NEURO_COSMIC

#!/usr/bin/env python3
"""
NEURO-COSMIC DATA ANALYSIS FRAMEWORK v1.0
Production version: Core scientific analysis framework for neural and cosmological data.
"""

import hashlib
import hmac
import os
import secrets
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Tuple
import logging

import numpy as np
import pandas as pd
from scipy import stats, signal

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger('neuro_cosmic_framework')

# =============================================================================
# CONFIGURATION & SECURITY
# =============================================================================

@dataclass
class FrameworkConfig:
    max_requests_per_minute: int = 100
    cache_ttl_hours: int = 24
    max_file_size_mb: int = 100
    allowed_data_types: Tuple[str, ...] = ('eeg', 'cosmological', 'behavioral')
    secret_key: str = field(default_factory=lambda: os.environ.get('FRAMEWORK_SECRET', secrets.token_hex(32)))
    
    def __post_init__(self):
        if len(self.secret_key) < 32:
            raise ValueError("Secret key must be at least 32 characters")

class SecurityError(Exception):
    pass

class DataValidationError(Exception):
    pass

# =============================================================================
# SECURE AUTHENTICATION
# =============================================================================

class SecureAuthManager:
    def __init__(self, secret_key: str):
        if len(secret_key) < 32:
            raise SecurityError("Insufficient secret key length")
        self.secret_key = secret_key.encode('utf-8')
        self.user_keys: Dict[str, str] = {}

    def create_user(self, user_id: str) -> str:
        if not user_id or not isinstance(user_id, str):
            raise SecurityError("Invalid user ID")
        api_key = secrets.token_urlsafe(32)
        key_hash = self._hash_key(api_key)
        self.user_keys[user_id] = key_hash
        return api_key

    def authenticate(self, user_id: str, api_key: str) -> bool:
        if not user_id or not api_key:
            return False
        stored_hash = self.user_keys.get(user_id)
        if not stored_hash:
            return False
        return hmac.compare_digest(stored_hash, self._hash_key(api_key))

    def _hash_key(self, key: str) -> str:
        return hmac.new(self.secret_key, key.encode('utf-8'), 'sha256').hexdigest()

# =============================================================================
# DATA VALIDATION & SANITIZATION
# =============================================================================

class DataValidator:
    @staticmethod
    def validate_eeg_data(df: pd.DataFrame) -> bool:
        required_columns = {'timestamp', 'channel_1'}
        if not required_columns.issubset(df.columns):
            return False
        if not pd.api.types.is_numeric_dtype(df['timestamp']):
            return False
        if len(df) > 1:
            time_diff = np.diff(df['timestamp'])
            if np.any(time_diff <= 0):
                return False
        return True

    @staticmethod
    def validate_cosmological_data(df: pd.DataFrame) -> bool:
        required_columns = {'redshift', 'distance'}
        if not required_columns.issubset(df.columns):
            return False
        if np.any(df['redshift'] < 0):
            return False
        if np.any(df['distance'] <= 0):
            return False
        return True

    @staticmethod
    def sanitize_input_data(df: pd.DataFrame) -> pd.DataFrame:
        numeric_df = df.select_dtypes(include=[np.number])
        sanitized_df = numeric_df.fillna(method='ffill').fillna(method='bfill')
        return sanitized_df

# =============================================================================
# SCIENTIFIC ANALYSIS ENGINES
# =============================================================================

class NeuralAnalysisEngine:
    def __init__(self):
        self.supported_metrics = ['power_spectrum', 'coherence', 'correlation']

    def analyze_eeg_power_spectrum(self, eeg_data: pd.DataFrame) -> Dict[str, Any]:
        if not DataValidator.validate_eeg_data(eeg_data):
            raise DataValidationError("Invalid EEG data format")
        signal_columns = [col for col in eeg_data.columns if col.startswith('channel_')]
        results = {}
        for channel in signal_columns:
            signal_data = eeg_data[channel].values
            freqs, psd = signal.welch(signal_data, fs=250)
            bands = {
                'delta': (0.5, 4),
                'theta': (4, 8),
                'alpha': (8, 13),
                'beta': (13, 30),
                'gamma': (30, 50)
            }
            band_powers = {}
            for band, (low, high) in bands.items():
                band_mask = (freqs >= low) & (freqs <= high)
                band_powers[band] = float(np.trapz(psd[band_mask], freqs[band_mask]))
            results[channel] = {
                'total_power': float(np.trapz(psd, freqs)),
                'band_powers': band_powers,
                'peak_frequency': float(freqs[np.argmax(psd)])
            }
        return results

    def compute_functional_connectivity(self, eeg_data: pd.DataFrame) -> pd.DataFrame:
        signal_columns = [col for col in eeg_data.columns if col.startswith('channel_')]
        signals = eeg_data[signal_columns].values.T
        correlation_matrix = np.corrcoef(signals)
        return pd.DataFrame(
            correlation_matrix,
            index=signal_columns,
            columns=signal_columns
        )

class CosmologicalAnalysisEngine:
    def analyze_hubble_relation(self, cosmic_data: pd.DataFrame) -> Dict[str, Any]:
        if not DataValidator.validate_cosmological_data(cosmic_data):
            raise DataValidationError("Invalid cosmological data format")
        c = 299792.458
        redshifts = cosmic_data['redshift'].values
        distances = cosmic_data['distance'].values
        velocities = c * redshifts
        valid_mask = (distances > 0) & (redshifts > 0) & (redshifts < 0.1)
        if np.sum(valid_mask) < 2:
            return {'error': 'Insufficient valid data for Hubble relation analysis'}
        v_valid = velocities[valid_mask]
        d_valid = distances[valid_mask]
        slope, intercept, r_value, p_value, std_err = stats.linregress(d_valid, v_valid)
        return {
            'hubble_constant_estimate': float(slope),
            'correlation_coefficient': float(r_value),
            'p_value': float(p_value),
            'standard_error': float(std_err),
            'data_points_used': int(np.sum(valid_mask))
        }

class CrossDomainAnalyzer:
    def __init__(self):
        self.neural_engine = NeuralAnalysisEngine()
        self.cosmic_engine = CosmologicalAnalysisEngine()

    def analyze_correlations(self, neural_data: pd.DataFrame, cosmic_data: pd.DataFrame, neural_metric: str = 'total_power') -> Dict[str, Any]:
        neural_results = self.neural_engine.analyze_eeg_power_spectrum(neural_data)
        cosmic_results = self.cosmic_engine.analyze_hubble_relation(cosmic_data)
        neural_metrics = [channel_data[neural_metric] for channel_data in neural_results.values() if neural_metric in channel_data]
        return {
            'neural_analysis': neural_results,
            'cosmological_analysis': cosmic_results,
            'correlation_analysis': {
                'status': 'exploratory',
                'disclaimer': 'Cross-domain correlations are speculative and require extensive validation',
                'neural_metrics_available': list(neural_metrics),
                'cosmic_metrics_available': list(cosmic_results.keys()) if isinstance(cosmic_results, dict) else []
            }
        }

# =============================================================================
# PRODUCTION-READY API FRAMEWORK
# =============================================================================

@dataclass
class AnalysisRequest:
    user_id: str
    analysis_type: str
    neural_data: Optional[pd.DataFrame] = None
    cosmic_data: Optional[pd.DataFrame] = None
    parameters: Dict[str, Any] = None

    def __post_init__(self):
        if self.analysis_type not in ['neural', 'cosmological', 'cross_domain']:
            raise ValueError(f"Invalid analysis type: {self.analysis_type}")
        if self.analysis_type in ['neural', 'cross_domain'] and self.neural_data is None:
            raise ValueError("Neural data required for neural analysis")
        if self.analysis_type in ['cosmological', 'cross_domain'] and self.cosmic_data is None:
            raise ValueError("Cosmological data required for cosmological analysis")

@dataclass
class AnalysisResult:
    request_id: str
    timestamp: str
    analysis_type: str
    results: Dict[str, Any]
    processing_time: float
    warnings: List[str]
    metadata: Dict[str, Any]

class AnalysisFramework:
    def __init__(self, config: FrameworkConfig):
        self.config = config
        self.auth_manager = SecureAuthManager(config.secret_key)
        self.validator = DataValidator()
        self.cross_analyzer = CrossDomainAnalyzer()
        self.rate_limiter = RateLimiter(config.max_requests_per_minute)
        self.neural_engine = NeuralAnalysisEngine()
        self.cosmic_engine = CosmologicalAnalysisEngine()
        logger.info("Analysis framework initialized")

    async def process_request(self, request: AnalysisRequest) -> AnalysisResult:
        start_time = datetime.utcnow()
        request_id = hashlib.sha256(f"{request.user_id}{start_time.isoformat()}".encode()).hexdigest()[:16]
        try:
            if not self.rate_limiter.check_limit(request.user_id):
                raise SecurityError("Rate limit exceeded")
            if request.neural_data is not None:
                if not self.validator.validate_eeg_data(request.neural_data):
                    raise DataValidationError("Invalid EEG data format")
                neural_data = self.validator.sanitize_input_data(request.neural_data)
            else:
                neural_data = None
            if request.cosmic_data is not None:
                if not self.validator.validate_cosmological_data(request.cosmic_data):
                    raise DataValidationError("Invalid cosmological data format")
                cosmic_data = self.validator.sanitize_input_data(request.cosmic_data)
            else:
                cosmic_data = None
            if request.analysis_type == 'neural':
                results = self.neural_engine.analyze_eeg_power_spectrum(neural_data)
            elif request.analysis_type == 'cosmological':
                results = self.cosmic_engine.analyze_hubble_relation(cosmic_data)
            elif request.analysis_type == 'cross_domain':
                results = self.cross_analyzer.analyze_correlations(neural_data, cosmic_data)
            else:
                raise ValueError(f"Unsupported analysis type: {request.analysis_type}")
            processing_time = (datetime.utcnow() - start_time).total_seconds()
            return AnalysisResult(
                request_id=request_id,
                timestamp=start_time.isoformat(),
                analysis_type=request.analysis_type,
                results=results,
                processing_time=processing_time,
                warnings=self._generate_warnings(results),
                metadata={
                    'data_points_neural': len(neural_data) if neural_data is not None else 0,
                    'data_points_cosmic': len(cosmic_data) if cosmic_data is not None else 0,
                    'framework_version': '1.0'
                }
            )
        except Exception as e:
            logger.error(f"Analysis failed for request {request_id}: {str(e)}")
            raise

    def _generate_warnings(self, results: Dict[str, Any]) -> List[str]:
        warnings = []
        if 'correlation_analysis' in results:
            warnings.append("Cross-domain correlations are exploratory and require rigorous validation")
        if isinstance(results, dict) and 'p_value' in results:
            if results['p_value'] > 0.05:
                warnings.append("Results are not statistically significant (p > 0.05)")
        return warnings

class RateLimiter:
    def __init__(self, requests_per_minute: int):
        self.requests_per_minute = requests_per_minute
        self.requests: Dict[str, List[datetime]] = {}
    def check_limit(self, user_id: str) -> bool:
        now = datetime.utcnow()
        minute_ago = now - timedelta(minutes=1)
        if user_id not in self.requests:
            self.requests[user_id] = []
        self.requests[user_id] = [req_time for req_time in self.requests[user_id] if req_time > minute_ago]
        if len(self.requests[user_id]) >= self.requests_per_minute:
            return False
        self.requests[user_id].append(now)
        return True