GNN_graphs_prep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: pytorch\n"
     ]
    }
   ],
   "source": [
    "import dgl\n",
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Node file\n",
    "gse = pd.read_csv('../data/GSE_full_pkd.csv', sep=',')\n",
    "\n",
    "# Edge file\n",
    "ppi = pd.read_csv('../data/PPI_net.txt', sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "             Unnamed: 0  5'nucleotidase increased       AGEP  \\\n",
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       "\n",
       "   AIDS dementia complex       AION  AML progression  ANA increased    Abasia  \\\n",
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       "\n",
       "   Abdominal abscess  Abdominal adhesions  ...  Xerophthalmia    Xerosis  \\\n",
       "0           0.000000                  0.0  ...      68.786461  12.542385   \n",
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       "\n",
       "      Yawning  Yeast infection  Yellow nail syndrome  Yellow skin  \\\n",
       "0  127.915960              0.0                   0.0    12.006820   \n",
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       "4   48.194148              0.0                   0.0     0.000000   \n",
       "\n",
       "   Zinc deficiency  Zollinger-Ellison syndrome  pH urine decreased  \\\n",
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       "\n",
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       "\n",
       "[5 rows x 5806 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gse.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "               protein1              protein2  neighborhood\n",
       "0  9606.ENSP00000000233  9606.ENSP00000272298             1\n",
       "1  9606.ENSP00000000233  9606.ENSP00000253401             1\n",
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      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppi = ppi[['protein1', 'protein2', 'neighborhood']]\n",
    "ppi['neighborhood'] = 1\n",
    "ppi.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
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       " '9606.ENSP00000229771': 937,\n",
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       " '9606.ENSP00000230732': 967,\n",
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       " '9606.ENSP00000230792': 969,\n",
       " '9606.ENSP00000230859': 970,\n",
       " '9606.ENSP00000230895': 971,\n",
       " '9606.ENSP00000230990': 972,\n",
       " '9606.ENSP00000231004': 973,\n",
       " '9606.ENSP00000231009': 974,\n",
       " '9606.ENSP00000231021': 975,\n",
       " '9606.ENSP00000231061': 976,\n",
       " '9606.ENSP00000231121': 977,\n",
       " '9606.ENSP00000231130': 978,\n",
       " '9606.ENSP00000231134': 979,\n",
       " '9606.ENSP00000231136': 980,\n",
       " '9606.ENSP00000231137': 981,\n",
       " '9606.ENSP00000231173': 982,\n",
       " '9606.ENSP00000231188': 983,\n",
       " '9606.ENSP00000231198': 984,\n",
       " '9606.ENSP00000231228': 985,\n",
       " '9606.ENSP00000231238': 986,\n",
       " '9606.ENSP00000231368': 987,\n",
       " '9606.ENSP00000231420': 988,\n",
       " '9606.ENSP00000231449': 989,\n",
       " '9606.ENSP00000231454': 990,\n",
       " '9606.ENSP00000231461': 991,\n",
       " '9606.ENSP00000231484': 992,\n",
       " '9606.ENSP00000231487': 993,\n",
       " '9606.ENSP00000231498': 994,\n",
       " '9606.ENSP00000231509': 995,\n",
       " '9606.ENSP00000231512': 996,\n",
       " '9606.ENSP00000231524': 997,\n",
       " '9606.ENSP00000231572': 998,\n",
       " '9606.ENSP00000231656': 999,\n",
       " '9606.ENSP00000231668': 1000,\n",
       " ...}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Nodes need to be numerical and ordered corectly, so we make a id dic\n",
    "genes = gse['Unnamed: 0'].tolist()\n",
    "gene_ids = {}\n",
    "for i, gene in enumerate(genes):\n",
    "    gene_ids[gene] = i+1\n",
    "gene_ids  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#There are 11 genes that are in GSE, so we remove them.\n",
    "drop_list =[] \n",
    "\n",
    "for gene in ppi['protein1'].unique().tolist():\n",
    "    if gene not in gene_ids:\n",
    "        drop_list.append(gene)\n",
    "    else: pass\n",
    "    \n",
    "for gene in ppi['protein2'].unique().tolist():\n",
    "    if gene not in gene_ids and gene not in drop_list:\n",
    "        drop_list.append(gene)\n",
    "    else: pass\n",
    "    \n",
    "drop_list\n",
    "\n",
    "for gene in drop_list: \n",
    "    ppi.drop(ppi.loc[ppi['protein1'] == gene].index, inplace=True)\n",
    "    ppi.drop(ppi.loc[ppi['protein2'] == gene].index, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9606.ENSP00000222330',\n",
       " '9606.ENSP00000241041',\n",
       " '9606.ENSP00000324464',\n",
       " '9606.ENSP00000346484',\n",
       " '9606.ENSP00000349458',\n",
       " '9606.ENSP00000369126',\n",
       " '9606.ENSP00000380044',\n",
       " '9606.ENSP00000384262',\n",
       " '9606.ENSP00000421689',\n",
       " '9606.ENSP00000435460',\n",
       " '9606.ENSP00000470087']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting to gene ids\n",
    "#%%time\n",
    "def gene2id (gene):\n",
    "    return(gene_ids[gene])\n",
    "\n",
    "gse['Unnamed: 0'] = gse.apply(lambda x: gene2id(x['Unnamed: 0']), axis=1)\n",
    "ppi['protein1'] = ppi.apply(lambda x: gene2id(x['protein1']), axis=1)\n",
    "ppi['protein2'] = ppi.apply(lambda x: gene2id(x['protein2']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>protein1</th>\n",
       "      <th>protein2</th>\n",
       "      <th>neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3469</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1759</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>15624</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>16446</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7409</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11759449</th>\n",
       "      <td>19555</td>\n",
       "      <td>6049</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11759450</th>\n",
       "      <td>19555</td>\n",
       "      <td>8711</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11759451</th>\n",
       "      <td>19555</td>\n",
       "      <td>9472</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11759452</th>\n",
       "      <td>19555</td>\n",
       "      <td>12490</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11759453</th>\n",
       "      <td>19555</td>\n",
       "      <td>9437</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11738330 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          protein1  protein2  neighborhood\n",
       "0                1      3469             1\n",
       "1                1      1759             1\n",
       "2                1     15624             1\n",
       "3                1     16446             1\n",
       "4                1      7409             1\n",
       "...            ...       ...           ...\n",
       "11759449     19555      6049             1\n",
       "11759450     19555      8711             1\n",
       "11759451     19555      9472             1\n",
       "11759452     19555     12490             1\n",
       "11759453     19555      9437             1\n",
       "\n",
       "[11738330 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppi.to_csv('../data/GNN-PPI-net.csv', index=False, sep = ',')\n",
    "ppi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "      <th>PSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Temazepam</td>\n",
       "      <td>sildenafil</td>\n",
       "      <td>Arthralgia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bumetanide</td>\n",
       "      <td>Oxytocin</td>\n",
       "      <td>Arthralgia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tamoxifen</td>\n",
       "      <td>Prednisone</td>\n",
       "      <td>Diarrhoea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Temazepam</td>\n",
       "      <td>sildenafil</td>\n",
       "      <td>Diarrhoea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bumetanide</td>\n",
       "      <td>Oxytocin</td>\n",
       "      <td>Diarrhoea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18492593</th>\n",
       "      <td>Ketoprofen</td>\n",
       "      <td>montelukast</td>\n",
       "      <td>Pulmonary embolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18492594</th>\n",
       "      <td>Ketoprofen</td>\n",
       "      <td>montelukast</td>\n",
       "      <td>Cough</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18492595</th>\n",
       "      <td>Ketoprofen</td>\n",
       "      <td>montelukast</td>\n",
       "      <td>Rash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18492596</th>\n",
       "      <td>Ketoprofen</td>\n",
       "      <td>montelukast</td>\n",
       "      <td>Alopecia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18492597</th>\n",
       "      <td>Ketoprofen</td>\n",
       "      <td>montelukast</td>\n",
       "      <td>Hypertension</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18492598 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Drug1        Drug2                 PSE\n",
       "0          Temazepam   sildenafil          Arthralgia\n",
       "1         Bumetanide     Oxytocin          Arthralgia\n",
       "2          Tamoxifen   Prednisone           Diarrhoea\n",
       "3          Temazepam   sildenafil           Diarrhoea\n",
       "4         Bumetanide     Oxytocin           Diarrhoea\n",
       "...              ...          ...                 ...\n",
       "18492593  Ketoprofen  montelukast  Pulmonary embolism\n",
       "18492594  Ketoprofen  montelukast               Cough\n",
       "18492595  Ketoprofen  montelukast                Rash\n",
       "18492596  Ketoprofen  montelukast            Alopecia\n",
       "18492597  Ketoprofen  montelukast        Hypertension\n",
       "\n",
       "[18492598 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PSE = pd.read_csv('../data/TWOSIDE-train-PSE-964.csv', sep = ',')\n",
    "PSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'Arthralgia',\n",
       " 'Diarrhoea',\n",
       " 'Headache',\n",
       " 'Vomiting',\n",
       " 'Dyspepsia',\n",
       " 'Renal impairment',\n",
       " 'Cough',\n",
       " 'Rhinorrhoea',\n",
       " 'Malaise',\n",
       " 'Back pain',\n",
       " 'Fluid retention',\n",
       " 'Oedema peripheral',\n",
       " 'Oedema',\n",
       " 'Death',\n",
       " 'Cerebrovascular accident',\n",
       " 'Pain',\n",
       " 'Wound',\n",
       " 'Fatigue',\n",
       " 'Gastrooesophageal reflux disease',\n",
       " 'Sinusitis',\n",
       " 'Urinary tract infection',\n",
       " 'Fall',\n",
       " 'Bone pain',\n",
       " 'Nausea',\n",
       " 'Paraesthesia',\n",
       " 'General physical health deterioration',\n",
       " 'Abdominal pain',\n",
       " 'Anxiety',\n",
       " 'Emotional distress',\n",
       " 'Injury',\n",
       " 'Memory impairment',\n",
       " 'Rash',\n",
       " 'Alanine aminotransferase increased',\n",
       " 'Drug eruption',\n",
       " 'Chest pain',\n",
       " 'Dyspnoea',\n",
       " 'Stress',\n",
       " 'Asthenia',\n",
       " 'Depression',\n",
       " 'Dysgeusia',\n",
       " 'Weight decreased',\n",
       " 'Pruritus',\n",
       " 'Feeling abnormal',\n",
       " 'Muscle spasms',\n",
       " 'Nightmare',\n",
       " 'Convulsion',\n",
       " 'Hyperhidrosis',\n",
       " 'Urinary retention',\n",
       " 'Dizziness',\n",
       " 'Haemoglobin decreased',\n",
       " 'White blood cell count decreased',\n",
       " 'Burning sensation',\n",
       " 'Flushing',\n",
       " 'Anhedonia',\n",
       " 'Gastrointestinal haemorrhage',\n",
       " 'Depressed mood',\n",
       " 'Drug ineffective',\n",
       " 'Deep vein thrombosis',\n",
       " 'Dehydration',\n",
       " 'Hip fracture',\n",
       " 'Injection site pain',\n",
       " 'Insomnia',\n",
       " 'Intestinal obstruction',\n",
       " 'Musculoskeletal pain',\n",
       " 'Nasal congestion',\n",
       " 'Neck pain',\n",
       " 'Rotator cuff syndrome',\n",
       " 'Spinal fracture',\n",
       " 'Rash macular',\n",
       " 'Erythema',\n",
       " 'Irritability',\n",
       " 'Leukopenia',\n",
       " 'Injection site pruritus',\n",
       " 'Anaemia',\n",
       " 'Suicidal ideation',\n",
       " 'Thrombocytopenia',\n",
       " 'Drug intolerance',\n",
       " 'Gastritis',\n",
       " 'Herpes zoster',\n",
       " 'Hypertension',\n",
       " 'Osteoarthritis',\n",
       " 'Osteopenia',\n",
       " 'Rib fracture',\n",
       " 'Anger',\n",
       " 'Ear infection',\n",
       " 'Rheumatoid arthritis',\n",
       " 'Arthritis',\n",
       " 'Diabetes mellitus',\n",
       " 'Abdominal discomfort',\n",
       " 'Nasopharyngitis',\n",
       " 'Pain in extremity',\n",
       " 'Aggression',\n",
       " 'Emotional disorder',\n",
       " 'Gynaecomastia',\n",
       " 'Migraine',\n",
       " 'Obesity',\n",
       " 'Tardive dyskinesia',\n",
       " 'Abdominal pain upper',\n",
       " 'Gastric ulcer',\n",
       " 'Multi-organ failure',\n",
       " 'Subdural haematoma',\n",
       " 'Upper gastrointestinal haemorrhage',\n",
       " 'Disorientation',\n",
       " 'Loss of consciousness',\n",
       " 'Somnolence',\n",
       " 'Road traffic accident',\n",
       " 'Confusional state',\n",
       " 'Heart rate irregular',\n",
       " 'Platelet count decreased',\n",
       " 'White blood cell count increased',\n",
       " 'Tremor',\n",
       " 'Aphagia',\n",
       " 'Infection',\n",
       " 'Neutropenia',\n",
       " 'Haematochezia',\n",
       " 'Rectal haemorrhage',\n",
       " 'Vision blurred',\n",
       " 'Visual acuity reduced',\n",
       " 'Inflammation',\n",
       " 'Hypoaesthesia',\n",
       " 'Mobility decreased',\n",
       " 'Myalgia',\n",
       " 'Conjunctivitis',\n",
       " 'Chest discomfort',\n",
       " 'Viral infection',\n",
       " 'Musculoskeletal stiffness',\n",
       " 'Pneumonia',\n",
       " 'Condition aggravated',\n",
       " 'Disease progression',\n",
       " 'Ascites',\n",
       " 'Cardiac failure',\n",
       " 'Renal failure',\n",
       " 'Atrial fibrillation',\n",
       " 'Device dislocation',\n",
       " 'Scar',\n",
       " 'Blood glucose increased',\n",
       " 'Hepatic failure',\n",
       " 'Asthma',\n",
       " 'Amnesia',\n",
       " 'Diplopia',\n",
       " 'Hypothyroidism',\n",
       " 'Angina pectoris',\n",
       " 'Pancytopenia',\n",
       " 'Wheezing',\n",
       " 'Lung infiltration',\n",
       " 'Osteonecrosis of jaw',\n",
       " 'Poor quality sleep',\n",
       " 'Cataract',\n",
       " 'Decreased appetite',\n",
       " 'Dry mouth',\n",
       " 'Dyspnoea exertional',\n",
       " 'Ear pain',\n",
       " 'Eczema',\n",
       " 'Hyperglycaemia',\n",
       " 'Hypokalaemia',\n",
       " 'Oral pain',\n",
       " 'Pain in jaw',\n",
       " 'Pollakiuria',\n",
       " 'Shock',\n",
       " 'Skin discolouration',\n",
       " 'Snoring',\n",
       " 'Speech disorder',\n",
       " 'Urinary incontinence',\n",
       " 'Urticaria',\n",
       " 'Muscular weakness',\n",
       " 'Ulcer',\n",
       " 'Diverticulum',\n",
       " 'Hypoglycaemia',\n",
       " 'Gastric disorder',\n",
       " 'Hypercholesterolaemia',\n",
       " 'Nephrolithiasis',\n",
       " 'Osteoporosis',\n",
       " 'Pancreatitis',\n",
       " 'Chills',\n",
       " 'Gait disturbance',\n",
       " 'Influenza like illness',\n",
       " 'Toothache',\n",
       " 'Depressed level of consciousness',\n",
       " 'Staphylococcal infection',\n",
       " 'Tinnitus',\n",
       " 'Pericardial effusion',\n",
       " 'Drug abuse',\n",
       " 'Hypoxia',\n",
       " 'Renal failure acute',\n",
       " 'Rhabdomyolysis',\n",
       " 'Stomatitis',\n",
       " 'Pulmonary oedema',\n",
       " 'Glaucoma',\n",
       " 'Balance disorder',\n",
       " 'Pulmonary congestion',\n",
       " 'Feeling hot',\n",
       " 'Bundle branch block left',\n",
       " 'Chronic obstructive pulmonary disease',\n",
       " 'Hyperkalaemia',\n",
       " 'Syncope',\n",
       " 'Upper respiratory tract infection',\n",
       " 'Bruxism',\n",
       " 'Dental caries',\n",
       " 'Diverticulitis',\n",
       " 'Drug effect decreased',\n",
       " 'Gastrointestinal disorder',\n",
       " 'Hiatus hernia',\n",
       " 'Injection site reaction',\n",
       " 'Mouth ulceration',\n",
       " 'Influenza',\n",
       " 'Neuralgia',\n",
       " 'Neuropathy peripheral',\n",
       " 'Osteomyelitis',\n",
       " 'Tooth fracture',\n",
       " 'Sleep disorder',\n",
       " 'Hepatic cirrhosis',\n",
       " 'Lethargy',\n",
       " 'Blood pressure increased',\n",
       " 'Constipation',\n",
       " 'Glossodynia',\n",
       " 'Weight increased',\n",
       " 'Blood potassium decreased',\n",
       " 'Cellulitis',\n",
       " 'Exostosis',\n",
       " 'Oral infection',\n",
       " 'Tooth abscess',\n",
       " 'Intervertebral disc protrusion',\n",
       " 'Swelling',\n",
       " 'Sepsis',\n",
       " 'Oropharyngeal pain',\n",
       " 'Dysphagia',\n",
       " 'Ecchymosis',\n",
       " 'Eyelid oedema',\n",
       " 'Productive cough',\n",
       " 'Bone disorder',\n",
       " 'Panic attack',\n",
       " 'Epistaxis',\n",
       " 'Local swelling',\n",
       " 'Thrombosis',\n",
       " 'Respiratory failure',\n",
       " 'Suicide attempt',\n",
       " 'Pancreatitis acute',\n",
       " 'Presyncope',\n",
       " 'Pulmonary embolism',\n",
       " 'Mania',\n",
       " 'Palpitations',\n",
       " 'Cardiac disorder',\n",
       " 'Dyskinesia',\n",
       " 'Nervousness',\n",
       " 'Compression fracture',\n",
       " 'Pleural effusion',\n",
       " 'Dysstasia',\n",
       " 'Blood creatinine increased',\n",
       " 'Cardio-respiratory arrest',\n",
       " 'Mental status changes',\n",
       " 'Eye disorder',\n",
       " 'Ocular hyperaemia',\n",
       " 'Ejection fraction decreased',\n",
       " 'Leukocytosis',\n",
       " 'Blister',\n",
       " 'Drug withdrawal syndrome',\n",
       " 'Mood swings',\n",
       " 'Oral disorder',\n",
       " 'Bradycardia',\n",
       " 'Electrocardiogram QT prolonged',\n",
       " 'Vertigo',\n",
       " 'Hepatomegaly',\n",
       " 'Splenomegaly',\n",
       " 'Hot flush',\n",
       " 'Atrioventricular block complete',\n",
       " 'Cardiac arrest',\n",
       " 'Coronary artery occlusion',\n",
       " 'Coronary artery stenosis',\n",
       " 'Haematocrit decreased',\n",
       " 'Sinus bradycardia',\n",
       " 'Sinus tachycardia',\n",
       " 'Dysuria',\n",
       " 'Myocardial infarction',\n",
       " 'Abdominal distension',\n",
       " 'Drug dependence',\n",
       " 'Haemorrhage',\n",
       " 'Oesophagitis',\n",
       " 'Renal failure chronic',\n",
       " 'Cardiac failure congestive',\n",
       " 'Coronary artery disease',\n",
       " 'Hallucination, visual',\n",
       " 'Dysarthria',\n",
       " 'Incontinence',\n",
       " 'Myocardial ischaemia',\n",
       " 'Localised infection',\n",
       " 'Major depression',\n",
       " 'Liver function test abnormal',\n",
       " 'Cardiomegaly',\n",
       " 'Interstitial lung disease',\n",
       " 'Flatulence',\n",
       " 'Mitral valve incompetence',\n",
       " 'Pulmonary hypertension',\n",
       " 'Bronchitis',\n",
       " 'Aspartate aminotransferase increased',\n",
       " 'Muscle tightness',\n",
       " 'Hypotension',\n",
       " 'Blindness',\n",
       " 'Abnormal dreams',\n",
       " 'Oral candidiasis',\n",
       " 'Encephalopathy',\n",
       " 'Skin ulcer',\n",
       " 'Lacrimation increased',\n",
       " 'Erectile dysfunction',\n",
       " 'Hepatic steatosis',\n",
       " 'Arrhythmia',\n",
       " 'Atelectasis',\n",
       " 'Cardiac valve disease',\n",
       " 'Deafness',\n",
       " 'Dyslipidaemia',\n",
       " 'Gingival bleeding',\n",
       " 'Haematuria',\n",
       " 'Haemorrhoids',\n",
       " 'Hepatic cyst',\n",
       " 'Hypercalcaemia',\n",
       " 'Hyperlipidaemia',\n",
       " 'Hyperthyroidism',\n",
       " 'Hyponatraemia',\n",
       " 'Left ventricular hypertrophy',\n",
       " 'Libido decreased',\n",
       " 'Plasma cell myeloma',\n",
       " 'Skin lesion',\n",
       " 'Tachycardia',\n",
       " 'Thyroid neoplasm',\n",
       " 'Tooth infection',\n",
       " 'Tooth loss',\n",
       " 'Tricuspid valve incompetence',\n",
       " 'Arthropathy',\n",
       " 'Tooth disorder',\n",
       " 'Blood cholesterol increased',\n",
       " 'Aphthous stomatitis',\n",
       " 'Epilepsy',\n",
       " 'Retching',\n",
       " 'Impaired healing',\n",
       " 'Tendon rupture',\n",
       " 'Acute myocardial infarction',\n",
       " 'Acute respiratory failure',\n",
       " 'Coagulopathy',\n",
       " 'Device malfunction',\n",
       " 'Emphysema',\n",
       " 'Extrasystoles',\n",
       " 'Fluid overload',\n",
       " 'Gingival disorder',\n",
       " 'Haematoma',\n",
       " 'Metabolic acidosis',\n",
       " 'Paraesthesia oral',\n",
       " 'Peritonitis',\n",
       " 'Ventricular tachycardia',\n",
       " 'Colitis',\n",
       " 'Sedation',\n",
       " 'Disseminated intravascular coagulation',\n",
       " 'Jaundice',\n",
       " 'Sinus congestion',\n",
       " 'Blood alkaline phosphatase increased',\n",
       " 'Cystitis',\n",
       " 'Eye pain',\n",
       " 'Faecal incontinence',\n",
       " 'Small intestinal obstruction',\n",
       " 'Visual impairment',\n",
       " 'Joint range of motion decreased',\n",
       " 'Cachexia',\n",
       " 'Oral discomfort',\n",
       " 'Pelvic pain',\n",
       " 'Thyroid disorder',\n",
       " 'Angiopathy',\n",
       " 'Blood glucose decreased',\n",
       " 'Prostate cancer',\n",
       " 'Lymphadenopathy',\n",
       " 'Acute myeloid leukaemia',\n",
       " 'Thirst',\n",
       " 'Myopathy',\n",
       " 'Blood bilirubin increased',\n",
       " 'Ventricular hypertrophy',\n",
       " 'Ataxia',\n",
       " 'Blood glucose fluctuation',\n",
       " 'Gallbladder disorder',\n",
       " 'Fibrosis',\n",
       " 'Upper limb fracture',\n",
       " 'Atrial flutter',\n",
       " 'Muscle twitching',\n",
       " 'Lower limb fracture',\n",
       " 'Orthopnoea',\n",
       " 'Abdominal pain lower',\n",
       " 'Sinus disorder',\n",
       " 'Haematemesis',\n",
       " 'Cardiovascular disorder',\n",
       " 'Jaw disorder',\n",
       " 'Hepatitis',\n",
       " 'International normalised ratio increased',\n",
       " 'Febrile neutropenia',\n",
       " 'Disability',\n",
       " 'Renal tubular necrosis',\n",
       " 'Colon cancer',\n",
       " 'Dermatitis contact',\n",
       " 'Clostridium difficile colitis',\n",
       " 'Pregnancy',\n",
       " 'Irritable bowel syndrome',\n",
       " 'Type 2 diabetes mellitus',\n",
       " 'C-reactive protein increased',\n",
       " 'Lower gastrointestinal haemorrhage',\n",
       " 'Blood urine present',\n",
       " 'Gastritis erosive',\n",
       " 'Cyanosis',\n",
       " 'Kidney infection',\n",
       " 'Hypernatraemia',\n",
       " 'Neoplasm malignant',\n",
       " 'Acidosis',\n",
       " 'Paranoia',\n",
       " 'Diabetic retinopathy',\n",
       " 'Neoplasm progression',\n",
       " 'Gingival pain',\n",
       " 'Gamma-glutamyltransferase increased',\n",
       " 'Supraventricular tachycardia',\n",
       " 'Lymphoma',\n",
       " 'Blood urea increased',\n",
       " \"Dementia Alzheimer's type\",\n",
       " 'Ventricular extrasystoles',\n",
       " 'Eyelid ptosis',\n",
       " 'Subarachnoid haemorrhage',\n",
       " 'Agranulocytosis',\n",
       " 'Dilatation atrial',\n",
       " 'Obsessive-compulsive disorder',\n",
       " 'Adrenal insufficiency',\n",
       " 'Intentional drug misuse',\n",
       " 'Diarrhoea haemorrhagic',\n",
       " 'Diabetes mellitus inadequate control',\n",
       " 'Extrapyramidal disorder',\n",
       " 'Diabetic coma',\n",
       " 'Ventricular dysfunction',\n",
       " 'Application site erythema',\n",
       " 'Angina unstable',\n",
       " 'Pulmonary haemorrhage',\n",
       " 'Right ventricular failure',\n",
       " 'Lung neoplasm',\n",
       " 'Anorexia',\n",
       " 'Drug toxicity',\n",
       " 'Mucosal inflammation',\n",
       " 'Face oedema',\n",
       " 'Procedural pain',\n",
       " 'Tenderness',\n",
       " 'Mass',\n",
       " 'Cholecystitis',\n",
       " 'Cholelithiasis',\n",
       " 'Liver disorder',\n",
       " 'Hypoalbuminaemia',\n",
       " 'Hepatitis C',\n",
       " 'Hepatotoxicity',\n",
       " 'Metastases to liver',\n",
       " 'Stevens-Johnson syndrome',\n",
       " 'Dermatitis exfoliative',\n",
       " 'Drug hypersensitivity',\n",
       " 'Toxic epidermal necrolysis',\n",
       " 'Rhinitis allergic',\n",
       " 'Systemic lupus erythematosus',\n",
       " 'Immune system disorder',\n",
       " 'Bacterial infection',\n",
       " 'Fungal infection',\n",
       " 'Abscess',\n",
       " 'Device related infection',\n",
       " 'Wound infection',\n",
       " 'Lung infection',\n",
       " 'Bacteraemia',\n",
       " 'Septic shock',\n",
       " 'Chronic sinusitis',\n",
       " 'Rhinitis',\n",
       " 'Herpes simplex',\n",
       " 'Joint injury',\n",
       " 'Limb injury',\n",
       " 'Femoral neck fracture',\n",
       " 'Femur fracture',\n",
       " 'Foot fracture',\n",
       " 'Spinal compression fracture',\n",
       " 'Nephropathy toxic',\n",
       " 'Laceration',\n",
       " 'Back injury',\n",
       " 'Head injury',\n",
       " 'Contusion',\n",
       " 'Skin laceration',\n",
       " 'Cardiac murmur',\n",
       " 'Electrocardiogram abnormal',\n",
       " 'Pulse absent',\n",
       " 'Blood lactate dehydrogenase increased',\n",
       " 'Red blood cell count decreased',\n",
       " 'Neutrophil count increased',\n",
       " 'Laboratory test abnormal',\n",
       " 'Blood triglycerides increased',\n",
       " 'Blood albumin decreased',\n",
       " 'Protein total decreased',\n",
       " 'Malnutrition',\n",
       " 'Electrolyte imbalance',\n",
       " 'Hypovolaemia',\n",
       " 'Metabolic disorder',\n",
       " 'Bone lesion',\n",
       " 'Osteonecrosis',\n",
       " 'Spinal disorder',\n",
       " 'Joint swelling',\n",
       " 'Myositis',\n",
       " 'Fibromyalgia',\n",
       " 'Intervertebral disc disorder',\n",
       " 'Scoliosis',\n",
       " 'Musculoskeletal chest pain',\n",
       " 'Fistula',\n",
       " 'Groin pain',\n",
       " 'Bursitis',\n",
       " 'Breast cancer',\n",
       " 'Breast cancer female',\n",
       " 'Renal cyst',\n",
       " 'Ovarian cyst',\n",
       " 'Lung neoplasm malignant',\n",
       " 'Basal cell carcinoma',\n",
       " 'Skin cancer',\n",
       " 'Transient ischaemic attack',\n",
       " 'Sinus headache',\n",
       " 'Dementia',\n",
       " 'Cognitive disorder',\n",
       " 'Disturbance in attention',\n",
       " \"Parkinson's disease\",\n",
       " 'Coma',\n",
       " 'Motor dysfunction',\n",
       " 'Agitation',\n",
       " 'Unresponsive to stimuli',\n",
       " 'Hyperaesthesia',\n",
       " 'Peroneal nerve palsy',\n",
       " 'Sleep apnoea syndrome',\n",
       " 'Hallucination',\n",
       " 'Mental disorder',\n",
       " 'Anuria',\n",
       " 'Oliguria',\n",
       " 'Nocturia',\n",
       " 'Breast mass',\n",
       " 'Vaginal haemorrhage',\n",
       " 'Obstructive airways disorder',\n",
       " 'Acute respiratory distress syndrome',\n",
       " 'Pleurisy',\n",
       " 'Pneumothorax',\n",
       " 'Apnoea',\n",
       " 'Respiratory arrest',\n",
       " 'Aspiration',\n",
       " 'Lung disorder',\n",
       " 'Pulmonary mass',\n",
       " 'Dermatitis bullous',\n",
       " 'Skin disorder',\n",
       " 'Swelling face',\n",
       " 'Dermatitis',\n",
       " 'Skin exfoliation',\n",
       " 'Psoriasis',\n",
       " 'Rash generalised',\n",
       " 'Alopecia',\n",
       " 'Arteriosclerosis',\n",
       " 'Blood pressure fluctuation',\n",
       " 'Peripheral vascular disorder',\n",
       " 'Vitreous floaters',\n",
       " 'Iron deficiency anaemia',\n",
       " 'Normochromic normocytic anaemia',\n",
       " 'Increased tendency to bruise',\n",
       " 'Petechiae',\n",
       " 'Purpura',\n",
       " 'Blood disorder',\n",
       " 'Myelodysplastic syndrome',\n",
       " 'Lymphoedema',\n",
       " 'Lymphopenia',\n",
       " 'Neutropenic sepsis',\n",
       " 'Atrioventricular block',\n",
       " 'Cardiac flutter',\n",
       " 'Supraventricular extrasystoles',\n",
       " 'Torsade de pointes',\n",
       " 'Dizziness postural',\n",
       " 'Haemoptysis',\n",
       " 'Aortic valve incompetence',\n",
       " 'Arteriosclerosis coronary artery',\n",
       " 'Acute pulmonary oedema',\n",
       " 'Cardiomyopathy',\n",
       " 'Ischaemic cardiomyopathy',\n",
       " 'Diastolic dysfunction',\n",
       " 'Left ventricular dysfunction',\n",
       " 'Ventricular hypokinesia',\n",
       " 'Pericarditis',\n",
       " 'Peripheral swelling',\n",
       " 'Hearing impaired',\n",
       " 'Otitis media',\n",
       " 'Diabetic ketoacidosis',\n",
       " 'Diabetic neuropathy',\n",
       " 'Amenorrhoea',\n",
       " 'Type 1 diabetes mellitus',\n",
       " 'Goitre',\n",
       " 'Dry eye',\n",
       " 'Eye swelling',\n",
       " 'Eye haemorrhage',\n",
       " 'Eye pruritus',\n",
       " 'Optic neuritis',\n",
       " 'Photophobia',\n",
       " 'Retinal detachment',\n",
       " 'Retinal haemorrhage',\n",
       " 'Blindness transient',\n",
       " 'Blindness unilateral',\n",
       " 'Visual disturbance',\n",
       " 'Inguinal hernia',\n",
       " 'Proctalgia',\n",
       " 'Colonic polyp',\n",
       " 'Gingivitis',\n",
       " 'Melaena',\n",
       " 'Gastroenteritis',\n",
       " 'Colitis ischaemic',\n",
       " 'Colitis ulcerative',\n",
       " \"Crohn's disease\",\n",
       " 'Duodenitis',\n",
       " 'Impaired gastric emptying',\n",
       " 'Ileus paralytic',\n",
       " 'Eructation',\n",
       " 'Faeces discoloured',\n",
       " 'Abdominal tenderness',\n",
       " 'Gastrointestinal pain',\n",
       " 'Ileus',\n",
       " 'Duodenal ulcer',\n",
       " 'Intestinal perforation',\n",
       " 'Large intestine perforation',\n",
       " 'Oesophageal ulcer',\n",
       " 'Peptic ulcer',\n",
       " 'Intestinal ischaemia',\n",
       " 'Pancreatic carcinoma',\n",
       " 'Cheilitis',\n",
       " 'Lip swelling',\n",
       " 'Pharyngitis',\n",
       " 'Odynophagia',\n",
       " 'Hypoaesthesia oral',\n",
       " 'Gingival swelling',\n",
       " 'Oedema mouth',\n",
       " 'Tongue disorder',\n",
       " 'Swollen tongue',\n",
       " 'Infusion related reaction',\n",
       " 'Injection site erythema',\n",
       " 'Injection site haematoma',\n",
       " 'Injection site haemorrhage',\n",
       " 'Injection site mass',\n",
       " 'Injection site swelling',\n",
       " 'Feeling cold',\n",
       " 'Peripheral coldness',\n",
       " 'Temperature intolerance',\n",
       " 'Abasia',\n",
       " 'Cold sweat',\n",
       " 'Disease recurrence',\n",
       " 'Night sweats',\n",
       " 'Organ failure',\n",
       " 'Pallor',\n",
       " 'Hernia',\n",
       " 'Polyp',\n",
       " 'Generalised oedema',\n",
       " 'Discomfort',\n",
       " 'Facial pain',\n",
       " 'Flank pain',\n",
       " 'Non-cardiac chest pain',\n",
       " 'Drug interaction',\n",
       " 'Therapeutic response decreased',\n",
       " 'Therapeutic response unexpected',\n",
       " 'Cyst',\n",
       " 'Nodule',\n",
       " 'Necrosis',\n",
       " 'Cholecystitis acute',\n",
       " 'Cholecystitis chronic',\n",
       " 'Cholestasis',\n",
       " 'Liver injury',\n",
       " 'Hepatic function abnormal',\n",
       " 'Acute hepatic failure',\n",
       " 'Bronchospasm',\n",
       " 'Hypersensitivity',\n",
       " 'Angioedema',\n",
       " 'Vasculitis',\n",
       " 'Gangrene',\n",
       " 'Clostridial infection',\n",
       " 'Enterococcal infection',\n",
       " 'Pseudomonas infection',\n",
       " 'Meningitis',\n",
       " 'Respiratory tract infection',\n",
       " 'Bronchopneumonia',\n",
       " 'Lobar pneumonia',\n",
       " 'Furuncle',\n",
       " 'Laryngitis',\n",
       " 'Pyelonephritis',\n",
       " 'Tuberculosis',\n",
       " 'Cytomegalovirus infection',\n",
       " 'Fracture',\n",
       " 'Joint dislocation',\n",
       " 'Stress fracture',\n",
       " 'Ankle fracture',\n",
       " 'Humerus fracture',\n",
       " 'Wrist fracture',\n",
       " 'Muscle strain',\n",
       " 'Tendonitis',\n",
       " 'Nerve compression',\n",
       " 'Nerve injury',\n",
       " 'Excoriation',\n",
       " 'Drug administration error',\n",
       " 'Accidental overdose',\n",
       " 'Phlebitis',\n",
       " 'Medical device complication',\n",
       " 'Post procedural complication',\n",
       " 'Post procedural haemorrhage',\n",
       " 'Procedural complication',\n",
       " 'Blood pressure abnormal',\n",
       " 'Blood pressure systolic increased',\n",
       " 'Blood thyroid stimulating hormone increased',\n",
       " 'Troponin increased',\n",
       " 'Lipase increased',\n",
       " 'Activated partial thromboplastin time prolonged',\n",
       " 'International normalised ratio decreased',\n",
       " 'Eosinophil count increased',\n",
       " 'Lymphocyte count decreased',\n",
       " 'Neutrophil count decreased',\n",
       " 'Hepatic enzyme increased',\n",
       " 'Transaminases increased',\n",
       " 'Oxygen saturation decreased',\n",
       " 'Blood glucose abnormal',\n",
       " 'Glycosylated haemoglobin increased',\n",
       " 'Blood uric acid increased',\n",
       " 'Blood culture positive',\n",
       " 'Body temperature decreased',\n",
       " 'Body temperature increased',\n",
       " 'Breath sounds abnormal',\n",
       " 'Drug level increased',\n",
       " 'Blood calcium decreased',\n",
       " 'Blood chloride decreased',\n",
       " 'Blood magnesium decreased',\n",
       " 'Blood potassium increased',\n",
       " 'Lactic acidosis',\n",
       " 'Eating disorder',\n",
       " 'Increased appetite',\n",
       " 'Failure to thrive',\n",
       " 'Hypocalcaemia',\n",
       " 'Hypomagnesaemia',\n",
       " 'Hypophosphataemia',\n",
       " 'Vitamin B12 deficiency',\n",
       " 'Osteitis',\n",
       " 'Joint effusion',\n",
       " 'Psoriatic arthropathy',\n",
       " 'Muscle atrophy',\n",
       " 'Restless legs syndrome',\n",
       " 'Muscle rigidity',\n",
       " 'Intervertebral disc degeneration',\n",
       " 'Sciatica',\n",
       " 'Musculoskeletal discomfort',\n",
       " 'Back disorder',\n",
       " 'Musculoskeletal disorder',\n",
       " 'Sensation of heaviness',\n",
       " 'Synovial cyst',\n",
       " 'Tendon disorder',\n",
       " 'Metastatic neoplasm',\n",
       " 'Squamous cell carcinoma',\n",
       " 'Neoplasm',\n",
       " 'Uterine leiomyoma',\n",
       " 'Malignant melanoma',\n",
       " 'Cerebral haemorrhage',\n",
       " 'Cerebral infarction',\n",
       " 'Haemorrhage intracranial',\n",
       " 'Lacunar infarction',\n",
       " 'Carotid artery stenosis',\n",
       " 'Multiple sclerosis',\n",
       " 'Multiple sclerosis relapse',\n",
       " 'Hydrocephalus',\n",
       " 'Brain oedema',\n",
       " 'Akathisia',\n",
       " 'Movement disorder',\n",
       " 'Hemiparesis',\n",
       " 'Parkinsonism',\n",
       " 'Coordination abnormal',\n",
       " 'Aphasia',\n",
       " 'Altered state of consciousness',\n",
       " 'Nervous system disorder',\n",
       " 'Myoclonus',\n",
       " 'Neurological symptom',\n",
       " 'Personality change',\n",
       " 'Thinking abnormal',\n",
       " 'Formication',\n",
       " 'Sensory disturbance',\n",
       " 'Sensory loss',\n",
       " 'Aphonia',\n",
       " 'Dysphonia',\n",
       " 'Incoherent',\n",
       " 'Carpal tunnel syndrome',\n",
       " 'Grand mal convulsion',\n",
       " 'Hypersomnia',\n",
       " 'Brain injury',\n",
       " 'Cerebral atrophy',\n",
       " 'Neuropathy',\n",
       " 'Abortion spontaneous',\n",
       " 'Fear',\n",
       " 'Delirium',\n",
       " 'Delusion',\n",
       " 'Hallucination, auditory',\n",
       " 'Bipolar disorder',\n",
       " 'Affect lability',\n",
       " 'Frustration',\n",
       " 'Affective disorder',\n",
       " 'Apathy',\n",
       " 'Social avoidant behaviour',\n",
       " 'Abnormal behaviour',\n",
       " 'Psychotic disorder',\n",
       " 'Schizophrenia',\n",
       " 'Sexual dysfunction',\n",
       " 'Completed suicide',\n",
       " 'Bladder disorder',\n",
       " 'Tubulointerstitial nephritis',\n",
       " 'Nephropathy',\n",
       " 'Azotaemia',\n",
       " 'Hydronephrosis',\n",
       " 'Proteinuria',\n",
       " 'Polyuria',\n",
       " 'Renal pain',\n",
       " 'Acute kidney injury',\n",
       " 'Breast pain',\n",
       " 'Metrorrhagia',\n",
       " 'Menorrhagia',\n",
       " 'Benign prostatic hyperplasia',\n",
       " 'Vaginal discharge',\n",
       " 'Pneumonia aspiration',\n",
       " 'Pulmonary fibrosis',\n",
       " 'Respiratory distress',\n",
       " 'Tachypnoea',\n",
       " 'Sputum discoloured',\n",
       " 'Rales',\n",
       " 'Respiratory tract congestion',\n",
       " 'Choking',\n",
       " 'Increased upper airway secretion',\n",
       " 'Sneezing',\n",
       " 'Pharyngeal oedema',\n",
       " 'Hyperkeratosis',\n",
       " 'Dry skin',\n",
       " 'Rash erythematous',\n",
       " 'Rash papular',\n",
       " 'Photosensitivity reaction',\n",
       " 'Pruritus generalised',\n",
       " 'Rash maculo-papular',\n",
       " 'Decubitus ulcer',\n",
       " 'Acne',\n",
       " 'Skin haemorrhage',\n",
       " 'Bedridden',\n",
       " 'Aortic aneurysm',\n",
       " 'Aortic arteriosclerosis',\n",
       " 'Peripheral arterial occlusive disease',\n",
       " 'Peripheral ischaemia',\n",
       " 'Orthostatic hypotension',\n",
       " 'Thrombophlebitis superficial',\n",
       " 'Haemodynamic instability',\n",
       " 'Essential hypertension',\n",
       " 'Varicose vein',\n",
       " 'Large intestine polyp',\n",
       " 'Injection site bruising',\n",
       " 'Candida infection',\n",
       " 'Sudden death',\n",
       " 'Gastric haemorrhage',\n",
       " 'Application site pruritus',\n",
       " 'Neuroleptic malignant syndrome',\n",
       " 'Withdrawal syndrome',\n",
       " 'Cytolytic hepatitis',\n",
       " 'Hepatitis acute',\n",
       " 'Skin infection',\n",
       " 'Blood creatine phosphokinase increased',\n",
       " 'Blood iron decreased',\n",
       " 'VIIth nerve paralysis',\n",
       " 'Hemiplegia',\n",
       " 'Polyneuropathy',\n",
       " 'Status epilepticus',\n",
       " 'Respiratory depression',\n",
       " 'Arterial occlusive disease',\n",
       " 'Anaphylactic shock',\n",
       " 'Paralysis',\n",
       " 'Chromaturia',\n",
       " 'Generalised erythema',\n",
       " 'Throat irritation',\n",
       " 'Hyperbilirubinaemia',\n",
       " 'Sick sinus syndrome',\n",
       " 'Ventricular fibrillation',\n",
       " 'Acute coronary syndrome',\n",
       " 'Cardiac failure acute',\n",
       " 'Congestive cardiomyopathy',\n",
       " 'Glucose tolerance impaired',\n",
       " 'Eye irritation',\n",
       " 'Macular degeneration',\n",
       " 'Frequent bowel movements',\n",
       " 'Hiccups',\n",
       " 'Mouth haemorrhage',\n",
       " 'Oral herpes',\n",
       " 'Ageusia',\n",
       " 'Pharyngolaryngeal pain',\n",
       " 'Injection site urticaria',\n",
       " 'Injection site warmth',\n",
       " 'Erythema multiforme',\n",
       " 'Onychomycosis',\n",
       " 'Pelvic fracture',\n",
       " 'Blood count abnormal',\n",
       " 'Red blood cell sedimentation rate increased',\n",
       " 'Low density lipoprotein increased',\n",
       " 'Bone density decreased',\n",
       " 'Electroencephalogram abnormal',\n",
       " 'Body height decreased',\n",
       " 'Hypertriglyceridaemia',\n",
       " 'Gout',\n",
       " 'Joint stiffness',\n",
       " 'Hypotonia',\n",
       " 'Muscle spasticity',\n",
       " 'Limb discomfort',\n",
       " 'Metastases to spine',\n",
       " 'Cerebral ischaemia',\n",
       " 'Ischaemic stroke',\n",
       " 'Skin burning sensation',\n",
       " 'Initial insomnia',\n",
       " 'Psychiatric symptom',\n",
       " 'Throat tightness',\n",
       " 'Micturition urgency',\n",
       " 'Scab',\n",
       " 'Skin hyperpigmentation',\n",
       " 'Ischaemia',\n",
       " 'Blood pressure inadequately controlled',\n",
       " 'Embolism',\n",
       " 'Serotonin syndrome',\n",
       " 'Haemorrhagic anaemia',\n",
       " 'Cardiogenic shock',\n",
       " 'Gastric ulcer haemorrhage',\n",
       " 'Hypertonia',\n",
       " 'Feeling jittery',\n",
       " 'Haemolytic anaemia',\n",
       " 'Eosinophilia',\n",
       " 'Inappropriate antidiuretic hormone secretion',\n",
       " 'Toxic skin eruption',\n",
       " 'Rash pustular',\n",
       " 'Hand fracture',\n",
       " 'Concussion',\n",
       " 'Blood creatine increased',\n",
       " 'Hyperuricaemia',\n",
       " 'Lower respiratory tract infection',\n",
       " 'Blood pressure diastolic decreased',\n",
       " 'Hypokinesia',\n",
       " 'Pain of skin',\n",
       " \"Non-Hodgkin's lymphoma\",\n",
       " 'Atrioventricular block first degree',\n",
       " 'Bundle branch block right',\n",
       " 'Endocarditis',\n",
       " 'Ear discomfort',\n",
       " 'Mydriasis',\n",
       " 'Abdominal hernia',\n",
       " 'Diverticulum intestinal',\n",
       " 'Appendicitis',\n",
       " 'Ulcer haemorrhage',\n",
       " 'Hepatic encephalopathy',\n",
       " 'Nephrogenic systemic fibrosis',\n",
       " 'Streptococcal infection',\n",
       " 'Urosepsis',\n",
       " 'Accident',\n",
       " 'Wound dehiscence',\n",
       " 'Prostatic specific antigen increased',\n",
       " 'Glomerular filtration rate decreased',\n",
       " 'Chest X-ray abnormal',\n",
       " 'Bladder cancer',\n",
       " 'Cerebrovascular disorder',\n",
       " 'Middle insomnia',\n",
       " 'Decreased interest',\n",
       " 'Hyperventilation',\n",
       " 'Pleuritic pain',\n",
       " 'Nail disorder',\n",
       " 'Aneurysm',\n",
       " 'Thrombophlebitis',\n",
       " 'Hypertensive crisis',\n",
       " 'Pneumonia bacterial',\n",
       " 'Hepatocellular injury',\n",
       " 'Shock haemorrhagic',\n",
       " 'Haemorrhagic stroke']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PSEs = PSE['PSE'].unique().tolist()\n",
    "PSEs = ['Unnamed: 0'] + PSEs\n",
    "PSEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Arthralgia</th>\n",
       "      <th>Diarrhoea</th>\n",
       "      <th>Headache</th>\n",
       "      <th>Vomiting</th>\n",
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       "      <th>Hepatocellular injury</th>\n",
       "      <th>Shock haemorrhagic</th>\n",
       "      <th>Haemorrhagic stroke</th>\n",
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       "      <td>11.557043</td>\n",
       "      <td>159.488805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.131207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2097.678625</td>\n",
       "      <td>3469.919959</td>\n",
       "      <td>3942.120674</td>\n",
       "      <td>3739.098323</td>\n",
       "      <td>2166.102151</td>\n",
       "      <td>598.497136</td>\n",
       "      <td>1792.400738</td>\n",
       "      <td>450.065121</td>\n",
       "      <td>1732.131649</td>\n",
       "      <td>...</td>\n",
       "      <td>357.130567</td>\n",
       "      <td>84.966805</td>\n",
       "      <td>299.834820</td>\n",
       "      <td>31.816050</td>\n",
       "      <td>698.467021</td>\n",
       "      <td>130.788080</td>\n",
       "      <td>17.369556</td>\n",
       "      <td>290.119394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.647387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2169.295754</td>\n",
       "      <td>3590.788212</td>\n",
       "      <td>4079.078193</td>\n",
       "      <td>3869.892186</td>\n",
       "      <td>2235.652521</td>\n",
       "      <td>618.186508</td>\n",
       "      <td>1847.730402</td>\n",
       "      <td>461.310440</td>\n",
       "      <td>1795.426913</td>\n",
       "      <td>...</td>\n",
       "      <td>370.564360</td>\n",
       "      <td>88.271348</td>\n",
       "      <td>314.431532</td>\n",
       "      <td>31.401490</td>\n",
       "      <td>720.579870</td>\n",
       "      <td>133.591341</td>\n",
       "      <td>17.107904</td>\n",
       "      <td>314.949263</td>\n",
       "      <td>4.900596</td>\n",
       "      <td>70.222325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>131.475935</td>\n",
       "      <td>214.448113</td>\n",
       "      <td>272.905665</td>\n",
       "      <td>238.337711</td>\n",
       "      <td>139.492185</td>\n",
       "      <td>23.672694</td>\n",
       "      <td>173.859116</td>\n",
       "      <td>56.985875</td>\n",
       "      <td>130.850475</td>\n",
       "      <td>...</td>\n",
       "      <td>40.935875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.565521</td>\n",
       "      <td>7.856576</td>\n",
       "      <td>65.308120</td>\n",
       "      <td>7.856576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.821060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.821060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>300.003438</td>\n",
       "      <td>472.430492</td>\n",
       "      <td>555.908618</td>\n",
       "      <td>535.897454</td>\n",
       "      <td>332.465184</td>\n",
       "      <td>71.658370</td>\n",
       "      <td>305.196001</td>\n",
       "      <td>89.677354</td>\n",
       "      <td>245.429943</td>\n",
       "      <td>...</td>\n",
       "      <td>65.823370</td>\n",
       "      <td>7.773126</td>\n",
       "      <td>57.448701</td>\n",
       "      <td>8.148609</td>\n",
       "      <td>99.051288</td>\n",
       "      <td>23.995306</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.170209</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.948546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19550</th>\n",
       "      <td>19551</td>\n",
       "      <td>1002.150453</td>\n",
       "      <td>1448.841850</td>\n",
       "      <td>1703.825703</td>\n",
       "      <td>1558.113483</td>\n",
       "      <td>978.236699</td>\n",
       "      <td>257.906524</td>\n",
       "      <td>846.163221</td>\n",
       "      <td>224.892148</td>\n",
       "      <td>697.356179</td>\n",
       "      <td>...</td>\n",
       "      <td>133.170510</td>\n",
       "      <td>34.904913</td>\n",
       "      <td>142.228214</td>\n",
       "      <td>20.721509</td>\n",
       "      <td>248.871392</td>\n",
       "      <td>56.899097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.995135</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.639241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19551</th>\n",
       "      <td>19552</td>\n",
       "      <td>26.726783</td>\n",
       "      <td>44.415508</td>\n",
       "      <td>62.073964</td>\n",
       "      <td>44.425961</td>\n",
       "      <td>18.047588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.318659</td>\n",
       "      <td>8.668741</td>\n",
       "      <td>17.699179</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19552</th>\n",
       "      <td>19553</td>\n",
       "      <td>44.099734</td>\n",
       "      <td>79.031392</td>\n",
       "      <td>96.983502</td>\n",
       "      <td>79.016809</td>\n",
       "      <td>35.346855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.703207</td>\n",
       "      <td>8.767463</td>\n",
       "      <td>34.955981</td>\n",
       "      <td>...</td>\n",
       "      <td>8.688242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.964596</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.652838</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19553</th>\n",
       "      <td>19554</td>\n",
       "      <td>3102.454655</td>\n",
       "      <td>5135.359395</td>\n",
       "      <td>5871.016637</td>\n",
       "      <td>5670.388742</td>\n",
       "      <td>3200.975464</td>\n",
       "      <td>882.941648</td>\n",
       "      <td>2693.372110</td>\n",
       "      <td>651.813588</td>\n",
       "      <td>2578.409495</td>\n",
       "      <td>...</td>\n",
       "      <td>481.710675</td>\n",
       "      <td>122.345326</td>\n",
       "      <td>409.303916</td>\n",
       "      <td>60.257496</td>\n",
       "      <td>1073.531794</td>\n",
       "      <td>177.268953</td>\n",
       "      <td>25.170222</td>\n",
       "      <td>438.237176</td>\n",
       "      <td>4.888619</td>\n",
       "      <td>95.994955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19554</th>\n",
       "      <td>19555</td>\n",
       "      <td>232.005372</td>\n",
       "      <td>400.063869</td>\n",
       "      <td>481.678012</td>\n",
       "      <td>446.997762</td>\n",
       "      <td>278.465165</td>\n",
       "      <td>54.479375</td>\n",
       "      <td>259.793225</td>\n",
       "      <td>72.367485</td>\n",
       "      <td>216.139824</td>\n",
       "      <td>...</td>\n",
       "      <td>48.911530</td>\n",
       "      <td>7.560104</td>\n",
       "      <td>48.299540</td>\n",
       "      <td>7.922473</td>\n",
       "      <td>88.927698</td>\n",
       "      <td>23.318393</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.402248</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.412655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19555 rows × 965 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0   Arthralgia    Diarrhoea     Headache     Vomiting  \\\n",
       "0               1  1435.311474  2188.937071  2499.321092  2312.106667   \n",
       "1               2  2097.678625  3469.919959  3942.120674  3739.098323   \n",
       "2               3  2169.295754  3590.788212  4079.078193  3869.892186   \n",
       "3               4   131.475935   214.448113   272.905665   238.337711   \n",
       "4               5   300.003438   472.430492   555.908618   535.897454   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "19550       19551  1002.150453  1448.841850  1703.825703  1558.113483   \n",
       "19551       19552    26.726783    44.415508    62.073964    44.425961   \n",
       "19552       19553    44.099734    79.031392    96.983502    79.016809   \n",
       "19553       19554  3102.454655  5135.359395  5871.016637  5670.388742   \n",
       "19554       19555   232.005372   400.063869   481.678012   446.997762   \n",
       "\n",
       "         Dyspepsia  Renal impairment        Cough  Rhinorrhoea      Malaise  \\\n",
       "0      1386.644430        356.284702  1174.944377   310.639288  1084.879668   \n",
       "1      2166.102151        598.497136  1792.400738   450.065121  1732.131649   \n",
       "2      2235.652521        618.186508  1847.730402   461.310440  1795.426913   \n",
       "3       139.492185         23.672694   173.859116    56.985875   130.850475   \n",
       "4       332.465184         71.658370   305.196001    89.677354   245.429943   \n",
       "...            ...               ...          ...          ...          ...   \n",
       "19550   978.236699        257.906524   846.163221   224.892148   697.356179   \n",
       "19551    18.047588          0.000000    44.318659     8.668741    17.699179   \n",
       "19552    35.346855          0.000000    61.703207     8.767463    34.955981   \n",
       "19553  3200.975464        882.941648  2693.372110   651.813588  2578.409495   \n",
       "19554   278.465165         54.479375   259.793225    72.367485   216.139824   \n",
       "\n",
       "       ...  Hyperventilation  Pleuritic pain  Nail disorder   Aneurysm  \\\n",
       "0      ...        223.630871       58.239659     173.643861  20.867455   \n",
       "1      ...        357.130567       84.966805     299.834820  31.816050   \n",
       "2      ...        370.564360       88.271348     314.431532  31.401490   \n",
       "3      ...         40.935875        0.000000      24.565521   7.856576   \n",
       "4      ...         65.823370        7.773126      57.448701   8.148609   \n",
       "...    ...               ...             ...            ...        ...   \n",
       "19550  ...        133.170510       34.904913     142.228214  20.721509   \n",
       "19551  ...          0.000000        0.000000       0.000000   0.000000   \n",
       "19552  ...          8.688242        0.000000      16.964596   0.000000   \n",
       "19553  ...        481.710675      122.345326     409.303916  60.257496   \n",
       "19554  ...         48.911530        7.560104      48.299540   7.922473   \n",
       "\n",
       "       Thrombophlebitis  Hypertensive crisis  Pneumonia bacterial  \\\n",
       "0            417.227247            86.881479            11.557043   \n",
       "1            698.467021           130.788080            17.369556   \n",
       "2            720.579870           133.591341            17.107904   \n",
       "3             65.308120             7.856576             0.000000   \n",
       "4             99.051288            23.995306             0.000000   \n",
       "...                 ...                  ...                  ...   \n",
       "19550        248.871392            56.899097             0.000000   \n",
       "19551          0.000000             0.000000             0.000000   \n",
       "19552         25.652838             0.000000             0.000000   \n",
       "19553       1073.531794           177.268953            25.170222   \n",
       "19554         88.927698            23.318393             0.000000   \n",
       "\n",
       "       Hepatocellular injury  Shock haemorrhagic  Haemorrhagic stroke  \n",
       "0                 159.488805            0.000000            50.131207  \n",
       "1                 290.119394            0.000000            66.647387  \n",
       "2                 314.949263            4.900596            70.222325  \n",
       "3                   7.821060            0.000000             7.821060  \n",
       "4                  24.170209            0.000000            31.948546  \n",
       "...                      ...                 ...                  ...  \n",
       "19550              87.995135            0.000000            49.639241  \n",
       "19551               0.000000            0.000000             0.000000  \n",
       "19552               0.000000            0.000000             0.000000  \n",
       "19553             438.237176            4.888619            95.994955  \n",
       "19554              23.402248            0.000000            23.412655  \n",
       "\n",
       "[19555 rows x 965 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gse[PSEs].to_csv('../data/GNN-GSE_full_pkd.csv', index=False, sep = ',')\n",
    "gse[PSEs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making drugs specific graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-00ad9c6ba85b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Node file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnodes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../data/GNN-GSE_full_pkd_norm.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# Edge file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0medges\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../data/GNN-PPI-net.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "# Node file\n",
    "nodes = pd.read_csv('../data/GNN-GSE_full_pkd_norm.csv', sep=',')\n",
    "\n",
    "# Edge file\n",
    "edges = pd.read_csv('../data/GNN-PPI-net.csv', sep=',')\n",
    "\n",
    "# Drug-protein file (DTI)\n",
    "dti = pd.read_csv('../data/GNN-DTI_full.csv', sep=',')\n",
    "\n",
    "# DrugIds\n",
    "DrugID = pd.read_csv('../data/DrugID.csv', sep = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProteinID</th>\n",
       "      <th>Arthralgia</th>\n",
       "      <th>Diarrhoea</th>\n",
       "      <th>Headache</th>\n",
       "      <th>Vomiting</th>\n",
       "      <th>Dyspepsia</th>\n",
       "      <th>Renal impairment</th>\n",
       "      <th>Cough</th>\n",
       "      <th>Rhinorrhoea</th>\n",
       "      <th>Malaise</th>\n",
       "      <th>...</th>\n",
       "      <th>Hyperventilation</th>\n",
       "      <th>Pleuritic pain</th>\n",
       "      <th>Nail disorder</th>\n",
       "      <th>Aneurysm</th>\n",
       "      <th>Thrombophlebitis</th>\n",
       "      <th>Hypertensive crisis</th>\n",
       "      <th>Pneumonia bacterial</th>\n",
       "      <th>Hepatocellular injury</th>\n",
       "      <th>Shock haemorrhagic</th>\n",
       "      <th>Haemorrhagic stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.358786</td>\n",
       "      <td>0.331214</td>\n",
       "      <td>0.331587</td>\n",
       "      <td>0.317190</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.312375</td>\n",
       "      <td>0.338038</td>\n",
       "      <td>0.366366</td>\n",
       "      <td>0.324478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.369491</td>\n",
       "      <td>0.367760</td>\n",
       "      <td>0.331027</td>\n",
       "      <td>0.258567</td>\n",
       "      <td>0.298505</td>\n",
       "      <td>0.376681</td>\n",
       "      <td>0.326366</td>\n",
       "      <td>0.283259</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.406277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.524359</td>\n",
       "      <td>0.525044</td>\n",
       "      <td>0.523004</td>\n",
       "      <td>0.512955</td>\n",
       "      <td>0.523873</td>\n",
       "      <td>0.524737</td>\n",
       "      <td>0.515684</td>\n",
       "      <td>0.530804</td>\n",
       "      <td>0.518065</td>\n",
       "      <td>...</td>\n",
       "      <td>0.590064</td>\n",
       "      <td>0.536531</td>\n",
       "      <td>0.571592</td>\n",
       "      <td>0.394230</td>\n",
       "      <td>0.499718</td>\n",
       "      <td>0.567042</td>\n",
       "      <td>0.490509</td>\n",
       "      <td>0.515264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.540129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.542261</td>\n",
       "      <td>0.543332</td>\n",
       "      <td>0.541174</td>\n",
       "      <td>0.530898</td>\n",
       "      <td>0.540694</td>\n",
       "      <td>0.542000</td>\n",
       "      <td>0.531602</td>\n",
       "      <td>0.544067</td>\n",
       "      <td>0.536996</td>\n",
       "      <td>...</td>\n",
       "      <td>0.612260</td>\n",
       "      <td>0.557398</td>\n",
       "      <td>0.599418</td>\n",
       "      <td>0.389093</td>\n",
       "      <td>0.515539</td>\n",
       "      <td>0.579196</td>\n",
       "      <td>0.483120</td>\n",
       "      <td>0.559363</td>\n",
       "      <td>0.741488</td>\n",
       "      <td>0.569101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.032865</td>\n",
       "      <td>0.032449</td>\n",
       "      <td>0.036207</td>\n",
       "      <td>0.032697</td>\n",
       "      <td>0.033736</td>\n",
       "      <td>0.020755</td>\n",
       "      <td>0.050020</td>\n",
       "      <td>0.067209</td>\n",
       "      <td>0.039136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046831</td>\n",
       "      <td>0.097350</td>\n",
       "      <td>0.046725</td>\n",
       "      <td>0.034063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013891</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.063384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.074992</td>\n",
       "      <td>0.071485</td>\n",
       "      <td>0.073753</td>\n",
       "      <td>0.073518</td>\n",
       "      <td>0.080407</td>\n",
       "      <td>0.062827</td>\n",
       "      <td>0.087807</td>\n",
       "      <td>0.105765</td>\n",
       "      <td>0.073406</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108756</td>\n",
       "      <td>0.049084</td>\n",
       "      <td>0.109518</td>\n",
       "      <td>0.100969</td>\n",
       "      <td>0.070866</td>\n",
       "      <td>0.104034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042927</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.258920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19550</th>\n",
       "      <td>19551</td>\n",
       "      <td>0.250509</td>\n",
       "      <td>0.219228</td>\n",
       "      <td>0.226048</td>\n",
       "      <td>0.213752</td>\n",
       "      <td>0.236587</td>\n",
       "      <td>0.226122</td>\n",
       "      <td>0.243446</td>\n",
       "      <td>0.265237</td>\n",
       "      <td>0.208573</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220029</td>\n",
       "      <td>0.220411</td>\n",
       "      <td>0.271138</td>\n",
       "      <td>0.256758</td>\n",
       "      <td>0.178055</td>\n",
       "      <td>0.246690</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.402290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19551</th>\n",
       "      <td>19552</td>\n",
       "      <td>0.006681</td>\n",
       "      <td>0.006721</td>\n",
       "      <td>0.008235</td>\n",
       "      <td>0.006095</td>\n",
       "      <td>0.004365</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012751</td>\n",
       "      <td>0.010224</td>\n",
       "      <td>0.005294</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19552</th>\n",
       "      <td>19553</td>\n",
       "      <td>0.011024</td>\n",
       "      <td>0.011958</td>\n",
       "      <td>0.012867</td>\n",
       "      <td>0.010840</td>\n",
       "      <td>0.008549</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017752</td>\n",
       "      <td>0.010340</td>\n",
       "      <td>0.010455</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014355</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032341</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018353</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19553</th>\n",
       "      <td>19554</td>\n",
       "      <td>0.775524</td>\n",
       "      <td>0.777046</td>\n",
       "      <td>0.778912</td>\n",
       "      <td>0.777902</td>\n",
       "      <td>0.774158</td>\n",
       "      <td>0.774126</td>\n",
       "      <td>0.774898</td>\n",
       "      <td>0.768745</td>\n",
       "      <td>0.771179</td>\n",
       "      <td>...</td>\n",
       "      <td>0.795900</td>\n",
       "      <td>0.772562</td>\n",
       "      <td>0.780279</td>\n",
       "      <td>0.746645</td>\n",
       "      <td>0.768059</td>\n",
       "      <td>0.768563</td>\n",
       "      <td>0.710797</td>\n",
       "      <td>0.778327</td>\n",
       "      <td>0.739676</td>\n",
       "      <td>0.777970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19554</th>\n",
       "      <td>19555</td>\n",
       "      <td>0.057995</td>\n",
       "      <td>0.060535</td>\n",
       "      <td>0.063905</td>\n",
       "      <td>0.061322</td>\n",
       "      <td>0.067347</td>\n",
       "      <td>0.047765</td>\n",
       "      <td>0.074744</td>\n",
       "      <td>0.085350</td>\n",
       "      <td>0.064645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080813</td>\n",
       "      <td>0.047739</td>\n",
       "      <td>0.092076</td>\n",
       "      <td>0.098167</td>\n",
       "      <td>0.063623</td>\n",
       "      <td>0.101099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041563</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.189743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19555 rows × 965 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ProteinID  Arthralgia  Diarrhoea  Headache  Vomiting  Dyspepsia  \\\n",
       "0              1    0.358786   0.331214  0.331587  0.317190   0.335361   \n",
       "1              2    0.524359   0.525044  0.523004  0.512955   0.523873   \n",
       "2              3    0.542261   0.543332  0.541174  0.530898   0.540694   \n",
       "3              4    0.032865   0.032449  0.036207  0.032697   0.033736   \n",
       "4              5    0.074992   0.071485  0.073753  0.073518   0.080407   \n",
       "...          ...         ...        ...       ...       ...        ...   \n",
       "19550      19551    0.250509   0.219228  0.226048  0.213752   0.236587   \n",
       "19551      19552    0.006681   0.006721  0.008235  0.006095   0.004365   \n",
       "19552      19553    0.011024   0.011958  0.012867  0.010840   0.008549   \n",
       "19553      19554    0.775524   0.777046  0.778912  0.777902   0.774158   \n",
       "19554      19555    0.057995   0.060535  0.063905  0.061322   0.067347   \n",
       "\n",
       "       Renal impairment     Cough  Rhinorrhoea   Malaise  ...  \\\n",
       "0              0.312375  0.338038     0.366366  0.324478  ...   \n",
       "1              0.524737  0.515684     0.530804  0.518065  ...   \n",
       "2              0.542000  0.531602     0.544067  0.536996  ...   \n",
       "3              0.020755  0.050020     0.067209  0.039136  ...   \n",
       "4              0.062827  0.087807     0.105765  0.073406  ...   \n",
       "...                 ...       ...          ...       ...  ...   \n",
       "19550          0.226122  0.243446     0.265237  0.208573  ...   \n",
       "19551          0.000000  0.012751     0.010224  0.005294  ...   \n",
       "19552          0.000000  0.017752     0.010340  0.010455  ...   \n",
       "19553          0.774126  0.774898     0.768745  0.771179  ...   \n",
       "19554          0.047765  0.074744     0.085350  0.064645  ...   \n",
       "\n",
       "       Hyperventilation  Pleuritic pain  Nail disorder  Aneurysm  \\\n",
       "0              0.369491        0.367760       0.331027  0.258567   \n",
       "1              0.590064        0.536531       0.571592  0.394230   \n",
       "2              0.612260        0.557398       0.599418  0.389093   \n",
       "3              0.067636        0.000000       0.046831  0.097350   \n",
       "4              0.108756        0.049084       0.109518  0.100969   \n",
       "...                 ...             ...            ...       ...   \n",
       "19550          0.220029        0.220411       0.271138  0.256758   \n",
       "19551          0.000000        0.000000       0.000000  0.000000   \n",
       "19552          0.014355        0.000000       0.032341  0.000000   \n",
       "19553          0.795900        0.772562       0.780279  0.746645   \n",
       "19554          0.080813        0.047739       0.092076  0.098167   \n",
       "\n",
       "       Thrombophlebitis  Hypertensive crisis  Pneumonia bacterial  \\\n",
       "0              0.298505             0.376681             0.326366   \n",
       "1              0.499718             0.567042             0.490509   \n",
       "2              0.515539             0.579196             0.483120   \n",
       "3              0.046725             0.034063             0.000000   \n",
       "4              0.070866             0.104034             0.000000   \n",
       "...                 ...                  ...                  ...   \n",
       "19550          0.178055             0.246690             0.000000   \n",
       "19551          0.000000             0.000000             0.000000   \n",
       "19552          0.018353             0.000000             0.000000   \n",
       "19553          0.768059             0.768563             0.710797   \n",
       "19554          0.063623             0.101099             0.000000   \n",
       "\n",
       "       Hepatocellular injury  Shock haemorrhagic  Haemorrhagic stroke  \n",
       "0                   0.283259            0.000000             0.406277  \n",
       "1                   0.515264            0.000000             0.540129  \n",
       "2                   0.559363            0.741488             0.569101  \n",
       "3                   0.013891            0.000000             0.063384  \n",
       "4                   0.042927            0.000000             0.258920  \n",
       "...                      ...                 ...                  ...  \n",
       "19550               0.156283            0.000000             0.402290  \n",
       "19551               0.000000            0.000000             0.000000  \n",
       "19552               0.000000            0.000000             0.000000  \n",
       "19553               0.778327            0.739676             0.777970  \n",
       "19554               0.041563            0.000000             0.189743  \n",
       "\n",
       "[19555 rows x 965 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# featurs need normalization\n",
    "'''\n",
    "cols = nodes.columns.values.tolist()[1:] # exclude ProteinID\n",
    "for PSE in tqdm(cols):\n",
    "    nodes[PSE] = nodes[PSE]/nodes[PSE].max() #Norm by PSE dist.\n",
    "'''\n",
    "nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protein1</th>\n",
       "      <th>protein2</th>\n",
       "      <th>neighborhood</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1759</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>15624</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>11738325</th>\n",
       "      <td>19555</td>\n",
       "      <td>6049</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11738326</th>\n",
       "      <td>19555</td>\n",
       "      <td>8711</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11738327</th>\n",
       "      <td>19555</td>\n",
       "      <td>9472</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11738328</th>\n",
       "      <td>19555</td>\n",
       "      <td>12490</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11738329</th>\n",
       "      <td>19555</td>\n",
       "      <td>9437</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11738330 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          protein1  protein2  neighborhood\n",
       "0                1      3469             1\n",
       "1                1      1759             1\n",
       "2                1     15624             1\n",
       "3                1     16446             1\n",
       "4                1      7409             1\n",
       "...            ...       ...           ...\n",
       "11738325     19555      6049             1\n",
       "11738326     19555      8711             1\n",
       "11738327     19555      9472             1\n",
       "11738328     19555     12490             1\n",
       "11738329     19555      9437             1\n",
       "\n",
       "[11738330 rows x 3 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DrugID</th>\n",
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       "  <tbody>\n",
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       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8914333</th>\n",
       "      <td>71306834</td>\n",
       "      <td>19532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8914334</th>\n",
       "      <td>71306834</td>\n",
       "      <td>19535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8914335</th>\n",
       "      <td>71306834</td>\n",
       "      <td>19539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8914336</th>\n",
       "      <td>71306834</td>\n",
       "      <td>19544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8914337</th>\n",
       "      <td>71306834</td>\n",
       "      <td>19554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8914338 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DrugID  ProteinID\n",
       "0              85         32\n",
       "1              85         90\n",
       "2              85        100\n",
       "3              85        119\n",
       "4              85        153\n",
       "...           ...        ...\n",
       "8914333  71306834      19532\n",
       "8914334  71306834      19535\n",
       "8914335  71306834      19539\n",
       "8914336  71306834      19544\n",
       "8914337  71306834      19554\n",
       "\n",
       "[8914338 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Dataset for Graph Classification from CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. graph_edges.csv\n",
    "\n",
    "containing three columns:\n",
    "- graph_id: the ID of the graph.\n",
    "- src: the source node of an edge of the given graph.\n",
    "- dst: the destination node of an edge of the given graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate drug specific ppi subgraph for GNN edges\n",
    "def drug2ppi(drug):\n",
    "    genes = dti['ProteinID'].loc[dti['DrugID'] == drug].tolist()\n",
    "    df = edges[['protein1','protein2']].loc[edges['protein1'].isin(genes)]\n",
    "    df = df.loc[df['protein2'].isin(genes)]\n",
    "    df['graph_id'] = DrugID.loc[DrugID['DrugID'] == drug]['GraphID'].tolist()[0]  #DrugID\n",
    "    df = df.rename(columns={'protein1': 'src_prot', 'protein2': 'dst_prot'}) # prot: actual protein id\n",
    "    final_genes =df['src_prot'].unique().tolist() # final genes that have ppi data\n",
    "    dic = {gene:final_genes.index(gene) for gene in final_genes} # conversion dic, starts at 0\n",
    "    df['src'] = df['src_prot'].map(dic) #local ids\n",
    "    df['dst'] = df['dst_prot'].map(dic) #local ids\n",
    "    return(df[['graph_id', 'src', 'dst', 'src_prot', 'dst_prot']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2049bf3a877a46ef90f5c541e4c6be13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=20.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Edges of the garphs, graph_edges.csv\n",
    "GNN_edges = pd.DataFrame(columns=['graph_id', 'src', 'dst', 'src_prot', 'dst_prot'])\n",
    "\n",
    "for drug in tqdm(dti['DrugID'].unique().tolist()[:20]):\n",
    "    GNN_edges = pd.concat([GNN_edges, drug2ppi(drug)], ignore_index=True)\n",
    "    #drug2ppi(drug).to_csv('../data/GNN_edges/'+str(drug)+'.csv', index=False, sep = ',')\n",
    "    \n",
    "GNN_edges.to_csv('../data/GNN_edges-toy2.csv', index=False, sep = ',')\n",
    "GNN_edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.graph_properties.csv: containing three columns:\n",
    "\n",
    "- graph_id: the ID of the graph.\n",
    "-label: the label of the graph.\n",
    "-num_nodes: the number of nodes in the grap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# counts how many nodes a drug2ppi has\n",
    "def drug2num_nodes(drug):\n",
    "    genes = dti['ProteinID'].loc[dti['DrugID'] == drug].tolist()\n",
    "    df = edges[['protein1','protein2']].loc[edges['protein1'].isin(genes)]\n",
    "    df = df.loc[df['protein2'].isin(genes)]\n",
    "    return(len(df['protein1'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "937fee0733014835bc47672ff3a933f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=1.0, bar_style='info', layout=Layout(width='20px'), max=1.0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#labes of the garphs, graph_properties.csv\n",
    "rows = []\n",
    "for i,drug in tqdm(enumerate(dti['DrugID'].unique().tolist())):\n",
    "    row = {'graph_id': i+1,\n",
    "           'label': drug, #drugID\n",
    "           'num_nodes': drug2num_nodes(drug)}\n",
    "    rows.append(row)\n",
    "    \n",
    "GNN_properties = pd.DataFrame.from_dict(rows)   \n",
    "GNN_properties.to_csv('../data/GNN_properties.csv', index=False, sep = ',')\n",
    "GNN_properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 11.9 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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zkcvliEajO1RGFOW0VCq1S6SxbDYb6XQat9stT2gCs9mMz+fDZDJt1wCMy+Uim81is9l2usj37kZkQ7WmVROJREWwMJvNIyytdrSMl8lksFgs29Vf3dbnJ5MZYO7cyhJkNWLpvZpIJILT6Ry1V5tKpeQCfnmQ7ujooLm5edT7iYAXi8WwWCxks1kWLXJu06vv/ZJIJEgkEvh8vvdUdVHsPajAt4/icrnwer3EYrEdmoJ0OBxcdpltl/RDbDabDGg+n494PD6iTyd0S0Wva2u+hS6Xi3w+T6lUes+TonuarY3uw7bNXrc34xKUSiWy2ex2n5gTicSoJWhN00in0/z1r/lRg0oul8NoNFZkPpqmMTg4iMfjGTVj6+9PYTAYsFgsMtsF6O7upr6+vmZfUqxliCEdh8NBKBRizJgxwK7LuLLZLNFoFLvdvlPWPRR7HhX49mFEGdFms233+kMiAQ8+qGcCCxdCOLxz1x0cDofM0AKBAJFIpGZWIQZ30un00Mls5G3ESdNut8vhiH2J7Ql6hUJhq0HKZErLMt6cOaltnty3totXa0BE07RR+4nBYBCPx4PNZqv5vJqmkUqlcLlcpFIpHA4HpVJJrmaMlnFeeGGB5mYnV1yhf1ZEJaKnpwev1zti0EeUF4WGqNfrJZ/PMzAwQGtr6y5bcRCvpVZZU7FvowLffoAoI26P+7vbjZxAu/DCIrnc4A6XTLeG1Wqt6D+K4FcLTdPQNI2Pf9yK12vgggtGBmG32y3LS9FodKcc4+5A7CNuzTJKWFWNRjKZRNM0br89QjwOf/5zdqtOHMlkEpfLtd2BIJfLVWRb5RSLRQqFglyFqEV5kNWHr/TvBQKBUY+hry/Jgw/qAWThQigU7EPf78PpdFZknyLgJZNJGfAMBgO5XI5QKERjY+MuUydKJpOqrLkfo6Y690O2x/1dTKCJVQdN097zxGg1+XyeXC4ne1fFYpFIJFKzpBcO5xk/vk5+vXp1N62tnoqeTy6XI5/PY7fb5Ulwbyafz5NOp7fLNdxsNo8oNcbjcQwGg+z35XI5Od3a39+P2+0ekRVVv+fViIGj8vttLTvs7+/H4/FIQ9hqMpkMmqbJz8u8eVkeecS21clPIajw6U97ePBBE/Pm5XjoISvBYFCWwGF4gtloNI547lwuJyX5dsWEsvDOdDqdo14UKPZ9VMa3H7I97u/iXCvcIQwGA0ajkUgkstWMcXsQ/bt4PE48HpdL1AAej0f2Z4rFImPHBmQGesEFBQ46qI1isUhXV9eQ9JUekIvFIgaDAavVutOHXXbmoE950Nva44pl7lqBKh6Py0nIfD5fkdX4/X5SqdSIsrbI9kZjW9llOblcjlKpNOrtq/uI4XCeRx7Rs8LR+pDiYsxsNvPAAyZ6e5MsXGgkFArJiWXhV5lOp/H5fCOCXj6fl1J8OzvoiWpJoVDA5/OpoLefowLfforRaJR9iW1NUYpML5vNyhNrYpSzdvm3NU2Ty/Xl/xmNRpqbmzEYDDLQ+f1+ueAudCmFXJeQPrvrrgz5fB6/3097eztOp5Pu7m4GBwcxmUxShaZUKu00Kbadufgsgp7b7eaCCwpbfdxcLofVah2RkUciERwOB1arVS7xl2eEVqsVi8VSMTUbjUa3mV1qmlbxXFsbail/vFofg+pMMZ0OsmCBXjiqNfkpVh2KxSJut3toZ7BAPB4fklHTh7QymQw+n6/mceXzeWKxGE6nc6cv+yeTSfmaxGPvDYbAil2HKnUeIAgZsa3Jn+XzeamjKAYz7Ha77PGIReZyPzen0zlqn6WWyn4wGMRsNo+6mB6JRPD5fCNO0tlsllKphKZpciim+nY7yo4uPhcKhVH3EHO5HNlsFo/HQzJpqPCLCwYzNDRUZijRaBSDwSADiLDoEYo3Ho+HaDRas9wHyKEhm81WIRgwGtXBarQyZzqdJpFI0NjYyIIFGvffb6goX6ZSKcxmsyxb5/N5BgcHaW5urrnALZbaU6mU/J3HYjGKxaK8SKi1/1dOPp+XwXNnlrnF5726rLmz7LYUey8q8B1AbI/7e/UkYiqVIpfLYTL5dtgotVQqkUwm8Xg80n7H7XaTzWaxWCyjjqzXEmwWx55IJAgEAuRyOWKxGK2tre9r2m7OnBSPP+7kzDPj3Hnn6FkQ6Iv1DodjRLAtD3qC+fN1Y91583L87ndhSqUSra2tgJ5hCM1Kk8lUMQlpMBhkqTMe17DZ8jUDXzablf0uYdOzNcoDXT6fJ5/P18ycBgcHsdlsaJprxEWB3a4H/vLj2VpwjsfjFbud4rV2dHRQKBRobW3dZvYmglOpVNppKj5bc0FRupsHBqrUeQAhMgyHw0E0Gq2pECJOBmJ6UOhulkoxLrhAL63NnZuhWNz2hKXRaJRlSSGfZbFYZPArX3AvP0aPxzNCm1Qce1NTE6FQCIvFQmtrK6FQSC4zl1/DlUolensTI8qw5f/19PTwu98NsnJlJ0884ZGSa6I8W/2fUBcpp1bQA7jtthDhcIGFC424XC7q6+vp6OiQU7cGg0GW/4R2Z/ljX3KJgcZGO5/4RO0+m81mIxwO09raul3TruWPLYY3qkkkEmiahsvloliMcs45+n6oKF8mEomK1ykCUq3HEkFPDCWZTCZyuRzr16+nWCwyefLk7Qp6oty7s6yqhH6s1+sdEfR02b2k0t08AFAZ3wFMLb1BgTAKLb/KzuVyBINpGhsdMnMUMmqjkU6nCYfDtLW1jfiZmM6rdf90Oo3BYBhRwhNBtFAoyL0+i8WC3W6XfUmz2cznPx/YqumqOKmKE7LNZsPhcDAwMIDJZNrqSL4gm81KF4pquru75WtOJpOyNxcOh+nr66OpqUk6WVSXg7dsGayYdK2VdcTjcTnl6nQ6yeVyW81Wy50ZapU5RanVarXicrlYv349Y8aMoVCw43br93c4HPJzIrKmWtme6MVqmkaxWMRkMsnPWiwWY8qUKVt9X6Ey6I32GdkRROYo+qfVVEurKd3N/RuV8R3ACPmzbDY7ImMS/ZRwOCy/Z7VaaW/X5dKE6kY8Hh91cGZ4QMZdc0/Q7/fLBeF0Ol2RjRUKBcLhMOFweMT3GxoacDgctLW1UV9fTz6fx2w209bWRltbG1ZrHQ89pPdsRpsyFJOhIugXCgUMBgMNDQ2y3Lq1AZqtBb1qXC6XVNcxmUyMGzeOgYEBOjs7RwQ93ZZpgHnz9Pf03HOzmEyVfcVsNovRaJTl4lKphMlk2i4d1GqJNEF5Cbyrq4u6ujrsdj3o1VJnGa1HGIvFpMNGLBYjn8/LydyBgQEmT568zWMUQQ/0CeX3E/TEtGYul5Nel+UUCoWak6Li16qGXPZPVOBT4HK58Hg8I+TPxJj54OBgReByu934fD5ZGhPmogIROGw2G1dc4aC93cu552ZqlhvNZjNdXV2YzeYRpUXRE3O73RVrEEKxRmQ97e3thMNhGUT9frMsV519dpJ0OkgsFpOl1dFO/qCXBEWWm8lkagaTbQW96glKoKJ8m0wmaW9vp6WlhY6ODpLJpJTFAmhqauJ3vwvT1RXjz3/OSKNasfCfTqfl8TscDjKZDHa7Xe78bQ0RJMsRXopGo1G+p+WZnVBnEYgLjUKhMCIYOhwO6Zbu8XhkRaCrq4tJkyZtM4suz8RtNtv76t8K14daZU3xczFdXCu4KnPZ/RdV6lRUIEpC5dOc1cMX5YhdO5fLJVX2hQtBMmmoGJCIRks1HcDFwnItlRNxxV6rx1M+YShKdWI6z+PxMDiYw2bLy5KtCAzpdJr6+nppxSMCUnnJrtwGSozhJxJgNo9upiooH9MvR1xYiHUP0DOOLVu2oGkaEydOJB6P4/P5WL9+PRMnTpTH7HQ6pSxdU1NTxe+hVCqRSCTwer1Eo9GaBsAioNUaahEB1+v10tnZid/vx+12YzAYiEajIyaBxbRueflUDLlomkYmk6GxsRGDwUA+n6e3txe3273N4RQR9MTv8L2KKdSaSC5HlGn192OkHFsmk2FwMEd7+/DvTw257F+ojE9RUc6xWCxDwyzD9itGo5FAIEA4HB5RsmxoaMBisQzJSGXp6emR+3nl8mjnn59H02qb6ZpMJhkwqzEYDDidzppC3E6nU2Zk5asBLpeLaDSKzZanWCwOBeEkZrMZo9Eoh2LKM1Wj0VixuC+OSYhIn39+Do8HPvaxkUor1ZRKpZqZTTKZlIo64v3NZrNMmjSJCRMm0NnZKRe4rVarLGcWCgVZLhRO6uWIMqQo59Uy/xWZYnXQy+VymM1mDAYDPT09tLS0yIw1k8nIdQmB6JOV09vbKw1vRcZsMBgoFAr09vbi9Xq3+Z6JoCf2NN9r0CvfB6wV9PL5vAzm5WLtYuhFrFrk82Hmz9d7yGrIZf9DZXwHOBddpLFwoWHUIZDybEio7tfV1Y04sc+dm2LRIifnnJPiN78J0tzcXDEoUCxGpcfeaOUrMelZqywl1F+qT2bCtFZkHiIQipJbf38/9fX1ZDIZ6Rcoyrper5dCoUB/f790gmhoaKh4fBHQ29qGs8HOzijt7aNrcPb19VFfXz+iDJjP5zEajfJ4qyXlRMDq7u7G5XIxbtw4YNjtwGq14vF4pLefy+Wq2D8Tk7PVWbJQYsnlciOyUJHtmUwmOWgjgqzIPgUiUxK36e/vl++Z3W6vGEgSQa+pqUnuXI6GCHpOp5NUKvWedvXS6TS5XG6re6qpVIpUKoXNZiORoOJ3OjiYJxDQe9bRaFQ6Pqghl/0TlfEdwOiWN/qJd7QhEDHGL9Yf6urqCIfDFU4JiQQsWqRnEo8/7sRqraO7u5v+/v6hsp+uPRmLxeT3yv8TvmwiY6jVV3M6nWQymRFyasK0VgyiOBwOCoWCNHcVAbFUKsnsTp8K1YOy6C2Kk7m46hf9QKvVSl2dVQ6bzJmTJpsN0dnZSXd3d015NyHNJRDL6gaDgWQyid/vl69VIPp3Pp9vyIk8SU9PD4AsGYrBDJPJhN/vJ5fLVSjsiD6inm275UWLkEerzvbS6bS8kBgcHKSpqUma0tYaXhHfKz+21tZW7HY7uVyOYrEoe41icjWTyWzVELdc7UaUa3cEIXNWPWGczWbl5ysWi9HV1SUvnvR+Zorzz9c/MwsWQCBgIRgMkk6nZdADFfT2V1TGd4AjVCouuKDIH/8Y36oaihhJdzgccsldnGiGH6fAAw+YZUZgMBhoamoC9KGSYrE4QimjUChI0WPQS4IWi6Xm2HkkEqGpqWmE3Fe1Skw4HMbv98sgKLQt0+k0V17pY9Eip8xyxbBL+Z5aMpmU+3Zut5uBgQHicY2GBl3XVBjLioEaTXMxZoyfTCZDMpmUPcRIJILFYiESidDc3CwDQbVCjcjgcrmcXH+w2Wz09fVhNBppaWmp2QcVJrdutxuz2VyxOpHNZqXLAoDR6K04kYuAnEgkqKurk2VUk8k04v3P5/OEw2HsdjtOp5Pe3l5aW1vlUrrIDvX9yV7q6uqwWq1SwKAWIuh5PB7C4TB1dXU1bzcaYsq3VgVBvH/ZbJaBgQE5HepyueRqhtVqlRldV1cXHo9nrxdAV+wcVOBTyD9+USKz2+1blcASyiPFYlG6qovHgQQ2m00GtmQySSgUoq2tTeqGAlstfQFSl7H6pCbWHKqPTwQ3USbVNI3e3gStrXoWJMbz16/vY8qUZnm/eBzZCxSSbuWUK8YUCgVsNpsMYGIM/lOfcrNwoa4Cc8stXTQ0NOD3++nv78dms8lsFZDlzfKyIQwH7lgsJoOhx+Mhk8kQDocpFAo0NjaOOlijZ85GmptdsuQJenkvHo/zhS/U89BD5opgb7fbCYVCADQ3N8upzOosLZFIEIlEZKALh8MYDAb5HOFwmEAgIMu0dXV1UiRha16EQsy7lkxdNaKkDXoGK5woxP5lOWLYJ5VKyQsvg8FAqVSSWau4YCsUCnR2dg6twYxuCKzYv1CBTzEC0Q8T/me1KA8Izc3NFVOEItsS99U0ja6urqHJTzsWi95r29aJZrQFd5HJVJfuxMncbDbLDHTevDw/+9lmedtoNMpXv9rOU095+chHgvzhDzEaGxvlFGCxWKSurm7Ec5brS+ZyORkEOjrCTJrUKG+3cmUnmqaXb1tbW2lra6OvL0l9vW1E/0oEQzEYIi4MRFlUZCeiDNvd3U0+n2fs2LEjpjaHX2+Oe++lYqH9tdfe5bjjDpa37eyM4nAUsVgsDAwMMHHiRPkahUqKpmlyXcVgMMiLGSGiLSyJxBSp0WiURrJut3urNknlQa/6Akc8bzVGoxGbzUYymZSOIuUUi8WKASgxiSsuwMQ0b3kgTiaTDA4OMmbMmF1mZqvYO1GBT1GT7c3+CoUCPT091NXVVWRbtVYQhGfbOeckuf326FZ7P4LRsoHykl45eo/LUzG4sHTpBg45ZKI03DUajQwO5nA4ioRCIVwuF1arlba2NlmaLO9hCi874ZTQ398vs4zPf76Oxx7T3585c9L86ld9xGIxxo8fD8Cll5p5/HEXF15Y5M47kzXLjZqm4ff7yWazUotUlFqrX3symWTLli2MHz9eKrdUD2p0dkbJ5Qbx+/2YzWY2bdrEZz/r45VXxnHOOSl+97tBPB4PHR0dNDU1yYsF0X8sXx4XvVkhZiAmZHUxbr0kbbFY6Ovrk/ug4nXVyvbKy5vBYBCTyVRxASSmeKsvPGqtnAjRcPE7FVJ4mUym4qJNKMCUB+GBgQFKpZIswysOLFTgU2yVdDpNNpvdZimqr69PrkKYTCby+TzZbFba0HR3xxgzZvhE+Pbb65k2rX2brgKjiVZD7aCYy+XIZDJccYWDhx+2cNppYe65Rx/lF8MWoK9tRKNRaX1UKNjxevWsovpkKE78Ym9N+OFpmov6+uGT9osvvoXJlGbGjBkYjUb6+pJMmzYs1XbaaQn++U/3CLeDRCJBU1OTLHN6PB76+vqoq6uTgV2U90DPeAcHBzEajUyePJlCocB552V58kk9KMyZk+L224tksyE0TWPt2rW43W6KRQfjxtXh9/ulV+O0adOIxzXCYV042m6343K55C6gGP2vFtEWFwdOp5O+vj4cDofMZsV7LH63wkIpn8/LMqpYwt+aK4N4rZlMRh6PeAxN0zCbzRVZv+hPVn9PSNoJurq6pAiD4sBETXUqtorD4cDn840qai0Q5U4xFWk2m4nHNTnF2dbmlTt98+bpi+UbN26UYtijIfbzagkx+3y+ivuLoJdKpXjgARMdHREeesjKli1byGazpFIpWf4TmWw+n+e668Yxa9Z4vvzlZpLJJN3d3UQiIwW0BcKKyWzOMGeO3nc655wkRqNucSP6ga2tHjkN+pGPDPLPf+rPvXAhrFvXS6lUksanIssqlUoMDg6SSCTIZIbVboxGI16vF6/XS2NjIy0tLbS3t7Ny5Uo2btzId76zRR7fokVO2to8fP7zdaxbtw6r1UpTUxMTJjTIXlkikWDy5Mmcc04Sr9fAZz/rZeLEibS1teH1eoeWuAdJp9N0dnbKDErTtAo3940bdfd0r9crJykHBwfJ5/MV6jwiqxS9tG0FPbHnKEQGhGeecIQv984TAgsOh0N+T1wwlVcsisUimzZtoqGhQQW9AxyV8Sm2G9Hf2lrvLxaLYTKZuPhiA48/7mTevBz332+qGIDRNN12p7OzUzqxBwKBrS45l2cL5YhhF9GDEiXIrq4uWlpapCxVd3c39fX1jBkzpkJxpHrYZfnyzXzta36eesrH2WcnefRRu9y7K7+fGFyJRCIMDuaYPLmZnp4eWQ5sb2+XS/EGg4eenrV85SutPPGEh7POSvDnP2fo7Owkn89TX18vtU+tVitms7liFaP6dyAEupPJJG63m5UrV5LNZvnpT4/giScq73P//U/hdJawWCxMnz4do9HIunXraGhooKcnzqmnHi9vu2ZND/X1wzJh5YouYpVEuK/X19fz8Y9bePxxl/RntFqtFAqFESXoQqEg+5uj/R7LSSQSFasW5eLR1YhMuLx3KvYcy6sBYshq7Nixqp+nUIFvf2VXLd6K/p3D4RixTC4GCAYGMhXB5PXXVzFtWrvsGYFepjQajXI9IZVKSd3E0TKB6gV30QcrlUoV033CuFTTNJxOJ3a7nQ0bNpBMJpk4cSKaplX0oj72MTP/+IeLc85JcfXVb3P66R+Uz/mf/yylsdFBc3OzXOHQJ0gtWK16hrl582YZVNLptHxtdXV10oB1y5YtQ6/BQkuLm2g0itlsZvLkyYRCoSGn+RxTprTgdruHdhtdtLS45Z4j6JmqxWIhn89LpZdMJsO4ceNYv349uZyVm26awjPP1PORj/Rz6619mM1mIpGIDCAbN24kHtfI58PcfvupLF7cyoIFGn//u545ZbNZ0um0HBAxGAzkcjlyuRzr1q0jm82yZcsgX/7yJ+X71N+vu3ZU9/bK/R23Jk0nPhPJZBKHw4Hb7d7m8JPIJsunOrPZrBzMEogMVMjEKRQq8O2HvFcH6R0JluXZnygxWiwWWWq64II8Dz1k4bzzsvz0p5sBKspTYuk5nU7LiT2HwyGX4x0Ox4isIJEAk0l3CBdrAiI7KbfNiUajWK1WotEozc3NGAwGWT4MhbKMHRugoaFBmqKKE/lRR03D4XBwxhkR/v3vFs4/P8+tt/ZJ5wi3201DQwOf/rSHhx+2MHv2AH/5S05OPRqNRrk3GAqFpB+hyWSiUCgwduxY3G43fX19NDY2Mjg4KP3/Lr/czsMPWzjjjBh33BHlqqsaWLTIwdy5ae68Mzkkr2bE5dKIxUoEgxuxWCy4XC6pnCI0PwcHB0kmdb+/xsZGSqUYoVCI1tZWOjo6uPnmo3jppTYaG9MEgw5mz47yv/+7EY/HI/cjN24MEghYpCC30O0UmXs+n+f220/hkUesnH9+nnvvhWAwiMvlkoMp+upDnvZ236i92mQyST6flwMz27NHJzI6j8dTMd0q1h3Ke3w9PT04HI6d5uen2D9QgW8/I5Fgu4ShqxHB8sILi9x/v2mbt4fhcXCv11vzKr63N0EgYJE7b0KyTJxcxUK7CHKpVAq/3y/7dAaDQfa2Lr5YV5mZNy/PXXfpZbDqjDMcDks9TtBPgGJXLhaLyYGX2bMH+Otf81LTsa6ujtWrV+N0OvF6vQSDQeJxDZdLV1IR+pEbNmwgkzFzwgmHyed8552NlEox3G63VCAZN26cDOCDg4Py2FwuF2PGjCEcDmMymairq6NYLNLZGeGQQybIx3zkkVc477zhEuSyZZu48cZmFi1y0Nyco6/PyqmnDvLb3w7S29srve/q6uoYGBjA5/Nx2WU2Xn9dX1M44YRuvvjFl/B4PGzY0M/VV18x4nf1n/8spaXFTSiU5brr6njxxRbOOCPKLbd0SSeGYrFIMBjE4XBwxBFHDAUsAz6fSToqiM9BMpnknHOSvPBCE+efn+eXv+yVFz7ZbFZOzYp1iWr5ttEQpd5aHoDlnwn9fe2skM5TKAQq8O1naJrG+efneeQRKwsWaNx5Z7JiNL/WCaY6WK5e3Y3HM7yDB5UO3kJeS+hNlvdZRj52QgooRyIRORgiglqhUJBu3WKPzu/3y0EZTdNGTEd2dkYxGnXNxXLHiHw+T1eXvkAu+oXi2Lq7i8yYMZxtvPbaSg46qE2eqGOxGKtWdTJ2rL5KMHnyZN544w1KpRKHHHII2WyWYDBIoWBn5sxx8nH+9a9XKJVieDwe6VDR1NQ0NNXZJ3tdov+Yy+Vobm4mGAwyadIkQB90mTq1RT7m/fc/xa23HsJ//tPOqaeG+dWvksyYMSyjJejoiJBK9VNXVyd7WJqm8e67W7jssnkVt/3Zz/6CxTJIJpPh3nvnsXTpNAKBBOGwm5NO6uUb33ibn/3sSJ5/vnKidXBQFxfv7e2VwyLNzc1SEk24NIjPmHBbf/nlpcyZc3LFsXq9Rtm7KxaLZDIZnE5nxc7n1qg2i4XhIRav1yuzv1QqRTAYZNy4cXt9P09pge4Z3rvZlWKvJBqN8tBDPpJJcLsNwPBfVflSMiAlnNxuXaRaL49qNDU5a+7v9fYmsNsLcsFaWM4AUqrKbrfL4QZx9R2NRuWwRiymL4zncjlZAhRuBj6fj97ehJTpEplMIGDhvPNyPPKIlVNPDTMwsEV69QltStB7OcIVAPR+Ty6XY968LM8910hLS57eXr2caDLpAy8i8C1YUOLppw/m1FMH+cEPtpDJZGhqamLt2rW89tprTJw4UZbhTj89ytNP+zjllAE8HgNNTVOlOazVaqWvrw+v1ytP8MLIVXjUvfvuuzQ2Nsqpxbo6K6edFuGf//Rz8sl9tLZ6uPba1/nudzvxeo309aX48IeNvPRSm8z4zj03y8DAJnw+H319fSxfvpzW1tah4aI0Rx65ljffnAqA3x/n61+/jFmzVnD11S9z1VUvkki8yNixAXI5Kx6PgY0bIyOC3llnJXjnndXk82Fp/eT3+3E4HENTrWa5Q2c2m2lqamJgYIDFixfjdDo455wUjz/u5IwzdFFvkdmJHrGQdSv/TMKwM4b4vQpZtmoxA1EiLQ+c4XCYTCYj9yh3hN0dhN5rS0Lx/lEZ337E1jQuayGknQTptInmZtfQv9NySCCRSHDFFXYeftha84+0/ISRSqXI5/OyhycQTu1icEL0k0A/0YVCIa6/fgJPPulhzpw0d9wRlZOOwyofJZqanGzevJlCoSADzdixY+nvT2GxZPH7/XR2dgJ6lhWLlTj44LHyON54owuDoQ+Px0Nvby8ul4ti0cGxx86Qt1m8eBmTJjVJibJyqxufz0dXVxelkhOzOSMlxbxeL01NTdKNIBwOy9UBoRgiemV2u51gUDfHPeiggwiHwyQSCZJJA2PG6PqffX19HHnkkXIox2w2k0oZ8flMBINpDj98ytBSt5t0OkhPT4/Mwjs6OrBarfT0xCkWHXznO5+Vr+0Xv/gjXq8u6Cx6YXrWrfHb3x7Fyy+3c9JJvVxzzXK+//1xvPXWQcyePcAPfrCWSKTAIYdMxGKxyAsMt1sf1BHeh6tWrcLj8Qz1M1vIZrM0NNjl3l6hUNjqVDBUqrCI5fNAIFBxIZbJZEZIzPX29soqwPYihrUuu8xaod+6K9E/H3nl97cHUXt8+wkiyOyI3mD5bpjX66WhwS738MTgSVdXF5mMmYcf1h934ULo7x92W7/oIg2PB+bOTRONRikWi0Mn6ZS00fF4PLS1tdHU1MS4ceNoa2vD7/fLMhfo045iAXvRIgfBYJrBwUFWr15NOp1m06ZNhEKbWbJkiZRKi0QidHR0cMopQdraPHziE05CoRA2m41cLje0UG+SKvynnDJAMLiMQqEgVUsGBgYoFqOcfro+uj979gDZbIhQKEQqlZI9yGKxSDgclgMc06frMletra3SFzAWi8nsZdiTUJ9OHDNmDG1tbaRSKdatW0ddXR1udwtr1qyR2pqTJjXh9/tpbGzE7Xbz2muvydUGu70BiyU7JMtWYNmyZVx1VQPHHjuDa68dQzablesVwrHC6SzR0FDgkEPeBeD447fQ2OiQ07HieX/5yw/wsY/NpVgs8sADT3PZZU+wZs0a3nrrIACefbaBG2+cxllnfZgvfKFeOmoMDAywdu1aLr4YDj98MpddZpP7exdeWKS93cuXvqTraQpLoG0JIYB+ISRssPx+P62trRiNRvnZ7OnpkT090C/gNm/ejM/n22bQy2azxGIx4vE4AwMD9PT00N+fku4io7mUvF+EZqrYcS3fa1V+f7sflfHtB1RLhL2fko1Q2heZSSajZzWf+pRb7uUtXGgcWoKm4qq1pyeBy6VJWap8Pi8XkAVCdSOZTMpyVkNDA9lslosvNvDccw2cc06K3/42JHVARZ9QLFBHo1EikQhmsxmz2c+RR06Vj//KKyuYNWuCtMYR6icDAyYmTHCwefNm3n13C5MmNZHL5SgUCqxatWpo1N1NLjfIlClTiEQijB07FofDIffzSqUSoVCIcePGSbcEk8nE4OCgXFz3eDxyHL+jQ1dDaWhowGazycA5ceJEPv1pD888U8fs2SG+8Y23cLlcOJ1OfD4fwWCQaDRKQ0MDa9eu5Xe/O4nnn2/iuOM28/GPPz7kPFDP5ZefL1/3H/94HxZLVk5JWiwWmTnp/U43jY0O+b4LG6hUysill54rH+fOOxdiNKbIZDLceedpvPHGpBGfkeeff4NUqp9sNktHR7hireHWW/9EKpXihhu+KL/32GPPM3ZsQKrniOnY8onM8s9sOJzHYEjWDJKiTGq1WuW0bSgUoqWlBZPJhMvlko9brfspXnM+n6dUKsnH8Hq9/M//mHZ62VGs2wA1BderX7di96F6fPsB5btT2+oblEol8vm8DEoCoXsoshTQr44NBgMOh4O//a1AR8dmHI4i69fncTqdOJ1O5sxJs2iRg/POy6FpcXm1LHp0FotFunbbbDZaWlrIZDIyiNTX1zMwMEA6neb3vzfR39+F01licFC//+DgoBymaWpqwuFw0NamD7p0dHQQDocrem52e4Hly5fj9XrlvtsXvlAv35Ncro1HHpnISSf1ccstndjtdo4//ng2bdpEe7ufVMpKLBYjmUySyWQwGo3U19fLwLR69Wo55Vnu2q73IgPkcjlsNhuDg4M0NzdLO6RgMCgnRJNJA888o1vwPPtsPb/+9TRaWtwsXbp0SHtTX9dYt24dDkej7L29+up4rrlm/NBtejn66PW88cZkjjpqPc3NzcTjPdLhXQhqi+A3ZUqr/D2LidlQKMQdd5wiPwNHHrmOWCyGxaIf8wUXPMEbbwwHMIATT+wlmeyTO4uFQoSjj97AG29M4rjjNmO3F/D5PPj9cSIRD83NOdravDK73LRpE/X19SxfvgmnsySVc157bQLnnJPCYrHw8MMWFizwV3x+hZJLtedeoVBgxowZ8ms9gx/2SHS5XHi9XrnwXy6jF4vFZGZ+331w553vPwhtT7ArRwW9PYPK+PZxEokEVqtVeouVT2d2d8dH/GEZjUYsFosc+8/n89Kgc2vKKQLRoxPZl7475cbt1uXNyhU7RE8vl8tJB3KbzYbVapX7YalUiqlTpw6pr9STTgex2+1Sv1L0C/P5PH19fYD+PGKsvVgsDgXZBiIRXQlFLF1brVYikQJHHz2t5mu5774ncLthwoQJTJo0ibfeeguDwcDUqVPp7OwkGAwyc+ZMWRLt7u7G6XRKdRKxJpFKpejtTXDiiUdKm5tk0oDRmKK3t5dx48ZJZRmXy0U4HOYLX6jjmWfq+dCHurj++jdpbW0dWrS2kc2GpEGuxWLhhhsm8Z//tHHkkWux2+0sXjyWww9fw2WX/YNczsrf/34a77wzlcMOW80Xv/iStA7K5/OYTCbsdjsOh4NUKiUDtv7eG/nWt66R78eRR27izTcncNRR6/n8518gHo/z5z+fxdtvH8Sxx27k2mtX4nSWZNCIRCIyY49GixQK+te9vQm+972vycd96611GAxJNm3aRCqV4mc/O4q33z6Io45az4IFT3P99V+o+fsRfa9azgo9PT1SxFucwmopvKRSKXmc5SLqsG1rrO0lkUjIqdbyjFOx96Iyvn2YXC4nT/CgnyTEdOa8eTm8XuNWVVDE0vmOnABE4BInfH2R3CKlw8REJSAXyl0uF/X19ZRKJQYGBuRQihAVXrZsGd///kyeeMLORz/q4re/DdHe3k5/fz9ms1kGyfp6vb+UTCYZP348NptNlk4jkQjt7e1omkYoFKJQKAwFczj11DD/+leAD32oi1KpxH//O5YTT+xh/Ph6jEYjW7ZswWg00tDQQEdHBz09PdTX10ttSDHoI6YSW1tb2bRpkwzs118/gaee8nLGGTF+9askX/vaGP7xDzcf+Ug/d91VLwdg0um0vDD43veCXHFFcGghX2PVqlX87ncns3jxGD74wU6++92VNDY20t/fz403rieZfIfe3l65g/f22wcxf/5T3HvvaSxbppd6ly6dRk/PE1itOVnuFKorYhLTZDIxMJDBbi9QV+dm1qx3Wb78YA4/fD1vvjkZgCVLJjMw8BQ2GzK70jRdPCCd1kvhogQuplNdLheRiEYmk6G11cOHPtTJyy+P4bjjNtHfv4FSqTSk+xnh7bcPks9zzTVtfPCDHSxePJbTTovg9/tYuNAg+14iaxU7mcKeKRAI0NbWNsLFQSCCkc1mkxUCQB636BmWf66r90JHQ5TqVbDbd1GBbx9FZFvVihTDJRsr+bxhhFCv8NoTGcv2Pk8tmx5xJW232wkEAiMMQcVEo8gOXS6XzNaMRiPjx48nm82yfPkmnnhCzzafeaaOV199kQkT+ggEApRKJXniEx5zusddH06nE7fbLU/AXV36fp8YRqmvr6dQKHDTTRu59tp3yefDjB07lsHBXnp712G3Hyy1RXt7e+US+qZNm/D7/TQ3N7N582bpSCC0JuPxOA0NDXR3dxOLlXjqKb3P+dRTXg46aLjn+fzzTbzyyhOMG1dHJFLA6YTGxkY8Hg/9/f1YLJ1YLPrUqNHoZfFifVdv8eIx9Pa+Tjy+hp///DhefrmdY47ZwCc+sZHDDlvF0qXTOfTQVfh8Phn0ALxevUxZKmlYLBZ5vHp520YmE+X//u90li+fyVFHredjH3uMYlHvoZVKeWbOXM6KFbM49NBVOBxF8nkHb745BYDXX5/I+vVP0tzswm5vIJvtkkFP/K5LJSc+nx2/388tt3QxOLiRYjFKOl0cmlpNMnVqK8cdt5lXXx3PiSf24nAU+f3vI5jNMG5cHS6XgT/+UcNgSNLdHZOSZGLCtre3l6lTp9YsIZZPgwqPwOrPI1BTxUUMvZT3FEWpXvwdJBIJ4nENt7v24yv2HVTg20fR5aNqBy5RsdRLmn4gQ29vL1arFafTKRv8YvFYnBBGQ2QLApvNhtfrlVY0Ivvz+/0VpabyUXNxlSwGSkqlkhSpbm52cfbZSf7xDxcXXFDguONmyiysWCzi9XrlwrOmafT09MghEHGyWrCgxFNP1XPWWVZ+9av+oUxyI3Z7AYfDwbhxDgYH9Qwmmw1htVpJpVJSMLnc2T2ZTNLR0YHFYsFoNLJ+/XoAGWjFsI0+GdjDBz7g57//HV6ZEHz4w91Mnz6Ga65p4fnnmzjppF4uu+wJKXpdLBblwr7RWOKoo9azZMlkTjihm/p6G9mshZdfbgfg9dcncfHFJi699AX+53+eG1prCHLwwat4993pAMRiXopFBzZbXprEZrNZHnjgQpYvn8mMGStZuVLvhy1ZMpl4/EzWrNHv+8470/n2t29m/vynh1Y8ihiNKQ47bDVLl07jiCPWYjKlufnmE1mxYhazZr3LJz/5NAaDLo12//0X8vLL7ZxySpD/9/+W09nZKR0m7HY7Xq+XWbNmEYlEuOWWTnK5fgYGNtHa2o7L5ULTclKKTNM08nndcV4Enmg0KjP9aoRIgclkGlXyrJY9UTm1Mr5kMsnAwIBUl7nqqvpRV3oU+xaqx7cPkkgk+MQnnDzwgJE5c9Lcfnuk5u2uvNLPokUOTjstwq239sqr/0AgIKckhfHnjipciKENcbLI5XJ0dUWpqxvOJIUnHwz3VcQVvAiEQonfYrEQDKaZOLGRUqlEMBgkmUwyYcIEcrmclDnL5XJomkYup5fzisUiAwMZzjjjQ/LYnnjiJX7968N58kkPs2eHuPnmLfh8PnK5nFSJESsR4mIgm80SjUZJpYz4/WbpRhAIBAiFQhgMBg4//HDpTed2u3G5XESjUV566SV6euI8/PDZLF48lg98oIOrr34bkylNT0+ca675lDy2Bx54Gk2Ls3FjkDFj/LS1tWGxWOjp6SGfzwNuLJas7Fd9+9sH8cor4zniiLVomsbbbx/EzJkruPjih3E6ncTjce69dz6rVh3MrFnvcvHFD+Pz+SiVSkMXJEZ+8pMb5fMfeug63nlnSkXABJgxYyUXXbSQ+++/YCjrW8mnPvUvMpkMNls9dnuBYDBd0RO86abbMBiSRKNFfvzjG+T3b7vtISZNcsrVD7PZTEtLC4WCHU2LSw1Vk8kkrZmmTZuGyWTC4XCMcFbo79cvZOrr6+VzlC++b6tMKXwXq02La1E9CSr6xaFQloaG4ed4v3t3appzz6IC3z6GfoIu0txcbrY58o+oetCltzdJc7NLDiYYjcbtckAfDWHOKhDTpOeck+LnP++iqakJq9U6ovxZTSQSkSalwWCQbDbL2LFjcTqd9PT0SBUP4ZYtApJYmYjFdLmwq65q4Ikn3MyeHeLGG9cze/ax8jnuuedxnM4Sfr9fZpxmsxmr1SrNVEOhEDfddDgvvtjKySf3873vrZLC16mUkXBYzwAnTZpEc3MzRqORgYEM3d1rMBqNsoScSEAgEEDT4lLd5le/+hD//e9YjjtuM1//+hJuueVYFi8ew+mnJ7n55g10dXUxceJEli9fjtPpJBAIEIvp+p89PT1kMmZMJhOXXHKOfE3f/e4t2Gz5oewoTyZjxukskU6baGnRT9Yio1248HxWrJjF4Yev5oorniKRAKMxxX33nc/SpdM47LB1XHLJg6RSRr7//evkc9x0020YjSkpT1YsFrn77nm8+65ear3kkkeGgjU89NBFvP32QXi9UWIxH8cfv4WrrnpRDlHddtuHefHFVj784R7uvVc3Bq6rqyOdTkvXeZGl1tfXS9Wezs7OCssqMZUK2y431pIzG+12wuFdTDWXq8Ykk0msVitXXOHYKSsPSrFlz6MC325mZ+zY+Xw++cczf36JO+6I4XK5RlzRituU796BXgK1WCzyD9rhcOzwcZV701UH2WAwQyrVL9cARkPTNFavXk1rayt2u51IJFLh95bL5diyZYuUqhKrBZlMRjowCGeEaDRKOJwnELDQ0NDAQQfZCQbt1NenuP32J2lsbCSbzWIymbBYLITDefx+fXKyvr6eSKTAYYcN76zde+8/KBQi3Hbbifz3v2M54YQubr65Qz7n1Vc38vLLYzj11DC33NLJli1bCIVC3Hbbibz22gQ+9KEufvCDbuJxfdnabm/A5zPR35+q2Jv74Ad7+PrXX6OhoYHOzk7ZD21ubpamqf39/aTTae688zTeekvv6c2atYJzz12E1aoPOJVKJZmtzZy5nIsvfkTuqekDSXVSiq6/v5+BgYGh7ErD4SgOSY95uffeU1m6dBqHHrqSiy9+BJPJRLGo//zvf5/LihWzmD59JRdfvBCz2YzNZmP69OlEIhG2bMnyzW8OZ7d33/0o8XgP8bjG17/+efn9tWt7pcpOoVAgEAiwbt06nE4nzc3NMluNRqOMHz8ei8VSUc7clms76P2+8nWFaspVi6qDnfi5sD0qf75t/Z1omiZVZWo9bzicZ/z4Ovm1UmzZM6ju7G7koov0AHHRRe/t/tFoVGZZ992n/9EsXGjE7/cPZRuVkhPiNvfdZ5CLul6vVyqriNLh+efndui4xMSkQEyTAlx4YYl0OojX6yWZTMoVhOr7x2Ix1q1bx4QJE/D5fNhsNurr63G73ZjNZgYGBnA6nUycOFHqfAaDQZYuXSqXuwOBAIWCnYGBATKZDBMnNmK1Wtm8OUQwqPdyQiEnJpNP6j2mUin+3/+bwrnnnsIPf3gIbrebNWvWAAlOPLEHgKOOWk8y2YfB4JG9u//8p52VKztYtmwZS5as5uWX9UGUf/0rwCuvLB+SSXPz2msTAIb6Xcfwq199kLa2NpxO/UQ6blwdxxwz7Ja+eHErVmsdoVCIeFyTi9WZTIZ02sTmzZvlqsn8+U/J+y1fPpMf/egbLFx4vhxeWbFiFgArVswil7MOlRKbMJvNQxOZaUKhkOyLFgoF7HY92/773+dyww1fxGg08MMf3srHPrZIyqQBDA5a5OOvWjUDl6uZtrY2PB4Pa9asobu7m5YWA8cfr7+2Y47ZQDodpFgs0t7u4yMf6Qfgox8NEY/3UCgU2LRpE0ajkXg8LrVXBwYydHVFpZh3f3+/dPbwer3bFfSEH2O5gDkgA6rY0xQ2SOXC7SL7i0aj0lZKOMnr/cd4xdfV/4n+r3hc8Z/JZELTNBoa7EqxZS9AZXy7ieqsaM2aHgKBYSHnch+zWsTj8Yops1qU+5pVl3aqTVwF1b2LVau6tmljlEwm5eMYDIYhBRUz6bRJ/iGLwQp9v1BXYBGCzbmcVe6D1dfXy2xOTE96PB46OztJpVKMGTNmyCYoTn19PeFwHrtdL1VeeaWPp57yMWdOit/8ZkB63wWDQb761Tb+/e8WTjqpl+uuW0JbWxsDAwNks5YK14C77nqQlhY3kUhkaNm5jTFj/PT19REMBvnud2fw1ltTOeKINVx55fNyKvXOO0/l9dcnccIJXXz5y/+lWCwSCoX4059OZ8mSyRXv1113PciCBWexadMmSqUSq1ev5qabjuH11/W1iquueolf/OI4XnlFn3T82Mce4/bbP8Kbb07lqKPWc9ll/5B9zXvuOVdKiQluuOHH+P1m7r77HJYtO5hZs1awYMFD/O//folYzIfPF+PrX/8NDQ0NZDIZEomEXN/QLxrMFXt33/rWzygUCjz2mJ7hifKlzxcjGvVy+OFr+Oxnn8VqtdLQ0CD7ra2trWSzWbq6ohQKEXmRlcvl+NWvPsRrr03grLPi3HTTJorFIi6Xi4GBAQqFApqmccMNk+Rk6znnpLj7bn0nM5lMylWSbZHJZKSGqMi+wuEwwJCM2+j97FQqJe8rBmHez+RmeVZZveOqenx7FhX4diPVtX3hFq1pmlyKLqd82VuM9G8PYj+u+vapVEoqqtQ6rjlzUvz4xxtk9qVPG478wy/v7wklGDGpWU4ulyMajcoJ0kAgwHXXjePhhy3MnZvh1lt75S6eULtIpVLyuPv6+qSPnc/n47rrxvHccw185CNBbrxxPaeeOuxZ9847G2losMuTpF56zNLU5CSVSpHNZmlvn0Yy2cf3vjeD559v4qij1vOtb+kqL729vTQ3N5NOp6X2Zjabpbu7G5utXroemM1m6urq+NWvPsQrr4zjAx/o4KKLHpIL4i6Xi1AoywMPnMmSJZM55piNfOlLL+Pz+YaWyRuGJM5KbN4cGlIvsfGZz1wsX8sddzzGZz87V379s5/9jmKxiN1ekENADzxwhixrzp37GHV1VpLJJDZb/ZDbQopbbhnu133nO79n3DgrAwMDWK1Wmpub6eyMALojugi0hx66kmKxKLO7am666S6s1rA0D7ZarUMZpUlmRw6Hg4aGBvnvTZsG+OpXPyMf4y9/eRiHoyj9GfP5PP39qYr3AHQro+Zm/YJQDEJVO66Xk06nAeTELugrCaN9jgWijLo9Sivbg9iRNRqNI0qoir0DFfh2E+IKb7QrPREEYViBQgxMiCwOkJNv2/pj0h27UyOsXMqVXsrp6ori8ej9smQySTqdJpPJ4HA45Bi/sKERmpRbQwwhaJpevtM0jf7+VIWv3Ntvr5dOAeJqWOhfitUFseje2RmpGFhZu7aXr3zFy+OPOznvvCzf/e4qcrkckyZNwmAwsG7dOpndFAp2fvGLWbzwQhNnnhnnF7/oZtmyjVIPUghBt7S0MDAwQDgcxuFw0NHRQS6X47bbTuTNN6dw1FHrufzyJygWHXzlK5+Wx/KjH92J369n1I2NjRQKhaFFaTNjxvjl0M5f/nIWb7yhZ4NHHbWeq656EU3TGD9+PNddN4633tIzvEsvXcTf/z6X11+fxOGHryGfz1UMp0QikSHXiJLMyg49dBULFjxIoVCQFw4//OFnicV8+P1xbrnlfsLhMJqmYTKZ+POfz2Tp0unMmrWCSy55FJvNRiikf/7Kp0ABmekdccRaPvOZZ2SGL7z3Ojsj8rMjsnmDwUAsFqNYLJLNWioC33//uxyv1zgUxIMkEglaWlr49ren8eijevXh/PPz3HVXhmQyKU17TSaTVHGx2+0Vk5yRSEQqA21vH3C0x3qviKX5Wgoyir0LFfh2Azs6xVUeBNPp9JCAso5QEqlmtFKprr1oqbhKjsViI65uxf6UmJQTJy1hFivkwUqlEg0NDaP+YQtx6uqTj1DMuPxyO489Zufss5P87W8FGfDEnp4uXWbD5/Ph8Xjo6uqis7OTUqnEhRceQ1+fjdbWAn//+3+Geo0+2tt12Sqn00lXVxd+v1+O0V98sYEXX2ypOMbHHnueGTPGsnTpUhoaGoZOWE7Gjg1IE9NIJDI0Ru/j2muHbX1uvfVPFItRGThEGfCww1Zx2WVPyNWI+vp6uW4Ri+m7kuWrAAA33/x7HI6iHJiJxUqYTGl5UTI4mKNUKvHtb39F3keUIV0ujWTSUBGkvvvdWzAYkuRyVlwubWh9w0l9fYFi0UGxGMVkMo1YcfjBD34JJGTWff/9F7JixUxmzlzO/PlP4/frZeyGBl36LJPRA1IsFuPee89jxYpZHHLIu5hMJt5+exrHHbeJ669/i/Hjx9PV1YXVauVnPzuK555r4KMfHeQ3vwlis9no6uoiEAgwffp0+dldt66XlpaWiotDkUGV2xml02lZwRBC3LUW02tRPqm5ranj7X0s0KdMlYLLvoEKfLuYWr09v98srzBFL6FWBidKlkIGrDqACcROXPWvUpSiRNAp340Kh8MVE2/xeBy3200sFqvYw0smk7LJ73K5CAaDUvzZ5XLJq1uxUyWMaGtRKpXo6emhVHISDG6kqUm34RFlKbPZPCR51UoymWT9+vWYzeahSU4zEyc2yMdataqLaLRL2rw0NjbKHcXNmzcP7ZA1c9ppH6g4hmOP3cQNN7yNzWajVCqxadMm7r57Dq+8Mo7jj9/C9763CqfTyfLlm+TE5B/+8FH++9+xfPjD3XzqU//CbrdTKBRYv17jO9+5VD72j350J06nfgHhcDgwGo389rcnsXTpNI46ah3FYpG339Z1Q4e1NnNyklPsVYoMWZQP//73c3nnnenMnLkcQJY4589/sGKS89JLH+fuu8+RGeD55+tXWeI2s2at4MILHwDgwQfns2zZwfJxRAlcqOpEIjY8Hn2VoVAojFj+jkajJBJU7O+V89RTL+NwFMlms0Ol3SYaGxvx+82EQiF8Ph8zZ84c8bnv6emRgy7Vnx1hRSRWKIQzhsvlGjHIUguxyrO9urRbI51OyynT9/tYit2PCny7geqMT2g8AjIrsNls8gQIyN5ffX29vIrM5XJkMhlg9CBYjhirhuFpNYfDIT3LIpGI/LcYKkkkEiOUWoTivH5C1DUxQQ+eQny6sbFRjn5v7QQUj8fp70/JHk8wGBzyXnPh9epDLsK7bMKECfJkGwwG+dSn3Cxa5OD447fwy1/2yF1B4fQgLiBaW1sJhULkcjk+9Sk3L7/czgkndHHlla/jdJaw2+1S0mzJktV86UufkMf3i1/8kb//fTavvz6RQw5Zy403LhnSGM3g95uZMGECHR0d3HLLB3nrrYmyDDg8+LGaL31p8dCOYanCnueXv7wTi8VCNpulVIrJgRshASc8FYW4QDar+wnqg0J6ZlyeqX3zmzehadpQ2VE3ky3/+Y03/oRs1lzR67v66l9SX6/LjBUKdux23dBX9Jk1TStbYl/FZz/7LBMmTCASiUhty3Q6jcPhoLGxkdtvP4VnnqnjhBO6cTgcUhP1q199VfoZ/u53J/HqqxM46aRefvObAWbMmFEzMyoUCgwODtLUVOkEL3Y2Qf+7cLlc0iBYZHniAmi0EqeoaJRPcO4o4m9IXNjsiPelYu9CBb7dxNamuMoDISClujweT4UYbjmi9Giz2TCbzds9+JJIJOTSt5g6E31Aq9WKxWKpsDkSlEol+vr65DEJZXwhbyW+l8/nZUCu1di/4II8Dz1kYe7cND//ue528OlPe3jiCTdz52b46U83EYkUOPLIgypOLLFYbOh4IZ8P884779De3o7f7x/aQRt2mggGg0yePBmr1YqmaaxYsZlUqp+6ujqCwSBut1s6K5jNZr75zSnSd+6ww9azdOnwVOYhh6zlyiv/SbFYxGKxUCqV+L//O5slSybI21xzza/55S+vll/fcMOPaWpy4nQ2ceWV/yO//81v3oTNlh/y07MNvddFnM4SuVxOZtaplJGHHjqrIiMD3axX9PQqs78VzJ+vZ3IiaB122CoKhULFVKbZnKNQsOL1Rrn22lux2Wxks1k5VatbGZkqgud99z1BONwhjX2FHZXT6aSurm7ItNgqJ2MzGTPt7T7GjRtHd3c3L774Jl/84uXy8ba2t5ZIJOTnSgQ7MTVcfpEnTIjHjBlTcX/x+7fZbPKCKZVKSceO91qGLL/gfD+BU7H3oLQ6dxNbq4ZUD5sIAWYxCj1as1z8QZaPbJcHwVp9P7fbLZXuxQlM7PSJ7EuUp8SVNVBhQ2QwGGhsbJRDMEKzEyqHWoLBoDxup9NJJmPmoYf0zOWxxxx86UsDNDQ08MQTrUPfsxONNvLvf9czd26a3/62S6q2RKPRoZUJvYfY3t5OJBIhFApRV1cn7XZMJh9Wa5SlS5cCDA2bRHA4HLLf1NfXx+BgFputQDweZ/78dbzxhm6Ns3TpZA49dCPvvDMRYEgEejH5/MBQcHBUBL0ZM1ZSXx+VwcXjieL1GocmSTsqRKUDAQvFot4z7e9PVQSxyy9/kmw2yz33nFsxUblixSzmzn2s4rY33PBjYDj7W7FiJnPnPorNlmf+/AeZO/exip/HYj6uvPI2br/9Kvl1qdQERKW7h+jL2mw2Dj98jbQN6u/fIAOPz+eTKzFTpkyht7cXu91OMBjm2GMPZsaMGSxbtgyz2cySJUvo6emhudnFmWfGefJJDwsWaLjdowcNYWFlMBikyHY1IgC1tbVJ6bhMxozbjXQayWQyUubM6/Vu90VhNeJibrRjUey7qMC3lxGLxUa4UwuNSYHot9SazhRXysICqDpgigAmxshLpZLsWZUv3HZ1RXG5kHY6QIXDQjwex2q1YjQaK7QQxfKuOG5xoorH46TTaU49VS+HnXtuBp/PRKkU46MfDfHMM/V89KMxnnlG12PUA2MnTmdJPq7H45ErEi0tLWSzWZxOXROypaWFr31tLP/6V4CTT/bzhS8sweEoMjg4SKFQkJ54vb29/Pa3J7JkyRSOPHIdn/3ss9TX26RjwLHHbuLaa1/n859vZnDQid8fx24vUCgMZx2zZq1m+fJpzJq1ho9/fBGJhJVYzDf0On385S9z+fjHHyWf1wPRWWcZ8PlMeL119Pf31wxu4fBjgGHEGoHXG5W3Ef+fO/cxbLa8DLZebxSbLV9xv4cfPq/i67q6aMXtLZYQxaJBihgUiw4eeOAMli+fyZFHruU3v/kz2WwIj6deloZTqRT19fWYzWaWLVtGIBDgW986iJdfbufssxN897sryWQyMuseN24cU6dO5ZRT8qTT0aGLv8pKQvkgl+gjCyeO8s88DK8rOBwOaT11ySUGnnrKJ9sIQh9WCJuLyebtzdLESobQsN2R9Qa1m7fvoALfXoQQa64uyVQHuNECofi3mJQs1zWsnrLU+0z6Dp5QqRAC0J/4hIOHHvJx7rkZ/vY3K4FAQJ6czGazHHgRKw8ul4t0Ok0ymaxYnyg3yI3FOikWi9x6axSzOUNTk5NIRPebu/nmDkymGKlUP5o2hWefrefMM3UTXZtNX/wV5WCj0UhjYyPFYpExY8bQ399PQ0MDy5dv4l//OhSAF15o4oUXzuT44zfz/e/rWprd3d1DLgAulizRrXbefHMKmvYKiUSIyy9/gm98Yyr9/RtYvz7L4KCeJUQiHiKRAhaLgUQiwR/+MJsVK6Yxa9ZqLr30ETweL3Z7nunT32XVqoMBWLnyYOLxR/D5LPj9fkymMAaDgf7+fkol54jgNnPmChm4hDWQIBbz8cgj55Xddjk2W55s1iKDbSzmI5u1YLPlZamznBkz3sVmy3PVVbeRy1nweFII20S73c5f/3oOK1bMlLd/882pzJ//NPX1HrkSIi60DAaD3Kd7990tvPzyRwD4xz/cXHedQbrHezweWlpaZI/ZYEiSTCJtpkQ/s/yzm06nZc/SarXKvwPR8xUSacCQO0eap57S34OFC+HnP48SCFT6S4q+3LYW4MUun8FgeE8Gteefn1PODfsQKvDtJYj+2PaUVLY3EJZnX0ImDIaDoDiJ1NXVkUgkCAaDFAp2WY589FE73d091NXp+4Rut5tsNjticjMWi0n7md7eXkDvhVitVi67zMbChXDmmT5++cueocGNJO++uwmTySQFnj0eC8VikW9+8x0+//kc48fX09TUSjgcJpfLSbFjMaAg+pSapqFpGqefPp4zzohJbzyAV14ZTzK5luZmp3w/YrEYxxyzgddfn8SHPtTFmDF+OjriDAxkyGSW4Xa7CQQsHHHEWt56ayrHH7+FujorxaKJbHZYtmv58mmAW+6CXXbZo9xzj5llyw7isMNW4XLpqiHi/SiVnFgsGjZbnpkzV7BixUx8vijRqA8YbrOLUqUobU6f/i4rVx4sf14qDZeuyzM4oOL4BNOnv8tFF91fMf150UUPy75xNFqsCHqg7xg6naUK2yQxTCUCSH9//4jdN+HxqK8JNEp1Fqu1DocjTTgcJpVK0dDQUHNvTqgT6b3G4ZUdYUIsLuhE6bulxcuCBRoLFxo4//w87e0jA5bBYJDZn9B3FZl7tcPDtv72RHAU6jDi8RMJePhhvVy/cKHww9zqQyn2MGq4ZS8hHA7LCcv3S/WwTPUoenkmKNQlAAYHB0kmk3z1q2088ICJs85KcPfd+rDK4OCgvGIWSiviRChcCIRyidfrHRqiKTFu3PBrevTR56TrebFY5OCDDyadTkuvQL/fLzUPRb+xvb29YldKjNxnMhlKpRIzZ86kv1/XgfzCF+p4/HEX9fUpQiEnJ5zQxTe+8TbJZJJkMjm0W2fD5dJYtaoTgyFJoVDgvvvO5/XXJ3LEEWu44oqnaGpqGtJ0LDFmjB+/X5cwM5lM/Pznx/HWW1P50Ie6uOKKJ6WfYSIBDkeReFzDatUHQRIJsNnyUtxZDKpksxZyOUvFtOUNN/y4olxZHqiKRaPMJoGK7LKc8oEXERSFskv5wEr1c91333xWrjyYww5bxaWXvoDFou8hjhkzZkgFZ5D2dt+Qwko/brcbu92Oz+fjhhsm8uKLrcyePcC3vrUCu93O1742lv/8p42TTuolk8nw6qsTuPDCInfemZQXTEKkoJz+/n4pQAC13RXEio3P55ODXwaDB49n+0qZ4iJRiCNUOzyISWhxQSX61eKzJ/6WRhOEVxnfvoEKfHsBsZjurrCrll+3FgjLXatTqRQtLS1DJqwBmptdbNiwQSq3lJeKRlukF30Vt9tNPp/nE59wsGiRrq7y+c//G5vNxpgxY+RumJjkExOTTU1NQzY7GRlMhRqJKOMKDUihfdnY2IjR6K0IsrfeuhCHY3h4Q9M0fve7k3n11fEcd9xmrrzyOUKhEEajt2JB/Wc/+x0ul+5UYDAMLyY7nU76+vqGSsRO7Hb9ij+fz3PHHaezYsU0DjlkJZdcorsi/P3v5/Lmm1NkP1AwY4aewc2cuZyOjrEyY/vqV38pb5PNWioC1bXX3swTT5zNypUHy/uPxg03/LhmUC0fjhFTojAcYA85ZA2f+tSTGAwGJk+ejN/vx+Px8MUvNvDSS2188IOdfPWrr8oBEtHfLRaLDA7myOX0XmpXV7RCpaWcNWt6aGnRd0Xr6upGrOOU7/DVcldIp9NSIF0M2ezI34z4LIlSq3iM4YlW4WQxXM3YloZuOarHt++gSp17mGAwjcdj3aWKD9sqjYqp0fIr7MHBIJFInrFjx9Ld3S1PPiL4iWGTcsQVssvlksvGf/tbkf7+PjKZAZzOqfh8PjZt2gRAS0uLVAsRsmGhUAhN0+Rkq6ZpcnJULOOHQiFaWlo47LDD2LBhA5s2DRCLvcMpp3yA555rGHIwzxMK6YFZH+hx8eqrunv3q6+O5xOfsOLxeMjncxx11DqWLJnC0UdvwOcz8ZvffJhly2Ywc+ZyFix4aMhWyC4toex2I+m0rrhyxx2nsWKFvv6wbNkM5s17nGxW48039T7i8uXTZIY2ffpKGbSq+3iiRwcMlUOXy8ztlluuk9Oc5X08kc2VBzWbLc9jjw3rfIrBF1FCLc/0ykujy5YdRKn0bxoa7IRCIQYHB8lkzLz00iUALF48hk9+8mUmTgywatUq6urqcLlcUoPV46mnu7sbt1u3Wlq8uJVzz9U1Zh9/3MkFFxSYMKGBQqFAc3PziGyvHHHBU14BERdI+vtj2+akpihJCnEHcT8xKCWGVkSp0+v1jmphtL2ooLfvoALfHmTBAo3773fs9vLI1gJhJpPB7/fLgCfEgdva2ojH41LHstZ6hcFgkAM0Pp+PVCrFwMAALpcVTbPJq+opU6YQiUTo6+ujvr5eTpiKx8xmda82n8+H0Whk7dq1bNy4UYorGwwG3njjDaxWKz/84SE888xUTjllMl/4wrNcdhmk00HSaTNHHHGELJtmMlH8/jiRiIdAIIHFkqVU0rUfSyW96JHP5wgG0yxbNgPQg5Pd/iKpVBqzuSCHHiwWC5FIhMHBHG+/Pbzzd/DBK3G7IZvNMH36GlatOqiivFkdtMRziH+XM3/+g5xxxpMycxPTnABz5z5WEcTKg1p1n686qJaTy410+hgcHCzrERc49thNvPbaBI49dtOQsPZmJkyYQKFQkG72uVyOlpYWUqkUv/nNCSxe3Mr558Of/qSrBcXjGqlUiETCKrVmhcdfdaARmX75gMnAwACALLEOH39uRAAVjyd6mKI3Xf485RUPsc8aj8flRZti/0eVOvcQ1VJmXV0x3O6R05e7G3EyEw18Ic2kS5Q1AwmcTqfsDY6WqaZSKTklKoSALRYLbrdbTpHG43FWrerE4SjKQCeyPFFWbG1tZXAwR0uL7kbe09MjFW0GBjIVkmRHHLGRz372aVwuF6lUSkqr6T0bV4VCy0033TYUDNwVgtO/+MUf+b//O4F33pnO0UevJ5/PSzHn889/Qv6OxESscDGfMeNdLr30EQqFArfc8mW503fttb8c8d4UCnbMZv2EXb2UXl6GhMpeX7VEWfVtq+8DyDLqaFmi6AUeddR6Pv7xx3G73QSDafx+s1x1sNnqCQQsMnNqamoikzHj85mkgIKmaWzaNMD//M8ceRyhUI66uuELrHA4TCQSYezYsZhMpgp5PICNGzfS3Nwss7l8Ps+WLVtwOBwjZMHErp8IYCKzE0LR4mfbsvIqR2SaQrRalS73X1TGt4cQ5q2iId7Wpk+UFYtF2XyHnRMIt/YHXOtn5RmhcOq+/HI7jz/u5LTTStxyi74ILk5c5dNwQlNR9EX0cX6TdP4Wi9DRaJQrrnDwzDPHcc45KW68cSmhUIj29nYMBoOc4LzwwgL/+c9YzjorwR/+4JeBU5f6qvz4vvXWRLzeNlwuTQY/MV3a29vL0Udv4I03JnH44aspFCIYDBbc7pz8/mGHrcbpLPGpT/2TaPRJbDYb11+vL7YvXz6T5ctnSpUUg8GDzWZk3rx7mTvXMbRX56O7u1ix0xePO/F4UhXHKYKeoNaOnkBkfh5PqiKbq3Vbwdy5j8nbxWL6MZTfrzoj/P7378DpjDMwUOCPfzyV5csP5vjjt3DVVS/icDhwuWy43W6KxSImk4kf/GAWr7wyjlNOGeBXv+qjVCqxePFifD7dcPb553XJsU9/2sCf/xyXU5OBQIB8Pi/dJcxm81DmqJHJDJBOp+X0ZSQSIZvNMnbs2BG9wGi0iMGQkhc1oq/sdDppbGx8z20D0T8OhUJccolBrSfsx6iMbw+zravK8uET2PFAOG9ejkcesUqbl3I+8QkHDz5o5sILS9x3H3JHq7x3F4vFyGYtNDUNn3yWLFmD01mSWaHRaJTDCsViEYPBMHTycdPYqC+7G41GQqGQ3OtyOBoZM2b4av/111dhtebK+mh2cjkrM2eOk7dZuPBJmpqcchovFArxzW9OZvFi3SX9qKM284UvPIvBYMBub6CpyUk0GsVms2Gz2SgUCnR0hNG0+JBdUUEqvmSzFkymtBzBF3Y6DzxwBkuXTq9432bMWMnKlTM49NCVLFjwkBTpFmP/N974MaJRHz5flK9//Tb5msspLz9WZ2OjTXfOn/8gP//5NTKwVpdRyym/X6lkHHUgRqjG/PWvZ7NsWeVtfv3ru2hsdFBfX088HqdYLNLTE6/InH/967swGlNSOs5iCXDRRWfJny9evIy6OiuFQkEqEUWj0aGdSo1vf3sazz/fyBlnxPje91ZJ9we73Y7T6SzL2DWy2Szf+tZUnn7az4IFGnfdldkpQtHFYpFEIlFWDnXj8w0PtGxNZk2xb6IC3z6GuLoVGI3GUQNhLWcIl0vD7/dTLDrweof7HkuXbsDr1eW0ROCBYfHqSy+18NhjdubNy/OnPyWJx+MVJrSxWExqKzqdTr7//ZksWuTgox8d5PvfX42madTV1WE2mykWi0QiEW68cTLPPdfA6adH+d//7ZCO7UIyTdM0fvzjQ3nxxVZOPrmPb3zjbalKIxRl9HKrl9tuO45XX23nuOM2YTZbePnldj74wQ6uu24JkUgEu90uhb9jsRjNzc0kEgnS6bR8TrHsLKYy3377IA455F0+/vF/89e/nsSyZQczffpqVq0antL81rd+RiBgkb8TXUIrQyhkJhAYFp4upzqYGQwGMhnziJJnrenO8mlNqJwSrS591hK29niixOPDaw617I0Ajj9+C//v/y3DYDCQTqeJx+NMnz4di8XCZz7j4a23DuLDH+7me99bXTEU1dTUxFe+0spzzzUye3aI3/8+LO2shDhDIpEgl8vR15fk7LNPlM/56qvv0tbmlUvsoGdhNpsNTdMIh/NMmFAvb19dSt0RyvuD4m+ovA+o1hP2b1Spcx/DZDJVZGRi7Lv8506nE4PBMKKcOnWqPiquT+xFmDMnwKJFDs4/P8+hh04il8sNjfgb5Ylc1wyFe+4pMTgYGerH1Q8pZwQplUpyei4QCOByuUgmDSxapGeIzzxTx803N+JyafKqXFxr/epXfQwOdtDW5qVQ0KftBgYG8Hq9NDbqqi7XX/8Wv/xlgvp6G5FIK5lMhmg0KqXW2traiMVKvPqq7hjx6qsT5HuxePFYotHX8Pv9QwoyCTnAIMrJQnLNYrHIna1k0sDbbx8EwLJlB5NOP8Xllz/JH/8Iq1YdLJ0YZs5cPuRVaJALzUI71eWCXE4PPg5HsWzYYvRyZa3vi+nOmTOX4/GkKpRdypfbRyt9lj/GjBnvct55j0j1FoBCARwOI0cdtZ4lSyZz9NHrue661UNu9iU6OsI4HEW55rJ+/XouvbSOb397C6VSjPXrBzEajbS2tkp92T/8IU48HiOXGySXc1UMnGSzWUKhEE6nk+ZmF2efneQf/9D/bzKlcTpb8Hg8FX25fD5POBzG57Myb16ehx+2sGABOxz0xDoD6OX8rS2s33efWkTfn1EZ335GrdJoqeQcdcE3kYBCISIDXTKZpLm5GZfLRV9fH1df3chDD1k466w4P/nJRgYHByt8/bxeL8lkksbGRjnMYjQa+exnvTz1lI9zzklx2226q3lTU5MMOsIhYWBggEwmI1X4xYCBuBo3mUysX7+efN7GxImNdHR0UCwWh7zd/HR0dGC32/nud6fz0kttHHPMRux2Gy+91MbRR2/gG994a0gwOiu1F4UWpMFgGOoXZqQhrCit/elPp0sPvI99bBH5vI3vfOer8n279tqbeeqpM6UJ6wUX3C97VoJKQ9eRu3Pl3zcYDCxceH7NwRVRyhQZnEBkiEDFfbJZC488cl5FJlj+vfLbu91umV2l0yZ8PpPUc73ttg+zYsUsTj65j8svf5JgMMjf/jaXpUuncdxxm/nZzzZLbcxy/8CWlhb8fj8NDQ10dXUNXQwliUZ1hRlhKaT3D114vW1Eo1H8fvMIx4VQKCSnfMXQy/YOnYgMXrCj2puK/RcV+PZzykujIihtzTNPiEAXCgU6OyMce+wM+bPOTt15oKOjA79/DIGARSq6CP1OQPZpVq/uYtKkJlpaWqTSfTQalVJRYgAlEAjIiT+Hw0E2m6Wuro5UKoXZbOarX23n2WfrOeaYjdx000ZAd7Boa2ujqamJvr6+oczTzIYN72C32zGZfGQyA/L12mz1tLZ6ZHlUlM5+/OPJLF48hiOPXMcVVzxJoVCQ5b183oaYYrVarXz725cTjeoL51dddduoaii1SozVaikimIkyZ/l9aw2slE9qbk2NpZZW5w03/JiHHz5vhNrLDTf8mKlTW4cuOGzk8/rvQJ+41fjBD74ub/vzn/+BQqHA17/+efm9u+56EJPJxMyZ4/F6vcTjcUKhkOzxCnWVbDYrL1RENSGRSOB2u9m8eTPf+tZBvPLKOE4/Pcrf/64P0JSr7dTV1dX8rNaiXJUIlI2Qojbq8mc/p1ZptPwquDoQimX2TCbD1KmtslQ6Z06aRKKXYDDNN74xgaef9nL22Qn+/Ge9F+h267qVqVSKSCRCsVjEbM4M+eGtIJPJ0NLSQi6Xw+FwSNNRId7c1NTE2LFj6e3tpVAoYLVaWbeuF5PJxLPP6uLTr78+kZ6edzCZ0kyePJlMJjO0NC1cwkscfvjh2Gw2Ojs7KRZ1V/bf/OYEXnqpjQ99qItvfWs5xWKR664bxyuvDA/OvPnmFM45J0MgoPeU9H5lEjAAbsLh5JCuJhXDJeWL4zAcoIR+prhdrRIkMGqWV071bt6KFbM444wnt3k70HuAwIigJ44pHA7z17+ex9tvT+LYYzdx+eVPkEqlMJlSctXB74/T1OSkvb2dj350kGeeqeODH+zk178+nCVLJnPKKUGuu24JPT1xJkxooKGhgXA4TF9fHw5HI36/A5vNxoYNG2SJWuyOWq118vfw9NM+1q5dgc+nT2W6XC5yuZws5YvPqMPhqMjchIYmIK2IFIqtoQLfAUb1iUFMtAnE9J3b7cbv98teBxTRtDYMBg9PP63f9h//cNPbG6K+3obL5cLlclFfX09XVxcej4eBgQw2m43x48fT29vL5s2bsVqthMNhbDabDISDg4PSIFf0YS6/3MZ//nMyJ53Uy8kn9/HCC82ceGIvbjc4nfXSGHXs2LHyqt7hcPDSSy8RjUZpaGggGo0Sj2u89FIbAC+/3M7q1U/T0NDAK6+cUPG+HH30Bg4+WDdPHRzMUV8fIBaL8Ze/nMWKFbM44oi10ltv1qwVNdVQygOPCI6AXDyvpnrNQExfVpctbbZ8hVRZdb+vPLBW9wAvuuh+slmLDGImU45iUe+NGY1G/vCHM1i1Sjfhfe21CZx5ZgqDIVnh/hCJeIjHtSHT3nf4zGdSLFu2kR/+UPf3e+65RpLJQ3j11XZOPLGXz33ueRKJBHfcMZs33pjE7NkDfOMbb3PQQQdhNvux2wtyGKa+vp5583RngzPOiDFlSov0ghR7mH19fUDloEsqlZJ9U6tVdxCptcagdvEUtVCB7wCnPCMU1i8mk4l0Oi17NpqmD6aIgYPyLDAQ0E9OsViMQqEglV0uv9zGs88eyemnR/jRj9ZJs1phJiumLEUQTqfTdHSEhxzK6/jPf/RhlX//u4V//esVbrxxA1ZrjljMKFVD/H4/mzZtQtM0nE4noVCWYDBOS4ubdDpNIBBA0walG4PY0+vrWy9lykB3Xb/ssifo6Mhy991zWLp0GrNmrWDBgn/KIPLWW1P57ndv4ayzHqrI3sr/XS0VJkSiq7O98qV1cbtagyqiJyduA3oGJ3p2W2P69He5+OL7R5Q+RdBbsWIW8fjTFZngjBnvyqBXPhRz6KGriMd7ePPNQdxuN7/4xXEsX34WdXVJBgddHHroWl59dSoAL77Ywqc/rQtYC1f7Z59t4POfL3LFFXaef97LaaeF+fOfnQwODpLL5fj1r1185zsdOBxFwmEjLS0tAHJHz+v1ymnceDxOV5duUCwCnbDFKu/aaJrGpz/t5v77jWoyUzECFfgUUvHe4/FIAeB83obRWJJ9QeGIYDAYuPdeF7/4hR5gent1nc1EIkE2m8VkMhGNFnn22QYAnn7az+c+F6ahIc2MGTOwWCyEw2Gy2aycSM1kMlx9dSPPPz+T2bNDfP/7qzn11DD/+peu1XjTTVP43//txGh0yZOb8OITtkU///lxvP667pr+4Q/38OUvL6anpwe73c7nPvcCF130L5zOEkajjebmZi666F8y8C1dOpl02kQuZ2bpUn1VYfnymcyZ82iF9Y/RmMJmK1W8d0ajcWhasbLMeNVVt9V8r6sDUSzm49prb8ZqzfPYYyWZwZX35Mqzx5UrD5ZuCoJyOTPx2KtWHVyRUQrM5hyFghWvN4rLlZDB7eCDV3LJJQ9x770XyAz3yiufIpl8gbo6KzZbE4ODg6xb18vy5bqN0eCgi//3/27D4YhSV/cpXnihmblz0xx//CwGBgaYPTvEs8/WM3u2rr8qFtv/+c8AS5e+jNdr5GtfG8uiRQ5OO03jr3/VS+XJZFLu+YlVCIPBQENDgxxYKZ/Q7OrqYuXKldLqSLdTcnD//ccDyipIMRIV+A5wxOKu3++X3/v4x6384x9O5s8vsXBhpTK9rsY/SCjUTSiElDNraWnBZqunWIxSV2dlzpw0ixY5OOusOM3N+hX5smXLMJvNuN1uxo4dSy6XIxKJ0Nub4Pnn9ZPps8/Wc/PNjXz3ux0y8D37bAObNr2NyZSmpaVFLq+XSiUmT55MX19SBj2Al15qZc6cflwujWnTpg1pdXbQ2Ng0NPhior29XbquH3roKmy2PE6nmaOO2sySJeM57LBVQ7Jaw329aNQu1wBEGS6dNmGzlUasHpQPmojsq1YPbubM5XI6VAhRQ+3dO2CEP594DGDEMVSXQ8u1P2MxH6WSk/nzH+STn1yM2ZxhcNBekeFeeunzwCCDgwxNwBZpaLBzzDEbef31iRxyyLtMm+bj6KNnM3v2JgqFXgqFCLGYm4aGBn75yx4slgx9fX24XI2cdlqYf/4zwIIFcPzxs3jnnQ1y7eWf//STSg1IKTtxIVarX9fc3Izdbq8YWimVSgSDQYrFIkajkUQiwVlnxXniCQ9nnhknHk+gae4KRSSx9jMaqky6/6KmOg9Qynt55YMC1Uvv3d1xLJasXCoXk5ai/CRctj/+cStPPunhzDNj3HlnErvdTl9fkni8h6ampiGx4jjpdJq+vj5sNpssVVmtVr761XaeeaaOs89O8v3vr6auro4vfKGeJ5/0cOKJPVx99csYjUaamvTMIxwO09jYiM/no66uji9/uUUGyuOP38IFFyyUpVqxvuDxeGTgLRQK2Gw2otEipVKMZDLJHXecwjvvzGDGjJVcfvljmM1m/u//Tq/pb1ctOF3dk6tePp8xo9IQFvQgNm/eI9uczhTPCWzTnaGWkstoKjFieb6urg6LxUJvby+PPHIxb789jaOOWsdnPvMsxWKRYDAo73fssZu44IKFFIsOTjnlFPL5sBQfd7lcBAIB1q1bRyRSwGhM8etfn8Abb0zijDNi3HprLxs29NPU5MTr9eJ2u/nkJ508+aSX2bND3HuvNjTlmsFisci+8dYoX1mota4wOJjDYsnK/rFAmObqQbIywLlcLv7nf0xqgX0/RgW+A5BkMin7dlB5ZatpGuedl+Wxx/RpzY9+dJBbb+3FZrNRV1dXYRUzMDAgxXzb2oaj5XPPvU4goOserl+/nsbGRpqamuS0pMvlkisT4sTV29tLqeSkvd3HxIl69tbV1UVfX5KGBntFedTpdBIIBNi8efOQhqSDtjYv0WiRNWvWAAkcDgc9PT0UCgXGjx+P2Wymt7d3yE+vhNfrZcKECaxatYpAIMDatT1897vXytdwww0/HvLyyxGPO0coplSbwYpMrTzg3Hvv/BG3qV4rKPfKEwvmtQLntdfeXKH5OVqArV6bKL+t+Hf5cY4ZM2bIAaMHj0cvJ0YiBVwuDbvdTjAYJJ+38a1vXSMf74477uF3vzuat96aynHHbeJrX3tDTlUmEgn+9rc5vPhiK8cd1yWFBfTfZwynsySVfpxOJ6VSCbe7hWi0i0AgIC9K0um09Hu02+1y2ligZ9v6z9/LykIoFKK7u5uvfW0M//xngPPOy/GHP8SwWq0MDGSYPLlJ3lZJlu0+dleWrUqdBxBilUFMzIGwRjIwZ06a22+PkEwm+fWv62Tge+aZOqzWHI2N+vJwLBaTrutiMMbjgfnzS9x/v5Fzzklx5JEHkUgk6OnpoVgs0tXVJQOO0WjE4/GQSqXwer3ShLe1tZWmpiYSiQQrV64kkUhIM1iPp5Genh7y+Txms5np06cTDAYZN24cX/lKK88/38QHPrCF665bwqGHTqKxsVEuVq9cuZLNmzcD+glUd3UYIB6P8/rrrw/t84V55JEF8n067LBVI7Ko8mlJ0Hto5XJhtRwW9GxuOMjlcpYRwyRiOrRY1Cc6xeOVU+7JJx5bBK7ygZpagzSj2SCJx0kmk/zpT6ezdOk0PvCBLVxxxVMEAhb6+vooFApD9kQZGeiPPHIt+Xyet97Sh1lefXUCZvM6nM7c0LK5hRdfbB36WTuzZ0d59lkf8+blh05oRsLhMO3t7UOWUCW8XiPJ5PBQlRClrq+vJ5PJYDbrThGiHyxsi7ZnbUFMgIqsVAxr6X6Srfzzn/qF3COPWPnDH9wYDHkaGuxlCjEabvfevQe4r5VkNU2Tf5/ia4BPftLFgw+adkuWrTK+A4RkMkmpVKrY6asua8bjUCrF8Hq9XHBBgYceMnPhhUXuuUdfOPd6vXJJvVQq0d/fL/sken/FQakUI5vN0tCgm46KvT2j0YjRaGTdunVy2T2ZTOJ0OnE6ndTV1cm+Szqd5uqrm3j6aR8nntjLZz/7LG53Cw0Ndrq7uymVShx22GHE41rFgv3vf/836uqsWCwWOaizadMmotEo+XxeOj6IcmcmkyEc1icHy7O9ak3MWn03cTurNT/iZ+VZlxhEEWVJkfGJvh+MdFzfGuWZ39ayvVpL9OWI21Y/xo9//Osha6qRQtnHHNPB9de/RjKZ5PbbP8LixWM55ZQBvvWt5TKTd7vd3HDDJP75Tz9z52b43e8Gicc17Ha9tCysgoQYeCQSkXqvra2tJBIJaRgbjUZliVxc9AibIVG1gGF/PbFDWp39ORwO+dkS5fVMJkOxWOSTn3SNWtKMxzUgUeEbaLVa5RDN3sCe0hTVNE1ehNTKtsXvZrRMXKymiJ/XOhftymCuAt9+jsjyhPpINeIPZ+7cDH/5i34F5vP50DSN3t4ETmdJeqYJpwNRcioUCgSDwaFyp+5OYLfbSafTUgi6t7eXQCBANBolHA4TDudpbtb7NuJ2QrvRaDRitVoxm/2ccMJh8hgPO2w9S5dO5thjN3LNNa9IWbGGhgZ+/vNjefHFVk44oYtrr32dTEbfHRwcHCSfz+N06v2kTCaD0Wikq6tLBr877zyNd96ZziGHvEuppEl5se1RRSnv+QEV/TiRTZUPt5hMJXkb0e8rp3raUzx++WBL+XOK56gOrNsyu621LF/+GOW3+djHFuHxtHL11VfI2/7qV//H2LGBod9/PYFATjopeDweKfrd0xPn4IPHSYUev98vJ3CFvJ3X68VgMDA4OMjAwACTJ0/GZDLJHb9sNiulzUqlEi6XS8qjCQsjGB6wstls+Hw+3G63vECrRbkOLexYxpTNZqU0HzCkieve4VKrmFjVJ6hHKvVsD9Uthh0NFoVCgUwmI6dmRztOQMrYiX8bDAZpLbWzlHF2ZxBXgW8/Rox8b6skNDCQkQMsZrMZm80mNTWdTqf0tQOkUr/o15VKJTkoIMqnmqaxefNmfD7fkCB2ZkjV38uLL7Zw+ulRfvtbXdrK5/ORyWSIRCLyCj8Wi3H99eN58cVWjj++m1deaZPH+vvf/w2zOVNxXNGoncbGEnZ7A6lUvxSiFi4QZrOZVColTXWFTNj3vvc1+bg//OGtmM1mMpmBrZrDZrMWcjnLiJ6fQGSHMHq2JW5XXZa8554FrF49nMFOmbKGdesOGvX+4hjLA+tovUdAZni1srkZM1Zy3nkPVxzzL37xR8zmDH/60+m89dZUjj56A5/85NP09/fL+33oQ5184xtv09bWJsURDIZh0W6Px0MoFMJm09dI3G43/f39FAqFoQDllcNF4neWz+cZHBwkEAjIFRmDwSAlzCwWywj1FoEwpBWnNbPZXOHnl0wmpT+kuL0IPiKQ7gjlzycCgMhmtoXZbJZ7s1sL1Ft77gULSjzwgIl58/LcdVd6u4JQ+Xtjt9vf03PvKlSPT/GeKZVKRKNRWUbcFg0NdmIxfThBlJJEeVDX4TRgs+Wk84AYSGlubpbBRAwjGAwGenriNDR4WbZsGZlMZsg/r4UXX9QXk59+2kdn53omTWqSrtmtra2k02kikQgAt98epVTKk0r1c/31Vp57roGPfCTIlCkt0tamubmZH/3oUF55ZRx+f5xIxMMRR6zhC194UbovZDJm2tr08lpnZwSTqTjkGZgsG/VfMSTUbeXRR4ezpVrByWbLY7PlZSbm9UYZO7ZD3qdcHFo8fnXGV6sXF487K4LeQQetZM2ayq8tluKI7A0q5ciqe4+jTZ9CpdLMypUzOOusxznyyLW8+eZUPvzhbsaM8dPZ2cl5593DGWfop4r+/jyl0vB+4MsvjyEQCJFKxeXuXV3dOCyWLGvXrpXVgvr6egYHB9m8ebPU77z++vH8979e5s618pOfdJJMJkmlUhiNRnkB5PF4KBaLQ71kD9FoVGaW20P5ZyqZTOL1enE4HDLLEuLoVqtVBtgdpfrCUmSF5bhcrpoBRribCI3YbZUIq7n7bit/+pMRj8cCbJ/T/N7M7upVqoxvP0MEInHlvb2IspIQcHY6nfT39/OJTzh54gk3s2eH+O1v9St3UTJNJBJkMhk8Hg91dXUYDAYuukjjySe9nHxyHzff3EEoFMLtdjNp0iSuvNIvPfruuCM6ZHKblXZA+nCDCbdbt07S9T7NTJo0iXTahNNZkmr9oVCInp4411zzqRGv5ac//S02W56//vVsliyZwqxZ76Jpw8vhl176OJqmkcvlZAZUvRQOlVlZuZN69bTm1Vf/Erc7NWJiszx4iUA5bdpKLrlkYcXzlOt7luuAAhXlz2qj2lql1OqVhm1NfQpjW7HYrjtNPAkMy9iVP9exx24csoo6npdeauPYYzdx1VUv0tg4EYslyw9/eAj//nczJ53Uxxe+8O+h4RizdGG3WCxD/V0DZ545LBv34IMvc8opM+WEplhRMJlMMiMa9uULEwgEdujzHYvFpEboaNnge8k2RMYoSrDl34fhEmF1ViiyLd002a5cI3YzKvDtJ2iaRjQaxeFwvKfmeyaTIR6P09jYKHsg3d0xpk5tkbdZu7aX5mZd0FoIWwtboMbGRoLB9AindpstT39/P4cccggWi4WuriiQkDtUop9jtVr5zGc8PPqonY98pJ8bbliKpmlMmDABm81GPp8nEolIJ26bzYbb7ebznw/w73+3UF+fIhRy8sEPdvCtb62gszPCZz5zcc3XWj7YYbPlRwQyqOzVieBQy5VBBIzyXty2KB9sqbUqIahlPFt97LV6fNXlzPLbnHHGkyQSTtzuFFZrvmY5tjo4lkpOvv/94eP4/vd/MTTEZKnIKMXyv+Dxx/9NIGDB7/eTTBpoadFXFRwOBxaLhcsus/LEEx5aWvL09lo4//w8Dz44nLXow0dh/H4/uVxOZo8i+G2va0MkEhmxrwr6SoSwvxJGy+efn+euuzLyNqP1uMoR7YEdKRnqF13Ziu+NlhUqdj7qMmM/IJPJkM1mK3zydpTBwRwNDT7C4bAcK6+rs3LhhUUeeMDEhRcWSSb7CAY9uN1uqZifyWRIpVJ0dnaSzWaZPXsqzz5bz9lnJzn00Emk02m8Xi+9vb1DAS6H1eqWQtLle2SPPtoIwPPPN/Gzn43FZstLqxt9eKEet1uf0hscHKS1tZXf/S5MKNSL3V5g9eouzOYMRmOAQw6ZyOmnR3n6aR/HHLMBo9HEq6+O5/DDV1cEjBkzKnti1QEsHndWqLfkcsOCzy5XlGTSN3Q7PZsrL1eOhpATE4otguos7x//OLtijaKWIow4duHYUN2bvP/+C1i5Up8i7egYWxFIvd6ofPzRtEX14DasAHPIIe9isWQxGOw4nUaKRa88viVLxnPKKWGeey7ARz4SJBLpZOPGMH/+85m88cZkZs8O8ZOf6A4N6XSab37Type/7Ob003X3jYcesrBmTTcNDXbZL3a5XHR1ddHY2Eg8HpcZod/vJxKJ4Pf7R83Uyp3hhdZneQATn99QKMtjj42Vx/DnP1t2ecnNarVWDJuJrLB80ERMqyp2Pirj24cRWZ4YEX+vXHSRxsKFBk47LcIPf7iWhoYGLBYLuVxuaDAlgNerl5rC4bCcigQ9CK1Zs0b2Bp1OJ11dUcaODRAIBDCbzfIYOzs7ZXZnsVgoFAp0d3djs9nwer1cc00rjz5q45xzknzlK6/S1NSE1WqlUCjwta+N4cknvcybl+eee0q888479Pf3Y7FYSCQSBAIBvF4v69atw2w24/ePIZ0OyinSYDBIX5/u8v373586IsODkX01sa5QnhVdffUv+fWvr5Ffi0Dp8cT47GdvH5GhHXTQKjZsmEyhYAFKgFFmidWIBfZqxZfzznsEoGbpcmvUyhhr3cZqzW918OWII9bw5S//l3jcRSCQJZ+3YTAkcTgc9PUlueeej/Lf/47lhBO6+dGP1mE2+6mvt9HW1sbmzSEOOWSCfMzXXltJQ4Od1lZ916+vr4/PfS7AU095OeOMKL/6Vb88+bvdbkwm09Dz9KFpGvX19ZhMJjkV+YlPOHn8cV1e7847h8uJIuiJyoJ4HPEz0QM2mUwUi0U++1nfXqfUorLCXYcKfPso2WyWdDr9vrI8gJ6eeMVIdCiUw2RKYzabpf6h1+uV5VMhVJ1KpVi7todiUc/cxo0bR29vL0ajUf5cBE+x4yWmMBsbG0kkEiQSesmzUChIA9NCwU5dnW5d1NLSMrR4bOKII6bIY7zlljtobnbJYG82m4nF9EXoXC7HT35yOG+9NZXDD1/D5z//ArlcjmKxiMViIZ028bWvXVnzvSiflNzaOkElRdzuBIlE5ffN5vxQsNsxbrjhx9x883UV960uPZavPoxWYq3OHmtlo253lKuv1sW0a6m9VAfgcqeIWbNWAAaWLz+YD3+4m5/8JEihoPswjhkzhmKxKLOs66+fwIsvtjBnTpq//U1XZdH9Ey2yR2YweNA0PaMT/nriNsJMWfR3PR7P0EqDiZNOOlIeYzSqfwaE+Lnf76/42xCfRYPBgMfjIZfLSSk72PsXwWtlhdV9Stj7X8fegCp17mOIk4nVaq0Qlt5RkskkhUKB5maPtBmaNy9HXZ2VaDRdoZFoMBjkiLbVaiUYDHLNNa08/PA4zjknxR//GKdUKpHP5zn0UL1stXbt2iHvvTxms7483tjYSCwWY8uWLbJvs2HDBux2O+3t7XIZfsOGDVitVrmfZTAgy5Zz52b4wAcOkZOrBoOHH/5wIosXj+EDH+jgiitekqoib799EMHgk0OuDMahEluu5vshmD//wQox5/KgMjLoAZhGBD1gq0HP7Y5W3MflipFMevF6ozz44LxR7ysysrlzH5MBLR73jVh7qJWpnnPO46xZcxCaZho6hhjjx3fI21QPzpQLXpcLY4v3QDg0ALz0Uhua1s+kSZOIxWJs3LgRr9dLS0sLfr+fO+9MAL34fCaKRYv83AaDQZxOJw0NupNHb68+0CIqGGJSWJ8UraOhoWFI0ceI3+/H4xn+7J5/fp5kcoBIRO/bud1uGeREsHA6nbS0tEhrLDEtOvx7GfVXtlcgsuBy8vm8zF4BLr/cziOPWPeqzHVvRGV8+xAiyytXUNlRRD/Q6XTKPgromZ/PZ5JTdNlsVu7y6aayA2iahsfjIZu14PcPm36+8cZqwuEOZsyYIfspnZ2dfO5zfl56qY2PfKSf669/i3HjxklH7XQ6jcUSIJMZoL6+XsoYCYWO+vp6AoGAHKLRNI0tWwYxGlPE43GMRiPXXz+B559vrHh95VnbIYes5H/+59Ghx7ahafqJtdbCNlSKQdcqJ+7IAMvWKM8ct+cxZ8x4F6NxeGqzWph67tzH+M1vrqrITKuHcLaXWisP5X1F4cF30kkDmEzw3HMNnHxyPzfdtBG32y2XuUWJemBAd1xwOp0kEgnGjh0rfRtbW1sZGBjA7XbjcrnkOoHI5EXml06nSaVS+Hw+nE4n0WiUuro6ksnkkAKPGbd7WL/T6/VSKBTkkJZ47Gw2SzgcJpVKyQVsqBxcMZlM+P3+vUqdpZpSqVSxqyiOP5GA9vbh1QqlMTo6KvDtI8RiMcxm83bt5dUin8+TSqWw2+01/6hLpZLUNPT5fHKZvL+/H6vVitPplNOU+XyeSy4xsGiRkxNP7OH226Nks1laWlqkQktvb4LzzpstH/+JJ15i3Lg62be58ko/Tzzh5swzY/zkJxvl0nwikaCpqUkqdoi1AyEzpnutFdm8OcQxx0yveA3HH7+FL33pZTRNo68vicOhl8j+9KfTpSoLMMIqqDpAzJy5nHzezJo1lY+/OxBBccqUVaxbN/rzVwfAaq699uYRwzPbS639RWDEFOzcuWl+8Ys4kUiETGYAn89HIBDAYrEQiUTI5XJDDhhRWfqeNGkS0WiUpqYm+bsNBAJyxy4YDMrSnc1mq/isxuNxotEoVqsVg8GAw9GIpukXQWazmVwuJ/0kTSZTTWeHRCIhVwlqIYKykOKqNdUJ+u6euCjbFVQv4lerpoz23HtKwmxfQ5U693JyuZzsjQmdwR2hWCySSCSwWCxyHLwWQsfS4/HIHaqenh4CgQDFYlFOoYXDYQD+9jczfX29FIsxwuEIkYidhoaCFDduaLBz8sn9vPBCE6eeGsbr1ctTLpeL7u4YTzyhX4o++aSXH/zAzrhxTZjNZvL5vJxmMxgMFItFHA4HuVyO/v5+YrEYJpOJujovZ5+d4B//cPPBD3bw1a++i9drpKFhOmvXrqW52UVdXR3ptIkVK/SyXLXItNjNqxagru6fVZcmq6nVP3O54iSTnlHuMYzbHSORGL5KF8a0Hk+q5m5h+TGeccaTowa2J544m/POe2SrgU+8Pn0AZxKFghWzefRSsBiAEYNBjz3mYO3ajaxceTBnnNHMD36wFovFQltbGx6PR66fTJkyhS1bttDT08OmTZtoaWkhHo9LBSAhWN7b20sqZaS5efj9KJVK8j+RjYVCIT71KRfPPgsLFni4444oXV1dNDU1Sfm0WohdvlrSfQKz2UxjY+OoP4fhobKenp6tBkebzUYgEBi1OqNpGv39KRyOory/wGAwSBm/HeG++5Tp7vagMr69GFHS25YnWS1EL1C4IWwPes9MH0zx+/1yRUKUO8tvI4YHCoUC06Y56O+3UV+f4qmn3iUQ0Cc6Ozo6sFrr8HqN9Pb2kslkiMVizJgxg2uuaeHRR+2cemqY3/42hNFoJBaLMXHiRKnc7vV65WCDyWQin88TDofJZDL09vZSKBRoaZmC3a7b3BSLRZLJJKCXhe12O5s2DXDnnR/k7bcPqsj4qnUvy2XKyjnooFW7JfOrXl4X5cZbbrlGKsRcddVtFcdYPWxjMuUoFodP6uVl39HY2nBMLW8/t9vNPfecx5Ilkzn44HW8++7w0NGzz76GyZSu8FoU2Vg2m8XtdhOPx3E4HITDYWkxlcvliMfj/O//HsMLLzRx5plx/vjHuAwEYipT/JdKGWltHT6zb9gQZMKEBuLxuJQzq2a0Xb5dSTqdlp/XWnJo117bzqJFTpWd7QFUxrcXks/npQP1e8nyEokExWJxu9VbRJAU0mFer1dKQ4nsD/SBGIfDIQcD7HY7a9fG6O/Xy1GhkJOOjhx2u37ya2xsHNLh1G/f2NjIhAkTWL9+Pdde28FXvuJk0qQm8nmjPO6NGzficDhIpVJEo1E53ZfNZqVuo6ZpjBkzZshotkgkEiUSKeDzmWTJNpVK8b3vHczy5efg9Ubla5g//0FOOeUZuZKwYsUsOdRRPjQCI9cb3i8eT5QxY7pqZnHlAUwcEwwP14gdwvJjrB62KQ96wvJIMNqU6Wj9xRUrZlEsGlm16uAK09pEIsGcOXeTSs3n3XcPpqkpS3+/jRNP7CEe75GOC83NzQAyCBoMBjZuDA453TuZMGECfX19FItF6urqmDTpUF54wQ/Ak096KJWKNDWNFFbXpzn7OfdcM48+aufcczNMnKhnaELYWqwxlK81eL3e9/S3tL0Ip4JqPB4PTU1NIwJuIgGLFun/XrhQZWm7GxX49jISCV0q6r1MbAq5MtFH2xblGZJYWSgUCrKnJ0SoRclRDA+IctHatWsplUo0NjoIBh0EAgnM5hDJpEm6Lgj5tP7+fpqbm6mrq2Ps2LGsWbMGozHD8uX9MtC3trZSKpVIJBIEg2nGjbPJsXjR64lEItjtdjnVaTKZ+Pa3D+Kll9o4+uj1XH75k+TzeXI5K8uXV04irlgxi1LJWDGWX76wnUg4ZdlTSItVlxtH28HbHuJxHytX+rY50DJt2kp5TOJ4PJ5hT77q0mx1KXbq1FVcdNH9xOPDmprbu1ohjk2sL0DlxQGA2dwuf9bfb+Pxx9/A6YxjsdSTSqVobm6WvbREIkEqleKmm47gv/89lLPOivOzn21B0zSmTp1KIBAgFAoRCvUzZ46NRYsczJmTxuczybJ6Y2OjNKctFAo4nU7uvrswlNUXZXkckJPHsVgMi8VCJpPZYXmz0ai1Vyew2WzbXVkBPciJidQFC1TQ292oUudeQiRSABI7VI4plXQ3a6GZKZbIt4U4idQaj04kEmXO7LqT+cDAAHa7XfYAxd6d19tGb+86JkyYwFtv9bBhw2K5lpDL5WhrayMYDEr/NavVKoNcLBajUChITy6RTXo8Hn70o0N54YUmPvjBTq699jU5WCOyUK/Xy+DgIMlkkmLRwac/fZE8/ltuuQO/30wmk+GOO2azdOk0KWB91FGbWLJkgrxtubfdD394A4WCFYOhKEf+RaZTS9JMsL1qLVvD49EzUr2fGONrX/uF/Fk2a+HBB+eNWKyvXkwfXqQfFs8u1+Asf13Tpq1E0wxbLeFWv65qa6ZAIEE47Oa44zbzox+tkxdGoicl+mmHHXYYGzb0M3v2sfKx3nprHU1NTtlXFm4JsViMYDBNY6ODMWPGAEjLIq/Xi9PplOX7csEEsQ9ajviM2u32HdKtFRd3tW6/K7z41M7dnkFlfHsBugu6mTlzbNx+e3C77ydONsILbVsIbULhnF6NUL0QprGBQECagBaLRZqbm6UN0Y03TuKhhyx89KPjuPnmzVitCdrb29myZQtNTU34/X42btwop9CEQLDdbpeBt1yQ2GKxEIvFWLu2hxde+CgAixeP4WtfW0mplMdkMjFx4kSMRi/RaBcejwe/34/T6eSUUwZ47rkGTjqpj+OPn0Umk6Gzs5Of/nQTsdgGnM4SmzeHsNny3HrrB1m2bAaHHrpKBr1QyCezOBEcYDjTqXZSF5hMuYrgcOWVt3H77Vdt8/cgyo5eb5TPfOYOOX3p8cSIx73cf/8FUm6suj83Y8a7NQdyRAZZXhoFRrwutzsqj3lrQzurV8+QQVRkxeVuDuGwm2uvvZmTT54BOCgU7FgsSINft7sFkynNW2+9xZQpU6Sj+ezZAzQ1OYnH40yZMkVOYQoxBrfbTWdnJ+vXr8fn89Hc3ExLSwvZbFauL4iLw3J39f7+ftnbE5/zpqYmjEajdCoRZVPRN679uzFvl7P7zkIFvT2DCnx7mEQC7r9fv7pctMjB3//u2OYfQ6lUIh6PYzabt2vwJZvNyoxwW8K+ulu2nXw+L9VVxFRpf38/JpOJeFzjoYf0oYZnnqnH4cjI1YeGhgksXfqyNCA1m810dXVhNBqlv19jYyNNTU1yWlPTNFwuFw6HgxkzLBx77EZee20iJ5/cR7EYJZXSX8N3vzudJUumcMIJjfz8510ygP/pT0n6+4NMmtREX5++GJ9MJoecI2xkMnnq622k0yWGbOIoFgvyNdfXR8tKmLqsGCB7gzZbXpb+ypfFy3tqBkOR22+/atRgIjKu8lJpeQlWf++98utTTnlmRNCbPn3YwLa6H7m9lB9bIuFj6tRVrF1bO/OLx33cc88LOBxR0ulppNNpDjtsFUuXTmfmzOV4PCmWLFlS5s3XxY03LuUHP5jFK6+M46ST+vjylxcTj8f54hdf4tvfHsfg4Bby+clYrdaKoQ8RMDVNY+LEiYwbN06KUYtyd3UGJxbZXS4XsVgMTXPJcqzP5yMUCsnHF48jdvr8fv9WpzsV+zeq1LkXIHZv5s3L8dBDo/8xCo3B7XV9TqfT5PN5rFbrdml5LlhQ4v77jcyZk+L226OYzWbC4TBGo5FgMMi0adOw2+1ccYWd++/Xg8MZZ8S46aaNlEolrrqqnv/+dxyHH76Gyy77Bw0NDdKuSLiwO51OisUinZ2duN1uaUAq5KsyGd1kNhTKUl9vo76+nnQ6zebNIT7xiQvlsb755lomTmzE4/HQ2dmJ2Wymr68Pr9dLOBzm1Vdf5eCDD8ZoNNLX10coFOIPfziDt9+eJB/jxht/gss1/PHfssVXM2Orli/bVp/P7Y5hMGgy+xptSKZcSHrFilnyccX/y/uQ5T02Qa0SbLURrX48o2d21f3Go4/eTKmk8eabEzj55H6+8513K/wd9azfSVfXajZs2EBfX7JiD/L3v3+Ez33uPPn14sXLCIc7SCQS+P1+gsE0LpdGW1sbra2tstyZTqelbZHY5xRVCd2CqocxY8bg9XoZGNDNgn0+kyyZX3fdWJ5+2s/cuRnuvjsvl+bL/0aKxSLxuO4bWCgUpFluNbuipKnYu1CBby8hkQCLJStHvKsREmMej2ebuz2pVIp8Po/D4djuq9pEAsqrn++8sxGzOUOpVJKZGegZ4bHHDp/EX3rpbTweA+m0iQ98YDgDueOOezCbM/IK2+FwyNKsWDUQPT+xwpBIJKQAtjjRulwuSqUSmqbx//7fFJ5/vom5czP89Keb8Hg8dHd343a76e3tpampiTVr1rBp0wCHHTaZfD4vXSPAzVVXXSaP75BD1vKZzzyFyWQikUjIE+HChee/p0xqW9QaaKn2+/v/7X13mJx1ufY9vb7T287upjfSIBBAiQQhikRIIIVEBEQB4fOIiHjwCGI7CvpxOIgFPkFBUFABkxBiMHQBiRBaIASSbLJ9dqe3d9o79fvj3d+zM7OzyW6SJWXf+7q8MMnstJ15n9/zPHdpZCjN9pDV+XrsZxqJ72+++XYKw61GNVmFoVFB/t73HoTTyUOlsmLaNA+NFKPRKNnOlctlKJVKuFwuhEJZfOpTJ9LPP/HEFtx99zy89lozFi/ux09+0kbF6Wc/Ownbtk3Caad14j//800YDAZYra3Q68tU9PR6PYrFIonf9Xo9AoEAmpubkclkkEwmcdNNEyhCaN061ZDP7oEcSxiBrH6/zdAoSJZBSkw4PiCNOo8SiN9BDenRGCuTMTUNBsMBx5pMxjDS5PX6x2csMwC44QYOt9zSDrvdjlKphEhEgMdjhMulx3nnJbFlixg2K5dnkEgUEQwGsWiRBa+91oJTTtmHk0+egR07dpCsIpFIwGQykXOHXC4fIMiIHVokEqGix1xcxMDSNIl577svgVIpD5NJjmhUTHbgOA4+nw+JRALd3d24996zsG3bJCxYsAcrVmyGTlcaYLkK+NSnfPjXv5pxyimduPHGN1EuW+Hz+chnVKlU4pJLNiKdFqUETAPXqGixQjISnd9IZBEclxlCSGHjxPqgWtYp6vXlml3frFkf4qmnlg88r6GPefPNtwMY9F5VqQTceef1iMfFqqFWF7F4cQsSiQQCgQD27BGjp9iBCxDZv0zbuXfvXigUCpx33mRs2WLCkiVhWK0q/PCHH6JQ2AeZLI1oVIBarQbPV7Bt2yQAGPjvR/jRj2bhzTcnY/nyHH772xiZLcjlcuh0OvA8D7/fT8xPrVYLudyEp54SC8/69Sr09sZhNAKrVnFYt04xIoak0SjmAsbj8YaSoXrHmGowb8xGqE6AqIZEYDn6IHV8RyHi8TjJCYazGGNg409g0JNwtBB9/lLw+yuYMWNwse/zJWE0Al/8ohybNxuxaJEPP/rRRwPdmw6nnDIT8Xgcfr8fer0eRqMRb7+9G5lMEHK5nGQM5XIZRqMRarUaLpcLsVgMGo0GuVwOFosFgUAAdrudqOI8z0Or1dIoNJ/P45Of/CTtdGKxGBKJBFKpFHbv3g2j0YhUKoVoNI/rrrui5rV9+tMB3HrrBwiHw0gkEshmFZDLM0SoEHV/cmi1RdKbyeVyei5snFjvl8nE3QCGJK9XjxtZrFB96kMjT8zapPRfQaOJDRtDdPPNt4PjZJQi36jLmzVrN3btmlnzM4C4s2TjPNGEwI5QKAOOy6K5WbQd8/l8NII2Go3o7+9HOBwmO7ByuQybzQa1Wg2bzYZMRk5jY6fTCbvdDkAsBsx156abJuDVV5vw6U8Hcf/9JcyY0UTP7Y03PkRzc/X+MQWfzweLxQKHw0H7YplMhssvV+Opp7RYvjyL3/wmTAelSsUw6gLD8zwUCsVBWwFWg32mqvHlL2uxfr1KEqkfZZAK31GGUqmEeDw+oI8b3jqJic4BjGj8Wf1zgiAQbZvtOa67zoENG9SUhn3uuXH86leBIZFAr7++ExpNAX19SVQqIsGG4zgafymVSvT09IDneUydOpUsqeLxOBVmdlF0uVyIRqPkyBIKhaBSqWAymaDT6ZBOp8HzPNRq9YAPaImih/z+FMLhTsjlcmQyGZTLZbS0tOBXv1qEV17x1Lzm2277FWSyND2/QqEArVYLQRDw8MNLqZitXfscVKpBnVa5rMf3vnc9/bm6cACoCYSVyUDG19VFsdpM+rzz/lEztmRgovzHHluBnTvnYu7cnVi9+m/07/U7QJMpgRtvvBsA6HHqi+P8+bvwpS9txoMPXogPP5yG+fN3oVQqUpjsF7/4FLFs5XI5/vSnC/D++7Nw3nlJ3HrrDsTjceh0OhozG41GhEIhJJNJlMtlmEwm9Pf3QxAEOiwAYj6jyeTF4sUnU4iw0WiEXq9HuVxGJCKA42TYvXs37r33LDz/vA0XXSTgvvsSiEQiNN5miQp2u51MCdLpNAqFwgAZyg25XHT0MZvNCIVE7Z/FYhm1zRezBTwU8/dGGO0IVsLHB6nwHSVghUyhUFAHw/YewOC4pFp0zuQA9ffDCkEj5HI5pNNp2lWIQa61ru7//ncH5s93k23Y2rUVrFunxLJlWdx/fwIrVgh4/fWJOOecML7//Q+g1+vpIsnS2XO5HHp6etDc3IxoNAq3243u7m7Mnj0bPp8PlUoFJpMJRqMHsVgPkVuUSiWKxSKUSuWAc78H+XwU3/3uZLz11hSccso+AMDbb0/FvHkf4YornqbukkktBEGFBx88A2+8MQkAMHfuh7j00qdQqVQgl8upg85mFbjllm/UvD9z5nyASy7ZSBfAP/95OUXwVBecfF6N22+/mX6OdXYaTYHkINmsArfd9t0h91/d6QGD5s82mw3hcI4y5ViOoEajQTKpw89+9k36mdoUig8ByLBjxwmYM2cnli/fCKtVhb/85UK8++4MLFjQgSuueBE33HAV/fyPf3wXLBYl7PaJiMVi+MY3vkz/tm3bR8jlwjWHlUQigWKxiJaWFqTTabjdbgQCAVgsFhrPR6NR3HbbfGzfPgMLFrTh2mtfpKQDq9VKLM0JEyYgkUigpaUFxaIWWq24XzWbzRAEAeFwGBzHYdq0aUilUshmFdBqxQMaYwb39vZSdNWNNzbj+edtuPBCAffcE4bNZiNpw2jGjEz03sjy7GAhmUYfnZCifI8CMMNesRCI31I25gPELw/HAStX5pFKpUgbl0ql6GfZ/9LpNBFHKpUKKpUKyR+SySRkMhkmTpyI5uZm2O12mEwmeL0mrFkjPpcLLxQwe7a411Mqlejr68Nf/lIBzwOPPloEYMTrr08EIEbSxGJi6kMqlUK5XEY4HCb5QktLC6LRKJqbmxEKhWAwGPDBBx8Q0/SmmyZg3rxJuPXW6VAoFFCr1chkMohGo3jnnXfwne9MxOLFC3DDDa146y2Rjfn221Px9ttTAQA7dpwAjmvClClTyJKqUBDTxC+77J/0/n7wwWxoNHZYLBaYzWaoVKoBazY5Fixoq/ldiO4uehoZL1v2PP1bMmkGz4txThwnw6xZH9K/VY8ZWRiv2awgf9Dq+xcEFXVqTzyxCj/72S144olViEajUKvF33EwKKO8OLVaDa9XgRNP3A0AAxpEjnZ7O3bMxlVXbcXPf34Pvv71VzBhgg1qtQ3vvivKLt59dzJKpRJOPbWdfl6pzOF3v1uCyy67EH/+8zn0Pixe7Ee5nCRhOYvCYskgpVIJ06ZNQzAYRFNTE6xWK9xuN7xeLxYt+hy2b2ePOR0nnrgIZrMZ8XgcnZ2d2LNnD3ieRzgchkwmQ09PD/x+UfxeLuvR19eH3t5eJBIJhMNhvPnmm7jkEjncbj2++lUTOI6DwWBAPp/HhAkT0NTUBIdjEp5/XpTobNyoQaViQH9/P3w+H31v1g76G+wXTCaRSCRwuPqBxx4TOz2p6B1dkDq+IwjGvhzOYqxUKiEQSNd0Y35/Gi7XIE2bSRbqwdicjJ1mNBqHjHHqo08iEQFmswJWq7XmNvF4HCqVFZFIF2QyGa65xoxnnjFjyZIIfvObEHValUqFTuNTpkxBV1cXEokEcrkc1Go1wuEwCgUNeSl++cur6HF+97u/0p4tk8kgm1XUdCEnnbQP27dPxRln9AIQxe2nndaJm2/eTq+B6bRY4Oj3vjcNW7aYsGiRD7feuoMs1AAj4vFeeL1eRKNRpFLAn/+8BNu3z8CJJ+7GVVc9S4bKuVwOt9/+NSQSHBlFV48pqy3N5sz5ANde+xLZvsXjYifzox99m25/wgkfQS4vYefOuZg/fy/ef39wjPzDH94JuTyDO+/8JnjeDIuFx69/vZESA5LJJHp740il/NDpdHjooaV4772ZOPnkNnztay/TWFilsqJcTuLmmy9FPM7B5RLw4IPPI5vNwudLQKHIolIx4Prrv0KPfc89fxxwQFHA7Z6KpiaOtJmJRAKlUgkymYxS0VnxkctN0GgKaG5uRj6fx9e+ZsWTT2qwdCmPu+/uh8lkIi0oMzF46623kEqlaMT76KPLsXVrC847L4m77vLBZDIhm82iszOMz372E/QcP/ywB0bjUDu/L31JgyefVGPpUh733RcfkOEUMGfOBLrNaMaMbPpyoP26hGMXUuE7AmCC8vow2EbIZDL40pc0WLdOgRUrCnjoodrluU6nq7mP6lFofQRLI5o2Y0+yqJVGHqGis4wMixf78f3vdw44b1Tg8QwWU+ahmMvl4PP5qGPVarXw+XzQ6/W47bZ5eOUVkdBw8slt0Ol0eO21Fpx+ehduueU92Gw2+P1+RCIRcByH++47G6+80oQzzujBl7+8BUqlhcaA8XgRbreBfBqVSiWNqVimWiaTgc02AXJ5Bt3d3TAYDPjNb87E669PwMKF+3DJJRspd81qtaK3twiHo0jdLgA89NB5ePvtaZgx4yOoVCXa111yyUaUSiXkcsqa/drddz8AmSyNhx46D+++OwPz5++CTIYB0fdeXHHFc/jOd77W8Hc9b96HUKk4vPNOK/3d/fdvhE4nEjCcTidisRiNxGOxGCoVA5TKHFQqFdRqG37965Pw3nszccYZfmzdOrjrfOWVd+Hz7SZHk1AohPvvPwdbt7Zi4cJ9WLt2A3K5HP7614sGBPQhXHPNi1CpVLDZbDSG9vl8pCW9775z8NZbU3D22UH87//6UCwWB4qbGmazgkKPWcJIMBiEXq+n5y7GXKlxwQUL6Xnu3t0Hg6FCJJrrrnNi40YNliwJ47rr/oWTTjoJdrt9iI6VjTSTySQ0Gg1CoRCuv96FDRvUWLEij8cfl486mYEdKj9OJxcJHw+kwvcxgoXBajSahloglqwMoGbUIopu3bBYGn9xGSuTjaeY9o3dF8P+hLnJZHJIVygIArq7ozXsOwA455ww7riji3Zx7L6z2Syx2vL5PCwWCxF1uroiuPbaL9bcz//+7/1wOBwwmeQ1MTWlkhZTprjR1dWFTEaOfD6KYrFI4Z/5fB6TJ0+m0SbLCmRjOXaxzeVyRI9nHdiVV15Mj//gg09ApRKgUqlw112nY9u2SZg/fxduvPENOBwOVCoGLFv26YbvF+vOZDIOTz55Ad55ZzpOPnkvbrzxDcjlJnzxi8votnfeeR+eeOJ8vPFGCxYubIdWq8G//tXc8H6rYbNlcNddT8BoNFLEjVqthkKhQDrNYfZsGyKRCKLRKO69d/EQ/eHixWG88ooDixb58JWvbIFSqcRJJ52E9nZx5Gm328HzFdhsavT19SGfV9c874cfXo+WFgslZZhMJmi1WvA8j0Agjf/4j8vpts88sxUcJ47RM5kMRWqxNA+Hw4FCQbSe02g0iMViuPJKA7ZubYHbnUcgoMYFF2Tw1FO1cgBBENDZGUapJOo6mfzE6XRCoTDD5dIP8eLs6+uD1WqFTqdDMllGqZQgA3aGkZo6lMtlJJPJGsszCcc+JB3fGCOVAvT6MvlRms1mFItFYmRWQy6XN3Rk4Thu4PbmmmV9LpejDo4VLLbPG6m7CyBeXBhVPJFIUMFUqVSYNs1To+8DxN3ejBl2YkBWpzYw0+Bdu3bB5XJBJpPB4XBAq+3CmWf24dVXvQDEXZLDoUUy2Qe12gGj0QOrVYWf/nQuXn7ZgwUL2nD99W2QyUqw2+3weDw1VPF0Ok2WbRaLhZh/TAeYTqfR0tJC2jCe5zF9uhOLFvlIXC2TpaHXm2AwuElj9v77s6BQ7EI4HEa5HMTpp0/GG29MxIIFe6BSqbBt22TMnv0RZLI0/va3i7Fjx2wsXNiO++77MxwOLQRBDpNJDpstjWjUAKs1Bb1ejzfeEE2X33prCu6778+4/nor7r57LrZubYXdnkEkosfChfugVCrx+usTccYZIdx5517wfBNisRhUKhVJOb7//SuQTJphtaawfv1b4LimIUVv6dIkfvCDvVAqw1Aqc+jpcaFYLOLdd98dCHMVkE53D2hDPTjhhBNQLBZxzjkhvPiiE5/9bAxz5kxEJBKB0+mkwpFKpWAymTB9+nTySD3jjF4oFFkIggJtbW3weDxwOp005tZoNOjrS2L69CbanwmCClu3iu9JIKDGhg3/xpQpBvT0WGCxWGpG+RaLEqWShXZ80WgU/+f/WPDPf7qxbFkGd921DzqdjiQJLpeLpiomkxalkmmIkTXLAKz+7tU7vbC/t1jE4i8IwqgSGCQcvZA6vjEEY3QtW5bFI49UZ6MpGwpd94dcLofLLlNi3TolLrpIwD33RGo6OJ1Od1Ahm7lcDuFwGEqlkliZjfaBqRRwxRUFrF+vwrJlWfzud0kKj2VmwOxiksvlEAqF4PF4oNVqEY1GYTQasW/fPuTzYneRzYbQ0tKCQqGAO+88FW++ORmnnNKFt9+eSI+5desOFAoiC7Czs5OKnUKhgMPhoJyzri6x+2RsVZVKRZ1FNBpFPB6HTCZDMBgcsEBTQKMpkDuIXC7HL35xOl57rQWLFvnwgx/spMeKxWJIp2XgOBkMBgN+/ON5eO+9aVi4sBtvvTW4Q9q06Z/I56MDjFxg1apz6d8efng9HnzwDLz8sgenntqBG298g+KeeL4CmSyNYlFLz0mvd8HtNkCvd0GvL6OnpwfBYHBAgmHGNddcSPd9xx1/hMUi4OGHP4/XXmvGwoX7cNVV/8Ypp5wCs1nU0PX09GDGjBlwuVyIRCK49FIltm5twSc/2YNrr30RRqMRc+fOpYNUoaCBWp0npi1jZGazWbKHy2QykMvlKJf1aGmxIBqNolTSobXVSh2SIAiIRCL46U/n4ZVXPFi2LIPf/jZOo+9vf7sZzzxjwXnnJfHLX/oHshvjsFgsmDx5MkKhLDhORvFCzMAhlQK83sECFI+XAKQGDitl6PV6Yiyz8Tczrh6ucDHj6urLYf33gBV+ZpItCdOPXUiFb4xwuDU8h+v+WN4dABpNms3mEQd1+v0plEpiCjuzs+rr64NMJiNNlcViQWdnJ+bOnQue57F7925wHIddu3YhFouR84f48wZ86Usr6P7POiuCl1+249xzY/jv/96DPXv2QKt1QKnMUffY3NyMbDaLdDpNxZ/R/41GIwoFURrw0UcfkaM/Kxws26+/v5/IC4y+vnevfyC7LU15ckzzp1QqoVbbsHbt5+m5nnaaH9u2ebB4cT++970dJJdQKpX4+c9PwrPPWnHuuTH84AcfDuzILIhGu6FUKmG329Hf34/p06dj+/btdMFOpVLQarX42c9OxNtvT8OiRT7cfPN2qNVqOBwO6HQ6nHnmRITDOrhcAn79643geR4TJ85Ba2srisU4LrtMie3bZ2Lp0iS+9a03aE8ndkv5Gsu5J598Ae3t78PrnYGZM5sxf/589PX1wWQyIRQKUSYeS1FnjNXm5mYUCgXs27cPfr8fd9/9CWzbNhlnnx3C//2/nVCpVLBarQiFsjj11EFnmxdffBNNTRycTieUSiV4voJYrAcajQYmkwmFQgGBQADf+c4EvPSSCxdfXMbvf5+q2bNVKhWsWVPG3/6mwMqVBTz0UI66fZlMhlwuR/tF5oLkcrkAiNOJkRi7M1mQQqGASqWisa0o/0nhiiu0WL9eKckUjlFIhW8Mcbg1PAd7f5lMhvYb+bx4kq8uXCO5EFSTZorFIgKBAMUY5fN5OBwO0uAxu6lUyoBksg19fX2IRqNwOBwAxDGlxWKhxIjbbpuP11+fgCVLwvj5zzuRyykBpJBMJnHnnQvx0ksuLFzYjh//eBfMZjN1t+w0zsgoKpWKLtRMd+bxeBCNRpHP58FxHFKpFFQqFdrb22G1WqHVamG32yGTyfDNb7rx5ptTsHBhO+65J4xIJEI2VLt27YLNZsPtt5+Id96ZRnl0n/xkP+64Yx8qlQolTVQqFbjdbvT0xGA2K1Aq6aDRFChHMBaLYdasWeQTmslk8PbbuzFzZjNF+px99iDh4+2398BmUxMLVyaTob+/Ao5LQxAEfPObHmzfPgMrVxbws5+FMHOml372pZfewvz5U5BOpyGXy6FSqXDllQZs3mzAsmVZ3HlnD6680oDXXmvG+eencdddomOL0WiESqVCJBJBOp2m93DSpEngeR4c14TmZlEaEgpl4XINat+eeuol6HQiC/S22+bjpZdEI4aLLhJw1119AwUxRHrTQqGAlpYW5PN5qNVq+HyJmuK8e3cfWlutQ/R1rONi3Wf9zk4QBAiCgFQqhXQ6Db1eT2SVA6WUAGLxKxaLFMuVzWYHvgcyTJ8+SByShOnHHqTCN8Y43OOQkdxfsVisyRtTKBQUz1LN9GRjpf2BXTxkMhmy2Szi8TiKxSIRRpxOJ0UWsfTrSCSCU091we9Xw27P4LnndsPr9RLx5NVXX8XMmTPR19dHsTIfftiN5mYzdXNGoxE7dnTg0kuX03N5/vk3YDSKIygWRFsoFDBz5kw6oTP/xWQyCbVaDavVing8jnA4DLlcjunTp+Ppp1+BRiNebN1uNyKRCILBTA3xZcOG55FI+GjMVy6XUSqVBsZfXnzuc/Ppts88sxVNTRyRQCZNmgSFQoHe3l5897uTByJ6/Pj5zztpJJvNZuF2u1EoFHDTTROwbdsknHWWHzfd9DZaWlpw443NePFFB849N4577xWLz4QJE6iDZmO4cllfYw7+/PNv4Cc/mYSXX3Zj0SIfHnxQZNay9yGdToPjOGSzCrhceuzZ01fTke3bF0Q2G4JCoYBSqYTX60VPTw/a24OYOlVMVv/udyfjueesWLmyiAcfFAOEL79cjccfB1avLuH//b8YOjs7EQ7nsHTpp+i+eV40Yg+Hw3A4HIhGozAYDPQ7Y6NKjUaD6693YdMmPS64IIMnn9QgkShBqcwBqDVuyOVy1C3vD2x3rdPpkEgk6HFYBuBowQ6hK1cW8Ic/iLtnxl49HGnvEsYWUuE7DsA6AebWwqy5cjnxQtHIdJc5YAw33mSC9HQ6PeCekYXJZKIuSaPRDPhjRmGxWAao/Uyvp8CsWRa6r5de+gg6XRKxWAxerxf9/f1kPZXL5eBwOKBSqeiClEyWMWGCDRqNBrfcMhUvvGDHWWf58f3v74TT6SQfTSaobmtrIycaZmzNcRymTp2K9957D1arFe3tQZhMcvzP/yzEv//dikWLevFf//UujEYjbDYbbDYbvvlNNzZsUOPTnw7iW996HU6nEzabDR988AEmTJiAYDAIm80Gl8uFK6/U45VXmnD22SH89rcx8DwPu91OXa+YWu/AvHmT6H149929sFjEkWEwGITb7YZGY8ekSXa6zbZtH6FUSiCfz6O19QSo1Xnk83n6nVYqFUqcaGlpgV6vx5VX6vH00xyWLIngl7/0IxqNoq2tH06nDqeddtqAjZgJlUqF9muxWIwK29q1FWzYoMbq1WX86U959Pf3UyJHIBDAt77lxWuvNeOzn43hjjsiNRZ2PA/I5ZkB27kCmpvNyGazUCgUiEajuPJKA55/3oazzvLjjju6MX/+fNr9scgqAMTCVavVMJvNSCaT8PtTSKcDuPXW6Xj6aSPWrAH++tdBb1rW8Y+0cDESlpgpyZPDDjvYGQyGIUnu+0P9IbR6KsLQaF8u4chDKnzHKPL5PBU2AAMmvRXq9PZnvcSigeovGGz/l0qlkEgkIAgCDAYDXC4XnWTZF5tdsAqFAgqFAtxud00xbW0to7dXDo+niM2b368RMbPIJPZYTqcTPp8PLpcLP/zhLGzd2oIzzujB/fcnIQgCfL4EPB5RUM48QR0OB1KpFI1u2Xi1v78fXq8XSqWSyCw//ek8bNliwumn9xK7EhALUS4XhtPpRC4nauECAdEggN03x3FIJAazCZlMQxxrGjBtmoeKP3tPGbnDarXi4ovLeOEFOy68MId7741St8o0k+FwGLfeOh1btphw7rkx3HFHNzo6OrBkyRIiYmSzWfT09BDxIplM0l42EolAqVQim1Wgre1dTJkyBeFwmPSZbG/GcRy8Xi8FugYCAcyZMwd6vR6VSgXbt+/FggXTAYCE8JFIBKkUakan773XjltucWPzZgPOPz+Fv/51MO0gFAohl8tRzl4oFIJOp0M0msekSQ50dHSgo6MDcrkcLpeLkjoqFQPK5SRaW1vptbHPeCJRqhmjsrEi83dl+9eRmkwzggqzWquW5DBZkEwmg1qtphH4waLeIAIAkW4AKbXhSEIqfMcIqrV6wKAOqfqUOdILQP2IUxAEJJNJ9PX1EfnD6/WSC3/1qbwR5ZvneWQyGbjd7pr7XrIkhRdfNOLzn+fxhz+IJ+1QKIQPPgihUOgl30wmRbDbJ+Lznz+T7vfpp1/FjBleStKOx+OYNWsWFWbG9NPpdPD5fCgWtXA6Rc9QNgITBBWWLz+b7vMzn0ni+edNOOecMH7+8w5YrVai3IsEFjXa2tpgtVoHOsV2TJs2Df39/YhEIujt7cW8efOI+MJ2p06nEx6PBzKZjKQcVqsVgUAAHNcElUpAuVyGVquli29HRwcmTJhA3WEi4aOx3ZQpUwaINjJ6jwEgGAwiGAySGTNL8QgEAshkMjAYDAgEApg3bx7y+TztvsSirYbJJEcqlcK0adPovTeZTGhra8PEiRNJ+8h0cJVKBcuXZ/H3v+tx8cUVPPaY+By6u6MwmxXIZrNwOp0wm82UhM7y9FiBS6fTCIfDsFqtKBbFOCmfz4dsNotf/vIM/POfLqxYUcC6dUqaHBiNRiQSCZjN5iG7bTbKZ4QX1skBjf1r61EqlZBMJqn4MdkPI30pFArI5fIBg4AKkWMOR+fGjOGl1IYjC6nwHcWo1ulV6/Kqo4iY9m+kYKxBpVKJZDKJffv2kcWWw+GooXvr9foRSyQSCXE8ZzKZyHuxmoW6desOTJxox4IFNgSDWtjtGTz99AcIhUIAQJ3cXXedjueft5FTR2tr60Dx4Eibx5h7RqMR4XAY4XAYd965EP/6VzNOP70Lv/ylHwqFAs3Nokj8qquM+Mc/OJx7bhx33eVDNJpHNhvC3Llz4XQ6sXfvXgCDnqkcx5FTf3t7O8xm84CgvYIXXngBn/nMRdi37z3ysFy4UCSiOJ1OGicqlUpotVrE43G0traC53liFIoyBh7d3d1Qq9UolUpQq9Xo7hZHgdlslqQezD4ukUgQSYldmFnBy+fzUChEp5Te3l643VNx8skz0N3djXQ6jcmTJ+Pqqzk895wV554bxy9/6ScxPMdxJFXgOA4ul4so+9Wfl2xWAZksTZKXcrmMzs5OIn4wH9RUKkWj62g0WuOow9I32O9w584uzJ07KF/Zs6cfra1WMkdn/rXi8xC7IyYob7Sbrv5e1LsWNbotO6QxC71qF6LqyYlSqUQoFCIyF5PRHCyk1IYjD6nwHUWolhoAtV/e6o5vNOL0asTjRchkaXR2diKRSEChUGDSpEngOG5U46LhEIvFqICZTCZ84QsyPP44cPHFFfzkJ3tgMs2A1zv4nF9+eTccjhJ6enrAcRw6OjoG9i0cliw5HX6/Hzt27IDb7YZWq6UsP9Y5sE6xXNbXsCA3bnwRCxZMh8ViAc9XIAgRxGIF6HQlYlIyb8lwOIxcLgev14tMJoOenh60trYOGG8DVqsKyWQSXq8XPM/jy1/WYfv2GVi82I+rr96KefOmQK/X08We2a319PTA6XQiGo3C4/GQLyt7j1l227vvvgubbQKAFDiOg0ajgcPhQDwex8SJE4mFKggCikXRTk2r1VLnyMbWOp0O27Ztw513LsTWra04//wUHn20hB07dkCvd+GUU2bQ+7NzZzdcLj36+/thNpupYE6ePBmTJk1CKpUaIMBkIZPJatiS5XIZfr8fAODxeBCLxRAOhwGIn1GVSgWHwwG1Wk0BtsyxKJVKkacrkxesXl3CunUKfPrTQTzySAFOp5N2xx6Pp+ZQx4pVtZfscGABziztpBHY/TEj7eoOm6F6pcDIW4FAgMhidrt9CJt0JCNMKbXhyEIqfEcY1VKDRqywdDpNX7JDWZSzL9rixX7cf39iRKdWJnlgovGRFNpIJAJAHLuKHQAApKhQnXSSFT09Mthsadx++yPweDy0DxJ3XgooFGG6OLJ/E/ctHJqbzYhGo7DZbEgkRBsrk8mEW26Zgief1OD881P4yU9Esst3vjMB//iHCWefHcIdd3TRRd1ms0GpVCISiSCRSMBqtaK7u5sex2Aw4LvfnYxnnjFj6VIeP/7xroHYJT1OPHFKzeu96CIBf/xjnuzTWMHq7OykvSNzsEmlUjAYDEilUvB4POjt7cVllynx6qtefPazMTz6qCgTcbvdyGQycDqdKJfL9HpzuRz9f5PJhFwuNyCkTqG7uxulkg6f+MQcem579wagVIpmArfeOh3PPGPGsmVZ/P73PO0imQdoX18f0uk0kskk7VBNJhNNAFg3VX3oYjtXcQ/ro/zISqVCxZvtGhmLkmnsCoUCmpqaBtivcSST4uMrFArMnj0b4XCYDgwMTJ85mgMfY9Ey8kojMlc8HidT8v0V1eoJDNP0hUIh5PN5yGQymEwmXHWVccQFTdrxHTlIhe9jRr3UoN5kGqgthgebql6Ngx2tMAo/6zRG+lFhOzmz2UzsT0AcnYrOLmYAASQSCbzzzjsUdnrLLZchFjPCak3hf/7nMbjdbmSzCvj9e3HffWdj5865OOssP773vR3I5XKwWq1wuVy0i8xk5OQ4AhhrtFY+XxKxmNjN9ff317h06PV60v15vV7s2xeoSQXw+ZLQ68sIBAK44YYmbNlSa1rc25tAc7MZkUiEdGlWqxX9/f3IZrOYPFmMBeJ5Hvl8Hm63e0BfBrS2Wuh+2tr80OvLpEdkgb1MH8jy59joUKfT0Wdp+/btaGlpwX/910Q88YQcq1YV8Ytf9KNYLKK7u5u8RwuFGLFYy+Uy+vr60NLSglQqhaamJgQCAZTLZdKssS7OaDTC4XDUFB22qwTEYsdMqNPpNBUo1sFls1loNBrIZDLodDqSmEycOBE6nQ7t7e30Xejs7AQAzJw5kwpvNBqtOXwdzPeCMZUb+XQmEomBkW52WIlPsVhELpej0TD7jjJCV19fErNmDXqwSiPMoxeSV+cYo5HUoJHbe3W8kF6vP+SxYzWMRpDf5po1I/8yMrr3gRIk6sF0fYIgwOFwoFQqQaVSkTBazOwTSQWXXnopdu7ciZ07w4jFxCcWixkRDivwwAMn4t//bsXChV3YuVPcBb38sgc33bQbHo8FbrcbOp2OTul+vx8tLVORTCZhMpmwfHkOTz2lxapVRQhCBK2trQgGg4jH42hqakKxWKQOm0kVxLgdMdR040YNVq4sIJXyQy43oVgs4vbb9+GBB6biW9/i8PjjMqxZI5o8s5FYc3Mz+VEWi0XqSlm0T7FYpLGlx2PB8uVZPPWUDueeG4dGU4BSqYHFYkEgEEA4HEapVMKUKWKXyQg9bHwXj8dhMpnw+uuv4+STT4Zer8cdd3ThBz8ADIYKymU5PB4PaeXcbit4Xol4PA61Wk3J6O3t7cTgtVgs2LNnD1paWqiw5HKiM0pbWxsZFbDPR3WRYIWQMS5tNhu6uiIwmxU13SAbebrdohE5GxUHg0EYjUZMnz4dKpUK27dvh81mg8fjgdlsril0rIgBB97nMbDdZCKRQDQapS6QJciz4trX19fQ2oytA+Ryec2/s+fS1MRhzZrKwOdCKnpHM6SObwzQSGrQ6HTKxOHAyL+8h4KPe7TCrMwAcR8EAB0dHUSCYYQaAOjs7MSMGc0oFFRQqQq4665Ha/L4zjsvjS1bDDjvvAR++9s4MpnMQCAsR+QDr9dLQnuj0YhcLodEooRcLkxkCZYlx3Rter0eO3fuhEqlou6jUqkQG9NmU9PBpbu7mxw/xHGkHE6njl4rIwPpdDoim7z00ktYtGgRkslkFXvVDofDQZZaO3d2wWSSEwuSGSy3t7fj9NNPJ3edrq4uErJXP3er1UoSgGrLLjZadDgc8Pv9cDqdkMvl6OrqIsswnU4HQRDQ29sLi8WCVCpFGrtKpYKpU6fS57JcLlM+H3sP2OeajT/j8ThyuRy6u7vx3/89B5s3G7BiRR6PPFJseJhLp9OIRCLw+XyYOnUqOjs74fXOQEuLhZ5XJBKBw+GgAwDTf7KOq7r7YnrO4cBYrmxKUD2NYGNjdkkcrSE1GweLExZZFVtUKoJHG6TCdxhQLzXYn4aufll+vAdd7tq1C6WSDhaLyCKdOHEi9Ho9isUi2tvbMWOGSLqoH8eedNI+KJUySl4//fRO3H13Cg6HFmazGVarlbSBTBfIDg+JRALxeByCINCIkFmpRSIRupiz/Vl7ezvmzp0LjuMgl8vR398PpVIJl8uFfD5PLjBMpM52VKzYcByH9vZ2tLa2kvcpIOrttm/fjk9+8pNoa2uDIAjQaDSYPXt2DSX/gw8+oP0uM9p2OBzIZDI0MmYdpN/vR2trK2XjxWIxzJkzp4bAwSzlOI5DJBIh2QIAIggxHaXBYADP84jHi1R8y+UyOE4Mou3o6CBmY6FQoOKsUqmI5MKeO7Nry2az4PlKTZxVb28CBkOF2LLVYKL8cDiMm25qxQsvOLByZQH33BNBLpeD3W5HMBhEIBCAw+GA1+tFoaCB1Tp0EsG+X6yLG4kdHzC4S1epVDSuZlOKgwErqldeacC6dQqJxHKUQSp8B4nhpAaNUE+PHq4oHo9YubKADRtU+PznU9i8ufbY293dDavVSifr5csL2LRp8GL28MPP4IorPkd/fuOND6HRiI4vFosFdrsdqZTo6el2uyGXy1EoFJBMJhGN5qFU5kiqwcyVmfA7Go2iUqlg165dmD59OlpbWyEIAhEdmCZOr9ejq6uLRm3MpFij0VD3FwqFSKg8YcIEGgUmk0l0dnbSeNdiscBkMpErChv/dXZ2orOzE3a7HV6vF8FgEDNmzEAgEIDL5UJ7ezs4joNOp0NnZycMBgNMJhN2796NM888s+HnLhqNDoTTqrF3717qcnQ6HRVvnU6HcDhMzi9Ll/J48kkNBEEAz/OkzeN5npij9fstg8FApB7GNmXSjJtvnoFNm1RYvbqEu+/2U3Yi6zQZWFFXKMywWAYnI6FQDkaj+P1iB8Suri58/et2bN5sPGAxqda4jpQJzQonk6ro9fqD/r5KsoWjF1LhGyHqpQYH6tYORlh+vKH+i9/eHoLdriF2al9fH438GOlhxYoSNm1SY80a4IEHan/+pZfegsdjpO4iFouhv7+fxONGoxEajQZf+IIMGzdqcNZZAdx/vziWMxqNaGpqAs/zNCoLh8Po7+/HqaeeSgxFMeRVNNFmzEeWusBGaMxnk7mM9PX1oampCX19feTMwTrCYDCI7du3Y8aMGdDr9eA4jky12etOJBLIZrMIBoNYsGABVCoV4vE4dWXsOZVKJYTDOSxadCJeeOEFnHbaacOmgzP3FRb+um/fPhpLzpo1C6FQaCC7sbYz+/DDHtjtGpJisA6QJRTUywvYpIOJwtme74YbPAN5eQU88IAYRlwul0nXWCqVagJkS6USIpEIrrvOgSeekOOCCzLYtEn8zjAhe6PP1EiLSf1U5kDkmEqlQgcau91+0Cnskmzh6IRU+PaDA0kN6lFdHEcrLD9esWpVgRwq/vAHsetlbMFoNIpJkyaRd6XRaITZbIbfn4LXa0I6ncbll6uwYYMaK1cWsW6dSMwIhUJIJpOIRCKYPn06jQDFWBquxrR59+4+8v3s7e0lAXY6LcNbb/0TF1xwAXUwzGklmUwSDR8AdetMoK5QKMBxHPL5PEU7cRwHtVqNTCYDs9lM2rZdu3ahUqlg4sSJJGoHQN0ji3UqFArYs6cPS5eeCbVazCyMx+NQKBSYOXMmeJ7HxReX8eyzFnzuc3Hcd1+CutdGEpdEIkH+l2x0193djWxWgZYWC4n0NRoN1q4FNmxQkRdmR0cH7HY7JRqwDpb5b7IOvf67MOhTmsOUKU76++riVCqVsHfvXhiNRlQqFeh0OmKA9vX1DchUStBqi+B5Hs3NzUToYbvGw1FM2EGCdZPD7dcFQUB/fz+MRiN0Ot1BjT6lHd/RB6nwVWEkUoN6HA5h+fEK9t7wfAVer3hizmQy1N35/X7qFiqVSlVmnMiuYz6SwWAGJpN4cWfds8/no7FjKpWCIAikpfv+92fgueesWLIkgr/9TQGDwUCHGLvdjpUr89iwQY3zzkvi4YfFcajb7QYgjl/L5TKxF5n1VjKZRD6fJ4sz5njCAneLxSLMZnNN1A5LoyiXy+jo6IDF0oKWFgvtyQDxsLRv3z5cd50Tzz5rwbJlGTzySBHxeHxAWrEPM2fORCxWgM02+FlkSQeBQABmsxkymWyI/tPtdkMmk9F7vnJlAVu2mEjczsy8xbgm8eKcy+VIoM3ChT0eD1mJWa1WMiwfrmPieR5XX83h8ceBFSsKeOgh0U6sWkYQDodRLBbJmoyFBttsNiI8FQoF+Hw+SvaoNpA+nMWkmlHdaBXBjA4Y67dSqdA+WMKxiXFf+KoF4gqFYsQnOkZhlord8EilUhSuWj0qYr6KGo0GbW1tMJvN5DnJLLkcDgeSySRyuRwsFgslMTCRNQA0NzfXjMoYYzQejyObVYDjBi/6kyZNgsVigUJhrhmV7d7dB7fbQCPHWCwGi8WCCRMm0J4MAPr7++Fyucjf0WQyoVwuo729ncyg2ei7u7ubtHHM5PoLX5Bh69YWrFxZwK9/HQLHcTAYDNi3bx8yGTlOOmkqPaeurigsFiWMRiPa2troM3nttRY8/bQRK1YU8PDDYpKBWq1u6GbCrNcAsZj5fAlMm+amf9+3LwhBiECv15PvJ1Db1bHfHeuKdDodMVzL5TJ0Ol3DTok9dn1xqmc7A6J43OVyYe9ePzhOhqamJmI6M+ZqIBCAQqGA3W6HIKjGtHuqfo7V14NCoQC/3w+v1wu5XE6H3fFAUDseMe6OLIylx/6n1WphMplgMpkOWPQymQz9HHMMGYkp7nhFpVJBoVAYcmGojrwxGAwolXTkEJPP5xGJRNDZGaYCWCwWB4TvIkVfoVCQ3yMg/k5TqRTcbjesVitOOOEEnHzyDNhsNqL6d3R0YOvWrdi58w189rNiWO0FF2QwaZKDRoZ+fwpyuRxms5nE52wnF4kI1HGwfMNwOIyWlhaKIzIYDBAEAVqtFn19fdDpdAgEAujv57F1q5gKsX69CpmMHKVSCe3t7SgWi1CpBCxbJh4GPvOZKOXOBQIB6iaNRiOuu+5VdHSE8cgjBbI3Y44h8Xic3l9GvPL5fPD5fEilUnC59PQYy5ZlUanwOOGEE2peE3tdZrOZfDeZh6VCoaDOjO2r0+l0zYSkHvUFSq1W03eNyVksFgtWrixg3rxJ+PrX7eT8A4ijVKa3i8ViuOCCNDgOuPji0qgMFUaD6ueo1WqRTCbps8fcdsTnxpEkJplM1uz/JRz9OO47vtFIDRqhXlh+KOa04w3MMLi+I2ZdhFwux4UX5vDcc1ZceKGARx8VBeWrVhWxfr0SS5fyuPfewdw2tg9raWmpKZSMXcvIKIlEAl6vt6bgsp0Z+2+5rMfEiXZMmTIFTqeT9kYrVog7yWAwSJ+T1auLePZZK9asqeCxx2T0mUomk7BarVAqxVSBaDSKUkkHhUL0iXS5XETV//a3W/D002JSxS9+0V/zObJYLEin0+jujmL27AkUZst0h/39/UilUojH4zjzzDOJRFI9hmfen9UWY/l8nsaGDO3tQWg04ueZ+Y8ajUYqOGyUyUa5bFfIDnjpdBrFYpEs03iep0DX6t/7SDVw9WSVeLyESoWHwWCoeX31t/P709DryyN6jHqwa8H+Dqz1t2FGFMViEVdfzeHZZy01O8ZqMtvhcFuSMLY4rgofG61UC8OB0XtcNvLkkzB6sMLHLoLMNcNoNA7s54Yy9MTbD/5dMllBpSL+QzQaRXNzM3Um4kgzS5Za/f395HqTz+drOng2du3o6IDRaITP50OpVEImk0E6LcMXvnA+3Xbv3gBUKmFAKwh4vYNPqK3ND4OhQkWP7StlMhm+9CUN1q9X4vOfT+GBB0R/0kqlgv7+fpxyyinIZhUwGkH2aEzkLggC+T2ybpPt7RQKBdrbg6hUeDidTpJ/CIIwxFSZFWObzQatVjukADEvTMb4zGQysNlsCAaD1EGLO1keTU0i07PaHJuNq1mXww6R0WiUMhkBwO9Poalp5OLv6jTzdetU9FwrlUoNQexoYEiOhFW6P2s0CUcHjptR59q14gdy5UrxNFs9UhlJ0asegbLxkclkkoreQYJ5SjKwzD+LxUJ/z6zUgEErtfq/4zjxd8EE6IxFybp4j8cDm82GWCyGSZMmoaWlBTabDRzHQavVEnmDZcW53W4YDAacddZZOPvss3HmmWfiE5+Yi3PPFcefixb1Yu/e7fRzTU0cfabWrAGmTfOQeTIrvul0Gn19SaxfL3ZxTz9tRDgsjiuZpZfoDcpTWGswGKQRWSKRIBIKGw339fUhGAzi6qs5LF68AL/+9SI4HA7a5VmtVnJQKRQKlLZhs9loZ10NnueJVep0OpFIJJBMJofIChh7kRkAaDQaGllXd0Fms5mirUwmE8xmM3p6erBqVQFeL4e1a0f+WXnsMbGA/OUvFdqvGQwGaLVaOiRU3+5IygIafWaH3sZI7ym7phxH/cVxgeOi4ztYbU91gKV0Oju8YN0G6/oYS7MRGjH0qv8um80ikUjA4/FQ3h0LUC2XyzRWrD/gsLw15tDS1RWBx2MkazMGUc/GIxjMwGYTBd/RaJRidWbPng293kXPJxjMIJ0O0AGJjSSvvprDP/7BYc0a4I9/FOD3+6HVatHV1YUTTjiBLMry+TwlXuj1eiJJVXuailZkrWhuHiQF9fXxaGriSKLAOi9GDmJghB72/JPJZM3kgplTMwNqrdYBlUqgDpn97hgrlcX2KBSKhno2NgKUyTiYzYO/g4MRbDcak7LR+MG6qIwFRsMqZTInRtaREtiPPI6Ljq/6FHbRRfn9fpiY0DaZTKJQKNQssiUcXrCOSKfT7VfA3/jULP63WjKQz+fR09MDu90+EFMk7py0Wi2NpqthMpmQSCTA8zy++lUOCxfOxA03eIbsdhihxeHQIhQKYc6cOVi6dCmWLVuGqVOn4rXXXsNLL23C5s2bsXp1EW63HtdcYyYhPEuheOihLNrbQ/jFL/oQiURoFJlOiyNLrVYLjUZD41rxdQ7uP5l/ZD6fR3NzM9LpAHWbn/tcAna7uLNUKpVEuDCZTEP2zqyYCoJAJtnsYssS1ltaWmAymXDllQZYrUpcfvnQyYZOp4PZbKaxJutc6nkczEmmUuGxapXYnR1Ok2Y2Go/H4w272SOB0bw2xvo1m83I5/NIJBJYvboIjsOoOmMJhw/HRcfHkEoBen2Z7JaqU5sZ62o0kgUJB49gMFjDOjxY9PT0wOv1kl7P4XAgl8tRxBErLmyvWy8vyeVyaGvrx/z5k+k+G3Ui5XIZPp8PFouFaPsqlYoeh+M49PUlh3RgHo94R4lEYkicTT6fx+rVJWzapMP556fw29+K1P1CoYB0Og2dTkd+msz6q1KpoFwuw2KxoK2tDVarFVqtA+3t72POnDlIpVLEIM1kMiRSZztBhkqlgj179mDixIl0qGOeo+zP9a8nHBZgt2sadl35fB7RaBTXXGPCpk36Yfds+XweoVAWLpf+oNYEByLGsLzAY9kJSbIyO/I4Ljo+BqNRPF1pNBqsWlWknR8T6o5EsiDh0MF2T/UX49GChdOGQiEUCgUYjUbaSVUXPUDUBLLfL8/zxHwExIieCy8UyU7DdSJsFMXSHsxmM1QqFUUBsfs5/3yRubdyZQFms4L0f9WsRoZ8Xo1Nm0Rm6ObNRhQKGnR2diIcDsNsNtOYnUkg5HI5JYIDwJQpUxAMBiGTibIB5grDPsNM26jVamvkDMzOrampiZIaWChs9WTD6dTVdGhMRtEIYoyRh2zEHn8cQzo/drvm5sHO5nCfq5lwPh6PH7N7s5HsCSWMLY6rjo9BOlEdGTC9HaO/y+XyUVHbqxEIpFEsxsnWiuUYMoq9xWI5YFFNp9NkHSZaitmG/Rzs2NGB6dObGo68g8EgxfvEYjEARjQ3m6n7UCgUw4qYGRNx1aoSHnwwTQWuo6MDgBjtw6y50uk0YrEYjEYPLBYlyuUy3n//fQqNZWGw9WDvOdshsfeHjZpzuRwJr+uRTCYhl5tgNIrFn4WxNvqd5fN5fPGLcqxbpxwRs5LtIFmHOhKM9PPC7lutVh+zpu/Sju/I4bjq+BikE9XHD9aRWK1WCrA9WKxZU4HHY8C114pjR7YfYXKIehr/cCgWi9BqtZQtx/P9DbuEtWuB+fMn44orhhY9RuefMGECgsHgQFEXH7tcLqNYLO7XuePhh3Nobw/h97/nyelEqVTC4/HA4XDQa4rFYkgmk/jGN1ywWpVYuVIU5c+fPx+RSASFgqbhHhMAiewFQUA4HCbmJyOFtLS0DNshibtHsetjqRTDabFzuRz+9jfliJmVjP3JoqKGe/4HA3bfrOM+Fs/v0nXpyOG47PgYpBPV2IOdvDUaDXVL7NReKBRQKBRGtY8ZrlsvlUrgeX7IHm04CIJAEUVer3fgvlPEqGTxOfubDqTTaWJbchwHn88Ho9EItVpMXGcSiuGQz+eJYNLU1FQjaq4W93d3dw/sJj2w29VVtxGfy+c+Fx/w8RTT2huhUCggEAiQkTLb+zHbtEql0tDaDABJEgBgxYo8nnxSPaSjK5fLtDI4WDC5RrV8otFt2OFgpGCfQUYekiDhQDguOz4GqeiNLZh+jGnm6sEy30aDUimBCy4QbbBYt85GqCMteiwzju0FGRjpheM4lMvlAc/P1JDpACsSjNwCiOJ5vV5PozWFQgGlUlmjM6tGsVikwFqO44Z18mBdkNfrhU5XwrJlta89lQKefVZ83Zs26cDzQ8+pzF+ypaUFgiDQbrS6uNRbmxUKBdqFMtF6X18STz4pFt76HV4qlTrktBFm85dMJsnlpB5MqzkasO5v8HcqQcL+cVx3fBLGDo2cNQDxIlwulxs6h+wPxWIRkUgEKpUKNpuNuvVCoUCkjpGC0e5ZlE21kXL1jhAYtJpKpQC320B2ZNXGBzzPo7+fh9ttIEp6oVAgok0qlaKuUC6Xk2SmUqnAarUik8lQECx7rfl8HhqNBn6/HwqFAh6Phx4rGMzA4dDS/VU7m9x7b5QcUgDxcFEsFqHX65FMJmE0GhGPx6FUiibXwWCQOj722CxlgR1KSqUSstksjEbjsO4o1V3h4QD7vTYyuj7YvTAAKn7skCJBQiNIhU/CqMAKx3AXluqL1kgvYDzPU/hrded4MEVPEARks1mo1WrylKxHLpcj0kw1IpEIcrkc7HZ7zfNYvjyLTZtEBuQTTyiocNaP5XieR6lUQqFQgFKppLEiK3Sse2TvS6VSQVtbGzweDz3P6hFoteVbOi1DqZRAOp2m0S1Ls6juLNnXORgMwmKxkOlyNZi/afWhpT7stfo8k06nodFoxsSnttH481AKX/X9ssOIBAn1kByXJYwY+XyeCtHhSKRgPpUsJ69a98XGd6MpesCgawnrgBqBue6r1WrqwpiXp91uJ/s68TUaSZKwbp0SfX0JNDWJhUSn04HneSosHMchEAggn8/DbDZTRqNSqWw4vmNpB/n80AMEG00y8gvbTYppBRwUCvH+SqUSaQ+rwXEcent7ByJ0aguZWq1GuVxGJpOhYsxS40Xf09rnwhLjxwIsc5ExU1kxPtT9PJN6MBmHZD0ooRrH9Y5PwuFDKpVCoVAYkYzgQGAXc5brZrVah/h6CoIw6tEaGy+yorM/MC0g2+exvDlgMJpGTAnncdFFogZw1aoS7HYNjUDr3wcm2m9tbSWrMNa11YOlm//Hf9jgdGqxdm1lYOSaop0b64QVCsVAlqAC//mfrfB6OXzta1ao1Wq4XK6Ge1TG5rz0UmVDhxDW0bKCzPIQ6yEIwpiPDFmRZ96cl1+uPiyuJux9EwRBig2SUANp1Clhv6hPVNgf2H5sf6NOFmSq1WppvFkNxsYcLZGCCaYZfZ5Zme0vHiaXyyESiQzx7qyHKGJXolCIwWq11mjS2O4rGAxSvlw1SqUSUqkUstksPB4PstksQqEQ1Go1KhVDTfJDPF6CXJ5BpVJpWPRHq08daZKASqWCRqNBIpEYwrg83Lu9A2GsNLjFYpEIOlK0mASp45MwLARBIDblSEZF6XR62ILFCpNGo4FKpaJk9WqwXdXBsAd5nq8hSpRKpQMWvUKhAKvVSnmLjcDGfxpNAS0tLdBoNOT1Wi6LeXAdHSGoVCp6Pex943mebMUKhQJ8Ph/tRz0eD5qauBpGqdksmlbXp5QzGAwVrFiRp9sf6G0aaZIAI+uIqemDnREzz/44MVYaXKVSCYvFgmw2OyyjVML4gdTxSWgInuf3m6gw3M9Ud3gs5LTaYYNp4+rvN5vNolwuH5SlHM/zyOfzsNvt9Hf761RSqRQUCgWNCJlNWKOLfCKRgFKphFqtHlL8eZ7HmjVlbNlixoUXCvjTn8SipFarh+jJ2HvT1dVFYz32+PX7rP7+fsrDY6jer6bTslEVhJHsy1hXn06n6X37uLu9aoylBpe9lyONLJNw/EH6rUuoQalUQiwWO2CiwkiQzWaJ/KHT6cjlv1HRq1QqB1X08vk80ul0jTB7uE6l0T4PED1Fmb9oNViQLIvvqb5/MfWhgi1bRPLNxo0a0r3l83nwPD9kZ8bMtZm7DfMBPdAFvn6/OtqCMJLbsxQGpVJZE+J8pDCWGlw2kmYjaAnjD9KwWwKBib4buXuMBkwczUgizDSZGQxXI5MRBdsHW2RjsRgsFkvNyb3RyJUlIjTq7FiKejXLsfq5sfsqFovEGhVt1GRYs2ZQ91afOs7eBwDEEpXJZKhUKtBoNNBoNMjlckgkEg31bEyT9nGxEi0WC2KxGBQKBWKxApzO49vQ3WQyQRCE/Xb8Eo5PSKNOCQBA3djBGv6Ke60K1GqRds/Glozc0ejCworIwT4m85+sL9T1I9dsNotSqXTA3SHbEyqVShSLRYRCIbhcLpRKJeRyuWEjrUYylvP5fGhqaiL/zHrI5XISoofDYahUVqhUwqjlHIeKSqWC5csz+PvfDSMyoj4e0Mh2T8LxDWnUOc5RLBZJ63QoLveXXCKD06nFV7/KkTaL7VIaSSDS6TTkcvlBPybr4Bp1p9VnOZ7nKaPvQKgmd/T0xIh5yhxahhvFjmRUqdfryeuS4ziyPWPPlxFhenp68JWv6OB0anHNNR9v0QOAdFqGv/9dfJ3DRQ8db2CWZwCOWcNrCaOD1PGNYxwud4tGFPRMJgiDwTBMh5Qio+iDhc/ng9vtHkJNZ8bYOp0O8Xic8vVGimKxiJUrC9i0SYfVq0t44gmxOLFE80a+nPtDPp9HsVhEuVymvD8AFGVUP/o9GiK1hrMtGw9gzkQ6nU4yvD6OIRW+cYix+HJXXyx//3seuVwOTqdzyO2qdWMHi3A43NCKCxBTxZ1O3bD7vP1BlCBUhqSss4BjZk820vusTkRgYvmRsCSPhsIz3pNN2L77SLFaJYwtpMI3zlAoFGjndrip3DxfQbEYh9FoRC6XG9JJ8jwPjUZzSE4g+Xwe4XCY/CqrUW3mvG7dyMggbMzIDLcVCsVhKzzVgnBWVL3ekV1Ix3vhORogGV4fv5AK3zjCcIkKhwMskYAV1HqCCctLO5QLSCoFRKPdaG1tHdJ1jXZEyFIZAHG31+j+DuVtymazkMvl1NledJGAjRs143J8eKyDuQ2JO2DpQHI8QCK3jANU58uNRdHL58W0cKZPq0cymWxI1x8N1q4VC9uNNza2Fxup4wfLEMxmszCZTMOGoh7K21QqlWqS2VMpUecHjB/CyPEEg8EAvV6P1auLh8VDVMKRh9TxHec43IkK9RhOKsA6PmbRdSg6tPpuLhjMwulszAYd7kQuCAJyuVxDAf3hRjweH2LHdjTs7SQcPI4G0pGEwwep4zuOcTgTFYa7fwDDdpGHKxKmvpuzWJTkhcnzfA3Tsv6pZDKZmtSGsS56zKatHo89Jl4spaJ3bGKsPEQlHBlIHd9xiNEkKhwshnMbYY/f398Pj8ezX6Po0WK4bi6TyVDxk8vlMBgMpL871G5zNBAEgR5TwvEJacd3fEAqfMcZWLjrcLurQwXbFzayH2Pw+XxwOp0fOxOOyTQymQwJxJk12Mf12PUjTgkSJBx9kArfcYSDSVQYDVim2XD7QubJqVAoPlarrXK5TA4t9QxNQRCQz+fpzwaDYUwc+SW/RwkSjh1Ihe84QKlUot3SWIVssvy64VxeWNGzWq01YbRjiUKhQHl3I3m8SqVC1HRAzGg7HIeEdDoNlUolab0kSDhGIKUzHONgDhNjOWJjGXojKXofR8cjCAIEQYBSqRxVZ1nv2VksFpFMJunftFrtqPeBhUIBlUpFKnoSJBxDkDq+Yxgs4HUsHeWZa/1we7JksoxiMV5T9OrF64cL2WwW+Xx+zFz0s9lsHUPUeMBC3ki6IEGChKMbkpzhGER1osJYFT3Wxen1+mGL3tq1gNksx9e+ZhvTTi+dTiORSNDucKxes06no/QEg8EAnueRTCaRTCaRy+WG3L6v78gllEuQIOHgIXV8xxgOV6LC/sDsx/an/9ufoPdwdHzMQxMQQ2rHanc5UrDxKsNXvqLH+vVKSZAuQcIxCKnwHQNIpQCDoUJ+l2NJz8/n88jlcoeUInAohY8xNAHRQ3MsGJiHCp6vwGSSVf1Z0nZJkHAs4ei7qkioAfOoXLVKZFSOZdEbbRTL4XQjYUSTdDoNk8k0JukRhwscJ5NcPCRIOIYhdXxHMT5Of8DDEQ4LiCPK0cgZ8vk8stkslErlsAnnRyskFw8JEo5NHJ1HagkAPj5/wHg8ftiYktlsFjpdYwPpauRyOSQSCZRKJZjN5mOu6AFS0ZMg4ViF1PEdAxirzoJ5eh7OseKB9nvMV/PjshKTIEGChHpIAvZjAGNR9AqFAtLpNKxW6+G/8wZgKed6vV4ycZYgQcIRhVT4xiGY/dhYC68rlQo5oxytDE0JEiSMP0iFb5zhQPZjh4pUCtDrS0in0wAwZikREiRIkHCwkI7g4wSplGg/NpYJ5GvXAl4vhzVryjCZTFLRkyBBwlEJidwyDsCE5hdfXMbjj4/NWefjlF5IkCBBwqFA6viOc6RSYtEDgCeekCOVGpvH+bikFxIkSJBwqJA6vnGA4azFxgKSqFuCBAlHO6TCN04gFSQJEiRIECEVPgkSJEiQMK4g7fgkSJAgQcK4glT4JEiQIEHCuIJU+CRIkCBBwriCVPgkSJAgQcK4glT4JEiQIEHCuIJU+CRIkCBBwriCVPgkSJAgQcK4glT4JEiQIEHCuIJU+CRIkCBBwriCVPgkSJAgQcK4glT4JEiQIEHCuIJU+CRIkCBBwriCVPgkSJAgQcK4glT4JEiQIEHCuIJU+CRIkCBBwriCVPgkSJAgQcK4wv8H6RrIiHU+L5UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "x = drug2ppi(137).reset_index(drop=True)   \n",
    "G = nx.from_pandas_edgelist(x, 'src', 'dst')\n",
    "#print(list(nx.isolates(G)))\n",
    "\n",
    "options = { \"node_color\": \"blue\",\n",
    "            \"edge_color\": \"gray\",\n",
    "            \"node_size\": 10,\n",
    "            \"linewidths\": 0,\n",
    "            \"width\": 0.1 }\n",
    "#nx.draw(G, with_labels=True, **options)\n",
    "nx.draw(G, **options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>graph_id</th>\n",
       "      <th>src</th>\n",
       "      <th>dst</th>\n",
       "      <th>src_prot</th>\n",
       "      <th>dst_prot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>176</td>\n",
       "      <td>32</td>\n",
       "      <td>4621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>32</td>\n",
       "      <td>1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>32</td>\n",
       "      <td>1129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>876</td>\n",
       "      <td>32</td>\n",
       "      <td>18028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>756</td>\n",
       "      <td>32</td>\n",
       "      <td>16363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50265</th>\n",
       "      <td>3</td>\n",
       "      <td>1024</td>\n",
       "      <td>132</td>\n",
       "      <td>19514</td>\n",
       "      <td>3408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50266</th>\n",
       "      <td>3</td>\n",
       "      <td>1024</td>\n",
       "      <td>716</td>\n",
       "      <td>19514</td>\n",
       "      <td>15709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50267</th>\n",
       "      <td>3</td>\n",
       "      <td>1024</td>\n",
       "      <td>700</td>\n",
       "      <td>19514</td>\n",
       "      <td>15442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50268</th>\n",
       "      <td>3</td>\n",
       "      <td>1027</td>\n",
       "      <td>441</td>\n",
       "      <td>19530</td>\n",
       "      <td>10008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50269</th>\n",
       "      <td>3</td>\n",
       "      <td>1027</td>\n",
       "      <td>636</td>\n",
       "      <td>19530</td>\n",
       "      <td>14336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50270 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       graph_id   src  dst  src_prot  dst_prot\n",
       "0             3     1  176        32      4621\n",
       "1             3     1   52        32      1027\n",
       "2             3     1   56        32      1129\n",
       "3             3     1  876        32     18028\n",
       "4             3     1  756        32     16363\n",
       "...         ...   ...  ...       ...       ...\n",
       "50265         3  1024  132     19514      3408\n",
       "50266         3  1024  716     19514     15709\n",
       "50267         3  1024  700     19514     15442\n",
       "50268         3  1027  441     19530     10008\n",
       "50269         3  1027  636     19530     14336\n",
       "\n",
       "[50270 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "935\n"
     ]
    }
   ],
   "source": [
    "print(len(x['src'].unique().tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>GraphID</th>\n",
       "      <th>DrugID</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>carnitine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>119</td>\n",
       "      <td>gamma-aminobutyric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>5-aminolevulinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>143</td>\n",
       "      <td>leucovorin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>146</td>\n",
       "      <td>5-methyltetrahydrofolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>1426</td>\n",
       "      <td>56603655</td>\n",
       "      <td>pegaptanib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>1427</td>\n",
       "      <td>56842239</td>\n",
       "      <td>n-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>1428</td>\n",
       "      <td>70683024</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>1429</td>\n",
       "      <td>70695640</td>\n",
       "      <td>colestyramine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>1430</td>\n",
       "      <td>71306834</td>\n",
       "      <td>K779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1430 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      GraphID    DrugID                      Name\n",
       "0           1        85                 carnitine\n",
       "1           2       119        gamma-aminobutyric\n",
       "2           3       137          5-aminolevulinic\n",
       "3           4       143                leucovorin\n",
       "4           5       146  5-methyltetrahydrofolate\n",
       "...       ...       ...                       ...\n",
       "1425     1426  56603655                pegaptanib\n",
       "1426     1427  56842239                       n-3\n",
       "1427     1428  70683024                         x\n",
       "1428     1429  70695640             colestyramine\n",
       "1429     1430  71306834                      K779\n",
       "\n",
       "[1430 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DrugIDs, Names and GraphIDs\n",
    "drugs = pd.read_csv('../data/drug_names.tsv', sep='\\t')\n",
    "temp = []\n",
    "for i in range(len(drugs)):\n",
    "    row = {'GraphID': i+1,\n",
    "           'DrugID': drugs['DrugID'][i][4:],\n",
    "           'Name': drugs['name'][i]}\n",
    "    temp.append(row)\n",
    "    \n",
    "DrugID = pd.DataFrame.from_dict(temp)\n",
    "DrugID['DrugID'] = pd.to_numeric(DrugID['DrugID'])\n",
    "DrugID.to_csv('../data/DrugID.csv', index=False, sep = ',')\n",
    "DrugID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Name</th>\n",
       "      <th>GeneID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9606.ENSP00000000233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9606.ENSP00000000412</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9606.ENSP00000001008</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9606.ENSP00000001146</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9606.ENSP00000002125</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19550</th>\n",
       "      <td>9606.ENSP00000485671</td>\n",
       "      <td>19551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19551</th>\n",
       "      <td>9606.ENSP00000485672</td>\n",
       "      <td>19552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19552</th>\n",
       "      <td>9606.ENSP00000485673</td>\n",
       "      <td>19553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19553</th>\n",
       "      <td>9606.ENSP00000485675</td>\n",
       "      <td>19554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19554</th>\n",
       "      <td>9606.ENSP00000485678</td>\n",
       "      <td>19555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19555 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name  GeneID\n",
       "0      9606.ENSP00000000233       1\n",
       "1      9606.ENSP00000000412       2\n",
       "2      9606.ENSP00000001008       3\n",
       "3      9606.ENSP00000001146       4\n",
       "4      9606.ENSP00000002125       5\n",
       "...                     ...     ...\n",
       "19550  9606.ENSP00000485671   19551\n",
       "19551  9606.ENSP00000485672   19552\n",
       "19552  9606.ENSP00000485673   19553\n",
       "19553  9606.ENSP00000485675   19554\n",
       "19554  9606.ENSP00000485678   19555\n",
       "\n",
       "[19555 rows x 2 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GeneIDs and names\n",
    "gse = pd.read_csv('../data/GSE_full_pkd.csv', sep=',')\n",
    "genes = gse['Unnamed: 0'].tolist()\n",
    "temp = []\n",
    "for i in range(len(genes)):\n",
    "    row = {'Name': genes[i],\n",
    "           'GeneID': i+1}\n",
    "    temp.append(row)\n",
    "    \n",
    "GeneID = pd.DataFrame.from_dict(temp)\n",
    "GeneID.to_csv('../data/GeneID.csv', index=False, sep = ',')\n",
    "GeneID "
   ]
  }
 ],
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