This documentation is for development version 0.18.dev0.

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.

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