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The unpaired comparison could not answer the question it was asked. The two uncle models draw independent RNG streams -- uncle_model is in the config key, which is what makes --old bit-reproduce earlier runs -- so the arms differed in stake draw, peering graph and every lottery outcome, each comparison paid the between-run variance twice, and the per-cell floor (+-0.0015) sat an order of magnitude above the effect. Only delta_max = 5 resolved, and only after pooling. Adds `paired_streams`: the RNG root is derived from the model- independent part of the key, so a countable cell and its --old twin get the SAME stake, graph and lottery draws and the uncle rule is the only difference. Each replicate is then a matched pair and the shared variance cancels. Trajectories still diverge after epoch 0 through the genuine feedback (a different counted density changes the next epoch's difficulty), which is the signal. The flag is deliberately NOT in key(): it selects which key the seed is derived from, so including it would perturb every historical seed. Re-verified that --old still bit-reproduces the committed 2026-07-27 rho-boundary parquet, max |delta| = 0. Results (configs/fine-delay-paired.yaml, 40 replicates per arm): - Negative control becomes an IDENTITY check. With U = 0 no reference is taken, so shared streams must give bit-identical trajectories. All 200 replicate pairs differ by exactly 0.0. Unpaired, the same control only had to agree within +-0.025 and drifted by 0.016. - Per-cell SE shrinks by a median 1.6x (1.2-2.1x); widest 95% CI goes +-0.0015 -> +-0.0010. 5/15 cells resolve at |t| >= 2 (0.75 expected by chance); the largest, U=2 at delta_max=4, is t = 4.32 and clears Bonferroni for 15 tests. - The cost is a STEP, not the ramp the unpaired data suggested: delta_max 1-3 unresolved (t = 1.1, 1.8, 1.4), then delta_max 4 AND 5 both resolve at -0.0011 (t = 4.7) and -0.0009 (t = 3.7). Whole-band pooled -0.00060 +- 0.00021, t = 5.7 -- where the unpaired estimate of the same quantity (t = 2.8) had failed correction. So the first-fork restriction costs nothing measurable up to delta_max = 3 and about 0.1% at 4-5 -- an order of magnitude below the +-0.9% per-epoch sampling noise. Two bugs found while building this, both of which would have silently produced a wrong answer: - paired_streams was missing from metrics._CONFIG_FIELDS, so it never reached the parquet; plot_fine_delay.py falls back to the unpaired test when it cannot confirm pairing, so the sweep would have completed and quietly reported the old result. Caught before the run finished; the sweep was restarted and a test now pins the field. - The U=0 control check reported FAILS on a PERFECT control: paired, the gap is exactly 0 so its SE is 0 and t is 0/0. It now checks the gap itself when the streams are shared, and falls back to the t-test only when there is real spread. §3.2a is rewritten around the paired measurement; the unpaired sweep is retained in §9 as the power comparison that motivated it. Figures 34-35 regenerated, with the control annotation and provenance reflecting the design actually used. Tests: 214 passed (was 209). ruff clean. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
69 lines
2.8 KiB
Python
69 lines
2.8 KiB
Python
"""Per-epoch per-node divergence rows."""
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from __future__ import annotations
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from typing import Any
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import numpy as np
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from .config import SimConfig
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from .epoch import EpochResult
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# Config fields recorded on every row for grouping/plotting.
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_CONFIG_FIELDS = (
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"n_nodes", "stake_dist", "pareto_shape", "latency", "topology", "degree",
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"link_latency_mean", "link_latency_dist", "blend_hops", "blend_delay_max",
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"init_dest", "init_spread", "uncle_model", "window_absorption",
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"uncle_window", "max_uncles", "uncle_strategy",
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# Recorded so downstream analysis can TELL whether a countable/--old pair actually shared
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# its RNG streams. The paired test is only valid on paired runs, and without this column
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# the analysis silently falls back to the much weaker unpaired test.
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"paired_streams",
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"f", "beta", "k", "genesis_d_factor", "epochs", "fixed_point", "legacy_block_count",
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"replicate",
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"adversary_frac", "adversary_strategy", "adversary_period", "adversary_withhold_epochs",
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)
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def divergence_row(
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config: SimConfig, epoch: int, d_in: np.ndarray, er: EpochResult, d_true: float
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) -> dict[str, Any]:
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"""One row per (config, epoch): per-node D_est spread + chain agreement."""
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ratio = np.asarray(er.d_next, dtype=float) / d_true # (N,)
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row: dict[str, Any] = {field: getattr(config, field) for field in _CONFIG_FIELDS}
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row.update(
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epoch=epoch,
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mean_ratio=float(ratio.mean()),
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median_ratio=float(np.median(ratio)),
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std_ratio=float(ratio.std()),
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min_ratio=float(ratio.min()),
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max_ratio=float(ratio.max()),
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range_ratio=float(ratio.max() - ratio.min()), # the headline divergence measure
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iqr_ratio=float(np.percentile(ratio, 75) - np.percentile(ratio, 25)),
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p10_ratio=float(np.percentile(ratio, 10)),
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p90_ratio=float(np.percentile(ratio, 90)),
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mean_ratio_in=float((np.asarray(d_in, dtype=float) / d_true).mean()),
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range_ratio_in=float(np.ptp(np.asarray(d_in, dtype=float) / d_true)),
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mean_m=float(np.mean(er.m)),
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mean_q=float(np.nanmean(er.q)),
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mean_q_eff=float(np.nanmean(er.q_eff)),
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std_q=float(np.nanstd(er.q)),
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agreement_window=er.agreement_window,
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agreement_tip=er.agreement_tip,
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mean_orphan_rate=er.mean_orphan_rate,
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n_active_window=er.n_active_window,
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n_blocks=er.n_blocks,
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adv_blocks=er.adv_blocks,
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honest_blocks=er.honest_blocks,
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adv_block_share=(
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er.adv_blocks / (er.adv_blocks + er.honest_blocks)
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if (er.adv_blocks + er.honest_blocks) > 0 else 0.0
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),
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fork_rate=er.fork_rate,
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max_reorg_depth=er.max_reorg_depth,
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mean_reorg_depth=er.mean_reorg_depth,
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p_ref=er.p_ref,
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deep_ref_share=er.deep_ref_share,
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)
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return row
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