def set_background_estimator(self):
if self.background_type == 'Power Law':
self.background_estimator = components1d.PowerLaw()
self.bg_line_range = 'from_left_range'
elif self.background_type == 'Gaussian':
self.background_estimator = components1d.Gaussian()
self.bg_line_range = 'full'
elif self.background_type == 'Offset':
self.background_estimator = components1d.Offset()
self.bg_line_range = 'full'
elif self.background_type == 'Polynomial':
self.background_estimator = components1d.Polynomial(
self.polynomial_order)
self.bg_line_range = 'full'
def add_polynomial_background(self, order=6):
"""
Add a polynomial background.
the background is added to self.background_components
Parameters
----------
order: int
The order of the polynomial
"""
background = create_component.Polynomial(order=order)
background.name = 'background_order_' + str(order)
background.isbackground = True
self.append(background)
self.background_components.append(background)
def add_polynomial_background(self, order=6):
"""
Add a polynomial background.
the background is added to self.background_components
Parameters
----------
order: int
The order of the polynomial
"""
with ignore_warning(message="The API of the `Polynomial` component"):
background = create_component.Polynomial(order=order, legacy=False)
background.name = 'background_order_' + str(order)
background.isbackground = True
self.append(background)
self.background_components.append(background)
def test_plot_BackgroundRemoval_change_background():
pl = components1d.PowerLaw()
pl.A.value = 1e10
pl.r.value = 3
s = signals.Signal1D(pl.function(np.arange(100, 200)))
s.axes_manager[0].offset = 100
s.add_gaussian_noise(100)
br = BackgroundRemoval(s,
background_type='Power Law',
polynomial_order=2,
fast=False,
plot_remainder=True)
br.span_selector.extents = (105, 150)
# will draw the line
br.span_selector_changed()
# will update the right axis
br.span_selector_changed()
assert isinstance(br.background_estimator, components1d.PowerLaw)
br.background_type = 'Polynomial'
assert isinstance(br.background_estimator,
type(components1d.Polynomial(legacy=False)))
def remove_background(self,
signal_range='interactive',
background_type='PowerLaw',
polynomial_order=2,
fast=True,
show_progressbar=None):
"""Remove the background, either in place using a gui or returned as a new
spectrum using the command line.
Parameters
----------
signal_range : tuple, optional
If this argument is not specified, the signal range has to be
selected using a GUI. And the original spectrum will be replaced.
If tuple is given, the a spectrum will be returned.
background_type : string
The type of component which should be used to fit the background.
Possible components: PowerLaw, Gaussian, Offset, Polynomial
If Polynomial is used, the polynomial order can be specified
polynomial_order : int, default 2
Specify the polynomial order if a Polynomial background is used.
fast : bool
If True, perform an approximative estimation of the parameters.
If False, the signal is fitted using non-linear least squares
afterwards.This is slower compared to the estimation but
possibly more accurate.
show_progressbar : None or bool
If True, display a progress bar. If None the default is set in
`preferences`.
Examples
--------
Using gui, replaces spectrum s
>>>> s.remove_background()
Using command line, returns a spectrum
>>>> s = s.remove_background(signal_range=(400,450), background_type='PowerLaw')
Using a full model to fit the background
>>>> s = s.remove_background(signal_range=(400,450), fast=False)
Raises
------
SignalDimensionError if the signal dimension is not 1.
"""
self._check_signal_dimension_equals_one()
if signal_range == 'interactive':
br = BackgroundRemoval(self)
br.edit_traits()
else:
if background_type == 'PowerLaw':
background_estimator = components1d.PowerLaw()
elif background_type == 'Gaussian':
background_estimator = components1d.Gaussian()
elif background_type == 'Offset':
background_estimator = components1d.Offset()
elif background_type == 'Polynomial':
background_estimator = components1d.Polynomial(
polynomial_order)
else:
raise ValueError("Background type: " + background_type +
" not recognized")
spectra = self._remove_background_cli(
signal_range=signal_range,
background_estimator=background_estimator,
fast=fast,
show_progressbar=show_progressbar)
return spectra
