Create NEURO_COSMIC

NEURO_COSMIC

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+#!/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