Inventor Nader Maleki

import os import glob import json import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from sklearn.metrics import classification_report from datetime import datetime

plt.style.use('seaborn-v0_8')

---------- Part 1: Multi-run network processing ----------

results_dir = "results" os.makedirs(results_dir, exist_ok=True)

Reading files by listing once

all_files = glob.glob(os.path.join(results_dir, '*')) seed_metrics = sorted([f for f in all_files if 'metrics_seed' in f and f.endswith('.json')]) seed_histories = sorted([f for f in all_files if 'history_seed' in f and f.endswith('.csv')]) seed_confmats = sorted([f for f in all_files if 'confmat_seed' in f and f.endswith('.csv')])

Create PDF

pdf_path = os.path.join(results_dir, 'summary_dual_hemisphere_2031.pdf')

with PdfPages(pdf_path) as pdf:

Title page

fig, ax = plt.subplots(figsize=(11, 8.5)) ax.axis('off') ax.text(0.5, 0.6, "Dual Hemisphere Network Report – Code 2031", fontsize=20, ha='center', fontweight='bold') ax.text(0.5, 0.4, f"Generation date: {datetime.now().strftime('%Y-%m-%d %H:%M')}", fontsize=14, ha='center') pdf.savefig(fig) plt.close()

Process each run

for metrics_file, hist_file, conf_file in zip(seed_metrics, seed_histories, seed_confmats): seed_id = os.path.splitext(os.path.basename(metrics_file))[0].split("_")[-1]

Read data

with open(metrics_file, 'r', encoding='utf-8') as f: metrics = json.load(f)

history = pd.read_csv(hist_file) confmat = pd.read_csv(conf_file, index_col=0).values

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