mne.viz.plot_arrowmap

mne.viz.plot_arrowmap(data, info_from, info_to=None, scale=1e-10, vmin=None, vmax=None, cmap=None, sensors=True, res=64, axes=None, names=None, show_names=False, mask=None, mask_params=None, outlines='head', contours=6, image_interp='bilinear', show=True, head_pos=None, onselect=None, extrapolate='box')[source]

Plot arrow map.

Compute arrowmaps, based upon the Hosaka-Cohen transformation [1], these arrows represents an estimation of the current flow underneath the MEG sensors. They are a poor man’s MNE.

Since planar gradiometers takes gradients along latitude and longitude, they need to be projected to the flatten manifold span by magnetometer or radial gradiometers before taking the gradients in the 2D Cartesian coordinate system for visualization on the 2D topoplot. You can use the info_from and info_to parameters to interpolate from gradiometer data to magnetometer data.

Parameters
dataarray, shape (n_channels,)

The data values to plot.

info_frominstance of Info

The measurement info from data to interpolate from.

info_toinstance of Info | None

The measurement info to interpolate to. If None, it is assumed to be the same as info_from.

scalefloat, default 1e-10

To scale the arrows

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 | None

Colormap to use. If None, ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’.

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.

resint

The resolution of the topomap image (n pixels along each side).

axesinstance of Axes | None

The axes to plot to. If None, a new figure will be created.

nameslist | None

List of channel names. If None, channel names are not plotted.

show_namesbool | 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. If True, a list of names must be provided (see names keyword).

maskndarray of bool, shape (n_channels, n_times) | None

The channels to be marked as significant at a given time point. Indices set to True will be considered. Defaults to None.

mask_paramsdict | None

Additional plotting parameters for plotting significant sensors. Default (None) equals:

dict(marker='o', markerfacecolor='w', markeredgecolor='k',
     linewidth=0, markersize=4)
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-axes 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. If an array, the values represent the levels for the contours. The values are in uV for EEG, fT for magnetometers and fT/m for gradiometers. Defaults to 6.

image_interpstr

The image interpolation to be used. All matplotlib options are accepted.

showbool

Show figure if True.

head_posdict | 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.

onselectcallable() | None

Handle for a function that is called when the user selects a set of channels by rectangle selection (matplotlib RectangleSelector). If None interactive selection is disabled. Defaults to None.

extrapolatestr

If ‘box’ (default) 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. If ‘head’ extrapolate to the edges of the head circle (or to the edges of the skirt if outlines='skirt'). If ‘local’ extrapolate only to nearby points (approximately to points closer than median inter-electrode distance).

New in version 0.18.

Returns
figmatplotlib.figure.Figure

The Figure of the plot

Notes

New in version 0.17.

References

1

D. Cohen, H. Hosaka “Part II magnetic field produced by a current dipole”, Journal of electrocardiology, Volume 9, Number 4, pp. 409-417, 1976. DOI: 10.1016/S0022-0736(76)80041-6