"""Leap-frog prediction algorithm for mechanical lag compensation.

The Trav'ler dish has inherent mechanical lag — by the time the motors
reach the commanded position, a satellite in motion has already moved on.
This module applies a small predictive overshoot so the dish "leaps ahead"
of the target, reducing tracking error during a pass.
"""


def apply_leapfrog(
    target_az: float,
    target_el: float,
    current_az: float,
    current_el: float,
) -> tuple[float, float]:
    """Apply predictive overshoot to compensate for mechanical lag.

    For each axis, if the delta exceeds a threshold, the target is nudged
    further in the direction of travel. This keeps the dish slightly ahead
    of fast-moving satellites.

    Args:
        target_az: Desired azimuth from tracking software (degrees).
        target_el: Desired elevation from tracking software (degrees).
        current_az: Last-known azimuth of the dish (degrees).
        current_el: Last-known elevation of the dish (degrees).

    Returns:
        Adjusted (azimuth, elevation) with overshoot applied.

    Note:
        The original upstream code had a copy-paste bug where the elevation
        delta adjustments modified target_az instead of target_el.
        See docs/bugs.md for details.
    """
    # Azimuth compensation
    az_delta = target_az - current_az
    if abs(az_delta) > 2:
        target_az += 1.0 if az_delta > 0 else -1.0
    elif abs(az_delta) > 1:
        target_az += 0.5 if az_delta > 0 else -0.5

    # Elevation compensation (bug fix: original modified target_az here)
    el_delta = target_el - current_el
    if abs(el_delta) > 2:
        target_el += 1.0 if el_delta > 0 else -1.0
    elif abs(el_delta) > 1:
        target_el += 0.5 if el_delta > 0 else -0.5

    return target_az, target_el