How I can split a two column data into chunks like this snap shot

Hello,

I have attached a mat file and I want to chunk my data in equal number of arrays like the attached image. Since I am dealing with a large data set of around 200 files of data and I have to do this iteration for every file and plot it. so kindly guide me and help me in chunking and plotting automatically as currently i am doing it manually with every file and using excel tool for data chunking and plotting.

Looking forward to your comments.

Answer 1

[Voss]

S = load('Data_Chunking.mat');
A = S.Alg2_mLat_maxEE;

C = 9; % chunk size

[M,N] = size(A);

% in case A is not an integer number of chunks tall,
% append A with rows of NaNs to make it an integer 
% number of chunks tall
m = mod(M,C);
if m
    A = [A; NaN(C-m,N)];
    M = M+C-m;
end

% now do the chunking:
A_chunked = reshape(permute(reshape(A(:,[2 1]),C,[],2),[1 3 2]),C,[]);

% make a table out of the chunked matrix (may be easier to see/deal with):
T = array2table(A_chunked,'VariableNames',["Number of Iterations";"Value"]+(1:M/C));

disp(T);

    Number of Iterations1    Value1    Number of Iterations2    Value2    Number of Iterations3    Value3    Number of Iterations4    Value4    Number of Iterations5    Value5    Number of Iterations6    Value6    Number of Iterations7    Value7    Number of Iterations8    Value8    Number of Iterations9    Value9    Number of Iterations10    Value10
    _____________________    ______    _____________________    ______    _____________________    ______    _____________________    ______    _____________________    ______    _____________________    ______    _____________________    ______    _____________________    ______    _____________________    ______    ______________________    _______

              6              1.8523              60             150.95             114             653.51             168             1255.6             222             1742.1             276             3096.1             330             5273.5             384             7019.8             438             11964              492               38570 
             12              19.743              66             253.64             120             700.77             174             1295.8             228             1844.4             282             3262.5             336             5717.7             390             8261.9             444             12519              498               53695 
             18              29.175              72             461.59             126             715.14             180             1362.6             234             1865.4             288             3444.9             342             6003.9             396             8451.5             450             12572              NaN                 NaN 
             24              32.305              78             498.31             132             782.68             186               1476             240             1888.7             294             3827.6             348             6040.7             402             8513.5             456             14272              NaN                 NaN 
             30               43.65              84             501.85             138             843.13             192             1479.2             246             1919.9             300             3971.6             354               6346             408             9886.9             462             16234              NaN                 NaN 
             36               74.47              90             521.24             144             1007.4             198               1561             252             2036.2             306             4210.5             360             6380.8             414              10094             468             23964              NaN                 NaN 
             42              87.369              96             529.57             150             1130.1             204             1569.2             258             2477.3             312             4406.3             366             6536.9             420              10157             474             28829              NaN                 NaN 
             48              118.96             102             585.18             156             1193.9             210             1700.7             264             2741.2             318             4577.2             372               6549             426              10180             480             32492              NaN                 NaN 
             54              142.44             108             614.52             162             1216.2             216             1707.2             270               3020             324               4890             378             6997.9             432              10934             486             33161              NaN                 NaN 

Answer 2

[Fabio Freschi]

If I have understood correctly, this code should to the job. Note that 83 is a prime number, so it is difficult to create chunks. I have kept the first 80 rows

load('Data_Chunking.mat');

% remove three rows to make it "chunkable"
A = Alg2_mLat_maxEE(1:80,:);

% number of rows per chunk
N = 5;

% reshape to have N rows
B = reshape(permute(reshape(A,N,[],2),[1 3 2]),N,[])

B = 5×32

1.0e+04 * 0.0002 0.0006 0.0074 0.0036 0.0254 0.0066 0.0530 0.0096 0.0715 0.0126 0.1194 0.0156 0.1476 0.0186 0.1707 0.0216 0.1920 0.0246 0.3096 0.0276 0.4210 0.0306 0.5718 0.0336 0.6537 0.0366 0.8451 0.0396 1.0180 0.0426 0.0020 0.0012 0.0087 0.0042 0.0462 0.0072 0.0585 0.0102 0.0783 0.0132 0.1216 0.0162 0.1479 0.0192 0.1742 0.0222 0.2036 0.0252 0.3263 0.0282 0.4406 0.0312 0.6004 0.0342 0.6549 0.0372 0.8513 0.0402 1.0934 0.0432 0.0029 0.0018 0.0119 0.0048 0.0498 0.0078 0.0615 0.0108 0.0843 0.0138 0.1256 0.0168 0.1561 0.0198 0.1844 0.0228 0.2477 0.0258 0.3445 0.0288 0.4577 0.0318 0.6041 0.0348 0.6998 0.0378 0.9887 0.0408 1.1964 0.0438 0.0032 0.0024 0.0142 0.0054 0.0502 0.0084 0.0654 0.0114 0.1007 0.0144 0.1296 0.0174 0.1569 0.0204 0.1865 0.0234 0.2741 0.0264 0.3828 0.0294 0.4890 0.0324 0.6346 0.0354 0.7020 0.0384 1.0094 0.0414 1.2519 0.0444 0.0044 0.0030 0.0151 0.0060 0.0521 0.0090 0.0701 0.0120 0.1130 0.0150 0.1363 0.0180 0.1701 0.0210 0.1889 0.0240 0.3020 0.0270 0.3972 0.0300 0.5273 0.0330 0.6381 0.0360 0.8262 0.0390 1.0157 0.0420 1.2572 0.0450