def test_estimate_parameters_binned(only_current, binned, lazy, uniform): s = Signal1D(np.empty((200,))) s.axes_manager.signal_axes[0].is_binned = binned axis = s.axes_manager.signal_axes[0] axis.scale = .05 axis.offset = -5 g1 = PESVoigt() g1.centre.value = 1 g1.area.value = 5. g1.gamma.value = 0.001 g1.FWHM.value = 0.5 s.data = g1.function(axis.axis) if not uniform: axis.convert_to_non_uniform_axis() if lazy: s = s.as_lazy() g2 = PESVoigt() 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(g2.FWHM.value, 1, 0.5) np.testing.assert_allclose(g1.area.value, g2.area.value * factor, 0.04) np.testing.assert_allclose(g2.centre.value, 1, 1e-3)
def test_function(): g = PESVoigt() g.area.value = 5 g.FWHM.value = 0.5 g.gamma.value = 0.2 g.centre.value = 1 np.testing.assert_allclose(g.function(0), 0.35380168) np.testing.assert_allclose(g.function(1), 5.06863535)
def test_estimate_parameters_binned(only_current, binned, lazy): s = Signal1D(np.empty((200, ))) s.metadata.Signal.binned = binned axis = s.axes_manager.signal_axes[0] axis.scale = .05 axis.offset = -5 g1 = PESVoigt() g1.centre.value = 1 g1.area.value = 5. g1.gamma.value = 0.001 g1.FWHM.value = 0.5 s.data = g1.function(axis.axis) if lazy: s = s.as_lazy() g2 = PESVoigt() 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(g2.FWHM.value, 1, 0.5) np.testing.assert_allclose(g1.area.value, g2.area.value * factor, 0.04) np.testing.assert_allclose(g2.centre.value, 1, 1e-3)