2021-04-13 14:13:15 +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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2021-05-27 14:00:37 +00:00
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"execution_count": 1,
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2021-04-13 14:13:15 +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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2021-05-20 10:33:36 +00:00
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"import numpy as np\n",
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2021-05-27 14:00:37 +00:00
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"import matplotlib.pyplot as plt\n",
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"pandas.options.display.float_format = '{:,.4f}'.format"
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2021-04-13 14:13:15 +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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2021-05-27 14:00:37 +00:00
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"execution_count": 2,
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2021-04-13 14:13:15 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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2021-05-27 14:00:37 +00:00
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"DIR = \"/Users/maggicl/git/bachelorThesis/spatial_resource_waste/\""
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2021-04-13 14:13:15 +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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2021-05-27 14:00:37 +00:00
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"execution_count": 6,
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2021-04-13 14:13:15 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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2021-05-20 10:33:36 +00:00
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"NAMES = {4: 'EVICT', 5: 'FAIL', 6: 'FINISH', 7: 'KILL'}\n",
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"\n",
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2021-04-13 14:13:15 +00:00
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"def plot_df(df, cluster, type_of_data):\n",
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2021-05-20 10:33:36 +00:00
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" \n",
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" df = df[df[\"term\"].isin(range(4,8))].sort_values(\"term\")\n",
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" \n",
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2021-04-13 14:13:15 +00:00
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" s = df.sum()\n",
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2021-05-20 10:33:36 +00:00
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" #print(\"Cluster \" + cluster + \":\")\n",
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2021-04-13 14:13:15 +00:00
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" df[\"cpu\"] = df[\"cpu\"] / s[\"cpu\"]\n",
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" df[\"ram\"] = df[\"ram\"] / s[\"ram\"]\n",
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2021-05-12 13:24:57 +00:00
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" \n",
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" latex = df.copy()\n",
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2021-05-20 10:33:36 +00:00
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" latex[\"Task termination\"] = latex[\"term\"].apply(lambda x: NAMES[x])\n",
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" del latex[\"term\"]\n",
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" latex[\"% CPU\"] = (latex[\"cpu\"] * 100).round(2).apply(lambda x: \"%2.02f\" % x) + \"%\"\n",
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" del latex[\"cpu\"]\n",
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" latex[\"% Memory\"] = (latex[\"ram\"] * 100).round(2).apply(lambda x: \"%2.02f\" % x) + \"%\"\n",
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" del latex[\"ram\"]\n",
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" #print(latex.to_latex(index=False)) \n",
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2021-04-13 14:13:15 +00:00
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"\n",
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" df2 = df.copy()\n",
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2021-05-20 10:33:36 +00:00
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" df[\"kind\"] = \"CPU\"\n",
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" df[\"percent\"] = df[\"cpu\"] * 100\n",
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2021-04-13 14:13:15 +00:00
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" del df[\"cpu\"]\n",
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" del df[\"ram\"]\n",
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" \n",
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2021-05-20 10:33:36 +00:00
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" df2[\"kind\"] = \"Memory\"\n",
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" df2[\"percent\"] = df2[\"ram\"] * 100\n",
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2021-04-13 14:13:15 +00:00
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" del df2[\"cpu\"]\n",
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" del df2[\"ram\"]\n",
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" \n",
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" df = pd.concat([df, df2])\n",
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" \n",
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2021-05-27 14:00:37 +00:00
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" print(df)\n",
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" \n",
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2021-05-20 10:33:36 +00:00
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" fig, ax = plt.subplots()\n",
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" fig.set_size_inches(4, 4)\n",
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" \n",
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2021-05-22 14:18:14 +00:00
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" colors = plt.cm.Spectral([0.9, 0.3, 0.8, 0.1])\n",
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2021-05-20 10:33:36 +00:00
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" \n",
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" a = pd.plotting.table(ax, latex.set_index(\"Task termination\"), rowColours=colors,\n",
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" bbox=[0,-0,1,0.3])\n",
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" ax.set_ylim(bottom=-50, top=105)\n",
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" \n",
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2021-04-13 14:13:15 +00:00
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" bottom = [0, 0]\n",
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" lines = []\n",
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2021-05-20 10:33:36 +00:00
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" for t in [4,5,6,7]:\n",
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" dft = df[df.term==t]\n",
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" line = plt.bar(x=dft[\"kind\"], bottom=bottom, height=dft[\"percent\"], \n",
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" color=colors[t-4], width=0.85)\n",
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" #ax.bar_label(line, label_type='center', fmt=\"%.02f%%\")\n",
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" lines.append(line)\n",
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2021-04-13 14:13:15 +00:00
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" bottom += df[df.term==t][\"percent\"].values\n",
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2021-05-20 10:33:36 +00:00
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" #plt.legend(lines, [\"EVICT\", \"FAIL\", \"FINISH\", \"KILL\"],\n",
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" # bbox_to_anchor=(1,1))\n",
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" if cluster == \"2011\":\n",
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" plt.title(\"2011 data (%s resources)\" % type_of_data)\n",
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" elif cluster == \"all\":\n",
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" plt.title(\"2019 data (%s resources)\" % type_of_data)\n",
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" else:\n",
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" plt.title(\"Cluster %s (%s resources)\" % (cluster.upper(), type_of_data))\n",
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" \n",
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" ax.set_xticks([])\n",
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" ax.set_yticks([0,20,40,60,80,100])\n",
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" fig.savefig('../report/figures/spatial_resource_waste/%s-%s.pgf' % \n",
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2021-05-22 14:18:14 +00:00
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" (type_of_data, cluster), bbox_inches='tight')\n",
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2021-05-20 10:33:36 +00:00
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" plt.show()\n",
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" "
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2021-04-13 14:13:15 +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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2021-05-27 14:00:37 +00:00
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"execution_count": 7,
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2021-04-13 14:13:15 +00:00
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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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2021-05-22 14:18:14 +00:00
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"a\n",
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"3 4 CPU 13.534758\n",
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"2 5 CPU 2.295446\n",
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"1 6 CPU 1.406182\n",
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"5 7 CPU 82.763613\n",
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"3 4 Memory 11.944255\n",
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"2 5 Memory 2.868907\n",
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"1 6 Memory 1.118353\n",
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"5 7 Memory 84.068484\n"
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2021-05-20 10:33:36 +00:00
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]
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},
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{
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"data": {
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2021-05-27 14:00:37 +00:00
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"image/png": "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2021-05-20 10:33:36 +00:00
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"text/plain": [
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"<Figure size 288x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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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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2021-05-22 14:18:14 +00:00
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"b\n",
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"3 4 CPU 4.846675\n",
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"2 5 CPU 6.311480\n",
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"1 6 CPU 2.594542\n",
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"5 7 CPU 86.247303\n",
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"3 4 Memory 7.346297\n",
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"2 5 Memory 8.423371\n",
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"1 6 Memory 1.228843\n",
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"5 7 Memory 83.001490\n"
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2021-05-20 10:33:36 +00:00
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]
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},
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{
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"data": {
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2021-05-27 14:00:37 +00:00
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2021-05-20 10:33:36 +00:00
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"text/plain": [
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},
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2021-05-22 14:18:14 +00:00
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"c\n",
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"3 4 CPU 8.238472\n",
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"2 5 CPU 1.233646\n",
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"1 6 CPU 2.950120\n",
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"5 7 CPU 87.577761\n",
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"3 4 Memory 8.076928\n",
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"2 5 Memory 2.089036\n",
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"1 6 Memory 3.337954\n",
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"5 7 Memory 86.496082\n"
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2021-05-27 14:00:37 +00:00
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2021-04-13 14:13:15 +00:00
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2021-05-20 10:33:36 +00:00
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"<Figure size 288x288 with 1 Axes>"
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2021-04-13 14:13:15 +00:00
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]
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2021-05-22 14:18:14 +00:00
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"d\n",
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"3 4 CPU 7.638602\n",
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"5 7 Memory 86.053968\n"
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2021-04-13 14:13:15 +00:00
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2021-05-27 14:00:37 +00:00
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2021-04-13 14:13:15 +00:00
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"text/plain": [
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"<Figure size 288x288 with 1 Axes>"
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2021-04-13 14:13:15 +00:00
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]
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},
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"text": [
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"1 4 CPU 8.528859\n",
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"2 5 CPU 3.165632\n",
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"3 6 CPU 2.020153\n",
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"4 7 CPU 86.285355\n",
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"1 4 Memory 9.025691\n",
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"2 5 Memory 4.451117\n",
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"3 6 Memory 1.660565\n",
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"4 7 Memory 84.862627\n"
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2021-05-27 14:00:37 +00:00
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2021-04-13 14:13:15 +00:00
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"text/plain": [
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2021-05-20 10:33:36 +00:00
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"<Figure size 288x288 with 1 Axes>"
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2021-04-13 14:13:15 +00:00
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]
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},
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"text": [
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 2.850959\n",
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"1 5 CPU 0.058594\n",
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"2 6 CPU 0.000102\n",
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"3 7 CPU 97.090345\n",
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"1 5 Memory 0.070269\n",
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"2 6 Memory 0.000152\n",
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"3 7 Memory 96.505443\n"
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2021-04-13 14:13:15 +00:00
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2021-05-27 14:00:37 +00:00
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2021-04-13 14:13:15 +00:00
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"text/plain": [
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2021-05-20 10:33:36 +00:00
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"<Figure size 288x288 with 1 Axes>"
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2021-04-13 14:13:15 +00:00
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]
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},
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"text": [
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 0.003693\n",
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"1 5 CPU 0.003359\n",
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"2 6 CPU 0.013934\n",
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"3 7 CPU 99.979014\n",
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"0 4 Memory 0.005485\n",
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"2 6 Memory 0.020615\n",
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"3 7 Memory 99.968099\n"
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2021-05-27 14:00:37 +00:00
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"text/plain": [
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2021-05-20 10:33:36 +00:00
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"<Figure size 288x288 with 1 Axes>"
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2021-04-13 14:13:15 +00:00
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]
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},
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"text": [
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 0.010445\n",
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"1 5 CPU 0.001528\n",
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"3 7 Memory 99.968072\n"
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2021-04-13 14:13:15 +00:00
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2021-05-27 14:00:37 +00:00
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2021-04-13 14:13:15 +00:00
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 0.048160\n",
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"3 7 Memory 99.977441\n"
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2021-05-27 14:00:37 +00:00
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2021-05-20 10:33:36 +00:00
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"<Figure size 288x288 with 1 Axes>"
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2021-04-13 14:13:15 +00:00
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"text": [
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 0.362143\n",
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2021-04-13 14:13:15 +00:00
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2021-05-27 14:00:37 +00:00
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2021-04-13 14:13:15 +00:00
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2021-04-13 14:13:15 +00:00
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 0.008458\n",
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"1 5 CPU 0.000967\n",
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"2 6 Memory 0.024097\n",
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"3 7 Memory 99.958699\n"
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2021-05-20 10:33:36 +00:00
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"<Figure size 288x288 with 1 Axes>"
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2021-04-13 14:13:15 +00:00
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"text": [
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 0.123373\n",
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2021-04-13 14:13:15 +00:00
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2021-05-27 14:00:37 +00:00
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2021-04-13 14:13:15 +00:00
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2021-04-13 14:13:15 +00:00
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 0.007628\n",
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"3 7 Memory 99.997168\n"
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2021-05-20 10:33:36 +00:00
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"<Figure size 288x288 with 1 Axes>"
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2021-04-13 14:13:15 +00:00
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"text": [
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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 2.727769\n",
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2021-04-13 14:13:15 +00:00
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2021-05-27 14:00:37 +00:00
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2021-04-13 14:13:15 +00:00
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"text/plain": [
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2021-05-20 10:33:36 +00:00
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"<Figure size 288x288 with 1 Axes>"
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]
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},
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"metadata": {
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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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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 28.2\n",
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"1 5 CPU 31.7\n",
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"2 6 CPU 13.9\n",
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"3 7 CPU 26.2\n",
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"0 4 Memory 30.4\n",
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"1 5 Memory 23.5\n",
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"2 6 Memory 17.3\n",
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"3 7 Memory 28.8\n"
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2021-05-20 10:33:36 +00:00
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]
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},
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{
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"data": {
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2021-05-27 14:00:37 +00:00
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAARQAAAD7CAYAAACrFWuaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqw0lEQVR4nO3dd3gU1frA8e+bhBBCCwjSAoKCQhotNBElIiCICoIUEeGiov7shaKgWKgXEPSichWugChVL8hVQZCioHRCEZUiKIGAoYMB0s7vj5ksm7AJKZNsQt7P8+yT7NQzc3bfPXPmnDlijEEppZzg4+0EKKWuHhpQlFKO0YCilHKMBhSllGM0oCilHKMBRSnlGA0oBYSI9BORNfm4v4oi8quIlMivfXqbiLQWkRhvp8PbRGSCiDyRF9suUgFFRIqLyDQR+UNEzopItIh0SLdMG/uLFi8iK0XkOrd53UXkR3veKg/b/1BEfhORFBHpl4fH8bqIzMrlZoYA040x551IU34QkekiMsLb6bgKjAdeERF/pzdcpAIK4AccBG4DygLDgHkiUhNARCoAXwCvAuWBTcBct/VPAJOAMRlsfxvwf8AW55PuHBEpDvQFPAYlsRS1z0YaIuLn7TQAiIiv09s0xsQCvwL3OL1tjDFF+gVsB7ra/w8AfnSbVxI4D9RNt84jwKpMtrkG6HeF/V4DfAmcATYAbwFr3Oa/gxX8zgCbgVb29DuBBCAROAdss6f/A/gFOAv8DjyWyb5vBfamm7YKGAmstY+5NlAXWIYVSH8Dumcl/UBNwAB+6bb/iNv7/nZ6TwJLgevs6QJMBP6yt70DCLPzJtE+9nPAYnv5qsDnQBywH3jGbR8lgOn2PnYBA4GYTM6LAZ4E9gD77WmdgGjgFPAjEOG2/GDgkH3OfwPa2NOLY/3wHLZfk4Di9rx+7vnstt/a9v/TgQ+Ar4G/gTuA6lg/dHHAcWByTs+j23pDgY8d/z55+wvtzRdQCbiAHTCwvsQfpFtmJ3bAcZvmRECZA8zDClph9gfTPaA8iPWl9QNeBI4AAfa814FZ6bZ3F3CD/UG6DYgHGmWw7yeBr9JNWwX8CYTa+yyLFdD+Yb9vCBwDQq6Ufq4QUIB7gb1APXvbw7ADOdAeK4AG2cdSD6hiz5sOjHDbpo+97GuAP3A9VjBtb88fA/yAVdqsbufllQLKMnv5EvYx/wU0A3yxSnUHsALGTfb5qep2zDfY/78JrAOuBSpiBaK37Hn9uHJAOQ20tI+vJFbJd6L9fwBwS27Ooz3/PmCL09+pIlusFZFiwKfADGPMr/bkUliZ6e40UNrhffsCXYHXjDF/G2N2AjPclzHGzDLGHDfGJBljJnDpQ+yRMeYrY8w+Y1kNfAu0ymDxIKxf1fSmG2N+NsYkYZWEDhhjPrbTsBWrJHB/VtJ/BY8Do40xv9j7GgU0sOurErHOd11A7GViM9hOE6CiMeZNY0yCMeZ34COgpz2/OzDSGHPCGHMQeDcLaRttL38eq1T0b2PMemNMsjFmBnARaA4kY+VJiIgUM8YcMMbss7fRG3jTGPOXMSYOeAPok43zs8gYs9YYkwJEYJXCBtrn+oIxJrXyPjfn8SzW58BRRTKg2PUDn2AVn59ym3UOKJNu8TJ4/vLlRkUu1eek+iNdGl8SkV9E5LSInMIqMVTIaIMi0kFE1onICXv5jpksfxLPQdI9PdcBzUTkVOoL64tSOSvpv4LrgHfctnsC61e0mjFmBTAZeA/4y67oTp8n7tupmi6Nr2CVPMH6ImY3jenPwYvptl8dq1SyF3gOq7T4l4jMEZGqbvt139cf9rSsck9DdeAPO2Ckl5vzWBrrMs5RRS6giIgA07A+dF2NMYlus38G6rstWxLrMuJnh5MRByRhfVhS1XDbbytgENYvbDljTBBWSUnsRdJ0EbcrWT/Hqr2vZC//tdvy6W0HbvQw3X27B4HVxpggt1cpY8wTV0o/1rU/QKDbtMrptv1Yum2XMMb8CGCMedcY0xgIsdM50NNx29vZn247pY0xHe35sZmkMSPpz8HIdNsPNMbMttP5mTHmFqwvtgHG2usdtqe57/ew/f/f7udFRNzPS0ZpqJFBJXFOzyNYl0DbMjkPOVLkAgpWhVc94G5z+S3T/wJhItJVRAKwrs23p14SiYivPd0P8BGRAPvSCXu+vz1fgGL2/MvOsTEmGauS7XURCRSREKzr81Slsb6wcYCfiLxG2pLTUaCm27b9sYrfcUCSfSu8XSbnYAMQJCLVMlnmf8CNItJHRIrZryYiUu9K6beL+YeAB+1z1h8rMKeaArwsIqH2eSsrIvfb/zcRkWb2ef0bq44rxe24r093HGdFZLCIlLD3FSYiTez58+z9lBORYODpTI7Xk4+Ax+30iIiUFJG7RKS0iNwkIrfbwfwCVkV2ajpnA8PEautTAetzlHpHbRsQKiIN7M/K61dIwwaswDjG3n+AiLS05+X0PIJVz/ZNNs/HlTldKVOQX1z6JbmAdXmT+urttswdWLfUzmNVJNZ0m9fPXt/9Nd1t/ioP81tnkJaKWF9aT3dJfIH/2PNisUorB4A77PnXYFX8nsSuWMOqaD2KVYz9BKvSdEQm52IcMDhd2h9Jt8xNwFdcuruwAmhwpfTb8ztg3XU5BUwAVpP2Lk8frDsPZ7B+af9jT2+DVYI6h1UJ/ClQyp5Xh0t3XBba06pifYGP2Odjndt5CgRm2stn9S5P7XTT7gQ22tuIBeZjBfwI+7jPYl1q/I9LFbQBWPU1sfbrXewKdXv+UPvYDmJVvqevlB2RLg01gIV2HhwD3s3leawCxAD+Tn/HxN6BKmJEpCLWHZCGxoHGbXZDvkeMdQmgCjARmQDsM8a87/S2C0TjHZX/jHVZUtfb6VD5zxjzYl5tuyjWoSil8ohe8iilHKMlFKWUYzSgKKUcU6grZStUqGBq1qzp7WQoVeRs3rz5mDGmYvrphTqg1KxZk02bNnk7GUoVOSLisRuDXvIopRyTZwFFRP4jIn+JyE63aeVFZJmI7LH/lrOni4i8KyJ7RWS7iDTKq3QppfJOXpZQpmM1W3Y3BPjOGFMH+M5+D1Yz7Tr2awBWfxulVCGTZwHFGPM9Vh8Hd/dy6bkZM4DObtNnGss6rI5rVfIqbUqpvJHfdSiVzKWHvBzh0nMrqpH2GRAx9jSlVCHitbs8xhgjItlupisiA7Aui6hRIyuPt4Cfb+2c3d2oXAr9fmGu1tc8847c5lt+l1COpl7K2H//sqcfIu2DcILtaZcxxnxojIk0xkRWrHjZbXCllBfld0D5kksP4ukLLHKb/pB9t6c5cNpk/BxRpVQBlWeXPCIyG2gNVBBrtLbhWE8hnyciD2M9Z7O7vfjXWM9A3Yv1tPZ/5FW6lFJ5J88CijGmVwaz2nhYNnU8FKVUIaYtZZVSjtGAopRyjAYUpZRjNKAopRyjAUUp5RgNKEopx2hAUUo5RgOKUsoxGlCUUo7RgKKUcowGFKWUYzSgKKUcowFFKeUYDShKKcdoQFFKOUYDilLKMRpQlFKO0YCilHKMBhSllGO8ElBE5HkR+VlEdorIbBEJEJFaIrLeHt94roj4eyNtSqmcy/eAIiLVgGeASGNMGOAL9ATGAhONMbWBk8DD+Z02pVTueOuSxw8oISJ+QCAQC9wOLLDnu497rJQqJPI9oBhjDgHjgT+xAslpYDNwyhiTZC+mYxsrVQh545KnHHAvUAuoCpQE7szG+gNEZJOIbIqLi8ujVCqlcsIblzx3APuNMXHGmETgC6AlEGRfAoGObaxUoeSNgPIn0FxEAkVEsEYS3AWsBLrZy7iPe6yUKiS8UYeyHqvydQuww07Dh8Bg4AUR2QtcA0zL77QppXInz8Y2zowxZjjW4OnufgeaeiE5SimHaEtZpZRjNKAopRyjAUUp5RgNKEopx3ilUja/vTuyu7eTUOT829sJUF6hJRSllGM0oCilHFMkLnlU4aOXqd6R20tVLaEopRyjAUUp5RgNKEopx2hAUUo5RgOKUsoxGlCUUo7RgKKUcowGFKWUYzSgKKUcowFFKeUYDShKKcd4a2z
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2021-05-20 10:33:36 +00:00
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"text/plain": [
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"<Figure size 288x288 with 1 Axes>"
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]
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},
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"metadata": {
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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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2021-05-27 14:00:37 +00:00
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" term kind percent\n",
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"0 4 CPU 19.2\n",
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"1 5 CPU 13.7\n",
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"2 6 CPU 23.2\n",
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"3 7 CPU 43.9\n",
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"0 4 Memory 21.0\n",
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"1 5 Memory 14.2\n",
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"2 6 Memory 32.1\n",
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"3 7 Memory 32.7\n"
|
2021-05-20 10:33:36 +00:00
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]
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},
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{
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"data": {
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2021-05-27 14:00:37 +00:00
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"image/png": "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2021-05-20 10:33:36 +00:00
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"text/plain": [
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"<Figure size 288x288 with 1 Axes>"
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2021-04-13 14:13:15 +00:00
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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2021-05-20 10:33:36 +00:00
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"\n",
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"dft = None\n",
|
2021-04-13 14:13:15 +00:00
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|
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"for cluster in \"abcd\":\n",
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2021-05-22 14:18:14 +00:00
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" print(cluster)\n",
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2021-04-13 14:13:15 +00:00
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|
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" df = pd.read_csv(glob.glob(DIR + cluster + \"_actual/part-*\")[0], header=None,\n",
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|
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" names=[\"term\", \"cpu\", \"ram\"])\n",
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2021-05-20 10:33:36 +00:00
|
|
|
" plot_df(df, cluster, \"used\")\n",
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|
|
" if dft is None:\n",
|
|
|
|
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" dft = df\n",
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" else:\n",
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|
|
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" dft = dft.append(df)\n",
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"\n",
|
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|
|
"dft = dft.groupby(\"term\").sum().reset_index()\n",
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|
|
"plot_df(dft, \"all\", \"used\")\n",
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"\n",
|
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|
|
|
"dft = None\n",
|
2021-04-13 14:13:15 +00:00
|
|
|
"for cluster in \"abcdefgh\":\n",
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|
|
|
|
" data = None\n",
|
|
|
|
|
" with open(DIR + cluster + \"_res_micros_requested.json\", \"r\") as f:\n",
|
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|
|
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" data = json.loads(f.read())\n",
|
|
|
|
|
" dfd = {'term': [], 'cpu': [], 'ram': []}\n",
|
2021-05-20 10:33:36 +00:00
|
|
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" for term in [4,5,6,7]:\n",
|
2021-04-13 14:13:15 +00:00
|
|
|
" dfd['term'].append(term)\n",
|
|
|
|
|
" dfd['cpu'].append(float(data[\"cpu-\" + (\"None\" if term == -1 else str(term))]))\n",
|
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|
|
|
" dfd['ram'].append(float(data[\"ram-\" + (\"None\" if term == -1 else str(term))]))\n",
|
2021-05-12 13:24:57 +00:00
|
|
|
" df = pd.DataFrame(dfd, columns=['term', 'cpu', 'ram'])\n",
|
2021-05-20 10:33:36 +00:00
|
|
|
" if dft is None:\n",
|
|
|
|
|
" dft = df\n",
|
|
|
|
|
" else:\n",
|
|
|
|
|
" dft = dft.append(df)\n",
|
|
|
|
|
" plot_df(df, cluster, \"requested\")\n",
|
|
|
|
|
" \n",
|
|
|
|
|
"dft = dft.groupby(\"term\").sum().reset_index()\n",
|
|
|
|
|
"plot_df(dft, \"all\", \"requested\")\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"dfr2011 = {'term': [4,5,6,7], 'cpu': [28.2, 31.7, 13.9, 26.2], 'ram': [30.4,23.5, 17.3, 28.8]}\n",
|
|
|
|
|
"dfr2011 = pd.DataFrame(dfr2011, columns=dfr2011.keys())\n",
|
|
|
|
|
"dfa2011 = {'term': [4,5,6,7],'cpu': [19.2, 13.7, 23.2, 43.9], 'ram': [21.0,14.2,32.1,32.7]}\n",
|
|
|
|
|
"dfa2011 = pd.DataFrame(dfa2011, columns=dfa2011.keys())\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plot_df(dfr2011, \"2011\", \"requested\")\n",
|
|
|
|
|
"plot_df(dfa2011, \"2011\", \"used\")"
|
2021-04-13 14:13:15 +00:00
|
|
|
]
|
2021-05-12 13:24:57 +00:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"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",
|
|
|
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|
"execution_count": null,
|
|
|
|
|
"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,
|
|
|
|
|
"metadata": {},
|
|
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|
"outputs": [],
|
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|
|
"source": []
|
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|
},
|
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|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2021-04-13 14:13:15 +00:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
2021-05-24 12:41:16 +00:00
|
|
|
"display_name": "Python 3",
|
2021-04-13 14:13:15 +00:00
|
|
|
"language": "python",
|
2021-05-24 12:41:16 +00:00
|
|
|
"name": "python3"
|
2021-04-13 14:13:15 +00:00
|
|
|
},
|
|
|
|
|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
"version": 3
|
|
|
|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
2021-05-24 12:41:16 +00:00
|
|
|
"version": "3.8.3"
|
2021-04-13 14:13:15 +00:00
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 5
|
|
|
|
|
}
|
