syncopy.load_ft_raw(filename, list_only=False, select_structures=None, include_fields=None, mem_use=2000)[source]

Imports raw time-series data from Field Trip into potentially multiple AnalogData objects, one for each structure found within the MAT-file.

For MAT-File < v7.3 the MAT-file gets loaded completely into RAM, but its size should be capped by Matlab at 2GB. The v7.3 is in hdf5 format and will be read in trial-by-trial, this should be the Matlab default for MAT-Files exceeding 2GB.

The aim is to parse each FT data structure, which have the following fields (Syncopy analogon on the right):









fsample (optional)




The FT cfg contains a lot of meta data which at the moment we don’t import into Syncopy.

This is still experimental code, use with caution!!

  • filename (str) – Path to the MAT-file

  • list_only (bool, optional) – Set to True to return only a list containing the names of the structures found

  • select_structures (sequence or None, optional) – Sequence of strings, one for each structure, the default None will load all structures found

  • include_fields (sequence, optional) – Additional MAT-File fields within each structure to be imported. They can be accessed via a purpose-generated AnalogData.info attribute.

  • mem_use (int) – The amount of RAM requested for the import process in MB. Note that < v7.3 MAT-File formats can only be loaded at once. For MAT-File v7.3 this should be at least twice the size of a single trial.


out_dict – Dictionary with keys being the names of the structures loaded from the MAT-File, and its values being the AnalogData datasets

Return type



Load two structures ‘Data_K’ and ‘Data_KB’ from a MAT-File example.mat:

>>> dct = load_ft_raw('example.mat', select_structures=('Data_K', 'Data_KB'))

Access the individual AnalogData datasets:

>>> data_kb = dct['Data_KB']
>>> data_k = dct['Data_K']

Load all structures from example.mat plus additional field ‘chV1’:

>>> dct = load_ft_raw('example.mat', include_fields=('chV1',))

Access the additionally loaded field:

>>> dct['Data_K'].info['chV1']

Just peek into the MAT-File and get a list of the contained structures:

>>> load_ft_raw('example.mat', list_only=True)
>>> ['Data_K', 'Data_KB']