Deleting 3rd column of all cell double inside a variable than combine the data together

I got a variable called Pr_error_2, each data represent the pseudorange error of satellites in each second. I would like to delete the 3rd column of all cells double inside the variable. Afterwards combine the data to form something like the expected result. How can I do so? Originally I tried the loop to delete the 3rd column but don’t know why it only deletes 3rd columns in the first data.

In Pr_error_2, the first column inside each cell is the number representing each satellite, second column is the pseudorange error, thrid row is something can be ignored and need to be deleted.

Answer 1

[Star Strider]

I limited the run to the first 20 ‘Pr_error_2’ cells because the full set required more time than the 55 second limit here permits, so I ahve not tested it for all of them. I ran it offline (MATLAB Online) for all the cells in ‘Pr_error_2’ and it ran successfully, requiring 108.05 seconds to complete. The full ‘PrJ’ table is (5048x1876) so there are apparently many more satellites than the 25 showin in the first 20.

files = dir('*.mat');

for k = 1:numel(files)
    load(files(k).name)
end
% whos
% Pr_error_2
% Pr_error_2{1:5}
Expected_result

Expected_result = 18×6

4.0000 1.2762 1.2721 1.2657 1.2657 1.2571 8.0000 -1.4158 -1.4161 -1.4164 NaN NaN 16.0000 0 0 0 0 0 26.0000 1.3146 1.3146 1.3146 1.3146 1.3146 27.0000 -3.8309 -3.8323 -3.8334 -3.8349 -3.8361 28.0000 2.2691 2.2688 2.2684 2.2680 2.2677 29.0000 NaN NaN NaN NaN 2.5593 31.0000 1.9395 1.9391 1.9390 1.9388 1.9384 32.0000 4.5824 4.5784 4.5739 4.5696 4.5655 89.0000 1.2268 1.2266 1.2265 1.2261 1.2559

Pr_error_2{[1 2 end-1 end]}                             % Check Data

ans = 17×3

1.0e+04 * 0.0004 0.1276 0.0034 0.0008 -1.4158 0.0014 0.0016 0 0.0066 0.0026 1.3146 0.0054 0.0027 -0.3831 0.0045 0.0028 2.2691 0.0033 0.0031 1.9395 0.0047 0.0032 0.4582 0.0018 0.0089 0.1227 0.0062 0.0093 0 0.0064

ans = 17×3

1.0e+04 * 0.0004 0.1272 0.0034 0.0008 -1.4161 0.0014 0.0016 0 0.0066 0.0026 1.3146 0.0054 0.0027 -0.3832 0.0045 0.0028 2.2688 0.0033 0.0031 1.9391 0.0047 0.0032 0.4578 0.0018 0.0089 0.1227 0.0062 0.0093 0 0.0064

ans = 16×3

1.0e+04 * 0.0008 -1.8709 0.0027 0.0016 0 0.0061 0.0026 1.2043 0.0044 0.0027 -0.6256 0.0061 0.0028 1.5452 0.0027 0.0031 1.4481 0.0044 0.0032 -0.2811 0.0005 0.0087 1.4568 0.0046 0.0088 -0.7967 0.0047 0.0089 0.0826 0.0062

ans = 16×3

1.0e+04 * 0.0008 -1.8711 0.0027 0.0016 0 0.0061 0.0026 1.2038 0.0044 0.0027 -0.6257 0.0061 0.0028 1.5448 0.0027 0.0031 1.4477 0.0044 0.0032 -0.2817 0.0005 0.0087 1.4568 0.0046 0.0088 -0.7967 0.0047 0.0089 0.0826 0.0062

for k = 1:20%numel(Pr_error_2)
    PrT{k} = array2table(Pr_error_2{k}(:,[1 2]), 'VariableNames',{'Satellite',sprintf('Error_%04d',k)});
end
PrJ = PrT{1};
for k = 1:numel(PrT)-1
    PrJ = outerjoin(PrJ,PrT{k+1},'Keys',1);
end

% PrJ
PrJ = removevars(PrJ, 3:2:size(PrJ,2))

PrJ = 25×21 table
    Satellite_PrJ    Error_0001    Error_0002    Error_0003    Error_0004    Error_0005    Error_0006    Error_0007    Error_0008    Error_0009    Error_0010    Error_0011    Error_0012    Error_0013    Error_0014    Error_0015    Error_0016    Error_0017    Error_0018    Error_0019    Error_0020
    _____________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________    __________

           4           1276.2        1272.1        1265.7        1260.7        1257.1        1248.9        1245.7        1240.3        1237.3        1233.7        1231.7        1230.2        1229.3        1223.4          1218        1215.9        1212.8        1209.2           NaN           NaN  
           8           -14158        -14161        -14164           NaN           NaN           NaN        -14182        -14186        -14189        -14188        -14190        -14189        -14190        -14193        -14196        -14197        -14200        -14199        -14199        -14202  
          16                0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0  
          26            13146         13146         13146         13146         13146         13146         13145         13145         13146         13145         13145         13145         13145         13145         13145         13144         13144         13144         13144         13143  
          27          -3830.9       -3832.3       -3833.4       -3834.9       -3836.1       -3837.7       -3838.9       -3840.1       -3841.1       -3842.6       -3843.9         -3845       -3846.1       -3847.6       -3848.6       -3850.2       -3851.3       -3852.9       -3854.3       -3855.9  
          28            22691         22688         22684         22680         22677         22673         22670         22666         22663         22659         22656         22653         22650         22647         22643         22640         22637         22634         22630         22627  
          31            19395         19391         19390         19388         19384         19379         19382         19382         19379         19374         19372         19371         19368         19367         19365         19361         19358         19358         19355         19355  
          32           4582.4        4578.4        4573.9        4569.6        4565.5        4561.3        4557.1        4552.5        4548.4        4544.4        4540.2        4536.1        4531.9        4527.7        4523.8        4519.6        4515.8        4511.6        4507.5        4503.2  
          89           1226.8        1226.6        1226.5        1226.1        1225.9          1226          1226        1225.7        1225.4        1225.4        1224.9        1224.2          1224        1223.8        1223.7        1223.7        1223.7        1223.5        1223.4        1223.5  
          93                0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0             0  
          95            12539         12540         12540         12540         12540         12541         12541         12541         12541         12541         12541         12541         12541         12541         12541         12541         12541         12542         12542         12543  
          96          -4207.6       -4207.5       -4207.7       -4207.9       -4207.8       -4207.5       -4207.5       -4207.6         -4208       -4208.3       -4208.7       -4209.4       -4209.7       -4209.5       -4209.1       -4209.5       -4209.4       -4209.3         -4209       -4208.9  
          97            10845         10846         10848         10849         10851         10853         10855         10857         10858         10859         10861         10862         10863         10865         10867         10868         10870         10870         10874         10874  
          98            27899         27900         27901         27902         27903         27904         27905         27906         27907         27908         27909         27909         27909         27910         27911         27912         27913         27914         27915         27916  
         100          -1344.6         -1345       -1345.1       -1345.2       -1345.1       -1344.7       -1344.9       -1345.1       -1345.6       -1345.7       -1346.2       -1346.9       -1347.2       -1347.5       -1347.2       -1347.4       -1347.3         -1347       -1347.4       -1347.6  
         102            17405         17404         17404         17404         17404         17404         17403         17403         17403         17403         17402         17401         17401         17400         17400         17400         17400         17400         17400         17400  

There is of course nothing specific about the names I chose.

EDIT — (25 Feb 2024 at 17:35)

The variable names I chose allow straightforward tracking of different variables to their associated original cell arrays. To convert the ‘PrJ’ table to a matrix, use the table2array function.

.

[Yat Chi]

Thanks very much for your help, the code works successfully with my need. Still I find some of the problems in the code and I wonder it can be fixed. For example in 11 second of data there should be data of the 29 satellite but it didnt show at the final result. Also satellite 87 and 88 successfully read but satellite number is incorrectly shown as NaN, it also only copy the last second of data whereas data before is not copied. I wonder if it can be fixed but if not that is also fine for me. Anyway thanks again for your help.

[Star Strider]

As always, my pleasure!

Thank you for noticing tthat Satellite 29 was missing. I went back over my code, and noticed that I should have specified 'MergeKeys' in my outerjoin call:

rJ = outerjoin(PrJ,PrT{k+1},'Keys',1, 'MergeKeys',1);

With that change, the code is much more efficient and returns the correct result —

files = dir('*.mat');

for k = 1:numel(files)
    load(files(k).name)
end

tic

for k = 1:numel(Pr_error_2)
    PrT{k,:} = array2table(Pr_error_2{k}(:,[1 2]), 'VariableNames',{'Satellite',sprintf('Error_%04d',k)});
end
PrJ = PrT{1};
for k = 1:numel(PrT)-1
    PrJ = outerjoin(PrJ,PrT{k+1},'Keys',1, 'MergeKeys',1);
end

toc

Elapsed time is 15.352364 seconds.

PrJ_size = size(PrJ)

PrJ_size = 1×2

22 1876

First_Sample = PrJ(1:11, 1:8)

First_Sample = 11×8 table
    Satellite    Error_0001    Error_0002    Error_0003    Error_0004    Error_0005    Error_0006    Error_0007
    _________    __________    __________    __________    __________    __________    __________    __________

        3             NaN           NaN           NaN           NaN           NaN           NaN           NaN  
        4          1276.2        1272.1        1265.7        1260.7        1257.1        1248.9        1245.7  
        8          -14158        -14161        -14164           NaN           NaN           NaN        -14182  
       16               0             0             0             0             0             0             0  
       26           13146         13146         13146         13146         13146         13146         13145  
       27         -3830.9       -3832.3       -3833.4       -3834.9       -3836.1       -3837.7       -3838.9  
       28           22691         22688         22684         22680         22677         22673         22670  
       29             NaN           NaN           NaN           NaN         25593         25588         25583  
       31           19395         19391         19390         19388         19384         19379         19382  
       32          4582.4        4578.4        4573.9        4569.6        4565.5        4561.3        4557.1  
       87             NaN           NaN           NaN           NaN           NaN           NaN           NaN  

Second_Sample = PrJ(12:22, 1:8)

Second_Sample = 11×8 table
    Satellite    Error_0001    Error_0002    Error_0003    Error_0004    Error_0005    Error_0006    Error_0007
    _________    __________    __________    __________    __________    __________    __________    __________

        88            NaN           NaN           NaN           NaN           NaN           NaN           NaN  
        89         1226.8        1226.6        1226.5        1226.1        1225.9          1226          1226  
        93              0             0             0             0             0             0             0  
        95          12539         12540         12540         12540         12540         12541         12541  
        96        -4207.6       -4207.5       -4207.7       -4207.9       -4207.8       -4207.5       -4207.5  
        97          10845         10846         10848         10849         10851         10853         10855  
        98          27899         27900         27901         27902         27903         27904         27905  
       100        -1344.6         -1345       -1345.1       -1345.2       -1345.1       -1344.7       -1344.9  
       102          17405         17404         17404         17404         17404         17404         17403  
       110          29916         29916         29917         29918         29918         29919         29920  
       111            NaN           NaN           NaN           NaN           NaN           NaN           NaN  

PrJm = table2array(PrJ);
sat = PrJm(:,1);
col = 1:size(PrJm,2);

figure
waterfall(sat,col,PrJm.')
colormap(turbo)
colorbar
xlabel('Satellite')
ylabel('Column (Variable) #')
zlabel('Error')

All 22 satellites are now present in ‘PrJ’.

.

[Yat Chi]

Ah, that works perfectly!

[Star Strider]

Thank you!