def reconstruct_potential_sd(grid_in,
refdens_in,
startpot_in,
occs_in,
denserr=1e-4,
method=None):
from bfgs_only_fprime import fmin_bfgs_onlygrad, fmin_newton_onlygrad
from scipy.optimize import fmin_bfgs
funcs = CalcFuncGrad(grid_in,
occs_in,
refdens_in,
startpot_in * grid_in.r,
lambd=1e-8)
#funcs = CalcFuncGrad(grid_in, occs_in, refdens_in, startpot_in*grid_in.r, lambd=0.0)
if (method is None) or (method == 'BFGS'):
recpot = fmin_bfgs(funcs.lagrangian,
startpot_in * grid_in.r,
fprime=funcs.gradient,
callback=funcs.info,
norm=2,
disp=True,
gtol=1e-8) / grid_in.r
elif (method == 'BFGS_OnlyGrad'):
recpot = fmin_bfgs_onlygrad(funcs.gradient,
startpot_in * grid_in.r,
invhess=funcs.invhess,
callback=funcs.info,
gtol=1e-10) / grid_in.r
else:
raise Exception('Unknown optimization method')
return recpot - startpot_in
def reconstruct_spinpotential_sd(grid_in,
refdens_alpha_in,
refdens_beta_in,
startpot_in,
occs_alpha_in,
occs_beta_in,
lamb_tot=0.0,
lamb_spin=0.0,
gradnorm=1e-8):
from bfgs_only_fprime import fmin_bfgs_onlygrad, fmin_newton_onlygrad
from scipy.optimize import fmin_bfgs
funcs = CalcFuncGradSpin(grid_in, occs_alpha_in, occs_beta_in,
refdens_alpha_in, refdens_beta_in,
startpot_in * grid_in.r, lamb_tot, lamb_spin)
startpot = numpy.concatenate(
[startpot_in * grid_in.r, startpot_in * grid_in.r])
recpot = fmin_bfgs_onlygrad(funcs.gradient,
startpot,
invhess=funcs.invhess,
callback=funcs.info,
gtol=gradnorm)
return recpot[:grid_in.N] / grid_in.r - startpot_in, recpot[
grid_in.N:] / grid_in.r - startpot_in,
def reconstruct_restrpotential_sd(grid_in, refdens_tot_in, startpot_in, occs_in, ons, lamb, gradnorm=1e-8):
from bfgs_only_fprime import fmin_bfgs_onlygrad, fmin_newton_onlygrad
from scipy.optimize import fmin_bfgs
funcs = CalcFuncGrad(grid_in, occs_in, refdens_tot_in, startpot_in * grid_in.r, ons, lamb)
recpot = fmin_bfgs_onlygrad(funcs.gradient, startpot_in * grid_in.r, invhess=funcs.invhess, callback=funcs.info,
gtol=gradnorm)
return recpot / grid_in.r - startpot_in