Merge branch 'lukas_issue451_feat_robust-apriori-estimate-for-short-timeseries' into 'develop'

Resolve "robust apriori estimate for short timeseries"

See merge request !513

mlair/helpers/filter.py

@@ -382,7 +382,7 @@ class ClimateFIRFilter(FIRFilter):
                                monthly_mean.sel(month=month, drop=True),
                                monthly)
         # transform monthly information into original sampling rate
-        return monthly.resample({time_dim: sampling}).interpolate()
+        return monthly.dropna(dim=time_dim).resample({time_dim: sampling}).interpolate()
 
     @staticmethod
     def _compute_hourly_mean_per_month(data: xr.DataArray, time_dim: str, as_anomaly: bool) -> Dict[int, xr.DataArray]:
@@ -422,7 +422,7 @@ class ClimateFIRFilter(FIRFilter):
         for month in means.keys():
             hourly_mean_single_month = means[month].sel(hour=hour, drop=True)
             h_coll = xr.where((h_coll[f"{time_dim}.month"] == month), hourly_mean_single_month, h_coll)
-        h_coll = h_coll.resample({time_dim: sampling}).interpolate()
+        h_coll = h_coll.dropna(time_dim).resample({time_dim: sampling}).interpolate()
         h_coll = h_coll.sel({time_dim: (h_coll[f"{time_dim}.hour"] == hour)})
         return h_coll
 

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)

test/test_run_modules/test_model_setup.py

@@ -27,7 +27,7 @@ class TestModelSetup:
         obj.data_store.set("lr_decay", "dummy_str", "general.model")
         obj.data_store.set("hist", "dummy_str", "general.model")
         obj.data_store.set("epochs", 2)
-        obj.model_name = "%s.h5"
+        obj.model_path = "%s.h5"
         yield obj
         RunEnvironment().__del__()