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import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})
warnings.filterwarnings('ignore')
%matplotlib inline
dfcr = pd.read_csv('docker_all-Chameleon-remote-20170422032837.csv', names=list(range(1,500)))
dfcr=dfcr.set_index(1)
sns.set_style("whitegrid")
dfplot=dfcr.T.dropna(how='all')
sns.set_style("whitegrid")
ax = sns.boxplot(dfplot,showfliers=False)
ax.set_xticklabels(list(dfcr.index.values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Chameleon Docker Mode Remote Client')
plt.savefig("Images/Chameleon-Docker-Mode-Remote-Client.png")
dfplot.describe().dropna()
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dfcl = pd.read_csv('docker_all-Chameleon-local-20170422202501.csv', names=list(range(1,500)))
dfcl=dfcl.set_index(1)
sns.set_style("whitegrid")
dfplot=dfcl.T.dropna(how='all')
sns.set_style("whitegrid")
ax = sns.boxplot(dfplot,showfliers=False)
ax.set_xticklabels(list(dfcl.index.values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Chameleon Docker Mode Local Client')
plt.savefig("Images/Chameleon-Docker-Mode-Local-Client.png")
dfplot.describe().dropna()
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dfar = pd.read_csv('docker_all-Aws-remote-20170422195527.csv', names=list(range(1,500)))
dfar=dfar.set_index(1)
sns.set_style("whitegrid")
dfplot=dfar.T.dropna(how='all')
sns.set_style("whitegrid")
ax = sns.boxplot(dfplot,showfliers=False)
ax.set_xticklabels(list(dfar.index.values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Aws Docker Mode Remote Client')
plt.savefig("Images/Aws-Docker-Mode-Remote-Client.png")
dfplot.describe().dropna()
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dfal = pd.read_csv('docker_all-Aws-local-20170422204102.csv', names=list(range(1,500)))
dfal=dfal.set_index(1)
sns.set_style("whitegrid")
dfplot=dfal.T.dropna(how='all')
sns.set_style("whitegrid")
ax = sns.boxplot(dfplot,showfliers=False)
ax.set_xticklabels(list(dfal.index.values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Aws Docker Mode Local Client')
plt.savefig("Images/Aws-Docker-Mode-Local-Client.png")
dfplot.describe().dropna()
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dfscr = pd.read_csv('swarm_all-Chameleon-remote-20170422233318.csv', names=list(range(1,500)))
dfscr=dfscr.set_index(1)
dfplot=dfscr.T.dropna(how='all')
sns.set_style("whitegrid")
ax = sns.boxplot(dfplot,showfliers=False)
ax.set_xticklabels(list(dfscr.index.values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Chameleon Swarm Mode Remote Client')
plt.savefig("Images/Chameleon-Swarm-Mode-Remote-Client.png")
dfplot.describe().dropna()
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dfscl = pd.read_csv('swarm_all-Chameleon-local-20170423003245.csv', names=list(range(1,500)))
dfscl=dfscl.set_index(1)
dfplot=dfscl.T.dropna(how='all')
sns.set_style("whitegrid")
ax = sns.boxplot(dfplot,showfliers=False)
ax.set_xticklabels(list(dfscl.index.values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Chameleon Swarm Mode Local Client')
plt.savefig("Images/Chameleon-Swarm-Mode-Local-Client.png")
dfplot.describe().dropna()
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dfsar = pd.read_csv('swarm_all-Aws-remote-20170423003923.csv', names=list(range(1,500)))
dfsar=dfsar.set_index(1)
sns.set_style("whitegrid")
dfplot=dfsar.T.dropna(how='all')
sns.set_style("whitegrid")
ax = sns.boxplot(dfplot,showfliers=False)
ax.set_xticklabels(list(dfsar.index.values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Aws Swarm Mode Remote Client')
plt.savefig("Images/Aws-Swarm-Mode-Remote-Client.png")
dfplot.describe().dropna()
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dfsal = pd.read_csv('swarm_all-Aws-local-20170422235928.csv', names=list(range(1,500)))
dfsal=dfsal.set_index(1)
sns.set_style("whitegrid")
dfplot=dfsal.T.dropna(how='all')
sns.set_style("whitegrid")
ax = sns.boxplot(dfplot,showfliers=False)
ax.set_xticklabels(list(dfsal.index.values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Aws Swarm Mode Local Client')
plt.savefig("Images/Aws-Swarm-Mode-Local-Client.png")
dfplot.describe().dropna()
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#Merge the data frames
dfcr1=dfcr.T.describe().dropna().loc[['mean','std']]
dfcr1['cloud'] = 'Chameleon'
dfcr1['type'] = 'Remote'
dfcl1=dfcl.T.describe().dropna().loc[['mean','std']]
dfcl1['cloud'] = 'Chameleon'
dfcl1['type'] = 'Local'
dfar1=dfar.T.describe().dropna().loc[['mean','std']]
dfar1['cloud'] = 'Aws'
dfar1['type'] = 'Remote'
dfal1=dfal.T.describe().dropna().loc[['mean','std']]
dfal1['cloud'] = 'Aws'
dfal1['type'] = 'Local'
dfmerged_docker=pd.concat([dfcr1,dfcl1,dfar1,dfal1])
dfmerged_docker.reset_index(level=0, inplace=True)
dfmerged_docker.set_index(['cloud', 'type','index']).T.stack().to_csv('Docker.csv',float_format='%.3f')
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#Merge the data frames
dfscr1=dfscr.T.describe().dropna().loc[['mean','std']]
dfscr1['cloud'] = 'Chameleon'
dfscr1['type'] = 'Remote'
dfscl1=dfscl.T.describe().dropna().loc[['mean','std']]
dfscl1['cloud'] = 'Chameleon'
dfscl1['type'] = 'Local'
dfsar1=dfsar.T.describe().dropna().loc[['mean','std']]
dfsar1['cloud'] = 'Aws'
dfsar1['type'] = 'Remote'
dfsal1=dfsal.T.describe().dropna().loc[['mean','std']]
dfsal1['cloud'] = 'Aws'
dfsal1['type'] = 'Local'
dfmerged_docker=pd.concat([dfscr1,dfscl1,dfsar1,dfsal1])
dfmerged_docker=pd.concat([dfscr1,dfscl1,dfsar1,dfsal1])
dfmerged_docker.set_index(['cloud', 'type','index']).T.stack().to_csv('Swarm.csv',float_format='%.3f')
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dfes_dockerl = pd.read_csv('elasticsearch/Elastic-Docker-BenchmarkResult.txt',sep='|',skiprows=[1],skipinitialspace=True,dtype={'Value ':str},keep_default_na ='')
dfes_docker= dfes_dockerl[['Metric ','Operation ','Value ','Unit ']]
dfes_swarml = pd.read_csv('elasticsearch/Elastic-Swarm-BenchmarkResult.txt',sep='|',skiprows=[1],skipinitialspace=True,dtype={'Value ':str},keep_default_na ='')
dfes_swarm= dfes_swarml[['Metric ','Operation ','Value ','Unit ']]
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dfes_docker['Mode'] = 'Docker'
dfes_swarm['Mode'] = 'Swarm'
dfesmerged=pd.concat([dfes_docker,dfes_swarm])
dfesmerged.set_index(['Mode','Unit ','Metric ', 'Operation ',]).T.stack().stack().stack().to_csv('escompare.csv',float_format='%.3f')
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dfcr1=dfcr.T.dropna(how='all')
dfcr1['cloud'] = 'Chameleon'
dfcr1['type'] = 'Remote'
dfcl1=dfcl.T.dropna(how='all')
dfcl1['cloud'] = 'Chameleon'
dfcl1['type'] = 'Local'
dfar1=dfar.T.dropna(how='all')
dfar1['cloud'] = 'Aws'
dfar1['type'] = 'Remote'
dfal1=dfal.T.dropna(how='all')
dfal1['cloud'] = 'Aws'
dfal1['type'] = 'Local'
dfmerged_docker_all=pd.concat([dfcr1,dfcl1,dfar1,dfal1])
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dfplot=dfmerged_docker_all.set_index(['cloud','type'],append=True).stack().reset_index()
sns.set_style("whitegrid")
plt.figure(figsize=(8, 6))
ax = sns.boxplot(x=1,y=0 , hue='cloud',data = dfplot[dfplot['cloud'] == 'Remote'], showfliers=False)
ax.set_xticklabels(list(dfplot[1].values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Aws Swarm Mode Local Client')
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dfes = pd.read_csv('escompare.csv')
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sns.set_style("whitegrid")
plt.figure(figsize=(8, 6))
ax = sns.boxplot(x=1,y=0 , hue='cloud',data = dfplot[dfplot['type'] == 'Remote'], showfliers=False)
ax.set_xticklabels(list(dfplot[1].values),rotation=90)
ax.set_ylabel('Time Taken in s')
sns.plt.title('Aws Swarm Mode Local Client')
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