"""
f2py.compile(prob_def_f, modulename='problemdef', verbose=0)
"""
Part 2
Using the compiled callback routines to solve the problem.
"""
import bacoli_py
import numpy
import time
from problemdef import f, bndxa, bndxb, uinit
# Initialize the Solver object.
solver = bacoli_py.Solver()
# Specify the number of PDE's in this system.
npde = 1
# Pack all of these callbacks and the number of PDE's into a
# ProblemDefinition object.
problem_definition = bacoli_py.ProblemDefinition(npde,
f=f._cpointer,
bndxa=bndxa._cpointer,
bndxb=bndxb._cpointer,
uinit=uinit._cpointer)
# Specify initial mesh, output_points and output_times.
# Set t0.
# Solving the nonlinear Schrodinger system.
# ------------------------------------------------------------------
import bacoli_py
import numpy
# Create Solver object. Here use the Runge-Kutta method for time
# integration and allow a large number of spatial subintervals to be
# used.
solver = bacoli_py.Solver(t_int='r', nint_max=2000)
# The number of PDEs in this system.
npde = 4
# Initialize problem-dependent parameters.
tempt1 = numpy.sqrt(6.0 / 5.0)
tempt2 = numpy.sqrt(2.0)
# Function defining the PDE system.
def f(t, x, u, ux, uxx, fval):
fval[0] = -0.5*ux[0] - 0.5*uxx[1] - u[1] \
* ((u[0] * u[0] + u[1] * u[1]) + 2.0/3.0 \
* ((u[2] * u[2] + u[3] * u[3])))
fval[1] = - 0.5 * ux[1] + 0.5 * uxx[0] + u[0] \
* ((u[0] * u[0] + u[1] * u[1]) + 2.0/3.0 \
* ((u[2] * u[2] + u[3] * u[3])))
fval[2] = 0.5 * ux[2] - 0.5 * uxx[3] - u[3] \