Add cadillac_detector.py with detection stub

cadillac_detector.py

@@ -0,0 +1,58 @@
+"""
+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