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74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
"""Window sufficiency at scale + the W-as-buffer question (fig25, report §3.4).
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From the window-scale sweep (N = 1 000 vs 10 000, W = 50..600, delta in {8, 16, 32} s, U in
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{1, 2}, k = 256): the window floor's position is N-invariant, a wider window buys back the
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near-boundary (rho ~ 1) undershoot at U = 1, and no window fixes sustained overload (rho > U).
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Run: python scripts/window_scale_analysis.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
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from tsi_sim.plotting import style # noqa: E402
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HERE = Path(__file__).resolve().parent.parent
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RUNS = HERE / "runs"
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FIGS = HERE / "report-figures"
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BAR = 0.98
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def main() -> None:
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src = sorted(RUNS.glob("*_window-scale/results.parquet"))[-1]
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df = pd.read_parquet(src)
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# Early stop terminates each config once it converges (~epoch 16), well short of the
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# nominal ``epochs`` (40). A fixed ``epoch >= 20`` tail would drop almost every config;
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# take the second half of each config's *actually-run* epochs instead (matches the
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# burn_frac=0.5 tail used elsewhere).
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keys = ["n_nodes", "blend_delay_max", "uncle_window", "max_uncles", "replicate"]
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tail_from = df.groupby(keys).epoch.transform("max") * 0.5
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t = df[df.epoch >= tail_from]
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eq = (t.groupby(["n_nodes", "blend_delay_max", "uncle_window", "max_uncles"],
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as_index=False).mean_ratio.mean())
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import matplotlib.pyplot as plt
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style.apply_style()
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fig, axes = plt.subplots(1, 3, figsize=(13.2, 4.0), sharey=True)
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for ax, delay in zip(axes, (8.0, 16.0, 32.0), strict=True):
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for n, u in ((1000, 1), (10000, 1), (1000, 2), (10000, 2)):
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s = eq[(eq.blend_delay_max == delay) & (eq.n_nodes == n)
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& (eq.max_uncles == u)].sort_values("uncle_window")
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ax.plot(s.uncle_window, s.mean_ratio,
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"-o" if u == 1 else "--s", ms=4, lw=1.3,
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color=style.OKABE_ITO[0 if n == 1000 else 1],
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label=f"N={n:,}, U={u}" if delay == 8.0 else None)
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ax.axhline(BAR, color="0.7", lw=0.8, ls="--")
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ax.set_xlabel("uncle window W (slots)")
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ax.set_title(f"blending budget δ = {delay:g} s")
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axes[0].set_ylabel(r"$\hat D / D$")
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axes[0].text(60, BAR + 0.006, "0.98 recovery bar", fontsize=7, color="0.5")
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axes[0].legend(fontsize=8, loc="lower right")
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fig.suptitle("Window sufficiency at scale: a wider W buys back the ρ ≈ 1 boundary (middle) "
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"but cannot fix sustained overload (right)", y=1.03)
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style.save(fig, FIGS / "fig25_window_scale",
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provenance=f"scripts/window_scale_analysis.py ({src.parent.name})")
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plt.close(fig)
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print("wrote fig25_window_scale")
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for delay in (8.0, 16.0, 32.0):
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s = eq[(eq.blend_delay_max == delay) & (eq.max_uncles == 1)]
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piv = s.pivot(index="n_nodes", columns="uncle_window", values="mean_ratio")
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print(f"\nU=1 δ={delay:g}:")
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print(piv.round(3).to_string())
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if __name__ == "__main__":
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main()
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