Flavor-network-via-Plotly.ipynb

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   "cell_type": "markdown",
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   "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."
   ]
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       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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       "<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"
   ]
  },
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{\"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>"
      ]
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     "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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   "source": [
    "category_graph3d(V, Edges, ingr_category, ingr_preval, d_index_ingr, category='meat')"
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[0.6676771965851551, 2.1286420691158314, 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, 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   "source": [
    "category_graph3d(V, Edges, ingr_category, ingr_preval, d_index_ingr, category='fish/seafood')"
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