def flat_pattern(self):
pd = Correlation2D(data=np.ones(shape=(2, 2, 5, 5)))
pd.axes_manager.signal_axes[0].scale = 0.5
pd.axes_manager.signal_axes[0].name = "theta"
pd.axes_manager.signal_axes[1].scale = 2
pd.axes_manager.signal_axes[1].name = "k"
return pd
def test_2d_data_as_lazy(self):
data = np.random.random((100, 150))
s = Correlation2D(data)
scale0, scale1, metadata_string = 0.5, 1.5, "test"
s.axes_manager[0].scale = scale0
s.axes_manager[1].scale = scale1
s.metadata.Test = metadata_string
s_lazy = s.as_lazy()
assert s_lazy.__class__ == LazyCorrelation2D
assert hasattr(s_lazy.data, "compute")
assert s_lazy.axes_manager[0].scale == scale0
assert s_lazy.axes_manager[1].scale == scale1
assert s_lazy.metadata.Test == metadata_string
assert data.shape == s_lazy.data.shape
def test_4d_data_as_lazy(self):
data = np.random.random((4, 10, 15))
s = Correlation2D(data)
s_lazy = s.as_lazy()
assert s_lazy.__class__ == LazyCorrelation2D
assert data.shape == s_lazy.data.shape
def test_decomposition_class_assignment(self, diffraction_pattern):
s = Correlation2D(diffraction_pattern)
s.decomposition()
assert isinstance(s, Correlation2D)
def test_decomposition_is_performed(self, diffraction_pattern):
s = Correlation2D(diffraction_pattern)
s.decomposition()
assert s.learning_results is not None