def test_orthomax(whiten_method):
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition()
s.blind_source_separation(2,
algorithm="orthomax",
whiten_method=whiten_method)
s.learning_results.bss_factors[:, 0] *= -1
s._auto_reverse_bss_component("loadings")
np.testing.assert_array_less(s.learning_results.bss_factors[:, 0], 0)
np.testing.assert_array_less(0, s.learning_results.bss_factors[:, 1])
s._auto_reverse_bss_component("factors")
np.testing.assert_array_less(0, s.learning_results.bss_factors)
# Verify that we can change gamma for orthomax method
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition()
s.blind_source_separation(2, algorithm="orthomax", gamma=2)
def test_num_components_error():
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition()
s.learning_results.output_dimension = None
with pytest.raises(
ValueError, match="No `number_of_components` or `comp_list` provided"
):
s.blind_source_separation(number_of_components=None)
def test_factors_error():
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition()
factors = s.get_decomposition_factors().data
with pytest.raises(TypeError, match="`factors` must be a BaseSignal instance"):
s.blind_source_separation(2, factors=factors)
factors = BaseSignal(s.get_decomposition_factors().data)
with pytest.raises(ValueError, match="`factors` must have navigation dimension"):
s.blind_source_separation(2, factors=factors)
def test_orthomax(whiten_method):
rng = np.random.RandomState(123)
S = rng.laplace(size=(3, 500))
A = rng.random((3, 3))
s = Signal1D(A @ S)
s.decomposition()
s.blind_source_separation(3, algorithm="orthomax", whiten_method=whiten_method)
W = s.learning_results.unmixing_matrix
assert amari(W, A) < 0.5
# Verify that we can change gamma for orthomax method
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition()
s.blind_source_separation(2, algorithm="orthomax", gamma=2)
def test_get_core_loss_eels_line_scan_signal(add_powerlaw, add_noise):
s = ad.get_core_loss_eels_line_scan_signal(add_powerlaw, add_noise)
assert s.metadata.Signal.signal_type == 'EELS'
def setup_method(self, method):
s = get_core_loss_eels_line_scan_signal(True)
self.signal = s
er = EdgesRange(self.signal)
self.er = er
def test_algorithm_error():
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition()
with pytest.raises(ValueError, match="'algorithm' not recognised"):
s.blind_source_separation(2, algorithm="uniform")
def test_components_list():
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition(output_dimension=3)
s.blind_source_separation(comp_list=[0, 2])
assert s.learning_results.unmixing_matrix.shape == (2, 2)
def test_num_components(num_components):
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition(output_dimension=2)
s.blind_source_separation(number_of_components=num_components)
def test_no_decomposition_error():
s = artificial_data.get_core_loss_eels_line_scan_signal()
with pytest.raises(AttributeError, match="A decomposition must be performed"):
s.blind_source_separation(2)
def test_plot_eels_labels_nav():
s = get_core_loss_eels_line_scan_signal(True)
s.add_elements(['Cr', 'Fe'])
s.plot(plot_edges=True)
s.axes_manager.indices = (10, )
s._plot.close()
def test_plot_eels_labels():
s = get_core_loss_eels_line_scan_signal(True, random_state=10)
s.add_elements(['Cr'])
s.plot(plot_edges=True)
return s._plot.signal_plot.figure
def setup_method(self, method):
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition()
s.blind_source_separation(2)
self.s = s
def test_get_core_loss_eels_line_scan_signal():
s = ad.get_core_loss_eels_line_scan_signal()
assert s.metadata.Signal.signal_type == 'EELS'
