fix(NumpyModel): Equality operator when the fields are heterogeneous.

patch(NumpyModel): Migrated to new __eq__ method in Pydantic

pydantic_numpy/model.py

@@ -45,24 +45,29 @@ class NumpyModel(BaseModel):
     _directory_suffix: ClassVar[str] = ".pdnp"
 
     def __eq__(self, other: Any) -> bool:
-        if isinstance(other, NumpyModel):
-            self_type = self.__pydantic_generic_metadata__["origin"] or self.__class__
-            other_type = other.__pydantic_generic_metadata__["origin"] or other.__class__
+        if not isinstance(other, BaseModel):
+            return NotImplemented  # delegate to the other item in the comparison
+
+        self_type = self.__pydantic_generic_metadata__["origin"] or self.__class__
+        other_type = other.__pydantic_generic_metadata__["origin"] or other.__class__
 
+        if not (
+            self_type == other_type
+            and getattr(self, "__pydantic_private__", None) == getattr(other, "__pydantic_private__", None)
+            and self.__pydantic_extra__ == other.__pydantic_extra__
+        ):
+            return False
+
+        if isinstance(other, NumpyModel):
             self_ndarray_field_to_array, self_other_field_to_value = self._dump_numpy_split_dict()
             other_ndarray_field_to_array, other_other_field_to_value = other._dump_numpy_split_dict()
 
-            return (
-                self_type == other_type
-                and self_other_field_to_value == other_other_field_to_value
-                and self.__pydantic_private__ == other.__pydantic_private__
-                and self.__pydantic_extra__ == other.__pydantic_extra__
-                and _compare_np_array_dicts(self_ndarray_field_to_array, other_ndarray_field_to_array)
+            return self_other_field_to_value == other_other_field_to_value and _compare_np_array_dicts(
+                self_ndarray_field_to_array, other_ndarray_field_to_array
             )
-        elif isinstance(other, BaseModel):
-            return super().__eq__(other)
-        else:
-            return NotImplemented  # delegate to the other item in the comparison
+
+        # Self is NumpyModel, other is not; likely unequal; checking anyway.
+        return super().__eq__(other)
 
     @classmethod
     @validate_call
@@ -156,10 +161,10 @@ def _dump_numpy_split_dict(self) -> tuple[dict, dict]:
         ndarray_field_to_array = {}
         other_field_to_value = {}
 
-        for k, v in self.model_dump(exclude_unset=True).items():
+        for k, v in self.model_dump().items():
             if isinstance(v, np.ndarray):
                 ndarray_field_to_array[k] = v
-            else:
+            elif v:
                 other_field_to_value[k] = v
 
         return ndarray_field_to_array, other_field_to_value
@@ -259,16 +264,13 @@ def _compare_np_array_dicts(
     keys2 = frozenset(dict_b.keys())
 
     if keys1 != keys2:
-        raise ValueError("Dictionaries have different keys")
+        return False
 
     for key in keys1:
         arr_a = dict_a[key]
         arr_b = dict_b[key]
 
-        if arr_a.shape != arr_b.shape:
-            raise ValueError(f"Arrays for key '{key}' have different shapes")
-
-        if not np_general_all_close(arr_a, arr_b, rtol, atol):
+        if arr_a.shape != arr_b.shape or not np_general_all_close(arr_a, arr_b, rtol, atol):
             return False
 
     return True

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pydantic_numpy"
-version = "5.0.1"
+version = "5.0.2"
 description = "Pydantic Model integration of the NumPy array"
 authors = ["Can H. Tartanoglu", "Christoph Heindl"]
 maintainers = ["Can H. Tartanoglu <[email protected]>"]
@@ -30,6 +30,7 @@ semver = "^3.0.1"
 pytest = "^7.4.0"
 parameterized = "^0.9.0"
 orjson = "*"
+coverage = "^7.5.1"
 
 [tool.poetry.group.format.dependencies]
 black = "^23.7.0"

tests/helper/testing_groups.py

@@ -1,4 +1,4 @@
-import os
+import platform
 
 import numpy as np
 
@@ -185,7 +185,7 @@
     (np.array([[[0]]]), np.int64, Np3DArrayInt64, 3),
 ]
 
-if os.name != "nt":
+if platform.system() != "Windows":
 
     def get_strict_data_type_nd_array_typing_dimensions_128_bit():
         return [

tests/test_np_model.py

@@ -1,11 +1,11 @@
-import os
+import platform
 import tempfile
 from pathlib import Path
 
 import numpy as np
 import pytest
 
-from pydantic_numpy.model import model_agnostic_load
+from pydantic_numpy.model import NumpyModel, model_agnostic_load
 from pydantic_numpy.typing import NpNDArray
 from tests.model import (
     NpNDArrayModelWithNonArray,
@@ -17,28 +17,19 @@
 NON_ARRAY_VALUE = 5
 
 
-def _numpy_model():
+@pytest.fixture
+def numpy_model() -> NpNDArrayModelWithNonArray:
     return NpNDArrayModelWithNonArray(array=np.array([0.0]), non_array=NON_ARRAY_VALUE)
 
 
 @pytest.fixture
-def numpy_model():
-    return _numpy_model()
-
-
-@pytest.fixture(
-    params=[
-        _numpy_model(),
-        NpNDArrayModelWithNonArrayWithArbitrary(
-            array=np.array([0.0]), non_array=NON_ARRAY_VALUE, my_arbitrary_slice=slice(0, 10)
-        ),
-    ]
-)
-def numpy_model_with_arbitrary(request):
-    return request.param
+def numpy_model_with_arbitrary() -> NpNDArrayModelWithNonArrayWithArbitrary:
+    return NpNDArrayModelWithNonArrayWithArbitrary(
+        array=np.array([0.0]), non_array=NON_ARRAY_VALUE, my_arbitrary_slice=slice(0, 1)
+    )
 
 
-if os.name != "nt":
+if platform.system() != "Windows":
 
     def test_io_yaml(numpy_model: NpNDArrayModelWithNonArray) -> None:
         with tempfile.TemporaryDirectory() as tmp_dirname:
@@ -80,3 +71,30 @@ class NumpyModelBForTest(NpNDArrayModelWithNonArray):
             models = [NumpyModelAForTest, NumpyModelBForTest]
             assert model_a == model_agnostic_load(tmp_dir_path, TEST_MODEL_OBJECT_ID, models=models)
             assert model_b == model_agnostic_load(tmp_dir_path, OTHER_TEST_MODEL_OBJECT_ID, models=models)
+
+    def test_simple_eq(numpy_model: NpNDArrayModelWithNonArray) -> None:
+        assert numpy_model == numpy_model
+
+    def test_not_eq_different_fields(numpy_model, numpy_model_with_arbitrary) -> None:
+        assert numpy_model != numpy_model_with_arbitrary
+
+        class AnotherModel(NumpyModel):
+            yarra: NpNDArray
+
+        assert numpy_model != AnotherModel(yarra=np.array([0.0]))
+
+    def test_not_eq_different_inner(numpy_model: NpNDArrayModelWithNonArray) -> None:
+        assert numpy_model != NpNDArrayModelWithNonArray(array=np.array([1.0]), non_array=NON_ARRAY_VALUE)
+
+    def test_not_eq_different_shape(numpy_model: NpNDArrayModelWithNonArray) -> None:
+        assert numpy_model != NpNDArrayModelWithNonArray(array=np.array([0.0, 1.0]), non_array=NON_ARRAY_VALUE)
+
+    def test_random_not_eq(numpy_model: NpNDArrayModelWithNonArray) -> None:
+        for r in (0, 5, 1.0, "1"):
+            assert numpy_model != r
+
+    def test_serde_eq(numpy_model: NpNDArrayModelWithNonArray) -> None:
+        ser = numpy_model.model_dump_json()
+        reread_data = numpy_model.model_validate_json(ser)
+
+        assert numpy_model == reread_data

tests/test_typing.py

@@ -1,4 +1,4 @@
-import os
+import platform
 import tempfile
 from pathlib import Path
 from typing import Optional
@@ -54,7 +54,7 @@ def test_wrong_dimension():
         get_numpy_type_model(Np1DArrayInt64)(array_field=np.array([[0]]))
 
 
-if os.name != "nt":
+if platform.system() != "Windows":
     from tests.helper.testing_groups import (
         get_strict_data_type_nd_array_typing_dimensions_128_bit,
     )