Add artifact-1/tests/test_main.py

artifact-1/tests/test_main.py

@@ -0,0 +1,67 @@
+import json
+import pytest
+from pathlib import Path
+import pandas as pd
+
+import src.artifact_1.main as main
+
+
+def load_test_data():
+    test_data_path = Path(__file__).parent / 'data' / 'test_timing_data.json'
+    if not test_data_path.exists():
+        # fallback synthetic data
+        return [
+            {"expires_at_dist_hours": 48.0, "delta_t": 1.2, "pinned": True, "unpinned": False},
+            {"expires_at_dist_hours": 12.0, "delta_t": -0.5, "pinned": False, "unpinned": True},
+            {"expires_at_dist_hours": 5.0, "delta_t": 0.3, "pinned": False, "unpinned": True}
+        ]
+    with open(test_data_path, encoding='utf-8') as f:
+        return json.load(f)
+
+
+@pytest.fixture(scope='module')
+def sample_data():
+    return load_test_data()
+
+
+def test_analyze_timing_data_basic(sample_data):
+    result = main.analyze_timing_data(sample_data)
+    assert isinstance(result, dict)
+    assert 'negative_dt_count' in result
+    assert 'total_count' in result
+    assert result['total_count'] == len(sample_data)
+    assert result['negative_dt_count'] <= result['total_count']
+
+
+def test_analyze_timing_data_near_expiry_threshold(sample_data):
+    result = main.analyze_timing_data(sample_data)
+    threshold = result.get('recommended_near_expiry_threshold')
+    assert isinstance(threshold, (int, float))
+    assert 0 <= threshold <= 72
+
+
+def test_analyze_timing_data_invalid_input():
+    # wrong type
+    with pytest.raises((ValueError, TypeError)):
+        main.analyze_timing_data('not a list')
+    # missing keys
+    bad_data = [{"delta_t": 1.0}]
+    with pytest.raises((KeyError, ValueError)):
+        main.analyze_timing_data(bad_data)
+
+
+def test_analyze_timing_data_pandas_consistency(sample_data):
+    # Ensure that conversion to pandas does not break the structure assumptions
+    df = pd.DataFrame(sample_data)
+    assert all(col in df.columns for col in ['expires_at_dist_hours', 'delta_t'])
+    result = main.analyze_timing_data(sample_data)
+    assert isinstance(result.get('mean_delta_t'), (float, int))
+
+
+def test_analyze_timing_data_negative_dt_flag(sample_data):
+    has_negative = any(d['delta_t'] < 0 for d in sample_data)
+    result = main.analyze_timing_data(sample_data)
+    if has_negative:
+        assert result['negative_dt_count'] > 0
+    else:
+        assert result['negative_dt_count'] == 0