def flat_pattern(self):
pd = PolarDiffraction2D(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 flat_pattern(self):
rng = np.random.default_rng(seed=1)
pd = PolarDiffraction2D(data=rng.random((2, 2, 50, 15)))
pd.axes_manager.signal_axes[0].scale = 0.5
pd.axes_manager.signal_axes[0].name = "theta"
pd.axes_manager.signal_axes[1].scale = 0.1
pd.axes_manager.signal_axes[1].name = "k"
return pd
def test_2_fold_corr(self):
rand_number = np.random.random(10)
tiled_random = np.tile(rand_number, (2, 3, 2))
p = PolarDiffraction2D(tiled_random)
cor = p.get_pearson_correlation()
sym_coeff = cor.get_symmetry_coefficient(
method="average",
angular_range=0.0,
include_duplicates=True,
normalize=False,
)
np.testing.assert_almost_equal(sym_coeff.data[0, 0], 1)
def test_2d_data_as_lazy(self):
data = np.random.random((100, 150))
s = PolarDiffraction2D(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__ == LazyPolarDiffraction2D
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 = PolarDiffraction2D(data)
s_lazy = s.as_lazy()
assert s_lazy.__class__ == LazyPolarDiffraction2D
assert data.shape == s_lazy.data.shape
def test_decomposition_class_assignment(self, diffraction_pattern):
s = PolarDiffraction2D(diffraction_pattern)
s.decomposition()
assert isinstance(s, PolarDiffraction2D)
def test_decomposition_is_performed(self, diffraction_pattern):
s = PolarDiffraction2D(diffraction_pattern)
s.decomposition()
assert s.learning_results is not None