Three things the hardware offers that the code ignored.
The joystick has never had a line of code. Left records a dry label, right a
wet one, middle cycles the LED scene, and a full-panel flash acknowledges the
press because a headless box gives no other sign and a button you cannot tell
worked gets pressed twice. Precipitation is the weakest head in the bank and
strong labels are its binding constraint: this station has 80 of them against
thousands of proxy ones, entirely because the only label control lives in a web
page, and a web page is not where anyone is standing when it starts raining.
The board has no RTC, so a power cut without a network gives a clock somewhere
in 1970 on the next boot. Solar elevation, the diurnal harmonics and a sample's
position on the 5-minute grid then all lie with complete confidence, and unlike
a gap in the record the damage cannot be identified afterwards. train() now
refuses a clock below 2025 or one that has stepped behind the newest stored
row, and logs the refusal rather than training on fiction.
Undervoltage and thermal capping both shift the SoC temperature, which is the
regressor in the self-heating compensation, so a weak power supply presents as
an unexplained temperature bias rather than as anything resembling a power
problem. get_throttled is now sampled hourly and logged when set.
Measured and deliberately not done: colour features. r, g and b are logged and
74% of rows carry usable colour, but adding blue/red, green/red and saturation
made MAE 1.50% worse and helped in only 13 of 72 cases. Three more regressors
on a 33-feature model whose longest horizon trains on 13 independent pairs is
straightforwardly overfitting. That also prompted a sweep of the RLS prior and
forgetting factor in both directions; delta = 100 with lambda = 0.9985 is a
local optimum on both axes, so neither moved.
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.
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.
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