Extracting Data from Table

I am attempting to sort a large set of 3D data, into 2D frames. For context, the data I have imported is a text file and I want to seperate the text file into seperate tables, which all have the same X value. I had previously done this by creating several ID variables...

idx_250 = FFF1.x_coordinate == 0.25;
x_250 = FF1(idx_250,:);

This was repeated for each X value from 0.25 to 4, in 0.25 intervals. In attempt to neaten this up I tried to create a for loop.

for i = 1 :length(xv1)
    id = FFF1.x_coordinate == xv1(i);
    idTable = FFF1(id,:);
    %Here I will process the data within idTable and save it in an appropriate array
end

Here, xv1 is a vector containing all of my x values [0.25 0.5 ... 4].

However, when i = 1 and xv1(i) therefore equals 0.25, the id vector only has one true value. However if I put: id = FFF1.x_coordinate == 0.25, there is no problem. Could someone please explain why it would work if i directly type 0.25 vs a variable with the same value? Thanks!

[Stephen23]

@Rhiannon: please click the paperclip button to upload your data in a MAT file.

Answer 1

[Star Strider]

It would help to have your data.

Lacking it, consider doing something like this —

T1 = table(reshape(repmat((1:5),5,1),[],1), sort(rand(25,1)), randn(25,1), VariableNames=["Class","X","Y"])

T1 = 25x3 table
    Class       X           Y    
    _____    ________    ________

      1      0.043269    0.028793
      1      0.053658     0.12042
      1       0.12265    -0.58972
      1       0.15962    -0.64789
      1       0.16202      1.2526
      2       0.19557     0.81594
      2       0.24494      1.0379
      2       0.25965     0.38801
      2       0.28422      0.9758
      2        0.3173     0.95883
      3        0.3423      1.9717
      3       0.40383     0.85093
      3       0.40679    0.047132
      3       0.48328     -2.1233
      3        0.5016     0.95197
      4       0.55621     -1.2707

[UClass,~,idx] = unique(T1.Class,'stable');
XYc = accumarray(idx, (1:numel(idx)).', [], @(x){T1(x,:)})

XYc = 5x1 cell array
    {5x3 table}
    {5x3 table}
    {5x3 table}
    {5x3 table}
    {5x3 table}

XYc{3}

ans = 5x3 table
    Class       X          Y    
    _____    _______    ________

      3       0.3423      1.9717
      3      0.40383     0.85093
      3      0.40679    0.047132
      3      0.48328     -2.1233
      3       0.5016     0.95197

In this instance, all the ‘XYc’ tables are the same sizes, however they don’t have to be, and ‘Class’ do not have to be integers, providing there is a limited number of different values for it. (The'stable' argument simply returns them in the order the unique function encountered them, rather than sorting them as well.)

Example —

T1 = table(randi(5,25,1)*rand, sort(rand(25,1)), randn(25,1), VariableNames=["Class","X","Y"])

T1 = 25x3 table
     Class        X            Y    
    _______    ________    _________

    0.15684    0.010617      -1.0611
    0.62738    0.022529      0.84091
    0.47053     0.05813     -0.89505
    0.31369     0.07921     -0.69426
    0.31369    0.085088    0.0017908
    0.47053     0.11078     0.067547
    0.62738     0.11444         1.26
    0.15684      0.2486       1.0515
    0.47053     0.33631      -0.2269
    0.15684     0.34201      -1.9554
    0.62738     0.35836      -1.2822
    0.47053     0.39938     -0.16623
    0.31369      0.4293        1.398
    0.31369     0.43839      0.95893
    0.47053     0.46436      -0.2051
    0.47053     0.48195       0.3377

[UClass,~,idx] = unique(T1.Class,'stable');
XYc = accumarray(idx, (1:numel(idx)).', [], @(x){T1(x,:)})

XYc = 5x1 cell array
    {3x3 table}
    {6x3 table}
    {9x3 table}
    {5x3 table}
    {2x3 table}

XYc{1}

ans = 3x3 table
     Class        X           Y   
    _______    ________    _______

    0.15684    0.010617    -1.0611
    0.15684      0.2486     1.0515
    0.15684     0.34201    -1.9554

XYc{3}

ans = 9x3 table
     Class        X          Y    
    _______    _______    ________

    0.47053    0.05813    -0.89505
    0.47053    0.11078    0.067547
    0.47053    0.33631     -0.2269
    0.47053    0.39938    -0.16623
    0.47053    0.46436     -0.2051
    0.47053    0.48195      0.3377
    0.47053    0.58899       1.198
    0.47053     0.8856      1.1108
    0.47053    0.95445     0.60166

XYc{5}

ans = 2x3 table
     Class        X          Y    
    _______    _______    ________

    0.78422    0.52813     -0.2259
    0.78422    0.90808    -0.30591

.

EDIT — Corrected typographical errors.

.

[Rhiannon]

Hi,

Thank you for your answer, I appreciate you taking the time. I have implemented successfully implemented your procedure!

All the best

[Star Strider]

As always, my pleasure!

Answer 2

[Voss]

If FFF1.x_coordinate == xv1(i) gives a different result than FFF1.x_coordinate == 0.25, then xv1(i) is not equal to 0.25. You don't show how xv1 is constructed, so I can't comment on why xv1(1) is not exactly 0.25.

Regardless, here is a way to split a table into a cell array of tables based on the value of one table variable (in this case 'x'):

x = randi(16,100,1)/4; % x: random values amongst {0.25, 0.5, ..., 3.75, 4}
y = randn(100,1);      % y: some other random values
z = randn(100,1);      % z: some other random values
T = table(x,y,z)

T = 100x3 table
     x          y            z    
    ____    _________    _________

    2.25      0.11897      -1.5427
       1      0.40671       1.9308
    2.25     -0.56589      0.93971
    3.25      0.17427       1.2856
     3.5    -0.093726       1.0214
       4     0.031567      0.25919
    1.75       1.2622       1.5087
    1.25       1.1321      0.72386
       1      -1.6124      -0.5274
     0.5     -0.16188     -0.66869
    0.25      -0.2583    -0.079574
     0.5     -0.98999      -1.9855
     1.5      -1.0284     -0.31855
     0.5    -0.072366      0.23192
    1.25     -0.23006     -0.90122
       3       -1.375      0.82931

% split T into cell array of tables C, based on value of T.x
vars = T.Properties.VariableNames;
C = splitapply(@(varargin){table(varargin{:},'VariableNames',vars)},T,findgroups(T.x))

C = 15x1 cell array
    { 8x3 table}
    { 5x3 table}
    { 5x3 table}
    { 7x3 table}
    { 5x3 table}
    { 7x3 table}
    { 7x3 table}
    { 5x3 table}
    { 6x3 table}
    { 6x3 table}
    { 5x3 table}
    {11x3 table}
    { 8x3 table}
    { 6x3 table}
    { 9x3 table}

% show the tables contained in cell array C
C{:}

ans = 8x3 table
     x         y            z     
    ____    ________    __________

    0.25     -0.2583     -0.079574
    0.25        1.11      -0.65081
    0.25     -0.9458       -1.1575
    0.25      1.5975     -0.030275
    0.25       -1.55       0.39269
    0.25    -0.73644    -0.0045602
    0.25    -0.32405        0.1475
    0.25    -0.77887       0.23531

ans = 5x3 table
     x         y           z    
    ___    _________    ________

    0.5     -0.16188    -0.66869
    0.5     -0.98999     -1.9855
    0.5    -0.072366     0.23192
    0.5       1.1782     0.18323
    0.5      -1.7053    -0.98583

ans = 5x3 table
    x       y           z    
    _    ________    ________

    1     0.40671      1.9308
    1     -1.6124     -0.5274
    1      1.9243    -0.02964
    1     -1.7024     0.30671
    1    -0.72628    -0.45578

ans = 7x3 table
     x          y           z    
    ____    _________    ________

    1.25       1.1321     0.72386
    1.25     -0.23006    -0.90122
    1.25      0.14016    -0.41135
    1.25    -0.083017     -1.6199
    1.25      0.49901    -0.22053
    1.25      -1.3333    -0.24507
    1.25     -0.88313     0.68772

ans = 5x3 table
     x        y           z    
    ___    ________    ________

    1.5     -1.0284    -0.31855
    1.5     0.78943     -1.8405
    1.5      1.1048       1.665
    1.5     0.44321    -0.35164
    1.5    -0.53269    -0.13423

ans = 7x3 table
     x         y          z   
    ____    _______    _______

    1.75     1.2622     1.5087
    1.75    -1.4937     1.1163
    1.75    0.90235     2.1495
    1.75     2.2662     1.2343
    1.75    0.65392    0.63818
    1.75    0.25772     1.1014
    1.75    -1.4749     1.4902

ans = 7x3 table
    x       y           z    
    _    ________    ________

    2     -1.0289      -0.478
    2      0.1122       0.434
    2      1.9323    -0.95664
    2    -0.88587     0.64639
    2      1.5465     -0.1029
    2    -0.30542     0.12073
    2      1.4805    0.015096

ans = 5x3 table
     x         y           z    
    ____    ________    ________

    2.25     0.11897     -1.5427
    2.25    -0.56589     0.93971
    2.25      1.7936    -0.90235
    2.25      1.0833    -0.93631
    2.25     0.90805      1.9975

ans = 6x3 table
     x        y           z    
    ___    ________    ________

    2.5     0.20708    -0.92896
    2.5      1.6666      -1.289
    2.5     -1.0514    -0.25642
    2.5    -0.71928     -0.5503
    2.5     -1.7355    -0.41085
    2.5     0.46468     0.19258

ans = 6x3 table
     x         y           z    
    ____    ________    ________

    2.75      1.2641     0.32202
    2.75      2.5691     0.34345
    2.75      1.2653    -0.15336
    2.75    -0.87514      1.4729
    2.75       1.943     -1.2511
    2.75      0.4632     0.54318

ans = 5x3 table
    x       y           z    
    _    ________    ________

    3      -1.375     0.82931
    3    -0.46188      1.5111
    3     -1.3897     -1.7481
    3     0.43772    -0.13668
    3    -0.32444     -1.2576

ans = 11x3 table
     x         y            z    
    ____    ________    _________

    3.25     0.17427       1.2856
    3.25     0.53059     -0.57649
    3.25     0.22523      0.45576
    3.25    -0.24738      0.53447
    3.25     -0.6151      0.23264
    3.25       1.517     -0.34507
    3.25       1.047       1.6549
    3.25      1.6717      0.90479
    3.25     0.48414     -0.90945
    3.25      1.3229    -0.019966
    3.25     -1.1691     -0.91074

ans = 8x3 table
     x         y            z    
    ___    _________    _________

    3.5    -0.093726       1.0214
    3.5      0.51092       2.2424
    3.5      -1.2459      0.47417
    3.5      0.72009     -0.31976
    3.5      -1.4277     -0.35091
    3.5     -0.34765       1.6445
    3.5     -0.33764      0.80268
    3.5       1.6172    -0.020338

ans = 6x3 table
     x         y          z    
    ____    _______    ________

    3.75    -0.1864     -1.2987
    3.75    0.21496     -1.1633
    3.75    -1.4814     0.78726
    3.75     1.9162    -0.11848
    3.75    0.79221     -1.4294
    3.75     1.2749    0.023442

ans = 9x3 table
    x        y            z    
    _    _________    _________

    4     0.031567      0.25919
    4     -0.44974      0.72412
    4      0.19819     -0.48513
    4    -0.010109    -0.006922
    4     -0.54603        1.055
    4      -1.7345       1.5203
    4    -0.045592      0.87809
    4      0.50318      0.67836
    4     -0.12952     -0.27911

[Rhiannon]

Thank you for taking the time to answer my question, i really appreciate it