test/feat: tested weight generator more thoroughly

tests/test_for_utils/test_whittaker_base.py

@@ -229,11 +229,12 @@ def test_get_checked_lambda(
         ("error", TypeError),  # Number 5
     ],
 )
-def test_weight_generator(
+def test_weight_generator_identical_weights(
     combination: Tuple[Any, Union[np.ndarray, float, Type[Exception]]]
 ) -> None:
     """
-    Tests the weight generator.
+    Tests the weight generator when provided with weights that are identical for all
+    signals.
 
     The ``combination`` parameter defines
 
@@ -260,16 +261,38 @@ def test_weight_generator(
     # otherwise, the output is compared to the expected output
     # Case 1: the expected output is a scalar
     if isinstance(expected_output, (float, int)):
-        for w in get_weight_generator(weights=weights, n_series=n_series):
-            assert isinstance(w, (float, int))
-            assert w == expected_output
+        for wght in get_weight_generator(weights=weights, n_series=n_series):
+            assert isinstance(wght, (float, int))
+            assert wght == expected_output
 
         return
 
     # Case 2: the expected output is an array
-    for w in get_weight_generator(weights=weights, n_series=n_series):
-        assert isinstance(w, np.ndarray)
-        assert np.array_equal(w, expected_output)
+    for wght in get_weight_generator(weights=weights, n_series=n_series):
+        assert isinstance(wght, np.ndarray)
+        assert np.array_equal(wght, expected_output)
+
+
+def test_weight_generator_different_weights() -> None:
+    """
+    Tests the weight generator when provided with weights that are different for each
+    signal.
+
+    """
+
+    # the weights are defined
+    weights = np.array(
+        [
+            [1.0, 2.0, 3.0, 4.0, 5.0],
+            [6.0, 7.0, 8.0, 9.0, 10.0],
+            [11.0, 12.0, 13.0, 14.0, 15.0],
+        ]
+    )
+    weights_ref = weights.copy()
+
+    # the generator is tested
+    for idx, wght in enumerate(get_weight_generator(weights=weights, n_series=3)):
+        assert np.array_equal(wght, weights_ref[idx, ::])
 
 
 @pytest.mark.parametrize("combination", [(True, 244_9755_000.0), (False, 490_000.0)])