def test_function_nd(binned, lazy):
s = Signal1D(np.empty((200, )))
s.metadata.Signal.binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = .05
axis.offset = -5
g1 = Voigt(centre=1, area=5, gamma=0, sigma=0.5, legacy=False)
s.data = g1.function(axis.axis)
s2 = stack([s] * 2)
if lazy:
s2 = s2.as_lazy()
g2 = Voigt(legacy=False)
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data)
def test_estimate_parameters_binned(only_current, binned, lazy, uniform):
s = Signal1D(np.empty((200, )))
s.axes_manager.signal_axes[0].is_binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = .05
axis.offset = -5
g1 = Voigt(centre=1, area=5, gamma=0.001, sigma=0.5, legacy=False)
s.data = g1.function(axis.axis)
if lazy:
s = s.as_lazy()
g2 = Voigt(legacy=False)
if not uniform:
axis.convert_to_non_uniform_axis()
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(g2.sigma.value, 0.5, 0.01)
np.testing.assert_allclose(g1.area.value, g2.area.value * factor, 0.01)
np.testing.assert_allclose(g2.centre.value, 1, 1e-3)
def test_estimate_parameters_binned(only_current, binned, lazy):
s = Signal1D(np.empty((200, )))
s.metadata.Signal.binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = .05
axis.offset = -5
g1 = Voigt(centre=1, area=5, gamma=0.001, sigma=0.5, legacy=False)
s.data = g1.function(axis.axis)
if lazy:
s = s.as_lazy()
g2 = Voigt(legacy=False)
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(g2.sigma.value, 0.5, 0.01)
assert_allclose(g1.area.value, g2.area.value * factor, 0.01)
assert_allclose(g2.centre.value, 1, 1e-3)