def _setup_sim(self, **kwargs):
""" Create a simulation interface to Assimulo using CVODE, given
a problem definition
"""
# Create Assimulo interface
sim = CVode(self.prob)
sim.discr = 'BDF'
sim.maxord = 5
# Setup some default arguments for the ODE solver, or override
# if available. This is very hackish, but it's fine for now while
# the number of anticipated tuning knobs is small.
if 'maxh' in kwargs:
sim.maxh = kwargs['maxh']
else:
sim.maxh = np.min([0.1, self.output_dt])
if 'minh' in kwargs:
sim.minh = kwargs['minh']
# else: sim.minh = 0.001
if "iter" in kwargs:
sim.iter = kwargs['iter']
else:
sim.iter = 'Newton'
if "linear_solver" in kwargs:
sim.linear_solver = kwargs['linear_solver']
if "max_steps" in kwargs: # DIFFERENT NAME!!!!
sim.maxsteps = kwargs['max_steps']
else:
sim.maxsteps = 1000
if "time_limit" in kwargs:
sim.time_limit = kwargs['time_limit']
sim.report_continuously = True
else:
sim.time_limit = 0.0
# Don't save the [t_-, t_+] around events
sim.store_event_points = False
# Setup tolerances
nr = self.args[0]
sim.rtol = state_rtol
sim.atol = state_atol + [1e-12] * nr
if not self.console:
sim.verbosity = 50
else:
sim.verbosity = 40
# sim.report_continuously = False
# Save the Assimulo interface
return sim
def _setup_sim(self, **kwargs):
""" Create a simulation interface to Assimulo using CVODE, given
a problem definition
"""
# Create Assimulo interface
sim = CVode(self.prob)
sim.discr = 'BDF'
sim.maxord = 5
# Setup some default arguments for the ODE solver, or override
# if available. This is very hackish, but it's fine for now while
# the number of anticipated tuning knobs is small.
if 'maxh' in kwargs:
sim.maxh = kwargs['maxh']
else:
sim.maxh = np.min([0.1, self.output_dt])
if 'minh' in kwargs:
sim.minh = kwargs['minh']
# else: sim.minh = 0.001
if "iter" in kwargs:
sim.iter = kwargs['iter']
else:
sim.iter = 'Newton'
if "linear_solver" in kwargs:
sim.linear_solver = kwargs['linear_solver']
if "max_steps" in kwargs: # DIFFERENT NAME!!!!
sim.maxsteps = kwargs['max_steps']
else:
sim.maxsteps = 1000
if "time_limit" in kwargs:
sim.time_limit = kwargs['time_limit']
sim.report_continuously = True
else:
sim.time_limit = 0.0
# Don't save the [t_-, t_+] around events
sim.store_event_points = False
# Setup tolerances
nr = self.args[0]
sim.rtol = state_rtol
sim.atol = state_atol + [1e-12]*nr
if not self.console:
sim.verbosity = 50
else:
sim.verbosity = 40
# sim.report_continuously = False
# Save the Assimulo interface
return sim
def simulate(self, t:numpy.ndarray, parameters:dict=None, verbosity:int=30, reset_afterwards:bool=False, suppress_stdout:bool=True) -> List[TimeSeries]:
"""
Runs a forward simulation for the fully specified model and its observation functions (if specified).
Arguments
---------
t : numpy.ndarray or float
The time points for integration. In case a single time point is provided,
the solver will treat this as final integration time and chooses the intermediate steps on its own.
Keyword arguments
-----------------
parameters : dict
In case a simulation for specific parameter values is wanted.
Default is None.
verbosity : int
Prints solver statistics (quiet = 50, whisper = 40, normal = 30, loud = 20, scream = 10).
Default is 30.
reset_afterwards : bool
After simulation, reset the Simulator instance to its state directly after instantiation.
Useful, if parameters argument is used.
suppress_stdout : bool
No printouts of integrator warnings, which are directed to stdout by the assimulo package.
Set to False for model debugging purposes.
Default is True.
Returns
-------
simulations : List[TimeSeries]
The collection of simulations results as `ModelState` objects
and `Observation` objects (if at least one `ObservationFunction` has been specified)
Raises
------
ValueError
Not all parameters have values.
"""
if parameters is not None:
self.set_parameters(parameters)
# Create the solver from problem instance
exp_sim = CVode(self.bioprocess_model)
exp_sim.verbosity = verbosity # QUIET = 50 WHISPER = 40 NORMAL = 30 LOUD = 20 SCREAM = 10
exp_sim.store_event_points = False
exp_sim.discr = 'BDF'
exp_sim.stablimit = True
if self.integrator_kwargs is not None:
for key in list(self.integrator_kwargs.keys()):
exec(f'exp_sim.{key} = self.integrator_kwargs["{key}"]')
# Do the actual forward simulation
_t = numpy.array(t, dtype=numpy.float64).flatten()
if len(_t) == 1:
tfinal = float(_t)
ncp_list = None
elif len(_t) > 1:
ncp_list = _t
tfinal = numpy.max(_t)
try:
if suppress_stdout:
f = io.StringIO()
with redirect_stdout(f):
t, y = exp_sim.simulate(tfinal=tfinal, ncp_list=ncp_list)
else:
t, y = exp_sim.simulate(tfinal=tfinal, ncp_list=ncp_list)
except CVodeError as e:
print(f'CVodeError occured with flag {e.value}. CVodeError message was: {e}.')
raise e
# clean double entry artifacts due to event detection
unq, unq_idx = numpy.unique(t, return_index=True)
# build model prediction dictionary
model_predictions = [
ModelState(
name=_name,
timepoints=numpy.array(t)[unq_idx],
values=numpy.array(_y)[unq_idx],
replicate_id=self.replicate_id,
)
for _name, _y in zip(self.bioprocess_model.states, y.T)
]
if self.observer is not None:
observations = self.observer.get_observations(model_predictions)
else:
observations = []
if reset_afterwards:
self.reset()
simulations = []
simulations.extend(model_predictions)
simulations.extend(observations)
return simulations
