Two changes to the same signal path, one large and one small.
The large one: all three filters were tuned to track one to three decades
faster than their signals move. In a still room the temperature filter
reported a median rate of 12.4 C/h while the air moved 0.4 C/h, and it
overshot a real -36 C/h event by 77%. Sweeping q against the RMSE of the
reported rate versus the true rate, using noise measured on the board
(temperature 0.088 C, pressure 0.022 hPa, humidity 0.40 %):
temperature 6.45 -> 0.37 C/h RMSE 2e-6 -> 1e-9
pressure 2.15 -> 0.24 hPa/h RMSE 1e-5 -> 1e-8
humidity 27.94 -> 3.55 %/h RMSE 5e-5 -> 2e-8
Tracking does not suffer. Lag against a genuine 2 C/h ramp is 0.003 C at both
the old and new values, and the peak response to a five-minute event moves
closer to the truth rather than further from it, because the overshoot goes
away. What is given up is response to sub-minute transients, which for a
station forecasting fifteen minutes to a day ahead is noise to reject.
This matters most for pressure, whose tendency drives the precipitation
forecast, and which was the worst tuned of the three.
config.yaml shadowed kalman_q_temp, so editing the dataclass alone changed
nothing. All six values are now listed there with that hazard spelled out,
because a silent shadow cost real time here.
The small one: temp_raw was the plain average of two thermometers whose
white-noise sds differ by 7x (LPS25HB 0.007 C, HTS221 0.049 C), which throws
the quiet one away. Inverse-variance weighting cuts the raw noise 3.5x.
The trap is that the chips do not agree. They sit at different distances from
the SoC and stand about 1.3 C apart, so weighting by variance alone drags
temp_raw 0.48 C onto the LPS25HB, which after the 1.55x gain of the inverse
compensator is 0.75 C of silent bias on every reading, since k was fitted
against the mean of the two. The gradient is therefore tracked and removed
before weighting and only the deviations are fused: measured mean shift
0.0001 C, noise still 3.5x lower. The tracked gradient is retained because it
is a second observation of self-heating.
Also corrected: the earlier claim that the HTS221 was the quieter channel was
wrong, taken from twelve samples at a cadence slow enough that real drift
dominated. At 0.5 s over 120 samples the LPS25HB is quieter by 7x and takes
98% of the weight.
A room held at a setpoint is a different process from one left to drift. It is
a closed loop, and persistence, the baseline everything here is scored against,
is the wrong statement about it: the truth is not that it stays where it is, it
is that it returns to the setpoint.
So site.heating adds a fourth ensemble member, first order because that is what
a controlled system is:
dT_set(h) = (T_set - T_now) * (1 - exp(-h / tau))
Humidity follows and is the part that is easy to get wrong. Heating adds no
moisture, so vapour pressure is conserved and not relative humidity:
RH(h) = RH_now * es(T_now) / es(T_now + dT_set(h))
Warm the air and RH falls although nothing was dried, which is why a heated
house in winter is dry. The test asserts the dew point is unchanged to 1e-6.
Pressure gets zero: a thermostat cannot move the synoptic field.
Offered, not imposed. Hedge scores this member on realised error like any
other, so a wrong tau or a stale setpoint costs accuracy and gets down-weighted
rather than quietly biasing every forecast. Verified: on history with no
heating the ensemble assigned it weight 0.000. With heating off it returns zero
and is identical to persistence.
Going from three members to four means old saved heads must migrate.
from_dict reinitialises weights and member_mae. I missed member_mae first time
and it did not fail on load, it failed later inside learn() on a broadcast
error, which is a much worse place to find out; the migration test now covers
both and calls learn() to prove it.
Settings tab gains the toggle, setpoint and time constant. Turning heating on
or off is treated as a regime change like a door: discontinuity marker plus a
queued retrain.
1. POST /api/recompute re-derives every compensated column from the untouched
raw values, removing the step a calibration otherwise leaves through the
history. Possible because temp_raw, cpu_temp and hum are never overwritten.
Idempotent by construction and tested per row: 0 of 6051 rows change on a
second run. 6069 rows in 0.25 s here, so a few seconds on the Pi.
2. Calibration now emits a 'discontinuity' event alongside the calibration log,
so downstream views can find the boundary without parsing prose.
3. Vendored Tailwind, Chart.js, hammer, the zoom plugin, KaTeX with its 20
woff2 faces, and both Google fonts into ashvale/static, served by the
station. 1.4 MB. Verified with every non-localhost request aborted in the
browser: zero external requests, equations still render, fonts still load.
The dashboard no longer needs internet.
4. 54 pytest cases over the pure numerics: physics closed forms and round
trips, both compensator inverse properties, the Kalman covariance
invariants and NIS consistency, the RLS trace cap under a deliberately
unexcited regressor, conformal coverage, and the Zambretti ordering. Wired
into CI after the seed step so the recompute cases have history. Writing
them caught my own sign error on the conformal update: a hit raises alpha
and narrows the band, which reads backwards until you follow it through.
5. Stats for Nerds gains the condition number of each head's covariance, a
standardised innovation histogram per Kalman filter from a bounded 600
sample ring buffer, and a reliability strip of realised against nominal
coverage. All arithmetic on data already in memory.
6. OutdoorProbe reads a DS18B20 over the kernel 1-Wire driver, no new
dependency. Polled on its own slower cadence because the sensor blocks for
up to 750 ms during conversion, which would eat a third of the 2 s sample
budget. Rejects the 85000 power-on sentinel and out-of-range values, and
reports age so a dead probe cannot masquerade as fresh.
kemal