Tests for class_load_container v2 (#247)

* Tests for class_load_container v2
  - file as same name as class
  - useless numpy conversations removed
  - switched to built-in type tests
  - human readable tests included
* ruff
* removed dupicate empty list
* load_aggregator: collections.abc.Sequence

---------

Co-authored-by: Dominique Lasserre <lasserre.d@gmail.com>

src/akkudoktoreos/prediction/load_aggregator.py

@@ -0,0 +1,37 @@
+from collections import defaultdict
+from collections.abc import Sequence
+
+
+class LoadAggregator:
+    def __init__(self, prediction_hours: int = 24) -> None:
+        """Initializes the LoadAggregator object with the number of prediction hours.
+
+        :param prediction_hours: Number of hours to predict (default: 24)
+        """
+        self.loads: defaultdict[str, list[float]] = defaultdict(
+            list
+        )  # Dictionary to hold load arrays for different sources
+        self.prediction_hours: int = prediction_hours
+
+    def add_load(self, name: str, last_array: Sequence[float]) -> None:
+        """Adds a load array for a specific source. Accepts a Sequence of floats.
+
+        :param name: Name of the load source (e.g., "Household", "Heat Pump").
+        :param last_array: Sequence of loads, where each entry corresponds to an hour.
+        :raises ValueError: If the length of last_array doesn't match the prediction hours.
+        """
+        # Check length of the array without converting
+        if len(last_array) != self.prediction_hours:
+            raise ValueError(f"Total load inconsistent lengths in arrays: {name} {len(last_array)}")
+        self.loads[name] = list(last_array)
+
+    def calculate_total_load(self) -> list[float]:
+        """Calculates the total load for each hour by summing up the loads from all sources.
+
+        :return: A list representing the total load for each hour.
+                 Returns an empty list if no loads have been added.
+        """
+        # Optimize the summation using a single loop with zip
+        total_load = [sum(hourly_loads) for hourly_loads in zip(*self.loads.values())]
+
+        return total_load

src/akkudoktoreos/prediction/load_container.py

@@ -1,39 +0,0 @@
-import numpy as np
-
-
-class Gesamtlast:
-    def __init__(self, prediction_hours: int = 24):
-        self.lasten: dict[
-            str, np.ndarray
-        ] = {}  # Contains names and load arrays for different sources
-        self.prediction_hours = prediction_hours
-
-    def hinzufuegen(self, name: str, last_array: np.ndarray) -> None:
-        """Adds an array of loads for a specific source.
-
-        :param name: Name of the load source (e.g., "Household", "Heat Pump")
-        :param last_array: Array of loads, where each entry corresponds to an hour
-        """
-        if len(last_array) != self.prediction_hours:
-            raise ValueError(f"Total load inconsistent lengths in arrays: {name} {len(last_array)}")
-        self.lasten[name] = last_array
-
-    def gesamtlast_berechnen(self) -> np.ndarray:
-        """Calculates the total load for each hour and returns an array of total loads.
-
-        :return: Array of total loads, where each entry corresponds to an hour
-        """
-        if not self.lasten:
-            return np.ndarray(0)
-
-        # Assumption: All load arrays have the same length
-        stunden = len(next(iter(self.lasten.values())))
-        gesamtlast_array = [0] * stunden
-
-        for last_array in self.lasten.values():
-            gesamtlast_array = [
-                gesamtlast + stundenlast
-                for gesamtlast, stundenlast in zip(gesamtlast_array, last_array)
-            ]
-
-        return np.array(gesamtlast_array)

src/akkudoktoreos/server/fastapi_server.py

@@ -23,7 +23,7 @@ from akkudoktoreos.optimization.genetic import (
 )
 
 # Still to be adapted
-from akkudoktoreos.prediction.load_container import Gesamtlast
+from akkudoktoreos.prediction.load_aggregator import LoadAggregator
 from akkudoktoreos.prediction.load_corrector import LoadPredictionAdjuster
 from akkudoktoreos.prediction.load_forecast import LoadForecast
 from akkudoktoreos.prediction.prediction import get_prediction
@@ -160,14 +160,13 @@ def fastapi_gesamtlast(request: GesamtlastRequest) -> list[float]:
     future_predictions = adjuster.predict_next_hours(hours)
 
     leistung_haushalt = future_predictions["Adjusted Pred"].to_numpy()
-    gesamtlast = Gesamtlast(prediction_hours=hours)
-    gesamtlast.hinzufuegen(
+    gesamtlast = LoadAggregator(prediction_hours=hours)
+    gesamtlast.add_load(
         "Haushalt",
-        leistung_haushalt,
+        tuple(leistung_haushalt),
     )
 
-    last = gesamtlast.gesamtlast_berechnen()
-    return last.tolist()
+    return gesamtlast.calculate_total_load()
 
 
 @app.get("/gesamtlast_simple")
@@ -183,8 +182,10 @@ def fastapi_gesamtlast_simple(year_energy: float) -> list[float]:
     )[0]  # Get expected household load for the date range
 
     prediction_hours = config_eos.prediction_hours if config_eos.prediction_hours else 48
-    gesamtlast = Gesamtlast(prediction_hours=prediction_hours)  # Create Gesamtlast instance
-    gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)  # Add household to total load calculation
+    gesamtlast = LoadAggregator(prediction_hours=prediction_hours)  # Create Gesamtlast instance
+    gesamtlast.add_load(
+        "Haushalt", tuple(leistung_haushalt)
+    )  # Add household to total load calculation
 
     # ###############
     # # WP (Heat Pump)
@@ -192,8 +193,7 @@ def fastapi_gesamtlast_simple(year_energy: float) -> list[float]:
     # leistung_wp = wp.simulate_24h(temperature_forecast)  # Simulate heat pump load for 24 hours
     # gesamtlast.hinzufuegen("Heatpump", leistung_wp)  # Add heat pump load to total load calculation
 
-    last = gesamtlast.gesamtlast_berechnen()  # Calculate total load
-    return last.tolist()  # Return total load as JSON
+    return gesamtlast.calculate_total_load()
 
 
 class ForecastResponse(PydanticBaseModel):