// ════════════════════════════════════════════════════════════════════════
//  compute.js — Build the per-hour display rows from the raw API data.
//
//  Pure-ish function: feed in (forecast, airQuality, location, vehicleType,
//  vehicleVent, buildingType) and get back { hourlyRows, days, utcOffsetMs }.
//
//  Open-Meteo with timezone=auto returns local wall-clock strings like
//  "2026-05-13T14:00" — no Z suffix. Two forms are used in each row:
//    • String slices (iso.slice(...)) for display & day grouping
//    • A true UTC Date (dt) for solarElevationDeg (which uses .getUTC*).
// ════════════════════════════════════════════════════════════════════════

import {
  vaporPressureHpa, solarElevationDeg, calcTmrt, utciApprox,
  calcConcreteTemp, calcVehicleInteriorTemp,
  calcIndoorTempPass, calcManagedIndoorTempPass,
} from './physics.js';
import { windCompass8, uvSplit, cloudCategory, precipPenalty } from './utils.js';

export function buildHourlyRows({ forecast, airQuality, location, vehicleType, vehicleVent, buildingType }) {
  const utcOffsetMs = (forecast?.utc_offset_seconds ?? 0) * 1000;

  // Build a fast lookup map from the air quality hourly data: ISO string → index.
  // Normalise to "YYYY-MM-DDTHH" (13 chars) so forecast timestamps like
  // "2026-05-17T14:00" match AQ timestamps like "2026-05-17T14".
  const aqTimeMap = {};
  if (airQuality?.hourly?.time) {
    airQuality.hourly.time.forEach((t, i) => { aqTimeMap[t.slice(0, 13)] = i; });
  }
  const getAq = (field, iso) => {
    if (!airQuality?.hourly?.[field]) return null;
    const i = aqTimeMap[iso.slice(0, 13)];
    if (i === undefined) return null;
    return airQuality.hourly[field][i] ?? null;
  };

  const hourlyRows = forecast ? forecast.hourly.time.map((iso, i) => {
    const h = forecast.hourly;
    const Ta   = h.temperature_2m[i];
    const RH   = h.relative_humidity_2m[i];
    const dew  = h.dew_point_2m ? h.dew_point_2m[i] : null;
    const va   = h.wind_speed_10m[i];
    const wd   = h.wind_direction_10m ? h.wind_direction_10m[i] : null;
    const gust = h.wind_gusts_10m    ? h.wind_gusts_10m[i]    : null;
    const dir  = h.direct_radiation[i]    || 0;
    const dif  = h.diffuse_radiation[i]   || 0;
    const glob = h.shortwave_radiation[i] || 0;
    const cc     = h.cloud_cover[i];
    const ccLow  = h.cloud_cover_low  ? h.cloud_cover_low[i]  : null;
    const ccMid  = h.cloud_cover_mid  ? h.cloud_cover_mid[i]  : null;
    const ccHigh = h.cloud_cover_high ? h.cloud_cover_high[i] : null;
    const uv     = h.uv_index ? (h.uv_index[i] || 0) : 0;
    const precip = h.precipitation[i] || 0;
    const snow   = h.snowfall[i] || 0;
    const soilT0 = h.soil_temperature_0cm    ? h.soil_temperature_0cm[i]    : null;
    const soilT6 = h.soil_temperature_6cm    ? h.soil_temperature_6cm[i]    : null;
    const soilM  = h.soil_moisture_0_to_1cm  ? h.soil_moisture_0_to_1cm[i]  : null;
    // Scale radiation by cloud cover before passing to concrete calc.
    // shortwave_radiation and cloud_cover are separate forecast fields that
    // don't always agree — this prevents hot concrete values on cloudy days.
    // 100% cloud -> 85% reduction, 0% cloud -> full radiation.
    const effectiveRad = glob * (1 - (cc / 100) * 0.85);
    const concreteT = calcConcreteTemp(Ta, effectiveRad, va);
    // iso is a local wall-clock string e.g. "2026-05-13T14:00" (no Z).
    // For display we slice the string directly — no Date object needed.
    // For solarElevationDeg (which uses .getUTC* internally) we need the
    // true UTC instant: treat the local time as UTC then subtract the offset.
    // e.g. Brisbane UTC+10: local 14:00 → parse as UTC 14:00 → subtract 10h → UTC 04:00 ✓
    const dt   = new Date(Date.parse(iso + 'Z') - utcOffsetMs);
    const elev = solarElevationDeg(location.lat, location.lon, dt);
    const vehicleT = calcVehicleInteriorTemp(Ta, glob, elev, vehicleType, vehicleVent);
    const eh       = vaporPressureHpa(Ta, RH);
    const Tmrt     = calcTmrt(Ta, dir, dif, glob, elev);
    const utci     = utciApprox(Ta, Tmrt, va, eh);
    const utciAdj = utci + precipPenalty(precip, snow, va);
    // Derived
    const compass = windCompass8(wd);
    const { uvA, uvB } = uvSplit(uv, elev);
    const cloudCat = cloudCategory(cc, ccLow, ccMid, ccHigh);
    // Visibility from the main forecast API (metres → km).
    const visKm        = (() => { const v = h.visibility ? h.visibility[i] : null; return v != null ? v / 1000 : null; })();
    const aqi          = getAq('european_aqi', iso);
    const grassPollen  = getAq('grass_pollen', iso);
    const birchPollen  = getAq('birch_pollen', iso);
    const alderPollen  = getAq('alder_pollen', iso);
    const mugwortPollen= getAq('mugwort_pollen', iso);
    const olivePollen  = getAq('olive_pollen', iso);
    const ragweedPollen= getAq('ragweed_pollen', iso);
    // ── FUTURE FEATURE: Activity "What If" Modifier ───────────────────────────
    // Add two extra columns driven by a user-selected activity level. These are
    // intentionally kept SEPARATE from the core columns above so that baseline
    // profile data stays consistent and comparable across profiles.
    //
    // The user picks an activity from a simple UI picker (no live data needed —
    // this is a forecast/planning tool, not a tracker):
    //   Resting → Walking → Cycling → Running → Sport/Intense
    //
    // Two output columns only (keep it clean):
    //
    //   adjustedSafeTime  — baseline UV safe exposure time × an activity multiplier.
    //                       Higher activity = shorter safe time, because:
    //                         • metabolic heat raises core body temp
    //                         • sweating washes away sunscreen faster
    //                         • more skin blood flow = higher UV sensitivity
    //                       Suggested multipliers (tune with real data):
    //                         Resting: 1.0 (no change)
    //                         Walking: 0.85
    //                         Cycling: 0.75
    //                         Running: 0.60
    //                         Sport:   0.50
    //
    //   heatStressLevel   — a simple label: 'Low' | 'Moderate' | 'High' | 'Very High'
    //                       Derived from UTCI + activity heat load. A runner at
    //                       UTCI 28°C should read 'High' even if a resting person
    //                       would read 'Moderate' at the same UTCI.
    //                       Colour code in the UI: 🟢 🟡 🟠 🔴
    //
    // Implementation sketch:
    //   1. Accept `activityLevel` as a new param to buildHourlyRows() alongside
    //      vehicleType, buildingType etc.
    //   2. Define ACTIVITY_PRESETS in utils.js (multiplier + utciOffset per level).
    //   3. Compute adjustedSafeTime = baseSafeTime * preset.multiplier
    //   4. Compute heatStressLevel from (utci + preset.utciOffset) banded into labels.
    //   5. Add both fields to the returned row object below.
    //   6. In components.js, render these as optional columns that only appear when
    //      an activity other than 'Resting' is selected — keeps the default table clean.
    // ─────────────────────────────────────────────────────────────────────────────

    return {
      iso, dt, Ta, RH, dew, va, wd, gust, dir, dif, glob,
      cc, ccLow, ccMid, ccHigh, cloudCat,
      uv, uvA, uvB,
      precip, snow,
      soilT0, soilT6, soilM, concreteT, vehicleT,
      elev, Tmrt, utci, utciAdj, eh, compass,
      visKm, aqi,
      grassPollen, birchPollen, alderPollen, mugwortPollen, olivePollen, ragweedPollen,
    };
  }) : [];

  // Two-pass indoor temperature: needs the full hourly arrays so thermal
  // lag can look back at previous hours. Run after hourlyRows is built,
  // then stamp each row with its indoorT value.
  if (hourlyRows.length > 0) {
    const TaArr   = hourlyRows.map(r => r.Ta);
    const globArr = hourlyRows.map(r => r.glob);
    const elevArr = hourlyRows.map(r => r.elev);
    const indoorTemps = calcIndoorTempPass(TaArr, globArr, elevArr, buildingType);
    const managedTemps = calcManagedIndoorTempPass(TaArr, globArr, elevArr, buildingType);
    hourlyRows.forEach((r, i) => { r.indoorT = indoorTemps[i]; r.managedT = managedTemps[i]; });
  }

  // Group those hourly rows into days for the day tabs.
  const days = [];
  hourlyRows.forEach(row => {
    const key = row.iso.slice(0, 10);
    let day = days.find(d => d.key === key);
    if (!day) { day = { key, rows: [] }; days.push(day); }
    day.rows.push(row);
  });

  // ── FUTURE FEATURE v2: Today Summary + Alert System ──────────────────────────
  // After grouping rows into days, generate a per-day summary object that powers
  // a stylish "Today at a Glance" panel shown above or below the main dial.
  //
  // The summary is NOT a live alert/push system — it's a forecast digest that
  // refreshes with the forecast data. Think of it as a smart briefing card.
  //
  // WHAT TO COMPUTE (per day, from that day's rows):
  //   • Peak UTCI+P and time it occurs        → heat stress headline
  //   • Min UTCI+P and time                   → cold stress headline
  //   • Peak UV index and time                → UV warning
  //   • Max precipitation rate and time       → rain/ice warning
  //   • Max vehicle cabin temp                → "dangerous to leave pets/children in car"
  //   • Max pollen level + type               → pollen advisory
  //   • Road condition risk (low air temp + precip → ice risk)
  //
  // ALERT CATEGORIES (each generates a styled warning card if threshold exceeded):
  //   🌡️  Heat warning      UTCI+P > 32°C
  //   🥶  Cold warning       UTCI+P < 0°C
  //   ☀️  UV warning         UV index > 6
  //   🌧️  Heavy rain         precip > 4mm/h
  //   🧊  Ice/road risk      Ta < 3°C + any precip (or recent precip overnight)
  //   🚗  Vehicle danger     vehicleT > 35°C ("don't leave pets or children in car")
  //   🌿  High pollen        any pollen type > 50 grains/m³
  //
  // DESIGN NOTES:
  //   • Cards should be concise — one line of bold text + a short explanation
  //   • Colour-coded to match the existing UTCI stress band palette
  //   • Collapsible — show top 2-3 alerts by default, expand for full list
  //   • For today only (days[0]); optionally extend to day tabs in a later pass
  //   • The "X°C above seasonal norm" historical context line (see app.js comment)
  //     could live here too, as a subtle subheading under the dial temperature
  //
  // Suggested return shape — add to the return value below:
  //   daySummaries: days.map(day => buildDaySummary(day.rows))
  //
  // where buildDaySummary() is a new helper in this file (or a separate
  // summary.js module if it grows large).
  // ─────────────────────────────────────────────────────────────────────────────

  return { hourlyRows, days, utcOffsetMs };
}