Live_RC/server/public_training_profile.js

function findPublicTrainingShare(state, shareToken) {
  if (!shareToken || shareToken.length < 24) return null;
  const shares = Array.isArray(state?.training?.shares) ? state.training.shares : [];
  return shares.find((share) => share?.enabled !== false && String(share?.token || "") === shareToken) || null;
}

function hasPublicTrainingShare(state, shareToken) {
  return Boolean(findPublicTrainingShare(state, shareToken));
}

function getPublicDriverProfileId(driver) {
  const explicitId = String(driver?.profileId || "").trim();
  if (explicitId) return explicitId;
  const name = String(driver?.name || "").trim().toLocaleLowerCase().replace(/\s+/g, " ");
  return name ? `legacy:${name}` : `driver:${String(driver?.id || "unknown")}`;
}

function getPublicDriverProfile(drivers, reference) {
  const normalizedReference = String(reference || "");
  const anchor = drivers.find((driver) => (
    String(driver?.id || "") === normalizedReference
    || getPublicDriverProfileId(driver) === normalizedReference
  ));
  if (!anchor) return null;
  const profileId = getPublicDriverProfileId(anchor);
  return {
    id: profileId,
    drivers: drivers.filter((driver) => getPublicDriverProfileId(driver) === profileId),
  };
}

const PUBLIC_TRAINING_SECTION_KEYS = ["progress", "achievements", "performance", "goals", "history"];

function normalizePublicTrainingVisibility(value) {
  const source = value && typeof value === "object" ? value : {};
  return Object.fromEntries(PUBLIC_TRAINING_SECTION_KEYS.map((key) => [key, source[key] !== false]));
}

function filterPublicTrainingStats(stats, visibility) {
  const filtered = {
    totalLaps: stats.totalLaps, totalSessions: stats.totalSessions, trainingDays: stats.trainingDays,
    bestLapMs: stats.bestLapMs, averageLapMs: stats.averageLapMs, recentLapCount: stats.recentLapCount,
    consistencySpreadPct: stats.consistencySpreadPct,
  };
  if (visibility.progress) Object.assign(filtered, {
    recentAverageMs: stats.recentAverageMs, previousRecentAverageMs: stats.previousRecentAverageMs,
    averageTrendMs: stats.averageTrendMs, previousConsistencySpreadPct: stats.previousConsistencySpreadPct,
    consistencyTrendPct: stats.consistencyTrendPct, trendSampleReady: stats.trendSampleReady,
    controlledLapStreak: stats.controlledLapStreak, todayControlledLapStreak: stats.todayControlledLapStreak,
    todayStrongFinishStints: stats.todayStrongFinishStints, todayCleanFiveMinuteStints: stats.todayCleanFiveMinuteStints,
    todayCompletedFiveMinuteStints: stats.todayCompletedFiveMinuteStints, paceHistory: stats.dailySummaries.slice(0, 12),
  });
  if (visibility.achievements) filtered.achievements = stats.achievements;
  if (visibility.performance) Object.assign(filtered, {
    controlZoneMinMs: stats.controlZoneMinMs, controlZoneMaxMs: stats.controlZoneMaxMs,
    outlierLaps: stats.outlierLaps, strongFinishStints: stats.strongFinishStints,
    cleanFiveMinuteStints: stats.cleanFiveMinuteStints, stintCount: stats.stintCount,
    stintSummaries: stats.stintSummaries, bestThreeConsecutive: stats.bestThreeConsecutive,
    bestFiveMinuteResult: stats.bestFiveMinuteResult, paceBuckets: stats.paceBuckets,
  });
  if (visibility.history) filtered.dailySummaries = stats.dailySummaries;
  return filtered;
}

function buildPublicTrainingProfile(state, shareToken, updatedAt, sinceTimestamp = 0, windowMs = 0, evenLapsSince = 0, stepTrainingSince = 0) {
  const share = findPublicTrainingShare(state, shareToken);
  if (!share) return null;
  const visibility = normalizePublicTrainingVisibility(share.visibility);
  const profileReference = String(share.driverId || "");
  const drivers = Array.isArray(state?.drivers) ? state.drivers : [];
  const driverProfile = getPublicDriverProfile(drivers, profileReference);
  if (!driverProfile?.drivers.length) return null;
  const driver = driverProfile.drivers[0];

  const classes = Array.isArray(state?.classes) ? state.classes : [];
  const ledger = Array.isArray(state?.training?.ledger) ? state.training.ledger : [];
  const profileReferences = new Set([driverProfile.id, ...driverProfile.drivers.map((item) => String(item.id || ""))]);
  const profileClassIds = [...new Set(driverProfile.drivers.map((item) => String(item.classId || "")).filter(Boolean))];
  const classProfiles = profileClassIds.map((classId) => {
    const classDrivers = driverProfile.drivers.filter((item) => String(item.classId || "") === classId);
    const classDriverIds = new Set(classDrivers.map((item) => String(item.id || "")));
    const classLaps = ledger
      .filter((entry) => classDriverIds.has(String(entry?.driverId || "")) && Number(entry?.lapMs || 0) > 500)
      .map((entry) => ({
        timestamp: Math.max(0, Number(entry.timestamp || 0) || 0),
        lapMs: Math.max(0, Math.round(Number(entry.lapMs || 0) || 0)),
        sessionId: String(entry.sessionId || ""),
        sourceId: String(entry.sourceId || entry.driverId || ""),
        excludedFromPace: Boolean(entry.excludedFromPace),
        stintBoundary: Boolean(entry.stintBoundary),
      }))
      .sort((left, right) => left.timestamp - right.timestamp);
    const stats = buildPublicTrainingStats(classLaps);
    const classMarkers = (Array.isArray(state?.training?.markers) ? state.training.markers : []).filter((marker) => classDriverIds.has(String(marker?.driverId || "")) && String(marker?.classId || "") === classId);
    stats.paceBuckets = buildPublicTrainingPaceBuckets(classLaps, classMarkers);
    const channelStats = buildPublicTimingChannelStats(state, classDriverIds);
    const classGoals = (Array.isArray(state?.training?.goals) ? state.training.goals : [])
      .filter((goal) => profileReferences.has(String(goal?.driverId || "")) && (!goal?.classId || String(goal.classId) === classId))
      .map((goal) => buildPublicTrainingGoal(goal, stats.totalLaps));
    const brands = [...new Set(classDrivers.map((item) => String(item.brand || "").trim()).filter(Boolean))];
    const evenLapsConfig = sanitizePublicEvenLapsConfig(
      (Array.isArray(state?.training?.evenLaps) ? state.training.evenLaps : []).find((entry) => classDriverIds.has(String(entry?.driverId || "")) && String(entry?.classId || "") === classId)
    );
    const stepTrainingConfig = sanitizePublicStepTrainingConfig(
      (Array.isArray(state?.training?.stepTraining) ? state.training.stepTraining : []).find((entry) => classDriverIds.has(String(entry?.driverId || "")) && String(entry?.classId || "") === classId)
    );
    const todayLaps = classLaps.filter((lap) => isPublicTrainingToday(lap.timestamp));
    return {
      classId,
      className: String(classes.find((item) => String(item?.id || "") === classId)?.name || "").trim(),
      brand: brands.join(" / "),
      stats: filterPublicTrainingStats(stats, visibility),
      channelStats: visibility.performance ? channelStats : {},
      nextMilestone: getPublicTrainingMilestone(stats.totalLaps),
      recommendation: getPublicTrainingRecommendation(stats),
      goals: visibility.goals ? classGoals : [],
      currentSession: sinceTimestamp
        ? buildPublicTrainingWindowStats(classLaps.filter((lap) => lap.timestamp >= sinceTimestamp && (!windowMs || lap.timestamp <= sinceTimestamp + windowMs)))
        : null,
      evenLapsConfig,
      evenLapsProgress: buildPublicEvenLapsProgress(classLaps, todayLaps, evenLapsConfig, evenLapsSince),
      stepTrainingConfig,
      stepTrainingProgress: buildPublicStepTrainingProgress(classLaps, stepTrainingConfig, stepTrainingSince),
    };
  });
  const activeClass = classProfiles[0] || {
    classId: "", className: "", brand: "",
    stats: filterPublicTrainingStats(buildPublicTrainingStats([]), visibility), nextMilestone: getPublicTrainingMilestone(0),
    channelStats: visibility.performance ? buildPublicTimingChannelStats({}, new Set()) : {},
    recommendation: getPublicTrainingRecommendation({ totalLaps: 0 }), goals: [], currentSession: null,
  };
  const settings = state?.settings && typeof state.settings === "object" ? state.settings : {};

  return {
    updatedAt: String(updatedAt || ""),
    visibility,
    profile: {
      name: String(driver.name || "Driver").trim(),
      brand: activeClass.brand,
      className: activeClass.className,
    },
    branding: {
      clubName: String(settings.clubName || "JMK RB").trim(),
      clubTagline: String(settings.clubTagline || "Live Timing & Race Control").trim(),
      logoDataUrl: normalizePublicLogoDataUrl(settings.logoDataUrl),
    },
    classProfiles,
    stats: activeClass.stats,
    channelStats: activeClass.channelStats,
    nextMilestone: activeClass.nextMilestone,
    recommendation: activeClass.recommendation,
    goals: activeClass.goals,
    currentSession: activeClass.currentSession,
  };
}

function buildPublicTimingChannelStats(state, classDriverIds) {
  const splits = [];
  const pitTimes = [];
  Object.values(state?.resultsBySession || {}).forEach((result) => {
    (Array.isArray(result?.splits) ? result.splits : []).forEach((split) => {
      const splitMs = Math.round(Number(split?.splitMs || 0) || 0);
      if (classDriverIds.has(String(split?.driverId || "")) && splitMs > 0) splits.push({ splitMs, timestamp: Number(split?.timestamp || 0) });
    });
    (Array.isArray(result?.pitStops) ? result.pitStops : []).forEach((stop) => {
      const pitStopMs = Math.round(Number(stop?.pitStopMs || 0) || 0);
      if (classDriverIds.has(String(stop?.driverId || "")) && pitStopMs > 0) pitTimes.push(pitStopMs);
    });
  });
  const average = (values) => values.length
    ? Math.round(values.reduce((sum, value) => sum + value, 0) / values.length)
    : 0;
  splits.sort((left, right) => left.timestamp - right.timestamp);
  const splitTimes = splits.map((split) => split.splitMs);
  let runningBestSplitMs = 0;
  let latestSplitGainMs = 0;
  splits.forEach((split) => {
    if (runningBestSplitMs && split.splitMs < runningBestSplitMs) latestSplitGainMs = runningBestSplitMs - split.splitMs;
    if (!runningBestSplitMs || split.splitMs < runningBestSplitMs) runningBestSplitMs = split.splitMs;
  });
  return {
    splitCount: splitTimes.length,
    bestSplitMs: splitTimes.length ? Math.min(...splitTimes) : 0,
    averageSplitMs: average(splitTimes),
    latestSplitGainMs,
    pitStopCount: pitTimes.length,
    totalPitMs: pitTimes.reduce((sum, value) => sum + value, 0),
    bestPitStopMs: pitTimes.length ? Math.min(...pitTimes) : 0,
    averagePitStopMs: average(pitTimes),
  };
}

function normalizePublicTrainingSince(value, startedAgoValue) {
  const now = Date.now();
  if (startedAgoValue !== undefined) {
    const startedAgoMs = Math.round(Number(startedAgoValue));
    if (Number.isFinite(startedAgoMs) && startedAgoMs >= 0 && startedAgoMs <= 24 * 60 * 60 * 1000) {
      return now - startedAgoMs;
    }
  }
  const timestamp = Math.round(Number(value || 0) || 0);
  return timestamp >= now - 24 * 60 * 60 * 1000 && timestamp <= now + 60 * 1000 ? timestamp : 0;
}

function normalizePublicTrainingWindow(value) {
  const windowMs = Math.round(Number(value || 0) || 0);
  return Math.min(24 * 60 * 60 * 1000, Math.max(0, windowMs));
}

function buildPublicTrainingWindowStats(laps) {
  const lapTimes = laps.filter((lap) => !lap.excludedFromPace).map((lap) => Number(lap.lapMs || 0)).filter((lapMs) => lapMs > 500);
  return {
    laps: laps.length,
    bestLapMs: lapTimes.length ? Math.min(...lapTimes) : 0,
    averageLapMs: publicTrainingAverage(lapTimes),
    // A spread needs a handful of laps to mean anything - fewer than that, report it as
    // unavailable (0 alongside consistencySampleReady:false) rather than a misleading number.
    consistencySpreadPct: lapTimes.length >= 5 ? publicTrainingSpread(lapTimes) : 0,
    consistencySampleReady: lapTimes.length >= 5,
  };
}

function isPublicTrainingToday(timestamp) {
  const date = new Date(Number(timestamp || 0));
  if (Number.isNaN(date.getTime())) return false;
  const now = new Date();
  return date.getFullYear() === now.getFullYear() && date.getMonth() === now.getMonth() && date.getDate() === now.getDate();
}

function sanitizePublicEvenLapsConfig(entry) {
  if (!entry) return null;
  return {
    targetMs: Math.max(0, Math.round(Number(entry.targetMs || 0))),
    toleranceMs: Math.max(0, Math.round(Number(entry.toleranceMs || 0))),
    lapCount: Math.max(1, Math.round(Number(entry.lapCount || 5))),
  };
}

function sanitizePublicStepTrainingConfig(entry) {
  if (!entry || !Array.isArray(entry.steps) || !entry.steps.length) return null;
  return {
    steps: entry.steps.map((step) => ({
      effort: ["calm", "controlled", "fast"].includes(String(step?.effort || "")) ? String(step.effort) : "calm",
      lapCount: Math.max(0, Math.round(Number(step?.lapCount || 0) || 0)),
    })).filter((step) => step.lapCount > 0),
  };
}

// Mirrors buildEvenLapsProgress in src/training_logic.js (the admin implementation), but
// takes an already-sanitized, class-scoped lap array instead of doing its own state/session
// lookups: server/public_training_profile.js never sends individual lap rows to the client,
// only the computed progress, so this must stay a server-side re-implementation rather than
// a shared import from the browser-only admin module.
function buildPublicEvenLapsProgress(classLaps, todayLaps, config, sinceTimestamp) {
  if (!config || !(config.targetMs > 0)) return null;
  const withinTolerance = (lapMs) => Math.abs(lapMs - config.targetMs) <= config.toleranceMs;
  const longestStreak = (laps) => {
    let longest = 0;
    let current = 0;
    laps.forEach((lap) => {
      if (!lap.excludedFromPace && withinTolerance(lap.lapMs)) {
        current += 1;
        longest = Math.max(longest, current);
      } else {
        current = 0;
      }
    });
    return longest;
  };
  const liveLaps = sinceTimestamp ? classLaps.filter((lap) => lap.timestamp >= sinceTimestamp) : [];
  let currentStreak = 0;
  for (let index = liveLaps.length - 1; index >= 0; index -= 1) {
    if (!liveLaps[index].excludedFromPace && withinTolerance(liveLaps[index].lapMs)) currentStreak += 1;
    else break;
  }
  const bestStreakToday = Math.max(longestStreak(todayLaps), currentStreak);
  const lastLap = liveLaps.at(-1) || null;
  return {
    active: Boolean(sinceTimestamp),
    targetMs: config.targetMs,
    toleranceMs: config.toleranceMs,
    lapCount: config.lapCount,
    currentStreak,
    bestStreakToday,
    lastLapMs: lastLap ? lastLap.lapMs : null,
    lastLapWithinTolerance: lastLap ? (!lastLap.excludedFromPace && withinTolerance(lastLap.lapMs)) : null,
  };
}

// Mirrors buildStepTrainingProgress in src/training_logic.js - see the note above
// buildPublicEvenLapsProgress for why this is a separate server-side implementation.
function buildPublicStepTrainingProgress(classLaps, config, sinceTimestamp) {
  const steps = config?.steps || [];
  if (!steps.length) return null;
  const liveLaps = sinceTimestamp ? classLaps.filter((lap) => lap.timestamp >= sinceTimestamp) : [];
  const totalLaps = steps.reduce((sum, step) => sum + step.lapCount, 0);
  let cursor = 0;
  const stepStates = steps.map((step, index) => {
    const stepLaps = liveLaps.slice(cursor, cursor + step.lapCount);
    cursor += step.lapCount;
    const paceValues = stepLaps.filter((lap) => !lap.excludedFromPace).map((lap) => lap.lapMs);
    return {
      index,
      effort: step.effort,
      lapCount: step.lapCount,
      lapsCompleted: stepLaps.length,
      done: stepLaps.length >= step.lapCount,
      averageLapMs: paceValues.length ? Math.round(paceValues.reduce((sum, value) => sum + value, 0) / paceValues.length) : null,
    };
  });
  const complete = liveLaps.length >= totalLaps;
  const currentStepIndex = complete ? steps.length - 1 : stepStates.findIndex((step) => !step.done);
  const currentStepState = stepStates[currentStepIndex] || null;
  const controlledSteps = stepStates.filter((step) => step.effort === "controlled" && step.averageLapMs != null);
  const controlledComparison = controlledSteps.length >= 2
    ? {
        firstAvgMs: controlledSteps[0].averageLapMs,
        lastAvgMs: controlledSteps.at(-1).averageLapMs,
        diffMs: controlledSteps.at(-1).averageLapMs - controlledSteps[0].averageLapMs,
      }
    : null;
  return {
    active: Boolean(sinceTimestamp),
    totalSteps: steps.length,
    totalLaps,
    completedLaps: Math.min(liveLaps.length, totalLaps),
    complete,
    currentStepIndex,
    currentStep: currentStepState && {
      index: currentStepState.index,
      effort: currentStepState.effort,
      lapCount: currentStepState.lapCount,
      lapsCompleted: currentStepState.lapsCompleted,
      lapsRemaining: Math.max(0, currentStepState.lapCount - currentStepState.lapsCompleted),
    },
    steps: stepStates,
    controlledComparison,
  };
}

function resolvePublicClassDriver(state, shareToken, classId) {
  const share = findPublicTrainingShare(state, shareToken);
  if (!share) return null;
  const drivers = Array.isArray(state?.drivers) ? state.drivers : [];
  const profile = getPublicDriverProfile(drivers, share.driverId);
  return profile?.drivers.find((driver) => String(driver?.classId || "") === String(classId || "")) || null;
}

function savePublicEvenLapsConfig(state, shareToken, input) {
  const classDriver = resolvePublicClassDriver(state, shareToken, input?.classId);
  if (!classDriver) return null;
  const classId = String(input.classId);
  const targetMs = Math.max(0, Math.round(Number(input?.targetMs || 0)));
  const toleranceMs = Math.max(0, Math.round(Number(input?.toleranceMs || 0)));
  const lapCount = Math.max(1, Math.round(Number(input?.lapCount || 0) || 5));
  if (!(targetMs > 0)) return null;
  if (!state.training || typeof state.training !== "object") state.training = {};
  state.training.evenLaps = Array.isArray(state.training.evenLaps) ? state.training.evenLaps : [];
  const existing = state.training.evenLaps.find((entry) => String(entry?.driverId || "") === String(classDriver.id) && String(entry?.classId || "") === classId);
  const updatedAt = new Date().toISOString();
  if (existing) {
    existing.targetMs = targetMs;
    existing.toleranceMs = toleranceMs;
    existing.lapCount = lapCount;
    existing.updatedAt = updatedAt;
    return existing;
  }
  const entry = { id: "even_laps_public_" + Date.now() + "_" + Math.random().toString(36).slice(2, 9), driverId: String(classDriver.id), classId, targetMs, toleranceMs, lapCount, updatedAt };
  state.training.evenLaps.push(entry);
  return entry;
}

function savePublicStepTrainingConfig(state, shareToken, input) {
  const classDriver = resolvePublicClassDriver(state, shareToken, input?.classId);
  if (!classDriver) return null;
  const classId = String(input.classId);
  const steps = Array.isArray(input?.steps)
    ? input.steps.map((step) => ({
        effort: ["calm", "controlled", "fast"].includes(String(step?.effort || "")) ? String(step.effort) : "calm",
        lapCount: Math.max(0, Math.round(Number(step?.lapCount || 0) || 0)),
      })).filter((step) => step.lapCount > 0)
    : [];
  if (!steps.length) return null;
  if (!state.training || typeof state.training !== "object") state.training = {};
  state.training.stepTraining = Array.isArray(state.training.stepTraining) ? state.training.stepTraining : [];
  const existing = state.training.stepTraining.find((entry) => String(entry?.driverId || "") === String(classDriver.id) && String(entry?.classId || "") === classId);
  const updatedAt = new Date().toISOString();
  if (existing) {
    existing.steps = steps;
    existing.updatedAt = updatedAt;
    return existing;
  }
  const entry = { id: "step_training_public_" + Date.now() + "_" + Math.random().toString(36).slice(2, 9), driverId: String(classDriver.id), classId, steps, updatedAt };
  state.training.stepTraining.push(entry);
  return entry;
}

function buildPublicTrainingGoal(goal, currentLaps) {
  const targetLaps = Math.max(1, Math.round(Number(goal?.targetLaps || 1) || 1));
  return {
    title: String(goal?.title || "").trim(),
    reward: String(goal?.reward || "").trim(),
    targetLaps,
    currentLaps,
    remainingLaps: Math.max(0, targetLaps - currentLaps),
    percent: Math.min(100, Math.max(0, (currentLaps / targetLaps) * 100)),
    reached: currentLaps >= targetLaps,
    claimed: Boolean(goal?.claimedAt),
  };
}

function buildPublicTrainingPaceBuckets(laps, markers, bucketMinutes = 30) {
  const bucketMs = Math.max(5, Number(bucketMinutes || 30)) * 60 * 1000;
  const buckets = new Map();
  laps.filter((lap) => !lap.excludedFromPace).forEach((lap) => {
    const date = new Date(lap.timestamp);
    const minuteBlock = bucketMs / 60000;
    const startTs = new Date(date.getFullYear(), date.getMonth(), date.getDate(), date.getHours(), Math.floor(date.getMinutes() / minuteBlock) * minuteBlock, 0, 0).getTime();
    const bucket = buckets.get(startTs) || { startTs, values: [] };
    bucket.values.push(lap.lapMs);
    buckets.set(startTs, bucket);
  });
  return [...buckets.values()].sort((left, right) => right.startTs - left.startTs).slice(0, 24).map((bucket) => {
    const sorted = [...bucket.values].sort((left, right) => left - right);
    const trim = sorted.length >= 5 ? Math.max(1, Math.floor(sorted.length * 0.1)) : 0;
    const kept = trim ? sorted.slice(trim, sorted.length - trim) : sorted;
    return {
      startTs: bucket.startTs,
      lapCount: bucket.values.length,
      paceMs: kept.length ? Math.round(kept.reduce((sum, value) => sum + value, 0) / kept.length) : 0,
      bestLapMs: bucket.values.length ? Math.min(...bucket.values) : 0,
      markerCount: markers.filter((marker) => Number(marker?.timestamp || 0) >= bucket.startTs && Number(marker?.timestamp || 0) < bucket.startTs + bucketMs).length,
    };
  });
}

function publicTrainingAverage(values) {
  return values.length ? Math.round(values.reduce((sum, value) => sum + value, 0) / values.length) : 0;
}

function publicTrainingPercentile(values, ratio) {
  const sorted = [...values].sort((left, right) => left - right);
  return sorted.length
    ? sorted[Math.min(sorted.length - 1, Math.max(0, Math.round((sorted.length - 1) * ratio)))]
    : 0;
}

function publicTrainingSpread(values) {
  const mean = publicTrainingAverage(values);
  return mean ? ((publicTrainingPercentile(values, 0.9) - publicTrainingPercentile(values, 0.1)) / mean) * 100 : 0;
}

function buildPublicTrainingStats(laps) {
  const paceLaps = laps.filter((lap) => !lap.excludedFromPace);
  const lapTimes = paceLaps.map((lap) => lap.lapMs);
  const recent = lapTimes.slice(-30);
  const previous = lapTimes.slice(Math.max(0, lapTimes.length - 60), Math.max(0, lapTimes.length - 30));
  const average = publicTrainingAverage;
  const spread = publicTrainingSpread;
  const recentAverageMs = average(recent);
  const previousRecentAverageMs = average(previous);
  const consistencySpreadPct = spread(recent);
  const previousConsistencySpreadPct = spread(previous);
  const trendSampleReady = recent.length >= 10 && previous.length >= 10;
  const daily = new Map();
  paceLaps.forEach((lap) => {
    const date = new Date(lap.timestamp);
    const dateKey = Number.isNaN(date.getTime()) ? "unknown" : `${date.getFullYear()}-${String(date.getMonth() + 1).padStart(2, "0")}-${String(date.getDate()).padStart(2, "0")}`;
    const day = daily.get(dateKey) || { dateKey, laps: 0, totalMs: 0, bestLapMs: 0, latestTimestamp: 0 };
    day.laps += 1;
    day.totalMs += lap.lapMs;
    day.bestLapMs = day.bestLapMs ? Math.min(day.bestLapMs, lap.lapMs) : lap.lapMs;
    day.latestTimestamp = Math.max(day.latestTimestamp, lap.timestamp);
    daily.set(dateKey, day);
  });
  const dailySummaries = [...daily.values()]
    .map((day) => ({
      dateKey: day.dateKey,
      laps: day.laps,
      bestLapMs: day.bestLapMs,
      latestTimestamp: day.latestTimestamp,
      averageLapMs: day.laps ? Math.round(day.totalMs / day.laps) : 0,
    }))
    .sort((left, right) => right.latestTimestamp - left.latestTimestamp)
    .slice(0, 30);
  const bestLapMs = lapTimes.length ? Math.min(...lapTimes) : 0;
  const controlTargetMs = bestLapMs ? Math.round(bestLapMs * 1.08) : 0;
  const stints = splitPublicTrainingStints(laps);
  const summarizeStints = (items) => {
    let controlledLapStreak = 0;
    let outlierLaps = 0;
    let strongFinishStints = 0;
    let cleanFiveMinuteStints = 0;
    let completedFiveMinuteStints = 0;
    items.forEach((stint) => {
      let currentStreak = 0;
      const values = stint.laps.filter((lap) => !lap.excludedFromPace).map((lap) => lap.lapMs);
      values.forEach((lapMs) => {
        currentStreak = controlTargetMs && lapMs <= controlTargetMs ? currentStreak + 1 : 0;
        controlledLapStreak = Math.max(controlledLapStreak, currentStreak);
      });
      const medianMs = publicTrainingPercentile(values, 0.5);
      const stintOutliers = values.filter((lapMs) => medianMs && lapMs > medianMs * 1.15).length;
      outlierLaps += stintOutliers;
      const comparisonSize = values.length >= 6 ? Math.max(3, Math.floor(values.length / 3)) : 0;
      const startPaceMs = comparisonSize ? average(values.slice(0, comparisonSize)) : 0;
      const endPaceMs = comparisonSize ? average(values.slice(-comparisonSize)) : 0;
      if (comparisonSize && endPaceMs <= startPaceMs * 1.01) strongFinishStints += 1;
      const fiveMinuteResult = getPublicBestDurationResult([stint], 5 * 60 * 1000);
      if (fiveMinuteResult) completedFiveMinuteStints += 1;
      if (!stintOutliers && fiveMinuteResult) cleanFiveMinuteStints += 1;
    });
    return { controlledLapStreak, outlierLaps, strongFinishStints, cleanFiveMinuteStints, completedFiveMinuteStints };
  };
  const stintSummary = summarizeStints(stints);
  const today = new Date();
  const todayKey = [today.getFullYear(), String(today.getMonth() + 1).padStart(2, "0"), String(today.getDate()).padStart(2, "0")].join("-");
  const todayStintSummary = summarizeStints(splitPublicTrainingStints(laps.filter((lap) => {
    const date = new Date(lap.timestamp);
    const dateKey = [date.getFullYear(), String(date.getMonth() + 1).padStart(2, "0"), String(date.getDate()).padStart(2, "0")].join("-");
    return !Number.isNaN(date.getTime()) && dateKey === todayKey;
  })));
  const bestThreeConsecutive = getPublicBestConsecutiveLaps(stints, 3);
  const bestFiveMinuteResult = getPublicBestDurationResult(stints, 5 * 60 * 1000);
  const stintSummaries = stints.map((stint, index) => {
    const paceLaps = stint.laps.filter((lap) => !lap.excludedFromPace);
    const values = paceLaps.map((lap) => lap.lapMs);
    const startedAt = Math.max(0, Number(stint.laps[0]?.timestamp || 0) - Number(stint.laps[0]?.lapMs || 0));
    const endedAt = Number(stint.laps.at(-1)?.timestamp || startedAt);
    return {
      number: index + 1, startedAt, lapCount: stint.laps.length,
      bestLapMs: values.length ? Math.min(...values) : 0, averageLapMs: average(values),
      durationMs: Math.max(0, endedAt - startedAt),
    };
  }).sort((left, right) => right.startedAt - left.startedAt).slice(0, 8);

  const stats = {
    totalLaps: laps.length,
    totalSessions: new Set(laps.map((lap) => lap.sessionId).filter(Boolean)).size,
    trainingDays: daily.size,
    bestLapMs,
    averageLapMs: average(lapTimes),
    recentAverageMs,
    previousRecentAverageMs,
    averageTrendMs: trendSampleReady ? previousRecentAverageMs - recentAverageMs : 0,
    recentLapCount: recent.length,
    consistencySpreadPct,
    previousConsistencySpreadPct,
    consistencyTrendPct: trendSampleReady ? previousConsistencySpreadPct - consistencySpreadPct : 0,
    trendSampleReady,
    controlZoneMinMs: bestLapMs,
    controlZoneMaxMs: controlTargetMs,
    controlledLapStreak: stintSummary.controlledLapStreak,
    outlierLaps: stintSummary.outlierLaps,
    strongFinishStints: stintSummary.strongFinishStints,
    cleanFiveMinuteStints: stintSummary.cleanFiveMinuteStints,
    todayControlledLapStreak: todayStintSummary.controlledLapStreak,
    todayStrongFinishStints: todayStintSummary.strongFinishStints,
    todayCleanFiveMinuteStints: todayStintSummary.cleanFiveMinuteStints,
    todayCompletedFiveMinuteStints: todayStintSummary.completedFiveMinuteStints,
    dailySummaries,
    stintCount: stints.length,
    stintSummaries,
    bestThreeConsecutive,
    bestFiveMinuteResult,
  };
  return { ...stats, achievements: buildPublicTrainingAchievements(stats) };
}

function buildPublicTrainingAchievements(stats) {
  const definitions = [
    ["first_session", Number(stats.totalSessions || 0), 1],
    ["laps_100", Number(stats.totalLaps || 0), 100],
    ["laps_500", Number(stats.totalLaps || 0), 500],
    ["laps_1000", Number(stats.totalLaps || 0), 1000],
    ["laps_1500", Number(stats.totalLaps || 0), 1500],
    ["days_3", Number(stats.trainingDays || 0), 3],
    ["days_10", Number(stats.trainingDays || 0), 10],
    ["controlled_5", Number(stats.controlledLapStreak || 0), 5],
    ["strong_finish", Number(stats.strongFinishStints || 0), 1],
    ["clean_five_minute", Number(stats.cleanFiveMinuteStints || 0), 1],
    ["five_minute", stats.bestFiveMinuteResult ? 1 : 0, 1],
  ];
  return definitions.map(([id, current, target]) => ({
    id,
    percent: Math.min(100, Math.max(0, (current / target) * 100)),
    unlocked: current >= target,
  }));
}

function splitPublicTrainingStints(laps, breakThresholdMs = 2 * 60 * 1000) {
  const streams = new Map();
  (Array.isArray(laps) ? laps : []).forEach((lap) => {
    const normalizedLap = {
      ...lap,
      sessionId: String(lap.sessionId || ""),
      sourceId: String(lap.sourceId || lap.driverId || "default"),
      timestamp: Number(lap.timestamp || 0),
      lapMs: Number(lap.lapMs || 0),
    };
    const key = `${normalizedLap.sessionId}:${normalizedLap.sourceId}`;
    if (!streams.has(key)) streams.set(key, []);
    streams.get(key).push(normalizedLap);
  });

  const stints = [];
  streams.forEach((stream) => {
    let current = null;
    stream.sort((left, right) => left.timestamp - right.timestamp).forEach((lap) => {
      const previousLap = current?.laps.at(-1);
      const breakMs = previousLap ? Math.max(0, lap.timestamp - previousLap.timestamp) : 0;
      const detectedBreak = Boolean(previousLap && breakMs > breakThresholdMs);
      const explicitBoundary = Boolean(lap.stintBoundary);
      const normalizedLap = {
        ...lap,
        excludedFromPace: Boolean(lap.excludedFromPace || explicitBoundary || (detectedBreak && lap.lapMs > breakThresholdMs)),
        stintBoundary: Boolean(explicitBoundary || detectedBreak),
      };
      if (!current || detectedBreak || explicitBoundary) {
        current = { sessionId: lap.sessionId, sourceId: lap.sourceId, laps: [normalizedLap] };
        stints.push(current);
      } else {
        current.laps.push(normalizedLap);
      }
    });
  });
  return stints;
}

function getPublicBestConsecutiveLaps(stints, lapCount) {
  let best = null;
  (Array.isArray(stints) ? stints : []).forEach((stint) => {
    const paceLaps = stint.laps.filter((lap) => !lap.excludedFromPace);
    for (let index = 0; index <= paceLaps.length - lapCount; index += 1) {
      const laps = paceLaps.slice(index, index + lapCount);
      const totalMs = laps.reduce((sum, lap) => sum + Number(lap.lapMs || 0), 0);
      const candidate = { lapCount, totalMs, averageLapMs: Math.round(totalMs / lapCount) };
      if (!best || candidate.totalMs < best.totalMs) best = candidate;
    }
  });
  return best;
}

function getPublicBestDurationResult(stints, targetMs) {
  let best = null;
  (Array.isArray(stints) ? stints : []).forEach((stint) => {
    const paceLaps = stint.laps.filter((lap) => !lap.excludedFromPace);
    for (let startIndex = 0; startIndex < paceLaps.length; startIndex += 1) {
      const firstLap = paceLaps[startIndex];
      const startedAt = Math.max(0, Number(firstLap.timestamp || 0) - Number(firstLap.lapMs || 0));
      const cutoffAt = startedAt + targetMs;
      const counted = [];
      for (let index = startIndex; index < paceLaps.length; index += 1) {
        const lap = paceLaps[index];
        const lapStartedAt = Math.max(0, Number(lap.timestamp || 0) - Number(lap.lapMs || 0));
        if (lapStartedAt >= cutoffAt) break;
        counted.push(lap);
      }
      const lastLap = counted.at(-1);
      if (!lastLap || Number(lastLap.timestamp || 0) < cutoffAt) continue;
      const totalMs = Number(lastLap.timestamp || 0) - startedAt;
      if (totalMs > targetMs + Number(lastLap.lapMs || 0)) continue;
      const lapCount = counted.length;
      const candidate = {
        targetMs,
        lapCount,
        totalMs,
        averageLapMs: Math.round(counted.reduce((sum, lap) => sum + Number(lap.lapMs || 0), 0) / lapCount),
      };
      if (!best || candidate.lapCount > best.lapCount || (candidate.lapCount === best.lapCount && candidate.totalMs < best.totalMs)) best = candidate;
    }
  });
  return best;
}

function addPublicTrainingMarker(state, shareToken, input, timestamp = Date.now()) {
  const share = findPublicTrainingShare(state, shareToken);
  if (!share) return null;
  const drivers = Array.isArray(state?.drivers) ? state.drivers : [];
  const profile = getPublicDriverProfile(drivers, share.driverId);
  const classId = String(input?.classId || "");
  const classDriver = profile?.drivers.find((driver) => String(driver?.classId || "") === classId);
  if (!classDriver) return null;
  const type = ["setup", "tires", "suspension", "car", "note"].includes(String(input?.type || "")) ? String(input.type) : "car";
  const marker = {
    id: "training_marker_public_" + Math.round(timestamp) + "_" + Math.random().toString(36).slice(2, 9),
    driverId: String(classDriver.id || ""), classId, sessionId: "", timestamp: Math.round(timestamp), type,
    note: String(input?.note || "").trim().slice(0, 160), createdAt: new Date(timestamp).toISOString(),
  };
  if (!state.training || typeof state.training !== "object") state.training = {};
  state.training.markers = [...(Array.isArray(state.training.markers) ? state.training.markers : []), marker];
  return marker;
}

function normalizePublicLogoDataUrl(value) {
  const logo = String(value || "").trim();
  return logo.length <= 2_000_000 && /^data:image\/(?:png|jpeg|webp);base64,/i.test(logo) ? logo : "";
}

function getPublicTrainingMilestone(currentLaps) {
  const presets = [100, 250, 500, 1000, 1500, 2500, 5000];
  const targetLaps = presets.find((target) => target > currentLaps) || (Math.floor(currentLaps / 1000) + 1) * 1000;
  return {
    currentLaps,
    targetLaps,
    remainingLaps: Math.max(0, targetLaps - currentLaps),
    percent: Math.min(100, Math.max(0, (currentLaps / targetLaps) * 100)),
  };
}

function getPublicTrainingRecommendation(stats) {
  if (stats.totalLaps < 10) return { level: "baseline", targetLapMs: 0 };
  if (stats.recentLapCount >= 10 && stats.consistencySpreadPct > 8) {
    return { level: "consistency", targetLapMs: Math.round(stats.bestLapMs * 1.08) };
  }
  return { level: "progression", targetLapMs: Math.round(stats.bestLapMs * 1.05) };
}

module.exports = {
  buildPublicTrainingProfile,
  findPublicTrainingShare,
  hasPublicTrainingShare,
  normalizePublicTrainingSince,
  normalizePublicTrainingWindow,
  splitPublicTrainingStints,
  getPublicBestConsecutiveLaps,
  getPublicBestDurationResult,
  addPublicTrainingMarker,
  buildPublicEvenLapsProgress,
  buildPublicStepTrainingProgress,
  savePublicEvenLapsConfig,
  savePublicStepTrainingConfig,
};