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 onFitPeaks(self, event=None):
print( 'Fit Peaks')
opts = {}
filters, peaks = [], []
sig, det, bgr = {}, {}, {}
opts['flyield'] = self.wids.flyield_use.IsChecked()
opts['xray_en'] = self.wids.xray_en.GetValue()
opts['emin'] = self.wids.fit_emin.GetValue()
opts['emax'] = self.wids.fit_emax.GetValue()
det['use'] = self.wids.det_use.IsChecked()
det['thickness'] = self.wids.det_thk.GetValue()
det['material'] = self.wids.det_mat.GetStringSelection()
bgr['use'] = self.wids.bgr_use.IsChecked()
bgr['width'] = self.wids.bgr_width.GetValue()
bgr['compress'] = int(self.wids.bgr_compress.GetStringSelection())
bgr['exponent'] = int(self.wids.bgr_exponent.GetStringSelection())
sig['offset'] = self.wids.sig_offset.param
sig['slope'] = self.wids.sig_slope.param
sig['quad'] = self.wids.sig_quad.param
for k in self.wids.filters:
f = (k[0].GetStringSelection(), k[1].GetValue(), k[2].param)
filters.append(f)
for k in self.wids.peaks:
p = (k[0].IsChecked(), k[1].param, k[2].param, k[3].param)
peaks.append(p)
opts = {'det': det, 'bgr': bgr, 'sig': sig,
'filters': filters, 'peaks': peaks}
for key, val in opts.items():
print( key, val)
mca = self.mca
mca.data = mca.counts*1.0
energy = mca.energy
_larch = self.parent.larch
if bgr.pop('use'):
xrf_background(energy=mca.energy, counts=mca.counts,
group=mca, _larch=_larch, **bgr)
mca.data = mca.data - mca.bgr
if det.pop('use'):
# mu in 1/mm, note energy is needed in eV
mu = material_mu(det['material'], energy*1000.0, _larch=_larch)/10.0
t = det['thickness']
mca.det_atten = np.exp(-t*mu)
mca.data = mca.data / np.maximum(1.e-49, (1.0 - mca.det_atten))
def onFitPeaks(self, event=None):
opts = {}
filters, peaks = [], []
sig, det, bgr = {}, {}, {}
opts['flyield'] = self.wids.flyield_use.IsChecked()
opts['xray_en'] = self.wids.xray_en.GetValue()
opts['emin'] = self.wids.fit_emin.GetValue()
opts['emax'] = self.wids.fit_emax.GetValue()
det['use'] = self.wids.det_use.IsChecked()
det['thickness'] = self.wids.det_thk.GetValue()
det['material'] = self.wids.det_mat.GetStringSelection()
bgr['use'] = self.wids.bgr_use.IsChecked()
bgr['width'] = self.wids.bgr_width.GetValue()
bgr['compress'] = int(self.wids.bgr_compress.GetStringSelection())
bgr['exponent'] = int(self.wids.bgr_exponent.GetStringSelection())
sig['offset'] = self.wids.sig_offset.param
sig['slope'] = self.wids.sig_slope.param
sig['quad'] = self.wids.sig_quad.param
for k in self.wids.filters:
f = (k[0].GetStringSelection(), k[1].GetValue(), k[2].param)
filters.append(f)
for k in self.wids.peaks:
use = k[0].IsChecked()
p = (k[0].IsChecked(), k[1].param, k[2].param, k[3].param)
peaks.append(p)
if not use:
k[1].param.vary = False
k[2].param.vary = False
k[3].param.vary = False
opts['det'] = det
opts['bgr'] = bgr
opts['sig'] = sig
opts['filters'] = filters
opts['peaks'] = peaks
mca = self.mca
mca.data = mca.counts * 1.0
energy = mca.energy
_larch = self.parent.larch
if bgr['use']:
bgr.pop('use')
xrf_background(energy=mca.energy,
counts=mca.counts,
group=mca,
_larch=_larch,
**bgr)
opts['use_bgr'] = True
opts['mca'] = mca
if det['use']:
mu = material_mu(det['material'], energy * 1000.0,
_larch=_larch) / 10.0
t = det['thickness']
mca.det_atten = np.exp(-t * mu)
fit = Minimizer(xrf_resid,
self.paramgroup,
toler=1.e-4,
_larch=_larch,
fcn_kws=opts)
fit.leastsq()
parent = self.parent
parent.oplot(mca.energy,
mca.model,
label='fit',
style='solid',
color='#DD33DD')
print(fitting.fit_report(self.paramgroup, _larch=_larch))
def onFitPeaks(self, event=None):
opts = {}
filters, peaks = [], []
sig, det, bgr = {}, {}, {}
opts['flyield'] = self.wids.flyield_use.IsChecked()
opts['xray_en'] = self.wids.xray_en.GetValue()
opts['emin'] = self.wids.fit_emin.GetValue()
opts['emax'] = self.wids.fit_emax.GetValue()
det['use'] = self.wids.det_use.IsChecked()
det['thickness'] = self.wids.det_thk.GetValue()
det['material'] = self.wids.det_mat.GetStringSelection()
bgr['use'] = self.wids.bgr_use.IsChecked()
bgr['width'] = self.wids.bgr_width.GetValue()
bgr['compress'] = int(self.wids.bgr_compress.GetStringSelection())
bgr['exponent'] = int(self.wids.bgr_exponent.GetStringSelection())
sig['offset'] = self.wids.sig_offset.param
sig['slope'] = self.wids.sig_slope.param
sig['quad'] = self.wids.sig_quad.param
for k in self.wids.filters:
f = (k[0].GetStringSelection(), k[1].GetValue(), k[2].param)
filters.append(f)
for k in self.wids.peaks:
use = k[0].IsChecked()
p = (k[0].IsChecked(), k[1].param, k[2].param, k[3].param)
peaks.append(p)
if not use:
k[1].param.vary = False
k[2].param.vary = False
k[3].param.vary = False
opts['det'] = det
opts['bgr'] = bgr
opts['sig'] = sig
opts['filters'] = filters
opts['peaks'] = peaks
mca = self.mca
mca.data = mca.counts*1.0
energy = mca.energy
_larch = self.parent.larch
if bgr['use']:
bgr.pop('use')
xrf_background(energy=mca.energy, counts=mca.counts,
group=mca, _larch=_larch, **bgr)
opts['use_bgr']=True
opts['mca'] = mca
if det['use']:
mu = material_mu(det['material'], energy*1000.0,
_larch=_larch)/10.0
t = det['thickness']
mca.det_atten = np.exp(-t*mu)
fit = Minimizer(xrf_resid, self.paramgroup, toler=1.e-4,
_larch=_larch, fcn_kws = opts)
fit.leastsq()
parent = self.parent
parent.oplot(mca.energy, mca.model,
label='fit', style='solid',
color='#DD33DD')
print( fitting.fit_report(self.paramgroup, _larch=_larch))
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 calc_mu(self, energy):
self.mu_total = material_mu(self.material, energy, kind='total')
self.mu_photo = material_mu(self.material, energy, kind='photo')