Converting wind/wave data to hourly data by interpolation

2022 6 月 30
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2022 6 月 30 に更新
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Dear All,
I have wind and wave data that as can be seen attached. I want to convert it to hourly data acording to duration (time) values in column A. The value of duration here represents the duration of the wind blowing and its unit is hour. I want to interpolate all the other parameters while converting to hourly data. For example, I have formulated this in Excel for the line as follows,
D2-(($D$2-$D$3)/$A$2)
D3-(($D$2-$D$3)/$A$2)
D4-(($D$2-$D$3)/$A$2)
......
D8-(($D$2-$D$3)/$A$2)
The problem is, this is a very large set of data and doing this manually in Excel will take an enourmous amount of time. Is there a command in MATLAB where I can add (A-1) number of columns to each line and interpolate the rest of columns as described above?
Thank you in advance!

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Adam Danz
Adam Danz 2022 年 6 月 30 日
Are the durations consecutive? In other words, the first 3 durations are 8,1,2, does that account for a consecutive 11 hour period?
Seda Cora Özdemir
Seda Cora Özdemir 2022 年 6 月 30 日
Hello Adam,
Thank you very much for your prompt response!
The durations are consecutive. But the yellow ones were added by me to better explain the situation. So the durations go as 8, 9, 14, 7, ...., etc. I have to insert 7 lines and get 8 lines in total for the first data point, insert 8 lines and get 9 lines in total for the second, insert 13 lines and get 14 lines in total for the third, etc. The other values have to be interpolated as was done in the code you shared.
I hope I could clarify the situation a bit. Do you think this can be done with MATLAB?
Adam Danz
Adam Danz 2022 年 6 月 30 日
I've updated my answer with the following changes.
  1. I replaced your demo file with a clean version that removes the added highlighted rows.
  2. Critically, the timestamps are computed so that they start with 0.
I've spot-checked a few rows of the results and they seem to match your expected results from the original data file.
Seda Cora Özdemir
Seda Cora Özdemir 2022 年 6 月 30 日
Thank you very much Adam!!! You saved my life :)
Adam Danz
Adam Danz 2022 年 6 月 30 日
Look at the last row of data; it may not be included in the final interpolated timetable. I don't know how you would interpolate that last row but it's just something to keep in mind.

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Adam Danz
Adam Danz 2022 年 6 月 30 日
編集済み: Adam Danz 2022 年 6 月 30 日
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Ran in:
If the durations are consecutive, then I suggest creating an time stamps based on the durations which allows you to use retime to interpolate the timetable to hourly samples.
I've created a "clean" version of your file by removing the rows that were highlighted.
T = readtable('sample_data_clean.xlsx')
Warning: Column headers from the file were modified to make them valid MATLAB identifiers before creating variable names for the table. The original column headers are saved in the VariableDescriptions property.
Set 'VariableNamingRule' to 'preserve' to use the original column headers as table variable names.
T = 109×6 table
Duration WindDirection_degree_ WindSpeed_m_s_ Hs Tp Tm ________ _____________________ ______________ ________ _______ _______ 8 206.2 8.9288 0.11265 1.0981 0.91139 9 228.66 8.1667 0.10047 1.0481 0.86994 14 224.28 6.6657 0.082006 0.97952 0.813 7 226.63 9.29 0.11429 1.0941 0.90813 3 241.58 3.99 0.052364 0.86184 0.71533 2 331.51 2.045 0.11948 1.8665 1.5492 5 19.992 4.712 0.15356 1.6705 1.3865 3 9.01 7.4567 0.3187 2.3324 1.9359 19 21.945 5.3311 0.17374 1.7407 1.4448 6 19.252 2.6983 0.087938 1.3872 1.1514 2 140.3 1.365 0.076353 1.5845 1.3152 2 181.79 2.535 0.036398 0.78666 0.65293 3 224.42 3.4667 0.042649 0.78771 0.6538 2 248.24 3.555 0.046655 0.82931 0.68832 3 204.89 2.7 0.034065 0.73703 0.61173 7 179.76 4.1971 0.060263 0.93064 0.77243
% Create time stamps with arbitrary starting date/time
% Note that the time stamps start at hour 0.
arbitraryDateTime = datetime(2000,1,1,0,0,0) + [0;cumsum(hours(T.Duration(1:end-1)))];;
TT = table2timetable(T,'RowTimes',arbitraryDateTime)
TT = 109×6 timetable
Time Duration WindDirection_degree_ WindSpeed_m_s_ Hs Tp Tm ____________________ ________ _____________________ ______________ ________ _______ _______ 01-Jan-2000 00:00:00 8 206.2 8.9288 0.11265 1.0981 0.91139 01-Jan-2000 08:00:00 9 228.66 8.1667 0.10047 1.0481 0.86994 01-Jan-2000 17:00:00 14 224.28 6.6657 0.082006 0.97952 0.813 02-Jan-2000 07:00:00 7 226.63 9.29 0.11429 1.0941 0.90813 02-Jan-2000 14:00:00 3 241.58 3.99 0.052364 0.86184 0.71533 02-Jan-2000 17:00:00 2 331.51 2.045 0.11948 1.8665 1.5492 02-Jan-2000 19:00:00 5 19.992 4.712 0.15356 1.6705 1.3865 03-Jan-2000 00:00:00 3 9.01 7.4567 0.3187 2.3324 1.9359 03-Jan-2000 03:00:00 19 21.945 5.3311 0.17374 1.7407 1.4448 03-Jan-2000 22:00:00 6 19.252 2.6983 0.087938 1.3872 1.1514 04-Jan-2000 04:00:00 2 140.3 1.365 0.076353 1.5845 1.3152 04-Jan-2000 06:00:00 2 181.79 2.535 0.036398 0.78666 0.65293 04-Jan-2000 08:00:00 3 224.42 3.4667 0.042649 0.78771 0.6538 04-Jan-2000 11:00:00 2 248.24 3.555 0.046655 0.82931 0.68832 04-Jan-2000 13:00:00 3 204.89 2.7 0.034065 0.73703 0.61173 04-Jan-2000 16:00:00 7 179.76 4.1971 0.060263 0.93064 0.77243
% Interpolate to hourly samples
TThourly = retime(TT,'hourly','linear')
TThourly = 844×6 timetable
Time Duration WindDirection_degree_ WindSpeed_m_s_ Hs Tp Tm ____________________ ________ _____________________ ______________ ________ _______ _______ 01-Jan-2000 00:00:00 8 206.2 8.9288 0.11265 1.0981 0.91139 01-Jan-2000 01:00:00 8.125 209.01 8.8335 0.11113 1.0918 0.90621 01-Jan-2000 02:00:00 8.25 211.81 8.7382 0.10961 1.0856 0.90103 01-Jan-2000 03:00:00 8.375 214.62 8.643 0.10808 1.0793 0.89585 01-Jan-2000 04:00:00 8.5 217.43 8.5477 0.10656 1.0731 0.89067 01-Jan-2000 05:00:00 8.625 220.24 8.4524 0.10504 1.0668 0.88548 01-Jan-2000 06:00:00 8.75 223.04 8.3572 0.10352 1.0606 0.8803 01-Jan-2000 07:00:00 8.875 225.85 8.2619 0.10199 1.0544 0.87512 01-Jan-2000 08:00:00 9 228.66 8.1667 0.10047 1.0481 0.86994 01-Jan-2000 09:00:00 9.5556 228.17 7.9999 0.09842 1.0405 0.86361 01-Jan-2000 10:00:00 10.111 227.69 7.8331 0.096368 1.0329 0.85729 01-Jan-2000 11:00:00 10.667 227.2 7.6663 0.094317 1.0253 0.85096 01-Jan-2000 12:00:00 11.222 226.71 7.4996 0.092265 1.0176 0.84463 01-Jan-2000 13:00:00 11.778 226.22 7.3328 0.090213 1.01 0.83831 01-Jan-2000 14:00:00 12.333 225.74 7.166 0.088161 1.0024 0.83198 01-Jan-2000 15:00:00 12.889 225.25 6.9993 0.08611 0.99476 0.82565
Convince yourself that the results are reasonable by plotting the original and interp'd data
figure
plot(TT.Time, TT.WindDirection_degree_,'-b','linewidth',2,'displayname','original');
hold on
plot(TThourly.Time, TThourly.WindDirection_degree_,'-r','displayname','interp''d');
legend

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