def xas_deconvolve(energy, norm=None, group=None, form='gaussian',
esigma=1.0, eshift=0.0, _larch=None):
"""XAS spectral deconvolution
This function de-convolves a normalized mu(E) spectra with a
peak shape, enhancing separation of XANES features.
This can be unstable -- Use results with caution!
Arguments
----------
energy: array of x-ray energies, in eV or group
norm: array of normalized mu(E)
group: output group
form: form of deconvolution function. One of
'gaussian' (default) or 'lorentzian'
esigma energy sigma to pass to gaussian() or lorentzian()
[in eV, default=1.0]
eshift energy shift to apply to result. [in eV, default=0]
Returns
-------
None
The array 'deconv' will be written to the output group.
Notes
-----
Support See First Argument Group convention, requiring group
members 'energy' and 'norm'
"""
if _larch is None:
raise Warning("cannot deconvolve -- larch broken?")
energy, mu, group = parse_group_args(energy, members=('energy', 'norm'),
defaults=(norm,), group=group,
fcn_name='xas_deconv')
eshift = eshift + 0.5 * esigma
en = remove_dups(energy)
en = en - en[0]
estep = max(0.001, 0.001*int(min(en[1:]-en[:-1])*1000.0))
npts = 1 + int(max(en) / estep)
x = np.arange(npts)*estep
y = _interp(en, mu, x, kind='linear', _larch=_larch)
kernel = gaussian
if form.lower().startswith('lor'):
kernel = lorentzian
yext = np.concatenate((y, np.arange(len(y))*y[-1]))
ret, err = deconvolve(yext, kernel(x, 0, esigma))
nret = min(len(x), len(ret))
ret = ret[:nret]*yext[nret-1]/ret[nret-1]
out = _interp(x+eshift, ret, en, kind='linear', _larch=_larch)
group = set_xafsGroup(group, _larch=_larch)
group.deconv = out
def xas_deconvolve(energy, norm=None, group=None, form='gaussian',
esigma=1.0, eshift=0.0, _larch=None):
"""XAS spectral deconvolution
This function de-convolves a normalized mu(E) spectra with a
peak shape, enhancing separation of XANES features.
This can be unstable -- Use results with caution!
Arguments
----------
energy: array of x-ray energies, in eV or group
norm: array of normalized mu(E)
group: output group
form: form of deconvolution function. One of
'gaussian' (default) or 'lorentzian'
esigma energy sigma to pass to gaussian() or lorentzian()
[in eV, default=1.0]
eshift energy shift to apply to result. [in eV, default=0]
Returns
-------
None
The array 'deconv' will be written to the output group.
Notes
-----
Support See First Argument Group convention, requiring group
members 'energy' and 'norm'
"""
if _larch is None:
raise Warning("cannot deconvolve -- larch broken?")
energy, mu, group = parse_group_args(energy, members=('energy', 'norm'),
defaults=(norm,), group=group,
fcn_name='xas_deconv')
eshift = eshift + 0.5 * esigma
en = remove_dups(energy)
en = en - en[0]
estep = max(0.001, 0.001*int(min(en[1:]-en[:-1])*1000.0))
npts = 1 + int(max(en) / estep)
x = np.arange(npts)*estep
y = _interp(en, mu, x, kind='linear', _larch=_larch)
kernel = gaussian
if form.lower().startswith('lor'):
kernel = lorentzian
yext = np.concatenate((y, np.arange(len(y))*y[-1]))
ret, err = deconvolve(yext, kernel(x, 0, esigma))
nret = min(len(x), len(ret))
ret = ret[:nret]*yext[nret-1]/ret[nret-1]
out = _interp(x+eshift, ret, en, kind='linear', _larch=_larch)
group = set_xafsGroup(group, _larch=_larch)
group.deconv = out
def kk(self,
energy=None,
mu=None,
z=None,
edge='K',
how='scalar',
mback_kws=None):
"""
Convert mu(E) data into f'(E) and f"(E). f"(E) is made by
matching mu(E) to the tabulated values of the imaginary part
of the scattering factor (Cromer-Liberman), f'(E) is then
obtained by performing a differential Kramers-Kronig transform
on the matched f"(E).
Attributes
energy: energy array
mu: array with mu(E) data
z: Z number of absorber
edge: absorption edge, usually 'K' or 'L3'
mback_kws: arguments for the mback algorithm
Returns
self.f1, self.f2: CL values over on the input energy grid
self.fp, self.fpp: matched and KK transformed data on the input energy grid
References:
* Cromer-Liberman: http://dx.doi.org/10.1063/1.1674266
* KK computation: Ohta and Ishida, Applied Spectroscopy 42:6 (1988) 952-957
* diffKK implementation: http://dx.doi.org/10.1103/PhysRevB.58.11215
* MBACK (Weng, Waldo, Penner-Hahn): http://dx.doi.org/10.1086/303711
* Lee and Xiang: http://dx.doi.org/10.1088/0004-637X/702/2/970
"""
if type(energy).__name__ == 'ndarray': self.energy = energy
if type(mu).__name__ == 'ndarray': self.mu = mu
if z != None: self.z = z
if edge != None: self.edge = edge
if mback_kws != None: self.mback_kws = mback_kws
if self.z == None:
Exception("Z for absorber not provided for diffKK")
if self.edge == None:
Exception("absorption edge not provided for diffKK")
mb_kws = dict(order=3,
z=self.z,
edge=self.edge,
e0=None,
emin=None,
emax=None,
whiteline=False,
leexiang=False,
tables='cl',
fit_erfc=False,
return_f1=True)
if self.mback_kws is not None:
mb_kws.update(self.mback_kws)
start = time.clock()
mback(self.energy, self.mu, group=self, _larch=self._larch, **mb_kws)
## interpolate matched data onto an even grid with an even number of elements (about 1 eV)
npts = int(self.energy[-1] - self.energy[0]) + (
int(self.energy[-1] - self.energy[0]) % 2)
self.grid = np.linspace(self.energy[0], self.energy[-1], npts)
fpp = _interp(self.energy,
self.f2 - self.fpp,
self.grid,
fill_value=0.0)
## do difference KK
if repr(how).startswith('sca'):
fp = kkmclr_sca(self.grid, fpp)
else:
fp = kkmclr(self.grid, fpp)
## interpolate back to original grid and add diffKK result to f1 to make fp array
self.fp = self.f1 + _interp(self.grid, fp, self.energy, fill_value=0.0)
## clean up group
#for att in ('normalization_function', 'weight', 'grid'):
# if hasattr(self, att): delattr(self, att)
finish = time.clock()
self.time_elapsed = float(finish - start)
def kk(self, energy=None, mu=None, z=None, edge='K', how='scalar', mback_kws=None):
"""
Convert mu(E) data into f'(E) and f"(E). f"(E) is made by
matching mu(E) to the tabulated values of the imaginary part
of the scattering factor (Cromer-Liberman), f'(E) is then
obtained by performing a differential Kramers-Kronig transform
on the matched f"(E).
Attributes
energy: energy array
mu: array with mu(E) data
z: Z number of absorber
edge: absorption edge, usually 'K' or 'L3'
mback_kws: arguments for the mback algorithm
Returns
self.f1, self.f2: CL values over on the input energy grid
self.fp, self.fpp: matched and KK transformed data on the input energy grid
References:
* Cromer-Liberman: http://dx.doi.org/10.1063/1.1674266
* KK computation: Ohta and Ishida, Applied Spectroscopy 42:6 (1988) 952-957
* diffKK implementation: http://dx.doi.org/10.1103/PhysRevB.58.11215
* MBACK (Weng, Waldo, Penner-Hahn): http://dx.doi.org/10.1086/303711
* Lee and Xiang: http://dx.doi.org/10.1088/0004-637X/702/2/970
"""
if type(energy).__name__ == 'ndarray': self.energy = energy
if type(mu).__name__ == 'ndarray': self.mu = mu
if z != None: self.z = z
if edge != None: self.edge = edge
if mback_kws != None: self.mback_kws = mback_kws
if self.z == None:
Exception("Z for absorber not provided for diffKK")
if self.edge == None:
Exception("absorption edge not provided for diffKK")
mb_kws = dict(order=3, z=self.z, edge=self.edge, e0=None, emin=None, emax=None,
whiteline=False, leexiang=False, tables='chantler',
fit_erfc=False, return_f1=True)
if self.mback_kws is not None:
mb_kws.update(self.mback_kws)
start = time.clock()
mback(self.energy, self.mu, group=self, _larch=self._larch, **mb_kws)
## interpolate matched data onto an even grid with an even number of elements (about 1 eV)
npts = int(self.energy[-1] - self.energy[0]) + (int(self.energy[-1] - self.energy[0])%2)
self.grid = np.linspace(self.energy[0], self.energy[-1], npts)
fpp = _interp(self.energy, self.f2-self.fpp, self.grid, fill_value=0.0)
## do difference KK
if repr(how).startswith('sca'):
fp = kkmclr_sca(self.grid, fpp)
else:
fp = kkmclr(self.grid, fpp)
## interpolate back to original grid and add diffKK result to f1 to make fp array
self.fp = self.f1 + _interp(self.grid, fp, self.energy, fill_value=0.0)
## clean up group
#for att in ('normalization_function', 'weight', 'grid'):
# if hasattr(self, att): delattr(self, att)
finish = time.clock()
self.time_elapsed = float(finish-start)
