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