Compute meeting-room utilisation from a versioned booking export across time zones and clock changes

# Meeting-room utilisation from a booking-system export Inputs: `rooms.csv` (room id and the building's time zone), `bookings.csv` (an export with one row per saved version of each booking; times are UTC). Rules: - Each booking_id can appear several times; only the row with the highest version number counts (versions are integers: 10 is later than 9). File order means nothing. - A booking whose counting row has status "cancelled" is ignored. - Business time is 08:00-18:00 local wall-clock time in the room's time zone, Monday to Friday, from 2026-10-19 to 2026-11-06 inclusive. Only booked time inside business time counts. - When bookings in the same room overlap, the overlapping time counts once. - booked_minutes = booked business minutes for the room over the whole period. - utilisation_pct = booked_minutes / total business minutes in the period x 100, rounded half-up to 2 decimals. - peak_day = the local date with the most booked business minutes for that room (on a tie, the earliest); null if the room has none. - active_bookings = the number of booking_ids whose counting row is not cancelled (whether or not they fall in business time). Write `out/rooms.json`: `{"rooms": {"<room_id>": {"booked_minutes": <int>, "utilisation_pct": <n>, "peak_day": "<YYYY-MM-DD>" | null}, ...}, "active_bookings": <int>}` covering every room in rooms.csv. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.