def test_time_event(self):
f = lambda t,y,yd: y-yd
global tnext
global nevent
tnext = 0.0
nevent = 0
def time_events(t,y,yd,sw):
global tnext,nevent
events = [1.0, 2.0, 2.5, 3.0]
for ev in events:
if t < ev:
tnext = ev
break
else:
tnext = None
nevent += 1
return tnext
def handle_event(solver, event_info):
solver.y+= 1.0
global tnext
nose.tools.assert_almost_equal(solver.t, tnext)
assert event_info[0] == []
assert event_info[1] == True
exp_mod = Implicit_Problem(f,0.0,0.0)
exp_mod.time_events = time_events
exp_mod.handle_event = handle_event
#CVode
exp_sim = IDA(exp_mod)
exp_sim(5.,100)
assert nevent == 5
def test_time_event(self):
f = lambda t, y, yd: y - yd
global tnext
global nevent
tnext = 0.0
nevent = 0
def time_events(t, y, yd, sw):
global tnext, nevent
events = [1.0, 2.0, 2.5, 3.0]
for ev in events:
if t < ev:
tnext = ev
break
else:
tnext = None
nevent += 1
return tnext
def handle_event(solver, event_info):
solver.y += 1.0
global tnext
nose.tools.assert_almost_equal(solver.t, tnext)
assert event_info[0] == []
assert event_info[1] == True
exp_mod = Implicit_Problem(f, 0.0, 0.0)
exp_mod.time_events = time_events
exp_mod.handle_event = handle_event
#CVode
exp_sim = IDA(exp_mod)
exp_sim(5., 100)
assert nevent == 5
def simulate(self, Tend, nIntervals, gridWidth):
problem = Implicit_Problem(self.rhs, self.y0, self.yd0)
problem.name = 'IDA'
# solver.rhs = self.right_hand_side
problem.handle_result = self.handle_result
problem.state_events = self.state_events
problem.handle_event = self.handle_event
problem.time_events = self.time_events
problem.finalize = self.finalize
# Create IDA object and set additional parameters
simulation = IDA(problem)
simulation.atol = self.atol
simulation.rtol = self.rtol
simulation.verbosity = self.verbosity
if hasattr(simulation, 'continuous_output'):
simulation.continuous_output = False # default 0, if one step approach should be used
elif hasattr(simulation, 'report_continuously'):
simulation.report_continuously = False # default 0, if one step approach should be used
simulation.tout1 = self.tout1
simulation.lsoff = self.lsoff
# Calculate nOutputIntervals:
if gridWidth <> None:
nOutputIntervals = int((Tend - self.t0) / gridWidth)
else:
nOutputIntervals = nIntervals
# Check for feasible input parameters
if nOutputIntervals == 0:
print 'Error: gridWidth too high or nIntervals set to 0! Continue with nIntervals=1'
nOutputIntervals = 1
# Perform simulation
simulation.simulate(Tend, nOutputIntervals) # to get the values: t_new, y_new, yd_new = simulation.simulate
def simulate(self, Tend, nIntervals, gridWidth):
problem = Implicit_Problem(self.rhs, self.y0, self.yd0)
problem.name = 'IDA'
# solver.rhs = self.right_hand_side
problem.handle_result = self.handle_result
problem.state_events = self.state_events
problem.handle_event = self.handle_event
problem.time_events = self.time_events
problem.finalize = self.finalize
# Create IDA object and set additional parameters
simulation = IDA(problem)
simulation.atol = self.atol
simulation.rtol = self.rtol
simulation.verbosity = self.verbosity
if hasattr(simulation, 'continuous_output'):
simulation.continuous_output = False # default 0, if one step approach should be used
elif hasattr(simulation, 'report_continuously'):
simulation.report_continuously = False # default 0, if one step approach should be used
simulation.tout1 = self.tout1
simulation.lsoff = self.lsoff
# Calculate nOutputIntervals:
if gridWidth <> None:
nOutputIntervals = int((Tend - self.t0) / gridWidth)
else:
nOutputIntervals = nIntervals
# Check for feasible input parameters
if nOutputIntervals == 0:
print 'Error: gridWidth too high or nIntervals set to 0! Continue with nIntervals=1'
nOutputIntervals = 1
# Perform simulation
simulation.simulate(Tend, nOutputIntervals) # to get the values: t_new, y_new, yd_new = simulation.simulate
def test_time_event(self):
"""
This tests the functionality of the time event function.
"""
f = lambda t, x, xd, sw: xd - x
def time_events(t, y, yd, sw):
if sw[0]:
return 1.0
if sw[1]:
return 3.0
return None
def handle_event(solver, event_info):
if event_info[1]:
solver.y = N.array([1.0])
solver.yd = N.array([1.0])
if not solver.sw[0]:
solver.sw[1] = False
if solver.sw[0]:
solver.sw[0] = False
mod = Implicit_Problem(f, [1.0], [1.0])
mod.time_events = time_events
mod.handle_event = handle_event
mod.switches0 = [True, True]
sim = IDA(mod)
sim.simulate(5.0)
nose.tools.assert_almost_equal(sim.y_sol[38], 1.0000000, 5)
nose.tools.assert_almost_equal(sim.y_sol[87], 1.0000000, 5)
sim = IDA(mod, [1.0], [1.0])
sim.simulate(2.0)
nose.tools.assert_almost_equal(sim.t_sol[-1], 2.0000000, 5)
def test_time_event(self):
"""
This tests the functionality of the time event function.
"""
f = lambda t,x,xd,sw: xd-x
def time_events(t, y, yd, sw):
if sw[0]:
return 1.0
if sw[1]:
return 3.0
return None
def handle_event(solver, event_info):
if event_info[1]:
solver.y = N.array([1.0])
solver.yd = N.array([1.0])
if not solver.sw[0]:
solver.sw[1] = False
if solver.sw[0]:
solver.sw[0] = False
mod = Implicit_Problem(f,[1.0],[1.0])
mod.time_events = time_events
mod.handle_event = handle_event
mod.switches0 = [True, True]
sim = IDA(mod)
sim.simulate(5.0)
nose.tools.assert_almost_equal(sim.y_sol[38], 1.0000000, 5)
nose.tools.assert_almost_equal(sim.y_sol[87], 1.0000000, 5)
sim = IDA(mod, [1.0],[1.0])
sim.simulate(2.0)
nose.tools.assert_almost_equal(sim.t_sol[-1], 2.0000000, 5)
def test_clear_event_log(self):
"""
This tests the functionality of the time event function.
"""
f = lambda t, x, xd, sw: xd - x
def time_events(t, y, yd, sw):
if sw[0]:
return 1.0
if sw[1]:
return 3.0
return None
def handle_event(solver, event_info):
if event_info[1]:
solver.y = N.array([1.0])
solver.yd = N.array([1.0])
if not solver.sw[0]:
solver.sw[1] = False
if solver.sw[0]:
solver.sw[0] = False
mod = Implicit_Problem(f, [1.0], [1.0], sw0=[True, True])
mod.time_events = time_events
mod.handle_event = handle_event
sim = IDA(mod)
sim.verbosity = 10
assert len(sim.event_data) == 0
sim.simulate(5.0)
assert len(sim.event_data) > 0
sim.reset()
assert len(sim.event_data) == 0
sim.simulate(5.0)
assert len(sim.event_data) > 0