master-thesis/reports/demos.ipynb

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{
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"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
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" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.graph_objs as go\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"init_notebook_mode(connected=True)"
]
},
{
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"execution_count": 3,
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"ingredients = [\n",
" \"noodles\",\n",
" \"boiled noodles\",\n",
" \"cheese powder\",\n",
" \"butter\",\n",
" \"milk\",\n",
" \"soup\",\n",
" \"drained boiled noodles\",\n",
" \"tuna\",\n",
" \"corn\",\n",
" \"drained tuna\",\n",
" \"drained corn\"\n",
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},
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"actions = [\n",
" \"boil\",\n",
" \"drain\"\n",
"]"
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"i = 0\n",
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"ing_act[ai[\"boil\"], ii[\"noodles\"]] = 1\n",
"ing_act[ai[\"drain\"], ii[\"boiled noodles\"]] = 1\n",
"ing_act[ai[\"drain\"], ii[\"tuna\"]] = 1\n",
"ing_act[ai[\"drain\"], ii[\"corn\"]] = 1\n",
"\n",
"ing_ing[ii[\"cheese powder\"], ii[\"butter\"]] = 1\n",
"ing_ing[ii[\"cheese powder\"], ii[\"milk\"]] = 1\n",
"ing_ing[ii[\"cheese powder\"], ii[\"soup\"]] = 1\n",
"ing_ing[ii[\"cheese powder\"], ii[\"drained boiled noodles\"]] = 1\n",
"ing_ing[ii[\"cheese powder\"], ii[\"drained tuna\"]] = 1\n",
"ing_ing[ii[\"cheese powder\"], ii[\"drained corn\"]] = 1\n",
"\n",
"\n",
"ing_ing[ii[\"butter\"], ii[\"milk\"]] = 1\n",
"ing_ing[ii[\"butter\"], ii[\"soup\"]] = 1\n",
"ing_ing[ii[\"butter\"], ii[\"drained boiled noodles\"]] = 1\n",
"ing_ing[ii[\"butter\"], ii[\"drained tuna\"]] = 1\n",
"ing_ing[ii[\"butter\"], ii[\"drained corn\"]] = 1\n",
"\n",
"ing_ing[ii[\"milk\"], ii[\"soup\"]] = 1\n",
"ing_ing[ii[\"milk\"], ii[\"drained boiled noodles\"]] = 1\n",
"ing_ing[ii[\"milk\"], ii[\"drained tuna\"]] = 1\n",
"ing_ing[ii[\"milk\"], ii[\"drained corn\"]] = 1\n",
"\n",
"ing_ing[ii[\"soup\"], ii[\"drained boiled noodles\"]] = 1\n",
"ing_ing[ii[\"soup\"], ii[\"drained tuna\"]] = 1\n",
"ing_ing[ii[\"soup\"], ii[\"drained corn\"]] = 1\n",
"\n",
"ing_ing[ii[\"drained boiled noodles\"], ii[\"drained tuna\"]] = 1\n",
"ing_ing[ii[\"drained boiled noodles\"], ii[\"drained corn\"]] = 1\n",
"\n"
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" {\"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\"}], \"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\":
" {\"responsive\": true}\n",
" ).then(function(){\n",
" \n",
"var gd = document.getElementById('0db72a0b-4e5b-40c4-89c6-404bff4bc40c');\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",
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"\n",
" })\n",
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" </script>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = go.Figure(data=go.Heatmap(\n",
" z=ing_ing,\n",
" x=ingredients,\n",
" y=ingredients,\n",
" xgap = 2,\n",
" ygap = 2,))\n",
"\n",
"fig.update_layout(\n",
" yaxis = dict(\n",
" scaleanchor = \"x\",\n",
" scaleratio = 1,\n",
" )\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"display_name": "Python 3",
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"name": "python3"
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"language_info": {
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"name": "ipython",
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