mirror of
https://github.com/lynchaos/ashvale-station.git
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kemal
50b29f8077
recompute replayed the Kalman over stored rows at their own spacing while q stays tuned for the live 2 s cadence. Q scales with dt^3, so at the 30 s persist interval the process noise was 3375x too large and the filter tracked noise instead of smoothing: it wrote indoor temperature rates of +/-20 C/h into the history. This is the exact trap DESIGN.md section 2 documents for simulate.py, which does scale q, and I walked into it anyway. Now rescaled per step, because tiering means the stored cadence is not constant. Mean |rate| on the real board dropped to 2.73 C/h; what remains above 10 is the filter's warm-up transient in the first four samples, which is honest. Readout scene puts the actual numbers between the animations: temperature, humidity, sea-level pressure and the signed three hour forecast, each in its channel colour, scrolling. Text is drawn whole-pixel on purpose. Everything else here is sub-pixel and that is what makes it look good, but splitting a 3 px glyph across two columns halves its peak and smears it illegible. Crisp beats smooth when the thing has to be read. site.environment and site.enclosure record where the sensor lives and what has changed around it, with POST /api/environment to change them at runtime. This is not cosmetic: closing a door changes how strongly the sensor couples to outside, which is a regime change in the process the heads are fitting, and at lambda 0.9985 they carry about 55 hours of memory. Left alone they keep predicting the old room for two days. Page-Hinkley would notice eventually but needs matured forecasts to do it, which at the long horizons is the same two days. So the endpoint marks a discontinuity and queues a retrain.
717 lines
31 KiB
Python
717 lines
31 KiB
Python
# Copyright 2026 Kemal Yaylali
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""The station: everything wired together and running on its own clocks.
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Four asynchronous loops, deliberately decoupled so a slow one cannot
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starve a fast one:
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sample (2 s) read hardware, run the Kalman bank, keep live state
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persist (30 s) one row to SQLite
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train (10 min) rebuild the feature grid, update every head, re-fit
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climatology, emit a fresh forecast bundle
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verify (5 min) score forecasts whose validity time has arrived, feed
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the errors to conformal calibration and drift
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detection, write the scorecard
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The verify loop is the one most projects skip and the one that makes the
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difference. A forecast that is never scored is an opinion; a forecast
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that is scored against persistence is a measurement.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import time
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import numpy as np
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from . import physics
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from .config import Config
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from .estimation import KalmanCV, SignalTracker
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from .features import build_features
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from .models.anomaly import AnomalyMonitor
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from .models.climatology import HarmonicClimatology
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from .models.nowcast import NowcastEnsemble
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from .models.precip import PrecipitationModel, proxy_wet_label, zambretti
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from .sensors import OutdoorProbe, SenseBoard, enrich
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from .storage import Store, resample
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STATE_VERSION = 1
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class Station:
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def __init__(self, cfg: Config):
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self.cfg = cfg
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self.store = Store(cfg.storage.db_path)
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self.board = SenseBoard(
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rotation=cfg.sensor.rotation_deg,
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low_light=cfg.sensor.low_light,
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tcs_addr=cfg.sensor.tcs3400_addr,
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latitude=cfg.site.latitude,
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longitude=cfg.site.longitude,
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)
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# Optional and entirely absent on a board without one wired up.
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self.probe = (OutdoorProbe(cfg.sensor.outdoor_probe_period_s)
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if cfg.sensor.outdoor_probe else None)
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self.tracker = SignalTracker(cfg)
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self.nowcast = NowcastEnsemble(cfg.model.targets, cfg.model.horizons_s, cfg.model)
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self.climatology = HarmonicClimatology(
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cfg.model.targets, min_days_annual=cfg.model.climatology_min_days_annual
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)
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self.precip = PrecipitationModel()
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self.monitor = AnomalyMonitor(cfg.model)
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self.live: Dict[str, Any] = {}
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self.forecast_bundle: Dict[str, Any] = {}
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self.outlook_bundle: Dict[str, Any] = {}
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self.precip_bundle: Dict[str, Any] = {}
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self.anomaly_bundle: Dict[str, Any] = {}
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self.last_train: float = 0.0
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self.last_persist: float = 0.0
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self.last_compact: float = 0.0
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self.training_log: List[Dict] = []
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self._tasks: List[asyncio.Task] = []
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self._stop = asyncio.Event()
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self.state_path = Path(cfg.storage.state_dir) / "station_state.json"
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self.load_state()
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# ------------------------------------------------------------ state
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def save_state(self) -> None:
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payload = {
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"version": STATE_VERSION,
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"saved_at": time.time(),
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"tracker": self.tracker.to_dict(),
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"nowcast": self.nowcast.to_dict(),
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"climatology": self.climatology.to_dict(),
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"precip": self.precip.to_dict(),
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"monitor": self.monitor.to_dict(),
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}
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tmp = self.state_path.with_suffix(".tmp")
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with open(tmp, "w", encoding="utf-8") as fh:
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json.dump(payload, fh)
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tmp.replace(self.state_path) # atomic, survives a power cut mid-write
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def load_state(self) -> bool:
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if not self.state_path.exists():
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return False
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try:
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with open(self.state_path, "r", encoding="utf-8") as fh:
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s = json.load(fh)
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if s.get("version") != STATE_VERSION:
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return False
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self.tracker.load_dict(s["tracker"])
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self.nowcast.load_dict(s["nowcast"])
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self.climatology.load_dict(s["climatology"])
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self.precip.load_dict(s["precip"])
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self.monitor.load_dict(s["monitor"])
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return True
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except Exception as exc:
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self.store.log_event("state", "warn", f"could not restore state: {exc}")
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return False
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# ----------------------------------------------------------- sample
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def sample_once(self) -> Dict[str, Any]:
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ts = time.time()
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raw = self.board.read()
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raw = enrich(raw, self.cfg.site.altitude_m)
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est = self.tracker.step(ts, raw.get("temp_raw", float("nan")),
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raw.get("hum", float("nan")),
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raw.get("press", float("nan")),
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raw.get("cpu_temp", float("nan")))
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temp_c = est["temp_smooth"]
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slp = float(physics.sea_level_pressure(est["press_smooth"], temp_c,
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self.cfg.site.altitude_m))
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dew = float(physics.dew_point(temp_c, est["hum_smooth"]))
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elev, azim = physics.solar_position(ts, self.cfg.site.latitude,
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self.cfg.site.longitude)
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expected = float(physics.clear_sky_irradiance(elev))
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lux = float(raw.get("lux", 0.0) or 0.0)
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cloud = (float(np.clip(1.0 - lux / max(expected * 45.0, 1.0), 0.0, 1.0))
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if elev > 5.0 else 0.5)
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row = {
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"ts": ts,
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"temp_raw": raw.get("temp_raw"),
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"temp_c": est["temp_c"],
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"temp_smooth": temp_c,
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"temp_rate": est["temp_rate"],
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"hum": raw.get("hum"),
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"hum_smooth": est["hum_smooth"],
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"press": raw.get("press"),
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"press_slp": slp,
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"press_smooth": est["press_smooth"],
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"press_rate": est["press_rate"],
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"cpu_temp": raw.get("cpu_temp"),
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"dew_c": dew,
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"lux": lux,
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"r": raw.get("r"), "g": raw.get("g"), "b": raw.get("b"),
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"pitch": raw.get("pitch"), "roll": raw.get("roll"),
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"yaw": raw.get("yaw"), "compass": raw.get("compass"),
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"ax": raw.get("ax"), "ay": raw.get("ay"), "az": raw.get("az"),
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"gx": raw.get("gx"), "gy": raw.get("gy"), "gz": raw.get("gz"),
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}
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anomaly = self.monitor.observe(ts, {
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"temp_c": temp_c, "hum": est["hum_smooth"], "press_slp": slp,
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"temp_rate": est["temp_rate"], "press_rate": est["press_rate"],
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"dew_c": dew, "cpu_temp": raw.get("cpu_temp"),
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})
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self.anomaly_bundle = anomaly
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self.live = {
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**row,
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"timestamp": time.strftime("%H:%M:%S", time.localtime(ts)),
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"colour": raw.get("colour", {}),
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"simulated": bool(raw.get("simulated", not self.board.available)),
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"dew_depression": temp_c - dew,
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"vpd": float(physics.vapour_pressure_deficit(temp_c, est["hum_smooth"])),
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"wet_bulb": float(physics.wet_bulb(temp_c, est["hum_smooth"])),
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"heat_index": float(physics.heat_index(temp_c, est["hum_smooth"])),
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"abs_humidity": float(physics.absolute_humidity(temp_c, est["hum_smooth"])),
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"solar_elevation": float(elev),
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"solar_azimuth": float(azim),
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"clear_sky_wm2": expected,
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"cloud_index": cloud,
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"cpu_offset": (raw.get("cpu_temp") or float("nan")) - (raw.get("temp_raw") or float("nan")),
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"compensator_k": self.tracker.compensator.k,
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"hum_offset": self.tracker.hum_compensator.offset,
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"outdoor_c": (self.probe.read() if self.probe is not None else None),
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"hum_psychrometric": float(est["hum_c"]) - float(raw.get("hum") or float("nan")),
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"health": anomaly["health_overall"],
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"novelty_d2": anomaly["novelty"].get("d2", 0.0),
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}
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self._update_precip()
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return self.live
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def _observation_vector(self) -> Dict[str, float]:
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live = self.live
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hist = self.store.window(8.0, ["ts", "press_slp", "temp_c", "dew_c"])
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tend = {"tend_1h": live.get("press_rate", 0.0),
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"tend_3h": live.get("press_rate", 0.0),
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"tend_6h": live.get("press_rate", 0.0)}
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if hist["ts"].size > 5:
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now = hist["ts"][-1]
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for key, hours in (("tend_1h", 1.0), ("tend_3h", 3.0), ("tend_6h", 6.0)):
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idx = np.searchsorted(hist["ts"], now - hours * 3600.0)
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if 0 <= idx < hist["ts"].size - 1:
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dtp = (now - hist["ts"][idx]) / 3600.0
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if dtp > 0.25:
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tend[key] = float((hist["press_slp"][-1] - hist["press_slp"][idx]) / dtp)
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dew_dep = live.get("dew_depression", 5.0)
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dew_dep_rate = 0.0
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if hist["ts"].size > 5:
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idx = np.searchsorted(hist["ts"], hist["ts"][-1] - 3600.0)
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if 0 <= idx < hist["ts"].size - 1:
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past = hist["temp_c"][idx] - hist["dew_c"][idx]
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dew_dep_rate = float(dew_dep - past)
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return {
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"slp": live.get("press_slp", 1013.25),
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"rh": live.get("hum_smooth", 60.0),
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"dew_depression": dew_dep,
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"dew_dep_rate": dew_dep_rate,
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"cloud_index": live.get("cloud_index", 0.5),
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"temp_dev": self.climatology.anomaly_now(
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"temperature", live.get("ts", time.time()), live.get("temp_smooth", 0.0)
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),
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"wet_bulb_depression": live.get("temp_smooth", 0.0) - live.get("wet_bulb", 0.0),
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**tend,
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}
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def _update_precip(self) -> None:
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obs = self._observation_vector()
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zam = zambretti(obs["slp"], obs["tend_3h"], self.live.get("ts"),
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self.cfg.site.latitude)
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self.precip_bundle = self.precip.predict(obs, zam)
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self.precip_bundle["indoors_caveat"] = self.cfg.site.indoors
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y = proxy_wet_label(obs["rh"], obs["dew_depression"], obs["cloud_index"])
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if y is not None and int(self.live.get("ts", 0)) % 300 < self.cfg.sensor.sample_period_s:
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self.precip.learn(obs, zam, y, strong=False)
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def add_label(self, kind: str, value: float, ts: Optional[float] = None,
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note: str = "") -> Dict:
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"""Human-in-the-loop ground truth. Worth ten times a proxy label."""
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ts = ts or time.time()
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self.store.insert_label(ts, kind, value, note)
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if kind == "rain":
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obs = self._observation_vector()
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zam = zambretti(obs["slp"], obs["tend_3h"], ts, self.cfg.site.latitude)
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loss = self.precip.learn(obs, zam, float(value), strong=True)
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self.store.log_event("label", "info",
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f"strong rain label {value} accepted, loss {loss:.3f}", ts)
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return {"accepted": True, "loss": loss, "strong_labels": self.precip.n_strong}
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return {"accepted": True}
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def calibrate_temperature(self, reference_c: float) -> Dict:
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raw = self.live.get("temp_raw")
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cpu = self.live.get("cpu_temp")
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if raw is None or cpu is None:
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return {"error": "no live reading yet"}
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result = self.tracker.compensator.calibrate(float(raw), float(cpu), float(reference_c))
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self.store.log_event("calibration", "info",
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f"k -> {result['k']:.3f} (residual {result['residual']:+.2f} C)")
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# Discontinuity marker: everything logged before this instant used a
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# different coefficient. Kept as its own event kind so the scorecard and
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# the records view can find it without parsing prose.
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self.store.log_event("discontinuity", "warn",
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f"temperature k {result['k']:.4f}")
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return result
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def calibrate_humidity(self, reference_pct: float) -> Dict:
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raw_h = self.live.get("hum")
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raw_t = self.live.get("temp_raw")
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temp_c = self.live.get("temp_c")
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if raw_h is None or raw_t is None or temp_c is None:
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return {"error": "no live reading yet"}
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result = self.tracker.hum_compensator.calibrate(
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float(raw_h), float(raw_t), float(temp_c), float(reference_pct))
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self.save_state()
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self.store.log_event("calibration", "info",
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f"rh offset -> {result['offset']:+.2f}% "
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f"(residual {result['residual']:+.2f}%)")
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self.store.log_event("discontinuity", "warn",
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f"humidity offset {result['offset']:+.4f}")
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return result
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def reset_humidity_calibration(self) -> Dict:
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from .estimation import HumidityCompensator
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self.tracker.hum_compensator = HumidityCompensator(
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self.cfg.sensor.hum_offset, self.cfg.sensor.hum_offset_min,
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self.cfg.sensor.hum_offset_max,
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psychrometric=self.cfg.sensor.hum_psychrometric,
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)
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self.save_state()
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self.store.log_event("calibration", "info",
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f"rh offset reset to prior {self.cfg.sensor.hum_offset}")
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return {"offset": self.tracker.hum_compensator.offset, "reset": True, "n": 0}
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def recompute_history(self) -> Dict:
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"""Re-derive every compensated column from the stored raw values.
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Why this exists: calibration only changes readings from that moment on,
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so a correction of any size leaves a step in the record. Measured on this
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station, one humidity calibration put a 25-point discontinuity through
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the middle of the day. That contaminates the all-time records with values
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that were never real weather, and makes the learners train across a jump.
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It is possible at all because the raw columns are never overwritten:
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`temp_raw`, `cpu_temp` and `hum` are exactly what the sensor reported, so
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the current coefficients can be applied to the whole history.
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The Kalman levels are re-run rather than shifted, because the filter is
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not a constant offset. That means the smoothing is *re-derived*, not bit
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identical to what was logged live: the replay sees the stored cadence,
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which for tiered rows is coarser than the 2 s the filter runs at. The
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levels are right, the fine texture of old raw rows is not recoverable.
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"""
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data = self.store.all_for_recompute()
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ts = data["ts"]
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if ts.size == 0:
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return {"rows": 0, "reason": "no history"}
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t0 = time.time()
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comp, hcomp = self.tracker.compensator, self.tracker.hum_compensator
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n = ts.size
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temp_c = np.empty(n)
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hum_c = np.empty(n)
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for i in range(n):
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tr, cp, hu = data["temp_raw"][i], data["cpu_temp"][i], data["hum"][i]
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temp_c[i] = comp.compensate(tr, cp) if np.isfinite(tr) and np.isfinite(cp) else tr
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hum_c[i] = (hcomp.compensate(hu, tr, temp_c[i])
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if np.isfinite(hu) and np.isfinite(tr) else hu)
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# Replay the filters over the corrected series. Fresh instances, so an
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# old contaminated state cannot leak into the re-derivation.
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kt = KalmanCV(self.cfg.sensor.kalman_q_temp, self.cfg.sensor.kalman_r_temp)
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kh = KalmanCV(self.cfg.sensor.kalman_q_hum, self.cfg.sensor.kalman_r_hum)
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q_temp = float(self.cfg.sensor.kalman_q_temp)
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q_hum = float(self.cfg.sensor.kalman_q_hum)
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live_dt = float(self.cfg.sensor.sample_period_s)
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temp_s = np.empty(n)
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temp_r = np.empty(n)
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hum_s = np.empty(n)
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prev = None
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for i in range(n):
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dt = live_dt if prev is None else max(ts[i] - prev, 1e-3)
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prev = ts[i]
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# q is tuned for the live 2 s cadence and Q scales with dt^3, so
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# replaying stored rows at their own spacing (30 s raw, 300 s and
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# 3600 s once tiered) inflates the process noise by up to seven
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# orders of magnitude. The filter then abandons smoothing and tracks
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# measurement noise, which showed up as indoor rates of +/-20 C/h.
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# Rescaled per step because tiers mean the cadence is not constant.
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scale = (live_dt / dt) ** 3
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kt.q = q_temp * scale
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kh.q = q_hum * scale
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lvl, rate = kt.update(temp_c[i], dt)
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temp_s[i], temp_r[i] = lvl, rate * 3600.0
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hum_s[i], _ = kh.update(hum_c[i], dt)
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dew = np.asarray(physics.dew_point(temp_s, hum_s), dtype=float)
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slp = np.asarray(physics.sea_level_pressure(
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data["press"], temp_s, self.cfg.site.altitude_m), dtype=float)
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written = self.store.apply_recompute(ts, {
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"temp_c": temp_c, "temp_smooth": temp_s, "temp_rate": temp_r,
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"hum_smooth": hum_s, "dew_c": dew, "press_slp": slp,
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})
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secs = time.time() - t0
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self.store.log_event(
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"recompute", "info",
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f"re-derived {written} rows from raw with k={comp.k:.4f}, "
|
|
f"rh offset={hcomp.offset:+.2f}% in {secs:.1f}s")
|
|
return {"rows": written, "seconds": round(secs, 2),
|
|
"k": comp.k, "hum_offset": hcomp.offset}
|
|
|
|
def set_environment(self, environment: Optional[str] = None,
|
|
enclosure: Optional[str] = None,
|
|
note: str = "") -> Dict:
|
|
"""Record a change in the sensor's surroundings and act on it.
|
|
|
|
Not cosmetic. A door closing changes how strongly the sensor couples to
|
|
outside, which is a regime change in the very process the heads are
|
|
fitting. Their forgetting factor is 0.9985 on a five minute grid, about
|
|
55 hours of memory, so left alone they keep predicting the old room for
|
|
two days. Page-Hinkley would eventually notice from forecast error, but
|
|
it needs matured forecasts to do it, which at the longer horizons is
|
|
exactly the two days you were trying to skip.
|
|
|
|
So this does three things: writes a discontinuity marker so the record
|
|
shows where the regime changed, requests a retrain so the fit is redone
|
|
against recent data rather than drifting, and stores the new state for
|
|
the API and the Methods page to report honestly.
|
|
"""
|
|
changed = []
|
|
if environment and environment != self.cfg.site.environment:
|
|
changed.append(f"environment {self.cfg.site.environment} -> {environment}")
|
|
self.cfg.site.environment = environment
|
|
if enclosure and enclosure != self.cfg.site.enclosure:
|
|
changed.append(f"enclosure {self.cfg.site.enclosure} -> {enclosure}")
|
|
self.cfg.site.enclosure = enclosure
|
|
if not changed:
|
|
return {"changed": False, "environment": self.cfg.site.environment,
|
|
"enclosure": self.cfg.site.enclosure}
|
|
|
|
detail = "; ".join(changed) + (f" ({note})" if note else "")
|
|
self.store.log_event("environment", "info", detail)
|
|
self.store.log_event("discontinuity", "warn", detail)
|
|
self.monitor.retrain_requested = True
|
|
return {"changed": True, "environment": self.cfg.site.environment,
|
|
"enclosure": self.cfg.site.enclosure,
|
|
"retrain_requested": True, "detail": detail}
|
|
|
|
def reset_calibration(self) -> Dict:
|
|
"""Return the self-heating coefficient to its configured prior.
|
|
|
|
Worth having: a single mistyped reference reading can drive `k`
|
|
to its clamp, and because state persists across restarts it will
|
|
stay there quietly biasing every reading until you notice.
|
|
"""
|
|
from .estimation import ThermalCompensator
|
|
self.tracker.compensator = ThermalCompensator(
|
|
self.cfg.sensor.cpu_heat_k, self.cfg.sensor.cpu_heat_k_min,
|
|
self.cfg.sensor.cpu_heat_k_max,
|
|
)
|
|
self.save_state()
|
|
self.store.log_event("calibration", "info",
|
|
f"coefficient reset to prior k={self.cfg.sensor.cpu_heat_k}")
|
|
return {"k": self.tracker.compensator.k, "reset": True, "n": 0}
|
|
|
|
# ------------------------------------------------------------ train
|
|
|
|
def build_training_grid(self, hours: float = 24 * 30):
|
|
raw = self.store.window(hours, ["ts", "temp_smooth", "hum_smooth",
|
|
"press_slp", "lux"])
|
|
if raw["ts"].size < 10:
|
|
return None
|
|
grid_ts, cols = resample(
|
|
raw["ts"],
|
|
{"temperature": raw["temp_smooth"], "humidity": raw["hum_smooth"],
|
|
"pressure": raw["press_slp"], "lux": raw["lux"]},
|
|
self.cfg.model.grid_s,
|
|
)
|
|
if grid_ts.size < self.cfg.model.min_rows_to_train:
|
|
return None
|
|
X, valid = build_features(
|
|
grid_ts, cols["temperature"], cols["humidity"], cols["pressure"],
|
|
cols["lux"], self.cfg.model.grid_s,
|
|
self.cfg.site.latitude, self.cfg.site.longitude,
|
|
)
|
|
return grid_ts, cols, X, valid
|
|
|
|
def train(self, hours: float = 24 * 30) -> Dict:
|
|
t_start = time.time()
|
|
built = self.build_training_grid(hours)
|
|
if built is None:
|
|
return {"trained": False,
|
|
"reason": f"need at least {self.cfg.model.min_rows_to_train} grid rows"}
|
|
grid_ts, cols, X, valid = built
|
|
|
|
clim_scores = self.climatology.fit(grid_ts, cols, valid)
|
|
counts = self.nowcast.fit(X, valid, cols, self.climatology, grid_ts)
|
|
|
|
self.last_train = time.time()
|
|
self.monitor.clear_retrain_flag()
|
|
entry = {
|
|
"ts": self.last_train,
|
|
"grid_rows": int(grid_ts.size),
|
|
"valid_rows": int(valid.sum()),
|
|
"span_days": round(float((grid_ts[-1] - grid_ts[0]) / 86400.0), 2),
|
|
"pairs": counts,
|
|
"climatology_resid_std": {k: round(v, 3) for k, v in clim_scores.items()},
|
|
"annual_terms": self.climatology.use_annual,
|
|
"seconds": round(time.time() - t_start, 2),
|
|
}
|
|
self.training_log = ([entry] + self.training_log)[:20]
|
|
self.store.log_event("train", "info",
|
|
f"retrained on {grid_ts.size} grid rows in {entry['seconds']}s")
|
|
self.refresh_forecasts()
|
|
self.save_state()
|
|
return {"trained": True, **entry}
|
|
|
|
# --------------------------------------------------------- forecast
|
|
|
|
def refresh_forecasts(self, persist: bool = True) -> Dict:
|
|
built = self.build_training_grid(hours=48.0)
|
|
now = time.time()
|
|
if built is None or not self.live:
|
|
return {}
|
|
grid_ts, cols, X, valid = built
|
|
x_now = X[-1]
|
|
|
|
anchors = {
|
|
"temperature": float(self.live.get("temp_smooth", cols["temperature"][-1])),
|
|
"humidity": float(self.live.get("hum_smooth", cols["humidity"][-1])),
|
|
"pressure": float(self.live.get("press_slp", cols["pressure"][-1])),
|
|
}
|
|
fc = self.nowcast.forecast(x_now, anchors, now, self.climatology)
|
|
|
|
bundle: Dict[str, Any] = {"issued_ts": now, "anchors": anchors, "targets": {}}
|
|
for target, per_h in fc.items():
|
|
series = []
|
|
for h in sorted(per_h):
|
|
p = per_h[h]
|
|
series.append({
|
|
"horizon_s": h,
|
|
"horizon_label": _fmt_horizon(h),
|
|
"valid_ts": now + h,
|
|
"mu": round(p["mu"], 3),
|
|
"lo": round(p["lo"], 3),
|
|
"hi": round(p["hi"], 3),
|
|
"delta": round(p["delta"], 3),
|
|
"weights": {k: round(v, 3) for k, v in p["weights"].items()},
|
|
})
|
|
if persist:
|
|
self.store.insert_forecast(now, h, target, p["mu"], p["lo"],
|
|
p["hi"], "ensemble")
|
|
bundle["targets"][target] = series
|
|
self.forecast_bundle = bundle
|
|
|
|
self.outlook_bundle = {
|
|
"issued_ts": now,
|
|
"ready": self.climatology.ready,
|
|
"annual_terms": self.climatology.use_annual,
|
|
"history_days": round(self.store.span_days(), 2),
|
|
"targets": {
|
|
t: self.climatology.outlook(
|
|
t, now, days=7,
|
|
anomaly=self.climatology.anomaly_now(t, now, anchors.get(t, 0.0)),
|
|
)
|
|
for t in self.cfg.model.targets
|
|
},
|
|
}
|
|
return bundle
|
|
|
|
# ----------------------------------------------------------- verify
|
|
|
|
def verify(self) -> Dict:
|
|
"""Score matured forecasts against truth and against persistence."""
|
|
due = self.store.due_forecasts()
|
|
if not due:
|
|
return {"scored": 0}
|
|
|
|
hist = self.store.window(24 * 8, ["ts", "temp_smooth", "hum_smooth", "press_slp"])
|
|
if hist["ts"].size < 5:
|
|
return {"scored": 0}
|
|
series = {"temperature": hist["temp_smooth"], "humidity": hist["hum_smooth"],
|
|
"pressure": hist["press_slp"]}
|
|
|
|
def value_at(target: str, ts: float) -> Optional[float]:
|
|
idx = int(np.searchsorted(hist["ts"], ts))
|
|
if idx <= 0 or idx >= hist["ts"].size:
|
|
return None
|
|
if abs(hist["ts"][idx] - ts) > 900:
|
|
return None
|
|
return float(series[target][idx])
|
|
|
|
buckets: Dict[tuple, Dict[str, List[float]]] = {}
|
|
scored = 0
|
|
for row in due:
|
|
target, h = row["target"], int(row["horizon_s"])
|
|
truth = value_at(target, row["valid_ts"])
|
|
anchor = value_at(target, row["issued_ts"])
|
|
if truth is None or anchor is None:
|
|
continue
|
|
key = (target, h)
|
|
b = buckets.setdefault(key, {"err": [], "pers": [], "cov": []})
|
|
err = truth - row["mu"]
|
|
b["err"].append(err)
|
|
b["pers"].append(truth - anchor)
|
|
b["cov"].append(1.0 if row["lo"] <= truth <= row["hi"] else 0.0)
|
|
head = self.nowcast.heads.get(key)
|
|
if head is not None:
|
|
head.conformal.observe(err, covered=bool(row["lo"] <= truth <= row["hi"]))
|
|
if h <= 10800:
|
|
self.monitor.observe_error(row["valid_ts"], abs(err))
|
|
scored += 1
|
|
|
|
now = time.time()
|
|
for (target, h), b in buckets.items():
|
|
e = np.asarray(b["err"], dtype=float)
|
|
p = np.asarray(b["pers"], dtype=float)
|
|
mae = float(np.mean(np.abs(e)))
|
|
mae_p = float(np.mean(np.abs(p)))
|
|
self.store.insert_score(
|
|
now, target, h,
|
|
mae=mae,
|
|
rmse=float(np.sqrt(np.mean(e ** 2))),
|
|
bias=float(np.mean(e)),
|
|
mae_persistence=mae_p,
|
|
skill=float(1.0 - mae / mae_p) if mae_p > 1e-9 else 0.0,
|
|
coverage=float(np.mean(b["cov"])),
|
|
n=int(e.size),
|
|
)
|
|
|
|
with self.store._conn() as conn:
|
|
conn.execute("DELETE FROM forecasts WHERE valid_ts <= ?", (now - 3600,))
|
|
return {"scored": scored, "buckets": len(buckets)}
|
|
|
|
# ------------------------------------------------------------ loops
|
|
|
|
async def _loop_sample(self):
|
|
period = self.cfg.sensor.sample_period_s
|
|
while not self._stop.is_set():
|
|
try:
|
|
self.sample_once()
|
|
now = time.time()
|
|
if now - self.last_persist >= self.cfg.sensor.persist_period_s:
|
|
self.store.insert_telemetry(self.live)
|
|
self.last_persist = now
|
|
except Exception as exc:
|
|
self.store.log_event("sample", "error", repr(exc))
|
|
await asyncio.sleep(period)
|
|
|
|
async def _loop_train(self):
|
|
await asyncio.sleep(5)
|
|
try:
|
|
self.train()
|
|
except Exception as exc:
|
|
self.store.log_event("train", "error", repr(exc))
|
|
while not self._stop.is_set():
|
|
await asyncio.sleep(30)
|
|
now = time.time()
|
|
due = (now - self.last_train) >= self.cfg.model.train_period_s
|
|
if due or self.monitor.retrain_requested:
|
|
try:
|
|
await asyncio.to_thread(self.train)
|
|
except Exception as exc:
|
|
self.store.log_event("train", "error", repr(exc))
|
|
|
|
async def _loop_verify(self):
|
|
await asyncio.sleep(60)
|
|
while not self._stop.is_set():
|
|
try:
|
|
await asyncio.to_thread(self.verify)
|
|
except Exception as exc:
|
|
self.store.log_event("verify", "error", repr(exc))
|
|
await asyncio.sleep(300)
|
|
|
|
async def _loop_maintenance(self):
|
|
while not self._stop.is_set():
|
|
await asyncio.sleep(3600)
|
|
now = time.time()
|
|
if now - self.last_compact >= self.cfg.storage.vacuum_period_s:
|
|
try:
|
|
removed = await asyncio.to_thread(
|
|
self.store.compact,
|
|
self.cfg.storage.raw_retention_days,
|
|
self.cfg.storage.five_min_retention_days,
|
|
)
|
|
self.last_compact = now
|
|
self.store.log_event("compact", "info", json.dumps(removed))
|
|
except Exception as exc:
|
|
self.store.log_event("compact", "error", repr(exc))
|
|
self.save_state()
|
|
|
|
def start(self) -> None:
|
|
self._stop.clear()
|
|
self._tasks = [
|
|
asyncio.create_task(self._loop_sample()),
|
|
asyncio.create_task(self._loop_train()),
|
|
asyncio.create_task(self._loop_verify()),
|
|
asyncio.create_task(self._loop_maintenance()),
|
|
]
|
|
|
|
async def stop(self) -> None:
|
|
self._stop.set()
|
|
for t in self._tasks:
|
|
t.cancel()
|
|
for t in self._tasks:
|
|
try:
|
|
await t
|
|
except (asyncio.CancelledError, Exception):
|
|
pass
|
|
try:
|
|
self.save_state()
|
|
except Exception:
|
|
pass
|
|
|
|
# ------------------------------------------------------------ views
|
|
|
|
def status(self) -> Dict:
|
|
return {
|
|
"site": self.cfg.site.name,
|
|
"hardware": "sense-hat-v2" if self.board.available else "simulator",
|
|
"colour_sensor": self.board.has_colour,
|
|
"rows": self.store.row_count(),
|
|
"history_days": round(self.store.span_days(), 3),
|
|
"last_train": self.last_train,
|
|
"next_train_in_s": max(0.0, self.cfg.model.train_period_s
|
|
- (time.time() - self.last_train)),
|
|
"climatology_ready": self.climatology.ready,
|
|
"annual_terms": self.climatology.use_annual,
|
|
"compensator_k": round(self.tracker.compensator.k, 4),
|
|
"calibrations": self.tracker.compensator.n_calibrations,
|
|
"health": self.monitor.health.overall,
|
|
"drift_stress": round(self.monitor.drift.stress, 3),
|
|
"retrain_requested": self.monitor.retrain_requested,
|
|
"training_log": self.training_log[:5],
|
|
}
|
|
|
|
|
|
def _fmt_horizon(seconds: int) -> str:
|
|
if seconds < 3600:
|
|
return f"{seconds // 60}m"
|
|
if seconds < 86400:
|
|
return f"{seconds // 3600}h"
|
|
return f"{seconds // 86400}d"
|