preprocessing.preprocessing

logger_config

logger

preprocessing

@save_state(model=PreprocessingFunctionSchema)
def preprocessing(config: dict,
                  centered_reference_keypoint: str,
                  orientation_reference_keypoint: str,
                  run_lowconf_cleaning: bool = True,
                  run_egocentric_alignment: bool = True,
                  run_outlier_cleaning: bool = True,
                  run_savgol_filtering: bool = True,
                  run_rescaling: bool = True,
                  save_logs: bool = True) -> str

Preprocess the data by:

• Cleaning low confidence data points
• Egocentric alignment
• Outlier cleaning using IQR
• Savitzky-Golay filtering
• Rescaling

Parameters

• config (dict): Configuration dictionary.
• centered_reference_keypoint (str, optional): Keypoint to use as centered reference.
• orientation_reference_keypoint (str, optional): Keypoint to use as orientation reference.
• run_lowconf_cleaning (bool, optional): Whether to run low confidence cleaning.
• run_egocentric_alignment (bool, optional): Whether to run egocentric alignment.
• run_outlier_cleaning (bool, optional): Whether to run outlier cleaning.
• run_savgol_filtering (bool, optional): Whether to run Savitzky-Golay filtering.
• run_rescaling (bool, optional): Whether to run rescaling. Defaults to True. Requires run_egocentric_alignment.
• save_logs (bool, optional): Whether to save logs.

Returns

• variable name of the last-executed preprocessing step output, also saved
• `to the config as ``preprocessed_variable```

preprocess_session

def preprocess_session(file_path: str, steps: List[Tuple[Callable,
                                                         dict]]) -> None

Run preprocessing steps on one session's processed file: read once, apply the steps in order, and replace the file once.

Parameters

• file_path (str): Path to the session's processed netCDF file.
• steps (list of (function, kwargs)): Dataset functions to apply in order, each called as function(ds=ds, **kwargs).

Returns

• None