- All menus and UI text now say Contacts instead of QSOs
- Add 68, 122, 134, 241 GHz bands to submit form and validation
- Add info box on submit page explaining why contacts matter
- Larger submit button with icon
- Make HRRR partition migration idempotent for partial re-runs
Rename all modules, functions, variables, routes, and UI text from
qso/qsos to contact/contacts. Database table stays as "qsos" to avoid
migration. Add /qsos -> /contacts redirects for old URLs.
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
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.
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.
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)
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
- Add enqueue_for_qso/1 to directly enqueue weather/HRRR/terrain/IEMRE
jobs for a single user-submitted QSO (no cron, no bulk processing)
- Submit flow calls enqueue_for_qso instead of generic enqueue worker
- Add enrichment queues to prod config for on-demand processing
- Guard against HRRR fill values in store_hrrr_profiles (fixes badarith)
- Filter QSOs without pos2 in build_terrain_jobs
- Port grid square rendering from gridmap-web with toggle control
- Consolidate band selector, grid toggle, data timestamp, and links
into unified control panel
- Add "How this works" (/algo) and "Submit a QSO" (/submit) links
- Add back-to-map link on algo page
- Darken range circles for visibility over propagation overlay
- Fix popup blocking double-click zoom with pointer-events passthrough
- Prevent control panel from intercepting map click/scroll events
Click anywhere on the propagation map to see a detailed popup with:
- Overall score and tier label with color
- Estimated range for the selected band (CW mode)
- All 9 scoring factors with visual bar charts, individual scores,
and weight percentages
- Grid point coordinates and data timestamp
Factors are displayed in weight order so users can immediately see
which atmospheric conditions are driving the prediction.
Replace circle markers with a canvas tile layer that renders smooth,
flowing colored regions using bilinear interpolation between grid
points. Colors interpolate between tiers for gradients. ~95k grid
points at 0.125 degree resolution with wgrib2 extraction.
95k points at 0.125 degree resolution caused the GRIB2 extraction to
take too long. 0.5 degree (~55 km) resolution gives 6k points which
completes in under a minute. Can increase resolution later once the
extraction is optimized.
QSO enrichment now groups all path points by HRRR hour and creates
one batch job per hour instead of one job per point. The batch job
downloads the GRIB2 data once and extracts all needed points from
the same binary. Legacy single-point jobs are still supported for
backward compatibility.
Vendor Leaflet 1.9.4 (JS, CSS, marker images) and wire it into
the esbuild/Tailwind asset pipeline. Create MapLive with band
selector buttons, auto-refresh, and a colocated JS hook that
renders propagation scores as color-coded circle markers with a
legend. Stub Propagation context and BandConfig modules provide
the data interface for the scoring pipeline.
Add nav bar with links to Map, QSOs, and Submit pages.
Add fetch_grid/3 for batch HRRR data retrieval across multiple lat/lon
points in a single download pass. Expand surface messages to include
wind (UGRD/VGRD), cloud cover (TCDC), and precipitation (APCP). Add
extract_grid/2 to the GRIB2 extractor for multi-point extraction from
a single GRIB binary, and add GRIB2 variable identifiers for the new
surface fields.
Define 0.125-degree CONUS grid (25-50N, 125-66W) for propagation
scoring and create propagation_scores table with composite unique
index on lat/lon/valid_time/band_mhz for upsert support.
Add extract_values/3 to SimplePacking and ComplexPacking for batch
index extraction from a single GRIB2 message. Add extract_grid/2 to
Extractor which takes a list of {lat, lon} points and returns all
variable values for each point, skipping points outside the grid.
This enables extracting weather data for many grid points from a
single HRRR download instead of re-parsing per point.
Single source of truth for all scoring parameters: weights, thresholds,
seasonal tables, and per-band coefficients for 8 microwave bands
(10G through 241G). Includes ITU-R P.838-3 rain attenuation
coefficients, humidity effects, refractivity scoring thresholds,
and sunrise/tier definitions.
Enqueue worker now gathers atmospheric data at pos1, midpoint, and pos2
along each QSO path instead of only pos1. Existing has_* guards prevent
duplicate fetches at each grid point.
- Add Radio.qso_path_points/1 for path point extraction
- Update hrrr_job_for_qso, iemre_job_for_qso, jobs_for_qso to iterate path points
- Refactor Weather into find_nearest_hrrr/3 and find_nearest_iemre/3
- Add hrrr_profiles_for_path/1 and iemre_for_path/1 query functions
- Add mix reset_enrichment task to trigger re-processing
- Add precip_1h_in and wx_codes fields to ASOS surface observations
- Add IEMRE reanalysis schema for radar-derived hourly precipitation
- Add IemreFetchWorker with exponential backoff and idempotency
- Integrate IEMRE enqueue into cron weather backfill pipeline
- All existing QSOs marked iemre_queued=false for automatic backfill
Poll UBNT AirFiber radios (AF11X + AF60-LR) every 5 minutes via
net-snmp CLI, storing signal metrics in commercial_samples. Fetches
ASOS weather alongside each cycle for propagation correlation.
Includes 7 seeded link definitions, Oban cron worker, and net-snmp
in the Docker image.
extract_n_values_array used Enum.take(count) on a reversed list
before reversing it, which included padding values from byte
alignment and dropped actual values. When group count * bits
wasn't a multiple of 8, the extra padding bits produced a
phantom value that shifted the entire array by one position.
This caused cascading errors in spatial differencing — values
started correct but diverged exponentially (DPT decoded as
38 billion K instead of 275 K).
Fix: reverse the list first, then take count, so padding values
at the end are discarded instead of actual values at the start.
AWS S3 doesn't support multi-range HTTP requests. When given
Range: bytes=0-999, 2000-2999 it ignores the header and returns
the full file (200 instead of 206). Download each range separately.
Instead of returning "malformed section" on trailing bytes or truncated
sections, attempt to return parsed results. This handles older HRRR
files (2019) that have padding bytes after the last section.
When a local .hgt tile is missing, download it from the public AWS S3
skadi bucket, decompress with zlib, and write to the tiles directory
before retrying the lookup. Falls back to Open-Meteo/OpenTopo APIs if
the download fails.
HTTP 404 means the HRRR data doesn't exist on NOAA S3 (pre-2014
dates, etc.) and will never succeed. Return {:cancel, reason}
instead of {:error, reason} so Oban stops retrying immediately.
Eliminates the external wgrib2 C tool dependency that blocked HRRR
processing. Implements Lambert Conformal projection, simple packing
(Template 5.0), complex packing with spatial differencing (Template 5.3),
and GRIB2 section parsing — enough to extract point values from HRRR
grid data using only Elixir.
Maidenhead grid module converts grid squares to lat/lon coordinates.
Submission form validates grids, bands, modes, and email, computes
positions and distance, then triggers the weather/HRRR/terrain
processing pipeline via Oban.
