integrate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integrate resources to replicate hetionet (AlzKB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting hetio\n",
      "  Downloading hetio-0.2.11-py3-none-any.whl (30 kB)\n",
      "Requirement already satisfied: regex in /opt/anaconda3/lib/python3.9/site-packages (from hetio) (2022.7.9)\n",
      "Installing collected packages: hetio\n",
      "Successfully installed hetio-0.2.11\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#pip install hetio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/__init__.py:11: FutureWarning: \n",
      "PACKAGE HAS BEEN RELOCATED:\n",
      "v0.2.11 is the last release before the hetio package is renamed to hetnetpy.\n",
      "Consider switching to the hetnetpy package or specifying hetio<=0.2.10 to silence this warning.\n",
      "Future releases and development will only occur for the hetnetpy package.\n",
      "\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "import seaborn\n",
    "\n",
    "import hetio.hetnet\n",
    "import hetio.readwrite\n",
    "import hetio.stats\n",
    "\n",
    "#from utils import rawgit, obo_iri\n",
    "def obo_iri(identifier):\n",
    "    return 'http://purl.obolibrary.org/obo/' + identifier.replace(':', '_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the metagraph and instantiate the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "kind_to_abbev = {\n",
    "    \n",
    "    # metanodes\n",
    "    'Compound': 'C',\n",
    "    'Disease': 'D',\n",
    "    'Gene': 'G',\n",
    "    'Anatomy': 'A',\n",
    "    'Symptom': 'S',\n",
    "    #'Side Effect': 'SE',\n",
    "    #'Pathway': 'PW',\n",
    "    'Pharmacologic Class': 'PC',\n",
    "    'Biological Process': 'BP',\n",
    "    'Cellular Component': 'CC',\n",
    "    'Molecular Function': 'MF',\n",
    "    \n",
    "    # metaedges\n",
    "    'treats': 't',\n",
    "    'palliates': 'p',\n",
    "    'binds': 'b',\n",
    "    'expresses': 'e',\n",
    "    'regulates': 'r',\n",
    "    'upregulates': 'u',\n",
    "    'downregulates': 'd',\n",
    "    'interacts': 'i',\n",
    "    'includes': 'i',\n",
    "    'covaries': 'c',\n",
    "    'regulates': 'r',\n",
    "    'participates': 'p',\n",
    "    'resembles': 'r',\n",
    "    'associates': 'a',\n",
    "    'localizes': 'l',\n",
    "    'presents': 'p',\n",
    "    'causes': 'c',\n",
    "}\n",
    "\n",
    "metaedge_tuples = [ #only those we're updating this time\n",
    "    #('Compound', 'Disease', 'treats', 'both'),\n",
    "    #('Compound', 'Disease', 'palliates', 'both'),\n",
    "    ('Compound', 'Gene', 'binds', 'both'),\n",
    "    #('Compound', 'Gene', 'upregulates', 'both'),\n",
    "    #('Compound', 'Gene', 'downregulates', 'both'),\n",
    "    #('Compound', 'Compound', 'resembles', 'both'),\n",
    "    #('Compound', 'Side Effect', 'causes', 'both'),\n",
    "    ('Pharmacologic Class', 'Compound', 'includes', 'both'),\n",
    "    #('Anatomy', 'Gene', 'expresses', 'both'),\n",
    "    #('Anatomy', 'Gene', 'upregulates', 'both'),\n",
    "    #('Anatomy', 'Gene', 'downregulates', 'both'),\n",
    "    #('Gene', 'Gene', 'interacts', 'both'),\n",
    "    #('Gene', 'Gene', 'covaries', 'both'),\n",
    "    #('Gene', 'Gene', 'regulates', 'forward'),\n",
    "    #('Gene', 'Pathway', 'participates', 'both'),\n",
    "    ('Gene', 'Biological Process', 'participates', 'both'),\n",
    "    ('Gene', 'Cellular Component', 'participates', 'both'),\n",
    "    ('Gene', 'Molecular Function', 'participates', 'both'),\n",
    "    ('Disease', 'Disease', 'resembles', 'both'),\n",
    "    #('Disease', 'Gene', 'associates', 'both'),\n",
    "    #('Disease', 'Gene', 'upregulates', 'both'),\n",
    "    #('Disease', 'Gene', 'downregulates', 'both'),\n",
    "    ('Disease', 'Anatomy', 'localizes', 'both'),\n",
    "    ('Disease', 'Symptom', 'presents', 'both'),\n",
    "]\n",
    "metagraph = hetio.hetnet.MetaGraph.from_edge_tuples(metaedge_tuples, kind_to_abbev)\n",
    "graph = hetio.hetnet.Graph(metagraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "path ='../data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tax_id</th>\n",
       "      <th>GeneID</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>map_location</th>\n",
       "      <th>type_of_gene</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9606</td>\n",
       "      <td>1</td>\n",
       "      <td>A1BG</td>\n",
       "      <td>19</td>\n",
       "      <td>19q13.43</td>\n",
       "      <td>protein-coding</td>\n",
       "      <td>alpha-1-B glycoprotein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9606</td>\n",
       "      <td>2</td>\n",
       "      <td>A2M</td>\n",
       "      <td>12</td>\n",
       "      <td>12p13.31</td>\n",
       "      <td>protein-coding</td>\n",
       "      <td>alpha-2-macroglobulin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tax_id  GeneID Symbol chromosome map_location    type_of_gene  \\\n",
       "0    9606       1   A1BG         19     19q13.43  protein-coding   \n",
       "1    9606       2    A2M         12     12p13.31  protein-coding   \n",
       "\n",
       "              description  \n",
       "0  alpha-1-B glycoprotein  \n",
       "1   alpha-2-macroglobulin  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'EntrezGene/genes-human.tsv')\n",
    "gene_df = pandas.read_table(url)\n",
    "gene_df = gene_df[gene_df.type_of_gene == 'protein-coding']\n",
    "coding_genes = set(gene_df.GeneID)\n",
    "gene_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in gene_df.iterrows():\n",
    "    if row.type_of_gene != 'protein-coding':\n",
    "        continue\n",
    "    data = {\n",
    "        'description': row['description'],\n",
    "        'source': 'Entrez Gene',\n",
    "        'url': 'http://identifiers.org/ncbigene/{}'.format(row.GeneID),\n",
    "        'license': 'CC0 1.0',\n",
    "    }\n",
    "    if pandas.notnull(row['chromosome']):\n",
    "        data['chromosome'] = row['chromosome']\n",
    "    graph.add_node(kind = 'Gene', identifier=row.GeneID, name=row.Symbol, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disease Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doid</th>\n",
       "      <th>name</th>\n",
       "      <th>source</th>\n",
       "      <th>pathophysiology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:2531</td>\n",
       "      <td>hematologic cancer</td>\n",
       "      <td>DOcancerslim</td>\n",
       "      <td>neoplastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:1319</td>\n",
       "      <td>brain cancer</td>\n",
       "      <td>DOcancerslim</td>\n",
       "      <td>neoplastic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        doid                name        source pathophysiology\n",
       "0  DOID:2531  hematologic cancer  DOcancerslim      neoplastic\n",
       "1  DOID:1319        brain cancer  DOcancerslim      neoplastic"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'DiseaseOntology/slim-terms.tsv')\n",
    "disease_df = pandas.read_table(url)\n",
    "disease_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in disease_df.iterrows():\n",
    "    data = {\n",
    "        'source': 'Disease Ontology',\n",
    "        'url': obo_iri(row.doid),\n",
    "        'license': 'CC BY 3.0',\n",
    "    }\n",
    "    graph.add_node(kind='Disease', identifier=row.doid, name=row['name'], data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compound Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drugbank_id</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>groups</th>\n",
       "      <th>atc_codes</th>\n",
       "      <th>categories</th>\n",
       "      <th>inchikey</th>\n",
       "      <th>inchi</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>small molecule</td>\n",
       "      <td>approved|investigational</td>\n",
       "      <td>B01AE06</td>\n",
       "      <td>Amino Acids, Peptides, and Proteins|Anticoagul...</td>\n",
       "      <td>OIRCOABEOLEUMC-GEJPAHFPSA-N</td>\n",
       "      <td>InChI=1S/C98H138N24O33/c1-5-52(4)82(96(153)122...</td>\n",
       "      <td>Bivalirudin is a synthetic 20 residue peptide ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB00007</td>\n",
       "      <td>Leuprolide</td>\n",
       "      <td>small molecule</td>\n",
       "      <td>approved|investigational</td>\n",
       "      <td>L02AE51|L02AE02</td>\n",
       "      <td>Adrenal Cortex Hormones|Agents Causing Muscle ...</td>\n",
       "      <td>GFIJNRVAKGFPGQ-LIJARHBVSA-N</td>\n",
       "      <td>InChI=1S/C59H84N16O12/c1-6-63-57(86)48-14-10-2...</td>\n",
       "      <td>Leuprolide is a synthetic 9-residue peptide an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drugbank_id         name            type                    groups  \\\n",
       "0     DB00006  Bivalirudin  small molecule  approved|investigational   \n",
       "1     DB00007   Leuprolide  small molecule  approved|investigational   \n",
       "\n",
       "         atc_codes                                         categories  \\\n",
       "0          B01AE06  Amino Acids, Peptides, and Proteins|Anticoagul...   \n",
       "1  L02AE51|L02AE02  Adrenal Cortex Hormones|Agents Causing Muscle ...   \n",
       "\n",
       "                      inchikey  \\\n",
       "0  OIRCOABEOLEUMC-GEJPAHFPSA-N   \n",
       "1  GFIJNRVAKGFPGQ-LIJARHBVSA-N   \n",
       "\n",
       "                                               inchi  \\\n",
       "0  InChI=1S/C98H138N24O33/c1-5-52(4)82(96(153)122...   \n",
       "1  InChI=1S/C59H84N16O12/c1-6-63-57(86)48-14-10-2...   \n",
       "\n",
       "                                         description  \n",
       "0  Bivalirudin is a synthetic 20 residue peptide ...  \n",
       "1  Leuprolide is a synthetic 9-residue peptide an...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'DrugBank/drugbank-slim.tsv')\n",
    "compound_df = pandas.read_table(url)\n",
    "compound_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in compound_df.iterrows():\n",
    "    url = 'http://www.drugbank.ca/drugs/' + row.drugbank_id\n",
    "    data = {\n",
    "        'source': 'DrugBank',\n",
    "        'inchikey': row.inchikey,\n",
    "        'inchi': row.inchi, 'url': url,\n",
    "        'license': 'CC BY-NC 4.0',\n",
    "    }\n",
    "    graph.add_node(kind='Compound', identifier=row.drugbank_id, name=row['name'], data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anotomy nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uberon_id</th>\n",
       "      <th>uberon_name</th>\n",
       "      <th>mesh_id</th>\n",
       "      <th>mesh_name</th>\n",
       "      <th>bto_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UBERON:0000002</td>\n",
       "      <td>uterine cervix</td>\n",
       "      <td>D002584</td>\n",
       "      <td>Cervix Uteri</td>\n",
       "      <td>BTO:0001421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UBERON:0000004</td>\n",
       "      <td>nose</td>\n",
       "      <td>D009666</td>\n",
       "      <td>Nose</td>\n",
       "      <td>BTO:0000840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        uberon_id     uberon_name  mesh_id     mesh_name       bto_id\n",
       "0  UBERON:0000002  uterine cervix  D002584  Cervix Uteri  BTO:0001421\n",
       "1  UBERON:0000004            nose  D009666          Nose  BTO:0000840"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'Uberon/hetio-slim.tsv')\n",
    "uberon_df = pandas.read_table(url)\n",
    "uberon_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in uberon_df.iterrows():\n",
    "    data = {\n",
    "        'source': 'Uberon',\n",
    "        'url': obo_iri(row['uberon_id']),\n",
    "        'license': 'CC BY 3.0',\n",
    "    }\n",
    "    for xref in 'mesh_id', 'bto_id':\n",
    "        if pandas.notnull(row[xref]):\n",
    "            data[xref] = row[xref]\n",
    "    graph.add_node(kind='Anatomy', identifier=row['uberon_id'], name=row['uberon_name'], data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Symptom Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mesh_id</th>\n",
       "      <th>mesh_name</th>\n",
       "      <th>in_hsdn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D000006</td>\n",
       "      <td>Abdomen, Acute</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D000067329</td>\n",
       "      <td>Obesity, Metabolically Benign</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mesh_id                      mesh_name  in_hsdn\n",
       "0     D000006                 Abdomen, Acute        1\n",
       "1  D000067329  Obesity, Metabolically Benign        0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'MeSH/symptoms.tsv')\n",
    "symptom_df = pandas.read_table(url)\n",
    "symptom_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in symptom_df.iterrows():\n",
    "    url = 'http://identifiers.org/mesh/{}'.format(row.mesh_id)\n",
    "    data = {\n",
    "        'source': 'MeSH',\n",
    "        'url': url,\n",
    "        'license': 'CC0 1.0',\n",
    "    }\n",
    "    graph.add_node(kind='Symptom', identifier=row.mesh_id, name=row.mesh_name, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pharmacologic Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_id</th>\n",
       "      <th>class_name</th>\n",
       "      <th>class_source</th>\n",
       "      <th>class_type</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>M0000499</td>\n",
       "      <td>Adrenocorticotropic Hormone</td>\n",
       "      <td>FDA</td>\n",
       "      <td>Chemical Structure</td>\n",
       "      <td>http://purl.bioontology.org/ontology/NDFRT/M00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>M0000713</td>\n",
       "      <td>Alkaloids</td>\n",
       "      <td>FDA</td>\n",
       "      <td>Chemical Structure</td>\n",
       "      <td>http://purl.bioontology.org/ontology/NDFRT/M00...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     class_id                   class_name class_source          class_type  \\\n",
       "639  M0000499  Adrenocorticotropic Hormone          FDA  Chemical Structure   \n",
       "640  M0000713                    Alkaloids          FDA  Chemical Structure   \n",
       "\n",
       "                                                   url  \n",
       "639  http://purl.bioontology.org/ontology/NDFRT/M00...  \n",
       "640  http://purl.bioontology.org/ontology/NDFRT/M00...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'DrugCentral/classes.tsv')\n",
    "class_df = pandas.read_table(url)\n",
    "class_types = {'Physiologic Effect', 'Mechanism of Action', 'Chemical/Ingredient', 'Chemical Structure'}\n",
    "class_df = class_df.query(\"class_type in @class_types\")\n",
    "class_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in class_df.itertuples():\n",
    "    data = {\n",
    "        'source': '{} via DrugCentral'.format(row.class_source),\n",
    "        'class_type': row.class_type,\n",
    "        'license': 'CC BY 4.0',\n",
    "        'url': row.url,\n",
    "    }\n",
    "    graph.add_node(kind='Pharmacologic Class', identifier=row.class_id, name=row.class_name, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drugbank_id</th>\n",
       "      <th>drugbank_name</th>\n",
       "      <th>class_id</th>\n",
       "      <th>class_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB11791</td>\n",
       "      <td>Capmatinib</td>\n",
       "      <td>N0000182138</td>\n",
       "      <td>Cytochrome P450 1A2 Inhibitors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DB11791</td>\n",
       "      <td>Capmatinib</td>\n",
       "      <td>N0000185503</td>\n",
       "      <td>P-Glycoprotein Inhibitors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drugbank_id drugbank_name     class_id                      class_name\n",
       "1     DB11791    Capmatinib  N0000182138  Cytochrome P450 1A2 Inhibitors\n",
       "2     DB11791    Capmatinib  N0000185503       P-Glycoprotein Inhibitors"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'DrugCentral/drug-to-class.tsv')\n",
    "drug_class_df = pandas.read_table(url)\n",
    "drug_class_df = drug_class_df.query(\"class_id in @class_df.class_id\")\n",
    "drug_class_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in drug_class_df.itertuples():\n",
    "    data = {\n",
    "        'source': 'DrugCentral',\n",
    "        'license': 'CC BY 4.0',\n",
    "        'unbiased': False,\n",
    "    }\n",
    "    source_id = 'Pharmacologic Class', row.class_id\n",
    "    target_id = 'Compound', row.drugbank_id\n",
    "    graph.add_edge(source_id, target_id, 'includes', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene Ontology Domains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>go_id</th>\n",
       "      <th>go_name</th>\n",
       "      <th>go_domain</th>\n",
       "      <th>tax_id</th>\n",
       "      <th>annotation_type</th>\n",
       "      <th>size</th>\n",
       "      <th>gene_ids</th>\n",
       "      <th>gene_symbols</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GO:0000002</td>\n",
       "      <td>mitochondrial genome maintenance</td>\n",
       "      <td>biological_process</td>\n",
       "      <td>9606</td>\n",
       "      <td>inferred</td>\n",
       "      <td>26</td>\n",
       "      <td>142|291|1763|1890|2021|4205|4358|4976|5428|624...</td>\n",
       "      <td>PARP1|SLC25A4|DNA2|TYMP|ENDOG|MEF2A|MPV17|OPA1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO:0000009</td>\n",
       "      <td>alpha-1,6-mannosyltransferase activity</td>\n",
       "      <td>molecular_function</td>\n",
       "      <td>9606</td>\n",
       "      <td>inferred</td>\n",
       "      <td>2</td>\n",
       "      <td>55650|79087</td>\n",
       "      <td>PIGV|ALG12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        go_id                                 go_name           go_domain  \\\n",
       "0  GO:0000002        mitochondrial genome maintenance  biological_process   \n",
       "1  GO:0000009  alpha-1,6-mannosyltransferase activity  molecular_function   \n",
       "\n",
       "   tax_id annotation_type  size  \\\n",
       "0    9606        inferred    26   \n",
       "1    9606        inferred     2   \n",
       "\n",
       "                                            gene_ids  \\\n",
       "0  142|291|1763|1890|2021|4205|4358|4976|5428|624...   \n",
       "1                                        55650|79087   \n",
       "\n",
       "                                        gene_symbols  \n",
       "0  PARP1|SLC25A4|DNA2|TYMP|ENDOG|MEF2A|MPV17|OPA1...  \n",
       "1                                         PIGV|ALG12  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'GeneOntology/GO_annotations-9606-inferred-allev.tsv')\n",
    "go_df = pandas.read_table(url)\n",
    "go_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in go_df.iterrows():\n",
    "    genes = coding_genes & set(map(int, row.gene_ids.split('|')))\n",
    "    if 2 > len(genes) or len(genes) > 1000:\n",
    "        continue\n",
    "    kind = row['go_domain'].replace('_', ' ').title()\n",
    "    data = {'source': 'Gene Ontology', 'url': obo_iri(row.go_id), 'license': 'CC BY 4.0'}\n",
    "    target = graph.add_node(kind=kind, identifier=row['go_id'], name=row['go_name'], data=data)\n",
    "    target_id = target.get_id()\n",
    "    for gene in genes:\n",
    "        source_id = 'Gene', gene\n",
    "        data = {'source': 'NCBI gene2go', 'unbiased': False, 'license': 'CC BY 4.0'}\n",
    "        graph.add_edge(source_id, target_id, 'participates', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MEDLINE cooccurrence data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "medline_data = {\n",
    "    'source': 'MEDLINE cooccurrence',\n",
    "    'unbiased': False,\n",
    "    'license': 'CC0 1.0',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Symptom edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doid_code</th>\n",
       "      <th>doid_name</th>\n",
       "      <th>mesh_id</th>\n",
       "      <th>mesh_name</th>\n",
       "      <th>cooccurrence</th>\n",
       "      <th>expected</th>\n",
       "      <th>enrichment</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>p_fisher</th>\n",
       "      <th>n_source</th>\n",
       "      <th>n_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>D000647</td>\n",
       "      <td>Amnesia</td>\n",
       "      <td>489</td>\n",
       "      <td>19.641482</td>\n",
       "      <td>24.896288</td>\n",
       "      <td>48.294582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10199</td>\n",
       "      <td>1042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>D004314</td>\n",
       "      <td>Down Syndrome</td>\n",
       "      <td>1102</td>\n",
       "      <td>56.021580</td>\n",
       "      <td>19.670991</td>\n",
       "      <td>34.268557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10199</td>\n",
       "      <td>2972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    doid_code            doid_name  mesh_id      mesh_name  cooccurrence  \\\n",
       "0  DOID:10652  Alzheimer's disease  D000647        Amnesia           489   \n",
       "1  DOID:10652  Alzheimer's disease  D004314  Down Syndrome          1102   \n",
       "\n",
       "    expected  enrichment  odds_ratio  p_fisher  n_source  n_target  \n",
       "0  19.641482   24.896288   48.294582       0.0     10199      1042  \n",
       "1  56.021580   19.670991   34.268557       0.0     10199      2972  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'MEDLINE/disease-symptom-cooccurrence.tsv')\n",
    "disease_symptom_df = pandas.read_table(url)\n",
    "disease_symptom_df = disease_symptom_df[disease_symptom_df.p_fisher < 0.005]\n",
    "disease_symptom_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in disease_symptom_df.iterrows():\n",
    "    source_id = 'Disease', row.doid_code\n",
    "    target_id = 'Symptom', row.mesh_id\n",
    "    data = medline_data.copy()\n",
    "    graph.add_edge(source_id, target_id, 'presents', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disease-localization edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doid_code</th>\n",
       "      <th>doid_name</th>\n",
       "      <th>uberon_id</th>\n",
       "      <th>uberon_name</th>\n",
       "      <th>cooccurrence</th>\n",
       "      <th>expected</th>\n",
       "      <th>enrichment</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>p_fisher</th>\n",
       "      <th>n_source</th>\n",
       "      <th>n_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>UBERON:0000955</td>\n",
       "      <td>brain</td>\n",
       "      <td>21745</td>\n",
       "      <td>3071.649615</td>\n",
       "      <td>7.079258</td>\n",
       "      <td>27.695942</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30744</td>\n",
       "      <td>97944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>UBERON:0000956</td>\n",
       "      <td>cerebral cortex</td>\n",
       "      <td>3590</td>\n",
       "      <td>594.986283</td>\n",
       "      <td>6.033753</td>\n",
       "      <td>8.029408</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30744</td>\n",
       "      <td>18972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    doid_code            doid_name       uberon_id      uberon_name  \\\n",
       "0  DOID:10652  Alzheimer's disease  UBERON:0000955            brain   \n",
       "1  DOID:10652  Alzheimer's disease  UBERON:0000956  cerebral cortex   \n",
       "\n",
       "   cooccurrence     expected  enrichment  odds_ratio  p_fisher  n_source  \\\n",
       "0         21745  3071.649615    7.079258   27.695942       0.0     30744   \n",
       "1          3590   594.986283    6.033753    8.029408       0.0     30744   \n",
       "\n",
       "   n_target  \n",
       "0     97944  \n",
       "1     18972  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'MEDLINE/disease-uberon-cooccurrence.tsv')\n",
    "disease_anatomy_df = pandas.read_table(url)\n",
    "disease_anatomy_df = disease_anatomy_df[disease_anatomy_df.p_fisher < 0.005]\n",
    "disease_anatomy_df = disease_anatomy_df[disease_anatomy_df.uberon_id.isin(uberon_df['uberon_id'])]\n",
    "disease_anatomy_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in disease_anatomy_df.iterrows():\n",
    "    source_id = 'Disease', row.doid_code\n",
    "    target_id = 'Anatomy', row.uberon_id\n",
    "    data = medline_data.copy()\n",
    "    graph.add_edge(source_id, target_id, 'localizes', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disease-disease similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doid_code_0</th>\n",
       "      <th>doid_name_0</th>\n",
       "      <th>doid_code_1</th>\n",
       "      <th>doid_name_1</th>\n",
       "      <th>cooccurrence</th>\n",
       "      <th>expected</th>\n",
       "      <th>enrichment</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>p_fisher</th>\n",
       "      <th>n_source</th>\n",
       "      <th>n_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>DOID:14330</td>\n",
       "      <td>Parkinson's disease</td>\n",
       "      <td>2521</td>\n",
       "      <td>1562.065225</td>\n",
       "      <td>1.613889</td>\n",
       "      <td>1.651428</td>\n",
       "      <td>8.873145e-115</td>\n",
       "      <td>104084</td>\n",
       "      <td>73639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>DOID:11949</td>\n",
       "      <td>Creutzfeldt-Jakob disease</td>\n",
       "      <td>157</td>\n",
       "      <td>116.223134</td>\n",
       "      <td>1.350850</td>\n",
       "      <td>1.361746</td>\n",
       "      <td>1.558560e-04</td>\n",
       "      <td>104084</td>\n",
       "      <td>5479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  doid_code_0          doid_name_0 doid_code_1                doid_name_1  \\\n",
       "0  DOID:10652  Alzheimer's disease  DOID:14330        Parkinson's disease   \n",
       "1  DOID:10652  Alzheimer's disease  DOID:11949  Creutzfeldt-Jakob disease   \n",
       "\n",
       "   cooccurrence     expected  enrichment  odds_ratio       p_fisher  n_source  \\\n",
       "0          2521  1562.065225    1.613889    1.651428  8.873145e-115    104084   \n",
       "1           157   116.223134    1.350850    1.361746   1.558560e-04    104084   \n",
       "\n",
       "   n_target  \n",
       "0     73639  \n",
       "1      5479  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'MEDLINE/disease-disease-cooccurrence.tsv')\n",
    "disease_similarity_df = pandas.read_table(url)\n",
    "disease_similarity_df = disease_similarity_df[-disease_similarity_df[['doid_code_0', 'doid_code_1']].apply(frozenset, 1).duplicated()]\n",
    "disease_similarity_df = disease_similarity_df[disease_similarity_df.p_fisher < 0.005]\n",
    "disease_similarity_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doid_code_0</th>\n",
       "      <th>doid_name_0</th>\n",
       "      <th>doid_code_1</th>\n",
       "      <th>doid_name_1</th>\n",
       "      <th>cooccurrence</th>\n",
       "      <th>expected</th>\n",
       "      <th>enrichment</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>p_fisher</th>\n",
       "      <th>n_source</th>\n",
       "      <th>n_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>DOID:14330</td>\n",
       "      <td>Parkinson's disease</td>\n",
       "      <td>2521</td>\n",
       "      <td>1562.065225</td>\n",
       "      <td>1.613889</td>\n",
       "      <td>1.651428</td>\n",
       "      <td>8.873145e-115</td>\n",
       "      <td>104084</td>\n",
       "      <td>73639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>DOID:11949</td>\n",
       "      <td>Creutzfeldt-Jakob disease</td>\n",
       "      <td>157</td>\n",
       "      <td>116.223134</td>\n",
       "      <td>1.350850</td>\n",
       "      <td>1.361746</td>\n",
       "      <td>1.558560e-04</td>\n",
       "      <td>104084</td>\n",
       "      <td>5479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  doid_code_0          doid_name_0 doid_code_1                doid_name_1  \\\n",
       "0  DOID:10652  Alzheimer's disease  DOID:14330        Parkinson's disease   \n",
       "1  DOID:10652  Alzheimer's disease  DOID:11949  Creutzfeldt-Jakob disease   \n",
       "\n",
       "   cooccurrence     expected  enrichment  odds_ratio       p_fisher  n_source  \\\n",
       "0          2521  1562.065225    1.613889    1.651428  8.873145e-115    104084   \n",
       "1           157   116.223134    1.350850    1.361746   1.558560e-04    104084   \n",
       "\n",
       "   n_target  \n",
       "0     73639  \n",
       "1      5479  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_similarity_df[disease_similarity_df['doid_name_0']==\"Alzheimer's disease\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in disease_similarity_df.iterrows():\n",
    "    source_id = 'Disease', row.doid_code_0\n",
    "    target_id = 'Disease', row.doid_code_1\n",
    "    data = medline_data.copy()\n",
    "    graph.add_edge(source_id, target_id, 'resembles', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compound bindings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drugbank_id</th>\n",
       "      <th>entrez_gene_id</th>\n",
       "      <th>sources</th>\n",
       "      <th>pubmed_ids</th>\n",
       "      <th>actions</th>\n",
       "      <th>affinity_nM</th>\n",
       "      <th>license</th>\n",
       "      <th>urls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>2147</td>\n",
       "      <td>DrugBank (target)|DrugCentral (IUPHAR)</td>\n",
       "      <td>11060732|11504570|11752352|11833835|11923794|1...</td>\n",
       "      <td>inhibitor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC BY-NC 4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>4353</td>\n",
       "      <td>DrugBank (enzyme)</td>\n",
       "      <td>18701766</td>\n",
       "      <td>inhibitor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC BY-NC 4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drugbank_id  entrez_gene_id                                 sources  \\\n",
       "0     DB00006            2147  DrugBank (target)|DrugCentral (IUPHAR)   \n",
       "1     DB00006            4353                       DrugBank (enzyme)   \n",
       "\n",
       "                                          pubmed_ids    actions  affinity_nM  \\\n",
       "0  11060732|11504570|11752352|11833835|11923794|1...  inhibitor          NaN   \n",
       "1                                           18701766  inhibitor          NaN   \n",
       "\n",
       "        license urls  \n",
       "0  CC BY-NC 4.0  NaN  \n",
       "1  CC BY-NC 4.0  NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'BindingDB/CbG-binding.tsv')\n",
    "binding_df = pandas.read_table(url)\n",
    "binding_df = binding_df.merge(compound_df[['drugbank_id']])\n",
    "binding_df = binding_df[binding_df.entrez_gene_id.isin(coding_genes)]\n",
    "binding_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in binding_df.iterrows():\n",
    "    source_id = 'Compound', row.drugbank_id\n",
    "    target_id = 'Gene', row.entrez_gene_id\n",
    "    data = {'unbiased': False}\n",
    "    # singular fields\n",
    "    for key in 'affinity_nM', 'license':\n",
    "        value = row[key]\n",
    "        if pandas.notnull(value):\n",
    "            data[key] = value\n",
    "    # compound fields\n",
    "    for key in 'sources', 'pubmed_ids', 'actions', 'urls':\n",
    "        value = row[key]\n",
    "        if pandas.notnull(value):\n",
    "            data[key] = value.split('|')\n",
    "    graph.add_edge(source_id, target_id, 'binds', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network visualizations and stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export node degree tables\n",
    "hetio.stats.degrees_to_excel(graph, os.path.join(path,'degrees.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V2NjYWEVGRlrrv/jFL6qts3PnzlUeVj0ziJ09SJxp5+rVq6vU5ePj47R+dj/PvB9n9q+srNSsWbM0ZsyYKm1p2rRptW0E3IVjZMMdI8/XlmbNmtWqHRfq/8X0rbKyUh4eHsrKypKHh4fTthYtWkiSfvvb3yo6OlqrV6/WunXrlJKSojlz5ighIaFKfbV5b7p27ap33nlHHh4eCgkJqXIMlc5/HJ0zZ47mzZun+fPnKzw8XL6+vkpMTFRZWZlTHWf/Y36+ui/0Ozt69GiFhoZq0aJFCgkJUWVlpcLCwqzXu5jP79zf6fP9nl/M30BNddaktr9jZ5LcnJwc9e7d+6L3+8c//qFHH31Uc+bMUVRUlPz8/PTiiy9q+/btVsySJUs0depUZWRk6K233tIf/vAHZWZmql+/fkpOTlZcXJxWr16t999/X08//bTS0tJ0xx131Kr9l4IrFqjC399f0dHR+tvf/qbTp09X2f7dd99J+unM27Fjx5zOrn/yySdyOBzq1q2by+3Ytm1blfVrr73Weu3s7Gyn9v3rX/9SkyZNrD/kNm3aOGXwFRUV2r9/f63a0L17d/373/9WSUlJje06l7+/vzp16mQtnp415+/V1d2iRQtdffXVtWpnbdvbpk0b3X///Vq+fLnmz5+v119/vdr6/Pz8nPpS00H1nnvuUWZmpvbs2XPB9vn4+OjYsWNO9Xbq1MnprO6FXHfddcrNza1SR6dOnZzO8gJ1gWPk/6nrY2Rt9OzZUydOnNDBgwcvKv7c/kty+h6BTp06ycvLy6k/hYWFTvX36dNHFRUVKigoqHIsCg4OtuJCQ0M1efJkrVy5UklJSVq0aFG1barNe+Pt7a1OnTqpQ4cO1SYVF/Lxxx/rtttu03333adevXqpY8eOOnToUK3ruRjffPONcnJy9Ic//EFDhw5Vt27dVFhY6BTTs2dPZWdn69tvv622jm7dumnz5s1OZVu2bKnxb+lCfwN2u11BQUHasWOHtf3MDFs1CQ8PV2Vl5UXP5NS7d291795dc+bMqfbEwJljxbk+/vhj9e/fX1OmTFGfPn3UqVOnaq869OnTRzNnztSWLVsUFhamFStWWNu6dOmiRx99VOvWrdOYMWO0ZMmSi2qzqxiBUa1XXnlFFRUVuuGGG/T222/r0KFDysnJ0V//+lfrsvCwYcPUs2dP3Xvvvdq9e7d27NihcePGadCgQTVeOq2N//mf/9Hf//53HTx4UE8//bR27Nihhx9+WJJ07733qmnTpho/frz279+vjz76SAkJCYqPj1dQUJAk6eabb9bq1au1evVqffrpp5oyZUqNf8Q1iYuLU5MmTTRhwgR98sknWrNmTbVn9i9VWVmZVfeZswoPP/zwJf9zfDHt/eMf/6h//vOfOnz4sA4cOKD33nvP5X9yHn30UUVFRenmm2/WSy+9pN27dysvL09r167V+++/b53J8/Pz0/Tp0/Xoo49q6dKlOnLkiPbs2aO//e1vWrp06UW/3h//+Ee98cYbSk5O1oEDB5STk2OdsQHqA8fIn9T1MbI2Bg0apIEDB+pXv/qVMjMzlZeXp/fff18ZGRnVxt98883atWuX3njjDR06dEhPP/20U2LVokULTZgwQTNmzNAHH3yg/fv36/7773c6Pnfp0kX33nuvxo0bp5UrVyovL087d+7U888/rzVr1kiSEhMTtXbtWuXl5Wn37t368MMP3ZJYuqpTp07KzMzUli1blJOTo0mTJik/P79OXqtVq1YKCAjQ66+/rsOHD+vDDz/UtGnTnGLuueceBQcH6/bbb9e//vUvffbZZ3r77be1detWSdKMGTOUmpqq1157TYcOHdLcuXO1cuVKTZ8+vdrXvJi/gYSEBKWkpOif//yncnNz9cgjj6iwsLDGKx3XXHONxo8frwceeECrVq1SXl6eNmzYoH/84x/VxttsNi1ZskQHDx7UwIEDtWbNGn322Wfau3evnn32Wd12223V7tepUyft2rVLa9eu1cGDB/XUU09p586d1va8vDzNnDlTW7du1dGjR7Vu3TodPHhQ3bp1U0lJiR5++GFt2LBBR48e1b/+9S/t3Lmz3n7nSCxQrQ4dOmj37t0aMmSIkpKSFBYWpuHDh+uDDz7Qq6++Kun/vuGyVatWGjhwoIYNG6aOHTvqrbfecksbZs2apbS0NPXs2VNLly7Vm2++qe7du0uSmjdvrrVr1+rbb79V3759deedd2ro0KFasGCBtf8DDzyg8ePHWwN5hw4dNGTIkFq1oUWLFnr33Xf1ySefqE+fPnryySf1/PPPu6V/kjR06FB17txZAwcO1NixYzV69GglJydfcn0X015vb2/NnDlTPXv21MCBA+Xh4aG0tDSX+tG0aVN98MEHeuKJJ7RkyRLdeOON6tatmxITEzVgwACnb0L985//rD/+8Y9KSUlRt27dFB0drXfffde63/ZiREdH67333lNmZqb69u2rfv36ae7cuWrfvr1L/QAuFsfIn9T1MbK23n77bfXt21f33HOPunfvrscee0wVFRXVxkZHR+upp57SY489pr59++rUqVMaN26cU8yLL76ogQMHKjY2VsOGDdONN95Y5R78JUuWaNy4cUpKSlLXrl0VGxur7du3W1dhKyoq9NBDD6lbt24aOXKkunbtqldeeaVu3oBaeOqpp3TdddcpOjpagwcPtv6prwtNmjRRWlqasrKyFBYWpkcffVQvvviiU4y3t7fWrVunwMBA3XLLLQoPD9dzzz1nnZi6/fbb9dJLL+nFF19Ujx49tHDhQi1ZskSDBw+u9jUv5m/g8ccf1z333KNx48YpKipKLVq0UHR09HlvqX311Vd15513asqUKbr22ms1ceLEaq9cnnHDDTdo165d+uUvf6mJEyeqW7duio2N1YEDBzR//vxq95k8ebLGjBmju+66S5GRkfrmm280ZcoUp759+umn+tWvfqUuXbrowQcf1MMPP6xJkybJw8ND33zzjcaNG6cuXbpo7NixGjVqlGbNmlVjG93JZi7lBkmgjtlsNqWnp9fZQQ4ALmccIwH3q6ysVLdu3TR27FiXv23754qHtwEAAPCzc+Y2okGDBqm0tFQLFixQXl6e4uLiGrpply1uhQIAAMDPTpMmTZSamqq+fftqwIAB2rdvn9avX98onoG5XHErFAAAAACXccUCAAAAgMtILAAAAAC4jMQCAAAAgMuu2MTCGKOioqLzfjU7AODniTECANzvik0sTp06JbvdrlOnTjV0UwAAjQxjBAC43xWbWAAAAACoPyQWAAAAAFxGYgEAAADAZSQWAAAAAFxGYgEAAADAZSQWAAC32LRpk0aPHq2QkBDZbDatWrWqSkxOTo5iY2Nlt9vl5+enfv366dixY9b20tJSJSQkqHXr1vL19VVsbKxOnDjhVEdhYaHi4+Nlt9tlt9sVHx+v7777ro57BwC4EBILAIBbnD59Wr169dKCBQuq3X7kyBHdeOONuvbaa7Vhwwb9+9//1lNPPaWmTZtaMYmJiUpPT1daWpo2b96s4uJixcTEqKKiwoqJi4tTdna2MjIylJGRoezsbMXHx9d5/wAA52czV+i3AxUVFclut8vhcKhly5YN3RwA+Fmx2WxKT0/X7bffbpXdfffd8vLy0rJly6rdx+FwqE2bNlq2bJnuuusuSdKXX36p0NBQrVmzRtHR0crJyVH37t21bds2RUZGSpK2bdumqKgoffrpp+ratetFtY8xAgDcjysWAIA6V1lZqdWrV6tLly6Kjo5WYGCgIiMjnW6XysrKUnl5uUaMGGGVhYSEKCwsTFu2bJEkbd26VXa73UoqJKlfv36y2+1WDACgYZBYAADqXEFBgYqLi/Xcc89p5MiRWrdune644w6NGTNGGzdulCTl5+fL29tbrVq1cto3KChI+fn5VkxgYGCV+gMDA62Y6pSWlqqoqMhpAQC4l2dDNwAAcOWrrKyUJN1222169NFHJUm9e/fWli1b9Nprr2nQoEE17muMkc1ms9bP/rmmmHOlpKRo1qxZl9p8AMBF4IoFAKDOtW7dWp6enurevbtTebdu3axZoYKDg1VWVqbCwkKnmIKCAgUFBVkxX331VZX6v/76ayumOjNnzpTD4bCW48ePu9olAMA5SCwAAHXO29tbffv2VW5urlP5wYMH1b59e0lSRESEvLy8lJmZaW0/efKk9u/fr/79+0uSoqKi5HA4tGPHDitm+/btcjgcVkx1fHx81LJlS6cFAOBe3AoFAHCL4uJiHT582FrPy8tTdna2/P391a5dO82YMUN33XWXBg4cqCFDhigjI0PvvvuuNmzYIEmy2+2aMGGCkpKSFBAQIH9/f02fPl3h4eEaNmyYpJ+ucIwcOVITJ07UwoULJUkPPvigYmJiLnpGKABA3WC62Rqs2H6s2vK4yHauNg0ArkgbNmzQkCFDqpSPHz9eqampkqS///3vSklJ0YkTJ9S1a1fNmjVLt912mxX7ww8/aMaMGVqxYoVKSko0dOhQvfLKKwoNDbVivv32W02dOlXvvPOOJCk2NlYLFizQVVddddFtZYwAAPcjsagBgwYAXLkYIwDA/XjGAgAAAIDLSCwAAAAAuIzEAgAAAIDLSCwAAAAAuIzEAgAAAIDLap1YbNq0SaNHj1ZISIhsNptWrVpVJSYnJ0exsbGy2+3y8/NTv379rG9WlaTS0lIlJCSodevW8vX1VWxsrE6cOOFUR2FhoeLj42W322W32xUfH6/vvvuu1h0EAAAAUPdqnVicPn1avXr10oIFC6rdfuTIEd1444269tprtWHDBv373//WU089paZNm1oxiYmJSk9PV1pamjZv3qzi4mLFxMSooqLCiomLi1N2drYyMjKUkZGh7OxsxcfHX0IXAQAAANS1Wn/z9qhRozRq1Kgatz/55JO65ZZb9MILL1hlHTt2tH52OBxavHixli1bZn2T6vLlyxUaGqr169crOjpaOTk5ysjI0LZt2xQZGSlJWrRokaKiopSbm8u3qwIAAACNjFufsaisrNTq1avVpUsXRUdHKzAwUJGRkU63S2VlZam8vFwjRoywykJCQhQWFqYtW7ZIkrZu3Sq73W4lFZLUr18/2e12KwYAAABA4+HWxKKgoEDFxcV67rnnNHLkSK1bt0533HGHxowZo40bN0qS8vPz5e3trVatWjntGxQUpPz8fCsmMDCwSv2BgYFWzLlKS0tVVFTktAAAAACoH7W+Fep8KisrJUm33XabHn30UUlS7969tWXLFr322msaNGhQjfsaY2Sz2az1s3+uKeZsKSkpmjVrlivNBwAAAHCJ3HrFonXr1vL09FT37t2dyrt162bNChUcHKyysjIVFhY6xRQUFCgoKMiK+eqrr6rU//XXX1sx55o5c6YcDoe1HD9+3B1dAgAAAHAR3JpYeHt7q2/fvsrNzXUqP3jwoNq3by9JioiIkJeXlzIzM63tJ0+e1P79+9W/f39JUlRUlBwOh3bs2GHFbN++XQ6Hw4o5l4+Pj1q2bOm0AAAAAKgftb4Vqri4WIcPH7bW8/LylJ2dLX9/f7Vr104zZszQXXfdpYEDB2rIkCHKyMjQu+++qw0bNkiS7Ha7JkyYoKSkJAUEBMjf31/Tp09XeHi4NUtUt27dNHLkSE2cOFELFy6UJD344IOKiYlhRigAAACgEap1YrFr1y4NGTLEWp82bZokafz48UpNTdUdd9yh1157TSkpKZo6daq6du2qt99+WzfeeKO1z7x58+Tp6amxY8eqpKREQ4cOVWpqqjw8PKyYN998U1OnTrVmj4qNja3xuzMAAAAANCybMcY0dCPqQlFRkex2uxwOxyXdFrVi+7Fqy+Mi27naNABAA2OMAAD3c+szFgAAAAB+nkgsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIA4BabNm3S6NGjFRISIpvNplWrVtUYO2nSJNlsNs2fP9+pvLS0VAkJCWrdurV8fX0VGxurEydOOMUUFhYqPj5edrtddrtd8fHx+u6779zfIQBArZBYAADc4vTp0+rVq5cWLFhw3rhVq1Zp+/btCgkJqbItMTFR6enpSktL0+bNm1VcXKyYmBhVVFRYMXFxccrOzlZGRoYyMjKUnZ2t+Ph4t/cHAFA7ng3dAADAlWHUqFEaNWrUeWO++OILPfzww1q7dq1uvfVWp20Oh0OLFy/WsmXLNGzYMEnS8uXLFRoaqvXr1ys6Olo5OTnKyMjQtm3bFBkZKUlatGiRoqKilJubq65du9ZN5wAAF8QVCwBAvaisrFR8fLxmzJihHj16VNmelZWl8vJyjRgxwioLCQlRWFiYtmzZIknaunWr7Ha7lVRIUr9+/WS3260YAEDD4IoFAKBePP/88/L09NTUqVOr3Z6fny9vb2+1atXKqTwoKEj5+flWTGBgYJV9AwMDrZjqlJaWqrS01FovKiq6lC4AAM6DKxYAgDqXlZWll156SampqbLZbLXa1xjjtE91+58bc66UlBTrYW+73a7Q0NBatQEAcGEkFgCAOvfxxx+roKBA7dq1k6enpzw9PXX06FElJSXpmmuukSQFBwerrKxMhYWFTvsWFBQoKCjIivnqq6+q1P/1119bMdWZOXOmHA6HtRw/ftx9nQMASCKxAADUg/j4eO3du1fZ2dnWEhISohkzZmjt2rWSpIiICHl5eSkzM9Pa7+TJk9q/f7/69+8vSYqKipLD4dCOHTusmO3bt8vhcFgx1fHx8VHLli2dFgCAe/GMBQDALYqLi3X48GFrPS8vT9nZ2fL391e7du0UEBDgFO/l5aXg4GBrJie73a4JEyYoKSlJAQEB8vf31/Tp0xUeHm7NEtWtWzeNHDlSEydO1MKFCyVJDz74oGJiYpgRCgAaGIkFAMAtdu3apSFDhljr06ZNkySNHz9eqampF1XHvHnz5OnpqbFjx6qkpERDhw5VamqqPDw8rJg333xTU6dOtWaPio2NveB3ZwAA6p7NGGMauhF1oaioSHa7XQ6H45Iuea/Yfqza8rjIdq42DQDQwBgjAMD9eMYCAAAAgMtILAAAAAC4jMQCAAAAgMtqnVhs2rRJo0ePVkhIiGw2m1atWlVj7KRJk2Sz2TR//nyn8tLSUiUkJKh169by9fVVbGysTpw44RRTWFio+Ph468uM4uPj9d1339W2uQAAAADqQa0Ti9OnT6tXr14XnIFj1apV2r59u0JCQqpsS0xMVHp6utLS0rR582YVFxcrJiZGFRUVVkxcXJyys7OVkZGhjIwMZWdnKz4+vrbNBQAAAFAPaj3d7KhRozRq1KjzxnzxxRd6+OGHtXbtWt16661O2xwOhxYvXqxly5ZZ85IvX75coaGhWr9+vaKjo5WTk6OMjAxt27ZNkZGRkqRFixYpKipKubm5zFUOAAAANDJuf8aisrJS8fHxmjFjhnr06FFle1ZWlsrLy635xyUpJCREYWFh2rJliyRp69atstvtVlIhSf369ZPdbrdiAAAAADQebv+CvOeff16enp6aOnVqtdvz8/Pl7e2tVq1aOZUHBQUpPz/figkMDKyyb2BgoBVzrtLSUpWWllrrRUVFl9oFAAAAALXk1isWWVlZeumll5SamiqbzVarfY0xTvtUt/+5MWdLSUmxHvS22+0KDQ2tXeMBAAAAXDK3JhYff/yxCgoK1K5dO3l6esrT01NHjx5VUlKSrrnmGklScHCwysrKVFhY6LRvQUGBgoKCrJivvvqqSv1ff/21FXOumTNnyuFwWMvx48fd2TUAAAAA5+HWxCI+Pl579+5Vdna2tYSEhGjGjBlau3atJCkiIkJeXl7KzMy09jt58qT279+v/v37S5KioqLkcDi0Y8cOK2b79u1yOBxWzLl8fHzUsmVLpwUAAABA/aj1MxbFxcU6fPiwtZ6Xl6fs7Gz5+/urXbt2CggIcIr38vJScHCwNZOT3W7XhAkTlJSUpICAAPn7+2v69OkKDw+3Zonq1q2bRo4cqYkTJ2rhwoWSpAcffFAxMTHMCAUAAAA0QrVOLHbt2qUhQ4ZY69OmTZMkjR8/XqmpqRdVx7x58+Tp6amxY8eqpKREQ4cOVWpqqjw8PKyYN998U1OnTrVmj4qNjb3gd2cAAAAAaBg2Y4xp6EbUhaKiItntdjkcjku6LWrF9mPVlsdFtnO1aQCABsYYAQDu5/bvsQAAAADw80NiAQAAAMBlJBYAAAAAXEZiAQAAAMBlJBYAAAAAXEZiAQAAAMBlJBYAAAAAXEZiAQAAAMBlJBYAAAAAXEZiAQAAAMBlJBYAAAAAXEZiAQAAAMBlJBYAALfYtGmTRo8erZCQENlsNq1atcraVl5erscff1zh4eHy9fVVSEiIxo0bpy+//NKpjtLSUiUkJKh169by9fVVbGysTpw44RRTWFio+Ph42e122e12xcfH67vvvquHHgIAzofEAgDgFqdPn1avXr20YMGCKtu+//577d69W0899ZR2796tlStX6uDBg4qNjXWKS0xMVHp6utLS0rR582YVFxcrJiZGFRUVVkxcXJyys7OVkZGhjIwMZWdnKz4+vs77BwA4P8+GbgAA4MowatQojRo1qtptdrtdmZmZTmUvv/yybrjhBh07dkzt2rWTw+HQ4sWLtWzZMg0bNkyStHz5coWGhmr9+vWKjo5WTk6OMjIytG3bNkVGRkqSFi1apKioKOXm5qpr165120kAQI24YgEAaBAOh0M2m01XXXWVJCkrK0vl5eUaMWKEFRMSEqKwsDBt2bJFkrR161bZ7XYrqZCkfv36yW63WzEAgIbBFQsAQL374Ycf9MQTTyguLk4tW7aUJOXn58vb21utWrVyig0KClJ+fr4VExgYWKW+wMBAK6Y6paWlKi0ttdaLiorc0Q0AwFm4YgEAqFfl5eW6++67VVlZqVdeeeWC8cYY2Ww2a/3sn2uKOVdKSor1sLfdbldoaOilNR4AUCMSCwBAvSkvL9fYsWOVl5enzMxM62qFJAUHB6usrEyFhYVO+xQUFCgoKMiK+eqrr6rU+/XXX1sx1Zk5c6YcDoe1HD9+3E09AgCcQWIBAKgXZ5KKQ4cOaf369QoICHDaHhERIS8vL6eHvE+ePKn9+/erf//+kqSoqCg5HA7t2LHDitm+fbscDocVUx0fHx+1bNnSaQEAuBfPWAAA3KK4uFiHDx+21vPy8pSdnS1/f3+FhITozjvv1O7du/Xee++poqLCeibC399f3t7estvtmjBhgpKSkhQQECB/f39Nnz5d4eHh1ixR3bp108iRIzVx4kQtXLhQkvTggw8qJiaGGaEAoIGRWAAA3GLXrl0aMmSItT5t2jRJ0vjx45WcnKx33nlHktS7d2+n/T766CMNHjxYkjRv3jx5enpq7NixKikp0dChQ5WamioPDw8r/s0339TUqVOt2aNiY2Or/e4MAED9shljTEM3oi4UFRXJbrfL4XBc0iXvFduPVVseF9nO1aYBABoYYwQAuB/PWAAAAABwWa0Ti02bNmn06NEKCQmRzWbTqlWrrG3l5eV6/PHHFR4eLl9fX4WEhGjcuHH68ssvneooLS1VQkKCWrduLV9fX8XGxurEiRNOMYWFhYqPj7emBoyPj9d33313SZ0EAAAAULdqnVicPn1avXr1qvZ+1u+//167d+/WU089pd27d2vlypU6ePCgYmNjneISExOVnp6utLQ0bd68WcXFxYqJiVFFRYUVExcXp+zsbGVkZCgjI0PZ2dmKj4+/hC4CAAAAqGu1fnh71KhRGjVqVLXb7Ha70zSBkvTyyy/rhhtu0LFjx9SuXTs5HA4tXrxYy5Yts2b5WL58uUJDQ7V+/XpFR0crJydHGRkZ2rZtmyIjIyVJixYtUlRUlHJzc5n5AwAAAGhk6vwZC4fDIZvNpquuukqSlJWVpfLycms2D0kKCQlRWFiYtmzZIknaunWr7Ha7lVRIUr9+/WS3260YAAAAAI1HnU43+8MPP+iJJ55QXFycNetGfn6+vL291apVK6fYoKAga07z/Px8BQYGVqkvMDDQijlXaWmpSktLrfWioiJ3dQMAAADABdTZFYvy8nLdfffdqqys1CuvvHLBeGOMbDabtX72zzXFnC0lJcV60Ntutys0NPTSGw8AAACgVuoksSgvL9fYsWOVl5enzMxMpznCg4ODVVZWpsLCQqd9CgoKFBQUZMV89dVXVer9+uuvrZhzzZw5Uw6Hw1qOHz/uxh4BAAAAOB+3JxZnkopDhw5p/fr1CggIcNoeEREhLy8vp4e8T548qf3796t///6SpKioKDkcDu3YscOK2b59uxwOhxVzLh8fH7Vs2dJpAQAAAFA/av2MRXFxsQ4fPmyt5+XlKTs7W/7+/goJCdGdd96p3bt367333lNFRYX1TIS/v7+8vb1lt9s1YcIEJSUlKSAgQP7+/po+fbrCw8OtWaK6deumkSNHauLEiVq4cKEk6cEHH1RMTAwzQgEAAACNUK0Ti127dmnIkCHW+rRp0yRJ48ePV3Jyst555x1JUu/evZ32++ijjzR48GBJ0rx58+Tp6amxY8eqpKREQ4cOVWpqqjw8PKz4N998U1OnTrVmj4qNja32uzMAAAAANDybMcY0dCPqQlFRkex2uxwOxyXdFrVi+7Fqy+Mi27naNABAA2OMAAD3q/PvsQAAAABw5SOxAAAAAOAyEgsAAAAALiOxAAAAAOAyEgsAAAAALiOxAAAAAOAyEgsAAAAALiOxAAAAAOAyEgsAAAAALiOxAAAAAOAyEgsAAAAALiOxAAAAAOAyEgsAAAAALiOxAAAAAOAyEgsAgFts2rRJo0ePVkhIiGw2m1atWuW03Rij5ORkhYSEqFmzZho8eLAOHDjgFFNaWqqEhAS1bt1avr6+io2N1YkTJ5xiCgsLFR8fL7vdLrvdrvj4eH333Xd13DsAwIWQWAAA3OL06dPq1auXFixYUO32F154QXPnztWCBQu0c+dOBQcHa/jw4Tp16pQVk5iYqPT0dKWlpWnz5s0qLi5WTEyMKioqrJi4uDhlZ2crIyNDGRkZys7OVnx8fJ33DwBwfp4N3QAAwJVh1KhRGjVqVLXbjDGaP3++nnzySY0ZM0aStHTpUgUFBWnFihWaNGmSHA6HFi9erGXLlmnYsGGSpOXLlys0NFTr169XdHS0cnJylJGRoW3btikyMlKStGjRIkVFRSk3N1ddu3atn84CAKrgigUAoM7l5eUpPz9fI0aMsMp8fHw0aNAgbdmyRZKUlZWl8vJyp5iQkBCFhYVZMVu3bpXdbreSCknq16+f7Ha7FQMAaBhcsQAA1Ln8/HxJUlBQkFN5UFCQjh49asV4e3urVatWVWLO7J+fn6/AwMAq9QcGBlox1SktLVVpaam1XlRUdGkdAQDUiCsWAIB6Y7PZnNaNMVXKznVuTHXxF6onJSXFetjbbrcrNDS0li0HAFwIiQUAoM4FBwdLUpWrCgUFBdZVjODgYJWVlamwsPC8MV999VWV+r/++usqV0PONnPmTDkcDms5fvy4S/0BAFRFYgEAqHMdOnRQcHCwMjMzrbKysjJt3LhR/fv3lyRFRETIy8vLKebkyZPav3+/FRMVFSWHw6EdO3ZYMdu3b5fD4bBiquPj46OWLVs6LQAA9+IZCwCAWxQXF+vw4cPWel5enrKzs+Xv76927dopMTFRs2fPVufOndW5c2fNnj1bzZs3V1xcnCTJbrdrwoQJSkpKUkBAgPz9/TV9+nSFh4dbs0R169ZNI0eO1MSJE7Vw4UJJ0oMPPqiYmBhmhAKABkZiAQBwi127dmnIkCHW+rRp0yRJ48ePV2pqqh577DGVlJRoypQpKiwsVGRkpNatWyc/Pz9rn3nz5snT01Njx45VSUmJhg4dqtTUVHl4eFgxb775pqZOnWrNHhUbG1vjd2cAAOqPzRhjGroRdaGoqEh2u10Oh+OSLnmv2H6s2vK4yHauNg0A0MAYIwDA/Wr9jMWmTZs0evRohYSEyGazadWqVU7bjTFKTk5WSEiImjVrpsGDB+vAgQNOMaWlpUpISFDr1q3l6+ur2NhYnThxwimmsLBQ8fHx1gwe8fHx+u6772rdQQAAAAB1r9aJxenTp9WrV68aLzu/8MILmjt3rhYsWKCdO3cqODhYw4cP16lTp6yYxMREpaenKy0tTZs3b1ZxcbFiYmJUUVFhxcTFxSk7O1sZGRnKyMhQdna24uPjL6GLAAAAAOparZ+xGDVqlEaNGlXtNmOM5s+fryeffFJjxoyRJC1dulRBQUFasWKFJk2aJIfDocWLF2vZsmXWw3jLly9XaGio1q9fr+joaOXk5CgjI0Pbtm2zvl110aJFioqKUm5uLg/oAQAAAI2MW6ebzcvLU35+vvVAnfTTFH+DBg3Sli1bJElZWVkqLy93igkJCVFYWJgVs3XrVtntdiupkKR+/frJbrdbMecqLS1VUVGR0wIAAACgfrg1sTjzxUfnfklRUFCQtS0/P1/e3t5q1arVeWMCAwOr1B8YGFjly5XO4FtVAQAAgIZTJ1+QZ7PZnNaNMVXKznVuTHXx56uHb1UFAAAAGo5bE4vg4GBJqnJVoaCgwLqKERwcrLKyMhUWFp435quvvqpS/9dff13lasgZfKsqAAAA0HDcmlh06NBBwcHByszMtMrKysq0ceNG9e/fX5IUEREhLy8vp5iTJ09q//79VkxUVJQcDod27NhhxWzfvl0Oh8OKAQAAANB41HpWqOLiYh0+fNhaz8vLU3Z2tvz9/dWuXTslJiZq9uzZ6ty5szp37qzZs2erefPmiouLkyTZ7XZNmDBBSUlJCggIkL+/v6ZPn67w8HBrlqhu3bpp5MiRmjhxohYuXChJevDBBxUTE8OMUAAAAEAjVOvEYteuXRoyZIi1Pm3aNEnS+PHjlZqaqscee0wlJSWaMmWKCgsLFRkZqXXr1snPz8/aZ968efL09NTYsWNVUlKioUOHKjU1VR4eHlbMm2++qalTp1qzR8XGxtb43RkAAAAAGpbNGGMauhF1oaioSHa7XQ6H45Ket1ix/Vi15XGR7VxtGgCggTFGAID71cmsUAAAAAB+XkgsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAyzwbugEAAPxcrdh+rNryuMh29dwSAHAdVywAAAAAuIzEAgAAAIDLSCwAAAAAuIzEAgAAAIDLSCwAAPXixx9/1B/+8Ad16NBBzZo1U8eOHfWnP/1JlZWVVowxRsnJyQoJCVGzZs00ePBgHThwwKme0tJSJSQkqHXr1vL19VVsbKxOnDhR390BAJyDxAIAUC+ef/55vfbaa1qwYIFycnL0wgsv6MUXX9TLL79sxbzwwguaO3euFixYoJ07dyo4OFjDhw/XqVOnrJjExESlp6crLS1NmzdvVnFxsWJiYlRRUdEQ3QIA/C+mmwUA1IutW7fqtttu06233ipJuuaaa/Tf//3f2rVrl6SfrlbMnz9fTz75pMaMGSNJWrp0qYKCgrRixQpNmjRJDodDixcv1rJlyzRs2DBJ0vLlyxUaGqr169crOjq6YToHAOCKBQCgftx444364IMPdPDgQUnSv//9b23evFm33HKLJCkvL0/5+fkaMWKEtY+Pj48GDRqkLVu2SJKysrJUXl7uFBMSEqKwsDArpjqlpaUqKipyWgAA7sUVCwBAvXj88cflcDh07bXXysPDQxUVFXr22Wd1zz33SJLy8/MlSUFBQU77BQUF6ejRo1aMt7e3WrVqVSXmzP7VSUlJ0axZs9zZHQDAObhiAQCoF2+99ZaWL1+uFStWaPfu3Vq6dKn+8pe/aOnSpU5xNpvNad0YU6XsXBeKmTlzphwOh7UcP3780jsCAKgWVywAAPVixowZeuKJJ3T33XdLksLDw3X06FGlpKRo/PjxCg4OlvTTVYm2bdta+xUUFFhXMYKDg1VWVqbCwkKnqxYFBQXq379/ja/t4+MjHx+fuugWAOB/ccUCAFAvvv/+ezVp4jzseHh4WNPNdujQQcHBwcrMzLS2l5WVaePGjVbSEBERIS8vL6eYkydPav/+/edNLAAAdc/tiQXzlAMAqjN69Gg9++yzWr16tT7//HOlp6dr7ty5uuOOOyT9dAtUYmKiZs+erfT0dO3fv1/333+/mjdvrri4OEmS3W7XhAkTlJSUpA8++EB79uzRfffdp/DwcGuWKABAw3D7rVBn5ilfunSpevTooV27duk3v/mN7Ha7HnnkEUn/N095amqqunTpomeeeUbDhw9Xbm6u/Pz8JP00T/m7776rtLQ0BQQEKCkpSTExMcrKypKHh4e7mw0AqGMvv/yynnrqKU2ZMkUFBQUKCQnRpEmT9Mc//tGKeeyxx1RSUqIpU6aosLBQkZGRWrdunTU2SNK8efPk6empsWPHqqSkREOHDlVqaipjAwA0MJsxxrizwpiYGAUFBWnx4sVW2a9+9Ss1b95cy5YtkzFGISEhSkxM1OOPPy7pp6sTQUFBev755615ytu0aaNly5bprrvukiR9+eWXCg0N1Zo1ay5qnvKioiLZ7XY5HA61bNmy1v1Ysf1YteVxke1qXRcAoHFpLGMEYw2AK4nbb4VqqHnKmaMcAAAAaDhuvxWqoeYpZ45yAAAAoOG4/YpFQ81TzhzlAAAAQMNx+xWLhpqnnDnKAQAAgIbj9isWzFMOAAAA/Py4/YrFmXnK27Vrpx49emjPnj2aO3euHnjgAUnO85R37txZnTt31uzZs2ucpzwgIED+/v6aPn0685QDAAAAjZTbEwvmKQcAAAB+ftz+PRaNRWOZoxwA0Pg0ljGCsQbAlcTtz1gAAAAA+PkhsQAAAADgMhILAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILAEC9+eKLL3TfffcpICBAzZs3V+/evZWVlWVtN8YoOTlZISEhatasmQYPHqwDBw441VFaWqqEhAS1bt1avr6+io2N1YkTJ+q7KwCAc5BYAADqRWFhoQYMGCAvLy+9//77+uSTTzRnzhxdddVVVswLL7yguXPnasGCBdq5c6eCg4M1fPhwnTp1yopJTExUenq60tLStHnzZhUXFysmJkYVFRUN0CsAwBmeDd0AAMDPw/PPP6/Q0FAtWbLEKrvmmmusn40xmj9/vp588kmNGTNGkrR06VIFBQVpxYoVmjRpkhwOhxYvXqxly5Zp2LBhkqTly5crNDRU69evV3R0dL32CQDwf7hiAQCoF++8846uv/56/frXv1ZgYKD69OmjRYsWWdvz8vKUn5+vESNGWGU+Pj4aNGiQtmzZIknKyspSeXm5U0xISIjCwsKsmOqUlpaqqKjIaQEAuBeJBQCgXnz22Wd69dVX1blzZ61du1aTJ0/W1KlT9cYbb0iS8vPzJUlBQUFO+wUFBVnb8vPz5e3trVatWtUYU52UlBTZ7XZrCQ0NdWfXAAAisQAA1JPKykpdd911mj17tvr06aNJkyZp4sSJevXVV53ibDab07oxpkrZuS4UM3PmTDkcDms5fvz4pXcEAFCtOkksmPUDAHCutm3bqnv37k5l3bp107FjxyRJwcHBklTlykNBQYF1FSM4OFhlZWUqLCysMaY6Pj4+atmypdMCAHAvtycWzPoBAKjOgAEDlJub61R28OBBtW/fXpLUoUMHBQcHKzMz09peVlamjRs3qn///pKkiIgIeXl5OcWcPHlS+/fvt2IAAA3D7bNCMesHAKA6jz76qPr376/Zs2dr7Nix2rFjh15//XW9/vrrkn66BSoxMVGzZ89W586d1blzZ82ePVvNmzdXXFycJMlut2vChAlKSkpSQECA/P39NX36dIWHh1vjBQCgYbj9ikVDzfrBjB8A0Lj17dtX6enp+u///m+FhYXpz3/+s+bPn697773XinnssceUmJioKVOm6Prrr9cXX3yhdevWyc/Pz4qZN2+ebr/9do0dO1YDBgxQ8+bN9e6778rDw6MhugUA+F9uv2JxZtaPadOm6fe//7127NihqVOnysfHR+PGjTvvrB9Hjx6VdGmzfqSkpGjWrFnu7g4AwI1iYmIUExNT43abzabk5GQlJyfXGNO0aVO9/PLLevnll+ughQCAS+X2KxYNNesHM34AAAAADcftiUVDzfrBjB8AAABAw3F7YsGsHwAAAMDPj9ufsWDWDwAAAODnx+2JxZlZP2bOnKk//elP6tChQ7WzfpSUlGjKlCkqLCxUZGRktbN+eHp6auzYsSopKdHQoUOVmprKrB8AAABAI2QzxpiGbkRdKCoqkt1ul8PhuKTnLVZsP1ZteVxkO1ebBgBoYI1ljGCsAXAlcfszFgAAAAB+fkgsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAyzwbugGXmxXbj1VbHhfZrp5bAgAAADQeXLEAAAAA4DISCwAAAAAuI7EAAAAA4DISCwAAAAAuI7EAAAAA4DISCwAAAAAuI7EAAAAA4DISCwAAAAAuI7EAADSIlJQU2Ww2JSYmWmXGGCUnJyskJETNmjXT4MGDdeDAAaf9SktLlZCQoNatW8vX11exsbE6ceJEPbceAHAuEgsAQL3buXOnXn/9dfXs2dOp/IUXXtDcuXO1YMEC7dy5U8HBwRo+fLhOnTplxSQmJio9PV1paWnavHmziouLFRMTo4qKivruBgDgLCQWAIB6VVxcrHvvvVeLFi1Sq1atrHJjjObPn68nn3xSY8aMUVhYmJYuXarvv/9eK1askCQ5HA4tXrxYc+bM0bBhw9SnTx8tX75c+/bt0/r16xuqSwAAkVgAAOrZQw89pFtvvVXDhg1zKs/Ly1N+fr5GjBhhlfn4+GjQoEHasmWLJCkrK0vl5eVOMSEhIQoLC7NiqlNaWqqioiKnBQDgXnWeWHAPLQDgjLS0NO3evVspKSlVtuXn50uSgoKCnMqDgoKsbfn5+fL29na60nFuTHVSUlJkt9utJTQ01NWuAADOUaeJBffQAgDOOH78uB555BEtX75cTZs2rTHOZrM5rRtjqpSd60IxM2fOlMPhsJbjx4/XrvEAgAuqs8SCe2gBAGfLyspSQUGBIiIi5OnpKU9PT23cuFF//etf5enpaV2pOPfKQ0FBgbUtODhYZWVlKiwsrDGmOj4+PmrZsqXTAgBwrzpLLOr7HlrunwWAxm3o0KHat2+fsrOzreX666/Xvffeq+zsbHXs2FHBwcHKzMy09ikrK9PGjRvVv39/SVJERIS8vLycYk6ePKn9+/dbMQCAhuFZF5WeuYd2586dVbad7x7ao0ePWjG1vYc2JSVFs2bNckfzAQB1wM/PT2FhYU5lvr6+CggIsMoTExM1e/Zsde7cWZ07d9bs2bPVvHlzxcXFSZLsdrsmTJigpKQkBQQEyN/fX9OnT1d4eHiVE1kAgPrl9sTizD2069atq9d7aGfOnKlp06ZZ60VFRTycBwCXmccee0wlJSWaMmWKCgsLFRkZqXXr1snPz8+KmTdvnjw9PTV27FiVlJRo6NChSk1NlYeHRwO2HADg9sTi7Htoz6ioqNCmTZu0YMEC5ebmSvrpqkTbtm2tmJruoT37qkVBQUGNl7p9fHzk4+Pj7u4AAOrQhg0bnNZtNpuSk5OVnJxc4z5NmzbVyy+/rJdffrluGwcAqBW3P2PBPbQAAADAz4/br1hwDy0AAADw81MnD29fCPfQAgAAAFcWmzHGNHQj6kJRUZHsdrscDsclzVe+YvuxWsXHRbar9WsAABpGXY0RtR0L3FUPADQGdfrN2wAAAAB+HkgsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAy0gsAAAAALiMxAIAAACAyzwbugFXihXbj1VbHhfZrp5bAgAAANQ/rlgAAAAAcBmJBQAAAACXkVgAAOpFSkqK+vbtKz8/PwUGBur2229Xbm6uU4wxRsnJyQoJCVGzZs00ePBgHThwwCmmtLRUCQkJat26tXx9fRUbG6sTJ07UZ1cAANUgsQAA1IuNGzfqoYce0rZt25SZmakff/xRI0aM0OnTp62YF154QXPnztWCBQu0c+dOBQcHa/jw4Tp16pQVk5iYqPT0dKWlpWnz5s0qLi5WTEyMKioqGqJbAID/5fbEgjNSAIDqZGRk6P7771ePHj3Uq1cvLVmyRMeOHVNWVpakn8aG+fPn68knn9SYMWMUFhampUuX6vvvv9eKFSskSQ6HQ4sXL9acOXM0bNgw9enTR8uXL9e+ffu0fv36huweAPzsuT2x4IwUAOBiOBwOSZK/v78kKS8vT/n5+RoxYoQV4+Pjo0GDBmnLli2SpKysLJWXlzvFhISEKCwszIoBADQMt083m5GR4bS+ZMkSBQYGKisrSwMHDqxyRkqSli5dqqCgIK1YsUKTJk2yzkgtW7ZMw4YNkyQtX75coaGhWr9+vaKjo93dbABAPTLGaNq0abrxxhsVFhYmScrPz5ckBQUFOcUGBQXp6NGjVoy3t7datWpVJebM/tUpLS1VaWmptV5UVOSWfgAA/k+dP2PBGSkAwLkefvhh7d27V//93/9dZZvNZnNaN8ZUKTvXhWJSUlJkt9utJTQ09NIaDgCoUZ1+QV59npHibBQAXB4SEhL0zjvvaNOmTbr66qut8uDgYEk/jQFt27a1ygsKCqwxIzg4WGVlZSosLHQaIwoKCtS/f/8aX3PmzJmaNm2atV5UVHRFJRd8SSuAxqBOr1jU5xkpzkYBQONmjNHDDz+slStX6sMPP1SHDh2ctnfo0EHBwcHKzMy0ysrKyrRx40YraYiIiJCXl5dTzMmTJ7V///7zJhY+Pj5q2bKl0wIAcK86SyzOnJH66KOPajwjdbaazkjVFHOumTNnyuFwWMvx48fd2R0AgIseeughLV++XCtWrJCfn5/y8/OVn5+vkpISST+dcEpMTNTs2bOVnp6u/fv36/7771fz5s0VFxcnSbLb7ZowYYKSkpL0wQcfaM+ePbrvvvsUHh5uPZMHAGgYbk8sGuqMFGejAKBxe/XVV+VwODR48GC1bdvWWt566y0r5rHHHlNiYqKmTJmi66+/Xl988YXWrVsnPz8/K2bevHm6/fbbNXbsWA0YMEDNmzfXu+++Kw8Pj4boFgDgf7n9GYuHHnpIK1as0D//+U/rjJT001mmZs2aOZ2R6ty5szp37qzZs2fXeEYqICBA/v7+mj59OmekAOAyZoy5YIzNZlNycrKSk5NrjGnatKlefvllvfzyy25sHQDAVW5PLF599VVJ0uDBg53KlyxZovvvv1/ST2ekSkpKNGXKFBUWFioyMrLaM1Kenp4aO3asSkpKNHToUKWmpnJGCgAAAGiE3J5YcEYKAAAA+Pmp8++xAAAAAHDlI7EAAAAA4DISCwAAAAAuI7EAAAAA4DISCwAAAAAuI7EAAAAA4DISCwAAAAAuI7EAAAAA4DK3f0EenK3Yfqza8rjIdvXcEgAAAKDucMUCAAAAgMtILAAAAAC4jMQCAAAAgMtILAAAAAC4jMQCAAAAgMuYFaqB1DRblMSMUQAAALj8cMUCAAAAgMu4YgEAACTx3UsAXMMVCwAAAAAuI7EAAAAA4DISCwAAAAAu4xmLRoh7XAEAAHC5IbEAAABuxQky4OeJxOIywoEaAAAAjRXPWAAAAABwWaNPLF555RV16NBBTZs2VUREhD7++OOGbhIAoBFgfACAxqVRJxZvvfWWEhMT9eSTT2rPnj266aabNGrUKB07Vv0tQQCAnwfGBwBofBr1MxZz587VhAkT9Nvf/laSNH/+fK1du1avvvqqUlJSGrh1jQfPXgD4uWF8+Hlw5/jGWAnUvUabWJSVlSkrK0tPPPGEU/mIESO0ZcuWBmrV5YWDKIArEeMDGhJjK1CzRptY/Oc//1FFRYWCgoKcyoOCgpSfn18lvrS0VKWlpda6w+GQJBUVFV3S639/+tQl7Xc5+K8PDzR0E5yMvT7ULfX8Y9dxt9RT2/bU9Lru6hdQn/z8/GSz2Rq6GedV2/FBqr8xorb1UU/91NNY21Qdd40pV+rYdKX263JwMeNDo00szji3A8aYajuVkpKiWbNmVSkPDeUXrbGb2NANOIe72tPY+gVcDIfDoZYtWzZ0My7KxY4PUv2NEY3t+EE99VdXXR/zL5d2NpQrtV+NycWMD402sWjdurU8PDyqnH0qKCiocpZKkmbOnKlp06ZZ65WVlfr2228VEBBQ67NvRUVFCg0N1fHjxy+bAbY69KPxuBL6INGPxqSu+uDn5+e2uupKbccHiTHiSsD73nB47xtGY3vfL2Z8aLSJhbe3tyIiIpSZmak77rjDKs/MzNRtt91WJd7Hx0c+Pj5OZVdddZVLbWjZsmWj+CBdRT8ajyuhDxL9aEyuhD7UVm3HB4kx4krC+95weO8bxuX0vjfaxEKSpk2bpvj4eF1//fWKiorS66+/rmPHjmny5MkN3TQAQANifACAxqdRJxZ33XWXvvnmG/3pT3/SyZMnFRYWpjVr1qh9+/YN3TQAQANifACAxqdRJxaSNGXKFE2ZMqVeX9PHx0dPP/10lcvmlxv60XhcCX2Q6EdjciX0wVUNMT5IvPcNhfe94fDeN4zL8X23GWNMQzcCAAAAwOWtSUM3AAAAAMDlj8QCAAAAgMtILAAAAAC4jMSiGq+88oo6dOigpk2bKiIiQh9//HFDN6lGycnJstlsTktwcLC13Rij5ORkhYSEqFmzZho8eLAOHDjQgC3+yaZNmzR69GiFhITIZrNp1apVTtsvpt2lpaVKSEhQ69at5evrq9jYWJ04caIee3Hhftx///1VPp9+/fo5xTR0P1JSUtS3b1/5+fkpMDBQt99+u3Jzc51iGvvncTF9uBw+i1dffVU9e/a05iyPiorS+++/b21v7J/Dz8HlND5cKS40zsE93DEu49K443+JxoLE4hxvvfWWEhMT9eSTT2rPnj266aabNGrUKB07dqyhm1ajHj166OTJk9ayb98+a9sLL7yguXPnasGCBdq5c6eCg4M1fPhwnTp1qgFbLJ0+fVq9evXSggULqt1+Me1OTExUenq60tLStHnzZhUXFysmJkYVFRX11Y0L9kOSRo4c6fT5rFmzxml7Q/dj48aNeuihh7Rt2zZlZmbqxx9/1IgRI3T69GkrprF/HhfTB6nxfxZXX321nnvuOe3atUu7du3SzTffrNtuu80avBv753CluxzHhyvF+cY5uIc7xmVcGnf8L9FoGDi54YYbzOTJk53Krr32WvPEE080UIvO7+mnnza9evWqdltlZaUJDg42zz33nFX2ww8/GLvdbl577bV6auGFSTLp6enW+sW0+7vvvjNeXl4mLS3Nivniiy9MkyZNTEZGRr21/Wzn9sMYY8aPH29uu+22GvdpjP0oKCgwkszGjRuNMZfn53FuH4y5PD8LY4xp1aqV+a//+q/L8nO40lxu48OV4nzjHOrGpYzLcI9L+V+iMeGKxVnKysqUlZWlESNGOJWPGDFCW7ZsaaBWXdihQ4cUEhKiDh066O6779Znn30mScrLy1N+fr5Tf3x8fDRo0KBG3Z+LaXdWVpbKy8udYkJCQhQWFtbo+rZhwwYFBgaqS5cumjhxogoKCqxtjbEfDodDkuTv7y/p8vw8zu3DGZfTZ1FRUaG0tDSdPn1aUVFRl+XncCW5XMeHK0VN4xzqx+X6/8SV5HzjV2NCYnGW//znP6qoqFBQUJBTeVBQkPLz8xuoVecXGRmpN954Q2vXrtWiRYuUn5+v/v3765tvvrHafDn1R9JFtTs/P1/e3t5q1apVjTGNwahRo/Tmm2/qww8/1Jw5c7Rz507dfPPNKi0tldT4+mGM0bRp03TjjTcqLCzMauOZNtXUxsbUj+r6IF0+n8W+ffvUokUL+fj4aPLkyUpPT1f37t0vu8/hSnM5jg9XivONc6gfl+v/E1eKC41fjUmj/+bthmCz2ZzWjTFVyhqLUaNGWT+Hh4crKipKv/zlL7V06VLrwZ7LqT9nu5R2N7a+3XXXXdbPYWFhuv7669W+fXutXr1aY8aMqXG/hurHww8/rL1792rz5s1Vtl0un0dNfbhcPouuXbsqOztb3333nd5++22NHz9eGzdutLZfLp/DlepyPZ5ezs43zk2bNq0BW/bzw+9/w7jU8ashcMXiLK1bt5aHh0eV7LugoKBKlt5Y+fr6Kjw8XIcOHbJmzbjc+nMx7Q4ODlZZWZkKCwtrjGmM2rZtq/bt2+vQoUOSGlc/EhIS9M477+ijjz7S1VdfbZVfTp9HTX2oTmP9LLy9vdWpUyddf/31SklJUa9evfTSSy9dVp/DlehKGB+uFGePc6gfl+v/E1eqc8evxoTE4ize3t6KiIhQZmamU3lmZqb69+/fQK2qndLSUuXk5Kht27bq0KGDgoODnfpTVlamjRs3Nur+XEy7IyIi5OXl5RRz8uRJ7d+/v1H37ZtvvtHx48fVtm1bSY2jH8YYPfzww1q5cqU+/PBDdejQwWn75fB5XKgP1WmMn0V1jDEqLS29LD6HK9mVMD5cKc4e51A/Ltf/J65U545fjUq9Py7eyKWlpRkvLy+zePFi88knn5jExETj6+trPv/884ZuWrWSkpLMhg0bzGeffWa2bdtmYmJijJ+fn9Xe5557ztjtdrNy5Uqzb98+c88995i2bduaoqKiBm33qVOnzJ49e8yePXuMJDN37lyzZ88ec/To0Ytu9+TJk83VV19t1q9fb3bv3m1uvvlm06tXL/Pjjz82in6cOnXKJCUlmS1btpi8vDzz0UcfmaioKPOLX/yiUfXjd7/7nbHb7WbDhg3m5MmT1vL9999bMY3987hQHy6Xz2LmzJlm06ZNJi8vz+zdu9f8/ve/N02aNDHr1q0zxjT+z+FKd7mND1eKC41zcA93jMu4NO74X6KxILGoxt/+9jfTvn174+3tba677jqnKSsbm7vuusu0bdvWeHl5mZCQEDNmzBhz4MABa3tlZaV5+umnTXBwsPHx8TEDBw40+/bta8AW/+Sjjz4ykqos48ePN8ZcXLtLSkrMww8/bPz9/U2zZs1MTEyMOXbsWKPpx/fff29GjBhh2rRpY7y8vEy7du3M+PHjq7SxoftRXfslmSVLllgxjf3zuFAfLpfP4oEHHrCOPW3atDFDhw61kgpjGv/n8HNwOY0PV4oLjXNwD3eMy7g07vhforGwGWNM3V4TAQAAAHCl4xkLAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILAAAAAC4jsQAAAADgMhILoBYGDx6sxMTEhm4GAKCRYXwASCwAAAAAuAGJBdCIlJeXN3QTAACNEOMDLgckFkANTp8+rXHjxqlFixZq27at5syZ47S9rKxMjz32mH7xi1/I19dXkZGR2rBhg1PMokWLFBoaqubNm+uOO+7Q3LlzddVVV1nbk5OT1bt3b/39739Xx44d5ePjI2OMHA6HHnzwQQUGBqply5a6+eab9e9//9up7nfffVcRERFq2rSpOnbsqFmzZunHH3+sq7cDAPC/GB+A6pFYADWYMWOGPvroI6Wnp2vdunXasGGDsrKyrO2/+c1v9K9//UtpaWnau3evfv3rX2vkyJE6dOiQJOlf//qXJk+erEceeUTZ2dkaPny4nn322Sqvc/jwYf3jH//Q22+/rezsbEnSrbfeqvz8fK1Zs0ZZWVm67rrrNHToUH377beSpLVr1+q+++7T1KlT9cknn2jhwoVKTU2ttn4AgHsxPgA1MACqOHXqlPH29jZpaWlW2TfffGOaNWtmHnnkEXP48GFjs9nMF1984bTf0KFDzcyZM40xxtx1113m1ltvddp+7733Grvdbq0//fTTxsvLyxQUFFhlH3zwgWnZsqX54YcfnPb95S9/aRYuXGiMMeamm24ys2fPdtq+bNky07Zt20vvNADgghgfgJp5NnRiAzRGR44cUVlZmaKioqwyf39/de3aVZK0e/duGWPUpUsXp/1KS0sVEBAgScrNzdUdd9zhtP2GG27Qe++951TWvn17tWnTxlrPyspScXGxVc8ZJSUlOnLkiBWzc+dOpzNQFRUV+uGHH/T999+refPml9p1AMB5MD4ANSOxAKphjDnv9srKSnl4eCgrK0seHh5O21q0aGHVYbPZLlivr69vlbrbtm1b5X5cSdb9t5WVlZo1a5bGjBlTJaZp06bnbTsA4NIxPgA1I7EAqtGpUyd5eXlp27ZtateunSSpsLBQBw8e1KBBg9SnTx9VVFSooKBAN910U7V1XHvttdqxY4dT2a5duy742tddd53y8/Pl6empa665psaY3NxcderUqXYdAwC4hPEBqBmJBVCNFi1aaMKECZoxY4YCAgIUFBSkJ598Uk2a/DTfQZcuXXTvvfdq3LhxmjNnjvr06aP//Oc/+vDDDxUeHq5bbrlFCQkJGjhwoObOnavRo0frww8/1Pvvv1/lLNW5hg0bpqioKN1+++16/vnn1bVrV3355Zdas2aNbr/9dl1//fX64x//qJiYGIWGhurXv/61mjRpor1792rfvn165pln6uMtAoCfJcYHoGbMCgXU4MUXX9TAgQMVGxurYcOG6cYbb1RERIS1fcmSJRo3bpySkpLUtWtXxcbGavv27QoNDZUkDRgwQK+99prmzp2rXr16KSMjQ48++ugFL0XbbDatWbNGAwcO1AMPPKAuXbro7rvv1ueff66goCBJUnR0tN577z1lZmaqb9++6tevn+bOnav27dvX3RsCAJDE+ADUxGYudLMgALeZOHGiPv30U3388ccN3RQAQCPC+IArAbdCAXXoL3/5i4YPHy5fX1+9//77Wrp0qV555ZWGbhYAoIExPuBKxBULoA6NHTtWGzZs0KlTp9SxY0clJCRo8uTJDd0sAEADY3zAlYjEAgAAAIDLeHgbAAAAgMtILAAAAAC4jMQCAAAAgMtILAAAAAC4jMQCAAAAgMtILAAAAAC4jMQCAAAAgMtILAAAAAC4jMQCAAAAgMv+Pxh3AHJIOkPfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oaKg1bNjQ+vfv77/qEMHlMTvJE+uotg4ePKjY2Fi98MILuvbaa8/6fKtWrdLll1+ugoKCc/pTDKrKOIDKgKfd4FdaWqq8vDw98cQTioqK0tVXX13RJQGooggf+O3cuVMJCQm64IILtGDBgrN6cRsAfg5PuwEAnONNpgAA5wgfAIBzhA8AwLlzMnzMTEVFRSf9+BUAQOV2TobPvn37FBUVpX379lV0KQCAM3BOhg8A4NxG+AAAnCN8AADOET4AAOcIHwCAc4QPAMA5wgcA4BzhAwBwjvABADhH+AAAnCN8AADOET4AAOdOO3xWr16twYMHKzY2Vh6PR6+//nrAfjNTWlqaYmNjFRYWpt69e2vLli0BfYqLizVx4kSdd955qlOnjq6++mr985//PKuBAADOHacdPgcOHFCHDh00Z86cE+6fNWuWZs+erTlz5ignJ0c+n0/JyckBn0CdkpKiJUuWaPHixXr//fe1f/9+DRo0SEePHj3zkQAAzhkeO4svxfF4PFqyZImGDh0q6cdVT2xsrFJSUnTfffdJ+nGVExMTo5kzZ2rMmDEqLCxUw4YN9Ze//EXDhg2TJO3evVtxcXFatmyZ+vfv/4v3W1RUpKioKBUWFioyMvJMyz9tiz7aedrHDO/WpBwqAYBzW1Bf89m+fbvy8vLUr18/f5vX61VSUpLWrFkjSVq/fr1++OGHgD6xsbFq27atvw8AoGoLCebJ8vLyJEkxMTEB7TExMdqxY4e/T61atVS/fv0yfY4d/1PFxcUqLi72bxcVFQWzbACAY+VytZvH4wnYNrMybT/1c30yMjIUFRXlv8XFxQWtVgCAe0ENH5/PJ0llVjD5+fn+1ZDP51NJSYkKCgpO2uenUlNTVVhY6L/t2rUrmGUDABwLavgkJCTI5/MpKyvL31ZSUqLs7Gz16NFDktS5c2eFhoYG9NmzZ4/+3//7f/4+P+X1ehUZGRlwAwCcu077NZ/9+/dr27Zt/u3t27dr48aNatCggZo0aaKUlBSlp6crMTFRiYmJSk9PV3h4uIYPHy5JioqK0h133KF77rlH0dHRatCgge699161a9dOffv2Dd7IAACV1mmHz7p163T55Zf7t6dMmSJJGjVqlBYsWKBp06bp0KFDGjdunAoKCtStWzdlZmYqIiLCf8yTTz6pkJAQ3XjjjTp06JD69OmjBQsWqGbNmkEYEgCgsjur9/lUFN7nAwDnNj7bDQDgHOEDAHCO8AEAOEf4AACcI3wAAM4RPgAA5wgfAIBzhA8AwDnCBwDgHOEDAHCO8AEAOEf4AACcI3wAAM4RPgAA5wgfAIBzhA8AwDnCBwDgHOEDAHCO8AEAOEf4AACcI3wAAM4RPgAA5wgfAIBzhA8AwDnCBwDgHOEDAHCO8AEAOEf4AACcI3wAAM4RPgAA5wgfAIBzhA8AwDnCBwDgHOEDAHCO8AEAOEf4AACcI3wAAM4RPgAA5wgfAIBzhA8AwDnCBwDgHOEDAHCO8AEAOEf4AACcI3wAAM4RPgAA50IquoCqbtFHO0+r//BuTcqpEgCoPIK+8jly5Ih++9vfKiEhQWFhYWrWrJkeeughlZaW+vuYmdLS0hQbG6uwsDD17t1bW7ZsCXYpAIBKKujhM3PmTD333HOaM2eOtm7dqlmzZumxxx7TM8884+8za9YszZ49W3PmzFFOTo58Pp+Sk5O1b9++YJcDAKiEgh4+a9eu1ZAhQzRw4EA1bdpU119/vfr166d169ZJ+nHV89RTT2n69Om69tpr1bZtWy1cuFAHDx7UokWLgl0OAKASCnr49OrVS8uXL1dubq4k6ZNPPtH777+vq666SpK0fft25eXlqV+/fv5jvF6vkpKStGbNmmCXAwCohIJ+wcF9992nwsJCtWrVSjVr1tTRo0f1yCOP6Oabb5Yk5eXlSZJiYmICjouJidGOHTtOeM7i4mIVFxf7t4uKioJdNgDAoaCvfF566SX99a9/1aJFi7RhwwYtXLhQjz/+uBYuXBjQz+PxBGybWZm2YzIyMhQVFeW/xcXFBbtsAIBDQQ+fqVOn6v7779dNN92kdu3a6dZbb9XkyZOVkZEhSfL5fJL+swI6Jj8/v8xq6JjU1FQVFhb6b7t27Qp22QAAh4IePgcPHlSNGoGnrVmzpv9S64SEBPl8PmVlZfn3l5SUKDs7Wz169DjhOb1eryIjIwNuAIBzV9Bf8xk8eLAeeeQRNWnSRBdddJE+/vhjzZ49W7fffrukH59uS0lJUXp6uhITE5WYmKj09HSFh4dr+PDhwS4HAFAJBT18nnnmGf3ud7/TuHHjlJ+fr9jYWI0ZM0YPPPCAv8+0adN06NAhjRs3TgUFBerWrZsyMzMVERER7HIAAJWQx8ysoos4XUVFRYqKilJhYaHTp+BO96NyzgQfrwOgOuCDRQEAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwL+iccnEtcvGkUAFAWKx8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwrl/D55ptvdMsttyg6Olrh4eG6+OKLtX79ev9+M1NaWppiY2MVFham3r17a8uWLeVRCgCgEgp6+BQUFKhnz54KDQ3V22+/rU8//VRPPPGE6tWr5+8za9YszZ49W3PmzFFOTo58Pp+Sk5O1b9++YJcDAKiEQoJ9wpkzZyouLk7z58/3tzVt2tT/bzPTU089penTp+vaa6+VJC1cuFAxMTFatGiRxowZE+ySAACVTNBXPkuXLlWXLl10ww03qFGjRurYsaPmzZvn3799+3bl5eWpX79+/jav16ukpCStWbMm2OUAACqhoIfPV199pWeffVaJiYl69913NXbsWN1999168cUXJUl5eXmSpJiYmIDjYmJi/Pt+qri4WEVFRQE3AMC5K+hPu5WWlqpLly5KT0+XJHXs2FFbtmzRs88+q5EjR/r7eTyegOPMrEzbMRkZGXrwwQeDXSoAoIIEfeXTuHFjtWnTJqCtdevW2rlzpyTJ5/NJUplVTn5+fpnV0DGpqakqLCz033bt2hXssgEADgU9fHr27KnPP/88oC03N1fx8fGSpISEBPl8PmVlZfn3l5SUKDs7Wz169DjhOb1eryIjIwNuAIBzV9Cfdps8ebJ69Oih9PR03XjjjfrHP/6huXPnau7cuZJ+fLotJSVF6enpSkxMVGJiotLT0xUeHq7hw4cHu5xzzqKPdp72McO7NSmHSgCg/AQ9fLp27aolS5YoNTVVDz30kBISEvTUU09pxIgR/j7Tpk3ToUOHNG7cOBUUFKhbt27KzMxUREREsMsBAFRCHjOzii7idBUVFSkqKkqFhYVn9RTcmawyKiNWPgDONXy2GwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnQiq6AJy9RR/tPK3+w7s1KadKAODUsPIBADhH+AAAnCN8AADOET4AAOcIHwCAc4QPAMA5wgcA4BzhAwBwjvABADhH+AAAnCN8AADOET4AAOcIHwCAc4QPAMA5wgcA4BzhAwBwjvABADhH+AAAnCN8AADOET4AAOcIHwCAc4QPAMA5wgcA4Fy5h09GRoY8Ho9SUlL8bWamtLQ0xcbGKiwsTL1799aWLVvKuxQAQCVRruGTk5OjuXPnqn379gHts2bN0uzZszVnzhzl5OTI5/MpOTlZ+/btK89yAACVRLmFz/79+zVixAjNmzdP9evX97ebmZ566ilNnz5d1157rdq2bauFCxfq4MGDWrRoUXmVAwCoRMotfMaPH6+BAweqb9++Ae3bt29XXl6e+vXr52/zer1KSkrSmjVryqscAEAlElIeJ128eLE2bNignJycMvvy8vIkSTExMQHtMTEx2rFjxwnPV1xcrOLiYv92UVFREKsFALgW9JXPrl27NGnSJP31r39V7dq1T9rP4/EEbJtZmbZjMjIyFBUV5b/FxcUFtWYAgFtBD5/169crPz9fnTt3VkhIiEJCQpSdna0//OEPCgkJ8a94jq2AjsnPzy+zGjomNTVVhYWF/tuuXbuCXTYAwKGgP+3Wp08fbd68OaDttttuU6tWrXTfffepWbNm8vl8ysrKUseOHSVJJSUlys7O1syZM094Tq/XK6/XG+xSAQAVJOjhExERobZt2wa01alTR9HR0f72lJQUpaenKzExUYmJiUpPT1d4eLiGDx8e7HIAAJVQuVxw8EumTZumQ4cOady4cSooKFC3bt2UmZmpiIiIiigHAOCYx8ysoos4XUVFRYqKilJhYaEiIyPP+DyLPtoZxKrOHcO7NanoEgBUc3y2GwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwLqegC4N6ij3ae9jHDuzUph0oAVFesfAAAzrHyQbk53RUWqyug+mDlAwBwjvABADjH0244JWdykQIAnAwrHwCAc4QPAMA5wgcA4BzhAwBwjvABADhH+AAAnCN8AADOET4AAOcIHwCAc4QPAMA5wgcA4BzhAwBwLujhk5GRoa5duyoiIkKNGjXS0KFD9fnnnwf0MTOlpaUpNjZWYWFh6t27t7Zs2RLsUgAAlVTQwyc7O1vjx4/Xhx9+qKysLB05ckT9+vXTgQMH/H1mzZql2bNna86cOcrJyZHP51NycrL27dsX7HIAAJWQx8ysPO/gX//6lxo1aqTs7GxddtllMjPFxsYqJSVF9913nySpuLhYMTExmjlzpsaMGfOL5ywqKlJUVJQKCwsVGRl5xrXxNQGVC99kClQf5f6aT2FhoSSpQYMGkqTt27crLy9P/fr18/fxer1KSkrSmjVryrscAEAlUK5fJmdmmjJlinr16qW2bdtKkvLy8iRJMTExAX1jYmK0Y8eOE56nuLhYxcXF/u2ioqJyqhgA4EK5rnwmTJigTZs26X//93/L7PN4PAHbZlam7ZiMjAxFRUX5b3FxceVSLwDAjXILn4kTJ2rp0qVauXKlLrjgAn+7z+eT9J8V0DH5+fllVkPHpKamqrCw0H/btWtXeZUNAHAg6OFjZpowYYJee+01rVixQgkJCQH7ExIS5PP5lJWV5W8rKSlRdna2evToccJzer1eRUZGBtwAAOeuoL/mM378eC1atEh///vfFRER4V/hREVFKSwsTB6PRykpKUpPT1diYqISExOVnp6u8PBwDR8+PNjlAAAqoaCHz7PPPitJ6t27d0D7/PnzNXr0aEnStGnTdOjQIY0bN04FBQXq1q2bMjMzFREREexyAACVULm/z6c88D6fqon3+QDVB5/tBgBwjvABADhH+AAAnCN8AADOET4AAOfK9bPdgNNxJlcfcoUccG5i5QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJwjfAAAzhE+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI5vMsU57XS//ZRvPgUqB1Y+AADnCB8AgHOEDwDAOcIHAOAc4QMAcI7wAQA4R/gAAJzjfT6oVk73fUHS6b83yMV9AOc6Vj4AAOcIHwCAc4QPAMA5wgcA4BzhAwBwjqvdgF9wJlevAfh5rHwAAM4RPgAA5wgfAIBzhA8AwDnCBwDgHFe7Aeeo070Kz8Xnx/G5djhVrHwAAM4RPgAA53jaDcBJ8QZblJcKXfn86U9/UkJCgmrXrq3OnTvrvffeq8hyAACOVFj4vPTSS0pJSdH06dP18ccf69JLL9WAAQO0cyd/aQFAVecxM6uIO+7WrZs6deqkZ5991t/WunVrDR06VBkZGT97bFFRkaKiolRYWKjIyMgzroGnFFBZnMkVX1Xl97eqXIVXWa/0q4xXRUoVtPIpKSnR+vXr1a9fv4D2fv36ac2aNRVREgDAoQq54OC7777T0aNHFRMTE9AeExOjvLy8Mv2Li4tVXFzs3y4sLJT04wrobBw8sO+sjgeC5Ux+l6vK7+/Z/j8+FWcyV6dbl4v7OBOnW1ewaoqIiJDH4znp/gq92u2nhZnZCYvNyMjQgw8+WKY9Li6u3GoDXLqroguoQJV17C7qqoxjD1ZNv/SySIWEz3nnnaeaNWuWWeXk5+eXWQ1JUmpqqqZMmeLfLi0t1ffff6/o6OifTdafU1RUpLi4OO3ateusXjc6VzF+xs/4GX95jj8iIuJn91dI+NSqVUudO3dWVlaWrrnmGn97VlaWhgwZUqa/1+uV1+sNaKtXr15QaomMjKyWv3zHMH7Gz/gZf0WosKfdpkyZoltvvVVdunRR9+7dNXfuXO3cuVNjx46tqJIAAI5UWPgMGzZMe/fu1UMPPaQ9e/aobdu2WrZsmeLj4yuqJACAIxV6wcG4ceM0bty4Crlvr9erGTNmlHk6r7pg/Iyf8TP+ihx/hb3JFABQffGp1gAA5wgfAIBzhA8AwLlqGT7V5ascVq9ercGDBys2NlYej0evv/56wH4zU1pammJjYxUWFqbevXtry5YtFVNsOcjIyFDXrl0VERGhRo0aaejQofr8888D+lTlOXj22WfVvn17/3s5unfvrrffftu/vyqP/UQyMjLk8XiUkpLib6vKc5CWliaPxxNw8/l8/v0VPfZqFz7V6ascDhw4oA4dOmjOnDkn3D9r1izNnj1bc+bMUU5Ojnw+n5KTk7VvX9X4zLDs7GyNHz9eH374obKysnTkyBH169dPBw4c8PepynNwwQUX6NFHH9W6deu0bt06XXHFFRoyZIj/AaYqj/2ncnJyNHfuXLVv3z6gvarPwUUXXaQ9e/b4b5s3b/bvq/CxWzVzySWX2NixYwPaWrVqZffff38FVeSGJFuyZIl/u7S01Hw+nz366KP+tsOHD1tUVJQ999xzFVBh+cvPzzdJlp2dbWbVcw7q169v//3f/12txr5v3z5LTEy0rKwsS0pKskmTJplZ1f/5z5gxwzp06HDCfZVh7NVq5cNXOfzH9u3blZeXFzAXXq9XSUlJVXYujn0aeoMGDSRVrzk4evSoFi9erAMHDqh79+7Vauzjx4/XwIED1bdv34D26jAHX3zxhWJjY5WQkKCbbrpJX331laTKMfYKfZOpa6f7VQ5V2bHxnmguduzYUREllSsz05QpU9SrVy+1bdtWUvWYg82bN6t79+46fPiw6tatqyVLlqhNmzb+B5iqPHZJWrx4sTZs2KCcnJwy+6r6z79bt2568cUX1aJFC3377bd6+OGH1aNHD23ZsqVSjL1ahc8xp/pVDtVBdZmLCRMmaNOmTXr//ffL7KvKc9CyZUtt3LhR//73v/Xqq69q1KhRys7O9u+vymPftWuXJk2apMzMTNWuXfuk/arqHAwYMMD/73bt2ql79+668MILtXDhQv3qV7+SVLFjr1ZPu53uVzlUZceueqkOczFx4kQtXbpUK1eu1AUXXOBvrw5zUKtWLTVv3lxdunRRRkaGOnTooKeffrpajH39+vXKz89X586dFRISopCQEGVnZ+sPf/iDQkJC/OOsynNwvDp16qhdu3b64osvKsXPv1qFz/Ff5XC8rKws9ejRo4KqqhgJCQny+XwBc1FSUqLs7OwqMxdmpgkTJui1117TihUrlJCQELC/OszBT5mZiouLq8XY+/Tpo82bN2vjxo3+W5cuXTRixAht3LhRzZo1q/JzcLzi4mJt3bpVjRs3rhw/fyeXNVQiixcvttDQUPvzn/9sn376qaWkpFidOnXs66+/rujSgm7fvn328ccf28cff2ySbPbs2fbxxx/bjh07zMzs0UcftaioKHvttdds8+bNdvPNN1vjxo2tqKiogisPjl//+tcWFRVlq1atsj179vhvBw8e9PepynOQmppqq1evtu3bt9umTZvsN7/5jdWoUcMyMzPNrGqP/WSOv9rNrGrPwT333GOrVq2yr776yj788EMbNGiQRURE+B/rKnrs1S58zMz++Mc/Wnx8vNWqVcs6derkv/S2qlm5cqVJKnMbNWqUmf14ueWMGTPM5/OZ1+u1yy67zDZv3lyxRQfRicYuyebPn+/vU5Xn4Pbbb/f/njds2ND69OnjDx6zqj32k/lp+FTlORg2bJg1btzYQkNDLTY21q699lrbsmWLf39Fj51PtQYAOFetXvMBAFQOhA8AwDnCBwDgHOEDAHCO8AEAOEf4AACcI3wAAM4RPgAA5wgf4Cz07t074GuZAZwawgcA4BzhA1RiP/zwQ0WXAJQLwgc4RQcOHNDIkSNVt25dNW7cWE888UTA/pKSEk2bNk3nn3++6tSpo27dumnVqlUBfebNm6e4uDiFh4frmmuu0ezZs1WvXj3//rS0NF188cV64YUX1KxZM3m9XpmZCgsL9V//9V9q1KiRIiMjdcUVV+iTTz4JOPcbb7yhzp07q3bt2mrWrJkefPBBHTlypLymAzgrhA9wiqZOnaqVK1dqyZIlyszM1KpVq7R+/Xr//ttuu00ffPCBFi9erE2bNumGG27QlVdeqS+++EKS9MEHH2js2LGaNGmSNm7cqOTkZD3yyCNl7mfbtm16+eWX9eqrr2rjxo2SpIEDByovL0/Lli3T+vXr1alTJ/Xp00fff/+9JOndd9/VLbfcorvvvluffvqpnn/+eS1YsOCE5wcqBWefnw2cw/bt22e1atWyxYsX+9v27t1rYWFhNmnSJNu2bZt5PB775ptvAo7r06ePpaammtmPH3E/cODAgP0jRoywqKgo//aMGTMsNDTU8vPz/W3Lly+3yMhIO3z4cMCxF154oT3//PNmZnbppZdaenp6wP6//OUv1rhx4zMfNFCOQio6/IBzwZdffqmSkhJ1797d39agQQO1bNlSkrRhwwaZmVq0aBFwXHFxsaKjoyVJn3/+ua655pqA/ZdcconefPPNgLb4+Hg1bNjQv71+/Xrt37/ff55jDh06pC+//NLfJycnJ2Clc/ToUR0+fFgHDx5UeHj4mQ4dKBeED3AK7Be+9qq0tFQ1a9bU+vXrVbNmzYB9devW9Z/D4/H84nnr1KlT5tyNGzcu8/qRJP/rRaWlpXrwwQd17bXXlulTu3btn60dqAiED3AKmjdvrtDQUH344Ydq0qSJJKmgoEC5ublKSkpSx44ddfToUeXn5+vSSy894TlatWqlf/zjHwFt69at+8X77tSpk/Ly8hQSEqKmTZuetM/nn3+u5s2bn97AgApC+ACnoG7durrjjjs0depURUdHKyYmRtOnT1eNGj9es9OiRQuNGDFCI0eO1BNPPKGOHTvqu+++04oVK9SuXTtdddVVmjhxoi677DLNnj1bgwcP1ooVK/T222+XWQ39VN++fdW9e3cNHTpUM2fOVMuWLbV7924tW7ZMQ4cOVZcuXfTAAw9o0KBBiouL0w033KAaNWpo06ZN2rx5sx5++GEXUwScFq52A07RY489pssuu0xXX321+vbtq169eqlz587+/fPnz9fIkSN1zz33qGXLlrr66qv10UcfKS4uTpLUs2dPPffcc5o9e7Y6dOigd955R5MnT/7Fp8U8Ho+WLVumyy67TLfffrtatGihm266SV9//bViYmIkSf3799ebb76prKwsde3aVb/61a80e/ZsxcfHl9+EAGfBY7/0ZDaAcnPXXXfps88+03vvvVfRpQBO8bQb4NDjjz+u5ORk1alTR2+//bYWLlyoP/3pTxVdFuAcKx/AoRtvvFGrVq3Svn371KxZM02cOFFjx46t6LIA5wgfAIBzXHAAAHCO8AEAOEf4AACcI3wAAM4RPgAA5wgfAIBzhA8AwDnCBwDgHOEDAHDu/wOU0/PCAMP54QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and save degree distribution vizualizations\n",
    "# Commented out due to https://github.com/ContinuumIO/anaconda-issues/issues/368\n",
    "hetio.stats.plot_degrees(graph, os.path.join(path,'degrees.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metanode</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>metaedges</th>\n",
       "      <th>nodes</th>\n",
       "      <th>unconnected_nodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>652</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Biological Process</td>\n",
       "      <td>BP</td>\n",
       "      <td>1</td>\n",
       "      <td>12322</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cellular Component</td>\n",
       "      <td>CC</td>\n",
       "      <td>1</td>\n",
       "      <td>1695</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Compound</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>2778</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Disease</td>\n",
       "      <td>D</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gene</td>\n",
       "      <td>G</td>\n",
       "      <td>4</td>\n",
       "      <td>20608</td>\n",
       "      <td>3232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Molecular Function</td>\n",
       "      <td>MF</td>\n",
       "      <td>1</td>\n",
       "      <td>3460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pharmacologic Class</td>\n",
       "      <td>PC</td>\n",
       "      <td>1</td>\n",
       "      <td>478</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Symptom</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>505</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              metanode abbreviation  metaedges  nodes  unconnected_nodes\n",
       "3              Anatomy            A          1    652                115\n",
       "6   Biological Process           BP          1  12322                  0\n",
       "8   Cellular Component           CC          1   1695                  0\n",
       "2             Compound            C          2   2778                535\n",
       "1              Disease            D          3    137                 16\n",
       "0                 Gene            G          4  20608               3232\n",
       "7   Molecular Function           MF          1   3460                  0\n",
       "5  Pharmacologic Class           PC          1    478                  0\n",
       "4              Symptom            S          1    505                 35"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of metanodes and cooresponding nodes\n",
    "metanode_df = hetio.stats.get_metanode_df(graph)\n",
    "metanode_df.to_csv(os.path.join(path,'metanodes.tsv'), sep='\\t', index=False)\n",
    "metanode_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42635"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of nodes\n",
    "metanode_df.nodes.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metaedge</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>edges</th>\n",
       "      <th>source_nodes</th>\n",
       "      <th>target_nodes</th>\n",
       "      <th>unbiased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Compound - binds - Gene</td>\n",
       "      <td>CbG</td>\n",
       "      <td>25733</td>\n",
       "      <td>2167</td>\n",
       "      <td>2705</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Disease - localizes - Anatomy</td>\n",
       "      <td>DlA</td>\n",
       "      <td>4335</td>\n",
       "      <td>121</td>\n",
       "      <td>537</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Disease - presents - Symptom</td>\n",
       "      <td>DpS</td>\n",
       "      <td>3758</td>\n",
       "      <td>121</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Disease - resembles - Disease</td>\n",
       "      <td>DrD</td>\n",
       "      <td>250</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gene - participates - Biological Process</td>\n",
       "      <td>GpBP</td>\n",
       "      <td>548342</td>\n",
       "      <td>16061</td>\n",
       "      <td>12322</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gene - participates - Cellular Component</td>\n",
       "      <td>GpCC</td>\n",
       "      <td>88885</td>\n",
       "      <td>12241</td>\n",
       "      <td>1695</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gene - participates - Molecular Function</td>\n",
       "      <td>GpMF</td>\n",
       "      <td>104777</td>\n",
       "      <td>14213</td>\n",
       "      <td>3460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pharmacologic Class - includes - Compound</td>\n",
       "      <td>PCiC</td>\n",
       "      <td>1948</td>\n",
       "      <td>478</td>\n",
       "      <td>1202</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    metaedge abbreviation   edges  \\\n",
       "0                    Compound - binds - Gene          CbG   25733   \n",
       "1              Disease - localizes - Anatomy          DlA    4335   \n",
       "2               Disease - presents - Symptom          DpS    3758   \n",
       "3              Disease - resembles - Disease          DrD     250   \n",
       "4   Gene - participates - Biological Process         GpBP  548342   \n",
       "5   Gene - participates - Cellular Component         GpCC   88885   \n",
       "6   Gene - participates - Molecular Function         GpMF  104777   \n",
       "7  Pharmacologic Class - includes - Compound         PCiC    1948   \n",
       "\n",
       "   source_nodes  target_nodes  unbiased  \n",
       "0          2167          2705         0  \n",
       "1           121           537         0  \n",
       "2           121           470         0  \n",
       "3            87            81         0  \n",
       "4         16061         12322         0  \n",
       "5         12241          1695         0  \n",
       "6         14213          3460         0  \n",
       "7           478          1202         0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of metaedges and cooresponding edges\n",
    "metaedge_df = hetio.stats.get_metaedge_df(graph)\n",
    "\n",
    "# Calculate number of unbiased edges\n",
    "rows = list()\n",
    "for metaedge, edges in graph.get_metaedge_to_edges(exclude_inverts=True).items():\n",
    "    unbiased = sum(edge.data['unbiased'] for edge in edges)\n",
    "    rows.append({'metaedge': str(metaedge), 'unbiased': unbiased})\n",
    "\n",
    "metaedge_df = metaedge_df.merge(pandas.DataFrame(rows))\n",
    "metaedge_df.to_csv(os.path.join(path,'metaedges.tsv'), sep='\\t', index=False)\n",
    "metaedge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Summary of different styles for representing each metaedge\n",
    "#metaedge_style_df = hetio.stats.get_metaedge_style_df(metagraph)\n",
    "#metaedge_style_df.to_csv('data/summary/metaedge-styles.tsv', sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "778028"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of edges in the network\n",
    "metaedge_df.edges.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Save graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "path ='../data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write nodes to a table\n",
    "hetio.readwrite.write_nodetable(graph, os.path.join(path,'nodes.tsv'))\n",
    "\n",
    "# Write edges to a table\n",
    "hetio.readwrite.write_sif(graph, os.path.join(path,'edges.sif.gz'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Barplots of metaedge and metanode counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = seaborn.barplot(x='metanode', y='nodes', data=metanode_df.sort_values('nodes'))\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(90)\n",
    "ax.set_xlabel(''); ax.set_ylabel('nodes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = seaborn.barplot(x='metaedge', y='edges', data=metaedge_df.sort_values('edges'))\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(90)\n",
    "ax.set_xlabel(''); ax.set_ylabel('edges');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}