Separate array_equal_with_nan

src/power_grid_model_ds/_core/fancypy.py

@@ -8,6 +8,8 @@
 
 import numpy as np
 
+from power_grid_model_ds._core.utils.misc import array_equal_with_nan
+
 if TYPE_CHECKING:
     from power_grid_model_ds._core.model.arrays.base.array import FancyArray
 
@@ -44,23 +46,5 @@ def sort(array: "FancyArray", axis=-1, kind=None, order=None) -> "FancyArray":
 def array_equal(array1: "FancyArray", array2: "FancyArray", equal_nan: bool = True) -> bool:
     """Return True if two arrays are equal."""
     if equal_nan:
-        return _array_equal_with_nan(array1, array2)
+        return array_equal_with_nan(array1.data, array2.data)
     return np.array_equal(array1.data, array2.data)
-
-
-def _array_equal_with_nan(array1: "FancyArray", array2: "FancyArray") -> bool:
-    # np.array_equal does not work with NaN values in structured arrays, so we need to compare column by column.
-    # related issue: https://github.com/numpy/numpy/issues/21539
-
-    if array1.columns != array2.columns:
-        return False
-
-    for column in array1.columns:
-        column_dtype = array1.dtype[column]
-        if np.issubdtype(column_dtype, np.str_):
-            if not np.array_equal(array1[column], array2[column]):
-                return False
-            continue
-        if not np.array_equal(array1[column], array2[column], equal_nan=True):
-            return False
-    return True

src/power_grid_model_ds/_core/utils/misc.py

@@ -39,3 +39,24 @@ def get_inherited_attrs(cls: Type, *private_attributes):
             retrieved_attributes[private_attr] = attr_dict
 
     return retrieved_attributes
+
+
+def array_equal_with_nan(array1: np.ndarray, array2: np.ndarray) -> bool:
+    """Compare two structured arrays for equality, treating NaN values as equal.
+
+    np.array_equal does not work with NaN values in structured arrays, so we need to compare column by column.
+    related issue: https://github.com/numpy/numpy/issues/21539
+    """
+    if array1.dtype.names != array2.dtype.names:
+        return False
+
+    columns: Sequence[str] = array1.dtype.names
+    for column in columns:
+        column_dtype = array1.dtype[column]
+        if np.issubdtype(column_dtype, np.str_):
+            if not np.array_equal(array1[column], array2[column]):
+                return False
+            continue
+        if not np.array_equal(array1[column], array2[column], equal_nan=True):
+            return False
+    return True