2021-04-27 14:37:09 +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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2021-05-16 10:22:27 +00:00
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"execution_count": 5,
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2021-04-27 14:37:09 +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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2021-05-16 10:22:27 +00:00
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"DIR = \"/Users/maggicl/Git/bachelorThesis/figure_9/\""
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]
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},
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{
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"cell_type": "code",
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2021-05-16 10:22:27 +00:00
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"execution_count": 7,
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2021-04-27 14:37:09 +00:00
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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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" plt.figure(figsize=(11,8))\n",
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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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" 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((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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2021-05-16 10:22:27 +00:00
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"execution_count": 8,
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2021-04-27 14:37:09 +00:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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2021-05-16 10:22:27 +00:00
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"image/png": "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2021-04-27 14:37:09 +00:00
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"text/plain": [
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"<Figure size 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +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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"data": {
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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"<Figure size 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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"<Figure size 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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},
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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"<Figure size 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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},
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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"<Figure size 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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"<Figure size 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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"data": {
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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"<Figure size 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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"<Figure size 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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"<Figure size 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-16 10:22:27 +00:00
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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-16 10:22:27 +00:00
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"image/png": "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2021-04-27 14:37:09 +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 792x576 with 1 Axes>"
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2021-04-27 14:37:09 +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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|
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"def to_9a_bin(col):\n",
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" # 1 [2,10] [11,50] [51,100] 101+\n",
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" if col <= 0:\n",
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" return \"None known\"\n",
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" elif col == 1:\n",
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" return \"1\"\n",
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" elif col <= 10:\n",
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|
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" return \"2-10\"\n",
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" elif col <= 50:\n",
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" return \"11-50\"\n",
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" elif col <= 100:\n",
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" return \"51-100\"\n",
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" else:\n",
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|
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" return \"101+\"\n",
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" \n",
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|
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"def to_9c_bin(col):\n",
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|
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" # 1 [2,10] [11,50] [51,100] 101+\n",
|
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|
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" if col <= 0:\n",
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|
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" return \"Unknown\"\n",
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|
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" elif col < 0.5:\n",
|
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|
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" return \"(0,0.5)\"\n",
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|
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" elif col < 0.9:\n",
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|
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" return \"[0.5,0.9)\"\n",
|
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|
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" elif col < 1.1:\n",
|
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|
|
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" return \"[0.9,1.1)\"\n",
|
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|
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" elif col < 1.5:\n",
|
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|
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" return \"[1.1,1.5)\"\n",
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" else:\n",
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|
|
" return \">=1.5\"\n",
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"\n",
|
|
|
|
|
"for cluster in \"abcdefgh\":\n",
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|
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" dfa = pd.read_csv(DIR + cluster + \"_term_task_count.csv\") \\\n",
|
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|
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" .sort_values([\"term\", \"task_count\"])\n",
|
|
|
|
|
" dfa[\"task_count\"] = dfa[\"task_count\"].apply(to_9a_bin)\n",
|
|
|
|
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" dfa = dfa.groupby([\"term\", \"task_count\"]).sum().reset_index()\n",
|
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" \n",
|
|
|
|
|
" categorical_plot(dfa, \"task_count\", \"# of tasks w.r.t. job rate (cluster \" + \n",
|
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" cluster + \")\", [to_9a_bin(x) for x in [-1,1,10,50,100,101]])\n",
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" \n",
|
|
|
|
|
" # column should be called machine_count here, typo in analysis scripts\n",
|
|
|
|
|
" dfb = pd.read_csv(DIR + cluster + \"_priority_exectime/part-00000\", header=None,\n",
|
|
|
|
|
" names=[\"term\", \"priority\", \"interval\", \"count\"]).sort_values([\"term\", \"interval\"])\n",
|
|
|
|
|
" del dfb[\"priority\"]\n",
|
|
|
|
|
" dfb = dfb.groupby([\"term\", \"interval\"]).sum().reset_index()\n",
|
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" \n",
|
|
|
|
|
" xs = [\"<1\", \"1-2\", \"2-4\", \"4-10\", \"10-60\", \"60-1d\", \">=1d\"]\n",
|
|
|
|
|
" categorical_plot(dfb, \"interval\", \"Job execution time (in mins) w.r.t. job rate (cluster \" + \n",
|
|
|
|
|
" cluster + \")\", xs)\n",
|
|
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|
" \n",
|
|
|
|
|
" dfc = pd.read_csv(DIR + cluster + \"_term_machine_locality.csv\") \\\n",
|
|
|
|
|
" .sort_values([\"term\", \"machine_locality\"])\n",
|
|
|
|
|
" dfc[\"machine_locality\"] = dfc[\"machine_locality\"].apply(to_9c_bin)\n",
|
|
|
|
|
" dfc = dfc.groupby([\"term\", \"machine_locality\"]).sum().reset_index()\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" categorical_plot(dfc, \"machine_locality\", \"Machine locality w.r.t. job rate (cluster \" + \n",
|
|
|
|
|
" cluster + \")\", [to_9c_bin(x) for x in [-1,0.49,0.99,1.09,1.49,1.5]])"
|
|
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|
|
]
|
|
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|
|
},
|
2021-05-16 10:22:27 +00:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
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|
|
"source": []
|
|
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|
|
},
|
2021-04-27 14:37:09 +00:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
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|
|
"source": []
|
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|
}
|
|
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|
],
|
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|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
2021-05-11 12:48:51 +00:00
|
|
|
"display_name": "Python 3",
|
2021-04-27 14:37:09 +00:00
|
|
|
"language": "python",
|
2021-05-11 12:48:51 +00:00
|
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|
"name": "python3"
|
2021-04-27 14:37:09 +00:00
|
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|
},
|
|
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|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
"version": 3
|
|
|
|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
2021-05-11 12:48:51 +00:00
|
|
|
"version": "3.8.3"
|
2021-04-27 14:37:09 +00:00
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 5
|
|
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|
|
}
|
