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	Numpy array of Noise covariance matrix.
ch_names	Channel names.
nfree	Number of degrees of freedom.

Methods

__add__(cov)	Add Covariance taking into account number of degrees of freedom.
__contains__((k) -> True if D has a key k, …)
__getitem__	x.__getitem__(y) <==> x[y]
__iter__() <==> iter(x)
__len__() <==> len(x)
as_diag()	Set covariance to be processed as being diagonal.
clear(() -> None.  Remove all items from D.)
copy()	Copy the Covariance object.
fromkeys(…)	v defaults to None.
get((k[,d]) -> D[k] if k in D, …)
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, …)
iteritems(() -> an iterator over the (key, …)
iterkeys(() -> an iterator over the keys of D)
itervalues(…)
keys(() -> list of D’s keys)
plot(info[, exclude, colorbar, proj, …])	Plot Covariance data.
pop((k[,d]) -> v, …)	If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), …)	2-tuple; but raise KeyError if D is empty.
save(fname)	Save covariance matrix in a FIF file.
setdefault((k[,d]) -> D.get(k,d), …)
update(([E, …)	If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)
viewitems(…)
viewkeys(…)
viewvalues(…)

__add__(cov)[source]¶

Add Covariance taking into account number of degrees of freedom.

__contains__(k) → True if D has a key k, else False¶

__getitem__()¶

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

__iter__() <==> iter(x)¶

__len__() <==> len(x)¶

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(S[, v]) → New dict with keys from S and values equal to v.¶

v defaults to None.

get(k[, d]) → D[k] if k in D, else d. d defaults to None.¶

has_key(k) → True if D has a key k, else False¶

items() → list of D’s (key, value) pairs, as 2-tuples¶

iteritems() → an iterator over the (key, value) items of D¶

iterkeys() → an iterator over the keys of D¶

itervalues() → an iterator over the values of D¶

keys() → list of 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.pyplot.Figure The covariance plot. fig_svd : instance of matplotlib.pyplot.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(k[, d]) → D.get(k,d), also set D[k]=d if k not in D¶

update([E, ]**F) → None. Update D from dict/iterable E and F.¶

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, 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() → list of D’s values¶

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

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

viewvalues() → an object providing a view on D’s values¶