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 = Spectrum(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)
np.testing.assert_almost_equal(
s_out.data, ref)
def test_estimate_parameters_binned():
s = Spectrum(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)
nt.assert_almost_equal(
g1.height.value / axis.scale,
g2.height.value)
nt.assert_almost_equal(g2.centre.value, g1.centre.value, delta=1e-3)
nt.assert_almost_equal(g2.fwhm.value, g1.fwhm.value, delta=0.1)
def test_util_fwhm_get():
g1 = GaussianHF(fwhm=0.33)
g1.height.value = 1.0
nt.assert_almost_equal(g1.A, 1.0 * sqrt2pi * 0.33 / sigma2fwhm)
def test_util_fwhm_set():
g1 = GaussianHF(fwhm=0.33)
g1.A = 1.0
nt.assert_almost_equal(g1.height.value, 1.0 * sigma2fwhm / (
0.33 * sqrt2pi))
def test_util_sigma_getset():
g1 = GaussianHF()
g1.sigma = 1.0
nt.assert_almost_equal(g1.sigma, 1.0)
def test_util_sigma_get():
g1 = GaussianHF()
g1.fwhm.value = 1.0
nt.assert_almost_equal(g1.sigma, 1.0 / sigma2fwhm)
def test_util_sigma_set():
g1 = GaussianHF()
g1.sigma = 1.0
nt.assert_almost_equal(g1.fwhm.value, 1.0 * sigma2fwhm)
def test_util_fwhm_getset():
g1 = GaussianHF(fwhm=0.33)
g1.A = 1.0
nt.assert_almost_equal(g1.A, 1.0)