def test_function():
g = PowerLaw()
g.A.value = 1
g.r.value = 2
g.origin.value = 3
assert g.function(2) == 1
assert g.function(1) == 0.25
def __init__(self,
signal1D,
auto_background=True,
auto_add_edges=True,
ll=None,
GOS=None,
dictionary=None):
Model1D.__init__(self, signal1D)
# When automatically setting the fine structure energy regions,
# the fine structure of an EELS edge component is automatically
# disable if the next ionisation edge onset distance to the
# higher energy side of the fine structure region is lower that
# the value of this parameter
self._min_distance_between_edges_for_fine_structure = 0
self._preedge_safe_window_width = 2
self.signal1D = signal1D
self._suspend_auto_fine_structure_width = False
self.convolved = False
self.low_loss = ll
self.GOS = GOS
self.edges = []
self._background_components = []
if dictionary is not None:
auto_background = False
auto_add_edges = False
self._load_dictionary(dictionary)
if auto_background is True:
background = PowerLaw()
self.append(background)
if self.signal.subshells and auto_add_edges is True:
self._add_edges_from_subshells_names()
def __init__(self,
signal1D,
auto_background=True,
auto_add_edges=True,
ll=None,
GOS=None,
dictionary=None):
Model1D.__init__(self, signal1D)
self._suspend_auto_fine_structure_width = False
self.convolved = False
self.low_loss = ll
self.GOS = GOS
self.edges = []
self._background_components = []
if dictionary is not None:
auto_background = False
auto_add_edges = False
self._load_dictionary(dictionary)
if auto_background is True:
background = PowerLaw()
self.append(background)
if self.signal.subshells and auto_add_edges is True:
self._add_edges_from_subshells_names()
def test_linear_override():
g = PowerLaw()
for para in g.parameters:
if para is g.A:
assert para._linear
else:
assert not para._linear
def test_function_nd(binned):
s = Signal1D(np.empty((100, )))
axis = s.axes_manager.signal_axes[0]
axis.scale = 0.02
axis.offset = 1
g1 = PowerLaw(50015.156, 1.2)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
g2 = PowerLaw()
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, 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 = 0.02
axis.offset = 1
g1 = PowerLaw(50015.156, 1.2)
s.data = g1.function(axis.axis)
g2 = PowerLaw()
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
# error of the estimate function is rather large, esp. when binned=FALSE
assert_allclose(g1.A.value, g2.A.value * factor, rtol=0.05)
assert abs(g2.r.value - g1.r.value) <= 2e-2