9-scalability-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aNImnn36aZ599lscff5zKlSuzbNmy7HZ//PEHI0aMYP78+ZQvX56nnnqK0aNH079/fz744ANWr17tex87dxbq85bg0BG6FIkmTZrwySef8MADD/D111+zadMmli9fTqdOnYiKimLEiBGkpKTku53pCZO5IuZCOrdqxK9bfO1r1j6d+o2aANCgSVO2bNrE7l2p7N6VSrvzWwBwQ+/ufPXdDwBENjiba+8cwtvTZlGqVMG7KmJ6xjBn+hwAX6Bf2oUKlSoQVqYM/YYNY/r8+ZQLz/thFz2zgrlZw4Zs2LIlzzbz5s3jySefJCoqivbt27N//342bvQNBNapU6fsMM8tJSWFmJgYmjRpwqhRo1ixYgUA8+fPp3///tntTjrpJL799ltWrlxJmzZtiIqK4o033mDDhg1UrlyZsLAwbr75Zt5//33KZZ2zkOJBgS5F4pxzzmHx4sU0adKEoUOHMm3aNBo1akRycjLJycksW7aMefPmHbHeWWedxcaNG0nbsxuAS6+8lilzv6ZCxUpkZh4EoHSZHMPjhoSSmZlxzFpmvfkC/fteweJlq2nZ9ToyMo5sf9a5Z7Fyyco81+9yaRfmzpjL+rXrMTNOP/N0SpUqxVfvvEOvTp34+Msv6dmvX57rHhqaNzQ0lIzMzDzbOOeYNm1a9tdm48aNNGjQAIDy5csf9X0NGDCAO++8k2XLljF+/Hj2799/1LbOOTp16pS9j5UrV/Lqq69SqlQpvv/+e2JjY/noo4/o0qXLUbchJx4FuhSJLVu2UK5cOa677jri4uL47rvv2LZtGwsX+q5OSU9Pzz6irFixIrt3+wK8XLly3HzzzTzx8P38lRVQmZmZpKenH3N/FStVplLlKnz93WIA3po2i3atm3Pw4EE2bfmNi9q05KkhA0ndvYc9afuoWL48u/ekZa9/y1238Oyjz/LHb3/46juQztS3pgJQp14dQkJDeOXZV+hyqS/w9u7ZS+ru3XRp25an77+fZWvW+P21yfl+AWJiYnjxxRdxzjfY1w8//ODXdlJTU6lZsyYAb7zx9xOUOnXqxNixf59E3rFjB61bt2bBggX8/PPPgO8cx48//siePXtITU2la9euPPfccyxZssTv9yHBpz70EijQlxn6Y9myZcTFxRESEkLp0qUZN24cpUqVYuDAgaSmppKRkcGgQYNo1KgRffv2pV+/foSHh7Nw4UJGjhzJ7YPiuLzjBZSrUIGwsDB6xF5F9VNO5ffffj3qPh9/bhxxD/6bvfv3c0adWrw++lEyMzO5bsBQUnfvwTnHwJuupkrlivxfp7bE3h7HjLlfcs+TQ2jbqS3bt23nlthbcM5hZvS6plf2trtc2oVnH32WuUlzAUjbk8bldw7gr7/+wjnHk3Fxfn9tLrroouwulgcffJCHH36YQYMGERkZycGDB6lXrx4fffRRvtt59NFH6d27NyeddBIdOnRg3bp1gO8qof79+9O4cWNCQ0N55JFHuOyyy4iPj+fqq6/mr7/+AmDEiBFUrFiRnj17sn//fpxzjB492u/3IcFnh44Cilp0dLRLTEwssv0V9trr9WHXFGq9JvXqFGq9KU8cu7vgaBqsXnXUZatWrcr+k724Wpqys1DrRYasK/A6K3J04RTEGYUcPje8ceNCrXciOdb3WEn+2TsezCzJORed1zJ1uYiIeIQCXUTEIxToIiIeoUAXEfEIBbqIiEco0EVEPELXoZdEj1YO8PZSA7s9ESkUHaFLkXPOcfDgwWCXIeI5CnQpEuvXr+fcc8+lT58+2XcsHjJ16lT6Zj0Aom/fvgwcOJALLriAM844g6lTpwapYpHiR4EuReann37i3//+NytWrDjmIFNbt27lm2++4aOPPmLw4MFHbScih/Mr0M2si5mtMbOfzeyInzAzu8fMVprZUjP71MxOD3ypUtydfvrptG7dOt92l156KSEhITRs2DD7IQ0ikr98A93MQoGxwCVAQ+BqM8s98v8PQLRzLhKYCjwd6EKl+Mt5VG5m2a9zD/NatmzZ7NfBGmtIpDjy5wi9FfCzc+4X59wB4F2gZ84GzrnPnXN7sya/BWoFtkzxmlNOOYVVq1Zx8OBBPvjgg2CXI+IJ/ly2WBPYlGM6BTjvGO1vBj7Oa4GZ3QbcBlCnTuFGRJMAOAEuM3zyySfp3r07ERERREdHZz/PUkQKL6DXoZvZdUA00C6v5c65CcAE8A2fG8h9y4mtbt26LF++PHs6NjaW2NjYI9rFx8cfNq2gF/GfP4G+GaidY7pW1rzDmFlHYAjQzjn3V2DKExERf/nTh74IONvM6plZGeAqYGbOBmbWDBgP9HDO/R74MkVEJD/5BrpzLgO4E5gLrAKmOOdWmNlwM+uR1WwUUAF4z8ySzWzmUTYnIiLHiV996M652cDsXPOG5XjdMcB1iYhIAelOURERj1Cgi4h4hIbPLYGavNEkoNtbdsOygG4vL63PrcW3a1IA2LBuLaMee4h1P/1IxcqVKV+hIv++ZzAtWrdh3OgnKVeuPDf0G/CP9vfWK28R2yeW8HLhAOzds5dRj4xi4VcLqVSpEuUqlOOeYfcQ2SKyQNutHxPDN+++S7WTTjpqm/j4eDp37kyNGjUKtO1t27bRvXt3Dhw4wAsvvMCFF15YoPWl+FOgS7Hy1/793HnDldw7dDjtO3cF4KfVK1m5NJkWrdsEbD9vTXiL7r27Zwf6sLuHUbNOTWZ/N5uQkBBSNqSwds3agO0vp/j4eBo3blygQM/IyODTTz+lSZMmTJo06bjUJSc+dblIkXn77bdp1aoVUVFR3H777WRmZlKhQgWGDBlC06ZNad26dfZgXOvWreP888+nSZMmDB06NHsbs6a/R9MWLbPDHODs+g3pecU12dNrf1rDzb2707VNFC+8+k72/NHj36Zxh9407tCb5ydOBiBt7z66XT+Qph2vpHGH3iTMmMvbE97m919/56ZeN3HjpTeycd1Gli1exsCHBhIS4vuRqXV6Ldp1bsdzw5/jnRz7GPHyyzwfH8/WbdvodMMNnBcbS3SvXixISjrsa7Fh82aa9ejBvx99lEaNGtG5c2f27dvH1KlTSUxM5NprryUqKop9+/aRlJREu3btaNGiBTExMWzduhWA9u3bM2jQIKKjoxkzZgz3338/M2bMyF7vjjvuIDo6mkaNGvHII49k73vRokVccMEFNG3alFatWrF7924yMzOJi4ujZcuWREZGMn78eMA38mXbtm2JioqicePGfP311//sm0COKwW6FIlVq1aRkJDAggULSE5OJjQ0lMmTJ5OWlkbr1q1ZsmQJbdu2ZeLEiQDcdddd3HHHHSxbtozTTjsteztr16ymQeOmx9zX+rU/Mu7taUz+8FMeGz2B9PR0kpau5PUpM/nuozf59sM3mPjfD/hh+WrmfP4/apwawZL5CSz/7D26XHQB1912HdVPrc5rH7zG69NfZ+2atZzb+NzDxnA/pMulXZg7Y2729Ptz5xLbpQtTZs+mY5s2fDd1Kt9NnUpk/fpHrPvzxo3cftVVrFixgipVqjBt2jRiY2OJjo5m8uTJJCcnU6pUKQYMGMDUqVNJSkripptuYsiQIdnbOHDgAImJidx7770MHz6cK6+8kuTkZMLDwxk5ciSJiYksXbqUL7/8kqVLl3LgwAGuvPJKxowZw5IlS5g/fz7h4eG8+uqrVK5cmUWLFrFo0SImTpzIunXr+O9//0tMTAzJycksWbKEqKiogn70UoTU5SJF4tNPPyUpKYmWLVsCsG/fPqpXr06ZMmXo3r07AC1atOCTTz4BYMGCBUybNg2A66+/nrj7H8hzu4NuuY6N69Zy+hln8dzEtwC4sENnypQtS5myZale7SR+2/Yn33yfTK8uF1E+qwvlsks68PV3P9Cl/QXcO3w0D4wcQ/eOF3Lhec1JKcD7ahDZgO1/bOf3X39nz5o/qVKpErVOPZUWjRrRb9gw0jMy+L8OHWiaR6DXrVkze36LFi1Yv379EW3WrFnD8uXL6dSpEwCZmZmH/YK78sorj1rblClTmDBhAhkZGWzdupWVK1diZpx22mnZn0OlSpUAmDdvHkuXLs1+oEhqaio//fQTLVu25KabbiI9PZ1LL71UgX6CU6BLkXDOccMNN/DEE08cNv+ZZ57JHko3NDSUjIyM7GU5h9g95Mxz67P4u/9lTz8/6W1WLPmB0SMezp5Xpszfw++GhoaSkZl51LrOOfN0Fs/5L7M/+4ahT7/Mxf9qxeUP3Jlrn2fy44ofyczMzPMoPaZHDPNmziNj3R/EdukCwL+io5kXH8+cr77itqFDGdinD9f26HHYemXLlDmszn379h2xbeccjRo1YuHChXnWf7QHhaxbt45nnnmGRYsWcdJJJ9G3b98jhinOvZ8XX3yRmJiYI5Z99dVXzJo1i759+3LPPffQp0+fo25HgktdLlIkLr74YqZOncrvv/tGhvjzzz/ZsGHDUdu3adOGd999F4DJkydnz+/aM5bkRd/xxby/73Pbv2/vEevnduF5zZg+93P27ttH2t59fDDncy48rxlbft1GufAwrru8G3H9+rB42WoAylcoT9qeNADq1KtDo6aNGPvU2Ozx2Tdv3MyX874EfN0uH0//mOmffEKvzp0B2LhlC6dUrcpNsbH0vewyklet8vtrVbFiRXbv3g3Aueeey7Zt27IDPT09nRUrVuS7jV27dlG+fHkqV67Mb7/9xscff5y9va1bt7Jo0SIAdu/eTUZGBjExMYwbN4709HQAfvzxR9LS0tiwYQOnnHIKt956K7fccguLFy/2+31I0dMReglUFJcZ5tawYUNGjBhB586dOXjwIKVLl2bs2LFHbT9mzBiuueYannrqKXr2/Hv4/bDwcF6Mf5dnhg/h6cceomq16pQrX4FbB953zP03b9KAvr170Kqb7+jylqsvpVnj+sz94n/EjXieEAuhdOlSjHviIQBir4+l35X9iDg1gtenv85jzz/GqGGjuKTVJYSFhVHl5Crc++i9AJxV/yz27tlLjerVOS0iAoCvFi3i+fh4SpUqRYVy5Zg0cqTfX6u+ffvSr18/wsPDWbhwIVOnTmXgwIGkpqaSkZHBoEGDaNSo0TG30bRpU5o1a0b9+vWpXbs2bdr4rgAqU6YMCQkJDBgwgH379hEeHs78+fO55ZZbWL9+Pc2bN8c5R0REBNOnT+eLL75g1KhRlC5dmgoVKvDmm2/6/T6k6FmwnggTHR3tEhMTi2x/dQfPKtR668Ouyb9RHprUK9x471OeyMi/UR4arD76EeCqVato0KBBobZ7oliasrNQ60WGrCvwOitydIUUxBm/Fu5nKbxx40KtdyI51vdYSf7ZOx7MLMk5F53XMnW5iIh4hAJdRMQjFOgiIh6hQBcR8QgFuoiIRyjQRUQ8Qtehl0Cr6gf2Esaivmwrt0kvPsstA+4t8HrtY2/lmYfvJrppQ+qe142KFcpjBmWrVOE/Y/9Djdr+j3Y45M4hXNGqLb06d+aORx5hYJ8+NDjzzALXJPJP6Ahdir1JLz2X53znHAcPHvR7O5+/N56l86cQ3Saa8aPHF7qecY89pjCXoFCgS5FIS0ujW7duNG3alMaNG5OQkEDdunW5//77adKkCa1ateLnn38GfA9quPzyy2nZsiUtW7ZkwYIFAOxN28PD9/Tn8o4XENupDfNnz+T5Jx7lr/37uCLmQh4ccCubN22kR7uWDBnUj8s6XsCmLb9yx+D/EH3JtTS6KJZHnhmXb61R0VH8vtU3RMHmjZvp070PvTv0pneH3vzw/Q+A75fFyAdG0r11d265/Bb+/OPP7PVjbryRpKzb8yNatcqe/8G8edyWNVLi+3PnEt2rF+ddfjlt27YNwFdYRF0uUkTmzJlDjRo1mDXLd9dgamoqDzzwAJUrV2bZsmW8+eabDBo0iI8++oi77rqLu+++m3/9619s3LiRmJgYEj5ZyPgxo6hYqRLT5vsG59q1cycdu/bg3fhJTJnrG6d786aNbFy3lhHPvUxk85acHrKOkQ/05+STKpOZmcnFV/Zj6cofiWx4zlFr/eazb+jQtQMAJ1c7mYlTJ1I2rCwb1m4g7vY4psyfwvxZ81m3dh0zFsxg+7bt9GzTE7pe6vfX44lXXmHGK69Q85RT+KtWrUJ+VUUOp0CXItGkSRPuvfdeHnjgAbp37579eLSrr746+/+7774bgPnz57Ny5crsdXft2sXetD18982XPDX21ez5lapUyXNfp9WqTWTzltnTUz78hAmT3ycjM4Otv/3Byp/W5RnoF/W+nT93plKmfHkGPOh7hF1GRgYjB49kzfI1hISEsOEX34BiSQuT6NqrK6GhoVQ/tTqtLmx1xPaOpXWzZtw+dCiXxcRw1YB/9rg8kUMU6FIkzjnnHBYvXszs2bMZOnQoF198MXD4ELmHXh88eJBvv/2WsLCw7GUFGcslPLxc9ut1GzfzzPg3WTTrbU6qUom+gx5h//6/8lzv8/fGU6VSRboPfJixT43l/sfv581X3qRqRFWmfTGNgwcP0qJWi4K87cPe3/6//t7vi8OG8f3Spcz56itatGhBUlISVatWLdC2RXJTH7oUiS1btlCuXDmuu+464uLisodhTUhIyP7//PPPB6Bz5868+OKL2esmJycD0PrC9iS88ffzMnft3AlAqdKlsod9zW3X7jTKh4dTuVIFftu2nY8/X3DMOkuVKsXgEYOZOWUmqTtS2bNrDxGnRBASEsKHUz4kM2ts9Rbnt2DO9DlkZmay7ddtLPpmUZ7bq161Kqt/+YWDBw8y87PPsuf/smkTrSIjGXbnnURERLBp06Zj1iXiDx2hl0DBuMxw2bJlxMXFERISQunSpRk3bhyxsbHs2LGDyMhIypYtyzvv+J7N+cILL9C/f38iIyPJyMigbdu2/Hvok9w28D7+MzSOyy4+n9DQUG6/+wE6XvJ/XH7NDfTu/C8aNI7kzvsfPmy/TRudQ7PG9anf9jJq1ziFNi2P/fg6gIhTI+jaqyvvvPYOV914FYNuGsTMKTP5V4d/ZT80umO3jnz/9ff0bNOTU2udStPovLf7+KBBXN6/P9VOPpnmDRuSttc3dvtDzz7L2g0bcEDHrl1p2jT/ukTyo+Fz8+GFITxP1OFz69atS2JiItWqVcu3rYbPPbFp+Nyio+FzRURKAHW5SNDk9VBkESk8HaGXEMHqWhPv0/fWiUOBXgKEhYWxfft2/eBJwDnn2L59+2GXmErwqMulBKhVqxYpKSls27Yt2KUU2m879hVqvVVW8Pf8a6nC/Vhk7irUapQODS3ciieIsLAwaulu1xOCAr0EKF26NPXq1Qt2Gf/IJUV4pcQVHr9KQrzLry4XM+tiZmvM7GczG5zH8rJmlpC1/DszqxvwSkVE5JjyDXQzCwXGApcADYGrzaxhrmY3Azucc2cBzwFPBbpQERE5Nn+O0FsBPzvnfnHOHQDeBXrmatMTeCPr9VTgYss5iIWIiBx3/vSh1wRyDjSRApx3tDbOuQwzSwWqAn/kbGRmtwG3ZU3uMbM1hSm6KBX+t9LyauR6//7I/aeP3/T7M0+F+6roszsR6GfvqE4/2oIiPSnqnJsATCjKfQaLmSUe7fZcObHpsyveSvLn50+Xy2agdo7pWlnz8mxjZqWAysD2QBQoIiL+8SfQFwFnm1k9MysDXAXMzNVmJnBD1utY4DOnu1hERIpUvl0uWX3idwJzgVDgNefcCjMbDiQ652YCrwJvmdnPwJ/4Qr+kKxFdSx6lz654K7GfX9CGzxURkcDSWC4iIh6hQBcR8QgFuoiIRyjQRUQ8QoEeQGZ2ipm9amYfZ003NLObg12X+M/Mws3s3GDXIQVnZqebWces1+FmVjHYNRU1BXpgxeO7vLNG1vSPwKBgFSMFY2b/ByQDc7Kmo8ws9z0XcgIys1vxjSM1PmtWLWB60AoKEgV6YFVzzk0BDoLvGn4gM7glSQE8im8wup0AzrlkoHgPJF9y9AfaALsAnHM/AdWDWlEQKNADK83MqgIOwMxaA6nBLUkKIN05l/vz0o0axcNfWaPBAtlDkJS4z05PLAqse/ANg3CmmS0AIvANhSDFwwozuwYINbOzgYHA/4Jck/jnSzN7CAg3s07Av4EPg1xTkdOdogGWdWRwLr7RP9c459KDXJL4yczKAUOAzvg+v7nA4865/UEtTPJlZiH4HrST87ObVNLGlFKgB1DW0526AXXJ8dePc250sGoSkZJDXS6B9SGwH1hG1olRKT7MLBp4iCN/IUcGqybxj5l1Bx7H9/CHUviO0p1zrlJQCytiOkIPIDNbqh/+4ivrCVpx5PqF7JzbELSixC9ZI71eBiwrad0sOekIPbA+NrPOzrl5wS5ECmVb1nDQUvxsApaX5DAHHaEHlJn1At7GdzloOiX0z77iyswuBq4GPgX+OjTfOfd+0IoSv5hZS3xdLl9y+GdXos5f6Qg9sEYD51PC/+wrxm4E6gOl+bvLxQEK9BPfSGAPEAaUCXItQaNADyz92Ve8tXTOaRyX4qmGc65xsIsINgV6YP0CfJE1OFeJ/bOvGPufmTV0zq0MdiFSYLN1/kp96AFlZo/kNd8591hR1yIFZ2argDOBdfh+IR86B6Irl05wZrYbKI/vcyux568U6CJZzOz0vObrskUpLtTlEgBm9rxzbpCZfUgeAwI553oEoSzxk5lVcs7tAnYHuxYpGDOr75xbbWbN81runFtc1DUFk47QA8DMWjjnksysXV7LnXNfFnVN4j8z+8g5193M1uH7hWw5Fjvn3BlBKk3yYWYTnHO3mdnneSx2zrkORV5UECnQA8jM7nLOjclvnogElpmF5R5ELa95Xqfx0APrhjzm9S3qIqRwzOxTf+bJCSmvYY5L3NDH6kMPADO7GrgGqJfrkWUVgT+DU5X4y8zCgHJANTM7ib+7XCoBNYNWmOTLzE7F9xmFm1kzDv/sygWtsCBRoAfG/4CtQDXg2RzzdwNLg1KRFMTt+J79WgNI4u9Q2AW8FKSaxD8x+P4KroXvZy/nZ/dQkGoKGvWhi2QxswHOuReDXYcUnJld7pybFuw6gk2BfpwdOgsf7DqkcMzsVOfcr8GuQwrOzJqXtMsWdVL0+Bsf7ALkH3k12AVIod0R7AKKmo7QjxMzq+6c+z3YdYhIyaFADwAzOzn3LHwn15rh+xrrSpcTnJnVAXY553aaWV0gGljtnFse3MrkWMysDJB+aIRTM7sIaA6sdM59HNTigkCBHgBmdhDIPd5HLSAF3Wl4wjOzwfiudPkLeAa4D1gAtAZe1WiZJy4zWwK0d87tMLM4oBcwG2gHJDnnBge1wCKmQA8AM7sX6ATEOeeWZc1b55yrF9zKxB9mtgLfEXk5YD1whnNum5mVB77TONsnLjNbfujzMbNE4ELn3D4zKwUsLmkjZeqkaAA4554FbgGGmdloM6tIHoN0yQkr0zm3D9gJ7AO2Azjn0oJZlPhll5kd+oX7B74nFoHvHpsSl286Qg8wM+uB74aGus65U4Ndj+TPzOLxPbasPLAXyADmAB2Ais65K4JXnRyLmUUCbwFLsma1Ab4CmgCjnXP/DVZtwaBADwAzOw9Y5ZzbZWblgEfxnZhJAv7jnEsNZn1ybFl/nvfG91fVVKAVvqEcNgJjdaR+YjOzUKAzcA6+I/MUYK5zbmcw6woGBXoAZPXBNnXOZZjZBCANmAZcnDX/sqAWKCIlQonrYzpOQpxzGVmvo51zdzvnvsl69JyucDnBmVklM3vCzN4ys2tyLXs5WHVJ/sysS47Xlc3sVTNbamb/NbNTgllbMCjQA2O5md2Y9XqJmUUDmNk5+J5vKCe21/HdOzANuMrMpplZ2axlrYNXlvjhPzleP4tvkLz/AxZRAu/SVpdLAJhZZWAMcCG+M+3NgU1Z/wY655YcY3UJMjNLds5F5ZgeAnQFegCfOOfyfLyZBJ+ZLT70+eTxOR42XRJo+NwAyDrp2dfMKgH1yDox45z7LbiViZ/KmlmIc+4ggHNupJltxne1RIXglib5qG5m9+D7C6uSmZn7+yi1xPVAlLg3fDw553Y555Y455IU5sXKh/guUczmnIsH7gUOBKMg8dtEfA+SqQC8ge+ZBIcefJEcvLKCQ10uIsdgZjc6514Pdh1ydGZWH99Ti75zzu3JMb+Lc25O8CorejpCFzm2x4JdgBydmQ0AZgAD8F2c0DPH4v/kvZZ3qQ9dSjwzO9pjAg0ocZe+FTO3AS2cc3uyRsmcamZ1nXNj+PtxdCWGAl3EF9oxwI5c840S+OT4YibkUDeLc269mbXHF+qnUwIDXV0uIvARUME5tyHXv/XAF8EtTfLxm5lFHZrICvfu+E6ONglWUcGik6IiUmyZWS0gI6/nvppZG+fcgiCUFTQKdBERj1CXi4iIRyjQRUQ8QoEuIuIRCnQREY/4fz0moXRrhbJYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import hatchet as ht\n",
    "import glob\n",
    "\n",
    "dirs = glob.glob(\n",
    "    \"/mnt/c/Users/onurc/Desktop/Work/Projects/chopper-data/tortuga-weak-scaling/cubex-32ppn-nopapi/tortuga-cubex*\",\n",
    "    recursive=True,\n",
    ")\n",
    "\n",
    "p_dir = []\n",
    "for dir in dirs:\n",
    "    p_dir.append([int(dir.split(\"-\")[-1]), dir])\n",
    "dirs = sorted(p_dir, key=lambda l: l[0])\n",
    "\n",
    "data = [path[1] + \"/profile.cubex\" for path in dirs[1:]]\n",
    "gfs = ht.GraphFrame.construct_from(data)\n",
    "\n",
    "for idx in range(len(gfs)):\n",
    "    gfs[idx].update_metadata(int(dirs[idx+1][0]))\n",
    "\n",
    "tortuga_eff = ht.Chopper.speedup_efficiency(gfs, weak=True, efficiency=True)\n",
    "\n",
    "disp_mets = [\"name\",\"64.time\", \"128.time\", \"256.time\"]\n",
    "tortuga_eff = tortuga_eff[disp_mets]\n",
    "tortuga_eff_filt = tortuga_eff.loc[~(tortuga_eff['name'].str.contains(\"MPI\"))]\n",
    "tortuga_eff_filt = tortuga_eff_filt.loc[~(tortuga_eff_filt['name'].str.contains(\"MEASUREMENT\"))]\n",
    "tortuga_eff_filt = tortuga_eff_filt.loc[~(tortuga_eff_filt['name'].str.contains(\"write\"))]\n",
    "tortuga_eff_filt = tortuga_eff_filt.loc[~(tortuga_eff_filt['name'].str.contains(\"cLesSolver_v3.0\"))]\n",
    "tortuga_eff_filt.reset_index(drop=True, inplace=True)\n",
    "tortuga_eff_filt.set_index(\"name\", inplace=True)\n",
    "tortuga_eff_filt = tortuga_eff_filt[[\"64.time\", \"128.time\", \"256.time\"]]\n",
    "# display(tortuga_eff_filt)\n",
    "\n",
    "tortuga_eff_filt.head(4).T.loc[: , :].plot.bar()"
   ]
  }
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