Add measurements_analysis/tests/test_core.py

measurements_analysis/tests/test_core.py

@@ -0,0 +1,36 @@
+import pytest
+import math
+from measurements_analysis import core
+
+# Beispielmessdaten entsprechend dem MeasurementData-Datenmodell
+data_sample = [
+    {"spacing_mm": 0.0, "hf_amplitude": 1.0, "spike_outlier_rate": 0.02, "levenes_test_p": 0.8},
+    {"spacing_mm": 0.5, "hf_amplitude": 0.95, "spike_outlier_rate": 0.03, "levenes_test_p": 0.75},
+    {"spacing_mm": 1.0, "hf_amplitude": 0.9, "spike_outlier_rate": 0.05, "levenes_test_p": 0.7},
+    {"spacing_mm": 1.5, "hf_amplitude": 0.85, "spike_outlier_rate": 0.04, "levenes_test_p": 0.65}
+]
+
+def test_analyze_measurements_returns_dict():
+    result = core.analyze_measurements(data_sample)
+    assert isinstance(result, dict)
+
+
+def test_analyze_measurements_contains_expected_keys():
+    result = core.analyze_measurements(data_sample)
+    expected_keys = {"mean_hf_amplitude", "variance_hf_amplitude", "mean_outlier_rate", "mean_levenes_p"}
+    assert expected_keys.issubset(result.keys())
+
+
+def test_analyze_measurements_mean_values():
+    result = core.analyze_measurements(data_sample)
+    expected_mean_amp = sum(d["hf_amplitude"] for d in data_sample) / len(data_sample)
+    expected_mean_outlier = sum(d["spike_outlier_rate"] for d in data_sample) / len(data_sample)
+    expected_mean_levene = sum(d["levenes_test_p"] for d in data_sample) / len(data_sample)
+    assert math.isclose(result["mean_hf_amplitude"], expected_mean_amp, rel_tol=1e-9)
+    assert math.isclose(result["mean_outlier_rate"], expected_mean_outlier, rel_tol=1e-9)
+    assert math.isclose(result["mean_levenes_p"], expected_mean_levene, rel_tol=1e-9)
+
+
+def test_analyze_measurements_variance_positive():
+    result = core.analyze_measurements(data_sample)
+    assert result["variance_hf_amplitude"] >= 0.0