pytest parametrize

Signed-off-by: Maria Teresa Ortega <teresa.ortega0903@gmail.com>

Author: teresa-ortega

test/common/test_named_product.py

@@ -16,85 +16,81 @@
 
 """Unit tests for the named_product function in lambkin.common."""
 
-from lambkin.common.named_product import named_product
-
-
-def test_two_parameters_correct_length():
-    """Tests if named_product generates the correct number of combinations.
-
-    It verifies that two parameters with 2 values each produce
-    4 combinations (2 × 2).
-    """
-    result = named_product(
-        sensor_model=["likelihood_field", "beam"], num_particles=[1, 10]
-    )
-    assert len(result) == 4
-
-
-def test_two_parameters_correct_combinations():
-    """Tests if named_product generates all expected combinations.
-
-    It verifies that every possible combination of the given
-    parameters is present in the result.
-    """
-    result = named_product(
-        sensor_model=["likelihood_field", "beam"], num_particles=[1, 10]
-    )
-    assert {"sensor_model": "likelihood_field", "num_particles": 1} in result
-    assert {"sensor_model": "likelihood_field", "num_particles": 10} in result
-    assert {"sensor_model": "beam", "num_particles": 1} in result
-    assert {"sensor_model": "beam", "num_particles": 10} in result
-
-
-def test_three_parameters_correct_length():
-    """Tests if named_product scales correctly with three parameters.
-
-    It verifies that three parameters with 2 values each produce
-    8 combinations (2³).
-    """
-    result = named_product(
-        sensor_model=["likelihood_field", "beam"],
-        num_particles=[1, 10],
-        rate=[10, 100],
-    )
-    assert len(result) == 8
-
+import pytest
 
-def test_single_parameter():
-    """Tests if named_product works correctly with a single parameter.
-
-    It verifies that a single parameter returns one dict per value.
-    """
-    result = named_product(sensor_model=["likelihood_field", "beam"])
-    assert result == [{"sensor_model": "likelihood_field"}, {"sensor_model": "beam"}]
-
-
-def test_returns_list_of_dicts():
-    """Tests if named_product always returns a list of dictionaries.
-
-    It verifies the return type is always a list and each
-    element is a dictionary regardless of input size.
-    """
-    result = named_product(sensor_model=["likelihood_field"])
-    assert isinstance(result, list)
-    assert isinstance(result[0], dict)
-
-
-def test_empty_list_returns_no_combinations():
-    """Tests if named_product handles an empty parameter list correctly.
-
-    It verifies that an empty list in any parameter produces
-    no combinations at all.
-    """
-    result = named_product(sensor_model=[], num_particles=[1])
-    assert result == []
-
-
-def test_no_parameters_returns_one_empty_dict():
-    """Tests if named_product handles no parameters gracefully.
+from lambkin.common.named_product import named_product
 
-    It verifies that calling named_product with no arguments
-    returns a list with a single empty dictionary.
-    """
-    result = named_product()
-    assert result == [{}]
+# Membership test
+
+
+@pytest.mark.parametrize(
+    "kwargs, expected_combinations",
+    [
+        (
+            {"sensor_model": ["likelihood_field", "beam"], "num_particles": [1, 10]},
+            [
+                {"sensor_model": "likelihood_field", "num_particles": 1},
+                {"sensor_model": "likelihood_field", "num_particles": 10},
+                {"sensor_model": "beam", "num_particles": 1},
+                {"sensor_model": "beam", "num_particles": 10},
+            ],
+        ),
+        (
+            {"sensor_model": ["likelihood_field", "beam"]},
+            [
+                {"sensor_model": "likelihood_field"},
+                {"sensor_model": "beam"},
+            ],
+        ),
+        (
+            {},
+            [{}],
+        ),
+        (
+            {"sensor_model": [], "num_particles": [1]},
+            [],
+        ),
+    ],
+)
+def test_named_product_combinations(kwargs, expected_combinations):
+    """named_product contains exactly the expected combination dicts."""
+    result = named_product(**kwargs)
+    assert result == expected_combinations
+
+
+# Length test
+
+
+@pytest.mark.parametrize(
+    "kwargs, expected_len",
+    [
+        (
+            {"sensor_model": ["likelihood_field", "beam"], "num_particles": [1, 10]},
+            4,
+        ),
+        (
+            {
+                "sensor_model": ["likelihood_field", "beam"],
+                "num_particles": [1, 10],
+                "rate": [10, 100],
+            },
+            8,
+        ),
+        (
+            {"sensor_model": ["likelihood_field", "beam"]},
+            2,
+        ),
+        (
+            {"sensor_model": [], "num_particles": [1]},
+            0,
+        ),
+        (
+            {},
+            1,
+        ),
+    ],
+)
+def test_named_product_length(kwargs, expected_len):
+    """named_product returns the correct number of combinations (cartesian product)."""
+    result = named_product(**kwargs)
+    assert len(result) == expected_len