In [1]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

import pandas as pd
import seaborn as sns
from ficus import FigureManager


    

In [12]:

    

data = pd.read_csv('storage_bench_results.txt', skipinitialspace=True)


    

In [13]:

    

data.columns


    

    
Out[13]:





Index(['storage_type', 'n_hashes', 'bench', 'time'], dtype='object')

In [16]:

    

#with FigureManager(show=True, figsize=(12,8)) as (fig, ax):    
sns.catplot(x='storage_type', y='time', hue='n_hashes', col='bench', kind='swarm', data=data,)


    

    
Out[16]:





<seaborn.axisgrid.FacetGrid at 0x7f0cb93d4d68>






    

In [25]:

    

data = pd.read_csv('storage_bench_results_2.txt', skipinitialspace=True)


    

In [26]:

    

sns.catplot(x='storage_type', y='time', hue='n_hashes', col='bench', kind='swarm', data=data,)


    

    
Out[26]:





<seaborn.axisgrid.FacetGrid at 0x7f0cb8c52978>






    

In [77]:

    

data = pd.read_csv('storage_bench_results_3.txt', skipinitialspace=True)


    

In [78]:

    

sns.catplot(x='storage_type', y='time', hue='n_hashes', col='bench', kind='swarm', data=data,)


    

    
Out[78]:





<seaborn.axisgrid.FacetGrid at 0x7f0cb7972d68>






    

In [79]:

    

comp = data.pivot_table(index=['storage_type', 'n_hashes'], columns='bench', values=['time', 'storage_type']).reset_index()
comp.columns = [' '.join(col).strip() for col in comp.columns.values]


    

In [80]:

    

comp


    

    
Out[80]:







  

    

      

      
storage_type
      
n_hashes
      
time insert
      
time insert_and_query
      
time insert_second
      
time query
    
  
  

    

      
0
      
BitStorage
      
1000000
      
0.059829
      
0.051519
      
0.050041
      
0.033410
    
    

      
1
      
BitStorage
      
10000000
      
0.663944
      
0.532553
      
0.527301
      
0.335135
    
    

      
2
      
BitStorage
      
100000000
      
13.421067
      
11.873967
      
11.839533
      
8.085970
    
    

      
3
      
ByteStorage
      
1000000
      
0.063725
      
0.087318
      
0.053965
      
0.035305
    
    

      
4
      
ByteStorage
      
10000000
      
1.044263
      
1.392820
      
0.952653
      
0.716201
    
    

      
5
      
ByteStorage
      
100000000
      
17.751100
      
23.662567
      
16.653267
      
12.055467
    
    

      
6
      
NibbleStorage
      
1000000
      
0.114838
      
0.185971
      
0.110701
      
0.057718
    
    

      
7
      
NibbleStorage
      
10000000
      
1.382013
      
2.108183
      
1.331357
      
0.628721
    
    

      
8
      
NibbleStorage
      
100000000
      
35.791900
      
42.657133
      
35.254100
      
12.048467
    
    

      
9
      
SparseppSetStorage
      
1000000
      
0.171973
      
0.082023
      
0.083155
      
0.058892
    
    

      
10
      
SparseppSetStorage
      
10000000
      
3.296693
      
1.212373
      
1.213280
      
0.951652
    
    

      
11
      
SparseppSetStorage
      
100000000
      
41.073300
      
18.007200
      
17.957800
      
13.985600
    
  

In [82]:

    

sns.relplot(x='time insert', y='time insert_and_query',  style='n_hashes', hue='storage_type', data=comp)


    

    
Out[82]:





<seaborn.axisgrid.FacetGrid at 0x7f0cb7d50f60>






    

In [84]:

    

sns.relplot(x='time insert', y='time insert_second',  style='n_hashes', hue='storage_type', data=comp)


    

    
Out[84]:





<seaborn.axisgrid.FacetGrid at 0x7f0cb7697400>






    

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