modius_performance/visualization/apr_charts.py

"""
APR 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 DATE_RANGES, Y_AXIS_RANGES, FILE_PATHS
from .base_chart import BaseChart

logger = logging.getLogger(__name__)


class APRChart(BaseChart):
    """Chart for APR visualizations."""
    
    def create_chart(self, df: pd.DataFrame, **kwargs) -> go.Figure:
        """Create APR time series chart."""
        if df.empty:
            return self._create_empty_chart("No APR data available")
        
        # Filter for APR data only
        apr_data = df[df['metric_type'] == 'APR'].copy()
        if apr_data.empty:
            return self._create_empty_chart("No APR data available")
        
        # Apply daily median aggregation to reduce outliers
        apr_data = self.data_processor.aggregate_daily_medians(apr_data, ['apr', 'adjusted_apr'])
        if apr_data.empty:
            return self._create_empty_chart("No APR data available after aggregation")
        
        # Apply high APR filtering (400% threshold) with forward filling
        apr_data, _ = self.data_processor.filter_high_apr_values(apr_data)
        if apr_data.empty:
            return self._create_empty_chart("No APR data available after high APR filtering")
        
        # Save processed APR data for verification (after all processing steps)
        processed_csv_path = self.data_processor.save_to_csv(apr_data, FILE_PATHS['apr_processed_csv'])
        if processed_csv_path:
            logger.info(f"Saved processed APR data to {processed_csv_path} for verification")
            logger.info(f"Processed APR data contains {len(apr_data)} rows after agent exclusion, zero filtering, daily median aggregation, and high APR filtering")
        
        # Filter outliers (disabled but keeping for compatibility)
        apr_data = self._filter_outliers(apr_data, 'apr')
        
        # Get time range
        min_time = apr_data['timestamp'].min()
        max_time = apr_data['timestamp'].max()
        x_start_date = DATE_RANGES['apr_start']
        
        # Create figure
        fig = self._create_base_figure()
        
        # Add background shapes
        y_range = Y_AXIS_RANGES['apr']
        self._add_background_shapes(fig, min_time, max_time, y_range['min'], y_range['max'])
        self._add_zero_line(fig, min_time, max_time)
        
        # Calculate moving averages
        avg_apr_data = self._calculate_moving_average(apr_data, 'apr')
        
        # Add individual agent data points
        unique_agents = apr_data['agent_name'].unique()
        color_map = self._get_color_map(unique_agents)
        self._add_agent_data_points(fig, apr_data, 'apr', color_map)
        
        # Add APR moving average line
        self._add_moving_average_line(
            fig, avg_apr_data, 'apr', 
            'Average APR (7d window)', 
            self.colors['apr'], 
            width=3
        )
        
        # Add adjusted APR moving average if available
        if 'adjusted_apr' in apr_data.columns and apr_data['adjusted_apr'].notna().any():
            # Calculate adjusted APR moving average
            adjusted_avg_data = self._calculate_moving_average(apr_data, 'adjusted_apr')
            
            # Handle missing values with forward fill (fix the column name)
            import warnings
            with warnings.catch_warnings():
                warnings.simplefilter("ignore", FutureWarning)
                # Fix: Use the correct column name 'moving_avg' not 'adjusted_moving_avg'
                adjusted_avg_data['moving_avg'] = adjusted_avg_data['moving_avg'].ffill()
            
            self._add_moving_average_line(
                fig, adjusted_avg_data, 'adjusted_apr',
                'Average ETH Adjusted APR (7d window)',
                self.colors['adjusted_apr'],
                width=3
            )
        
        # Update layout and axes
        self._update_layout(
            fig, 
            title="Modius Agents",
            y_axis_title=None,  # Remove single y-axis title to use region-specific labels
            y_range=[y_range['min'], y_range['max']]
        )
        
        # Use auto-range for both x-axis and y-axis to show all available data
        self._update_axes(
            fig,
            x_range=None,  # Let plotly auto-determine the best x-axis range
            y_auto=True    # Let plotly auto-determine the best y-axis range
        )
        
        # Add region-specific annotations for positive and negative areas
        self._add_region_annotations(fig, y_range)
        
        # Save chart
        self._save_chart(
            fig, 
            FILE_PATHS['apr_graph_html'], 
            FILE_PATHS['apr_graph_png']
        )
        
        return fig
    
    def _add_region_annotations(self, fig: go.Figure, y_range: Dict[str, float]) -> None:
        """Add annotations for positive and negative regions."""
        # Annotation for negative region
        fig.add_annotation(
            x=-0.08,
            y=-25,
            xref="paper",
            yref="y",
            text="Percent drawdown [%]",
            showarrow=False,
            font=dict(
                size=16, 
                family=self.config['font_family'], 
                color="black", 
                weight="bold"
            ),
            textangle=-90,
            align="center"
        )
        
        # Annotation for positive region
        fig.add_annotation(
            x=-0.08,
            y=50,
            xref="paper",
            yref="y",
            text="Agent APR [%]",
            showarrow=False,
            font=dict(
                size=16, 
                family=self.config['font_family'], 
                color="black", 
                weight="bold"
            ),
            textangle=-90,
            align="center"
        )


class APRHashChart(BaseChart):
    """Chart for APR vs Agent Hash visualizations."""
    
    def create_chart(self, df: pd.DataFrame, **kwargs) -> go.Figure:
        """Create APR vs agent hash bar chart."""
        if df.empty:
            return self._create_empty_chart("No agent hash data available")
        
        # Data is already filtered and processed by the calling function
        # Just filter for data with agent hash
        apr_data = df[df['agent_hash'].notna()].copy()
        if apr_data.empty:
            return self._create_empty_chart("No valid APR data with agent_hash found")
        
        logger.info(f"APR Hash Chart: Using {len(apr_data)} processed data points")
        
        # Create figure
        fig = self._create_base_figure()
        
        # Get unique hashes and create version mapping
        unique_hashes = apr_data['agent_hash'].unique()
        version_map = self._create_version_map(unique_hashes)
        
        # Sort hashes by version
        sorted_hashes = sorted(unique_hashes, key=lambda h: "1" if h.endswith("tby") else "2" if h.endswith("vq") else h)
        
        # Add zero line for bar chart
        self._add_zero_line(fig, -0.5, len(version_map) - 0.5)
        
        # Version colors
        version_colors = {
            "v0.4.1": "rgba(31, 119, 180, 0.7)",
            "v0.4.2": "rgba(44, 160, 44, 0.7)",
        }
        default_color = "rgba(214, 39, 40, 0.7)"
        
        # Aggregate data by version for bar chart
        version_data = {}
        version_stats = {}
        
        # Aggregate data by version
        for agent_hash in sorted_hashes:
            hash_data = apr_data[apr_data['agent_hash'] == agent_hash]
            version = version_map[agent_hash]
            
            # Calculate statistics
            apr_values = hash_data['apr'].tolist()
            
            # Store statistics for version comparison
            if version not in version_stats:
                version_stats[version] = {'apr_values': [], 'count': 0, 'hashes': []}
            
            version_stats[version]['apr_values'].extend(apr_values)
            version_stats[version]['count'] += len(apr_values)
            version_stats[version]['hashes'].append(agent_hash)
        
        # Create bar chart data
        versions = list(version_stats.keys())
        medians = []
        colors = []
        hover_texts = []
        
        for version in versions:
            # Calculate median APR for this version
            all_values = version_stats[version]['apr_values']
            median_apr = pd.Series(all_values).median()
            medians.append(median_apr)
            
            # Choose color
            color = version_colors.get(version, default_color)
            colors.append(color)
            
            # Create hover text
            count = version_stats[version]['count']
            mean_apr = pd.Series(all_values).mean()
            min_apr = pd.Series(all_values).min()
            max_apr = pd.Series(all_values).max()
            
            hover_text = (
                f"Version: {version}<br>"
                f"Median APR: {median_apr:.2f}%<br>"
                f"Mean APR: {mean_apr:.2f}%<br>"
                f"Min APR: {min_apr:.2f}%<br>"
                f"Max APR: {max_apr:.2f}%<br>"
                f"Data points: {count}"
            )
            hover_texts.append(hover_text)
        
        # Add bar chart
        fig.add_trace(
            go.Bar(
                x=versions,
                y=medians,
                marker=dict(
                    color=colors,
                    line=dict(width=1, color='black')
                ),
                hoverinfo='text',
                hovertext=hover_texts,
                showlegend=False,
                name="Median APR"
            )
        )
        
        # Add median value annotations on top of bars
        for i, (version, median_apr) in enumerate(zip(versions, medians)):
            fig.add_annotation(
                x=version,
                y=median_apr + (max(medians) * 0.05),  # 5% above the bar
                text=f"{median_apr:.1f}%",
                showarrow=False,
                font=dict(
                    family=self.config['font_family'],
                    size=14,
                    color="black",
                    weight="bold"
                )
            )
        
        # Add version comparison annotation
        self._add_version_comparison(fig, version_stats, len(versions))
        
        # Update layout
        self._update_layout(
            fig,
            title="Performance Graph",
            height=900
        )
        
        # Update axes
        self._update_axes(fig, y_auto=True)
        
        # Update x-axis for bar chart
        fig.update_xaxes(
            tickangle=-45
        )
        
        # Save chart
        self._save_chart(
            fig,
            FILE_PATHS['apr_hash_graph_html'],
            FILE_PATHS['apr_hash_graph_png']
        )
        
        return fig
    
    def _create_version_map(self, hashes: list) -> Dict[str, str]:
        """Create version mapping for agent hashes."""
        version_map = {}
        for hash_val in hashes:
            if hash_val.endswith("tby"):
                version_map[hash_val] = "v0.4.1"
            elif hash_val.endswith("vq"):
                version_map[hash_val] = "v0.4.2"
            else:
                version_map[hash_val] = f"Hash: {hash_val[-6:]}"
        return version_map
    
    def _add_version_comparison(self, fig: go.Figure, version_stats: Dict, num_hashes: int) -> None:
        """Add version comparison annotation."""
        if "v0.4.1" in version_stats and "v0.4.2" in version_stats:
            v041_values = version_stats["v0.4.1"]["apr_values"]
            v042_values = version_stats["v0.4.2"]["apr_values"]
            
            v041_median = pd.Series(v041_values).median()
            v042_median = pd.Series(v042_values).median()
            
            improvement = v042_median - v041_median
            change_text = "improvement" if improvement > 0 else "decrease"
            
            fig.add_annotation(
                x=(num_hashes - 1) / 2,
                y=90,
                text=f"<b>Version Comparison:</b> {abs(improvement):.2f}% {change_text} from v0.4.1 to v0.4.2",
                showarrow=False,
                font=dict(
                    family=self.config['font_family'],
                    size=16,
                    color="black",
                    weight="bold"
                ),
                bgcolor="rgba(255, 255, 255, 0.9)",
                bordercolor="black",
                borderwidth=2,
                borderpad=6,
                opacity=0.9
            )


def generate_apr_visualizations(data_processor=None) -> Tuple[go.Figure, Optional[str]]:
    """Generate APR 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()
    
    # Create chart
    apr_chart = APRChart(data_processor)
    fig, csv_path = apr_chart.generate_visualization(
        apr_df, 
        csv_filename=FILE_PATHS['apr_csv']
    )
    
    return fig, csv_path


def generate_apr_vs_agent_hash_visualizations(df: pd.DataFrame) -> Tuple[go.Figure, Optional[str]]:
    """Generate APR vs agent hash visualizations."""
    from ..data.data_processor import DataProcessor
    
    data_processor = DataProcessor()
    
    # Use the same processed data as the APR time series chart
    # First apply the same filtering pipeline to get consistent data
    apr_data = df[df['metric_type'] == 'APR'].copy()
    if not apr_data.empty:
        # Apply daily median aggregation
        apr_data = data_processor.aggregate_daily_medians(apr_data, ['apr', 'adjusted_apr'])
        # Apply high APR filtering
        apr_data, _ = data_processor.filter_high_apr_values(apr_data)
    
    # Create chart using the processed data
    apr_hash_chart = APRHashChart(data_processor)
    fig, csv_path = apr_hash_chart.generate_visualization(
        apr_data,
        csv_filename=FILE_PATHS['apr_hash_csv']
    )
    
    return fig, csv_path