I would like to compose several arrays into one table. Since they have different data types, it seems not very straightforward. Consider this simple MWE:
daysTotal = 2;
hoursTotal = daysTotal*24;
timeStepHours = 1/4;
nhi = 1/timeStepHours;
q_in_mean = 0.82;
% You may need to change this, according to your locale
startDateTimeStr = '02.09.2019 00:00:00';
startDateTime = datetime(startDateTimeStr);
dateTimeVector = startDateTime:hours(timeStepHours): ...
(startDateTime+days(daysTotal)-hours(timeStepHours));
var1 = ones(hoursTotal*nhi, 3);
var2 = q_in_mean*ones(hoursTotal*nhi, 1);
tableFull = table(dateTimeVector.', var1, var2);
While this works, I get tableFull.var2 in it's old shape, though I would like to have a (hoursTotal*nhi, 5) table, i.e., one separate table column for each array column.
This would work if all elements were from the same data type:
tableFull = array2table([dateTimeVector.', var1, var2])
And while this works, it does not seem very elegant:
tableFull = horzcat(table(dateTimeVector.'), array2table(var1), table(var2));
Is there a better way to get a (hoursTotal*nhi, 5) table from those three variables with different data types?
Starting in R2018a, you can use splitvars to do this, i.e.
>> splitvars(tableFull)
ans =
192×5 table
Var1 var1_1 var1_2 var1_3 var2
____________________ ______ ______ ______ ____
02-Sep-2019 00:00:00 1 1 1 0.82
02-Sep-2019 00:15:00 1 1 1 0.82
...
...
Another option for this particular case might be making a timetable like this
>> array2timetable([var1, var2], 'RowTimes', dateTimeVector.')
ans =
192×4 timetable
Time Var1 Var2 Var3 Var4
____________________ ____ ____ ____ ____
02-Sep-2019 00:00:00 1 1 1 0.82
02-Sep-2019 00:15:00 1 1 1 0.82
...
...
BTW, to parse the date strings independent of locale, you can use 'InputFormat' to write
startDateTime = datetime(startDateTimeStr, 'InputFormat', 'dd.MM.yyyy HH:mm:ss');