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IS there an alternative way to store data in matlab other than .mat?

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Elias Unk
Elias Unk on 21 Jun 2017
Edited: Elias Unk on 22 Jun 2017
The kind of data i'll be saving is a type double matrix of around 120 lines and columns and prefferrably i'll need to fill each line seperately using a loop i already wrote.
Elias Unk
Elias Unk on 22 Jun 2017
Stephen,if you read bellow before dropping this comment you'd realize that
by itself wasn't enough then Jan guided through the last commend,Regards.

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Accepted Answer

Jan on 21 Jun 2017
Edited: Jan on 21 Jun 2017
Open MAT files by the Matlab:
FileData = load('File.mat')
Or use the matfile command.
I never needed to open a MAT file by a double click, but if MS Office was installed after Matlab, the .mat extensions is associated to MS Access. There are several methods to associate it with Matlab again, simply ask your favorite internet search engine for these terms.

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More Answers (1)

Walter Roberson
Walter Roberson on 21 Jun 2017
MATLAB supports the following:
  • arbitrary binary files, using fread() and fwrite()
  • structured binary files, using memmapfile()
  • binary .mat files, using save(), which supports all MATLAB data types
  • rectangular blocks of plain text, using save -ASCII, which supports numeric scalars and vectors and 2D arrays only
  • .csv for numeric values, using csvwrite(); characters are not supported
  • text files for numeric values with arbitrary delimiter between fields, using dlmwrite(); characters are not supported
  • .csv and .xls for numeric values, using xlswrite(); on systems that are not MS Windows or which do not have Microsoft Excel installed, characters are not supported
  • .xlsx for mixed values, using xlswrite(), including on systems that are not MS Windows or which do not have Microsoft Excel installed; full support for this started in roughly 2012
  • HDF4 files (read-only), and HDF5 files (read and write), using hdf*() and h5*() functions
  • NETCDF files
  • general xml documents
  • standard ODBC-compliant and JDBC-compliant databases, including Oracle®, SAS®, MySQL®, Sybase®, Microsoft® SQL Server®, Microsoft® Access™, and PostgreSQL®, using the Database Toolbox
As well as a number of other more specialized formats.

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