fix: restore NaN validation in polars-based variable bounds checking

The polars migration broke NaN validation because check_has_nulls_polars
only checked for null values, not NaN values. In polars, these are distinct
concepts. This fix enhances the validation to detect both null and NaN values
in numeric columns while avoiding type errors on non-numeric columns.

Fixes failing tests in test_inconsistency_checks.py that expected ValueError
to be raised when variables have NaN bounds.

Author: FabianHofmann

linopy/common.py

@@ -363,22 +363,35 @@ def to_polars(ds: Dataset, **kwargs: Any) -> pl.DataFrame:
 
 def check_has_nulls_polars(df: pl.DataFrame, name: str = "") -> None:
     """
-    Checks if the given DataFrame contains any null values and raises a ValueError if it does.
+    Checks if the given DataFrame contains any null or NaN values and raises a ValueError if it does.
 
     Args:
     ----
-        df (pl.DataFrame): The DataFrame to check for null values.
+        df (pl.DataFrame): The DataFrame to check for null or NaN values.
         name (str): The name of the data container being checked.
 
     Raises:
     ------
-        ValueError: If the DataFrame contains null values,
-        a ValueError is raised with a message indicating the name of the constraint and the fields containing null values.
+        ValueError: If the DataFrame contains null or NaN values,
+        a ValueError is raised with a message indicating the name of the constraint and the fields containing null/NaN values.
     """
+    # Check for null values in all columns
     has_nulls = df.select(pl.col("*").is_null().any())
     null_columns = [col for col in has_nulls.columns if has_nulls[col][0]]
-    if null_columns:
-        raise ValueError(f"{name} contains nan's in field(s) {null_columns}")
+
+    # Check for NaN values only in numeric columns (avoid enum/categorical columns)
+    numeric_cols = [
+        col for col, dtype in zip(df.columns, df.dtypes) if dtype.is_numeric()
+    ]
+
+    nan_columns = []
+    if numeric_cols:
+        has_nans = df.select(pl.col(numeric_cols).is_nan().any())
+        nan_columns = [col for col in has_nans.columns if has_nans[col][0]]
+
+    invalid_columns = list(set(null_columns + nan_columns))
+    if invalid_columns:
+        raise ValueError(f"{name} contains nan's in field(s) {invalid_columns}")
 
 
 def filter_nulls_polars(df: pl.DataFrame) -> pl.DataFrame: