lucidia sync: providers registry, db, main updates

cadillac_detector.py

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