mne.MixedSourceEstimate¶

class
mne.
MixedSourceEstimate
(data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None)[source]¶ Container for mixed surface and volume source estimates.
 Parameters
 data
array
of shape (n_dipoles, n_times) tuple
, shape (2,) The data in source space. The data can either be a single array or a tuple with two arrays: “kernel” shape (n_vertices, n_sensors) and “sens_data” shape (n_sensors, n_times). In this case, the source space data corresponds to
np.dot(kernel, sens_data)
. vertices
list
ofarray
Vertex numbers corresponding to the data. The list contains arrays with one array per source space.
 tminscalar
Time point of the first sample in data.
 tstepscalar
Time step between successive samples in data.
 subject
str
None
The subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 data
See also
SourceEstimate
A container for surface source estimates.
VectorSourceEstimate
A container for vector source estimates.
VolSourceEstimate
A container for volume source estimates.
VolVectorSourceEstimate
A container for Volume vector source estimates.
Notes
New in version 0.9.0.
 Attributes
 subject
str
None
The subject name.
times
array
of shape (n_times,)A timestamp for each sample.
 vertices
list
ofarray
Vertex numbers corresponding to the data. The list contains arrays with one array per source space.
data
array
of shape (n_dipoles, n_times)Numpy array of source estimate data.
shape
tuple
Shape of the data.
 subject
Methods
__add__
(self, a)Add source estimates.
__div__
(self, a)Divide source estimates.
__hash__
(self, /)Return hash(self).
__mul__
(self, a)Multiply source estimates.
__neg__
(self)Negate the source estimate.
__sub__
(self, a)Subtract source estimates.
bin
(self, width[, tstart, tstop, func])Return a source estimate object with data summarized over time bins.
copy
(self)Return copy of source estimate instance.
crop
(self[, tmin, tmax, include_tmax])Restrict SourceEstimate to a time interval.
extract_label_time_course
(self, labels, src)Extract label time courses for lists of labels.
get_peak
(self[, tmin, tmax, mode, …])Get location and latency of peak amplitude.
mean
(self)Make a summary stc file with mean over time points.
plot_surface
(*args, **kwargs)Warning
DEPRECATED: stc_mixed.plot_surface(…) is deprecated and will be removed in 0.22, use stc_mixed.surface().plot(…)
resample
(self, sfreq[, npad, window, …])Resample data.
save
(self, fname[, ftype, verbose])Save the full source estimate to an HDF5 file.
sqrt
(self)Take the square root.
sum
(self)Make a summary stc file with sum over time points.
surface
(self)Return the cortical surface source estimate.
time_as_index
(self, times[, use_rounding])Convert time to indices.
to_data_frame
(self[, index, scalings, …])Export data in tabular structure as a pandas DataFrame.
transform
(self, func[, idx, tmin, tmax, copy])Apply linear transform.
transform_data
(self, func[, idx, tmin_idx, …])Get data after a linear (time) transform has been applied.
volume
(self)Return the volume surface source estimate.

__hash__
(self, /)¶ Return hash(self).

bin
(self, width, tstart=None, tstop=None, func=<function mean at 0x7f66b61534c0>)[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
 widthscalar
Width of the individual bins in seconds.
 tstartscalar 
None
Time point where the first bin starts. The default is the first time point of the stc.
 tstopscalar 
None
Last possible time point contained in a bin (if the last bin would be shorter than width it is dropped). The default is the last time point of the stc.
 func
callable()
Function that is applied to summarize the data. Needs to accept a numpy.array as first input and an
axis
keyword argument.
 Returns
 stc
SourceEstimate
VectorSourceEstimate
The binned source estimate.
 stc

copy
(self)[source]¶ Return copy of source estimate instance.
 Returns
 stcinstance of
SourceEstimate
A copy of the source estimate.
 stcinstance of

crop
(self, tmin=None, tmax=None, include_tmax=True)[source]¶ Restrict SourceEstimate to a time interval.
 Parameters
 tmin
float
None
The first time point in seconds. If None the first present is used.
 tmax
float
None
The last time point in seconds. If None the last present is used.
 include_tmaxbool
If True (default), include tmax. If False, exclude tmax (similar to how Python indexing typically works).
New in version 0.19.
 tmin
 Returns
 stcinstance of
SourceEstimate
The cropped source estimate.
 stcinstance of

property
data
¶ Numpy array of source estimate data.

extract_label_time_course
(self, labels, src, mode='auto', allow_empty=False, verbose=None)[source]¶ Extract label time courses for lists of labels.
This function will extract one time course for each label. The way the time courses are extracted depends on the mode parameter.
 Parameters
 labels
Label
BiHemiLabel
list
tuple
str
If using a surface or mixed source space, this should be the
Label
’s for which to extract the time course. If working with wholebrain volume source estimates, this must be one of:a string path to a FreeSurfer atlas for the subject (e.g., their ‘aparc.a2009s+aseg.mgz’) to extract time courses for all volumes in the atlas
a twoelement list or tuple, the first element being a path to an atlas, and the second being a list or dict of
volume_labels
to extract (seemne.setup_volume_source_space()
for details).
Changed in version 0.21.0: Support for volume source estimates.
 srcinstance of
SourceSpaces
The source spaces for the source time courses.
 mode
str
Extraction mode, see Notes.
 allow_emptybool 
str
False
(default) will emit an error if there are labels that have no vertices in the source estimate.True
and'ignore'
will return allzero time courses for labels that do not have any vertices in the source estimate, and True will emit a warning while and “ignore” will just log a message.Changed in version 0.21.0: Support for “ignore”.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). Defaults to self.verbose.
 labels
 Returns
See also
extract_label_time_course
Extract time courses for multiple STCs.
Notes
Valid values for
mode
are:'max'
Maximum value across vertices at each time point within each label.
'mean'
Average across vertices at each time point within each label. Ignores orientation of sources for standard source estimates, which varies across the cortical surface, which can lead to cancellation. Vector source estimates are always in XYZ / RAS orientation, and are thus already geometrically aligned.
'mean_flip'
Finds the dominant direction of source space normal vector orientations within each label, applies a signflip to time series at vertices whose orientation is more than 180° different from the dominant direction, and then averages across vertices at each time point within each label.
'pca_flip'
Applies singular value decomposition to the time courses within each label, and uses the first rightsingular vector as the representative label time course. This signal is scaled so that its power matches the average (pervertex) power within the label, and signflipped by multiplying by
np.sign(u @ flip)
, whereu
is the first leftsingular vector andflip
is the same signflip vector used whenmode='mean_flip'
. This signflip ensures that extracting time courses from the same label in similar STCs does not result in 180° direction/phase changes.
'auto'
(default)Uses
'mean_flip'
when a standard source estimate is applied, and'mean'
when a vector source estimate is supplied.New in version 0.21: Support for
'auto'
, vector, and volume source estimates.
The only modes that work for vector and volume source estimates are
'mean'
,'max'
, and'auto'
.

get_peak
(self, tmin=None, tmax=None, mode='abs', vert_as_index=False, time_as_index=False)[source]¶ Get location and latency of peak amplitude.
 Parameters
 tmin
float
None
The minimum point in time to be considered for peak getting.
 tmax
float
None
The maximum point in time to be considered for peak getting.
 mode{‘pos’, ‘neg’, ‘abs’}
How to deal with the sign of the data. If ‘pos’ only positive values will be considered. If ‘neg’ only negative values will be considered. If ‘abs’ absolute values will be considered. Defaults to ‘abs’.
 vert_as_indexbool
Whether to return the vertex index (True) instead of of its ID (False, default).
 time_as_indexbool
Whether to return the time index (True) instead of the latency (False, default).
 tmin
 Returns

mean
(self)[source]¶ Make a summary stc file with mean over time points.
 Returns
 stc
SourceEstimate
VectorSourceEstimate
The modified stc.
 stc

plot_surface
(*args, **kwargs)[source]¶ Warning
DEPRECATED: stc_mixed.plot_surface(…) is deprecated and will be removed in 0.22, use stc_mixed.surface().plot(…)
Plot surface source estimates with PySurfer.
Note: PySurfer currently needs the SUBJECTS_DIR environment variable, which will automatically be set by this function. Plotting multiple SourceEstimates with different values for subjects_dir will cause PySurfer to use the wrong FreeSurfer surfaces when using methods of the returned Brain object. It is therefore recommended to set the SUBJECTS_DIR environment variable or always use the same value for subjects_dir (within the same Python session).
 Parameters
 src
SourceSpaces
The source spaces to plot.
 subject
str
None
The subject name corresponding to FreeSurfer environment variable SUBJECT. If None stc.subject will be used. If that is None, the environment will be used.
 surface
str
The type of surface (inflated, white etc.).
 hemi
str
, ‘lh’  ‘rh’  ‘split’  ‘both’ The hemisphere to display. Using ‘both’ or ‘split’ requires PySurfer version 0.4 or above.
 colormap
str
np.ndarray
offloat
, shape(n_colors, 3  4) Name of colormap to use. See
plot_source_estimates
. time_label
str
How to print info about the time instant visualized.
 smoothing_steps
int
The amount of smoothing.
 transparentbool 
None
If True, use a linear transparency between fmin and fmid. None will choose automatically based on colormap type.
 alpha
float
Alpha value to apply globally to the overlay.
 time_viewerbool
Display time viewer GUI.
 subjects_dir
str
None
The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR.
 figureinstance of
mayavi.mlab.Figure
None
If None, the last figure will be cleaned and a new figure will be created.
 views
str
list
View to use. See
surfer.Brain
. colorbarbool
If True, display colorbar on scene.
 clim
str
dict
Colorbar properties specification. See
plot_source_estimates
.
 src
 Returns
 braininstance of
surfer.Brain
A instance of
surfer.Brain
from PySurfer.
 braininstance of

resample
(self, sfreq, npad='auto', window='boxcar', n_jobs=1, verbose=None)[source]¶ Resample data.
 Parameters
 sfreq
float
New sample rate to use.
 npad
int
str
Amount to pad the start and end of the data. Can also be “auto” to use a padding that will result in a poweroftwo size (can be much faster).
 window
str
tuple
Window to use in resampling. See
scipy.signal.resample()
. n_jobs
int
The number of jobs to run in parallel (default 1). Requires the joblib package.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). Defaults to self.verbose.
 sfreq
 Returns
 stcinstance of
SourceEstimate
The resampled source estimate.
 stcinstance of
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
(self, fname, ftype='h5', verbose=None)[source]¶ Save the full source estimate to an HDF5 file.
 Parameters
 fname
str
The file name to write the source estimate to, should end in ‘stc.h5’.
 ftype
str
File format to use. Currently, the only allowed values is “h5”.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). Defaults to self.verbose.
 fname

property
sfreq
¶ Sample rate of the data.

property
shape
¶ Shape of the data.

sqrt
(self)[source]¶ Take the square root.
 Returns
 stcinstance of
SourceEstimate
A copy of the SourceEstimate with sqrt(data).
 stcinstance of

sum
(self)[source]¶ Make a summary stc file with sum over time points.
 Returns
 stc
SourceEstimate
VectorSourceEstimate
The modified stc.
 stc

surface
(self)[source]¶ Return the cortical surface source estimate.
 Returns
 stcinstance of
SourceEstimate
orVectorSourceEstimate
The surface source estimate.
 stcinstance of
Examples using
surface
:

property
times
¶ A timestamp for each sample.

property
tmin
¶ The first timestamp.

to_data_frame
(self, index=None, scalings=None, long_format=False, time_format='ms')[source]¶ Export data in tabular structure as a pandas DataFrame.
Vertices are converted to columns in the DataFrame. By default, an additional column “time” is added, unless
index='time'
(in which case time values form the DataFrame’s index). Parameters
 index‘time’ 
None
Kind of index to use for the DataFrame. If
None
, a sequential integer index (pandas.RangeIndex
) will be used. If'time'
, apandas.Float64Index
,pandas.Int64Index
, orpandas.TimedeltaIndex
will be used (depending on the value oftime_format
). Defaults toNone
. scalings
dict
None
Scaling factor applied to the channels picked. If
None
, defaults todict(eeg=1e6, mag=1e15, grad=1e13)
— i.e., converts EEG to µV, magnetometers to fT, and gradiometers to fT/cm. long_formatbool
If True, the DataFrame is returned in long format where each row is one observation of the signal at a unique combination of time point and vertex. Defaults to
False
. time_format
str
None
Desired time format. If
None
, no conversion is applied, and time values remain as float values in seconds. If'ms'
, time values will be rounded to the nearest millisecond and converted to integers. If'timedelta'
, time values will be converted topandas.Timedelta
values. Defaults to'ms'
.New in version 0.20.
 index‘time’ 
 Returns
 dfinstance of
pandas.DataFrame
A dataframe suitable for usage with other statistical/plotting/analysis packages.
 dfinstance of

transform
(self, func, idx=None, tmin=None, tmax=None, copy=False)[source]¶ Apply linear transform.
The transform is applied to each source time course independently.
 Parameters
 func
callable()
The transform to be applied, including parameters (see, e.g.,
functools.partial()
). The first parameter of the function is the input data. The first two dimensions of the transformed data should be (i) vertices and (ii) time. See Notes for details. idx
array
None
Indices of source time courses for which to compute transform. If None, all time courses are used.
 tmin
float
int
None
First time point to include (ms). If None, self.tmin is used.
 tmax
float
int
None
Last time point to include (ms). If None, self.tmax is used.
 copybool
If True, return a new instance of SourceEstimate instead of modifying the input inplace.
 func
 Returns
 stcs
SourceEstimate
VectorSourceEstimate
list
The transformed stc or, in the case of transforms which yield Ndimensional output (where N > 2), a list of stcs. For a list, copy must be True.
 stcs
Notes
Transforms which yield 3D output (e.g. timefrequency transforms) are valid, so long as the first two dimensions are vertices and time. In this case, the copy parameter must be True and a list of SourceEstimates, rather than a single instance of SourceEstimate, will be returned, one for each index of the 3rd dimension of the transformed data. In the case of transforms yielding 2D output (e.g. filtering), the user has the option of modifying the input inplace (copy = False) or returning a new instance of SourceEstimate (copy = True) with the transformed data.
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
(self, 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
 func
callable()
The transform to be applied, including parameters (see, e.g.,
functools.partial()
). The first parameter of the function is the input data. The first return value is the transformed data, remaining outputs are ignored. The first dimension of the transformed data has to be the same as the first dimension of the input data. idx
array
None
Indicices of source time courses for which to compute transform. If None, all time courses are used.
 tmin_idx
int
None
Index of first time point to include. If None, the index of the first time point is used.
 tmax_idx
int
None
Index of the first time point not to include. If None, time points up to (and including) the last time point are included.
 func
 Returns
 data_t
ndarray
The transformed data.
 data_t
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).

property
tstep
¶ The change in time between two consecutive samples (1 / sfreq).

volume
(self)[source]¶ Return the volume surface source estimate.
 Returns
 stcinstance of
VolSourceEstimate
orVolVectorSourceEstimate
The volume source estimate.
 stcinstance of
Examples using
volume
: