# Ashvale Station configuration. Every field is optional: anything omitted
# falls back to the dataclass default in ashvale/config.py.

site:
  name: ashvale-labs-weather-station
  latitude: 52.2053            # Cambridge, UK
  longitude: 0.1218
  altitude_m: 15.0             # matters more than you would think, see README
  timezone: Europe/London
  indoors: true                # be honest here, it changes how forecasts are worded

sensor:
  sample_period_s: 2.0
  persist_period_s: 30.0
  rotation_deg: 90
  cpu_heat_k: 0.55             # starting point only, calibrate from the dashboard
  # Process noise. Raise to track faster, lower to smooth harder. These were
  # retuned against noise measured on a real board by sweeping each q against
  # the RMSE of the reported rate versus the true rate. The originals tracked
  # two to three decades faster than any of these 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. All three are listed because this file shadows the defaults
  # in ashvale/config.py, and a value present here silently wins.
  kalman_q_temp: 1.0e-9
  kalman_r_temp: 0.02
  kalman_q_press: 1.0e-8
  kalman_r_press: 0.05
  kalman_q_hum: 2.0e-8
  kalman_r_hum: 0.60

model:
  grid_s: 300
  horizons_s: [900, 3600, 10800, 21600, 43200, 86400]
  rls_forgetting: 0.9985       # effective memory ~ 11 h on a 5-minute grid
  conformal_alpha: 0.10        # 90% prediction intervals
  train_period_s: 600
  min_rows_to_train: 120

storage:
  raw_retention_days: 7.0
  five_min_retention_days: 90.0

server:
  host: 0.0.0.0
  port: 8000
  led_enabled: true