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Merge wave-demo-seed-aug: full-coverage demo across every domain
Extend the demo tenant with body composition, blood glucose with meal contexts, cardio vitals, recovery and sleep scores, and realistic workouts with heart-rate samples, all reconciled against the existing series and dated relative to now. Replace the stale achievement ids the prior seed referenced with verified registry keys.
This commit is contained in:
Marc-André Bombeck committed
commit
bc31787053
1 file changed
+292
-13
+292
-13
@@ -11,6 +11,16 @@
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* sleep, steps) — flat-or-improving trends, most-recent point from today
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* - A full week of per-stage sleep nights (awake/REM/light(core)/deep),
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* ~7.5 h each, including last night
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* - 90 days of body composition (fat/lean/muscle mass, total body water,
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* bone mass, visceral fat, BMI) derived from the same-day weight + body fat
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* - 90 days of blood glucose in a healthy non-diabetic band (fasting +
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* post-meal + bedtime, contexts tagged)
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* - 90 days of cardio fitness + vitals (HRV SDNN + RMSSD, SpO2, respiratory
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* rate, VO2 max, active energy, walking/running distance, flights climbed)
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* - 90 days of WHOOP-style scores (recovery, day strain, sleep
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* performance/efficiency/consistency) coherent with the sleep nights
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* - ~3-4 workouts/week (running, strength, cycling) with per-workout HR
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* samples and per-workout strain
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* - 3 medications with schedules and ~90 days of intake history at high
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* compliance, with today scheduled on-track (taken or not-yet-due)
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* - 90 days of mood entries
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@@ -118,6 +128,8 @@ async function seed() {
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await client.query("DELETE FROM lab_results");
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await client.query("DELETE FROM measurement_reminders");
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await client.query("DELETE FROM mood_entries");
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await client.query("DELETE FROM workout_samples");
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await client.query("DELETE FROM workouts");
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await client.query("DELETE FROM medication_intake_events");
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await client.query("DELETE FROM medication_schedules");
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await client.query("DELETE FROM reminder_phase_configs");
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@@ -292,6 +304,248 @@ async function seed() {
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}
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}
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// ── Body composition (smart-scale series) ──
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// A full scale-style breakdown, written every 2–3 days like a real
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// body-composition scale would record. Every component is DERIVED from
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// the same-day weight + body-fat series above so the numbers reconcile:
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// fat mass = weight × bodyFat%, fat-free mass = weight − fat mass, and
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// the muscle / water / bone / BMI components hang off those so the demo
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// never contradicts its own headline weight + body-fat tiles. Source
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// APPLE_HEALTH with device_type 'scale' so they read as device-tracked.
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// Canonical units (see prisma/schema.prisma + VALUE_RANGES): mass
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// components in kg, VISCERAL_FAT a 1–12 rating, BODY_MASS_INDEX kg/m².
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console.log("Creating body composition series...");
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const HEIGHT_M = 1.82;
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for (let i = 0; i < span; i++) {
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// Roughly every other day, jittered, so the cadence reads organic.
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if (i % 2 !== 0 && Math.random() > 0.4) continue;
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const date = daysAgo(days - i);
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const weightKg = weights[i];
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const fatPct = bodyFat[i];
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const fatMass = Math.round(weightKg * (fatPct / 100) * 10) / 10;
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const fatFree = Math.round((weightKg - fatMass) * 10) / 10;
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// Skeletal muscle is a subset of fat-free mass (~75% of it for a lean
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// adult male); lean body mass ≈ fat-free mass.
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const muscleMass = Math.round(fatFree * 0.75 * 10) / 10;
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const leanMass = fatFree;
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// Total body water ≈ 73% of fat-free mass (kg of water).
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const bodyWater = Math.round(fatFree * 0.73 * 10) / 10;
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const boneMass = Math.round((3.1 + (Math.random() - 0.5) * 0.2) * 10) / 10;
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// Visceral-fat rating on Withings' 1–12 scale; a healthy ~6, easing
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// down slightly as the body fat trends down.
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const visceralFat = Math.round(7 - (i / span) * 1.5);
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const bmi = Math.round((weightKg / (HEIGHT_M * HEIGHT_M)) * 10) / 10;
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const composition: Array<{ type: string; value: number; unit: string }> =
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[
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{ type: "FAT_MASS", value: fatMass, unit: "kg" },
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{ type: "FAT_FREE_MASS", value: fatFree, unit: "kg" },
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{ type: "LEAN_BODY_MASS", value: leanMass, unit: "kg" },
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{ type: "MUSCLE_MASS", value: muscleMass, unit: "kg" },
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{ type: "TOTAL_BODY_WATER", value: bodyWater, unit: "kg" },
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{ type: "BONE_MASS", value: boneMass, unit: "kg" },
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{ type: "VISCERAL_FAT", value: visceralFat, unit: "rating" },
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{ type: "BODY_MASS_INDEX", value: bmi, unit: "kg/m²" },
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];
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for (const c of composition) {
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, $3, $4, $5, 'APPLE_HEALTH', 'scale', $6, $6, $6)`,
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[cuid(), userId, c.type, c.value, c.unit, date],
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);
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}
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}
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// ── Blood glucose (healthy non-diabetic series) ──
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// A few readings per day over the full window, every value in a healthy
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// non-diabetic band: fasting ~85–95 mg/dL, a well-controlled post-meal
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// peak ~110–135, and a calm bedtime ~95–105. Canonical storage is mg/dL
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// (schema note on BLOOD_GLUCOSE); the per-reading context lands in the
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// glucose_context column (FASTING / POSTPRANDIAL / BEDTIME) so the
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// glucose panel can apply the right target band per reading.
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console.log("Creating blood glucose series...");
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const glucoseReadings: Array<{
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context: string;
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hour: number;
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base: number;
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jitter: number;
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}> = [
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{ context: "FASTING", hour: 7, base: 89, jitter: 6 },
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{ context: "POSTPRANDIAL", hour: 13, base: 122, jitter: 12 },
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{ context: "POSTPRANDIAL", hour: 19, base: 118, jitter: 12 },
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{ context: "BEDTIME", hour: 22, base: 99, jitter: 6 },
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];
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for (let i = 0; i < span; i++) {
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for (const r of glucoseReadings) {
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const at = daysAgoAt(days - i, r.hour, Math.floor(Math.random() * 30));
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const value = Math.round(r.base + (Math.random() - 0.5) * r.jitter);
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, glucose_context, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'BLOOD_GLUCOSE', $3, 'mg/dL', 'APPLE_HEALTH', $4, 'phone', $5, $5, $5)`,
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[cuid(), userId, value, r.context, at],
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);
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}
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}
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// ── Cardio fitness + vitals ───────────────
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// The dashboard + insights surface these device-tracked vitals. All
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// recent (full window, latest point today), all in healthy/aspirational
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// bands. Canonical units per schema + VALUE_RANGES: HRV ms,
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// OXYGEN_SATURATION percent, RESPIRATORY_RATE breaths/min, VO2_MAX
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// mL/(kg·min), ACTIVE_ENERGY_BURNED kcal, WALKING_RUNNING_DISTANCE metres,
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// FLIGHTS_CLIMBED count. Source APPLE_HEALTH device_type 'watch'.
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console.log("Creating cardio fitness + vitals series...");
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// HRV SDNN: a healthy ~55 ms, gently rising; also write the RMSSD variant.
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const hrvSdnn = randomWalk(48, 58, span, 5, VALUE_RANGES.HEART_RATE_VARIABILITY);
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const hrvRmssd = randomWalk(42, 52, span, 5, VALUE_RANGES.HRV_RMSSD);
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// SpO2: 97–99%.
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const spo2 = randomWalk(98, 98, span, 0.8, VALUE_RANGES.OXYGEN_SATURATION);
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// Respiratory rate: 13–16 breaths/min.
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const respRate = randomWalk(15, 14, span, 0.8, VALUE_RANGES.RESPIRATORY_RATE);
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// Active energy: ~550 kcal/day.
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const activeKcal = randomWalk(480, 600, span, 80, VALUE_RANGES.ACTIVE_ENERGY_BURNED);
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// Walking + running distance (metres): ~6–8 km/day, tracks steps.
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const distM = randomWalk(6200, 7800, span, 900, VALUE_RANGES.WALKING_RUNNING_DISTANCE);
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// Flights climbed: ~8–14/day.
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const flights = randomWalk(9, 13, span, 4, VALUE_RANGES.FLIGHTS_CLIMBED);
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for (let i = 0; i < span; i++) {
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const date = daysAgo(days - i);
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const vitals: Array<{ type: string; value: number; unit: string }> = [
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{ type: "HEART_RATE_VARIABILITY", value: Math.round(hrvSdnn[i]), unit: "ms" },
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{ type: "HRV_RMSSD", value: Math.round(hrvRmssd[i]), unit: "ms" },
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{ type: "OXYGEN_SATURATION", value: Math.round(spo2[i] * 10) / 10, unit: "%" },
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{ type: "RESPIRATORY_RATE", value: Math.round(respRate[i] * 10) / 10, unit: "count/min" },
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{ type: "ACTIVE_ENERGY_BURNED", value: Math.round(activeKcal[i]), unit: "kcal" },
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{ type: "WALKING_RUNNING_DISTANCE", value: Math.round(distM[i]), unit: "m" },
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{ type: "FLIGHTS_CLIMBED", value: Math.round(flights[i]), unit: "count" },
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];
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for (const v of vitals) {
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, $3, $4, $5, 'APPLE_HEALTH', 'watch', $6, $6, $6)`,
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[cuid(), userId, v.type, v.value, v.unit, date],
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);
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}
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// VO2 max: only every ~10 days (Apple samples it rarely), ~46–49.
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if (i % 10 === 0) {
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const vo2 = Math.round((46 + (i / span) * 3) * 10) / 10;
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'VO2_MAX', $3, 'mL/(kg·min)', 'APPLE_HEALTH', 'watch', $4, $4, $4)`,
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[cuid(), userId, vo2, date],
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);
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}
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}
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// ── Recovery / strain / sleep scores (WHOOP-style) ──
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// The WHOOP-style readiness surface. These are stored as Measurement rows
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// (type RECOVERY_SCORE / DAY_STRAIN / SLEEP_PERFORMANCE / SLEEP_EFFICIENCY
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// / SLEEP_CONSISTENCY, unit 'score' or '%') with source WHOOP, distinct
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// from the COMPUTED engine's own rows. Coherent with the seeded sleep:
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// well-slept nights pair with high recovery + efficiency, and day-strain
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// sits in a sustainable band. RECOVERY_SCORE / sleep percentages are
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// 0–100; DAY_STRAIN is on WHOOP's 0–21 scale.
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console.log("Creating recovery / strain / sleep scores...");
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const recovery = randomWalk(68, 78, span, 8, VALUE_RANGES.RECOVERY_SCORE);
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const dayStrain = randomWalk(11, 13, span, 2.5, VALUE_RANGES.DAY_STRAIN);
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const sleepPerf = randomWalk(82, 90, span, 6, VALUE_RANGES.SLEEP_PERFORMANCE);
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const sleepEff = randomWalk(88, 93, span, 4, VALUE_RANGES.SLEEP_EFFICIENCY);
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const sleepConsistency = randomWalk(74, 84, span, 6, VALUE_RANGES.SLEEP_CONSISTENCY);
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for (let i = 0; i < span; i++) {
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// Anchor scores to the morning wake instant so a night's recovery sits
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// on the day it belongs to.
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const at = daysAgoAt(days - i, 7, 0);
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const scores: Array<{ type: string; value: number; unit: string }> = [
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{ type: "RECOVERY_SCORE", value: Math.round(recovery[i]), unit: "score" },
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{ type: "DAY_STRAIN", value: Math.round(dayStrain[i] * 10) / 10, unit: "score" },
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{ type: "SLEEP_PERFORMANCE", value: Math.round(sleepPerf[i]), unit: "%" },
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{ type: "SLEEP_EFFICIENCY", value: Math.round(sleepEff[i]), unit: "%" },
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{ type: "SLEEP_CONSISTENCY", value: Math.round(sleepConsistency[i]), unit: "%" },
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];
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for (const s of scores) {
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, $3, $4, $5, 'WHOOP', 'band', $6, $6, $6)`,
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[cuid(), userId, s.type, s.value, s.unit, at],
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);
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}
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}
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// ── Workouts ──────────────────────────────
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// ~3–4 sessions/week across the window: a mix of running, strength, and
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// cycling, with the fields the workout tiles render — duration, energy,
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// avg/max HR, distance (running/cycling). Each workout carries a sparse
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// HR sample series in WorkoutSamples (the per-workout HR chart) and its
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// per-workout strain in metadata (WORKOUT_STRAIN lives on the workout row
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// per the schema note, not as a free-floating Measurement). Distances in
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// metres, energy in kcal, durations in seconds — matching the model.
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console.log("Creating workouts...");
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const workoutPlan: Array<{
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sport: string;
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durationMin: number;
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kcal: number;
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avgHr: number;
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maxHr: number;
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distanceM: number | null;
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strain: number;
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}> = [
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{ sport: "running", durationMin: 38, kcal: 420, avgHr: 148, maxHr: 171, distanceM: 6500, strain: 13.4 },
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{ sport: "strength", durationMin: 52, kcal: 360, avgHr: 118, maxHr: 152, distanceM: null, strain: 10.2 },
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{ sport: "cycling", durationMin: 65, kcal: 520, avgHr: 134, maxHr: 158, distanceM: 24000, strain: 12.1 },
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];
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// Step backwards through the window placing ~2 sessions a week per slot
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// so the cadence lands around 3–4/week without overlapping.
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let planIdx = 0;
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for (let dayOffset = days - 1; dayOffset >= 1; dayOffset -= 2) {
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// Skip ~25% of slots so the week isn't perfectly regular.
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if (Math.random() < 0.25) continue;
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const plan = workoutPlan[planIdx % workoutPlan.length];
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planIdx += 1;
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const startHour = 17 + Math.floor(Math.random() * 3);
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const startedAt = daysAgoAt(dayOffset, startHour, Math.floor(Math.random() * 50));
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const durationSec = plan.durationMin * 60;
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const endedAt = new Date(startedAt.getTime() + durationSec * 1000);
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const workoutId = cuid();
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await client.query(
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`INSERT INTO workouts (id, user_id, sport_type, started_at, ended_at, duration_sec, total_energy_kcal, total_distance_m, avg_heart_rate, max_heart_rate, source, metadata, created_at, updated_at)
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VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 'APPLE_HEALTH', $11, $4, $4)`,
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[
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workoutId,
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userId,
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plan.sport,
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startedAt,
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endedAt,
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durationSec,
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plan.kcal,
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plan.distanceM,
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plan.avgHr,
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plan.maxHr,
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JSON.stringify({ workoutStrain: plan.strain }),
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],
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);
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// A sparse HR series — one sample every ~5 minutes across the session,
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// rising into the working band and easing at the end. The per-workout
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// HR chart renders straight from this WorkoutSamples blob.
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const sampleCount = Math.max(2, Math.round(plan.durationMin / 5));
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const samples: Array<{ t: string; hr: number }> = [];
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for (let s = 0; s < sampleCount; s++) {
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const frac = s / (sampleCount - 1);
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// Warm up from ~60% to the avg, peak near the max around 70% through.
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const peakBias = 1 - Math.abs(frac - 0.7) * 0.6;
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const hr = Math.round(
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plan.avgHr + (plan.maxHr - plan.avgHr) * peakBias * 0.7,
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);
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const t = new Date(startedAt.getTime() + frac * durationSec * 1000);
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samples.push({ t: t.toISOString(), hr });
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}
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await client.query(
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`INSERT INTO workout_samples (id, workout_id, samples, sample_count, created_at)
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VALUES ($1, $2, $3, $4, NOW())`,
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[cuid(), workoutId, JSON.stringify(samples), samples.length],
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);
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}
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// ── Medications ───────────────────────────
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console.log("Creating medications...");
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@@ -541,19 +795,39 @@ async function seed() {
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// ── Achievements ─────────────────────────
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console.log("Creating achievements...");
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// Every id below is a real key from the achievement registry
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// (src/lib/gamification/achievements.ts) so the badge resolves to a real
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// title/icon rather than an "unknown" placeholder. Only milestones the
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// seeded history genuinely supports are unlocked (first-entry badges,
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// logging streaks, in-range BP/pulse/BMI streaks, compliance + miss-free
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// streaks, the self-context + 7-night sleep badges). The dynamic streak
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// families use the `<prefix>-<target>` naming the generator emits.
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const achievements = [
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{ id: "first-measurement", daysAgo: 89 },
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{ id: "first-medication", daysAgo: 89 },
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{ id: "first-mood", daysAgo: 89 },
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{ id: "week-streak-measurements", daysAgo: 82 },
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{ id: "week-streak-mood", daysAgo: 82 },
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{ id: "month-streak-measurements", daysAgo: 59 },
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{ id: "compliance-90", daysAgo: 45 },
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{ id: "weight-loss-5", daysAgo: 30 },
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{ id: "bp-normal", daysAgo: 20 },
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{ id: "steps-10k", daysAgo: 15 },
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{ id: "month-streak-mood", daysAgo: 10 },
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{ id: "three-month-streak", daysAgo: 2 },
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{ id: "weight-first", daysAgo: 89 },
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{ id: "bp-first", daysAgo: 89 },
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{ id: "pulse-first", daysAgo: 89 },
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{ id: "mood-first", daysAgo: 89 },
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{ id: "self-context-complete", daysAgo: 88 },
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{ id: "entry-streak-7", daysAgo: 82 },
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{ id: "mood-streak-7", daysAgo: 82 },
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{ id: "sleep-log-7", daysAgo: 70 },
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{ id: "mood-up-7", daysAgo: 68 },
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{ id: "weight-50", daysAgo: 60 },
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{ id: "bp-50", daysAgo: 55 },
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{ id: "entry-streak-30", daysAgo: 50 },
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{ id: "mood-streak-30", daysAgo: 50 },
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{ id: "miss-free-7", daysAgo: 48 },
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{ id: "consistent-month", daysAgo: 45 },
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{ id: "bp-green-7", daysAgo: 42 },
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{ id: "pulse-green-7", daysAgo: 40 },
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{ id: "bmi-green-7", daysAgo: 38 },
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{ id: "compliance-80-30", daysAgo: 30 },
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{ id: "miss-free-30", daysAgo: 30 },
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{ id: "on-time-perfect-7", daysAgo: 28 },
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{ id: "measurement-weeks-4", daysAgo: 25 },
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{ id: "bp-green-30", daysAgo: 12 },
|
||||
{ id: "pulse-green-30", daysAgo: 10 },
|
||||
{ id: "miss-free-90", daysAgo: 2 },
|
||||
];
|
||||
|
||||
for (const ach of achievements) {
|
||||
@@ -645,9 +919,14 @@ async function seed() {
|
||||
await client.query("COMMIT");
|
||||
console.log("\nDemo data seeded successfully!");
|
||||
console.log(` User: demo / demo123demo123`);
|
||||
console.log(` Measurements: ~${span * 8} entries (8 types, today included)`);
|
||||
console.log(` Measurements: weight, BP, pulse, resting HR, body fat, steps, sleep`);
|
||||
console.log(` Body composition: fat/lean/muscle mass, water, bone, visceral fat, BMI`);
|
||||
console.log(` Blood glucose: ${glucoseReadings.length} readings/day (fasting + post-meal + bedtime)`);
|
||||
console.log(` Cardio + vitals: HRV (SDNN + RMSSD), SpO2, resp. rate, VO2max, active energy, distance, flights`);
|
||||
console.log(` Scores: recovery, day strain, sleep performance/efficiency/consistency`);
|
||||
console.log(` Sleep: 7 per-stage nights (4 stages each) + daily aggregate`);
|
||||
console.log(` Medications: 3 (high compliance, today on-track)`);
|
||||
console.log(` Workouts: ~3-4/week (running, strength, cycling) with HR samples`);
|
||||
console.log(` Mood: ~${Math.round(span * 0.95)} entries`);
|
||||
console.log(` Vorsorge reminders: 2 (dental, annual physical)`);
|
||||
console.log(` Lab panel: ${labResults.length} biomarkers`);
|
||||
|
||||
Reference in new issue
Block a user