The live history path now tries NASA POWER first (keyless, independent of
Open-Meteo) and falls back to the Open-Meteo archive only when NASA is
unavailable — the reverse of before. Gusts NASA lacks are filled from Meteostat
inside _fetch_history_nasa (added in the prior change). Both sources must still
return a plausibly-full span (MIN_ARCHIVE_DAYS) before being cached as complete;
a short/partial response is rejected and the other source is tried.
_fetch_history_nasa now accepts a start/end range so it also serves the recent
tail top-up: _topup_tail fetches NASA-first via the new _fetch_history_tail
helper (Open-Meteo range as fallback, skipped during its rate-limit cooldown),
removing the last per-active-cell Open-Meteo call from the history path. The
Open-Meteo cooldown no longer gates the top-up, since NASA is not subject to it.
Open-Meteo remains wired as the fallback (deletion is a later step). Tests
updated to the new source order plus tail-fetch coverage.
NASA POWER (history) and MET Norway (forecast) carry no wind gusts, but gust is
a graded metric. This adds data/meteostat.py, which finds the nearest Meteostat
station to a cell and reads its daily peak gust (wpgt) from the keyless gzipped
bulk endpoints, filling the gust column on the NASA POWER history frame. Where no
station is within ~100 km it estimates from sustained wind (wind * GUST_FACTOR).
Bulk files (station list + per-station daily) are cached on disk so steady-state
network IO is near zero, and any lookup/fetch failure degrades to pure estimation
so a history fetch never fails. A constant estimate factor makes an estimated
gust redundant with wind, so real signal comes only where a station backs it.
Activates once NASA POWER becomes the primary history source; Open-Meteo already
carries its own gusts. New history_gust / meteostat_stations metrics phases map
to the meteostat source.
/suggest is now served purely from the local GeoNames index — no external
geocoder call per keystroke. /geocode answers from the local index first and
falls back to OSM Nominatim /search only on a miss (or while the index is still
loading), covering the neighbourhood/postcode/tiny-village/native-name long tail
the cities dump lacks.
Nominatim shares the reverse geocoder's lock and ~1/sec pacing since both hit
the same host, and only the low-volume /geocode miss path reaches it. Removes
the Open-Meteo geocoding dependency entirely; the "geocode" metrics phase now
maps to the nominatim source.
The frontend/ half of this commit (calendar.js, calendar.html, app.js,
style.css) is out of scope for this repo; a separate agent handles the
split frontend repo.
Claude-Session: https://claude.ai/code/session_01RdARHDJaYC1wSQRTYM6t3Z
Replace the per-cell parquet cache with TimescaleDB hypertables as the
production backend for the raw daily climate record, and drop pg_duckdb.
Parquet stays the backend whenever THERMOGRAPH_DATABASE_URL is not a
Postgres URL (dev, tests, offline tooling), the same dialect switch the
accounts DB and derived store already use, so CI stays Postgres-free.
- data/climate_store.py: psycopg + polars bridge over climate_history
(a hypertable), climate_recent, and climate_sync (per-cell freshness).
Reads via pl.read_database, writes via COPY + ON CONFLICT upsert;
fail-soft to a cache miss so a DB hiccup degrades to upstream refetch.
- data/climate.py: route every cache/mtime touchpoint through a backend
dispatch. recent_stamp becomes int(recent_synced_at) on Postgres; the
stale-serve path still avoids bumping it, so derived-payload tokens
invalidate on exactly the same events as before.
- alembic 0002: CREATE EXTENSION timescaledb plus the hypertable schema
(compression policy on year-old chunks), guarded to no-op off Postgres.
- migrate_cache_to_pg.py (make migrate-cache): idempotent backfill of the
parquet cache into the hypertables, preserving file mtimes as sync
timestamps so recent_stamp is unchanged across cutover.
- db image -> stock timescale/timescaledb:latest-pg18; drop the custom
pg_duckdb Dockerfile, the read-only /parquet mount, and the duckdb
tuning GUC. Docs updated for the new backend and cutover.
Co-authored-by: Claude <noreply@anthropic.com>
A self-hosted Open-Meteo instance that isn't backfilled yet answers historical
requests with all-null values (or only its recent sync window), which
_finalize_frame reduces to a near-empty frame. That frame is not None, so the
loader would accept it as source=open-meteo and cache it indefinitely as a
complete record — never falling back to NASA and never self-healing after the
backfill lands. Require at least MIN_ARCHIVE_DAYS before accepting an archive
result; below that, fall through to the NASA backup and don't cache the short
frame. Guards the prod cutover window.
Get the 45-year historical record off the rate-limited public Open-Meteo
archive API by running a private Open-Meteo instance that serves the
era5_seamless blend (0.1° ERA5-Land + 0.25° ERA5 for gusts) from the
compressed .om archive in object storage, mounted on the host with rclone.
- climate.py: make ARCHIVE_URL env-driven (THERMOGRAPH_ARCHIVE_URL) and pin
models=era5_seamless on the archive fetches only, so a self-hosted instance
serves the same 0.1° resolution; forecast path unchanged. The public API's
default is already seamless, so dev/beta (URL unset) behave identically.
- docker-compose.openmeteo.yml: open-meteo-api + two rolling sync workers
(era5_land 0.1°, era5 0.25° for gusts), bind-mounting the object-storage
mount; the overlay points the app at the local instance.
- Makefile: om-up / om-down / om-backfill (one-time full-history backfill).
- Terraform: per-host openmeteo flag layers the overlay, renders OM_DATA_DIR,
and provisions the host rclone systemd mount from the bucket credentials.
- deploy/openmeteo: operator runbook + rclone mount unit template.
Run Thermograph as a docker-compose stack (app + Postgres 18) and standardize the
data layer on Postgres, while keeping the test suite on SQLite.
- accounts/db.py: DSN-driven engines. On Postgres, a per-worker read-write +
read-only asyncpg pair (the RO engine pins read-only transactions, used by the
pure-GET endpoints) plus a sync psycopg engine for the notifier thread; the
SQLite path is preserved for tests/local (selected when THERMOGRAPH_DATABASE_URL
is unset). models.py: boolean server_default -> sa.false().
- store.py / metrics.py: dialect-flexible — Postgres UNLOGGED tables via psycopg
when configured, else the existing raw-sqlite3 paths byte-for-byte; sync
interfaces and every fail-soft contract preserved.
- Alembic (backend/alembic/) manages the accounts schema; the container entrypoint
runs `alembic upgrade head` before uvicorn (4 workers). migrate_accounts_to_pg.py
copies the accounts data SQLite->PG through the ORM (UUID/bool/JSON coerced),
skips access_token, and resets identity sequences.
- Dockerfile + docker-compose.yml: app image (uvicorn, 4 workers, loopback 8137)
and a Postgres 18 db (2 CPUs) running pg_duckdb (deploy/db/) so the parquet
climate cache is queryable in-DB via read_parquet('/parquet/cache/*.parquet').
- deploy.sh/thermograph.service rewired to manage the compose stack; env example,
Makefile targets (up/down/db-up), and deploy/POSTGRES-MIGRATION.md cutover runbook.
Tests stay on SQLite (dialect fallback) — 323 pass. The full Postgres stack was
verified via docker compose: alembic migrations, register/login, the RO endpoint,
store/metrics round-trips, and the accounts data migration.
The overall climate-shift score was a weighted roll-up (feels-like and humidity
2x, wind and gusts 0.5x, and so on). Make it an equal average instead: every
metric — and, through each metric's own mean over the 10/25/50/75/90 percentiles,
every percentile — counts the same.
- scoring._overall: plain mean of the present metrics' divergences, mapped to
0-100, replacing the weighted sum. Missing metrics still drop out cleanly.
- Drop the now-unused per-metric weights from the METRICS table and the scored
entries (nothing rendered them; only the roll-up read them).
- views.SCORE_VER s4 -> s5 so cached weighted scores are recomputed.
- score.js: the hero note now reads "Every metric counts equally" instead of
"Temperature, feels-like and humidity are weighted most".
Verified against Seattle: overall mad equals the equal mean of the eight metric
mads (7.9), and the entries no longer carry a weight.
Claude-Session: https://claude.ai/code/session_013dRZmX9D3JEntfMKWMTWZ8
* Centralize filesystem paths in a single module
Add paths.py, which resolves the repo root once and derives the cache,
accounts DB, logs, templates, frontend and bundled-city-data locations
from it. Replace the 13 per-module `dirname(__file__)/..` anchors with
references to it, so a module's location no longer determines where the
app reads its data. Env overrides (accounts DB, VAPID, IndexNow) are
unchanged; every resolved path is byte-identical to before.
Groundwork for moving modules into packages without re-pointing paths.
Claude-Session: https://claude.ai/code/session_01XXxmNFy9cZ6Gh8Y9thZn62
* Split the backend into domain packages
Group the flat backend modules into packages that mirror their concerns:
data/ climate, grading, scoring, grid, places, cities,
city_events, store
web/ app, views, homepage, content, schemas
notifications/ notify, digest, push, mailer, discord,
discord_interactions, discord_link
accounts/ models, users, api_accounts, db
core/ metrics, singleton, audit
Intra-project imports are rewritten to the package-qualified form. The
entry scripts (indexnow, warm_cities, migrate, gen_cities, gen_flavor)
and paths.py stay at the backend/ root, and backend/app.py becomes a
shim re-exporting web.app:app so the launch target stays `app:app` —
run.sh, the systemd units, and CI need no change.
Verified: full suite (318) passes, `uvicorn app:app` boots and serves
the home/SEO/static/API surfaces, and every root script imports clean.
Claude-Session: https://claude.ai/code/session_01XXxmNFy9cZ6Gh8Y9thZn62
