mirror of
https://github.com/blackboxprogramming/lucidia.git
synced 2026-03-17 09:37:56 -05:00
Alexa Amundson
855585cb0e
Synced from BlackRoad-OS-Inc/blackroad-operator/orgs/personal/lucidia BlackRoad OS — Pave Tomorrow. RoadChain-SHA2048: fe729062952871e7 RoadChain-Identity: alexa@sovereign RoadChain-Full: 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
59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
"""
|
|
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
|