def test_church_reverb_is_applied_correctly():
expected_output = np.genfromtxt('tests/data/reverb/bark_church.csv', dtype = 'float32')
output_signal = add_reverb(input_signal, 'church')
# Mean squared error between input and output signal
mse = ((expected_output - output_signal)**2).mean(axis=0)
print(mse)
assert mse < 0.00001, "Output does not match test data!"
def test_hall_reverb_is_applied_correctly():
expected_output = np.genfromtxt('data/reverb/bark_hall.csv',
dtype='float32')
output_signal = add_reverb(input_signal, 'hall')
assert np.array_equal(output_signal, expected_output)
def test_unsupported_input_signal_type_raises_error():
with pytest.raises(TypeError):
add_reverb(np.array(['1', '2', '3']), 'hall')
def test_unsupported_reverb_type_raises_error():
with pytest.raises(Exception):
add_reverb(input_signal, 'unsupported_reverb_type')