528 lines
228 KiB
Text
528 lines
228 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "fbd121e1",
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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 numpy as np\n",
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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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"execution_count": 7,
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"id": "e6213e81",
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"metadata": {},
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"outputs": [],
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"source": [
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"DIR = \"/home/claudio/hdd/git/bachelorThesis/spatial_resource_waste/\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "0ceab723",
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"metadata": {},
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"outputs": [],
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"source": [
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"NAMES = {4: 'EVICT', 5: 'FAIL', 6: 'FINISH', 7: 'KILL'}\n",
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"\n",
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"def plot_df(df, cluster, type_of_data):\n",
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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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" s = df.sum()\n",
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" #print(\"Cluster \" + cluster + \":\")\n",
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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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" \n",
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" latex = df.copy()\n",
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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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"\n",
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" df2 = df.copy()\n",
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" df[\"kind\"] = \"CPU\"\n",
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" df[\"percent\"] = df[\"cpu\"] * 100\n",
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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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" df2[\"kind\"] = \"Memory\"\n",
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" df2[\"percent\"] = df2[\"ram\"] * 100\n",
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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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" 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([0.9, 0.3, 0.8, 0.1])\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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" bottom = [0, 0]\n",
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" lines = []\n",
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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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" bottom += df[df.term==t][\"percent\"].values\n",
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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), bbox_inches='tight')\n",
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" plt.show()\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "d8b99ab4",
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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": [
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"a\n",
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"<matplotlib.table.Table object at 0x7fc351b56880>\n"
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]
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"b\n",
|
|
"<matplotlib.table.Table object at 0x7fc351c3f370>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"c\n",
|
|
"<matplotlib.table.Table object at 0x7fc33e9b51c0>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"d\n",
|
|
"<matplotlib.table.Table object at 0x7fc351a52940>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc351991730>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc351b66220>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc3517cd6a0>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc351924d30>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc351a555e0>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc3518d4850>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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2Vuoa5ZGAIiJ+OILJp8aYBdbiEyJS01pfEziZ17Y6t7FS1y5P/MojOKYa3WWM+afLqkU45jUGnd9YKa/kialI7wT6A9tEJN5aNhaYDHwuIoOBX4CHPJA3pVQJlHpAMcbEAZLP6g6lmRellL30SlmllG00oCilbKMBRSllGw0oSinbaEBRStlGA4pSyjYaUJRSttGAopSyjQYUpZRtNKAopWyjAUUpZRsNKEop22hAUUrZRgOKUso2GlCUUrbRgKKUso0GFKWUbTSgKKVsowFFKWWbayqgiEgnEfmfiCSKiE5FqpSX8cRd7/MkIr7AVKAjkARsEJFFxpidJd33OxP1Bvql7X1PZ0B5xDUTUIAWQKIxZj+AiMzFMd9xiQOK8j76JeAZJf0iuJaaPLWBwy6vk6xlSikvcS3VUApFRIYCQwHq1q1bqG3eb9PPnVlSbqBl5p2upRrKEaCOy+sga1kOOrexUteuaymgbAAaisjNIuIP9MEx37FSyktcM00eY0yGiDwJLAd8gf8YY3Z4OFtKqSK4ZgIKgDHmG+AbT+dDKVU8YozxdB6KLSAgwFy+fNnT2VDFEBgYSFpamqezoYpJRDKysrL8ci335oAiIsab8389ExG07LyXVX5y5fJrqVP2D+XUqVO0bt2akJAQvvrqK+fybt26cfTo0Xy3mzVrFiEhIYSGhtKsWTPefPNNAAYOHMjNN99MeHg4ERER/PzzzwC0b9+ejRs3Orc/ePAgISEh7jkoL1ecMnn55ZcRERITE53LYmJiEJEc5105aEBxkzlz5vD444+zfv16YmJiAFi8eDHNmjWjVq1aeW6zdOlSYmJiWLFiBdu2bWPt2rVUrFjRuf6NN94gPj6eyZMn89hjj5XGYfyhFKdMAEJDQ5k7d67z9bx58wgODnZ3dp0yMjJK7b1KSgOKm/j5+ZGWlsalS5fw9fUlIyODmJgYRo0ale82kyZN4s0333R+uAMCAnj00UdzpWvbtm2Ob0xVOMUpE4Du3buzcOFCAPbt20fFihWpVq2ac/2KFSto1aoVERERPPjgg5w7dw6AevXqMWbMGMLDw4mMjGTz5s1ERUVRv359pk2bBoAxhpEjRzprpbGxsQCsXr2aNm3a0LVrV2677TZefPFFZxAEGDduHFOmTLHz9NhCA4qb9OvXj4ULF9KxY0fGjh3Le++9R//+/QkMDMx3m+3bt9O8efOr7nvx4sWEhobamd3rQnHKBKBChQrUqVOH7du3M3fuXHr37u1cd/r0aaKjo1m5ciWbN28mMjKSf/7zn871devWJT4+njZt2jBw4EDmz5/P2rVreemllwBYsGAB8fHxbN26lZUrVzJy5EiOHTsGwObNm5kyZQp79uxh0KBBzJo1C4CsrCzmzp3Lww8/bPcpKrFr6mfjP5KKFSuyZMkSAJKTk5k8eTJffvkljz76KMnJyTz77LO0atWqSPscOXIk0dHRVK9enY8++ghwdI5dKa9lqmRl0qdPH+bOncvy5ctZtWoVH3/8MQBr165l586d3HnnnQBcvnw5xz66du0KOJpN586do3z58pQvX56AgABSUlKIi4ujb9+++Pr6UqNGDdq1a8eGDRuoUKECLVq04OabbwYctZ2qVauyZcsWTpw4QbNmzahatarbzlVxaUApBa+++irjxo1jzpw5tG7dml69etGzZ0+WL1+eI11wcDCbNm3i7rvvznM/b7zxBr169cqxrGrVqiQnJztf//rrrzmq4ypvhS2TbPfffz8jR44kMjKSChUqOJcbY+jYsSNz5szJc7uAgAAAfHx8nM+zX1+tb+SGG27I8XrIkCHMmDGD48ePM2jQoEIdZ2nTJo+b7d27l6SkJNq3b09aWho+Pj6ICBcuXMiVdsyYMYwcOZLjx48Djm+76dOnF7j/9u3bM3v2bOdPsDNnzuSuu+6y/0D+QIpSJtkCAwN57bXXGDduXI7lLVu25KeffnL2aZ0/f549e/YUOi9t2rQhNjaWzMxMTp06xZo1a2jRokWeaXv06MGyZcvYsGEDUVFRhX6P0qQ1FDcbN24cEydOBKBv3750796dyZMnM2HChFxpO3fuzIkTJ7jnnnswxiAiV/0mGjp0KLt376Zp06aICJGRkUyaNMktx/JHUZQycdWnT59cy6pXr86MGTPo27cvly5dAiA6Oppbb721UHnp0aMHP//8s7P8Xn/9df785z+ze/fuXGn9/f256667qFSpEr6+voXaf2nTC9uUR+iFbUWXlZVFREQE8+bNo2HDhh7Ni17YppQX27lzJw0aNKBDhw4eDyYF0RqK8gitoXi3/GooBfah+PgFHDcZl2u4L1slIyL6E6mXKlu2rJadF8uv7AqsoYiIaT5ppbvyVGKbxtyj33JeSmso3s22PpRNY+9l5zuPOR/HV8/h6MpZHFmW8+fNtKOJ7Pin4xeKba/9jYzzqQCkn/2V/XOi2fZGf3b96/+x9+OxpB3b59xf/IQebHv9YXa+8xh7po8s1sFe75YtW0ajRo1o0KABkydPzrV+2rRphIaGEh4eTuvWrdm58/eJBRISEmjVqhXBwcGEhoZy8eJFLl26RKdOnQgJCeG9995zph06dCibN28ulWO6nhS3/L799luaN29OaGgozZs357vvvgMo1fIr8s/GPn7+3DY85832L55KYu/Hz1O70xDnsl8Tvqdy05zXQxhj2PfJS1Rtfi+39H0BgLRj+8i8mObc58F5r1OxcUsqh7Yt8sEoyMzMZNiwYXz77bcEBQVx++23O8eDZOvXrx+PP/44AIsWLWLEiBEsW7aMjIwMHn74YT755BOaNm3KmTNn8PPzY8mSJbRu3ZqxY8dy55138sQTT7B161YyMzOJiIjw1KH+IZWk/KpVq8bixYupVasW27dvJyoqiiNHjrB8+fJSKz9bfuUpWz2IMuXKc/7QLuey5IQfqHJFQDm7Px7xLUP1O/7qXBZYsz7lb9ZxKXZZv349DRo04JZbbsHf358+ffo4B7Zlc73S8/z588728IoVKwgLC6Np06aA4ypcX19f56C69PR0ZzNl/PjxvPrqq6V0VNePkpSf66jp4OBgLly4wKVLl0q1/IocULLSL+do8vya8D0AlZve5Xx+7tBOygRWoGy1oBzbXjx+kMDa1+5PXn8ER44coU6d3ycPCAoK4siRXJMHMHXqVOrXr8+oUaN45513ANizZw8iQlRUFBEREbz++usAdOzYkYMHD9KyZUuGDx/OokWLiIiIKHDIvyqekpSfqy+++IKIiAgCAgJKtfxsafIAVAlrz+5pTxPU+XGSt67OVTtR15Zhw4YxbNgwPvvsM6Kjo5k5cyYZGRnExcWxYcMGAgMD6dChA82bN6dDhw589tlnAKSnpxMVFcXChQsZMWIEhw4dYsCAAc5BcKp05FV+2Xbs2MHo0aNZsWIFAGXKlCm18rPtwjb/SjcSUPnPnDuQQPKOH6kc1j5XmrI1biLtyF673lLloXbt2hw+/PsEjElJSdSunf8EjH369HHevSwoKIi2bdtSrVo1AgMD6dy5c65Ou/fee48BAwY4b/4UGxvLW2+95ZZjuR6VpPyy0/fo0YNZs2ZRv379XOndXX62XilbpeldHF7ybwKq1MS/Yu5JuMrXb0ZWRjqn1n/tXJZ2bD9nD2yzMxvXtdtvv529e/dy4MABLl++zNy5c3N9++zd+3tQX7JkifPKy6ioKLZt20ZaWhoZGRn88MMPOToDk5OT+frrrxkwYEChB9WpoilJ+aWkpNClSxcmT57svJ2Cq9IovyI3ebL7ULJVuDWSoE6Ou4pVDm3HocVTqdv1yTy3FRHq93+ZpK/f4/gPsfiU8ce/cg3q3P9EMbOvrlSmTBneffddoqKiyMzMZNCgQQQHB/Piiy8SGRlJ165deffdd1m5ciV+fn5UrlzZWV2uXLkyI0aM4Pbbb0dE6Ny5M126dHHue8KECYwbNw4fHx+ioqKYOnUqoaGhzl8cVMmVpPzeffddEhMTmTBhgnOg44oVK7jxxhuB0ik/vbBNeYRe2ObddHCgUsrtNKAopWxTYJOnrH+ZzEvpmdds0NFqs/cqW7YsFy9e9HQ2VDGJCFlZWbmaPFftQ8n8YqA781Uivg/M0IDipfTLwLvZ1ofi9+BMIp5d6HwcPHkWgClf7yCwzyxSz/8+1/Dq7cf46z8cnbozvtvLUx+uLfYBqMK72uCyS5cu0bt3bxo0aMAdd9zBwYMHAcesg+XKlSM8PJzw8HBn778ODixd3lx+RQ4o5fx92fxWN+ej3o3lAZgbd4DbG1RjwbpfbM2gKprswWVLly5l586dzJkzJ8doYoCPPvqIypUrk5iYyDPPPMPo0aOd6+rXr098fDzx8fHOyaiyB5clJCTwySefAOjgQDfx9vKzpX9k3/HfOHchgwl9I5gbd8COXapiKszgsoULF/LII48A0KtXL1atWlVg80MHB5Yeby+/IgeUC5cznc2dnq857rcQG3eA3q1vpk2TGuw5ksqJFL1y0lMKM7jMNU2ZMmWoWLEiZ86cAeDAgQM0a9aMdu3a8eOPPwI6OLA0eXv5FflK2ewmj6u5cQf4YtTd+PgIPVvexPz/HmRY5ya2ZVKVjpo1a3Lo0CGqVq3Kpk2b6N69Ozt27KBChQo6ONALXAvlV+Imz7Zfktl77DeiJiznlsfnEfvTAW32eFBhBpe5psnIyCA1NZWqVasSEBDgnN6yefPm1K9fP9ekVTo40L28vfxKHFDmxu3npd7h7J/2IPunPUjS9N4cTU7jl5Pn7MifKqLCDC7r2rWrc/zH/PnzufvuuxERTp06RWZmJgD79+9n79693HLLLc7tdHCg+3l7+ZV45sDYuAN8Pe6eHMu6t6hL7E8HaNEw5xy7M1cnsnD9Iefr/07uQlDVnPO3qpIpzOCywYMH079/fxo0aECVKlWYO3cuAGvWrOHFF1/Ez88PHx8fpk2bRpUqVZz71sGB7uft5acXtimP0AvbvJsODlRKuZ0GFKWUbQps8pQJ8M/MvJx+zQYdrTZ7Lx0c6N2KPThw6JpP3Zqxkvig7d80oHgp/TLwbrb1oXzY/mG+GDTG+Th77BRHt+xk2eg3APjf0h/4oN3DnNn3+6858x4ZzdljpwD47KGnuZjiGFC4edZXzBswivkDn+eLQWM4uTMRgMXDozm1e79z+7PHTjHvkd/HK6j8DRo0iBtvvJGQkJB806xevZrw8HCCg4Np164dAP/73/+cg8rCw8OpUKECMTExAIwePZqwsDAGDBjg3Mfs2bOd65V9rlZ+CxcuJCwsjPDwcCIjI4mLi3Ou69SpE5UqVeL+++/Psc3f/vY3wsLCGDt2rHNZdHR0jptb26XIPxv7BvjzwH8m5Vh29vipHK9vqF6FLbO+4p5Xhue7nxPb93Lo5y30nD4RX38/LqacJTMjo6jZUVcYOHAgTz75ZI5/flcpKSk88cQTLFu2jLp163Ly5EkAGjVqRHx8POAYoFa7dm169OhBamoqmzdvJiEhgSFDhrBt2zYaNGjAxx9/zLJly0rrsK4bVyu/Dh060LVrV0SEhIQEHnroIXbv3g3AyJEjSUtL4/33f5/mJiEhgXLlypGQkEDHjh1JTU0lLS2NdevW8cILL9ief7f0j9z0l2YkHzxCyqGj+aZJO5NC2Yrl8fX3A6BspfLcUK2yO7JzXWnbtm2Oaw+u9Nlnn9GzZ0/q1q0L4LyBsatVq1ZRv359brrpJnx8fJyDytLS0vDz8+PNN9/kqaeews/Pz23Hcb26Wvn96U9/cs4U6DprIDiCTfny5XOk9/Pz48KFC2RlZZGeno6vry8vvvgir7zyilvyX+QaSualy3wxaAwA5WveyL0Tn8mdSISmfe9nyyeLuGtc3hfNBN0eyuaZC4jt9yy1I0O45e6W1Ar/ffzPd6++R5kAxwc2Mz0D8blm+4a9yp49e0hPT6d9+/acPXuWp59+Ote34dy5c+nbty8A5cuXp3PnzjRr1owOHTpQsWJF1q1bx/jx4z2RfQV8+eWXjBkzhpMnT7JkyZIC0zZp0oTq1asTERFB//79SUxMJCsry223nbClyZOXBvf8hS2fLOS3oyfzXO8XWJYeH07keMJujm7ZyaqX/0WLx3rT6D5Hm/7u8U9QvbHjsuGzx06x7Pk3i5pVlYeMjAw2bdrEqlWruHDhAq1ataJly5bceuutAFy+fJlFixYxadLvZTxq1ChGjRoFwJAhQ5gwYQLTp093zoXsjqqzyl+PHj3o0aMHa9asYfz48axcWfDMFK59XX/96195//33mThxIlu3bqVjx448+uijtuXNbV/7PmV8Cevdma2fLc4/ja8PtZrdRuSgXtz590c48MMGd2VHWYKCgoiKiuKGG26gWrVqtG3blq1btzrXL126lIiICGrUqJFr2y1btmCMoVGjRsybN4/PP/+cffv25Zh4SpWetm3bsn//fk6fPl2o9AsXLqR58+acO3eOffv28fnnnzN//nzS0tJsy5Nb2xG33teWI5t2cDHlt1zrUg4dJfXwcefrM3t/4U81quVKp+zVrVs34uLiyMjIcHbONWnye1Nzzpw5zubOlbJvypOenu4chObj42PrB1IVLDEx0flz++bNm7l06ZJzhHFB0tPTiYmJYdSoUVy4cMHZ95KZmcnly5evsnXhlXhwYEF8/coQ8sC9/PedT3KtS79wif/GzOTSufP4+PpSoXYN2o4c4s7sXBf69u3L6tWrOX36NEFBQbzyyiukp6cD8Pjjj9OkSRM6depEWFgYPj4+DBkyxPkT5fnz5/n2229z/EqQ7auvviIyMtJ5U57w8HBCQ0MJCwujadOmpXeAf3BXK78vvviCWbNm4efnR7ly5YiNjXUGhzZt2rB7927OnTtHUFAQH330EVFRUQBMnTqVRx55hMDAQMLCwkhLSyM0NJTOnTtTqVIl2/KvF7Ypj9AL27ybDg5USrmdBhSllG0KbPIE+PpmXs7KumaDjlabvZcODvRuxR4cuL1Nt3zXe1rIjws1oHgp/TLwbrb1odz+09fO52t+PUGXjSs5ejGNqb/s5uMkx+C+cf/bzIpTOS+7P3Ixje6bvityxlXepkyZQkhICMHBwc4Ll3r37u0c3FevXj3Cw8MLvS3AvHnzCA4OxsfHh40bNzqX//TTT4SFhREZGem85iQlJYV7772XrKwsdx3iH1peZRAfH0/Lli2dA//Wr1+fa7tffvmFiIgI5+DO7Mm80tLS6NKlC40bNyY4OJjnn3/euc2//vUvQkJC6Ny5s/Mn4ri4OJ55Jo+r3Euo2M2ZtcmnmLRvG/8ObkWtsoF25kldxfbt2/nwww9Zv349W7du5euvvyYxMZHY2FjnrHEPPPAAPXv2LPS2ACEhISxYsIC2bdvm2Oatt97im2++ISYmxvkBjo6OZuzYsfjokIgiy68MRo0axUsvvUR8fDwTJkxwXp3sqmbNmvz888/Ex8ezbt06Jk+ezNGjji/v5557jt27d7NlyxZ++uknli5dCsCnn35KQkICf/nLX1i+fDnGGF599VW3DJ8o1qdhY+ppXk6MZ2rwHdQtpzeZLm27du3ijjvuIDAwkDJlytCuXTsWLFjgXG+M4fPPP8/zArWCtm3SpAmNGjXKtU32zHPZgwP37dvH4cOHad++vduO8Y8svzIQEX77zXERaGpqap4Tcfn7+xMQEAA45izOriEGBgZy1113OdNERESQlJQEOD4P6enpzvKbPXs29913X4GDEIuryAEl3WQxfOd6ptzWglsCy199A2W7kJAQfvzxR86cOUNaWhrffPNNjrlcfvzxR2rUqEHDhg2LvG1exowZw4ABA5g0aRJPPvkk48aNIzo62vbjul7kVwYxMTGMHDmSOnXq8Nxzz+UYT+Xq8OHDhIWFUadOHUaPHp0r8KSkpLB48WI6dOgAwJNPPknLli05dOgQd955Jx9//DHDhg1zy7EVOaCUER/CK1RhwfFDV0+s3KJJkyaMHj2ae++9l06dOhEeHo6vr69zfUGXz19t27yEh4ezdu1avv/+e/bv30/NmjUxxtC7d28efvhhTpw4Yevx/dHlVwb//ve/efvttzl8+DBvv/02gwcPznP7OnXqkJCQQGJiIjNnzsxx/jMyMujbty/Dhw93zsnTv39/tmzZwuzZs3n77bcZPnw4S5cupVevXjzzzDO29oMVOaAI8FbjSLafTeaDQ3uuml65x+DBg9m0aRNr1qyhcuXKztHCGRkZLFiwgN69exd526sxxhAdHc348eN55ZVXeP3113n00Ud55513bDmm60leZTBz5kxnv9eDDz6YZ6esq1q1ajlrO9mGDh1Kw4YN+fvf/54r/dGjR1m/fj3du3fnrbfeIjY2lkqVKrFq1SrbjqtYfSjlfMvwXnBLlpxK4ovjv9iWGVV42XdaO3ToEAsWLKBfv34ArFy5ksaNGxMUFFTkba9m1qxZdO7cmSpVqjhnntPBgcWTVxnUqlWLH374AYDvvvsuzyZrUlKSc6a/5ORk4uLinP1eL7zwAqmpqfnemnP8+PFMmDABwDlA0O7yK/bgwIp+/kwLacXArXFU8fPPtf6VxHhe278NgD8HlOP1xpEcvHCODuuWO9OMuiWEqOq1c22rru6BBx7gzJkz+Pn5MXXqVOcAL9ebI2U7evQoQ4YM4Ztvvilw2y+//JKnnnqKU6dO0aVLF8LDw1m+3FFeaWlpzJgxgxUrVgAwYsQIOnfujL+/v3MiblV4eZXBhx9+yNNPP01GRgZly5blgw8+AGDjxo1MmzaN6dOns2vXLp599lnndTzPPfccoaGhJCUlMXHiRBo3buy8edKTTz7JkCGOAbdbtmwBcK7r168foaGh1KlTJ89fk4pLL2xTHqEXtnk3HRyolHI7DShKKdsU2OQp6+t7/FJWVu57ASpVQtrk8XrGGJOrQlJgQFFKqaLQJo9SyjYaUJRSttGAopSyjQYUpZRtNKAopWyjAUUpZRsNKEop22hAUUrZRgOKUso2/x82R86PJn/4GAAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc351ace250>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc351864c70>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc3518edc70>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc351b5f550>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc3519c77f0>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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8WnzOf7ktOqvCSUsoSinHaEBRSjlGA4pSyjEaUJRSjtGAopRyjAYUpZRjvDFyYHURWSkiu0TkZxF51p5eXkSWicge+2+5/E6bUip3vFFCSQJeNMaEAM2BJ0UkBBgCfGeMqQN8Z79XShUi+R5QjDGxxpgt9v9nsQZNrwbcizWuMej4xkoVSl6tQxGRmkBDYD1QyRgTa886AlTyVrqUUjnjtYAiIqWAz4HnjDFn3OcZYwxgMlhPxzZWqoDySkARkWJYweRTY8wX9uSjIlLFnl8F+MvTujq2sVIFlzfu8gjWUKO/GGPedpv1Jda4xqDjGytVKHmjt3FLoA+wQ0Si7WmvAGOAeSLyMPAH0N0LaVNK5UK+BxRjzBpAMpjdJj/TopRylraUVUo5RgOKUsoxGlCUUo7RgKKUcowGFKWUYzSgKKUcowFFKeUYDShKKcdoQFFKOUYDilLKMRpQlFKO0YCilHKMBhSllGM0oCilHKMBRSnlGA0oSinHaEBRSjlGA4pSyjEaUJRSjilQAUVE7hSR30Rkr4joUKRKFTIFJqCIiC/wHtABCAF62WMeK6UKiQITUICmwF5jzO/GmARgDtZ4x0qpQqIgBZRqwEG39zH2NKVUIeGNgb5yRUQGAAMAatSokaV1Ng3R4X4KG82zwqkglVAOAdXd3gfb09LQsY2VKrgKUkDZCNQRkVoi4g/0xBrvWClVSBSYSx5jTJKIPAUsBXyB/xhjfvZyspRS2VBgAgqAMeZr4Gtvp0MplTNijPF2GnKsePHiJiEhwdvJUDkQGBhIfHy8t5OhckhEklJSUopdNr0wBxQRMYU5/UWZiKB5V3jZ+SfppxekStmrSlxcHLfccgthYWEsXLjQNf3ee+/l8OHDGa43c+ZMwsLCCA8Pp2HDhowfPx6Afv36UatWLRo0aECjRo346aefAGjdujWbNm1yrX/gwAHCwsLy5qAKuZzkyeuvv46IsHfvXte0SZMmISJpzruyaEDJI7Nnz+bxxx9nw4YNTJo0CYDFixfTsGFDqlat6nGdb775hkmTJvHtt9+yY8cO1q1bR9myZV3zx40bR3R0NGPGjOGxxx7Lj8O4quQkTwDCw8OZM2eO6/38+fMJDQ3N6+S6JCUl5du+cksDSh4pVqwY8fHxXLx4EV9fX5KSkpg0aRKDBg3KcJ3Ro0czfvx414e7ePHiPProo5ctd+utt6b5xVRZk5M8AejcuTOLFi0CYN++fZQtW5YKFSq45n/77be0aNGCRo0acf/993Pu3DkAatasycsvv0yDBg2IjIxky5YttG/fnhtuuIEpU6YAYIxh4MCBrlLp3LlzAVi1ahWtWrXinnvuISQkhNdee80VBAGGDh3KO++84+TpcYQGlDzywAMPsGjRItq2bcsrr7zC+++/T58+fQgMDMxwnZ07d9K4ceMrbnvx4sWEh4c7mdwiISd5AlCmTBmqV6/Ozp07mTNnDj169HDNO3bsGCNGjGD58uVs2bKFyMhI3n77bdf8GjVqEB0dTatWrejXrx8LFixg3bp1DB8+HIAvvviC6Ohotm3bxvLlyxk4cCCxsbEAbNmyhXfeeYfdu3fTv39/Zs6cCUBKSgpz5szhwQcfdPoU5VqBum18NSlbtixfffUVACdPnmTMmDH897//5dFHH+XkyZO8+OKLtGjRIlvbHDhwICNGjKBixYpMmzYNsCrH0vM0TeUuT3r27MmcOXNYunQp3333HR9//DEA69atY9euXbRs2RKAhISENNu45557AOuy6dy5c5QuXZrSpUtTvHhxTp06xZo1a+jVqxe+vr5UqlSJ2267jY0bN1KmTBmaNm1KrVq1AKu0c80117B161aOHj1Kw4YNueaaa/LsXOWUBpR88NZbbzF06FBmz57NLbfcQrdu3bjvvvtYunRpmuVCQ0PZvHkzt99+u8ftjBs3jm7duqWZds0113Dy5EnX+xMnTqQpjivPsponqTp16sTAgQOJjIykTJkyrunGGNq2bcvs2bM9rle8eHEAfHx8XP+nvr9S3UjJkiXTvH/kkUeYPn06R44coX///lk6zvymlzx5bM+ePcTExNC6dWvi4+Px8fFBRDh//vxly7788ssMHDiQI0eOANav3dSpUzPdfuvWrZk1a5brFuyMGTOIiopy/kCuItnJk1SBgYGMHTuWoUOHppnevHlz1q5d66rT+vvvv9m9e3eW09KqVSvmzp1LcnIycXFxfP/99zRt2tTjsl26dGHJkiVs3LiR9u3bZ3kf+UlLKHls6NChjBw5EoBevXrRuXNnxowZw5tvvnnZsh07duTo0aPccccdGGMQkSv+Eg0YMIBff/2V+vXrIyJERkYyevToPDmWq0V28sRdz549L5tWsWJFpk+fTq9evbh48SIAI0aM4MYbb8xSWrp06cJPP/3kyr9//vOfVK5cmV9//fWyZf39/YmKiiIoKAhfX98sbT+/acM25RXasC37UlJSaNSoEfPnz6dOnTpeTYs2bFOqENu1axe1a9emTZs2Xg8mmdESivIKLaEUbhmVUDKtQ/EpVvyISUqolHfJyh0R0VukhVRAQIDmXSGWUd5lWkIREdN49PK8SlOubX75Dv2VK6S0hFK4OVaHsvmVdux69zHX68iq2RxePpNDS9Le3ow/vJef37buUOwY25ukv08DkHj2BL/PHsGOcX345V9PsOfjV4iP3efaXvSbXdjxzwfZ9e5j7J46MEcHWxQcPHiQqKgoQkJCCA0NdTXDjo6Opnnz5q7m3hs2bLhs3ejoaFq0aEFoaCgRERGu5t4A+/fvp1mzZtSuXZsePXqQ+niIf/3rX4SFhdGxY0fXtDVr1vD888/nw9FefS5cuEDTpk2pX78+oaGhrpazGZ1/T/78809KlSrl6kAKsGTJEm666SZq167NmDFjXNN79+5NREQEr7zyimvaiBEj0nSSdEK2bxv7FPMn5Jl/p5l2IS6GPR8Podqdj7imndi+knL107aHMMaw75PhXNO4Hdf3GgZAfOw+ki/Eu7Z5YP4/KVu3OeXCb832wRQlfn5+TJgwgUaNGnH27FkaN25M27ZtGTRoEMOHD6dDhw58/fXXDBo0iFWrVqVZNzAwkJkzZ1KnTh0OHz5M48aNad++PUFBQQwePJjnn3+enj178vjjjzNt2jSeeOIJPv30U7Zv386oUaNYunQpnTp14q233sqwQZfKXPHixVmxYgWlSpUiMTGRW265hQ4dOvD22297PP+evPDCC3To0MH1Pjk5mSeffJJly5YRHBxMkyZNuOeee0hKSqJEiRJs376dtm3bcvr0aeLj41m/fj3Dhg1z9LgcucsTUDEYvxKl+fvPX1zTTm5fTfl0AeXs79GIrx8Vm93tmhZY5QZK19J+KdlVpUoVGjVqBEDp0qWpV68ehw4dQkQ4c+YMAKdPn/bYi/bGG2903SmoWrUq1157LXFxcRhjWLFihas1bt++fV2/YMYYEhMTiY+Pp1ixYsyaNYsOHTpQvnz5fDjaq4+IUKpUKQASExNJTExERDI8/+ktXLiQWrVqpen1vGHDBmrXrs3111+Pv78/PXv2ZNGiRRQrVozz58+TkpJCYmIivr6+vPbaa7zxxhuOH1e2SygpiQnsevdS1/nKrXtSPiKKcvWjOLF9JSVr1OPcn7vwCyxDQIXgNOteOHKAwGoF95ZXYXXgwAG2bt1Ks2bNmDRpEu3bt+ell14iJSWFH3/8MdN1N2zYQEJCAjfccAPHjx8nKCgIPz/rYxEcHMyhQ9bAA0899RTNmzcnNDSUli1bcu+992bYTF1lTXJyMo0bN2bv3r08+eST3HDDDRmef3fnzp1j7NixLFu2LM3lzqFDh6he/dLAEcHBwaxfv5569epRsWJFGjVqRJ8+fdi7d6+rTYvTHLnkASgf0ZpfpzxLcMfHOblt1WWlE5U3zp07R9euXZk0aRJlypRh2LBhTJw4ka5duzJv3jwefvhhli/3XLEeGxtLnz59mDFjBj4+mRdW+/TpQ58+fQB48803eeaZZ/jmm2+YOXMm1atXZ8KECVfchkrL19eX6OhoTp06RZcuXTy2jvXk9ddf5/nnn3eVcLLC/dEHd999N//+978ZOXIk27Zto23bth4fk5ETjn0C/IOupXi5ypzbv52TP/9AuYjWly0TUOk64g/tcWqXRV5iYiJdu3ald+/e3HfffYDVlyf1//vvv99jpSzAmTNnuOuuuxg5ciTNmzcHrI6Gp06dcnVai4mJoVq1tIM3Hj58mA0bNtC5c2cmTJjA3LlzCQoK4rvvvsurw7zqBQUFERUVxU8//XTF8w+wfv16Bg0aRM2aNZk0aRKjRo1i8uTJVKtWjYMHLw2+6Wn9RYsW0bhxY86dO8e+ffuYN28eCxYscOz5vo7+pJSvH8XBrz6gePkq+Je9fBCu0jc0JCUpkbgN/3NNi4/9nbP7dziZjCLBGMPDDz9MvXr1eOGFF1zTq1atyurVqwFYsWKFx1aVCQkJdOnShYceeihN72URISoqigULFgBWcLr33rTDS7/66quuPi/nz59HRPDx8dEHTmdTXFwcp06dAqzzuGzZMurVq3fF8w/www8/cODAAQ4cOMBzzz3HK6+8wlNPPUWTJk3Ys2cP+/fvJyEhgTlz5rgenwDWD1DqA6VS8w6sSy+nHvae6zqUMjdGEnynVVwqF34bfy5+jxr3POVxXRHhhj6vE/O/9zmyei4+fv74l6tE9U7/l8PkF11r167lk08+ITw8nAYNGgAwatQoPvroI5599lmSkpIICAjgww8/BGDTpk1MmTKFqVOnMm/ePL7//nuOHz/O9OnTAZg+fToNGjRg7Nix9OzZk2HDhtGwYUMefvhh1z63bt0K4Lr2fuCBBwgPD6d69epXfOqZSis2Npa+ffuSnJxMSkoK3bt3p1OnToSEhHg8/19++SWbNm3KtAOjn58fkydPpn379iQnJ9O/f/80lbbvvfceffv2JTAwkIiICOLj4wkPD6djx44EBQU5clzasE15hTZsK9y0c6BSKs9pQFFKOSbTS54Af7/ki4nJBTboaLG58AoICODChQveTobKIREhJSXlskueK9ahJH/eLy/TlSu+XadrQCmk9MegcHOsDqXY/TNo9OIi1+vAX2cBeOd/PxPYcyan/750+2nVzljuHmVV6k5fsYenP1qX4wNQaWXUuWzy5MnUrl0bEeHYsWMe1125ciUNGjRwvQICAlxNvLVzYN7LqGPnq6++SkREBA0aNKBdu3YZjmbo6+vryjv328IFIe+yHVBK+PuyZcK9rlfNa0sDMGfNfprUrsAX6/9wPJHqcqmdy7Zt20Z0dDRLlixh3bp1tGzZkuXLl3PddddluG5UVBTR0dFER0ezYsUKAgMDadeuHYCrc+DevXspV66ca7iO1M6BN998M0uXLsUYw1tvvcWrr76aL8d7NUnt2Llr1y7WrVvHe++9x65duxg4cCDbt28nOjqaTp06ZXiLuESJEq78+/LLL13TC0LeOVI/su/IGc6dT+LNXo2Ys2a/E5tUV5BR57KGDRtSs2bNLG9nwYIFdOjQgcDAQO0cmE8y6tjpPjzH33//na0HUBWUvMt2w7bzCck0etEalrHmtaX5YvDtzF2znx631KJVvUrsPnSao6fOUymohOOJVWml71zWrFmzbG9jzpw5rpa22jkw/7l37ATrifwzZ86kbNmyrFy50uM6Fy5cIDIyEj8/P4YMGULnzp0LTN7l6pLni8HWgFRz1uynR8ta+PgI9zW/jgU/HnA6ncqD1M5lMTExbNiwgZ07d2Zr/djYWHbs2JGlMV769OnD1q1bmTVrFhMnTnR1DuzWrRvPP/88KSkpOT2MIit9x06AkSNHcvDgQXr37s3kyZM9rvfHH3+wadMmPvvsM5577jn27duX6X7yM+9yfcmz44+T7Ik9Q/s3l3L94/OZu3a/Xvbks9TOZUuWLMnWevPmzaNLly4UK1YM0M6B+clTx053vXv35vPPP/e4bmqeXH/99bRu3ZqtW7cWmLzLdUCZs+Z3hvdowO9T7uf3KfcTM7UHh0/G88df55xIn8qAp85ldevWzdY2Zs+eTa9evVzvtXNg/sioY+eePZd64i9atMhjfp48edI1oNixY8dYu3YtISEhBSbvch1Q5q7ZT+emNdJM69y0BnPXXl5KmbFqLzUened6xRz/O7e7L7JiY2OJiooiIiKCJk2a0LZtWzp16sS7775LcHAwMTExRERE8Mgj1mM5N23a5PofrGv3gwcPctttt6XZ7tixY3n77bepXbs2x48fz1LnwLVr13LnnXfm9SFfNVI7dq5YscJ1+/frr79myJAhhIWFERERwbfffuu6neyed7/88guRkZHUr1+fqKgohgwZQkhICFAw8k4btimv0IZthZt2DlRK5TkNKEopx2R6yeNX3D85OSGxwAYdLTYXXto5sHDLcefAAd9/mqcJy40Pb+2tAaWQ0h+Dws2xOpSPWj/I5/1fdr3OxsZxeOsulgweB8Bv36zmw9se5Pi+P13rzO87mLOxcQB81v1ZLpyyOhRumbmQ+Q8NYkG/IXze/2X+2rUXgMXPjCDu199d65+NjWN+38HZTepVrX///lx77bWEhYW5pmW1c9ngwYMJCwsjLCxMRw30Ek/516NHD9ddn5o1a7oe7ekuo06hUDDyL9sBxbe4P13/M9r1Kl3l8odRl6xYnq0zF2a6naM79/DnT1u5b+pIuk0fw11vv0LJa6/JbnKKrH79+l3WkC0rncu++uortmzZQnR0NOvXr2f8+PGugcEKQueyosJT/s2dO9fV6a9r164eG7xl1CkUCkb+5Un9yHU3N+TkgUOc+tPzLyRA/PFTBJQtja+/1UozIKg0JSuUy4vkXJVuvfXWyzp3ZaVz2a5du7j11lvx8/OjZMmSREREsGTJkgLTuayo8JR/qYwxzJs3L02jw1QZdQotKPmX7c6ByRcT+Lz/ywCUrnIt7UZ6KDaJUL9XJ7Z+8iVRQx/3uJ3gJuFsmfEFcx94kWqRYVx/e3OqNqjnmr/irffxK24Fm+TEJEQHkcqSK3Uuq1+/Pm+88QYvvvgi8fHxrFy5kpCQkALTuUxZw2RUqlTJ4xAo4LlT6LFjxwpE/mU7oKRe8lxJ7TtuZusnizhz+C+P84sFBtDlo5Ec2f4rh7fu4rvX/0XTx3pwUwer5ebtr/4fFeteD1h1KEuGjPe4HZXWyJEjGTlyJKNHj2by5MmXjV/brl07Nm7cyM0330zFihVp0aIFvr6+mW5TRw3MX+m7RKSXfsTBnTt3Urly5QyXz8/8y7NPgY+fLxE9OrLts8UZL+PrQ9WGIUT270bL5/qyf/XGvEpOkZNZ57KhQ4cSHR3NsmXLMMZw4403FpjOZUVdUlISX3zxBT169Ljisu6dQgtK/uXpz8qNHW7l0OafuXDqzGXzTv15mNMHj7jeH9/zB6UqVcjL5Fz1stK5LDk5mePHjwOwfft2tm/fTrt27QpM57Kibvny5dStW5fg4GCP8zPqFFpQ8i9PA4pvMT/Curbj/MnLA0ri+YusGjWFeX0GsqDfEE4eOETkP7rmZXKuKr169aJFixb89ttvBAcHM23atCx1LktMTKRVq1aEhIQwYMAAZs2a5bruLgidy4oKT/kH1gOv0l/uHD58mI4dOwIZdwqFgpF/2rBNeYU2bCvctHOgUirPaUBRSjkm00ue4r6+yQkpKQU26GixufDSzoGFW447B+5sdW+G870t7IdFGlAKKf0xKNwcq0NpsvZ/rv+/P3GUuzYt5/CFeN7741c+jrE69w39bQvfxqVtdn/oQjydN6/IdsKVZxmNPgdWZ7C6desSGhrKoEGDsrXuiRMnaNu2LXXq1KFt27acPHkSgM8//5zQ0FBatWrluu28b9++LLWXUJfLKA+io6Np3rw5DRo0IDIykg0bNnhcf9CgQYSGhlKvXj2eeeYZV3DevHkz4eHh1K5dO830wYMHExERwUMPPeTaxqxZs5g0aZKjx5Xjy5l1J+MYvW8HH4S2oGpAoJNpUlmQ0ehzK1euZNGiRWzbto2ff/6Zl156KcvrAowZM4Y2bdqwZ88e2rRpw5gxYwArSG3cuJHHHnuMzz77DIBhw4YxYsSI/Dvoq0hGeTBo0CCGDx9OdHQ0b775pscfhB9//JG1a9eyfft2du7cycaNG1m9ejUATzzxBB999BF79uxhz549LFmyhNOnT7Nlyxa2b9+Ov78/O3bs4Pz583z88cc8+eSTjh5XjgLKptPHeH1vNO+FNqNGiZKOJkhlTUajz33wwQcMGTKE4sWLA3DttddmeV2wGsT17dsXSNvBzMfHh4sXL7o6mP3www9Urlw5w/4mKnMZ5YGIuHp/nz59mqpVq162rohw4cIFEhISuHjxIomJiVSqVInY2FjOnDlD8+bNEREeeughFi5ciI+PD4mJiRhjXPk3fvx4nn76adcQKk7JdkBJNCk8s2sD74Q05frA0o4mRuWM++hzu3fv5ocffqBZs2bcdtttbNyYeXeG9CPXHT16lCpVqgBQuXJljh49CsDLL7/MHXfcweLFi+nVq5c+vsBB7nkwadIkBg4cSPXq1XnppZcYPfryfnMtWrQgKiqKKlWqUKVKFdq3b+8KSO4tbFM7CJYuXZqOHTvSsGFDqlSpQtmyZVm/fj2dO3d2/FiyHVD8xIcGZcrzxZE/r7ywynPpR59LSkrixIkTrFu3jnHjxtG9e/cMKz89jVznTkRcj0Bo27YtmzdvZvHixSxatIiOHTuye/duunXrxqOPPqrN73MofR588MEHTJw4kYMHDzJx4sQ0rV1T7d27l19++YWYmBgOHTrEihUr+OGHHzLdz6BBg4iOjmbChAmuJvhTp06le/fujl62ZjugCDChbiQ7z57kwz93O5YQlX2eRp8LDg7mvvvuQ0Ro2rQpPj4+HDt2LEvrAq6iM1jNvNNfMsXHxzN9+nSefPJJhg8fzowZM7jlllv49NOC26K6oPKUBzNmzHD9f//993uslP3vf/9L8+bNKVWqFKVKlaJDhw789NNPVKtWjZiYGNdynjoIbt26FWMMN910E/Pnz2fevHns27cvTT+w3MhRHUoJXz/eD23OV3ExfH7kD0cSorIno9HnOnfu7HoOyu7du0lISKBChQpZWhfgnnvuYcaMGYDnDmbjxo3jmWeeoVixYtpBMBcyyoOqVau6KlhXrFjhsY6qRo0arF69mqSkJBITE1m9ejX16tWjSpUqlClThnXr1mGMYebMmR47CL711lskJiaSnJwM4Gj+Zft5KKnKFvNnSlgL+m1bQ/li/pfNf2NvNGN/3wFA5eIl+GfdSA6cP0eb9Zce7DLo+jDaV6x22brqylJHnwsPD3c9e3TUqFH079+f/v37ExYWhr+/PzNmzEBEOHz4MI888ghff/11hut27NiRIUOG0L17d6ZNm8Z1113HvHnzXPtM7f6e+hzTp59+miZNmhAUFOSqvFVZk1EefPTRRzz77LMkJSUREBDAhx9+CFgdPKdMmcLUqVPp1q0bK1asIDw8HBHhzjvv5O677wbg/fffp1+/fpw/f54OHTrQoUMH1z4XLlxIZGSkq6K3QYMGhIeHExERQf369R05Lm3YprxCG7YVbto5UCmV5zSgKKUck+klT4Cv75GLKSmV8jE9qojQS55CzxhjLiuQZBpQlFIqO/SSRynlGA0oSinHaEBRSjlGA4pSyjEaUJRSjtGAopRyjAYUpZRjNKAopRyjAUUp5Zj/B50rhs9LTKYTAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<matplotlib.table.Table object at 0x7fc3518c31f0>\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 288x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"dft = None\n",
|
|
"for cluster in \"abcd\":\n",
|
|
" print(cluster)\n",
|
|
" df = pd.read_csv(glob.glob(DIR + cluster + \"_actual/part-*\")[0], header=None,\n",
|
|
" names=[\"term\", \"cpu\", \"ram\"])\n",
|
|
" 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",
|
|
"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",
|
|
" for term in [4,5,6,7]:\n",
|
|
" 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",
|
|
" df = pd.DataFrame(dfd, columns=['term', 'cpu', 'ram'])\n",
|
|
" 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\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "6242cbc1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2b8d0814",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ca43e76c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "031484e2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "60740e10",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "venv",
|
|
"language": "python",
|
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"name": "venv"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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|
"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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