Add data_analysis/tests/test_core.py

data_analysis/tests/test_core.py

@@ -0,0 +1,58 @@
+import pytest
+import math
+from data_analysis import core
+
+
+class RunData:
+    def __init__(self, policy_hash: str, warn_rate: float, unknown_rate: float, delta_time: float):
+        self.policy_hash = policy_hash
+        self.warn_rate = warn_rate
+        self.unknown_rate = unknown_rate
+        self.delta_time = delta_time
+
+
+@pytest.fixture
+def sample_run_data():
+    return [
+        RunData(policy_hash="abc123", warn_rate=0.1, unknown_rate=0.05, delta_time=1.2),
+        RunData(policy_hash="def456", warn_rate=0.3, unknown_rate=0.10, delta_time=1.8),
+        RunData(policy_hash="ghi789", warn_rate=0.2, unknown_rate=0.02, delta_time=2.0),
+    ]
+
+
+def test_calculate_warn_rate_valid(sample_run_data):
+    result = core.calculate_warn_rate(sample_run_data)
+    expected = (0.1 + 0.3 + 0.2) / 3
+    assert math.isclose(result, expected, rel_tol=1e-9)
+
+
+def test_calculate_warn_rate_empty():
+    with pytest.raises(ValueError):
+        core.calculate_warn_rate([])
+
+
+def test_calculate_warn_rate_malformed():
+    data = [object()]
+    with pytest.raises(AttributeError):
+        core.calculate_warn_rate(data)
+
+
+def test_delta_time_distribution_valid(sample_run_data):
+    result = core.delta_time_distribution(sample_run_data)
+    assert isinstance(result, dict)
+    assert set(result.keys()) >= {"mean", "variance"}
+    mean_expected = sum(rd.delta_time for rd in sample_run_data) / 3
+    var_expected = sum((rd.delta_time - mean_expected) ** 2 for rd in sample_run_data) / 3
+    assert math.isclose(result["mean"], mean_expected, rel_tol=1e-9)
+    assert math.isclose(result["variance"], var_expected, rel_tol=1e-9)
+
+
+def test_delta_time_distribution_empty():
+    with pytest.raises(ValueError):
+        core.delta_time_distribution([])
+
+
+def test_delta_time_distribution_malformed():
+    data = [object()]
+    with pytest.raises(AttributeError):
+        core.delta_time_distribution(data)