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 : array of shape (n_channels, n_channels)

The covariance.

ch_names : list of string

List of channels’ names.

nfree : int

Number of degrees of freedom i.e. number of time points used.

Methods

as_diag([copy]) 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(...)
__init__(data, names, bads, projs, nfree, eig=None, eigvec=None, method=None, loglik=None)

Init of covariance.

as_diag(copy=True)

Set covariance to be processed as being diagonal.

Parameters:

copy : bool

If True, return a modified copy of the covarince. If False, the covariance is modified in place.

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

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

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose).

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)

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