bachelorThesis/spatial_resource_waste/spatial_resource_waste.ipynb

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{
"cells": [
{
"cell_type": "code",
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"execution_count": 6,
"id": "fbd121e1",
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"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import sys\n",
"import glob\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib as mpl\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
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"execution_count": 7,
"id": "e6213e81",
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"metadata": {},
"outputs": [],
"source": [
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"DIR = \"/home/claudio/hdd/git/bachelorThesis/spatial_resource_waste/\""
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]
},
{
"cell_type": "code",
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"execution_count": 16,
"id": "0ceab723",
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"metadata": {},
"outputs": [],
"source": [
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"NAMES = {4: 'EVICT', 5: 'FAIL', 6: 'FINISH', 7: 'KILL'}\n",
"\n",
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"def plot_df(df, cluster, type_of_data):\n",
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" \n",
" df = df[df[\"term\"].isin(range(4,8))].sort_values(\"term\")\n",
" \n",
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" s = df.sum()\n",
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" #print(\"Cluster \" + cluster + \":\")\n",
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" df[\"cpu\"] = df[\"cpu\"] / s[\"cpu\"]\n",
" df[\"ram\"] = df[\"ram\"] / s[\"ram\"]\n",
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" \n",
" latex = df.copy()\n",
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" latex[\"Task termination\"] = latex[\"term\"].apply(lambda x: NAMES[x])\n",
" del latex[\"term\"]\n",
" latex[\"% CPU\"] = (latex[\"cpu\"] * 100).round(2).apply(lambda x: \"%2.02f\" % x) + \"%\"\n",
" del latex[\"cpu\"]\n",
" latex[\"% Memory\"] = (latex[\"ram\"] * 100).round(2).apply(lambda x: \"%2.02f\" % x) + \"%\"\n",
" del latex[\"ram\"]\n",
" #print(latex.to_latex(index=False)) \n",
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"\n",
" df2 = df.copy()\n",
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" df[\"kind\"] = \"CPU\"\n",
" df[\"percent\"] = df[\"cpu\"] * 100\n",
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" del df[\"cpu\"]\n",
" del df[\"ram\"]\n",
" \n",
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" df2[\"kind\"] = \"Memory\"\n",
" df2[\"percent\"] = df2[\"ram\"] * 100\n",
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" del df2[\"cpu\"]\n",
" del df2[\"ram\"]\n",
" \n",
" df = pd.concat([df, df2])\n",
" \n",
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" fig, ax = plt.subplots()\n",
" fig.set_size_inches(4, 4)\n",
" \n",
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" colors = plt.cm.Spectral([0.9, 0.3, 0.8, 0.1])\n",
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" \n",
" a = pd.plotting.table(ax, latex.set_index(\"Task termination\"), rowColours=colors,\n",
" bbox=[0,-0,1,0.3])\n",
" ax.set_ylim(bottom=-50, top=105)\n",
" print(a)\n",
" \n",
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" bottom = [0, 0]\n",
" lines = []\n",
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" for t in [4,5,6,7]:\n",
" dft = df[df.term==t]\n",
" line = plt.bar(x=dft[\"kind\"], bottom=bottom, height=dft[\"percent\"], \n",
" color=colors[t-4], width=0.85)\n",
" #ax.bar_label(line, label_type='center', fmt=\"%.02f%%\")\n",
" lines.append(line)\n",
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" bottom += df[df.term==t][\"percent\"].values\n",
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" #plt.legend(lines, [\"EVICT\", \"FAIL\", \"FINISH\", \"KILL\"],\n",
" # bbox_to_anchor=(1,1))\n",
" if cluster == \"2011\":\n",
" plt.title(\"2011 data (%s resources)\" % type_of_data)\n",
" elif cluster == \"all\":\n",
" plt.title(\"2019 data (%s resources)\" % type_of_data)\n",
" else:\n",
" plt.title(\"Cluster %s (%s resources)\" % (cluster.upper(), type_of_data))\n",
" \n",
" ax.set_xticks([])\n",
" ax.set_yticks([0,20,40,60,80,100])\n",
" fig.savefig('../report/figures/spatial_resource_waste/%s-%s.pgf' % \n",
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" (type_of_data, cluster), bbox_inches='tight')\n",
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" plt.show()\n",
" "
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]
},
{
"cell_type": "code",
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"execution_count": 17,
"id": "d8b99ab4",
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"a\n",
"<matplotlib.table.Table object at 0x7fc351b56880>\n"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"b\n",
"<matplotlib.table.Table object at 0x7fc351c3f370>\n"
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]
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAARQAAAD7CAYAAACrFWuaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAArQElEQVR4nO3deXgURfrA8e+bg0C4hXBGBEEx5CCEyKGACCKnCIgIsiByyU/XY3ERFUEXQfBaQAVxRUFECQqsAW9EEWG5IdzIfQSCBCRcgRCS+v3RnTFDDkjSySTwfp5nnqS7q3uq05N3qqqrusQYg1JKOcHL0xlQSl07NKAopRyjAUUp5RgNKEopx2hAUUo5RgOKUsoxGlDykYi8IiKzPJ0Pp4jIbBHpUsDvuUREBhbkexY2IlJZRLaLiJ+n83IlGlDySEQeFpG1InJWROJE5DsRaebg8WuKiBERn3w45ln79YeITBER32z2CQPqA9FO5UNdHWPMH8AvwGBP5+VKNKDkgYgMBSYCrwGVgRrAFOB+D2bLzRUCUTljTCkgFGgKPJFN2seAz0wh7gnpZNAthHn4DOsaFGoaUHJJRMoCo4EnjDHzjTHnjDHJxpiFxphhmaRvKSKxl63bLyL32L83sks6p+0Sw7/tZEvtnwl2aaKpnb6/XQw+KSI/iMhN6Y5rROQJEdkF7LrSuRhjjgGLgHrZJGsP/JruPdyqc5eXpESkn4jsFZEzIrJPRHqnS5td3tuIyA4ROSUi7wGSVYbsPMwVkVkichroJyJlReQju7R4WETGiIi3nb6OiPxqH/u4iMxJd6w7RGSNvW2NiNyRbpvrOl1+7unOe4CIHAR+ttcPss/xjIhsE5EIe301EZknIvH23+WpdMfN6jMAsAq4Of3fqlAyxugrFy+gHXAJ8MkmzSvALPv3lkDsZdv3A/fYv68A+ti/lwKa2L/XBEz698EqAe0GggAf4CXgf+m2G6wAcQNQIpN8uR0TqAZsBPpncR4l7fQBmZ3b5ce0058G6trbqgLBV8o7UBE4A3QHfIF/2H/jgdn8fZOBLlhfjiWA/wIf2HmoBKwGHrPTzwZG2GmLA83s9TcAJ4E+dp562csVLr9OmVzXtPOeab9nCeBB4DBwO1ZArAPcZL/vOmAUUAy4GdgLtM3uM5DufTcBnT392c/upSWU3KsAHDfGXHLoeMlAHRGpaIw5a4xZmU3aIcA4Y8x2+/1fA8Iv+/YaZ4z50xhzPpvjHBeRBKwP/zlgbhbpytk/z1zNidhSgRARKWGMiTPGbL2KvHcAthpj5hpjkrGqk0ev8D4rjDFfGWNSgTL2MZ4xVonxGDAB6GmnTcb6x65mjLlgjFlmr+8I7DLGfGqMuWSMmQ3sAO7Lwfm+Yr/neWAg8IYxZo2x7DbGHMAKMAHGmNHGmIvGmL3Ah5flL7vPwBn+uhaFkgaU3DsBVHSwzjwAuBXYYRe5O2WT9iZgkogk2AHhT6xvwurp0hy6ivesaIwpB/gDy4EfskiXYP8sfRXHxBhzDngIK3jEicg3InLbVeS9Wvp8G+tr+UrnkX77TVglm7h0x/8Aq6QC8Jz9XqtFZKuI9LfXVwMOXHbcA7j/Pa8kfT5uBPZkkuYmoFpa3uz8vYjV/gZX/gyU5q9rUSh5vBGrCFsBJGEVt7P6Zk/vHNY/LgB2vT4gbdkYswvoJSJeQDdgrohUwCpOX+4QMNYY81k273fVjafGmPMiMgP4p/3tePyy7edEZA/Whz0+s/MBqly2zw/ADyJSAhiD9U3cPLu8i8gtWP+MacuSfjmr7Kf7/RDWNamYWcnRGHMUGGQfuxnwk4gsBY5g/bOnVwP4/mrONYt81M4kzSFgnzHmlkxPJIvPgP3398GqOm3MbN/CQksouWSMOYVVF54sIl1ExF9EfEWkvYi8kckuO4HiItJRrNuzLwGufgUi8jcRCbCL7gn26lSsf+BUrPp2mqnACyISbO9bVkQezO25iNW/oQ9W9eJEFsm+Be5KtxwDtBCRGmI1UL+Q7niVReR+ESmJ9Q9+1j6HK+X9GyBYRLrZ/0BPkfk/b6aMMXHAj8DbIlJGRLxEpLaI3GW/14MiEmgnP4kVBFLtc7tVrC4APiLyEFYD9dfpzrWnfX0jsdp4sjMNKzg3FEsdu0q3GjgjIsNFpISIeItIiIjcbucvq88AQCNgv111Krw83YhT1F9Ab2At1rfYUax/ijvMZY139nI/IA44BvwT90bZWfb6s8BWoEu6/UZjBZYE/mqs7QNsxmr8PAR8nC69Aepkk+eadpqz9isB6w7O7dnsE2LnS9Ktm2zvuxvrmz+tUbaqfbxT9vYlQL10+2WX93ZYwfcU8J59nOwaZWddtq4s8D4Qax9jA9DT3vYGVnvRWawqyeB0+zXDajA9Zf9slm7bzVh3Wc7a1/cdMjbK+lyWjyHA7/Y+W4AG9vpqWI3DR7GC2sqr/AxMBp7y9Of9Si+xM6vUFYnI58AXxpivPJ2X64mIVMIKrA2MMRc8nZ/saEBRSjlG21CUUo7RgKKUcowGFKWUYzSgKKUcU6Q7tlWsWNHUrFnT09lQ6rqzbt2648aYgMvXF+mAUrNmTdauXevpbCh13RGRTDvYaZVHKeWYfAsoIvKxiBwTkS3p1t0gIotEZJf9s7y9XkTkHRHZLSKb0p4doZQqWvKzhDIDqxt1es8Di401OGqxvQzWw3tusV+DsbpOK6WKmHwLKMaYpVhD09O7H/jE/v0TrJG6aetnGstKoJyIVM2vvCml8kdBt6FUNtaIULAGR6U9B6I67s+TiCVnz6JQShUCHrvLY4wxIpLjgUQiMhj76d81atS4qn22tuiS07dReRS89Ks87a/XzDPyet0KuoTyR1pVxv55zF5/GPcH6QTa6zIwxvzHGBNpjIkMCMhwG1wp5UEFHVAWAI/Yvz/CX3O8LAD62nd7mgCn0lWNlFJFRL5VeURkNtaT3iuKNX3Ey8B44AsRGYD1zM4edvJvsR4uvBtIBB7Nr3wppfJPvgUUY0yvLDa1ziStIftJppRSRYD2lFVKOUYDilLKMRpQlFKO0YCilHKMBhSllGM0oCilHKMBRSnlGA0oSinHaEBRSjlGA4pSyjEaUJRSjtGAopRyjAYUpZRjNKAopRyjAUUp5RgNKEopx2hAUUo5RgOKUsoxGlCUUo7xSEARkX+IyFYR2SIis0WkuIjUEpFV9vzGc0SkmCfyppTKvQIPKCJSHXgKiDTGhADeQE/gdWCCMaYOcBIYUNB5U0rljaeqPD5ACRHxAfyBOKAVMNfenn7eY6VUEVHgAcUYcxh4CziIFUhOAeuABGPMJTuZzm2sVBHkiSpPeeB+oBZQDSgJtMvB/oNFZK2IrI2Pj8+nXCqlcsMTVZ57gH3GmHhjTDIwH7gTKGdXgUDnNlaqSPJEQDkINBERfxERrJkEtwG/AN3tNOnnPVZKFRGeaENZhdX4uh7YbOfhP8BwYKiI7AYqAB8VdN6UUnmTb3MbZ8cY8zLW5Onp7QUaeSA7SimHaE9ZpZRjNKAopRyjAUUp5RgNKEopx2hAUUo5RgOKUsoxGlCUUo7RgKKUcowGFKWUYzSgKKUcowFFKeUYDShKKcdoQFFKOUYDilLKMRpQlFKO0YCilHKMBhSllGM0oCilHKMBRSnlGE/NbVxOROaKyA4R2S4iTUXkBhFZJCK77J/lPZE3pVTueaqEMgn43hhzG1Af2A48Dyw2xtwCLLaXlVJFiCdmDiwLtMCeJsMYc9EYk4A1m+AndjKd21ipIsgTJZRaQDwwXUQ2iMg0ESkJVDbGxNlpjgKVPZA3pVQeeCKg+AARwPvGmAbAOS6r3hhjDGAy21nnNlaq8PJEQIkFYu0ZBMGaRTAC+ENEqgLYP49ltrPObaxU4eWJqUiPAodEpK69Km1u4wVYcxqDzm2sVJHkkalIgSeBz0SkGNYUpI9iBbcvRGQAcADo4aG8KaVyyVNzG8cAkZlsal3AWVFKOUh7yiqlHKMBRSnlGA0oSinHaEBRSjlGA4pSyjEaUJRSjtGAopRyjAYUpZRjNKAopRyjAUUp5RgNKEopx2hAUUo
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"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"c\n",
"<matplotlib.table.Table object at 0x7fc33e9b51c0>\n"
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]
},
{
"data": {
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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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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"d\n",
"<matplotlib.table.Table object at 0x7fc351a52940>\n"
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]
},
{
"data": {
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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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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc351991730>\n"
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]
},
{
"data": {
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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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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc351b66220>\n"
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]
},
{
"data": {
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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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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc3517cd6a0>\n"
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]
},
{
"data": {
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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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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc351924d30>\n"
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]
},
{
"data": {
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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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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc351a555e0>\n"
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]
},
{
"data": {
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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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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc3518d4850>\n"
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]
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAARQAAAD7CAYAAACrFWuaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAoiklEQVR4nO3deXwURfr48c+TkATicoMsEBAFBMxBCBFh5VLEILhcohwryBcQ/YniigIC4oFhwWsNrriouAKiEEEUEDkERYwrNyGcC+EQwg0mEQhHjvr9MZ1xQg5y9GQYed6v17wy013dU901eaaqpqtLjDEopZQdfDydAaXUH4cGFKWUbTSgKKVsowFFKWUbDShKKdtoQFFK2UYDylWIyMsiMtvT+bCDiDwmIjGezkdpEpEZIhLt6Xx4kogEiMhuEanu7vfSgAKISD8R2Sgi50TkmIgsFZHWNu6/nogYESnjhn2eu+LRO5/0/sALwBt25aE0WMfYwNP58GbGmEvAf4Dn3f1e131AEZERQAzwD6AGUBd4D+jmwWzlcJVAVMkY8yeXR2w+6boBu40xR4rxHteFa+EcuDEPnwGPiEiAm/YPXOcBRUQqAhOAYcaYBcaY88aYdGPMYmPMyDzStxeRpCuWHRSRe6znLayazm8ickJE/mklW2P9TbFqEa2s9INEZJeIJIvIchG5yWW/RkSGicheYK8Nh3sf8IPL/rNrOINF5BDwXSHy1NGqOqeKyLsi8oOIDLHW5WgaXlkrE5GKIvKRVQM8IiLRIuJrrWtg7StVRE6LSKy1PPu8bXWtfYnI/SISLyIpIvJfEQlzed9mIrJZRM5a+ymb3wkRkYEi8pOIvC0iZ4CXrebBmyJyyCrDaSJSzkpfTUS+tt73VxH5UUR8rHVNRGS1tW6HiHR1eZ/V2efJ5X3jXF7nKmsR6WYd428isk9EOhX3PAIYY5KAZKBl/h+RkruuAwrQCscH7kub9jcFmGKMqQDUBz63lre1/mbXJn4WkW7AWKAnUB34EZhzxf66A3cAt9mQt1Dgf3ksbwc0AaIKypOIVAMW4Gg2VQP2AXcW4f1nABlAA6AZcC+Q/U/2KrACqAwEAf8CMMZkn7em2bUvEWmGo/r+GFAVeB9YZAUCf+Ar4BOgCjAPeOAq+boD2I+jdjoRmAzcCoRbea0NvGilfRZIwnFuauA4V0ZE/IDF1jHcCDwFfCoijYpwfrpbeblNRFoAs4CRQCUcn5+DVroZFPE8utgFNC1Cnorseg8oVYHTxpgMm/aXDjQQkWrGmHPGmLUFpH0cmGSM2WW9/z+AcNcagbX+V2PMhQL2c9r6Vsx+NMknXSXgbB7LX7ZqZheukqfOwA5jzHxjTDqOZuLxAvLlJCI1rO3/br3XSeBtoI+VJB24CahljLlojInLZ1cAQ4H3jTHrjDGZxpiZwCUc37wtAT8gxqppzgc2XCV7R40x/7KO96K1/2es837WOgeu+awJ3GTt/0fjGAzXEvgTMNkYc9kY8x3wNdC3MOfH4lrWg4H/GGO+NcZkGWOOGGN223Aez+L4HLjN9R5QzgDVxL5262Ac3267RWSDiNxfQNqbgCnZgQD4FRAc34jZDhfiPasZYyq5PHblky4ZKJ/Hctf3KChPtVzTWv9Ihclf9n79gGMu+34fx7c5wCjrfdZbzYVBV9nXs65BFKhj5a8WcMTkHPH6y1Xy5noM1YFAYJPLvpdZy8HRoZ0IrBCR/SKS3clZCzhsjMm64n1dy/JqXPNRB0cN8EolPY/lgZQi5KnIPN4J5WE/4/h26w7ML0T68zg+cABYbVfnT3HGmL1AX6td3ROYLyJVgbyGdB8GJhpjPi3g/ewcCp6AI9gV9B755klEGuL4oGe/FtfXXHFugD9fsd9LOIJfrtqgMeY48Ki139bAShFZY4xJzCO/2XmcmEce2wG1RURcgkpd8v7ndL69y/PTwAUgOK/Oa6vG8iyOgBYCfCciG4CjQB0R8XEJKnWBPdbzgs5NXvk4jKPJfKWSnscmwFt57Nc213UNxRiTiqN9PFVEuotIoIj4ich9IvJ6HpvsAcqKSBer3fwC4Ow1F5GHRaS69aFKsRZnAaesv7e47GsaMEZEgq1tK4rIg3Yfo4tvcPSXFKSgPC0BgkWkp1WjG07Of4x4oK2I1BVHZ/eY7BXGmGM42vZviUgFEfERkfpWAEBEHhSRICt5Mo5/rux/zBPkPG8fAo+LyB3icINVHuVxfEFkAMOtcuwJtCjsCbLK7UPgbRG50cpbbRGJsp7fb3V8CpAKZFr5XAekAaOs920P/BWY63JuelqfrwY4arIF+Qj4PxHpYJ2r2iLSuCTnUURq4+hXKqgZXnLGmOv+AfwN2Ijjm+Q4jn+ev1jrXgZmu6QdCBwDTgLP4egsu8daN9tafg7YAXR32W4CjsCSArS0lvUHtgG/4fj2+Y9LegM0KCDP9aw05654jMgnvR9wCEf72nX7MlekKyhPnXAE1VTgXRy/Gg1xWT/VOr5EHN+Uzv0DFYF/4+jUTAW2AH2sda8DR6z87wOGuuzzcet8pwAPueRjg7XsGI7O1/LWukhr32eBWOsRnc85GQjEXbGsLI5+k/3WOdgFDLfWPWOV93nrOMa7bBdsnY9UYCfQw2VdNRyB4CzwE47PVJzL+lxlDfTAUas8a53PqBKex5HAP939vyTWm6nrgIgMBW4zxvzdpv2txhFsp9uxP+Ue4rj2ZCvQ1jg6ct3meu9Dua4YYz7wdB5U6TOOK2Ubl8Z7Xdd9KEope2mTRyllG62hKKVsowFFKWUbr+6UrVatmqlXr56ns6HUdWfTpk2njTG57q/i1QGlXr16bNy40dPZUOq6IyJ5DmnQJo9SyjZuCygi8h8ROSki212WVRGRb0Vkr/W3srVcROQdEUkUkQQRiXBXvpRS7uPOGsoMHJdIu3oeWGWMaQis4vdb0t0HNLQeQ3FcWqyU8jJuCyjGmDU4hr+76gbMtJ7PxDHKN3v5LOOwFqgkIjXdlTellHuUdh9KDeMYMQmOQXg1rOe1yXk/iCSKdi8JpdQ1wGO/8hhjjIgU+TJda4DbUIC6desWapsdbbsX9W1UCQWv+apE22uZeUZJy620aygnspsy1t/skY9HyHmzniBrWS7GmA+MMZHGmMjq1d0+zYhSqghKO6AsAh6xnj8CLHRZPsD6taclkOrSNFJKeQm3NXlEZA7QHsc9W5OAl3DcUfxzERmM456bD1nJv8Fx891EHHe++j935Usp5T5uCyjGmPzu+N0hj7QGGOauvCilSodeKauUso0GFKWUbTSgKKVsowFFKWUbDShKKdtoQFFK2UYDilLKNhpQlFK20YCilLKNBhSllG00oCilbKMBRSllGw0oSinbaEBRStlGA4pSyjYaUJRSttGAopSyjQYUpZRtNKAopWzjkYAiIs+IyA4R2S4ic0SkrIjcLCLrrPmNY0XE3xN5U0oVX6kHFBGpDQwHIo0xIYAv0Ad4DXjbGNMASAYGl3belFIl46kmTxmgnIiUAQKBY8DdwHxrveu8x0opL1HqAcUYcwR4EziEI5CkApuAFGNMhpVM5zZWygt5oslTGegG3AzUAm4AOhVh+6EislFENp46dcpNuVRKFYcnmjz3AAeMMaeMMenAAuBOoJLVBAKd21gpr+SJgHIIaCkigSIiOGYS3Al8D/Sy0rjOe6yU8hKe6ENZh6PzdTOwzcrDB8BoYISIJAJVgY9KO29KqZJx29zGBTHGvIRj8nRX+4EWHsiOUsomeqWsUso2GlCUUrbRgKKUso0GFKWUbTSgKKVsowFFKWUbDShKKdtoQFFK2UYDilLKNhpQlFK20YCilLKNBhSllG00oCilbKMBRSllGw0oSinbaEBRStlGA4pSyjYaUJRSttGAopSyjafmNq4kIvNFZLeI7BKRViJSRUS+FZG91t/KnsibUqr4PFVDmQIsM8Y0BpoCu4DngVXGmIbAKuu1UsqLeGLmwIpAW6xpMowxl40xKThmE5xpJdO5jZXyQp6oodwMnAI+FpEtIjJdRG4AahhjjllpjgM1PJA3pVQJeCKglAEigH8
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"text/plain": [
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"<Figure size 288x288 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc351ace250>\n"
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]
},
{
"data": {
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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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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc351864c70>\n"
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]
},
{
"data": {
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"text/plain": [
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"<Figure size 288x288 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc3518edc70>\n"
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]
},
{
"data": {
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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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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc351b5f550>\n"
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]
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAARQAAAD7CAYAAACrFWuaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAoxElEQVR4nO3dd3wU1fr48c+TipHQESkiaFRCAoTQf9KrRkUEpCjFC4rcr+Ve8SICCqgoIKIBRb2WK0UvQRCNIIKKoICG3tFLjXSINAmhpJzfHzNZNg1IMslmyfN+vfaV7NQzc3afPefMnDlijEEppZzg4+kEKKWuHRpQlFKO0YCilHKMBhSllGM0oCilHKMBRSnlGA0oRYSIPCIiKwpxfxVF5HcRua6w9ulpItJaRA54Oh2eJiKTROTvBbHtYhVQRCRQRD4WkT9E5IyIbBSRuzMt087+oiWJyFIRudltXg8R+cWetyyb7d8nIltFJNFernYBHccYEfk0n5t5HphmjDnnRJoKg4hME5Gxnk7HNeANYISIBDi94WIVUAA/YD/QCigNvAB8LiI1AESkAjAPeBEoB6wFZrutfwKIBsZn3rCI3AZ8BgwGygDzga9FxK9AjiQfRCQQ6A9kG5TEUtw+GxkUlXwTEV+nt2mMOQz8DnR2etsYY4r1C9gMdLP/HwT84jbveuAcUCvTOo8CyzJNexL4xu29j71uuxz2Wx74GvgLWA28Aqxwmz8ZK/j9BawDWtjT7wIuAslAIrDJnv434DfgDLAHePwyx9wS2JVp2jLgVWClne4QoBbwPVYg/R/Q42rSD9QADOCXafuPur0fYKf3JLAYuNmeLsBbwDF721uAcDtvku1jTwTm28tXAb4AEoC9wNNu+7gOmGbvYzswFDhwmfNigCeAncBee9q9wEbgFPALUNdt+WHAQfuc/y89r4FArB+eQ/YrGgi05z3ins9u+w2x/58GvAcsBM4C7YGbsH7oEoDjwDt5PY9u640EPnH8++TpL7QnX0Al4Dx2wMD6Er+XaZmt2AHHbVpOAWWh23tfe9v/yGHfMcDnWEEr3P5gugeUPlhfWj/gWeAIUMKeNwb4NNP27gFutT9IrYAkIDKHfT+BW/Czpy0D9gFh9j5LYwW0v9nv6wN/ArWvlH6uEFCA+4FdQKi97RewAznQCSuAlrGPJRSobC592ca6bdPHXnYUEADcghVMO9nzxwPLsUqbN9l5eaWA8r29/HX2MR8Dmtj52R+IxwoYd9jnp4rbMd9q//8yEAfcAFTECkSv2PMe4coB5TRwp3181wObsILD9UAJoHl+zqM9vyuw3unvVLEt1oqIP1YVZbox5nd7ckmszHR3Ggi+ik3+ALSyG/4CgBFYH/KgbPbtC3QDRhljzhpjtgLT3ZcxxnxqjDlujEkxxkzi0oc4W8aYb4wxu43lJ+A7oEUOi5fB+lXNbJoxZpsxJgWrJBRvjPnETsMGrJLAg1eT/isYDIwzxvxm7+s1IMJur0rGOt+1ALGXOZzDdhoBFY0xLxtjLhpj9gAfAr3s+T2AV40xJ4wx+4EpV5G2cfby57BKRf82xqwyxqQaY6YDF4CmQCpWntQWEX9jTLwxZre9jYeBl40xx4wxCcBLQN9cnJ9YY8xKY0waUBerFDbUPtfnjTHpjff5OY9nsD4HjiqWAcVuH5iJVXx+0m1WIlAq0+KlyP7Ll4EdlPoD7wCHgQpYxezsripU5FJ7Tro/MqXxXyLym4icFpFTWCWGCpc5prtFJE5ETtjLR11m+ZNkHyTd03Mz0ERETqW/sL4oN15N+q/gZmCy23ZPYP2KVjXG/Ih1DqcCx0TkAxHJnCfu26mSKY0jsEqeYH0Rc5vGzOfg2UzbvwmrVLIL+CdWafGYiMSISBW3/brv6w972tVyT8NNwB92wMgsP+cxGKsa56hiF1BERICPsT503YwxyW6ztwH13Ja9Hqsase1qtm2MmWuMCTfGlAdGYxWD12SzaAKQgvVhSVfdbb8tgOewfmHLGmPKYJWUJH1XmY4pEKv08AZQyV5+odvymW0Gbs/uENz+3w/8ZIwp4/YqaYz5+5XSj1X3h4ylsxszbfvxTNu+zhjzC4AxZooxpgFQ207n0OyO297O3kzbCTbGRNnzD18mjTnJfA5ezbT9IGPMLDud/zXGNMf6Yhtggr3eIXua+34P2f+fdT8vIuJ+XnJKQ/UcGonzeh7BqgJtusx5yJNiF1CwGrxCgftM1kumXwLhItJNREpg1c03p1eJRMTXnu4H+IhICbvqhD2/gb1MReAD4Gu36pSLMSYVq5FtjIgE2ZeX+7stEoz1hU0A/ERkFBlLTkeBGm5XYgKwit8JQIp9KbzjZc7BaqCMiFS9zDILgNtFpK+I+NuvRiISeqX028X8g0Af+3wMwArM6d4HhotImH3eSovIg/b/jUSkiX1ez2K1Q6W5HfctmY7jjIgME5Hr7H2Fi0gje/7n9n7Kikg14KnLHG92PgQG2+kREbleRO4RkWARuUNE2trB/DxWQ3Z6OmcBL4h1r08FrM9R+hW1TUCYiETYn6UxV0jDaqzAON7efwkRudOel9fzCFY727e5PB9X5nSjTFF+cemX5DxW9Sb99bDbMu2xLqmdw2pIrOE27xF7fffXNLf5K7CqRyeAfwPXXyYtFbG+tNldJfEF/mPPO4xVWokH2tvzy9v7OondsIbV0HoUqxg7E6vRdOxl9j8RGOb2fhluV2HsaXcA33Dp6sKPQMSV0m/PvxvrqsspYBLwExmv8vTFuvLwF9Yv7X/s6e2wSlCJWI3AnwEl7Xm3cemKy1f2tCpYX+Aj9vmIcztPQcAMe/mrvcoTkmnaXVilzFN2XszBCvh17eNOz+8FXGqgLYHVXnPYfk3BblC354+0j20/VuN75kbZsZnSUB34ys6DP4Ep+TyPlbGq4gFOf8fE3oEqZuxS1HKgvnHg5jYReQQrYDTP77ZUwRKRScBuY8y7Tm+7SNy8owqfsaoltTydDlX4jDHPFtS2i2MbilKqgGiVRynlGC2hKKUcowFFKeUYr26UrVChgqlRo4ank6FUsbNu3bo/jTEVM0/36oBSo0YN1q5d6+lkKFXsiEi23Ri0yqOUckyBBRQR+Y+IHBORrW7TyonI9yKy0/5b1p4uIjJFRHaJyGYRiSyodCmlCk5BllCmYd227O55YIkx5jZgif0erNu0b7Nfg7D62yilvEyBBRRjzM9YfRzc3c+l52ZMB7q4TZ9hLHFYHdcqF1TalFIFo7DbUCqZSw95OcKl51ZUJeMzIA7Y05RSXsRjV3mMMUZEcn2brogMwqoWUb361TzeAra17JLb3ah8Cvv5q3ytr3nmGfnNt8IuoRxNr8rYf4/Z0w+S8UE41expWRhjPjDGNDTGNKxYMctlcKWUBxV2QPmaSw/i6Q/Euk3vZ1/taQqcNjk/R1QpVUQVWJVHRGYBrYEKYo3WNhrrKeSfi8hArOds9rAXX4j1DNRdWE9r/1tBpUspVXAKLKAYY3rnMKtdNsumj4eilPJieqesUsoxGlCUUo7RgKKUcowGFKWUYzSgKKUcowFFKeUYDShKKcdoQFFKOUYDilLKMRpQlFKO0YCilHKMBhSllGM0oCilHKMBRSnlGA0oSinHaEBRSjlGA4pSyjEaUJRSjtGAopRyjEcCiog8IyLbRGSriMwSkRIiUlNEVtnjG88WkQBPpE0plXeFHlBEpCrwNNDQGBMO+AK9gAnAW8aYEOAkMLCw06aUyh9PVXn8gOtExA8IAg4DbYG59nz3cY+VUl6i0AOKMeYg8AawDyuQnAbWAaeMMSn2Yjq2sVJeyBNVnrLA/UBNoApwPXBXLtYfJCJrRWRtQkJCAaVSKZUXnqjytAf2GmMSjDHJwDzgTqCMXQUCHdtYKa/kiYCyD2gqIkEiIlgjCW4HlgLd7WXcxz1WSnkJT7ShrMJqfF0PbLHT8AEwDBgiIruA8sDHhZ02pVT+FNjYxpdjjBmNNXi6uz1AYw8kRynlEL1TVinlGA0oSinHaEBRSjlGA4pSyjEaUJRSjtGAopRyjAYUpZRjNKAopRyjAUUp5Rg
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"text/plain": [
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"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc3519c77f0>\n"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"<matplotlib.table.Table object at 0x7fc3518c31f0>\n"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 288x288 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"\n",
"dft = None\n",
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"for cluster in \"abcd\":\n",
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" print(cluster)\n",
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" df = pd.read_csv(glob.glob(DIR + cluster + \"_actual/part-*\")[0], header=None,\n",
" names=[\"term\", \"cpu\", \"ram\"])\n",
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" plot_df(df, cluster, \"used\")\n",
" if dft is None:\n",
" dft = df\n",
" else:\n",
" dft = dft.append(df)\n",
"\n",
"dft = dft.groupby(\"term\").sum().reset_index()\n",
"plot_df(dft, \"all\", \"used\")\n",
"\n",
"dft = None\n",
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"for cluster in \"abcdefgh\":\n",
" data = None\n",
" with open(DIR + cluster + \"_res_micros_requested.json\", \"r\") as f:\n",
" data = json.loads(f.read())\n",
" dfd = {'term': [], 'cpu': [], 'ram': []}\n",
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" for term in [4,5,6,7]:\n",
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" dfd['term'].append(term)\n",
" dfd['cpu'].append(float(data[\"cpu-\" + (\"None\" if term == -1 else str(term))]))\n",
" dfd['ram'].append(float(data[\"ram-\" + (\"None\" if term == -1 else str(term))]))\n",
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" df = pd.DataFrame(dfd, columns=['term', 'cpu', 'ram'])\n",
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" if dft is None:\n",
" dft = df\n",
" else:\n",
" dft = dft.append(df)\n",
" plot_df(df, cluster, \"requested\")\n",
" \n",
"dft = dft.groupby(\"term\").sum().reset_index()\n",
"plot_df(dft, \"all\", \"requested\")\n",
"\n",
"dfr2011 = {'term': [4,5,6,7], 'cpu': [28.2, 31.7, 13.9, 26.2], 'ram': [30.4,23.5, 17.3, 28.8]}\n",
"dfr2011 = pd.DataFrame(dfr2011, columns=dfr2011.keys())\n",
"dfa2011 = {'term': [4,5,6,7],'cpu': [19.2, 13.7, 23.2, 43.9], 'ram': [21.0,14.2,32.1,32.7]}\n",
"dfa2011 = pd.DataFrame(dfa2011, columns=dfa2011.keys())\n",
"\n",
"plot_df(dfr2011, \"2011\", \"requested\")\n",
"plot_df(dfa2011, \"2011\", \"used\")"
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]
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},
{
"cell_type": "code",
"execution_count": null,
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"id": "6242cbc1",
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"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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"id": "2b8d0814",
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"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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"id": "ca43e76c",
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"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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"id": "031484e2",
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"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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"id": "60740e10",
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"metadata": {},
"outputs": [],
"source": []
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}
],
"metadata": {
"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "venv"
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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",
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"version": "3.9.5"
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}
},
"nbformat": 4,
"nbformat_minor": 5
}