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fix(demo): seed each reading with the source that writes it

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The 90-day core series (weight, blood pressure, pulse, body fat, resting
heart rate, sleep, steps) were all tagged MANUAL. The measurements list
badges every row that is not MANUAL, so the newest weight and
blood-pressure rows carried no source while Settings > Sources named
Withings for those metrics (from the older history rows). The body
composition series claimed Apple Health for fat mass, fat-free mass,
muscle, water, bone and visceral fat, none of which HealthKit has.

Weight, body fat, blood pressure and its pulse are now WITHINGS with no
device type, the way the Withings sync writes them; resting heart rate
and the sleep aggregate are APPLE_HEALTH from the watch; steps are the
iOS daily total under its `stats:` key, so the legacy-step consolidation
leaves them alone. The Withings body-composition components move to
WITHINGS; lean body mass and BMI stay APPLE_HEALTH from the scale. The
waist stays MANUAL, since nothing syncs a tape measure.

An integration test runs the real seed and holds every Withings and
Apple Health row against the type maps those integrations ingest
through, and pins a device source on the newest reading of each synced
vital.
This commit is contained in:
Marc-André Bombeck committed 2026-10-24 13:11:07 +02:00
1 parent 2b9315ad8b
commit 6748a5b843
2 files changed
+207 -29
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scripts/seed-demo.ts
+97 -29
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@@ -91,6 +91,7 @@ import {
} from "../src/lib/crypto";
import { ENVIRONMENT_LOCATION_AAD } from "../src/lib/crypto/encrypted-columns";
import { withBriefingGeneratedAt } from "../src/lib/insights/briefing-generated-at";
import { dailyStatsExternalId } from "../src/lib/measurements/apple-health-mapping";
import { sealInsightsCachedText } from "../src/lib/insights/cached-text-cipher";
import {
DEFAULT_CHART_OVERLAY_PREFS,
@@ -600,39 +601,49 @@ export async function seed() {
settleLatest(series);
}
// Each series carries the source that writes it on a real account, the
// way the integration writes it: the Withings scale and cuff (weight,
// body fat, blood pressure with its pulse) land as WITHINGS with no
// device type (`src/lib/withings/sync.ts`), the watch's resting heart
// rate, sleep and steps as APPLE_HEALTH. The list badges every row that
// is not MANUAL, and Settings > Sources names Withings for these
// metrics, so a hand-entered tag here left the demo's newest weight and
// blood-pressure rows without a source while the ladder named one. The
// waist stays MANUAL: it is a tape measure, and nothing syncs it.
for (let i = 0; i < span; i++) {
const date = daysAgo(days - i);
// Weight (daily)
await client.query(
`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
VALUES ($1, $2, 'WEIGHT', $3, 'kg', 'MANUAL', $4, $4, $4)`,
VALUES ($1, $2, 'WEIGHT', $3, 'kg', 'WITHINGS', $4, $4, $4)`,
[cuid(), userId, weights[i], date],
);
// Blood pressure (daily)
await client.query(
`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
VALUES ($1, $2, 'BLOOD_PRESSURE_SYS', $3, 'mmHg', 'MANUAL', $4, $4, $4)`,
VALUES ($1, $2, 'BLOOD_PRESSURE_SYS', $3, 'mmHg', 'WITHINGS', $4, $4, $4)`,
[cuid(), userId, Math.round(sysBP[i]), date],
);
await client.query(
`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
VALUES ($1, $2, 'BLOOD_PRESSURE_DIA', $3, 'mmHg', 'MANUAL', $4, $4, $4)`,
VALUES ($1, $2, 'BLOOD_PRESSURE_DIA', $3, 'mmHg', 'WITHINGS', $4, $4, $4)`,
[cuid(), userId, Math.round(diaBP[i]), date],
);
// Spot pulse (daily)
// Spot pulse (daily), the heart pulse the cuff reports with each
// reading.
await client.query(
`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
VALUES ($1, $2, 'PULSE', $3, 'bpm', 'MANUAL', $4, $4, $4)`,
VALUES ($1, $2, 'PULSE', $3, 'bpm', 'WITHINGS', $4, $4, $4)`,
[cuid(), userId, Math.round(pulse[i]), date],
);
// Resting heart rate (daily) — scored by the resting-pulse tile
await client.query(
`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
VALUES ($1, $2, 'RESTING_HEART_RATE', $3, 'bpm', 'MANUAL', $4, $4, $4)`,
`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
VALUES ($1, $2, 'RESTING_HEART_RATE', $3, 'bpm', 'APPLE_HEALTH', 'watch', $4, $4, $4)`,
[cuid(), userId, Math.round(restingHr[i]), date],
);
@@ -640,7 +651,7 @@ export async function seed() {
if (i % 2 === 0 || Math.random() > 0.5) {
await client.query(
`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
VALUES ($1, $2, 'BODY_FAT', $3, '%', 'MANUAL', $4, $4, $4)`,
VALUES ($1, $2, 'BODY_FAT', $3, '%', 'WITHINGS', $4, $4, $4)`,
[cuid(), userId, bodyFat[i], date],
);
}
@@ -653,17 +664,27 @@ export async function seed() {
const nightsAgo = days - i;
if (nightsAgo >= 7) {
await client.query(
`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
VALUES ($1, $2, 'SLEEP_DURATION', $3, 'minutes', 'MANUAL', $4, $4, $4)`,
`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
VALUES ($1, $2, 'SLEEP_DURATION', $3, 'minutes', 'APPLE_HEALTH', 'watch', $4, $4, $4)`,
[cuid(), userId, Math.round(sleepMin[i]), date],
);
}
// Steps (daily)
// Steps (daily), as the one daily total the iOS app writes per day
// under its `stats:` key, so the legacy-step consolidation leaves it be.
await client.query(
`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
VALUES ($1, $2, 'ACTIVITY_STEPS', $3, 'steps', 'MANUAL', $4, $4, $4)`,
[cuid(), userId, Math.round(steps[i]), date],
`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, external_id, measured_at, created_at, updated_at)
VALUES ($1, $2, 'ACTIVITY_STEPS', $3, 'steps', 'APPLE_HEALTH', 'watch', $4, $5, $5, $5)`,
[
cuid(),
userId,
Math.round(steps[i]),
dailyStatsExternalId(
"HKQuantityTypeIdentifierStepCount",
berlinDayKey(date),
),
date,
],
);
// Waist circumference (fortnightly, plus today). The Health Score's
@@ -784,8 +805,12 @@ export async function seed() {
// the same-day weight + body-fat series above so the numbers reconcile:
// fat mass = weight × bodyFat%, fat-free mass = weight − fat mass, and
// the muscle / water / bone / BMI components hang off those so the demo
// never contradicts its own headline weight + body-fat tiles. Source
// APPLE_HEALTH with device_type 'scale' so they read as device-tracked.
// never contradicts its own headline weight + body-fat tiles. The
// components the Withings scale measures arrive as WITHINGS, the way its
// sync writes them; lean body mass and BMI, which Withings does not
// report but its app writes to Apple Health, arrive as APPLE_HEALTH with
// device_type 'scale'. HealthKit has no fat-mass, muscle, water, bone or
// visceral-fat type, so none of those can come from Apple Health.
// Canonical units (see prisma/schema.prisma + VALUE_RANGES): mass
// components in kg, VISCERAL_FAT a 1–12 rating, BODY_MASS_INDEX kg/m².
console.log("Creating body composition series...");
@@ -811,22 +836,65 @@ export async function seed() {
const visceralFat = Math.round(7 - (i / span) * 1.5);
const bmi = Math.round((weightKg / (HEIGHT_M * HEIGHT_M)) * 10) / 10;
const composition: Array<{ type: string; value: number; unit: string }> =
[
{ type: "FAT_MASS", value: fatMass, unit: "kg" },
{ type: "FAT_FREE_MASS", value: fatFree, unit: "kg" },
{ type: "LEAN_BODY_MASS", value: leanMass, unit: "kg" },
{ type: "MUSCLE_MASS", value: muscleMass, unit: "kg" },
{ type: "TOTAL_BODY_WATER", value: bodyWater, unit: "kg" },
{ type: "BONE_MASS", value: boneMass, unit: "kg" },
{ type: "VISCERAL_FAT", value: visceralFat, unit: "rating" },
{ type: "BODY_MASS_INDEX", value: bmi, unit: "kg/m²" },
];
const composition: Array<{
type: string;
value: number;
unit: string;
source: "WITHINGS" | "APPLE_HEALTH";
}> = [
{ type: "FAT_MASS", value: fatMass, unit: "kg", source: "WITHINGS" },
{
type: "FAT_FREE_MASS",
value: fatFree,
unit: "kg",
source: "WITHINGS",
},
{
type: "LEAN_BODY_MASS",
value: leanMass,
unit: "kg",
source: "APPLE_HEALTH",
},
{
type: "MUSCLE_MASS",
value: muscleMass,
unit: "kg",
source: "WITHINGS",
},
{
type: "TOTAL_BODY_WATER",
value: bodyWater,
unit: "kg",
source: "WITHINGS",
},
{ type: "BONE_MASS", value: boneMass, unit: "kg", source: "WITHINGS" },
{
type: "VISCERAL_FAT",
value: visceralFat,
unit: "rating",
source: "WITHINGS",
},
{
type: "BODY_MASS_INDEX",
value: bmi,
unit: "kg/m²",
source: "APPLE_HEALTH",
},
];
for (const c of composition) {
await client.query(
`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
VALUES ($1, $2, $3, $4, $5, 'APPLE_HEALTH', 'scale', $6, $6, $6)`,
[cuid(), userId, c.type, c.value, c.unit, date],
VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $8, $8)`,
[
cuid(),
userId,
c.type,
c.value,
c.unit,
c.source,
c.source === "APPLE_HEALTH" ? "scale" : null,
date,
],
);
}
}