Divide timeseries to monthly ones

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Ancalagon8
Ancalagon8 il 15 Gen 2023
Modificato: Ancalagon8 il 6 Gen 2025 alle 21:49
For a specific year I have created an hourly timetable using retime.
How can I divide into monthly ?

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Star Strider
Star Strider il 15 Gen 2023
Ran in:
A for loop is the easiest way to do this —
LD = load(websave('dataset','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1255052/dataset.mat'));
T = LD.TT1;
VarPerHour = retime(T, 'hourly', 'sum')
VarPerHour = 8760×1 timetable
date_time Temperature __________________ ___________ 01-Jan-19 00:00:00 588 01-Jan-19 01:00:00 608.11 01-Jan-19 02:00:00 608.25 01-Jan-19 03:00:00 608.33 01-Jan-19 04:00:00 608.25 01-Jan-19 05:00:00 608.4 01-Jan-19 06:00:00 608.59 01-Jan-19 07:00:00 608.9 01-Jan-19 08:00:00 609.32 01-Jan-19 09:00:00 599.51 01-Jan-19 10:00:00 609.61 01-Jan-19 11:00:00 609.51 01-Jan-19 12:00:00 609.39 01-Jan-19 13:00:00 609.44 01-Jan-19 14:00:00 609.58 01-Jan-19 15:00:00 609.83
for k = 1:12
MMidx = month(VarPerHour.date_time) == k;
VarPerHourMonth{k,:} = VarPerHour(MMidx,:);
end
VarPerHourMonth
VarPerHourMonth = 12×1 cell array
{744×1 timetable} {672×1 timetable} {744×1 timetable} {720×1 timetable} {744×1 timetable} {720×1 timetable} {744×1 timetable} {744×1 timetable} {720×1 timetable} {744×1 timetable} {720×1 timetable} {744×1 timetable}
VarPerHourMonth{1}(1:5,:)
ans = 5×1 timetable
date_time Temperature __________________ ___________ 01-Jan-19 00:00:00 588 01-Jan-19 01:00:00 608.11 01-Jan-19 02:00:00 608.25 01-Jan-19 03:00:00 608.33 01-Jan-19 04:00:00 608.25
VarPerHourMonth{12}(1:5,:)
ans = 5×1 timetable
date_time Temperature __________________ ___________ 01-Dec-19 00:00:00 590.64 01-Dec-19 01:00:00 193.5 01-Dec-19 02:00:00 0 01-Dec-19 03:00:00 0 01-Dec-19 04:00:00 0
This uses an existing timetable. It should work with the one you are currently using as well.
.
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Star Strider
Star Strider il 24 Gen 2023
Ran in:
Let’s do that experiment —
LD = load(websave('dataset','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1255052/dataset.mat'));
T = LD.TT1;
VarPerHour = retime(T, 'hourly', 'sum')
VarPerHour = 8760×1 timetable
date_time Temperature __________________ ___________ 01-Jan-19 00:00:00 588 01-Jan-19 01:00:00 608.11 01-Jan-19 02:00:00 608.25 01-Jan-19 03:00:00 608.33 01-Jan-19 04:00:00 608.25 01-Jan-19 05:00:00 608.4 01-Jan-19 06:00:00 608.59 01-Jan-19 07:00:00 608.9 01-Jan-19 08:00:00 609.32 01-Jan-19 09:00:00 599.51 01-Jan-19 10:00:00 609.61 01-Jan-19 11:00:00 609.51 01-Jan-19 12:00:00 609.39 01-Jan-19 13:00:00 609.44 01-Jan-19 14:00:00 609.58 01-Jan-19 15:00:00 609.83
for k = 1:12
MMidx = month(VarPerHour.date_time) == k;
VarPerHourMonth{k,:} = VarPerHour(MMidx,:);
end
for k = 1:12
TTTemp = VarPerHourMonth{k}; % Create Temporary 'timetable'
Hours = hour(TTTemp.date_time); % Create 'Hours' Variable
[y,m,d] = ymd(TTTemp.date_time); % Begin To Create 'Date' Variable
Date = datetime(y,m,d); % Finish Creating 'Date' Variable
TTTemp = addvars(TTTemp, Date, Hours,'Before','Temperature'); % Add 'Hours' & 'Date' Variables
TTTemp.Properties.VariableNames(1:2) = {'Date','Hours'}; % Name 'Hours' & 'Date' Variables
TTTempT = timetable2table(TTTemp); % Convert To 'table'
VarPerHourMonthT{k,:} = unstack(TTTempT(:,2:end),'Temperature','Hours', 'VariableNamingRule','preserve'); % Unstack & Write To Cell Array
MMM{k,:} = month(TTTemp.date_time(1,:),'shortname');
end
Filename = 'RainPerHourMonth.xlsx';
for k = 1:12
writetable(VarPerHourMonthT{k}, Filename, 'Sheet',string(MMM{k}))
end
T6 = readtable(Filename, 'Sheet','Jun', 'VariableNamingRule','preserve') % Check 6
T6 = 30×25 table
Date 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ___________ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ 01-Jun-2019 590.12 610.21 609.91 609.69 609.39 609.26 609.45 609.56 609.62 609.79 609.73 609.8 609.85 609.93 609.99 610.05 609.95 609.9 609.82 609.79 609.87 610.11 610.22 610.32 02-Jun-2019 589.91 610 609.75 609.56 609.36 609.43 609.4 609.48 609.66 609.82 609.93 610.05 610.08 610.03 609.77 609.73 609.75 609.61 609.64 609.86 610.03 610.06 610.54 559.69 03-Jun-2019 590.07 610.38 610.19 609.93 609.65 609.56 609.55 609.72 610.1 610.23 610.29 610.35 610.37 610.34 610.3 610.31 610.13 610.13 609.77 609.69 609.61 609.7 609.99 610.16 04-Jun-2019 589.7 609.81 609.39 609.14 608.96 608.84 608.93 608.96 609.02 609.24 609.31 609.47 609.6 609.56 609.54 609.37 609.11 608.87 608.7 608.59 608.64 608.66 608.87 608.92 05-Jun-2019 588.63 608.9 608.82 608.6 608.47 608.43 608.46 608.49 608.58 608.8 608.85 608.95 609.06 608.94 608.78 608.62 608.55 608.46 608.38 608.45 608.63 608.85 609.36 609.43 06-Jun-2019 589.19 609.53 609.6 609.66 609.68 609.82 609.87 610.12 610.34 610.58 610.71 610.7 610.89 610.93 610.89 610.83 610.69 610.58 610.6 610.62 610.87 610.99 611.3 611.32 07-Jun-2019 591.04 611.48 611.42 611.23 611.21 611.2 611.38 611.55 611.61 611.72 611.79 611.83 611.97 611.91 611.84 611.74 611.61 611.56 611.45 611.39 611.56 611.71 612.04 612.22 08-Jun-2019 591.75 612.06 611.75 611.64 611.57 611.44 611.45 611.56 611.67 611.79 611.78 611.53 611.46 611.41 611.2 610.94 610.69 610.4 610.22 610.18 610.24 610.43 610.6 610.84 09-Jun-2019 590.49 610.77 610.68 610.37 610.12 609.74 609.8 609.97 610.16 610.2 609.81 609.51 609.18 172.6 0 0 0 0 0 0 0 0 0 0 10-Jun-2019 0 0 0 0 0 0 0 0 0 577.48 607.67 607.64 607.41 607.18 606.86 606.63 363.89 606.34 606.1 606.23 606.5 606.62 606.69 606.93 11-Jun-2019 586.4 606.44 606.44 606.31 606.12 605.98 606.08 606.26 606.4 606.48 606.49 606.62 606.75 606.81 606.55 606.3 606.37 606.63 607.04 607.08 607.06 607.5 607.95 607.93 12-Jun-2019 587.48 607.62 607.39 607.33 607.21 607.23 607.5 607.91 608.08 608.17 608.36 608.57 608.55 608.54 608.4 608.31 608.3 608.16 608.33 608.3 608.33 608.55 609.12 609.08 13-Jun-2019 588.75 609.03 608.79 608.66 608.59 608.66 608.89 609.09 609.46 609.72 609.8 609.93 609.9 609.72 609.48 609.34 609.24 609.14 609.03 608.98 609.02 609.13 609.46 609.63 14-Jun-2019 589.38 609.6 609.52 609.36 609.17 609.03 609.18 609.22 609.3 609.42 609.47 609.48 609.43 609.39 588.9 588.49 608.61 608.54 608.32 608.25 608.37 608.58 608.49 608.46 15-Jun-2019 588.22 608.57 608.22 607.96 607.86 607.64 607.61 607.68 607.83 607.89 607.87 607.7 607.65 607.37 607.15 606.85 606.86 606.62 606.46 606.43 606.35 606.53 606.74 607.02 16-Jun-2019 586.81 606.99 607.09 607.03 606.91 606.99 607.15 607.26 607.16 607.16 607.21 607.25 587.09 607.3 606.87 606.55 606.65 607.31 606.8 606.56 606.81 607.04 607.25 607.52
For some reason, writetable doesn’t like the cell array (even though indexing into it should produce a character array), however it accepts the string argument. Referring to it by name in the readtable test works. (The ‘MMM’ cell array didn’t initially appear in this version of my code, so I added it in the loop.)
So, an emphatic ‘Yes!’
.
Star Strider
Star Strider il 24 Gen 2023
As always, my pleasure!

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Più risposte (1)

Christopher McCausland
Christopher McCausland il 15 Gen 2023
Hi Ancalogon,
Having a look at the documentation here; you can just replace hourly with any timestep such as:
Time Step
'yearly'
'quarterly'
'monthly'
'weekly'
'daily'
'hourly'
'minutely'
'secondly'
Have you tried;
VarPermonth = retime(T, 'monthly', 'sum');
Let me know if this is what you are looking for, if not please provide a snippit of the data and the expected output.
Christopher
  1 Commento
Christopher McCausland
Christopher McCausland il 15 Gen 2023
Hi Ancalagon,
I get what you want now.
What you really need to do is filter ValPerHour by months, heres an example of how to do so:
https://uk.mathworks.com/matlabcentral/answers/892852-filter-timetable-by-month
And also an ealier suggestion from Walter;
https://uk.mathworks.com/matlabcentral/answers/1561736-extract-data-from-timetable-for-specific-months
I hope this helps!
Christopher

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