paper-ParallelPython-Short/data/plots.ipynb

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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datasize</th>\n",
       "      <th>coldruntime</th>\n",
       "      <th>hotruntime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1K</td>\n",
       "      <td>0.100575</td>\n",
       "      <td>0.055373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10K</td>\n",
       "      <td>0.091807</td>\n",
       "      <td>0.054508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100K</td>\n",
       "      <td>0.130599</td>\n",
       "      <td>0.077568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1M</td>\n",
       "      <td>0.755035</td>\n",
       "      <td>0.330882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10M</td>\n",
       "      <td>4.626190</td>\n",
       "      <td>3.199181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  datasize  coldruntime  hotruntime\n",
       "0       1K     0.100575    0.055373\n",
       "1      10K     0.091807    0.054508\n",
       "2     100K     0.130599    0.077568\n",
       "3       1M     0.755035    0.330882\n",
       "4      10M     4.626190    3.199181"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "x = ['1K','10K','100K','1M','10M']\n",
    "\n",
    "term = pow (10,-6)\n",
    "\n",
    "# read data\n",
    "df=pd.read_csv(\"./scalability.csv\")\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Users/lord_pretzel/.pyenv/versions/3.9.0/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, df[\"coldruntime\"],color='blue', label=\"Cold cache\")\n",
    "plt.plot(x, df[\"hotruntime\"],color='red', label=\"Hot cache\")\n",
    "\n",
    "#plt.xticks(x)\n",
    "plt.yscale(\"log\")\n",
    "plt.tight_layout()\n",
    "#plt.savefig('write-size.jpeg')\n",
    "plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "argv": [
    "python",
    "-m",
    "ipykernel_launcher",
    "-f",
    "{connection_file}"
   ],
   "display_name": "Python 3",
   "env": null,
   "interrupt_mode": "signal",
   "language": "python",
   "metadata": null,
   "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": "3.9.0"
  },
  "name": "plots.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}