manuscript_shared_environment_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-28T18:14:14.860473Z",
     "start_time": "2023-11-28T18:14:14.845286Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import pymysql\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from collections import defaultdict\n",
    "import paramiko\n",
    "\n",
    "\n",
    "from sshtunnel import SSHTunnelForwarder\n",
    "from collections import defaultdict\n",
    "%matplotlib inline  \n",
    "import seaborn as sns\n",
    "import gzip\n",
    "\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "#All results used in figures output in a single file\n",
    "figure_results = '/home/kel2158/Projects/COVIDHerit/figures/sharedenv_figuredate.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:13.619709Z",
     "start_time": "2023-11-03T20:27:13.608820Z"
    }
   },
   "outputs": [],
   "source": [
    "def return_sign_in(login_loc = '/home/kel2158/login.txt'):\n",
    "    login_dict = dict()\n",
    "    with open(login_loc, \"r\") as file:\n",
    "        for line in file.readlines():\n",
    "            login_dict[ line.split(\",\")[0]] = line.split(\",\")[1].strip()\n",
    "    return(login_dict)\n",
    "\n",
    "login_dict = return_sign_in()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:25:04.174997Z",
     "start_time": "2023-11-03T20:25:04.060687Z"
    }
   },
   "outputs": [],
   "source": [
    "#Technically 2 binary outcomes (covid, negative covid OR hosp/not )\n",
    "limit_to_covid = 1\n",
    "\n",
    "database = \"user_kel2158\"\n",
    "hhid_folder = '/home/kel2158/Projects/hhid_update/data/'\n",
    "base_pedigree_folder = '/home/kel2158/Projects/Heritability_Base_Data/pedigree_v3_updated_results/'\n",
    "\n",
    "#Figure output directory\n",
    "figure_directory = '/home/kel2158/Projects/COVIDHerit/figures'\n",
    "\n",
    "#Input files\n",
    "filename_hhid =  hhid_folder+'hhid_trimmed_notcln.txt.gz'\n",
    "filename_buildingid =  hhid_folder+'buildid_notcln.txt.gz'\n",
    "finename_fam_input = base_pedigree_folder+ \"family_v3.txt.gz\"\n",
    "\n",
    "output_folder= '/home/kel2158/Projects/COVIDHerit/data/12_29_21/'\n",
    "covid_nopro_file = output_folder+'covid_noproband.txt.gz'\n",
    "covid_file = output_folder+'covid.txt.gz'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:16.187302Z",
     "start_time": "2023-11-03T20:27:15.668413Z"
    }
   },
   "outputs": [],
   "source": [
    "server = SSHTunnelForwarder(\n",
    "    '10.144.220.21',\n",
    "    ssh_username='kel2158',\n",
    "    ssh_password=login_dict['mimir_pw'],\n",
    "    remote_bind_address=('127.0.0.1', 3306)\n",
    ")\n",
    "server.start()\n",
    "\n",
    "db = pymysql.connect(\n",
    "    host='127.0.0.1',\n",
    "    port=server.local_bind_port,\n",
    "    user=login_dict['user'],\n",
    "    password=login_dict[\"db_pw\"],\n",
    "    db=database\n",
    ")\n",
    "\n",
    "cur = db.cursor()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:17.208684Z",
     "start_time": "2023-11-03T20:27:17.193976Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute( \"SET sql_mode=(SELECT REPLACE(@@sql_mode,'ONLY_FULL_GROUP_BY',''));\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:18.486815Z",
     "start_time": "2023-11-03T20:27:17.636433Z"
    }
   },
   "outputs": [],
   "source": [
    "#Read in family file \n",
    "file_name = finename_fam_input\n",
    "gfile = gzip.open(file_name)\n",
    "\n",
    "fam_df = pd.read_csv(gfile,sep='\\t')\n",
    "mrn2famid = dict(zip (fam_df[\"ptid\"],fam_df[\"famid\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:20.008211Z",
     "start_time": "2023-11-03T20:27:18.964106Z"
    }
   },
   "outputs": [],
   "source": [
    "#Read in hhid file (apt level)\n",
    "file_name = filename_hhid\n",
    "gfile = gzip.open(file_name)\n",
    "hhid_df = pd.read_csv(gfile,sep='\\t')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:33.148626Z",
     "start_time": "2023-11-03T20:27:20.012737Z"
    }
   },
   "outputs": [],
   "source": [
    "#Read in buildingID file (apt level)\n",
    "file_name = filename_buildingid\n",
    "gfile = gzip.open(file_name)\n",
    "build_df = pd.read_csv(gfile,sep='\\t')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:33.212533Z",
     "start_time": "2023-11-03T20:27:33.156875Z"
    }
   },
   "outputs": [],
   "source": [
    "#Read in time to first pcr trait file \n",
    "file_name = covid_nopro_file\n",
    "gfile = gzip.open(file_name)\n",
    "covnopro_df = pd.read_csv(gfile,sep='\\t')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:42.014793Z",
     "start_time": "2023-11-03T20:27:33.216901Z"
    }
   },
   "outputs": [],
   "source": [
    "#Make relevant \n",
    "mrn2hhid = dict( zip( hhid_df[\"mrn\"], hhid_df[\"trimmed_hhid\"] ) )\n",
    "mrn2buildid = dict( zip( build_df[\"mrn\"], build_df[\"building_id\"] ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:43.406693Z",
     "start_time": "2023-11-03T20:27:42.019403Z"
    }
   },
   "outputs": [],
   "source": [
    "#Equivalent to first_pcr_nohospreq_df in pull pheno script\n",
    "\n",
    "#Get earliest PCR! Positive covid test for subsetting on patients that have\n",
    "#gotten infected within 30 days of relative's infection\n",
    "pull_data_query = \"\"\" SELECT DISTINCT pat_mrn_id, MIN(order_time) AS order_time, MAX(order_time)\n",
    "                      FROM user_kel2158.covid_lab_table\n",
    "                      WHERE test_result = \"pos\" \n",
    "                    GROUP BY pat_mrn_id;  \"\"\"\n",
    "\n",
    "cur.execute(pull_data_query)\n",
    "rawdata = cur.fetchall()\n",
    "\n",
    "db.close()\n",
    "server.stop()\n",
    "\n",
    "\n",
    "pheno_df = pd.DataFrame(list( rawdata ), columns=[\"mrn\",\n",
    "                           \"order_time\",\"latest_pcr_order_time\"\n",
    "                           ] )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:43.425289Z",
     "start_time": "2023-11-03T20:27:43.410076Z"
    }
   },
   "outputs": [],
   "source": [
    "def pull_fam_count( pheno, df, print_res = True, \\\n",
    "                   return_fams_df=False, type_pheno=\"binary_withproband\",\n",
    "                   return_patient_limited_df = False ):\n",
    "    \n",
    "    \"\"\"Returns the number of families in a phenotype for SOLAR based on pheno \n",
    "     Needs phenotype column, famid column and ptid column\"\"\"    \n",
    "    \n",
    "    #subset on phenotype of interest and those in our pedigree\n",
    "    df_phenosub = df[ (df[\"pheno\"] == pheno ) \n",
    "                       &  (~df[\"famid\"].isnull())] \n",
    "    \n",
    "    #Get the total number of  members in each fam and then number of cases\n",
    "    df_phenogrouped = df_phenosub.groupby( 'famid' )\\\n",
    "        .agg({ \"ptid\":\"count\",\"value\":sum })\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns={ \"ptid\":\"num_fam_members\",\"value\":\"num_cases\" })\n",
    "    \n",
    "    #Subset to fams that will be included in SOLAR\n",
    "    #ie has more than one family member an at least one case in each family\n",
    "    if type_pheno == \"binary_withproband\":\n",
    "        solar_fams_df = df_phenogrouped[ (df_phenogrouped[\"num_fam_members\"] > 1) \\\n",
    "                                        & (df_phenogrouped[\"num_cases\"] > 0) ]\n",
    "    \n",
    "    else:\n",
    "         solar_fams_df = df_phenogrouped[ (df_phenogrouped[\"num_fam_members\"] > 1)]\n",
    "    \n",
    "    solar_limited_df = df_phenosub[ df_phenosub[\"famid\"].isin(solar_fams_df[\"famid\"].unique())]\n",
    "    solar_limited_df.drop_duplicates(inplace=True)\n",
    "    solar_patients = set( solar_limited_df[\"ptid\"] )\n",
    "    \n",
    "    \n",
    "    \n",
    "    if print_res:\n",
    "            print(f'There are {len(solar_fams_df)} SOLAR families')\n",
    "    \n",
    "    if return_fams_df:\n",
    "        return solar_fams_df, solar_patients\n",
    "    \n",
    "    elif return_patient_limited_df == True:\n",
    "        return solar_limited_df\n",
    "    \n",
    "    else:\n",
    "        return(len( solar_fams_df) )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:43.555000Z",
     "start_time": "2023-11-03T20:27:43.428876Z"
    }
   },
   "outputs": [],
   "source": [
    "pheno_df.rename(columns={\"mrn\":\"ptid\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:43.675112Z",
     "start_time": "2023-11-03T20:27:43.558470Z"
    }
   },
   "outputs": [],
   "source": [
    "#Add in controls\n",
    "pheno_df = pd.concat([ pheno_df, covnopro_df[covnopro_df[\"value\"] == 0]])\n",
    "\n",
    "pheno_df[\"value\"].fillna(1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:56.820523Z",
     "start_time": "2023-11-03T20:27:43.678144Z"
    }
   },
   "outputs": [],
   "source": [
    "pheno_df[\"pheno\"] = \"covid_noproband\"\n",
    "pheno_df[\"famid\"] = pheno_df[\"ptid\"].map(mrn2famid)\n",
    "pheno_df[\"hhid\"] = pheno_df[\"ptid\"].map(mrn2hhid)\n",
    "pheno_df[\"buildid\"] = pheno_df[\"ptid\"].map(mrn2buildid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:56.985537Z",
     "start_time": "2023-11-03T20:27:56.829405Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 5676 SOLAR families\n"
     ]
    }
   ],
   "source": [
    "#Subset to families used by solar (ie at least 2 patients per family)\n",
    "\n",
    "solar_nopro_df = pull_fam_count(\"covid_noproband\", pheno_df,\\\n",
    "                                return_patient_limited_df=True,type_pheno=\"no_proband\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:56.995018Z",
     "start_time": "2023-11-03T20:27:56.988017Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize a list to keep track of all figures\n",
    "all_figures = []\n",
    "\n",
    "# Create a function to save a figure and add it to the list\n",
    "def save_and_track_figure(fig, filename, save_directory = figure_directory):\n",
    "    # Combine the save_directory and filename to create the full path\n",
    "    save_path = os.path.join(save_directory, filename)\n",
    "    \n",
    "    fig.patch.set_facecolor('white')\n",
    "    # Save the figure with the full path\n",
    "    fig.savefig(save_path, dpi=300, bbox_inches=\"tight\")\n",
    "    \n",
    "    # Append the saved file path to the list of saved figures\n",
    "    all_figures.append(save_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Shared Environment Groupings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-06T14:22:55.281853Z",
     "start_time": "2023-11-06T14:22:54.624975Z"
    }
   },
   "outputs": [],
   "source": [
    "#Limit to cases\n",
    "pheno_cases_df = solar_nopro_df[ solar_nopro_df[\"value\"] == 1 ]\n",
    "\n",
    "group_dict = defaultdict(list)\n",
    "\n",
    "samplesize_groupings_dict = dict()\n",
    "\n",
    "for bubble in [\"famid\",\"buildid\",\"hhid\"]:\n",
    "    \n",
    "    time_from_last_pos = list()\n",
    "    groups = pheno_cases_df.groupby(bubble)\n",
    "    \n",
    "    patients_n = 0\n",
    "    \n",
    "    for num,group in enumerate(groups):\n",
    "        \n",
    "        df = group[1]\n",
    "        #If there is more than one individual in the group\n",
    "        if len(df) > 1:\n",
    "            \n",
    "            patients_n += len(df)\n",
    "            \n",
    "            df.sort_values(by=\"order_time\",inplace=True)\n",
    "            counter = 0\n",
    "\n",
    "            #For each first positive pcr test for a patient\n",
    "            for subsequent_pcr_time in df[\"order_time\"]:\n",
    "\n",
    "                #If first in group continue\n",
    "                if counter == 0:\n",
    "                    counter += 1\n",
    "                    last_pcr_time = subsequent_pcr_time\n",
    "                    continue\n",
    "                \n",
    "                #If not first pos test get time from last group member's test\n",
    "                else:\n",
    "                    time_diff = subsequent_pcr_time - last_pcr_time\n",
    "                    time_from_last_pos.append(time_diff.days)\n",
    "                    counter += 1\n",
    "\n",
    "                    last_pcr_time = subsequent_pcr_time\n",
    "    \n",
    "    group_dict[bubble] = time_from_last_pos\n",
    "    samplesize_groupings_dict[bubble] = patients_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-06T14:22:56.477643Z",
     "start_time": "2023-11-06T14:22:56.469144Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'famid': 485, 'buildid': 519, 'hhid': 269}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samplesize_groupings_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:57.753619Z",
     "start_time": "2023-11-03T20:27:57.744678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.1796875"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean( group_dict[\"famid\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:57.832622Z",
     "start_time": "2023-11-03T20:27:57.756728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121.15188901706132"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std( group_dict[\"famid\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:57.945922Z",
     "start_time": "2023-11-03T20:27:57.835989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.44285714285714"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean( group_dict[\"hhid\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:58.077390Z",
     "start_time": "2023-11-03T20:27:57.949636Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.59678503403477"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std( group_dict[\"hhid\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:58.155898Z",
     "start_time": "2023-11-03T20:27:58.080860Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.45578231292517"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean( group_dict[\"buildid\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:58.243470Z",
     "start_time": "2023-11-03T20:27:58.159098Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137.32905174419363"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std( group_dict[\"buildid\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:58.320138Z",
     "start_time": "2023-11-03T20:27:58.247116Z"
    }
   },
   "outputs": [],
   "source": [
    "#See if there is a difference\n",
    "f_val, p_val = stats.f_oneway( group_dict[\"hhid\"],\n",
    "                              group_dict[\"famid\"],\n",
    "                              group_dict[\"buildid\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:58.444652Z",
     "start_time": "2023-11-03T20:27:58.324062Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f value:  11.770135799891959\n",
      "p value:  9.417194371448607e-06\n"
     ]
    }
   ],
   "source": [
    "print( \"f value: \",f_val)\n",
    "print( \"p value: \",p_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:58.537617Z",
     "start_time": "2023-11-03T20:27:58.448912Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_95_ci(data1,data2):\n",
    "    #n greater than 30 so use z score for 95% ci (1.96())\n",
    "    #ci for difference of the means\n",
    "    mean1 = np.mean(data1)\n",
    "    mean2 = np.mean(data2)\n",
    "    \n",
    "    n1 = len(data1)\n",
    "    n2 = len(data2)\n",
    "    \n",
    "    sd1 = np.std(data1)\n",
    "    sd2 = np.std(data2)\n",
    "    \n",
    "    pooled_sd = ( (n1-1)*sd1 + (n2-1)*sd2 )/ (n1+n2-2)\n",
    "\n",
    "    \n",
    "    high_ci = (mean1-mean2) + 1.96*pooled_sd*(1/n1 + 1/n2)**.5\n",
    "    low_ci = (mean1-mean2) - 1.96*pooled_sd*(1/n1 + 1/n2)**.5\n",
    "    \n",
    "    degrees_freedom = n1+n2-2\n",
    "    \n",
    "    return(high_ci,low_ci,degrees_freedom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T01:53:29.472536Z",
     "start_time": "2023-11-07T01:53:29.460717Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2.559206532596979, pvalue=0.01092357015218149)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#perform two sample t-test hhid vs famid\n",
    "stats.ttest_ind(a=group_dict[\"famid\"], b=group_dict[\"hhid\"], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:58.747242Z",
     "start_time": "2023-11-03T20:27:58.652866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51.98505650555608, 5.488604208729633, 394)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_95_ci(group_dict[\"famid\"],group_dict[\"hhid\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:58.824569Z",
     "start_time": "2023-11-03T20:27:58.750840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=5.205890036846074, pvalue=3.215389623116209e-07)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#perform two sample t-test hhid vs buildID\n",
    "stats.ttest_ind(a=group_dict[\"buildid\"], b=group_dict[\"hhid\"], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:58.950023Z",
     "start_time": "2023-11-03T20:27:58.827706Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85.07915093468498, 34.94669940545107, 432)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_95_ci(group_dict[\"buildid\"],group_dict[\"hhid\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:59.044528Z",
     "start_time": "2023-11-03T20:27:58.953264Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.8324634274599827, pvalue=0.004789060501311233)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#perform two sample t-test famid vs buildID\n",
    "stats.ttest_ind(a=group_dict[\"famid\"], b=group_dict[\"buildid\"], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:59.123275Z",
     "start_time": "2023-11-03T20:27:59.047508Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53.02427899922239, 9.52791062662794, 548)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_95_ci(group_dict[\"buildid\"],group_dict[\"famid\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:59.221433Z",
     "start_time": "2023-11-03T20:27:59.130951Z"
    }
   },
   "outputs": [],
   "source": [
    "#Create dataframe for graphing\n",
    "shared_env_df = pd.DataFrame( list( zip( group_dict[\"hhid\"], len(group_dict[\"hhid\"]) * [\"hhid\"]) ) +\\\n",
    "list( zip( group_dict[\"famid\"], len(group_dict[\"famid\"]) * [\"famid\"]) ) +\\\n",
    "list( zip( group_dict[\"buildid\"], len(group_dict[\"buildid\"]) * [\"buildid\"]) ),\\\n",
    "                               columns=[\"Time to Last Pos PCR\",\"Shared Environment Grouping\"] )\n",
    "\n",
    "\n",
    "shared_env_df[\"Shared Environment Grouping\"].replace( {\"hhid\":\"Household\",\n",
    "                                                      \"famid\":\"Family\",\n",
    "                                                      \"buildid\":\"Building\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-03T20:27:59.325663Z",
     "start_time": "2023-11-03T20:27:59.223657Z"
    }
   },
   "outputs": [],
   "source": [
    "#Manuscript figure width requirements\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "figsize_2colw = 175/25.4\n",
    "figsize_1colw= 85/25.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T04:30:59.892585Z",
     "start_time": "2023-11-07T04:30:59.500044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAADICAYAAAD8zJ39AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAe9klEQVR4nO3deVQUV/YH8G93swmKohhFZwImGNyOOiNCYisuGI0yRskEFYdNETBuk4gZ0RjcjUt03BgWjSLgiBghx5PMGE0jIJAg5GhcYnAFtaVVpIWfytbL7w9CD81SDUpXFV33c06OedjLLZvb71XVfe+JtFqtFoQQXhFzHQAhpClKTEJ4iBKTEB6ixCSEhygxCeEh3iXme++9x3UIhHCOd4lZWlrKdQiEcI53iUkIocQkhJcoMYlgKZVKrF+/Hk+fPuU6lCYoMYlgpaWlobCwEKmpqVyH0gQlJhEkpVKJzMxMaLVaZGVl8a7XpMQkvB7SGUtaWhrq529oNBre9ZqUmITXQzpjycnJgUqlAgCoVCrk5ORwHJE+SkyB4/uQzlikUikkEgkAQCKRQCqVchyRPkrM3wlxOAfU9ZYajQYAoFarBdNrent7645bo9Hggw8+4DgifZSYvxPicA6oG9Kp1WoAdYnJtyGdMdWfY/JxrQBKTOgP5zIzMwXVa7q6ujK2TVVycrJe++jRoxxF0jxKTNT1lg0vBAit12xIJBJxHQIrcnNzGdtco8QEkJ2drTesyc7O5jgi9uTn5+u1z58/z1EkpCFKTAA9evRgbJsyoR77a6+9xtjmGiUmgCdPnjC2TVnjaXZCmXanVCoZ21yjxAQwcuRIxrYp6969O2PbVA0bNoyxzTVKzGYI5QIIADx8+JCxbaru3r3L2OaaWXu90C+//IIlS5bgjTfewLRp0/D9999Do9EgJiYGsbGxuHbtGiwsLLBz5872est2U1BQoNfOz8/HggULOIqGXY2/hITypVRSUsLY5lq79Zh5eXno3bs3JBIJoqKiEBcXBw8PD8hkMpw7dw779u1Dt27dUFhY2F5v2W74Xp5lTKNGjdJrC+XYe/fuzdjmWrsl5ujRo7F//36sWLFCd2XPwcEBJSUlMDc312s3FhcXB1dXV7i6uuLx48ftFVKreXt7Qyyu+6eQSCS8K88ypilTpjC2TZW9vT1jm2vtlpgXL16ERqOBnZ2drsSrpKQEvXv31v3S17cbCw0NRUFBAQoKCtCzZ8/2CqnV7OzsMHbsWIhEInh4eKBbt26sx8CV9PR03fBVJBJBJpNxHBE7rly5wtjmWrudYzo5OWHJkiUwNzeHp6cnQkJCoNVqERsbC7lcjkWLFsHKygoDBgxor7dsV97e3rh//76gekugrla2YXFFTk4O5s2bx3FURMS33b5cXV2bXIwhxnPw4EFkZGRApVLBzMwM48aNE0Rizpkzp8nP/v3vf3MQSfPodonAeXt764ayYrFYMCMGd3d3xjbXKDEFTqjn1wEBAXrtwMBAjiJpHiUmgbe3N1xcXATTWwJ1X0j1vaS7uzvvvpDa7eIP6bjs7OwQGRnJdRisCwgIQHl5Oe96S4Au/hDCSzSUJYSHKDEJ4SFKTCJYfF4ZkRKTCBafV0akxCSCxPeFrikxCa+HdMZCe5cQ3uPzkM5YOvzeJeXl5cjOzkZWVhaysrLYiImwiO9DOmORSqUwM6urrzEzM+PdBHGDiblw4UL89ttvuHPnDu7cucNGTIRFfB/SGQvfi/cNluQNGDAA8+fPZyMWwoHmhnQdcdpXQkICiouL2/Sc+sS0trbG3r17W/UcR0fHJgXwxmAwMa9cuYIPP/wQ1tbWAOr+AYjpkEqlevMx+TakMyaJRAKxWMzJqhmGGKyVLS0tRVJSEjQaDYKCgoy+7ijVyrJLqVTi448/Rm1tLSwsLLBr1y7ezbQwlg0bNgAAPv/8c44jacrgOeayZcswevRojBkzBhEREWzERFgk1PmYfGcwMS0sLODq6oqRI0fqFtUipkWI8zH5zuA55l/+8hf4+/tDq9Xiww8/ZCMmwjKhzsfkM8bE/O677/DkyROMGzcOAFBWVsZGTIQIHmNi9u3bF2q1Wnfe8eDBA8YXe/jwIby8vLBq1SqcOnWqw2yRQAjftHjSWFJSghMnTmDHjh344YcfcObMGcTGxrb4QlqtFtu2bYOjoyNSUlI61BYJhPBNiz2mg4MDQkJCMHDgQLi7u8Pc3Bz/+Mc/WnyhmJgY+Pn5YceOHbobtw4ODpDL5U22SHBxcdF7blxcHOLi4gCAky0SCOEbxsusr7/+uu7m871797Bq1aoWH1vfo54/f15X1tVRtkgghG8MXpW1traGi4sLXFxckJyc3OLj6pPRz88Pvr6+HW6LBEL4xGBiDhw4EHPnzoVarYabm5vBF0xKSgIAeHl56X4WHByM4ODgVwiTEGExmJjvvfceunbtChcXF/Tt25eNmAgRPIOlPGvWrEFOTg6sra2xdOlSNmIiRPAM9pi2trbo3LkzXFxcdFdXCSHGxdhj1tbWYvLkybh8+TLef/99/PWvf2UrLsIiIa75w3eMiRkYGIiff/4Zu3fvxj//+U8MHTqUrbhYJ+RfzuTkZPz222+MV90JuxgT891334VMJkNSUhKOHDmiu+JqioS4IBVQ94VUvxBVdna2IL+Y+IgxMefOnYsDBw6gX79+cHR0hJOTE0thsUuoC1IBdb2lRqMBULfmD/Wa/GDwquyGDRvQp08fODk5wdHRkY2YWCfUBakAIDc3V6/Nt2UchcrgVVlnZ2e4u7vr1vwxRaayIBUxHQYT88GDB3r3Lw8ePGjUgLgglUpx9uxZqNVqSCQSQS1I1bNnTygUCr024R5jYqanp8PHx4etWDjj7e0NmUwGoG76mpCW2Gh8Pi2k82s+Y0zMe/fu6bXrp3OZIpFIBJ5trs2KkSNH4ty5c7p2a+qhifExJiYf96Y3hrS0NN2XjkgkQmpqqmDOMWtqahjbhBu07B3qLv6o1WoAgFqtFtSVycZr+Obn53MUCWmoxcRUKpWQyWRYunQp0tPTIZPJMGfOHDZjYw3fN5gxpvovpJbahBstJqa1tTXkcjmqqqpw//59PHjwAAsWLGAzNtbwfYMZY2q8VjCtHcwPLX4KlpaWCAgIwNy5c1FcXIzbt28jIyODxdDYI+TVyIcNG6bXHj58ODeBED0G72MeOnQIK1as0A31TNWECROQk5MDT09PrkNhVcN7mEDdukyEewbHLXZ2dqisrIRarTbp84/09HRUVVXp7mcKReNEpMTkB4OJaW1tjdTUVCQlJZns7BKlUomMjAxotVpkZmYK6iZ741JLUy697EgMJqabmxsqKysxdOhQjB07lo2YWJeWlqYbDahUKkEVsVdWVjK2CTcMJuY333wDkUiEKVOm4KuvvmrxcT///DOCgoIQGBiIzMxMBAYGIjAwEE+fPkVqaipCQ0Mxf/58XbE4n2RnZ+uqfrRaLbKzszmOiD2Nq52EWP3ERwav6EgkElRXV6OoqIjx27Sqqgq7du3C6dOnMX36dCgUCpw/fx7Hjh3D2bNnkZycjISEBMhkMkyePLldD+JV9ejRA3K5XK9NCJcM9pjh4eGQSCTYv38/vvjiixYfJ5VKce3aNXz55ZeYNm0arKysdFsiNN4iobG4uDi4urrC1dWVky0Snjx5wtg2ZY3rn025HrojMZiYGzduxOPHj/HkyRPs3r27xcfl5+djxIgR+O9//wsrKytUV1frtkSQSCQA+LtFwujRoxnbpqzxfcs//elP3ARC9LTqPiYAlJeXY/PmzS0+rqKiAvPmzYOFhQWCg4MRFhaG2tpaxMbG4o9//KPelgl8Uz/tS6vVQiQSCaryp7a2Vq9NRez8YDAx09PTAdR9gFeuXGnxcZ6enno35xvWm3p5eeltmcBH9dO+hDaUa/yZMn3GhD0Gh7L37t3D/fv38eTJE0RFRbERE+uam/ZFCJcM9pi5ubmoqamBWCzGqVOnAAAJCQlGD4xNzU37Esp8TMJPBntMW1tbREdHIzo6Gj179jS5pAQAV1dXvfbIkSM5ioR93bt3Z2wTbhjsMcvKynD16lWoVCrB7PYspJvsffr0QVlZmV6bcM9gYm7duhVff/01NBoNdu3axUJI7Gs8i79x25TRxR9+MjiUPXz4MDIyMmBnZ4f4+HgWQmKfVCrV3WsV2vKVhJ8MJmZRURHeeOMN+Pr64s6dO2zExDpvb2/dzH2JRCKo+5iEnwwmpkQiwY0bNxAbG2uypWpCXsGASvL4yeA55vbt23HmzBmo1WocPnyYjZg44e3tjfv37wuut3Rzc0NeXp5em3CvVVu9v/7669i1axfCw8PZiIkTdnZ2iIyMFFRvCdC6snxlMDGfP3+OjIwMREZGCuo2glBcuHCBsU24YTAxu3XrhoKCAtja2sLGxoaNmAgRPIOJuWjRIgwbNgw5OTn49NNP2YiJsMjKyoqxTbhhMDGXL18ODw8PjB49GmvXrmUhJMImOsfkJ8arshqNBnZ2dhg4cCAsLCxgZ2fHVlyEJXS7hJ8YEzM4OBgikQh///vfIRaL6eKPCRo1apTeNnxU9cQPjIl56NAhvXuX9G1qeqRSqV5iCmlZFT4zeI7p5OQEJycndOvWDVlZWWzERFiUmJio1zblIpKOxGDlz5gxYwDULS1y9OhRowdE2NVw2c7m2oQbBhOz/jzTzMwMs2bNYiMmwiJra2u8ePFCr024x5iY5eXlWLFiBezt7bF//34MGTKErbgIS6qqqhjbhBuMifnxxx/jk08+wcaNG+Hv749Vq1bh+PHjzT5WJpMhOTkZL168wFtvvQW5XA6NRoOYmBjExsbi2rVrsLCwwM6dO41yIOTl0O0SfmK8+GNhYYGhQ4dCq9VixIgRjFUhL168QFxcHJYvX47169cjLi4OHh4ekMlkOHfuHPbt24du3bqhsLCw3Q+iPSiVSqxfv15QO30BdbdLmNqEG4yJWVtbi/DwcHh6euptvNOcadOm4cWLF9izZw8mTZoEAB1miwSgbgnLwsJCwS1dOXv2bL22r68vR5GQhhgTMzo6GiEhIXj//fchEokQHR3d4mNLS0uxZMkSrF+/Hr169QLwvy0R6lcH4OsWCUqlEpmZmdBqtcjKyhJcr1n/+dT/SbjH+ElYWlpiwIABAOpuRHfp0qXFxy5btgwlJSVYuXIlxo8fj5CQEGRlZeHdd9+Fh4cHFi1ahPLyct3r8UlaWppuNKDRaATVa6alpem1hXTsfCbS8qzOztXVlfVV6oKDg/W2GOzUqRPjXqCmRMjHvmHDBgDA559/znEkTRkcu/zrX/+Cr68vfH19ERcXx0ZMrJNKpXpbJAipXrTxYteN24QbBhPz6tWrOHr0KI4ePYrr16+zERPrJkyYoLejdMPNkUxd42lejXf/ItxgvI9ZU1ODyspKHDt2DEDdRRJTlJ6errfbl0wmE8zeJY1PG/Lz8zmKRF9CQgKKi4uN+h71r18/pDUWR0dHBAQEtOk5jIk5f/58HDx4EKdPn4ZWq0VMTMwrBchXOTk5ej0mbSrEveLiYly/cQs2tvZGew+Vuu70Rf6w3Gjv8byi9KWex5iYN2/eREJCgu6XNjEx0SR/YaVSKTIyMqBSqWBmZiaoc0w+z8e0sbXH0LdncB3GK7n00zcv9TzGc8zOnTvD2dlZ99+bb775Um/Cd97e3rqLP2KxWFBryzZORJqPyQ+MPaaLiws8PDzYioUz9Suxy2Qywa3E3tx8zO3bt3MUDanHmJh79+5lKw7OCXUldpqPyU9UgyVwffv2ZWwTbhhMzMzMTJSWliI8PBwnTpxgIyZOCLWI3d/fX68dGBjIUSSkIYOJGR8fj8zMTAwaNAhnzpxhIybWCbmIPScnR6/d8Aot4Y7BxKypqcHhw4fh5uaG0tKXuyfDd0IuYs/NzWVsE24YXPMnPDwcPXr0QJcuXRinfXVkOTk5UKlUAACVSkUFBoRzBnvMU6dOYfPmzTh16hTjtK+OTCqVwsys7jtKiAUGDQnp2PnMYGKuWrVKV5rn7++PHTt2sBEXq4RcYDB79my9idKNVzQg3DCYmL6+vsjLy8PXX3+N48ePm+Q2CULe6t3Ozk634kSvXr0Edex8ZvAcMzIyEvn5+Th58iSePn2K5cuXsxEX64RaYKBUKnXrLJWWluLp06eUnDxgsMeMiIjApUuXoNVqeTMlyBiEutV7w6VFtFqtoK5I85nBxOzduzemT5+Orl270jZ8Jqi5K9KEewYT08fHB7169UJ6ejocHBzYiImwSMhXpPmMMTGrq6tx7do17NmzB87Ozli6dClbcRGWeHt76/5fJBIJ7hybrxgTMywsDKNGjcKOHTswYsQILFiwgPHF0tPTMX/+fKSmpiI0NBTz58+HSqVCVFQUFi9ejGXLlrVr8OTV2dnZ6dbytbe3F9w5Nl8xJqZarcaIESNgaWkJqVTKeKvk5s2buHDhAqqqqpCSktLhtkgQKqVSiYcPHwIAHj58KKg6YT5jTMxnz57h9u3buH37Nm7duqW3XVtjzs7OCA8PB4AmWyJ0hC0ShCo5ORkajQZAXZ1wcnIyxxERwEBiDh8+HImJiUhMTERSUhKGDRvWqheVSCQAOs4WCYBwNxVqXLROV2X5gbHAYM2aNS/1oj4+PggJCYFWq0VsbCzkcjkWLVoEKysrXm6RAOjPx6QCdsI1g5U/bZWUlAQA8PLy0v0sODgYwcHB7f1W7abxfMwPPvhAMBdBGq+SR9vw8UO7J2ZH1Nx8zI7aa7Z1oeTGK68rFIpWLYD8MosYk9ajNX8g7OoXc3Nz3TUAW1tbXbEB4RZ9CjCtBZ9fpheLjIyEXC7Hli1bBDOE5zvqMSHs+ZhAXa/p5ORESckjlJioq355++23AQDu7u70C0o4R4nZSH3PSQiXKDFRd7vkp59+AgD8+OOPgisyIPxDiYm62yUNr8rSZGHCNUpMANnZ2Xr7Y2ZnZ3McERE6SkzU3b9jahPCNkpMAI8ePWJsE8I2SkxCeIgSE01vkdAtE8I1KskDMHjwYFy5ckXXHjJkCIfR6GtrUfrLqH/91hSvvwoqfG89Skzw+xyzuLgYd67/hr6dzY32Hua/3yqqeXDLaO8hf1Zr+EFEhxITTROxfg0cvujb2RxLhr7GdRivZO8l/nzZdQQmm5ivOgRs7bCOhmfEGOjiD4DOnTsztglhm8n2mG3pxZRKJRYtWqRrb9u2jWaYEE6ZbGK2hZ2dHTp37oxnz57RtC+eUCgUeP5/z3Hpp2+4DuWVPK8ohUJb2ebndZjENPZtA7VaDbFYjLKyMqPeNqBzUtIarCVmbm4uYmNjAQC7d+9uc69UXFyM327egHl342w3rxJpACtzFFeUGuX1AaC27P/a/ByFQoHKZ7Ud/qqm/FktOikUrX587969oRaVY+jbM4wXFAsu/fQNevfq2ubnsZaYcXFxiI2Nxfnz53Hs2DGEhYW16fkKhQJalarVv9xatRp4id2vayqr2/YEkQii3xe4NhiTSgVFG34561Wrta2+D1ir0ULDwqbfYhFgLm59hVS1WotObXyP5xWlrR7KVj0vh1pt/HulEok5rGxan2jPK0oBPiemWq2GlZUVHBwckJ6ervd3cXFxiIuLA4AWt0iwtbVFdXXrk6ZGW6Nb+t+YxGIxLMwtWvdgc4s2z1xxd3dv0xBeoVC06d/pZVlaWja7qj4TR0dHozwWABSKSlRXG/8bydLSom09YK+ubT4WABBpmXYKakdhYWHYs2cP8vLy8Ouvv7a4c5irqysKCgrYCIkQ3mKtxwwNDUVYWBhqa2t155qEkOax1mO2FvWYhFDlDyG8RIlJCA9RYhLCQ7w7x7S3t4eTkxMn7/348WPONs7lmlCPncvjtre3x6lTp5r9O94lJpeEfOFJqMfO1+OmoSwhPESJSQgPUWI2EBoaynUInBHqsfP1uOkckxAeoh6TEB6ixCSEh0wqMdeuXYubN28CADIyMnDgwIGXfq2ioiKsXr26xb9v/Prx8fH44YcfXvr9jCU+Ph4TJ06En58f5s2b16rnHDx4EHK5HH5+fkaOrv2tXbsWM2bMgJ+fHxYuXNjk7zds2NDiZ2fsBa/bosMsLfIySkpK4O/vD41Gg8jISHzxxRc4cOAAsrOzUVRUhPLycty8eRNVVVXYv38/PvvsM1RUVMDGxgYLFizA6dOnoVAo0KtXL6xZswZhYWGwsrLCkCFDMHjwYABASkoKTp8+jeLiYqxYsYLjI25eREQEJk6cCLlcjqCgINTW1mLq1Kmora3FuXPn8OLFC7z55pt49OgRfHx8cPfuXVRW1q1Ts3PnTkilUjg4OCAhIYHxy4ovvvzySzg7O2PGjBnw9fXF0aNHsXbtWgQFBaGwsBBjxowB0PSzKywsRFFREebNm4dx48ahpqYGkZGRCAsLg42NDS5duoSsrCxWjsHkEnPFihXo0qULFAoFrly5gjt37kCpVGLTpk1NHltcXIw+ffpgwoQJuHr1KvLy8uDq6opbt26hvLwcb7/9Nvbs2YOAgACcOXMGkyZNgq+vL2bPnq1LzG+//RYJCQmIjo5m+1Bbbfv27UhKSsLgwYMxd+5c3Lp1CzKZDB4eHpgxYwZsbGxw7do1BAcH49ChQxCL/zeQCggIwKZNm9C9e/cOs1bRihUroNVqYWZmxrgPTUuf3fDhwxEZGQl/f3+cPXsWkydPxuzZs+Hl5WXs0HVMaigLAFu3bkV8fDwiIiLQ8IKzRqOBmZkZVCqVbit3f39/eHp6Yt26dSgtLcWIESOwZcsW+Pj4wMbGRrfagFgs1lsNoeHr1n/wklYuL8KFTz/9FPHx8ejTpw9ycnLw5z//WXcMnTp1glgshqWlJUQiUZNVH+zt7VFTU4OSkpKXmonPha1btyI1NRX9+/dHZmYmAOg+84Za+uysra11P6+pqdE9js3Npkyux2woNDQUoaGhEIvFWLlyJS5duoTAwEDY2NjAw8MD6enpuHHjBnr27Il33nkHR44cwdKlS2FlZQU3Nze915o0aRI++ugj5OfnY/z48bqfT58+HaGhoZDL5fjkk0/YPsQ2ee211/Dtt9/i3r17bVp2ZdSoUUaMqv0tX74ctra2UCqVmDlzJhYuXIgbN240eVxrPrtJkyYhLCwMeXl5KC8vN3boOnQfkzA6f/48tm3bhiNHjsDS0pLrcFj3+PFjrFu3DlZWVujfv3+bF5F7WZSYhPCQyZ1jEmIKKDEJ4SFKTEJ4iBKTvDK5XM51CCaHErOdXL9+Hb6+vggJCcHy5csB4JVL2hqWGAKAm5sb/Pz84Ofnh+3btxt8PlslZhEREXrtlJQU/O1vf8P8+fPx0UcfQa1Wv/Rr15cHCo1J38dkk0wmw6xZszBjxgycPHkSlZWVKCoqwqJFi6BQKJCSkoLIyEgolUo8f/4chw8fxqBBg+Dl5YWxY8fi+++/R0VFBdatW4eTJ0+isLAQ+fn5esn91ltvISkpSdfOyMjA3r174eLiAgcHB1RUVGDmzJmQSCRITEzEjRs3UFRUhFmzZsHb2xtdu3bFL7/8gqqqKsTGxmLq1KkYP348CgsLER8fj0mTJkEqleL69esYMmQICgsLkZiY2KRUsWHJ2pQpU3Dx4kXcuXMH/fr1Q1lZGb777jscOXIEAHDx4kUolUp89tlnUKlUCA4ORlRUFKysrODl5YWKigr84Q9/wMSJExEUFIRx48YhMzMT9vb2mDZtmq480NPTUy/WzZs349GjR8jLy0NKSgpn60QZC/WY7SQkJAR3797F4sWLcfHiRVhYWKB79+6IiorCwIED8eDBA0ydOhVTp07Fr7/+CqAu0bZv346YmBjY2Nigc+fO+PHHH1FQUICoqCi9QgYAuHHjBoKCghAUFITjx48DAMaOHYvNmzfj/PnzmDVrFk6cOIETJ05g1qxZuue98847iIiIQHZ2NmJiYjB16lT85z//QdeuXbF69Wq8/vrrkMvl6NatG9avX4/a2lqsXr0aEolEV6poY2OjK1WsL1krLi6GVCrFsGHD0K9fPwBAYWEhhg8fDgDYsmULNm7ciNzcXFRXV2PHjh24cOECFi9ejK+++gopKSnN/lvOmTMH27Zt0/sSahyrQqHA3r17MXLkyHb7DPmEErOd7N+/H4GBgdi3bx+sra2Rl5enK+kzMzNDRUUFduzYgR49eqBPnz4A9LeU37x5M5YsWQIXF5cWS8X69++P+Ph4xMfHw8fHB4B++ZizszPu3r2L27dvY8CAAbrn1b9P4xLF+ueam5tDo9GgU6e6/bjq/wTqNoNqXKrY8D0b69+/P7Kzs6HVahEREYGQkBCUlZXp4mhYcdRcmWR9nCqVSu+xDWNVKpW6n7NZJscmSsx2MmbMGAQEBMDf3x+XL1/W9Rr1zMzMoFarcfz4cdy/f1/vF3HevHkICAhAZGQk+vTpA3d3dyxZsqTJTIbr16/rzjFXrlzZbBzOzs663qux0aNHY+nSpcjIyMDUqVNbdVyDBg2CUqnE0qVLUVBQADOzpmc/CoUCly9fBlBXWztz5kxMnz4dvr6+OHLkCCZNmqR7rK+vL6Kjo7F48WL4+vpi1KhR2LNnDzZt2gQbGxsAQExMDIKDgxESEtJsTF27dkX37t0RHh6Oc+fOwcrKqlXH0pFQ5Q/hlfj4eN05J5OVK1dCpVIBQKsuhHU0lJiE8BANZQnhIUpMQniIEpMQHqLEJISHKDEJ4aH/B5SSO0i8q74EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 240.945x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(figsize_1colw,3))\n",
    "\n",
    "sns.boxplot(data=shared_env_df, x=\"Shared Environment Grouping\", y=\"Time to Last Pos PCR\",palette=\"Set2\",\\\n",
    "                 order=[ \"Household\",\"Family\",\"Building\"])\\\n",
    ".set( \n",
    "    ylabel='Days To Subsequent Infection',\n",
    "    xlabel='Shared Environment Grouping'\n",
    ")\n",
    "sns.despine()\n",
    "\n",
    "\n",
    "save_and_track_figure(fig, \"shared_env_comparison.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
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