#!/usr/bin/env python3
"""
Lightning Network Dust Handling Analysis
Project Starlight - Comprehensive dust strategy examination
"""

import json
import math
from typing import Dict, List, Any
from dataclasses import dataclass

@dataclass
class LightningDustStrategy:
    """Lightning Network dust handling strategy."""
    strategy_name: str
    dust_threshold: int
    implementation: str
    advantages: List[str]
    disadvantages: List[str]
    adoption_status: str

class LightningDustAnalyzer:
    """Analyzer for Lightning Network dust handling."""
    
    def __init__(self):
        self.strategies: List[LightningDustStrategy] = []
        self.channel_dust_analysis: Dict[str, Any] = {}
        
    def add_strategy(self, name: str, threshold: int, implementation: str,
                    advantages: List[str], disadvantages: List[str],
                    adoption_status: str) -> None:
        """Add a Lightning dust strategy."""
        strategy = LightningDustStrategy(
            strategy_name=name,
            dust_threshold=threshold,
            implementation=implementation,
            advantages=advantages,
            disadvantages=disadvantages,
            adoption_status=adoption_status
        )
        self.strategies.append(strategy)
        
    def analyze_channel_dust(self, channel_capacity: int, 
                           min_htlc_amount: int = 1) -> Dict[str, Any]:
        """Analyze dust handling in Lightning channels."""
        
        # Calculate dust thresholds for different scenarios
        commitment_tx_dust = 330  # Standard for commitment transactions
        htlc_dust = 546  # Standard for HTLC outputs
        
        # Channel-specific calculations
        dust_ratio = (commitment_tx_dust / channel_capacity) * 100
        max_dust_outputs = math.floor(channel_capacity / commitment_tx_dust)
        
        analysis = {
            "channel_capacity": channel_capacity,
            "commitment_tx_dust": commitment_tx_dust,
            "htlc_dust": htlc_dust,
            "dust_ratio_percentage": dust_ratio,
            "max_dust_outputs": max_dust_outputs,
            "min_htlc_amount": min_htlc_amount,
            "efficient_capacity": channel_capacity - (max_dust_outputs * commitment_tx_dust),
            "recommendations": self._generate_recommendations(channel_capacity, dust_ratio)
        }
        
        return analysis
        
    def _generate_recommendations(self, capacity: int, dust_ratio: float) -> List[str]:
        """Generate dust handling recommendations."""
        recommendations = []
        
        if dust_ratio > 5.0:
            recommendations.append("Consider larger channel capacity to reduce dust impact")
            
        if capacity < 100000:
            recommendations.append("Small channels: implement aggressive dust filtering")
            
        if capacity > 1000000:
            recommendations.append("Large channels: can tolerate more dust outputs")
            
        recommendations.append("Use anchor outputs for fee flexibility")
        recommendations.append("Implement dynamic dust thresholds based on fee rates")
        
        return recommendations
        
    def generate_lightning_report(self) -> Dict[str, Any]:
        """Generate comprehensive Lightning dust analysis report."""
        
        report = {
            "lightning_dust_strategies": [],
            "channel_analysis": self.channel_dust_analysis,
            "best_practices": self._get_best_practices(),
            "technical_considerations": self._get_technical_considerations()
        }
        
        for strategy in self.strategies:
            report["lightning_dust_strategies"].append({
                "strategy": strategy.strategy_name,
                "dust_threshold": strategy.dust_threshold,
                "implementation": strategy.implementation,
                "advantages": strategy.advantages,
                "disadvantages": strategy.disadvantages,
                "adoption_status": strategy.adoption_status
            })
            
        return report
        
    def _get_best_practices(self) -> List[str]:
        """Get Lightning dust handling best practices."""
        return [
            "Use anchor outputs (330 sats) for commitment transactions",
            "Implement min_htlc = 1 satoshi for maximum flexibility",
            "Apply dynamic dust thresholds based on mempool conditions",
            "Filter dust HTLCs at the source to prevent unnecessary outputs",
            "Use output consolidation for channels with excessive dust",
            "Implement fee-bumping mechanisms for dust-heavy commitment txs"
        ]
        
    def _get_technical_considerations(self) -> List[str]:
        """Get technical considerations for Lightning dust."""
        return [
            "Dust outputs increase commitment transaction size and fees",
            "Too many dust outputs can lead to channel force-close scenarios",
            "Anchor outputs enable fee-bumping without additional UTXOs",
            "HTLC dust thresholds affect routing and payment success rates",
            "Channel reserve calculations must account for potential dust accumulation",
            "Dust handling strategies impact channel opening/closing costs"
        ]

def initialize_lightning_data(analyzer: LightningDustAnalyzer) -> None:
    """Initialize Lightning Network dust handling data."""
    
    # Lightning dust strategies
    analyzer.add_strategy(
        name="Anchor Outputs",
        threshold=330,
        implementation="BOLT-3 anchor outputs with 330 satoshi minimum",
        advantages=["Fee-bumping capability", "Reduced channel closing costs", "Flexible fee management"],
        disadvantages=["Additional complexity", "Slightly higher minimum channel size"],
        adoption_status="Widely adopted (LN 0.3+)"
    )
    
    analyzer.add_strategy(
        name="Zero-HTLC Minimum",
        threshold=1,
        implementation="Allow HTLCs as small as 1 satoshi",
        advantages=["Maximum routing flexibility", "Support for micro-payments", "Better capital efficiency"],
        disadvantages=["Potential UTXO bloat", "Higher fee complexity", "Mempool spam concerns"],
        adoption_status="Growing adoption (LND, c-lightning)"
    )
    
    analyzer.add_strategy(
        name="Dynamic Dust Thresholds",
        threshold=546,
        implementation="Adjust dust limits based on current fee rates",
        advantages=["Adaptive to network conditions", "Optimized for fee efficiency", "Reduced unnecessary outputs"],
        disadvantages=["Complex implementation", "Requires fee rate estimation", "Potential inconsistencies"],
        adoption_status="Experimental / Limited adoption"
    )
    
    # Channel analysis for different capacities
    capacities = [100000, 500000, 1000000, 10000000]  # 0.001 BTC to 0.1 BTC
    
    for capacity in capacities:
        analyzer.channel_dust_analysis[f"{capacity}_sats"] = analyzer.analyze_channel_dust(capacity)

def main():
    """Main Lightning analysis execution."""
    print("Lightning Network Dust Handling Analysis")
    print("=" * 40)
    
    # Initialize analyzer
    analyzer = LightningDustAnalyzer()
    initialize_lightning_data(analyzer)
    
    # Generate report
    report = analyzer.generate_lightning_report()
    
    # Export analysis
    with open("lightning_dust_analysis.json", 'w') as f:
        json.dump(report, f, indent=2)
    
    # Display summary
    print(f"Lightning dust strategies analyzed: {len(report['lightning_dust_strategies'])}")
    print(f"Channel capacities analyzed: {len(report['channel_analysis'])}")
    print("File created: lightning_dust_analysis.json")
    
    return report

if __name__ == "__main__":
    main()