results_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "\n",
    "gap_df = pd.read_csv(Path('times_result_0.csv'))\n",
    "grid_size_df = pd.read_csv(Path('times_result_1.csv'))\n",
    "energy_df = pd.read_csv(Path('times_result_2.csv'))\n",
    "voltage_df = pd.read_csv(Path('times_result_3.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_times(data_df: pd.DataFrame, column_name: str, column_name_label: str, yscale: str = 'linear'):\n",
    "    data = [\n",
    "        data_df.loc[data_df['particle']=='proton']['cython_time'].tolist(),\n",
    "        data_df.loc[data_df['particle']=='proton']['numba_time'].tolist(),\n",
    "        data_df.loc[data_df['particle']=='proton']['numba_parallel_time'].tolist(),\n",
    "    ]\n",
    "\n",
    "    x_labels = data_df.loc[data_df['particle']=='proton'][column_name].tolist()\n",
    "    X = np.arange(len(x_labels))\n",
    "    fig = plt.figure()\n",
    "\n",
    "    ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "    ax.bar(X + 0.0, data[0], color = 'tab:orange', label='cython', width = 0.25, zorder=3)\n",
    "    ax.bar(X + 0.25, data[1], color = 'tab:blue', label='numba single core', width = 0.25, zorder=3)\n",
    "    ax.bar(X + 0.5, data[2], color = 'tab:purple', label='numba parallel', width = 0.25, zorder=3)\n",
    "\n",
    "    plt.xticks([r + 0.25 for r in range(len(x_labels))], x_labels)\n",
    "\n",
    "    plt.xlabel(column_name_label, fontweight ='bold', fontsize = 15)\n",
    "    plt.ylabel('Execution Time [s]', fontweight ='bold', fontsize = 15)\n",
    "\n",
    "    plt.grid(axis='y', color='0.95', zorder=0)\n",
    "\n",
    "    plt.legend()\n",
    "\n",
    "    plt.yscale(yscale)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_times(gap_df, 'electrode_gap_cm', 'Electrode Gap [cm]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_times(grid_size_df, 'grid_size_um', 'Grid Size [um]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_times(energy_df, 'E_MeV_u', 'Energy [MeV/u]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6dOigBg0aaPbs2Vq4cKHuuOMOh9oXEBCg5cuXKz4+Xk2bNtXf//53jR07VpJyfaBP+nPUeN26dTp06JDuvfdeRUZGauzYsdbDeLlp166dFi1apGXLlqlp06bq0KGDtm3bZh2fO3eumjdvroceekhRUVEyTVMrV660+6KWuXPnqn///nrppZdUv359de/eXdu3b89zmkhRMczrJ/S4IMMw9M0336h79+43LTd37lz94x//0KZNmzRp0iQtXbr0pv+rJSMjw+Z/S6SmpiosLEznzp1TQEBAEbXePmNCJafUY44755R6AADu4/Lly0pMTFR4eHieYQruYf78+Ro0aJDOnTtXJHORXcnNfh+npqaqUqVKOn/+vN385xYPAh4+fFijR4/Wf/7zH5Url79bmjx5ss0SKNekpaU59U0zFZ1UT1pampNqAgC4iytXrig7O9v6wH189tlnql27tqpXr649e/Zo9OjRevzxx+Xt7e12fX3t9296eroyMzNtjhUkH5X60JyVlaW+fftqwoQJqlevXr7PGzNmjGJiYqztayPN15YucTfueE8AgOJ1+fJleXh4WB+4j5SUFI0fP17JyckKDQ1Vz5499cYbb7hlP1/7/VuhQoUcI80F+QdCqQ/NFy5c0I4dO7R7925rMnp2drZM01S5cuX0/fffq0OHDjnO8/b2lre3d479hmHkOQm+NHPHewIAFK9rf3e469+NZdmoUaPsvgXQXdzs93FBfl+X+tAcEBCgvXv32uybNWuW1qxZo6+//lq1a9cuoZYBAADAXbhkaE5LS9ORI0es7YSEBMXHx6ty5cqqWbOmxowZoxMnTuhf//qXPDw8dOedd9qcX7VqVfn4+OTYDwAAADjCJUPzjh071L59e2v72tzjAQMGaN68eTp58mSer5oEAAAAiprLLznnLKmpqQoMDMzXkiNFanygk+o575x6AABu4/Lly0pISFDt2rVZcg6l1s1+Hxck/7nfI5IAAABAESM0AwAAAHYQmgEAAIpIu3btNHLkyJJuRpEzDENLly6VJCUmJsowjJu+eflG7vC9uOSDgAAAwLXNHLLGaXUNm53zfQuAszHSDAAAUAZlZWW53SuzixOhGQAAuJV27drphRde0F//+ldVrlxZISEhGj9+vHU8t+kF586dk2EYiouLkyTFxcXJMAytXr1akZGR8vX1VYcOHXTq1Cl99913atCggQICAtS3b1+lp6fb1J+Zmanhw4crMDBQQUFBeu2113T9YmWfffaZWrRooYoVKyokJER9+/bVqVOnbnpP4eHhev3119WnTx/5+fmpevXqmjlzpk2Z6dOnq1GjRvLz81NYWJiGDh2qtLQ06/i8efNUqVIlLVu2TA0bNpS3t7eSkpK0fft2derUSUFBQQoMDFTbtm21a9euAn3nP//8s7p06SJ/f38FBwfrySef1JkzZwp0DVdHaAYAAG7n008/lZ+fn7Zu3aqpU6dq4sSJ+uGHHwp8nfHjx+vDDz/Upk2bdPz4cfXq1UszZszQggULtGLFCn3//ff64IMPctRdrlw5bdu2Te+9956mT5+uTz75xDp+9epVvf766/rpp5+0dOlSJSYm6qmnnrLblrfffltNmjTR7t27NXr0aI0YMcLmnjw8PPT+++9r3759+vTTT7VmzRr99a9/tblGenq63nrrLX3yySfat2+fqlatqgsXLmjAgAHasGGDtmzZooiICD344IO6cOFCvr6jc+fOqUOHDoqMjNSOHTu0atUqpaSkqFevXvk6v7RgTjMAAHA7jRs31rhx4yRJERER+vDDDxUbG6tOnToV6DqTJk1SmzZtJElPP/20xowZo6NHj6pOnTqSpJ49e2rt2rUaNWqUdU5YWJjeffddGYah+vXra+/evXr33Xc1ePBgSdKgQYOssnXq1NH777+vli1bKi0tTf7+/nm2pU2bNho9erQkqV69etq4caPeffdd656uf9AuPDxckyZN0pAhQzRr1ixr/9WrVzVr1iw1adLE2tehg+2c8Y8++kiVKlXSunXr9NBDD9n9jj788ENFRkbqzTfftPbNmTNHYWFhOnTokOrVq2f3GqUBI80AAMDtNG7c2GY7NDTU7hQIe9cJDg5WhQoVrMB8bd+N173rrrtkGIa1HRUVpcOHDysrK0uStHPnTnXr1k01a9ZUxYoV1bZtW0my+7bjqKioHNv79++3tn/88Ufdf//9ql69uipWrKgnn3xSZ8+etZk+4uXlleO7SUlJ0eDBgxUREaHAwEAFBAQoLS0t329f/umnn7R27Vr5+/tbn9tvv12SdPTo0XxdozRgpBkAALid8uXL22wbhmE99Obh8eeY4fXzjK9evWr3OoZh3PS6+XHx4kVFR0crOjpa8+fPV5UqVZSUlKTo6GhduXIl39e5UWJioh566CE9//zzeuONN1S5cmVt2LBBTz/9tK5cuaIKFSpIknx9fW0CvSQNGDBAZ8+e1XvvvadatWrJ29tbUVFR+W5PWlqaunXrprfeeivHsdDQUIfvydUQmgEAQJlSpUoVSdLJkycVGRkpSQVac9ierVu32mxfmyfs6empAwcO6OzZs5oyZYrCwsIkSTt27MjXdbds2ZJju0GDBpL+HL3Ozs7WO++8Y/2j4KuvvsrXdTdu3KhZs2bpwQcflCQdP368QA/xNWvWTIsXL1Z4eLjKlXPfaMn0DAAAUKb4+vrqrrvu0pQpU7R//36tW7dOr776apFdPykpSTExMTp48KAWLlyoDz74QCNGjJAk1axZU15eXvrggw/066+/atmyZXr99dfzdd2NGzdq6tSpOnTokGbOnKlFixZZ161bt66uXr1qXfezzz7T7Nmz83XdiIgIffbZZ9q/f7+2bt2qfv36ydfXN9/3O2zYMP3xxx/q06ePtm/frqNHj2r16tUaOHCgNSXFHRCaAQBAmTNnzhxlZmaqefPmGjlypCZNmlRk1+7fv78uXbqkVq1aadiwYRoxYoSeffZZSX+Ocs+bN0+LFi1Sw4YNNWXKFE2bNi1f133ppZe0Y8cORUZGatKkSZo+fbqio6MlSU2aNNH06dP11ltv6c4779T8+fM1efLkfF33n//8p/773/+qWbNmevLJJ/XCCy+oatWq+b7fatWqaePGjcrKytIDDzygRo0aaeTIkapUqZI16u0ODPP6CT1lWGpqqgIDA3X+/HkFBAQ4r+LxgU6q57xz6gEAuI3Lly8rISFBtWvXlo+PT0k3p0wLDw/XyJEjS/2rqEvCzX4fFyT/uU/8BwAAAIoJoRkAAACww30fcQQAAHATiYmJJd2EMo+RZgAAAMAOQjMAAABgB6EZAADcFAttoTQrqt+/hGYAAJCra6+MTk9PL+GWAI679jpwT0/PQl2HBwEBAECuPD09ValSJZ06dUqSVKFCBRmGUcKtAvIvOztbp0+fVoUKFQr9im9CMwAAyFNISIgkWcEZKG08PDxUs2bNQv+Dj9AMAADyZBiGQkNDVbVqVV29erWkmwMUmJeXV5G8zpvQDAAA7PL09Cz0nFCgNONBQAAAAMAOQjMAAABgB6EZAAAAsIPQDAAAANhBaAYAAADsIDQDAAAAdhCaAQAAADsIzQAAAIAdhGYAAADADkIzAAAAYAehGQAAALCD0AwAAADYQWgGAAAA7CA0AwAAAHYQmgEAAAA7CM0AAACAHYRmAAAAwA5CMwAAAGAHoRkAAACwg9AMAAAA2EFoBgAAAOwgNAMAAAB2EJoBAAAAOwjNAAAAgB2EZgAAAMAOQjMAAABgh0uG5vXr16tbt26qVq2aDMPQ0qVLb1p+yZIl6tSpk6pUqaKAgABFRUVp9erVzmksAAAA3J5LhuaLFy+qSZMmmjlzZr7Kr1+/Xp06ddLKlSu1c+dOtW/fXt26ddPu3buLuaUAAAAoC8qVdANy06VLF3Xp0iXf5WfMmGGz/eabb+rbb7/V8uXLFRkZWcStAwAAQFnjkqG5sLKzs3XhwgVVrlw5zzIZGRnKyMiwtlNTUyVJpmnKNM1ib+M1hpPqceY9AQAAlAYFyUduGZqnTZumtLQ09erVK88ykydP1oQJE3LsT0tLk4eH82atVHRSPWlpaU6qCQAAoHQoSD5yu9C8YMECTZgwQd9++62qVq2aZ7kxY8YoJibG2k5NTVVYWJj8/f3l7+/vjKY6lTveEwAAQGFkZ2fnu6xbheYvvvhCzzzzjBYtWqSOHTvetKy3t7e8vb1z7DcMQ4bhrEkTzuOO9wQAAFAYBclHLrl6hiMWLlyogQMHauHCheratWtJNwcAAABuxCVHmtPS0nTkyBFrOyEhQfHx8apcubJq1qypMWPG6MSJE/rXv/4l6c8pGQMGDNB7772n1q1bKzk5WZLk6+urwMDAErkHAAAAuA+XHGnesWOHIiMjreXiYmJiFBkZqbFjx0qSTp48qaSkJKv8Rx99pMzMTA0bNkyhoaHWZ8SIESXSfgAAALgXw2QtMkl/PggYGBio8+fPKyAgwHkVj3fSSPj4886pBwAAoJQoSP5zyZFmAAAAwJUQmgEAAAA7CM0AAACAHYRmAAAAwA5CMwAAAGAHoRkAAACwg9AMAAAA2EFoBgAAAOwgNAMAAAB2EJoBAAAAOwjNAAAAgB2EZgAAAMAOQjMAAABgB6EZAAAAsIPQDAAAANhBaAYAAADsIDQDAAAAdhCaAQAAADvK5bfg+vXri6zSKlWqqEGDBkV2PQAAAKA45Ts0t2vXToZhFEmlPXr00FdffVUk1wIAAACKW75D8zWmaRZHOwAAAACX5fQ5zUU1Wg0AAAA4S4FHmtu2bavx48cXuCLTNNWhQ4cCnwcAAACUtAKH5ipVqqht27bF0RYAAADAJRVoekZRzGdmTjQAAABKm3yPNCckJEiS/Pz8HK6sKK4BOMX4QCfUcb746wAAAEUi36G5Vq1aha6sKK4BAAAAOFuB5zTbk5aWppUrV+rYsWOqW7euHnnkEXl48OJBAAAAlF4Oh+YffvhBb7/9tiTpmWeeUa9evXT8+HG1a9dOiYmJVrn77rtP33//vcqXL1/oxgIAAAAlweEh4BUrVujHH39UbGysateuLUl68803rXnL0p8P/a1fv14fffRR4VsKAAAAlBCHQ/O2bdskSZUrV1bLli0lScuXL5dhGDJN02aVjK+//rqQzQQAAABKjsOh+fjx4zIMwxplTk5O1u+//y5Jmjhxoo4cOaKAgACZpqlffvmlaFoLAAAAlACHQ/OZM2ckSdWqVZMkHThwwDr22GOPqU6dOrrrrrskSefOnStEEwEAAICS5XBoNgxDkvTHH39Ikvbv32/tr1u3riTJ19dXkuTj41OoRgIAAAAlyeHQHBoaKtM0tWXLFk2ZMkUffvihJKl27dry8vKS9H+j0UFBQUXQVAAAAKBkOLzk3N13362EhARlZWXp73//u6Q/R5nbt28vScrOzta+ffts5j0DAAAApZHDI80vvviitfbytdUyPD09NWzYMEnSunXrrLnM99xzT+FbCgAAAJQQh0eamzVrphUrVmjq1Kn67bffVLduXY0ePVpNmjSRJB07dkxdu3aVJD300ENF01oAAACgBBjm9Qsql2GpqakKDAzU+fPnFRAQ4LyKxwc6qZ7zzqnHXTijX+gTAABKVEHyn8PTMwAAAICyIt/TM2JiYiRJTZs2Vf/+/R2qrCiuAQAAADhbvkPzjBkzZBiGevTo4XDgLYprAAAAAM7G9AwAAADAjgKvnrFu3Tp16NChONoCAAAAuKQCh+YzZ85o3bp1xdEWAAAAwCUVODSzQh0AAADKmnyH5vvuu0+GYRRJpXfccUeRXAcAAABwhnyH5ri4uGJsBgAAAOC6WD0DAAAAsIPQDAAAANhBaAYAAADscMnQvH79enXr1k3VqlWTYRhaunSp3XPi4uLUrFkzeXt7q27dupo3b16xtxMAAABlg0uG5osXL6pJkyaaOXNmvsonJCSoa9euat++veLj4zVy5Eg988wzWr16dTG3FAAAAGVBgddpdoYuXbqoS5cu+S4/e/Zs1a5dW++8844kqUGDBtqwYYPeffddRUdHF1czAQAAUEa4ZGguqM2bN6tjx442+6KjozVy5Mg8z8nIyFBGRoa1nZqaKunPl7c48wUuRbPytX28lKZgnNEv9AkAACWrIH8Xu0VoTk5OVnBwsM2+4OBgpaam6tKlS/L19c1xzuTJkzVhwoQc+9PS0uTh4bxZKxWdVE9aWpqTanIPzugX+gQAgJJVkL+Liyw0Z2Rk6OTJk0pPT1fDhg2L6rLFZsyYMYqJibG2U1NTFRYWJn9/f/n7+5dgy4qHO95TaUefAABQsrKzs/NdttChecuWLRo/frzi4uJ09epVGYahzMxMTZo0Sb/++qvKlSunWbNmqVy54hvUDgkJUUpKis2+lJQUBQQE5DrKLEne3t7y9vbOsd8wjCJ7Xbgrccd7Ku3oEwAASlZB/i4uVJKdN2+enn32WWVlZeWYE+Ln56d58+bJMAx16dJFjz76aGGquqmoqCitXLnSZt8PP/ygqKioYqsTAAAAZYfDoXnfvn167rnnlJmZaY3OXh+ce/XqpZdfflmStHLlygKF5rS0NB05csTaTkhIUHx8vCpXrqyaNWtqzJgxOnHihP71r39JkoYMGaIPP/xQf/3rXzVo0CCtWbNGX331lVasWOHo7QFwNeMDnVDH+eKvAwBQKjn8xNs777xjTccICwtT9erVbY5Xr15dderUkSTt2LGjQNfesWOHIiMjFRkZKUmKiYlRZGSkxo4dK0k6efKkkpKSrPK1a9fWihUr9MMPP6hJkyZ655139Mknn7DcHAAAAIqEwyPNa9eulSQFBgZq165deu655/Tbb7/ZlKlbt66OHj2qxMTEAl27Xbt2N10CJLe3/bVr1067d+8uUD0AAABAfjg80nzy5EkZhqGoqChVrlw51zLXHv67ePGio9UAAAAAJc7h0Hxt5Yn09PQ8yxw6dEgSS2sBAACgdHM4NNepU0emaWrz5s06fPhwjuPz58/X4cOHZRiGIiIiCtVIAAAAoCQ5PKe5U6dO+umnn3T16lXddddd8vHxsY517NhR69atsykLAAAAlFYOjzSPGDFCFSv++bLh//73v0pOTpb05zu8165dq6ysLEl/Ts0YOnRoETQVAAAAKBkOh+bq1avrs88+s3mr3o1v0/Py8tKnn36qatWqFa6VAAAAQAlyODRL0sMPP6xt27bpsccek5+fn0zTlGmaqlChgh599FFt3bpV3bt3L6KmAgAAACWjUK/RlqQ777xTX3/9tUzT1JkzZyRJt956qzw8CpXHAQAAAJdR6NB8jWEYqlKlSlFdDgAAAHAZhQ7N6enp2rFjh06ePKmMjIw8y/Xv37+wVQEAAAAlolCh+Y033tBbb72Vrzf+EZoBAABQWjkcmt977z299tprNy1jGIZM07RZUQMAAAAobRx+Wm/27NmSdNNAbJqmo5cHAAAAXIbDI80JCQnWuswvv/yy7rvvPvn7+xdl2wAAAACX4HBoDgkJ0fHjx9WuXTu99dZbRdkmAAAAwKU4HJr79u2rKVOm6Pjx48xbBoCyaHygE+o4X/x1AEA+ODyneezYsWrVqpWOHDmiHj16aOvWrbpw4UJRtg0AAABwCQ6HZh8fH/3P//yPfHx89O233+ruu+9WpUqV5OnpmeNTrlyRvUMFAAAAcDqH0+yePXt033336fLly6ySAQAAALfm8Ejz6NGjrekYzGcGAACAO3N4pHnTpk3Wy0vCw8MVGRkpf39/eXg4nMMBAAAAl+RwaL42T7lBgwb66aefmLcMAAAAt+XwsPD9998v0zQVFBREYAYAAIBbczjtTpkyRbGxsdqyZYsWL16sHj16FGW7AABAQbF2NlBsHA7Nr7/+uurXr6/NmzerV69eqlevnho2bKjAwJw/sIZh6J///GehGgoAAACUFIdD87x582QYhvUw4MGDB3Xo0KEc5a69LZDQDAAAgNKqSCYjs+QcAAAA3FmhQjMvNQEAAEBZ4HBozs7OLsp2AAAAAC6LN5EAAAAAdrDAMgAAQHFxxjKAEksBOkG+Q/PEiRMlSQ0bNlTPnj2t7fwaO3ZswVoGAAAAuIh8h+bx48fLMAz16NFDPXv2tLbzi9AMAACA0qrY5zSzwgYAAABKuwLNab4xABOIAQAAUBbkOzQnJCRIkvz8/Gy2AQAAAHeX79Bcq1atm24DAAAA7irfc5o9PDzk6emp3r17F2d7AAAAAJdT4AcBmccMAACAsoY3AgIAAAB2EJoBAAAAOwjNAAAAgB0FWqdZkhYvXixPT88CnWMYhjIzMwtaFQAAAOASChyaeRAQAAAAZU2BQ7NhGAUqT8gGAABAaVfg0Ozr66sqVaoUR1sAAAAAl1Tg0Pzggw/qq6++Ko62AAAAAC6J1TMAAAAAOwjNAAAAgB2EZgAAAMCOAoVmVsIAAABAWZTv0Jydna3s7GynPQQ4c+ZMhYeHy8fHR61bt9a2bdtuWn7GjBmqX7++fH19FRYWphdffFGXL192SlsBAADg3lxyesaXX36pmJgYjRs3Trt27VKTJk0UHR2tU6dO5Vp+wYIFGj16tMaNG6f9+/frn//8p7788kv97W9/c3LLAQAA4I5cMjRPnz5dgwcP1sCBA9WwYUPNnj1bFSpU0Jw5c3Itv2nTJrVp00Z9+/ZVeHi4HnjgAfXp08fu6DQAAACQHwVep7m4XblyRTt37tSYMWOsfR4eHurYsaM2b96c6zl33323Pv/8c23btk2tWrXSr7/+qpUrV+rJJ5/Ms56MjAxlZGRY26mpqZL+nLftzLnbBXu/ouOYj14wzugX+qRg6BPXQ5+4HvrE9fD3vGsryPfmcqH5zJkzysrKUnBwsM3+4OBgHThwINdz+vbtqzNnzuiee+6RaZrKzMzUkCFDbjo9Y/LkyZowYUKO/WlpafLwcN4AfEUn1ZOWluakmtyDM/qFPikY+sT10Ceuhz5xPfw979oK8r25XGh2RFxcnN58803NmjVLrVu31pEjRzRixAi9/vrreu2113I9Z8yYMYqJibG2U1NTFRYWJn9/f/n7+zur6U7jjvdU2tEnroc+cT30ieuhT1wT/eKY7OzsfJd1udAcFBQkT09PpaSk2OxPSUlRSEhIrue89tprevLJJ/XMM89Ikho1aqSLFy/q2Wef1d///vdcR469vb3l7e2dY79hGDIMZ/3PFOdxx3sq7egT10OfuB76xPXQJ66JfnFMQb43l3sQ0MvLS82bN1dsbKy1Lzs7W7GxsYqKisr1nPT09BzB2NPTUxJzfAAAAFB4LjfSLEkxMTEaMGCAWrRooVatWmnGjBm6ePGiBg4cKEnq37+/qlevrsmTJ0uSunXrpunTpysyMtKanvHaa6+pW7duVngGAAAAHFWo0Hzo0CFNmTJFcXFxOnnypK5cuZJrOcMwlJmZme/r9u7dW6dPn9bYsWOVnJyspk2batWqVdbDgUlJSTYjy6+++qoMw9Crr76qEydOqEqVKurWrZveeOONwtweAAAAIKkQoXnnzp1q166d0tPTi2UKxPDhwzV8+PBcj8XFxdlslytXTuPGjdO4ceOKvB0AAACAw3Oax4wZo4sXL0pi8jkAAADcm8MjzVu2bLHCcnBwsFq2bKnAwEDmEAMAAMDtFPpBwJCQEO3du1eVK1cuivYAAAAALsfh6RmRkZGSpDvvvJPADAAAALfmcGh+6aWXZJqmduzYodOnTxdlmwAAAACX4vD0jKZNm+qJJ57QF198oXvvvVejRo1SZGRknqPONWvWdLiRAAAAQElyODSHh4dbDwIeOnTIeoV1bgq6TjMAAADgSgr9IOC14MzrqgEAAOCuChWaCcoAAAAoCxwOzbx9DwAAAGUFoRkAAACww+El5wAAAICyotAPAmZnZ+urr77SqlWrdOzYMUl/Li/XuXNn9erVi9dqAwAAoNQrVGg+duyYHn74Yf388885jn3++eeaPHmyvv32W9WuXbsw1QAAAAAlyuHpGZcuXVLnzp21d+/eXFfRME1TP//8sx588EGlp6cXqpEAAABASXI4NH/00Uc6ePCgzTrN13+uOXTokD7++OPCtxQAAAAoIQ6H5iVLlli/7tSpkzZt2qTz58/r/Pnz2rhxox544AHr+OLFiwvXSgAAAKAEOTyned++fTIMQzVq1NDy5cvl5eVlHYuKitKyZcsUERGh48eP65dffimSxgIAAAAlweGR5gsXLkiSGjZsaBOYr/Hy8lLDhg1tygIAAAClkcOhOTAwUKZpas+ePbp06VKO45cuXdKePXskSQEBAY63EAAAAChhDofmxo0bS5KSk5PVpUsXxcbGKjk5WcnJyVqzZo0efPBBnTx5UoZhWGUBAACA0sjhOc09e/bUmjVrJEn/+c9/bB78u9Hjjz/uaDUAAABAiXN4pHnQoEFq3LixtbxcXkvONWrUSIMGDSp8SwEAAIAS4nBo9vLy0nfffaeWLVvm+XKT5s2ba8WKFbk+KAgAAACUFoV6jXZoaKi2bt2q5cuX67vvvtOxY8ckSTVr1lSXLl3UrVs36+UnAAAAQGlVqNB8Tbdu3dStW7eiuBQAAADgchyengEAAACUFfkeab72MF/Lli31/PPPF+jhPsMw9M9//rPgrQMAAABcQL5D87x582QYhtLS0vT8889b2/aYpkloBgAAQKnG9AwAAADAjgI9CHjj0nK5LTUHAAAAuJt8h+bs7OybbgMAAADuiukZAAAAgB0Or9PcoUMHSVLbtm01bty4XMt89913Onz4sCTphRdecLQqAAAAoEQ5HJrj4uJkGIaCgoLyLDNnzhwtXrxYhmEQmgEAAFBqFev0jKysrOK8PAAAAOAUxRaak5OTFR8fX1yXBwAAAJymQNMzPD09bbZN09TixYtz7L9RQEBAwVsGAAAAuIhCrdOc175rDMOQYRhq0aJFwVsGAAAAuIgCT8/Iz6uzrzFNUz4+Pho/fnxBqwEAAABcRoFGmvv372+F5k8//VSGYahWrVpq27atTTnDMOTr66u6deuqZ8+eCgsLK7oWAwAAAE5WoNA8b94869effvqpTNNUixYtNHfu3KJuFwAAAOAyHF6neezYsTIMQw0bNizK9gAAAAAux+HQzDxlAAAAlBUOh+aJEycWqPzYsWMdrQoAAAAoUYUaaS7IShqEZgAAAJRWDodme65fv7kg4RoAAABwNYUKzfZebGKvDAAAAFAaOByaExISct2fkpKiTZs26a233tKpU6f03HPPafTo0Q43EAAAAChpDofmWrVq5bm/VatWuv/++9W0aVN99NFH6t69e57lAQAAAFdX4Ndo51ejRo1Uo0YNmaapKVOmFFc1AAAAQLErttB8+PBhnTx5UpK0c+fOAp8/c+ZMhYeHy8fHR61bt9a2bdtuWv7cuXMaNmyYQkND5e3trXr16mnlypUOtR0AAAC4nsPTMwYNGpTr/qysLP33v//Vf/7zH2VlZTl07S+//FIxMTGaPXu2WrdurRkzZig6OloHDx5U1apVc5S/cuWKOnXqpKpVq+rrr79W9erVdezYMVWqVMmh+gEAAIDrORya582bd9Ol5EzTlGEYMgxDUVFRBbr29OnTNXjwYA0cOFCSNHv2bK1YsUJz5szJ9aHCOXPm6I8//tCmTZtUvnx5SVJ4eHiB6gQAAADyUmzrNBuGIdM05evrqzfeeCPf5125ckU7d+7UmDFjrH0eHh7q2LGjNm/enOs5y5YtU1RUlIYNG6Zvv/1WVapUUd++fTVq1Ch5enrmek5GRoYyMjKs7dTUVEl/hn1nLpPnrBWsWfqvYJzRL/RJwdAnroc+cT30ievh73nXVpDvrVjWaS5fvrxCQkLUrl07jR49Wg0aNMj3Nc+cOaOsrCwFBwfb7A8ODtaBAwdyPefXX3/VmjVr1K9fP61cuVJHjhzR0KFDdfXqVY0bNy7XcyZPnqwJEybk2J+WliYPj2Kb6p1DRSfVk5aW5qSa3IMz+oU+KRj6xPXQJ66HPnE9/D3v2gryvTkcmrOzsx09tchlZ2eratWq+uijj+Tp6anmzZvrxIkTevvtt/MMzWPGjFFMTIy1nZqaqrCwMPn7+8vf399ZTXcad7yn0o4+cT30ieuhT1wPfeKa6BfHFCTPFtv0DEcFBQXJ09NTKSkpNvtTUlIUEhKS6zmhoaEqX768zVSMBg0aKDk5WVeuXJGXl1eOc7y9veXt7Z1j/7V52O7GHe+ptKNPXA994nroE9dDn7gm+sUxBfnenDcPIZ+8vLzUvHlzxcbGWvuys7MVGxub5wOFbdq00ZEjR2z+tXDo0CGFhobmGpgBAACAgnA4NE+bNk2VK1dW5cqVtWDBghzHFy5caB1/5513CnTtmJgYffzxx/r000+1f/9+Pf/887p48aK1mkb//v1tHhR8/vnn9ccff2jEiBE6dOiQVqxYoTfffFPDhg1z9PYAAAAAi8Oh+d///rfOnTsnDw8P9erVK8fxXr16qVy5cjp37pyWL19eoGv37t1b06ZN09ixY9W0aVPFx8dr1apV1sOBSUlJ1otTJCksLEyrV6/W9u3b1bhxY73wwgsaMWJErsvTAQAAAAXl8JzmQ4cOyTAMtWjRQuXK5bzMtQfyVq9erUOHDhX4+sOHD9fw4cNzPRYXF5djX1RUlLZs2VLgegAAAAB7HB5pPnv2rCTd9K1/mZmZNmUBAACA0sjh0Ozn5yfTNPXTTz/p0qVLOY6np6crPj7eKgsAAACUVg6H5nr16kn6cxR5yJAhSk9Pt46lp6dryJAhOnv2rAzDUEREROFbCgAAAJQQh+c0R0dHa9u2bZKkzz//XCtXrlTjxo0lSXv27NEff/xhle3cuXMhmwkAAACUHIdHmocNG6ZbbrnF2j579qzi4uIUFxdnM4c5MDBQQ4cOLVwrAQAAgBLkcGiuWrWqPv/8c/n4+Mg0TZs3qhiGIdM05ePjo88//9xaKg4AAAAojQr1RsAuXbpo27Zt6tmzp/VgoGma8vPzU8+ePbVt2zY9+OCDRdVWAAAAoEQ4PKf5mjvuuENfffWVsrOzrWkZt956qzw8XO4N3QAAAIBDCh2ar/Hw8FCVKlWK6nIAAACAyyh0aE5NTdWsWbP03Xff6dixY7p06ZJSUlK0bNkynTt3Tp6enurXr19RtBUAAAAoEYUKzbt27dLDDz+skydPSpLNA4EbNmzQO++8I0mqXbu27r777kI2FQAAACgZDk88PnPmjLp27arff/891+N/+ctfZJqmJGnp0qWOVgMAAACUOIdD8/Tp05WSkmKNLHt5edkcb9y4sbXU3KZNmwrRRAAAAKBkORyaly9fLkkqV66ctmzZooceeihHmYYNG8o0TR0+fNjxFgIAAAAlzOHQnJCQIMMw1KZNG7Vs2TLXMhUrVpQknTt3ztFqAAAAgBLncGi+Nl/Zx8cnzzLX5jt7e3s7Wg0AAABQ4hwOzaGhoTJNU9u2bVN6enqO43v27NHOnTtlGIZq1KhRqEYCAAAAJcnh0HzvvfdKkv773/+qQ4cONvOWJ02apE6dOlmj0ffcc08hmwkAAACUHIdD87Bhw6yVM7Zv3669e/dK+nPaxrhx43T69GlJkmEYGjJkSBE0FQAAACgZDofmFi1a6O9//7s1miz9GZCvBelr/x09erSaNWtWyGYCAAAAJcfh0CxJEydO1P/8z/+oatWqMk3T5hMUFKSZM2dq0qRJRdVWAAAAoEQU6jXakjR48GA9/fTT2rlzpxITEyVJtWrVUvPmzeXp6VnYy6OIhI9eUex1JE7pWux1AAAAlASHQ/PVq1dVvnx5SZKHh4datmyZ53rNJ06cUPXq1R2tCgAAAChRDk/PaNmypfbv32+33MKFC9W4cWNHqwEAAABKnMOhec+ePWrRooU+/PDDXI+npqbqL3/5i/7yl7/wRkAAAACUaoV6EPDy5csaMWKEHnzwQSUnJ1v7169fryZNmmjhwoU2q2sAAAAApZHDc5pvvfVWnT17VoZhaPXq1WrcuLFmzZqlHTt26J133lF2drZVNq+5zgAAAEBp4PBI8/79+9WjRw9ribkzZ86od+/eevvtt5WVlSXTNFW+fHlNmjRJmzZtKso2AwAAAE7lcGgOCgrSokWL9NVXX6lq1aoyDMMK0IZhKDIyUtu3b9ff/vY3eXgUahYIAAAAUKIKvU5zpUqV5OvrK+n/3gIoSf7+/vL39y/s5QEAAIAS5/AQcGpqqp555hlFR0crKSlJkqyRZknasGGDGjdunOfqGgAAAEBp4XBobtCggebOnWsF5YoVK2ru3Ll64YUXrDIXL17UiBEj1K5du6JoKwAAAFAiHA7NJ0+etH59zz336KefftKAAQM0Y8YMrVq1SqGhoZL+HH3+z3/+U/iWAgAAACWkUE/olStXTm+88YbWrVunWrVqWfs7deqkPXv26NFHHy10AwEAAICS5nBorl+/vrZs2aIxY8bYPAB4TeXKlbV48WJ98skn8vPzK1QjAQAAgJLk8OoZu3btslbNuJlBgwapffv2jlYDAAAAlDiHR5rzE5ivqV27tqPVAAAAACUu3yPN77//viQpIiJCXbp0sfZfvnxZV65ckSQFBATYnDNt2jRt2rRJhmFo8eLFRdFeAAAAwOnyHZpHjhwpwzDUo0cPm9D85JNPasmSJTIMQ5mZmTbnbN26VUuXLs11zjMAAABQWhT6jYCSrBeaAAAAAO6oUEvOAQAAAGUBoRkAAACwg9AMAAAA2EFoBgAAAOwo8IOA69atU4cOHaztffv2Wb++fv+NxwAAAIDSqsCh+cyZM1q3bl2O/aZp5rofAAAAKO2KZMk51mEGAACAOytQaGY9ZgAAAJRF+Q7NCQkJxdkOAAAAwGXlOzTXqlWrONsBAAAAuCyWnAMAAADscOnQPHPmTIWHh8vHx0etW7fWtm3b8nXeF198IcMw1L179+JtIAAAAMoElw3NX375pWJiYjRu3Djt2rVLTZo0UXR0tE6dOnXT8xITE/Xyyy/r3nvvdVJLAQAA4O5cNjRPnz5dgwcP1sCBA9WwYUPNnj1bFSpU0Jw5c/I8JysrS/369dOECRNUp04dJ7YWAAAA7qxI1mkualeuXNHOnTs1ZswYa5+Hh4c6duyozZs353nexIkTVbVqVT399NP6z3/+c9M6MjIylJGRYW2npqZK+nNZPWcuredOK1y705KEzugXd/q+nIE+cT30ieuhT1yPs/6ep18cU5DvzSVD85kzZ5SVlaXg4GCb/cHBwTpw4ECu52zYsEH//Oc/FR8fn686Jk+erAkTJuTYn5aWJg8P5w3AV3RaTcUvLS2tpJtQZJzRL+70fTkDfeJ66BPXQ5+4Hmf9PU+/OKYg35tLhuaCunDhgp588kl9/PHHCgoKytc5Y8aMUUxMjLWdmpqqsLAw+fv7y9/fv7ia6tb43gqG78v10Ceuhz5xPfSJa6JfHJOdnZ3vsi4ZmoOCguTp6amUlBSb/SkpKQoJCclR/ujRo0pMTFS3bt2sfde+hHLlyungwYO67bbbbM7x9vaWt7d3jmsZhsFrwR3E91YwfF+uhz5xPfSJ66FPXBP94piCfG8u+SCgl5eXmjdvrtjYWGtfdna2YmNjFRUVlaP87bffrr179yo+Pt76PPzww2rfvr3i4+MVFhbmzOYDAADAzbjkSLMkxcTEaMCAAWrRooVatWqlGTNm6OLFixo4cKAkqX///qpevbomT54sHx8f3XnnnTbnV6pUSZJy7AcAAAAKymVDc+/evXX69GmNHTtWycnJatq0qVatWmU9HJiUlOTUB/YAAABQdrlsaJak4cOHa/jw4bkei4uLu+m58+bNK/oGAUUofPSKYq8jcUrXYq8DAICygKFaAAAAwA5CMwAAAGAHoRkAAACwg9AMAAAA2EFoBgAAAOwgNAMAAAB2EJoBAAAAOwjNAAAAgB2EZgAAAMAOQjMAAABgB6EZAAAAsKNcSTcAAFxF+OgVxV5H4pSuxV4HAKDoMdIMAAAA2EFoBgAAAOwgNAMAAAB2EJoBAAAAOwjNAAAAgB2EZgAAAMAOlpwDALgsZywDKLEUIAD7GGkGAAAA7GCkGQAA5Buj/yirGGkGAAAA7CA0AwAAAHYwPQMAAKCUc8a0mbI+ZYaRZgAAAMAOQjMAAABgB6EZAAAAsIPQDAAAANhBaAYAAADsIDQDAAAAdhCaAQAAADtYpxlFZuaQNcVex7DZHYq9DgAAgBsx0gwAAADYQWgGAAAA7CA0AwAAAHYQmgEAAAA7CM0AAACAHYRmAAAAwA5CMwAAAGAHoRkAAACwg9AMAAAA2EFoBgAAAOwgNAMAAAB2EJoBAAAAOwjNAAAAgB2EZgAAAMAOQjMAAABgB6EZAAAAsIPQDAAAANhBaAYAAADsIDQDAAAAdrh0aJ45c6bCw8Pl4+Oj1q1ba9u2bXmW/fjjj3Xvvffqlltu0S233KKOHTvetDwAAACQXy4bmr/88kvFxMRo3Lhx2rVrl5o0aaLo6GidOnUq1/JxcXHq06eP1q5dq82bNyssLEwPPPCATpw44eSWAwAAwN24bGiePn26Bg8erIEDB6phw4aaPXu2KlSooDlz5uRafv78+Ro6dKiaNm2q22+/XZ988omys7MVGxvr5JYDAADA3bhkaL5y5Yp27typjh07Wvs8PDzUsWNHbd68OV/XSE9P19WrV1W5cuXiaiYAAADKiHIl3YDcnDlzRllZWQoODrbZHxwcrAMHDuTrGqNGjVK1atVsgvf1MjIylJGRYW2npqZKkkzTlGmaDra84Ayn1eQenNU37tIvzvy9XNzoE9fjLn0iuU+/0Ceuhz5xbQW5J5cMzYU1ZcoUffHFF4qLi5OPj0+uZSZPnqwJEybk2J+WliYPD+cNwFd0Wk3uIS0tzSn1uEu/zHp+bbHX0f/tlsVeh+Q+feKs38PO4C59IrlPv9Anroc+cW0FuSeXDM1BQUHy9PRUSkqKzf6UlBSFhITc9Nxp06ZpypQp+vHHH9W4ceM8y40ZM0YxMTHWdmpqqsLCwuTv7y9/f//C3QCKDX3jeuiTguH7ck30i+uhT1yPO/ZJdnZ2vsu6ZGj28vJS8+bNFRsbq+7du0uS9VDf8OHD8zxv6tSpeuONN7R69Wq1aNHipnV4e3vL29s7x37DMGQY7vQ/U9wLfeN66JOC4ftyTfSL66FPXI879klB7sklQ7MkxcTEaMCAAWrRooVatWqlGTNm6OLFixo4cKAkqX///qpevbomT54sSXrrrbc0duxYLViwQOHh4UpOTpYkRo4BAABQaC4bmnv37q3Tp09r7NixSk5OVtOmTbVq1Srr4cCkpCSbucf/+Mc/dOXKFfXs2dPmOuPGjdP48eOd2XQAAAC4GZcNzZI0fPjwPKdjxMXF2WwnJiYWf4MAAABQJrl0aAYAdzNzyBqn1DNsdgen1AMAZYVLvtwEAAAAcCWEZgAAAMAOQjMAAABgB3OaAQBlnjPmmjPPHCjdCM0AAMDl8A8ZuBpCMwAAAOwq6/+QYU4zAAAAYAehGQAAALCD0AwAAADYQWgGAAAA7CA0AwAAAHYQmgEAAAA7CM0AAACAHYRmAAAAwA5CMwAAAGAHoRkAAACwg9AMAAAA2EFoBgAAAOwgNAMAAAB2EJoBAAAAOwjNAAAAgB2EZgAAAMAOQjMAAABgB6EZAAAAsIPQDAAAANhBaAYAAADsIDQDAAAAdhCaAQAAADsIzQAAAIAdhGYAAADADkIzAAAAYAehGQAAALCD0AwAAADYQWgGAAAA7CA0AwAAAHYQmgEAAAA7CM0AAACAHYRmAAAAwA5CMwAAAGAHoRkAAACwg9AMAAAA2EFoBgAAAOwgNAMAAAB2EJoBAAAAOwjNAAAAgB2EZgAAAMAOQjMAAABgB6EZAAAAsIPQDAAAANhBaAYAAADscOnQPHPmTIWHh8vHx0etW7fWtm3bblp+0aJFuv322+Xj46NGjRpp5cqVTmopAAAA3JnLhuYvv/xSMTExGjdunHbt2qUmTZooOjpap06dyrX8pk2b1KdPHz399NPavXu3unfvru7du+vnn392cssBAADgblw2NE+fPl2DBw/WwIED1bBhQ82ePVsVKlTQnDlzci3/3nvvqXPnznrllVfUoEEDvf7662rWrJk+/PBDJ7ccAAAA7sYlQ/OVK1e0c+dOdezY0drn4eGhjh07avPmzbmes3nzZpvykhQdHZ1neQAAACC/ypV0A3Jz5swZZWVlKTg42GZ/cHCwDhw4kOs5ycnJuZZPTk7OtXxGRoYyMjKs7fPnz1v/NU2zMM0vkECn1eQervVTcaNf8o8+cU3O6Bf6pGDoE9dDn7geZ/2dck1qaqok5S/7mS7oxIkTpiRz06ZNNvtfeeUVs1WrVrmeU758eXPBggU2+2bOnGlWrVo11/Ljxo0zJfHhw4cPHz58+PAp45/jx4/bzacuOdIcFBQkT09PpaSk2OxPSUlRSEhIrueEhIQUqPyYMWMUExNjbWdnZ+uPP/7QrbfeKsMwCnkHpVtqaqrCwsJ0/PhxBQQElHRzIPrEVdEvroc+cT30ieuhT/6PaZq6cOGCqlWrZresS4ZmLy8vNW/eXLGxserevbukP0NtbGyshg8fnus5UVFRio2N1ciRI619P/zwg6KionIt7+3tLW9vb5t9lSpVKormu42AgIAy/8PkaugT10S/uB76xPXQJ66HPvlTYGBgvsq5ZGiWpJiYGA0YMEAtWrRQq1atNGPGDF28eFEDBw6UJPXv31/Vq1fX5MmTJUkjRoxQ27Zt9c4776hr16764osvtGPHDn300UcleRsAAABwAy4bmnv37q3Tp09r7NixSk5OVtOmTbVq1SrrYb+kpCR5ePzf4h933323FixYoFdffVV/+9vfFBERoaVLl+rOO+8sqVsAAACAm3DZ0CxJw4cPz3M6RlxcXI59jz/+uB5//PFibpX78/b21rhx43JMX0HJoU9cE/3ieugT10OfuB76xDGGaTpxfTUAAACgFHLJl5sAAAAAroTQDAAAANhBaAYAAADsIDQDAAAAdhCay7Dx48fLMAybz+23324dv3z5soYNG6Zbb71V/v7+6tGjR463LqJw1q9fr27duqlatWoyDENLly61OW6apsaOHavQ0FD5+vqqY8eOOnz4sE2ZP/74Q/369VNAQIAqVaqkp59+WmlpaU68C/dir0+eeuqpHD83nTt3tilDnxStyZMnq2XLlqpYsaKqVq2q7t276+DBgzZl8vPnVVJSkrp27aoKFSqoatWqeuWVV5SZmenMW3Eb+emTdu3a5fhZGTJkiE0Z+qTo/OMf/1Djxo2tF5ZERUXpu+++s47zM1J4hOYy7o477tDJkyetz4YNG6xjL774opYvX65FixZp3bp1+v333/XYY4+VYGvdz8WLF9WkSRPNnDkz1+NTp07V+++/r9mzZ2vr1q3y8/NTdHS0Ll++bJXp16+f9u3bpx9++EH//ve/tX79ej377LPOugW3Y69PJKlz5842PzcLFy60OU6fFK1169Zp2LBh2rJli3744QddvXpVDzzwgC5evGiVsffnVVZWlrp27aorV65o06ZN+vTTTzVv3jyNHTu2JG6p1MtPn0jS4MGDbX5Wpk6dah2jT4pWjRo1NGXKFO3cuVM7duxQhw4d9Mgjj2jfvn2S+BkpEibKrHHjxplNmjTJ9di5c+fM8uXLm4sWLbL27d+/35Rkbt682UktLFskmd988421nZ2dbYaEhJhvv/22te/cuXOmt7e3uXDhQtM0TfOXX34xJZnbt2+3ynz33XemYRjmiRMnnNZ2d3Vjn5imaQ4YMMB85JFH8jyHPil+p06dMiWZ69atM00zf39erVy50vTw8DCTk5OtMv/4xz/MgIAAMyMjw7k34IZu7BPTNM22bduaI0aMyPMc+qT43XLLLeYnn3zCz0gRYaS5jDt8+LCqVaumOnXqqF+/fkpKSpIk7dy5U1evXlXHjh2tsrfffrtq1qypzZs3l1Rzy5SEhAQlJyfb9EFgYKBat25t9cHmzZtVqVIltWjRwirTsWNHeXh4aOvWrU5vc1kRFxenqlWrqn79+nr++ed19uxZ6xh9UvzOnz8vSapcubKk/P15tXnzZjVq1Mh6q6wkRUdHKzU11RqJg+Nu7JNr5s+fr6CgIN15550aM2aM0tPTrWP0SfHJysrSF198oYsXLyoqKoqfkSLi0m8ERPFq3bq15s2bp/r16+vkyZOaMGGC7r33Xv38889KTk6Wl5eXKlWqZHNOcHCwkpOTS6bBZcy17/n6P8CubV87lpycrKpVq9ocL1eunCpXrkw/FZPOnTvrscceU+3atXX06FH97W9/U5cuXbR582Z5enrSJ8UsOztbI0eOVJs2bXTnnXdKUr7+vEpOTs71Z+naMTgutz6RpL59+6pWrVqqVq2a9uzZo1GjRungwYNasmSJJPqkOOzdu1dRUVG6fPmy/P399c0336hhw4aKj4/nZ6QIEJrLsC5duli/bty4sVq3bq1atWrpq6++kq+vbwm2DHBdTzzxhPXrRo0aqXHjxrrtttsUFxen+++/vwRbVjYMGzZMP//8s83zFyhZefXJ9fP4GzVqpNDQUN1///06evSobrvtNmc3s0yoX7++4uPjdf78eX399dcaMGCA1q1bV9LNchtMz4ClUqVKqlevno4cOaKQkBBduXJF586dsymTkpKikJCQkmlgGXPte77x6ebr+yAkJESnTp2yOZ6Zmak//viDfnKSOnXqKCgoSEeOHJFEnxSn4cOH69///rfWrl2rGjVqWPvz8+dVSEhIrj9L147BMXn1SW5at24tSTY/K/RJ0fLy8lLdunXVvHlzTZ48WU2aNNF7773Hz0gRITTDkpaWpqNHjyo0NFTNmzdX+fLlFRsbax0/ePCgkpKSFBUVVYKtLDtq166tkJAQmz5ITU3V1q1brT6IiorSuXPntHPnTqvMmjVrlJ2dbf0FheL122+/6ezZswoNDZVEnxQH0zQ1fPhwffPNN1qzZo1q165tczw/f15FRUVp7969Nv+g+eGHHxQQEKCGDRs650bciL0+yU18fLwk2fys0CfFKzs7WxkZGfyMFJWSfhIRJeell14y4+LizISEBHPjxo1mx44dzaCgIPPUqVOmaZrmkCFDzJo1a5pr1qwxd+zYYUZFRZlRUVEl3Gr3cuHCBXP37t3m7t27TUnm9OnTzd27d5vHjh0zTdM0p0yZYlaqVMn89ttvzT179piPPPKIWbt2bfPSpUvWNTp37mxGRkaaW7duNTds2GBGRESYffr0KalbKvVu1icXLlwwX375ZXPz5s1mQkKC+eOPP5rNmjUzIyIizMuXL1vXoE+K1vPPP28GBgaacXFx5smTJ61Penq6Vcben1eZmZnmnXfeaT7wwANmfHy8uWrVKrNKlSrmmDFjSuKWSj17fXLkyBFz4sSJ5o4dO8yEhATz22+/NevUqWPed9991jXok6I1evRoc926dWZCQoK5Z88ec/To0aZhGOb3339vmiY/I0WB0FyG9e7d2wwNDTW9vLzM6tWrm7179zaPHDliHb906ZI5dOhQ85ZbbjErVKhgPvroo+bJkydLsMXuZ+3ataakHJ8BAwaYpvnnsnOvvfaaGRwcbHp7e5v333+/efDgQZtrnD171uzTp4/p7+9vBgQEmAMHDjQvXLhQAnfjHm7WJ+np6eYDDzxgVqlSxSxfvrxZq1Ytc/DgwTZLNJkmfVLUcusPSebcuXOtMvn58yoxMdHs0qWL6evrawYFBZkvvfSSefXqVSffjXuw1ydJSUnmfffdZ1auXNn09vY269ata77yyivm+fPnba5DnxSdQYMGmbVq1TK9vLzMKlWqmPfff78VmE2Tn5GiYJimaTpvXBsAAAAofZjTDAAAANhBaAYAAADsIDQDAAAAdhCaAQAAADsIzQAAAIAdhGYAAADADkIzAAAAYAehGUCZ1qVLFxmGYX22bt2aZ9kzZ86ofPnyVtlatWqpMEvdh4eHW9dq166dw9dxB/PmzbPph2ufSpUqSZK+/PJLm/3PP//8Ta/XvXt3m/Jr1qxRYmJirnUYhqG4uLjiv0kApRqhGUCZ1rdvX5vtL7/8Ms+yixcvVmZmprXdp08fGYZRLO26MUSW9VD38MMPy9/f39pevHixsrKyci2bmpqqVatWWdvVqlUr8/8oAVB4hGYAZdqjjz4qX19fa/urr77Kc/T4iy++sNm+MXCj6HTp0kU9evTQI488Ikny9fVV9+7dreOnT59WbGxsrud+8803ysjIsLZ79+4tDw8P+fn5qUePHurRo4fuu+++Ym0/APdDaAZQpvn7+6tbt27W9okTJ7Rhw4Yc5U6ePKn169db23fccYcaN27slDaWRbNmzdLXX3+tTz/91NqX3/8rcOP+a+dVqVJFX3/9tb7++mtNmDChiFsMwN0RmgGUefkJY19//bWys7Ot7X79+tkcT0hI0IsvvqhGjRqpYsWK8vb2Vs2aNdWrV688R0RzExcXJ8MwNHDgQJv97du3t5mucc0PP/ygoUOH6q677lLNmjXl5+cnb29vhYaGqnPnzvrss8/yHDk/fvy4BgwYoCpVqsjX11eRkZH66KOPZJqmTV1PPfVUjnOvXr2qOXPm6IEHHlDVqlXl5eWlW2+9VR07dtTnn39u810VlU6dOqlKlSrW9jfffKOrV6/alPnjjz/0448/Wtv16tVTixYtirwtAMogEwDKuIyMDPOWW24xJZmSzODgYDMzM9OmzN13320dNwzDTEhIsI4tWrTIrFChgnU8t89zzz1nZmdn21yzVq1a1vG2bduapmmaa9euvel1rn2u6devn92yjz32mJmVlWVT9+HDh82QkJBcyw8aNMhme8CAATbnJicnmy1btrxpnV27djUvX76c7z6YO3euzfnXf7/XGzp0qE255cuX2xz/6KOPbI6PHz8+1+vc+D2vXbs2320FUDYx0gygzPPy8lKPHj2s7ZSUFJsH744fP67Nmzdb21FRUQoPD5ck7d69W/369VN6erp1vGnTpmrfvr3NXOn/+Z//0dSpU+22pUqVKurRo0eO0dH77rvPmo97fVuvtb9Jkybq0KGDHnnkEbVv395adUKSlixZoq+++srmnAEDBig5Odnarly5sjp16qQaNWpozpw5ebbPNE316NFD27dvt/bdfvvteuihh1S/fn1r34oVK/Tiiy/avd+Csvd/BfKamgEAhVbSqR0AXMGaNWtsRh4HDx5sHZs2bZrNsQ8//NA69uijj9oce++996xje/bsMf38/KxjAQEBZnp6unU8t5Hma24cec1rJPTQoUPmxYsXc+xPS0sz69SpY53fs2dP69imTZtsrh0REWGmpKSYpmmaly5dMu+///48R5qXL19uc2zKlCk29Q4bNsw65unpaSYlJeX9pd/kfvMaac7OzjbDw8OtchUrVjQvXbpkmqZppqSkmJ6entaxFi1a5FkfI80ACoqRZgCQ1LZtW1WvXt3aXrJkiTVf9vpVM8qVK6devXpJkrKysvT9999bx8LCwjR8+HBru1GjRjZzn1NTU21GrItCeHi45s+fr+joaFWrVk0+Pj4yDEP+/v769ddfrXKHDh2yfr1mzRqba7z00kuqWrWqJMnHx+emD8mtXLnSZnvjxo3q2bOn9YmPj7eO3fj9FAXDMPTEE09Y2xcuXLDatGjRIptl6G6cdw4AhVGupBsAAK7Aw8NDvXv31vTp0yVJZ8+e1Y8//qj69etrx44dVrnrH0Y7e/asLl68aB27/fbb5eFhOxZxxx132GwfO3asyNqclZWlzp075wjBuUlNTbV+/dtvv9kca9Sokc32nXfemed1EhMTbbaXL19+03qL8n6v6du3r6ZMmWJtf/nll3rsscdspmZ4eHjYhGsAKCxGmgHgf904//WLL75w6bWZFy1aZBOYvby8dN999+mxxx5Tjx49FBQUZB0zb/LmwuJ6QYskm7neRaVRo0Y2wf7f//63Dh48aLNUYPv27RUSElLkdQMouxhpBoD/1bx5c9WvX18HDx6UJC1dulQ1atSwjleoUMHmBRu33nqr/Pz8rNHmAwcOKDs722a0ed++fTZ11KxZM19tyU+Q3bRpk832xo0bbR4gvP3223XmzJkc593YhgMHDigqKsra/vnnn/Oss1atWjbbx44dy/c9FaW+ffvqb3/7m6Q/g/nAgQNt/mHgSv+4AeAeGGkGgOv06dPH+nVqaqp++eUXa/vGVzl7enqqU6dO1vbx48c1a9Ysa3vfvn2aP3++tV2xYkXdfffd+WrH9StvSNLvv/+eo8yNaxT7+flZv/7kk0+s8H+jDh062Gy/++67OnfunCTp8uXLGj9+fJ7t6tKli832yJEjlZaWZrPv8uXLWr58uR566KE8r1NYN77C/Pq54t7e3jlWGAGAwiI0A8B1bjZCmduxV199VeXLl7e2/9//+39q1qyZOnTooJYtW9rMeR49enSOMJyXunXr2mwPHTpUDz30kHr27GktXdeyZUubMnfddZe6du2qyMhIDR48OM/R6tatW9uE971796pevXqKjo5WRESEzctBbtStWze1bt3a2v7mm28UFhamDh066OGHH1arVq10yy236OGHH9aKFSvyda+OCA8Ptxkdv17Xrl0VGBhYbHUDKJsIzQBwnYiIiFzfIFe5cmV17tw5x/7mzZvrX//6l00Y3r17t9auXatLly5Z+55++mmNHj063+2IjIy0mbd7/vx5rVixQosXL7amZfTr18/mVd6pqalauXKl4uPjFR0drTZt2uR5/U8//VTBwcHW9unTp/X999/rt99+05AhQ2zKenl5Wb82DEPffPONmjdvbu07d+6c1q5dq+XLl2v79u26fPmyJOV4KLKo5fUPHKZmACgOhGYAuEFuoatnz542I8rXe+KJJ/Tzzz9rxIgRatiwofz8/OTl5aXq1aurZ8+eWr16tT755JMChUjDMLRy5Uo98cQTqlq1aq7nent7Ky4uTs8995yCg4Pl5eWl2267TePGjdOyZcvk6emZ5/Xr1q2r7du368knn1RQUJB8fHzUpEkTffzxx3rllVdsyt74QF1oaKg2b96sefPmqUuXLgoJCVH58uXl4+Oj8PBwPfTQQ5o+fbqSkpLyfb+O6NWrl8qVs300JyAgQF27di3WegGUTYZ5s0eqAQBu6cKFC8rMzNQtt9xis980TT333HP6+OOPrX1r1qxR+/bti7U98+bN08CBA63thIQE662LxSEuLs7mntauXat27doVW30ASj9WzwCAMmjfvn267777dO+996p+/foKCgrSmTNntHbtWh04cMAqd8899xR7YM5N7dq1JUmBgYHWQ4qFlZiYaF0XAAqK0AwAZdTVq1e1Zs2aPF+O0qpVKy1ZssTJrQIA10RoBoAyKCIiQuPGjVNcXJyOHj2q06dPyzAMVa1aVc2aNVOvXr3Uq1evm86LLkrh4eG5LhN3/TJ6heXn55fnUnTX3vIIAHlhTjMAAABgB6tnAAAAAHYQmgEAAAA7CM0AAACAHYRmAAAAwA5CMwAAAGAHoRkAAACwg9AMAAAA2EFoBgAAAOwgNAMAAAB2/H9NkffC+PQ5ggAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_times(voltage_df, 'voltage_V', 'Voltage [V]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>Unnamed: 0</th>\n",
       "      <th>E_MeV_u</th>\n",
       "      <th>voltage_V</th>\n",
       "      <th>electrode_gap_cm</th>\n",
       "      <th>particle</th>\n",
       "      <th>grid_size_um</th>\n",
       "      <th>numba_parallel_time</th>\n",
       "      <th>numba_time</th>\n",
       "      <th>cython_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.1</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.012378</td>\n",
       "      <td>0.013449</td>\n",
       "      <td>0.035368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.233058</td>\n",
       "      <td>0.305448</td>\n",
       "      <td>0.880478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.864676</td>\n",
       "      <td>1.355035</td>\n",
       "      <td>3.904035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>1.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.278747</td>\n",
       "      <td>3.381052</td>\n",
       "      <td>9.747164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.128516</td>\n",
       "      <td>6.629515</td>\n",
       "      <td>19.091478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>2.0</td>\n",
       "      <td>437.939377</td>\n",
       "      <td>403.682301</td>\n",
       "      <td>1117.775306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>2.5</td>\n",
       "      <td>162.405034</td>\n",
       "      <td>154.443905</td>\n",
       "      <td>412.150912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>3.0</td>\n",
       "      <td>43.374619</td>\n",
       "      <td>64.042103</td>\n",
       "      <td>180.727412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.560315</td>\n",
       "      <td>31.125785</td>\n",
       "      <td>88.952417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.301532</td>\n",
       "      <td>17.472970</td>\n",
       "      <td>50.302202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.162344</td>\n",
       "      <td>0.305735</td>\n",
       "      <td>0.880352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.161715</td>\n",
       "      <td>0.305495</td>\n",
       "      <td>0.880418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.162664</td>\n",
       "      <td>0.305291</td>\n",
       "      <td>0.880393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.161861</td>\n",
       "      <td>0.305312</td>\n",
       "      <td>0.880239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.161918</td>\n",
       "      <td>0.305542</td>\n",
       "      <td>0.880531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.184659</td>\n",
       "      <td>0.305268</td>\n",
       "      <td>0.880402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.278821</td>\n",
       "      <td>0.511798</td>\n",
       "      <td>1.471607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>100</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.198771</td>\n",
       "      <td>0.381067</td>\n",
       "      <td>1.098199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>250</td>\n",
       "      <td>150</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.178433</td>\n",
       "      <td>0.342305</td>\n",
       "      <td>0.986167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>250</td>\n",
       "      <td>200</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.168027</td>\n",
       "      <td>0.322576</td>\n",
       "      <td>0.930033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.163392</td>\n",
       "      <td>0.312988</td>\n",
       "      <td>0.902328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.161413</td>\n",
       "      <td>0.305596</td>\n",
       "      <td>0.880380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  E_MeV_u  voltage_V  electrode_gap_cm particle  grid_size_um  \\\n",
       "0           0      250        300               0.1   proton          10.0   \n",
       "1           1      250        300               0.5   proton          10.0   \n",
       "2           2      250        300               1.0   proton          10.0   \n",
       "3           3      250        300               1.5   proton          10.0   \n",
       "4           4      250        300               2.0   proton          10.0   \n",
       "0           0      250        300               0.5   proton           2.0   \n",
       "1           1      250        300               0.5   proton           2.5   \n",
       "2           2      250        300               0.5   proton           3.0   \n",
       "3           3      250        300               0.5   proton           3.5   \n",
       "4           4      250        300               0.5   proton           4.0   \n",
       "0           0       10        300               0.5   proton          10.0   \n",
       "1           1       50        300               0.5   proton          10.0   \n",
       "2           2      100        300               0.5   proton          10.0   \n",
       "3           3      150        300               0.5   proton          10.0   \n",
       "4           4      200        300               0.5   proton          10.0   \n",
       "5           5      250        300               0.5   proton          10.0   \n",
       "0           0      250         50               0.5   proton          10.0   \n",
       "1           1      250        100               0.5   proton          10.0   \n",
       "2           2      250        150               0.5   proton          10.0   \n",
       "3           3      250        200               0.5   proton          10.0   \n",
       "4           4      250        250               0.5   proton          10.0   \n",
       "5           5      250        300               0.5   proton          10.0   \n",
       "\n",
       "   numba_parallel_time  numba_time  cython_time  \n",
       "0             0.012378    0.013449     0.035368  \n",
       "1             0.233058    0.305448     0.880478  \n",
       "2             0.864676    1.355035     3.904035  \n",
       "3             5.278747    3.381052     9.747164  \n",
       "4             8.128516    6.629515    19.091478  \n",
       "0           437.939377  403.682301  1117.775306  \n",
       "1           162.405034  154.443905   412.150912  \n",
       "2            43.374619   64.042103   180.727412  \n",
       "3             6.560315   31.125785    88.952417  \n",
       "4             4.301532   17.472970    50.302202  \n",
       "0             0.162344    0.305735     0.880352  \n",
       "1             0.161715    0.305495     0.880418  \n",
       "2             0.162664    0.305291     0.880393  \n",
       "3             0.161861    0.305312     0.880239  \n",
       "4             0.161918    0.305542     0.880531  \n",
       "5             0.184659    0.305268     0.880402  \n",
       "0             0.278821    0.511798     1.471607  \n",
       "1             0.198771    0.381067     1.098199  \n",
       "2             0.178433    0.342305     0.986167  \n",
       "3             0.168027    0.322576     0.930033  \n",
       "4             0.163392    0.312988     0.902328  \n",
       "5             0.161413    0.305596     0.880380  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df = pd.concat([gap_df, grid_size_df, energy_df, voltage_df])\n",
    "\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.559167867575894, 1.8464920820594897, 5.210009826272289)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speedups = combined_df.apply(lambda row: row['cython_time']/row['numba_parallel_time'], axis=1)\n",
    "\n",
    "speedups.max(), speedups.min(), speedups.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>Unnamed: 0</th>\n",
       "      <th>E_MeV_u</th>\n",
       "      <th>voltage_V</th>\n",
       "      <th>electrode_gap_cm</th>\n",
       "      <th>particle</th>\n",
       "      <th>grid_size_um</th>\n",
       "      <th>numba_parallel_time</th>\n",
       "      <th>numba_time</th>\n",
       "      <th>cython_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>2.0</td>\n",
       "      <td>437.939377</td>\n",
       "      <td>403.682301</td>\n",
       "      <td>1117.775306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>2.5</td>\n",
       "      <td>162.405034</td>\n",
       "      <td>154.443905</td>\n",
       "      <td>412.150912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>3.0</td>\n",
       "      <td>43.374619</td>\n",
       "      <td>64.042103</td>\n",
       "      <td>180.727412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>250</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>proton</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.560315</td>\n",
       "      <td>31.125785</td>\n",
       "      <td>88.952417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  E_MeV_u  voltage_V  electrode_gap_cm particle  grid_size_um  \\\n",
       "0           0      250        300               0.5   proton           2.0   \n",
       "1           1      250        300               0.5   proton           2.5   \n",
       "2           2      250        300               0.5   proton           3.0   \n",
       "3           3      250        300               0.5   proton           3.5   \n",
       "\n",
       "   numba_parallel_time  numba_time  cython_time  \n",
       "0           437.939377  403.682301  1117.775306  \n",
       "1           162.405034  154.443905   412.150912  \n",
       "2            43.374619   64.042103   180.727412  \n",
       "3             6.560315   31.125785    88.952417  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slow_runs = combined_df.loc[combined_df['cython_time']>60]\n",
    "slow_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.559167867575894, 2.5377964122351555, 5.703994678716387)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speedups_for_slow = slow_runs.apply(lambda row: row['cython_time']/row['numba_parallel_time'], axis=1)\n",
    "speedups_for_slow.max(), speedups_for_slow.min(), speedups_for_slow.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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