369 lines
16 KiB
JavaScript
369 lines
16 KiB
JavaScript
function findPublicTrainingShare(state, shareToken) {
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if (!shareToken || shareToken.length < 24) return null;
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const shares = Array.isArray(state?.training?.shares) ? state.training.shares : [];
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return shares.find((share) => share?.enabled !== false && String(share?.token || "") === shareToken) || null;
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}
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function hasPublicTrainingShare(state, shareToken) {
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return Boolean(findPublicTrainingShare(state, shareToken));
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}
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function getPublicDriverProfileId(driver) {
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const explicitId = String(driver?.profileId || "").trim();
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if (explicitId) return explicitId;
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const name = String(driver?.name || "").trim().toLocaleLowerCase().replace(/\s+/g, " ");
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return name ? `legacy:${name}` : `driver:${String(driver?.id || "unknown")}`;
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}
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function getPublicDriverProfile(drivers, reference) {
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const normalizedReference = String(reference || "");
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const anchor = drivers.find((driver) => (
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String(driver?.id || "") === normalizedReference
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|| getPublicDriverProfileId(driver) === normalizedReference
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));
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if (!anchor) return null;
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const profileId = getPublicDriverProfileId(anchor);
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return {
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id: profileId,
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drivers: drivers.filter((driver) => getPublicDriverProfileId(driver) === profileId),
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};
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}
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function buildPublicTrainingProfile(state, shareToken, updatedAt, sinceTimestamp = 0, windowMs = 0) {
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const share = findPublicTrainingShare(state, shareToken);
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if (!share) return null;
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const profileReference = String(share.driverId || "");
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const drivers = Array.isArray(state?.drivers) ? state.drivers : [];
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const driverProfile = getPublicDriverProfile(drivers, profileReference);
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if (!driverProfile?.drivers.length) return null;
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const driver = driverProfile.drivers[0];
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const classes = Array.isArray(state?.classes) ? state.classes : [];
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const ledger = Array.isArray(state?.training?.ledger) ? state.training.ledger : [];
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const profileReferences = new Set([driverProfile.id, ...driverProfile.drivers.map((item) => String(item.id || ""))]);
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const profileClassIds = [...new Set(driverProfile.drivers.map((item) => String(item.classId || "")).filter(Boolean))];
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const classProfiles = profileClassIds.map((classId) => {
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const classDrivers = driverProfile.drivers.filter((item) => String(item.classId || "") === classId);
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const classDriverIds = new Set(classDrivers.map((item) => String(item.id || "")));
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const classLaps = ledger
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.filter((entry) => classDriverIds.has(String(entry?.driverId || "")) && Number(entry?.lapMs || 0) > 500)
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.map((entry) => ({
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timestamp: Math.max(0, Number(entry.timestamp || 0) || 0),
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lapMs: Math.max(0, Math.round(Number(entry.lapMs || 0) || 0)),
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sessionId: String(entry.sessionId || ""),
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}))
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.sort((left, right) => left.timestamp - right.timestamp);
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const stats = buildPublicTrainingStats(classLaps);
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const channelStats = buildPublicTimingChannelStats(state, classDriverIds);
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const classGoals = (Array.isArray(state?.training?.goals) ? state.training.goals : [])
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.filter((goal) => profileReferences.has(String(goal?.driverId || "")) && (!goal?.classId || String(goal.classId) === classId))
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.map((goal) => buildPublicTrainingGoal(goal, stats.totalLaps));
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const brands = [...new Set(classDrivers.map((item) => String(item.brand || "").trim()).filter(Boolean))];
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return {
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classId,
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className: String(classes.find((item) => String(item?.id || "") === classId)?.name || "").trim(),
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brand: brands.join(" / "),
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stats,
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channelStats,
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nextMilestone: getPublicTrainingMilestone(stats.totalLaps),
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recommendation: getPublicTrainingRecommendation(stats),
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goals: classGoals,
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currentSession: sinceTimestamp
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? buildPublicTrainingWindowStats(classLaps.filter((lap) => lap.timestamp >= sinceTimestamp && (!windowMs || lap.timestamp <= sinceTimestamp + windowMs)))
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: null,
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};
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});
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const activeClass = classProfiles[0] || {
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classId: "", className: "", brand: "",
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stats: buildPublicTrainingStats([]), nextMilestone: getPublicTrainingMilestone(0),
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channelStats: buildPublicTimingChannelStats({}, new Set()),
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recommendation: getPublicTrainingRecommendation({ totalLaps: 0 }), goals: [], currentSession: null,
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};
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const settings = state?.settings && typeof state.settings === "object" ? state.settings : {};
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return {
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updatedAt: String(updatedAt || ""),
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profile: {
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name: String(driver.name || "Driver").trim(),
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brand: activeClass.brand,
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className: activeClass.className,
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},
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branding: {
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clubName: String(settings.clubName || "JMK RB").trim(),
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clubTagline: String(settings.clubTagline || "Live Timing & Race Control").trim(),
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logoDataUrl: normalizePublicLogoDataUrl(settings.logoDataUrl),
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},
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classProfiles,
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stats: activeClass.stats,
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channelStats: activeClass.channelStats,
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nextMilestone: activeClass.nextMilestone,
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recommendation: activeClass.recommendation,
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goals: activeClass.goals,
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currentSession: activeClass.currentSession,
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};
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}
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function buildPublicTimingChannelStats(state, classDriverIds) {
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const splitTimes = [];
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const pitTimes = [];
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Object.values(state?.resultsBySession || {}).forEach((result) => {
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(Array.isArray(result?.splits) ? result.splits : []).forEach((split) => {
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const splitMs = Math.round(Number(split?.splitMs || 0) || 0);
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if (classDriverIds.has(String(split?.driverId || "")) && splitMs > 0) splitTimes.push(splitMs);
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});
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(Array.isArray(result?.pitStops) ? result.pitStops : []).forEach((stop) => {
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const pitStopMs = Math.round(Number(stop?.pitStopMs || 0) || 0);
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if (classDriverIds.has(String(stop?.driverId || "")) && pitStopMs > 0) pitTimes.push(pitStopMs);
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});
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});
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const average = (values) => values.length
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? Math.round(values.reduce((sum, value) => sum + value, 0) / values.length)
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: 0;
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return {
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splitCount: splitTimes.length,
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bestSplitMs: splitTimes.length ? Math.min(...splitTimes) : 0,
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averageSplitMs: average(splitTimes),
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pitStopCount: pitTimes.length,
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totalPitMs: pitTimes.reduce((sum, value) => sum + value, 0),
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bestPitStopMs: pitTimes.length ? Math.min(...pitTimes) : 0,
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averagePitStopMs: average(pitTimes),
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};
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}
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function normalizePublicTrainingSince(value, startedAgoValue) {
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const now = Date.now();
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if (startedAgoValue !== undefined) {
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const startedAgoMs = Math.round(Number(startedAgoValue));
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if (Number.isFinite(startedAgoMs) && startedAgoMs >= 0 && startedAgoMs <= 24 * 60 * 60 * 1000) {
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return now - startedAgoMs;
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}
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}
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const timestamp = Math.round(Number(value || 0) || 0);
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return timestamp >= now - 24 * 60 * 60 * 1000 && timestamp <= now + 60 * 1000 ? timestamp : 0;
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}
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function normalizePublicTrainingWindow(value) {
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const windowMs = Math.round(Number(value || 0) || 0);
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return Math.min(24 * 60 * 60 * 1000, Math.max(0, windowMs));
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}
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function buildPublicTrainingWindowStats(laps) {
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const lapTimes = laps.map((lap) => Number(lap.lapMs || 0)).filter((lapMs) => lapMs > 500);
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return {
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laps: lapTimes.length,
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bestLapMs: lapTimes.length ? Math.min(...lapTimes) : 0,
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averageLapMs: lapTimes.length ? Math.round(lapTimes.reduce((sum, lapMs) => sum + lapMs, 0) / lapTimes.length) : 0,
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};
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}
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function buildPublicTrainingGoal(goal, currentLaps) {
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const targetLaps = Math.max(1, Math.round(Number(goal?.targetLaps || 1) || 1));
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return {
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title: String(goal?.title || "").trim(),
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reward: String(goal?.reward || "").trim(),
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targetLaps,
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currentLaps,
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remainingLaps: Math.max(0, targetLaps - currentLaps),
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percent: Math.min(100, Math.max(0, (currentLaps / targetLaps) * 100)),
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reached: currentLaps >= targetLaps,
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claimed: Boolean(goal?.claimedAt),
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};
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}
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function buildPublicTrainingStats(laps) {
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const lapTimes = laps.map((lap) => lap.lapMs);
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const recent = lapTimes.slice(-30);
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const previous = lapTimes.slice(Math.max(0, lapTimes.length - 60), Math.max(0, lapTimes.length - 30));
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const average = (values) => values.length ? Math.round(values.reduce((sum, value) => sum + value, 0) / values.length) : 0;
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const percentile = (values, ratio) => {
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const sorted = [...values].sort((left, right) => left - right);
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return sorted.length
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? sorted[Math.min(sorted.length - 1, Math.max(0, Math.round((sorted.length - 1) * ratio)))]
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: 0;
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};
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const spread = (values) => {
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const mean = average(values);
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return mean ? ((percentile(values, 0.9) - percentile(values, 0.1)) / mean) * 100 : 0;
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};
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const recentAverageMs = average(recent);
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const previousRecentAverageMs = average(previous);
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const consistencySpreadPct = spread(recent);
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const previousConsistencySpreadPct = spread(previous);
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const trendSampleReady = recent.length >= 10 && previous.length >= 10;
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const daily = new Map();
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laps.forEach((lap) => {
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const date = new Date(lap.timestamp);
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const dateKey = Number.isNaN(date.getTime()) ? "unknown" : `${date.getFullYear()}-${String(date.getMonth() + 1).padStart(2, "0")}-${String(date.getDate()).padStart(2, "0")}`;
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const day = daily.get(dateKey) || { dateKey, laps: 0, totalMs: 0, bestLapMs: 0, latestTimestamp: 0 };
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day.laps += 1;
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day.totalMs += lap.lapMs;
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day.bestLapMs = day.bestLapMs ? Math.min(day.bestLapMs, lap.lapMs) : lap.lapMs;
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day.latestTimestamp = Math.max(day.latestTimestamp, lap.timestamp);
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daily.set(dateKey, day);
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});
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const dailySummaries = [...daily.values()]
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.map((day) => ({
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dateKey: day.dateKey,
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laps: day.laps,
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bestLapMs: day.bestLapMs,
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latestTimestamp: day.latestTimestamp,
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averageLapMs: day.laps ? Math.round(day.totalMs / day.laps) : 0,
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}))
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.sort((left, right) => right.latestTimestamp - left.latestTimestamp)
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.slice(0, 30);
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const bestLapMs = lapTimes.length ? Math.min(...lapTimes) : 0;
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const controlTargetMs = bestLapMs ? Math.round(bestLapMs * 1.08) : 0;
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let controlledLapStreak = 0;
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for (let index = lapTimes.length - 1; index >= 0; index -= 1) {
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if (!controlTargetMs || lapTimes[index] > controlTargetMs) break;
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controlledLapStreak += 1;
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}
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const stints = splitPublicTrainingStints(laps);
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const bestThreeConsecutive = getPublicBestConsecutiveLaps(stints, 3);
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const bestFiveMinuteResult = getPublicBestDurationResult(stints, 5 * 60 * 1000);
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const stats = {
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totalLaps: laps.length,
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totalSessions: new Set(laps.map((lap) => lap.sessionId).filter(Boolean)).size,
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trainingDays: daily.size,
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bestLapMs,
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averageLapMs: average(lapTimes),
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recentAverageMs,
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previousRecentAverageMs,
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averageTrendMs: trendSampleReady ? previousRecentAverageMs - recentAverageMs : 0,
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recentLapCount: recent.length,
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consistencySpreadPct,
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previousConsistencySpreadPct,
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consistencyTrendPct: trendSampleReady ? previousConsistencySpreadPct - consistencySpreadPct : 0,
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trendSampleReady,
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controlledLapStreak,
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dailySummaries,
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stintCount: stints.length,
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bestThreeConsecutive,
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bestFiveMinuteResult,
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};
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return { ...stats, achievements: buildPublicTrainingAchievements(stats) };
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}
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function buildPublicTrainingAchievements(stats) {
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const definitions = [
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["first_session", Number(stats.totalSessions || 0), 1],
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["laps_100", Number(stats.totalLaps || 0), 100],
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["laps_500", Number(stats.totalLaps || 0), 500],
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["laps_1000", Number(stats.totalLaps || 0), 1000],
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["laps_1500", Number(stats.totalLaps || 0), 1500],
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["days_3", Number(stats.trainingDays || 0), 3],
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["days_10", Number(stats.trainingDays || 0), 10],
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["controlled_5", Number(stats.controlledLapStreak || 0), 5],
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["five_minute", stats.bestFiveMinuteResult ? 1 : 0, 1],
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];
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return definitions.map(([id, current, target]) => ({
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id,
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percent: Math.min(100, Math.max(0, (current / target) * 100)),
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unlocked: current >= target,
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}));
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}
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function splitPublicTrainingStints(laps, breakThresholdMs = 2 * 60 * 1000) {
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const stints = [];
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(Array.isArray(laps) ? laps : []).forEach((lap) => {
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const previousStint = stints.at(-1);
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const previousLap = previousStint?.laps.at(-1);
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const lapStartedAt = Math.max(0, Number(lap.timestamp || 0) - Number(lap.lapMs || 0));
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const breakMs = previousLap ? Math.max(0, Number(lap.timestamp || 0) - Number(previousLap.timestamp || 0)) : 0;
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const startsNewStint = !previousStint
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|| String(previousLap.sessionId || "") !== String(lap.sessionId || "")
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|| breakMs > breakThresholdMs;
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const normalizedLap = {
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...lap,
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excludedFromPace: Boolean(previousLap && breakMs > breakThresholdMs && Number(lap.lapMs || 0) > breakThresholdMs),
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};
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if (startsNewStint) {
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stints.push({ sessionId: String(lap.sessionId || ""), laps: [normalizedLap] });
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} else {
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previousStint.laps.push(normalizedLap);
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}
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});
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return stints;
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}
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function getPublicBestConsecutiveLaps(stints, lapCount) {
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let best = null;
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(Array.isArray(stints) ? stints : []).forEach((stint) => {
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const paceLaps = stint.laps.filter((lap) => !lap.excludedFromPace);
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for (let index = 0; index <= paceLaps.length - lapCount; index += 1) {
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const laps = paceLaps.slice(index, index + lapCount);
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const totalMs = laps.reduce((sum, lap) => sum + Number(lap.lapMs || 0), 0);
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const candidate = { lapCount, totalMs, averageLapMs: Math.round(totalMs / lapCount) };
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if (!best || candidate.totalMs < best.totalMs) best = candidate;
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}
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});
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return best;
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}
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function getPublicBestDurationResult(stints, targetMs) {
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let best = null;
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(Array.isArray(stints) ? stints : []).forEach((stint) => {
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const paceLaps = stint.laps.filter((lap) => !lap.excludedFromPace);
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for (let startIndex = 0; startIndex < paceLaps.length; startIndex += 1) {
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const firstLap = paceLaps[startIndex];
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const startedAt = Math.max(0, Number(firstLap.timestamp || 0) - Number(firstLap.lapMs || 0));
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const cutoffAt = startedAt + targetMs;
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const counted = [];
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for (let index = startIndex; index < paceLaps.length; index += 1) {
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const lap = paceLaps[index];
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const lapStartedAt = Math.max(0, Number(lap.timestamp || 0) - Number(lap.lapMs || 0));
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if (lapStartedAt >= cutoffAt) break;
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counted.push(lap);
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}
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const lastLap = counted.at(-1);
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if (!lastLap || Number(lastLap.timestamp || 0) < cutoffAt) continue;
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const totalMs = Number(lastLap.timestamp || 0) - startedAt;
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const lapCount = counted.length;
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const candidate = {
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targetMs,
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lapCount,
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totalMs,
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averageLapMs: Math.round(counted.reduce((sum, lap) => sum + Number(lap.lapMs || 0), 0) / lapCount),
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};
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if (!best || candidate.lapCount > best.lapCount || (candidate.lapCount === best.lapCount && candidate.totalMs < best.totalMs)) best = candidate;
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}
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});
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return best;
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}
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function normalizePublicLogoDataUrl(value) {
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const logo = String(value || "").trim();
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return logo.length <= 2_000_000 && /^data:image\/(?:png|jpeg|webp);base64,/i.test(logo) ? logo : "";
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}
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function getPublicTrainingMilestone(currentLaps) {
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const presets = [100, 250, 500, 1000, 1500, 2500, 5000];
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const targetLaps = presets.find((target) => target > currentLaps) || (Math.floor(currentLaps / 1000) + 1) * 1000;
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return {
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currentLaps,
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targetLaps,
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remainingLaps: Math.max(0, targetLaps - currentLaps),
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percent: Math.min(100, Math.max(0, (currentLaps / targetLaps) * 100)),
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};
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}
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function getPublicTrainingRecommendation(stats) {
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if (stats.totalLaps < 10) return { level: "baseline", targetLapMs: 0 };
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if (stats.recentLapCount >= 10 && stats.consistencySpreadPct > 8) {
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return { level: "consistency", targetLapMs: Math.round(stats.bestLapMs * 1.08) };
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}
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return { level: "progression", targetLapMs: Math.round(stats.bestLapMs * 1.05) };
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}
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module.exports = {
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buildPublicTrainingProfile,
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findPublicTrainingShare,
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hasPublicTrainingShare,
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normalizePublicTrainingSince,
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normalizePublicTrainingWindow,
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splitPublicTrainingStints,
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getPublicBestConsecutiveLaps,
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getPublicBestDurationResult,
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};
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