figures/supplement/S3-data_distrib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y8pCQkIA2bdqgoKAA1auXvc0qERERvXzUfpvj0qVLcHd3x/jx45GSkgIXFxecP3/eELER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\n0sr/Azs09PMk/G17AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f81090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['w']+['g' for g in range(19)]\n",
    "main_counts = [i for i in [1, 25, 25, 7, 25, 25, 25, 6, 6, 11, 22, 5, 1, 5, 17, 7, 2, 6, 16, 3, 1, 25, 24, 1, 3, 32, 8, 25, 25, 25, 2, 25, 4] if i >= 5]\n",
    "supplement_counts = [40,27,40,40,40,22,16,16,40,34,52,40,40,34,29]\n",
    "fig, (main_ax, supp_ax) = plt.subplots(figsize=(7.5,3), nrows=1, ncols=2, facecolor='w')\n",
    "\n",
    "sns.distplot(main_counts, color='g', bins=20, kde=False, ax=main_ax)\n",
    "sns.distplot(supplement_counts, color='g', bins=20, kde=False, ax=supp_ax)\n",
    "\n",
    "main_ax.set_xlabel('Main dataset:\\nSequences per host', size=12)\n",
    "supp_ax.set_xlabel('Supplemental dataset:\\nSequences per host', size=12)\n",
    "main_ax.set_ylabel('Number of hosts', size=12)\n",
    "supp_ax.set_ylabel(None, visible=False, size=12)\n",
    "main_ax.set_title('A', fontsize=18, ha='left', x=-0.12, y=1.08, fontweight='bold')\n",
    "supp_ax.set_title('B', fontsize=18, ha='left', x=-0.12, y=1.08, fontweight='bold')\n",
    "\n",
    "main_ax.set_xlim(0, 53)\n",
    "supp_ax.set_xlim(0, 53)\n",
    "main_ax.set_ylim(0, 12)\n",
    "supp_ax.set_ylim(0, 12)\n",
    "\n",
    "main_ax.tick_params(axis='both', which='major', labelsize=10, width=0.5, length=3, top=\"off\", right=\"off\")\n",
    "supp_ax.tick_params(axis='both', which='major', labelsize=10, width=0.5, length=3, top=\"off\", right=\"off\")\n",
    "\n",
    "main_ax.spines['left'].set_linewidth(0.5)\n",
    "main_ax.spines['bottom'].set_linewidth(0.5)\n",
    "supp_ax.spines['left'].set_linewidth(0.5)\n",
    "supp_ax.spines['bottom'].set_linewidth(0.5)\n",
    "supp_ax.spines['top'].set_linewidth(0.5)\n",
    "supp_ax.spines['right'].set_linewidth(0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../png/FigS3.png', bbox_inches='tight', dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Figure S3 Distribution of the number of sequences per host included in analyses\n",
    "A: All available high-quality lentivirus sequences were randomly subsampled up to 25 sequences per host for the main dataset. We included the 24 hosts with at least 5 sequences available in this dataset. B: For the supplemental dataset, we randomly subsampled up to 40 sequences per host, and included the 15 hosts with at least 16 sequences available in this dataset. For both datasets, a small number of additional sequences were permitted for the few hosts that are infected by multiple viral lineages in order to represent the full breadth of known genetic diversity of lentiviruses in each host population."
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
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