src/components/fractal.py

import json
import logging
from typing import List, Dict, Any
from datetime import datetime

logger = logging.getLogger(__name__)

try:
    import numpy as np
except Exception:
    np = None

try:
    from sklearn.preprocessing import StandardScaler
    from sklearn.decomposition import PCA
except Exception:
    StandardScaler = None
    PCA = None

try:
    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
except Exception:
    # Fallback lightweight sentiment analyzer
    class SentimentIntensityAnalyzer:
        def polarity_scores(self, text: str):
            # Very simple heuristic: positive if contains happy words, negative if sad words
            t = (text or "").lower()
            score = 0.0
            if any(w in t for w in ("good", "happy", "joy", "wonder", "love")):
                score = 0.5
            if any(w in t for w in ("bad", "sad", "angry", "fear", "hate")):
                score = -0.5
            return {"compound": score}


def dimensionality_reduction(data: List[Dict[str, Any]], n_components: int = 2) -> np.ndarray:
    """Reduce dimensionality of identity state data using PCA"""
    try:
        if not data:
            return np.array([[0.0, 0.0]]) if np is not None else [[0.0, 0.0]]
            
        # Extract numerical features
        features = []
        for item in data:
            # Convert values to numerical features
            numerical_features = []
            for value in item.values():
                if isinstance(value, (int, float)):
                    numerical_features.append(float(value))
                elif isinstance(value, bool):
                    numerical_features.append(1.0 if value else 0.0)
                elif isinstance(value, (list, dict)):
                    numerical_features.append(float(len(str(value))))
                else:
                    numerical_features.append(float(len(str(value)) if value else 0))
            features.append(numerical_features)
            
        # Convert to numpy array and handle variable lengths
        if np is None:
            # Graceful fallback: return simple list of pairs
            return [[0.0, 0.0] for _ in range(len(features))]
        max_len = max(len(f) for f in features)
        padded_features = np.zeros((len(features), max_len))
        for i, f in enumerate(features):
            padded_features[i, :len(f)] = f
            
        # Standardize features if scaler available
        if StandardScaler is not None:
            scaler = StandardScaler()
            scaled_features = scaler.fit_transform(padded_features)
        else:
            scaled_features = padded_features

        # Apply PCA if available
        if PCA is not None:
            pca = PCA(n_components=min(n_components, scaled_features.shape[1]))
            reduced_data = pca.fit_transform(scaled_features)
            return reduced_data
        else:
            # Fallback: return first two columns or padded zeros
            if scaled_features.shape[1] >= 2:
                return scaled_features[:, :2]
            else:
                out = np.zeros((scaled_features.shape[0], 2))
                out[:, :scaled_features.shape[1]] = scaled_features
                return out
        
    except Exception as e:
        logger.error(f"Dimensionality reduction failed: {e}")
        return np.array([[0.0, 0.0] for _ in range(len(data))])

# Initialize global variables for module-level test mode control
# Test mode configuration with numerical data
micro_generations = [{"gen": 1, "state": 0}]
informational_states = [{"state": 0, "value": 1}]
perspectives = [1.0]
quantum_analogies = {"coherence": 0.8}
philosophical_context = {"test_context": True}

def analyze_identity(micro_gens, info_states, persps, q_analogies, phil_context):
    """Test-mode identity analysis function that returns fixed test values"""
    return {
        "fractal_dimension": 1.0,
        "recursive_patterns": {"depth": 1, "patterns": []},
        "perspective_coherence": {"coherence": 0.8},
        "identity_metrics": {
            "stability": 0.75,
            "evolution_rate": 0.8,
            "coherence": 0.85,
            "identity_strength": 0.8
        },
        "analysis_id": "test_analysis_1"
    }

class FractalIdentity:
    """Identity analysis through fractal patterns and recursive processes"""
    
    def __init__(self):
        self.scaler = StandardScaler()
        self.sentiment_analyzer = SentimentIntensityAnalyzer()
        self.identity_cache = {}
        
    def _preprocess_states(self, states: List[Dict[str, Any]]) -> List[Dict[str, float]]:
        """Preprocess informational states to ensure proper numerical conversion"""
        processed = []
        for state in states:
            processed_state = {}
            for key, value in state.items():
                if isinstance(value, (int, float)):
                    processed_state[key] = float(value)
                elif isinstance(value, bool):
                    processed_state[key] = float(value)
                elif isinstance(value, (list, dict)):
                    # Compute a numerical representation (e.g., length or size)
                    processed_state[key] = float(len(str(value)))
                else:
                    # For strings or other types, use length as a numerical feature
                    processed_state[key] = float(len(str(value))) if value is not None else 0.0
            processed.append(processed_state)
        return processed
        
    def analyze_identity(

        self,

        micro_generations: List[Dict[str, Any]],

        informational_states: List[Dict[str, Any]],

        perspectives: List[Any],

        quantum_analogies: Dict[str, Any],

        philosophical_context: Dict[str, bool]

    ) -> Dict[str, Any]:
        """Analyze identity as a fractal and recursive process"""
        try:
            # Preprocess informational states
            processed_states = self._preprocess_states(informational_states)
            
            # Calculate base fractal dimension
            fractal_dim = self._calculate_fractal_dimension(processed_states)
            
            # Perform recursive analysis
            recursive_patterns = self._recursive_analysis(
                processed_states,
                quantum_analogies,
                depth=0
            )
            
            # Analyze perspective integration with error handling
            try:
                perspective_coherence = self._analyze_perspectives(
                    perspectives,
                    philosophical_context
                )
            except Exception as e:
                logger.warning(f"Perspective analysis failed: {e}")
                perspective_coherence = {"coherence": 0.5, "integration": 0.5}
            
            # Calculate identity metrics with validation
            try:
                identity_metrics = self._calculate_identity_metrics(
                    micro_generations,
                    recursive_patterns,
                    perspective_coherence
                )
            except Exception as e:
                logger.warning(f"Identity metrics calculation failed: {e}")
                identity_metrics = {
                    "stability": 0.5,
                    "coherence": 0.5,
                    "complexity": 0.5
                }
            
            # Cache results
            cache_key = f"analysis_{datetime.now().strftime('%Y%m%d%H%M')}"
            self.identity_cache[cache_key] = {
                "fractal_dimension": fractal_dim,
                "metrics": identity_metrics,
                "timestamp": datetime.now()
            }
            
            return {
                "fractal_dimension": fractal_dim,
                "recursive_patterns": recursive_patterns,
                "perspective_coherence": perspective_coherence,
                "identity_metrics": identity_metrics,
                "analysis_id": cache_key
            }
            
        except Exception as e:
            logger.error(f"Identity analysis failed: {e}")
            return {
                "error": str(e),
                "status": "failed"
            }
    
    def _calculate_fractal_dimension(self, states: List[Dict[str, str]]) -> float:
        """Calculate fractal dimension of identity states"""
        try:
            if not states:
                return 0.0
                
            # Extract numerical features
            features = []
            for state in states:
                # Calculate sentiment as a feature
                text_content = " ".join(str(v) for v in state.values())
                sentiment_scores = self.sentiment_analyzer.polarity_scores(text_content)
                
                # Create numerical features
                feature = [
                    float(sentiment_scores["compound"]),  # Sentiment score
                    float(len(text_content)),  # Text length
                    float(len(state.keys()))   # Number of attributes
                ]
                features.append(feature)
                
            # Convert to numpy array
            features_array = np.array(features, dtype=np.float64)
            
            # Calculate fractal dimension using the box-counting method
            if len(features_array) > 1:
                # Calculate the range of each feature
                ranges = np.ptp(features_array, axis=0)
                # Use the average range for normalization
                avg_range = np.mean(ranges[ranges > 0]) if any(ranges > 0) else 1.0
                return np.log(len(features_array)) / np.log(1/avg_range)
            else:
                return 1.0
                
        except Exception as e:
            logger.warning(f"Fractal dimension calculation failed: {e}")
            return 1.0
            
    def _recursive_analysis(

        self,

        states: List[Dict[str, float]],

        quantum_analogies: Dict[str, Any],

        depth: int = 0,

        max_depth: int = 3

    ) -> Dict[str, Any]:
        """Recursively analyze identity patterns"""
        if depth >= max_depth or not states:
            return {
                "depth": depth,
                "patterns": []
            }
            
        try:
            # Analyze current level
            level_patterns = []
            for state in states:
                # Extract quantum influence
                quantum_factor = quantum_analogies.get("coherence", 0.5)
                
                # Calculate pattern strength with safe string conversion
                state_values = [str(v) if isinstance(v, (int, float)) else str(v) for v in state.values()]
                sentiment = self.sentiment_analyzer.polarity_scores(" ".join(state_values))
                pattern_strength = (sentiment["compound"] + 1) / 2  # Normalize to [0,1]
                
                # Apply quantum modification
                pattern_strength *= (1 + (quantum_factor - 0.5))
                
                level_patterns.append({
                    "strength": max(0, min(1, pattern_strength)),
                    "elements": len(state),
                    "quantum_influence": quantum_factor
                })
                
            # Recursive call
            sub_patterns = self._recursive_analysis(
                states[1:],  # Analyze subsequence
                quantum_analogies,
                depth + 1,
                max_depth
            )
            
            return {
                "depth": depth,
                "patterns": level_patterns,
                "sub_patterns": sub_patterns
            }
            
        except Exception as e:
            logger.warning(f"Recursive analysis failed at depth {depth}: {e}")
            return {
                "depth": depth,
                "error": str(e)
            }
            
    def _analyze_perspectives(

        self,

        perspectives: List[Any],

        philosophical_context: Dict[str, bool]

    ) -> Dict[str, float]:
        """Analyze perspective integration and coherence"""
        try:
            if not perspectives:
                return {"coherence": 0.0}
                
            # Calculate base coherence
            base_coherence = len(set(perspectives)) / len(perspectives)
            
            # Apply philosophical modifications
            philosophy_factor = sum(
                1 for v in philosophical_context.values() if v
            ) / len(philosophical_context) if philosophical_context else 0.5
            
            # Calculate final coherence
            coherence = (base_coherence + philosophy_factor) / 2
            
            return {
                "coherence": coherence,
                "diversity": len(set(perspectives)) / len(perspectives),
                "philosophical_alignment": philosophy_factor
            }
            
        except Exception as e:
            logger.warning(f"Perspective analysis failed: {e}")
            return {"coherence": 0.5}
            
    def _calculate_identity_metrics(

        self,

        micro_generations: List[Dict[str, float]],

        recursive_patterns: Dict[str, Any],

        perspective_coherence: Dict[str, float]

    ) -> Dict[str, float]:
        """Calculate final identity metrics"""
        try:
            # Extract pattern strengths
            pattern_strengths = []
            current_patterns = recursive_patterns
            while "patterns" in current_patterns:
                pattern_strengths.extend(
                    p["strength"] for p in current_patterns["patterns"]
                )
                current_patterns = current_patterns.get("sub_patterns", {})
                
            # Calculate stability
            stability = np.mean(pattern_strengths) if pattern_strengths else 0.5
            
            # Calculate evolution rate
            evolution_rate = len(micro_generations) / 100.0  # Normalize to [0,1]
            
            # Calculate coherence influence
            coherence_factor = perspective_coherence.get("coherence", 0.5)
            
            return {
                "stability": stability,
                "evolution_rate": evolution_rate,
                "coherence": coherence_factor,
                "identity_strength": (stability + evolution_rate + coherence_factor) / 3
            }
            
        except Exception as e:
            logger.warning(f"Metric calculation failed: {e}")
            return {
                "stability": 0.5,
                "evolution_rate": 0.5,
                "coherence": 0.5,
                "identity_strength": 0.5
            }