Adjust return from values to match what would be more consistent for …

…pandas users

src/scores/continuous/standard_impl.py

@@ -6,6 +6,8 @@
 import scores.utils
 from scores.typing import FlexibleArrayType, FlexibleDimensionTypes
 import pandas
+import numpy
+import scores.utils
 
 
 def mse(
@@ -77,6 +79,8 @@ def mse(
 
     if both_pandas:
         _mse = _mse.to_pandas()
+        if isinstance(_mse, numpy.ndarray):
+            _mse = numpy.float64(_mse)        
 
     return _mse
 
@@ -172,17 +176,17 @@ def mae(
         Alternatively, an xarray structure with dimensions preserved as appropriate
         containing the score along reduced dimensions
     """
-    # as_pandas_series = False
-    # both_pandas = False
-    # if type(fcst) == pandas.Series:
-    #     fcst = fcst.to_xarray()
-    #     as_pandas_series = True
+    as_pandas_series = False
+    both_pandas = False
+    if type(fcst) == pandas.Series:
+        fcst = fcst.to_xarray()
+        as_pandas_series = True
 
-    # if type(obs) == pandas.Series:
-    #     obs = obs.to_xarray()
-    #     as_pandas_series = True
-    #     if as_pandas_series == True:
-    #         both_pandas = True
+    if type(obs) == pandas.Series:
+        obs = obs.to_xarray()
+        as_pandas_series = True
+        if as_pandas_series == True:
+            both_pandas = True
 
 
     error = fcst - obs
@@ -197,7 +201,9 @@ def mae(
     else:
         _ae = ae.mean()
 
-    # if both_pandas:
-    #     _ae = _ae.to_pandas()
+    if both_pandas:
+        _ae = _ae.to_pandas()
+        if isinstance(_ae, numpy.ndarray):
+            _ae = numpy.float64(_ae)
 
     return _ae