Resolve "robust apriori estimate for short timeseries"

Closes #451 (closed)

test/test_helpers/test_filter.py

@@ -178,8 +178,8 @@ class TestClimateFIRFilter:
         obj = object.__new__(ClimateFIRFilter)
         sel_opts = {time_dim: slice("2010-05", "2010-08")}
         res = obj.create_monthly_mean(xr_array_long, time_dim, sel_opts=sel_opts)
-        assert res.dropna(time_dim)[f"{time_dim}.month"].min() == 5
-        assert res.dropna(time_dim)[f"{time_dim}.month"].max() == 8
+        assert res.dropna(time_dim)[f"{time_dim}.month"].min() == 1
+        assert res.dropna(time_dim)[f"{time_dim}.month"].max() == 12
         mean_jun_2010 = xr_array_long[xr_array_long[f"{time_dim}.month"] == 6].sel({time_dim: "2010"}).mean()
         assert res.sel({time_dim: "2010-06-15T00:00:00"}) == mean_jun_2010
 
@@ -226,8 +226,8 @@ class TestClimateFIRFilter:
         xr_array_long = xr_array_long.resample({time_dim: "1H"}).interpolate()
         sel_opts = {time_dim: slice("2010-05", "2010-08")}
         res = obj.create_seasonal_hourly_mean(xr_array_long, time_dim, sel_opts=sel_opts)
-        assert res.dropna(time_dim)[f"{time_dim}.month"].min() == 5
-        assert res.dropna(time_dim)[f"{time_dim}.month"].max() == 8
+        assert res.dropna(time_dim)[f"{time_dim}.month"].min() == 1
+        assert res.dropna(time_dim)[f"{time_dim}.month"].max() == 12
 
     def test_create_unity_array(self, xr_array, time_dim):
         obj = object.__new__(ClimateFIRFilter)