src/fastnondominatedsort.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fast Non-dominated Sort for Multiobjective Optimization\n",
    "### Adapted from the paper `A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II`, by Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan\n",
    "\n",
    "DOI: *10.1109/4235.996017*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "mode": "markers",
         "type": "scatter",
         "x": [
          1,
          4,
          7
         ],
         "y": [
          7,
          5,
          7
         ]
        },
        {
         "mode": "lines",
         "type": "scatter",
         "x": [
          1,
          2,
          3,
          4,
          5,
          6,
          7
         ],
         "y": [
          3,
          2,
          1,
          1,
          1,
          2,
          3
         ]
        }
       ],
       "layout": {
        "autosize": false,
        "height": 450,
        "showlegend": false,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Initialization Makes Me Smile<br>(and it's fun to show off a little...)"
        },
        "width": 500,
        "xaxis": {
         "autorange": true,
         "range": [
          0.5829145728643216,
          7.417085427135678
         ],
         "showgrid": false,
         "showticklabels": false,
         "type": "linear"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          0.6614420062695925,
          7.432601880877743
         ],
         "showgrid": false,
         "showticklabels": false,
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"d8062b96-21eb-4c38-aba4-c976742b4614\" class=\"plotly-graph-div\" style=\"height:525px; width:500px;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    \n",
       "                if (document.getElementById(\"d8062b96-21eb-4c38-aba4-c976742b4614\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        'd8062b96-21eb-4c38-aba4-c976742b4614',\n",
       "                        [{\"mode\": \"markers\", \"type\": \"scatter\", \"x\": [1, 4, 7], \"y\": [7, 5, 7]}, {\"mode\": \"lines\", \"type\": \"scatter\", \"x\": [1, 2, 3, 4, 5, 6, 7], \"y\": [3, 2, 1, 1, 1, 2, 3]}],\n",
       "                        {\"autosize\": false, \"showlegend\": false, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Initialization Makes Me Smile<br>(and it's fun to show off a little...)\"}, \"width\": 500, \"xaxis\": {\"showgrid\": false, \"showticklabels\": false}, \"yaxis\": {\"showgrid\": false, \"showticklabels\": false}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('d8062b96-21eb-4c38-aba4-c976742b4614');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import chart_studio.plotly as ply\n",
    "import chart_studio.tools as plytool\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as plyoff\n",
    "\n",
    "# plotly initialization\n",
    "# if using jupyter lab, must have the plotly extension installed: https://github.com/jupyterlab/jupyter-renderers/tree/master/packages/plotly-extension\n",
    "# jupyter labextension install @jupyterlab/plotly-extension\n",
    "plyoff.init_notebook_mode(connected=True)\n",
    "x1 = [1,4,7]; y1 = [7,5,7]\n",
    "x2 = [1,2,3,4,5,6,7]; y2 = [3,2,1,1,1,2,3]\n",
    "plyoff.iplot(go.Figure(data=[go.Scatter({\"x\":x1, \"y\":y1, 'mode':'markers'}),\n",
    "                             go.Scatter({\"x\":x2, \"y\":y2, 'mode':'lines'})],\n",
    "                                        layout=go.Layout(autosize=False,width=500,title=\"Initialization Makes Me Smile<br>(and it's fun to show off a little...)\",\n",
    "                                                         showlegend=False, xaxis={'showgrid':False, 'showticklabels':False},\n",
    "                                                         yaxis={'showgrid':False, 'showticklabels':False})))\n",
    "\n",
    "# plotly authentication - not needed for offline mode\n",
    "#plytool.set_credentials_file(username='ahowe42', api_key='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dominates(i,j):\n",
    "    ''' i dom j if\n",
    "    i >= j for all dimensions and\n",
    "    i > j for at least 1 dimension '''\n",
    "    M = i.shape[0]\n",
    "    return (np.sum(i>=j)==M) & (np.sum(i>j)>=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FastNonDomSort(objs):\n",
    "    ''' implements a fast non-dominated sort algorithm for multi-objective maximization'''\n",
    "    N,M = objs.shape\n",
    "    parFronts = [[]]  # pareto fronts\n",
    "    parRnks = [np.inf]*N                  # pareto front membership\n",
    "    domSets = {i:[] for i in range(N)}    # set of items dominated\n",
    "    domCnts = [0]*N                       # number dominating items\n",
    "    f = 0                                 # pareto front counter\n",
    "    \n",
    "    # create the dominance sets & counts for all items & create the first dominance set\n",
    "    for i,xi in enumerate(objs):\n",
    "        for j,xj in enumerate(objs):\n",
    "            # no need to test dominance of i wrt i\n",
    "            if i == j:\n",
    "                continue\n",
    "            if dominates(xi,xj):\n",
    "                # if i dominates j, add j to i's dominance list...\n",
    "                domSets[i].append(j)\n",
    "            elif dominates(xj,xi):\n",
    "                # ... otherwise increase i's dominance counter\n",
    "                domCnts[i] += 1\n",
    "        # i is not dominated, so put in this pareto front\n",
    "        if domCnts[i] == 0:\n",
    "            parRnks[i] = f\n",
    "            parFronts[f].append(i)\n",
    "    keepDomCounts = domCnts.copy() # let's remember the non-discounted dominance counts\n",
    "    \n",
    "    # now create all additional pareto fronts\n",
    "    while parFronts[f] != []:\n",
    "        these = []\n",
    "        # iterate over the items in the current pareto front\n",
    "        for i in parFronts[f]:\n",
    "            # now iterate over the item's dominance set\n",
    "            for j in domSets[i]:\n",
    "                # discount the dominance by item i ...\n",
    "                domCnts[j] -= 1\n",
    "                # ... and check if j is no longer dominated\n",
    "                if domCnts[j] == 0:\n",
    "                    parRnks[j] = f+1\n",
    "                    these.append(j)\n",
    "        # put these nondominated items in the next pareto front; if none then exit\n",
    "        if these != []:\n",
    "            f += 1\n",
    "            parFronts.append(these)\n",
    "        else:\n",
    "            break\n",
    "        \n",
    "    return parFronts,parRnks,domSets,keepDomCounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.98772496 0.76637773]\n",
      " [0.93141789 0.9702976 ]\n",
      " [0.92852015 0.89347165]\n",
      " [0.90938365 0.99491186]\n",
      " [0.89041519 0.02023819]\n",
      " [0.86731537 0.85928532]\n",
      " [0.8454234  0.90821434]\n",
      " [0.84487304 0.57566001]\n",
      " [0.83458785 0.34028632]\n",
      " [0.83100785 0.43503704]\n",
      " [0.82953076 0.75236556]\n",
      " [0.81278281 0.87230449]\n",
      " [0.81254976 0.63329074]\n",
      " [0.80964233 0.54507774]\n",
      " [0.77586745 0.51285867]\n",
      " [0.76849623 0.65563649]\n",
      " [0.76408694 0.35403081]\n",
      " [0.70810663 0.27608576]\n",
      " [0.70743273 0.52060427]\n",
      " [0.69595655 0.18219911]\n",
      " [0.69406538 0.81260293]\n",
      " [0.67199075 0.68481652]\n",
      " [0.61219045 0.871679  ]\n",
      " [0.61129826 0.56320003]\n",
      " [0.58167492 0.61565357]\n",
      " [0.55920291 0.69453789]\n",
      " [0.54757767 0.76198679]\n",
      " [0.53634803 0.34291543]\n",
      " [0.53254832 0.97249147]\n",
      " [0.4927027  0.24440731]\n",
      " [0.48185628 0.03839081]\n",
      " [0.46944187 0.5729996 ]\n",
      " [0.46780566 0.0805379 ]\n",
      " [0.46407054 0.21717209]\n",
      " [0.46194603 0.37510874]\n",
      " [0.41423039 0.19169056]\n",
      " [0.40918196 0.20825787]\n",
      " [0.39642833 0.93681204]\n",
      " [0.38033256 0.26348672]\n",
      " [0.34889839 0.84962166]\n",
      " [0.31157818 0.17546395]\n",
      " [0.27925874 0.21163155]\n",
      " [0.26884107 0.13666738]\n",
      " [0.22557076 0.99725703]\n",
      " [0.22430689 0.29945132]\n",
      " [0.21920379 0.71860365]\n",
      " [0.21837438 0.58994792]\n",
      " [0.19942414 0.36968814]\n",
      " [0.14000104 0.69978434]\n",
      " [0.10088227 0.85114571]]\n"
     ]
    }
   ],
   "source": [
    "# create some random bi-objective data\n",
    "N = 50\n",
    "M = 2\n",
    "np.random.seed(1211)\n",
    "sols = np.random.rand(N,M)\n",
    "sols = sols[sols[:,0].argsort()][::-1]\n",
    "print(sols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 1, 3, 43], [2, 6, 28], [4, 5, 11, 37], [7, 10, 20, 22], [8, 9, 12, 15, 21, 25, 26, 39, 49], [13, 23, 24, 45], [14, 18, 31, 46, 48], [16, 34], [17, 27, 47], [19, 29, 38, 44], [30, 32, 33], [35, 36, 41], [40], [42]]\n",
      "[0, 0, 1, 0, 2, 2, 1, 3, 4, 4, 3, 2, 4, 5, 6, 4, 7, 8, 6, 9, 3, 4, 3, 5, 5, 4, 4, 8, 1, 9, 10, 6, 10, 10, 7, 11, 11, 2, 9, 4, 12, 11, 13, 0, 9, 5, 6, 8, 6, 4]\n"
     ]
    }
   ],
   "source": [
    "# identify the pareto frontiers for each object\n",
    "pfs,prs,ds,dc = FastNonDomSort(sols)\n",
    "print(pfs)\n",
    "print(prs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "line": {
          "color": "red"
         },
         "marker": {
          "color": "red",
          "symbol": "square"
         },
         "mode": "lines+markers",
         "name": "0",
         "type": "scatter",
         "x": [
          0.9877249556973117,
          0.9314178895498846,
          0.9093836538006194,
          0.22557076163507528
         ],
         "y": [
          0.7663777322377984,
          0.9702976028714669,
          0.994911858775635,
          0.9972570317866557
         ]
        },
        {
         "line": {
          "color": "orange"
         },
         "marker": {
          "color": "orange",
          "symbol": "star"
         },
         "mode": "lines+markers",
         "name": "1",
         "type": "scatter",
         "x": [
          0.9285201539025842,
          0.8454233996888489,
          0.532548318962121
         ],
         "y": [
          0.8934716491272268,
          0.9082143376819494,
          0.972491466693152
         ]
        },
        {
         "line": {
          "color": "tan"
         },
         "marker": {
          "color": "tan",
          "symbol": "circle"
         },
         "mode": "lines+markers",
         "name": "2",
         "type": "scatter",
         "x": [
          0.8904151916590889,
          0.8673153667472997,
          0.8127828098455792,
          0.3964283340005803
         ],
         "y": [
          0.020238189009668894,
          0.8592853247272766,
          0.8723044926003046,
          0.9368120433173509
         ]
        },
        {
         "line": {
          "color": "green"
         },
         "marker": {
          "color": "green",
          "symbol": "cross"
         },
         "mode": "lines+markers",
         "name": "3",
         "type": "scatter",
         "x": [
          0.8448730440244426,
          0.8295307579996861,
          0.6940653752397318,
          0.6121904518024561
         ],
         "y": [
          0.5756600094404372,
          0.7523655626234955,
          0.8126029298885974,
          0.8716789964381014
         ]
        },
        {
         "line": {
          "color": "blue"
         },
         "marker": {
          "color": "blue",
          "symbol": "triangle-up"
         },
         "mode": "lines+markers",
         "name": "4",
         "type": "scatter",
         "x": [
          0.8345878458821252,
          0.8310078526011888,
          0.8125497595658715,
          0.7684962277544862,
          0.6719907459540229,
          0.5592029112322169,
          0.5475776745415745,
          0.34889838833889564,
          0.10088227335466582
         ],
         "y": [
          0.3402863188378148,
          0.43503704144878697,
          0.6332907442439869,
          0.6556364947997526,
          0.6848165185087691,
          0.6945378871707912,
          0.7619867919947008,
          0.8496216633244763,
          0.8511457105146961
         ]
        },
        {
         "line": {
          "color": "purple"
         },
         "marker": {
          "color": "purple",
          "symbol": "diamond"
         },
         "mode": "lines+markers",
         "name": "5",
         "type": "scatter",
         "x": [
          0.8096423289606177,
          0.6112982571631709,
          0.5816749245970237,
          0.2192037902741697
         ],
         "y": [
          0.5450777377781831,
          0.563200033212886,
          0.6156535726159528,
          0.718603653197232
         ]
        },
        {
         "line": {
          "color": "black"
         },
         "marker": {
          "color": "black",
          "symbol": "square"
         },
         "mode": "lines+markers",
         "name": "6",
         "type": "scatter",
         "x": [
          0.7758674522367336,
          0.7074327274235348,
          0.4694418662119785,
          0.2183743823457659,
          0.1400010365317924
         ],
         "y": [
          0.5128586716819752,
          0.5206042670731301,
          0.5729995972544093,
          0.5899479158666152,
          0.6997843373238815
         ]
        },
        {
         "line": {
          "color": "red"
         },
         "marker": {
          "color": "red",
          "symbol": "star"
         },
         "mode": "lines+markers",
         "name": "7",
         "type": "scatter",
         "x": [
          0.7640869425293744,
          0.4619460333876194
         ],
         "y": [
          0.3540308115126355,
          0.37510873812196976
         ]
        },
        {
         "line": {
          "color": "orange"
         },
         "marker": {
          "color": "orange",
          "symbol": "circle"
         },
         "mode": "lines+markers",
         "name": "8",
         "type": "scatter",
         "x": [
          0.7081066332699772,
          0.5363480349467532,
          0.1994241412865334
         ],
         "y": [
          0.2760857578363435,
          0.3429154312741065,
          0.3696881429927802
         ]
        },
        {
         "line": {
          "color": "tan"
         },
         "marker": {
          "color": "tan",
          "symbol": "cross"
         },
         "mode": "lines+markers",
         "name": "9",
         "type": "scatter",
         "x": [
          0.6959565471872674,
          0.49270269749261764,
          0.3803325637467221,
          0.22430688573487922
         ],
         "y": [
          0.18219911025789826,
          0.2444073051565525,
          0.26348672360243175,
          0.299451322674804
         ]
        },
        {
         "line": {
          "color": "green"
         },
         "marker": {
          "color": "green",
          "symbol": "triangle-up"
         },
         "mode": "lines+markers",
         "name": "10",
         "type": "scatter",
         "x": [
          0.48185627965479627,
          0.4678056558105903,
          0.46407053978955737
         ],
         "y": [
          0.038390812620181514,
          0.08053789935427391,
          0.21717209482826583
         ]
        },
        {
         "line": {
          "color": "blue"
         },
         "marker": {
          "color": "blue",
          "symbol": "diamond"
         },
         "mode": "lines+markers",
         "name": "11",
         "type": "scatter",
         "x": [
          0.4142303866849786,
          0.4091819556847718,
          0.2792587422477223
         ],
         "y": [
          0.19169056006345053,
          0.20825786587329398,
          0.21163154719062638
         ]
        },
        {
         "line": {
          "color": "purple"
         },
         "marker": {
          "color": "purple",
          "symbol": "square"
         },
         "mode": "lines+markers",
         "name": "12",
         "type": "scatter",
         "x": [
          0.31157818119729774
         ],
         "y": [
          0.17546395270138493
         ]
        },
        {
         "line": {
          "color": "black"
         },
         "marker": {
          "color": "black",
          "symbol": "star"
         },
         "mode": "lines+markers",
         "name": "13",
         "type": "scatter",
         "x": [
          0.26884106985624556
         ],
         "y": [
          0.13666738240791554
         ]
        }
       ],
       "layout": {
        "autosize": true,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Pareto Frontiers"
        },
        "xaxis": {
         "autorange": true,
         "range": [
          0.04592397009940563,
          1.0426832589525719
         ],
         "type": "linear"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          -0.05132688546125688,
          1.0688221062575816
         ],
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"1cf6a0e6-39d8-4981-95c6-edf9904815b1\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    \n",
       "                if (document.getElementById(\"1cf6a0e6-39d8-4981-95c6-edf9904815b1\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        '1cf6a0e6-39d8-4981-95c6-edf9904815b1',\n",
       "                        [{\"line\": {\"color\": \"red\"}, \"marker\": {\"color\": \"red\", \"symbol\": \"square\"}, \"mode\": \"lines+markers\", \"name\": \"0\", \"type\": \"scatter\", \"x\": [0.9877249556973117, 0.9314178895498846, 0.9093836538006194, 0.22557076163507528], \"y\": [0.7663777322377984, 0.9702976028714669, 0.994911858775635, 0.9972570317866557]}, {\"line\": {\"color\": \"orange\"}, \"marker\": {\"color\": \"orange\", \"symbol\": \"star\"}, \"mode\": \"lines+markers\", \"name\": \"1\", \"type\": \"scatter\", \"x\": [0.9285201539025842, 0.8454233996888489, 0.532548318962121], \"y\": [0.8934716491272268, 0.9082143376819494, 0.972491466693152]}, {\"line\": {\"color\": \"tan\"}, \"marker\": {\"color\": \"tan\", \"symbol\": \"circle\"}, \"mode\": \"lines+markers\", \"name\": \"2\", \"type\": \"scatter\", \"x\": [0.8904151916590889, 0.8673153667472997, 0.8127828098455792, 0.3964283340005803], \"y\": [0.020238189009668894, 0.8592853247272766, 0.8723044926003046, 0.9368120433173509]}, {\"line\": {\"color\": \"green\"}, \"marker\": {\"color\": \"green\", \"symbol\": \"cross\"}, \"mode\": \"lines+markers\", \"name\": \"3\", \"type\": \"scatter\", \"x\": [0.8448730440244426, 0.8295307579996861, 0.6940653752397318, 0.6121904518024561], \"y\": [0.5756600094404372, 0.7523655626234955, 0.8126029298885974, 0.8716789964381014]}, {\"line\": {\"color\": \"blue\"}, \"marker\": {\"color\": \"blue\", \"symbol\": \"triangle-up\"}, \"mode\": \"lines+markers\", \"name\": \"4\", \"type\": \"scatter\", \"x\": [0.8345878458821252, 0.8310078526011888, 0.8125497595658715, 0.7684962277544862, 0.6719907459540229, 0.5592029112322169, 0.5475776745415745, 0.34889838833889564, 0.10088227335466582], \"y\": [0.3402863188378148, 0.43503704144878697, 0.6332907442439869, 0.6556364947997526, 0.6848165185087691, 0.6945378871707912, 0.7619867919947008, 0.8496216633244763, 0.8511457105146961]}, {\"line\": {\"color\": \"purple\"}, \"marker\": {\"color\": \"purple\", \"symbol\": \"diamond\"}, \"mode\": \"lines+markers\", \"name\": \"5\", \"type\": \"scatter\", \"x\": [0.8096423289606177, 0.6112982571631709, 0.5816749245970237, 0.2192037902741697], \"y\": [0.5450777377781831, 0.563200033212886, 0.6156535726159528, 0.718603653197232]}, {\"line\": {\"color\": \"black\"}, \"marker\": {\"color\": \"black\", \"symbol\": \"square\"}, \"mode\": \"lines+markers\", \"name\": \"6\", \"type\": \"scatter\", \"x\": [0.7758674522367336, 0.7074327274235348, 0.4694418662119785, 0.2183743823457659, 0.1400010365317924], \"y\": [0.5128586716819752, 0.5206042670731301, 0.5729995972544093, 0.5899479158666152, 0.6997843373238815]}, {\"line\": {\"color\": \"red\"}, \"marker\": {\"color\": \"red\", \"symbol\": \"star\"}, \"mode\": \"lines+markers\", \"name\": \"7\", \"type\": \"scatter\", \"x\": [0.7640869425293744, 0.4619460333876194], \"y\": [0.3540308115126355, 0.37510873812196976]}, {\"line\": {\"color\": \"orange\"}, \"marker\": {\"color\": \"orange\", \"symbol\": \"circle\"}, \"mode\": \"lines+markers\", \"name\": \"8\", \"type\": \"scatter\", \"x\": [0.7081066332699772, 0.5363480349467532, 0.1994241412865334], \"y\": [0.2760857578363435, 0.3429154312741065, 0.3696881429927802]}, {\"line\": {\"color\": \"tan\"}, \"marker\": {\"color\": \"tan\", \"symbol\": \"cross\"}, \"mode\": \"lines+markers\", \"name\": \"9\", \"type\": \"scatter\", \"x\": [0.6959565471872674, 0.49270269749261764, 0.3803325637467221, 0.22430688573487922], \"y\": [0.18219911025789826, 0.2444073051565525, 0.26348672360243175, 0.299451322674804]}, {\"line\": {\"color\": \"green\"}, \"marker\": {\"color\": \"green\", \"symbol\": \"triangle-up\"}, \"mode\": \"lines+markers\", \"name\": \"10\", \"type\": \"scatter\", \"x\": [0.48185627965479627, 0.4678056558105903, 0.46407053978955737], \"y\": [0.038390812620181514, 0.08053789935427391, 0.21717209482826583]}, {\"line\": {\"color\": \"blue\"}, \"marker\": {\"color\": \"blue\", \"symbol\": \"diamond\"}, \"mode\": \"lines+markers\", \"name\": \"11\", \"type\": \"scatter\", \"x\": [0.4142303866849786, 0.4091819556847718, 0.2792587422477223], \"y\": [0.19169056006345053, 0.20825786587329398, 0.21163154719062638]}, {\"line\": {\"color\": \"purple\"}, \"marker\": {\"color\": \"purple\", \"symbol\": \"square\"}, \"mode\": \"lines+markers\", \"name\": \"12\", \"type\": \"scatter\", \"x\": [0.31157818119729774], \"y\": [0.17546395270138493]}, {\"line\": {\"color\": \"black\"}, \"marker\": {\"color\": \"black\", \"symbol\": \"star\"}, \"mode\": \"lines+markers\", \"name\": \"13\", \"type\": \"scatter\", \"x\": [0.26884106985624556], \"y\": [0.13666738240791554]}],\n",
       "                        {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Pareto Frontiers\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('1cf6a0e6-39d8-4981-95c6-edf9904815b1');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the different pareto frontiers\n",
    "colrs = ('red', 'orange', 'tan', 'green', 'blue', 'purple', 'black')\n",
    "symbs = ('square', 'star', 'circle', 'cross', 'triangle-up', 'diamond')\n",
    "\n",
    "trc = [None]*len(pfs)\n",
    "c = 0; s = 0\n",
    "for i,front in enumerate(pfs):\n",
    "    these = sols[front]\n",
    "    col = colrs[c]; sym = symbs[s]\n",
    "    trc[i] = go.Scatter({'x':these[:,0], 'y':these[:,1], 'mode':'lines+markers', 'name':i,\n",
    "                          'line':{'color':col}, 'marker':{'symbol':sym, 'color':col}})\n",
    "    # increment the color & symbol counters, modulo the enumeration lengths\n",
    "    c = (c+1)%len(colrs); s = (s+1)%len(symbs)\n",
    "\n",
    "plyoff.iplot(go.Figure(data=trc, layout=go.Layout(title='Pareto Frontiers')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}