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59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
"""
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Cadillac detector for consciousness navigation.
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This module provides a stub implementation for detecting "Cadillac" segments in a consciousness trajectory.
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The idea: slide a window, compute capability ratio and energy metrics, and flag segments that are smooth and efficient.
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"""
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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):
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"""
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Detect Cadillac segments in a time series.
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Parameters
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----------
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x : 1-D numpy array
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Signal samples for a consciousness trajectory.
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Fs : float
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Sampling rate in Hz.
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G : numpy.ndarray
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Metric tensor (as estimated by fit_metric in consciousness_nav_scaffold).
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window_samples : int, optional
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Number of samples per sliding window (default 1000).
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C_threshold : float, optional
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Maximum deviation |C - 1| allowed for a segment to be considered efficient.
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energy_threshold : float, optional
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Maximum average energy per sample allowed (user-defined).
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phase_slip_threshold : float, optional
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Maximum phase-slip allowed (if using keeper metrics).
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energy_drift_threshold : float, optional
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Maximum energy drift allowed (if using keeper metrics).
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Returns
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-------
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list of tuple
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List of (start_index, end_index) pairs indicating segments satisfying the criteria.
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Note
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----
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This function is a template; users should implement the actual metric calculations
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using functions from breath_keeper or consciousness_nav_scaffold to evaluate
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capability ratio and energy metrics within each window.
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"""
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import numpy as np
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segments = []
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N = len(x)
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step = max(1, window_samples // 2)
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for start in range(0, N - window_samples + 1, step):
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end = start + window_samples
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# Extract window
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seg = x[start:end]
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# TODO: compute capability ratio C for seg (e.g., using apparent_length and true_length)
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# TODO: compute average energy, phase-slip, energy-drift metrics for seg
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# Placeholder condition: accept all segments (for demonstration)
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segments.append((start, end))
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return segments
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