mne_connectivity.SpectralConnectivity#
 class mne_connectivity.SpectralConnectivity(data, freqs, n_nodes, names=None, indices='all', method=None, spec_method=None, n_epochs_used=None, **kwargs)[source]#
Spectral connectivity class.
This class stores connectivity data that varies over frequencies. The underlying data is an array of shape (n_connections, n_freqs), or (n_nodes, n_nodes, n_freqs).
 Parameters
data :
np.ndarray
([epochs], n_estimated_nodes, [freqs], [times])The connectivity data that is a raveled array of
(n_estimated_nodes, ...)
shape. Then_estimated_nodes
is equal ton_nodes_in * n_nodes_out
if one is computing the full connectivity, or a subset of nodes equal to the length ofindices
passed in.freqs :
list
np.ndarray
The frequencies at which the connectivity data is computed over. If the frequencies are “frequency bands” (i.e. gamma band), then these are the median of those bands.
n_nodes :
int
The number of nodes in the dataset used to compute connectivity. This should be equal to the number of signals in the original dataset.
names :
list
np.ndarray
None
The names of the nodes of the dataset used to compute connectivity. If ‘None’ (default), then names will be a list of integers from 0 to
n_nodes
. If a list of names, then it must be equal in length ton_nodes
.indices :
tuple
of arrays str
None
The indices of relevant connectivity data. If
'all'
(default), then data is connectivity between all nodes. If'symmetric'
, then data is symmetric connectivity between all nodes. If a tuple, then the first list represents the “in nodes”, and the second list represents the “out nodes”. See “Notes” for more information.method :
str
, optionalThe method name used to compute connectivity.
spec_method :
str
, optionalThe type of method used to compute spectral analysis, by default None.
n_epochs_used :
int
, optionalThe number of epochs used in the computation of connectivity, by default None.
**kwargs :
dict
Extra connectivity parameters. These may include
freqs
for spectral connectivity, and/ortimes
for connectivity over time. In addition, these may include extra parameters that are stored as xarrayattrs
.
 Attributes

Xarray attributes of connectivity.
Generate block companion matrix.
The coordinates of the xarray data.
The dimensions of the xarray data.
The frequency points of the connectivity data.
Indices of connectivity data.
The method used to compute connectivity.
The number of epochs the connectivity data varies over.
Number of epochs used in computation of connectivity.
The number of nodes in the original dataset.
Node names.
Shape of raveled connectivity.
Xarray of the connectivity data.
Methods
append
(epoch_conn)Append another connectivity structure.
combine
([combine])Combine connectivity data over epochs.
get_data
([output])Get connectivity data as a numpy array.
plot_circle
(**kwargs)Visualize connectivity as a circular graph.
predict
(data)Predict samples on actual data.
rename_nodes
(mapping)Rename nodes.
save
(fname)Save connectivity data to disk.
simulate
(n_samples[, noise_func, random_state])Simulate vector autoregressive (VAR) model.
copy
eigvals
get_epoch_annotations
is_stable
 append(epoch_conn)#
Append another connectivity structure.
 Parameters
epoch_conn : instance of
Connectivity
The Epoched Connectivity class to append.
 Returns
self : instance of
Connectivity
The altered Epoched Connectivity class.
 property attrs#
Xarray attributes of connectivity.
See
xarray
’sattrs
.
 combine(combine='mean')#
Combine connectivity data over epochs.
 Parameters
combine : ‘mean’  ‘median’ 
callable()
How to combine correlation estimates across epochs. Default is ‘mean’. If callable, it must accept one positional input. For example:
combine = lambda data: np.median(data, axis=0)
 Returns
conn : instance of
Connectivity
The combined connectivity data structure.
 property companion#
Generate block companion matrix.
Returns the data matrix if the model is VAR(1).
 property coords#
The coordinates of the xarray data.
 property dims#
The dimensions of the xarray data.
 property freqs#
The frequency points of the connectivity data.
If these are computed over a frequency band, it will be the median frequency of the frequency band.
 get_data(output='compact')#
Get connectivity data as a numpy array.
 Parameters
output :
str
, optionalHow to format the output, by default ‘raveled’, which will represent each connectivity matrix as a
(n_nodes_in * n_nodes_out,)
list. If ‘dense’, then will return each connectivity matrix as a 2D array. If ‘compact’ (default) then will return ‘raveled’ ifindices
were defined as a list of tuples, ordense
if indices is ‘all’. Returns
data :
np.ndarray
The output connectivity data.
 property indices#
Indices of connectivity data.
 property method#
The method used to compute connectivity.
 property n_epochs#
The number of epochs the connectivity data varies over.
 property n_epochs_used#
Number of epochs used in computation of connectivity.
Can be ‘None’, if there was no epochs used. This is equivalent to the number of epochs, if there is no combining of epochs.
 property n_nodes#
The number of nodes in the original dataset.
Even if
indices
defines a subset of nodes that were computed, this should be the total number of nodes in the original dataset.
 property names#
Node names.
 plot_circle(**kwargs)#
Visualize connectivity as a circular graph.
 Parameters

Node names. The order corresponds to the order in con.
indices :
tuple
ofarray
None
Two arrays with indices of connections for which the connections strengths are defined in con. Only needed if con is a 1D array.
If not None, only the n_lines strongest connections (strength=abs(con)) are drawn.
node_angles :
array
, shape (n_node_names,) None
Array with node positions in degrees. If None, the nodes are equally spaced on the circle. See mne.viz.circular_layout.
Width of each node in degrees. If None, the minimum angle between any two nodes is used as the width.
node_colors :
list
oftuple
list
ofstr
List with the color to use for each node. If fewer colors than nodes are provided, the colors will be repeated. Any color supported by matplotlib can be used, e.g., RGBA tuples, named colors.
facecolor :
str
Color to use for background. See matplotlib.colors.
textcolor :
str
Color to use for text. See matplotlib.colors.
node_edgecolor :
str
Color to use for lines around nodes. See matplotlib.colors.
linewidth :
float
Line width to use for connections.
colormap :
str
 instance ofmatplotlib.colors.LinearSegmentedColormap
Colormap to use for coloring the connections.
Minimum value for colormap. If None, it is determined automatically.
Maximum value for colormap. If None, it is determined automatically.
colorbar : bool
Display a colorbar or not.
title :
str
The figure title.
colorbar_size :
float
Size of the colorbar.
colorbar_pos :
tuple
, shape (2,)Position of the colorbar.
fontsize_title :
int
Font size to use for title.
fontsize_names :
int
Font size to use for node names.
fontsize_colorbar :
int
Font size to use for colorbar.
padding :
float
Space to add around figure to accommodate long labels.
fig :
None
 instance ofmatplotlib.figure.Figure
The figure to use. If None, a new figure with the specified background color will be created.
subplot :
int
tuple
, shape (3,)Location of the subplot when creating figures with multiple plots. E.g. 121 or (1, 2, 1) for 1 row, 2 columns, plot 1. See matplotlib.pyplot.subplot.
interactive : bool
When enabled, leftclick on a node to show only connections to that node. Rightclick shows all connections.
node_linewidth :
float
Line with for nodes.
show : bool
Show figure if True.
 Returns
fig : instance of
matplotlib.figure.Figure
The figure handle.
axes : instance of
matplotlib.projections.polar.PolarAxes
The subplot handle.
Notes
This code is based on a circle graph example by Nicolas P. Rougier
By default,
matplotlib.pyplot.savefig()
does not takefacecolor
into account when saving, even if set when a figure is generated. This can be addressed via, e.g.:>>> fig.savefig(fname_fig, facecolor='black')
If
facecolor
is not set viamatplotlib.pyplot.savefig()
, the figure labels, title, and legend may be cut off in the output figure.
 predict(data)#
Predict samples on actual data.
The result of this function is used for calculating the residuals.
 Parameters
data :
array
Epoched or continuous data set. Has shape (n_epochs, n_signals, n_times) or (n_signals, n_times).
 Returns
predicted :
array
Data as predicted by the VAR model of shape same as
data
.
Notes
Residuals are obtained by r = x  var.predict(x).
To compute residual covariances:
# compute the covariance of the residuals # row are observations, columns are variables t = residuals.shape[0] sampled_residuals = np.concatenate( np.split(residuals[:, :, lags:], t, 0), axis=2 ).squeeze(0) rescov = np.cov(sampled_residuals)
 rename_nodes(mapping)#
Rename nodes.
 Parameters
mapping :
dict
Mapping from original node names (keys) to new node names (values).
 save(fname)#
Save connectivity data to disk.
 Parameters
fname :
str
pathlib.Path
The filepath to save the data. Data is saved as netCDF files (
.nc
extension).
 property shape#
Shape of raveled connectivity.
 simulate(n_samples, noise_func=None, random_state=None)#
Simulate vector autoregressive (VAR) model.
This function generates data from the VAR model.
 Parameters
n_samples :
int
Number of samples to generate.
noise_func : func, optional
This function is used to create the generating noise process. If set to None, Gaussian white noise with zero mean and unit variance is used.
random_state :
None
int
 instance ofRandomState
If
random_state
is anint
, it will be used as a seed forRandomState
. IfNone
, the seed will be obtained from the operating system (seeRandomState
for details). Default isNone
. Returns
data :
array
, shape (n_samples, n_channels)Generated data.
 property xarray#
Xarray of the connectivity data.