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61 lines
2.6 KiB
Python
61 lines
2.6 KiB
Python
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"""fig3 — uncle recovery under the Blend cascade: hops × per-hop delay × U (4-panel grid).
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Committed generator for report fig3. Previously fig3 was produced ad hoc (via make_figures on the
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blend-hops-delay run, then hand-copied into report-figures/) and had NO reproducible source in the
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repo; this script closes that gap. Per uncle cap U it plots mean D̂/D vs the per-hop budget δ_max,
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one curve per hop count, at N=1000 from the canonical blend-hops-delay sweep (per-trajectory 50%
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burn-in via figures_pernode.equilibrium). Accuracy is bounded by 1 (slot-counting cannot over-count
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occupied slots), so the y-axis is capped at the exact-recovery bound — no above-1 headroom.
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Run: python scripts/hops_delay_grid.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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from tsi_sim.plotting.figures_pernode import equilibrium # 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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def main() -> None:
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import matplotlib.pyplot as plt
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src = sorted(RUNS.glob("*_blend-hops-delay/results.parquet"))[-1]
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eq = equilibrium(pd.read_parquet(src))
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eq = eq[(eq.n_nodes == 1000) & (eq.topology == "blend") & (eq.stake_dist == "pareto")]
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us = sorted(eq.max_uncles.unique())
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hops = sorted(eq.blend_hops.unique())
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style.apply_style()
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fig, axes = plt.subplots(1, len(us), figsize=(3.2 * len(us), 3.6), sharey=True)
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for ax, U in zip(axes, us, strict=True):
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s = eq[eq.max_uncles == U]
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for i, h in enumerate(hops):
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g = s[s.blend_hops == h].groupby("blend_delay_max").mean_ratio.agg(["mean", "sem"])
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ax.errorbar(g.index, g["mean"], yerr=g["sem"], fmt="-o", ms=4, capsize=2,
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color=style.OKABE_ITO[i], label=f"{int(h)} hops")
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ax.axhline(1.0, color="0.4", lw=1.0, ls="--", zorder=0)
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ax.axhline(0.98, color="0.75", lw=0.8, ls=":", zorder=0)
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ax.set_title(f"U = {int(U)}")
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ax.set_xlabel(r"per-hop budget $\delta_{max}$ (s)")
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axes[0].set_ylabel(r"mean $\hat D / D$")
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axes[0].set_ylim(top=1.01) # bounded by 1: cap at the exact-recovery bound
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axes[0].legend(fontsize=8, loc="lower left")
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fig.suptitle(r"Uncle recovery under Blend cascade: hops × per-hop delay × U "
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r"(pareto, N=1000, f=1/30)", y=1.02)
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style.save(fig, FIGS / "fig3_hops_delay", provenance="scripts/hops_delay_grid.py")
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plt.close(fig)
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print("wrote fig3_hops_delay")
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if __name__ == "__main__":
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main()
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