mypy, req

src/akkudoktoreos/prediction/elecpriceakkudoktor.py

@@ -12,13 +12,13 @@ import numpy as np
 import requests
 from numpydantic import NDArray, Shape
 from pydantic import Field, ValidationError
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
 
 from akkudoktoreos.core.logging import get_logger
 from akkudoktoreos.core.pydantic import PydanticBaseModel
 from akkudoktoreos.prediction.elecpriceabc import ElecPriceDataRecord, ElecPriceProvider
 from akkudoktoreos.utils.cacheutil import cache_in_file
 from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
-from statsmodels.tsa.holtwinters import ExponentialSmoothing
 
 logger = get_logger(__name__)
 
@@ -122,22 +122,22 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
         self.update_datetime = to_datetime(in_timezone=self.config.timezone)
         return akkudoktor_data
 
-        def cap_outliers(data: np.ndarray, sigma: int = 2) -> np.ndarray:
-            mean = data.mean()
-            std = data.std()
-            lower_bound = mean - sigma * std
-            upper_bound = mean + sigma * std
-            capped_data = data.clip(lower=lower_bound, upper=upper_bound)
-            return capped_data
+    def _cap_outliers(self, data: np.ndarray, sigma: int = 2) -> np.ndarray:
+        mean = data.mean()
+        std = data.std()
+        lower_bound = mean - sigma * std
+        upper_bound = mean + sigma * std
+        capped_data = data.clip(min=lower_bound, max=upper_bound)
+        return capped_data
 
-        def predict_ets(
-            history: np.ndarray, seasonal_periods: int, prediction_hours: int
-        ) -> np.ndarray:
-            clean_history = cap_outliers(history)
-            model = ExponentialSmoothing(
-                clean_history, seasonal="add", seasonal_periods=seasonal_periods
-            ).fit()
-            return model.forecast(prediction_hours)
+    def _predict_ets(
+        self, history: np.ndarray, seasonal_periods: int, prediction_hours: int
+    ) -> np.ndarray:
+        clean_history = self._cap_outliers(history)
+        model = ExponentialSmoothing(
+            clean_history, seasonal="add", seasonal_periods=seasonal_periods
+        ).fit()
+        return model.forecast(prediction_hours)
 
     def _update_data(self, force_update: Optional[bool] = False) -> None:
         """Update forecast data in the ElecPriceDataRecord format.