Expose guided training exercises on the driver's mobile profile link
"Jämna varv" and "Stegträning" previously only lived on the admin Training page. A driver's own personal mobile profile link now offers the same self-service configure/start/stop flow already used for the 5-minute practice timer there, with live progress computed server-side from the driver's own share token so raw lap rows and driver IDs never reach the client. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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larssand
@@ -62,7 +62,7 @@ function filterPublicTrainingStats(stats, visibility) {
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return filtered;
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}
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function buildPublicTrainingProfile(state, shareToken, updatedAt, sinceTimestamp = 0, windowMs = 0) {
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function buildPublicTrainingProfile(state, shareToken, updatedAt, sinceTimestamp = 0, windowMs = 0, evenLapsSince = 0, stepTrainingSince = 0) {
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const share = findPublicTrainingShare(state, shareToken);
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if (!share) return null;
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const visibility = normalizePublicTrainingVisibility(share.visibility);
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@@ -98,6 +98,13 @@ function buildPublicTrainingProfile(state, shareToken, updatedAt, sinceTimestamp
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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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const evenLapsConfig = sanitizePublicEvenLapsConfig(
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(Array.isArray(state?.training?.evenLaps) ? state.training.evenLaps : []).find((entry) => classDriverIds.has(String(entry?.driverId || "")) && String(entry?.classId || "") === classId)
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);
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const stepTrainingConfig = sanitizePublicStepTrainingConfig(
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(Array.isArray(state?.training?.stepTraining) ? state.training.stepTraining : []).find((entry) => classDriverIds.has(String(entry?.driverId || "")) && String(entry?.classId || "") === classId)
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);
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const todayLaps = classLaps.filter((lap) => isPublicTrainingToday(lap.timestamp));
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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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@@ -110,6 +117,10 @@ function buildPublicTrainingProfile(state, shareToken, updatedAt, sinceTimestamp
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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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evenLapsConfig,
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evenLapsProgress: buildPublicEvenLapsProgress(classLaps, todayLaps, evenLapsConfig, evenLapsSince),
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stepTrainingConfig,
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stepTrainingProgress: buildPublicStepTrainingProgress(classLaps, stepTrainingConfig, stepTrainingSince),
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};
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});
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const activeClass = classProfiles[0] || {
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@@ -205,6 +216,181 @@ function buildPublicTrainingWindowStats(laps) {
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};
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}
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function isPublicTrainingToday(timestamp) {
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const date = new Date(Number(timestamp || 0));
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if (Number.isNaN(date.getTime())) return false;
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const now = new Date();
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return date.getFullYear() === now.getFullYear() && date.getMonth() === now.getMonth() && date.getDate() === now.getDate();
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}
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function sanitizePublicEvenLapsConfig(entry) {
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if (!entry) return null;
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return {
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targetMs: Math.max(0, Math.round(Number(entry.targetMs || 0))),
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toleranceMs: Math.max(0, Math.round(Number(entry.toleranceMs || 0))),
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lapCount: Math.max(1, Math.round(Number(entry.lapCount || 5))),
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};
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}
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function sanitizePublicStepTrainingConfig(entry) {
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if (!entry || !Array.isArray(entry.steps) || !entry.steps.length) return null;
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return {
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steps: entry.steps.map((step) => ({
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effort: ["calm", "controlled", "fast"].includes(String(step?.effort || "")) ? String(step.effort) : "calm",
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lapCount: Math.max(0, Math.round(Number(step?.lapCount || 0) || 0)),
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})).filter((step) => step.lapCount > 0),
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};
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}
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// Mirrors buildEvenLapsProgress in src/training_logic.js (the admin implementation), but
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// takes an already-sanitized, class-scoped lap array instead of doing its own state/session
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// lookups: server/public_training_profile.js never sends individual lap rows to the client,
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// only the computed progress, so this must stay a server-side re-implementation rather than
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// a shared import from the browser-only admin module.
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function buildPublicEvenLapsProgress(classLaps, todayLaps, config, sinceTimestamp) {
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if (!config || !(config.targetMs > 0)) return null;
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const withinTolerance = (lapMs) => Math.abs(lapMs - config.targetMs) <= config.toleranceMs;
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const longestStreak = (laps) => {
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let longest = 0;
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let current = 0;
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laps.forEach((lap) => {
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if (!lap.excludedFromPace && withinTolerance(lap.lapMs)) {
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current += 1;
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longest = Math.max(longest, current);
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} else {
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current = 0;
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}
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});
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return longest;
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};
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const liveLaps = sinceTimestamp ? classLaps.filter((lap) => lap.timestamp >= sinceTimestamp) : [];
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let currentStreak = 0;
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for (let index = liveLaps.length - 1; index >= 0; index -= 1) {
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if (!liveLaps[index].excludedFromPace && withinTolerance(liveLaps[index].lapMs)) currentStreak += 1;
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else break;
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}
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const bestStreakToday = Math.max(longestStreak(todayLaps), currentStreak);
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const lastLap = liveLaps.at(-1) || null;
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return {
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active: Boolean(sinceTimestamp),
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targetMs: config.targetMs,
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toleranceMs: config.toleranceMs,
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lapCount: config.lapCount,
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currentStreak,
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bestStreakToday,
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lastLapMs: lastLap ? lastLap.lapMs : null,
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lastLapWithinTolerance: lastLap ? (!lastLap.excludedFromPace && withinTolerance(lastLap.lapMs)) : null,
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};
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}
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// Mirrors buildStepTrainingProgress in src/training_logic.js - see the note above
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// buildPublicEvenLapsProgress for why this is a separate server-side implementation.
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function buildPublicStepTrainingProgress(classLaps, config, sinceTimestamp) {
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const steps = config?.steps || [];
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if (!steps.length) return null;
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const liveLaps = sinceTimestamp ? classLaps.filter((lap) => lap.timestamp >= sinceTimestamp) : [];
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const totalLaps = steps.reduce((sum, step) => sum + step.lapCount, 0);
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let cursor = 0;
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const stepStates = steps.map((step, index) => {
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const stepLaps = liveLaps.slice(cursor, cursor + step.lapCount);
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cursor += step.lapCount;
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const paceValues = stepLaps.filter((lap) => !lap.excludedFromPace).map((lap) => lap.lapMs);
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return {
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index,
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effort: step.effort,
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lapCount: step.lapCount,
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lapsCompleted: stepLaps.length,
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done: stepLaps.length >= step.lapCount,
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averageLapMs: paceValues.length ? Math.round(paceValues.reduce((sum, value) => sum + value, 0) / paceValues.length) : null,
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};
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});
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const complete = liveLaps.length >= totalLaps;
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const currentStepIndex = complete ? steps.length - 1 : stepStates.findIndex((step) => !step.done);
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const currentStepState = stepStates[currentStepIndex] || null;
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const controlledSteps = stepStates.filter((step) => step.effort === "controlled" && step.averageLapMs != null);
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const controlledComparison = controlledSteps.length >= 2
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? {
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firstAvgMs: controlledSteps[0].averageLapMs,
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lastAvgMs: controlledSteps.at(-1).averageLapMs,
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diffMs: controlledSteps.at(-1).averageLapMs - controlledSteps[0].averageLapMs,
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}
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: null;
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return {
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active: Boolean(sinceTimestamp),
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totalSteps: steps.length,
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totalLaps,
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completedLaps: Math.min(liveLaps.length, totalLaps),
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complete,
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currentStepIndex,
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currentStep: currentStepState && {
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index: currentStepState.index,
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effort: currentStepState.effort,
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lapCount: currentStepState.lapCount,
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lapsCompleted: currentStepState.lapsCompleted,
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lapsRemaining: Math.max(0, currentStepState.lapCount - currentStepState.lapsCompleted),
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},
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steps: stepStates,
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controlledComparison,
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};
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}
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function resolvePublicClassDriver(state, shareToken, classId) {
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const share = findPublicTrainingShare(state, shareToken);
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if (!share) return null;
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const drivers = Array.isArray(state?.drivers) ? state.drivers : [];
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const profile = getPublicDriverProfile(drivers, share.driverId);
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return profile?.drivers.find((driver) => String(driver?.classId || "") === String(classId || "")) || null;
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}
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function savePublicEvenLapsConfig(state, shareToken, input) {
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const classDriver = resolvePublicClassDriver(state, shareToken, input?.classId);
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if (!classDriver) return null;
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const classId = String(input.classId);
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const targetMs = Math.max(0, Math.round(Number(input?.targetMs || 0)));
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const toleranceMs = Math.max(0, Math.round(Number(input?.toleranceMs || 0)));
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const lapCount = Math.max(1, Math.round(Number(input?.lapCount || 0) || 5));
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if (!(targetMs > 0)) return null;
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if (!state.training || typeof state.training !== "object") state.training = {};
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state.training.evenLaps = Array.isArray(state.training.evenLaps) ? state.training.evenLaps : [];
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const existing = state.training.evenLaps.find((entry) => String(entry?.driverId || "") === String(classDriver.id) && String(entry?.classId || "") === classId);
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const updatedAt = new Date().toISOString();
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if (existing) {
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existing.targetMs = targetMs;
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existing.toleranceMs = toleranceMs;
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existing.lapCount = lapCount;
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existing.updatedAt = updatedAt;
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return existing;
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}
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const entry = { id: "even_laps_public_" + Date.now() + "_" + Math.random().toString(36).slice(2, 9), driverId: String(classDriver.id), classId, targetMs, toleranceMs, lapCount, updatedAt };
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state.training.evenLaps.push(entry);
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return entry;
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}
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function savePublicStepTrainingConfig(state, shareToken, input) {
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const classDriver = resolvePublicClassDriver(state, shareToken, input?.classId);
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if (!classDriver) return null;
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const classId = String(input.classId);
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const steps = Array.isArray(input?.steps)
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? input.steps.map((step) => ({
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effort: ["calm", "controlled", "fast"].includes(String(step?.effort || "")) ? String(step.effort) : "calm",
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lapCount: Math.max(0, Math.round(Number(step?.lapCount || 0) || 0)),
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})).filter((step) => step.lapCount > 0)
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: [];
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if (!steps.length) return null;
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if (!state.training || typeof state.training !== "object") state.training = {};
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state.training.stepTraining = Array.isArray(state.training.stepTraining) ? state.training.stepTraining : [];
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const existing = state.training.stepTraining.find((entry) => String(entry?.driverId || "") === String(classDriver.id) && String(entry?.classId || "") === classId);
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const updatedAt = new Date().toISOString();
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if (existing) {
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existing.steps = steps;
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existing.updatedAt = updatedAt;
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return existing;
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}
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const entry = { id: "step_training_public_" + Date.now() + "_" + Math.random().toString(36).slice(2, 9), driverId: String(classDriver.id), classId, steps, updatedAt };
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state.training.stepTraining.push(entry);
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return entry;
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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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@@ -529,4 +715,8 @@ module.exports = {
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getPublicBestConsecutiveLaps,
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getPublicBestDurationResult,
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addPublicTrainingMarker,
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buildPublicEvenLapsProgress,
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buildPublicStepTrainingProgress,
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savePublicEvenLapsConfig,
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savePublicStepTrainingConfig,
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};
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