pymovements.gaze.GazeDataFrame.__init__#

GazeDataFrame.__init__(data: pl.DataFrame | None = None, experiment: Experiment | None = None, events: pm.EventDataFrame | None = None, *, trial_columns: str | list[str] | None = None, time_column: str | None = None, pixel_columns: list[str] | None = None, position_columns: list[str] | None = None, velocity_columns: list[str] | None = None, acceleration_columns: list[str] | None = None, distance_column: str | None = None)[source]#

Initialize a pymovements.gaze.gaze_dataframe.GazeDataFrame.

Parameters:

  • data (pl.DataFrame) – A dataframe to be transformed to a polars dataframe.

  • experiment (Experiment) – The experiment definition.

  • events (EventDataFrame) – A dataframe of events in the gaze signal.

  • trial_columns – The name of the trial columns in the input data frame. If the list is empty or None, the input data frame is assumed to contain only one trial. If the list is not empty, the input data frame is assumed to contain multiple trials and the transformation methods will be applied to each trial separately.

  • time_column – The name of the timestamp column in the input data frame.

  • pixel_columns – The name of the pixel position columns in the input data frame. These columns will be nested into the column pixel. If the list is empty or None, the nested pixel column will not be created.

  • position_columns – The name of the dva position columns in the input data frame. These columns will be nested into the column position. If the list is empty or None, the nested position column will not be created.

  • velocity_columns – The name of the velocity columns in the input data frame. These columns will be nested into the column velocity. If the list is empty or None, the nested velocity column will not be created.

  • acceleration_columns – The name of the acceleration columns in the input data frame. These columns will be nested into the column acceleration. If the list is empty or None, the nested acceleration column will not be created.

  • distance_column – The name of the column containing eye-to-screen distance in millimeters for each sample in the input data frame. If specified, the column will be used for pixel to dva transformations. If not specified, the constant eye-to-screen distance will be taken from the experiment definition.

Notes

About using the arguments pixel_columns, position_columns, velocity_columns, and acceleration_columns:

By passing a list of columns as any of these arguments, these columns will be merged into a single column with the corresponding name , e.g. using pixel_columns will merge the respective columns into the column pixel.

The supported number of component columns with the expected order are:

  • zero columns: No nested component column will be created.

  • two columns: monocular data; expected order: x-component, y-component

  • four columns: binocular data; expected order: x-component left eye, y-component left eye, x-component right eye, y-component right eye,

  • six columns: binocular data with additional cyclopian data; expected order: x-component left eye, y-component left eye, x-component right eye, y-component right eye, x-component cyclopian eye, y-component cyclopian eye,

Examples

First let’s create an example DataFrame with three columns: the timestamp t and x and y for the pixel position.

>>> df = pl.from_dict(
...     data={'t': [1000, 1001, 1002], 'x': [0.1, 0.2, 0.3], 'y': [0.1, 0.2, 0.3]},
... )
>>> df
shape: (3, 3)
┌──────┬─────┬─────┐
│ t    ┆ x   ┆ y   │
│ ---  ┆ --- ┆ --- │
│ i64  ┆ f64 ┆ f64 │
╞══════╪═════╪═════╡
│ 1000 ┆ 0.1 ┆ 0.1 │
│ 1001 ┆ 0.2 ┆ 0.2 │
│ 1002 ┆ 0.3 ┆ 0.3 │
└──────┴─────┴─────┘

We can now initialize our GazeDataFrame by specyfing the names of the pixel position columns.

>>> gaze = GazeDataFrame(data=df, pixel_columns=['x', 'y'])
>>> gaze.frame
shape: (3, 2)
┌──────┬────────────┐
│ t    ┆ pixel      │
│ ---  ┆ ---        │
│ i64  ┆ list[f64]  │
╞══════╪════════════╡
│ 1000 ┆ [0.1, 0.1] │
│ 1001 ┆ [0.2, 0.2] │
│ 1002 ┆ [0.3, 0.3] │
└──────┴────────────┘