Hi Magsud,
I understand that you want to create a big csv file and create 17 csv files for each day.
Create a Big CSV File
- Use tabularTextDatastore to read all the data from the CSV files into a single table, which will be your big dataset.
- Create timestamps for all the measurements. It's important to make sure that the timestamps match the order of the measurements.
- Add the timestamps as a new variable (column) in your table.
- Finally, save the table as a single CSV file for machine learning.
Here is how you can do it:
% Assuming mSWW is loaded already
ds = tabularTextDatastore(mSWW.fFullPath, 'FileExtensions', '.csv');
% Read all data into a table
all_data = readall(ds);
% Create timestamps based on your time components
timeStamp = datetime(mSWW.fYear, mSWW.fMonth, mSWW.fDay, ...
mSWW.fHour, mSWW.fMinute, mSWW.fSecond);
% Add the timestamps to the table
all_data.Timestamp = timeStamp;
% Save the combined table as a big CSV file
writetable(all_data, 'big_data.csv');
Create Daily CSV Files
To create separate CSV files for each of the 17 days, you can use a loop to filter the data for each day and save it as a separate CSV file. Here is how you can do it:
unique_dates = unique(dateshift(all_data.Timestamp, 'start', 'day'));
for i = 1:length(unique_dates)
date_filter = all_data.Timestamp >= unique_dates(i) & ...
all_data.Timestamp < unique_dates(i+1);
daily_data = all_data(date_filter, :);
% Save daily data as CSV
filename = sprintf('day_%02d.csv', i);
writetable(daily_data, filename);
end
This code will create separate CSV files named day_01.csv, day_02.csv, and so on for each day.
Thank you,
Rishav Saha
