* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
cache.js contained a JavaScript clone of backend/grid.py's snapping math
(its own comment said so) to compute the 8 surrounding cells and fire 8
staggered prefetch requests — grid geometry had two homes, one per
language, plus a client-side guess at Nominatim pacing.
The server now owns it: grid.neighbors(cell) steps one cell width from
the center and re-snaps (adjacent rows have different longitude steps;
poles and the antimeridian handled by snap), and /api/v2/cell grew a
neighbors=1 flag that enqueues those cells for a single background
worker. The warm-only guarantee matches prefetch=1 — a cell with no
cached archive is skipped, so no weather-API quota is ever spent — and
reverse_geocode's own lock paces the at-most-one Nominatim call per
never-labeled cell. Re-enqueues are TTL-deduped; the worker starts from
the lifespan hook, so tests and offline importers never spawn it.
The client now sends its one conditional bundle request with
neighbors=1 (a warm spot costs an empty 304) instead of skipping the
bundle and firing 8 extra requests; the grid-math clone and the
now-unused hasFreshCache are deleted.
Tests (114): grid.neighbors mid-latitude/pole/antimeridian, the
neighbors=1 enqueue + TTL dedupe, _warm_cell materializing the
history-derived store rows, and the never-fetch-upstream guarantee.
Verified with the headless-Chromium smoke across all five pages.
Rate-limit handling crossed the climate→app boundary as prose: climate
raised RuntimeError(cooldown text), and app._weather_fetch_error re-parsed
it by keyword ('429'/'rate-limited'/'daily'/'tomorrow') while also calling
climate's private _is_rate_limit/_rate_limit_reason — and the user-facing
daily-quota copy existed verbatim in both modules.
climate now raises WeatherUnavailable (a RuntimeError subclass carrying
the user-facing text and a daily flag): from _load_history when both
sources fail with no stale cache, and from the forecast fetch on a 429.
limit_message() is the single home of the rate-limit copy; is_rate_limit
is public for the one remaining raw-429 fallback. app maps the typed
error to 503 by isinstance — no message parsing, no private imports.
The burst-limit copy is unified on 'The weather service is rate-limited…'
(the archive path previously said 'weather archive' internally but the
API always rewrote it; user-visible text is unchanged).
Tests: daily-quota 503 carries the 'tomorrow' copy, an unclassified raw
429 still maps to a clean 503, and a genuine fault stays a 502 with the
raw error.
The derived store's key/token formats existed in three places — each
endpoint, api_cell's slice assembly, and migrate.py — where any drift
would silently split the cache (endpoints missing rows the bundle wrote,
migrate materializing rows nobody reads). They are now defined once in
views.py (grade_key/calendar_key/day_key/forecast_key, history_token/
recent_token/day_token) and consumed everywhere, with a pinning test so
a format change is always deliberate.
The four data endpoints shared two copy-pasted sequences, now helpers:
- _fetch_history: history (+ optional recent bundle) fetch with upstream
failures mapped to clean HTTP errors and an empty record to 404.
- _cached_response: If-None-Match 304 / token-valid store replay /
build + persist + serve, with calendar's dont-persist-placeless rule
as an explicit flag.
Each endpoint is now its audit run + identity + a build callback (~10
lines); api_day's hourly-token special case moved into day_token. New
tests: identity format pins, rate-limit 503 parametrized across all five
data routes, prefetch=1 never touching upstream (cold 204, warm
history-only slices), and calendar's placeless-payload retry behavior.
- backend/tests: 74 hermetic tests (no network, no repo data//logs/ writes)
covering grid snapping/round-trips, grading percentiles/bands/windows/
dry streaks, the places index (norm, one-edit matchers, search,
corrections), the derived store (token validity, cache=False, degraded
mode), and route-level API tests over a faked climate layer — routing,
validation, ETag/304 revalidation, store replay, the /cell bundle, and
the v1/v2 aliases. The API tests would have caught the /place
AttributeError regression.
- requirements-dev.txt + make test (venv prefers uv-pinned 3.12, matching
deploy-dev.sh — pyarrow wheels stop at 3.12 and some pyenv builds lack
sqlite).
- CI: extract the build job into a reusable build.yml, add the test run
and an API health probe (page-only curl can't catch route wiring
faults); deploy-dev.yml now runs the same build gate before deploying
direct pushes, which previously deployed with no CI at all.
- Deploys serialize under one dev-lan-deploy concurrency group across
both workflows (previously per-PR groups could interleave two deploys
to the same checkout), and are never cancelled mid-restart.
- deploy-dev.sh health check also probes /api/v2/place — best-effort
externals mean a failure there is a genuine server bug.
