def test_one_reaction_constant_species_pop(self):
# test statics
init_volume = 1E-16
init_density = 1000
molecular_weight = 100.
default_species_copy_number = 10_000
init_accounted_mass = molecular_weight * default_species_copy_number / Avogadro
init_accounted_density = init_accounted_mass / init_volume
expected_initial_values_compt_1 = dict(init_volume=init_volume,
init_accounted_mass=init_accounted_mass,
init_mass= init_volume * init_density,
init_density=init_density,
init_accounted_density=init_accounted_density,
accounted_fraction = init_accounted_density / init_density)
expected_initial_values = {'compt_1': expected_initial_values_compt_1}
model = MakeModel.make_test_model('1 species, 1 reaction',
init_vols=[expected_initial_values_compt_1['init_volume']],
init_vol_stds=[0],
density=init_density,
molecular_weight=molecular_weight,
default_species_copy_number=default_species_copy_number,
default_species_std=0)
multialgorithm_simulation = MultialgorithmSimulation(model, self.wc_sim_config)
_, dynamic_model = multialgorithm_simulation.build_simulation()
check_simul_results(self, dynamic_model, None, expected_initial_values=expected_initial_values)
def make_sim_w_nrm_submodel(self, model, auto_initialize):
wc_lang.transform.PrepForWcSimTransform().run(model)
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config)
nrm_options = dict(auto_initialize=auto_initialize)
options = {'NrmSubmodel': dict(options=nrm_options)}
multialgorithm_simulation = MultialgorithmSimulation(model, wc_sim_config, options)
return multialgorithm_simulation.build_simulation()
def setUp(self):
self.model_file = os.path.join(os.path.dirname(__file__), 'fixtures',
'test_dynamic_expressions.xlsx')
self.model = Reader().run(self.model_file)[Model][0]
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config)
multialgorithm_simulation = MultialgorithmSimulation(
self.model, wc_sim_config)
_, self.dynamic_model = multialgorithm_simulation.build_simulation()
def make_dynamic_model(self, model_filename):
# read and initialize a model
self.model = TestDynamicModel.models[model_filename]
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config)
multialgorithm_simulation = MultialgorithmSimulation(
self.model, wc_sim_config)
multialgorithm_simulation.initialize_components()
self.dynamic_model = DynamicModel(
self.model, multialgorithm_simulation.local_species_population,
multialgorithm_simulation.temp_dynamic_compartments)
def setUp(self):
# read and initialize a model
self.model = Reader().run(self.MODEL_FILENAME, ignore_extra_models=True)[Model][0]
for conc in self.model.distribution_init_concentrations:
conc.std = 0.
PrepForWcSimTransform().run(self.model)
de_simulation_config = SimulationConfig(time_max=10)
self.wc_sim_config = WCSimulationConfig(de_simulation_config, dfba_time_step=1)
self.multialgorithm_simulation = MultialgorithmSimulation(self.model, self.wc_sim_config)
self.test_dir = tempfile.mkdtemp()
self.results_dir = tempfile.mkdtemp(dir=self.test_dir)
def make_model_and_simulation(self, model_type, num_submodels, species_copy_numbers=None,
species_stds=None, init_vols=None,
submodel_framework='WC:stochastic_simulation_algorithm'):
# make simple model
if init_vols is not None:
if not isinstance(init_vols, list):
init_vols = [init_vols]*num_submodels
model = MakeModel.make_test_model(model_type, num_submodels=num_submodels,
species_copy_numbers=species_copy_numbers,
species_stds=species_stds,
init_vols=init_vols,
submodel_framework=submodel_framework)
multialgorithm_simulation = MultialgorithmSimulation(model, self.wc_sim_config)
simulation_engine, _ = multialgorithm_simulation.build_simulation()
return (model, multialgorithm_simulation, simulation_engine)
def test_init(self):
self.assertTrue(isinstance(self.nrm_submodel.options, dict))
# test NrmSubmodel() with default options=None
wc_sim_config = WCSimulationConfig(SimulationConfig(time_max=10))
_, dynamic_model = MultialgorithmSimulation(self.model, wc_sim_config).build_simulation()
nrm_submodel = dynamic_model.dynamic_submodels['nrm_submodel']
self.assertEquals(nrm_submodel.options, None)
def make_ode_submodel(self,
model,
ode_time_step=1.0,
submodel_name='submodel_1'):
""" Make a MultialgorithmSimulation from a wc lang model """
# assume a single submodel
# todo: test concurrent OdeSubmodels, perhaps
self.ode_time_step = ode_time_step
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config,
ode_time_step=ode_time_step)
multialgorithm_simulation = MultialgorithmSimulation(
model, wc_sim_config)
simulation_engine, dynamic_model = multialgorithm_simulation.build_simulation(
)
simulation_engine.initialize()
submodel_1 = dynamic_model.dynamic_submodels[submodel_name]
return submodel_1
def make_ssa_submodel(self, model, default_center_of_mass=None):
PrepForWcSimTransform().run(model)
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config)
multialgorithm_simulation = MultialgorithmSimulation(
model, wc_sim_config)
multialgorithm_simulation.build_simulation()
# todo: don't call SsaSubmodel(); return dynamic_model.dynamic_submodels['submodel name here'] will work; see test_nrm.py
wc_lang_ssa_submodel = model.submodels[0]
ssa_submodel = SsaSubmodel(
model.id,
multialgorithm_simulation.dynamic_model,
list(wc_lang_ssa_submodel.reactions),
wc_lang_ssa_submodel.get_children(kind='submodel', __type=Species),
multialgorithm_simulation.get_dynamic_compartments(
wc_lang_ssa_submodel),
multialgorithm_simulation.local_species_population,
default_center_of_mass=default_center_of_mass)
return ssa_submodel
def test_dynamic_model(self):
self.make_dynamic_model(self.MODEL_FILENAME)
self.assertEqual(len(self.dynamic_model.cellular_dyn_compartments), 1)
self.assertEqual(self.dynamic_model.cellular_dyn_compartments[0].id,
'c')
self.assertEqual(self.dynamic_model.get_num_submodels(), 2)
model = TestDynamicModel.models[self.MODEL_FILENAME]
for compartment in self.model.get_compartments():
compartment.biological_type = onto['WC:extracellular_compartment']
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config)
multialgorithm_simulation = MultialgorithmSimulation(
model, wc_sim_config)
multialgorithm_simulation.initialize_components()
with self.assertRaisesRegex(
MultialgorithmError,
'must have at least 1 cellular compartment'):
DynamicModel(model,
multialgorithm_simulation.local_species_population,
multialgorithm_simulation.temp_dynamic_compartments)
def test_calc_reaction_rates(self):
# set standard deviation of initial conc. to 0
self.setUp(std_init_concentrations=0.)
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config, dfba_time_step=1)
multialgorithm_simulation = MultialgorithmSimulation(self.model, wc_sim_config)
_, dynamic_model = multialgorithm_simulation.build_simulation()
# rate law for reaction_4-forward: k_cat_4_for * max(species_4[c], p_4)
k_cat_4_for = 1
p_4 = 2
species_4_c_pop = \
multialgorithm_simulation.local_species_population.read_one(0, 'species_4[c]')
expected_rate_reaction_4_forward = k_cat_4_for * max(species_4_c_pop, p_4)
expected_rates = {
'reaction_2': 0.0,
'reaction_4': expected_rate_reaction_4_forward
}
for dynamic_submodel in multialgorithm_simulation.dynamic_model.dynamic_submodels.values():
rates = dynamic_submodel.calc_reaction_rates()
for index, rxn in enumerate(dynamic_submodel.reactions):
if rxn.id in expected_rates:
self.assertAlmostEqual(list(rates)[index], expected_rates[rxn.id])
def test_prepare_skipped_submodels(self):
multialgorithm_simulation = MultialgorithmSimulation(self.model, self.wc_sim_config)
self.assertEqual(multialgorithm_simulation.skipped_submodels(), set())
submodels_to_skip = ['submodel_1']
self.wc_sim_config.submodels_to_skip = submodels_to_skip
multialgorithm_simulation = MultialgorithmSimulation(self.model, self.wc_sim_config)
self.assertEqual(multialgorithm_simulation.skipped_submodels(), set(submodels_to_skip))
submodels_to_skip = ['no_such_submodel']
self.wc_sim_config.submodels_to_skip = submodels_to_skip
with self.assertRaisesRegex(MultialgorithmError,
"'submodels_to_skip' contains submodels that aren't in the model:"):
MultialgorithmSimulation(self.model, self.wc_sim_config)
def test_initialize_infrastructure(self):
self.multialgorithm_simulation.initialize_components()
self.multialgorithm_simulation.initialize_infrastructure()
self.assertTrue(isinstance(self.multialgorithm_simulation.dynamic_model, DynamicModel))
de_simulation_config = SimulationConfig(time_max=10, output_dir=self.results_dir)
wc_sim_config = WCSimulationConfig(de_simulation_config, dfba_time_step=1, checkpoint_period=10)
multialg_sim = MultialgorithmSimulation(self.model, wc_sim_config)
multialg_sim.initialize_components()
multialg_sim.initialize_infrastructure()
self.assertEqual(multialg_sim.checkpointing_sim_obj.checkpoint_dir, self.results_dir)
self.assertTrue(multialg_sim.checkpointing_sim_obj.access_state_object is not None)
self.assertTrue(isinstance(multialg_sim.checkpointing_sim_obj, MultialgorithmicCheckpointingSimObj))
self.assertTrue(isinstance(multialg_sim.dynamic_model, DynamicModel))
def make_dynamic_submodel_params(model, lang_submodel):
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config)
multialgorithm_simulation = MultialgorithmSimulation(model, wc_sim_config)
multialgorithm_simulation.initialize_components()
multialgorithm_simulation.dynamic_model = \
DynamicModel(multialgorithm_simulation.model,
multialgorithm_simulation.local_species_population,
multialgorithm_simulation.temp_dynamic_compartments)
return (lang_submodel.id,
multialgorithm_simulation.dynamic_model,
lang_submodel.reactions,
lang_submodel.get_children(kind='submodel', __type=Species),
multialgorithm_simulation.get_dynamic_compartments(lang_submodel),
multialgorithm_simulation.local_species_population)
def test_build_simulation(self):
de_simulation_config = SimulationConfig(time_max=10, output_dir=self.results_dir)
wc_sim_config = WCSimulationConfig(de_simulation_config, dfba_time_step=1, checkpoint_period=10)
multialgorithm_simulation = MultialgorithmSimulation(self.model, wc_sim_config)
simulation_engine, _ = multialgorithm_simulation.build_simulation()
# 3 objects: 2 submodels, and the checkpointing obj:
expected_sim_objs = set(['CHECKPOINTING_SIM_OBJ', 'submodel_1', 'submodel_2'])
self.assertEqual(expected_sim_objs, set(list(simulation_engine.simulation_objects)))
self.assertEqual(type(multialgorithm_simulation.checkpointing_sim_obj),
MultialgorithmicCheckpointingSimObj)
self.assertEqual(multialgorithm_simulation.dynamic_model.get_num_submodels(), 2)
# check that submodels receive options
dfba_options = dict(dfba='fast but inaccurate')
ssa_options = dict(ssa='accurate but slow')
options = {'DfbaSubmodel': dict(options=dfba_options),
'SsaSubmodel': dict(options=ssa_options)
}
multialgorithm_simulation = MultialgorithmSimulation(self.model, wc_sim_config, options)
multialgorithm_simulation.build_simulation()
dfba_submodel = multialgorithm_simulation.dynamic_model.dynamic_submodels['submodel_1']
ssa_submodel = multialgorithm_simulation.dynamic_model.dynamic_submodels['submodel_2']
self.assertEqual(dfba_submodel.options, dfba_options)
self.assertEqual(ssa_submodel.options, ssa_options)
# test skipped submodel
submodels_to_skip = ['submodel_2']
self.wc_sim_config.submodels_to_skip = submodels_to_skip
ma_sim = MultialgorithmSimulation(self.model, self.wc_sim_config)
_, dynamic_model = ma_sim.build_simulation()
expected_dynamic_submodels = set([sm.id for sm in self.model.get_submodels()]) - ma_sim.skipped_submodels()
self.assertEqual(expected_dynamic_submodels, set(dynamic_model.dynamic_submodels))
submodel_1 = self.model.submodels.get(id='submodel_1')[0]
# WC:modeling_framework is not an instance of a modeling framework
submodel_1.framework = onto['WC:modeling_framework']
ma_sim = MultialgorithmSimulation(self.model, self.wc_sim_config)
with self.assertRaisesRegex(MultialgorithmError, 'Unsupported lang_submodel framework'):
ma_sim.build_simulation()
def test_check_simul_results(self):
init_volume = 1E-16
init_density = 1000
molecular_weight = 100.
default_species_copy_number = 10_000
init_accounted_mass = molecular_weight * default_species_copy_number / Avogadro
init_accounted_density = init_accounted_mass / init_volume
expected_initial_values_compt_1 = dict(
init_volume=init_volume,
init_accounted_mass=init_accounted_mass,
init_mass=init_volume * init_density,
init_density=init_density,
init_accounted_density=init_accounted_density,
accounted_fraction=init_accounted_density / init_density)
expected_initial_values = {'compt_1': expected_initial_values_compt_1}
model = MakeModel.make_test_model(
'1 species, 1 reaction',
init_vols=[expected_initial_values_compt_1['init_volume']],
init_vol_stds=[0],
density=init_density,
molecular_weight=molecular_weight,
default_species_copy_number=default_species_copy_number,
default_species_std=0,
submodel_framework='WC:deterministic_simulation_algorithm')
multialgorithm_simulation = MultialgorithmSimulation(
model, self.wc_sim_config)
_, dynamic_model = multialgorithm_simulation.build_simulation()
check_simul_results(self,
dynamic_model,
None,
expected_initial_values=expected_initial_values)
# test dynamics
simulation = Simulation(model)
_, results_dir = simulation.run(time_max=2, **self.args)
nan = float('NaN')
check_simul_results(self, dynamic_model, results_dir,
expected_initial_values=expected_initial_values,
expected_species_trajectories=\
{'spec_type_0[compt_1]':[10000., 9999., 9998.]})
check_simul_results(self, dynamic_model, results_dir,
expected_initial_values=expected_initial_values,
expected_species_trajectories=\
{'spec_type_0[compt_1]':[nan, nan, nan]})
with self.assertRaises(AssertionError):
check_simul_results(self, dynamic_model, results_dir,
expected_initial_values=expected_initial_values,
expected_species_trajectories=\
{'spec_type_0[compt_1]':[10000., 10000., 9998.]})
with self.assertRaises(AssertionError):
check_simul_results(self, dynamic_model, results_dir,
expected_initial_values=expected_initial_values,
expected_species_trajectories=\
{'spec_type_0[compt_1]':[10000., 10000.]})
check_simul_results(self, dynamic_model, results_dir,
expected_initial_values=expected_initial_values,
expected_species_trajectories=\
{'spec_type_0[compt_1]':[10000., 9999., 9998.]},
rel_tol=1E-5)
check_simul_results(self,
dynamic_model,
results_dir,
expected_property_trajectories={
'compt_1': {
'mass': [1.000e-13, 9.999e-14, 9.998e-14]
}
})
check_simul_results(self,
dynamic_model,
results_dir,
expected_property_trajectories={
'compt_1': {
'mass': [nan, nan, nan]
}
})
with self.assertRaises(AssertionError):
check_simul_results(self,
dynamic_model,
results_dir,
expected_property_trajectories={
'compt_1': {
'mass':
[1.000e-13, 1.000e-13, 9.999e-14]
}
},
rel_tol=0)
plots_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'tests',
'results'))
os.makedirs(plots_dir, exist_ok=True)
plot_expected_vs_simulated(dynamic_model,
'ordinary_differential_equations',
results_dir,
trajectory_times=[0, 1, 2],
plots_dir=plots_dir,
expected_species_trajectories=\
{'spec_type_0[compt_1]':[10000., 10000., 9998.]},
expected_property_trajectories=\
{'compt_1':
{'mass':[1.000e-13, 1.000e-13, 9.999e-14]}})
plot_expected_vs_simulated(dynamic_model,
'ordinary_differential_equations',
results_dir,
trajectory_times=[0, 1, 2],
plots_dir=plots_dir,
expected_property_trajectories=\
{'compt_1':
{'mass':[1.000e-13, 1.000e-13, 9.999e-14]}})
plot_expected_vs_simulated(dynamic_model,
'ordinary_differential_equations',
results_dir,
trajectory_times=[0, 1, 2],
plots_dir=plots_dir,
expected_species_trajectories=\
{'spec_type_0[compt_1]':[10000., 10000., 9998.]})
plot_expected_vs_simulated(dynamic_model,
'ordinary_differential_equations',
results_dir,
trajectory_times=[0, 1, 2],
plots_dir=plots_dir,
expected_species_trajectories=\
{'spec_type_0[compt_1]':[nan, nan, nan]},
expected_property_trajectories=\
{'compt_1':
{'mass':[nan, nan, nan]}})
def test_deterministic_simulation_algorithm_submodel_statics(self):
self.transform_model_for_dsa_simulation(self.model)
prepare_model(self.model)
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config, dfba_time_step=1)
multialgorithm_simulation = MultialgorithmSimulation(self.model, wc_sim_config)
simulation_engine, _ = multialgorithm_simulation.build_simulation()
simulation_engine.initialize()
dsa_submodel_name = 'submodel_2'
dsa_submodel = multialgorithm_simulation.dynamic_model.dynamic_submodels[dsa_submodel_name]
self.assertTrue(isinstance(dsa_submodel, DsaSubmodel))
# test init: is reaction_table correct?
self.assertEqual(len(dsa_submodel.reaction_table), len(dsa_submodel.reactions))
for rxn_id, rxn_index in dsa_submodel.reaction_table.items():
# map reaction id to index
self.assertEqual(dsa_submodel.reactions[rxn_index].id, rxn_id)
# test send_initial_events(), schedule_next_reaction_execution() & schedule_ExecuteDsaReaction()
# all of dsa_submodel's reactions should be scheduled to execute
events = simulation_engine.event_queue.render(sim_obj=dsa_submodel, as_list=True)
reaction_indices = set()
send_time_idx, _, sender_idx, receiver_idx, event_type_idx, reaction_idx = list(range(6))
for event_record in events[1:]:
self.assertEqual(event_record[send_time_idx], (0.0,))
self.assertEqual(event_record[sender_idx], dsa_submodel_name)
self.assertEqual(event_record[receiver_idx], dsa_submodel_name)
self.assertEqual(event_record[event_type_idx], ExecuteDsaReaction.__name__)
reaction_indices.add(event_record[reaction_idx])
self.assertEqual(reaction_indices, set([str(i) for i in range(len(dsa_submodel.reactions))]))
# test handle_ExecuteDsaReaction_msg(): execute next reaction
# reaction_3_forward has the highest reaction rate
events = simulation_engine.event_queue.next_events()
self.assertEqual(len(events), 1)
event = events[0]
self.assertEqual(dsa_submodel.reactions[event.message.reaction_index].id, 'reaction_3_forward')
# reaction_3_forward: [c]: species_2 + (2) species_4 ==> species_5
# check population changes
species = ['species_2[c]', 'species_4[c]', 'species_5[c]']
pops_before = {}
populations = multialgorithm_simulation.local_species_population
for species_id in species:
pops_before[species_id] = populations.read_one(event.event_time, species_id)
expected_pop_changes = dict(zip(species, [-1, -2, +1]))
# set time of dsa_submodel to time of the event
dsa_submodel.time = event.event_time
dsa_submodel.handle_ExecuteDsaReaction_msg(event)
for s_id, expected_pop_change in expected_pop_changes.items():
self.assertEqual(pops_before[s_id] + expected_pop_changes[s_id],
populations.read_one(event.event_time, s_id))
# zero populations and test exception
for species_id in species:
pop = populations.read_one(event.event_time, species_id)
populations.adjust_discretely(event.event_time, {species_id: -pop})
with self.assertRaises(DynamicMultialgorithmError):
dsa_submodel.handle_ExecuteDsaReaction_msg(event)
# test DsaSubmodel options
expected = dict(a=1)
options = dict(DsaSubmodel=dict(optiona=expected))
options = {'DsaSubmodel': {'options': expected
}
}
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config, dfba_time_step=1)
multialgorithm_simulation = MultialgorithmSimulation(self.model, wc_sim_config, options)
multialgorithm_simulation.build_simulation()
dsa_submodel = multialgorithm_simulation.dynamic_model.dynamic_submodels['submodel_2']
self.assertEqual(dsa_submodel.options, expected)
class TestMultialgorithmSimulationStatically(unittest.TestCase):
MODEL_FILENAME = os.path.join(os.path.dirname(__file__), 'fixtures', 'test_model.xlsx')
def setUp(self):
# read and initialize a model
self.model = Reader().run(self.MODEL_FILENAME, ignore_extra_models=True)[Model][0]
for conc in self.model.distribution_init_concentrations:
conc.std = 0.
PrepForWcSimTransform().run(self.model)
de_simulation_config = SimulationConfig(time_max=10)
self.wc_sim_config = WCSimulationConfig(de_simulation_config, dfba_time_step=1)
self.multialgorithm_simulation = MultialgorithmSimulation(self.model, self.wc_sim_config)
self.test_dir = tempfile.mkdtemp()
self.results_dir = tempfile.mkdtemp(dir=self.test_dir)
def tearDown(self):
shutil.rmtree(self.test_dir)
def test_init(self):
self.model.submodels = []
with self.assertRaises(MultialgorithmError):
MultialgorithmSimulation(self.model, self.wc_sim_config)
def test_prepare_skipped_submodels(self):
multialgorithm_simulation = MultialgorithmSimulation(self.model, self.wc_sim_config)
self.assertEqual(multialgorithm_simulation.skipped_submodels(), set())
submodels_to_skip = ['submodel_1']
self.wc_sim_config.submodels_to_skip = submodels_to_skip
multialgorithm_simulation = MultialgorithmSimulation(self.model, self.wc_sim_config)
self.assertEqual(multialgorithm_simulation.skipped_submodels(), set(submodels_to_skip))
submodels_to_skip = ['no_such_submodel']
self.wc_sim_config.submodels_to_skip = submodels_to_skip
with self.assertRaisesRegex(MultialgorithmError,
"'submodels_to_skip' contains submodels that aren't in the model:"):
MultialgorithmSimulation(self.model, self.wc_sim_config)
def test_molecular_weights_for_species(self):
multi_alg_sim = self.multialgorithm_simulation
expected = {
'species_6[c]': float('nan'),
'H2O[c]': 18.0152
}
actual = multi_alg_sim.molecular_weights_for_species(set(expected.keys()))
self.assertEqual(actual['H2O[c]'], expected['H2O[c]'])
self.assertTrue(np.isnan(actual['species_6[c]']))
# add a species_type without a structure
species_type_wo_structure = self.model.species_types.create(
id='st_wo_structure',
name='st_wo_structure')
cellular_compartment = self.model.compartments.get(**{'id': 'c'})[0]
species_wo_structure = self.model.species.create(
species_type=species_type_wo_structure,
compartment=cellular_compartment)
species_wo_structure.id = species_wo_structure.gen_id()
actual = multi_alg_sim.molecular_weights_for_species([species_wo_structure.id])
self.assertTrue(np.isnan(actual[species_wo_structure.id]))
# test obtain weights for all species
actual = multi_alg_sim.molecular_weights_for_species()
self.assertEqual(actual['H2O[c]'], expected['H2O[c]'])
self.assertTrue(np.isnan(actual['species_6[c]']))
self.assertEqual(len(actual), len(self.model.get_species()))
def test_create_dynamic_compartments(self):
self.multialgorithm_simulation.create_dynamic_compartments()
self.assertEqual(set(['c', 'e']), set(self.multialgorithm_simulation.temp_dynamic_compartments))
for id, dynamic_compartment in self.multialgorithm_simulation.temp_dynamic_compartments.items():
self.assertEqual(id, dynamic_compartment.id)
self.assertTrue(0 < dynamic_compartment.init_density)
def test_prepare_dynamic_compartments(self):
self.multialgorithm_simulation.create_dynamic_compartments()
self.multialgorithm_simulation.init_species_pop_from_distribution()
self.multialgorithm_simulation.local_species_population = \
self.multialgorithm_simulation.make_local_species_population(retain_history=False)
self.multialgorithm_simulation.prepare_dynamic_compartments()
for dynamic_compartment in self.multialgorithm_simulation.temp_dynamic_compartments.values():
self.assertTrue(dynamic_compartment._initialized())
self.assertTrue(0 < dynamic_compartment.accounted_mass())
self.assertTrue(0 < dynamic_compartment.mass())
def test_init_species_pop_from_distribution(self):
self.multialgorithm_simulation.create_dynamic_compartments()
self.multialgorithm_simulation.init_species_pop_from_distribution()
species_wo_init_conc = ['species_1[c]', 'species_3[c]']
for species_id in species_wo_init_conc:
self.assertEqual(self.multialgorithm_simulation.init_populations[species_id], 0)
for concentration in self.model.get_distribution_init_concentrations():
self.assertTrue(0 <= self.multialgorithm_simulation.init_populations[concentration.species.id])
# todo: statistically evaluate sampled population
# ensure that over multiple runs of init_species_pop_from_distribution():
# mean(species population) ~= mean(volume) * mean(concentration)
def test_make_local_species_population(self):
self.multialgorithm_simulation.create_dynamic_compartments()
self.multialgorithm_simulation.init_species_pop_from_distribution()
local_species_population = self.multialgorithm_simulation.make_local_species_population()
self.assertEqual(local_species_population._molecular_weights,
self.multialgorithm_simulation.molecular_weights_for_species())
# test the initial population slopes
# continuous adjustments are only allowed on species used by continuous submodels
used_by_continuous_submodels = \
['species_1[e]', 'species_2[e]', 'species_1[c]', 'species_2[c]', 'species_3[c]']
adjustments = {species_id: 0. for species_id in used_by_continuous_submodels}
self.assertEqual(local_species_population.adjust_continuously(1, adjustments), None)
not_in_a_reaction = ['H2O[e]', 'H2O[c]']
used_by_discrete_submodels = ['species_4[c]', 'species_5[c]', 'species_6[c]']
adjustments = {species_id: 0. for species_id in used_by_discrete_submodels + not_in_a_reaction}
with self.assertRaises(DynamicSpeciesPopulationError):
local_species_population.adjust_continuously(2, adjustments)
def test_set_simultaneous_execution_priorities(self):
expected_order_of_sim_obj_classes = [SsaSubmodel,
NrmSubmodel,
DsaSubmodel,
DfbaSubmodel,
OdeSubmodel,
MultialgorithmicCheckpointingSimObj]
self.multialgorithm_simulation.set_simultaneous_execution_priorities()
# ensure that expected_order_of_sim_obj_classes are arranged in decreasing priority
for i in range(len(expected_order_of_sim_obj_classes) - 1):
simulation_object_class = expected_order_of_sim_obj_classes[i]
next_simulation_object_class = expected_order_of_sim_obj_classes[i+1]
self.assertLess(simulation_object_class.metadata.class_priority,
next_simulation_object_class.metadata.class_priority)
def test_initialize_components(self):
self.multialgorithm_simulation.initialize_components()
self.assertTrue(isinstance(self.multialgorithm_simulation.local_species_population,
LocalSpeciesPopulation))
for dynamic_compartment in self.multialgorithm_simulation.temp_dynamic_compartments.values():
self.assertTrue(isinstance(dynamic_compartment.species_population, LocalSpeciesPopulation))
def test_initialize_infrastructure(self):
self.multialgorithm_simulation.initialize_components()
self.multialgorithm_simulation.initialize_infrastructure()
self.assertTrue(isinstance(self.multialgorithm_simulation.dynamic_model, DynamicModel))
de_simulation_config = SimulationConfig(time_max=10, output_dir=self.results_dir)
wc_sim_config = WCSimulationConfig(de_simulation_config, dfba_time_step=1, checkpoint_period=10)
multialg_sim = MultialgorithmSimulation(self.model, wc_sim_config)
multialg_sim.initialize_components()
multialg_sim.initialize_infrastructure()
self.assertEqual(multialg_sim.checkpointing_sim_obj.checkpoint_dir, self.results_dir)
self.assertTrue(multialg_sim.checkpointing_sim_obj.access_state_object is not None)
self.assertTrue(isinstance(multialg_sim.checkpointing_sim_obj, MultialgorithmicCheckpointingSimObj))
self.assertTrue(isinstance(multialg_sim.dynamic_model, DynamicModel))
def test_build_simulation(self):
de_simulation_config = SimulationConfig(time_max=10, output_dir=self.results_dir)
wc_sim_config = WCSimulationConfig(de_simulation_config, dfba_time_step=1, checkpoint_period=10)
multialgorithm_simulation = MultialgorithmSimulation(self.model, wc_sim_config)
simulation_engine, _ = multialgorithm_simulation.build_simulation()
# 3 objects: 2 submodels, and the checkpointing obj:
expected_sim_objs = set(['CHECKPOINTING_SIM_OBJ', 'submodel_1', 'submodel_2'])
self.assertEqual(expected_sim_objs, set(list(simulation_engine.simulation_objects)))
self.assertEqual(type(multialgorithm_simulation.checkpointing_sim_obj),
MultialgorithmicCheckpointingSimObj)
self.assertEqual(multialgorithm_simulation.dynamic_model.get_num_submodels(), 2)
# check that submodels receive options
dfba_options = dict(dfba='fast but inaccurate')
ssa_options = dict(ssa='accurate but slow')
options = {'DfbaSubmodel': dict(options=dfba_options),
'SsaSubmodel': dict(options=ssa_options)
}
multialgorithm_simulation = MultialgorithmSimulation(self.model, wc_sim_config, options)
multialgorithm_simulation.build_simulation()
dfba_submodel = multialgorithm_simulation.dynamic_model.dynamic_submodels['submodel_1']
ssa_submodel = multialgorithm_simulation.dynamic_model.dynamic_submodels['submodel_2']
self.assertEqual(dfba_submodel.options, dfba_options)
self.assertEqual(ssa_submodel.options, ssa_options)
# test skipped submodel
submodels_to_skip = ['submodel_2']
self.wc_sim_config.submodels_to_skip = submodels_to_skip
ma_sim = MultialgorithmSimulation(self.model, self.wc_sim_config)
_, dynamic_model = ma_sim.build_simulation()
expected_dynamic_submodels = set([sm.id for sm in self.model.get_submodels()]) - ma_sim.skipped_submodels()
self.assertEqual(expected_dynamic_submodels, set(dynamic_model.dynamic_submodels))
submodel_1 = self.model.submodels.get(id='submodel_1')[0]
# WC:modeling_framework is not an instance of a modeling framework
submodel_1.framework = onto['WC:modeling_framework']
ma_sim = MultialgorithmSimulation(self.model, self.wc_sim_config)
with self.assertRaisesRegex(MultialgorithmError, 'Unsupported lang_submodel framework'):
ma_sim.build_simulation()
def test_get_dynamic_compartments(self):
expected_compartments = dict(
submodel_1=['c', 'e'],
submodel_2=['c']
)
self.multialgorithm_simulation.build_simulation()
for submodel_id in ['submodel_1', 'submodel_2']:
submodel = self.model.submodels.get_one(id=submodel_id)
submodel_dynamic_compartments = self.multialgorithm_simulation.get_dynamic_compartments(submodel)
self.assertEqual(set(submodel_dynamic_compartments.keys()), set(expected_compartments[submodel_id]))
def test_str(self):
self.multialgorithm_simulation.create_dynamic_compartments()
self.multialgorithm_simulation.init_species_pop_from_distribution()
self.multialgorithm_simulation.local_species_population = \
self.multialgorithm_simulation.make_local_species_population(retain_history=False)
self.assertIn('species_1[e]', str(self.multialgorithm_simulation))
self.assertIn('model:', str(self.multialgorithm_simulation))
def test_dynamic_components(self):
# test agregate properties like mass and volume against independent calculations of their values
# calculations made in the model's spreadsheet
# read model while ignoring missing models
model = read_model_for_test(self.MODEL_FILENAME)
# create dynamic model
de_simulation_config = SimulationConfig(time_max=10)
wc_sim_config = WCSimulationConfig(de_simulation_config)
multialgorithm_simulation = MultialgorithmSimulation(
model, wc_sim_config)
multialgorithm_simulation.initialize_components()
dynamic_model = DynamicModel(
model, multialgorithm_simulation.local_species_population,
multialgorithm_simulation.temp_dynamic_compartments)
# a Model to store expected initial values
class ExpectedInitialValue(obj_tables.Model):
component = obj_tables.StringAttribute()
attribute = obj_tables.StringAttribute()
expected_initial_value = obj_tables.FloatAttribute()
comment = obj_tables.StringAttribute()
class Meta(obj_tables.Model.Meta):
attribute_order = ('component', 'attribute',
'expected_initial_value', 'comment')
# get calculations of expected initial values from the workbook
expected_initial_values = \
obj_tables.io.Reader().run(self.MODEL_FILENAME, models=[ExpectedInitialValue],
ignore_extra_models=True)[ExpectedInitialValue]
for cellular_compartment in dynamic_model.cellular_dyn_compartments:
compartment = dynamic_model.dynamic_compartments[
cellular_compartment.id]
actual_values = {
'mass': compartment.mass(),
'volume': compartment.volume(),
'accounted mass': compartment.accounted_mass(),
'accounted volume': compartment.accounted_volume()
}
for expected_initial_value in expected_initial_values:
if expected_initial_value.component == cellular_compartment.id:
expected_value = expected_initial_value.expected_initial_value
actual_value = actual_values[
expected_initial_value.attribute]
numpy.testing.assert_approx_equal(actual_value,
expected_value)
# cell mass, cell volume, etc.
actual_values = {
'cell mass': dynamic_model.cell_mass(),
'cell volume': dynamic_model.cell_volume(),
'cell accounted mass': dynamic_model.cell_accounted_mass(),
'cell accounted volume': dynamic_model.cell_accounted_volume()
}
for expected_initial_value in expected_initial_values:
if expected_initial_value.component == 'whole_cell':
expected_value = expected_initial_value.expected_initial_value
actual_value = actual_values[
f"cell {expected_initial_value.attribute}"]
numpy.testing.assert_approx_equal(actual_value, expected_value)
# test dynamic_model.get_aggregate_state()
aggregate_state = dynamic_model.get_aggregate_state()
for eiv_record in expected_initial_values:
expected_value = eiv_record.expected_initial_value
if eiv_record.component == 'whole_cell':
actual_value = aggregate_state[f"cell {eiv_record.attribute}"]
numpy.testing.assert_approx_equal(actual_value, expected_value)
else:
actual_value = aggregate_state['compartments'][
eiv_record.component][eiv_record.attribute]
numpy.testing.assert_approx_equal(actual_value, expected_value)
def test_init(self):
self.model.submodels = []
with self.assertRaises(MultialgorithmError):
MultialgorithmSimulation(self.model, self.wc_sim_config)
def run(self,
time_max,
results_dir=None,
progress_bar=True,
checkpoint_period=None,
seed=None,
ode_time_step=None,
dfba_time_step=None,
profile=False,
submodels_to_skip=None,
verbose=True,
options=None):
""" Run one simulation
Args:
time_max (:obj:`float`): the maximum time of a simulation; a stop condition may end it
earlier (sec)
results_dir (:obj:`str`, optional): path to a directory in which results are stored
progress_bar (:obj:`bool`, optional): whether to show the progress of a simulation in
in a real-time bar on a terminal
checkpoint_period (:obj:`float`, optional): the period between simulation state checkpoints (sec)
ode_time_step (:obj:`float`, optional): time step length of ODE submodel (sec)
dfba_time_step (:obj:`float`, optional): time step length of dFBA submodel (sec)
profile (:obj:`bool`, optional): whether to output a profile of the simulation's performance
created by a Python profiler
seed (:obj:`object`, optional): a seed for the simulation's `numpy.random.RandomState`;
if provided, `seed` will reseed the simulator's PRNG
submodels_to_skip (:obj:`list` of :obj:`str`, optional): submodels that should not be run,
identified by their ids
verbose (:obj:`bool`, optional): whether to print success output
options (:obj:`dict`, optional): options for submodels, passed to `MultialgorithmSimulation`
Returns:
:obj:`tuple` of (`int`, `str`): number of simulation events, pathname of directory
containing the results, or :obj:`tuple` of (`int`, `None`): number of simulation events,
`None` if `results_dir is None`, or :obj:`tuple` of (`pstats.Stats`, `None`): profile
stats, `None` if `profile is True`
Raises:
:obj:`MultialgorithmError`: if the simulation raises an exception
"""
self._prepare()
# create simulation configurations
# create and validate DE sim configuration
self.de_sim_config = SimulationConfig(time_max,
output_dir=results_dir,
progress=progress_bar,
profile=profile)
self.de_sim_config.validate()
# create and validate WC configuration
self.wc_sim_config = WCSimulationConfig(
de_simulation_config=self.de_sim_config,
random_seed=seed,
ode_time_step=ode_time_step,
dfba_time_step=dfba_time_step,
checkpoint_period=checkpoint_period,
submodels_to_skip=submodels_to_skip,
verbose=verbose)
self.wc_sim_config.validate()
# create author metadata for DE sim
try:
username = getpass.getuser()
except KeyError: # pragma: no cover
username = '******'
self.author_metadata = AuthorMetadata(
name='Unknown name',
email='Unknown email',
username=username,
organization='Unknown organization')
# create WC sim metadata
wc_simulation_metadata = WCSimulationMetadata(self.wc_sim_config)
if self.model_path is not None:
wc_simulation_metadata.set_wc_model_repo(self.model_path)
if seed is not None:
RandomStateManager.initialize(seed=seed)
# create a multi-algorithmic simulator
multialgorithm_simulation = MultialgorithmSimulation(
self.model, self.wc_sim_config, options)
self.simulation_engine, self.dynamic_model = multialgorithm_simulation.build_simulation(
)
self.simulation_engine.initialize()
# set stop_condition after the dynamic model is created
self.de_sim_config.stop_condition = self.dynamic_model.get_stop_condition(
)
# run simulation
try:
# provide DE config and author metadata to DE sim
simulate_rv = self.simulation_engine.simulate(
sim_config=self.de_sim_config,
author_metadata=self.author_metadata)
# add WC sim metadata to the output after the simulation, which requires an empty output dir
# TODO: have simulation_engine.simulate() allow certain files in self.de_sim_config.output_dir, and move
# this code above
if self.de_sim_config.output_dir is not None:
WCSimulationMetadata.write_dataclass(
wc_simulation_metadata, self.de_sim_config.output_dir)
if profile:
stats = simulate_rv
return stats, None
else:
num_events = simulate_rv
except SimulatorError as e: # pragma: no cover
raise MultialgorithmError(
f'Simulation terminated with simulator error:\n{e}')
except BaseException as e: # pragma: no cover
raise MultialgorithmError(
f'Simulation terminated with error:\n{e}')
if verbose:
print(f'Simulated {num_events} events')
if results_dir:
# summarize results in an HDF5 file in results_dir
RunResults(results_dir)
if verbose:
print(f"Saved checkpoints and run results in '{results_dir}'")
return (num_events, results_dir)
else:
return (num_events, None)
