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 _estimate_gain(ns, cs,
weighted=False,
higher_than=None,
plot_results=False,
binning=0,
pol_order=1):
if binning > 0:
factor = 2 ** binning
remainder = np.mod(ns.shape[1], factor)
if remainder != 0:
ns = ns[:, remainder:]
cs = cs[:, remainder:]
new_shape = (ns.shape[0], ns.shape[1] / factor)
ns = rebin(ns, new_shape)
cs = rebin(cs, new_shape)
noise = ns - cs
variance = np.var(noise, 0)
average = np.mean(cs, 0).squeeze()
# Select only the values higher_than for the calculation
if higher_than is not None:
sorting_index_array = np.argsort(average)
average_sorted = average[sorting_index_array]
average_higher_than = average_sorted > higher_than
variance_sorted = variance.squeeze()[sorting_index_array]
variance2fit = variance_sorted[average_higher_than]
average2fit = average_sorted[average_higher_than]
else:
variance2fit = variance
average2fit = average
fit = np.polyfit(average2fit, variance2fit, pol_order)
if weighted is True:
from hyperspy._signals.spectrum import Spectrum
from hyperspy.model import Model
from hyperspy.components import Line
s = Spectrum(variance2fit)
s.axes_manager.signal_axes[0].axis = average2fit
m = Model(s)
l = Line()
l.a.value = fit[1]
l.b.value = fit[0]
m.append(l)
m.fit(weights=True)
fit[0] = l.b.value
fit[1] = l.a.value
if plot_results is True:
plt.figure()
plt.scatter(average.squeeze(), variance.squeeze())
plt.xlabel('Counts')
plt.ylabel('Variance')
plt.plot(average2fit, np.polyval(fit, average2fit), color='red')
results = {'fit': fit, 'variance': variance.squeeze(),
'counts': average.squeeze()}
return results
def test_3d_signal(self):
# This code should run smoothly, any exceptions should trigger failure
s = self.m_3d.as_signal()
model = Model(s)
p = hs.components.Polynomial(order=2)
model.append(p)
p.estimate_parameters(s, 0, 100, only_current=False)
np.testing.assert_allclose(p.coefficients.map['values'],
np.tile([0.5, 2, 3], (2, 5, 1)))
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 create_model(signal, *args, **kwargs):
from hyperspy.signals.eels import EELSSpectrum
from hyperspy.models.eelsmodel import EELSModel
from hyperspy.model import Model
if isinstance(signal, EELSSpectrum):
return EELSModel(signal, *args, **kwargs)
else:
return Model(signal, *args, **kwargs)
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 create_model(self):
"""Create a model for the current data.
Returns
-------
model : `Model` instance.
"""
from hyperspy.model import Model
model = Model(self)
return model
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 create_model(signal, *args, **kwargs):
"""Create a model object
Any extra argument is passes to the Model constructor.
Parameters
----------
signal: A signal class
If the signal is an EELS signal the following extra parameters
are available:
auto_background : boolean
If True, and if spectrum is an EELS instance adds automatically
a powerlaw to the model and estimate the parameters by the
two-area method.
auto_add_edges : boolean
If True, and if spectrum is an EELS instance, it will
automatically add the ionization edges as defined in the
Spectrum instance. Adding a new element to the spectrum using
the components.EELSSpectrum.add_elements method automatically
add the corresponding ionisation edges to the model.
ll : {None, EELSSpectrum}
If an EELSSPectrum is provided, it will be assumed that it is
a low-loss EELS spectrum, and it will be used to simulate the
effect of multiple scattering by convolving it with the EELS
spectrum.
GOS : {'hydrogenic', 'Hartree-Slater', None}
The GOS to use when auto adding core-loss EELS edges.
If None it will use the Hartree-Slater GOS if
they are available, otherwise it will use the hydrogenic GOS.
Returns
-------
A Model class
"""
from hyperspy._signals.eels import EELSSpectrum
from hyperspy.models.eelsmodel import EELSModel
from hyperspy.model import Model
if isinstance(signal, EELSSpectrum):
return EELSModel(signal, *args, **kwargs)
else:
return Model(signal, *args, **kwargs)
def fit(
self,
fitter=None,
method="ls",
grad=False,
bounded=False,
ext_bounding=False,
update_plot=False,
kind="std",
**kwargs
):
"""Fits the model to the experimental data
Parameters
----------
fitter : {None, "leastsq", "odr", "mpfit", "fmin"}
The optimizer to perform the fitting. If None the fitter
defined in the Preferences is used. leastsq is the most
stable but it does not support bounding. mpfit supports
bounding. fmin is the only one that supports
maximum likelihood estimation, but it is less robust than
the Levenberg–Marquardt based leastsq and mpfit, and it is
better to use it after one of them to refine the estimation.
method : {'ls', 'ml'}
Choose 'ls' (default) for least squares and 'ml' for
maximum-likelihood estimation. The latter only works with
fitter = 'fmin'.
grad : bool
If True, the analytical gradient is used if defined to
speed up the estimation.
ext_bounding : bool
If True, enforce bounding by keeping the value of the
parameters constant out of the defined bounding area.
bounded : bool
If True performs bounded optimization if the fitter
supports it. Currently only mpfit support bounding.
update_plot : bool
If True, the plot is updated during the optimization
process. It slows down the optimization but it permits
to visualize the optimization evolution.
kind : {'std', 'smart'}
If 'std' (default) performs standard fit. If 'smart'
performs smart_fit
**kwargs : key word arguments
Any extra key word argument will be passed to the chosen
fitter
See Also
--------
multifit, smart_fit
"""
if kind == "smart":
self.smart_fit(
fitter=fitter,
method=method,
grad=grad,
bounded=bounded,
ext_bounding=ext_bounding,
update_plot=update_plot,
**kwargs
)
elif kind == "std":
Model.fit(
self,
fitter=fitter,
method=method,
grad=grad,
bounded=bounded,
ext_bounding=ext_bounding,
update_plot=update_plot,
**kwargs
)
else:
raise ValueError("kind must be either 'std' or 'smart'." "'%s' provided." % kind)
def fit(self, *args, **kwargs):
if 'kind' in kwargs and kwargs['kind'] == 'smart':
self.smart_fit(*args, **kwargs)
else:
Model.fit(self, *args, **kwargs)
def fit(self,
fitter=None,
method='ls',
grad=False,
bounded=False,
ext_bounding=False,
update_plot=False,
kind='std',
**kwargs):
"""Fits the model to the experimental data
Parameters
----------
fitter : {None, "leastsq", "odr", "mpfit", "fmin"}
The optimizer to perform the fitting. If None the fitter
defined in the Preferences is used. leastsq is the most
stable but it does not support bounding. mpfit supports
bounding. fmin is the only one that supports
maximum likelihood estimation, but it is less robust than
the Levenberg–Marquardt based leastsq and mpfit, and it is
better to use it after one of them to refine the estimation.
method : {'ls', 'ml'}
Choose 'ls' (default) for least squares and 'ml' for
maximum-likelihood estimation. The latter only works with
fitter = 'fmin'.
grad : bool
If True, the analytical gradient is used if defined to
speed up the estimation.
ext_bounding : bool
If True, enforce bounding by keeping the value of the
parameters constant out of the defined bounding area.
bounded : bool
If True performs bounded optimization if the fitter
supports it. Currently only mpfit support bounding.
update_plot : bool
If True, the plot is updated during the optimization
process. It slows down the optimization but it permits
to visualize the optimization evolution.
kind : {'std', 'smart'}
If 'std' (default) performs standard fit. If 'smart'
performs smart_fit
**kwargs : key word arguments
Any extra key word argument will be passed to the chosen
fitter
See Also
--------
multifit, smart_fit
"""
if kind == 'smart':
self.smart_fit(fitter=fitter,
method=method,
grad=grad,
bounded=bounded,
ext_bounding=ext_bounding,
update_plot=update_plot,
**kwargs)
elif kind == 'std':
Model.fit(self,
fitter=fitter,
method=method,
grad=grad,
bounded=bounded,
ext_bounding=ext_bounding,
update_plot=update_plot,
**kwargs)
else:
raise ValueError('kind must be either \'std\' or \'smart\'.'
'\'%s\' provided.' % kind)
def _estimate_gain(ns,
cs,
weighted=False,
higher_than=None,
plot_results=False,
binning=0,
pol_order=1):
if binning > 0:
factor = 2**binning
remainder = np.mod(ns.shape[1], factor)
if remainder != 0:
ns = ns[:, remainder:]
cs = cs[:, remainder:]
new_shape = (ns.shape[0], ns.shape[1] / factor)
ns = rebin(ns, new_shape)
cs = rebin(cs, new_shape)
noise = ns - cs
variance = np.var(noise, 0)
average = np.mean(cs, 0).squeeze()
# Select only the values higher_than for the calculation
if higher_than is not None:
sorting_index_array = np.argsort(average)
average_sorted = average[sorting_index_array]
average_higher_than = average_sorted > higher_than
variance_sorted = variance.squeeze()[sorting_index_array]
variance2fit = variance_sorted[average_higher_than]
average2fit = average_sorted[average_higher_than]
else:
variance2fit = variance
average2fit = average
fit = np.polyfit(average2fit, variance2fit, pol_order)
if weighted is True:
from hyperspy._signals.spectrum import Spectrum
from hyperspy.model import Model
from hyperspy.components import Line
s = Spectrum(variance2fit)
s.axes_manager.signal_axes[0].axis = average2fit
m = Model(s)
l = Line()
l.a.value = fit[1]
l.b.value = fit[0]
m.append(l)
m.fit(weights=True)
fit[0] = l.b.value
fit[1] = l.a.value
if plot_results is True:
plt.figure()
plt.scatter(average.squeeze(), variance.squeeze())
plt.xlabel('Counts')
plt.ylabel('Variance')
plt.plot(average2fit, np.polyval(fit, average2fit), color='red')
results = {
'fit': fit,
'variance': variance.squeeze(),
'counts': average.squeeze()
}
return results
def fit(self, *args, **kwargs):
if 'kind' in kwargs and kwargs['kind'] == 'smart':
self.smart_fit(*args, **kwargs)
else:
Model.fit(self, *args, **kwargs)
def fit(self, fitter=None, method='ls', grad=False, weights=None,
bounded=False, ext_bounding=False, update_plot=False,
kind='std', **kwargs):
"""Fits the model to the experimental data
Parameters
----------
fitter : {None, "leastsq", "odr", "mpfit", "fmin"}
The optimizer to perform the fitting. If None the fitter
defined in the Preferences is used. leastsq is the most
stable but it does not support bounding. mpfit supports
bounding. fmin is the only one that supports
maximum likelihood estimation, but it is less robust than
the Levenberg–Marquardt based leastsq and mpfit, and it is
better to use it after one of them to refine the estimation.
method : {'ls', 'ml'}
Choose 'ls' (default) for least squares and 'ml' for
maximum-likelihood estimation. The latter only works with
fitter = 'fmin'.
grad : bool
If True, the analytical gradient is used if defined to
speed up the estimation.
weights : {None, True, numpy.array}
If None, performs standard least squares. If True
performs weighted least squares where the weights are
calculated using spectrum.Spectrum.estimate_poissonian_noise_variance.
Alternatively, external weights can be supplied by passing
a weights array of the same dimensions as the signal.
ext_bounding : bool
If True, enforce bounding by keeping the value of the
parameters constant out of the defined bounding area.
bounded : bool
If True performs bounded optimization if the fitter
supports it. Currently only mpfit support bounding.
update_plot : bool
If True, the plot is updated during the optimization
process. It slows down the optimization but it permits
to visualize the optimization evolution.
kind : {'std', 'smart'}
If 'std' (default) performs standard fit. If 'smart'
performs smart_fit
**kwargs : key word arguments
Any extra key word argument will be passed to the chosen
fitter
See Also
--------
multifit, smart_fit
"""
if kind == 'smart':
self.smart_fit(fitter=fitter,
method=method,
grad=grad,
weights=weights,
bounded=bounded,
ext_bounding=ext_bounding,
update_plot=update_plot,
**kwargs)
elif kind == 'std':
Model.fit(self,
fitter=fitter,
method=method,
grad=grad,
weights=weights,
bounded=bounded,
ext_bounding=ext_bounding,
update_plot=update_plot,
**kwargs)
else:
raise ValueError('kind must be either \'std\' or \'smart\'.'
'\'%s\' provided.' % kind)
def fit(self, *args, **kwargs):
if "kind" in kwargs and kwargs["kind"] == "smart":
self.smart_fit(*args, **kwargs)
else:
Model.fit(self, *args, **kwargs)
