",
"ema_logging.log_to_stderr(ema_logging.INFO);"
]
},
{
"cell_type": "markdown",
"metadata": {},
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"Next, we need to instantiate the prim algorithm. To mimic the original work of Ben Bryant and Rob Lempert, we set the peeling alpha to 0.1. The peeling alpha determines how much data is peeled off in each iteration of the algorithm. The lower the value, the less data is removed in each iteration. The minimium coverage threshold that a box should meet is set to 0.8. Next, we can use the instantiated algorithm to find a first box. "
]
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"[INFO] 882 points remaining, containing 89 cases of interest\n",
"[INFO] mean: 1.0, mass: 0.0510204081633, coverage: 0.505617977528, density: 1.0 restricted_dimensions: 6.0\n"
]
}
],
"source": [
"prim_alg = prim.Prim(x, y, threshold=0.8, peel_alpha=0.1)\n",
"box1 = prim_alg.find_box()"
]
},
{
"cell_type": "markdown",
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"Let's investigate this first box is some detail. A first thing to look at is the trade off between coverage and density. The box has a convenience function for this called `show_tradeoff`. To support working in the ipython notebook, this method returns a matplotlib figure with some additional information than can be used by mpld3. "
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<th></th>\\n <th>box 5</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>0.17</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.58</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 6</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>0.52</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 7</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>0.22</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>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 8</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>0.24</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.42</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 9</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>0.27</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>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 10</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>0.30</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.34</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 11</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.99</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.33</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>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 12</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.98</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.37</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 13</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.97</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.40</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.24</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 14</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.96</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.45</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.22</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 15</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.92</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.48</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>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 16</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.91</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.53</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.17</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 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>0.57</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>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 18</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>0.62</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>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 19</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>0.69</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>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 20</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.80</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.73</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.11</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 21</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>0.77</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>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 22</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>0.83</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>\", \"<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>0.69</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.87</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>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 24</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.64</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.90</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.07</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 25</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.58</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.93</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.06</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 26</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>0.96</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>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 27</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>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>6.00</td>\\n </tr>\\n </tbody>\\n</table>\"], \"type\": \"htmltooltip\", \"id\": \"el24444466968144\", \"hoffset\": 10}], \"data\": {\"data02\": [[0.0, 0.0]], \"data01\": [[1.0, 0.10090702947845805], [1.0, 0.11237373737373738], [1.0, 0.12535211267605634], [1.0, 0.13949843260188088], [1.0, 0.15532286212914484], [1.0, 0.17348927875243664], [1.0, 0.1943231441048035], [1.0, 0.21601941747572814], [1.0, 0.2418478260869565], [1.0, 0.270516717325228], [1.0, 0.30067567567567566], [0.9887640449438202, 0.3333333333333333], [0.9775280898876404, 0.3670886075949367], [0.9662921348314607, 0.40375586854460094], [0.9550561797752809, 0.44502617801047123], [0.9213483146067416, 0.4823529411764706], [0.9101123595505618, 0.5328947368421053], [0.8764044943820225, 0.5735294117647058], [0.8426966292134831, 0.6198347107438017], [0.8314606741573034, 0.6851851851851852], [0.797752808988764, 0.7319587628865979], [0.7528089887640449, 0.7701149425287356], [0.7303370786516854, 0.8333333333333334], [0.6853932584269663, 0.8714285714285714], [0.6404494382022472, 0.9047619047619048], [0.5842696629213483, 0.9285714285714286], [0.5393258426966292, 0.96], [0.5056179775280899, 1.0]]}, \"id\": \"el24444461300176\"});\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.v0.2.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, hoffset:0, 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_el244444613001763248762984\", {\"axes\": [{\"xlim\": [0.0, 1.2], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#262626\", \"text\": \"coverage\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 11.0, \"position\": [0.5, -0.047043010752688158], \"rotation\": -0.0, \"id\": \"el24444461548112\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#262626\", \"text\": \"density\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 11.0, \"position\": [-0.056367607526881691, 0.5], \"rotation\": -90.0, \"id\": \"el24444464048656\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 1.2], \"ylim\": [0.0, 1.2], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#CCCCCC\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#CCCCCC\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el24444457904784\", \"ydomain\": [0.0, 1.2], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], 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</thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>1.00</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.10</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>1.00</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.11</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.90</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>1.00</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.13</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.80</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>1.00</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.14</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.72</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>1.00</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.16</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.65</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 5</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>0.17</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.58</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 6</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>0.52</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 7</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>0.22</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>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 8</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>0.24</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.42</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 9</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>0.27</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>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 10</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>0.30</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.34</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 11</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.99</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.33</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>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 12</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.98</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.37</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 13</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.97</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.40</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.24</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 14</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.96</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.45</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.22</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 15</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.92</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.48</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>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 16</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.91</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.53</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.17</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 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>0.57</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>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 18</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>0.62</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>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 19</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>0.69</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>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 20</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.80</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.73</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.11</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 21</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>0.77</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>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 22</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>0.83</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>\", \"<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>0.69</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.87</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>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 24</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.64</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.90</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.07</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 25</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.58</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.93</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.06</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 26</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>0.96</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>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 27</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>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>6.00</td>\\n </tr>\\n </tbody>\\n</table>\"], \"type\": \"htmltooltip\", \"id\": \"el24444466968144\", \"hoffset\": 10}], \"data\": {\"data02\": [[0.0, 0.0]], \"data01\": [[1.0, 0.10090702947845805], [1.0, 0.11237373737373738], [1.0, 0.12535211267605634], [1.0, 0.13949843260188088], [1.0, 0.15532286212914484], [1.0, 0.17348927875243664], [1.0, 0.1943231441048035], [1.0, 0.21601941747572814], [1.0, 0.2418478260869565], [1.0, 0.270516717325228], [1.0, 0.30067567567567566], [0.9887640449438202, 0.3333333333333333], [0.9775280898876404, 0.3670886075949367], [0.9662921348314607, 0.40375586854460094], [0.9550561797752809, 0.44502617801047123], [0.9213483146067416, 0.4823529411764706], [0.9101123595505618, 0.5328947368421053], [0.8764044943820225, 0.5735294117647058], [0.8426966292134831, 0.6198347107438017], [0.8314606741573034, 0.6851851851851852], [0.797752808988764, 0.7319587628865979], [0.7528089887640449, 0.7701149425287356], [0.7303370786516854, 0.8333333333333334], [0.6853932584269663, 0.8714285714285714], [0.6404494382022472, 0.9047619047619048], [0.5842696629213483, 0.9285714285714286], [0.5393258426966292, 0.96], [0.5056179775280899, 1.0]]}, \"id\": \"el24444461300176\"});\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.v0.2.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, hoffset:0, 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_el244444613001763248762984\", {\"axes\": [{\"xlim\": [0.0, 1.2], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#262626\", \"text\": \"coverage\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 11.0, \"position\": [0.5, -0.047043010752688158], \"rotation\": -0.0, \"id\": \"el24444461548112\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#262626\", \"text\": \"density\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 11.0, \"position\": [-0.056367607526881691, 0.5], \"rotation\": -90.0, \"id\": \"el24444464048656\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 1.2], \"ylim\": [0.0, 1.2], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#CCCCCC\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#CCCCCC\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el24444457904784\", \"ydomain\": [0.0, 1.2], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [0.3], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el24444466968144\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#081D58\", \"#24419A\", \"#1E80B8\", \"#1E80B8\", \"#40B5C3\", \"#40B5C3\", \"#40B5C3\", \"#40B5C3\", \"#40B5C3\", \"#40B5C3\", \"#40B5C3\", \"#40B5C3\", \"#40B5C3\", \"#40B5C3\", \"#97D6B8\", \"#97D6B8\", \"#97D6B8\", \"#97D6B8\", \"#97D6B8\", \"#97D6B8\", \"#97D6B8\", \"#97D6B8\", \"#97D6B8\", \"#97D6B8\", \"#97D6B8\", \"#DFF2B2\", \"#FFFFD9\", \"#FFFFD9\"]}], \"xscale\": \"linear\", \"bbox\": [0.22833333333333339, 0.125, 0.51666666666666661, 0.77500000000000002]}, {\"xlim\": [0.0, 1.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#262626\", \"text\": \"nr. of restricted dimensions\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 11.0, \"position\": [1.7600386424731163, 0.5], \"rotation\": -90.0, \"id\": \"el24444467125456\"}], \"zoomable\": false, \"images\": [], \"xdomain\": [0.0, 1.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": null, \"position\": \"bottom\", \"nticks\": 0, \"tickvalues\": []}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"right\", \"nticks\": 7, \"tickvalues\": [0.071428571428571425, 0.21428571428571427, 0.3571428571428571, 0.5, 0.64285714285714279, 0.78571428571428559, 0.9285714285714286]}], \"lines\": [], \"markers\": [], \"id\": \"el24444466970512\", \"ydomain\": [0.0, 1.0], \"collections\": [{\"paths\": [[[[0.0, 0.0], [1.0, 0.0], [1.0, 0.14285714285714285], [0.0, 0.14285714285714285], [0.0, 0.0]], [\"M\", \"L\", \"L\", \"L\", \"L\"]], [[[0.0, 0.14285714285714285], [1.0, 0.14285714285714285], [1.0, 0.2857142857142857], [0.0, 0.2857142857142857], [0.0, 0.14285714285714285]], [\"M\", \"L\", \"L\", \"L\", \"L\"]], [[[0.0, 0.2857142857142857], [1.0, 0.2857142857142857], [1.0, 0.42857142857142855], [0.0, 0.42857142857142855], [0.0, 0.2857142857142857]], [\"M\", \"L\", \"L\", \"L\", \"L\"]], [[[0.0, 0.42857142857142855], [1.0, 0.42857142857142855], [1.0, 0.5714285714285714], [0.0, 0.5714285714285714], [0.0, 0.42857142857142855]], [\"M\", \"L\", \"L\", \"L\", \"L\"]], [[[0.0, 0.5714285714285714], [1.0, 0.5714285714285714], [1.0, 0.7142857142857142], [0.0, 0.7142857142857142], [0.0, 0.5714285714285714]], [\"M\", \"L\", \"L\", \"L\", \"L\"]], [[[0.0, 0.7142857142857142], [1.0, 0.7142857142857142], [1.0, 0.8571428571428571], [0.0, 0.8571428571428571], [0.0, 0.7142857142857142]], [\"M\", \"L\", \"L\", \"L\", \"L\"]], [[[0.0, 0.8571428571428571], [1.0, 0.8571428571428571], [1.0, 1.0], [0.0, 1.0], [0.0, 0.8571428571428571]], [\"M\", \"L\", \"L\", \"L\", \"L\"]]], \"edgecolors\": [], \"edgewidths\": [0.3], \"offsets\": \"data02\", \"yindex\": 1, \"id\": \"el24444467289232\", \"pathtransforms\": [], \"pathcoordinates\": \"data\", \"offsetcoordinates\": \"display\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#081D58\", \"#24419A\", \"#1E80B8\", \"#40B5C3\", \"#97D6B8\", \"#DFF2B2\", \"#FFFFD9\"]}, {\"paths\": [[[[0.0, 0.14285714285714285], [1.0, 0.14285714285714285]], [\"M\", \"L\"]], [[[0.0, 0.2857142857142857], [1.0, 0.2857142857142857]], [\"M\", \"L\"]], [[[0.0, 0.42857142857142855], [1.0, 0.42857142857142855]], [\"M\", \"L\"]], [[[0.0, 0.5714285714285714], [1.0, 0.5714285714285714]], [\"M\", \"L\"]], [[[0.0, 0.7142857142857142], [1.0, 0.7142857142857142]], [\"M\", \"L\"]], [[[0.0, 0.8571428571428571], [1.0, 0.8571428571428571]], [\"M\", \"L\"]]], \"edgecolors\": [\"#CCCCCC\"], \"edgewidths\": [0.5], \"offsets\": \"data02\", \"yindex\": 0, \"id\": \"el24444467687120\", \"pathtransforms\": [], \"pathcoordinates\": \"data\", \"offsetcoordinates\": \"display\", \"zorder\": 2, \"xindex\": 0, \"alphas\": [null], \"facecolors\": []}], \"xscale\": \"linear\", \"bbox\": [0.78374999999999995, 0.12499999999999989, 0.025833333333333375, 0.77500000000000013]}], \"height\": 640.0, \"width\": 960.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}, {\"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>0.10</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>1.00</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.11</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.90</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>1.00</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.13</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.80</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>1.00</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.14</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.72</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>1.00</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.16</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.65</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 5</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>0.17</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.58</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 6</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>0.52</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 7</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>0.22</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>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 8</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>0.24</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.42</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 9</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>0.27</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>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 10</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>0.30</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.34</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 11</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.99</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.33</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>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 12</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.98</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.37</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 13</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.97</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.40</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.24</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 14</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.96</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.45</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.22</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 15</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.92</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.48</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>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 16</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.91</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.53</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.17</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 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>0.57</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>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 18</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>0.62</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>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 19</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>0.69</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>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 20</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.80</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.73</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.11</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 21</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>0.77</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>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 22</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>0.83</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>\", \"<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>0.69</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.87</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>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 24</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.64</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.90</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.07</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 25</th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>coverage</th>\\n <td>0.58</td>\\n </tr>\\n <tr>\\n <th>density</th>\\n <td>0.93</td>\\n </tr>\\n <tr>\\n <th>mass</th>\\n <td>0.06</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 26</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>0.96</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>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 27</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>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>6.00</td>\\n </tr>\\n </tbody>\\n</table>\"], \"type\": \"htmltooltip\", \"id\": \"el24444466968144\", \"hoffset\": 10}], \"data\": {\"data02\": [[0.0, 0.0]], \"data01\": [[1.0, 0.10090702947845805], [1.0, 0.11237373737373738], [1.0, 0.12535211267605634], [1.0, 0.13949843260188088], [1.0, 0.15532286212914484], [1.0, 0.17348927875243664], [1.0, 0.1943231441048035], [1.0, 0.21601941747572814], [1.0, 0.2418478260869565], [1.0, 0.270516717325228], [1.0, 0.30067567567567566], [0.9887640449438202, 0.3333333333333333], [0.9775280898876404, 0.3670886075949367], [0.9662921348314607, 0.40375586854460094], [0.9550561797752809, 0.44502617801047123], [0.9213483146067416, 0.4823529411764706], [0.9101123595505618, 0.5328947368421053], [0.8764044943820225, 0.5735294117647058], [0.8426966292134831, 0.6198347107438017], [0.8314606741573034, 0.6851851851851852], [0.797752808988764, 0.7319587628865979], [0.7528089887640449, 0.7701149425287356], [0.7303370786516854, 0.8333333333333334], [0.6853932584269663, 0.8714285714285714], [0.6404494382022472, 0.9047619047619048], [0.5842696629213483, 0.9285714285714286], [0.5393258426966292, 0.96], [0.5056179775280899, 1.0]]}, \"id\": \"el24444461300176\"});\n",
" })\n",
" });\n",
"}\n",
"</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import mpld3\n",
"\n",
"box1.show_tradeoff()\n",
"mpld3.display()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, mpdl3 takes a regular matplotlib figure and transforms it into a d3 figure. In this case we use that to add pop up boxes to the tradeoff scatter plot with additional information on each point. We can also inspect each individual point in more detail. So let's look at point 21, just as in the original paper. For this, we can use the `inspect` method. By default this will display two tables, but we can also make a nice graph instead that contains the same information. "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"coverage 0.752809\n",
"density 0.770115\n",
"mass 0.098639\n",
"mean 0.770115\n",
"res dim 4.000000\n",
"Name: 21, dtype: float64\n",
"\n",
" box 21 \n",
" min max qp values\n",
"Demand elasticity -0.422000 -0.202000 1.184930e-16\n",
"Biomass backstop price 150.049995 199.600006 3.515113e-11\n",
"Total biomass 450.000000 755.799988 4.716969e-06\n",
"Cellulosic cost 72.650002 133.699997 1.574133e-01\n",
"\n"
]
},
{
"data": {
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p00bPPfecsrKySqBKAChqzRpntWnjqb/9zVMLFvznoetbtzrpwQfLOL5fvtxFgYGeat/e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\n9Gg3ffZZlho14h0fwJ9JQkK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k7e0tSfL09FRlZaXKyspUWFioadOmKTAwUOPGjVNISIju\nuOMOBQUFqaCg4OdtxCZk/Pjxevrpp+Xn53fR8ec+OHr88cd18uRJ3XnnnerVq5fS0tI0YcIEDRo0\nSBs2bFB6erqmTZumcePG6fTp0/ruu++cp7LX1taqffv2znl6e3s3+FCkqKhITz/9tNasWXPB60+a\nZNGKFT3k6vrzTgPFtcVqDWNbNxOmt/WwYWc1e/bljzKfPSu9+qqrNm8+c8G4kyct2rrVRRs2fK8z\nZ6T//m8Pbdp0RhkZ9UezBwywadMmF02f7qbJk8/q8cfdVV5u0dGjFsXHt9LAgTadPSv5+p4PEC8v\nqbLSoptvrn9s3z6LnnnGXXl531/dlQd+A9q2basBAwbo9ddfdz526tQpzZgxQwUFBWrVqpVSUlL0\n0Ucf6d///rdWr16tvXv3qrCwUFlZWVq0aJEqKirk4+MjSfLy8lJ5ebkkycXFRe+8847Gjx+voUOH\nysPDo1HWEbjWNfkQLy4uVp8+fSTVB2NQUJBKSkoknT9q7uvrq8mTJ8vDw0PffvutevTocdF5WSwW\nRUREyGKxqFWrVgoKCtKRI0cavNawYcMkSR07dpSXl5dOnjwpSerWrZskqU2bNurSpYvz/2tra9Wh\nQwfl5uZq5cqVslgszlOWs7OzlZ2drdLSUvXr10+SNHfuXC1dulSHDx9WWFjYBUdVy8rKnBG2du1a\ndezYUStXrtSJEyeUmprqPBX9h9577z3l5OQ4h/ft26dt27bpgw8+kCRVVFSooKBAzz//vCTp4Ycf\nlpeXl6qqquTr66vq6mq1adNGbdu2VWBgoDP4+vbtq927d+uOO+6Qi4vLj/oKm6YS6xaLRV5eXg22\nz/Hjx/Xxxx9r165dkqSTJ09q6NChWrBggXOauro6xcTEaP/+/ZKkLl266MMPP9TOnTtVXFys6dOn\ny+FwyNXVVQ6HQ7m5ucrPz1deXp4WLlwoSVq+fLmKi4tVVFQkqf4ar9LSUlmtVn3++eeaPXu25s+f\nL7vdrqKiIlksFlVVVcnhcOh///f/SWonq9Vq6CeFxsa2bj5MbuuyslMqKiqRxWKRt7f3Rc8A2rLF\nS2FhVpWVHVVZWcNxFktbhYW56+TJ45KkwEB/ffrpCX35pZ8OHrRr7lyLJLtcXWvl7n5IubnS9u0e\neuutdsrOrv+7/MYb7VRS0kKHDtX/HT516mZVVpbo0CG7tm3z0LPPdlJW1gG5udXo0KH69+3KyspL\nnhF3rTj33o+my+FwqGXLliooKLjo/sYPHT16VCdPnlRRUZG++uorHTt2TNHR0ZKk6upqdenSRaNH\nj9bAgQM1ZcoUDRkyxHmZopubm3bu3ClfX1/t3btXLVu2dP5+devWTR9//LHS09OVlZWlkSNHOvcl\nAPw4TT7Eg4KCtGPHDsXFxamqqkr79u2Tv7+/LBaL7Ha7KisrlZOTo82bN8tut+uhhx665JuIw+HQ\nrl275HA4dObMGRUXF+vGG290ju/cubO2b9+url276vjx46qsrHQeZb6cRYsW6fe//7369eunVatW\n6d1339XZs2e1bt06LVy4UA6HQ0OGDNGQIUP09ttva+bMmXJzc1Nqaqq++OKLBte5t2/fXhUVFfL0\n9NT69eudj8fGxuqvf/3rBa9dWVmps2fPNjgDICgoSCEhIRo6dKiOHz+utWvXKiIiokHEf/LJJ9q8\nebOGDx+uLVu2qGfPngoICNCZM2d06NAhBQYGqqCgwHl0/twfjSuJiIi44jTXquDgYBUXFzuHO3Xq\npPfee6/BNOvWrVNOTo7ef/99VVVV6eDBgxo4cKBWrVqltLQ0RUZGavfu3dq2bZuCg4MlSceOHZOP\nj49zeMSIEZo0aZKysrJUUlIiV1dX9e7dWxs3btTChQu1ceNGBQQEXHQZX3vN5vwdRtNXVFTk/L1B\n02Z+W3tIqn+9S733797tpuHDbRe9cWV8fAs99JC7OnZ0V12ddPCgh/r0aaFu3VpqwgS7eve269//\nbqHt212czz90qIW8vFydw4MHt1BmppumT/fU4cMWtWzpptBQf23Z4qKFC930/vs18ve/vsHrXuvf\nI86/6ebBbreruLj4R+0zderUSeXl5QoODpavr69uuukmffbZZ3JxcdHSpUt1++23O39nfHx8FBAQ\n4By+6667tG/fPo0dO1arV69WfHy8/Pz8NHToUH344Ydyc3OTn5+f/P39deutt/6q6/xb1VQOIKFx\nNNkQP3djtYSEBGVmZiopKUk1NTUaP368fH19FRISovnz5ysoKEjh4eEaPXq0fH19dfPNN6u0tNQZ\n6/85T4vFogcffFBVVVV64okn1KZNG+e4Rx99VFOnTtU///lP1dTUaNasWXJxcbnkTd7OPW/QoEGa\nP3++li1bprCwMJ0+fVpubm7y8fFRQkKC3N3dFRUVJT8/P916661KSkqSp6enbrjhBoWGhjaYX69e\nvQT1m6AAABSdSURBVPTll1+qU6dOF/15SNJrr72mwMBAxcbG6ttvv5W/v3+DaR999FFlZGTorbfe\nUnV1tf70pz9dsNyPPfaYJk+erLffflu+vr5asGCBXF1dNWfOHD311FNyOBwKDw93fupaVFR0yTMN\nmqsfbpPJkydr1KhRGjRokD766CNFRkbKxcVF8+bNU/v27ZWdna3HHntMNTU1+v777xvcpTQ6Otr5\nc5ak8PBw9e3bV5GRkbLb7XrppZckSRMnTpTValVKSoqk+g8GcnNzDa0tADT0zTctNGZMw/DNzHTT\nvffWKSLCrpSUOg0Y0FoOh5Seflbt2klz5tRq4kR31dRYVFMjzZ9//iZvUVF2RUWdHw4Ls6tPH5ti\nY+vnsXBh/bj0dDdZrRY98kj99eS33OLQCy80vFkc0JSc29+47rrr9OSTT6pfv36y2Wy6+eabG9xj\n5tVXX23wvGeeeUZjx47VX/7yF1133XVasWKFWrdurTFjxqhfv35ydXVV9+7dNWbMGKPrAzQVFgfn\nkDQp3333nbKysvTCCy809qI0MH/+fMXFxSk8PPyS0xQUFDTpI+LXApuNI+LNCUfPmo/G3NY/5myo\n3wqOiONacO6I+KW++QfmsO+KX6LJ3jW9ufLz81NwcHCD7xFvbCdOnFB1dfVlIxwAAAAAmotr52Nq\n/Gh//OMfG3sRGujQoYNmzpzZ2IsBAAAAAL8JhDgAAPjVXOunewMA8Gvg1HQAAAAAAAwixAEAAAAA\nMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAA\nAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAA\nAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAA\nAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEA\nAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEA\nAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIc\nAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQ\nBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwi\nxAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACD\nCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADA\nIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAA\nMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAA\nAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAA\nAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAA\nAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEA\nAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEA\nAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIc\nAAAAAACDCHEAAAAAAAwixAEAAAAAMIgQBwAAAADAIEIcAAAAAACDCHEAAAAAAAxq2dgLAKDpa9ny\n6rzV1NXVXZX5AAAAAI2JI+IAAAAAABhEiAMAAAAAYFCzD/H9+/dr3LhxSklJ0ahRo5STk3PZ6ZOT\nk1VcXKycnBzl5eVdcrr09HR98sknP2lZXnnlFX311Vc/6Tk/x44dO1RUVHTB42fPntWkSZPkcDj0\nxRdfKCEhQffdd58WL158yXl9+OGHeuqpp5zDBw8e1IMPPqgxY8YoNTVV5eXlqqmpUXp6+q+yLri4\n6upqpaSkqF+/foqMjFRBQYGOHz+umJgY53/t2rX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"text/plain": [
"<matplotlib.figure.Figure at 0x10af352d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"box1.inspect(21)\n",
"box1.inspect(21, style='graph')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If one where to do a detailed comparison with the results reported in the original article, one would see small numerical differences. These differences arise out of subtle differences in implementation. The most important difference is that the exploratory modeling workbench uses a custom objective function inside prim which is different from the one used in the scenario discovery toolkit. Other differences have to do with details about the hill climbing optimization that is used in prim, and in particular how ties are handled in selected the next step. The differences between the two implementations are only numerical, and don't affect the overarching conclusions drawn from the analysis. \n",
"\n",
"Let's select this 21 box, and get a more detailed view of what the box looks like. Following Bryant et al., we can use scatter plots for this. "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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c3SQ0YgQ7J1p4yLcuP58HatXUhG6ry8/evRuGgM0hQ0K3JQG19UfmxpUhJrmR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FAAAg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\n9Cva7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FVV6d1bXNHi35MlEBAk+DyGnaDu8Ck5hPevXaihO1dcYbTo//a3YjOQlxe40eku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3+KhoYG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\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4KI6yaNJgd5eFyMlhOzr0G6UZ93pZ1CPAmH6eYqLX1/O+ez4L1iK00KFnZ7UAbmzkPiTi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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cc1f050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"box1.select(21)\n",
"fig = box1.show_pairs_scatter()\n",
"fig.set_size_inches((12,12))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have now found a first box that explains close to 80% of the cases of interest. Let's see if we can find a second box that explains the remainder of the cases."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO] 795 points remaining, containing 22 cases of interest\n",
"[INFO] box does not meet threshold criteria, value is 0.34693877551, returning dump box\n"
]
}
],
"source": [
"box2 = prim_alg.find_box()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, we are unable to find a second box. The best coverage we can achieve is 0.35, which is well below the specified 0.8 threshold. Let's look at the final overal results from interactively fitting PRIM to the data. For this, we can use to convenience functions that transform the stats and boxes to pandas data frames."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" coverage density mass res_dim\n",
"box 1 0.752809 0.770115 0.098639 4\n",
"box 2 0.247191 0.027673 0.901361 0\n",
" box 1 box 2 \n",
" min max min max\n",
"Demand elasticity -0.422000 -0.202000 -0.8 -0.202000\n",
"Biomass backstop price 150.049995 199.600006 90.0 199.600006\n",
"Total biomass 450.000000 755.799988 450.0 997.799988\n",
"Cellulosic cost 72.650002 133.699997 67.0 133.699997\n"
]
}
],
"source": [
"print prim_alg.stats_to_dataframe()\n",
"print prim_alg.boxes_to_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CART"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The way of interacting with CART is quite similar to how we setup the prim analysis. We import cart from the analysis package. We instantiate the algorithm, and next fit CART to the data. This is done via the `build_tree` method."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from analysis import cart\n",
"cart_alg = cart.CART(x,y, 0.05)\n",
"cart_alg.build_tree()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have trained CART on the data, we can investigate its results. Just like PRIM, we can use `stats_to_dataframe` and `boxes_to_dataframe` to get an overview. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" coverage density mass res dim\n",
"box 1 0.011236 0.021739 0.052154 2\n",
"box 2 0.000000 0.000000 0.546485 2\n",
"box 3 0.000000 0.000000 0.103175 2\n",
"box 4 0.044944 0.090909 0.049887 2\n",
"box 5 0.224719 0.434783 0.052154 2\n",
"box 6 0.112360 0.227273 0.049887 3\n",
"box 7 0.000000 0.000000 0.051020 3\n",
"box 8 0.606742 0.642857 0.095238 2\n",
" box 1 box 2 box 3 \\\n",
" min max min max min \n",
"Cellulosic yield 80.0 81.649994 81.649994 99.900002 80.000 \n",
"Demand elasticity -0.8 -0.439000 -0.800000 -0.439000 -0.439 \n",
"Biomass backstop price 90.0 199.600006 90.000000 199.600006 90.000 \n",
"\n",
" box 4 box 5 \\\n",
" max min max min \n",
"Cellulosic yield 99.900002 80.000000 99.900002 80.000 \n",
"Demand elasticity -0.316500 -0.439000 -0.316500 -0.439 \n",
"Biomass backstop price 144.350006 144.350006 170.750000 170.750 \n",
"\n",
" box 6 box 7 \\\n",
" max min max min \n",
"Cellulosic yield 99.900002 80.0000 89.050003 89.050003 \n",
"Demand elasticity -0.316500 -0.3165 -0.202000 -0.316500 \n",
"Biomass backstop price 199.600006 90.0000 148.300003 90.000000 \n",
"\n",
" box 8 \n",
" max min max \n",
"Cellulosic yield 99.900002 80.000000 99.900002 \n",
"Demand elasticity -0.202000 -0.316500 -0.202000 \n",
"Biomass backstop price 148.300003 148.300003 199.600006 \n"
]
}
],
"source": [
"print cart_alg.stats_to_dataframe()\n",
"print cart_alg.boxes_to_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, we might want to look at the classification tree directly. For this, we can use the `show_tree` method. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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CGCGSxSyG6npOuqxmnlvM7r333qx16QX6L9Q3xRQQg/r16xfGUU6V1o4dO5rnRgthpbDG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NsPjczCYZm3PwA/A6ULwvJqVJqabgFptzaAhG+11Bgp9+wCOg7Qb58CA+aX4qum0P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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(cart_alg.show_tree()) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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