looking-at-counts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdaa48db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7deb9bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('counts.json', 'r') as fd:\n",
    "    counts = json.loads(fd.read())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3715b21",
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(list(counts.values()), bins = 100, log=True, color=\"darkorange\")\n",
    "plt.title(\"Frequency (y) of BioContainers executables by count (x)\", fontsize=20)\n",
    "plt.ylabel(\"Frequency\", fontsize=18)\n",
    "plt.xlabel(\"Count\", fontsize=18)\n",
    "plt.tight_layout()\n",
    "\n",
    "fig1 = plt.gcf()\n",
    "plt.show()\n",
    "plt.draw()\n",
    "fig1.savefig('biocontainers-counts.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c8b9334",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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