Graham McIntire
cfc5f7583f
Subscribes to mqtt.pskreporter.info:1883 over plain TCP and folds every reception report into hourly aggregates per (band, sender_grid_4, receiver_grid_4). Each spot is the empirical "propagation actually occurred on this path right now" signal we'll correlate against HRRR atmospheric state to recalibrate the scoring weights. Aggregating at the path-hour bucket collapses 50-reporter QSOs to one row instead of 50. Topic filter anchors on USA (DXCC 291) on either sender or receiver side so the broker pre-filters out everything outside our HRRR domain. Bands subscribed: VHF and up (6m, 2m, 70cm, 23cm, + microwave) — HF is dominated by ionospheric propagation, which this project doesn't model. Pskr.Client cluster-elects a singleton via :global.register_name — all replicas start the GenServer but only one connects to MQTT; the rest stay in :standby and watch for the leader's nodedown to re-run election. Off in dev/test, on by default in prod (PSKR_MQTT_ENABLED=false as kill-switch). Pskr.Aggregator buffers in memory keyed by Pskr.path_key/1 and flushes every 60s via Repo.insert_all/3 with an additive ON CONFLICT (count summed, SNR envelope by GREATEST/LEAST, modes unioned via unnest+DISTINCT). Idempotent across overlapping flushes and across leader handover. Dockerfile sets BUILD_WITHOUT_QUIC=1 — emqtt's transitive quicer dep wants CMake + OpenSSL headers we'd otherwise have to add to the builder image just to never use QUIC over plain MQTT. Base image is unchanged; the new dep compiles cleanly into the existing prop-base runtime.
68 lines
2.4 KiB
Elixir
68 lines
2.4 KiB
Elixir
defmodule Microwaveprop.Pskr.SpotHourly do
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@moduledoc """
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Hourly aggregate of PSK Reporter spots between two 4-character
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Maidenhead grid squares on a single band.
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A single QSO often produces 50+ reception reports (one per
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monitoring station that decoded it). Storing each report verbatim
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would dwarf the rest of the database — instead `Pskr.Aggregator`
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collapses every spot inside `(hour_utc, band, sender_grid,
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receiver_grid)` into one row, retaining `spot_count`, SNR
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envelope, and the set of modes observed. Downstream weather
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correlation cares about *did propagation occur in this cell during
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this hour*, not about each individual reporter — so the lossy
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aggregation is the desired shape, not just a space optimization.
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`midpoint_lat/lon` is the great-circle midpoint of the path,
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pre-computed at insert time so HRRR / IEMRE grid lookups are a
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cheap `WHERE midpoint_lat BETWEEN …` rather than a per-row
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recomputation.
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"""
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use Ecto.Schema
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import Ecto.Changeset
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@primary_key {:id, :binary_id, autogenerate: true}
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@foreign_key_type :binary_id
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schema "pskr_spots_hourly" do
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field :hour_utc, :utc_datetime
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field :band, :string
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field :sender_grid, :string
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field :receiver_grid, :string
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field :sender_country, :integer
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field :receiver_country, :integer
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field :sender_lat, :float
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field :sender_lon, :float
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field :receiver_lat, :float
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field :receiver_lon, :float
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field :midpoint_lat, :float
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field :midpoint_lon, :float
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field :distance_km, :float
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field :spot_count, :integer, default: 0
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field :max_snr_db, :integer
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field :min_snr_db, :integer
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field :modes, {:array, :string}, default: []
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field :first_spot_at, :utc_datetime
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field :last_spot_at, :utc_datetime
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timestamps(type: :utc_datetime)
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end
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@type t :: %__MODULE__{}
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@cast_fields ~w(hour_utc band sender_grid receiver_grid sender_country receiver_country
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sender_lat sender_lon receiver_lat receiver_lon midpoint_lat midpoint_lon
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distance_km spot_count max_snr_db min_snr_db modes first_spot_at
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last_spot_at)a
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@required_fields ~w(hour_utc band sender_grid receiver_grid spot_count)a
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@spec changeset(t(), map()) :: Ecto.Changeset.t()
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def changeset(record, attrs) do
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record
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|> cast(attrs, @cast_fields)
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|> validate_required(@required_fields)
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|> unique_constraint([:hour_utc, :band, :sender_grid, :receiver_grid])
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end
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end
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