mne.SourceEstimate¶

class
mne.
SourceEstimate
(data, vertices, tmin, tstep, subject=None, verbose=None)[source]¶ Container for surface source estimates.
 Parameters
 data
array
of shape (n_dipoles, n_times) tuple
, shape (2,) The data in source space. When it is a single array, the left hemisphere is stored in data[:len(vertices[0])] and the right hemisphere is stored in data[len(vertices[1]):]. When data is a tuple, it contains 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
, shape (2,) Vertex numbers corresponding to the data. The first element of the list contains vertices of left hemisphere and the second element contains vertices of right hemisphere.
 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). If used, it should be passed as a keywordargument only.
 data
See also
VectorSourceEstimate
A container for vector source estimates.
VolSourceEstimate
A container for volume source estimates.
MixedSourceEstimate
A container for mixed surface + volume source estimates.
 Attributes
Methods
__add__
(a)Add source estimates.
__div__
(a)Divide source estimates.
__hash__
(/)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.
center_of_mass
([subject, hemi, …])Compute the center of mass of activity.
copy
()Return copy of source estimate instance.
crop
([tmin, tmax, include_tmax])Restrict SourceEstimate to a time interval.
estimate_snr
(info, fwd, cov[, verbose])Compute timevarying SNR in the source space.
expand
(vertices)Expand SourceEstimate to include more vertices.
extract_label_time_course
(labels, src[, …])Extract label time courses for lists of labels.
get_peak
([hemi, tmin, tmax, mode, …])Get location and latency of peak amplitude.
in_label
(label)Get a source estimate object restricted to a label.
mean
()Make a summary stc file with mean over time points.
plot
([subject, surface, hemi, colormap, …])Plot SourceEstimate.
resample
(sfreq[, npad, window, n_jobs, verbose])Resample data.
save
(fname[, ftype, verbose])Save the source estimates to a 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
([index, scalings, …])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.

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
 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

center_of_mass
(subject=None, hemi=None, restrict_vertices=False, subjects_dir=None, surf='sphere')[source]¶ Compute the center of mass of activity.
This function computes the spatial center of mass on the surface as well as the temporal center of mass as in [1].
Note
All activity must occur in a single hemisphere, otherwise an error is raised. The “mass” of each point in space for computing the spatial center of mass is computed by summing across time, and viceversa for each point in time in computing the temporal center of mass. This is useful for quantifying spatiotemporal cluster locations, especially when combined with
mne.vertex_to_mni()
. Parameters
 subject
str
None
The subject the stc is defined for.
 hemi
int
, orNone
Calculate the center of mass for the left (0) or right (1) hemisphere. If None, one of the hemispheres must be all zeroes, and the center of mass will be calculated for the other hemisphere (useful for getting COM for clusters).
 restrict_verticesbool 
array
ofint
 instance ofSourceSpaces
If True, returned vertex will be one from stc. Otherwise, it could be any vertex from surf. If an array of int, the returned vertex will come from that array. If instance of SourceSpaces (as of 0.13), the returned vertex will be from the given source space. For most accuruate estimates, do not restrict vertices.
 subjects_dir
str
None
The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR.
 surf
str
The surface to use for Euclidean distance center of mass finding. The default here is “sphere”, which finds the center of mass on the spherical surface to help avoid potential issues with cortical folding.
 subject
 Returns
 vertex
int
Vertex of the spatial center of mass for the inferred hemisphere, with each vertex weighted by the sum of the stc across time. For a boolean stc, then, this would be weighted purely by the duration each vertex was active.
 hemi
int
Hemisphere the vertex was taken from.
 t
float
Time of the temporal center of mass (weighted by the sum across source vertices).
 vertex
References
 1
Larson and Lee, “The cortical dynamics underlying effective switching of auditory spatial attention”, NeuroImage 2012.
Examples using
center_of_mass
:

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

crop
(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
Examples using
crop
:

property
data
¶ Numpy array of source estimate data.

estimate_snr
(info, fwd, cov, verbose=None)[source]¶ Compute timevarying SNR in the source space.
This function should only be used with source estimates with units nanoAmperes (i.e., MNElike solutions, not dSPM or sLORETA).
Warning
This function currently only works properly for fixed orientation.
 Parameters
 infoinstance
Info
The measurement info.
 fwdinstance of
Forward
The forward solution used to create the source estimate.
 covinstance of
Covariance
The noise covariance used to estimate the resting cortical activations. Should be an evoked covariance, not empty room.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keywordargument only.
 infoinstance
 Returns
 snr_stcinstance of
SourceEstimate
The source estimate with the SNR computed.
 snr_stcinstance of
Notes
We define the SNR in decibels for each source location at each time point as:
\[{\rm SNR} = 10\log_10[\frac{a^2}{N}\sum_k\frac{b_k^2}{s_k^2}]\]where \(\\b_k\) is the signal on sensor \(k\) provided by the forward model for a source with unit amplitude, \(a\) is the source amplitude, \(N\) is the number of sensors, and \(s_k^2\) is the noise variance on sensor \(k\).
References
 1
Goldenholz, D. M., Ahlfors, S. P., Hämäläinen, M. S., Sharon, D., Ishitobi, M., Vaina, L. M., & Stufflebeam, S. M. (2009). Mapping the SignalToNoiseRatios of Cortical Sources in Magnetoencephalography and Electroencephalography. Human Brain Mapping, 30(4), 1077–1086. doi:10.1002/hbm.20571
Examples using
estimate_snr
:

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
 stc
SourceEstimate
VectorSourceEstimate
The modified stc (note: method operates inplace).
 stc

extract_label_time_course
(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). If used, it should be passed as a keywordargument only. 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'
.Examples using
extract_label_time_course
:

get_peak
(hemi=None, tmin=None, tmax=None, mode='abs', vert_as_index=False, time_as_index=False)[source]¶ Get location and latency of peak amplitude.
 Parameters
 hemi{‘lh’, ‘rh’,
None
} The hemi to be considered. If None, the entire source space is considered.
 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).
 hemi{‘lh’, ‘rh’,
 Returns
Examples using
get_peak
:

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
 label
Label
BiHemiLabel
The label (as created for example by mne.read_label). If the label does not match any sources in the SourceEstimate, a ValueError is raised.
 label
 Returns
 stc
SourceEstimate
VectorSourceEstimate
The source estimate restricted to the given label.
 stc
Examples using
in_label
:

property
lh_data
¶ Left hemisphere data.

property
lh_vertno
¶ Left hemisphere vertno.

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

plot
(subject=None, surface='inflated', hemi='lh', colormap='auto', time_label='auto', smoothing_steps=10, transparent=True, alpha=1.0, time_viewer='auto', subjects_dir=None, figure=None, views='auto', colorbar=True, clim='auto', cortex='classic', size=800, background='black', foreground=None, initial_time=None, time_unit='s', backend='auto', spacing='oct6', title=None, show_traces='auto', src=None, volume_options=1.0, view_layout='vertical', add_data_kwargs=None, verbose=None)[source]¶ Plot SourceEstimate.
 Parameters
 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
Hemisphere id (ie ‘lh’, ‘rh’, ‘both’, or ‘split’). In the case of ‘both’, both hemispheres are shown in the same window. In the case of ‘split’ hemispheres are displayed sidebyside in different viewing panes.
 colormap
str
np.ndarray
offloat
, shape(n_colors, 3  4) Name of colormap to use or a custom look up table. If array, must be (n x 3) or (n x 4) array for with RGB or RGBA values between 0 and 255. The default (‘auto’) uses ‘hot’ for onesided data and ‘mne’ for twosided data.
 time_label
str
callable()
None
Format of the time label (a format string, a function that maps floating point time values to strings, or None for no label). The default is
'auto'
, which will usetime=%0.2f ms
if there is more than one time point. smoothing_steps
int
The amount of smoothing.
 transparentbool 
None
If True: use a linear transparency between fmin and fmid and make values below fmin fully transparent (symmetrically for divergent colormaps). None will choose automatically based on colormap type.
 alpha
float
Alpha value to apply globally to the overlay. Has no effect with mpl backend.
 time_viewerbool 
str
Display time viewer GUI. Can also be ‘auto’, which will mean True for the PyVista backend and False otherwise.
Changed in version 0.20.0: “auto” mode added.
 subjects_dir
str
None
The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR.
 figureinstance of
mayavi.core.api.Scene
 instance ofmatplotlib.figure.Figure
list
int
None
If None, a new figure will be created. If multiple views or a split view is requested, this must be a list of the appropriate length. If int is provided it will be used to identify the Mayavi figure by it’s id or create a new figure with the given id. If an instance of matplotlib figure, mpl backend is used for plotting.
 views
str
list
View to use. Can be any of:
['lateral', 'medial', 'rostral', 'caudal', 'dorsal', 'ventral', 'frontal', 'parietal', 'axial', 'sagittal', 'coronal']
Three letter abbreviations (e.g.,
'lat'
) are also supported. Using multiple views (list) is not supported for mpl backend.When plotting a standard SourceEstimate (not volume, mixed, or vector) and using the PyVista backend,
views='flat'
is also supported to plot cortex as a flatmap.Changed in version 0.21.0: Support for flatmaps.
 colorbarbool
If True, display colorbar on scene.
 clim
str
dict
Colorbar properties specification. If ‘auto’, set clim automatically based on data percentiles. If dict, should contain:
kind
‘value’  ‘percent’Flag to specify type of limits.
lims
list  np.ndarray  tuple of float, 3 elementsLower, middle, and upper bounds for colormap.
pos_lims
list  np.ndarray  tuple of float, 3 elementsLower, middle, and upper bound for colormap. Positive values will be mirrored directly across zero during colormap construction to obtain negative control points.
Note
Only one of
lims
orpos_lims
should be provided. Only sequential colormaps should be used withlims
, and only divergent colormaps should be used withpos_lims
. cortex
str
ortuple
Specifies how binarized curvature values are rendered. Either the name of a preset PySurfer cortex colorscheme (one of ‘classic’, ‘bone’, ‘low_contrast’, or ‘high_contrast’), or the name of mayavi colormap, or a tuple with values (colormap, min, max, reverse) to fully specify the curvature colors. Has no effect with mpl backend.
 size
float
ortuple
offloat
The size of the window, in pixels. can be one number to specify a square window, or the (width, height) of a rectangular window. Has no effect with mpl backend.
 backgroundmatplotlib color
Color of the background of the display window.
 foregroundmatplotlib color 
None
Color of the foreground of the display window. Has no effect with mpl backend. None will choose white or black based on the background color.
 initial_time
float
None
The time to display on the plot initially.
None
to display the first time sample (default). time_unit‘s’  ‘ms’
Whether time is represented in seconds (“s”, default) or milliseconds (“ms”).
 backend‘auto’  ‘mayavi’  ‘pyvista’  ‘matplotlib’
Which backend to use. If
'auto'
(default), tries to plot with pyvista, but resorts to matplotlib if no 3d backend is available.New in version 0.15.0.
 spacing
str
The spacing to use for the source space. Can be
'ico#'
for a recursively subdivided icosahedron,'oct#'
for a recursively subdivided octahedron, or'all'
for all points. In general, you can speed up the plotting by selecting a sparser source space. Has no effect with mayavi backend. Defaults to ‘oct6’.New in version 0.15.0.
 title
str
None
Title for the figure. If None, the subject name will be used.
New in version 0.17.0.
 show_tracesbool 
str
float
If True, enable interactive picking of a point on the surface of the brain and plot its time course. This feature is only available with the PyVista 3d backend, and requires
time_viewer=True
. Defaults to ‘auto’, which will use True if and only iftime_viewer=True
, the backend is PyVista, and there is more than one time point. If float (between zero and one), it specifies what proportion of the total window should be devoted to traces (True is equivalent to 0.25, i.e., it will occupy the bottom 1/4 of the figure).New in version 0.20.0.
 srcinstance of
SourceSpaces
None
The source space corresponding to the source estimate. Only necessary if the STC is a volume or mixed source estimate.
 volume_options
float
dict
None
Options for volumetric source estimate plotting, with key/value pairs:
'resolution'
float  NoneResolution (in mm) of volume rendering. Smaller (e.g., 1.) looks better at the cost of speed. None (default) uses the volume source space resolution, which is often something like 7 or 5 mm, without resampling.
'blending'
strCan be “mip” (default) for maximum intensity projection or “composite” for composite blending using alpha values.
'alpha'
float  NoneAlpha for the volumetric rendering. Defaults are 0.4 for vector source estimates and 1.0 for scalar source estimates.
'surface_alpha'
float  NoneAlpha for the surface enclosing the volume(s). None (default) will use half the volume alpha. Set to zero to avoid plotting the surface.
'silhouette_alpha'
float  NoneAlpha for a silhouette along the outside of the volume. None (default) will use
0.25 * surface_alpha
.
'silhouette_linewidth'
floatThe line width to use for the silhouette. Default is 2.
A float input (default 1.) or None will be used for the
'resolution'
entry. view_layout
str
Can be “vertical” (default) or “horizontal”. When using “horizontal” mode, the PyVista backend must be used and hemi cannot be “split”.
 add_data_kwargs
dict
None
Additional arguments to brain.add_data (e.g.,
dict(time_label_size=10)
). verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keywordargument only.
 subject
 Returns
 figureinstance of
surfer.Brain
matplotlib.figure.Figure
An instance of
surfer.Brain
from PySurfer or matplotlib figure.
 figureinstance of
Notes
Flatmaps are available by default for
fsaverage
but not for other subjects reconstructed by FreeSurfer. We recommend usingmne.compute_source_morph()
to morph source estimates tofsaverage
for flatmap plotting. If you want to construct your own flatmap for a given subject, these links might help:Examples using
plot
:

resample
(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). If used, it should be passed as a keywordargument only. 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.
Examples using
resample
:

property
rh_data
¶ Right hemisphere data.

property
rh_vertno
¶ Right hemisphere vertno.

save
(fname, ftype='stc', verbose=None)[source]¶ Save the source estimates to a file.
 Parameters
 fname
str
The stem of the file name. The file names used for surface source spaces are obtained by adding “lh.stc” and “rh.stc” (or “lh.w” and “rh.w”) to the stem provided, for the left and the right hemisphere, respectively.
 ftype
str
File format to use. Allowed values are “stc” (default), “w”, and “h5”. The “w” format only supports a single time point.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keywordargument only. Defaults to self.verbose.
 fname
Examples using
save
:

property
sfreq
¶ Sample rate of the data.

property
shape
¶ Shape of the data.

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

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

property
times
¶ A timestamp for each sample.

property
tmin
¶ The first timestamp.

to_data_frame
(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

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
 src_originstance of
SourceSpaces
The original source spaces that were morphed to the current subject.
 subject_orig
str
None
The original subject. For most source spaces this shouldn’t need to be provided, since it is stored in the source space itself.
 subjects_dir
str
None
The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keywordargument only. Defaults to self.verbose.
 src_originstance of
 Returns
 stc
SourceEstimate
VectorSourceEstimate
The transformed source estimate.
 stc
See also
Notes
New in version 0.10.0.
Examples using
to_original_src
:

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
 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
(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).