[2024-03-05 16:17] Fix for unit conversion issues that occurred due t…

…o package updates. (#122)

Signed-off-by: Raoul Linnenbank <[email protected]>

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "weather_provider_api"
-version = "2.48.0"
+version = "2.49.0"
 description = "Weather Provider Libraries and API"
 authors = ["Verbindingsteam", "Raoul Linnenbank <[email protected]>"]
 license = "MPL-2.0"
@@ -42,6 +42,7 @@ pytest-cov = "^4.0.0"
 isort = "^5.10.1"
 pylint = "^2.15.10"
 black = "^23.10.1"
+ruff = {version = "^0.1.8", source = "pypi"}
 
 [tool.poetry.scripts]
 wpla_update_era5sl = "weather_provider_api.scripts.update_era5sl_repository:main"
@@ -54,6 +55,10 @@ wpla_clear_arome = "weather_provider_api.scripts.erase_arome_repository:main"
 wpla_clear_waarnemingen = "weather_provider_api.scripts.erase_waarnemingen_register:main"
 wpla_run_api = "weather_provider_api.main:main"
 
+[[tool.poetry.source]]
+name = "PyPI"
+priority = "primary"
+
 [tool.pylint]
 max-line-length = 120
 
@@ -64,3 +69,35 @@ line-length = 120
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
+[tool.ruff]
+select = [
+    "ANN",  # flake8-annotations
+    "E",  # flake8
+    "F",  # flake8
+    "I",  # isort
+    "D",  # pydocstyle
+    "S",  # flake8-bandit
+    "NPY",  # numpy-specific rules
+    "RUF",  # ruff specific rules
+]
+ignore = [
+    "E501",
+    "E712",
+
+    "ANN101",  # Missing type annotation for `self` in method
+    "ANN202",  # Missing return type annotation for private function
+    "ANN204",  # Missing return type annotation for special function
+    "ANN401",  # Dynamically typed expressions (typing.Any) are disallowed
+
+    # pydocstyle
+    "D100",  # Missing docstring in public module
+    "D104",  # Missing docstring in public package
+    "D106",  # Missing docstring in public nested class
+]
+
+[tool.ruff.per-file-ignores]
+"__init__.py" = ["F401"]
+"tests/**" = ["S", "ANN"]
+
+[tool.ruff.pydocstyle]
+convention = "google"

weather_provider_api/routers/weather/base_models/model.py

@@ -71,8 +71,9 @@ def convert_names_and_units(self, weather_data: xr.Dataset, unit: OutputUnit):
 
             new_data = weather_data[var_name]
             if "convert" in conversion_dict[var_name]:
+                dtype_value = new_data.dtype
                 converter: callable = conversion_dict[var_name]["convert"]
-                new_data = converter(weather_data[var_name])
+                new_data = converter(weather_data[var_name]).astype(dtype_value)
 
             weather_data = weather_data.drop_vars(var_name)
             weather_data[new_name] = new_data

weather_provider_api/routers/weather/base_models/source.py

@@ -6,8 +6,7 @@
 
 
 class WeatherSourceBase(object):  # pragma: no cover
-    """
-    Base class that contains the basic functionality for all sources. Any new sources should implement this as their
+    """Base class that contains the basic functionality for all sources. Any new sources should implement this as their
     base class!
     """
 

weather_provider_api/routers/weather/sources/knmi/models/daggegevens.py

@@ -179,12 +179,12 @@ def __init__(self):
         logger.debug(f"Weather model [{self.id}] initialized successfully")
 
     def get_weather(
-        self,
-        coords: List[GeoPosition],
-        begin: datetime,
-        end: datetime,
-        inseason=False,
-        weather_factors: List[str] = None,
+            self,
+            coords: List[GeoPosition],
+            begin: datetime,
+            end: datetime,
+            inseason=False,
+            weather_factors: List[str] = None,
     ) -> xr.Dataset:
         """
             The function that gathers and processes the requested Daggegevens weather data from the KNMI site
@@ -222,18 +222,19 @@ def get_weather(
 
         # The KNMI model isn't working properly yet, so we have to cut out any overflow time-wise
         ds = ds.sel(time=slice(begin, end))
+
         return ds
 
-    def is_async(self):  # pragma: no cover
+    def is_async(self) -> bool:  # pragma: no cover
         return self.async_model
 
     def _download_weather(
-        self,
-        stations: List[int],
-        start: datetime,
-        end: datetime,
-        inseason=False,
-        weather_factors: List[str] = None,
+            self,
+            stations: List[int],
+            start: datetime,
+            end: datetime,
+            inseason=False,
+            weather_factors: List[str] = None,
     ):
         """
             A function that downloads the weather from the KNMI download location and returns it as a text
@@ -306,7 +307,8 @@ def _parse_raw_weather_data(self, raw_data: str) -> xr.Dataset:
         return dataframe_data.to_xarray()
 
     @staticmethod
-    def _prepare_weather_data(coordinates: List[GeoPosition], station_id, raw_ds):
+    def _prepare_weather_data(coordinates: List[GeoPosition], station_id: list[np.int64],
+                              raw_ds: xr.Dataset) -> xr.Dataset:
         # A function that prepares the weather data for return by the API, by replacing the matching station with the
         # lat/lon location that was requested, and properly formatting the dimensions.
 
@@ -322,6 +324,7 @@ def _prepare_weather_data(coordinates: List[GeoPosition], station_id, raw_ds):
             coords={"time": timeline, "coord": coords_to_pd_index(coordinates)},
         )
         ds = ds.unstack("coord")
+
         return ds
 
     def _request_weather_factors(self, factors: Optional[List[str]]) -> List[str]:

weather_provider_api/routers/weather/sources/knmi/utils.py

@@ -25,7 +25,9 @@
 from weather_provider_api.routers.weather.utils.geo_position import GeoPosition
 
 
-def find_closest_stn_list(stn_stations: pd.DataFrame, coords: List[GeoPosition]):
+def find_closest_stn_list(
+        stn_stations: pd.DataFrame, coords: List[GeoPosition]
+) -> tuple[list[np.int64], list[np.int64], list[int]]:
     """
             A function that finds the closest stations to the locations in the given list of GeoPositions
     Args:
@@ -45,7 +47,7 @@ def find_closest_stn_list(stn_stations: pd.DataFrame, coords: List[GeoPosition])
     return coords_stn, stns, coords_stn_ind
 
 
-def _find_closest_stn_single(stn_stations: pd.DataFrame, coord: GeoPosition):
+def _find_closest_stn_single(stn_stations: pd.DataFrame, coord: GeoPosition) -> np.int64:
     """
         A function that finds the closest station to a single GeoPosition
     Args: