mne.preprocessing.eyetracking.Calibration#

class mne.preprocessing.eyetracking.Calibration(*, onset, model, eye, avg_error, max_error, positions, offsets, gaze, screen_size=None, screen_distance=None, screen_resolution=None)[source]#

Eye-tracking calibration info.

This data structure behaves like a dictionary. It contains information regarding a calibration that was conducted during an eye-tracking recording.

Note

When possible, a Calibration instance should be created with a helper function, such as read_eyelink_calibration().

Parameters:
onsetfloat

The onset of the calibration in seconds. If the calibration was performed before the recording started, the the onset can be negative.

modelstr

A string, which is the model of the eye-tracking calibration that was applied. For example 'H3' for a horizontal only 3-point calibration, or 'HV3' for a horizontal and vertical 3-point calibration.

eyestr

The eye that was calibrated. For example, 'left', or 'right'.

avg_errorfloat

The average error in degrees between the calibration positions and the actual gaze position.

max_errorfloat

The maximum error in degrees that occurred between the calibration positions and the actual gaze position.

positionsarray_like of float, shape (n_calibration_points, 2)

The x and y coordinates of the calibration points.

offsetsarray_like of float, shape (n_calibration_points,)

The error in degrees between the calibration position and the actual gaze position for each calibration point.

gazearray_like of float, shape (n_calibration_points, 2)

The x and y coordinates of the actual gaze position for each calibration point.

screen_sizearray_like of shape (2,)

The width and height (in meters) of the screen that the eyetracking data was collected with. For example (.531, .298) for a monitor with a display area of 531 x 298 mm.

screen_distancefloat

The distance (in meters) from the participant’s eyes to the screen.

screen_resolutionarray_like of shape (2,)

The resolution (in pixels) of the screen that the eyetracking data was collected with. For example, (1920, 1080) for a 1920x1080 resolution display.

Methods

__contains__(key, /)

True if the dictionary has the specified key, else False.

__getitem__(key, /)

Return self[key].

__iter__(/)

Implement iter(self).

__len__(/)

Return len(self).

clear()

copy()

Copy the instance.

fromkeys(iterable[, value])

Create a new dictionary with keys from iterable and values set to value.

get(key[, default])

Return the value for key if key is in the dictionary, else default.

items()

keys()

plot([show_offsets, axes, show])

Visualize calibration.

pop(key[, default])

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)

Remove and return a (key, value) pair as a 2-tuple.

setdefault(key[, default])

Insert key with a value of default if key is not in the dictionary.

update([E, ]**F)

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()

__contains__(key, /)#

True if the dictionary has the specified key, else False.

__getitem__(key, /)#

Return self[key].

__iter__(/)#

Implement iter(self).

__len__(/)#

Return len(self).

clear() None.  Remove all items from D.#
copy()[source]#

Copy the instance.

Returns:
calinstance of Calibration

The copied Calibration.

fromkeys(iterable, value=None, /)#

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#

Return the value for key if key is in the dictionary, else default.

items() a set-like object providing a view on D's items#
keys() a set-like object providing a view on D's keys#
plot(show_offsets=True, axes=None, show=True)[source]#

Visualize calibration.

Parameters:
show_offsetsbool

Whether to display the offset (in visual degrees) of each calibration point or not. Defaults to True.

axesinstance of matplotlib.axes.Axes | None

Axes to draw the calibration positions to. If None (default), a new axes will be created.

showbool

Whether to show the figure or not. Defaults to True.

Returns:
figinstance of matplotlib.figure.Figure

The resulting figure object for the calibration plot.

Examples using plot:

Importing Data from Eyetracking devices

Importing Data from Eyetracking devices

Working with eye tracker data in MNE-Python

Working with eye tracker data in MNE-Python
pop(key, default=<unrepresentable>, /)#

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) None.  Update D from dict/iterable E and F.#

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() an object providing a view on D's values#

Examples using mne.preprocessing.eyetracking.Calibration#

Importing Data from Eyetracking devices

Importing Data from Eyetracking devices

Working with eye tracker data in MNE-Python

Working with eye tracker data in MNE-Python

Plotting eye-tracking heatmaps in MNE-Python

Plotting eye-tracking heatmaps in MNE-Python