diff --git a/ir_gain_test/tests/test_core.py b/ir_gain_test/tests/test_core.py
new file mode 100644
index 0000000..b1fc2c9
--- /dev/null
+++ b/ir_gain_test/tests/test_core.py
@@ -0,0 +1,55 @@
+import pytest
+import pandas as pd
+from ir_gain_test import core
+
+@pytest.fixture
+def sample_intensity_data():
+    return [
+        {"timestamp": "2024-06-12T20:00:00", "gain_value": 1.0, "intensity": 200.0},
+        {"timestamp": "2024-06-12T20:00:01", "gain_value": 2.0, "intensity": 400.0},
+        {"timestamp": "2024-06-12T20:00:02", "gain_value": 3.0, "intensity": 380.0},
+        {"timestamp": "2024-06-12T20:00:03", "gain_value": 4.0, "intensity": 390.0}
+    ]
+
+
+def test_analyze_ir_gain_basic(sample_intensity_data):
+    gains = [1.0, 2.0, 3.0, 4.0]
+    report = core.analyze_ir_gain(gains, sample_intensity_data)
+
+    assert isinstance(report, dict)
+    assert set(report.keys()) == {"mean_intensity", "signal_to_noise_ratio", "optimal_gain"}
+    assert pytest.approx(report["mean_intensity"], rel=1e-3) == 342.5
+    assert report["signal_to_noise_ratio"] > 0
+    assert 1.0 <= report["optimal_gain"] <= 4.0
+
+
+def test_analyze_ir_gain_missing_data():
+    gains = [1.0, 2.0]
+    data = [
+        {"timestamp": "2024-06-12T20:00:00", "gain_value": 1.0, "intensity": 150.0},
+        # Missing gain 2.0 intentionally
+    ]
+    report = core.analyze_ir_gain(gains, data)
+
+    assert isinstance(report, dict)
+    assert report["mean_intensity"] == pytest.approx(150.0)
+    assert report["signal_to_noise_ratio"] >= 0
+
+
+def test_analyze_ir_gain_edge_zero_intensity():
+    gains = [1.0, 2.0, 3.0]
+    data = [
+        {"timestamp": "2024-06-12T20:00:00", "gain_value": 1.0, "intensity": 0.0},
+        {"timestamp": "2024-06-12T20:00:01", "gain_value": 2.0, "intensity": 0.0},
+        {"timestamp": "2024-06-12T20:00:02", "gain_value": 3.0, "intensity": 0.0},
+    ]
+    report = core.analyze_ir_gain(gains, data)
+
+    assert report["mean_intensity"] == 0.0
+    assert report["signal_to_noise_ratio"] == 0.0
+    assert report["optimal_gain"] == pytest.approx(1.0)
+
+
+def test_analyze_ir_gain_invalid_input():
+    with pytest.raises((TypeError, ValueError)):
+        core.analyze_ir_gain([], [])
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