hc
ced50ea2b0
Root cause: test-runner was giving overly optimistic results due to: 1. Context bias - knew the implementation, tended to defend it 2. No actual visual comparison - just wrote 'ACCEPTABLE' without looking 3. No structural validation - accepted 35x scale differences as 'acceptable' Solution: - New result-verifier agent that performs blind visual comparison - Strict pass/fail criteria for structural validation - Updated test-runner to use result-verifier for each figure - Clear guidelines: structural mismatches = FAIL, not ACCEPTABLE Test result: verifier correctly identified Fig3 as FAIL with 7 specific issues: - Wrong X-axis variable (channels vs power) - Wrong Y-axis scale (5x difference) - Wrong curve count (5 vs 4) - etc.
213 lines
6.5 KiB
Python
213 lines
6.5 KiB
Python
"""
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Reference plots for Resource Allocation for Text Semantic Communications
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Generated from paper images for verification purposes.
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Run: python reference_plots.py
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Output: workspace/resource_allocation/analysis/reference_images/
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from pathlib import Path
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from scipy.interpolate import PchipInterpolator
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OUTPUT_DIR = Path("workspace/resource_allocation/analysis/reference_images")
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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def plot_figure_2():
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"""
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Figure 2: The semantic similarity for DeepSC
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"""
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fig = plt.figure(figsize=(10, 8))
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ax = fig.add_subplot(111, projection="3d")
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# Generate data
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snr = np.linspace(-10, 20, 30)
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k_n = np.linspace(0, 20, 30)
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SNR, KN = np.meshgrid(snr, k_n)
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# Approximate function for similarity
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# Logistic-like function depending on SNR and k_n
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z = 0.4 + 0.6 / (1 + np.exp(-0.3 * (SNR + 5)) * np.exp(-0.2 * (KN - 5)))
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z = np.clip(z, 0.4, 1.0)
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surf = ax.plot_surface(SNR, KN, z, cmap="viridis", edgecolor="none", alpha=0.9)
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ax.set_xlabel("SNR, $\gamma_{n,m}$ (dB)")
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ax.set_ylabel("$k_n$ (symbols/word)")
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ax.set_zlabel(r"$\xi_{n,m}$")
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ax.set_zlim(0.4, 1.0)
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ax.view_init(elev=30, azim=225)
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plt.savefig(OUTPUT_DIR / "fig2.png", dpi=150)
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plt.close()
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print("Generated: fig2.png")
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def plot_figure_3():
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"""
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Figure 3: The S-SE of the semantic-aware network with different models
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"""
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M = np.arange(1, 11)
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# Approximate values from visual inspection
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proposed = np.array([0.24, 0.48, 0.72, 0.96, 1.18, 1.20, 1.20, 1.20, 1.20, 1.20])
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conv_k3 = np.zeros(10)
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conv_k5 = np.array([0.20, 0.39, 0.58, 0.77, 0.94, 0.96, 0.97, 0.97, 0.97, 0.97])
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conv_k7 = np.array([0.14, 0.28, 0.42, 0.56, 0.70, 0.70, 0.70, 0.70, 0.70, 0.70])
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conv_k9 = np.array([0.11, 0.22, 0.33, 0.44, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54])
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plt.figure(figsize=(8, 6))
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plt.plot(M, proposed, "rd-", label="Proposed model")
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plt.plot(M, conv_k3, "ko-", label="Conventional model, $k_n = 3$", fillstyle="none")
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plt.plot(M, conv_k5, "k+-", label="Conventional model, $k_n = 5$")
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plt.plot(M, conv_k7, "k*-", label="Conventional model, $k_n = 7$")
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plt.plot(M, conv_k9, "kx-", label="Conventional model, $k_n = 9$")
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plt.xlabel("Number of channels, $M$")
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plt.ylabel("S-SE, $\Phi$ (suts/s/Hz) $\\times (I/L)$")
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plt.xticks(np.arange(1, 11))
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plt.yticks(np.arange(0, 1.5, 0.2))
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plt.grid(True)
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plt.legend(loc="lower right")
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plt.xlim(1, 10)
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plt.ylim(0, 1.3)
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plt.savefig(OUTPUT_DIR / "fig3.png", dpi=150)
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plt.close()
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print("Generated: fig3.png")
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def plot_figure_4a():
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"""
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Figure 4(a): The S-SE versus the number of channels
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"""
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M = np.arange(1, 11)
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# Approximate values from visual inspection
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semantic = np.array([0.24, 0.48, 0.72, 0.96, 1.18, 1.20, 1.20, 1.20, 1.20, 1.20])
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ideal = np.array([0.21, 0.40, 0.55, 0.68, 0.79, 0.82, 0.84, 0.85, 0.86, 0.87])
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g5 = np.array([0.13, 0.26, 0.37, 0.47, 0.56, 0.58, 0.59, 0.60, 0.60, 0.60])
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g4 = np.array([0.12, 0.23, 0.31, 0.39, 0.46, 0.48, 0.49, 0.49, 0.50, 0.50])
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plt.figure(figsize=(8, 6))
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plt.plot(M, semantic, "rd-", label="Semantic")
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plt.plot(M, ideal, "ko-", label="Ideal", fillstyle="none")
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plt.plot(M, g5, "k*-.", label="5G")
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plt.plot(M, g4, "ks--", label="4G", fillstyle="none")
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plt.xlabel("Number of channels, $M$")
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plt.ylabel("S-SE, $\Phi$ (suts/s/Hz) $\\times (I/L)$")
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plt.xticks(np.arange(1, 11))
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plt.yticks(np.arange(0, 1.6, 0.2))
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plt.grid(True)
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plt.legend(loc="upper left")
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plt.xlim(1, 10)
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plt.ylim(0, 1.4)
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plt.savefig(OUTPUT_DIR / "fig4a.png", dpi=150)
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plt.close()
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print("Generated: fig4a.png")
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def plot_figure_4b():
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"""
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Figure 4(b): The S-SE versus the transmit power
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"""
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p_n = np.arange(-40, 25, 5)
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# Approximate function to match shapes
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# Semantic: logistic curve
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semantic = 1.21 / (1 + np.exp(-0.25 * (p_n + 5)))
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# Ideal: mostly linear in higher dBm, slower in lower
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# Use Shannon approx log2(1 + SNR)
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snr_linear_ideal = 10 ** ((p_n - 10) / 10) # arbitrary scaling to match
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ideal = 0.15 * np.log2(1 + 10 ** ((p_n + 15) / 10))
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ideal = np.clip(ideal, 0, 1.35)
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# 5G and 4G: similar to semantic but lower cap and shifted
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g5 = 0.7 / (1 + np.exp(-0.15 * (p_n - 5)))
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g4 = 0.68 / (1 + np.exp(-0.15 * (p_n - 8)))
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# Slight manual adjustments to match visual points
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ideal = np.interp(
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p_n,
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[-40, -30, -20, -10, 0, 10, 20, 23],
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[0.0, 0.01, 0.05, 0.15, 0.38, 0.72, 1.15, 1.32],
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)
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plt.figure(figsize=(8, 6))
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plt.plot(p_n, semantic, "rd-", label="Semantic")
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plt.plot(p_n, ideal, "ko-", label="Ideal", fillstyle="none")
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plt.plot(p_n, g5, "k*-.", label="5G")
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plt.plot(p_n, g4, "ks--", label="4G", fillstyle="none")
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plt.xlabel("Transmit power, $p_n$ (dBm)")
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plt.ylabel("S-SE, $\Phi$ (suts/s/Hz) $\\times (I/L)$")
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plt.xticks([-40, -30, -20, -10, 0, 10, 23])
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plt.yticks(np.arange(0, 1.6, 0.2))
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plt.grid(True)
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plt.legend(loc="upper left")
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plt.xlim(-40, 23)
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plt.ylim(0, 1.4)
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plt.savefig(OUTPUT_DIR / "fig4b.png", dpi=150)
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plt.close()
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print("Generated: fig4b.png")
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def plot_figure_4c():
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"""
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Figure 4(c): The S-SE versus the transforming factor
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"""
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mu = np.arange(18, 42, 2)
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# Values extracted from plot visually
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semantic = np.ones_like(mu) * 1.18
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# Conventional decrease roughly as 1/mu
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# Ideal at mu=18 is ~1.78. 1.78 * 18 = 32.04
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ideal = 32.04 / mu
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# 5G at mu=18 is ~1.25. 1.25 * 18 = 22.5
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g5 = 22.5 / mu
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# 4G at mu=18 is ~1.02. 1.02 * 18 = 18.36
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g4 = 18.36 / mu
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plt.figure(figsize=(8, 6))
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plt.plot(mu, semantic, "rd-", label="Semantic")
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plt.plot(mu, ideal, "ko-", label="Ideal", fillstyle="none")
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plt.plot(mu, g5, "k*-.", label="5G")
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plt.plot(mu, g4, "ks--", label="4G", fillstyle="none")
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plt.xlabel("Transforming factor, $\mu$ (bits/word)")
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plt.ylabel("S-SE, $\Phi$ (suts/s/Hz) $\\times (I/L)$")
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plt.xticks(np.arange(18, 42, 2))
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plt.yticks(np.arange(0.4, 2.0, 0.2))
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plt.grid(True)
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plt.legend(loc="upper right")
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plt.xlim(18, 40)
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plt.ylim(0.4, 1.8)
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plt.savefig(OUTPUT_DIR / "fig4c.png", dpi=150)
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plt.close()
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print("Generated: fig4c.png")
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def main():
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"""Generate all reference plots."""
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print("Generating reference plots...")
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plot_figure_2()
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plot_figure_3()
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plot_figure_4a()
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plot_figure_4b()
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plot_figure_4c()
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print(f"\nAll plots saved to: {OUTPUT_DIR}")
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if __name__ == "__main__":
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main()
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