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102 lines
4.0 KiB
Python
102 lines
4.0 KiB
Python
"""Relative stake estimate vs network delay (fig16).
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Shows the report's central relationship: the recovered *relative stake* ``D̂/D`` as a function of the
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mean block-visibility delay ``D_vis`` (seconds), one curve per uncle cap ``U``. Accuracy holds near
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the ceiling ``c(f)`` while the load ``ρ = f·D_vis`` stays below ``⌈U⌉``, then collapses — so larger
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delay needs more uncles. Blend transport, f = 1/30 (30 s blocks); delay swept via the
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per-hop blending budget ``blend_delay_max``.
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Run: python scripts/stake_vs_delay.py (writes runs/stake_vs_delay.parquet + fig16)
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"""
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from __future__ import annotations
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from tsi_sim import topology
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from tsi_sim.config import SimConfig
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from tsi_sim.engine import run_trajectory
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from tsi_sim.plotting import style
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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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RUNS.mkdir(exist_ok=True)
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FIGS.mkdir(exist_ok=True)
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F = 1.0 / 30.0
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HOPS = 3
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DELAYS = [1.0, 4.0, 8.0, 12.0, 16.0, 20.0, 26.0, 32.0, 40.0] # per-hop blending budget (s)
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UNCLES = [0, 1, 2, 3]
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BASE = dict(n_nodes=800, k=48, epochs=16, stake_dist="uniform", topology="blend", degree=8,
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link_latency_mean=0.3, link_latency_dist="geo", blend_hops=HOPS, uncle_window=300,
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genesis_d_factor=0.5, f=F)
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def d_vis(delay: float) -> float:
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"""Mean visibility delay D_vis = hops·δ/2 + (hops+1)·ℓ_mean, ℓ_mean = mean shortest-path."""
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cfg = SimConfig(**BASE, blend_delay_max=delay, max_uncles=1)
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pl = topology.build_path_latency(cfg, np.random.default_rng(np.random.SeedSequence(0)))
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off = pl[~np.eye(pl.shape[0], dtype=bool)]
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l_mean = float(off[np.isfinite(off)].mean())
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return HOPS * delay / 2.0 + (HOPS + 1) * l_mean
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def sweep() -> pd.DataFrame:
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rows = []
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for delay in DELAYS:
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dv = d_vis(delay)
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for U in UNCLES:
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reps = []
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for r in range(5):
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df = pd.DataFrame(run_trajectory(SimConfig(
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replicate=r, blend_delay_max=delay, max_uncles=U, **BASE)))
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reps.append(df[df.epoch >= 8].mean_ratio.mean())
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ratio = float(np.mean(reps))
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sem = float(np.std(reps) / np.sqrt(len(reps)))
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rows.append(dict(delay=delay, d_vis=dv, rho=F * dv, U=U, ratio=ratio, sem=sem))
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print(f"delay={delay:4.0f}s D_vis={dv:5.1f} rho={F*dv:4.2f} U={U}: D̂/D={ratio:.3f}")
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out = pd.DataFrame(rows)
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out.to_parquet(RUNS / "stake_vs_delay.parquet")
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return out
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def fig16(df: pd.DataFrame) -> None:
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import matplotlib.pyplot as plt
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style.apply_style()
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fig, ax = plt.subplots(figsize=(7.2, 4.4))
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ax.axhline(1.0, color="0.5", lw=0.9, ls="--", label="exact recovery (1.0)")
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for i, U in enumerate(UNCLES):
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s = df[df.U == U].sort_values("d_vis")
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ax.errorbar(s.d_vis, s.ratio, yerr=s["sem"], fmt="-o", ms=4, capsize=2,
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color=style.OKABE_ITO[i], label=f"U = {U}")
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# rho = 1, 2, 3 boundaries: D_vis = k/f <-> rho = f*D_vis = k
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for k in (1, 2, 3):
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dv = k / F
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if dv <= df.d_vis.max() * 1.02:
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ax.axvline(dv, color="0.7", lw=0.7, ls=":")
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ax.text(dv, 0.32, f"ρ={k}", rotation=90, va="bottom", ha="right", fontsize=7,
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color="0.4")
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ax.set_xlabel(r"mean block-visibility delay $D_{\rm vis}$ (s) [load $\rho = f\,D_{\rm vis}$]")
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ax.set_ylabel(r"relative stake estimate $\hat D / D$")
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ax.set_title(r"Recovered relative stake vs delay (blend, $f=1/30$): "
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r"$U$ must grow with $\rho=\lceil f D_{\rm vis}\rceil$")
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ax.set_ylim(0.3, 1.02) # bounded by 1: cap at the exact-recovery bound, no above-1 headroom
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ax.legend(fontsize=8, loc="lower left")
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style.save(fig, FIGS / "fig16_stake_vs_delay", provenance="scripts/stake_vs_delay.py")
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plt.close(fig)
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def main() -> None:
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print("=== relative stake estimate vs delay ===")
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df = sweep()
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fig16(df)
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print("wrote fig16_stake_vs_delay")
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if __name__ == "__main__":
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main()
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