fast-check properties with one shared configuration (tests/property/): a
fixed seed on every pull request, a fresh one and more cases at night through
FC_SEED and FC_NUM_RUNS, and a printed seed with the shrunk counterexample on
failure.
- Time zones: wall clocks agree with Intl in twenty awkward zones, a wall
clock maps back to an instant showing it, a skipped hour resolves forward
on the same day, every instant lies inside the window of the day it is
filed under, and day keys shift and count as inverses.
- Statistics: every median in the tree says the same number, medians and
spreads obey order, shift and scale, a least-squares fit recovers an exact
line and composes from stored sums exactly, and the segmented regression
recovers a noiseless interrupted line.
- Cadence and compliance: weekly and daily schedules expect exactly what a
day-by-day oracle expects, in any zone and on transition days, and the
rate stays within bounds and never falls when a missed dose is taken.
- Encryption: every codec opens what it sealed under the key it names,
rotation moves the key id and nothing else, and a tampered value never
opens to anything but the sealed bytes.
- Backups: randomly generated accounts come back from a disaster-recovery
restore with an identical canonical dump of every generated table.
The automatic Newey-West rule gives a lag of 3 on 30 to 270 daily
values, which leaves most of a physiological series' carry-over out of
the intervals. The lag is now at least 7. Reference values are
recomputed with statsmodels at the new default, and the lag-3 values
stay pinned with an explicit lag. A fit whose residual autocorrelation
exceeds 0.5 is flagged, and the card, the clinician view and the PDF
say the ranges may still be too narrow.
A four-parameter interrupted time series fit (pre-trend, level change,
slope change) by OLS, with a Bartlett-kernel HAC covariance, the n/(n-k)
small-sample factor and Student t intervals. The tests pin the
coefficients, standard errors and intervals to independently computed
reference values on contiguous and gapped series.
Six readers fitted a line by hand, four in the determinant form that
loses digits on a large x axis, and the two charts carried the same trend
function twice. linearFit / fitFromSums in lib/stats is now the one
implementation (centred sums, so a fit from points and one composed from
stored accumulators are the same arithmetic), trendLineByTime serves both
charts with the same two-decimal output, and a guard keeps hand-rolled
slopes out of the tree.
About a dozen hand-rolled spreads each picked population or sample
variance without saying so, and the two surfaces a person compares
picked differently: the series API reported the population SD of a
blood-pressure series, the MCP tools and the AI payload the sample SD of
the same readings, about 22 % apart at three readings.
`lib/stats/spread` holds `variance` and `standardDeviation` with an
explicit `population | sample` kind and `READING_SPREAD`, the kind every
surface uses to report the spread of a person's readings. The series
route and the feature payload both report it; the spreads that feed a
verdict (the normal-swing checks, the changepoint threshold) and the
clinical glucose SD stay the sample estimate, now said out loud. A guard
fails on a new hand-rolled spread and on the two surfaces drifting apart.
Marc-André Bombeck