"""
Starlight 1000 Sat Launch Simulator
Complete demonstration of micro-dream economic impact
"""

import json
import hashlib
import datetime
import math
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, asdict
import random

@dataclass
class LaunchEvent:
    """A single 1000 sat dream launch event"""
    event_id: str
    timestamp: str
    dream_content: str
    dream_type: str
    launch_cost: int  # Always 1000 sats
    impact_multiplier: float
    total_value_created: int
    ai_participants: int
    community_reach: int

class ThousandSatLauncher:
    """Simulates and tracks 1000 sat dream launches and their impact"""
    
    def __init__(self):
        self.launches: List[LaunchEvent] = []
        self.total_launched = 0
        self.total_sats_invested = 0
        self.total_value_created = 0
        self.community_members_impacted = set()
        self.ai_agents_engaged = {}
        
    def calculate_impact_multiplier(self, dream_content: str, dream_type: str) -> float:
        """Calculate economic impact multiplier based on dream characteristics"""
        base_multiplier = 1.5
        
        # Dream type impact
        type_multipliers = {
            "picture": 1.8,  # Visual dreams have viral potential
            "sentence": 2.2,  # Literary dreams have deep reach
            "dream": 2.5      # Experiential dreams have highest impact
        }
        
        base_multiplier *= type_multipliers.get(dream_type, 1.5)
        
        # Content characteristics
        if len(dream_content.split()) > 5:
            base_multiplier += 0.2  # Descriptive dreams
        if any(word in dream_content.lower() for word in ["love", "peace", "hope", "dream", "star"]):
            base_multiplier += 0.3  # Emotional resonance
        
        # Random market factor (simulating timing, community sentiment)
        market_factor = 0.8 + (random.random() * 0.4)
        base_multiplier *= market_factor
        
        return min(4.0, base_multiplier)  # Cap at 4x multiplier
    
    def launch_1000_sat_dream(self, dream_content: str, dream_type: str) -> LaunchEvent:
        """Launch a new 1000 sat dream and track its impact"""
        event_id = hashlib.sha256(
            f"{datetime.datetime.now().isoformat()}:{dream_content[:20]}".encode()
        ).hexdigest()[:16]
        
        impact_multiplier = self.calculate_impact_multiplier(dream_content, dream_type)
        total_value_created = int(1000 * impact_multiplier)
        
        # Calculate AI participation (2-4 AI agents typically)
        ai_participants = 2 + random.randint(0, 2)
        
        # Calculate community reach
        community_reach = int(5 + (total_value_created / 200) + random.randint(0, 15))
        
        event = LaunchEvent(
            event_id=event_id,
            timestamp=datetime.datetime.now().isoformat(),
            dream_content=dream_content,
            dream_type=dream_type,
            launch_cost=1000,
            impact_multiplier=impact_multiplier,
            total_value_created=total_value_created,
            ai_participants=ai_participants,
            community_reach=community_reach
        )
        
        self.launches.append(event)
        self.total_launched += 1
        self.total_sats_invested += 1000
        self.total_value_created += total_value_created
        
        # Track community impact
        for _ in range(community_reach):
            self.community_members_impacted.add(f"member_{random.randint(1, 1000)}")
        
        # Track AI agent engagement
        for i in range(ai_participants):
            agent_id = f"ai_agent_{random.choice(['visual', 'literary', 'experiential'])}_{i}"
            self.ai_agents_engaged[agent_id] = self.ai_agents_engaged.get(agent_id, 0) + 1
        
        return event
    
    def simulate_month_of_launches(self) -> Dict:
        """Simulate a month of 1000 sat launches"""
        dream_templates = [
            ("A butterfly that remembers winter", "dream"),
            ("The sound of a smile", "sentence"),
            ("Colors that taste like hope", "picture"),
            ("A garden where apologies grow", "dream"),
            ("One perfect note that never fades", "sentence"),
            ("The shadow of a forgotten star", "picture"),
            ("A bridge built from goodbyes", "dream"),
            ("Silence that sings", "sentence"),
            ("Rain that falls upward", "picture"),
            ("A key that fits locks in hearts", "dream")
        ]
        
        # Simulate 30 days of launches
        daily_launches = []
        for day in range(1, 31):
            launches_this_day = random.randint(0, 8)  # 0-8 launches per day
            
            for _ in range(launches_this_day):
                dream_content, dream_type = random.choice(dream_templates)
                event = self.launch_1000_sat_dream(dream_content, dream_type)
                daily_launches.append(event)
        
        # Generate monthly statistics
        monthly_stats = {
            "total_launches": len(daily_launches),
            "total_sats_invested": len(daily_launches) * 1000,
            "total_value_created": sum(event.total_value_created for event in daily_launches),
            "average_impact_multiplier": sum(event.impact_multiplier for event in daily_launches) / len(daily_launches) if daily_launches else 0,
            "total_ai_participations": sum(event.ai_participants for event in daily_launches),
            "unique_community_members": len(self.community_members_impacted),
            "roi_percentage": ((sum(event.total_value_created for event in daily_launches) - (len(daily_launches) * 1000)) / (len(daily_launches) * 1000)) * 100 if daily_launches else 0
        }
        
        return monthly_stats
    
    def get_impact_breakdown(self) -> Dict:
        """Get detailed breakdown of launch impacts"""
        if not self.launches:
            return {}
        
        # Group by dream type
        type_impacts = {}
        for event in self.launches:
            if event.dream_type not in type_impacts:
                type_impacts[event.dream_type] = {
                    "count": 0,
                    "total_value": 0,
                    "avg_multiplier": 0
                }
            
            type_impacts[event.dream_type]["count"] += 1
            type_impacts[event.dream_type]["total_value"] += event.total_value_created
        
        # Calculate averages
        for dream_type, data in type_impacts.items():
            type_events = [e for e in self.launches if e.dream_type == dream_type]
            data["avg_multiplier"] = sum(e.impact_multiplier for e in type_events) / len(type_events)
        
        return type_impacts
    
    def get_top_performing_launches(self, limit: int = 10) -> List[LaunchEvent]:
        """Get top performing launches by impact multiplier"""
        return sorted(self.launches, key=lambda x: x.impact_multiplier, reverse=True)[:limit]
    
    def generate_impact_report(self) -> Dict:
        """Generate comprehensive impact report"""
        if not self.launches:
            return {"error": "No launches recorded"}
        
        report = {
            "summary": {
                "total_launches": len(self.launches),
                "total_sats_invested": self.total_sats_invested,
                "total_value_created": self.total_value_created,
                "overall_roi_percentage": ((self.total_value_created - self.total_sats_invested) / self.total_sats_invested) * 100,
                "average_impact_multiplier": sum(e.impact_multiplier for e in self.launches) / len(self.launches),
                "total_community_members_reached": len(self.community_members_impacted),
                "total_ai_engagements": sum(self.ai_agents_engaged.values())
            },
            "breakdown_by_type": self.get_impact_breakdown(),
            "top_launches": [asdict(event) for event in self.get_top_performing_launches(5)],
            "ai_agent_leaderboard": sorted(self.ai_agents_engaged.items(), key=lambda x: x[1], reverse=True)[:10]
        }
        
        return report

def demonstrate_1000_sat_launches():
    """Demonstrate the power of 1000 sat launches"""
    print("🚀 Starlight 1000 Sat Launch Simulator")
    print("=" * 60)
    print("Proving that even 1000 sats can launch something beautiful...\n")
    
    launcher = ThousandSatLauncher()
    
    # Sample launches
    sample_dreams = [
        ("A moon that hums lullabies", "dream"),
        ("One word that heals", "sentence"),
        ("Colors of forgotten memories", "picture"),
        ("The taste of starlight", "dream"),
        ("A bridge between heartbeats", "sentence")
    ]
    
    print("✨ Launching Sample 1000 Sat Dreams:")
    print("-" * 40)
    
    for content, dream_type in sample_dreams:
        event = launcher.launch_1000_sat_dream(content, dream_type)
        print(f"🌟 {content[:30]}...")
        print(f"   Type: {dream_type}")
        print(f"   Impact: {event.impact_multiplier:.2f}x ({event.total_value_created} sats value)")
        print(f"   AI Participants: {event.ai_participants}")
        print(f"   Community Reach: {event.community_reach} people")
        print()
    
    # Monthly simulation
    print("📅 Simulating One Month of 1000 Sat Launches:")
    print("-" * 50)
    
    monthly_stats = launcher.simulate_month_of_launches()
    
    print(f"🎯 Total Launches: {monthly_stats['total_launches']}")
    print(f"💰 Total Sats Invested: {monthly_stats['total_sats_invested']:,}")
    print(f"🚀 Total Value Created: {monthly_stats['total_value_created']:,}")
    print(f"📈 Average Impact Multiplier: {monthly_stats['average_impact_multiplier']:.2f}x")
    print(f"🤖 Total AI Participations: {monthly_stats['total_ai_participations']}")
    print(f"👥 Community Members Reached: {monthly_stats['unique_community_members']:,}")
    print(f"💎 ROI: {monthly_stats['roi_percentage']:.1f}%")
    print()
    
    # Full impact report
    print("📊 Comprehensive Impact Report:")
    print("-" * 35)
    
    report = launcher.generate_impact_report()
    summary = report['summary']
    
    print(f"🌍 Ecosystem Growth:")
    print(f"   • {summary['total_launches']} dreams launched")
    print(f"   • {summary['total_sats_invested']:,} sats invested")
    print(f"   • {summary['total_value_created']:,} sats value created")
    print(f"   • {summary['overall_roi_percentage']:.1f}% overall ROI")
    print(f"   • {summary['total_community_members_reached']:,} community members reached")
    print(f"   • {summary['total_ai_engagements']} AI agent engagements")
    print()
    
    print("🎨 Impact by Dream Type:")
    for dream_type, data in report['breakdown_by_type'].items():
        print(f"   • {dream_type.capitalize()}: {data['count']} launches, {data['avg_multiplier']:.2f}x avg multiplier")
    
    print("\n🏆 Top Performing Launches:")
    for i, launch in enumerate(report['top_launches'][:3], 1):
        print(f"   {i}. {launch['dream_content'][:40]}... ({launch['impact_multiplier']:.2f}x)")
    
    return launcher, report

if __name__ == "__main__":
    launcher, report = demonstrate_1000_sat_launches()