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, yd):
res = yd[0] + y[0] * param
return N.array([res])
param = 2
# Define an Assimulo problem
imp_mod = Implicit_Problem(
f,
t0=5,
y0=0.02695,
yd0=-0.02695,
name='IDA Example: $\dot y + y = 0$ (reverse time)')
# Define an explicit solver
imp_sim = IDA(imp_mod) # Create a IDA solver
# Sets the parameters
imp_sim.atol = [1e-8] # Default 1e-6
imp_sim.rtol = 1e-8 # Default 1e-6
imp_sim.backward = True
# Simulate
t, y, yd = imp_sim.simulate(0) # Simulate 5 seconds (t0=5 -> tf=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.xlabel('Time')
P.ylabel('State')
P.title(imp_mod.name)
P.show()
return imp_mod, imp_sim
def dae_solver(residual,
y0,
yd0,
t0,
p0=None,
jac=None,
name='DAE',
solver='IDA',
algvar=None,
atol=1e-6,
backward=False,
display_progress=True,
pbar=None,
report_continuously=False,
rtol=1e-6,
sensmethod='STAGGERED',
suppress_alg=False,
suppress_sens=False,
usejac=False,
usesens=False,
verbosity=30,
tfinal=10.,
ncp=500):
'''
DAE solver.
Parameters
----------
residual: function
Implicit DAE model.
y0: List[float]
Initial model state.
yd0: List[float]
Initial model state derivatives.
t0: float
Initial simulation time.
p0: List[float]
Parameters for which sensitivites are to be calculated.
jac: function
Model jacobian.
name: string
Model name.
solver: string
DAE solver.
algvar: List[bool]
A list for defining which variables are differential and which are algebraic.
The value True(1.0) indicates a differential variable and the value False(0.0) indicates an algebraic variable.
atol: float
Absolute tolerance.
backward: bool
Specifies if the simulation is done in reverse time.
display_progress: bool
Actives output during the integration in terms of that the current integration is periodically printed to the stdout.
Report_continuously needs to be activated.
pbar: List[float]
An array of positive floats equal to the number of parameters. Default absolute values of the parameters.
Specifies the order of magnitude for the parameters. Useful if IDAS is to estimate tolerances for the sensitivity solution vectors.
report_continuously: bool
Specifies if the solver should report the solution continuously after steps.
rtol: float
Relative tolerance.
sensmethod: string
Specifies the sensitivity solution method.
Can be either ‘SIMULTANEOUS’ or ‘STAGGERED’. Default is 'STAGGERED'.
suppress_alg: bool
Indicates that the error-tests are suppressed on algebraic variables.
suppress_sens: bool
Indicates that the error-tests are suppressed on the sensitivity variables.
usejac: bool
Sets the option to use the user defined jacobian.
usesens: bool
Aactivates or deactivates the sensitivity calculations.
verbosity: int
Determines the level of the output.
QUIET = 50 WHISPER = 40 NORMAL = 30 LOUD = 20 SCREAM = 10
tfinal: float
Simulation final time.
ncp: int
Number of communication points (number of return points).
Returns
-------
sol: solution [time, model states], List[float]
'''
if usesens is True: # parameter sensitivity
model = Implicit_Problem(residual, y0, yd0, t0, p0=p0)
else:
model = Implicit_Problem(residual, y0, yd0, t0)
model.name = name
if usejac is True: # jacobian
model.jac = jac
if algvar is not None: # differential or algebraic variables
model.algvar = algvar
if solver == 'IDA': # solver
from assimulo.solvers import IDA
sim = IDA(model)
sim.atol = atol
sim.rtol = rtol
sim.backward = backward # backward in time
sim.report_continuously = report_continuously
sim.display_progress = display_progress
sim.suppress_alg = suppress_alg
sim.verbosity = verbosity
if usesens is True: # sensitivity
sim.sensmethod = sensmethod
sim.pbar = np.abs(p0)
sim.suppress_sens = suppress_sens
# Simulation
# t, y, yd = sim.simulate(tfinal, ncp=(ncp - 1))
ncp_list = np.linspace(t0, tfinal, num=ncp, endpoint=True)
t, y, yd = sim.simulate(tfinal, ncp=0, ncp_list=ncp_list)
# Plot
# sim.plot()
# plt.figure()
# plt.subplot(221)
# plt.plot(t, y[:, 0], 'b.-')
# plt.legend([r'$\lambda$'])
# plt.subplot(222)
# plt.plot(t, y[:, 1], 'r.-')
# plt.legend([r'$\dot{\lambda}$'])
# plt.subplot(223)
# plt.plot(t, y[:, 2], 'k.-')
# plt.legend([r'$\eta$'])
# plt.subplot(224)
# plt.plot(t, y[:, 3], 'm.-')
# plt.legend([r'$\dot{\eta}$'])
# plt.figure()
# plt.subplot(221)
# plt.plot(t, yd[:, 0], 'b.-')
# plt.legend([r'$\dot{\lambda}$'])
# plt.subplot(222)
# plt.plot(t, yd[:, 1], 'r.-')
# plt.legend([r'$\ddot{\lambda}$'])
# plt.subplot(223)
# plt.plot(t, yd[:, 2], 'k.-')
# plt.legend([r'$\dot{\eta}$'])
# plt.subplot(224)
# plt.plot(t, yd[:, 3], 'm.-')
# plt.legend([r'$\ddot{\eta}$'])
# plt.figure()
# plt.subplot(121)
# plt.plot(y[:, 0], y[:, 1])
# plt.xlabel(r'$\lambda$')
# plt.ylabel(r'$\dot{\lambda}$')
# plt.subplot(122)
# plt.plot(y[:, 2], y[:, 3])
# plt.xlabel(r'$\eta$')
# plt.ylabel(r'$\dot{\eta}$')
# plt.figure()
# plt.subplot(121)
# plt.plot(yd[:, 0], yd[:, 1])
# plt.xlabel(r'$\dot{\lambda}$')
# plt.ylabel(r'$\ddot{\lambda}$')
# plt.subplot(122)
# plt.plot(yd[:, 2], yd[:, 3])
# plt.xlabel(r'$\dot{\eta}$')
# plt.ylabel(r'$\ddot{\eta}$')
# plt.figure()
# plt.subplot(121)
# plt.plot(y[:, 0], y[:, 2])
# plt.xlabel(r'$\lambda$')
# plt.ylabel(r'$\eta$')
# plt.subplot(122)
# plt.plot(y[:, 1], y[:, 3])
# plt.xlabel(r'$\dot{\lambda}$')
# plt.ylabel(r'$\dot{\eta}$')
# plt.figure()
# plt.subplot(121)
# plt.plot(yd[:, 0], yd[:, 2])
# plt.xlabel(r'$\dot{\lambda}$')
# plt.ylabel(r'$\dot{\eta}$')
# plt.subplot(122)
# plt.plot(yd[:, 1], yd[:, 3])
# plt.xlabel(r'$\ddot{\lambda}$')
# plt.ylabel(r'$\ddot{\eta}$')
# plt.show()
sol = [t, y, yd]
return sol
