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 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')
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 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