Add laser_echo_analysis/tests/test_core.py

laser_echo_analysis/tests/test_core.py

@@ -0,0 +1,61 @@
+import pytest
+import math
+from laser_echo_analysis import core
+
+
+@pytest.fixture
+def sample_data():
+    # Simulated sample data based on LaserMeasurement model
+    # Mix of signal and background noise values
+    return [
+        {'timestamp': '2024-01-01T00:00:00Z', 'pixel_value': 5, 'delta_t': 0.1},
+        {'timestamp': '2024-01-01T00:00:01Z', 'pixel_value': 8, 'delta_t': 0.1},
+        {'timestamp': '2024-01-01T00:00:02Z', 'pixel_value': 12, 'delta_t': 0.1},
+        {'timestamp': '2024-01-01T00:00:03Z', 'pixel_value': 6, 'delta_t': 0.1},
+        {'timestamp': '2024-01-01T00:00:04Z', 'pixel_value': 10, 'delta_t': 0.1},
+    ]
+
+
+def test_analyze_data_basic(sample_data):
+    results = core.analyze_data(sample_data)
+
+    assert isinstance(results, dict), "Result should be a dictionary"
+    assert set(results.keys()) == {'peak', 'average_noise', 'signal_to_noise_ratio'}
+
+    # Validate expected numeric ranges
+    assert results['peak'] == 12
+    assert results['average_noise'] > 0
+    assert results['signal_to_noise_ratio'] > 1
+    assert math.isfinite(results['signal_to_noise_ratio'])
+
+
+def test_analyze_data_with_constant_signal():
+    constant_data = [{'timestamp': '2024-01-01T00:00:00Z', 'pixel_value': 10, 'delta_t': 0.1} for _ in range(10)]
+    results = core.analyze_data(constant_data)
+
+    assert results['peak'] == 10
+    # With constant signal, noise should be zero or near-zero
+    assert abs(results['average_noise']) < 1e-9
+    # Avoid division by zero, ensure SNR behaves correctly
+    assert results['signal_to_noise_ratio'] == pytest.approx(float('inf')) or results['signal_to_noise_ratio'] > 1e9
+
+
+def test_analyze_data_empty():
+    with pytest.raises((ValueError, AssertionError)):
+        core.analyze_data([])
+
+
+def test_analyze_data_invalid_input():
+    # Missing required field
+    invalid_data = [{'timestamp': '2024-01-01T00:00:00Z', 'delta_t': 0.2}]
+    with pytest.raises((KeyError, ValueError, AssertionError)):
+        core.analyze_data(invalid_data)
+
+
+def test_signal_to_noise_ratio_behavior(sample_data):
+    noisy_data = [dict(d, pixel_value=d['pixel_value'] + i % 3 - 1) for i, d in enumerate(sample_data)]
+    results_noisy = core.analyze_data(noisy_data)
+    results_clean = core.analyze_data(sample_data)
+
+    # More noise -> lower SNR expected
+    assert results_noisy['signal_to_noise_ratio'] < results_clean['signal_to_noise_ratio']