Refactor and consolidate generator set functionality in the domain layer

src/ecalc_cli/infrastructure/file_resource_service.py

@@ -3,8 +3,8 @@
 
 from libecalc.common.errors.exceptions import EcalcError, InvalidHeaderException
 from libecalc.common.logger import logger
+from libecalc.domain.resource import Resource
 from libecalc.infrastructure.file_io import read_facility_resource, read_timeseries_resource
-from libecalc.presentation.yaml.resource import Resource
 from libecalc.presentation.yaml.resource_service import ResourceService
 from libecalc.presentation.yaml.yaml_entities import MemoryResource
 from libecalc.presentation.yaml.yaml_models.yaml_model import YamlValidator

src/libecalc/common/utils/nan_handling.py

@@ -0,0 +1,26 @@
+import numpy as np
+import pandas as pd
+from numpy.typing import NDArray
+
+
+def clean_nan_values(values: NDArray[np.float64]) -> NDArray[np.float64]:
+    """
+    Handles NaN values in a consistent and predictable way by:
+
+    1. Forward filling all NaN values using the last valid observation.
+    2. Replacing any remaining NaNs (e.g., if the first value was NaN) with 0.0.
+
+    This function is immutable – it does not modify the input array in-place.
+
+    Args:
+        values: A NumPy array of float64 values (may contain NaNs).
+
+    Returns:
+        A cleaned NumPy array with no NaN values.
+    """
+
+    # Step 1: Forward-fill NaNs using pandas
+    filled = pd.Series(values).ffill().to_numpy()
+
+    result = np.nan_to_num(filled)  # By convention, we change remaining NaN-values to 0 regardless of extrapolation
+    return result

src/libecalc/common/utils/rates.py

@@ -70,19 +70,6 @@ def to_calendar_day(stream_day_rates: NDArray[np.float64], regularity: list[floa
         """
         return stream_day_rates * np.asarray(regularity, dtype=np.float64)
 
-    @staticmethod
-    def forward_fill_nan_values(rates: NDArray[np.float64]) -> NDArray[np.float64]:
-        """
-        Forward fill Nan-values
-
-        Args:
-            rates: Production rates (possibly containing NaN values)
-
-        Returns:
-            The production rates where all NaN values are replaced with the last value that was not NaN
-        """
-        return np.array(pd.Series(rates).ffill())
-
     @staticmethod
     def to_volumes(
         rates: list[float] | list[TimeSeriesValue] | NDArray[np.float64],