#!/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