from typing import Union, Optional, Dict
import numpy as np
import pandas as pd

class UNREP:
    """Unreplicated Trial Analysis using Moving Grid Method"""
    
    def __init__(self,
                 data: Union[np.ndarray, pd.DataFrame, None],
                 row: Union[str, int] = 'Row',
                 column: Union[str, int] = 'Column',
                 response: str = 'Yield',
                 design: str = 'moving_grid',
                 heterogeneity: float = 0.3,
                 mean: float = 5.0,
                 sd: float = 0.2,
                 ne: float = 0.3):
        """
        Initialize UNREP analysis
        
        Parameters
        ----------
        data : array-like or DataFrame or None
            Input data. If None, triggers simulation
        row : str or int
            Row column name or number of rows for simulation
        column : str or int
            Column column name or number of columns for simulation
        response : str
            Response variable column name
        design : str
            Analysis method ('moving_grid' only for now)
        heterogeneity : float
            Spatial trend intensity (0-1) for simulation
        mean : float
            Base response level for simulation
        sd : float
            Random variation for simulation
        ne : float
            Neighbor effects (0-1) for simulation
        """
        # Validate design
        if design not in ['moving_grid']:
            raise ValueError(f"Unsupported design: {design}")
        self.design = design
        
        # Set simulation parameters
        self.is_simulated = data is None
        if self.is_simulated:
            if not isinstance(row, int) or not isinstance(column, int):
                raise ValueError("For simulation, row and column must be integers")
            if not 0 <= heterogeneity <= 1:
                raise ValueError("Heterogeneity must be between 0 and 1")
            if not 0 <= ne <= 1:
                raise ValueError("Neighbor effects must be between 0 and 1")
                
            self.rows = row
            self.columns = column
            self.data = self._simulate_field(heterogeneity, mean, sd, ne)
            self.filepath = "simulated_results.csv"
            
        else:
            # Handle real data
            if isinstance(data, pd.DataFrame):
                self.row = row
                self.column = column
                self.response = response
                self.data = self._convert_dataframe(data)
            else:
                self.data = np.asarray(data)
                
            self.rows, self.columns = self.data.shape
            self.filepath = None
            
    def _convert_dataframe(self, df: pd.DataFrame) -> np.ndarray:
        """Convert DataFrame to numpy array"""
        if self.row in df.columns and self.column in df.columns:
            # Long format
            pivot = df.pivot(
                index=self.row,
                columns=self.column,
                values=self.response
            )
            return pivot.values
        else:
            # Wide format
            return df.values
            
    def _simulate_field(self, heterogeneity: float, mean: float,
                       sd: float, ne: float) -> np.ndarray:
        """Simulate field with spatial trends"""
        # Basic implementation - can be enhanced
        trend = heterogeneity * np.random.normal(mean, sd, (self.rows, self.columns))
        noise = (1 - heterogeneity) * np.random.normal(0, sd, (self.rows, self.columns))
        return trend + noise
        
    def analyze(self) -> Dict:
        """Analyze unreplicated trial"""
        results = {
            'adjusted_values': np.copy(self.data),  # Placeholder
            'regression_coefficient': 0.5,  # Placeholder
            'error_variance': np.var(self.data),
            'overall_mean': np.mean(self.data),
            'neighbor_effects': np.zeros_like(self.data),  # Placeholder
            'relative_efficiency': 1.1,  # Placeholder
            'summary': {
                'mean': np.mean(self.data),
                'std': np.std(self.data),
                'cv': np.std(self.data) / np.mean(self.data) * 100
            }
        }
        return results


