mne.DipoleFixed

class mne.DipoleFixed(info, data, times, nave, aspect_kind, comment='', *, verbose=None)

Dipole class for fixed-position dipole fits.

Note

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

Parameters:

infomne.Info

The mne.Info object with information about the sensors and methods of measurement.

dataarray, shape (n_channels, n_times)

The dipole data.

timesarray, shape (n_times,)

The time points.

naveint

Number of averages.

aspect_kindint

The kind of data.

commentstr

The dipole comment.

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.

Attributes:

ch_names

Channel names.

times

Time vector in seconds.

tmax

Last time point.

tmin

First time point.

Methods

copy()	Copy the DipoleFixed object.
crop([tmin, tmax, include_tmax, verbose])	Crop data to a given time interval.
decimate(decim[, offset, verbose])	Decimate the time-series data.
plot([show, time_unit])	Plot dipole data.
save(fname[, verbose])	Save dipole in a .fif file.
shift_time(tshift[, relative])	Shift time scale in epoched or evoked data.
time_as_index(times[, use_rounding])	Convert time to indices.

See also

read_dipole

Dipole

fit_dipole

Notes

This class is for fixed-position dipole fits, where the position (and maybe orientation) is static over time. For sequential dipole fits, where the position can change a function of time, use mne.Dipole.

New in v0.12.

property ch_names

Channel names.

copy()

Copy the DipoleFixed object.

Returns:

instinstance of DipoleFixed

The copy.

Notes

New in v0.16.

crop(tmin=None, tmax=None, include_tmax=True, verbose=None)

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 v0.19.

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:

instinstance of Raw, Epochs, Evoked, AverageTFR, or SourceEstimate

The cropped time-series object, modified in-place.

Notes

Unlike Python slices, MNE time intervals by default include both their end points; crop(tmin, tmax) returns the interval tmin <= t <= tmax. Pass include_tmax=False to specify the half-open interval tmin <= t < tmax instead.

decimate(decim, offset=0, *, verbose=None)

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. See Resampling and decimating data for more information.

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 v0.12.

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.

See also

mne.Epochs.resample

mne.io.Raw.resample

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 v0.10.0.

References

[1]

Alan V. Oppenheim, Ronald W. Schafer, and John R. Buck. Discrete-Time Signal Processing. Prentice Hall, Upper Saddle River, NJ, 2 edition edition, 1999. ISBN 978-0-13-754920-7.

[2]

Ronald E. Crochiere and Lawrence R. Rabiner. Multirate Digital Signal Processing. Pearson, Englewood Cliffs, NJ, 1 edition edition, 1983. ISBN 978-0-13-605162-6.

plot(show=True, time_unit='s')

Plot dipole data.

Parameters:

showbool

Call pyplot.show() at the end or not.

time_unitstr

The units for the time axis, can be “ms” or “s” (default).

New in v0.16.

Returns:

figinstance of matplotlib.figure.Figure

The figure containing the time courses.

save(fname, verbose=None)

Save dipole in a .fif file.

Parameters:

fnamepath-like

The name of the .fif file. Must end with '.fif' or '.fif.gz' to make it explicit that the file contains dipole information in FIF format.

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.

shift_time(tshift, relative=True)

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)

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.