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($self, key, default=None, /) Return the value for key if key is in the dictionary, else default. items() → a set-like object providing a view on D's items keys() → a set-like object providing a view on D's keys nfree Number of degrees of freedom. plot(info, exclude=[], colorbar=True, proj=False, show_svd=True, show=True, verbose=None)[source] Plot Covariance data. Parameters: info: dict Measurement info. exclude : list of string | str List of channels to exclude. If empty do not exclude any channel. If ‘bads’, exclude info[‘bads’]. colorbar : bool Show colorbar or not. proj : bool Apply projections or not. show_svd : bool Plot also singular values of the noise covariance for each sensor type. We show square roots ie. standard deviations. show : bool Show figure if True. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more). fig_cov : instance of matplotlib.figure.Figure The covariance plot. fig_svd : instance of matplotlib.figure.Figure | None The SVD spectra plot of the covariance. pop(k[, d]) → v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised popitem() → (k, v), remove and return some (key, value) pair as a 2-tuple; but raise KeyError if D is empty. save(fname)[source] Save covariance matrix in a FIF file. Parameters: fname : str Output filename. setdefault($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