"""
Visualization module for the Business Intelligence Dashboard.
Contains functions for creating various charts and plots.
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
from typing import Optional, List, Tuple, Any
import warnings

warnings.filterwarnings('ignore')

# Set style defaults
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")

# Color schemes
COLORS = {
    'primary': '#2E86AB',
    'secondary': '#A23B72',
    'success': '#28A745',
    'warning': '#F18F01',
    'danger': '#C73E1D',
    'info': '#17A2B8',
    'palette': ['#2E86AB', '#A23B72', '#F18F01', '#28A745', '#C73E1D', '#17A2B8', '#6C757D', '#563D7C']
}


def create_time_series_plot(
    df: pd.DataFrame,
    date_column: str,
    value_column: str,
    agg_method: str = 'sum',
    freq: str = 'D',
    title: Optional[str] = None
) -> Tuple[plt.Figure, Any]:
    """
    Create a time series plot showing trends over time.
    
    Args:
        df: pandas DataFrame
        date_column: Name of the date column
        value_column: Name of the value column to plot
        agg_method: Aggregation method ('sum', 'mean', 'count')
        freq: Frequency for resampling ('D'=daily, 'W'=weekly, 'M'=monthly)
        title: Plot title
        
    Returns:
        Tuple of (matplotlib Figure, axes)
    """
    if df is None or df.empty:
        fig, ax = plt.subplots(figsize=(12, 6))
        ax.text(0.5, 0.5, 'No data available', ha='center', va='center', fontsize=14)
        return fig, ax
    
    try:
        # Ensure date column is datetime
        plot_df = df.copy()
        plot_df[date_column] = pd.to_datetime(plot_df[date_column])
        plot_df = plot_df.set_index(date_column)
        
        # Resample and aggregate
        if agg_method == 'sum':
            ts_data = plot_df[value_column].resample(freq).sum()
        elif agg_method == 'mean':
            ts_data = plot_df[value_column].resample(freq).mean()
        elif agg_method == 'count':
            ts_data = plot_df[value_column].resample(freq).count()
        else:
            ts_data = plot_df[value_column].resample(freq).sum()
        
        # Create plot
        fig, ax = plt.subplots(figsize=(12, 6))
        
        ax.plot(ts_data.index, ts_data.values, color=COLORS['primary'], linewidth=2, marker='o', markersize=4)
        ax.fill_between(ts_data.index, ts_data.values, alpha=0.3, color=COLORS['primary'])
        
        # Formatting
        ax.set_xlabel('Date', fontsize=12)
        ax.set_ylabel(f'{value_column} ({agg_method})', fontsize=12)
        ax.set_title(title or f'{value_column} Over Time ({agg_method.capitalize()})', fontsize=14, fontweight='bold')
        
        # Format x-axis dates
        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
        plt.xticks(rotation=45)
        
        ax.grid(True, alpha=0.3)
        plt.tight_layout()
        
        return fig, ax
        
    except Exception as e:
        fig, ax = plt.subplots(figsize=(12, 6))
        ax.text(0.5, 0.5, f'Error creating plot: {str(e)}', ha='center', va='center', fontsize=12)
        return fig, ax


def create_distribution_plot(
    df: pd.DataFrame,
    column: str,
    plot_type: str = 'histogram',
    bins: int = 30,
    title: Optional[str] = None
) -> Tuple[plt.Figure, Any]:
    """
    Create a distribution plot (histogram or box plot).
    
    Args:
        df: pandas DataFrame
        column: Column to visualize
        plot_type: 'histogram' or 'boxplot'
        bins: Number of bins for histogram
        title: Plot title
        
    Returns:
        Tuple of (matplotlib Figure, axes)
    """
    if df is None or df.empty:
        fig, ax = plt.subplots(figsize=(10, 6))
        ax.text(0.5, 0.5, 'No data available', ha='center', va='center', fontsize=14)
        return fig, ax
    
    try:
        fig, ax = plt.subplots(figsize=(10, 6))
        data = df[column].dropna()
        
        if plot_type == 'histogram':
            ax.hist(data, bins=bins, color=COLORS['primary'], edgecolor='white', alpha=0.7)
            ax.axvline(data.mean(), color=COLORS['danger'], linestyle='--', linewidth=2, label=f'Mean: {data.mean():.2f}')
            ax.axvline(data.median(), color=COLORS['success'], linestyle='--', linewidth=2, label=f'Median: {data.median():.2f}')
            ax.legend()
            ax.set_ylabel('Frequency', fontsize=12)
        else:  # boxplot
            bp = ax.boxplot(data, patch_artist=True)
            bp['boxes'][0].set_facecolor(COLORS['primary'])
            bp['boxes'][0].set_alpha(0.7)
            ax.set_ylabel(column, fontsize=12)
        
        ax.set_xlabel(column if plot_type == 'histogram' else '', fontsize=12)
        ax.set_title(title or f'Distribution of {column}', fontsize=14, fontweight='bold')
        ax.grid(True, alpha=0.3)
        
        plt.tight_layout()
        return fig, ax
        
    except Exception as e:
        fig, ax = plt.subplots(figsize=(10, 6))
        ax.text(0.5, 0.5, f'Error creating plot: {str(e)}', ha='center', va='center', fontsize=12)
        return fig, ax


def create_category_bar_chart(
    df: pd.DataFrame,
    category_column: str,
    value_column: str,
    agg_method: str = 'sum',
    top_n: int = 10,
    title: Optional[str] = None,
    horizontal: bool = True
) -> Tuple[plt.Figure, Any]:
    """
    Create a bar chart for categorical analysis.
    
    Args:
        df: pandas DataFrame
        category_column: Column to group by
        value_column: Column to aggregate
        agg_method: Aggregation method
        top_n: Number of top categories to show
        title: Plot title
        horizontal: Whether to create horizontal bars
        
    Returns:
        Tuple of (matplotlib Figure, axes)
    """
    if df is None or df.empty:
        fig, ax = plt.subplots(figsize=(10, 8))
        ax.text(0.5, 0.5, 'No data available', ha='center', va='center', fontsize=14)
        return fig, ax
    
    try:
        # Aggregate data
        if agg_method == 'count':
            agg_data = df.groupby(category_column)[value_column].count()
        else:
            agg_data = df.groupby(category_column)[value_column].agg(agg_method)
        
        agg_data = agg_data.sort_values(ascending=False).head(top_n)
        
        fig, ax = plt.subplots(figsize=(10, 8))
        
        colors = [COLORS['palette'][i % len(COLORS['palette'])] for i in range(len(agg_data))]
        
        if horizontal:
            bars = ax.barh(range(len(agg_data)), agg_data.values, color=colors, alpha=0.8)
            ax.set_yticks(range(len(agg_data)))
            ax.set_yticklabels([str(x)[:30] for x in agg_data.index])
            ax.set_xlabel(f'{value_column} ({agg_method})', fontsize=12)
            ax.invert_yaxis()
            
            # Add value labels
            for i, bar in enumerate(bars):
                width = bar.get_width()
                ax.text(width, bar.get_y() + bar.get_height()/2, f'{width:,.0f}', 
                       ha='left', va='center', fontsize=10, fontweight='bold')
        else:
            bars = ax.bar(range(len(agg_data)), agg_data.values, color=colors, alpha=0.8)
            ax.set_xticks(range(len(agg_data)))
            ax.set_xticklabels([str(x)[:15] for x in agg_data.index], rotation=45, ha='right')
            ax.set_ylabel(f'{value_column} ({agg_method})', fontsize=12)
        
        ax.set_title(title or f'Top {top_n} {category_column} by {value_column} ({agg_method})', 
                    fontsize=14, fontweight='bold')
        ax.grid(True, alpha=0.3, axis='x' if horizontal else 'y')
        
        plt.tight_layout()
        return fig, ax
        
    except Exception as e:
        fig, ax = plt.subplots(figsize=(10, 8))
        ax.text(0.5, 0.5, f'Error creating plot: {str(e)}', ha='center', va='center', fontsize=12)
        return fig, ax


def create_pie_chart(
    df: pd.DataFrame,
    category_column: str,
    value_column: str,
    agg_method: str = 'sum',
    top_n: int = 8,
    title: Optional[str] = None
) -> Tuple[plt.Figure, Any]:
    """
    Create a pie chart for category distribution.
    
    Args:
        df: pandas DataFrame
        category_column: Column to group by
        value_column: Column to aggregate
        agg_method: Aggregation method
        top_n: Number of top categories to show
        title: Plot title
        
    Returns:
        Tuple of (matplotlib Figure, axes)
    """
    if df is None or df.empty:
        fig, ax = plt.subplots(figsize=(10, 8))
        ax.text(0.5, 0.5, 'No data available', ha='center', va='center', fontsize=14)
        return fig, ax
    
    try:
        # Aggregate data
        if agg_method == 'count':
            agg_data = df.groupby(category_column)[value_column].count()
        else:
            agg_data = df.groupby(category_column)[value_column].agg(agg_method)
        
        agg_data = agg_data.sort_values(ascending=False).head(top_n)
        
        # Group remaining as "Others" if needed
        if len(df[category_column].unique()) > top_n:
            if agg_method == 'count':
                others_value = df.groupby(category_column)[value_column].count().sort_values(ascending=False).iloc[top_n:].sum()
            else:
                others_value = df.groupby(category_column)[value_column].agg(agg_method).sort_values(ascending=False).iloc[top_n:].sum()
            agg_data['Others'] = others_value
        
        fig, ax = plt.subplots(figsize=(10, 8))
        
        colors = COLORS['palette'][:len(agg_data)]
        wedges, texts, autotexts = ax.pie(
            agg_data.values, 
            labels=[str(x)[:20] for x in agg_data.index],
            autopct='%1.1f%%',
            colors=colors,
            explode=[0.02] * len(agg_data),
            shadow=True
        )
        
        ax.set_title(title or f'{category_column} Distribution by {value_column}', 
                    fontsize=14, fontweight='bold')
        
        plt.tight_layout()
        return fig, ax
        
    except Exception as e:
        fig, ax = plt.subplots(figsize=(10, 8))
        ax.text(0.5, 0.5, f'Error creating plot: {str(e)}', ha='center', va='center', fontsize=12)
        return fig, ax


def create_scatter_plot(
    df: pd.DataFrame,
    x_column: str,
    y_column: str,
    color_column: Optional[str] = None,
    title: Optional[str] = None
) -> Tuple[plt.Figure, Any]:
    """
    Create a scatter plot to show relationships between variables.
    
    Args:
        df: pandas DataFrame
        x_column: Column for x-axis
        y_column: Column for y-axis
        color_column: Optional column for color coding
        title: Plot title
        
    Returns:
        Tuple of (matplotlib Figure, axes)
    """
    if df is None or df.empty:
        fig, ax = plt.subplots(figsize=(10, 8))
        ax.text(0.5, 0.5, 'No data available', ha='center', va='center', fontsize=14)
        return fig, ax
    
    try:
        fig, ax = plt.subplots(figsize=(10, 8))
        
        # Sample data if too large
        plot_df = df.sample(n=min(1000, len(df)), random_state=42) if len(df) > 1000 else df
        
        if color_column and color_column in plot_df.columns:
            unique_cats = plot_df[color_column].unique()[:8]
            for i, cat in enumerate(unique_cats):
                mask = plot_df[color_column] == cat
                ax.scatter(
                    plot_df.loc[mask, x_column], 
                    plot_df.loc[mask, y_column],
                    c=COLORS['palette'][i % len(COLORS['palette'])],
                    label=str(cat)[:20],
                    alpha=0.6,
                    s=50
                )
            ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
        else:
            ax.scatter(plot_df[x_column], plot_df[y_column], c=COLORS['primary'], alpha=0.6, s=50)
        
        ax.set_xlabel(x_column, fontsize=12)
        ax.set_ylabel(y_column, fontsize=12)
        ax.set_title(title or f'{x_column} vs {y_column}', fontsize=14, fontweight='bold')
        ax.grid(True, alpha=0.3)
        
        plt.tight_layout()
        return fig, ax
        
    except Exception as e:
        fig, ax = plt.subplots(figsize=(10, 8))
        ax.text(0.5, 0.5, f'Error creating plot: {str(e)}', ha='center', va='center', fontsize=12)
        return fig, ax


def create_correlation_heatmap(
    df: pd.DataFrame,
    columns: Optional[List[str]] = None,
    title: Optional[str] = None
) -> Tuple[plt.Figure, Any]:
    """
    Create a correlation heatmap for numerical columns.
    
    Args:
        df: pandas DataFrame
        columns: List of columns to include (None for all numeric)
        title: Plot title
        
    Returns:
        Tuple of (matplotlib Figure, axes)
    """
    if df is None or df.empty:
        fig, ax = plt.subplots(figsize=(10, 8))
        ax.text(0.5, 0.5, 'No data available', ha='center', va='center', fontsize=14)
        return fig, ax
    
    try:
        # Select numeric columns
        if columns:
            numeric_df = df[columns].select_dtypes(include=[np.number])
        else:
            numeric_df = df.select_dtypes(include=[np.number])
        
        if numeric_df.shape[1] < 2:
            fig, ax = plt.subplots(figsize=(10, 8))
            ax.text(0.5, 0.5, 'Need at least 2 numeric columns for correlation', 
                   ha='center', va='center', fontsize=14)
            return fig, ax
        
        corr_matrix = numeric_df.corr()
        
        fig, ax = plt.subplots(figsize=(10, 8))
        
        mask = np.triu(np.ones_like(corr_matrix, dtype=bool))
        sns.heatmap(
            corr_matrix, 
            mask=mask,
            annot=True, 
            cmap='RdBu_r', 
            center=0,
            fmt='.2f',
            square=True,
            linewidths=0.5,
            ax=ax,
            vmin=-1,
            vmax=1
        )
        
        ax.set_title(title or 'Correlation Heatmap', fontsize=14, fontweight='bold')
        
        plt.tight_layout()
        return fig, ax
        
    except Exception as e:
        fig, ax = plt.subplots(figsize=(10, 8))
        ax.text(0.5, 0.5, f'Error creating plot: {str(e)}', ha='center', va='center', fontsize=12)
        return fig, ax


def save_plot(fig: plt.Figure, filename: str = "chart.png", dpi: int = 150) -> str:
    """
    Save a matplotlib figure to a file.
    
    Args:
        fig: matplotlib Figure to save
        filename: Output filename
        dpi: Resolution
        
    Returns:
        Path to saved file
    """
    try:
        fig.savefig(filename, dpi=dpi, bbox_inches='tight', facecolor='white')
        return filename
    except Exception as e:
        return None