add model training

Dockerfile.train

@@ -0,0 +1,10 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY scripts/requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r /app/requirements.txt
+
+COPY scripts/train_rain_model.py /app/train_rain_model.py
+
+ENTRYPOINT ["python", "/app/train_rain_model.py"]

cmd/ingestd/web/app.js

@@ -289,6 +289,60 @@ function updateText(id, text) {
   if (el) el.textContent = text;
 }
 
+function lastNonNull(points, key) {
+  for (let i = points.length - 1; i >= 0; i -= 1) {
+    const v = points[i][key];
+    if (v !== null && v !== undefined) {
+      return v;
+    }
+  }
+  return null;
+}
+
+function computeRainProbability(latest, pressureTrend1h) {
+  if (!latest) {
+    return null;
+  }
+
+  let prob = 0.1;
+  if (pressureTrend1h !== null && pressureTrend1h !== undefined) {
+    if (pressureTrend1h <= -3.0) {
+      prob += 0.5;
+    } else if (pressureTrend1h <= -2.0) {
+      prob += 0.35;
+    } else if (pressureTrend1h <= -1.0) {
+      prob += 0.2;
+    } else if (pressureTrend1h <= -0.5) {
+      prob += 0.1;
+    }
+  }
+
+  if (latest.rh !== null && latest.rh !== undefined) {
+    if (latest.rh >= 95) {
+      prob += 0.2;
+    } else if (latest.rh >= 90) {
+      prob += 0.15;
+    } else if (latest.rh >= 85) {
+      prob += 0.1;
+    }
+  }
+
+  if (latest.wind_m_s !== null && latest.wind_m_s !== undefined && latest.wind_m_s >= 6) {
+    prob += 0.05;
+  }
+
+  prob = Math.max(0.05, Math.min(0.95, prob));
+
+  let label = "Low";
+  if (prob >= 0.6) {
+    label = "High";
+  } else if (prob >= 0.35) {
+    label = "Medium";
+  }
+
+  return { prob, label };
+}
+
 function updateSiteMeta(site, model, tzLabel) {
   const home = document.getElementById("site-home");
   const suffix = document.getElementById("site-meta-suffix");
@@ -400,6 +454,13 @@ function renderDashboard(data) {
 
   const obsFiltered = filterRange(obs, rangeStart, rangeEnd);
   const forecast = filterRange(forecastAll, rangeStart, rangeEnd);
+  const lastPressureTrend = lastNonNull(obsFiltered, "pressure_trend_1h");
+  const rainProb = computeRainProbability(latest, lastPressureTrend);
+  if (rainProb) {
+    updateText("live-rain-prob", `${Math.round(rainProb.prob * 100)}% (${rainProb.label})`);
+  } else {
+    updateText("live-rain-prob", "--");
+  }
 
   const obsTemps = obsFiltered.map((p) => p.temp_c);
   const obsWinds = obsFiltered.map((p) => p.wind_m_s);

cmd/ingestd/web/index.html

@@ -58,6 +58,10 @@
             <div class="label">Pressure hPa</div>
             <div class="value" id="live-pressure">--</div>
           </div>
+          <div class="metric">
+            <div class="label">Rain 1h %</div>
+            <div class="value" id="live-rain-prob">--</div>
+          </div>
           <div class="metric">
             <div class="label">Wind m/s</div>
             <div class="value" id="live-wind">--</div>

docs/rain_prediction.md

@@ -0,0 +1,102 @@
+# Rain Prediction (Next 1 Hour)
+
+This project now includes a starter training script for a **binary rain prediction**:
+
+> **Will we see >= 0.2 mm of rain in the next hour?**
+
+It uses local observations (WS90 + barometric pressure) and trains a lightweight
+logistic regression model. This is a baseline you can iterate on as you collect
+more data.
+
+## What the script does
+- Pulls data from TimescaleDB.
+- Resamples observations to 5-minute buckets.
+- Derives **pressure trend (1h)** from barometer data.
+- Computes **future 1-hour rainfall** from the cumulative `rain_mm` counter.
+- Trains a model and prints evaluation metrics.
+
+The output is a saved model file (optional) you can use later for inference.
+
+## Requirements
+Python 3.10+ and:
+
+```
+pandas
+numpy
+scikit-learn
+psycopg2-binary
+joblib
+```
+
+Install with:
+
+```sh
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r scripts/requirements.txt
+```
+
+## Usage
+
+```sh
+python scripts/train_rain_model.py \
+  --db-url "postgres://postgres:postgres@localhost:5432/micrometeo?sslmode=disable" \
+  --site "home" \
+  --start "2026-01-01" \
+  --end "2026-02-01" \
+  --out "models/rain_model.pkl"
+```
+
+You can also provide the connection string via `DATABASE_URL`:
+
+```sh
+export DATABASE_URL="postgres://postgres:postgres@localhost:5432/micrometeo?sslmode=disable"
+python scripts/train_rain_model.py --site home
+```
+
+## Output
+The script prints metrics including:
+- accuracy
+- precision / recall
+- ROC AUC
+- confusion matrix
+
+If `joblib` is installed, it saves a model bundle:
+
+```
+models/rain_model.pkl
+```
+
+This bundle contains:
+- The trained model pipeline
+- The feature list used during training
+
+## Data needs / when to run
+For a reliable model, you will want:
+- **At least 2-4 weeks** of observations
+- A mix of rainy and non-rainy periods
+
+Training with only a few days will produce an unstable model.
+
+## Features used
+The baseline model uses:
+- `pressure_trend_1h` (hPa)
+- `humidity` (%)
+- `temperature_c` (C)
+- `wind_avg_m_s` (m/s)
+- `wind_max_m_s` (m/s)
+
+These are easy to expand once you have more data (e.g. add forecast features).
+
+## Notes / assumptions
+- Rain detection is based on **incremental rain** derived from the WS90
+  `rain_mm` cumulative counter.
+- Pressure comes from `observations_baro`.
+- All timestamps are treated as UTC.
+
+## Next improvements
+Ideas once more data is available:
+- Add forecast precipitation and cloud cover as features
+- Try gradient boosted trees (e.g. XGBoost / LightGBM)
+- Train per-season models
+- Calibrate probabilities (Platt scaling / isotonic regression)

internal/db/series.go

@@ -15,15 +15,17 @@ type ObservationPoint struct {
 	TempC       *float64  `json:"temp_c,omitempty"`
 	RH          *float64  `json:"rh,omitempty"`
 	PressureHPA *float64  `json:"pressure_hpa,omitempty"`
-	WindMS      *float64  `json:"wind_m_s,omitempty"`
-	WindGustMS  *float64  `json:"wind_gust_m_s,omitempty"`
-	WindDirDeg  *float64  `json:"wind_dir_deg,omitempty"`
-	UVI         *float64  `json:"uvi,omitempty"`
-	LightLux    *float64  `json:"light_lux,omitempty"`
-	BatteryMV   *float64  `json:"battery_mv,omitempty"`
-	SupercapV   *float64  `json:"supercap_v,omitempty"`
-	RainMM      *float64  `json:"rain_mm,omitempty"`
-	RainStart   *int64    `json:"rain_start,omitempty"`
+	// PressureTrend1h is the change in pressure over the last hour (hPa).
+	PressureTrend1h *float64 `json:"pressure_trend_1h,omitempty"`
+	WindMS          *float64 `json:"wind_m_s,omitempty"`
+	WindGustMS      *float64 `json:"wind_gust_m_s,omitempty"`
+	WindDirDeg      *float64 `json:"wind_dir_deg,omitempty"`
+	UVI             *float64 `json:"uvi,omitempty"`
+	LightLux        *float64 `json:"light_lux,omitempty"`
+	BatteryMV       *float64 `json:"battery_mv,omitempty"`
+	SupercapV       *float64 `json:"supercap_v,omitempty"`
+	RainMM          *float64 `json:"rain_mm,omitempty"`
+	RainStart       *int64   `json:"rain_start,omitempty"`
 }
 
 type ForecastPoint struct {
@@ -147,6 +149,23 @@ func (d *DB) ObservationSeries(ctx context.Context, site, bucket string, start,
 		return nil, rows.Err()
 	}
 
+	indexByTime := make(map[time.Time]int, len(points))
+	for i := range points {
+		indexByTime[points[i].TS] = i
+	}
+	for i := range points {
+		if points[i].PressureHPA == nil {
+			continue
+		}
+		target := points[i].TS.Add(-1 * time.Hour)
+		j, ok := indexByTime[target]
+		if !ok || points[j].PressureHPA == nil {
+			continue
+		}
+		trend := *points[i].PressureHPA - *points[j].PressureHPA
+		points[i].PressureTrend1h = &trend
+	}
+
 	return points, nil
 }
 

scripts/requirements.txt

@@ -0,0 +1,5 @@
+pandas>=2.2.0
+numpy>=1.26.0
+scikit-learn>=1.4.0
+psycopg2-binary>=2.9.0
+joblib>=1.3.0

scripts/train_rain_model.py

@@ -0,0 +1,178 @@
+#!/usr/bin/env python3
+import argparse
+import os
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+import psycopg2
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+
+try:
+    import joblib
+except ImportError:  # pragma: no cover - optional dependency
+    joblib = None
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Train a simple rain prediction model (next 1h >= 0.2mm).")
+    parser.add_argument("--db-url", default=os.getenv("DATABASE_URL"), help="Postgres connection string.")
+    parser.add_argument("--site", required=True, help="Site name (e.g. home).")
+    parser.add_argument("--start", help="Start time (RFC3339 or YYYY-MM-DD).")
+    parser.add_argument("--end", help="End time (RFC3339 or YYYY-MM-DD).")
+    parser.add_argument("--out", default="models/rain_model.pkl", help="Path to save model.")
+    return parser.parse_args()
+
+
+def parse_time(value: str) -> str:
+    if not value:
+        return ""
+    try:
+        datetime.fromisoformat(value.replace("Z", "+00:00"))
+        return value
+    except ValueError:
+        raise ValueError(f"invalid time format: {value}")
+
+
+def fetch_ws90(conn, site, start, end):
+    sql = """
+        SELECT ts, temperature_c, humidity, wind_avg_m_s, wind_max_m_s, wind_dir_deg, rain_mm
+        FROM observations_ws90
+        WHERE site = %s
+          AND (%s = '' OR ts >= %s::timestamptz)
+          AND (%s = '' OR ts <= %s::timestamptz)
+        ORDER BY ts ASC
+    """
+    return pd.read_sql_query(sql, conn, params=(site, start, start, end, end), parse_dates=["ts"])
+
+
+def fetch_baro(conn, site, start, end):
+    sql = """
+        SELECT ts, pressure_hpa
+        FROM observations_baro
+        WHERE site = %s
+          AND (%s = '' OR ts >= %s::timestamptz)
+          AND (%s = '' OR ts <= %s::timestamptz)
+        ORDER BY ts ASC
+    """
+    return pd.read_sql_query(sql, conn, params=(site, start, start, end, end), parse_dates=["ts"])
+
+
+def build_dataset(ws90: pd.DataFrame, baro: pd.DataFrame) -> pd.DataFrame:
+    if ws90.empty:
+        raise RuntimeError("no ws90 observations found")
+    if baro.empty:
+        raise RuntimeError("no barometer observations found")
+
+    ws90 = ws90.set_index("ts").sort_index()
+    baro = baro.set_index("ts").sort_index()
+
+    ws90_5m = ws90.resample("5min").agg(
+        {
+            "temperature_c": "mean",
+            "humidity": "mean",
+            "wind_avg_m_s": "mean",
+            "wind_max_m_s": "max",
+            "wind_dir_deg": "mean",
+            "rain_mm": "last",
+        }
+    )
+    baro_5m = baro.resample("5min").mean()
+
+    df = ws90_5m.join(baro_5m, how="outer")
+    df["pressure_hpa"] = df["pressure_hpa"].interpolate(limit=3)
+
+    # Compute incremental rain and future 1-hour sum.
+    df["rain_inc"] = df["rain_mm"].diff().clip(lower=0)
+    window = 12  # 12 * 5min = 1 hour
+    df["rain_next_1h_mm"] = df["rain_inc"].rolling(window=window, min_periods=1).sum().shift(-(window - 1))
+    df["rain_next_1h"] = df["rain_next_1h_mm"] >= 0.2
+
+    # Pressure trend over the previous hour.
+    df["pressure_trend_1h"] = df["pressure_hpa"] - df["pressure_hpa"].shift(12)
+
+    return df
+
+
+def train_model(df: pd.DataFrame):
+    feature_cols = [
+        "pressure_trend_1h",
+        "humidity",
+        "temperature_c",
+        "wind_avg_m_s",
+        "wind_max_m_s",
+    ]
+
+    df = df.dropna(subset=feature_cols + ["rain_next_1h"])
+    if len(df) < 200:
+        raise RuntimeError("not enough data after filtering (need >= 200 rows)")
+
+    X = df[feature_cols]
+    y = df["rain_next_1h"].astype(int)
+
+    split_idx = int(len(df) * 0.8)
+    X_train, X_test = X.iloc[:split_idx], X.iloc[split_idx:]
+    y_train, y_test = y.iloc[:split_idx], y.iloc[split_idx:]
+
+    model = Pipeline(
+        [
+            ("scaler", StandardScaler()),
+            ("clf", LogisticRegression(max_iter=1000, class_weight="balanced")),
+        ]
+    )
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    y_prob = model.predict_proba(X_test)[:, 1]
+
+    metrics = {
+        "rows": len(df),
+        "train_rows": len(X_train),
+        "test_rows": len(X_test),
+        "accuracy": accuracy_score(y_test, y_pred),
+        "precision": precision_score(y_test, y_pred, zero_division=0),
+        "recall": recall_score(y_test, y_pred, zero_division=0),
+        "roc_auc": roc_auc_score(y_test, y_prob),
+        "confusion_matrix": confusion_matrix(y_test, y_pred).tolist(),
+    }
+
+    return model, metrics, feature_cols
+
+
+def main() -> int:
+    args = parse_args()
+    if not args.db_url:
+        raise SystemExit("missing --db-url or DATABASE_URL")
+
+    start = parse_time(args.start) if args.start else ""
+    end = parse_time(args.end) if args.end else ""
+
+    with psycopg2.connect(args.db_url) as conn:
+        ws90 = fetch_ws90(conn, args.site, start, end)
+        baro = fetch_baro(conn, args.site, start, end)
+
+    df = build_dataset(ws90, baro)
+    model, metrics, features = train_model(df)
+
+    print("Rain model metrics:")
+    for k, v in metrics.items():
+        print(f"  {k}: {v}")
+
+    if args.out:
+        out_dir = os.path.dirname(args.out)
+        if out_dir:
+            os.makedirs(out_dir, exist_ok=True)
+        if joblib is None:
+            print("joblib not installed; skipping model save.")
+        else:
+            joblib.dump({"model": model, "features": features}, args.out)
+            print(f"Saved model to {args.out}")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())