The board carries two independent thermometers and the code averaged them
into temp_raw without ever recording either. Measured over 12 samples on a
real station: HTS221 30.973 C at sd 0.060, LPS25HB 29.810 C at sd 0.443, a
standing gradient of 1.163 C with the SoC at 44.55 C.
Two things follow from that and neither is possible without the raw channels.
A plain average of a quiet sensor and one seven times noisier lands at sd
0.223 where inverse-variance weighting reaches 0.060, and the gradient between
two chips at different distances from the SoC is a second observation of
self-heating that could identify the compensator's k with no reference
thermometer. Both need history, and history cannot be backfilled, so the
columns land on their own ahead of the work that consumes them.
CREATE TABLE IF NOT EXISTS is a no-op against a table that already exists, so
adding to COLUMNS would have reached a fresh install and silently missed every
station already running, then surfaced as an OperationalError inside
insert_telemetry. That sits on the sample loop, so it takes a station down
rather than leaving a gap. Store now reconciles the table against COLUMNS on
open, which makes every future column addition safe rather than just this one.
The simulator gains the same two channels, with couplings solved so their
forward models average to exactly the k = 0.55 the compensator is tuned
against. Aggregate behaviour is unchanged; only the per-channel detail is new.
Simulated temp_raw noise does rise from 0.05 to 0.223, which is not a
regression but the end of an over-optimistic figure: it was modelling the
quiet sensor and calling it the average.
Reported from a real station after 1.5 days: a six hour temperature forecast of
53 C in a 24 C room, and 9 C at one day, both carrying a plus or minus of 0.43.
Confidently wrong is the one failure this project is supposed to refuse.
Root cause. fit() replayed history into the live RLS on every retrain tick and
never reset, so 453 grid rows had produced 64,676 updates in a day and a half.
RLS with forgetting reads every update as fresh evidence, so the model believed
it had a hundred times the data it had: P collapsed, in-sample error looked
excellent, and the weights drifted without bound in directions the data never
excited. Measured: cond(P) 3.1e9 and ||theta|| 1680 against a median |theta| of
1.67. A refit now starts from the prior, which makes retraining idempotent.
Across 25 refits on the real data ||theta|| holds at 11.35, drifting 0.03, where
before it grew without limit.
The two largest weights were sin_doy and cos_doy at +1174 and +1191. Annual
harmonics were in the design matrix from the first sample, where they are
near-constant, near-collinear with each other and with the bias, and a
rank-deficient regressor is what RLS answers with enormous cancelling weights.
They are now held at zero until the record spans the same 120 days the
climatology fit already requires, because a day and a half of data says nothing
whatsoever about the season.
Also raised the standardiser's variance floor from 1e-8, which only caught a
bit-exactly constant column, to 1e-3. A feature that merely barely moves was
being divided by its own noise.
The conformal calibrators and Hedge weights are deliberately not reset by a
refit: those are earned from scored forecasts, not from this regression.
Backtest unchanged within noise, coverage still 89 to 91 across all 18 heads.
Four regression tests added, including that refitting the same history twice
must give the same model.
Seven day outlook moves from History to Live, which now runs four rows.
Conditions ahead tightened so the Live column no longer needs a scroller.
Adds HumidityCompensator: an additive RH offset estimated by one-step RLS from
a trusted hygrometer, clamped to +/-35%, persisted, exposed at
POST /api/calibrate/humidity and on the renamed Models and calibration tab.
It also implements the psychrometric term (RH moved from element temperature
onto air temperature via conserved vapour pressure) but leaves it OFF by
default. The thermal argument predicts a hot element reads low; measured
against a reference hygrometer this board read 75.4% where the truth was
50.4%, so it reads HIGH and that correction would push it the wrong way. When
the flag is enabled, simulate.py applies the exact inverse, per the
simulator/compensator trap in DESIGN.md section 2.
Models pane rebuilt: the scorecard is one column per target so all 18 heads
are visible, and no panel on the tab uses an internal scroller. Verified in
Chromium at 1600x900: Live, History and Models all report zero scrollbars,
zero clipping, no page scroll, zero console errors. Backtest is numerically
identical to the previous commit, confirming the humidity work is a no-op
while the flag is off.
kemal