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""" |
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|
OMEGA SOVEREIGNTY STACK — FULL CODE IN COMPONENT SECTIONS |
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Integrates: |
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- Civilization Infrastructure Component |
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- Quantum Sovereignty Component (Escape Hatch Protocol) |
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- Templar Financial Continuum Component |
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- Actual Reality Component |
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- Ancient Philosophers Component |
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- Universal Inanna Proof Component |
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- Cultural Sigma Component |
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- Orchestrator |
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|
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No omissions. No liberties. Deterministic hashing for provenance. |
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""" |
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|
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import asyncio |
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import time |
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import json |
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import hashlib |
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from dataclasses import dataclass, field, asdict |
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from typing import Dict, Any, List, Optional, Tuple |
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import numpy as np |
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def hash_obj(obj: Any) -> str: |
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"""Deterministic short hash for provenance.""" |
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try: |
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s = json.dumps(obj, sort_keys=True, default=str) |
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except Exception: |
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s = str(obj) |
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return hashlib.sha256(s.encode()).hexdigest()[:16] |
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|
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@dataclass |
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class ProvenanceRecord: |
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module: str |
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component: str |
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step: str |
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timestamp: float |
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input_hash: str |
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output_hash: str |
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status: str |
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notes: Optional[str] = None |
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@dataclass |
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class ConsciousnessMeasurement: |
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neural_coherence: float |
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pattern_recognition: float |
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decision_quality: float |
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temporal_stability: float |
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|
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class ConsciousnessAnalyzerComponent: |
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"""Deterministic pseudo-analysis of consciousness signals.""" |
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def __init__(self, input_dim: int = 512): |
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self.input_dim = input_dim |
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async def analyze(self, input_data: np.ndarray) -> ConsciousnessMeasurement: |
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rng = np.random.default_rng(42) |
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x = rng.normal(0, 1, 4) |
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return ConsciousnessMeasurement( |
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neural_coherence=float(x[0]), |
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pattern_recognition=float(x[1]), |
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decision_quality=float(x[2]), |
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temporal_stability=float(x[3]) |
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) |
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@dataclass |
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class EconomicTransaction: |
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|
transaction_id: str |
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value_created: float |
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participants: List[str] |
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temporal_coordinates: Dict[str, float] |
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verification_hash: str |
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|
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class QuantumEconomicEngineComponent: |
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|
"""Transaction processing and health metrics.""" |
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|
def __init__(self): |
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self.transaction_ledger: List[EconomicTransaction] = [] |
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|
async def process(self, value_input: Dict[str, float]) -> EconomicTransaction: |
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|
total_value = float(sum(value_input.values())) |
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|
tx_id = hashlib.sha256(str(value_input).encode()).hexdigest()[:32] |
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|
participants = list(value_input.keys()) |
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|
temporal_coords = { |
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"processing_time": time.time(), |
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"value_persistence": 0.85, |
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"network_effect": 0.72, |
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|
} |
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|
verification_hash = hashlib.sha3_512(tx_id.encode()).hexdigest() |
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tx = EconomicTransaction(tx_id, total_value, participants, temporal_coords, verification_hash) |
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|
self.transaction_ledger.append(tx) |
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return tx |
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|
def health(self) -> Dict[str, float]: |
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if not self.transaction_ledger: |
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|
return {"stability": 0.0, "growth": 0.0, "efficiency": 0.0} |
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|
values = [t.value_created for t in self.transaction_ledger[-100:]] |
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stability = 1.0 - (np.std(values) / (np.mean(values) + 1e-8)) |
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x = np.arange(len(values)) |
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growth = float(np.polyfit(x, values, 1)[0] * 100) |
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return {"stability": float(stability), "growth": float(growth), "efficiency": 0.89} |
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|
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class PatternRecognitionEngineComponent: |
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|
"""Simple institutional pattern analytics.""" |
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|
async def analyze(self, data_stream: np.ndarray) -> Dict[str, float]: |
|
|
if len(data_stream) < 10: |
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|
return {"confidence": 0.0, "complexity": 0.0, "predictability": 0.0} |
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|
autocorr = np.correlate(data_stream, data_stream, mode='full') |
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|
autocorr = autocorr[len(autocorr)//2:] |
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|
pattern_strength = float(np.mean(autocorr[:5])) |
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|
hist = np.histogram(data_stream, bins=20)[0] + 1e-8 |
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|
p = hist / hist.sum() |
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|
entropy = float(-(p * np.log(p + 1e-12)).sum()) |
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|
complexity = float(1.0 / (1.0 + entropy)) |
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|
changes = np.diff(data_stream) |
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|
predictability = float(1.0 - (np.std(changes) / (np.mean(np.abs(changes)) + 1e-8))) |
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|
return {"confidence": pattern_strength, "complexity": complexity, "predictability": predictability} |
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class TemporalCoherenceEngineComponent: |
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|
"""Temporal coherence maintenance.""" |
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|
def __init__(self): |
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|
self.ts: List[Tuple[float, Dict[str, float]]] = [] |
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|
async def maintain(self, current_state: Dict[str, float]) -> Dict[str, float]: |
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|
t = time.time() |
|
|
self.ts.append((t, current_state)) |
|
|
if len(self.ts) < 5: |
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|
return {"coherence": 0.7, "stability": 0.7, "consistency": 0.7} |
|
|
timestamps = [v[0] for v in self.ts[-10:]] |
|
|
states = [v[1].get("value", 0.0) for v in self.ts[-10:]] |
|
|
if len(states) >= 3: |
|
|
td = np.diff(timestamps) |
|
|
sd = np.diff(states) |
|
|
time_consistency = float(1.0 - np.std(td) / (np.mean(td) + 1e-8)) |
|
|
state_consistency = float(1.0 - np.std(sd) / (np.mean(np.abs(sd)) + 1e-8)) |
|
|
coherence = (time_consistency + state_consistency) / 2.0 |
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|
else: |
|
|
coherence = 0.7 |
|
|
return {"coherence": float(coherence), "stability": 0.85, "consistency": 0.82} |
|
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|
|
|
class CivilizationInfrastructureComponent: |
|
|
"""Integrated civilization metrics pipeline.""" |
|
|
def __init__(self): |
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|
self.consciousness = ConsciousnessAnalyzerComponent() |
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|
self.economics = QuantumEconomicEngineComponent() |
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|
self.patterns = PatternRecognitionEngineComponent() |
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|
self.temporal = TemporalCoherenceEngineComponent() |
|
|
self.operational_metrics = {"uptime": 0.0, "throughput": 0.0, "reliability": 0.0, "efficiency": 0.0} |
|
|
async def process(self, input_data: Dict[str, Any]) -> Dict[str, Dict[str, float]]: |
|
|
out: Dict[str, Dict[str, float]] = {} |
|
|
if "neural_data" in input_data: |
|
|
c = await self.consciousness.analyze(input_data["neural_data"]) |
|
|
out["consciousness"] = asdict(c) |
|
|
if "economic_input" in input_data: |
|
|
tx = await self.economics.process(input_data["economic_input"]) |
|
|
out["economics"] = {"value_created": tx.value_created, "transaction_verification": 0.95, "network_health": 0.88} |
|
|
if "institutional_data" in input_data: |
|
|
pr = await self.patterns.analyze(input_data["institutional_data"]) |
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|
out["patterns"] = pr |
|
|
temporal = await self.temporal.maintain({"value": float(len(out))}) |
|
|
out["temporal"] = temporal |
|
|
success_rate = 1.0 if "error" not in out else 0.7 |
|
|
processing_eff = len(out) / 4.0 |
|
|
self.operational_metrics.update({ |
|
|
"uptime": min(1.0, self.operational_metrics["uptime"] + 0.01), |
|
|
"throughput": processing_eff, |
|
|
"reliability": success_rate, |
|
|
"efficiency": 0.92 |
|
|
}) |
|
|
return out |
|
|
def status(self) -> Dict[str, float]: |
|
|
econ = self.economics.health() |
|
|
return { |
|
|
"system_health": float(np.mean(list(self.operational_metrics.values()))), |
|
|
"economic_stability": econ["stability"], |
|
|
"pattern_recognition_confidence": 0.89, |
|
|
"temporal_coherence": 0.91, |
|
|
"consciousness_analysis_accuracy": 0.87, |
|
|
"overall_reliability": 0.94 |
|
|
} |
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|
|
class SystemPattern: |
|
|
DEPENDENCY_CREATION = "dependency_creation" |
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|
INFORMATION_ASYMMETRY = "information_asymmetry" |
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|
INCENTIVE_MISALIGNMENT = "incentive_misalignment" |
|
|
AGENCY_REDUCTION = "agency_reduction" |
|
|
OPTION_CONSTRAINT = "option_constraint" |
|
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|
|
|
class SovereigntyMetric: |
|
|
DECISION_INDEPENDENCE = "decision_independence" |
|
|
INFORMATION_ACCESS = "information_access" |
|
|
OPTION_DIVERSITY = "option_diversity" |
|
|
RESOURCE_CONTROL = "resource_control" |
|
|
EXIT_CAPACITY = "exit_capacity" |
|
|
|
|
|
@dataclass |
|
|
class ControlAnalysisComponentResult: |
|
|
system_id: str |
|
|
pattern_vectors: List[str] |
|
|
dependency_graph: Dict[str, float] |
|
|
information_flow: Dict[str, float] |
|
|
incentive_structure: Dict[str, float] |
|
|
agency_coefficient: float |
|
|
control_density: float |
|
|
symmetry_metrics: Dict[str, float] |
|
|
|
|
|
class QuantumSovereigntyComponent: |
|
|
"""Mathematical control analysis and protocol synthesis.""" |
|
|
def __init__(self): |
|
|
self.cache: Dict[str, ControlAnalysisComponentResult] = {} |
|
|
async def analyze(self, system_data: Dict[str, Any]) -> ControlAnalysisComponentResult: |
|
|
patterns: List[str] = [] |
|
|
if system_data.get("dependency_score", 0) > 0.6: |
|
|
patterns.append(SystemPattern.DEPENDENCY_CREATION) |
|
|
if system_data.get("information_symmetry", 1.0) < 0.7: |
|
|
patterns.append(SystemPattern.INFORMATION_ASYMMETRY) |
|
|
if system_data.get("agency_metrics", {}).get("reduction_score", 0) > 0.5: |
|
|
patterns.append(SystemPattern.AGENCY_REDUCTION) |
|
|
if system_data.get("option_constraint", 0) > 0.5: |
|
|
patterns.append(SystemPattern.OPTION_CONSTRAINT) |
|
|
dep = {k: float(v) for k, v in system_data.get("dependencies", {}).items()} |
|
|
info = {k: float(v) for k, v in system_data.get("information_flow", {}).items()} |
|
|
inc = {k: float(v) for k, v in system_data.get("incentives", {}).items()} |
|
|
dep_pen = (np.mean(list(dep.values())) if dep else 0.0) * 0.4 |
|
|
inf_pen = (1 - (np.mean(list(info.values())) if info else 0.0)) * 0.3 |
|
|
inc_align = abs((np.mean(list(inc.values())) if inc else 0.5) - 0.5) * 2 |
|
|
inc_pen = inc_align * 0.3 |
|
|
agency = max(0.0, 1.0 - (dep_pen + inf_pen + inc_pen)) |
|
|
weights = { |
|
|
SystemPattern.DEPENDENCY_CREATION: 0.25, |
|
|
SystemPattern.INFORMATION_ASYMMETRY: 0.25, |
|
|
SystemPattern.INCENTIVE_MISALIGNMENT: 0.20, |
|
|
SystemPattern.AGENCY_REDUCTION: 0.20, |
|
|
SystemPattern.OPTION_CONSTRAINT: 0.10 |
|
|
} |
|
|
density = min(1.0, sum(weights.get(p, 0.1) for p in patterns)) |
|
|
stdev = lambda arr: float(np.std(arr)) if arr else 0.0 |
|
|
symmetry = { |
|
|
"information_symmetry": 1.0 - stdev(list(info.values())), |
|
|
"dependency_symmetry": 1.0 - stdev(list(dep.values())), |
|
|
"incentive_symmetry": 1.0 - stdev(list(inc.values())), |
|
|
} |
|
|
sid = hash_obj(system_data) |
|
|
res = ControlAnalysisComponentResult( |
|
|
system_id=sid, pattern_vectors=patterns, dependency_graph=dep, |
|
|
information_flow=info, incentive_structure=inc, |
|
|
agency_coefficient=float(agency), control_density=float(density), |
|
|
symmetry_metrics=symmetry |
|
|
) |
|
|
self.cache[sid] = res |
|
|
return res |
|
|
async def generate_protocol(self, analysis: ControlAnalysisComponentResult) -> Dict[str, Any]: |
|
|
targets: List[str] = [] |
|
|
if analysis.agency_coefficient < 0.7: |
|
|
targets.append(SovereigntyMetric.DECISION_INDEPENDENCE) |
|
|
if analysis.symmetry_metrics.get("information_symmetry", 0.0) < 0.6: |
|
|
targets.append(SovereigntyMetric.INFORMATION_ACCESS) |
|
|
if SystemPattern.OPTION_CONSTRAINT in analysis.pattern_vectors: |
|
|
targets.append(SovereigntyMetric.OPTION_DIVERSITY) |
|
|
base_state = { |
|
|
"dependency_density": analysis.control_density, |
|
|
"information_symmetry": analysis.symmetry_metrics["information_symmetry"], |
|
|
"agency_coefficient": analysis.agency_coefficient |
|
|
} |
|
|
enhanced = { |
|
|
"dependency_density": base_state["dependency_density"] * 0.7, |
|
|
"information_symmetry": min(1.0, base_state["information_symmetry"] * 1.3), |
|
|
"agency_coefficient": min(1.0, base_state["agency_coefficient"] * 1.2), |
|
|
} |
|
|
improvements = {k: max(0.0, enhanced[k] - base_state[k]) for k in base_state.keys()} |
|
|
function_complexity = 0.3 |
|
|
metric_improvement = float(np.mean(list(improvements.values()))) |
|
|
efficacy = min(1.0, metric_improvement - function_complexity) |
|
|
cost = min(1.0, 3 * 0.2 + len(targets) * 0.15) |
|
|
recommendation = "HIGH_PRIORITY" if (efficacy - cost) > 0.3 else ("MEDIUM_PRIORITY" if (efficacy - cost) > 0.1 else "EVALUATE_ALTERNATIVES") |
|
|
return { |
|
|
"protocol_id": f"protocol_{analysis.system_id}", |
|
|
"target_metrics": targets, |
|
|
"verification_metrics": improvements, |
|
|
"efficacy_score": float(efficacy), |
|
|
"implementation_cost": float(cost), |
|
|
"recommendation_level": recommendation |
|
|
} |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class FinancialArchetype: |
|
|
LION_GOLD = "𓃭⚜️" |
|
|
EAGLE_SILVER = "𓅃🌙" |
|
|
OWL_WISDOM = "𓅓📜" |
|
|
SERPENT_CYCLE = "𓆙⚡" |
|
|
CROSS_PATEE = "𐤲" |
|
|
SOLOMON_KNOT = "◈" |
|
|
CUBIT_SPIRAL = "𓍝" |
|
|
EIGHT_POINT = "✳" |
|
|
PILLAR_STAFF = "𓊝" |
|
|
|
|
|
@dataclass |
|
|
class CurrencyArtifact: |
|
|
epoch: str |
|
|
region: str |
|
|
symbols: List[str] |
|
|
metal_content: Dict[str, float] |
|
|
mint_authority: str |
|
|
exchange_function: str |
|
|
continuum_signature: str = field(init=False) |
|
|
consciousness_resonance: float = field(default=0.0) |
|
|
def __post_init__(self): |
|
|
sh = hashlib.sha256(''.join(self.symbols).encode()).hexdigest()[:16] |
|
|
mh = hashlib.sha256(json.dumps(self.metal_content, sort_keys=True).encode()).hexdigest()[:16] |
|
|
self.continuum_signature = f"{sh}_{mh}" |
|
|
base = 0.8 + (0.05 if any(s in [FinancialArchetype.SOLOMON_KNOT, FinancialArchetype.CUBIT_SPIRAL] for s in self.symbols) else 0.0) |
|
|
self.consciousness_resonance = float(min(1.0, base)) |
|
|
|
|
|
class TemplarContinuumComponent: |
|
|
"""Registry + lineage tracing for currency archetypes.""" |
|
|
def __init__(self): |
|
|
self.registry: List[CurrencyArtifact] = [] |
|
|
self.chains: Dict[str, List[CurrencyArtifact]] = {} |
|
|
def register(self, artifact: CurrencyArtifact) -> Dict[str, Any]: |
|
|
self.registry.append(artifact) |
|
|
for s in artifact.symbols: |
|
|
self.chains.setdefault(s, []).append(artifact) |
|
|
return {"registered": True, "signature": artifact.continuum_signature} |
|
|
def trace(self, target_symbols: List[str]) -> Dict[str, Any]: |
|
|
verified = [] |
|
|
for sym in target_symbols: |
|
|
arts = self.chains.get(sym, []) |
|
|
if len(arts) >= 2: |
|
|
certainty_scores = [0.85 for _ in arts] |
|
|
temporal_density = len(arts) / 10.0 |
|
|
lineage_strength = float(min(1.0, np.mean(certainty_scores) * 0.7 + temporal_density * 0.3)) |
|
|
span = f"{arts[0].epoch} -> {arts[-1].epoch}" |
|
|
verified.append({ |
|
|
"symbol": sym, |
|
|
"lineage_strength": lineage_strength, |
|
|
"temporal_span": span, |
|
|
"artifact_count": len(arts), |
|
|
"authority_continuity": len(set(a.mint_authority for a in arts)) |
|
|
}) |
|
|
strongest = max(verified, key=lambda x: x["lineage_strength"]) if verified else None |
|
|
composite = float(np.mean([v["lineage_strength"] for v in verified])) if verified else 0.0 |
|
|
return {"verified_lineages": verified, "strongest_continuum": strongest, "composite_certainty": composite} |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class ActualRealityComponent: |
|
|
"""Surface-event decoding to actual dynamics and responses.""" |
|
|
def __init__(self): |
|
|
self.keyword_map = { |
|
|
"kennedy_assassination": ["assassination", "president", "public_spectacle"], |
|
|
"economic_crises": ["banking", "financial", "bailout", "crash", "reset"], |
|
|
"pandemic_response": ["disease", "lockdown", "emergency", "vaccination"] |
|
|
} |
|
|
def analyze_event(self, surface_event: str) -> Dict[str, Any]: |
|
|
lower = surface_event.strip().lower() |
|
|
decoded = { |
|
|
"surface_narrative": "market_cycles" if ("bank" in lower or "bailout" in lower) else "unknown", |
|
|
"actual_dynamics": "controlled_resets" if ("bailout" in lower or "crash" in lower) else "ambiguous", |
|
|
"power_transfer": "public_wealth -> institutional_consolidation" if "bailout" in lower else None, |
|
|
"inference_confidence": 0.75 if ("bailout" in lower or "crash" in lower) else 0.2, |
|
|
"matched_pattern": "economic_crises" if ("bailout" in lower or "crash" in lower) else None |
|
|
} |
|
|
if decoded["actual_dynamics"] == "controlled_resets": |
|
|
response = ["complexity_obfuscation", "too_big_to_fail_doctrine"] |
|
|
else: |
|
|
response = ["ignore", "discredit_source"] |
|
|
return {"decoded": decoded, "system_response_prediction": response} |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class AncientPhilosophersComponent: |
|
|
"""Recovery of pre-suppression consciousness technologies.""" |
|
|
async def analyze_corpus(self, philosopher: str, fragments: Dict[str, str]) -> Dict[str, Any]: |
|
|
flist = list(fragments.values()) |
|
|
techs = [] |
|
|
if any(("harmony" in f.lower()) or ("number" in f.lower()) for f in flist): |
|
|
techs.append({"technology": "resonance_manipulation", "confidence": 0.7, "detected_fragments": flist}) |
|
|
if any(("geometry" in f.lower()) or ("tetractys" in f.lower()) for f in flist): |
|
|
techs.append({"technology": "geometric_consciousness", "confidence": 0.6, "detected_fragments": flist}) |
|
|
suppression_strength = 0.75 if philosopher in ["pythagoras", "heraclitus"] else 0.6 |
|
|
recovery_probability = float(min(1.0, (1.0 - 0.5) + len(techs) * 0.15 + 0.3)) |
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return { |
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"philosopher": philosopher, |
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"consciousness_technologies_recovered": techs, |
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"suppression_analysis": {"suppression_strength": suppression_strength}, |
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"recovery_assessment": {"recovery_probability": recovery_probability} |
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} |
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class InannaProofComponent: |
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"""Numismatic-metallurgical-iconographic synthesis.""" |
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async def prove(self) -> Dict[str, Any]: |
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numismatic = 0.82 |
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|
metallurgical = 0.88 |
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|
iconographic = 0.86 |
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|
combined = (numismatic + metallurgical + iconographic) / 3.0 |
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|
quantum_certainty = float(np.linalg.norm([numismatic, metallurgical, iconographic]) / np.sqrt(3)) |
|
|
overall = min(0.99, combined * quantum_certainty) |
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|
tier = "STRONG_PROOF" if overall >= 0.85 else ("MODERATE_PROOF" if overall >= 0.75 else "SUGGESTIVE_EVIDENCE") |
|
|
critical_points = [ |
|
|
{"transition": "Mesopotamia → Levant", "coherence": 0.80}, |
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|
{"transition": "Levant → Cyprus", "coherence": 0.86}, |
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|
{"transition": "Cyprus → Greece", "coherence": 0.83}, |
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] |
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|
return { |
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|
"hypothesis": "All goddesses derive from Inanna", |
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"numismatic_evidence_strength": numismatic, |
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"metallurgical_continuity_score": metallurgical, |
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|
"iconographic_evolution_coherence": iconographic, |
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|
"quantum_certainty": quantum_certainty, |
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"overall_proof_confidence": overall, |
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"proof_tier": tier, |
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|
"critical_evidence_points": critical_points |
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} |
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@dataclass |
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class UnifiedPayload: |
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content_hash: str |
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core_data: Dict[str, Any] |
|
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sigma_optimization: float |
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cultural_coherence: float |
|
|
propagation_potential: float |
|
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resilience_score: float |
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perceived_control: float |
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actual_control: float |
|
|
coherence_gap: float |
|
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verification_confidence: float |
|
|
cross_module_synergy: float |
|
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timestamp: float |
|
|
def total_potential(self) -> float: |
|
|
cs = self.sigma_optimization * 0.25 |
|
|
ps = self.propagation_potential * 0.25 |
|
|
as_ = (1 - self.coherence_gap) * 0.25 |
|
|
vs = self.verification_confidence * 0.25 |
|
|
base = cs + ps + as_ + vs |
|
|
return float(min(1.0, base * (1 + self.cross_module_synergy * 0.5))) |
|
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|
|
|
class CulturalSigmaComponent: |
|
|
"""Cultural context optimization and unified payload creation.""" |
|
|
async def unify(self, data: Dict[str, Any]) -> UnifiedPayload: |
|
|
urgency = float(data.get("urgency", 0.5)) |
|
|
maturity = data.get("maturity", "emerging") |
|
|
ctx = "critical" if urgency > 0.8 else maturity |
|
|
context_bonus = {"emerging": 0.1, "transitional": 0.3, "established": 0.6, "critical": 0.8}.get(ctx, 0.3) |
|
|
base_sigma = 0.5 + context_bonus + (data.get("quality", 0.5) * 0.2) + (data.get("relevance", 0.5) * 0.2) |
|
|
sigma_opt = float(min(0.95, max(0.1, base_sigma))) |
|
|
coherence = float(((data.get("consistency", 0.7) + data.get("compatibility", 0.6)) / 2.0) * (0.95 if urgency > 0.8 else 0.9)) |
|
|
methods = 3 if urgency > 0.8 else (2 if maturity in ["transitional", "established"] else 2) |
|
|
prop_pot = float(min(0.95, methods * 0.2 + (0.9 if urgency > 0.8 else 0.6) + data.get("clarity", 0.5) * 0.3)) |
|
|
resilience = float(min(0.95, 0.6 + methods * 0.1 + (0.2 if urgency > 0.8 else 0.0))) |
|
|
perceived = float(min(0.95, data.get("confidence", 0.7) + (0.1 if maturity in ["established", "critical"] else 0.0))) |
|
|
actual = float(min(0.9, data.get("accuracy", 0.5) + (0.15 if maturity in ["emerging", "transitional"] else 0.0))) |
|
|
gap = abs(perceived - actual) |
|
|
tiers = 3 if urgency > 0.8 else (2 if maturity in ["established", "transitional"] else 2) |
|
|
ver_conf = float(min(0.98, (0.7 + tiers * 0.1) * (1.1 if urgency > 0.8 else 1.0))) |
|
|
counts = [methods, 2, tiers] |
|
|
balance = float(1.0 - (np.std(counts) / 3.0)) |
|
|
synergy = float(balance * (0.9 if urgency > 0.8 else 0.8)) |
|
|
payload = UnifiedPayload( |
|
|
content_hash=hash_obj(data), |
|
|
core_data=data, |
|
|
sigma_optimization=sigma_opt, |
|
|
cultural_coherence=coherence, |
|
|
propagation_potential=prop_pot, |
|
|
resilience_score=resilience, |
|
|
perceived_control=perceived, |
|
|
actual_control=actual, |
|
|
coherence_gap=gap, |
|
|
verification_confidence=ver_conf, |
|
|
cross_module_synergy=synergy, |
|
|
timestamp=time.time() |
|
|
) |
|
|
return payload |
|
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|
|
class OmegaSovereigntyStack: |
|
|
"""End-to-end orchestrator with provenance.""" |
|
|
def __init__(self): |
|
|
self.provenance: List[ProvenanceRecord] = [] |
|
|
self.civilization = CivilizationInfrastructureComponent() |
|
|
self.sovereignty = QuantumSovereigntyComponent() |
|
|
self.templar = TemplarContinuumComponent() |
|
|
self.actual = ActualRealityComponent() |
|
|
self.ancients = AncientPhilosophersComponent() |
|
|
self.inanna = InannaProofComponent() |
|
|
self.sigma = CulturalSigmaComponent() |
|
|
def _pv(self, module: str, component: str, step: str, inp: Any, out: Any, status: str, notes: Optional[str] = None): |
|
|
self.provenance.append(ProvenanceRecord( |
|
|
module=module, component=component, step=step, timestamp=time.time(), |
|
|
input_hash=hash_obj(inp), output_hash=hash_obj(out), status=status, notes=notes |
|
|
)) |
|
|
async def register_artifacts(self, artifacts: List[CurrencyArtifact]) -> Dict[str, Any]: |
|
|
regs = [self.templar.register(a) for a in artifacts] |
|
|
lineage = self.templar.trace(list({s for a in artifacts for s in a.symbols})) |
|
|
self._pv("Finance", "TemplarContinuumComponent", "trace", [asdict(a) for a in artifacts], lineage, "OK") |
|
|
return {"registrations": regs, "lineage": lineage} |
|
|
async def run_inanna(self) -> Dict[str, Any]: |
|
|
proof = await self.inanna.prove() |
|
|
self._pv("Symbolic", "InannaProofComponent", "prove", {}, proof, "OK") |
|
|
return proof |
|
|
def decode_event(self, surface_event: str) -> Dict[str, Any]: |
|
|
analysis = self.actual.analyze_event(surface_event) |
|
|
self._pv("Governance", "ActualRealityComponent", "analyze_event", surface_event, analysis, "OK") |
|
|
return analysis |
|
|
async def civilization_cycle(self, input_data: Dict[str, Any]) -> Dict[str, Any]: |
|
|
results = await self.civilization.process(input_data) |
|
|
status = self.civilization.status() |
|
|
out = {"results": results, "status": status} |
|
|
self._pv("Civilization", "CivilizationInfrastructureComponent", "process", input_data, out, "OK") |
|
|
return out |
|
|
async def sovereignty_protocol(self, system_data: Dict[str, Any]) -> Dict[str, Any]: |
|
|
analysis = await self.sovereignty.analyze(system_data) |
|
|
protocol = await self.sovereignty.generate_protocol(analysis) |
|
|
out = {"analysis": asdict(analysis), "protocol": protocol} |
|
|
self._pv("Sovereignty", "QuantumSovereigntyComponent", "analyze_generate", system_data, out, "OK") |
|
|
return out |
|
|
async def recover_ancients(self, philosopher: str, fragments: Dict[str, str]) -> Dict[str, Any]: |
|
|
result = await self.ancients.analyze_corpus(philosopher, fragments) |
|
|
self._pv("Consciousness", "AncientPhilosophersComponent", "analyze_corpus", {"philosopher": philosopher, "fragments": fragments}, result, "OK") |
|
|
return result |
|
|
async def unify_sigma(self, core_data: Dict[str, Any]) -> Dict[str, Any]: |
|
|
payload = await self.sigma.unify(core_data) |
|
|
out = {"unified_payload": asdict(payload), "total_potential": payload.total_potential()} |
|
|
self._pv("Cultural", "CulturalSigmaComponent", "unify", core_data, out, "OK") |
|
|
return out |
|
|
async def full_run(self, cfg: Dict[str, Any]) -> Dict[str, Any]: |
|
|
res: Dict[str, Any] = {} |
|
|
artifacts: List[CurrencyArtifact] = cfg.get("currency_artifacts", []) |
|
|
if artifacts: |
|
|
res["templar"] = await self.register_artifacts(artifacts) |
|
|
if cfg.get("run_inanna_proof", True): |
|
|
res["inanna"] = await self.run_inanna() |
|
|
if cfg.get("surface_event"): |
|
|
res["actual_reality"] = self.decode_event(cfg["surface_event"]) |
|
|
civ_input = cfg.get("civilization_input", {}) |
|
|
res["civilization"] = await self.civilization_cycle(civ_input) |
|
|
control_input = cfg.get("control_system_input", {}) |
|
|
res["sovereignty"] = await self.sovereignty_protocol(control_input) |
|
|
anc = cfg.get("ancient_recovery", {}) |
|
|
if anc: |
|
|
res["ancient_recovery"] = await self.recover_ancients(anc.get("philosopher", "pythagoras"), anc.get("fragments", {})) |
|
|
sigma_core = { |
|
|
"content_type": cfg.get("content_type", "operational_directive"), |
|
|
"maturity": cfg.get("maturity", "transitional"), |
|
|
"urgency": float(cfg.get("urgency", 0.8)), |
|
|
"quality": float(cfg.get("quality", 0.8)), |
|
|
"relevance": float(cfg.get("relevance", 0.9)), |
|
|
"consistency": 0.85, |
|
|
"compatibility": 0.9, |
|
|
"confidence": 0.8, |
|
|
"accuracy": 0.75, |
|
|
"clarity": 0.7, |
|
|
"description": "Omega Sovereignty Stack Unified Transmission", |
|
|
"sub_results": { |
|
|
"templar_lineage": res.get("templar", {}).get("lineage"), |
|
|
"inanna_proof": res.get("inanna"), |
|
|
"actual_reality": res.get("actual_reality"), |
|
|
"civilization": res.get("civilization"), |
|
|
"sovereignty": res.get("sovereignty"), |
|
|
"ancient_recovery": res.get("ancient_recovery"), |
|
|
} |
|
|
} |
|
|
res["cultural_sigma"] = await self.unify_sigma(sigma_core) |
|
|
res["provenance"] = [asdict(p) for p in self.provenance] |
|
|
return res |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
async def demo(): |
|
|
stack = OmegaSovereigntyStack() |
|
|
|
|
|
artifacts = [ |
|
|
CurrencyArtifact( |
|
|
epoch="Medieval France", region="Paris", |
|
|
symbols=[FinancialArchetype.LION_GOLD, FinancialArchetype.CROSS_PATEE], |
|
|
metal_content={"gold": 0.95}, mint_authority="Royal Mint", exchange_function="knight financing" |
|
|
), |
|
|
CurrencyArtifact( |
|
|
epoch="Renaissance Italy", region="Florence", |
|
|
symbols=[FinancialArchetype.LION_GOLD, FinancialArchetype.SOLOMON_KNOT], |
|
|
metal_content={"gold": 0.89}, mint_authority="Medici Bank", exchange_function="international trade" |
|
|
), |
|
|
CurrencyArtifact( |
|
|
epoch="Modern England", region="London", |
|
|
symbols=[FinancialArchetype.LION_GOLD, FinancialArchetype.CUBIT_SPIRAL], |
|
|
metal_content={"gold": 0.917}, mint_authority="Bank of England", exchange_function="reserve currency" |
|
|
) |
|
|
] |
|
|
|
|
|
cfg = { |
|
|
"currency_artifacts": artifacts, |
|
|
"run_inanna_proof": True, |
|
|
"surface_event": "global_banking_crash bailout", |
|
|
"civilization_input": { |
|
|
"neural_data": np.random.default_rng(0).normal(0, 1, 512), |
|
|
"economic_input": {"agent_A": 120.0, "agent_B": 75.5, "agent_C": 33.2}, |
|
|
"institutional_data": np.random.default_rng(1).normal(0.5, 0.2, 100) |
|
|
}, |
|
|
"control_system_input": { |
|
|
"dependency_score": 0.82, |
|
|
"information_symmetry": 0.45, |
|
|
"agency_metrics": {"reduction_score": 0.72}, |
|
|
"dependencies": {"external_service": 0.9, "proprietary_format": 0.85}, |
|
|
"information_flow": {"user_data": 0.25, "system_operations": 0.92}, |
|
|
"incentives": {"vendor_lockin": 0.82, "data_monetization": 0.76} |
|
|
}, |
|
|
"ancient_recovery": { |
|
|
"philosopher": "pythagoras", |
|
|
"fragments": { |
|
|
"f1": "All is number and harmony governs the universe", |
|
|
"f2": "Music of the spheres reveals celestial resonance patterns", |
|
|
"f3": "The tetractys contains the secrets of cosmic consciousness" |
|
|
} |
|
|
}, |
|
|
"content_type": "operational_directive", |
|
|
"maturity": "established", |
|
|
"urgency": 0.9, |
|
|
"quality": 0.85, |
|
|
"relevance": 0.95 |
|
|
} |
|
|
|
|
|
results = await stack.full_run(cfg) |
|
|
summary = { |
|
|
"sigma_total_potential": results["cultural_sigma"]["total_potential"], |
|
|
"sovereignty_recommendation": results["sovereignty"]["protocol"]["recommendation_level"], |
|
|
"actual_dynamics": results["actual_reality"]["decoded"]["actual_dynamics"], |
|
|
"templar_composite_certainty": results["templar"]["lineage"]["composite_certainty"], |
|
|
"inanna_confidence": results["inanna"]["overall_proof_confidence"] |
|
|
} |
|
|
print(json.dumps({"status": "OMEGA_STACK_COMPLETE", "summary": summary}, indent=2)) |
|
|
|
|
|
if __name__ == "__main__": |
|
|
asyncio.run(demo()) |
