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mne.viz.plot_arrowmap#

mne.viz.plot_arrowmap(data, info_from, info_to=None, scale=3e-10, vlim=(None, None), cnorm=None, cmap=None, sensors=True, res=64, axes=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.

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 3e-10

To scale the arrows.

vlimtuple of length 2

Colormap limits to use. If a tuple of floats, specifies the lower and upper bounds of the colormap (in that order); providing None for either entry will set the corresponding boundary at the min/max of the data. Defaults to (None, None).

New in version 1.2.

cnormmatplotlib.colors.Normalize | None

How to normalize the colormap. If None, standard linear normalization is performed. If not None, vmin and vmax will be ignored. See Matplotlib docs for more details on colormap normalization, and the ERDs example for an example of its use.

New in version 1.2.

cmapmatplotlib colormap | None

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

sensorsbool | str

Whether to add markers for sensor locations. If str, should be a valid matplotlib format string (e.g., 'r+' for red plusses, see the Notes section of plot()). If True (the default), black circles will be used.

resint

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

axesinstance of Axes | None

The axes to plot to. If None, a new Figure will be created. Default is None.

show_namesbool | callable()

If True, show channel names next to each sensor marker. If callable, 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 non-masked sensor names 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’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. 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_like

The number of contour lines to draw. If 0, no contours will be drawn. If a positive integer, that number of contour levels are chosen using the matplotlib tick locator (may sometimes be inaccurate, use array for accuracy). If array-like, the array values are used as the contour levels. The values should be in µV for EEG, fT for magnetometers and fT/m for gradiometers. If colorbar=True, the colorbar will have ticks corresponding to the contour levels. Default is 6.

image_interpstr

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.

showbool

Show the 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.

extrapolatestr

Options:

  • '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.

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.

Returns:
figmatplotlib.figure.Figure

The Figure of the plot.

Notes

New in version 0.17.

References

Examples using mne.viz.plot_arrowmap#

Visualizing Evoked data

Visualizing Evoked data

Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data
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