analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from modules.TCXFile import TCXFile\n",
    "from collections import defaultdict\n",
    "\n",
    "tcxdir = 'F:/jooks'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def default_value():\n",
    "    return []\n",
    "\n",
    "categories = defaultdict(default_value)\n",
    "\n",
    "for file in os.listdir(tcxdir):\n",
    "    tcx = TCXFile(os.path.join(tcxdir, file))\n",
    "    activities = tcx.get_activies()\n",
    "    sport = \"\"\n",
    "    if 'walk' in activities[0].sport.lower():\n",
    "        sport = 'walk'\n",
    "    elif 'run' in activities[0].sport.lower():\n",
    "        sport = 'run'\n",
    "    else:\n",
    "        sport = activities[0].sport.lower()\n",
    "    \n",
    "    distance = sum([x.distancemeters for x in activities])\n",
    "    timeelapsed = sum([x.totaltimeseconds for x in activities])\n",
    "\n",
    "    categories[sport].append((distance, timeelapsed, int(activities[0].id[:4])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "walk: 282224.4 minutes : 3403.9 kilometers\n",
      "biking: 1367.0 minutes : 129.5 kilometers\n",
      "run: 17348.9 minutes : 3010.0 kilometers\n",
      "hiking: 264.4 minutes : 24.6 kilometers\n",
      "skiing.cross_country: 105.8 minutes : 12.9 kilometers\n"
     ]
    }
   ],
   "source": [
    "for category in categories.keys():\n",
    "    distances, times, _ = zip(*categories[category])\n",
    "    print(f'{category}: {sum(times)/60.0:.1f} minutes : {sum(distances)/1000.0:.1f} kilometers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = []\n",
    "distances = []\n",
    "\n",
    "for distance, time, year in categories['run']:\n",
    "    if str(year) not in years:\n",
    "        years.append(str(year))\n",
    "        distances.append(distance/1000.0)\n",
    "    else:\n",
    "        distances[-1] += distance/1000.0\n",
    "\n",
    "pyplot.bar(years, distances)\n",
    "pyplot.tight_layout()\n",
    "pyplot.show()"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}