def test_normalize(self):
"""Test that by adding a normalization node we get a signal between 1 and 1."""
processor = DataProcessor("memory", [SVD(), MinMaxNormalize()])
self.assertFalse(processor.is_trained)
processor.train([self.data.data(idx) for idx in [0, 1]])
self.assertTrue(processor.is_trained)
# Test processing of all data
processed = processor(self.data.data())
np.testing.assert_array_almost_equal(
unp.nominal_values(processed),
np.array([[1.0, 0.0], [0.0, 1.0], [0.5, 0.5], [0.25, 0.75]]),
)
def test_normalize(self):
"""Test that by adding a normalization node we get a signal between 1 and 1."""
processor = DataProcessor(
"memory", [AverageData(axis=1),
SVD(), MinMaxNormalize()])
self.assertFalse(processor.is_trained)
processor.train([self.data.data(idx) for idx in [0, 1]])
self.assertTrue(processor.is_trained)
all_expected = np.array([[0.0, 1.0, 0.5, 0.75], [1.0, 0.0, 0.5, 0.25]])
# Test processing of all data
processed = processor(self.data.data())[0]
self.assertTrue(np.allclose(processed, all_expected))
def test_averaging_and_svd(self):
"""Test averaging followed by a SVD."""
processor = DataProcessor("memory", [AverageData(axis=1), SVD()])
# Test training using the calibration points
self.assertFalse(processor.is_trained)
processor.train([self.data.data(idx) for idx in [0, 1]])
self.assertTrue(processor.is_trained)
# Test the excited state
processed = processor(self.data.data(0))
np.testing.assert_array_almost_equal(
unp.nominal_values(processed),
self._sig_es,
)
# Test the ground state
processed = processor(self.data.data(1))
np.testing.assert_array_almost_equal(
unp.nominal_values(processed),
self._sig_gs,
)
# Test the x90p rotation
processed = processor(self.data.data(2))
np.testing.assert_array_almost_equal(
unp.nominal_values(processed),
self._sig_x90,
)
np.testing.assert_array_almost_equal(
unp.std_devs(processed),
np.array([0.25, 0.25]),
)
# Test the x45p rotation
processed = processor(self.data.data(3))
np.testing.assert_array_almost_equal(
unp.nominal_values(processed),
self._sig_x45,
)
np.testing.assert_array_almost_equal(
unp.std_devs(processed),
np.array([np.std([1, 1, 1, -1]) / np.sqrt(4.0) / 2] * 2),
)
def test_train_svd_processor(self):
"""Test that we can train a DataProcessor with an SVD."""
processor = DataProcessor("memory", [SVD()])
self.assertFalse(processor.is_trained)
iq_data = [[[0.0, 0.0], [0.0, 0.0]], [[1.0, 1.0], [-1.0, 1.0]],
[[-1.0, -1.0], [1.0, -1.0]]]
self.create_experiment(iq_data)
processor.train(self.iq_experiment.data())
self.assertTrue(processor.is_trained)
# Check that we can use the SVD
iq_data = [[[2, 2], [2, -2]]]
self.create_experiment(iq_data)
processed, _ = processor(self.iq_experiment.data(0))
expected = np.array([-2, -2]) / np.sqrt(2)
self.assertTrue(np.allclose(processed, expected))