Fetch the Open-Meteo air-quality feed (CAMS, domains=auto) with the weather:
17 hourly variables folded into local days by fixed rules (means and maxima
only with 18 of 24 hours, ozone as the highest 8-hour running mean, UV only
with the midday hours), a value the feed did not cover stored as null, never
as zero. Requests are cut into 90-day chunks, newest first, and chunks before
mid 2022 ask for the eight pollutant and index variables only. An air-quality
failure never costs the weather row; the day stays unfetched and the nightly
gap fill (up to 90 days per account, newest first) asks for it again.
Every Open-Meteo request asks an instance-wide budget kept in Postgres first
(500 a minute, 4000 an hour, 8000 a day, weighted by the hosted pricing
rule). A refused request is not sent and the run ends as done with
budget_blocked. OPENMETEO_AIR_QUALITY_URL and ENVIRONMENT_AIR_QUALITY_DISABLED
are honoured; the account switch lives on PATCH /api/environment/preferences.
The home, each dated location period and each stored day keep their coarse
location sealed as one value under healthlog/environment-location/v1. The
writers write the sealed value only, the readers open it and fall back to the
readable columns for rows the free-text encryption backfill has not reached,
and the backfill now seals all three and clears the readable columns. The
backup carries the location readable in a portable file and sealed in a
disaster-recovery file; a restore seals either under the receiving key.
Three correlation channels join the environment family (PM2.5, the ozone
8-hour high, the day's highest pollen count), read through the averaged
same-day window and left empty while air quality is off. The Coach and MCP
gain get_environment: module-gated, clamped to the lookback limit, no
coordinate or place name anywhere, with provenance and the attribution
lines. The settings section gets the switch, the coverage and the newest
day's values; the dashboard shows a quiet chip on a day with high pollen,
a warm night or very poor air. Copernicus and Open-Meteo are credited next
to the values.
Two daily series that both follow the season (temperature and blood
pressure, daylight and sleep) correlated strongly with nothing between
them. In a seeded simulation of such a pair the engine surfaced a
finding in 58 % of 400-day runs and 30 % of 180-day runs, and a real
next-day effect under the season came back with the wrong sign.
Every daily pair now goes through seasonallyAdjustedPearson: a linear
trend and one annual harmonic are removed from both series (trend only
under 120 paired days), the residuals are correlated, and the p-value
uses the Pyper-Peterman effective sample size. Shrinkage and the
phrasing tier read the effective size too. BH-FDR and the effect floor
are unchanged. Pairs too short to fit the basis are not tested.
The weather channels are tested once over the mean of the day before
and the day itself against the same day's outcome (lagDays 0), which
pairs the overnight low with the night it shaped now that sleep is
keyed on the wake day. Discovered pairs carry an additive nEff, the
same-day templates are wired, and the matrix skips the weather read
when the environment module is off.
scripts/compare-correlation-findings.ts counts findings per account
before and after, read-only, for the release-candidate dry run.
The correlation-narrative localisation replaced the env-channel narration
labels (which read through getEnvironmentField().narrationLabel) with the
localised insights.correlation.channel.ENV_* keys, leaving these two helpers
dead.
Add the keyless Open-Meteo client (historical archive + geocoding) routed
through safeFetch, the per-day location-resolution + fetch/upsert service
(travel override wins over home, then degrades silently), and the shared
environmental-exposure field table the registry and correlation engine both
read. The base URL is operator-overridable for a self-hosted instance; the
fetch stays server-side so a private/LAN host is not blocked.
Marc-André Bombeck