literature_search/data_exploration/sankey_diagram.ipynb

{
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "df = pd.read_csv(\"defined_articles.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import random\n",
    "\n",
    "def pick_colour(name):\n",
    "   color_wheel = 'grey'\n",
    "   #color_wheel = random.choice(px.colors.qualitative.Pastel)\n",
    "   color_wheel = px.colors.qualitative.Alphabet[8]\n",
    "   #if name == \"Clostridium\":\n",
    "    #   color_wheel = px.colors.qualitative.Pastel[6]\n",
    "   if name == \"Escherichia\":\n",
    "       color_wheel = px.colors.qualitative.Pastel[1]\n",
    "   #else:\n",
    "   #   color_wheel = random.choice(px.colors.qualitative.Pastel)\n",
    "   return color_wheel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a new dataframe to use in building the sankey chart\n",
    "\n",
    "#create an empty dataframe\n",
    "sankey_data = pd.DataFrame({'Source': [],\n",
    "                   'Target': [],\n",
    "                   'Value': [],\n",
    "                   'Color': []})\n",
    "\n",
    "organism_colouring = dict()\n",
    "colour_counter = 0\n",
    "\n",
    "#iterate over the rows of the dataframe\n",
    "for index, row in df.iterrows():\n",
    "\n",
    "    #get lists of substrates, products, and organisms\n",
    "    substrates = row[\"Substrate\"].split(\", \")\n",
    "    organisms = row[\"Organisms\"].split(\", \")\n",
    "    products = row[\"Product\"].split(\", \")\n",
    "\n",
    "    #adds colour to each organism using the pick_colour function\n",
    "    for organism in organisms:\n",
    "        if organism not in organism_colouring:\n",
    "            organism_colouring[organism] = pick_colour(organism)\n",
    "\n",
    "    #add links from each substrate to each organism\n",
    "    for substrate in substrates:\n",
    "        for organism in organisms:\n",
    "            sankey_data.loc[len(sankey_data.index)] = [substrate, organism, 1, organism_colouring[organism]]\n",
    "\n",
    "    #add links from each organism to each substrate\n",
    "    for organism in organisms:\n",
    "        for product in products:\n",
    "            sankey_data.loc[len(sankey_data.index)] = [organism, product, 1, organism_colouring[organism]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "#needed modification for building the plot\n",
    "\n",
    "#get each unique source_target and a mapping to their index\n",
    "#unique_source_target = list(pd.unique(sankey_data[['Source', 'Target']].values.ravel('K')))\n",
    "unique_source_target = list(pd.unique(sankey_data[['Source', 'Target', 'Color']].values.ravel('K')))\n",
    "mapping_dict = {k: v for v, k in enumerate(unique_source_target)}\n",
    "sankey_data['Source'] = sankey_data['Source'].map(mapping_dict)\n",
    "sankey_data['Target'] = sankey_data['Target'].map(mapping_dict)\n",
    "sankey_dict = sankey_data.to_dict(orient='list')\n",
    "\n",
    "#setting colours for the nodes\n",
    "node_colours = [organism_colouring.get(i, \"grey\") for i in unique_source_target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
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            "showbackground": true,
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            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
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           "x": 0.05
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           "ticks": "",
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           "zerolinewidth": 2
          },
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           "automargin": true,
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        },
        "title": {
         "text": "Sankey Diagram for consortia uses"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create diagram\n",
    "import plotly.express as px\n",
    "\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    orientation = \"h\",\n",
    "    node = dict(\n",
    "      pad = 15,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"black\", width = 0.5),\n",
    "      label = unique_source_target,\n",
    "      #color = \"grey\"\n",
    "      color = node_colours\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = sankey_dict[\"Source\"],\n",
    "      target = sankey_dict[\"Target\"],\n",
    "      value = sankey_dict[\"Value\"],\n",
    "      color = sankey_dict[\"Color\"]\n",
    "      #color = [px.colors.qualitative.Plotly[unique_source_target.index(i) % len(px.colors.qualitative.Plotly)] for i in unique_source_target]\n",
    "  ))])\n",
    "\n",
    "layout = dict(\n",
    "        title = \"Sankey Diagram for consortia uses\",\n",
    "    height = 850,\n",
    "    font = dict(\n",
    "      size = 8),)\n",
    "\n",
    "#show figure\n",
    "fig.update_layout(layout)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the figure as a .svg file\n",
    "fig.write_image(\"plots/sankey_diagram.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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           "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": "Sankey Diagram for consortia uses"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#new sankey chart for genus only\n",
    "\n",
    "#creating a new dataframe to use in building the sankey chart\n",
    "\n",
    "#create an empty dataframe\n",
    "sankey_data = pd.DataFrame({'Source': [],\n",
    "                   'Target': [],\n",
    "                   'Value': [],\n",
    "                   'Color': []})\n",
    "\n",
    "organism_colouring = dict()\n",
    "colour_counter = 0\n",
    "\n",
    "#iterate over the rows of the dataframe\n",
    "for index, row in df.iterrows():\n",
    "\n",
    "    #get lists of substrates, products, and organisms\n",
    "    all_substrates = row[\"Substrate\"].split(\", \")\n",
    "    organisms = row[\"Organisms\"].split(\", \")\n",
    "    all_products = row[\"Product\"].split(\", \")\n",
    "\n",
    "    substrates = [substrate_map[substrate] for substrate in all_substrates]\n",
    "    products = [product_map[product] for product in all_products]\n",
    "\n",
    "\n",
    "    #taking the first word to get the genus instead of the organisms\n",
    "    genuses = [organism.split(' ')[0] for organism in organisms]\n",
    "\n",
    "    #adds colour to each organism using the pick_colour function\n",
    "    for genus in genuses:\n",
    "        if genus not in organism_colouring:\n",
    "            organism_colouring[genus] = pick_colour(genus)\n",
    "\n",
    "    #add links from each substrate to each organism\n",
    "    for substrate in substrates:\n",
    "        for genus in genuses:\n",
    "            sankey_data.loc[len(sankey_data.index)] = [substrate, genus, 1, organism_colouring[genus]]\n",
    "\n",
    "    #add links from each organism to each substrate\n",
    "    for genus in genuses:\n",
    "        for product in products:\n",
    "            sankey_data.loc[len(sankey_data.index)] = [genus, product, 1, organism_colouring[genus]]\n",
    "\n",
    "\n",
    "#needed modification for building the plot\n",
    "\n",
    "#get each unique source_target and a mapping to their index\n",
    "#unique_source_target = list(pd.unique(sankey_data[['Source', 'Target']].values.ravel('K')))\n",
    "unique_source_target = list(pd.unique(sankey_data[['Source', 'Target', 'Color']].values.ravel('K')))\n",
    "mapping_dict = {k: v for v, k in enumerate(unique_source_target)}\n",
    "sankey_data['Source'] = sankey_data['Source'].map(mapping_dict)\n",
    "sankey_data['Target'] = sankey_data['Target'].map(mapping_dict)\n",
    "sankey_dict = sankey_data.to_dict(orient='list')\n",
    "\n",
    "#setting colours for the nodes\n",
    "node_colours = [organism_colouring.get(i, \"grey\") for i in unique_source_target]\n",
    "\n",
    "#create diagram\n",
    "import plotly.express as px\n",
    "\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    orientation = \"h\",\n",
    "    node = dict(\n",
    "      pad = 15,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"black\", width = 0.5),\n",
    "      label = unique_source_target,\n",
    "      #color = \"grey\"\n",
    "      color = node_colours\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = sankey_dict[\"Source\"],\n",
    "      target = sankey_dict[\"Target\"],\n",
    "      value = sankey_dict[\"Value\"],\n",
    "      color = sankey_dict[\"Color\"]\n",
    "      #color = [px.colors.qualitative.Plotly[unique_source_target.index(i) % len(px.colors.qualitative.Plotly)] for i in unique_source_target]\n",
    "  ))])\n",
    "\n",
    "layout = dict(\n",
    "        title = \"Sankey Diagram for consortia uses\",\n",
    "    height = 850,\n",
    "    font = dict(\n",
    "      size = 8),)\n",
    "\n",
    "#show figure\n",
    "fig.update_layout(layout)\n",
    "fig.show()\n",
    "#fig.write_image(\"plots/sankey_diagram_genus.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_substrates = []\n",
    "all_products = []\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    substrates = row[\"Substrate\"].split(\", \")\n",
    "    products = row[\"Product\"].split(\", \")\n",
    "    all_substrates.extend(substrates)\n",
    "    all_products.extend(products)\n",
    "\n",
    "all_unique_substrates = set(all_substrates)\n",
    "all_unique_products = set(all_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Light', 'Xylose', 'Tryptophan', 'Cellulose', 'Lignocellulose', 'CO2', 'Syngas', 'Saccharides', 'VFA_s', 'Wastewater sludge', 'CO', 'Glucose', 'Meat extract', 'Industrial waste', 'Amino acid medium', 'Food waste', 'Glycerol', 'Phenanthrene', 'Methanol_s', 'Starch', 'Sucrose'}\n",
      "{'Antilisterial compounds', '7-methylxanthine', 'L-lysine', 'Fructo-oligosaccharide', 'Caproate', 'Glycosides', 'coniferyl alcohol', 'O-methylated phenylpropanoids', 'MCFA', 'Hydrogen', 'caffeyl alcohol', '3-hydroxypropionic acid', 'Polysaccharide', 'Polyhydroxyalkanoate', 'Bacterial cellulose', 'Indigo', 'Anthocyanins', 'SCFA', '3-hydroxybenzoic acid', 'lovastatin', 'Antibodies', 'Rosmarinic acid', 'Butanol', 'Fumaric acid', 'Microbial proteases', 'Recombinant protein', 'Glucose_p', 'VFA', 'Ethanol', 'Pyranoanthocyanins', 'Itaconic acid', 'P-coumaric acid', 'Methanol_p', 'Electricity', 'Lipopeptides', 'Phenol', 'Biosurfactants', 'Resveratrol', 'Biomass', 'Cercosporin', 'Pharmaceutically active compounds', 'Caffeic acid', 'Vitamins', 'Butyrate', 'Flavonoids', 'Monacolin', 'Glionitrin B', 'Hyaluronic acid nanocomposite', '1,3-Propanediol', 'Propionate', 'PHB', 'Butyl acetate', 'Fatty acid'}\n"
     ]
    }
   ],
   "source": [
    "print(all_unique_substrates)\n",
    "print(all_unique_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df = pd.read_csv(\"all_articles.csv\")\n",
    "\n",
    "all_s = []\n",
    "all_p = []\n",
    "\n",
    "for index, row in all_df.iterrows():\n",
    "    substrates = row[\"Substrate\"].split(\", \")\n",
    "    products = row[\"Product\"].split(\", \")\n",
    "    all_s.extend(substrates)\n",
    "    all_p.extend(products)\n",
    "\n",
    "all_u_s = set(all_s)\n",
    "all_u_p = set(all_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Organic waste', 'Ethanol', 'Acetate', 'Crude oil', 'Arabinose', 'Antibiotic fermentation residue', 'Activated sludge', 'Galactose', 'Bovine milk', 'Amino acids', 'Glycerin', 'Sludge compost', 'Fructose', 'Lupin flour', 'Manure compost'}\n",
      "{'Lactate', 'Caproic acid', 'Organic acids', 'Siderophores', 'Hyaluronic acid', 'Bioflocculant', 'Fermented food product', 'Lactic acid', 'Glutamic acid', 'SCCA', 'Methane', 'SCOA'}\n"
     ]
    }
   ],
   "source": [
    "print(all_u_s - all_unique_substrates)\n",
    "print(all_u_p - all_unique_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "substrate_map = {\n",
    "    'Light': 'Light', \n",
    "    'Xylose': 'Sugars', \n",
    "    'Tryptophan': 'Amino acid medium', \n",
    "    'Cellulose': 'Lignocellulose', \n",
    "    'Lignocellulose': 'Lignocellulose', \n",
    "    'CO2': 'Gas', \n",
    "    'Syngas': 'Gas', \n",
    "    'Saccharides': 'Sugars', \n",
    "    'VFA_s': 'Short-chain fatty acids', \n",
    "    'Wastewater sludge': 'Waste', \n",
    "    'CO': 'Gas', \n",
    "    'Glucose': 'Sugars', \n",
    "    'Meat extract': 'Waste', \n",
    "    'Industrial waste': 'Waste', \n",
    "    'Amino acid medium': 'Amino acid medium', \n",
    "    'Food waste': 'Waste', \n",
    "    'Glycerol': 'Alcohols', \n",
    "    'Phenanthrene': 'Other substrate', \n",
    "    'Methanol_s': 'Alcohols', \n",
    "    'Starch': 'Sugars', \n",
    "    'Sucrose': 'Sugars',\n",
    "    'Organic waste': 'Waste', \n",
    "    'Ethanol': 'Alcohols', \n",
    "    'Acetate': 'Short-chain fatty acids', \n",
    "    'Crude oil': 'Crude oil', \n",
    "    'Arabinose': 'Sugars', \n",
    "    'Antibiotic fermentation residue': 'Waste', \n",
    "    'Activated sludge': 'Waste', \n",
    "    'Galactose': 'Sugars', \n",
    "    'Bovine milk': 'Complex media', \n",
    "    'Amino acids': 'Amino acid medium', \n",
    "    'Glycerin': 'Alcohols', \n",
    "    'Sludge compost': 'Waste', \n",
    "    'Fructose': 'Sugars', \n",
    "    'Lupin flour': 'Complex media', \n",
    "    'Manure compost': 'Waste'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "product_map_old = {\n",
    "    'Antilisterial compounds': 'Antibiotics', \n",
    "    '7-methylxanthine': 'Pharmaceuticals', \n",
    "    'L-lysine': 'Amino acids', \n",
    "    'Fructo-oligosaccharide': 'Polysaccharides', \n",
    "    'Caproate': 'Carboxylic acids', \n",
    "    'Glycosides': 'Glycosides', \n",
    "    'coniferyl alcohol': 'Phenols', \n",
    "    'O-methylated phenylpropanoids': 'Pharmaceuticals', \n",
    "    'MCFA': 'Carboxylic acids', \n",
    "    'Hydrogen': 'Hydorgen gas', \n",
    "    'caffeyl alcohol': 'Phenols', \n",
    "    '3-hydroxypropionic acid': 'Carboxylic acids', \n",
    "    'Polysaccharide': 'Polysaccharides', \n",
    "    'Polyhydroxyalkanoate': 'Polyester polymers', \n",
    "    'Bacterial cellulose': 'Cellulose', \n",
    "    'Indigo': 'Dye', \n",
    "    'Anthocyanins': 'Pigments', \n",
    "    'SCFA': 'Carboxylic acids', \n",
    "    '3-hydroxybenzoic acid': 'Phenols', \n",
    "    'lovastatin': 'Pharmaceuticals', \n",
    "    'Antibodies': 'Proteins', \n",
    "    'Rosmarinic acid': 'Phenols', \n",
    "    'Butanol': 'Primary alcohols', \n",
    "    'Fumaric acid': 'Carboxylic acids', \n",
    "    'Microbial proteases': 'Proteins', \n",
    "    'Recombinant protein': 'Proteins', \n",
    "    'Glucose_p': 'Metabolites to support growth', \n",
    "    'VFA': 'Carboxylic acids', \n",
    "    'Ethanol': 'Primary alcohols', \n",
    "    'Pyranoanthocyanins': 'Phenols', \n",
    "    'Itaconic acid': 'Carboxylic acids', \n",
    "    'P-coumaric acid': 'Carboxylic acids', \n",
    "    'Methanol_p': 'Primary alcohols', \n",
    "    'Electricity': 'Electricity', \n",
    "    'Lipopeptides': 'Proteins', \n",
    "    'Phenol': 'Phenols', \n",
    "    'Biosurfactants': 'Surfactants', \n",
    "    'Resveratrol': 'Phenols', \n",
    "    'Biomass': 'Biomass', \n",
    "    'Cercosporin': 'Pharmaceuticals', \n",
    "    'Pharmaceutically active compounds': 'Pharmaceuticals', \n",
    "    'Caffeic acid': 'Phenols', \n",
    "    'Vitamins': 'Metabolites to support growth', \n",
    "    'Butyrate': 'Carboxylic acids', \n",
    "    'Flavonoids': 'Phenols', \n",
    "    'Monacolin': 'Pharmaceuticals', \n",
    "    'Glionitrin B': 'Pharmaceuticals', \n",
    "    'Hyaluronic acid': 'Pharmaceuticals', \n",
    "    '1,3-Propanediol': 'Glycols', \n",
    "    'Propionate': 'Carboxylic acids', \n",
    "    'PHB': 'Polyester polymers', \n",
    "    'Butyl acetate': 'Carboxylic esters', \n",
    "    'Fatty acid': 'Carboxylic acids',\n",
    "    'Lactate': 'Carboxylic acids', \n",
    "    'Caproic acid': 'Carboxylic acids', \n",
    "    'Organic acids': 'Carboxylic acids', \n",
    "    'Siderophores': 'Bacterial extracellular compounds', \n",
    "    'Bioflocculant': 'Bacterial extracellular compounds', \n",
    "    'Fermented food product': 'Fermented food products', \n",
    "    'Lactic acid': 'Carboxylic acids',  \n",
    "    'Glutamic acid': 'Amino acids', \n",
    "    'SCCA': 'Carboxylic acids', \n",
    "    'Methane': 'Methane', \n",
    "    'SCOA': 'Carboxylic acids'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "product_map = {\n",
    "    'Antilisterial compounds': 'Antibiotics', \n",
    "    '7-methylxanthine': 'Pharmaceuticals', \n",
    "    'L-lysine': 'Amino acids', \n",
    "    'Fructo-oligosaccharide': 'Polysaccharides', \n",
    "    'Caproate': 'Carboxylic acids and esters', \n",
    "    'Glycosides': 'Other', \n",
    "    'coniferyl alcohol': 'Phenols', \n",
    "    'O-methylated phenylpropanoids': 'Pharmaceuticals', \n",
    "    'MCFA': 'Carboxylic acids and esters', \n",
    "    'Hydrogen': 'Hydorgen gas', \n",
    "    'caffeyl alcohol': 'Phenols', \n",
    "    '3-hydroxypropionic acid': 'Carboxylic acids and esters', \n",
    "    'Polysaccharide': 'Polysaccharides', \n",
    "    'Polyhydroxyalkanoate': 'Polyester polymers', \n",
    "    'Bacterial cellulose': 'Cellulose', \n",
    "    'Indigo': 'Dyes and pigments', \n",
    "    'Anthocyanins': 'Dyes and pigments', \n",
    "    'SCFA': 'Carboxylic acids and esters', \n",
    "    '3-hydroxybenzoic acid': 'Phenols', \n",
    "    'lovastatin': 'Pharmaceuticals', \n",
    "    'Antibodies': 'Proteins', \n",
    "    'Rosmarinic acid': 'Phenols', \n",
    "    'Butanol': 'Primary alcohols', \n",
    "    'Fumaric acid': 'Carboxylic acids and esters', \n",
    "    'Microbial proteases': 'Proteins', \n",
    "    'Recombinant protein': 'Proteins', \n",
    "    'Glucose_p': 'Metabolites to support growth', \n",
    "    'VFA': 'Carboxylic acids and esters', \n",
    "    'Ethanol': 'Primary alcohols', \n",
    "    'Pyranoanthocyanins': 'Phenols', \n",
    "    'Itaconic acid': 'Carboxylic acids and esters', \n",
    "    'P-coumaric acid': 'Carboxylic acids and esters', \n",
    "    'Methanol_p': 'Primary alcohols', \n",
    "    'Electricity': 'Electricity', \n",
    "    'Lipopeptides': 'Proteins', \n",
    "    'Phenol': 'Phenols', \n",
    "    'Biosurfactants': 'Other', \n",
    "    'Resveratrol': 'Phenols', \n",
    "    'Biomass': 'Biomass', \n",
    "    'Cercosporin': 'Pharmaceuticals', \n",
    "    'Pharmaceutically active compounds': 'Pharmaceuticals', \n",
    "    'Caffeic acid': 'Phenols', \n",
    "    'Vitamins': 'Metabolites to support growth', \n",
    "    'Butyrate': 'Carboxylic acids and esters', \n",
    "    'Flavonoids': 'Phenols', \n",
    "    'Monacolin': 'Pharmaceuticals', \n",
    "    'Glionitrin B': 'Pharmaceuticals', \n",
    "    'Hyaluronic acid': 'Pharmaceuticals', \n",
    "    '1,3-Propanediol': 'Glycols', \n",
    "    'Propionate': 'Carboxylic acids and esters', \n",
    "    'PHB': 'Polyester polymers', \n",
    "    'Butyl acetate': 'Carboxylic acids and esters', \n",
    "    'Fatty acid': 'Carboxylic acids and esters',\n",
    "    'Lactate': 'Carboxylic acids and esters', \n",
    "    'Caproic acid': 'Carboxylic acids and esters', \n",
    "    'Organic acids': 'Carboxylic acids and esters', \n",
    "    'Siderophores': 'Bacterial extracellular compounds', \n",
    "    'Bioflocculant': 'Bacterial extracellular compounds', \n",
    "    'Fermented food product': 'Fermented food products', \n",
    "    'Lactic acid': 'Carboxylic acids and esters',  \n",
    "    'Glutamic acid': 'Amino acids', \n",
    "    'SCCA': 'Carboxylic acids and esters', \n",
    "    'Methane': 'Methane', \n",
    "    'SCOA': 'Carboxylic acids and esters'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sankey visualisation of mapping to share with Daniel\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "def SankeyVisualisation(d):\n",
    "  # Get unique nodes and their indices\n",
    "  nodes = list(set(list(d.keys()) + list(d.values())))\n",
    "  node_indices = {node: i for i, node in enumerate(nodes)}\n",
    "\n",
    "  # Define link sources and targets\n",
    "  link_sources = [node_indices[key] for key in d.keys()]\n",
    "  link_targets = [node_indices[value] for value in d.values()]\n",
    "\n",
    "  # Define link values\n",
    "  link_values = [1]*len(d)\n",
    "\n",
    "  fig = go.Figure(data=[go.Sankey(\n",
    "      #orientation = \"h\",\n",
    "      node = dict(\n",
    "        pad = 15,\n",
    "        thickness = 40,\n",
    "        line = dict(color = \"black\", width = 0.5),\n",
    "        label = nodes\n",
    "      ),\n",
    "      link = dict(\n",
    "      arrowlen=20,\n",
    "        source = link_sources,\n",
    "        target = link_targets,\n",
    "        value = link_values\n",
    "    ))])\n",
    "\n",
    "  layout = dict(\n",
    "      #title = \"Substrate mapping\",\n",
    "      height = 1000,\n",
    "      font = dict(\n",
    "      size = 15),)\n",
    "\n",
    "  #show figure\n",
    "  fig.update_layout(layout)\n",
    "  fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "link": {
          "arrowlen": 20,
          "source": [
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          "target": [
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   "source": [
    "#new sankey chart for FAMILY of organisms\n",
    "\n",
    "#creating a new dataframe to use in building the sankey chart\n",
    "\n",
    "#create an empty dataframe\n",
    "sankey_data = pd.DataFrame({'Source': [],\n",
    "                   'Target': [],\n",
    "                   'Value': [],\n",
    "                   'Color': []})\n",
    "\n",
    "organism_colouring = dict()\n",
    "colour_counter = 0\n",
    "\n",
    "#iterate over the rows of the dataframe\n",
    "#for index, row in all_df.iterrows():\n",
    "for index, row in df.iterrows():\n",
    "\n",
    "    #get lists of substrates, products, and organisms\n",
    "    substrates = row[\"Substrate\"].split(\", \")\n",
    "    organisms = row[\"Family\"].split(\", \")\n",
    "    products = row[\"Product\"].split(\", \")\n",
    "\n",
    "    #taking the first word to get the genus instead of the organisms\n",
    "    genuses = [organism.split(' ')[0] for organism in organisms]\n",
    "    #cluster the substrates and products using dictionaries\n",
    "    new_substrates = [substrate_map[substrate] for substrate in substrates]\n",
    "    new_products = [product_map[product] for product in products]\n",
    "\n",
    "    #adds colour to each organism using the pick_colour function\n",
    "    for genus in genuses:\n",
    "        if genus not in organism_colouring:\n",
    "            organism_colouring[genus] = pick_colour(genus)\n",
    "\n",
    "    #add links from each substrate to each organism\n",
    "    for substrate in new_substrates:\n",
    "        for genus in genuses:\n",
    "            sankey_data.loc[len(sankey_data.index)] = [substrate, genus, 1, organism_colouring[genus]]\n",
    "\n",
    "    #add links from each organism to each substrate\n",
    "    for genus in genuses:\n",
    "        for product in new_products:\n",
    "            sankey_data.loc[len(sankey_data.index)] = [genus, product, 1, organism_colouring[genus]]\n",
    "\n",
    "\n",
    "#needed modification for building the plot\n",
    "\n",
    "#get each unique source_target and a mapping to their index\n",
    "#unique_source_target = list(pd.unique(sankey_data[['Source', 'Target']].values.ravel('K')))\n",
    "unique_source_target = list(pd.unique(sankey_data[['Source', 'Target', 'Color']].values.ravel('K')))\n",
    "mapping_dict = {k: v for v, k in enumerate(unique_source_target)}\n",
    "sankey_data['Source'] = sankey_data['Source'].map(mapping_dict)\n",
    "sankey_data['Target'] = sankey_data['Target'].map(mapping_dict)\n",
    "sankey_dict = sankey_data.to_dict(orient='list')\n",
    "\n",
    "#setting colours for the nodes\n",
    "node_colours = [organism_colouring.get(i, \"grey\") for i in unique_source_target]\n",
    "\n",
    "#create diagram\n",
    "import plotly.express as px\n",
    "\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    orientation = \"h\",\n",
    "    node = dict(\n",
    "      pad = 15,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"black\", width = 0.5),\n",
    "      label = unique_source_target,\n",
    "      #color = \"grey\"\n",
    "      color = node_colours\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = sankey_dict[\"Source\"],\n",
    "      target = sankey_dict[\"Target\"],\n",
    "      value = sankey_dict[\"Value\"],\n",
    "      color = sankey_dict[\"Color\"]\n",
    "  ))])\n",
    "\n",
    "layout = dict(\n",
    "        title = \"Sankey Diagram for consortia uses\",\n",
    "    height = 850,\n",
    "    font = dict(\n",
    "      size = 8),)\n",
    "\n",
    "#show figure\n",
    "fig.update_layout(layout)\n",
    "fig.show()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
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         "text": "Sankey Diagram for consortia uses"
        },
        "width": 1300
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     "metadata": {},
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   "source": [
    "#sankey chart for genus of organisms, with updated groupings\n",
    "\n",
    "#creating a new dataframe to use in building the sankey chart\n",
    "\n",
    "#create an empty dataframe\n",
    "sankey_data = pd.DataFrame({'Source': [],\n",
    "                   'Target': [],\n",
    "                   'Value': [],\n",
    "                   'Color': []})\n",
    "\n",
    "organism_colouring = dict()\n",
    "colour_counter = 0\n",
    "\n",
    "#iterate over the rows of the dataframe\n",
    "#for index, row in all_df.iterrows():\n",
    "for index, row in df.iterrows():\n",
    "\n",
    "    #get lists of substrates, products, and organisms\n",
    "    substrates = row[\"Substrate\"].split(\", \")\n",
    "    organisms = row[\"Organisms\"].split(\", \")\n",
    "    products = row[\"Product\"].split(\", \")\n",
    "\n",
    "    #taking the first word to get the genus instead of the organisms\n",
    "    genuses = [organism.split(' ')[0] for organism in organisms]\n",
    "    #cluster the substrates and products using dictionaries\n",
    "    new_substrates = [substrate_map[substrate] for substrate in substrates]\n",
    "    new_products = [product_map[product] for product in products]\n",
    "\n",
    "    #adds colour to each organism using the pick_colour function\n",
    "    for genus in genuses:\n",
    "        if genus not in organism_colouring:\n",
    "            organism_colouring[genus] = pick_colour(genus)\n",
    "\n",
    "    #add links from each substrate to each organism\n",
    "    for substrate in new_substrates:\n",
    "        for genus in genuses:\n",
    "            sankey_data.loc[len(sankey_data.index)] = [substrate, genus, 1, organism_colouring[genus]]\n",
    "\n",
    "    #add links from each organism to each substrate\n",
    "    for genus in genuses:\n",
    "        for product in new_products:\n",
    "            sankey_data.loc[len(sankey_data.index)] = [genus, product, 1, organism_colouring[genus]]\n",
    "\n",
    "\n",
    "#needed modification for building the plot\n",
    "\n",
    "#get each unique source_target and a mapping to their index\n",
    "#unique_source_target = list(pd.unique(sankey_data[['Source', 'Target']].values.ravel('K')))\n",
    "unique_source_target = list(pd.unique(sankey_data[['Source', 'Target', 'Color']].values.ravel('K')))\n",
    "mapping_dict = {k: v for v, k in enumerate(unique_source_target)}\n",
    "sankey_data['Source'] = sankey_data['Source'].map(mapping_dict)\n",
    "sankey_data['Target'] = sankey_data['Target'].map(mapping_dict)\n",
    "sankey_dict = sankey_data.to_dict(orient='list')\n",
    "\n",
    "#setting colours for the nodes\n",
    "node_colours = [organism_colouring.get(i, \"grey\") for i in unique_source_target]\n",
    "\n",
    "#create diagram\n",
    "import plotly.express as px\n",
    "\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    orientation = \"h\",\n",
    "    node = dict(\n",
    "      pad = 15,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"black\", width = 0.5),\n",
    "      label = unique_source_target,\n",
    "      #color = \"grey\"\n",
    "      color = node_colours\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = sankey_dict[\"Source\"],\n",
    "      target = sankey_dict[\"Target\"],\n",
    "      value = sankey_dict[\"Value\"],\n",
    "      color = sankey_dict[\"Color\"]\n",
    "  ))])\n",
    "\n",
    "layout = dict(\n",
    "        title = \"Sankey Diagram for consortia uses\",\n",
    "    height = 850,\n",
    "    width = 1300,\n",
    "    font = dict(\n",
    "      size = 8),)\n",
    "\n",
    "#show figure\n",
    "fig.update_layout(layout)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.write_image('plots/sankey_genus_grouped_ecoli.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Family\n",
       "Enterobacteriaceae        28\n",
       "Clostridiaceae            17\n",
       "Bacillaceae                7\n",
       "Saccharomycetaceae         7\n",
       "Hypocreaceae               5\n",
       "Eubacteriaceae             5\n",
       "Lactobacillaceae           3\n",
       "Acetobacteraceae           2\n",
       "Saccotheciaceae            2\n",
       "Aspergillaceae             2\n",
       "Enterococcaceae            2\n",
       "Veillonellaceae            2\n",
       "Dipodascaceae              1\n",
       "Phanerochaetaceae          1\n",
       "Thermoanaerobacterales     1\n",
       "Synechococcaceae           1\n",
       "Streptomycetaceae          1\n",
       "Streptococcaceae           1\n",
       "Sphingomonadaceae          1\n",
       "Shewanellaceae             1\n",
       "Schizophyllaceae           1\n",
       "Beijerinckiaceae           1\n",
       "Burkholderiaceae           1\n",
       "Rhizopodaceae              1\n",
       "Rhizobiaceae               1\n",
       "Pseudomonas                1\n",
       "Pseudomonadaceae           1\n",
       "Promicromonosporaceae      1\n",
       "Phaffomycetaceae           1\n",
       "Desulfuromonadaceae        1\n",
       "Pasteurellaceae            1\n",
       "Paenibacillaceae           1\n",
       "Oscillospiraceae           1\n",
       "Nitrobacteraceae           1\n",
       "Neocallimastigaceae        1\n",
       "Caulobacteraceae           1\n",
       "Moraxellaceae              1\n",
       "Methylocystaceae           1\n",
       "Corynebacteriaceae         1\n",
       "Lachnospiraceae            1\n",
       "Desulfobacteriaceae        1\n",
       "Halomonadaceae             1\n",
       "Geobacteraceae             1\n",
       "Fomitopsidaceae            1\n",
       "Mycosphaerellaceae         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plot showing most common organisms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "o_df = df.copy()\n",
    "\n",
    "o_df[\"Family\"] = o_df[\"Family\"].str.split(\", \")\n",
    "\n",
    "# Use the explode function to create a new row for each animal\n",
    "o_df = o_df.explode(\"Family\")\n",
    "\n",
    "#group\n",
    "organism_per_journal = o_df.groupby(\"Family\").size()\n",
    "\n",
    "#sort by decending order\n",
    "organism_per_journal = organism_per_journal.sort_values(ascending=False)\n",
    "\n",
    "organism_per_journal\n",
    "\n",
    "# #make the plot\n",
    "# organism_per_journal[:14].plot.bar()\n",
    "# plt.style.use('default')\n",
    "\n",
    "# #plt.setp(plt.gca().get_xticklabels(), rotation=60, ha=\"right\", va=\"top\")\n",
    "\n",
    "# plt.ylabel(\"Number of publications\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Thermoanaerobacterales'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/Projects/BIOTBT3900/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3803\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m   3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/Projects/BIOTBT3900/venv/lib/python3.10/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/Projects/BIOTBT3900/venv/lib/python3.10/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Thermoanaerobacterales'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/Users/andreastallvik/Projects/BIOTBT3900/literature search/sankey_diagram.ipynb Cell 18\u001b[0m in \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/andreastallvik/Projects/BIOTBT3900/literature%20search/sankey_diagram.ipynb#X30sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m organism_per_journal[\u001b[39m'\u001b[39;49m\u001b[39mThermoanaerobacterales\u001b[39;49m\u001b[39m'\u001b[39;49m]\n",
      "File \u001b[0;32m~/Projects/BIOTBT3900/venv/lib/python3.10/site-packages/pandas/core/series.py:981\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    978\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_values[key]\n\u001b[1;32m    980\u001b[0m \u001b[39melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m--> 981\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_value(key)\n\u001b[1;32m    983\u001b[0m \u001b[39mif\u001b[39;00m is_hashable(key):\n\u001b[1;32m    984\u001b[0m     \u001b[39m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[1;32m    985\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    986\u001b[0m         \u001b[39m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
      "File \u001b[0;32m~/Projects/BIOTBT3900/venv/lib/python3.10/site-packages/pandas/core/series.py:1089\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1086\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_values[label]\n\u001b[1;32m   1088\u001b[0m \u001b[39m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1089\u001b[0m loc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex\u001b[39m.\u001b[39;49mget_loc(label)\n\u001b[1;32m   1090\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindex\u001b[39m.\u001b[39m_get_values_for_loc(\u001b[39mself\u001b[39m, loc, label)\n",
      "File \u001b[0;32m~/Projects/BIOTBT3900/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3803\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   3806\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   3807\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3810\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Thermoanaerobacterales'"
     ]
    }
   ],
   "source": [
    "organism_per_journal['Thermoanaerobacterales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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