Graham McIntire
2defa3db7a
- Unify haversine_km / deg_to_rad: Geo is the canonical home (atan2
form for antipodal stability); Radio, common_volume, rain_scatter,
ionosphere, terrain/srtm, terrain/elevation_client now delegate.
- Drop safe_min/safe_max wrappers in favor of Enum.min/max defaults.
- Move parse_float / parse_int to MicrowavepropWeb.LiveHelpers; eme,
path, and rover LiveViews import from there.
- Extract MicrowavepropWeb.LocationResolver from the eme/path
resolve_source / resolve_location duplicate. Standardize the kind
key and "Invalid grid square" error message.
- Convert Scorer cond chains (humidity, td_depression, sky, wind,
rain, pwat, pressure) to multi-clause guarded defs, matching the
existing classify_time_period style.
- extract_profile_fields uses a named map accumulator instead of a
6-tuple; build_path_conditions guards on %{temps: []} / %{dewpoints: []}.
- Collapse scores_file clamp/3 to max |> min.
- humidity_effect_label lives in BandConfig; map_live and path_live
both use it.
104 lines
3.5 KiB
Elixir
104 lines
3.5 KiB
Elixir
defmodule Microwaveprop.Rover.PathTerrain do
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@moduledoc """
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Per-link terrain clearance for the rover scoring pipeline.
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For each (rover cell, fixed station) pair, samples elevation along the
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great-circle path between them and reports how far the rover sits
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above the highest intermediate terrain. Positive clearance means the
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rover should clear trees/buildings between it and the station; negative
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clearance means the path is blocked by an intervening ridge.
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"""
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alias Microwaveprop.Buildings.Index, as: BuildingsIndex
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alias Microwaveprop.Canopy
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alias Microwaveprop.Rover.Elevation
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# Number of intermediate samples between rover and station.
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@sample_count 12
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# Search radius around each path sample point when looking for
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# buildings that obstruct the line of sight. ~150 m catches the
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# typical width of a downtown high-rise plus a margin for building
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# centroid imprecision.
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@building_search_radius_m 150
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@type latlon :: {float(), float()}
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@doc """
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Returns `%{ {rover_pt, station_pt} => clearance_m | nil }`.
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`clearance_m` is `rover_elev - max(intermediate_path_elev)`. `nil` when
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the rover cell or the entire intermediate path lacks SRTM coverage.
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"""
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@spec clearance_map([map()], [map()], keyword()) :: %{{latlon(), latlon()} => integer() | nil}
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def clearance_map(cells, stations, opts \\ []) when is_list(cells) and is_list(stations) do
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elev_lookup = Keyword.get(opts, :elev_lookup, &Elevation.lookup_many/1)
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buildings_lookup = Keyword.get(opts, :buildings_lookup, &default_building_height/1)
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canopy_lookup = Keyword.get(opts, :canopy_lookup, &default_canopy_height/1)
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pairs =
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for cell <- cells, station <- stations do
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{{cell.lat, cell.lon}, {station.lat, station.lon}}
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end
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sample_points =
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pairs
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|> Enum.flat_map(fn {a, b} -> intermediate_samples(a, b) end)
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|> Enum.uniq()
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rover_points = Enum.map(cells, fn c -> {c.lat, c.lon} end)
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elev = elev_lookup.(rover_points ++ sample_points)
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Map.new(pairs, fn {rover_pt, station_pt} ->
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{{rover_pt, station_pt}, clearance(rover_pt, station_pt, elev, buildings_lookup, canopy_lookup)}
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end)
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end
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defp clearance(rover_pt, station_pt, elev, buildings_lookup, canopy_lookup) do
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rover_elev = Map.get(elev, rover_pt)
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case rover_elev do
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nil ->
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nil
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_ ->
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path_max =
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rover_pt
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|> intermediate_samples(station_pt)
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|> Enum.map(&obstacle_top(&1, elev, buildings_lookup, canopy_lookup))
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|> Enum.reject(&is_nil/1)
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|> Enum.max(fn -> nil end)
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if path_max, do: rover_elev - path_max
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end
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end
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defp obstacle_top({lat, lon} = pt, elev, buildings_lookup, canopy_lookup) do
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case Map.get(elev, pt) do
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nil ->
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nil
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ground ->
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building_m = buildings_lookup.({lat, lon})
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canopy_m = canopy_lookup.({lat, lon})
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ground + round(max(building_m, canopy_m))
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end
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end
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defp default_building_height({lat, lon}) do
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BuildingsIndex.max_height_near(lat, lon, @building_search_radius_m)
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end
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defp default_canopy_height({lat, lon}) do
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Canopy.lookup(lat, lon)
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end
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defp intermediate_samples({lat1, lon1}, {lat2, lon2}) do
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# Linear interpolation in lat/lon — fine at the distances we work with
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# (≤200 mi). Skip endpoints (i=0 is the rover, i=N+1 is the station).
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for i <- 1..@sample_count do
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f = i / (@sample_count + 1)
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{lat1 + f * (lat2 - lat1), lon1 + f * (lon2 - lon1)}
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end
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end
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end
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