Smoothing for multiple csv files

Question

Oliver Steiner 2023-2-20

0
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https://ww2.mathworks.cn/matlabcentral/answers/1915625-smoothing-for-multiple-csv-files

编辑： Mathieu NOE 2023-3-6

Hi! I smoothed some data for an experiment for one partipants, now I want to do that for multiple participants and save this to a table. Can you sopport me with this? Thanks. The code for one participant looks like this:

% Set the filename
filename = 'P01.xlsx';
% Import the data from the Xlsx file
data = xlsread(filename);
% assess the data 
y = (data(:,1));
x = (data(:,5));
% Now I will smooth the data after the minimal pupil size
smoothdata = round(smooth(x(380:2000),y(380:2000),0.1,'loess'))
% Combine the smoothed data with the real data
combinedData = [y(1:379);smoothdata]

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Dyuman Joshi 2023-2-20

You can run the code through a for loop and save the data in a table according to the number of participants.

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Answer 1

Mathieu NOE 2023-3-6

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https://ww2.mathworks.cn/matlabcentral/answers/1915625-smoothing-for-multiple-csv-files#answer_1186365

编辑：Mathieu NOE 2023-3-6

在 MATLAB Online 中打开

hello

xlsread is a bit outdated , you could try more recent alternatives (readtable, readcell , readmatrix,...)

suggestion below (a simple for loop)

you can improve the sorting process by using this Fex submission (the native dir function is not perfect for that)

Natural-Order Filename Sort - File Exchange - MATLAB Central (mathworks.com)

the result is stored as a cell array (combinedData{k})

but you can also use vertical or horizontal concatenation if the data has always same size

fileDir = pwd; % current directory (or specify which one is the working directory)
S = dir(fullfile(fileDir,'P*.xlsx')); % get list of data files in directory
S = natsortfiles(S); % sort file names into natural order , see : 
           %(https://fr.mathworks.com/matlabcentral/fileexchange/47434-natural-order-filename-sort)
for k = 1:length(S)
    filename = S(k).name % to actually show filenames are sorted (see command window)
    % Import the data from the Xlsx file
    data = readmatrix(fullfile(fileDir, filename));
    % assess the data 
    y = (data(:,1));
    x = (data(:,5));
    % Now I will smooth the data after the minimal pupil size
    smoothdata = round(smooth(x(380:2000),y(380:2000),0.1,'loess'))
    % Combine the smoothed data with the real data
    combinedData{k} = [y(1:379);smoothdata]
end