fix(docs): Render docs

src/nwp_consumer/internal/entities/parameters.py

@@ -85,7 +85,7 @@ def __str__(self) -> str:
 class Parameter(StrEnum):
     """Parameters of interest to OCF.
 
-    Inheriting from StrEnum and using `auto()` makes the values
+    Inheriting from StrEnum and using ``auto()`` makes the values
     of the enums equal to the lowercased enum name.
 
     See Also:

src/nwp_consumer/internal/ports/repositories.py

@@ -54,7 +54,7 @@ def fetch_init_data(self, it: dt.datetime) \
 
         As per the typing, the return value is a generator of functions that
         may produce one or more xarray datasets.
-        The generator-of-functions approach (typed here as `Iterator[Callable...]`)
+        The generator-of-functions approach (typed here as ``Iterator[Callable...]``)
         is important, as it allows for lazy evaluation:
         by returning a generator of delayed objects, joblib can parallelize
         the download and the results can be accumulated in a low-memory fashion (see
@@ -82,22 +82,23 @@ def fetch_init_data(self, it: dt.datetime) \
         ...         '''Download and convert a raw file to an xarray dataset.'''
         ...         return Success([xr.open_dataset(file).to_dataarray()])
 
-        .. important:: No downloading or processing should be done in this method*. All of that
+        .. warning:: No downloading or processing should be done in this method*. All of that
           should be handled in the function that is yielded by the generator -
           ``_download_and_convert`` in the example above.
           This is to allow for parallelization of the download and processing.
 
-        *It is however, worth considering the most efficient way to download and process the data.
-        The above assumes that the data comes in many files, but there is a possibility of the
-        case where the source provides one large file with many underlying datasets within.
-        In this case, it may be more efficient to download the large file in the
-        `fetch_init_data` method and then process the datasets within via the yielded functions.
-
-        TODO: For the moment, this returns a list of `xarray.DataArray` objects. It may be
-        TODO: more efficient to return a generator here to avoid reading all the datasets into
-        TODO: memory at once, however, often the source of these datasets is `cfgrib.open_datasets`
-        TODO: which has no option for returning a generator, hence the current choice of `list`.
-        TODO: This may be revisited in the future.
+        .. note:: It is however, worth considering the most efficient way to download and process the data.
+          The above assumes that the data comes in many files, but there is a possibility of the
+          case where the source provides one large file with many underlying datasets within.
+          In this case, it may be more efficient to download the large file in the
+          `fetch_init_data` method and then process the datasets within via the yielded functions.
+
+        .. note:: For the moment, this returns a list of ``xarray.DataArray`` objects. It may be
+          more efficient to return a generator here to avoid reading all the datasets into
+          memory at once, however, often the source of these datasets is ``cfgrib.open_datasets``
+          which has no option for returning a generator, hence the current choice of ``list``.
+          This may be revisited in the future, for instance by recreating the ``open_datasets``
+          function in a manner which returns a generator of datasets.
 
         Args:
             it: The initialization time for which to fetch data.

src/nwp_consumer/internal/repositories/model_repositories/ecmwf_realtime.py

@@ -14,7 +14,7 @@
 - 8 digits representing the target time in the format mmddHHMM
 - 1 digit representing the file number(?)
 
-So a file named `A2D10250000D10260100` would be for an initialization
+So a file named ``A2D10250000D10260100`` would be for an initialization
 time of 2024-10-25 00:00 and a target time of 2024-10-26 01:00 (step of 25 hours).
 
 The file contents is specific to the order agreed with the data provider.
@@ -225,7 +225,6 @@ def _convert(path: pathlib.Path) -> ResultE[list[xr.DataArray]]:
         """Convert a grib file to an xarray DataArray.
 
         Args:
-            model: Metadata of the model producing the data
             path: The path to the grib file.
         """
         try:

src/nwp_consumer/internal/repositories/model_repositories/metoffice_global.py

@@ -11,7 +11,7 @@
 `this PDF <https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/data/global-atmospheric-model-17-km-resolution.pdf>`_.
 
 For further details on the repository, see the
-`CedaMetOfficeGlobalModelRepository.metadata` implementation.
+`CedaMetOfficeGlobalModelRepository.repository` implementation.
 
 Data discrepancies and corrections
 ==================================