"""
Volume chart implementations.
"""
import plotly.graph_objects as go
import pandas as pd
import logging
from datetime import datetime
from typing import Dict, Any, Optional, Tuple

from ..config.constants import Y_AXIS_RANGES, FILE_PATHS
from .base_chart import BaseChart

logger = logging.getLogger(__name__)


class VolumeChart(BaseChart):
    """Chart for Volume visualizations."""
    
    def create_chart(self, df: pd.DataFrame, **kwargs) -> go.Figure:
        """Create Cumulative Volume bar chart."""
        if df.empty:
            return self._create_empty_chart("No volume data available")
        
        # Calculate daily volume changes using the new method
        daily_data = self.data_processor.calculate_daily_volume_changes(df)
        if daily_data.empty:
            return self._create_empty_chart("No daily volume data available")
        
        # Create figure
        fig = self._create_base_figure()
        
        # Get time range
        min_time = daily_data['date'].min()
        max_time = daily_data['date'].max()
        
        # Determine bar colors based on daily volume (all bars will be blue for cumulative volume)
        bar_colors = ['blue'] * len(daily_data)
        
        # Create custom hover text for bars
        bar_hover_text = []
        for _, row in daily_data.iterrows():
            date_str = row['date'].strftime('%Y-%m-%d')
            bar_hover_text.append(
                f"Date: {date_str}<br>"
                f"Daily Volume: {row['volume']:.2f}<br>"
                f"Cumulative Volume: {row['cumulative_volume']:.2f}<br>"
                f"Day-over-Day Change: {row['day_over_day_pct']:.1f}%<br>"
                f"7-Day SMA: {row['sma_7d']:.1f}%"
            )
        
        # Add cumulative volume bar chart
        fig.add_trace(
            go.Bar(
                x=daily_data['date'],
                y=daily_data['cumulative_volume'],
                marker=dict(
                    color=bar_colors,
                    line=dict(width=1, color='black'),
                    opacity=0.7
                ),
                name='Daily Volume Change',
                hovertext=bar_hover_text,
                hoverinfo='text',
                customdata=daily_data[['volume', 'day_over_day_pct']].values
            )
        )
        
        # Add 7-Day SMA line
        fig.add_trace(
            go.Scatter(
                x=daily_data['date'],
                y=daily_data['sma_7d'],
                mode='lines',
                line=dict(color='red', width=3),
                name='7-Day SMA',
                hovertemplate='Date: %{x}<br>7-Day SMA: %{y:.1f}%<extra></extra>',
                yaxis='y2'
            )
        )
        
        # Update layout and axes
        self._update_layout(
            fig, 
            title="Daily Volume Change (%) with 7-Day SMA",
            y_axis_title="Cumulative Volume"
        )
        
        # Update axes
        self._update_axes(
            fig,
            x_range=[min_time, max_time],
            y_auto=True
        )
        
        # Update primary y-axis to show volume format
        fig.update_yaxes(tickformat=".2f")
        
        # Add secondary y-axis for SMA
        fig.update_layout(
            yaxis2=dict(
                title="Percentage Change (%)",
                overlaying='y',
                side='right',
                tickformat=".1f"
            )
        )
        
        # Save chart
        self._save_chart(
            fig, 
            FILE_PATHS['volume_graph_html'], 
            FILE_PATHS['volume_graph_png']
        )
        
        return fig


def generate_volume_visualizations(data_processor=None) -> Tuple[go.Figure, Optional[str]]:
    """Generate Volume visualizations."""
    from ..data.data_processor import DataProcessor
    
    if data_processor is None:
        data_processor = DataProcessor()
    
    # Fetch data
    apr_df, _ = data_processor.fetch_apr_data_from_db()
    
    # Filter for records with volume data
    volume_df = apr_df[apr_df['volume'].notna()].copy()
    
    # Create chart
    volume_chart = VolumeChart(data_processor)
    fig, csv_path = volume_chart.generate_visualization(
        volume_df, 
        csv_filename=FILE_PATHS['volume_csv']
    )
    
    return fig, csv_path