clinical_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import datetime as dt\n",
    "from datetime import date as date_func\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import glob\n",
    "import re\n",
    "import os\n",
    "import copy\n",
    "import subprocess\n",
    "import timeit\n",
    "from datetime import datetime, timedelta\n",
    "from collections import defaultdict, Counter\n",
    "from functools import reduce\n",
    "\n",
    "import vcf\n",
    "import pysam\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import dask.dataframe as dd\n",
    "from Bio import SeqIO\n",
    "from Bio.SeqUtils import seq1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import matplotlib.ticker as mtick\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_plot_data(mut_label, clinical_cryptic_df):\n",
    "    clinical_mutation_df = clinical_cryptic_df[clinical_cryptic_df['Nt Mutations'] == mut_label]\n",
    "    #clinical_mutation_df = clinical_cryptic_df[(clinical_cryptic_df['Nt Mutations'] == mut_label) & (clinical_cryptic_df['Combined Freq'] >= 0.02)]\n",
    "    ww_df = wastewater_df[wastewater_df['Nt Mutations'] == mut_label]\n",
    "    x_ticklabels = []\n",
    "    p_rates = []\n",
    "    ww_counts = []\n",
    "    has_data = []\n",
    "    sample_counts = []\n",
    "    freqs = []\n",
    "\n",
    "    for i in range(0, 8):\n",
    "        start = sampling_date_start + i*week_offset\n",
    "        end = sampling_date_start + (i+1)*week_offset\n",
    "\n",
    "        weekly_clinical_df = clinical_mutation_df[(clinical_mutation_df['Collection_Date'] >= start) & (clinical_mutation_df['Collection_Date'] < end)]\n",
    "        ww_count = ww_df[(ww_df['Date'] >= start) & (ww_df['Date'] < end)].shape[0]\n",
    "\n",
    "        weekly_sample_count = weekly_clinical_df.shape[0]\n",
    "        sample_counts.append(weekly_sample_count)\n",
    "        #count_present = weekly_clinical_df[weekly_clinical_df['Support DP'] >= 10].shape[0]\n",
    "        supported_sample_df = weekly_clinical_df[(weekly_clinical_df['Support DP'] >= 10) & (weekly_clinical_df['Combined Freq'] >= freq_cutoff)]\n",
    "        supported_sample_df = supported_sample_df[(supported_sample_df['SB_p_value'] > 0.05)]\n",
    "        #supported_sample_df = weekly_clinical_df[(weekly_clinical_df['Overlapping Support DP'] >= 10) & (weekly_clinical_df['Combined Freq'] >= freq_cutoff)]\n",
    "        count_present = supported_sample_df.shape[0]\n",
    "        count_no_data = weekly_clinical_df[weekly_clinical_df['Total DP'] == 0].shape[0]\n",
    "        \n",
    "        #freq = weekly_clinical_df[weekly_clinical_df['Support DP'] >= 10]['Combined Freq'].mean()\n",
    "        freq = supported_sample_df['Combined Freq'].values\n",
    "        freqs.append(freq)\n",
    "\n",
    "        count_exclude_no_data = weekly_sample_count - count_no_data\n",
    "\n",
    "        if count_exclude_no_data > 0:\n",
    "            p_rate = count_present/count_exclude_no_data\n",
    "            has_data.append(2)\n",
    "        else:\n",
    "            p_rate = np.nan\n",
    "            has_data.append(0)\n",
    "\n",
    "        #time_label = start.strftime('%m/%d/%y') + ' - ' + (end-timedelta(days=1)).strftime('%m/%d/%y')\n",
    "        time_label = start.strftime('%m-%d-%y')\n",
    "        x_ticklabels.append(time_label)\n",
    "        p_rates.append(p_rate)\n",
    "        if ww_count > 0:\n",
    "            ww_counts.append(2)\n",
    "        else:\n",
    "            ww_counts.append(np.nan)\n",
    "            \n",
    "    return x_ticklabels, p_rates, has_data, ww_counts, sample_counts, freqs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selected Mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_cryptic_mutations = ['T28823G;G28881A;G28882A;G28883C',\n",
    "                              'C6402T;G6456A',\n",
    "                              'A26530G;C26533T;C26542A;T26545G',\n",
    "                              'A26530G;T26545G',\n",
    "                              'A27259C;C27335T;A27344T;A27345T',\n",
    "                              'T29029C;A29039T',\n",
    "                              'A26530G;C26577G;C26625A',\n",
    "                              'C10449A;T10459C',\n",
    "                              'T15682A;T15685A',\n",
    "                              'A24966T;C25000T',\n",
    "                              'A27344T;A27345T;A27354G',\n",
    "                              'A29039T;G29049A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampling_date_start =  pd.to_datetime('12/06/2021')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_offset = timedelta(days=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_cutoff = 0.02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6818"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wastewater_df = pd.read_csv('/home/Users/yl181/wastewater/quarc_figures/merged_df.csv', index_col=0)\n",
    "wastewater_df['Date'] = wastewater_df['Date'].apply(str)\n",
    "wastewater_df['Date'] = pd.to_datetime(wastewater_df['Date'], format='%Y-%m-%d')\n",
    "len(wastewater_df['Nt Mutations'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Houston"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "houston_df = pd.read_csv('filtered_houston_df.csv', index_col=0)\n",
    "houston_df['Collection_Date'] =  pd.to_datetime(houston_df['Collection_Date'], format='%Y-%m-%d')\n",
    "houston_df['Date'] = houston_df['Collection_Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRA Accession ID</th>\n",
       "      <th>Run Index</th>\n",
       "      <th>Nt Mutations</th>\n",
       "      <th>Support DP</th>\n",
       "      <th>Total DP</th>\n",
       "      <th>DP1</th>\n",
       "      <th>DP2</th>\n",
       "      <th>DP3</th>\n",
       "      <th>DP4</th>\n",
       "      <th>Overlapping Support DP</th>\n",
       "      <th>...</th>\n",
       "      <th>SRA Study</th>\n",
       "      <th>collected_by</th>\n",
       "      <th>Host_age</th>\n",
       "      <th>amplicon__PCR_primer_scheme (run)</th>\n",
       "      <th>host_sex</th>\n",
       "      <th>taxon</th>\n",
       "      <th>lineage</th>\n",
       "      <th>SB_p_value</th>\n",
       "      <th>SB</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>0</td>\n",
       "      <td>A29039T;G29049A;A29301G</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>FBCCHS - COVID Testing - 4520 READING ROAD\\, S...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>BA.1.15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-12-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>0</td>\n",
       "      <td>G29050A;A29301G</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>FBCCHS - COVID Testing - 4520 READING ROAD\\, S...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>BA.1.15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-12-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>0</td>\n",
       "      <td>A26530G;C26577G;G26634A</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>FBCCHS - COVID Testing - 4520 READING ROAD\\, S...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>BA.1.15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-12-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>0</td>\n",
       "      <td>T13078C;T13195C</td>\n",
       "      <td>0</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>FBCCHS - COVID Testing - 4520 READING ROAD\\, S...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>BA.1.15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-12-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>0</td>\n",
       "      <td>A26530G;C26533T;C26542A;T26545G</td>\n",
       "      <td>0</td>\n",
       "      <td>1508</td>\n",
       "      <td>242</td>\n",
       "      <td>1195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>FBCCHS - COVID Testing - 4520 READING ROAD\\, S...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>SRR17385361</td>\n",
       "      <td>BA.1.15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-12-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101195</th>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>5059</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>6</td>\n",
       "      <td>1517</td>\n",
       "      <td>1080</td>\n",
       "      <td>1105</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>GALE\\, LETOSHA</td>\n",
       "      <td>31.0</td>\n",
       "      <td>VarSkip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>BA.1.20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101196</th>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>5059</td>\n",
       "      <td>A24966T;C25000T</td>\n",
       "      <td>10</td>\n",
       "      <td>1818</td>\n",
       "      <td>1034</td>\n",
       "      <td>851</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>GALE\\, LETOSHA</td>\n",
       "      <td>31.0</td>\n",
       "      <td>VarSkip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>BA.1.20</td>\n",
       "      <td>0.028278</td>\n",
       "      <td>1.448688</td>\n",
       "      <td>2021-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101197</th>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>5059</td>\n",
       "      <td>A27344T;A27345T;A27354G</td>\n",
       "      <td>13</td>\n",
       "      <td>1343</td>\n",
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       "      <td>838</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>GALE\\, LETOSHA</td>\n",
       "      <td>31.0</td>\n",
       "      <td>VarSkip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>BA.1.20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101198</th>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>5059</td>\n",
       "      <td>T23599G;C23604A;G23642T</td>\n",
       "      <td>0</td>\n",
       "      <td>670</td>\n",
       "      <td>258</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>GALE\\, LETOSHA</td>\n",
       "      <td>31.0</td>\n",
       "      <td>VarSkip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>BA.1.20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101199</th>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>5059</td>\n",
       "      <td>A29039T;G29049A</td>\n",
       "      <td>30</td>\n",
       "      <td>1557</td>\n",
       "      <td>1035</td>\n",
       "      <td>1113</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>SRP337649</td>\n",
       "      <td>GALE\\, LETOSHA</td>\n",
       "      <td>31.0</td>\n",
       "      <td>VarSkip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRR18133357</td>\n",
       "      <td>BA.1.20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-12-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101200 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SRA Accession ID  Run Index                     Nt Mutations  \\\n",
       "0           SRR17385361          0          A29039T;G29049A;A29301G   \n",
       "1           SRR17385361          0                  G29050A;A29301G   \n",
       "2           SRR17385361          0          A26530G;C26577G;G26634A   \n",
       "3           SRR17385361          0                  T13078C;T13195C   \n",
       "4           SRR17385361          0  A26530G;C26533T;C26542A;T26545G   \n",
       "...                 ...        ...                              ...   \n",
       "101195      SRR18133357       5059                  T29029C;A29039T   \n",
       "101196      SRR18133357       5059                  A24966T;C25000T   \n",
       "101197      SRR18133357       5059          A27344T;A27345T;A27354G   \n",
       "101198      SRR18133357       5059          T23599G;C23604A;G23642T   \n",
       "101199      SRR18133357       5059                  A29039T;G29049A   \n",
       "\n",
       "        Support DP  Total DP   DP1   DP2  DP3  DP4  Overlapping Support DP  \\\n",
       "0                0       113     0     0    0    0                       0   \n",
       "1                0       164     0     0    0    0                       0   \n",
       "2                0         1     0     0    0    0                       0   \n",
       "3                0       231     0     0    0    0                       0   \n",
       "4                0      1508   242  1195    0    0                       0   \n",
       "...            ...       ...   ...   ...  ...  ...                     ...   \n",
       "101195           6      1517  1080  1105    3    6                       3   \n",
       "101196          10      1818  1034   851   10    1                       1   \n",
       "101197          13      1343   877   838   11    4                       2   \n",
       "101198           0       670   258   186    0    0                       0   \n",
       "101199          30      1557  1035  1113   30    8                       8   \n",
       "\n",
       "        ...  SRA Study                                       collected_by  \\\n",
       "0       ...  SRP337649  FBCCHS - COVID Testing - 4520 READING ROAD\\, S...   \n",
       "1       ...  SRP337649  FBCCHS - COVID Testing - 4520 READING ROAD\\, S...   \n",
       "2       ...  SRP337649  FBCCHS - COVID Testing - 4520 READING ROAD\\, S...   \n",
       "3       ...  SRP337649  FBCCHS - COVID Testing - 4520 READING ROAD\\, S...   \n",
       "4       ...  SRP337649  FBCCHS - COVID Testing - 4520 READING ROAD\\, S...   \n",
       "...     ...        ...                                                ...   \n",
       "101195  ...  SRP337649                                     GALE\\, LETOSHA   \n",
       "101196  ...  SRP337649                                     GALE\\, LETOSHA   \n",
       "101197  ...  SRP337649                                     GALE\\, LETOSHA   \n",
       "101198  ...  SRP337649                                     GALE\\, LETOSHA   \n",
       "101199  ...  SRP337649                                     GALE\\, LETOSHA   \n",
       "\n",
       "       Host_age  amplicon__PCR_primer_scheme (run)  host_sex        taxon  \\\n",
       "0          34.0                                NaN    female  SRR17385361   \n",
       "1          34.0                                NaN    female  SRR17385361   \n",
       "2          34.0                                NaN    female  SRR17385361   \n",
       "3          34.0                                NaN    female  SRR17385361   \n",
       "4          34.0                                NaN    female  SRR17385361   \n",
       "...         ...                                ...       ...          ...   \n",
       "101195     31.0                            VarSkip       NaN  SRR18133357   \n",
       "101196     31.0                            VarSkip       NaN  SRR18133357   \n",
       "101197     31.0                            VarSkip       NaN  SRR18133357   \n",
       "101198     31.0                            VarSkip       NaN  SRR18133357   \n",
       "101199     31.0                            VarSkip       NaN  SRR18133357   \n",
       "\n",
       "        lineage SB_p_value        SB       Date  \n",
       "0       BA.1.15   1.000000  0.000000 2021-12-14  \n",
       "1       BA.1.15   1.000000  0.000000 2021-12-14  \n",
       "2       BA.1.15   1.000000  0.000000 2021-12-14  \n",
       "3       BA.1.15   1.000000  0.000000 2021-12-14  \n",
       "4       BA.1.15   1.000000  0.000000 2021-12-14  \n",
       "...         ...        ...       ...        ...  \n",
       "101195  BA.1.20   1.000000  0.000000 2021-12-22  \n",
       "101196  BA.1.20   0.028278  1.448688 2021-12-22  \n",
       "101197  BA.1.20   1.000000  0.000000 2021-12-22  \n",
       "101198  BA.1.20   1.000000  0.000000 2021-12-22  \n",
       "101199  BA.1.20   1.000000  0.000000 2021-12-22  \n",
       "\n",
       "[101200 rows x 58 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houston_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lineage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filtering(clinical_mutation_df, mut_label):\n",
    "    clinical_mutation_df = clinical_mutation_df[clinical_mutation_df['Nt Mutations'] == mut_label].copy()\n",
    "    start = sampling_date_start + 0*week_offset\n",
    "    end = sampling_date_start + (7+1)*week_offset\n",
    "    clinical_mutation_df = clinical_mutation_df[(clinical_mutation_df['Collection_Date'] >= start) & (clinical_mutation_df['Collection_Date'] < end)]\n",
    "    supported_sample_df = clinical_mutation_df[(clinical_mutation_df['Support DP'] >= 10) & (clinical_mutation_df['Combined Freq'] >= freq_cutoff)]\n",
    "    supported_sample_df = supported_sample_df[(supported_sample_df['SB_p_value'] > 0.05)]\n",
    "    \n",
    "    return supported_sample_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lineage_values(df, mut_label, state):\n",
    "    lineage_mutation_df = pd.pivot_table(filtering(df, mut_label), values='SRA Accession ID', index='lineage', columns='Nt Mutations', \n",
    "                   aggfunc='count').fillna(0)\n",
    "    lineage_mutation_df.index.unique()\n",
    "\n",
    "    group_labels = []\n",
    "    for idx, _ in lineage_mutation_df.iterrows():\n",
    "        if idx.startswith('AY.'):\n",
    "            group_labels.append('Delta')\n",
    "        elif idx == 'BA.1.15' or idx.startswith('BA.1.15.'):\n",
    "            group_labels.append('BA.1.15')\n",
    "        elif idx == 'BA.1.17' or idx.startswith('BA.1.17.'):\n",
    "            group_labels.append('BA.1.17')\n",
    "        elif idx == 'BA.1.18' or idx.startswith('BA.1.18.'):\n",
    "            group_labels.append('BA.1.18')\n",
    "        elif idx == 'BA.1.20' or idx.startswith('BA.1.20.'):\n",
    "            group_labels.append('BA.1.20')\n",
    "        elif idx == 'BA.1.1' or idx.startswith('BA.1.1.'):\n",
    "            group_labels.append('BA.1.1')\n",
    "        else:\n",
    "            group_labels.append('Omicron')\n",
    "    lineage_mutation_df['Label'] = group_labels\n",
    "    \n",
    "    lineage_mutation_df = lineage_mutation_df.groupby(lineage_mutation_df['Label']).sum()\n",
    "    \n",
    "    # if lineage_mutation_df[mut_label].values.sum() != 0:\n",
    "    #     lineage_values = lineage_mutation_df[mut_label].values/lineage_mutation_df[mut_label].values.sum()\n",
    "    # else:\n",
    "    #     lineage_values = lineage_mutation_df[mut_label].values\n",
    "    # lineage_labels = lineage_mutation_df[mut_label].index\n",
    "    # return lineage_values, lineage_labels\n",
    "    \n",
    "    lineage_mutation_df = lineage_mutation_df.rename({mut_label: state}, axis=1).transpose()\n",
    "    return lineage_mutation_df\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lineage_plot_data(mut_label):\n",
    "    states = ['Houston', 'Maryland', 'Massachusetts', 'California', 'Colorado', 'Utah'][::-1]\n",
    "    lineage_array = []\n",
    "    for state in states:\n",
    "        if state == 'Houston':\n",
    "            lineage_mutation_df = get_lineage_values(houston_df, mut_label, state).copy()\n",
    "        else:\n",
    "            temp_df = us_df[us_df['Region'] == state]\n",
    "            lineage_mutation_df = get_lineage_values(temp_df, mut_label, state).copy()\n",
    "        \n",
    "        if not lineage_mutation_df.empty:\n",
    "            lineage_array.append(lineage_mutation_df)\n",
    "    \n",
    "    return lineage_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from operator import add"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-cryptic Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filtering_full(clinical_mutation_df):\n",
    "    clinical_mutation_df = clinical_mutation_df.copy()\n",
    "    start = sampling_date_start + 0*week_offset\n",
    "    end = sampling_date_start + (7+1)*week_offset\n",
    "    clinical_mutation_df = clinical_mutation_df[(clinical_mutation_df['Collection_Date'] >= start) & (clinical_mutation_df['Collection_Date'] < end)]\n",
    "    supported_sample_df = clinical_mutation_df[(clinical_mutation_df['Support DP'] >= 10) & (clinical_mutation_df['Combined Freq'] >= freq_cutoff)]\n",
    "    supported_sample_df = supported_sample_df[(supported_sample_df['SB_p_value'] > 0.05)]\n",
    "    \n",
    "    return supported_sample_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lineage_mutation_df = pd.pivot_table(filtering_full(houston_df), values='SRA Accession ID', index='lineage', columns='Nt Mutations', \n",
    "               aggfunc='count').fillna(0)\n",
    "lineage_mutation_df.index.unique()\n",
    "\n",
    "group_labels = []\n",
    "for idx, _ in lineage_mutation_df.iterrows():\n",
    "    if idx.startswith('AY.'):\n",
    "        group_labels.append('Delta')\n",
    "    elif idx == 'BA.1.15' or idx.startswith('BA.1.15.'):\n",
    "        group_labels.append('BA.1.15')\n",
    "    elif idx == 'BA.1.17' or idx.startswith('BA.1.17.'):\n",
    "        group_labels.append('BA.1.17')\n",
    "    elif idx == 'BA.1.18' or idx.startswith('BA.1.18.'):\n",
    "        group_labels.append('BA.1.18')\n",
    "    elif idx == 'BA.1.20' or idx.startswith('BA.1.20.'):\n",
    "        group_labels.append('BA.1.20')\n",
    "    elif idx == 'BA.1.1' or idx.startswith('BA.1.1.'):\n",
    "        group_labels.append('BA.1.1')\n",
    "    else:\n",
    "        group_labels.append('Omicron')\n",
    "lineage_mutation_df['Label'] = group_labels\n",
    "lineage_mutation_df = lineage_mutation_df.groupby(lineage_mutation_df['Label']).sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.815, 0.5, 'Mean Allele Frequency')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_cutoff = 0.02\n",
    "\n",
    "fontsize = 14\n",
    "num = int(len(selected_cryptic_mutations)/2)\n",
    "fig, axes  = plt.subplots(num, 4, figsize=(12, num*2), sharex=False, constrained_layout=True, gridspec_kw={'width_ratios': [5, 1, 5, 1], 'wspace':0.35})\n",
    "\n",
    "x = np.arange(8)\n",
    "\n",
    "plotted_mut = []\n",
    "\n",
    "for ax_idx, mut_label in enumerate(selected_cryptic_mutations[-num:]):\n",
    "    ax = axes[ax_idx][2]\n",
    "    x_ticklabels, p_rates, has_data, ww_counts, sample_counts, freqs = get_plot_data(mut_label, houston_df)\n",
    "\n",
    "    ax.bar(x, ww_counts, color='gray', alpha=0.2, width=1)\n",
    "    bar = ax.bar(x, p_rates, color='c')\n",
    "    ax.scatter(x, ww_counts, marker=\"P\", s=60, color='black')\n",
    "    ax.set_ylim(0,1.3)\n",
    "    ax.set_title(\", \".join(mut_label.split(';')))\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    plotted_mut.append(mut_label)\n",
    "\n",
    "    ax.text(0.01, 0.95, f'CR{ax_idx+7}', transform=ax.transAxes,\n",
    "        fontsize=14, fontweight='bold', va='top')\n",
    "    ax_twin = ax.twinx()\n",
    "    ax_twin.plot(x, [a.mean() for a in np.array(freqs)], 'b.--')\n",
    "    #ax_twin.set_ylim(0, np.nanmax(freqs)*2.5)\n",
    "    ax_twin.set_ylim(0, 0.13)\n",
    "    ax_twin.set_yticks([0,0.05,0.1])\n",
    "    ax_twin.set_yticklabels(['0','.05','.10'])\n",
    "    ax.set_xlim(-0.5,7.5)\n",
    "    ax.set_xticks(np.arange(9)-0.5)\n",
    "    ax.set_xticklabels([])\n",
    "    \n",
    "    ax2 = axes[ax_idx][3]\n",
    "    bottom = 0\n",
    "    lineage_values = lineage_mutation_df[mut_label].values/lineage_mutation_df[mut_label].values.sum()\n",
    "    lineage_labels = lineage_mutation_df[mut_label].index\n",
    "    for idx, label in enumerate(lineage_labels):\n",
    "        p = ax2.bar(0, lineage_values[idx], 1, label=label, bottom=bottom, edgecolor='black')\n",
    "        bottom += lineage_values[idx]\n",
    "    \n",
    "    ax2.set_ylim(0,1)\n",
    "    ax2.set_xlim(-0.2,0.2)\n",
    "    ax2.xaxis.set_ticks_position('none')\n",
    "    ax2.set_yticks([])\n",
    "    ax2.set_xticklabels([])\n",
    "    \n",
    "ax2.set_xticks([0])\n",
    "ax2.set_xticklabels(['Lineage'], rotation=90, fontsize=12)    \n",
    "x_ticklabels.append((sampling_date_start+8*week_offset).strftime('%m-%d-%y'))\n",
    "ax.set_xlim(-0.5,7.5)\n",
    "ax.set_xticks(np.arange(9)-0.5)\n",
    "ax.set_xticklabels(x_ticklabels, rotation=90, fontsize=12)\n",
    "\n",
    "ax2.legend(loc=4, bbox_to_anchor=(2.75, 0, 0, 0))\n",
    "\n",
    "for ax_idx, mut_label in enumerate(selected_cryptic_mutations[:num]):\n",
    "    ax = axes[ax_idx][0]\n",
    "    x_ticklabels, p_rates, has_data, ww_counts, sample_counts, freqs = get_plot_data(mut_label, houston_df)\n",
    "\n",
    "    ax.bar(x, ww_counts, color='gray', alpha=0.2, width=1)\n",
    "    ax.bar(x, p_rates, color='c')\n",
    "    ax.scatter(x, ww_counts, marker=\"P\", s=60, color='black')\n",
    "    ax.set_ylim(0,1.3)\n",
    "    ax.set_title(\", \".join(mut_label.split(';')))\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    plotted_mut.append(mut_label)\n",
    "    \n",
    "    ax.text(0.01, 0.95, f'CR{ax_idx+1}', transform=ax.transAxes,\n",
    "        fontsize=14, fontweight='bold', va='top')\n",
    "        \n",
    "    ax_twin = ax.twinx()\n",
    "    ax_twin.plot(x, [a.mean() for a in np.array(freqs)], 'b.--')\n",
    "    #ax_twin.set_ylim(0, np.nanmax(freqs)*2.5)\n",
    "    ax_twin.set_ylim(0, 0.13)\n",
    "    ax_twin.set_yticks([0,0.05,0.1])\n",
    "    ax_twin.set_yticklabels(['0','.05','.10'])\n",
    "    ax.set_xlim(-0.5,7.5)\n",
    "    ax.set_xticks(np.arange(9)-0.5)\n",
    "    ax.set_xticklabels([])\n",
    "    \n",
    "    ax2 = axes[ax_idx][1]\n",
    "    bottom = 0\n",
    "    lineage_values = lineage_mutation_df[mut_label].values/lineage_mutation_df[mut_label].values.sum()\n",
    "    lineage_labels = lineage_mutation_df[mut_label].index\n",
    "    for idx, label in enumerate(lineage_labels):\n",
    "        p = ax2.bar(0, lineage_values[idx], 1, label=label, bottom=bottom, edgecolor='black')\n",
    "        bottom += lineage_values[idx]\n",
    "    \n",
    "    ax2.set_ylim(0,1)\n",
    "    ax2.set_xlim(-0.2,0.2)\n",
    "    ax2.xaxis.set_ticks_position('none')\n",
    "    ax2.set_yticks([])\n",
    "    ax2.set_xticklabels([])\n",
    "    \n",
    "ax2.set_xticks([0])\n",
    "ax2.set_xticklabels(['Lineage'], rotation=90, fontsize=12)\n",
    "x_ticklabels.append((sampling_date_start+8*week_offset).strftime('%m-%d-%y'))\n",
    "ax.set_xlim(-0.5,7.5)\n",
    "ax.set_xticks(np.arange(9)-0.5)\n",
    "ax.set_xticklabels(x_ticklabels, rotation=90, fontsize=12)\n",
    "\n",
    "fig.text(-0.01, 0.5, 'Prevalence Rate in Clinical Samples', va='center', rotation='vertical', fontsize=12)\n",
    "#fig.text(.365, 0.5, 'Mean Allele Frequency', va='center', rotation='vertical', fontsize=12)\n",
    "#fig.text(0.445, 0.5, 'Prevalence Rate in Clinical Samples', va='center', rotation='vertical', fontsize=12)\n",
    "fig.text(.815, 0.5, 'Mean Allele Frequency', va='center', rotation='vertical', fontsize=12)\n",
    "\n",
    "#fig.suptitle(f\"Houston Clinical Samples (Filter SB/DP=10/AF={freq_cutoff})\", fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_df = pd.read_csv('filtered_us_df.csv', index_col=0)\n",
    "us_df['Collection_Date'] =  pd.to_datetime(us_df['Collection_Date'], format='%Y-%m-%d')\n",
    "us_df['Date'] = us_df['Collection_Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRA Accession ID</th>\n",
       "      <th>Run Index</th>\n",
       "      <th>Nt Mutations</th>\n",
       "      <th>Support DP</th>\n",
       "      <th>Total DP</th>\n",
       "      <th>DP1</th>\n",
       "      <th>DP2</th>\n",
       "      <th>DP3</th>\n",
       "      <th>DP4</th>\n",
       "      <th>Overlapping Support DP</th>\n",
       "      <th>...</th>\n",
       "      <th>Region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Pango</th>\n",
       "      <th>CDC</th>\n",
       "      <th>Date</th>\n",
       "      <th>taxon</th>\n",
       "      <th>lineage</th>\n",
       "      <th>SB_p_value</th>\n",
       "      <th>SB</th>\n",
       "      <th>Collection_Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>SRR18124929</td>\n",
       "      <td>10</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>22</td>\n",
       "      <td>4346</td>\n",
       "      <td>2565</td>\n",
       "      <td>2088</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>SRR18124929</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>SRR18125553</td>\n",
       "      <td>20</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>14</td>\n",
       "      <td>4849</td>\n",
       "      <td>3117</td>\n",
       "      <td>2545</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>SRR18125553</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>SRR18125588</td>\n",
       "      <td>21</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>21</td>\n",
       "      <td>3995</td>\n",
       "      <td>2556</td>\n",
       "      <td>2603</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1.1.17</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>SRR18125588</td>\n",
       "      <td>BA.1.1.17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>SRR18131167</td>\n",
       "      <td>30</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>48</td>\n",
       "      <td>3985</td>\n",
       "      <td>2619</td>\n",
       "      <td>2521</td>\n",
       "      <td>28</td>\n",
       "      <td>42</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1.18</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>SRR18131167</td>\n",
       "      <td>BA.1.18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>SRR18125585</td>\n",
       "      <td>43</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>24</td>\n",
       "      <td>4061</td>\n",
       "      <td>2546</td>\n",
       "      <td>2706</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>SRR18125585</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168355</th>\n",
       "      <td>SRR18124865</td>\n",
       "      <td>8420</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>11</td>\n",
       "      <td>4144</td>\n",
       "      <td>3080</td>\n",
       "      <td>2923</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>SRR18124865</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168375</th>\n",
       "      <td>SRR18124875</td>\n",
       "      <td>8421</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>35</td>\n",
       "      <td>4308</td>\n",
       "      <td>2543</td>\n",
       "      <td>2436</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>SRR18124875</td>\n",
       "      <td>BA.1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170295</th>\n",
       "      <td>SRR18124863</td>\n",
       "      <td>8517</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>18</td>\n",
       "      <td>4205</td>\n",
       "      <td>2521</td>\n",
       "      <td>2691</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>SRR18124863</td>\n",
       "      <td>BA.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171415</th>\n",
       "      <td>SRR18124872</td>\n",
       "      <td>8573</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>22</td>\n",
       "      <td>3926</td>\n",
       "      <td>2511</td>\n",
       "      <td>2544</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1.17</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>SRR18124872</td>\n",
       "      <td>BA.1.17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178115</th>\n",
       "      <td>SRR18128002</td>\n",
       "      <td>8908</td>\n",
       "      <td>T29029C;A29039T</td>\n",
       "      <td>13</td>\n",
       "      <td>4633</td>\n",
       "      <td>3161</td>\n",
       "      <td>2720</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>BA.1.18</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>2022-01-07</td>\n",
       "      <td>SRR18128002</td>\n",
       "      <td>BA.1.18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022-01-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>285 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SRA Accession ID  Run Index     Nt Mutations  Support DP  Total DP  \\\n",
       "215         SRR18124929         10  T29029C;A29039T          22      4346   \n",
       "415         SRR18125553         20  T29029C;A29039T          14      4849   \n",
       "435         SRR18125588         21  T29029C;A29039T          21      3995   \n",
       "615         SRR18131167         30  T29029C;A29039T          48      3985   \n",
       "875         SRR18125585         43  T29029C;A29039T          24      4061   \n",
       "...                 ...        ...              ...         ...       ...   \n",
       "168355      SRR18124865       8420  T29029C;A29039T          11      4144   \n",
       "168375      SRR18124875       8421  T29029C;A29039T          35      4308   \n",
       "170295      SRR18124863       8517  T29029C;A29039T          18      4205   \n",
       "171415      SRR18124872       8573  T29029C;A29039T          22      3926   \n",
       "178115      SRR18128002       8908  T29029C;A29039T          13      4633   \n",
       "\n",
       "         DP1   DP2  DP3  DP4  Overlapping Support DP  ...    Region  Country  \\\n",
       "215     2565  2088    4   22                       4  ...  Maryland      USA   \n",
       "415     3117  2545    4   14                       4  ...  Maryland      USA   \n",
       "435     2556  2603   12   18                       9  ...  Maryland      USA   \n",
       "615     2619  2521   28   42                      23  ...  Maryland      USA   \n",
       "875     2546  2706   16   24                      16  ...  Maryland      USA   \n",
       "...      ...   ...  ...  ...                     ...  ...       ...      ...   \n",
       "168355  3080  2923    8   11                       8  ...  Maryland      USA   \n",
       "168375  2543  2436   10   33                       8  ...  Maryland      USA   \n",
       "170295  2521  2691    9   18                       9  ...  Maryland      USA   \n",
       "171415  2511  2544    9   19                       6  ...  Maryland      USA   \n",
       "178115  3161  2720    8   13                       8  ...  Maryland      USA   \n",
       "\n",
       "            Pango      CDC       Date        taxon    lineage SB_p_value   SB  \\\n",
       "215        BA.1.1  Omicron 2022-01-13  SRR18124929     BA.1.1        1.0  0.0   \n",
       "415        BA.1.1  Omicron 2022-01-13  SRR18125553     BA.1.1        1.0  0.0   \n",
       "435     BA.1.1.17  Omicron 2022-01-13  SRR18125588  BA.1.1.17        1.0  0.0   \n",
       "615       BA.1.18  Omicron 2022-01-13  SRR18131167    BA.1.18        1.0  0.0   \n",
       "875        BA.1.1  Omicron 2022-01-13  SRR18125585     BA.1.1        1.0  0.0   \n",
       "...           ...      ...        ...          ...        ...        ...  ...   \n",
       "168355     BA.1.1  Omicron 2021-12-27  SRR18124865     BA.1.1        1.0  0.0   \n",
       "168375     BA.1.1  Omicron 2021-12-27  SRR18124875     BA.1.1        1.0  0.0   \n",
       "170295       BA.1  Omicron 2021-12-27  SRR18124863       BA.1        1.0  0.0   \n",
       "171415    BA.1.17  Omicron 2021-12-27  SRR18124872    BA.1.17        1.0  0.0   \n",
       "178115    BA.1.18  Omicron 2022-01-07  SRR18128002    BA.1.18        1.0  0.0   \n",
       "\n",
       "       Collection_Date  \n",
       "215         2022-01-13  \n",
       "415         2022-01-13  \n",
       "435         2022-01-13  \n",
       "615         2022-01-13  \n",
       "875         2022-01-13  \n",
       "...                ...  \n",
       "168355      2021-12-27  \n",
       "168375      2021-12-27  \n",
       "170295      2021-12-27  \n",
       "171415      2021-12-27  \n",
       "178115      2022-01-07  \n",
       "\n",
       "[285 rows x 24 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_df[(us_df['Region'] == 'Maryland') & (us_df['Nt Mutations'] == mut_label)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_heatmap_data(mut_label, clinical_cryptic_df, regions, exclude=False):\n",
    "    clinical_mutation_df = clinical_cryptic_df[(clinical_cryptic_df['Nt Mutations'] == mut_label)]\n",
    "    \n",
    "    if not exclude:\n",
    "        for region in regions:\n",
    "            clinical_mutation_df = clinical_mutation_df[clinical_mutation_df['Region'] == region]\n",
    "    else:\n",
    "        for region in regions:\n",
    "            clinical_mutation_df = clinical_mutation_df[clinical_mutation_df['Region'] != region]\n",
    "\n",
    "    x_ticklabels = []\n",
    "    p_rates = []\n",
    "    has_data = []\n",
    "    sample_counts = []\n",
    "    freqs = []\n",
    "\n",
    "    for i in range(0, 8):\n",
    "        start = sampling_date_start + i*week_offset\n",
    "        end = sampling_date_start + (i+1)*week_offset\n",
    "\n",
    "        weekly_clinical_df = clinical_mutation_df[(clinical_mutation_df['Collection_Date'] >= start) & (clinical_mutation_df['Collection_Date'] < end)]\n",
    "        weekly_sample_count = weekly_clinical_df.shape[0]\n",
    "        sample_counts.append(weekly_sample_count)\n",
    "        #count_present = weekly_clinical_df[weekly_clinical_df['Support DP'] >= 10].shape[0]\n",
    "        supported_sample_df = weekly_clinical_df[(weekly_clinical_df['Support DP'] >= 10) & (weekly_clinical_df['Combined Freq'] >= freq_cutoff)]\n",
    "        supported_sample_df = supported_sample_df[(supported_sample_df['SB_p_value'] > 0.05)]\n",
    "        #supported_sample_df = weekly_clinical_df[(weekly_clinical_df['Overlapping Support DP'] >= 10) & (weekly_clinical_df['Combined Freq'] >= freq_cutoff)]\n",
    "        count_present = supported_sample_df.shape[0]\n",
    "        count_no_data = weekly_clinical_df[weekly_clinical_df['Total DP'] == 0].shape[0]\n",
    "        \n",
    "        #freq = weekly_clinical_df[weekly_clinical_df['Support DP'] >= 10]['Combined Freq'].mean()\n",
    "        freq = supported_sample_df['Combined Freq'].mean()\n",
    "        freqs.append(freq)\n",
    "\n",
    "        count_exclude_no_data = weekly_sample_count - count_no_data\n",
    "\n",
    "        if count_exclude_no_data > 0:\n",
    "            p_rate = count_present/count_exclude_no_data\n",
    "            has_data.append(2)\n",
    "        else:\n",
    "            p_rate = np.nan\n",
    "            has_data.append(0)\n",
    "\n",
    "        #time_label = start.strftime('%m/%d/%y') + ' - ' + (end-timedelta(days=1)).strftime('%m/%d/%y')\n",
    "        time_label = start.strftime('%m-%d-%y')\n",
    "        x_ticklabels.append(time_label)\n",
    "        p_rates.append(p_rate)\n",
    "\n",
    "    return x_ticklabels, p_rates, has_data, sample_counts, freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Colorado', 'California', 'New Mexico', 'Minnesota',\n",
       "       'Pennsylvania', 'Georgia', 'Maryland', 'New Hampshire',\n",
       "       'New Jersey', 'Massachusetts', 'Utah', 'Louisiana',\n",
       "       'North Carolina', 'Florida', 'Illinois', 'Alabama', 'Ohio',\n",
       "       'New York', 'South Carolina', 'Rhode Island', 'South Dakota',\n",
       "       'Vermont', 'Montana', 'Connecticut', 'Michigan', 'Puerto Rico',\n",
       "       'Maine', 'Arkansas', 'Idaho', 'West Virginia', 'Mississippi',\n",
       "       'Arizona', 'Oklahoma', 'Indiana', 'Missouri', 'Wisconsin'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_df['Region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_heatmap_array(mut_label):\n",
    "    states = ['Maryland', 'Massachusetts', 'California', 'Colorado', 'Utah']\n",
    "    \n",
    "    heatmap_array = []\n",
    "\n",
    "    for state in states:\n",
    "        _, p_rates, _, _, freqs = get_heatmap_data(mut_label, us_df, [state])\n",
    "        heatmap_array.append(p_rates)\n",
    "\n",
    "    _, p_rates, _, _, freqs = get_heatmap_data(mut_label, us_df, states, exclude=True)\n",
    "    heatmap_array.append(p_rates)\n",
    "    \n",
    "    return heatmap_array, states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A27259C;C27335T;A27344T;A27345T'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_label = selected_cryptic_mutations[4]\n",
    "mut_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_cutoff = 0.02\n",
    "\n",
    "fontsize = 12\n",
    "fig, axes  = plt.subplots(3, 1, figsize=(8, 10), sharex=False, constrained_layout=True,\n",
    "                         gridspec_kw={'height_ratios': [5, 3, 3], 'hspace':0.35})\n",
    "\n",
    "# Barplot Prevalence Rate in Houston\n",
    "\n",
    "x_ticklabels, p_rates, has_data, ww_counts, sample_counts, freqs = get_plot_data(mut_label, houston_df)\n",
    "\n",
    "ax = axes[0]\n",
    "x = np.arange(8)\n",
    "ax.bar(x, ww_counts, color='gray', alpha=0.3, width=1)\n",
    "bar = ax.bar(x, p_rates, color='c')\n",
    "#ax.scatter(x, ww_counts, marker=\"P\", s=60, color='black')\n",
    "\n",
    "ax.set_ylim(0,1)\n",
    "ax.set_ylabel('Prevalence Rate in\\nHouston Samples', fontsize=fontsize)\n",
    "ax.set_title(\", \".join(mut_label.split(';')))\n",
    "\n",
    "# Boxplot of AF in Houston\n",
    "\n",
    "ax_twin = ax.twinx()\n",
    "ax_twin.boxplot(freqs, \n",
    "                positions=x,\n",
    "                showmeans=False)\n",
    "ax_twin.set_ylim(0, 0.5)\n",
    "ax_twin.set_ylabel('Intra-host\\nAllele Frequency', fontsize=fontsize)\n",
    "\n",
    "x_ticklabels.append((sampling_date_start+8*week_offset).strftime('%m-%d-%y'))\n",
    "ax.set_xlim(-0.5,7.5)\n",
    "ax.set_xticks(np.arange(9)-0.5)\n",
    "ax.set_xticklabels(x_ticklabels, rotation=90, fontsize=12)\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.set_xlabel(' ', fontsize=fontsize)\n",
    "\n",
    "ax.text(-0.25, 1.1, f'a', transform=ax.transAxes,\n",
    "        fontsize=20, fontweight='bold', va='top')\n",
    "# Heatmap\n",
    "\n",
    "heatmap_array, state_labels = get_heatmap_array(mut_label)\n",
    "state_labels.append('Other States')\n",
    "\n",
    "sns.heatmap(heatmap_array, linewidth=2, \n",
    "            ax=axes[1],\n",
    "            vmin=0,\n",
    "            vmax=1,\n",
    "            cmap='flare',\n",
    "            yticklabels=state_labels,\n",
    "            xticklabels=[],\n",
    "            annot=True, fmt=\".2f\",\n",
    "            cbar_kws={'label': 'Prevalence Rate\\nin non-Texas Samples'},)\n",
    "\n",
    "axes[1].figure.axes[-1].yaxis.label.set_size(fontsize)\n",
    "\n",
    "axes[1].xaxis.set_ticks_position('none')\n",
    "axes[1].yaxis.set_ticks_position('none')\n",
    "axes[1].set_yticklabels(state_labels, fontsize=fontsize)\n",
    "axes[1].set_xlabel(' ', fontsize=fontsize)\n",
    "\n",
    "# bar\n",
    "df = pd.concat(get_lineage_plot_data(mut_label), axis=0).fillna(0)\n",
    "df = df.div(df.sum(axis=1), axis=0)\n",
    "df = df[['BA.1.1', 'BA.1.15', 'BA.1.17', 'BA.1.18', 'BA.1.20', 'Omicron']]\n",
    "\n",
    "ax = axes[2]\n",
    "y = df.index.to_list()\n",
    "lineages = df.columns.to_list()\n",
    "for idx, lineage in enumerate(lineages):\n",
    "    if idx == 0: \n",
    "        ax.barh(y, df[lineage].to_list(), align='center', height=.5, label=lineage, edgecolor='black')\n",
    "        sum_array = df[lineage].to_list()\n",
    "    else:\n",
    "        ax.barh(y, df[lineage].to_list(), align='center', height=.5, label=lineage, left=sum_array, edgecolor='black')\n",
    "        sum_array = list(map(add, sum_array, df[lineage].to_list())) \n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels(y, fontsize=fontsize)\n",
    "ax.set_xlabel('Consensus Pango Lineage of Cryptic Supported Samples', fontsize=fontsize)\n",
    "\n",
    "ax.set_xticks([])\n",
    "#ax.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
    "#ax.set_title('Run Time by Job')\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "#ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"bottom\"].set_visible(False)\n",
    "#ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "ax.set_xlim(0,1)\n",
    "ax.legend(loc=4, bbox_to_anchor=(1.25, 0, 0, 0))\n",
    "\n",
    "fig.savefig('c1.png', transparent=False, facecolor='white', dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C10449A;T10459C'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_label = selected_cryptic_mutations[7]\n",
    "mut_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_cutoff = 0.02\n",
    "\n",
    "fontsize = 12\n",
    "fig, axes  = plt.subplots(3, 1, figsize=(8, 10), sharex=False, constrained_layout=True,\n",
    "                         gridspec_kw={'height_ratios': [5, 3, 3], 'hspace':0.35})\n",
    "\n",
    "# Barplot Prevalence Rate in Houston\n",
    "\n",
    "x_ticklabels, p_rates, has_data, ww_counts, sample_counts, freqs = get_plot_data(mut_label, houston_df)\n",
    "\n",
    "ax = axes[0]\n",
    "x = np.arange(8)\n",
    "ax.bar(x, ww_counts, color='gray', alpha=0.3, width=1)\n",
    "bar = ax.bar(x, p_rates, color='c')\n",
    "#ax.scatter(x, ww_counts, marker=\"P\", s=60, color='black')\n",
    "\n",
    "ax.set_ylim(0,1)\n",
    "ax.set_ylabel('Prevalence Rate in\\nHouston Samples', fontsize=fontsize)\n",
    "ax.set_title(\", \".join(mut_label.split(';')))\n",
    "\n",
    "# Boxplot of AF in Houston\n",
    "\n",
    "ax_twin = ax.twinx()\n",
    "ax_twin.boxplot(freqs, \n",
    "                positions=x,\n",
    "                showmeans=False)\n",
    "ax_twin.set_ylim(0, 0.5)\n",
    "ax_twin.set_ylabel('Intra-host\\nAllele Frequency', fontsize=fontsize)\n",
    "\n",
    "x_ticklabels.append((sampling_date_start+8*week_offset).strftime('%m-%d-%y'))\n",
    "ax.set_xlim(-0.5,7.5)\n",
    "ax.set_xticks(np.arange(9)-0.5)\n",
    "ax.set_xticklabels(x_ticklabels, rotation=90, fontsize=12)\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.set_xlabel(' ', fontsize=fontsize)\n",
    "\n",
    "ax.text(-0.25, 1.1, f'b', transform=ax.transAxes,\n",
    "        fontsize=20, fontweight='bold', va='top')\n",
    "\n",
    "# Heatmap\n",
    "\n",
    "heatmap_array, state_labels = get_heatmap_array(mut_label)\n",
    "state_labels.append('Other States')\n",
    "\n",
    "sns.heatmap(heatmap_array, linewidth=2, \n",
    "            ax=axes[1],\n",
    "            vmin=0,\n",
    "            vmax=1,\n",
    "            cmap='flare',\n",
    "            yticklabels=state_labels,\n",
    "            xticklabels=[],\n",
    "            annot=True, fmt=\".2f\",\n",
    "            cbar_kws={'label': 'Prevalence Rate\\nin non-Texas Samples'},)\n",
    "\n",
    "axes[1].figure.axes[-1].yaxis.label.set_size(fontsize)\n",
    "\n",
    "axes[1].xaxis.set_ticks_position('none')\n",
    "axes[1].yaxis.set_ticks_position('none')\n",
    "axes[1].set_yticklabels(state_labels, fontsize=fontsize)\n",
    "axes[1].set_xlabel(' ', fontsize=fontsize)\n",
    "\n",
    "# bar\n",
    "df = pd.concat(get_lineage_plot_data(mut_label), axis=0).fillna(0)\n",
    "df = df.div(df.sum(axis=1), axis=0)\n",
    "df = df[['BA.1.1', 'BA.1.15', 'BA.1.17', 'BA.1.18', 'BA.1.20', 'Omicron']]\n",
    "\n",
    "ax = axes[2]\n",
    "y = df.index.to_list()\n",
    "lineages = df.columns.to_list()\n",
    "for idx, lineage in enumerate(lineages):\n",
    "    if idx == 0: \n",
    "        ax.barh(y, df[lineage].to_list(), align='center', height=.5, label=lineage, edgecolor='black')\n",
    "        sum_array = df[lineage].to_list()\n",
    "    else:\n",
    "        ax.barh(y, df[lineage].to_list(), align='center', height=.5, label=lineage, left=sum_array, edgecolor='black')\n",
    "        sum_array = list(map(add, sum_array, df[lineage].to_list())) \n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels(y, fontsize=fontsize)\n",
    "ax.set_xlabel('Consensus Pango Lineage of Cryptic Supported Samples', fontsize=fontsize)\n",
    "\n",
    "ax.set_xticks([])\n",
    "#ax.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
    "#ax.set_title('Run Time by Job')\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "#ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"bottom\"].set_visible(False)\n",
    "#ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "ax.set_xlim(0,1)\n",
    "ax.legend(loc=4, bbox_to_anchor=(1.25, 0, 0, 0))\n",
    "\n",
    "fig.savefig('c2.png', transparent=False, facecolor='white', dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A27344T;A27345T;A27354G'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_label = selected_cryptic_mutations[-2]\n",
    "mut_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_cutoff = 0.02\n",
    "\n",
    "fontsize = 12\n",
    "fig, axes  = plt.subplots(3, 1, figsize=(8, 10), sharex=False, constrained_layout=True,\n",
    "                         gridspec_kw={'height_ratios': [5, 3, 3], 'hspace':0.35})\n",
    "\n",
    "# Barplot Prevalence Rate in Houston\n",
    "\n",
    "x_ticklabels, p_rates, has_data, ww_counts, sample_counts, freqs = get_plot_data(mut_label, houston_df)\n",
    "\n",
    "ax = axes[0]\n",
    "x = np.arange(8)\n",
    "ax.bar(x, ww_counts, color='gray', alpha=0.3, width=1)\n",
    "bar = ax.bar(x, p_rates, color='c')\n",
    "#ax.scatter(x, ww_counts, marker=\"P\", s=60, color='black')\n",
    "\n",
    "ax.set_ylim(0,1)\n",
    "ax.set_ylabel('Prevalence Rate in\\nHouston Samples', fontsize=fontsize)\n",
    "ax.set_title(\", \".join(mut_label.split(';')))\n",
    "\n",
    "# Boxplot of AF in Houston\n",
    "\n",
    "ax_twin = ax.twinx()\n",
    "ax_twin.boxplot(freqs, \n",
    "                positions=x,\n",
    "                showmeans=False)\n",
    "ax_twin.set_ylim(0, 0.5)\n",
    "ax_twin.set_ylabel('Intra-host\\nAllele Frequency', fontsize=fontsize)\n",
    "\n",
    "x_ticklabels.append((sampling_date_start+8*week_offset).strftime('%m-%d-%y'))\n",
    "ax.set_xlim(-0.5,7.5)\n",
    "ax.set_xticks(np.arange(9)-0.5)\n",
    "ax.set_xticklabels(x_ticklabels, rotation=90, fontsize=12)\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.set_xlabel(' ', fontsize=fontsize)\n",
    "\n",
    "# Heatmap\n",
    "\n",
    "heatmap_array, state_labels = get_heatmap_array(mut_label)\n",
    "state_labels.append('Other States')\n",
    "\n",
    "sns.heatmap(heatmap_array, linewidth=2, \n",
    "            ax=axes[1],\n",
    "            vmin=0,\n",
    "            vmax=1,\n",
    "            cmap='flare',\n",
    "            yticklabels=state_labels,\n",
    "            xticklabels=[],\n",
    "            annot=True, fmt=\".2f\",\n",
    "            cbar_kws={'label': 'Prevalence Rate\\nin non-Texas Samples'},)\n",
    "\n",
    "axes[1].figure.axes[-1].yaxis.label.set_size(fontsize)\n",
    "\n",
    "axes[1].xaxis.set_ticks_position('none')\n",
    "axes[1].yaxis.set_ticks_position('none')\n",
    "axes[1].set_yticklabels(state_labels, fontsize=fontsize)\n",
    "axes[1].set_xlabel(' ', fontsize=fontsize)\n",
    "\n",
    "# bar\n",
    "df = pd.concat(get_lineage_plot_data(mut_label), axis=0).fillna(0)\n",
    "df = df.div(df.sum(axis=1), axis=0)\n",
    "df = df[['BA.1.1', 'BA.1.15', 'BA.1.17', 'BA.1.18', 'BA.1.20', 'Omicron', 'Delta']]\n",
    "\n",
    "ax = axes[2]\n",
    "y = df.index.to_list()\n",
    "lineages = df.columns.to_list()\n",
    "for idx, lineage in enumerate(lineages):\n",
    "    if idx == 0: \n",
    "        ax.barh(y, df[lineage].to_list(), align='center', height=.5, label=lineage, edgecolor='black')\n",
    "        sum_array = df[lineage].to_list()\n",
    "    else:\n",
    "        ax.barh(y, df[lineage].to_list(), align='center', height=.5, label=lineage, left=sum_array, edgecolor='black')\n",
    "        sum_array = list(map(add, sum_array, df[lineage].to_list())) \n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels(y, fontsize=fontsize)\n",
    "ax.set_xlabel('Consensus Pango Lineage of Cryptic Supported Samples', fontsize=fontsize)\n",
    "\n",
    "ax.set_xticks([])\n",
    "#ax.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
    "#ax.set_title('Run Time by Job')\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "#ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"bottom\"].set_visible(False)\n",
    "#ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "ax.set_xlim(0,1)\n",
    "ax.legend(loc=4, bbox_to_anchor=(1.25, 0, 0, 0))\n",
    "\n",
    "fig.savefig('c3.png', transparent=False, facecolor='white', dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}