2015-11-15 00:00:00
36
80
49
51.299
2015-11-15 00:30:00
36
80
48.967
51.366
2015-11-15 01:00:00
36
80
48.934
51.432
2015-11-15 01:30:00
36.224
79.776
48.9
51.499
2015-11-15 02:00:00
36.634
79.366
48.9
51.5
Synchronize and Aggregate Data Values
Synchronize the timetables and calculate the daily means for all variables in the output timetable.
ttDaily = synchronize(indoors,outdoors,'daily','mean');
ttDaily
ttDaily=4×5 timetable
Time
Humidity_indoors
AirQuality
Humidity_outdoors
TemperatureF
___________________
________________
__________
_________________
____________
2015-11-15 00:00:00
36.5
80.05
48.931
51.394
2015-11-16 00:00:00
36.85
80.35
47.924
51.571
2015-11-17 00:00:00
36.85
79.45
48.45
51.238
2015-11-18 00:00:00
NaN
NaN
49.5
50.8
Synchronize the timetables to six-hour time intervals and calculate a mean for each interval.
tt6Hours = synchronize(indoors,outdoors,'regular','mean','TimeStep',hours(6));
tt6Hours(1:5,:)
ans=5×5 timetable
Time
Humidity_indoors
AirQuality
Humidity_outdoors
TemperatureF
___________________
________________
__________
_________________
____________
2015-11-15 00:00:00
36.4
80.2
48.9
51.45
2015-11-15 06:00:00
36.4
79.8
48.9
51.45
2015-11-15 12:00:00
36.6
80.4
49.025
51.45
2015-11-15 18:00:00
36.6
79.8
48.9
51.225
2015-11-16 00:00:00
36.6
80.2
48.5
51.4
As an alternative, specify a time vector that has the same six-hour time intervals.
tv = [datetime(2015,11,15):hours(6):datetime(2015,11,18)];
tv.Format = indoors.Time.Format;
tt6Hours = synchronize(indoors,outdoors,tv,'mean');
tt6Hours(1:5,:)
ans=5×5 timetable
Time
Humidity_indoors
AirQuality
Humidity_outdoors
TemperatureF
___________________
________________
__________
_________________
____________
2015-11-15 00:00:00
36.4
80.2
48.9
51.45
2015-11-15 06:00:00
36.4
79.8
48.9
51.45
2015-11-15 12:00:00
36.6
80.4
49.025
51.45
2015-11-15 18:00:00
36.6
79.8
48.9
51.225
10 Timetables
10-12