"""
Cadillac detector for consciousness navigation.

This module provides a stub implementation for detecting "Cadillac" segments in a consciousness trajectory.

The idea: slide a window, compute capability ratio and energy metrics, and flag segments that are smooth and efficient.
"""


def detect_cadillac_segments(x, Fs, G, window_samples=1000, C_threshold=0.1, energy_threshold=0.05, phase_slip_threshold=1e-3, energy_drift_threshold=1e-4):
    """
    Detect Cadillac segments in a time series.

    Parameters
    ----------
    x : 1-D numpy array
        Signal samples for a consciousness trajectory.
    Fs : float
        Sampling rate in Hz.
    G : numpy.ndarray
        Metric tensor (as estimated by fit_metric in consciousness_nav_scaffold).
    window_samples : int, optional
        Number of samples per sliding window (default 1000).
    C_threshold : float, optional
        Maximum deviation |C - 1| allowed for a segment to be considered efficient.
    energy_threshold : float, optional
        Maximum average energy per sample allowed (user-defined).
    phase_slip_threshold : float, optional
        Maximum phase-slip allowed (if using keeper metrics).
    energy_drift_threshold : float, optional
        Maximum energy drift allowed (if using keeper metrics).

    Returns
    -------
    list of tuple
        List of (start_index, end_index) pairs indicating segments satisfying the criteria.

    Note
    ----
    This function is a template; users should implement the actual metric calculations
    using functions from breath_keeper or consciousness_nav_scaffold to evaluate
    capability ratio and energy metrics within each window.
    """
    import numpy as np

    segments = []
    N = len(x)
    step = max(1, window_samples // 2)
    for start in range(0, N - window_samples + 1, step):
        end = start + window_samples
        # Extract window
        seg = x[start:end]
        # TODO: compute capability ratio C for seg (e.g., using apparent_length and true_length)
        # TODO: compute average energy, phase-slip, energy-drift metrics for seg
        # Placeholder condition: accept all segments (for demonstration)
        segments.append((start, end))

    return segments