"""
Data Pipeline Module - Environmental data processing
"""

import numpy as np
import json
import csv
from typing import List, Dict, Optional, Any, Tuple
from dataclasses import dataclass
from datetime import datetime
import os


@dataclass
class DataSource:
    """Data source configuration"""
    name: str
    type: str  # 'csv', 'json', 'netcdf'
    path: str
    update_frequency: float  # seconds
    last_update: float


class DataPipeline:
    """
    Data ingestion and processing pipeline for environmental data
    
    Handles CTD profiles, bathymetry, sediment cores, and sensor data
    """
    
    def __init__(self, data_dir: str = "./data"):
        """
        Initialize data pipeline
        
        Args:
            data_dir: Root data directory
        """
        self.data_dir = data_dir
        self.sources = {}
        self.cache = {}
        
        # Create data directories if they don't exist
        os.makedirs(f"{data_dir}/validation", exist_ok=True)
        os.makedirs(f"{data_dir}/missions", exist_ok=True)
        os.makedirs(f"{data_dir}/calibration", exist_ok=True)
    
    def register_source(self, source: DataSource):
        """Register a data source"""
        self.sources[source.name] = source
    
    def load_ctd(self,
                source: str,
                trench: str,
                time_range: Optional[Tuple[str, str]] = None) -> Dict:
        """
        Load CTD (Conductivity-Temperature-Depth) data
        
        Args:
            source: Data source name
            trench: Trench name
            time_range: (start_time, end_time) as strings
            
        Returns:
            CTD data dictionary
        """
        # Simulate loading CTD data
        # In production, this would read from actual files
        
        # Generate synthetic CTD profile for Challenger Deep
        depths = np.linspace(0, 11000, 100)
        temperatures = 2.0 + 0.001 * depths  # Simplified profile
        salinities = 34.7 + 0.0001 * depths
        pressures = depths * 0.01007
        
        data = {
            'metadata': {
                'source': source,
                'trench': trench,
                'timestamp': datetime.now().isoformat(),
                'num_profiles': 1
            },
            'profiles': [{
                'depth_m': depths.tolist(),
                'temperature_c': temperatures.tolist(),
                'salinity_psu': salinities.tolist(),
                'pressure_mpa': pressures.tolist()
            }]
        }
        
        return data
    
    def load_bathymetry(self,
                       source: str,
                       trench: str,
                       resolution: str = "15s") -> Dict:
        """
        Load bathymetry data
        
        Args:
            source: Data source name
            trench: Trench name
            resolution: Grid resolution
            
        Returns:
            Bathymetry data dictionary
        """
        # Generate synthetic bathymetry grid
        x = np.linspace(-500, 500, 100)
        y = np.linspace(-500, 500, 100)
        X, Y = np.meshgrid(x, y)
        
        # Simulate trench topography
        Z = -11000 + 100 * np.sin(X/100) * np.cos(Y/100) + 50 * np.random.randn(100, 100)
        
        data = {
            'metadata': {
                'source': source,
                'trench': trench,
                'resolution': resolution,
                'timestamp': datetime.now().isoformat()
            },
            'grid': {
                'x': x.tolist(),
                'y': y.tolist(),
                'z': Z.tolist()
            }
        }
        
        return data
    
    def load_sediment_data(self,
                          source: str,
                          trench: str,
                          core_id: str) -> Dict:
        """
        Load sediment core data
        
        Args:
            source: Data source name
            trench: Trench name
            core_id: Core identifier
            
        Returns:
            Sediment data dictionary
        """
        # Generate synthetic sediment core data
        depths = np.linspace(0, 10, 50)  # cm
        porosity = 0.7 - 0.02 * depths
        density = 1.5 + 0.05 * depths
        carbon = 2.0 * np.exp(-depths/3)
        
        data = {
            'metadata': {
                'source': source,
                'trench': trench,
                'core_id': core_id,
                'length_cm': 10.0,
                'timestamp': datetime.now().isoformat()
            },
            'core': {
                'depth_cm': depths.tolist(),
                'porosity': porosity.tolist(),
                'density_gcm3': density.tolist(),
                'carbon_percent': carbon.tolist()
            }
        }
        
        return data
    
    def process_timeseries(self,
                          data: List[Dict],
                          parameters: List[str],
                          resample_rate: Optional[float] = None) -> Dict:
        """
        Process time series data
        
        Args:
            data: List of data points
            parameters: Parameters to extract
            resample_rate: Resampling rate in seconds
            
        Returns:
            Processed time series
        """
        if not data:
            return {}
        
        processed = {}
        
        for param in parameters:
            values = [d.get(param, np.nan) for d in data]
            
            # Remove NaN
            values = [v for v in values if not np.isnan(v)]
            
            if values:
                processed[param] = {
                    'mean': float(np.mean(values)),
                    'std': float(np.std(values)),
                    'min': float(np.min(values)),
                    'max': float(np.max(values)),
                    'count': len(values)
                }
        
        return processed
    
    def merge_datasets(self,
                      datasets: List[Dict],
                      on: str = "timestamp") -> Dict:
        """
        Merge multiple datasets
        
        Args:
            datasets: List of datasets to merge
            on: Key to merge on
            
        Returns:
            Merged dataset
        """
        if not datasets:
            return {}
        
        merged = datasets[0].copy()
        
        for ds in datasets[1:]:
            for key, value in ds.items():
                if key not in merged:
                    merged[key] = value
                elif isinstance(value, dict) and isinstance(merged[key], dict):
                    # Recursively merge dictionaries
                    for k, v in value.items():
                        if k not in merged[key]:
                            merged[key][k] = v
        
        return merged
    
    def save_mission_data(self,
                         mission_id: str,
                         data: Dict,
                         format: str = "json"):
        """Save mission data to file"""
        filename = f"{self.data_dir}/missions/{mission_id}/data.{format}"
        os.makedirs(os.path.dirname(filename), exist_ok=True)
        
        if format == "json":
            with open(filename, 'w') as f:
                json.dump(data, f, indent=2)
        elif format == "csv":
            # Simplified CSV export
            pass
    
    def load_mission_data(self,
                         mission_id: str,
                         format: str = "json") -> Optional[Dict]:
        """Load mission data from file"""
        filename = f"{self.data_dir}/missions/{mission_id}/data.{format}"
        
        if not os.path.exists(filename):
            return None
        
        if format == "json":
            with open(filename, 'r') as f:
                return json.load(f)
        
        return None
    
    def get_calibration(self, sensor_type: str) -> Dict:
        """Get calibration coefficients for sensor"""
        # Default calibration coefficients
        calibrations = {
            'pressure': {
                'a0': 0.0,
                'a1': 1.0,
                'a2': 0.0,
                'units': 'MPa'
            },
            'temperature': {
                'a0': 0.0,
                'a1': 1.0,
                'units': '°C'
            },
            'oxygen': {
                'a0': 0.0,
                'a1': 1.0,
                'a2': 0.0,
                'units': 'μM'
            }
        }
        
        return calibrations.get(sensor_type, {})
    
    def __repr__(self) -> str:
        return f"DataPipeline(data_dir={self.data_dir}, sources={len(self.sources)})"
