Scaffold plus the Layer 1 analytical core and the first two experiments. - knowledge/: truth, metrics, teachers (2.7.1 shared-switch construction), step, lineage, experiment, config, seeding (imported as `knowledge`). - Validation spine green: neutral decay (Pred 1), fixation (Pred 2), exact mutation-drift equilibrium (Pred 3), union coverage (Pred 5). 68 tests pass. - E1 reproduces tail-first collapse. E2 delivers the headline: a grounding phase boundary g* << 1, with stationary H tracking the exact H_eq closed form (g=0.005 -> 68% of truth diversity; g=0.05 -> 96%). - Reproducibility: uv venv from a hash-pinned uv.lock is the source of truth; every run writes results.parquet + resolved_config.yaml + manifest.json (lib versions, git commit, sha256). Figures and manifests tracked; the large regenerable parquet is gitignored. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
"""Shared helpers for Layer-1 figure scripts.
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Figures are a pure function of a committed results bundle (``results/EX/``:
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``results.parquet`` + ``resolved_config.yaml``). No simulation is rerun here.
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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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import yaml
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def load_bundle(results_dir: str | Path) -> tuple[pd.DataFrame, dict]:
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"""Load a results bundle: (long-form DataFrame, source experiment config)."""
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results_dir = Path(results_dir)
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df = pd.read_parquet(results_dir / "results.parquet")
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resolved = yaml.safe_load((results_dir / "resolved_config.yaml").read_text())
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return df, resolved["source_config"]
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def mean_ci(df: pd.DataFrame, by: str, value: str, ci: float = 0.95):
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"""Return (index, mean, half-width) for a normal-approx CI of ``value`` grouped by ``by``."""
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from scipy import stats
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g = df.groupby(by)[value]
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mean = g.mean()
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sem = g.sem()
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z = stats.norm.ppf(0.5 + ci / 2.0)
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return mean.index.to_numpy(), mean.to_numpy(), (z * sem).to_numpy()
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def savefig(fig, results_dir: str | Path, name: str) -> None:
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"""Write a figure as both PNG (150 dpi) and PDF next to its results bundle."""
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results_dir = Path(results_dir)
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fig.savefig(results_dir / f"{name}.png", dpi=150, bbox_inches="tight")
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fig.savefig(results_dir / f"{name}.pdf", bbox_inches="tight")
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print(f"wrote {results_dir}/{name}.png and .pdf")
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