mirror of
https://github.com/lynchaos/ashvale-station.git
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kemal
b9a468542d
The generator added 0.05 hPa of measurement noise where a real board shows 0.0224 hPa as the sd of the change between 30 s samples. Twice the noise flatters any smoother tested against it and understates the skill available at short lead, which is where the pressure heads were already weakest. Correcting my own earlier claim: I reported the simulator's pressure as 60x too noisy. That figure came from comparing the old local database, whose rows are hourly-tier aggregates spanning 251 days, against 30 s rows from the station. Generated like for like at matched cadence the gap is 1.9x, not 60x, and the simulator was never the blocker on pressure work that I described. A gap remains after this change: press_slp still steps 0.0415 hPa per 30 s against the station's 0.0224. That residue is the synoptic OU process moving faster than Cambridge did over these four days, which is a weather-realism question rather than a sensor one. Four days of one room is not enough to retune a synoptic model against, so it is left alone and recorded here.
251 lines
11 KiB
Python
251 lines
11 KiB
Python
#!/usr/bin/env python3
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# 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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"""Seed the database with synthetic history.
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Why this exists: a freshly flashed Pi has no history, and a forecaster
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with no history is a random number generator with a nice dashboard. This
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script writes physically plausible past telemetry so you can exercise
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training, verification and the whole dashboard before the real station
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has logged its first night.
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The generator is not a toy. It is a three-component stochastic model:
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pressure Ornstein-Uhlenbeck, tau = 30 h, sigma = 9 hPa
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(roughly the observed synoptic variability of NW Europe)
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temperature seasonal harmonic + solar-driven diurnal cycle
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+ OU anomaly (tau = 6 h), with a nocturnal inversion term
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humidity driven inversely by temperature about a dew point that
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itself performs a slow random walk, which is what makes
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RH and T correlate the way they actually do
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Everything is then pushed through the same CPU-heating and noise model
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the real sensor suffers from, so a model trained here does not fall over
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when it meets real data.
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python scripts/simulate.py --days 21 --wipe
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"""
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from __future__ import annotations
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import argparse
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import math
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import sys
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import time
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from pathlib import Path
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import numpy as np
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from ashvale.config import load_config # noqa: E402
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from ashvale.estimation import SignalTracker # noqa: E402
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from ashvale.physics import ( # noqa: E402
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clear_sky_irradiance,
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dew_point,
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sea_level_pressure,
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solar_position,
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)
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from ashvale.sensors import ( # noqa: E402
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K_HTS221,
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K_LPS25HB,
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SD_HTS221,
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SD_LPS25HB,
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)
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from ashvale.storage import Store # noqa: E402
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def generate(days: float, step_s: int, lat: float, lon: float,
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seed: int = 11, end: float | None = None,
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psychrometric: bool = False) -> dict:
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rng = np.random.default_rng(seed)
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n = int(days * 86400 / step_s)
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# Anchoring to wall clock makes a fixed seed insufficient for reproducibility:
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# the OU realisation repeats, but the timestamps shift, which moves solar
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# elevation, day of year and the seasonal harmonic. Those feed the temperature
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# model directly, so two same-seed runs produce different data. Pin end as well
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# and the backfill becomes bit-reproducible, which is what before/after
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# evidence on a model change actually requires.
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end = time.time() if end is None else end
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ts = end - np.arange(n)[::-1] * step_s
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# --- synoptic pressure: OU process
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tau_p, sigma_p = 30 * 3600.0, 9.0
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press = np.zeros(n)
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a = math.exp(-step_s / tau_p)
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noise_scale = sigma_p * math.sqrt(1 - a * a)
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for i in range(1, n):
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press[i] = a * press[i - 1] + noise_scale * rng.normal()
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press_slp = 1013.0 + press
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# --- solar forcing
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elev, _ = solar_position(ts, lat, lon)
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elev = np.atleast_1d(elev)
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ghi = clear_sky_irradiance(elev)
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cloud = np.clip(0.45 + 0.35 * np.sin(2 * np.pi * ts / (4.5 * 86400)) +
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0.25 * rng.normal(size=n).cumsum() / math.sqrt(n), 0.0, 1.0)
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lux = np.maximum(ghi * 45.0 * (1.0 - 0.85 * cloud), 0.0) + 6.0
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# --- temperature: season + diurnal + OU anomaly + inversion at night
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doy = np.array([time.gmtime(float(t)).tm_yday for t in ts])
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seasonal = 6.5 * np.sin(2 * np.pi * (doy - 105) / 365.25)
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diurnal = 0.011 * ghi * (1.0 - 0.6 * cloud)
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inversion = -1.8 * (elev < -3).astype(float) * (1.0 - cloud)
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tau_t, sigma_t = 6 * 3600.0, 1.9
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at = math.exp(-step_s / tau_t)
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anom = np.zeros(n)
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for i in range(1, n):
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anom[i] = at * anom[i - 1] + sigma_t * math.sqrt(1 - at * at) * rng.normal()
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# pressure and temperature anomalies are correlated in the real world
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anom += 0.12 * press
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temp = 11.5 + seasonal + diurnal + inversion + anom
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# --- humidity via a slowly wandering dew point
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dew = temp - 4.5 + 2.5 * np.sin(2 * np.pi * ts / (3.2 * 86400))
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dew -= 0.10 * press
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dew = np.minimum(dew, temp - 0.2)
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es_t = 6.112 * np.exp(17.625 * temp / (243.04 + temp))
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es_d = 6.112 * np.exp(17.625 * dew / (243.04 + dew))
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rh = np.clip(100.0 * es_d / es_t, 8.0, 100.0)
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# CPU temperature: a slow AR(1) load process, not white noise. A Zero 2 W
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# under a steady FastAPI load drifts by a degree or two over minutes, it
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# does not jitter by four degrees between samples.
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cpu_load = np.zeros(n)
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a_cpu = math.exp(-step_s / (900.0))
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for i in range(1, n):
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cpu_load[i] = a_cpu * cpu_load[i - 1] + 1.6 * math.sqrt(1 - a_cpu * a_cpu) * rng.normal()
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cpu = temp + 21.0 + cpu_load
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# The sensor sits in a thermal gradient between the room and the SoC.
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# The compensator inverts T = T_raw - k (T_cpu - T_raw), so the forward
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# model must be its exact inverse: T_raw = (T + k T_cpu) / (1 + k).
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# Generating it any other way bakes a bias into the synthetic data that
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# no amount of calibration can remove, and quietly caps your skill score.
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# Two thermometers, not one, because the board has two. Their forward
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# models average to the k = 0.55 case this used to generate directly, so
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# temp_raw is unchanged in expectation. Its noise is not: a real board
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# averages sd 0.060 with sd 0.443 and lands at 0.223, where this used to
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# claim 0.05. Simulating the quiet sensor and calling it the average is
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# what let an over-optimistic measurement noise go unnoticed.
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temp_h = (temp + K_HTS221 * cpu) / (1.0 + K_HTS221) + SD_HTS221 * rng.normal(size=n)
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temp_p = (temp + K_LPS25HB * cpu) / (1.0 + K_LPS25HB) + SD_LPS25HB * rng.normal(size=n)
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temp_raw = (temp_h + temp_p) / 2.0
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# If the compensator will move RH from the element temperature onto the air
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# temperature, the forward model here must be its exact inverse, or the
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# synthetic data bakes in a bias no calibration can remove. Same trap as the
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# thermal algebra above. Off by default, matching sensor.hum_psychrometric.
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if psychrometric:
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es_raw = 6.112 * np.exp(17.625 * temp_raw / (243.04 + temp_raw))
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rh_sensor = np.clip(rh * es_t / es_raw, 0.0, 100.0)
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else:
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rh_sensor = rh
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press_station = press_slp / (1.0 + 0.0) - 1.8 # nominal 15 m offset
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# 0.02 hPa, measured on a real LPS25HB as the sd of the change between
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# 30 s samples. The 0.05 this used to carry made simulated pressure about
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# twice as noisy as the real thing, which flatters any smoother tested
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# against it and understates the skill available at short lead.
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press_station += 0.02 * rng.normal(size=n)
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return {
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"ts": ts, "temp": temp, "temp_raw": temp_raw,
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"temp_h": temp_h, "temp_p": temp_p,
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"rh": rh_sensor + 0.4 * rng.normal(size=n),
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"press": press_station, "press_slp": press_slp, "cpu": cpu,
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"lux": lux * (0.85 + 0.3 * rng.random(n)), "dew": dew, "cloud": cloud,
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}
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def main() -> None:
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--days", type=float, default=14.0)
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ap.add_argument("--step", type=int, default=300, help="seconds between rows")
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ap.add_argument("--seed", type=int, default=11)
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ap.add_argument("--end", type=float, default=None,
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help="unix timestamp the history ends at; defaults to now. "
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"Pin it with --seed for a bit-reproducible backfill")
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ap.add_argument("--wipe", action="store_true", help="clear existing telemetry first")
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ap.add_argument("--config", default=None)
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args = ap.parse_args()
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cfg = load_config(args.config)
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store = Store(cfg.storage.db_path)
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# The Kalman process noise is tuned for the real sampling cadence (2 s).
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# Backfilling at 300 s steps with the same q gives Q_level = q*dt^3/3, which
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# is five orders of magnitude larger, so the filter abandons smoothing and
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# tracks measurement noise. Its rate estimates then blow past anything
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# physical and poison the all-time records. Scale q by (real_dt/step)^3 so
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# the synthetic history has the same effective smoothing as the live station.
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scale = (cfg.sensor.sample_period_s / float(args.step)) ** 3
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cfg.sensor.kalman_q_temp *= scale
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cfg.sensor.kalman_q_hum *= scale
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cfg.sensor.kalman_q_press *= scale
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if args.wipe:
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with store._conn() as conn:
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conn.execute("DELETE FROM telemetry")
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conn.execute("DELETE FROM forecasts")
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conn.execute("DELETE FROM scores")
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print("cleared existing telemetry, forecasts and scores")
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data = generate(args.days, args.step, cfg.site.latitude, cfg.site.longitude,
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args.seed, args.end, cfg.sensor.hum_psychrometric)
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tracker = SignalTracker(cfg)
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n = data["ts"].size
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t0 = time.time()
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for i in range(n):
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ts = float(data["ts"][i])
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est = tracker.step(ts, float(data["temp_raw"][i]), float(data["rh"][i]),
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float(data["press"][i]), float(data["cpu"][i]))
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slp = float(sea_level_pressure(est["press_smooth"], est["temp_smooth"],
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cfg.site.altitude_m))
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store.insert_telemetry({
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"ts": ts,
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"temp_raw": data["temp_raw"][i],
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"temp_h": data["temp_h"][i],
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"temp_p": data["temp_p"][i],
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"temp_c": est["temp_c"],
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"temp_smooth": est["temp_smooth"],
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"temp_rate": est["temp_rate"],
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"hum": data["rh"][i],
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"hum_smooth": est["hum_smooth"],
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"press": data["press"][i],
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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": data["cpu"][i],
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"dew_c": float(dew_point(est["temp_smooth"], est["hum_smooth"])),
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"lux": data["lux"][i],
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"r": data["lux"][i] * 0.30, "g": data["lux"][i] * 0.34, "b": data["lux"][i] * 0.28,
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"pitch": 0.0, "roll": 0.0, "yaw": 180.0, "compass": 180.0,
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"ax": 0.0, "ay": 0.0, "az": 1.0, "gx": 0.0, "gy": 0.0, "gz": 0.0,
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})
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if i % 500 == 0:
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print(f" {i}/{n} rows", end="\r", flush=True)
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print(f"\nwrote {n} rows spanning {args.days:.1f} days in {time.time() - t0:.1f}s")
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print(f"database: {cfg.storage.db_path}")
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print("next: python scripts/evaluate.py (or just start the server)")
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if __name__ == "__main__":
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main()
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