def solve(self, T = None, tol = None, nl_tol = 1e-5):
def time_finish(t):
if T:
if t >= T:
return True
return False
def converged(du):
if tol:
if du < tol:
return True
return False
tic()
delta = 1e10
while not (time_finish(self.t) or converged(delta)):
# ADAPTIVE TIMESTEP
if self.adapt_timestep and (self.t > 0.0 or self.adapt_initial_timestep):
solve(self.a_k == self.L_k, self.k)
self.timestep = self.k.vector().array()[0]
# SOLVE COUPLED EQUATIONS
# solve(self.F == 0, self.w[0], bcs=self.bc, solver_parameters=solver_parameters)
solve(self.F == 0, self.w[0], bcs=self.bc, J=self.J, solver_parameters=solver_parameters)
if tol:
delta = 0.0
f_list = [[self.w[0].split()[i], self.w[1].split()[i]] for i in range(len(self.w[0].split()))]
for f_0, f_1 in f_list:
delta = max(errornorm(f_0, f_1, norm_type="L2", degree_rise=1)/self.timestep, delta)
self.w[1].assign(self.w[0])
self.t += self.timestep
# display results
if self.plot:
if self.t > self.plot_t:
self.plotter.update_plot(self)
self.plot_t += self.plot
# save data
if self.write:
if self.t > self.write_t:
sw_io.write_model_to_files(self, 'a', file=self.save_loc)
self.write_t += self.write
# write timestep info
sw_io.print_timestep_info(self, delta)
print "\n* * * Initial forward run finished: time taken = {}".format(toc())
list_timings(True)
if self.plot:
self.plotter.clean_up()
def solve(self, T = None, tol = None, nl_tol = 1e-5):
def time_finish(t):
if T:
if t >= T:
return True
return False
def converged(du):
if tol:
if du < tol:
return True
return False
tic()
delta = 1e10
while not (time_finish(self.t) or converged(delta)):
# ADAPTIVE TIMESTEP
if self.adapt_timestep and (self.t > 0.0 or self.adapt_initial_timestep):
solve(self.a_k == self.L_k, self.k)
self.timestep = self.k.vector().array()[0]
# M = assemble(self.J)
# U, s, Vh = scipy.linalg.svd(M.array())
# cond = s.max()/s.min()
# print cond, s.min(), s.max()
# SOLVE COUPLED EQUATIONS
if self.time_discretise.im_func == runge_kutta:
# store previous solution
self.w[2].assign(self.w[1])
# runge kutta timestep
solve(self.F == 0, self.w[0], bcs=self.bc, J=self.J, solver_parameters=solver_parameters)
if self.slope_limiter:
self.slope_limit()
# # 2nd order
# self.w[1].assign(self.w[0])
# solve(self.F_RK == 0, self.w[0], bcs=self.bc, J=self.J_RK,
# solver_parameters=solver_parameters)
# 3rd order
self.w[1].assign(self.w[0])
solve(self.F_RK1 == 0, self.w[0], bcs=self.bc, J=self.J_RK1,
solver_parameters=solver_parameters)
if self.slope_limiter:
self.slope_limit()
self.w[1].assign(self.w[0])
solve(self.F_RK2 == 0, self.w[0], bcs=self.bc, J=self.J_RK2,
solver_parameters=solver_parameters)
# replace previous solution
self.w[1].assign(self.w[2])
else:
solve(self.F == 0, self.w[0], bcs=self.bc, J=self.J, solver_parameters=solver_parameters)
if self.slope_limiter:
self.slope_limit()
if tol:
delta = 0.0
f_list = [[self.w[0].split()[i], self.w[1].split()[i]] for i in range(len(self.w[0].split()))]
for f_0, f_1 in f_list:
delta = max(errornorm(f_0, f_1, norm_type="L2", degree_rise=1)/self.timestep, delta)
self.w[1].assign(self.w[0])
self.t += self.timestep
# display results
if self.plot:
if self.t > self.plot_t:
self.plotter.update_plot(self)
self.plot_t += self.plot
# save data
if self.write:
if self.t > self.write_t:
sw_io.write_model_to_files(self, 'a', file=self.save_loc)
self.write_t += self.write
# write timestep info
sw_io.print_timestep_info(self, delta)
print "\n* * * Initial forward run finished: time taken = {}".format(toc())
list_timings(True)
if self.plot:
self.plotter.clean_up()
# error calc callback
if self.error_callback:
return self.error_callback(self)
