305 lines
52 KiB
Plaintext
305 lines
52 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "german-marathon",
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"metadata": {},
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"source": [
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"# Statuses total time\n",
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"Sums the times instances spend in one of each states in the diagram saved as\n",
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"\"statuses.drawio\". Unknown times are summed as \"unknown\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "boolean-microwave",
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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 pandas\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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "activated-messenger",
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"metadata": {},
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"outputs": [],
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"source": [
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"# QUEUE = set([\"0-2\", \"1-2\", \"assumptions:\", \"1-1\", \"1-0\"])\n",
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"# RESUB = set([\"4-1\", \"4-0\", \"5-1\", \"6-1\", \"7-1\", \"8-1\", \"assumptions:\", \"5-0\", \"6-0\", \"7-0\", \\\n",
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"# \"8-0\"])\n",
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"# READY = set([\"0-3\", \"2-3\", \"0-9\", \"2-9\", \"9-3\", \"2-7\", \"2-8\", \"9-7\", \"9-8\", \\\n",
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"# \"9-9\", \"0-7\", \"0-8\", \"assumptions:\", \"2-0\", \"2-4\", \"9-4\", \"9-1\"])\n",
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"# RUN = set([\"3-1\", \"3-10\", \"3-4\", \"3-5\", \"3-6\", \"3-7\", \"3-8\", \"10-5\", \"10-6\", \\\n",
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"# \"10-7\", \"10-8\", \"10-4\", \"10-10\", \"10-1\", \"assumptions:\", \"3-0\", \"10-0\", \"3-3\"])\n",
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"\n",
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"QUEUE = set([\"0-2\", \"1-2\"])\n",
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"ENDED = set([\"5-1\", \"6-1\", \"7-1\", \"8-1\"])\n",
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"READY = set([\"0-3\", \"0-9\", \"2-3\", \"2-9\", \"9-3\", \"9-9\"])\n",
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"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",
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"EVICT = set([\"4-1\", \"4-0\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "novel-commercial",
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"metadata": {},
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"outputs": [],
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"source": [
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"obj = {}\n",
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"\n",
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"filename=\"/home/claudio/google_2019/thesis_queries/machine_time_waste/a_state_changes.json\"\n",
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"\n",
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"with open(filename, 'r') as f:\n",
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" obj = json.loads(f.read())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "casual-oasis",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = {'Last termination': [], 'time_type': [], 'time_ms': []}\n",
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"totals = {}\n",
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"\n",
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"def to_name(et):\n",
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" if et == 4: \n",
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" return 'EVICT'\n",
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" elif et == 5: \n",
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" return 'FAIL'\n",
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" elif et == 6:\n",
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" return 'FINISH'\n",
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" elif et == 7:\n",
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" return 'KILL'\n",
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" elif et == 8:\n",
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" return 'LOST'\n",
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" else:\n",
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" return 'NO_TERM'\n",
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"\n",
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"def add_record(et, tt, time):\n",
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" data['Last termination'].append(to_name(et))\n",
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" data['time_type'].append(tt)\n",
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" data['time_ms'].append(time)\n",
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" \n",
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" \n",
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"for pair in obj[\"data\"]:\n",
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" s = set()\n",
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" qt = et = rt = xt = vt = ut = 0\n",
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" \n",
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" # print(\"End type \" + str(pair[0])) \n",
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" \n",
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" pair[0] = 0 if pair[0] is None else pair[0]\n",
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" \n",
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" x = pair[1]\n",
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" for k in x.keys():\n",
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" if k in QUEUE:\n",
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" qt += x[k]\n",
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" elif k in ENDED:\n",
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" et += x[k]\n",
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" elif k in READY:\n",
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" rt += x[k]\n",
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" elif k in RUN:\n",
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" xt += x[k]\n",
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" elif k in EVICT:\n",
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" vt += x[k]\n",
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" else:\n",
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" s.add(k)\n",
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" ut += x[k]\n",
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" \n",
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" add_record(pair[0], 'queued', qt)\n",
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" add_record(pair[0], 'ended', et)\n",
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" add_record(pair[0], 'ready', rt)\n",
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" add_record(pair[0], 'running', xt)\n",
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" add_record(pair[0], 'evicted', vt)\n",
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" add_record(pair[0], 'unknown', ut)\n",
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" totals[pair[0]] = qt + et + rt + xt + vt + ut\n",
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" # print(s) \n",
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" \n",
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"df = pandas.DataFrame(data, columns=['Last termination', 'time_type', 'time_ms'])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "encouraging-acoustic",
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"metadata": {},
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"source": [
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"## Graph 1: Absolute total time spent per status per \"last termination\" type"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "romance-complex",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Last termination time_type time_ms\n",
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"0 NO_TERM queued 1.515995e-02\n",
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"1 NO_TERM ended 0.000000e+00\n",
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"2 NO_TERM ready 3.365563e-02\n",
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"3 NO_TERM running 9.511017e-01\n",
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"4 NO_TERM evicted 0.000000e+00\n",
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"5 NO_TERM unknown 8.273546e-05\n",
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"6 EVICT queued 2.199883e-02\n",
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"7 EVICT ended 7.361000e-08\n",
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"8 EVICT ready 6.672357e-03\n",
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"9 EVICT running 8.789100e-01\n",
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"10 EVICT evicted 1.503149e-05\n",
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"11 EVICT unknown 9.240368e-02\n",
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"12 FAIL queued 8.517812e-02\n",
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"13 FAIL ended 0.000000e+00\n",
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"14 FAIL ready 9.300481e-04\n",
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"15 FAIL running 6.596810e-01\n",
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"16 FAIL evicted 6.492666e-12\n",
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"17 FAIL unknown 2.542109e-01\n",
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"18 FINISH queued 3.237908e-01\n",
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"19 FINISH ended 1.907057e-07\n",
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"20 FINISH ready 2.215952e-02\n",
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"21 FINISH running 3.951182e-01\n",
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"22 FINISH evicted 3.313964e-11\n",
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"23 FINISH unknown 2.589313e-01\n",
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"24 KILL queued 3.455137e-02\n",
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"25 KILL ended 3.509624e-05\n",
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"26 KILL ready 4.547064e-03\n",
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"27 KILL running 3.297613e-01\n",
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"28 KILL evicted 2.820098e-05\n",
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"29 KILL unknown 6.310770e-01\n",
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"30 LOST queued 3.119403e-05\n",
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"31 LOST ended 1.203386e-01\n",
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"32 LOST ready 8.201877e-04\n",
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"33 LOST running 2.690978e-04\n",
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"34 LOST evicted 1.693104e-13\n",
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"35 LOST unknown 8.785409e-01\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"Text(0, 0.5, 'Total microseconds')"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
|
||
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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||
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"metadata": {},
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||
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"output_type": "display_data"
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}
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||
|
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],
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"source": [
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"sns.set_theme(style=\"ticks\")\n",
|
||
|
|
"\n",
|
||
|
|
"print(df)\n",
|
||
|
|
"\n",
|
||
|
|
"g = sns.histplot(df, x=\"Last termination\", weights=\"time_ms\", shrink=.5,\n",
|
||
|
|
" hue=\"time_type\", multiple=\"dodge\", discrete=True)\n",
|
||
|
|
"g.set_yscale(\"log\")\n",
|
||
|
|
"g.set_ylabel(\"Total microseconds\") "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "temporal-alabama",
|
||
|
|
"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",
|
||
|
|
"execution_count": 6,
|
||
|
|
"id": "actual-penetration",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/home/claudio/python-venv/lib/python3.6/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
|
||
|
|
"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
||
|
|
"\n",
|
||
|
|
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
|
|
" iloc._setitem_with_indexer(indexer, value)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for i in [0,4,5,6,7,8]:\n",
|
||
|
|
" df[\"time_ms\"].loc[df[\"Last termination\"] == to_name(i)] = df[\"time_ms\"].loc[df[\"Last termination\"] == to_name(i)] / totals[i]\n",
|
||
|
|
"\n",
|
||
|
|
"h = sns.histplot(df, x=\"Last termination\", weights=\"time_ms\", shrink=.5, common_bins=True,\n",
|
||
|
|
" hue=\"time_type\", multiple=\"stack\", discrete=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "disabled-relative",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "successful-guidance",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.6.9"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|