Updated tests

Author: abchoudh-amd

projects/rocprofiler-compute/tests/test_analyze_commands.py

@@ -1706,22 +1706,6 @@ def import_db_evaluate():
     return db_analysis.evaluate
 
 
-def create_metric_evaluator(raw_pmc_df=None, sys_vars=None, empirical_peaks=None):
-    """Create a MetricEvaluator instance with test data."""
-    import sys
-
-    sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
-    from utils.parser import MetricEvaluator
-
-    if raw_pmc_df is None:
-        raw_pmc_df = pd.DataFrame({"a": [10.0], "b": [2.0]})
-    if sys_vars is None:
-        sys_vars = {}
-    if empirical_peaks is None:
-        empirical_peaks = {}
-    return MetricEvaluator(raw_pmc_df, sys_vars, empirical_peaks)
-
-
 @pytest.mark.misc
 def test_evaluate_division_by_zero_returns_inf_with_warning():
     """Division by zero should produce inf and emit a warning."""
@@ -1787,27 +1771,6 @@ def test_evaluate_none_expression_returns_none():
     assert result is None
 
 
-@pytest.mark.misc
-def test_evaluate_errstate_suppresses_runtime_warnings():
-    """np.errstate should suppress RuntimeWarning during evaluation."""
-    import warnings as warn_module
-
-    evaluate = import_db_evaluate()
-    pmc_df = pd.DataFrame({"a": [0.0], "b": [0.0]})
-    with warn_module.catch_warnings():
-        warn_module.simplefilter("error")
-        with patch("rocprof_compute_analyze.analysis_db.console_warning"):
-            # Should not raise RuntimeWarning thanks to np.errstate
-            # 0/0 -> NaN -> scalar NA -> returns None
-            result = evaluate(
-                "test_metric",
-                "pmc_df['a'][0] / pmc_df['b'][0]",
-                pmc_df,
-                {},
-            )
-            assert result is None
-
-
 @pytest.mark.misc
 def test_evaluate_failed_expression_returns_none():
     """An expression that raises an exception should return None."""
@@ -1833,12 +1796,27 @@ def test_evaluate_scalar_na_returns_none():
 # =============================================================================
 
 
+def create_metric_evaluator(raw_pmc_df=None, sys_vars=None, empirical_peaks=None):
+    """Create a MetricEvaluator instance with test data."""
+    from utils.parser import MetricEvaluator
+
+    if raw_pmc_df is None:
+        raw_pmc_df = pd.DataFrame({"a": [10.0], "b": [2.0]})
+    if sys_vars is None:
+        sys_vars = {}
+    if empirical_peaks is None:
+        empirical_peaks = {}
+    return MetricEvaluator(raw_pmc_df, sys_vars, empirical_peaks)
+
+
 @pytest.mark.misc
 def test_metric_evaluator_valid_expression():
     """Valid expressions should evaluate correctly."""
     evaluator = create_metric_evaluator()
-    result = evaluator.eval_expression("raw_pmc_df['a'][0] / raw_pmc_df['b'][0]")
-    assert result == 5.0
+    with patch("utils.parser.console_warning") as mock_warn:
+        result = evaluator.eval_expression("raw_pmc_df['a'][0] + raw_pmc_df['b'][0]")
+    assert result == 12.0
+    mock_warn.assert_not_called()
 
 
 @pytest.mark.misc
@@ -1868,24 +1846,6 @@ def test_metric_evaluator_zero_divided_by_zero_returns_na_with_warning():
     assert len(nan_warnings) > 0
 
 
-@pytest.mark.misc
-def test_metric_evaluator_errstate_suppresses_runtime_warnings():
-    """np.errstate should suppress RuntimeWarning during evaluation."""
-    import warnings as warn_module
-
-    raw_pmc_df = pd.DataFrame({"a": [0.0], "b": [0.0]})
-    evaluator = create_metric_evaluator(raw_pmc_df=raw_pmc_df)
-    with warn_module.catch_warnings():
-        warn_module.simplefilter("error")
-        with patch("utils.parser.console_warning"):
-            # Should not raise RuntimeWarning thanks to np.errstate
-            # 0/0 -> NaN -> scalar NA -> returns "N/A"
-            result = evaluator.eval_expression(
-                "raw_pmc_df['a'][0] / raw_pmc_df['b'][0]"
-            )
-            assert result == "N/A"
-
-
 @pytest.mark.misc
 def test_metric_evaluator_na_expression_returns_na_string():
     """Expression evaluating to NA should return 'N/A'."""
@@ -1903,29 +1863,3 @@ def test_metric_evaluator_none_expression_returns_na_string():
     with patch("utils.parser.console_debug"):
         result = evaluator.eval_expression("None")
     assert result == "N/A"
-
-
-@pytest.mark.misc
-def test_metric_evaluator_sys_vars_accessible():
-    """System variables should be accessible in expressions."""
-    evaluator = create_metric_evaluator(sys_vars={"my_var": 42})
-    result = evaluator.eval_expression("my_var")
-    assert result == 42
-
-
-@pytest.mark.misc
-def test_metric_evaluator_empirical_peaks_accessible():
-    """Empirical peaks should be accessible in expressions."""
-    evaluator = create_metric_evaluator(empirical_peaks={"peak_val": 100.0})
-    result = evaluator.eval_expression("peak_val")
-    assert result == 100.0
-
-
-@pytest.mark.misc
-def test_metric_evaluator_valid_expression_no_warning():
-    """A valid expression should not produce warnings."""
-    evaluator = create_metric_evaluator()
-    with patch("utils.parser.console_warning") as mock_warn:
-        result = evaluator.eval_expression("raw_pmc_df['a'][0] + raw_pmc_df['b'][0]")
-    assert result == 12.0
-    mock_warn.assert_not_called()