"""
📈 Analysis Visualization Functions
==================================

This module provides visualization functions for statistical analysis,
performance analysis, and clustering results in holographic memory systems.

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_performance_analysis(performance_data: Dict[str, Any],
                            title: str = "Performance Analysis",
                            figsize: Tuple[int, int] = (12, 8),
                            save_path: Optional[str] = None) -> plt.Figure:
    """Plot performance analysis results."""
    # Implementation placeholder
    fig, ax = plt.subplots(figsize=figsize)
    ax.text(0.5, 0.5, 'Performance 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_clustering_results(clustering_data: Dict[str, Any],
                          title: str = "Clustering Analysis",
                          figsize: Tuple[int, int] = (12, 8),
                          save_path: Optional[str] = None) -> plt.Figure:
    """Plot clustering analysis results."""
    # Implementation placeholder
    fig, ax = plt.subplots(figsize=figsize)
    ax.text(0.5, 0.5, 'Clustering 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_dimensionality_analysis(dim_data: Dict[str, Any],
                                title: str = "Dimensionality Analysis", 
                                figsize: Tuple[int, int] = (12, 8),
                                save_path: Optional[str] = None) -> plt.Figure:
    """Plot dimensionality analysis results."""
    # Implementation placeholder
    fig, ax = plt.subplots(figsize=figsize)
    ax.text(0.5, 0.5, 'Dimensionality 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_statistical_distribution(distribution_data: Dict[str, Any],
                                 title: str = "Statistical Distribution",
                                 figsize: Tuple[int, int] = (12, 8), 
                                 save_path: Optional[str] = None) -> plt.Figure:
    """Plot statistical distribution analysis."""
    # Implementation placeholder
    fig, ax = plt.subplots(figsize=figsize)
    ax.text(0.5, 0.5, 'Statistical 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


def plot_correlation_matrix(correlation_data: np.ndarray,
                           title: str = "Correlation Matrix",
                           figsize: Tuple[int, int] = (10, 8),
                           save_path: Optional[str] = None) -> plt.Figure:
    """Plot correlation matrix."""
    # Implementation placeholder
    fig, ax = plt.subplots(figsize=figsize)
    ax.text(0.5, 0.5, 'Correlation Matrix\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