Merge pull request #329 from raybellwaves/just-docs

docs/source/conf.py

@@ -32,7 +32,6 @@
 # ones.
 extensions = [
     "sphinx.ext.autodoc",
-    "sphinx.ext.autodoc.typehints",
     "sphinx.ext.autosummary",
     "sphinx.ext.intersphinx",
     "sphinx.ext.extlinks",
@@ -49,13 +48,12 @@
     "pr": ("https://github.com/xarray-contrib/xskillscore/pull/%s", "GH#"),
 }
 
-autodoc_typehints = "description"
+autodoc_typehints = "none"
 
 nbsphinx_timeout = 60
 nbsphinx_execute = "always"
 
 autosummary_generate = True
-autodoc_typehints = "none"
 
 napoleon_use_param = True
 napoleon_use_rtype = True

xskillscore/core/comparative.py

@@ -8,6 +8,8 @@
 
 from . import deterministic as dm
 
+XArray = Union[xr.Dataset, xr.DataArray]
+
 
 def sign_test(
     forecasts1,
@@ -69,14 +71,11 @@ def sign_test(
     Returns
     -------
     xarray.DataArray or xarray.Dataset
-        Boolean returns answering whether forecast1 is significantly different to
-        forecast2.
+        boolean whether ``forecast1`` is significantly different to ``forecast2``.
     xarray.DataArray or xarray.Dataset
-        Positive (negative) walk values shows how often ``forecast1`` is better (worse)
-        than ``forecast2`` according to metric computed over ``dim``.
+        walk values shows how often ``forecast1`` is better ``forecast2``.
     xarray.DataArray or xarray.Dataset
-        confidence shows the positive boundary for a random walk at significance
-        level ``alpha``.
+        confidence boundary for a random walk at significance level ``alpha``.
 
     Examples
     --------
@@ -196,19 +195,15 @@ def _categorical_metric(observations, forecasts, dim):
 
 
 def halfwidth_ci_test(
-    forecasts1: Union[xr.Dataset, xr.DataArray],
-    forecasts2: Union[xr.Dataset, xr.DataArray],
-    observations: Optional[Union[xr.Dataset, xr.DataArray]] = None,
+    forecasts1: XArray,
+    forecasts2: XArray,
+    observations: Optional[XArray] = None,
     metric: Optional[str] = None,
     dim: Optional[Union[str, List[str]]] = None,
     time_dim: str = "time",
     alpha: float = 0.05,
     **kwargs: Mapping,
-) -> Tuple[
-    Union[xr.Dataset, xr.DataArray],
-    Union[xr.Dataset, xr.DataArray],
-    Union[xr.Dataset, xr.DataArray],
-]:
+) -> Tuple[XArray, XArray, XArray]:
     """
     Returns the Jolliffe and Ebert significance test.
 
@@ -238,7 +233,7 @@ def halfwidth_ci_test(
     metric : str, optional
         Name of distance metric function to be used for computing the error between
         forecasts and observation. It can be any of the xskillscore distance metric
-        function except for``mape``. Valid metrics are ``me``, ``rmse``, ``mse``,
+        function except for ``mape``. Valid metrics are ``me``, ``rmse``, ``mse``,
         ``mae``, ``median_absolute_error`` and ``smape``. Note that if metric is None,
         observations must also be None.
         Defaults to None.
@@ -258,11 +253,9 @@ def halfwidth_ci_test(
     Returns
     -------
     xarray.DataArray or xarray.Dataset
-        Is the difference in scores (score(f2) - score(f1)) significant?
-        (returns boolean).
+        boolean whether the difference in scores (score(f2) - score(f1)) are significant.
     xarray.DataArray or xarray.Dataset
-        Difference in scores (score(f2) - score(f1)) reduced by ``dim`` and
-        ``time_dim``.
+        difference in scores (score(f2) - score(f1)) reduced by ``dim`` and ``time_dim``.
     xarray.DataArray or xarray.Dataset
         half-width of the confidence interval at the significance level ``alpha``.
 
@@ -296,7 +289,6 @@ def halfwidth_ci_test(
     <xarray.DataArray ()>
     array(True)
 
-
     References
     ----------
         * https://www.cawcr.gov.au/projects/verification/CIdiff/FAQ-CIdiff.html