Serialize all NumPy integer, float and bool scalars in json_serial

.uncover-agent/NOTE.txt

@@ -0,0 +1,3 @@
+# uncover-agent test diff
+# original: https://github.com/geopython/pygeoapi/pull/2418
+# generated 2026-10-21 18:56:14 UTC

.uncover-agent/added.py

@@ -0,0 +1,43 @@
+    elif _is_numpy_scalar(obj, 'integer'):
+    elif _is_numpy_scalar(obj, 'floating'):
+    elif _is_numpy_scalar(obj, 'bool', 'bool_'):
+        return bool(obj)
+def _is_numpy_scalar(obj: Any, *type_names: str) -> bool:
+    """
+    helper function to check whether an object is a NumPy scalar of
+    a given abstract type (e.g. `integer` covers `int8` to `uint64`),
+    without requiring NumPy to be installed
+
+    :param obj: `object` to be evaluated
+    :param type_names: NumPy type names to match
+
+    :returns: `bool` of whether the object is a matching NumPy scalar
+    """
+
+    return any(cls.__module__ == 'numpy' and cls.__name__ in type_names
+               for cls in type(obj).__mro__)
+
+
+import json
+import numpy as np
+
+
+@pytest.mark.parametrize('value,expected', [
+    (np.int8(-3), -3),
+    (np.int16(7), 7),
+    (np.int32(7), 7),
+    (np.int64(7), 7),
+    (np.uint8(255), 255),
+    (np.uint64(2**64 - 1), 2**64 - 1),
+    (np.float16(0.5), 0.5),
+    (np.float32(0.5), 0.5),
+    (np.float64(0.5), 0.5),
+    (np.bool_(True), True),
+])
+def test_json_serial_numpy_scalars(value, expected):
+    result = util.json_serial(value)
+    assert result == expected
+    assert type(result) is type(expected)
+    assert json.loads(json.dumps({'value': value},
+                                 default=util.json_serial)) == {
+        'value': expected}