mne.
VolSourceEstimate
(data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None)¶Container for volume source estimates
Parameters:  data : array of shape (n_dipoles, n_times)  2tuple (kernel, sens_data)
vertices : array
tmin : scalar
tstep : scalar
subject : str  None
verbose : bool, str, int, or None


Notes
New in version 0.9.0.
Attributes
data 
Numpy array of source estimate data 
shape 
Shape of the data 
subject  (str  None) The subject name. 
times  (array of shape (n_times,)) The time vector. 
vertices  (array of shape (n_dipoles,)) The indices of the dipoles in the source space. 
Methods
__add__ (a) 

__div__ (a) 

__hash__ () <==> hash(x) 

__mul__ (a) 

__neg__ () 

__sub__ (a) 

as_volume (src[, dest, mri_resolution]) 
Export volume source estimate as a nifti object 
bin (width[, tstart, tstop, func]) 
Returns a SourceEstimate object with data summarized over time bins 
copy () 
Return copy of SourceEstimate instance 
crop ([tmin, tmax]) 
Restrict SourceEstimate to a time interval 
get_peak ([tmin, tmax, mode, vert_as_index, ...]) 
Get location and latency of peak amplitude 
mean () 
Make a summary stc file with mean power between tmin and tmax. 
resample (sfreq[, npad, window, n_jobs, verbose]) 
Resample data 
save (fname[, ftype, verbose]) 
Save the source estimates to a file 
save_as_volume (fname, src[, dest, ...]) 
Save a volume source estimate in a nifti file 
sqrt () 
Take the square root 
time_as_index (times[, use_rounding]) 
Convert time to indices 
to_data_frame ([picks, index, scale_time, ...]) 
Export data in tabular structure as a pandas DataFrame. 
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__
() <==> hash(x)¶as_volume
(src, dest='mri', mri_resolution=False)¶Export volume source estimate as a nifti object
Parameters:  src : list
dest : ‘mri’  ‘surf’
mri_resolution: bool


Returns:  img : instance Nifti1Image

Notes
New in version 0.9.0.
bin
(width, tstart=None, tstop=None, func=<function mean>)¶Returns a SourceEstimate 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:  width : scalar
tstart : scalar  None
tstop : scalar  None
func : callable


Returns:  stc : instance of SourceEstimate

copy
()¶Return copy of SourceEstimate instance
crop
(tmin=None, tmax=None)¶Restrict SourceEstimate to a time interval
Parameters:  tmin : float  None
tmax : float  None


data
¶Numpy array of source estimate data
get_peak
(tmin=None, tmax=None, mode='abs', vert_as_index=False, time_as_index=False)¶Get location and latency of peak amplitude
Parameters:  tmin : float  None
tmax : float  None
mode : {‘pos’, ‘neg’, ‘abs’}
vert_as_index : bool
time_as_index : bool


Returns:  pos : int
latency : float

mean
()¶Make a summary stc file with mean power between tmin and tmax.
Returns:  stc : instance of SourceEstimate


resample
(sfreq, npad='auto', window='boxcar', n_jobs=1, verbose=None)¶Resample data
Parameters:  sfreq : float
npad : int  str
window : string or tuple
n_jobs : int
verbose : bool, str, int, or None


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.
save
(fname, ftype='stc', verbose=None)¶Save the source estimates to a file
Parameters:  fname : string
ftype : string
verbose : bool, str, int, or None


save_as_volume
(fname, src, dest='mri', mri_resolution=False)¶Save a volume source estimate in a nifti file
Parameters:  fname : string
src : list
dest : ‘mri’  ‘surf’
mri_resolution: bool


Returns:  img : instance Nifti1Image

Notes
New in version 0.9.0.
sfreq
¶Sample rate of the data
shape
¶Shape of the data
sqrt
()¶Take the square root
Returns:  stc : instance of SourceEstimate


time_as_index
(times, use_rounding=False)¶Convert time to indices
Parameters:  times : listlike  float  int
use_rounding : boolean


Returns:  index : ndarray

to_data_frame
(picks=None, index=None, scale_time=1000.0, scalings=None, copy=True, start=None, stop=None)¶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:  picks : arraylike of int  None
index : tuple of str  None
scale_time : float
scalings : dict  None
copy : bool
start : int  None
stop : int  None


Returns:  df : instance of pandas.core.DataFrame

transform
(func, idx=None, tmin=None, tmax=None, copy=False)¶Apply linear transform
The transform is applied to each source time course independently.
Parameters:  func : callable
idx : array  None
tmin : float  int  None
tmax : float  int  None
copy : bool


Returns:  stcs : instance of SourceEstimate  list

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 “lcmv_epochs” do this automatically (if possible).
transform_data
(func, idx=None, tmin_idx=None, tmax_idx=None)¶Get data after a linear (time) transform has been applied
The transorm is applied to each source time course independently.
Parameters:  func : callable
idx : array  None
tmin_idx : int  None
tmax_idx : int  None


Returns:  data_t : ndarray

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 “lcmv_epochs” do this automatically (if possible).