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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2021-05-20 15:30:06 +00:00
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"execution_count": 57,
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2021-04-17 09:37:58 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import sys\n",
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"import glob\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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2021-05-20 15:30:06 +00:00
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"execution_count": 58,
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2021-04-17 09:37:58 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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2021-05-12 12:15:49 +00:00
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"DIR = \"/Users/maggicl/Git/bachelorThesis/figure_7/\""
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2021-04-17 09:37:58 +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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2021-05-20 15:30:06 +00:00
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"execution_count": 69,
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2021-04-17 09:37:58 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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2021-05-20 15:30:06 +00:00
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"def tier(p):\n",
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" if p <= 99:\n",
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" return \"Free\"\n",
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" elif p <= 115:\n",
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" return \"Best effort batch\"\n",
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" elif p <= 119:\n",
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" return \"Mid\"\n",
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" elif p <= 359:\n",
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" return \"Production\"\n",
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" else:\n",
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" return \"Monitoring\"\n",
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"\n",
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2021-04-17 09:37:58 +00:00
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"def categorical_plot(df, key, title, sorted_xs = None):\n",
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2021-05-20 15:30:06 +00:00
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" \n",
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2021-04-17 09:37:58 +00:00
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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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2021-05-20 15:30:06 +00:00
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" for t in [4,5,6,7]:\n",
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2021-05-12 12:15:49 +00:00
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" xs = xs.union(np.array(df[df[\"term\"] == t][key]).reshape(-1))\n",
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2021-04-17 09:37:58 +00:00
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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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2021-05-16 10:22:27 +00:00
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" plt.figure(figsize=(10,8))\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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2021-05-20 15:30:06 +00:00
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" for t in [4,5,6,7]:\n",
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2021-04-17 09:37:58 +00:00
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" ys = []\n",
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2021-05-20 15:30:06 +00:00
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" if xs[0] == \"-1\":\n",
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" del xs[0]\n",
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2021-04-17 09:37:58 +00:00
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" for x in xs:\n",
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2021-05-20 15:30:06 +00:00
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" partsum = df[(df[\"term\"] != -1) & (df[key] == x)][\"count\"].sum().astype(int)\n",
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" y = df[(df[\"term\"] == t) & (df[key] == x)][\"count\"]\n",
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" ys.append(100 * ((y.values.astype(int)[0] / partsum if partsum > 0 else y.values.astype(int)[0]) if y.any()\n",
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" else 0))\n",
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2021-04-17 09:37:58 +00:00
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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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2021-05-20 15:30:06 +00:00
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" plt.savefig('../report/figures/figure_7/%s-%s.pgf' % \n",
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" (key, cluster))\n",
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2021-04-17 09:37:58 +00:00
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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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2021-05-20 15:30:06 +00:00
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"execution_count": 70,
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2021-04-17 09:37:58 +00:00
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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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2021-05-20 15:30:06 +00:00
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"image/png": "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2021-04-17 10:50:40 +00:00
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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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2021-05-20 15:30:06 +00:00
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2021-04-17 10:50:40 +00:00
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2021-04-18 20:15:26 +00:00
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2021-05-20 15:30:06 +00:00
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2021-04-18 20:15:26 +00:00
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"text/plain": [
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2021-05-16 10:22:27 +00:00
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"<Figure size 720x576 with 1 Axes>"
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2021-04-18 20:15:26 +00:00
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]
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},
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"output_type": "display_data"
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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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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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},
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2021-04-20 07:15:19 +00:00
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{
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"data": {
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2021-05-20 15:30:06 +00:00
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2021-04-20 07:15:19 +00:00
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"text/plain": [
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2021-05-16 10:22:27 +00:00
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"<Figure size 720x576 with 1 Axes>"
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2021-04-20 07:15:19 +00:00
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]
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},
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2021-04-17 09:37:58 +00:00
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"data": {
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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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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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"output_type": "display_data"
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},
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{
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"data": {
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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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2021-04-20 07:15:19 +00:00
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2021-05-20 15:30:06 +00:00
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2021-04-20 07:15:19 +00:00
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"text/plain": [
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2021-05-16 10:22:27 +00:00
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"<Figure size 720x576 with 1 Axes>"
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2021-04-20 07:15:19 +00:00
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]
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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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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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},
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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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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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},
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2021-04-20 07:15:19 +00:00
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{
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"data": {
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2021-05-20 15:30:06 +00:00
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2021-04-20 07:15:19 +00:00
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"text/plain": [
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2021-05-16 10:22:27 +00:00
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"<Figure size 720x576 with 1 Axes>"
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2021-04-20 07:15:19 +00:00
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]
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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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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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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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"output_type": "display_data"
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},
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{
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"data": {
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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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2021-04-20 07:15:19 +00:00
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2021-05-20 15:30:06 +00:00
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2021-04-20 07:15:19 +00:00
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"text/plain": [
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2021-05-16 10:22:27 +00:00
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"<Figure size 720x576 with 1 Axes>"
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2021-04-20 07:15:19 +00:00
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]
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},
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2021-04-17 09:37:58 +00:00
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{
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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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"<Figure size 720x576 with 1 Axes>"
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},
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2021-05-20 15:30:06 +00:00
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"image/png": "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2021-04-17 09:37:58 +00:00
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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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},
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"output_type": "display_data"
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},
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2021-04-20 07:15:19 +00:00
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{
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"data": {
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2021-05-20 15:30:06 +00:00
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2021-04-20 07:15:19 +00:00
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"text/plain": [
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2021-05-16 10:22:27 +00:00
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"<Figure size 720x576 with 1 Axes>"
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2021-04-20 07:15:19 +00:00
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]
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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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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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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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},
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{
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"data": {
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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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2021-04-20 07:15:19 +00:00
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2021-05-20 15:30:06 +00:00
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2021-04-20 07:15:19 +00:00
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2021-05-16 10:22:27 +00:00
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"<Figure size 720x576 with 1 Axes>"
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2021-04-20 07:15:19 +00:00
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]
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2021-04-17 09:37:58 +00:00
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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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2021-05-20 15:30:06 +00:00
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2021-04-17 09:37:58 +00:00
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"<Figure size 720x576 with 1 Axes>"
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},
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2021-04-20 07:15:19 +00:00
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},
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{
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"data": {
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2021-05-20 15:30:06 +00:00
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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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|
|
},
|
|
|
|
|
"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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
|
2021-04-20 07:15:19 +00:00
|
|
|
"text/plain": [
|
2021-05-16 10:22:27 +00:00
|
|
|
"<Figure size 720x576 with 1 Axes>"
|
2021-04-20 07:15:19 +00:00
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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"
|
2021-04-17 09:37:58 +00:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2021-05-20 15:30:06 +00:00
|
|
|
"dfatot = None\n",
|
|
|
|
|
"dfbtot = None\n",
|
|
|
|
|
"dfctot = None\n",
|
|
|
|
|
"\n",
|
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",
|
2021-05-20 15:30:06 +00:00
|
|
|
" dfa[\"tier\"] = dfa[\"priority\"].apply(tier)\n",
|
|
|
|
|
" dfa = dfa[dfa[\"priority\"].ge(0)]\n",
|
|
|
|
|
" dfa = dfa.groupby([\"tier\", \"term\"]).sum().reset_index()\n",
|
|
|
|
|
" dfa = dfa[dfa[\"term\"].isin(range(4,8))]\n",
|
|
|
|
|
" del dfa[\"priority\"]\n",
|
|
|
|
|
" if dfatot is None:\n",
|
|
|
|
|
" dfatot = dfa\n",
|
|
|
|
|
" else:\n",
|
|
|
|
|
" dfatot.append(dfa)\n",
|
2021-04-17 09:37:58 +00:00
|
|
|
" \n",
|
2021-05-20 15:30:06 +00:00
|
|
|
" xs = [\"Free\", \"Best effort batch\", \"Mid\", \"Production\", \"Monitoring\"]\n",
|
|
|
|
|
" categorical_plot(dfa, \"tier\", \"Figure 7a: Priorities w.r.t. event rate (cluster \" + cluster + \")\", xs)\n",
|
2021-04-17 09:37:58 +00:00
|
|
|
" \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",
|
2021-05-20 15:30:06 +00:00
|
|
|
" if dfbtot is None:\n",
|
|
|
|
|
" dfbtot = dfb\n",
|
|
|
|
|
" else:\n",
|
|
|
|
|
" dfbtot.append(dfb)\n",
|
2021-04-17 09:37:58 +00:00
|
|
|
" \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",
|
2021-05-12 12:15:49 +00:00
|
|
|
" bins = [\"0\", \"1\", \"2-3\", \"4-5\", \"6-7\", \"8-9\", \"10-11\", \"12-13\", \">=14\"]\n",
|
|
|
|
|
" bin_max = [1, 3, 5, 7, 9, 11, 13]\n",
|
|
|
|
|
" def map_bins(value):\n",
|
|
|
|
|
" for i, m in enumerate(bin_max):\n",
|
|
|
|
|
" if value < m:\n",
|
|
|
|
|
" return bins[i-1]\n",
|
|
|
|
|
" return bins[-1]\n",
|
|
|
|
|
" \n",
|
2021-04-20 07:15:19 +00:00
|
|
|
" dfc = pd.read_csv(DIR + cluster + \"_figure7c.csv\", header=0)\n",
|
|
|
|
|
" dfc = dfc.sort_values([\"term\", \"n_exec\"])\n",
|
2021-05-12 12:15:49 +00:00
|
|
|
" dfc[\"n_exec\"] = dfc[\"n_exec\"].map(map_bins)\n",
|
|
|
|
|
" dfc = dfc.groupby([\"term\", \"n_exec\"]).sum().reset_index()\n",
|
2021-04-20 07:15:19 +00:00
|
|
|
" categorical_plot(dfc, \"n_exec\", \"Figure 7c: Priorities w.r.t. machine concurrency (cluster \" + cluster + \")\")\n",
|
2021-05-20 15:30:06 +00:00
|
|
|
" if dfctot is None:\n",
|
|
|
|
|
" dfctot = dfc\n",
|
|
|
|
|
" else:\n",
|
|
|
|
|
" dfctot.append(dfc)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"cluster = \"all\"\n",
|
|
|
|
|
" \n",
|
|
|
|
|
"dfatot = dfatot.groupby([\"tier\", \"term\"]).sum().reset_index()\n",
|
|
|
|
|
"dfbtot = dfbtot.groupby([\"term\", \"exectime\"]).sum().reset_index()\n",
|
|
|
|
|
"dfctot = dfctot.groupby([\"term\", \"n_exec\"]).sum().reset_index()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"xs = [\"Free\", \"Best effort batch\", \"Mid\", \"Production\", \"Monitoring\"]\n",
|
|
|
|
|
"categorical_plot(dfatot, \"tier\", \"2019 data\", xs)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"xs = [\"<1\", \"1-2\", \"2-4\", \"4-10\", \"10-60\", \"60-1d\", \">=1d\"]\n",
|
|
|
|
|
"categorical_plot(dfbtot, \"exectime\", \"2019 data\", xs)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"categorical_plot(dfctot, \"n_exec\", \"2019 data\")\n",
|
|
|
|
|
"\n"
|
2021-04-17 09:37:58 +00:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2021-04-18 20:15:26 +00:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
2021-05-12 12:15:49 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
2021-04-18 20:15:26 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2021-04-17 09:37:58 +00:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
2021-05-12 12:15:49 +00:00
|
|
|
"display_name": "Python 3",
|
2021-04-17 09:37:58 +00:00
|
|
|
"language": "python",
|
2021-05-12 12:15:49 +00:00
|
|
|
"name": "python3"
|
2021-04-17 09:37:58 +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",
|
2021-05-12 12:15:49 +00:00
|
|
|
"version": "3.8.3"
|
2021-04-17 09:37:58 +00:00
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 5
|
|
|
|
|
}
|
