pycartool.ris.SourceEstimate🔗

class pycartool.ris.SourceEstimate(sources_tc, sfreq, source_space=None, subject=None, filename=None)🔗

Container for source estimate data.

Parameters:

sources_tcarray: The sources time courses. Can be either scalar (ndim=1) or vectorial (ndim=3). shape (n_solution_points, n_dim, n_timeframes).
sfreqfloat: The sampling frequency.
source_spacepycartool.spi.SourceSpace: The SourceSpace corresponding to the source estimate.
subjectstr: The subject used to create the source estimate.
filenamestr: Filename from which the source estimate was imported.

Attributes

is_scalar	(bool) True if estimate is scalar, False is estimate is vectorial.
n_sources	(int) Number of sources.
sources_tc	(`array`) The sources time courses. Can be either scalar (`ndim=1`) or vectorial (`ndim=3`). shape (`n_solution_points`, `n_dim`, `n_timeframes`).
source_space	(`pycartool.spi.SourceSpace`) The SourceSpace corresponding to the source estimate.
subject	(str) The subject used to create the source estimate.
filename	(str) Filename from which the source estimate was imported.

Methods

`compute_rois_tc`(region_of_interest[, method])	Compute time course in Region of interest.
`compute_tc`([method])	Compute time course.
`per_roi`(region_of_interest)	Get source in specific rois.
`save`(filename[, export_spi])	Write SourceEstimate to Cartool ris file.

compute_rois_tc(region_of_interest, method='svd')🔗

Compute time course in Region of interest.

Parameters:

region_of_interestpycartool.rois.RegionsOfInterest: The region of interest used to split the source estimate.
methodstr: The method use to compute the time course. Can be either ‘mean’, ‘median’ or ‘svd’. Default to ‘svd’.

Returns:

Roi_source_estimatepycartool.ris.SourceEstimate: A source estimate instance where each source correspond to a region of interest.

compute_tc(method='svd')🔗

Compute time course.

Parameters:

methodstr: The method use to compute the time course. Can be either ‘mean’, ‘median’ or ‘svd’. Default to ‘svd’.

Returns:

tcarray: The global source estimate time course. Can be either Vectorial or Scalar depending of the source estimate and the method. shape(n_dim, n_times).

per_roi(region_of_interest)🔗

Get source in specific rois.

Parameters:

region_of_interestpycartool.rois.RegionsOfInterest: The region of interest used to split the source estimate.

Returns:

rois_source_estimatelist of pycartool.ris.SourceEstimate: A list of Source estimate instance restricted to a Region of interest.

save(filename, export_spi=False)🔗

Write SourceEstimate to Cartool ris file.

Parameters:

filenamestr or file-like: The ris file to write.
export_spibool: If True, also export the corresponding source space as ris file.

Examples using `pycartool.ris.SourceEstimate`🔗