Two steps:
1) Find the nearest sensor. This easy using interpolation:
>> load("SL.mat")
>> load("SensorLoc.mat")
>> SensorLoc.Index = (1:height(SensorLoc))'
SensorLoc =
9×3 table
Sensor Loc Index
_____________ ___ _____
{'Sensor_02'} 10 1
{'Sensor_04'} 20 2
{'Sensor_05'} 30 3
{'Sensor_07'} 50 4
{'Sensor_08'} 60 5
{'Sensor_09'} 80 6
{'Sensor_11'} 100 7
{'Sensor_12'} 150 8
{'Sensor_13'} 200 9
>> SL2 = SL(:,"BL_TS");
>> SL2.SensorIndex = interp1(SensorLoc.Loc,SensorLoc.Index,SL2.BL_TS,"nearest","extrap")
SL2 =
527×2 timetable
Times_TS BL_TS SensorIndex
____________________ _____ ___________
08-Oct-2021 15:15:00 205 9
08-Oct-2021 15:30:00 229 9
08-Oct-2021 16:00:00 214 9
08-Oct-2021 16:15:00 220 9
08-Oct-2021 17:15:00 183 9
08-Oct-2021 17:30:00 0 1
: : :
18-Oct-2021 17:30:00 109 7
18-Oct-2021 17:45:00 10 1
18-Oct-2021 18:00:00 217 9
18-Oct-2021 18:15:00 175 9
18-Oct-2021 18:30:00 199 9
18-Oct-2021 18:45:00 198 9
Display all 527 rows.
2) For each row of SL, pick out the data from the right sensor. Several ways to do this, simplest involves turning the (SL row index,sensor index) pairs into linear indices:
>> SensorData = SL{:,2:end};
>> SL2.SensorData = SensorData(sub2ind(size(SensorData),(1:height(SensorData))',SL2.SensorIndex))
SL2 =
527×3 timetable
Times_TS BL_TS SensorIndex SensorData
____________________ _____ ___________ __________
08-Oct-2021 15:15:00 205 9 2.1119
08-Oct-2021 15:30:00 229 9 2.1945
08-Oct-2021 16:00:00 214 9 2.3331
08-Oct-2021 16:15:00 220 9 2.4322
08-Oct-2021 17:15:00 183 9 2.4121
08-Oct-2021 17:30:00 0 1 2.4047
: : : :
18-Oct-2021 17:30:00 109 7 0.19229
18-Oct-2021 17:45:00 10 1 0.22684
18-Oct-2021 18:00:00 217 9 0.14355
18-Oct-2021 18:15:00 175 9 0.11803
18-Oct-2021 18:30:00 199 9 0.084789
18-Oct-2021 18:45:00 198 9 0.05123
Display all 527 rows.
