Calculate daily standard deviation from timetable

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Ancalagon8 on 21 Dec 2022

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Commented: Star Strider on 30 Jan 2023

Hello, I want to calculate the mean max and mean min standard deviation of temperature data from a daily timetable.

t=timetable(dt, Temperature);
temperaturePerDayMin = retime(t2, 'daily', 'min');
tmpPerDayMin_std= std(temperaturePerDayMin.Temperature);
temperaturePerDayMax = retime(t2, 'daily', 'max');
tmpPerDayMax_std= std(temperaturePerDayMax.Temperature); 

Right now tmpPerDayMin_std and tmpPerDayMax_std return only one value. How can I calculate the standard deviation for each day?

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Answer 1

Star Strider on 21 Dec 2022

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Try something like this —

DT = datetime('01-Dec-2022 00:00:00') + hours(0:720).';

Temperature = sin(2*pi*(0:numel(DT)-1)/24-0.5).'*12+5 + randn(size(DT))*1.5;

TT1 = timetable(DT, Temperature)

TT1 = 721×1 timetable

DT Temperature ____________________ ___________ 01-Dec-2022 00:00:00 -0.6988 01-Dec-2022 01:00:00 1.2667 01-Dec-2022 02:00:00 3.0934 01-Dec-2022 03:00:00 7.2162 01-Dec-2022 04:00:00 10.372 01-Dec-2022 05:00:00 12.655 01-Dec-2022 06:00:00 15.885 01-Dec-2022 07:00:00 18.277 01-Dec-2022 08:00:00 16.86 01-Dec-2022 09:00:00 16.837 01-Dec-2022 10:00:00 19.089 01-Dec-2022 11:00:00 14.336 01-Dec-2022 12:00:00 12.287 01-Dec-2022 13:00:00 9.4703 01-Dec-2022 14:00:00 2.5353 01-Dec-2022 15:00:00 -1.0999

figure

plot(TT1.DT, TT1.Temperature)

grid

DailyMax = retime(TT1, 'daily', 'max');

DailyMin = retime(TT1, 'daily', 'min');

DailySTD = retime(TT1, 'daily', @std);

TT2 = table(DailyMax.DT, DailyMax.Temperature, DailyMin.Temperature, DailySTD.Temperature, 'VariableNames',{'DT','Max','Min','StDv'})

TT2 = 31×4 table

DT Max Min StDv ___________ ______ _______ ______ 01-Dec-2022 19.089 -8.976 9.209 02-Dec-2022 16.877 -7.9704 8.9778 03-Dec-2022 18.891 -8.2106 8.3563 04-Dec-2022 17.053 -7.7505 8.9039 05-Dec-2022 19.937 -8.5247 8.9055 06-Dec-2022 17.545 -9.3624 8.7491 07-Dec-2022 19.835 -6.6562 8.0717 08-Dec-2022 18.21 -8.5766 8.8617 09-Dec-2022 17.416 -6.1877 8.1288 10-Dec-2022 17.298 -8.2496 8.4282 11-Dec-2022 18.21 -6.3769 8.891 12-Dec-2022 19.579 -8.3538 9.035 13-Dec-2022 20.088 -7.3316 8.5688 14-Dec-2022 19.397 -8.5091 8.7861 15-Dec-2022 17.578 -8.363 8.9894 16-Dec-2022 17.249 -8.5435 9.1342

The standard deviation of a single value is zero by definition, so it is only possible to calculate the standard deviation of all temperatures for a single day for that day.

It is possible to calculate the standard deviation for the maximum and minimum temperatures for several days (at least more than 1) and of course for the entire month.

.

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Star Strider on 21 Dec 2022

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Thank you!

Yes!

Example —

DT = datetime('01-Dec-2022 00:00:00') + hours(0:720).';

Temperature = sin(2*pi*(0:numel(DT)-1)/24-0.5).'*12+5 + randn(size(DT))*1.5;

TT1 = timetable(DT, Temperature)

TT1 = 721×1 timetable

DT Temperature ____________________ ___________ 01-Dec-2022 00:00:00 -0.91782 01-Dec-2022 01:00:00 3.4756 01-Dec-2022 02:00:00 5.1349 01-Dec-2022 03:00:00 5.9696 01-Dec-2022 04:00:00 10.697 01-Dec-2022 05:00:00 13.404 01-Dec-2022 06:00:00 14.426 01-Dec-2022 07:00:00 14.551 01-Dec-2022 08:00:00 16.658 01-Dec-2022 09:00:00 16.201 01-Dec-2022 10:00:00 14.351 01-Dec-2022 11:00:00 11.749 01-Dec-2022 12:00:00 10.131 01-Dec-2022 13:00:00 8.4033 01-Dec-2022 14:00:00 0.49336 01-Dec-2022 15:00:00 0.1879

figure

plot(TT1.DT, TT1.Temperature)

grid

DailyMax = retime(TT1, 'daily', 'max');

DailyMin = retime(TT1, 'daily', 'min');

DailySTD = retime(TT1, 'daily', @std);

T2 = table(DailyMax.DT, DailyMax.Temperature, DailyMin.Temperature, DailySTD.Temperature, 'VariableNames',{'DT','Max','Min','StDv'})

T2 = 31×4 table

DT Max Min StDv ___________ ______ _______ ______ 01-Dec-2022 16.658 -8.8893 8.2913 02-Dec-2022 16.515 -6.5274 8.2742 03-Dec-2022 18.799 -7.4772 8.7142 04-Dec-2022 17.145 -9.1443 8.4744 05-Dec-2022 18.821 -8.4418 8.5831 06-Dec-2022 18.391 -5.9905 8.9501 07-Dec-2022 15.976 -8.7264 8.6104 08-Dec-2022 15.344 -7.7227 8.062 09-Dec-2022 18.631 -9.265 8.6461 10-Dec-2022 19.929 -7.4301 9.0601 11-Dec-2022 17.767 -9.7136 8.8605 12-Dec-2022 17.554 -7.4757 8.7277 13-Dec-2022 18.164 -7.6267 9.016 14-Dec-2022 20.333 -8.9258 9.2568 15-Dec-2022 16.008 -9.7254 8.7095 16-Dec-2022 20.091 -7.3932 9.2243

DaysMeetingThreshold = nnz(T2.Max >= 18) % Days Equal To Or Higher Than 18°C

DaysMeetingThreshold = 17

Adjust the conditions to select either greater than (>) or greater than or equal to (>=) and the desired temperature threshold (here 18°C). I did not include that in the timetable because it is for the entire time period and so would have to have a column length equal to the entire timetable. This could also be done for calendar months or calendar weeks or for the entire table. That would require extra code and probably an additional timetable as well.

An example of doing that using retime wouild be:

Threshold_1 = retime(TT1, 'weekly', @(x)nnz(x>=18))
Threshold_1 = 5×1 timetable
        DT         Temperature
    ___________    ___________

    27-Nov-2022         1     
    04-Dec-2022         6     
    11-Dec-2022         8     
    18-Dec-2022         4     
    25-Dec-2022         7     
TT2 = table2timetable(T2);
Threshold2 = retime(TT2, 'weekly', @(x)nnz(x>=18))
Threshold2 = 5×3 timetable
        DT         Max    Min    StDv
    ___________    ___    ___    ____

    27-Nov-2022     1      0      0  
    04-Dec-2022     4      0      0  
    11-Dec-2022     4      0      0  
    18-Dec-2022     3      0      0  
    25-Dec-2022     5      0      0  

The ‘Threshold1_1’ timetable counts the number of times each week any ‘Temperature’ equalled or exceeded 18°C.

The ‘Threshold_2’ timetable counts the number of times the maximum ‘Temperature’ equalled or exceeded 18°C.

It could be added to the existing timetable, however that would require additional code and that code would not be trivial, since the lengths of the constant values would have to match the entries of the corresponding dates in position and length.

.

Star Strider on 23 Dec 2022

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As always, my pleasure!

I am not certain that I understand what you want to do.

You would likely need a loop for the various thresholds. After that, putting them into a table is straightforward —

DT = datetime('01-Dec-2022 00:00:00') + hours(0:720).';

Temperature = sin(2*pi*(0:numel(DT)-1)/24-0.5).'*12+5 + randn(size(DT))*1.5;

TT1 = timetable(DT, Temperature)

TT1 = 721×1 timetable

DT Temperature ____________________ ___________ 01-Dec-2022 00:00:00 -2.4124 01-Dec-2022 01:00:00 1.8933 01-Dec-2022 02:00:00 2.5341 01-Dec-2022 03:00:00 6.1807 01-Dec-2022 04:00:00 9.8868 01-Dec-2022 05:00:00 14.528 01-Dec-2022 06:00:00 14.26 01-Dec-2022 07:00:00 15.974 01-Dec-2022 08:00:00 14.304 01-Dec-2022 09:00:00 17.543 01-Dec-2022 10:00:00 12.134 01-Dec-2022 11:00:00 14.879 01-Dec-2022 12:00:00 11.453 01-Dec-2022 13:00:00 6.3793 01-Dec-2022 14:00:00 5.644 01-Dec-2022 15:00:00 2.349

figure

plot(TT1.DT, TT1.Temperature)

grid

DailyMax = retime(TT1, 'daily', 'max');

DailyMin = retime(TT1, 'daily', 'min');

DailySTD = retime(TT1, 'daily', @std);

T2 = table(DailyMax.DT, DailyMax.Temperature, DailyMin.Temperature, DailySTD.Temperature, 'VariableNames',{'DT','Max','Min','StDv'})

T2 = 31×4 table

DT Max Min StDv ___________ ______ _______ ______ 01-Dec-2022 17.543 -9.0929 8.8102 02-Dec-2022 19.193 -6.685 8.5592 03-Dec-2022 19.767 -11.037 9.8884 04-Dec-2022 18.123 -8.7118 8.8951 05-Dec-2022 17.171 -7.8953 8.8536 06-Dec-2022 18.443 -8.2063 8.3054 07-Dec-2022 18.364 -8.341 8.5502 08-Dec-2022 18.076 -6.3634 8.7735 09-Dec-2022 18.339 -8.4784 8.7009 10-Dec-2022 17.352 -8.1611 8.4984 11-Dec-2022 18.944 -7.9201 8.536 12-Dec-2022 17.78 -7.6084 8.6725 13-Dec-2022 18.489 -8.9344 9.1313 14-Dec-2022 19.052 -7.7588 8.8545 15-Dec-2022 17.135 -7.8149 8.897 16-Dec-2022 19.373 -8.0265 9.1146

Thresholdv = [16; 17; 18; 19; 20]; % Column Vector

for k = 1:numel(Thresholdv)

DaysMeetingThreshold(k,:) = nnz(T2.Max >= Thresholdv(k)); % Days Equal To Or Higher Than Thresholdv (°C)

end

ThresholdTable = table(Thresholdv, DaysMeetingThreshold)

ThresholdTable = 5×2 table

Thresholdv DaysMeetingThreshold __________ ____________________ 16 30 17 30 18 17 19 5 20 0

.

Star Strider on 23 Dec 2022

Edited: Star Strider on 30 Dec 2022

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As always, my pleasure!

EDIT — (30 Dec 2022 at 16:56)

Not easily.

DT = datetime('01-Dec-2022 00:00:00') + hours(0:720).';

Temperature = sin(2*pi*(0:numel(DT)-1)/24-0.5).'*12+5 + randn(size(DT))*1.5;

TT1 = timetable(DT, Temperature)

TT1 = 721×1 timetable

DT Temperature ____________________ ___________ 01-Dec-2022 00:00:00 -1.1057 01-Dec-2022 01:00:00 1.3984 01-Dec-2022 02:00:00 6.8774 01-Dec-2022 03:00:00 6.4973 01-Dec-2022 04:00:00 12.631 01-Dec-2022 05:00:00 14.64 01-Dec-2022 06:00:00 16.174 01-Dec-2022 07:00:00 17.047 01-Dec-2022 08:00:00 17.984 01-Dec-2022 09:00:00 13.908 01-Dec-2022 10:00:00 12.703 01-Dec-2022 11:00:00 12.548 01-Dec-2022 12:00:00 11.678 01-Dec-2022 13:00:00 8.3992 01-Dec-2022 14:00:00 4.2721 01-Dec-2022 15:00:00 1.7873

figure

plot(TT1.DT, TT1.Temperature)

grid

DailyMax = retime(TT1, 'daily', 'max');

HourlyMax = retime(TT1, 'hourly', 'max')

HourlyMax = 721×1 timetable

DT Temperature ____________________ ___________ 01-Dec-2022 00:00:00 -1.1057 01-Dec-2022 01:00:00 1.3984 01-Dec-2022 02:00:00 6.8774 01-Dec-2022 03:00:00 6.4973 01-Dec-2022 04:00:00 12.631 01-Dec-2022 05:00:00 14.64 01-Dec-2022 06:00:00 16.174 01-Dec-2022 07:00:00 17.047 01-Dec-2022 08:00:00 17.984 01-Dec-2022 09:00:00 13.908 01-Dec-2022 10:00:00 12.703 01-Dec-2022 11:00:00 12.548 01-Dec-2022 12:00:00 11.678 01-Dec-2022 13:00:00 8.3992 01-Dec-2022 14:00:00 4.2721 01-Dec-2022 15:00:00 1.7873

DailyMin = retime(TT1, 'daily', 'min');

DailySTD = retime(TT1, 'daily', @std);

T2 = table(DailyMax.DT, DailyMax.Temperature, DailyMin.Temperature, DailySTD.Temperature, 'VariableNames',{'DT','Max','Min','StDv'})

T2 = 31×4 table

DT Max Min StDv ___________ ______ _______ ______ 01-Dec-2022 17.984 -9.2499 9.0444 02-Dec-2022 18.206 -8.5237 9.1837 03-Dec-2022 15.431 -7.5063 8.2329 04-Dec-2022 19.696 -9.143 9.0854 05-Dec-2022 19.471 -7.37 8.7003 06-Dec-2022 19.677 -10.246 9.3604 07-Dec-2022 16.639 -6.4818 8.3036 08-Dec-2022 18.539 -6.9418 8.6188 09-Dec-2022 17.33 -9.3591 8.8737 10-Dec-2022 17.894 -7.7901 8.513 11-Dec-2022 18.26 -8.2763 8.9268 12-Dec-2022 18.52 -9.1745 9.0402 13-Dec-2022 18.13 -8.7439 8.5561 14-Dec-2022 19.723 -8.7907 8.8667 15-Dec-2022 19.534 -9.033 9.2443 16-Dec-2022 19.735 -7.8816 8.3044

Thresholdv = [16; 17; 18; 19; 20]; % Column Vector

for k = 1:numel(Thresholdv)

DaysMeetingThreshold(k,:) = nnz(T2.Max >= Thresholdv(k)); % Days Equal To Or Higher Than Thresholdv (°C)

end

ThresholdTable = table(Thresholdv, DaysMeetingThreshold)

ThresholdTable = 5×2 table

Thresholdv DaysMeetingThreshold __________ ____________________ 16 29 17 26 18 17 19 9 20 0

[yh,mh,dh] = ymd(HourlyMax.DT); % Return Hour At Which Maximum Temperature Occurred

for k = 1:numel(DailyMax)

[yd,md,dd] = ymd(DailyMax.DT(k));

Lv = ismember([yh,mh,dh], [yd,md,dd], 'rows');

idx = HourlyMax.Temperature(Lv) == DailyMax.Temperature(k);

HM = HourlyMax(Lv,:);

DayHourMax(k,:) = timetable2table(HM(idx,:));

end

DayHourMax % Table Of Hour In Each Day At Which Maximum Temperature Occurred

DayHourMax = 31×2 table

DT Temperature ____________________ ___________ 01-Dec-2022 08:00:00 17.984 02-Dec-2022 07:00:00 18.206 03-Dec-2022 07:00:00 15.431 04-Dec-2022 08:00:00 19.696 05-Dec-2022 06:00:00 19.471 06-Dec-2022 08:00:00 19.677 07-Dec-2022 10:00:00 16.639 08-Dec-2022 09:00:00 18.539 09-Dec-2022 08:00:00 17.33 10-Dec-2022 08:00:00 17.894 11-Dec-2022 08:00:00 18.26 12-Dec-2022 09:00:00 18.52 13-Dec-2022 08:00:00 18.13 14-Dec-2022 08:00:00 19.723 15-Dec-2022 09:00:00 19.534 16-Dec-2022 07:00:00 19.735

This should be easier!

.

Star Strider on 28 Jan 2023

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Probably something like this —

MonthlyMax = retime(TT1, 'monthly', @(x)mean(max(x)));

MonthlyMin = retime(TT1, 'monthly', @(x)mean(min(x)));

although I am not certain what you want.

Testing that to see what it returns —

DT = datetime('01-Dec-2022 00:00:00') + caldays(1:364).';

Temperature = sin(2*pi*(0:numel(DT)-1)/24-0.5).'*12+5 + randn(size(DT))*1.5;

TT1 = timetable(DT, Temperature)

TT1 = 364×1 timetable

DT Temperature ___________ ___________ 02-Dec-2022 1.8812 03-Dec-2022 2.0567 04-Dec-2022 6.6711 05-Dec-2022 12.135 06-Dec-2022 10.489 07-Dec-2022 13.718 08-Dec-2022 13.921 09-Dec-2022 17.49 10-Dec-2022 16.473 11-Dec-2022 16.773 12-Dec-2022 16.449 13-Dec-2022 10.577 14-Dec-2022 9.2101 15-Dec-2022 9.5114 16-Dec-2022 5.1743 17-Dec-2022 0.10695

figure

plot(TT1.DT, TT1.Temperature)

grid

hold on

MonthlyMax = retime(TT1, 'monthly', @(x)mean(max(x)))

MonthlyMax = 12×1 timetable

DT Temperature ___________ ___________ 01-Dec-2022 17.49 01-Jan-2023 18.8 01-Feb-2023 20.47 01-Mar-2023 17.53 01-Apr-2023 18.049 01-May-2023 18.325 01-Jun-2023 19.46 01-Jul-2023 18.845 01-Aug-2023 19.449 01-Sep-2023 19.491 01-Oct-2023 20.38 01-Nov-2023 17.817

MonthlyMin = retime(TT1, 'monthly', @(x)mean(min(x)))

MonthlyMin = 12×1 timetable

DT Temperature ___________ ___________ 01-Dec-2022 -8.1543 01-Jan-2023 -8.6214 01-Feb-2023 -8.5488 01-Mar-2023 -8.1802 01-Apr-2023 -8.1751 01-May-2023 -9.2394 01-Jun-2023 -9.6803 01-Jul-2023 -7.3772 01-Aug-2023 -6.802 01-Sep-2023 -8.406 01-Oct-2023 -7.7957 01-Nov-2023 -7.0099

MthV = DT(1) + calmonths(0:11).';

hold on

plot(MthV, [MonthlyMax.Temperature, MonthlyMin.Temperature], '-r')

hold off

.

Star Strider on 28 Jan 2023

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I forgot about that. It’s taking the standard deviation of a scalar, and that’s zero by definition. The maximum and minimum of a standard deviation are going to be the same (since it’s also a scalar), and the standard deviation of the maxima or minima for the entire year is going to be scalars representing the entire year, plotting a straight line over all the months. so a different sort of metric may be necessary.

There may be other ways, for example the standard error of the mean for each month, presented as confidence intervals —

DT = datetime('01-Dec-2022 00:00:00') + caldays(1:364).';

Temperature = sin(2*pi*(0:numel(DT)-1)/24-0.5).'*12+5 + randn(size(DT))*1.5;

TT1 = timetable(DT, Temperature)

TT1 = 364×1 timetable

DT Temperature ___________ ___________ 02-Dec-2022 0.82051 03-Dec-2022 1.9947 04-Dec-2022 4.6657 05-Dec-2022 7.8428 06-Dec-2022 11.934 07-Dec-2022 14.881 08-Dec-2022 15.027 09-Dec-2022 15.659 10-Dec-2022 15.119 11-Dec-2022 16.932 12-Dec-2022 17.403 13-Dec-2022 13.246 14-Dec-2022 11.374 15-Dec-2022 7.0888 16-Dec-2022 4.5771 17-Dec-2022 -0.48706

figure

plot(TT1.DT, TT1.Temperature)

grid

hold on

MonthlyMax = retime(TT1, 'monthly', @(x)mean(x)+std(x)/sqrt(numel(x))*1.96)

MonthlyMax = 12×1 timetable

DT Temperature ___________ ___________ 01-Dec-2022 8.1303 01-Jan-2023 10.545 01-Feb-2023 8.4384 01-Mar-2023 5.0567 01-Apr-2023 8.5555 01-May-2023 10.352 01-Jun-2023 7.3369 01-Jul-2023 5.7303 01-Aug-2023 9.5159 01-Sep-2023 9.2639 01-Oct-2023 6.069 01-Nov-2023 7.6833

MonthlyMin = retime(TT1, 'monthly', @(x)mean(x)-std(x)/sqrt(numel(x))*1.96)

MonthlyMin = 12×1 timetable

DT Temperature ___________ ___________ 01-Dec-2022 2.3238 01-Jan-2023 4.3452 01-Feb-2023 2.2066 01-Mar-2023 -0.90149 01-Apr-2023 2.2013 01-May-2023 3.7726 01-Jun-2023 1.3024 01-Jul-2023 -0.49234 01-Aug-2023 3.4644 01-Sep-2023 3.3579 01-Oct-2023 0.20044 01-Nov-2023 1.7348

MthV = DT(1) + days(15) + calmonths(0:11).';

hold on

plot(MthV, [MonthlyMax.Temperature, MonthlyMin.Temperature], '-r')

hold off

Other options could be calculating the monthly standard deviation of the maxima and minima for each day during the month. That would initially require one aggregation to calculate the daily maxima and minima, and a second aggretation to calculate the standard deviation of the monthly values of the daily data. That may be the only way to get a standard deviation aggregated over a month.

The aggregation depends on what you want, and what you want to do with the aggregated data after calculating it.

.

Star Strider on 28 Jan 2023

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I was thinking of something like this —

DT = datetime('01-Dec-2022 00:00:00') + hours(1:364*24).'; % Hours

Temperature = sin(2*pi*(0:numel(DT)-1)/24-0.5).'*12+5 + randn(size(DT))*1.5; % Temperatures

TT1 = timetable(DT, Temperature)

TT1 = 8736×1 timetable

DT Temperature ____________________ ___________ 01-Dec-2022 01:00:00 -1.4502 01-Dec-2022 02:00:00 -0.14428 01-Dec-2022 03:00:00 5.977 01-Dec-2022 04:00:00 8.8143 01-Dec-2022 05:00:00 9.1807 01-Dec-2022 06:00:00 12.38 01-Dec-2022 07:00:00 15.92 01-Dec-2022 08:00:00 16.297 01-Dec-2022 09:00:00 16.229 01-Dec-2022 10:00:00 16.797 01-Dec-2022 11:00:00 15.728 01-Dec-2022 12:00:00 12.875 01-Dec-2022 13:00:00 9.361 01-Dec-2022 14:00:00 9.4571 01-Dec-2022 15:00:00 5.3876 01-Dec-2022 16:00:00 0.11136

DailyMax = retime(TT1, 'daily', 'max') % Daily Aggregation

DailyMax = 365×1 timetable

DT Temperature ___________ ___________ 01-Dec-2022 16.797 02-Dec-2022 17.7 03-Dec-2022 17.013 04-Dec-2022 16.641 05-Dec-2022 17.55 06-Dec-2022 18.994 07-Dec-2022 20.201 08-Dec-2022 17.164 09-Dec-2022 17.171 10-Dec-2022 18.355 11-Dec-2022 16.357 12-Dec-2022 15.898 13-Dec-2022 17.103 14-Dec-2022 17.685 15-Dec-2022 20.491 16-Dec-2022 20.725

DailyMin = retime(TT1, 'daily', 'min') % Daily Aggregation

DailyMin = 365×1 timetable

DT Temperature ___________ ___________ 01-Dec-2022 -7.4328 02-Dec-2022 -5.7989 03-Dec-2022 -8.8891 04-Dec-2022 -8.6656 05-Dec-2022 -8.6631 06-Dec-2022 -8.6668 07-Dec-2022 -8.3697 08-Dec-2022 -9.3005 09-Dec-2022 -7.7954 10-Dec-2022 -8.7234 11-Dec-2022 -10.109 12-Dec-2022 -9.815 13-Dec-2022 -7.3838 14-Dec-2022 -8.13 15-Dec-2022 -7.6904 16-Dec-2022 -7.6138

DailyTT = DailyMax;

DailyTT = addvars(DailyTT, DailyMin.Temperature)

DailyTT = 365×2 timetable

DT Temperature Var2 ___________ ___________ _______ 01-Dec-2022 16.797 -7.4328 02-Dec-2022 17.7 -5.7989 03-Dec-2022 17.013 -8.8891 04-Dec-2022 16.641 -8.6656 05-Dec-2022 17.55 -8.6631 06-Dec-2022 18.994 -8.6668 07-Dec-2022 20.201 -8.3697 08-Dec-2022 17.164 -9.3005 09-Dec-2022 17.171 -7.7954 10-Dec-2022 18.355 -8.7234 11-Dec-2022 16.357 -10.109 12-Dec-2022 15.898 -9.815 13-Dec-2022 17.103 -7.3838 14-Dec-2022 17.685 -8.13 15-Dec-2022 20.491 -7.6904 16-Dec-2022 20.725 -7.6138

MonthlyTT = retime(DailyTT, 'monthly', @(x)std(x)) % Monthly Aggregation Of Daily Data

MonthlyTT = 12×2 timetable

DT Temperature Var2 ___________ ___________ _______ 01-Dec-2022 1.1873 1.0935 01-Jan-2023 1.0858 0.82949 01-Feb-2023 1.3895 0.80469 01-Mar-2023 1.2204 1.1097 01-Apr-2023 0.79565 1.0219 01-May-2023 0.94119 1.1451 01-Jun-2023 1.0995 1.0555 01-Jul-2023 0.9984 0.96406 01-Aug-2023 0.91378 1.1695 01-Sep-2023 1.1669 0.99959 01-Oct-2023 1.0167 0.82053 01-Nov-2023 3.8584 1.4976

MthV = DT(1) + calmonths(0:11).';

figure

hold on

plot(MthV, MonthlyTT{:,1}, '-r')

plot(MthV, MonthlyTT{:,2}, '-b')

hold off

legend('Max Monthly SD','Min Monthly SD', 'Location','best')

This plots the standard deviations, not the actual temperature values.

.

Star Strider on 30 Jan 2023

Open in MATLAB Online

Try something like this —

DT = datetime('01-Dec-2022 00:00:00') + hours(1:364*24).'; % Hours

Temperature = sin(2*pi*(0:numel(DT)-1)/24-0.5).'*12+5 + randn(size(DT))*1.5; % Temperatures

TT1 = timetable(DT, Temperature)

TT1 = 8736×1 timetable

DT Temperature ____________________ ___________ 01-Dec-2022 01:00:00 -0.37909 01-Dec-2022 02:00:00 0.28714 01-Dec-2022 03:00:00 6.4977 01-Dec-2022 04:00:00 7.7289 01-Dec-2022 05:00:00 12.292 01-Dec-2022 06:00:00 15.373 01-Dec-2022 07:00:00 14.676 01-Dec-2022 08:00:00 15.309 01-Dec-2022 09:00:00 15.836 01-Dec-2022 10:00:00 16.44 01-Dec-2022 11:00:00 16.672 01-Dec-2022 12:00:00 14.35 01-Dec-2022 13:00:00 8.6757 01-Dec-2022 14:00:00 9.1514 01-Dec-2022 15:00:00 6.0855 01-Dec-2022 16:00:00 2.4084

DailyMax = retime(TT1, 'daily', 'max'); % Daily Aggregation

DailyMin = retime(TT1, 'daily', 'min'); % Daily Aggregation

DailyMean = retime(TT1, 'daily', 'mean'); % Daily Aggregation

DailyRange = DailyMax.Temperature - DailyMin.Temperature; % Daily Aggregation

DailyTT = timetable(DailyMax.DT, DailyMax.Temperature, DailyMin.Temperature, DailyRange, DailyMean.Temperature, 'VariableNames',{'Max_Temperature','Min_Temperature','Range_Temperature','Mean_Temperature'})

DailyTT = 365×4 timetable

Time Max_Temperature Min_Temperature Range_Temperature Mean_Temperature ___________ _______________ _______________ _________________ ________________ 01-Dec-2022 16.672 -9.4342 26.106 5.1988 02-Dec-2022 18.117 -8.7949 26.911 5.3819 03-Dec-2022 18.611 -7.9121 26.523 5.1666 04-Dec-2022 18.68 -8.1488 26.829 4.8373 05-Dec-2022 17.69 -7.1424 24.832 5.3975 06-Dec-2022 19.069 -8.1585 27.228 5.2481 07-Dec-2022 15.966 -9.9869 25.952 4.8057 08-Dec-2022 18.182 -9.3876 27.57 5.4564 09-Dec-2022 17.444 -8.3602 25.804 4.8884 10-Dec-2022 19.302 -7.5362 26.838 5.391 11-Dec-2022 16.696 -10.497 27.193 4.528 12-Dec-2022 18.3 -6.9914 25.291 5.1051 13-Dec-2022 18.151 -8.5995 26.75 5.4419 14-Dec-2022 17.836 -10.976 28.811 5.0321 15-Dec-2022 17.282 -9.1337 26.416 4.8376 16-Dec-2022 18.325 -8.0825 26.408 4.8119

MonthlyTTMax = retime(DailyTT, 'monthly', 'max')

MonthlyTTMax = 12×4 timetable

Time Max_Temperature Min_Temperature Range_Temperature Mean_Temperature ___________ _______________ _______________ _________________ ________________ 01-Dec-2022 20.326 -6.2942 28.852 5.4564 01-Jan-2023 20.882 -6.5416 30.436 5.486 01-Feb-2023 21.771 -6.7069 29.895 5.42 01-Mar-2023 20.752 -6.3323 30.718 5.7682 01-Apr-2023 19.842 -6.1608 28.69 5.81 01-May-2023 19.771 -6.3754 29.726 5.3571 01-Jun-2023 20.162 -6.2147 29.415 5.839 01-Jul-2023 21.085 -6.1956 30.339 5.8512 01-Aug-2023 20.793 -5.192 29.184 5.6606 01-Sep-2023 20.346 -6.2424 29.367 5.5407 01-Oct-2023 20.827 -5.951 29.933 5.4544 01-Nov-2023 20.722 -5.9304 30.332 5.5216

MonthlyTTMin = retime(DailyTT, 'monthly', 'min')

MonthlyTTMin = 12×4 timetable

Time Max_Temperature Min_Temperature Range_Temperature Mean_Temperature ___________ _______________ _______________ _________________ ________________ 01-Dec-2022 15.966 -10.976 22.838 4.4957 01-Jan-2023 16.409 -10.684 24.377 4.5691 01-Feb-2023 15.547 -10.91 24.217 4.3471 01-Mar-2023 15.748 -10.449 23.368 4.5982 01-Apr-2023 15.941 -10.591 22.533 4.2688 01-May-2023 16.111 -10.972 23.687 4.3221 01-Jun-2023 16.484 -11.179 23.418 4.3588 01-Jul-2023 16.605 -10.795 24.173 4.5544 01-Aug-2023 15.518 -10.379 22.288 4.3416 01-Sep-2023 15.359 -10.131 22.941 4.5646 01-Oct-2023 16.226 -10.036 22.756 4.4768 01-Nov-2023 -5.9304 -10.465 0 -5.9304

MonthlyTTMean = retime(DailyTT, 'monthly', 'mean')

MonthlyTTMean = 12×4 timetable

Time Max_Temperature Min_Temperature Range_Temperature Mean_Temperature ___________ _______________ _______________ _________________ ________________ 01-Dec-2022 17.894 -8.3725 26.267 5.0415 01-Jan-2023 18.351 -8.3832 26.735 4.9523 01-Feb-2023 18.018 -8.3188 26.337 4.923 01-Mar-2023 18.023 -8.1983 26.222 5.0542 01-Apr-2023 17.919 -8.011 25.93 5.0405 01-May-2023 18.123 -8.3906 26.513 4.902 01-Jun-2023 17.953 -8.0177 25.971 5.0389 01-Jul-2023 18.193 -8.1407 26.334 5.0607 01-Aug-2023 18.041 -8.2389 26.28 4.9959 01-Sep-2023 18.051 -7.866 25.917 5.0096 01-Oct-2023 17.892 -7.9565 25.848 4.9462 01-Nov-2023 17.38 -8.1048 25.485 4.6034

MonthlyTTSD = retime(DailyTT, 'monthly', @(x)std(x)) % Monthly Aggregation Of Daily Data

MonthlyTTSD = 12×4 timetable

Time Max_Temperature Min_Temperature Range_Temperature Mean_Temperature ___________ _______________ _______________ _________________ ________________ 01-Dec-2022 0.89012 1.0981 1.2396 0.28508 01-Jan-2023 1.1506 1.0405 1.3665 0.27683 01-Feb-2023 1.1575 1.0735 1.6071 0.29298 01-Mar-2023 1.2434 0.99746 1.8445 0.28534 01-Apr-2023 1 1.1311 1.6295 0.34489 01-May-2023 0.99102 1.1128 1.4308 0.28412 01-Jun-2023 0.8422 1.0872 1.5087 0.3929 01-Jul-2023 0.87463 1.033 1.4316 0.31694 01-Aug-2023 1.0816 1.1013 1.7648 0.32041 01-Sep-2023 1.1947 1.0865 1.6608 0.27119 01-Oct-2023 1.043 0.97543 1.5385 0.24107 01-Nov-2023 4.5398 1.0787 5.0632 2.007

MthV = DT(1) + calmonths(0:11).';

figure

hold on

plot(MthV, MonthlyTTSD{:,1}, '-r')

plot(MthV, MonthlyTTSD{:,2}, '-b')

hold off

legend('Max Monthly SD','Min Monthly SD', 'Location','best')

The ‘MonthlyTT’ set of timetable arrays should have everything you need. If necessary, it should be straightforward to abstract the various variables from them and import them to a new summary timetable, similar to what I did to create ‘DailyTT’.

Creating numbered variables such as:

MeanDailyRange_January=mean(DailyRange_January);

is generally discouraged. You can get the various monthly values from the timetable arrays that calculated them.

I am doing my best to provide what you want, however that is not always clear to me. The result is that we need to iterate until we converge on the correct arrays.

.

Ancalagon8 on 30 Jan 2023

Thank you @Star Strider. So far things are fine, but my concern is how can I add the time information of the min and max.

Star Strider on 30 Jan 2023

As always, my pleasure!

I thought we already covered that problem at some point. By now I’ve lost track of where it is (although searching for it, I believe it was here).

It would be essentially the same approach.

.

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Calculate daily standard deviation from timetable

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Calculate daily standard deviation from timetable

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