Add set data test cases

src/diffpy/srmise/dataclusters.py

@@ -140,6 +140,8 @@ def setdata(self, x, y, res):
         self.x = x
         self.y = y
         self.res = res
+        if x.size > 0 and res == 0:
+            raise ValueError("Make trivial clustering, please make positive resolution.")
         # If x sequence size is empty, set the object into Initialized state.
         if x.size == 0 and res == 0:
             self.data_order = np.array([])

src/diffpy/srmise/tests/test_dataclusters.py

@@ -1,6 +1,7 @@
 from copy import copy
 
 import numpy as np
+import pytest
 
 from diffpy.srmise.dataclusters import DataClusters
 
@@ -15,8 +16,8 @@ def test_clear():
 
 
 def test___eq__():
-    actual = DataClusters(np.array([1, 2, 3]), np.array([3, 2, 1]), 0)
-    expected = DataClusters(np.array([1, 2, 3]), np.array([3, 2, 1]), 0)
+    actual = DataClusters(np.array([1, 2, 3]), np.array([3, 2, 1]), 1)
+    expected = DataClusters(np.array([1, 2, 3]), np.array([3, 2, 1]), 1)
     assert expected == actual
     attributes = vars(actual)
     for attr_key, attr_val in attributes.items():
@@ -32,3 +33,86 @@ def test___eq__():
             print(f"not-equal test failed on {attr_key}")
             assert not expected == actual
         attributes.update({attr_key: reset})
+
+    # In the set data test, we test for these cases.
+    # (1) x and y are non-empty array values, and res is positive (the most generic case)
+    # (2) x and y are non-empty array values, and res is 0 (will produce a msg that makes trivial clustering)
+    # (3) x and y are non-empty array values, and res is negative (will produce a ValueError,
+    # msg = please enter a non-negative res value)
+    # (4, 5) One of x and y is empty array, and res is positive
+    # (produce ValueError & msg "Sequences x and y must have the same length.", something like that)
+    # (6) Both x and y are empty array, and res is zero.
+
+
+@pytest.mark.parametrize(
+    "inputs, expected",
+    [
+        (
+            # case (1)
+            {
+                "input_x": np.array([1, 2, 3]),
+                "input_y": np.array([3, 2, 1]),
+                "input_res": 4,
+            },
+            DataClusters(np.array([1, 2, 3]), np.array([3, 2, 1]), 4),
+        ),
+        (
+            # case (6)
+            {
+                "input_x": np.array([]),
+                "input_y": np.array([]),
+                "input_res": 0,
+            },
+            DataClusters(np.array([]), np.array([]), 0),
+        ),
+    ],
+)
+def test_set_data(inputs, expected):
+    actual = DataClusters(x=inputs["input_x"], y=inputs["input_y"], res=inputs["input_res"])
+    assert actual == expected
+
+
+@pytest.mark.parametrize(
+    "inputs, msg",
+    [
+        (
+            # case (4)
+            {
+                "input_x": np.array([]),
+                "input_y": np.array([3, 2]),
+                "input_res": 4,
+            },
+            "Sequences x and y must have the same length.",
+        ),
+        (
+            # case (5)
+            {
+                "input_x": np.array([1, 2]),
+                "input_y": np.array([]),
+                "input_res": 4,
+            },
+            "Sequences x and y must have the same length.",
+        ),
+        (
+            # case (3)
+            {
+                "input_x": np.array([1]),
+                "input_y": np.array([3]),
+                "input_res": -1,
+            },
+            "Resolution res must be non-negative.",
+        ),
+        (
+            # case (2)
+            {
+                "input_x": np.array([1, 2, 3]),
+                "input_y": np.array([3, 2, 1]),
+                "input_res": 0,
+            },
+            "Make trivial clustering, please make positive resolution.",
+        ),
+    ],
+)
+def test_set_data_order_bad(inputs, msg):
+    with pytest.raises(ValueError, match=msg):
+        DataClusters(x=inputs["input_x"], y=inputs["input_y"], res=inputs["input_res"])