import assert from 'node:assert/strict';
import test from 'node:test';
import { computeHealthBaselines, deviationFromBaseline } from './health-baseline-engine.mjs';
import { evaluateHealthEvents } from './health-event-engine.mjs';
import { createHealthObservationService } from './health-observation-service.mjs';
import { createInMemoryHealthObservationStore } from './health-observation-store.mjs';
import { buildHealthAssessSummary } from './health-assess-summary.mjs';
import { buildHealthTimeline } from './health-timeline.mjs';

const now = Date.UTC(2026, 8, 2, 8, 0, 0);

function seedBp(store, userId, dayOffset, systolic, diastolic, context = 'morning') {
  const observedAt = now - dayOffset * 24 * 60 * 60 * 1000;
  return store.insert(userId, {
    confirmed: true,
    observedAt,
    metricType: 'bp_systolic',
    valueNum: systolic,
    unit: 'mmHg',
    context,
    source: 'manual',
    qualityFlag: 'ok',
  }).then(() => store.insert(userId, {
    confirmed: true,
    observedAt,
    metricType: 'bp_diastolic',
    valueNum: diastolic,
    unit: 'mmHg',
    context,
    source: 'manual',
    qualityFlag: 'ok',
  }));
}

test('health observation service commits blood pressure pair', async () => {
  const service = createHealthObservationService({ store: createInMemoryHealthObservationStore() });
  const result = await service.commit('u1', {
    confirmed: true,
    metricSet: 'blood_pressure',
    systolic: 128,
    diastolic: 76,
    context: 'morning',
  });
  assert.equal(result.observations.length, 2);
});

test('timeline groups observations by day', async () => {
  const store = createInMemoryHealthObservationStore();
  await seedBp(store, 'u1', 0, 130, 80);
  const service = createHealthObservationService({ store });
  const timeline = await service.listTimeline('u1');
  assert.equal(timeline.length, 1);
  assert.ok(timeline[0].metrics.bp_systolic);
});

test('baseline engine reaches stable after 30 samples', () => {
  const observations = Array.from({ length: 30 }, (_, i) => ({
    metricType: 'bp_systolic',
    valueNum: 120 + (i % 3),
    observedAt: now - i * 24 * 60 * 60 * 1000,
    context: 'morning',
    qualityFlag: 'ok',
    deletedAt: null,
  }));
  const baselines = computeHealthBaselines(observations, { now });
  const target = baselines.find((b) => b.metricType === 'bp_systolic' && b.windowDays === 30 && b.context === 'morning');
  assert.equal(target.maturity, 'stable');
  assert.ok(target.mean > 0);
});

test('event engine detects absolute threshold breach', () => {
  const observations = [{
    metricType: 'bp_systolic',
    valueNum: 185,
    observedAt: now,
    context: 'morning',
    qualityFlag: 'ok',
    deletedAt: null,
  }];
  const events = evaluateHealthEvents(observations, [], { now });
  assert.ok(events.some((e) => e.ruleId === 'bp_sys_high'));
});

test('assess summary mentions open events when drift exists', () => {
  const observations = Array.from({ length: 30 }, (_, i) => ({
    metricType: 'bp_systolic',
    valueNum: i < 5 ? 150 : 120,
    observedAt: now - i * 24 * 60 * 60 * 1000,
    context: 'morning',
    qualityFlag: 'ok',
    deletedAt: null,
  }));
  const summary = buildHealthAssessSummary(observations, { now });
  assert.match(summary, /基线|记录|诊断/);
});

test('deviationFromBaseline computes percent change', () => {
  assert.equal(deviationFromBaseline(132, 120), 10);
});