def simulate(self, Tend, nIntervals, gridWidth):
problem = Explicit_Problem(self.rhs, self.y0)
problem.name = 'CVode'
# 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
simulation = CVode(problem)
# Change multistep method: 'adams' or 'VDF'
if self.discr == 'Adams':
simulation.discr = 'Adams'
simulation.maxord = 12
else:
simulation.discr = 'BDF'
simulation.maxord = 5
# Change iteration algorithm: functional(FixedPoint) or newton
if self.iter == 'FixedPoint':
simulation.iter = 'FixedPoint'
else:
simulation.iter = 'Newton'
# Sets additional parameters
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
# '''Initialize problem '''
# self.t_cur = self.t0
# self.y_cur = self.y0
# 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 = simulation.simulate
def simulate(self, Tend, nIntervals, gridWidth):
problem = Explicit_Problem(self.rhs, self.y0)
problem.name = 'CVode'
# 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
simulation = CVode(problem)
# Change multistep method: 'adams' or 'VDF'
if self.discr == 'Adams':
simulation.discr = 'Adams'
simulation.maxord = 12
else:
simulation.discr = 'BDF'
simulation.maxord = 5
# Change iteration algorithm: functional(FixedPoint) or newton
if self.iter == 'FixedPoint':
simulation.iter = 'FixedPoint'
else:
simulation.iter = 'Newton'
# Sets additional parameters
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
# '''Initialize problem '''
# self.t_cur = self.t0
# self.y_cur = self.y0
# 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 = simulation.simulate
def simulate_ode(fun, y_initial, tf, opts):
"function to run CVode solver on given problem"
# get options
ode_opts, ode_system_options = opts
# iter, discretization_method, atol, rtol, time_points = ode_opts
iter = ode_opts["iter"]
discretization_method = ode_opts["discr"]
atol = ode_opts["atol"]
rtol = ode_opts["rtol"]
time_points = ode_opts["time_points"]
try:
display_progress = ode_opts["display_progress"]
except KeyError:
display_progress = True
try:
verbosity = ode_opts["verbosity"]
except KeyError:
verbosity = 10
# define explicit assimulo problem
prob = Explicit_Problem(lambda t, x: fun(t, x, ode_system_options),
y0=y_initial)
# create solver instance
solver = CVode(prob)
# set solver options
solver.iter, solver.discr, solver.atol, solver.rtol, solver.display_progress, solver.verbosity = \
iter, discretization_method, atol, rtol, display_progress, verbosity
# simulate system
time_course, y_result = solver.simulate(tf, time_points)
return time_course, y_result, prob, solver
def run_example(with_plots=True):
r"""
Demonstration of the use of CVode by solving the
linear test equation :math:`\dot y = - y`
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
#Define the rhs
def f(t, y):
ydot = -y[0]
return N.array([ydot])
#Define an Assimulo problem
exp_mod = Explicit_Problem(f,
y0=4,
name=r'CVode Test Example: $\dot y = - y$')
#Define an explicit solver
exp_sim = CVode(exp_mod) #Create a CVode solver
#Sets the parameters
exp_sim.iter = 'Newton' #Default 'FixedPoint'
exp_sim.discr = 'BDF' #Default 'Adams'
exp_sim.atol = [1e-4] #Default 1e-6
exp_sim.rtol = 1e-4 #Default 1e-6
#Simulate
t1, y1 = exp_sim.simulate(5, 100) #Simulate 5 seconds
t2, y2 = exp_sim.simulate(7) #Simulate 2 seconds more
#Plot
if with_plots:
import pylab as P
P.plot(t1, y1, color="b")
P.plot(t2, y2, color="r")
P.title(exp_mod.name)
P.ylabel('y')
P.xlabel('Time')
P.show()
#Basic test
nose.tools.assert_almost_equal(float(y2[-1]), 0.00347746, 5)
nose.tools.assert_almost_equal(exp_sim.get_last_step(), 0.0222169642893, 3)
return exp_mod, exp_sim
def run_example(with_plots=True):
"""
The same as example :doc:`EXAMPLE_cvode_basic` but now integrated backwards in time.
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
#Define the rhs
def f(t, y):
ydot = -y[0]
return N.array([ydot])
#Define an Assimulo problem
exp_mod = Explicit_Problem(
f,
t0=5,
y0=0.02695,
name=r'CVode Test Example (reverse time): $\dot y = - y$ ')
#Define an explicit solver
exp_sim = CVode(exp_mod) #Create a CVode solver
#Sets the parameters
exp_sim.iter = 'Newton' #Default 'FixedPoint'
exp_sim.discr = 'BDF' #Default 'Adams'
exp_sim.atol = [1e-8] #Default 1e-6
exp_sim.rtol = 1e-8 #Default 1e-6
exp_sim.backward = True
#Simulate
t, y = exp_sim.simulate(0) #Simulate 5 seconds (t0=5 -> tf=0)
#print 'y(5) = {}, y(0) ={}'.format(y[0][0],y[-1][0])
#Basic test
nose.tools.assert_almost_equal(float(y[-1]), 4.00000000, 3)
#Plot
if with_plots:
P.plot(t, y, color="b")
P.title(exp_mod.name)
P.ylabel('y')
P.xlabel('Time')
P.show()
return exp_mod, exp_sim
def make_explicit_sim(self):
explicit_sim = CVode(self.explicit_problem)
explicit_sim.iter = 'Newton'
explicit_sim.discr = 'BDF'
explicit_sim.rtol = 1e-7
explicit_sim.atol = 1e-7
explicit_sim.sensmethod = 'SIMULTANEOUS'
explicit_sim.suppress_sens = True
explicit_sim.report_continuously = False
explicit_sim.usesens = False
explicit_sim.verbosity = 50
if self.use_jac and self.model_jac is not None:
explicit_sim.usejac = True
else:
explicit_sim.usejac = False
return explicit_sim
def setUp(self): """ Configures the solver """ #Define an explicit solver simSolver = CVode(self) #Create a CVode solver #Sets the parameters #simSolver.verbosity = LOUD #simSolver.report_continuously = True simSolver.iter = 'Newton' #Default 'FixedPoint' simSolver.discr = 'BDF' #Default 'Adams' #simSolver.discr = 'Adams' simSolver.atol = [1e-6] #Default 1e-6 simSolver.rtol = 1e-6 #Default 1e-6 #simSolver.problem_info['step_events'] = True # activates step events #simSolver.maxh = 1.0 #simSolver.store_event_points = True self.simSolver = simSolver
def setUp(self):
"""
Configures the solver
"""
#Define an explicit solver
simSolver = CVode(self)
#Create a CVode solver
#Sets the parameters
#simSolver.verbosity = LOUD
#simSolver.report_continuously = True
simSolver.iter = 'Newton' #Default 'FixedPoint'
simSolver.discr = 'BDF' #Default 'Adams'
#simSolver.discr = 'Adams'
simSolver.atol = [1e-6] #Default 1e-6
simSolver.rtol = 1e-6 #Default 1e-6
#simSolver.problem_info['step_events'] = True # activates step events
#simSolver.maxh = 1.0
#simSolver.store_event_points = True
self.simSolver = simSolver
def prepareSimulation(self, params = None):
if params == None:
params = AttributeDict({
'absTol' : 1e-6,
'relTol' : 1e-6,
})
#Define an explicit solver
simSolver = CVode(self)
#Create a CVode solver
#Sets the parameters
#simSolver.verbosity = LOUD
simSolver.report_continuously = True
simSolver.iter = 'Newton' #Default 'FixedPoint'
simSolver.discr = 'BDF' #Default 'Adams'
#simSolver.discr = 'Adams'
simSolver.atol = [params.absTol] #Default 1e-6
simSolver.rtol = params.relTol #Default 1e-6
simSolver.problem_info['step_events'] = True # activates step events
#simSolver.maxh = 1.0
simSolver.store_event_points = True
self.simSolver = simSolver
def prepareSimulation(self, params=None):
if params == None:
params = AttributeDict({
'absTol': 1e-6,
'relTol': 1e-6,
})
#Define an explicit solver
simSolver = CVode(self)
#Create a CVode solver
#Sets the parameters
#simSolver.verbosity = LOUD
simSolver.report_continuously = True
simSolver.iter = 'Newton' #Default 'FixedPoint'
simSolver.discr = 'BDF' #Default 'Adams'
#simSolver.discr = 'Adams'
simSolver.atol = [params.absTol] #Default 1e-6
simSolver.rtol = params.relTol #Default 1e-6
simSolver.problem_info['step_events'] = True # activates step events
#simSolver.maxh = 1.0
simSolver.store_event_points = True
self.simSolver = simSolver
def simulate(self, Tend, nIntervals, gridWidth):
# define assimulo problem:(has to be done here because of the starting value in Explicit_Problem
solver = Explicit_Problem(self.rhs, self.y0)
''' *******DELETE LATER '''''''''
# problem.handle_event = handle_event
# problem.state_events = state_events
# problem.init_mode = init_mode
solver.handle_result = self.handle_result
solver.name = 'Simple Explicit Example'
simulation = CVode(solver) # Create a RungeKutta34 solver
# simulation.inith = 0.1 #Sets the initial step, default = 0.01
# Change multistep method: 'adams' or 'VDF'
if self.discr == 'Adams':
simulation.discr = 'Adams'
simulation.maxord = 12
else:
simulation.discr = 'BDF'
simulation.maxord = 5
# Change iteration algorithm: functional(FixedPoint) or newton
if self.iter == 'FixedPoint':
simulation.iter = 'FixedPoint'
else:
simulation.iter = 'Newton'
# Sets additional parameters
simulation.atol = self.atol
simulation.rtol = self.rtol
simulation.verbosity = 0
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
# Create Solver and set settings
# noRootFunctions = np.size(self.state_events(self.t0, np.array(self.y0)))
# solver = sundials.CVodeSolver(RHS = self.f, ROOT = self.rootf, SW = [False]*noRootFunctions,
# abstol = self.atol, reltol = self.rtol)
# solver.settings.JAC = None #Add user-dependent jacobian here
'''Initialize problem '''
# solver.init(self.t0, self.y0)
self.handle_result(self.t0, self.y0)
nextTimeEvent = self.time_events(self.t0, self.y0)
self.t_cur = self.t0
self.y_cur = self.y0
state_event = False
#
#
if gridWidth <> None:
nOutputIntervals = int((Tend - self.t0) / gridWidth)
else:
nOutputIntervals = nIntervals
# Define step length depending on if gridWidth or nIntervals has been chosen
if nOutputIntervals > 0:
# Last point on grid (does not have to be Tend:)
if(gridWidth <> None):
dOutput = gridWidth
else:
dOutput = (Tend - self.t0) / nIntervals
else:
dOutput = Tend
outputStepCounter = long(1)
nextOutputPoint = min(self.t0 + dOutput, Tend)
while self.t_cur < Tend:
# Time-Event detection and step time adjustment
if nextTimeEvent is None or nextOutputPoint < nextTimeEvent:
time_event = False
self.t_cur = nextOutputPoint
else:
time_event = True
self.t_cur = nextTimeEvent
try:
# #Integrator step
# self.y_cur = solver.step(self.t_cur)
# self.y_cur = np.array(self.y_cur)
# state_event = False
# Simulate
# take a step to next output point:
t_new, y_new = simulation.simulate(self.t_cur) # 5, 10) #5, 10 self.t_cur self.t_cur 2. argument nsteps Simulate 5 seconds
# t_new, y_new are both vectors of the time and states at t_cur and all intermediate
# points before it! So take last values:
self.t_cur = t_new[-1]
self.y_cur = y_new[-1]
state_event = False
except:
import sys
print "Unexpected error:", sys.exc_info()[0]
# except CVodeRootException, info:
# self.t_cur = info.t
# self.y_cur = info.y
# self.y_cur = np.array(self.y_cur)
# time_event = False
# state_event = True
#
#
# Depending on events have been detected do different tasks
if time_event or state_event:
event_info = [state_event, time_event]
if not self.handle_event(self, event_info):
break
solver.init(self.t_cur, self.y_cur)
nextTimeEvent = self.time_events(self.t_cur, self.y_cur)
# If no timeEvent happens:
if nextTimeEvent <= self.t_cur:
nextTimeEvent = None
if self.t_cur == nextOutputPoint:
# Write output if not happened before:
if not time_event and not state_event:
self.handle_result(nextOutputPoint, self.y_cur)
outputStepCounter += 1
nextOutputPoint = min(self.t0 + outputStepCounter * dOutput, Tend)
self.finalize()
switches0 = [True] #Initial switches #Create an Assimulo Problem mod = Explicit_Problem(pendulum, y0, t0, sw0=switches0) mod.state_events = state_events #Sets the state events to the problem mod.handle_event = handle_event #Sets the event handling to the problem mod.name = 'Pendulum with events' #Sets the name of the problem #Create an Assimulo solver (CVode) sim = CVode(mod) #Specifies options sim.discr = 'Adams' #Sets the discretization method sim.iter = 'FixedPoint' #Sets the iteration method sim.rtol = 1.e-8 #Sets the relative tolerance sim.atol = 1.e-6 #Sets the absolute tolerance #Simulation ncp = 200 #Number of communication points tfinal = 10.0 #Final time t, y = sim.simulate(tfinal, ncp) #Simulate #Plots the result P.plot(t,y)
gamma = p[3]
flux = np.array(
[alpha * y[0], beta * y[0] * y[1], delta * y[0] * y[1], gamma * y[1]])
rhs = np.array([flux[0] - flux[1], flux[2] - flux[3]])
return rhs
if __name__ == '__main__':
p = np.array([.5, .02, .4, .004])
ode_function = lambda t, x: rhs_fun(t, x, p)
# define explicit assimulo problem
prob = Explicit_Problem(ode_function, y0=np.array([10, .0001]))
# create solver instance
solver = CVode(prob)
solver.iter = 'Newton'
solver.discr = 'Adams'
solver.atol = 1e-10
solver.rtol = 1e-10
solver.display_progress = True
solver.verbosity = 10
# simulate system
time_course, y_result = solver.simulate(10, 200)
print time_course
print y_result
def run_example(with_plots=True):
"""
Simulations for the Gyro (Heavy Top) example in Celledoni/Safstrom:
Journal of Physics A, Vol 39, 5463-5478, 2006
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
def curl(v):
return array([[0, v[2], -v[1]], [-v[2], 0, v[0]], [v[1], -v[0], 0]])
#Defines the rhs
def f(t, u):
"""
Simulations for the Gyro (Heavy Top) example in Celledoni/Safstrom:
Journal of Physics A, Vol 39, 5463-5478, 2006
"""
I1 = 1000.
I2 = 5000.
I3 = 6000.
u0 = [0, 0, 1.]
pi = u[0:3]
Q = (u[3:12]).reshape((3, 3))
Qu0 = dot(Q, u0)
f = array([Qu0[1], -Qu0[0], 0.])
f = 0
omega = array([pi[0] / I1, pi[1] / I2, pi[2] / I3])
pid = dot(curl(omega), pi) + f
Qd = dot(curl(omega), Q)
return hstack([pid, Qd.reshape((9, ))])
def energi(state):
energi = []
for st in state:
Q = (st[3:12]).reshape((3, 3))
pi = st[0:3]
u0 = [0, 0, 1.]
Qu0 = dot(Q, u0)
V = Qu0[2] # potential energy
T = 0.5 * (pi[0]**2 / 1000. + pi[1]**2 / 5000. + pi[2]**2 / 6000.)
energi.append([T])
return energi
#Initial conditions
y0 = hstack([[1000. * 10, 5000. * 10, 6000 * 10], eye(3).reshape((9, ))])
#Create an Assimulo explicit problem
exp_mod = Explicit_Problem(f, y0, name="Gyroscope Example")
#Create an Assimulo explicit solver (CVode)
exp_sim = CVode(exp_mod)
#Sets the parameters
exp_sim.discr = 'BDF'
exp_sim.iter = 'Newton'
exp_sim.maxord = 2 #Sets the maxorder
exp_sim.atol = 1.e-10
exp_sim.rtol = 1.e-10
#Simulate
t, y = exp_sim.simulate(0.1)
#Plot
if with_plots:
import pylab as P
P.plot(t, y / 10000.)
P.xlabel('Time')
P.ylabel('States, scaled by $10^4$')
P.title(exp_mod.name)
P.show()
#Basic tests
nose.tools.assert_almost_equal(y[-1][0], 692.800241862)
nose.tools.assert_almost_equal(y[-1][8], 7.08468221e-1)
return exp_mod, exp_sim
def run_example(with_plots=True):
"""
This is the same example from the Sundials package (cvsRoberts_FSA_dns.c)
This simple example problem for CVode, due to Robertson,
is from chemical kinetics, and consists of the following three
equations:
.. math::
\dot y_1 &= -p_1 y_1 + p_2 y_2 y_3 \\
\dot y_2 &= p_1 y_1 - p_2 y_2 y_3 - p_3 y_2^2 \\
\dot y_3 &= p_3 y_2^2
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
def f(t, y, p):
p3 = 3.0e7
yd_0 = -p[0] * y[0] + p[1] * y[1] * y[2]
yd_1 = p[0] * y[0] - p[1] * y[1] * y[2] - p3 * y[1]**2
yd_2 = p3 * y[1]**2
return N.array([yd_0, yd_1, yd_2])
#The initial conditions
y0 = [1.0, 0.0, 0.0] #Initial conditions for y
#Create an Assimulo explicit problem
exp_mod = Explicit_Problem(f,
y0,
name='Sundials test example: Chemical kinetics')
#Sets the options to the problem
exp_mod.p0 = [0.040, 1.0e4] #Initial conditions for parameters
exp_mod.pbar = [0.040, 1.0e4]
#Create an Assimulo explicit solver (CVode)
exp_sim = CVode(exp_mod)
#Sets the paramters
exp_sim.iter = 'Newton'
exp_sim.discr = 'BDF'
exp_sim.rtol = 1.e-4
exp_sim.atol = N.array([1.0e-8, 1.0e-14, 1.0e-6])
exp_sim.sensmethod = 'SIMULTANEOUS' #Defines the sensitvity method used
exp_sim.suppress_sens = False #Dont suppress the sensitivity variables in the error test.
exp_sim.report_continuously = True
#Simulate
t, y = exp_sim.simulate(
4, 400) #Simulate 4 seconds with 400 communication points
#Plot
if with_plots:
import pylab as P
P.plot(t, y)
P.xlabel('Time')
P.ylabel('State')
P.title(exp_mod.name)
P.show()
#Basic test
nose.tools.assert_almost_equal(y[-1][0], 9.05518032e-01, 4)
nose.tools.assert_almost_equal(y[-1][1], 2.24046805e-05, 4)
nose.tools.assert_almost_equal(y[-1][2], 9.44595637e-02, 4)
nose.tools.assert_almost_equal(
exp_sim.p_sol[0][-1][0], -1.8761,
2) #Values taken from the example in Sundials
nose.tools.assert_almost_equal(exp_sim.p_sol[1][-1][0], 2.9614e-06, 8)
return exp_mod, exp_sim
def run_example(with_plots=True):
r"""
Example for demonstrating the use of a user supplied Jacobian (sparse).
Note that this will only work if Assimulo has been configured with
Sundials + SuperLU. Based on the SUNDIALS example cvRoberts_sps.c
ODE:
.. math::
\dot y_1 &= -0.04y_1 + 1e4 y_2 y_3 \\
\dot y_2 &= - \dot y_1 - \dot y_3 \\
\dot y_3 &= 3e7 y_2^2
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
#Defines the rhs
def f(t, y):
yd_0 = -0.04 * y[0] + 1e4 * y[1] * y[2]
yd_2 = 3e7 * y[1] * y[1]
yd_1 = -yd_0 - yd_2
return N.array([yd_0, yd_1, yd_2])
#Defines the Jacobian
def jac(t, y):
colptrs = [0, 3, 6, 9]
rowvals = [0, 1, 2, 0, 1, 2, 0, 1, 2]
data = [
-0.04, 0.04, 0.0, 1e4 * y[2], -1e4 * y[2] - 6e7 * y[1], 6e7 * y[1],
1e4 * y[1], -1e4 * y[1], 0.0
]
J = SP.csc_matrix((data, rowvals, colptrs))
return J
#Defines an Assimulo explicit problem
y0 = [1.0, 0.0, 0.0] #Initial conditions
exp_mod = Explicit_Problem(f,
y0,
name='Example using analytic (sparse) Jacobian')
exp_mod.jac = jac #Sets the Jacobian
exp_mod.jac_nnz = 9
exp_sim = CVode(exp_mod) #Create a CVode solver
#Set the parameters
exp_sim.iter = 'Newton' #Default 'FixedPoint'
exp_sim.discr = 'BDF' #Default 'Adams'
exp_sim.atol = [1e-8, 1e-14, 1e-6] #Default 1e-6
exp_sim.rtol = 1e-4 #Default 1e-6
exp_sim.linear_solver = "sparse"
#Simulate
t, y = exp_sim.simulate(0.4) #Simulate 0.4 seconds
#Basic tests
nose.tools.assert_almost_equal(y[-1][0], 0.9851, 3)
#Plot
if with_plots:
P.plot(t, y[:, 1], linestyle="dashed", marker="o") #Plot the solution
P.xlabel('Time')
P.ylabel('State')
P.title(exp_mod.name)
P.show()
return exp_mod, exp_sim
def simulate(self, Tend, nIntervals, gridWidth):
# define assimulo problem:(has to be done here because of the starting value in Explicit_Problem
solver = Explicit_Problem(self.rhs, self.y0)
''' *******DELETE LATER '''''''''
# problem.handle_event = handle_event
# problem.state_events = state_events
# problem.init_mode = init_mode
solver.handle_result = self.handle_result
solver.name = 'Simple Explicit Example'
simulation = CVode(solver) # Create a RungeKutta34 solver
# simulation.inith = 0.1 #Sets the initial step, default = 0.01
# Change multistep method: 'adams' or 'VDF'
if self.discr == 'Adams':
simulation.discr = 'Adams'
simulation.maxord = 12
else:
simulation.discr = 'BDF'
simulation.maxord = 5
# Change iteration algorithm: functional(FixedPoint) or newton
if self.iter == 'FixedPoint':
simulation.iter = 'FixedPoint'
else:
simulation.iter = 'Newton'
# Sets additional parameters
simulation.atol = self.atol
simulation.rtol = self.rtol
simulation.verbosity = 0
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
# Create Solver and set settings
# noRootFunctions = np.size(self.state_events(self.t0, np.array(self.y0)))
# solver = sundials.CVodeSolver(RHS = self.f, ROOT = self.rootf, SW = [False]*noRootFunctions,
# abstol = self.atol, reltol = self.rtol)
# solver.settings.JAC = None #Add user-dependent jacobian here
'''Initialize problem '''
# solver.init(self.t0, self.y0)
self.handle_result(self.t0, self.y0)
nextTimeEvent = self.time_events(self.t0, self.y0)
self.t_cur = self.t0
self.y_cur = self.y0
state_event = False
#
#
if gridWidth <> None:
nOutputIntervals = int((Tend - self.t0) / gridWidth)
else:
nOutputIntervals = nIntervals
# Define step length depending on if gridWidth or nIntervals has been chosen
if nOutputIntervals > 0:
# Last point on grid (does not have to be Tend:)
if(gridWidth <> None):
dOutput = gridWidth
else:
dOutput = (Tend - self.t0) / nIntervals
else:
dOutput = Tend
outputStepCounter = long(1)
nextOutputPoint = min(self.t0 + dOutput, Tend)
while self.t_cur < Tend:
# Time-Event detection and step time adjustment
if nextTimeEvent is None or nextOutputPoint < nextTimeEvent:
time_event = False
self.t_cur = nextOutputPoint
else:
time_event = True
self.t_cur = nextTimeEvent
try:
# #Integrator step
# self.y_cur = solver.step(self.t_cur)
# self.y_cur = np.array(self.y_cur)
# state_event = False
# Simulate
# take a step to next output point:
t_new, y_new = simulation.simulate(self.t_cur) # 5, 10) #5, 10 self.t_cur self.t_cur 2. argument nsteps Simulate 5 seconds
# t_new, y_new are both vectors of the time and states at t_cur and all intermediate
# points before it! So take last values:
self.t_cur = t_new[-1]
self.y_cur = y_new[-1]
state_event = False
except:
import sys
print "Unexpected error:", sys.exc_info()[0]
# except CVodeRootException, info:
# self.t_cur = info.t
# self.y_cur = info.y
# self.y_cur = np.array(self.y_cur)
# time_event = False
# state_event = True
#
#
# Depending on events have been detected do different tasks
if time_event or state_event:
event_info = [state_event, time_event]
if not self.handle_event(self, event_info):
break
solver.init(self.t_cur, self.y_cur)
nextTimeEvent = self.time_events(self.t_cur, self.y_cur)
# If no timeEvent happens:
if nextTimeEvent <= self.t_cur:
nextTimeEvent = None
if self.t_cur == nextOutputPoint:
# Write output if not happened before:
if not time_event and not state_event:
self.handle_result(nextOutputPoint, self.y_cur)
outputStepCounter += 1
nextOutputPoint = min(self.t0 + outputStepCounter * dOutput, Tend)
self.finalize()
### Mesh N = 60 X = 165e-6 # [m] ### Initial conditions c_init = 1000.0 # [mol/m^3] c_centered = c_init*numpy.ones( N, dtype='d' ) exp_mod = MyProblem( N, X, c_centered, 'ce only model, explicit CVode' ) #exp_mod = MyProblem( N, X, numpy.linspace(c_init-c_init/5.,c_init+c_init/5.,N), 'ce only model, explicit CVode' ) # Set the ODE solver exp_sim = CVode(exp_mod) #Create a CVode solver #Set the parameters exp_sim.iter = 'Newton' #Default 'FixedPoint' exp_sim.discr = 'BDF' #Default 'Adams' exp_sim.atol = 1e-5 #Default 1e-6 exp_sim.rtol = 1e-5 #Default 1e-6 #Simulate exp_mod.set_j_vec( 1.e-4 ) t1, y1 = exp_sim.simulate(100, 100) exp_mod.set_j_vec( 0.0 ) t2, y2 = exp_sim.simulate(200, 100) #exp_mod.set_j_vec( 0.0 ) #t3, y3 = exp_sim.simulate(10000, 200) #Plot
def run_example(with_plots=True):
r"""
Example for demonstrating the use of a user supplied Jacobian
ODE:
.. math::
\dot y_1 &= y_2 \\
\dot y_2 &= -9.82
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
#Defines the rhs
def f(t, y):
yd_0 = y[1]
yd_1 = -9.82
return N.array([yd_0, yd_1])
#Defines the Jacobian
def jac(t, y):
j = N.array([[0, 1.], [0, 0]])
return j
#Defines an Assimulo explicit problem
y0 = [1.0, 0.0] #Initial conditions
exp_mod = Explicit_Problem(f, y0, name='Example using analytic Jacobian')
exp_mod.jac = jac #Sets the Jacobian
exp_sim = CVode(exp_mod) #Create a CVode solver
#Set the parameters
exp_sim.iter = 'Newton' #Default 'FixedPoint'
exp_sim.discr = 'BDF' #Default 'Adams'
exp_sim.atol = 1e-5 #Default 1e-6
exp_sim.rtol = 1e-5 #Default 1e-6
#Simulate
t, y = exp_sim.simulate(
5, 1000) #Simulate 5 seconds with 1000 communication points
#Plot
if with_plots:
import pylab as P
P.plot(t, y, linestyle="dashed", marker="o") #Plot the solution
P.xlabel('Time')
P.ylabel('State')
P.title(exp_mod.name)
P.show()
#Basic tests
nose.tools.assert_almost_equal(y[-1][0], -121.75000000, 4)
nose.tools.assert_almost_equal(y[-1][1], -49.100000000)
return exp_mod, exp_sim
def run_example(with_plots=True):
r"""
Example to demonstrate the use of a preconditioner
.. math::
\dot y_1 & = 2 t \sin y_1 + t \sin y_2 \\
\dot y_2 & = 3 t \sin y_1 + 2 t \sin y_2
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
#Define the rhs
def rhs(t, y):
A = np.array([[2.0, 1.0], [3.0, 2.0]])
yd = np.dot(A * t, np.sin(y))
return yd
#Define the preconditioner setup function
def prec_setup(t, y, fy, jok, gamma, data):
A = np.array([[2.0, 1.0], [3.0, 2.0]])
#If jok is false the jacobian data needs to be recomputed
if jok == False:
#Extract the diagonal of the jacobian to form a Jacobi preconditioner
a0 = A[0, 0] * t * np.cos(y[0])
a1 = A[1, 1] * t * np.cos(y[1])
a = np.array([(1. - gamma * a0), (1. - gamma * a1)])
#Return true (jacobian data was recomputed) and the new data
return [True, a]
#If jok is true the existing jacobian data can be reused
if jok == True:
#Return false (jacobian data was reused) and the old data
return [False, data]
#Define the preconditioner solve function
def prec_solve(t, y, fy, r, gamma, delta, data):
#Solve the system Pz = r
z0 = r[0] / data[0]
z1 = r[1] / data[1]
z = np.array([z0, z1])
return z
#Initial conditions
y0 = [1.0, 2.0]
#Define an Assimulo problem
exp_mod = Explicit_Problem(
rhs, y0, name="Example of using a preconditioner in SUNDIALS")
#Set the preconditioner setup and solve function for the problem
exp_mod.prec_setup = prec_setup
exp_mod.prec_solve = prec_solve
#Create a CVode solver
exp_sim = CVode(exp_mod)
#Set the parameters for the solver
exp_sim.iter = 'Newton'
exp_sim.discr = 'BDF'
exp_sim.atol = 1e-5
exp_sim.rtol = 1e-5
exp_sim.linear_solver = 'SPGMR'
exp_sim.precond = "PREC_RIGHT" #Set the desired type of preconditioning
#Simulate
t, y = exp_sim.simulate(5)
if with_plots:
exp_sim.plot()
#Basic verification
nose.tools.assert_almost_equal(y[-1, 0], 3.11178295, 4)
nose.tools.assert_almost_equal(y[-1, 1], 3.19318992, 4)
return exp_mod, exp_sim
def run_example(with_plots=True):
r"""
An example for CVode with scaled preconditioned GMRES method
as a special linear solver.
Note, how the operation Jacobian times vector is provided.
ODE:
.. math::
\dot y_1 &= y_2 \\
\dot y_2 &= -9.82
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
#Defines the rhs
def f(t, y):
yd_0 = y[1]
yd_1 = -9.82
return N.array([yd_0, yd_1])
#Defines the Jacobian*vector product
def jacv(t, y, fy, v):
j = N.array([[0, 1.], [0, 0]])
return N.dot(j, v)
y0 = [1.0, 0.0] #Initial conditions
#Defines an Assimulo explicit problem
exp_mod = Explicit_Problem(
f, y0, name='Example using the Jacobian Vector product')
exp_mod.jacv = jacv #Sets the Jacobian
exp_sim = CVode(exp_mod) #Create a CVode solver
#Set the parameters
exp_sim.iter = 'Newton' #Default 'FixedPoint'
exp_sim.discr = 'BDF' #Default 'Adams'
exp_sim.atol = 1e-5 #Default 1e-6
exp_sim.rtol = 1e-5 #Default 1e-6
exp_sim.linear_solver = 'SPGMR' #Change linear solver
#exp_sim.options["usejac"] = False
#Simulate
t, y = exp_sim.simulate(
5, 1000) #Simulate 5 seconds with 1000 communication points
#Basic tests
nose.tools.assert_almost_equal(y[-1][0], -121.75000000, 4)
nose.tools.assert_almost_equal(y[-1][1], -49.100000000)
#Plot
if with_plots:
P.plot(t, y)
P.xlabel('Time')
P.ylabel('State')
P.title(exp_mod.name)
P.show()
return exp_mod, exp_sim
def run_sim_ice(Y, YLIQ):
def dy_dt_func(t, Y):
dy_dt = np.zeros(len(Y))
svp = f.svp_liq(Y[ITEMP])
svp_ice = f.svp_ice(Y[ITEMP])
# vapour mixing ratio
WV = c.eps * Y[IRH_ICE] * svp / (Y[IPRESS_ICE] - svp)
# liquid mixing ratio
WL = sum(YLIQ[IND1:IND2] * YLIQ[0:IND1])
# ice mixing ratio
WI = sum(Y[IND1:IND2] * Y[0:IND1])
Cpm = c.CP + WV * c.CPV + WL * c.CPW + WI * c.CPI
# RH with respect to ice
RH_ICE = WV / (c.eps * svp_ice / (Y[IPRESS_ICE] - svp_ice))
# ------------------------- growth rate of ice --------------------------
RH_EQ = 1e0 # from ACPIM, FPARCELCOLD - MICROPHYSICS.f90
CAP = f.CAPACITANCE01(Y[0:IND1], np.exp(Y[IND2:IND3]))
growth_rate = f.ICEGROWTHRATE(Y[ITEMP_ICE], Y[IPRESS_ICE],
RH_ICE, RH_EQ, Y[0:IND1],
np.exp(Y[IND2:IND3]), CAP)
growth_rate[np.isnan(growth_rate)] = 0 # get rid of nans
growth_rate = np.where(Y[IND1:IND2] < 1e-6, 0.0, growth_rate)
# Mass of water condensing
dy_dt[:IND1] = growth_rate
#---------------------------aspect ratio---------------------------------------
DELTA_RHO = c.eps * svp / (Y[IPRESS_ICE] - svp)
DELTA_RHOI = c.eps * svp_ice / (Y[IPRESS_ICE] - svp_ice)
DELTA_RHO = Y[IRH_ICE] * DELTA_RHO - DELTA_RHOI
DELTA_RHO = DELTA_RHO * Y[IPRESS_ICE] / Y[ITEMP_ICE] / c.RA
RHO_DEP = f.DEP_DENSITY(DELTA_RHO, Y[ITEMP_ICE])
# this is the rate of change of LOG of the aspect ratio
dy_dt[IND2:IND3] = (dy_dt[0:IND1] *
((f.INHERENTGROWTH(Y[ITEMP_ICE]) - 1) /
(f.INHERENTGROWTH(Y[ITEMP_ICE]) + 2)) /
(Y[0:IND1] * c.rhoi * RHO_DEP))
#------------------------------------------------------------------------------
# Change in vapour content
dwv_dt = -1 * sum(Y[IND1:IND2] * dy_dt[0:IND1])
# change in water vapour mixing ratio
DRI = -1 * dwv_dt
dy_dt[ITEMP_ICE] = 0.0 #+c.LS/Cpm*DRI
# if n.Simulation_type.lower() == 'parcel':
# dy_dt[ITEMP_ICE]=dy_dt[ITEMP_ICE] + c.LS/Cpm*DRI
#---------------------------RH change------------------------------------------
dy_dt[IRH_ICE] = (Y[IPRESS_ICE] - svp) * svp * dwv_dt
dy_dt[IRH_ICE] = (
dy_dt[IRH_ICE] - WV * Y[IPRESS_ICE] *
derivative(f.svp_liq, Y[ITEMP_ICE], dx=1.0) * dy_dt[ITEMP_ICE])
dy_dt[IRH_ICE] = dy_dt[IRH_ICE] / (c.eps * svp**2)
#------------------------------------------------------------------------------
return dy_dt
#--------------------- SET-UP solver --------------------------------------
y0 = Y
t0 = 0.0
#define assimulo problem
exp_mod = Explicit_Problem(dy_dt_func, y0, t0)
# define an explicit solver
exp_sim = CVode(exp_mod)
exp_sim.iter = 'Newton'
exp_sim.discr = 'BDF'
# set tolerance for each dydt function
tol_list = np.zeros_like(Y)
tol_list[0:IND1] = 1e-30 # mass
tol_list[IND1:IND2] = 10 # number
tol_list[IND2:IND3] = 1e-30 # aspect ratio
# tol_list[IND3:IRH_SV_ICE] = 1e-26
#tol_list[IRH_SV_ICE] = 1e-26
tol_list[IPRESS_ICE] = 10
tol_list[ITEMP_ICE] = 1e-4
tol_list[IRH_ICE] = 1e-8
exp_sim.atol = tol_list
exp_sim.rtol = 1.0e-8
exp_sim.inith = 1.0e-2 # initial time step-size
exp_sim.usejac = False
exp_sim.maxncf = 100 # max number of convergence failures allowed by solver
exp_sim.verbosity = 40
t_output, y_output = exp_sim.simulate(1)
return y_output[-1, :]
def run_example(with_plots=True):
r"""
This example shows how to use Assimulo and CVode for simulating sensitivities
for initial conditions.
.. math::
\dot y_1 &= -(k_{01}+k_{21}+k_{31}) y_1 + k_{12} y_2 + k_{13} y_3 + b_1\\
\dot y_2 &= k_{21} y_1 - (k_{02}+k_{12}) y_2 \\
\dot y_3 &= k_{31} y_1 - k_{13} y_3
with the parameter dependent inital conditions
:math:`y_1(0) = 0, y_2(0) = 0, y_3(0) = 0` . The initial values are taken as parameters :math:`p_1,p_2,p_3`
for the computation of the sensitivity matrix,
see http://sundials.2283335.n4.nabble.com/Forward-sensitivities-for-initial-conditions-td3239724.html
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
def f(t, y, p):
y1, y2, y3 = y
k01 = 0.0211
k02 = 0.0162
k21 = 0.0111
k12 = 0.0124
k31 = 0.0039
k13 = 0.000035
b1 = 49.3
yd_0 = -(k01 + k21 + k31) * y1 + k12 * y2 + k13 * y3 + b1
yd_1 = k21 * y1 - (k02 + k12) * y2
yd_2 = k31 * y1 - k13 * y3
return N.array([yd_0, yd_1, yd_2])
#The initial conditions
y0 = [0.0, 0.0, 0.0] #Initial conditions for y
p0 = [0.0, 0.0, 0.0] #Initial conditions for parameters
yS0 = N.array([[1, 0, 0], [0, 1, 0], [0, 0, 1.]])
#Create an Assimulo explicit problem
exp_mod = Explicit_Problem(f,
y0,
p0=p0,
name='Example: Computing Sensitivities')
#Sets the options to the problem
exp_mod.yS0 = yS0
#Create an Assimulo explicit solver (CVode)
exp_sim = CVode(exp_mod)
#Sets the paramters
exp_sim.iter = 'Newton'
exp_sim.discr = 'BDF'
exp_sim.rtol = 1e-7
exp_sim.atol = 1e-6
exp_sim.pbar = [
1, 1, 1
] #pbar is used to estimate the tolerances for the parameters
exp_sim.report_continuously = True #Need to be able to store the result using the interpolate methods
exp_sim.sensmethod = 'SIMULTANEOUS' #Defines the sensitvity method used
exp_sim.suppress_sens = False #Dont suppress the sensitivity variables in the error test.
#Simulate
t, y = exp_sim.simulate(400) #Simulate 400 seconds
#Basic test
nose.tools.assert_almost_equal(y[-1][0], 1577.6552477, 5)
nose.tools.assert_almost_equal(y[-1][1], 611.9574565, 5)
nose.tools.assert_almost_equal(y[-1][2], 2215.88563217, 5)
nose.tools.assert_almost_equal(exp_sim.p_sol[0][1][0], 1.0)
#Plot
if with_plots:
title_text = r"Sensitivity w.r.t. ${}$"
legend_text = r"$\mathrm{{d}}{}/\mathrm{{d}}{}$"
P.figure(1)
P.subplot(221)
P.plot(t,
N.array(exp_sim.p_sol[0])[:, 0], t,
N.array(exp_sim.p_sol[0])[:, 1], t,
N.array(exp_sim.p_sol[0])[:, 2])
P.title(title_text.format('p_1'))
P.legend((legend_text.format('y_1',
'p_1'), legend_text.format('y_1', 'p_2'),
legend_text.format('y_1', 'p_3')))
P.subplot(222)
P.plot(t,
N.array(exp_sim.p_sol[1])[:, 0], t,
N.array(exp_sim.p_sol[1])[:, 1], t,
N.array(exp_sim.p_sol[1])[:, 2])
P.title(title_text.format('p_2'))
P.legend((legend_text.format('y_2',
'p_1'), legend_text.format('y_2', 'p_2'),
legend_text.format('y_2', 'p_3')))
P.subplot(223)
P.plot(t,
N.array(exp_sim.p_sol[2])[:, 0], t,
N.array(exp_sim.p_sol[2])[:, 1], t,
N.array(exp_sim.p_sol[2])[:, 2])
P.title(title_text.format('p_3'))
P.legend((legend_text.format('y_3',
'p_1'), legend_text.format('y_3', 'p_2'),
legend_text.format('y_3', 'p_3')))
P.subplot(224)
P.title('ODE Solution')
P.plot(t, y)
P.suptitle(exp_mod.name)
P.show()
return exp_mod, exp_sim
def run_sim(Y, time, Y_AER1, YICE):
def dy_dt_func(t, Y):
dy_dt = np.zeros(len(Y))
# add condensed semi-vol mass into bins
if n.SV_flag:
MBIN2[-1 * n.n_sv:, :] = np.reshape(Y[INDSV1:INDSV2],
[n.n_sv, nbins * nmodes])
# calculate saturation vapour pressure over liquid
svp1 = f.svp_liq(Y[ITEMP])
# saturation ratio
SL = svp1 * Y[IRH] / (Y[IPRESS] - svp1)
SL = (SL * Y[IPRESS] / (1 + SL)) / svp1
# water vapour mixing ratio
WV = c.eps * Y[IRH] * svp1 / (Y[IPRESS] - svp1)
WL = np.sum(Y[IND1:IND2] * Y[:IND1]) # LIQUID MIXING RATIO
WI = np.sum(YICE[IND1:IND2] * YICE[:IND1]) # ice mixing ratio
RM = c.RA + WV * c.RV
CPM = c.CP + WV * c.CPV + WL * c.CPW + WI * c.CPI
if simulation_type.lower() == 'chamber':
# CHAMBER MODEL - pressure change
dy_dt[IPRESS] = -100 * PRESS1 * PRESS2 * np.exp(-PRESS2 *
(time + t))
elif simulation_type.lower() == 'parcel':
# adiabatic parcel
dy_dt[IPRESS] = -Y[IPRESS] / RM / Y[
ITEMP] * c.g * w #! HYDROSTATIC EQUATION
else:
print('simulation type unknown')
return
# ----------------------------change in vapour content: -----------------------
# 1. equilibruim size of particles
if n.kappa_flag:
#if n.SV_flag:
# need to recalc kappa taking into acount the condensed semi-vols
Kappa = np.sum(
(MBIN2[:, :] / RHOBIN2[:, :]) * KAPPABIN2[:, :],
axis=0) / np.sum(MBIN2[:, :] / RHOBIN2[:, :], axis=0)
# print(Kappa)
# print(MBIN2/RHOBIN2)
KK01 = f.kk01(Y[0:IND1], Y[ITEMP], MBIN2, RHOBIN2, Kappa)
else:
KK01 = f.K01(Y[0:IND1], Y[ITEMP], MBIN2, n.n_sv, RHOBIN2,
NUBIN2, MOLWBIN2)
# print(KK01[0])
Dw = KK01[2] # wet diameter
RHOAT = KK01[1] # density of particles inc water and aerosol mass
RH_EQ = KK01[0] # equilibrium RH
# print(MBIN2/MOLWBIN2)
# 2. growth rate of particles, Jacobson p455
# rate of change of radius
growth_rate = f.DROPGROWTHRATE(Y[ITEMP], Y[IPRESS], SL, RH_EQ,
RHOAT, Dw)
growth_rate[np.isnan(growth_rate)] = 0 # get rid of nans
growth_rate = np.where(Y[IND1:IND2] < 1e-9, 0.0, growth_rate)
# 3. Mass of water condensing
# change in mass of water per particle
dy_dt[:IND1] = (np.pi * RHOAT * Dw**2) * growth_rate
# 4. Change in vapour content
# change in water vapour mixing ratio
dwv_dt = -1 * np.sum(
Y[IND1:IND2] *
dy_dt[:IND1]) # change to np.sum for speed # mass
# -----------------------------------------------------------------------------
if simulation_type.lower() == 'chamber':
# CHAMBER MODEL - temperature change
dy_dt[ITEMP] = -Temp1 * Temp2 * np.exp(-Temp2 * (time + t))
elif simulation_type.lower() == 'parcel':
# adiabatic parcel
dy_dt[ITEMP] = RM / Y[IPRESS] * dy_dt[IPRESS] * Y[
ITEMP] / CPM # TEMPERATURE CHANGE: EXPANSION
dy_dt[ITEMP] = dy_dt[ITEMP] - c.LV / CPM * dwv_dt
else:
print('simulation type unknown')
return
# --------------------------------RH change------------------------------------
dy_dt[IRH] = svp1 * dwv_dt * (Y[IPRESS] - svp1)
dy_dt[IRH] = dy_dt[IRH] + svp1 * WV * dy_dt[IPRESS]
dy_dt[IRH] = (
dy_dt[IRH] - WV * Y[IPRESS] *
derivative(f.svp_liq, Y[ITEMP], dx=1.0) * dy_dt[ITEMP])
dy_dt[IRH] = dy_dt[IRH] / (c.eps * svp1**2)
# -----------------------------------------------------------------------------
# ------------------------------ SEMI-VOLATILES -------------------------------
if n.SV_flag:
# SV_mass = np.reshape(Y[INDSV1:INDSV2],[n.n_sv,n.nmodes*n.nbins])
# SV_mass = np.where(SV_mass == 0.0,1e-30,SV_mass)
# MBIN2[n.n_sv*-1:,:] = SV_mass
RH_EQ_SV = f.K01SV(Y[:IND1], Y[ITEMP], MBIN2, n.n_sv, RHOBIN2,
NUBIN2, MOLWBIN2)
RH_EQ = RH_EQ_SV[0]
RHOAT = RH_EQ_SV[1]
DW = RH_EQ_SV[2]
SVP_ORG = f.SVP_GASES(n.semi_vols, Y[ITEMP],
n.n_sv) #C-C equation
#RH_ORG = [x*Y[IPRESS]/c.RA/Y[ITEMP] for x in Y[IRH_SV]]
RH_ORG = [x for x in Y[IRH_SV]]
RH_ORG = [(x / c.aerosol_dict[key][0]) * c.R * Y[ITEMP]
for x, key in zip(RH_ORG, n.semi_vols[:n.n_sv])
] # just for n_sv keys in dictionary
RH_ORG = [RH_ORG[x] / SVP_ORG[x] for x in range(n.n_sv)]
dy_dt[INDSV1:INDSV2] = f.SVGROWTHRATE(Y[ITEMP], Y[IPRESS],
SVP_ORG, RH_ORG, RH_EQ,
DW, n.n_sv, n.nbins,
n.nmodes, MOLWBIN2)
dy_dt[IRH_SV] = -np.sum(np.reshape(
dy_dt[INDSV1:INDSV2], [n.n_sv, IND1]) * Y[IND1:IND2],
axis=1) #see line 137
return dy_dt
#--------------------- SET-UP solver ------------------------------------------
y0 = Y
t0 = 0.0
#define assimulo problem
exp_mod = Explicit_Problem(dy_dt_func, y0, t0)
# define an explicit solver
exp_sim = CVode(exp_mod)
exp_sim.iter = 'Newton'
exp_sim.discr = 'BDF'
#set parameters
tol_list = np.zeros_like(Y)
tol_list[0:IND1] = 1e-40 # this is now different to ACPIM (1e-25)
tol_list[IND1:IND2] = 10 # number
tol_list[IND2:IND3] = 1e-30 # capacitance
tol_list[IND3:INDSV2] = 1e-26 #condendensed semi-vol mass
tol_list[IRH_SV] = 1e-26 # RH of each semi-vol compound
tol_list[IPRESS] = 10
tol_list[ITEMP] = 1e-4
tol_list[IRH] = 1e-8 # set tolerance for each dydt function
exp_sim.atol = tol_list
exp_sim.rtol = 1.0e-8
exp_sim.inith = 0 # initial time step-size
exp_sim.usejac = False
exp_sim.maxncf = 100 # max number of convergence failures allowed by solver
exp_sim.verbosity = 40
t_output, y_output = exp_sim.simulate(1)
return y_output[-1, :], t_output[:]
def run_example(with_plots=True):
r"""
This is the same example from the Sundials package (cvsRoberts_FSA_dns.c)
Its purpose is to demonstrate the use of parameters in the differential equation.
This simple example problem for CVode, due to Robertson
see http://www.dm.uniba.it/~testset/problems/rober.php,
is from chemical kinetics, and consists of the system:
.. math::
\dot y_1 &= -p_1 y_1 + p_2 y_2 y_3 \\
\dot y_2 &= p_1 y_1 - p_2 y_2 y_3 - p_3 y_2^2 \\
\dot y_3 &= p_3 y_ 2^2
on return:
- :dfn:`exp_mod` problem instance
- :dfn:`exp_sim` solver instance
"""
def f(t, y, p):
yd_0 = -p[0]*y[0]+p[1]*y[1]*y[2]
yd_1 = p[0]*y[0]-p[1]*y[1]*y[2]-p[2]*y[1]**2
yd_2 = p[2]*y[1]**2
return N.array([yd_0,yd_1,yd_2])
def jac(t,y, p):
J = N.array([[-p[0], p[1]*y[2], p[1]*y[1]],
[p[0], -p[1]*y[2]-2*p[2]*y[1], -p[1]*y[1]],
[0.0, 2*p[2]*y[1],0.0]])
return J
def fsens(t, y, s, p):
J = N.array([[-p[0], p[1]*y[2], p[1]*y[1]],
[p[0], -p[1]*y[2]-2*p[2]*y[1], -p[1]*y[1]],
[0.0, 2*p[2]*y[1],0.0]])
P = N.array([[-y[0],y[1]*y[2],0],
[y[0], -y[1]*y[2], -y[1]**2],
[0,0,y[1]**2]])
return J.dot(s)+P
#The initial conditions
y0 = [1.0,0.0,0.0] #Initial conditions for y
#Create an Assimulo explicit problem
exp_mod = Explicit_Problem(f,y0, name='Robertson Chemical Kinetics Example')
exp_mod.rhs_sens = fsens
exp_mod.jac = jac
#Sets the options to the problem
exp_mod.p0 = [0.040, 1.0e4, 3.0e7] #Initial conditions for parameters
exp_mod.pbar = [0.040, 1.0e4, 3.0e7]
#Create an Assimulo explicit solver (CVode)
exp_sim = CVode(exp_mod)
#Sets the solver paramters
exp_sim.iter = 'Newton'
exp_sim.discr = 'BDF'
exp_sim.rtol = 1.e-4
exp_sim.atol = N.array([1.0e-8, 1.0e-14, 1.0e-6])
exp_sim.sensmethod = 'SIMULTANEOUS' #Defines the sensitvity method used
exp_sim.suppress_sens = False #Dont suppress the sensitivity variables in the error test.
exp_sim.report_continuously = True
#Simulate
t, y = exp_sim.simulate(4,400) #Simulate 4 seconds with 400 communication points
#Basic test
nose.tools.assert_almost_equal(y[-1][0], 9.05518032e-01, 4)
nose.tools.assert_almost_equal(y[-1][1], 2.24046805e-05, 4)
nose.tools.assert_almost_equal(y[-1][2], 9.44595637e-02, 4)
nose.tools.assert_almost_equal(exp_sim.p_sol[0][-1][0], -1.8761, 2) #Values taken from the example in Sundials
nose.tools.assert_almost_equal(exp_sim.p_sol[1][-1][0], 2.9614e-06, 8)
nose.tools.assert_almost_equal(exp_sim.p_sol[2][-1][0], -4.9334e-10, 12)
#Plot
if with_plots:
P.plot(t, y)
P.title(exp_mod.name)
P.xlabel('Time')
P.ylabel('State')
P.show()
return exp_mod, exp_sim
def stiff_ode_solver(matrix,
y_initial,
forward_rate,
rev_rate,
third_body=None,
iteration='Newton',
discr='BDF',
atol=1e-10,
rtol=1e-6,
sim_time=0.001,
num_data_points=500):
"""
Sets up the initial condition for solving the odes
Parameters
----------
matrix : ndarray
stoichiometric matrix
y_initial : list
A list of initial concentrations
forward_rate : list
A list of forward reaction rates
for all the reactions in the mechanism
rev_rate : list
A list of reverse reaction rates
for all the reactions in the mechanism
sim_time : float
total time to simulate in seconds
third_body : ndarray
third body matrix, default = None
iteration : str
determines the iteration method that is be
used by the solver, default='Newton'
discr : determines the discretization method,
default='BDF'
atol : float
absolute tolerance(s) that is to be used
by the solver, default=1e-10
rtol : float
relative tolerance that is to be
used by the solver, default= 1e-7
num_data_points : integer
number of even space data points in output
arrays, default = 500
Returns
----------
t1 : list
A list of time-points at which
the system of ODEs is solved
[t1, t2, t3,...]
y1 : list of lists
A list of concentrations of all the species
at t1 time-points
[[y1(t1), y2(t1),...], [y1(t2), y2(t2),...],...]
"""
# y0[0] = 0
# y0[0] = 0
# dydt = np.zeros((len(species_list)), dtype=float)
# Define the rhs
kf = forward_rate
kr = rev_rate
mat_reac = np.abs(np.asarray(np.where(matrix < 0, matrix, 0)))
mat_prod = np.asarray(np.where(matrix > 0, matrix, 0))
# d = kf * np.prod(y0**np.abs(mat_reac), axis = 1) - kr * np.prod(y0**mat_prod, axis = 1)
def rhs(t, concentration):
# print(t)
y = concentration
if third_body is not None:
third_body_eff = np.dot(third_body, y)
third_body_eff = np.where(third_body_eff > 0, third_body_eff, 1)
# print(third_body_eff)
else:
third_body_eff = np.ones(len(forward_rate))
# print(len(third_body_matrix))
rate_concentration = (kf * np.prod(np.power(y, mat_reac), axis=1) -
kr * np.prod(np.power(y, mat_prod), axis=1))
dydt = np.dot(
third_body_eff,
np.multiply(matrix, rate_concentration.reshape(matrix.shape[0],
1)))
# dydt = [np.sum(third_body_eff * (matrix[:, i] * rate_concentration)) for i in
# range(len(species_list))]
del t
del y
return dydt
t0 = 0
# Define an Assimulo problem
exp_mod = Explicit_Problem(rhs, y_initial, t0)
# Define an explicit solver
exp_sim = CVode(exp_mod) # Create a CVode solver
# Sets the parameters
exp_sim.iter = iteration # Default 'FixedPoint'
exp_sim.discr = discr # Default 'Adams'
exp_sim.atol = [atol] # Default 1e-6
exp_sim.rtol = rtol # Default 1e-6
exp_sim.maxh = 0.1
exp_sim.minh = 1e-18
exp_sim.num_threads = 1
while True:
try:
t1, y1 = exp_sim.simulate(sim_time, num_data_points)
break
except CVodeError:
# reduce absolute error by two orders of magnitude
# and try to solve again.
print("next process started")
atol = atol * 1e-2
exp_sim.atol = atol
# if atol < 1e-15:
# t1 = 0
# y1 = 0
# break
# print(exp_sim.atol)
return t1, y1
