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 nt.assert_equal(g.function(2), 1.5) nt.assert_equal(g.function(1), 3)
def on_spanselect(self, x0, x1): self.span.disconnect_events() self.span = None ax = self.drag_data[3] window = ax.figure.canvas.parent() mw = window.property('hyperspyUI.ModelWrapper') if mw is None: sw = window.property('hyperspyUI.SignalWrapper') mw = sw.make_model() self._wire_wrapper(mw) m = mw.model g = GaussianHF() mw.add_component(g) g._model_plot_line.line.set_picker(True) m.fit_component(g, signal_range=(x0, x1)) i = m.axis.value2index(g.centre.value) g.active = False h = m.signal()[i] - m(onlyactive=True)[i] g.active = True if m.signal.metadata.Signal.binned: h /= m.axis.scale g.height.value = h g.height.free = False g.centre.free = False m.fit_component(g, signal_range=(x0, x1), estimate_parameters=False) g.height.free = True g.centre.free = True if self.drag_data is None: return self.drag_data = None
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 on_mousedown(self, event): if event.inaxes is None: return ax = event.inaxes f = ax.figure x, y = event.xdata, event.ydata if event.button == 1 and event.dblclick: # Add Gaussian here! window = f.canvas.parent() mw = window.property('hyperspyUI.ModelWrapper') if mw is None: sw = window.property('hyperspyUI.SignalWrapper') mw = sw.make_model() self._wire_wrapper(mw) m = mw.model i = m.axis.value2index(x) h = m.signal()[i] - m()[i] if m.signal.metadata.Signal.binned: h /= m.axis.scale g = GaussianHF(height=h * np.sqrt(2 * np.pi), centre=x) g.height.free = False g.centre.free = False mw.add_component(g) g._model_plot_line.line.set_picker(True) m.fit_component(g, signal_range=None, estimate_parameters=False) g.height.free = True g.centre.free = True else: self.dragging = True self.drag_data = [x, y, event.button, ax, event]
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_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 setup_method(self, method): s = Signal1D(range(100)) m = s.create_model() m.append(Gaussian(A=13)) m[-1].name = 'something' m.append(GaussianHF(module="numpy")) m[-1].height.value = 3 m.append( Expression(name="Line", expression="a * x + b", a=1, c=0, rename_pars={"b": "c"})) self.m = m
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(): 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_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_util_fwhm_getset(): g1 = GaussianHF(fwhm=0.33) g1.A = 1.0 nt.assert_almost_equal(g1.A, 1.0)
def test_util_fwhm_getset(): g1 = GaussianHF(fwhm=0.33) g1.A = 1.0 assert_allclose(g1.A, 1.0)
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_set(): g1 = GaussianHF(fwhm=0.33) g1.A = 1.0 assert_allclose(g1.height.value, 1.0 * sigma2fwhm / (0.33 * sqrt2pi))
def test_util_fwhm_get(): g1 = GaussianHF(fwhm=0.33) g1.height.value = 1.0 assert_allclose(g1.A, 1.0 * sqrt2pi * 0.33 / sigma2fwhm)
def test_util_sigma_get(): g1 = GaussianHF() g1.fwhm.value = 1.0 assert_allclose(g1.sigma, 1.0 / sigma2fwhm)
def test_util_sigma_getset(): g1 = GaussianHF() g1.sigma = 1.0 assert_allclose(g1.sigma, 1.0)
def test_util_sigma_getset(): g1 = GaussianHF() g1.sigma = 1.0 nt.assert_almost_equal(g1.sigma, 1.0)
def test_util_sigma_set(): g1 = GaussianHF() g1.sigma = 1.0 assert_allclose(g1.fwhm.value, 1.0 * 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_fwhm_set(): g1 = GaussianHF(fwhm=0.33) g1.A = 1.0 assert_allclose(g1.height.value, 1.0 * sigma2fwhm / ( 0.33 * sqrt2pi))