"""
🔗 Binding Operation Visualization Functions
==========================================

This module provides visualization functions for holographic binding operations,
quality assessment, and binding accuracy analysis.

Author: Benedict Chen (benedict@benedictchen.com)
"""

import numpy as np
import matplotlib.pyplot as plt
from typing import Dict, Any, List, Optional, Tuple


def plot_binding_quality(binding_results: Dict[str, Any],
                        title: str = "Binding Quality Analysis",
                        figsize: Tuple[int, int] = (15, 10),
                        save_path: Optional[str] = None) -> plt.Figure:
    """Plot binding quality analysis results."""
    # Implementation placeholder
    fig, ax = plt.subplots(figsize=figsize)
    ax.text(0.5, 0.5, 'Binding Quality Analysis\n(Implementation in progress)', 
           ha='center', va='center', fontsize=16)
    ax.set_title(title)
    
    if save_path:
        fig.savefig(save_path, dpi=300, bbox_inches='tight')
    
    return fig


def plot_binding_accuracy(accuracies: List[float],
                         title: str = "Binding Accuracy",
                         figsize: Tuple[int, int] = (10, 6),
                         save_path: Optional[str] = None) -> plt.Figure:
    """Plot binding accuracy histogram."""
    # Implementation placeholder
    fig, ax = plt.subplots(figsize=figsize)
    ax.text(0.5, 0.5, 'Binding Accuracy Plot\n(Implementation in progress)', 
           ha='center', va='center', fontsize=16)
    ax.set_title(title)
    
    if save_path:
        fig.savefig(save_path, dpi=300, bbox_inches='tight')
    
    return fig


def plot_unbinding_accuracy(accuracies: List[float],
                           title: str = "Unbinding Accuracy", 
                           figsize: Tuple[int, int] = (10, 6),
                           save_path: Optional[str] = None) -> plt.Figure:
    """Plot unbinding accuracy histogram."""
    # Implementation placeholder
    fig, ax = plt.subplots(figsize=figsize)
    ax.text(0.5, 0.5, 'Unbinding Accuracy Plot\n(Implementation in progress)', 
           ha='center', va='center', fontsize=16)
    ax.set_title(title)
    
    if save_path:
        fig.savefig(save_path, dpi=300, bbox_inches='tight')
    
    return fig


def plot_binding_similarity_distribution(similarities: List[float],
                                        title: str = "Binding Similarity Distribution",
                                        figsize: Tuple[int, int] = (10, 6),
                                        save_path: Optional[str] = None) -> plt.Figure:
    """Plot distribution of binding similarities."""
    # Implementation placeholder
    fig, ax = plt.subplots(figsize=figsize)
    ax.text(0.5, 0.5, 'Binding Similarity Distribution\n(Implementation in progress)', 
           ha='center', va='center', fontsize=16)
    ax.set_title(title)
    
    if save_path:
        fig.savefig(save_path, dpi=300, bbox_inches='tight')
    
    return fig