delete text and redundant tests

src/diffpy/srmise/dataclusters.py

@@ -135,7 +135,10 @@ def setdata(self, x, y, res):
         if len(x) != len(y):
             raise ValueError("Sequences x and y must have the same length.")
         if res < 0:
-            raise ValueError("Resolution res must be non-negative.")
+            raise ValueError(
+                "Resolution is the Determines how closely clusters are formed in the clustering algorithm. "
+                "Please set it to be non-negative."
+            )
         # Test for sorting?
         self.x = x
         self.y = y

src/diffpy/srmise/tests/test_dataclusters.py

@@ -34,85 +34,47 @@ def test___eq__():
             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,
+                "x": np.array([1, 2, 3]),
+                "y": np.array([3, 2, 1]),
+                "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"])
+    actual = DataClusters(x=inputs["x"], y=inputs["y"], res=inputs["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,
+                "x": np.array([1]),
+                "y": np.array([3, 2]),
+                "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,
+                "x": np.array([1]),
+                "y": np.array([3]),
+                "res": -1,
             },
-            "Make trivial clustering, please make positive resolution.",
+            "Resolution is the Determines how closely clusters are formed in the clustering algorithm. "
+            "Please set it to be non-negative.",
         ),
     ],
 )
 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"])
+        DataClusters(x=inputs["x"], y=inputs["y"], res=inputs["res"])