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https://github.com/MBombeck/HealthLog.git
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Marc-André Bombeck
8f730484e0
The insights comparison snapshot took its current and baseline pulse figures from a reading AVG, and the Coach's data inventory told the model a reading mean of the whole pulse history. Both now read the mean of the user's local days, each the mean of its hours' means, through the day-mean helper. Every other type keeps its aggregate.
92 lines
3.0 KiB
TypeScript
92 lines
3.0 KiB
TypeScript
/**
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* The comparison snapshot's window average and the Coach's availability mean
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* read pulse as the mean of its days, each the mean of its hours' means, on a
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* real Postgres.
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*
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* Two days, three days back and two days back (inside the 30-day window): a
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* workout day with one hour of 12 readings at 150 and three resting hours of
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* one reading at 60 (day value 82.5), and a resting day of two hours at 70.
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* Mean of the days: 76.25. Mean of the readings: (12 x 150 + 3 x 60 + 2 x 70)
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* / 17 = 124.7.
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*/
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import { beforeAll, describe, expect, it } from "vitest";
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import { getPrismaClient, truncateAllTables } from "./setup";
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import type { Prisma } from "@/generated/prisma/client";
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import { probeCoachAvailability } from "@/lib/ai/coach/tools/availability";
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import { hourlyMeanWindowAverage } from "@/lib/insights/comprehensive-generate";
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import { invalidateUserTimezone } from "@/lib/tz/resolver";
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const prisma = getPrismaClient();
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const USER = "pulse-comparison-availability";
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const DAY = 86_400_000;
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const now = new Date();
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const D1 = Date.UTC(
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now.getUTCFullYear(),
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now.getUTCMonth(),
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now.getUTCDate() - 3,
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);
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const D2 = D1 + DAY;
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const DAY_MEAN = (82.5 + 70) / 2;
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const READING_MEAN = (12 * 150 + 3 * 60 + 2 * 70) / 17;
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beforeAll(async () => {
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await truncateAllTables(prisma);
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await prisma.user.create({
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data: { id: USER, username: USER, timezone: "UTC" },
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});
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invalidateUserTimezone(USER);
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const rows: Prisma.MeasurementCreateManyInput[] = [];
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const add = (at: number, value: number) =>
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rows.push({
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id: `${USER}-${rows.length}`,
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userId: USER,
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type: "PULSE",
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unit: "bpm",
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source: "APPLE_HEALTH",
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value,
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measuredAt: new Date(at),
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});
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for (let i = 0; i < 12; i += 1) add(D1 + 10 * 3_600_000 + i * 300_000, 150);
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for (const h of [12, 14, 16]) add(D1 + h * 3_600_000, 60);
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for (const h of [8, 9]) add(D2 + h * 3_600_000, 70);
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await prisma.measurement.createMany({ data: rows });
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}, 120_000);
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describe("pulse window means on a real database", () => {
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it("the fixture separates the two statistics", () => {
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expect(READING_MEAN).toBeCloseTo(124.71, 2);
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expect(DAY_MEAN).toBe(76.25);
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});
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it("the comparison snapshot's window average is the mean of the days", async () => {
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const avg = await hourlyMeanWindowAverage(USER, "PULSE", {
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gt: new Date(Date.now() - 30 * DAY),
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});
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expect(avg).toBeCloseTo(DAY_MEAN, 9);
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const empty = await hourlyMeanWindowAverage(USER, "PULSE", {
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gt: new Date(Date.now() - 60 * DAY),
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lte: new Date(Date.now() - 30 * DAY),
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});
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expect(empty).toBeNull();
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});
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it("the Coach availability mean is the mean of the days", async () => {
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const probed = await probeCoachAvailability(
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USER,
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new Map([["pulse", { kind: "measurement", types: ["PULSE"] }]]),
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{ now },
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);
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const series = probed.get("pulse")?.series;
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expect(series).toEqual([
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expect.objectContaining({
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series: "PULSE",
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count: 17,
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mean: Math.round(DAY_MEAN * 10) / 10,
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min: 60,
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max: 150,
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}),
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]);
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});
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});
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