During a first test the Status entity only said "learning" for the
first half hour (the radiator-lag wait after a restart), and the
attributes explaining why were hard to find. Add two sensors:
- Learning Progress (%): for the least trained room, the mean of its
three readiness requirements (200 samples, 30 ON, 30 OFF), each capped
at 100 %; 100 % means ready. Attributes show the counts.
- Last Sample: an enum of the last poll's learning outcome, with English
and Romanian state names (e.g. "Waiting for radiators to settle"). Its
history shows when learning happens.
Polls blocked by missing shared inputs now report waiting_for_inputs.
Readiness thresholds move to const.py.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
A single stale, unavailable, missing or invalid room sensor paused
learning and forecasts for every room. That is not acceptable once the
MPC may control heating.
Now such a room is left out: the weighted temperature, learning and the
optimizer use the remaining rooms with their weights re-normalized, and
a room learns only across poll pairs where it was usable at both ends.
Readiness considers only the rooms used. Everything pauses only when no
room is usable or a shared input (relay, thermostat, outdoor
temperature) is missing.
A new setup/Configure checkbox, room_stale_use_last_value (default off),
keeps using a stale room sensor's last value instead, for sensors that
report only on change. It never applies to unavailable or invalid
sensors, or to a sensor silent for more than 24 hours.
Status adds excluded_rooms, weight_coverage and a per-room "used" flag.
English and Romanian labels and help text added.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
buzeraandClaude Opus 5.5