.. currentmodule:: pypillometry Data classes ============ See :doc:`/user_guide/data_model` for an introduction. The methods for pupil and gaze data are in the namespaces ``d.pupil`` (:doc:`pupil`) and ``d.gaze`` (:doc:`gaze`), the plots in ``d.plot`` (:doc:`plotting`). Main class ---------- :class:`EyeData` holds the data. Its methods for any channel (crop, downsample, masks and layers, events, intervals, segments, saving, ...) are listed on its page. .. autosummary:: :toctree: generated :template: class.rst :nosignatures: EyeData Channels, layers and mask policies ---------------------------------- The time series (``d["left_pupil"]``) and their mask layers are stored in an :class:`EyeDataDict` (``d.data``). A :class:`MaskPolicy` says which layers make a sample unusable (``"default"``, ``"strict"``, ``"raw"``). .. autosummary:: :toctree: generated :template: class.rst :nosignatures: EyeDataDict MaskPolicy Simulated data and history -------------------------- .. autosummary:: :toctree: generated :template: class.rst :nosignatures: FakeEyeData .. autosummary:: :toctree: generated :template: function.rst :nosignatures: keephistory Quality report -------------- See :meth:`EyeData.quality_report`. .. autosummary:: :toctree: generated :template: class.rst :nosignatures: QualityReport QualityMetric Tables ------ Some results are dicts that are displayed as a table (:class:`pandas.DataFrame`): - results with one row per event or trial (e.g., :meth:`Events.to_table`, :meth:`SegmentedEyeData.gaze.dwell_time `) are a :class:`Table`, a dict of numpy columns - results per channel (e.g., :meth:`EyeData.layer_stats`: channel -> layer -> statistics) and the list of example datasets are a :class:`RecordDict`, a dict of records .. autosummary:: :toctree: generated :template: class.rst :nosignatures: Table RecordDict