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_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