Source code for suboptimumg.log_analysis.drivetrain

import numpy as np
from perda.core_data_structures.data_instance import (
    DataInstance,
    left_join_data_instances,
)
from perda.core_data_structures.single_run_data import SingleRunData

from .kinematics import GROUND_SPEED

REAR_SLIP_RATIO = "rear.slipRatio"




def add_rear_slip_ratio(
    data: SingleRunData,
    wheel_speed_col: str = "pcm.moc.motor.wheelSpeed",
    low_speed_thresh: float = 1.0,
) -> SingleRunData:
    """Add the rear axle longitudinal slip ratio: ``(v_wheel - v_ground) / v_ground``.

    Positive values mean driven wheels spinning faster than ground speed (traction loss).
    Requires ``add_groundspeed`` and the ``correct_motor_data`` preprocessing step.
    Stores ``rear.slipRatio``.

    Parameters
    ----------
    low_speed_thresh : float
        Ground speed (m/s) below which slip ratio is forced to zero.
    """
    missing = [c for c in (GROUND_SPEED,) if c not in data]
    if missing:
        raise KeyError(
            f"add_rear_slip_ratio: missing variables(s) {missing}.\n"
            f"Try running add_groundspeed first."
        )

    ws = data[wheel_speed_col]
    ws, gs_aln = left_join_data_instances(ws, [data[GROUND_SPEED]])
    vg = gs_aln.value_np

    slip = np.zeros_like(vg, dtype=np.float64)
    mask = np.abs(vg) > low_speed_thresh
    slip[mask] = (ws.value_np[mask] - vg[mask]) / vg[mask]

    data[REAR_SLIP_RATIO] = DataInstance(
        timestamp_np=ws.timestamp_np,
        value_np=slip,
        label="Rear Slip Ratio",
        cpp_name=REAR_SLIP_RATIO,
    )
    return data