mne.viz.plot_ica_components#

mne.viz.plot_ica_components(ica, picks=None, ch_type=None, res=64, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=False, title=None, show=True, outlines='head', contours=6, image_interp='bilinear', inst=None, plot_std=True, topomap_args=None, image_args=None, psd_args=None, reject='auto', sphere=None, *, verbose=None)[source]#

Project mixing matrix on interpolated sensor topography.

Parameters

icainstance of mne.preprocessing.ICA: The ICA solution.
picksint | list of int | slice | None: Indices of the ICA components to visualize. If None, all components are plotted in batches of 20.
ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ | None: The channel type to plot. For ‘grad’, the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then channels are chosen in the order given above.
resint: The resolution of the topomap image (n pixels along each side).
vminfloat | callable() | None: The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data). If callable, the output equals vmin(data). Defaults to None.
vmaxfloat | callable() | None: The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If callable, the output equals vmax(data). Defaults to None.
cmapmatplotlib colormap | (colormap, bool) | ‘interactive’ | 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. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’. If ‘interactive’, translates to (None, True). Defaults to ‘RdBu_r’.

Warning

Interactive mode works smoothly only for a small amount of topomaps.
sensorsbool | 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.
colorbarbool: Plot a colorbar.
titlestr | None: Title to use.
showbool: Show figure if True.
outlines‘head’ | ‘skirt’ | 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’.
contoursint | 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. Defaults to 6.
image_interpstr: The image interpolation to be used. All matplotlib options are accepted.
instRaw | Epochs | None: To be able to see component properties after clicking on component topomap you need to pass relevant data - instances of Raw or Epochs (for example the data that ICA was trained on). This takes effect only when running matplotlib in interactive mode.
plot_stdbool | float: Whether to plot standard deviation in ERP/ERF and spectrum plots. Defaults to True, which plots one standard deviation above/below. If set to float allows to control how many standard deviations are plotted. For example 2.5 will plot 2.5 standard deviation above/below.
topomap_argsdict | None: Dictionary of arguments to plot_topomap. If None, doesn’t pass any additional arguments. Defaults to None.
image_argsdict | None: Dictionary of arguments to plot_epochs_image. If None, doesn’t pass any additional arguments. Defaults to None.
psd_argsdict | None: Dictionary of arguments to psd_multitaper. If None, doesn’t pass any additional arguments. Defaults to None.
reject‘auto’ | dict | None: Allows to specify rejection parameters used to drop epochs (or segments if continuous signal is passed as inst). If None, no rejection is applied. The default is ‘auto’, which applies the rejection parameters used when fitting the ICA object.
spherefloat | array-like | str | None: The sphere parameters to use for the cartoon head. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give the radius (origin assumed 0, 0, 0). Can also be a spherical ConductorModel, which will use the origin and radius. Can be “auto” to use a digitization-based fit. Can also be None (default) to use ‘auto’ when enough extra digitization points are available, and 0.095 otherwise. Currently the head radius does not affect plotting.

New in version 0.20.
verbosebool | 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.

Returns

figinstance of matplotlib.figure.Figure | list of matplotlib.figure.Figure: The figure object(s). Components are plotted on a grid with maximum dimensions of 5⨉4. If more than 20 components are plotted, a new figure will be created for each batch of 20, and a list of those figures will be returned.

Notes

When run in interactive mode, plot_ica_components allows to reject components by clicking on their title label. The state of each component is indicated by its label color (gray: rejected; black: retained). It is also possible to open component properties by clicking on the component topomap (this option is only available when the inst argument is supplied).

mne.viz.plot_ica_sources

mne.viz.plot_ica_properties