def nadkin(self):
if isinstance(self._nadkin, xcfun.Functional):
return self._nadkin
elif self._nadkin.upper() == 'TF':
return xcfun.Functional({'tfk': 1.0})
elif self._nadkin.upper() == 'PW91K':
return xcfun.Functional({'pw91k': 1.0})
else:
raise PyAdfError('Unknown nonadditive kinetic energy functional '
+ str(self._nadkin) + 'in db-MBE job')
def nadxc(self):
if isinstance(self._nadxc, xcfun.Functional):
return self._nadxc
elif self._nadxc.upper() == 'LDA':
return xcfun.Functional({'lda': 1.0})
elif self._nadxc.upper() in ['BP', 'BP86']:
return xcfun.Functional({'BeckeX': 1.0, 'P86C': 1.0})
elif self._nadxc.upper() in ['BLYP']:
return xcfun.Functional({'BeckeX': 1.0, 'LYP': 1.0})
else:
raise PyAdfError('Unknown nonadditive xc functional '
+ str(self._nadxc) + ' in db-MBE job')
def test_lda_old():
fun_xc_name = 'LDA'
fun_xc_weight = 1.0
fun_xc = xcfun.Functional({fun_xc_name: fun_xc_weight})
n = numpy.array([1.0])
result = fun_xc.eval_potential_n(n)
energy = result[0, 0]
potential = result[0, 1]
print("Functional used: ", fun_xc_name, " weight ", fun_xc_weight)
print(" density: ", n)
print(" energy: ", energy)
print(" potential: ", potential)
assert_allclose(energy, -0.8101513)
assert_allclose(potential, -1.06468341)
def main():
version = xcfun.xcfun_version()
splash = xcfun.xcfun_splash()
print "\n Sample use of python interface to xcfun"
print "\n\nXCFun version ",version
print splash
fun_xc_name = 'LDA'
fun_xc_weigth = 1.0
fun_xc = xcfun.Functional({fun_xc_name : fun_xc_weigth})
n_gn_gnn = numpy.zeros((1,10))
n_gn_gnn[0,:] = [1.0, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0]
n = numpy.zeros((1,1))
n[0,:] = [1.0]
result = fun_xc.eval_potential_n(n)
energy = result[0,0]
potential = result[0,1]
print "Functional used: ",fun_xc_name," weight ",fun_xc_weigth
print " density: ",n
print " energy: ",energy
print " potential: ",potential
resut_energy = -0.8101513
resut_potential = -1.06468341
tol = 1.0e-7
energy_diff = energy - resut_energy
potential_diff = potential - resut_potential
if ((abs(energy_diff) > tol) or (abs(potential_diff) > tol)):
sys.exit(EXIT_FAILURE)
sys.exit()
def pbe_fun():
return xcfun.Functional({'PBE': 1.0})
def lda_fun():
return xcfun.Functional({'LDA': 1.0})
def set_fun_nad_xc(self):
self.fun_nad_xc = xcfun.Functional(self.settings.nad_xc)
def set_fun_nad_kin(self):
self.fun_nad_kin = xcfun.Functional(self.settings.nad_kin)
def set_fun_nad_xc(self):
self.fun_nad_xc = xcfun.Functional(self.settings.nad_xc)
self.check_fun_type(self.fun_nad_xc)
def set_fun_nad_kin(self):
self.fun_nad_kin = xcfun.Functional(self.settings.nad_kin)
self.check_fun_type(self.fun_nad_kin)
