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Tides, weather, water, and shellfish status for oyster growers in Thorndyke Bay, Hood Canal, WA.
DataPython · JavaScriptGitHub @RobertsLabOnlineOwner unconfirmed
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The maker's introduction consists of the line above. Read the repository README for more details. Repository README ↗
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Read the full README ↗A map-based portal of near-real-time conditions for oyster growers in Thorndyke Bay (Hood Canal, WA): tides and low-tide work windows, weather and marine alerts, radar, rain, water quality and the LiveOcean water forecast, shellfish status, and Sentinel-2 satellite imagery. The map also shows the farm layout, tide-height lines, geotagged farm photos, and on-farm temperature sensors. See PLAN.md for the full roadmap.
It started as the Phase 1 MVP (public data only) and now includes the first Phase 2 pieces (farm sensors via The Things Network). It is a static site with no server or database:
GitHub Actions: update.yml (every ~20 min)
└─ ingest/fetch.py → pulls NOAA, NWS, WA DOH, NANOOS/IOOS, IEM, LiveOcean, The Things Network
└─ writes web/data/*.json
└─ GitHub Pages serves web/ (MapLibre map + uPlot charts)
GitHub Actions: imagery.yml (twice daily)
└─ imagery/run.py → Sentinel-2 products on the imagery-data branch, copied into web/data/imagery/
Python 3.9+ with only the standard library, no installs needed.
python3 ingest/fetch.py
python3 serve.py
Then open http://localhost:8000. Re-run fetch.py to refresh the data. --only tides forecast fetches a subset.
Tests:
python3 -m unittest discover ingest/tests
ingest/
config.py site location, station IDs, NWS grid/zone
fetch.py runs every source, writes web/data/*.json + meta.json
sources/ one module per data source
common.py shared helpers: HTTP, time parsing, unit conversion
tides.py NOAA CO-OPS predictions (Lofall 9445088)
nws.py NWS gridded forecast, alerts (PZZ135), surface observations
rain.py observed rain: Stage IV at the site + CoCoRaHS gauges (via IEM)
sun.py sunrise/sunset (computed)
doh.py WA DOH biotoxin status + commercial growing areas
water.py NANOOS/IOOS ERDDAP water temp, salinity, oxygen
liveocean.py UW LiveOcean model surface temp/salinity nowcast + forecast via OPeNDAP
(shown as the "Water forecast" card and map markers; see docs/liveocean-plan.md)
sensors.py farm LoRaWAN temperature nodes via The Things Network
(SENSORS_MOCK=1 python3 ingest/fetch.py --only sensors for fake data;
adding gateways and sensors: docs/sensors.md)
archive_sensors.py appends every sensor reading to monthly CSVs on the sensor-data branch
tests/ unittest tests (sensor archive, LiveOcean ingest, HOBO archive)
imagery/ satellite pipeline (needs numpy/rasterio: pip install -r imagery/requirements.txt)
run.py Sentinel-2 true color + clear-sky % + tide at capture -> imagery.json
grid.py shared UTM 10N 10 m grid every sensor/model output is resampled onto
(shown as the "Satellite view" map layer and Satellite card; see docs/imagery-plan.md)
stac.py STAC scene search with catalog fallback and retries
s2.py Sentinel-2 L2A: tiles -> scenes, read onto the grid, clear-sky stats, true color
settings.py catalogs, lookback/retention days, clear-sky thresholds
baseline/ scripts run by hand; outputs are committed to web/baseline/ and not re-run by CI
(pip install -r baseline/requirements.txt)
liveocean_climatology.py LiveOcean monthly climatology (2014-2024) at the model cells:
surface + bottom temp/salinity/oxygen -> liveocean_climatology.json (one-off)
tide_contours.py tide-height lines (ft MLLW) from the NOAA NCEI Puget Sound DEM
-> tide_contours.geojson (one-off)
hobo_archive.py archival HOBO loggers in the Step 4 bag (2024-26) -> thorndyke_hobo.json
(stdlib only; re-run after each logger download; see docs/archival-temperature-plan.md)
photos/ farm photos for the map (originals git-ignored; see photos/README.md)
build.py geotagged photos -> web/photos/ copies + photos.geojson (needs Pillow)
web/
index.html map (radar, satellite, farm layout, tide lines, photos, sensors) +
"Today at Thorndyke" panel
tides.html low-tide work-window calendar with .ics export
about.html data sources and freshness
farm-layout.geojson hand-drawn farm layout shown on the map
baseline/ committed static data (not regenerated by the workflow)
photos/ web copies of farm photos + photos.geojson (from photos/build.py)
js/, css/, icon.svg, manifest.webmanifest
docs/ plans (LiveOcean, imagery, archival temperature), sensor how-to, solar gateway assembly
.github/workflows/update.yml scheduled fetch + Pages deploy (copies the imagery-data branch in,
archives sensor readings to the sensor-data branch)
.github/workflows/imagery.yml twice-daily imagery build -> imagery-data branch
ingest/sources/<name>.py with a fetch() -> dict function that returns JSON-serializable data.
A source that needs its previous output or another source's output takes fetch(ctx) instead
and is listed in WITH_CONTEXT.SOURCES in ingest/fetch.py.load("<name>") from web/js/common.js.A failing source never blocks the others. In CI the previously published file is reused, and the page shows how old the data is.
main, every ~20 minutes, and on demand (Actions → Run workflow).GitHub turns off scheduled workflows in public repos after 60 days with no commits. Push a commit or re-enable the workflow when that happens.
Advisory only. Official harvest closures come from the WA Department of Health.