def __init__(self, spectrum, auto_background=True, auto_add_edges=True, ll=None, GOS=None, dictionary=None):
Model.__init__(self, spectrum)
self._suspend_auto_fine_structure_width = False
self.convolved = False
self.low_loss = ll
self.GOS = GOS
self.edges = list()
if dictionary is not None:
auto_background = False
auto_add_edges = False
self._load_dictionary(dictionary)
if auto_background is True:
interactive_ns = get_interactive_ns()
background = PowerLaw()
background.name = "background"
warnings.warn(
'Adding "background" to the user namespace. ' "This feature will be removed in HyperSpy 0.9.",
VisibleDeprecationWarning,
)
interactive_ns["background"] = background
self.append(background)
if self.spectrum.subshells and auto_add_edges is True:
self._add_edges_from_subshells_names()
def power_law_extrapolation(self,
window_size=20,
extrapolation_size=1024,
add_noise=False,
fix_neg_r=False):
"""Extrapolate the spectrum to the right using a powerlaw
Parameters
----------
window_size : int
The number of channels from the right side of the
spectrum that are used to estimate the power law
parameters.
extrapolation_size : int
Size of the extrapolation in number of channels
add_noise : bool
If True, add poissonian noise to the extrapolated spectrum.
fix_neg_r : bool
If True, the negative values for the "components.PowerLaw"
parameter r will be flagged and the extrapolation will be
done with a constant zero-value.
Returns
-------
A new spectrum, with the extrapolation.
"""
self._check_signal_dimension_equals_one()
axis = self.axes_manager.signal_axes[0]
s = self.deepcopy()
s.mapped_parameters.title += (' %i channels extrapolated' %
extrapolation_size)
if s.tmp_parameters.has_item('filename'):
s.tmp_parameters.filename += ('_%i_channels_extrapolated' %
extrapolation_size)
new_shape = list(self.data.shape)
new_shape[axis.index_in_array] += extrapolation_size
s.data = np.zeros((new_shape))
s.get_dimensions_from_data()
s.data[..., :axis.size] = self.data
pl = PowerLaw()
pl._axes_manager = self.axes_manager
pl.estimate_parameters(s, axis.index2value(axis.size - window_size),
axis.index2value(axis.size - 1))
if fix_neg_r is True:
_r = pl.r.map['values']
_A = pl.A.map['values']
_A[_r <= 0] = 0
pl.A.map['values'] = _A
s.data[..., axis.size:] = (
pl.A.map['values'][..., np.newaxis] *
s.axes_manager.signal_axes[0].axis[np.newaxis, axis.size:]
**(-pl.r.map['values'][..., np.newaxis]))
return s
def __init__(self, spectrum, auto_background=True,
auto_add_edges=True, ll=None,
GOS=None, *args, **kwargs):
Model.__init__(self, spectrum, *args, **kwargs)
self.convolved = False
self.low_loss = ll
self.GOS = GOS
if auto_background is True:
background = PowerLaw()
background.name = 'background'
interactive_ns['background'] = background
self.append(background)
if self.spectrum.subshells and auto_add_edges is True:
self._add_edges_from_subshells_names()
def __init__(self, spectrum, auto_background=True,
auto_add_edges=True, ll=None,
GOS=None, *args, **kwargs):
Model.__init__(self, spectrum, *args, **kwargs)
self.convolved = False
self.low_loss = ll
self.GOS = GOS
if auto_background is True:
background = PowerLaw()
background.name = 'background'
interactive_ns['background'] = background
self.append(background)
if self.spectrum.subshells and auto_add_edges is True:
self._add_edges_from_subshells_names()
def __init__(self, spectrum, auto_background=True, auto_add_edges=True, ll=None, GOS=None, *args, **kwargs):
Model.__init__(self, spectrum, *args, **kwargs)
self._suspend_auto_fine_structure_width = False
self.convolved = False
self.low_loss = ll
self.GOS = GOS
self.edges = list()
if auto_background is True:
interactive_ns = get_interactive_ns()
background = PowerLaw()
background.name = "background"
interactive_ns["background"] = background
self.append(background)
if self.spectrum.subshells and auto_add_edges is True:
self._add_edges_from_subshells_names()
def __init__(self,
spectrum,
auto_background=True,
auto_add_edges=True,
ll=None,
GOS=None,
dictionary=None):
Model1D.__init__(self, spectrum)
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.spectrum.subshells and auto_add_edges is True:
self._add_edges_from_subshells_names()
def __init__(self, spectrum, auto_background=True, auto_add_edges=True, ll=None, *args, **kwargs):
Model.__init__(self, spectrum, *args, **kwargs)
self.ll = ll
if auto_background is True:
background = PowerLaw()
background.name = "background"
interactive_ns["background"] = background
self.append(background)
if self.ll is not None:
self.convolved = True
if self.experiments.convolution_axis is None:
self.experiments.set_convolution_axis()
else:
self.convolved = False
if self.spectrum.subshells and auto_add_edges is True:
self._add_edges_from_subshells_names()
def __init__(self,
spectrum,
auto_background=True,
auto_add_edges=True,
ll=None,
*args,
**kwargs):
Model.__init__(self, spectrum, *args, **kwargs)
self.ll = ll
if auto_background is True:
bg = PowerLaw()
interactive_ns['bg'] = bg
self.append(bg)
if self.ll is not None:
self.convolved = True
if self.experiments.convolution_axis is None:
self.experiments.set_convolution_axis()
else:
self.convolved = False
if self.spectrum.subshells and auto_add_edges is True:
self._add_edges_from_subshells_names()
def power_law_extrapolation(self, window_size=20,
extrapolation_size=1024,
add_noise=False,
fix_neg_r=False):
"""Extrapolate the spectrum to the right using a powerlaw
Parameters
----------
window_size : int
The number of channels from the right side of the
spectrum that are used to estimate the power law
parameters.
extrapolation_size : int
Size of the extrapolation in number of channels
add_noise : bool
If True, add poissonian noise to the extrapolated spectrum.
fix_neg_r : bool
If True, the negative values for the "components.PowerLaw"
parameter r will be flagged and the extrapolation will be
done with a constant zero-value.
Returns
-------
A new spectrum, with the extrapolation.
"""
self._check_signal_dimension_equals_one()
axis = self.axes_manager.signal_axes[0]
s = self.deepcopy()
s.mapped_parameters.title += (
' %i channels extrapolated' %
extrapolation_size)
if s.tmp_parameters.has_item('filename'):
s.tmp_parameters.filename += (
'_%i_channels_extrapolated' % extrapolation_size)
new_shape = list(self.data.shape)
new_shape[axis.index_in_array] += extrapolation_size
s.data = np.zeros((new_shape))
s.get_dimensions_from_data()
s.data[...,:axis.size] = self.data
pl = PowerLaw()
pl._axes_manager = self.axes_manager
pl.estimate_parameters(
s, axis.index2value(axis.size - window_size),
axis.index2value(axis.size - 1))
if fix_neg_r is True:
_r = pl.r.map['values']
_A = pl.A.map['values']
_A[_r<=0] = 0
pl.A.map['values'] = _A
s.data[...,axis.size:] = (
pl.A.map['values'][...,np.newaxis]*
s.axes_manager.signal_axes[0].axis[np.newaxis,axis.size:]**(
-pl.r.map['values'][...,np.newaxis]))
return s