def guess_starting_values(self, y, x):
"""could probably improve this"""
ymax, ymin = max(y), min(y)
yex = ymax
self.set_initval(self.params.amplitude, 5.*(ymax-ymin))
if self.negative:
yex = ymin
self.params.amplitude.value = -(ymax - ymin)*5.0
iyex = index_nearest(y, yex)
self.set_initval(self.params.center, x[iyex])
halfy = yex /2.0
ihalfy = index_of(y, halfy)
sig0 = abs(x[iyex] - x[ihalfy])
sig1 = 0.15*(max(x) - min(x))
if sig1 < sig0 : sig0 = sig1
self.set_initval(self.params.sigma, sig0)
bkg0 = ymin
if self.negative: bkg0 = ymax
self.set_init_bkg(bkg0)
def preedge(energy,
mu,
e0=None,
step=None,
nnorm=3,
nvict=0,
pre1=None,
pre2=-50,
norm1=100,
norm2=None):
"""pre edge subtraction, normalization for XAFS (straight python)
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
mu: array of mu(E)
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
Returns
-------
dictionary with elements (among others)
e0 energy origin in eV
edge_step edge step
norm normalized mu(E)
pre_edge determined pre-edge curve
post_edge determined post-edge, normalization curve
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].
"""
energy = remove_dups(energy)
if e0 is None or e0 < energy[0] or e0 > energy[-1]:
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]
nnorm = max(min(nnorm, MAX_NNORM), 0)
ie0 = index_nearest(energy, e0)
e0 = energy[ie0]
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)
if p2 - p1 < 2:
p2 = min(len(energy), p1 + 2)
omu = mu * energy**nvict
ex, mx = remove_nans2(energy[p1:p2], omu[p1:p2])
precoefs = polyfit(ex, mx, 1)
pre_edge = (precoefs[0] * energy + precoefs[1]) * energy**(-nvict)
# normalization
p1 = index_of(energy, norm1 + e0)
p2 = index_nearest(energy, norm2 + e0)
if p2 - p1 < 2:
p2 = min(len(energy), p1 + 2)
coefs = polyfit(energy[p1:p2], omu[p1:p2], nnorm)
post_edge = 0
norm_coefs = []
for n, c in enumerate(reversed(list(coefs))):
post_edge += c * energy**(n - nvict)
norm_coefs.append(c)
edge_step = step
if edge_step is None:
edge_step = post_edge[ie0] - pre_edge[ie0]
norm = (mu - pre_edge) / edge_step
out = {
'e0': e0,
'edge_step': edge_step,
'norm': norm,
'pre_edge': pre_edge,
'post_edge': post_edge,
'norm_coefs': norm_coefs,
'nvict': nvict,
'nnorm': nnorm,
'norm1': norm1,
'norm2': norm2,
'pre1': pre1,
'pre2': pre2,
'precoefs': precoefs
}
return 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 = 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.nnorm = pre_dat['nnorm']
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 preedge(energy, mu, e0=None, step=None,
nnorm=3, nvict=0, pre1=None, pre2=-50,
norm1=100, norm2=None):
"""pre edge subtraction, normalization for XAFS (straight python)
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
mu: array of mu(E)
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
Returns
-------
dictionary with elements (among others)
e0 energy origin in eV
edge_step edge step
norm normalized mu(E)
pre_edge determined pre-edge curve
post_edge determined post-edge, normalization curve
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].
"""
energy = remove_dups(energy)
if e0 is None or e0 < energy[0] or e0 > energy[-1]:
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]
nnorm = max(min(nnorm, MAX_NNORM), 1)
ie0 = index_nearest(energy, e0)
e0 = energy[ie0]
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)
if p2-p1 < 2:
p2 = min(len(energy), p1 + 2)
omu = mu*energy**nvict
ex, mx = remove_nans2(energy[p1:p2], omu[p1:p2])
precoefs = polyfit(ex, mx, 1)
pre_edge = (precoefs[0] * energy + precoefs[1]) * energy**(-nvict)
# normalization
p1 = index_of(energy, norm1+e0)
p2 = index_nearest(energy, norm2+e0)
if p2-p1 < 2:
p2 = min(len(energy), p1 + 2)
coefs = polyfit(energy[p1:p2], omu[p1:p2], nnorm)
post_edge = 0
norm_coefs = []
for n, c in enumerate(reversed(list(coefs))):
post_edge += c * energy**(n-nvict)
norm_coefs.append(c)
edge_step = step
if edge_step is None:
edge_step = post_edge[ie0] - pre_edge[ie0]
norm = (mu - pre_edge)/edge_step
out = {'e0': e0, 'edge_step': edge_step, 'norm': norm,
'pre_edge': pre_edge, 'post_edge': post_edge,
'norm_coefs': norm_coefs, 'nvict': nvict,
'nnorm': nnorm, 'norm1': norm1, 'norm2': norm2,
'pre1': pre1, 'pre2': pre2, 'precoefs': precoefs}
return 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')
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 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 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
# Fourier transform to R space, again passing in a Group (here,
# 'k' and 'chi' are expected, and writitng out 'r', 'chir_mag',
# and so on
xftf(xafsdat, kmin=2, kmax=15, dk=3, kweight=2, _larch=my_larch)
#
# plot grid of results:
# mu + bkg
pylab.subplot(2, 2, 1)
pylab.plot(xafsdat.energy, xafsdat.bkg, 'r--')
pylab.plot(xafsdat.energy, xafsdat.mu)
pylab.xlabel('Energy (eV)')
# normalized XANES
# find array bounds for normalized mu(E) for [e0 - 25: e0 + 75]
j0 = index_nearest(xafsdat.energy, xafsdat.e0 - 25.0)
j1 = index_nearest(xafsdat.energy, xafsdat.e0 + 75.0)
pylab.subplot(2, 2, 2)
pylab.plot(xafsdat.energy[j0:j1], xafsdat.norm[j0:j1])
pylab.xlabel('Energy (eV)')
# chi(k)
pylab.subplot(2, 2, 3)
pylab.plot(xafsdat.k, xafsdat.chi*xafsdat.k**2)
pylab.plot(xafsdat.k, xafsdat.kwin, 'r--')
pylab.xlabel(r'$ k (\AA^{-1}) $')
pylab.ylabel(r'$ k^2 \chi(\AA^{-2}) $')
# chi(R)
pylab.subplot(2, 2, 4)
pylab.plot(xafsdat.r, xafsdat.chir_mag)
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 = 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: can be fairly slow.
if calc_uncertainties:
nchi = len(chi)
nmue = iemax - ie0 + 1
redchi = params.chi_reduced
covar = params.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 = getattr(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
# Fourier transform to R space, again passing in a Group (here,
# 'k' and 'chi' are expected, and writitng out 'r', 'chir_mag',
# and so on
xftf(xafsdat, kmin=2, kmax=15, dk=3, kweight=2, _larch=my_larch)
#
# plot grid of results:
# mu + bkg
pylab.subplot(2, 2, 1)
pylab.plot(xafsdat.energy, xafsdat.bkg, 'r--')
pylab.plot(xafsdat.energy, xafsdat.mu)
pylab.xlabel('Energy (eV)')
# normalized XANES
# find array bounds for normalized mu(E) for [e0 - 25: e0 + 75]
j0 = index_nearest(xafsdat.energy, xafsdat.e0 - 25.0)
j1 = index_nearest(xafsdat.energy, xafsdat.e0 + 75.0)
pylab.subplot(2, 2, 2)
pylab.plot(xafsdat.energy[j0:j1], xafsdat.norm[j0:j1])
pylab.xlabel('Energy (eV)')
# chi(k)
pylab.subplot(2, 2, 3)
pylab.plot(xafsdat.k, xafsdat.chi * xafsdat.k**2)
pylab.plot(xafsdat.k, xafsdat.kwin, 'r--')
pylab.xlabel(r'$ k (\AA^{-1}) $')
pylab.ylabel(r'$ k^2 \chi(\AA^{-2}) $')
# chi(R)
pylab.subplot(2, 2, 4)
pylab.plot(xafsdat.r, xafsdat.chir_mag)
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 = 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: can be fairly slow.
if calc_uncertainties:
nchi = len(chi)
nmue = iemax-ie0 + 1
redchi = params.chi_reduced
covar = params.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 = getattr(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
