BF 550_PROJECT-2_apal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "cd6aeced",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import statsmodels.api as sm\n",
    "df = pd.read_excel('/Users/aasthapal/Downloads/SALAMANDER.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63adcae",
   "metadata": {},
   "source": [
    "# Plot 1B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "cf1ffcce",
   "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>Bacterial abundance estimates on the skin of healthy, wild salamanders in Germany</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sample Name</td>\n",
       "      <td>Site</td>\n",
       "      <td>Region</td>\n",
       "      <td>Bacterial abundance (qPCR)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KAZ031</td>\n",
       "      <td>KAZ</td>\n",
       "      <td>Eifel</td>\n",
       "      <td>7330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KAZ033</td>\n",
       "      <td>KAZ</td>\n",
       "      <td>Eifel</td>\n",
       "      <td>31600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KAZ034</td>\n",
       "      <td>KAZ</td>\n",
       "      <td>Eifel</td>\n",
       "      <td>12600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KAZ035</td>\n",
       "      <td>KAZ</td>\n",
       "      <td>Eifel</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bacterial abundance estimates on the skin of healthy, wild salamanders in Germany  \\\n",
       "0                                        Sample Name                                  \n",
       "1                                             KAZ031                                  \n",
       "2                                             KAZ033                                  \n",
       "3                                             KAZ034                                  \n",
       "4                                             KAZ035                                  \n",
       "\n",
       "  Unnamed: 1 Unnamed: 2                  Unnamed: 3  \n",
       "0       Site     Region  Bacterial abundance (qPCR)  \n",
       "1        KAZ      Eifel                        7330  \n",
       "2        KAZ      Eifel                       31600  \n",
       "3        KAZ      Eifel                       12600  \n",
       "4        KAZ      Eifel                        5200  "
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd ##IN THIS PLOT I HAVE TRIED TO identify the bacterial abundance on salamaander skin  at various sites\n",
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import statsmodels.api as sm\n",
    "# df = pd.read_excel('SALAMANDER.xlsx', sheet_name=3)\n",
    "df = pd.read_excel('SALAMANDER.xlsx', sheet_name='Bacterial Abundance (Field)') \n",
    "# print (df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "5a2342c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q statsmodels\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "# data = sm.datasets.get_rdataset('dietox', 'geepack').data\n",
    "# md = smf.mixedlm(\"Weight ~ Time\", data, groups=data[\"Pig\"], re_formula=\"~Time\")\n",
    "# mdf = md.fit(method=[\"lbfgs\"])\n",
    "# print(mdf.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "cfa0d20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns =['Sample Name', 'Site', 'Region', 'Bacterial abundance (qPCR)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "9b546113",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "10bd7707",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ax = sns.boxplot(x='Site', y='Bacterial abundance (qPCR)', data=df)\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "# ax = df.boxplot(x = 1, y = 3, data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "1067f8f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sample Name', 'Site', 'Region', 'Bacterial abundance (qPCR)'], dtype='object')"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "24429d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.get_loc(\"Site\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "ec098639",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grouped = df.groupby(\"Site\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "b5743a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Fi = df_grouped.get_group(\"Fi\")['Bacterial abundance (qPCR)']\n",
    "df_KAZ = df_grouped.get_group(\"KAZ\")['Bacterial abundance (qPCR)']\n",
    "df_KB = df_grouped.get_group(\"KB\")['Bacterial abundance (qPCR)']\n",
    "df_SO = df_grouped.get_group(\"SO\")['Bacterial abundance (qPCR)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "494beb76",
   "metadata": {},
   "outputs": [],
   "source": [
    "###for plotting\n",
    "# fig, axs = plt.subplots(4)\n",
    "# plt.figure(figsize=(15,8))\n",
    "\n",
    "# axs[0].boxplot(x=df_Fi)\n",
    "# axs[1].boxplot(x=df_KAZ)\n",
    "# axs[2].boxplot(x=df_KB)\n",
    "# axs[3].boxplot(x=df_SO)\n",
    "\n",
    "# axs[0].semilogy(base = 10)\n",
    "# axs[1].semilogy(base = 10)\n",
    "# axs[2].semilogy(base = 10)\n",
    "# axs[3].semilogy(base = 10)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "f563e32f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# boxplot = df.boxplot(column=['Site'])\n",
    "# columns=['Col1', 'Col2', 'Col3', 'Col4'])\n",
    "# plt.boxplot(x=df_Fi)\n",
    "# plt.semilogy(base = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "5a940d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.boxplot(x=df_KAZ)\n",
    "\n",
    "# plt.semilogy(base = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "4241d7f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.boxplot(x=df_KB)\n",
    "\n",
    "# plt.semilogy(base = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "6ec06101",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.boxplot(x=df_SO)\n",
    "# plt.semilogy(base = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "id": "313115cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Plotting the various sites with abundance\n",
    "box_plot_dict = {'Fi': df_Fi, 'KAZ': df_KAZ, 'KB': df_KB, 'SO': df_SO}\n",
    "fig, ax = plt.subplots()\n",
    "bp = ax.boxplot(box_plot_dict.values(), patch_artist=True)\n",
    "for box in bp['boxes']:\n",
    "    box.set(facecolor = 'grey' )\n",
    "ax.set_xticklabels(box_plot_dict.keys())\n",
    "ax.set_facecolor('lightgrey')\n",
    "plt.semilogy(base = 10)\n",
    "plt.title('Box plot showing qPCR estimates of bacterial abundance on fire salamander skin')\n",
    "plt.xlabel('site')\n",
    "plt.ylabel('log bacterial abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96328974",
   "metadata": {},
   "source": [
    "# Figure 3B of Abundance of bacteria on Bsal Expose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "f107df38",
   "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>Bacterial abundance and density estimates associated with salamanders in the bacterial addition experiment</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>before start experiment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>after 3 days of bacterial treatments</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7 days after Bsal infection</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14 days after Bsal infection</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21 days after Bsal infection</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28days after Bsal infection</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Treatment</td>\n",
       "      <td>Individual ID</td>\n",
       "      <td>Trunk Length (mm)</td>\n",
       "      <td>Trunk width (mm)</td>\n",
       "      <td>Trunk Surface Area (mm2)</td>\n",
       "      <td>total bacteria/swab</td>\n",
       "      <td>total bacteria/mm^2</td>\n",
       "      <td>total bacteria/swab</td>\n",
       "      <td>total bacteria/mm^2</td>\n",
       "      <td>total bacteria/swab</td>\n",
       "      <td>total bacteria/mm^2</td>\n",
       "      <td>total bacteria/swab</td>\n",
       "      <td>total bacteria/mm^2</td>\n",
       "      <td>total bacteria/swab</td>\n",
       "      <td>total bacteria/mm^2</td>\n",
       "      <td>total bacteria/swab</td>\n",
       "      <td>total bacteria/mm^2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steno</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>330</td>\n",
       "      <td>571000</td>\n",
       "      <td>1730.30303</td>\n",
       "      <td>17500000</td>\n",
       "      <td>53030.30303</td>\n",
       "      <td>2670000</td>\n",
       "      <td>8090.909091</td>\n",
       "      <td>3780000</td>\n",
       "      <td>11454.545455</td>\n",
       "      <td>2760000</td>\n",
       "      <td>8363.636364</td>\n",
       "      <td>1100000</td>\n",
       "      <td>3333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Steno</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>297</td>\n",
       "      <td>670000</td>\n",
       "      <td>2255.892256</td>\n",
       "      <td>536000</td>\n",
       "      <td>1804.713805</td>\n",
       "      <td>8950000</td>\n",
       "      <td>30134.680135</td>\n",
       "      <td>8130000</td>\n",
       "      <td>27373.737374</td>\n",
       "      <td>13300000</td>\n",
       "      <td>44781.144781</td>\n",
       "      <td>5950000</td>\n",
       "      <td>20033.670034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steno</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>232</td>\n",
       "      <td>84800</td>\n",
       "      <td>365.517241</td>\n",
       "      <td>1270000</td>\n",
       "      <td>5474.137931</td>\n",
       "      <td>2010000</td>\n",
       "      <td>8663.793103</td>\n",
       "      <td>1120000</td>\n",
       "      <td>4827.586207</td>\n",
       "      <td>6500000</td>\n",
       "      <td>28017.241379</td>\n",
       "      <td>3190000</td>\n",
       "      <td>13750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bacterial abundance and density estimates associated with salamanders in the bacterial addition experiment   \\\n",
       "0                                                NaN                                                            \n",
       "1                                          Treatment                                                            \n",
       "2                                              Steno                                                            \n",
       "3                                              Steno                                                            \n",
       "4                                              Steno                                                            \n",
       "\n",
       "       Unnamed: 1         Unnamed: 2        Unnamed: 3  \\\n",
       "0             NaN                NaN               NaN   \n",
       "1   Individual ID  Trunk Length (mm)  Trunk width (mm)   \n",
       "2               1                 30                11   \n",
       "3               2                 33                 9   \n",
       "4               3                 29                 8   \n",
       "\n",
       "                 Unnamed: 4               Unnamed: 5           Unnamed: 6  \\\n",
       "0                       NaN  before start experiment                  NaN   \n",
       "1  Trunk Surface Area (mm2)      total bacteria/swab  total bacteria/mm^2   \n",
       "2                       330                   571000           1730.30303   \n",
       "3                       297                   670000          2255.892256   \n",
       "4                       232                    84800           365.517241   \n",
       "\n",
       "                              Unnamed: 7           Unnamed: 8  \\\n",
       "0  after 3 days of bacterial treatments                   NaN   \n",
       "1                    total bacteria/swab  total bacteria/mm^2   \n",
       "2                               17500000          53030.30303   \n",
       "3                                 536000          1804.713805   \n",
       "4                                1270000          5474.137931   \n",
       "\n",
       "                    Unnamed: 9          Unnamed: 10  \\\n",
       "0  7 days after Bsal infection                  NaN   \n",
       "1          total bacteria/swab  total bacteria/mm^2   \n",
       "2                      2670000          8090.909091   \n",
       "3                      8950000         30134.680135   \n",
       "4                      2010000          8663.793103   \n",
       "\n",
       "                    Unnamed: 11          Unnamed: 12  \\\n",
       "0  14 days after Bsal infection                  NaN   \n",
       "1           total bacteria/swab  total bacteria/mm^2   \n",
       "2                       3780000         11454.545455   \n",
       "3                       8130000         27373.737374   \n",
       "4                       1120000          4827.586207   \n",
       "\n",
       "                    Unnamed: 13          Unnamed: 14  \\\n",
       "0  21 days after Bsal infection                  NaN   \n",
       "1           total bacteria/swab  total bacteria/mm^2   \n",
       "2                       2760000          8363.636364   \n",
       "3                      13300000         44781.144781   \n",
       "4                       6500000         28017.241379   \n",
       "\n",
       "                   Unnamed: 15          Unnamed: 16  \n",
       "0  28days after Bsal infection                  NaN  \n",
       "1          total bacteria/swab  total bacteria/mm^2  \n",
       "2                      1100000          3333.333333  \n",
       "3                      5950000         20033.670034  \n",
       "4                      3190000                13750  "
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##in this plot, i tried to find the Bacterial abundance and density estimates associated with salamanders in the bacterial addition experiment. \n",
    "\n",
    "df_new = pd.read_excel('SALAMANDER.xlsx', sheet_name='Bacterial Abundance (Exp2)') #load the data \n",
    "# df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "137faf7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_new.drop([0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "788cd362",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.columns = [\"Treatment\", \"Individual ID\", \"Trunk Length (mm)\", \"Trunk width (mm)\", \"Trunk Surface Area (mm2)\", \"total bacteria/swab\", \"total bacteria/mm^2\", \"total bacteria/swab\", \"total bacteria/mm^2\", \"total bacteria/swab\", \"total bacteria/mm^2\", \"total bacteria/swab\", \"total bacteria/mm^2\", \"total bacteria/swab\", \"total bacteria/mm^2\", \"total bacteria/swab\", \"total bacteria/mm^2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "1ae151a5",
   "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",
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       "    }\n",
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       "    .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>Treatment</th>\n",
       "      <th>Individual ID</th>\n",
       "      <th>Trunk Length (mm)</th>\n",
       "      <th>Trunk width (mm)</th>\n",
       "      <th>Trunk Surface Area (mm2)</th>\n",
       "      <th>total bacteria/swab</th>\n",
       "      <th>total bacteria/mm^2</th>\n",
       "      <th>total bacteria/swab</th>\n",
       "      <th>total bacteria/mm^2</th>\n",
       "      <th>total bacteria/swab</th>\n",
       "      <th>total bacteria/mm^2</th>\n",
       "      <th>total bacteria/swab</th>\n",
       "      <th>total bacteria/mm^2</th>\n",
       "      <th>total bacteria/swab</th>\n",
       "      <th>total bacteria/mm^2</th>\n",
       "      <th>total bacteria/swab</th>\n",
       "      <th>total bacteria/mm^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steno</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>330</td>\n",
       "      <td>571000</td>\n",
       "      <td>1730.30303</td>\n",
       "      <td>17500000</td>\n",
       "      <td>53030.30303</td>\n",
       "      <td>2670000</td>\n",
       "      <td>8090.909091</td>\n",
       "      <td>3780000</td>\n",
       "      <td>11454.545455</td>\n",
       "      <td>2760000</td>\n",
       "      <td>8363.636364</td>\n",
       "      <td>1100000</td>\n",
       "      <td>3333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Steno</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>297</td>\n",
       "      <td>670000</td>\n",
       "      <td>2255.892256</td>\n",
       "      <td>536000</td>\n",
       "      <td>1804.713805</td>\n",
       "      <td>8950000</td>\n",
       "      <td>30134.680135</td>\n",
       "      <td>8130000</td>\n",
       "      <td>27373.737374</td>\n",
       "      <td>13300000</td>\n",
       "      <td>44781.144781</td>\n",
       "      <td>5950000</td>\n",
       "      <td>20033.670034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steno</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>232</td>\n",
       "      <td>84800</td>\n",
       "      <td>365.517241</td>\n",
       "      <td>1270000</td>\n",
       "      <td>5474.137931</td>\n",
       "      <td>2010000</td>\n",
       "      <td>8663.793103</td>\n",
       "      <td>1120000</td>\n",
       "      <td>4827.586207</td>\n",
       "      <td>6500000</td>\n",
       "      <td>28017.241379</td>\n",
       "      <td>3190000</td>\n",
       "      <td>13750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Steno</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>288</td>\n",
       "      <td>522000</td>\n",
       "      <td>1812.5</td>\n",
       "      <td>1690000</td>\n",
       "      <td>5868.055556</td>\n",
       "      <td>2930000</td>\n",
       "      <td>10173.611111</td>\n",
       "      <td>6770000</td>\n",
       "      <td>23506.944444</td>\n",
       "      <td>2490000</td>\n",
       "      <td>8645.833333</td>\n",
       "      <td>2860000</td>\n",
       "      <td>9930.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Agar</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>216</td>\n",
       "      <td>131000</td>\n",
       "      <td>606.481481</td>\n",
       "      <td>2630000</td>\n",
       "      <td>12175.925926</td>\n",
       "      <td>5960000</td>\n",
       "      <td>27592.592593</td>\n",
       "      <td>11600000</td>\n",
       "      <td>53703.703704</td>\n",
       "      <td>1810000</td>\n",
       "      <td>8379.62963</td>\n",
       "      <td>4210000</td>\n",
       "      <td>19490.740741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Agar</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>232</td>\n",
       "      <td>506000</td>\n",
       "      <td>2181.034483</td>\n",
       "      <td>2860000</td>\n",
       "      <td>12327.586207</td>\n",
       "      <td>14800000</td>\n",
       "      <td>63793.103448</td>\n",
       "      <td>9780000</td>\n",
       "      <td>42155.172414</td>\n",
       "      <td>651000</td>\n",
       "      <td>2806.034483</td>\n",
       "      <td>7810000</td>\n",
       "      <td>33663.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Steno</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>196</td>\n",
       "      <td>386000</td>\n",
       "      <td>1969.387755</td>\n",
       "      <td>1070000</td>\n",
       "      <td>5459.183673</td>\n",
       "      <td>1630000</td>\n",
       "      <td>8316.326531</td>\n",
       "      <td>9580000</td>\n",
       "      <td>48877.55102</td>\n",
       "      <td>4000000</td>\n",
       "      <td>20408.163265</td>\n",
       "      <td>8280000</td>\n",
       "      <td>42244.897959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pseudo</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>270</td>\n",
       "      <td>260000</td>\n",
       "      <td>962.962963</td>\n",
       "      <td>1200000</td>\n",
       "      <td>4444.444444</td>\n",
       "      <td>1400000</td>\n",
       "      <td>5185.185185</td>\n",
       "      <td>5490000</td>\n",
       "      <td>20333.333333</td>\n",
       "      <td>7450000</td>\n",
       "      <td>27592.592593</td>\n",
       "      <td>1430000</td>\n",
       "      <td>5296.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Agar</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>196</td>\n",
       "      <td>275000</td>\n",
       "      <td>1403.061224</td>\n",
       "      <td>3150000</td>\n",
       "      <td>16071.428571</td>\n",
       "      <td>17200000</td>\n",
       "      <td>87755.102041</td>\n",
       "      <td>6730000</td>\n",
       "      <td>34336.734694</td>\n",
       "      <td>2730000</td>\n",
       "      <td>13928.571429</td>\n",
       "      <td>7410000</td>\n",
       "      <td>37806.122449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pseudo</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>232</td>\n",
       "      <td>258000</td>\n",
       "      <td>1112.068966</td>\n",
       "      <td>3150000</td>\n",
       "      <td>13577.586207</td>\n",
       "      <td>1340000</td>\n",
       "      <td>5775.862069</td>\n",
       "      <td>5500000</td>\n",
       "      <td>23706.896552</td>\n",
       "      <td>2650000</td>\n",
       "      <td>11422.413793</td>\n",
       "      <td>2130000</td>\n",
       "      <td>9181.034483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pseudo</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>216</td>\n",
       "      <td>859000</td>\n",
       "      <td>3976.851852</td>\n",
       "      <td>2340000</td>\n",
       "      <td>10833.333333</td>\n",
       "      <td>3000000</td>\n",
       "      <td>13888.888889</td>\n",
       "      <td>5900000</td>\n",
       "      <td>27314.814815</td>\n",
       "      <td>222</td>\n",
       "      <td>1.027778</td>\n",
       "      <td>6330000</td>\n",
       "      <td>29305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Agar</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>182</td>\n",
       "      <td>226000</td>\n",
       "      <td>1241.758242</td>\n",
       "      <td>1210000</td>\n",
       "      <td>6648.351648</td>\n",
       "      <td>5140000</td>\n",
       "      <td>28241.758242</td>\n",
       "      <td>12200000</td>\n",
       "      <td>67032.967033</td>\n",
       "      <td>3060000</td>\n",
       "      <td>16813.186813</td>\n",
       "      <td>21500000</td>\n",
       "      <td>118131.868132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Steno</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>279</td>\n",
       "      <td>329000</td>\n",
       "      <td>1179.21147</td>\n",
       "      <td>342000</td>\n",
       "      <td>1225.806452</td>\n",
       "      <td>901000</td>\n",
       "      <td>3229.390681</td>\n",
       "      <td>15700000</td>\n",
       "      <td>56272.401434</td>\n",
       "      <td>4710000</td>\n",
       "      <td>16881.72043</td>\n",
       "      <td>5800000</td>\n",
       "      <td>20788.530466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pseudo</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>192</td>\n",
       "      <td>76100</td>\n",
       "      <td>396.354167</td>\n",
       "      <td>463000</td>\n",
       "      <td>2411.458333</td>\n",
       "      <td>295000</td>\n",
       "      <td>1536.458333</td>\n",
       "      <td>4000000</td>\n",
       "      <td>20833.333333</td>\n",
       "      <td>4950000</td>\n",
       "      <td>25781.25</td>\n",
       "      <td>1880000</td>\n",
       "      <td>9791.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Control</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>192</td>\n",
       "      <td>174000</td>\n",
       "      <td>906.25</td>\n",
       "      <td>43500</td>\n",
       "      <td>226.5625</td>\n",
       "      <td>520000</td>\n",
       "      <td>2708.333333</td>\n",
       "      <td>361000</td>\n",
       "      <td>1880.208333</td>\n",
       "      <td>12000000</td>\n",
       "      <td>62500</td>\n",
       "      <td>8600000</td>\n",
       "      <td>44791.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pseudo</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>248</td>\n",
       "      <td>146000</td>\n",
       "      <td>588.709677</td>\n",
       "      <td>131000</td>\n",
       "      <td>528.225806</td>\n",
       "      <td>2100000</td>\n",
       "      <td>8467.741935</td>\n",
       "      <td>3280000</td>\n",
       "      <td>13225.806452</td>\n",
       "      <td>1430000</td>\n",
       "      <td>5766.129032</td>\n",
       "      <td>897000</td>\n",
       "      <td>3616.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pseudo</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>240</td>\n",
       "      <td>194000</td>\n",
       "      <td>808.333333</td>\n",
       "      <td>335000</td>\n",
       "      <td>1395.833333</td>\n",
       "      <td>506000</td>\n",
       "      <td>2108.333333</td>\n",
       "      <td>1810000</td>\n",
       "      <td>7541.666667</td>\n",
       "      <td>1570000</td>\n",
       "      <td>6541.666667</td>\n",
       "      <td>1880000</td>\n",
       "      <td>7833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Agar</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>232</td>\n",
       "      <td>94100</td>\n",
       "      <td>405.603448</td>\n",
       "      <td>461000</td>\n",
       "      <td>1987.068966</td>\n",
       "      <td>1240000</td>\n",
       "      <td>5344.827586</td>\n",
       "      <td>2050000</td>\n",
       "      <td>8836.206897</td>\n",
       "      <td>6290000</td>\n",
       "      <td>27112.068966</td>\n",
       "      <td>2990000</td>\n",
       "      <td>12887.931034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Agar</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>232</td>\n",
       "      <td>399000</td>\n",
       "      <td>1719.827586</td>\n",
       "      <td>994000</td>\n",
       "      <td>4284.482759</td>\n",
       "      <td>1510000</td>\n",
       "      <td>6508.62069</td>\n",
       "      <td>4740000</td>\n",
       "      <td>20431.034483</td>\n",
       "      <td>2320000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2310000</td>\n",
       "      <td>9956.896552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Control</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>200</td>\n",
       "      <td>318000</td>\n",
       "      <td>1590</td>\n",
       "      <td>55600</td>\n",
       "      <td>278</td>\n",
       "      <td>598000</td>\n",
       "      <td>2990</td>\n",
       "      <td>394000</td>\n",
       "      <td>1970</td>\n",
       "      <td>1430000</td>\n",
       "      <td>7150</td>\n",
       "      <td>746000</td>\n",
       "      <td>3730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Steno</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>182</td>\n",
       "      <td>69900</td>\n",
       "      <td>384.065934</td>\n",
       "      <td>339000</td>\n",
       "      <td>1862.637363</td>\n",
       "      <td>254000</td>\n",
       "      <td>1395.604396</td>\n",
       "      <td>766000</td>\n",
       "      <td>4208.791209</td>\n",
       "      <td>2330000</td>\n",
       "      <td>12802.197802</td>\n",
       "      <td>5370000</td>\n",
       "      <td>29505.494505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Control</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>279</td>\n",
       "      <td>522000</td>\n",
       "      <td>1870.967742</td>\n",
       "      <td>114</td>\n",
       "      <td>0.408602</td>\n",
       "      <td>738</td>\n",
       "      <td>2.645161</td>\n",
       "      <td>3320000</td>\n",
       "      <td>11899.641577</td>\n",
       "      <td>8740000</td>\n",
       "      <td>31326.164875</td>\n",
       "      <td>3230000</td>\n",
       "      <td>11577.060932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Agar</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>232</td>\n",
       "      <td>136000</td>\n",
       "      <td>586.206897</td>\n",
       "      <td>202000</td>\n",
       "      <td>870.689655</td>\n",
       "      <td>1570000</td>\n",
       "      <td>6767.241379</td>\n",
       "      <td>677000</td>\n",
       "      <td>2918.103448</td>\n",
       "      <td>6450000</td>\n",
       "      <td>27801.724138</td>\n",
       "      <td>1950000</td>\n",
       "      <td>8405.172414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pseudo</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>175</td>\n",
       "      <td>408000</td>\n",
       "      <td>2331.428571</td>\n",
       "      <td>584000</td>\n",
       "      <td>3337.142857</td>\n",
       "      <td>2240000</td>\n",
       "      <td>12800</td>\n",
       "      <td>3490000</td>\n",
       "      <td>19942.857143</td>\n",
       "      <td>1700000</td>\n",
       "      <td>9714.285714</td>\n",
       "      <td>1740000</td>\n",
       "      <td>9942.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Control</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>196</td>\n",
       "      <td>13000</td>\n",
       "      <td>66.326531</td>\n",
       "      <td>77900</td>\n",
       "      <td>397.44898</td>\n",
       "      <td>58900</td>\n",
       "      <td>300.510204</td>\n",
       "      <td>130000</td>\n",
       "      <td>1016</td>\n",
       "      <td>184000</td>\n",
       "      <td>938.77551</td>\n",
       "      <td>1170000</td>\n",
       "      <td>5969.387755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Control</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>341</td>\n",
       "      <td>138000</td>\n",
       "      <td>404.692082</td>\n",
       "      <td>65300</td>\n",
       "      <td>191.495601</td>\n",
       "      <td>80400</td>\n",
       "      <td>235.777126</td>\n",
       "      <td>138000</td>\n",
       "      <td>1016</td>\n",
       "      <td>1350000</td>\n",
       "      <td>3958.944282</td>\n",
       "      <td>1010000</td>\n",
       "      <td>2961.876833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Treatment Individual ID Trunk Length (mm) Trunk width (mm)  \\\n",
       "2      Steno             1                30               11   \n",
       "3      Steno             2                33                9   \n",
       "4      Steno             3                29                8   \n",
       "5      Steno             4                32                9   \n",
       "6       Agar             5                27                8   \n",
       "7       Agar             6                29                8   \n",
       "8      Steno             7                28                7   \n",
       "9     Pseudo             8                30                9   \n",
       "10      Agar             9                28                7   \n",
       "11    Pseudo            11                29                8   \n",
       "12    Pseudo            12                27                8   \n",
       "13      Agar            14                26                7   \n",
       "14     Steno            15                31                9   \n",
       "15    Pseudo            16                24                8   \n",
       "16   Control            17                24                8   \n",
       "17    Pseudo            18                31                8   \n",
       "18    Pseudo            19                30                8   \n",
       "19      Agar            20                29                8   \n",
       "20      Agar            21                29                8   \n",
       "21   Control            23                25                8   \n",
       "22     Steno            24                26                7   \n",
       "23   Control            25                31                9   \n",
       "24      Agar            26                29                8   \n",
       "25    Pseudo            27                25                7   \n",
       "26   Control            28                28                7   \n",
       "27   Control            29                31               11   \n",
       "\n",
       "   Trunk Surface Area (mm2) total bacteria/swab total bacteria/mm^2  \\\n",
       "2                       330              571000          1730.30303   \n",
       "3                       297              670000         2255.892256   \n",
       "4                       232               84800          365.517241   \n",
       "5                       288              522000              1812.5   \n",
       "6                       216              131000          606.481481   \n",
       "7                       232              506000         2181.034483   \n",
       "8                       196              386000         1969.387755   \n",
       "9                       270              260000          962.962963   \n",
       "10                      196              275000         1403.061224   \n",
       "11                      232              258000         1112.068966   \n",
       "12                      216              859000         3976.851852   \n",
       "13                      182              226000         1241.758242   \n",
       "14                      279              329000          1179.21147   \n",
       "15                      192               76100          396.354167   \n",
       "16                      192              174000              906.25   \n",
       "17                      248              146000          588.709677   \n",
       "18                      240              194000          808.333333   \n",
       "19                      232               94100          405.603448   \n",
       "20                      232              399000         1719.827586   \n",
       "21                      200              318000                1590   \n",
       "22                      182               69900          384.065934   \n",
       "23                      279              522000         1870.967742   \n",
       "24                      232              136000          586.206897   \n",
       "25                      175              408000         2331.428571   \n",
       "26                      196               13000           66.326531   \n",
       "27                      341              138000          404.692082   \n",
       "\n",
       "   total bacteria/swab total bacteria/mm^2 total bacteria/swab  \\\n",
       "2             17500000         53030.30303             2670000   \n",
       "3               536000         1804.713805             8950000   \n",
       "4              1270000         5474.137931             2010000   \n",
       "5              1690000         5868.055556             2930000   \n",
       "6              2630000        12175.925926             5960000   \n",
       "7              2860000        12327.586207            14800000   \n",
       "8              1070000         5459.183673             1630000   \n",
       "9              1200000         4444.444444             1400000   \n",
       "10             3150000        16071.428571            17200000   \n",
       "11             3150000        13577.586207             1340000   \n",
       "12             2340000        10833.333333             3000000   \n",
       "13             1210000         6648.351648             5140000   \n",
       "14              342000         1225.806452              901000   \n",
       "15              463000         2411.458333              295000   \n",
       "16               43500            226.5625              520000   \n",
       "17              131000          528.225806             2100000   \n",
       "18              335000         1395.833333              506000   \n",
       "19              461000         1987.068966             1240000   \n",
       "20              994000         4284.482759             1510000   \n",
       "21               55600                 278              598000   \n",
       "22              339000         1862.637363              254000   \n",
       "23                 114            0.408602                 738   \n",
       "24              202000          870.689655             1570000   \n",
       "25              584000         3337.142857             2240000   \n",
       "26               77900           397.44898               58900   \n",
       "27               65300          191.495601               80400   \n",
       "\n",
       "   total bacteria/mm^2 total bacteria/swab total bacteria/mm^2  \\\n",
       "2          8090.909091             3780000        11454.545455   \n",
       "3         30134.680135             8130000        27373.737374   \n",
       "4          8663.793103             1120000         4827.586207   \n",
       "5         10173.611111             6770000        23506.944444   \n",
       "6         27592.592593            11600000        53703.703704   \n",
       "7         63793.103448             9780000        42155.172414   \n",
       "8          8316.326531             9580000         48877.55102   \n",
       "9          5185.185185             5490000        20333.333333   \n",
       "10        87755.102041             6730000        34336.734694   \n",
       "11         5775.862069             5500000        23706.896552   \n",
       "12        13888.888889             5900000        27314.814815   \n",
       "13        28241.758242            12200000        67032.967033   \n",
       "14         3229.390681            15700000        56272.401434   \n",
       "15         1536.458333             4000000        20833.333333   \n",
       "16         2708.333333              361000         1880.208333   \n",
       "17         8467.741935             3280000        13225.806452   \n",
       "18         2108.333333             1810000         7541.666667   \n",
       "19         5344.827586             2050000         8836.206897   \n",
       "20          6508.62069             4740000        20431.034483   \n",
       "21                2990              394000                1970   \n",
       "22         1395.604396              766000         4208.791209   \n",
       "23            2.645161             3320000        11899.641577   \n",
       "24         6767.241379              677000         2918.103448   \n",
       "25               12800             3490000        19942.857143   \n",
       "26          300.510204              130000                1016   \n",
       "27          235.777126              138000                1016   \n",
       "\n",
       "   total bacteria/swab total bacteria/mm^2 total bacteria/swab  \\\n",
       "2              2760000         8363.636364             1100000   \n",
       "3             13300000        44781.144781             5950000   \n",
       "4              6500000        28017.241379             3190000   \n",
       "5              2490000         8645.833333             2860000   \n",
       "6              1810000          8379.62963             4210000   \n",
       "7               651000         2806.034483             7810000   \n",
       "8              4000000        20408.163265             8280000   \n",
       "9              7450000        27592.592593             1430000   \n",
       "10             2730000        13928.571429             7410000   \n",
       "11             2650000        11422.413793             2130000   \n",
       "12                 222            1.027778             6330000   \n",
       "13             3060000        16813.186813            21500000   \n",
       "14             4710000         16881.72043             5800000   \n",
       "15             4950000            25781.25             1880000   \n",
       "16            12000000               62500             8600000   \n",
       "17             1430000         5766.129032              897000   \n",
       "18             1570000         6541.666667             1880000   \n",
       "19             6290000        27112.068966             2990000   \n",
       "20             2320000               10000             2310000   \n",
       "21             1430000                7150              746000   \n",
       "22             2330000        12802.197802             5370000   \n",
       "23             8740000        31326.164875             3230000   \n",
       "24             6450000        27801.724138             1950000   \n",
       "25             1700000         9714.285714             1740000   \n",
       "26              184000           938.77551             1170000   \n",
       "27             1350000         3958.944282             1010000   \n",
       "\n",
       "   total bacteria/mm^2  \n",
       "2          3333.333333  \n",
       "3         20033.670034  \n",
       "4                13750  \n",
       "5          9930.555556  \n",
       "6         19490.740741  \n",
       "7         33663.793103  \n",
       "8         42244.897959  \n",
       "9          5296.296296  \n",
       "10        37806.122449  \n",
       "11         9181.034483  \n",
       "12        29305.555556  \n",
       "13       118131.868132  \n",
       "14        20788.530466  \n",
       "15         9791.666667  \n",
       "16        44791.666667  \n",
       "17         3616.935484  \n",
       "18         7833.333333  \n",
       "19        12887.931034  \n",
       "20         9956.896552  \n",
       "21                3730  \n",
       "22        29505.494505  \n",
       "23        11577.060932  \n",
       "24         8405.172414  \n",
       "25         9942.857143  \n",
       "26         5969.387755  \n",
       "27         2961.876833  "
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.drop([0, 1]) #dropping the first 2 row \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "ce572856",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_new.columns[1]\n",
    "# df_new[\"Treatment\"]\n",
    "df_tbu = df_new.get(['Treatment','total bacteria/mm^2'])\n",
    "df_tbu = df_tbu.drop([0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "cbc45dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tbu.columns = [\"Treatment\", \"0 days bacteria/mm^2\", \"3 days bacteria/mm^2\", \"7 days bacteria/mm^2\", \"14 days bacteria/mm^2\", \"21 days bacteria/mm^2\", \"28 days bacteria/mm^2\"]\n",
    "# df_tbu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "18a6136d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grouped = df_tbu.groupby(['Treatment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "c4af9d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_steno = df_grouped.get_group(\"Steno\")\n",
    "# print(df_steno)\n",
    "df_agar = df_grouped.get_group(\"Agar\")\n",
    "df_control = df_grouped.get_group(\"Control\")\n",
    "df_pseudo = df_grouped.get_group(\"Pseudo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "a077a1f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_steno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "fe53f112",
   "metadata": {},
   "outputs": [],
   "source": [
    "timeLine = [0, 3, 7, 14, 21, 28] # x axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "4de409b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#take the mean of each sampls\n",
    "plt.plot(timeLine, [df_steno['0 days bacteria/mm^2'].mean(), df_steno['3 days bacteria/mm^2'].mean(), df_steno['7 days bacteria/mm^2'].mean(0), \n",
    "                     df_steno['14 days bacteria/mm^2'].mean(), df_steno['21 days bacteria/mm^2'].mean(), df_steno['28 days bacteria/mm^2'].mean()],marker='*', color='gold')\n",
    "\n",
    "plt.plot(timeLine, [df_agar['0 days bacteria/mm^2'].mean(), df_agar['3 days bacteria/mm^2'].mean(), df_agar['7 days bacteria/mm^2'].mean(0), \n",
    "                     df_agar['14 days bacteria/mm^2'].mean(), df_agar['21 days bacteria/mm^2'].mean(), df_agar['28 days bacteria/mm^2'].mean()],marker='*', color='darkgrey')\n",
    "plt.plot(timeLine, [df_control['0 days bacteria/mm^2'].mean(), df_control['3 days bacteria/mm^2'].mean(), df_control['7 days bacteria/mm^2'].mean(0), \n",
    "                     df_control['14 days bacteria/mm^2'].mean(), df_control['21 days bacteria/mm^2'].mean(), df_control['28 days bacteria/mm^2'].mean()],marker='*', color='dimgrey')\n",
    "plt.plot(timeLine, [df_pseudo['0 days bacteria/mm^2'].mean(), df_pseudo['3 days bacteria/mm^2'].mean(), df_pseudo['7 days bacteria/mm^2'].mean(0), \n",
    "                     df_pseudo['14 days bacteria/mm^2'].mean(), df_pseudo['21 days bacteria/mm^2'].mean(), df_pseudo['28 days bacteria/mm^2'].mean()],marker='*', color='red')\n",
    "plt.semilogy(base = 10)\n",
    "plt.xticks(np.arange(0, 30, 7))\n",
    "plt.legend([\"df_steno\" , \"df_control*\", \"df_control\",\"df_pseudo\"])\n",
    "plt.xlabel(\"time(days)\")\n",
    "plt.ylabel(\"Log bacterial density\")\n",
    "# ax.annotate(\"\", xy=(0.5, 0.5), xytext=(4, 104),arrowprops=dict(arrowstyle=\"->\"))\n",
    "# plt.annotate('Brackmard minimum',ha = 'center', va = 'bottom',xytext = (),xy = (),arrowprops = {'facecolor' : 'black'}))\n",
    "plt.title('Density plot')\n",
    "plt.errorbar()\n",
    "plt.yscale('log')\n",
    "# plt.locator_params(numticks=12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "id": "e496f301",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_pos = np.arange(len(labels))\n",
    "\n",
    "# dataf = [steno_mean,agar_mean,control_mean,pseudo_mean]\n",
    "\n",
    "# error = [steno_std, agar_std, control_std,pseudo_std]\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# ax.errorbar(x_pos, dataf,yerr=0.8,align='center',alpha=0.5,ecolor='black', capsize=10)\n",
    "# ax.set_ylabel('density')\n",
    "# ax.set_xticks(x_pos)\n",
    "# ax.set_xticklabels(labels)\n",
    "# ax.set_title('bacterial abundance')\n",
    "# ax.yaxis.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "id": "d50be89c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Standard error of the mean : 171.450\n",
      "The Standard error of the mean : 2079.251\n",
      "The Standard error of the mean : 3697.772\n",
      "The Standard error of the mean : 2833.500\n",
      "The Standard error of the mean : 4620.064\n",
      "The Standard error of the mean : 171.450\n"
     ]
    }
   ],
   "source": [
    "##standard error of means \n",
    "\n",
    "import scipy.stats as stat\n",
    "#define dataset 1\n",
    "data1 = [1.73E+03,2.26E+03,3.66E+02,1.81E+03,6.06E+02,2.18E+03,1.97E+03,9.63E+02,1.40E+03,1.11E+03,3.98E+03,1.24E+03,1.18E+03,3.96E+02,9.06E+02,5.89E+02,8.08E+02,4.06E+02,1.72E+03,1.59E+03,3.84E+02, 1.87E+03, 5.86E+02,2.33E+03,6.63E+01,4.05E+02]\n",
    "#define dataset 2 by repeated the first dataset twice\n",
    "data2=[5.30E+04,1.80E+03,5.47E+03,5.87E+03,1.22E+04,1.23E+04,5.46E+03,4.44E+03,1.61E+04,1.36E+04,1.08E+04,6.65E+03,1.23E+03,2.41E+03,2.27E+02,5.28E+02,1.40E+03,1.99E+03,4.28E+03,2.78E+02,1.86E+03,4.09E-01,8.71E+02,3.34E+03,3.97E+02,1.91E+02]\n",
    "data3=[1.15E+04,2.74E+04,4.83E+03,2.35E+04,5.37E+04,4.22E+04,4.89E+04,2.03E+04,3.43E+04,2.37E+04,2.73E+04,6.70E+04,5.63E+04,2.08E+04,1.88E+03,1.32E+04,7.54E+03,8.84E+03,2.04E+04,1.97E+03,4.21E+03,1.19E+04,2.92E+03,1.99E+04,1016,1016]\n",
    "data4=[8.36E+03,4.48E+04,2.80E+04,8.65E+03,8.38E+03,2.81E+03,2.04E+04,2.76E+04,1.39E+04,1.14E+04,1.03E+00,1.68E+04,1.69E+04,2.58E+04,6.25E+04,5.77E+03,6.54E+03,2.71E+04,1.00E+04,7.15E+03,1.28E+04,3.13E+04,2.78E+04,9.71E+03,9.39E+02,3.96E+03]\n",
    "data5=[3.33E+03,2.00E+04,1.38E+04,9.93E+03,1.95E+04,3.37E+04,4.22E+04,5.30E+03,3.78E+04,9.18E+03,2.93E+04,1.18E+05,2.08E+04,9.79E+03,4.48E+04,3.62E+03,7.83E+03,1.29E+04,9.96E+03,3.73E+03,2.95E+04,1.16E+04,8.41E+03,9.94E+03,5.97E+03,2.96E+03]\n",
    "data6=[1.73E+03,2.26E+03,3.66E+02,1.81E+03,6.06E+02,2.18E+03,1.97E+03,9.63E+02,1.40E+03,1.11E+03,3.98E+03,1.24E+03,1.18E+03,3.96E+02,9.06E+02,5.89E+02,8.08E+02,4.06E+02,1.72E+03,1.59E+03,3.84E+02,1.87E+03,5.86E+02,2.33E+03,6.63E+01,4.05E+02]\n",
    "#calculate standard error of the mean \n",
    "result1 = stat.sem(data1)\n",
    "result2=stat.sem(data2)\n",
    "result3=stat.sem(data3)\n",
    "result4=stat.sem(data4)\n",
    "result5=stat.sem(data5)\n",
    "result6=stat.sem(data6)\n",
    "\n",
    "#Print the result\n",
    "print(\"The Standard error of the mean : %.3f\"%result1)\n",
    "print(\"The Standard error of the mean : %.3f\"%result2)\n",
    "print(\"The Standard error of the mean : %.3f\"%result3)\n",
    "print(\"The Standard error of the mean : %.3f\"%result4)\n",
    "print(\"The Standard error of the mean : %.3f\"%result5)\n",
    "print(\"The Standard error of the mean : %.3f\"%result6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "id": "c1d72814",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x= np.arange(0, 30, 7)\n",
    "# n_nts = len(x)\n",
    "# SEMs = np.arange(171.450,2079.251,3697.772,2833.500,4620.064,171.450)\n",
    "# ax.plot(nts, SEMs, 'b-', label='SEM for Normal dist.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e9a936",
   "metadata": {},
   "source": [
    "# Figure 3D: Survival plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "357f7944",
   "metadata": {},
   "outputs": [
    {
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       "        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>Bsal Infection Intensity, clinical scores, time to death, and survival data ssociated with salamanders in the bacterial addition experiment</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <th>Unnamed: 23</th>\n",
       "      <th>Unnamed: 24</th>\n",
       "      <th>Unnamed: 25</th>\n",
       "      <th>Unnamed: 26</th>\n",
       "      <th>Unnamed: 27</th>\n",
       "      <th>Unnamed: 28</th>\n",
       "      <th>Unnamed: 29</th>\n",
       "      <th>Unnamed: 30</th>\n",
       "      <th>Unnamed: 31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Treatment</td>\n",
       "      <td>Individual ID</td>\n",
       "      <td>Bsal load D7</td>\n",
       "      <td>Bsal load D14</td>\n",
       "      <td>Bsal load D21</td>\n",
       "      <td>Bsal load D28</td>\n",
       "      <td>Bsal load D30</td>\n",
       "      <td>Bsal load D32</td>\n",
       "      <td>Bsal load D35</td>\n",
       "      <td>Bsal load D39</td>\n",
       "      <td>...</td>\n",
       "      <td>clinical score D39</td>\n",
       "      <td>clinical score D42</td>\n",
       "      <td>Clinical score D49</td>\n",
       "      <td>clinical score D56</td>\n",
       "      <td>Clinical score D63</td>\n",
       "      <td>Clinical score D70</td>\n",
       "      <td>Clinical score D77</td>\n",
       "      <td>Rate of Change in Infection Intensity (Day 0-28)</td>\n",
       "      <td>Days until Death</td>\n",
       "      <td>Survival Status</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sten.rhizo</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>1030</td>\n",
       "      <td>888</td>\n",
       "      <td>196.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.542857</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sten.rhizo</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>894</td>\n",
       "      <td>2340</td>\n",
       "      <td>9980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>649</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sten.rhizo</td>\n",
       "      <td>3</td>\n",
       "      <td>-</td>\n",
       "      <td>71.6</td>\n",
       "      <td>1096</td>\n",
       "      <td>11800</td>\n",
       "      <td>12340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>837.742857</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sten.rhizo</td>\n",
       "      <td>4</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>3800</td>\n",
       "      <td>13460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>961.428571</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bsal Infection Intensity, clinical scores, time to death, and survival data ssociated with salamanders in the bacterial addition experiment   \\\n",
       "0                                          Treatment                                                                                             \n",
       "1                                         Sten.rhizo                                                                                             \n",
       "2                                         Sten.rhizo                                                                                             \n",
       "3                                         Sten.rhizo                                                                                             \n",
       "4                                         Sten.rhizo                                                                                             \n",
       "\n",
       "       Unnamed: 1    Unnamed: 2     Unnamed: 3     Unnamed: 4     Unnamed: 5  \\\n",
       "0   Individual ID  Bsal load D7  Bsal load D14  Bsal load D21  Bsal load D28   \n",
       "1               1             -           1030            888          196.4   \n",
       "2               2             -            894           2340           9980   \n",
       "3               3             -           71.6           1096          11800   \n",
       "4               4             -              -           3800          13460   \n",
       "\n",
       "      Unnamed: 6     Unnamed: 7     Unnamed: 8     Unnamed: 9  ...  \\\n",
       "0  Bsal load D30  Bsal load D32  Bsal load D35  Bsal load D39  ...   \n",
       "1            NaN            NaN           2520            NaN  ...   \n",
       "2            NaN            NaN            NaN            NaN  ...   \n",
       "3          12340            NaN            NaN            NaN  ...   \n",
       "4            NaN            NaN            NaN            NaN  ...   \n",
       "\n",
       "          Unnamed: 22         Unnamed: 23         Unnamed: 24  \\\n",
       "0  clinical score D39  clinical score D42  Clinical score D49   \n",
       "1                 NaN                   3                   4   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "\n",
       "          Unnamed: 25         Unnamed: 26         Unnamed: 27  \\\n",
       "0  clinical score D56  Clinical score D63  Clinical score D70   \n",
       "1                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "\n",
       "          Unnamed: 28                                       Unnamed: 29  \\\n",
       "0  Clinical score D77  Rate of Change in Infection Intensity (Day 0-28)   \n",
       "1                 NaN                                        -59.542857   \n",
       "2                 NaN                                               649   \n",
       "3                 NaN                                        837.742857   \n",
       "4                 NaN                                        961.428571   \n",
       "\n",
       "        Unnamed: 30      Unnamed: 31  \n",
       "0  Days until Death  Survival Status  \n",
       "1                49                1  \n",
       "2                28                1  \n",
       "3                30                1  \n",
       "4                28                1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## this it the plot Bsal Infection Intensity, clinical scores, time to death, and survival data ssociated with salamanders in the bacterial addition experiment. \n",
    "##We see that there is a correlation in the bacterial density and salander survival\n",
    "df = pd.read_excel('SALAMANDER.xlsx', sheet_name=9)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "c86fd268",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tbu = df.get([\"Bsal Infection Intensity, clinical scores, time to death, and survival data ssociated with salamanders in the bacterial addition experiment \", \"Unnamed: 30\", \"Unnamed: 31\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "a54c9450",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tbu = df_tbu.drop([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "8bde8d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grouped = df_tbu.groupby(\"Bsal Infection Intensity, clinical scores, time to death, and survival data ssociated with salamanders in the bacterial addition experiment \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "68816c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_pseudo = df_grouped.get_group(\"Pseudo\")['Unnamed: 30']\n",
    "list_StenRizo = df_grouped.get_group(\"Sten.rhizo\")['Unnamed: 30']\n",
    "list_controlNoTreatment = df_grouped.get_group(\"Control (No Treatment)\")['Unnamed: 30']\n",
    "list_steno = df_grouped.get_group(\"Steno\")['Unnamed: 30']\n",
    "list_agarRinse = df_grouped.get_group(\"Control (Agar rinse)\")['Unnamed: 30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "1ca70854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     28\n",
       "10    35\n",
       "11    63\n",
       "14    32\n",
       "16    39\n",
       "17    28\n",
       "Name: Unnamed: 30, dtype: object"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_pseudo = list_pseudo.dropna(axis=0, how=\"any\", inplace=False)\n",
    "list_pseudo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "7d4dfe86",
   "metadata": {},
   "outputs": [],
   "source": [
    "##created a list to store the probabilities of each type \n",
    "prob_pseudo = []\n",
    "prob_StenRizo = []\n",
    "prob_controlNoTreatment = []\n",
    "prob_steno = []\n",
    "prob_agarRinse = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "310f4f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(80): # for the entire timeframe\n",
    "  # Pseudo\n",
    "  alive = list_pseudo[list_pseudo > i] #calculating the probability of people who will be alive in pseudo if it's above the value given in the dataset, it will die.\n",
    "  prob_pseudo.append(len(alive)/list_pseudo.size) #probability is equal to alive/total\n",
    "  # StenRizo\n",
    "  alive = list_StenRizo[list_StenRizo > i]\n",
    "  prob_StenRizo.append(len(alive)/list_StenRizo.size)\n",
    "  # controlNoTreatment\n",
    "  alive = list_controlNoTreatment[list_controlNoTreatment > i]\n",
    "  prob_controlNoTreatment.append(len(alive)/list_controlNoTreatment.size)\n",
    "  # Steno\n",
    "  alive = list_steno[list_steno > i]\n",
    "  prob_steno.append(len(alive)/list_steno.size)\n",
    "  # AgarRinse\n",
    "  alive = list_agarRinse[list_agarRinse > i]\n",
    "  prob_agarRinse.append(len(alive)/list_agarRinse.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e6549f3",
   "metadata": {},
   "source": [
    "STENO= ORANGE \n",
    "PSEUDO==BLUE\n",
    "purple== agarRinse== CONTROL.\n",
    "RED== CONTROL* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "id": "bee8aa62",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_axis = [i for i in range(80)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "id": "104a9ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_axis, prob_pseudo) #plotting the probabilities OF how many were alive on each day\n",
    "plt.plot(x_axis, prob_StenRizo)\n",
    "plt.plot(x_axis, prob_controlNoTreatment)\n",
    "plt.plot(x_axis, prob_steno)\n",
    "plt.plot(x_axis, prob_agarRinse)\n",
    "plt.yticks(np.arange(0, 1.25, 0.25))\n",
    "plt.xticks(np.arange(30, 90,10 ))\n",
    "plt.legend([\"prob_pseudo\" , \"prob_steno\",\"prob_StenRizo\", \"CONTROL*\",\"CONTROL\"])\n",
    "plt.xlabel(\"time(days)\")\n",
    "plt.ylabel(\"survival probability\")\n",
    "plt.title('Survival curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d756b1f7",
   "metadata": {},
   "source": [
    "# PCA for figure 2C "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "0d1e3b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "###in this plot, I have tried to reduce the dimensions of multiple samples present in the dataframe. \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "f8185994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "452"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_excel('SALAMANDER.xlsx', sheet_name='rarified OTUTable (Exp1)')\n",
    "# print(dataset)\n",
    "X = dataset.iloc[1:,1:-1].values\n",
    "len(X)\n",
    "# y = dataset.iloc[:, -1].values \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "id": "1e27fa66",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "bde43b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = X.iloc[0, :]\n",
    "# columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "3dcfabab",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "id": "c05d34bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2987,    4,    0,    2,    5,    3, 3145,   21,    6,   12,   10,\n",
       "               6,    6,    0, 2956,    9,    1,    2,    0,  877,   69,   24,\n",
       "             650, 1745],\n",
       "           dtype='int64', name=0)"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns = columns\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "ce6f746d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.74491022, -0.14707408, -0.07803285, ..., -0.10083799,\n",
       "         1.72803573,  8.76264708],\n",
       "       [10.45465357,  2.63342059,  5.16641026, ...,  0.09539058,\n",
       "        19.81089939,  1.43095821],\n",
       "       [ 5.18998224,  0.70045638,  0.26070858, ...,  0.64483056,\n",
       "         2.96284717,  2.42876202],\n",
       "       ...,\n",
       "       [-0.14306145, -0.19168095, -0.07803285, ..., -0.10083799,\n",
       "        -0.12418142, -0.19589583],\n",
       "       [-0.14306145, -0.19168095, -0.07803285, ..., -0.10083799,\n",
       "        -0.12418142, -0.19589583],\n",
       "       [-0.14306145, -0.19168095, -0.07803285, ..., -0.10083799,\n",
       "        -0.10043505, -0.19589583]])"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler #standard scaling\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X)\n",
    "X_train\n",
    "# X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "3ab052f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             0         1\n",
      "0     7.579115 -9.228242\n",
      "1    10.256071 -6.332933\n",
      "2    11.191093 -9.093267\n",
      "3     0.727204 -1.228583\n",
      "4     8.248411 -2.414428\n",
      "..         ...       ...\n",
      "446  -0.641652  0.261510\n",
      "447  -0.641367  0.259557\n",
      "448  -0.625201  0.248433\n",
      "449  -0.635321  0.256379\n",
      "450  -0.652577  0.269514\n",
      "\n",
      "[451 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components = 2) #pca for 2 principal components\n",
    "X_train = pca.fit_transform(X_train)\n",
    "pca_df = pd.DataFrame(data = X_train, columns=range(0,2))\n",
    "print(pca_df)\n",
    "# X_test = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "65a5b0e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25888762, 0.1825569 ])"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explained_variance=pca.explained_variance_ratio_ ##variance in the PCs\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "id": "8dd6a0d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "PC_values = np.arange(pca.n_components_) + 1\n",
    "# plt.plot(PC_values, pca.explained_variance_ratio_, 'ro-',linewidth=1)\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.bar(PC_values, pca.explained_variance_ratio_)\n",
    "plt.title('PCA')\n",
    "plt.xlabel('PCA1')\n",
    "plt.ylabel('PCA2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "796df9be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.decomposition import PCA \n",
    "# # sklearn_pca = PCA(n_components=2)\n",
    "# # Y_sklearn = sklearn_pca.fit_transform(df)\n",
    "# # print(Y_sklearn)\n",
    "# plt.figure(figsize=(15,8))\n",
    "# plt.scatter(pca_df['0'],pca_df['1'])\n",
    "\n",
    "# plt.xlabel(\"PC1\")\n",
    "# plt.ylabel(\"PC2\")\n",
    "# plt.title('PCA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "id": "53f70b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(451, 2)"
      ]
     },
     "execution_count": 719,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, X_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "# plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "#              alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "# for i, j in enumerate(np.unique(y_set)):\n",
    "#     plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "#                 c = ListedColormap(('red', 'green', 'blue'))(i), label = j)\n",
    "\n",
    "plt.scatter(X_train[:, 0], X_train[:, 1])\n",
    "plt.title('PCA Treated sample')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602442d2",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "##from the control sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "3f73c1e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_excel('SALAMANDER.xlsx', sheet_name='rarified OTUTable(Field)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "88d9f393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[510, 0, 0, ..., 0, 0, 0],\n",
       "       [88, 0, 0, ..., 0, 0, 0],\n",
       "       [11, 0, 7, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 7, 0]], dtype=object)"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xi = dataset.iloc[1:, 1:-1].values\n",
    "Xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "aa6e0187",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xi = pd.DataFrame(Xi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "7edc351b",
   "metadata": {},
   "outputs": [],
   "source": [
    "col = Xi.iloc[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "ec3a6715",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xi = Xi.drop([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "1b01bc02",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xi.columns = col\n",
    "# Xi.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "f4f8f86c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.34766015e+01, -1.65001599e-01, -1.90212212e-01, ...,\n",
       "        -1.00334111e-01, -1.64613509e-02, -4.51081805e-02],\n",
       "       [ 9.12632222e+00, -1.65001599e-01,  7.99975502e+00, ...,\n",
       "        -1.00334111e-01, -1.64613509e-02, -4.51081805e-02],\n",
       "       [ 4.94773265e+00,  2.82528938e+01,  3.31977374e+00, ...,\n",
       "         1.65628540e+01,  9.96076343e-01, -4.51081805e-02],\n",
       "       ...,\n",
       "       [-6.65748297e-02, -1.65001599e-01, -1.90212212e-01, ...,\n",
       "        -1.00334111e-01, -1.64613509e-02, -4.51081805e-02],\n",
       "       [-6.65748297e-02, -1.65001599e-01, -1.90212212e-01, ...,\n",
       "        -1.00334111e-01, -1.64613509e-02, -4.51081805e-02],\n",
       "       [-6.65748297e-02, -1.65001599e-01, -1.90212212e-01, ...,\n",
       "        -1.00334111e-01,  6.92315035e-01, -4.51081805e-02]])"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "Xi_train = sc.fit_transform(Xi)\n",
    "Xi_train\n",
    "# X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "94a62f5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0          1\n",
      "0       0.523394  -0.575146\n",
      "1       7.427444  12.280494\n",
      "2     373.831116 -57.813572\n",
      "3      51.683844  -4.397139\n",
      "4      -0.801984  -0.704871\n",
      "...          ...        ...\n",
      "6146   -0.830752  -0.424063\n",
      "6147   -0.833018  -0.448170\n",
      "6148   -0.827352  -0.387904\n",
      "6149   -0.831885  -0.436117\n",
      "6150   -0.856071  -0.713017\n",
      "\n",
      "[6151 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components = 2)\n",
    "Xi_train = pca.fit_transform(Xi_train)\n",
    "PCA_df = pd.DataFrame(data = Xi_train, columns=range(0,2))\n",
    "print(PCA_df)\n",
    "# X_test = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "75ff96df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.21331489, 0.0718017 ])"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explained_vari=pca.explained_variance_ratio_\n",
    "explained_vari"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "id": "5e74bfa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "PC_val = np.arange(pca.n_components_) + 1\n",
    "# plt.plot(PC_values, pca.explained_variance_ratio_, 'ro-',linewidth=1)\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.bar(PC_val, pca.explained_variance_ratio_)\n",
    "plt.title('PCA')\n",
    "plt.xlabel('PCA1')\n",
    "plt.ylabel('PCA2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "266d00f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.decomposition import PCA \n",
    "# # sklearn_pca = PCA(n_components=2)\n",
    "# # Y_sklearn = sklearn_pca.fit_transform(df)\n",
    "# # print(Y_sklearn)\n",
    "# plt.figure(figsize=(15,8))\n",
    "# plt.scatter(PCA_DF['0'],PCA_DF['1'])\n",
    "\n",
    "# plt.xlabel(\"PC1\")\n",
    "# plt.ylabel(\"PC2\")\n",
    "# plt.title('PCA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "id": "c2af884b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(6151, 2)"
      ]
     },
     "execution_count": 661,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "Xi_set, yi_set = Xi_train, Xi_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = Xi_set[:, 0].min() - 1, stop = Xi_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = Xi_set[:, 1].min() - 1, stop = Xi_set[:, 1].max() + 1, step = 0.01))\n",
    "# plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "#              alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "# for i, j in enumerate(np.unique(y_set)):\n",
    "#     plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "#                 c = ListedColormap(('red', 'green', 'blue'))(i), label = j)\n",
    "\n",
    "plt.scatter(Xi_train[:, 0], Xi_train[:, 1])\n",
    "plt.title('PCA Control sample')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "\n",
    "Xi_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "id": "b5b0f8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7fda1e1040>"
      ]
     },
     "execution_count": 693,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Both the control and exposed\n",
    "\n",
    "plt.scatter(X_train[:, 0], X_train[:, 1]) #EXP DATA\n",
    "plt.scatter(Xi_train[:, 0], Xi_train[:, 1]) #feild data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ad786d",
   "metadata": {},
   "source": [
    "# Figure 2d: abundance of different bacterial species\n",
    "In this plot, i have taken a filtered dataframe with only those 6 types of bacteria and their abundance on different samples "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "id": "8256bda3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import statsmodels.api as sm\n",
    "# df = pd.read_excel('SALAMANDER.xlsx', sheet_name=3)\n",
    "control = pd.read_excel('SALAMANDER.xlsx', sheet_name = 'rarified OTUTable(Field)') #loading the data- control\n",
    "bsal_exposed = pd.read_excel('SALAMANDER.xlsx', sheet_name = 'rarified OTUTable (Exp1)')\n",
    "#loading the data- treatment (bsal_exposed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "id": "ee783b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "id": "25881976",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q statsmodels\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "# data = sm.datasets.get_rdataset('dietox', 'geepack').data\n",
    "# md = smf.mixedlm(\"Weight ~ Time\", data, groups=data[\"Pig\"], re_formula=\"~Time\")\n",
    "# mdf = md.fit(method=[\"lbfgs\"])\n",
    "# print(mdf.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "id": "e90cddfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "control_columns = control.iloc[0,:]  \n",
    "# print(control_columns)\n",
    "bsal_exposed_columns = bsal_exposed.iloc[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "id": "ac5936d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "control = control.drop([0]) \n",
    "\n",
    "bsal_exposed = bsal_exposed.drop([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "id": "9fc2e0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "control.columns = control_columns\n",
    "bsal_exposed.columns = bsal_exposed_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "id": "d08464ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "taxonomies = ['k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales', \n",
    "         'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Actinomycetaceae; g__Actinomyces; s__', \n",
    "         'k__Bacteria; p__Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Dyadobacter; s__',\n",
    "         'k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',\n",
    "         'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Lactococcus; s__',\n",
    "         'k__Bacteria; p__Fusobacteria; c__Fusobacteriia; o__Fusobacteriales; f__Fusobacteriaceae; g__Fusobacterium; s__',\n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Aeromonadales; f__Aeromonadaceae; g__; s__',\n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas', \n",
    "         'k__Bacteria; p__Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Dyadobacter; s__', \n",
    "         'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Microbacteriaceae; g__Leucobacter; s__', \n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas', \n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__', \n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__acidaminiphila',\n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__maltophilia',\n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__panacihumi']\n",
    "control = control[control.taxonomy.isin(taxonomies)] # Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "id": "1b8635ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "taxonomies = ['k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales',\n",
    "              'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Actinomycetaceae; g__Actinomyces; s__',\n",
    "              'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Brevibacteriaceae; g__Brevibacterium; s__',\n",
    "              'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Corynebacteriaceae; g__Corynebacterium',\n",
    "              'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dermabacteraceae; g__Brachybacterium',\n",
    "              'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dermacoccaceae; g__Dermacoccus; s__',\n",
    "              'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dietziaceae; g__Dietzia; s__timorensis',\n",
    "              'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Microbacteriaceae; g__Leucobacter; s__',\n",
    "              'k__Bacteria; p__Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Dyadobacter; s__',\n",
    "              'k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',\n",
    "              'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Lactococcus; s__',\n",
    "              'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Lactococcus; s__garvieae',\n",
    "              'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__']\n",
    "bsal_exposed = bsal_exposed[bsal_exposed.taxonomy.isin(taxonomies)] # Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "id": "800aea6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "control.drop('#OTU ID', axis=1, inplace=True)\n",
    "bsal_exposed.drop('#OTU ID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "id": "a9421200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The count values\n",
    "control_counts = control.iloc[:, :-1].values\n",
    "exposed_counts = bsal_exposed.iloc[:, :-1].values\n",
    "control_counts = pd.DataFrame(control_counts)\n",
    "exposed_counts = pd.DataFrame(exposed_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "id": "42d877bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The taxonomy column\n",
    "control = control.iloc[:, -1].values\n",
    "bsal_exposed = bsal_exposed.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "id": "fa84fe1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "control_replaced = []\n",
    "bsal_exposed_replaced = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "id": "f526dd41",
   "metadata": {},
   "outputs": [],
   "source": [
    "##for loop for every type of bacteria from the taxonomy \n",
    "\n",
    "stenos = ['k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas', \n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__', \n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__acidaminiphila',\n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__maltophilia',\n",
    "         'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__panacihumi']\n",
    "\n",
    "for element in control:\n",
    "  if element == \"k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__\":\n",
    "    control_replaced.append(\"Chryseobacterium\")\n",
    "  elif element == \"k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Lactococcus; s__\":\n",
    "    control_replaced.append(\"Lactococcus\") \n",
    "  elif element in stenos:\n",
    "    control_replaced.append(\"Stenotrophomonas\")\n",
    "  elif element == \"k__Bacteria; p__Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Dyadobacter; s__\":\n",
    "    control_replaced.append(\"Dyadobacter\")\n",
    "  elif element == \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Actinomycetaceae; g__Actinomyces; s__\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Brevibacteriaceae; g__Brevibacterium; s__\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Brevibacteriaceae; g__Brevibacterium; s__aureum\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Corynebacteriaceae; g__Corynebacterium\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Corynebacteriaceae; g__Corynebacterium; s__\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dermabacteraceae; g__Brachybacterium\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dermacoccaceae; g__Dermacoccus; s__\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dietziaceae; g__Dietzia; s__timorensis\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dietziaceae; g__Dietzia; s__timorensis\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Microbacteriaceae; g__Leucobacter; s__\":\n",
    "    control_replaced.append(\"Actinobacteria\")\n",
    "  elif element == \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Microbacteriaceae; g__Leucobacter; s__\":\n",
    "    control_replaced.append(\"Leucobacter\")\n",
    "  # else:\n",
    "  #   control_replaced.append(element)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "id": "108ed4f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for element in control:\n",
    "#   if element == \"k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__\":\n",
    "#     control_replaced.append(\"Chryseobacterium\")\n",
    "#   elif element == \"k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Lactococcus; s__\":\n",
    "#     control_replaced.append(\"Lactococcus\") \n",
    "#   elif element == \"k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__\":\n",
    "#     control_replaced.append(\"Stenotrophomonas\")\n",
    "#   elif element == \"k__Bacteria; p__Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Dyadobacter; s__\":\n",
    "#     control_replaced.append(\"Dyadobacter\")\n",
    "#   elif element == \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Actinomycetaceae; g__Actinomyces; s__\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Brevibacteriaceae; g__Brevibacterium; s__\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Brevibacteriaceae; g__Brevibacterium; s__aureum\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Corynebacteriaceae; g__Corynebacterium\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Corynebacteriaceae; g__Corynebacterium; s__\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dermabacteraceae; g__Brachybacterium\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dermacoccaceae; g__Dermacoccus; s__\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dietziaceae; g__Dietzia; s__timorensis\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dietziaceae; g__Dietzia; s__timorensis\" or \"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Microbacteriaceae; g__Leucobacter; s__\":\n",
    "#     control_replaced.append(\"Actinobacteria\")\n",
    "#   elif element ==\"k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Microbacteriaceae; g__Leucobacter; s__\":\n",
    "#     control_replaced.append(\"Leucobacter\")\n",
    "\n",
    "#   # else:\n",
    "#   #   control_replaced.append(element)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "id": "cda00b77",
   "metadata": {},
   "outputs": [],
   "source": [
    "##list of all the taxonomy found after filtering out \n",
    "Actinomycetales = ['k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales',\n",
    "       'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales',\n",
    "       'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales',\n",
    "       'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Actinomycetaceae; g__Actinomyces; s__',\n",
    "       'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Brevibacteriaceae; g__Brevibacterium; s__',\n",
    "       'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Corynebacteriaceae; g__Corynebacterium',\n",
    "       'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dermabacteraceae; g__Brachybacterium',\n",
    "       'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dermacoccaceae; g__Dermacoccus; s__',\n",
    "       'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Dietziaceae; g__Dietzia; s__timorensis']\n",
    "\n",
    "Leucobacter = ['k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Microbacteriaceae; g__Leucobacter; s__',\n",
    "       'k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Microbacteriaceae; g__Leucobacter; s__']\n",
    "\n",
    "Dyadobacter = ['k__Bacteria; p__Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Dyadobacter; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Dyadobacter; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Dyadobacter; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Dyadobacter; s__']\n",
    "\n",
    "Chryseobacterium = ['k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',\n",
    "       'k__Bacteria; p__Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__[Weeksellaceae]; g__Chryseobacterium; s__',]\n",
    "\n",
    "Lactococcus = [  'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Lactococcus; s__',\n",
    "       'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Lactococcus; s__garvieae',]    \n",
    "\n",
    "Stenotrophomonas = ['k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__',\n",
    "       'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__',\n",
    "       'k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae; g__Stenotrophomonas; s__']\n",
    "\n",
    "for element in bsal_exposed:\n",
    "  if element in Chryseobacterium:\n",
    "    bsal_exposed_replaced.append(\"Chryseobacterium\")\n",
    "  elif element in Dyadobacter:\n",
    "    bsal_exposed_replaced.append(\"Dyadobacter\")\n",
    "  elif element in Stenotrophomonas:\n",
    "    bsal_exposed_replaced.append(\"Stenotrophomonas\")\n",
    "  elif element in Leucobacter:\n",
    "    bsal_exposed_replaced.append(\"Leucobacter\")\n",
    "  elif element in Lactococcus:\n",
    "    bsal_exposed_replaced.append(\"Lactococcus\") \n",
    "  elif element in Actinomycetales:\n",
    "    bsal_exposed_replaced.append(\"Actinomycetales\")  \n",
    "  else:\n",
    "    bsal_exposed_replaced.append(element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "id": "08a374bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "control_counts[\"taxonomy\"] = control_replaced\n",
    "exposed_counts[\"taxonomy\"] = bsal_exposed_replaced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "id": "9258bb30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2  3  4  5  6  7  8  9   ... 14  15 16 17 18 19 20 21 22 23\n",
      "7   0  0  0  6  2  2  0  0  0  0  ...  1  16  1  1  2  5  4  0  1  2\n",
      "22  0  0  0  0  0  0  2  0  0  0  ...  0   1  0  2  0  0  0  0  0  0\n",
      "\n",
      "[2 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "# Creating a DataFrame for each microbe to do the further analysis\n",
    "chryseobacteriumControl = control_counts[control_counts.taxonomy == \"Chryseobacterium\"].iloc[:, :-1]\n",
    "# print(chryseobacteriumControl)\n",
    "lactococcusControl = control_counts[control_counts.taxonomy == \"Lactococcus\"].iloc[:, :-1]\n",
    "stenotrophomonasControl = control_counts[control_counts.taxonomy == \"Stenotrophomonas\"].iloc[:, :-1]\n",
    "# print(lactococcusControl)\n",
    "dyadobacterControl = control_counts[control_counts.taxonomy == \"Dyadobacter\"].iloc[:, :-1]\n",
    "actinobacteriaControl = control_counts[control_counts.taxonomy == \"Actinobacteria\"].iloc[:, :-1]\n",
    "leucobacterControl = control_counts[control_counts.taxonomy == \"Leucobacter\"].iloc[:, :-1]\n",
    "# print(dyadobacterControl)\n",
    "# print(stenotrophomonasControl)\n",
    "# minValuesstenotrophomonasControl= stenotrophomonasControl.min() ##Min values are zero and hence their relative abundance cant be calculated.\n",
    "# print(minValuesstenotrophomonasControl)\n",
    "actinobacteriaExposed = exposed_counts[exposed_counts.taxonomy == \"Actinomycetales\"].iloc[:, :-1]\n",
    "chryseobacteriumExposed = exposed_counts[exposed_counts.taxonomy == \"Chryseobacterium\"].iloc[:, :-1]\n",
    "# print(chryseobacteriumExposed)\n",
    "dyadobacterExposed = exposed_counts[exposed_counts.taxonomy == \"Dyadobacter\"].iloc[:, :-1] \n",
    "stenotrophomonasExposed = exposed_counts[exposed_counts.taxonomy == \"Stenotrophomonas\"].iloc[:, :-1]\n",
    "# print(stenotrophomonasExposed)\n",
    "leucobacterExposed = exposed_counts[exposed_counts.taxonomy == \"Leucobacter\"].iloc[:, :-1]\n",
    "# print(leucobacterExposed)\n",
    "lactococcusExposed = exposed_counts[exposed_counts.taxonomy == \"Lactococcus\"].iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "id": "c6945e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEXCAYAAACtTzM+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAeeklEQVR4nO3deZRdVZn38W8lJkESBrWhIWiQl+EnsSCRyguiQdGAQlCRwdaFqwVZEHCB8toM2oJC8AVakaGFgAjYIBKRZjQQg0YiDS9zSQhl9KG1TSuQ7hbQtmWohKTeP/a+5KZI3To1nHuqbn6ftVjcMz/73NR57t77nH3aenp6MDMzG1N1AGZmNjI4IZiZGeCEYGZmmROCmZkBTghmZpY5IZiZGeCEYKOMpH0ldVUdR81wxyPpK5IOHsR2CyVNHa44bOP0uqoDMLP1vB9YPtCNImJ2CbHYRsYJwUY0SUcDJwNrgGeBfwImSboBeBuwCXBsRNwr6RrgjcCOwELgGGCviHgy72sxcAnwHHAhMBboAc6LiJsljQe+Brw3L3sM+FxE/FnS24FLgTflbS6IiO/mMCdJugnYCfgTMCcinpS0CzAP2AzYFlgKfDwiXpa0F/BNYCKwCjgF2BWYAZwvaQ1wZ4N4VgAPAbsDXwIuAg4HJgGXRkR7LvO+tWlJZ+Vzs12OpxNYAhwJ7ACcFhHfH/i3ZK3CTUY2YkmaRrogHhARuwM/BE4H3gxcFBHTgSuAs+o22zQi3h4RpwLXkpICknYEdgHuAOYCF0ZEB3A06Vc5wBeBV4COiJgGPAP8g6TX5WNfkuM4EDhX0t55u7fk/U0H5gPX5fnHAtdGxDtJyWIH4CBJ44DbgLPzhftY4B+By4FHgVMj4ta+4qkra1dE7JrXLWomcAiwBzAbmBoR7wFOzOfFNmJOCDaSzQLuiojfA0TExcDxwG8i4qG8zlJg67pt7qv7fBnwqXwBngNcFRFrgBuBeZKuBzpIv7ABPgQcDDwmaSnwUWAqKZFsEhG35DieAW4GDsjbLYuI+/Pna4AZkrYAvgD8QdJppIv9ZNIv+N2ANRFxZ95fZ0TsFhFre5W/r3hq7u371PVpcUT8d0S8REowi/L835BqV7YRc5ORjWSvkJpnAJD0elIz0eq6dXqAtrrpv9Q+5GabZaSL6hHAXnn+FZIWAB8gXdTPkiRSs8xJEfGjfLxJpCapbevjyMYA4/LnNb2W9eQYv0/6G7uR1PwzJce6XrnysdqBX/XaT1/xvKasDc7H+F7Lu3tNr8Yscw3BRrIlwH6Sts3TxwFfH+A+5gHnAw/nX/ZIuh94R0RcQ6o5bAlsA9wFnChpvKQxwJXAeaQL9WpJh+btJwOHAT/Jx5gmaXpdjPdFxIvAB0nNQj/Iy/YiXeQD6JG0f97fHsDdpL/HV1iXaPqKp5E/AFMkbS2pDfjEAM6VbeScEGzEiogngFOBRZIeJ/2aP36Au7mD1Ezzrbp5pwFnS3oM+BkwNyJWAF8FVpA6b5eTfmmfHBGrSc01J+Uax2LShX5J3t8vgTNzjB8hddJCaoq6VdITpL6Oe4CdIqIbODRvszTHdmhErCL1VZwn6ci+4mlU2IhYno/1KPAg8NvCZ8o2em0e/tpaWe74vQpojwj/YzdrwH0I1rIkXQvsS7rV08nArB+uIZiZGeA+BDMzy5wQzMwMGKV9CJ2dnROA/w2s5LX3gJuZ2YaNJT1X80hHR0fvZ1JGZ0IgJYPBPKVpZmawD+s/1Q+M3oSwEmCXXXZh/PjeD2JWo6uri/b29qrDGFatVqZWKw+0XplarTwwssq0atUqnnzyScjX0N5Ga0JYAzB+/HgmTJhQdSyvGkmxDJdWK1OrlQdar0ytVh4YkWXaYFO7O5XNzAxwQjAzs8wJwczMACcEMzPLnBDMzAxwQjAzs8wJwczMACcEMxukVauHf9SYjo6OYd9nGXG2qtH6YJqZVWz8uLF8+OTbqw6jXwsuOLjqEEYN1xDMzAxwQjAzs8wJwczMACcEMzPLnBDMzAxwQjAzs8wJwczMgJKfQ5B0BHAGMA64OCLm9Vp+JnA08Mc868re65iZWXOUlhAkbQecA3QA3cD9kpZExPK61WYAn4iIB8qKw8zMiimzyWg/4O6IeD4iXgBuAg7vtc4M4EuSlkm6VNImJcZjZmYNlJkQJrP+i5xXAm+uTUiaBDwGnArsAWwJfLnEeMzMrIEy+xDGAD11023A2tpERPwFmF2blnQB8B3g9KIH6OrqGnqUw6izs7PqEIZdq5Wp1coD1ZWpjIHoylL191718YsqMyE8BexTN70N8ExtQtIUYL+I+E6e1QasHsgB2tvbmTBhwlDjHBadnZ2j6g+kiFYrU6uVB1qzTGWo8hyNpO+ou7u74Q/pMhPCYuAsSVsBLwCHAXPqlr8EfF3SEmAFcAJwa4nxmJlZA6X1IUTE06TmnyXAUmB+RDwsaaGkGRHxB+A4YAEQpBrCBWXFY2ZmjZX6HEJEzAfm95o3u+7zzcDNZcZgZmbF+EllMzMDnBDMzCxzQjAzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwczMstcVWUnSm4HdgbuA7SLid6VGZWZmTddvDUHSQcD9wDxga2C5pIPLDszMzJqrSJPRV4C9gD9FxEpgJnB2kZ1LOkLSckn/KumEBusdJOm3hSI2M7NSFEkIY3MiACAilgI9/W0kaTvgHFICmQ7MkTR1A+v9NfANoK1YyGZmVoYiCeFFSVPISUDSPsDLBbbbD7g7Ip6PiBeAm4DDN7DeVcDcgvGamVlJinQqfxH4MbCtpAeAnYHDCmw3GVhZN70S2LN+BUmfA34OPFgoWjMzK02/CSEi7pf0TmBvYCzwQEQ8V2DfY1i/aakNWFubkNROSiyzgDcPJOiarq6uwWxWms7OzqpDGHatVqZWKw9UV6aOjo5KjjsYVX/vVR+/qH4TgqTdgfMi4iBJuwE/lfTxiIh+Nn0K2KduehvgmbrpjwHbAo8C44HJku6NiPptGmpvb2fChAlFVy9VZ2fnqPoDKaLVytRq5YHWLFMZqjxHI+k76u7ubvhDukgfwuWkdn4i4gngLOCKAtstBmZJ2krSpqTawKLawog4MyJ2iYjpwGzgmYEkAzMzG15FEsLEiLi1NhERtwGb97dRRDwNnA4sAZYC8yPiYUkLJc0YXLhmZlaWIp3KPZJ2j4hlAJJ2BdYU2XlEzAfm95o3ewPrrQDeWmSfZmZWjiIJ4cvAPZKeyNNvAz5ZXkhmZlaFIncZ3SFJwLuBV4CHIuK/So/MzMyaquhop7vm/48DZko6tKR4zMysIkVuO70SOBD4NeueK+gBbikxLjMza7IifQizgF0j4n/KDsbMzKpTpMno904GZmatr0gN4f9JugFYALxUmxkRbjIyM2shRRLC3vn/x9TNcx+CmVmLKXLb6fuaEYiZmVWryF1GOwMnApNII5aOBXaKiHeXHJuZmTVRkU7l+aTRSN8FrACmAk802sDMzEafIglhs4j4DHAX8CNgf9b1K5iZWYsokhBqL8P5NdAeEX+iwDuVzcxsdClyl9GvJV0MXAtcLWkSaQgLMzNrIUVqCJ8B7o2Ix4ArgfcDc0qNyszMmq7IbacvAjfnz5eT3qBmZmYtps+EIGktffcV9EREkeYmMzMbJRpd1LciPXfwVeDfSe9RXgMcBWxfemRmZtZUfSaEiHgOQNKMfNtpzTclPVp6ZGZm1lRFOpUn5jemASBpN2BCeSGZmVkVivQDnAE8KGkZKYFMBY4oNSozM2u6IncZ3SLpPmBmnvUvEfFsuWGZmVmz9dtkJGlT0is0twfeCnxK0t+VHJeZmTVZkSaj60iJ4AnWf6eymZm1kCIJYXfSO5VfKTsYMzOrTqF3KpcehZmZVa5IDeEJYImkRaz/TuULS4vKzMyarkhC2Jw09PVOJcdiZmYVKnLb6aebEYiZmVWryDuVF7CBu4oi4iOlRGRmZpUo0mR0U93n8cDhwCNFdi7pCNKTzuOAiyNiXq/lhwBzgbF5n3MiYlWRfZuZ2fAq0mR0bf20pGuAn/W3naTtgHOADqAbuF/SkohYnpdPBC4F9oiI/5R0A2kk1W8PrAhmZjYcitx22lsbMLnAevsBd0fE8xHxAqmmcXhtYZ731pwMNgW2Bv44iHjMzGwYDLQPoQ1oB+4psO/JwMq66ZXAnvUrRMRqSQcC3wOeBn5cYL9mZlaCgfYh9ACXUezCPYb1O6PbgLW9V4qIHwFvknQu6fWchUdS7erqKrpqU3R2dlYdwrBrtTK1WnmgujJ1dHRUctzBqPp7r/r4RRXqQ5D0JmBvYDXwUESsKbDvp4B96qa3AZ6pTUh6IzAjImrJ5XrgB0UDB2hvb2fChJHxaobOzs5R9QdSRKuVqdXKA61ZpjJUeY5G0nfU3d3d8Id0kdFODwICOBX4MvBLSe8pcOzFwCxJW+U+gsOARXXL24DvSZqSpz8G3Fdgv2ZmVoIincpfBd4bEe+NiJnAQUC/w1ZExNPA6cASYCkwPyIelrQwv5bzOWAOcIekxwEBXxhkOczMbIiK9CH0RMQvahMR8XNJhe5Oioj5wPxe82bXfb4NuK1QpGZmVqo+E0Ju4wd4RNIpwLdIncJHAXeXH5qZmTVToxrCs6S7hNry9NfrpnuAU8oNzczMmqnPhBARfTYL5buOzMyshRTpQ3iVpF2Bk4C/BSaWEpGZmVWiUEKQ9EHg88D+wL3Ah8sMyszMmq9Rp/ImwJGkGsHWpIfG2iNi3+aEZmZmzdTo9tHfkZ45OAOYHBEnAB6a2sysRTVqMrqXNPTEX4CXJN3VnJDMzKwKfdYQIuIwYBpp2IorSWMTvUHSDk2KzczMmqjhE8cRsTIi5gLbA58FOoHIL7MxM7MWUuguozy66c3AzZJ2AY4vNSozM2u6AT2HABARTwJ/V0IsZmZWocG8QtPMzFqQE4KZmQHF3qm8DXA1sDPpNtTvAkdFxMqGG5qZ2ahSpIZwGemdBS8Bz5NednNVeSGZmVkViiSEt0bElcDaiFgdEV8ApvS3kZmZjS5FEsLa+jekSdqs4HZmZjaKFLmw3wJcD2wh6TjS29JuLDUqMzNrun4TQkScCywEHiENf/1t4OyS4zIzsyYrcpfRccD8iLiuCfGYmVlFijQZvQ/4N0lXS3pn2QGZmVk1ijQZfQLYhTSw3TcldUk6qfTIzMysqQrdLRQRfyT1HZxHej/CF8sMyszMmq9IH8I7gKOBjwE/B74O/LDkuMzMrMmKjHZ6O/AdYM+I+F3J8ZiZWUWKJITtI6Kn9EjMzKxSfSYESfdFxEzgz5LqE0Ib0BMRm5cenZmZNU2jGsLH8v/bN7CsrYRYzMysQn0mhLrhrb8VEQfWL5P0IOBnEszMWkijJqObSM8f7ChpWd2icUB3kZ1LOgI4I29zcUTM67X8YGAuqcbxW+DT+RZXMzNrskbPIZwCfA54Bvhs3X/HkJ5ebkjSdsA5wExgOjBH0tS65ZsDlwMHRcQ0YBlw1mAKYWZmQ9eoyWgFsEKSImJt/TJJEwvsez/g7oh4Pm9zE3A46wbGGwecEBFP5+llwCcHFr6ZmQ2XIredfljS2cAkUtPOWOCNwGb9bDcZqH/N5kpgz9pERDwH3Aog6fWkp58vKRy5mZkNqyIJ4RukfoDjga8BhwB/LrDdGKD37apre68kaQtSYng8Iq4tsN9XdXV1DWT10nV2dlYdwrBrtTK1WnmgujJ1dHRUctzBqPp7r/r4RRVJCC9ExA8kTQdeBj4D/AI4tZ/tngL2qZvehtQf8SpJ2wJ3kV668/mCMb+qvb2dCRMmDHSzUnR2do6qP5AiWq1MrVYeaM0ylaHKczSSvqPu7u6GP6SLDG73sqQJwK+B6bk/ociTy4uBWZK2krQpcBiwqLZQ0lhgAXBjRPwfPw1tZlatIjWEHwJ3AkcCD0jaB3i2v40i4mlJpwNLgPHAVRHxsKSFwFeAtwB7AK+TdHje7NGIOGYQ5TAzsyHqNyFExLmSvpcv8AcD7wG+X2TnETEfmN9r3uz88VEKDr9tZmbla/Rg2qG9pmfkj78nPVtwS4lxmZlZkzWqIXy2wbIenBDMzFpKowfT+n0a2czMWkeRN6ZtA1wN7ExqKroOODIi/qPk2MzMrImKdOpeBtwGvAT8EVhKShBmZtZCiiSEt0bElcDaiFgdEV8AppQcl5mZNVmRhLBW0qvrSdqs4HZmZjaKFLmw3wJcD2wh6TjSMBM3lhqVmZk1Xb8JISLOBRYCjwD7A9+OiLllB2ZmZs1VZOgKIuI60t1FAEjaPyJ+UlpUNmCrVq9h/Lixw7rPMgbkKiNOMxsejZ5U7iC9n+A50qstn5U0BbgYOBB4fVMitELGjxvLh0++veow+rXggoOrDsHM+tCoyegy4Gbg34Az8jhGy4CJwLQmxGZmZk3UqMloi4i4IA9T/STwN8DxEXFDc0IzM7NmalRDeBEgItYAmwAHORmYmbWuRgmhre7zsxHxWNnBmJlZdRo1GY2R9AZyYqj/DBARz5ccm5mZNVGjhLAb6c1otSTwXN2yHsD3DpqZtZBGw197eAozs42IL/pmZgY4IZiZWeaEYGZmgBOCmZllTghmZgY4IZiZWeaEYGZmgBOCmZllTghmZgY4IZiZWeaEYGZmgBOCmZlljUY7HTJJRwBnAOOAiyNiXh/rfRe4OyKuKTMeMzPrW2k1BEnbAecAM4HpwBxJU3utM1nSAuDwsuIwM7Niymwy2o/0q//5iHgBuInXXvg/CdwO3FhiHGZmVkCZTUaTgZV10yuBPetXiIjzASTNHMwBurq6Bh1cGTo7Oys7dkdHR2XHHqgqz1OVxy5LVWXyv7nRc/yiykwIY0hvVqtpA9YO5wHa29uZMGHCcO5y0Do7O0fVH0iVqjpPrfgdtWKZylDlORpJ31F3d3fDH9JlNhk9BWxbN70N8EyJxzMzsyEoMyEsBmZJ2krSpsBhwKISj2ctZNXqNcO+zzJ+pZURp1lVSmsyioinJZ0OLAHGA1dFxMOSFgJfiYhHyzq2jX7jx43lwyffXnUY/VpwwcFVh2A2bEp9DiEi5gPze82bvYH1jiozDjMz65+fVDYzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwaxp/I4HG+lKHf7azNbxOx5spHMNwczMACcEMzPLNsqE4LZcM7PX2ij7ENyWa2b2WhtlDcHMbEM29taDjbKGYGa2IRt764FrCGZmBjghmJlZ5oRgZmaAE4KZmWVOCGZmBjghmJlZ5oRgZmaAE4KZmWVOCGZmBpT8pLKkI4AzgHHAxRExr9fy6cBVwObAvwDHR8QrZcZkZmYbVloNQdJ2wDnATGA6MEfS1F6rfQ84MSJ2AdqAY8uKx8zMGiuzhrAfcHdEPA8g6SbgcODsPL098PqIeDCvfw0wF7i8wL7HAqxatWrQwW05ceygt22W7u7uAa3famVqtfJA65Wp1coDrVmmmrpr5gYL2dbT0zPIkBqT9PfAxIg4I08fA+wZEXPy9N7A+RExM0/vBCzMtYWGOjs7ZwL3lhK4mVnr26ejo+O+3jPLrCGMAeqzTRuwdgDLG3kE2AdYCfgtMmZmxYwFtiVdQ1+jzITwFOmiXbMN8Eyv5ds2WN6njo6ObuA12c3MzPr1m74WlHnb6WJglqStJG0KHAYsqi2MiH8HXpb07jzrb4EflRiPmZk1UFpCiIingdOBJcBSYH5EPCxpoaQZebVPAhdJ+hUwCfhmWfGYmVljpXUqm5nZ6OInlc3MDHBCMDOzzAnBzMwAJwQzM8tKHdxuY9HfIH6jkaTNgfuBD0XEiorDGRJJZwJ/kyfvjIjTqoxnOEg6mzQUTA9wdURcWHFIw0LSN4C/ioijqo5lqCQtAbYGVudZx0XEQxWG1C/XEIao4CB+o4qkvUgP/vU7jMhIJ2k/4APAO0jfT4ekQyoNaogkvRd4P7A7MAP4rCRVG9XQSZoFHFl1HMNBUhvp72daREzP/43oZABOCMPh1UH8IuIFoDaI32h2LHACBZ8cH+FWAidHxKqIWA38EphScUxDEhH3AO/LQ8VvTarpv1BtVEMj6Y2kH1bnVh3LMKkl6B9LelzSiZVGU5CbjIZuMumiU7MS2LOiWIZFRBwD0AI/OomIX9Q+S9qZ1HT07r63GB0iYrWkucApwD8DT1cc0lBdQXqQ9S1VBzJM3gD8FPgsqSn5Z5IiIn5SbViNuYYwdEMZpM+aRNLbgZ8Ap0bEv1Ydz3CIiDOBrUgX0VH7LpE8EvLvI+KnVccyXCLigYj4VET8d0Q8C1wNzK46rv44IQzdoAfps+bI42X9FPhiRFxbdTxDJelt+W2DRMSLwC2k/oTR6uPAByQtJb0v5SOSLqo2pKGRNDP3idS0sa5zecRyk9HQLQbOkrQVqR33MGBOtSFZjaS3ALcBH4+IuysOZ7j8L2CupJmk2unBwHeqDWnwImL/2mdJRwH7RsTnq4toWGwJnC3pXaQmoyOB4yuNqADXEIaor0H8Kg3K6p0CbAJcKGlp/m/E/2E2EhELgTuBx4BO4P6IuKHaqKxeRNzB+t/RdyLigWqj6p8HtzMzM8A1BDMzy5wQzMwMcEIwM7PMCcHMzAAnBDMzy5wQrDSS9pXUVXUcNcMdj6SvSDp4ENstLHMARElHSnog32K7XNK3JW05xH3+WNJfDXCbEfX9W/+cEMwG7/2kh44GJCJmR8TyEuJB0peAY4CPRsR0YBrpCdkFQ9z1/v2vYqOdn1S2YSHpaOBkYA3wLOuGMZ4k6QbgbaQHxI6NiHslXQO8EdgRWEi6iO0VEU/m/S0GLgGeAy4ExpKeyj0vIm6WNB74GvDevOwx4HMR8ec8btGlwJvyNhdExHfr4rkJ2An4EzAnIp6UtAswD9iMNBTJUtLTzS/n4cC/CUwEVpEedtuVNPT0+ZLWkB5C6iueFcBDpOElvgRcRBoRdxJwaUS05zLvW5uWdFY+N9vleDpJDz8eCewAnBYR3+/1HUwE/h7YIyL+E14dBO9U4JB8znry+ZyVv6uHgM9HxP/kOK/Jy6YA342IL0v6p3yIJZJmA/f2Ks+TDc63jSKuIdiQSZpGuhgeEBG7Az8kPb0N8Gbgovxr9QrgrLpNN42It0fEqcC1pKSApB1JY8nfAcwFLoyIDuBo0q9ygC8CrwAdETGNNH7UP0h6XT7+JTmWA4FzJe2dt3tL3t90YD5wXZ5/LHBtRLyTlCx2AA6SNI409MXZ+cJ9LPCPwOXAo6TB8m7tK566snZFxK553aJmAocAe5AGRpsaEe8BTsznpbe3AS/1HrwvIl6MiOsjYhXpRU6TSTWHaaRrwPl1q0+KiH2AdwGnSNohIj6dl70vIn5fXx5SzaPR+bZRxAnBhsMs4K7axSIiLo6I2vAQv6l7MchS0vj9NffVfb4M+FS+AM8BroqINcCNwDxJ1wMdpF+kAB8ijeHzWB4U7aPAVFIi2SQibsmxPAPcDByQt1sWEffnz9cAMyRtAXwB+IOk00gX+8mkX/C7AWsi4s68v86I2C0ieo9o21c8Nff2dfIaWJxHy3yJlGAW5fm/IdWueltL/3/TBwLfiojVuQyX5Hk1t8OrQ7L8Vx/HgXXl6e982yjiJiMbDq9QNwS4pNcD2+fJ+hEee0ijPtb8pfYhN9ssI11UjwD2yvOvkLSA9NazA0gDCYrULHNSRPwoH3MSqUlqW9YfjhzSRbLW1r+m17KeHOP3SX8PN5Kaf6bkWNcrWz5WO/CrXvvpK57XlLXB+Rjfa3l3r+n+RstcDoyTtHN9LUHSJqQRUY9hXdNbTf25AXipQXz1auXpvb8N7dNGCdcQbDgsAfaTVBsG/Djg64PYzzxS88XD+Zcmku4H3hER15BqDluShhi/CzhR0nhJY4ArgfNIF+rVkg7N208mjUBbezHJtNrQ0TnO+/IQ0h8kNQv9IC/bi3SxC6BH0v55f3sAd5P+dl5h3YWvr3ga+QMwRdLW+ZWLnxjAuXqNiOgmNd1dLemvc7wTSH0WE/M5XQR8RtK4HOcJrDs3jaxhwxf5/s63jSJOCDZkEfEEcCqwSNLjpF/ygxlR9A5SM8236uadRhpG+DHgZ8DciFgBfBVYQeq8XU76JXtyfk3mR4GTco1jMelCvyTv75fAmTnOj7Cu8/tLwK2SniD1ddwD7JQvsofmbZbm2A7N7fE/BM6TdGRf8TQqbL7T6ApSX8SDwG8Ln6m+93kuqcnmrhzv4zmW2u2x/xf4D1Lz3S9JF/mTCuz6n4F7cu2o/nj9nW8bRTzaqY0YuSPyKqA9IvwP06zJ3IdgI4Kka4F9Sbd6OhmYVcA1BDMzA9yHYGZmmROCmZkBTghmZpY5IZiZGeCEYGZmmROCmZkB8P8BTLe+3TBLek4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria.\n",
    "for i in range(len(chryseobacteriumControl)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(chryseobacteriumControl.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(chryseobacteriumControl.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('chryseobacterium')\n",
    "plt.xlabel('chryseobacterium Control')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a42665ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "id": "be1dfdd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria.\n",
    "for i in range(len(lactococcusControl)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(lactococcusControl.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(lactococcusControl.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('Lactococcus')\n",
    "plt.xlabel('lactococcus Control')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "id": "49656d2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria.\n",
    "for i in range(len(dyadobacterControl)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(dyadobacterControl.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(dyadobacterControl.iloc[i, :].max()/sum[i])#relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('dyadobacter')\n",
    "plt.xlabel('dyadobacter Control')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "id": "47a5aedf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(actinobacteriaControl)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(actinobacteriaControl.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(actinobacteriaControl.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('actinobacteria')\n",
    "plt.xlabel('actinobacteria Control')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "id": "826a26b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(actinobacteriaExposed)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(actinobacteriaExposed.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(actinobacteriaExposed.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('actinobacteria')\n",
    "plt.xlabel('actinobacteria Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "id": "bda38490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(chryseobacteriumExposed)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(chryseobacteriumExposed.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(chryseobacteriumExposed.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('chryseobacterium')\n",
    "plt.xlabel('chryseobacterium Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "id": "38c07c48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(dyadobacterExposed)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(dyadobacterExposed.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(dyadobacterExposed.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('dyadobacter')\n",
    "plt.xlabel('dyadobacter Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "id": "10b4eaf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(stenotrophomonasExposed)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(stenotrophomonasExposed.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(stenotrophomonasExposed.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('stenotrophomonas')\n",
    "plt.xlabel('stenotrophomonas Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "id": "49e3a16f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(leucobacterExposed)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(leucobacterExposed.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "   toBePlotted.append(leucobacterExposed.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('leucobacter')\n",
    "plt.xlabel('leucobacter Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()\n",
    "# print(leucobacterExposed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "id": "f4558d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(lactococcusExposed)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(lactococcusExposed.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(lactococcusExposed.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('Lactococcus')\n",
    "plt.xlabel('lactococcus Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "id": "2d01b372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(leucobacterControl)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "    sum.append(leucobacterControl.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:2]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "    toBePlotted.append(leucobacterControl.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "# plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.bar([1, 2, 3, 4], [0.28, 0.28, 0.28, 0.14]) \n",
    "plt.title('leucobacter')\n",
    "plt.xlabel('leucobacter Control')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()\n",
    "###i was unable to filter out values from leucobacter in control for some weird reason so, i manually calulted the value and plotted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "id": "91d242bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(stenotrophomonasControl)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(stenotrophomonasControl.iloc[i, :].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(stenotrophomonasControl.iloc[i, :].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('stenotrophomonas')\n",
    "plt.xlabel('stenotrophomonas Control') \n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fea6bb8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5362de16",
   "metadata": {},
   "source": [
    "# PLOTS With the ABUNDANCE ALONG THE COLUMN (SAMPLES\n",
    "\n",
    "Note: Same as above plots just that it is considering the values along the axis 0 which are the samples having all the bacterial population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "id": "03d7c294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###newplot\n",
    "sum = []\n",
    "for i in range(len(chryseobacteriumControl[0])):\n",
    "  sum.append(chryseobacteriumControl.iloc[:, i].sum())\n",
    "\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(chryseobacteriumControl.iloc[:,i].max()/sum[i])\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('chryseobacterium')\n",
    "plt.xlabel('chryseobacterium Control')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 748,
   "id": "272c639b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# sum = []\n",
    "# for i in range(len(lactococcusControl[0])):\n",
    "#   sum.append(lactococcusControl.iloc[:,i].sum())\n",
    "# indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "# toBePlotted = []\n",
    "# for i in indices:\n",
    "#   toBePlotted.append(lactococcusControl.iloc[:, i].max()/sum[i])\n",
    "# plt.bar([i for i in range(len(indices))], toBePlotted) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "id": "6792d8e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = []\n",
    "for i in range(len(dyadobacterControl[0])):\n",
    "  sum.append(dyadobacterControl.iloc[:,i].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(dyadobacterControl.iloc[:,i].max()/sum[i])\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('dyadobacterControl')\n",
    "plt.xlabel('dyadobacterControl')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "id": "8a797845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = []\n",
    "for i in range(len(actinobacteriaControl[0])):\n",
    "  sum.append(actinobacteriaControl.iloc[:,i].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(actinobacteriaControl.iloc[:,i].max()/sum[i])\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('actinobacteria')\n",
    "plt.xlabel('actinobacteria Control')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "id": "7191a796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = []\n",
    "for i in range(len(actinobacteriaExposed[0])):\n",
    "  sum.append(actinobacteriaExposed.iloc[:,i].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(actinobacteriaExposed.iloc[:,i].max()/sum[i])\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('actinobacteria')\n",
    "plt.xlabel('actinobacteria Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "id": "455facc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = []\n",
    "for i in range(len(chryseobacteriumExposed[0])):\n",
    "  sum.append(chryseobacteriumExposed.iloc[:,i].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(chryseobacteriumExposed.iloc[:,i].max()/sum[i])\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('chryseobacterium')\n",
    "plt.xlabel('chryseobacterium Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "id": "f23f48a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEXCAYAAACtTzM+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAep0lEQVR4nO3deZxcVZ338U8nJA2EzQWFoAEega9AQ2IaCWhAGMLwAvRhNCwaBBEx4oI+KioPoAIOjoIKiixKUCJDRCYCCgZwkNVBDGllacFf1JmgQJhhEzRAQ5b545yCotJVdbs7t6q7832/XnlR9567/OpQXb8659x7bseqVaswMzMb0+4AzMxseHBCMDMzwAnBzMwyJwQzMwOcEMzMLHNCMDMzwAnB1gKSDpZ08xo4zqslNb1OW9LNkg4e6vnysbaW9OM1cSyzZpwQzIa3LQG1OwhbO3T4xjQbjSSdBhwOPA78AdgO2AaYFhGL8zY3AOcA9wPnAhsCmwN3AYdFxHOS3gWcDjwD3Al8KCI68v6fB94DLAcWAx+LiEdya+Qh0hf5+sClEXF63udE4CBgPWACcHxEXClpHeAM4O35eLcDHwV+B2wB3BoR+0l6C/DVvO8K4NSIuEbSUcAH8vqnImLvNVqhtlZwC8FGHUkHATOBKcBbgI2BZcBc4Ji8zRtISeIa4IPA3IjYjZQ0tgYOlPRa4HvAzIjoBh6oOsf7gf2BN0fEzkAvcHFVGBsBu+V/75W0v6QtgRnAXnmfk4DT8vYfAbqByUAXKTkdmuP9U04GrwC+DxwREVNJieV8SZPyMXbMx3YysEFxQrDRaAZwRUT8LSKWk77UAc4DjpQ0DpgNzImIFcDngEclfRY4H5gIbABMB+6NiPvy/t+pOsf+wPcjYlle/iawj6TxeXlORCyPiKeB+cC+EfEAcCRwuKSvAMfm81RiviQino2IlRFxWERcUvO+die1YK6SdBewAFgF7JzL78nnMxuUddodgFlJOqpeLweIiMWS7iH9sp4FTMvlPyT9LVwO/AyYVLX/asfJxpK+jCvG5GNUtl9RU/aCpKnAT4CzgJ8Dt5ASUOXYLx4vt05qf7CNBe6PiGlV200EHiV1j/0dsyFwC8FGo2uBQyRtImkMcERV2bnAmcDCiHg4r9sPOC0ifpSXp5G+fG8FdpQ0Oa8/quo41wFHS5qQlz9O6ufvy8tHSurI3TyH5u33BBZFxDdIyeCf8nkAbgBmSerMMZ/PS+MT4/I2dwDbStoTQNIU0vjIFgOsH7N+OSHYqBMRC0jdRIuAXwNPVRVfQ+qmuaBq3YnAlZLuJXUL3QJsExGPkloSl0r6DWlsoeIi0pf4Qkn3A1NJv9IrngJ6SIPD50TETaSWyKvz9veRftG/UtKG+bw9+d+9wFLgW3m75yQtBB4jjY2cKelu4BLSeMKSQVaV2cv4KiNbq0jaHZgDdEWEP/xmVTyGYGsNSXOBvUiXlDoZmNVwC8HMzACPIZiZWeaEYGZmwAgdQ+jp6ekE3ky6EmNFk83NzCwZS7q58c7u7u6+2sIRmRBIyeC2dgdhZjZC7QH8snblSE0ISwG22247xo8fv1phb28vXV1dLQ+qKMc3NI5v6IZ7jI5vaOrF9/zzz7N48WLI36G1RmpCWAEwfvx4Ojs7+92g3vrhwvENjeMbuuEeo+Mbmibx9dvV7kFlMzMDnBDMzCxzQjAzM8AJwczMslIHlSVtRJrt8e21MzLmqXvnkJ4sdStwbH6YiZmZtUFpLQRJ00jXuW5XZ5N/JT2DdjvSQ0U+WFYsZmbWXJldRh8kPST84dqC/GzZ9SLijrzqYuCQEmMxM7MmSp/tVNIS0oO/l1St2x04MyKm5+VtgAW5tdBUT0/PVsB/DTamN26/IxPWX3ewu494y555jt/f/7t2h2E2aP4bHvLf8Nbd3d1Lale268a0Mbz8ebQdwMqBHqSrq6vfmy96enro7u5uuO87Pv2TgZ5u1Lj66wc1rJ8i9ddOjm/ohnuM/htubLB/w319ffT29tbdr11XGT1ImmCpYjP66VoyM7PWaUtCiIgHSM+JfWtedQTpwehmZtYmLU0IkhZI2iUvHg6cJen3pIeef6uVsZiZ2cuVPoYQEVtVvT6g6vXdwK5ln9/MzIrxncpmZgY4IZiZWeaEYGZmgBOCmZllTghmZgY4IZiZWeaEYGZmgBOCmZllTghmZgY4IZiZWeaEYGZmgBOCmZllTghmZgY4IZiZWeaEYGZmgBOCmZllTghmZgY4IZiZWeaEYGZmgBOCmZllTghmZgY4IZiZWeaEYGZmgBOCmZllTghmZgY4IZiZWeaEYGZmgBOCmZllTghmZgY4IZi13PMvrGh3CHR3d7f1/MOhDmx167Q7ALO1zfhxY3nHp3/S7jDa6uqvH9TuEKwfbiGYmRlQcgtB0izgZGAccHZEnFtTPhX4DjAe+Avw3oj4a5kxmZlZ/0prIUjaAjgdmA5MAWZL2qFms28CX4iIyUAAx5cVj5mZNVZml9EM4MaIeCIilgHzgYNrthkLbJRfrw88W2I8ZmbWQJldRhOBpVXLS4Fda7b5FPBzSWcDy4BpJcZjZmYNlJkQxgCrqpY7gJWVBUnrARcBMyJioaRPAT8ADix6gt7e3rplPT09dcvafcndcNCofoqUt9tIjs+fv2Qon0HXYTl/w2UmhAeBPaqWNwMerlruAp6NiIV5+TvAlwZygq6uLjo7O1db39PT4w9ME43qZ7jXn+MbHUbyZ3A4GEz99fX1NfwhXeYYwg3APpI2lbQ+MBO4rqr8j8DrJSkvHwTcWWI8toa0+6aidn9RtPv9m5WltBZCRDwk6STgJtJlpXNy19AC0pVFiyQdBVwuqQP4H+D9ZcVja87afmOVb6qy0apQQpD0OmBn4Hpgi4j4c5H9ImIeMK9m3QFVr68Fri0crZmZlaZpl5GkA4HbgXOB1wD3SfJPJDOzUabIGMIXSJeD/jUilpJuNDut1KjMzKzliiSEsTkRABARd/Hyy0nNzGwUKJIQnpE0iZwEJO0BPFdqVGZm1nJFBpVPAH4ObC7pV8C2pEtIzcxsFGmaECLidkm7AbuT5h76VUQ8XnpkZmbWUkWuMtoZuDRfIvoA8Iuqm8nMzGyUKDKGcD4wByAi7gVOIU0zYWZmo0iRhDAhIq6sLETEVbw0ZbWZmY0SRRLCqtxtBICk7QFP5mJmNsoUucro88Atku7Ny28EDi8vJDMza4ciVxldkweR3wosB34dEf9TemRmZtZSRae/3j7/dxwwXdK7SorHzMzapGkLQdKFwP6k5xdUpqxYBVxRYlxmZtZiRcYQ9gG2j4i/lR2MmZm1T5Euo784GZiZjX5FWgj/Ieky4Grg2crKiHCXkZnZKFIkIeye/3tM1TqPIZiZjTJFLjvduxWBmJlZexW5ymhb4GPABkAHacbTbSLirSXHZmZmLVRkUHkeMB54C7AE2AG4t9EOZmY28hRJCBtGxIeB64FrgX15aVzBzMxGiSIJofIwnD8CXRHxV/xMZTOzUafIVUZ/lHQ2MBe4SNIGpCkszMxsFCnSQvgwcFtE/Ba4EPgHYHapUZmZWcsVuez0GeDH+fX5pCeomZnZKFM3IUhaSf2xglURUaS7yczMRohGX+qbku47+BLwAOk5yiuAo4AtS4/MzMxaqm5CiIjHASTtki87rfiWpEWlR2ZmZi1VZFB5Qn5iGgCSdgI6ywvJzMzaocg4wMnAHZLuISWQHYBZpUZlZmYtV+Qqoysk/RKYnlfdGhGPlRuWmZm1WpHJ7dYnPULzlaRB5q0kERHfKLDvLFILYxxwdkScW1Mu0mD1K4BHgHdHxJMDfhdmZjZkRcYQLgE+DkwGdsr/uprtJGkL4HRSy2IKMFvSDlXlHcBPga9ExGTgt8AJA4zfzMzWkCJjCDuTnqm8fIDHngHcGBFPAEiaDxwMnJbLpwLLIuK6vPxlYJMBnsPMzNaQIgnhL4M89kRgadXyUmDXquVtgEckXQS8CbgfOG6Q5zIzsyEqkhDuBW6SdB0vf6ZyszGEMbz8TucOYGXNufcC9oyIRZK+BHyDdONbIb29vXXLenp66pZ1d3cXPcWo1ah+mpW7/lx/a4LrcGiGUn/1FEkIG5Gmvt5mgMd+ENijankz4OGq5UeAP0RE5Sa3HwLzB3KCrq4uOjtXvyWip6fHH5gmGtWP668519/QuQ6HZjD119fX1/CHdJHLTt9fML5aNwCnSNoUWAbM5OWzpN4ObCppckTcDbwDGHhKMzOzNaLIZadX088kdxHxfxvtFxEPSToJuIn0CM45EbFQ0gLgC7mb6J3AhZImkFoURwzmTZiZ2dAV6TKq7sYZT7pS6M4iB4+IeaRnMlevO6Dq9a95+UCzmZm1SZEuo7nVy5IuBm4uKR4zM2uTIjem1eogXVJqZmajyEDHEDpIdynfUmZQZmbWegMdQ1gFnAf8vJxwzMysXZp2GeUxhGuAx4H/Bn4VESvKDszMzFqraUKQdCAQwGeAzwP3S9qz7MDMzKy1inQZfQl4W0T8DkDSVOC7wC5lBmZmZq1V5CqjVZVkABARvym4n5mZjSB1WwiSXplf3inpeOAC0uR0RwE3lh+amZm1UqMuo8dIVxV15OUzqpZXAceXG5qZmbVS3YQQEXW7hSS9qpxwzMysXYoMKr9I0vbAJ0iT0E0oJSIzM2uLQglB0n7AJ4F9gdtIU1Wbmdko0mhQeV3gfaQWwWuAHwFdEbFXa0IzM7NWanT56J+BA4GTgYkR8VHg+ZZEZWZmLdeoy+g20iMw/w48K+n61oRkZmbtULeFEBEzgcmkaSsuJD3R7BWStm5RbGZm1kIN7ziOiKURcSqwJXAc6ZnHIemyVgRnZmatU+gqozy76Y+BH0vaDji21KjMzKzlBnQfAkBELAY+VUIsZmbWRp6kzszMACcEMzPLijxTeTPgImBb0mWoPwCOioilJcdmZmYtVKSFcB5wFfAs8ARwFzCnvJDMzKwdiiSErSLiQmBlRLwQEZ8DJpUcl5mZtViRhLBS0ovbSdqw4H5mZjaCFPlivwK4FNhY0odIT0u7vNSozMys5ZomhIj4MrAAuJM0/fV3gdNKjsvMzFqsyFVGHwLmRcQlLYjHzMzapEiX0d7Af0q6SNJuZQdkZmbtUaTL6N3AdqSJ7b4lqVfSJ0qPzMzMWqrQ1UIR8SRp7OBfSM9HOKHMoMzMrPWKjCG8CTgaOAT4DXAG8NOS4zIzsxYrMtvpT4DvAbtGxJ8HcnBJs0iP4BwHnB0R59bZ7kDg2xHhh++YmbVJkYSwZUSsGuiBJW0BnA50A33A7ZJuioj7arZ7LfA1oGOg5zAzszWn7hiCpF/ml09Lqv73N0lPFzj2DODGiHgiIpYB84GD+9luDnDqgCM3M7M1qlEL4ZD8365+yor8mp8IVM+IuhTYtXoDSR8njUvcUeB4q+nt7a1b1tPTU7esu7t7MKcbVRrVT7Ny15/rb01wHQ7NUOqvnroJoWp66wsiYv/qMkl3AM3uSRgDVHc1dQArq47RBcwE9gFeN4CYX9TV1UVnZ+dq63t6evyBaaJR/bj+mnP9DZ3rcGgGU399fX0Nf0jXTQiS5pPuP3iDpHuqisaRxgSaeZD0/ISKzYCHq5YPATYHFgHjgYmSbouI6n3MzKxFGnUZHQ9sBVwIHFe1fjlwX3871LgBOEXSpsAyUmtgdqUwIr4IfBFA0lbAzU4GZmbt06jLaAmwRJIiYmV1maQJzQ4cEQ9JOgm4idQCmBMRCyUtAL4QEYuGFrqZma1JRS47fYek04ANSOMAY4FXAhs22zEi5gHzatYd0M92S0itETMza5MiU1d8Dfgy8GfgI8B1wAVlBmVmZq1XJCEsi4gfkS4NfQ74MPD2UqMyM7OWK5IQnpPUCfwRmJLHEwZ857KZmQ1vRcYQfgr8DHgf8CtJewCPlRqVmZm1XNFHaB4dEQ8BBwG30v8UFGZmNoI1ujHtXTXLu+SXfwGmA1eUGJeZmbVYoy6j4xqUrcIJwcxsVGl0Y9rerQzEzMzaq8gT0zYDLgK2JXUVXQK8LyIeKTk2MzNroSKXnZ4HXAU8CzwJ3EVKEGZmNooUSQhbRcSFwMqIeCEiPgdMKjkuMzNrsSIJYaWkF7eTtGHB/czMbAQp8sV+BXApsLGkDwE3ApeXGpWZmbVc0RvTFgB3AvsC340IPwPZzGyUKTJ1BRFxCenqIgAk7RsR/15aVGZm1nKN7lTuBs4BHgfeHxGPSZoEnA3sD6zXkgjNzKwlGnUZnQf8GPhP4GRJBwH3ABOAyS2IzczMWqhRl9HGEfF1SWOBxcChwLERcVlrQjMzs1Zq1EJ4BiAiVgDrAgc6GZiZjV6NEkJH1evHIuK3ZQdjZmbt06jLaIykV5ATQ/VrgIh4ouTYzMyshRolhJ1IT0arJIHHq8pWAWPLCsrMzFqv0fTXnp7CzGwt4i99MzMDnBDMzCxzQjAzM8AJwczMMicEMzMDnBDMzCxzQjAzM8AJwczMMicEMzMDCj4xbbAkzQJOBsYBZ0fEuTXlBwGnkqbH+C/Sg3ieLDMmMzPrX2ktBElbAKcD04EpwGxJO1SVbwScT5pWezLp4TunlBWPmZk1VmaX0Qzgxoh4IiKWAfOBg6vKxwEfjYiH8vI9wKQS4zEzswbK7DKaCCytWl4K7FpZiIjHgSsBJK0HnEB6hrOZmbVBmQlhDGma7IoOYGXtRpI2JiWGuyNi7kBO0NvbW7esp6enbll3d/dATjMqNaqfZuWuP9ffmuA6HJqh1F89ZSaEB4E9qpY3Ax6u3kDS5sD1wI3AJwd6gq6uLjo7O1db39PT4w9ME43qx/XXnOtv6FyHQzOY+uvr62v4Q7rMhHADcIqkTYFlwExgdqVQ0ljgauDyiPjnEuMwM7MCSksIEfGQpJOAm4DxwJyIWChpAfAF4PXAVGAdSZXB5kURcUxZMZmZWX2l3ocQEfOAeTXrDsgvF+Eb48zMhg1/IZuZGeCEYGZmmROCmZkBTghmZpY5IZiZGeCEYGZmmROCmZkBTghmZpY5IZiZGeCEYGZmmROCmZkBTghmZpY5IZiZGeCEYGZmmROCmZkBTghmZpY5IZiZGeCEYGZmmROCmZkBTghmZpY5IZiZGeCEYGZmmROCmZkBTghmZpY5IZiZGeCEYGZmmROCmZkBTghmZpY5IZiZGeCEYGZmmROCmZkBTghmZpY5IZiZGQDrlHlwSbOAk4FxwNkRcW5N+RRgDrARcCtwbEQsLzMmMzPrX2ktBElbAKcD04EpwGxJO9Rs9q/AxyJiO6AD+GBZ8ZiZWWNlthBmADdGxBMAkuYDBwOn5eUtgfUi4o68/cXAqcD5BY49FuD555+vu0FfX1/DA2wyYWyB04xOzeqmyDauv6FtszbXH7gOh2qw9Vf1ndlv5XWsWrVqKHHVJen/AxMi4uS8fAywa0TMzsu7A2dGxPS8vA2wILcWGurp6ZkO3FZK4GZmo98e3d3dv6xdWWYLYQxQnW06gJUDKG/kTmAPYCmwYggxmpmtTcYCm5O+Q1dTZkJ4kPSlXbEZ8HBN+eYNyuvq7u7uA1bLbmZm1tSf6hWUednpDcA+kjaVtD4wE7iuUhgRDwDPSXprXnUEcG2J8ZiZWQOlJYSIeAg4CbgJuAuYFxELJS2QtEve7HDgLEm/BzYAvlVWPGZm1lhpg8pmZjay+E5lMzMDnBDMzCxzQjAzM8AJwczMslInt2sVSZNI8yK9Bgjg8Ij4e802WwK9vHQN7n9HxH4lxzWsJ/crEN8XgaOBJ/OqC2u3aUGMGwG3A2+PiCU1ZVNo8+SITeJra/3l8x+aF38WEZ+tKZ9Cez9/zeJrd/2dRppuZxVwUUR8o6Z8Cu2tv2bxDbj+RksL4TzgvIh4I7AI+Hw/2+xCuvR1Sv5XdjIY1pP7FYxvF+DdVXXW6mQwjXQDYr3pTNo6OWKB+NpWf5JmAP8IvIn0/7db0jtrNmvn569IfO2sv7cB/wDsnOM4TpJqNmtn/RWJb8D1N+ITgqRxwJ7A/LzqYuCQfjZ9M9Al6S5JN0raqeTQXpzcLyKW5fgOrhTWmdyvv7jbEl+2C3CipHskfVvSui2MD9If2Efp5w72YVB/0CC+rJ31txT4dEQ8HxEvAPcDkyqFw6D+GsaXta3+IuIWYO/8i/81pN6UZZXydtdfs/iyAdffiE8IwKuBp6uaakuB1/Wz3XOkjD4V+BpwlaTxJcY1McdSURtXs/KyNTy/pA2A3wKfIdXZJvTf8ipNRBwTEfUmMWx3/TWMr931FxG/q3xZSdqW1DWzoGqTttZfs/jaXX85xhcknQrcB/wCeKiqeDh8/urGN9j6G1FjCJIOAc6qWf0HXj5JHvQzSV5EnFK1uEDSvwDbA3evyRirlDm535rQ8Px5DOaAyrKkrwPfI919Phy0u/4aGi71J2lH4GfAZyLiD1VFw6L+6sU3XOovIr4o6avA1aQW4Xdz0bCov3rxDbb+RlQLISL+LSJeV/2P1A+5saTK/N6b038Xw3GSXlW1qgN4ocRwm03eN+jJ/daQhueXNEnS0VXlZdfXQLW7/hoaDvWX5wn7BXBCRMytKW57/TWKr931J+mNedCYiHgGuILUX1/R1vprFt9g629EJYT+5P7H24DD8qoj6X+SvLcBH4AXB2TGAr8vMbThPrlfw/iAZ4EzJG0tqYPUV35lC+NraBjUXzNtrT9JrweuAmZFxGW15e2uv2bx0f7P3/8BLpTUmbuWD6JqhuV211+z+Bhk/Y34hJB9hHSVzH2kKbcrD+U5Nl+aBfAJYF9JvaQxhPdERGlNvOE+uV+z+CLiUeBDpKZokH5hfL1V8dUzXOqvnmFUf8cD6wLfyBdS3JX/HoZL/TWMr931FxELSF1ZvwV6gNsj4rLhUn/N4hts/XlyOzMzA0ZPC8HMzIbICcHMzAAnBDMzy5wQzMwMcEIwM7NsRN2pbKOPpINJE4TttQaO9Wrg0YjoaLLdzcC3I2J+o+0KnnNr4GsRMXOIx7kZ2BJ4qqbon9dEnGtS0Xq2kccJwWxotgRqZ5kcrM8Mty9/W7s4IVjL5ZsFDwceJ81FhaQJpOkApkXE4rzuBuAc0kyY5wIbkqYLuAs4LCKek/Qu0jTezwB31pzn88B7gOXAYlJL5JFc/E5JJwDrA5dGxOl5nxNJd32uB0wAjo+IKyWtA5wBvD0f73bS3Z9zgC0kXR8R+0l6C/DVvO8K4NSIuEbSUaQ75ScAT0XE3gOor+3z+faOiLsk/QB4ISI+IGk58BVg/3zsEyPiikbvP9fZyaS5d1aQEtGtkjYGvgnsRHpGxi9y2fJG9Wyjh8cQrKUkHUSaJmMK8BZgY4A8Bfdc4Ji83RtIzxm4hjRp19yI2A3YBtgaOFDSa0kTds2MiG7ggarzvJ/0JfnmiNiZ9HCki6tC2QjYLf97r6T985TGM4C98j4nAZU73T8CdAOTgS5Scjo0x/unnAxeAXwfOCIippISy/lKD3AC2DEfu14yOLPqrt3Kv1dFxP3AZ4G5kj6QY/hY3mcs8Ex+/4cC38vTkTR6/2cCH4mIXUgzYO6V158F9ORjvYk0k/CnGtWzjS5uIVirzQCuiIi/AUj6HvDxXHYecKukk4DZwJyIWCHpc6RpRz5LShITSVMFTAfujYj78v7fAb6cX+8PfD8nGki/fE/SS1Oez8lTpj8taT6wb0RcK+lI4HBJ25CSxQZVcV8SEc/m5cNy/HtVvbfdSS2Yq/TSs0pW8dKkY/dExNMN6qZul1FEXChpP1KLaXJVHADfztvcI+le0vNBGr3/y4ArJf0M+HdSywdS62fXnHQgtZKgcT3bKOKEYO1QPRj54iMHI2KxpHtIv6xnAdNy0Q9Jn9XLSfO3TKo6Rr/HIv1yrp6XZUw+RmX7FTVlL0iaCvyE9Ev558AtwPlVx37xePlXc20Leyxwf0RMq9puIvAoqYvs7wySpE5S6+gpUuuqeirr6vc9hvTe6r7/iDgpJ+J9gaOATwO75n0OyS0SJG2SjzGD+vVso4i7jKzVrgUOkbSJpDGkWSKrnUvq0lgYEZXphPcDTouIH+XlaaQvr1uBHSVNzuuPqjrOdcDReWwCUivk1ojoy8tHSurI3TyH5u33BBZFejbtLcA/5fNAmh12Vp5dcgwpUVT658flbe4AtpW0J7z4zN0/AFsMoH7qOZPU7fOPwDm5e6viyHy+qcAbc+z9vn9ghaQlwPoRcQGpK2znnHCuBz6Z66UT+Cmpa6pRPdso4haCtVRELFB6fOki0sO/7wY2rdrkGtJA7QVV604kdXEsI/1CvgXYJiIelTQLuFTS83l9xUXA64GF+Qv8j6Rf6RVPkWaJXA84JyJuyrPlzpR0P+nH0jXAKyVtSOom2Srv0wHcTJrdciPSNMgLSYlqJmksYN18jCMiYolWe9xtv86UdHLNuivyOd8J7BQRf5V0FvDDSuIB3ippdj7fYRHxpKR+338eIP5/wDxJL5AGlo+OiD5JHyd1Ld1LSnI3AGfkJ3PVq2cbRTzbqQ0rknYnJYSuiPCHswlJq4BNI+KxdsdiI59bCDZsSJpLuuLlMCcDs9ZzC8HMzAAPKpuZWeaEYGZmgBOCmZllTghmZgY4IZiZWeaEYGZmAPwveNUHf7zIHOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = []\n",
    "for i in range(len(dyadobacterExposed[0])):\n",
    "  sum.append(dyadobacterExposed.iloc[:,i].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(dyadobacterExposed.iloc[:,i].max()/sum[i])\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('dyadobacter')\n",
    "plt.xlabel('dyadobacter Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "id": "07427308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = []\n",
    "for i in range(len(stenotrophomonasExposed[0])):\n",
    "  sum.append(stenotrophomonasExposed.iloc[:,i].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(stenotrophomonasExposed.iloc[:,i].max()/sum[i])\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('stenotrophomonas')\n",
    "plt.xlabel('stenotrophomonas Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "id": "e307cd72",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# sum = []\n",
    "# for i in range(len(leucobacterExposed[0])):\n",
    "#   sum.append(leucobacterExposed.iloc[:,i].sum())\n",
    "# indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "# toBePlotted = []\n",
    "# for i in indices:\n",
    "#   toBePlotted.append(leucobacterExposed.iloc[:,i].max()/sum[i]) \n",
    "# plt.bar([i for i in range(len(indices))], toBePlotted) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "id": "825ca17c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = []\n",
    "for i in range(len(lactococcusExposed[0])):\n",
    "  sum.append(lactococcusExposed.iloc[:,i].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(lactococcusExposed.iloc[:,i].max()/sum[i])\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('lactococcus')\n",
    "plt.xlabel('lactococcus Exposed')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "id": "c42a07b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##ALONG THE COLUMN\n",
    "for i in range(len(stenotrophomonasControl[0])): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "  sum.append(stenotrophomonasControl.iloc[:,i].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "  toBePlotted.append(stenotrophomonasControl.iloc[:,i].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.title('stenotrophomonas')\n",
    "plt.xlabel('stenotrophomonas Control') \n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "id": "3b1e92e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "for i in range(len(leucobacterControl[0])): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "    sum.append(leucobacterControl.iloc[:,i].sum())\n",
    "indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:2]\n",
    "toBePlotted = []\n",
    "for i in indices:\n",
    "    toBePlotted.append(leucobacterControl.iloc[:,i].max()/sum[i]) #relative abundance. no. divided by the total of that bacteria\n",
    "# plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "plt.bar([1, 2, 3, 4], [0.28, 0.28, 0.28, 0.14]) \n",
    "plt.title('leucobacter')\n",
    "plt.xlabel('leucobacter Control')\n",
    "plt.ylabel('Relative Abundance')\n",
    "plt.show()\n",
    "###i was unable to filter out values from leucobacter in control for some weird reason so, i manually calulted the value and plotted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "id": "20f75b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for minimum value of control samples. But all the min values are zerors.\n",
    "# sum = [] ##taking the sum of all the values in the row to get the abundace of each of the sample in that bacteria\n",
    "# for i in range(len(lactococcusControl)): #running a loop to get all the values with highest no. and then summing the total to divide and get the relative abundance.\n",
    "#   sum.append(lactococcusControl.iloc[i, :].sum())\n",
    "# indices = sorted(range(len(sum)), key=lambda i: sum[i], reverse=True)[:6]\n",
    "# toBePlotted = []\n",
    "# for i in indices:\n",
    "#   toBePlotted.append(np.min(lactococcusControl.iloc[i, :])) #relative abundance. no. divided by the total of that bacteria\n",
    "# plt.bar([i for i in range(len(indices))], toBePlotted) \n",
    "# plt.title('Lactococcus')\n",
    "# plt.xlabel('lactococcus Control')\n",
    "# plt.ylabel('Relative Abundance')\n",
    "# plt.show()\n",
    "\n",
    "# minValueslactococcusControl=lactococcusControl.min()\n",
    "# print(minValueslactococcusControl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b0bbe0",
   "metadata": {},
   "source": [
    "# Figure 2A (extra content)\n",
    "In the datafile, it was only given that they used this file to do the analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "id": "cb2ef86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('/Users/aasthapal/Downloads/SALAMANDER.xlsx', sheet_name=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "id": "8b7925c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tbu = df.get([\"Bsal Infection Intensity, clinical scores, time to death, and survival data ssociated with salamanders in the bacterial addition experiment \", \"Unnamed: 30\", \"Unnamed: 31\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "id": "deb9ef78",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tbu = df_tbu.drop([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "id": "3997e8e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grouped = df_tbu.groupby(\"Bsal Infection Intensity, clinical scores, time to death, and survival data ssociated with salamanders in the bacterial addition experiment \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "id": "ee1860aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_pseudo = df_grouped.get_group(\"Pseudo\")['Unnamed: 30']\n",
    "list_StenRizo = df_grouped.get_group(\"Sten.rhizo\")['Unnamed: 30']\n",
    "list_controlNoTreatment = df_grouped.get_group(\"Control (No Treatment)\")['Unnamed: 30']\n",
    "list_steno = df_grouped.get_group(\"Steno\")['Unnamed: 30']\n",
    "list_agarRinse = df_grouped.get_group(\"Control (Agar rinse)\")['Unnamed: 30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "id": "9fd01d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     28\n",
       "10    35\n",
       "11    63\n",
       "14    32\n",
       "16    39\n",
       "17    28\n",
       "Name: Unnamed: 30, dtype: object"
      ]
     },
     "execution_count": 593,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_pseudo = list_pseudo.dropna(axis=0, how=\"any\", inplace=False)\n",
    "list_pseudo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "id": "fa829b6f",
   "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>Bacterial abundance and density estimates for salamanders in Bsal Exposure Experiment</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sample ID</td>\n",
       "      <td>Individual ID</td>\n",
       "      <td>Bacterial Abundance Estimate (qPCR)</td>\n",
       "      <td>Bacterial Density (per mm2)</td>\n",
       "      <td>Log Bacterial Density</td>\n",
       "      <td>Timepoint</td>\n",
       "      <td>Treatment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>10200</td>\n",
       "      <td>2550</td>\n",
       "      <td>3.40654</td>\n",
       "      <td>Before</td>\n",
       "      <td>Inf.ind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4A</td>\n",
       "      <td>4</td>\n",
       "      <td>78700</td>\n",
       "      <td>21270</td>\n",
       "      <td>4.327767</td>\n",
       "      <td>Before</td>\n",
       "      <td>Inf.ind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6A</td>\n",
       "      <td>6</td>\n",
       "      <td>8240</td>\n",
       "      <td>1017</td>\n",
       "      <td>3.007321</td>\n",
       "      <td>Before</td>\n",
       "      <td>Inf.ind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8A</td>\n",
       "      <td>8</td>\n",
       "      <td>99600</td>\n",
       "      <td>17025</td>\n",
       "      <td>4.231087</td>\n",
       "      <td>Before</td>\n",
       "      <td>Inf.ind</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bacterial abundance and density estimates for salamanders in Bsal Exposure Experiment   \\\n",
       "0                                          Sample ID                                       \n",
       "1                                                 1A                                       \n",
       "2                                                 4A                                       \n",
       "3                                                 6A                                       \n",
       "4                                                 8A                                       \n",
       "\n",
       "      Unnamed: 1                           Unnamed: 2  \\\n",
       "0  Individual ID  Bacterial Abundance Estimate (qPCR)   \n",
       "1              1                                10200   \n",
       "2              4                                78700   \n",
       "3              6                                 8240   \n",
       "4              8                                99600   \n",
       "\n",
       "                    Unnamed: 3             Unnamed: 4 Unnamed: 5 Unnamed: 6  \n",
       "0  Bacterial Density (per mm2)  Log Bacterial Density  Timepoint  Treatment  \n",
       "1                         2550                3.40654     Before    Inf.ind  \n",
       "2                        21270               4.327767     Before    Inf.ind  \n",
       "3                         1017               3.007321     Before    Inf.ind  \n",
       "4                        17025               4.231087     Before    Inf.ind  "
      ]
     },
     "execution_count": 594,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/aasthapal/Downloads/SALAMANDER.xlsx', sheet_name=5)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "id": "c973ad12",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_plot = df.iloc[:,[4,5]]\n",
    "# df_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "d24dfe11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_plot.columns = [\"Log Bacterial Density\", \"Timepoint\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "6b3c282a",
   "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>Log Bacterial Density</th>\n",
       "      <th>Timepoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.40654</td>\n",
       "      <td>Before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.327767</td>\n",
       "      <td>Before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.007321</td>\n",
       "      <td>Before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.231087</td>\n",
       "      <td>Before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.472318</td>\n",
       "      <td>Before</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Log Bacterial Density Timepoint\n",
       "1               3.40654    Before\n",
       "2              4.327767    Before\n",
       "3              3.007321    Before\n",
       "4              4.231087    Before\n",
       "5              3.472318    Before"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot = df_plot.drop([0])\n",
    "df_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "id": "4f430724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.boxplot(x=df_plot[\"Log Bacterial Density\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "id": "fc13b43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_grouped = df_plot.groupby(\"Timepoint\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "id": "7f2e388b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_before = df_grouped.get_group(\"Before\") \n",
    "# df_after = df_grouped.get_group(\"After\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "id": "598beb36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.boxplot(x=df_before[\"Log Bacterial Density\"]) #both the plots on one \n",
    "# plt.boxplot(x=df_after[\"Log Bacterial Density\"])\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "id": "0fcf1678",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axs = plt.subplots(2)\n",
    "\n",
    "# axs[0].boxplot(x=df_before[\"Log Bacterial Density\"])\n",
    "# axs[1].boxplot(x=df_after[\"Log Bacterial Density\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "85f05781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plot_dict = {'control1' : df_plot[\"Log Bacterial Density\"], 'control2': df_before[\"Log Bacterial Density\"], 'control3': df_after[\"Log Bacterial Density\"]}\n",
    "fig, ax = plt.subplots()\n",
    "bp = ax.boxplot(box_plot_dict.values(), patch_artist=True)\n",
    "bp['boxes'][0].set(facecolor = 'dodgerblue')\n",
    "bp['boxes'][1].set(facecolor = 'orange')\n",
    "bp['boxes'][2].set(facecolor = 'dodgerblue')\n",
    "# ax.set_xticklabels(box_plot_dict.keys())\n",
    "ax.set_facecolor('lightgrey')\n",
    "plt.semilogy(base = 10)\n",
    "# plt.xlabel('')\n",
    "# plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3f08adf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4fc79bdc",
   "metadata": {},
   "source": [
    "# BRAY-CURTIS (EXTRA Content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ef88c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import statsmodels.api as sm\n",
    "df_otu = pd.read_excel('SALAMANDER.xlsx', sheet_name='rarified OTUTable(Field)')\n",
    "df_otu.head()\n",
    "df_otu.dropna() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "id": "cb0e00a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dropped=df_otu.drop([0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "id": "fa35c0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_col_list= dropped.iloc[:, 0].tolist() #create the otus as list\n",
    "# print(first_col_list)\n",
    "# d= dataframe of all the values\n",
    "# print(DistanceMatrix(d, ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "id": "6e6a44e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "otuid=[] #otu as list\n",
    "for word in first_col_list:\n",
    "    word = word.split(\",\")\n",
    "    otuid.append(word)\n",
    "# print(otuid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d0d0484",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_list = otuid #otu as string\n",
    "\n",
    "# printing original list \n",
    "# print(\"The original list : \" + str(test_list))\n",
    "  \n",
    "# Convert List of lists to list of Strings\n",
    "# using map() + join()\n",
    "otu_str = list(map(''.join, test_list))\n",
    "  \n",
    "# printing result\n",
    "print(str(otu_str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2acd16a",
   "metadata": {},
   "outputs": [],
   "source": [
    "abundance = dropped.iloc[:, 1:-1]\n",
    "# abundance\n",
    "new_array = abundance.to_numpy()\n",
    "new_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "216c63e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_list = df_otu['Rarified OTUTable for Wild-caught, healthy fire salamanders in Germany'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a7bb146",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_list = test_list[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18f8a799",
   "metadata": {},
   "outputs": [],
   "source": [
    "bc_dm = beta_diversity(\"braycurtis\", new_array, test_list[0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a65f5873",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install scikit-bio\n",
    "\n",
    "from skbio.diversity import beta_diversity #bray-cutis distances\n",
    "\n",
    "bc_dm = beta_diversity(\"braycurtis\", new_array, otu_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a64922ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy_array = np.array(new_array)\n",
    "transpose = numpy_array.T\n",
    "\n",
    "transpose_list = transpose.tolist()\n",
    "\n",
    "print(transpose_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f10d9e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "bc_dm = beta_diversity(\"braycurtis\", transpose_list, otu_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "766922af",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_array[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85e61b15",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = [[510, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,0, 0, 1, 0, 0, 0, 825, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "            [88, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1aaebbbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_ids = ['TACGTAGGGTGCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTCTGTCGCGTCTGCTGTGAAATCCCGAGGCTCAACCTCGGGCCAGCAGTGGGTACGGGCAGACTAGAGTGCGGTAGGGGAGATTGGAATTCCTGG', 'TACGTAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCGCTAAGACCGATGTGAAATCCCCGGGCTTAACCTGGGAACTGCATTGGTGACTGGCGGGCTAGAGTATGGCAGAGGGGGGTAGAATTCCACG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "083e69d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_new_id = list('TAG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dcd2fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_new = test_list[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f617e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f29fc16",
   "metadata": {},
   "outputs": [],
   "source": [
    "type(new_array.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef30e032",
   "metadata": {},
   "outputs": [],
   "source": [
    "bc_dm2 = beta_diversity(\"braycurtis\", new_array[0:11].tolist(), test_list[0:11])\n",
    "bc_dm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c24542a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bc_dm = beta_diversity(\"braycurtis\", new_array[0:201].tolist(), test_list[0:201])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6161faf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bc_dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "575db9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "wu_dm = beta_diversity(\"weighted_unifrac\", new_array[0:201].tolist(), test_list[0:201], otu_ids=test_list[0:201]) #unifrac distances\n",
    "print(wu_dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f61cdfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skbio.stats.distance import mantel\n",
    "r, p_value, n = mantel(wu_dm, bc_dm)"
   ]
  }
 ],
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