Noise covariance matrix.
Warning
This class should not be instantiated directly, but instead should be created using a covariance reading or computation function.
The data.
list
of str
Channel names.
list
of str
Bad channels.
list
Projection vectors.
int
Degrees of freedom.
None
Eigenvalues.
None
Eigenvectors.
str
| None
The method used to compute the covariance.
float
The log likelihood.
str
| int
| None
Control verbosity of the logging output. If None
, use the default
verbosity level. See the logging documentation and
mne.verbose()
for details. Should only be passed as a keyword
argument.
Methods
|
Add Covariance taking into account number of degrees of freedom. |
|
True if the dictionary has the specified key, else False. |
x.__getitem__(y) <==> x[y] |
|
|
Implement iter(self). |
|
Return len(self). |
|
Set covariance to be processed as being diagonal. |
|
|
|
Copy the Covariance object. |
|
Create a new dictionary with keys from iterable and values set to value. |
|
Return the value for key if key is in the dictionary, else default. |
|
|
|
|
|
Pick channels from this covariance matrix. |
|
Plot Covariance data. |
|
Plot a topomap of the covariance diagonal. |
|
If key is not found, d is returned if given, otherwise KeyError is raised |
|
Remove and return a (key, value) pair as a 2-tuple. |
|
Save covariance matrix in a FIF file. |
|
Insert key with a value of default if key is not in the dictionary. |
|
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] |
|
True if the dictionary has the specified key, else False.
x.__getitem__(y) <==> x[y]
Implement iter(self).
Return len(self).
Set covariance to be processed as being diagonal.
dict
The covariance.
Notes
This function allows creation of inverse operators equivalent to using the old “–diagnoise” mne option.
This function operates in place.
Channel names.
Copy the Covariance object.
Covariance
The copied object.
Examples using copy
:
Computing source timecourses with an XFit-like multi-dipole model
Numpy array of Noise covariance matrix.
Create a new dictionary with keys from iterable and values set to value.
Return the value for key if key is in the dictionary, else default.
Number of degrees of freedom.
Pick channels from this covariance matrix.
The modified covariance matrix.
Notes
Operates in-place.
New in version 0.20.0.
Plot Covariance data.
mne.Info
The mne.Info
object with information about the sensors and methods of measurement.
list
of str
| str
List of channels to exclude. If empty do not exclude any channel. If ‘bads’, exclude info[‘bads’].
Show colorbar or not.
Apply projections or not.
Plot also singular values of the noise covariance for each sensor type. We show square roots ie. standard deviations.
Show figure if True.
str
| int
| None
Control verbosity of the logging output. If None
, use the default
verbosity level. See the logging documentation and
mne.verbose()
for details. Should only be passed as a keyword
argument.
matplotlib.figure.Figure
The covariance plot.
matplotlib.figure.Figure
| None
The SVD spectra plot of the covariance.
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
:
Working with CTF data: the Brainstorm auditory dataset
Source reconstruction using an LCMV beamformer
Reading/Writing a noise covariance matrix
Plot a topomap of the covariance diagonal.
mne.Info
The mne.Info
object with information about the sensors and methods of measurement.
str
The channel type being plotted. Determines the 'auto'
extrapolation mode.
New in version 0.21.
float
| callable()
| None
Lower and upper bounds of the colormap, in the same units as the data.
If vmin
and vmax
are both None
, they are set at ± the
maximum absolute value of the data (yielding a colormap with midpoint
at 0). If only one of vmin
, vmax
is None
, will use
min(data)
or max(data)
, respectively. If callable, should
accept a NumPy array
of data and return a
float.
None
Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range (zoom). The mouse scroll can also be used to adjust the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None (default), ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’. If ‘interactive’, translates to (None, True).
Warning
Interactive mode works smoothly only for a small amount of topomaps. Interactive mode is disabled by default for more than 2 topomaps.
str
Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., ‘r+’ for red plusses). If True (default), circles will be used.
Plot a colorbar in the rightmost column of the figure.
dict
| float
| None
The scalings of the channel types to be applied for plotting.
If None, defaults to dict(eeg=1e6, grad=1e13, mag=1e15)
.
dict
| str
| None
The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined.
int
The resolution of the topomap image (n pixels along each side).
float
Side length per topomap in inches.
str
String format for colorbar values.
If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be shown. If ‘reconstruct’, projection vectors will be applied and then M/EEG data will be reconstructed via field mapping to reduce the signal bias caused by projection.
Changed in version 0.21: Support for ‘reconstruct’ was added.
Show the figure if True
.
callable()
If True, show channel names on top of the map. If a callable is
passed, channel names will be formatted using the callable; e.g., to
delete the prefix ‘MEG ‘ from all channel names, pass the function
lambda x: x.replace('MEG ', '')
. If mask
is not None, only
significant sensors will be shown.
str
| None
The title of the generated figure. If None
(default), no title is
displayed.
ndarray
of bool, shape (n_channels,) | None
Array indicating channel(s) to highlight with a distinct
plotting style. Array elements set to True
will be plotted
with the parameters given in mask_params
. Defaults to None
,
equivalent to an array of all False
elements.
dict
| None
Additional plotting parameters for plotting significant sensors. Default (None) equals:
dict(marker='o', markerfacecolor='w', markeredgecolor='k',
linewidth=0, markersize=4)
dict
| None
The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If ‘skirt’ the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.
int
| array
of float
The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. The values are in µV for EEG, fT for magnetometers and fT/m for gradiometers. If colorbar=True, the ticks in colorbar correspond to the contour levels. Defaults to 6.
str
The image interpolation to be used. Options are 'cubic'
(default)
to use scipy.interpolate.CloughTocher2DInterpolator
,
'nearest'
to use scipy.spatial.Voronoi
or
'linear'
to use scipy.interpolate.LinearNDInterpolator
.
Axes
| list
| None
The axes to plot to. If list, the list must be a list of Axes of the
same length as times
(unless times
is None). If instance of
Axes, times
must be a float or a list of one float.
Defaults to None.
str
Options:
'box'
Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.
'local'
(default for MEG sensors)Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.
'head'
(default for non-MEG sensors)Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.
Changed in version 0.21:
The default was changed to 'local'
for MEG sensors.
'local'
was changed to use a convex hull mask
'head'
was changed to extrapolate out to the clipping circle.
float
| array-like | instance of ConductorModel
| None
| ‘auto’ | ‘eeglab’The sphere parameters to use for the head outline. Can be array-like of
shape (4,) to give the X/Y/Z origin and radius in meters, or a single float
to give just the radius (origin assumed 0, 0, 0). Can also be an instance
of a spherical ConductorModel
to use the origin and
radius from that object. If 'auto'
the sphere is fit to digitization
points. If 'eeglab'
the head circle is defined by EEG electrodes
'Fpz'
, 'Oz'
, 'T7'
, and 'T8'
(if 'Fpz'
is not present,
it will be approximated from the coordinates of 'Oz'
). None
(the
default) is equivalent to 'auto'
when enough extra digitization points
are available, and (0, 0, 0, 0.095) otherwise. Currently the head radius
does not affect plotting.
New in version 0.20.
Changed in version 1.1: Added 'eeglab'
option.
float
| ‘mean’Value to extrapolate to on the topomap borders. If 'mean'
(default),
then each extrapolated point has the average value of its neighbours.
New in version 0.20.
Covariance
| None
If not None, whiten the instance with noise_cov
before
plotting.
str
| int
| None
Control verbosity of the logging output. If None
, use the default
verbosity level. See the logging documentation and
mne.verbose()
for details. Should only be passed as a keyword
argument.
Figure
The matplotlib figure.
Notes
New in version 0.21.
Examples using plot_topomap
:
Compute source power estimate by projecting the covariance with MNE
If key is not found, d is returned if given, otherwise KeyError is raised
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.
Save covariance matrix in a FIF file.
str
Output filename.
If True (default False), overwrite the destination file if it exists.
New in version 1.0.
str
| int
| None
Control verbosity of the logging output. If None
, use the default
verbosity level. See the logging documentation and
mne.verbose()
for details. Should only be passed as a keyword
argument.
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.
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]
mne.Covariance
#Working with CTF data: the Brainstorm auditory dataset
Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization
Computing various MNE solutions
Source reconstruction using an LCMV beamformer
EEG source localization given electrode locations on an MRI
Brainstorm Elekta phantom dataset tutorial
Brainstorm CTF phantom dataset tutorial
4D Neuroimaging/BTi phantom dataset tutorial
Corrupt known signal with point spread
Reading/Writing a noise covariance matrix
Generate simulated evoked data
Simulate raw data using subject anatomy
Generate simulated source data
Cortical Signal Suppression (CSS) for removal of cortical signals
Whitening evoked data with a noise covariance
Compute source power spectral density (PSD) of VectorView and OPM data
Source localization with a custom inverse solver
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute MNE inverse solution on evoked data with a mixed source space
Compute source power estimate by projecting the covariance with MNE
Computing source timecourses with an XFit-like multi-dipole model
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers
Compute Rap-Music on evoked data
Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG
Compute MxNE with time-frequency sparse prior