def test_get_scaling_parameter(is_binned, non_uniform, dim):
shape = [10 + i for i in range(dim)]
signal = Signal1D(np.arange(np.prod(shape)).reshape(shape[::-1]))
axis = signal.axes_manager.signal_axes[0]
axis.is_binned = is_binned
axis.scale = 0.5
if non_uniform:
axis.convert_to_non_uniform_axis()
centre = np.ones(shape[::-2])
scaling_factor = _get_scaling_factor(signal, axis, centre)
if is_binned:
assert np.all(scaling_factor == 0.5)
else:
assert scaling_factor == 1
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 Lorentzian by calculating the median (centre) and half
the interquartile range (gamma).
Note that an insufficient range will affect the accuracy of this
method.
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 gaussian.py and
https://en.wikipedia.org/wiki/Cauchy_distribution
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, gamma = _estimate_lorentzian_parameters(signal, x1, x2,
only_current)
scaling_factor = _get_scaling_factor(signal, axis, centre)
if only_current is True:
self.centre.value = centre
self.gamma.value = gamma
self.A.value = height * gamma * np.pi
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 * gamma * np.pi
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.gamma.map['values'][:] = gamma
self.gamma.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 skew normal distribution 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 Lin, Lee and Yen, Statistica Sinica 17, 909-927 (2007)
https://www.jstor.org/stable/24307705
Examples
--------
>>> g = hs.model.components1D.SkewNormal()
>>> 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._axes[-1].offset = -10
>>> s.axes_manager._axes[-1].scale = 0.01
>>> g.estimate_parameters(s, -10, 10, False)
"""
super()._estimate_parameters(signal)
axis = signal.axes_manager.signal_axes[0]
x0, height, scale, shape = _estimate_skewnormal_parameters(
signal, x1, x2, only_current
)
scaling_factor = _get_scaling_factor(signal, axis, x0)
if only_current is True:
self.x0.value = x0
self.A.value = height * sqrt2pi
self.scale.value = scale
self.shape.value = shape
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 * sqrt2pi
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.x0.map['values'][:] = x0
self.x0.map['is_set'][:] = True
self.scale.map['values'][:] = scale
self.scale.map['is_set'][:] = True
self.shape.map['values'][:] = shape
self.shape.map['is_set'][:] = True
self.fetch_stored_values()
return True