# mne.Covariance¶

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

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.

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)

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()

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()

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)

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). 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)

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