def autobk(energy, mu, rbkg=1, nknots=None, group=None, e0=None,
kmin=0, kmax=None, kw=1, dk=0, win=None, vary_e0=True,
chi_std=None, nfft=2048, kstep=0.05, _larch=None):
if _larch is None:
raise Warning("cannot calculate autobk spline -- larch broken?")
# get array indices for rkbg and e0: irbkg, ie0
rgrid = np.pi/(kstep*nfft)
if rbkg < 2*rgrid: rbkg = 2*rgrid
irbkg = int(1.01 + rbkg/rgrid)
if e0 is None:
e0 = find_e0(energy, mu, group=group, _larch=_larch)
ie0 = _index_nearest(energy, e0)
# save ungridded k (kraw) and grided k (kout)
# and ftwin (*k-weighting) for FT in residual
kraw = np.sqrt(ETOK*(energy[ie0:] - e0))
if kmax is None:
kmax = max(kraw)
kout = kstep * np.arange(int(1.01+kmax/kstep))
ftwin = kout**kw * ftwindow(kout, xmin=kmin, xmax=kmax,
window=win, dx=dk)
# calc k-value and initial guess for y-values of spline params
nspline = max(4, min(60, 2*int(rbkg*(kmax-kmin)/np.pi) + 1))
spl_y = np.zeros(nspline)
spl_k = np.zeros(nspline)
for i in range(nspline):
q = kmin + i*(kmax-kmin)/(nspline - 1)
ik = _index_nearest(kraw, q)
i1 = min(len(kraw)-1, ik + 5)
i2 = max(0, ik - 5)
spl_k[i] = kraw[ik]
spl_y[i] = (2*mu[ik] + mu[i1] + mu[i2] ) / 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
fparams = Parameters()
for i, v in enumerate(coefs):
fparams.add("c%i" % i, value=v, vary=i<len(spl_y))
fitkws = dict(knots=knots, order=order, kraw=kraw, mu=mu[ie0:],
irbkg=irbkg, kout=kout, ftwin=ftwin, nfft=nfft)
# do fit
fit = Minimizer(__resid, fparams, fcn_kws=fitkws)
fit.leastsq()
# write final results
coefs = [p.value for p in fparams.values()]
bkg, chi = spline_eval(kraw, mu[ie0:], knots, coefs, order, kout)
obkg = np.zeros(len(mu))
obkg[:ie0] = mu[:ie0]
obkg[ie0:] = bkg
if _larch.symtable.isgroup(group):
setattr(group, 'bkg', obkg)
setattr(group, 'chie', mu-obkg)
setattr(group, 'k', kout)
setattr(group, 'chi', chi)
def fft(self, k, chi):
if self.kwin_array is None:
self.kwin_array = ftwindow(k, xmin=self.kmin, xmax=self.kmax,
dx=self.dk, dx2=self.dk2,
window=self.window)
cchi = zeros(self.nfft, dtype='complex128')
cchi[0:len(chi)] = chi
out = fft(cchi * self.kwin_array * k**self.kw)
return self.kstep*sqrt(pi) * out[:self.nfft/2]
def ffti(self, chir):
" reverse FT -- meant to be used internally"
self.make_karrays()
if self.rwin is None:
self.rwin = ftwindow(self.r_, xmin=self.rmin, xmax=self.rmax,
dx=self.dr, dx2=self.dr2, window=self.rwindow)
cx = chir * self.rwin[:len(chir)] * self.r_[:len(chir)]**self.rw,
return xafsift_fast(cx, kstep=self.kstep, nfft=self.nfft)
def ifft(self, r, chir):
if self.rwin_array is None:
self.rwin_array = ftwindow(r, xmin=self.rmin, xmax=self.rmax,
dx=self.dr, dx2=self.dr2,
window=self.rwindow)
cchir = zeros(self.nfft, dtype='complex128')
cchir[0:len(chir)] = chir
out = ifft(cchir * self.rwin_array * r**self.rw)
return sqrt(pi)/(2*self.kstep) * out[:self.nfft/2]
def fftf(self, chi, kweight=None):
""" forward FT -- meant to be used internally.
chi must be on self.k_ grid"""
self.make_karrays()
if self.kwin is None:
self.kwin = ftwindow(self.k_, xmin=self.kmin, xmax=self.kmax,
dx=self.dk, dx2=self.dk2, window=self.window)
if kweight is None:
kweight = self.get_kweight()
cx = chi * self.kwin[:len(chi)] * self.k_[:len(chi)]**kweight
return xafsft_fast(cx, kstep=self.kstep, nfft=self.nfft)
def fft(self, k, chi):
if self.kwin_array is None:
self.kwin_array = ftwindow(k,
xmin=self.kmin,
xmax=self.kmax,
dx=self.dk,
dx2=self.dk2,
window=self.window)
cchi = zeros(self.nfft, dtype='complex128')
cchi[0:len(chi)] = chi
out = fft(cchi * self.kwin_array * k**self.kw)
return self.kstep * sqrt(pi) * out[:self.nfft / 2]
def ifft(self, r, chir):
if self.rwin_array is None:
self.rwin_array = ftwindow(r,
xmin=self.rmin,
xmax=self.rmax,
dx=self.dr,
dx2=self.dr2,
window=self.rwindow)
cchir = zeros(self.nfft, dtype='complex128')
cchir[0:len(chir)] = chir
out = ifft(cchir * self.rwin_array * r**self.rw)
return sqrt(pi) / (2 * self.kstep) * out[:self.nfft / 2]
def autobk(energy,
mu,
rbkg=1,
nknots=None,
group=None,
e0=None,
kmin=0,
kmax=None,
kw=1,
dk=0,
win=None,
vary_e0=True,
chi_std=None,
nfft=2048,
kstep=0.05,
_larch=None):
if _larch is None:
raise Warning("cannot calculate autobk spline -- larch broken?")
# get array indices for rkbg and e0: irbkg, ie0
rgrid = np.pi / (kstep * nfft)
if rbkg < 2 * rgrid: rbkg = 2 * rgrid
irbkg = int(1.01 + rbkg / rgrid)
if e0 is None:
e0 = find_e0(energy, mu, group=group, _larch=_larch)
ie0 = _index_nearest(energy, e0)
# save ungridded k (kraw) and grided k (kout)
# and ftwin (*k-weighting) for FT in residual
kraw = np.sqrt(ETOK * (energy[ie0:] - e0))
if kmax is None:
kmax = max(kraw)
kout = kstep * np.arange(int(1.01 + kmax / kstep))
ftwin = kout**kw * ftwindow(kout, xmin=kmin, xmax=kmax, window=win, dx=dk)
# calc k-value and initial guess for y-values of spline params
nspline = max(4, min(60, 2 * int(rbkg * (kmax - kmin) / np.pi) + 1))
spl_y = np.zeros(nspline)
spl_k = np.zeros(nspline)
for i in range(nspline):
q = kmin + i * (kmax - kmin) / (nspline - 1)
ik = _index_nearest(kraw, q)
i1 = min(len(kraw) - 1, ik + 5)
i2 = max(0, ik - 5)
spl_k[i] = kraw[ik]
spl_y[i] = (2 * mu[ik] + mu[i1] + mu[i2]) / 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
fparams = Parameters()
for i, v in enumerate(coefs):
fparams.add("c%i" % i, value=v, vary=i < len(spl_y))
fitkws = dict(knots=knots,
order=order,
kraw=kraw,
mu=mu[ie0:],
irbkg=irbkg,
kout=kout,
ftwin=ftwin,
nfft=nfft)
# do fit
fit = Minimizer(__resid, fparams, fcn_kws=fitkws)
fit.leastsq()
# write final results
coefs = [p.value for p in fparams.values()]
bkg, chi = spline_eval(kraw, mu[ie0:], knots, coefs, order, kout)
obkg = np.zeros(len(mu))
obkg[:ie0] = mu[:ie0]
obkg[ie0:] = bkg
if _larch.symtable.isgroup(group):
setattr(group, 'bkg', obkg)
setattr(group, 'chie', mu - obkg)
setattr(group, 'k', kout)
setattr(group, 'chi', chi)
def autobk(energy, mu, 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, debug=False, _larch=None, **kws):
"""Use Autobk algorithm to remove XAFS background
Options are:
rbkg -- distance out to which the chi(R) is minimized
"""
if _larch is None:
raise Warning("cannot calculate autobk spline -- larch broken?")
if 'kw' in kws:
kweight = kws['kw']
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()
if edge_step is None:
if _larch.symtable.isgroup(group) and hasattr(group, 'edge_step'):
edge_step = group.edge_step
if e0 is None:
if _larch.symtable.isgroup(group) and hasattr(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)
edge_step, e0 = pre_edge(energy, mu, group=group,
_larch=_larch, **pre_kws)
# get array indices for rkbg and e0: irbkg, ie0
ie0 = index_nearest(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
kraw = np.sqrt(ETOK*(energy[ie0:] - e0))
if kmax is None:
kmax = max(kraw)
kout = kstep * np.arange(int(1.01+kmax/kstep), dtype='float64')
# 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)
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
nspline = max(4, min(60, 2*int(rbkg*(kmax-kmin)/np.pi) + 1))
spl_y = np.zeros(nspline)
spl_k = np.zeros(nspline)
spl_e = np.zeros(nspline)
for i in range(nspline):
q = kmin + i*(kmax-kmin)/(nspline - 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
ncoefs = len(coefs)
params = Group()
for i in range(ncoefs):
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, mu[ie0:], knots, coefs, order, kout)
fitkws = dict(ncoefs=len(coefs), chi_std=chi_std,
knots=knots, order=order, kraw=kraw, mu=mu[ie0:],
irbkg=irbkg, kout=kout, ftwin=ftwin, nfft=nfft)
# do fit
fit = Minimizer(__resid, params, fcn_kws=fitkws, _larch=_larch, toler=1.e-4)
fit.leastsq()
# write final results
coefs = [getattr(params, FMT_COEF % i) for i in range(ncoefs)]
bkg, chi = spline_eval(kraw, mu[ie0:], knots, coefs, order, kout)
obkg = np.zeros(len(mu))
obkg[:ie0] = mu[:ie0]
obkg[ie0:] = bkg
if _larch.symtable.isgroup(group):
group.bkg = obkg
group.chie = (mu-obkg)/edge_step
group.k = kout
group.chi = chi/edge_step
if debug:
group.spline_params = params
ix_bkg = np.zeros(len(mu))
ix_bkg[:ie0] = mu[:ie0]
ix_bkg[ie0:] = initbkg
group.init_bkg = ix_bkg
group.init_chi = initchi/edge_step
group.spline_e = spl_e
group.spline_y = np.array([coefs[i] for i in range(nspline)])
group.spline_yinit = spl_y
