2021-04-28 12:59:45 +00:00
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{
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"cells": [
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{
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"cell_type": "code",
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2021-05-24 10:06:24 +00:00
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"execution_count": 2,
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2021-05-22 14:18:14 +00:00
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"id": "20840d16",
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2021-04-28 12:59:45 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import sys\n",
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"import glob\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from IPython.display import display, Markdown"
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]
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},
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{
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"cell_type": "code",
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2021-05-24 10:06:24 +00:00
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"execution_count": 4,
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2021-05-22 14:18:14 +00:00
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"id": "3de236df",
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2021-04-28 12:59:45 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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2021-05-22 14:18:14 +00:00
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"DIR = \"/home/claudio/hdd/git/bachelorThesis/table_iii/\""
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2021-04-28 12:59:45 +00:00
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]
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},
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{
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"cell_type": "code",
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2021-05-24 10:06:24 +00:00
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"execution_count": 5,
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2021-05-22 14:18:14 +00:00
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"id": "ff58edfc",
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2021-04-28 12:59:45 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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"NAMES = {-1: \"No termination\", 0: \"SUBMIT\", 1: \"QUEUE\", 2: \"ENABLE\", \n",
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" 3: \"SCHEDULE\", 4: \"EVICT\", 5: \"FAIL\", 6: \"FINISH\",\n",
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" 7: \"KILL\", 8: \"LOST\", 9: \"UPDATE_PENDING\", 10: \"UPDATE_RUNNING\"}\n",
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"\n",
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"def rename(df, new, old):\n",
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" df.rename(columns={old: new}, inplace=True)"
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]
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},
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{
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"cell_type": "code",
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2021-05-24 10:06:24 +00:00
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"execution_count": 28,
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2021-05-22 14:18:14 +00:00
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"id": "5dd86c47",
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2021-04-28 12:59:45 +00:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"# Table III"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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2021-05-16 10:22:27 +00:00
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\\tableIII{A}{\n",
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2021-05-22 14:18:14 +00:00
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"\\begin{tabular}{llrrrr}\n",
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2021-05-16 10:22:27 +00:00
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"\\toprule\n",
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2021-05-24 10:06:24 +00:00
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" EVICT & 103.228 (719) & 73.694 & 0.769 & 0.000 & 28.766 \\\\\n",
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" FAIL & 11.819 (26) & 0.288 & 11.062 & 0.002 & 0.468 \\\\\n",
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"FINISH & 2.185 (1) & 0.019 & 0.004 & 2.153 & 0.008 \\\\\n",
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" KILL & 5.963 (11) & 2.350 & 0.214 & 0.003 & 3.396 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"}\n",
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"\\tableIII{B}{\n",
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2021-05-22 14:18:14 +00:00
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"\\begin{tabular}{llrrrr}\n",
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2021-05-16 10:22:27 +00:00
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"\\toprule\n",
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2021-05-24 10:06:24 +00:00
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" EVICT & 83.018 (394) & 64.817 & 0.240 & 0.000 & 17.962 \\\\\n",
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" FAIL & 20.851 (62) & 0.518 & 19.657 & 0.001 & 0.675 \\\\\n",
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"FINISH & 2.995 (4) & 0.020 & 0.021 & 2.943 & 0.012 \\\\\n",
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" KILL & 9.173 (12) & 3.351 & 0.276 & 0.004 & 5.541 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"}\n",
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"\\tableIII{C}{\n",
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2021-05-22 14:18:14 +00:00
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"\\begin{tabular}{llrrrr}\n",
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2021-05-16 10:22:27 +00:00
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"\\toprule\n",
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2021-05-24 10:06:24 +00:00
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" EVICT & 98.437 (444) & 73.716 & 1.813 & 0.000 & 22.908 \\\\\n",
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" FAIL & 52.010 (30) & 0.773 & 48.446 & 2.035 & 0.756 \\\\\n",
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"FINISH & 2.507 (2) & 0.018 & 0.013 & 2.471 & 0.006 \\\\\n",
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" KILL & 5.452 (6) & 1.533 & 0.116 & 0.004 & 3.799 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"}\n",
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"\\tableIII{D}{\n",
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2021-05-22 14:18:14 +00:00
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"\\begin{tabular}{llrrrr}\n",
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2021-05-16 10:22:27 +00:00
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"\\toprule\n",
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2021-05-24 10:06:24 +00:00
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" EVICT & 76.759 (366) & 62.001 & 0.700 & 0.000 & 14.058 \\\\\n",
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" FAIL & 62.314 (62) & 0.496 & 58.968 & 0.810 & 2.040 \\\\\n",
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"FINISH & 3.877 (2) & 0.059 & 0.019 & 3.789 & 0.010 \\\\\n",
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" KILL & 6.795 (6) & 1.960 & 0.151 & 0.002 & 4.682 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"}\n",
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"\\tableIII{E}{\n",
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2021-05-22 14:18:14 +00:00
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"\\begin{tabular}{llrrrr}\n",
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2021-05-16 10:22:27 +00:00
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"\\toprule\n",
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2021-05-24 10:06:24 +00:00
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" EVICT & 17.678 (72) & 11.781 & 0.106 & 0.000 & 5.791 \\\\\n",
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" FAIL & 112.384 (28) & 0.458 & 111.471 & 0.000 & 0.456 \\\\\n",
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"FINISH & 2.029 (2) & 0.014 & 0.008 & 1.999 & 0.008 \\\\\n",
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" KILL & 13.505 (64) & 1.288 & 0.057 & 0.000 & 12.160 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"}\n",
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"\\tableIII{F}{\n",
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2021-05-22 14:18:14 +00:00
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"\\begin{tabular}{llrrrr}\n",
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2021-05-16 10:22:27 +00:00
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"\\toprule\n",
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2021-05-24 10:06:24 +00:00
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" EVICT & 70.146 (114) & 23.974 & 0.192 & 0.000 & 45.980 \\\\\n",
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" FAIL & 41.087 (54) & 0.279 & 39.257 & 0.000 & 1.550 \\\\\n",
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"FINISH & 3.129 (4) & 0.019 & 0.004 & 3.008 & 0.098 \\\\\n",
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" KILL & 10.288 (38) & 0.384 & 0.098 & 0.001 & 9.804 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"}\n",
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"\\tableIII{G}{\n",
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2021-05-22 14:18:14 +00:00
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"\\begin{tabular}{llrrrr}\n",
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2021-05-16 10:22:27 +00:00
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"\\toprule\n",
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2021-05-24 10:06:24 +00:00
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" EVICT & 136.032 (490) & 77.429 & 0.303 & 0.000 & 58.299 \\\\\n",
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" FAIL & 8.948 (8) & 0.016 & 8.593 & 0.000 & 0.339 \\\\\n",
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"FINISH & 14.176 (2) & 0.015 & 0.002 & 14.154 & 0.005 \\\\\n",
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" KILL & 32.320 (164) & 6.909 & 0.135 & 0.000 & 25.276 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"}\n",
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"\\tableIII{H}{\n",
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2021-05-22 14:18:14 +00:00
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"\\begin{tabular}{llrrrr}\n",
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2021-05-16 10:22:27 +00:00
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"\\toprule\n",
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2021-05-24 10:06:24 +00:00
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" EVICT & 14.734 (40) & 6.733 & 0.837 & 0.000 & 7.165 \\\\\n",
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" FAIL & 41.067 (120) & 0.600 & 37.600 & 0.000 & 2.867 \\\\\n",
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"FINISH & 3.681 (2) & 0.024 & 0.014 & 3.633 & 0.011 \\\\\n",
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" KILL & 17.976 (98) & 0.633 & 0.170 & 0.000 & 17.173 \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"}\n",
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"\\tableIII{ALL}{\n",
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"\\begin{tabular}{llrrrr}\n",
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"\\toprule\n",
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" EVICT & 78.710 (342) & 52.242 & 0.673 & 0.000 & 25.795 \\\\\n",
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" FAIL & 24.962 (26) & 0.290 & 23.635 & 0.348 & 0.691 \\\\\n",
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"FINISH & 2.962 (2) & 0.022 & 0.012 & 2.915 & 0.013 \\\\\n",
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" KILL & 8.763 (16) & 1.876 & 0.143 & 0.003 & 6.741 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"}\n"
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]
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2021-04-28 12:59:45 +00:00
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}
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],
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"source": [
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"display(Markdown(\"# Table III\"))\n",
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2021-05-24 10:06:24 +00:00
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"for cluster in list(\"abcdefgh\") + [\"all\"]:\n",
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2021-04-28 12:59:45 +00:00
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" df = pd.read_csv(glob.glob(DIR + \"/table-iii-\" + cluster + \".csv/part-00000-*\")[0])\n",
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2021-05-22 14:18:14 +00:00
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" df = df[df[\"task_term\"].isin(range(4,8))]\n",
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" df = df.sort_values(\"task_term\")\n",
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2021-05-24 10:06:24 +00:00
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" df[\"mean\"] = df[\"mean\"].round(3).apply(lambda x: \"%.03f\" % x) + \" (\" + df[\"%95\"].apply(lambda x: \"%d\" % x) + \")\"\n",
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2021-05-22 14:18:14 +00:00
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" rename(df, \"# Evts. mean (95-th percentile)\", \"mean\")\n",
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" del df[\"%95\"]\n",
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2021-04-28 12:59:45 +00:00
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" \n",
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2021-05-22 14:18:14 +00:00
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" for i in [4,5,6,7]:\n",
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2021-04-28 12:59:45 +00:00
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" df.loc[df.task_term == i, \"task_term\"] = NAMES[i]\n",
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2021-05-24 10:06:24 +00:00
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" df[\"avg_count_%d\" % i] = df[\"avg_count_%d\" % i].round(3)\n",
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2021-05-22 14:18:14 +00:00
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" rename(df, \"mean # \" + NAMES[i] + \" evts.\", \"avg_count_\" + str(i))\n",
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" for i in [0,1,2,3,8,9,10]:\n",
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2021-04-28 12:59:45 +00:00
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" del df[\"avg_count_\" + str(i)]\n",
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" rename(df, \"Task termination\", \"task_term\")\n",
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2021-05-16 10:22:27 +00:00
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" print((\"\\\\tableIII{\" + cluster.upper() + \"}{\"))\n",
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2021-05-24 10:06:24 +00:00
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" print(df.to_latex(index=False, header=False), end=\"}\\n\")\n"
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2021-04-28 12:59:45 +00:00
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]
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},
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{
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"cell_type": "code",
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2021-05-24 10:44:49 +00:00
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"execution_count": 40,
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2021-05-22 14:18:14 +00:00
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"id": "cea0e71e",
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2021-04-28 12:59:45 +00:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"# Table IV"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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2021-05-16 10:22:27 +00:00
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\\tableIV{A}{\n",
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2021-05-24 10:44:49 +00:00
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" EVICT & 0.000 (0) & NaN & NaN & NaN & NaN \\\\\n",
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" FAIL & 90.793 (499) & 0.695 & 0.684 & 0.086 & 1.850 \\\\\n",
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"FINISH & 1.187 (1) & 0.005 & 0.001 & 1.073 & 0.024 \\\\\n",
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" KILL & 16.533 (10) & 1.045 & 0.074 & 0.461 & 1.189 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"}\n",
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"\\tableIV{B}{\n",
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2021-05-24 10:44:49 +00:00
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" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
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" FAIL & 74.368 (374) & 2.003 & 1.994 & 0.267 & 4.944 \\\\\n",
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"FINISH & 6.304 (10) & 0.022 & 0.008 & 2.349 & 0.013 \\\\\n",
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" KILL & 69.853 (234) & 1.696 & 0.158 & 0.614 & 3.009 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"}\n",
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"\\tableIV{C}{\n",
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2021-05-24 10:44:49 +00:00
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" EVICT & 1.000 (1) & 1.001 & 0.000 & 0.000 & 0.000 \\\\\n",
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" FAIL & 41.982 (200) & 3.484 & 0.998 & 0.376 & 3.998 \\\\\n",
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"FINISH & 1.991 (1) & 0.022 & 0.017 & 1.565 & 0.017 \\\\\n",
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" KILL & 110.681 (652) & 0.627 & 0.059 & 0.656 & 2.267 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"}\n",
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"\\tableIV{D}{\n",
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2021-05-24 10:44:49 +00:00
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" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
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" FAIL & 43.356 (250) & 6.112 & 0.949 & 0.531 & 6.498 \\\\\n",
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"FINISH & 2.109 (2) & 0.268 & 0.013 & 1.723 & 0.019 \\\\\n",
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" KILL & 89.648 (283) & 1.013 & 0.054 & 0.283 & 3.256 \\\\\n",
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2021-05-16 10:22:27 +00:00
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"}\n",
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"\\tableIV{E}{\n",
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2021-05-24 10:44:49 +00:00
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" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
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" FAIL & 23.081 (25) & 0.247 & 0.666 & 0.717 & 1.588 \\\\\n",
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"FINISH & 7.776 (2) & 0.019 & 0.029 & 1.934 & 0.021 \\\\\n",
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" KILL & 88.790 (309) & 0.706 & 0.029 & 0.461 & 7.572 \\\\\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
"}\n",
|
|
|
|
|
"\\tableIV{F}{\n",
|
2021-05-24 10:44:49 +00:00
|
|
|
" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
|
|
|
|
|
" FAIL & 17.161 (8) & 0.621 & 0.546 & 0.426 & 7.559 \\\\\n",
|
|
|
|
|
"FINISH & 2.941 (2) & 0.015 & 0.051 & 1.670 & 0.162 \\\\\n",
|
|
|
|
|
" KILL & 103.889 (361) & 0.183 & 0.064 & 0.417 & 5.824 \\\\\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
"}\n",
|
|
|
|
|
"\\tableIV{G}{\n",
|
2021-05-24 10:44:49 +00:00
|
|
|
" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
|
|
|
|
|
" FAIL & 51.835 (250) & 0.556 & 3.335 & 0.608 & 20.352 \\\\\n",
|
|
|
|
|
"FINISH & 8.519 (36) & 0.002 & 0.630 & 1.760 & 0.005 \\\\\n",
|
|
|
|
|
" KILL & 37.055 (100) & 5.687 & 0.065 & 0.080 & 19.166 \\\\\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
"}\n",
|
|
|
|
|
"\\tableIV{H}{\n",
|
2021-05-24 10:44:49 +00:00
|
|
|
" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
|
|
|
|
|
" FAIL & 20.504 (1) & 0.114 & 2.300 & 0.981 & 12.833 \\\\\n",
|
|
|
|
|
"FINISH & 4.278 (14) & 0.005 & 0.153 & 1.778 & 0.014 \\\\\n",
|
|
|
|
|
" KILL & 11.023 (3) & 0.235 & 0.103 & 0.288 & 11.337 \\\\\n",
|
|
|
|
|
"}\n",
|
|
|
|
|
"\\tableIV{ALL}{\n",
|
|
|
|
|
" EVICT & 0.989 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
|
|
|
|
|
" FAIL & 43.126 (200) & 0.114 & 2.300 & 0.981 & 12.833 \\\\\n",
|
|
|
|
|
"FINISH & 3.074 (2) & 0.005 & 0.153 & 1.778 & 0.014 \\\\\n",
|
|
|
|
|
" KILL & 53.919 (178) & 0.235 & 0.103 & 0.288 & 11.337 \\\\\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
"}\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"display(Markdown(\"# Table IV\"))\n",
|
2021-05-24 10:44:49 +00:00
|
|
|
"for cluster in list(\"abcdefgh\") + [\"all\"]:\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" 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",
|
2021-05-24 10:44:49 +00:00
|
|
|
" names=[\"term\", \"mean\", \"%95\"])\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" 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",
|
2021-05-24 10:44:49 +00:00
|
|
|
" df = df[df[\"term\"].isin(range(4,8))]\n",
|
|
|
|
|
" df.loc[df[\"mean\"] == -1, \"mean\"] = 0\n",
|
|
|
|
|
" df.loc[df[\"%95\"] == -1, \"%95\"] = 0\n",
|
|
|
|
|
" df[\"mean\"] = df[\"mean\"].round(3).apply(lambda x: \"%.03f\" % x) + \" (\" + df[\"%95\"].apply(lambda x: \"%d\" % x) + \")\"\n",
|
|
|
|
|
" rename(df, \"# Tasks. mean (95-th p)\", \"mean\")\n",
|
|
|
|
|
" del df[\"%95\"]\n",
|
|
|
|
|
" \n",
|
2021-05-16 10:22:27 +00:00
|
|
|
"\n",
|
|
|
|
|
" rename(df, \"# Evts. mean\", \"mean\")\n",
|
|
|
|
|
" rename(df, \"# Evts. 95% p.tile\", \"%95\")\n",
|
|
|
|
|
" \n",
|
2021-05-24 10:44:49 +00:00
|
|
|
" for i in [4,5,6,7]:\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" df.loc[df.term == i, \"term\"] = NAMES[i]\n",
|
2021-05-24 10:44:49 +00:00
|
|
|
" df[str(i)] = df[str(i)].round(3)\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" rename(df, \"# \" + NAMES[i] + \" Evts. mean\", str(i))\n",
|
2021-05-24 10:44:49 +00:00
|
|
|
" for i in [0,1,2,3,8,9,10]:\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" del df[str(i)]\n",
|
|
|
|
|
" rename(df, \"Job termination\", \"term\")\n",
|
|
|
|
|
" print((\"\\\\tableIV{\" + cluster.upper() + \"}{\"))\n",
|
2021-05-24 10:44:49 +00:00
|
|
|
" s = df.to_latex(index=False,header=False)\n",
|
|
|
|
|
" s = s.split(\"\\\\toprule\\n\")[1]\n",
|
|
|
|
|
" s = s.split(\"\\\\bottomrule\")[0]\n",
|
|
|
|
|
" print(s, end=\"}\\n\")\n"
|
2021-05-16 10:22:27 +00:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2021-05-24 10:06:24 +00:00
|
|
|
"execution_count": 7,
|
2021-05-22 14:18:14 +00:00
|
|
|
"id": "6763138f",
|
2021-05-16 10:22:27 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"max_count = 50"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2021-05-24 10:06:24 +00:00
|
|
|
"execution_count": 18,
|
2021-05-22 14:18:14 +00:00
|
|
|
"id": "dc0714c9",
|
2021-05-16 10:22:27 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
2021-04-28 12:59:45 +00:00
|
|
|
{
|
2021-05-24 10:06:24 +00:00
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 288x216 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
2021-04-28 12:59:45 +00:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2021-05-24 10:06:24 +00:00
|
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2021-04-28 12:59:45 +00:00
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"text/plain": [
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2021-05-24 10:06:24 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-28 12:59:45 +00:00
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},
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2021-05-16 10:22:27 +00:00
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2021-04-28 12:59:45 +00:00
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2021-05-24 10:06:24 +00:00
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2021-04-28 12:59:45 +00:00
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2021-05-24 10:06:24 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-28 12:59:45 +00:00
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]
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},
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2021-05-16 10:22:27 +00:00
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2021-04-28 12:59:45 +00:00
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{
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"data": {
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2021-05-24 10:06:24 +00:00
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2021-04-28 12:59:45 +00:00
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2021-05-24 10:06:24 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-28 12:59:45 +00:00
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},
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2021-05-16 10:22:27 +00:00
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2021-04-28 12:59:45 +00:00
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2021-05-24 10:06:24 +00:00
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2021-04-28 12:59:45 +00:00
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2021-05-24 10:06:24 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-28 12:59:45 +00:00
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]
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},
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2021-05-16 10:22:27 +00:00
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2021-04-28 12:59:45 +00:00
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{
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2021-05-24 10:06:24 +00:00
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2021-04-28 12:59:45 +00:00
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2021-05-24 10:06:24 +00:00
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2021-04-28 12:59:45 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-28 12:59:45 +00:00
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2021-05-24 10:06:24 +00:00
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2021-04-28 12:59:45 +00:00
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2021-05-24 10:06:24 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-28 12:59:45 +00:00
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]
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},
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2021-05-16 10:22:27 +00:00
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2021-04-28 12:59:45 +00:00
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{
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2021-05-24 10:06:24 +00:00
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"image/png": "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
|
2021-04-28 12:59:45 +00:00
|
|
|
"text/plain": [
|
2021-05-24 10:06:24 +00:00
|
|
|
"<Figure size 288x216 with 1 Axes>"
|
2021-04-28 12:59:45 +00:00
|
|
|
]
|
|
|
|
|
},
|
2021-05-16 10:22:27 +00:00
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
2021-04-28 12:59:45 +00:00
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
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|
|
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{
|
|
|
|
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"data": {
|
2021-05-24 10:06:24 +00:00
|
|
|
"image/png": "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
|
2021-04-28 12:59:45 +00:00
|
|
|
"text/plain": [
|
2021-05-24 10:06:24 +00:00
|
|
|
"<Figure size 288x216 with 1 Axes>"
|
2021-04-28 12:59:45 +00:00
|
|
|
]
|
|
|
|
|
},
|
2021-05-16 10:22:27 +00:00
|
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"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
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|
},
|
2021-04-28 12:59:45 +00:00
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2021-05-24 10:06:24 +00:00
|
|
|
"def figure_5_plot(df, cluster):\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" dft = {}\n",
|
2021-05-24 10:06:24 +00:00
|
|
|
" plt.figure(figsize=(4,3))\n",
|
|
|
|
|
" for i in [4,5,7]:\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" 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",
|
2021-05-24 10:06:24 +00:00
|
|
|
" dft[i].loc[dfi[\"succ\"].eq(0) & dfi[\"non\"].eq(0), [\"perc\"]] = 0\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" \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",
|
2021-04-28 12:59:45 +00:00
|
|
|
"\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" 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",
|
2021-05-24 10:06:24 +00:00
|
|
|
" ys.append(0 if a.empty else a[\"perc\"].squeeze() * 100)\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" \n",
|
|
|
|
|
" plt.plot([x if x < 51 else \">50\" for x in range(0,52)], ys)\n",
|
2021-05-24 10:06:24 +00:00
|
|
|
" 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",
|
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|
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"\n",
|
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|
|
|
"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",
|
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|
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" \n",
|
|
|
|
|
"dftot = dftot.groupby([\"count_4\", \"count_5\", \"count_7\", \"count_8\"]).sum().reset_index()\n",
|
|
|
|
|
"figure_5_plot(df, \"all\")"
|
2021-04-28 12:59:45 +00:00
|
|
|
]
|
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|
|
},
|
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{
|
|
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"cell_type": "code",
|
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|
"execution_count": null,
|
2021-05-22 14:18:14 +00:00
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"id": "e095a31e",
|
2021-05-16 10:22:27 +00:00
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|
"metadata": {},
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"outputs": [],
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"source": []
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},
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{
|
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|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
2021-05-22 14:18:14 +00:00
|
|
|
"id": "a4898bed",
|
2021-04-28 12:59:45 +00:00
|
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"metadata": {},
|
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"outputs": [],
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"source": []
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}
|
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],
|
|
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|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
2021-05-22 14:18:14 +00:00
|
|
|
"display_name": "venv",
|
2021-04-28 12:59:45 +00:00
|
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|
"language": "python",
|
2021-05-22 14:18:14 +00:00
|
|
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"name": "venv"
|
2021-04-28 12:59:45 +00:00
|
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},
|
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"language_info": {
|
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|
"codemirror_mode": {
|
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|
"name": "ipython",
|
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|
"version": 3
|
|
|
|
|
},
|
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|
"file_extension": ".py",
|
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|
"mimetype": "text/x-python",
|
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|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
2021-05-22 14:18:14 +00:00
|
|
|
"version": "3.9.5"
|
2021-04-28 12:59:45 +00:00
|
|
|
}
|
|
|
|
|
},
|
|
|
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|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 5
|
|
|
|
|
}
|
