obspy_pypi_usage_stats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obspy PyPI Download Analysis\n",
    "\n",
    "Information comes from pypistats.org. Obspy page can be found [here](https://pypistats.org/packages/obspy). As stated by the documentation all aggregations encompass the past 180 days unless otherwise stated. \n",
    "\n",
    "### Limitations\n",
    "1. There may be significant amounts of dropped data until [July 26th of 2018](https://pypistats.org/faqs#why-are-there-so-many-more-downloads-after-july-26-2018)\n",
    "2. The service does not exclude CI downloads\n",
    "3. PyPI is only one of the many ways to install ObsPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import obspy\n",
    "import pypistats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# approximate dates for which data apply\n",
    "t2 = obspy.UTCDateTime()\n",
    "t1 = t2 - 3600 * 24 * 180\n",
    "str(t1.datetime.date())\n",
    "date1 = str(t1.datetime.date())\n",
    "date2 = str(t2.datetime.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_json(json, xlabel=None, show=True):\n",
    "    \"\"\" Make a bar plot from the download stats \"\"\"\n",
    "    fig = plt.figure()\n",
    "    downloads = [x['downloads'] for x in json['data']]\n",
    "    names = [x['category'] for x in json['data']]\n",
    "    xposition = range(len(names))\n",
    "    plt.bar(xposition, downloads)\n",
    "    plt.xticks(range(len(names)), names)\n",
    "    plt.xlabel((xlabel or json['type']) + f' [{date1} to {date2}]')\n",
    "    plt.ylabel('downloads')\n",
    "    if show:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get downloads by OS\n",
    "system_downloads = json.loads(pypistats.system('obspy', format='json',))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get downloads by python version\n",
    "python_downloads = json.loads(pypistats.python_minor('obspy', format='json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "plot_json(system_downloads, xlabel='OS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "plot_json(python_downloads, xlabel='python version')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
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