// ------------------------------------------------------------------------
//  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,
  calcConcreteTempPass, calcVehicleInteriorTemp,
  calcIndoorTempPass, calcManagedIndoorTempPass,
} from './physics.js';
import { windCompass8, uvSplit, cloudCategory, precipPenalty } from './utils.js';
import { UTCI_ENVIRONMENTS } from './config.js';

export function buildHourlyRows({ forecast, airQuality, location, vehicleType, vehicleVent, buildingType, utciEnv }) {
  const env = UTCI_ENVIRONMENTS[utciEnv] ?? UTCI_ENVIRONMENTS.open;
  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 precipProb  = h.precipitation_probability ? (h.precipitation_probability[i] ?? 0) : 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;
    // 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 -
    // iso is a local wall-clock string e.g. "2026-05-13T14:00" (no Z).
    // For solarElevationDeg (which uses .getUTC* internally) we need the
    // true UTC instant: treat the local time as UTC then subtract the offset.
    const dt   = new Date(Date.parse(iso + 'Z') - utcOffsetMs);
    const elev = solarElevationDeg(location.lat, location.lon, dt);
    // Use direct_radiation (beam sunlight) + a fraction of diffuse for concrete.
    // direct_radiation is zero on fully overcast days - far more accurate than
    // shortwave_radiation which can be unreliably high even at 100% cloud cover.
    // Diffuse (scattered light through cloud) contributes ~20% as much heat to
    // a surface as direct beam, so we weight it accordingly.
    const effectiveRad = dir + dif * 0.2;
    // concreteT is now stamped in the two-pass section below (thermal lag).
    const vehicleT = calcVehicleInteriorTemp(Ta, glob, elev, vehicleType, vehicleVent);
    const eh       = vaporPressureHpa(Ta, RH);
    // Apply environment modifier - adjust solar inputs and air temp for shaded environments.
    const TaEnv   = Ta   + env.taOffset;
    const dirEnv  = dir  * env.dirFactor;
    const difEnv  = dif  * env.difFactor;
    const globEnv = glob * env.globFactor;
    const Tmrt     = calcTmrt(TaEnv, dirEnv, difEnv, globEnv, elev);
    const utci     = utciApprox(TaEnv, 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, precipProb, snow,
      soilT0, soilT6, soilM, vehicleT,
      effectiveRad,
      elev, Tmrt, utci, utciAdj, eh, compass,
      visKm, aqi,
      grassPollen, birchPollen, alderPollen, mugwortPollen, olivePollen, ragweedPollen,
    };
  }) : [];

  // Two-pass calculations: concrete thermal lag + indoor temperature.
  // Both need the full hourly arrays so they can look back at previous
  // hours. Run after hourlyRows is built, then stamp each row.
  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 radArr      = hourlyRows.map(r => r.effectiveRad);
    const vaArr       = hourlyRows.map(r => r.va);
    const uvArr       = hourlyRows.map(r => r.uv);
    const cloudCatArr = hourlyRows.map(r => r.cloudCat);
    const soilMArr    = hourlyRows.map(r => r.soilM);
    const precipArr   = hourlyRows.map(r => r.precip);
    const snowArr     = hourlyRows.map(r => r.snow);

    // Concrete surface temperature with thermal lag (1.5 h time constant).
    // A slab baking in the sun retains heat when cloud rolls in, and takes
    // a couple of hours of sunshine to fully heat up from a cold start.
    const concreteTemps = calcConcreteTempPass(
      TaArr, radArr, vaArr, elevArr, uvArr, cloudCatArr, soilMArr, precipArr, snowArr
    );
    hourlyRows.forEach((r, i) => { r.concreteT = concreteTemps[i]; });

    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/m-
  //
  // 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 };
}