def ode_julia_naive(coords,
pot,
tol=1e-4,
nsteps=20000,
convergence_check=None,
solver_type=de.CVODE_BDF(),
rtol=1e-4,
atol=1e-4,
**kwargs):
class feval_pot:
""" wrapper class that interfaces base potential to functions for ode solver
"""
def __init__(self):
self.nfev = 0
self.nhev = 0
def get_negative_grad(self, x, p, t):
"""
negative grad is f(u, p, t)
"""
self.nfev += 1
return -pot.getEnergyGradient(x.copy())[1]
def get_energy_gradient(self, x):
self.nfev += 1
return pot.getEnergyGradient(x.copy())
def get_jacobian(self, x, p, t):
self.nhev += 1
return -pot.getEnergyGradientHessian(x.copy())[2]
function_evaluate_pot = feval_pot()
converged = False
n = 0
if convergence_check == None:
convergence_check = lambda g: np.linalg.norm(g) < tol
# odefunc = de.ODEFunction(function_evaluate_pot.get_negative_grad, function_evaluate_pot.get_jacobian)
# initialize ode problem
tspan = (0, 10000.0)
f_bound = de.ODEFunction(function_evaluate_pot.get_negative_grad)
# f_free = de.ODEFunction(get_negative_grad,jac = get_jacobian)
prob = de.ODEProblem(f_bound, coords, tspan)
solver = Main.eval("CVODE_BDF(linear_solver=:GMRES)")
integrator = de.init(prob, solver, reltol=rtol, abstol=atol)
x_ = np.full(len(coords), np.nan)
while not converged and n < nsteps:
xold = x_
de.step_b(integrator)
x_ = integrator.u
n += 1
converged = convergence_check(de.get_du(integrator))
res = Result()
res.coords = x_
res.energy = pot.getEnergy(x_)
res.grad = 0
res.nfev = function_evaluate_pot.nfev
res.nsteps = n
res.nhev = function_evaluate_pot.nhev
res.success = converged
# res.nhev = function_evaluate_pot.nhev
return res
