mne.Dipole#

class mne.Dipole(times, pos, amplitude, ori, gof, name=None, conf=None, khi2=None, nfree=None, *, verbose=None)[source]#

Dipole class for sequential dipole fits.

Note

This class should usually not be instantiated directly via mne.Dipole(...). Instead, use one of the functions listed in the See Also section below.

Used to store positions, orientations, amplitudes, times, goodness of fit of dipoles, typically obtained with Neuromag/xfit, mne_dipole_fit or certain inverse solvers. Note that dipole position vectors are given in the head coordinate frame.

Parameters:
timesarray, shape (n_dipoles,)

The time instants at which each dipole was fitted (sec).

posarray, shape (n_dipoles, 3)

The dipoles positions (m) in head coordinates.

amplitudearray, shape (n_dipoles,)

The amplitude of the dipoles (Am).

oriarray, shape (n_dipoles, 3)

The dipole orientations (normalized to unit length).

gofarray, shape (n_dipoles,)

The goodness of fit.

namestr | None

Name of the dipole.

confdict

Confidence limits in dipole orientation for “vol” in m^3 (volume), “depth” in m (along the depth axis), “long” in m (longitudinal axis), “trans” in m (transverse axis), “qlong” in Am, and “qtrans” in Am (currents). The current confidence limit in the depth direction is assumed to be zero (although it can be non-zero when a BEM is used).

New in version 0.15.

khi2array, shape (n_dipoles,)

The χ^2 values for the fits.

New in version 0.15.

nfreearray, shape (n_dipoles,)

The number of free parameters for each fit.

New in version 0.15.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Notes

This class is for sequential dipole fits, where the position changes as a function of time. For fixed dipole fits, where the position is fixed as a function of time, use mne.DipoleFixed.

Attributes:
times

Time vector in seconds.

tmax

Last time point.

tmin

First time point.

Methods

__getitem__(item)

Get a time slice.

__len__()

Return the number of dipoles.

copy()

Copy the Dipoles object.

crop([tmin, tmax, include_tmax, verbose])

Crop data to a given time interval.

decimate(decim[, offset, verbose])

Decimate the time-series data.

plot_amplitudes([color, show])

Plot the dipole amplitudes as a function of time.

plot_locations(trans, subject[, ...])

Plot dipole locations.

save(fname[, overwrite, verbose])

Save dipole in a .dip or .bdip file.

shift_time(tshift[, relative])

Shift time scale in epoched or evoked data.

time_as_index(times[, use_rounding])

Convert time to indices.

to_mni(subject, trans[, subjects_dir, verbose])

Convert dipole location from head to MNI coordinates.

to_mri(subject, trans[, subjects_dir, verbose])

Convert dipole location from head to MRI surface RAS coordinates.

to_volume_labels(trans[, subject, aseg, ...])

Find an ROI in atlas for the dipole positions.

__getitem__(item)[source]#

Get a time slice.

Parameters:
itemarray_like or slice

The slice of time points to use.

Returns:
dipinstance of Dipole

The sliced dipole.

__len__()[source]#

Return the number of dipoles.

Returns:
lenint

The number of dipoles.

Examples

This can be used as:

>>> len(dipoles)  
10
copy()[source]#

Copy the Dipoles object.

Returns:
dipinstance of Dipole

The copied dipole instance.

crop(tmin=None, tmax=None, include_tmax=True, verbose=None)[source]#

Crop data to a given time interval.

Parameters:
tminfloat | None

Start time of selection in seconds.

tmaxfloat | None

End time of selection in seconds.

include_tmaxbool

If True (default), include tmax. If False, exclude tmax (similar to how Python indexing typically works).

New in version 0.19.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
selfinstance of Dipole

The cropped instance.

Examples using crop:

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior
decimate(decim, offset=0, verbose=None)[source]#

Decimate the time-series data.

Parameters:
decimint

Factor by which to subsample the data.

Warning

Low-pass filtering is not performed, this simply selects every Nth sample (where N is the value passed to decim), i.e., it compresses the signal (see Notes). If the data are not properly filtered, aliasing artifacts may occur.

offsetint

Apply an offset to where the decimation starts relative to the sample corresponding to t=0. The offset is in samples at the current sampling rate.

New in version 0.12.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
instMNE-object

The decimated object.

Notes

For historical reasons, decim / “decimation” refers to simply subselecting samples from a given signal. This contrasts with the broader signal processing literature, where decimation is defined as (quoting [1], p. 172; which cites [2]):

“… a general system for downsampling by a factor of M is the one shown in Figure 4.23. Such a system is called a decimator, and downsampling by lowpass filtering followed by compression [i.e, subselecting samples] has been termed decimation (Crochiere and Rabiner, 1983).”

Hence “decimation” in MNE is what is considered “compression” in the signal processing community.

Decimation can be done multiple times. For example, inst.decimate(2).decimate(2) will be the same as inst.decimate(4).

If decim is 1, this method does not copy the underlying data.

New in version 0.10.0.

References

plot_amplitudes(color='k', show=True)[source]#

Plot the dipole amplitudes as a function of time.

Parameters:
colormatplotlib color

Color to use for the trace.

showbool

Show figure if True.

Returns:
figmatplotlib.figure.Figure

The figure object containing the plot.

plot_locations(trans, subject, subjects_dir=None, mode='orthoview', coord_frame='mri', idx='gof', show_all=True, ax=None, block=False, show=True, scale=None, color=None, *, highlight_color='r', fig=None, title=None, head_source='seghead', surf='pial', width=None, verbose=None)[source]#

Plot dipole locations.

If mode is set to ‘arrow’ or ‘sphere’, only the location of the first time point of each dipole is shown else use the show_all parameter.

Parameters:
transdict | None

The mri to head trans. Can be None with mode set to ‘3d’.

subjectstrNone

The FreeSurfer subject name (will be used to set the FreeSurfer environment variable SUBJECT). Can be None with mode set to '3d'.

subjects_dirpath-like | None

The path to the directory containing the FreeSurfer subjects reconstructions. If None, defaults to the SUBJECTS_DIR environment variable.

modestr

Can be:

'arrow' or 'sphere'

Plot in 3D mode using PyVista with the given glyph type.

'orthoview'

Plot in matplotlib Axes3D using matplotlib with MRI slices shown on the sides of a cube, with the dipole(s) shown as arrows extending outward from a dot (i.e., the arrows pivot on the tail).

'outlines'

Plot in matplotlib Axes using a quiver of arrows for the dipoles in three axes (axial, coronal, and sagittal views), with the arrow pivoting in the middle of the arrow.

Changed in version 1.1: Added support for 'outlines'.

coord_framestr

Coordinate frame to use: ‘head’ or ‘mri’. Can also be ‘mri_rotated’ when mode equals 'outlines'. Defaults to ‘mri’.

New in version 0.14.0.

Changed in version 1.1: Added support for 'mri_rotated'.

idxint | ‘gof’ | ‘amplitude’

Index of the initially plotted dipole. Can also be ‘gof’ to plot the dipole with highest goodness of fit value or ‘amplitude’ to plot the dipole with the highest amplitude. The dipoles can also be browsed through using up/down arrow keys or mouse scroll. Defaults to ‘gof’. Only used if mode equals ‘orthoview’.

New in version 0.14.0.

show_allbool

Whether to always plot all the dipoles. If True (default), the active dipole is plotted as a red dot and its location determines the shown MRI slices. The non-active dipoles are plotted as small blue dots. If False, only the active dipole is plotted. Only used if mode='orthoview'.

New in version 0.14.0.

axinstance of matplotlib Axes3D | list of matplotlib Axes | None

Axes to plot into. If None (default), axes will be created. If mode equals 'orthoview', must be a single Axes3D. If mode equals 'outlines', must be a list of three Axes.

New in version 0.14.0.

blockbool

Whether to halt program execution until the figure is closed. Defaults to False. Only used if mode equals ‘orthoview’.

New in version 0.14.0.

showbool

Show figure if True. Defaults to True. Only used if mode equals ‘orthoview’.

scalefloat

The scale (size in meters) of the dipoles if mode is not 'orthoview'. The default is 0.03 when mode is 'outlines' and 0.005 otherwise.

colortuple

The color of the dipoles. The default (None) will use 'y' if mode is 'orthoview' and show_all is True, else ‘r’. Can also be a list of colors to use when mode is 'outlines'.

Changed in version 0.19.0: Color is now passed in orthoview mode.

highlight_colorcolor

The highlight color. Only used in orthoview mode with show_all=True.

New in version 0.19.0.

figinstance of Figure3D | None

3D figure in which to plot the alignment. If None, creates a new 600x600 pixel figure with black background. Only used when mode is 'arrow' or 'sphere'.

New in version 0.19.0.

titlestr | None

The title of the figure if mode='orthoview' (ignored for all other modes). If None, dipole number and its properties (amplitude, orientation etc.) will be shown. Defaults to None.

New in version 0.21.0.

head_sourcestr | list of str

Head source(s) to use. See the source option of mne.get_head_surf() for more information. Only used when mode equals 'outlines'.

New in version 1.1.

surfstr | None

Brain surface to show outlines for, can be 'white', 'pial', or None. Only used when mode is 'outlines'.

New in version 1.1.

widthfloat | None

Width of the matplotlib quiver arrow, see matplotlib.axes.Axes.quiver(). If None (default), when mode is 'outlines' 0.015 will be used, and when mode is 'orthoview' the matplotlib default is used.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
figinstance of Figure3D or matplotlib.figure.Figure

The PyVista figure or matplotlib Figure.

Notes

New in version 0.9.0.

Examples using plot_locations:

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit

Visualize source time courses (stcs)

Visualize source time courses (stcs)

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data
save(fname, overwrite=False, *, verbose=None)[source]#

Save dipole in a .dip or .bdip file.

Parameters:
fnamestr

The name of the .dip or .bdip file.

overwritebool

If True (default False), overwrite the destination file if it exists.

New in version 0.20.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Notes

Changed in version 0.20: Support for writing bdip (Xfit binary) files.

shift_time(tshift, relative=True)[source]#

Shift time scale in epoched or evoked data.

Parameters:
tshiftfloat

The (absolute or relative) time shift in seconds. If relative is True, positive tshift increases the time value associated with each sample, while negative tshift decreases it.

relativebool

If True, increase or decrease time values by tshift seconds. Otherwise, shift the time values such that the time of the first sample equals tshift.

Returns:
epochsMNE-object

The modified instance.

Notes

This method allows you to shift the time values associated with each data sample by an arbitrary amount. It does not resample the signal or change the data values in any way.

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

Convert time to indices.

Parameters:
timeslist-like | float | int

List of numbers or a number representing points in time.

use_roundingbool

If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices.

Returns:
indexndarray

Indices corresponding to the times supplied.

property times#

Time vector in seconds.

property tmax#

Last time point.

property tmin#

First time point.

to_mni(subject, trans, subjects_dir=None, verbose=None)[source]#

Convert dipole location from head to MNI coordinates.

Parameters:
subjectstr

The FreeSurfer subject name.

transstr | dict | instance of Transform

If str, the path to the head<->MRI transform *-trans.fif file produced during coregistration. Can also be 'fsaverage' to use the built-in fsaverage transformation.

subjects_dirpath-like | None

The path to the directory containing the FreeSurfer subjects reconstructions. If None, defaults to the SUBJECTS_DIR environment variable.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
pos_mniarray, shape (n_pos, 3)

The MNI coordinates (in mm) of pos.

Examples using to_mni:

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit
to_mri(subject, trans, subjects_dir=None, verbose=None)[source]#

Convert dipole location from head to MRI surface RAS coordinates.

Parameters:
subjectstr

The FreeSurfer subject name.

transstr | dict | instance of Transform

If str, the path to the head<->MRI transform *-trans.fif file produced during coregistration. Can also be 'fsaverage' to use the built-in fsaverage transformation.

subjects_dirpath-like | None

The path to the directory containing the FreeSurfer subjects reconstructions. If None, defaults to the SUBJECTS_DIR environment variable.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
pos_mriarray, shape (n_pos, 3)

The Freesurfer surface RAS coordinates (in mm) of pos.

Examples using to_mri:

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit
to_volume_labels(trans, subject='fsaverage', aseg='aparc+aseg', subjects_dir=None, verbose=None)[source]#

Find an ROI in atlas for the dipole positions.

Parameters:
transpath-like | dict | instance of Transform | None

If str, the path to the head<->MRI transform *-trans.fif file produced during coregistration. Can also be 'fsaverage' to use the built-in fsaverage transformation. If trans is None, an identity matrix is assumed.

Changed in version 0.19: Support for ‘fsaverage’ argument.

subjectstr

The FreeSurfer subject name.

asegstr

The anatomical segmentation file. Default aparc+aseg. This may be any anatomical segmentation file in the mri subdirectory of the Freesurfer subject directory.

subjects_dirpath-like | None

The path to the directory containing the FreeSurfer subjects reconstructions. If None, defaults to the SUBJECTS_DIR environment variable.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
labelslist

List of anatomical region names from anatomical segmentation atlas.

Notes

New in version 0.24.

Examples using to_volume_labels:

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit

Examples using mne.Dipole#

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

Visualize source time courses (stcs)

Visualize source time courses (stcs)

Brainstorm Elekta phantom dataset tutorial

Brainstorm Elekta phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

Plotting with mne.viz.Brain

Plotting with mne.viz.Brain

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data