How to compute monthly average and standard deviation with for loop
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Hi everyone,
I have to compute the monthly average and standard deviation on each year. I tryed to do this one but I obtain the error 'The logical indices contain a true value outside of the array bounds.' and I don't know how to fix it.
format long g
folder = 'D:\Valerio\data\IPCC_midcent\RCP4.5\BCC_CSM\BCC_CSM.xlsx';
file = xlsread(folder);
start_yy = 2026;
end_yy = 2044;
range_yy = (start_yy:end_yy).';
r_yy = length(range_yy);
dt = datetime([file(:,1:3) file(:,4)/1E4 repmat([0 0],size(file,1),1)]);
tt = timetable(dt, file(:,5:end));
data = tt.Var1;
a = datenum({'01-Jan-2026','31-Dec-2026'});
aa = datevec(a(1):1:a(2));
Out = aa(:,1:3);
mm = Out(:,2);
for i = 1:r_yy
s1 = sprintf('01-Jan-%d',2025+i);
s2 = sprintf('01-Jan-%d',2026+i);
TR = timerange(s1,s2);
tt_TR = tt(TR,:);
data_TR = tt_TR.Var1;
Hs = data_TR(:,1);
Tp = data_TR(:,2);
for j = 1:12
mm_Hs(j,i) = mean(Hs(mm == j));
mm_Tp(j,i) = mean(Tp(mm == j));
dev_Hs(j,i) = std(Hs(mm == j));
dev_Tp(j,i) = std(Tp(mm == j));
end
end
The error is at the first iteration of the second for loop. Is there someone that can help me. Thank you so much.
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Réponses (1)
Ameer Hamza
le 25 Mai 2020
If you have Statistics and Machine Learning Toolbox, then you can use grpstat(). For example
format long g
folder = 'BCC_CSM.xlsx';
T = readtable(folder);
T(:,2:4) = []; % delete month day and hour columns as they are not important for yearly mean
T.Properties.VariableNames = {'year', 'data1', 'data2', 'data3'};
result = grpstats(T, 'year', {'mean', 'std'});
result_mean = grpstats(T, 'year', 'mean');
result_std = grpstats(T, 'year', 'std');
Otherwise you can use splitapply()
data = xlsread('BCC_CSM.xlsx');
data(:, 2:4) = []; % delete month day and hour columns as they are not important for yearly mean
[grps, years] = findgroups(data(:,1));
result_mean = splitapply(@mean, data(:,2:end), grps);
result_mean = [years result_mean];
result_std = splitapply(@std, data(:,2:end), grps);
result_std = [years result_std];
4 commentaires
Ameer Hamza
le 25 Mai 2020
In that case, try following codes.
grpstat()
format long g
folder = 'BCC_CSM.xlsx';
T = readtable(folder);
T(:,3:4) = []; % delete month day and hour columns as they are not important for yearly mean
T.Properties.VariableNames = {'year', 'months', 'data1', 'data2', 'data3'};
result = grpstats(T, {'year', 'months'}, {'mean', 'std'});
result_mean = grpstats(T, {'year', 'months'}, 'mean');
result_std = grpstats(T, {'year', 'months'}, 'std');
splitapply()
data = xlsread('BCC_CSM.xlsx');
data(:, 3:4) = []; % delete month day and hour columns as they are not important for yearly mean
[grps, Years, Months] = findgroups(data(:,1), data(:,2));
result_mean = splitapply(@(x) mean(x, 1), data(:,3:end), grps);
result_mean = [Years Months result_mean];
result_std = splitapply(@(x) std(x, [], 1), data(:,3:end), grps);
result_std = [Years Months result_std];
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