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 run_example():
#initial values
t0 = 0
y0 = np.array([0., -0.10344, -0.65, 0., 0. , 0., -0.628993, 0.047088]) #|phi_s| <= 0.1034 rad, |phi_b| <= 0.12 rad
yd0 = np.array([0., 0., 0., 0., 0., 0., 0., 0.])
sw = [False, True, False]
#problem
model = Implicit_Problem(pecker, y0, yd0, t0, sw0=sw)
model.state_events = state_events #from woodpecker.py
model.handle_event = handle_event #from woodpecker.py
model.name = 'Woodpeckermodel'
sim = IDA(model) #create IDA solver
tfinal = 2.0 #final time
ncp = 500 #number control points
sim.suppress_alg = True
sim.rtol=1.e-6
sim.atol[6:8] = 1e6
sim.algvar[6:8] = 0
t, y, yd = sim.simulate(tfinal, ncp) #simulate
#plot
fig, ax = P.subplots()
ax.plot(t, y[:, 0], label='z')
legend = ax.legend(loc='upper center', shadow=True)
P.grid()
P.figure(1)
fig2, ax2 = P.subplots()
ax2.plot(t, y[:, 1], label='phi_s')
ax2.plot(t, y[:, 2], label='phi_b')
legend = ax2.legend(loc='upper center', shadow=True)
P.grid()
P.figure(2)
fig3, ax3 = P.subplots()
ax3.plot(t, y[:, 4], label='phi_sp')
ax3.plot(t, y[:, 5], label='phi_bp')
legend = ax3.legend(loc='upper center', shadow=True)
P.grid()
P.figure(3)
fig4, ax4 = P.subplots()
ax4.plot(t, y[:, 6], label='lambda_1')
ax4.plot(t, y[:, 7], label='lambda_2')
legend = ax4.legend(loc='upper right', shadow=True)
P.grid()
#event data
sim.print_event_data()
#show plots
P.show()
print("...")
P.show()
def test_switches(self):
"""
This tests that the switches are actually turned when override.
"""
f = lambda t,x,xd,sw: N.array([xd[0]- 1.0])
state_events = lambda t,x,xd,sw: N.array([x[0]-1.])
def handle_event(solver, event_info):
solver.sw = [False] #Override the switches to point to another instance
mod = Implicit_Problem(f, [0.0],[1.0])
mod.f = f
mod.sw0 = [True]
mod.state_events = state_events
mod.handle_event = handle_event
sim = IDA(mod)
assert sim.sw[0] == True
sim.simulate(3)
assert sim.sw[0] == False
def test_switches(self):
"""
This tests that the switches are actually turned when override.
"""
res = lambda t,x,xd,sw: N.array([1.0 - xd])
state_events = lambda t,x,xd,sw: N.array([x[0]-1.])
def handle_event(solver, event_info):
solver.sw = [False] #Override the switches to point to another instance
mod = Implicit_Problem(res,[0.0], [1.0])
mod.sw0 = [True]
mod.state_events = state_events
mod.handle_event = handle_event
sim = Radau5DAE(mod)
assert sim.sw[0] == True
sim.simulate(3)
assert sim.sw[0] == False
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
# State IV => State III
if solver.sw[3]:
solver.sw[3] = not solver.sw[3]
solver.sw[2] = not solver.sw[2]
y0,yp0 = init_woodpecker()
t0 = 0.0
sw0 = np.array([1,0,0,0])
model = Implicit_Problem(woodpecker,y0,yp0,t0,sw0)
model.state_events = state_events
model.handle_event = handle_event
solver = IDA(model)
solver.simulate(3)
from woodpecker_model import init_woodpecker
from woodpecker_model import res
from state_event_woodpecker import state_event
from handle_event_woodpecker import handle_event
from assimulo.problem import Implicit_Problem
from assimulo.solvers import IDA
import matplotlib.pyplot as P
import numpy as N
t0 = 0.0
tfinal = 2.0
y0, yd0, switches0 = init_woodpecker()
model = Implicit_Problem(res, y0, yd0, t0, sw0=switches0)
model.state_events = state_event
model.handle_event = handle_event
sim = IDA(model)
sim.algvar = [1, 1, 1, 0, 0, 0, 0, 0]
sim.suppress_alg = True
sim.atol = [1e-5, 1e-5, 1e-5, 1e15, 1e15, 1e15, 1e15, 1e15]
t, y, yd = sim.simulate(tfinal)
#sim.print_event_data()
"""
Plotting
y[:,0] - plots z (height)
y[:,1] - plots sleeve angle
y[:,2] - plots bird angle
"""
elif state_info[3] != 0:
if solver.sw[2] == 1 and yp[2] > 0:
yp[2] = -yp[2]
print("DING")
solver.yd = yp
solver.y = y
t0 = 0
tfinal = 1
ncp = 500
y0 = [0., -0.10344, -0.65, 0. ,0. , 0., -0.6911, -0.14161]
yp0 = [0., 0., 0., 0., 0., 1.40059e2, 0., 0.]
switches0 = [False, True, False]
mod = Implicit_Problem(woodpeckertoy, y0, yp0, t0, sw0 = switches0)
mod.state_events = state_events
mod.handle_event = handle_event
sim = IDA(mod)
sim.suppress_alg = True
sim.rtol=1.e-6
sim.atol[3:8] = 1e6
sim.algvar[3:8] = 0.
t, y, yp = sim.simulate(tfinal, ncp)
P.plot(t,y[:,0:3])
P.legend(["z","phiS", "phiB", "z vel", "phiS vel", "phiB vel"])
P.ylabel('y')
P.xlabel('x')
fontlabel_size = 20
tick_size = 14
params = {'lines.markersize' : 0, 'axes.labelsize': fontlabel_size, 'text.fontsize': fontlabel_size, 'legend.fontsize': fontlabel_size, 'xtick.labelsize': tick_size, 'ytick.labelsize': tick_size}
startsw = [1,0,0,0] y0 = numpy.array([0.5, 0,0, -0, 0, 0.5,-1e-4,0]) yd0 = numpy.array([-0, 0, 0.5,-g, 1e-12, 0, 0, 0]) w0 = -0.91 y0 = numpy.array([0.5, 0,0, -0, w0, w0,-1e-4,0]) yd0 = numpy.array([-0, w0, w0,-g, 1e-12, 0, 0, 0]) #y0 = numpy.array([4.83617428e-01, -3.00000000e-02, -2.16050178e-01, 1.67315232e-16, -5.39725367e-14, -1.31300925e+01, -7.20313572e-02, -6.20545138e-02]) #yd0 = numpy.array([1.55140566e-17, -5.00453439e-15, -1.31302838e+01, 6.62087352e-13, -2.13577297e-10, 2.21484026e+02, -4.67637454e+00, -2.89824658e+00]) #startsw = [0,1, 0, 0] problem = Implicit_Problem(res, y0, yd0, t0, sw0=startsw) problem.state_events = state_events problem.handle_event = handle_event problem.name = 'Woodpecker' phipIndex = [4, 5] lambdaIndex = [6, 7] sim = IDA(problem) sim.rtol = 1e-6 sim.atol[phipIndex] = 1e8 sim.algvar[phipIndex] = 1 sim.atol[lambdaIndex] = 1e8 sim.algvar[lambdaIndex] = 1 sim.suppress_alg = True ncp = 500
startsw = [1,0,0,0] y0 = numpy.array([0.5, 0,0, -0, 0, 0.5,-1e-4,0]) yd0 = numpy.array([-0, 0, 0.5,-g, 1e-12, 0, 0, 0]) w0 = -0.91 y0 = numpy.array([0.5, 0,0, -0, w0, w0,-1e-4,0]) yd0 = numpy.array([-0, w0, w0,-g, 1e-12, 0, 0, 0]) #y0 = numpy.array([4.83617428e-01, -3.00000000e-02, -2.16050178e-01, 1.67315232e-16, -5.39725367e-14, -1.31300925e+01, -7.20313572e-02, -6.20545138e-02]) #yd0 = numpy.array([1.55140566e-17, -5.00453439e-15, -1.31302838e+01, 6.62087352e-13, -2.13577297e-10, 2.21484026e+02, -4.67637454e+00, -2.89824658e+00]) #startsw = [0,1, 0, 0] problem = Implicit_Problem(res, y0, yd0, t0, sw0=startsw) problem.state_events = state_events problem.handle_event = handle_event problem.name = 'Woodpecker' phipIndex = [4, 5] lambdaIndex = [6, 7] sim = IDA(problem) sim.rtol = 1e-6 sim.atol[phipIndex] = 1e8 sim.algvar[phipIndex] = 1 sim.atol[lambdaIndex] = 1e8 sim.algvar[lambdaIndex] = 1 sim.suppress_alg = True ncp = 500