add test for dataclusters

src/diffpy/srmise/dataclusters.py

@@ -68,12 +68,34 @@ def __init__(self, x, y, res):
     def __iter__(self):
         return self
 
+    def __eq__(self, other):
+        return (
+            np.array_equal(self.x, other.x)
+            and np.array_equal(self.y, other.y)
+            and np.array_equal(self.data_order, other.data_order)
+            and np.array_equal(self.clusters, other.clusters)
+            and self.res == other.res
+            and self.current_idx == other.current_idx
+            and self.lastcluster_idx == other.lastcluster_idx
+            and self.lastpoint_idx == other.lastpoint_idx
+            and self.status == other.status
+        )
+
     def clear(self):
-        """Clear all members, including user data."""
+        """
+        Clear all data and reset the cluster object to a transient initial state.
+
+        The purpose of this method is to provide a clean state before creating new clustering operations.
+        The object is updated in-place and no new instance is returned.
+
+        Returns
+        -------
+        None
+        """
         self.x = np.array([])
         self.y = np.array([])
-        self.data_order = np.array([], dtype=np.int32)
-        self.clusters = np.array([[]], dtype=np.int32)
+        self.data_order = np.array([])
+        self.clusters = np.array([[]])
         self.res = 0
         self.current_idx = 0
         self.lastcluster_idx = None

src/diffpy/srmise/tests/test_dataclusters.py

@@ -0,0 +1,43 @@
+import numpy as np
+import pytest
+
+from diffpy.srmise.dataclusters import DataClusters
+
+
+@pytest.mark.parametrize(
+    "inputs, expected",
+    [
+        (
+            {
+                "input_x": np.array([1, 2, 3]),
+                "input_y": np.array([3, 2, 1]),
+                "input_res": 4,
+            },
+            {
+                "x": np.array([]),
+                "y": np.array([]),
+                "data_order": np.array([]),
+                "clusters": np.array([[]]),
+                "res": 0,
+                "current_idx": 0,
+                "lastcluster_idx": None,
+                "lastpoint_idx": None,
+                "status": 0,
+            },
+        ),
+    ],
+)
+def test_clear(inputs, expected):
+    # Initialize DataClusters with input parameters
+    actual = DataClusters(x=inputs["input_x"], y=inputs["input_y"], res=inputs["input_res"])
+
+    # Perform the clear operation
+    actual.clear()
+
+    # Assert each expected attribute against its actual value after clearing
+    for attr, expected_value in expected.items():
+        assert (
+            np.array_equal(getattr(actual, attr), expected_value)
+            if isinstance(expected_value, np.ndarray)
+            else getattr(actual, attr) == expected_value
+        )