differentiate between month in dataset.

Hi, I have multiple data output from simulink, which i have saved in matlab as .mat, but this data is for a whole year in seconds but i want to differentiate between months. so my data set is just a datapoint each second for a whole, year and i want to be able to divide it into january, Feburary, ect... so that i can make a barchart that compare each month. I tried using findgoups, but then i need to create a collumn in the data set where the frist 31*24*3600 seconds is number 1 for january, and then the next 29*24*3600 seconds is number 2 for february. Do anybody know a smart way to do this?

Answer 1

[Mathieu NOE]

hello

maybe this ?

% simplified code for one year scalar input
years = 2023; % 
mm = (1:12); % monthes

days_per_month = datenum(years, mm(:)+1, 1) - datenum(years, mm(:), 1); % adding 1 to the month
seconds_per_month = 24*60*60*days_per_month;

% dummy data example
% one full year of data should have generated 31536000 samples , that can
% be now splited in 12 separate chuncks
samples = sum(seconds_per_month);
data_one_year = (1:samples) + rand(1,samples);

% do some math like averaging each month data 

for ci = 1:12
    
    duration = seconds_per_month(ci);
    
    if ci == 1
        start = 1;
    else
        start = stop +1;
    end
    
    stop = start + duration -1;
    
    data_extract(ci) = mean(data_one_year(start:stop));

end

name = monthName(mm);

plot(mm,data_extract,'*-')
set(gca,'XTick',mm);
set(gca,'XTickLabel',name);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function name = monthName(num)
    name = month(datetime(1,num,1), 'name');
end

[Mathieu NOE]

hello

problem solved ?

Answer 2

[Star Strider]

Perhaps something like this —

secondsperyear = 60*60*24*365;
DT = datetime(2023,1,1,0,0,0) + seconds(0:secondsperyear-1).';
Ends = [DT(1); DT(end)]

Ends = 2×1 datetime array
   01-Jan-2023 00:00:00
   31-Dec-2023 23:59:59

Data = randn(secondsperyear,1);
T1 = table(DT, Data);

TT1 = table2timetable(T1)

TT1 = 31536000×1 timetable
             DT               Data  
    ____________________    ________

    01-Jan-2023 00:00:00    -0.10644
    01-Jan-2023 00:00:01     0.25004
    01-Jan-2023 00:00:02    -0.30974
    01-Jan-2023 00:00:03    -0.38354
    01-Jan-2023 00:00:04     0.94189
    01-Jan-2023 00:00:05     0.76489
    01-Jan-2023 00:00:06     0.98377
    01-Jan-2023 00:00:07     0.51009
    01-Jan-2023 00:00:08     -2.4735
    01-Jan-2023 00:00:09     -1.0738
    01-Jan-2023 00:00:10     -0.1024
    01-Jan-2023 00:00:11    -0.64083
    01-Jan-2023 00:00:12     -1.2975
    01-Jan-2023 00:00:13      2.0611
    01-Jan-2023 00:00:14     -0.4892
    01-Jan-2023 00:00:15     0.26055

HCounts = retime(TT1, 'monthly', 'count')

HCounts = 12×1 timetable
        DT            Data   
    ___________    __________

    01-Jan-2023    2.6784e+06
    01-Feb-2023    2.4192e+06
    01-Mar-2023    2.6784e+06
    01-Apr-2023     2.592e+06
    01-May-2023    2.6784e+06
    01-Jun-2023     2.592e+06
    01-Jul-2023    2.6784e+06
    01-Aug-2023    2.6784e+06
    01-Sep-2023     2.592e+06
    01-Oct-2023    2.6784e+06
    01-Nov-2023     2.592e+06
    01-Dec-2023    2.6784e+06

figure
bar(HCounts.DT,HCounts.Data)
xtickformat('MMM')
xlabel('Months')
ylabel('Counts')

The retime function has several aggregation and calculation options. Counting the number of occurrences in each month is only one of them.

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