prop/lib/microwaveprop/rover/compute.ex

defmodule Microwaveprop.Rover.Compute do
  @moduledoc """
  End-to-end Calculate pipeline for the rover planner.

  Given a home QTH, a list of selected fixed stations, a band/time/mode,
  and drive/elevation constraints, returns the per-cell quality scores
  plus the top 5 candidate parking spots.
  """

  alias Microwaveprop.Propagation
  alias Microwaveprop.Radio.Maidenhead
  alias Microwaveprop.Rover.Aggregator
  alias Microwaveprop.Rover.DriveTime
  alias Microwaveprop.Rover.Elevation
  alias Microwaveprop.Rover.LinkMargin

  @avg_speed_kmh 65.0
  @drive_penalty_db_per_hour 2.0
  @elev_bonus_db_per_100m 1.0
  @elev_bonus_cap_db 5.0
  @top_n 5

  @tier_excellent 10.0
  @tier_good 3.0
  @tier_marginal 0.0

  @color_excellent "#16a34a"
  @color_good "#eab308"
  @color_marginal "#f97316"

  @type run_args :: %{
          home: %{lat: float(), lon: float(), elev_m: integer() | nil},
          stations: [%{callsign: String.t(), lat: float(), lon: float(), selected: boolean()}],
          band_mhz: non_neg_integer(),
          valid_time: DateTime.t(),
          mode: atom(),
          max_drive_min: integer(),
          min_elev_gain: integer()
        }

  @spec run(run_args(), keyword()) :: %{cells: [map()], top_candidates: [map()]}
  def run(args, deps \\ []) do
    scores_at = Keyword.get(deps, :scores_at, &Propagation.scores_at/3)
    elev_lookup = Keyword.get(deps, :elev_lookup, &Elevation.lookup_many/1)

    %{
      home: home,
      stations: stations,
      band_mhz: band_mhz,
      valid_time: valid_time,
      mode: mode,
      max_drive_min: max_drive_min,
      min_elev_gain: min_elev_gain
    } = args

    selected_stations = Enum.filter(stations, & &1.selected)
    radius_km = max_drive_min * @avg_speed_kmh / 60.0
    bbox = bbox_around(home, radius_km)

    raw_cells = scores_at.(band_mhz, valid_time, bbox)

    # First filter: drive radius
    in_radius =
      Enum.filter(raw_cells, fn cell ->
        DriveTime.haversine_km({home.lat, home.lon}, {cell.lat, cell.lon}) <= radius_km
      end)

    # Bulk elevation lookup for surviving cells
    points = Enum.map(in_radius, &{&1.lat, &1.lon})
    elev_map = elev_lookup.(points)
    home_elev = home.elev_m || 0

    cells =
      in_radius
      |> Enum.map(fn cell -> annotate_cell(cell, elev_map, home, home_elev, mode, selected_stations) end)
      |> Enum.filter(&keep_cell?(&1, home_elev, min_elev_gain))

    top_candidates =
      cells |> Enum.sort_by(& &1.score, :desc) |> Enum.take(@top_n) |> Enum.map(&candidate_payload(&1, home))

    %{cells: cells, top_candidates: top_candidates}
  end

  defp keep_cell?(nil, _home_elev, _min_gain), do: false

  defp keep_cell?(%{elev_m: elev_m, score: score}, home_elev, min_gain) when is_integer(elev_m) do
    elev_m - home_elev >= min_gain and score >= 0
  end

  defp keep_cell?(_, _, _), do: false

  defp annotate_cell(cell, elev_map, home, home_elev, mode, stations) do
    elev_m = Map.get(elev_map, {cell.lat, cell.lon})

    case elev_m do
      nil ->
        nil

      _ ->
        margins = Enum.map(stations, fn _s -> LinkMargin.link_margin_from_score(cell.score, mode) end)
        agg = Aggregator.cell_margin_db(margins)

        case agg do
          nil ->
            nil

          agg_db ->
            dist_km = DriveTime.haversine_km({home.lat, home.lon}, {cell.lat, cell.lon})
            elev_bonus = elev_bonus(elev_m, home_elev)
            drive_penalty = drive_penalty(dist_km)
            score = agg_db + elev_bonus - drive_penalty

            %{
              lat: cell.lat,
              lon: cell.lon,
              elev_m: elev_m,
              score: score,
              distance_km: dist_km,
              tier_color: tier_color(score)
            }
        end
    end
  end

  defp candidate_payload(cell, home) do
    drive_min = DriveTime.drive_min(cell.distance_km)
    bearing = DriveTime.bearing_compass({home.lat, home.lon}, {cell.lat, cell.lon})
    grid = Maidenhead.from_latlon(cell.lat, cell.lon, 10)

    %{
      grid: grid,
      lat: cell.lat,
      lon: cell.lon,
      elev_m: cell.elev_m,
      drive_min: drive_min,
      score: cell.score,
      tier_color: cell.tier_color,
      distance_km: cell.distance_km,
      bearing_compass: bearing,
      name: "#{grid} — #{round(cell.distance_km)} km #{bearing} of home"
    }
  end

  defp elev_bonus(elev_c, elev_home) do
    diff = (elev_c - elev_home) / 100.0 * @elev_bonus_db_per_100m
    diff |> max(0.0) |> min(@elev_bonus_cap_db)
  end

  defp drive_penalty(dist_km), do: dist_km / @avg_speed_kmh * @drive_penalty_db_per_hour

  defp tier_color(score) do
    cond do
      score >= @tier_excellent -> @color_excellent
      score >= @tier_good -> @color_good
      score >= @tier_marginal -> @color_marginal
      true -> @color_marginal
    end
  end

  # Approximate bounding box. 1 deg lat ≈ 111 km; 1 deg lon ≈ 111·cos(lat) km.
  defp bbox_around(%{lat: lat, lon: lon}, radius_km) do
    dlat = radius_km / 111.0
    dlon = radius_km / (111.0 * max(:math.cos(lat * :math.pi() / 180.0), 0.1))

    %{
      "south" => lat - dlat,
      "north" => lat + dlat,
      "west" => lon - dlon,
      "east" => lon + dlon
    }
  end
end