rename notes to references

xskillscore/core/probabilistic.py

@@ -327,8 +327,7 @@ def threshold_brier_score(
     if isinstance(threshold, (xr.DataArray, xr.Dataset)):
         if "threshold" not in threshold.dims:
             raise ValueError(
-                "please provide threshold with threshold dim, found",
-                threshold.dims,
+                "please provide threshold with threshold dim, found", threshold.dims,
             )
         input_core_dims = [[], [member_dim], ["threshold"]]
         output_core_dims = [["threshold"]]
@@ -553,34 +552,28 @@ def discrimination(
         ...                       forecast_event_likelihood,
         ...                       dim=['x','y'])
 
-        Notes
-        -----
-        See http://www.cawcr.gov.au/projects/verification/
+        References
+        ----------
+        http://www.cawcr.gov.au/projects/verification/
     """
 
     _fail_if_dim_empty(dim)
 
-    hist_event = (
-        histogram(
-            forecasts.where(observations),
-            bins=[probability_bin_edges],
-            bin_names=[FORECAST_PROBABILITY_DIM],
-            bin_dim_suffix="",
-            dim=dim,
-        )
-        / (observations).sum(dim=dim)
-    )
-
-    hist_no_event = (
-        histogram(
-            forecasts.where(np.logical_not(observations)),
-            bins=[probability_bin_edges],
-            bin_names=[FORECAST_PROBABILITY_DIM],
-            bin_dim_suffix="",
-            dim=dim,
-        )
-        / (np.logical_not(observations)).sum(dim=dim)
-    )
+    hist_event = histogram(
+        forecasts.where(observations),
+        bins=[probability_bin_edges],
+        bin_names=[FORECAST_PROBABILITY_DIM],
+        bin_dim_suffix="",
+        dim=dim,
+    ) / (observations).sum(dim=dim)
+
+    hist_no_event = histogram(
+        forecasts.where(np.logical_not(observations)),
+        bins=[probability_bin_edges],
+        bin_names=[FORECAST_PROBABILITY_DIM],
+        bin_dim_suffix="",
+        dim=dim,
+    ) / (np.logical_not(observations)).sum(dim=dim)
 
     return xr.concat([hist_event, hist_no_event], dim="event").assign_coords(
         {"event": [True, False]}