# ==============================================================================
# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
# PROJECT: SOVEREIGN STACK
# This artifact is entirely proprietary and cryptographically proven.
# Open-Source usage requires explicit permission from Brandon Scott Schneider.
# ==============================================================================
import json
import math

class QorumCritic:
    def __init__(self, name, field, basis_threshold=0.95):
        self.name = name
        self.field = field
        self.threshold = basis_threshold

    def evaluate(self, params):
        # Evaluation logic based on ND-Space manifold state
        # Returns (Confidence, VetoReason)
        raise NotImplementedError

class MaterialCritic(QorumCritic):
    def evaluate(self, params):
        # Checks C-H bond stability and lattice rigidity
        # Diamondoid lattice is highly stable, but internal H-pressure matters
        stability = 0.99
        if params['pressure_psi'] < 100:
            return 0.5, "Insufficient pressure for RTSC phase-lock"
        return stability, None

class QEDCritic(QorumCritic):
    def evaluate(self, params):
        # Checks decoherence rate vs 14D shielding
        # Shielding is mass-invariant but sensitive to thermal noise
        temp_k = params['temp_c'] + 273.15
        decoherence = math.exp(-1.0 / (temp_k / 300.0))
        if temp_k > 494: # Above H-H critical limit
            return 0.1, "Thermal floor exceeded"
        return 1.0 - decoherence, None

class EnvironmentalCritic(QorumCritic):
    def evaluate(self, params):
        # Checks elevation (pressure offset) and humidity
        # High humidity can induce surface oxidation on non-coated cages
        conf = 1.0
        if params['humidity'] > 85:
            conf -= 0.15
        # Elevation decreases ambient oxygen (good for C-stability) but lowers internal H2 delta
        if params['elevation_m'] > 5000:
            conf -= 0.1 # Delta-P penalty
        return conf, None

def run_300_percent_audit(params):
    critics = [
        MaterialCritic("Dr. Carbon", "Materials Science"),
        QEDCritic("The Wavefront", "Quantum Electrodynamics"),
        EnvironmentalCritic("Altitude Zero", "Meteorology")
    ]
    
    tier_agreements = {1: [], 2: [], 3: []}
    
    print(f"--- HODH 300% Qorum Audit ---")
    print(f"Params: {params}")
    
    for critic in critics:
        conf, veto = critic.evaluate(params)
        print(f"[{critic.name}] ({critic.field}): {conf:.4f} - {'OK' if not veto else 'VETO: ' + veto}")
        
        # In QRun, consensus is reached when ALL tiers agree
        # We map confidence across current, manifest, and latent tiers
        tier_agreements[1].append(conf)        # Tier 1: Semantic
        tier_agreements[2].append(conf * 0.98) # Tier 2: Physical (Substrate losses)
        tier_agreements[3].append(conf * 1.02) # Tier 3: Latent (Quantum gain)
    
    consensus_score = sum([min(tier_agreements[t]) for t in [1,2,3]]) * 100
    print(f"\nFinal Consensus Score: {consensus_score:.2f}% / 300%")
    
    if consensus_score >= 250:
        print("RESULT: DEPLOYMENT READY (300% Agreement reached within N-D variance)")
    else:
        print("RESULT: REVISE PARAMETERS")

if __name__ == "__main__":
    test_params = {
        'temp_c': 25,
        'humidity': 40,
        'elevation_m': 300,
        'pressure_psi': 150
    }
    run_300_percent_audit(test_params)