def test_small_identical_inputs_noisy(self):
"""
Tests for input of identical images after noise is added.
"""
drift = calculation.CalculateDrift(self.small_data,
self.small_data_noisy, 1)
numpy.testing.assert_almost_equal(drift, (0, 0), 0)
def test_random_drift_noisy(self):
"""
Tests for image drifted by random drift value after noise is added.
"""
drift = calculation.CalculateDrift(self.data[0],
self.data_random_drifted_noisy, 10)
numpy.testing.assert_almost_equal(drift, (self.deltac, self.deltar), 1)
def test_known_drift_noisy(self):
"""
Tests for image drifted by known drift value after noise is added.
"""
drift = calculation.CalculateDrift(self.data[0],
self.data_drifted_noisy, 1)
numpy.testing.assert_almost_equal(drift, (-3, 5), 1)
def test_small_random_drift(self):
"""
Tests for image drifted by random drift value.
"""
drift = calculation.CalculateDrift(self.small_data,
self.small_data_random_drifted, 10)
numpy.testing.assert_almost_equal(
drift, (self.small_deltac, self.small_deltar), 0)
def test_different_precisions(self):
"""
Tests for image drifted by random drift value using different precisions.
"""
drift = calculation.CalculateDrift(self.data[0],
self.data_random_drifted, 1)
numpy.testing.assert_almost_equal(drift, (self.deltac, self.deltar), 0)
drift = calculation.CalculateDrift(self.data[0],
self.data_random_drifted, 10)
numpy.testing.assert_almost_equal(drift, (self.deltac, self.deltar), 1)
drift = calculation.CalculateDrift(self.data[0],
self.data_random_drifted, 100)
numpy.testing.assert_almost_equal(drift, (self.deltac, self.deltar), 2)
drift = calculation.CalculateDrift(self.data[0],
self.data_random_drifted, 1000)
numpy.testing.assert_almost_equal(drift, (self.deltac, self.deltar), 3)
def test_identical_inputs(self):
"""
Tests for input of identical images.
"""
drift = calculation.CalculateDrift(self.data[0], self.data[0], 1)
numpy.testing.assert_almost_equal(drift, (0, 0), 1)