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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Flavor Network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We plot the backbone of the flavor network, presented in [Nature](https://www.nature.com/articles/srep00196)\n",
" \n",
"The file `'flavor-network.json'` \n",
"was created from the files provided by the authors, here: [https://www.nature.com/articles/srep00196](https://www.nature.com/articles/srep00196), \n",
"and here [http://yongyeol.com/pub/](http://yongyeol.com/pub/).\n",
"\n",
"The json file is a dict with three keys: `'name'`, `'nodes'` and `'edges'`. \n",
"We also saved in this file the position of each node, assigned by the `fdp` `pygraphviz` layout algorithm.\n",
"\n",
"The nodes of the network are the ingredients and node color corresponds to the food category.\n",
"Two ingredients are connected if they share a number of flavor compounds. \n",
"Hovering the mouse over a node the ingredient name and its prevalence is displayed.\n",
"Since for some categories the edges are cluttered we provide the posibility to plot its \n",
"corresponding subgraph as a Python `igraph` with the Kamada-Kaway 3d layout."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/vnd.plotly.v1+html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import json\n",
"import igraph as ig\n",
"import copy\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, iplot\n",
"init_notebook_mode(connected=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The backbone of the flavor network"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def Plotly_edges(E, node_position):\n",
"\n",
" Xe=[]\n",
" Ye=[]\n",
" for e in E:\n",
" Xe+=[node_position[e[0]][0], node_position[e[1]][0], None]# x coordinates of the nodes defining the edge e\n",
" Ye+=[node_position[e[0]][1], node_position[e[1]][1], None]# y \n",
" return dict(type='scatter', \n",
" x=Xe,\n",
" y=Ye, \n",
" mode='lines',\n",
" line=dict(color='rgb(160,160,160)', width=0.5),\n",
" hoverinfo='none',\n",
" name='edge'\n",
" ) \n",
"\n",
"def layout2d(V, node_position):\n",
" axis=dict(showbackground=False,# \n",
" showline=False, \n",
" zeroline=False,\n",
" showgrid=False,\n",
" showticklabels=False,\n",
" title='' \n",
" )\n",
"\n",
" layout = dict(title=\"Backbone of the Flavor Network\", \n",
" font=dict(family='Balto', color='rgb(5,5,5)'),\n",
" width=900,\n",
" height=800,\n",
" showlegend=True,\n",
" xaxis=dict(axis),\n",
" yaxis=dict(axis), \n",
" \n",
" margin=dict(t=100, l=20),\n",
" hovermode='closest',\n",
" legend=dict(x=1., y=0.9),\n",
" paper_bgcolor='rgb(235,235,235)',\n",
" annotations=[dict(showarrow=False, \n",
" text=\"Data source: <a href='https://www.nature.com/articles/srep00196'> [1]</a>\", \n",
" xref='paper', \n",
" yref='paper', \n",
" x=0, \n",
" y=-0.05, \n",
" xanchor='left', \n",
" yanchor='top', \n",
" font=dict(family='Balto',\n",
" size=14) \n",
" )\n",
" ]\n",
" )\n",
"\n",
" anno_text=['flower', 'red_bean', 'onion', 'beef', 'fish', 'cottage_cheese', 'wine',\n",
" 'olive', 'citrus', 'fennel', 'pepper', 'basil', 'white_bread', 'mushroom']\n",
"\n",
"\n",
" x_annotext=[node_position[V.index(txt)][0] for txt in anno_text]\n",
" y_annotext=[node_position[V.index(txt)][1] for txt in anno_text]\n",
" x_anchor=['right', 'right', 'right', 'center', 'center', 'center', 'left', \n",
" 'center', 'left', 'left', 'right', 'right', 'left', 'left']\n",
" y_anchor=['bottom', 'center', 'center', 'bottom', 'top', 'bottom', 'center',\n",
" 'bottom', 'center', 'center', 'center','bottom', 'center', 'center']\n",
" step=4#a value we add/subtract to x_annotext, y_annotext in order to avoid overlaying the annotext and the node dot\n",
" for k in range(len(x_anchor)):\n",
" if x_anchor[k]=='right': x_annotext[k]-=step\n",
" elif x_anchor[k]=='left': x_annotext[k]+=step\n",
" else:pass\n",
" for k in range(len(y_anchor)):\n",
" if y_anchor[k]=='top': y_annotext[k]-=step\n",
" elif y_anchor[k]=='bottom': y_annotext[k]+=step \n",
" else: pass \n",
"\n",
" layout['annotations']=layout['annotations']+[dict(showarrow=False,\n",
" text=anno_text[k],\n",
" xref='x',\n",
" yref='y',\n",
" x=x_annotext[k],\n",
" y=y_annotext[k],\n",
" font=dict(family='Balto', size=11, color='rgb(5,5,5)'),\n",
" xanchor=x_anchor[k], \n",
" yanchor=y_anchor[k]\n",
" ) for k in range(len(anno_text))\n",
" ]\n",
" return layout\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
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\"size\": 9, \"color\": \"rgb(247,231,180)\", \"line\": {\"color\": \"rgb(140,140,140)\", \"width\": 0.5}}, \"text\": [\"rye_bread<br>prevalence=0.00434\", \"rice<br>prevalence=0.16204\", \"hop<br>prevalence=0.00014\", \"bread<br>prevalence=0.21732\", \"white_bread<br>prevalence=0.01766\", \"malt<br>prevalence=0.00177\", \"popcorn<br>prevalence=0.00463\", \"brown_rice<br>prevalence=0.01609\", \"wheat_bread<br>prevalence=0.00396\"], \"hoverinfo\": \"text\"}, {\"x\": [331.07, 431.77, 423.81, 393.36, 419.2, 481.26, 446.76, 338.78, 359.01, 155.03], \"y\": [609.33, 556.62, 477.31, 609.43, 654.95, 644.76, 751.15, 895.56, 699.01, 294.93], \"mode\": \"markers\", \"name\": \"plant derivative\", \"marker\": {\"symbol\": \"dot\", \"size\": 9, \"color\": \"#acae64\", \"line\": {\"color\": \"rgb(140,140,140)\", \"width\": 0.5}}, \"text\": [\"peppermint_oil<br>prevalence=0.00043\", \"coffee<br>prevalence=0.03417\", \"peanut_butter<br>prevalence=0.04749\", \"tea<br>prevalence=0.00482\", \"jasmine_tea<br>prevalence=0.00005\", \"cocoa<br>prevalence=0.22915\", \"black_tea<br>prevalence=0.00181\", \"lime_peel_oil<br>prevalence=0.00477\", \"green_tea<br>prevalence=0.00153\", \"coconut_oil<br>prevalence=0.00081\"], \"hoverinfo\": \"text\"}, {\"x\": [481.71, 611.79, 549.51, 582.05, 408.5, 485.84, 547.86, 548.6, 480.23, 596.78, 724.98], \"y\": [381.29, 367.43, 330.11, 418.12, 374.49, 461.45, 462.16, 374.12, 337.28, 550.27, 427.33], \"mode\": \"markers\", \"name\": \"meat\", \"marker\": {\"symbol\": \"dot\", \"size\": 9, \"color\": \"#d21717\", \"line\": {\"color\": \"rgb(140,140,140)\", \"width\": 0.5}}, \"text\": [\"roasted_pork<br>prevalence=0.00597\", \"chicken<br>prevalence=0.25268\", \"pork<br>prevalence=0.09374\", \"roasted_beef<br>prevalence=0.01060\", \"bacon<br>prevalence=0.10291\", \"raw_beef<br>prevalence=0.00010\", \"beef<br>prevalence=0.23016\", \"cured_pork<br>prevalence=0.01499\", \"pork_sausage<br>prevalence=0.06501\", \"pork_liver<br>prevalence=0.00024\", \"lamb<br>prevalence=0.02253\"], \"hoverinfo\": \"text\"}, {\"x\": [237.55, 171.67, 1084.6, 225.81, 299.59, 418.14, 163.77, 276.74, 361.66, 152.02, 377.54, 295.59, 1121.0, 994.94, 280.32, 992.96, 184.76, 1063.9, 482.95, 678.59, 1058.5, 236.91, 1101.6, 440.47, 420.0, 742.85], \"y\": [458.22, 435.37, 202.37, 502.31, 416.19, 325.02, 524.85, 887.49, 418.96, 480.48, 236.98, 523.79, 248.93, 1141.8, 829.41, 1204.0, 972.53, 1122.5, 275.91, 383.25, 1231.7, 389.39, 1178.2, 194.81, 280.98, 346.24], \"mode\": \"markers\", \"name\": \"vegetable\", \"marker\": {\"symbol\": \"dot\", \"size\": 9, \"color\": \"#009b00\", \"line\": {\"color\": \"rgb(140,140,140)\", \"width\": 0.5}}, \"text\": [\"mung_bean<br>prevalence=0.00091\", \"lima_bean<br>prevalence=0.00711\", \"vegetable<br>prevalence=0.07923\", \"bean<br>prevalence=0.09083\", \"kidney_bean<br>prevalence=0.02110\", \"onion<br>prevalence=0.86063\", \"red_bean<br>prevalence=0.00158\", \"bell_pepper<br>prevalence=0.27908\", \"potato_chip<br>prevalence=0.00310\", \"black_bean<br>prevalence=0.02262\", \"chive<br>prevalence=0.06277\", \"soybean<br>prevalence=0.04935\", \"watercress<br>prevalence=0.00630\", \"shiitake<br>prevalence=0.02439\", \"tomato<br>prevalence=0.47296\", \"matsutake<br>prevalence=0.00267\", \"green_bell_pepper<br>prevalence=0.12305\", \"mushroom<br>prevalence=0.15832\", \"shallot<br>prevalence=0.05666\", \"baked_potato<br>prevalence=0.00043\", \"enokidake<br>prevalence=0.00506\", \"red_kidney_bean<br>prevalence=0.00282\", \"porcini<br>prevalence=0.00506\", \"leek<br>prevalence=0.01962\", \"garlic<br>prevalence=0.80631\", \"potato<br>prevalence=0.16920\"], \"hoverinfo\": \"text\"}, {\"x\": [651.75, 588.68, 715.81, 876.95, 524.85, 586.89, 543.32, 667.56, 677.94, 648.9, 670.12, 549.05, 730.83, 605.56, 777.85, 738.41], \"y\": [747.83, 776.15, 713.52, 714.38, 735.59, 716.37, 652.83, 606.23, 562.21, 703.82, 659.82, 605.19, 615.81, 650.04, 757.31, 659.93], \"mode\": \"markers\", \"name\": \"alcoholic beverage\", \"marker\": {\"symbol\": \"dot\", \"size\": 9, \"color\": \"#840123\", \"line\": {\"color\": \"rgb(140,140,140)\", \"width\": 0.5}}, \"text\": [\"cabernet_sauvignon_wine<br>prevalence=0.00081\", \"white_wine<br>prevalence=0.10338\", \"cognac<br>prevalence=0.00315\", \"grape_brandy<br>prevalence=0.00038\", \"apple_brandy<br>prevalence=0.00177\", \"cider<br>prevalence=0.05346\", \"rum<br>prevalence=0.02873\", \"champagne_wine<br>prevalence=0.00473\", \"beer<br>prevalence=0.01446\", \"red_wine<br>prevalence=0.06649\", \"port_wine<br>prevalence=0.00234\", \"jamaican_rum<br>prevalence=0.00005\", \"whiskey<br>prevalence=0.00706\", \"sherry<br>prevalence=0.03007\", \"brandy<br>prevalence=0.01885\", \"wine<br>prevalence=0.04186\"], \"hoverinfo\": \"text\"}, {\"x\": [536.4, 678.62, 460.04, 609.91, 784.62, 676.49, 952.25, 826.09, 537.65, 732.32, 672.05, 362.65, 605.6, 467.16, 654.22, 904.47, 540.76, 829.49, 342.37, 786.0, 667.65, 778.7, 641.56, 466.96, 543.51, 338.54, 891.5, 848.09, 530.17, 480.62, 840.75, 722.15, 734.12, 301.35, 745.2, 531.98, 931.96, 418.54, 274.79, 869.91, 910.22, 602.79, 670.28, 547.57, 719.95], \"y\": [908.79, 791.87, 960.78, 1056.8, 855.82, 1067.1, 759.56, 982.74, 1123.3, 975.71, 1022.7, 565.33, 1011.4, 1130.0, 879.91, 847.61, 963.73, 811.8, 851.53, 931.66, 978.41, 1070.1, 835.89, 1086.0, 1016.1, 1079.3, 803.6, 938.71, 1060.2, 1007.8, 1081.2, 889.72, 1023.0, 653.36, 1114.7, 820.23, 1044.8, 1041.9, 741.39, 1026.8, 896.5, 923.92, 934.22, 864.76, 836.91], \"mode\": \"markers\", \"name\": \"fruit\", \"marker\": {\"symbol\": \"dot\", \"size\": 9, \"color\": \"#FFF700\", \"line\": {\"color\": \"rgb(140,140,140)\", \"width\": 0.5}}, \"text\": [\"orange_juice<br>prevalence=0.08114\", \"pineapple<br>prevalence=0.07713\", \"guava<br>prevalence=0.00062\", \"berry<br>prevalence=0.00869\", \"grape<br>prevalence=0.01647\", \"huckleberry<br>prevalence=0.00048\", \"cherry<br>prevalence=0.05174\", \"melon<br>prevalence=0.00749\", \"citrus<br>prevalence=0.00778\", \"juniper_berry<br>prevalence=0.00158\", \"blackberry<br>prevalence=0.00807\", \"okra<br>prevalence=0.00477\", \"elderberry<br>prevalence=0.00024\", \"mandarin<br>prevalence=0.01327\", \"quince<br>prevalence=0.00134\", \"bartlett_pear<br>prevalence=0.00110\", \"raspberry<br>prevalence=0.03742\", \"concord_grape<br>prevalence=0.00057\", \"lingonberry<br>prevalence=0.00043\", \"plum<br>prevalence=0.01322\", \"black_raspberry<br>prevalence=0.00038\", \"cranberry<br>prevalence=0.04372\", \"apple<br>prevalence=0.11541\", \"lime<br>prevalence=0.05169\", \"bitter_orange<br>prevalence=0.00396\", \"grapefruit<br>prevalence=0.00558\", \"banana<br>prevalence=0.04697\", \"kiwi<br>prevalence=0.00501\", \"tangerine<br>prevalence=0.00210\", \"orange<br>prevalence=0.08157\", \"olive<br>prevalence=0.08577\", \"apricot<br>prevalence=0.02959\", \"blueberry<br>prevalence=0.02219\", \"japanese_plum<br>prevalence=0.00062\", \"black_currant<br>prevalence=0.00053\", \"passion_fruit<br>prevalence=0.00095\", \"durian<br>prevalence=0.00005\", \"lemon<br>prevalence=0.14539\", \"strawberry_jam<br>prevalence=0.00005\", \"papaya<br>prevalence=0.00220\", \"pear<br>prevalence=0.02234\", \"nectarine<br>prevalence=0.00239\", \"strawberry<br>prevalence=0.05145\", \"mango<br>prevalence=0.01852\", \"muscat_grape<br>prevalence=0.00005\"], \"hoverinfo\": \"text\"}, {\"x\": [458.58, 601.63, 663.7, 476.58, 574.44, 591.95, 529.44, 509.99, 562.94, 575.89, 572.15, 239.25, 634.83, 538.59, 591.71], \"y\": [600.7, 108.73, 106.57, 104.4, 17.671, 196.78, 156.58, 59.016, 284.82, 240.8, 64.675, 611.86, 62.372, 108.72, 152.75], \"mode\": \"markers\", \"name\": \"fish/seafood\", \"marker\": {\"symbol\": \"dot\", \"size\": 9, \"color\": \"#5aadff\", \"line\": {\"color\": \"rgb(140,140,140)\", \"width\": 0.5}}, \"text\": [\"katsuobushi<br>prevalence=0.00234\", \"mackerel<br>prevalence=0.00210\", \"smoked_fish<br>prevalence=0.00029\", \"eel<br>prevalence=0.00095\", \"fish<br>prevalence=0.08969\", \"salmon<br>prevalence=0.02072\", \"smoked_salmon<br>prevalence=0.00463\", \"herring<br>prevalence=0.00048\", \"caviar<br>prevalence=0.00124\", \"sturgeon_caviar<br>prevalence=0.00005\", \"haddock<br>prevalence=0.00148\", \"squid<br>prevalence=0.01088\", \"catfish<br>prevalence=0.00325\", \"cod<br>prevalence=0.00831\", \"tuna<br>prevalence=0.02114\"], \"hoverinfo\": \"text\"}, {\"x\": [213.02, 32.24, 401.73, 88.719, 26.695, 212.75, 106.65, 90.712, 150.74], \"y\": [681.46, 582.74, 1086.7, 699.57, 665.5, 731.25, 743.59, 626.78, 692.4], \"mode\": \"markers\", \"name\": \"flower\", \"marker\": {\"symbol\": \"dot\", \"size\": 9, \"color\": \"#f500f5\", \"line\": {\"color\": \"rgb(140,140,140)\", \"width\": 0.5}}, \"text\": [\"geranium<br>prevalence=0.00005\", \"lavender<br>prevalence=0.00296\", \"bergamot<br>prevalence=0.00033\", \"flower<br>prevalence=0.00143\", \"rose<br>prevalence=0.00301\", \"jasmine<br>prevalence=0.00033\", \"orange_flower<br>prevalence=0.00086\", \"carnation<br>prevalence=0.00014\", \"gardenia<br>prevalence=0.00043\"], \"hoverinfo\": \"text\"}], {\"title\": \"Backbone of the Flavor Network\", \"font\": {\"family\": \"Balto\", \"color\": \"rgb(5,5,5)\"}, \"width\": 900, \"height\": 800, \"showlegend\": true, \"xaxis\": {\"showbackground\": false, \"showline\": false, \"zeroline\": false, \"showgrid\": false, \"showticklabels\": false, \"title\": \"\"}, \"yaxis\": {\"showbackground\": false, \"showline\": false, \"zeroline\": false, \"showgrid\": false, \"showticklabels\": false, \"title\": \"\"}, \"margin\": {\"t\": 100, \"l\": 20}, \"hovermode\": \"closest\", \"legend\": {\"x\": 1.0, \"y\": 0.9}, \"paper_bgcolor\": \"rgb(235,235,235)\", \"annotations\": [{\"showarrow\": false, \"text\": \"Data source: <a href='https://www.nature.com/articles/srep00196'> [1]</a>\", \"xref\": \"paper\", \"yref\": \"paper\", \"x\": 0, \"y\": -0.05, \"xanchor\": \"left\", \"yanchor\": \"top\", \"font\": {\"family\": \"Balto\", \"size\": 14}}, {\"showarrow\": false, \"text\": \"flower\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 84.719, \"y\": 703.57, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"right\", \"yanchor\": \"bottom\"}, {\"showarrow\": false, \"text\": \"red_bean\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 159.77, \"y\": 524.85, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"right\", \"yanchor\": \"center\"}, {\"showarrow\": false, \"text\": \"onion\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 414.14, \"y\": 325.02, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"right\", \"yanchor\": \"center\"}, {\"showarrow\": false, \"text\": \"beef\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 547.86, \"y\": 466.16, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"center\", \"yanchor\": \"bottom\"}, {\"showarrow\": false, \"text\": \"fish\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 574.44, \"y\": 13.671, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"center\", \"yanchor\": \"top\"}, {\"showarrow\": false, \"text\": \"cottage_cheese\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 895.12, \"y\": 611.39, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"center\", \"yanchor\": \"bottom\"}, {\"showarrow\": false, \"text\": \"wine\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 742.41, \"y\": 659.93, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"left\", \"yanchor\": \"center\"}, {\"showarrow\": false, \"text\": \"olive\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 840.75, \"y\": 1085.2, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"center\", \"yanchor\": \"bottom\"}, {\"showarrow\": false, \"text\": \"citrus\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 541.65, \"y\": 1123.3, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"left\", \"yanchor\": \"center\"}, {\"showarrow\": false, \"text\": \"fennel\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 517.4, \"y\": 1218.1, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"left\", \"yanchor\": \"center\"}, {\"showarrow\": false, \"text\": \"pepper\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 138.01, \"y\": 927.14, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"right\", \"yanchor\": \"center\"}, {\"showarrow\": false, \"text\": \"basil\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 401.03, \"y\": 894.64, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"right\", \"yanchor\": \"bottom\"}, {\"showarrow\": false, \"text\": \"white_bread\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 1002.54, \"y\": 997.71, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"left\", \"yanchor\": \"center\"}, {\"showarrow\": false, \"text\": \"mushroom\", \"xref\": \"x\", \"yref\": \"y\", \"x\": 1067.9, \"y\": 1122.5, \"font\": {\"family\": \"Balto\", \"size\": 11, \"color\": \"rgb(5,5,5)\"}, \"xanchor\": \"left\", \"yanchor\": \"center\"}]}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#### Process data read from the json file\n",
"with open(\"Data/flavor-network.json\") as json_file:\n",
" network = json.load(json_file)\n",
"#print (network.keys())\n",
"\n",
"V=[net['ingredient'] for net in network['nodes']]#list of nodes\n",
"N=len(V)\n",
"d_index_ingr=dict(zip(range(N), V))\n",
"ingr_preval=[net['prevalence'] for net in network['nodes']]# list of prevalence values for each ingredient(node)\n",
"ingr_category={net['ingredient']: net['category'] for net in network['nodes']}# dict ingredient: category\n",
"categories=list(set(ingr_category.values()))# list of categories\n",
"n_cat=len(categories)\n",
"cat_indices=[[V.index(v) for v in V if ingr_category[v]==categories[k]] for k in range(n_cat)]\n",
"node_position=[net['position'] for net in network['nodes']]#node position assigned via pygraphviz fdp layout\n",
"\n",
"Edges=[tuple(e['edge']) for e in network['edges']]\n",
"E=[(V.index(e[0]), V.index(e[1])) for e in Edges]# edges represented as tuple of linked node indices\n",
"\n",
"\n",
"colors={'fruit': '#FFF700',\n",
" 'herb':'rgb(102,194,163)',\n",
" 'cereal/crop':'rgb(247,231,180)' ,\n",
" 'flower': '#f500f5',\n",
" 'spice': '#ffa517', \n",
" 'alcoholic beverage':'#840123',\n",
" 'meat': '#d21717',\n",
" 'animal product':'#d2823b',\n",
" 'vegetable': '#009b00', \n",
" 'fish/seafood': '#5aadff',\n",
" 'plant':'#7b9f20',\n",
" 'plant derivative': '#acae64',\n",
" 'dairy':'rgb(250,250,250)',\n",
" 'nut/seed/pulse': '#8D6645'\n",
" }\n",
" \n",
"\n",
"ingr_color=[colors[ingr_category[v]] for v in V]#list of ingredient(node) colors\n",
"\n",
"edges=Plotly_edges(E, node_position)\n",
"nodes=[dict(x=[node_position[idx][0] for idx in cat_indices[k]],\n",
" y=[node_position[idx][1] for idx in cat_indices[k]],\n",
" mode='markers',\n",
" name=categories[k],\n",
" marker=dict(symbol='dot',\n",
" size=9, \n",
" color= colors[categories[k]],\n",
" line=dict(color='rgb(140,140,140)', width=0.5)\n",
" ),\n",
" text=[V[idx]+'<br>prevalence='+'{:0.5f}'.format(ingr_preval[idx]) for idx in cat_indices[k]],\n",
" hoverinfo='text'\n",
" ) for k in range(n_cat)\n",
" ]\n",
"\n",
"\n",
"\n",
"data=[edges]+nodes\n",
"layout=layout2d(V, node_position)\n",
"fig=dict(data=data, layout=layout)\n",
"iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Flavor category subnetwork"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to plot the 3d graph of a category define the following functions:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def category_data(V, Edges, ingr_category, ingr_preval, d_index_ingr, category):\n",
" #prepare data for the subgraph of a category\n",
" #categ is a string\n",
" index_thiscat=categories.index(category)\n",
" \n",
" subg_Edges=[e for e in Edges if ingr_category[e[0]]==category or ingr_category[e[1]]==category]\n",
" subg_E=[(V.index(e[0]), V.index(e[1])) for e in subg_Edges]# edges as tuples of ints (indices of linked nodes\n",
" #in the list of global nodes)\n",
"\n",
" subg_nodes=np.array([list(e) for e in subg_E]).reshape(2*len(subg_E))\n",
" subg_nodes=np.unique(subg_nodes)# all nodes linked with nodes in this category\n",
" nodes_categ=list(set([ingr_category[V[idx]] for idx in subg_nodes]))\n",
" nr_cat=len(nodes_categ)\n",
" \n",
" subg_g=ig.Graph()\n",
" subg_g.add_vertices(subg_nodes.tolist())\n",
" L=len(subg_g.vs)\n",
" ig_nodes=dict(zip(subg_g.vs['name'], range(L)))\n",
" J= [[n for n in subg_nodes if ingr_category[d_index_ingr[n]]==nodes_categ[k]] for k in range(nr_cat)]\n",
" I=[[ig_nodes[n] for n in subg_nodes if ingr_category[d_index_ingr[n]]==nodes_categ[k]] for k in range(nr_cat)]\n",
" ig_edges=[(ig_nodes[e[0]], ig_nodes[e[1]]) for e in subg_E]\n",
" subg_g.add_edges(ig_edges)\n",
" subg_layt=np.array(subg_g.layout('kk3d') )\n",
"\n",
" Xe=[]\n",
" Ye=[]\n",
" Ze=[]\n",
" for e in ig_edges:\n",
" Xe+=[subg_layt[e[0]][0], subg_layt[e[1]][0], None]\n",
" Ye+=[subg_layt[e[0]][1], subg_layt[e[1]][1], None] \n",
" Ze+=[subg_layt[e[0]][2], subg_layt[e[1]][2], None]\n",
" subg_edges= [dict(type='scatter3d', \n",
" x=Xe,\n",
" y=Ye,\n",
" z=Ze,\n",
" mode='lines',\n",
" line=dict(color='rgb(160,160,160)', width=0.75),\n",
" hoverinfo='none',\n",
" name='edge'\n",
" )]\n",
"\n",
" subg_nodes=[dict(type='scatter3d',\n",
" x=subg_layt[I[k],0],\n",
" y=subg_layt[I[k],1],\n",
" z=subg_layt[I[k],2],\n",
" mode='markers',\n",
" name=nodes_categ[k],\n",
" marker=dict(symbol='dot',\n",
" size=9, \n",
" color= colors[nodes_categ[k]],\n",
" line=dict(color='rgb(140,140,140)', width=0.5)\n",
" ),\n",
" text=[V[idx]+'<br>prevalence='+'{:0.5f}'.format(ingr_preval[idx]) for idx in J[k]],\n",
" hoverinfo='text' \n",
" ) for k in range(nr_cat)]\n",
" return subg_edges+ subg_nodes\n",
"\n",
"#layout for 3d subnetworks\n",
"def layout3d(layout2d, subgraph_title):\n",
" noaxis=dict(showbackground=False,\n",
" showgrid=False,\n",
" showline=False,\n",
" showticklabels=False,\n",
" ticks='',\n",
" title='',\n",
" zeroline=False)\n",
" \n",
" layout_cat=copy.deepcopy(layout2d)\n",
" layout_cat['annotations'][1:]=[]\n",
" layout_cat.update(title=subgraph_title,\n",
" width=800,\n",
" height=700,\n",
" scene=dict(camera = dict(eye=dict(x=1., y=1., z=1.)),\n",
" aspectratio=dict(x=1, y=1, z=0.95),\n",
" xaxis=dict(noaxis),\n",
" yaxis=dict(noaxis), \n",
" zaxis=dict(noaxis),\n",
" \n",
" )\n",
" )\n",
" return layout_cat"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def category_graph3d(V, Edges, ingr_category, ingr_preval, d_index_ingr, category='meat'):\n",
" data_cat=category_data(V, Edges, ingr_category, ingr_preval, d_index_ingr, category)\n",
" title_cat='3D Subgraph of the ingredients in the '+category+' category'\n",
" layout_cat=layout3d(layout, title_cat)\n",
" fig3d=dict(data=data_cat, layout=layout_cat)\n",
" iplot(fig3d)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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]
},
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],
"source": [
"category_graph3d(V, Edges, ingr_category, ingr_preval, d_index_ingr, category='meat')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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0.4275082175243571], \"y\": [0.4258743815356649, 0.9040995192017403, 1.158475522750573], \"z\": [0.25491551329753936, 0.7328351170008863, 1.7006787679917144], \"mode\": \"markers\", \"name\": \"plant derivative\", \"marker\": {\"symbol\": \"dot\", \"size\": 9, \"color\": \"#acae64\", \"line\": {\"color\": \"rgb(140,140,140)\", \"width\": 0.5}}, \"text\": [\"coffee<br>prevalence=0.03417\", \"cocoa<br>prevalence=0.22915\", \"black_tea<br>prevalence=0.00181\"], \"hoverinfo\": \"text\"}], {\"title\": \"3D Subgraph of the ingredients in the fish/seafood category\", \"font\": {\"family\": \"Balto\", \"color\": \"rgb(5,5,5)\"}, \"width\": 800, \"height\": 700, \"showlegend\": true, \"xaxis\": {\"showbackground\": false, \"showline\": false, \"zeroline\": false, \"showgrid\": false, \"showticklabels\": false, \"title\": \"\"}, \"yaxis\": {\"showbackground\": false, \"showline\": false, \"zeroline\": false, \"showgrid\": false, \"showticklabels\": false, \"title\": \"\"}, \"margin\": {\"t\": 100, \"l\": 20}, \"hovermode\": \"closest\", \"legend\": {\"x\": 1.0, \"y\": 0.9}, \"paper_bgcolor\": \"rgb(235,235,235)\", \"annotations\": [{\"showarrow\": false, \"text\": \"Data source: <a href='https://www.nature.com/articles/srep00196'> [1]</a>\", \"xref\": \"paper\", \"yref\": \"paper\", \"x\": 0, \"y\": -0.05, \"xanchor\": \"left\", \"yanchor\": \"top\", \"font\": {\"family\": \"Balto\", \"size\": 14}}], \"scene\": {\"camera\": {\"eye\": {\"x\": 1.0, \"y\": 1.0, \"z\": 1.0}}, \"aspectratio\": {\"x\": 1, \"y\": 1, \"z\": 0.95}, \"xaxis\": {\"showbackground\": false, \"showgrid\": false, \"showline\": false, \"showticklabels\": false, \"ticks\": \"\", \"title\": \"\", \"zeroline\": false}, \"yaxis\": {\"showbackground\": false, \"showgrid\": false, \"showline\": false, \"showticklabels\": false, \"ticks\": \"\", \"title\": \"\", \"zeroline\": false}, \"zaxis\": {\"showbackground\": false, \"showgrid\": false, \"showline\": false, \"showticklabels\": false, \"ticks\": \"\", \"title\": \"\", \"zeroline\": false}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
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],
"source": [
"category_graph3d(V, Edges, ingr_category, ingr_preval, d_index_ingr, category='fish/seafood')"
]
},
{
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