figures/Fig4/abundance/abundance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abundance of plasmid-dependent phages in wastewater"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cm = 1/2.54"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/counts.tsv', sep='\\t')\n",
    "df = df.rename(columns={'x': 'strain'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# replace counts 0 for 1, for log scale\n",
    "df['titer(pfu/mL)'] = df['titer(pfu/mL)'].replace(0.0, 1)\n",
    "\n",
    "# get mean for line\n",
    "df_mean = df.groupby('strain', sort=False)['titer(pfu/mL)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "strain\n",
       "ppu                 4.500000\n",
       "sen               110.000000\n",
       "eco              4200.000000\n",
       "ppuRP4+senRP4    1016.666667\n",
       "Name: titer(pfu/mL), dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 472.441x472.441 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"notebook\", font_scale=1)\n",
    "plt.figure(figsize=(12*cm,12*cm))\n",
    "\n",
    "g = sns.stripplot(x='strain', \n",
    "                  y='titer(pfu/mL)',\n",
    "                  data=df,\n",
    "                  hue='location',\n",
    "                  dodge=True, \n",
    "                  marker='v',\n",
    "                  size=8,\n",
    "                  palette=sns.color_palette(['#648fff', '#dc267f']))\n",
    "\n",
    "_ = [g.hlines(y, i-.3, i+.3, colors=['#000000']) for i, y in df_mean.reset_index()['titer(pfu/mL)'].items()]\n",
    "\n",
    "\n",
    "plt.ylim([0.8, 10000])\n",
    "plt.gca().set_yscale('log')\n",
    "\n",
    "sns.despine()\n",
    "g.get_legend().remove()\n",
    "#plt.savefig('data/svg/abundance.svg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}