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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": [
""
],
"text/vnd.plotly.v1+html": [
""
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"metadata": {},
"output_type": "display_data"
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],
"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: [1]\", \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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prevalence=0.00005",
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prevalence=0.00296",
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prevalence=0.00033",
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prevalence=0.00143",
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prevalence=0.00301",
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prevalence=0.00033",
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prevalence=0.00086",
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prevalence=0.00014",
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prevalence=0.00043"
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"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]+'
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]+'
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": {},
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