Add data_analysis_tool/tests/test_core.py

data_analysis_tool/tests/test_core.py

@@ -0,0 +1,50 @@
+import pytest
+import pandas as pd
+from pathlib import Path
+from data_analysis_tool import core
+
+
+@pytest.fixture
+def tmp_csv(tmp_path):
+    data = pd.DataFrame({
+        'timestamp': ['2024-05-01T00:00:00Z', '2024-05-01T00:01:00Z', '2024-05-01T00:02:00Z'],
+        'Bx': [10.0, 20.0, 30.0],
+        'By': [5.0, 7.0, 9.0],
+        'Bz': [1.0, 2.0, 3.0]
+    })
+    csv_path = tmp_path / 'sample.csv'
+    data.to_csv(csv_path, index=False)
+    return csv_path
+
+
+def test_analyze_data_returns_analysisresults(tmp_csv):
+    result = core.analyze_data(str(tmp_csv))
+    assert isinstance(result, core.AnalysisResults)
+
+
+def test_analyze_data_correct_means_and_variance(tmp_csv):
+    result = core.analyze_data(str(tmp_csv))
+    expected_mean_Bx = pytest.approx((10.0 + 20.0 + 30.0) / 3, rel=1e-3)
+    expected_mean_By = pytest.approx((5.0 + 7.0 + 9.0) / 3, rel=1e-3)
+    values_Bx = [10.0, 20.0, 30.0]
+    mean_Bx = sum(values_Bx) / 3
+    expected_variance_Bx = pytest.approx(sum((x - mean_Bx) ** 2 for x in values_Bx) / len(values_Bx), rel=1e-3)
+
+    assert result.mean_Bx == expected_mean_Bx
+    assert result.mean_By == expected_mean_By
+    assert result.variance_Bx == expected_variance_Bx
+
+
+def test_to_json_output_contains_expected_keys(tmp_csv):
+    result = core.analyze_data(str(tmp_csv))
+    result_json = result.to_json()
+    assert isinstance(result_json, str)
+
+    for key in ["mean_Bx", "mean_By", "variance_Bx", "spikes"]:
+        assert key in result_json
+
+
+def test_invalid_file_path_raises(tmp_path):
+    bad_path = tmp_path / 'does_not_exist.csv'
+    with pytest.raises((FileNotFoundError, pd.errors.EmptyDataError, ValueError)):
+        core.analyze_data(str(bad_path))