def fit_background(self,
start_energy=None,
end_energy=None,
windows_sigma=(4., 3.),
kind='single',
**kwargs):
"""
Fit the background in the energy range containing no X-ray line.
After the fit, the background is fixed.
Parameters
----------
start_energy : {float, None}
If float, limit the range of energies from the left to the
given value.
end_energy : {float, None}
If float, limit the range of energies from the right to the
given value.
windows_sigma: tuple of two float
The (lower, upper) bounds around each X-ray line, each as a float,
to define the energy range free of X-ray lines.
kind : {'single', 'multi'}
If 'single' fit only the current location. If 'multi'
use multifit.
**kwargs : extra key word arguments
All extra key word arguments are passed to fit or
See also
--------
free_background
"""
if end_energy is None:
end_energy = self.end_energy
if start_energy is None:
start_energy = self.start_energy
# disactivate line
self.free_background()
with stash_active_state(self):
self.disable_xray_lines()
self.set_signal_range(start_energy, end_energy)
for component in self:
if component.isbackground is False:
self.remove_signal_range(
component.centre.value -
windows_sigma[0] * component.sigma.value,
component.centre.value +
windows_sigma[1] * component.sigma.value)
if kind == 'single':
self.fit(**kwargs)
if kind == 'multi':
self.multifit(**kwargs)
self.reset_signal_range()
self.fix_background()
def fit_background(self,
start_energy=None,
end_energy=None,
windows_sigma=(4., 3.),
kind='single',
**kwargs):
"""
Fit the background in the energy range containing no X-ray line.
After the fit, the background is fixed.
Parameters
----------
start_energy : {float, None}
If float, limit the range of energies from the left to the
given value.
end_energy : {float, None}
If float, limit the range of energies from the right to the
given value.
windows_sigma: tuple of two float
The (lower, upper) bounds around each X-ray line, each as a float,
to define the energy range free of X-ray lines.
kind : {'single', 'multi'}
If 'single' fit only the current location. If 'multi'
use multifit.
**kwargs : extra key word arguments
All extra key word arguments are passed to fit or
See also
--------
free_background
"""
if end_energy is None:
end_energy = self.end_energy
if start_energy is None:
start_energy = self.start_energy
# disactivate line
self.free_background()
with stash_active_state(self):
self.disable_xray_lines()
self.set_signal_range(start_energy, end_energy)
for component in self:
if component.isbackground is False:
self.remove_signal_range(
component.centre.value -
windows_sigma[0] * component.sigma.value,
component.centre.value +
windows_sigma[1] * component.sigma.value)
if kind == 'single':
self.fit(**kwargs)
if kind == 'multi':
self.multifit(**kwargs)
self.reset_signal_range()
self.fix_background()
def test_stash_array(self):
g = self.gaussian
g.active_is_multidimensional = True
g._create_arrays()
g._active_array[2, 0] = False
g._active_array[0, 0] = False
with stash_active_state([g]):
g.active_is_multidimensional = False
assert not g._active_is_multidimensional
np.testing.assert_array_equal(
g.A.as_signal('values').data, np.zeros((3, 3)))
assert g._active_array is None
assert g._active_is_multidimensional
np.testing.assert_allclose(
g._active_array,
np.array([[0, 1, 1], [1, 1, 1], [0, 1, 1]], dtype=bool))
assert np.isnan(g.A.as_signal('values').data[[0, 2], [0]]).all()
def test_stash_array(self):
g = self.gaussian
g.active_is_multidimensional = True
g._create_arrays()
g._active_array[2, 0] = False
g._active_array[0, 0] = False
with stash_active_state([g]):
g.active_is_multidimensional = False
assert not g._active_is_multidimensional
np.testing.assert_array_equal(g.A.as_signal('values').data,
np.zeros((3, 3)))
assert g._active_array is None
assert g._active_is_multidimensional
np.testing.assert_allclose(
g._active_array, np.array([[0, 1, 1], [1, 1, 1], [0, 1, 1]],
dtype=bool))
assert np.isnan(g.A.as_signal('values').data[[0, 2], [0]]).all()
def test_stash_array(self):
g = self.gaussian
g.active_is_multidimensional = True
g._create_arrays()
g._active_array[2, 0] = False
g._active_array[0, 0] = False
with stash_active_state([g]):
g.active_is_multidimensional = False
nt.assert_false(g._active_is_multidimensional)
nt.assert_true(
np.all(g.A.as_signal('values').data == np.zeros((3, 3))))
nt.assert_equal(g._active_array, None)
nt.assert_true(g._active_is_multidimensional)
np.testing.assert_almost_equal(
g._active_array,
np.array([[0, 1, 1], [1, 1, 1], [0, 1, 1]], dtype=bool))
nt.assert_true(
np.isnan(g.A.as_signal('values').data[[0, 2], [0]]).all())
def test_stash_array(self):
g = self.gaussian
g.active_is_multidimensional = True
g._create_arrays()
g._active_array[2, 0] = False
g._active_array[0, 0] = False
with stash_active_state([g]):
g.active_is_multidimensional = False
nt.assert_false(g._active_is_multidimensional)
nt.assert_true(
np.all(
g.A.as_signal('values').data == np.zeros(
(3, 3))))
nt.assert_equal(g._active_array, None)
nt.assert_true(g._active_is_multidimensional)
np.testing.assert_almost_equal(
g._active_array, np.array([[0, 1, 1], [1, 1, 1], [0, 1, 1]],
dtype=bool))
nt.assert_true(
np.isnan(g.A.as_signal('values').data[[0, 2], [0]]).all())
