Graham McIntire 5fd37a17fd feat(propagation): per-band weight calibration from full-corpus correlation ... Ran recalibrate_algo.py against the full local prop_dev (81,994 contacts, 18.6M HRRR rows) and derived per-band composite weights for the nine bands with >=200 matched contacts. Moisture (dewpoint/PWAT/surface N) is consistently beneficial through 5.76 GHz, reverses at 24 GHz; rain scales sqrt(rain_k) not linearly so 24 GHz gets 0.215 rain weight instead of the linear-ratio 0.95. 10 GHz stays as the reference band (defaults); 47+ GHz inherits defaults (n<200). - BandConfig.weights/1 returns per-band override or default fallback - @band_configs carries :weights on 222/432/902/1296/2304/3400/5760/24G - Recalibrator.compute_factors/3 + fit(band_mhz:) band-aware fitting - Scorer + ContactLive.Show pass band_config to weights/1 - algo.md Part 2d documents the 2026-04-18 analysis + derivation rule - scripts/derive_band_weights.py turns correlations into weight maps - report preserved at docs/algo-reports/2026-04-18-recalibration.md		2026-04-18 10:19:26 -05:00
..
backup_prop_db.sh	postgres backup script added	2026-04-17 17:34:15 -05:00
derive_band_weights.py	feat(propagation): per-band weight calibration from full-corpus correlation	2026-04-18 10:19:26 -05:00
recalibrate_algo.py	feat(propagation): per-band weight calibration from full-corpus correlation	2026-04-18 10:19:26 -05:00
restore_prod_to_local.sh	chore(scripts): source restore script config from .envrc, pin pg tools	2026-04-18 09:36:52 -05:00