def test_function():
g = PESVoigt()
g.area.value = 5
g.FWHM.value = 0.5
g.gamma.value = 0.2
g.centre.value = 1
np.testing.assert_allclose(g.function(0), 0.35380168)
np.testing.assert_allclose(g.function(1), 5.06863535)
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 = PESVoigt()
g1.centre.value = 1
g1.area.value = 5.
g1.gamma.value = 0.001
g1.FWHM.value = 0.5
s.data = g1.function(axis.axis)
if not uniform:
axis.convert_to_non_uniform_axis()
if lazy:
s = s.as_lazy()
g2 = PESVoigt()
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.FWHM.value, 1, 0.5)
np.testing.assert_allclose(g1.area.value, g2.area.value * factor, 0.04)
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 = PESVoigt()
g1.centre.value = 1
g1.area.value = 5.
g1.gamma.value = 0.001
g1.FWHM.value = 0.5
s.data = g1.function(axis.axis)
if lazy:
s = s.as_lazy()
g2 = PESVoigt()
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
np.testing.assert_allclose(g2.FWHM.value, 1, 0.5)
np.testing.assert_allclose(g1.area.value, g2.area.value * factor, 0.04)
np.testing.assert_allclose(g2.centre.value, 1, 1e-3)