def test_exponential_running_demean(mock_data):
mock_input, _, expected_data = mock_data
demeaned_data = exponential_moving_demean(mock_input)
assert mock_input.shape == demeaned_data.shape == expected_data.shape
np.testing.assert_allclose(demeaned_data,
expected_data,
rtol=1e-4,
atol=1e-4)
def test_exponential_running_init_block_size(mock_data):
mock_input, _, _ = mock_data
init_block_size = 3
standardized_data = exponential_moving_standardize(
mock_input, init_block_size=init_block_size)
np.testing.assert_allclose(standardized_data[:, :init_block_size].sum(),
[0],
rtol=1e-4,
atol=1e-4)
demeaned_data = exponential_moving_demean(mock_input,
init_block_size=init_block_size)
np.testing.assert_allclose(demeaned_data[:, :init_block_size].sum(), [0],
rtol=1e-4,
atol=1e-4)
def test_exponential_running_init_block_size(mock_data):
mock_input, _, _ = mock_data
init_block_size = 3
standardized_data = exponential_moving_standardize(
mock_input, init_block_size=init_block_size)
# mean over time axis (1!) should give 0 per channel
np.testing.assert_allclose(
standardized_data[:, :init_block_size].mean(axis=1), 0,
rtol=1e-4, atol=1e-4)
np.testing.assert_allclose(
standardized_data[:, :init_block_size].std(axis=1), 1,
rtol=1e-4, atol=1e-4)
# mean over time axis (1!) should give 0 per channel
demeaned_data = exponential_moving_demean(
mock_input, init_block_size=init_block_size)
np.testing.assert_allclose(
demeaned_data[:, :init_block_size].mean(axis=1), 0, rtol=1e-4, atol=1e-4)