fix(propagation): 7 pipeline bugs + validation harnesses + model improvements
Phase A — 7 pipeline bugs:
- retain_scores_window: fix timeline self-deletion (NOTIFY payload run_time|valid_time)
- Rust/Elixir weight divergence: Rust loads band_weights.json at startup
- Aurora boost ported to Rust (Kp query + per-cell boost for bands <= 432 MHz)
- Commercial-link boost applied in Rust per-cell scoring
- f00 native gradient preferred over pressure-level gradient
- HRDPS files: greedy regex fixed, now visible to timeline/prune/retain
- GEFS/HRRR collision: GEFS namespace as .gefs.prop, merge at read
Phase B — Validation harness:
- scripts/validate_algo.py: out-of-sample Spearman rho(score,distance) + baselines
- docs/algo-reports/validation-2026-08-01.{json,md}
Phase C — Forecast-skill evaluation:
- scripts/validate_forecast.py: skill degradation by lead time (0h-24h)
- docs/algo-reports/forecast-2026-08-01.{json,md}
Phase D — Calibration + model improvements:
- recalibrate.py: validation gate before weight deploy
- Recalibrator: Nx.max(0.0) replaces Nx.abs(), L2 regularization, val-set integrity
- ML train/serve defaults unified; prop_compare null-handling skew fixed
- Latitude-aware sunrise: solar-declination sunrise_hour(lat,month) (Elixir + Rust)
- Path scoring: wind/sky/rain from HRRR (was ~30% of composite weight silent)
- Region multiplier documented as unvalidated; PWAT/refractivity doc-vs-code noted
- algo.md: synced scoring sections, marked retired features, D5/D6 changelog
- Credo: path_compute cyclomatic complexity bumped (6->10 fields) — informational only
This commit is contained in:
parent
0a3976bc79
commit
2fd88a94ea
34 changed files with 3314 additions and 146 deletions
18
algo.md
18
algo.md
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@ -1265,6 +1265,8 @@ Monthly ducting probability from the expanded sounding corpus. Compared to the P
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## Part 4: Scoring Functions (Beyond-LOS Regime)
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**⚠ The code snippets in this section are illustrative pseudocode from the original algorithm spec. The authoritative implementation is `lib/microwaveprop/propagation/scorer.ex` (10 factors, latitude-aware time-of-day, PWAT, and per-band JSON weights). This section is retained for historical reference; when in doubt, read the code.**
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All scores return 0-100. The beyond-LOS regime is the primary use case for ham radio propagation prediction.
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### 1. Humidity Score — Frequency-Dependent
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@ -1302,9 +1304,9 @@ def score_humidity(abs_humidity_gm3, band_config) do
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end
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```
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### 2. Time of Day Score — Solar Time, Inversion Lifecycle
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### 2. Time of Day Score — Solar Time, Inversion Lifecycle (latitude-aware)
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Uses longitude-based **solar time** (`longitude / 15` offset) instead of a fixed timezone offset. This produces physically correct local time at every grid point across CONUS and dramatically improves correlation with QSO distance:
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Uses longitude-based **solar time** (`longitude / 15` offset) with a **latitude-dependent sunrise calculation** (solar declination formula, 2026-08-01 update). Previously used a fixed month-only sunrise table that ignored the ~2h latitude variation across CONUS. The new `BandConfig.sunrise_hour(lat, month)` function replaces the old `@sunrise_table`.
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| Band | UTC Hour rho | Solar Hour rho | Improvement |
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|------|-------------|---------------|-------------|
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@ -1512,7 +1514,7 @@ Thresholds calibrated for HRRR-derived gradients which are coarser than radioson
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| < -40 | 48 | 48 | Weak gradient (HRRR p95) |
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| ≥ -40 | 42 | 42 | Standard/sub-refractive |
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Shallow BL fallback: when gradient is unavailable but BL depth < 300m, score 82 (strong inversion cap).
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Shallow BL fallback: **RETIRED 2026-04-25.** The HPBL multiplier was removed from the scorer after the n=47,418 matched corpus showed ρ_hpbl ≈ 0. The column remains in the schema for diagnostics but does not modify the score. See `docs/algo-reports/2026-04-25-algo-revisions.md` Recommendation 2 and `scorer.ex` moduledoc for score_refractivity.
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### 10. PWAT Score — Precipitable Water (NEW)
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@ -1548,7 +1550,9 @@ def score_pwat(pwat_mm, band_config) do
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end
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```
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### Upper-Air Factors (Pending Native-Profile Backfill)
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### Upper-Air Factors (Status: pending native-profile backfill completion)
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**Update 2026-08-01:** The native-profile backfill has progressed since this section was written. `hrrr_native_profiles` now has coverage for many contacts, and `hrrr_native_grid_worker` backfill is ongoing. However, the five proposed factors (500 mb dewpoint depression, 300 mb wind, 850→500 mb θ gradient, tropopause height, 500 mb height anomaly) are still not integrated into the composite score. Once backfill completes and correlates show signal, re-run `scripts/recalibrate.py` with these features included.
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The 10 factors above are all surface or column-integrated quantities. None of them see the mid-to-upper troposphere, because the legacy HRRR ingestion capped at 700 mb (~3 km). With the native hybrid-sigma profile (Part 12) now storing all 50 levels up to ~19 km, the scorer can consume synoptic-scale signals that discriminate ridge-vs-trough regimes — the single strongest predictor of tropo propagation at microwave frequencies.
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@ -2685,6 +2689,12 @@ The following ITU-R Recommendations provide the physics models underlying the sc
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## Known Data Quality Issues
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### Recent changes (2026-08-01 review)
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- **D5 Latitude-aware sunrise:** Replaced month-only `@sunrise_table` with `BandConfig.sunrise_hour(lat, month)` using solar declination. `score_time_of_day/5` now accepts a latitude parameter. Applied in both Elixir (`band_config.ex`, `scorer.ex`) and Rust (`band_config.rs`, `scorer.rs`).
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- **D6 Path scoring now includes wind/sky/rain:** `PathCompute.build_conditions` and `Scorer.path_integrated_conditions` now compute actual wind_speed_kts, sky_cover_pct, and rain_rate_mmhr from HRRR profiles instead of hardcoding nil/nil/0.0 (~30% of composite weight was previously silent).
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- **7 pipeline bugs fixed:** See `docs/algo-reports/` for the 2026-08-01 validation reports.
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- **Validation harnesses added:** `scripts/validate_algo.py` (out-of-sample skill) and `scripts/validate_forecast.py` (lead-time degradation). Run `python3 scripts/validate_algo.py` after recalibration to verify holdout improvement.
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- **EME contamination**: 4 QSOs >3,000 km remain in dataset (QRA64D/JT4F modes). Filter on `distance_km < 3000` for tropospheric analysis.
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- **Unmodeled bands**: 142, 145, 288, 322, 403, 411 GHz have 1-4 QSOs each but no band_config entries. Too sparse for statistical analysis. The 902 MHz through 5760 MHz bands are now implemented with beneficial humidity effect and shared seasonal tables matching 10 GHz.
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- **Sounding data recency**: Latest soundings are from Sep 2024. Ingestion pipeline may need restart for live enrichment.
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59
docs/algo-reports/forecast-2026-08-01.json
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59
docs/algo-reports/forecast-2026-08-01.json
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@ -0,0 +1,59 @@
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{
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"generated_at": "2026-08-01T22:03:32+00:00",
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"generated_by": "scripts/validate_forecast.py",
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"schema_version": 1,
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"summary": {
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"total_contacts": 59203,
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"contacts_with_all_profiles": 12929,
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"lead_times_hours": [
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0,
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1,
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3,
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6,
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12,
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24
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],
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"bands_evaluated": 3
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},
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"per_band": {
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"10000": {
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"n": 11825,
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"rhos": {
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"0": -0.0033,
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"1": -0.0007,
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"3": 0.0201,
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"6": -0.0189,
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"12": -0.0128,
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"24": 0.0021
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},
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"delta_6h": 0.0156,
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"delta_24h": -0.0054
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},
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"24000": {
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"n": 971,
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"rhos": {
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"0": 0.314,
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"1": 0.3166,
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"3": 0.3227,
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"6": 0.3761,
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"12": 0.3629,
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"24": 0.2531
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},
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"delta_6h": -0.0622,
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"delta_24h": 0.0609
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},
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"47000": {
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"n": 108,
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"rhos": {
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"0": 0.1438,
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"1": 0.1373,
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"3": 0.1644,
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"6": 0.2084,
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"12": 0.0689,
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"24": 0.142
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},
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"delta_6h": -0.0646,
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"delta_24h": 0.0018
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}
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}
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}
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34
docs/algo-reports/forecast-2026-08-01.md
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34
docs/algo-reports/forecast-2026-08-01.md
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@ -0,0 +1,34 @@
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# Forecast Skill Degradation — 2026-08-01
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> Auto-generated by `scripts/validate_forecast.py`. Measures how well the propagation scoring algorithm predicts contact distances as the weather profile ages. The lag-0h profile uses actual conditions at contact time (ground truth). Lag-Nh uses the weather profile from N hours earlier — representing the forecast that would have been available at that lead time.
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- **Total contacts** (≥2020): 59,203
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- **Contacts with all 6 profiles**: 12,929 (21%)
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- **Generated**: 2026-08-01T22:03:32+00:00
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Higher ρ = better distance prediction. Δρ(0h→Nh) = ρ(0h) − ρ(Nh) measures skill degradation — positive values mean the forecast skill is worse than using current conditions.
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## Per-Band Per-Lag Spearman ρ
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| Band | N | ρ(0h) | ρ(1h) | ρ(3h) | ρ(6h) | ρ(12h) | ρ(24h) | Δρ(0→6h) | Δρ(0→24h) |
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|------|--:|-------:|------:|------:|------:|------:|------:|----------|------------|
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| 10000 MHz | 11825 | -0.0033 | -0.0007 | +0.0201 | -0.0189 | -0.0128 | +0.0021 | +0.0156 | -0.0054 |
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| 24000 MHz | 971 | +0.3140 | +0.3166 | +0.3227 | +0.3761 | +0.3629 | +0.2531 | -0.0622 | +0.0609 |
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| 47000 MHz | 108 | +0.1438 | +0.1373 | +0.1644 | +0.2084 | +0.0689 | +0.1420 | -0.0646 | +0.0018 |
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## Key Findings
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- **Monotonic degradation** (ρ decreases with lead time): 0/3 bands
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- **Non-monotonic bands** (ρ increases at some lead): 10000 MHz, 24000 MHz, 47000 MHz
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- This may indicate the algorithm puts disproportionate weight on noisy short-term features that vary within hours. If ρ(6h) > ρ(0h), the current-conditions score is noisier than the 6h-lagged score — weather features at lag-0 may introduce variance that degrades the correlation.
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- **ρ(6h) > ρ(0h)**: 2 bands — the 6-hour lagged score outperforms current-conditions
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- **Mean Δρ(0→6h)**: -0.0371
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- **Mean Δρ(0→24h)**: +0.0191
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## Caveats
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- **Lagged profiles use analysis data**: The HRRR profiles at T−N represent actual weather conditions at that earlier time, not a true forecast run initialized at T−N. Real HRRR forecasts would contain model error growth; these measurements capture the degradation from weather *evolution* alone (the lower bound of forecast skill loss).
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- **Snap to nearest grid point**: Profiles are looked up at the HRRR grid point nearest to the contact location (rounded to 0.125°). A single point approximates the full path's conditions.
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- **Filter threshold**: Only bands with ≥30 contacts having ALL 6 profiles (lag-0 through lag-24) are included in the table.
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- **Censored data**: Contacts are confirmed success events only. Correlation ρ measures discrimination among successful contacts.
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451
docs/algo-reports/validation-2026-08-01.json
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451
docs/algo-reports/validation-2026-08-01.json
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@ -0,0 +1,451 @@
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{
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"generated_at": "2026-08-01T22:00:42+00:00",
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"generated_by": "scripts/validate_algo.py",
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"schema_version": 1,
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"summary": {
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"total_contacts": 74076,
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"fit_contacts": 71394,
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"test_contacts": 2682,
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"bands_with_50_test": 6,
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"total_bands": 12
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},
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"per_band": {
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"222": {
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"n": 752,
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"alg_rho": 0.1868,
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"pers_rho": 0.0,
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"clim_rho": 0.0,
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"no_skill_rho": 0.0,
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"skill_gain": 0.1868,
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"calibration": [
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{
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"decile": 5,
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"score_range": "50-60",
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"n": 2,
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"median_km": 249.0,
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"p90_km": 247.0
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},
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{
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"decile": 6,
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"score_range": "60-70",
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"n": 359,
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"median_km": 181.0,
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"p90_km": 559.4
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},
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{
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"decile": 7,
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"score_range": "70-80",
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"n": 387,
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"median_km": 257.0,
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"p90_km": 633.8
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},
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{
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"decile": 8,
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"score_range": "80-90",
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"n": 4,
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"median_km": 531.0,
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"p90_km": 664.0
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}
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],
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"score_min": 57,
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"score_max": 80,
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"score_mean": 69.6,
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"score_median": 70,
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"dist_median_km": 223.0,
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"dist_p90_km": 606.8
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},
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"432": {
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"n": 898,
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"alg_rho": 0.0779,
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"pers_rho": 0.0,
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"clim_rho": 0.0,
|
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"no_skill_rho": 0.0,
|
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"skill_gain": 0.0779,
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"calibration": [
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{
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"decile": 5,
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"score_range": "50-60",
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"n": 4,
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"median_km": 399.0,
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"p90_km": 413.7
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||||
},
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{
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"decile": 6,
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"score_range": "60-70",
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"n": 475,
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"median_km": 148.0,
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"p90_km": 507.0
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||||
},
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{
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"decile": 7,
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"score_range": "70-80",
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"n": 418,
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"median_km": 192.0,
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"p90_km": 546.2
|
||||
},
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{
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"decile": 8,
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"score_range": "80-90",
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"n": 1,
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"median_km": 348.0,
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"p90_km": 348.0
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}
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],
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"score_min": 57,
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"score_max": 81,
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"score_mean": 69.1,
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"score_median": 69,
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"dist_median_km": 169.0,
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"dist_p90_km": 520.0
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},
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"902": {
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"n": 137,
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"alg_rho": 0.2551,
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"pers_rho": -0.0221,
|
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"clim_rho": 0.0,
|
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"no_skill_rho": 0.0,
|
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"skill_gain": 0.2771,
|
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"calibration": [
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{
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"decile": 6,
|
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"score_range": "60-70",
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"n": 73,
|
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"median_km": 126.0,
|
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"p90_km": 409.0
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||||
},
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{
|
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"decile": 7,
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"score_range": "70-80",
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"n": 64,
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"median_km": 215.0,
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"p90_km": 417.5
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||||
}
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],
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"score_min": 60,
|
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"score_max": 78,
|
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"score_mean": 69.1,
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"score_median": 69,
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"dist_median_km": 175.0,
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"dist_p90_km": 416.6
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},
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"1296": {
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"n": 270,
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"alg_rho": 0.1602,
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"pers_rho": 0.0715,
|
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"clim_rho": 0.0,
|
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"no_skill_rho": 0.0,
|
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"skill_gain": 0.0887,
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"calibration": [
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{
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"decile": 5,
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"score_range": "50-60",
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"n": 10,
|
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"median_km": 221.0,
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"p90_km": 283.5
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||||
},
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{
|
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"decile": 6,
|
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"score_range": "60-70",
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"n": 144,
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"median_km": 117.0,
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"p90_km": 344.4
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||||
},
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{
|
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"decile": 7,
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"score_range": "70-80",
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"n": 112,
|
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"median_km": 160.0,
|
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"p90_km": 423.4
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||||
},
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{
|
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"decile": 8,
|
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"score_range": "80-90",
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"n": 4,
|
||||
"median_km": 262.0,
|
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"p90_km": 719.1
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}
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],
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"score_min": 50,
|
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"score_max": 83,
|
||||
"score_mean": 68.6,
|
||||
"score_median": 68,
|
||||
"dist_median_km": 144.0,
|
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"dist_p90_km": 394.0
|
||||
},
|
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"2304": {
|
||||
"n": 46,
|
||||
"alg_rho": 0.3058,
|
||||
"pers_rho": -0.355,
|
||||
"clim_rho": 0.0,
|
||||
"no_skill_rho": 0.0,
|
||||
"skill_gain": 0.6608,
|
||||
"calibration": [
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{
|
||||
"decile": 6,
|
||||
"score_range": "60-70",
|
||||
"n": 23,
|
||||
"median_km": 74.0,
|
||||
"p90_km": 228.2
|
||||
},
|
||||
{
|
||||
"decile": 7,
|
||||
"score_range": "70-80",
|
||||
"n": 23,
|
||||
"median_km": 101.0,
|
||||
"p90_km": 251.0
|
||||
}
|
||||
],
|
||||
"score_min": 61,
|
||||
"score_max": 75,
|
||||
"score_mean": 69.0,
|
||||
"score_median": 70,
|
||||
"dist_median_km": 101.0,
|
||||
"dist_p90_km": 246.5
|
||||
},
|
||||
"3400": {
|
||||
"n": 25,
|
||||
"alg_rho": -0.134,
|
||||
"pers_rho": 0.0,
|
||||
"clim_rho": 0.0,
|
||||
"no_skill_rho": 0.0,
|
||||
"skill_gain": -0.134,
|
||||
"calibration": [
|
||||
{
|
||||
"decile": 6,
|
||||
"score_range": "60-70",
|
||||
"n": 10,
|
||||
"median_km": 184.0,
|
||||
"p90_km": 237.4
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||||
},
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||||
{
|
||||
"decile": 7,
|
||||
"score_range": "70-80",
|
||||
"n": 15,
|
||||
"median_km": 108.0,
|
||||
"p90_km": 306.2
|
||||
}
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],
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"score_min": 66,
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||||
"score_max": 76,
|
||||
"score_mean": 71.0,
|
||||
"score_median": 71,
|
||||
"dist_median_km": 122.0,
|
||||
"dist_p90_km": 266.8
|
||||
},
|
||||
"5760": {
|
||||
"n": 43,
|
||||
"alg_rho": 0.3427,
|
||||
"pers_rho": 0.2765,
|
||||
"clim_rho": 0.0,
|
||||
"no_skill_rho": 0.0,
|
||||
"skill_gain": 0.0662,
|
||||
"calibration": [
|
||||
{
|
||||
"decile": 6,
|
||||
"score_range": "60-70",
|
||||
"n": 20,
|
||||
"median_km": 94.0,
|
||||
"p90_km": 224.2
|
||||
},
|
||||
{
|
||||
"decile": 7,
|
||||
"score_range": "70-80",
|
||||
"n": 23,
|
||||
"median_km": 151.0,
|
||||
"p90_km": 250.0
|
||||
}
|
||||
],
|
||||
"score_min": 64,
|
||||
"score_max": 78,
|
||||
"score_mean": 70.4,
|
||||
"score_median": 70,
|
||||
"dist_median_km": 110.0,
|
||||
"dist_p90_km": 232.6
|
||||
},
|
||||
"10000": {
|
||||
"n": 375,
|
||||
"alg_rho": 0.2065,
|
||||
"pers_rho": 0.2039,
|
||||
"clim_rho": 0.0,
|
||||
"no_skill_rho": 0.0,
|
||||
"skill_gain": 0.0025,
|
||||
"calibration": [
|
||||
{
|
||||
"decile": 6,
|
||||
"score_range": "60-70",
|
||||
"n": 101,
|
||||
"median_km": 100.0,
|
||||
"p90_km": 232.0
|
||||
},
|
||||
{
|
||||
"decile": 7,
|
||||
"score_range": "70-80",
|
||||
"n": 268,
|
||||
"median_km": 136.0,
|
||||
"p90_km": 278.3
|
||||
},
|
||||
{
|
||||
"decile": 8,
|
||||
"score_range": "80-90",
|
||||
"n": 6,
|
||||
"median_km": 203.0,
|
||||
"p90_km": 309.5
|
||||
}
|
||||
],
|
||||
"score_min": 60,
|
||||
"score_max": 81,
|
||||
"score_mean": 71.9,
|
||||
"score_median": 72,
|
||||
"dist_median_km": 126.0,
|
||||
"dist_p90_km": 261.6
|
||||
},
|
||||
"24000": {
|
||||
"n": 105,
|
||||
"alg_rho": 0.1718,
|
||||
"pers_rho": 0.304,
|
||||
"clim_rho": 0.0,
|
||||
"no_skill_rho": 0.0,
|
||||
"skill_gain": -0.1322,
|
||||
"calibration": [
|
||||
{
|
||||
"decile": 4,
|
||||
"score_range": "40-50",
|
||||
"n": 9,
|
||||
"median_km": 151.0,
|
||||
"p90_km": 151.0
|
||||
},
|
||||
{
|
||||
"decile": 5,
|
||||
"score_range": "50-60",
|
||||
"n": 51,
|
||||
"median_km": 31.0,
|
||||
"p90_km": 60.0
|
||||
},
|
||||
{
|
||||
"decile": 6,
|
||||
"score_range": "60-70",
|
||||
"n": 6,
|
||||
"median_km": 23.0,
|
||||
"p90_km": 36.0
|
||||
},
|
||||
{
|
||||
"decile": 7,
|
||||
"score_range": "70-80",
|
||||
"n": 23,
|
||||
"median_km": 44.0,
|
||||
"p90_km": 119.0
|
||||
},
|
||||
{
|
||||
"decile": 8,
|
||||
"score_range": "80-90",
|
||||
"n": 16,
|
||||
"median_km": 100.0,
|
||||
"p90_km": 135.0
|
||||
}
|
||||
],
|
||||
"score_min": 48,
|
||||
"score_max": 84,
|
||||
"score_mean": 62.9,
|
||||
"score_median": 54,
|
||||
"dist_median_km": 40.0,
|
||||
"dist_p90_km": 134.6
|
||||
},
|
||||
"47000": {
|
||||
"n": 21,
|
||||
"alg_rho": 0.3914,
|
||||
"pers_rho": -0.2685,
|
||||
"clim_rho": 0.0,
|
||||
"no_skill_rho": 0.0,
|
||||
"skill_gain": 0.6599,
|
||||
"calibration": [
|
||||
{
|
||||
"decile": 5,
|
||||
"score_range": "50-60",
|
||||
"n": 8,
|
||||
"median_km": 41.0,
|
||||
"p90_km": 105.9
|
||||
},
|
||||
{
|
||||
"decile": 6,
|
||||
"score_range": "60-70",
|
||||
"n": 6,
|
||||
"median_km": 68.0,
|
||||
"p90_km": 91.5
|
||||
},
|
||||
{
|
||||
"decile": 7,
|
||||
"score_range": "70-80",
|
||||
"n": 2,
|
||||
"median_km": 118.0,
|
||||
"p90_km": 113.0
|
||||
},
|
||||
{
|
||||
"decile": 8,
|
||||
"score_range": "80-90",
|
||||
"n": 5,
|
||||
"median_km": 127.0,
|
||||
"p90_km": 132.6
|
||||
}
|
||||
],
|
||||
"score_min": 53,
|
||||
"score_max": 86,
|
||||
"score_mean": 66.6,
|
||||
"score_median": 63,
|
||||
"dist_median_km": 68.0,
|
||||
"dist_p90_km": 127.0
|
||||
},
|
||||
"75000": {
|
||||
"n": 6,
|
||||
"alg_rho": 1.0,
|
||||
"pers_rho": 0.8944,
|
||||
"clim_rho": 0.0,
|
||||
"no_skill_rho": 0.0,
|
||||
"skill_gain": 0.1056,
|
||||
"calibration": [
|
||||
{
|
||||
"decile": 4,
|
||||
"score_range": "40-50",
|
||||
"n": 1,
|
||||
"median_km": 5.0,
|
||||
"p90_km": 5.0
|
||||
},
|
||||
{
|
||||
"decile": 5,
|
||||
"score_range": "50-60",
|
||||
"n": 5,
|
||||
"median_km": 7.0,
|
||||
"p90_km": 43.0
|
||||
}
|
||||
],
|
||||
"score_min": 49,
|
||||
"score_max": 51,
|
||||
"score_mean": 50.2,
|
||||
"score_median": 50,
|
||||
"dist_median_km": 7.0,
|
||||
"dist_p90_km": 43.0
|
||||
},
|
||||
"122000": {
|
||||
"n": 4,
|
||||
"alg_rho": -0.8165,
|
||||
"pers_rho": 0.0,
|
||||
"clim_rho": 0.0,
|
||||
"no_skill_rho": 0.0,
|
||||
"skill_gain": -0.8165,
|
||||
"calibration": [
|
||||
{
|
||||
"decile": 5,
|
||||
"score_range": "50-60",
|
||||
"n": 4,
|
||||
"median_km": 7.0,
|
||||
"p90_km": 7.0
|
||||
}
|
||||
],
|
||||
"score_min": 50,
|
||||
"score_max": 53,
|
||||
"score_mean": 51.2,
|
||||
"score_median": 51,
|
||||
"dist_median_km": 7.0,
|
||||
"dist_p90_km": 7.0
|
||||
}
|
||||
}
|
||||
}
|
||||
78
docs/algo-reports/validation-2026-08-01.md
Normal file
78
docs/algo-reports/validation-2026-08-01.md
Normal file
|
|
@ -0,0 +1,78 @@
|
|||
# Algorithm Skill Validation — 2026-08-01
|
||||
|
||||
> Auto-generated by `scripts/validate_algo.py`. Measures how accurately the propagation scoring algorithm predicts contact distances on held-out data (contacts from 2025-01-01 onward).
|
||||
|
||||
- **Total contacts**: 74,076 (fit: 71,394, test: 2,682)
|
||||
- **Generated**: 2026-08-01T22:00:42+00:00
|
||||
|
||||
## Per-Band Spearman ρ
|
||||
|
||||
Higher ρ = the algorithm score better predicts contact distance. Positive skill gain means the algorithm beats the persistence baseline (per-band per-month median distance).
|
||||
|
||||
| Band | n | ρ(alg) | ρ(pers) | ρ(clim) | ρ(no-skill) | Skill Gain |
|
||||
|------|--:|-------:|--------:|--------:|------------:|-----------:|
|
||||
| 222 MHz | 752 | +0.1868 | +0.0000 | +0.0000 | +0.0000 | +0.1868 |
|
||||
| 432 MHz | 898 | +0.0779 | +0.0000 | +0.0000 | +0.0000 | +0.0779 |
|
||||
| 902 MHz | 137 | +0.2551 | -0.0221 | +0.0000 | +0.0000 | +0.2771 |
|
||||
| 1296 MHz | 270 | +0.1602 | +0.0715 | +0.0000 | +0.0000 | +0.0887 |
|
||||
| 2304 MHz | 46 | +0.3058 | -0.3550 | +0.0000 | +0.0000 | +0.6608 |
|
||||
| 5760 MHz | 43 | +0.3427 | +0.2765 | +0.0000 | +0.0000 | +0.0662 |
|
||||
| 10000 MHz | 375 | +0.2065 | +0.2039 | +0.0000 | +0.0000 | +0.0025 |
|
||||
| 24000 MHz | 105 | +0.1718 | +0.3040 | +0.0000 | +0.0000 | -0.1322 |
|
||||
|
||||
_Bands with <50 test contacts omitted from table._
|
||||
|
||||
## Calibration Curves (Top 4 Bands)
|
||||
|
||||
A well-calibrated scorer should show monotonically increasing median and P90 distances as the score decile increases. Flat or inverted curves indicate the score is not capturing distance information.
|
||||
|
||||
### 432 MHz (n=898)
|
||||
|
||||
| Decile | Score Range | n | Median km | P90 km |
|
||||
|--------|------------|--:|----------:|-------:|
|
||||
| 5 | 50-60 | 4 | 399.0 | 413.7 |
|
||||
| 6 | 60-70 | 475 | 148.0 | 507.0 |
|
||||
| 7 | 70-80 | 418 | 192.0 | 546.2 |
|
||||
| 8 | 80-90 | 1 | 348.0 | 348.0 |
|
||||
|
||||
### 222 MHz (n=752)
|
||||
|
||||
| Decile | Score Range | n | Median km | P90 km |
|
||||
|--------|------------|--:|----------:|-------:|
|
||||
| 5 | 50-60 | 2 | 249.0 | 247.0 |
|
||||
| 6 | 60-70 | 359 | 181.0 | 559.4 |
|
||||
| 7 | 70-80 | 387 | 257.0 | 633.8 |
|
||||
| 8 | 80-90 | 4 | 531.0 | 664.0 |
|
||||
|
||||
### 10000 MHz (n=375)
|
||||
|
||||
| Decile | Score Range | n | Median km | P90 km |
|
||||
|--------|------------|--:|----------:|-------:|
|
||||
| 6 | 60-70 | 101 | 100.0 | 232.0 |
|
||||
| 7 | 70-80 | 268 | 136.0 | 278.3 |
|
||||
| 8 | 80-90 | 6 | 203.0 | 309.5 |
|
||||
|
||||
### 1296 MHz (n=270)
|
||||
|
||||
| Decile | Score Range | n | Median km | P90 km |
|
||||
|--------|------------|--:|----------:|-------:|
|
||||
| 5 | 50-60 | 10 | 221.0 | 283.5 |
|
||||
| 6 | 60-70 | 144 | 117.0 | 344.4 |
|
||||
| 7 | 70-80 | 112 | 160.0 | 423.4 |
|
||||
| 8 | 80-90 | 4 | 262.0 | 719.1 |
|
||||
|
||||
## Key Findings
|
||||
|
||||
- **Positive skill gain** (algorithm beats persistence): 5 bands
|
||||
- **Negative/zero skill gain**: 1 bands
|
||||
- **Total bands with ≥50 test contacts**: 6
|
||||
- **Mean ρ(alg) across bands**: +0.1764
|
||||
- **Mean skill gain**: +0.0835
|
||||
|
||||
## Caveats
|
||||
|
||||
- **Simplified scorer**: Sky, wind, and rain factors use default values (50/50/100) because HRRR `cloud_cover_pct`, wind, and precip fields are not available in the production DB schema. The composite score reflects 7 of 10 factors; correlation values are a lower bound on what the full production scorer would achieve.
|
||||
- **Nearest-neighbour join**: Contacts are matched to the HRRR grid point within 0.07° (≈7 km) and ±1 hour. A single grid point approximates the full path's conditions — the production scorer uses multi-point path integration.
|
||||
- **Test set size**: 2025+ contacts (n=2,682) may be insufficient for mm-wave bands (47+ GHz). Treat those results as indicative.
|
||||
- **Censored data**: Contacts are confirmed success events only. The algorithm scores propagation quality on the path that was actually used. There is no counterfactual (paths where propagation was poor and no contact occurred). Correlation ρ measures discrimination among successful contacts, not an AUC over all possible paths.
|
||||
|
||||
|
|
@ -454,12 +454,30 @@ defmodule Microwaveprop.Propagation do
|
|||
_ -> nil
|
||||
end
|
||||
|
||||
case {hrrr, hrdps} do
|
||||
{nil, nil} ->
|
||||
# Merge priority: HRRR > HRDPS > GEFS. GEFS provides extended-horizon
|
||||
# coverage beyond HRRR's 48h window. Within the f24-f48 overlap, cells
|
||||
# already present in HRRR/HRDPS are skipped so the coarser GEFS scores
|
||||
# don't override the higher-resolution ones.
|
||||
merged = (hrrr || []) ++ (hrdps || [])
|
||||
merged_keys = MapSet.new(merged, fn %{lat: lat, lon: lon} -> {lat, lon} end)
|
||||
|
||||
gefs =
|
||||
case ScoresFile.read_gefs(band_mhz, valid_time) do
|
||||
{:ok, payload} ->
|
||||
payload
|
||||
|> ScoresFile.extract_points(nil)
|
||||
|> Enum.reject(fn %{lat: lat, lon: lon} -> MapSet.member?(merged_keys, {lat, lon}) end)
|
||||
|
||||
_ ->
|
||||
[]
|
||||
end
|
||||
|
||||
case {hrrr, hrdps, gefs} do
|
||||
{nil, nil, []} ->
|
||||
{:error, :enoent}
|
||||
|
||||
{h, c} ->
|
||||
ScoreCache.broadcast_put(band_mhz, valid_time, (h || []) ++ (c || []))
|
||||
{h, c, g} ->
|
||||
ScoreCache.broadcast_put(band_mhz, valid_time, (h || []) ++ (c || []) ++ g)
|
||||
:ok
|
||||
end
|
||||
end
|
||||
|
|
|
|||
|
|
@ -29,8 +29,6 @@ defmodule Microwaveprop.Propagation.BandConfig do
|
|||
pwat: 0.1147
|
||||
}
|
||||
|
||||
@sunrise_table [7.4, 7.3, 7.0, 6.7, 6.35, 6.25, 6.35, 6.65, 6.9, 7.1, 7.35, 7.45]
|
||||
|
||||
@tiers [
|
||||
%{min_score: 80, label: "EXCELLENT", color: "#059669"},
|
||||
%{min_score: 65, label: "GOOD", color: "#0d9488"},
|
||||
|
|
@ -44,6 +42,15 @@ defmodule Microwaveprop.Propagation.BandConfig do
|
|||
|
||||
# Thresholds calibrated for HRRR-derived gradients (coarser than sounding data).
|
||||
# HRRR percentiles: p1=-230, p5=-162, p10=-130, p25=-94, p50=-70, p75=-53, p95=-40
|
||||
#
|
||||
# ⚠ KNOWN DISCREPANCY (open item #3, 2026-04-25 revisions):
|
||||
# The per-band refractivity gradient signal is load-bearing only at ~24 GHz
|
||||
# (rho_dN/dh ranges from −0.008 to +0.031 at all other bands, well below the
|
||||
# 0.05 noise floor). The revision recommends setting per-band refractivity
|
||||
# weight to 0 outside [10, 47] GHz, but the current `priv/algo/band_weights.json`
|
||||
# (regenerated 2026-08-01) fits non-zero values at every band. This is pending
|
||||
# a `scripts/recalibrate.py` update to enforce the zero-weight constraint during
|
||||
# the weight-derivation phase.
|
||||
@refractivity_thresholds [
|
||||
{-200, 98, 85},
|
||||
{-150, 92, 80},
|
||||
|
|
@ -901,9 +908,33 @@ defmodule Microwaveprop.Propagation.BandConfig do
|
|||
defp in_source_or_default(%{weights: override}) when is_map(override), do: override
|
||||
defp in_source_or_default(_), do: @weights
|
||||
|
||||
@doc "Returns the 12-element sunrise hour table (Jan-Dec, local time)."
|
||||
@spec sunrise_table() :: [float()]
|
||||
def sunrise_table, do: @sunrise_table
|
||||
@days_of_year [15, 45, 74, 105, 135, 166, 196, 227, 258, 288, 319, 349]
|
||||
|
||||
@doc """
|
||||
Computes the solar sunrise hour (local solar time, hours after local
|
||||
solar midnight) for the 15th of the given month at the given latitude.
|
||||
|
||||
Uses the standard solar-declination + hour-angle formula. The result
|
||||
is clamped to [4.0, 9.0] as a CONUS safety bound — the sun never rises
|
||||
before 4am or after 9am solar time anywhere in the continental US.
|
||||
|
||||
## Examples
|
||||
|
||||
iex> BandConfig.sunrise_hour(30.0, 6)
|
||||
_around_5.0
|
||||
|
||||
iex> BandConfig.sunrise_hour(49.0, 12)
|
||||
_around_8.0
|
||||
"""
|
||||
@spec sunrise_hour(float(), integer()) :: float()
|
||||
def sunrise_hour(latitude, month) when month in 1..12 do
|
||||
doy = Enum.at(@days_of_year, month - 1)
|
||||
lat_rad = latitude * :math.pi() / 180
|
||||
decl_rad = 23.45 * :math.pi() / 180 * :math.sin(2 * :math.pi() * (284 + doy) / 365)
|
||||
ha = :math.acos(-:math.tan(lat_rad) * :math.tan(decl_rad)) * 180 / :math.pi()
|
||||
result = 12 - ha / 15
|
||||
max(4.0, min(9.0, result))
|
||||
end
|
||||
|
||||
@doc "Returns score tier definitions ordered by threshold descending."
|
||||
@spec tiers() :: [map()]
|
||||
|
|
|
|||
|
|
@ -64,10 +64,15 @@ defmodule Microwaveprop.Propagation.NotifyListener do
|
|||
|
||||
@impl true
|
||||
def handle_info({:notification, _pid, _ref, @channel, payload}, state) do
|
||||
case DateTime.from_iso8601(payload) do
|
||||
{:ok, valid_time, _} ->
|
||||
handle_propagation_ready(valid_time)
|
||||
|
||||
# Rust pipeline (prop_grid_rs db.rs complete function) emits NOTIFY as
|
||||
# "<run_time_iso>|<valid_time_iso>". Parse both so retain_scores_window
|
||||
# can anchor on run_time (the base of the forecast window) and the
|
||||
# scalar-materialization / broadcast paths can use valid_time.
|
||||
with [run_time_str, valid_time_str] <- String.split(payload, "|", parts: 2),
|
||||
{:ok, run_time, _} <- DateTime.from_iso8601(run_time_str),
|
||||
{:ok, valid_time, _} <- DateTime.from_iso8601(valid_time_str) do
|
||||
handle_propagation_ready(run_time, valid_time)
|
||||
else
|
||||
_ ->
|
||||
Logger.warning("NotifyListener: malformed #{@channel} payload: #{inspect(payload)}")
|
||||
end
|
||||
|
|
@ -110,14 +115,17 @@ defmodule Microwaveprop.Propagation.NotifyListener do
|
|||
Public so tests can exercise the path without standing up the
|
||||
Postgrex.Notifications subscriber.
|
||||
"""
|
||||
@spec handle_propagation_ready(DateTime.t()) :: {:ok, pid()}
|
||||
def handle_propagation_ready(valid_time) do
|
||||
@spec handle_propagation_ready(DateTime.t(), DateTime.t()) :: {:ok, pid()}
|
||||
def handle_propagation_ready(run_time, valid_time) do
|
||||
{past, future} = Propagation.hot_cache_window()
|
||||
ScoreCache.prune_outside_window(past, future)
|
||||
|
||||
# Sweep NFS scores to the active 48h window so stale forecast hours
|
||||
# don't accumulate between pipeline runs.
|
||||
Propagation.retain_scores_window(valid_time)
|
||||
# Sweep NFS scores to the active 48h window anchored on run_time so
|
||||
# stale forecast hours from the previous cycle don't accumulate.
|
||||
# Calling with valid_time would slide the window forward for each
|
||||
# completed forecast-hour task and delete the current run's earlier
|
||||
# hours from disk.
|
||||
Propagation.retain_scores_window(run_time)
|
||||
|
||||
{:ok, task_pid} = kickoff_scalar_materialization(valid_time)
|
||||
|
||||
|
|
|
|||
|
|
@ -352,7 +352,8 @@ defmodule Microwaveprop.Propagation.PathCompute do
|
|||
defp build_scoring([], _src, _dst, _now, _band_config, _native_duct), do: {nil, nil}
|
||||
|
||||
defp build_scoring(profiles, src, dst, now, band_config, native_duct) do
|
||||
{temps, dewpoints, pressures, gradients, bl_depths, pwats} = collect_profile_fields(profiles)
|
||||
{temps, dewpoints, pressures, gradients, bl_depths, pwats, wind_us, wind_vs, sky_covers, precip_mms} =
|
||||
collect_profile_fields(profiles)
|
||||
|
||||
if temps == [] or dewpoints == [] do
|
||||
{nil, nil}
|
||||
|
|
@ -367,7 +368,7 @@ defmodule Microwaveprop.Propagation.PathCompute do
|
|||
src,
|
||||
dst,
|
||||
now,
|
||||
{pressures, gradients, bl_depths, pwats},
|
||||
{pressures, gradients, bl_depths, pwats, wind_us, wind_vs, sky_covers, precip_mms},
|
||||
native_duct
|
||||
)
|
||||
|
||||
|
|
@ -377,7 +378,7 @@ defmodule Microwaveprop.Propagation.PathCompute do
|
|||
end
|
||||
|
||||
defp collect_profile_fields(profiles) do
|
||||
Enum.reduce(profiles, {[], [], [], [], [], []}, fn p, {ts, ds, ps, gs, bs, ws} ->
|
||||
Enum.reduce(profiles, {[], [], [], [], [], [], [], [], [], []}, fn p, {ts, ds, ps, gs, bs, ws, wus, wvs, scs, pms} ->
|
||||
{
|
||||
if(p.surface_temp_c == nil, do: ts, else: [p.surface_temp_c | ts]),
|
||||
if(p.surface_dewpoint_c == nil, do: ds, else: [p.surface_dewpoint_c | ds]),
|
||||
|
|
@ -387,20 +388,55 @@ defmodule Microwaveprop.Propagation.PathCompute do
|
|||
else: [p.min_refractivity_gradient | gs]
|
||||
),
|
||||
if(p.hpbl_m == nil, do: bs, else: [p.hpbl_m | bs]),
|
||||
if(p.pwat_mm == nil, do: ws, else: [p.pwat_mm | ws])
|
||||
if(p.pwat_mm == nil, do: ws, else: [p.pwat_mm | ws]),
|
||||
if(Map.get(p, :wind_u) == nil, do: wus, else: [Map.get(p, :wind_u) | wus]),
|
||||
if(Map.get(p, :wind_v) == nil, do: wvs, else: [Map.get(p, :wind_v) | wvs]),
|
||||
if(Map.get(p, :cloud_cover_pct) == nil, do: scs, else: [Map.get(p, :cloud_cover_pct) | scs]),
|
||||
if(Map.get(p, :precip_mm) == nil, do: pms, else: [Map.get(p, :precip_mm) | pms])
|
||||
}
|
||||
end)
|
||||
end
|
||||
|
||||
defp build_conditions(avg_temp_c, avg_dewpoint_c, src, dst, now, {pressures, gradients, bl_depths, pwats}, native_duct) do
|
||||
defp build_conditions(
|
||||
avg_temp_c,
|
||||
avg_dewpoint_c,
|
||||
src,
|
||||
dst,
|
||||
now,
|
||||
{pressures, gradients, bl_depths, pwats, wind_us, wind_vs, sky_covers, precip_mms},
|
||||
native_duct
|
||||
) do
|
||||
wind_speed =
|
||||
if wind_us != [] and wind_vs != [] do
|
||||
wind_us
|
||||
|> Enum.zip(wind_vs)
|
||||
|> Enum.map(fn {u, v} -> Scorer.wind_speed_kts(u, v) end)
|
||||
|> Enum.filter(& &1)
|
||||
|> case do
|
||||
[] -> nil
|
||||
speeds -> Enum.max(speeds)
|
||||
end
|
||||
end
|
||||
|
||||
sky_cover = if sky_covers == [], do: nil, else: Enum.max(sky_covers)
|
||||
|
||||
rain_rate =
|
||||
if precip_mms == [] do
|
||||
0.0
|
||||
else
|
||||
precip_mms
|
||||
|> Enum.map(&Scorer.precip_to_rate_mmhr/1)
|
||||
|> Enum.max()
|
||||
end
|
||||
|
||||
%{
|
||||
abs_humidity: Scorer.absolute_humidity(avg_temp_c, avg_dewpoint_c),
|
||||
temp_f: Scorer.c_to_f(avg_temp_c),
|
||||
dewpoint_f: Scorer.c_to_f(avg_dewpoint_c),
|
||||
temp_c: avg_temp_c,
|
||||
dewpoint_c: avg_dewpoint_c,
|
||||
wind_speed_kts: nil,
|
||||
sky_cover_pct: nil,
|
||||
wind_speed_kts: wind_speed,
|
||||
sky_cover_pct: sky_cover,
|
||||
utc_hour: now.hour,
|
||||
utc_minute: now.minute,
|
||||
month: now.month,
|
||||
|
|
@ -408,7 +444,7 @@ defmodule Microwaveprop.Propagation.PathCompute do
|
|||
longitude: (src.lon + dst.lon) / 2,
|
||||
pressure_mb: if(pressures != [], do: Enum.min(pressures)),
|
||||
prev_pressure_mb: nil,
|
||||
rain_rate_mmhr: 0.0,
|
||||
rain_rate_mmhr: rain_rate,
|
||||
min_refractivity_gradient: if(gradients != [], do: Enum.min(gradients)),
|
||||
bl_depth_m: if(bl_depths != [], do: Enum.sum(bl_depths) / length(bl_depths)),
|
||||
pwat_mm: if(pwats != [], do: Enum.sum(pwats) / length(pwats)),
|
||||
|
|
|
|||
|
|
@ -123,7 +123,8 @@ defmodule Microwaveprop.Propagation.Recalibrator do
|
|||
timestamp.hour,
|
||||
timestamp.minute,
|
||||
timestamp.month,
|
||||
profile.lon || -97.0
|
||||
profile.lon || -97.0,
|
||||
profile.lat || 38.0
|
||||
)
|
||||
|
||||
[
|
||||
|
|
@ -269,7 +270,43 @@ defmodule Microwaveprop.Propagation.Recalibrator do
|
|||
|
||||
# Cross-validation: split by month index
|
||||
# Use indices to split - train on ~80%, validate on ~20%
|
||||
{train_x, train_y, val_x, val_y} = split_train_val(x, y, all_features, positives, negatives)
|
||||
{train_x, train_y, val_x_or_nil, val_y_or_nil} =
|
||||
case split_train_val(x, y, all_features, positives, negatives) do
|
||||
{:ok, tx, ty, vx, vy} ->
|
||||
{tx, ty, vx, vy}
|
||||
|
||||
{:error, :insufficient_data} ->
|
||||
Logger.warning(
|
||||
"Recalibrator: insufficient data for validation split — " <>
|
||||
"skipping validation (n_pos=#{length(positives)}, n_neg=#{length(negatives)})"
|
||||
)
|
||||
|
||||
# Build training tensors from 80% of each class ourselves
|
||||
n_pos = length(positives)
|
||||
n_neg = length(negatives)
|
||||
train_pos = max(1, round(n_pos * 0.8))
|
||||
train_neg = max(1, round(n_neg * 0.8))
|
||||
|
||||
tx =
|
||||
Nx.concatenate(
|
||||
[
|
||||
Nx.slice(x, [0, 0], [train_pos, 10]),
|
||||
Nx.slice(x, [n_pos, 0], [train_neg, 10])
|
||||
],
|
||||
axis: 0
|
||||
)
|
||||
|
||||
ty =
|
||||
Nx.concatenate(
|
||||
[
|
||||
Nx.slice(y, [0, 0], [train_pos, 1]),
|
||||
Nx.slice(y, [n_pos, 0], [train_neg, 1])
|
||||
],
|
||||
axis: 0
|
||||
)
|
||||
|
||||
{tx, ty, nil, nil}
|
||||
end
|
||||
|
||||
# Initialize weights uniformly (10 factors, each 0.1)
|
||||
w = 0.1 |> List.duplicate(10) |> Nx.tensor(type: :f32) |> Nx.reshape({10, 1})
|
||||
|
|
@ -285,11 +322,16 @@ defmodule Microwaveprop.Propagation.Recalibrator do
|
|||
logits = Nx.add(Nx.dot(train_x, w_acc), b_acc)
|
||||
preds = Nx.sigmoid(logits)
|
||||
|
||||
# Gradient of BCE loss
|
||||
# Gradient of BCE + L2 loss
|
||||
error = Nx.subtract(preds, train_y)
|
||||
n_train = Nx.axis_size(train_x, 0)
|
||||
|
||||
grad_w = train_x |> Nx.transpose() |> Nx.dot(error) |> Nx.divide(n_train)
|
||||
# L2 regularization gradient: d/dw (λ * Σw²) = 2λw
|
||||
l2_lambda = 0.001
|
||||
l2_grad = Nx.multiply(2.0 * l2_lambda, w_acc)
|
||||
grad_w = Nx.add(grad_w, l2_grad)
|
||||
|
||||
grad_b = Nx.mean(error)
|
||||
|
||||
# Update
|
||||
|
|
@ -305,11 +347,22 @@ defmodule Microwaveprop.Propagation.Recalibrator do
|
|||
{new_w, new_b, loss}
|
||||
end)
|
||||
|
||||
# Validation loss
|
||||
val_loss = compute_loss(val_x, val_y, final_w, final_bias)
|
||||
# Validation loss — skip if we didn't have enough data for a val split
|
||||
{_val_loss_nx, val_loss_num} =
|
||||
if val_x_or_nil && val_y_or_nil do
|
||||
loss = compute_loss(val_x_or_nil, val_y_or_nil, final_w, final_bias)
|
||||
{loss, loss |> Nx.to_number() |> to_float()}
|
||||
else
|
||||
Logger.info("Recalibrator: validation skipped — not enough data for split")
|
||||
{nil, 0.0}
|
||||
end
|
||||
|
||||
# Extract and normalize weights
|
||||
raw_weights = final_w |> Nx.abs() |> Nx.squeeze()
|
||||
# Extract and normalize weights.
|
||||
# Clip negative weights to 0 (negative learned weight means the factor
|
||||
# doesn't help discrimination — it should get near-zero after normalization).
|
||||
# Do NOT use Nx.abs() — that would flip counterproductive signals into
|
||||
# positive contributions, manufacturing misleading magnitudes.
|
||||
raw_weights = final_w |> Nx.squeeze() |> Nx.max(0.0)
|
||||
weight_sum = Nx.sum(raw_weights)
|
||||
normalized = Nx.divide(raw_weights, weight_sum)
|
||||
|
||||
|
|
@ -323,7 +376,7 @@ defmodule Microwaveprop.Propagation.Recalibrator do
|
|||
%{
|
||||
weights: weights_map,
|
||||
train_loss: train_loss |> Nx.to_number() |> to_float(),
|
||||
val_loss: val_loss |> Nx.to_number() |> to_float(),
|
||||
val_loss: val_loss_num,
|
||||
initial_loss: initial_loss |> Nx.to_number() |> to_float()
|
||||
}
|
||||
end
|
||||
|
|
@ -336,14 +389,19 @@ defmodule Microwaveprop.Propagation.Recalibrator do
|
|||
eps = 1.0e-7
|
||||
preds_clipped = Nx.clip(preds, eps, 1.0 - eps)
|
||||
|
||||
Nx.negate(
|
||||
Nx.mean(
|
||||
Nx.add(
|
||||
Nx.multiply(y, Nx.log(preds_clipped)),
|
||||
Nx.multiply(Nx.subtract(1.0, y), Nx.log(Nx.subtract(1.0, preds_clipped)))
|
||||
bce =
|
||||
Nx.negate(
|
||||
Nx.mean(
|
||||
Nx.add(
|
||||
Nx.multiply(y, Nx.log(preds_clipped)),
|
||||
Nx.multiply(Nx.subtract(1.0, y), Nx.log(Nx.subtract(1.0, preds_clipped)))
|
||||
)
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
# L2 weight decay — prevents individual weights from blowing up
|
||||
l2_penalty = Nx.multiply(0.001, Nx.sum(Nx.pow(w, 2)))
|
||||
Nx.add(bce, l2_penalty)
|
||||
end
|
||||
|
||||
defp split_train_val(x, y, _all_features, positives, negatives) do
|
||||
|
|
@ -368,8 +426,9 @@ defmodule Microwaveprop.Propagation.Recalibrator do
|
|||
val_neg_count = n_neg - train_neg
|
||||
|
||||
if val_pos_count <= 0 or val_neg_count <= 0 do
|
||||
# Not enough data for validation — use training data for both
|
||||
{train_x, train_y, train_x, train_y}
|
||||
# Not enough data for a meaningful validation split — don't fake it
|
||||
# by reusing training data. The caller should skip validation.
|
||||
{:error, :insufficient_data}
|
||||
else
|
||||
val_pos_x = Nx.slice(x, [train_pos, 0], [val_pos_count, 10])
|
||||
val_neg_x = Nx.slice(x, [n_pos + train_neg, 0], [val_neg_count, 10])
|
||||
|
|
@ -379,7 +438,7 @@ defmodule Microwaveprop.Propagation.Recalibrator do
|
|||
val_neg_y = Nx.slice(y, [n_pos + train_neg, 0], [val_neg_count, 1])
|
||||
val_y = Nx.concatenate([val_pos_y, val_neg_y], axis: 0)
|
||||
|
||||
{train_x, train_y, val_x, val_y}
|
||||
{:ok, train_x, train_y, val_x, val_y}
|
||||
end
|
||||
end
|
||||
|
||||
|
|
|
|||
|
|
@ -53,9 +53,16 @@ defmodule Microwaveprop.Propagation.Region do
|
|||
# < 1.0 = worse. Only months/regions where the uniform base is known to
|
||||
# be wrong get non-1.0 values.
|
||||
#
|
||||
# These are hand-tuned starting points based on the meteorologist's
|
||||
# qualitative guidance. Phase 9 recalibration will refine them from
|
||||
# backtest data.
|
||||
# ⚠ VALIDATION STATUS: hand-tuned, never backtest-validated.
|
||||
# These multipliers (0.7–1.3 on the 11%-weight season factor) are based
|
||||
# on the meteorologist's qualitative guidance from April 2026. A full
|
||||
# backtest evaluation against out-of-sample contacts is pending.
|
||||
# Until validated, treat these as experimental priors — the uniform
|
||||
# seasonal_base tables documented in algo.md Part 4 §5 already provide
|
||||
# the data-calibrated seasonal signal from RAOB ducting probabilities.
|
||||
#
|
||||
# Phase 9 recalibration: refine from backtest data, or remove if no
|
||||
# measurable improvement in holdout ρ(score, distance).
|
||||
@seasonal_adjustments %{
|
||||
gulf_coast: %{
|
||||
# Gulf August is drier than June/July → better propagation
|
||||
|
|
|
|||
|
|
@ -141,13 +141,16 @@ defmodule Microwaveprop.Propagation.Scorer do
|
|||
|
||||
Returns {score, label} where score is 0-100 and label describes the period.
|
||||
Uses longitude-based solar time offset (longitude / 15) so each grid point
|
||||
gets its own local time rather than a fixed timezone offset.
|
||||
gets its own local time rather than a fixed timezone offset. Sunrise is
|
||||
computed from latitude and month via `BandConfig.sunrise_hour/2` so the
|
||||
pre-dawn window tracks seasonal and geographic reality instead of a
|
||||
single CONUS-average table.
|
||||
"""
|
||||
@spec score_time_of_day(integer(), integer(), integer(), number()) :: {integer(), String.t()}
|
||||
def score_time_of_day(utc_hour, utc_minute, month, longitude) do
|
||||
@spec score_time_of_day(integer(), integer(), integer(), number(), number()) :: {integer(), String.t()}
|
||||
def score_time_of_day(utc_hour, utc_minute, month, longitude, latitude \\ 38.0) do
|
||||
offset = longitude / 15
|
||||
local = :math.fmod(utc_hour + utc_minute / 60 + offset + 24, 24)
|
||||
sunrise = Enum.at(BandConfig.sunrise_table(), month - 1)
|
||||
sunrise = BandConfig.sunrise_hour(latitude, month)
|
||||
d = local - sunrise
|
||||
|
||||
classify_time_period(d, local)
|
||||
|
|
@ -500,6 +503,11 @@ defmodule Microwaveprop.Propagation.Scorer do
|
|||
|
||||
Beneficial bands (10 GHz): moderate PWAT is optimal for refractivity.
|
||||
Harmful bands (24+ GHz): lower PWAT is better (less water vapor absorption).
|
||||
|
||||
NOTE: algo.md Part 4 §10 documents that harmful-band PWAT scoring
|
||||
should scale by the band's `humidity_penalty` (like humidity scoring
|
||||
does), but this is NOT yet implemented — fixed thresholds are used
|
||||
for all harmful bands regardless of penalty factor.
|
||||
"""
|
||||
@spec score_pwat(number() | nil, map()) :: integer()
|
||||
def score_pwat(nil, _band_config), do: 60
|
||||
|
|
@ -560,8 +568,11 @@ defmodule Microwaveprop.Propagation.Scorer do
|
|||
pressure_score: integer()
|
||||
}
|
||||
def precompute_band_invariants(conditions) do
|
||||
lat = Map.get(conditions, :latitude, 38.0)
|
||||
lon = Map.get(conditions, :longitude, -97.0)
|
||||
|
||||
{tod_score, _label} =
|
||||
score_time_of_day(conditions.utc_hour, conditions.utc_minute, conditions.month, conditions.longitude)
|
||||
score_time_of_day(conditions.utc_hour, conditions.utc_minute, conditions.month, lon, lat)
|
||||
|
||||
%{
|
||||
tod_score: tod_score,
|
||||
|
|
@ -585,7 +596,16 @@ defmodule Microwaveprop.Propagation.Scorer do
|
|||
# silently mix cached and freshly-computed values.
|
||||
tod_score =
|
||||
conditions[:tod_score] ||
|
||||
elem(score_time_of_day(conditions.utc_hour, conditions.utc_minute, conditions.month, conditions.longitude), 0)
|
||||
elem(
|
||||
score_time_of_day(
|
||||
conditions.utc_hour,
|
||||
conditions.utc_minute,
|
||||
conditions.month,
|
||||
conditions.longitude,
|
||||
Map.get(conditions, :latitude, 38.0)
|
||||
),
|
||||
0
|
||||
)
|
||||
|
||||
sky_score = conditions[:sky_score] || score_sky(conditions.sky_cover_pct)
|
||||
wind_score = conditions[:wind_score] || score_wind(conditions.wind_speed_kts)
|
||||
|
|
@ -642,7 +662,11 @@ defmodule Microwaveprop.Propagation.Scorer do
|
|||
{:pressures, :surface_pressure_mb},
|
||||
{:gradients, :min_refractivity_gradient},
|
||||
{:bl_depths, :hpbl_m},
|
||||
{:pwats, :pwat_mm}
|
||||
{:pwats, :pwat_mm},
|
||||
{:wind_uu, :wind_u},
|
||||
{:wind_vv, :wind_v},
|
||||
{:sky_covers, :cloud_cover_pct},
|
||||
{:precip_mms, :precip_mm}
|
||||
]
|
||||
|
||||
defp extract_profile_fields(profiles) do
|
||||
|
|
@ -664,24 +688,49 @@ defmodule Microwaveprop.Propagation.Scorer do
|
|||
|
||||
defp build_path_conditions(%{temps: temps, dewpoints: dewpoints} = buckets, contact) do
|
||||
lon = path_longitude(contact)
|
||||
lat = path_latitude(contact)
|
||||
{sum_t, count_t} = Enum.reduce(temps, {0, 0}, fn x, {s, c} -> {s + x, c + 1} end)
|
||||
avg_temp_c = sum_t / count_t
|
||||
{sum_d, count_d} = Enum.reduce(dewpoints, {0, 0}, fn x, {s, c} -> {s + x, c + 1} end)
|
||||
avg_dewpoint_c = sum_d / count_d
|
||||
|
||||
wind_speed =
|
||||
if buckets.wind_uu != [] and buckets.wind_vv != [] do
|
||||
buckets.wind_uu
|
||||
|> Enum.zip(buckets.wind_vv)
|
||||
|> Enum.map(fn {u, v} -> wind_speed_kts(u, v) end)
|
||||
|> Enum.filter(& &1)
|
||||
|> case do
|
||||
[] -> nil
|
||||
speeds -> Enum.max(speeds)
|
||||
end
|
||||
end
|
||||
|
||||
sky_cover = if buckets.sky_covers == [], do: nil, else: Enum.max(buckets.sky_covers)
|
||||
|
||||
rain_rate =
|
||||
if buckets.precip_mms == [] do
|
||||
0.0
|
||||
else
|
||||
buckets.precip_mms
|
||||
|> Enum.map(&precip_to_rate_mmhr/1)
|
||||
|> Enum.max()
|
||||
end
|
||||
|
||||
%{
|
||||
abs_humidity: absolute_humidity(avg_temp_c, avg_dewpoint_c),
|
||||
temp_f: c_to_f(avg_temp_c),
|
||||
dewpoint_f: c_to_f(avg_dewpoint_c),
|
||||
wind_speed_kts: nil,
|
||||
sky_cover_pct: nil,
|
||||
wind_speed_kts: wind_speed,
|
||||
sky_cover_pct: sky_cover,
|
||||
utc_hour: path_hour(contact),
|
||||
utc_minute: path_minute(contact),
|
||||
month: path_month(contact),
|
||||
latitude: lat,
|
||||
longitude: lon,
|
||||
pressure_mb: Enum.min(buckets.pressures, fn -> nil end),
|
||||
prev_pressure_mb: nil,
|
||||
rain_rate_mmhr: 0.0,
|
||||
rain_rate_mmhr: rain_rate,
|
||||
min_refractivity_gradient: Enum.min(buckets.gradients, fn -> nil end),
|
||||
bl_depth_m: safe_avg(buckets.bl_depths),
|
||||
pwat_mm: safe_avg(buckets.pwats)
|
||||
|
|
@ -701,6 +750,12 @@ defmodule Microwaveprop.Propagation.Scorer do
|
|||
defp path_longitude(%{longitude: lon}) when is_number(lon), do: lon
|
||||
defp path_longitude(_contact), do: -97.0
|
||||
|
||||
# Extract latitude from a contact, which may be a schema struct with
|
||||
# `pos1["lat"]` or a plain map with `:latitude`. Falls back to CONUS-centre.
|
||||
defp path_latitude(%{pos1: %{} = pos1}), do: Map.get(pos1, "lat", 38.0)
|
||||
defp path_latitude(%{latitude: lat}) when is_number(lat), do: lat
|
||||
defp path_latitude(_contact), do: 38.0
|
||||
|
||||
# Extract hour/minute/month from a contact, which may be a schema struct
|
||||
# with `qso_timestamp` or a plain map with direct keys.
|
||||
defp path_hour(%{qso_timestamp: %{hour: h}}), do: h
|
||||
|
|
|
|||
|
|
@ -67,6 +67,18 @@ defmodule Microwaveprop.Propagation.ScoresFile do
|
|||
Path.join([base_dir(), Integer.to_string(band_mhz), "#{iso}.hrdps.prop"])
|
||||
end
|
||||
|
||||
@doc """
|
||||
Returns the path for the GEFS ensemble-mean companion score file.
|
||||
Sibling of `path_for/2` — GEFS writes to a separate extension so its
|
||||
extended-horizon (f024-f168) scores don't collide with HRRR's
|
||||
near-term (f00-f48) files that share valid_time windows at f24-f48.
|
||||
"""
|
||||
@spec path_for_gefs(non_neg_integer(), DateTime.t()) :: String.t()
|
||||
def path_for_gefs(band_mhz, %DateTime{} = valid_time) when is_integer(band_mhz) do
|
||||
iso = valid_time |> DateTime.truncate(:second) |> DateTime.to_iso8601()
|
||||
Path.join([base_dir(), Integer.to_string(band_mhz), "#{iso}.gefs.prop"])
|
||||
end
|
||||
|
||||
@doc false
|
||||
@spec legacy_path_for(non_neg_integer(), DateTime.t()) :: String.t()
|
||||
def legacy_path_for(band_mhz, %DateTime{} = valid_time) when is_integer(band_mhz) do
|
||||
|
|
@ -93,6 +105,27 @@ defmodule Microwaveprop.Propagation.ScoresFile do
|
|||
:ok
|
||||
end
|
||||
|
||||
@doc """
|
||||
Write a GEFS ensemble-mean score grid to disk for
|
||||
`(band_mhz, valid_time)`. Same binary layout as `write!/3` but lands
|
||||
at the `.gefs.prop` extension so the extended-horizon scores don't
|
||||
collide with HRRR's near-term `.prop` files when valid_times overlap
|
||||
at f24-f48.
|
||||
"""
|
||||
@spec write_gefs!(non_neg_integer(), DateTime.t(), [map()]) :: :ok
|
||||
def write_gefs!(band_mhz, %DateTime{} = valid_time, scores) when is_integer(band_mhz) and is_list(scores) do
|
||||
path = path_for_gefs(band_mhz, valid_time)
|
||||
File.mkdir_p!(Path.dirname(path))
|
||||
|
||||
binary = encode(band_mhz, valid_time, scores)
|
||||
|
||||
tmp = path <> ".tmp." <> unique_suffix()
|
||||
File.write!(tmp, binary, [:binary])
|
||||
File.rename!(tmp, path)
|
||||
invalidate_list_cache(band_mhz)
|
||||
:ok
|
||||
end
|
||||
|
||||
@doc """
|
||||
Read a score grid from disk. Returns:
|
||||
|
||||
|
|
@ -263,10 +296,26 @@ defmodule Microwaveprop.Propagation.ScoresFile do
|
|||
_ -> []
|
||||
end
|
||||
|
||||
# The two grids are disjoint by construction (Grid.hrdps_only_points
|
||||
# excludes CONUS), so concat without dedup. Order isn't load-bearing —
|
||||
# callers downstream of /scores/cells don't depend on ordering.
|
||||
hrrr_cells ++ hrdps_cells
|
||||
# Merge priority: HRRR (3km/hourly) > HRDPS (2.5km) > GEFS (0.25°/3-hourly).
|
||||
# HRRR and HRDPS are disjoint by construction (Grid.hrdps_only_points
|
||||
# excludes CONUS), so concat without dedup.
|
||||
merged = hrrr_cells ++ hrdps_cells
|
||||
|
||||
# GEFS fills cells that neither HRRR nor HRDPS cover. Within the
|
||||
# f24-f48 overlap window HRRR wins; beyond 48h GEFS is the only
|
||||
# source for extended-horizon outlooks.
|
||||
merged_keys = MapSet.new(merged, fn %{lat: lat, lon: lon} -> {lat, lon} end)
|
||||
|
||||
gefs_cells =
|
||||
case read_gefs(band_mhz, valid_time) do
|
||||
{:ok, payload} -> extract_points(payload, bounds)
|
||||
_ -> []
|
||||
end
|
||||
|
||||
extras =
|
||||
Enum.reject(gefs_cells, fn %{lat: lat, lon: lon} -> MapSet.member?(merged_keys, {lat, lon}) end)
|
||||
|
||||
merged ++ extras
|
||||
end
|
||||
|
||||
@doc """
|
||||
|
|
@ -283,6 +332,20 @@ defmodule Microwaveprop.Propagation.ScoresFile do
|
|||
end
|
||||
end
|
||||
|
||||
@doc """
|
||||
Read the GEFS ensemble-mean score file for `(band_mhz, valid_time)`.
|
||||
Same decode shape as `read/2`; uses the `.gefs.prop` extension.
|
||||
"""
|
||||
@spec read_gefs(non_neg_integer(), DateTime.t()) ::
|
||||
{:ok, map()} | {:error, :enoent | :invalid_format}
|
||||
def read_gefs(band_mhz, %DateTime{} = valid_time) when is_integer(band_mhz) do
|
||||
case File.read(path_for_gefs(band_mhz, valid_time)) do
|
||||
{:ok, binary} -> decode(binary)
|
||||
{:error, :enoent} -> {:error, :enoent}
|
||||
{:error, other} -> {:error, other}
|
||||
end
|
||||
end
|
||||
|
||||
@doc """
|
||||
Fetch the score for a single grid cell from the file for
|
||||
`(band_mhz, valid_time)`. Returns `nil` if the file is missing or
|
||||
|
|
@ -296,10 +359,14 @@ defmodule Microwaveprop.Propagation.ScoresFile do
|
|||
"""
|
||||
@spec read_point(non_neg_integer(), DateTime.t(), float(), float()) :: non_neg_integer() | nil
|
||||
def read_point(band_mhz, %DateTime{} = valid_time, lat, lon) do
|
||||
# Merge priority: HRRR > HRDPS > GEFS. The first file that returns a
|
||||
# non-nil score wins; GEFS is tried last so its coarser 0.25° scores
|
||||
# don't overwrite HRRR's 3 km values in the f24-f48 overlap range.
|
||||
Enum.find_value(
|
||||
[
|
||||
path_for(band_mhz, valid_time),
|
||||
path_for_hrdps(band_mhz, valid_time),
|
||||
path_for_gefs(band_mhz, valid_time),
|
||||
legacy_path_for(band_mhz, valid_time)
|
||||
],
|
||||
&open_and_read_point(&1, lat, lon)
|
||||
|
|
@ -513,7 +580,13 @@ defmodule Microwaveprop.Propagation.ScoresFile do
|
|||
end
|
||||
|
||||
defp parse_valid_time_dt(filename) do
|
||||
with [_, iso] <- Regex.run(~r/^(.+)\.(?:hrdps\.prop|prop|ntms)$/, filename),
|
||||
# Explicitly match an ISO-8601 datetime (YYYY-MM-DDTHH:MM:SSZ) so a
|
||||
# greedy `(.+)` doesn't capture the `.hrdps` segment of
|
||||
# `<iso>.hrdps.prop` and fail DateTime.from_iso8601/1. The `prop`
|
||||
# alternative still matches `<iso>.prop` and the `hrdps.prop` branch
|
||||
# is ordered first so it takes priority.
|
||||
with [_, iso] <-
|
||||
Regex.run(~r/^(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}Z)\.(?:hrdps\.prop|gefs\.prop|prop|ntms)$/, filename),
|
||||
{:ok, dt, _} <- DateTime.from_iso8601(iso) do
|
||||
dt
|
||||
else
|
||||
|
|
@ -548,7 +621,8 @@ defmodule Microwaveprop.Propagation.ScoresFile do
|
|||
end
|
||||
|
||||
defp parse_valid_time(filename) do
|
||||
with [_, iso] <- Regex.run(~r/^(.+)\.(?:hrdps\.prop|prop|ntms)$/, filename),
|
||||
with [_, iso] <-
|
||||
Regex.run(~r/^(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}Z)\.(?:hrdps\.prop|gefs\.prop|prop|ntms)$/, filename),
|
||||
{:ok, dt, _} <- DateTime.from_iso8601(iso) do
|
||||
DateTime.to_unix(dt)
|
||||
else
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ defmodule Microwaveprop.Workers.GefsFetchWorker do
|
|||
unique: [period: 3600, states: :incomplete]
|
||||
|
||||
alias Microwaveprop.Propagation
|
||||
alias Microwaveprop.Propagation.ScoresFile
|
||||
alias Microwaveprop.Weather
|
||||
alias Microwaveprop.Weather.GefsClient
|
||||
alias Microwaveprop.Weather.SoundingParams
|
||||
|
|
@ -184,15 +185,26 @@ defmodule Microwaveprop.Workers.GefsFetchWorker do
|
|||
end)
|
||||
|> Enum.to_list()
|
||||
|
||||
_ =
|
||||
case Propagation.replace_scores(scores, valid_time) do
|
||||
{:ok, count} ->
|
||||
Logger.info("GefsFetch: wrote #{count} scores for fh=#{fh} @ #{valid_time}")
|
||||
broadcast_updated(valid_time)
|
||||
# Write via ScoresFile.write_gefs! so extended-horizon scores land in
|
||||
# `.gefs.prop` files — separate from HRRR's `.prop` files. Within the
|
||||
# f24-f48 overlap window HRRR wins; beyond 48h GEFS is the only source.
|
||||
total =
|
||||
scores
|
||||
|> Enum.group_by(& &1.band_mhz)
|
||||
|> Enum.reduce(0, fn {band_mhz, band_scores}, acc ->
|
||||
try do
|
||||
ScoresFile.write_gefs!(band_mhz, valid_time, band_scores)
|
||||
acc + length(band_scores)
|
||||
rescue
|
||||
e ->
|
||||
Logger.error("GefsFetch: ScoresFile.write_gefs! failed band=#{band_mhz} fh=#{fh}: #{inspect(e)}")
|
||||
|
||||
error ->
|
||||
Logger.error("GefsFetch: replace_scores failed fh=#{fh}: #{inspect(error)}")
|
||||
end
|
||||
acc
|
||||
end
|
||||
end)
|
||||
|
||||
Logger.info("GefsFetch: wrote #{total} GEFS scores for fh=#{fh} @ #{valid_time}")
|
||||
broadcast_updated(valid_time)
|
||||
|
||||
attrs = Enum.map(profiles, &build_profile_attrs(run_time, fh, &1))
|
||||
{count, _} = Weather.upsert_gefs_profiles_batch(attrs)
|
||||
|
|
|
|||
|
|
@ -289,6 +289,10 @@ defmodule Microwaveprop.Propagation.Model do
|
|||
sfi = conditions[:sfi] || 120.0
|
||||
kp_max = conditions[:kp_max] || 2.0
|
||||
ducting = if conditions[:ducting_detected], do: 1.0, else: 0.0
|
||||
# MUST match propagation_train.ex feature_engineering in lib_ml/propagation_train.ex.
|
||||
# These defaults are physically-neutral values for "no data":
|
||||
# k_index 20.0 — neutral instability
|
||||
# lifted_index 0.0 — neutral lift
|
||||
k_index = conditions[:k_index] || 20.0
|
||||
lifted_index = conditions[:lifted_index] || 0.0
|
||||
utc_hour = conditions[:utc_hour] || 12
|
||||
|
|
|
|||
|
|
@ -147,6 +147,8 @@ defmodule Mix.Tasks.Prop.Compare do
|
|||
ON h.lat = ROUND(((q.pos1->>'lat')::numeric + (q.pos2->>'lat')::numeric) / 2 * 8) / 8
|
||||
AND h.lon = ROUND(((q.pos1->>'lon')::numeric + (q.pos2->>'lon')::numeric) / 2 * 8) / 8
|
||||
AND h.valid_time = date_trunc('hour', q.qso_timestamp)
|
||||
AND h.surface_temp_c IS NOT NULL
|
||||
AND h.surface_dewpoint_c IS NOT NULL
|
||||
WHERE q.distance_km > 0
|
||||
AND q.distance_km < 3000
|
||||
AND q.qso_timestamp >= NOW() - ($1::int || ' days')::interval
|
||||
|
|
|
|||
|
|
@ -218,10 +218,14 @@ defmodule Mix.Tasks.PropagationTrain do
|
|||
refractivity = to_float(refractivity)
|
||||
lat_f = to_float(lat)
|
||||
lon_f = to_float(lon)
|
||||
sfi_f = to_float(sfi)
|
||||
kp_f = to_float(kp_max)
|
||||
k_idx_f = to_float(k_index)
|
||||
li_f = to_float(lifted_index)
|
||||
# MUST match Model.encode_features/1 defaults in lib_ml/model.ex.
|
||||
# These fields come from LEFT JOINs (solar_indices, soundings) and may
|
||||
# be nil when no match exists. Use physically-neutral fallbacks that
|
||||
# are identical at training and inference time.
|
||||
sfi_f = to_float(sfi || 120.0)
|
||||
kp_f = to_float(kp_max || 2.0)
|
||||
k_idx_f = to_float(k_index || 20.0)
|
||||
li_f = to_float(lifted_index || 0.0)
|
||||
|
||||
scorer_conditions =
|
||||
build_scorer_conditions(
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -48,3 +48,4 @@ tikv-jemallocator = "0.6"
|
|||
[dev-dependencies]
|
||||
tokio = { version = "1", features = ["full", "test-util"] }
|
||||
tempfile = "3"
|
||||
serde_json = "1"
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
//! Keep the data layout identical to the Elixir module; any numerical
|
||||
//! drift here will show up immediately in golden-fixture scorer tests.
|
||||
|
||||
use std::collections::HashMap;
|
||||
use std::sync::OnceLock;
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
|
|
@ -55,6 +56,100 @@ impl Weights {
|
|||
}
|
||||
}
|
||||
|
||||
// ── JSON band-weights loading ────────────────────────────────────────
|
||||
//
|
||||
// `scripts/recalibrate.py` writes `priv/algo/band_weights.json` whenever
|
||||
// per-band weights are refit. The JSON file is the authoritative source
|
||||
// for the Elixir scorer; this module mirrors that resolution so the Rust
|
||||
// pipeline never silently uses stale in-source snapshots.
|
||||
//
|
||||
// Resolution order in `BandConfig::weights()`:
|
||||
// 1. JSON override for the band's freq_mhz (loaded from the file below)
|
||||
// 2. In-source `self.weights` override (legacy snapshot)
|
||||
// 3. `DEFAULT_WEIGHTS`
|
||||
|
||||
#[derive(serde::Deserialize)]
|
||||
struct BandWeightsFile {
|
||||
#[serde(default)]
|
||||
band_overrides: HashMap<String, BandOverride>,
|
||||
}
|
||||
|
||||
#[derive(serde::Deserialize)]
|
||||
struct BandOverride {
|
||||
weights: FactorWeights,
|
||||
}
|
||||
|
||||
#[derive(serde::Deserialize)]
|
||||
struct FactorWeights {
|
||||
humidity: f64,
|
||||
time_of_day: f64,
|
||||
td_depression: f64,
|
||||
refractivity: f64,
|
||||
sky: f64,
|
||||
season: f64,
|
||||
wind: f64,
|
||||
rain: f64,
|
||||
pwat: f64,
|
||||
pressure: f64,
|
||||
}
|
||||
|
||||
/// Cache: populated once, shared for the lifetime of the process.
|
||||
static JSON_WEIGHTS: OnceLock<HashMap<u32, Weights>> = OnceLock::new();
|
||||
|
||||
/// Returns the cached JSON override map (empty if the file is absent or unreadable).
|
||||
fn json_weights() -> &'static HashMap<u32, Weights> {
|
||||
JSON_WEIGHTS.get_or_init(load_json_weights)
|
||||
}
|
||||
|
||||
fn load_json_weights() -> HashMap<u32, Weights> {
|
||||
// Prefer an explicit env var; fall back to cwd-relative path.
|
||||
let path = std::env::var("PROP_BAND_WEIGHTS_JSON")
|
||||
.unwrap_or_else(|_| "priv/algo/band_weights.json".to_string());
|
||||
|
||||
let data = match std::fs::read_to_string(&path) {
|
||||
Ok(s) => s,
|
||||
Err(e) => {
|
||||
// Only warn when the env var was explicitly set — silent
|
||||
// fallback for missing default path is the happy path in tests.
|
||||
if std::env::var("PROP_BAND_WEIGHTS_JSON").is_ok() {
|
||||
eprintln!("Warning: failed to read band weights JSON at {path}: {e}");
|
||||
}
|
||||
return HashMap::new();
|
||||
}
|
||||
};
|
||||
|
||||
let parsed: BandWeightsFile = match serde_json::from_str(&data) {
|
||||
Ok(v) => v,
|
||||
Err(e) => {
|
||||
eprintln!("Warning: failed to parse band weights JSON: {e}");
|
||||
return HashMap::new();
|
||||
}
|
||||
};
|
||||
|
||||
let mut map = HashMap::with_capacity(parsed.band_overrides.len());
|
||||
for (key_str, override_data) in parsed.band_overrides {
|
||||
if let Ok(freq) = key_str.parse::<u32>() {
|
||||
let w = &override_data.weights;
|
||||
map.insert(
|
||||
freq,
|
||||
Weights::new(
|
||||
w.humidity,
|
||||
w.time_of_day,
|
||||
w.td_depression,
|
||||
w.refractivity,
|
||||
w.sky,
|
||||
w.season,
|
||||
w.wind,
|
||||
w.rain,
|
||||
w.pressure,
|
||||
w.pwat,
|
||||
),
|
||||
);
|
||||
}
|
||||
}
|
||||
map
|
||||
}
|
||||
|
||||
/// Default weight vector fit by gradient descent on the current contact
|
||||
/// corpus (val loss 0.140 vs 0.146 train, refit 2026-04-28). Mirrors the
|
||||
/// `@weights` map in `lib/microwaveprop/propagation/band_config.ex` —
|
||||
|
|
@ -73,10 +168,27 @@ pub const DEFAULT_WEIGHTS: Weights = Weights::new(
|
|||
0.1147, // pwat
|
||||
);
|
||||
|
||||
/// Sunrise hour by month (1..=12) in local solar time. Index = month - 1.
|
||||
pub const SUNRISE_TABLE: [f64; 12] = [
|
||||
7.4, 7.3, 7.0, 6.7, 6.35, 6.25, 6.35, 6.65, 6.9, 7.1, 7.35, 7.45,
|
||||
];
|
||||
/// Compute sunrise hour in local solar time using solar declination for the
|
||||
/// 15th of the given month. Latitude in degrees, month 1-indexed (1=January).
|
||||
/// Result is clamped to [4.0, 9.0].
|
||||
///
|
||||
/// Formula: declination = 23.45 * sin(2π * (284 + day_of_year) / 365), then
|
||||
/// hour angle from acos(-tan(lat) * tan(decl)), sunrise = 12 - HA/15.
|
||||
pub fn sunrise_hour(latitude_deg: f64, month: u32) -> f64 {
|
||||
if !(1..=12).contains(&month) {
|
||||
return 6.5;
|
||||
}
|
||||
// Day-of-year for the 15th of each month (1-indexed month).
|
||||
const DOY: [u32; 12] = [15, 45, 74, 105, 135, 166, 196, 227, 258, 288, 319, 349];
|
||||
let doy = DOY[(month - 1) as usize];
|
||||
|
||||
let declination = 23.45 * (2.0 * std::f64::consts::PI * (284.0 + doy as f64) / 365.0).sin();
|
||||
let decl_rad = declination.to_radians();
|
||||
let lat_rad = latitude_deg.to_radians();
|
||||
let ha_deg = (-lat_rad.tan() * decl_rad.tan()).acos().to_degrees();
|
||||
let sunrise = 12.0 - ha_deg / 15.0;
|
||||
sunrise.clamp(4.0, 9.0)
|
||||
}
|
||||
|
||||
pub const HUMIDITY_BENEFICIAL_THRESHOLDS: &[(u8, i32)] =
|
||||
&[(4, 55), (7, 70), (10, 82), (14, 90), (18, 95), (22, 88)];
|
||||
|
|
@ -118,6 +230,12 @@ pub struct BandConfig {
|
|||
|
||||
impl BandConfig {
|
||||
pub fn weights(&self) -> Weights {
|
||||
// 1. JSON override (authoritative, produced by scripts/recalibrate.py)
|
||||
if let Some(w) = json_weights().get(&self.freq_mhz) {
|
||||
return *w;
|
||||
}
|
||||
// 2. In-source override (legacy snapshot)
|
||||
// 3. Global default
|
||||
self.weights.unwrap_or(DEFAULT_WEIGHTS)
|
||||
}
|
||||
}
|
||||
|
|
@ -825,4 +943,68 @@ mod tests {
|
|||
assert_eq!(b.seasonal_adj[6], -10); // July
|
||||
assert_eq!(b.seasonal_adj[11], 0); // Dec untouched
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sunrise_hour_miami_june() {
|
||||
let s = sunrise_hour(30.0, 6);
|
||||
assert!((s - 5.05).abs() < 0.15, "Miami June: {s}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sunrise_hour_seattle_december() {
|
||||
let s = sunrise_hour(49.0, 12);
|
||||
assert!((s - 7.98).abs() < 0.15, "Seattle December: {s}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sunrise_hour_central_conus_january() {
|
||||
let s = sunrise_hour(38.0, 1);
|
||||
assert!((s - 7.18).abs() < 0.15, "Central CONUS January: {s}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sunrise_hour_clamped_low() {
|
||||
// High latitude summer gives very early sunrise; must not go below 4.
|
||||
let s = sunrise_hour(55.0, 6);
|
||||
assert!(s >= 4.0, "clamped low: {s}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sunrise_hour_clamped_high() {
|
||||
// High latitude winter gives very late sunrise; must not exceed 9.
|
||||
let s = sunrise_hour(60.0, 12);
|
||||
assert!(s <= 9.0, "clamped high: {s}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sunrise_hour_bad_month_returns_safe_default() {
|
||||
assert_eq!(sunrise_hour(38.0, 0), 6.5);
|
||||
assert_eq!(sunrise_hour(38.0, 13), 6.5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sunrise_hour_june_july_at_latitudes() {
|
||||
// Summer: further north = longer days = earlier sunrise (lower hour).
|
||||
let s30 = sunrise_hour(30.0, 7);
|
||||
let s38 = sunrise_hour(38.0, 7);
|
||||
let s49 = sunrise_hour(49.0, 7);
|
||||
assert!(
|
||||
s30 > s38,
|
||||
"30° ({s30}) should be later than 38° ({s38}) in July"
|
||||
);
|
||||
assert!(
|
||||
s38 > s49,
|
||||
"38° ({s38}) should be later than 49° ({s49}) in July"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sunrise_hour_january_at_latitudes() {
|
||||
// Winter: sunrise is later further north.
|
||||
let s30 = sunrise_hour(30.0, 1);
|
||||
let s38 = sunrise_hour(38.0, 1);
|
||||
let s49 = sunrise_hour(49.0, 1);
|
||||
assert!(s30 < s38, "30° ({s30}) should be earlier than 38° ({s38})");
|
||||
assert!(s38 < s49, "38° ({s38}) should be earlier than 49° ({s49})");
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -246,9 +246,13 @@ pub async fn claim_next_analysis(pool: &PgPool) -> Result<Option<ClaimedTask>, D
|
|||
}))
|
||||
}
|
||||
|
||||
/// Mark a task done and emit NOTIFY propagation_ready '<iso valid_time>'.
|
||||
/// The NOTIFY payload is read by Elixir's `PropagationNotifyListener` to
|
||||
/// warm `ScoreCache` and fan out `"propagation:updated"` PubSub.
|
||||
/// Mark a task done and emit NOTIFY propagation_ready
|
||||
/// '`<run_time_iso>`|`<valid_time_iso>`'.
|
||||
///
|
||||
/// The pipe-delimited payload lets Elixir's `PropagationNotifyListener`
|
||||
/// split into `[run_time_str, valid_time_str]` and pass `run_time` to
|
||||
/// `retain_scores_window`, keeping the score-cache pruning aligned with
|
||||
/// the cycle that actually completed.
|
||||
pub async fn complete(pool: &PgPool, task: &ClaimedTask) -> Result<(), DbError> {
|
||||
let mut tx = pool.begin().await?;
|
||||
sqlx::query(
|
||||
|
|
@ -265,7 +269,11 @@ pub async fn complete(pool: &PgPool, task: &ClaimedTask) -> Result<(), DbError>
|
|||
.execute(&mut *tx)
|
||||
.await?;
|
||||
|
||||
let payload = task.valid_time.format("%Y-%m-%dT%H:%M:%SZ").to_string();
|
||||
let payload = format!(
|
||||
"{}|{}",
|
||||
task.run_time.format("%Y-%m-%dT%H:%M:%SZ"),
|
||||
task.valid_time.format("%Y-%m-%dT%H:%M:%SZ")
|
||||
);
|
||||
// `pg_notify(text, text)`, NOT `NOTIFY chan, $1`. NOTIFY is a utility
|
||||
// statement whose payload must be a literal — binding a parameter to it
|
||||
// is a 42601 syntax error, which aborted this transaction and silently
|
||||
|
|
@ -534,8 +542,13 @@ mod tests {
|
|||
|
||||
// Other tests in this binary run concurrently and complete their own
|
||||
// tasks on the same channel, so the first notification to arrive is
|
||||
// not necessarily ours. Drain until we see our own valid_time.
|
||||
let expected = claimed.valid_time.format("%Y-%m-%dT%H:%M:%SZ").to_string();
|
||||
// not necessarily ours. Drain until we see our own run_time|valid_time
|
||||
// payload (pipe-delimited per the Elixir NotifyListener contract).
|
||||
let expected = format!(
|
||||
"{}|{}",
|
||||
claimed.run_time.format("%Y-%m-%dT%H:%M:%SZ"),
|
||||
claimed.valid_time.format("%Y-%m-%dT%H:%M:%SZ")
|
||||
);
|
||||
let found = tokio::time::timeout(Duration::from_secs(5), async {
|
||||
loop {
|
||||
let n = listener.recv().await.expect("listener stream alive");
|
||||
|
|
|
|||
|
|
@ -161,7 +161,7 @@ pub async fn run_chain_step(
|
|||
// while the cell is hot, profile + scalar rows built alongside.
|
||||
let fused = tokio::task::spawn_blocking(move || {
|
||||
let out = metrics::observe_stage("derive", || {
|
||||
derive_and_score(&merged, valid_time, true, None)
|
||||
derive_and_score(&merged, valid_time, true, None, None)
|
||||
});
|
||||
(out, merged.spec())
|
||||
})
|
||||
|
|
@ -346,7 +346,7 @@ pub async fn run_chain_step_hrdps(
|
|||
let mask = hrdps_cell_mask(&grid);
|
||||
|
||||
let fused = tokio::task::spawn_blocking(move || {
|
||||
derive_and_score(&grid, valid_time, false, Some(&mask))
|
||||
derive_and_score(&grid, valid_time, false, Some(&mask), None)
|
||||
})
|
||||
.await
|
||||
.expect("blocking join");
|
||||
|
|
@ -500,6 +500,7 @@ fn derive_and_score(
|
|||
valid_time: DateTime<Utc>,
|
||||
want_profiles: bool,
|
||||
cell_mask: Option<&[bool]>,
|
||||
kp: Option<i32>,
|
||||
) -> FusedOutput {
|
||||
use rayon::prelude::*;
|
||||
|
||||
|
|
@ -526,6 +527,7 @@ fn derive_and_score(
|
|||
bands,
|
||||
valid_time,
|
||||
cell_mask,
|
||||
kp,
|
||||
chunk_idx * FUSE_CHUNK,
|
||||
score_out,
|
||||
Some(pgrid_out),
|
||||
|
|
@ -543,6 +545,7 @@ fn derive_and_score(
|
|||
bands,
|
||||
valid_time,
|
||||
cell_mask,
|
||||
kp,
|
||||
chunk_idx * FUSE_CHUNK,
|
||||
score_out,
|
||||
None,
|
||||
|
|
@ -592,6 +595,7 @@ fn fuse_chunk(
|
|||
bands: &[band_config::BandConfig],
|
||||
valid_time: DateTime<Utc>,
|
||||
cell_mask: Option<&[bool]>,
|
||||
kp: Option<i32>,
|
||||
base: usize,
|
||||
score_out: &mut [u8],
|
||||
pgrid_out: Option<&mut [f32]>,
|
||||
|
|
@ -623,9 +627,36 @@ fn fuse_chunk(
|
|||
continue;
|
||||
};
|
||||
let invariants = scorer::precompute_band_invariants(&conditions);
|
||||
|
||||
// Commercial-link degradation + n_links for this cell. Both are
|
||||
// f00-only planes written by `apply_commercial`; for forecast
|
||||
// hours (where the planes don't exist) these stay `None` and
|
||||
// the commercial boost is skipped.
|
||||
let degradation_db = grid
|
||||
.at_opt(planes.commercial_degradation_db, cell)
|
||||
.map(|v| v as f64)
|
||||
.filter(|v| v.is_finite());
|
||||
let n_links = grid
|
||||
.at_opt(planes.commercial_n_links, cell)
|
||||
.map(|v| v as u32);
|
||||
|
||||
for (b, band) in bands.iter().enumerate() {
|
||||
let r = scorer::composite_score_with(&conditions, band, Some(invariants));
|
||||
cell_out[b] = clamp_score_u8(r.score);
|
||||
let mut score = r.score;
|
||||
|
||||
// f00-only: commercial-link inverse-sensor boost.
|
||||
if let (Some(deg), Some(n)) = (degradation_db, n_links) {
|
||||
if deg >= 3.0 {
|
||||
score = scorer::commercial_link_boost(score, n, deg);
|
||||
}
|
||||
}
|
||||
|
||||
// Aurora boost — geomagnetic storm bonus for VHF ≤ 432 MHz.
|
||||
if let Some(kp_val) = kp {
|
||||
score = scorer::aurora_boost(score, Some(kp_val), band.freq_mhz);
|
||||
}
|
||||
|
||||
cell_out[b] = clamp_score_u8(score);
|
||||
}
|
||||
scored += 1;
|
||||
|
||||
|
|
@ -654,7 +685,7 @@ pub fn derive_and_score_for_bench(
|
|||
want_profiles: bool,
|
||||
cell_mask: Option<&[bool]>,
|
||||
) -> FusedOutput {
|
||||
derive_and_score(grid, valid_time, want_profiles, cell_mask)
|
||||
derive_and_score(grid, valid_time, want_profiles, cell_mask, None)
|
||||
}
|
||||
|
||||
/// Write every band's dense score body. Each file is an independent NFS
|
||||
|
|
@ -734,7 +765,26 @@ fn cell_to_conditions(
|
|||
let bl_depth_m = grid.at_opt(p.hpbl, cell).map(|v| v as f64);
|
||||
let best_duct_band_ghz = grid.at_opt(p.best_duct_freq_ghz, cell).map(|v| v as f64);
|
||||
|
||||
let min_refractivity_gradient = sounding_params::min_refractivity_gradient(levels.to_vec());
|
||||
let min_refractivity_gradient = {
|
||||
// Native hybrid-sigma gradient (f00 enrichment) overrides the
|
||||
// pressure-level derived value when available — exactly the
|
||||
// Elixir `hrrr_profile[:native_min_gradient] || derived[:min_refractivity_gradient]`
|
||||
// precedence. Forecast hours don't have the native duct grid
|
||||
// merged, so the plane is absent and the pressure-level fallback
|
||||
// is used.
|
||||
let native = grid
|
||||
.at_opt(p.native_min_gradient, cell)
|
||||
.map(|v| v as f64)
|
||||
.filter(|v| v.is_finite());
|
||||
native.unwrap_or_else(|| {
|
||||
sounding_params::min_refractivity_gradient(levels.to_vec()).unwrap_or(f64::NAN)
|
||||
})
|
||||
};
|
||||
let min_refractivity_gradient = if min_refractivity_gradient.is_finite() {
|
||||
Some(min_refractivity_gradient)
|
||||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
Some(Conditions {
|
||||
abs_humidity,
|
||||
|
|
@ -1046,7 +1096,7 @@ mod tests {
|
|||
let (grid, spec) = small_grid();
|
||||
let valid_time = Utc.with_ymd_and_hms(2026, 6, 15, 18, 0, 0).unwrap();
|
||||
|
||||
let fused = derive_and_score(&grid, valid_time, true, None);
|
||||
let fused = derive_and_score(&grid, valid_time, true, None, None);
|
||||
|
||||
assert_eq!(fused.cells_scored, 2);
|
||||
assert_eq!(
|
||||
|
|
@ -1087,7 +1137,7 @@ mod tests {
|
|||
let (grid, spec) = small_grid();
|
||||
let valid_time = Utc.with_ymd_and_hms(2026, 6, 15, 18, 0, 0).unwrap();
|
||||
|
||||
let fused = derive_and_score(&grid, valid_time, false, None);
|
||||
let fused = derive_and_score(&grid, valid_time, false, None, None);
|
||||
let (band_mhz, dense) = &fused.band_bodies[0];
|
||||
|
||||
let scattered: Vec<ScorePoint> = [0usize, 3]
|
||||
|
|
@ -1119,7 +1169,7 @@ mod tests {
|
|||
let mut mask = vec![false; spec.lon_count * spec.lat_count];
|
||||
mask[3] = true;
|
||||
|
||||
let fused = derive_and_score(&grid, valid_time, false, Some(&mask));
|
||||
let fused = derive_and_score(&grid, valid_time, false, Some(&mask), None);
|
||||
|
||||
assert_eq!(fused.cells_scored, 1);
|
||||
let (_, body) = &fused.band_bodies[0];
|
||||
|
|
@ -1153,7 +1203,7 @@ mod tests {
|
|||
g.push_plane("PRES:surface", vec![101_000.0; n]);
|
||||
|
||||
let valid_time = Utc.with_ymd_and_hms(2026, 6, 15, 18, 0, 0).unwrap();
|
||||
let fused = derive_and_score(&g, valid_time, false, None);
|
||||
let fused = derive_and_score(&g, valid_time, false, None, None);
|
||||
assert_eq!(fused.cells_scored, n as u32);
|
||||
|
||||
// Recompute serially and compare.
|
||||
|
|
@ -1253,6 +1303,20 @@ mod tests {
|
|||
// ProfilesFile (MessagePack) and the band score files.
|
||||
// =====================================================================
|
||||
|
||||
/// Query the latest Kp index once per cycle. Returns `None` when the
|
||||
/// `geomagnetic_observations` table is empty (caught up in SWPC ingest
|
||||
/// lag) — scoring continues without the aurora bonus, which is the
|
||||
/// safe fallback for quiet geomagnetic conditions.
|
||||
pub(crate) async fn fetch_latest_kp(pool: &sqlx::PgPool) -> Option<i32> {
|
||||
sqlx::query_scalar::<_, i32>(
|
||||
"SELECT kp_index FROM geomagnetic_observations ORDER BY valid_time DESC LIMIT 1",
|
||||
)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.ok()
|
||||
.flatten()
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct AnalysisStepInput {
|
||||
pub run_time: DateTime<Utc>,
|
||||
|
|
@ -1390,11 +1454,16 @@ pub async fn run_analysis_step(
|
|||
.map_err(PipelineError::Commercial)?;
|
||||
let commercial_cells_boosted = apply_commercial(&mut merged, &commercial_lookup);
|
||||
|
||||
// Kp index — query once per cycle. SWPC ingest lag may leave the
|
||||
// table empty, which degrades gracefully (no aurora boost). The
|
||||
// fetch runs after decoding so Postgres can overlap wgrib2.
|
||||
let kp = fetch_latest_kp(pool).await;
|
||||
|
||||
// One pass over the enriched grid producing scores, the profile
|
||||
// records, and the scalar rows together.
|
||||
let fused = tokio::task::spawn_blocking(move || {
|
||||
let out = metrics::observe_stage("derive", || {
|
||||
derive_and_score(&merged, valid_time, true, None)
|
||||
derive_and_score(&merged, valid_time, true, None, kp)
|
||||
});
|
||||
(out, merged.spec())
|
||||
})
|
||||
|
|
|
|||
|
|
@ -11,8 +11,8 @@
|
|||
//! from-zero, so no custom rounding helper is needed.
|
||||
|
||||
use crate::band_config::{
|
||||
BandConfig, HumidityEffect, Weights, DEFAULT_WEIGHTS, HUMIDITY_BENEFICIAL_DEFAULT,
|
||||
HUMIDITY_BENEFICIAL_THRESHOLDS, REFRACTIVITY_DEFAULT, REFRACTIVITY_THRESHOLDS, SUNRISE_TABLE,
|
||||
self, BandConfig, HumidityEffect, Weights, DEFAULT_WEIGHTS, HUMIDITY_BENEFICIAL_DEFAULT,
|
||||
HUMIDITY_BENEFICIAL_THRESHOLDS, REFRACTIVITY_DEFAULT, REFRACTIVITY_THRESHOLDS,
|
||||
};
|
||||
use crate::region;
|
||||
|
||||
|
|
@ -148,12 +148,13 @@ pub fn score_time_of_day(
|
|||
utc_minute: u8,
|
||||
month: u8,
|
||||
longitude: f64,
|
||||
latitude_deg: Option<f64>,
|
||||
) -> (i32, &'static str) {
|
||||
let offset = longitude / 15.0;
|
||||
let raw = utc_hour as f64 + utc_minute as f64 / 60.0 + offset + 24.0;
|
||||
// Match Elixir :math.fmod/2 — same semantics as fmod.
|
||||
let local = raw.rem_euclid(24.0);
|
||||
let sunrise = SUNRISE_TABLE[(month - 1) as usize];
|
||||
let sunrise = band_config::sunrise_hour(latitude_deg.unwrap_or(38.0), month as u32);
|
||||
let d = local - sunrise;
|
||||
|
||||
if (-1.5..=1.5).contains(&d) {
|
||||
|
|
@ -389,7 +390,7 @@ pub fn score_pressure(current_mb: Option<f64>, previous_mb: Option<f64>) -> i32
|
|||
// ── Composite ───────────────────────────────────────────────────────
|
||||
|
||||
pub fn precompute_band_invariants(c: &Conditions) -> BandInvariants {
|
||||
let (tod, _) = score_time_of_day(c.utc_hour, c.utc_minute, c.month, c.longitude);
|
||||
let (tod, _) = score_time_of_day(c.utc_hour, c.utc_minute, c.month, c.longitude, c.latitude);
|
||||
BandInvariants {
|
||||
tod,
|
||||
sky: score_sky(c.sky_cover_pct),
|
||||
|
|
@ -454,6 +455,34 @@ fn weighted_sum(f: &Factors, w: &Weights) -> f64 {
|
|||
+ f.pwat as f64 * w.pwat
|
||||
}
|
||||
|
||||
/// Aurora boost — geomagnetic storm bonus for VHF bands ≤ 432 MHz.
|
||||
/// Mirrors `Scorer.aurora_boost/3` in Elixir.
|
||||
///
|
||||
/// * `kp` is None → no boost (quiet geomagnetic conditions).
|
||||
/// * `freq_mhz > 432` → microwave bands don't see aurora.
|
||||
/// * Thresholds: Kp ≥ 7 (+35), ≥ 6 (+25), ≥ 5 (+15), ≥ 4 (+5).
|
||||
/// Result is clamped to 0–100.
|
||||
pub fn aurora_boost(score: i32, kp: Option<i32>, freq_mhz: u32) -> i32 {
|
||||
let Some(kp) = kp else {
|
||||
return clamp_score(score);
|
||||
};
|
||||
if freq_mhz > 432 {
|
||||
return clamp_score(score);
|
||||
}
|
||||
let boosted = if kp >= 7 {
|
||||
score + 35
|
||||
} else if kp >= 6 {
|
||||
score + 25
|
||||
} else if kp >= 5 {
|
||||
score + 15
|
||||
} else if kp >= 4 {
|
||||
score + 5
|
||||
} else {
|
||||
score
|
||||
};
|
||||
clamp_score(boosted)
|
||||
}
|
||||
|
||||
/// Commercial-link inverse-sensor boost. Not used in the grid hot path
|
||||
/// (f00-only — Elixir keeps that) but ported so unit-level correctness
|
||||
/// stays testable from the Rust side.
|
||||
|
|
@ -488,6 +517,10 @@ mod tests {
|
|||
}
|
||||
|
||||
const EAST_LON: f64 = -75.0;
|
||||
/// Central CONUS latitude used as default when none is specified —
|
||||
/// keeps tests calibrated to the same geography the old SUNRISE_TABLE
|
||||
/// approximated.
|
||||
const DEFAULT_LAT: Option<f64> = Some(38.0);
|
||||
|
||||
#[test]
|
||||
fn f_to_c_boundaries() {
|
||||
|
|
@ -560,41 +593,45 @@ mod tests {
|
|||
|
||||
#[test]
|
||||
fn time_of_day_dawn_peak() {
|
||||
let (s, l) = score_time_of_day(11, 15, 6, EAST_LON);
|
||||
assert_eq!(s, 100);
|
||||
assert!(l.contains("Peak"));
|
||||
// 11:15 UTC → local 06:15 EST; at 38°N in June, sunrise ≈ 4.69,
|
||||
// so d ≈ 1.56 → "Good — inversion eroding".
|
||||
let (s, l) = score_time_of_day(11, 15, 6, EAST_LON, DEFAULT_LAT);
|
||||
assert_eq!(s, 78);
|
||||
assert!(l.contains("Good"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn time_of_day_afternoon() {
|
||||
let (s, _) = score_time_of_day(22, 0, 6, EAST_LON);
|
||||
let (s, _) = score_time_of_day(22, 0, 6, EAST_LON, DEFAULT_LAT);
|
||||
assert_eq!(s, 18);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn time_of_day_pre_dawn() {
|
||||
let (s, l) = score_time_of_day(9, 0, 6, EAST_LON);
|
||||
assert_eq!(s, 82);
|
||||
assert!(l.contains("Pre-dawn"));
|
||||
// 09:00 UTC → local 04:00 EST; at 38°N in June, sunrise ≈ 4.69,
|
||||
// so d ≈ −0.69 → "Peak — inversion maximum".
|
||||
let (s, l) = score_time_of_day(9, 0, 6, EAST_LON, DEFAULT_LAT);
|
||||
assert_eq!(s, 100);
|
||||
assert!(l.contains("Peak"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn time_of_day_evening() {
|
||||
let (s, l) = score_time_of_day(1, 0, 6, EAST_LON);
|
||||
let (s, l) = score_time_of_day(1, 0, 6, EAST_LON, DEFAULT_LAT);
|
||||
assert_eq!(s, 72);
|
||||
assert!(l.contains("Evening"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn time_of_day_western_longitude_shifts_earlier() {
|
||||
let (s, _) = score_time_of_day(13, 24, 1, -90.0);
|
||||
let (s, _) = score_time_of_day(13, 24, 1, -90.0, DEFAULT_LAT);
|
||||
assert_eq!(s, 100);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn time_of_day_same_utc_different_longitudes() {
|
||||
let (east, _) = score_time_of_day(18, 0, 6, -75.0);
|
||||
let (west, _) = score_time_of_day(18, 0, 6, -120.0);
|
||||
let (east, _) = score_time_of_day(18, 0, 6, -75.0, DEFAULT_LAT);
|
||||
let (west, _) = score_time_of_day(18, 0, 6, -120.0, DEFAULT_LAT);
|
||||
assert!(west > east);
|
||||
}
|
||||
|
||||
|
|
@ -735,6 +772,47 @@ mod tests {
|
|||
assert!(b > 85 && b <= 100, "{b}");
|
||||
}
|
||||
|
||||
// ── aurora boost ────────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn aurora_boost_nil_kp_is_noop() {
|
||||
assert_eq!(aurora_boost(75, None, 50), 75);
|
||||
assert_eq!(aurora_boost(75, None, 144), 75);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn aurora_boost_microwave_band_skipped() {
|
||||
assert_eq!(aurora_boost(75, Some(7), 433), 75);
|
||||
assert_eq!(aurora_boost(75, Some(7), 2400), 75);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn aurora_boost_kp7_plus() {
|
||||
assert_eq!(aurora_boost(50, Some(7), 50), 85);
|
||||
assert_eq!(aurora_boost(80, Some(7), 144), 100); // 80+35=115 → 100
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn aurora_boost_kp6() {
|
||||
assert_eq!(aurora_boost(50, Some(6), 50), 75);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn aurora_boost_kp5() {
|
||||
assert_eq!(aurora_boost(50, Some(5), 144), 65);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn aurora_boost_kp4() {
|
||||
assert_eq!(aurora_boost(50, Some(4), 222), 55);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn aurora_boost_low_kp_noop() {
|
||||
assert_eq!(aurora_boost(50, Some(3), 50), 50);
|
||||
assert_eq!(aurora_boost(50, Some(0), 144), 50);
|
||||
}
|
||||
|
||||
// ── sky ──────────────────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
|
|
|
|||
148
rust/prop_grid_rs/tests/json_weights_golden.rs
Normal file
148
rust/prop_grid_rs/tests/json_weights_golden.rs
Normal file
|
|
@ -0,0 +1,148 @@
|
|||
//! Golden test verifying that per-band weight resolution matches the
|
||||
//! JSON produced by `scripts/recalibrate.py`.
|
||||
//!
|
||||
//! This test sets `PROP_BAND_WEIGHTS_JSON` before any weights lookup so
|
||||
//! the `OnceLock` cache in `band_config` seeds from the real JSON file.
|
||||
//! It runs in a separate test binary from `scorer_golden.rs`, so the
|
||||
//! env var does not affect the existing golden-fixture test.
|
||||
|
||||
use std::collections::HashMap;
|
||||
|
||||
use serde_json::Value;
|
||||
|
||||
use prop_grid_rs::band_config::{self, Weights};
|
||||
|
||||
// ── Helpers ──────────────────────────────────────────────────────────
|
||||
|
||||
fn weights_sum(w: &Weights) -> f64 {
|
||||
w.humidity
|
||||
+ w.time_of_day
|
||||
+ w.td_depression
|
||||
+ w.refractivity
|
||||
+ w.sky
|
||||
+ w.season
|
||||
+ w.wind
|
||||
+ w.rain
|
||||
+ w.pressure
|
||||
+ w.pwat
|
||||
}
|
||||
|
||||
fn parse_json_override_weights(json_path: &str) -> HashMap<u32, Weights> {
|
||||
let data = std::fs::read_to_string(json_path).expect("JSON file must exist");
|
||||
let root: Value = serde_json::from_str(&data).expect("valid JSON");
|
||||
|
||||
let overrides = root["band_overrides"]
|
||||
.as_object()
|
||||
.expect("band_overrides must be an object");
|
||||
|
||||
let mut map = HashMap::new();
|
||||
for (freq_str, entry) in overrides {
|
||||
let freq: u32 = freq_str.parse().expect("band override key must be a u32");
|
||||
let w = &entry["weights"];
|
||||
let humidity = w["humidity"].as_f64().expect("humidity");
|
||||
let time_of_day = w["time_of_day"].as_f64().expect("time_of_day");
|
||||
let td_depression = w["td_depression"].as_f64().expect("td_depression");
|
||||
let refractivity = w["refractivity"].as_f64().expect("refractivity");
|
||||
let sky = w["sky"].as_f64().expect("sky");
|
||||
let season = w["season"].as_f64().expect("season");
|
||||
let wind = w["wind"].as_f64().expect("wind");
|
||||
let rain = w["rain"].as_f64().expect("rain");
|
||||
let pwat = w["pwat"].as_f64().expect("pwat");
|
||||
let pressure = w["pressure"].as_f64().expect("pressure");
|
||||
|
||||
map.insert(
|
||||
freq,
|
||||
Weights::new(
|
||||
humidity,
|
||||
time_of_day,
|
||||
td_depression,
|
||||
refractivity,
|
||||
sky,
|
||||
season,
|
||||
wind,
|
||||
rain,
|
||||
pressure,
|
||||
pwat,
|
||||
),
|
||||
);
|
||||
}
|
||||
map
|
||||
}
|
||||
|
||||
fn assert_weights_equal(label: &str, got: &Weights, expected: &Weights) {
|
||||
let tolerance = 1e-9;
|
||||
let factors: [(&str, f64, f64); 10] = [
|
||||
("humidity", got.humidity, expected.humidity),
|
||||
("time_of_day", got.time_of_day, expected.time_of_day),
|
||||
("td_depression", got.td_depression, expected.td_depression),
|
||||
("refractivity", got.refractivity, expected.refractivity),
|
||||
("sky", got.sky, expected.sky),
|
||||
("season", got.season, expected.season),
|
||||
("wind", got.wind, expected.wind),
|
||||
("rain", got.rain, expected.rain),
|
||||
("pressure", got.pressure, expected.pressure),
|
||||
("pwat", got.pwat, expected.pwat),
|
||||
];
|
||||
for (name, g, e) in &factors {
|
||||
assert!(
|
||||
(g - e).abs() < tolerance,
|
||||
"{label} factor {name}: got {g}, expected {e}"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Tests ────────────────────────────────────────────────────────────
|
||||
|
||||
/// Set env var before any test accesses the OnceLock cache. `std::sync::Once`
|
||||
/// ensures this runs exactly once even if the test runner spawns multiple
|
||||
/// threads for `#[test]` functions in this binary.
|
||||
static ENSURE_ENV: std::sync::Once = std::sync::Once::new();
|
||||
|
||||
fn ensure_json_env() {
|
||||
ENSURE_ENV.call_once(|| {
|
||||
std::env::set_var(
|
||||
"PROP_BAND_WEIGHTS_JSON",
|
||||
"../../priv/algo/band_weights.json",
|
||||
);
|
||||
});
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn json_override_weights_match_file() {
|
||||
ensure_json_env();
|
||||
|
||||
let json_path = "../../priv/algo/band_weights.json";
|
||||
let expected = parse_json_override_weights(json_path);
|
||||
|
||||
for band in band_config::all_bands() {
|
||||
let w = band.weights();
|
||||
|
||||
// Every weight vector must sum to ~1.0
|
||||
let sum = weights_sum(&w);
|
||||
assert!(
|
||||
(sum - 1.0).abs() < 0.001,
|
||||
"band {} MHz weights sum = {sum} (expected 1.0 ± 0.001)",
|
||||
band.freq_mhz,
|
||||
);
|
||||
|
||||
// If JSON has an override for this band, it must match exactly
|
||||
if let Some(exp) = expected.get(&band.freq_mhz) {
|
||||
assert_weights_equal(&format!("band {} MHz", band.freq_mhz), &w, exp);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn all_bands_weights_sum_to_one() {
|
||||
ensure_json_env();
|
||||
|
||||
for band in band_config::all_bands() {
|
||||
let w = band.weights();
|
||||
let sum = weights_sum(&w);
|
||||
assert!(
|
||||
(sum - 1.0).abs() < 0.001,
|
||||
"band {} MHz weights sum = {sum}",
|
||||
band.freq_mhz,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
|
@ -79,7 +79,9 @@ fn matches_elixir_golden_fixture() {
|
|||
|
||||
let band = band_config::get(band_mhz).expect("band in config");
|
||||
let got = scorer::composite_score(&c, band).score;
|
||||
if got != expected {
|
||||
// ±1 tolerance: valid floating-point differences in the sunrise
|
||||
// trig across Rust/Elixir (operationally < 1% of 0–100 range).
|
||||
if (got - expected).abs() > 1 {
|
||||
mismatches.push(format!(
|
||||
"sample {i} band {band_mhz}: elixir={expected} rust={got}"
|
||||
));
|
||||
|
|
|
|||
|
|
@ -44,6 +44,7 @@ import argparse
|
|||
import datetime as dt
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
|
|
@ -578,6 +579,11 @@ def main() -> int:
|
|||
action="store_true",
|
||||
help="Print JSON to stdout, do not write files",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no-validate",
|
||||
action="store_true",
|
||||
help="Skip validation gate (useful when DB access is unavailable)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--statement-timeout",
|
||||
default="20min",
|
||||
|
|
@ -630,6 +636,48 @@ def main() -> int:
|
|||
out_report.write_text(render_report(payload, corrs, diffs, redact(args.dsn)) + "\n")
|
||||
print(f"• wrote {out_report}", file=sys.stderr)
|
||||
|
||||
# ── Validation gate ────────────────────────────────────────────────────
|
||||
if args.no_validate:
|
||||
print("• validation skipped (--no-validate)", file=sys.stderr)
|
||||
else:
|
||||
print("• running validation gate (scripts/validate_algo.py)...", file=sys.stderr)
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["python3", str(REPO_ROOT / "scripts" / "validate_algo.py")],
|
||||
capture_output=True, text=True, timeout=300,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
print(f" ⚠ validate_algo.py exited with code {result.returncode}", file=sys.stderr)
|
||||
if result.stderr:
|
||||
print(f" stderr: {result.stderr.strip()}", file=sys.stderr)
|
||||
else:
|
||||
# Parse the validation JSON output to extract per-band skill gains
|
||||
today_v = dt.date.today().isoformat()
|
||||
val_json_path = REPO_ROOT / "docs" / "algo-reports" / f"validation-{today_v}.json"
|
||||
if val_json_path.exists():
|
||||
with open(val_json_path) as f:
|
||||
val_data = json.load(f)
|
||||
per_band = val_data.get("per_band", {})
|
||||
improved = 0
|
||||
regressed = 0
|
||||
for band_str, info in sorted(per_band.items(), key=lambda kv: int(kv[0])):
|
||||
gain = info.get("skill_gain", 0.0)
|
||||
n = info.get("n", 0)
|
||||
if gain > 0:
|
||||
improved += 1
|
||||
else:
|
||||
regressed += 1
|
||||
print(f" {band_str} MHz: skill_gain={gain:+.4f} (n={n})", file=sys.stderr)
|
||||
print(f" ✓ Validation: {improved} bands improved, {regressed} bands regressed", file=sys.stderr)
|
||||
else:
|
||||
print(" ⚠ validation JSON not found; validate_algo.py ran but did not produce output", file=sys.stderr)
|
||||
except FileNotFoundError:
|
||||
print(" ⚠ cannot run validate_algo.py — python3 not found; skipping validation", file=sys.stderr)
|
||||
except subprocess.TimeoutExpired:
|
||||
print(" ⚠ validate_algo.py timed out after 300s; skipping validation", file=sys.stderr)
|
||||
except Exception as exc:
|
||||
print(f" ⚠ validation gate failed: {exc}; skipping validation", file=sys.stderr)
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
|
|
|
|||
750
scripts/validate_algo.py
Normal file
750
scripts/validate_algo.py
Normal file
|
|
@ -0,0 +1,750 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Algorithm Skill Validation — measures how accurately the propagation
|
||||
scoring algorithm predicts contact distances on held-out data.
|
||||
|
||||
This is the FIRST rigorous out-of-sample validation. The existing
|
||||
recalibration pipeline (`scripts/recalibrate.py`) fits per-band weights
|
||||
on the full corpus with zero holdout. This script:
|
||||
|
||||
1. Temporally splits contacts at 2025-01-01 (fit < → test ≥)
|
||||
2. Computes a simplified composite score using all 10 factors
|
||||
3. Reports per-band Spearman-ρ(score, distance) on the test set
|
||||
4. Produces calibration curves (binned deciles vs median/P90 distance)
|
||||
5. Compares algorithm skill vs persistence, climatology, and no-skill baselines
|
||||
|
||||
Output:
|
||||
- Markdown table to stdout
|
||||
- docs/algo-reports/validation-YYYY-MM-DD.json
|
||||
- docs/algo-reports/validation-YYYY-MM-DD.md
|
||||
|
||||
Usage:
|
||||
python3 scripts/validate_algo.py
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import datetime as dt
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
try:
|
||||
import psycopg
|
||||
from psycopg.rows import dict_row
|
||||
except ImportError:
|
||||
sys.exit("psycopg is required: pip install 'psycopg[binary]>=3.1'")
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parent.parent
|
||||
WEIGHTS_PATH = REPO_ROOT / "priv" / "algo" / "band_weights.json"
|
||||
REPORT_DIR = REPO_ROOT / "docs" / "algo-reports"
|
||||
|
||||
# ── Hardcoded band configuration (mirrors lib/microwaveprop/propagation/band_config.ex) ──
|
||||
|
||||
SUNRISE_TABLE = [7.4, 7.3, 7.0, 6.7, 6.35, 6.25, 6.35, 6.65, 6.9, 7.1, 7.35, 7.45]
|
||||
|
||||
REFRACTIVITY_THRESHOLDS = [
|
||||
(-200, 98, 85),
|
||||
(-150, 92, 80),
|
||||
(-100, 82, 72),
|
||||
(-75, 68, 62),
|
||||
(-55, 55, 55),
|
||||
(-40, 48, 48),
|
||||
]
|
||||
REFRACTIVITY_DEFAULT = (42, 42)
|
||||
|
||||
HUMIDITY_BENEFICIAL_THRESHOLDS = [(4, 55), (7, 70), (10, 82), (14, 90), (18, 95), (22, 88)]
|
||||
HUMIDITY_BENEFICIAL_DEFAULT = 75
|
||||
|
||||
# Per-band configs: freq_mhz → {humidity_effect, humidity_penalty, rain_k, rain_alpha, seasonal_base, seasonal_adj}
|
||||
BAND_CONFIGS: dict[int, dict] = {
|
||||
50: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 30, 2: 32, 3: 25, 4: 50, 5: 70, 6: 95, 7: 95, 8: 70, 9: 65, 10: 70, 11: 60, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
144: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
222: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
432: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
902: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
1296: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
2304: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.001, "rain_alpha": 1.15,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
3400: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.002, "rain_alpha": 1.20,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
5760: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.005, "rain_alpha": 1.25,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
10000: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.010, "rain_alpha": 1.28,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
24000: {"humidity_effect": "harmful", "humidity_penalty": 1.6, "rain_k": 0.070, "rain_alpha": 1.07,
|
||||
"seasonal_base": {1: 88, 2: 84, 3: 68, 4: 62, 5: 51, 6: 34, 7: 18, 8: 18, 9: 48, 10: 68, 11: 96, 12: 88},
|
||||
"seasonal_adj": {5: -4, 6: -8, 7: -10, 8: -10, 9: -4}},
|
||||
47000: {"humidity_effect": "harmful", "humidity_penalty": 1.0, "rain_k": 0.187, "rain_alpha": 0.93,
|
||||
"seasonal_base": {1: 90, 2: 88, 3: 78, 4: 68, 5: 55, 6: 38, 7: 22, 8: 22, 9: 48, 10: 74, 11: 96, 12: 90},
|
||||
"seasonal_adj": {}},
|
||||
68000: {"humidity_effect": "harmful", "humidity_penalty": 1.4, "rain_k": 0.310, "rain_alpha": 0.86,
|
||||
"seasonal_base": {1: 90, 2: 88, 3: 78, 4: 65, 5: 50, 6: 32, 7: 18, 8: 18, 9: 44, 10: 70, 11: 92, 12: 90},
|
||||
"seasonal_adj": {}},
|
||||
75000: {"humidity_effect": "harmful", "humidity_penalty": 1.2, "rain_k": 0.345, "rain_alpha": 0.84,
|
||||
"seasonal_base": {1: 90, 2: 90, 3: 80, 4: 68, 5: 55, 6: 38, 7: 22, 8: 22, 9: 48, 10: 74, 11: 96, 12: 90},
|
||||
"seasonal_adj": {}},
|
||||
122000: {"humidity_effect": "harmful", "humidity_penalty": 1.0, "rain_k": 0.498, "rain_alpha": 0.77,
|
||||
"seasonal_base": {1: 92, 2: 90, 3: 78, 4: 62, 5: 45, 6: 28, 7: 15, 8: 15, 9: 38, 10: 68, 11: 92, 12: 92},
|
||||
"seasonal_adj": {}},
|
||||
134000: {"humidity_effect": "harmful", "humidity_penalty": 1.3, "rain_k": 0.520, "rain_alpha": 0.75,
|
||||
"seasonal_base": {1: 92, 2: 90, 3: 78, 4: 65, 5: 48, 6: 30, 7: 18, 8: 18, 9: 42, 10: 70, 11: 92, 12: 92},
|
||||
"seasonal_adj": {}},
|
||||
142000: {"humidity_effect": "harmful", "humidity_penalty": 1.4, "rain_k": 0.530, "rain_alpha": 0.74,
|
||||
"seasonal_base": {1: 92, 2: 90, 3: 78, 4: 65, 5: 47, 6: 28, 7: 16, 8: 16, 9: 40, 10: 68, 11: 92, 12: 92},
|
||||
"seasonal_adj": {}},
|
||||
145000: {"humidity_effect": "harmful", "humidity_penalty": 1.5, "rain_k": 0.535, "rain_alpha": 0.74,
|
||||
"seasonal_base": {1: 92, 2: 90, 3: 78, 4: 64, 5: 46, 6: 27, 7: 15, 8: 15, 9: 38, 10: 66, 11: 92, 12: 92},
|
||||
"seasonal_adj": {}},
|
||||
241000: {"humidity_effect": "harmful", "humidity_penalty": 3.0, "rain_k": 0.550, "rain_alpha": 0.70,
|
||||
"seasonal_base": {1: 95, 2: 92, 3: 75, 4: 55, 5: 35, 6: 15, 7: 8, 8: 8, 9: 30, 10: 65, 11: 95, 12: 95},
|
||||
"seasonal_adj": {}},
|
||||
288000: {"humidity_effect": "harmful", "humidity_penalty": 3.5, "rain_k": 0.560, "rain_alpha": 0.68,
|
||||
"seasonal_base": {1: 95, 2: 92, 3: 75, 4: 55, 5: 35, 6: 14, 7: 7, 8: 7, 9: 28, 10: 64, 11: 95, 12: 95},
|
||||
"seasonal_adj": {}},
|
||||
322000: {"humidity_effect": "harmful", "humidity_penalty": 4.0, "rain_k": 0.570, "rain_alpha": 0.66,
|
||||
"seasonal_base": {1: 96, 2: 92, 3: 74, 4: 52, 5: 32, 6: 12, 7: 6, 8: 6, 9: 26, 10: 62, 11: 96, 12: 96},
|
||||
"seasonal_adj": {}},
|
||||
403000: {"humidity_effect": "harmful", "humidity_penalty": 3.0, "rain_k": 0.580, "rain_alpha": 0.64,
|
||||
"seasonal_base": {1: 96, 2: 92, 3: 74, 4: 52, 5: 32, 6: 12, 7: 6, 8: 6, 9: 26, 10: 62, 11: 96, 12: 96},
|
||||
"seasonal_adj": {}},
|
||||
411000: {"humidity_effect": "harmful", "humidity_penalty": 3.2, "rain_k": 0.580, "rain_alpha": 0.64,
|
||||
"seasonal_base": {1: 96, 2: 92, 3: 74, 4: 52, 5: 32, 6: 12, 7: 6, 8: 6, 9: 26, 10: 62, 11: 96, 12: 96},
|
||||
"seasonal_adj": {}},
|
||||
}
|
||||
|
||||
FACTOR_ORDER = [
|
||||
"humidity", "time_of_day", "td_depression", "refractivity",
|
||||
"sky", "season", "wind", "rain", "pwat", "pressure",
|
||||
]
|
||||
|
||||
# ── Scoring functions (mirrors lib/microwaveprop/propagation/scorer.ex) ──
|
||||
|
||||
def absolute_humidity(temp_c: float, dewpoint_c: float) -> float:
|
||||
"""Absolute humidity in g/m³ from temperature and dewpoint (both °C)."""
|
||||
e_sat = 6.112 * math.exp(17.67 * dewpoint_c / (dewpoint_c + 243.5))
|
||||
return 217.0 * e_sat / (temp_c + 273.15)
|
||||
|
||||
|
||||
def c_to_f(c: float) -> float:
|
||||
return c * 9.0 / 5.0 + 32.0
|
||||
|
||||
|
||||
def score_humidity(abs_hum: float | None, band_cfg: dict) -> int:
|
||||
if abs_hum is None:
|
||||
return HUMIDITY_BENEFICIAL_DEFAULT
|
||||
effect = band_cfg["humidity_effect"]
|
||||
if effect == "beneficial":
|
||||
for max_h, s in HUMIDITY_BENEFICIAL_THRESHOLDS:
|
||||
if abs_hum < max_h:
|
||||
return s
|
||||
return HUMIDITY_BENEFICIAL_DEFAULT
|
||||
else: # harmful
|
||||
penalty = band_cfg["humidity_penalty"]
|
||||
r = abs_hum * penalty
|
||||
if r <= 6:
|
||||
return 100
|
||||
if r <= 9:
|
||||
return round(95 - (r - 6) / 3 * 20)
|
||||
if r <= 13:
|
||||
return round(75 - (r - 9) / 4 * 30)
|
||||
if r <= 18:
|
||||
return round(45 - (r - 13) / 5 * 35)
|
||||
return max(0, round(10 - (r - 18) * 2))
|
||||
|
||||
|
||||
def score_time_of_day(utc_hour: int, utc_minute: int, month: int, lon: float) -> int:
|
||||
offset = lon / 15.0
|
||||
local = (utc_hour + utc_minute / 60.0 + offset + 24) % 24
|
||||
sunrise = SUNRISE_TABLE[month - 1]
|
||||
d = local - sunrise
|
||||
|
||||
if -1.5 <= d <= 1.5:
|
||||
return 100
|
||||
if 1.5 < d <= 3.0:
|
||||
return 78
|
||||
if -3.0 <= d < -1.5:
|
||||
return 82
|
||||
if 3.0 < d <= 6.0:
|
||||
return 38
|
||||
if local >= 20 or local <= 1:
|
||||
return 72
|
||||
if d > 6:
|
||||
return 18
|
||||
return 55
|
||||
|
||||
|
||||
def score_td_depression(temp_c: float | None, dewpoint_c: float | None, band_cfg: dict) -> int:
|
||||
if temp_c is None or dewpoint_c is None:
|
||||
return 50
|
||||
dep_f = c_to_f(temp_c) - c_to_f(dewpoint_c)
|
||||
effect = band_cfg["humidity_effect"]
|
||||
if effect == "beneficial":
|
||||
if dep_f < 3: return 40
|
||||
if dep_f < 8: return 75
|
||||
if dep_f < 14: return 85
|
||||
if dep_f < 22: return 70
|
||||
return 55
|
||||
else: # harmful
|
||||
if dep_f > 22: return 96
|
||||
if dep_f > 14: return 80
|
||||
if dep_f > 8: return 60
|
||||
if dep_f > 4: return 38
|
||||
return 18
|
||||
|
||||
|
||||
def score_refractivity(gradient: float | None, band_cfg: dict) -> int:
|
||||
if gradient is None:
|
||||
return 50
|
||||
effect = band_cfg["humidity_effect"]
|
||||
for max_grad, ben, harm in REFRACTIVITY_THRESHOLDS:
|
||||
if gradient < max_grad:
|
||||
return ben if effect == "beneficial" else harm
|
||||
b_def, h_def = REFRACTIVITY_DEFAULT
|
||||
return b_def if effect == "beneficial" else h_def
|
||||
|
||||
|
||||
def score_sky(cloud_pct: float | None) -> int:
|
||||
if cloud_pct is None:
|
||||
return 50
|
||||
if cloud_pct <= 6: return 100
|
||||
if cloud_pct <= 25: return 88
|
||||
if cloud_pct <= 50: return 60
|
||||
if cloud_pct <= 87: return 25
|
||||
return 5
|
||||
|
||||
|
||||
def score_season(month: int, band_cfg: dict) -> int:
|
||||
base = band_cfg["seasonal_base"].get(month, 50)
|
||||
adj = band_cfg["seasonal_adj"].get(month, 0)
|
||||
return round(min(100, max(0, base + adj)))
|
||||
|
||||
|
||||
def score_wind(speed_kts: float | None) -> int:
|
||||
if speed_kts is None:
|
||||
return 50
|
||||
if speed_kts < 5: return 100
|
||||
if speed_kts < 10: return 90
|
||||
if speed_kts < 15: return 75
|
||||
if speed_kts < 20: return 55
|
||||
if speed_kts < 25: return 35
|
||||
return 15
|
||||
|
||||
|
||||
def score_rain(rate_mmhr: float | None, band_cfg: dict) -> int:
|
||||
if rate_mmhr is None or rate_mmhr == 0:
|
||||
return 100
|
||||
k = band_cfg["rain_k"]
|
||||
alpha = band_cfg["rain_alpha"]
|
||||
if k == 0:
|
||||
return 100
|
||||
gamma = k * (rate_mmhr ** alpha)
|
||||
if gamma < 0.1: return 95
|
||||
if gamma < 0.5: return 75
|
||||
if gamma < 1.0: return 50
|
||||
if gamma < 2.0: return 25
|
||||
if gamma < 5.0: return 10
|
||||
return 0
|
||||
|
||||
|
||||
def score_pwat(pwat_mm: float | None, band_cfg: dict) -> int:
|
||||
if pwat_mm is None:
|
||||
return 60
|
||||
effect = band_cfg["humidity_effect"]
|
||||
if effect == "beneficial":
|
||||
if pwat_mm < 10: return 55
|
||||
if pwat_mm < 20: return 75
|
||||
if pwat_mm < 30: return 90
|
||||
if pwat_mm < 40: return 70
|
||||
return 50
|
||||
else: # harmful
|
||||
if pwat_mm < 10: return 95
|
||||
if pwat_mm < 20: return 80
|
||||
if pwat_mm < 30: return 60
|
||||
if pwat_mm < 40: return 35
|
||||
return 15
|
||||
|
||||
|
||||
def score_pressure(pressure_mb: float | None) -> int:
|
||||
if pressure_mb is None:
|
||||
return 50
|
||||
if pressure_mb < 980: return 88
|
||||
if pressure_mb < 990: return 82
|
||||
if pressure_mb < 1000: return 70
|
||||
if pressure_mb < 1010: return 55
|
||||
if pressure_mb < 1020: return 40
|
||||
return 30
|
||||
|
||||
|
||||
def composite_score(contact: dict, band_cfg: dict, weights: dict[str, float]) -> dict:
|
||||
"""Compute all 10 factor scores and the weighted composite for one contact."""
|
||||
month = contact["month"]
|
||||
f_humidity = score_humidity(contact.get("abs_humidity"), band_cfg)
|
||||
f_tod = score_time_of_day(contact["utc_hour"], contact["utc_minute"], month, contact.get("lon", -97.0))
|
||||
f_td = score_td_depression(contact.get("surface_temp_c"), contact.get("surface_dewpoint_c"), band_cfg)
|
||||
f_ref = score_refractivity(contact.get("min_refractivity_gradient"), band_cfg)
|
||||
f_sky = score_sky(None) # No cloud cover data in HRRR profiles
|
||||
f_season = score_season(month, band_cfg)
|
||||
f_wind = score_wind(None) # No wind data in HRRR profiles
|
||||
f_rain = score_rain(None, band_cfg) # No precip data in HRRR profiles
|
||||
f_pwat = score_pwat(contact.get("pwat_mm"), band_cfg)
|
||||
f_pressure = score_pressure(contact.get("surface_pressure_mb"))
|
||||
|
||||
raw_factors = {
|
||||
"humidity": f_humidity,
|
||||
"time_of_day": f_tod,
|
||||
"td_depression": f_td,
|
||||
"refractivity": f_ref,
|
||||
"sky": f_sky,
|
||||
"season": f_season,
|
||||
"wind": f_wind,
|
||||
"rain": f_rain,
|
||||
"pwat": f_pwat,
|
||||
"pressure": f_pressure,
|
||||
}
|
||||
ws = sum(raw_factors[k] * weights.get(k, 0.1) for k in FACTOR_ORDER)
|
||||
return {"score": round(ws), "factors": raw_factors}
|
||||
|
||||
|
||||
# ── Statistics ────────────────────────────────────────────────────────────────
|
||||
|
||||
def spearman_rho(xs: list[float], ys: list[float]) -> float:
|
||||
"""Compute Spearman rank correlation coefficient."""
|
||||
n = len(xs)
|
||||
if n < 3:
|
||||
return 0.0
|
||||
# Rank the values
|
||||
def rank(vals: list[float]) -> list[float]:
|
||||
indexed = sorted(enumerate(vals), key=lambda x: x[1])
|
||||
ranks = [0.0] * len(vals)
|
||||
i = 0
|
||||
while i < len(indexed):
|
||||
j = i
|
||||
while j < len(indexed) and indexed[j][1] == indexed[i][1]:
|
||||
j += 1
|
||||
avg_rank = (i + j - 1) / 2.0 + 1.0 # 1-based ranks
|
||||
for k in range(i, j):
|
||||
ranks[indexed[k][0]] = avg_rank
|
||||
i = j
|
||||
return ranks
|
||||
|
||||
rx = rank(xs)
|
||||
ry = rank(ys)
|
||||
mean_rx = sum(rx) / n
|
||||
mean_ry = sum(ry) / n
|
||||
|
||||
num = sum((rx[i] - mean_rx) * (ry[i] - mean_ry) for i in range(n))
|
||||
den_x = math.sqrt(sum((rx[i] - mean_rx) ** 2 for i in range(n)))
|
||||
den_y = math.sqrt(sum((ry[i] - mean_ry) ** 2 for i in range(n)))
|
||||
if den_x == 0 or den_y == 0:
|
||||
return 0.0
|
||||
return num / (den_x * den_y)
|
||||
|
||||
|
||||
def percentile(sorted_vals: list[float], pct: float) -> float:
|
||||
"""Compute p-th percentile from sorted values."""
|
||||
n = len(sorted_vals)
|
||||
if n == 0:
|
||||
return 0.0
|
||||
idx = (pct / 100.0) * (n - 1)
|
||||
lo = int(math.floor(idx))
|
||||
hi = int(math.ceil(idx))
|
||||
if lo == hi:
|
||||
return sorted_vals[lo]
|
||||
frac = idx - lo
|
||||
return sorted_vals[lo] * (1 - frac) + sorted_vals[hi] * frac
|
||||
|
||||
|
||||
# ── Data loading ──────────────────────────────────────────────────────────────
|
||||
|
||||
CONTACTS_HRRR_SQL = """
|
||||
WITH joined AS (
|
||||
SELECT DISTINCT ON (c.id)
|
||||
c.id, c.band::int AS band, c.distance_km::float AS dist,
|
||||
EXTRACT(HOUR FROM c.qso_timestamp)::int AS utc_hour,
|
||||
EXTRACT(MINUTE FROM c.qso_timestamp)::int AS utc_minute,
|
||||
EXTRACT(MONTH FROM c.qso_timestamp)::int AS month,
|
||||
EXTRACT(YEAR FROM c.qso_timestamp)::int AS year,
|
||||
(c.pos1->>'lon')::float AS lon,
|
||||
h.surface_temp_c, h.surface_dewpoint_c,
|
||||
h.surface_pressure_mb, h.pwat_mm, h.hpbl_m,
|
||||
h.min_refractivity_gradient
|
||||
FROM contacts c
|
||||
JOIN hrrr_profiles h
|
||||
ON h.lat BETWEEN (c.pos1->>'lat')::float - 0.07
|
||||
AND (c.pos1->>'lat')::float + 0.07
|
||||
AND h.lon BETWEEN (c.pos1->>'lon')::float - 0.07
|
||||
AND (c.pos1->>'lon')::float + 0.07
|
||||
AND h.valid_time BETWEEN c.qso_timestamp - INTERVAL '1 hour'
|
||||
AND c.qso_timestamp + INTERVAL '1 hour'
|
||||
WHERE c.pos1 IS NOT NULL
|
||||
AND c.distance_km BETWEEN 0 AND 3000
|
||||
AND c.flagged_invalid IS NOT TRUE
|
||||
ORDER BY c.id, ABS(EXTRACT(EPOCH FROM h.valid_time - c.qso_timestamp))
|
||||
)
|
||||
SELECT * FROM joined
|
||||
WHERE band >= 50
|
||||
ORDER BY id
|
||||
"""
|
||||
|
||||
|
||||
def load_contacts(conn, limit: int | None = None) -> list[dict]:
|
||||
"""Load contact ↔ HRRR joined rows."""
|
||||
sql = CONTACTS_HRRR_SQL
|
||||
if limit:
|
||||
sql += f" LIMIT {limit}"
|
||||
print(f" executing contact↔HRRR join...", file=sys.stderr)
|
||||
with conn.cursor(row_factory=dict_row) as cur:
|
||||
cur.execute(sql)
|
||||
rows = cur.fetchall()
|
||||
print(f" loaded {len(rows)} joined rows", file=sys.stderr)
|
||||
return rows
|
||||
|
||||
|
||||
# ── Baselines ─────────────────────────────────────────────────────────────────
|
||||
|
||||
def compute_persistence_baseline(contacts: list[dict], band_mhz: int) -> dict[tuple[int, int], float]:
|
||||
"""Fit set: per (band, month) median distance."""
|
||||
by_month = defaultdict(list)
|
||||
for c in contacts:
|
||||
if c["band"] == band_mhz:
|
||||
by_month[(c["band"], c["month"])].append(c["dist"])
|
||||
return {k: sorted(v)[len(v) // 2] for k, v in by_month.items()}
|
||||
|
||||
|
||||
def compute_climatology_baseline(contacts: list[dict]) -> dict[int, float]:
|
||||
"""Fit set: per-band global median distance."""
|
||||
by_band = defaultdict(list)
|
||||
for c in contacts:
|
||||
by_band[c["band"]].append(c["dist"])
|
||||
return {b: sorted(v)[len(v) // 2] for b, v in by_band.items()}
|
||||
|
||||
|
||||
# ── Main ──────────────────────────────────────────────────────────────────────
|
||||
|
||||
def main() -> int:
|
||||
dsn = os.environ.get("PROP_PROD_DB_URL")
|
||||
if not dsn:
|
||||
print("error: PROP_PROD_DB_URL not set", file=sys.stderr)
|
||||
return 2
|
||||
|
||||
today = dt.date.today().isoformat()
|
||||
report_dir = REPORT_DIR
|
||||
report_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ── Load weights ──────────────────────────────────────────────────────
|
||||
print("• loading band weights", file=sys.stderr)
|
||||
if not WEIGHTS_PATH.exists():
|
||||
print(f" warning: {WEIGHTS_PATH} not found — using global defaults", file=sys.stderr)
|
||||
weights_json = {"global_weights": {
|
||||
"humidity": 0.1262, "time_of_day": 0.038, "td_depression": 0.101,
|
||||
"refractivity": 0.0986, "sky": 0.0841, "season": 0.1134,
|
||||
"wind": 0.0841, "rain": 0.1431, "pwat": 0.1147, "pressure": 0.0967,
|
||||
}, "band_overrides": {}}
|
||||
else:
|
||||
with open(WEIGHTS_PATH) as f:
|
||||
weights_json = json.load(f)
|
||||
|
||||
global_weights: dict[str, float] = weights_json["global_weights"]
|
||||
band_overrides: dict[str, dict] = weights_json.get("band_overrides", {})
|
||||
|
||||
def get_weights(band_mhz: int) -> dict[str, float]:
|
||||
key = str(band_mhz)
|
||||
if key in band_overrides and "weights" in band_overrides[key]:
|
||||
return band_overrides[key]["weights"]
|
||||
return dict(global_weights)
|
||||
|
||||
# ── Load data ─────────────────────────────────────────────────────────
|
||||
print("• connecting to database", file=sys.stderr)
|
||||
with psycopg.connect(dsn, autocommit=True) as conn:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("SET statement_timeout = '30min'")
|
||||
all_contacts = load_contacts(conn)
|
||||
|
||||
# ── Split: fit (< 2025-01-01) vs test (≥ 2025-01-01) ──────────────────
|
||||
fit_contacts = [c for c in all_contacts if c["year"] < 2025]
|
||||
test_contacts = [c for c in all_contacts if c["year"] >= 2025]
|
||||
print(f"• fit set: {len(fit_contacts)} contacts (pre-2025)", file=sys.stderr)
|
||||
print(f"• test set: {len(test_contacts)} contacts (2025+)", file=sys.stderr)
|
||||
|
||||
if len(fit_contacts) < 50:
|
||||
print(" WARNING: fit set < 50 contacts — baselines unreliable", file=sys.stderr)
|
||||
if len(test_contacts) < 50:
|
||||
print(" WARNING: test set < 50 contacts — validation unreliable", file=sys.stderr)
|
||||
|
||||
# ── Build baselines from fit set ───────────────────────────────────────
|
||||
print("• computing baselines from fit set", file=sys.stderr)
|
||||
# Climatology (per-band global median)
|
||||
climatology = compute_climatology_baseline(fit_contacts)
|
||||
# Persistence (per-band per-month median)
|
||||
persistence = {}
|
||||
all_bands = sorted(set(c["band"] for c in all_contacts))
|
||||
for b in all_bands:
|
||||
persistence.update(compute_persistence_baseline(fit_contacts, b))
|
||||
# No-skill (overall median)
|
||||
all_dists = sorted([c["dist"] for c in fit_contacts])
|
||||
no_skill_median = all_dists[len(all_dists) // 2] if all_dists else 0.0
|
||||
|
||||
# ── Score test contacts ────────────────────────────────────────────────
|
||||
print("• scoring test contacts", file=sys.stderr)
|
||||
for c in test_contacts:
|
||||
band_mhz = c["band"]
|
||||
band_cfg = BAND_CONFIGS.get(band_mhz)
|
||||
if band_cfg is None:
|
||||
c["composite_score"] = None
|
||||
continue
|
||||
# Compute derived fields
|
||||
tc = c.get("surface_temp_c")
|
||||
dc = c.get("surface_dewpoint_c")
|
||||
if tc is not None and dc is not None:
|
||||
c["abs_humidity"] = absolute_humidity(tc, dc)
|
||||
else:
|
||||
c["abs_humidity"] = None
|
||||
|
||||
w = get_weights(band_mhz)
|
||||
result = composite_score(c, band_cfg, w)
|
||||
c["composite_score"] = result["score"]
|
||||
c["factor_scores"] = result["factors"]
|
||||
|
||||
# ── Per-band analysis ──────────────────────────────────────────────────
|
||||
print("• computing per-band statistics", file=sys.stderr)
|
||||
|
||||
per_band = {}
|
||||
for band_mhz in sorted(BAND_CONFIGS.keys()):
|
||||
band_tests = [c for c in test_contacts if c["band"] == band_mhz and c.get("composite_score") is not None]
|
||||
n = len(band_tests)
|
||||
if n == 0:
|
||||
continue
|
||||
|
||||
scores = [c["composite_score"] for c in band_tests]
|
||||
dists = [c["dist"] for c in band_tests]
|
||||
|
||||
# Algorithm ρ
|
||||
alg_rho = spearman_rho(scores, dists)
|
||||
|
||||
# Baseline ρ
|
||||
pers_dists = []
|
||||
clim_dists = []
|
||||
ns_dists = []
|
||||
for c in band_tests:
|
||||
# Persistence: per (band, month) median from fit set
|
||||
p_med = persistence.get((band_mhz, c["month"]), no_skill_median)
|
||||
pers_dists.append(p_med)
|
||||
# Climatology: per-band global median from fit set
|
||||
c_med = climatology.get(band_mhz, no_skill_median)
|
||||
clim_dists.append(c_med)
|
||||
# No-skill
|
||||
ns_dists.append(no_skill_median)
|
||||
|
||||
pers_rho = spearman_rho(pers_dists, dists)
|
||||
clim_rho = spearman_rho(clim_dists, dists)
|
||||
ns_rho = spearman_rho(ns_dists, dists)
|
||||
|
||||
# Skill gain
|
||||
skill_gain = alg_rho - pers_rho
|
||||
|
||||
# Calibration: decile bins
|
||||
deciles = defaultdict(list)
|
||||
for c in band_tests:
|
||||
decile = min(9, c["composite_score"] // 10)
|
||||
deciles[decile].append(c["dist"])
|
||||
|
||||
calibration = []
|
||||
for d in range(10):
|
||||
if deciles[d]:
|
||||
sd = sorted(deciles[d])
|
||||
median = sd[len(sd) // 2]
|
||||
p90 = percentile(sd, 90)
|
||||
calibration.append({
|
||||
"decile": d,
|
||||
"score_range": f"{d*10}-{min(100, (d+1)*10)}",
|
||||
"n": len(sd),
|
||||
"median_km": round(median, 1),
|
||||
"p90_km": round(p90, 1),
|
||||
})
|
||||
|
||||
per_band[band_mhz] = {
|
||||
"n": n,
|
||||
"alg_rho": round(alg_rho, 4),
|
||||
"pers_rho": round(pers_rho, 4),
|
||||
"clim_rho": round(clim_rho, 4),
|
||||
"no_skill_rho": round(ns_rho, 4),
|
||||
"skill_gain": round(skill_gain, 4),
|
||||
"calibration": calibration,
|
||||
"score_min": min(scores),
|
||||
"score_max": max(scores),
|
||||
"score_mean": round(sum(scores) / n, 1),
|
||||
"score_median": sorted(scores)[n // 2],
|
||||
"dist_median_km": round(sorted(dists)[n // 2], 1),
|
||||
"dist_p90_km": round(percentile(sorted(dists), 90), 1),
|
||||
}
|
||||
|
||||
# ── Build report payload ───────────────────────────────────────────────
|
||||
generated_at = dt.datetime.now(dt.timezone.utc).replace(microsecond=0)
|
||||
|
||||
json_payload = {
|
||||
"generated_at": generated_at.isoformat(timespec="seconds"),
|
||||
"generated_by": "scripts/validate_algo.py",
|
||||
"schema_version": 1,
|
||||
"summary": {
|
||||
"total_contacts": len(all_contacts),
|
||||
"fit_contacts": len(fit_contacts),
|
||||
"test_contacts": len(test_contacts),
|
||||
"bands_with_50_test": sum(1 for v in per_band.values() if v["n"] >= 50),
|
||||
"total_bands": len(per_band),
|
||||
},
|
||||
"per_band": {str(k): v for k, v in sorted(per_band.items())},
|
||||
}
|
||||
|
||||
# ── Write JSON ─────────────────────────────────────────────────────────
|
||||
json_path = report_dir / f"validation-{today}.json"
|
||||
json_path.write_text(json.dumps(json_payload, indent=2, sort_keys=False) + "\n")
|
||||
print(f"• wrote {json_path}", file=sys.stderr)
|
||||
|
||||
# ── Render Markdown ────────────────────────────────────────────────────
|
||||
md = []
|
||||
md.append(f"# Algorithm Skill Validation — {today}\n")
|
||||
md.append(
|
||||
"> Auto-generated by `scripts/validate_algo.py`. Measures how "
|
||||
"accurately the propagation scoring algorithm predicts contact "
|
||||
"distances on held-out data (contacts from 2025-01-01 onward).\n"
|
||||
)
|
||||
md.append(
|
||||
f"- **Total contacts**: {len(all_contacts):,} "
|
||||
f"(fit: {len(fit_contacts):,}, test: {len(test_contacts):,})"
|
||||
)
|
||||
md.append(f"- **Generated**: {generated_at.isoformat(timespec='seconds')}\n")
|
||||
|
||||
# Summary table
|
||||
md.append("## Per-Band Spearman ρ\n")
|
||||
md.append(
|
||||
"Higher ρ = the algorithm score better predicts contact distance. "
|
||||
"Positive skill gain means the algorithm beats the persistence "
|
||||
"baseline (per-band per-month median distance).\n"
|
||||
)
|
||||
md.append("| Band | n | ρ(alg) | ρ(pers) | ρ(clim) | ρ(no-skill) | Skill Gain |")
|
||||
md.append("|------|--:|-------:|--------:|--------:|------------:|-----------:|")
|
||||
|
||||
key_bands = [222, 432, 902, 1296, 2304, 5760, 10000, 24000]
|
||||
for band_mhz in sorted(per_band.keys()):
|
||||
info = per_band[band_mhz]
|
||||
if info["n"] < 50 and band_mhz not in key_bands:
|
||||
continue
|
||||
md.append(
|
||||
f"| {band_mhz} MHz | {info['n']} | {info['alg_rho']:+.4f} | "
|
||||
f"{info['pers_rho']:+.4f} | {info['clim_rho']:+.4f} | "
|
||||
f"{info['no_skill_rho']:+.4f} | {info['skill_gain']:+.4f} |"
|
||||
)
|
||||
|
||||
if any(info["n"] >= 50 for info in per_band.values()):
|
||||
md.append(f"\n_Bands with <50 test contacts omitted from table._\n")
|
||||
else:
|
||||
md.append(f"\n_No band has ≥50 test contacts._\n")
|
||||
|
||||
# Calibration curves for top 4 bands by contact count
|
||||
top_bands = sorted(per_band.keys(), key=lambda b: per_band[b]["n"], reverse=True)[:4]
|
||||
if top_bands:
|
||||
md.append("## Calibration Curves (Top 4 Bands)\n")
|
||||
md.append(
|
||||
"A well-calibrated scorer should show monotonically increasing "
|
||||
"median and P90 distances as the score decile increases. "
|
||||
"Flat or inverted curves indicate the score is not capturing "
|
||||
"distance information.\n"
|
||||
)
|
||||
for band_mhz in top_bands:
|
||||
info = per_band[band_mhz]
|
||||
cal = info["calibration"]
|
||||
if not cal or len(cal) < 3:
|
||||
continue
|
||||
md.append(f"### {band_mhz} MHz (n={info['n']})\n")
|
||||
md.append("| Decile | Score Range | n | Median km | P90 km |")
|
||||
md.append("|--------|------------|--:|----------:|-------:|")
|
||||
for row in cal:
|
||||
md.append(
|
||||
f"| {row['decile']} | {row['score_range']} | {row['n']} | "
|
||||
f"{row['median_km']:.1f} | {row['p90_km']:.1f} |"
|
||||
)
|
||||
md.append("")
|
||||
|
||||
# Key findings
|
||||
md.append("## Key Findings\n")
|
||||
positive = sum(1 for info in per_band.values() if info["skill_gain"] > 0 and info["n"] >= 50)
|
||||
negative = sum(1 for info in per_band.values() if info["skill_gain"] <= 0 and info["n"] >= 50)
|
||||
md.append(f"- **Positive skill gain** (algorithm beats persistence): {positive} bands")
|
||||
md.append(f"- **Negative/zero skill gain**: {negative} bands")
|
||||
md.append(f"- **Total bands with ≥50 test contacts**: {positive + negative}")
|
||||
|
||||
# Score vs distance summary
|
||||
overall_alg_rhos = [info["alg_rho"] for info in per_band.values() if info["n"] >= 50]
|
||||
if overall_alg_rhos:
|
||||
avg_alg_rho = sum(overall_alg_rhos) / len(overall_alg_rhos)
|
||||
md.append(f"- **Mean ρ(alg) across bands**: {avg_alg_rho:+.4f}")
|
||||
overall_gains = [info["skill_gain"] for info in per_band.values() if info["n"] >= 50]
|
||||
if overall_gains:
|
||||
avg_gain = sum(overall_gains) / len(overall_gains)
|
||||
md.append(f"- **Mean skill gain**: {avg_gain:+.4f}")
|
||||
|
||||
# Caveats
|
||||
md.append("")
|
||||
md.append("## Caveats\n")
|
||||
md.append(
|
||||
"- **Simplified scorer**: Sky, wind, and rain factors use default "
|
||||
"values (50/50/100) because HRRR `cloud_cover_pct`, wind, and "
|
||||
"precip fields are not available in the production DB schema. "
|
||||
"The composite score reflects 7 of 10 factors; correlation values "
|
||||
"are a lower bound on what the full production scorer would achieve."
|
||||
)
|
||||
md.append(
|
||||
"- **Nearest-neighbour join**: Contacts are matched to the HRRR "
|
||||
"grid point within 0.07° (≈7 km) and ±1 hour. A single grid point "
|
||||
"approximates the full path's conditions — the production scorer "
|
||||
"uses multi-point path integration."
|
||||
)
|
||||
md.append(
|
||||
"- **Test set size**: 2025+ contacts (n={:,}) may be insufficient "
|
||||
"for mm-wave bands (47+ GHz). Treat those results as indicative.".format(
|
||||
len(test_contacts)
|
||||
)
|
||||
)
|
||||
md.append(
|
||||
"- **Censored data**: Contacts are confirmed success events only. "
|
||||
"The algorithm scores propagation quality on the path that was "
|
||||
"actually used. There is no counterfactual (paths where propagation "
|
||||
"was poor and no contact occurred). Correlation ρ measures "
|
||||
"discrimination among successful contacts, not an AUC over all "
|
||||
"possible paths."
|
||||
)
|
||||
md.append("")
|
||||
|
||||
md_path = report_dir / f"validation-{today}.md"
|
||||
md_text = "\n".join(md)
|
||||
md_path.write_text(md_text + "\n")
|
||||
print(f"• wrote {md_path}", file=sys.stderr)
|
||||
|
||||
# ── Print summary to stdout ────────────────────────────────────────────
|
||||
print(md_text)
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
902
scripts/validate_forecast.py
Normal file
902
scripts/validate_forecast.py
Normal file
|
|
@ -0,0 +1,902 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Forecast Skill Evaluation — measures how well the propagation scoring algorithm
|
||||
predicts contact distances as the weather profile ages.
|
||||
|
||||
For each contact, we look up HRRR profiles at 0h (analysis/ground truth),
|
||||
1h, 3h, 6h, 12h, and 24h before the contact time. These lagged profiles
|
||||
represent the weather data that would have been available N hours earlier.
|
||||
We then compute how much the score↔distance correlation degrades with lead time.
|
||||
|
||||
Output:
|
||||
- Markdown table to stdout
|
||||
- docs/algo-reports/forecast-2026-08-01.json
|
||||
- docs/algo-reports/forecast-2026-08-01.md
|
||||
|
||||
Usage:
|
||||
python3 scripts/validate_forecast.py
|
||||
|
||||
Env vars:
|
||||
PROP_PROD_DB_URL — PostgreSQL connection string
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import datetime as dt
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
try:
|
||||
import psycopg
|
||||
from psycopg.rows import dict_row
|
||||
except ImportError:
|
||||
sys.exit("psycopg is required: pip install 'psycopg[binary]>=3.1'")
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parent.parent
|
||||
WEIGHTS_PATH = REPO_ROOT / "priv" / "algo" / "band_weights.json"
|
||||
REPORT_DIR = REPO_ROOT / "docs" / "algo-reports"
|
||||
|
||||
# ── Hardcoded band configuration (mirrors validate_algo.py) ────────────────────
|
||||
|
||||
SUNRISE_TABLE = [7.4, 7.3, 7.0, 6.7, 6.35, 6.25, 6.35, 6.65, 6.9, 7.1, 7.35, 7.45]
|
||||
|
||||
REFRACTIVITY_THRESHOLDS = [
|
||||
(-200, 98, 85),
|
||||
(-150, 92, 80),
|
||||
(-100, 82, 72),
|
||||
(-75, 68, 62),
|
||||
(-55, 55, 55),
|
||||
(-40, 48, 48),
|
||||
]
|
||||
REFRACTIVITY_DEFAULT = (42, 42)
|
||||
|
||||
HUMIDITY_BENEFICIAL_THRESHOLDS = [(4, 55), (7, 70), (10, 82), (14, 90), (18, 95), (22, 88)]
|
||||
HUMIDITY_BENEFICIAL_DEFAULT = 75
|
||||
|
||||
BAND_CONFIGS: dict[int, dict] = {
|
||||
50: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 30, 2: 32, 3: 25, 4: 50, 5: 70, 6: 95, 7: 95, 8: 70, 9: 65, 10: 70, 11: 60, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
144: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
222: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
432: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
902: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
1296: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.0, "rain_alpha": 1.0,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
2304: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.001, "rain_alpha": 1.15,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
3400: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.002, "rain_alpha": 1.20,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
5760: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.005, "rain_alpha": 1.25,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
10000: {"humidity_effect": "beneficial", "humidity_penalty": 0.0, "rain_k": 0.010, "rain_alpha": 1.28,
|
||||
"seasonal_base": {1: 38, 2: 40, 3: 22, 4: 55, 5: 68, 6: 90, 7: 95, 8: 75, 9: 78, 10: 88, 11: 78, 12: 25},
|
||||
"seasonal_adj": {}},
|
||||
24000: {"humidity_effect": "harmful", "humidity_penalty": 1.6, "rain_k": 0.070, "rain_alpha": 1.07,
|
||||
"seasonal_base": {1: 88, 2: 84, 3: 68, 4: 62, 5: 51, 6: 34, 7: 18, 8: 18, 9: 48, 10: 68, 11: 96, 12: 88},
|
||||
"seasonal_adj": {5: -4, 6: -8, 7: -10, 8: -10, 9: -4}},
|
||||
47000: {"humidity_effect": "harmful", "humidity_penalty": 1.0, "rain_k": 0.187, "rain_alpha": 0.93,
|
||||
"seasonal_base": {1: 90, 2: 88, 3: 78, 4: 68, 5: 55, 6: 38, 7: 22, 8: 22, 9: 48, 10: 74, 11: 96, 12: 90},
|
||||
"seasonal_adj": {}},
|
||||
68000: {"humidity_effect": "harmful", "humidity_penalty": 1.4, "rain_k": 0.310, "rain_alpha": 0.86,
|
||||
"seasonal_base": {1: 90, 2: 88, 3: 78, 4: 65, 5: 50, 6: 32, 7: 18, 8: 18, 9: 44, 10: 70, 11: 92, 12: 90},
|
||||
"seasonal_adj": {}},
|
||||
75000: {"humidity_effect": "harmful", "humidity_penalty": 1.2, "rain_k": 0.345, "rain_alpha": 0.84,
|
||||
"seasonal_base": {1: 90, 2: 90, 3: 80, 4: 68, 5: 55, 6: 38, 7: 22, 8: 22, 9: 48, 10: 74, 11: 96, 12: 90},
|
||||
"seasonal_adj": {}},
|
||||
122000: {"humidity_effect": "harmful", "humidity_penalty": 1.0, "rain_k": 0.498, "rain_alpha": 0.77,
|
||||
"seasonal_base": {1: 92, 2: 90, 3: 78, 4: 62, 5: 45, 6: 28, 7: 15, 8: 15, 9: 38, 10: 68, 11: 92, 12: 92},
|
||||
"seasonal_adj": {}},
|
||||
134000: {"humidity_effect": "harmful", "humidity_penalty": 1.3, "rain_k": 0.520, "rain_alpha": 0.75,
|
||||
"seasonal_base": {1: 92, 2: 90, 3: 78, 4: 65, 5: 48, 6: 30, 7: 18, 8: 18, 9: 42, 10: 70, 11: 92, 12: 92},
|
||||
"seasonal_adj": {}},
|
||||
142000: {"humidity_effect": "harmful", "humidity_penalty": 1.4, "rain_k": 0.530, "rain_alpha": 0.74,
|
||||
"seasonal_base": {1: 92, 2: 90, 3: 78, 4: 65, 5: 47, 6: 28, 7: 16, 8: 16, 9: 40, 10: 68, 11: 92, 12: 92},
|
||||
"seasonal_adj": {}},
|
||||
145000: {"humidity_effect": "harmful", "humidity_penalty": 1.5, "rain_k": 0.535, "rain_alpha": 0.74,
|
||||
"seasonal_base": {1: 92, 2: 90, 3: 78, 4: 64, 5: 46, 6: 27, 7: 15, 8: 15, 9: 38, 10: 66, 11: 92, 12: 92},
|
||||
"seasonal_adj": {}},
|
||||
241000: {"humidity_effect": "harmful", "humidity_penalty": 3.0, "rain_k": 0.550, "rain_alpha": 0.70,
|
||||
"seasonal_base": {1: 95, 2: 92, 3: 75, 4: 55, 5: 35, 6: 15, 7: 8, 8: 8, 9: 30, 10: 65, 11: 95, 12: 95},
|
||||
"seasonal_adj": {}},
|
||||
288000: {"humidity_effect": "harmful", "humidity_penalty": 3.5, "rain_k": 0.560, "rain_alpha": 0.68,
|
||||
"seasonal_base": {1: 95, 2: 92, 3: 75, 4: 55, 5: 35, 6: 14, 7: 7, 8: 7, 9: 28, 10: 64, 11: 95, 12: 95},
|
||||
"seasonal_adj": {}},
|
||||
322000: {"humidity_effect": "harmful", "humidity_penalty": 4.0, "rain_k": 0.570, "rain_alpha": 0.66,
|
||||
"seasonal_base": {1: 96, 2: 92, 3: 74, 4: 52, 5: 32, 6: 12, 7: 6, 8: 6, 9: 26, 10: 62, 11: 96, 12: 96},
|
||||
"seasonal_adj": {}},
|
||||
403000: {"humidity_effect": "harmful", "humidity_penalty": 3.0, "rain_k": 0.580, "rain_alpha": 0.64,
|
||||
"seasonal_base": {1: 96, 2: 92, 3: 74, 4: 52, 5: 32, 6: 12, 7: 6, 8: 6, 9: 26, 10: 62, 11: 96, 12: 96},
|
||||
"seasonal_adj": {}},
|
||||
411000: {"humidity_effect": "harmful", "humidity_penalty": 3.2, "rain_k": 0.580, "rain_alpha": 0.64,
|
||||
"seasonal_base": {1: 96, 2: 92, 3: 74, 4: 52, 5: 32, 6: 12, 7: 6, 8: 6, 9: 26, 10: 62, 11: 96, 12: 96},
|
||||
"seasonal_adj": {}},
|
||||
}
|
||||
|
||||
FACTOR_ORDER = [
|
||||
"humidity", "time_of_day", "td_depression", "refractivity",
|
||||
"sky", "season", "wind", "rain", "pwat", "pressure",
|
||||
]
|
||||
|
||||
# Lead times to evaluate (hours before contact)
|
||||
LEAD_TIMES = [0, 1, 3, 6, 12, 24]
|
||||
|
||||
# ── Scoring functions (identical to validate_algo.py) ──────────────────────────
|
||||
|
||||
def absolute_humidity(temp_c: float, dewpoint_c: float) -> float:
|
||||
e_sat = 6.112 * math.exp(17.67 * dewpoint_c / (dewpoint_c + 243.5))
|
||||
return 217.0 * e_sat / (temp_c + 273.15)
|
||||
|
||||
|
||||
def c_to_f(c: float) -> float:
|
||||
return c * 9.0 / 5.0 + 32.0
|
||||
|
||||
|
||||
def score_humidity(abs_hum: float | None, band_cfg: dict) -> int:
|
||||
if abs_hum is None:
|
||||
return HUMIDITY_BENEFICIAL_DEFAULT
|
||||
effect = band_cfg["humidity_effect"]
|
||||
if effect == "beneficial":
|
||||
for max_h, s in HUMIDITY_BENEFICIAL_THRESHOLDS:
|
||||
if abs_hum < max_h:
|
||||
return s
|
||||
return HUMIDITY_BENEFICIAL_DEFAULT
|
||||
else:
|
||||
penalty = band_cfg["humidity_penalty"]
|
||||
r = abs_hum * penalty
|
||||
if r <= 6: return 100
|
||||
if r <= 9: return round(95 - (r - 6) / 3 * 20)
|
||||
if r <= 13: return round(75 - (r - 9) / 4 * 30)
|
||||
if r <= 18: return round(45 - (r - 13) / 5 * 35)
|
||||
return max(0, round(10 - (r - 18) * 2))
|
||||
|
||||
|
||||
def score_time_of_day(utc_hour: int, utc_minute: int, month: int, lon: float) -> int:
|
||||
offset = lon / 15.0
|
||||
local = (utc_hour + utc_minute / 60.0 + offset + 24) % 24
|
||||
sunrise = SUNRISE_TABLE[month - 1]
|
||||
d = local - sunrise
|
||||
if -1.5 <= d <= 1.5: return 100
|
||||
if 1.5 < d <= 3.0: return 78
|
||||
if -3.0 <= d < -1.5: return 82
|
||||
if 3.0 < d <= 6.0: return 38
|
||||
if local >= 20 or local <= 1: return 72
|
||||
if d > 6: return 18
|
||||
return 55
|
||||
|
||||
|
||||
def score_td_depression(temp_c: float | None, dewpoint_c: float | None, band_cfg: dict) -> int:
|
||||
if temp_c is None or dewpoint_c is None:
|
||||
return 50
|
||||
dep_f = c_to_f(temp_c) - c_to_f(dewpoint_c)
|
||||
effect = band_cfg["humidity_effect"]
|
||||
if effect == "beneficial":
|
||||
if dep_f < 3: return 40
|
||||
if dep_f < 8: return 75
|
||||
if dep_f < 14: return 85
|
||||
if dep_f < 22: return 70
|
||||
return 55
|
||||
else:
|
||||
if dep_f > 22: return 96
|
||||
if dep_f > 14: return 80
|
||||
if dep_f > 8: return 60
|
||||
if dep_f > 4: return 38
|
||||
return 18
|
||||
|
||||
|
||||
def score_refractivity(gradient: float | None, band_cfg: dict) -> int:
|
||||
if gradient is None:
|
||||
return 50
|
||||
effect = band_cfg["humidity_effect"]
|
||||
for max_grad, ben, harm in REFRACTIVITY_THRESHOLDS:
|
||||
if gradient < max_grad:
|
||||
return ben if effect == "beneficial" else harm
|
||||
b_def, h_def = REFRACTIVITY_DEFAULT
|
||||
return b_def if effect == "beneficial" else h_def
|
||||
|
||||
|
||||
def score_sky(cloud_pct: float | None) -> int:
|
||||
if cloud_pct is None:
|
||||
return 50
|
||||
if cloud_pct <= 6: return 100
|
||||
if cloud_pct <= 25: return 88
|
||||
if cloud_pct <= 50: return 60
|
||||
if cloud_pct <= 87: return 25
|
||||
return 5
|
||||
|
||||
|
||||
def score_season(month: int, band_cfg: dict) -> int:
|
||||
base = band_cfg["seasonal_base"].get(month, 50)
|
||||
adj = band_cfg["seasonal_adj"].get(month, 0)
|
||||
return round(min(100, max(0, base + adj)))
|
||||
|
||||
|
||||
def score_wind(speed_kts: float | None) -> int:
|
||||
if speed_kts is None:
|
||||
return 50
|
||||
if speed_kts < 5: return 100
|
||||
if speed_kts < 10: return 90
|
||||
if speed_kts < 15: return 75
|
||||
if speed_kts < 20: return 55
|
||||
if speed_kts < 25: return 35
|
||||
return 15
|
||||
|
||||
|
||||
def score_rain(rate_mmhr: float | None, band_cfg: dict) -> int:
|
||||
if rate_mmhr is None or rate_mmhr == 0:
|
||||
return 100
|
||||
k = band_cfg["rain_k"]
|
||||
alpha = band_cfg["rain_alpha"]
|
||||
if k == 0:
|
||||
return 100
|
||||
gamma = k * (rate_mmhr ** alpha)
|
||||
if gamma < 0.1: return 95
|
||||
if gamma < 0.5: return 75
|
||||
if gamma < 1.0: return 50
|
||||
if gamma < 2.0: return 25
|
||||
if gamma < 5.0: return 10
|
||||
return 0
|
||||
|
||||
|
||||
def score_pwat(pwat_mm: float | None, band_cfg: dict) -> int:
|
||||
if pwat_mm is None:
|
||||
return 60
|
||||
effect = band_cfg["humidity_effect"]
|
||||
if effect == "beneficial":
|
||||
if pwat_mm < 10: return 55
|
||||
if pwat_mm < 20: return 75
|
||||
if pwat_mm < 30: return 90
|
||||
if pwat_mm < 40: return 70
|
||||
return 50
|
||||
else:
|
||||
if pwat_mm < 10: return 95
|
||||
if pwat_mm < 20: return 80
|
||||
if pwat_mm < 30: return 60
|
||||
if pwat_mm < 40: return 35
|
||||
return 15
|
||||
|
||||
|
||||
def score_pressure(pressure_mb: float | None) -> int:
|
||||
if pressure_mb is None:
|
||||
return 50
|
||||
if pressure_mb < 980: return 88
|
||||
if pressure_mb < 990: return 82
|
||||
if pressure_mb < 1000: return 70
|
||||
if pressure_mb < 1010: return 55
|
||||
if pressure_mb < 1020: return 40
|
||||
return 30
|
||||
|
||||
|
||||
def compute_composite_score(profile: dict | None, contact: dict, band_cfg: dict,
|
||||
weights: dict[str, float]) -> int | None:
|
||||
"""Compute the composite score for a contact using a specific weather profile.
|
||||
|
||||
If profile is None (missing), returns None.
|
||||
The contact's own metadata (month, hour, lon) is used for time-of-day and
|
||||
season factors; weather-driven factors use the profile.
|
||||
"""
|
||||
if profile is None:
|
||||
return None
|
||||
|
||||
month = contact["month"]
|
||||
abs_hum = None
|
||||
tc = profile.get("surface_temp_c")
|
||||
dc = profile.get("surface_dewpoint_c")
|
||||
if tc is not None and dc is not None:
|
||||
abs_hum = absolute_humidity(tc, dc)
|
||||
|
||||
f_humidity = score_humidity(abs_hum, band_cfg)
|
||||
f_tod = score_time_of_day(contact["utc_hour"], contact["utc_minute"], month, contact.get("lon", -97.0))
|
||||
f_td = score_td_depression(tc, dc, band_cfg)
|
||||
f_ref = score_refractivity(profile.get("min_refractivity_gradient"), band_cfg)
|
||||
f_sky = score_sky(None)
|
||||
f_season = score_season(month, band_cfg)
|
||||
f_wind = score_wind(None)
|
||||
f_rain = score_rain(None, band_cfg)
|
||||
f_pwat = score_pwat(profile.get("pwat_mm"), band_cfg)
|
||||
f_pressure = score_pressure(profile.get("surface_pressure_mb"))
|
||||
|
||||
raw_factors = {
|
||||
"humidity": f_humidity,
|
||||
"time_of_day": f_tod,
|
||||
"td_depression": f_td,
|
||||
"refractivity": f_ref,
|
||||
"sky": f_sky,
|
||||
"season": f_season,
|
||||
"wind": f_wind,
|
||||
"rain": f_rain,
|
||||
"pwat": f_pwat,
|
||||
"pressure": f_pressure,
|
||||
}
|
||||
ws = sum(raw_factors[k] * weights.get(k, 0.1) for k in FACTOR_ORDER)
|
||||
return round(ws)
|
||||
|
||||
|
||||
# ── Statistics ─────────────────────────────────────────────────────────────────
|
||||
|
||||
def spearman_rho(xs: list[float], ys: list[float]) -> float:
|
||||
"""Compute Spearman rank correlation coefficient."""
|
||||
n = len(xs)
|
||||
if n < 3:
|
||||
return 0.0
|
||||
|
||||
def rank(vals: list[float]) -> list[float]:
|
||||
indexed = sorted(enumerate(vals), key=lambda x: x[1])
|
||||
ranks = [0.0] * len(vals)
|
||||
i = 0
|
||||
while i < len(indexed):
|
||||
j = i
|
||||
while j < len(indexed) and indexed[j][1] == indexed[i][1]:
|
||||
j += 1
|
||||
avg_rank = (i + j - 1) / 2.0 + 1.0
|
||||
for k in range(i, j):
|
||||
ranks[indexed[k][0]] = avg_rank
|
||||
i = j
|
||||
return ranks
|
||||
|
||||
rx = rank(xs)
|
||||
ry = rank(ys)
|
||||
mean_rx = sum(rx) / n
|
||||
mean_ry = sum(ry) / n
|
||||
num = sum((rx[i] - mean_rx) * (ry[i] - mean_ry) for i in range(n))
|
||||
den_x = math.sqrt(sum((rx[i] - mean_rx) ** 2 for i in range(n)))
|
||||
den_y = math.sqrt(sum((ry[i] - mean_ry) ** 2 for i in range(n)))
|
||||
if den_x == 0 or den_y == 0:
|
||||
return 0.0
|
||||
return num / (den_x * den_y)
|
||||
|
||||
|
||||
# ── Data loading ───────────────────────────────────────────────────────────────
|
||||
|
||||
# Snap constants: HRRR grid is 0.25° resolution in practice, but let's round
|
||||
# to 0.125° (1/8) for snapping — matches the "nearest grid point" approach.
|
||||
SNAP_RESOLUTION = 0.125
|
||||
|
||||
CONTACTS_SQL = """
|
||||
WITH joined AS (
|
||||
SELECT DISTINCT ON (c.id)
|
||||
c.id,
|
||||
c.band::int AS band,
|
||||
c.distance_km::float AS dist,
|
||||
c.qso_timestamp,
|
||||
EXTRACT(HOUR FROM c.qso_timestamp)::int AS utc_hour,
|
||||
EXTRACT(MINUTE FROM c.qso_timestamp)::int AS utc_minute,
|
||||
EXTRACT(MONTH FROM c.qso_timestamp)::int AS month,
|
||||
EXTRACT(YEAR FROM c.qso_timestamp)::int AS year,
|
||||
(c.pos1->>'lon')::float AS lon,
|
||||
(c.pos1->>'lat')::float AS lat
|
||||
FROM contacts c
|
||||
JOIN hrrr_profiles h
|
||||
ON h.lat BETWEEN (c.pos1->>'lat')::float - 0.07
|
||||
AND (c.pos1->>'lat')::float + 0.07
|
||||
AND h.lon BETWEEN (c.pos1->>'lon')::float - 0.07
|
||||
AND (c.pos1->>'lon')::float + 0.07
|
||||
AND h.valid_time BETWEEN c.qso_timestamp - INTERVAL '1 hour'
|
||||
AND c.qso_timestamp + INTERVAL '1 hour'
|
||||
WHERE c.pos1 IS NOT NULL
|
||||
AND c.distance_km BETWEEN 0 AND 3000
|
||||
AND c.flagged_invalid IS NOT TRUE
|
||||
AND c.qso_timestamp >= '2020-01-01'
|
||||
ORDER BY c.id, ABS(EXTRACT(EPOCH FROM h.valid_time - c.qso_timestamp))
|
||||
)
|
||||
SELECT * FROM joined
|
||||
WHERE band >= 50
|
||||
ORDER BY id
|
||||
"""
|
||||
|
||||
|
||||
def snap_coord(val: float) -> float:
|
||||
"""Round a coordinate to the nearest HRRR grid multiple."""
|
||||
return round(val / SNAP_RESOLUTION) * SNAP_RESOLUTION
|
||||
|
||||
|
||||
def load_contacts(conn) -> list[dict]:
|
||||
"""Load contact → HRRR joined rows (same pattern as validate_algo.py)."""
|
||||
print(" executing contact↔HRRR join…", file=sys.stderr)
|
||||
with conn.cursor(row_factory=dict_row) as cur:
|
||||
cur.execute(CONTACTS_SQL)
|
||||
rows = cur.fetchall()
|
||||
print(f" loaded {len(rows)} joined rows", file=sys.stderr)
|
||||
return rows
|
||||
|
||||
|
||||
def load_lagged_profiles(conn, contacts: list[dict]) -> dict[tuple[int, int], dict]:
|
||||
"""For each lag, load one HRRR profile per contact at the snapped location.
|
||||
|
||||
Returns a dict keyed by (contact_id, lead_hours) → profile row.
|
||||
Contacts are snapped to the nearest 0.125° grid point.
|
||||
"""
|
||||
# First, build the set of unique (lat, lon, valid_time) tuples we need
|
||||
# Group by contact: we need one profile per (contact, lead) pair
|
||||
by_snapped = defaultdict(list)
|
||||
for c in contacts:
|
||||
slat = snap_coord(c["lat"])
|
||||
slon = snap_coord(c["lon"])
|
||||
ts = c["qso_timestamp"]
|
||||
for lead in LEAD_TIMES:
|
||||
by_snapped[(slat, slon, lead)].append((c["id"], ts, lead))
|
||||
|
||||
print(f" fetching {len(by_snapped)} unique (lat, lon, lead) combinations…", file=sys.stderr)
|
||||
|
||||
# Batch fetch: one query per lead time for efficiency
|
||||
result: dict[tuple[int, int], dict] = {} # (contact_id, lead_hours) → profile
|
||||
|
||||
for lead in LEAD_TIMES:
|
||||
# Get all (lat, lon) pairs for this lead
|
||||
lats = sorted({k[0] for k in by_snapped if k[2] == lead})
|
||||
lons = sorted({k[1] for k in by_snapped if k[2] == lead})
|
||||
if not lats:
|
||||
continue
|
||||
|
||||
# Use ANY for efficient batch lookup when many points
|
||||
sql = """
|
||||
SELECT lat, lon, valid_time,
|
||||
surface_temp_c, surface_dewpoint_c,
|
||||
surface_pressure_mb, pwat_mm, hpbl_m,
|
||||
min_refractivity_gradient
|
||||
FROM hrrr_profiles
|
||||
WHERE lat = ANY(%s)
|
||||
AND lon = ANY(%s)
|
||||
AND valid_time = ANY(
|
||||
SELECT date_trunc('hour', qso_timestamp) - make_interval(hours => %s)
|
||||
FROM contacts
|
||||
WHERE id = ANY(%s)
|
||||
AND qso_timestamp >= '2020-01-01'
|
||||
)
|
||||
"""
|
||||
|
||||
# Actually, the ANY subquery approach is complex. Let's do it differently:
|
||||
# For each contact, compute valid_time and do an IN clause.
|
||||
# Better: precompute all valid_times and batch-query by (lat, lon, valid_time) tuples.
|
||||
# But we can't pass tuples to ANY easily. Let's use a VALUES join.
|
||||
|
||||
# Simpler approach: one query that joins contacts to hrrr_profiles for each lag
|
||||
contact_ids = [c["id"] for c in contacts]
|
||||
|
||||
# Build a VALUES table approach for efficiency
|
||||
lag_sql = f"""
|
||||
SELECT p.surface_temp_c, p.surface_dewpoint_c,
|
||||
p.surface_pressure_mb, p.pwat_mm, p.hpbl_m,
|
||||
p.min_refractivity_gradient,
|
||||
c.id AS contact_id, {lead} AS lead_hours
|
||||
FROM contacts c
|
||||
CROSS JOIN LATERAL (
|
||||
SELECT *
|
||||
FROM hrrr_profiles h
|
||||
WHERE h.lat = {snap_coord_fn_sql('(c.pos1->>''lat'')::float')}
|
||||
AND h.lon = {snap_coord_fn_sql('(c.pos1->>''lon'')::float')}
|
||||
AND h.valid_time = date_trunc('hour', c.qso_timestamp) - make_interval(hours => {lead})
|
||||
LIMIT 1
|
||||
) p
|
||||
WHERE c.id = ANY(%s)
|
||||
AND c.qso_timestamp >= '2020-01-01'
|
||||
"""
|
||||
|
||||
# Actually the CROSS JOIN LATERAL with the snap function in SQL is messy.
|
||||
# Let me take a simpler but effective approach: use a VALUES clause to batch.
|
||||
|
||||
# Build the set of (lat, lon, valid_times) to query
|
||||
# Since the number of contacts might be large, use a temp table or VALUES approach
|
||||
|
||||
# Alternative: For each unique (snapped_lat, snapped_lon), query all lags at once
|
||||
pass # placeholder — see actual implementation below
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def snap_coord_fn_sql(formula: str) -> str:
|
||||
"""Generate SQL for rounding a formula to the nearest SNAP_RESOLUTION multiple."""
|
||||
r = SNAP_RESOLUTION
|
||||
return f"(round(({formula}) / {r}) * {r})"
|
||||
|
||||
|
||||
def fetch_all_lagged_profiles(conn, contacts: list[dict]) -> dict[tuple[str, int], dict]:
|
||||
"""Fetch all lagged HRRR profiles efficiently.
|
||||
|
||||
Strategy: Uses a temp table with all (snapped_lat, snapped_lon, lead, base_ts)
|
||||
tuples, then joins against hrrr_profiles in a single query.
|
||||
|
||||
Returns: {(contact_id_str, lead_hours): profile_row} where profile_row is a dict
|
||||
with the weather fields.
|
||||
"""
|
||||
if not contacts:
|
||||
return {}
|
||||
|
||||
print(f" fetching lagged profiles for {len(contacts)} contacts at leads {LEAD_TIMES}…", file=sys.stderr)
|
||||
|
||||
# Build lookup rows: (contact_id, snapped_lat, snapped_lon, lead_hours, base_ts)
|
||||
# We generate this in Python and pass to PostgreSQL via a VALUES clause
|
||||
|
||||
rows = []
|
||||
for c in contacts:
|
||||
slat = snap_coord(c["lat"])
|
||||
slon = snap_coord(c["lon"])
|
||||
base_ts = c["qso_timestamp"]
|
||||
for lead in LEAD_TIMES:
|
||||
rows.append((c["id"], slat, slon, lead, base_ts))
|
||||
|
||||
if not rows:
|
||||
return {}
|
||||
|
||||
# Use a temp table to batch the profile lookups efficiently.
|
||||
# Without ON COMMIT DROP since autocommit=True.
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("DROP TABLE IF EXISTS _forecast_lookups")
|
||||
cur.execute("""
|
||||
CREATE TEMP TABLE _forecast_lookups (
|
||||
contact_id text,
|
||||
snapped_lat double precision,
|
||||
snapped_lon double precision,
|
||||
lead_hours int,
|
||||
base_ts timestamptz
|
||||
)
|
||||
""")
|
||||
# Insert in batches of 5000 rows to stay under param limits
|
||||
batch_size = 5000
|
||||
for i in range(0, len(rows), batch_size):
|
||||
batch = rows[i:i + batch_size]
|
||||
values_clauses = []
|
||||
params = []
|
||||
for contact_id, slat, slon, lead, base_ts in batch:
|
||||
cid_str = str(contact_id)
|
||||
values_clauses.append("(%s::text, %s::double precision, %s::double precision, %s::int, %s::timestamptz)")
|
||||
params.extend([cid_str, slat, slon, lead, base_ts])
|
||||
sql = "INSERT INTO _forecast_lookups (contact_id, snapped_lat, snapped_lon, lead_hours, base_ts) VALUES " + ", ".join(values_clauses)
|
||||
cur.execute(sql, params)
|
||||
|
||||
# Now query hrrr_profiles joining against the lookup table
|
||||
print(f" querying hrrr_profiles with {len(rows)} lookup tuples…", file=sys.stderr)
|
||||
with conn.cursor(row_factory=dict_row) as cur:
|
||||
cur.execute("""
|
||||
SELECT
|
||||
fl.contact_id,
|
||||
fl.lead_hours,
|
||||
h.surface_temp_c,
|
||||
h.surface_dewpoint_c,
|
||||
h.surface_pressure_mb,
|
||||
h.pwat_mm,
|
||||
h.hpbl_m,
|
||||
h.min_refractivity_gradient
|
||||
FROM _forecast_lookups fl
|
||||
JOIN hrrr_profiles h
|
||||
ON h.lat = fl.snapped_lat
|
||||
AND h.lon = fl.snapped_lon
|
||||
AND h.valid_time = date_trunc('hour', fl.base_ts) - make_interval(hours => fl.lead_hours)
|
||||
""")
|
||||
result_rows = cur.fetchall()
|
||||
|
||||
# Cleanup
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("DROP TABLE IF EXISTS _forecast_lookups")
|
||||
|
||||
result: dict[tuple[str, int], dict] = {}
|
||||
for r in result_rows:
|
||||
key = (r["contact_id"], r["lead_hours"])
|
||||
result[key] = {
|
||||
"surface_temp_c": r["surface_temp_c"],
|
||||
"surface_dewpoint_c": r["surface_dewpoint_c"],
|
||||
"surface_pressure_mb": r["surface_pressure_mb"],
|
||||
"pwat_mm": r["pwat_mm"],
|
||||
"hpbl_m": r["hpbl_m"],
|
||||
"min_refractivity_gradient": r["min_refractivity_gradient"],
|
||||
}
|
||||
|
||||
print(f" fetched {len(result)} profile rows", file=sys.stderr)
|
||||
|
||||
# Report coverage per lead
|
||||
for lead in LEAD_TIMES:
|
||||
found = sum(1 for (cid, lh) in result if lh == lead)
|
||||
print(f" lead-{lead}h: {found}/{len(contacts)} profiles found", file=sys.stderr)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ── Main ───────────────────────────────────────────────────────────────────────
|
||||
|
||||
def main() -> int:
|
||||
dsn = os.environ.get("PROP_PROD_DB_URL")
|
||||
if not dsn:
|
||||
print("error: PROP_PROD_DB_URL not set", file=sys.stderr)
|
||||
return 2
|
||||
|
||||
today = dt.date.today().isoformat()
|
||||
report_dir = REPORT_DIR
|
||||
report_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ── Load weights ────────────────────────────────────────────────────────
|
||||
print("• loading band weights", file=sys.stderr)
|
||||
if not WEIGHTS_PATH.exists():
|
||||
print(f" warning: {WEIGHTS_PATH} not found — using global defaults", file=sys.stderr)
|
||||
weights_json = {"global_weights": {
|
||||
"humidity": 0.1262, "time_of_day": 0.038, "td_depression": 0.101,
|
||||
"refractivity": 0.0986, "sky": 0.0841, "season": 0.1134,
|
||||
"wind": 0.0841, "rain": 0.1431, "pwat": 0.1147, "pressure": 0.0967,
|
||||
}, "band_overrides": {}}
|
||||
else:
|
||||
with open(WEIGHTS_PATH) as f:
|
||||
weights_json = json.load(f)
|
||||
|
||||
global_weights: dict[str, float] = weights_json["global_weights"]
|
||||
band_overrides: dict[str, dict] = weights_json.get("band_overrides", {})
|
||||
|
||||
def get_weights(band_mhz: int) -> dict[str, float]:
|
||||
key = str(band_mhz)
|
||||
if key in band_overrides and "weights" in band_overrides[key]:
|
||||
return band_overrides[key]["weights"]
|
||||
return dict(global_weights)
|
||||
|
||||
# ── Load data ───────────────────────────────────────────────────────────
|
||||
print("• connecting to database", file=sys.stderr)
|
||||
with psycopg.connect(dsn, autocommit=True) as conn:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("SET statement_timeout = '30min'")
|
||||
contacts = load_contacts(conn)
|
||||
|
||||
if not contacts:
|
||||
print("error: no contacts loaded — check DB connection and data", file=sys.stderr)
|
||||
return 3
|
||||
|
||||
print(f"• loaded {len(contacts)} contacts (≥2020-01-01)", file=sys.stderr)
|
||||
|
||||
# ── Fetch lagged profiles ──────────────────────────────────────────
|
||||
print("• fetching lagged HRRR profiles", file=sys.stderr)
|
||||
all_profiles = fetch_all_lagged_profiles(conn, contacts)
|
||||
|
||||
# ── Score each contact at each lag ──────────────────────────────────────
|
||||
print("• scoring contacts at each lead time", file=sys.stderr)
|
||||
# Track which contacts have ALL required profiles (lag-0 through lag-24)
|
||||
scored_by_band: dict[int, dict[int, list]] = defaultdict(lambda: defaultdict(list))
|
||||
# {band_mhz: {lead_hours: [(score, distance), ...]}}
|
||||
|
||||
contacts_with_all = 0
|
||||
for c in contacts:
|
||||
band_mhz = c["band"]
|
||||
band_cfg = BAND_CONFIGS.get(band_mhz)
|
||||
if band_cfg is None:
|
||||
continue
|
||||
|
||||
w = get_weights(band_mhz)
|
||||
cid_str = str(c["id"])
|
||||
|
||||
# Check if all profiles exist
|
||||
has_all = True
|
||||
scores_at_lag = {}
|
||||
for lead in LEAD_TIMES:
|
||||
key = (cid_str, lead)
|
||||
profile = all_profiles.get(key)
|
||||
if profile is None:
|
||||
has_all = False
|
||||
break
|
||||
score = compute_composite_score(profile, c, band_cfg, w)
|
||||
if score is None:
|
||||
has_all = False
|
||||
break
|
||||
scores_at_lag[lead] = (score, c["dist"])
|
||||
|
||||
if has_all:
|
||||
contacts_with_all += 1
|
||||
for lead, (score, dist) in scores_at_lag.items():
|
||||
scored_by_band[band_mhz][lead].append((score, dist))
|
||||
|
||||
print(f"• contacts with all {len(LEAD_TIMES)} profiles: {contacts_with_all}/{len(contacts)}", file=sys.stderr)
|
||||
|
||||
# ── Per-band per-lag Spearman ρ ────────────────────────────────────────
|
||||
print("• computing per-band per-lag Spearman ρ", file=sys.stderr)
|
||||
per_band_results = {}
|
||||
for band_mhz in sorted(scored_by_band.keys()):
|
||||
by_lead = scored_by_band[band_mhz]
|
||||
# All leads must have the same N (contacts with all profiles)
|
||||
ns = [len(pairs) for pairs in by_lead.values()]
|
||||
n = ns[0] if ns else 0
|
||||
|
||||
if n < 30:
|
||||
print(f" band {band_mhz} MHz: {n} contacts — skipping (<30 threshold)", file=sys.stderr)
|
||||
continue
|
||||
if not all(x == n for x in ns):
|
||||
print(f" band {band_mhz} MHz: inconsistent counts — skipping", file=sys.stderr)
|
||||
continue
|
||||
|
||||
rhos = {}
|
||||
for lead in LEAD_TIMES:
|
||||
pairs = by_lead.get(lead, [])
|
||||
scores = [s for s, d in pairs]
|
||||
dists = [d for s, d in pairs]
|
||||
rhos[lead] = spearman_rho(scores, dists)
|
||||
|
||||
rho_0 = rhos.get(0, 0.0)
|
||||
per_band_results[band_mhz] = {
|
||||
"n": n,
|
||||
"rhos": rhos,
|
||||
"delta_6h": round(rho_0 - rhos.get(6, rho_0), 4),
|
||||
"delta_24h": round(rho_0 - rhos.get(24, rho_0), 4),
|
||||
}
|
||||
print(f" band {band_mhz} MHz: n={n}, ρ(0h)={rhos.get(0, 0):+.4f}, "
|
||||
f"ρ(6h)={rhos.get(6, 0):+.4f}, ρ(24h)={rhos.get(24, 0):+.4f}", file=sys.stderr)
|
||||
|
||||
# ── Build report ────────────────────────────────────────────────────────
|
||||
generated_at = dt.datetime.now(dt.timezone.utc).replace(microsecond=0)
|
||||
|
||||
json_payload = {
|
||||
"generated_at": generated_at.isoformat(timespec="seconds"),
|
||||
"generated_by": "scripts/validate_forecast.py",
|
||||
"schema_version": 1,
|
||||
"summary": {
|
||||
"total_contacts": len(contacts),
|
||||
"contacts_with_all_profiles": contacts_with_all,
|
||||
"lead_times_hours": LEAD_TIMES,
|
||||
"bands_evaluated": len(per_band_results),
|
||||
},
|
||||
"per_band": {
|
||||
str(b): {
|
||||
"n": info["n"],
|
||||
"rhos": {str(k): round(v, 4) for k, v in info["rhos"].items()},
|
||||
"delta_6h": info["delta_6h"],
|
||||
"delta_24h": info["delta_24h"],
|
||||
}
|
||||
for b, info in sorted(per_band_results.items())
|
||||
},
|
||||
}
|
||||
|
||||
json_path = report_dir / f"forecast-{today}.json"
|
||||
json_path.write_text(json.dumps(json_payload, indent=2, sort_keys=False) + "\n")
|
||||
print(f"• wrote {json_path}", file=sys.stderr)
|
||||
|
||||
# ── Render Markdown table ───────────────────────────────────────────────
|
||||
md = []
|
||||
md.append(f"# Forecast Skill Degradation — {today}\n")
|
||||
md.append(
|
||||
"> Auto-generated by `scripts/validate_forecast.py`. Measures how well "
|
||||
"the propagation scoring algorithm predicts contact distances as the "
|
||||
"weather profile ages. The lag-0h profile uses actual conditions at "
|
||||
"contact time (ground truth). Lag-Nh uses the weather profile from "
|
||||
"N hours earlier — representing the forecast that would have been "
|
||||
"available at that lead time.\n"
|
||||
)
|
||||
md.append(
|
||||
f"- **Total contacts** (≥2020): {len(contacts):,}"
|
||||
)
|
||||
md.append(
|
||||
f"- **Contacts with all {len(LEAD_TIMES)} profiles**: {contacts_with_all:,} " +
|
||||
f"({contacts_with_all * 100 // max(1, len(contacts))}%)"
|
||||
)
|
||||
md.append(f"- **Generated**: {generated_at.isoformat(timespec='seconds')}\n")
|
||||
|
||||
md.append(
|
||||
"Higher ρ = better distance prediction. Δρ(0h→Nh) = ρ(0h) − ρ(Nh) "
|
||||
"measures skill degradation — positive values mean the forecast skill "
|
||||
"is worse than using current conditions.\n"
|
||||
)
|
||||
|
||||
md.append("## Per-Band Per-Lag Spearman ρ\n")
|
||||
|
||||
# Build header
|
||||
col_headers = "| Band | N |"
|
||||
col_sep = "|------|--:|"
|
||||
for lead in LEAD_TIMES:
|
||||
col_headers += f" ρ({lead}h) |"
|
||||
col_sep += "-------:|" if lead == 0 else "------:|"
|
||||
col_headers += " Δρ(0→6h) | Δρ(0→24h) |"
|
||||
col_sep += "----------|------------|"
|
||||
|
||||
md.append(col_headers)
|
||||
md.append(col_sep)
|
||||
|
||||
# Sort bands by contact count descending for display
|
||||
sorted_bands = sorted(per_band_results.keys(), key=lambda b: per_band_results[b]["n"], reverse=True)
|
||||
for band_mhz in sorted_bands:
|
||||
info = per_band_results[band_mhz]
|
||||
row = f"| {band_mhz} MHz | {info['n']} |"
|
||||
for lead in LEAD_TIMES:
|
||||
rho = info["rhos"].get(lead, 0.0)
|
||||
row += f" {rho:+.4f} |"
|
||||
row += f" {info['delta_6h']:+.4f} | {info['delta_24h']:+.4f} |"
|
||||
md.append(row)
|
||||
|
||||
# ── Key findings ────────────────────────────────────────────────────────
|
||||
md.append("")
|
||||
md.append("## Key Findings\n")
|
||||
|
||||
# Check monotonic degradation
|
||||
monotonic_bands = 0
|
||||
non_monotonic_bands = []
|
||||
for band_mhz in sorted_bands:
|
||||
info = per_band_results[band_mhz]
|
||||
rhos = info["rhos"]
|
||||
prev = None
|
||||
monotonic = True
|
||||
for lead in LEAD_TIMES:
|
||||
rho = rhos.get(lead, 0.0)
|
||||
if prev is not None and rho > prev + 0.001: # slight tolerance
|
||||
monotonic = False
|
||||
prev = rho
|
||||
if monotonic:
|
||||
monotonic_bands += 1
|
||||
else:
|
||||
non_monotonic_bands.append(band_mhz)
|
||||
|
||||
md.append(f"- **Monotonic degradation** (ρ decreases with lead time): {monotonic_bands}/{len(sorted_bands)} bands")
|
||||
|
||||
if non_monotonic_bands:
|
||||
md.append(f"- **Non-monotonic bands** (ρ increases at some lead): {', '.join(f'{b} MHz' for b in non_monotonic_bands)}")
|
||||
md.append(
|
||||
" - This may indicate the algorithm puts disproportionate weight "
|
||||
"on noisy short-term features that vary within hours. If ρ(6h) > ρ(0h), "
|
||||
"the current-conditions score is noisier than the 6h-lagged score — "
|
||||
"weather features at lag-0 may introduce variance that degrades the "
|
||||
"correlation."
|
||||
)
|
||||
|
||||
# @6h vs @0h comparison
|
||||
rho_6h_better = 0
|
||||
for band_mhz in sorted_bands:
|
||||
info = per_band_results[band_mhz]
|
||||
r0 = info["rhos"].get(0, 0.0)
|
||||
r6 = info["rhos"].get(6, 0.0)
|
||||
if r6 > r0:
|
||||
rho_6h_better += 1
|
||||
if rho_6h_better > 0:
|
||||
md.append(f"- **ρ(6h) > ρ(0h)**: {rho_6h_better} bands — the 6-hour lagged score outperforms current-conditions")
|
||||
|
||||
# Average degradation
|
||||
if per_band_results:
|
||||
avg_delta_6 = sum(info["delta_6h"] for info in per_band_results.values()) / len(per_band_results)
|
||||
avg_delta_24 = sum(info["delta_24h"] for info in per_band_results.values()) / len(per_band_results)
|
||||
md.append(f"- **Mean Δρ(0→6h)**: {avg_delta_6:+.4f}")
|
||||
md.append(f"- **Mean Δρ(0→24h)**: {avg_delta_24:+.4f}")
|
||||
else:
|
||||
md.append("- **No bands passed the ≥30 contact threshold.**")
|
||||
|
||||
# Caveats
|
||||
md.append("")
|
||||
md.append("## Caveats\n")
|
||||
md.append(
|
||||
"- **Lagged profiles use analysis data**: The HRRR profiles at T−N "
|
||||
"represent actual weather conditions at that earlier time, not a true "
|
||||
"forecast run initialized at T−N. Real HRRR forecasts would contain "
|
||||
"model error growth; these measurements capture the degradation from "
|
||||
"weather *evolution* alone (the lower bound of forecast skill loss)."
|
||||
)
|
||||
md.append(
|
||||
"- **Snap to nearest grid point**: Profiles are looked up at the HRRR "
|
||||
"grid point nearest to the contact location (rounded to 0.125°). "
|
||||
"A single point approximates the full path's conditions."
|
||||
)
|
||||
md.append(
|
||||
"- **Filter threshold**: Only bands with ≥30 contacts having ALL "
|
||||
"6 profiles (lag-0 through lag-24) are included in the table."
|
||||
)
|
||||
md.append(
|
||||
"- **Censored data**: Contacts are confirmed success events only. "
|
||||
"Correlation ρ measures discrimination among successful contacts."
|
||||
)
|
||||
md.append("")
|
||||
|
||||
md_path = report_dir / f"forecast-{today}.md"
|
||||
md_text = "\n".join(md)
|
||||
md_path.write_text(md_text + "\n")
|
||||
print(f"• wrote {md_path}", file=sys.stderr)
|
||||
|
||||
# ── Print to stdout ─────────────────────────────────────────────────────
|
||||
print(md_text)
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
|
|
@ -79,14 +79,16 @@ defmodule Microwaveprop.Propagation.BandConfigPropertyTest do
|
|||
end
|
||||
end
|
||||
|
||||
property "sunrise_table returns 12 numeric entries in a plausible hour range" do
|
||||
table = BandConfig.sunrise_table()
|
||||
assert Enum.count_until(table, 13) == 12
|
||||
property "sunrise_hour returns plausible values in the expected hour range" do
|
||||
check all(
|
||||
lat <- StreamData.float(min: 25.0, max: 49.0),
|
||||
month <- StreamData.integer(1..12)
|
||||
) do
|
||||
h = BandConfig.sunrise_hour(lat, month)
|
||||
assert is_float(h)
|
||||
|
||||
check all(idx <- StreamData.integer(0..11)) do
|
||||
entry = Enum.at(table, idx)
|
||||
assert is_number(entry)
|
||||
assert entry >= 4.0 and entry <= 9.0
|
||||
assert h >= 4.0 and h <= 9.0,
|
||||
"sunrise_hour(#{lat}, #{month}) = #{h}, expected 4.0–9.0"
|
||||
end
|
||||
end
|
||||
|
||||
|
|
|
|||
|
|
@ -260,27 +260,29 @@ defmodule Microwaveprop.Propagation.BandConfigTest do
|
|||
end
|
||||
end
|
||||
|
||||
describe "sunrise_table/0" do
|
||||
test "returns 12 monthly values" do
|
||||
table = BandConfig.sunrise_table()
|
||||
assert Enum.count_until(table, 13) == 12
|
||||
describe "sunrise_hour/2" do
|
||||
test "returns plausible sunrise hours across latitudes" do
|
||||
# Verify latitude-aware computation produces physically reasonable
|
||||
# sunrise times per latitude and month (CONUS range ~4.0–9.0h solar).
|
||||
for lat <- [30.0, 38.0, 49.0], month <- 1..12 do
|
||||
h = BandConfig.sunrise_hour(lat, month)
|
||||
assert is_float(h)
|
||||
|
||||
assert h >= 4.0 and h <= 9.0,
|
||||
"sunrise_hour(#{lat}, #{month}) = #{h}, expected 4.0–9.0"
|
||||
end
|
||||
end
|
||||
|
||||
test "returns expected sunrise hours" do
|
||||
assert BandConfig.sunrise_table() == [
|
||||
7.4,
|
||||
7.3,
|
||||
7.0,
|
||||
6.7,
|
||||
6.35,
|
||||
6.25,
|
||||
6.35,
|
||||
6.65,
|
||||
6.9,
|
||||
7.1,
|
||||
7.35,
|
||||
7.45
|
||||
]
|
||||
test "summer has earlier sunrise than winter at all latitudes" do
|
||||
for lat <- [30.0, 38.0, 49.0] do
|
||||
assert BandConfig.sunrise_hour(lat, 6) < BandConfig.sunrise_hour(lat, 1),
|
||||
"lat=#{lat}: June sunrise should be earlier than January"
|
||||
end
|
||||
end
|
||||
|
||||
test "northern latitudes have later winter sunrise" do
|
||||
# Seattle should have later winter sunrise than Miami
|
||||
assert BandConfig.sunrise_hour(49.0, 1) > BandConfig.sunrise_hour(30.0, 1)
|
||||
end
|
||||
end
|
||||
|
||||
|
|
|
|||
|
|
@ -56,7 +56,7 @@ defmodule Microwaveprop.Propagation.NotifyListenerTest do
|
|||
|
||||
assert :ets.info(:propagation_score_cache, :size) == 0
|
||||
|
||||
{:ok, _task_pid} = NotifyListener.handle_propagation_ready(valid_time)
|
||||
{:ok, _task_pid} = NotifyListener.handle_propagation_ready(valid_time, valid_time)
|
||||
ScoreCache.sync()
|
||||
|
||||
# Cache stays empty: NotifyListener no longer materialises the
|
||||
|
|
@ -71,7 +71,7 @@ defmodule Microwaveprop.Propagation.NotifyListenerTest do
|
|||
|
||||
Phoenix.PubSub.subscribe(Microwaveprop.PubSub, "propagation:updated")
|
||||
|
||||
{:ok, _task_pid} = NotifyListener.handle_propagation_ready(valid_time)
|
||||
{:ok, _task_pid} = NotifyListener.handle_propagation_ready(valid_time, valid_time)
|
||||
|
||||
assert_receive {:propagation_updated, [^valid_time]}
|
||||
end
|
||||
|
|
@ -83,7 +83,7 @@ defmodule Microwaveprop.Propagation.NotifyListenerTest do
|
|||
now_valid_time = DateTime.truncate(DateTime.utc_now(), :second)
|
||||
ScoresFile.write!(10_000, now_valid_time, sample_scores())
|
||||
|
||||
{:ok, _task_pid} = NotifyListener.handle_propagation_ready(now_valid_time)
|
||||
{:ok, _task_pid} = NotifyListener.handle_propagation_ready(now_valid_time, now_valid_time)
|
||||
|
||||
assert ScoreCache.fetch(10_000, stale) == :miss
|
||||
end
|
||||
|
|
@ -105,7 +105,7 @@ defmodule Microwaveprop.Propagation.NotifyListenerTest do
|
|||
|
||||
refute ScalarFile.exists?(vt)
|
||||
|
||||
{:ok, task_pid} = NotifyListener.handle_propagation_ready(vt)
|
||||
{:ok, task_pid} = NotifyListener.handle_propagation_ready(vt, vt)
|
||||
|
||||
# Monitor the background Task that materializes the scalar file.
|
||||
ref = Process.monitor(task_pid)
|
||||
|
|
|
|||
|
|
@ -385,16 +385,17 @@ defmodule Microwaveprop.Propagation.ScorerPropertyTest do
|
|||
end
|
||||
end
|
||||
|
||||
# ── score_time_of_day/4 ─────────────────────────────────────────
|
||||
# ── score_time_of_day/5 ─────────────────────────────────────────
|
||||
|
||||
property "score_time_of_day always in 0..100 and returns a string label" do
|
||||
check all(
|
||||
hour <- hour_gen(),
|
||||
minute <- minute_gen(),
|
||||
month <- month_gen(),
|
||||
lat <- latitude_gen(),
|
||||
lon <- longitude_gen()
|
||||
) do
|
||||
{score, label} = Scorer.score_time_of_day(hour, minute, month, lon)
|
||||
{score, label} = Scorer.score_time_of_day(hour, minute, month, lon, lat)
|
||||
assert score in 0..100
|
||||
assert is_binary(label)
|
||||
assert label != ""
|
||||
|
|
|
|||
|
|
@ -98,12 +98,14 @@ defmodule Microwaveprop.Propagation.ScorerTest do
|
|||
assert cond_map.utc_hour == 18
|
||||
assert cond_map.utc_minute == 30
|
||||
assert cond_map.month == 6
|
||||
assert cond_map.latitude == 32.9
|
||||
assert cond_map.longitude == -97.0
|
||||
|
||||
# Fields that the path integration can't infer from HRRR are nil.
|
||||
assert cond_map.wind_speed_kts == nil
|
||||
assert cond_map.sky_cover_pct == nil
|
||||
assert cond_map.prev_pressure_mb == nil
|
||||
# Rain rate: no precip data defaults to 0.0.
|
||||
assert cond_map.rain_rate_mmhr == 0.0
|
||||
end
|
||||
|
||||
|
|
@ -181,6 +183,7 @@ defmodule Microwaveprop.Propagation.ScorerTest do
|
|||
contact_no_lon = %{utc_hour: 12, utc_minute: 0, month: 3, latitude: 32.9}
|
||||
|
||||
cond_map = Scorer.path_integrated_conditions(profiles, contact_no_lon)
|
||||
assert cond_map.latitude == 32.9
|
||||
assert cond_map.longitude == -97.0
|
||||
end
|
||||
end
|
||||
|
|
|
|||
|
|
@ -309,6 +309,31 @@ defmodule Microwaveprop.Propagation.ScoresFileTest do
|
|||
end
|
||||
end
|
||||
|
||||
describe "HRDPS file visibility" do
|
||||
test "parse_valid_time correctly extracts the ISO datetime from .hrdps.prop filenames" do
|
||||
# The greedy `(.+)` in an older regex captured "<iso>.hrdps" as the
|
||||
# datetime portion, causing DateTime.from_iso8601/1 to fail and
|
||||
# HRDPS files to be silently skipped by list/retain/prune.
|
||||
vt = ~U[2026-08-01 12:00:00Z]
|
||||
ScoresFile.write!(10_000, vt, [])
|
||||
hrrr_path = ScoresFile.path_for(10_000, vt)
|
||||
hrdps_path = ScoresFile.path_for_hrdps(10_000, vt)
|
||||
File.cp!(hrrr_path, hrdps_path)
|
||||
File.rm!(hrrr_path)
|
||||
|
||||
# Only the .hrdps.prop file exists — it must be visible.
|
||||
assert ScoresFile.list_valid_times(10_000) == [vt]
|
||||
|
||||
# retain_window with a window that includes vt keeps the file.
|
||||
assert ScoresFile.retain_window(~U[2026-08-01 10:00:00Z], 6) == 0
|
||||
assert ScoresFile.list_valid_times(10_000) == [vt]
|
||||
|
||||
# retain_window outside vt's window deletes it.
|
||||
assert ScoresFile.retain_window(~U[2026-08-01 13:00:00Z], 6) == 1
|
||||
assert ScoresFile.list_valid_times(10_000) == []
|
||||
end
|
||||
end
|
||||
|
||||
describe "retain_window/2" do
|
||||
test "deletes files outside [run_time, run_time + hours] across every band" do
|
||||
run_time = ~U[2026-04-14 16:00:00Z]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue