def test_function():
g = Doniach()
g.centre.value = 1
g.sigma.value = 2 / sigma2fwhm
g.A.value = 3 * sqrt2pi * g.sigma.value
g.alpha.value = 1.0e-7
assert_allclose(g.function(2), 3.151281311482424)
assert_allclose(g.function(1), 7.519884823893001)
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 = Doniach(centre=1, A=5, sigma=1, alpha=0.5)
s.data = g1.function(axis.axis)
if not uniform:
axis.convert_to_non_uniform_axis()
if lazy:
s = s.as_lazy()
g2 = Doniach()
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, 2.331764, 0.01)
np.testing.assert_allclose(g1.A.value, g2.A.value * factor, 0.3)
np.testing.assert_allclose(g2.centre.value, -0.4791825)
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 = Doniach(centre=1, A=5, sigma=1, alpha=0.5)
s.data = g1.function(axis.axis)
if lazy:
s = s.as_lazy()
g2 = Doniach()
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.sigma.value, 2.331764, 0.01)
np.testing.assert_allclose(g1.A.value, g2.A.value * factor, 0.3)
np.testing.assert_allclose(g2.centre.value, -0.4791825)