def test_function_nd(binned):
s = Signal1D(np.empty((100,)))
axis = s.axes_manager.signal_axes[0]
axis.scale = 1
axis.offset = -20
g1 = Gaussian(50015.156, 10/sigma2fwhm, 10)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
g2 = Gaussian()
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_negative_scale():
s = Signal1D(np.empty((100,)))
axis = s.axes_manager.signal_axes[0]
axis.scale = -1
axis.offset = 100
g1 = Gaussian(50015.156, 15/sigma2fwhm, 50)
s.data = g1.function(axis.axis)
g2 = Gaussian()
with pytest.raises(ValueError):
g2.estimate_parameters(s, 40, 60)
assert g2.estimate_parameters(s, 90, 10)
np.testing.assert_allclose(g1.A.value, g2.A.value)
assert abs(g2.centre.value - g1.centre.value) <= 1e-3
assert abs(g2.sigma.value - g1.sigma.value) <= 0.1
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 = Gaussian(50015.156, 10 / sigma2fwhm, 10)
s.data = g1.function(axis.axis)
if not uniform:
axis.convert_to_non_uniform_axis()
if lazy:
s = s.as_lazy()
g2 = Gaussian()
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)
assert abs(g2.centre.value - g1.centre.value) <= 1e-3
assert abs(g2.sigma.value - g1.sigma.value) <= 0.1
def test_estimate_parameters_binned(only_current, binned):
s = Signal1D(np.empty((100,)))
s.metadata.Signal.binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = 1
axis.offset = -20
g1 = Gaussian(50015.156, 10/sigma2fwhm, 10)
s.data = g1.function(axis.axis)
g2 = Gaussian()
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)
assert abs(g2.centre.value - g1.centre.value) <= 1e-3
assert abs(g2.sigma.value - g1.sigma.value) <= 0.1
