ica : instance of mne.preprocessing.ICA
picks : int | array-like | None
The indices of the sources to be plotted.
If None all 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.
res : int
The resolution of the topomap image (n pixels along each side).
layout : None | Layout
Layout instance specifying sensor positions (does not need to
be specified for Neuromag data). If possible, the correct layout is
inferred from the data.
vmin : float | 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.
vmax : float | 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.
cmap : matplotlib 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.
sensors : bool | str
Add markers for sensor locations to the plot. Accepts matplotlib
plot format string (e.g., ‘r+’ for red plusses). If True, a circle
will be used (via .add_artist). Defaults to True.
colorbar : bool
title : str | None
show : bool
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, and the ‘autoshrink’ (bool) field will trigger
automated shrinking of the positions due to points outside the outline.
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’.
contours : int | False | None
The number of contour lines to draw. If 0, no contours will be drawn.
image_interp : str
The image interpolation to be used. All matplotlib options are
accepted.
head_pos : dict | None
If None (default), the sensors are positioned such that they span
the head circle. If dict, can have entries ‘center’ (tuple) and
‘scale’ (tuple) for what the center and scale of the head should be
relative to the electrode locations.
inst : Raw | 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.
|
