def main():
res = driver(code=mp, job=job, mol=be, bsopt=event, opt=optimization)
print res.success
print res.x
print res.fun
#save the basis set
opt_basis = vars(BasisSet.from_optdict(res.x, event))
with open('optimized_functions.bas', 'wb') as ff:
ff.write(str(opt_basis))
def opt_shell_by_nf(shell=None, nfs=None, max_params=5, opt_tol=1.0e-4,
save=False, bsopt=None, **kwargs):
'''
For a given shell optimize the functions until the convergence criterion
is reached the energy difference for two consecutive function numbers is
less than the threshold
Kwargs:
shell : string
string label for the shell to be optimized
nfs : list of ints
list of integers representing the number of basis functions to be
inceremented in the optimization,
max_params : int
maximal number of parameters to be used in the legendre expansion,
(length of the expansion)
opt_tol : float
threshold controlling the termination of the shell optimization,
if energy difference between two basis sets with subsequent number
of functionsis larger than this threshold, another function is
added to this shell and parameters are reoptimized,
save : bool
a flag to trigger saving all the optimized basis functions for each
shell,
kwargs : dict
options for the basis set optimization driver, see driver function
from the basisopt module
Returns:
BasisSet object instance with optimized functions for the specified
shell
Raises:
ValueError:
when `shell` is different from `s`, `p`, `d`, `f`, `g`, `h`, `i`
when number of parameters equals 1 and there are more functions
when there are more parameters than functions to optimize
'''
_shells = ['s', 'p', 'd', 'f', 'g', 'h', 'i']
if shell not in _shells:
raise ValueError('shell must be one of the following: {}'.format(", ".join(_shells)))
if len(bsopt['params'][0]) == 1 and min(nfs) != 1:
raise ValueError("1 parameter and {0} functions doesn't make sense".format(min(nfs)))
if min(nfs) < len(bsopt['params'][0]):
raise ValueError("more parameters ({0}) than functions to optimize ({1})".format(len(bsopt['params'][0]), min(nfs)))
bsopt["typ"] = "legendre"
e_last = 0.0
x_last = bsopt['params'][0]
for nf in nfs:
bsopt['nfpshell'] = [0]*_shells.index(shell) + [nf]
res = driver(bsopt=bsopt, **kwargs)
print("Completed optimization for {0:d} {1}-type functions".format(nf, shell))
print("{s:<25s} : {v:>20.10f}".format(s="Current Function value", v=res.fun))
print("{s:<25s} : {v:>20.10f}".format(s="Previous Function value", v=e_last))
print("{s:<25s} : {v:>20.10f}".format(s="Difference", v=abs(res.fun-e_last)))
if abs(res.fun - e_last) < opt_tol:
print("Basis saturated with respect to threshold")
bsopt['nfpshell'] = nfps_last
if save:
save_basis(x_last, bsopt)
return BasisSet.from_optdict(x_last, bsopt)
else:
print("Threshold exceeded, continuing optimization.")
x_last = res.x.tolist()
nfps_last = bsopt['nfpshell']
if len(bsopt['params'][0]) < max_params:
print("adding more parameters")
restup = tuple(res.x)
# this assumption should be revised, improved or justified
# suggestion: maybe change to average of existing parameters
restup += tuple([abs(min(restup)*0.25)])
bsopt['params'] = [restup,]
else:
print("not adding more parameters")
bsopt['params'] = [tuple(res.x),]
e_last = res.fun
else:
print("Supplied number of functions exhausted but the required accuracy was not reached")
if save:
save_basis(res.x.tolist(), bsopt)
return BasisSet.from_optdict(res.x.tolist(), bsopt)