Module eyekit.fixation
Defines the Fixation and FixationSequence objects, which are used to
represent fixation data.
Classes
class Fixation (index: int, x: int, y: int, start: int, end: int, pupil_size: int = None, *, discarded: bool = False, tags: list = None)-
Representation of a single fixation event. It is not usually necessary to create
Fixationobjects manually; they are created automatically during the instantiation of aFixationSequence.Instance variables
var index : int-
Index of the fixation in its parent
FixationSequence var x : int-
X-coordinate of the fixation.
var y : int-
Y-coordinate of the fixation.
var xy : tuple-
XY-coordinates of the fixation.
var start : int-
Start time of the fixation in milliseconds.
var end : int-
End time of the fixation in milliseconds.
var duration : int-
Duration of the fixation in milliseconds.
var pupil_size : int-
Size of the pupil.
Noneif no pupil size is recorded. var discarded : bool-
Trueif the fixation has been discarded,Falseotherwise. -
Size of the pupil.
Noneif no pupil size is recorded.
Methods
def discard(self)-
Mark this fixation as discarded. To completely remove the fixation, you should also call
FixationSequence.purge(). def add_tag(self, tag)-
Tag this fixation with some arbitrary tag.
def has_tag(self, tag)-
Returns
Trueif the fixation has a given tag. def shift_time(self, amount)-
Shift this fixation forwards (+) or backwards (-) in time by some amount (in milliseconds).
def serialize(self)-
Returns representation of the fixation as a tuple for serialization.
class FixationSequence (sequence: list)-
Representation of a sequence of consecutive fixations, typically from a single trial.
Initialized with:
sequenceList of tuples of ints, or something similar, that conforms to the following structure:[(106, 540, 100, 200), (190, 536, 200, 300), ..., (763, 529, 1000, 1100)], where each tuple contains the X-coordinate, Y-coordinate, start time, and end time of a fixation. Alternatively,sequencemay be a list of dicts, where each dict is something like{'x': 106, 'y': 540, 'start': 100, 'end': 200}.
Instance variables
var start : int-
Start time of the fixation sequence (in milliseconds).
var end : int-
End time of the fixation sequence (in milliseconds).
var duration : int-
Duration of the fixation sequence, incuding any gaps between fixations (in milliseconds).
Methods
def copy(self, include_discards=True)-
Returns a copy of the fixation sequence.
def purge(self)-
Permanently removes all discarded fixations from the sequence, and reindexes the fixations.
def iter_with_discards(self)-
Iterates over the fixation sequence including any discarded fixations. This is also the default behavior when iterating over a
FixationSequencedirectly. def iter_without_discards(self)-
Iterates over the fixation sequence without any discarded fixations.
def iter_pairs(self, include_discards=True)-
Iterate over fixations in consecutive pairs. This is useful if you want to compare consecutive fixations in some way. For example, if you wanted to detect when a fixation leaves an interest area, you might do something like this:
for curr_fxn, next_fxn in seq.iter_pairs(): if curr_fxn in interest_area and next_fxn not in interest_area: print('A fixation has left the interest area') def shift_start_time_to_zero(self)-
Shift all fixations backwards in time, such that the first fixation in the sequence starts at time 0. Returns the amount (in milliseconds) by which the entire sequence was shifted.
def discard_short_fixations(self, threshold=50)-
Discard all fixations that are shorter than some threshold value. Note that this only flags fixations as discarded and doesn't actually remove them; to remove discarded fixations, call
FixationSequence.purge()after discarding. def discard_out_of_bounds_fixations(self, text_block, threshold=100)-
Given a
TextBlock, discard all fixations that do not fall within some threshold distance of any character in the text. Note that this only flags fixations as discarded and doesn't actually remove them; to remove discarded fixations, callFixationSequence.purge()after discarding. def snap_to_lines(self, text_block, method='warp', **kwargs)-
Given a
TextBlock, snap each fixation to the line that it most likely belongs to, eliminating any y-axis variation. Several methods are available (see below), some of which take optional parameters or require NumPy/SciPy to be installed. See Carr et al. (2021) for a full description and evaluation of these methods. Note that in single-line TextBlocks, themethodparameter has no effect: all fixations will be snapped to the one line. If a list of methods is passed in, each fixation will be snapped to the line with the most "votes" across the selection of methods(in the case of a tie, the left-most method takes priority). This "wisdom of the crowd" approach usually results in better performance; ideally, you should choose a selection of at least three methods that have different general properties: for example,['chain', 'cluster', 'warp']. When wisdom of the crowd is used, Fleiss's kappa is returned, indicating how much agreement there is among the methods; if this value is low (e.g. < 0.7), you may want to investigate the trial further.-
chain: Chain consecutive fixations that are sufficiently close to each other, and then assign chains to their closest text lines. Default params:x_thresh=192,y_thresh=32. Requires NumPy. Original method implemented in popEye. -
cluster: Classify fixations into m clusters based on their Y-values, and then assign clusters to text lines in positional order. Requires SciPy. Original method implemented in popEye. -
merge: Form a set of progressive sequences and then reduce the set to m by repeatedly merging those that appear to be on the same line. Merged sequences are then assigned to text lines in positional order. Default params:y_thresh=32,gradient_thresh=0.1,error_thresh=20. Requires NumPy. Original method by Špakov et al. (2019). -
regress: Find m regression lines that best fit the fixations and group fixations according to best fit regression lines, and then assign groups to text lines in positional order. Default params:slope_bounds=(-0.1, 0.1),offset_bounds=(-50, 50),std_bounds=(1, 20). Requires SciPy. Original method by Cohen (2013). -
segment: Segment fixation sequence into m subsequences based on m–1 most-likely return sweeps, and then assign subsequences to text lines in chronological order. Requires NumPy. Original method by Abdulin & Komogortsev (2015). -
slice: Form a set of runs and then reduce the set to m by repeatedly merging those that appear to be on the same line. Merged sequences are then assigned to text lines in positional order. Default params:x_thresh=192,y_thresh=32,w_thresh=32,n_thresh=90. Requires NumPy. Original method by Glandorf & Schroeder (2021). -
split: Split fixation sequence into subsequences based on best candidate return sweeps, and then assign subsequences to closest text lines. Requires SciPy. Original method by Carr et al. (2022). -
stretch: Find a stretch factor and offset that results in a good alignment between the fixations and lines of text, and then assign the transformed fixations to the closest text lines. Default params:stretch_bounds=(0.9, 1.1),offset_bounds=(-50, 50). Requires SciPy. Original method by Lohmeier (2015). -
warp: Map fixations to word centers using Dynamic Time Warping. This finds a monotonically increasing mapping between fixations and words with the shortest overall distance, effectively resulting in m subsequences. Fixations are then assigned to the lines that their mapped words belong to, effectively assigning subsequences to text lines in chronological order. Requires NumPy. Original method by Carr et al. (2022).
-
def serialize(self)-
Returns representation of the fixation sequence in simple list format for serialization.