mirror of
https://github.com/MBombeck/HealthLog.git
synced 2026-11-10 21:32:18 +00:00
Marc-André Bombeck
4dc3bd46bf
walkSourceFiles now lists each indexed folder (src, e2e, prisma) once per worker and filters it by prefix, readSource and readSourceFolder read through the same memoised file cache, and readSourceText is the cached read for guards that read files themselves. The guards project shares a module graph per worker, so a file is read once however many guards ask for it. sourcesContaining adds the substring pre-filter for sweeps that look for a few markers. Every guard over 1.5 s now reads through it; anything outside the indexed folders is read from disk as before.
99 lines
3.9 KiB
TypeScript
99 lines
3.9 KiB
TypeScript
/**
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* Structural guard: a standard deviation is computed in `lib/stats/spread`,
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* with its kind named at the call site.
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*
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* About a dozen hand-rolled spreads each picked population (÷ n) or sample
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* (÷ n − 1) without saying so, and the two surfaces a person compares picked
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* differently: the series API reported the population SD of a blood-pressure
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* series, the MCP and AI payload the sample SD of the same readings, about
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* 22 % apart at three readings. The reading spread both report is now
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* `READING_SPREAD`.
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*
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* The matcher flags a squared deviation from a mean in a file that also takes
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* a square root. The files below compute one for a test statistic or a fit,
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* never as a reported spread, and say so. The doctor report's glucose panel
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* composes its SD from per-bucket sums of squares (sample, the same rule as
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* `glucoseSD`) and is out of the matcher's reach by shape. Comments are
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* stripped first; the planted case proves the matcher fires.
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*/
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import { join } from "node:path";
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import { describe, expect, it } from "vitest";
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import {
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readSourceText,
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stripComments,
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walkSourceFiles,
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} from "./helpers/source-files";
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const SRC = join(process.cwd(), "src");
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const ALLOWED: Record<string, string> = {
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"lib/stats/spread.ts": "The implementation.",
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"lib/insights/correlations.ts":
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"The correlation engine's centred sums and the Welch t-test variances.",
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"lib/insights/mood-tag-influence.ts": "Welch t-test variances.",
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"lib/insights/mood-pattern-cards.ts": "A pooled variance for a t statistic.",
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"lib/stats/segmented-regression.ts":
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"The total sum of squares behind R² and the HAC standard errors of a segmented fit; neither is reported as a spread.",
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"lib/insights/derived/trajectory.ts":
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"The residual standard error of a regression fit (÷ n − 2).",
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"lib/insights/derived/sleep-score.ts":
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"A circular SD of the sleep midpoint around its circular mean, the input of the consistency score; `spread` has no circular distance, and the score is never shown as a spread.",
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};
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/**
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* A squared deviation: `(x - m) ** 2`, `(x - m) * (x - m)`, or a two-argument
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* distance squared, `dist(x, m) ** 2` (a circular or otherwise custom
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* deviation is still a hand-rolled spread).
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*/
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const SQUARED_DEVIATION =
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/\(\s*[\w.[\]]+\s*-\s*[\w.[\]]+\s*\)\s*\*\*\s*2|\(\s*([\w.[\]]+)\s*-\s*([\w.[\]]+)\s*\)\s*\*\s*\(\s*\1\s*-\s*\2\s*\)|\b[A-Za-z_]\w*\s*\(\s*[\w.[\]]+\s*,\s*[\w.[\]]+\s*\)\s*\*\*\s*2/;
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function computesSpread(code: string): boolean {
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return SQUARED_DEVIATION.test(code) && code.includes("Math.sqrt(");
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}
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function read(file: string): string {
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return stripComments(readSourceText(join(SRC, file)));
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}
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describe("one standard deviation", () => {
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it("the matcher fires on a hand-rolled spread", () => {
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expect(
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computesSpread(
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"const v = xs.reduce((s, x) => s + (x - m) ** 2, 0) / n; Math.sqrt(v);",
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),
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).toBe(true);
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expect(
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computesSpread("acc += (v - mean) * (v - mean); Math.sqrt(acc / n);"),
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).toBe(true);
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expect(
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computesSpread(
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"xs.reduce((s, v) => s + circularMinuteDistance(v, mean) ** 2, 0); Math.sqrt(x);",
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),
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).toBe(true);
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});
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it("no file outside the list computes its own", () => {
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const offenders = walkSourceFiles(SRC, { floor: 3000 })
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.filter(
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(file) =>
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!file.startsWith("generated/") &&
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!file.includes("__tests__/") &&
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!/\.test\.tsx?$/.test(file),
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)
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.filter((file) => computesSpread(read(file)) && !(file in ALLOWED));
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expect(offenders).toEqual([]);
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});
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it("the series API and the AI and MCP payload report the same spread", () => {
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expect(read("app/api/measurements/series/route.ts")).toMatch(
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/standardDeviation\(\s*values,\s*READING_SPREAD\s*\)/,
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);
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const features = read("lib/insights/features.ts");
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expect(
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features.match(/roundedSpread\(\s*vals,\s*READING_SPREAD\s*\)/g),
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).toHaveLength(2);
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});
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});
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