mne.Covariance¶

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

Noise covariance matrix.

Warning

This class should not be instantiated directly, but instead should be created using a covariance reading or computation function.

Parameters:	data : array-like The data. names : list of str Channel names. bads : list of str Bad channels. projs : list Projection vectors. nfree : int Degrees of freedom. eig : array-like | None Eigenvalues. eigvec : array-like | None Eigenvectors. method : str | None The method used to compute the covariance. loglik : float The log likelihood.

See also

compute_covariance, compute_raw_covariance, make_ad_hoc_cov, read_cov

Attributes:	data : array of shape (n_channels, n_channels) Numpy array of Noise covariance matrix. ch_names : list of string Channel names. nfree : int Number of degrees of freedom. dim : int The number of channels n_channels.

Methods

__add__(cov)	Add Covariance taking into account number of degrees of freedom.
__contains__($self, key, /)	True if the dictionary has the specified key, else False.
__getitem__	x.__getitem__(y) <==> x[y]
__iter__($self, /)	Implement iter(self).
__len__($self, /)	Return len(self).
as_diag()	Set covariance to be processed as being diagonal.
clear()
copy()	Copy the Covariance object.
fromkeys($type, iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get($self, key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
plot(info[, exclude, colorbar, proj, …])	Plot Covariance data.
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised
popitem()	2-tuple; but raise KeyError if D is empty.
save(fname)	Save covariance matrix in a FIF file.
setdefault($self, 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()

__add__(cov)[source]¶

Add Covariance taking into account number of degrees of freedom.

__contains__($self, key, /)¶

True if the dictionary has the specified key, else False.

__getitem__()¶

x.__getitem__(y) <==> x[y]

__iter__($self, /)¶

Implement iter(self).

__len__($self, /)¶

Return len(self).

as_diag()[source]¶

Set covariance to be processed as being diagonal.

Returns:	cov : dict The covariance.

Notes

This function allows creation of inverse operators equivalent to using the old “–diagnoise” mne option.

ch_names¶

Channel names.

clear() → None. Remove all items from D.¶

copy()[source]¶

Copy the Covariance object.

Returns:	cov : instance of Covariance The copied object.

data¶

Numpy array of Noise covariance matrix.

fromkeys($type, iterable, value=None, /)¶

Create a new dictionary with keys from iterable and values set to value.

get($self, key, default=None, /)¶

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

items() → a set-like object providing a view on D's items¶

keys() → a set-like object providing a view on D's keys¶

nfree¶

Number of degrees of freedom.

plot(info, exclude=[], colorbar=True, proj=False, show_svd=True, show=True, verbose=None)[source]¶

Plot Covariance data.

Parameters:

info: dict Measurement info. exclude : list of string | str List of channels to exclude. If empty do not exclude any channel. If ‘bads’, exclude info[‘bads’]. colorbar : bool Show colorbar or not. proj : bool Apply projections or not. show_svd : bool Plot also singular values of the noise covariance for each sensor type. We show square roots ie. standard deviations. show : bool Show figure if True. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

fig_cov : instance of matplotlib.figure.Figure The covariance plot. fig_svd : instance of matplotlib.figure.Figure | None The SVD spectra plot of the covariance.

pop(k[, d]) → v, remove specified key and return the corresponding value.¶

If key is not found, d is returned if given, otherwise KeyError is raised

popitem() → (k, v), remove and return some (key, value) pair as a¶

2-tuple; but raise KeyError if D is empty.

save(fname)[source]¶

Save covariance matrix in a FIF file.

Parameters:	fname : str Output filename.

setdefault($self, 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¶