gazel-devjeetr 0.0.7
Track fixations across edits for eyetracking experiments
Requires Python: >=3.8.5

# 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:

```python
# 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`.

```python
# 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:

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

### Gazes

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

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

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


A `SnapshotDiff` is defined as follows:
```python
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:

```python
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`.

```python
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`.

```python
from pytrace import transforms as T

diff = tracker.diff(2, 5)

# TODO
```