silence sphinx warnings

Author: keewis

xarray/backends/api.py

@@ -761,6 +761,7 @@ def open_mfdataset(
               'no_conflicts', 'override'}, optional
         String indicating how to compare variables of the same name for
         potential conflicts when merging:
+
          * 'broadcast_equals': all values must be equal when variables are
            broadcast against each other to ensure common dimensions.
          * 'equals': all values and dimensions must be the same.
@@ -770,6 +771,7 @@ def open_mfdataset(
            must be equal. The returned dataset then contains the combination
            of all non-null values.
          * 'override': skip comparing and pick variable from first dataset
+
     preprocess : callable, optional
         If provided, call this function on each dataset prior to concatenation.
         You can find the file-name from which each dataset was loaded in

xarray/core/alignment.py

@@ -108,7 +108,7 @@ def align(
 
     Returns
     -------
-    aligned : same as *objects
+    aligned : same as `*objects`
         Tuple of objects with aligned coordinates.
 
     Raises

xarray/core/combine.py

@@ -531,6 +531,7 @@ def combine_by_coords(
         * 'all': All data variables will be concatenated.
         * list of str: The listed data variables will be concatenated, in
           addition to the 'minimal' data variables.
+
         If objects are DataArrays, `data_vars` must be 'all'.
     coords : {'minimal', 'different', 'all' or list of str}, optional
         As per the 'data_vars' kwarg, but for coordinate variables.
@@ -747,6 +748,7 @@ def auto_combine(
              'no_conflicts', 'override'}, optional
         String indicating how to compare variables of the same name for
         potential conflicts:
+
         - 'broadcast_equals': all values must be equal when variables are
           broadcast against each other to ensure common dimensions.
         - 'equals': all values and dimensions must be the same.

xarray/core/common.py

@@ -463,7 +463,7 @@ def assign_coords(self, coords=None, **coords_kwargs):
     def assign_attrs(self, *args, **kwargs):
         """Assign new attrs to this object.
 
-        Returns a new object equivalent to self.attrs.update(*args, **kwargs).
+        Returns a new object equivalent to ``self.attrs.update(*args, **kwargs)``.
 
         Parameters
         ----------
@@ -490,7 +490,7 @@ def pipe(
         **kwargs,
     ) -> T:
         """
-        Apply func(self, *args, **kwargs)
+        Apply ``func(self, *args, **kwargs)``
 
         This method replicates the pandas method of the same name.
 
@@ -819,6 +819,7 @@ def rolling_exp(
         ----------
         window : A single mapping from a dimension name to window value,
                  optional
+
             dim : str
                 Name of the dimension to create the rolling exponential window
                 along (e.g., `time`).
@@ -857,6 +858,7 @@ def coarsen(
         ----------
         dim: dict, optional
             Mapping from the dimension name to the window size.
+
             dim : str
                 Name of the dimension to create the rolling iterator
                 along (e.g., `time`).
@@ -867,7 +869,7 @@ def coarsen(
             multiple of the window size. If 'trim', the excess entries are
             dropped. If 'pad', NA will be padded.
         side : 'left' or 'right' or mapping from dimension to 'left' or 'right'
-        coord_func: function (name) that is applied to the coordintes,
+        coord_func : function (name) that is applied to the coordintes,
             or a mapping from coordinate name to function (name).
 
         Returns

xarray/core/concat.py

@@ -45,6 +45,7 @@ def concat(
           * 'all': All data variables will be concatenated.
           * list of str: The listed data variables will be concatenated, in
             addition to the 'minimal' data variables.
+
         If objects are DataArrays, data_vars must be 'all'.
     coords : {'minimal', 'different', 'all' or list of str}, optional
         These coordinate variables will be concatenated together:

xarray/core/dataarray.py

@@ -2916,7 +2916,7 @@ def quantile(
             is a scalar. If multiple percentiles are given, first axis of
             the result corresponds to the quantile and a quantile dimension
             is added to the return array. The other dimensions are the
-             dimensions that remain after the reduction of the array.
+            dimensions that remain after the reduction of the array.
 
         See Also
         --------

xarray/core/dataset.py

@@ -2108,7 +2108,7 @@ def thin(
         indexers: Union[Mapping[Hashable, int], int] = None,
         **indexers_kwargs: Any,
     ) -> "Dataset":
-        """Returns a new dataset with each array indexed along every `n`th
+        """Returns a new dataset with each array indexed along every `n`-th
         value for the specified dimension(s)
 
         Parameters
@@ -3467,6 +3467,7 @@ def merge(
                   'no_conflicts'}, optional
             String indicating how to compare variables of the same name for
             potential conflicts:
+
             - 'broadcast_equals': all values must be equal when variables are
               broadcast against each other to ensure common dimensions.
             - 'equals': all values and dimensions must be the same.
@@ -3475,6 +3476,7 @@ def merge(
             - 'no_conflicts': only values which are not null in both datasets
               must be equal. The returned dataset then contains the combination
               of all non-null values.
+
         join : {'outer', 'inner', 'left', 'right', 'exact'}, optional
             Method for joining ``self`` and ``other`` along shared dimensions:
 
@@ -3615,7 +3617,7 @@ def drop_sel(self, labels=None, *, errors="raise", **labels_kwargs):
             in the dataset. If 'ignore', any given labels that are in the
             dataset are dropped and no error is raised.
         **labels_kwargs : {dim: label, ...}, optional
-            The keyword arguments form of ``dim`` and ``labels`
+            The keyword arguments form of ``dim`` and ``labels``
 
         Returns
         -------
@@ -3907,6 +3909,7 @@ def interpolate_na(
         method : {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
                   'polynomial', 'barycentric', 'krog', 'pchip',
                   'spline'}, optional
+
             String indicating which method to use for interpolation:
 
             - 'linear': linear interpolation (Default). Additional keyword
@@ -5218,7 +5221,7 @@ def integrate(self, coord, datetime_unit=None):
         datetime_unit
             Can be specify the unit if datetime coordinate is used. One of
             {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs',
-             'as'}
+            'as'}
 
         Returns
         -------

xarray/core/groupby.py

@@ -677,11 +677,13 @@ def map(self, func, shortcut=False, args=(), **kwargs):
             Callable to apply to each array.
         shortcut : bool, optional
             Whether or not to shortcut evaluation under the assumptions that:
+
             (1) The action of `func` does not depend on any of the array
                 metadata (attributes or coordinates) but only on the data and
                 dimensions.
             (2) The action of `func` creates arrays with homogeneous metadata,
                 that is, with the same dimensions and attributes.
+
             If these conditions are satisfied `shortcut` provides significant
             speedup. This should be the case for many common groupby operations
             (e.g., applying numpy ufuncs).