bachelorThesis/machine_time_waste/statuses_total_time.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Statuses total time\n",
"Sums the times instances spend in one of each states in the diagram saved as\n",
"\"statuses.drawio\". Unknown times are summed as \"unknown\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import sys\n",
"import pandas\n",
"import seaborn as sns\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# QUEUE = set([\"0-2\", \"1-2\", \"assumptions:\", \"1-1\", \"1-0\"])\n",
"# RESUB = set([\"4-1\", \"4-0\", \"5-1\", \"6-1\", \"7-1\", \"8-1\", \"assumptions:\", \"5-0\", \"6-0\", \"7-0\", \\\n",
"# \"8-0\"])\n",
"# READY = set([\"0-3\", \"2-3\", \"0-9\", \"2-9\", \"9-3\", \"2-7\", \"2-8\", \"9-7\", \"9-8\", \\\n",
"# \"9-9\", \"0-7\", \"0-8\", \"assumptions:\", \"2-0\", \"2-4\", \"9-4\", \"9-1\"])\n",
"# RUN = set([\"3-1\", \"3-10\", \"3-4\", \"3-5\", \"3-6\", \"3-7\", \"3-8\", \"10-5\", \"10-6\", \\\n",
"# \"10-7\", \"10-8\", \"10-4\", \"10-10\", \"10-1\", \"assumptions:\", \"3-0\", \"10-0\", \"3-3\"])\n",
"\n",
"QUEUE = set([\"0-2\", \"1-2\"])\n",
"ENDED = set([\"5-1\", \"6-1\", \"7-1\", \"8-1\"])\n",
"READY = set([\"0-3\", \"0-9\", \"2-3\", \"2-9\", \"9-3\", \"9-9\"])\n",
"RUN = set([\"3-1\", \"3-4\", \"3-5\", \"3-6\", \"3-7\", \"3-8\", \"3-10\", \"10-1\", \"10-4\", \"10-5\", \"10-6\", \"10-7\", \"10-8\", \"10-10\"])\n",
"EVICT = set([\"4-1\", \"4-0\"])"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [],
"source": [
"DIR = \"/Users/maggicl/git/bachelorThesis\""
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def to_name(et):\n",
" if et == 4: \n",
" return 'EVICT'\n",
" elif et == 5: \n",
" return 'FAIL'\n",
" elif et == 6:\n",
" return 'FINISH'\n",
" elif et == 7:\n",
" return 'KILL'\n",
" elif et == 8:\n",
" return 'LOST'\n",
" else:\n",
" return 'NO_TERM'\n",
"\n",
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"def create_df(cluster):\n",
" obj = {}\n",
"\n",
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" filename = DIR + \"/machine_time_waste/\" + cluster + \"_state_changes.json\"\n",
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"\n",
" with open(filename, 'r') as f:\n",
" obj = json.loads(f.read())\n",
" \n",
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" data = {'Last termination': [], 'time_type': [], 'time_ms': []}\n",
" totals = {}\n",
" \n",
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" def add_record(et, tt, time):\n",
" data['Last termination'].append(to_name(et))\n",
" data['time_type'].append(tt)\n",
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" data['time_ms'].append(time / 1000)\n",
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"\n",
" for pair in obj[\"data\"]:\n",
" qt = et = rt = xt = vt = ut = 0\n",
"\n",
" pair[0] = 0 if pair[0] is None else pair[0]\n",
"\n",
" x = pair[1]\n",
" for k in x.keys():\n",
" if k in QUEUE:\n",
" qt += x[k]\n",
" elif k in ENDED:\n",
" et += x[k]\n",
" elif k in READY:\n",
" rt += x[k]\n",
" elif k in RUN:\n",
" xt += x[k]\n",
" elif k in EVICT:\n",
" vt += x[k]\n",
" else:\n",
" ut += x[k]\n",
"\n",
" add_record(pair[0], 'queued', qt)\n",
" add_record(pair[0], 'ended', et)\n",
" add_record(pair[0], 'ready', rt)\n",
" add_record(pair[0], 'running', xt)\n",
" add_record(pair[0], 'evicted', vt)\n",
" add_record(pair[0], 'unknown', ut)\n",
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" totals[pair[0]] = (qt + et + rt + xt + vt + ut) / 1000\n",
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" \n",
" return (pandas.DataFrame(data, columns=['Last termination', 'time_type', 'time_ms']), totals)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graph 1: Absolute total time spent per status per \"last termination\" type"
]
},
{
"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
"outputs": [],
"source": [
"def graph_1(df, cluster):\n",
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" #sns.set_theme(style=\"ticks\")\n",
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" g = sns.histplot(df, x=\"Last termination\", weights=\"time_ms\", shrink=.5,\n",
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" hue=\"time_type\", multiple=\"dodge\", discrete=True, legend=False)\n",
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" g.set_yscale(\"log\")\n",
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" g.set_ylabel(\"Total (milliseconds)\") \n",
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" g.set_title(\"Cluster \" + cluster + \": Absolute total time spent per status per \\\"last termination\\\" type\")\n",
" #g.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graph 2: Relative total time spent per status per \"last termination\" type\n",
"\n",
"Values are proportions on total for each \"last termination\" type"
]
},
{
"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
"outputs": [],
"source": [
"def graph_2(df, cluster, totals):\n",
" df = df.copy()\n",
" for i in [0,4,5,6,7,8]:\n",
" df.loc[df[\"Last termination\"] == to_name(i), \"time_ms\"] = df[\"time_ms\"][df[\"Last termination\"] == to_name(i)] / totals[i]\n",
"\n",
" h = sns.histplot(df, x=\"Last termination\", \n",
" weights=\"time_ms\", shrink=.5, common_bins=True,\n",
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" hue=\"time_type\", multiple=\"stack\", discrete=True, legend=False)\n",
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" #ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
" h.set_title(\"Cluster \" + cluster + \": Relative total time spent per status per \\\"last termination\\\" type\") "
]
},
{
"cell_type": "code",
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"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
]
},
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"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
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},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
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},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
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},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
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{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
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},
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
},
{
"data": {
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"image/png": "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
"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
]
},
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"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"dft = None\n",
"tts = None\n",
"\n",
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"for cluster in \"abcdefgh\":\n",
" df, totals = create_df(cluster)\n",
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"\n",
" plt.figure(figsize=(10,8))\n",
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" graph_1(df, cluster)\n",
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" plt.figure(figsize=(10,8))\n",
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" graph_2(df, cluster, totals)\n",
" \n",
" if dft is None:\n",
" dft = df\n",
" tts = totals\n",
" else:\n",
" dft.loc[:, \"time_ms\"] = dft[\"time_ms\"].add(df[\"time_ms\"], fill_value=0)\n",
" for key in totals:\n",
" tts[key] += totals[key]\n",
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"\n",
"plt.figure(figsize=(10,8))\n",
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"graph_1(dft, \"all\")\n",
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"plt.figure(figsize=(10,8))\n",
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"graph_2(dft, \"all\", tts)"
]
},
{
"cell_type": "code",
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"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
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"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Last termination</th>\n",
" <th>time_type</th>\n",
" <th>time_ms</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NO_TERM</td>\n",
" <td>queued</td>\n",
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" <td>1.699297e+12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NO_TERM</td>\n",
" <td>ended</td>\n",
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" <td>0.000000e+00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NO_TERM</td>\n",
" <td>ready</td>\n",
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" <td>1.252067e+14</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NO_TERM</td>\n",
" <td>running</td>\n",
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" <td>3.842227e+13</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NO_TERM</td>\n",
" <td>evicted</td>\n",
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" <td>0.000000e+00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NO_TERM</td>\n",
" <td>unknown</td>\n",
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" <td>2.341963e+10</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>EVICT</td>\n",
" <td>queued</td>\n",
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" <td>1.435821e+12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>EVICT</td>\n",
" <td>ended</td>\n",
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" <td>4.767580e+07</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>EVICT</td>\n",
" <td>ready</td>\n",
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" <td>5.606274e+11</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>EVICT</td>\n",
" <td>running</td>\n",
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" <td>2.504113e+14</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>EVICT</td>\n",
" <td>evicted</td>\n",
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" <td>2.282988e+10</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>EVICT</td>\n",
" <td>unknown</td>\n",
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" <td>6.726776e+13</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>FAIL</td>\n",
" <td>queued</td>\n",
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" <td>1.578510e+12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>FAIL</td>\n",
" <td>ended</td>\n",
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" <td>0.000000e+00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>FAIL</td>\n",
" <td>ready</td>\n",
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" <td>2.613915e+10</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>FAIL</td>\n",
" <td>running</td>\n",
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" <td>4.833851e+13</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>FAIL</td>\n",
" <td>evicted</td>\n",
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" <td>5.572550e+02</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>FAIL</td>\n",
" <td>unknown</td>\n",
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" <td>1.659997e+13</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>FINISH</td>\n",
" <td>queued</td>\n",
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" <td>5.357666e+13</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>FINISH</td>\n",
" <td>ended</td>\n",
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" <td>8.581627e+07</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>FINISH</td>\n",
" <td>ready</td>\n",
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" <td>2.440088e+13</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>FINISH</td>\n",
" <td>running</td>\n",
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" <td>5.511501e+14</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>FINISH</td>\n",
" <td>evicted</td>\n",
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" <td>1.150760e+04</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>FINISH</td>\n",
" <td>unknown</td>\n",
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" <td>5.054797e+14</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>KILL</td>\n",
" <td>queued</td>\n",
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" <td>6.169786e+14</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>KILL</td>\n",
" <td>ended</td>\n",
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" <td>7.602624e+11</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>KILL</td>\n",
" <td>ready</td>\n",
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" <td>1.803627e+14</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>KILL</td>\n",
" <td>running</td>\n",
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" <td>3.653008e+15</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>KILL</td>\n",
" <td>evicted</td>\n",
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" <td>3.912275e+11</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>KILL</td>\n",
" <td>unknown</td>\n",
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" <td>2.110184e+16</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>LOST</td>\n",
" <td>queued</td>\n",
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" <td>2.866478e+08</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>LOST</td>\n",
" <td>ended</td>\n",
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" <td>1.830379e+12</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>LOST</td>\n",
" <td>ready</td>\n",
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" <td>4.515414e+09</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>LOST</td>\n",
" <td>running</td>\n",
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" <td>5.671707e+10</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>LOST</td>\n",
" <td>evicted</td>\n",
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" <td>2.014000e+00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>LOST</td>\n",
" <td>unknown</td>\n",
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" <td>1.173356e+13</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Last termination time_type time_ms\n",
"0 NO_TERM queued 1.699297e+12\n",
"1 NO_TERM ended 0.000000e+00\n",
"2 NO_TERM ready 1.252067e+14\n",
"3 NO_TERM running 3.842227e+13\n",
"4 NO_TERM evicted 0.000000e+00\n",
"5 NO_TERM unknown 2.341963e+10\n",
"6 EVICT queued 1.435821e+12\n",
"7 EVICT ended 4.767580e+07\n",
"8 EVICT ready 5.606274e+11\n",
"9 EVICT running 2.504113e+14\n",
"10 EVICT evicted 2.282988e+10\n",
"11 EVICT unknown 6.726776e+13\n",
"12 FAIL queued 1.578510e+12\n",
"13 FAIL ended 0.000000e+00\n",
"14 FAIL ready 2.613915e+10\n",
"15 FAIL running 4.833851e+13\n",
"16 FAIL evicted 5.572550e+02\n",
"17 FAIL unknown 1.659997e+13\n",
"18 FINISH queued 5.357666e+13\n",
"19 FINISH ended 8.581627e+07\n",
"20 FINISH ready 2.440088e+13\n",
"21 FINISH running 5.511501e+14\n",
"22 FINISH evicted 1.150760e+04\n",
"23 FINISH unknown 5.054797e+14\n",
"24 KILL queued 6.169786e+14\n",
"25 KILL ended 7.602624e+11\n",
"26 KILL ready 1.803627e+14\n",
"27 KILL running 3.653008e+15\n",
"28 KILL evicted 3.912275e+11\n",
"29 KILL unknown 2.110184e+16\n",
"30 LOST queued 2.866478e+08\n",
"31 LOST ended 1.830379e+12\n",
"32 LOST ready 4.515414e+09\n",
"33 LOST running 5.671707e+10\n",
"34 LOST evicted 2.014000e+00\n",
"35 LOST unknown 1.173356e+13"
]
},
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"execution_count": 48,
"metadata": {},
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"output_type": "execute_result"
}
],
"source": [
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"dft"
]
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},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"language": "python",
"name": "python3"
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