Add read load part w test.

scripts/license_checker_and_adder.py

@@ -1,5 +1,6 @@
 import os
 import re
+
 from pathlib import Path
 from typing import List
 

src/antares/model/area.py

@@ -372,3 +372,6 @@ def create_hydro(
         hydro = self._area_service.create_hydro(self.id, properties, matrices)
         self._hydro = hydro
         return hydro
+
+    def read_load(self) -> Load:
+        return self._area_service.read_load(self._id)

src/antares/model/study.py

@@ -198,7 +198,7 @@ def __init__(
     def service(self) -> BaseStudyService:
         return self._study_service
 
-    def read_areas(self) -> List[Area]:
+    def read_areas(self) -> list[Area]:
         return self._area_service.read_areas()
 
     def get_areas(self) -> MappingProxyType[str, Area]:

src/antares/service/base_services.py

@@ -71,13 +71,6 @@ def create_thermal_cluster(
         """
         pass
 
-    @abstractmethod
-    def read_areas(self) -> List[Area]:
-        """
-        Returns: Returns a list of areas
-        """
-        pass
-
     @abstractmethod
     def create_thermal_cluster_with_matrices(
         self,
@@ -285,6 +278,12 @@ def get_load_matrix(self, area: Area) -> pd.DataFrame:
         # Once it will, there will be no change to do in the code on our side.
         pass
 
+    @abstractmethod
+    def read_areas(self) -> list[Area]:
+        """
+        Returns: Returns a list of areas
+        """
+        pass
 
 class BaseLinkService(ABC):
     @abstractmethod

src/antares/service/local_services/area_local.py

@@ -14,6 +14,7 @@
 import os
 
 from configparser import ConfigParser
+from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 import pandas as pd
@@ -334,3 +335,35 @@ def read_areas(self) -> List[Area]:
                     )
                 )
         return areas
+
+    def _read_timeseries(self, ts_file_type: TimeSeriesFileType, study_path: Path, area_id: Optional[str] = None, group_id: Optional[str] = None,constraint_id: Optional[str] = None, time_series: Optional[pd.DataFrame] = None,) -> pd.DataFrame:
+        def _write_file(_file_path: Path, _time_series: pd.DataFrame) -> None:
+            _file_path.parent.mkdir(parents=True, exist_ok=True)
+            _time_series.to_csv(_file_path, sep="\t", header=False, index=False, encoding="utf-8")
+
+        file_path = study_path / (
+            ts_file_type.value
+            if not (area_id or constraint_id or group_id)
+            else ts_file_type.value.format(area_id=area_id, constraint_id=constraint_id, group_id=group_id)
+        )
+        if file_path.is_file() and time_series is not None:
+            raise ValueError(f"File {file_path} already exists and a time series was provided.")
+        elif file_path.is_file() and time_series is None:
+            if os.path.getsize(file_path) != 0:
+                _time_series = pd.read_csv(
+                    file_path,
+                    sep="\t",
+                    header=None,
+                    index_col=None,
+                    encoding="utf-8",
+                )
+            else:
+                _time_series = pd.DataFrame()
+        else:
+            _time_series = time_series if time_series is not None else pd.DataFrame([])
+            _write_file(file_path, _time_series)
+        return _time_series
+
+    def read_load(self, id: str) -> Load:
+        timeseries = self._read_timeseries(TimeSeriesFileType.LOAD, self.config.study_path, area_id=id)
+        return Load(timeseries)

tests/antares/services/local_services/test_area.py

@@ -1092,3 +1092,27 @@ def test_read_areas_local(self, local_study_w_areas):
         expected_areas = ["at", "it", "fr"]
         for area in actual_areas:
             assert area.id in expected_areas
+
+class TestReadLoad:
+    def test_read_load_local(self, local_study_w_areas):
+        study_path = local_study_w_areas.service.config.study_path
+        local_study_object = read_study_local(study_path)
+        areas = local_study_object.read_areas()
+
+        def _write_file(_file_path, _time_series) -> None:
+            _file_path.parent.mkdir(parents=True, exist_ok=True)
+            _time_series.to_csv(file_path, sep="\t", header=False, index=False, encoding="utf-8")
+
+        for area in areas:
+            expected_conversion = pd.DataFrame(
+                    [
+                        [-9999999980506447872, 0, 9999999980506447872],
+                        [0, area.id, 0],
+                    ]
+                , dtype="object")
+
+            file_path = study_path / "input" / "load" / "series" / f"load_{area.id}.txt".format(area_id=area.id)
+            _write_file(file_path, expected_conversion)
+
+            load_object = area.read_load()
+            pd.testing.assert_frame_equal(load_object.time_series.astype(str), expected_conversion.astype(str), check_dtype=False)