Updated value_clipping wrt rh corr

* The rh corrected clipping were asigning 0 to inpu nan
* Added unit test specifically for rh corr

src/pypromice/process/value_clipping.py

@@ -1,8 +1,5 @@
-from typing import Dict, Set, Mapping
-
 import numpy as np
 import pandas
-import pandas as pd
 import xarray
 
 from pypromice.utilities.dependency_graph import DependencyGraph
@@ -31,8 +28,6 @@ def clip_values(
     """
     cols = ["lo", "hi", "OOL"]
     assert set(cols) <= set(var_configurations.columns)
-    # TODO: Check if this is necessary
-    # variable_limits = var_configurations[cols].dropna(how="all")
 
     variable_limits = var_configurations[cols].assign(
         dependents=lambda df: df.OOL.fillna("").str.split(),
@@ -45,19 +40,16 @@ def clip_values(
     for var, row in variable_limits.iterrows():
         if var not in list(ds.variables):
             continue
-        # TODO: Check if this is necessary
-        # I guess the nan flagging is already handled below
-        # What if rh_u_cor is nan?
-        # What if row.lo/hi is nan?
 
+        # This is a special case for rh_u_cor and rh_l_cor where values are clipped to 0 and 100.
         if var in ["rh_u_cor", "rh_l_cor"]:
-            ds[var] = ds[var].where(ds[var] >= row.lo, other=0)
-            ds[var] = ds[var].where(ds[var] <= row.hi, other=100)
-
-            # Mask out invalid corrections based on uncorrected var
-            var_uncor = var.rstrip("_cor")
-            ds[var] = ds[var].where(~np.isnan(ds[var_uncor]), other=np.nan)
-
+            # Nan inputs should stay nan
+            was_nan = ds[var].isnull()
+            if ~np.isnan(row.lo):
+                ds[var] = ds[var].where(ds[var] >= row.lo, other=0)
+            if ~np.isnan(row.hi):
+                ds[var] = ds[var].where( ds[var] <= row.hi, other=100)
+            ds[var] = ds[var].where(~was_nan)
         else:
             if ~np.isnan(row.lo):
                 ds[var] = ds[var].where(ds[var] >= row.lo)

tests/unit/test_value_clippping.py

@@ -198,3 +198,78 @@ def test_circular_dependencies(self):
             check_dtype=True,
         )
 
+    def test_rh_corrected_case(self):
+        """
+        The rh corrected variables are treated differently in the clipping function.
+        """
+        data_index = pd.RangeIndex(2)
+        rh_u = pd.Series(index=data_index, data=[0, 54], name="rh_u")
+        rh_u_cor = pd.Series(index=data_index, data=[0, np.nan], name="rh_u_cor")
+        rh_l = pd.Series(index=data_index, data=[-20, 54], name="rh_l")
+        rh_l_cor = pd.Series(index=data_index, data=[0, 254], name="rh_l_cor")
+        data = pd.concat([rh_u, rh_u_cor, rh_l, rh_l_cor], axis=1)
+        variable_config = pd.DataFrame(
+            columns=["field", "lo", "hi", "OOL"],
+            data=[
+                ["rh_u", 0, 100, "rh_u_cor"],
+                ["rh_u_cor", 0, 150, ""],
+                ["rh_l_cor", np.nan, np.nan, ""],
+                ["rh_l", 0, 100, "rh_l_cor"],
+            ],
+        ).set_index("field")
+        data_set = xr.Dataset(data)
+
+        data_set_out = clip_values(data_set, variable_config)
+
+        # Convert to dataframe for easier comparison
+        data_frame_out = data_set_out.to_dataframe()
+        # The value of rh_u_cor should be nan since the input was nan
+        self.assertTrue(np.isnan(data_frame_out.iloc[1]["rh_u_cor"]))
+        # The value of rh_l_cor should not be changed since the hi threshold is not nan
+        self.assertEqual(data_frame_out.iloc[1]["rh_l_cor"], 254)
+        # The value of rh_l_cor should be nan since rh_l is below its threshold
+        self.assertTrue(np.isnan(data_frame_out.iloc[0]["rh_l_cor"]))
+
+    def test_nan_input(self):
+        """
+        Test that the function handles the case where nan input should cascade to child variables.
+        """
+        fields = ["a", "b"]
+        variable_config = pd.DataFrame(
+            columns=["field", "lo", "hi", "OOL"],
+            data=[
+                ["a", 0, 10, "b"],
+                ["b", 100, 110, ""],
+            ],
+        ).set_index("field")
+        data_index = pd.RangeIndex(2)
+        data = pd.DataFrame(
+            columns=fields,
+            data=[
+                [0, 100],  # All a withing range
+                [np.nan, 100],  # a is nan
+            ],
+            dtype=float,
+            index=data_index,
+        )
+        expected_output = pd.DataFrame(
+            columns=fields,
+            data=[
+                [0, 100],
+                [np.nan, np.nan],  # a is nan -> b
+            ],
+            dtype=float,
+            index=data.index,
+        )
+
+        data_set = xr.Dataset(data)
+
+        data_set_out = clip_values(data_set, variable_config)
+        data_frame_out = data_set_out.to_dataframe()
+
+        pd.testing.assert_frame_equal(
+            data_frame_out,
+            expected_output,
+            check_names=False,
+            check_dtype=True,
+        )