Files
HealthTracking/__tests__/trainingMetrics.test.ts
FightRate Agent 41b49ba442 feat(chart): TrainingChart dinâmico com viewport, zoom, pan e pin sobrevivente
Substitui o gráfico estático (últimos 120 samples + selectedIndex por
posição) por um modelo de viewport temporal que resolve os problemas de
qualidade em tempo real e abre zoom/pan nos 3 call sites (Captura,
Janela salva e Faixa customizada).

Adições:
- src/training/chartViewport.ts: funções puras (granularityForSpan,
  formatTickLabel, buildChartViewport, applyZoomIn/Out, applyPan,
  isLiveTailSnap, pickSampleAtTimestamp, xFromTimestampMs,
  timestampMsFromX, tipos Viewport/ChartPin/Granularity).
- __tests__/chartViewport.test.ts: 34 testes cobrindo limites,
  ordenação defensiva, snap zone, hit-test e granularidade adaptativa.
- src/components/TrainingChart.tsx: reescrito para consumir viewport.
  - Pin por timestampMs (sobrevive a novas amostras em tempo real).
  - Pinch (Gesture.Pinch) + Pan (Gesture.Pan) + Tap (Gesture.Tap) via
    react-native-gesture-handler v3.
  - Auto-follow: endMs estende com novos samples enquanto liveTail.
  - Snap zone (5% da duração visível) reanexa o live ao final do pan.
  - Eixo X com ticks adaptativos: minute/halfMinute/quarterMinute/
    tenSeconds/second/halfSecond, conforme duração visível.
  - Botões +/- (zoom 2x) e ↺ (reset) na lateral.
  - Indicador 'dados mais novos disponíveis' quando !liveTail.
  - Estado vazio preservado (samples < 2).
- jest.setup.js + jest.config.js: mocks de gesture-handler e
  reanimated para o App.test rodar em Jest (módulos nativos não
  disponíveis no host).

Modificações de setup (dependências aprovadas):
- package.json + package-lock.json: adiciona react-native-gesture-handler
  ^3.0.1, react-native-reanimated ^4.4.1, react-native-worklets ^0.9.2.
- index.js: import 'react-native-gesture-handler' no topo.
- App.tsx: <GestureHandlerRootView> envolvendo SafeAreaProvider.
- babel.config.js: plugin react-native-worklets/plugin.

Verificações:
- npm run typecheck: 0 errors.
- npm run lint: 0 errors, 0 warnings.
- npm test -- --runInBand: 55/55 passam.
- Build Android: não verificado nesta sessão (timeout); o usuário
  valida localmente antes de distribuir.

Não comitado neste commit (mudanças pré-existentes não relacionadas):
- android/* (migração fightratern → healthtracking/fightrate, mipmaps)
- README.md
- app.json
2026-06-15 00:10:55 -03:00

192 lines
5.0 KiB
TypeScript

import {
buildInsightDashboard,
calculateBpmVariability,
calculateHeartRateDrift,
calculateLiveMetrics,
calculatePostSessionRecovery,
filterReliableHeartRateSamples,
isReliableHeartRateSample,
summarizeRrAvailability,
} from '../src/training/trainingMetrics';
import {
type CompletedTrainingSession,
type HeartRateSample,
} from '../src/training/trainingTypes';
function sample(
timestamp: number,
bpm: number,
rrIntervalsMs: readonly number[] = [],
): HeartRateSample {
return {
timestamp,
bpm,
contactDetected: null,
rrIntervalsMs,
deviceName: 'Magene H003',
};
}
function session(
startedAt: number,
endedAt: number,
peakBpm = 180,
): CompletedTrainingSession {
return {
id: `session-${startedAt}-${endedAt}`,
startedAt,
endedAt,
durationMs: endedAt - startedAt,
sampleCount: 12,
averageBpm: 150,
peakBpm,
minBpm: 112,
deviceName: 'Magene H003',
};
}
describe('sample reliability guards', () => {
it('rejects zero BPM and explicit no-contact packets', () => {
expect(isReliableHeartRateSample(sample(1_000, 0))).toBe(false);
expect(
isReliableHeartRateSample({
...sample(2_000, 128),
contactDetected: false,
}),
).toBe(false);
expect(isReliableHeartRateSample(sample(3_000, 128))).toBe(true);
});
it('removes invalid readings before metrics are calculated', () => {
const reliable = filterReliableHeartRateSamples([
sample(1_000, 0),
{ ...sample(2_000, 125), contactDetected: false },
sample(3_000, 130),
]);
expect(reliable.map(item => item.bpm)).toEqual([130]);
});
it('marks live BPM as unavailable when the latest valid reading is stale', () => {
const metrics = calculateLiveMetrics([sample(1_000, 140)], {
now: 20_000,
staleAfterMs: 12_000,
});
expect(metrics.currentBpm).toBeNull();
expect(metrics.averageBpm).toBe(140);
});
});
describe('calculateBpmVariability', () => {
it('summarizes BPM range, standard deviation and coefficient of variation', () => {
const variability = calculateBpmVariability([
sample(1_000, 100),
sample(2_000, 120),
sample(3_000, 140),
]);
expect(variability).toMatchObject({
averageBpm: 120,
minBpm: 100,
maxBpm: 140,
rangeBpm: 40,
standardDeviationBpm: 16.3,
coefficientOfVariation: 0.136,
classification: 'oscilante',
});
});
});
describe('calculateHeartRateDrift', () => {
it('compares the first and last fifth of a long enough sample window', () => {
const start = Date.UTC(2026, 5, 10, 9, 0);
const samples = Array.from({ length: 12 }, (_, index) =>
sample(start + index * 60_000, index < 6 ? 130 + index : 150 + index),
);
expect(calculateHeartRateDrift(samples)).toMatchObject({
status: 'ok',
driftBpm: 30,
driftPercent: 23,
initialAverageBpm: 131,
finalAverageBpm: 161,
});
});
it('does not over-interpret short windows as cardiovascular drift', () => {
expect(
calculateHeartRateDrift([sample(1_000, 130), sample(60_000, 140)]),
).toMatchObject({ status: 'short-window' });
});
});
describe('calculatePostSessionRecovery', () => {
it('calculates one and two minute recovery from post-window continuous samples', () => {
const startedAt = Date.UTC(2026, 5, 10, 10, 0);
const endedAt = startedAt + 10 * 60_000;
expect(
calculatePostSessionRecovery(session(startedAt, endedAt, 182), [
sample(endedAt + 5_000, 176),
sample(endedAt + 61_000, 160),
sample(endedAt + 119_000, 148),
]),
).toMatchObject({
status: 'ok',
oneMinuteDropBpm: 22,
twoMinuteDropBpm: 34,
});
});
});
describe('summarizeRrAvailability', () => {
it('reports RR availability without calculating fake HRV', () => {
expect(
summarizeRrAvailability([
sample(1_000, 120, [1000, 980]),
sample(2_000, 121, [990]),
]),
).toMatchObject({ status: 'ok', intervalCount: 3, sampleCount: 2 });
expect(summarizeRrAvailability([sample(1_000, 120)])).toMatchObject({
status: 'needs-rr',
intervalCount: 0,
});
});
});
describe('buildInsightDashboard', () => {
it('builds compact scientific rows using persisted samples before live fallback', () => {
const start = Date.UTC(2026, 5, 10, 9, 0);
const persisted = [
sample(start, 120),
sample(start + 60_000, 140),
sample(start + 11 * 60_000, 160),
];
const dashboard = buildInsightDashboard({
liveSamples: [sample(start + 12 * 60_000, 170)],
continuousSamples: persisted,
sessions: [],
});
expect(dashboard.sampleCount).toBe(3);
expect(dashboard.rows.map(row => row.key)).toEqual([
'current-bpm',
'average-bpm',
'bpm-range',
'bpm-variability',
'zones',
'load',
'drift',
'recovery',
'profile',
'hrv',
]);
expect(dashboard.rows.find(row => row.key === 'hrv')).toMatchObject({
status: 'needs-rr',
});
});
});