def fluo_corr(energy, mu, formula, elem, group=None, edge='K', anginp=45,
angout=45, _larch=None, **pre_kws):
"""correct over-absorption (self-absorption) for fluorescene XAFS
using the FLUO alogrithm of D. Haskel.
Arguments
---------
energy array of energies
mu uncorrected fluorescence mu
formula string for sample stoichiometry
elem atomic symbol or Z of absorbing element
group output group [default None]
edge name of edge ('K', 'L3', ...) [default 'K']
anginp input angle in degrees [default 45]
angout output angle in degrees [default 45]
Additional keywords will be passed to pre_edge(), which will be used
to ensure consistent normalization.
Returns
--------
None, writes `mu_corr` and `norm_corr` (normalized `mu_corr`)
to output group.
Notes
-----
Support First Argument Group convention, requiring group
members 'energy' and 'mu'
"""
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='fluo_corr')
# generate normalized mu for correction
preinp = preedge(energy, mu, **pre_kws)
mu_inp = preinp['norm']
anginp = max(1.e-7, np.deg2rad(anginp))
angout = max(1.e-7, np.deg2rad(angout))
# find edge energies and fluorescence line energy
e_edge = xray_edge(elem, edge, _larch=_larch)[0]
e_fluor = xray_line(elem, edge, _larch=_larch)[0]
# calculate mu(E) for fluorescence energy, above, below edge
energies = np.array([e_fluor, e_edge-10.0, e_edge+10.0])
muvals = material_mu(formula, energies, density=1, _larch=_larch)
mu_fluor = muvals[0] * np.sin(anginp)/np.sin(angout)
mu_below = muvals[1]
mu_celem = muvals[2] - muvals[1]
alpha = (mu_fluor + mu_below)/mu_celem
mu_corr = mu_inp*alpha/(alpha + 1 - mu_inp)
preout = preedge(energy, mu_corr, **pre_kws)
if group is not None:
group = set_xafsGroup(group, _larch=_larch)
group.mu_corr = mu_corr
group.norm_corr = preout['norm']
def find_e0(energy, mu=None, group=None, _larch=None):
"""calculate E0 given mu(energy)
This finds the point with maximum derivative with some
checks to avoid spurious glitches.
Arguments
----------
energy: array of x-ray energies, in eV or group
mu: array of mu(E)
group: output group
Returns
-------
Value of e0. If provided, group.e0 will be set to this value.
Notes
-----
Supports First Argument Group convention, requiring group
members 'energy' and 'mu'
"""
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='find_e0')
e0 = _finde0(energy, mu)
if group is not None:
group = set_xafsGroup(group, _larch=_larch)
group.e0 = e0
return e0
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 xas_convolve(energy, norm=None, group=None, form='lorentzian',
esigma=1.0, eshift=0.0, _larch=None):
"""
convolve a normalized mu(E) spectra with a Lorentzian or Gaussian peak
shape, degrading separation of XANES features.
This is provided as a complement to xas_deconvolve, and to deliberately
broaden spectra to compare with spectra measured at lower resolution.
Arguments
----------
energy: array of x-ray energies (in eV) or XAFS data group
norm: array of normalized mu(E)
group: output group
form: form of deconvolution function. One of
'lorentzian' or 'gaussian' ['lorentzian']
esigma energy sigma (in eV) to pass to gaussian() or lorentzian() [1.0]
eshift energy shift (in eV) to apply to result [0]
Returns
-------
None
The array 'conv' will be written to the output group.
Notes
-----
Follows the First Argument Group convention, using group members named
'energy' and 'norm'
"""
energy, mu, group = parse_group_args(energy, members=('energy', 'norm'),
defaults=(norm,), group=group,
fcn_name='xas_convolve')
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))
npad = 1 + int(max(estep*2.01, 50*esigma)/estep)
npts = npad + int(max(en) / estep)
x = np.arange(npts)*estep
y = interp(en, mu, x, kind='cubic')
kernel = lorentzian
if form.lower().startswith('g'):
kernel = gaussian
k = kernel(x, center=0, sigma=esigma)
ret = np.convolve(y, k, mode='full')
out = interp(x-eshift, ret[:len(x)], en, kind='cubic')
group = set_xafsGroup(group, _larch=_larch)
group.conv = out / k.sum()
def find_e0(energy, mu=None, group=None, _larch=None):
"""calculate E0 given mu(energy)
This finds the point with maximum derivative with some
checks to avoid spurious glitches.
Arguments
----------
energy: array of x-ray energies, in eV or group
mu: array of mu(E)
group: output group
Returns
-------
Value of e0. If provided, group.e0 will be set to this value.
Notes
-----
Supports First Argument Group convention, requiring group
members 'energy' and 'mu'
"""
energy, mu, group = parse_group_args(energy,
members=('energy', 'mu'),
defaults=(mu, ),
group=group,
fcn_name='find_e0')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
energy = remove_dups(energy)
dmu = np.gradient(mu) / np.gradient(energy)
# find points of high derivative
high_deriv_pts = np.where(dmu > max(dmu) * 0.05)[0]
idmu_max, dmu_max = 0, 0
for i in high_deriv_pts:
if (dmu[i] > dmu_max and (i + 1 in high_deriv_pts)
and (i - 1 in high_deriv_pts)):
idmu_max, dmu_max = i, dmu[i]
e0 = energy[idmu_max]
if group is not None:
group = set_xafsGroup(group, _larch=_larch)
group.e0 = e0
return e0
def _ff2chi(pathlist,
group=None,
paramgroup=None,
_larch=None,
k=None,
kmax=None,
kstep=0.05,
**kws):
"""sum chi(k) for a list of FeffPath Groups.
Parameters:
------------
pathlist: a list of FeffPath Groups
paramgroup: a Parameter Group for calculating Path Parameters [None]
kmax: maximum k value for chi calculation [20].
kstep: step in k value for chi calculation [0.05].
k: explicit array of k values to calculate chi.
Returns:
---------
group contain arrays for k and chi
This essentially calls path2chi() for each of the paths in the
pathlist and writes the resulting arrays to group.k and group.chi.
"""
msg = _larch.writer.write
if (paramgroup is not None and _larch is not None
and _larch.symtable.isgroup(paramgroup)):
_larch.symtable._sys.paramGroup = paramgroup
for path in pathlist:
if not isNamedClass(path, FeffPathGroup):
msg('%s is not a valid Feff Path' % path)
return
path._calc_chi(k=k, kstep=kstep, kmax=kmax)
k = pathlist[0].k[:]
out = np.zeros_like(k)
for path in pathlist:
out += path.chi
if group is None:
group = Group()
else:
group = set_xafsGroup(group, _larch=_larch)
group.k = k
group.chi = out
return group
def find_e0(energy, mu=None, group=None, _larch=None):
"""calculate E0 given mu(energy)
This finds the point with maximum derivative with some
checks to avoid spurious glitches.
Arguments
----------
energy: array of x-ray energies, in eV or group
mu: array of mu(E)
group: output group
Returns
-------
Value of e0. If provided, group.e0 will be set to this value.
Notes
-----
Supports First Argument Group convention, requiring group
members 'energy' and 'mu'
"""
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='find_e0')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
energy = remove_dups(energy)
dmu = np.gradient(mu)/np.gradient(energy)
# find points of high derivative
high_deriv_pts = np.where(dmu > max(dmu)*0.05)[0]
idmu_max, dmu_max = 0, 0
for i in high_deriv_pts:
if (dmu[i] > dmu_max and
(i+1 in high_deriv_pts) and
(i-1 in high_deriv_pts)):
idmu_max, dmu_max = i, dmu[i]
e0 = energy[idmu_max]
if group is not None:
group = set_xafsGroup(group, _larch=_larch)
group.e0 = e0
return e0
def _ff2chi(pathlist, group=None, paramgroup=None, _larch=None,
k=None, kmax=None, kstep=0.05, **kws):
"""sum chi(k) for a list of FeffPath Groups.
Parameters:
------------
pathlist: a list of FeffPath Groups
paramgroup: a Parameter Group for calculating Path Parameters [None]
kmax: maximum k value for chi calculation [20].
kstep: step in k value for chi calculation [0.05].
k: explicit array of k values to calculate chi.
Returns:
---------
group contain arrays for k and chi
This essentially calls path2chi() for each of the paths in the
pathlist and writes the resulting arrays to group.k and group.chi.
"""
msg = _larch.writer.write
if (paramgroup is not None and _larch is not None and
_larch.symtable.isgroup(paramgroup)):
_larch.symtable._sys.paramGroup = paramgroup
for path in pathlist:
if not isNamedClass(path, FeffPathGroup):
msg('%s is not a valid Feff Path' % path)
return
path._calc_chi(k=k, kstep=kstep, kmax=kmax)
k = pathlist[0].k[:]
out = np.zeros_like(k)
for path in pathlist:
out += path.chi
if group is None:
group = Group()
else:
group = set_xafsGroup(group, _larch=_larch)
group.k = k
group.chi = out
return group
def _xafsft(self, chi, group=None, rmax_out=10, **kws):
"returns "
for key, val in kws:
if key == 'kw':
key = 'kweight'
setattr(self, key, val)
self.make_karrays()
out = self.fftf(chi)
irmax = min(self.nfft/2, int(1.01 + rmax_out/self.rstep))
group = set_xafsGroup(group, _larch=self._larch)
r = self.rstep * arange(irmax)
mag = sqrt(out.real**2 + out.imag**2)
group.kwin = self.kwin[:len(chi)]
group.r = r[:irmax]
group.chir = out[:irmax]
group.chir_mag = mag[:irmax]
group.chir_pha = complex_phase(out[:irmax])
group.chir_re = out.real[:irmax]
group.chir_im = out.imag[:irmax]
def _xafsft(self, chi, group=None, rmax_out=10, **kws):
"returns "
for key, val in kws:
if key == 'kw':
key = 'kweight'
setattr(self, key, val)
self.make_karrays()
out = self.fftf(chi)
irmax = min(self.nfft / 2, int(1.01 + rmax_out / self.rstep))
group = set_xafsGroup(group, _larch=self._larch)
r = self.rstep * arange(irmax)
mag = sqrt(out.real**2 + out.imag**2)
group.kwin = self.kwin[:len(chi)]
group.r = r[:irmax]
group.chir = out[:irmax]
group.chir_mag = mag[:irmax]
group.chir_pha = complex_phase(out[:irmax])
group.chir_re = out.real[:irmax]
group.chir_im = out.imag[:irmax]
def mback(energy, mu=None, group=None, z=None, edge='K', e0=None, pre1=None, pre2=-50,
norm1=100, norm2=None, order=3, leexiang=False, tables='chantler', fit_erfc=False,
return_f1=False, _larch=None):
"""
Match mu(E) data for tabulated f''(E) using the MBACK algorithm and,
optionally, the Lee & Xiang extension
Arguments
----------
energy: array of x-ray energies, in eV.
mu: array of mu(E).
group: output group.
z: atomic number of the absorber.
edge: x-ray absorption edge (default 'K')
e0: edge energy, in eV. If None, it will be determined here.
pre1: low E range (relative to e0) for pre-edge region.
pre2: high E range (relative to e0) for pre-edge region.
norm1: low E range (relative to e0) for post-edge region.
norm2: high E range (relative to e0) for post-edge region.
order: order of the legendre polynomial for normalization.
(default=3, min=0, max=5).
leexiang: boolean (default False) to use the Lee & Xiang extension.
tables: tabulated scattering factors: 'chantler' (default) or 'cl' (cromer-liberman)
fit_erfc: boolean (default False) to fit parameters of error function.
return_f1: boolean (default False) to include the f1 array in the group.
Returns
-------
None
The following attributes will be written to the output group:
group.f2: tabulated f2(E).
group.f1: tabulated f1(E) (if 'return_f1' is True).
group.fpp: mback atched spectrum.
group.edge_step: edge step of spectrum.
group.norm: normalized spectrum.
group.mback_params: group of parameters for the minimization.
References:
* 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
* Cromer-Liberman: http://dx.doi.org/10.1063/1.1674266
* Chantler: http://dx.doi.org/10.1063/1.555974
"""
order = max(min(order, MAXORDER), 0)
### implement the First Argument Group convention
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='mback')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
group = set_xafsGroup(group, _larch=_larch)
energy = remove_dups(energy)
if e0 is None or e0 < energy[1] or e0 > energy[-2]:
e0 = find_e0(energy, mu, group=group)
print(e0)
ie0 = index_nearest(energy, e0)
e0 = energy[ie0]
pre1_input = pre1
norm2_input = norm2
if pre1 is None: pre1 = min(energy) - e0
if norm2 is None: norm2 = max(energy) - e0
if norm2 < 0: norm2 = max(energy) - e0 - norm2
pre1 = max(pre1, (min(energy) - e0))
norm2 = min(norm2, (max(energy) - e0))
if pre1 > pre2:
pre1, pre2 = pre2, pre1
if norm1 > norm2:
norm1, norm2 = norm2, norm1
p1 = index_of(energy, pre1+e0)
p2 = index_nearest(energy, pre2+e0)
n1 = index_nearest(energy, norm1+e0)
n2 = index_of(energy, norm2+e0)
if p2 - p1 < 2:
p2 = min(len(energy), p1 + 2)
if n2 - n1 < 2:
p2 = min(len(energy), p1 + 2)
## theta is a boolean array indicating the
## energy values considered for the fit.
## theta=1 for included values, theta=0 for excluded values.
theta = np.zeros_like(energy, dtype='int')
theta[p1:(p2+1)] = 1
theta[n1:(n2+1)] = 1
## weights for the pre- and post-edge regions, as defined in the MBACK paper (?)
weight = np.ones_like(energy, dtype=float)
weight[p1:(p2+1)] = np.sqrt(np.sum(weight[p1:(p2+1)]))
weight[n1:(n2+1)] = np.sqrt(np.sum(weight[n1:(n2+1)]))
## get the f'' function from CL or Chantler
if tables.lower() == 'chantler':
f1 = f1_chantler(z, energy, _larch=_larch)
f2 = f2_chantler(z, energy, _larch=_larch)
else:
(f1, f2) = f1f2(z, energy, edge=edge, _larch=_larch)
group.f2 = f2
if return_f1:
group.f1 = f1
em = find_xray_line(z, edge)[0] # erfc centroid
params = Parameters()
params.add(name='s', value=1.0, vary=True) # scale of data
params.add(name='xi', value=50.0, vary=False, min=0) # width of erfc
params.add(name='a', value=0.0, vary=False) # amplitude of erfc
if fit_erfc:
params['a'].vary = True
params['a'].value = 0.5
params['xi'].vary = True
for i in range(order+1): # polynomial coefficients
params.add(name='c%d' % i, value=0, vary=True)
out = minimize(match_f2, params, method='leastsq',
gtol=1.e-5, ftol=1.e-5, xtol=1.e-5, epsfcn=1.e-5,
kws = dict(en=energy, mu=mu, f2=f2, e0=e0, em=em,
order=order, weight=weight, theta=theta, leexiang=leexiang))
opars = out.params.valuesdict()
eoff = energy - e0
norm_function = opars['a']*erfc((energy-em)/opars['xi']) + opars['c0']
for i in range(order):
attr = 'c%d' % (i + 1)
if attr in opars:
norm_function += opars[attr]* eoff**(i + 1)
group.e0 = e0
group.fpp = opars['s']*mu - norm_function
# calculate edge step and normalization from f2 + norm_function
pre_f2 = preedge(energy, group.f2+norm_function, e0=e0, pre1=pre1,
pre2=pre2, norm1=norm1, norm2=norm2, nnorm=2, nvict=0)
group.edge_step = pre_f2['edge_step'] / opars['s']
group.norm = (opars['s']*mu - pre_f2['pre_edge']) / pre_f2['edge_step']
group.mback_details = Group(params=opars, pre_f2=pre_f2,
f2_scaled=opars['s']*f2,
norm_function=norm_function)
def xas_deconvolve(energy, norm=None, group=None, form='lorentzian',
esigma=1.0, eshift=0.0, smooth=True,
sgwindow=None, sgorder=3, _larch=None):
"""XAS spectral deconvolution
de-convolve a normalized mu(E) spectra with a peak shape, enhancing the
intensity and separation of peaks of a XANES spectrum.
The results can be unstable, and noisy, and should be used
with caution!
Arguments
----------
energy: array of x-ray energies (in eV) or XAFS data group
norm: array of normalized mu(E)
group: output group
form: functional form of deconvolution function. One of
'gaussian' or 'lorentzian' [default]
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]
smooth whether to smooth result with savitzky_golay method [True]
sgwindow window size for savitzky_golay [found from data step and esigma]
sgorder order for savitzky_golay [3]
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'
Smoothing with savitzky_golay() requires a window and order. By
default, window = int(esigma / estep) where estep is step size for
the gridded data, approximately the finest energy step in the data.
"""
energy, mu, group = parse_group_args(energy, members=('energy', 'norm'),
defaults=(norm,), group=group,
fcn_name='xas_deconvolve')
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='cubic')
kernel = lorentzian
if form.lower().startswith('g'):
kernel = gaussian
yext = np.concatenate((y, np.arange(len(y))*y[-1]))
ret, err = deconvolve(yext, kernel(x, center=0, sigma=esigma))
nret = min(len(x), len(ret))
ret = ret[:nret]*yext[nret-1]/ret[nret-1]
if smooth:
if sgwindow is None:
sgwindow = int(1.0*esigma/estep)
sqwindow = int(sgwindow)
if sgwindow < (sgorder+1):
sgwindow = sgorder + 2
if sgwindow % 2 == 0:
sgwindow += 1
ret = savitzky_golay(ret, sgwindow, sgorder)
out = interp(x+eshift, ret, en, kind='cubic')
group = set_xafsGroup(group, _larch=_larch)
group.deconv = out
def mback_norm(energy, mu=None, group=None, z=None, edge='K', e0=None,
pre1=None, pre2=-50, norm1=100, norm2=None, nnorm=1, nvict=1,
_larch=None):
"""
simplified version of MBACK to Match mu(E) data for tabulated f''(E)
for normalization
Arguments:
energy, mu: arrays of energy and mu(E)
group: output group (and input group for e0)
z: Z number of absorber
e0: edge energy
pre1: low E range (relative to E0) for pre-edge fit
pre2: high E range (relative to E0) for pre-edge fit
norm1: low E range (relative to E0) for post-edge fit
norm2: high E range (relative to E0) for post-edge fit
nnorm: degree of polynomial (ie, nnorm+1 coefficients will be
found) for post-edge normalization curve fit to the
scaled f2. Default=1 (linear)
Returns:
group.norm_poly: normalized mu(E) from pre_edge()
group.norm: normalized mu(E) from this method
group.mback_mu: tabulated f2 scaled and pre_edge added to match mu(E)
group.mback_params: Group of parameters for the minimization
References:
* MBACK (Weng, Waldo, Penner-Hahn): http://dx.doi.org/10.1086/303711
* Chantler: http://dx.doi.org/10.1063/1.555974
"""
### implement the First Argument Group convention
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='mback')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
group = set_xafsGroup(group, _larch=_larch)
group.norm_poly = group.norm*1.0
if z is not None: # need to run find_e0:
e0_nominal = xray_edge(z, edge)[0]
if e0 is None:
e0 = getattr(group, 'e0', None)
if e0 is None:
find_e0(energy, mu, group=group)
e0 = group.e0
atsym = None
if z is None or z < 2:
atsym, edge = guess_edge(group.e0, _larch=_larch)
z = atomic_number(atsym)
if atsym is None and z is not None:
atsym = atomic_symbol(z)
if getattr(group, 'pre_edge_details', None) is None: # pre_edge never run
preedge(energy, mu, pre1=pre1, pre2=pre2, nvict=nvict,
norm1=norm1, norm2=norm2, e0=e0, nnorm=nnorm)
mu_pre = mu - group.pre_edge
f2 = f2_chantler(z, energy)
weights = np.ones(len(energy))*1.0
if norm2 is None:
norm2 = max(energy) - e0
if norm2 < 0:
norm2 = max(energy) - e0 - norm2
# avoid l2 and higher edges
if edge.lower().startswith('l'):
if edge.lower() == 'l3':
e_l2 = xray_edge(z, 'L2').edge
norm2 = min(norm2, e_l2-e0)
elif edge.lower() == 'l2':
e_l2 = xray_edge(z, 'L1').edge
norm2 = min(norm2, e_l1-e0)
ipre2 = index_of(energy, e0+pre2)
inor1 = index_of(energy, e0+norm1)
inor2 = index_of(energy, e0+norm2) + 1
weights[ipre2:] = 0.0
weights[inor1:inor2] = np.linspace(0.1, 1.0, inor2-inor1)
params = Parameters()
params.add(name='slope', value=0.0, vary=True)
params.add(name='offset', value=-f2[0], vary=True)
params.add(name='scale', value=f2[-1], vary=True)
out = minimize(f2norm, params, method='leastsq',
gtol=1.e-5, ftol=1.e-5, xtol=1.e-5, epsfcn=1.e-5,
kws = dict(en=energy, mu=mu_pre, f2=f2, weights=weights))
p = out.params.valuesdict()
model = (p['offset'] + p['slope']*energy + f2) * p['scale']
group.mback_mu = model + group.pre_edge
pre_f2 = preedge(energy, model, nnorm=nnorm, nvict=nvict, e0=e0,
pre1=pre1, pre2=pre2, norm1=norm1, norm2=norm2)
step_new = pre_f2['edge_step']
group.edge_step_poly = group.edge_step
group.edge_step_mback = step_new
group.norm_mback = mu_pre / step_new
group.mback_params = Group(e0=e0, pre1=pre1, pre2=pre2, norm1=norm1,
norm2=norm2, nnorm=nnorm, fit_params=p,
fit_weights=weights, model=model, f2=f2,
pre_f2=pre_f2, atsym=atsym, edge=edge)
if (abs(step_new - group.edge_step)/(1.e-13+group.edge_step)) > 0.75:
print("Warning: mback edge step failed....")
else:
group.edge_step = step_new
group.norm = group.norm_mback
def autobk(energy, mu=None, group=None, rbkg=1, nknots=None, e0=None,
edge_step=None, kmin=0, kmax=None, kweight=1, dk=0,
win='hanning', k_std=None, chi_std=None, nfft=2048, kstep=0.05,
pre_edge_kws=None, nclamp=4, clamp_lo=1, clamp_hi=1,
calc_uncertainties=False, _larch=None, **kws):
"""Use Autobk algorithm to remove XAFS background
Parameters:
-----------
energy: 1-d array of x-ray energies, in eV, or group
mu: 1-d array of mu(E)
group: output group (and input group for e0 and edge_step).
rbkg: distance (in Ang) for chi(R) above
which the signal is ignored. Default = 1.
e0: edge energy, in eV. If None, it will be determined.
edge_step: edge step. If None, it will be determined.
pre_edge_kws: keyword arguments to pass to pre_edge()
nknots: number of knots in spline. If None, it will be determined.
kmin: minimum k value [0]
kmax: maximum k value [full data range].
kweight: k weight for FFT. [1]
dk: FFT window window parameter. [0]
win: FFT window function name. ['hanning']
nfft: array size to use for FFT [2048]
kstep: k step size to use for FFT [0.05]
k_std: optional k array for standard chi(k).
chi_std: optional chi array for standard chi(k).
nclamp: number of energy end-points for clamp [2]
clamp_lo: weight of low-energy clamp [1]
clamp_hi: weight of high-energy clamp [1]
calc_uncertaintites: Flag to calculate uncertainties in
mu_0(E) and chi(k) [False]
Output arrays are written to the provided group.
Follows the 'First Argument Group' convention.
"""
msg = _larch.writer.write
if 'kw' in kws:
kweight = kws.pop('kw')
if len(kws) > 0:
msg('Unrecognized a:rguments for autobk():\n')
msg(' %s\n' % (', '.join(kws.keys())))
return
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='autobk')
energy = remove_dups(energy)
# if e0 or edge_step are not specified, get them, either from the
# passed-in group or from running pre_edge()
group = set_xafsGroup(group, _larch=_larch)
if edge_step is None and isgroup(group, 'edge_step'):
edge_step = group.edge_step
if e0 is None and isgroup(group, 'e0'):
e0 = group.e0
if e0 is None or edge_step is None:
# need to run pre_edge:
pre_kws = dict(nnorm=3, nvict=0, pre1=None,
pre2=-50., norm1=100., norm2=None)
if pre_edge_kws is not None:
pre_kws.update(pre_edge_kws)
pre_edge(energy, mu, group=group, _larch=_larch, **pre_kws)
if e0 is None:
e0 = group.e0
if edge_step is None:
edge_step = group.edge_step
if e0 is None or edge_step is None:
msg('autobk() could not determine e0 or edge_step!: trying running pre_edge first\n')
return
# get array indices for rkbg and e0: irbkg, ie0
ie0 = index_of(energy, e0)
rgrid = np.pi/(kstep*nfft)
if rbkg < 2*rgrid: rbkg = 2*rgrid
irbkg = int(1.01 + rbkg/rgrid)
# save ungridded k (kraw) and grided k (kout)
# and ftwin (*k-weighting) for FT in residual
enpe = energy[ie0:] - e0
kraw = np.sign(enpe)*np.sqrt(ETOK*abs(enpe))
if kmax is None:
kmax = max(kraw)
else:
kmax = max(0, min(max(kraw), kmax))
kout = kstep * np.arange(int(1.01+kmax/kstep), dtype='float64')
iemax = min(len(energy), 2+index_of(energy, e0+kmax*kmax/ETOK)) - 1
# interpolate provided chi(k) onto the kout grid
if chi_std is not None and k_std is not None:
chi_std = np.interp(kout, k_std, chi_std)
# pre-load FT window
ftwin = kout**kweight * ftwindow(kout, xmin=kmin, xmax=kmax,
window=win, dx=dk)
# calc k-value and initial guess for y-values of spline params
nspl = max(4, min(128, 2*int(rbkg*(kmax-kmin)/np.pi) + 1))
spl_y, spl_k, spl_e = np.zeros(nspl), np.zeros(nspl), np.zeros(nspl)
for i in range(nspl):
q = kmin + i*(kmax-kmin)/(nspl - 1)
ik = index_nearest(kraw, q)
i1 = min(len(kraw)-1, ik + 5)
i2 = max(0, ik - 5)
spl_k[i] = kraw[ik]
spl_e[i] = energy[ik+ie0]
spl_y[i] = (2*mu[ik+ie0] + mu[i1+ie0] + mu[i2+ie0] ) / 4.0
# get spline represention: knots, coefs, order=3
# coefs will be varied in fit.
knots, coefs, order = splrep(spl_k, spl_y)
# set fit parameters from initial coefficients
params = Group()
for i in range(len(coefs)):
name = FMT_COEF % i
p = Parameter(coefs[i], name=name, vary=i<len(spl_y))
p._getval()
setattr(params, name, p)
initbkg, initchi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
knots, coefs, order, kout)
# do fit
fit = Minimizer(__resid, params, _larch=_larch, toler=1.e-4,
fcn_kws = dict(ncoefs=len(coefs), chi_std=chi_std,
knots=knots, order=order,
kraw=kraw[:iemax-ie0+1],
mu=mu[ie0:iemax+1], irbkg=irbkg, kout=kout,
ftwin=ftwin, kweight=kweight,
nfft=nfft, nclamp=nclamp,
clamp_lo=clamp_lo, clamp_hi=clamp_hi))
fit.leastsq()
# write final results
coefs = [getattr(params, FMT_COEF % i) for i in range(len(coefs))]
bkg, chi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
knots, coefs, order, kout)
obkg = np.copy(mu)
obkg[ie0:ie0+len(bkg)] = bkg
# outputs to group
group = set_xafsGroup(group, _larch=_larch)
group.bkg = obkg
group.chie = (mu-obkg)/edge_step
group.k = kout
group.chi = chi/edge_step
# now fill in 'autobk_details' group
params.init_bkg = np.copy(mu)
params.init_bkg[ie0:ie0+len(bkg)] = initbkg
params.init_chi = initchi/edge_step
params.knots_e = spl_e
params.knots_y = np.array([coefs[i] for i in range(nspl)])
params.init_knots_y = spl_y
params.nfev = params.fit_details.nfev
params.kmin = kmin
params.kmax = kmax
group.autobk_details = params
# uncertainties in mu0 and chi: fairly slow!!
if HAS_UNCERTAIN and calc_uncertainties:
vbest, vstd = [], []
for n in fit.var_names:
par = getattr(params, n)
vbest.append(par.value)
vstd.append(par.stderr)
uvars = uncertainties.correlated_values(vbest, params.covar)
# uncertainty in bkg (aka mu0)
# note that much of this is working around
# limitations in the uncertainty package that make it
# 1. take an argument list (not array)
# 2. work on returned scalars (but not arrays)
# 3. not handle kw args and *args well (so use
# of global "index" is important here)
nkx = iemax-ie0 + 1
def my_dsplev(*args):
coefs = np.array(args)
return splev(kraw[:nkx], [knots, coefs, order])[index]
fdbkg = uncertainties.wrap(my_dsplev)
dmu0 = [fdbkg(*uvars).std_dev() for index in range(len(bkg))]
group.delta_bkg = np.zeros(len(mu))
group.delta_bkg[ie0:ie0+len(bkg)] = np.array(dmu0)
# uncertainty in chi (see notes above)
def my_dchi(*args):
coefs = np.array(args)
b,chi = spline_eval(kraw[:nkx], mu[ie0:iemax+1],
knots, coefs, order, kout)
return chi[index]
fdchi = uncertainties.wrap(my_dchi)
dchi = [fdchi(*uvars).std_dev() for index in range(len(kout))]
group.delta_chi = np.array(dchi)/edge_step
def cauchy_wavelet(k, chi=None, group=None, kweight=0, rmax_out=10,
nfft=2048, _larch=None):
"""
Cauchy Wavelet Transform for XAFS, following work of Munoz, Argoul, and Farges
Parameters:
-----------
k: 1-d array of photo-electron wavenumber in Ang^-1 or group
chi: 1-d array of chi
group: output Group
rmax_out: highest R for output data (10 Ang)
kweight: exponent for weighting spectra by k**kweight
nfft: value to use for N_fft (2048).
Returns:
---------
None -- outputs are written to supplied group.
Notes:
-------
Arrays written to output group:
r uniform array of R, out to rmax_out.
wcauchy complex cauchy wavelet(k, R)
wcauchy_mag magnitude of wavelet(k, R)
wcauchy_re real part of wavelet(k, R)
wcauchy_im imaginary part of wavelet(k, R)
Supports First Argument Group convention (with group
member names 'k' and 'chi')
"""
k, chi, group = parse_group_args(k, members=('k', 'chi'),
defaults=(chi,), group=group,
fcn_name='cauchy_wavelet')
kstep = np.round(1000.*(k[1]-k[0]))/1000.0
rstep = (np.pi/2048)/kstep
rmin = 1.e-7
rmax = rmax_out
nrpts = int(np.round((rmax-rmin)/rstep))
nkout = len(k)
if kweight != 0:
chi = chi * k**kweight
# extend EXAFS to 1024 data points...
NFT = nfft/2
if len(k) < NFT:
knew = np.arange(NFT) * kstep
xnew = np.zeros(NFT) * kstep
xnew[:len(k)] = chi
else:
knew = k[:NFT]
xnew = chi[:NFT]
# FT parameters
freq = (1.0/kstep)*np.arange(nfft)/(2*nfft)
omega = 2*np.pi*freq
# simple FT calculation
tff = np.fft.fft(xnew, n= 2*nfft)
# scale parameter
r = np.linspace(0, rmax, nrpts)
r[0] = 1.e-19
a = nrpts/(2*r)
# Characteristic values for Cauchy wavelet:
cauchy_sum = np.log(2*np.pi) - np.log(1.0+np.arange(nrpts)).sum()
# Main calculation:
out = np.zeros(nkout*nrpts,
dtype='complex128').reshape(nrpts, nkout)
for i in range(nrpts):
aom = a[i]*omega
aom[np.where(aom==0)] = 1.e-19
filt = cauchy_sum + nrpts*np.log(aom) - aom
tmp = np.conj(np.exp(filt))*tff[:nfft]
out[i, :] = np.fft.ifft(tmp, 2*nfft)[:nkout]
group = set_xafsGroup(group, _larch=_larch)
group.r = r
group.wcauchy = out
group.wcauchy_mag = np.sqrt(out.real**2 + out.imag**2)
group.wcauchy_re = out.real
group.wcauchy_im = out.imag
def autobk(energy,
mu=None,
group=None,
rbkg=1,
nknots=None,
e0=None,
edge_step=None,
kmin=0,
kmax=None,
kweight=1,
dk=0,
win='hanning',
k_std=None,
chi_std=None,
nfft=2048,
kstep=0.05,
pre_edge_kws=None,
nclamp=4,
clamp_lo=1,
clamp_hi=1,
calc_uncertainties=False,
_larch=None,
**kws):
"""Use Autobk algorithm to remove XAFS background
Parameters:
-----------
energy: 1-d array of x-ray energies, in eV, or group
mu: 1-d array of mu(E)
group: output group (and input group for e0 and edge_step).
rbkg: distance (in Ang) for chi(R) above
which the signal is ignored. Default = 1.
e0: edge energy, in eV. If None, it will be determined.
edge_step: edge step. If None, it will be determined.
pre_edge_kws: keyword arguments to pass to pre_edge()
nknots: number of knots in spline. If None, it will be determined.
kmin: minimum k value [0]
kmax: maximum k value [full data range].
kweight: k weight for FFT. [1]
dk: FFT window window parameter. [0]
win: FFT window function name. ['hanning']
nfft: array size to use for FFT [2048]
kstep: k step size to use for FFT [0.05]
k_std: optional k array for standard chi(k).
chi_std: optional chi array for standard chi(k).
nclamp: number of energy end-points for clamp [2]
clamp_lo: weight of low-energy clamp [1]
clamp_hi: weight of high-energy clamp [1]
calc_uncertaintites: Flag to calculate uncertainties in
mu_0(E) and chi(k) [False]
Output arrays are written to the provided group.
Follows the 'First Argument Group' convention.
"""
msg = _larch.writer.write
if 'kw' in kws:
kweight = kws.pop('kw')
if len(kws) > 0:
msg('Unrecognized a:rguments for autobk():\n')
msg(' %s\n' % (', '.join(kws.keys())))
return
energy, mu, group = parse_group_args(energy,
members=('energy', 'mu'),
defaults=(mu, ),
group=group,
fcn_name='autobk')
energy = remove_dups(energy)
# if e0 or edge_step are not specified, get them, either from the
# passed-in group or from running pre_edge()
group = set_xafsGroup(group, _larch=_larch)
if edge_step is None and isgroup(group, 'edge_step'):
edge_step = group.edge_step
if e0 is None and isgroup(group, 'e0'):
e0 = group.e0
if e0 is None or edge_step is None:
# need to run pre_edge:
pre_kws = dict(nnorm=3,
nvict=0,
pre1=None,
pre2=-50.,
norm1=100.,
norm2=None)
if pre_edge_kws is not None:
pre_kws.update(pre_edge_kws)
pre_edge(energy, mu, group=group, _larch=_larch, **pre_kws)
if e0 is None:
e0 = group.e0
if edge_step is None:
edge_step = group.edge_step
if e0 is None or edge_step is None:
msg('autobk() could not determine e0 or edge_step!: trying running pre_edge first\n'
)
return
# get array indices for rkbg and e0: irbkg, ie0
ie0 = index_of(energy, e0)
rgrid = np.pi / (kstep * nfft)
if rbkg < 2 * rgrid: rbkg = 2 * rgrid
irbkg = int(1.01 + rbkg / rgrid)
# save ungridded k (kraw) and grided k (kout)
# and ftwin (*k-weighting) for FT in residual
enpe = energy[ie0:] - e0
kraw = np.sign(enpe) * np.sqrt(ETOK * abs(enpe))
if kmax is None:
kmax = max(kraw)
else:
kmax = max(0, min(max(kraw), kmax))
kout = kstep * np.arange(int(1.01 + kmax / kstep), dtype='float64')
iemax = min(len(energy), 2 + index_of(energy, e0 + kmax * kmax / ETOK)) - 1
# interpolate provided chi(k) onto the kout grid
if chi_std is not None and k_std is not None:
chi_std = np.interp(kout, k_std, chi_std)
# pre-load FT window
ftwin = kout**kweight * ftwindow(
kout, xmin=kmin, xmax=kmax, window=win, dx=dk)
# calc k-value and initial guess for y-values of spline params
nspl = max(4, min(128, 2 * int(rbkg * (kmax - kmin) / np.pi) + 1))
spl_y, spl_k, spl_e = np.zeros(nspl), np.zeros(nspl), np.zeros(nspl)
for i in range(nspl):
q = kmin + i * (kmax - kmin) / (nspl - 1)
ik = index_nearest(kraw, q)
i1 = min(len(kraw) - 1, ik + 5)
i2 = max(0, ik - 5)
spl_k[i] = kraw[ik]
spl_e[i] = energy[ik + ie0]
spl_y[i] = (2 * mu[ik + ie0] + mu[i1 + ie0] + mu[i2 + ie0]) / 4.0
# get spline represention: knots, coefs, order=3
# coefs will be varied in fit.
knots, coefs, order = splrep(spl_k, spl_y)
# set fit parameters from initial coefficients
params = Group()
for i in range(len(coefs)):
name = FMT_COEF % i
p = Parameter(coefs[i], name=name, vary=i < len(spl_y))
p._getval()
setattr(params, name, p)
initbkg, initchi = spline_eval(kraw[:iemax - ie0 + 1], mu[ie0:iemax + 1],
knots, coefs, order, kout)
# do fit
fit = Minimizer(__resid,
params,
_larch=_larch,
toler=1.e-4,
fcn_kws=dict(ncoefs=len(coefs),
chi_std=chi_std,
knots=knots,
order=order,
kraw=kraw[:iemax - ie0 + 1],
mu=mu[ie0:iemax + 1],
irbkg=irbkg,
kout=kout,
ftwin=ftwin,
kweight=kweight,
nfft=nfft,
nclamp=nclamp,
clamp_lo=clamp_lo,
clamp_hi=clamp_hi))
fit.leastsq()
# write final results
coefs = [getattr(params, FMT_COEF % i) for i in range(len(coefs))]
bkg, chi = spline_eval(kraw[:iemax - ie0 + 1], mu[ie0:iemax + 1], knots,
coefs, order, kout)
obkg = np.copy(mu)
obkg[ie0:ie0 + len(bkg)] = bkg
# outputs to group
group = set_xafsGroup(group, _larch=_larch)
group.bkg = obkg
group.chie = (mu - obkg) / edge_step
group.k = kout
group.chi = chi / edge_step
# now fill in 'autobk_details' group
params.init_bkg = np.copy(mu)
params.init_bkg[ie0:ie0 + len(bkg)] = initbkg
params.init_chi = initchi / edge_step
params.knots_e = spl_e
params.knots_y = np.array([coefs[i] for i in range(nspl)])
params.init_knots_y = spl_y
params.nfev = params.fit_details.nfev
params.kmin = kmin
params.kmax = kmax
group.autobk_details = params
# uncertainties in mu0 and chi: fairly slow!!
if HAS_UNCERTAIN and calc_uncertainties:
vbest, vstd = [], []
for n in fit.var_names:
par = getattr(params, n)
vbest.append(par.value)
vstd.append(par.stderr)
uvars = uncertainties.correlated_values(vbest, params.covar)
# uncertainty in bkg (aka mu0)
# note that much of this is working around
# limitations in the uncertainty package that make it
# 1. take an argument list (not array)
# 2. work on returned scalars (but not arrays)
# 3. not handle kw args and *args well (so use
# of global "index" is important here)
nkx = iemax - ie0 + 1
def my_dsplev(*args):
coefs = np.array(args)
return splev(kraw[:nkx], [knots, coefs, order])[index]
fdbkg = uncertainties.wrap(my_dsplev)
dmu0 = [fdbkg(*uvars).std_dev() for index in range(len(bkg))]
group.delta_bkg = np.zeros(len(mu))
group.delta_bkg[ie0:ie0 + len(bkg)] = np.array(dmu0)
# uncertainty in chi (see notes above)
def my_dchi(*args):
coefs = np.array(args)
b, chi = spline_eval(kraw[:nkx], mu[ie0:iemax + 1], knots, coefs,
order, kout)
return chi[index]
fdchi = uncertainties.wrap(my_dchi)
dchi = [fdchi(*uvars).std_dev() for index in range(len(kout))]
group.delta_chi = np.array(dchi) / edge_step
def xftf(k,
chi=None,
group=None,
kmin=0,
kmax=20,
kweight=0,
dk=1,
dk2=None,
with_phase=False,
window='kaiser',
rmax_out=10,
nfft=2048,
kstep=0.05,
_larch=None,
**kws):
"""
forward XAFS Fourier transform, from chi(k) to chi(R), using
common XAFS conventions.
Parameters:
-----------
k: 1-d array of photo-electron wavenumber in Ang^-1 or group
chi: 1-d array of chi
group: output Group
rmax_out: highest R for output data (10 Ang)
kweight: exponent for weighting spectra by k**kweight
kmin: starting k for FT Window
kmax: ending k for FT Window
dk: tapering parameter for FT Window
dk2: second tapering parameter for FT Window
window: name of window type
nfft: value to use for N_fft (2048).
kstep: value to use for delta_k (0.05 Ang^-1).
with_phase: output the phase as well as magnitude, real, imag [False]
Returns:
---------
None -- outputs are written to supplied group.
Notes:
-------
Arrays written to output group:
kwin window function Omega(k) (length of input chi(k)).
r uniform array of R, out to rmax_out.
chir complex array of chi(R).
chir_mag magnitude of chi(R).
chir_re real part of chi(R).
chir_im imaginary part of chi(R).
chir_pha phase of chi(R) if with_phase=True
(a noticable performance hit)
Supports First Argument Group convention (with group member names 'k' and 'chi')
"""
# allow kweight keyword == kw
if 'kw' in kws:
kweight = kws['kw']
k, chi, group = parse_group_args(k,
members=('k', 'chi'),
defaults=(chi, ),
group=group,
fcn_name='xftf')
cchi, win = xftf_prep(k,
chi,
kmin=kmin,
kmax=kmax,
kweight=kweight,
dk=dk,
dk2=dk2,
nfft=nfft,
kstep=kstep,
window=window,
_larch=_larch)
out = xftf_fast(cchi * win, kstep=kstep, nfft=nfft)
rstep = pi / (kstep * nfft)
irmax = min(nfft / 2, int(1.01 + rmax_out / rstep))
group = set_xafsGroup(group, _larch=_larch)
r = rstep * arange(irmax)
mag = sqrt(out.real**2 + out.imag**2)
group.kwin = win[:len(chi)]
group.r = r[:irmax]
group.chir = out[:irmax]
group.chir_mag = mag[:irmax]
group.chir_re = out.real[:irmax]
group.chir_im = out.imag[:irmax]
if with_phase:
group.chir_pha = complex_phase(out[:irmax])
def fluo_corr(energy,
mu,
formula,
elem,
group=None,
edge='K',
anginp=45,
angout=45,
_larch=None,
**pre_kws):
"""correct over-absorption (self-absorption) for fluorescene XAFS
using the FLUO alogrithm of D. Haskel.
Arguments
---------
energy array of energies
mu uncorrected fluorescence mu
formula string for sample stoichiometry
elem atomic symbol or Z of absorbing element
group output group [default None]
edge name of edge ('K', 'L3', ...) [default 'K']
anginp input angle in degrees [default 45]
angout output angle in degrees [default 45]
Additional keywords will be passed to pre_edge(), which will be used
to ensure consistent normalization.
Returns
--------
None, writes `mu_corr` and `norm_corr` (normalized `mu_corr`)
to output group.
Notes
-----
Support First Argument Group convention, requiring group
members 'energy' and 'mu'
"""
energy, mu, group = parse_group_args(energy,
members=('energy', 'mu'),
defaults=(mu, ),
group=group,
fcn_name='fluo_corr')
# generate normalized mu for correction
preinp = preedge(energy, mu, **pre_kws)
mu_inp = preinp['norm']
anginp = max(1.e-7, np.deg2rad(anginp))
angout = max(1.e-7, np.deg2rad(angout))
# find edge energies and fluorescence line energy
e_edge = xray_edge(elem, edge, _larch=_larch)[0]
e_fluor = xray_line(elem, edge, _larch=_larch)[0]
# calculate mu(E) for fluorescence energy, above, below edge
energies = np.array([e_fluor, e_edge - 10.0, e_edge + 10.0])
muvals = material_mu(formula, energies, density=1, _larch=_larch)
mu_fluor = muvals[0] * np.sin(anginp) / np.sin(angout)
mu_below = muvals[1]
mu_celem = muvals[2] - muvals[1]
alpha = (mu_fluor + mu_below) / mu_celem
mu_corr = mu_inp * alpha / (alpha + 1 - mu_inp)
preout = preedge(energy, mu_corr, **pre_kws)
if group is not None:
group = set_xafsGroup(group, _larch=_larch)
group.mu_corr = mu_corr
group.norm_corr = preout['norm']
def mback(energy, mu, group=None, order=3, z=None, edge='K', e0=None, emin=None, emax=None,
whiteline=None, leexiang=False, tables='chantler', fit_erfc=False, return_f1=False,
_larch=None):
"""
Match mu(E) data for tabulated f''(E) using the MBACK algorithm and,
optionally, the Lee & Xiang extension
Arguments:
energy, mu: arrays of energy and mu(E)
order: order of polynomial [3]
group: output group (and input group for e0)
z: Z number of absorber
edge: absorption edge (K, L3)
e0: edge energy
emin: beginning energy for fit
emax: ending energy for fit
whiteline: exclusion zone around white lines
leexiang: flag to use the Lee & Xiang extension
tables: 'chantler' (default) or 'cl'
fit_erfc: True to float parameters of error function
return_f1: True to put the f1 array in the group
Returns:
group.f2: tabulated f2(E)
group.f1: tabulated f1(E) (if return_f1 is True)
group.fpp: matched data
group.mback_params: Group of parameters for the minimization
References:
* 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
* Cromer-Liberman: http://dx.doi.org/10.1063/1.1674266
* Chantler: http://dx.doi.org/10.1063/1.555974
"""
order=int(order)
if order < 1: order = 1 # set order of polynomial
if order > MAXORDER: order = MAXORDER
### implement the First Argument Group convention
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='mback')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
group = set_xafsGroup(group, _larch=_larch)
if e0 is None: # need to run find_e0:
e0 = xray_edge(z, edge, _larch=_larch)[0]
if e0 is None:
e0 = group.e0
if e0 is None:
find_e0(energy, mu, group=group)
### theta is an array used to exclude the regions <emin, >emax, and
### around white lines, theta=0.0 in excluded regions, theta=1.0 elsewhere
(i1, i2) = (0, len(energy)-1)
if emin is not None: i1 = index_of(energy, emin)
if emax is not None: i2 = index_of(energy, emax)
theta = np.ones(len(energy)) # default: 1 throughout
theta[0:i1] = 0
theta[i2:-1] = 0
if whiteline:
pre = 1.0*(energy<e0)
post = 1.0*(energy>e0+float(whiteline))
theta = theta * (pre + post)
if edge.lower().startswith('l'):
l2 = xray_edge(z, 'L2', _larch=_larch)[0]
l2_pre = 1.0*(energy<l2)
l2_post = 1.0*(energy>l2+float(whiteline))
theta = theta * (l2_pre + l2_post)
## this is used to weight the pre- and post-edge differently as
## defined in the MBACK paper
weight1 = 1*(energy<e0)
weight2 = 1*(energy>e0)
weight = np.sqrt(sum(weight1))*weight1 + np.sqrt(sum(weight2))*weight2
## get the f'' function from CL or Chantler
if tables.lower() == 'chantler':
f1 = f1_chantler(z, energy, _larch=_larch)
f2 = f2_chantler(z, energy, _larch=_larch)
else:
(f1, f2) = f1f2(z, energy, edge=edge, _larch=_larch)
group.f2=f2
if return_f1: group.f1=f1
n = edge
if edge.lower().startswith('l'): n = 'L'
params = Group(s = Parameter(1, vary=True, _larch=_larch), # scale of data
xi = Parameter(50, vary=fit_erfc, min=0, _larch=_larch), # width of erfc
em = Parameter(xray_line(z, n, _larch=_larch)[0], vary=False, _larch=_larch), # erfc centroid
e0 = Parameter(e0, vary=False, _larch=_larch), # abs. edge energy
## various arrays need by the objective function
en = energy,
mu = mu,
f2 = group.f2,
weight = weight,
theta = theta,
leexiang = leexiang,
_larch = _larch)
if fit_erfc:
params.a = Parameter(1, vary=True, _larch=_larch) # amplitude of erfc
else:
params.a = Parameter(0, vary=False, _larch=_larch) # amplitude of erfc
for i in range(order): # polynomial coefficients
setattr(params, 'c%d' % i, Parameter(0, vary=True, _larch=_larch))
fit = Minimizer(match_f2, params, _larch=_larch, toler=1.e-5)
fit.leastsq()
eoff = energy - params.e0.value
normalization_function = params.a.value*erfc((energy-params.em.value)/params.xi.value) + params.c0.value
for i in range(MAXORDER):
j = i+1
attr = 'c%d' % j
if hasattr(params, attr):
normalization_function = normalization_function + getattr(getattr(params, attr), 'value') * eoff**j
group.fpp = params.s*mu - normalization_function
group.mback_params = params
def autobk(energy, mu=None, group=None, rbkg=1, nknots=None, e0=None,
edge_step=None, kmin=0, kmax=None, kweight=1, dk=0,
win='hanning', k_std=None, chi_std=None, nfft=2048, kstep=0.05,
pre_edge_kws=None, nclamp=4, clamp_lo=1, clamp_hi=1,
calc_uncertainties=True, err_sigma=1, _larch=None, **kws):
"""Use Autobk algorithm to remove XAFS background
Parameters:
-----------
energy: 1-d array of x-ray energies, in eV, or group
mu: 1-d array of mu(E)
group: output group (and input group for e0 and edge_step).
rbkg: distance (in Ang) for chi(R) above
which the signal is ignored. Default = 1.
e0: edge energy, in eV. If None, it will be determined.
edge_step: edge step. If None, it will be determined.
pre_edge_kws: keyword arguments to pass to pre_edge()
nknots: number of knots in spline. If None, it will be determined.
kmin: minimum k value [0]
kmax: maximum k value [full data range].
kweight: k weight for FFT. [1]
dk: FFT window window parameter. [0]
win: FFT window function name. ['hanning']
nfft: array size to use for FFT [2048]
kstep: k step size to use for FFT [0.05]
k_std: optional k array for standard chi(k).
chi_std: optional chi array for standard chi(k).
nclamp: number of energy end-points for clamp [2]
clamp_lo: weight of low-energy clamp [1]
clamp_hi: weight of high-energy clamp [1]
calc_uncertaintites: Flag to calculate uncertainties in
mu_0(E) and chi(k) [True]
err_sigma: sigma level for uncertainties in mu_0(E) and chi(k) [1]
Output arrays are written to the provided group.
Follows the 'First Argument Group' convention.
"""
msg = _larch.writer.write
if 'kw' in kws:
kweight = kws.pop('kw')
if len(kws) > 0:
msg('Unrecognized a:rguments for autobk():\n')
msg(' %s\n' % (', '.join(kws.keys())))
return
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='autobk')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
energy = remove_dups(energy)
# if e0 or edge_step are not specified, get them, either from the
# passed-in group or from running pre_edge()
group = set_xafsGroup(group, _larch=_larch)
if edge_step is None and isgroup(group, 'edge_step'):
edge_step = group.edge_step
if e0 is None and isgroup(group, 'e0'):
e0 = group.e0
if e0 is None or edge_step is None:
# need to run pre_edge:
pre_kws = dict(nnorm=3, nvict=0, pre1=None,
pre2=-50., norm1=100., norm2=None)
if pre_edge_kws is not None:
pre_kws.update(pre_edge_kws)
pre_edge(energy, mu, group=group, _larch=_larch, **pre_kws)
if e0 is None:
e0 = group.e0
if edge_step is None:
edge_step = group.edge_step
if e0 is None or edge_step is None:
msg('autobk() could not determine e0 or edge_step!: trying running pre_edge first\n')
return
# get array indices for rkbg and e0: irbkg, ie0
ie0 = index_of(energy, e0)
rgrid = np.pi/(kstep*nfft)
if rbkg < 2*rgrid: rbkg = 2*rgrid
irbkg = int(1.01 + rbkg/rgrid)
# save ungridded k (kraw) and grided k (kout)
# and ftwin (*k-weighting) for FT in residual
enpe = energy[ie0:] - e0
kraw = np.sign(enpe)*np.sqrt(ETOK*abs(enpe))
if kmax is None:
kmax = max(kraw)
else:
kmax = max(0, min(max(kraw), kmax))
kout = kstep * np.arange(int(1.01+kmax/kstep), dtype='float64')
iemax = min(len(energy), 2+index_of(energy, e0+kmax*kmax/ETOK)) - 1
# interpolate provided chi(k) onto the kout grid
if chi_std is not None and k_std is not None:
chi_std = np.interp(kout, k_std, chi_std)
# pre-load FT window
ftwin = kout**kweight * ftwindow(kout, xmin=kmin, xmax=kmax,
window=win, dx=dk)
# calc k-value and initial guess for y-values of spline params
nspl = max(4, min(128, 2*int(rbkg*(kmax-kmin)/np.pi) + 1))
spl_y, spl_k, spl_e = np.zeros(nspl), np.zeros(nspl), np.zeros(nspl)
for i in range(nspl):
q = kmin + i*(kmax-kmin)/(nspl - 1)
ik = index_nearest(kraw, q)
i1 = min(len(kraw)-1, ik + 5)
i2 = max(0, ik - 5)
spl_k[i] = kraw[ik]
spl_e[i] = energy[ik+ie0]
spl_y[i] = (2*mu[ik+ie0] + mu[i1+ie0] + mu[i2+ie0] ) / 4.0
# get spline represention: knots, coefs, order=3
# coefs will be varied in fit.
knots, coefs, order = splrep(spl_k, spl_y)
# set fit parameters from initial coefficients
params = Parameters()
for i in range(len(coefs)):
params.add(name = FMT_COEF % i, value=coefs[i], vary=i<len(spl_y))
initbkg, initchi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
knots, coefs, order, kout)
# do fit
result = minimize(__resid, params, method='leastsq',
gtol=1.e-5, ftol=1.e-5, xtol=1.e-5, epsfcn=1.e-5,
kws = dict(ncoefs=len(coefs), chi_std=chi_std,
knots=knots, order=order,
kraw=kraw[:iemax-ie0+1],
mu=mu[ie0:iemax+1], irbkg=irbkg, kout=kout,
ftwin=ftwin, kweight=kweight,
nfft=nfft, nclamp=nclamp,
clamp_lo=clamp_lo, clamp_hi=clamp_hi))
# write final results
coefs = [result.params[FMT_COEF % i].value for i in range(len(coefs))]
bkg, chi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
knots, coefs, order, kout)
obkg = np.copy(mu)
obkg[ie0:ie0+len(bkg)] = bkg
# outputs to group
group = set_xafsGroup(group, _larch=_larch)
group.bkg = obkg
group.chie = (mu-obkg)/edge_step
group.k = kout
group.chi = chi/edge_step
# now fill in 'autobk_details' group
details = Group(params=result.params)
details.init_bkg = np.copy(mu)
details.init_bkg[ie0:ie0+len(bkg)] = initbkg
details.init_chi = initchi/edge_step
details.knots_e = spl_e
details.knots_y = np.array([coefs[i] for i in range(nspl)])
details.init_knots_y = spl_y
details.nfev = result.nfev
details.kmin = kmin
details.kmax = kmax
group.autobk_details = details
# uncertainties in mu0 and chi: can be fairly slow.
if calc_uncertainties:
nchi = len(chi)
nmue = iemax-ie0 + 1
redchi = result.redchi
covar = result.covar / redchi
jac_chi = np.zeros(nchi*nspl).reshape((nspl, nchi))
jac_bkg = np.zeros(nmue*nspl).reshape((nspl, nmue))
cvals, cerrs = [], []
for i in range(len(coefs)):
par = result.params[FMT_COEF % i]
cvals.append(getattr(par, 'value', 0.0))
cdel = getattr(par, 'stderr', 0.0)
if cdel is None:
cdel = 0.0
cerrs.append(cdel/2.0)
cvals = np.array(cvals)
cerrs = np.array(cerrs)
# find derivatives by hand!
_k = kraw[:nmue]
_m = mu[ie0:iemax+1]
for i in range(nspl):
cval0 = cvals[i]
cvals[i] = cval0 + cerrs[i]
bkg1, chi1 = spline_eval(_k, _m, knots, cvals, order, kout)
cvals[i] = cval0 - cerrs[i]
bkg2, chi2 = spline_eval(_k, _m, knots, cvals, order, kout)
cvals[i] = cval0
jac_chi[i] = (chi1 - chi2) / (2*cerrs[i])
jac_bkg[i] = (bkg1 - bkg2) / (2*cerrs[i])
dfchi = np.zeros(nchi)
dfbkg = np.zeros(nmue)
for i in range(nspl):
for j in range(nspl):
dfchi += jac_chi[i]*jac_chi[j]*covar[i,j]
dfbkg += jac_bkg[i]*jac_bkg[j]*covar[i,j]
prob = 0.5*(1.0 + erf(err_sigma/np.sqrt(2.0)))
dchi = t.ppf(prob, nchi-nspl) * np.sqrt(dfchi*redchi)
dbkg = t.ppf(prob, nmue-nspl) * np.sqrt(dfbkg*redchi)
group.delta_chi = dchi
group.delta_bkg = 0.0*mu
group.delta_bkg[ie0:ie0+len(dbkg)] = dbkg
def estimate_noise(k, chi=None, group=None, rmin=15.0, rmax=30.0,
kweight=1, kmin=0, kmax=20, dk=4, dk2=None, kstep=0.05,
kwindow='kaiser', nfft=2048, _larch=None, **kws):
"""
estimate noise levels in EXAFS spectrum and estimate highest k
where data is above the noise level
Parameters:
-----------
k: 1-d array of photo-electron wavenumber in Ang^-1 (or group)
chi: 1-d array of chi
group: output Group [see Note below]
rmin: minimum R value for high-R region of chi(R)
rmax: maximum R value for high-R region of chi(R)
kweight: exponent for weighting spectra by k**kweight [1]
kmin: starting k for FT Window [0]
kmax: ending k for FT Window [20]
dk: tapering parameter for FT Window [4]
dk2: second tapering parameter for FT Window [None]
kstep: value to use for delta_k ( Ang^-1) [0.05]
window: name of window type ['kaiser']
nfft: value to use for N_fft [2048].
Returns:
---------
None -- outputs are written to supplied group. Values (scalars) written
to output group:
epsilon_k estimated noise in chi(k)
epsilon_r estimated noise in chi(R)
kmax_suggest highest estimated k value where |chi(k)| > epsilon_k
Notes:
-------
1. This method uses the high-R portion of chi(R) as a measure of the noise
level in the chi(R) data and uses Parseval's theorem to convert this noise
level to that in chi(k). This method implicitly assumes that there is no
signal in the high-R portion of the spectrum, and that the noise in the
spectrum s "white" (independent of R) . Each of these assumptions can be
questioned.
2. The estimate for 'kmax_suggest' has a tendency to be fair but pessimistic
in how far out the chi(k) data goes before being dominated by noise.
3. Follows the 'First Argument Group' convention, so that you can either
specifiy all of (an array for 'k', an array for 'chi', option output Group)
OR pass a group with 'k' and 'chi' as the first argument
"""
k, chi, group = parse_group_args(k, members=('k', 'chi'),
defaults=(chi,), group=group,
fcn_name='esitmate_noise')
# save _sys.xafsGroup -- we want to NOT write to it here!
savgroup = set_xafsGroup(None, _larch=_larch)
tmpgroup = Group()
rmax_out = min(10*pi, rmax+2)
xftf(k, chi, kmin=kmin, kmax=kmax, rmax_out=rmax_out,
kweight=kweight, dk=dk, dk2=dk2, kwindow=kwindow,
nfft=nfft, kstep=kstep, group=tmpgroup, _larch=_larch)
chir = tmpgroup.chir
rstep = tmpgroup.r[1] - tmpgroup.r[0]
irmin = int(0.01 + rmin/rstep)
irmax = min(nfft/2, int(1.01 + rmax/rstep))
highr = realimag(chir[irmin:irmax])
# get average of window function value, scale eps_r scale by this
# this is imperfect, but improves the result.
kwin_ave = tmpgroup.kwin.sum()*kstep/(kmax-kmin)
eps_r = sqrt((highr*highr).sum() / len(highr)) / kwin_ave
# use Parseval's theorem to convert epsilon_r to epsilon_k,
# compensating for kweight
w = 2 * kweight + 1
scale = sqrt((2*pi*w)/(kstep*(kmax**w - kmin**w)))
eps_k = scale*eps_r
# do reverse FT to get chiq array
xftr(tmpgroup.r, tmpgroup.chir, group=tmpgroup, rmin=0.5, rmax=9.5,
dr=1.0, window='parzen', nfft=nfft, kstep=kstep, _larch=_larch)
# sets kmax_suggest to the largest k value for which
# | chi(q) / k**kweight| > epsilon_k
iq0 = index_of(tmpgroup.q, (kmax+kmin)/2.0)
tst = tmpgroup.chiq_mag[iq0:] / ( tmpgroup.q[iq0:])**kweight
kmax_suggest = tmpgroup.q[iq0 + where(tst < eps_k)[0][0]]
# restore original _sys.xafsGroup, set output variables
_larch.symtable._sys.xafsGroup = savgroup
group = set_xafsGroup(group, _larch=_larch)
group.epsilon_k = eps_k
group.epsilon_r = eps_r
group.kmax_suggest = kmax_suggest
def xftr(r, chir=None, group=None, rmin=0, rmax=20, with_phase=False,
dr=1, dr2=None, rw=0, window='kaiser', qmax_out=None,
nfft=2048, kstep=0.05, _larch=None, **kws):
"""
reverse XAFS Fourier transform, from chi(R) to chi(q).
calculate reverse XAFS Fourier transform
This assumes that chir_re and (optional chir_im are
on a uniform r-grid given by r.
Parameters:
------------
r: 1-d array of distance, or group.
chir: 1-d array of chi(R)
group: output Group
qmax_out: highest *k* for output data (30 Ang^-1)
rweight: exponent for weighting spectra by r^rweight (0)
rmin: starting *R* for FT Window
rmax: ending *R* for FT Window
dr: tapering parameter for FT Window
dr2: second tapering parameter for FT Window
window: name of window type
nfft: value to use for N_fft (2048).
kstep: value to use for delta_k (0.05).
with_phase: output the phase as well as magnitude, real, imag [False]
Returns:
---------
None -- outputs are written to supplied group.
Notes:
-------
Arrays written to output group:
rwin window Omega(R) (length of input chi(R)).
q uniform array of k, out to qmax_out.
chiq complex array of chi(k).
chiq_mag magnitude of chi(k).
chiq_re real part of chi(k).
chiq_im imaginary part of chi(k).
chiq_pha phase of chi(k) if with_phase=True
(a noticable performance hit)
Supports First Argument Group convention (with group member names 'r' and 'chir')
"""
if 'rweight' in kws:
rw = kws['rweight']
r, chir, group = parse_group_args(r, members=('r', 'chir'),
defaults=(chir,), group=group,
fcn_name='xftr')
rstep = r[1] - r[0]
kstep = pi/(rstep*nfft)
scale = 1.0
cchir = zeros(nfft, dtype='complex128')
r_ = rstep * arange(nfft, dtype='float64')
cchir[0:len(chir)] = chir
if chir.dtype == np.dtype('complex128'):
scale = 0.5
win = ftwindow(r_, xmin=rmin, xmax=rmax, dx=dr, dx2=dr2, window=window)
out = scale * xftr_fast( cchir*win * r_**rw, kstep=kstep, nfft=nfft)
if qmax_out is None: qmax_out = 30.0
q = linspace(0, qmax_out, int(1.05 + qmax_out/kstep))
nkpts = len(q)
group = set_xafsGroup(group, _larch=_larch)
group.q = q
mag = sqrt(out.real**2 + out.imag**2)
group.rwin = win[:len(chir)]
group.chiq = out[:nkpts]
group.chiq_mag = mag[:nkpts]
group.chiq_re = out.real[:nkpts]
group.chiq_im = out.imag[:nkpts]
if with_phase:
group.chiq_pha = complex_phase(out[:nkpts])
def mback(energy,
mu=None,
group=None,
order=3,
z=None,
edge='K',
e0=None,
emin=None,
emax=None,
whiteline=None,
leexiang=False,
tables='chantler',
fit_erfc=False,
return_f1=False,
_larch=None):
"""
Match mu(E) data for tabulated f''(E) using the MBACK algorithm and,
optionally, the Lee & Xiang extension
Arguments:
energy, mu: arrays of energy and mu(E)
order: order of polynomial [3]
group: output group (and input group for e0)
z: Z number of absorber
edge: absorption edge (K, L3)
e0: edge energy
emin: beginning energy for fit
emax: ending energy for fit
whiteline: exclusion zone around white lines
leexiang: flag to use the Lee & Xiang extension
tables: 'chantler' (default) or 'cl'
fit_erfc: True to float parameters of error function
return_f1: True to put the f1 array in the group
Returns:
group.f2: tabulated f2(E)
group.f1: tabulated f1(E) (if return_f1 is True)
group.fpp: matched data
group.mback_params: Group of parameters for the minimization
References:
* 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
* Cromer-Liberman: http://dx.doi.org/10.1063/1.1674266
* Chantler: http://dx.doi.org/10.1063/1.555974
"""
order = int(order)
if order < 1: order = 1 # set order of polynomial
if order > MAXORDER: order = MAXORDER
### implement the First Argument Group convention
energy, mu, group = parse_group_args(energy,
members=('energy', 'mu'),
defaults=(mu, ),
group=group,
fcn_name='mback')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
group = set_xafsGroup(group, _larch=_larch)
if e0 is None: # need to run find_e0:
e0 = xray_edge(z, edge, _larch=_larch)[0]
if e0 is None:
e0 = group.e0
if e0 is None:
find_e0(energy, mu, group=group)
### theta is an array used to exclude the regions <emin, >emax, and
### around white lines, theta=0.0 in excluded regions, theta=1.0 elsewhere
(i1, i2) = (0, len(energy) - 1)
if emin is not None: i1 = index_of(energy, emin)
if emax is not None: i2 = index_of(energy, emax)
theta = np.ones(len(energy)) # default: 1 throughout
theta[0:i1] = 0
theta[i2:-1] = 0
if whiteline:
pre = 1.0 * (energy < e0)
post = 1.0 * (energy > e0 + float(whiteline))
theta = theta * (pre + post)
if edge.lower().startswith('l'):
l2 = xray_edge(z, 'L2', _larch=_larch)[0]
l2_pre = 1.0 * (energy < l2)
l2_post = 1.0 * (energy > l2 + float(whiteline))
theta = theta * (l2_pre + l2_post)
## this is used to weight the pre- and post-edge differently as
## defined in the MBACK paper
weight1 = 1 * (energy < e0)
weight2 = 1 * (energy > e0)
weight = np.sqrt(sum(weight1)) * weight1 + np.sqrt(sum(weight2)) * weight2
## get the f'' function from CL or Chantler
if tables.lower() == 'chantler':
f1 = f1_chantler(z, energy, _larch=_larch)
f2 = f2_chantler(z, energy, _larch=_larch)
else:
(f1, f2) = f1f2(z, energy, edge=edge, _larch=_larch)
group.f2 = f2
if return_f1:
group.f1 = f1
em = xray_line(z, edge.upper(), _larch=_larch)[0] # erfc centroid
params = Parameters()
params.add(name='s', value=1, vary=True) # scale of data
params.add(name='xi', value=50, vary=fit_erfc, min=0) # width of erfc
params.add(name='a', value=0, vary=False) # amplitude of erfc
if fit_erfc:
params['a'].value = 1
params['a'].vary = True
for i in range(order): # polynomial coefficients
params.add(name='c%d' % i, value=0, vary=True)
out = minimize(match_f2,
params,
method='leastsq',
gtol=1.e-5,
ftol=1.e-5,
xtol=1.e-5,
epsfcn=1.e-5,
kws=dict(en=energy,
mu=mu,
f2=f2,
e0=e0,
em=em,
order=order,
weight=weight,
theta=theta,
leexiang=leexiang))
opars = out.params.valuesdict()
eoff = energy - e0
norm_function = opars['a'] * erfc(
(energy - em) / opars['xi']) + opars['c0']
for i in range(order):
j = i + 1
attr = 'c%d' % j
if attr in opars:
norm_function += opars[attr] * eoff**j
group.e0 = e0
group.fpp = opars['s'] * mu - norm_function
group.mback_params = opars
tmp = Group(energy=energy, mu=group.f2 - norm_function, e0=0)
# calculate edge step from f2 + norm_function: should be very smooth
pre_f2 = preedge(energy, group.f2 + norm_function, e0=e0, nnorm=2, nvict=0)
group.edge_step = pre_f2['edge_step'] / opars['s']
pre_fpp = preedge(energy, mu, e0=e0, nnorm=2, nvict=0)
group.norm = (mu - pre_fpp['pre_edge']) / group.edge_step
import larch from larch_plugins.xafs import pre_edge, find_e0 from larch_plugins.xafs import autobk, xftf, xftr from larch import Interpreter from larch_plugins.xafs import set_xafsGroup import numpy as np mylarch = Interpreter() group = set_xafsGroup(None, _larch=mylarch) # cu_metal_rt.xdi raw_data = \ '''8779.0 149013.7 550643.089065 -1.3070486 8789.0 144864.7 531876.119084 -1.3006104 8799.0 132978.7 489591.10592 -1.3033816 8809.0 125444.7 463051.104096 -1.3059724 8819.0 121324.7 449969.103983 -1.3107085 8829.0 119447.7 444386.117562 -1.3138152 8839.0 119100.7 440176.091039 -1.3072055 8849.0 117707.7 440448.106567 -1.3195882 8859.0 117754.7 442302.10637 -1.3233895 8869.0 117428.7 441944.116528 -1.3253521 8879.0 117383.7 442810.120466 -1.327693 8889.0 117185.7 443658.11566 -1.3312944 8899.0 117058.7 444213.107169 -1.3336289 8909.0 117276.7 443655.118274 -1.3305114 8919.0 117332.7 447444.089027 -1.3385381 8929.0 117332.7 448243.08656 -1.3403222 8939.0 117756.7 450590.088482 -1.3419374 8949.0 117199.7 445515.089564 -1.3353518 8959.0 117458.7 448318.080875 -1.3394162
def autobk(energy, mu=None, group=None, rbkg=1, nknots=None, e0=None,
edge_step=None, kmin=0, kmax=None, kweight=1, dk=0.1,
win='hanning', k_std=None, chi_std=None, nfft=2048, kstep=0.05,
pre_edge_kws=None, nclamp=4, clamp_lo=1, clamp_hi=1,
calc_uncertainties=True, err_sigma=1, _larch=None, **kws):
"""Use Autobk algorithm to remove XAFS background
Parameters:
-----------
energy: 1-d array of x-ray energies, in eV, or group
mu: 1-d array of mu(E)
group: output group (and input group for e0 and edge_step).
rbkg: distance (in Ang) for chi(R) above
which the signal is ignored. Default = 1.
e0: edge energy, in eV. If None, it will be determined.
edge_step: edge step. If None, it will be determined.
pre_edge_kws: keyword arguments to pass to pre_edge()
nknots: number of knots in spline. If None, it will be determined.
kmin: minimum k value [0]
kmax: maximum k value [full data range].
kweight: k weight for FFT. [1]
dk: FFT window window parameter. [0.1]
win: FFT window function name. ['hanning']
nfft: array size to use for FFT [2048]
kstep: k step size to use for FFT [0.05]
k_std: optional k array for standard chi(k).
chi_std: optional chi array for standard chi(k).
nclamp: number of energy end-points for clamp [2]
clamp_lo: weight of low-energy clamp [1]
clamp_hi: weight of high-energy clamp [1]
calc_uncertaintites: Flag to calculate uncertainties in
mu_0(E) and chi(k) [True]
err_sigma: sigma level for uncertainties in mu_0(E) and chi(k) [1]
Output arrays are written to the provided group.
Follows the 'First Argument Group' convention.
"""
msg = sys.stdout
if _larch is not None:
msg = _larch.writer.write
if 'kw' in kws:
kweight = kws.pop('kw')
if len(kws) > 0:
msg('Unrecognized a:rguments for autobk():\n')
msg(' %s\n' % (', '.join(kws.keys())))
return
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='autobk')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
energy = remove_dups(energy)
# if e0 or edge_step are not specified, get them, either from the
# passed-in group or from running pre_edge()
group = set_xafsGroup(group, _larch=_larch)
if edge_step is None and isgroup(group, 'edge_step'):
edge_step = group.edge_step
if e0 is None and isgroup(group, 'e0'):
e0 = group.e0
if e0 is None or edge_step is None:
# need to run pre_edge:
pre_kws = dict(nnorm=3, nvict=0, pre1=None,
pre2=-50., norm1=100., norm2=None)
if pre_edge_kws is not None:
pre_kws.update(pre_edge_kws)
pre_edge(energy, mu, group=group, _larch=_larch, **pre_kws)
if e0 is None:
e0 = group.e0
if edge_step is None:
edge_step = group.edge_step
if e0 is None or edge_step is None:
msg('autobk() could not determine e0 or edge_step!: trying running pre_edge first\n')
return
# get array indices for rkbg and e0: irbkg, ie0
ie0 = index_of(energy, e0)
rgrid = np.pi/(kstep*nfft)
if rbkg < 2*rgrid: rbkg = 2*rgrid
irbkg = int(1.01 + rbkg/rgrid)
# save ungridded k (kraw) and grided k (kout)
# and ftwin (*k-weighting) for FT in residual
enpe = energy[ie0:] - e0
kraw = np.sign(enpe)*np.sqrt(ETOK*abs(enpe))
if kmax is None:
kmax = max(kraw)
else:
kmax = max(0, min(max(kraw), kmax))
kout = kstep * np.arange(int(1.01+kmax/kstep), dtype='float64')
iemax = min(len(energy), 2+index_of(energy, e0+kmax*kmax/ETOK)) - 1
# interpolate provided chi(k) onto the kout grid
if chi_std is not None and k_std is not None:
chi_std = np.interp(kout, k_std, chi_std)
# pre-load FT window
ftwin = kout**kweight * ftwindow(kout, xmin=kmin, xmax=kmax,
window=win, dx=dk, dx2=dk)
# calc k-value and initial guess for y-values of spline params
nspl = max(5, min(64, int(2*rbkg*(kmax-kmin)/np.pi) + 2))
spl_y, spl_k, spl_e = np.zeros(nspl), np.zeros(nspl), np.zeros(nspl)
for i in range(nspl):
q = kmin + i*(kmax-kmin)/(nspl - 1)
ik = index_nearest(kraw, q)
i1 = min(len(kraw)-1, ik + 5)
i2 = max(0, ik - 5)
spl_k[i] = kraw[ik]
spl_e[i] = energy[ik+ie0]
spl_y[i] = (2*mu[ik+ie0] + mu[i1+ie0] + mu[i2+ie0] ) / 4.0
# get spline represention: knots, coefs, order=3
# coefs will be varied in fit.
knots, coefs, order = splrep(spl_k, spl_y)
# set fit parameters from initial coefficients
params = Parameters()
for i in range(len(coefs)):
params.add(name = FMT_COEF % i, value=coefs[i], vary=i<len(spl_y))
initbkg, initchi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
knots, coefs, order, kout)
# do fit
result = minimize(__resid, params, method='leastsq',
gtol=1.e-5, ftol=1.e-5, xtol=1.e-5, epsfcn=1.e-5,
kws = dict(ncoefs=len(coefs), chi_std=chi_std,
knots=knots, order=order,
kraw=kraw[:iemax-ie0+1],
mu=mu[ie0:iemax+1], irbkg=irbkg, kout=kout,
ftwin=ftwin, kweight=kweight,
nfft=nfft, nclamp=nclamp,
clamp_lo=clamp_lo, clamp_hi=clamp_hi))
# write final results
coefs = [result.params[FMT_COEF % i].value for i in range(len(coefs))]
bkg, chi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
knots, coefs, order, kout)
obkg = np.copy(mu)
obkg[ie0:ie0+len(bkg)] = bkg
# outputs to group
group = set_xafsGroup(group, _larch=_larch)
group.bkg = obkg
group.chie = (mu-obkg)/edge_step
group.k = kout
group.chi = chi/edge_step
group.e0 = e0
# now fill in 'autobk_details' group
details = Group(params=result.params)
details.init_bkg = np.copy(mu)
details.init_bkg[ie0:ie0+len(bkg)] = initbkg
details.init_chi = initchi/edge_step
details.knots_e = spl_e
details.knots_y = np.array([coefs[i] for i in range(nspl)])
details.init_knots_y = spl_y
details.nfev = result.nfev
details.kmin = kmin
details.kmax = kmax
group.autobk_details = details
# uncertainties in mu0 and chi: can be fairly slow.
if calc_uncertainties:
nchi = len(chi)
nmue = iemax-ie0 + 1
redchi = result.redchi
covar = result.covar / redchi
jac_chi = np.zeros(nchi*nspl).reshape((nspl, nchi))
jac_bkg = np.zeros(nmue*nspl).reshape((nspl, nmue))
cvals, cerrs = [], []
for i in range(len(coefs)):
par = result.params[FMT_COEF % i]
cvals.append(getattr(par, 'value', 0.0))
cdel = getattr(par, 'stderr', 0.0)
if cdel is None:
cdel = 0.0
cerrs.append(cdel/2.0)
cvals = np.array(cvals)
cerrs = np.array(cerrs)
# find derivatives by hand!
_k = kraw[:nmue]
_m = mu[ie0:iemax+1]
for i in range(nspl):
cval0 = cvals[i]
cvals[i] = cval0 + cerrs[i]
bkg1, chi1 = spline_eval(_k, _m, knots, cvals, order, kout)
cvals[i] = cval0 - cerrs[i]
bkg2, chi2 = spline_eval(_k, _m, knots, cvals, order, kout)
cvals[i] = cval0
jac_chi[i] = (chi1 - chi2) / (2*cerrs[i])
jac_bkg[i] = (bkg1 - bkg2) / (2*cerrs[i])
dfchi = np.zeros(nchi)
dfbkg = np.zeros(nmue)
for i in range(nspl):
for j in range(nspl):
dfchi += jac_chi[i]*jac_chi[j]*covar[i,j]
dfbkg += jac_bkg[i]*jac_bkg[j]*covar[i,j]
prob = 0.5*(1.0 + erf(err_sigma/np.sqrt(2.0)))
dchi = t.ppf(prob, nchi-nspl) * np.sqrt(dfchi*redchi)
dbkg = t.ppf(prob, nmue-nspl) * np.sqrt(dfbkg*redchi)
group.delta_chi = dchi
group.delta_bkg = 0.0*mu
group.delta_bkg[ie0:ie0+len(dbkg)] = dbkg
def xftr(r,
chir=None,
group=None,
rmin=0,
rmax=20,
with_phase=False,
dr=1,
dr2=None,
rw=0,
window='kaiser',
qmax_out=None,
nfft=2048,
kstep=0.05,
_larch=None,
**kws):
"""
reverse XAFS Fourier transform, from chi(R) to chi(q).
calculate reverse XAFS Fourier transform
This assumes that chir_re and (optional chir_im are
on a uniform r-grid given by r.
Parameters:
------------
r: 1-d array of distance, or group.
chir: 1-d array of chi(R)
group: output Group
qmax_out: highest *k* for output data (30 Ang^-1)
rweight: exponent for weighting spectra by r^rweight (0)
rmin: starting *R* for FT Window
rmax: ending *R* for FT Window
dr: tapering parameter for FT Window
dr2: second tapering parameter for FT Window
window: name of window type
nfft: value to use for N_fft (2048).
kstep: value to use for delta_k (0.05).
with_phase: output the phase as well as magnitude, real, imag [False]
Returns:
---------
None -- outputs are written to supplied group.
Notes:
-------
Arrays written to output group:
rwin window Omega(R) (length of input chi(R)).
q uniform array of k, out to qmax_out.
chiq complex array of chi(k).
chiq_mag magnitude of chi(k).
chiq_re real part of chi(k).
chiq_im imaginary part of chi(k).
chiq_pha phase of chi(k) if with_phase=True
(a noticable performance hit)
Supports First Argument Group convention (with group member names 'r' and 'chir')
"""
if 'rweight' in kws:
rw = kws['rweight']
r, chir, group = parse_group_args(r,
members=('r', 'chir'),
defaults=(chir, ),
group=group,
fcn_name='xftr')
rstep = r[1] - r[0]
kstep = pi / (rstep * nfft)
scale = 1.0
cchir = zeros(nfft, dtype='complex128')
r_ = rstep * arange(nfft, dtype='float64')
cchir[0:len(chir)] = chir
if chir.dtype == np.dtype('complex128'):
scale = 0.5
win = ftwindow(r_, xmin=rmin, xmax=rmax, dx=dr, dx2=dr2, window=window)
out = scale * xftr_fast(cchir * win * r_**rw, kstep=kstep, nfft=nfft)
if qmax_out is None: qmax_out = 30.0
q = linspace(0, qmax_out, int(1.05 + qmax_out / kstep))
nkpts = len(q)
group = set_xafsGroup(group, _larch=_larch)
group.q = q
mag = sqrt(out.real**2 + out.imag**2)
group.rwin = win[:len(chir)]
group.chiq = out[:nkpts]
group.chiq_mag = mag[:nkpts]
group.chiq_re = out.real[:nkpts]
group.chiq_im = out.imag[:nkpts]
if with_phase:
group.chiq_pha = complex_phase(out[:nkpts])
def pre_edge(energy, mu=None, group=None, e0=None, step=None,
nnorm=3, nvict=0, pre1=None, pre2=-50,
norm1=100, norm2=None, make_flat=True, _larch=None):
"""pre edge subtraction, normalization for XAFS
This performs a number of steps:
1. determine E0 (if not supplied) from max of deriv(mu)
2. fit a line of polymonial to the region below the edge
3. fit a polymonial to the region above the edge
4. extrapolae the two curves to E0 to determine the edge jump
Arguments
----------
energy: array of x-ray energies, in eV, or group (see note)
mu: array of mu(E)
group: output group
e0: edge energy, in eV. If None, it will be determined here.
step: edge jump. If None, it will be determined here.
pre1: low E range (relative to E0) for pre-edge fit
pre2: high E range (relative to E0) for pre-edge fit
nvict: energy exponent to use for pre-edg fit. See Note
norm1: low E range (relative to E0) for post-edge fit
norm2: high E range (relative to E0) for post-edge fit
nnorm: degree of polynomial (ie, nnorm+1 coefficients will be found) for
post-edge normalization curve. Default=3 (quadratic), max=5
make_flat: boolean (Default True) to calculate flattened output.
Returns
-------
None
The following attributes will be written to the output group:
e0 energy origin
edge_step edge step
norm normalized mu(E)
flat flattened, normalized mu(E)
pre_edge determined pre-edge curve
post_edge determined post-edge, normalization curve
dmude derivative of mu(E)
(if the output group is None, _sys.xafsGroup will be written to)
Notes
-----
1 nvict gives an exponent to the energy term for the fits to the pre-edge
and the post-edge region. For the pre-edge, a line (m * energy + b) is
fit to mu(energy)*energy**nvict over the pre-edge region,
energy=[e0+pre1, e0+pre2]. For the post-edge, a polynomial of order
nnorm will be fit to mu(energy)*energy**nvict of the post-edge region
energy=[e0+norm1, e0+norm2].
2 If the first argument is a Group, it must contain 'energy' and 'mu'.
If it exists, group.e0 will be used as e0.
See First Argrument Group in Documentation
"""
energy, mu, group = parse_group_args(energy, members=('energy', 'mu'),
defaults=(mu,), group=group,
fcn_name='pre_edge')
pre_dat = preedge(energy, mu, e0=e0, step=step, nnorm=nnorm,
nvict=nvict, pre1=pre1, pre2=pre2, norm1=norm1,
norm2=norm2)
group = set_xafsGroup(group, _larch=_larch)
e0 = pre_dat['e0']
norm = pre_dat['norm']
norm1 = pre_dat['norm1']
norm2 = pre_dat['norm2']
# generate flattened spectra, by fitting a quadratic to .norm
# and removing that.
flat = norm
ie0 = index_nearest(energy, e0)
p1 = index_of(energy, norm1+e0)
p2 = index_nearest(energy, norm2+e0)
if p2-p1 < 2:
p2 = min(len(energy), p1 + 2)
if make_flat and p2-p1 > 4:
enx, mux = remove_nans2(energy[p1:p2], norm[p1:p2])
# enx, mux = (energy[p1:p2], norm[p1:p2])
fpars = Group(c0 = Parameter(0, vary=True),
c1 = Parameter(0, vary=True),
c2 = Parameter(0, vary=True),
en=enx, mu=mux)
fit = Minimizer(flat_resid, fpars, _larch=_larch, toler=1.e-5)
try:
fit.leastsq()
except (TypeError, ValueError):
pass
fc0, fc1, fc2 = fpars.c0.value, fpars.c1.value, fpars.c2.value
flat_diff = fc0 + energy * (fc1 + energy * fc2)
flat = norm - flat_diff + flat_diff[ie0]
flat[:ie0] = norm[:ie0]
group.e0 = e0
group.norm = norm
group.flat = flat
group.dmude = np.gradient(mu)/np.gradient(energy)
group.edge_step = pre_dat['edge_step']
group.pre_edge = pre_dat['pre_edge']
group.post_edge = pre_dat['post_edge']
group.pre_edge_details = Group()
group.pre_edge_details.pre1 = pre_dat['pre1']
group.pre_edge_details.pre2 = pre_dat['pre2']
group.pre_edge_details.norm1 = pre_dat['norm1']
group.pre_edge_details.norm2 = pre_dat['norm2']
group.pre_edge_details.pre_slope = pre_dat['precoefs'][0]
group.pre_edge_details.pre_offset = pre_dat['precoefs'][1]
for i in range(MAX_NNORM):
if hasattr(group, 'norm_c%i' % i):
delattr(group, 'norm_c%i' % i)
for i, c in enumerate(pre_dat['norm_coefs']):
setattr(group.pre_edge_details, 'norm_c%i' % i, c)
return
def estimate_noise(k,
chi=None,
group=None,
rmin=15.0,
rmax=30.0,
kweight=1,
kmin=0,
kmax=20,
dk=4,
dk2=None,
kstep=0.05,
kwindow='kaiser',
nfft=2048,
_larch=None,
**kws):
"""
estimate noise levels in EXAFS spectrum and estimate highest k
where data is above the noise level
Parameters:
-----------
k: 1-d array of photo-electron wavenumber in Ang^-1 (or group)
chi: 1-d array of chi
group: output Group [see Note below]
rmin: minimum R value for high-R region of chi(R)
rmax: maximum R value for high-R region of chi(R)
kweight: exponent for weighting spectra by k**kweight [1]
kmin: starting k for FT Window [0]
kmax: ending k for FT Window [20]
dk: tapering parameter for FT Window [4]
dk2: second tapering parameter for FT Window [None]
kstep: value to use for delta_k ( Ang^-1) [0.05]
window: name of window type ['kaiser']
nfft: value to use for N_fft [2048].
Returns:
---------
None -- outputs are written to supplied group. Values (scalars) written
to output group:
epsilon_k estimated noise in chi(k)
epsilon_r estimated noise in chi(R)
kmax_suggest highest estimated k value where |chi(k)| > epsilon_k
Notes:
-------
1. This method uses the high-R portion of chi(R) as a measure of the noise
level in the chi(R) data and uses Parseval's theorem to convert this noise
level to that in chi(k). This method implicitly assumes that there is no
signal in the high-R portion of the spectrum, and that the noise in the
spectrum s "white" (independent of R) . Each of these assumptions can be
questioned.
2. The estimate for 'kmax_suggest' has a tendency to be fair but pessimistic
in how far out the chi(k) data goes before being dominated by noise.
3. Follows the 'First Argument Group' convention, so that you can either
specifiy all of (an array for 'k', an array for 'chi', option output Group)
OR pass a group with 'k' and 'chi' as the first argument
"""
k, chi, group = parse_group_args(k,
members=('k', 'chi'),
defaults=(chi, ),
group=group,
fcn_name='esitmate_noise')
# save _sys.xafsGroup -- we want to NOT write to it here!
savgroup = set_xafsGroup(None, _larch=_larch)
tmpgroup = Group()
rmax_out = min(10 * pi, rmax + 2)
xftf(k,
chi,
kmin=kmin,
kmax=kmax,
rmax_out=rmax_out,
kweight=kweight,
dk=dk,
dk2=dk2,
kwindow=kwindow,
nfft=nfft,
kstep=kstep,
group=tmpgroup,
_larch=_larch)
chir = tmpgroup.chir
rstep = tmpgroup.r[1] - tmpgroup.r[0]
irmin = int(0.01 + rmin / rstep)
irmax = min(nfft / 2, int(1.01 + rmax / rstep))
highr = realimag(chir[irmin:irmax])
# get average of window function value, scale eps_r scale by this
# this is imperfect, but improves the result.
kwin_ave = tmpgroup.kwin.sum() * kstep / (kmax - kmin)
eps_r = sqrt((highr * highr).sum() / len(highr)) / kwin_ave
# use Parseval's theorem to convert epsilon_r to epsilon_k,
# compensating for kweight
w = 2 * kweight + 1
scale = sqrt((2 * pi * w) / (kstep * (kmax**w - kmin**w)))
eps_k = scale * eps_r
# do reverse FT to get chiq array
xftr(tmpgroup.r,
tmpgroup.chir,
group=tmpgroup,
rmin=0.5,
rmax=9.5,
dr=1.0,
window='parzen',
nfft=nfft,
kstep=kstep,
_larch=_larch)
# sets kmax_suggest to the largest k value for which
# | chi(q) / k**kweight| > epsilon_k
iq0 = index_of(tmpgroup.q, (kmax + kmin) / 2.0)
tst = tmpgroup.chiq_mag[iq0:] / (tmpgroup.q[iq0:])**kweight
kmax_suggest = tmpgroup.q[iq0 + where(tst < eps_k)[0][0]]
# restore original _sys.xafsGroup, set output variables
_larch.symtable._sys.xafsGroup = savgroup
group = set_xafsGroup(group, _larch=_larch)
group.epsilon_k = eps_k
group.epsilon_r = eps_r
group.kmax_suggest = kmax_suggest
def pre_edge_baseline(energy, norm=None, group=None, form='lorentzian',
emin=None, emax=None, elo=None, ehi=None,
with_line=True, _larch=None):
"""remove baseline from main edge over pre edge peak region
This assumes that pre_edge() has been run successfully on the spectra
and that the spectra has decent pre-edge subtraction and normalization.
Arguments
----------
energy: array of x-ray energies, in eV, or group (see note 1)
norm: array of normalized mu(E)
group: output group
elo: low energy of pre-edge peak region to not fit baseline [e0-20]
ehi: high energy of pre-edge peak region ot not fit baseline [e0-10]
emax: max energy (eV) to use for baesline fit [e0-5]
emin: min energy (eV) to use for baesline fit [e0-40]
form: form used for baseline (see note 2) ['lorentzian']
with_line: whether to include linear component in baseline ['True']
Returns
-------
None
A group named 'prepeaks' will be created in the output group, with the following
attributes:
energy energy array for pre-edge peaks = energy[emin:emax]
baseline fitted baseline array over pre-edge peak energies
mu spectrum over pre-edge peak energies
peaks baseline-subtraced spectrum over pre-edge peak energies
dmu estimated uncertainty in peaks from fit
centroid estimated centroid of pre-edge peaks (see note 3)
peak_energies list of predicted peak energies (see note 4)
fit_details details of fit to extract pre-edge peaks.
(if the output group is None, _sys.xafsGroup will be written to)
Notes
-----
1 If the first argument is a Group, it must contain 'energy' and 'norm'.
See First Argrument Group in Documentation
2 A function will be fit to the input mu(E) data over the range between
[emin:elo] and [ehi:emax], ignorng the pre-edge peaks in the
region [elo:ehi]. The baseline function is specified with the `form`
keyword argument, which can be one of
'lorentzian', 'gaussian', or 'voigt',
with 'lorentzian' the default. In addition, the `with_line` keyword
argument can be used to add a line to this baseline function.
3 The value calculated for `prepeaks.centroid` will be found as
(prepeaks.energy*prepeaks.peaks).sum() / prepeaks.peaks.sum()
4 The values in the `peak_energies` list will be predicted energies
of the peaks in `prepeaks.peaks` as found by peakutils.
"""
energy, norm, group = parse_group_args(energy, members=('energy', 'norm'),
defaults=(norm,), group=group,
fcn_name='pre_edge_baseline')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(norm.shape) > 1:
norm = norm.squeeze()
dat_emin, dat_emax = min(energy), max(energy)
dat_e0 = getattr(group, 'e0', -1)
if dat_e0 > 0:
if emin is None:
emin = dat_e0 - 30.0
if emax is None:
emax = dat_e0 - 1.0
if elo is None:
elo = dat_e0 - 15.0
if ehi is None:
ehi = dat_e0 - 5.0
if emin < 0:
emin += dat_e0
if elo < 0:
elo += dat_e0
if emax < dat_emin:
emax += dat_e0
if ehi < dat_emin:
ehi += dat_e0
if emax is None or emin is None or elo is None or ehi is None:
raise ValueError("must provide emin and emax to pre_edge_baseline")
# get indices for input energies
if emin > emax:
emin, emax = emax, emin
if emin > elo:
elo, emin = emin, elo
if ehi > emax:
ehi, emax = emax, ehi
imin = index_of(energy, emin)
ilo = index_of(energy, elo)
ihi = index_of(energy, ehi)
imax = index_of(energy, emax)
# build xdat, ydat: dat to fit (skipping pre-edge peaks)
xdat = np.concatenate((energy[imin:ilo+1], energy[ihi:imax+1]))
ydat = np.concatenate((norm[imin:ilo+1], norm[ihi:imax+1]))
# build fitting model: note that we always include
# a LinearModel but may fix slope and intercept
form = form.lower()
if form.startswith('voig'):
model = VoigtModel()
elif form.startswith('gaus'):
model = GaussianModel()
else:
model = LorentzianModel()
model += LinearModel()
params = model.make_params(amplitude=1.0, sigma=2.0,
center=emax,
intercept=0, slope=0)
params['amplitude'].min = 0.0
params['sigma'].min = 0.25
params['sigma'].max = 50.0
params['center'].max = emax + 25.0
params['center'].min = emax - 25.0
if not with_line:
params['slope'].vary = False
params['intercept'].vary = False
# run fit
result = model.fit(ydat, params, x=xdat)
# energy including pre-edge peaks, for output
edat = energy[imin: imax+1]
# get baseline and resulting mu over edat range
bline = result.eval(result.params, x=edat)
mu = norm[imin:imax+1]
peaks = mu-bline
# estimate centroid
cen = (edat*peaks).sum() / peaks.sum()
# uncertainty in mu includes only uncertainties in baseline fit
# and uncertainty in centroid:
try:
dpeaks = result.eval_uncertainty(result.params, x=edat)
except:
dbpeaks = 0.0
cen_plus = (edat*(peaks+dpeaks)).sum()/ (peaks+dpeaks).sum()
cen_minus = (edat*(peaks-dpeaks)).sum()/ (peaks-dpeaks).sum()
dcen = abs(cen_minus - cen_plus) / 2.0
# locate peak positions
peak_energies = []
if HAS_PEAKUTILS:
peak_ids = peakutils.peak.indexes(peaks, thres=0.05, min_dist=2)
peak_energies = [edat[pid] for pid in peak_ids]
group = set_xafsGroup(group, _larch=_larch)
group.prepeaks = Group(energy=edat, mu=mu, baseline=bline,
peaks=peaks, delta_peaks=dpeaks,
centroid=cen, delta_centroid=dcen,
peak_energies=peak_energies,
fit_details=result,
emin=emin, emax=emax, elo=elo, ehi=ehi,
form=form, with_line=with_line)
return
def xas_convolve(energy,
norm=None,
group=None,
form='lorentzian',
esigma=1.0,
eshift=0.0,
_larch=None):
"""
convolve a normalized mu(E) spectra with a Lorentzian or Gaussian peak
shape, degrading separation of XANES features.
This is provided as a complement to xas_deconvolve, and to deliberately
broaden spectra to compare with spectra measured at lower resolution.
Arguments
----------
energy: array of x-ray energies (in eV) or XAFS data group
norm: array of normalized mu(E)
group: output group
form: form of deconvolution function. One of
'lorentzian' or 'gaussian' ['lorentzian']
esigma energy sigma (in eV) to pass to gaussian() or lorentzian() [1.0]
eshift energy shift (in eV) to apply to result [0]
Returns
-------
None
The array 'conv' will be written to the output group.
Notes
-----
Follows the First Argument Group convention, using group members named
'energy' and 'norm'
"""
if _larch is None:
raise Warning("cannot xas_convolve -- larch broken?")
energy, mu, group = parse_group_args(energy,
members=('energy', 'norm'),
defaults=(norm, ),
group=group,
fcn_name='xas_convolve')
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))
npad = 1 + int(max(estep * 2.01, 50 * esigma) / estep)
npts = npad + int(max(en) / estep)
x = np.arange(npts) * estep
y = interp(en, mu, x, kind='cubic', _larch=_larch)
kernel = lorentzian
if form.lower().startswith('g'):
kernel = gaussian
k = kernel(x, center=0, sigma=esigma)
ret = np.convolve(y, k, mode='full')
out = interp(x - eshift, ret[:len(x)], en, kind='cubic', _larch=_larch)
group = set_xafsGroup(group, _larch=_larch)
group.conv = out / k.sum()
def xftf(k, chi=None, group=None, kmin=0, kmax=20, kweight=0,
dk=1, dk2=None, with_phase=False, window='kaiser', rmax_out=10,
nfft=2048, kstep=0.05, _larch=None, **kws):
"""
forward XAFS Fourier transform, from chi(k) to chi(R), using
common XAFS conventions.
Parameters:
-----------
k: 1-d array of photo-electron wavenumber in Ang^-1 or group
chi: 1-d array of chi
group: output Group
rmax_out: highest R for output data (10 Ang)
kweight: exponent for weighting spectra by k**kweight
kmin: starting k for FT Window
kmax: ending k for FT Window
dk: tapering parameter for FT Window
dk2: second tapering parameter for FT Window
window: name of window type
nfft: value to use for N_fft (2048).
kstep: value to use for delta_k (0.05 Ang^-1).
with_phase: output the phase as well as magnitude, real, imag [False]
Returns:
---------
None -- outputs are written to supplied group.
Notes:
-------
Arrays written to output group:
kwin window function Omega(k) (length of input chi(k)).
r uniform array of R, out to rmax_out.
chir complex array of chi(R).
chir_mag magnitude of chi(R).
chir_re real part of chi(R).
chir_im imaginary part of chi(R).
chir_pha phase of chi(R) if with_phase=True
(a noticable performance hit)
Supports First Argument Group convention (with group member names 'k' and 'chi')
"""
# allow kweight keyword == kw
if 'kw' in kws:
kweight = kws['kw']
k, chi, group = parse_group_args(k, members=('k', 'chi'),
defaults=(chi,), group=group,
fcn_name='xftf')
cchi, win = xftf_prep(k, chi, kmin=kmin, kmax=kmax, kweight=kweight,
dk=dk, dk2=dk2, nfft=nfft, kstep=kstep,
window=window, _larch=_larch)
out = xftf_fast(cchi*win, kstep=kstep, nfft=nfft)
rstep = pi/(kstep*nfft)
irmax = min(nfft/2, int(1.01 + rmax_out/rstep))
group = set_xafsGroup(group, _larch=_larch)
r = rstep * arange(irmax)
mag = sqrt(out.real**2 + out.imag**2)
group.kwin = win[:len(chi)]
group.r = r[:irmax]
group.chir = out[:irmax]
group.chir_mag = mag[:irmax]
group.chir_re = out.real[:irmax]
group.chir_im = out.imag[:irmax]
if with_phase:
group.chir_pha = complex_phase(out[:irmax])
def xas_deconvolve(energy,
norm=None,
group=None,
form='lorentzian',
esigma=1.0,
eshift=0.0,
smooth=True,
sgwindow=None,
sgorder=3,
_larch=None):
"""XAS spectral deconvolution
de-convolve a normalized mu(E) spectra with a peak shape, enhancing the
intensity and separation of peaks of a XANES spectrum.
The results can be unstable, and noisy, and should be used
with caution!
Arguments
----------
energy: array of x-ray energies (in eV) or XAFS data group
norm: array of normalized mu(E)
group: output group
form: functional form of deconvolution function. One of
'gaussian' or 'lorentzian' [default]
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]
smooth whether to smooth result with savitzky_golay method [True]
sgwindow window size for savitzky_golay [found from data step and esigma]
sgorder order for savitzky_golay [3]
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'
Smoothing with savitzky_golay() requires a window and order. By
default, window = int(esigma / estep) where estep is step size for
the gridded data, approximately the finest energy step in the data.
"""
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_deconvolve')
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='cubic', _larch=_larch)
kernel = lorentzian
if form.lower().startswith('g'):
kernel = gaussian
yext = np.concatenate((y, np.arange(len(y)) * y[-1]))
ret, err = deconvolve(yext, kernel(x, center=0, sigma=esigma))
nret = min(len(x), len(ret))
ret = ret[:nret] * yext[nret - 1] / ret[nret - 1]
if smooth:
if sgwindow is None:
sgwindow = int(1.0 * esigma / estep)
sqwindow = int(sgwindow)
if sgwindow < (sgorder + 1):
sgwindow = sgorder + 2
if sgwindow % 2 == 0:
sgwindow += 1
ret = savitzky_golay(ret, sgwindow, sgorder)
out = interp(x + eshift, ret, en, kind='cubic', _larch=_larch)
group = set_xafsGroup(group, _larch=_larch)
group.deconv = out
def pre_edge(energy,
mu=None,
group=None,
e0=None,
step=None,
nnorm=3,
nvict=0,
pre1=None,
pre2=-50,
norm1=100,
norm2=None,
make_flat=True,
_larch=None):
"""pre edge subtraction, normalization for XAFS
This performs a number of steps:
1. determine E0 (if not supplied) from max of deriv(mu)
2. fit a line of polymonial to the region below the edge
3. fit a polymonial to the region above the edge
4. extrapolae the two curves to E0 to determine the edge jump
Arguments
----------
energy: array of x-ray energies, in eV, or group (see note)
mu: array of mu(E)
group: output group
e0: edge energy, in eV. If None, it will be determined here.
step: edge jump. If None, it will be determined here.
pre1: low E range (relative to E0) for pre-edge fit
pre2: high E range (relative to E0) for pre-edge fit
nvict: energy exponent to use for pre-edg fit. See Note
norm1: low E range (relative to E0) for post-edge fit
norm2: high E range (relative to E0) for post-edge fit
nnorm: degree of polynomial (ie, nnorm+1 coefficients will be found) for
post-edge normalization curve. Default=3 (quadratic), max=5
make_flat: boolean (Default True) to calculate flattened output.
Returns
-------
None
The following attributes will be written to the output group:
e0 energy origin
edge_step edge step
norm normalized mu(E)
flat flattened, normalized mu(E)
pre_edge determined pre-edge curve
post_edge determined post-edge, normalization curve
dmude derivative of mu(E)
(if the output group is None, _sys.xafsGroup will be written to)
Notes
-----
1 nvict gives an exponent to the energy term for the fits to the pre-edge
and the post-edge region. For the pre-edge, a line (m * energy + b) is
fit to mu(energy)*energy**nvict over the pre-edge region,
energy=[e0+pre1, e0+pre2]. For the post-edge, a polynomial of order
nnorm will be fit to mu(energy)*energy**nvict of the post-edge region
energy=[e0+norm1, e0+norm2].
2 If the first argument is a Group, it must contain 'energy' and 'mu'.
If it exists, group.e0 will be used as e0.
See First Argrument Group in Documentation
"""
energy, mu, group = parse_group_args(energy,
members=('energy', 'mu'),
defaults=(mu, ),
group=group,
fcn_name='pre_edge')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
pre_dat = preedge(energy,
mu,
e0=e0,
step=step,
nnorm=nnorm,
nvict=nvict,
pre1=pre1,
pre2=pre2,
norm1=norm1,
norm2=norm2)
group = set_xafsGroup(group, _larch=_larch)
e0 = pre_dat['e0']
norm = pre_dat['norm']
norm1 = pre_dat['norm1']
norm2 = pre_dat['norm2']
# generate flattened spectra, by fitting a quadratic to .norm
# and removing that.
flat = norm
ie0 = index_nearest(energy, e0)
p1 = index_of(energy, norm1 + e0)
p2 = index_nearest(energy, norm2 + e0)
if p2 - p1 < 2:
p2 = min(len(energy), p1 + 2)
if make_flat and p2 - p1 > 4:
enx, mux = remove_nans2(energy[p1:p2], norm[p1:p2])
# enx, mux = (energy[p1:p2], norm[p1:p2])
fpars = Parameters()
fpars.add('c0', value=0, vary=True)
fpars.add('c1', value=0, vary=True)
fpars.add('c2', value=0, vary=True)
fit = Minimizer(flat_resid, fpars, fcn_args=(enx, mux))
result = fit.leastsq(xtol=1.e-6, ftol=1.e-6)
fc0 = result.params['c0'].value
fc1 = result.params['c1'].value
fc2 = result.params['c2'].value
flat_diff = fc0 + energy * (fc1 + energy * fc2)
flat = norm - flat_diff + flat_diff[ie0]
flat[:ie0] = norm[:ie0]
group.e0 = e0
group.norm = norm
group.flat = flat
group.dmude = np.gradient(mu) / np.gradient(energy)
group.edge_step = pre_dat['edge_step']
group.pre_edge = pre_dat['pre_edge']
group.post_edge = pre_dat['post_edge']
group.pre_edge_details = Group()
group.pre_edge_details.pre1 = pre_dat['pre1']
group.pre_edge_details.pre2 = pre_dat['pre2']
group.pre_edge_details.nnorm = pre_dat['nnorm']
group.pre_edge_details.norm1 = pre_dat['norm1']
group.pre_edge_details.norm2 = pre_dat['norm2']
group.pre_edge_details.pre1_input = pre_dat['pre1_input']
group.pre_edge_details.norm2_input = pre_dat['norm2_input']
group.pre_edge_details.pre_slope = pre_dat['precoefs'][0]
group.pre_edge_details.pre_offset = pre_dat['precoefs'][1]
for i in range(MAX_NNORM):
if hasattr(group, 'norm_c%i' % i):
delattr(group, 'norm_c%i' % i)
for i, c in enumerate(pre_dat['norm_coefs']):
setattr(group.pre_edge_details, 'norm_c%i' % i, c)
return
def mback(energy,
mu,
group=None,
order=3,
z=None,
edge='K',
e0=None,
emin=None,
emax=None,
whiteline=None,
leexiang=False,
tables='chantler',
fit_erfc=False,
return_f1=False,
_larch=None):
"""
Match mu(E) data for tabulated f''(E) using the MBACK algorithm and,
optionally, the Lee & Xiang extension
Arguments:
energy, mu: arrays of energy and mu(E)
order: order of polynomial [3]
group: output group (and input group for e0)
z: Z number of absorber
edge: absorption edge (K, L3)
e0: edge energy
emin: beginning energy for fit
emax: ending energy for fit
whiteline: exclusion zone around white lines
leexiang: flag to use the Lee & Xiang extension
tables: 'chantler' (default) or 'cl'
fit_erfc: True to float parameters of error function
return_f1: True to put the f1 array in the group
Returns:
group.f2: tabulated f2(E)
group.f1: tabulated f1(E) (if return_f1 is True)
group.fpp: matched data
group.mback_params: Group of parameters for the minimization
References:
* 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
* Cromer-Liberman: http://dx.doi.org/10.1063/1.1674266
* Chantler: http://dx.doi.org/10.1063/1.555974
"""
order = int(order)
if order < 1: order = 1 # set order of polynomial
if order > MAXORDER: order = MAXORDER
### implement the First Argument Group convention
energy, mu, group = parse_group_args(energy,
members=('energy', 'mu'),
defaults=(mu, ),
group=group,
fcn_name='mback')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(mu.shape) > 1:
mu = mu.squeeze()
group = set_xafsGroup(group, _larch=_larch)
if e0 is None: # need to run find_e0:
e0 = xray_edge(z, edge, _larch=_larch)[0]
if e0 is None:
e0 = group.e0
if e0 is None:
find_e0(energy, mu, group=group)
### theta is an array used to exclude the regions <emin, >emax, and
### around white lines, theta=0.0 in excluded regions, theta=1.0 elsewhere
(i1, i2) = (0, len(energy) - 1)
if emin is not None: i1 = index_of(energy, emin)
if emax is not None: i2 = index_of(energy, emax)
theta = np.ones(len(energy)) # default: 1 throughout
theta[0:i1] = 0
theta[i2:-1] = 0
if whiteline:
pre = 1.0 * (energy < e0)
post = 1.0 * (energy > e0 + float(whiteline))
theta = theta * (pre + post)
if edge.lower().startswith('l'):
l2 = xray_edge(z, 'L2', _larch=_larch)[0]
l2_pre = 1.0 * (energy < l2)
l2_post = 1.0 * (energy > l2 + float(whiteline))
theta = theta * (l2_pre + l2_post)
## this is used to weight the pre- and post-edge differently as
## defined in the MBACK paper
weight1 = 1 * (energy < e0)
weight2 = 1 * (energy > e0)
weight = np.sqrt(sum(weight1)) * weight1 + np.sqrt(sum(weight2)) * weight2
## get the f'' function from CL or Chantler
if tables.lower() == 'chantler':
f1 = f1_chantler(z, energy, _larch=_larch)
f2 = f2_chantler(z, energy, _larch=_larch)
else:
(f1, f2) = f1f2(z, energy, edge=edge, _larch=_larch)
group.f2 = f2
if return_f1: group.f1 = f1
n = edge
if edge.lower().startswith('l'): n = 'L'
params = Group(
s=Parameter(1, vary=True, _larch=_larch), # scale of data
xi=Parameter(50, vary=fit_erfc, min=0, _larch=_larch), # width of erfc
em=Parameter(xray_line(z, n, _larch=_larch)[0],
vary=False,
_larch=_larch), # erfc centroid
e0=Parameter(e0, vary=False, _larch=_larch), # abs. edge energy
## various arrays need by the objective function
en=energy,
mu=mu,
f2=group.f2,
weight=weight,
theta=theta,
leexiang=leexiang,
_larch=_larch)
if fit_erfc:
params.a = Parameter(1, vary=True, _larch=_larch) # amplitude of erfc
else:
params.a = Parameter(0, vary=False, _larch=_larch) # amplitude of erfc
for i in range(order): # polynomial coefficients
setattr(params, 'c%d' % i, Parameter(0, vary=True, _larch=_larch))
fit = Minimizer(match_f2, params, _larch=_larch, toler=1.e-5)
fit.leastsq()
eoff = energy - params.e0.value
normalization_function = params.a.value * erfc(
(energy - params.em.value) / params.xi.value) + params.c0.value
for i in range(MAXORDER):
j = i + 1
attr = 'c%d' % j
if hasattr(params, attr):
normalization_function = normalization_function + getattr(
getattr(params, attr), 'value') * eoff**j
group.fpp = params.s * mu - normalization_function
group.mback_params = params
def pre_edge_baseline(energy,
norm=None,
group=None,
form='lorentzian',
emin=None,
emax=None,
elo=None,
ehi=None,
with_line=True,
_larch=None):
"""remove baseline from main edge over pre edge peak region
This assumes that pre_edge() has been run successfully on the spectra
and that the spectra has decent pre-edge subtraction and normalization.
Arguments
----------
energy: array of x-ray energies, in eV, or group (see note 1)
norm: array of normalized mu(E)
group: output group
elo: low energy of pre-edge peak region to not fit baseline [e0-20]
ehi: high energy of pre-edge peak region ot not fit baseline [e0-10]
emax max energy (eV) to use for baesline fit [e0-5]
emin: min energy (eV) to use for baesline fit [e0-40]
form: form used for baseline (see note 2) ['lorentzian']
with_line: whether to include linear component in baseline ['True']
Returns
-------
None
A group named 'prepeaks' will be created in the output group, with the following
attributes:
energy energy array for pre-edge peaks = energy[emin-eneg:emax+epos]
baseline fitted baseline array over pre-edge peak energies
mu baseline-subtraced spectrum over pre-edge peak energies
dmu estimated uncertainty in mu from fit
centroid estimated centroid of pre-edge peaks (see note 3)
peak_energies list of predicted peak energies (see note 4)
fit_details details of fit to extract pre-edge peaks.
(if the output group is None, _sys.xafsGroup will be written to)
Notes
-----
1 If the first argument is a Group, it must contain 'energy' and 'norm'.
See First Argrument Group in Documentation
2 A function will be fit to the input mu(E) data over the range between
[emin:elo] and [ehi:emax], ignorng the pre-edge peaks in the
region [elo:ehi]. The baseline function is specified with the `form`
keyword argument, which can be one of
'lorentzian', 'gaussian', or 'voigt',
with 'lorentzian' the default. In addition, the `with_line` keyword
argument can be used to add a line to this baseline function.
3 The value calculated for `prepeaks.centroid` will be found as
(prepeaks.energy*prepeaks.mu).sum() / prepeaks.mu.sum()
4 The values in the `peak_energies` list will be predicted energies
of the peaks in `prepeaks.mu` as found by peakutils.
"""
energy, norm, group = parse_group_args(energy,
members=('energy', 'norm'),
defaults=(norm, ),
group=group,
fcn_name='pre_edge_baseline')
if len(energy.shape) > 1:
energy = energy.squeeze()
if len(norm.shape) > 1:
norm = norm.squeeze()
dat_emin, dat_emax = min(energy), max(energy)
dat_e0 = getattr(group, 'e0', -1)
if dat_e0 > 0:
if emin is None:
emin = dat_e0 - 30.0
if emax is None:
emax = dat_e0 - 1.0
if elo is None:
elo = dat_e0 - 15.0
if ehi is None:
ehi = dat_e0 - 5.0
if emin < 0:
emin += dat_e0
if elo < 0:
elo += dat_e0
if emax < dat_emin:
emax += dat_e0
if ehi < dat_emin:
ehi += dat_e0
if emax is None or emin is None or elo is None or ehi is None:
raise ValueError("must provide emin and emax to pre_edge_baseline")
# get indices for input energies
if emin > emax:
emin, emax = emax, emin
if emin > elo:
elo, emin = emin, elo
if ehi > emax:
ehi, emax = emax, ehi
imin = index_of(energy, emin)
ilo = index_of(energy, elo)
ihi = index_of(energy, ehi)
imax = index_of(energy, emax)
# build xdat, ydat: dat to fit (skipping pre-edge peaks)
xdat = np.concatenate((energy[imin:ilo + 1], energy[ihi:imax + 1]))
ydat = np.concatenate((norm[imin:ilo + 1], norm[ihi:imax + 1]))
# build fitting model: note that we always include
# a LinearModel but may fix slope and intercept
form = form.lower()
if form.startswith('voig'):
model = VoigtModel()
elif form.startswith('gaus'):
model = GaussianModel()
else:
model = LorentzianModel()
model += LinearModel()
params = model.make_params(amplitude=1.0,
sigma=2.0,
center=emax,
intercept=0,
slope=0)
params['amplitude'].min = 0.0
params['sigma'].min = 0.25
params['sigma'].max = 50.0
params['center'].max = emax + 25.0
params['center'].min = emax - 25.0
if not with_line:
params['slope'].vary = False
params['intercept'].vary = False
# run fit
result = model.fit(ydat, params, x=xdat)
# energy including pre-edge peaks, for output
edat = energy[imin:imax + 1]
# get baseline and resulting mu over edat range
bline = result.eval(result.params, x=edat)
mu = norm[imin:imax + 1] - bline
# uncertainty in mu includes only uncertainties in baseline fit
dmu = result.eval_uncertainty(result.params, x=edat)
# estimate centroid and its uncertainty
cen = (edat * mu).sum() / mu.sum()
cen_plus = (edat * (mu + dmu)).sum() / (mu + dmu).sum()
cen_minus = (edat * (mu - dmu)).sum() / (mu - dmu).sum()
dcen = abs(cen_minus - cen_plus) / 2.0
# locate peak positions
peak_energies = []
if HAS_PEAKUTILS:
peak_ids = peakutils.peak.indexes(mu, thres=0.05, min_dist=2)
peak_energies = [edat[pid] for pid in peak_ids]
group = set_xafsGroup(group, _larch=_larch)
group.prepeaks = Group(energy=edat,
mu=mu,
delta_mu=dmu,
baseline=bline,
centroid=cen,
delta_centroid=dcen,
peak_energies=peak_energies,
fit_details=result,
emin=emin,
emax=emax,
elo=elo,
ehi=ehi,
form=form,
with_line=with_line)
return
def cauchy_wavelet(k,
chi=None,
group=None,
kweight=0,
rmax_out=10,
nfft=2048,
_larch=None):
"""
Cauchy Wavelet Transform for XAFS, following work of Munoz, Argoul, and Farges
Parameters:
-----------
k: 1-d array of photo-electron wavenumber in Ang^-1 or group
chi: 1-d array of chi
group: output Group
rmax_out: highest R for output data (10 Ang)
kweight: exponent for weighting spectra by k**kweight
nfft: value to use for N_fft (2048).
Returns:
---------
None -- outputs are written to supplied group.
Notes:
-------
Arrays written to output group:
r uniform array of R, out to rmax_out.
wcauchy complex cauchy wavelet(k, R)
wcauchy_mag magnitude of wavelet(k, R)
wcauchy_re real part of wavelet(k, R)
wcauchy_im imaginary part of wavelet(k, R)
Supports First Argument Group convention (with group
member names 'k' and 'chi')
"""
k, chi, group = parse_group_args(k,
members=('k', 'chi'),
defaults=(chi, ),
group=group,
fcn_name='cauchy_wavelet')
kstep = np.round(1000. * (k[1] - k[0])) / 1000.0
rstep = (np.pi / 2048) / kstep
rmin = 1.e-7
rmax = rmax_out
nrpts = int(np.round((rmax - rmin) / rstep))
nkout = len(k)
if kweight != 0:
chi = chi * k**kweight
# extend EXAFS to 1024 data points...
NFT = nfft / 2
if len(k) < NFT:
knew = np.arange(NFT) * kstep
xnew = np.zeros(NFT) * kstep
xnew[:len(k)] = chi
else:
knew = k[:NFT]
xnew = chi[:NFT]
# FT parameters
freq = (1.0 / kstep) * np.arange(nfft) / (2 * nfft)
omega = 2 * np.pi * freq
# simple FT calculation
tff = np.fft.fft(xnew, n=2 * nfft)
# scale parameter
r = np.linspace(0, rmax, nrpts)
r[0] = 1.e-19
a = nrpts / (2 * r)
# Characteristic values for Cauchy wavelet:
cauchy_sum = np.log(2 * np.pi) - np.log(1.0 + np.arange(nrpts)).sum()
# Main calculation:
out = np.zeros(nkout * nrpts, dtype='complex128').reshape(nrpts, nkout)
for i in range(nrpts):
aom = a[i] * omega
aom[np.where(aom == 0)] = 1.e-19
filt = cauchy_sum + nrpts * np.log(aom) - aom
tmp = np.conj(np.exp(filt)) * tff[:nfft]
out[i, :] = np.fft.ifft(tmp, 2 * nfft)[:nkout]
group = set_xafsGroup(group, _larch=_larch)
group.r = r
group.wcauchy = out
group.wcauchy_mag = np.sqrt(out.real**2 + out.imag**2)
group.wcauchy_re = out.real
group.wcauchy_im = out.imag