mirror of
https://github.com/logos-blockchain/research.git
synced 2026-08-07 03:33:33 +00:00
91 lines
3.6 KiB
Python
91 lines
3.6 KiB
Python
"""Deficit-vs-load figure (fig26) from the rho-boundary sweep (configs/rho-boundary.yaml).
|
|
|
|
The "region below the block rate": the estimator equilibrium is bounded by 1 (it cannot over-count
|
|
occupied slots), so the signal of interest is the UNDER-COUNT DEFICIT 1 - D̂/D >= 0 as a function of
|
|
the load rho = f*D_vis, per uncle cap U. hops is fixed at 3 in the sweep so rho ∝ blend_delay_max.
|
|
|
|
Left panel: deficit 1 - D̂/D vs rho, per U (log-y), with the U=⌈ρ⌉ boundary visible.
|
|
Right panel: the same as accuracy D̂/D vs rho, y-axis capped at the 1.0 bound — no above-1 headroom;
|
|
residual above-1 shows only as ±σ error bars (sampling noise around ≤1).
|
|
|
|
Run: python scripts/rho_boundary_analysis.py
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
|
|
|
|
from tsi_sim.plotting import style # noqa: E402
|
|
|
|
HERE = Path(__file__).resolve().parent.parent
|
|
RUNS = HERE / "runs"
|
|
FIGS = HERE / "report-figures"
|
|
F = 1.0 / 30.0
|
|
HOPS = 3
|
|
LMEAN = 1.2 # degree-6, N=1000 geo graph (matches §4)
|
|
|
|
|
|
def load() -> pd.DataFrame:
|
|
src = sorted(RUNS.glob("*_rho-boundary/results.parquet"))[-1]
|
|
df = pd.read_parquet(src)
|
|
keys = ["blend_delay_max", "max_uncles", "replicate"]
|
|
df["emax"] = df.groupby(keys).epoch.transform("max")
|
|
tail = df[df.epoch >= df.emax // 2]
|
|
g = (tail.groupby(["blend_delay_max", "max_uncles"])
|
|
.mean_ratio.agg(["mean", "sem"]).reset_index())
|
|
g["rho"] = F * (HOPS * g.blend_delay_max / 2.0 + (HOPS + 1) * LMEAN)
|
|
g["deficit"] = 1.0 - g["mean"]
|
|
return g
|
|
|
|
|
|
def fig26(g: pd.DataFrame) -> None:
|
|
import matplotlib.pyplot as plt
|
|
style.apply_style()
|
|
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9.6, 4.2))
|
|
for i, U in enumerate((0, 1, 2, 3)):
|
|
s = g[g.max_uncles == U].sort_values("rho")
|
|
c = style.OKABE_ITO[i]
|
|
# left: deficit (floored at a small positive value for the log axis)
|
|
d = np.clip(s.deficit.values, 3e-4, None)
|
|
ax1.plot(s.rho, d, "-o", ms=4, color=c, label=f"U = {U}")
|
|
# right: accuracy, capped at 1.0
|
|
ax2.errorbar(s.rho, s["mean"], yerr=s["sem"], fmt="-o", ms=4, capsize=2,
|
|
color=c, label=f"U = {U}")
|
|
ax1.set_yscale("log")
|
|
ax1.set_xlabel(r"load $\rho = f\,D_{vis}$")
|
|
ax1.set_ylabel(r"under-count deficit $1 - \hat D/D$")
|
|
ax1.set_title(r"deficit grows once $\rho$ exceeds the uncle cap")
|
|
ax1.axvline(1.0, color="0.6", lw=0.8, ls=":")
|
|
ax1.legend(fontsize=8, title="uncle cap")
|
|
ax2.axhline(1.0, color="0.4", lw=1.0, ls="--")
|
|
ax2.text(g.rho.min(), 1.001, r"$\hat D/D = 1$ bound (cannot over-count)",
|
|
fontsize=7, color="0.4", va="bottom")
|
|
ax2.set_ylim(0.0, 1.02) # cap at the bound: no above-1 headroom
|
|
ax2.set_xlabel(r"load $\rho = f\,D_{vis}$")
|
|
ax2.set_ylabel(r"accuracy $\hat D/D$ (bounded by 1)")
|
|
ax2.set_title("equilibrium sits at or below 1 at every load")
|
|
ax2.legend(fontsize=8, loc="lower left", title="uncle cap")
|
|
fig.suptitle(r"The region below the block rate: under-count deficit vs load "
|
|
r"(blend, N=1000, f=1/30, hops=3)", y=1.02)
|
|
style.save(fig, FIGS / "fig26_deficit_vs_rho", provenance="scripts/rho_boundary_analysis.py")
|
|
plt.close(fig)
|
|
|
|
|
|
def main() -> None:
|
|
g = load()
|
|
fig26(g)
|
|
above = g[g["mean"] > 1 + 2 * g["sem"]]
|
|
print(f"bounded-by-1 check: {len(above)}/{len(g)} cells above 1 by >2 SEM; "
|
|
f"max D̂/D = {g['mean'].max():.4f}")
|
|
print("wrote fig26_deficit_vs_rho")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|