Is it possible to use the for-loop for this purpose?

Although now I'm doing this work manually by my hand if there is any chance to know I want to ask is there any way to do this using for loop?

I want to polyfit two by two based on a rule and know slope and y-intercept for each one that runs.

I have a table that describes the name of a couple of station names that should be used in polyfit together. on the other hand, I have a cell including monthly tables for these stations data.

I wonder if any way available to do this like in the code below automatically: (In the table that describes the name of couple station name that should be used in polyfit together there is Ahvaz and Ramhormoz is beside each other)

%calculate P for January (thanks to Adam Danz)
Ahvaz_Jan = getfield(finalStations,'Ahvaz_Jan'); 
Ramhormoz_Jan = getfield(finalStations,'Ramhormoz_Jan');
nanIdx = isnan(Ahvaz_Jan.tmax_m) | isnan(Ramhormoz_Jan.tmax_m);
P = polyfit(Ahvaz_Jan.tmax_m(~nanIdx), Ramhormoz_Jan.tmax_m(~nanIdx), 1); % January P (slope and Y intercept)

% calculate P for Feburary
Ahvaz_Feb = getfield(finalStations,'Ahvaz_Feb'); 
Ramhormoz_Feb = getfield(finalStations,'Ramhormoz_Feb');
nanIdx = isnan(Ahvaz_Feb.tmax_m) | isnan(Ramhormoz_Feb.tmax_m);
P = polyfit(Ahvaz_Feb.tmax_m(~nanIdx), Ramhormoz_Feb.tmax_m(~nanIdx), 1); % Feburary P (slope and Y intercept)

% For March ...
% ...For December

% looking for next two stations name from table_of_couple_stations.xlsx and do above steps for them

and then looking at the table that describes the name of a couple of station names that should be used in polyfit together and find the next couple and do this for it, and repeat this process until the end, and gives me a table of all P with corresponding stations name.

I don't know if this could be done in Matlab but if there is any way it's a fabulous moment for me.

[Adam Danz]

I didn't recommend using getfield.

Ahvaz_Jan = getfield(finalStations,'Ahvaz_Jan'); 
Ramhormoz_Jan = getfield(finalStations,'Ramhormoz_Jan');

That can be replaced by

Ahvaz_Jan = finalStations.Ahvaz_Jan;           % or finalStations(1).Ahvaz_Jan
Ramhormoz_Jan = finalStations.Ramhormoz_Jan;   % or finalStations(1).Ramhormoz_Jan

That's described in the first line of text in getfield().

[Adam Danz]

It looks like you went with the structure approach in this question/answer.

Now you have a structure with 852 uniquely named fields, each containing a table for a station and a month. If you want to access a specific field such as the Ahvaz station for the month of January ("Ahvaz_Jan"), you'll have to construct the field name strings like this.

fieldString = sprintf('%s_%s','Ahvaz','Jan');
T = finalStations.(fieldString);

I think the cell array approach in that question/answer would be easier to work with (I'm biased because I wrote that answer). With that approach, you have a nx12 cell array of n-stations and 12 months. You also have a 1xn cell array of station names. So identifying the table would look like this.

T = Cmo{strcmp(stationList,'Ahvaz'), 1}
%  Where "stationList" is your cell array of station names 
% and the 1 is for January.  

Answer 1

[Adam Danz]

Using the cell approach where Cmo is a nx12 cell array of n station-tables for 12 months.

stationList is read in as a table. The months are added to the table.

All stations listed in the excel file are located in your data except for "Qom".

See inline comments for details.

% Load data
load('C')

% Change "data" column to "date"
for i = 1:numel(C)
    C{i}.Properties.VariableNames = strrep(C{1}.Properties.VariableNames,'data','date'); 
end

% Create the nx12 cell array of tables (n stations, 12 months)
Cmo = cell(numel(C),12);  
for i = 1:numel(C)
    Cmo(i,:) = arrayfun(@(m){C{i}(month(C{i}.date) == m, :)},1:12);
end

% Get station name from each row of Cmo
stationNames = cellfun(@(T)unique((T.station_name(~ismissing(T.station_name)))),Cmo(:,1)); 

% Get station pairs table from excel file 
stationList = readtable('table_of_couple_stations.xlsx');

% select which months you want to access
months = 1:12;  

% Add month columns to the stationNames table
% Each element of the month columns will be a 1x2 vector that will
% contain the linear regression coeffs.
monthNames = month(datetime(1, months, 1), 's');
newColumns = repmat({nan(height(stationList),2)},1,numel(monthNames)); 
stationList = [stationList, table(newColumns{:},'VariableNames', monthNames)]; 

% Loop through each pair in stationList
for i = 1:height(stationList)
    % Locate the pairs or stations in Cmo
    rowIdx1 = strcmp(stationNames, stationList.station_name{i});        % use find(rowIdx1) to see row number
    rowIdx2 = strcmp(stationNames, stationList.closest_station{i});     % use find(rowIdx2) to see row number
    % If station could not be located, skip this iteration and throw warning. 
    if ~any(rowIdx1)
        warning('%s could not be located.',stationList.station_name{i})
        continue
    end
    if ~any(rowIdx2)
        warning('%s could not be located.',stationList.closest_station{i})
        continue
    end
    % Loop through the selected months
    for j = 1:numel(months)
        % Extract tables for given station and month
        T1 = Cmo{rowIdx1, months(j)}; 
        T2 = Cmo{rowIdx2, months(j)}; 
        % Remove missing values and compute linear reg coeffs.
        nanIdx = isnan(T1.tmax_m) | isnan(T2.tmax_m);
        stationList.(monthNames{j})(i,:) = polyfit(T1.tmax_m(~nanIdx), T2.tmax_m(~nanIdx), 1);
    end
end

Look at the first few rows of the updated stationList table.

>> head(stationList)
ans =
  8×14 table
       station_name              closest_station                   Jan                    Feb                   Mar                   Apr                   May                   Jun                     Jul                     Aug                    Sep                    Oct                    Nov                    Dec         
    __________________    _____________________________    ___________________    ___________________    __________________    __________________    __________________    __________________    _____________________    ____________________    __________________    ___________________    ___________________    ____________________
    {'Abadan'        }    {'Bandar-E- Mahshahr'       }     1.0029    -0.56805    0.99101     -0.3693     0.9674    0.03199    0.92499     1.8964    0.89697     3.4205    0.82895     6.7491    0.38668        27.331    0.016655      43.889    0.47415     21.034    0.66668      11.245    0.92907      1.4489    0.97751     0.032892
    {'Abali'         }    {'Tehran (Mehrabad Airport)'}      1.101      8.0531     1.0942      9.6046    0.88167     11.602     0.8317      13.04     0.9531     12.168    0.70668     18.054    0.69229        18.706      0.7263      17.201    0.81482     13.946    0.84358      12.264    0.88557      10.139    0.91919       7.9292
    {'Abumusa Island'}    {'Siri Island'              }     0.8699      3.0804    0.76932      5.3086     1.0443    -1.3821    0.90781     3.1134    0.69469     10.979     0.6954     10.825    0.76521        8.1086      0.6392      12.767    0.76524     8.2608     1.0202    -0.51768    0.89973      3.1358     0.8468        3.855
    {'Ahar'          }    {'Tabriz'                   }    0.89695     -0.2543    0.82347      1.7484    0.82964     2.8538    0.77454     5.0873     0.7649     7.0762    0.79252     8.9474    0.83043        9.8787     0.72213      12.924    0.71801     10.897    0.62234      9.3066    0.73612      3.7588     0.8854    -0.044669
    {'Ahvaz'         }    {'Ramhormoz'                }     1.0585      -1.064    0.99794    -0.28061     1.0827    -2.6469      1.061    -2.3963    0.96587     1.2413    0.95975     1.5445    0.99822       -0.2165     0.99632    -0.20757     0.8994     3.9369    0.89535      3.6672     1.0306    -0.84648     1.0153     -0.12595
    {'Anar'          }    {'Shahrebabak'              }    0.85936      -0.978    0.93761     -2.4127     0.9901    -3.5843     1.0178    -4.3328    0.87629    0.62758    0.87015     1.6203    0.82648        3.6828     0.86516      2.1822    0.75321     5.5296    0.73567      4.7125    0.69743      3.6285    0.90427      -1.0359
    {'Arak'          }    {'Qom'                      }        NaN         NaN        NaN         NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN           NaN         NaN         NaN        NaN        NaN        NaN         NaN        NaN         NaN        NaN          NaN
    {'Babolsar'      }    {'Nowshahr'                 }     1.0055    -0.92578     1.0112     -1.1519    0.88316    0.23299     1.0007     -1.699     1.0348    -2.5691    0.99892     -1.713     0.9417    0.00020517      0.9914     -1.4848     1.0085     -2.026    0.98938     -1.0535     0.9681    -0.34382     1.0042     -0.58969

To access the coefficients for Ahar-Tabriz & February,

rowIdx = strcmp(stationList.station_name,'Ahar') & strcmp(stationList.closest_station,'Tabriz');
coeffs = stationList.Feb(rowIdx,:);

[BN]

Thank you so much. It's perfect, clear, and precise, I really appreciate your time and attention.

Best Regards and Best Wishes.

[Adam Danz]

Happy to help.

Answer 2

[Mohammad Sami]

You can use the for loop. Modify it as you deem fit.

% load from xlsx or from your mat file.
matching_station = readtable('table_of_couple_stations.xlsx');
monthnames = {'Jan' 'Feb' 'Mar' 'Apr'} % and the rest of names
for i = 1:height(matching_station);
    for j = 1:length(monthnames)
        nameA = [matching_station.station_name{i} '_' monthnames{j}]; % {i} assumed its a cell array
        nameB = [matching_station.closest_station{i} '_' monthnames{j}];
        tempA = getfield(finalStations,nameA); 
        tempB = getfield(finalStations,nameB);
        nanIdx = isnan(tempA.tmax_m) | isnan(tempB.tmax_m);
        P = polyfit(tempA.tmax_m(~nanIdx), tempB.tmax_m(~nanIdx), 1); 
    end
end

[Adam Danz]

The stations names in table_of_couple_stations do not match the field names, though. For example, "'Tehran (Mehrabad Airport)'" cannot be a field name.

This is another problem using the structure approach that wouldn't be a problem with the nx12 cell array approach.

[BN]

Dear Adam Danz, You are right the structure approach in this question/answer does not match some of the field names. I was picked this approach because I thought thanks to the abbreviation of month name in front of station names, it would be easy to use in the loop.

May I ask you if you please guide me on how to use the cell approach in this problem?

Thank you

[BN]

Here I can manually calculate polyfit for stations based on each month using cell array approach.

Cmo = cell(numel(C),12);
for i = 1:numel(C)
    Cmo(i,:) = arrayfun(@(m){C{i}(month(C{i}.date) == m, :)},1:12);
end
stationList = readtable('table_of_couple_stations.xlsx');
stationList = table2cell(stationList);
Ahvaz_jan = Cmo{strcmp(stationList,'Ahvaz'), 1};
Ramhormoz_jan = Cmo{strcmp(stationList,'Ramhormoz'), 1};
%remove pairs of data that contain a NaN value
nanIdx = isnan(Ahvaz_jan.tmax_m) | isnan(Ramhormoz_jan.tmax_m);
P = polyfit(Ahvaz_jan.tmax_m(~nanIdx), Ramhormoz_jan.tmax_m(~nanIdx), 1);

But still, I think about how to write the loop-for in order to make it automatically run for each one.

Thanks