2.5

Data table compact hours 1h, 2h, 3h or 4h. Calc max/min or average

assets/js/compute.js

@@ -231,6 +231,80 @@ export function buildHourlyRows({ forecast, airQuality, location, vehicleType, v
   return { hourlyRows, days, utcOffsetMs };
 }
 
+// ------------------------------------------------------------------------
+//  aggregateRows(rows, interval) - Compress a day's hourly rows into
+//  multi-hour buckets for the table view (1h / 2h / 3h / 4h).
+//
+//  Buckets are CLOCK-ALIGNED from midnight, so a 2h view groups 00-01,
+//  02-03, ...; 3h groups 00-02, 03-05, ...; etc. Each output row keeps the
+//  LANDING (first) hour's positional fields - iso, dt, solar elevation,
+//  global radiation, wind direction, sky/cloud category - so the little
+//  scope, clock label and wind vane all show the time the column lands on.
+//
+//  Every other column is aggregated with the rule that fits its meaning:
+//    - SUM  : precipitation totals (1mm/h x 3h = 3mm)
+//    - MAX  : risk/peak columns (rain %, UV, gusts, cabin/surface/indoor)
+//    - MEAN : smooth continuous quantities (air temp, RH, wind, cloud, ...)
+//    - FELT : worst-case felt temp - the warmest hour if it reaches >=20C
+//             (heat is the concern), otherwise the coldest (cold concern)
+//
+//  Derived render values (Delta, Burn) are recomputed downstream from the
+//  aggregated Air / UTCI / UV, so they stay consistent automatically.
+//
+//  interval <= 1 returns the input untouched (zero behaviour change).
+// ------------------------------------------------------------------------
+const AGG_MEAN = [
+  'Ta', 'RH', 'dew', 'va', 'dir', 'dif', 'cc', 'ccLow', 'ccMid', 'ccHigh',
+  'soilT0', 'soilT6', 'soilM', 'Tmrt', 'eh', 'visKm', 'aqi', 'effectiveRad',
+  'grassPollen', 'birchPollen', 'alderPollen', 'mugwortPollen', 'olivePollen', 'ragweedPollen',
+];
+const AGG_MAX = [
+  'precipProb', 'uv', 'uvA', 'uvB', 'gust',
+  'vehicleT', 'concreteT', 'indoorT', 'managedT',
+];
+const AGG_SUM  = ['precip', 'snow'];
+const AGG_FELT = ['utci', 'utciAdj'];
+
+export function aggregateRows(rows, interval) {
+  if (!rows || rows.length === 0 || !interval || interval <= 1) return rows;
+
+  // Numbers for a field across the group, skipping null / NaN.
+  const nums = (group, f) => group.map(r => r[f]).filter(v => v != null && !isNaN(v));
+  const mean = (vals) => vals.length ? vals.reduce((a, b) => a + b, 0) / vals.length : null;
+  const maxV = (vals) => vals.length ? Math.max(...vals) : null;
+  const felt = (vals) => {
+    if (!vals.length) return null;
+    const hi = Math.max(...vals);
+    return hi >= 20 ? hi : Math.min(...vals);
+  };
+
+  // Split rows into clock-aligned buckets by floor(localHour / interval).
+  const buckets = [];
+  let cur = null, curKey = null;
+  for (const r of rows) {
+    const hour = parseInt(r.iso.slice(11, 13), 10);
+    const key  = Math.floor(hour / interval);
+    if (key !== curKey) { cur = []; buckets.push(cur); curKey = key; }
+    cur.push(r);
+  }
+
+  return buckets.map(group => {
+    // Start from the landing row so positional fields (iso, dt, elev, glob,
+    // wd, compass, cloudCat) carry through unchanged, then overwrite the
+    // aggregatable columns.
+    const out = { ...group[0] };
+    AGG_MEAN.forEach(f => { out[f] = mean(nums(group, f)); });
+    AGG_MAX.forEach(f  => { out[f] = maxV(nums(group, f)); });
+    AGG_SUM.forEach(f  => { out[f] = group.reduce((a, r) => a + (r[f] || 0), 0); });
+    AGG_FELT.forEach(f => { out[f] = felt(nums(group, f)); });
+    // Bucket span markers used by the table for "now" highlighting and for
+    // matching event cell-tags that fall on any hour within the bucket.
+    out.isoHours = group.map(r => r.iso.slice(0, 13));
+    out.isoEnd   = group[group.length - 1].iso;
+    return out;
+  });
+}
+
 // ------------------------------------------------------------------------
 //  computeWhyFeelsLike(row, env) - Break down the felt-temp delta into its
 //  contributing factors for the "Why it feels like this" panel.