Add artifact.1/tests/test_core.py

artifact.1/tests/test_core.py

@@ -0,0 +1,61 @@
+import pytest
+import pandas as pd
+from typing import List, Dict
+from artifact_1 import core
+
+
+@pytest.fixture
+def sample_metric_data() -> List[Dict[str, float]]:
+    return [
+        {"band_width": 10.0, "retry_tail_p99": 5.0, "band_center": 100.0},
+        {"band_width": 15.0, "retry_tail_p99": 6.0, "band_center": 105.0},
+        {"band_width": 20.0, "retry_tail_p99": 4.5, "band_center": 98.5},
+    ]
+
+
+@pytest.fixture
+def edge_metric_data() -> List[Dict[str, float]]:
+    return [
+        {"band_width": 0.0, "retry_tail_p99": 0.0, "band_center": 0.0},
+        {"band_width": 100.0, "retry_tail_p99": 200.0, "band_center": 300.0},
+    ]
+
+
+def test_analyse_metrics_returns_dict(sample_metric_data):
+    result = core.analyse_metrics(sample_metric_data)
+    assert isinstance(result, dict), "Result should be a dictionary"
+    for key in ("band_width", "retry_tail_p99", "band_center"):
+        assert key in result, f"Missing expected key: {key}"
+
+
+def test_analyse_metrics_computation_consistency(sample_metric_data):
+    result = core.analyse_metrics(sample_metric_data)
+    df = pd.DataFrame(sample_metric_data)
+    # Vergleich grundlegender statistischer Kenngrößen
+    assert pytest.approx(result["band_width"]["mean"]) == df["band_width"].mean()
+    assert pytest.approx(result["retry_tail_p99"]["mean"]) == df["retry_tail_p99"].mean()
+    assert pytest.approx(result["band_center"]["mean"]) == df["band_center"].mean()
+
+
+def test_analyse_metrics_with_edge_cases(edge_metric_data):
+    result = core.analyse_metrics(edge_metric_data)
+    assert all(isinstance(v, dict) for v in result.values())
+    for metric_values in result.values():
+        for v in metric_values.values():
+            assert not pd.isna(v), "Analysis should not contain NaN values"
+
+
+def test_analyse_metrics_invalid_input_raises():
+    with pytest.raises((TypeError, ValueError)):
+        core.analyse_metrics([{"band_width": "x", "retry_tail_p99": 1, "band_center": 2}])
+
+    with pytest.raises((KeyError, ValueError)):
+        core.analyse_metrics([{"band_width": 1.0, "retry_tail_p99": 2.0}])
+
+
+@pytest.mark.parametrize("field", ["band_width", "retry_tail_p99", "band_center"])
+def test_analyse_metrics_field_level_validation(sample_metric_data, field):
+    # Manipuliere ein Feld, um Fehler zu erzwingen
+    corrupted = [{**d, field: None} for d in sample_metric_data]
+    with pytest.raises((TypeError, ValueError)):
+        core.analyse_metrics(corrupted)