mne.Covariance#

class mne.Covariance(data, names, bads, projs, nfree, eig=None, eigvec=None, method=None, loglik=None, *, verbose=None)[source]#

Noise covariance matrix.

Note

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

Parameters:

dataarray_like: The data.
nameslist of str: Channel names.
badslist of str: Bad channels.
projslist: Projection vectors.
nfreeint: Degrees of freedom.
eigarray_like | None: Eigenvalues.
eigvecarray_like | None: Eigenvectors.
methodstr | None: The method used to compute the covariance.
loglikfloat: The log likelihood.
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:

dataarray of shape (n_channels, n_channels): Numpy array of Noise covariance matrix.
ch_nameslist of str: Channel names.
nfreeint: Number of degrees of freedom.
dimint: The number of channels n_channels.

Methods

`__add__`(cov)	Add Covariance taking into account number of degrees of freedom.
`__contains__`(key, /)	True if the dictionary has the specified key, else False.
`__getitem__`(key, /)	Return self[key].
`__iter__`(/)	Implement iter(self).
`__len__`(/)	Return len(self).
`as_diag`()	Set covariance to be processed as being diagonal.
`clear`()
`copy`()	Copy the Covariance object.
`fromkeys`(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
`get`(key[, default])	Return the value for key if key is in the dictionary, else default.
`items`()
`keys`()
`pick_channels`(ch_names[, ordered, verbose])	Pick channels from this covariance matrix.
`plot`(info[, exclude, colorbar, proj, ...])	Plot Covariance data.
`plot_topomap`(info[, ch_type, scalings, ...])	Plot a topomap of the covariance diagonal.
`pop`(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
`popitem`(/)	Remove and return a (key, value) pair as a 2-tuple.
`save`(fname, *[, overwrite, verbose])	Save covariance matrix in a FIF file.
`setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`update`([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`values`()

See also

mne.compute_rank

Notes

For each channel type, the rank is estimated using mne.compute_rank().

Changed in version 0.19: Approximate ranks for each channel type are shown with red dashed lines.

Examples using plot:

Working with CTF data: the Brainstorm auditory dataset

Computing a covariance matrix

Source reconstruction using an LCMV beamformer

Reading/Writing a noise covariance matrix

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

plot_topomap(info, ch_type=None, *, scalings=None, proj=False, noise_cov=None, sensors=True, show_names=False, mask=None, mask_params=None, contours=6, outlines='head', sphere=None, image_interp='cubic', extrapolate='auto', border='mean', res=64, size=1, cmap=None, vlim=(None, None), cnorm=None, colorbar=True, cbar_fmt='%3.1f', units=None, axes=None, show=True, verbose=None)[source]#

Plot a topomap of the covariance diagonal.

Parameters:

infomne.Info

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

The channel type to plot. For 'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If None the first available channel type from order shown above is used. Defaults to None.

New in v0.21.

scalingsdict | float | None

The scalings of the channel types to be applied for plotting. If None, defaults to dict(eeg=1e6, grad=1e13, mag=1e15).

projbool | ‘interactive’ | ‘reconstruct’

If true SSP projections are applied before display. If 'interactive', a check box for reversible selection of SSP projection vectors will be shown. If 'reconstruct', projection vectors will be applied and then M/EEG data will be reconstructed via field mapping to reduce the signal bias caused by projection.

Changed in version 0.21: Support for ‘reconstruct’ was added.

noise_covinstance of Covariance | None

If not None, whiten the instance with noise_cov before plotting.

sensorsbool | str

Whether to add markers for sensor locations. If str, should be a valid matplotlib format string (e.g., 'r+' for red plusses, see the Notes section of plot()). If True (the default), black circles will be used.

show_namesbool | callable()

If True, show channel names next to each sensor marker. If callable, channel names will be formatted using the callable; e.g., to delete the prefix ‘MEG ‘ from all channel names, pass the function lambda x: x.replace('MEG ', ''). If mask is not None, only non-masked sensor names will be shown.

maskndarray of bool, shape (n_channels,) | None

Array indicating channel(s) to highlight with a distinct plotting style. Array elements set to True will be plotted with the parameters given in mask_params. Defaults to None, equivalent to an array of all False elements.

mask_paramsdict | None

Additional plotting parameters for plotting significant sensors. Default (None) equals:

dict(marker='o', markerfacecolor='w', markeredgecolor='k',
        linewidth=0, markersize=4)

contoursint | array_like

The number of contour lines to draw. If 0, no contours will be drawn. If a positive integer, that number of contour levels are chosen using the matplotlib tick locator (may sometimes be inaccurate, use array for accuracy). If array-like, the array values are used as the contour levels. The values should be in µV for EEG, fT for magnetometers and fT/m for gradiometers. If colorbar=True, the colorbar will have ticks corresponding to the contour levels. Default is 6.

outlines‘head’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.

The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in v0.20.

Changed in version 1.1: Added 'eeglab' option.

image_interpstr

The image interpolation to be used. Options are 'cubic' (default) to use scipy.interpolate.CloughTocher2DInterpolator, 'nearest' to use scipy.spatial.Voronoi or 'linear' to use scipy.interpolate.LinearNDInterpolator.

extrapolatestr

Options:

'box'
Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.
'local' (default for MEG sensors)
Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.
'head' (default for non-MEG sensors)
Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.

Changed in version 0.21:

The default was changed to 'local' for MEG sensors.
'local' was changed to use a convex hull mask
'head' was changed to extrapolate out to the clipping circle.

borderfloat | ‘mean’

Value to extrapolate to on the topomap borders. If 'mean' (default), then each extrapolated point has the average value of its neighbours.

New in v0.20.

resint

The resolution of the topomap image (number of pixels along each side).

sizefloat

Side length of each subplot in inches.

cmapmatplotlib colormap | (colormap, bool) | ‘interactive’ | None

Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, 'Reds' is used for data that is either all-positive or all-negative, and 'RdBu_r' is used otherwise. 'interactive' is equivalent to (None, True). Defaults to None.

Warning

Interactive mode works smoothly only for a small amount of topomaps. Interactive mode is disabled by default for more than 2 topomaps.

vlimtuple of length 2

Lower and upper bounds of the colormap, typically a numeric value in the same units as the data. If both entries are None, the bounds are set at (min(data), max(data)). Providing None for just one entry will set the corresponding boundary at the min/max of the data. Defaults to (None, None).

New in v1.2.

cnormmatplotlib.colors.Normalize | None

How to normalize the colormap. If None, standard linear normalization is performed. If not None, vmin and vmax will be ignored. See Matplotlib docs for more details on colormap normalization, and the ERDs example for an example of its use.

New in v1.2.

colorbarbool

Plot a colorbar in the rightmost column of the figure.

cbar_fmtstr

Formatting string for colorbar tick labels. See Format Specification Mini-Language for details.

unitsdict | str | None

The units to use for the colorbar label. Ignored if colorbar=False. If None and scalings=None the unit is automatically determined, otherwise the label will be “AU” indicating arbitrary units. Default is None.

axesinstance of Axes | list of Axes | None

The axes to plot into. If None, a new Figure will be created with the correct number of axes. If Axes are provided (either as a single instance or a list of axes), the number of axes provided must be length 1. Default is None.

showbool

Show the figure if True.

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 Figure: The matplotlib figure.

Notes

New in v0.21.

Examples using plot_topomap:

Compute source power estimate by projecting the covariance with MNE

pop(key, default=<unrepresentable>, /)#: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

save(fname, *, overwrite=False, verbose=None)[source]#

Save covariance matrix in a FIF file.

Parameters:

fnamepath-like: Output filename.
overwritebool: If True (default False), overwrite the destination file if it exists.

New in v1.0.
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.

setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.#: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values#

Examples using `mne.Covariance`#

Getting started with mne.Report

Working with CTF data: the Brainstorm auditory dataset

Repairing artifacts with ICA

Preprocessing optically pumped magnetometer (OPM) MEG data

Plotting whitened data

Computing a covariance matrix

Source localization with MNE, dSPM, sLORETA, and eLORETA

The role of dipole orientations in distributed source localization

Computing various MNE solutions

Source reconstruction using an LCMV beamformer

EEG source localization given electrode locations on an MRI

Brainstorm Elekta phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

KIT phantom dataset tutorial

Decoding (MVPA)

Corrupt known signal with point spread

DICS for power mapping

Reading/Writing a noise covariance matrix

Compare simulated and estimated source activity

Generate simulated evoked data

Generate simulated raw data

Simulate raw data using subject anatomy

Generate simulated source data

Cortical Signal Suppression (CSS) for removal of cortical signals

XDAWN Denoising

Whitening evoked data with a noise covariance

Compute source power spectral density (PSD) of VectorView and OPM data

Decoding source space data

Source localization with a custom inverse solver

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute MNE inverse solution on evoked data with a mixed source space

Compute source power estimate by projecting the covariance with MNE

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

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Compute cross-talk functions for LCMV beamformers

Plot point-spread functions (PSFs) for a volume

Compute Rap-Music on evoked data

Compute spatial resolution metrics in source space

Compute spatial resolution metrics to compare MEG with EEG+MEG

Computing source space SNR

Compute MxNE with time-frequency sparse prior

Compute Trap-Music on evoked data

Kernel OPM phantom data

From raw data to dSPM on SPM Faces dataset

mne.Covariance#

Examples using mne.Covariance#

Examples using `mne.Covariance`#