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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2021-05-20 10:33:36 +00:00
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"execution_count": 43,
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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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2021-04-13 14:13:15 +00:00
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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2021-05-20 10:33:36 +00:00
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"execution_count": 44,
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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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2021-05-12 13:24:57 +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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2021-05-20 10:33:36 +00:00
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"execution_count": 156,
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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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2021-05-20 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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" colors = plt.cm.Spectral([0.2, 0.4, 0.8, 0.9])\n",
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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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" print(a)\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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" (type_of_data, cluster))\n",
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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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2021-05-20 10:33:36 +00:00
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"execution_count": 157,
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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": [
|
2021-05-20 10:33:36 +00:00
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"<matplotlib.table.Table object at 0x7fd57ebc5820>\n"
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]
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAARQAAAD7CAYAAACrFWuaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqlklEQVR4nO3dd3xUxfr48c+TQgkl9F41gKRACAHhUsSLkSIiVYkoINWfCl9FQZALKKJYQBFF0YsKiBKkaOCKgCCKopRQBIRIkUiHgKEGQkjm98eerAnpyUk2gef9eu0r2XPmnJ2zkzw7MztzRowxKKWUHdxcnQGl1M1DA4pSyjYaUJRSttGAopSyjQYUpZRtNKAopWyjASUPiciLIjLf1fmwi4gsEJFu+fyaP4jI4Px8zYJGRCqLyF4RKerqvGRGA0ouicjDIhIhIpdE5ISIfCsirW08fx0RMSLiYdc5k527pJXvb7OQthHQGAi3Ox8qY8aYU8A6YKir85IZDSi5ICIjgenAq0BloBbwPvCAC7OVQiaBqCcQB4SISJVMTjUM+NwU4JGQeRF0C1AePsdRBgWaBpQcEhFvYBLwpDFmqTHmsjEm3hiz3BgzKo307UTk6A3bokTkHuv35lZN54KInBKRt6xk662f56zaREsr/UCrGhwjIqtEpHay8xoReVJE9gP7M7iM/sAsYCfwSCaX3An4MdlrpGjO3ViTEpEBIvKniFwUkUMi0jdZ2ozyHiIikSJyXkTeAyS9DFl5WCwi80XkAjBARLxF5GOrtnhMRCaLiLuV3kdEfrTOfUZEFiY7179EZIu1b4uI/CvZPmc53Xjtya57kIgcBr63tg+xrvGiiOwRkSBrezURWSIi0db7MiLZedP7GwDYBNyW/L0qkIwx+sjBA+gIXAc8MkjzIjDf+r0dcPSG/VHAPdbvvwKPWr+XBFpYv9cBTPLXwVEDOgA0BDyA/wC/JNtvgO+AckDxdPJWG0gEfIFngZ0ZXEcJ65wV07q2G/Nppb8ANLD2VQX8Mss7UAG4CPQCPIFnrPd4cAbvbzzQDceHY3HgK+BDKw+VgM3AMCv9AmCclbYY0NraXg6IAR618hRqPS9/YzmlUa5J1z3Pes3iQG/gGNAMR0D0sd5vN2ArMAEoAtwG/Al0yOhvINnr7gS6uvpvP6OH1lByrjxwxhhz3abzxQM+IlLBGHPJGLMxg7SPA1OMMXut138VCLzh02uKMeZvY8yVdM7xKI4gsgcIA/xEpEk6actYPy9m+WocwcpfRIobY04YY37PQt47A78bYxYbY+JxNCdPZvI6vxpjvjbGJAKlrXM8bRw1xtPA20AfK208jn/sasaYq8aYn63t9wH7jTGfGWOuG2MWAJHA/dm43het17wCDAbeMMZsMQ4HjDF/4QgwFY0xk4wx14wxfwL/vSF/Gf0NXOSfsiiQNKDk3Fmggo1t5kFAfSDSqnJ3ySBtbeAdETknIueAv3F8ElZPluZIJq/XD0e7HGPMMRzNmf7ppD1n/SyVyTmxzncZeAhH8DghIt+IyB1ZyHu15Pk2jo/lzK4j+f7aOGo2J5Kd/0McNRWA0dZrbRaR30VkoLW9GvDXDef9i5TvZ2aS56MmcDCNNLWBakl5s/L3Ao7+N8j8b6AU/5RFgeTyTqxC7FccHZrdgMVZSH8Z8Ep6YrXrKyY9N8bsB0JFxA3oASwWkfI4qtM3OgK8Yoz5PIPXS7fz1OofqAeMFZFnrc2lcNQonrux1mWMuSwiB3H8sUendT1AlRuOWQWsEpHiwGQcn8RtMsq7iNTD8c+Y9FySP8/CdR7BUSYV0qo5GmNOAkOsc7cG1ojIeuA4jn/25GoBK7Nyrenk4/Y00hwBDhlj6qV5Ien8DVjvvweOptNvaR1bUGgNJYeMMedxtIVnikg3EfESEU8R6SQib6RxyD6gmIjcJyKeOPoOnOMKROQREaloVd3PWZsTcfwDJ+JobyeZhSMY+FnHeotI72xkvz+OPhZfINB6+ONo/3dK55gVwF3Jnu8A2opILXF0UI9Ndi2VReQBESmB4x/8knUNmeX9GxxNrx7WP9AI0v7nTZMx5gSwGpgmIqVFxE1EbheRu6zX6i0iNazkMTiCQKJ1bfXFMQTAQ0Qest6b/yW71j5W+Qbj6OPJyGzgORFpKg4+VpNuM3BRRJ4XkeIi4i4i/iLSzMpfen8DAM2BKKvpVHC5uhOnsD+AvkAEjk+xkzj+Kf5lbui8s54PAE4Ap4HnSNkpO9/afgn4HeiW7LhJOALLOf7prH0U2IWj8/MI8Emy9AbwSSe/xXD8M92fxr73gcXpHOdv5UuSbZtp5ekAjk/+pE7ZqjiaUOet/T8AvsmOyyjvHXEE3/PAe9Z5MuqUnX/DNm/gA+CodY7tQB9r3xs4Oksv4WiSDE12XGscHabnrZ+tk+27Dce3LJes8p1B6k5Zjxvy8Tjwh3XMbqCJtb0ajs7hk1Y5bMzi38BMYISr/94ze4iVWaUyJSJfAF8aY752dV5uJSJSCUdgbWKMuerq/GREA4pSyjbah6KUso0GFKWUbTSgKKVsowFFKWWbQj2wrUKFCqZOnTquzoZSt5ytW7eeMcZUvHF7oQ4oderUISIiwtXZUOqWIyJpDrDTJo9SyjZ5FlBE5BMROS0iu5NtKyci34nIfutnWWu7iMgMETkgIjuT7h2hlCpc8rKGMgfHMOrkxgBrjWNy1FrrOTjmj9SzHkNxDJ1WShUyeRZQjDHrcUxNT+4BYK71+1wcM3WTts8zDhuBMiJSNa/yppTKG/ndh1LZOGaEgmNyVNJ9IKqT8n4SR8nevSiUUgWAy77lMcYYEcn2RCIRGYp19+9atWpl6Zjg19Zm92VULkWMaZ+r47XMXCO35ZbfNZRTSU0Z6+dpa/sxUt5Ip4a1LRVjzEfGmGBjTHDFiqm+BldKuVB+B5Rl/HObwf78s8bLMqCf9W1PC+B8sqaRUqqQyLMmj4gswHGn9wriWD5iIvAa8KWIDMJxz84HreQrcNxc+AAQCzyWV/lSSuWdPAsoxpjQdHalaqQZx01ZnsyrvCil8oeOlFVK2UYDilLKNhpQlFK20YCilLKNBhSllG00oCilbKMBRSllGw0oSinbaEBRStlGA4pSyjYaUJRSttGAopSyjQYUpZRtNKAopWyjAUUpZRsNKEop22hAUUrZRgOKUso2GlCUUrZxSUARkWdE5HcR2S0iC0SkmIjUFZFN1vrGC0WkiCvyppTKuXwPKCJSHRgBBBtj/AF3oA/wOvC2McYHiAEG5XfelFK546omjwdQXEQ8AC/gBPBvYLG1P/m6x0qpQiLfA4ox5hgwFTiMI5CcB7YC54wx161kuraxUoWQK5o8ZYEHgLpANaAE0DEbxw8VkQgRiYiOjs6jXCqlcsIVTZ57gEPGmGhjTDywFGgFlLGaQKBrGytVKLkioBwGWoiIl4gIjpUE9wDrgF5WmuTrHiulCglX9KFswtH5ug3YZeXhI+B5YKSIHADKAx/nd96UUrmTZ2sbZ8QYMxHH4unJ/Qk0d0F2lFI20ZGySinbaEBRStlGA4pSyjYaUJRSttGAopSyjQYUpZRtNKAopWyjAUUpZRsNKEop22hAUUrZRgOKUso2GlCUUrbRgKKUso0GFKWUbTSgKKVsowFFKWUbDShKKdtoQFFK2UYDilLKNq5a27iMiCwWkUgR2SsiLUWknIh8JyL7rZ9lXZE3pVTOuaqG8g6w0hhzB9AY2AuMAdYaY+oBa63nSqlCxBUrB3oDbbGWyTDGXDPGnMOxmuBcK5mubaxUIeSKGkpdIBr4VES2i8hsESkBVDbGnLDSnAQquyBvSqlccEVA8QCCgA+MMU2Ay9zQvDHGGMCkdbCubaxUweWKgHIUOGqtIAiOVQSDgFMiUhXA+nk6rYN1bWOlCi5XLEV6EjgiIg2sTUlrGy/DsaYx6NrGShVKLlmKFBgOfC4iRXAsQfoYjuD2pYgMAv4CHnRR3pRSOeSqtY13AMFp7Gqfz1lRStlIR8oqpWyjAUUpZRsNKEop22hAUUrZRgOKUso2GlCUUrbRgKKUso0GFKWUbTS
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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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"<matplotlib.table.Table object at 0x7fd57f9d65e0>\n"
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]
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},
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{
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"data": {
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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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2021-04-13 14:13:15 +00:00
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2021-05-20 10:33:36 +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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2021-04-13 14:13:15 +00:00
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{
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2021-05-20 10:33:36 +00:00
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2021-05-20 10:33:36 +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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2021-04-13 14:13:15 +00:00
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2021-05-20 10:33:36 +00:00
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2021-05-20 10:33:36 +00:00
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2021-05-20 10:33:36 +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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2021-05-20 10:33:36 +00:00
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2021-05-20 10:33:36 +00:00
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2021-05-20 10:33:36 +00:00
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2021-05-20 10:33:36 +00:00
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2021-05-20 10:33:36 +00:00
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2021-05-20 10:33:36 +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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"image/png": "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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": [
|
2021-05-20 10:33:36 +00:00
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"\n",
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"dft = None\n",
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2021-04-13 14:13:15 +00:00
|
|
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"for cluster in \"abcd\":\n",
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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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" names=[\"term\", \"cpu\", \"ram\"])\n",
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2021-05-20 10:33:36 +00:00
|
|
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" plot_df(df, cluster, \"used\")\n",
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|
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" if dft is None:\n",
|
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|
|
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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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|
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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",
|
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|
|
|
" 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",
|
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|
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" 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
|
|
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" dfd['term'].append(term)\n",
|
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" dfd['cpu'].append(float(data[\"cpu-\" + (\"None\" if term == -1 else str(term))]))\n",
|
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|
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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",
|
|
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|
|
" \n",
|
|
|
|
|
"dft = dft.groupby(\"term\").sum().reset_index()\n",
|
|
|
|
|
"plot_df(dft, \"all\", \"requested\")\n",
|
|
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|
|
"\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",
|
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|
|
|
"execution_count": null,
|
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|
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|
"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,
|
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"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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|
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|
"execution_count": null,
|
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|
"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,
|
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|
"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,
|
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|
"metadata": {},
|
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|
"outputs": [],
|
|
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|
|
"source": []
|
2021-04-13 14:13:15 +00:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
2021-05-12 12:15:49 +00:00
|
|
|
"display_name": "Python 3",
|
2021-04-13 14:13:15 +00:00
|
|
|
"language": "python",
|
2021-05-12 12:15:49 +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",
|
2021-05-12 12:15:49 +00:00
|
|
|
"version": "3.8.3"
|
2021-04-13 14:13:15 +00:00
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 5
|
|
|
|
|
}
|
