In [1]:

    

import os
files = []
for root, _, fnames in os.walk("No Videos"):
    files.extend([os.path.join(root, filename) for filename in fnames if filename.endswith('.csv')])


    

In [3]:

    

%%capture
import pandas as pd
df = pd.DataFrame()
list_ = []
for f in files:
    fpd = pd.read_csv(f,index_col=None, header=0)
    fpd.participantId = os.path.dirname(f).split('/')[-1]
    list_.append(fpd)
frame = pd.concat(list_)


    

In [3]:

    

frame.head()


    

    
Out[3]:






  

    

      

      
participantId
      
condition
      
timeSinceStartup
      
correctNodeHit
      
keypressed
      
calibrationData
      
bubbleSize
      
numberNodes
      
targetNode
      
currentSelectedNode
      
currentState
      
correctedEyeX
      
correctedEyeY
      
rawEyeX
      
rawEyeY
    
  
  

    

      
0
      
1
      
NaN
      
3.825378
      
NaN
      
HAPRING_TIP
      
noCalibrationDataSet
      
NaN
      
0.0
      
NaN
      
-1.0
      
Introduction
      
0.0
      
0.0
      
960.0
      
540.0
    
    

      
1
      
1
      
NaN
      
7.018346
      
NaN
      
HAPRING_TIP
      
noCalibrationDataSet
      
NaN
      
0.0
      
NaN
      
-1.0
      
Introduction
      
0.0
      
0.0
      
960.0
      
540.0
    
    

      
2
      
1
      
NaN
      
7.033599
      
NaN
      
NaN
      
noCalibrationDataSet
      
NaN
      
0.0
      
NaN
      
-1.0
      
Introduction
      
0.0
      
0.0
      
960.0
      
540.0
    
    

      
3
      
1
      
NaN
      
7.034281
      
NaN
      
NaN
      
noCalibrationDataSet
      
NaN
      
0.0
      
NaN
      
-1.0
      
Introduction
      
0.0
      
0.0
      
960.0
      
540.0
    
    

      
4
      
1
      
NaN
      
7.036498
      
NaN
      
NaN
      
noCalibrationDataSet
      
NaN
      
0.0
      
NaN
      
-1.0
      
Introduction
      
0.0
      
0.0
      
960.0
      
540.0
    
  

In [4]:

    

frame.condition.unique()


    

    
Out[4]:





array([nan, 'WITHCUSTOMCALIB', 'MOUSE', 'EYE', 'noCalibrationDataSet'], dtype=object)

In [5]:

    

import numpy as np
df  = pd.DataFrame(frame, columns=['participantId', 'condition','timeSinceStartup']).groupby(['participantId', 'condition'])

total_time_df = df.agg({'condition': 'first','timeSinceStartup': lambda x: max(x) - min(x)})
total_time_df = total_time_df[total_time_df.condition.isin(['WITHCUSTOMCALIB', 'MOUSE', 'EYE'])]


    

In [9]:

    

import matplotlib.pyplot as plt
plt.style.use('ggplot')

pd.DataFrame.boxplot(total_time_df, by='condition')
plt.show()