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https://github.com/kevinveenbirkenbach/computer-playbook.git
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kevinveenbirkenbachandClaude Opus 5
d4ae5cf9f0
suppressed_line_numbers matches the marker on the flagged line itself, and ruff had split two of the calls so the comment landed behind the closing paren, one line too low. The reads that can be cached now go through utils.cache.files, which the bare interpreter imports fine; the two that cannot keep a marker on a line short enough to stay whole. The parser also gains the two guards utils/install/lint/pinned.py already carries for the same bootstrap problem: its reading is cross-checked against PyYAML on both requirements files, and a subprocess with yaml made unimportable proves the module still works without it. That turns the manual venv check into a standing test. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
116 lines
3.6 KiB
Python
116 lines
3.6 KiB
Python
"""Which requirements source ``scripts/install/ansible.sh`` should try first.
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Both sources carry the same pinned collections, so the only thing separating
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them is how long they take and whether they answer at all. A single slow day
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says nothing: galaxy.ansible.com degrades and recovers, and a git clone can
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stall on one runner. The default is therefore retuned only when a rolling
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window of daily samples shows one source durably behind the other.
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A sample whose install failed is recorded as ``None`` and ranks worse than
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any duration, so an outage counts fully rather than being dropped.
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from statistics import median
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from pathlib import Path
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SOURCES = ("galaxy", "git")
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WINDOW = 12
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"""Samples the median is taken over; below this the default is left alone."""
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RATIO = 3.0
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"""How many times slower the preferred source must be before it is swapped."""
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@dataclass(frozen=True)
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class Verdict:
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"""The outcome of comparing the two sources.
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Args:
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preferred: the source in force when the comparison ran.
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proposed: the source the samples favour, equal to preferred when
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nothing should change.
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medians: median seconds per source, infinite when every sample failed.
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samples: how many samples the medians were taken over.
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"""
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preferred: str
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proposed: str
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medians: dict[str, float]
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samples: int
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@property
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def changed(self) -> bool:
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return self.proposed != self.preferred
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def load_history(path: Path) -> list[dict[str, float | None]]:
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"""Samples recorded so far, oldest first, or an empty list.
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Args:
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path: JSON file holding the rolling window.
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"""
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try:
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raw = path.read_text() # nocheck: cache-read -- rewritten every run
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payload = json.loads(raw)
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except (OSError, ValueError):
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return []
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return payload if isinstance(payload, list) else []
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def append_sample(
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history: list[dict[str, float | None]], sample: dict[str, float | None]
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) -> list[dict[str, float | None]]:
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"""The history with SAMPLE appended, truncated to the newest WINDOW entries.
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Args:
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history: samples recorded so far, oldest first.
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sample: seconds per source for this run, None where the install failed.
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"""
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return [*history, sample][-WINDOW:]
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def _median_seconds(history: list[dict[str, float | None]], source: str) -> float:
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values = [
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float("inf") if entry.get(source) is None else float(entry[source])
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for entry in history
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]
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return median(values) if values else float("inf")
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def decide(history: list[dict[str, float | None]], preferred: str) -> Verdict:
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"""Whether the samples justify swapping the preferred source.
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Args:
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history: samples recorded so far, oldest first.
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preferred: the source currently tried first.
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"""
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medians = {source: _median_seconds(history, source) for source in SOURCES}
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verdict = Verdict(
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preferred=preferred,
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proposed=preferred,
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medians=medians,
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samples=len(history),
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)
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if len(history) < WINDOW:
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return verdict
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other = next(source for source in SOURCES if source != preferred)
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mine, theirs = medians[preferred], medians[other]
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if theirs == float("inf") or theirs <= 0:
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return verdict
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if mine >= theirs * RATIO:
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return Verdict(
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preferred=preferred,
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proposed=other,
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medians=medians,
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samples=len(history),
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)
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return verdict
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