docs: rm param types from docstrings

nci · Oct 19, 2023 · fbd66eb · fbd66eb
1 parent 21da685
commit fbd66eb
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Showing 2 changed files with 36 additions and 39 deletions.
diff --git a/src/scores/continuous/standard_impl.py b/src/scores/continuous/standard_impl.py
@@ -71,7 +71,7 @@ def rmse(
     reduce_dims: FlexibleDimensionTypes = None,
     preserve_dims: FlexibleDimensionTypes = None,
     weights=None,
-):
+) -> FlexibleArrayType:
     """Calculate the Root Mean Squared Error from xarray or pandas objects.
 
     A detailed explanation is on [Wikipedia](https://en.wikipedia.org/wiki/Root-mean-square_deviation)
@@ -84,13 +84,13 @@ def rmse(
 
 
     Args:
-        fcst Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]: Forecast
+        fcst: Forecast
             or predicted variables in xarray or pandas.
-        obs (Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]): Observed
+        obs: Observed
             variables in xarray or pandas.
-        reduce_dims (Union[str, Iterable[str]): Optionally specify which dimensions to reduce when
+        reduce_dims: Optionally specify which dimensions to reduce when
             calculating RMSE. All other dimensions will be preserved.
-        preserve_dims (Union[str, Iterable[str]): Optionally specify which dimensions to preserve
+        preserve_dims: Optionally specify which dimensions to preserve
             when calculating RMSE. All other dimensions will be reduced.
             As a special case, 'all' will allow all dimensions to be
             preserved. In this case, the result will be in the same
@@ -102,7 +102,7 @@ def rmse(
           area, by latitude, by population, custom)
 
     Returns:
-        Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]:  An object containing
+        An object containing
             a single floating point number representing the root mean squared
             error for the supplied data. All dimensions will be reduced.
             Otherwise: Returns an object representing the root mean squared error,
@@ -122,7 +122,7 @@ def mae(
     reduce_dims: FlexibleDimensionTypes = None,
     preserve_dims: FlexibleDimensionTypes = None,
     weights=None,
-):
+) -> FlexibleArrayType:
     """Calculates the mean absolute error from forecast and observed data.
 
     A detailed explanation is on [Wikipedia](https://en.wikipedia.org/wiki/Mean_absolute_error)
@@ -133,25 +133,23 @@ def mae(
     Specifying both will result in an exception.
 
     Args:
-        fcst (Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]): Forecast
-            or predicted variables in xarray or pandas.
-        obs (Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]): Observed
-            variables in xarray or pandas.
-        reduce_dims (Union[str, Iterable[str]]): Optionally specify which dimensions
-            to reduce when calculating MAE. All other dimensions will be preserved.
-        preserve_dims (Union[str, Iterable[str]]): Optionally specify which
-            dimensions to preserve when calculating MAE. All other dimensions
-            will be reduced. As a special case, 'all' will allow all dimensions
-            to be preserved. In this case, the result will be in the same
-            shape/dimensionality as the forecast, and the errors will be
-            the absolute error at each point (i.e. single-value comparison
-            against observed), and the forecast and observed dimensions
-            must match precisely.
-        weights: Not yet implemented. Allow weighted averaging (e.g. by
-            area, by latitude, by population, custom).
+        fcst: Forecast or predicted variables in xarray or pandas.
+        obs: Observed variables in xarray or pandas.
+        reduce_dims: Optionally specify which dimensions to reduce when
+            calculating MAE. All other dimensions will be preserved.
+        preserve_dims: Optionally specify which dimensions to preserve when
+            calculating MAE. All other dimensions will be reduced. As a
+            special case, 'all' will allow all dimensions to be preserved. In
+            this case, the result will be in the same shape/dimensionality
+            as the forecast, and the errors will be the absolute error at each
+            point (i.e. single-value comparison against observed), and the
+            forecast and observed dimensions must match precisely.
+        weights:
+            Not yet implemented. Allow weighted averaging (e.g. by area, by
+            latitude, by population, custom).
 
     Returns:
-        Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]: By default an xarray DataArray containing
+        By default an xarray DataArray containing
         a single floating point number representing the mean absolute error for the
         supplied data. All dimensions will be reduced.
 

diff --git a/src/scores/utils.py b/src/scores/utils.py
@@ -44,18 +44,18 @@ def gather_dimensions(
     obs_dims: Iterable[Hashable],
     reduce_dims: FlexibleDimensionTypes = None,
     preserve_dims: FlexibleDimensionTypes = None,
-):
+) -> Optional[set[Hashable]]:
     """
     Establish which dimensions to reduce when calculating errors but before taking means
 
     Args:
-        fcst_dims (Iterable[str]): Forecast dimensions inputs
-        obs_dims (Iterable[str]): Observation dimensions inputs.
-        reduce_dims (Union[str, Iterable[str]]): Dimensions to reduce.
-        preserve_dims (Union[str, Iterable[str]]): Dimensions to preserve.
+        fcst_dims: Forecast dimensions inputs
+        obs_dims: Observation dimensions inputs.
+        reduce_dims: Dimensions to reduce.
+        preserve_dims: Dimensions to preserve.
 
     Returns:
-        Tuple[str]: Dimensions based on optional args.
+        Dimensions based on optional args.
     Raises:
         ValueError: When `preserve_dims and `reduce_dims` are both specified.
     """
@@ -110,15 +110,15 @@ def gather_dimensions(
     return reduce_dims
 
 
-def dims_complement(data, dims=None):
+def dims_complement(data, dims=None) -> list[str]:
     """Returns the complement of data.dims and dims
 
     Args:
-        data (Union[xr.Dataset, xr.DataArray]): Input xarray object
-        dims (Iterable[str]): an Iterable of strings corresponding to dimension names
+        data: Input xarray object
+        dims: an Iterable of strings corresponding to dimension names
 
     Returns:
-        List[str]: A sorted list of dimension names, the complement of data.dims and dims
+        A sorted list of dimension names, the complement of data.dims and dims
     """
 
     if dims is None:
@@ -131,15 +131,15 @@ def dims_complement(data, dims=None):
     return sorted(list(complement))
 
 
-def check_dims(xr_data: XarrayLike, expected_dims: List[str], mode: Optional[str] = None):
+def check_dims(xr_data: XarrayLike, expected_dims: list[str], mode: Optional[str] = None):
     """
     Checks the dimensions xr_data with expected_dims, according to `mode`.
 
     Args:
-        xr_data (XarrayLike): if a Dataset is supplied,
+        xr_data: if a Dataset is supplied,
             all of its data variables (DataArray objects) are checked.
-        expected_dims (Iterable[str]): an Iterable of dimension names.
-        mode (Optional[str]): one of 'equal' (default), 'subset' or 'superset'.
+        expected_dims: an Iterable of dimension names.
+        mode: one of 'equal' (default), 'subset' or 'superset'.
             If 'equal', checks that the data object has the same dimensions
             as `expected_dims`.
             If 'subset', checks that the dimensions of the data object is a
@@ -163,7 +163,6 @@ def check_dims(xr_data: XarrayLike, expected_dims: List[str], mode: Optional[str
         ValueError: `expected_dims` cannot be coerced into a set.
         ValueError: `mode` is not one of 'equal', 'subset', 'superset',
             'proper subset', 'proper superset', or 'disjoint'
-
     """
 
     if isinstance(expected_dims, str):