bachelorThesis/table_iii/table_iii_iv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20840d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import sys\n",
    "import glob\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import display, Markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3de236df",
   "metadata": {},
   "outputs": [],
   "source": [
    "DIR = \"/home/claudio/hdd/git/bachelorThesis/table_iii/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff58edfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "NAMES = {-1: \"No termination\", 0: \"SUBMIT\", 1: \"QUEUE\", 2: \"ENABLE\", \n",
    "          3: \"SCHEDULE\", 4: \"EVICT\", 5: \"FAIL\", 6: \"FINISH\",\n",
    "          7: \"KILL\", 8: \"LOST\", 9: \"UPDATE_PENDING\", 10: \"UPDATE_RUNNING\"}\n",
    "\n",
    "def rename(df, new, old):\n",
    "    df.rename(columns={old: new}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5dd86c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# Table III"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\tableIII{A}{\n",
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      " EVICT & 103.228 (719) & 73.694 &  0.769 & 0.000 & 28.766 \\\\\n",
      "  FAIL &   11.819 (26) &  0.288 & 11.062 & 0.002 &  0.468 \\\\\n",
      "FINISH &     2.185 (1) &  0.019 &  0.004 & 2.153 &  0.008 \\\\\n",
      "  KILL &    5.963 (11) &  2.350 &  0.214 & 0.003 &  3.396 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIII{B}{\n",
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      " EVICT & 83.018 (394) & 64.817 &  0.240 & 0.000 & 17.962 \\\\\n",
      "  FAIL &  20.851 (62) &  0.518 & 19.657 & 0.001 &  0.675 \\\\\n",
      "FINISH &    2.995 (4) &  0.020 &  0.021 & 2.943 &  0.012 \\\\\n",
      "  KILL &   9.173 (12) &  3.351 &  0.276 & 0.004 &  5.541 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIII{C}{\n",
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      " EVICT & 98.437 (444) & 73.716 &  1.813 & 0.000 & 22.908 \\\\\n",
      "  FAIL &  52.010 (30) &  0.773 & 48.446 & 2.035 &  0.756 \\\\\n",
      "FINISH &    2.507 (2) &  0.018 &  0.013 & 2.471 &  0.006 \\\\\n",
      "  KILL &    5.452 (6) &  1.533 &  0.116 & 0.004 &  3.799 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIII{D}{\n",
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      " EVICT & 76.759 (366) & 62.001 &  0.700 & 0.000 & 14.058 \\\\\n",
      "  FAIL &  62.314 (62) &  0.496 & 58.968 & 0.810 &  2.040 \\\\\n",
      "FINISH &    3.877 (2) &  0.059 &  0.019 & 3.789 &  0.010 \\\\\n",
      "  KILL &    6.795 (6) &  1.960 &  0.151 & 0.002 &  4.682 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIII{E}{\n",
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      " EVICT &  17.678 (72) & 11.781 &   0.106 & 0.000 &  5.791 \\\\\n",
      "  FAIL & 112.384 (28) &  0.458 & 111.471 & 0.000 &  0.456 \\\\\n",
      "FINISH &    2.029 (2) &  0.014 &   0.008 & 1.999 &  0.008 \\\\\n",
      "  KILL &  13.505 (64) &  1.288 &   0.057 & 0.000 & 12.160 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIII{F}{\n",
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      " EVICT & 70.146 (114) & 23.974 &  0.192 & 0.000 & 45.980 \\\\\n",
      "  FAIL &  41.087 (54) &  0.279 & 39.257 & 0.000 &  1.550 \\\\\n",
      "FINISH &    3.129 (4) &  0.019 &  0.004 & 3.008 &  0.098 \\\\\n",
      "  KILL &  10.288 (38) &  0.384 &  0.098 & 0.001 &  9.804 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIII{G}{\n",
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      " EVICT & 136.032 (490) & 77.429 & 0.303 &  0.000 & 58.299 \\\\\n",
      "  FAIL &     8.948 (8) &  0.016 & 8.593 &  0.000 &  0.339 \\\\\n",
      "FINISH &    14.176 (2) &  0.015 & 0.002 & 14.154 &  0.005 \\\\\n",
      "  KILL &  32.320 (164) &  6.909 & 0.135 &  0.000 & 25.276 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIII{H}{\n",
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      " EVICT &  14.734 (40) & 6.733 &  0.837 & 0.000 &  7.165 \\\\\n",
      "  FAIL & 41.067 (120) & 0.600 & 37.600 & 0.000 &  2.867 \\\\\n",
      "FINISH &    3.681 (2) & 0.024 &  0.014 & 3.633 &  0.011 \\\\\n",
      "  KILL &  17.976 (98) & 0.633 &  0.170 & 0.000 & 17.173 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIII{ALL}{\n",
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      " EVICT & 78.710 (342) & 52.242 &  0.673 & 0.000 & 25.795 \\\\\n",
      "  FAIL &  24.962 (26) &  0.290 & 23.635 & 0.348 &  0.691 \\\\\n",
      "FINISH &    2.962 (2) &  0.022 &  0.012 & 2.915 &  0.013 \\\\\n",
      "  KILL &   8.763 (16) &  1.876 &  0.143 & 0.003 &  6.741 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "display(Markdown(\"# Table III\"))\n",
    "for cluster in list(\"abcdefgh\") + [\"all\"]:\n",
    "    df = pd.read_csv(glob.glob(DIR + \"/table-iii-\" + cluster + \".csv/part-00000-*\")[0])\n",
    "    df = df[df[\"task_term\"].isin(range(4,8))]\n",
    "    df = df.sort_values(\"task_term\")\n",
    "    df[\"mean\"] = df[\"mean\"].round(3).apply(lambda x: \"%.03f\" % x) + \" (\" + df[\"%95\"].apply(lambda x: \"%d\" % x) + \")\"\n",
    "    rename(df, \"# Evts. mean (95-th percentile)\", \"mean\")\n",
    "    del df[\"%95\"]\n",
    "    \n",
    "    for i in [4,5,6,7]:\n",
    "        df.loc[df.task_term == i, \"task_term\"] = NAMES[i]\n",
    "        df[\"avg_count_%d\" % i] = df[\"avg_count_%d\" % i].round(3)\n",
    "        rename(df, \"mean # \" + NAMES[i] + \" evts.\", \"avg_count_\" + str(i))\n",
    "    for i in [0,1,2,3,8,9,10]:\n",
    "        del df[\"avg_count_\" + str(i)]\n",
    "    rename(df, \"Task termination\", \"task_term\")\n",
    "    print((\"\\\\tableIII{\" + cluster.upper() + \"}{\"))\n",
    "    print(df.to_latex(index=False, header=False), end=\"}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cea0e71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# Table IV"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\tableIV{A}{\n",
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "Job termination &  \\# Tasks mean &  \\# Tasks 95\\% p.tile &  \\# EVICT Evts. mean &  \\# FAIL Evts. mean &  \\# FINISH Evts. mean &  \\# KILL Evts. mean &  \\# LOST Evts. mean \\\\\n",
      "\\midrule\n",
      " No termination &     92.359436 &               174.3 &           23.263951 &           3.454474 &            23.047597 &          34.565608 &           0.707709 \\\\\n",
      "          EVICT &     -1.000000 &                -1.0 &                 NaN &                NaN &                  NaN &                NaN &                NaN \\\\\n",
      "           FAIL &     90.792728 &               499.0 &            0.694942 &           0.683556 &             0.085957 &           1.849587 &           0.009730 \\\\\n",
      "         FINISH &      1.187092 &                 1.0 &            0.004696 &           0.001341 &             1.072623 &           0.024396 &           0.000952 \\\\\n",
      "           KILL &     16.533171 &                10.0 &            1.045419 &           0.073867 &             0.461387 &           1.188720 &           0.044610 \\\\\n",
      "           LOST &    223.206593 &              1689.6 &            0.000000 &           0.000000 &             0.000000 &           1.034082 &           0.974598 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIV{B}{\n",
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "Job termination &  \\# Tasks mean &  \\# Tasks 95\\% p.tile &  \\# EVICT Evts. mean &  \\# FAIL Evts. mean &  \\# FINISH Evts. mean &  \\# KILL Evts. mean &  \\# LOST Evts. mean \\\\\n",
      "\\midrule\n",
      " No termination &    112.422759 &               169.8 &           34.681161 &           0.711242 &            13.379533 &          38.794188 &           0.780483 \\\\\n",
      "          EVICT &      1.000000 &                 1.0 &            1.000000 &           0.000000 &             0.000000 &           0.000000 &           0.000000 \\\\\n",
      "           FAIL &     74.367804 &               374.0 &            2.003355 &           1.993765 &             0.266584 &           4.944145 &           0.034526 \\\\\n",
      "         FINISH &      6.304299 &                10.0 &            0.022380 &           0.008476 &             2.349304 &           0.012729 &           0.006484 \\\\\n",
      "           KILL &     69.853370 &               234.0 &            1.696449 &           0.157833 &             0.613748 &           3.008678 &           0.012092 \\\\\n",
      "           LOST &    320.020202 &               459.8 &            0.000000 &           0.000000 &             0.000000 &           2.959946 &           1.996875 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIV{C}{\n",
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "Job termination &  \\# Tasks mean &  \\# Tasks 95\\% p.tile &  \\# EVICT Evts. mean &  \\# FAIL Evts. mean &  \\# FINISH Evts. mean &  \\# KILL Evts. mean &  \\# LOST Evts. mean \\\\\n",
      "\\midrule\n",
      " No termination &     96.399561 &               100.0 &           55.276973 &           7.552906 &            23.848867 &          41.578669 &           0.664107 \\\\\n",
      "          EVICT &      1.000000 &                 1.0 &            1.000829 &           0.000000 &             0.000000 &           0.000415 &           0.000000 \\\\\n",
      "           FAIL &     41.982301 &               200.0 &            3.483606 &           0.997592 &             0.376438 &           3.998369 &           0.046439 \\\\\n",
      "         FINISH &      1.991485 &                 1.0 &            0.021806 &           0.016914 &             1.565034 &           0.017401 &           0.001803 \\\\\n",
      "           KILL &    110.680808 &               652.0 &            0.627334 &           0.059076 &             0.656426 &           2.266794 &           0.006258 \\\\\n",
      "           LOST &     38.870091 &                48.6 &            0.000031 &           0.000311 &             0.000000 &           2.620721 &           1.833872 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIV{D}{\n",
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "Job termination &  \\# Tasks mean &  \\# Tasks 95\\% p.tile &  \\# EVICT Evts. mean &  \\# FAIL Evts. mean &  \\# FINISH Evts. mean &  \\# KILL Evts. mean &  \\# LOST Evts. mean \\\\\n",
      "\\midrule\n",
      " No termination &    103.889987 &              120.00 &           41.421532 &           7.604808 &            18.179476 &          47.603502 &           0.661826 \\\\\n",
      "          EVICT &      1.000000 &                1.00 &            1.000000 &           0.000000 &             0.000000 &           0.000000 &           0.000000 \\\\\n",
      "           FAIL &     43.355682 &              250.00 &            6.111993 &           0.948602 &             0.531390 &           6.497784 &           0.041077 \\\\\n",
      "         FINISH &      2.109260 &                2.00 &            0.268375 &           0.012614 &             1.723392 &           0.018567 &           0.005052 \\\\\n",
      "           KILL &     89.647948 &              283.00 &            1.013114 &           0.054374 &             0.283313 &           3.255675 &           0.006664 \\\\\n",
      "           LOST &    271.441748 &             2620.75 &            0.000000 &           0.000000 &             0.000000 &           5.938069 &           1.647084 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIV{E}{\n",
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "Job termination &  \\# Tasks mean &  \\# Tasks 95\\% p.tile &  \\# EVICT Evts. mean &  \\# FAIL Evts. mean &  \\# FINISH Evts. mean &  \\# KILL Evts. mean &  \\# LOST Evts. mean \\\\\n",
      "\\midrule\n",
      " No termination &    350.929407 &               596.0 &            7.204391 &           2.074423 &             0.126290 &          46.646065 &           0.378274 \\\\\n",
      "          EVICT &      1.000000 &                 1.0 &            1.000000 &           0.000000 &             0.000000 &           0.000000 &           0.000000 \\\\\n",
      "           FAIL &     23.081125 &                25.0 &            0.246529 &           0.665546 &             0.716720 &           1.588119 &           0.066467 \\\\\n",
      "         FINISH &      7.776085 &                 2.0 &            0.018677 &           0.029073 &             1.934488 &           0.020929 &           0.064920 \\\\\n",
      "           KILL &     88.790215 &               309.0 &            0.706293 &           0.028618 &             0.461084 &           7.572301 &           0.029122 \\\\\n",
      "           LOST &      5.374150 &                 5.0 &            0.000000 &           0.000000 &             0.000000 &           3.234494 &           1.813924 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIV{F}{\n",
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "Job termination &  \\# Tasks mean &  \\# Tasks 95\\% p.tile &  \\# EVICT Evts. mean &  \\# FAIL Evts. mean &  \\# FINISH Evts. mean &  \\# KILL Evts. mean &  \\# LOST Evts. mean \\\\\n",
      "\\midrule\n",
      " No termination &    217.718640 &               379.4 &            4.304676 &           1.315021 &             4.971122 &          48.118465 &           0.464429 \\\\\n",
      "          EVICT &      1.000000 &                 1.0 &            1.000000 &           0.000000 &             0.000000 &           0.000000 &           0.000000 \\\\\n",
      "           FAIL &     17.161251 &                 8.0 &            0.621327 &           0.546356 &             0.426265 &           7.559244 &           0.034773 \\\\\n",
      "         FINISH &      2.940843 &                 2.0 &            0.014704 &           0.051014 &             1.669860 &           0.162042 &           0.002623 \\\\\n",
      "           KILL &    103.888843 &               361.0 &            0.182630 &           0.063914 &             0.416684 &           5.824311 &           0.014161 \\\\\n",
      "           LOST &   3736.500000 &             18823.4 &            0.001491 &           0.000038 &             0.000000 &           6.298140 &           1.429604 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIV{G}{\n",
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "Job termination &  \\# Tasks mean &  \\# Tasks 95\\% p.tile &  \\# EVICT Evts. mean &  \\# FAIL Evts. mean &  \\# FINISH Evts. mean &  \\# KILL Evts. mean &  \\# LOST Evts. mean \\\\\n",
      "\\midrule\n",
      " No termination &    342.090034 &              599.10 &           14.184405 &           0.626186 &            23.836017 &          46.002917 &           0.735801 \\\\\n",
      "          EVICT &      1.000000 &                1.00 &            1.000000 &           0.000000 &             0.000000 &           0.000000 &           0.000000 \\\\\n",
      "           FAIL &     51.834803 &              250.00 &            0.555532 &           3.334848 &             0.607560 &          20.351992 &           0.176242 \\\\\n",
      "         FINISH &      8.519166 &               36.00 &            0.001733 &           0.629809 &             1.759677 &           0.005452 &           0.004575 \\\\\n",
      "           KILL &     37.054914 &              100.00 &            5.687172 &           0.064640 &             0.080370 &          19.166260 &           0.059132 \\\\\n",
      "           LOST &    190.500000 &              358.35 &            0.000000 &           0.000000 &             0.000000 &           1.994751 &           1.994751 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n",
      "\\tableIV{H}{\n",
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "Job termination &  \\# Tasks mean &  \\# Tasks 95\\% p.tile &  \\# EVICT Evts. mean &  \\# FAIL Evts. mean &  \\# FINISH Evts. mean &  \\# KILL Evts. mean &  \\# LOST Evts. mean \\\\\n",
      "\\midrule\n",
      " No termination &    321.133053 &               546.9 &            3.470078 &           0.907801 &             3.316902 &          44.535824 &           0.315120 \\\\\n",
      "          EVICT &      1.000000 &                 1.0 &            1.000000 &           0.000000 &             0.000000 &           0.000000 &           0.000000 \\\\\n",
      "           FAIL &     20.504293 &                 1.0 &            0.114090 &           2.300036 &             0.980635 &          12.833466 &           0.046833 \\\\\n",
      "         FINISH &      4.278193 &                14.0 &            0.005406 &           0.152814 &             1.778038 &           0.013567 &           0.012663 \\\\\n",
      "           KILL &     11.022705 &                 3.0 &            0.235500 &           0.102899 &             0.287701 &          11.336956 &           0.031148 \\\\\n",
      "           LOST &      3.400000 &                10.6 &            0.000000 &           0.000000 &             0.000000 &           0.235294 &           1.705882 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "display(Markdown(\"# Table IV\"))\n",
    "for cluster in \"abcdefgh\":\n",
    "    df = pd.read_csv(glob.glob(DIR + \"/table-iv-evts-\" + cluster + \".csv/part-00000-*\")[0], header=None,\n",
    "                names=[\"term\"] + [str(i) for i in range(0,11)])\n",
    "    df2 = pd.read_csv(glob.glob(DIR + \"/table-iv-tasks-\" + cluster + \".csv/part-00000-*\")[0], header=None,\n",
    "            names=[\"term\", \"# Tasks mean\", \"# Tasks 95% p.tile\"])\n",
    "    df[\"term\"] = df[\"term\"].astype(int)\n",
    "    df2[\"term\"] = df2[\"term\"].astype(int)\n",
    "    df.sort_values(by=\"term\", inplace=True)\n",
    "    df2.sort_values(by=\"term\", inplace=True)\n",
    "    \n",
    "    df = df2.merge(df, on=\"term\", how=\"outer\")\n",
    "\n",
    "    rename(df, \"# Evts. mean\", \"mean\")\n",
    "    rename(df, \"# Evts. 95% p.tile\", \"%95\")\n",
    "    \n",
    "    for i in [-1,4,5,6,7,8]:\n",
    "        df.loc[df.term == i, \"term\"] = NAMES[i]\n",
    "        rename(df, \"# \" + NAMES[i] + \" Evts. mean\", str(i))\n",
    "    for i in [0,1,2,3,9,10]:\n",
    "        del df[str(i)]\n",
    "    rename(df, \"Job termination\", \"term\")\n",
    "    print((\"\\\\tableIV{\" + cluster.upper() + \"}{\"))\n",
    "    print(df.to_latex(index=False), end=\"}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6763138f",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_count = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dc0714c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def figure_5_plot(df, cluster):\n",
    "    dft = {}\n",
    "    plt.figure(figsize=(4,3))\n",
    "    for i in [4,5,7]:\n",
    "        dft[i] = df[[\"count_\" + str(i), \"succ\", \"non\"]].copy()\n",
    "        dft[i] = dft[i].groupby(\"count_\" + str(i)).sum().reset_index()\n",
    "        \n",
    "        over = dft[i][dft[i][\"count_\" + str(i)] > max_count].sum()\n",
    "        if over[\"succ\"] == 0 and over[\"non\"] == 0:\n",
    "            percover = 0\n",
    "        else:\n",
    "            percover = over[\"succ\"] / (over[\"succ\"] + over[\"non\"])\n",
    "        \n",
    "        dft[i][\"perc\"] = dft[i][\"succ\"] / (dft[i][\"succ\"] + dft[i][\"non\"])\n",
    "        dfi = dft[i]\n",
    "        dft[i].loc[dfi[\"succ\"].eq(0) & dfi[\"non\"].eq(0), [\"perc\"]] = 0\n",
    "        \n",
    "        dft[i] = dft[i].drop(dft[i][dft[i][\"count_\" + str(i)] > max_count].index)\n",
    "        #dft[i][\"count_\" + str(i)] = dft[i][\"count_\" + str(i)].astype(str)\n",
    "        dft[i] = dft[i].append({\"count_\" + str(i): max_count + 1, \"perc\": percover}, ignore_index=True)\n",
    "\n",
    "        del dft[i][\"succ\"]\n",
    "        del dft[i][\"non\"]\n",
    "        plt.xticks([0,5,10,15,20,25,30,35,40,45,50,51])\n",
    "        \n",
    "        ys = []\n",
    "        for j in range(0, max_count + 2):\n",
    "            a = dft[i][dft[i][\"count_\" + str(i)] == j]\n",
    "            ys.append(0 if a.empty else a[\"perc\"].squeeze() * 100)\n",
    "     \n",
    "        plt.plot([x if x < 51 else \">50\" for x in range(0,52)], ys)\n",
    "        if cluster == \"all\":\n",
    "            plt.title(\"2019 data\")\n",
    "        elif cluster == \"2011\":\n",
    "            plt.title(\"2011 data\")\n",
    "        else:\n",
    "            plt.title(\"Cluster \" + cluster.upper())\n",
    "    lgd = plt.legend([\"EVICT\", \"FAIL\", \"KILL\"])\n",
    "    plt.savefig('../report/figures/figure_5/figure-5-%s.pgf' % cluster, \n",
    "                bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "dftot = None\n",
    "for cluster in \"abcdefgh\":\n",
    "    df = pd.read_csv(glob.glob(DIR + \"fig-5-\" + cluster + \".csv/part-00000-*\")[0], \n",
    "                     names=[\"count_4\", \"count_5\", \"count_7\", \"count_8\", \"succ\", \"non\"])\n",
    "    figure_5_plot(df, cluster)\n",
    "    if dftot is None:\n",
    "        dftot = df\n",
    "    else:\n",
    "        dftot = dftot.append(df)\n",
    "    \n",
    "dftot = dftot.groupby([\"count_4\", \"count_5\", \"count_7\", \"count_8\"]).sum().reset_index()\n",
    "figure_5_plot(df, \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e095a31e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4898bed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}