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) | 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 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).

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	(list of arrays of shape (n_dipoles,)) The indices of the dipoles in each 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.
copy()	Return copy of SourceEstimate instance.
crop([tmin, tmax])	Restrict SourceEstimate to a time interval.
mean()	Make a summary stc file with mean power between tmin and tmax.
plot_surface(src[, subject, surface, hemi, …])	Plot surface source estimates with PySurfer.
resample(sfreq[, npad, window, n_jobs, verbose])	Resample data.
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.

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.

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).

plot_surface(src, subject=None, surface=’inflated’, hemi=’lh’, colormap=’auto’, time_label=’time=%02.f ms’, smoothing_steps=10, transparent=None, alpha=1.0, time_viewer=False, config_opts=None, subjects_dir=None, figure=None, views=’lat’, colorbar=True, clim=’auto’)[source]¶

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 of float, 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. 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. config_opts : dict Keyword arguments for Brain initialization. See pysurfer.viz.Brain. 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 | 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(). colorbar : bool If True, display colorbar on scene. clim : str | dict Colorbar properties specification. See plot_source_estimates.

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.

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).