- 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
AnalogDataobjects, 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):
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 the 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 the names of the structures as keys loaded from the MAT-File, and
AnalogDatadatasets as values
- 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
>>> 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:
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']