def extended_simulator(request):
modelclass = ModelLibrary.modelclasses[request.param]
model_parameters = ModelLibrary.model_parameters[request.param]
initial_values = ModelLibrary.initial_values[request.param]
return ExtendedSimulator(bioprocess_model_class=modelclass,
model_parameters=model_parameters,
initial_values=initial_values)
def test_init(self, name):
modelclass = ModelLibrary.modelclasses[name]
initial_values = ModelLibrary.initial_values[name]
model_parameters = ModelLibrary.model_parameters[name]
ExtendedSimulator(bioprocess_model_class=modelclass,
model_parameters=model_parameters,
initial_values=initial_values)
def test_bad_observation_function_checking(self, model_checker, model,
bad_obsfun):
# Get all building blocks for the bioprocess model
name = 'model03'
model_parameters = ModelLibrary.model_parameters[name]
initial_values = ModelLibrary.initial_values[name]
# Get all buidling blocks for the observation function
name = 'obsfun01'
observed_state = ObservationFunctionLibrary.observed_states[name]
observation_parameters = ObservationFunctionLibrary.observation_function_parameters[
name]
# Create an extended simulator for checking
extended_simulator = ExtendedSimulator(
bioprocess_model_class=model,
model_parameters=model_parameters,
initial_values=initial_values,
observation_functions_parameters=[
(bad_obsfun, {
**observation_parameters,
'observed_state': observed_state,
}),
])
# These checks sould raise any warnings
with pytest.warns(UserWarning):
model_checker.check_model_consistency(extended_simulator)
def test_bioprocess_model_checking(self, model_checker, model):
name = 'model03'
model_parameters = ModelLibrary.model_parameters[name]
initial_values = ModelLibrary.initial_values[name]
extended_simulator = ExtendedSimulator(
bioprocess_model_class=model,
model_parameters=model_parameters,
initial_values=initial_values)
# These models should not raise any warnings
model_checker.check_model_consistency(extended_simulator)
def test_bioprocess_bad_model_checking(self, model_checker, bad_model):
name = 'model03'
model_parameters = ModelLibrary.model_parameters[name]
initial_values = ModelLibrary.initial_values[name]
extended_simulator = ExtendedSimulator(
bioprocess_model_class=bad_model,
model_parameters=model_parameters,
initial_values=initial_values)
# These models should raise warnings for different reasons (cf. the specific model in the modelling library for details)
with pytest.warns(UserWarning):
model_checker.check_model_consistency(extended_simulator)
def test_model06_variants(self, model_checker, model_variant,
initial_switches, expected_behavior):
name = 'model06'
model_parameters = ModelLibrary.model_parameters[name]
initial_values = ModelLibrary.initial_values[name]
extended_simulator = ExtendedSimulator(
bioprocess_model_class=model_variant,
model_parameters=model_parameters,
initial_values=initial_values,
initial_switches=initial_switches,
)
if expected_behavior == 'UserWarning':
with pytest.warns(UserWarning):
model_checker.check_model_consistency(extended_simulator)
elif expected_behavior == 'NameError':
with pytest.raises(NameError):
model_checker.check_model_consistency(extended_simulator)
else:
model_checker.check_model_consistency(extended_simulator)
def test_extended_simulator_with_observations(self):
# Get building blocks for BioprocessModel
name = 'model01'
modelclass = ModelLibrary.modelclasses[name]
initial_values = ModelLibrary.initial_values[name]
model_parameters = ModelLibrary.model_parameters[name]
# Get building blocks for ObservationFunctions
obsfun_name = 'obsfun01'
obsfun = ObservationFunctionLibrary.observation_functions[obsfun_name]
obsfun_parameters = ObservationFunctionLibrary.observation_function_parameters[
obsfun_name]
observed_state = ObservationFunctionLibrary.observed_states[
obsfun_name]
obsfuns_params = [(obsfun, {
**obsfun_parameters, 'observed_state':
observed_state
})]
# Set new values for parameters, using an extended simulator
extended_simulator = ExtendedSimulator(
bioprocess_model_class=modelclass,
model_parameters=model_parameters,
initial_values=initial_values,
observation_functions_parameters=obsfuns_params,
)
params = extended_simulator.get_all_parameters()
extended_simulator.set_parameters(
{_p: params[_p] * 1.05
for _p in params})
# Get some prediction to be used as artifical data
predicitions = extended_simulator.simulate(t=24)
measurements = [
Measurement(name=_prediction.name,
timepoints=_prediction.timepoints,
values=_prediction.values,
errors=np.ones_like(_prediction.values))
for _prediction in predicitions
]
extended_simulator._get_loss(metric='negLL', measurements=measurements)
def test_with_model_enforcing_CVodeError(self):
name = 'model06'
modelclass = ModelLibrary.modelclasses[name]
model_parameters = ModelLibrary.model_parameters[name]
initial_values = ModelLibrary.initial_values[name]
extended_simulator = ExtendedSimulator(
bioprocess_model_class=modelclass,
model_parameters=model_parameters,
initial_values=initial_values)
# The chosen model will create an integration error for rate = 0. A RuntimeWarning is tehrefore raised before the CVodeError is raised
with pytest.warns(RuntimeWarning):
with pytest.raises(CVodeError):
extended_simulator.simulate(t=24, parameters={'rate0': 0})
with pytest.warns(RuntimeWarning):
with pytest.raises(CVodeError):
extended_simulator._get_loss_for_minimzer(
metric='negLL',
guess_dict={'rate0': 0},
measurements=[
Measurement(name='y0',
timepoints=[1, 2, 3],
values=[10, 20, 30]),
],
handle_CVodeError=False,
verbosity_CVodeError=False,
)
# For toxic parameters causing integration errors, CVodeError handling results in inf loss
with pytest.warns(RuntimeWarning):
assert np.isinf(
extended_simulator._get_loss_for_minimzer(
metric='negLL',
guess_dict={'rate0': 0},
measurements=[
Measurement(name='y0',
timepoints=[1, 2, 3],
values=[10, 20, 30]),
],
handle_CVodeError=True,
verbosity_CVodeError=True,
))