Add audio_visualizer/tests/test_core.py

audio_visualizer/tests/test_core.py

@@ -0,0 +1,51 @@
+import pytest
+import numpy as np
+from audio_visualizer import core
+
+@pytest.fixture
+def dummy_data():
+    # Dummy data simulating SensorDataRow entries
+    timestamps = np.linspace(0, 1, 100)
+    frequency = np.linspace(20, 2000, 100)
+    amplitude = np.sin(np.linspace(0, 10 * np.pi, 100))
+    data = np.column_stack((timestamps, frequency, amplitude))
+    return data
+
+def test_compute_spectrum_valid_data(dummy_data):
+    # Ensure compute_spectrum runs without errors and returns numpy array
+    spectrum = core.compute_spectrum(dummy_data[:, 2])
+    assert isinstance(spectrum, np.ndarray), "Returned spectrum should be a numpy array"
+    assert spectrum.size == dummy_data[:, 2].size // 2 + 1, "Unexpected spectrum length"
+
+def test_compute_spectrum_zero_input():
+    data = np.zeros(256)
+    spectrum = core.compute_spectrum(data)
+    assert np.all(spectrum == 0), "Spectrum of all-zero input should be all zeros"
+
+def test_generate_visualization(monkeypatch, tmp_path, dummy_data):
+    test_file = tmp_path / "input.csv"
+    np.savetxt(test_file, dummy_data, delimiter=",")
+
+    # Patch load_audio_data and Visualizer.render to avoid real I/O or rendering
+    monkeypatch.setattr(core, "load_audio_data", lambda path: dummy_data[:, 2])
+    called = {}
+    def fake_render(self, spectrum):
+        called['rendered'] = isinstance(spectrum, np.ndarray)
+    monkeypatch.setattr(core.Visualizer, "render", fake_render)
+
+    core.generate_visualization(str(test_file))
+    assert called.get('rendered', False), "Visualizer.render should be called with a numpy array"
+
+def test_visualizer_render_executes(dummy_data):
+    vis = core.Visualizer()
+    spectrum = np.abs(np.fft.rfft(dummy_data[:, 2]))
+    # Ensure that render executes without raising exceptions
+    try:
+        vis.render(spectrum)
+    except Exception as e:
+        pytest.fail(f"Visualizer.render raised an exception: {e}")
+
+@pytest.mark.parametrize("invalid_input", [None, [], "invalid", np.array([])])
+def test_compute_spectrum_invalid_inputs(invalid_input):
+    with pytest.raises((ValueError, TypeError)):
+        core.compute_spectrum(invalid_input)