Graham McIntire
48000b0dca
`Pskr.Recalibrator.run/0` reads `pskr_calibration_samples`, bins
each sample per (band × feature), and writes spot-density stats to
`pskr_feature_bins` so an operator can read whether a feature
actually discriminates propagation at the threshold granularity
the scorer uses.
Bins, not regression: `BandConfig` already encodes scoring as
discrete thresholds, so the bin output matches that shape and an
operator can copy adjusted thresholds directly without translating
from regression coefficients.
Self-healing: corpus too thin ⇒ run row written with status
`skipped_insufficient_data` and the analysis is a no-op until next
fire. `min_total_samples = 1000` (≈ 4-5 days of CONUS PSKR
activity); per-band threshold is 100. Both surface in the run row's
`notes`.
Auto-applies nothing. Weight changes still go through human review
of `BandConfig.@band_configs` and a code commit. The recalibrator
is a read-only analyst that stays out of the production scoring
path.
Features binned (matching the scorer's discriminating fields):
* pwat_mm — humidity U-shape candidate
* hpbl_m — boundary layer (mechanism vs scoring re-eval)
* min_refractivity_gradient — refractivity threshold validation
* surface_pressure_mb — pressure-front proxy
* kp_index — aurora boost magnitude tuning
Schema: two tables.
* `pskr_recalibration_runs` — one row per fire with corpus
stats, status, notes
* `pskr_feature_bins` — one row per (run, band, feature, bin)
with sample_count, spot_count_total/avg/p50/p90
Cron: `0 4 * * 0` (Sundays 04:00 UTC, off-peak, post-climatology).
Manual reruns enqueue with no args.
Tests cover the empty-corpus skip path, sub-threshold totals,
per-band threshold gating, the actual bin emission, nil-feature
handling, spot-count averaging, and the always-records-a-run
audit invariant. 8 new tests, 3282 total passing.
Backfill pipeline untouched.
58 lines
1.9 KiB
Elixir
58 lines
1.9 KiB
Elixir
defmodule Microwaveprop.Pskr.FeatureBin do
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@moduledoc """
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One feature-bucket row from a `Pskr.RecalibrationRun`. Stores the
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spot-density statistics for a (band, feature, bin) cell of the
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corpus so an operator can read whether a feature actually
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discriminates propagation at the granularity the scorer cares
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about.
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Example: at run R, on the 10 GHz band, the `pwat_mm` feature has
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five rows (one per bin label: `<15`, `15-25`, `25-40`, `40-55`,
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`>55`). Each row carries:
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* `sample_count` — how many corpus samples landed in the bin
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* `spot_count_total` — sum of spot_count across those samples
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* `spot_count_avg` / `spot_count_p50` / `spot_count_p90` —
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central tendency + tail for the spot-density distribution
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The unique key (run_id, band, feature, bin_label) means a rerun
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of the same recalibration run is idempotent.
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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_feature_bins" do
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belongs_to :run, Microwaveprop.Pskr.RecalibrationRun, foreign_key: :run_id
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field :band, :string
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field :feature, :string
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field :bin_label, :string
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field :bin_min, :float
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field :bin_max, :float
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field :sample_count, :integer, default: 0
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field :spot_count_total, :integer, default: 0
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field :spot_count_avg, :float
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field :spot_count_p50, :float
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field :spot_count_p90, :float
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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(run_id band feature bin_label bin_min bin_max sample_count
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spot_count_total spot_count_avg spot_count_p50 spot_count_p90)a
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@required_fields ~w(run_id band feature bin_label sample_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([:run_id, :band, :feature, :bin_label])
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end
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end
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