class TestInitialDynamicComponentsComprehensively(unittest.TestCase):
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 test(self):
# test all DynamicComponents that implement eval()
### Test DynamicExpressions ###
# each one is tested using each of the objects it uses in some instance in self.model_file
# DynamicFunction
for id, dynamic_function in self.dynamic_model.dynamic_functions.items(
):
expected_value = float(self.model.get_functions(id=id)[0].comments)
numpy.testing.assert_approx_equal(dynamic_function.eval(0),
expected_value)
# test eval_dynamic_functions()
for func_id, func_val in self.dynamic_model.eval_dynamic_functions(
0).items():
expected_value = float(
self.model.get_functions(id=func_id)[0].comments)
numpy.testing.assert_approx_equal(func_val, expected_value)
a_func_id = list(self.dynamic_model.dynamic_functions)[0]
for func_id, func_val in \
self.dynamic_model.eval_dynamic_functions(0, functions_to_eval=[a_func_id]).items():
expected_value = float(
self.model.get_functions(id=func_id)[0].comments)
numpy.testing.assert_approx_equal(func_val, expected_value)
# DynamicStopCondition
for id, dynamic_stop_condition in self.dynamic_model.dynamic_stop_conditions.items(
):
expected_val_in_comment = self.model.get_stop_conditions(
id=id)[0].comments
if expected_val_in_comment == 'True':
expected_value = True
elif expected_val_in_comment == 'False':
expected_value = False
self.assertEqual(expected_value, dynamic_stop_condition.eval(0))
# DynamicObservable
for id, dynamic_observable in self.dynamic_model.dynamic_observables.items(
):
expected_value = float(
self.model.get_observables(id=id)[0].comments)
numpy.testing.assert_approx_equal(dynamic_observable.eval(0),
expected_value)
# test eval_dynamic_observables()
for obs_id, obs_val in self.dynamic_model.eval_dynamic_observables(
0).items():
expected_value = float(
self.model.get_observables(id=obs_id)[0].comments)
numpy.testing.assert_approx_equal(obs_val, expected_value)
an_obs_id = list(self.dynamic_model.dynamic_observables)[0]
for obs_id, obs_val in \
self.dynamic_model.eval_dynamic_observables(0, observables_to_eval=[an_obs_id]).items():
expected_value = float(
self.model.get_observables(id=obs_id)[0].comments)
numpy.testing.assert_approx_equal(obs_val, expected_value)
# DynamicRateLaw
for id, dynamic_rate_law in self.dynamic_model.dynamic_rate_laws.items(
):
expected_value = float(self.model.get_rate_laws(id=id)[0].comments)
numpy.testing.assert_approx_equal(dynamic_rate_law.eval(0),
expected_value)
### Test DynamicComponents ###
# DynamicCompartment
for id, dynamic_compartment in self.dynamic_model.dynamic_compartments.items(
):
expected_value = float(
self.model.get_compartments(id=id)[0].comments)
numpy.testing.assert_approx_equal(dynamic_compartment.eval(0),
expected_value)
# DynamicParameter
for id, dynamic_parameter in self.dynamic_model.dynamic_parameters.items(
):
expected_value = float(
self.model.get_parameters(id=id)[0].comments)
numpy.testing.assert_approx_equal(dynamic_parameter.eval(0),
expected_value)
# DynamicSpecies
for id, dynamic_species in self.dynamic_model.dynamic_species.items():
expected_value = float(self.model.get_species(id=id)[0].comments)
numpy.testing.assert_approx_equal(dynamic_species.eval(0),
expected_value)
# FIX FOR DE-SIM CHANGES: need to make a few dynamic models to test all branches of get_stop_condition()
# a dynamic model with no stop concitions
# a dynamic model with stop condidtions that all evaluate false
# a dynamic model with at least one stop condidtions that evaluates true
def test_get_stop_condition(self):
all_stop_conditions = self.dynamic_model.get_stop_condition()
self.assertTrue(callable(all_stop_conditions))
self.assertTrue(all_stop_conditions(0))
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))
class TestModelUtilities(unittest.TestCase):
def get_submodel(self, model, id_val):
return model.submodels.get_one(id=id_val)
MODEL_FILENAME = os.path.join(os.path.dirname(__file__), 'fixtures',
'test_model.xlsx')
def setUp(self):
# read a model
self.model = Reader().run(self.MODEL_FILENAME,
ignore_extra_models=True)[wc_lang.Model][0]
def test_find_private_species(self):
# since set() operations are being used, this test does not ensure that the methods being
# tested are deterministic and repeatable; repeatability should be tested by running the
# code under different circumstances and ensuring identical output
private_species = ModelUtilities.find_private_species(self.model)
submodel_1_exp_species_ids = [
'species_1[c]', 'species_1[e]', 'species_2[e]'
]
mod1_expected_species = wc_lang.Species.get(submodel_1_exp_species_ids,
self.model.get_species())
self.assertEqual(
set(private_species[self.get_submodel(self.model, 'submodel_1')]),
set(mod1_expected_species))
submodel_2_exp_species_ids = [
'species_5[c]', 'species_4[c]', 'species_6[c]'
]
mod2_expected_species = wc_lang.Species.get(submodel_2_exp_species_ids,
self.model.get_species())
self.assertEqual(
set(private_species[self.get_submodel(self.model, 'submodel_2')]),
set(mod2_expected_species))
private_species = ModelUtilities.find_private_species(self.model,
return_ids=True)
self.assertEqual(set(private_species['submodel_1']),
set(submodel_1_exp_species_ids))
self.assertEqual(set(private_species['submodel_2']),
set(submodel_2_exp_species_ids))
def test_find_shared_species(self):
self.assertEqual(
set(ModelUtilities.find_shared_species(self.model)),
set(
wc_lang.Species.get(
['species_2[c]', 'species_3[c]', 'H2O[e]', 'H2O[c]'],
self.model.get_species())))
self.assertEqual(
set(ModelUtilities.find_shared_species(self.model,
return_ids=True)),
set(['species_2[c]', 'species_3[c]', 'H2O[e]', 'H2O[c]']))
def test_sample_copy_num_from_concentration(self):
model = wc_lang.Model()
submodel = model.submodels.create(
id='submodel',
framework=onto['WC:stochastic_simulation_algorithm'])
compartment_c = model.compartments.create(
id='c', init_volume=wc_lang.InitVolume(mean=1.))
structure = wc_lang.ChemicalStructure(molecular_weight=10.)
species_types = {}
cus_species_types = {}
for cu in wc_lang.DistributionInitConcentration.units.choices:
id = str(cu).replace(' ', '_')
species_types[id] = model.species_types.create(id=id,
structure=structure)
cus_species_types[id] = cu
for other in ['no_units', 'no_concentration', 'no_std']:
species_types[other] = model.species_types.create(
id=other, structure=structure)
species = {}
for key, species_type in species_types.items():
species[key] = wc_lang.Species(species_type=species_type,
compartment=compartment_c)
species[key].id = species[key].gen_id()
conc_value = 2_000.
std_value = 0.
for key, sp in species.items():
if key in cus_species_types:
wc_lang.DistributionInitConcentration(
species=sp,
mean=conc_value,
std=std_value,
units=cus_species_types[key])
elif key == 'no_units':
wc_lang.DistributionInitConcentration(species=sp,
mean=conc_value,
std=std_value)
elif key == 'no_std':
wc_lang.DistributionInitConcentration(
species=sp,
mean=conc_value,
std=float('NaN'),
units=cus_species_types['molecule'])
elif key == 'no_concentration':
continue
conc_to_molecules = ModelUtilities.sample_copy_num_from_concentration
random_state = numpy.random.RandomState()
copy_number = conc_to_molecules(
species['molecule'],
species['molecule'].compartment.init_volume.mean, random_state)
self.assertEqual(copy_number, conc_value)
copy_number = conc_to_molecules(
species['molar'], species['molar'].compartment.init_volume.mean,
random_state)
self.assertEqual(copy_number, conc_value * Avogadro)
copy_number = conc_to_molecules(
species['no_units'],
species['no_units'].compartment.init_volume.mean, random_state)
self.assertEqual(copy_number, conc_value * Avogadro)
copy_number = conc_to_molecules(
species['millimolar'],
species['millimolar'].compartment.init_volume.mean, random_state)
self.assertEqual(copy_number, 10**-3 * conc_value * Avogadro)
copy_number = conc_to_molecules(
species['micromolar'],
species['micromolar'].compartment.init_volume.mean, random_state)
self.assertEqual(copy_number, 10**-6 * conc_value * Avogadro)
copy_number = conc_to_molecules(
species['nanomolar'],
species['nanomolar'].compartment.init_volume.mean, random_state)
self.assertEqual(copy_number, 10**-9 * conc_value * Avogadro)
copy_number = conc_to_molecules(
species['picomolar'],
species['picomolar'].compartment.init_volume.mean, random_state)
self.assertEqual(copy_number, 10**-12 * conc_value * Avogadro)
copy_number = conc_to_molecules(
species['femtomolar'],
species['femtomolar'].compartment.init_volume.mean, random_state)
self.assertAlmostEqual(copy_number,
10**-15 * conc_value * Avogadro,
delta=1)
copy_number = conc_to_molecules(
species['attomolar'],
species['attomolar'].compartment.init_volume.mean, random_state)
self.assertAlmostEqual(copy_number,
10**-18 * conc_value * Avogadro,
delta=1)
copy_number = conc_to_molecules(
species['no_concentration'],
species['no_concentration'].compartment.init_volume.mean,
random_state)
self.assertEqual(copy_number, 0)
conc = species['no_std'].distribution_init_concentration
copy_number = conc_to_molecules(
species['no_std'], species['no_std'].compartment.init_volume.mean,
random_state)
self.assertNotEqual(copy_number, conc_value)
with self.assertRaises(KeyError):
conc_to_molecules(
species['mol dm^-2'],
species['no_concentration'].compartment.init_volume.mean,
random_state)
species_tmp = wc_lang.Species(species_type=species_type,
compartment=compartment_c)
species_tmp.id = species_tmp.gen_id()
wc_lang.DistributionInitConcentration(
species=species_tmp,
mean=conc_value,
std=std_value,
units='not type(unit_registry.Unit)')
with self.assertRaisesRegex(ValueError, 'Unsupported unit type'):
conc_to_molecules(species_tmp,
species_tmp.compartment.init_volume.mean,
random_state)
species_tmp2 = wc_lang.Species(species_type=species_type,
compartment=compartment_c)
species_tmp2.id = species_tmp2.gen_id()
wc_lang.DistributionInitConcentration(
species=species_tmp2,
mean=conc_value,
std=std_value,
units=wc_lang.InitVolume.units.choices[0])
with self.assertRaisesRegex(ValueError, 'Unsupported unit'):
conc_to_molecules(species_tmp2,
species_tmp2.compartment.init_volume.mean,
random_state)
def test_get_species_types(self):
self.assertEqual(ModelUtilities.get_species_types([]), [])
species_type_ids = [
species_type.id for species_type in self.model.get_species_types()
]
species_ids = [
species.serialize() for species in self.model.get_species()
]
self.assertEqual(sorted(ModelUtilities.get_species_types(species_ids)),
sorted(species_type_ids))
def test_get_species_types(self):
self.assertEqual(
ModelUtilities.parse_species_id('species_type_id[compartment_id]'),
('species_type_id', 'compartment_id'))
with self.assertRaisesRegex(ValueError, 'Species id format should be'):
ModelUtilities.parse_species_id('compartment_id]')
with self.assertRaisesRegex(ValueError, 'Species id format should be'):
ModelUtilities.parse_species_id('[compartment_id]')
with self.assertRaisesRegex(ValueError, 'Species id format should be'):
ModelUtilities.parse_species_id('species_type_id[]')
with self.assertRaisesRegex(ValueError, 'Species id format should be'):
ModelUtilities.parse_species_id(
'species_type_id[compartment_id]extra')
