Exp_fin_7_ChemSimilarity_postclustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "690099b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle as pk\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools as it\n",
    "import networkx as nx\n",
    "from collections import (defaultdict,Counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6914fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's import the chemical-gene interactions from CTD (downloaded on 5th April 2021)\n",
    "chem_gene_df = pd.read_csv(\"input/CTD/CTD_chem_gene_ixns.tsv\",delimiter= '\\t', skipinitialspace=True)\n",
    "#Here, we filter for only the interactions that regards the H. Sapiens\n",
    "chem_homo = chem_gene_df[(chem_gene_df['Organism'] == 'Homo sapiens')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c31e151d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This cells create a dictionary where each key is a chemical compound and the correspondent value is a genelist \n",
    "chem_gene = {}\n",
    "for i,v in chem_homo.iterrows():\n",
    "    try:\n",
    "        chem_gene[v[\"ChemicalID\"]] |= {v[\"GeneSymbol\"]}\n",
    "    except KeyError as e:\n",
    "        chem_gene[v[\"ChemicalID\"]] = set([v[\"GeneSymbol\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10cadc0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we keep only the exposures which perturb at least one gene\n",
    "chem_gene_cleaned = {}\n",
    "tot_gene_list=[]\n",
    "for k,v in chem_gene.items():\n",
    "    if len(v)>0:\n",
    "        chem_gene_cleaned[k]=v\n",
    "        for gene in v:\n",
    "            tot_gene_list.append(gene)\n",
    "    else:\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "be41423d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's import the EEN backbone network\n",
    "backbone_ss_exposure_network = nx.read_weighted_edgelist(\"output/backbone_exp_graph_significant_weighted.edgelist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f8cc3610",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's import the resnik score\n",
    "with open('intermediate/parenthood_similarity_score_pairs_dictio.pickle', 'rb') as handle:\n",
    "    parenthood_similarity_score_pairs_dictio = pk.load(handle)\n",
    "with open('intermediate/wo_parentalhood_resnik.pickle', 'rb') as handle:\n",
    "    wo_parenthood_similarity_score_pairs_dictio = pk.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b489665",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's merge the resnik score without and with parenthood relationships\n",
    "tot_resnik={**wo_parenthood_similarity_score_pairs_dictio, **parenthood_similarity_score_pairs_dictio}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1380ddb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's import the genetic overlap JI\n",
    "    \n",
    "with open('intermediate/parenthood_overlap_pairs_dictio.pickle', 'rb') as handle:\n",
    "    parenthood_overlap_pairs_dictio = pk.load(handle)\n",
    "    \n",
    "with open('intermediate/wo_parentalhood_gene_overlap.pickle', 'rb') as handle:\n",
    "    wo_parenthood_overlap_pairs_dictio = pk.load(handle)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8b02c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's merge them together\n",
    "\n",
    "tot_overlap={**wo_parenthood_overlap_pairs_dictio, **parenthood_overlap_pairs_dictio}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26efbd6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's import the Louvain communities\n",
    "\n",
    "with open('output/Communities/Louvain/weighted_ji_fine_grained_communities_dict.pickle', 'rb') as handle:\n",
    "    fine_grained_communities_dict = pk.load(handle)\n",
    "with open('output/Communities/Louvain/weighted_ji_middle_communities_dict.pickle', 'rb') as handle:\n",
    "    middle_communities_dict = pk.load(handle)\n",
    "with open('output/Communities/Louvain/weighted_ji_broad_communities_dict.pickle', 'rb') as handle:\n",
    "    broad_communities_dict = pk.load(handle)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b38cc45",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we are evaluating the resnik score across exposures for the finest level of hierarchy in louvain\n",
    "#distinguishing the exposures that are part of the same community and those ones that are not part\n",
    "fine_grained_communities_resnik={}\n",
    "\n",
    "for com,exp_list in fine_grained_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            resnik_list_in=[]\n",
    "            resnik_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            resnik_list_out.append(tot_resnik[exp1,exp2])\n",
    "                        except:\n",
    "                            resnik_list_out.append(tot_resnik[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_resnik_in=sum(resnik_list_in)/len(resnik_list_in)\n",
    "            avg_resnik_out=sum(resnik_list_out)/len(resnik_list_out)\n",
    "            fine_grained_communities_resnik[com]=[avg_resnik_in,avg_resnik_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "            \n",
    "middle_communities_resnik={}\n",
    "\n",
    "for com,exp_list in middle_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            resnik_list_in=[]\n",
    "            resnik_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            resnik_list_out.append(tot_resnik[exp1,exp2])\n",
    "                        except:\n",
    "                            resnik_list_out.append(tot_resnik[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_resnik_in=sum(resnik_list_in)/len(resnik_list_in)\n",
    "            avg_resnik_out=sum(resnik_list_out)/len(resnik_list_out)\n",
    "            middle_communities_resnik[com]=[avg_resnik_in,avg_resnik_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "broad_communities_resnik={}\n",
    "\n",
    "for com,exp_list in broad_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            resnik_list_in=[]\n",
    "            resnik_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            resnik_list_out.append(tot_resnik[exp1,exp2])\n",
    "                        except:\n",
    "                            resnik_list_out.append(tot_resnik[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_resnik_in=sum(resnik_list_in)/len(resnik_list_in)\n",
    "            avg_resnik_out=sum(resnik_list_out)/len(resnik_list_out)\n",
    "            broad_communities_resnik[com]=[avg_resnik_in,avg_resnik_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "30e08186",
   "metadata": {},
   "outputs": [
    {
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jRo1SiRIlHNo3ICBAPXv2tKT7+Pjo3XffdbjOFi1aWAKrmzZtcji/rTfffNOpgFeHDh3Ur18/U9qZM2c0e/Zsl9vgiGLFiumuu+4ypa1YscLl8gYPHqxGjRq52yyHXbp0ybTtapAxN8nJyZo0aZIpzd/fX2PHjnWqnAEDBqhDhw6mtGPHjrn0Od9www0aMWKEw/v7+flpwIABprQTJ05kBeYBwFUEGgEAAAAPym4lYld7prlj4cKFljRHegHaat++verVq2dK2759u6Kjo50qJzg42OlFWOwFtrp27aoqVaq4Vc7evXudyn9FsWLFNHjwYKfzPfLII5Y0d3pVOqpmzZqm7TVr1rhc1oMPPuhuc5xiG1h0J0iak/Xr11sWnOndu7elh6Aj7B1f9o7D3AwdOlTe3s7d3jdv3tyS5ur3HACuYNVpAAAAwIPsDZuW/h3CW5AyMjIsKzfXqVPHEjB01F133WVZ3XblypW64447HC6jZcuW8vPzc6reyMhIS1rbtm2dKkOSqlatatq2DSw5qkOHDi4Fjbt06aKQkBDFx8dnpbkS9Dt37pyWL1+u7du3a/fu3bpw4YIuXbqkhIQEu9+98+fPm7aPHz/udJ2SVLx4cZdWynZHixYtTO/RihUr9Nprr+mtt97K0xXcV65caUm78847XSqrT58+CggIMK3Ybq/83LRv397pPPZWuI6Li3O6HAC4GoFGAAAAwIOKFy9uN/3ixYsqW7ZsgbVj7969SkhIMKU1bdrU5fKaNWtmSdu0aZNTgcYaNWo4XW9wcHC+lONqAKZJkyYu5fP29taNN96o5cuXZ6Xt3r1biYmJuc4fKEl///23PvvsMy1YsMChuR2zk56ersuXLysoKMipfA0bNpSPj4/L9bpiyJAhGjt2rCmAOnr0aP3444964IEH1LdvX8v8oa6wN4ze1WPF399fDRs21Pr167PSdu7cqZSUFAUEBDhcjm1PVEfYC4ATaATgLoZOAwAAAB5UunRpu+mu9qBzVWxsrCWtbt26LpcXFRXlUB05sV3YxRH2ekDmRTnp6elOlyFJtWvXdimf9G+P0qsZhpHrexgfH68BAwaoS5cu+uOPP9wKMl7hSvApIiLC7XqdddNNN+nJJ5+0pB85ckSjRo1Sw4YNVbZsWfXr10/vv/++li1bZupJ6Cjbz8Df399u70BH2R4rmZmZ2c7dmp3sziM5sXes5MX3BcD1jUAjAAAA4EEVK1a0O0za2fkM3WUvsOHOgjT2gnu2w3Jz4+yw6fwuxxXuzLVpL29OAej4+Hh1795d06dPd7lOe1wJPoWEhORpGxz18ccf69lnn832+ZiYGP3666965ZVX1L59e5UsWVLdu3fX5MmTLYvJZMf2WHF3PtXCdKwAgLsINAIAAAAe5O/vb5kPUJI2bNhQoO2wF2RxdLVne+zldTSQcy0pyPfw2WeftTuPY82aNfXMM89o+vTpWrdunU6dOqW4uDilpKTIMAzTY/LkyS6392p5OSeiM3x8fPTxxx9r3bp16tWrV67tSE5O1l9//aWhQ4eqatWqGjNmTK69V20/A3c+4+zyX4/HCoBrA3M0AgAAAB7WpEkTHT582JS2bt26Am2DvbkNbedsdIa9vPbquNYV1Hu4fft2ffvtt6a0oKAgTZgwQffcc4/Diwtltwp6UdOsWTPNmTNHMTEx+uOPP7RkyRKtXLnScpxd7fz583r11Vc1e/ZszZ8/P9sevbafgTufcXb5r8djBcC1gR6NAAAAgId16NDBkrZs2TJlZGQUWBvsDd90Z55Ie3ldmUeuqHNncQ17ebMLfk2bNs2yivR3332ne++916kVzJ0dslvYRUREaOjQoZo6daoOHTqkM2fOaMaMGXryySezXUBl7dq16t+/f7Zl2h4r7i6gwrEC4FpCoBEAAADwsF69elmCQadPn9acOXMKrA3h4eGWtN27d7tc3q5duyxpYWFhLpdXVO3bt8/lvHv37jVte3l5ZfseLly40LRdr149p1b4vuLQoUNO5ylKriwG8/nnn2vfvn3auHGj7r77bst+Cxcu1Lx58+yWYXuspKam6uDBgy63yfZY8fb2JtAIoMgi0AgAAAB4WJUqVdS5c2dL+vjx4wusDbVq1VJQUJApzZ15ItevX29Ja9KkicvlFVUbN250KV9mZqa2bNliSqtbt64CAwPt7n/8+HHTdtu2bV2qd/Xq1S7lK6oaN26sn376Se+9957luZkzZ2abx5arx0pqaqq2bdtmSqtfv778/f1dKg8API1AIwAAAFAIPP3005a0RYsWZRvsyGs+Pj5q3ry5KW3Pnj3auXOnS+X98ssvlrTWrVu7VFZRtmTJEpeG1i5atEjx8fGmtJYtW2a7f2xsrGnblR5x27dvd6sXa1H20ksvqUyZMqY02wDgFfa+xzNmzHCp3t9//13Jycm5lg8ARQWBRgAAAKAQuO2229SmTRtL+rBhw3T69Ok8reunn37SqVOnLOndu3e3pE2YMMHp8pcvX64dO3aY0ho2bKiyZcs6XVZRl5ycrO+//97pfP/9738taT169Mh2f9uVi20Dj4745JNPnM5zrfDx8bHM2ZhdgLh58+aWuTJ///13u8dUbuz1Wu7WrZvT5QBAYUGgEQAAACgkJk6cqGLFipnSzp07p86dO1uGxroiNTVVzz//vO655x6lpqZanh86dKil/gkTJmTbs8ue9PR0PfHEE5Z0e2nXizfffFMXLlxweP9ly5ZZesiVK1dOffr0yTZP+fLlTduLFi1SZmamw3UuWrRI3333ncP7X4tsA/r25i2VpICAAD300EOmtJSUFLu9knMyY8YMLV682JQWGRmp3r17O1UOABQmBBoBAACAQiIqKkpjx461pO/evVutWrXS0qVLXS7777//VuPGjfXxxx9nu09YWJjuu+8+U1p6err69evnUK9KwzD00EMPaevWrab0iIgIDRo0yLWGXwNiY2N11113KSUlJdd9jxw5onvvvdeS/uijj8rPzy/bfLZzMh46dMjh3qibNm3SwIEDLatWFyWXL1/WCy+8oGPHjrmUf/bs2Tp69Kgp7cYbb8x2/8cff9zyefzyyy/6z3/+41B9mzdvtgQrJempp56Sj4+PQ2UAQGFEoBEAAAAoRB5++GG9+uqrlvSTJ0+qQ4cOuu2227Rq1SqHeqslJSVp+vTpatu2rbp06eLQfItjxoxRxYoVTWkHDhzQzTffrEWLFmWb7+TJk+rTp4/dXnFff/21pafk9eLK6/7777/VuXPnHFehXrBggdq1a6cTJ06Y0uvUqaMXX3wxx3r69+9vSXvqqac0fvz4bAOIGRkZGjdunDp27Jg11DokJCTHegqr9PR0ffTRR7rhhhvUt29f/fDDDw7NjZmZmakpU6bYDYTnFByPjIzUu+++a0l/8cUXNWLECMv8mlf73//+p86dO1va17x58+u65y+Aa4OvpxuAwmPcuHEaN26cMjIyPN0UAACA69p7772nEiVKaOTIkZYg0dy5czV37lyVLl1aXbp0UVRUlMLCwhQWFiZJunjxog4dOqSNGzdq9erVSkxMdKru0qVLa+rUqerWrZvS09Oz0g8fPqyuXbuqcePG6tWrl6pWrapixYrp9OnTWrZsmebPn29Z1EKSRowYkeOQ32vdqFGjNHLkSGVkZGjlypWqV6+eunTpos6dO6tixYpKTU3VkSNH9Pvvv9tdoTogIECTJ0/ONVDbuXNntWvXTsuWLctKS09P14gRI/T555+rb9++ioqKUvHixXX27Fnt2LFDs2fPNs0rWLZsWT333HO5BjULs4yMDP3222/67bff5Ofnp4YNG6pRo0aqU6eOSpUqpdDQUKWlpSkmJkY7d+7U/Pnz7faCvPfee9WqVasc63r++ee1cOFCSwB+/Pjx+uGHH9S7d281a9ZMZcuWVXx8vA4cOKBff/1V+/fvt5QVGhqqH374Qb6+3KIDKNo4iyHLiBEjsv76Fhoa6unmAAAAXNdeffVVNWnSREOHDrW7yMT58+c1ffp0p8stXry4nnrqKcucflfr2LGjZs+erf79+yshIcH03KZNm7Rp0yaH6nriiSf02WefOd3Ga0nLli31+eef6/HHH5f0b/Bv/vz5mj9/fq55/f39NWPGjBxXm77a1KlT1bx5c0VHR5vS9+3bpw8++CDHvCEhIfrzzz+1fft2h+oqCtLS0rRx40a7AdycdOjQwe4iLba8vb01e/ZsDRgwQH/88Yfpubi4OE2dOlVTp07NtZwKFSpo7ty5qlGjhlPtBIDCiKHTAAAAQCHVvXt37d27V6+99ppllVtnBQUF6ZFHHtG+ffs0ZswYBQQE5Lj/rbfeqmXLlqlp06ZO11WmTBl99dVXGjt2rLy9ueUYMWKE/vvf/zo1fLxy5cr6888/1bNnT4fzVKlSRYsXL1bt2rWdal/t2rW1atUqNWnSxKl8hYmvr6/KlCnjVhl+fn569tlnNX/+fIeHkAcGBuq3337TyJEjVbx4cafrvOWWW7R69eoc54MEgKKEX30AAACgEAsKCtK7776rEydOaPLkybrtttscDoJERESod+/emjp1qqKjozVx4kRVqlTJ4bobN26sdevWadq0aerSpUuuwcmoqCi98cYbOnjwoIYPH+5wPdeDhx56SNu2bdPgwYNzDEhVrlxZr776qnbt2qUuXbo4XU9UVJQ2bNig9957T+XKlctx37p162rs2LHatm2b6tWr53RdhUlQUJCio6O1fPlyvfrqq2rXrp0CAwMdyhsZGakXX3xRu3fv1scff5zr99yWj4+P3nnnHe3fv19PPvmkqlatmmtb+/Tpo8WLF2vevHmqUqWKU/UBQGHmZRTlpcWQL64MnY6Liyuyk0Ffi45djldiWnruOxZSgX6+qhLE9wmwJzk5WYcPH1a1atWu28USADgnMzNTe/fu1YEDB3T8+HFdvnxZKSkpCg4OVqlSpVSmTBk1aNBAkZGReVpvQkKC1qxZo9OnTysmJkapqakKCwtTRESEGjVqpMqVK+dpfUXNP//8o44dO5rSlixZog4dOpjSkpKStHbtWu3Zs0fnz59XQECAypcvr5o1a6pp06by8vLKk/YYhqFt27Zpy5Ytio2NVVJSkoKDgxUZGambbrop14BYUZeenq6DBw/q4MGDOnHihOLj45WUlKTAwEAFBwerSpUqatCggWXxo7ywd+9e7dy5U2fPntW5c+dUokQJhYeHq0qVKmrevLn8/f3zvE4AcFde3JcQaIQFgcbC59jleN311++ebobbfunWi2AjYAeBRgC4NjgaaAQAoDDKi/sSFoMBioArPRmH1K6ncoElPNwa551JTNCUvTuLdI9MAAAAAACQMwKNQBFSLrAEPQIBAAAAAEChxGIwAAAAAAAAANxGoBEAAAAAAACA2wg0AgAAAAAAAHAbgUYAAAAAAAAAbiPQCAAAAAAAAMBtBBoBAAAAAAAAuM3X0w0AAAAAgGtBhw4dZBiGp5sBAIDH0KMRAAAAAAAAgNsINAIAAAAAAABwG4FGAAAAAAAAAG4j0AgAAAAAAADAbQQaAQAAAAAAALiNQCMAAICkqlWrysvLK+sxZMiQAm/DlClTTG3w8vLSkSNHCqz+N99801I/Ci8+L+TG9vvx5ptverpJAK4RQ4YMMZ1fqlat6ukmoZAg0AgAAAAAAADAbQQaAQCAS9auXWvpLdOmTRuXy9uxY4elPC8vLz300EMulzljxgxLeXfddZfL5QEAAADIHoFGAADgkiZNmig4ONiUtn79eiUmJrpU3pIlS+ym//PPPy6Vl12ZHTt2dLk8exiaCAAA4JgOHTqYrps6dOjg6SYhjxFoBAAALvH19VXbtm1NaampqVq5cqVL5WUXUDx48KCOHz+eZ2XmdaARAAAAwL8INAIAAJfZC9q50gPRMAwtW7Ys2+ddKTMmJka7du0ypZUrV05169Z1uiwAAAAAuSPQCAAAXGYv0JjdEOicbN++XbGxsVnbPj4+bpdpLziZ0/CcI0eOyDCMrMeUKVOcrrOoe/PNN03vgWEYnm4SADfYHs9M7QAgr0yZMsV0fjly5Iinm4RCgkAjAABwWaNGjVSyZElT2oYNG5SQkOBUObZBwYEDB8rLyyvb510pU2LYNAAAAJCfCDQCAACXeXt7q127dqa0tLQ0rVixwqlybIOC/fr1U/369bO2Dx8+rKNHj7pVpkSgEQAAAMhPBBoBAIBb3J2n0XZ+Ri8vL7Vr107t27d3uczo6Gjt3r3blFaxYkXVrFnT4TIAAAAAOMfX0w0AAABFm7vzNG7btk3nzp3L2m7QoIFKly6tDh066MsvvzSVef/99ztU5rXemzEtLU3r1q3Trl27FBsbKz8/P4WHh6tWrVpq3ry5ZY7LwuLs2bNas2aNDh06pMuXLys0NFQRERFq0aKFIiMjPd08k5iYGO3Zs0cHDx7UxYsXlZCQoODgYJUuXVoVK1ZU8+bNFRQU5Olm5rmLFy9q3bp1OnPmjM6ePauMjAxFREQoIiJCzZo1U3h4uKebeE1ISkrSvn37tGfPHsXGxio+Pl6+vr4qVaqUwsLCdNNNN6lq1aqebmaOMjMztWnTJm3fvl0xMTHy8vJSWFiYbrjhBrVu3Vr+/v6ebqJdZ8+e1caNG3X27FnFxsYqMTFRwcHBCgsLU+3atVW3bl0FBga6XY9hGNq+fbv279+vs2fP6vz581nnvGrVqqlx48by9s7/fj8xMTFau3atDh8+rEuXLikwMFCRkZFq3bq1ypUr53A5hw8f1oYNG3TixAklJSUpLCxMkZGRat++vYoVK5aPr0Dav3+/Nm3apBMnTig5OVkhISGqW7euWrVqpRIlSjhUhmEY2rp1q7Zu3aqYmBhlZGSobNmyioqKUvPmzU3TtVxrkpKStGbNGu3Zs0cXLlxQ8eLFFR4ervr16+vGG2+8pl87PMQAbMTFxRmSjLi4OE83Bf/f7vPnjOYzvzd+PbTP2BBzpsg9fj20z2g+83tj9/lznn4rgUIpKSnJ2LVrl5GUlOTpprgkMzPTKFOmjCEp6+Hr62tcunTJofyfffaZKe8TTzxhGIZhxMTEmNIjIyMdbtPw4cNNeSUZkyZNyjFPZGSkaf/777/f7n5LliyxlO3sI7vXMnnyZMu+hw8fznr+9OnTxlNPPWWEhoZmW3bJkiWNp59+2oiNjXX4/bpi1KhRlvIc0b59e1Oe9u3bW96zrl27Gt7e3tm2Oyoqyvj++++NzMxMp9udF+Lj443vv//euO+++4wqVark+hn6+PgYTZs2Nf773/8aKSkpHmmzq5+XrbS0NOPrr7822rRpY/j4+GT7mr28vIymTZsaH3/8sZGcnOx0Pbl9v51hW86oUaPs7jdjxgzLvp9++qlLdV6tTp06pjLLlStnpKWlZbt/ZmamsWLFCuPFF180mjdvbvj6+ub6HatUqZLx1FNPGUePHnW5nY6+T1ezd45bsmRJ1vNxcXHGG2+8YZQtWzbbtpcoUcIYMmSIcezYMZfbnpdOnz5tvPLKK0bDhg0NLy+vHN93f39/o2PHjsann35qnDvn/LXr3r17jQcffNAoV65cjvWUKVPGuOeee4xNmza59Jruv//+HH9X/vrrL6Njx47Znnd9fHyM3r17G7t27cq2joyMDOO7774zbrzxxhw/62HDhrn0m5PT7256eroxYcIEy7F29SMoKMh44okncvyc4uPjjbfeesuoWLFituWULVvW+Oijj3I8hu3xxDntarn9Bhw4cMAYMmSIERgYmONrf+utt4zLly873ebcvoM5vT5XHlfe24yMDKNq1aqm56pVq+b29cOnn35qqfO3335zq8yiKC/uSxg6jSzjxo1TVFSUmjVr5ummAACKEC8vL8sw5/T0dC1fvtyh/La9D6+sDB0eHq6oqKis9KNHj+rw4cMulSkV/R6NM2fOVN26dfX5558rLi4u2/0uXryozz77TLVr19aaNWsKsIVWKSkpevjhh9WxY0ctXLhQmZmZ2e67a9cuDRo0SLfccovTiwm568UXX1RERIQGDRqk//3vfzp27FiueTIyMrRhwwY9/PDDql69usPf98Lm77//Vv369fXII49oxYoVysjIyHZfwzC0YcMGPffcc6pVq5ZmzZpVgC11Ta9evVSmTBlTmrsryq9evVp79uwxpd13333y9bU/WGzp0qWKjIxUmzZt9OGHH2rdunVKT0/PtZ4TJ07o888/V/Xq1fX666/nePwUlOXLlysqKkpvv/22oqOjs90vISFBU6ZMUe3atTVnzpwCbKFZYmKinn32Wd1www0aM2aMtm3bJsMwcsyTmpqqJUuW6JlnnlGFChV06tQph+t67LHHVK9ePU2aNElnzpzJcf9z587pxx9/VJMmTTR48GBduHDB4deVk9TUVD300EPq1q2blixZku33JiMjQ3PmzFGjRo30008/WZ4/deqUOnbsqPvvv19bt27Ntr6EhARNnDhRdevW1bZt2/LkNZw6dUo333yzhg8fbjnWrnb58mV98cUXuvHGG+3WvWrVKtWvX1+jRo3SyZMnsy0nOjpazz//vNq0aaOLFy/mxUvwuHHjxql+/fqaMmWKEhMTs90vOjpao0aNUv369bV///4CbKHrvL29NXz4cFPa4cOHtWDBArfKnTBhgmm7UqVK6tmzp1tlXq8INCLLiBEjtGvXLq1fv97TTQEAFDGuztNoZDM/4xWuzNN45swZy41JZGSkqlWrlmvewmrChAm66667nLoBOnfunLp06aItW7bkW7tykpycrB49euibb75xKt9ff/2lW2+9NceAV15bt26dkpOTXc5/4sQJde7cWVOnTs3DVuW/yZMn65ZbbtHevXudznvs2DH169dP//nPf/KhZXnH399f9957rylt69at2rx5s8tlTp482ZL2wAMPZLv/4cOHdfz4cZfrS09P17vvvqs+ffo4FKDML3/88Ye6dOmSY8DGVlJSkvr166f58+fnY8vsO3bsmG6++WZ9+umnSkpKcqmMlJQUpaam5rpfbGysOnXqpK+++srpz8gwDH3//fdq06aNQ3/kyEl6err69u2rSZMmOZwnJSVFgwcP1rx587LSjh07pjZt2ph+n3Nz9uxZderUSUeOHHGmyRanTp1S69attXbtWofznDhxQt26dTMdZ3/99Ze6dOni1Hu6du1a3XLLLR49zvLCq6++qscff9yp37UjR46oTZs2Th3fnvTQQw9Zhux/9dVXLpe3ePFiy2/hI488UminoinsmKMRAAC4zdV5Grdu3arz589nbderV09hYWFZ2+3btzddOC5ZsiTHG3op/3szBgUF6cYbb8zatu3pUbZs2VznvapQoYLD9c2bN0+PP/54Vi+c0NBQdevWTa1bt1ZERIQyMzN15MgR/fHHH5Ybs4SEBD3wwANav359tr2t8svQoUNN34HatWurR48eqlOnjkqXLq24uDht3rxZM2fOtPSMWrZsmT799FM9//zzBdpm6d9gd4MGDdSgQQPVrVtX4eHhCgkJkY+Pjy5duqRDhw5p/fr1WrJkidLS0rLypaWl6eGHH1b9+vXVqFGjAm+3s6ZOnaqhQ4da0r28vNSqVSv16NFDlStXlq+vr06ePKmFCxdqyZIlpgCwYRh68cUX5eXl5ZHPylFDhw7V2LFjTWlTpkxx6XNKSkrS9OnTTWktW7ZUnTp1HC6jXLlyatKkierWrauqVasqJCREgYGBSkhIUExMjLZv36758+crJibGlO+PP/7Qq6++qg8//NDpdrtry5YteuWVV7KCbsWLF1fnzp3Vrl07lStXTr6+vjp+/Lj++usv/f3336a86enpeuihh7Rz506FhoYWSHuPHTum5s2b2+11Wbx4cbVt21bt2rVT2bJlFRoaqkuXLunMmTPavHmzVq1a5XAvRunf70THjh21Y8cOy3NhYWHq27evGjZsqIiICJ07d067d+/WzJkzLXXs2rVLbdq00ZYtW1S6dGnnX7SkF154QXPnzs3arlu3rnr27KmaNWsqNDRU586d04oVKzRz5kylpKRk7ZeRkaGhQ4dq37598vHxUa9evbJGEHh5ealNmzbq2rWrKleurOLFi+vkyZOaN2+eFi1aZKr/3LlzGjFihP7880+X2p+Wlqbbb79dR48ezaq7bdu2WXUHBAToxIkTmj9/vuV7Fh0drUcffVR//PGHdu/erTvvvDMrwFy8eHF169Yt6/uakZGhgwcPasaMGdq5c6epnLVr1+qTTz7Riy++6NJr8LSJEydqzJgxWdvh4eHq0aNH1hy7ycnJOnDggGbNmmV57TExMRo2bJj++OOPPG/X1ddNBw4cMI1cKFGihGrUqJFrGVfP+1qmTBndfffdph7qf/75p44fP67KlSs73T7bIKWvr68eeughp8vB/5cng7hxTWGOxsKHORqBa1tRn6PxioiICMv8T7n9ltjOhzNixAjT86dPnzY9X7ly5VzbMWzYMMscO999912u+Rydo9GWbV2OzKuUHXvzPRUrVsyQ/p0f77nnnjMuXLiQbf5p06Zl7X/146effnKo/ryao/HqNpQrV86YMWNGtnkvXbpk3HfffZZ6S5YsaSQmJjpUv7s6duxodOvWzfjhhx+Ms2fPOpTn7NmzxhNPPGGZ761+/fr53Nr/4+rntX//fiMoKMiSt379+sbatWuzzbdr1y6jZcuWlnx+fn7G+vXrc63Xk/OZNWrUyLR/WFiYkZqa6nS933//vaXur7/+Osc8kydPNm644Qbj3XffNbZu3epQPenp6cb3339vmefPy8vLWLduncPtdeX8ZG+OxquP6cGDBxunTp3KMX/p0qUtZYwZM8bhdrsjKSnJaNq0qaV+f39/44UXXjDOnz+fY/7MzExj1apVxvDhw41ixYrl+h2195vj4+NjvPrqq9n+rqenpxsffvihERAQYMnbt29fh16n7fx4/v7+WeejsLAwY/r06dnmPXDggFG7dm1L3aNHjzaeeOKJrO3GjRsbGzZsyLacefPm2Z3/b9WqVQ69Btvf3avfj0aNGuVY9/z5840SJUpY6l62bJnpeL/77ruz/b5mZGQY7777rqWM0NBQh35/CuMcjVeOVT8/P2PMmDHZfgczMzONzz//3O4cnqtXr3aozc7M0Xi13OZ1dtSGDRssbX/99dedLuf06dOGn5+fqZw777zTpTZdC5ijEQAAFBpX5la8IiMjI9d567Kbn/GKcuXKqVatWlnbx48f18GDB50qUyra8zMmJyfLy8tL3333nT766COVLFky23379+9vd8ics8OX3XVluNYNN9ygNWvWqF+/ftnuGxQUpClTpqh79+6m9IsXL2rmzJn52s4rZs2apQULFuiee+4x9ajNSVhYmMaOHWsZRrtjxw799ddf+dHMPPPYY4/p8uXLprSmTZtqxYoVat68ebb56tatqyVLlqhTp06m9LS0NLu9IwsT257QsbGx+v33350ux3Z+x+LFi2vAgAE55unXr58OHDig1157TQ0bNnSoHh8fH917771au3atqlSpkpVuGIY+/fRTp9vtrivH9DvvvKP//e9/Kl++fLb7dujQQbNnz7asZOvMcF53vPnmm9qwYYMpLTQ0VPPnz9eHH36oUqVK5Zj/Sq/er776SkePHlVERES2+65cuVITJ040pXl7e2vy5Ml67733sl2N2cfHRy+88IJmzpwpPz8/03OzZs1y6dyXmpoqwzBUrlw5rVixQnfddVe2+1avXl3z5s2ztO/jjz/WuHHjJP37OS5btkxNmjTJtpxbbrlFX3/9tSXd3vQCjrjSy7Jdu3a51t29e3f997//taTfeeedWVMjvPDCC/rpp5+y/b56e3vrtddes/Rci4uLKxJz0NqTnJysgIAAzZs3Ty+//HK230EvLy89+eSTeueddyzPFfQ1g6uaNGmili1bmtK++eYbp4e+f/PNN6YRCpL06KOPut2+6xmBRgAAkCecnacxMzPTMv+T7ZyM9tJyKvPMmTOWOXaqV6/u0jCawuSZZ57R4MGDHdr3nnvusQSLli5d6tYchK7w8/PT9OnTFRkZmeu+Xl5e+uSTTyzp7k7s7ih3hnPef//9uvPOO01phfkmbceOHVq4cKEpLSQkRL/99ptD70OxYsX066+/WqYH2L59e6EOsN57770KCAgwpTkbDDl27JgWL15sSuvXr59CQkJyzBccHGwJujmqSpUqGj9+vCntl19+yXFBqPxyxx13aOTIkQ7t26ZNG0ug68CBA7n+ochd58+fzwqUXeHl5aXp06e79AeniIgIBQYGZvu8vaDv008/7fD5+rbbbrMb6Pn4448db6SN7777TrVr1851v2rVqln+QHDu3DllZmYqLCxMP//8s0qUKJFrOffee6/q1q1rSrt6vkdnlSlTRj///LOCgoJy3XfgwIGmReMkZU050L59e73//vsO1fnWW2/J29scGnHnNXjaf/7zH3Xu3NmhfV944QVVrFjRlFZQv715YcSIEabt06dP67fffnM4f0ZGhiVYXrt2bcsf1OAcAo0AACBPODtP49atW02rbEZFRSk8PNyyn22gMacy7T1XlHszSv8GgUaNGuVUnkGDBpm209PT82w1UEfdc889OfZGsRUVFaXGjRub0jZu3JjXzcoX9913n2l75cqVHmpJ7r788ktL2siRIy03mjkJDQ21ewNvr+zConTp0urdu7cpbf78+bmuDHy17777zrKCb0H05OzRo4epp216erpTC2XkBW9vb6fnhrQ9D0n5f0yPGzfO0lv3kUceUbdu3fK8rpMnT1oCGhEREXr77bedKufZZ59VzZo1TWmrV6/Wpk2bnG5Tly5dnHqt2fU2f+aZZ1S2bFmXyzlx4oTOnj3rcH7bunPqMZtb3VeMHj3aEjzMToUKFdS6dWtTmivvf2Fwww03WIJvOfHz87P0yj5x4oRljtjCqn///pZex84sCnNlXserDRs2LE/adj0j0AgAAPJE7dq1LTcHW7ZsybbnjW3PRHu9GSXrcOqcejRea8OmpX8vonPrMWXL3vBXV1YWdsfDDz/sdB7bdu/bty+vmpOvbIMEp06dcnv12Pxi25sxICBADz74oNPl3H333ZYFK2wXiylsbIdPp6en6/vvv3c4/3fffWfarlq1quX8lB+8vb1VvXp1U9qaNWvyvd6rderUydKG3HjiPDRnzhzTdn4uVLR48WLL9/2+++5zqBfg1fz8/OyeL22PVUc4eyxntyCSswF0e+W4+lnnxWuoU6eOJXDobDlF5ffH1tChQx0OsF5RGK4ZXOXv7285fpYsWeLw5zdhwgTTdvHixTVkyJC8at51i1WnAQBAnunYsaN+/PHHrO2MjAwtW7ZMvXr1suyb2/yMV1SsWFHVq1fPGnJ38uRJ7d+/3xLcsVfmlTYVZdkFYHNiLyBQkEMtixcvnuNcf9mxbXdGRoYuX77s0BC6vJSSkqIVK1Zo69at2rFjh86ePav4+HhdvnzZbiDtykq8Vzt27Jhpbr3C4MyZMzp06JAprWPHji6tcBsQEKDevXub5iy8fPmytm7daumZWlh069ZNFStW1MmTJ7PSpkyZ4lAgatmyZZZhv0OGDHF5SPSOHTu0fv16bdu2TUePHlV8fLwuXbpkWgn4agcOHDBtF3Qg25XzUNmyZVWiRAnT6rL5eR6Kj4/PmpvvilatWjm0mq0r7PVctp1GwVH9+/e3rHLsSs/odu3aObV/qVKlFBwcrEuXLmWl1axZ0zI1Qm6qVq1qSbt48aJTZbhat73pOdq2bet03bavIT093SO/P+4qitcM7ho+fLg++OCDrLkZDcPQhAkT7E7JcrXDhw9bhokPGDAg13lckTsCjQAAIM/YBhqlf4N/toHGzMxMy0IxOV0ct2/f3nST/88//1gCjadPn7b8BdteL8uixl5ANTf25toryJuGyMhIywIHjsiu3QV1o3fgwAG9//77mjFjhtvvlys32fnN3lDApk2bulxes2bNLIujbNq0qdAGGn18fHTfffdpzJgxWWk7d+7U+vXr1axZsxzz2r5OLy8vp3u9pKSk6IsvvtDkyZO1a9cup/LaKujvlyvnIenfY7qgAo3r1q2z/CHg5ptvzrf6bI8nPz8/3XjjjS6VFRkZqfDwcNNwY2eH7hYvXlwVKlRwum7bQKMrgdng4GBLmiufdV7VnZevoagFGoviNYO7KlWqpD59+pgWUZoyZYree+89FS9ePNt8EydOtEyHwSIweYOh0wAAIM84Ok/jli1bTPMz1qlTJ8f5oByZp/FanJ9Rkku9zewF+WxXVMxPrrRZ8my73377bdWvX1+TJk3KkxuswniTFhsba0mzXcTBGbaLMGRXR2FiO3xayn1RmISEBP3yyy+mtE6dOjm00NEVK1euVL169fTCCy+4HWSUCv77lVfHdH4ez9HR0Za0Bg0a5Ft9tt/1qlWrZrvCryNsjydnjyVXe2HZfkaulJNX5+68qtuTr8HTiuI1Q154/PHHTdsXLlzQtGnTst0/NTVV3377rSmtcePGLo3GgBWBRgAAkGfsrfBsu+iL5Pj8jNk9b2+I9LU4bFqyfwNQ2BW1No8YMUKjRo3KdtiqKwrjTZrtcShJJUuWdLk8ezfz58+fd7m8glCzZk21adPGlPbzzz/n+NnPmDHDssCIvYBldpYsWaJu3brl6YrLBf39KgrHtL3vXn4OgbQ9ntw5liRrW1NSUpSYmOhw/rz6jDz5WV8Lr8HTrtfX3qFDB9WrV8+UZjv/4tVmzpxpWbCI3ox5h0AjAADIU7bBvczMTC1btsyU5uj8jFdERkaaeg+dPn3aMlG5vUBjQSzUgKLt+++/1/jx4y3ppUuX1oMPPqhvv/1Wy5cv15EjR3ThwgUlJSXJMAzT4/Dhwx5oufOuHh55hbMLV+SW114dhY1tkPDChQuW1YOvZtvjMTQ0VHfccYdDdV24cEEDBgywBIy8vb3VrVs3jRkzRvPnz9fOnTsVGxubNQ+o7XfMlXnXrjfx8fGWtPwc9mr7XXfnWMouf1E4noDCwna17bVr11rmbb3CdmXq0NBQDRw4MN/adr0h0AgAAPJUbsOn7QUeHbmJzmn49JUFYq5Wr149RUREONRmXJ/S0tIsCzBI0ssvv6wTJ07om2++0QMPPKA2bdooMjJSJUuWtDs0MikpqSCa6zZ7c5BdPX+es+zltVdHYdO/f39LUCe74dOHDh2ynK/uvvvuHOf9utro0aMtvWaaNm2qPXv2aMGCBXr55ZfVvXt3RUVFqUyZMipRooTdFWOLynfMk0JCQixptj1R85Ltd92dYym7/EXheAIKi8GDB1vmm7QNKEr/zs1rO0/44MGD3f5jAf4PgUYAAJCn7AUar+5tuHnzZtP8YrVq1XJowZachk9fq8Omkb+WLl2q06dPm9KeeOIJjRkzxuFAklT4hwtfYW8YqTuLitjL6+pcfs5wd9hwUFCQ7rrrLlPawoULTatRX/Hdd9/JMAxTmjPDpn/++WfTduXKlbVo0SKnF2woKt8xTypTpowlzd50AXnF9nhyd4Ee2/wBAQEKDAx0q0wUDYVxqo2iKCgoSPfff78p7ccff7T0drYXfBw+fHi+tu16Q6ARAIBCyrgQr8wTZwrsYVywDjtzRWRkpKpVq2ZK27ZtW9aNsrPzM2a3H4FGuGvhwoWmbR8fH7322mtOl3Po0KG8alK+Cg8Pt6Tt3r3b5fLsLWoSFhaW7f55teDAuXPnnM5jyzZYmJmZqf/973+mNMMw9N1335nSoqKi1KJFC4fq2L17t06cOGFKe/LJJ+2u8JqTtLQ0SzmwKleunCVt27Zt+Vaf7fF0+PBht+Z5tT2ecjqWUDgUpnMa/jVixAh5eXllbSckJGjq1KnZbktSu3btLPM7wj2+nm4AAACwMi7EK+X9b6S09IKr1M9XAS8/JK9S1uFnzurYsaNp3jrDMLR06VL17dvX6fkZr6hevboqVaqUdcMdHR2t3bt3q27dupYyvby8mNMMuTp+/Lhpu2bNmjmufp6d1atX51WT8lXjxo0taRs2bHC5vPXr11vSmjRpku3+9oa22ptXLzcHDhxwOo+ttm3bqnr16qYFWr777ju98sorWdtLlizR0aNHTfmc6c1o+/26Uq+zNm/erOTkZKfzXW+aNWsmX19fpaf/3+/mypUr862+xo0ba926dVnb6enp2rJli8OB6KsdO3ZMMTExprScjiUUDoXpnIZ/1apVS126dDH9IXHChAlZ8zfa6+HIIjB5j0AjAACFkJGQKKWly6dLyzwJ/OVa34V4ZSxaIyMhMc8Cjd9++60pbcmSJerdu7dlXhxnAoLt27fXDz/8YCozODjYcpHesGFDu8Po8oOPj48yMjKytq/+Pwq32NhY07Yrw37T0tJyXEikMClbtqxuuOEGUw/MJUuW6Pz5806/9tTUVM2ZM8eUFhQUpIYNG2abx96qvIcOHXI6oLJ06VKn9rfHy8tLDzzwgEaOHJmVtnfvXq1atUqtW7eWZJ230dfXV4MHD3a4Dtvvl+Tad2zatGlO57keBQcHW4J/a9as0YEDB1SjRo08r69169aWVW1nzJjhUqDxl19+sVs+CrfCdE4rSnx9zWGovL5uevzxx02Bxh07dmj58uVq27at5ZiNiIhweHEvOI6h0wAAFGJepULkFV46/x95HMzMbp5G2/kZa9SooYoVKzpcrr3h054eNm07WX9+Lj6AvGU78bu9wFBufvzxR8s8j4VZ9+7dTdspKSnZLoSSk2nTplmG+3Xq1Ek+Pj7Z5qldu7Yl7eqgkCMyMjI0adIkp/Jk57777rMsvDJlyhRJ/672++uvv5qeu/XWW53q8WpvYQFnv2MXL160/NEG2evbt69p2zAM/ec//8mXujp37mz5vk+dOtXpRWHS09P13//+15LerVs3t9qH/FfYzmlFRX5fN/Xs2VNVq1Y1pU2YMEHr1q3Tpk2bTOkPPvig/P3987R+EGgEAAD5oGLFipbFDnbs2KEZM2aY0pwd3lwYA422CwIUlfn6IMsiRPv27dORI0cczh8dHa3nn38+j1uVvx577DFL2jvvvONUsDQ+Pl4vvfSSJf2JJ57IMV9ERIQqVapkSps+fbpTvVnGjRtnmpbBHZUrV1aXLl1MadOmTVNSUpKmTZumxMRE03PODJuWrN8vSfrrr7+cKuPxxx93e5GR68mjjz5qGc763//+1+n33REVKlSwBDajo6P11ltvOVXOZ599pr1795rSbr75ZjVq1MjtNiJ/FbZzWlFhe910+PBhy6Jb7vD29rYMh54xY4beeecdy36PPPJIntWL/0OgEQAA5AvbYJ9hGBo3bpwpzdH5Ga+oVauWacL/s2fPWoYVent7q127ds411g0NGjQwbS9dutTpHi3wDHvz5dkLoNlz7tw59ezZ06VekJ5Uv359S0+puLg49e3b16FeJSkpKbrzzjstgcmGDRtagnb29OjRw7R97NgxffbZZ7k3XNLff/+tF1980aF9HTV06FDTdnx8vGbOnGnp5RkREaHbbrvNqbIbNWqkoKAgU9rnn3/u8MIub7/9tmmqCOQuNDRUTz31lCnNMAwNGDDA7h+lchMTE2MJOF/tmWeesaR9/PHHltXGszN//ny7C1A999xzjjcSHlXYzmlFge11U1xcnFatWpWndTz44IMqVqxY1nZqaqr++OMP0z633HKLpecj8gaBRgAAkC/s9Sq0DWS4smCLbR7bMhs1amR33qT8YjuPVlxcnAYMGODWar4oGLfccotlCNf06dP10EMP5Rgs/uuvv9SqVaushVTsLQhQmI0fP94SAFu7dq3atm1rGVZ2tT179qhTp06W1br9/PwcHvr30EMPWdJeeuklTZw4MdseLcnJyfrggw/Uo0cPpaSkmG4e3XX77bdbeteMHj3actM7aNAguyvM5sTPz0+33367Ke3ChQvq3Llzjqshnzp1Svfcc49GjRqVlVbUvmOe9Prrr6tly5amtIsXL6p79+56+eWXdeHChVzLWLdunR577DFVrVrVskjL1Vq3bm3pOZWZmanBgwfrzTffVGpqqt18GRkZ+vjjj9W3b1/LPn379rX0lEThVdjOaUWBvflHhw4dqiVLligzMzNP6ihTpowGDhyY4z4sApN/WAwGAADki9x6K95www2qXLmy0+W2b98+x8URCnLYtPTvPG8jR440rXT6559/6s8//1SpUqVUtmxZBQQEmPJUqFBBc+fOLdB2wqpUqVJ65pln9Pbbb5vSJ02apN9++0133XWXGjdurFKlSunixYs6dOiQ/vjjD23fvj1rXx8fH33++edOD6v1pOrVq2v8+PG67777TOlbtmxRs2bNdPPNN+uWW25R5cqV5ePjo5MnT2rRokVavHix6Xt+xejRo9W0aVOH6m7evLn69Omj2bNnZ6VlZGRo+PDhGjdunPr27asaNWrI399fZ8+e1caNGzV37lxTsOezzz7T8OHDXXz1ZgEBARo4cKDGjx+flWbvjwSufr6vv/66pk2bprS0tKy0ffv2qVGjRrrlllvUqVMnVapUSenp6Tp9+rT++ecfLVq0SCkpKVn7Dx06VAcPHrzuFoxwlZ+fn3755Rc1b97c1PM2NTVVH3zwgcaOHav27durbdu2KleunEJCQnTp0iVFR0dry5YtWrVqld0Vw7Pz8ccfa/ny5dqxY0dWWnp6ut566y199dVX6tu3rxo2bKiwsDBduHBBu3bt0q+//mq3Z2vlypX1zTffuPcGoEAVtnNaUdCiRQtFRUVp165dWWn79u1Tp06dVLx4cVWqVEmBgYGWfHPnzlWFChUcrmfEiBHZzkEcGRmpW2+91fnGwyEEGgEAQL4oV66c6tatm23PPld6MzqSr6ADjeXLl9fIkSP15ptvWp67cOGC3d4zzLlWeIwcOVL//POPli1bZko/d+6cZXVKW15eXho/frzTUwAUBoMHD1ZaWpqGDRtmCh5mZmZq+fLlltXh7fHy8tL777/v9DyVEyZM0Pr163Xq1ClT+vbt201BXHteeOEFDRs2LE9vyocOHWoKNNpq2rSp6tev71LZtWrV0hdffGFpb2ZmpubOnZvrHxw6deqk8ePHWxbxQc4qVaqktWvXqlevXtq6davpuaSkJM2fP1/z58/Pk7qKFy+uxYsXq2fPnpaFQGJiYjRx4kSHyqlbt67mz5/v0srk8KzCdk4rCr744gt169bNMp9lUlKS9u/fbzdPdj2Es9OkSRO1bNlSa9assTz3yCOPWBYDQ97hnQUAAPkmp6Cfq8GZqKgohYeH233O19fX7rx7+e3111/Xe++9x8qFRZCfn5/mzJmjnj17OpWvZMmSmj59epGeSH7o0KGaN2+eatWq5XTeypUra8aMGS7NL1auXDmtWLFCNWrUcDiPv7+/Pv74Y3344YdO15ebJk2aWOYMu5rtPI7OGjZsmMaPH+/0+WHo0KGaO3eupUc0HFO5cmWtXLlSTzzxhMvvYUhIiEPDWsPDw7VkyRINHz5cvr7O9eXx8vLSPffco5UrV6pKlSoutROeVdjOaUVBp06dNGvWLJUtWzZf63n88cctaX5+fnrwwQfztd7rHYFGAAAKMeNCvIyz5/P/cSE+X9qfU6DR1R6NkrJd7KVJkyaWOfcKgre3t1599VWdPHlSX375pQYMGKD69esrLCzsupt7qSgKDQ3VnDlz9MMPP6hhw4Y57hsREaEXXnhBe/fu1Z133llALcw/Xbp00c6dOzVx4kTdfPPN8vHxyXZfLy8vNWnSRB9//LH27dunO+64w+V6q1Wrpm3btmn06NF2V2e+wt/fXwMGDNDmzZv17LPPulxfbrIbGl2sWLFc5/lyxKOPPqpNmzZpwIABOc716O/vr169emnZsmWaNGkSQUY3lShRQmPHjtX+/fv1zDPPqGbNmrnmCQwMVI8ePfTf//5Xp06dMi1Allu+r776Sjt27NDQoUNzzVe6dGkNHDhQGzdu1A8//GCZKxRFS2E7pxUFvXr10pEjRzR9+nQ9+OCDat68ucqVK6cSJUrIy8srT+qwN63HHXfcke8Bzuudl5GX64jjmhAfH6/Q0FDFxcUx8XQhsefCed2/ZJ5ebtRcVYKK3mdy7HK83t+8Tt917KE6pRgOAthKTk7W4cOHVa1atayglHEhXinvfyOlWedDyzd+vgp4+SF5lSp65xkgLx07dkyrV69WdHS04uPjVaxYMVWoUEH16tVTw4YN8+wGqDC6ePGi1qxZo+joaJ09e1YZGRkKDw9X2bJl1axZM0VERORLvdu2bdPWrVsVGxurxMREhYaGqnbt2mrVqpVl4ZqiLiEhQatWrdKhQ4d0/vx5eXl5qXTp0qpZs6aaNWt2zb3ewubo0aPatm2bzp49q9jYWGVkZCg4OFhly5ZV7dq1VadOnTzpnW4YhrZu3aoDBw4oJiZGFy9eVEhIiMLDw1WtWjU1bdqUoZvXsOvpnFaYvfrqqxozZowp7Z9//nHrj93XOnv3Jc4i0AgLAo2FD4FG4NqW3Q+6cSFeRkJigbXDq0QgQUYAAAAUeWlpaapSpYrOnDmTlVa3bl3TIjSwyotAI4vBAABQSHmVCiHwBwAAADjpl19+MQUZJemxxx7zUGuuL/TVBgAAAAAAwDUhMzPTMmQ6NDRUQ4YM8UyDrjMEGgEAAAAAAHBNGDdunHbs2GFKe/jhh5kfs4AQaAQAAAAAAECRlpiYqPfff1/PPfecKT04OFgvvfSSh1p1/WGORgAAAAAAABQpb7zxhubMmSNJiouL0/Hjx5WRkWF3v7CwsIJu3nWLQCMAAAAAAACKlGPHjmnr1q057tOxY0c9++yzBdQiSAydBgAAAAAAwDWma9eu+u233+TtTeirINGjEQAAAAAAAEVaQECAypUrp2bNmmnQoEHq06ePp5t0XSLQCAAAAAAAgCJlypQpmjJliqebARv0HwUAAAAAAADgNgKNAAAAAAAAANxGoBEAAAAAAACA2wg0Isu4ceMUFRWlZs2aebopAAAAAAAAKGIINCLLiBEjtGvXLq1fv97TTQEAAAAAAEARQ6ARAAAAAAAAgNsINAIAAAAAAABwG4FGAAAA4DoxZMgQeXl5ZT2qVq3q6SahEDly5Ijp++Hl5aUpU6Z4ulkArhEdOnQwnV86dOjgkXbYnufefPNNj7TjWkWgEQAAAAAAAIDbfD3dAAAAUPQdOXJE1apVcyqPt7e3goKCFBISoooVK+rGG29Us2bNdMcdd6h06dL51FIAAAAA+YUejQAAwCMyMzMVHx+vEydOaO3atfr666/18MMPq3z58rr77rt19OhRTzfRKVOmTLEMxTly5IinmwUAAFCkVa1a1XR9NWTIEE83CTkg0AgAAAqV1NRUTZs2TVFRUfrf//7n6eYAAAAAcBBDpwEAQL4oUaKEatSoke3zaWlpiouL0+nTp5WZmWl5PjExUUOGDFFGRoYeeOCB/GwqAAAAgDxAoBEAAOSLpk2b6p9//sl1v8TERK1Zs0aTJk3STz/9JMMwsp4zDENPPPGEOnXqpMjIyHxsLQCgatWqpnMwAOQlR64LCwLnufzF0GkAAOBRgYGB6tSpk3744Qf9+eefKlasmOn5hIQEjRkzxkOtAwAAAOAoAo0AAKDQ6NGjh95++21L+m+//WZ3eDUAAACAwoNAIwAAKFSGDRsmPz8/U1p0dLROnTrloRYBAAAAcARzNAIAgEIlJCREtWrV0s6dO03pZ86cUaVKldwqOzU1VevXr9fJkycVExOj+Ph4lSpVSuHh4YqKilJUVJRb5cNs79692rt3r2JiYhQbG6uAgACFh4ercuXKatGihWWYfF7JyMjQpk2bdPToUZ09e1YXLlxQSEiIwsPDVbNmTTVq1EheXl75UvcVaWlpWrdunXbt2qXY2Fj5+fkpPDxctWrVUvPmzeXj45Ov9RekmJgYbdiwQTExMYqJiZGPj48iIiJUtmxZtWzZUiEhIZ5u4jXh8uXL2rNnj/bt26dz587p0qVLCggIUKlSpRQREaGmTZuqXLlynm5mjorqcXH8+HFt3bpVsbGxOnfunFJSUhQcHKyyZcuqTp06qlOnjvz9/d2u58q56/Dhwzp79qzi4uJUunTprPeoQYMGefBqcnfs2DFt2LBBR48eVUJCgoKDg1WjRg21bt1apUqVcric3bt3a/PmzTp9+rRSU1MVERGh6tWrq02bNvL1zd9QxPbt27V161adOXNGKSkpKlWqlOrXr68WLVooICDAoTLS09O1ceNG7dixQ7GxsfL29lbZsmV10003qWHDhvnafk+Li4vTmjVrtH//fsXFxSkoKEjh4eFq3Lix6tSp4+nmFUoFfY0ZHR2trVu36ujRo4qPj1dSUpKKFSumwMBAlStXTlWrVlWtWrUUFBSUp/U6xABsxMXFGZKMuLg4TzcF/9/u8+eM5jO/N349tM/YEHOmyD1+PbTPaD7ze2P3+XOefiuBQikpKcnYtWuXkZSU5OmmuOzw4cOGJNOjffv2LpfXqlUrS3lr1651qazMzEzjl19+MW677TajRIkSlnKvflSoUMEYMWKEcfz4cYfKvv/++3Msz5HHqFGjssq7fPmy4efnZ3r+vffec/i1Pvroo5byS5QoYaSmpjqUPzo62vDy8jLlHzdunMP1G4Zh7N271xg+fLgRGRmZ4+suVqyY0b17d2PBggVOlZ+ThQsXGnfeeadRsmTJHOsuU6aMcd999xm7d+92uo7Jkydbyjt8+HDW86dPnzaeeuopIzQ0NNv6S5YsaTz99NNGbGxsnr12R9l+ZyMjI10qJzEx0fjwww+NJk2aWL4zVz98fX2Ntm3bGpMmTTLS09OdrmfUqFGWMl1h7xw1efJku/t+9NFHln1nzZrlUr1XJCUlWb6XTZs2zTFPWlqasWDBAuPJJ580GjZsmOP7fOVRvXp14/XXXzfOnj3rUjudeZ+uVtSPC3sOHjxoPPHEE0bNmjVzfd8DAwONHj16GN98841x+fJlp+tav369cffddxulSpXK9Tfq4YcfNg4cOODSa2rfvn2Ov9PTpk0zmjZtmm39AQEBxuDBg3P8jUxOTjY+//xzo0aNGjl+1q+88oqRkJDg9GvI6Tc0KSnJeP/9943KlSvneP5/4403jMTExGzriI6ONp599lmjTJky2ZZTrVo1Y/LkyUZmZqZT7ffEOe1quf0GbN682ejXr5/lWsQ2zxdffOHwtcXVcvsO5vT6XHlkJ6fvkTPy8xrTngsXLhjvvfeeERUV5dDr9/HxMW688Ubj6aefNlasWOHQ9zUv7ksINMKCQGPhQ6ARuLYRaLSyd2N36NAhp8tZtmyZ0bhxY6cvTAMCAozXX3/dyMjIyLH8vA40GoZhtGnTxvR8x44d3XrfJBlLly51KP8PP/xgybt3716H8sbGxhoPPvig4ePj4/R70L59e7cuvLdt22Z06tTJ6Xp9fHyMYcOGGcnJyQ7XlVNAZcaMGbkGOa9+lClTxli9erXLr9sVeRFo/Pnnn42KFSs6/X7Xq1fP4e/iFZ64KT9z5ozh6+tr2rdPnz4u1XvFjz/+aKl//Pjx2e4/bdo0IywszOXzSmBgoPHll1863c78CDQWhePiamfPnjWGDBli+Q44+ggODnaqrgEDBjgURL764efnZzz55JNOnbsMI/sgT1xcnHHbbbc5XH9oaKjx999/W8rftWuXUb9+fYfLqVGjhnHs2DGnXoNtGVd+Q3ft2mXUqlXLqfPR0aNHLeXPnj3bKF26tMPl9OrVy0hJSXG4/YU10JiZmWmMHDnSqd/wRo0aGdHR0U61+1oKNOb3Naat6dOnG+Hh4W69H478gTUv7kuYoxEAABQqZ86c0YEDB0xpoaGhqlq1qlPlfP311+rUqZM2bdrkdBtSUlL0zjvvqG/fvkpISHA6vzu6dOli2l61apWSkpJyzXfs2DHt37/f7nOLFi1yqG7b/SpXrqxatWrlmm/fvn1q2bKlJk2apIyMDIfqutrSpUvVrFkzrV+/3um8v//+u1q1aqXFixc7nTcjI0MTJ05Uhw4dFBMT43T+q02YMEF33XWXLl686HCec+fOqUuXLtqyZYtbdRekd955R3fffbdOnjzpdN6dO3eqa9eu+umnn/KhZXmnbNmyuvXWW01pc+fO1dmzZ10uc/LkyabtYsWKaeDAgdnuf2VosasSExP1+OOP69FHH3W5jLxQ1I6Lbdu2qVmzZpoyZYrS09NdKuPSpUsO7Xfo0CG1bt1a06ZNk2EYTtWRlpamsWPHqmvXrrpw4YIrzcxy6dIldezYUX/++afDeeLi4tSrVy/TZ7Rlyxa1bdtWO3bscLicAwcOqEOHDoqLi3OmyRY7duzQzTffrH379jmcZ+fOnerevbvi4+Oz0qZMmaK+ffvq/PnzDpfz+++/65577nGqvYVNZmamBg8erHfffdep3/DNmzerXbt2unz5cj62rnAq6GvMb775RgMGDHDrd6ggMUcjAAAoVN5//33LTdftt9/u1Jx677//vl555RVLeokSJdS1a1c1a9ZM5cuXV3BwsOLi4rR//34tXLjQcsE4Z84cPfjgg/r555/t1lOlShXdeOONkqTz58/r+PHjpufr1q2b67xdtnOqdenSRW+++WbWdkpKipYvX65u3brlWE5OwcSFCxfaXc3b1t9//21pS2527dqlNm3a2L3Zbd68uW6++WbVrl1bpUqVUmpqqk6fPq1Vq1Zp3rx5SklJydr3zJkzuvXWW7Vp0yZVrlw513ol6ccff9TgwYMtK5L7+/urU6dOatGihSpXrqzQ0FBdvnxZR44c0d9//60VK1aY9l+zZo3uuOMOLVmyxLIQkSPmzZunxx9/POt7Gxoaqm7duql169aKiIhQZmamjhw5oj/++ENr16415U1ISNADDzyg9evX5/ucZe5655139MYbb1jSfX191bFjR3Xp0kUVK1ZUenq6jh8/rrlz52rNmjWm4zk1NVX33nuvfHx81L9//4JsvlOGDh2qOXPmZG2npaXp+++/1zPPPON0WSdOnLAcW7fffrtKlizpcBmRkZFq1KiRoqKiVKlSJQUHB6t48eK6fPmyTp06pS1btmjBggWWgM2ECRPUoEEDPfbYY063211F7bjYtGmT2rVrZ/fGPyQkRB07dlSrVq0UERGhoKAgxcXF6eTJk9q4caNWrVqlc+fOOVxXTEyM2rRpo9OnT1ueq1Spku644w7VrVtXpUuXVkxMjLZu3apZs2ZZ6li+fLm6dOmiVatWOTzvoK377rvP9NvXpEkT9ejRQ9WqVVNQUJDOnDmjxYsX6/fffzedaxMTE3X//fdr06ZNio2NVc+ePbPa5+fnp44dO6pTp06qUKGCfH19deTIEc2ePdvyWR86dEivvPKKxo8f71L74+Li1LNnz6zfID8/P3Xq1Cmrbm9vbx05ckRz5syx1L1nzx698sorGjdunP755x89/PDDWa8xNDRUPXr0yPrMU1JStHv3bk2bNk1HjhwxlTNz5kxNnz69UJ/TcvLaa6/phx9+yNquXLmybrvtNjVo0EBhYWG6fPmydu/erZkzZ+rw4cOmvHv37tXLL7+sL7/8Mk/b5O/vn3V9Jf17rZGWlpa1XapUKVWpUiVP63RUQV1jXrF3717TufSK4sWLq1OnTmrWrJkiIyMVFBSk9PR0xcfH6/Tp09q5c6fWr19v+b4WCJf7QuKaxdDpwoeh08C1jaHT/8rMzLQ7N5q/v7+xc+dOh8tZtGiR4e3tbSqjePHixpgxY3L9bVuyZIlRvXp1SxscGYKY2xxljkpLSzOCg4NN5bzwwgu55hs4cKApz9VD8Xx8fHJ97fv27bO0/4cffsgxz+XLl+3OE9SzZ09j165dOeY9ffq0MWjQIEve5s2bOzScaMeOHUZgYKApr6+vr/HCCy8YMTExOebdvHmz0aRJE0vdzz//fK712vucixUrlvWeP/fcc8aFCxeyzT9t2rSs/a9+/PTTT7nWnRdcHTq9YsUKu0Pq2rRpk+Pw+lWrVhl16tSx5CtZsqTdYYu2PDXMMC0tzYiIiDDt37BhQ5fqfvfddy11//XXXznmGTVqlNGgQQPjs88+M/bt2+dQPcnJycbYsWONkJAQyzC9EydOOFRGXg6dLkrHRWxsrN15ZYODg43Ro0fnOpdgenq6sXDhQmPgwIGGj49PjvtmZmYaPXr0sNRVvHhx45NPPsn2/JeYmGi8+OKLlt83ScbTTz/t0Ou0HbYaEBCQ9f9q1aoZCxcuzDbvhg0bjLJly1rq/vHHH41evXplbXfp0iXH7+y3335rOZd4e3s7PH2Gbf1Xv4bOnTvnWPeUKVMsQ+J9fHyMHTt2mKaDePLJJ7P9viYnJ9udD7lWrVoOtb+wDZ329/fPul4IDg42vv7662zn001JSTFeeuklS70+Pj4On2McHTpty/b4vP/++x3Klx3b1+Do0GlPXGM++OCDljyDBg3K9Vrnip07dxpvvfWWUbFixQIbOk2gERYEGgsfAo3Ate16DTSmpqYa586dM9auXWt8+umnxo033mgpQ5IxYcIEh9sRHx9vuRGKiIgwtm3b5nAZFy9eNBo2bGgqIywsLNcbzbwKNBqGYfTs2dNUTqNGjXLcPzMz0/S6fX19jdtvv91Uxm+//ZZjGePHjzft7+XlZZw5cybHPPZutj744AOnXqu9m65p06blmCcjI8MyD1iJEiWMxYsXO1xvSkqK0bVrV8sNV243u/Y+5yvv1//+9z+H6rY3F2bnzp0dbrs7XAk0ZmZmGrVr17a0+bbbbnNobrJz587ZnbetZ8+eueb15E35s88+a8mzceNGp+u2nTu1cuXKuQbTL1686HQ9V2zdutUSbHzllVccypuXgcaidFwMGDDAUnelSpWc+u24Irfzvr3XWaxYMWPRokUOlT9x4kS77/P69etzzWsb5LnyqFu3rnH69Olc869cudIyn+TVAfmBAwcaaWlpuZZjL/ju6MJn9tovyejfv79DdY8ePdqS9+rX4MgCaJmZmZbfD0nGypUrc81b2AKNVx6lS5c2tmzZ4lDdDz/8sCX/O++841Deohxo9NQ1pu18vZ06dXJ6ESLD+PcPaI78ZjNHIwAAKLSWLl0qLy+vbB/+/v4qU6aMWrRooWeeeUZbt2415a9UqZJmzZqlYcOGOVznhAkTFB0dnbXt7e2t2bNnq0GDBg6XERoaqlmzZpmGPMfGxuqbb75xuAx32Q5Z3rJlS47ztW3fvt30ulu0aKG+ffua9lm4cGGOddoOva5fv77Kli2b7f4nT57UpEmTTGmPPfaYXnzxxRzrsfXmm2+qa9euprT3338/xzwzZ860zAM2efJkdezY0eF6/f399csvvygsLCwrLTU1VZ988onDZVztmWee0eDBgx3a95577lHz5s1NaUuXLlVycrJLdee3P//8U3v37jWlValSRdOmTct1agBJKl26tObMmaPixYvnWm5hMnToUEua7VyLuVmxYoVl7tT7779f3t4534aFhoY6Vc/VGjZsqNGjR5vSbI/VglIUjos9e/bol19+MaUVK1ZMc+fOdeq344rc5hO2d4758MMP1blzZ4fKf+SRRzR8+HBTmmEYLp+7AgICNG3aNMs0Hva0bt1aPXr0MKVdmd+2du3a+uabbxwa6v7cc89Zpg6YN2+e4422UbNmTX377bcO1f3ss8+qVKlSprQrr2Hw4MEOTTPg5eWld955x5LuzmvwtMmTJ5uGKufk/fffV7FixUxpCxYsyI9mFSqeuMa8dOmS5frvwQcfdGo6oSt8fX0d+s3OCwQaAQBAodKmTRv9+uuvOnz4sG6//XaH86Wmpuqzzz4zpd13331q2bKl02244YYbLDfHs2bNcrocV9necBqGYZnj7Wq2QcIuXbpYgpU5zeGYmZmpJUuW5NgGW59++qlSU1OztkNCQnINEGbHdt6/zZs36+jRo9nu/8EHH5i2O3TooLvuusvpekNDQ/XUU0+Z0lz5nENCQjRq1Cin8gwaNMi0nZ6erm3btjldd0GwN/fWRx99pBIlSjhcRrVq1fTSSy+Z0gzD0Lhx49xuX36pV6+emjVrZkr78ccfTd/73NgGJr28vPTAAw/kSftyMmjQINONaExMjFMLZeSFonJc/Oc//7HM8zpq1CiXgoy5WbNmjTZu3GhKa9CggUaMGOFUOWPGjLEEy2bMmGEKgjhq8ODBTr3Wfv362U1/4403FBgY6FAZxYoVU8+ePU1pW7dudXpRnCtGjRrl8PkoICDAUrck+fj46N1333W4zhYtWqhSpUqmNFcWBikM2rdvr969ezu8f+nSpS0LZm3ZssVyHF1LPHWNaW9xqTJlyjhdZ0Ej0AgAAAqVFStW6LHHHtM777zj1OT6q1at0qlTp0xpDz30kMvtuO2220zba9asMS1ekp/q169v6V2SU6DQXqCxQoUKioqKykrbu3evTpw4YTf/xo0bLYu55LYQzIwZM0zb/fv3V3BwcI55stO6dWtL75Z//vnH7r5Hjhyx3Kjn5ed85MiRHIOc9vTv318hISFO5bHtuSWpUPbuS01N1dKlS01p5cqVs/SYdcQjjzwiHx8fU1puPW09zTYoeP78edMiMTlJTEy09JRr166dbrjhhjxrX3ZCQ0MVERFhSluzZk2+13u1onJc/P7776btoKCgfFut2973fdiwYbn2cLVVsmRJy6rlaWlp2Z43c/Lggw86tX+jRo0sacHBwU7/sce2nEuXLrm0mn1wcLDTi7DYew1du3Z1enER23IK4zncEQ8//LDTeWyP1cuXL7v0+RUVnrrGLF26tKX3ou2CdoURgUYAAJAvSpQooRtvvDHHR/Xq1VW6dGlL3jNnzujtt99WVFSU/vjjD4fqsw2G+Pn5WXojOaNatWqm7eTkZO3evdvl8pxl26Mwux6NaWlpWrZsWdZ2cHBw1l/YbYOF2QV1bMv28/NT+/bts23b0aNHLcG4m2++Odv9c+Pt7a3IyEhT2ubNm+3ua/s5u1u37eecU93Zyem9yk716tUtabYrBhcGmzZtsgxdvf32211aCbh8+fJq27atKW3v3r1O/UGhoA0cONAyRNDR4dMzZsyw9EZxtTejYRjasGGDJk6cqMcff1y33367OnfurGbNmummm26y+zh//rypjGPHjrlUt6uKwnGxc+dOnT171pTWu3dvt4au52TlypWWtOx6CObGXnDNXvk5CQwMVNOmTZ3KY3uulqSWLVvKz8/PqXLsDTG/ePGiU2W4Wre912B7bnKE7Wtwpf2FQVE4Vj3NU9eYxYoVU8OGDU1pH330kX799VeX6y4Izl8hAAAAOKBp06YO9644ffq0Fi9erAkTJpj+UhsTE6Pbb79d//vf/3TPPffkWIa9Gyx7vWMcZW94ZE7zJOa1Ll266IcffsjaPnz4sA4ePGi5uF+9erUSEhKyttu3b58VBOrSpYvGjh2b9dyiRYvsBjpse0S2aNFCQUFB2bbN3nv97rvvWoYVOePAgQOm7ezea3t1OzPE3hHOfs41a9Z0ug57gYzCeJNmbyigs4GJqzVr1sx0XjAMQ5s3b861B62nlCxZUn379tVPP/2UlbZgwQKdPn1a5cuXzzHvlClTTNuu9PqKi4vTRx99pKlTpzrd09ZWQQdBisJxsXr1akuaO3+4yI3t8VSpUiWH5ka0p0mTJvL29jYNV3V26G5kZKTTfzSw13O9Ro0aTpWRXTmufNZ5VXdelFMYz+G5KVasmGUIuCOKym9YXvHkNeYDDzygp59+Oms7OTlZ/fr1U8uWLXX//ferZ8+eLn2G+YlAIwAA8Ljy5cvr3nvv1b333qsvvvhCTz31VNZcTRkZGRo6dKhuvPFG1atXL9sybIcFp6WlWRaYcVdB9ryyF3hZtGiRJdBob9j0FR06dJCvr6/S09Ml/dtz0TAM0zCc5ORkywV0bkEfe0OwDx48mGMeZ2X3Xtur29Ofs71eubmx1wMnLS3N6XLym70bn7p167pc3tXD+XOqozB54IEHTIHGjIwMTZ06NceFj44cOWL5Q0v//v0dnsNOkmbPnq1hw4a5NO+ePQUdBCgKx4W99zY/5maU/g2q255b3DmWgoKCVLlyZVMA2tljyXaeR0fY+4zyqhxXPuvC9Bqu/NYWJa4cp1LR+Q3LK568xhw+fLh+/PFHrVu3zpS+Zs0arVmzRo8++qhq1KihNm3aqGnTpmrTpo0aNmzo0oIxeYWh0wAAoFB54okn9MILL5jSUlJS9MQTT+SYryCCgElJSflexxWVKlVS7dq1TWn2hj7bBhqvXsE5ODjY9Bf36Ohobd++3bT/ihUrLENjcws0evK9Loyfs7PD9ooS27k7JVnm03SGvZt52yG+hU3nzp0tc7fZ9la09d1331kWtnBm2PSPP/6ofv365VmQUSr4IEBROC7sffdcCTg5Ij4+3rJYhjvHkmRtq7PHUl59Rp78rK+F1+BJ1+vrdpYnrz0CAgL0559/5nhtduDAAU2ZMkWPP/64brrpJoWFhWnw4MGaO3euRxbpIdAIAAAKnVdffdXS82fJkiXasWNHtnnsBUSKOtuLyiVLlpguGOPj47V+/fqsbdsFYOyVYRuYtN0ODg5WixYtcmyXJ9/ra/FzLszsrXjpzGrTjuS1V0dh4u3trfvvv9+Utnv3bq1du9bu/oZh6LvvvjOl1apVy+EhuQcPHtTQoUOVkZFhSvfz81Pfvn316aefatGiRdq7d6/Onz+vhIQEZWZmyjAM08PePHQwi4+Pt6TlNG2EO/L6WLKXv7AfS0BR5elrj7CwMP3111+aNm2amjRpkuv+58+f1/fff6/bbrtNUVFRmjlzZgG08v8QaAQAAIVOaGio3YnZFyxYkG2e4sWLm7bLli1rufF29zFkyJC8fqk5sl0Q5vz586Y5uP755x/TUC3b/SVzD0fJ2ivSdiGYdu3a5Tpnl+17LUnz5s3L0/c6u/k97dWdlJSUp3W/+eabOb7+64m9ucyunhPUWfbyurpaeUEaMmSIZRhadovC/PPPPzp8+LApzZnejC+//LJl9dFbbrlFR48e1a+//qqnn35anTt3Vq1atVSqVCkFBgbaHSJXkD2wiyp7q2Jfvnw5X+rK62PJXv6icCwBRVFhuMb08vJS//79tWHDBu3atUsfffSRevXqpTJlyuSYb+/evbrzzjv16KOPWnra5xfmaAQAoJA6k5igizY3m/mpZECAygW617siL9WsWdMSWMxpPpywsDBT7xRP//U5L3Ts2FE+Pj6mnk2LFi3KWowjp2HTV7Rs2VJBQUFZN8/Lly9Xamqq/P39deHCBcviAY4syhEWFmZJK6jhr9nVXaFChQKp/3pjbxipO4uK2Mvr6hxhznB32PANN9yg9u3bmwLg06ZN02effWZZldp2WLWPj4/uu+8+h+pJSEjQ77//bkpr3Lix5syZ4/QQx2vhHJjf7N2g59f7FhISYlm8xd0FemzzF8SxhMLhWp4PsTAqbNeYdevWVd26dfXcc89Jkvbv36/ly5frn3/+0bx58+zO1zphwgRVqlRJr732Wr63j0AjAACF0JnEBA1Y+LuSbYbO5adiPj6a1rVXoQk22utpktNE92XLltWhQ4eytlNTUx1aGbYwK1mypJo0aWKaAHzRokV6+eWXs/5/NXtBQl9fX7Vv315//vmnpH8DGatXr1b79u21ePFiy9w9jgQay5Yta0lzd0VcR2VXN4HG/BEeHm5J2717t1q3bu1Sebt27bKk2QseX5HdggPOBt3yYn6tBx54wBRovHjxombNmqWBAwdmpV2+fNkyRK179+4Ofz+XLVtm6c34yiuvOP16jx8/TiDCAfZWfN62bZvdHvXu8vLyUlhYmGJiYrLSdu/e7XJ5CQkJOnbsmCktp2MJhUNhOqfBcYX9GrNmzZqqWbNm1rQb8+bN09tvv22aXkeSRo8erWHDhuX7uYJAIwAAhdDFlBQlZ2RoSO16BRL4O5OYoCl7d+piSkqhCTTaWyHVx8cn2/1btGih1atXm9KWLVumAQMG5HnbClKXLl1MgcaVK1cqOTlZ58+fN92kRkVFZXvB26VLl6xAo/Tv8On27dtbApXlypVT/fr1c22TvTkcly1bpldeeSXXvO5q0aKFaQXgK3W3atUq3+u+HjVu3NiStmHDBj344IMulWd70+Pl5WW3jivs/cEhPj4+16Fitg4cOODU/vbceeedevzxx03z4E2ZMsUUaJw+fbplOKszw6aPHz9uSXMl6GV7LoR99s4bK1eu1IgRI/KlvsaNG2v+/PlZ2ydOnFB0dLTdP6DkZuPGjZY/FDkydxs8qzCd0+C4onSN6ePjo549e+qWW25R7969NW/evKznEhMTNX/+fA0aNChf28AcjQAAFGLlAkuoSlBIvj8KS3Dxanv27LGk2et9coW9YcO//vprnrYpJ/bmNbRdzMEVtj0Mk5OTtXz5coeGTWdXxpW8tmXYm+PRnoYNGyoiIsKUtmzZsgLpYeHpz/l607hxY8vQ4N9++82l73Z0dLSWL19uSqtdu3aOwz3trcp7da8SRy1dutTpPLYCAwMtN5WLFi3SiRMnsrZt520sU6aMevfu7XAd9nptuzIcdtq0aU7nuR7VrVvXEuT7/fff7f6hKy/Y6wk8Y8YMl8r65ZdfHCofhUthOqcVJbbXWHlxfeWMonjt4evrqzFjxljSt23blu91E2gEAACFzoULF7Ry5UpLet26dbPN0759e4WGhprSZs6cqf379+d5++yxNwl/Xiwq0Lp1a8sK3IsWLXJo2PQV9evXNwVpN2zYoG3btll6RDgybFr6txeabfAkMTFRn3/+uUP53REVFaWaNWua0tatW6fFixfne93XIz8/P3Xs2NGUdubMGf32229Ol/X111+bFi+SpG7duuWYp3bt2pa0q3v4OiIuLi7PAm+2vRMzMzP1v//9T9K/PYxWrFhhev7ee++Vv7+/w+XbW4U4pykj7Dl48KBmz57tVJ7r2e23327avnz5ssaPH58vdXXv3t2S9vXXX1t6JuYmLi5OP/74oynN3rGKwqewndOKCttrrPxatCk7nr7GdFWdOnUsafn1h5SrEWgEAACFzjvvvKPk5GRLek49g0qUKKFnnnnGlJaRkaFBgwZZ5jzLD/YWzXCll4KtgIAAtWnTxpS2aNEi02rRV+ZhzMnVQcSMjAy7k4E7GmiU/l0Z17aHwQcffKC1a9c6XIar7LX9oYceKrAFaa439oaRPv/880pMTHS4jKNHj+r99983pXl5eenxxx/PMV/Dhg0tc5fZBlhy89Zbb5mGO7ujdevWlkDBlcVfbBeBkZwbNi3J7vQHf/31l8P5MzMzs+bogmOef/55y7Qcb7/9trZv357ndTVv3jxrMa8rtm3bpgkTJjhVzmuvvWY53/Xv39/S0xyFT2E7pxUVttdYeXF95QxPX2O66vTp05Y0e3Mv5zUCjQAAoFD57LPP9Omnn1rSO3XqpBo1auSY95lnnrFMcL1u3TrdeeedLv8FNyYmRiNHjtTChQtz3M/e3IZz5851qU5btkOaN23apFOnTmVtt2zZ0m6PyqvZBhH/+OMP03atWrVUqVIlh9tUvXp1SxAlNTVVffr00apVqxwu52oZGRn6+eefc10RcdCgQZa/0h8+fFi33nqr6X1xRnx8vD788EN9//33LuW/lt16662W9/vIkSO65557LD0U7blw4YL69OljCUz26tXL0jvVVrFixdShQwdT2qpVqywLrmRn6tSp+uyzzxza11G23/v9+/dr2bJlWT0br7jpppt00003OVW2vfkY3333XdNqp9nJzMzUsGHDtGzZMqfqvN7VqFFD99xzjyktOTlZt956q3bs2OF0eUeOHMnx+WeffdaS9vzzzzv8uX377beWHpdeXl6WIAgKp8J4TisKGjRoYNresWOH3Tlt85MnrjFXrVqlzz//3OXAsr2RJjfeeKNLZTmDQCMAAPC406dP6/vvv9fNN99s92bJz89PX3zxRa7lhISE6Oeff7b0tPvjjz/UpEkT/fDDDw4FRpKTkzV79mwNGjRIkZGReu+993K9iCxdurQlGDN58mR9+umnbg9Tya2noSM9EfOiDFuffPKJ5eI/OjpaHTp00EsvvaQzZ844VM7WrVs1cuRI1ahRQwMHDtTWrVtz3N/Hx0e//PKLJbi6du1aNWrUSOPHj7fbI9ZWenq6Fi1apEceeURVqlRxqs3XEy8vL02aNMnS62v27Nnq1q1bjosSrF27Vm3atLF8piVLlnTomJb+7a1q67777stx+PbFixf1wgsv6P7775dhGJZ5Jt0xePBgy3sxfPhwy03v0KFDnS67fPnylh7MBw4cUPfu3XNc2X3v3r265ZZb9M0330j6t5ez7ZQLyN7YsWN1ww03mNJOnDihm2++WR988EGuvXczMjK0ZMkSDRo0KNfg+cCBA3Xrrbea0pKSknTrrbfqyy+/zHYYdXJysl555RU9/PDDMgzD9NzTTz/NQjBFSGE7pxUFtvOPZmZm6q677tKGDRsKrA2euMaMiYnR008/rUqVKmn48OH666+/lJaWlmsdly9f1ksvvWQJSoeGhqpnz5655ncXq04DAIB8sWHDhlx78yQkJOj8+fM5Dnn19fXVDz/8oKioKIfq7dy5s8aOHavHHnvMlH7w4EENGjRIzz33nDp06KAmTZooPDxcQUFBunTpki5evKhDhw5p48aN2rJli5KSkhyq72pDhw7Viy++mLWdkZGhZ599Vs8995wqVaqk0NBQuwGK4cOH51huo0aNVKZMmWwXW3EkSFixYkXVqVPH7iI7jpZhKygoSHPmzFHLli0VHR2dlZ6WlqYPP/xQn332mVq1aqW2bduqUqVKKl26tFJTU3Xx4kWdOXNGW7Zs0caNG+0O7clN/fr19cMPP+iOO+4wXdjHxMRoxIgReu2119S+fXu1aNFCERERCg0NVUJCgi5evKhjx45p48aN2rx5s0M9xfDvTd6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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "tot_communities_resnik = {**fine_grained_communities_resnik,**middle_communities_resnik, **broad_communities_resnik}\n",
    "\n",
    "avg_resnik_in_louvain=[]\n",
    "avg_resnik_out_louvain=[]\n",
    "\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    avg_resnik_in_louvain.append(float(res_list[0]))\n",
    "    avg_resnik_out_louvain.append(float(res_list[1]))\n",
    "\n",
    "state_resnik=[]\n",
    "for i in avg_resnik_in_louvain:\n",
    "    state_resnik.append(\"Within a louvain community\")\n",
    "for i in avg_resnik_out_louvain:\n",
    "    state_resnik.append(\"Between louvain communities\")\n",
    "\n",
    "\n",
    "tot_com_resnik_in_out_df=pd.DataFrame()\n",
    "\n",
    "tot_com_resnik_in_out_df['Resnik']=avg_resnik_in_louvain+avg_resnik_out_louvain\n",
    "\n",
    "tot_com_resnik_in_out_df['Comparison']=state_resnik\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "#plt.xlabel('Resnik',fontsize=18)\n",
    "#plt.ylabel('Frequency', fontsize=18)\n",
    "\n",
    "#plt.title('Resnik distribution of total genes in each hierarchical level of clustering of EEN', fontsize=28)\n",
    "g=sns.histplot(\n",
    "    tot_com_resnik_in_out_df, x=\"Resnik\", hue=\"Comparison\", element=\"step\",palette='husl',\n",
    "    stat=\"density\",bins=22,binrange=(0,14)\n",
    ")\n",
    "plt.xticks(fontsize=36) \n",
    "plt.yticks(fontsize=36) \n",
    "\n",
    "g.set(xlabel=None)\n",
    "g.set(ylabel=None)\n",
    "g.set(yscale=\"log\")\n",
    "plt.setp(ax.get_legend().get_texts(), fontsize='36') # for legend text\n",
    "plt.setp(ax.get_legend().get_title(), fontsize='36') # for legend title\n",
    "plt.savefig('output/Figures/Fig2F_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\", transparent=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "333b3423-540a-45eb-a657-a74a11a5c154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.4372801331131024e-38"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's run the T-test between the two distributions\n",
    "from scipy import stats\n",
    "t_value,p_value=stats.ttest_ind(avg_resnik_in_louvain,avg_resnik_out_louvain)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7cebac45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.440485312998367, 4.4372801331131024e-38, 0.8343461562647118)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's calculate the Cohen's d as an effect size\n",
    "def calculate_ttest_and_effect_size(list1, list2):\n",
    "    from scipy import stats\n",
    "    import numpy as np\n",
    "    t_value, p_value = stats.ttest_ind(list1, list2)\n",
    "\n",
    "    n1 = len(list1)\n",
    "    n2 = len(list2)\n",
    "    dof = n1 + n2 - 2\n",
    "\n",
    "    mean1 = np.mean(list1)\n",
    "    mean2 = np.mean(list2)\n",
    "    var1 = np.var(list1, ddof=1)\n",
    "    var2 = np.var(list2, ddof=1)\n",
    "\n",
    "    pooled_var = ((n1 - 1) * var1 + (n2 - 1) * var2) / dof\n",
    "    effect_size = (mean1 - mean2) / np.sqrt(pooled_var)\n",
    "\n",
    "    return t_value, p_value, effect_size\n",
    "\n",
    "calculate_ttest_and_effect_size(avg_resnik_in_louvain,avg_resnik_out_louvain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "54623442",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "finegrained_avg_resnik_in_out_ratio_louvain=[]\n",
    "middle_avg_resnik_in_out_ratio_louvain=[]\n",
    "broad_avg_resnik_in_out_ratio_louvain=[]\n",
    "state_resnik=[]\n",
    "\n",
    "for com,res_list in fine_grained_communities_resnik.items():\n",
    "    finegrained_avg_resnik_in_out_ratio_louvain.append(float(res_list[0]))\n",
    "    state_resnik.append(\"Fine-grained\")\n",
    "    \n",
    "for com,res_list in middle_communities_resnik.items():\n",
    "    middle_avg_resnik_in_out_ratio_louvain.append(float(res_list[0]))\n",
    "    state_resnik.append(\"Intermediate\")\n",
    "\n",
    "for com,res_list in broad_communities_resnik.items():\n",
    "    broad_avg_resnik_in_out_ratio_louvain.append(float(res_list[0]))\n",
    "    state_resnik.append(\"Broad\")\n",
    "    \n",
    "\n",
    "\n",
    "tot_com_resnik_in_out_df=pd.DataFrame()\n",
    "\n",
    "tot_com_resnik_in_out_df['Resnik']=finegrained_avg_resnik_in_out_ratio_louvain+middle_avg_resnik_in_out_ratio_louvain +broad_avg_resnik_in_out_ratio_louvain\n",
    "\n",
    "tot_com_resnik_in_out_df['Comparison']=state_resnik\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fcc9f5bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_1213/87037651.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  g=sns.boxplot(x=tot_com_resnik_in_out_df[\"Comparison\"], y=tot_com_resnik_in_out_df[\"Resnik\"], palette=\"Blues_d\", linewidth=5)\n",
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_1213/87037651.py:15: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([int(i) for i in list(ax.get_yticks())], size = 36)\n",
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_1213/87037651.py:17: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(labels, size=36)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "g=sns.boxplot(x=tot_com_resnik_in_out_df[\"Comparison\"], y=tot_com_resnik_in_out_df[\"Resnik\"], palette=\"Blues_d\", linewidth=5)\n",
    "\n",
    "ax.set_xlabel(\"\",fontsize=32)\n",
    "ax.set_ylabel(\"\",fontsize=32)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.set_yticklabels([int(i) for i in list(ax.get_yticks())], size = 36)\n",
    "labels = [\"Fine-grained\", \"Intermediate\", \"Broad\"]  # Modified labels\n",
    "ax.set_xticklabels(labels, size=36)\n",
    "plt.savefig('output/Figures/SF5D_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "37b1d249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013734215551003175"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "\n",
    "\n",
    "t_value,p_value=stats.ttest_ind(finegrained_avg_resnik_in_out_ratio_louvain,middle_avg_resnik_in_out_ratio_louvain)\n",
    "\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a41d99a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06846062769180661"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "\n",
    "\n",
    "t_value,p_value=stats.ttest_ind(finegrained_avg_resnik_in_out_ratio_louvain,broad_avg_resnik_in_out_ratio_louvain)\n",
    "\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bf7ab83-aafd-4362-9b04-5f092b3f9da8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f76be88-cae1-4042-ac3b-befc35e6a83d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_density_LSD(network, group):\n",
    "    # Create a subnetwork of the total network\n",
    "    G_subgraph = network.subgraph(group)\n",
    "    # Calculate the density of that subnetwork\n",
    "    density = nx.density(G_subgraph)\n",
    "    return density\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e2960b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tot_com_dict={**fine_grained_communities_dict,**middle_communities_dict, **broad_communities_dict}\n",
    "\n",
    "\n",
    "tot_com_resnik_in_out_list=[]\n",
    "density_com_list=[]\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    tot_com_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "    density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,tot_com_dict[com]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "30952486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's remove duplets\n",
    "import scipy\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy import stats\n",
    "\n",
    "density_com_list=[]\n",
    "tot_com_resnik_in_out_list=[]\n",
    "\n",
    "fine_grained_resnik_in_out_list=[]\n",
    "fine_grained_density_com_list=[]\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    if com in fine_grained_communities_dict.keys():\n",
    "        if len(fine_grained_communities_dict[com])>2:\n",
    "            fine_grained_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            fine_grained_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,fine_grained_communities_dict[com]))\n",
    "            tot_com_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,fine_grained_communities_dict[com]))\n",
    "\n",
    "middle_resnik_in_out_list=[]\n",
    "middle_density_com_list=[]\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    if com in middle_communities_dict.keys():\n",
    "        if len(middle_communities_dict[com])>2:\n",
    "            middle_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            middle_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,middle_communities_dict[com])) \n",
    "            tot_com_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,middle_communities_dict[com])) \n",
    "    \n",
    "broad_resnik_in_out_list=[]\n",
    "broad_density_com_list=[]\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    if com in broad_communities_dict.keys():\n",
    "        if len(broad_communities_dict[com])>2:\n",
    "            broad_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            broad_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,broad_communities_dict[com])) \n",
    "            tot_com_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,broad_communities_dict[com])) \n",
    "    \n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "slope, intercept, r, p, stderr = scipy.stats.linregress(density_com_list, tot_com_resnik_in_out_list)\n",
    "\n",
    "ax.plot(fine_grained_density_com_list,fine_grained_resnik_in_out_list, linewidth=0, marker='s',c='#D8C4C5', label='Fine-grained')\n",
    "ax.plot(middle_density_com_list,middle_resnik_in_out_list, linewidth=0, marker='s',c='#A47179', label='Intermediate')\n",
    "ax.plot(broad_density_com_list,broad_resnik_in_out_list, linewidth=0, marker='s',c='#7A2D45', label='Broad')\n",
    "\n",
    "y=[]\n",
    "for i in density_com_list:\n",
    "    g=(intercept + slope * i)\n",
    "    y.append(g)\n",
    "line = \"Regression line\" \n",
    "\n",
    "ax.plot(density_com_list, y,c='red', label=line)\n",
    "#ax.set_xlabel('Average biological similarity', fontsize=18)\n",
    "#ax.set_ylabel('Average chemical similarity',fontsize=18)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "plt.xticks(fontsize=36) \n",
    "plt.yticks(fontsize=36) \n",
    "\n",
    "# plt.title('Correlation between Resnik and Genetic overlap in %s' %met )\n",
    "ax.legend(facecolor='white',fontsize=36)\n",
    "plt.savefig('output/Figures/SF4B_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "81bc603b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.2166869385555397, pvalue=1.4671558989955178e-05)\n",
      "SignificanceResult(statistic=0.30858216753015305, pvalue=4.075188496624386e-10)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.pearsonr(density_com_list,tot_com_resnik_in_out_list))  # Pearson's r\n",
    "print(scipy.stats.spearmanr(density_com_list,tot_com_resnik_in_out_list)) # Spearman's rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d655971d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d2f3c62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a10b490",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we are evaluating the resnik score across exposures for the finest level of hierarchy in louvain\n",
    "#distinguishing the exposures that are part of the same community and those ones that are not part\n",
    "fine_grained_communities_overlap={}\n",
    "\n",
    "for com,exp_list in fine_grained_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            overlap_list_in=[]\n",
    "            overlap_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            overlap_list_out.append(tot_overlap[exp1,exp2])\n",
    "                        except:\n",
    "                            overlap_list_out.append(tot_overlap[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_overlap_in=sum(overlap_list_in)/len(overlap_list_in)\n",
    "            avg_overlap_out=sum(overlap_list_out)/len(overlap_list_out)\n",
    "            fine_grained_communities_overlap[com]=[avg_overlap_in,avg_overlap_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "            \n",
    "middle_communities_overlap={}\n",
    "\n",
    "for com,exp_list in middle_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            overlap_list_in=[]\n",
    "            overlap_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            overlap_list_out.append(tot_overlap[exp1,exp2])\n",
    "                        except:\n",
    "                            overlap_list_out.append(tot_overlap[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_overlap_in=sum(overlap_list_in)/len(overlap_list_in)\n",
    "            avg_overlap_out=sum(overlap_list_out)/len(overlap_list_out)\n",
    "            middle_communities_overlap[com]=[avg_overlap_in,avg_overlap_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "broad_communities_overlap={}\n",
    "\n",
    "for com,exp_list in broad_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            overlap_list_in=[]\n",
    "            overlap_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            overlap_list_out.append(tot_overlap[exp1,exp2])\n",
    "                        except:\n",
    "                            overlap_list_out.append(tot_overlap[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_overlap_in=sum(overlap_list_in)/len(overlap_list_in)\n",
    "            avg_overlap_out=sum(overlap_list_out)/len(overlap_list_out)\n",
    "            broad_communities_overlap[com]=[avg_overlap_in,avg_overlap_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "448e0a4a",
   "metadata": {},
   "outputs": [
    {
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S9erV5e3traioKC1dutTmvVm8eLFCQkL08ccfZ8bq1aunXr16qWHDhvL19dWpU6e0cuVK/fTTT8rIyMhsd+LECX3xxRcaMWKEw9wXLFigKVOm2MQbNGigO+64QzfeeKPKlCmjS5cu6ciRI1qzZo1hq7AkzZgxQ35+fho6dKjDuc6dO6fhw4crISHBEPf391efPn10yy23KDQ0VJcuXdLu3bv1zTffZL5vu3bt0u7dux2Ofz2+/vpru19fLVu2VOfOnVWrVi0FBQVJurLd9tChQ1q7dq22bdtm02fixIlq166dzRbutm3bZq6C27Nnj2FrbXBwsFq2bGlo7+vrazN2fHy8Bg4cqOjoaEPc29tbd955p1q3bq1KlSrJ3d1dZ86c0V9//aWVK1caCt/Hjh3TkCFDtGLFisyPCQAAAMD/Z7K6+udyXJer52ytW7cu3+ZITk5WVFSUwsPD5e3tnW/zFHWJF2P13aRpynCwSsjNw0M9xz1NIS+fZT3PTZImTZqkvn372m2f3RldoaGhmjx5stq0aWO3X3R0tB599FEdPnzYEH/qqaccFo1ye07Z+vXrbcYNDQ3VW2+9pdatW2fb79tvv9WECRN0+fLlzFhAQIC++eYbVa1a1W6frO+hv7+/oejUqFEjNW3aVOnp6YqMjNTWrVs1atQojRw5MrNN1jPxrvL19dW7776rdu3a2Z173Lhx2a74c3d319ixY/Xggw/aPR9uzZo1Gj16tGGrbeXKlbV+/Xq740lXCkn33XefLBaL4eOdOHGiunfvnm2/3377Tc8995xhW6bZbNbChQvVpEmTbPs99thj+vnnnw2xli1b6p133rF7OUhqaqpmzJihWbNm2R2vZcuWWrBggd3XXPlaS0xMVOfOnQ0fj7e3t6ZOnarOnTtn+/FIV96LMWPGKC4uzhD/6KOPbM57vNbzzz+vb775xqmP5VqjRo2y2brdoUMHvf766ypXrpzdPpcuXdKbb76pFStWGOIdO3Y0FIiRv/iZAQAAoPhgOy1KrJTERIcFPEnKSEtzuFIPRYe/v7/mz5+fbQFPkqpWraoZM2bIbDYb4hs2bMjzfFJTUzVhwgRDLCwsTMuWLXNYwJOkXr16ac6cOYZfmOPj4/Xuu+86Pf/VAp63t7dmzJihZcuW6aWXXtKECRP0xRdfaPXq1br77rtzHMfNzU2fffZZtgU8SXrhhReyvV32ueee0+DBg7O94KFr16666667DLETJ07o7Nmz2c43YcIEQwHPz89PCxcudFjAk66sKFu4cKFh1aXFYtGkSZOy7bN7926bAl7Tpk01e/bsbG/39fT01NNPP63Ro0c7zOd6ff/99zbnxD333HM5FvCkK+/F+PHjbeJ//vlnnuV31YYNG2wKeH369NHHH3+cbQFPkoKCgjRlyhSboub69eu1cePGPM8TAAAAKO4o4gEoFkaMGOFwa+hV4eHhuu222wwxVy9TcMbKlSttClFvv/22w1tAr9W8eXM9+eSThtiPP/6okydPupTHG2+8oS5dutjEw8PDnboJtnfv3mrevLnDNgEBAbr11ltt4nXq1NGDDz6Y4xx33nmnTezIkSN2227evFl79uwxxF566SXVq1cvx3kkqUaNGjbF1R07dmj79u1222c9M9HT01OTJk0ybKXOziOPPKIWLVo4lVdu/Pjjj4bnypUr695773W6/x133GGz9dXV8wWdkfUCmerVq+u1115z+ube5557TvXr13c4JgAAAACKeACKAQ8PD6dWlV3VtGlTw3NqaqrNeWfX68svvzQ8t2jRQq1atXJpjPvvv18BAQGZz2lpaTZbCx2pWbNmjqvTcpJ1FVR27BXRsq6wy054eLhNLLtLGrK+r5UrV1bv3r2dmueq2267TdWrVzfEvv76a5t2qamp+umnn2z61qhRw6l5TCaTnnrqKZdyc0Xr1q3Vs2dPNWvWTKGhoerRo4fc3d2d7u/h4WHzPuT1JRxHjhyx2aL98MMP26yGdcTDw8Pm3MI//vjDcA4mAAAAAC62AFAM1KxZU2XKlHG6fcWKFW1iSUlJ8vf3z5N8EhMTbVaLtW/f3uVxfHx89L///c9QSNq6davT/du1a+f0aid7/P39bVZAZSc4ONgm1qxZM6f62vvcXXse4LWyFoTatm0rNzfX/t5kMpnUoUMHzZ07NzNm733dvXu3TXG3W7duLs3VokULVa5c2e4Zjtdr+PDh1z1G1m3Q125TzgubNm2yiXXo0MHlcdq3by93d3fD2Ylbt25Vjx49ris/AAAAoCRhJR6AIq9u3boutbd3e+a1xYHrtXPnTpvxsq54clbWItrOnTsNt7k64mwRLTv2VshlJ+uNppKyvYQjK3ursuzdqXT06FGdP3/eEMur9/Xo0aM258vZu1XW0QUY9phMphy3IxeGhIQE/fzzzzbbs5392nJW1ltw/f39bW6CdkZgYKAqV65siGW3BRoAAAAorViJB6DICwoKcqm9h4ftP215WbyIjIy0ic2bN0/ff/+9y2MdP37c8JyYmKiLFy86VQipVauWy/Ndy9GlA864diuwI86uFsx6q7Akfffdd7kq5mQt2ElX3uuyZctmPh89etTwenBwcK7ek3r16uXqc58XLl++rOjoaEVHRysqKkqHDx/Wvn37FBkZafdrPq8vpM/6OUtLS9OoUaNyNVZ8fLzhOev3BgAAAFDaUcQDUOTlxTbYvCxe2DvPzZVtsDmJjY11qojnyhZje7K7cdZZrpx75ozY2Fib2N69e7V37958GT/rxST2tgw7w9nLTK7X5cuXtX79em3btk2HDh3S0aNHde7cuQKZOztZvxeSk5NtbqrNq7EBAACA0o4iHkosLz8/uXl4KCMtLds2bh4e8rrOQgbynyuH+ReE/C4uxMXFOdUuMDDwuubJ6yLc9Sro9zXruXy5LWpebzE0J5cuXdLHH3+sxYsXu3QxhclkkoeHR56fg5c1t+I4NgAAAFAcUcRDieUXXEY9xz2tlMTEbNt4+fnJL7hMwSWFEiE/iyLSlVtTnWFv23BxVtDv6/VcCnItLy+vPBnHnj179ujRRx91esVduXLl1LhxY918883q1KmTxo0bZ3NZSF7Kz89Zfn89AAAAoHiwpKQo3cHPhu5ms8z5+DN5UVKyfgMEsvALLkORDnnO3llwu3btytdiTmlg731dtWqVatasmS/z+fj4GJ6znsnmrEQHfyi4HlFRURo4cKDd1Xc+Pj5q0KCB6tSpo5o1ayo8PFy1a9dWWFiYoV1en4GXVUBAgOH8wW7dumn69On5OicAAABKD0tKiqK2bXf4c63JZFJ4RPNSUcijiAcALrJ3Ft2lS5dUvnz5gk+mBLH3vjq7tTg3QkNDDc/2LsNwRn7lOHbsWJsCXq1atTR69Gi1bdvW7o3BWSUnJ+dLblcFBQUZ3rf8/HwBAACg9Em3WHL8w7TValW6xVIqinhuhZ0AABQ3lStXtollvekUrivo97VGjRqG57i4uFzdiGrvtuLrtXXrVu3atcsQa9iwoZYuXarOnTs7VcCTcl+YdFbWz1lUVFS+zgcAAID/z5KSouSEhGz/s6SkFHaKyGOsxAMAF0VERNjEtm7dqpYtW7o81p9//qlTp06pUqVKqlKliipUqFDkLpwoKA0aNJC3t7dh9djWrVvVp08fl8fatWuXDhw4oEqVKqly5cqqVKmSzXbnZs2a2fTbunWrqlSp4vJceW3t2rU2sTFjxsjX19fpMeLj43Xy5ElDLK+310ZERGjjxo2ZzydOnNDp06dzdWPvwoULVbZsWVWuXFmVK1d26oZmAACA0optpqUTRTwApZ6rFxxUqFBBVapUMazaWrFihR599FG5uTm/wDk9PV3jx49XdHR0ZiwoKEgbN250eqVVSeLp6akmTZpo06ZNmbE1a9Zo3Lhx8vf3d2mst99+23Chg7u7u3799VfDFtqGDRsqJCREMTExmbHly5erd+/eTs8THR2t7du3u5SbM+ytCGzatKlLY6xbt87mh7o0B7d1S65/L9x00002sWXLlunxxx93aZxNmzZp4sSJhtgDDzygCRMmuDQOAABAacE209KJ7bQASj1XCm9X3XXXXYbnY8eOadmyZS6NsXTpUkMBT5I6duxYKgt4V2V9X+Pj4/XJJ5+4NMbvv/9ucyNrRESEzRl4bm5u6tWrlyG2adMm/fnnn07PNXv27Hy5PMLeDcUpLmyHSEpK0owZM2ziOd346ur3QkREhKpXr26ILViwQGfPnnV6DKvVavcyjNtvv92lXAAAAICSjiIegFLPXtEspwsBHnjgAZutjW+88Yb++ecfp+Y8fPiw3nrrLUPMZDLpwQcfdKp/SdW9e3dVrFjREJs9e7Z+/vlnp/qfO3dOL774ok188ODBdtsPGjTIZpvtCy+8oFOnTuU419q1a7VkyRKn8nJV1ltmJenHH390qm9KSoqefvppmwKxlPPXddbvhcuXLzts7+bmpiFDhhhisbGxGj16tN1bde356KOPtGPHDkOsYcOGudqeDgAAAJRkFPEAlHpBQUE2sZwuKyhTpoxGjRpliCUnJ2vQoEFatmyZMjIysu37999/a9CgQUpMTDTEe/Toofr167uQecnj4eGhcePGGWIZGRkaNWqUZs+ebXeF2lUHDhxQ//79debMGUM8IiJCnTp1stunYsWKeuihhwyxU6dOqX///g7Pulu6dKlGjx6d04eTazfffLNNbOrUqdq5c6fDfrt379b999+vDRs22H0969dcVoGBgYbn6Ohoh++5JPXt21cNGzY0xLZu3ar+/fvr0KFD2fazWCyaPn263n//fZvXnnnmGZe39gIAAAAlHWfiASj1KlSoIDc3N0PhbdKkScrIyFCNGjV0+fJlBQYGqlatWoZ+Q4YM0ZYtW7Ru3brMWHJysl588UXNmzdP3bp1U+PGjVW2bFklJyfr8OHDWrNmjeEigKuqVaum8ePH598HWYx07dpVAwcO1IIFCzJj6enpmjp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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's remove duplets and let's look at network modularity and genetic overlap\n",
    "import scipy\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy import stats\n",
    "\n",
    "tot_communities_overlap={**fine_grained_communities_overlap,**middle_communities_overlap,**broad_communities_overlap}\n",
    "density_com_list=[]\n",
    "tot_com_overlap_in_list=[]\n",
    "\n",
    "fine_grained_overlap_in_list=[]\n",
    "fine_grained_density_com_list=[]\n",
    "for com,res_list in tot_communities_overlap.items():\n",
    "    if com in fine_grained_communities_dict.keys():\n",
    "        if len(fine_grained_communities_dict[com])>2:\n",
    "            fine_grained_overlap_in_list.append(res_list[0])\n",
    "            fine_grained_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,fine_grained_communities_dict[com]))\n",
    "            tot_com_overlap_in_list.append(res_list[0])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,fine_grained_communities_dict[com]))\n",
    "\n",
    "\n",
    "middle_overlap_in_list=[]\n",
    "middle_density_com_list=[]\n",
    "for com,res_list in tot_communities_overlap.items():\n",
    "    if com in middle_communities_dict.keys():\n",
    "        if len(middle_communities_dict[com])>2:\n",
    "            middle_overlap_in_list.append(res_list[0])\n",
    "            middle_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,middle_communities_dict[com])) \n",
    "            tot_com_overlap_in_list.append(res_list[0])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,middle_communities_dict[com])) \n",
    "    \n",
    "    \n",
    "broad_overlap_in_list=[]\n",
    "broad_density_com_list=[]\n",
    "for com,res_list in tot_communities_overlap.items():\n",
    "    if com in broad_communities_dict.keys():\n",
    "        if len(broad_communities_dict[com])>2:\n",
    "            broad_overlap_in_list.append(res_list[0])\n",
    "            broad_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,broad_communities_dict[com])) \n",
    "            tot_com_overlap_in_list.append(res_list[0])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,broad_communities_dict[com])) \n",
    "    \n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "slope, intercept, r, p, stderr = scipy.stats.linregress(density_com_list, tot_com_overlap_in_list)\n",
    "\n",
    "ax.plot(fine_grained_density_com_list,fine_grained_overlap_in_list, linewidth=0, marker='s',c='#D8C4C5', label='Fine-grained')\n",
    "ax.plot(middle_density_com_list,middle_overlap_in_list, linewidth=0, marker='s',c='#A47179', label='Intermediate')\n",
    "ax.plot(broad_density_com_list,broad_overlap_in_list, linewidth=0, marker='s',c='#7A2D45', label='Broad')\n",
    "\n",
    "y=[]\n",
    "for i in density_com_list:\n",
    "    g=(intercept + slope * i)\n",
    "    y.append(g)\n",
    "line = \"Regression line\" \n",
    "\n",
    "ax.plot(density_com_list, y,c='red', label=line)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "plt.xticks(fontsize=36) \n",
    "plt.yticks(fontsize=36) \n",
    "\n",
    "ax.legend(facecolor='white',fontsize=36)\n",
    "plt.savefig('output/Figures/Figure2C_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0407c593",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.7812088075487331, pvalue=5.0429754454190797e-82)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.pearsonr(density_com_list, tot_com_overlap_in_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2207c7ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy import stats\n",
    "#Let's create two lists that will containing the genetic overlap and the resnik values for these communities\n",
    "\n",
    "avg_overlap_in_com=[]\n",
    "avg_resnik_in_com=[]\n",
    "for com,overlap_list in tot_communities_overlap.items():\n",
    "    avg_overlap_in_com.append(overlap_list[0])\n",
    "    avg_resnik_in_com.append(tot_communities_resnik[com][0])\n",
    "\n",
    "column_names = [\"Resnik\", \"Overlap\",'Group']\n",
    "\n",
    "geneoverlap_resnik_df = pd.DataFrame(columns = column_names)\n",
    "geneoverlap_resnik_df['Resnik']=avg_resnik_in_com\n",
    "geneoverlap_resnik_df['Overlap']=avg_overlap_in_com\n",
    "\n",
    "\n",
    "geneoverlap_resnik_df_sort_by_overlap=geneoverlap_resnik_df.sort_values(\"Overlap\")\n",
    "\n",
    "cat_list=[]\n",
    "for i in range(len(geneoverlap_resnik_df_sort_by_overlap)):\n",
    "    if i<len(geneoverlap_resnik_df_sort_by_overlap)/5:\n",
    "        cat_list.append(\"1st quintile\")\n",
    "    elif i>len(geneoverlap_resnik_df_sort_by_overlap)/5 and i<2*len(geneoverlap_resnik_df_sort_by_overlap)/5:\n",
    "        cat_list.append(\"2nd quintile\")\n",
    "    elif i>2*len(geneoverlap_resnik_df_sort_by_overlap)/5 and i<3*len(geneoverlap_resnik_df_sort_by_overlap)/5:\n",
    "        cat_list.append(\"3rd quintile\")\n",
    "    elif i>3*len(geneoverlap_resnik_df_sort_by_overlap)/5 and i<4*len(geneoverlap_resnik_df_sort_by_overlap)/5:\n",
    "        cat_list.append(\"4th quintile\")\n",
    "    else:\n",
    "        cat_list.append(\"5th quintile\")\n",
    "        \n",
    "geneoverlap_resnik_df_sort_by_overlap[\"Group\"]=cat_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6fa5ae11",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_1213/938415392.py:15: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  g=sns.boxplot(data=geneoverlap_resnik_df_sort_by_overlap,x=\"Group\", y=\"Resnik\",order=['1st quintile','2nd quintile', '3rd quintile','4th quintile',\n",
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_1213/938415392.py:21: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([int(i) for i in ax.get_yticks()], size = 36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's visualize as boxplot\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) \n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "\n",
    "sns.set_style('white')\n",
    "sns.set(style=\"white\",font_scale = 2)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "g=sns.boxplot(data=geneoverlap_resnik_df_sort_by_overlap,x=\"Group\", y=\"Resnik\",order=['1st quintile','2nd quintile', '3rd quintile','4th quintile',\n",
    " '5th quintile'],palette=\"Blues_d\",linewidth=5)\n",
    "plt.xlabel('',fontsize=22)\n",
    "plt.ylabel('', fontsize=22)\n",
    "#plt.xticks([], fontsize=22)\n",
    "ax.grid(False)\n",
    "ax.set_yticklabels([int(i) for i in ax.get_yticks()], size = 36)\n",
    "#ax.set_xticklabels(ax.get_xticks(), size = 36)\n",
    "plt.savefig('output/Figures/SF5E_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "#g.set_yscale(\"log\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eabab702",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.26913109842228816, pvalue=4.611347212301309e-10)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.pearsonr(avg_overlap_in_com, avg_resnik_in_com))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a718d41d-2da0-46b9-a5ba-a5d8a61931fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa85a243-70b2-4ebf-a96c-a09d287c135e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "564b607b-d525-4cfc-bea1-9d26343912ed",
   "metadata": {},
   "source": [
    "### Infomap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f28fc8a8-3692-414a-bdab-052d5cc48fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's import the infomap communities\n",
    "\n",
    "with open('output/Communities/Infomap/ji_infomap_fine_grained_communities_dict.pickle', 'rb') as handle:\n",
    "    fine_grained_communities_dict = pk.load(handle)\n",
    "with open('output/Communities/Infomap/ji_infomap_middle_communities_dict.pickle', 'rb') as handle:\n",
    "    middle_communities_dict = pk.load(handle)\n",
    "with open('output/Communities/Infomap/ji_infomap_broad_communities_dict.pickle', 'rb') as handle:\n",
    "    broad_communities_dict = pk.load(handle)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "165535df-721a-4908-9f6f-3184204c7e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we are evaluating the resnik score across exposures for the finest level of hierarchy in louvain\n",
    "#distinguishing the exposures that are part of the same community and those ones that are not part\n",
    "fine_grained_communities_resnik={}\n",
    "\n",
    "for com,exp_list in fine_grained_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            resnik_list_in=[]\n",
    "            resnik_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            resnik_list_out.append(tot_resnik[exp1,exp2])\n",
    "                        except:\n",
    "                            resnik_list_out.append(tot_resnik[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_resnik_in=sum(resnik_list_in)/len(resnik_list_in)\n",
    "            avg_resnik_out=sum(resnik_list_out)/len(resnik_list_out)\n",
    "            fine_grained_communities_resnik[com]=[avg_resnik_in,avg_resnik_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "            \n",
    "middle_communities_resnik={}\n",
    "\n",
    "for com,exp_list in middle_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            resnik_list_in=[]\n",
    "            resnik_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            resnik_list_out.append(tot_resnik[exp1,exp2])\n",
    "                        except:\n",
    "                            resnik_list_out.append(tot_resnik[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_resnik_in=sum(resnik_list_in)/len(resnik_list_in)\n",
    "            avg_resnik_out=sum(resnik_list_out)/len(resnik_list_out)\n",
    "            middle_communities_resnik[com]=[avg_resnik_in,avg_resnik_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "broad_communities_resnik={}\n",
    "\n",
    "for com,exp_list in broad_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            resnik_list_in=[]\n",
    "            resnik_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        resnik_list_in.append(tot_resnik[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            resnik_list_out.append(tot_resnik[exp1,exp2])\n",
    "                        except:\n",
    "                            resnik_list_out.append(tot_resnik[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_resnik_in=sum(resnik_list_in)/len(resnik_list_in)\n",
    "            avg_resnik_out=sum(resnik_list_out)/len(resnik_list_out)\n",
    "            broad_communities_resnik[com]=[avg_resnik_in,avg_resnik_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57a28d6c-d077-4593-94ef-3cc58de4fdff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "tot_communities_resnik = {**fine_grained_communities_resnik,**middle_communities_resnik, **broad_communities_resnik}\n",
    "\n",
    "avg_resnik_in_infomap=[]\n",
    "avg_resnik_out_infomap=[]\n",
    "\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    avg_resnik_in_infomap.append(float(res_list[0]))\n",
    "    avg_resnik_out_infomap.append(float(res_list[1]))\n",
    "\n",
    "state_resnik=[]\n",
    "for i in avg_resnik_in_infomap:\n",
    "    state_resnik.append(\"Within a infomap community\")\n",
    "for i in avg_resnik_out_infomap:\n",
    "    state_resnik.append(\"Between infomap communities\")\n",
    "\n",
    "\n",
    "tot_com_resnik_in_out_df=pd.DataFrame()\n",
    "\n",
    "tot_com_resnik_in_out_df['Resnik']=avg_resnik_in_infomap+avg_resnik_out_infomap\n",
    "\n",
    "tot_com_resnik_in_out_df['Comparison']=state_resnik\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "#plt.xlabel('Resnik',fontsize=18)\n",
    "#plt.ylabel('Frequency', fontsize=18)\n",
    "\n",
    "#plt.title('Resnik distribution of total genes in each hierarchical level of clustering of EEN', fontsize=28)\n",
    "g=sns.histplot(\n",
    "    tot_com_resnik_in_out_df, x=\"Resnik\", hue=\"Comparison\", element=\"step\",palette='husl',\n",
    "    stat=\"density\",bins=22,binrange=(0,14)\n",
    ")\n",
    "plt.xticks(fontsize=36) \n",
    "plt.yticks(fontsize=36) \n",
    "\n",
    "g.set(xlabel=None)\n",
    "g.set(ylabel=None)\n",
    "g.set(yscale=\"log\")\n",
    "plt.setp(ax.get_legend().get_texts(), fontsize='36') # for legend text\n",
    "plt.setp(ax.get_legend().get_title(), fontsize='36') # for legend title\n",
    "plt.savefig('output/Figures/SF16F_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\", transparent=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c0969dea-8292-441a-82d8-eac2e29eaebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7786338440966404e-37"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's run the T-test between the two distributions\n",
    "from scipy import stats\n",
    "t_value,p_value=stats.ttest_ind(avg_resnik_in_infomap,avg_resnik_out_infomap)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "50f2e70d-737a-4f93-a910-1df33df67e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.373879798078871, 1.7786338440966404e-37, 0.8705654232501644)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's calculate the Cohen's d as an effect size\n",
    "def calculate_ttest_and_effect_size(list1, list2):\n",
    "    from scipy import stats\n",
    "    import numpy as np\n",
    "    t_value, p_value = stats.ttest_ind(list1, list2)\n",
    "\n",
    "    n1 = len(list1)\n",
    "    n2 = len(list2)\n",
    "    dof = n1 + n2 - 2\n",
    "\n",
    "    mean1 = np.mean(list1)\n",
    "    mean2 = np.mean(list2)\n",
    "    var1 = np.var(list1, ddof=1)\n",
    "    var2 = np.var(list2, ddof=1)\n",
    "\n",
    "    pooled_var = ((n1 - 1) * var1 + (n2 - 1) * var2) / dof\n",
    "    effect_size = (mean1 - mean2) / np.sqrt(pooled_var)\n",
    "\n",
    "    return t_value, p_value, effect_size\n",
    "\n",
    "calculate_ttest_and_effect_size(avg_resnik_in_infomap,avg_resnik_out_infomap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5f162ebe-e400-45e2-a831-1fe89b08500e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "finegrained_avg_resnik_in_out_ratio_infomap=[]\n",
    "middle_avg_resnik_in_out_ratio_infomap=[]\n",
    "broad_avg_resnik_in_out_ratio_infomap=[]\n",
    "state_resnik=[]\n",
    "\n",
    "for com,res_list in fine_grained_communities_resnik.items():\n",
    "    finegrained_avg_resnik_in_out_ratio_infomap.append(float(res_list[0]))\n",
    "    state_resnik.append(\"Fine-grained\")\n",
    "    \n",
    "for com,res_list in middle_communities_resnik.items():\n",
    "    middle_avg_resnik_in_out_ratio_infomap.append(float(res_list[0]))\n",
    "    state_resnik.append(\"Intermediate\")\n",
    "\n",
    "for com,res_list in broad_communities_resnik.items():\n",
    "    broad_avg_resnik_in_out_ratio_infomap.append(float(res_list[0]))\n",
    "    state_resnik.append(\"Broad\")\n",
    "    \n",
    "\n",
    "\n",
    "tot_com_resnik_in_out_df=pd.DataFrame()\n",
    "\n",
    "tot_com_resnik_in_out_df['Resnik']=finegrained_avg_resnik_in_out_ratio_infomap+middle_avg_resnik_in_out_ratio_infomap +broad_avg_resnik_in_out_ratio_infomap\n",
    "\n",
    "tot_com_resnik_in_out_df['Comparison']=state_resnik\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2ef4b181-9000-49cb-bdec-4ee30239fa04",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_34128/3869115118.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  g=sns.boxplot(x=tot_com_resnik_in_out_df[\"Comparison\"], y=tot_com_resnik_in_out_df[\"Resnik\"], palette=\"Blues_d\", linewidth=5)\n",
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_34128/3869115118.py:15: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([int(i) for i in list(ax.get_yticks())], size = 36)\n",
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_34128/3869115118.py:17: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(labels, size=36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "g=sns.boxplot(x=tot_com_resnik_in_out_df[\"Comparison\"], y=tot_com_resnik_in_out_df[\"Resnik\"], palette=\"Blues_d\", linewidth=5)\n",
    "\n",
    "ax.set_xlabel(\"\",fontsize=32)\n",
    "ax.set_ylabel(\"\",fontsize=32)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.set_yticklabels([int(i) for i in list(ax.get_yticks())], size = 36)\n",
    "labels = [\"Fine-grained\", \"Intermediate\", \"Broad\"]  # Modified labels\n",
    "ax.set_xticklabels(labels, size=36)\n",
    "#plt.savefig('output/Figures/SF19A_v00.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7f3c083e-144c-45c8-841c-fd0c4652b2e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17314916713392736"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "\n",
    "\n",
    "t_value,p_value=stats.ttest_ind(finegrained_avg_resnik_in_out_ratio_infomap,middle_avg_resnik_in_out_ratio_infomap)\n",
    "\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7bb205fa-3265-4746-91a9-767493a71617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13230028388877504"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "\n",
    "\n",
    "t_value,p_value=stats.ttest_ind(finegrained_avg_resnik_in_out_ratio_infomap,broad_avg_resnik_in_out_ratio_infomap)\n",
    "\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eab0f4fd-fe7b-4d61-865f-4ea9ba099cc9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5b521c4b-6223-4413-b815-7f4cad7e5374",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_density_LSD(network, group):\n",
    "    # Create a subnetwork of the total network\n",
    "    G_subgraph = network.subgraph(group)\n",
    "    # Calculate the density of that subnetwork\n",
    "    density = nx.density(G_subgraph)\n",
    "    return density\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dee45210-59cb-4216-96f7-839a3a523928",
   "metadata": {},
   "outputs": [],
   "source": [
    "tot_com_dict={**fine_grained_communities_dict,**middle_communities_dict, **broad_communities_dict}\n",
    "\n",
    "\n",
    "tot_com_resnik_in_out_list=[]\n",
    "density_com_list=[]\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    tot_com_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "    density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,tot_com_dict[com]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "48196a66-0d3a-4c72-9303-cfc83715da56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's remove duplets\n",
    "import scipy\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy import stats\n",
    "\n",
    "density_com_list=[]\n",
    "tot_com_resnik_in_out_list=[]\n",
    "\n",
    "fine_grained_resnik_in_out_list=[]\n",
    "fine_grained_density_com_list=[]\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    if com in fine_grained_communities_dict.keys():\n",
    "        if len(fine_grained_communities_dict[com])>2:\n",
    "            fine_grained_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            fine_grained_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,fine_grained_communities_dict[com]))\n",
    "            tot_com_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,fine_grained_communities_dict[com]))\n",
    "\n",
    "middle_resnik_in_out_list=[]\n",
    "middle_density_com_list=[]\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    if com in middle_communities_dict.keys():\n",
    "        if len(middle_communities_dict[com])>2:\n",
    "            middle_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            middle_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,middle_communities_dict[com])) \n",
    "            tot_com_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,middle_communities_dict[com])) \n",
    "    \n",
    "broad_resnik_in_out_list=[]\n",
    "broad_density_com_list=[]\n",
    "for com,res_list in tot_communities_resnik.items():\n",
    "    if com in broad_communities_dict.keys():\n",
    "        if len(broad_communities_dict[com])>2:\n",
    "            broad_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            broad_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,broad_communities_dict[com])) \n",
    "            tot_com_resnik_in_out_list.append(res_list[0]/res_list[1])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,broad_communities_dict[com])) \n",
    "    \n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "slope, intercept, r, p, stderr = scipy.stats.linregress(density_com_list, tot_com_resnik_in_out_list)\n",
    "\n",
    "ax.plot(fine_grained_density_com_list,fine_grained_resnik_in_out_list, linewidth=0, marker='s',c='#D8C4C5', label='Fine-grained')\n",
    "ax.plot(middle_density_com_list,middle_resnik_in_out_list, linewidth=0, marker='s',c='#A47179', label='Intermediate')\n",
    "ax.plot(broad_density_com_list,broad_resnik_in_out_list, linewidth=0, marker='s',c='#7A2D45', label='Broad')\n",
    "\n",
    "\n",
    "\n",
    "y=[]\n",
    "for i in density_com_list:\n",
    "    g=(intercept + slope * i)\n",
    "    y.append(g)\n",
    "line = \"Regression line\" \n",
    "\n",
    "ax.plot(density_com_list, y,c='red', label=line)\n",
    "#ax.set_xlabel('Average biological similarity', fontsize=18)\n",
    "#ax.set_ylabel('Average chemical similarity',fontsize=18)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "plt.xticks(fontsize=36) \n",
    "plt.yticks(fontsize=36) \n",
    "\n",
    "# plt.title('Correlation between Resnik and Genetic overlap in %s' %met )\n",
    "ax.legend(facecolor='white',fontsize=36)\n",
    "plt.savefig('output/Figures/SF16D_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dffcc85e-d907-4a37-9ea5-df7302de6dd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.19607728678106903, pvalue=0.00015667415057980168)\n",
      "SignificanceResult(statistic=0.30451658757310196, pvalue=2.587492728580625e-09)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.pearsonr(density_com_list,tot_com_resnik_in_out_list))  # Pearson's r\n",
    "print(scipy.stats.spearmanr(density_com_list,tot_com_resnik_in_out_list)) # Spearman's rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d01cea8e-bed4-4272-a9af-56b7503bd3af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6bedafd-b4c2-4b0e-b453-4b8034dc152b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "958d8b81-d69c-423d-a3e6-da58c0fc84e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we are evaluating the resnik score across exposures for the finest level of hierarchy in louvain\n",
    "#distinguishing the exposures that are part of the same community and those ones that are not part\n",
    "fine_grained_communities_overlap={}\n",
    "\n",
    "for com,exp_list in fine_grained_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            overlap_list_in=[]\n",
    "            overlap_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            overlap_list_out.append(tot_overlap[exp1,exp2])\n",
    "                        except:\n",
    "                            overlap_list_out.append(tot_overlap[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_overlap_in=sum(overlap_list_in)/len(overlap_list_in)\n",
    "            avg_overlap_out=sum(overlap_list_out)/len(overlap_list_out)\n",
    "            fine_grained_communities_overlap[com]=[avg_overlap_in,avg_overlap_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "            \n",
    "middle_communities_overlap={}\n",
    "\n",
    "for com,exp_list in middle_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            overlap_list_in=[]\n",
    "            overlap_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            overlap_list_out.append(tot_overlap[exp1,exp2])\n",
    "                        except:\n",
    "                            overlap_list_out.append(tot_overlap[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_overlap_in=sum(overlap_list_in)/len(overlap_list_in)\n",
    "            avg_overlap_out=sum(overlap_list_out)/len(overlap_list_out)\n",
    "            middle_communities_overlap[com]=[avg_overlap_in,avg_overlap_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "broad_communities_overlap={}\n",
    "\n",
    "for com,exp_list in broad_communities_dict.items():\n",
    "    try:\n",
    "        if len(exp_list)>1:\n",
    "            overlap_list_in=[]\n",
    "            overlap_list_out=[]\n",
    "            pairwise_list = list(it.combinations(exp_list, 2))\n",
    "            for i in range(len(pairwise_list)):\n",
    "                try:\n",
    "                    try:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][0],pairwise_list[i][1]])\n",
    "                    except:\n",
    "                        overlap_list_in.append(tot_overlap[pairwise_list[i][1],pairwise_list[i][0]])\n",
    "                except:\n",
    "                    pass\n",
    "            not_exp_com_list=[]\n",
    "            for exp in backbone_ss_exposure_network.nodes():\n",
    "                if exp not in exp_list:\n",
    "                    not_exp_com_list.append(exp)\n",
    "                else:\n",
    "                    pass\n",
    "            for exp1 in exp_list:\n",
    "                for exp2 in not_exp_com_list:\n",
    "                    try:\n",
    "                        try:\n",
    "                            overlap_list_out.append(tot_overlap[exp1,exp2])\n",
    "                        except:\n",
    "                            overlap_list_out.append(tot_overlap[exp2,exp1])\n",
    "                    except:\n",
    "                        pass\n",
    "            avg_overlap_in=sum(overlap_list_in)/len(overlap_list_in)\n",
    "            avg_overlap_out=sum(overlap_list_out)/len(overlap_list_out)\n",
    "            broad_communities_overlap[com]=[avg_overlap_in,avg_overlap_out]\n",
    "        else:\n",
    "            pass\n",
    "    except:\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6017cc9a-d8b1-4f5e-a459-69e71b22febb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's remove duplets and let's look at network modularity and genetic overlap\n",
    "import scipy\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy import stats\n",
    "\n",
    "tot_communities_overlap={**fine_grained_communities_overlap,**middle_communities_overlap,**broad_communities_overlap}\n",
    "density_com_list=[]\n",
    "tot_com_overlap_in_list=[]\n",
    "\n",
    "fine_grained_overlap_in_list=[]\n",
    "fine_grained_density_com_list=[]\n",
    "for com,res_list in tot_communities_overlap.items():\n",
    "    if com in fine_grained_communities_dict.keys():\n",
    "        if len(fine_grained_communities_dict[com])>2:\n",
    "            fine_grained_overlap_in_list.append(res_list[0])\n",
    "            fine_grained_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,fine_grained_communities_dict[com]))\n",
    "            tot_com_overlap_in_list.append(res_list[0])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,fine_grained_communities_dict[com]))\n",
    "\n",
    "\n",
    "middle_overlap_in_list=[]\n",
    "middle_density_com_list=[]\n",
    "for com,res_list in tot_communities_overlap.items():\n",
    "    if com in middle_communities_dict.keys():\n",
    "        if len(middle_communities_dict[com])>2:\n",
    "            middle_overlap_in_list.append(res_list[0])\n",
    "            middle_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,middle_communities_dict[com])) \n",
    "            tot_com_overlap_in_list.append(res_list[0])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,middle_communities_dict[com])) \n",
    "    \n",
    "    \n",
    "broad_overlap_in_list=[]\n",
    "broad_density_com_list=[]\n",
    "for com,res_list in tot_communities_overlap.items():\n",
    "    if com in broad_communities_dict.keys():\n",
    "        if len(broad_communities_dict[com])>2:\n",
    "            broad_overlap_in_list.append(res_list[0])\n",
    "            broad_density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,broad_communities_dict[com])) \n",
    "            tot_com_overlap_in_list.append(res_list[0])\n",
    "            density_com_list.append(calculate_density_LSD(backbone_ss_exposure_network,broad_communities_dict[com])) \n",
    "    \n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "slope, intercept, r, p, stderr = scipy.stats.linregress(density_com_list, tot_com_overlap_in_list)\n",
    "\n",
    "ax.plot(fine_grained_density_com_list,fine_grained_overlap_in_list, linewidth=0, marker='s',c='#D8C4C5', label='Fine-grained')\n",
    "ax.plot(middle_density_com_list,middle_overlap_in_list, linewidth=0, marker='s',c='#A47179', label='Intermediate')\n",
    "ax.plot(broad_density_com_list,broad_overlap_in_list, linewidth=0, marker='s',c='#7A2D45', label='Broad')\n",
    "\n",
    "\n",
    "y=[]\n",
    "for i in density_com_list:\n",
    "    g=(intercept + slope * i)\n",
    "    y.append(g)\n",
    "line = \"Regression line\" \n",
    "\n",
    "ax.plot(density_com_list, y,c='red', label=line)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "plt.xticks(fontsize=36) \n",
    "plt.yticks(fontsize=36) \n",
    "\n",
    "ax.legend(facecolor='white',fontsize=36)\n",
    "plt.savefig('output/Figures/SF16C_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04a8a6ab-54a4-4d68-bbfa-a25ac375a6b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.8051953660347677, pvalue=7.611897955311582e-85)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.pearsonr(density_com_list, tot_com_overlap_in_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "714067ea-cc76-4cad-b523-490f3d6f0487",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy import stats\n",
    "#Let's create two lists that will containing the genetic overlap and the resnik values for these communities\n",
    "\n",
    "avg_overlap_in_com=[]\n",
    "avg_resnik_in_com=[]\n",
    "for com,overlap_list in tot_communities_overlap.items():\n",
    "    avg_overlap_in_com.append(overlap_list[0])\n",
    "    avg_resnik_in_com.append(tot_communities_resnik[com][0])\n",
    "\n",
    "column_names = [\"Resnik\", \"Overlap\",'Group']\n",
    "\n",
    "geneoverlap_resnik_df = pd.DataFrame(columns = column_names)\n",
    "geneoverlap_resnik_df['Resnik']=avg_resnik_in_com\n",
    "geneoverlap_resnik_df['Overlap']=avg_overlap_in_com\n",
    "\n",
    "\n",
    "geneoverlap_resnik_df_sort_by_overlap=geneoverlap_resnik_df.sort_values(\"Overlap\")\n",
    "\n",
    "cat_list=[]\n",
    "for i in range(len(geneoverlap_resnik_df_sort_by_overlap)):\n",
    "    if i<len(geneoverlap_resnik_df_sort_by_overlap)/5:\n",
    "        cat_list.append(\"1st quintile\")\n",
    "    elif i>len(geneoverlap_resnik_df_sort_by_overlap)/5 and i<2*len(geneoverlap_resnik_df_sort_by_overlap)/5:\n",
    "        cat_list.append(\"2nd quintile\")\n",
    "    elif i>2*len(geneoverlap_resnik_df_sort_by_overlap)/5 and i<3*len(geneoverlap_resnik_df_sort_by_overlap)/5:\n",
    "        cat_list.append(\"3rd quintile\")\n",
    "    elif i>3*len(geneoverlap_resnik_df_sort_by_overlap)/5 and i<4*len(geneoverlap_resnik_df_sort_by_overlap)/5:\n",
    "        cat_list.append(\"4th quintile\")\n",
    "    else:\n",
    "        cat_list.append(\"5th quintile\")\n",
    "        \n",
    "geneoverlap_resnik_df_sort_by_overlap[\"Group\"]=cat_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "627935ca-df52-4a54-b4ec-a14b761afb4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_62924/1439479375.py:15: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  g=sns.boxplot(data=geneoverlap_resnik_df_sort_by_overlap,x=\"Group\", y=\"Resnik\",order=['1st quintile','2nd quintile', '3rd quintile','4th quintile',\n",
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_62924/1439479375.py:21: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([int(i) for i in ax.get_yticks()], size = 36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's visualize as boxplot\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) \n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "\n",
    "sns.set_style('white')\n",
    "sns.set(style=\"white\",font_scale = 2)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "g=sns.boxplot(data=geneoverlap_resnik_df_sort_by_overlap,x=\"Group\", y=\"Resnik\",order=['1st quintile','2nd quintile', '3rd quintile','4th quintile',\n",
    " '5th quintile'],palette=\"Blues_d\",linewidth=5)\n",
    "plt.xlabel('',fontsize=22)\n",
    "plt.ylabel('', fontsize=22)\n",
    "#plt.xticks([], fontsize=22)\n",
    "ax.grid(False)\n",
    "ax.set_yticklabels([int(i) for i in ax.get_yticks()], size = 36)\n",
    "#ax.set_xticklabels(ax.get_xticks(), size = 36)\n",
    "#plt.savefig('output/Figures/SF7B_v03.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "#g.set_yscale(\"log\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6125226d-5bef-4d2f-9667-9161b9fe627d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.23964871122900497, pvalue=4.692919164357374e-08)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.pearsonr(avg_overlap_in_com, avg_resnik_in_com))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc0abfb6-5e86-4e04-9a9b-112b28638c63",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}