Move COPY priv after mix compile so ML model retraining doesn't bust
the compile cache. Create empty priv dirs before compile for
:code.priv_dir. Move algo.md after compile. Add docs/plans and
analysis outputs to .dockerignore.
CW advantage scales dramatically with frequency: +29% at 10G, +48% at
47G, +221% at 75G. At 24 GHz the pattern reverses (PH wins by 8%)
due to rainscatter favoring wider-bandwidth modes. Above 122 GHz,
100% of contacts are CW — SSB can't close the path.
The ML model undervalues conditions outside Aug/Sep training data
(e.g. April with excellent factors scored 37/100). Algorithm's
physics-based factors handle unseen seasons correctly.
- Algorithm is primary scorer, ML infrastructure kept for iteration
- Remove unused ML grid worker code path
- Add client-side propagation reach: BFS flood-fill from clicked point
through contiguous cells with score >= 50, drawn as convex hull polygon
ML Integration:
- Load trained model at app startup, cache compiled predict fn in persistent_term
- Grid worker uses batched ML prediction (10K chunks) when model loaded,
falls back to algorithm scorer when not
- ML score replaces composite, algorithm factor scores preserved for detail view
- Fix process explosion: single EXLA call per chunk instead of per-grid-point
QSO Features:
- Callsign search (ILIKE on station1/station2) with trigram indexes
- Reciprocal QSO grouping (same pair, same band, same hour)
- Wider layout (max-w-7xl) for data table pages
- QSO Training Data link on map page
Infrastructure:
- Re-enable hourly propagation grid worker in dev
- Track ML model weights in git for Docker builds
- Add btree indexes on qsos (timestamp, band, distance_km)
- Remove nav icons from layout header
Add SFI, Kp max (solar), K-index, lifted index (sounding stability),
and ducting_detected (HRRR) as model features. Training now joins to
solar_indices and nearest sounding (within 6 hours) for both phases.
Model can learn solar/geomagnetic effects if they exist in the data.
- 15 features: add surface_refractivity and latitude
- Bigger network: 128→64→32 (3 hidden layers)
- Phase 1: pretrain on 500K stratified algorithm scores (all seasons/locations)
- Phase 2: fine-tune on 57K real QSO-HRRR matched data (percentile target)
- Lower LR (0.0003) for fine-tuning to preserve pretrained knowledge
- Model.train accepts :initial_state option for transfer learning
- 3 hidden layers instead of 2 for better feature interaction learning
- Target is within-band distance percentile (0-1) instead of raw
normalized distance — reduces noise from operator/equipment variation
Features: HRRR conditions averaged at both QSO endpoints + solar time
Target: distance_km normalized per-band (distance / p99_range, capped at 1.0)
This trains on actual propagation outcomes from 57K+ QSOs, not the
hand-tuned algorithm output.
Raw features had vastly different scales (pressure ~1013, sin/cos ~[-1,1])
causing gradient explosion. Normalize all atmospheric features to ~[0,1]
using known physical bounds. Add Polaris dep for optimizer.
- Add polaris dep for Adam optimizer (Axon.Optimizers deprecated)
- Fix Model.train/3 to use Polaris.Optimizers.adam
- Training task: mix propagation_train with stratified band sampling
- Model encodes solar time (longitude/15) not fixed timezone
Solar time (longitude/15) replaces fixed CDT/CST offset for time-of-day
scoring. Correlation analysis shows dramatic improvement at higher
frequencies: 24 GHz rho jumps from 0.056 (UTC) to 0.188 (solar), and
75 GHz corrects from spurious -0.39 to physically correct +0.24.
Score time-of-day per grid point using longitude/15 solar offset instead of
hardcoded CST/CDT. Add PWAT as 10th scoring factor. Refine pressure thresholds.
Update ML model and training pipeline to use local solar time.
Comprehensive description of atmospheric science behind the predictions
for meteorologist review: refractivity-propagation connection, four
ducting mechanisms, HRRR model usage, frequency-dependent effects,
diurnal/seasonal cycles, and known limitations.
- point_detail response includes forecast array (score per valid_time)
- SVG sparkline shows score trend across all forecast hours
- Trend indicator: Improving/Declining/Steady based on first vs last score
- Add covering index (band_mhz, lat, lon, valid_time) INCLUDE (score) for
point_forecast queries
- HrrrClient.hrrr_url accepts forecast_hour param (wrfsfcfHH.grib2)
- PropagationGridWorker fetches all 19 forecast hours per run
- Propagation.scores_at/3 queries scores at specific valid_time
- Propagation.available_valid_times/1 returns all forecast times for timeline
- Pruning keeps scores with valid_time >= now - 2h (forecast-aware)
- MapLive: select_time event, timeline data pushed to JS
- JS: forecast timeline bar at bottom of map with clickable hour buttons
- PubSub broadcast sends list of valid_times instead of single time
- Add phx-update="ignore" to detail-panel so LiveView patches don't wipe it
- Re-request point_detail after scores update to refresh with new data
- Range circles persist across updates since they're on a separate layer
Previous thresholds (-500 to -60) were calibrated for radiosonde data.
HRRR profiles have coarser vertical resolution, with gradients clustering
between -40 and -130 N/km (median -70). Nearly all grid points were
falling through to the default score of 42, wasting the refractivity
factor. New thresholds (-200 to -40) spread across HRRR percentiles.
- Replace piecewise knife-edge loss with P.526-16 Eq. 31 single formula
- Fix diffraction parameter ν to use standard formula instead of ad-hoc approximation
- Implement Deygout 3-edge method for multiple obstacle diffraction
- Add dynamic k-factor from HRRR refractivity gradient (falls back to 4/3)
- Terrain worker now looks up nearest HRRR profile for atmospheric correction
- Update algo.md with P.526-16 methods and k-factor table
- Fix pre-existing map_live_test antenna height default (33 ft, not 8)
Deletes grid-aligned profiles (0.125 degree) older than 48 hours
while preserving QSO-linked profiles at arbitrary positions.
Called after each PropagationGridWorker run.
ASOS re-scoring was upserting 458K records every 10 min while losing
the refractivity gradient (the key ducting indicator). HRRR hourly
provides all surface variables plus vertical profile data.
458K-record upsert held a connection for the entire transaction,
exceeding the 15s Postgrex timeout on prod. Set transaction timeout
to infinity and increase prod pool from 10 to 20.
Req default 15s receive_timeout too short for GRIB2 byte-range
downloads on prod. Also reduce HRRR queue concurrency from 20 to 5
to avoid NOAA rate limiting (was causing burst-then-stall pattern).
- Viewshed uses propagation score to set max range (ducting/NLOS distance)
- Terrain checked within radio horizon; clear paths get full atmospheric range
- Detail panel moves from map popup to sidebar for better visibility
- Panel shows instantly on click with loading state
- Dark opaque background for readability over map
- Factor analysis section explains each score with contextual descriptions
Replace generic range circles with actual LOS coverage polygon computed
from SRTM elevation data. Casts 180 rays (every 2 degrees) from the
clicked point, runs Fresnel/diffraction analysis on each, and renders
the reachable area as a Leaflet polygon.
- Viewshed module with haversine forward, terrain sweep, async compute
- Antenna height control (default 8 ft) in map panel
- LiveView start_async/handle_async for non-blocking computation
- Remove signal icon from band selector
algo.md was read at runtime via File.read! but isn't included in
the release. Use @external_resource + compile-time module attribute
to bake the HTML into the module.
