MATLAB: Are Tables or Structures better for this scenario

MATLABstructurestables

I have an array (D) of numbers (double, 120×1). I want to search the data (UINT32) in a structure for occurrences of these numbers from the array and record the indices. In the 2nd column, I want to search in the data and record the channel of these indices.

Tables: I know that the number of indices for each of the 120 numbers in the array will not be the same, that leads me to think that trying to collect the data in a table is not a good idea because the number of rows will not be the same. Ideally I would like a 240 column table indices and channel for the 120 numbers in array D.

Structures: I am thinking of creating a for loop to achieve this. How can I get a structure with 120 tabs for each of the numbers in the array and two columns in each tab, one for the indices and another for channel?

following is the bit of code I am trying to fix:

if true
  % code
D = [1034;120;71;73;74;.....];
for ind = 1:length(D)
s1.(D{ind}) = find(Raw.Run1.message.id==(length(D{ind})));
s2.(D{ind}) = Raw.Run1.message.chan(s1.(D{ind}));
end

If there is a more efficient way to do this (neither structures or tables), I am open to that as well.

Thanks!

Best Answer

Perhaps something like this (untested, because you have not given us any sample data):

S = load('data.mat');
F = S.Raw.Run1.message.id;
D = [1034,120,71,73,74];
W = cell(size(D));
for k = 1:numel(D)
    W{k} = find(D(k)==F);
end

Read this to know about cell array indexing:

https://www.mathworks.com/help/matlab/cell-arrays.html

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For this application, I'd use fillmissing. It will do the same task as filling in locations using logical indexing with the output of isnan, but in the code I find it makes the intent clearer to the reader. In the example below I'm going to assign the output of fillmissing into a different struct array so you can compare the "before and after", but you could assign the output back into the struct you processed if you want. Let's define some sample data:

rating.pretest.s1 = [4;1;NaN;3];
rating.pretest.s2 = [5;2;0;NaN];

Now let's fill in the missing values with the constant 1. I used 'UniformOutput', false to return the output as a struct array with the same fields as the input.

fillingfunction = @(x) fillmissing(x, 'constant', 1);
rating2.pretest = structfun(fillingfunction, rating.pretest, ...
                            'UniformOutput', false);

Compare the before and after.

rating.pretest.s1
rating2.pretest.s1
rating.pretest.s2
rating2.pretest.s2

FYI you might want to use table arrays to store your data.

pretestScores = table(rating.pretest.s1, rating.pretest.s2, ...
    'VariableNames', {'Test1', 'Test2'}, ...
    'RowNames', {'Alice', 'Bob', 'Charlie', 'Doug'})
posttestScores = table(rating.pretest.s1+2, rating.pretest.s2+3, ...
    'VariableNames', {'Test1', 'Test2'}, ...
    'RowNames', {'Alice', 'Bob', 'Charlie', 'Doug'})

You can even store one or more table arrays inside another table!

allScores = table(pretestScores, posttestScores)
allScores.Properties.RowNames = pretestScores.Properties.RowNames

Though if you were to build that allScores table you probably wouldn't want to put the student names as the RowNames of the inner table arrays as well, as that looks kind of funny. You can trim them if you want.

allScores.pretestScores.Properties.RowNames = {}
allScores.posttestScores.Properties.RowNames = {}

MATLAB: Importing data files with the same name in different directories (seeds) , setting them equal to “loop able” variable

"I will say that I've written a verbose form of this code that sets variables for every import. I have 25 simulations with 20 seeds each to compare and its very error prone."

That is not a surprise, because your approach is one way that beginners force themselves into writing slow, complex, buggy code. The approach of "..creating some variable name that corresponds seed numbers...": magically creating/accessing variable numbers is the problem. Read this to know why:

https://www.mathworks.com/matlabcentral/answers/304528-tutorial-why-variables-should-not-be-named-dynamically-eval

Forcing meta-data into variable names is bad way to write code. Meta-data, like the sequential folder names, is data, so it should be stored as data and not forced awkwardly into variable names.

You should simply store the data in one array (e.g. a structure, or cell array, or an ND array) which is then trivial to access using efficient indexing. Here is something to get you started:

D = 'path to where the PATH* directories are located';
P = 'path1';
S = dir(fullfile(D,P,'seed*));
for jj = 1:numel(S)
    F = fullfile(D,P,S(jj).name,'Ca_cyt.dat');
    S(jj).data = csvread(F); % use whatever function imports your data files
end

And that is all. The data for the files are all stored in the structure S, along with the folder names. So you can access them trivially using indexing, e.g. the second folder's name and data:

S(2).name
S(2).data

It also means that you can use a comma-separated list to perform actions on all of the imported data or filenames. For example, to put all of the filenames into one cell array:

{S.name}

or to vertically concatenate all of the imported data:

vertcat(S.data)

or you can access it in other ways, depending on the size and classes of your data.

Read this to know more about how comma separated lists work:

https://www.mathworks.com/matlabcentral/answers/320713-how-to-operate-on-comma-separated-lists

You might also want to look at the examples in the MATLAB documentation:

https://www.mathworks.com/help/matlab/import_export/process-a-sequence-of-files.html

Because you did not upload any sample files or explain anything about them I have no idea how your data files are formatted, so you will have to pick the file importing function yourself. I just used csvread as an example. If you upload a sample data file then I could help you with picking a suitable data importing function.

"Lastly, I would like to compare the averages of different situations.... path1 ... path2 ... path3 ..."

Then you will probably have to put the entirety of my code into another loop, basically like this:

D = 'path to where the PATH* directories are located';
N = 3;
C = cell(1,N);
for ii = 1:N
    P = sprintf('path%d',ii);
    C{ii} = dir(fullfile(D,P,'seed*));
    for jj = 1:numel(C{ii})
        F = fullfile(D,P,C{ii}(jj).name,'Ca_cyt.dat');
        C{ii}(jj).data = .... use whatever function imports your data files
    end
end

And then access the structures inside the cell array C. You could generate one structure array from that cell array, which might make accessing the data easier:

S = [C{:}]

Best Answer

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