def test_function():
g = GaussianHF()
g.centre.value = 1
g.fwhm.value = 2
g.height.value = 3
assert g.function(2) == 1.5
assert g.function(1) == 3
def test_function():
g = GaussianHF()
g.centre.value = 1
g.fwhm.value = 2
g.height.value = 3
assert g.function(2) == 1.5
assert g.function(1) == 3
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 = 2.
axis.offset = -30
g1 = GaussianHF(50015.156, 23, 10)
s.data = g1.function(axis.axis)
if not uniform:
axis.convert_to_non_uniform_axis()
if lazy:
s = s.as_lazy()
g2 = GaussianHF()
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.height.value, g2.height.value * factor)
assert abs(g2.centre.value - g1.centre.value) <= 1e-3
assert abs(g2.fwhm.value - g1.fwhm.value) <= 0.1
def test_function():
g = GaussianHF()
g.centre.value = 1
g.fwhm.value = 2
g.height.value = 3
nt.assert_equal(g.function(2), 1.5)
nt.assert_equal(g.function(1), 3)
def test_integral_as_signal():
s = Signal1D(np.zeros((2, 3, 100)))
g1 = GaussianHF(fwhm=3.33, centre=20.)
h_ref = np.linspace(0.1, 3.0, s.axes_manager.navigation_size)
for d, h in zip(s._iterate_signal(), h_ref):
g1.height.value = h
d[:] = g1.function(s.axes_manager.signal_axes[0].axis)
m = s.create_model()
g2 = GaussianHF()
m.append(g2)
g2.estimate_parameters(s, 0, 100, True)
m.multifit()
s_out = g2.integral_as_signal()
ref = (h_ref * 3.33 * sqrt2pi / sigma2fwhm).reshape(s_out.data.shape)
assert_allclose(s_out.data, ref)
def test_integral_as_signal():
s = Signal1D(np.zeros((2, 3, 100)))
g1 = GaussianHF(fwhm=3.33, centre=20.)
h_ref = np.linspace(0.1, 3.0, s.axes_manager.navigation_size)
for d, h in zip(s._iterate_signal(), h_ref):
g1.height.value = h
d[:] = g1.function(s.axes_manager.signal_axes[0].axis)
m = s.create_model()
g2 = GaussianHF()
m.append(g2)
g2.estimate_parameters(s, 0, 100, True)
m.multifit()
s_out = g2.integral_as_signal()
ref = (h_ref * 3.33 * sqrt2pi / sigma2fwhm).reshape(s_out.data.shape)
assert_allclose(s_out.data, ref)
def test_estimate_parameters_binned():
s = Signal1D(np.empty((100,)))
axis = s.axes_manager.signal_axes[0]
axis.scale = 2.
axis.offset = -30
g1 = GaussianHF(50015.156, 23, 10)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = True
g2 = GaussianHF()
g2.estimate_parameters(s, axis.low_value, axis.high_value, True)
assert_allclose(
g1.height.value / axis.scale,
g2.height.value)
assert abs(g2.centre.value - g1.centre.value) <= 1e-3
assert abs(g2.fwhm.value - g1.fwhm.value) <= 0.1
def test_function_nd(binned):
s = Signal1D(np.empty((100, )))
axis = s.axes_manager.signal_axes[0]
axis.scale = 2.
axis.offset = -30
g1 = GaussianHF(50015.156, 23, 10)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
g2 = GaussianHF()
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
# TODO: sort out while the rtol to be so high...
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data, rtol=0.05)
def test_function_nd(binned):
s = Signal1D(np.empty((100,)))
axis = s.axes_manager.signal_axes[0]
axis.scale = 2.
axis.offset = -30
g1 = GaussianHF(50015.156, 23, 10)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
g2 = GaussianHF()
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
# TODO: sort out while the rtol to be so high...
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data, rtol=0.05)
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 = 2.
axis.offset = -30
g1 = GaussianHF(50015.156, 23, 10)
s.data = g1.function(axis.axis)
g2 = GaussianHF()
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.height.value, g2.height.value * factor)
assert abs(g2.centre.value - g1.centre.value) <= 1e-3
assert abs(g2.fwhm.value - g1.fwhm.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 = 2.
axis.offset = -30
g1 = GaussianHF(50015.156, 23, 10)
s.data = g1.function(axis.axis)
g2 = GaussianHF()
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.height.value, g2.height.value * factor)
assert abs(g2.centre.value - g1.centre.value) <= 1e-3
assert abs(g2.fwhm.value - g1.fwhm.value) <= 0.1