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.
array
, shape (n_channels,)The data values to plot.
Info
The measurement info from data to interpolate from.
Info
| None
The measurement info to interpolate to. If None, it is assumed to be the same as info_from.
float
, default 3e-10To scale the arrows.
float
| 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.
None
Colormap to use. If None, ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’.
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.
int
The resolution of the topomap image (n pixels along each side).
Axes
| None
The axes to plot to. If None, a new figure will be created.
list
| None
List of channel names. If None, channel names are not plotted.
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).
ndarray
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.
dict
| None
Additional plotting parameters for plotting significant sensors. Default (None) equals:
dict(marker='o', markerfacecolor='w', markeredgecolor='k',
linewidth=0, markersize=4)
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’.
int
| 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.
str
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.
callable()
| 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.
str
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.
float
| 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.
matplotlib.figure.Figure
The Figure of the plot.
Notes
New in version 0.17.
References
mne.viz.plot_arrowmap
#Plotting topographic arrowmaps of evoked data