publika träningslogik är nu utbruten och testbar
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larssand
195
server/public_training_profile.js
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195
server/public_training_profile.js
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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 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 driverId = String(share.driverId || "");
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const drivers = Array.isArray(state?.drivers) ? state.drivers : [];
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const driver = drivers.find((item) => String(item?.id || "") === driverId);
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if (!driver) return null;
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const classes = Array.isArray(state?.classes) ? state.classes : [];
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const driverClass = classes.find((item) => String(item?.id || "") === String(driver.classId || ""));
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const ledger = Array.isArray(state?.training?.ledger) ? state.training.ledger : [];
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const driverLaps = ledger
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.filter((entry) => String(entry?.driverId || "") === 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(driverLaps);
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const goals = (Array.isArray(state?.training?.goals) ? state.training.goals : [])
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.filter((goal) => String(goal?.driverId || "") === driverId)
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.map((goal) => buildPublicTrainingGoal(goal, stats.totalLaps));
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const rankedTotals = getRankedTrainingTotals(ledger, new Set(drivers.map((item) => String(item?.id || "")).filter(Boolean)));
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const rankIndex = rankedTotals.findIndex(([id]) => id === driverId);
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const settings = state?.settings && typeof state.settings === "object" ? state.settings : {};
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const currentSession = sinceTimestamp
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? buildPublicTrainingWindowStats(driverLaps.filter((lap) => lap.timestamp >= sinceTimestamp && (!windowMs || lap.timestamp <= sinceTimestamp + windowMs)))
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: null;
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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: String(driver.brand || "").trim(),
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className: String(driverClass?.name || "").trim(),
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clubRank: rankIndex >= 0 ? rankIndex + 1 : 0,
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rankedDrivers: rankedTotals.length,
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},
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branding: {
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clubName: String(settings.clubName || "JMK RB RaceController").trim(),
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clubTagline: String(settings.clubTagline || "RC Timing System").trim(),
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logoDataUrl: normalizePublicLogoDataUrl(settings.logoDataUrl),
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},
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stats,
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nextMilestone: getPublicTrainingMilestone(stats.totalLaps),
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recommendation: getPublicTrainingRecommendation(stats),
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goals,
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currentSession,
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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 getRankedTrainingTotals(ledger, registeredDriverIds) {
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const totals = new Map();
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ledger.forEach((entry) => {
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const driverId = String(entry?.driverId || "");
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if (!driverId || !registeredDriverIds.has(driverId) || Number(entry?.lapMs || 0) <= 500) return;
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totals.set(driverId, (totals.get(driverId) || 0) + 1);
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});
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return [...totals.entries()].sort((left, right) => right[1] - left[1]);
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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 average = (values) => values.length ? Math.round(values.reduce((sum, value) => sum + value, 0) / values.length) : 0;
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const sortedRecent = [...recent].sort((left, right) => left - right);
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const percentile = (ratio) => sortedRecent.length
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? sortedRecent[Math.min(sortedRecent.length - 1, Math.max(0, Math.round((sortedRecent.length - 1) * ratio)))]
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: 0;
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const recentAverageMs = average(recent);
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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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return {
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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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recentLapCount: recent.length,
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consistencySpreadPct: recentAverageMs ? ((percentile(0.9) - percentile(0.1)) / recentAverageMs) * 100 : 0,
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controlledLapStreak,
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dailySummaries,
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};
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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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};
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