convert_to_tfe#

convert_to_tfe(all_data: pd.DataFrame, features: list[tuple[str, str, str]], frame_spec: tuple[int, int, int]) → dict[source]#

Generate TFE manifest and feature data for simulation.

Parameters:

  • all_data – Simulation data containing ID, TICK, and time.

  • features – List of feature keys, names, and data types.

  • frame_spec – Specification for frames.

Returns:

TFE manifest and feature data

get_manifest_data(features: list[tuple[str, str, str]], frames: list[int]) → dict[source]#

Build manifest for TFE.

Parameters:

  • features – List of feature keys, names, and data types.

  • frames – List of frames.

Returns:

Manifest in TFE format.

get_tracks_from_data(data: pd.DataFrame) → dict[source]#

Extract track ids from data and format for TFE.

Parameters:

data – Simulation data for selected frames.

Returns:

Track data in TFE format.

get_times_from_data(data: pd.DataFrame) → dict[source]#

Extract time points from data and format for TFE.

Parameters:

data – Simulation data for selected frames.

Returns:

Time data in TFE format.

get_feature_from_data(data: pd.DataFrame, feature: str, categories: list | None = None) → dict[source]#

Extract specified feature from data and format for TFE.

Parameters:

  • data – Simulation data for selected frames.

  • feature – Feature key.

  • categories – List of data categories (if data is categorical).

Returns:

Feature data in TFE format.