def get_xdilib():
"""make initial connection to XDI dll"""
global XDILIB
if XDILIB is None:
XDILIB = get_dll('xdifile')
XDILIB.XDI_errorstring.restype = c_char_p
return XDILIB
def get_xdilib():
"""make initial connection to XDI dll"""
global XDILIB
if XDILIB is None:
XDILIB = get_dll('xdifile')
XDILIB.XDI_errorstring.restype = c_char_p
return XDILIB
def __init__(self, phase_file=None, title=''):
global F8LIB
if F8LIB is None:
try:
F8LIB = get_dll('feff8lpath')
except:
pass
self.reset(phase_file=phase_file, title=title)
def __init__(self, phase_file=None, title=''):
global F8LIB
if F8LIB is None:
try:
F8LIB = get_dll('feff8lpath')
except:
pass
self.reset(phase_file=phase_file, title=title)
def initializeLarchPlugin(_larch=None):
"""initialize F8LIB"""
if _larch is not None:
global F8LIB
if F8LIB is None:
try:
F8LIB = get_dll('feff8lpath')
except:
pass
def sigma2_debye(t, theta, path=None, _larch=None):
"""calculate sigma2 for a Feff Path wih the correlated Debye model
sigma2 = sigma2_debye(t, theta, path=None)
Parameters:
-----------
t sample temperature (in K)
theta Debye temperature (in K)
path FeffPath to cacluate sigma2 for [None]
if path is None, the 'current path'
(_sys.paramGroup._feffdat) is used.
"""
global FEFF6LIB
if FEFF6LIB is None:
FEFF6LIB = get_dll('feff6')
FEFF6LIB.sigma2_debye.restype = ctypes.c_double
if path is None:
try:
path = _larch.symtable._sys.paramGroup
except:
pass
try:
fdat = path._feffdat
except:
return 0.00
if theta < 1.e-5: theta = 1.e-5
if t < 1.e-5: t = 1.e-5
npts = len(fdat.geom)
nat = ctypes.pointer(ctypes.c_int(npts))
t = ctypes.pointer(ctypes.c_double(t))
th = ctypes.pointer(ctypes.c_double(theta))
rs = ctypes.pointer(ctypes.c_double(fdat.rnorman))
ax = (npts*ctypes.c_double)()
ay = (npts*ctypes.c_double)()
az = (npts*ctypes.c_double)()
am = (npts*ctypes.c_double)()
for i, dat in enumerate(fdat.geom):
s, iz, ip, x, y, z = dat
ax[i], ay[i], az[i], am[i] = x, y, z, atomic_mass(iz, _larch=_larch)
return FEFF6LIB.sigma2_debye(nat, t, th, rs, ax, ay, az, am)
def sigma2_correldebye(natoms, tk, theta, rnorm, x, y, z, atwt):
"""
internal sigma2 calc for a Feff Path wih the correlated Debye model
these routines come courtesy of jj rehr and si zabinsky.
Arguments:
natoms *int, lengths for x, y, z, atwt [in]
tk *double, sample temperature (K) [in]
theta *double, Debye temperature (K) [in]
rnorm *double, Norman radius (Ang) [in]
x *double, array of x coord (Ang) [in]
y *double, array of y coord (Ang) [in]
x *double, array of z coord (Ang) [in]
atwt *double, array of atomic_weight (amu) [in]
Returns:
sig2_cordby double, calculated sigma2
"""
global FEFF6LIB
if FEFF6LIB is None:
FEFF6LIB = get_dll('feff6')
FEFF6LIB.sigma2_debye.restype = ctypes.c_double
na = ctypes.pointer(ctypes.c_int(natoms))
t = ctypes.pointer(ctypes.c_double(tk))
th = ctypes.pointer(ctypes.c_double(theta))
rs = ctypes.pointer(ctypes.c_double(rnorm))
ax = (natoms * ctypes.c_double)()
ay = (natoms * ctypes.c_double)()
az = (natoms * ctypes.c_double)()
am = (natoms * ctypes.c_double)()
for i in range(natoms):
ax[i], ay[i], az[i], am[i] = x[i], y[i], z[i], atwt[i]
return FEFF6LIB.sigma2_debye(na, t, th, rs, ax, ay, az, am)
def sigma2_correldebye(natoms, tk, theta, rnorm, x, y, z, atwt):
"""
internal sigma2 calc for a Feff Path wih the correlated Debye model
these routines come courtesy of jj rehr and si zabinsky.
Arguments:
natoms *int, lengths for x, y, z, atwt [in]
tk *double, sample temperature (K) [in]
theta *double, Debye temperature (K) [in]
rnorm *double, Norman radius (Ang) [in]
x *double, array of x coord (Ang) [in]
y *double, array of y coord (Ang) [in]
x *double, array of z coord (Ang) [in]
atwt *double, array of atomic_weight (amu) [in]
Returns:
sig2_cordby double, calculated sigma2
"""
global FEFF6LIB
if FEFF6LIB is None:
FEFF6LIB = get_dll('feff6')
FEFF6LIB.sigma2_debye.restype = ctypes.c_double
na = ctypes.pointer(ctypes.c_int(natoms))
t = ctypes.pointer(ctypes.c_double(tk))
th = ctypes.pointer(ctypes.c_double(theta))
rs = ctypes.pointer(ctypes.c_double(rnorm))
ax = (natoms*ctypes.c_double)()
ay = (natoms*ctypes.c_double)()
az = (natoms*ctypes.c_double)()
am = (natoms*ctypes.c_double)()
for i in range(natoms):
ax[i], ay[i], az[i], am[i] = x[i], y[i], z[i], atwt[i]
return FEFF6LIB.sigma2_debye(na, t, th, rs, ax, ay, az, am)
def f1f2(z, energies, width=None, edge=None):
"""Return anomalous scattering factors f1, f2 from Cromer-Liberman
Look-up and return f1, f2 for an element and array of energies
from Cromer-Liberman (Cowan-Brennan implementation)
Parameters
----------
z: atomic number of element
energies: array of x-ray energies (in eV)
width: width used to convolve values with lorentzian profile
edge: x-ray edge ('K', 'L3', etc) used to lookup energy
width for convolution.
Returns:
---------
f1, f2: anomalous scattering factors
"""
global CLLIB
if CLLIB is None:
CLLIB = get_dll('cldata')
en = as_ndarray(energies)
if not isinstance(z, int):
z = atomic_number(z)
if z is None:
return None
if z > 92:
print( 'Cromer-Liberman data not available for Z>92')
return
if edge is not None or width is not None:
natwid = core_width(element=z, edge=edge)
if width is None and natwid not in (None, []):
width = natwid
if width is not None: # will convolve!
e_extra = int(width*80.0)
estep = (en[1:] - en[:-1]).min()
emin = min(en) - e_extra
emax = max(en) + e_extra
npts = 1 + abs(emax-emin+estep*0.02)/abs(estep)
en = np.linspace(emin, emax, int(npts))
nk = int(e_extra / estep)
sig = width/2.0
lor = (1./(1 + ((np.arange(2*nk+1)-nk*1.0)/sig)**2))/(np.pi*sig)
scale = lor.sum()
# create ctypes pointers for the C function
npts = len(en)
p_z = ctypes.pointer(ctypes.c_int(int(z)))
p_npts = ctypes.pointer(ctypes.c_int(npts))
p_en = (npts*ctypes.c_double)()
p_f1 = (npts*ctypes.c_double)()
p_f2 = (npts*ctypes.c_double)()
for i in range(npts):
p_en[i] = en[i]
nout = CLLIB.f1f2(p_z, p_npts, p_en, p_f1, p_f2)
f1 = np.array([i for i in p_f1[:]])
f2 = np.array([i for i in p_f2[:]])
if width is not None: # do the convolution
f1 = np.interp(energies, en, convolve(f1, lor)[nk:-nk])/scale
f2 = np.interp(energies, en, convolve(f2, lor)[nk:-nk])/scale
return (f1, f2)
import h5py
import Image
import sqlalchemy
import wx
import ctypes
import ctypes.util
import scipy.io.netcdf
from scipy.io.netcdf import netcdf_file
import scipy.constants
loadlib = ctypes.windll.LoadLibrary
# larch library bits...
from larch.larchlib import get_dll
cllib = get_dll('cldata')
# matplotlib, wxmplot
matplotlib.use('WXAgg')
mpl_data_files = matplotlib.get_py2exe_datafiles()
import wxmplot
# epics
import epics
ca = epics.ca.initialize_libca()
extra_files = ['inno_setup.iss', 'license.txt', 'readme.txt']
def gen_style(title):
if title.endswith('.py'):
title = title[:-3]
def f1f2(z, energies, width=None, edge=None, _larch=None):
"""Return anomalous scattering factors f1, f2 from Cromer-Liberman
Look-up and return f1, f2 for an element and array of energies
from Cromer-Liberman (Cowan-Brennan implementation)
Parameters
----------
z: atomic number of element
energies: array of x-ray energies (in eV)
width: width used to convolve values with lorentzian profile
edge: x-ray edge ('K', 'L3', etc) used to lookup energy
width for convolution.
Returns:
---------
f1, f2: anomalous scattering factors
"""
global CLLIB
if CLLIB is None:
CLLIB = get_dll('cldata')
en = as_ndarray(energies)
if not isinstance(z, int):
z = atomic_number(z, _larch=_larch)
if z is None:
return None
if z > 92:
print( 'Cromer-Liberman data not available for Z>92')
return
if edge is not None or width is not None and _larch is not None:
natwid = core_width(element=z, edge=edge, _larch=_larch)
if width is None and natwid not in (None, []):
width = natwid
if width is not None: # will convolve!
e_extra = int(width*80.0)
estep = (en[1:] - en[:-1]).min()
emin = min(en) - e_extra
emax = max(en) + e_extra
npts = 1 + abs(emax-emin+estep*0.02)/abs(estep)
en = np.linspace(emin, emax, npts)
nk = int(e_extra / estep)
sig = width/2.0
lor = (1./(1 + ((np.arange(2*nk+1)-nk*1.0)/sig)**2))/(np.pi*sig)
scale = lor.sum()
# create ctypes pointers for the C function
npts = len(en)
p_z = ctypes.pointer(ctypes.c_int(int(z)))
p_npts = ctypes.pointer(ctypes.c_int(npts))
p_en = (npts*ctypes.c_double)()
p_f1 = (npts*ctypes.c_double)()
p_f2 = (npts*ctypes.c_double)()
for i in range(npts):
p_en[i] = en[i]
nout = CLLIB.f1f2(p_z, p_npts, p_en, p_f1, p_f2)
f1 = np.array([i for i in p_f1[:]])
f2 = np.array([i for i in p_f2[:]])
if width is not None: # do the convolution
f1 = np.interp(energies, en, convolve(f1, lor)[nk:-nk])/scale
f2 = np.interp(energies, en, convolve(f2, lor)[nk:-nk])/scale
return (f1, f2)
import h5py
import Image
import sqlalchemy
import wx
import ctypes
import ctypes.util
import scipy.io.netcdf
from scipy.io.netcdf import netcdf_file
import scipy.constants
loadlib = ctypes.windll.LoadLibrary
# larch library bits...
from larch.larchlib import get_dll
cllib = get_dll('cldata')
# matplotlib, wxmplot
matplotlib.use('WXAgg')
mpl_data_files = matplotlib.get_py2exe_datafiles()
import wxmplot
# epics
import epics
ca = epics.ca.initialize_libca()
extra_files = ['inno_setup.iss', 'license.txt', 'readme.txt']
def gen_style(title):
if title.endswith('.py'):
