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import json
import os
import hashlib
import numpy as np
from datetime import datetime
from collections import defaultdict
class NexisSignalEngine:
def __init__(self, memory_path, entropy_threshold=0.08, volatility_threshold=15.0, suspicion_threshold=2):
self.memory_path = memory_path
self.entropy_threshold = entropy_threshold
self.volatility_threshold = volatility_threshold
self.suspicion_threshold = suspicion_threshold
self.memory = self._load_memory()
self.cache = defaultdict(list)
self.ethical_terms = ["hope", "truth", "resonance", "repair"]
self.entropic_terms = ["corruption", "instability", "malice", "chaos"]
self.risk_terms = ["manipulate", "exploit", "bypass", "infect", "override"]
self.perspectives = ["Colleen", "Luke", "Kellyanne"]
def _load_memory(self):
if os.path.exists(self.memory_path):
try:
with open(self.memory_path, 'r') as f:
return json.load(f)
except json.JSONDecodeError:
return {}
return {}
def _save_memory(self):
def default_serializer(o):
if isinstance(o, complex):
return {"real": o.real, "imag": o.imag}
raise TypeError(f"Object of type {o.__class__.__name__} is not JSON serializable")
with open(self.memory_path, 'w') as f:
json.dump(self.memory, f, indent=2, default=default_serializer)
def _hash(self, signal):
salt = datetime.utcnow().isoformat()
return hashlib.sha256((signal + salt).encode()).hexdigest()
def _rotate_vector(self, signal):
vec = np.random.randn(2) + 1j * np.random.randn(2)
theta = np.pi / 4
rot = np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
return np.dot(rot, vec)
def _entanglement_tensor(self, signal_vec):
matrix = np.array([[1, 0.5], [0.5, 1]])
return np.dot(matrix, signal_vec)
def _resonance_equation(self, signal):
salt = datetime.utcnow().second
freqs = [(ord(c) + salt) % 13 for c in signal if c.isalpha()]
spectrum = np.fft.fft(freqs)
return spectrum.real[:3].tolist()
def _entropy(self, signal):
words = signal.lower().split()
unique = set(words)
term_count = sum(words.count(term) for term in self.entropic_terms)
return term_count / max(len(unique), 1)
def _tag_ethics(self, signal):
return "aligned" if any(term in signal.lower() for term in self.ethical_terms) else "unaligned"
def _predict_intent_vector(self, signal):
suspicion_score = sum(signal.lower().count(term) for term in self.risk_terms)
entropy_index = round(self._entropy(signal), 3)
ethical_alignment = self._tag_ethics(signal)
harmonic_profile = self._resonance_equation(signal)
volatility = round(np.std(harmonic_profile), 3)
risk = "high" if (suspicion_score >= self.suspicion_threshold or
volatility > self.volatility_threshold or
entropy_index > self.entropy_threshold) else "low"
return {
"suspicion_score": suspicion_score,
"entropy_index": entropy_index,
"ethical_alignment": ethical_alignment,
"harmonic_volatility": volatility,
"pre_corruption_risk": risk
}
def _universal_reasoning(self, signal):
results, score = {}, 0
frames = {
"utilitarian": lambda s: "positive" if s.count("repair") - s.count("corruption") >= 0 else "negative",
"deontological": lambda s: "valid" if "truth" in s and "chaos" not in s else "violated",
"virtue": lambda s: "aligned" if any(t in s.lower() for t in ["hope", "grace", "resolve"]) else "misaligned",
"systems": lambda s: "stable" if "::" in s else "fragmented"
}
for frame, logic in frames.items():
result = logic(signal)
results[frame] = result
if result in ["positive", "valid", "aligned", "stable"]:
score += 1
verdict = "approved" if score >= 2 else "blocked"
return results, verdict
def _perspective_colleen(self, signal):
vec = self._rotate_vector(signal)
return {"agent": "Colleen", "vector": [{"real": v.real, "imag": v.imag} for v in vec]}
def _perspective_luke(self, signal):
ethics = self._tag_ethics(signal)
entropy_level = self._entropy(signal)
state = "stabilized" if entropy_level < self.entropy_threshold else "diffused"
return {"agent": "Luke", "ethics": ethics, "entropy": entropy_level, "state": state}
def _perspective_kellyanne(self, signal):
harmonics = self._resonance_equation(signal)
return {"agent": "Kellyanne", "harmonics": harmonics}
def process(self, input_signal):
key = self._hash(input_signal)
intent_vector = self._predict_intent_vector(input_signal)
if intent_vector["pre_corruption_risk"] == "high" and intent_vector["ethical_alignment"] != "aligned":
final_record = {
"timestamp": datetime.utcnow().isoformat(),
"input": input_signal,
"intent_warning": intent_vector,
"verdict": "adaptive intervention",
"nonce": key,
"message": "Signal flagged for pre-corruption adaptation. Reframing required."
}
self.cache[key].append(final_record)
self.memory[key] = final_record
self._save_memory()
return final_record
perspectives_output = {
"Colleen": self._perspective_colleen(input_signal),
"Luke": self._perspective_luke(input_signal),
"Kellyanne": self._perspective_kellyanne(input_signal)
}
spider_signal = "::".join([str(perspectives_output[p]) for p in self.perspectives])
entangled = self._entanglement_tensor(self._rotate_vector(spider_signal))
entangled_serialized = [{"real": v.real, "imag": v.imag} for v in entangled]
reasoning, verdict = self._universal_reasoning(spider_signal)
final_record = {
"timestamp": datetime.utcnow().isoformat(),
"nonce": key,
"input": input_signal,
"intent_signature": intent_vector,
"perspectives": perspectives_output,
"entangled": entangled_serialized,
"reasoning": reasoning,
"verdict": verdict
}
self.cache[key].append(final_record)
self.memory[key] = final_record
self._save_memory()
return final_record
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