2021-04-17 09:37:58 +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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2021-04-18 20:15:26 +00:00
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"execution_count": 11,
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2021-04-17 09:37:58 +00:00
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"id": "black-funeral",
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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"
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]
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},
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{
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"cell_type": "code",
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2021-04-18 20:15:26 +00:00
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"execution_count": 12,
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2021-04-17 09:37:58 +00:00
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"id": "hawaiian-cabin",
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"metadata": {},
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"outputs": [],
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"source": [
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"DIR = \"/home/claudio/hdd/git/bachelorThesis/figure_7/\""
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]
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},
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{
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"cell_type": "code",
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2021-04-18 20:15:26 +00:00
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"execution_count": 29,
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2021-04-17 09:37:58 +00:00
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"id": "c5d455be-278f-43e7-b98a-0d460be1328a",
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"metadata": {},
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"outputs": [],
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"source": [
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"def categorical_plot(df, key, title, sorted_xs = None):\n",
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" if sorted_xs == None:\n",
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" xs = set()\n",
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" lines = []\n",
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" for t in [4,5,6,7,8]:\n",
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" xs = xs.union(np.array(df[df.term == t][key]).reshape(-1))\n",
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" xs = [str(x) for x in sorted(xs)]\n",
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" else:\n",
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" xs = sorted_xs\n",
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" \n",
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2021-04-18 20:15:26 +00:00
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" plt.figure(figsize=(10 if key != \"n_exec\" else 22,8))\n",
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2021-04-17 09:37:58 +00:00
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" ax = plt.gca()\n",
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" lines = []\n",
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" for t in [4,5,6,7,8]:\n",
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" ys = []\n",
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" for x in xs:\n",
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2021-04-18 20:15:26 +00:00
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" partsum = df[(df.term != -1) & (df[key] == (int(x) if key != \"exectime\" else x))][\"count\"].sum().astype(int)\n",
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" y = df[(df.term == t) & (df[key] == (int(x) if key != \"exectime\" else x))][\"count\"]\n",
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2021-04-17 09:37:58 +00:00
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" ys.append((y.values.astype(int)[0] / partsum if partsum > 0 else y.values.astype(int)[0]) if y.any() else 0)\n",
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" lines.append(ax.plot(xs,ys, marker='.')[0])\n",
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"\n",
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" plt.xticks(xs)\n",
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" plt.legend(lines, [\"EVICT\", \"FAIL\", \"FINISH\", \"KILL\", \"LOST\"],\n",
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" bbox_to_anchor=(1,1))\n",
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" plt.title(title)\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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2021-04-18 20:15:26 +00:00
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"execution_count": 30,
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2021-04-17 09:37:58 +00:00
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"id": "d79f5580-3b46-40e5-863f-a40a3b325e72",
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"metadata": {},
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"outputs": [
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2021-04-17 10:50:40 +00:00
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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 720x576 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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"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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},
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"metadata": {
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2021-04-18 20:15:26 +00:00
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{
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"text/plain": [
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"<Figure size 1584x576 with 1 Axes>"
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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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2021-04-17 09:37:58 +00:00
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{
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"data": {
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"<Figure size 720x576 with 1 Axes>"
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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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|
]
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|
},
|
|
|
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|
"metadata": {
|
|
|
|
|
"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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"data": {
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"image/png": "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
|
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|
"text/plain": [
|
|
|
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
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|
|
]
|
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|
|
|
},
|
|
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|
"metadata": {
|
|
|
|
|
"needs_background": "light"
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|
},
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|
"output_type": "display_data"
|
|
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|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2021-04-17 10:50:40 +00:00
|
|
|
"for cluster in \"abcdefgh\":\n",
|
2021-04-17 09:37:58 +00:00
|
|
|
" # Processing figure 7a\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" dfa = pd.read_csv(glob.glob(DIR + cluster + \"_priority/part-*\")[0], header=None,\n",
|
|
|
|
|
" names=[\"term\", \"priority\", \"count\"])\n",
|
|
|
|
|
" dfa = dfa.sort_values([\"term\", \"priority\"])\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" categorical_plot(dfa, \"priority\", \"Figure 7a: Priorities w.r.t. event rate (cluster \" + cluster + \")\")\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" # Processing figure 7b\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" dfb = pd.read_csv(glob.glob(DIR + cluster + \"_exectime/part-*\")[0], header=None,\n",
|
|
|
|
|
" names=[\"term\", \"ignore\", \"exectime\", \"count\"])\n",
|
|
|
|
|
" del dfb[\"ignore\"]\n",
|
|
|
|
|
" dfb = dfb.sort_values([\"term\", \"exectime\"])\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" xs = [\"<1\", \"1-2\", \"2-4\", \"4-10\", \"10-60\", \"60-1d\", \">=1d\"]\n",
|
|
|
|
|
" categorical_plot(dfb, \"exectime\", \"Figure 7b: Execution times (mins) w.r.t. event rate (cluster \" + cluster + \")\", xs)\n",
|
2021-04-18 20:15:26 +00:00
|
|
|
"\n",
|
|
|
|
|
" # Processing figure 7c\n",
|
2021-04-17 09:37:58 +00:00
|
|
|
" \n",
|
2021-04-18 20:15:26 +00:00
|
|
|
" if cluster in \"a\":\n",
|
|
|
|
|
" dfc = pd.read_csv(DIR + cluster + \"_figure7c.csv\", header=0)\n",
|
|
|
|
|
" dfc = dfc.sort_values([\"term\", \"n_exec\"])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" categorical_plot(dfc, \"n_exec\", \"Figure 7c: Priorities w.r.t. machine concurrency (cluster \" + cluster + \")\")\n",
|
2021-04-17 09:37:58 +00:00
|
|
|
" "
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"id": "a9a09b9c-4281-41f4-a069-eb954e0a018f",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2021-04-18 20:15:26 +00:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"id": "717dc811-1a7d-468c-9038-e405914b20aa",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2021-04-17 09:37:58 +00:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"display_name": "venv",
|
|
|
|
|
"language": "python",
|
|
|
|
|
"name": "venv"
|
|
|
|
|
},
|
|
|
|
|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
"version": 3
|
|
|
|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
|
|
|
|
"version": "3.9.3"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 5
|
|
|
|
|
}
|
