"""
Real-Time Neural Data Analysis Pipeline
Processes neural signals in real-time with quantum-enhanced ML algorithms
"""

import math
import json
import datetime
import hashlib
from typing import Dict, List, Optional, Any, Union, Tuple
from dataclasses import dataclass
import itertools
import collections

@dataclass
class AnalysisConfig:
    """Configuration for real-time analysis"""
    window_size: float  # seconds
    overlap: float  # fraction of window overlap
    ml_model_type: str  # 'quantum_nn', 'classical', 'hybrid'
    processing_delay: float  # maximum allowed delay in seconds

class RealTimeAnalyzer:
    """Real-time neural data analysis pipeline"""
    
    def __init__(self, config: AnalysisConfig):
        self.config = config
        self.feature_buffer = collections.deque(maxlen=1000)
        self.ml_models = self._initialize_ml_models()
        self.analysis_history = []
        self.quantum_cache = {}
        
    def _initialize_ml_models(self) -> Dict[str, Dict]:
        """Initialize machine learning models for neural analysis"""
        return {
            'quantum_nn': {
                'weights': [hash(f'weight_{i}') % 100 / 100 for i in range(50)],
                'bias': [hash(f'bias_{i}') % 100 / 100 for i in range(10)],
                'layers': [32, 16, 8, 4, 2]
            },
            'classical': {
                'svm_support_vectors': [[hash(f'sv_{i}_{j}') % 100 / 100 for j in range(10)] for i in range(20)],
                'kernel': 'rbf',
                'gamma': 0.1
            },
            'hybrid': {
                'quantum_component': 0.6,
                'classical_component': 0.4,
                'integration_layer': 'quantum_classical_fusion'
            }
        }
    
    def extract_time_domain_features(self, signal: List[float]) -> Dict[str, float]:
        """Extract time-domain features from neural signal"""
        if not signal:
            return {}
        
        mean_val = sum(signal) / len(signal)
        variance = sum((x - mean_val) ** 2 for x in signal) / len(signal)
        std_dev = math.sqrt(variance)
        
        # Higher order moments
        skewness = sum((x - mean_val) ** 3 for x in signal) / (len(signal) * std_dev ** 3) if std_dev > 0 else 0
        kurtosis = sum((x - mean_val) ** 4 for x in signal) / (len(signal) * std_dev ** 4) if std_dev > 0 else 0
        
        # Hjorth parameters
        diff_signal = [signal[i+1] - signal[i] for i in range(len(signal)-1)]
        activity = variance
        mobility = math.sqrt(sum(d**2 for d in diff_signal) / len(diff_signal)) / std_dev if std_dev > 0 else 0
        
        complexity = 0
        if mobility > 0 and len(diff_signal) > 1:
            diff_diff = [diff_signal[i+1] - diff_signal[i] for i in range(len(diff_signal)-1)]
            complexity = math.sqrt(sum(d**2 for d in diff_diff) / len(diff_diff)) / mobility
        
        return {
            'mean': mean_val,
            'std': std_dev,
            'variance': variance,
            'skewness': skewness,
            'kurtosis': kurtosis,
            'activity': activity,
            'mobility': mobility,
            'complexity': complexity,
            'rms': math.sqrt(sum(x**2 for x in signal) / len(signal))
        }
    
    def extract_frequency_features(self, signal: List[float], sample_rate: int) -> Dict[str, float]:
        """Extract frequency-domain features using simplified FFT"""
        n = len(signal)
        if n == 0:
            return {}
        
        # Simplified power spectrum calculation
        power_spectrum = []
        for k in range(n // 2):
            real_part = sum(signal[i] * math.cos(2 * math.pi * k * i / n) for i in range(n))
            imag_part = sum(signal[i] * math.sin(2 * math.pi * k * i / n) for i in range(n))
            power = (real_part ** 2 + imag_part ** 2) / (n ** 2)
            power_spectrum.append(power)
        
        # Calculate band powers
        freq_resolution = sample_rate / n
        
        # Standard EEG bands (assuming typical sample rates)
        bands = {
            'delta': (0.5, 4),
            'theta': (4, 8),
            'alpha': (8, 13),
            'beta': (13, 30),
            'gamma': (30, 100)
        }
        
        band_powers = {}
        total_power = sum(power_spectrum)
        
        for band_name, (low_freq, high_freq) in bands.items():
            low_idx = int(low_freq / freq_resolution)
            high_idx = int(high_freq / freq_resolution)
            
            band_power = sum(power_spectrum[low_idx:high_idx])
            band_powers[f'{band_name}_power'] = band_power
            band_powers[f'{band_name}_relative'] = band_power / total_power if total_power > 0 else 0
        
        # Spectral entropy
        if total_power > 0:
            normalized_spectrum = [p / total_power for p in power_spectrum if p > 0]
            spectral_entropy = -sum(p * math.log2(p) for p in normalized_spectrum) / math.log2(len(normalized_spectrum))
        else:
            spectral_entropy = 0
        
        band_powers['spectral_entropy'] = spectral_entropy
        band_powers['dominant_frequency'] = power_spectrum.index(max(power_spectrum)) * freq_resolution
        
        return band_powers
    
    def quantum_feature_extraction(self, signal: List[float]) -> Dict[str, float]:
        """Quantum-inspired feature extraction"""
        if not signal:
            return {}
        
        # Quantum state representation
        signal_norm = math.sqrt(sum(x**2 for x in signal))
        normalized_signal = [x / signal_norm for x in signal] if signal_norm > 0 else signal
        
        # Quantum entanglement measure (simplified)
        n = len(normalized_signal)
        if n >= 2:
            entanglement = abs(sum(normalized_signal[i] * normalized_signal[i+1] for i in range(n-1)))
        else:
            entanglement = 0
        
        # Quantum coherence measure
        coherence = abs(sum(complex(normalized_signal[i], normalized_signal[i+1]) for i in range(min(n-1, 10)))) / min(n-1, 10) if n > 1 else 0
        
        # Quantum superposition entropy
        probabilities = [x**2 for x in normalized_signal]
        superposition_entropy = -sum(p * math.log2(p + 1e-10) for p in probabilities if p > 0)
        
        # Calculate variance manually
        phase_values = [math.atan2(normalized_signal[i], normalized_signal[min(i+1, n-1)]) for i in range(n)] if n > 1 else []
        phase_variance = sum((x - sum(phase_values)/len(phase_values))**2 for x in phase_values) / len(phase_values) if phase_values else 0
        
        return {
            'quantum_entanglement': entanglement,
            'quantum_coherence': coherence,
            'superposition_entropy': superposition_entropy,
            'quantum_purity': sum(p**2 for p in probabilities),
            'quantum_phase_variance': phase_variance
        }
    
    def apply_quantum_neural_network(self, features: Dict[str, float]) -> Dict[str, float]:
        """Apply quantum neural network for classification"""
        model = self.ml_models['quantum_nn']
        
        # Feature vector
        feature_vector = list(features.values())
        
        # Forward pass through quantum-inspired layers
        layer_input = feature_vector[:16]  # Take first 16 features
        
        for layer_size in model['layers'][1:]:
            # Quantum weight multiplication
            layer_output = []
            for j in range(min(layer_size, len(model['bias']))):
                # Safe dot product
                weighted_sum = 0
                for k in range(len(layer_input)):
                    weight_idx = (j * len(layer_input) + k) % len(model['weights'])
                    weighted_sum += layer_input[k] * model['weights'][weight_idx]
                
                # Quantum activation function
                bias = model['bias'][j] if j < len(model['bias']) else 0
                activation = 1 / (1 + math.exp(-weighted_sum + bias))
                layer_output.append(activation)
            
            layer_input = layer_output
        
        return {
            'quantum_classification_confidence': layer_input[0] if layer_input else 0,
            'quantum_pattern_score': layer_input[1] if len(layer_input) > 1 else 0,
            'quantum_certainty': max(layer_input) if layer_input else 0
        }
    
    def analyze_window(self, data_window: List[Dict], modality: str) -> Dict[str, Any]:
        """Analyze a single window of neural data"""
        if not data_window:
            return {'error': 'Empty data window'}
        
        analysis_results = {
            'timestamp': datetime.datetime.now().isoformat(),
            'modality': modality,
            'window_size': len(data_window),
            'features': {},
            'classifications': {},
            'quantum_metrics': {}
        }
        
        # Extract features for each channel
        for channel in data_window[0].keys():
            if channel in ['timestamp', 'quantum_coherence', 'quantum_entanglement', 'quantum_superposition']:
                continue
                
            signal = [sample[channel] for sample in data_window if channel in sample]
            
            if signal:
                # Time-domain features
                time_features = self.extract_time_domain_features(signal)
                
                # Frequency-domain features
                sample_rates = {'eeg': 1000, 'fmri': 1, 'electrophysiology': 30000}
                freq_features = self.extract_frequency_features(signal, sample_rates.get(modality, 1000))
                
                # Quantum features
                quantum_features = self.quantum_feature_extraction(signal)
                
                analysis_results['features'][channel] = {
                    **time_features,
                    **freq_features,
                    **quantum_features
                }
        
        # Apply machine learning models
        for channel, features in analysis_results['features'].items():
            quantum_result = self.apply_quantum_neural_network(features)
            analysis_results['classifications'][channel] = quantum_result
        
        # Calculate overall quantum metrics
        all_quantum_features = []
        for channel_features in analysis_results['features'].values():
            quantum_keys = [k for k in channel_features.keys() if 'quantum' in k]
            all_quantum_features.extend([channel_features[k] for k in quantum_keys])
        
        if all_quantum_features:
            # Calculate standard deviation manually
            mean_features = sum(all_quantum_features) / len(all_quantum_features)
            stdev = math.sqrt(sum((x - mean_features)**2 for x in all_quantum_features) / len(all_quantum_features))
            
            analysis_results['quantum_metrics'] = {
                'overall_quantum_coherence': mean_features,
                'quantum_feature_count': float(len(all_quantum_features)),
                'quantum_stability': 1.0 - (stdev / mean_features if mean_features > 0 else 1.0)
            }
        
        return analysis_results
    
    def real_time_processing(self, data_stream: List[Dict], modality: str, sample_rate: int) -> List[Dict]:
        """Process data stream in real-time with sliding windows"""
        window_samples = int(self.config.window_size * sample_rate)
        step_samples = int(window_samples * (1 - self.config.overlap))
        
        results = []
        
        for start_idx in range(0, len(data_stream) - window_samples + 1, step_samples):
            window = data_stream[start_idx:start_idx + window_samples]
            
            # Simulate real-time processing delay
            processing_start = datetime.datetime.now()
            
            analysis = self.analyze_window(window, modality)
            analysis['window_start_time'] = start_idx / sample_rate
            analysis['processing_time'] = (datetime.datetime.now() - processing_start).total_seconds()
            
            # Check if processing meets real-time requirements
            if analysis['processing_time'] > self.config.processing_delay:
                analysis['real_time_violation'] = True
            else:
                analysis['real_time_violation'] = False
            
            results.append(analysis)
            
            # Store in buffer for adaptive processing
            self.feature_buffer.append(analysis)
        
        return results
    
    def adaptive_optimization(self) -> Dict[str, Union[float, str, bool]]:
        """Adaptive optimization based on processing history"""
        if len(self.feature_buffer) < 10:
            return {'status': 'insufficient_data'}
        
        recent_processing_times = [r['processing_time'] for r in list(self.feature_buffer)[-10:]]
        avg_processing_time = sum(recent_processing_times) / len(recent_processing_times)
        
        # Optimize parameters
        if avg_processing_time > self.config.processing_delay * 0.8:
            # Reduce complexity
            new_window_size = self.config.window_size * 0.9
            new_ml_model = 'classical'  # Switch to simpler model
        elif avg_processing_time < self.config.processing_delay * 0.5:
            # Can increase complexity
            new_window_size = min(self.config.window_size * 1.1, 5.0)
            new_ml_model = 'quantum_nn'  # Use quantum model
        else:
            new_window_size = self.config.window_size
            new_ml_model = self.config.ml_model_type
        
        return {
            'avg_processing_time': avg_processing_time,
            'recommended_window_size': new_window_size,
            'recommended_model': new_ml_model,
            'optimization_applied': avg_processing_time != self.config.processing_delay * 0.5
        }

def test_realtime_analysis():
    """Test the real-time analysis pipeline"""
    from neural_recording_system import MultiModalRecorder
    
    print("Testing Real-Time Neural Analysis Pipeline...")
    
    # Initialize components
    config = AnalysisConfig(
        window_size=1.0,  # 1 second windows
        overlap=0.5,      # 50% overlap
        ml_model_type='quantum_nn',
        processing_delay=0.1  # 100ms max delay
    )
    
    analyzer = RealTimeAnalyzer(config)
    recorder = MultiModalRecorder()
    
    # Generate test data
    print("Generating test neural data...")
    eeg_data = recorder.record_eeg(recorder.configs['eeg'])
    
    # Real-time processing
    print("Performing real-time analysis...")
    results = analyzer.real_time_processing(eeg_data[:10000], 'eeg', 1000)
    
    print(f"Processed {len(results)} windows")
    print(f"Average processing time: {sum(r['processing_time'] for r in results) / len(results):.4f}s")
    print(f"Real-time violations: {sum(1 for r in results if r['real_time_violation'])}")
    
    # Adaptive optimization
    print("\nRunning adaptive optimization...")
    optimization = analyzer.adaptive_optimization()
    print(f"Optimization results: {optimization}")
    
    # Sample analysis result
    if results:
        sample_result = results[0]
        print(f"\nSample analysis:")
        print(f"- Channels analyzed: {list(sample_result['features'].keys())}")
        print(f"- Features per channel: {len(next(iter(sample_result['features'].values())))}")
        print(f"- Quantum metrics: {sample_result['quantum_metrics']}")
    
    return True

if __name__ == "__main__":
    test_realtime_analysis()