This documentation is for development version 0.18.dev0.

mne.VectorSourceEstimate

class 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:
data : array of shape (n_dipoles, 3, n_times)

The data in source space. Each dipole contains three vectors that denote the dipole strength in X, Y and Z directions over time.

vertices : array | list of shape (2,)

Vertex numbers corresponding to the data.

tmin : float

Time point of the first sample in data.

tstep : float

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).

See also

SourceEstimate
A container for surface source estimates.
VolSourceEstimate
A container for volume source estimates.
MixedSourceEstimate
A container for mixed surface + volume source estimates.

Notes

New in version 0.15.

Attributes:
subject : str | None

The subject name.

times : array of shape (n_times,)

A timestamp for each sample.

shape : tuple

Shape of the data.

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.
__add__(a)[source]

Add source estimates.

__div__(a)[source]

Divide source estimates.

__hash__($self, /)

Return hash(self).

__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 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:
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 : SourceEstimate | VectorSourceEstimate

The binned source estimate.

copy()[source]

Return copy of source estimate 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 : SourceEstimate | VectorSourceEstimate

The modified stc (note: method operates inplace).

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.

Returns:
stc : SourceEstimate | VectorSourceEstimate

The source estimate restricted to the given label.

lh_data

Left hemisphere data.

lh_vertno

Left hemisphere vertno.

magnitude()[source]

Compute magnitude of activity without directionality.

Returns:
stc : instance of SourceEstimate

The source estimate without directionality information.

mean()[source]

Make a summary stc file with mean over time points.

Returns:
stc : SourceEstimate | VectorSourceEstimate

The modified stc.

normal(src)[source]

Compute activity orthogonal to the cortex.

Parameters:
src : instance of SourceSpaces

The source space for which this source estimate is specified.

Returns:
stc : instance of SourceEstimate

The source estimate only retaining the activity orthogonal to the cortex.

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:
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.

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. This should be a sequential colormap.

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

If True, use a linear transparency between fmin and fmid.

brain_alpha : float

Alpha value to apply globally to the surface meshes. Defaults to 0.4.

overlay_alpha : float

Alpha value to apply globally to the overlay. Defaults to brain_alpha.

vector_alpha : float

Alpha value to apply globally to the vector glyphs. Defaults to 1.

scale_factor : float | None

Scaling factor for the vector glyphs. By default, an attempt is made to automatically determine a sane value.

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.api.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 : ‘value’ | ‘percent’

Flag to specify type of limits.

lims : list | np.ndarray | tuple of float, 3 elements

Left, middle, and right bound for colormap.

Unlike stc.plot, it cannot use pos_lims, as the surface plot must show the magnitude.

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 tuple of float

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 : surfer.Brain

A instance of surfer.Brain from PySurfer.

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:
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='h5', verbose=None)[source]

Save the full source estimate to an HDF5 file.

Parameters:
fname : string

The file name to write the source estimate to, should end in ‘-stc.h5’.

ftype : string

File format to use. Currently, the only allowed values is “h5”.

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).

sum()[source]

Make a summary stc file with sum over time points.

Returns:
stc : SourceEstimate | VectorSourceEstimate

The modified stc.

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.

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:
picks : str | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels.

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.

scaling_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.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 source estimate 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). Defaults to self.verbose.

Returns:
stc : SourceEstimate | VectorSourceEstimate

The transformed source estimate.

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 : SourceEstimate | VectorSourceEstimate | 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 “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.

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 “apply_lcmv_epochs” do this automatically (if possible).

tstep

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