eelbrain.Sensor

class eelbrain.Sensor(locations, names=None, sysname=None, proj2d='z root', adjacency='none')[source]

Dimension class for representing sensor information

Parameters:

  • locations (numpy.ndarray) – Sensor locations with shape (n_sensor, 3), in ALS coordinates, i.e., for each sensor a (anterior, left, superior) coordinate triplet.

  • names (list[str]) – Sensor names, same order as locs (default is ['0', '1', '2', ...]).

  • sysname (str) – Name of the sensor system.

  • proj2d (str) – Default 2d projection (default is 'z-root'; for options see notes below).

  • adjacency (AdjacencyArg) –

    Adjacency between elements, as array of int with shape (n_edges, 2). Can be specified as:

    • "none" for no connections

    • "grid" to use adjacency in the sensor names

    • list of connections (e.g., [('OZ', 'O1'), ('OZ', 'O2'), ...])

    • numpy.ndarray of int, shape (n_edges, 2), to specify connections in terms of indices. Each row should specify one connection [i, j] with i < j. If the array’s dtype is uint32, property checks are disabled to improve efficiency.

channel_idx

Dictionary mapping channel names to indexes.

Type:

dict

locations

Spatial position of all sensors, with shape (n_sensors, 3).

Type:

numpy.ndarray

names

Ordered list of sensor names.

Type:

list[str]

right

Sensor position along left-right axis.

Type:

NDVar

anterior

Sensor position along posterior-anterior axis, with shape (n_sensors,).

Type:

numpy.ndarray

superior

Sensor position along inferior-superior axis, with shape (n_sensors,).

Type:

numpy.ndarray

Notes

The following are possible 2d-projections:

'z root':

the radius of each sensor is set to equal the root of the vertical distance from the top of the net.

'cone':

derive x/y coordinate from height based on a cone transformation.

'lower cone':

only use cone for sensors with z < 0.

Axis and sign :

For example, x+ for anterior, x- for posterior.

Examples

>>> locations = [(0,  0,   0),
...              (0, -.25, -.45)]
>>> sensor_dim = Sensor(locations, names=["Cz", "Pz"])

Methods

adjacency()

Retrieve the dimension's adjacency graph

dimindex(arg)

Convert a dimension index to an array index

from_lout([path, transform_2d])

Create a Sensor instance from a *.lout file

from_montage(montage[, channels])

From DigMontage

from_sfp([path])

Create a Sensor instance from an sfp file

from_xyz([path])

Create a Sensor instance from a text file with xyz coordinates

get_adjacency()

Sensor adjacency as list of (name_1, name_2)

get_locs_2d([proj, extent, frame, invisible])

Compute a 2 dimensional projection of the sensor locations

index([include, exclude])

Construct an index for specified sensors

index_into_dim(dim)

Index into a subset dimension

intersect(dim[, check_dims])

Create a Sensor dimension that is the intersection with dim

neighbors(connect_dist)

Find neighboring sensors.

set_adjacency([neighbors, connect_dist])

Define the sensor adjacency through neighbors or distance