mne.SourceEstimate¶

class mne.SourceEstimate(data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None)[source]¶

Container for surface source estimates.

Parameters:

data : array of shape (n_dipoles, n_times) | 2-tuple (kernel, sens_data) 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 “numpy.dot(kernel, sens_data)”. vertices : list of two arrays Vertex numbers corresponding to the data. tmin : scalar Time point of the first sample in data. tstep : scalar 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. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

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	(list of two arrays of shape (n_dipoles,)) The indices of the dipoles in the left and right source space.

Methods

__add__(a)	Add source estimates.
__div__(a)	Divide source estimates.
__hash__() <==> hash(x)
__mul__(a)	Multiply source estimates.
__neg__()	Negate the source estimate.
__sub__(a)	Subtract source estimates.
bin(width[, tstart, tstop, func])	Return a SourceEstimate object with data summarized over time bins.
center_of_mass([subject, hemi, …])	Compute the center of mass of activity.
copy()	Return copy of SourceEstimate instance.
crop([tmin, tmax])	Restrict SourceEstimate to a time interval.
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 SourceEstimate object restricted to a label.
mean()	Make a summary stc file with mean power between tmin and tmax.
morph(subject_to[, grade, smooth, …])	Morph a source estimate from one subject to another.
morph_precomputed(subject_to, vertices_to, …)	Morph source estimate between subjects using a precomputed matrix.
plot([subject, surface, hemi, colormap, …])	Plot SourceEstimates with PySurfer.
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.
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.
to_original_src(src_orig[, subject_orig, …])	Get a SourceEstimate 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.

__add__(a)[source]¶

Add source estimates.

__div__(a)[source]¶

Divide source estimates.

__hash__() <==> hash(x)¶

__mul__(a)[source]¶

Multiply source estimates.

__neg__()[source]¶

Negate the source estimate.

__sub__(a)[source]¶

Subtract source estimates.

bin(width, tstart=None, tstop=None, func=<function mean>)[source]¶

Return 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 Width of the individual bins in seconds. tstart : scalar | None Time point where the first bin starts. The default is the first time point of the stc. tstop : scalar | 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 : instance of SourceEstimate The binned SourceEstimate.

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 [R17].

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 vice-versa for each point in time in computing the temporal center of mass. This is useful for quantifying spatio-temporal cluster locations, especially when combined with mne.vertex_to_mni().

Parameters:

subject : string | None The subject the stc is defined for. hemi : int, or None 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_vertices : bool | array of int | instance of SourceSpaces 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, or None Path to the SUBJECTS_DIR. If None, the path is obtained by using the 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.

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).

See also

mne.Label.center_of_mass, mne.vertex_to_mni

References

[R17]

(1, 2) Larson and Lee, “The cortical dynamics underlying effective switching of auditory spatial attention”, NeuroImage 2012.

copy()[source]¶

Return copy of SourceEstimate instance.

crop(tmin=None, tmax=None)[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.

data¶

Numpy array of source estimate data.

expand(vertices)[source]¶

Expand SourceEstimate to include more vertices.

This will add rows to stc.data (zero-filled) and modify stc.vertices to include all vertices in stc.vertices and the input vertices.

Parameters:	vertices : list of array New vertices to add. Can also contain old values.
Returns:	stc : instance of SourceEstimate The modified stc (note: method operates inplace).

extract_label_time_course(labels, src, mode=’mean_flip’, 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.

Valid values for mode are:

• ‘mean’: Average within each label.
• ‘mean_flip’: Average within each label with sign flip depending on source orientation.
• ‘pca_flip’: Apply an SVD to the time courses within each label and use the scaled and sign-flipped first right-singular vector as the label time course. The scaling is performed such that the power of the label time course is the same as the average per-vertex time course power within the label. The sign of the resulting time course is adjusted by multiplying it with “sign(dot(u, flip))” where u is the first left-singular vector, and flip is a sing-flip vector based on the vertex normals. This procedure assures that the phase does not randomly change by 180 degrees from one stc to the next.
• ‘max’: Max value within each label.

Parameters:

labels : Label | list of Label The labels for which to extract the time courses. src : list Source spaces for left and right hemisphere. mode : str Extraction mode, see explanation above. allow_empty : bool Instead of emitting an error, return all-zero time course for labels that do not have any vertices in the source estimate. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

label_tc : array, shape=(len(labels), n_times) Extracted time course for each label.

See also

extract_label_time_course

extract time courses for multiple STCs

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_index : bool whether to return the vertex index instead of of its ID. Defaults to False. time_as_index : bool Whether to return the time index instead of the latency. Defaults to False.

Returns:

pos : int The vertex exhibiting the maximum response, either ID or index. latency : float | int The time point of the maximum response, either latency in seconds or index.

in_label(label)[source]¶

Get a SourceEstimate 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.

lh_data¶

Left hemisphere data.

lh_vertno¶

Left hemisphere vertno.

mean()[source]¶

Make a summary stc file with mean power between tmin and tmax.

Returns:	stc : instance of SourceEstimate The modified stc (method operates inplace).

morph(subject_to, grade=5, smooth=None, subjects_dir=None, buffer_size=64, n_jobs=1, subject_from=None, sparse=False, verbose=None)[source]¶

Morph a source estimate from one subject to another.

Parameters:

subject_to : string Name of the subject on which to morph as named in the SUBJECTS_DIR grade : int, list (of two arrays), or None Resolution of the icosahedral mesh (typically 5). If None, all vertices will be used (potentially filling the surface). If a list, then values will be morphed to the set of vertices specified in in grade[0] and grade[1]. Note that specifying the vertices (e.g., grade=[np.arange(10242), np.arange(10242)] for fsaverage on a standard grade 5 source space) can be substantially faster than computing vertex locations. Note that if subject=’fsaverage’ and ‘grade=5’, this set of vertices will automatically be used (instead of computed) for speed, since this is a common morph. NOTE : If sparse=True, grade has to be set to None. smooth : int or None Number of iterations for the smoothing of the surface data. If None, smooth is automatically defined to fill the surface with non-zero values. subjects_dir : string, or None Path to SUBJECTS_DIR if it is not set in the environment. buffer_size : int Morph data in chunks of buffer_size time instants. Saves memory when morphing long time intervals. n_jobs : int Number of jobs to run in parallel. subject_from : string Name of the original subject as named in the SUBJECTS_DIR. If None, self.subject will be used. sparse : bool Morph as a sparse source estimate. If True the only parameters used are subject_to and subject_from, and grade has to be None. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

stc_to : SourceEstimate Source estimate for the destination subject.

morph_precomputed(subject_to, vertices_to, morph_mat, subject_from=None)[source]¶

Morph source estimate between subjects using a precomputed matrix.

Parameters:	subject_to : string Name of the subject on which to morph as named in the SUBJECTS_DIR. vertices_to : list of array of int The vertices on the destination subject’s brain. morph_mat : sparse matrix The morphing matrix, usually from compute_morph_matrix. subject_from : string | None Name of the original subject as named in the SUBJECTS_DIR. If None, self.subject will be used.
Returns:	stc_to : SourceEstimate Source estimate for the destination subject.

plot(subject=None, surface=’inflated’, hemi=’lh’, colormap=’auto’, time_label=’auto’, smoothing_steps=10, transparent=None, alpha=1.0, 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 SourceEstimates 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:

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. colormap : str | np.ndarray of float, 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. If ‘auto’, either ‘hot’ or ‘mne’ will be chosen based on whether ‘lims’ or ‘pos_lims’ are specified in clim. 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 time=%0.2f ms. smoothing_steps : int The amount of smoothing transparent : bool | 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_viewer : bool Display time viewer GUI. subjects_dir : str The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR. figure : instance of mayavi.core.scene.Scene | 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. views : str | list View to use. See surfer.Brain(). colorbar : bool 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 : str Flag to specify type of limits. ‘value’ or ‘percent’. lims : list | np.ndarray | tuple of float, 3 elements Note: Only use this if ‘colormap’ is not ‘mne’. Left, middle, and right bound for colormap. pos_lims : list | np.ndarray | tuple of float, 3 elements Note: Only use this if ‘colormap’ is ‘mne’. Left, middle, and right bound for colormap. Positive values will be mirrored directly across zero during colormap construction to obtain negative control points. cortex : str or tuple 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. size : float or pair of floats The size of the window, in pixels. can be one number to specify a square window, or the (width, height) of a rectangular window. background : matplotlib color Color of the background of the display window. foreground : matplotlib color Color of the foreground of the display window. 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”).

Returns:

brain : Brain A instance of surfer.viz.Brain from PySurfer.

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 power-of-two size (can be much faster). window : string or tuple Window to use in resampling. See scipy.signal.resample. n_jobs : int Number of jobs to run in parallel. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more). Defaults to self.verbose.

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=’stc’, verbose=None)[source]¶

Save the source estimates to a file.

Parameters:

fname : string 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 : string File format to use. Allowed values are “stc” (default), “w”, and “h5”. The “w” format only supports a single time point. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more). Defaults to self.verbose.

sfreq¶

Sample rate of the data.

shape¶

Shape of the data.

sqrt()[source]¶

Take the square root.

Returns:	stc : instance of SourceEstimate A copy of the SourceEstimate with sqrt(data).

time_as_index(times, use_rounding=False)[source]¶

Convert time to indices.

Parameters:	times : list-like | float | int List of numbers or a number representing points in time. use_rounding : boolean If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices.
Returns:	index : ndarray Indices corresponding to the times supplied.

to_data_frame(picks=None, index=None, scale_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:

picks : array-like of int | None If None only MEG and EEG channels are kept otherwise the channels indices in picks are kept. index : tuple of str | None Column to be used as index for the data. Valid string options are ‘epoch’, ‘time’ and ‘condition’. If None, all three info columns will be included in the table as categorial data. scale_time : float Scaling to be applied to time units. scalings : dict | None Scaling to be applied to the channels picked. If None, defaults to scalings=dict(eeg=1e6, grad=1e13, mag=1e15, misc=1.0). copy : bool If true, data will be copied. Else data may be modified in place. start : int | None If it is a Raw object, this defines a starting index for creating the dataframe from a slice. The times will be interpolated from the index and the sampling rate of the signal. stop : int | None If it is a Raw object, this defines a stop index for creating the dataframe from a slice. The times will be interpolated from the index and the sampling rate of the signal.

Returns:

df : instance of pandas.core.DataFrame A dataframe suitable for usage with other statistical/plotting/analysis packages. Column/Index values will depend on the object type being converted, but should be human-readable.

to_original_src(src_orig, subject_orig=None, subjects_dir=None, verbose=None)[source]¶

Get a SourceEstimate from morphed source to the original subject.

Parameters:

src_orig : instance 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 : string, or None Path to SUBJECTS_DIR if it is not set in the environment. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

See also

morph_source_spaces

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:

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. Transforms which yield 3D output (e.g. time-frequency transforms) are valid, so long as the first two dimensions are vertices and time. In this case, the copy parameter (see below) 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. 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. copy : bool If True, return a new instance of SourceEstimate instead of modifying the input inplace.

Returns:

stcs : instance of SourceEstimate | list The transformed stc or, in the case of transforms which yield N-dimensional output (where N > 2), a list of stcs. For a list, copy must be True.

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)[source]¶

Get data after a linear (time) transform has been applied.

The transorm 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.

Returns:

data_t : ndarray The transformed data.

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).