def test_function_nd(lazy):
s = Signal1D(np.empty((200, )))
axis = s.axes_manager.signal_axes[0]
axis.scale = .05
axis.offset = -5
A, sigma1, sigma2, fraction, centre = 5, 0.3, 0.75, 0.5, 1
g1 = SplitVoigt(A=A,
sigma1=sigma1,
sigma2=sigma2,
fraction=fraction,
centre=centre)
s.data = g1.function(axis.axis)
s2 = stack([s] * 2)
if lazy:
s2 = s2.as_lazy()
g2 = SplitVoigt()
assert g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
g2.A.map['values'] = [A] * 2
g2.sigma1.map['values'] = [sigma1] * 2
g2.sigma2.map['values'] = [sigma2] * 2
g2.fraction.map['values'] = [fraction] * 2
g2.centre.map['values'] = [centre] * 2
np.testing.assert_allclose(g2.function_nd(axis.axis), s2.data)
def test_estimate_parameters_binned(only_current, binned, lazy, uniform):
s = Signal1D(np.empty((100,)))
s.axes_manager.signal_axes[0].is_binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = 1
axis.offset = -20
g1 = SplitVoigt(A=20001.0, centre=10.0, sigma1=3.0, sigma2=3.0)
s.data = g1.function(axis.axis)
if not uniform:
axis.convert_to_non_uniform_axis()
if lazy:
s = s.as_lazy()
g2 = SplitVoigt()
if binned and uniform:
factor = axis.scale
elif binned:
factor = np.gradient(axis.axis)
else:
factor = 1
assert g2.estimate_parameters(s, axis.low_value, axis.high_value,
only_current=only_current)
assert g2._axes_manager[-1].is_binned == binned
np.testing.assert_allclose(g1.A.value, g2.A.value * factor, rtol=0.2)
assert abs(g2.centre.value - g1.centre.value) <= 0.1
assert abs(g2.sigma1.value - g1.sigma1.value) <= 0.1
assert abs(g2.sigma2.value - g1.sigma2.value) <= 0.1
def test_function():
g = SplitVoigt()
g.A.value = 2.5
g.centre.value = 0
g.sigma1.value = 2
g.sigma2.value = 2
np.testing.assert_allclose(g.function(0), 0.49867785, rtol=1e-3)
np.testing.assert_allclose(g.function(6), 0.00553981, rtol=1e-3)
def test_height_attribute():
s = Signal1D(np.empty((100, )))
axis = s.axes_manager.signal_axes[0]
axis.scale = 0.5
axis.offset = -20
g1 = SplitVoigt(A=20001.0, centre=10.0, sigma1=10.0, sigma2=4.0)
s.data = g1.function(axis.axis)
np.testing.assert_almost_equal(g1.height, 1139.8920786)
g1.height = 1000.0
np.testing.assert_almost_equal(g1.height, 1000.0)
np.testing.assert_almost_equal(g1.A.value, 17546.3979224)
def test_estimate_parameters_binned(only_current, binned, lazy):
s = Signal1D(np.empty((100,)))
s.metadata.Signal.binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = 1
axis.offset = -20
g1 = SplitVoigt(A=20001.0, centre=10.0, sigma1=3.0, sigma2=3.0)
s.data = g1.function(axis.axis)
if lazy:
s = s.as_lazy()
g2 = SplitVoigt()
factor = axis.scale if binned else 1
assert g2.estimate_parameters(s, axis.low_value, axis.high_value,
only_current=only_current)
assert g2.binned == binned
assert_allclose(g1.A.value, g2.A.value * factor, rtol=0.2)
assert abs(g2.centre.value - g1.centre.value) <= 0.1
assert abs(g2.sigma1.value - g1.sigma1.value) <= 0.1
assert abs(g2.sigma2.value - g1.sigma2.value) <= 0.1