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 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_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)
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)
