scripts/generate_paper_charts.py

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os

# Ensure output directory exists
# Ensure output directory exists
# Output to ../papers relative to this script
output_dir = os.path.join(os.path.dirname(__file__), "..", "papers")
os.makedirs(output_dir, exist_ok=True)

# Set style for scientific publication
plt.style.use('default') 
# Use a simple, clean style since 'seaborn-whitegrid' might not be available or 'seaborn' not installed
# We will manually set grid and aesthetic
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Liberation Sans']
plt.rcParams['figure.dpi'] = 300

# Color palette (Scientific Blue/Orange)
c_blue = '#1f77b4'
c_orange = '#ff7f0e'
c_green = '#2ca02c'
c_red = '#d62728'

# --- Metric 1: F1 Improvement (Bar Chart) ---
def plot_f1_improvement():
    data = {
        'Strategy': ['Baseline (Fixed τ)', 'Optimized (Adaptive τ)', 'Breakthrough (Argmax)'],
        'F1 Score': [81.3, 98.4, 99.6],
        'Recall': [75.8, 100.0, 99.5]
    }
    df = pd.DataFrame(data)
    
    fig, ax = plt.subplots(figsize=(8, 5))
    x = np.arange(len(df['Strategy']))
    width = 0.35
    
    rects1 = ax.bar(x - width/2, df['F1 Score'], width, label='F1 Score', color=c_blue, alpha=0.8, edgecolor='black')
    rects2 = ax.bar(x + width/2, df['Recall'], width, label='Recall', color=c_green, alpha=0.8, edgecolor='black')
    
    ax.set_ylabel('Performance (%)', fontsize=12, fontweight='bold')
    ax.set_title('Figure 1: Atomic VSA Optimization Trajectory', fontsize=14, fontweight='bold', pad=15)
    ax.set_xticks(x)
    ax.set_xticklabels(df['Strategy'], fontsize=10, rotation=0)
    ax.set_ylim(60, 105)
    ax.legend(loc='lower right')
    ax.grid(axis='y', linestyle='--', alpha=0.5)
    
    # Add value labels
    def autolabel(rects):
        for rect in rects:
            height = rect.get_height()
            ax.annotate(f'{height}%',
                        xy=(rect.get_x() + rect.get_width() / 2, height),
                        xytext=(0, 3),  # 3 points vertical offset
                        textcoords="offset points",
                        ha='center', va='bottom', fontweight='bold')

    autolabel(rects1)
    autolabel(rects2)
    
    plt.tight_layout()
    save_path = os.path.join(output_dir, "fig1_optimization_trajectory.png")
    plt.savefig(save_path)
    print(f"Generated: {save_path}")
    plt.close()

# --- Metric 2: SNR Gap Analysis (Horizontal Bar Chart) ---
def plot_snr_gap():
    # Data from Table 1 in the paper
    conditions = [
        'Acanthamoebiasis', 'Cholera', 'Resp. Tuberculosis', 
        'Malaria (P. falc)', 'Plasmodium w/ Complications', 
        'Acute Hep B', 'Acute Hep A', 'Typhoid Fever'
    ]
    snr_gaps = [0.603, 0.482, 0.324, 0.229, 0.188, 0.017, 0.000, 0.000]
    colors = [c_green if x > 0.1 else (c_orange if x > 0 else c_red) for x in snr_gaps]
    
    fig, ax = plt.subplots(figsize=(10, 6))
    y_pos = np.arange(len(conditions))
    
    bars = ax.barh(y_pos, snr_gaps, color=colors, edgecolor='black', alpha=0.8)
    ax.set_yticks(y_pos)
    ax.set_yticklabels(conditions)
    ax.invert_yaxis()  # labels read top-to-bottom
    ax.set_xlabel('Resonance Gap (SNR)', fontsize=12, fontweight='bold')
    ax.set_title('Figure 2: The Holographic Limit (Resonance Gap Analysis)', fontsize=14, fontweight='bold', pad=15)
    ax.axvline(x=0.05, color='red', linestyle='--', label='Noise Floor (0.05)')
    ax.legend()
    ax.grid(axis='x', linestyle='--', alpha=0.5)

    # Add value labels
    for i, v in enumerate(snr_gaps):
        ax.text(v + 0.01, i + 0.1, f'{v:.3f}', color='black', fontweight='bold')

    plt.tight_layout()
    save_path = os.path.join(output_dir, "fig2_snr_analysis.png")
    plt.savefig(save_path)
    print(f"Generated: {save_path}")
    plt.close()

# --- Metric 3: Speedup (Log Scale) ---
def plot_speedup():
    labels = ['Atomic VSA', 'Neural Net (Inference)']
    times_us = [42, 50000] # 42us vs 50ms (50,000us)
    
    fig, ax = plt.subplots(figsize=(8, 4))
    y_pos = np.arange(len(labels))
    
    rects = ax.barh(y_pos, times_us, color=[c_green, c_red], edgecolor='black')
    ax.set_yticks(y_pos)
    ax.set_yticklabels(labels)
    ax.invert_yaxis()
    ax.set_xlabel('Inference Time (microseconds) - Log Scale', fontsize=12, fontweight='bold')
    ax.set_title('Figure 3: Computational Efficiency (Log Scale)', fontsize=14, fontweight='bold')
    ax.set_xscale('log')
    ax.grid(axis='x', linestyle='--', alpha=0.5)
    
    for i, v in enumerate(times_us):
        label = f"{v} µs" if v < 1000 else f"{v/1000} ms"
        ax.text(v * 1.1, i, label, va='center', fontweight='bold')

    plt.tight_layout()
    save_path = os.path.join(output_dir, "fig3_speedup.png")
    plt.savefig(save_path)
    print(f"Generated: {save_path}")
    plt.close()

if __name__ == "__main__":
    plot_f1_improvement()
    plot_snr_gap()
    plot_speedup()