In [137]:

    

import numpy as np
import pandas as pd
import scipy.stats as stats
from matplotlib import pyplot as plt
import matplotlib.pylab as pylab
import seaborn as sns
import json
pylab.rcParams['figure.figsize'] = 16, 12
%matplotlib inline


    

In [136]:

    

y = np.random.randn(100)
plt.plot(y)
plt.autoscale(tight='x')
plt.tight_layout()


    

In [21]:

    

df = pd.read_json('/tmp/1.json', orient='index')
df


    

    
Out[21]:






  

    

      

      
endTime
      
groupBy
      
helpOpenedFromPopup
      
markTabFromHotkey
      
markTabFromPanelTree
      
markTabFromPopup
      
markTabFromVisualization
      
openFrequentFromPopup
      
openRecentFromPopup
      
popupOpened
      
startTime
      
switchToMarkedTabFromHotkey
      
switchToMarkedTabFromPopup
      
switchToTabFromPanelTree
      
switchToTabFromVisualization
      
tabsActivated
      
tabsClosed
      
tabsCreated
      
tabsMarked
      
tabsMoved
      

    
  
  

    

      
s_on_off
      
 1398983689204
      
 {u'id': [], u'time': []}
      
 1
      
 12
      
 0
      
 0
      
 0
      
 0
      
 0
      
 {u'numOpenTabs': [17, 17], u'time': [139898367...
      
 1398981819812
      
 11
      
 0
      
 0
      
 0
      
 {u'index': [2, 1, 0, 2, 3, 2, 3, 0, 1, 4, 1, 4...
      
 {u'window': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
      
 {u'index': [2, 3, 1, 5, 4, 5, 6, 9, 7, 5, 10, ...
      
 {u'numMarkedTabs': [3, 4, 5, 6, 7, 8, 9, 10, 1...
      
 {u'window': [1, 1, 1, 1, 1, 1], u'numOpenTabs'...
      
...
    
  

1 rows × 26 columns

In [16]:

    

j = json.load(open('1_on.json'))


    

In [17]:

    

j


    

    
Out[17]:





{u'endTime': 1398983689204,
 u'groupBy': {u'id': [], u'time': []},
 u'helpOpenedFromPopup': 1,
 u'markTabFromHotkey': 12,
 u'markTabFromPanelTree': 0,
 u'markTabFromPopup': 0,
 u'markTabFromVisualization': 0,
 u'openFrequentFromPopup': 0,
 u'openRecentFromPopup': 0,
 u'popupOpened': {u'numOpenTabs': [17, 17],
  u'time': [1398983679443, 1398983679444]},
 u'startTime': 1398981819812,
 u'switchToMarkedTabFromHotkey': 11,
 u'switchToMarkedTabFromPopup': 0,
 u'switchToTabFromPanelTree': 0,
 u'switchToTabFromVisualization': 0,
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 u'tabsClosed': {u'numOpenTabs': [3,
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  u'window': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]},
 u'tabsCreated': {u'index': [2,
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  u'numOpenTabs': [3,
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 u'tabsMarked': {u'numMarkedTabs': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
  u'numOpenTabs': [6, 9, 11, 12, 11, 13, 12, 16, 16, 16, 16, 17],
  u'time': [1398981979026,
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 u'tabsMoved': {u'numOpenTabs': [10, 10, 10, 10, 10, 10],
  u'time': [1398982111512,
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  u'window': [1, 1, 1, 1, 1, 1]},
 u'tabsOpen': {u'1': {u'numOpenTabs': [3,
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   u'time': [1398981822641,
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 u'urlsRevisited': 25,
 u'urlsVisited': 98,
 u'visualizationOpened': {u'numOpenTabs': [11, 12],
  u'time': [1398982593327, 1398982594756]},
 u'visualizationOpenedFromHotkey': 2,
 u'visualizationOpenedFromPopup': 0}

In [25]:

    

s = pd.Series([[1,2],[3,4]], index=['a', 'b'])
s


    

    
Out[25]:





a    [1, 2]
b    [3, 4]
dtype: object

In [ ]:

    




    

In [129]:

    

df = pd.read_json('/tmp/combined.json', orient='records')
pd.DataFrame(df, columns=['tabsActivated'])


    

    
Out[129]:






  

    

      

      
tabsActivated
    
  
  

    

      
0
      
 {u'index': [2, 1, 0, 2, 3, 2, 3, 0, 1, 4, 1, 4...
    
    

      
1
      
 {u'index': [16, 5, 6, 5, 6, 7, 7, 6, 7, 8, 9, ...
    
    

      
2
      
 {u'index': [16, 15, 14, 13, 14, 13, 14, 13, 14...
    
    

      
3
      
 {u'index': [1, 0, 1, 2, 3, 2, 3, 6, 11, 6, 4, ...
    
    

      
4
      
 {u'index': [7, 8, 15, 9, 16, 10, 17, 11, 18, 1...
    
    

      
5
      
 {u'index': [3, 2, 3, 2, 3, 2, 3, 3, 4, 3, 2, 1...
    
    

      
6
      
 {u'index': [2, 1, 0, 0, 1, 2, 3, 2, 1, 2, 1, 2...
    
    

      
7
      
 {u'index': [0, 0, 1, 2, 1, 4, 3, 3, 2, 2, 3, 3...
    
  

8 rows × 1 columns

In [89]:

    

df['startTime'].keys()


    

    
Out[89]:





Int64Index([0, 1, 2, 3], dtype='int64')

In [90]:

    

def to_time_delta(start=None, timestamps=None):
    return (np.array(timestamps) - start) / (1 * 1000) # ms to s

def scalarxy(ycol=None):
    """Get x and y arrays with one data point for each row.
    
    x axis represents the row index.
    y axis represents one scalar value for each row.
    
    Args:
      ycol: Column to be used as y (scalar data type).
      
    Returns:
      x, y: Arrays with x and y values.
      
    """
    x, y = df.index, df[ycol]
    return x, y

def xy(index=None, xcol=None, ycol=None):
    """Get x and y from values in columns.
    
    Args:
      index: Index of the row.
      xcol: Column to be used as x.
      ycol: Column to be used as y.
      
    Returns:
      x, y: Arrays with x and y values.
      
    """
    x, y = df[xcol][index], df[ycol][index]
    return x,y

def nestedxy(index=None, maincol=None, xcol=None, ycol=None):
    """Get x and y from values in nested columns.
    
    Args:
      index: Index of the row.
      maincol: Main column name.
      xcol: Nested column to be used as x.
      ycol: Nested column to be used as y.
      
    Returns:
      x, y: Arrays with x and y values.
      
    """
    series = df[maincol][index] # Values from the main column
    x, y = series[xcol], series[ycol]
    
    start_time = df['startTime'][index]
    if xcol == 'time':
        x = to_time_delta(start=start_time, timestamps=series['time'])
    if ycol == 'time':
        y = to_time_delta(start=start_time, timestamps=series['time'])
    return x, y


    

In [143]:

    

# Pattern of tab switching over time
fig = plt.figure(figsize=(16, 8))
nrows, ncols = len(df.index) / 2, 2
for index in df.index:
    x, y = nestedxy(index=index, maincol='tabsActivated', xcol='time', ycol='index')
    plt.subplot(nrows, ncols, index + 1)
    plt.hold(True)
    plt.plot(x, y, 'ro', markersize=3)
    plt.plot(x, y)
    plt.xlabel('Time (s)')
    plt.ylabel('Active tab index')
    plt.title('Run %d' % (index))
plt.autoscale(tight='x')
plt.tight_layout()


    

In [134]:

    

x, y = scalarxy(ycol='startTime')
plt.scatter(x, y)


    

    
Out[134]:





<matplotlib.collections.PathCollection at 0x6c61310>






    

In [ ]: