Fix docstrings

HISTORY.rst

@@ -23,7 +23,7 @@ New features and enhancements
 * Add more comments in the template. (:pull:`233`, :issue:`232`).
 * ``generate_weights`` now allows to split weights between experiments, and make them vary along the time/horizon axis. (:issue:`108`, :pull:`231`).
 * New independence_level, `institution`, added to ``generate_weights``. (:pull:`231`).
-* Updated ``produce_horizon`` so it can accept multiple periods or warming levels. (:pull:`231`).
+* Updated ``produce_horizon`` so it can accept multiple periods or warming levels. (:pull:`231`, :pull:`240`).
 * Add more comments in the template. (:pull:`233`,:pull:`235`, :issue:`232`).
 * New function ``diagnostics.health_checks`` that can perform multiple checkups on a dataset. (:pull:`238`).
 

tests/test_aggregate.py

@@ -455,6 +455,15 @@ def test_combine(self):
             out.horizon, ["1982-1988", "+0.8Cvs1850-1900", "+0.85Cvs1850-1900"]
         )
 
+    def test_single(self):
+        out = xs.produce_horizon(
+            self.ds,
+            indicators=self.yaml_file,
+            periods=[1982, 1988],
+        )
+        assert len(out.horizon) == 1
+        np.testing.assert_array_equal(out.horizon, ["1982-1988"])
+
     def test_warminglevel_in_ds(self):
         ds = self.ds.copy().expand_dims({"warminglevel": ["+1Cvs1850-1900"]})
         out = xs.produce_horizon(

xscen/aggregate.py

@@ -727,7 +727,7 @@ def produce_horizon(
 
     all_periods = []
     if periods is not None:
-        all_periods.extend(periods)
+        all_periods.extend(standardize_periods(periods))
     if warminglevels is not None:
         if isinstance(warminglevels["wl"], (int, float)):
             all_periods.append(warminglevels)
@@ -761,7 +761,12 @@ def produce_horizon(
 
         if ds_sub is not None:
             # compute indicators
-            ind_dict = compute_indicators(ds=ds_sub, indicators=indicators)
+            ind_dict = compute_indicators(
+                ds=ds_sub.squeeze(dim="warminglevel")
+                if "warminglevel" in ds_sub.dims
+                else ds_sub,
+                indicators=indicators,
+            )
 
             # Compute the window-year mean
             ds_merge = xr.Dataset()
@@ -807,10 +812,10 @@ def produce_horizon(
                     )
                     ds_mean["horizon"] = ds_mean["horizon"].astype(str)
 
-                if "warminglevel" in ds_mean.dims:
-                    wl = ds_mean["warminglevel"].values
+                if "warminglevel" in ds_mean.coords:
+                    wl = np.array([ds_mean["warminglevel"].item()])
                     wl_attrs = ds_mean["warminglevel"].attrs
-                    ds_mean = ds_mean.squeeze(dim="warminglevel", drop=True)
+                    ds_mean = ds_mean.drop_vars("warminglevel")
                     ds_mean["horizon"] = wl
                     ds_mean["horizon"].attrs.update(wl_attrs)