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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2021-05-16 10:22:27 +00:00
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"execution_count": 3,
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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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2021-05-16 10:22:27 +00:00
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"execution_count": 4,
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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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2021-05-16 10:22:27 +00:00
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"DIR = \"/Users/maggicl/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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2021-05-16 10:22:27 +00:00
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"execution_count": 5,
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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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2021-05-16 10:22:27 +00:00
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"execution_count": 6,
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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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"\\begin{tabular}{lrrrrrrr}\n",
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"\\toprule\n",
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"Task termination & \\# Evts. 95\\% p.tile & \\# Evts. mean & \\# EVICT Evts. mean & \\# FAIL Evts. mean & \\# FINISH Evts. mean & \\# KILL Evts. mean & \\# LOST Evts. mean \\\\\n",
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"\\midrule\n",
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" KILL & 58.0 & 27.395925 & 2.349579 & 0.213859 & 0.003412 & 3.395996 & 0.089576 \\\\\n",
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" FINISH & 9.0 & 12.405370 & 0.019321 & 0.003779 & 2.153432 & 0.008150 & 0.008989 \\\\\n",
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" FAIL & 108.0 & 50.039556 & 0.287778 & 11.061864 & 0.002098 & 0.467656 & 0.053144 \\\\\n",
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" LOST & 7.0 & 8.847145 & 0.083348 & 0.001821 & 0.384190 & 1.329910 & 1.007933 \\\\\n",
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" EVICT & 2924.0 & 428.550689 & 73.693595 & 0.768553 & 0.000179 & 28.766164 & 0.845501 \\\\\n",
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" No termination & 84.0 & 14.818523 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\\\\n",
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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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"\\begin{tabular}{lrrrrrrr}\n",
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"\\toprule\n",
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"Task termination & \\# Evts. 95\\% p.tile & \\# Evts. mean & \\# EVICT Evts. mean & \\# FAIL Evts. mean & \\# FINISH Evts. mean & \\# KILL Evts. mean & \\# LOST Evts. mean \\\\\n",
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"\\midrule\n",
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" KILL & 60.0 & 40.901041 & 3.351496 & 0.276305 & 0.003656 & 5.541079 & 0.033457 \\\\\n",
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" FINISH & 20.0 & 17.277596 & 0.020444 & 0.020628 & 2.942579 & 0.011640 & 0.016278 \\\\\n",
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" FAIL & 260.0 & 86.772419 & 0.518061 & 19.656798 & 0.000560 & 0.675392 & 0.088523 \\\\\n",
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" LOST & 14.0 & 25.690455 & 0.257231 & 0.007420 & 1.928351 & 3.515436 & 2.015153 \\\\\n",
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" EVICT & 1578.0 & 345.705559 & 64.816518 & 0.240214 & 0.000000 & 17.961539 & 1.028401 \\\\\n",
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" No termination & 32.0 & 13.018130 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\\\\n",
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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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"\\begin{tabular}{lrrrrrrr}\n",
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"\\toprule\n",
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"Task termination & \\# Evts. 95\\% p.tile & \\# Evts. mean & \\# EVICT Evts. mean & \\# FAIL Evts. mean & \\# FINISH Evts. mean & \\# KILL Evts. mean & \\# LOST Evts. mean \\\\\n",
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"\\midrule\n",
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" KILL & 32.0 & 24.230887 & 1.533237 & 0.116082 & 0.003994 & 3.799111 & 0.013670 \\\\\n",
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" FINISH & 18.0 & 15.242628 & 0.017929 & 0.012701 & 2.470654 & 0.006020 & 0.006414 \\\\\n",
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" FAIL & 156.0 & 187.030894 & 0.772823 & 48.445773 & 2.035378 & 0.756015 & 0.133687 \\\\\n",
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" LOST & 28.0 & 22.385446 & 0.411365 & 0.007569 & 1.412201 & 2.751353 & 1.998665 \\\\\n",
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" EVICT & 1748.0 & 404.108669 & 73.715527 & 1.812816 & 0.000166 & 22.908022 & 0.546198 \\\\\n",
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" No termination & 96.0 & 21.315166 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\\\\n",
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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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"\\begin{tabular}{lrrrrrrr}\n",
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"\\toprule\n",
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"Task termination & \\# Evts. 95\\% p.tile & \\# Evts. mean & \\# EVICT Evts. mean & \\# FAIL Evts. mean & \\# FINISH Evts. mean & \\# KILL Evts. mean & \\# LOST Evts. mean \\\\\n",
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"\\midrule\n",
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" KILL & 32.0 & 29.953873 & 1.960134 & 0.150521 & 0.002385 & 4.682411 & 0.016156 \\\\\n",
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" FINISH & 18.0 & 23.105615 & 0.058651 & 0.019051 & 3.789050 & 0.009785 & 0.018699 \\\\\n",
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" FAIL & 269.0 & 228.004975 & 0.496316 & 58.968210 & 0.809520 & 2.040396 & 0.324754 \\\\\n",
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" LOST & 20.0 & 17.065721 & 0.014760 & 0.003577 & 0.079289 & 4.636283 & 1.999794 \\\\\n",
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" EVICT & 1478.0 & 323.366130 & 62.000510 & 0.700268 & 0.000373 & 14.057514 & 0.627592 \\\\\n",
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" No termination & 103.0 & 27.867403 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\\\\n",
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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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"\\begin{tabular}{lrrrrrrr}\n",
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"\\toprule\n",
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"Task termination & \\# Evts. 95\\% p.tile & \\# Evts. mean & \\# EVICT Evts. mean & \\# FAIL Evts. mean & \\# FINISH Evts. mean & \\# KILL Evts. mean & \\# LOST Evts. mean \\\\\n",
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"\\midrule\n",
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" KILL & 258.0 & 55.877475 & 1.287917 & 0.056909 & 0.000185 & 12.159880 & 0.054997 \\\\\n",
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" FINISH & 14.0 & 11.976806 & 0.013879 & 0.008435 & 1.998677 & 0.008241 & 0.026641 \\\\\n",
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" FAIL & 138.0 & 450.526937 & 0.457703 & 111.471047 & 0.000000 & 0.455705 & 0.187991 \\\\\n",
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" LOST & 14.0 & 11.899908 & 0.000000 & 0.000000 & 0.033976 & 3.131007 & 1.792164 \\\\\n",
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" EVICT & 310.0 & 84.645189 & 11.780754 & 0.106119 & 0.000090 & 5.790960 & 0.654955 \\\\\n",
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" No termination & 34.0 & 7.349165 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\\\\n",
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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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"\\begin{tabular}{lrrrrrrr}\n",
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"\\toprule\n",
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"Task termination & \\# Evts. 95\\% p.tile & \\# Evts. mean & \\# EVICT Evts. mean & \\# FAIL Evts. mean & \\# FINISH Evts. mean & \\# KILL Evts. mean & \\# LOST Evts. mean \\\\\n",
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"\\midrule\n",
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" KILL & 162.0 & 45.039557 & 0.384065 & 0.098430 & 0.001178 & 9.804287 & 0.037783 \\\\\n",
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" FINISH & 20.0 & 19.899709 & 0.019381 & 0.003510 & 3.007839 & 0.097934 & 0.023707 \\\\\n",
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" FAIL & 220.0 & 164.043073 & 0.279352 & 39.257407 & 0.000023 & 1.549795 & 0.203997 \\\\\n",
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" LOST & 36.0 & 25.002219 & 0.011815 & 0.000909 & 0.149586 & 7.283534 & 2.000428 \\\\\n",
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" EVICT & 510.0 & 302.262347 & 23.973621 & 0.192394 & 0.000094 & 45.979997 & 0.374789 \\\\\n",
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" No termination & 24.0 & 7.784905 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\\\\n",
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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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"\\begin{tabular}{lrrrrrrr}\n",
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"\\toprule\n",
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"Task termination & \\# Evts. 95\\% p.tile & \\# Evts. mean & \\# EVICT Evts. mean & \\# FAIL Evts. mean & \\# FINISH Evts. mean & \\# KILL Evts. mean & \\# LOST Evts. mean \\\\\n",
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"\\midrule\n",
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" KILL & 641.00 & 130.054143 & 6.909204 & 0.135073 & 0.000033 & 25.275769 & 0.131106 \\\\\n",
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" FINISH & 18.00 & 105.240418 & 0.015228 & 0.001655 & 14.153775 & 0.004879 & 0.158300 \\\\\n",
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" FAIL & 40.00 & 40.121553 & 0.016111 & 8.592728 & 0.000000 & 0.338883 & 0.011310 \\\\\n",
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" LOST & 4602.25 & 576.384120 & 1.931330 & 0.360515 & 48.094421 & 35.596567 & 3.534335 \\\\\n",
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" EVICT & 2015.00 & 555.574743 & 77.429054 & 0.303127 & 0.000000 & 58.299330 & 0.653819 \\\\\n",
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" No termination & 30.00 & 9.503553 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\\\\n",
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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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"\\begin{tabular}{lrrrrrrr}\n",
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"\\toprule\n",
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"Task termination & \\# Evts. 95\\% p.tile & \\# Evts. mean & \\# EVICT Evts. mean & \\# FAIL Evts. mean & \\# FINISH Evts. mean & \\# KILL Evts. mean & \\# LOST Evts. mean \\\\\n",
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"\\midrule\n",
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" KILL & 388.0 & 74.425542 & 0.633338 & 0.169666 & 0.000231 & 17.172624 & 0.062799 \\\\\n",
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" FINISH & 22.0 & 23.978294 & 0.023700 & 0.014129 & 3.632529 & 0.011111 & 0.028482 \\\\\n",
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" FAIL & 487.0 & 170.153701 & 0.600483 & 37.599942 & 0.000000 & 2.866647 & 0.343806 \\\\\n",
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" LOST & 386.4 & 94.666667 & 1.493333 & 2.400000 & 0.573333 & 14.040000 & 3.480000 \\\\\n",
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" EVICT & 206.0 & 75.658064 & 6.732544 & 0.837154 & 0.000000 & 7.164722 & 0.421745 \\\\\n",
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" No termination & 18.0 & 8.123506 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\\\\n",
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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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"for cluster in \"abcdefgh\":\n",
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" df = pd.read_csv(glob.glob(DIR + \"/table-iii-\" + cluster + \".csv/part-00000-*\")[0])\n",
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" rename(df, \"# Evts. mean\", \"mean\")\n",
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" rename(df, \"# Evts. 95% p.tile\", \"%95\")\n",
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" \n",
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" for i in [-1,4,5,6,7,8]:\n",
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" df.loc[df.task_term == i, \"task_term\"] = NAMES[i]\n",
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" rename(df, \"# \" + NAMES[i] + \" Evts. mean\", \"avg_count_\" + str(i))\n",
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" for i in [0,1,2,3,9,10]:\n",
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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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" print(df.to_latex(index=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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2021-05-16 10:22:27 +00:00
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"execution_count": 7,
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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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"\\begin{tabular}{lrrrrrrr}\n",
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"\\toprule\n",
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"Job termination & \\# Tasks mean & \\# Tasks 95\\% p.tile & \\# EVICT Evts. mean & \\# FAIL Evts. mean & \\# FINISH Evts. mean & \\# KILL Evts. mean & \\# LOST Evts. mean \\\\\n",
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"\\midrule\n",
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" No termination & 92.359436 & 174.3 & 23.263951 & 3.454474 & 23.047597 & 34.565608 & 0.707709 \\\\\n",
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" EVICT & -1.000000 & -1.0 & NaN & NaN & NaN & NaN & NaN \\\\\n",
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" FAIL & 90.792728 & 499.0 & 0.694942 & 0.683556 & 0.085957 & 1.849587 & 0.009730 \\\\\n",
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" 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": 70,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"max_count = 50"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 101,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
2021-04-28 12:59:45 +00:00
|
|
|
{
|
2021-05-16 10:22:27 +00:00
|
|
|
"name": "stderr",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"<ipython-input-101-e0a194a108e9>:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
|
|
|
|
|
" dft[i][dfi.succ == 0][dfi.non == 0][\"perc\"] = 0\n"
|
|
|
|
|
]
|
2021-04-28 12:59:45 +00:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2021-05-16 10:22:27 +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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2021-04-28 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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2021-05-16 10:22:27 +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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2021-05-16 10:22:27 +00:00
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2021-04-28 12:59:45 +00:00
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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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2021-05-16 10:22:27 +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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2021-05-16 10:22:27 +00:00
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2021-04-28 12:59:45 +00:00
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"image/png": "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
|
2021-04-28 12:59:45 +00:00
|
|
|
"text/plain": [
|
2021-05-16 10:22:27 +00:00
|
|
|
"<Figure size 1224x720 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"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2021-05-16 10:22:27 +00:00
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA9oAAAJOCAYAAABIl3+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXiddZk//vcnJ805QIECLWvZZFNArbINjvs2MF9HxHEBREAZFBVE/Y1fl68iCoI67oqDOiiDIhVU1HEYccZtnFGUgoiCIDuUpS2Flq1JmuT5/dGmdEmak+SkJ5DX67pykfOcz/M8dy7xjzf3c3+eUlVVAAAAgNboaHcBAAAA8EQiaAMAAEALCdoAAADQQoI2AAAAtJCgDQAAAC0kaAMAAEALCdoAMImUUk4rpXyz3XUAAGMnaAPABlZKOaqUMq+U8nAp5Z5Syn+UUp7dwuvvUkqpSimdE33NUsp5pZQzWnUfAHgiELQBYAMqpbwryWeTnJlkmyQ7JflSksPaWdfqWhnQAWAqErQBYAMppWye5CNJ3lZV1feqqnqkqqrlVVX9W1VV7x5i/fNLKfPXOnZbKeXFK38/cGVn/MFSyoJSyqdXLvvvlf9csrJrfvDK9W8spfy5lPJAKeWyUsrOq123KqW8rZRyY5IbJ+DPB4ApQ9AGgA3n4CSNJJe06HqfS/K5qqo2S7JbkotWHn/uyn/OqKpqelVVvymlvCLJ+5O8MsmsJL9KcuFa13tFkoOS7N2i+gBgShK0AWDD2SrJfVVV9bXoesuT7F5KmVlV1cNVVV2+nrVvTnJWVVV/Xnn/M5PMWb2rvfL7+6uqWrae69xXSlky+JPkqPH/GQDwxCJoA8CGszjJzBbOQB+fZM8k15dSriilvGw9a3dO8rnVAvL9SUqSHVZbc2cT95xZVdWMwZ8k3xpr8QDwRCVoA8CG85sk3VnxiHYzHkmy8eCHUkotKx77TpJUVXVjVVVHJtk6yceTfKeUskmSaohr3ZnkzauH5KqqNqqq6terrRnqPABglARtANhAqqpamuTUJGeXUl5RStm4lDKtlHJoKeUTQ5zylySNUsr/KaVMS/KBJPXBL0spR5dSZlVVNZBkycrD/UkWJRlI8qTVrnVOkveVUvZZee7mpZRXt/yPBADi9R0AsAFVVfXpUsqCrAjNFyR5KMmVST46xNqlpZS3JvmXJLUkn0iy+i7khyT5dCll4yS3JzmiqqruJCmlfDTJ/64M6IdUVXVJKWV6krkr57KXJvnPJBdP0J8KAFNWqSpPiQEAAECreHQcAAAAWkjQBgAAgBYStAEAAKCFBG0AAABoobbtOj5z5sxql112adftAQAAYMyuvPLK+6qqmjXUd20L2rvsskvmzZvXrtsDAADAmJVSbh/uO4+OAwAAQAsJ2gAAANBCgjYAAAC0UNtmtAEAAJj8li9fnvnz56e7u7vdpbRFo9HI7NmzM23atKbPEbQBAAAY1vz587Pppptml112SSml3eVsUFVVZfHixZk/f3523XXXps8b8dHxUsrXSikLSyl/Gub7Ukr5fCnlplLKNaWUZ46ibgAAACax7u7ubLXVVlMuZCdJKSVbbbXVqLv5zcxon5fkkPV8f2iSPVb+vCnJP4+qAgAAACa1qRiyB43lbx8xaFdV9d9J7l/PksOSnF+tcHmSGaWU7UZdCQAAADwBtGJGe4ckd672ef7KY/esvbCU8qas6Hpnp512asGtAQAAeKKr1Wp56lOfuurzEUccke7u7vT09OSss85adfzqq6/OkUcemT//+c/ZZZddMm/evMycOTP33ntv3vGOd+SKK65IvV7PLrvskrPOOivHHntskuSOO+7I5ptvns033zwzZ87Mf/3Xf42r3lYE7aH66NVQC6uq+kqSryTJ/vvvP+QaAAAAWN1GG22Uq6++eo1jN9xwQw499NA1gvbcuXNz1FFHrbGuqqocfvjhOfbYYzN37twkKwL5gw8+uOqaxx13XF72spflVa96VUvqbUXQnp9kx9U+z05ydwuuCwAAAEPaa6+9MmPGjPz2t7/NQQcdlCS56KKLctlll62x7uc//3mmTZuWE088cdWxOXPmTGhtrQjaP0xyUillbpKDkiytqmqdx8YBAAB4fPvwv12b6+5+sKXX3Hv7zfKhv9tnvWuWLVu2Rjh+3/vel9e+9rU58sgjM3fu3Bx00EG5/PLLs9VWW2WPPfZY49w//elP2W+//Vpa80hGDNqllAuTPD/JzFLK/CQfSjItSaqqOifJpUn+NslNSR5N8oaJKhYAAICpZ6hHx5MVs9rPetaz8qlPfSpz587NkUce2Ybq1jVi0K6qar2VVlVVJXlbyyoCAABgUhqp87yh7bjjjtlll13yy1/+Mt/97nfzm9/8Zp01++yzT77zne9s0LqaeY82AAAATEpHHnlk3vnOd2a33XbL7Nmz1/n+hS98YXp6evLVr3511bErrrgiv/zlLyesJkEbAACASW1wRnvw573vfe+q71796lfn2muvzRFHHDHkuaWUXHLJJfnP//zP7Lbbbtlnn31y2mmnZfvtt5+weluxGRoAAABMmP7+/mG/mzVrVpYvX77O8dtuu23V79tvv30uuuiiYa9x3nnnjae8dehoAwAAQAsJ2gAAANBCgjYAAAC0kKANAAAALSRoAwAAQAsJ2iP44c0/zMsueVkeWf5Iu0sBAADgcUDQHsGjyx/N7Q/enmV9y9pdCgAAwJRUq9XWeI/24Ku7PvOZz6TRaGTp0qWr1v7iF7/Iy172siQrXtt10kknbfB6vUd7BPVaPUnS09/T5koAAACmpo022ihXX331OscvvPDCHHDAAbnkkkty3HHHbfjChqGjPQJBGwAAYPK5+eab8/DDD+eMM87IhRde2O5y1qCjPYJ658qg3SdoAwAAU9x/vDe594+tvea2T00O/dh6lyxbtixz5sxJkuy666655JJLcuGFF+bII4/Mc57znNxwww1ZuHBhtt5669bWNkaC9ggatUYSHW0AAIB2GerR8blz5+aSSy5JR0dHXvnKV+biiy/O2972tjZVuCZBewRdta4kSXd/d5srAQAAaLMROs8byjXXXJMbb7wxL3nJS5Ikvb29edKTnjRpgrYZ7REMdrR7+3vbXAkAAADJik3QTjvttNx222257bbbcvfdd+euu+7K7bff3u7SkgjaIxqc0e7u09EGAACYDObOnZvDDz98jWOHH3545s6du87a8847L7Nnz171M3/+/Amvz6PjIzCjDQAA0F4PP/zwGp9vvfXWddZ8+tOfXvX785///CTJcccd15bXfuloj8CMNgAAAKMhaI/AjDYAAACjIWiPwIw2AAAAoyFoj6BeWxG0zWgDAADQDEF7BB2lI10dXWa0AQAAaIqg3YR6rW5GGwAAgKYI2k2od9bNaAMAALTJ9OnTV/1+6aWXZo899sgdd9yR0047LZ/85CeTrHiV13e+8501zrvtttuy7777btBaE+/Rbkq9VjejDQAA0GY//elPc/LJJ+cnP/lJdtppp3aXMyxBuwmNWkPQBgAAaKNf/epXOeGEE3LppZdmt912a3c56yVoN6Gr1uXRcQAAYMr7+O8+nuvvv76l13zylk/Oew58z3rX9PT05LDDDssvfvGLPPnJT27p/SeCGe0mNDobNkMDAABok2nTpuVZz3pWzj333HaX0hQd7SbUa/Us61vW7jIAAADaaqTO80Tp6OjIRRddlBe/+MU588wz8/73v78tdTRLR7sJZrQBAADaa+ONN86PfvSjXHDBBZO+sy1oN8GMNgAAQPttueWW+fGPf5wzzjgjP/jBD9b5/s1vfnNmz56d2bNn5+CDD06S3HDDDauOzZ49OxdffPGE1+nR8SaY0QYAAGifhx9+eNXvO+64Y2699dYkyWGHHbbq+HnnnTfkucuXL5/Q2oaio92Eeq2e7n4dbQAAAEYmaDehXqub0QYAAKApgnYT6rV6evoEbQAAAEYmaDeh3llPX9WXvoG+dpcCAADAJCdoN6FRaySJDdEAAAAYkaDdhHqtniQ2RAM
|
2021-04-28 12:59:45 +00:00
|
|
|
"text/plain": [
|
2021-05-16 10:22:27 +00:00
|
|
|
"<Figure size 1224x720 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"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"for cluster in \"abcdefgh\":\n",
|
2021-05-16 10:22:27 +00:00
|
|
|
" 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",
|
|
|
|
|
" dft = {}\n",
|
|
|
|
|
" plt.figure(figsize=(17,10))\n",
|
|
|
|
|
" for i in [4,5,7,8]:\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][dfi.succ == 0][dfi.non == 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",
|
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",
|
|
|
|
|
" ys.append(0 if a.empty else a[\"perc\"].squeeze())\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" plt.plot([x if x < 51 else \">50\" for x in range(0,52)], ys)\n",
|
|
|
|
|
" plt.title(\"Cluster \" + cluster.upper())\n",
|
|
|
|
|
" plt.legend([\"EVICT\", \"FAIL\", \"KILL\", \"LOST\"])"
|
2021-04-28 12:59:45 +00:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
2021-05-16 10:22:27 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
2021-04-28 12:59:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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2021-05-16 10:22:27 +00:00
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"display_name": "Python 3",
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2021-04-28 12:59:45 +00:00
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"language": "python",
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2021-05-16 10:22:27 +00:00
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"name": "python3"
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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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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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2021-05-16 10:22:27 +00:00
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"version": "3.8.3"
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2021-04-28 12:59:45 +00:00
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|
