'''
MobiFall Dataset Loader.
Maintainer: @aharshit123456

This file contains the MobiFall dataset loader class that inherits from BaseDatasetLoader.
'''

import os
import pandas as pd
import numpy as np
from typing import List, Dict, Tuple
from ..core.base_classes import BaseDatasetLoader
from .utils import download_dataset, extract_dataset, sliding_window


class MobiFallLoader(BaseDatasetLoader):
    """
    MobiFall dataset loader class.
    
    This class handles loading and processing of the MobiFall dataset for gait analysis.
    """
    
    def __init__(self):
        super().__init__(
            name="mobifall",
            description="MobiFall Dataset - Contains accelerometer and gyroscope data for fall detection"
        )
        self.metadata = {
            'sensors': ['accelerometer', 'gyroscope'],
            'components': ['x', 'y', 'z'],
            'sampling_frequency': 100,  # Typical for MobiFall
            'activities': ['ADL', 'FALL']  # Activities of Daily Living and Falls
        }
    
    def load_data(self, data_dir: str, **kwargs) -> Tuple[List[pd.DataFrame], List[str]]:
        """
        Load MobiFall dataset from the specified directory.
        
        Args:
            data_dir: Directory to store/find the dataset
            **kwargs: Additional arguments (unused for MobiFall)
            
        Returns:
            Tuple of (data_list, names_list)
        """
        # TODO: Implement MobiFall data loading
        # This is a placeholder implementation
        print("MobiFall data loading is not yet implemented")
        return [], []
    
    def create_sliding_windows(self, data: List[pd.DataFrame], names: List[str], 
                             window_size: int = 192, step_size: int = 32) -> List[Dict]:
        """
        Create sliding windows from the MobiFall dataset.
        
        Args:
            data: List of DataFrames containing MobiFall data
            names: List of names corresponding to the data
            window_size: Size of the sliding window (default: 192)
            step_size: Step size for the sliding window (default: 32)
            
        Returns:
            List of dictionaries containing sliding windows for each DataFrame
        """
        # TODO: Implement MobiFall sliding window creation
        # This is a placeholder implementation
        print("MobiFall sliding window creation is not yet implemented")
        return []
    
    def get_supported_formats(self) -> List[str]:
        """
        Get list of supported file formats for MobiFall dataset.
        
        Returns:
            List of supported file extensions
        """
        return ['.csv', '.txt']
    
    def get_sensor_info(self) -> Dict[str, List[str]]:
        """
        Get information about sensors in the dataset.
        
        Returns:
            Dictionary containing sensor information
        """
        return {
            'sensors': self.metadata['sensors'],
            'components': self.metadata['components'],
            'sampling_frequency': self.metadata['sampling_frequency']
        }
    
    def get_activity_info(self) -> List[str]:
        """
        Get information about activities in the dataset.
        
        Returns:
            List of activity types
        """
        return self.metadata['activities']


# Legacy function wrapper for backward compatibility
def load_mobifall_data():
    """
    Legacy function for loading MobiFall data.
    
    Returns:
        Tuple of (data_list, names_list)
    """
    loader = MobiFallLoader()
    return loader.load_data("")