def estimate_parameters(self, signal, x1, x2, only_current=False):
"""Estimate the Voigt function by calculating the momenta of the
Gaussian.
Parameters
----------
signal : Signal1D instance
x1 : float
Defines the left limit of the spectral range to use for the
estimation.
x2 : float
Defines the right limit of the spectral range to use for the
estimation.
only_current : bool
If False estimates the parameters for the full dataset.
Returns
-------
: bool
Exit status required for the :meth:`remove_background` function.
Notes
-----
Adapted from http://www.scipy.org/Cookbook/FittingData
Examples
--------
>>> g = hs.model.components1D.Voigt(legacy=False)
>>> x = np.arange(-10, 10, 0.01)
>>> data = np.zeros((32, 32, 2000))
>>> data[:] = g.function(x).reshape((1, 1, 2000))
>>> s = hs.signals.Signal1D(data)
>>> s.axes_manager[-1].offset = -10
>>> s.axes_manager[-1].scale = 0.01
>>> g.estimate_parameters(s, -10, 10, False)
"""
super(Voigt, self)._estimate_parameters(signal)
axis = signal.axes_manager.signal_axes[0]
centre, height, sigma = _estimate_gaussian_parameters(
signal, x1, x2, only_current)
if only_current is True:
self.centre.value = centre
self.sigma.value = sigma
self.area.value = height * sigma * sqrt2pi
if self.binned:
self.area.value /= axis.scale
return True
else:
if self.area.map is None:
self._create_arrays()
self.area.map['values'][:] = height * sigma * sqrt2pi
if self.binned:
self.area.map['values'][:] /= axis.scale
self.area.map['is_set'][:] = True
self.sigma.map['values'][:] = sigma
self.sigma.map['is_set'][:] = True
self.centre.map['values'][:] = centre
self.centre.map['is_set'][:] = True
self.fetch_stored_values()
return True
def estimate_parameters(self, signal, x1, x2, only_current=False):
"""Estimate the gaussian by calculating the momenta.
Parameters
----------
signal : Signal1D instance
x1 : float
Defines the left limit of the spectral range to use for the
estimation.
x2 : float
Defines the right limit of the spectral range to use for the
estimation.
only_current : bool
If False estimates the parameters for the full dataset.
Returns
-------
bool
Notes
-----
Adapted from http://www.scipy.org/Cookbook/FittingData
Examples
--------
>>> g = hs.model.components1D.GaussianHF()
>>> x = np.arange(-10, 10, 0.01)
>>> data = np.zeros((32, 32, 2000))
>>> data[:] = g.function(x).reshape((1, 1, 2000))
>>> s = hs.signals.Signal1D(data)
>>> s.axes_manager[-1].offset = -10
>>> s.axes_manager[-1].scale = 0.01
>>> g.estimate_parameters(s, -10, 10, False)
"""
super()._estimate_parameters(signal)
axis = signal.axes_manager.signal_axes[0]
centre, height, sigma = _estimate_gaussian_parameters(
signal, x1, x2, only_current)
scaling_factor = _get_scaling_factor(signal, axis, centre)
if only_current is True:
self.centre.value = centre
self.fwhm.value = sigma * sigma2fwhm
self.height.value = float(height)
if is_binned(signal):
# in v2 replace by
#if axis.is_binned:
self.height.value /= scaling_factor
return True
else:
if self.height.map is None:
self._create_arrays()
self.height.map['values'][:] = height
if is_binned(signal):
# in v2 replace by
#if axis.is_binned:
self.height.map['values'][:] /= scaling_factor
self.height.map['is_set'][:] = True
self.fwhm.map['values'][:] = sigma * sigma2fwhm
self.fwhm.map['is_set'][:] = True
self.centre.map['values'][:] = centre
self.centre.map['is_set'][:] = True
self.fetch_stored_values()
return True
def estimate_parameters(self, signal, x1, x2, only_current=False):
"""Estimate the Donach by calculating the median (centre) and the
variance parameter (sigma).
Note that an insufficient range will affect the accuracy of this
method and that this method doesn't estimate the asymmetry parameter
(alpha).
Parameters
----------
signal : Signal1D instance
x1 : float
Defines the left limit of the spectral range to use for the
estimation.
x2 : float
Defines the right limit of the spectral range to use for the
estimation.
only_current : bool
If False estimates the parameters for the full dataset.
Returns
-------
bool
Returns True when the parameters estimation is successful
Examples
--------
>>> g = hs.model.components1D.Lorentzian()
>>> x = np.arange(-10, 10, 0.01)
>>> data = np.zeros((32, 32, 2000))
>>> data[:] = g.function(x).reshape((1, 1, 2000))
>>> s = hs.signals.Signal1D(data)
>>> s.axes_manager[-1].offset = -10
>>> s.axes_manager[-1].scale = 0.01
>>> g.estimate_parameters(s, -10, 10, False)
"""
super()._estimate_parameters(signal)
axis = signal.axes_manager.signal_axes[0]
centre, height, sigma = _estimate_gaussian_parameters(
signal, x1, x2, only_current)
scaling_factor = _get_scaling_factor(signal, axis, centre)
if only_current is True:
self.centre.value = centre
self.sigma.value = sigma
self.A.value = height * 1.3
if is_binned(signal):
# in v2 replace by
#if axis.is_binned:
self.A.value /= scaling_factor
return True
else:
if self.A.map is None:
self._create_arrays()
self.A.map['values'][:] = height * 1.3
if is_binned(signal):
# in v2 replace by
#if axis.is_binned:
self.A.map['values'][:] /= scaling_factor
self.A.map['is_set'][:] = True
self.sigma.map['values'][:] = sigma
self.sigma.map['is_set'][:] = True
self.centre.map['values'][:] = centre
self.centre.map['is_set'][:] = True
self.fetch_stored_values()
return True
def estimate_parameters(self, signal, x1, x2, only_current=False):
"""Estimate the gaussian by calculating the momenta.
Parameters
----------
signal : Signal instance
x1 : float
Defines the left limit of the spectral range to use for the
estimation.
x2 : float
Defines the right limit of the spectral range to use for the
estimation.
only_current : bool
If False estimates the parameters for the full dataset.
Returns
-------
bool
Notes
-----
Adapted from http://www.scipy.org/Cookbook/FittingData
Examples
--------
>>> g = hs.model.components.GaussianHF()
>>> x = np.arange(-10, 10, 0.01)
>>> data = np.zeros((32, 32, 2000))
>>> data[:] = g.function(x).reshape((1, 1, 2000))
>>> s = hs.signals.Spectrum(data)
>>> s.axes_manager._axes[-1].offset = -10
>>> s.axes_manager._axes[-1].scale = 0.01
>>> g.estimate_parameters(s, -10, 10, False)
"""
binned = signal.metadata.Signal.binned
axis = signal.axes_manager.signal_axes[0]
centre, height, sigma = _estimate_gaussian_parameters(signal, x1, x2,
only_current)
if only_current is True:
self.centre.value = centre
self.fwhm.value = sigma * sigma2fwhm
self.height.value = float(height)
if binned is True:
self.height.value /= axis.scale
return True
else:
if self.height.map is None:
self._create_arrays()
self.height.map['values'][:] = height
if binned is True:
self.height.map['values'][:] /= axis.scale
self.height.map['is_set'][:] = True
self.fwhm.map['values'][:] = sigma * sigma2fwhm
self.fwhm.map['is_set'][:] = True
self.centre.map['values'][:] = centre
self.centre.map['is_set'][:] = True
self.fetch_stored_values()
return True
