gazel 0.0.7

Track fixations across edits for eyetracking experiments

pyTrace

Pytrace provides fixation algorithms for aggregating gazes, as well as tools to help you deal with edits that occur during eyetracking sessions.

Getting started

Installation

Requirements

pytrace requires python 3.8.5

Installation

You can install pytrace using

pip install pytrace

pyTrace comes with a command line interface as well as a python library.

To use either version, you need 3 things:

• itrace core file output
• gaze file output
• changelog
• original source file(s)

To get started:

# import pytrace
from pytrace import fixation_filter, Tracker

# run fixation filter to get fixations
fixations = fixation_filter(gazes, changelog, sources, **opts)

# Now track edits using Tracker

tracker = Tracker(fixations, changelog, sources, language)
# Note that you need to pass in the source code language,
# to help `pytrace` determine which parser to use.

Base Functionality

The main goal of Tracker is to track fixations and source code tokens across edits. Once you create a Tracker you can query it to get snapshots etc.

Snapshots

Tracker maintains a list of snapshots corresponding to the original file and subsequent edits. The original version is stored at index 0 and the first edit is stored at index 1.

# get original
tracker.get_snapshot(0)

# get first edit
tracker.get_snapshot(1)

Each snapshot is represented by a Snapshot. A Snapshot is defined as:

class Snapshot:
    id: int
    source: Source
    tokens: Tuple[Token, ...]
    changes: Tuple[TokenChange, ...] = ()
    time: float = 0.0

Snapshot.time represents the time at which a snapshot was created. It corresponds to the timestamp in the changelog that was used to create this Snapshot.

Snapshot.tokens represents the parsed source code tokens. Snapshot.changes represents all the token changes that happened to this Snapshot since the last version. For the Snapshot representing the original source, Snapshot.changes is empty.

Snapshots.source is a Source object, containing the raw text of the source code, as well as mappings from text indices to line/column numbers and vice-versa. It is defined as follows:

@dataclass(frozen=True)
class Source:
    text: str
    mapping: PositionMapping
    language: str

Gazes

You can retreive gazes for a given time window as follows:

tracker.get_gazes()
# all gazes

tracker.get_gazes(start, end)
# returns a dataframe that is filtered

The gaze dataframe is simply a pandas.DataFrame containing the original gazes, with some additional columns:

• syntax_node - The syntax node associated with the gaze. None if the gaze doesn't fall on a token.
• syntax_node_id - A stable id for the token associated with this gaze. None if the gaze doesn't fall on a token.

syntax_node_id is a unique id that is assigned to each token in the source code across different snapshots. For a given token, this id is unique across time and space. Thus, you can use this id to determine how

diffs

tracker.diff(0, 2) # gives you the diff between version 0 & version 1

tracker.diff_time(2300, 2400)
    # gives you the diff between time unit 2300 & time unit 2400

A SnapshotDiff is defined as follows:

class SnapshotDiff(NamedTuple):
    old: Snapshot
    new: Snapshot
    token_changes: List[TokenChange]
    gaze_changes: List[GazeChange]
    gazes: pd.DataFrame

It gives you a list of all token changes and gaze changes.

Token changes can be of 3 types: inserted, moved or deleted.

Gaze changes can be of 2 types: deleted or moved. (deleted means that the token to which the gaze was mapped to has been removed from the source.)

By default, tracker.diff(start, end) will include all the gazes from the start of the experiment (the time at which the first gaze is recorded), up until the end snapshot time. If you want to only include gazes within the timespan of the start and end snapshots, you can pass an optional parameter:

diff = tracker.diff(0, 3, window_only=True)

pytrace also provides a pretty-printer to help you print diffs for inspecting the data. It supports print gaze changes, token changes and SnapshotDiffs.

from pytrace import pprint

diff = tracker.diff(2, 3)

pprint(diff)
pprint(diff.token_changes)
pprint(diff.gaze_changes)

pytrace provides a module transforms to help you manipulate pytrace structures including Snapshots and SnapshotDiff.

from pytrace import transforms as T

diff = tracker.diff(2, 5)

# TODO