mne.VolSourceEstimate¶

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

Container for volume 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 : array 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).

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)	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.
as_volume(src[, dest, mri_resolution])	Export volume source estimate as a nifti object.
bin(width[, tstart, tstop, func])	Return 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.

__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.

as_volume(src, dest=’mri’, mri_resolution=False)[source]¶

Export volume source estimate as a nifti object.

Parameters:	src : list The list of source spaces (should actually be of length 1) dest : ‘mri’ | ‘surf’ If ‘mri’ the volume is defined in the coordinate system of the original T1 image. If ‘surf’ the coordinate system of the FreeSurfer surface is used (Surface RAS). mri_resolution: bool It True the image is saved in MRI resolution. WARNING: if you have many time points the file produced can be huge.
Returns:	img : instance Nifti1Image The image object.

Notes

New in version 0.9.0.

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.

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.

get_peak(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_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 The latency in seconds.

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).

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.

save(fname, ftype=’stc’, verbose=None)[source]¶

Save the source estimates to a file.

Parameters:	fname : string The stem of the file name. The stem is extended with “-vl.stc” or “-vl.w”. ftype : string File format to use. Allowed values are “stc” (default) and “w”. 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.

save_as_volume(fname, src, dest=’mri’, mri_resolution=False)[source]¶

Save a volume source estimate in a NIfTI file.

Parameters:

fname : string The name of the generated nifti file. src : list The list of source spaces (should actually be of length 1) dest : ‘mri’ | ‘surf’ If ‘mri’ the volume is defined in the coordinate system of the original T1 image. If ‘surf’ the coordinate system of the FreeSurfer surface is used (Surface RAS). mri_resolution: bool It True the image is saved in MRI resolution. WARNING: if you have many time points the file produced can be huge.

Returns:

img : instance Nifti1Image The image object.

Notes

New in version 0.9.0.

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.

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).