MATLAB: Is it possible to use the for-loop for this purpose

for loopMATLAB

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.

Best Answer

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,:);

Related Solutions

MATLAB: How to write a loop for this case

The variable "C" is a 1xn cell array where each element contains a table. Each table contains a column "date" listing dates in datetime format (from your previous questions).

This solution seaprates each table by month to create an nx12 cell array where Cmo{i,j} contains a subtable from station i and month j.

% Loop through each table and break it apart by month
% creating 12 sub-tables that are stored in the rows 
% of Cmo.
Cmo = cell(numel(C),12);  
for i = 1:numel(C)
    Cmo(i,:) = arrayfun(@(m){C{i}(month(C{i}.date) == m, :)},1:12);
end

Depending on how you'll use these data, it may be more practical to keep the tables all together rather then splitting them by month and using logical indexing to isolate the rows of a table that are associated with a particular month.

MATLAB: How to have a loop-for to filling NaN rows

The best approach would be to do this within the j-loop from this answer (you just need to change 1 line and add 3 lines).

The top part of the loop below is unchanged. The bottom part computes the regression coefficients and then computes the estimate of the missing values in table T1 based on the values in T2. This assumes that the rows in T2 correspond with the rows in T1. Fortunately all of the pairs of tables in your data have matching date columns so this simple method is safe. An assumption-check is performed to verify this and if you ever run any data where the date columns between the two tables do not match, an error will be thrown.

After filling the missing values in T1, that table is loaded back into the Cmo array.

    % 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);
        
        
        %********** UPDATED / NEW SECTION BELOW *****************
        
        
        coefs = polyfit(T1.tmax_m(~nanIdx), T2.tmax_m(~nanIdx), 1);
        stationList.(monthNames{j})(i,:) = coefs;
        
        % This simple approach assumes the rows of T1 are from the
        % same dates as the rows of T2.  Here we check that assumption.
        % If this assumption-check ever fails, a more rigorous approach
        % will be needed that matches the rows by date.
        assert(isequal(T1.date,T2.date),'Assumption violation: dates do not match between tables.')
        % Compute missing values (if the values in T2 are also missing, it will return NaN.
        T1.tmax_m(nanIdx) = coefs(1) * T2.tmax_m(nanIdx) + coefs(2);
        % Update the original Cmo array of nx12 tables.
        Cmo{rowIdx1, months(j)} = T1;  
    end

Best Answer

Related Solutions

MATLAB: How to write a loop for this case

MATLAB: How to have a loop-for to filling NaN rows

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