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_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):
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_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_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_sigma_getset():
g1 = GaussianHF()
g1.sigma = 1.0
assert_allclose(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
assert_allclose(g1.height.value, 1.0 * sigma2fwhm / (
0.33 * sqrt2pi))
def test_util_fwhm_getset():
g1 = GaussianHF(fwhm=0.33)
g1.A = 1.0
assert_allclose(g1.A, 1.0)
