Accelerometers

labda.accelerometers.AutoCalibrate dataclass

Automated gravitational calibration of tri-axial accelerometer data.

This class corrects gain (scale) and offset errors by identifying stationary periods in the data where the magnitude of the acceleration vector should theoretically be exactly 1 g.

Attributes:

Name	Type	Description
window	str | timedelta	The window size for resampling and stationarity checks (e.g., "10s"). Defaults to "10s".
tolerance	float	Maximum standard deviation (in g) allowed for a window to be considered stationary. Defaults to 0.015.
samples	int	Minimum number of stationary windows required to attempt calibration. Defaults to 50.
calib_cube	float	Coverage threshold. Ensures stationary points exist at least calib_cube g away from the origin on all axes (both positive and negative) to prevent overfitting to a single orientation. Defaults to 0.3.
max_iter	int	Maximum number of optimization iterations.
improv_tol	float	Minimum relative error improvement required to continue optimization iterations.
err_tol	float	Target mean error (in g) for successful calibration.

compute

compute(df: DataFrame) -> pd.DataFrame

Applies calibration to the provided DataFrame.

If sufficient stationary data is found and the algorithm converges, the returned DataFrame will contain corrected values. If calibration fails (e.g., not enough stationary periods or poor coverage), the original data is returned, and a warning is logged.

Parameters:

Name	Type	Description	Default
df	DataFrame	Input dataframe containing raw accelerometer data. Must contain columns: acc_x, acc_y, acc_z.	required

Returns:

Type	Description
DataFrame	pd.DataFrame: A new DataFrame with calibrated data (float32). The index and column names are preserved.