mne.viz.plot_arrowmap(data, info_from, info_to=None, scale=3e-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='cubic', show=True, onselect=None, extrapolate='auto', sphere=None)[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 flattened 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.

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 3e-10

To scale the arrows.

vmin, vmaxfloat | 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.

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.


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,) | 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.

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. 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. 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. Defaults to 6.


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.


Show figure if True.

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.



  • '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.

New in version 0.18.

spherefloat | 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.


The Figure of the plot.


New in version 0.17.


Examples using mne.viz.plot_arrowmap#

Visualizing Evoked data

Visualizing Evoked data

Visualizing Evoked data
Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data