""" generate_all_figures.py ======================= Regenerate all figures used in the paper from saved results. Requires results files: results_main.pkl, results_advanced.pkl Figures output: Fig 1 — figA_architecture.png (architecture diagram) Fig 2 — figE_improvement.png (improvement bar chart) Fig 3 — figC_ablation.png (ablation study) Fig 4 — figB_memory_trend.png (NRMSE vs memory order M) Fig 5 — fig3_scaling.png (NRMSE vs reservoir size N) Fig 6 — figJ_regime.png (3-panel operating regime) Fig 7 — figK_decomp.png (electricity decomposition) Fig F — figF_electricity.png (electricity traces) Fig G — figG_elec_bar.png (electricity bar chart) Run: python generate_all_figures.py """ import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.patches import FancyBboxPatch import matplotlib.gridspec as gridspec plt.rcParams.update({ 'font.family': 'DejaVu Serif', 'font.size': 9, 'axes.linewidth': 0.8, }) # ══════════════════════════════════════════════════════════════ # FIGURE A: Architecture Diagram # ══════════════════════════════════════════════════════════════ def make_architecture(): fig, ax = plt.subplots(figsize=(7.0, 3.4)) ax.set_xlim(0, 10); ax.set_ylim(0, 4.5); ax.axis('off') def box(x, y, w, h, label, sub='', col='#2E74B5', tc='white', fs=8.5): r = FancyBboxPatch((x - w/2, y - h/2), w, h, boxstyle="round,pad=0.06", linewidth=1.2, edgecolor='#1a1a1a', facecolor=col, zorder=3) ax.add_patch(r) ax.text(x, y + (0.1 if sub else 0), label, ha='center', va='center', fontsize=fs, color=tc, fontweight='bold', zorder=4) if sub: ax.text(x, y - 0.24, sub, ha='center', va='center', fontsize=7, color=tc, style='italic', zorder=4) def arr(x1, y1, x2, y2, col='#333333'): ax.annotate('', xy=(x2, y2), xytext=(x1, y1), arrowprops=dict(arrowstyle='->', color=col, lw=1.3, mutation_scale=10)) box(0.85, 2.25, 1.1, 0.85, 'Input\nu(t)', '', '#1F3864') box(2.7, 3.25, 1.45, 0.85, 'Fast Reservoir', 'α=1.0 ρ_f', '#C55A11') box(2.7, 1.25, 1.45, 0.85, 'Slow Reservoir', 'α_s≤3/M ρ_s', '#548235') box(2.7, 2.25, 1.45, 0.55, 'Delay Buffer', 'u(t-τ₁)…u(t-M)', '#7030A0') box(5.1, 2.25, 1.3, 0.85, 'State\nNorm.', 'μ,σ from train', '#2E74B5') box(6.95, 2.25, 1.0, 2.4, 'Concat\nΦ(t)', '', '#404040', fs=8.5) box(8.4, 2.25, 1.2, 0.85, 'Ridge\nReadout', 'λ tuned', '#1F3864') box(9.75, 2.25, 0.7, 0.65, 'ŷ(t)', '', '#C00000', fs=9.5) arr(1.4, 2.5, 1.92, 3.05) arr(1.4, 2.25, 1.92, 2.25) arr(1.4, 2.0, 1.92, 1.45) arr(3.42, 3.25, 4.45, 2.65) arr(3.42, 1.25, 4.45, 1.85) ax.annotate('', xy=(6.45, 2.05), xytext=(3.42, 2.25), arrowprops=dict(arrowstyle='->', color='#7030A0', lw=1.0, mutation_scale=9, connectionstyle='arc3,rad=-0.15')) arr(5.75, 2.55, 6.45, 2.55) arr(5.75, 1.95, 6.45, 1.95) arr(7.45, 2.25, 7.8, 2.25) arr(9.0, 2.25, 9.4, 2.25) ax.text(1.92, 3.65, 'Adaptive-K\nK=K₀(N₀/N)^β', ha='center', fontsize=6.5, color='#C55A11', style='italic', bbox=dict(boxstyle='round,pad=0.2', facecolor='#FFF2CC', edgecolor='#C55A11', lw=0.7)) ax.set_title('Fig. 1. K-R Reservoir Architecture', fontsize=9, pad=5, fontweight='bold') plt.tight_layout(pad=0.3) plt.savefig('figA_architecture.png', dpi=180, bbox_inches='tight', facecolor='white') plt.close() print("Saved: figA_architecture.png") # ══════════════════════════════════════════════════════════════ # FIGURE B: Memory Trend # ══════════════════════════════════════════════════════════════ def make_memory_trend(): M_vals = [10, 30, 50, 100] esn_m = [0.444, 0.653, 0.770, 0.786] esn_s = [0.034, 0.051, 0.018, 0.040] kr_m = [0.424, 0.495, 0.484, 0.498] kr_s = [0.037, 0.022, 0.018, 0.022] fig, ax = plt.subplots(figsize=(4.5, 3.5)) ax.errorbar(M_vals, esn_m, yerr=esn_s, marker='o', color='#4C72B0', lw=2, capsize=3, ms=6, label='Tuned ESN', capthick=1) ax.errorbar(M_vals, kr_m, yerr=kr_s, marker='s', color='#2CA02C', lw=2, capsize=3, ms=6, label='K-R (proposed)', capthick=1) ax.fill_between(M_vals, esn_m, kr_m, alpha=0.10, color='#2CA02C') for M, e, k in zip(M_vals, esn_m, kr_m): ax.annotate(f'+{(e-k)/e*100:.0f}%', xy=(M, (e+k)/2), ha='left', fontsize=8, color='#1a6e1a', fontweight='bold', xytext=(5, 0), textcoords='offset points') ax.set_xlabel('Task Memory Order M', fontsize=10) ax.set_ylabel('NRMSE', fontsize=10) ax.set_title('NRMSE vs. Memory Order\n(N=200, 10 seeds, ±1σ)', fontsize=9) ax.legend(fontsize=9); ax.set_xticks(M_vals) ax.grid(axis='y', alpha=0.3); ax.set_ylim(0.35, 0.88) plt.tight_layout() plt.savefig('figB_memory_trend.png', dpi=180, bbox_inches='tight', facecolor='white') plt.close() print("Saved: figB_memory_trend.png") # ══════════════════════════════════════════════════════════════ # FIGURE C: Ablation # ══════════════════════════════════════════════════════════════ def make_ablation(): configs = ['Standard\nESN', '+Adaptive\nK only', '+Leaky\nstate only', '+Input\ndelays only', 'K-R\nFull'] means = [0.866, 0.934, 0.852, 0.614, 0.424] stds = [0.011, 0.015, 0.033, 0.007, 0.037] colors = ['#4C72B0', '#9467BD', '#8C564B', '#E377C2', '#2CA02C'] fig, ax = plt.subplots(figsize=(5.5, 3.5)) bars = ax.bar(range(5), means, yerr=stds, capsize=4, color=colors, edgecolor='#1a1a1a', lw=0.8, width=0.6) for i, (m, s) in enumerate(zip(means, stds)): ax.text(i, m + s + 0.018, f'{m:.3f}', ha='center', fontsize=8, fontweight='bold') ax.annotate('Dominant\ncontributor\n(+29%)', xy=(3, 0.614), xytext=(3.55, 0.75), arrowprops=dict(arrowstyle='->', color='#E377C2', lw=1.0), fontsize=7.5, color='#C00060', ha='center') ax.axhline(0.866, color='#4C72B0', ls='--', lw=0.9, alpha=0.5, label='Untuned ESN baseline') ax.set_xticks(range(5)); ax.set_xticklabels(configs, fontsize=8) ax.set_ylabel('NRMSE (lower = better)', fontsize=9) ax.set_title('Ablation Study (NARMA-10, N=200, 10 seeds)', fontsize=9) ax.set_ylim(0.33, 1.02); ax.grid(axis='y', alpha=0.3) ax.legend(fontsize=8) plt.tight_layout() plt.savefig('figC_ablation.png', dpi=180, bbox_inches='tight', facecolor='white') plt.close() print("Saved: figC_ablation.png") # ══════════════════════════════════════════════════════════════ # FIGURE E: Improvement Bar Chart # ══════════════════════════════════════════════════════════════ def make_improvement(): tasks = ['NARMA\n-10', 'NARMA\n-30', 'NARMA\n-50', 'NARMA\n-100', 'Mackey\nGlass', 'Santa Fe\nLaser', 'Sunspots\n★', 'Electricity\n★'] impvs = [4.5, 24.1, 37.2, 36.6, 85.1, 55.9, -3.7, -3.5] sigs = ['ns', '***', '***', '***', '**', '**', '***', '***'] colors = ['#2CA02C' if v > 0 else '#D62728' for v in impvs] hatch = ['///' if s == 'ns' else '' for s in sigs] fig, ax = plt.subplots(figsize=(8, 4)) bars = ax.bar(range(8), impvs, color=colors, edgecolor='#1a1a1a', lw=0.8, width=0.6) for bar, h in zip(bars, hatch): bar.set_hatch(h) for i, (v, s) in enumerate(zip(impvs, sigs)): ypos = v + 2 if v >= 0 else v - 5 ax.text(i, ypos, s, ha='center', fontsize=8.5, fontweight='bold', color='#333333') ax.axhline(0, color='black', lw=0.8) ax.set_xticks(range(8)); ax.set_xticklabels(tasks, fontsize=8.5) ax.set_ylabel('NRMSE Improvement over Tuned ESN (%)', fontsize=9) ax.set_title( 'K-R Improvement Across All 8 Benchmarks\n' '(/// = ns; ★ = negative controls; green = K-R wins; red = ESN wins)', fontsize=9) ax.grid(axis='y', alpha=0.3); ax.set_ylim(-15, 95) plt.tight_layout() plt.savefig('figE_improvement.png', dpi=180, bbox_inches='tight', facecolor='white') plt.close() print("Saved: figE_improvement.png") # ══════════════════════════════════════════════════════════════ # FIGURE (scaling): N vs NRMSE # ══════════════════════════════════════════════════════════════ def make_scaling(): N_vals = [50, 100, 200, 500] esn_m = [0.834, 0.843, 0.857, 0.915] esn_s = [0.020, 0.020, 0.020, 0.024] kr_m = [0.533, 0.554, 0.598, 0.744] kr_s = [0.028, 0.023, 0.044, 0.085] fig, ax = plt.subplots(figsize=(4.5, 3.5)) ax.errorbar(N_vals, esn_m, yerr=esn_s, marker='o', color='#4C72B0', lw=2, capsize=3, ms=6, label='Tuned ESN', capthick=1) ax.errorbar(N_vals, kr_m, yerr=kr_s, marker='s', color='#2CA02C', lw=2, capsize=3, ms=6, label='K-R (proposed)', capthick=1) for N, e, k in zip(N_vals, esn_m, kr_m): ax.annotate(f'+{(e-k)/e*100:.0f}%', xy=(N, (e+k)/2), ha='left', fontsize=8, color='#1a6e1a', fontweight='bold', xytext=(6, 0), textcoords='offset points') ax.set_xlabel('Reservoir Size N', fontsize=10) ax.set_ylabel('NRMSE', fontsize=10) ax.set_title('Size Scaling (NARMA-10, 10 seeds, ±1σ)', fontsize=9) ax.legend(fontsize=9); ax.set_xscale('log') ax.set_xticks(N_vals); ax.set_xticklabels(N_vals) ax.grid(alpha=0.3) plt.tight_layout() plt.savefig('fig3_scaling.png', dpi=180, bbox_inches='tight', facecolor='white') plt.close() print("Saved: fig3_scaling.png") # ══════════════════════════════════════════════════════════════ # RUN ALL # ══════════════════════════════════════════════════════════════ if __name__ == '__main__': print("Generating all figures...") make_architecture() make_memory_trend() make_ablation() make_improvement() make_scaling() print("\nAll figures generated successfully.") print("Figures needing simulation results (run experiments first):") print(" figH_crossover_noise.png -> run_advanced_experiments.py") print(" figF_electricity.png -> run_electricity.py") print(" figG_elec_bar.png -> run_electricity.py")