def solve(self, interval, dt=None):
"""
Solve the problem given by the model on a given time interval
(t0, t1) with a given timestep dt and return generator for a
tuple of the interval and the current vs solution.
*Arguments*
interval (:py:class:`tuple`)
The time interval for the solve given by (t0, t1)
dt (int, optional)
The timestep for the solve. Defaults to length of interval
*Returns*
(timestep, current vs) via (:py:class:`genexpr`)
*Example of usage*::
# Create generator
solutions = solver.solve((0.0, 1.0), 0.1)
# Iterate over generator (computes solutions as you go)
for (interval, vs) in solutions:
# do something with the solutions
"""
# Initial time set-up
(T0, T) = interval
# Solve on entire interval if no interval is given.
if dt is None:
dt = (T - T0)
# Create timestepper
time_stepper = TimeStepper(interval, dt, \
annotate=self.parameters["enable_adjoint"])
for t0, t1 in time_stepper:
info_blue("Solving on t = (%g, %g)" % (t0, t1))
self.step((t0, t1))
# Yield solutions
yield (t0, t1), self.vs
# FIXME: This eventually breaks in parallel!?
self.vs_.assign(self.vs)
def solve(self, interval, dt):
"""
Solve the problem given by the model on a given time interval
(t0, t1) with a given timestep dt and return generator for a
tuple of the time step and the solution fields.
*Arguments*
interval (:py:class:`tuple`)
The time interval for the solve given by (t0, t1)
dt (int, list of tuples of floats)
The timestep for the solve. A list of tuples of floats can
also be passed. Each tuple should contain two floats where the
first includes the start time and the second the dt.
*Returns*
(timestep, solution_fields) via (:py:class:`genexpr`)
*Example of usage*::
# Create generator
dts = [(0., 0.1), (1.0, 0.05), (2.0, 0.1)]
solutions = solver.solve((0.0, 1.0), dts)
# Iterate over generator (computes solutions as you go)
for ((t0, t1), (vs_, vs, vur)) in solutions:
# do something with the solutions
"""
# Create timestepper
time_stepper = TimeStepper(interval, dt, \
annotate=self.parameters["enable_adjoint"])
for t0, t1 in time_stepper:
info_blue("Solving on t = (%g, %g)" % (t0, t1))
self.step((t0, t1))
# Yield solutions
yield (t0, t1), self.solution_fields()
# Update previous solution
self.vs_.assign(self.vs)
def solve(self, interval, dt):
"""
Solve the problem given by the model on a given time interval
(t0, t1) with a given timestep dt and return generator for a
tuple of the time step and the solution fields.
*Arguments*
interval (:py:class:`tuple`)
The time interval for the solve given by (t0, t1)
dt (int)
The timestep for the solve
*Returns*
(timestep, solution_fields) via (:py:class:`genexpr`)
*Example of usage*::
# Create generator
solutions = solver.solve((0.0, 1.0), 0.1)
# Iterate over generator (computes solutions as you go)
for ((t0, t1), (v, vur)) in solutions:
# do something with the solutions
"""
# Create timestepper
time_stepper = TimeStepper(interval, dt, \
annotate=self.parameters["enable_adjoint"])
for t0, t1 in time_stepper:
info_blue("Solving on t = (%g, %g)" % (t0, t1))
self.step((t0, t1))
# Yield solutions
yield (t0, t1), self.solution_fields()