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
- dataarray_like
The data.
- names
list
ofstr
Channel names.
- bads
list
ofstr
Bad channels.
- projs
list
Projection vectors.
- nfree
int
Degrees of freedom.
- eigarray_like |
None
Eigenvalues.
- eigvecarray_like |
None
Eigenvectors.
- method
str
|None
The method used to compute the covariance.
- loglik
float
The log likelihood.
- Attributes
Methods
__add__
(self, cov)Add Covariance taking into account number of degrees of freedom.
__contains__
(self, key, /)True if the dictionary has the specified key, else False.
x.__getitem__(y) <==> x[y]
__iter__
(self, /)Implement iter(self).
__len__
(self, /)Return len(self).
as_diag
(self)Set covariance to be processed as being diagonal.
clear
()copy
(self)Copy the Covariance object.
fromkeys
(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
(self, info[, exclude, colorbar, proj, …])Plot Covariance data.
pop
()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
(self, 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
()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
()-
__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
(self)[source]¶ Set covariance to be processed as being diagonal.
- Returns
- cov
dict
The covariance.
- cov
Notes
This function allows creation of inverse operators equivalent to using the old “–diagnoise” mne option.
-
property
ch_names
¶ Channel names.
-
clear
()¶
-
copy
(self)[source]¶ Copy the Covariance object.
- Returns
- covinstance of
Covariance
The copied object.
- covinstance of
-
property
data
¶ Numpy array of Noise covariance matrix.
-
fromkeys
(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
()¶
-
keys
()¶
-
property
nfree
¶ Number of degrees of freedom.
-
plot
(self, info, exclude=[], colorbar=True, proj=False, show_svd=True, show=True, verbose=None)[source]¶ Plot Covariance data.
- Parameters
- info: dict
Measurement info.
- exclude
list
ofstr
|str
List of channels to exclude. If empty do not exclude any channel. If ‘bads’, exclude info[‘bads’].
- colorbarbool
Show colorbar or not.
- projbool
Apply projections or not.
- show_svdbool
Plot also singular values of the noise covariance for each sensor type. We show square roots ie. standard deviations.
- showbool
Show figure if True.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
- Returns
- fig_covinstance of
matplotlib.figure.Figure
The covariance plot.
- fig_svdinstance of
matplotlib.figure.Figure
|None
The SVD spectra plot of the covariance.
- fig_covinstance of
See also
Notes
For each channel type, the rank is estimated using
mne.compute_rank()
.Changed in version 0.19: Approximate ranks for each channel type are shown with red dashed lines.
Examples using
plot
:
-
pop
()¶ 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
(self, fname)[source]¶ Save covariance matrix in a FIF file.
- Parameters
- fname
str
Output filename.
- fname
-
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
()¶ 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
()¶