mne.
VectorSourceEstimate
(data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None)[source]¶Container for vector surface source estimates.
For each vertex, the magnitude of the current is defined in the X, Y and Z directions.
Parameters: 


See also
SourceEstimate
VolSourceEstimate
MixedSourceEstimate
Notes
New in version 0.15.
Attributes: 

Methods
__add__ (a) 
Add source estimates. 
__div__ (a) 
Divide source estimates. 
__hash__ ($self, /) 
Return hash(self). 
__mul__ (a) 
Multiply source estimates. 
__neg__ () 
Negate the source estimate. 
__sub__ (a) 
Subtract source estimates. 
bin (width[, tstart, tstop, func]) 
Return a source estimate object with data summarized over time bins. 
copy () 
Return copy of source estimate instance. 
crop ([tmin, tmax]) 
Restrict SourceEstimate to a time interval. 
expand (vertices) 
Expand SourceEstimate to include more vertices. 
in_label (label) 
Get a source estimate object restricted to a label. 
magnitude () 
Compute magnitude of activity without directionality. 
mean () 
Make a summary stc file with mean over time points. 
normal (src) 
Compute activity orthogonal to the cortex. 
plot ([subject, hemi, colormap, time_label, …]) 
Plot VectorSourceEstimate with PySurfer. 
resample (sfreq[, npad, window, n_jobs, verbose]) 
Resample data. 
save (fname[, ftype, verbose]) 
Save the full source estimate to an HDF5 file. 
sqrt () 
Take the square root. 
sum () 
Make a summary stc file with sum over time points. 
time_as_index (times[, use_rounding]) 
Convert time to indices. 
to_data_frame ([picks, index, scaling_time, …]) 
Export data in tabular structure as a pandas DataFrame. 
to_original_src (src_orig[, subject_orig, …]) 
Get a source estimate from morphed source to the original subject. 
transform (func[, idx, tmin, tmax, copy]) 
Apply linear transform. 
transform_data (func[, idx, tmin_idx, tmax_idx]) 
Get data after a linear (time) transform has been applied. 
__hash__
($self, /)¶Return hash(self).
bin
(width, tstart=None, tstop=None, func=<function mean>)[source]¶Return a source estimate object with data summarized over time bins.
Time bins of width
seconds. This method is intended for
visualization only. No filter is applied to the data before binning,
making the method inappropriate as a tool for downsampling data.
Parameters: 


Returns: 

crop
(tmin=None, tmax=None)[source]¶Restrict SourceEstimate to a time interval.
Parameters: 


data
¶Numpy array of source estimate data.
expand
(vertices)[source]¶Expand SourceEstimate to include more vertices.
This will add rows to stc.data (zerofilled) and modify stc.vertices to include all vertices in stc.vertices and the input vertices.
Parameters: 


Returns: 

in_label
(label)[source]¶Get a source estimate object restricted to a label.
SourceEstimate contains the time course of activation of all sources inside the label.
Parameters: 


Returns: 

lh_data
¶Left hemisphere data.
lh_vertno
¶Left hemisphere vertno.
magnitude
()[source]¶Compute magnitude of activity without directionality.
Returns: 


mean
()[source]¶Make a summary stc file with mean over time points.
Returns: 


normal
(src)[source]¶Compute activity orthogonal to the cortex.
Parameters: 


Returns: 

plot
(subject=None, hemi='lh', colormap='hot', time_label='auto', smoothing_steps=10, transparent=True, brain_alpha=0.4, overlay_alpha=None, vector_alpha=1.0, scale_factor=None, time_viewer=False, subjects_dir=None, figure=None, views='lat', colorbar=True, clim='auto', cortex='classic', size=800, background='black', foreground='white', initial_time=None, time_unit='s')[source]¶Plot VectorSourceEstimate with PySurfer.
A “glass brain” is drawn and all dipoles defined in the source estimate are shown using arrows, depicting the direction and magnitude of the current moment at the dipole. Additionally, an overlay is plotted on top of the cortex with the magnitude of the current.
Parameters: 


Returns: 

Notes
New in version 0.15.
If the current magnitude overlay is not desired, set overlay_alpha=0
and smoothing_steps=1
.
resample
(sfreq, npad='auto', window='boxcar', n_jobs=1, verbose=None)[source]¶Resample data.
Parameters: 


Notes
For some data, it may be more accurate to use npad=0 to reduce artifacts. This is dataset dependent – check your data!
Note that the sample rate of the original data is inferred from tstep.
rh_data
¶Right hemisphere data.
rh_vertno
¶Right hemisphere vertno.
save
(fname, ftype='h5', verbose=None)[source]¶Save the full source estimate to an HDF5 file.
Parameters: 


sfreq
¶Sample rate of the data.
shape
¶Shape of the data.
sqrt
()[source]¶Take the square root.
Returns: 


sum
()[source]¶Make a summary stc file with sum over time points.
Returns: 


time_as_index
(times, use_rounding=False)[source]¶Convert time to indices.
Parameters: 


Returns: 

times
¶A timestamp for each sample.
tmin
¶The first timestamp.
to_data_frame
(picks=None, index=None, scaling_time=1000.0, scalings=None, copy=True, start=None, stop=None)[source]¶Export data in tabular structure as a pandas DataFrame.
Columns and indices will depend on the object being converted. Generally this will include as much relevant information as possible for the data type being converted. This makes it easy to convert data for use in packages that utilize dataframes, such as statsmodels or seaborn.
Parameters: 


Returns: 

to_original_src
(src_orig, subject_orig=None, subjects_dir=None, verbose=None)[source]¶Get a source estimate from morphed source to the original subject.
Parameters: 


Returns: 

See also
Notes
New in version 0.10.0.
transform
(func, idx=None, tmin=None, tmax=None, copy=False)[source]¶Apply linear transform.
The transform is applied to each source time course independently.
Parameters: 


Returns: 

Notes
Applying transforms can be significantly faster if the SourceEstimate object was created using “(kernel, sens_data)”, for the “data” parameter as the transform is applied in sensor space. Inverse methods, e.g., “apply_inverse_epochs”, or “apply_lcmv_epochs” do this automatically (if possible).
transform_data
(func, idx=None, tmin_idx=None, tmax_idx=None)[source]¶Get data after a linear (time) transform has been applied.
The transform is applied to each source time course independently.
Parameters: 


Returns: 

Notes
Applying transforms can be significantly faster if the SourceEstimate object was created using “(kernel, sens_data)”, for the “data” parameter as the transform is applied in sensor space. Inverse methods, e.g., “apply_inverse_epochs”, or “apply_lcmv_epochs” do this automatically (if possible).
tstep
¶The change in time between two consecutive samples (1 / sfreq).