"""
Reality DNA
Unique fingerprint for each place
Enables global similarity search
"""

from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
import json


@dataclass
class RealityDNA:
    """DNA fingerprint of a place"""
    location_id: str
    location_name: str
    coordinates: Dict[str, float]
    
    # Core dimensions
    walkability: float
    tourism: float
    retail: float
    family: float
    noise: float
    history: float
    safety: float
    greenery: float
    connectivity: float
    affordability: float
    
    # Derived
    dna_vector: List[float]
    dna_hash: str
    
    def to_dict(self) -> Dict:
        return {
            "location_id": self.location_id,
            "location_name": self.location_name,
            "coordinates": self.coordinates,
            "dna": {
                "walkability": round(self.walkability, 1),
                "tourism": round(self.tourism, 1),
                "retail": round(self.retail, 1),
                "family": round(self.family, 1),
                "noise": round(self.noise, 1),
                "history": round(self.history, 1),
                "safety": round(self.safety, 1),
                "greenery": round(self.greenery, 1),
                "connectivity": round(self.connectivity, 1),
                "affordability": round(self.affordability, 1)
            },
            "dna_vector": [round(v, 2) for v in self.dna_vector],
            "dna_hash": self.dna_hash
        }


class RealityDNABuilder:
    """Builds Reality DNA from signals"""
    
    def build(self, location_id: str, location_name: str, signals: Dict[str, float]) -> RealityDNA:
        """Build DNA from signal values"""
        
        # Extract core dimensions
        walkability = signals.get("walkability", 50)
        tourism = signals.get("tourism", 50)
        retail = signals.get("retail_density", 50)
        family = signals.get("family_friendly", 50)
        noise = signals.get("noise_level", 50)
        history = signals.get("historical_value", 50)
        safety = signals.get("safety_index", 50)
        greenery = signals.get("greenery", 50)
        connectivity = signals.get("mobile_connectivity", 50)
        affordability = 100 - signals.get("rent_burden", 50)  # Invert
        
        # Create vector
        vector = [
            walkability, tourism, retail, family, noise,
            history, safety, greenery, connectivity, affordability
        ]
        
        # Create hash
        dna_hash = self._hash_vector(vector)
        
        return RealityDNA(
            location_id=location_id,
            location_name=location_name,
            coordinates=signals.get("coordinates", {"lat": 0, "lng": 0}),
            walkability=walkability,
            tourism=tourism,
            retail=retail,
            family=family,
            noise=noise,
            history=history,
            safety=safety,
            greenery=greenery,
            connectivity=connectivity,
            affordability=affordability,
            dna_vector=vector,
            dna_hash=dna_hash
        )
    
    def _hash_vector(self, vector: List[float]) -> str:
        """Create hash from DNA vector"""
        # Quantize to 10 levels
        quantized = [int(v / 10) for v in vector]
        return "".join(str(min(q, 9)) for q in quantized)


class GlobalSimilaritySearch:
    """Find similar places globally"""
    
    def __init__(self):
        self.dna_database: Dict[str, RealityDNA] = {}
    
    def add_place(self, dna: RealityDNA):
        """Add a place to the database"""
        self.dna_database[dna.location_id] = dna
    
    def find_similar(
        self,
        query_dna: RealityDNA,
        top_k: int = 5,
        exclude_self: bool = True
    ) -> List[Dict]:
        """
        Find places similar to query
        
        Returns:
            List of similar places with similarity scores
        """
        similarities = []
        
        for location_id, candidate in self.dna_database.items():
            if exclude_self and location_id == query_dna.location_id:
                continue
            
            # Calculate similarity
            similarity = self._calculate_similarity(query_dna.dna_vector, candidate.dna_vector)
            
            similarities.append({
                "location_id": candidate.location_id,
                "location_name": candidate.location_name,
                "coordinates": candidate.coordinates,
                "similarity": round(similarity, 3),
                "dna": candidate.to_dict()["dna"]
            })
        
        # Sort by similarity
        similarities.sort(key=lambda x: x["similarity"], reverse=True)
        
        return similarities[:top_k]
    
    def find_by_dna_pattern(
        self,
        pattern: Dict[str, float],
        top_k: int = 5
    ) -> List[Dict]:
        """
        Find places matching a DNA pattern
        
        Example:
        {"walkability": 90, "tourism": 80, "history": 95}
        → Finds places like Gamla Stan
        """
        # Create query vector from pattern
        query_vector = [
            pattern.get("walkability", 50),
            pattern.get("tourism", 50),
            pattern.get("retail", 50),
            pattern.get("family", 50),
            pattern.get("noise", 50),
            pattern.get("history", 50),
            pattern.get("safety", 50),
            pattern.get("greenery", 50),
            pattern.get("connectivity", 50),
            pattern.get("affordability", 50)
        ]
        
        similarities = []
        
        for location_id, candidate in self.dna_database.items():
            similarity = self._calculate_similarity(query_vector, candidate.dna_vector)
            
            similarities.append({
                "location_id": candidate.location_id,
                "location_name": candidate.location_name,
                "coordinates": candidate.coordinates,
                "similarity": round(similarity, 3),
                "dna": candidate.to_dict()["dna"]
            })
        
        similarities.sort(key=lambda x: x["similarity"], reverse=True)
        
        return similarities[:top_k]
    
    def find_contrasts(
        self,
        query_dna: RealityDNA,
        top_k: int = 3
    ) -> List[Dict]:
        """Find places that are opposite to query"""
        similarities = []
        
        for location_id, candidate in self.dna_database.items():
            if location_id == query_dna.location_id:
                continue
            
            similarity = self._calculate_similarity(query_dna.dna_vector, candidate.dna_vector)
            
            similarities.append({
                "location_id": candidate.location_id,
                "location_name": candidate.location_name,
                "coordinates": candidate.coordinates,
                "similarity": round(similarity, 3),
                "dna": candidate.to_dict()["dna"]
            })
        
        # Sort ascending (least similar = most contrast)
        similarities.sort(key=lambda x: x["similarity"])
        
        return similarities[:top_k]
    
    def _calculate_similarity(self, vector_a: List[float], vector_b: List[float]) -> float:
        """Calculate cosine similarity between two DNA vectors"""
        if len(vector_a) != len(vector_b):
            return 0.0
        
        # Cosine similarity
        dot_product = sum(a * b for a, b in zip(vector_a, vector_b))
        magnitude_a = sum(a * a for a in vector_a) ** 0.5
        magnitude_b = sum(b * b for b in vector_b) ** 0.5
        
        if magnitude_a == 0 or magnitude_b == 0:
            return 0.0
        
        return dot_product / (magnitude_a * magnitude_b)
    
    def get_stats(self) -> Dict:
        """Get database statistics"""
        return {
            "total_places": len(self.dna_database),
            "coverage": self._calculate_coverage()
        }
    
    def _calculate_coverage(self) -> Dict:
        """Calculate geographic coverage"""
        if not self.dna_database:
            return {}
        
        lats = [d.coordinates["lat"] for d in self.dna_database.values()]
        lngs = [d.coordinates["lng"] for d in self.dna_database.values()]
        
        return {
            "lat_range": [min(lats), max(lats)],
            "lng_range": [min(lngs), max(lngs)],
            "center": {
                "lat": sum(lats) / len(lats),
                "lng": sum(lngs) / len(lngs)
            }
        }


# Example usage
def example_reality_dna():
    """Example: Build DNA and search globally"""
    builder = RealityDNABuilder()
    search = GlobalSimilaritySearch()
    
    # Build DNA for Stockholm Gamla Stan
    gamla_stan_signals = {
        "walkability": 96,
        "tourism": 98,
        "retail_density": 88,
        "family_friendly": 51,
        "noise_level": 74,
        "historical_value": 100,
        "safety_index": 81,
        "greenery": 20,
        "mobile_connectivity": 85,
        "rent_burden": 80,
        "coordinates": {"lat": 59.325, "lng": 18.07}
    }
    
    gamla_stan = builder.build("SE-001", "Stockholm Gamla Stan", gamla_stan_signals)
    search.add_place(gamla_stan)
    
    print("=== Reality DNA: Gamla Stan ===")
    print(f"Hash: {gamla_stan.dna_hash}")
    print(f"Vector: {gamla_stan.dna_vector}")
    
    # Add more places
    places = [
        ("DK-001", "Copenhagen Nyhavn", {
            "walkability": 90, "tourism": 95, "retail_density": 85,
            "family_friendly": 60, "noise_level": 70, "historical_value": 90,
            "safety_index": 85, "greenery": 30, "mobile_connectivity": 90,
            "rent_burden": 75, "coordinates": {"lat": 55.68, "lng": 12.59}
        }),
        ("DE-001", "Berlin Mitte", {
            "walkability": 85, "tourism": 80, "retail_density": 90,
            "family_friendly": 65, "noise_level": 75, "historical_value": 75,
            "safety_index": 75, "greenery": 40, "mobile_connectivity": 88,
            "rent_burden": 70, "coordinates": {"lat": 52.52, "lng": 13.405}
        }),
        ("JP-001", "Tokyo Shibuya", {
            "walkability": 95, "tourism": 90, "retail_density": 95,
            "family_friendly": 55, "noise_level": 85, "historical_value": 40,
            "safety_index": 90, "greenery": 25, "mobile_connectivity": 95,
            "rent_burden": 85, "coordinates": {"lat": 35.66, "lng": 139.7}
        }),
        ("US-001", "Manhattan SoHo", {
            "walkability": 92, "tourism": 85, "retail_density": 95,
            "family_friendly": 45, "noise_level": 80, "historical_value": 70,
            "safety_index": 70, "greenery": 20, "mobile_connectivity": 90,
            "rent_burden": 90, "coordinates": {"lat": 40.72, "lng": -74.0}
        })
    ]
    
    for loc_id, name, signals in places:
        dna = builder.build(loc_id, name, signals)
        search.add_place(dna)
    
    # Find similar to Gamla Stan
    print("\n=== Places Similar to Gamla Stan ===")
    similar = search.find_similar(gamla_stan, top_k=3)
    for place in similar:
        print(f"  {place['location_name']}: {place['similarity']:.3f}")
    
    # Find by pattern
    print("\n=== Places with High History + Tourism ===")
    pattern_places = search.find_by_dna_pattern({
        "history": 90,
        "tourism": 90,
        "walkability": 80
    })
    for place in pattern_places:
        print(f"  {place['location_name']}: {place['similarity']:.3f}")
    
    # Find contrasts
    print("\n=== Places Most Different from Gamla Stan ===")
    contrasts = search.find_contrasts(gamla_stan)
    for place in contrasts:
        print(f"  {place['location_name']}: {place['similarity']:.3f}")
    
    return search


if __name__ == '__main__':
    example_reality_dna()