update

docs/rain_model_runbook.md

@@ -25,7 +25,7 @@ docker compose exec -T timescaledb \
 Recommended evaluation run (includes validation-only tuning, calibration comparison, naive baselines, and walk-forward folds):
 
 ```sh
-python scripts/train_rain_model.py \
+scripts/rainml_py.sh scripts/train_rain_model.py \
   --site "home" \
   --start "2026-02-01T00:00:00Z" \
   --end "2026-03-03T23:55:00Z" \
@@ -59,7 +59,7 @@ Review in report:
 2. Run one dry-run inference:
 
 ```sh
-python scripts/predict_rain_model.py \
+scripts/rainml_py.sh scripts/predict_rain_model.py \
   --site home \
   --model-path "models/rain_model.pkl" \
   --model-name "rain_next_4h" \
@@ -70,7 +70,7 @@ python scripts/predict_rain_model.py \
 3. Run live inference:
 
 ```sh
-python scripts/predict_rain_model.py \
+scripts/rainml_py.sh scripts/predict_rain_model.py \
   --site home \
   --model-path "models/rain_model.pkl" \
   --model-name "rain_next_4h" \
@@ -128,7 +128,7 @@ Alert heuristic: sustained Brier-score increase > 25% from trailing 30-day avera
 Use the health-check script in cron, systemd timer, or your alerting scheduler:
 
 ```sh
-python scripts/check_rain_pipeline_health.py \
+scripts/rainml_py.sh scripts/check_rain_pipeline_health.py \
   --site home \
   --model-name rain_next_4h \
   --horizon-hours 4 \
@@ -164,7 +164,7 @@ Recommended production defaults:
 To compare saved training reports and pick a deployment candidate automatically:
 
 ```sh
-python scripts/recommend_rain_model.py \
+scripts/rainml_py.sh scripts/recommend_rain_model.py \
   --reports-glob "models/rain_model_report*.json" \
   --require-walk-forward \
   --top-k 5 \
@@ -194,7 +194,7 @@ docker compose exec -T timescaledb \
 3. Run a full 4h training/evaluation cycle and save report:
 
 ```sh
-python scripts/train_rain_model.py \
+scripts/rainml_py.sh scripts/train_rain_model.py \
   --site "home" \
   --start "2026-02-01T00:00:00Z" \
   --end "2026-03-03T23:55:00Z" \
@@ -213,21 +213,21 @@ python scripts/train_rain_model.py \
 4. Compare 4h metrics against the latest 1h benchmark report before switching dashboard defaults:
 
 ```sh
-python scripts/compare_rain_reports.py \
+scripts/rainml_py.sh scripts/compare_rain_reports.py \
   --baseline "models/rain_model_report_1h.json" \
   --candidate "models/rain_model_report_4h.json"
 ```
 5. Run dry-run inference, then live inference with 4h model name/horizon:
 
 ```sh
-python scripts/predict_rain_model.py \
+scripts/rainml_py.sh scripts/predict_rain_model.py \
   --site home \
   --model-path "models/rain_model_4h.pkl" \
   --model-name "rain_next_4h" \
   --horizon-hours 4 \
   --dry-run
 
-python scripts/predict_rain_model.py \
+scripts/rainml_py.sh scripts/predict_rain_model.py \
   --site home \
   --model-path "models/rain_model_4h.pkl" \
   --model-name "rain_next_4h" \
@@ -237,7 +237,7 @@ python scripts/predict_rain_model.py \
 6. Validate health checks and dashboard data path for 4h:
 
 ```sh
-python scripts/check_rain_pipeline_health.py \
+scripts/rainml_py.sh scripts/check_rain_pipeline_health.py \
   --site home \
   --model-name rain_next_4h \
   --horizon-hours 4 \