9.2. XAFS: Preedge Subtraction, Normalization, and data treatment¶
After reading in data and constructing \(\mu(E)\), the principle
preprocessing steps for XAFS analysis are preedge subtraction and
normalization. Reading data and constructing \(\mu(E)\) are handled by
internal larch functions, especially read_ascii()
. The main
XAFSspecific function for preedge subtraction and normalization is
pre_edge()
.
This chapter also describes methods for the treatment of XAFS and XANES data including corrections for overabsorption (sometimes confusingly called selfabsorption) and for spectral convolution and deconvolution.
9.2.1. The pre_edge()
function¶

pre_edge
(energy, mu, group=None, ...)¶  Preedge subtraction and normalization. This performs a number of steps:
 determine \(E_0\) (if not supplied) from max of deriv(mu)
 fit a line of polynomial to the region below the edge
 fit a polynomial to the region above the edge
 extrapolate the two curves to \(E_0\) to determine the edge jump
Parameters:  energy – 1d array of xray energies, in eV
 mu – 1d array of \(\mu(E)\)
 group – output group
 e0 – edge energy, \(E_0\) 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 preedge fit
 pre2 – high E range (relative to E0) for preedge fit
 nvict – energy exponent to use for preedge fit. See Note below.
 norm1 – low E range (relative to E0) for postedge fit
 norm2 – high E range (relative to E0) for postedge fit
 nnorm – number of terms in polynomial (that is, 1+degree) for postedge, normalization curve. Default=3 (quadratic)
Returns: None.
Follows the First Argument Group convention, using group members named
energy
andmu
. The following data is put into the output group:attribute meaning e0 energy origin edge_step edge step norm normalized mu(E) (array) pre_edge preedge curve (array) post_edge postedge, normalization curve (array) pre_slope slope of preedge line pre_offset offset of preedge line nvict value of nvict used nnorm value of nnorm used norm_c0 constant of normalization polynomial norm_c1 linear coefficient of normalization polynomial norm_c2 quadratic coefficient of normalization polynomial norm_c* higher power coefficients of normalization polynomial
Notes:
 nvict gives an exponent to the energy term for the preedge fit. That is, a line \((m E + b)\) is fit to \(\mu(E) E^{nvict}\) over the predge region, E= [E0+pre1, E0+pre2].

find_e0
(energy, mu=None, group=None, ...)¶ Determine \(E_0\), the energy threshold of the absorption edge, from the arrays energy and mu for \(\mu(E)\).
This finds the point with maximum derivative with some checks to avoid spurious glitches.
Parameters:  energy – array of xray energies, in eV
 mu – array of \(\mu(E)\)
 group – output group
Follows the First Argument Group convention, using group members named
energy
andmu
. The value ofe0
will be written to the output group.
9.2.2. PreEdge Subtraction Example¶
A simple example of preedge subtraction:
fname = 'fe2o3_rt1.xmu'
dat = read_ascii(fname, labels='energy mu i0')
pre_edge(dat, group=dat)
show(dat)
newplot(dat.energy, dat.mu, label=' $ \mu(E) $ ',
xlabel='Energy (eV)',
title='%s PreEdge ' % fname,
show_legend=True)
plot(dat.energy, dat.pre_edge, label='preedge line',
color='black', style='dashed' )
plot(dat.energy, dat.post_edge, label='normalization line',
color='black', style='dotted' )
gives the following results:
9.2.3. The MBACK algorithm¶
Larch provides an implementation of the MBACK algorithm of Weng [Larch_9] with an option of using the modification proposed by Lee et al [Larch_10]. In MBACK, the data are matched to the tabulated values of the imaginary part of the energydependent correction to the Thompson scattering factor, \(f''(E)\). To account for any instrumental or sampledependent aspects of the shape of the measured data, \(\mu_{data}(E)\), a Legendre polynomial of order \(m\) centered around the absorption edge is subtracted from the data. To account for the sort of highly nonlinear preedge which often results from Compton scattering in the measurement window of an energydiscriminating detector, a complementary error function is added to the Legendre polynomial.
The form of the normalization function, then, is
where \(A\), \(E_{em}\), and \(\xi\) are the amplitude, centroid, and width of the complementary error function and \(s\) is a scaling factor for the measured data. \(E_{em}\) is typically the centroid of the emission line for the measured edge. This results in a function of \(3+m\) variables (a tabulated value of \(E_{em}\) is used). The function to be minimized, then is
To give weight in the fit to the preedge region, which typically has fewer measured points than the postedge region, the weight is adjusted by breaking the minimization function into two regions: the \(n_1\) data points below the absorption edge and the \(n_2\) data points above the absorption edge. \(n_1+n_2=N\), where N is the total number of data points.
If this is used in publication, a citation should be given to Weng [Larch_9].

mback
(energy, mu, group=None, ...)¶ Match measured \(\mu(E)\) data to tabulated crosssection data.
Parameters:  energy – 1d array of xray energies, in eV
 mu – 1d array of \(\mu(E)\)
 group – output group
 z – the Z number of the absorber
 edge – the absorption edge, usually ‘K’ or ‘L3’
 e0 – edge energy, \(E_0\) in eV. If None, the tabulated value is used.
 emin – the minimum energy to include in the fit. If None, use first energy point
 emax – the maximum energy to include in the fit. If None, use last energy point
 whiteline – a margin around the edge to exclude from the fit. If not None, must be a positive integer
 leexiang – flag for using the use the Lee&Xiang extension [False]
 tables – ‘CL’ (CromerLiberman) or ‘Chantler’, [‘CL’]
 fit_erfc – if True, fit the amplitude and width of the complementary error function [False]
 return_f1 – if True, put f1 in the output group [False]
 pre_edge_kws – dictionary containing keyword arguments to pass to
pre_edge()
.
Returns: None.
Follows the First Argument Group convention, using group members named
energy
andmu
. The following data is put into the output group:attribute meaning fpp matched \(\mu(E)\) data f2 tabulated \(f''(E)\) data f1 tabulated \(f'(E)\) data (if return_f1
is True)mback_params params group for the MBACK minimization function
Notes:
 The
whiteline
parameter is used to exclude the region around the white line in the data from the fit. The large spectral weight under the white line can skew the fit result, particularly in data measured over a short data range. The value is eV units. The
order
parameter is the order of the Legendre polynomial. Data measured over a very short data range are likely best processed withorder=2
. Extended XAS data are often better processed with a value of 3 or 4. The order is enforced to be an integer between 1 and 6. A call to
pre_edge()
is made ife0
is not supplied. The option to return \(f'(E)\) is used by
diffkk()
.
Here is an example of processing XANES data measured over an extended data range. This example is the K edge of copper foil, with the result shown in Figure 9.2.2.
data=read_ascii('../xafsdata/cu_10k.xmu')
mback(data.energy, data.mu, group=a, z=29, edge='K', order=4)
newplot(data.energy, data.f2, xlabel='Energy (eV)', ylabel='matched absorption', label='$f_2$',
legend_loc='lr', show_legend=True)
plot(data.energy, data.fpp, label='Copper foil')
Here is an example of processing XANES data measured over a rather
short data range. This example is the magnesium silicate mineral
talc, Mg_{3}Si_{4}O_{10}(OH)_{2},
measured at the Si K edge, with the result shown in
Figure 9.2.3. Note that the order of the Legendre polynomial
is set to 2 and that the whiteline
parameter is set to avoid the
large features near the edge.
data=read_ascii('Talc.xmu')
mback(data.e, data.xmu, group=a, z=14, edge='K', order=2, whiteline=50, fit_erfc=True)
newplot(data.e, data.f2, xlabel='Energy (eV)', ylabel='matched absorption', label='$f_2$',
legend_loc='lr', show_legend=True)
plot(data.e, data.fpp, label='Talc ($\mathrm{Mg}_3\mathrm{Si}_4\mathrm{O}_{10}\mathrm{(OH)}_2$)')
9.2.4. Preedge baseline subtraction¶
A common application of XAFS is the analysis of “preedge peaks” of transition metal oxides to determine oxidation state and molecular configuration. These peaks sit just below the main absorption edge (typically, due to metal 4p electrons) of a main K edge, and are due to overlaps of the metal delectrons and oxygen pelectrons, and are often described in terms of molecular orbital theory.
To analyze the energies and relative strengths of these preedge peaks, it is necessary to try to remove the contribution of the main edge. The main edge (or at least its low energy side) can be modeled reasonably well as a Lorentzian function for these purposes of describing the tail below the preedge peaks.

pre_edge_baseline
(energy, norm, group=None, form='lorentzian', ...)¶ 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 preedge subtraction and normalization.Parameters:  energy – array of xray energies, in eV, or group (see note 1)
 norm – array of normalized \(\mu(E)\)
 group – output group
 elo – low energy of preedge peak region to not fit baseline [e020]
 ehi – high energy of preedge peak region ot not fit baseline [e010]
 emax – max energy (eV) to use for baesline fit [e05]
 emin – min energy (eV) to use for baesline fit [e040]
 form – form used for baseline (see note 2) [‘lorentzian’]
 with_line – whether to include linear component in baseline [
True
]
Follows the First Argument Group convention, using group members named
energy
andnorm
.For output, a subgroup name
prepeaks
is created in the output group (if the output group isNone
,_sys.xafsGroup
will be used), with the following attributes:attribute meaning energy energy array for preedge peaks = energy[emin:emax] baseline fitted baseline array over preedge peak energies mu spectrum over preedge peak energies peaks baselinesubtraced spectrum over preedge peak energies dmu estimated uncertainty in peaks from fit centroid estimated centroid of preedge peaks (see note below) peak_energies list of predicted peak energies (see note belo) fit_details details of fit to extract preedge peaks.
Notes:
 A function will be fit to the input \(\mu(E)\) data over the range between [emin:elo] and [ehi:emax], ignorng the preedge 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.
 The value calculated for prepeaks.centroid will be found as (prepeaks.energy*prepeaks.peaks).sum() / prepeaks.peaks.sum()
 The values in the peak_energies list will be predicted energies of the peaks in prepeaks.peaks as found by peakutils.
9.2.5. Overabsorption Corrections¶
For XAFS data measured in fluorescence, a common problem of overabsorption in which too much of the total Xray absorption coefficient is from the absorbing element. In such cases, the implicit assumption in a fluorescence XAFS measurement that the fluorescence intensity is proportional to the absorption coefficient of the element of interest breaks down. This is often referred to as selfabsorption in the older XAFS literature, but the term should be avoided as it is quite a different effect from selfabsorption in Xray fluorescence analysis. In fact, the effect is more like extinction in that the fluorescence probability approaches a constant, with no XAFS oscillations, as the total absorption coefficient is dominated by the element of interest. Overabsorption most stongly effects the XAFS oscillation amplitude, and so coordination number and meansquare displacement parameters in the EXAFS, and edgeposition and preedge peak height for XANES. Fortunately, the effect can be corrected for small overabsorption.
For XANES, a common correction method from the FLUO program by D. Haskel
([Larch_11]) can be used. The algorithm is contained in the
fluo_corr()
function.

fluo_corr
(energy, mu, formula, elem, group=None, edge='K', anginp=45, angout=45, **pre_kws)¶ calculate \(\mu(E)\) corrected for overabsorption in fluorescence XAFS using the FLUO algorithm (suitabe for XANES, but questionable for EXAFS).
Parameters:  energy – 1d array of xray energies, in eV
 mu – 1d array of \(\mu(E)\)
 formula – string for sample stoichiometry
 group – output group
 elem – atomic symbol (‘Zn’) or Z of absorbing element
 edge – name of edge (‘K’, ‘L3’, …) [default ‘K’]
 anginp – input angle in degrees [default 45]
 angout – output angle in degrees [default 45]
 pre_kws – additional keywords for
pre_edge()
.
Returns: None
Follows the First Argument Group convention, using group members named
energy
andmu
. The value ofmu_corr
andnorm_corr
will be written to the output group, containing \(\mu(E)\) and normalized \(\mu(E)\) corrected for overabsorption.
9.2.6. Spectral deconvolution¶
In order to readily compare XAFS data from different sources, it is sometimes necessary to considert the energy resolution used to collect each spectum. To be clear, the resolution of an EXAFS spectrum includes contributions from the xray sourse, instrumental broadening from the xray optics (especially the Xray monochromator used in most measurements), and the intrinsic lifetime of the excited core electronic level. For data measured in Xray fluorescence or electron emission mode, the energy resolution can also includes the energy width of the decay channels measured.
For a large fraction of XAFS data, the energy resolution is dominated by the intrinsic width of the excited core level and by the resolution of a silicon (111) double crystal monochromator, and so does not vary appreciably between spectra taken at different facilities or at different times. Exceptions to this rule occur when using a higher order reflection of a silicon monochromator or a different monochromator altogether. Resolution can also be noticeably worse for data taken at older (first and second generation) sources and beamlines, either without a collimating mirror or slits before the monochromator to improve the resolution. In addition, highresolution Xray fluorescence measurements can be used to dramatically enhance the energy resolution of XAFS spectra, and are becoming widely available.
Because of these effects, it is sometimes useful to change the resolution
of XAFS spectra. For example, one may need to reduce the resolution to
match data measured with degraded resolution. This can be done with
xas_convolve()
which convolves an XAFS spectrum with either a
Gaussian or Lorentzian function with a known energy width. Note that
convolving with a Gaussian is less dramatic than using a Lorenztian, and
usually better reflects the effect of an incident Xray beam with degraded
resolution due to source or monochromator.
One may also want to try to improve the energy resolution of an XAFS
spectrum, either to compare it to data taken with higher resolution or to
better identify and enumerate peaks in a XANES spectrum. This can be done
with xas_deconvolve()
function which deconvolves either a Gaussian or
Lorentzian function from an XAFS spectrum. This usually requires fairly
good data. Whereas a Gaussian most closely reflects broadening from the
Xray source, broadening due to the natural energy width of the core levels
is better described by a Lorenztian. Therefore, to try to reduce the
influence of the core level in order better mimic highresolution
fluorescence data, deconvolving with a Lorenztian is often better.
9.2.6.1. xas_convolve()
¶

xas_convolve(energy, norm=None, group=None, form='lorentzian', esigma=1.0, eshift=0.0):
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.
Parameters:  energy – 1d array of \(E\)
 norm – 1d 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()
orlorentzian()
lineshape [1.0]  eshift – energy shift (in eV) to apply to result. [0.0]
Follows the First Argument Group convention, using group members named
energy
andnorm
. The following data is put into the output group:attribute meaning conv array of convolved, normalized \(\mu(E)\)
9.2.6.2. xas_deconvolve()
¶

xas_deconvolve
(energy, norm=None, group=None, form='lorentzian', esigma=1.0, eshift=0.0, smooth=True, sgwindow=None, sgorder=3)¶ XAS spectral deconvolution
deconvolve 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!
Parameters:  energy – 1d array of \(E\)
 norm – 1d 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()
orlorentzian()
lineshape [1.0]  eshift – energy shift (in eV) to apply to result. [0.0]
 smooth – whether to smooth the result with the SavitzkyGolay
method [
True
]  sgwindow – window size for SavitzkyGolay function [found from data step and esigma]
 sgorder – order for the SavitzkyGolay function [3]
Follows the First Argument Group convention, using group members named
energy
andnorm
.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.The following data is put into the output group:
attribute meaning deconv array of deconvolved, normalized \(\mu(E)\)
9.2.6.3. Examples using xas_deconvolve()
and xas_convolve()
¶
An example using xas_deconvolve()
to deconvolve a XAFS spectrum would
be:
## examples/xafs/doc_deconv1.lar
dat = read_ascii('../xafsdata/fe2o3_rt1.xmu', labels='energy mu i0')
pre_edge(dat)
xas_deconvolve(dat, esigma=1.0)
plot_mu(dat, show_norm=True, emin=30, emax=70)
plot(dat.energy, dat.deconv, label='deconvolved')
## end of examples/xafs/doc_deconv1.lar
resulting in deconvolved data:
To deconvolve an XAFS spectrum using the energy width of the core level,
we can use the _xray.core_width()
functiion, as shown below for Cu metal.
We can also test that the deconvolution is correct by using
xas_convolve()
to reconvolve the result and comparing it to original
data. This can be done with:
## examples/xafs/doc_deconv2.lar
dat = read_ascii('../xafsdata/cu_metal_rt.xdi', labels='energy i0 i1 mu')
dat.mu = log(dat.i1 / dat.i0)
pre_edge(dat)
esigma = core_width('Cu', edge='K')
xas_deconvolve(dat, esigma=esigma)
plot_mu(dat, show_norm=True, emin=50, emax=250)
plot(dat.energy, dat.deconv, label='deconvolved')
# Test convolution:
test = group(energy=dat.energy, norm=dat.deconv)
xas_convolve(test, esigma=esigma)
plot_mu(dat, show_norm=True, emin=50, emax=250, win=2)
plot(test.energy, 100*(test.convdat.norm),
label='(reconvolved  original)x100', win=2)
## end of examples/xafs/doc_deconv2.lar
with results shown below:
Finally, deconvolution of \(L_{\rm III}\) XAFS data can be particularly dramatic and useful. As with the copper spectrum above, we’ll deconvolve \(L_{\rm III}\) XAFS for platinum, using the nominal energy width of the core level (5.17 eV). For this example, we also see noticeable improvement in amplitude of the XAFS.
## examples/xafs/doc_deconv3.lar
data = read_ascii('../xafsdata/pt_metal_rt.xdi', labels='energy time i1 i0')
data.mu = log(data.i1 / data.i0)
pre_edge(data)
autobk(data, rbkg=1.1, kweight=2)
xftf(data, kmin=2, kmax=17, dk=5, kwindow='kaiser', kweight=2)
xas_deconvolve(data, esigma=core_width('Pt', edge='L3'))
decon = group(energy=data.energy, mu=data.deconv, filename=data.filename)
autobk(decon, rbkg=1.1, kweight=2)
xftf(decon, kmin=2, kmax=17, dk=5, kwindow='kaiser', kweight=2)
# plot in E
plot_mu(data, show_norm=True, emin=50, emax=250)
plot(data.energy, data.deconv, label='deconvolved', win=1)
# plot in k
plot_chik(data, kweight=2, show_window=False, win=2)
plot_chik(decon, kweight=2, show_window=False,
label='deconvolved', new=False, win=2)
# plot in R
plot_chir(data, win=3)
plot_chir(decon, label='deconvolved', new=False, win=3)
## end examples/xafs/doc_deconv3.lar
with results shown below:
References
[Larch_9]  (1, 2) TsuChien Weng, Geoffrey S. Waldo, and James E. PennerHahn. A method for normalization of Xray absorption spectra. Journal of Synchrotron Radiation, 12(4):506–510, Jul 2005. doi:10.1107/S0909049504034193. 
[Larch_10]  Julia C. Lee, Jingen Xiang, Bruce Ravel, Jeffrey Kortright, and Kathryn Flanagan. Condensed matter astrophysics: a prescription for determining the speciesspecific composition and quantity of interstellar dust using xrays. The Astrophysical Journal, 702(2):970, 2009. URL: http://stacks.iop.org/0004637X/702/i=2/a=970, doi:0004637X/702/i=2/a=970. 
[Larch_11]  D. Haskel. Fluo. http://www.aps.anl.gov/~haskel/fluo.html, 1999. 