mirror of
https://github.com/MBombeck/HealthLog.git
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Marc-André Bombeck
1f4ba5939d
One-shot prettier pass so the new format:check gate starts from a clean baseline. Formatting only — no behaviour change. Generator-owned and vendored artifacts stay untouched via .prettierignore.
1102 lines
45 KiB
TypeScript
1102 lines
45 KiB
TypeScript
/**
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* Demo Data Seed Script for HealthLog
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*
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* Creates a realistic, aspirational demo tenant for the public `demo`
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* account (demo / demo123demo123 — credentials are intentionally public).
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* The shape is tuned so a fresh seed reads healthy, engaged, and current
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* for App-Store / marketing screenshots:
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*
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* - 1 admin user (demo/demo123demo123)
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* - 90 days of measurements (weight, BP, pulse, resting HR, body fat,
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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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* - Vorsorge (preventive-care) reminders — upcoming dental + annual physical
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* - One lab panel of biomarkers in healthy ranges
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* - One resolved illness episode in the past
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* - An AI-configured Coach with one short sample conversation
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* - App settings (registration disabled, English locale)
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*
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* Every date is relative to "now" so the demo stays fresh on every re-seed.
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*
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* Usage: npx tsx scripts/seed-demo.ts
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* Requires DATABASE_URL env var. The Coach sample conversation additionally
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* needs ENCRYPTION_KEYS / ENCRYPTION_KEY (the same key the app runs with).
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*/
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import { Buffer } from "node:buffer";
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import pg from "pg";
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import { encryptToBytes } from "../src/lib/ai/coach/bytes-codec";
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import { encrypt } from "../src/lib/crypto";
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import { VALUE_RANGES } from "../src/lib/validations/measurement";
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const DATABASE_URL = process.env.DATABASE_URL;
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if (!DATABASE_URL) {
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console.error("DATABASE_URL is required");
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process.exit(1);
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}
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const pool = new pg.Pool({ connectionString: DATABASE_URL });
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// ── Helpers ──────────────────────────────────────
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function cuid(): string {
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const chars = "abcdefghijklmnopqrstuvwxyz0123456789";
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let id = "c";
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for (let i = 0; i < 24; i++) {
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id += chars[Math.floor(Math.random() * chars.length)];
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}
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return id;
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}
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function daysAgo(n: number): Date {
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const d = new Date();
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d.setDate(d.getDate() - n);
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d.setHours(
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7 + Math.floor(Math.random() * 3),
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Math.floor(Math.random() * 60),
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0,
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0,
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);
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return d;
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}
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// A specific local instant N days ago, at a fixed wall-clock time. Used for
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// the per-stage sleep segments and the on-track intake slots where the hour
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// matters (a random morning hour would scatter the sleep stages).
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function daysAgoAt(n: number, hour: number, minute = 0): Date {
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const d = new Date();
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d.setDate(d.getDate() - n);
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d.setHours(hour, minute, 0, 0);
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return d;
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}
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function formatDate(d: Date): string {
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return d.toISOString().split("T")[0];
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}
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// Smooth random walk with mean reversion
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function randomWalk(
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start: number,
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target: number,
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days: number,
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volatility: number,
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// Optional physiological band. A free walk can drift below the floor and
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// mint an out-of-range row (a systolic-0 BP reading was observed in a
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// fresh seed — iOS #33); clamping every step keeps the series inside the
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// same band the input validator enforces.
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clamp?: { min: number; max: number },
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): number[] {
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const values: number[] = [start];
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for (let i = 1; i < days; i++) {
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const prev = values[i - 1];
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const drift = (target - prev) * 0.03; // mean reversion
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const noise = (Math.random() - 0.5) * volatility;
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let next = Math.round((prev + drift + noise) * 10) / 10;
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if (clamp) next = Math.min(clamp.max, Math.max(clamp.min, next));
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values.push(next);
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}
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return values;
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}
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// ── Main ─────────────────────────────────────────
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async function seed() {
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const client = await pool.connect();
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try {
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await client.query("BEGIN");
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console.log("Cleaning existing data...");
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await client.query("DELETE FROM coach_messages");
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await client.query("DELETE FROM coach_conversations");
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await client.query("DELETE FROM illness_episodes");
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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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await client.query("DELETE FROM medications");
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await client.query("DELETE FROM measurements");
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await client.query("DELETE FROM user_achievements");
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await client.query("DELETE FROM audit_logs");
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await client.query("DELETE FROM sessions");
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await client.query("DELETE FROM passkeys");
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await client.query("DELETE FROM auth_challenges");
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await client.query("DELETE FROM notification_channels");
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await client.query("DELETE FROM push_subscriptions");
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await client.query("DELETE FROM api_tokens");
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await client.query("DELETE FROM data_backups");
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await client.query("DELETE FROM users");
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// ── User ──────────────────────────────────
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console.log("Creating demo user...");
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const userId = cuid();
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// argon2id hash of "demo123demo123" — pre-computed
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// We'll use a bcrypt-compatible approach: register via API later
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// For now, insert with a placeholder and we'll set it via the app
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const passwordHash =
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"$argon2id$v=19$m=65536,t=3,p=4$Kips6OxPAl0vmspO9SoKZQ$oX9gLgwHVnnENCqBloyM13ewuqmhPnw8EpLoemS3MNI";
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// AI Coach is configured to the Anthropic provider so the Coach surface
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// reads as connected. The stored key is a placeholder ciphertext: the
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// public demo never makes a live provider call (the sample conversation
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// below supplies the visible content), so no real credential is seeded.
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const aiProvider = "ANTHROPIC";
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const aiModel = "claude-3-5-sonnet";
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const aiKeyEncrypted = encrypt("sk-ant-demo-placeholder-not-a-live-key");
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await client.query(
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`INSERT INTO users (id, username, email, password_hash, role, height_cm, date_of_birth, gender, timezone, locale, ai_provider, ai_model, ai_anthropic_key_encrypted, onboarding_completed_at, created_at, updated_at)
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VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13, NOW(), NOW(), NOW())`,
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[
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userId,
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"demo",
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"demo@healthlog.app",
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passwordHash,
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"ADMIN",
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182.0,
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"1990-05-15",
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"MALE",
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"Europe/Berlin",
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"en",
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aiProvider,
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aiModel,
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aiKeyEncrypted,
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],
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);
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// ── Measurements (90 days) ────────────────
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// The loop runs i = 0..days (inclusive) so the final iteration lands a
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// point for TODAY (daysAgo(0)) — every headline metric has a current
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// reading for the dashboard, not one from yesterday.
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console.log("Creating 90 days of measurements...");
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const days = 90;
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const span = days + 1; // include today
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// Weight: 86.5 → trending down to a stable ~82 (healthy BMI ~24.7 at 182cm)
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const weights = randomWalk(86.5, 82.0, span, 0.6);
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// Systolic BP: 128 → settled in the optimal band ~118 (clamped)
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const sysBP = randomWalk(
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128,
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118,
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span,
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3,
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VALUE_RANGES.BLOOD_PRESSURE_SYS,
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);
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// Diastolic BP: 82 → settled ~76 (clamped)
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const diaBP = randomWalk(
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82,
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76,
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span,
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2.5,
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VALUE_RANGES.BLOOD_PRESSURE_DIA,
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);
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// Spot pulse (daytime heart rate): ~72, gently lower
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const pulse = randomWalk(74, 70, span, 3, VALUE_RANGES.PULSE);
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// Resting heart rate: clean ~60, the metric the resting-pulse tile scores
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const restingHr = randomWalk(
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64,
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58,
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span,
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2,
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VALUE_RANGES.RESTING_HEART_RATE,
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);
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// Body fat: 24% → trending to a healthy ~19%
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const bodyFat = randomWalk(24.0, 19.0, span, 0.4, VALUE_RANGES.BODY_FAT);
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// Sleep duration in MINUTES (the SLEEP_DURATION unit): ~7h → ~7h45m
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const sleepMin = randomWalk(
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420,
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465,
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span,
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25,
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VALUE_RANGES.SLEEP_DURATION,
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);
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// Steps: 6500 → a solid ~9000
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const steps = randomWalk(6500, 9000, span, 1500);
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for (let i = 0; i < span; i++) {
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const date = daysAgo(days - i);
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// Weight (daily)
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'WEIGHT', $3, 'kg', 'MANUAL', $4, $4, $4)`,
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[cuid(), userId, weights[i], date],
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);
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// Blood pressure (daily)
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'BLOOD_PRESSURE_SYS', $3, 'mmHg', 'MANUAL', $4, $4, $4)`,
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[cuid(), userId, Math.round(sysBP[i]), date],
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);
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'BLOOD_PRESSURE_DIA', $3, 'mmHg', 'MANUAL', $4, $4, $4)`,
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[cuid(), userId, Math.round(diaBP[i]), date],
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);
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// Spot pulse (daily)
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'PULSE', $3, 'bpm', 'MANUAL', $4, $4, $4)`,
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[cuid(), userId, Math.round(pulse[i]), date],
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);
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// Resting heart rate (daily) — scored by the resting-pulse tile
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'RESTING_HEART_RATE', $3, 'bpm', 'MANUAL', $4, $4, $4)`,
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[cuid(), userId, Math.round(restingHr[i]), date],
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);
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// Body fat (every 2-3 days)
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if (i % 2 === 0 || Math.random() > 0.5) {
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'BODY_FAT', $3, '%', 'MANUAL', $4, $4, $4)`,
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[cuid(), userId, bodyFat[i], date],
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);
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}
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// Sleep duration (daily, in minutes). This single aggregate row keeps
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// the 90-day sleep series continuous for the trend chart. The most
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// recent seven nights are instead written as per-stage breakdowns below
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// (one row per stage); skip the aggregate for those so a night is not
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// double-counted (aggregate + summed stages).
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const nightsAgo = days - i;
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if (nightsAgo >= 7) {
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'SLEEP_DURATION', $3, 'minutes', 'MANUAL', $4, $4, $4)`,
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[cuid(), userId, Math.round(sleepMin[i]), date],
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);
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}
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// Steps (daily)
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'ACTIVITY_STEPS', $3, 'steps', 'MANUAL', $4, $4, $4)`,
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[cuid(), userId, Math.round(steps[i]), date],
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);
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}
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// ── Per-stage sleep nights (last 7 nights, incl. last night) ──
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// One SLEEP_DURATION row per stage, all stamped at the same wake instant,
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// mirroring how a HealthKit night lands (AWAKE / REM / CORE / DEEP, with
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// the legacy aggregate kept above). The reader sums the stages for the
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// night's total. Durations target a healthy ~7.5 h with a normal stage
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// mix (~55% core, ~22% REM, ~18% deep, ~5% awake). Source APPLE_HEALTH
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// with device_type 'watch' so the night reads as device-tracked rather
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// than a manual estimate.
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console.log("Creating per-stage sleep nights (7 nights)...");
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const sleepStages: Array<{ stage: string; fraction: number }> = [
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{ stage: "AWAKE", fraction: 0.05 },
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{ stage: "REM", fraction: 0.22 },
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{ stage: "CORE", fraction: 0.55 },
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{ stage: "DEEP", fraction: 0.18 },
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];
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for (let n = 1; n <= 7; n++) {
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// Wake at ~06:50 with small per-night jitter; total ~7.5 h.
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const wake = daysAgoAt(n - 1, 6, 40 + Math.floor(Math.random() * 25));
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const totalMin = 440 + Math.floor(Math.random() * 40); // 7h20m – 8h
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for (const { stage, fraction } of sleepStages) {
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const stageMin = Math.round(totalMin * fraction);
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, sleep_stage, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'SLEEP_DURATION', $3, 'minutes', 'APPLE_HEALTH', $4, 'watch', $5, $5, $5)`,
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[cuid(), userId, stageMin, stage, wake],
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);
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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 =
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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) {
|
||
const at = daysAgoAt(days - i, r.hour, Math.floor(Math.random() * 30));
|
||
const value = Math.round(r.base + (Math.random() - 0.5) * r.jitter);
|
||
await client.query(
|
||
`INSERT INTO measurements (id, user_id, type, value, unit, source, glucose_context, device_type, measured_at, created_at, updated_at)
|
||
VALUES ($1, $2, 'BLOOD_GLUCOSE', $3, 'mg/dL', 'APPLE_HEALTH', $4, 'phone', $5, $5, $5)`,
|
||
[cuid(), userId, value, r.context, at],
|
||
);
|
||
}
|
||
}
|
||
|
||
// ── Cardio fitness + vitals ───────────────
|
||
// The dashboard + insights surface these device-tracked vitals. All
|
||
// recent (full window, latest point today), all in healthy/aspirational
|
||
// bands. Canonical units per schema + VALUE_RANGES: HRV ms,
|
||
// OXYGEN_SATURATION percent, RESPIRATORY_RATE breaths/min, VO2_MAX
|
||
// mL/(kg·min), ACTIVE_ENERGY_BURNED kcal, WALKING_RUNNING_DISTANCE metres,
|
||
// FLIGHTS_CLIMBED count. Source APPLE_HEALTH device_type 'watch'.
|
||
console.log("Creating cardio fitness + vitals series...");
|
||
// HRV SDNN: a healthy ~55 ms, gently rising; also write the RMSSD variant.
|
||
const hrvSdnn = randomWalk(
|
||
48,
|
||
58,
|
||
span,
|
||
5,
|
||
VALUE_RANGES.HEART_RATE_VARIABILITY,
|
||
);
|
||
const hrvRmssd = randomWalk(42, 52, span, 5, VALUE_RANGES.HRV_RMSSD);
|
||
// SpO2: 97–99%.
|
||
const spo2 = randomWalk(98, 98, span, 0.8, VALUE_RANGES.OXYGEN_SATURATION);
|
||
// Respiratory rate: 13–16 breaths/min.
|
||
const respRate = randomWalk(
|
||
15,
|
||
14,
|
||
span,
|
||
0.8,
|
||
VALUE_RANGES.RESPIRATORY_RATE,
|
||
);
|
||
// Active energy: ~550 kcal/day.
|
||
const activeKcal = randomWalk(
|
||
480,
|
||
600,
|
||
span,
|
||
80,
|
||
VALUE_RANGES.ACTIVE_ENERGY_BURNED,
|
||
);
|
||
// Walking + running distance (metres): ~6–8 km/day, tracks steps.
|
||
const distM = randomWalk(
|
||
6200,
|
||
7800,
|
||
span,
|
||
900,
|
||
VALUE_RANGES.WALKING_RUNNING_DISTANCE,
|
||
);
|
||
// Flights climbed: ~8–14/day.
|
||
const flights = randomWalk(9, 13, span, 4, VALUE_RANGES.FLIGHTS_CLIMBED);
|
||
|
||
for (let i = 0; i < span; i++) {
|
||
const date = daysAgo(days - i);
|
||
const vitals: Array<{ type: string; value: number; unit: string }> = [
|
||
{
|
||
type: "HEART_RATE_VARIABILITY",
|
||
value: Math.round(hrvSdnn[i]),
|
||
unit: "ms",
|
||
},
|
||
{ type: "HRV_RMSSD", value: Math.round(hrvRmssd[i]), unit: "ms" },
|
||
{
|
||
type: "OXYGEN_SATURATION",
|
||
value: Math.round(spo2[i] * 10) / 10,
|
||
unit: "%",
|
||
},
|
||
{
|
||
type: "RESPIRATORY_RATE",
|
||
value: Math.round(respRate[i] * 10) / 10,
|
||
unit: "count/min",
|
||
},
|
||
{
|
||
type: "ACTIVE_ENERGY_BURNED",
|
||
value: Math.round(activeKcal[i]),
|
||
unit: "kcal",
|
||
},
|
||
{
|
||
type: "WALKING_RUNNING_DISTANCE",
|
||
value: Math.round(distM[i]),
|
||
unit: "m",
|
||
},
|
||
{
|
||
type: "FLIGHTS_CLIMBED",
|
||
value: Math.round(flights[i]),
|
||
unit: "count",
|
||
},
|
||
];
|
||
for (const v of vitals) {
|
||
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', 'watch', $6, $6, $6)`,
|
||
[cuid(), userId, v.type, v.value, v.unit, date],
|
||
);
|
||
}
|
||
// VO2 max: only every ~10 days (Apple samples it rarely), ~46–49.
|
||
if (i % 10 === 0) {
|
||
const vo2 = Math.round((46 + (i / span) * 3) * 10) / 10;
|
||
await client.query(
|
||
`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
|
||
VALUES ($1, $2, 'VO2_MAX', $3, 'mL/(kg·min)', 'APPLE_HEALTH', 'watch', $4, $4, $4)`,
|
||
[cuid(), userId, vo2, date],
|
||
);
|
||
}
|
||
}
|
||
|
||
// ── Recovery / strain / sleep scores (WHOOP-style) ──
|
||
// The WHOOP-style readiness surface. These are stored as Measurement rows
|
||
// (type RECOVERY_SCORE / DAY_STRAIN / SLEEP_PERFORMANCE / SLEEP_EFFICIENCY
|
||
// / SLEEP_CONSISTENCY, unit 'score' or '%') with source WHOOP, distinct
|
||
// from the COMPUTED engine's own rows. Coherent with the seeded sleep:
|
||
// well-slept nights pair with high recovery + efficiency, and day-strain
|
||
// sits in a sustainable band. RECOVERY_SCORE / sleep percentages are
|
||
// 0–100; DAY_STRAIN is on WHOOP's 0–21 scale.
|
||
console.log("Creating recovery / strain / sleep scores...");
|
||
const recovery = randomWalk(68, 78, span, 8, VALUE_RANGES.RECOVERY_SCORE);
|
||
const dayStrain = randomWalk(11, 13, span, 2.5, VALUE_RANGES.DAY_STRAIN);
|
||
const sleepPerf = randomWalk(
|
||
82,
|
||
90,
|
||
span,
|
||
6,
|
||
VALUE_RANGES.SLEEP_PERFORMANCE,
|
||
);
|
||
const sleepEff = randomWalk(88, 93, span, 4, VALUE_RANGES.SLEEP_EFFICIENCY);
|
||
const sleepConsistency = randomWalk(
|
||
74,
|
||
84,
|
||
span,
|
||
6,
|
||
VALUE_RANGES.SLEEP_CONSISTENCY,
|
||
);
|
||
for (let i = 0; i < span; i++) {
|
||
// Anchor scores to the morning wake instant so a night's recovery sits
|
||
// on the day it belongs to.
|
||
const at = daysAgoAt(days - i, 7, 0);
|
||
const scores: Array<{ type: string; value: number; unit: string }> = [
|
||
{
|
||
type: "RECOVERY_SCORE",
|
||
value: Math.round(recovery[i]),
|
||
unit: "score",
|
||
},
|
||
{
|
||
type: "DAY_STRAIN",
|
||
value: Math.round(dayStrain[i] * 10) / 10,
|
||
unit: "score",
|
||
},
|
||
{
|
||
type: "SLEEP_PERFORMANCE",
|
||
value: Math.round(sleepPerf[i]),
|
||
unit: "%",
|
||
},
|
||
{ type: "SLEEP_EFFICIENCY", value: Math.round(sleepEff[i]), unit: "%" },
|
||
{
|
||
type: "SLEEP_CONSISTENCY",
|
||
value: Math.round(sleepConsistency[i]),
|
||
unit: "%",
|
||
},
|
||
];
|
||
for (const s of scores) {
|
||
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, 'WHOOP', 'band', $6, $6, $6)`,
|
||
[cuid(), userId, s.type, s.value, s.unit, at],
|
||
);
|
||
}
|
||
}
|
||
|
||
// ── Workouts ──────────────────────────────
|
||
// ~3–4 sessions/week across the window: a mix of running, strength, and
|
||
// cycling, with the fields the workout tiles render — duration, energy,
|
||
// avg/max HR, distance (running/cycling). Each workout carries a sparse
|
||
// HR sample series in WorkoutSamples (the per-workout HR chart) and its
|
||
// per-workout strain in metadata (WORKOUT_STRAIN lives on the workout row
|
||
// per the schema note, not as a free-floating Measurement). Distances in
|
||
// metres, energy in kcal, durations in seconds — matching the model.
|
||
console.log("Creating workouts...");
|
||
const workoutPlan: Array<{
|
||
sport: string;
|
||
durationMin: number;
|
||
kcal: number;
|
||
avgHr: number;
|
||
maxHr: number;
|
||
distanceM: number | null;
|
||
strain: number;
|
||
}> = [
|
||
{
|
||
sport: "running",
|
||
durationMin: 38,
|
||
kcal: 420,
|
||
avgHr: 148,
|
||
maxHr: 171,
|
||
distanceM: 6500,
|
||
strain: 13.4,
|
||
},
|
||
{
|
||
sport: "strength",
|
||
durationMin: 52,
|
||
kcal: 360,
|
||
avgHr: 118,
|
||
maxHr: 152,
|
||
distanceM: null,
|
||
strain: 10.2,
|
||
},
|
||
{
|
||
sport: "cycling",
|
||
durationMin: 65,
|
||
kcal: 520,
|
||
avgHr: 134,
|
||
maxHr: 158,
|
||
distanceM: 24000,
|
||
strain: 12.1,
|
||
},
|
||
];
|
||
// Step backwards through the window placing ~2 sessions a week per slot
|
||
// so the cadence lands around 3–4/week without overlapping.
|
||
let planIdx = 0;
|
||
for (let dayOffset = days - 1; dayOffset >= 1; dayOffset -= 2) {
|
||
// Skip ~25% of slots so the week isn't perfectly regular.
|
||
if (Math.random() < 0.25) continue;
|
||
const plan = workoutPlan[planIdx % workoutPlan.length];
|
||
planIdx += 1;
|
||
const startHour = 17 + Math.floor(Math.random() * 3);
|
||
const startedAt = daysAgoAt(
|
||
dayOffset,
|
||
startHour,
|
||
Math.floor(Math.random() * 50),
|
||
);
|
||
const durationSec = plan.durationMin * 60;
|
||
const endedAt = new Date(startedAt.getTime() + durationSec * 1000);
|
||
const workoutId = cuid();
|
||
await client.query(
|
||
`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)
|
||
VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 'APPLE_HEALTH', $11, $4, $4)`,
|
||
[
|
||
workoutId,
|
||
userId,
|
||
plan.sport,
|
||
startedAt,
|
||
endedAt,
|
||
durationSec,
|
||
plan.kcal,
|
||
plan.distanceM,
|
||
plan.avgHr,
|
||
plan.maxHr,
|
||
JSON.stringify({ workoutStrain: plan.strain }),
|
||
],
|
||
);
|
||
// A sparse HR series — one sample every ~5 minutes across the session,
|
||
// rising into the working band and easing at the end. The per-workout
|
||
// HR chart renders straight from this WorkoutSamples blob.
|
||
const sampleCount = Math.max(2, Math.round(plan.durationMin / 5));
|
||
const samples: Array<{ t: string; hr: number }> = [];
|
||
for (let s = 0; s < sampleCount; s++) {
|
||
const frac = s / (sampleCount - 1);
|
||
// Warm up from ~60% to the avg, peak near the max around 70% through.
|
||
const peakBias = 1 - Math.abs(frac - 0.7) * 0.6;
|
||
const hr = Math.round(
|
||
plan.avgHr + (plan.maxHr - plan.avgHr) * peakBias * 0.7,
|
||
);
|
||
const t = new Date(startedAt.getTime() + frac * durationSec * 1000);
|
||
samples.push({ t: t.toISOString(), hr });
|
||
}
|
||
await client.query(
|
||
`INSERT INTO workout_samples (id, workout_id, samples, sample_count, created_at)
|
||
VALUES ($1, $2, $3, $4, NOW())`,
|
||
[cuid(), workoutId, JSON.stringify(samples), samples.length],
|
||
);
|
||
}
|
||
|
||
// ── Medications ───────────────────────────
|
||
console.log("Creating medications...");
|
||
|
||
// Medication 1: Ramipril (blood pressure)
|
||
const med1Id = cuid();
|
||
await client.query(
|
||
`INSERT INTO medications (id, user_id, name, dose, active, notifications_enabled, created_at, updated_at)
|
||
VALUES ($1, $2, 'Ramipril', '5mg', true, true, $3, $3)`,
|
||
[med1Id, userId, daysAgo(120)],
|
||
);
|
||
const sched1Id = cuid();
|
||
await client.query(
|
||
`INSERT INTO medication_schedules (id, medication_id, window_start, window_end, label)
|
||
VALUES ($1, $2, '08:00', '10:00', 'Morning')`,
|
||
[sched1Id, med1Id],
|
||
);
|
||
|
||
// Medication 2: Vitamin D3
|
||
const med2Id = cuid();
|
||
await client.query(
|
||
`INSERT INTO medications (id, user_id, name, dose, active, notifications_enabled, created_at, updated_at)
|
||
VALUES ($1, $2, 'Vitamin D3', '2000 IU', true, true, $3, $3)`,
|
||
[med2Id, userId, daysAgo(90)],
|
||
);
|
||
const sched2Id = cuid();
|
||
await client.query(
|
||
`INSERT INTO medication_schedules (id, medication_id, window_start, window_end, label)
|
||
VALUES ($1, $2, '08:00', '10:00', 'Morning')`,
|
||
[sched2Id, med2Id],
|
||
);
|
||
|
||
// Medication 3: Magnesium (evening)
|
||
const med3Id = cuid();
|
||
await client.query(
|
||
`INSERT INTO medications (id, user_id, name, dose, active, notifications_enabled, created_at, updated_at)
|
||
VALUES ($1, $2, 'Magnesium', '400mg', true, true, $3, $3)`,
|
||
[med3Id, userId, daysAgo(60)],
|
||
);
|
||
const sched3Id = cuid();
|
||
await client.query(
|
||
`INSERT INTO medication_schedules (id, medication_id, window_start, window_end, label)
|
||
VALUES ($1, $2, '20:00', '22:00', 'Evening')`,
|
||
[sched3Id, med3Id],
|
||
);
|
||
|
||
// ── Medication Intake Events ──────────────
|
||
// High compliance with no overdue doses. Historical days (i < days) carry
|
||
// a taken/skip row at ~95%+ adherence. TODAY is handled separately below
|
||
// so the dashboard always reads on-track (morning doses taken, the
|
||
// evening dose left as a not-yet-due pending row — never a miss).
|
||
console.log("Creating intake events (90 days)...");
|
||
|
||
for (let i = 0; i < days; i++) {
|
||
const date = daysAgo(days - i);
|
||
|
||
// Ramipril: 98% compliance (miss ~1 every 50 days)
|
||
const takeMed1 = Math.random() > 0.02;
|
||
if (takeMed1) {
|
||
const takenTime = new Date(date);
|
||
takenTime.setHours(8, Math.floor(Math.random() * 45), 0);
|
||
await client.query(
|
||
`INSERT INTO medication_intake_events (id, user_id, medication_id, scheduled_for, taken_at, skipped, source, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, $5, false, 'WEB', $5, $5)`,
|
||
[cuid(), userId, med1Id, date, takenTime],
|
||
);
|
||
} else {
|
||
await client.query(
|
||
`INSERT INTO medication_intake_events (id, user_id, medication_id, scheduled_for, taken_at, skipped, source, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, NULL, true, 'WEB', $4, $4)`,
|
||
[cuid(), userId, med1Id, date],
|
||
);
|
||
}
|
||
|
||
// Vitamin D3: 95% compliance
|
||
const takeMed2 = Math.random() > 0.05;
|
||
if (takeMed2) {
|
||
const takenTime = new Date(date);
|
||
takenTime.setHours(8, Math.floor(5 + Math.random() * 50), 0);
|
||
await client.query(
|
||
`INSERT INTO medication_intake_events (id, user_id, medication_id, scheduled_for, taken_at, skipped, source, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, $5, false, 'WEB', $5, $5)`,
|
||
[cuid(), userId, med2Id, date, takenTime],
|
||
);
|
||
} else {
|
||
await client.query(
|
||
`INSERT INTO medication_intake_events (id, user_id, medication_id, scheduled_for, taken_at, skipped, source, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, NULL, true, 'WEB', $4, $4)`,
|
||
[cuid(), userId, med2Id, date],
|
||
);
|
||
}
|
||
|
||
// Magnesium: only last 60 days, 94% compliance
|
||
if (i >= 30) {
|
||
const takeMed3 = Math.random() > 0.06;
|
||
if (takeMed3) {
|
||
const takenTime = new Date(date);
|
||
takenTime.setHours(20, Math.floor(Math.random() * 60), 0);
|
||
await client.query(
|
||
`INSERT INTO medication_intake_events (id, user_id, medication_id, scheduled_for, taken_at, skipped, source, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, $5, false, 'WEB', $5, $5)`,
|
||
[cuid(), userId, med3Id, date, takenTime],
|
||
);
|
||
} else {
|
||
await client.query(
|
||
`INSERT INTO medication_intake_events (id, user_id, medication_id, scheduled_for, taken_at, skipped, source, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, NULL, true, 'WEB', $4, $4)`,
|
||
[cuid(), userId, med3Id, date],
|
||
);
|
||
}
|
||
}
|
||
}
|
||
|
||
// ── Today's doses (on-track, no overdue) ──
|
||
// Morning meds: taken inside their 08:00–10:00 window. Evening Magnesium:
|
||
// a pending row anchored on tonight's 20:00 slot, never auto-missed — it
|
||
// reads as "not yet due" regardless of the seed wall-clock, and the
|
||
// compliance engine excludes a plain pending row from the denominator, so
|
||
// "today" never shows 0% or an overdue dose.
|
||
console.log("Creating today's on-track doses...");
|
||
const todayMorning = daysAgoAt(0, 8, 30);
|
||
await client.query(
|
||
`INSERT INTO medication_intake_events (id, user_id, medication_id, scheduled_for, taken_at, skipped, source, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, $4, false, 'WEB', $4, $4)`,
|
||
[cuid(), userId, med1Id, todayMorning],
|
||
);
|
||
await client.query(
|
||
`INSERT INTO medication_intake_events (id, user_id, medication_id, scheduled_for, taken_at, skipped, source, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, $4, false, 'WEB', $4, $4)`,
|
||
[cuid(), userId, med2Id, todayMorning],
|
||
);
|
||
const todayEvening = daysAgoAt(0, 20, 0);
|
||
await client.query(
|
||
`INSERT INTO medication_intake_events (id, user_id, medication_id, scheduled_for, taken_at, skipped, auto_missed, source, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, NULL, false, false, 'WEB', $4, $4)`,
|
||
[cuid(), userId, med3Id, todayEvening],
|
||
);
|
||
|
||
// ── Mood Entries ─────────────────────────
|
||
console.log("Creating mood entries (90 days)...");
|
||
|
||
const moodOptions = ["SUPER_GUT", "GUT", "OKAY", "SCHLECHT", "LAUSIG"];
|
||
const tagOptions = [
|
||
["productive", "exercise", "well-rested"],
|
||
["focused", "social", "creative"],
|
||
["tired", "stressed"],
|
||
["anxious", "low-energy"],
|
||
["exhausted", "pain"],
|
||
];
|
||
|
||
// Mood trend: starts solid (~3.5) and improves to ~4.4 — flat-or-up, so
|
||
// the score never dips into an alarming band.
|
||
const moodTrend = randomWalk(3.5, 4.4, span, 0.7);
|
||
|
||
for (let i = 0; i < span; i++) {
|
||
// Skip ~5% of days (forgot to log)
|
||
if (Math.random() < 0.05) continue;
|
||
|
||
const date = daysAgo(days - i);
|
||
const rawScore = Math.round(Math.min(5, Math.max(1, moodTrend[i])));
|
||
const mood = moodOptions[5 - rawScore]; // invert: score 5 = SUPER_GUT
|
||
const tags = tagOptions[5 - rawScore];
|
||
const loggedAt = new Date(date);
|
||
loggedAt.setHours(21, Math.floor(Math.random() * 59), 0);
|
||
|
||
await client.query(
|
||
`INSERT INTO mood_entries (id, user_id, date, mood, score, tags, source, mood_logged_at, synced_at, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, $5, $6, 'WEB', $7, $7, $7, $7)`,
|
||
[
|
||
cuid(),
|
||
userId,
|
||
formatDate(date),
|
||
mood,
|
||
rawScore,
|
||
JSON.stringify(tags),
|
||
loggedAt,
|
||
],
|
||
);
|
||
}
|
||
|
||
// ── Vorsorge reminders (preventive care) ──
|
||
// User-created (origin VORSORGE) checklist reminders with no auto-resolve
|
||
// measurement target. Both anchored to fire comfortably in the future so
|
||
// the Vorsorge surface shows sensible upcoming items, not overdue ones.
|
||
console.log("Creating Vorsorge reminders...");
|
||
const dentalNextDue = daysAgo(-45); // ~6 weeks out
|
||
await client.query(
|
||
`INSERT INTO measurement_reminders (id, user_id, label, interval_days, anchor_date, origin, notify_hour, location, next_due_at, created_at, updated_at)
|
||
VALUES ($1, $2, 'Dental check-up', 365, $3, 'VORSORGE', 9, 'Dentist', $4, NOW(), NOW())`,
|
||
[cuid(), userId, daysAgo(320), dentalNextDue],
|
||
);
|
||
const physicalNextDue = daysAgo(-90); // ~3 months out
|
||
await client.query(
|
||
`INSERT INTO measurement_reminders (id, user_id, label, interval_days, anchor_date, origin, notify_hour, location, next_due_at, created_at, updated_at)
|
||
VALUES ($1, $2, 'Annual physical', 365, $3, 'VORSORGE', 9, 'Family doctor', $4, NOW(), NOW())`,
|
||
[cuid(), userId, daysAgo(275), physicalNextDue],
|
||
);
|
||
|
||
// ── Lab panel (biomarkers in healthy ranges) ──
|
||
// One grouped panel taken ~10 days ago, every analyte comfortably inside
|
||
// its reference range, so the labs surface reads clean.
|
||
console.log("Creating lab panel...");
|
||
const panelTakenAt = daysAgo(10);
|
||
const labResults: Array<{
|
||
analyte: string;
|
||
value: number;
|
||
unit: string;
|
||
low: number;
|
||
high: number;
|
||
}> = [
|
||
{
|
||
analyte: "Total Cholesterol",
|
||
value: 178,
|
||
unit: "mg/dL",
|
||
low: 0,
|
||
high: 200,
|
||
},
|
||
{ analyte: "LDL", value: 98, unit: "mg/dL", low: 0, high: 130 },
|
||
{ analyte: "HDL", value: 58, unit: "mg/dL", low: 40, high: 100 },
|
||
{ analyte: "Triglycerides", value: 92, unit: "mg/dL", low: 0, high: 150 },
|
||
{ analyte: "HbA1c", value: 5.2, unit: "%", low: 4.0, high: 5.6 },
|
||
{
|
||
analyte: "Fasting Glucose",
|
||
value: 89,
|
||
unit: "mg/dL",
|
||
low: 70,
|
||
high: 99,
|
||
},
|
||
{ analyte: "Vitamin D", value: 42, unit: "ng/mL", low: 30, high: 100 },
|
||
{ analyte: "TSH", value: 1.8, unit: "mIU/L", low: 0.4, high: 4.0 },
|
||
{ analyte: "Ferritin", value: 120, unit: "ng/mL", low: 30, high: 400 },
|
||
];
|
||
for (const lab of labResults) {
|
||
await client.query(
|
||
`INSERT INTO lab_results (id, user_id, panel, analyte, value, unit, reference_low, reference_high, taken_at, source, created_at, updated_at)
|
||
VALUES ($1, $2, 'Annual blood panel', $3, $4, $5, $6, $7, $8, 'MANUAL', NOW(), NOW())`,
|
||
[
|
||
cuid(),
|
||
userId,
|
||
lab.analyte,
|
||
lab.value,
|
||
lab.unit,
|
||
lab.low,
|
||
lab.high,
|
||
panelTakenAt,
|
||
],
|
||
);
|
||
}
|
||
|
||
// ── Resolved illness episode ──────────────
|
||
// A short cold ~7 weeks ago, fully recovered, so the illness journal shows
|
||
// a clean "resolved" entry rather than an active sickness.
|
||
console.log("Creating resolved illness episode...");
|
||
await client.query(
|
||
`INSERT INTO illness_episodes (id, user_id, label, type, lifecycle, onset_at, resolved_at, created_at, updated_at)
|
||
VALUES ($1, $2, 'Common cold', 'INFECTION', 'ACUTE', $3, $4, NOW(), NOW())`,
|
||
[cuid(), userId, daysAgo(49), daysAgo(43)],
|
||
);
|
||
|
||
// ── Achievements ─────────────────────────
|
||
console.log("Creating achievements...");
|
||
|
||
// Every id below is a real key from the achievement registry
|
||
// (src/lib/gamification/achievements.ts) so the badge resolves to a real
|
||
// title/icon rather than an "unknown" placeholder. Only milestones the
|
||
// seeded history genuinely supports are unlocked (first-entry badges,
|
||
// logging streaks, in-range BP/pulse/BMI streaks, compliance + miss-free
|
||
// streaks, the self-context + 7-night sleep badges). The dynamic streak
|
||
// families use the `<prefix>-<target>` naming the generator emits.
|
||
const achievements = [
|
||
{ id: "weight-first", daysAgo: 89 },
|
||
{ id: "bp-first", daysAgo: 89 },
|
||
{ id: "pulse-first", daysAgo: 89 },
|
||
{ id: "mood-first", daysAgo: 89 },
|
||
{ id: "self-context-complete", daysAgo: 88 },
|
||
{ id: "entry-streak-7", daysAgo: 82 },
|
||
{ id: "mood-streak-7", daysAgo: 82 },
|
||
{ id: "sleep-log-7", daysAgo: 70 },
|
||
{ id: "mood-up-7", daysAgo: 68 },
|
||
{ id: "weight-50", daysAgo: 60 },
|
||
{ id: "bp-50", daysAgo: 55 },
|
||
{ id: "entry-streak-30", daysAgo: 50 },
|
||
{ id: "mood-streak-30", daysAgo: 50 },
|
||
{ id: "miss-free-7", daysAgo: 48 },
|
||
{ id: "consistent-month", daysAgo: 45 },
|
||
{ id: "bp-green-7", daysAgo: 42 },
|
||
{ id: "pulse-green-7", daysAgo: 40 },
|
||
{ id: "bmi-green-7", daysAgo: 38 },
|
||
{ id: "compliance-80-30", daysAgo: 30 },
|
||
{ id: "miss-free-30", daysAgo: 30 },
|
||
{ id: "on-time-perfect-7", daysAgo: 28 },
|
||
{ id: "measurement-weeks-4", daysAgo: 25 },
|
||
{ id: "bp-green-30", daysAgo: 12 },
|
||
{ id: "pulse-green-30", daysAgo: 10 },
|
||
{ id: "miss-free-90", daysAgo: 2 },
|
||
];
|
||
|
||
for (const ach of achievements) {
|
||
const unlocked = daysAgo(ach.daysAgo);
|
||
await client.query(
|
||
`INSERT INTO user_achievements (id, user_id, achievement_id, unlocked_at, created_at)
|
||
VALUES ($1, $2, $3, $4, $4)`,
|
||
[cuid(), userId, ach.id, unlocked],
|
||
);
|
||
}
|
||
|
||
// ── Coach sample conversation ─────────────
|
||
// A short, encrypted two-turn exchange so the Coach surface shows real
|
||
// content. Messages use the same AES-256-GCM Bytes codec the app writes
|
||
// with (encryptToBytes → encrypt() under ENCRYPTION_KEYS), so they decrypt
|
||
// exactly like a live conversation — no faked / plaintext rows.
|
||
console.log("Creating Coach sample conversation...");
|
||
const convoId = cuid();
|
||
const convoStart = daysAgo(3);
|
||
await client.query(
|
||
`INSERT INTO coach_conversations (id, user_id, title, created_at, updated_at)
|
||
VALUES ($1, $2, $3, $4, $4)`,
|
||
[convoId, userId, "Blood pressure trend", convoStart],
|
||
);
|
||
|
||
const coachTurns: Array<{
|
||
role: string;
|
||
content: string;
|
||
metricSourceJson?: string;
|
||
}> = [
|
||
{
|
||
role: "user",
|
||
content: "How has my blood pressure been trending lately?",
|
||
},
|
||
{
|
||
role: "assistant",
|
||
content:
|
||
"Your blood pressure has been settling nicely. Over the last few " +
|
||
"weeks the morning readings have drifted into the optimal band, and " +
|
||
"your resting heart rate is sitting in a comfortable range too. " +
|
||
"Keeping up the steady sleep and daily movement looks like it is " +
|
||
"paying off — worth staying consistent with the morning measurements " +
|
||
"so the trend stays easy to read.",
|
||
metricSourceJson: JSON.stringify({
|
||
window: "last30days",
|
||
metrics: ["bloodPressure", "restingHeartRate", "sleep"],
|
||
}),
|
||
},
|
||
];
|
||
for (let t = 0; t < coachTurns.length; t++) {
|
||
const turn = coachTurns[t];
|
||
const ts = new Date(convoStart.getTime() + t * 60_000);
|
||
const encrypted = Buffer.from(encryptToBytes(turn.content));
|
||
await client.query(
|
||
`INSERT INTO coach_messages (id, conversation_id, role, encrypted_content, metric_source_json, provider_type, prompt_version, created_at)
|
||
VALUES ($1, $2, $3, $4, $5, $6, $7, $8)`,
|
||
[
|
||
cuid(),
|
||
convoId,
|
||
turn.role,
|
||
encrypted,
|
||
turn.metricSourceJson ?? null,
|
||
turn.role === "assistant" ? aiProvider : null,
|
||
turn.role === "assistant" ? "demo" : null,
|
||
ts,
|
||
],
|
||
);
|
||
}
|
||
|
||
// ── App Settings ─────────────────────────
|
||
console.log("Creating app settings...");
|
||
await client.query(
|
||
`INSERT INTO app_settings (id, registration_enabled, default_locale)
|
||
VALUES ('singleton', false, 'en')
|
||
ON CONFLICT (id) DO UPDATE SET registration_enabled = false, default_locale = 'en'`,
|
||
);
|
||
|
||
// ── Audit Log (some login entries) ──────
|
||
console.log("Creating audit log entries...");
|
||
for (let i = 0; i < 15; i++) {
|
||
const loginDate = daysAgo(Math.floor(Math.random() * 30));
|
||
await client.query(
|
||
`INSERT INTO audit_logs (id, user_id, action, details, ip_address, created_at)
|
||
VALUES ($1, $2, 'auth.login', '{"method":"password"}', '203.0.113.42', $3)`,
|
||
[cuid(), userId, loginDate],
|
||
);
|
||
}
|
||
|
||
await client.query("COMMIT");
|
||
console.log("\nDemo data seeded successfully!");
|
||
console.log(` User: demo / demo123demo123`);
|
||
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: ${labResults.length} biomarkers`);
|
||
console.log(` Illness: 1 resolved episode`);
|
||
console.log(` Coach: 1 conversation (${coachTurns.length} messages)`);
|
||
console.log(` Achievements: ${achievements.length}`);
|
||
} catch (err) {
|
||
await client.query("ROLLBACK");
|
||
console.error("Seed failed:", err);
|
||
throw err;
|
||
} finally {
|
||
client.release();
|
||
await pool.end();
|
||
}
|
||
}
|
||
|
||
seed();
|