def test_function_nd(binned, lazy):
s = Signal1D(np.empty((250,)))
axis = s.axes_manager.signal_axes[0]
axis.scale = .2
axis.offset = -15
g1 = Lorentzian(52342, 2, 10)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
if lazy:
s2 = s2.as_lazy()
g2 = Lorentzian()
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
np.testing.assert_allclose(g2.function_nd(axis.axis) * factor, s2.data,0.16)
def test_estimate_parameters_binned(only_current, binned, lazy, uniform):
s = Signal1D(np.empty((250, )))
s.axes_manager.signal_axes[0].is_binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = .2
axis.offset = -15
g1 = Lorentzian(52342, 2, 10)
s.data = g1.function(axis.axis)
if not uniform:
axis.convert_to_non_uniform_axis()
if lazy:
s = s.as_lazy()
g2 = Lorentzian()
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, 0.1)
assert abs(g2.centre.value - g1.centre.value) <= 0.2
assert abs(g2.gamma.value - g1.gamma.value) <= 0.1
def test_estimate_parameters_binned(only_current, binned, lazy):
s = Signal1D(np.empty((250,)))
s.metadata.Signal.binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = .2
axis.offset = -15
g1 = Lorentzian(52342, 2, 10)
s.data = g1.function(axis.axis)
if lazy:
s = s.as_lazy()
g2 = Lorentzian()
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(g1.A.value, g2.A.value * factor,0.1)
assert abs(g2.centre.value - g1.centre.value) <= 0.2
assert abs(g2.gamma.value - g1.gamma.value) <= 0.1