chore(qa): Fix docsigs

src/anemoi/datasets/create/functions/filters/rename.py

@@ -47,11 +47,7 @@ def __repr__(self) -> str:
 
 
 class RenamedFieldFormat:
-    """Rename a field based on a format string.
-
-    Args:
-        format (str): A string that defines the new name of the field.
-    """
+    """Rename a field based on a format string."""
 
     def __init__(self, field, what, format):
         self.field = field

src/anemoi/datasets/create/functions/filters/single_level_specific_humidity_to_relative_humidity.py

@@ -8,6 +8,8 @@
 # nor does it submit to any jurisdiction.
 
 
+from typing import Union
+
 import numpy as np
 from earthkit.data.indexing.fieldlist import FieldArray
 from earthkit.meteo import constants
@@ -43,7 +45,7 @@ def __getattr__(self, name):
         return getattr(self.field, name)
 
 
-def model_level_pressure(A, B, surface_pressure):
+def model_level_pressure(A, B, surface_pressure) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """Calculates:
      - pressure at the model full- and half-levels
      - delta: depth of log(pressure) at full levels
@@ -114,7 +116,7 @@ def model_level_pressure(A, B, surface_pressure):
     return p_full_level, p_half_level, delta, alpha
 
 
-def calc_specific_gas_constant(q):
+def calc_specific_gas_constant(q) -> Union[float, np.ndarray]:
     """Calculates the specific gas constant of moist air
     (specific content of cloud particles and hydrometeors are neglected)
 
@@ -133,7 +135,7 @@ def calc_specific_gas_constant(q):
     return R
 
 
-def relative_geopotential_thickness(alpha, q, T):
+def relative_geopotential_thickness(alpha, q, T) -> np.ndarray:
     """Calculates the geopotential thickness w.r.t the surface on model full-levels
 
     Parameters
@@ -158,7 +160,7 @@ def relative_geopotential_thickness(alpha, q, T):
     return dphi
 
 
-def pressure_at_height_level(height, q, T, sp, A, B):
+def pressure_at_height_level(height, q, T, sp, A, B) -> Union[float, np.ndarray]:
     """Calculates the pressure at a height level given in meters above surface.
     This is done by finding the model level above and below the specified height
     and interpolating the pressure

src/anemoi/datasets/create/functions/sources/xarray/coordinates.py

@@ -8,6 +8,8 @@
 # nor does it submit to any jurisdiction.
 
 
+from __future__ import annotations
+
 import datetime
 import logging
 
@@ -78,7 +80,7 @@ def __repr__(self):
             self.variable.shape,
         )
 
-    def reduced(self, i):
+    def reduced(self, i) -> Coordinate:
         """Create a new coordinate with a single value
 
         Parameters
@@ -96,7 +98,7 @@ def reduced(self, i):
             **self.kwargs,
         )
 
-    def index(self, value):
+    def index(self, value) -> Coordinate:
         """Return the index of the value in the coordinate
 
         Parameters

src/anemoi/datasets/create/functions/sources/xarray/fieldlist.py

@@ -128,9 +128,14 @@ def _skip_attr(v, attr_name):
 
         return cls(ds, variables)
 
-    def sel(self, **kwargs):
+    def sel(self, **kwargs) -> FieldList:
         """Override the FieldList's sel method
 
+        Parameters
+        ----------
+        kwargs : dict
+            The selection criteria
+
         Returns
         -------
         FieldList

src/anemoi/datasets/create/functions/sources/xarray/variable.py

@@ -162,13 +162,7 @@ def __init__(self, variable, **kwargs):
 
     @cached_property
     def fields(self):
-        """Filter the fields of a variable based on metadata.
-
-        Returns
-        -------
-        list
-            A list of fields that match the metadata.
-        """
+        """Filter the fields of a variable based on metadata."""
         return [
             field
             for field in self.variable

src/anemoi/datasets/create/statistics/__init__.py

@@ -27,7 +27,7 @@
 LOG = logging.getLogger(__name__)
 
 
-def default_statistics_dates(dates):
+def default_statistics_dates(dates) -> tuple:
     """Calculate default statistics dates based on the given list of dates.
 
     Args:

src/anemoi/datasets/data/complement.py

@@ -70,7 +70,7 @@ def missing(self):
         missing = missing | self.target.missing
         return set(missing)
 
-    def tree(self):
+    def tree(self) -> Node:
         """Generates a hierarchical tree structure for the `Cutout` instance and
         its associated datasets.
 

src/anemoi/datasets/data/dataset.py

@@ -14,12 +14,16 @@
 import pprint
 import warnings
 from functools import cached_property
+from typing import TYPE_CHECKING
 
 import numpy as np
 from anemoi.utils.dates import frequency_to_seconds
 from anemoi.utils.dates import frequency_to_string
 from anemoi.utils.dates import frequency_to_timedelta
 
+if TYPE_CHECKING:
+    import matplotlib
+
 LOG = logging.getLogger(__name__)
 
 
@@ -519,7 +523,7 @@ def _compute_constant_fields_from_statistics(self):
 
         return result
 
-    def plot(self, date, variable, member=0, **kwargs):
+    def plot(self, date, variable, member=0, **kwargs) -> "matplotlib.pyplot.Axes":
         """For debugging purposes, plot a field.
 
         Parameters
@@ -537,7 +541,7 @@ def plot(self, date, variable, member=0, **kwargs):
 
         Returns
         -------
-            matplotlib.pyplot.Axes
+        axes : matplotlib.pyplot.Axes
         """
 
         from anemoi.utils.devtools import plot_values

src/anemoi/datasets/data/grids.py

@@ -236,7 +236,7 @@ def _initialize_masks(self):
                         lam_current_mask[~lam_overlap_mask] = False
             self.masks.append(lam_current_mask)
 
-    def has_overlap(self, lats1, lons1, lats2, lons2, distance_threshold=1.0):
+    def has_overlap(self, lats1, lons1, lats2, lons2, distance_threshold=1.0) -> bool:
         """Checks for overlapping points between two sets of latitudes and
         longitudes within a specified distance threshold.
 
@@ -261,7 +261,7 @@ def has_overlap(self, lats1, lons1, lats2, lons2, distance_threshold=1.0):
         # Check if any distance is less than the specified threshold
         return np.any(distances < distance_threshold)
 
-    def __getitem__(self, index):
+    def __getitem__(self, index) -> np.ndarray:
         """Retrieves data from the masked LAMs and global dataset based on the
         given index.
 
@@ -276,7 +276,7 @@ def __getitem__(self, index):
             index = (index, slice(None), slice(None), slice(None))
         return self._get_tuple(index)
 
-    def _get_tuple(self, index):
+    def _get_tuple(self, index) -> np.ndarray:
         """Helper method that applies masks and retrieves data from each dataset
         according to the specified index.
 
@@ -300,7 +300,7 @@ def _get_tuple(self, index):
 
         return apply_index_to_slices_changes(result, changes)
 
-    def collect_supporting_arrays(self, collected, *path):
+    def collect_supporting_arrays(self, collected, *path) -> None:
         """Collects supporting arrays, including masks for each LAM and the global
         dataset.
 
@@ -316,12 +316,9 @@ def collect_supporting_arrays(self, collected, *path):
         collected.append((path + ("global",), "cutout_mask", self.global_mask))
 
     @cached_property
-    def shape(self):
+    def shape(self) -> tuple:
         """Returns the shape of the Cutout, accounting for retained grid points
         across all LAMs and the global dataset.
-
-        Returns:
-            tuple: Shape of the concatenated masked datasets.
         """
         shapes = [np.sum(mask) for mask in self.masks]
         global_shape = np.sum(self.global_mask)
@@ -333,24 +330,18 @@ def check_same_resolution(self, d1, d2):
         pass
 
     @property
-    def grids(self):
+    def grids(self) -> tuple:
         """Returns the number of grid points for each LAM and the global dataset
         after applying masks.
-
-        Returns:
-            tuple: Count of retained grid points for each dataset.
         """
         grids = [np.sum(mask) for mask in self.masks]
         grids.append(np.sum(self.global_mask))
         return tuple(grids)
 
     @property
-    def latitudes(self):
+    def latitudes(self) -> np.ndarray:
         """Returns the concatenated latitudes of each LAM and the global dataset
         after applying masks.
-
-        Returns:
-            np.ndarray: Concatenated latitude array for the masked datasets.
         """
         lam_latitudes = np.concatenate([lam.latitudes[mask] for lam, mask in zip(self.lams, self.masks)])
 
@@ -362,12 +353,9 @@ def latitudes(self):
         return latitudes
 
     @property
-    def longitudes(self):
+    def longitudes(self) -> np.ndarray:
         """Returns the concatenated longitudes of each LAM and the global dataset
         after applying masks.
-
-        Returns:
-            np.ndarray: Concatenated longitude array for the masked datasets.
         """
         lam_longitudes = np.concatenate([lam.longitudes[mask] for lam, mask in zip(self.lams, self.masks)])
 
@@ -378,7 +366,7 @@ def longitudes(self):
         longitudes = np.concatenate([lam_longitudes, self.globe.longitudes[self.global_mask]])
         return longitudes
 
-    def tree(self):
+    def tree(self) -> Node:
         """Generates a hierarchical tree structure for the `Cutout` instance and
         its associated datasets.