def test_2d_data_as_lazy(self): data = np.random.random((100, 150)) s = Power2D(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__ == LazyPower2D 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_decomposition_class_assignment(self, diffraction_pattern): s = Power2D(diffraction_pattern) s.decomposition() assert isinstance(s, Power2D)
def test_decomposition_is_performed(self, diffraction_pattern): s = Power2D(diffraction_pattern) s.decomposition() assert s.learning_results is not None
def flat_pattern(self): pd = Power2D(data=np.ones(shape=(2, 2, 5, 5))) return pd
def test_4d_data_as_lazy(self): data = np.random.random((4, 10, 15)) s = Power2D(data) s_lazy = s.as_lazy() assert s_lazy.__class__ == LazyPower2D assert data.shape == s_lazy.data.shape