MATLAB: Divide table in days and then in hours.

divide table

Hello everyone! I'm not very practical with matlab, so I hope that someone can help me. I have a big table [351496 x 5] that it contains 6 months of monitoring. Below the first five rows of my table.

The first column is the categorical variable (they are the name of my PIR sensors) the last column (ColumnDateTime) is the date time array. I want to divide my table for days, so as to create daily subtables. then, every daily subtable I would divide it by hours, creating 4 additional tables (which correspond to the times of the day: morning, afternoon, evening, night). Then I would like to analyze if there are 2 PIR sensors that detect movements in two different rooms (the different rooms are recognized in the first column, through the names of the sensors) in a time <= to 2 seconds. Thanks in advance.

Best Answer

Hi,

if you use a timetable for storing your data the function that might help you is

retime

With this function you can aggregate your data among other things. There are also examples given on the retime documentation site.

Best regards

Stephan

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If this is in a datetime variable type, how about you offset your original time by 1 hr (e.g., yourVarName - hours(1)). After that you can group them by date and perform average over each group (consider using findgroups function). This can be done easily using splitapply function if this is a table variable.

MATLAB: Create a single average day out of hourly data (33days)

"First row is date second is hourly mean temp."

That would be column, not row, as just a nit.

Yes, either groupsummary or varfun will do the job, but you need to create the grouping variable to use them...beginning with a timetable, tt that looks like the above, then

>> tt.Day=day(tt.Date);
>> varfun(@mean,tt,'groupingvariables','Day')
ans =
  2×3 timetable
            Date            Day    GroupCount    mean_Temp
    ____________________    ___    __________    _________
    07-Mar-2019 11:00:00     7         13         15.692  
    08-Mar-2019 00:00:00     8          8           13.5  
>> groupsummary(tt,'Day','mean')
ans =
  2×3 table
    Day    GroupCount    mean_Temp
    ___    __________    _________
     7         13         15.692  
     8          8           13.5  
>> [g,ig]=findgroups(tt.Day);
>> splitapply(@mean,tt.Temp,g)
ans =
   15.6923
   13.5000
>> table(tt.Date(ig),splitapply(@mean,tt.Temp,g))
ans =
  2×2 table
            Var1             Var2 
    ____________________    ______
    07-Mar-2019 17:00:00    15.692
    07-Mar-2019 18:00:00      13.5
>>

are various routes to the end...

>> retime(timetable(tt.Date(ig),splitapply(@mean,tt.Temp,g),'VariableNames',{'MeanTemp'}),'daily','nearest')
ans =
  2×1 timetable
            Time            MeanTemp
    ____________________    ________
    07-Mar-2019 00:00:00     15.692 
    08-Mar-2019 00:00:00       13.5 
>>

also gets rid of the odd beginning hour for each day if your data aren't all collected beginning at 00:00 each day.

You could add some error checking to ensure that don't include days that are just nightime or daytime measurements so those would bias results where there is some extreme from day to night.

ERRATUM:

Average over hours, not days as misread original intent:

tt.Hour=hour(tt.Time);
>> varfun(@mean,tt,"InputVariables",'Temp',"GroupingVariables",'Hour')
ans =
  21×3 timetable
            Time            Hour    GroupCount    mean_Temp
    ____________________    ____    __________    _________
    08-Mar-2019 00:00:00      0         1            16    
    08-Mar-2019 01:00:00      1         1            15    
    08-Mar-2019 02:00:00      2         1            13    
    08-Mar-2019 03:00:00      3         1            12    
    08-Mar-2019 04:00:00      4         1            16    
    08-Mar-2019 05:00:00      5         1            12    
    08-Mar-2019 06:00:00      6         1            12    
    08-Mar-2019 07:00:00      7         1            12    
    07-Mar-2019 11:00:00     11         1            16    
    07-Mar-2019 12:00:00     12         1            14    
    07-Mar-2019 13:00:00     13         1            12    
    07-Mar-2019 14:00:00     14         1            19    
    07-Mar-2019 15:00:00     15         1            23    
    07-Mar-2019 16:00:00     16         1            14    
    07-Mar-2019 17:00:00     17         1            15    
    07-Mar-2019 18:00:00     18         1            18    
    07-Mar-2019 19:00:00     19         1            17    
    07-Mar-2019 20:00:00     20         1            13    
    07-Mar-2019 21:00:00     21         1            14    
    07-Mar-2019 22:00:00     22         1            14    
    07-Mar-2019 23:00:00     23         1            15    
>>

Best Answer

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MATLAB: Convert hourly temperature data into daily in the specific time interval

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