bench_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load result files as dataframes\n",
    "df_win10 = pd.DataFrame( pd.read_json( 'bench_win10.json' ).launch.values.tolist() )\n",
    "df_pi4_sd = pd.DataFrame( pd.read_json( 'bench_pi4_sdcard.json' ).launch.values.tolist() )\n",
    "df_pi4_ram = pd.DataFrame( pd.read_json( 'bench_pi4_ramdisk.json' ).launch.values.tolist() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.831028</td>\n",
       "      <td>3.748991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.290453</td>\n",
       "      <td>4.585746</td>\n",
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      ],
      "text/plain": [
       "          0         1\n",
       "0  1.831028  3.748991\n",
       "1  2.290453  4.585746"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create result dataframes\n",
    "test_means = pd.concat( [ df_pi4_ram.mean(), df_pi4_sd.mean() ], axis = 1 )\n",
    "test_stds = pd.concat( [ df_pi4_ram.std(), df_pi4_sd.std() ], axis = 1 )\n",
    "\n",
    "test_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
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u98rAi5Rn6UbKs7AqsAdwJOXafi8ipmfmc/XwWZS/BydR7vM5lOeml+OBU4G/A6YDt7Dwyy+vTuDY2jagfN+vAH8JXFfPty3QftFCkiRJ0jhF5nj/rSNJkiRJWtJExNGUAGnHo5Qg3g2UoO8DOYJ/EEbEGcBRwAvA7pl5Q482awDXUwK612fmB1r7j6AEaKFk5+6Xmb/qc771M3N2Y/tdwB2UQO/9wPsy86nWMdtRgqBTgMeBjTPzlcb+jegG2qEEwQ5rB/Ij4kvAl+vmPpl5eWv/Z4Gz6uaFmXlwj/GfR8mW7tg5M4ca+78CnEAJqG2WmU/0uAydFwbmZ+bLvfYvDq3rdkhmXtDafzLwxbr5tcw8pkcfpwBfqJunZeaxjX3LUgL2qwFnZuZRrWOfoARr/42SBXxHZm7TanMZMBO4NzPf2dr3x5Ts99cp91w76N3JyP4uJXD+MLB5Zs5r7D+Ycr90fCYzz2n3Mxr1pZINad1DEbEiMBd4E61r1aOPqZn59BjOPUS3csJc4P2Z+fNWmw2Bm4B1KC+WbNw8V30R5TZK1vddwC697uOImE4JIE8BTsrME1r7L6CUC38kMzca4bh/kJkzBrSbqLF1zgfwBLBDZj48aIySJEmSxs85riVJkiRp6XQGJfu1YwNKluE/UwLAT0bEZVHmUu5ZjauWCD+8bn6lV9AaoJZ4/nzd3DEiOhm4nazXP6mbvwQO7Be0rn3Nbn10BN1/u366HbSux/yEkoUNsC79M7ehBKGO6JN9fjolsxUWLoveGQuUTMujeuyHksH7vwPOv05d3tcvaA2QUFT9FAAAC+dJREFUmS9NZtB6OBGxPN174wG633Hbn1Gy2gEOq4FiADLzdUoGK7Syq6PM4bw2Jeh8Yv14q2jMc12DlDvVzaHW8W+ie0+e0ytoXcfwK7rf60bAzn1+D4Ch8Qath7EGJWgNrezxtrEErXs4qR20rn0/Qvf7XAX4ZKvJnpTAMJQXGnrex5l5C90XVg4d/3BHZFGM7TiD1pIkSdLiYeBakiRJkpZCWXyaMh/ulXQDsh1vpWSqfgu4MyK27dHN7pQS3AD/Mswpf9hY36GxvjXdeWbPH1CSt59d6/LBzLx2QLtzexzTy6X9AsKZORe4r25u0twXEetSSl4DXFbb9urjBUo59X7m1OW0iNh+QLsl3XRg9bp+YbusekfNXu5kLK8KvLfVZKgut6wvSnTMqMvbapDxMbolxTsGzW+9PaUcNgxz72bm3ZTMb1jw3m0bdv70cXqabtnrg/q9UDJBkgUzydsuoVQFgIWfp33q8pH60sggnb8L60VEe77pRWGix/Yqg59nSZIkSRPIwLUkSZIkLcUy85rM3JuSzbkb8OeUuYSbGZtbAD+oZbmbpjfWH4qI7PdDKSncsU5j/d2N9esYhYhYge78yTcNalszKx+um1sNaHrPMKftzFu7SuvzZp/DBcRuHrDvIkowbAXgRxFxZUQcERFb1+z0MYuIt0TElv1+xtN3D83+Bn43rf3t72aoLoMFs65ntPZ3MpB37tGmub+jee9eO+jerffv1Np2Hfq7fcC+cavl7S+qm/sBD0TEqRGxV0SsPuDQsXioV/WCxlheppToh/LySVPn2m44gut6ReO4Qdd2okz02O4fVCFCkiRJ0sQycC1JkiRJbwCZ+UJmXp2ZX83MmZRAzccomaxQArWntw5ba4ynm9JYX7Ox/vgo+2kG654cQftOWeA1BrR5aZg+OiXEl2193uxzuLH8st+OzPxv4ADKiwPLAR+hlCy+HXgqIi6JiD2G6b+ffYA7B/xMpNFcj2a55vZ3cxvwf3W9GZTuZFYPtZYzGm066/f2KAk9Efdu27Nj7HM0/ojyYgmUSgXHUgKsT0fEzyLihIhYs+/RIzeS56lzH09tfb4oru1EmeixLY7vXJIkSVK1KMtOSZIkSZKWULWE87cj4n7gFmB54EMRsUadsxq6wdt5lMzpHGH3/YJiIz1+rMfGOPofTd/DjWXgODLz8oi4Btgf2AP4AOVFgtXrZ/tHxFXAfr8h2Z5jvh6ZOT8irgP2pgaiW/Nbd7L0O2XiOyXFn6bP/NZV88WDXVkweD7IoEDl6yPsY8wy83lgZkS8h/KCwwzKs7ccZe7mbYDPR8QnMvPK8ZxqBG36fW+da/tj4LBRnPOhUbQdq4ke2yL/ziVJkiR1GbiWJEmSpDewzLwzIn5MCZ4uA2xKt1x2p5TwcsDczHxkDKdoliNej9GVW24GEdceQftOm2cGthqbZp/DjWXYrM8aoDy//hARmwN7AUdS5tfeE/gqcMxIB5iZFwAXjLT9OI3mejT39/puhiiB62kRsTbdTOqfduYSz8wHImI2sD4lG/s++s9vDQved69l5s+HGeMSJTNvBW4FiIiVKM/nJ4ADKXOFfysiNu2RaT5SI3meOvfx063Pn6rHr70EXtcleWySJEmShmGpcEmSJEnSnMb6/Mb6bY31sZavvrWxvlPfVj3UOX/vr5vbD2pbA54b1c2JLovd7nO7YdoOt38hmXl/Zn6NMkdvp0TzAaPtZzFqBgXfO0zb5v5e381QY30G3cD1tX3aNdvAwvNbw8Tcu0uEzHwxM7+XmQdR5qiHUtp6r3F0u3HNXO+pzi/fmdu6/Z11ru1GEfH2cYxhNBUYRtp2osYmSZIkaRIYuJYkSZKkN7CICOA9dTOBZlb1VcCrdf3oGswarTuAR+v6IRGx2iiPv7ouN4uIQYHvZlngq/u2GqPMnAPcUzdnRsTKvdrV7NgxB5wz81ngp3Wzb2BxCXAL3Yz4gyKiZ0W3+vkhdXMupYRz289YcJ7r9vzWtLZ3ZvD81gDX0826PjwiBs17/pvkvxrr47k/AvjUgP0HACvV9fbz9K+N9T8dxxhersuR/F0ZaduJGpskSZKkSWDgWpIkSZKWMhGxckTcHBEfjYhlh2n+ZWCzun5dZv66xHJmPg6cVze3AC6IiOUHnHfViDiy+VlmzgdOqZtrA/8UEW8e0MfbWh/NopsFfnZErN7jmHfTDVI9Dlzar/9x+mZdTgXO6NPmNAaUCo+IfXv9Do39a9B9kWBxzAk8Jpn5KnBu3Xw7cGKfpidS7h2A83rN2V3vkR/Wzd+n3CfzKMHnpqG6nAbs0vqs3ecrdO+7qcClg16aiIgVIuKzEbFivzaLWkRsEhEzhmm2W2N9vPfHCRExrcc41gf+qm6+AFzYavIduhn3B0fEUYNOEhEbR8THe+x6vC7XiohVhhlrp+0m9WWbfiZqbJIkSZImgXNcS5IkSdLSaTvgcuDxiLgcuJES6JoLrEIpA3wQ8Du1/SvAsT36+QKwA7AtJag4PSLOAW6ufa1KCUzOAD5KyYw8q9XHLEpZ4z2AjwB3RcQ3ah/PA2tSSmQfQJkD++DOgZl5V0ScTAlMvxO4rW7fQsm+3K2OewolY/zwGrRcFL5JyR7eFji0BvhmUTLK1wf+ENi9jm16nz4+RwneXwVcQ8nifhZYDdiGMsd1J/A9a9H8GhPmK8C+wObAcRHxLsqLDo8Bb6Nkwe9d2z4I/MWAvoYo908nuHxrnQf81zLzwYh4FNiAct91juvnbyjZ2b9bl/dExNmUgPgzlIzizYEdgZnA6iwcpF2cNgCujYh7KZnDP6Fcy/mU+eH3oftszAauHMe57qfcZzdGxCmUsuzzgPcDx9G9B4/LzAXmuM7M+RFxAOVvymrAGRGxL/APwN2UKg1TKX9j9gA+RAkoX9Qaww11uQzlpZQzacxNnpn/02p7SB3XaRHxj8Bzdd9rmfnIBI9NkiRJ0iQwcC1JkiRJS595wBPAOsC6wGfqTz+PAZ/KzFvaOzLzpYjYmRLQ+z1KdvZfD+jryR59ZETMBP4W+DiwMXBqn+Nv7/HZ8ZTA9OeADekd0H2ZErT+9wFjG5fMnBcRe1ECzu8Adq0/Td+nBEz/Y0BXbwY+Vn/6+ToLvwCwRMnMFyPiw5SS8ltSgtR792h6N7BnOxDdMjTMdvPzTza2e81v3Rnf/Bq0PJMSRF+XUmGgnxeB1wfsX1y2oASP+5kN7J2ZL47jHHOAo4FLgJP6tDk5M7/Ra0dm3hMRO1CqG0xj4XnH2+b2+Owa4CbKyzMH1p+mZmb1xZSXVzap4z66se8RuvPbT9TYJEmSJE0CA9eSJEmStJTJzJcjYj1KQOjDdfkOSuBuReAlSmD7DkrW5iWZ+dKA/p4D9omIHSnz4u5IyQBdiRL0eRi4Ffgu0DNwXEtEHxgRs4BDgZ3qeBL4BSUD9DvAt3scm5Q5ti+mZDXvRAnKz6MErb4PnJ6Zj7aPnWiZOScitgWOoWSgb0rJVr8X+HvgnDq+fg6glLneBfhtyu/xVuA1Sub2jygltW9aVL/DRMrM2bVU+6HA/pRM1rdQsmHvoAQPz8vM14bp6nZK5nmnjPpQn3bX0g1c95vfujm+V4A/iIizKMHrD1Iym1ehBKofBW6j3EOX9yplvhhdR6lusAtlnBtSyqZPoVybO4ErgHPHGbQGIDOviojplIoFu1Cex+cp2cpfz8z/HOb4eyJia8o9PZNS5WEtyv81PUN5pm8ErsjM63ocPz8idgO+SHnhYVPK35SFSoFn5gsR8T5K8Ho3yrWZsqjGJkmSJGlyRPn3vyRJkiRJkiRJkiRJk2OZyR6AJEmSJEmSJEmSJOmNzcC1JEmSJEmSJEmSJGlSGbiWJEmSJEmSJEmSJE0qA9eSJEmSJEmSJEmSpEll4FqSJEmSJEmSJEmSNKkMXEuSJEmSJEmSJEmSJpWBa0mSJEmSJEmSJEnSpDJwLUmSJEmSJEmSJEmaVAauJUmSJEmSJEmSJEmTysC1JEmSJEmSJEmSJGlSGbiWJEmSJEmSJEmSJE0qA9eSJEmSJEmSJEmSpEn1/9M70nistBfDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2304x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = test_means.plot.barh( xerr = test_stds, \n",
    "                     capsize = 16, \n",
    "                     figsize = ( 32, 18 ), \n",
    "                     fontsize = 24,\n",
    "                     color = [ ( 0.01, 0.46, 0.37 ), ( 0.95, 0.75, 0.1 ) ] \n",
    ")\n",
    "\n",
    "graph.set_title( 'Raspberry Pi 4 - Chromium loading results \\n', fontsize = 40 )\n",
    "graph.legend( [ 'RAM Disk', 'SD Card' ], fontsize = 28, loc = 'lower right' )\n",
    "\n",
    "graph.set_xlabel( '\\nSeconds - lower is better', fontsize = 28 )\n",
    "graph.set_xticks( np.arange( 0, np.max( np.max( test_means ) ) * 1.25, 1 ) )\n",
    "graph.set_yticklabels( [ 'Startup ', 'Startup + \\n page load ' ], fontsize = 28 )\n",
    "\n",
    "# set individual bar lables using above list\n",
    "for i in graph.patches:\n",
    "    graph.text( 0.1, i.get_y() + 0.095, str( round( ( i.get_width() ), 3 ) ), fontsize = 32, color = 'black' )\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}