In [1]:

    

import pandas as pd


    

In [2]:

    

import emission.core.get_database as edb


    

In [3]:

    

import bson.objectid as boi


    

In [5]:

    

edb.get_trip_new_db().find_one({'_id': boi.ObjectId('56cc93a2f6858f75fa405986')})


    

In [6]:

    

edb.get_timeseries_db().distinct('metadata.key')


    

    
Out[6]:





[u'analysis/smoothing',
 u'statemachine/transition',
 u'background/motion_activity',
 u'background/location',
 u'background/filtered_location']

In [7]:

    

edb.get_timeseries_db().find({'metadata.key':'statemachine/transition'}).count()


    

    
Out[7]:





212

In [8]:

    

edb.get_timeseries_db().find({'metadata.key':'statemachine/transition', 'metadata.write_ts': {'$gte': 1456160320.494486, '$lte': 1456160557.006508}}).count()


    

    
Out[8]:





3

In [9]:

    

list(edb.get_timeseries_db().find({'metadata.key':'statemachine/transition', 'metadata.write_ts': {'$gte': 1456160320.494486, '$lte': 1456160557.006508}}).sort('metadata.write_ts'))


    

    
Out[9]:





[{u'_id': ObjectId('56cb5fdbeaedff78c7634e42'),
  u'data': {u'curr_state': 2,
   u'fmt_time': u'2016-02-22T08:58:55.208203-08:00',
   u'local_dt': datetime.datetime(2016, 2, 22, 8, 58, 55, 208000),
   u'transition': 14,
   u'ts': 1456160335.208203},
  u'metadata': {u'key': u'statemachine/transition',
   u'platform': u'ios',
   u'plugin': u'none',
   u'read_ts': 0,
   u'time_zone': u'America/Los_Angeles',
   u'type': u'message',
   u'write_fmt_time': u'2016-02-22T08:58:55.208203-08:00',
   u'write_local_dt': datetime.datetime(2016, 2, 22, 8, 58, 55, 208000),
   u'write_ts': 1456160335.208203},
  u'user_id': UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')},
 {u'_id': ObjectId('56cb5fdbeaedff78c7634e47'),
  u'data': {u'curr_state': 2,
   u'fmt_time': u'2016-02-22T09:00:26.348244-08:00',
   u'local_dt': datetime.datetime(2016, 2, 22, 9, 0, 26, 348000),
   u'transition': 7,
   u'ts': 1456160426.348244},
  u'metadata': {u'key': u'statemachine/transition',
   u'platform': u'ios',
   u'plugin': u'none',
   u'read_ts': 0,
   u'time_zone': u'America/Los_Angeles',
   u'type': u'message',
   u'write_fmt_time': u'2016-02-22T09:00:26.348244-08:00',
   u'write_local_dt': datetime.datetime(2016, 2, 22, 9, 0, 26, 348000),
   u'write_ts': 1456160426.348244},
  u'user_id': UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')},
 {u'_id': ObjectId('56cb5fdbeaedff78c7634e48'),
  u'data': {u'curr_state': 2,
   u'fmt_time': u'2016-02-22T09:00:26.900016-08:00',
   u'local_dt': datetime.datetime(2016, 2, 22, 9, 0, 26, 900000),
   u'transition': 12,
   u'ts': 1456160426.900016},
  u'metadata': {u'key': u'statemachine/transition',
   u'platform': u'ios',
   u'plugin': u'none',
   u'read_ts': 0,
   u'time_zone': u'America/Los_Angeles',
   u'type': u'message',
   u'write_fmt_time': u'2016-02-22T09:00:26.900016-08:00',
   u'write_local_dt': datetime.datetime(2016, 2, 22, 9, 0, 26, 900000),
   u'write_ts': 1456160426.900016},
  u'user_id': UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')}]

In [10]:

    

list(edb.get_timeseries_db().find({'metadata.key':'statemachine/transition', 'data.transition': {'$in': [13, 14]},
                                   'metadata.write_ts': {'$gte': (1456160320.494486 - 5 * 60),
                                                         '$lte': (1456160557.006508 + 5 * 60)}}).sort('metadata.write_ts'))


    

    
Out[10]:





[{u'_id': ObjectId('56cb5fdbeaedff78c7634e42'),
  u'data': {u'curr_state': 2,
   u'fmt_time': u'2016-02-22T08:58:55.208203-08:00',
   u'local_dt': datetime.datetime(2016, 2, 22, 8, 58, 55, 208000),
   u'transition': 14,
   u'ts': 1456160335.208203},
  u'metadata': {u'key': u'statemachine/transition',
   u'platform': u'ios',
   u'plugin': u'none',
   u'read_ts': 0,
   u'time_zone': u'America/Los_Angeles',
   u'type': u'message',
   u'write_fmt_time': u'2016-02-22T08:58:55.208203-08:00',
   u'write_local_dt': datetime.datetime(2016, 2, 22, 8, 58, 55, 208000),
   u'write_ts': 1456160335.208203},
  u'user_id': UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')}]

In [11]:

    

list(edb.get_timeseries_db().find({'metadata.key':'background/motion_activity',
                                   'metadata.write_ts': {'$gte': (1456160320.494486 - 5 * 60),
                                                         '$lte': (1456160557.006508 + 5 * 60)}}).sort('metadata.write_ts'))[0:5]


    

    
Out[11]:





[{u'_id': ObjectId('56cb5fdceaedff78c7635095'),
  u'data': {u'automotive': False,
   u'confidence': 50,
   u'confidence_level': u'low',
   u'cycling': False,
   u'fmt_time': u'2016-02-22T08:53:55.766853-08:00',
   u'local_dt': datetime.datetime(2016, 2, 22, 8, 53, 55, 766000),
   u'running': False,
   u'stationary': False,
   u'ts': 1456160035.766853,
   u'type': 7,
   u'unknown': False,
   u'walking': True},
  u'metadata': {u'key': u'background/motion_activity',
   u'platform': u'ios',
   u'plugin': None,
   u'read_ts': 0,
   u'time_zone': u'America/Los_Angeles',
   u'type': u'sensor-data',
   u'write_fmt_time': u'2016-02-22T08:53:55.766853-08:00',
   u'write_local_dt': datetime.datetime(2016, 2, 22, 8, 53, 55, 766000),
   u'write_ts': 1456160035.766853},
  u'user_id': UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')},
 {u'_id': ObjectId('56cb5fdceaedff78c7635096'),
  u'data': {u'automotive': False,
   u'confidence': 75,
   u'confidence_level': u'medium',
   u'cycling': False,
   u'fmt_time': u'2016-02-22T08:54:05.766853-08:00',
   u'local_dt': datetime.datetime(2016, 2, 22, 8, 54, 5, 766000),
   u'running': False,
   u'stationary': False,
   u'ts': 1456160045.766853,
   u'type': 7,
   u'unknown': False,
   u'walking': True},
  u'metadata': {u'key': u'background/motion_activity',
   u'platform': u'ios',
   u'plugin': None,
   u'read_ts': 0,
   u'time_zone': u'America/Los_Angeles',
   u'type': u'sensor-data',
   u'write_fmt_time': u'2016-02-22T08:54:05.766853-08:00',
   u'write_local_dt': datetime.datetime(2016, 2, 22, 8, 54, 5, 766000),
   u'write_ts': 1456160045.766853},
  u'user_id': UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')},
 {u'_id': ObjectId('56cb5fdceaedff78c7635097'),
  u'data': {u'automotive': False,
   u'confidence': 100,
   u'confidence_level': u'high',
   u'cycling': False,
   u'fmt_time': u'2016-02-22T08:54:10.766853-08:00',
   u'local_dt': datetime.datetime(2016, 2, 22, 8, 54, 10, 766000),
   u'running': False,
   u'stationary': False,
   u'ts': 1456160050.766853,
   u'type': 7,
   u'unknown': False,
   u'walking': True},
  u'metadata': {u'key': u'background/motion_activity',
   u'platform': u'ios',
   u'plugin': None,
   u'read_ts': 0,
   u'time_zone': u'America/Los_Angeles',
   u'type': u'sensor-data',
   u'write_fmt_time': u'2016-02-22T08:54:10.766853-08:00',
   u'write_local_dt': datetime.datetime(2016, 2, 22, 8, 54, 10, 766000),
   u'write_ts': 1456160050.766853},
  u'user_id': UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')},
 {u'_id': ObjectId('56cb5fdceaedff78c7635098'),
  u'data': {u'automotive': False,
   u'confidence': 75,
   u'confidence_level': u'medium',
   u'cycling': False,
   u'fmt_time': u'2016-02-22T09:00:42.049375-08:00',
   u'local_dt': datetime.datetime(2016, 2, 22, 9, 0, 42, 49000),
   u'running': False,
   u'stationary': False,
   u'ts': 1456160442.049375,
   u'type': 9,
   u'unknown': False,
   u'walking': False},
  u'metadata': {u'key': u'background/motion_activity',
   u'platform': u'ios',
   u'plugin': None,
   u'read_ts': 0,
   u'time_zone': u'America/Los_Angeles',
   u'type': u'sensor-data',
   u'write_fmt_time': u'2016-02-22T09:00:42.049375-08:00',
   u'write_local_dt': datetime.datetime(2016, 2, 22, 9, 0, 42, 49000),
   u'write_ts': 1456160442.049375},
  u'user_id': UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')},
 {u'_id': ObjectId('56cb5fdceaedff78c7635099'),
  u'data': {u'automotive': False,
   u'confidence': 50,
   u'confidence_level': u'low',
   u'cycling': False,
   u'fmt_time': u'2016-02-22T09:00:44.276170-08:00',
   u'local_dt': datetime.datetime(2016, 2, 22, 9, 0, 44, 276000),
   u'running': False,
   u'stationary': False,
   u'ts': 1456160444.27617,
   u'type': 7,
   u'unknown': False,
   u'walking': True},
  u'metadata': {u'key': u'background/motion_activity',
   u'platform': u'ios',
   u'plugin': None,
   u'read_ts': 0,
   u'time_zone': u'America/Los_Angeles',
   u'type': u'sensor-data',
   u'write_fmt_time': u'2016-02-22T09:00:44.276170-08:00',
   u'write_local_dt': datetime.datetime(2016, 2, 22, 9, 0, 44, 276000),
   u'write_ts': 1456160444.27617},
  u'user_id': UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')}]

In [12]:

    

import emission.storage.timeseries.abstract_timeseries as esta
import emission.net.usercache.abstract_usercache as enua


    

In [13]:

    

from uuid import UUID


    

In [14]:

    

motion_df = esta.TimeSeries.get_time_series(UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')) \
    .get_data_df('background/motion_activity', enua.UserCache.TimeQuery('write_ts', 
                                                                        1456160320.494486,
                                                                        1456160557.006508))


    

In [15]:

    

motion_df[motion_df.type != 9]


    

    
Out[15]:






  

    

      

      
_id
      
automotive
      
confidence
      
confidence_level
      
cycling
      
fmt_time
      
local_dt
      
metadata_write_ts
      
running
      
stationary
      
ts
      
type
      
unknown
      
walking
    
  
  

    

      
1
      
56cb5fdceaedff78c7635099
      
False
      
50
      
low
      
False
      
2016-02-22T09:00:44.276170-08:00
      
2016-02-22 09:00:44.276
      
1.456160e+09
      
False
      
False
      
1.456160e+09
      
7
      
False
      
True
    
    

      
3
      
56cb5fdceaedff78c763509b
      
False
      
50
      
low
      
False
      
2016-02-22T09:00:59.543755-08:00
      
2016-02-22 09:00:59.543
      
1.456160e+09
      
False
      
False
      
1.456160e+09
      
7
      
False
      
True
    
    

      
8
      
56cb5fdceaedff78c76350a0
      
False
      
50
      
low
      
False
      
2016-02-22T09:01:32.621500-08:00
      
2016-02-22 09:01:32.621
      
1.456160e+09
      
False
      
False
      
1.456160e+09
      
7
      
False
      
True
    
    

      
10
      
56cb5fdceaedff78c76350a2
      
False
      
50
      
low
      
False
      
2016-02-22T09:02:13.330235-08:00
      
2016-02-22 09:02:13.330
      
1.456161e+09
      
False
      
False
      
1.456161e+09
      
7
      
False
      
True
    
    

      
12
      
56cb5fdceaedff78c76350a4
      
False
      
50
      
low
      
False
      
2016-02-22T09:02:15.874864-08:00
      
2016-02-22 09:02:15.874
      
1.456161e+09
      
False
      
False
      
1.456161e+09
      
7
      
False
      
True
    
  

In [16]:

    

trip_ids = [boi.ObjectId('56cc93a2f6858f75fa405986'), boi.ObjectId('56cc93a2f6858f75fa405988'), boi.ObjectId('56cc93a2f6858f75fa40598a'),
            boi.ObjectId('56cc93a2f6858f75fa40598c'), boi.ObjectId('56cc93a2f6858f75fa40598e'), boi.ObjectId('56cc93a2f6858f75fa405990')]


    

In [17]:

    

import emission.storage.decorations.trip_queries as esdt


    

In [35]:

    

trips = [esdt.get_trip(tid) for tid in trip_ids]


    

In [46]:

    

ts = esta.TimeSeries.get_time_series(UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')) 
for trip in trips:
    print 20 * '=' + trip.start_fmt_time + 10 * '=' + trip.end_fmt_time + 20 * '='
    motion_df = ts.get_data_df('background/motion_activity', enua.UserCache.TimeQuery('write_ts', 
                                                                        trip.start_ts - 5 * 60,
                                                                        trip.end_ts + 5 * 60))
    if len(motion_df) != 0:
        print motion_df[(motion_df.type != 9) & (motion_df.type != 3)][["confidence", "confidence_level", "type", "fmt_time"]]
    else:
        print "No motion activities"
        
    print 50 * '-'
    transition_df = ts.get_data_df('statemachine/transition', enua.UserCache.TimeQuery('write_ts',
                                                                        trip.start_ts - 5 * 60,
                                                                        trip.end_ts + 5 * 60))
    if len(transition_df) != 0:
        print transition_df[transition_df.transition == 14][["transition", "fmt_time"]]
    else:
        print "No transition activities"


    

    




====================2016-02-22T08:58:40.494486-08:00==========2016-02-22T09:02:37.006508-08:00====================
    confidence confidence_level  type                          fmt_time
0           50              low     7  2016-02-22T08:53:55.766853-08:00
1           75           medium     7  2016-02-22T08:54:05.766853-08:00
2          100             high     7  2016-02-22T08:54:10.766853-08:00
4           50              low     7  2016-02-22T09:00:44.276170-08:00
6           50              low     7  2016-02-22T09:00:59.543755-08:00
11          50              low     7  2016-02-22T09:01:32.621500-08:00
13          50              low     7  2016-02-22T09:02:13.330235-08:00
15          50              low     7  2016-02-22T09:02:15.874864-08:00
18          50              low     7  2016-02-22T09:03:01.676397-08:00
31          50              low     7  2016-02-22T09:05:36.901761-08:00
32          50              low     7  2016-02-22T09:05:44.535384-08:00
34          50              low     7  2016-02-22T09:05:52.168616-08:00
36          50              low     7  2016-02-22T09:06:07.434659-08:00
38          50              low     7  2016-02-22T09:06:12.523704-08:00
40          50              low     7  2016-02-22T09:06:32.878869-08:00
--------------------------------------------------
   transition                          fmt_time
5          14  2016-02-22T08:58:55.208203-08:00
====================2016-02-22T10:07:00.000028-08:00==========2016-02-22T10:07:00.000028-08:00====================
    confidence confidence_level  type                          fmt_time
1           50              low     7  2016-02-22T10:07:26.764054-08:00
16          50              low     7  2016-02-22T10:11:31.025921-08:00
17          75           medium     7  2016-02-22T10:11:36.115304-08:00
19          50              low     7  2016-02-22T10:11:56.473527-08:00
--------------------------------------------------
   transition                          fmt_time
4          14  2016-02-22T10:07:32.198895-08:00
====================2016-02-22T10:27:22.000019-08:00==========2016-02-22T10:30:15.000020-08:00====================
    confidence confidence_level  type                          fmt_time
7           50              low     7  2016-02-22T10:24:32.033723-08:00
8           50              low     7  2016-02-22T10:24:39.664155-08:00
10          50              low     7  2016-02-22T10:24:44.751091-08:00
12          50              low     7  2016-02-22T10:24:47.294478-08:00
14          50              low     7  2016-02-22T10:25:20.360789-08:00
18          50              low     7  2016-02-22T10:26:44.299580-08:00
20          50              low     0  2016-02-22T10:26:57.017387-08:00
22          50              low     7  2016-02-22T10:27:24.998391-08:00
23          75           medium     7  2016-02-22T10:27:30.085855-08:00
24         100             high     7  2016-02-22T10:27:35.173142-08:00
26          50              low     0  2016-02-22T10:27:42.804651-08:00
27          50              low     0  2016-02-22T10:28:43.848877-08:00
29          50              low     1  2016-02-22T10:29:44.888668-08:00
31          50              low     7  2016-02-22T10:29:55.061765-08:00
33          50              low     0  2016-02-22T10:30:05.235626-08:00
35          50              low     1  2016-02-22T10:30:12.866482-08:00
36          75           medium     7  2016-02-22T10:30:25.584582-08:00
37         100             high     7  2016-02-22T10:30:30.672255-08:00
--------------------------------------------------
No transition activities
====================2016-02-22T13:07:22.999994-08:00==========2016-02-22T13:13:16.999949-08:00====================
    confidence confidence_level  type                          fmt_time
1           50              low     1  2016-02-22T13:03:03.738880-08:00
7           50              low     0  2016-02-22T13:05:32.902720-08:00
8           75           medium     0  2016-02-22T13:05:37.902720-08:00
9          100             high     0  2016-02-22T13:05:42.902720-08:00
10         100             high    10  2016-02-22T13:06:17.902720-08:00
11         100             high     0  2016-02-22T13:06:42.902720-08:00
12         100             high    10  2016-02-22T13:06:52.902720-08:00
13         100             high     0  2016-02-22T13:06:57.902720-08:00
15          50              low     7  2016-02-22T13:10:13.752606-08:00
17          50              low     7  2016-02-22T13:11:40.265408-08:00
19          50              low     7  2016-02-22T13:11:42.810011-08:00
22          50              low     7  2016-02-22T13:11:52.988741-08:00
--------------------------------------------------
   transition                          fmt_time
4          14  2016-02-22T13:09:38.211089-08:00
====================2016-02-22T15:36:23.000059-08:00==========2016-02-22T15:37:35.000060-08:00====================
    confidence confidence_level  type                          fmt_time
3           50              low     7  2016-02-22T15:31:50.638773-08:00
4           75           medium     7  2016-02-22T15:31:55.638773-08:00
5          100             high     7  2016-02-22T15:32:00.638773-08:00
8           50              low     0  2016-02-22T15:33:24.061914-08:00
9           75           medium     0  2016-02-22T15:33:44.061914-08:00
10         100             high     0  2016-02-22T15:33:49.061914-08:00
11         100             high    10  2016-02-22T15:35:51.226661-08:00
12         100             high     0  2016-02-22T15:35:58.222899-08:00
13         100             high    10  2016-02-22T15:36:06.491621-08:00
14         100             high     0  2016-02-22T15:36:09.353743-08:00
15         100             high    10  2016-02-22T15:36:13.169997-08:00
16          75           medium     0  2016-02-22T15:36:13.805993-08:00
17         100             high     0  2016-02-22T15:36:15.713970-08:00
19          50              low     7  2016-02-22T15:38:02.569237-08:00
21          50              low     7  2016-02-22T15:38:15.289601-08:00
23          50              low     7  2016-02-22T15:39:11.256662-08:00
29          50              low     7  2016-02-22T15:40:30.116855-08:00
31          50              low     7  2016-02-22T15:41:05.734211-08:00
34          50              low     7  2016-02-22T15:41:15.910509-08:00
36          50              low     7  2016-02-22T15:41:18.454599-08:00
38          50              low     7  2016-02-22T15:41:23.542452-08:00
40          50              low     7  2016-02-22T15:41:56.615520-08:00
41          75           medium     7  2016-02-22T15:42:14.424286-08:00
42         100             high     7  2016-02-22T15:42:19.512349-08:00
44         100             high     7  2016-02-22T15:42:29.689191-08:00
--------------------------------------------------
   transition                          fmt_time
4          14  2016-02-22T15:37:50.221577-08:00
====================2016-02-22T15:49:52.999964-08:00==========2016-02-22T16:04:02.000039-08:00====================
    confidence confidence_level  type                          fmt_time
1           50              low     7  2016-02-22T15:47:14.657470-08:00
3           50              low     7  2016-02-22T15:48:53.879601-08:00
6           50              low     7  2016-02-22T15:49:01.512029-08:00
10          50              low     0  2016-02-22T15:49:44.760739-08:00
11         100             high     0  2016-02-22T15:49:52.392630-08:00
12         100             high    10  2016-02-22T15:51:37.964272-08:00
13         100             high     0  2016-02-22T15:51:39.872442-08:00
14         100             high    10  2016-02-22T15:51:47.504071-08:00
15          75           medium     0  2016-02-22T15:51:48.140126-08:00
16         100             high     0  2016-02-22T15:51:49.412084-08:00
17         100             high    10  2016-02-22T15:51:54.500113-08:00
18         100             high     0  2016-02-22T15:52:05.311265-08:00
19         100             high    10  2016-02-22T15:52:09.126973-08:00
20         100             high     0  2016-02-22T15:52:14.850862-08:00
21         100             high    10  2016-02-22T15:52:19.620486-08:00
22         100             high     0  2016-02-22T15:53:07.637047-08:00
23         100             high    10  2016-02-22T15:53:58.836131-08:00
24         100             high     0  2016-02-22T15:54:01.698114-08:00
25         100             high    10  2016-02-22T15:54:05.514003-08:00
26         100             high     0  2016-02-22T15:54:49.079696-08:00
27         100             high    10  2016-02-22T15:54:52.895487-08:00
28         100             high     0  2016-02-22T15:54:59.890829-08:00
29         100             high    10  2016-02-22T15:57:59.554448-08:00
30         100             high     0  2016-02-22T15:58:15.771020-08:00
31         100             high    10  2016-02-22T15:58:19.586771-08:00
32         100             high     0  2016-02-22T15:58:36.757466-08:00
33         100             high    10  2016-02-22T15:58:43.753683-08:00
34         100             high     0  2016-02-22T15:58:56.792021-08:00
35         100             high    10  2016-02-22T16:01:57.404701-08:00
36         100             high     0  2016-02-22T16:02:37.472290-08:00
37         100             high    10  2016-02-22T16:04:17.958449-08:00
38         100             high     0  2016-02-22T16:04:49.123409-08:00
39         100             high    10  2016-02-22T16:04:55.165070-08:00
40         100             high     0  2016-02-22T16:06:24.522086-08:00
41         100             high    10  2016-02-22T16:06:28.337900-08:00
42         100             high     0  2016-02-22T16:06:37.559274-08:00
43         100             high    10  2016-02-22T16:06:41.374822-08:00
44         100             high     0  2016-02-22T16:07:09.673706-08:00
45         100             high    10  2016-02-22T16:07:13.489671-08:00
46          50              low     0  2016-02-22T16:08:01.505837-08:00
47         100             high     0  2016-02-22T16:08:03.731895-08:00
48         100             high    10  2016-02-22T16:08:07.865632-08:00
49         100             high     0  2016-02-22T16:08:10.091533-08:00
50         100             high    10  2016-02-22T16:08:15.179438-08:00
51         100             high     0  2016-02-22T16:08:22.810365-08:00
53          50              low     1  2016-02-22T16:08:49.521020-08:00
54          50              low     7  2016-02-22T16:08:52.065094-08:00
--------------------------------------------------
   transition                          fmt_time
0          14  2016-02-22T15:46:36.204119-08:00
1          14  2016-02-22T15:55:22.234766-08:00

In [25]:

    

transition_df = esta.TimeSeries.get_time_series(UUID('c528bcd2-a88b-3e82-be62-ef4f2396967a')) \
                        .get_data_df('statemachine/transition', None)


    

In [26]:

    

transition_df.tail()


    

    
Out[26]:






  

    

      

      
_id
      
curr_state
      
fmt_time
      
local_dt
      
metadata_write_ts
      
transition
      
ts
    
  
  

    

      
207
      
56cbcb48eaedff78c76364ac
      
2
      
2016-02-22T18:00:08.378537-08:00
      
2016-02-22 18:00:08.378
      
1.456193e+09
      
8
      
1.456193e+09
    
    

      
208
      
56cbcb48eaedff78c76364ae
      
2
      
2016-02-22T18:00:09.608475-08:00
      
2016-02-22 18:00:09.608
      
1.456193e+09
      
10
      
1.456193e+09
    
    

      
209
      
56cbcb48eaedff78c76364b0
      
2
      
2016-02-22T18:00:09.703326-08:00
      
2016-02-22 18:00:09.703
      
1.456193e+09
      
2
      
1.456193e+09
    
    

      
210
      
56cbcb4deaedff78c76366b7
      
1
      
2016-02-22T18:00:09.925963-08:00
      
2016-02-22 18:00:09.925
      
1.456193e+09
      
2
      
1.456193e+09
    
    

      
211
      
56cbcb4deaedff78c76366b9
      
1
      
2016-02-22T19:00:15.639098-08:00
      
2016-02-22 19:00:15.639
      
1.456196e+09
      
7
      
1.456196e+09
    
  

In [28]:

    

transition_df[(transition_df.ts > 1456189016) & (transition_df.transition == 2)]


    

    
Out[28]:






  

    

      

      
_id
      
curr_state
      
fmt_time
      
local_dt
      
metadata_write_ts
      
transition
      
ts
    
  
  

    

      
209
      
56cbcb48eaedff78c76364b0
      
2
      
2016-02-22T18:00:09.703326-08:00
      
2016-02-22 18:00:09.703
      
1.456193e+09
      
2
      
1.456193e+09
    
    

      
210
      
56cbcb4deaedff78c76366b7
      
1
      
2016-02-22T18:00:09.925963-08:00
      
2016-02-22 18:00:09.925
      
1.456193e+09
      
2
      
1.456193e+09
    
  

In [ ]: