Divide annual timeseries to monthly ones

Question

Ancalagon8 2023-1-15

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1894545-divide-annual-timeseries-to-monthly-ones

评论： Star Strider 2023-1-24

采纳的回答： Star Strider

在 MATLAB Online 中打开

For a specific year I have created an hourly timetable using retime:

VarPerHour = retime(T, 'hourly', 'sum');

How can I divide VarPerHour into 12 monthly timetables?

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Answer 1

Star Strider 2023-1-15

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1894545-divide-annual-timeseries-to-monthly-ones#answer_1149260

在 MATLAB Online 中打开

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_timeTemperature_____________________________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 2023-1-16

在 MATLAB Online 中打开

This was an adventure!

This looks a bit more complicated than it actually is.

I can’t get them exactly in the format you want, however this is reasonably close.

In any event, it’s the best I can do —

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_timeTemperature_____________________________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
end
VarPerHourMonthT                                                                                                % Display Results
VarPerHourMonthT = 12×1 cell array
    {31×25 table}
    {28×25 table}
    {31×25 table}
    {30×25 table}
    {31×25 table}
    {30×25 table}
    {31×25 table}
    {31×25 table}
    {30×25 table}
    {31×25 table}
    {30×25 table}
    {31×25 table}
VarPerHourMonthT{1}(1:5,:)
ans = 5×25 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-Jan-2019       588    608.11    608.25    608.33    608.25     608.4    608.59     608.9    609.32    599.51    609.61    609.51    609.39    609.44    609.58    609.83    609.97    610.21    610.35    610.42    610.52    610.54    610.72    610.54
    02-Jan-2019    589.97    610.02    609.79    609.47    609.06    608.76    608.66    608.54    608.66    608.75    608.57    608.08    607.77    607.33    606.85    606.66     606.4    606.22    606.29    606.31    606.17    605.95    605.57    514.48
    03-Jan-2019    574.56    604.75    604.71    604.53    604.05    603.61    603.22    603.14    603.77    604.38    604.76    604.79    604.45    604.75    604.73    604.92    605.31    605.53    606.32    606.63    607.06    607.59    607.94    608.23
    04-Jan-2019    588.18    608.74    608.99    609.25    609.32     609.3    609.35    609.49    609.92    610.62    610.78    610.59    610.29    610.01    609.96    609.88    609.79    610.11    610.26    610.51    610.71    610.28    610.17    610.06
    05-Jan-2019    589.38    609.63    609.23    609.37    609.24    608.86    608.74    608.44    608.65    609.31    609.42    609.41     608.9    608.53    608.53     608.6     608.7    608.82    609.15    609.34    609.36    609.36    609.39    609.23
VarPerHourMonthT{12}(1:5,:)
ans = 5×25 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-Dec-2019    590.64     193.5         0         0         0         0         0         0         0         0         0    603.21    613.27    613.16    613.12    613.12    613.17    163.52         0         0         0         0         0         0
    02-Dec-2019    583.83    614.47    614.55    614.53    614.32    614.18    614.23    614.26    614.48    614.56     614.6    614.43    614.13    613.97    613.75    613.47    613.22    613.05    612.97    613.07    613.12    613.15    613.04    613.09
    03-Dec-2019    592.37    612.51    612.56    612.35    611.93    611.72    611.59     611.5    611.43    611.41    611.53    611.47    611.56    611.59    611.35     611.2     611.2    611.22    611.07    611.13    611.06    611.07    611.08    610.76
    04-Dec-2019    589.94    610.24    610.45    610.26    609.94    609.84    609.82    610.14    610.25    610.56    611.45    600.92    610.75    610.62    610.46    590.17    152.67         0         0         0         0         0         0         0
    05-Dec-2019    583.18    613.92    614.01    614.16    614.15    614.12    614.21    614.31    614.68    604.68    614.93    614.73    614.95    614.92    614.91    614.97    614.94    615.21    615.25    615.63    615.94       616    616.06    616.11

The new ‘VarPerHourMonthT’ cell array is of table arrays, not timetable arrays because I doubt that timetable arrays would support this format. The essential function here is the unstack function, and most of the code in the loop iterations is devoted to creating table arrays that are compatible with it. Using unstack on the timetable arrays themselves is possible (I did that first), however they do not produce the result you want, so I adopted this approach instead. (The problem with using unstack on the timetable arrays themselves is that it produces a diagonal matrix of ‘Temperature’ sums in a matrix of NaN values for each hour, rather than a single row. To get the result you want required creating both the ‘Date’ and ‘Hour’ variables, converting the timetable arrays to table arrays, eliminating the ‘date_time’ variable, and then using unstack on the table arrays.)

I did my best to comment-document my code here, along with describing it.

.

Star Strider 2023-1-16

在 MATLAB Online 中打开

Yes.

See the documentation section on Spreadsheet Files Only . It will probably be necesary to use a for loop to write to each sheet. (I have never had to do this, so I have no experience with it.)

Testing it here —

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_timeTemperature_____________________________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
end
Filename = 'RainPerHourMonth.xlsx';
for k = 1:12
    writetable(VarPerHourMonthT{k}, Filename, 'Sheet',k)
end
Warning: Added specified worksheet.
Warning: Added specified worksheet.
Warning: Added specified worksheet.
Warning: Added specified worksheet.
Warning: Added specified worksheet.
Warning: Added specified worksheet.
Warning: Added specified worksheet.
Warning: Added specified worksheet.
Warning: Added specified worksheet.
Warning: Added specified worksheet.
Warning: Added specified worksheet.
T6 = readtable(Filename, 'Sheet',6, 'VariableNamingRule','preserve')            % Check 6
T6 = 30×25 table
Date01234567891011121314151617181920212223___________________________________________________________________________________________________________________________________________________________

    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

I do not understand the writetable warnings. It seems to work.

.

Star Strider 2023-1-24

在 MATLAB Online 中打开

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_timeTemperature_____________________________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
Date01234567891011121314151617181920212223___________________________________________________________________________________________________________________________________________________________

    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!’

.

Ancalagon8 2023-1-24

Worked perfect (as always)! Thanks!!

Star Strider 2023-1-24

As always, my pleasure!

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Answer 2

Christopher McCausland 2023-1-15

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1894545-divide-annual-timeseries-to-monthly-ones#answer_1149225

在 MATLAB Online 中打开

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

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Ancalagon8 2023-1-15

Thanks for your answer @Christopher McCausland.

VarPermonth = retime(T, 'monthly', 'sum');

returns me only one value per month (12X1 timetable).

VarPerHour = retime(T, 'hourly', 'sum') is a 8760X1 timetable (365 days X 24 hours).

I need to split VarPerHour per month but keep all values.

Christopher McCausland 2023-1-15

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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