def test_exec_sed_task_output_step_interval(self):
doc = self._build_sed_doc()
doc.models[0].source = os.path.join(os.path.dirname(__file__),
'fixtures', 'test.bngl')
sim = doc.simulations[0]
sim.initial_time = 0.
sim.output_start_time = 0.
sim.output_end_time = 0.1
sim.number_of_points = 10
sim.algorithm.changes.append(
sedml_data_model.AlgorithmParameterChange(kisao_id='KISAO_0000684',
new_value='2'), )
sim.algorithm.changes.append(
sedml_data_model.AlgorithmParameterChange(kisao_id='KISAO_0000415',
new_value='1000000'), )
variables = [data_gen.variables[0] for data_gen in doc.data_generators]
with mock.patch.dict('os.environ',
{'ALGORITHM_SUBSTITUTION_POLICY': 'NONE'}):
with self.assertRaises(NotImplementedError):
exec_sed_task(doc.tasks[0], variables)
with mock.patch.dict(
'os.environ',
{'ALGORITHM_SUBSTITUTION_POLICY': 'SIMILAR_VARIABLES'}):
results, _ = exec_sed_task(doc.tasks[0], variables)
numpy.testing.assert_allclose(
results['var_time'],
numpy.linspace(sim.output_start_time, sim.output_end_time,
sim.number_of_points + 1))
def test_exec_sed_task(self):
task = sedml_data_model.Task(
model=sedml_data_model.Model(
source=self.EXAMPLE_MODEL_FILENAME,
language=sedml_data_model.ModelLanguage.SBML.value,
changes=[],
),
simulation=sedml_data_model.UniformTimeCourseSimulation(
algorithm=sedml_data_model.Algorithm(
kisao_id='KISAO_0000560',
changes=[
sedml_data_model.AlgorithmParameterChange(
kisao_id='KISAO_0000209',
new_value='2e-6',
),
],
),
initial_time=0.,
output_start_time=10.,
output_end_time=20.,
number_of_points=20,
),
)
variables = [
sedml_data_model.Variable(id='time',
symbol=sedml_data_model.Symbol.time,
task=task),
sedml_data_model.Variable(
id='A',
target=
"/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='A']",
task=task),
sedml_data_model.Variable(
id='C',
target=
'/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id="C"]',
task=task),
sedml_data_model.Variable(
id='DA',
target=
"/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='DA']",
task=task),
]
variable_results = exec_sed_task(task, variables)
self.assertTrue(sorted(variable_results.keys()),
sorted([var.id for var in variables]))
self.assertEqual(variable_results[variables[0].id].shape,
(task.simulation.number_of_points + 1, ))
numpy.testing.assert_almost_equal(
variable_results['time'],
numpy.linspace(task.simulation.output_start_time,
task.simulation.output_end_time,
task.simulation.number_of_points + 1),
)
for results in variable_results.values():
self.assertFalse(numpy.any(numpy.isnan(results)))
def _build_sed_doc(self, algorithm=None):
if algorithm is None:
algorithm = sedml_data_model.Algorithm(
kisao_id='KISAO_0000019',
changes=[
sedml_data_model.AlgorithmParameterChange(
kisao_id='KISAO_0000211',
new_value='2e-8',
),
],
)
doc = sedml_data_model.SedDocument()
doc.models.append(
sedml_data_model.Model(
id='model_1',
source='model_1.bngl',
language=sedml_data_model.ModelLanguage.BNGL.value,
changes=[
sedml_data_model.ModelAttributeChange(
target='functions.gfunc.expression',
new_value='0.5*Atot^2/(10 + Atot^2)'),
sedml_data_model.ModelAttributeChange(
target='functions.gfunc().expression',
new_value='0.5*Atot^2/(10 + Atot^2)'),
sedml_data_model.ModelAttributeChange(
target='species.A().initialCount', new_value='4'),
sedml_data_model.ModelAttributeChange(
target='parameters.g1.value', new_value='18.0'),
],
))
doc.simulations.append(
sedml_data_model.UniformTimeCourseSimulation(
id='sim_1_time_course',
algorithm=algorithm,
initial_time=0.,
output_start_time=0.1,
output_end_time=0.2,
number_of_points=20,
))
doc.tasks.append(
sedml_data_model.Task(
id='task_1',
model=doc.models[0],
simulation=doc.simulations[0],
))
doc.data_generators.append(
sedml_data_model.DataGenerator(
id='data_gen_time',
variables=[
sedml_data_model.Variable(
id='var_time',
symbol=sedml_data_model.Symbol.time,
task=doc.tasks[0],
),
],
math='var_time',
))
doc.data_generators.append(
sedml_data_model.DataGenerator(
id='data_gen_A',
variables=[
sedml_data_model.Variable(
id='var_A',
target="species.A",
task=doc.tasks[0],
),
],
math='var_A',
))
doc.data_generators.append(
sedml_data_model.DataGenerator(
id='data_gen_B',
variables=[
sedml_data_model.Variable(
id='var_B',
target='species.B.count',
task=doc.tasks[0],
),
],
math='var_B',
))
doc.data_generators.append(
sedml_data_model.DataGenerator(
id='data_gen_GeneA_00',
variables=[
sedml_data_model.Variable(
id='var_GeneA_00',
target="molecules.GeneA_00()",
task=doc.tasks[0],
),
],
math='var_GeneA_00',
))
doc.data_generators.append(
sedml_data_model.DataGenerator(
id='data_gen_GeneA_01',
variables=[
sedml_data_model.Variable(
id='var_GeneA_01',
target="molecules.GeneA_01()",
task=doc.tasks[0],
),
],
math='var_GeneA_01',
))
doc.outputs.append(
sedml_data_model.Report(
id='report_1',
data_sets=[
sedml_data_model.DataSet(
id='data_set_time',
label='Time',
data_generator=doc.data_generators[0]),
sedml_data_model.DataSet(
id='data_set_A',
label='A',
data_generator=doc.data_generators[1]),
sedml_data_model.DataSet(
id='data_set_B',
label='B',
data_generator=doc.data_generators[2]),
sedml_data_model.DataSet(
id='data_set_GeneA_00',
label='GeneA_00',
data_generator=doc.data_generators[3]),
sedml_data_model.DataSet(
id='data_set_GeneA_01',
label='GeneA_01',
data_generator=doc.data_generators[4]),
],
))
append_all_nested_children_to_doc(doc)
return doc
def test(self):
model = data_model.Model(
id='model1',
name='Model1',
source='model.sbml',
language='urn:sedml:language:sbml',
changes=[
data_model.ModelAttributeChange(target='/sbml:sbml/sbml:model[id=\'a\']/@id', new_value='234'),
data_model.ModelAttributeChange(target='/sbml:sbml/sbml:model[id=\'b\']/@id', new_value='432'),
data_model.AddElementModelChange(target='/sbml:sbml/sbml:model[id=\'b\']/@id', new_elements='432'),
data_model.ReplaceElementModelChange(target='/sbml:sbml/sbml:model[id=\'b\']/@id', new_elements='432'),
data_model.RemoveElementModelChange(target='/sbml:sbml/sbml:model[id=\'b\']/@id'),
data_model.ComputeModelChange(
target='/sbml:sbml/sbml:model[id=\'b\']/@id',
parameters=[
data_model.Parameter(id='p_1', value=1.5),
data_model.Parameter(id='p_2', value=2.5),
],
variables=[
data_model.Variable(
id='v_1',
model=None,
target="/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='param_1']",
),
data_model.Variable(
id='v_1',
model=None,
target="/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='param_1']",
),
],
math='p_1 * v_1 + p_2 * v_2',
),
],
)
for var in model.changes[-1].variables:
var.model = model
ss_simulation = data_model.SteadyStateSimulation(
id='simulation1',
name='Simulation1',
algorithm=data_model.Algorithm(
kisao_id='KISAO_0000029',
changes=[
data_model.AlgorithmParameterChange(kisao_id='KISAO_0000001', new_value='1.234'),
]),
)
one_step_simulation = data_model.OneStepSimulation(
id='simulation1',
name='Simulation1',
algorithm=data_model.Algorithm(
kisao_id='KISAO_0000029',
changes=[
data_model.AlgorithmParameterChange(kisao_id='KISAO_0000001', new_value='1.234'),
]),
step=10.)
time_course_simulation = data_model.UniformTimeCourseSimulation(
id='simulation1',
name='Simulation1',
algorithm=data_model.Algorithm(
kisao_id='KISAO_0000029',
changes=[
data_model.AlgorithmParameterChange(kisao_id='KISAO_0000001', new_value='1.234'),
data_model.AlgorithmParameterChange(kisao_id='KISAO_0000002', new_value='4.321'),
]),
initial_time=10.,
output_start_time=20.,
output_end_time=30,
number_of_steps=10)
task = data_model.Task(id='task1', name='Task1', model=model, simulation=time_course_simulation)
for var in model.changes[-1].variables:
var.task = task
report = data_model.Report(
id='report1',
name='Report1',
data_sets=[
data_model.DataSet(
id='dataset1',
name='Dataset1',
label='Dataset-1',
data_generator=data_model.DataGenerator(
id='dataGen1',
name='DataGen1',
variables=[
data_model.Variable(
id='DataGenVar1', name='DataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model)
],
parameters=[
data_model.Parameter(
id='DataGenParam1', name='DataGenParam1', value=2.)
],
math='DataGenVar1 - DataGenParam1',
)
),
],
)
plot2d = data_model.Plot2D(
name='Plot2D',
curves=[
data_model.Curve(
id='curve1', name='Curve1',
x_scale=data_model.AxisScale.linear,
y_scale=data_model.AxisScale.log,
x_data_generator=data_model.DataGenerator(
id='xDataGen1',
name='XDataGen1',
variables=[
data_model.Variable(
id='xDataGenVar1', name='XDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model)
],
parameters=[
data_model.Parameter(
id='xDataGenParam1', name='XDataGenParam1', value=2.)
],
math='xDataGenVar1 * xDataGenParam1',
),
y_data_generator=None,
),
data_model.Curve(
id='curve2', name='Curve2',
x_scale=data_model.AxisScale.linear,
y_scale=data_model.AxisScale.log,
x_data_generator=None,
y_data_generator=data_model.DataGenerator(
id='yDataGen1',
name='yDataGen1',
variables=[
data_model.Variable(
id='yDataGenVar1', name='YDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model)
],
parameters=[
data_model.Parameter(
id='yDataGenParam1', name='YDataGenParam1', value=2.)
],
math='yDataGenParam1 + YDataGenParam1',
),
),
]
)
plot3d = data_model.Plot3D(
name='Plot3D',
surfaces=[
data_model.Surface(
id='curve1', name='Curve1',
x_scale=data_model.AxisScale.linear,
y_scale=data_model.AxisScale.log,
z_scale=data_model.AxisScale.linear,
x_data_generator=data_model.DataGenerator(
id='xDataGen1',
name='XDataGen1',
variables=[
data_model.Variable(
id='xDataGenVar1', name='XDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model)
],
parameters=[
data_model.Parameter(
id='xDataGenParam1', name='XDataGenParam1', value=2.)
],
math='xDataGenVar1 * xDataGenParam1',
),
y_data_generator=None,
z_data_generator=None,
),
data_model.Surface(
id='curve2',
name='Curve2',
x_scale=data_model.AxisScale.linear,
y_scale=data_model.AxisScale.log,
z_scale=data_model.AxisScale.log,
x_data_generator=None,
y_data_generator=None,
z_data_generator=data_model.DataGenerator(
id='zDataGen1',
name='zDataGen1',
variables=[
data_model.Variable(
id='zDataGenVar1', name='ZDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model)
],
parameters=[
data_model.Parameter(
id='zDataGenParam1', name='ZDataGenParam1', value=2.)
],
math='zDataGenParam1 / ZDataGenParam1',
),
),
]
)
document = data_model.SedDocument(
level=1,
version=3,
models=[model],
simulations=[ss_simulation, one_step_simulation, time_course_simulation],
tasks=[task],
data_generators=[plot2d.curves[0].x_data_generator],
outputs=[report, plot2d, plot3d],
metadata=Metadata(),
)
self.assertEqual(
ss_simulation.to_tuple(),
(
ss_simulation.id,
ss_simulation.name,
(
ss_simulation.algorithm.kisao_id,
(
(
ss_simulation.algorithm.changes[0].kisao_id,
ss_simulation.algorithm.changes[0].new_value,
),
),
),
)
)
self.assertEqual(
one_step_simulation.to_tuple(),
(
one_step_simulation.id,
one_step_simulation.name,
(
one_step_simulation.algorithm.kisao_id,
(
(
one_step_simulation.algorithm.changes[0].kisao_id,
one_step_simulation.algorithm.changes[0].new_value,
),
),
),
one_step_simulation.step
)
)
self.assertEqual(report.to_tuple()[0], report.id)
self.assertEqual(plot2d.to_tuple()[2][0][4], plot2d.curves[0].x_data_generator.id)
self.assertEqual(document.to_tuple(), (
document.level,
document.version,
(model.to_tuple(),),
tuple(none_sorted((ss_simulation.to_tuple(), one_step_simulation.to_tuple(), time_course_simulation.to_tuple()))),
(task.to_tuple(),),
(plot2d.curves[0].x_data_generator.to_tuple(),),
tuple(none_sorted((report.to_tuple(), plot2d.to_tuple(), plot3d.to_tuple()))),
document.metadata.to_tuple(),
))
change = model.changes[0]
change2 = copy.deepcopy(change)
self.assertTrue(change.is_equal(change2))
change2.target = None
self.assertFalse(change.is_equal(change2))
change = model.changes[-1]
change2 = copy.deepcopy(change)
self.assertTrue(change.is_equal(change2))
change2 = copy.deepcopy(change)
change2.target = None
self.assertFalse(change.is_equal(change2))
change2 = copy.deepcopy(change)
change2.parameters[0].value = -1
self.assertFalse(change.is_equal(change2))
change2 = copy.deepcopy(change)
change2.variables[0].id = 'variable_1'
self.assertFalse(change.is_equal(change2))
change2 = copy.deepcopy(change)
change2.variables[0].model.id = 'different_model'
self.assertFalse(change.is_equal(change2))
change2 = copy.deepcopy(change)
change2.math = 'x * y'
self.assertFalse(change.is_equal(change2))
model2 = copy.deepcopy(model)
self.assertTrue(model.is_equal(model2))
model2.id = None
self.assertFalse(model.is_equal(model2))
alg_change = time_course_simulation.algorithm.changes[0]
alg_change_2 = copy.deepcopy(alg_change)
self.assertTrue(alg_change.is_equal(alg_change_2))
alg_change_2.new_value = None
self.assertFalse(alg_change.is_equal(alg_change_2))
alg = time_course_simulation.algorithm
alg2 = copy.deepcopy(alg)
self.assertTrue(alg.is_equal(alg2))
alg2.kisao_id = None
self.assertFalse(alg.is_equal(alg2))
one_step_simulation_2 = copy.deepcopy(one_step_simulation)
self.assertTrue(one_step_simulation.is_equal(one_step_simulation_2))
one_step_simulation_2.step = -1.
self.assertFalse(one_step_simulation.is_equal(one_step_simulation_2))
time_course_simulation_2 = copy.deepcopy(time_course_simulation)
self.assertTrue(time_course_simulation.is_equal(time_course_simulation_2))
time_course_simulation_2.initial_time = -1.
self.assertFalse(time_course_simulation.is_equal(time_course_simulation_2))
task2 = copy.deepcopy(task)
self.assertTrue(task.is_equal(task2))
task2.id = None
self.assertFalse(task.is_equal(task2))
var = copy.deepcopy(report.data_sets[0].data_generator.variables[0])
var2 = copy.deepcopy(var)
self.assertTrue(var.is_equal(var2))
var2.target = None
self.assertFalse(var.is_equal(var2))
data_generator = copy.deepcopy(report.data_sets[0].data_generator)
data_generator_2 = copy.deepcopy(data_generator)
self.assertTrue(data_generator.is_equal(data_generator_2))
data_generator_2.variables = []
self.assertFalse(data_generator.is_equal(data_generator_2))
data_set = copy.deepcopy(report.data_sets[0])
data_set_2 = copy.deepcopy(data_set)
self.assertTrue(data_set.is_equal(data_set_2))
data_set_2.label = None
self.assertFalse(data_set.is_equal(data_set_2))
report2 = copy.deepcopy(report)
self.assertTrue(report.is_equal(report2))
report2.data_sets = []
self.assertFalse(report.is_equal(report2))
curve = copy.deepcopy(plot2d.curves[0])
curve2 = copy.deepcopy(curve)
self.assertTrue(curve.is_equal(curve2))
curve2.x_scale = None
self.assertFalse(curve.is_equal(curve2))
surface = copy.deepcopy(plot3d.surfaces[1])
surface2 = copy.deepcopy(surface)
self.assertTrue(surface.is_equal(surface2))
surface2.z_data_generator = None
self.assertFalse(surface.is_equal(surface2))
plot2d_2 = copy.deepcopy(plot2d)
self.assertTrue(plot2d.is_equal(plot2d_2))
plot2d_2.curves = []
self.assertFalse(plot2d.is_equal(plot2d_2))
plot3d_2 = copy.deepcopy(plot3d)
self.assertTrue(plot3d.is_equal(plot3d_2))
plot3d_2.name = None
self.assertFalse(plot3d.is_equal(plot3d_2))
document_2 = copy.deepcopy(document)
self.assertTrue(document.is_equal(document_2))
document_2.models = []
self.assertFalse(document.is_equal(document_2))
def _build_sed_doc(self, algorithm=None):
if algorithm is None:
algorithm = sedml_data_model.Algorithm(
kisao_id='KISAO_0000088',
changes=[
sedml_data_model.AlgorithmParameterChange(
kisao_id='KISAO_0000211',
new_value='1e-10',
),
],
)
doc = sedml_data_model.SedDocument()
doc.models.append(sedml_data_model.Model(
id='model_1',
source='model_1.xml',
language=sedml_data_model.ModelLanguage.SBML.value,
changes=[],
))
doc.simulations.append(sedml_data_model.UniformTimeCourseSimulation(
id='sim_1_time_course',
algorithm=algorithm,
initial_time=0.,
output_start_time=0.1,
output_end_time=0.2,
number_of_points=20,
))
doc.tasks.append(sedml_data_model.Task(
id='task_1',
model=doc.models[0],
simulation=doc.simulations[0],
))
doc.data_generators.append(sedml_data_model.DataGenerator(
id='data_gen_time',
variables=[
sedml_data_model.Variable(
id='var_time',
symbol=sedml_data_model.Symbol.time,
task=doc.tasks[0],
),
],
math='var_time',
))
doc.data_generators.append(sedml_data_model.DataGenerator(
id='data_gen_BE',
variables=[
sedml_data_model.Variable(
id='var_BE',
target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='BE']",
target_namespaces=self.NAMESPACES,
task=doc.tasks[0],
),
],
math='var_BE',
))
doc.data_generators.append(sedml_data_model.DataGenerator(
id='data_gen_Cdh1',
variables=[
sedml_data_model.Variable(
id='var_Cdh1',
target='/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id="Cdh1"]',
target_namespaces=self.NAMESPACES,
task=doc.tasks[0],
),
],
math='var_Cdh1',
))
doc.data_generators.append(sedml_data_model.DataGenerator(
id='data_gen_Cdc20',
variables=[
sedml_data_model.Variable(
id='var_Cdc20',
target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='Cdc20']",
target_namespaces=self.NAMESPACES,
task=doc.tasks[0],
),
],
math='var_Cdc20',
))
doc.outputs.append(sedml_data_model.Report(
id='report_1',
data_sets=[
sedml_data_model.DataSet(id='data_set_time', label='Time', data_generator=doc.data_generators[0]),
sedml_data_model.DataSet(id='data_set_BE', label='BE', data_generator=doc.data_generators[1]),
sedml_data_model.DataSet(id='data_set_Cdh1', label='Cdh1', data_generator=doc.data_generators[2]),
sedml_data_model.DataSet(id='data_set_Cdc20', label='Cdc20', data_generator=doc.data_generators[3]),
],
))
append_all_nested_children_to_doc(doc)
return doc
def test_exec_sed_task(self):
task = sedml_data_model.Task(
model=sedml_data_model.Model(
source=os.path.join(os.path.dirname(__file__), 'fixtures', 'BIOMD0000000297.edited', 'ex1', 'BIOMD0000000297.xml'),
language=sedml_data_model.ModelLanguage.SBML.value,
changes=[],
),
simulation=sedml_data_model.UniformTimeCourseSimulation(
algorithm=sedml_data_model.Algorithm(
kisao_id='KISAO_0000088',
changes=[
sedml_data_model.AlgorithmParameterChange(
kisao_id='KISAO_0000211',
new_value='1e-10',
),
],
),
initial_time=0.,
output_start_time=10.,
output_end_time=20.,
number_of_points=20,
),
)
variables = [
sedml_data_model.Variable(
id='time',
symbol=sedml_data_model.Symbol.time,
task=task,
),
sedml_data_model.Variable(
id='BE',
target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='BE']",
target_namespaces=self.NAMESPACES,
task=task,
),
sedml_data_model.Variable(
id='Cdh1',
target='/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id="Cdh1"]',
target_namespaces=self.NAMESPACES,
task=task,
),
sedml_data_model.Variable(
id='Cdc20',
target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='Cdc20']",
target_namespaces=self.NAMESPACES,
task=task,
),
sedml_data_model.Variable(
id='kswe_prime',
target="/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='kswe_prime']",
target_namespaces=self.NAMESPACES,
task=task,
),
sedml_data_model.Variable(
id='kswe',
target="/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='kswe']",
target_namespaces=self.NAMESPACES,
task=task,
),
sedml_data_model.Variable(
id='compartment',
target="/sbml:sbml/sbml:model/sbml:listOfCompartments/sbml:compartment[@id='compartment']",
target_namespaces=self.NAMESPACES,
task=task,
),
]
variable_results, _ = core.exec_sed_task(task, variables, log=TaskLog())
self.assertTrue(sorted(variable_results.keys()), sorted([var.id for var in variables]))
self.assertEqual(variable_results[variables[0].id].shape, (task.simulation.number_of_points + 1,))
numpy.testing.assert_allclose(
variable_results['time'],
numpy.linspace(task.simulation.output_start_time, task.simulation.output_end_time, task.simulation.number_of_points + 1),
)
for variable in variables:
self.assertFalse(numpy.any(numpy.isnan(variable_results[variable.id])))
numpy.testing.assert_allclose(
variable_results['kswe_prime'],
numpy.full((task.simulation.number_of_points + 1,), 2.)
)
numpy.testing.assert_allclose(
variable_results['compartment'],
numpy.full((task.simulation.number_of_points + 1,), 1.)
)
def test_exec_sedml_docs_in_combine_archive_with_all_algorithms(self):
algorithms = gen_algorithms_from_specs(os.path.join(os.path.dirname(__file__), '..', 'biosimulators.json')).values()
for i_alg, alg in enumerate(algorithms):
alg_props = KISAO_ALGORITHM_MAP[alg.kisao_id]
print('Testing algorithm {} of {}: {} ({})'.format(i_alg + 1, len(algorithms), alg_props.name, alg.kisao_id))
alg.changes = []
for param_kisao_id, param_props in alg_props.parameters.items():
if param_kisao_id == 'KISAO_0000488':
new_value = '1'
else:
new_value = param_props.default
if isinstance(new_value, enum.Enum):
new_value = new_value.value
if new_value is None:
new_value = ''
else:
new_value = str(new_value)
alg.changes.append(sedml_data_model.AlgorithmParameterChange(
kisao_id=param_kisao_id,
new_value=new_value,
))
doc, archive_filename = self._build_combine_archive(algorithm=alg)
variables = []
for data_gen in doc.data_generators:
for var in data_gen.variables:
variables.append(var)
doc.tasks[0].model.source = os.path.join(os.path.dirname(__file__),
'fixtures', 'BIOMD0000000297.edited', 'ex1', 'BIOMD0000000297.xml')
results, _ = core.exec_sed_task(doc.tasks[0], variables, log=TaskLog())
self.assertEqual(set(results.keys()), set(var.id for var in variables))
alg.changes = []
for param_kisao_id, param_props in alg_props.parameters.items():
new_value = param_props.default
if isinstance(new_value, enum.Enum):
new_value = new_value.value
if new_value is not None:
new_value = str(new_value)
alg.changes.append(sedml_data_model.AlgorithmParameterChange(
kisao_id=param_kisao_id,
new_value=new_value,
))
doc, archive_filename = self._build_combine_archive(algorithm=alg)
out_dir = os.path.join(self.dirname, alg.kisao_id)
config = get_config()
config.REPORT_FORMATS = [report_data_model.ReportFormat.h5, report_data_model.ReportFormat.csv]
config.BUNDLE_OUTPUTS = True
config.KEEP_INDIVIDUAL_OUTPUTS = True
_, log = core.exec_sedml_docs_in_combine_archive(archive_filename, out_dir, config=config)
if log.exception:
raise log.exception
self._assert_combine_archive_outputs(doc, out_dir)
def test_exec_sed_task_errors(self):
with mock.patch.dict('os.environ', {'ALGORITHM_SUBSTITUTION_POLICY': 'NONE'}):
task = sedml_data_model.Task()
task.model = sedml_data_model.Model(id='model')
task.model.source = os.path.join(self.dirname, 'valid-model.xml')
with open(task.model.source, 'w') as file:
file.write('<?xml version="1.0" encoding="UTF-8" standalone="no"?>')
file.write('<sbml2 xmlns="http://www.sbml.org/sbml/level2/version4" level="2" version="4">')
file.write(' <model id="model">')
file.write(' </model>')
file.write('</sbml2>')
task.model.language = sedml_data_model.ModelLanguage.SBML
task.model.changes = []
task.simulation = sedml_data_model.UniformTimeCourseSimulation(
id='simulation',
algorithm=sedml_data_model.Algorithm(kisao_id='KISAO_0000448'),
initial_time=-10.,
output_start_time=10.,
output_end_time=20.1,
number_of_points=10,
)
variables = []
with self.assertRaisesRegex(ValueError, 'could not be imported'):
core.exec_sed_task(task, variables, log=TaskLog())
task.model.source = os.path.join(os.path.dirname(__file__), 'fixtures', 'BIOMD0000000297.edited', 'ex1', 'BIOMD0000000297.xml')
with self.assertRaisesRegex(AlgorithmCannotBeSubstitutedException, 'Algorithms cannot be substituted'):
core.exec_sed_task(task, variables, log=TaskLog())
task.simulation.algorithm.kisao_id = 'KISAO_0000029'
task.simulation.algorithm.changes = [
sedml_data_model.AlgorithmParameterChange(kisao_id='KISAO_0000531'),
]
with self.assertRaisesRegex(NotImplementedError, 'is not supported. Parameter must'):
core.exec_sed_task(task, variables, log=TaskLog())
task.simulation.algorithm.changes[0].kisao_id = 'KISAO_0000488'
task.simulation.algorithm.changes[0].new_value = 'abc'
with self.assertRaisesRegex(ValueError, 'not a valid integer'):
core.exec_sed_task(task, variables, log=TaskLog())
task.simulation.algorithm.changes[0].new_value = '10'
with self.assertRaisesRegex(NotImplementedError, 'is not supported. Initial time must be >= 0'):
core.exec_sed_task(task, variables, log=TaskLog())
task.simulation.initial_time = 0.
with self.assertRaisesRegex(NotImplementedError, 'must specify an integer'):
core.exec_sed_task(task, variables, log=TaskLog())
task.simulation.output_end_time = 20.
variables = [
sedml_data_model.Variable(id='var_1', symbol='unsupported', task=task)
]
with self.assertRaisesRegex(NotImplementedError, 'Symbols must be'):
core.exec_sed_task(task, variables, log=TaskLog())
variables = [
sedml_data_model.Variable(id='var_1', symbol=sedml_data_model.Symbol.time, task=task),
sedml_data_model.Variable(id='var_2', target='/invalid:target', target_namespaces={'invalid': 'invalid'}, task=task),
]
with self.assertRaisesRegex(ValueError, 'XPaths must reference unique objects.'):
core.exec_sed_task(task, variables, log=TaskLog())
variables = [
sedml_data_model.Variable(id='var_1', symbol=sedml_data_model.Symbol.time, task=task),
sedml_data_model.Variable(
id='BE',
target="/sbml:sbml/sbml:model/sbml:listOfReactions/sbml:reaction[@id='R1']",
target_namespaces=self.NAMESPACES,
task=task
),
]
with self.assertRaisesRegex(ValueError, 'Targets must have'):
core.exec_sed_task(task, variables, log=TaskLog())
variables = [
sedml_data_model.Variable(
id='time',
symbol=sedml_data_model.Symbol.time,
task=task
),
sedml_data_model.Variable(
id='BE',
target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='BE']",
target_namespaces=self.NAMESPACES,
task=task,
),
sedml_data_model.Variable(
id='Cdh1',
target='/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id="Cdh1"]',
target_namespaces=self.NAMESPACES,
task=task,
),
sedml_data_model.Variable(
id='Cdc20',
target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='Cdc20']",
target_namespaces=self.NAMESPACES,
task=task,
),
]
task.simulation.algorithm.kisao_id = 'KISAO_0000088'
task.simulation.algorithm.changes[0].kisao_id = 'KISAO_0000211'
task.simulation.algorithm.changes[0].new_value = '1e-10'
variable_results, _ = core.exec_sed_task(task, variables, log=TaskLog())
self.assertTrue(sorted(variable_results.keys()), sorted([var.id for var in variables]))
self.assertEqual(variable_results[variables[0].id].shape, (task.simulation.number_of_points + 1,))
numpy.testing.assert_allclose(
variable_results['time'],
numpy.linspace(task.simulation.output_start_time, task.simulation.output_end_time, task.simulation.number_of_points + 1),
)
# algorithm substitution
task = sedml_data_model.Task(
model=sedml_data_model.Model(
source=os.path.join(os.path.dirname(__file__), 'fixtures', 'BIOMD0000000297.edited', 'ex1', 'BIOMD0000000297.xml'),
language=sedml_data_model.ModelLanguage.SBML.value,
changes=[],
),
simulation=sedml_data_model.UniformTimeCourseSimulation(
algorithm=sedml_data_model.Algorithm(
kisao_id='KISAO_0000088',
changes=[
sedml_data_model.AlgorithmParameterChange(
kisao_id='KISAO_0000211',
new_value='1e-10',
),
],
),
initial_time=0.,
output_start_time=10.,
output_end_time=20.,
number_of_points=20,
),
)
variables = []
task.simulation.algorithm.changes[0].new_value = 'not a number'
with mock.patch.dict('os.environ', {'ALGORITHM_SUBSTITUTION_POLICY': 'NONE'}):
with self.assertRaisesRegex(ValueError, 'is not a valid'):
core.exec_sed_task(task, variables, log=TaskLog())
with mock.patch.dict('os.environ', {'ALGORITHM_SUBSTITUTION_POLICY': 'SIMILAR_VARIABLES'}):
with self.assertWarnsRegex(BioSimulatorsWarning, 'Unsuported value'):
core.exec_sed_task(task, variables, log=TaskLog())
task.simulation.algorithm.changes[0].kisao_id = 'KISAO_0000531'
with mock.patch.dict('os.environ', {'ALGORITHM_SUBSTITUTION_POLICY': 'NONE'}):
with self.assertRaisesRegex(NotImplementedError, 'is not supported'):
core.exec_sed_task(task, variables, log=TaskLog())
with mock.patch.dict('os.environ', {'ALGORITHM_SUBSTITUTION_POLICY': 'SIMILAR_VARIABLES'}):
with self.assertWarnsRegex(BioSimulatorsWarning, 'was ignored because it is not supported'):
core.exec_sed_task(task, variables, log=TaskLog())
def test_write_read(self):
shutil.copy(
os.path.join(os.path.dirname(__file__), '..', 'fixtures',
'BIOMD0000000075.xml'),
os.path.join(self.tmp_dir, 'model.sbml'))
model1 = data_model.Model(
id='model1',
name='Model1',
source='model.sbml',
language='urn:sedml:language:sbml',
changes=[
data_model.ModelAttributeChange(
target='/sbml:sbml/sbml:model[id=\'a\']/@id',
new_value='234'),
data_model.ModelAttributeChange(
target='/sbml:sbml/sbml:model[id=\'b\']/@id',
new_value='432'),
data_model.AddElementModelChange(
target=
'/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters',
new_elements=
'<sbml:parameter xmlns:sbml="http://www.sbml.org/sbml/level2/version3" id="new_parameter" value="1.0"/>'
),
data_model.AddElementModelChange(
target=
'/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters',
new_elements=
'<parameter id="new_parameter_1" value="1.0"/><parameter id="new_parameter_2" value="1.0"/>'
),
data_model.ReplaceElementModelChange(
target=
'/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters/sbml:parameter[@id=\'p1\']',
new_elements='<parameter id="p1" value="1.0"/>'),
data_model.ReplaceElementModelChange(
target=
'/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters/sbml:parameter[@id=\'p1\']',
new_elements=
'<parameter id="p1" value="1.0"/><parameter id="p1" value="1.0"/>'
),
data_model.RemoveElementModelChange(
target=
'/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters/sbml:parameter[@id=\'p1\']',
),
data_model.ComputeModelChange(
target=
'/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters/sbml:parameter[@id=\'p1\']',
parameters=[
data_model.Parameter(id='a', value=1.5),
data_model.Parameter(id='b', value=2.25),
],
variables=[
data_model.Variable(
id='x',
target='variable_target_x',
),
data_model.Variable(
id='y',
target='variable_target_y',
),
],
math='a * x + b * y',
),
],
)
for var in model1.changes[-1].variables:
var.model = model1
model2 = data_model.Model(
id='model2',
name='Model2',
source='model.sbml',
language='urn:sedml:language:sbml',
changes=[
data_model.ModelAttributeChange(
target='/sbml:sbml/sbml:model[id=\'a\']/@id',
new_value='234',
),
data_model.ModelAttributeChange(
target='/sbml:sbml/sbml:model[id=\'b\']/@id',
new_value='432'),
],
)
ss_simulation = data_model.SteadyStateSimulation(
id='simulation1',
name='Simulation1',
algorithm=data_model.Algorithm(
kisao_id='KISAO_0000019',
changes=[
data_model.AlgorithmParameterChange(
kisao_id='KISAO_0000211', new_value='1.234'),
]),
)
one_step_simulation = data_model.OneStepSimulation(
id='simulation2',
name='Simulation2',
algorithm=data_model.Algorithm(
kisao_id='KISAO_0000019',
changes=[
data_model.AlgorithmParameterChange(
kisao_id='KISAO_0000211', new_value='1.234'),
]),
step=10.)
time_course_simulation = data_model.UniformTimeCourseSimulation(
id='simulation3',
name='Simulation3',
algorithm=data_model.Algorithm(
kisao_id='KISAO_0000019',
changes=[
data_model.AlgorithmParameterChange(
kisao_id='KISAO_0000209', new_value='1.234'),
data_model.AlgorithmParameterChange(
kisao_id='KISAO_0000211', new_value='4.321'),
]),
initial_time=10.,
output_start_time=20.,
output_end_time=30,
number_of_steps=10)
task1 = data_model.Task(id='task1',
name='Task1',
model=model1,
simulation=time_course_simulation)
task2 = data_model.Task(id='task2',
name='Task2',
model=model2,
simulation=time_course_simulation)
task3 = data_model.RepeatedTask(
id='task3',
name='Task3',
reset_model_for_each_iteration=True,
changes=[],
sub_tasks=[
data_model.SubTask(task=task1, order=1),
data_model.SubTask(task=task2, order=2),
],
ranges=[
data_model.UniformRange(
id='range1',
start=10.,
end=20.,
number_of_steps=4,
type=data_model.UniformRangeType.linear),
data_model.UniformRange(id='range2',
start=10.,
end=20.,
number_of_steps=4,
type=data_model.UniformRangeType.log),
data_model.VectorRange(id='range3',
values=[3., 5., 7., 11., 13.]),
],
)
task3.ranges.append(
data_model.FunctionalRange(
id='range4',
range=task3.ranges[0],
parameters=[
data_model.Parameter(
id='x2',
value=2.0,
),
],
variables=[
data_model.Variable(
id='y2',
model=model1,
target=
"/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:Parameter[@id='param_1']",
),
],
math='{} * {} + {}'.format(task3.ranges[0].id, 'x2', 'y2'),
), )
task3.range = task3.ranges[1]
task3.changes.append(
data_model.SetValueComputeModelChange(
model=model1,
target=
"/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='p1']",
range=task3.ranges[0],
parameters=[],
variables=[],
math='range1',
), )
task3.changes.append(
data_model.SetValueComputeModelChange(
model=model1,
target=
"/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='p1']",
range=task3.ranges[0],
parameters=[],
variables=[
data_model.Variable(
id='range1_var1',
model=model1,
target=
"/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='p1']",
)
],
math='range1 * range1_var1',
), )
task3.changes.append(
data_model.SetValueComputeModelChange(
model=model1,
target=
"/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='p1']",
range=task3.ranges[0],
parameters=[
data_model.Parameter(id='range1_p1', value=2.5),
],
variables=[],
math='range1 * range1_p1',
), )
report = data_model.Report(
id='report1',
name='Report1',
data_sets=[
data_model.DataSet(
id='dataset1',
name='Dataset1',
label='Dataset-1',
data_generator=data_model.DataGenerator(
id='dataGen1',
name='DataGen1',
variables=[
data_model.Variable(
id='DataGenVar1',
name='DataGenVar1',
target='/sbml:sbml/sbml:model/@id',
task=task1,
)
],
parameters=[
data_model.Parameter(id='DataGenParam1',
name='DataGenParam1',
value=2.)
],
math='DataGenVar1 - DataGenParam1',
)),
],
)
plot2d = data_model.Plot2D(
id='plot2d',
name='Plot2D',
curves=[
data_model.Curve(
id='curve1',
name='Curve1',
x_scale=data_model.AxisScale.linear,
y_scale=data_model.AxisScale.log,
x_data_generator=data_model.DataGenerator(
id='xDataGen2',
name='XDataGen2',
variables=[
data_model.Variable(id='xDataGenVar1',
name='XDataGenVar1',
symbol='urn:sedml:symbol:time',
task=task2)
],
parameters=[
data_model.Parameter(id='xDataGenParam1',
name='XDataGenParam1',
value=2.)
],
math='xDataGenVar1 * xDataGenParam1',
),
y_data_generator=data_model.DataGenerator(
id='yDataGen3',
name='yDataGen3',
variables=[
data_model.Variable(
id='xDataGenVar2',
name='XDataGenVar1',
target='/sbml:sbml/sbml:model/@id',
task=task2,
)
],
parameters=[
data_model.Parameter(id='xDataGenParam2',
name='XDataGenParam1',
value=2.)
],
math='xDataGenVar2 * xDataGenParam2',
),
),
data_model.Curve(
id='curve2',
name='Curve2',
x_scale=data_model.AxisScale.linear,
y_scale=data_model.AxisScale.log,
x_data_generator=data_model.DataGenerator(
id='yDataGen4',
name='yDataGen4',
variables=[
data_model.Variable(
id='yDataGenVar1',
target='/sbml:sbml/sbml:model/@id',
task=task1,
)
],
parameters=[
data_model.Parameter(id='YDataGenParam1', value=2.)
],
math='yDataGenVar1 + YDataGenParam1',
),
y_data_generator=data_model.DataGenerator(
id='yDataGen5',
name='yDataGen5',
variables=[
data_model.Variable(
id='yDataGenVar2',
target='/sbml:sbml/sbml:model/@id',
task=task1,
)
],
parameters=[
data_model.Parameter(id='YDataGenParam2',
value=2.),
],
math='yDataGenVar2 + YDataGenParam2',
),
),
])
plot3d = data_model.Plot3D(
id='plot3d',
name='Plot3D',
surfaces=[
data_model.Surface(
id='surface1',
name='Surface1',
x_scale=data_model.AxisScale.linear,
y_scale=data_model.AxisScale.log,
z_scale=data_model.AxisScale.linear,
x_data_generator=data_model.DataGenerator(
id='xDataGen6',
name='XDataGen6',
variables=[
data_model.Variable(
id='xDataGenVar3',
name='XDataGenVar2',
target='/sbml:sbml/sbml:model/@id',
task=task2,
)
],
parameters=[
data_model.Parameter(id='xDataGenParam3',
name='XDataGenParam1',
value=2.)
],
math='xDataGenVar3 * xDataGenParam3',
),
y_data_generator=data_model.DataGenerator(
id='xDataGen8',
name='XDataGen8',
variables=[
data_model.Variable(
id='xDataGenVar4',
name='XDataGenVar2',
target='/sbml:sbml/sbml:model/@id',
task=task2,
)
],
parameters=[
data_model.Parameter(id='xDataGenParam4',
name='XDataGenParam1',
value=2.)
],
math='xDataGenVar4 * xDataGenParam4',
),
z_data_generator=data_model.DataGenerator(
id='xDataGen9',
name='XDataGen9',
variables=[
data_model.Variable(
id='xDataGenVar5',
name='XDataGenVar2',
target='/sbml:sbml/sbml:model/@id',
task=task2,
)
],
parameters=[
data_model.Parameter(id='xDataGenParam5',
name='XDataGenParam1',
value=2.)
],
math='xDataGenVar5 * xDataGenParam5',
),
),
data_model.Surface(
id='surface2',
name='Surface2',
x_scale=data_model.AxisScale.linear,
y_scale=data_model.AxisScale.log,
z_scale=data_model.AxisScale.log,
x_data_generator=data_model.DataGenerator(
id='xDataGen10',
name='XDataGen10',
variables=[
data_model.Variable(
id='xDataGenVar6',
name='XDataGenVar2',
target='/sbml:sbml/sbml:model/@id',
task=task2,
)
],
parameters=[
data_model.Parameter(id='xDataGenParam6',
name='XDataGenParam1',
value=2.)
],
math='xDataGenVar6 * xDataGenParam6',
),
y_data_generator=data_model.DataGenerator(
id='xDataGen11',
name='XDataGen11',
variables=[
data_model.Variable(
id='xDataGenVar7',
name='XDataGenVar2',
target='/sbml:sbml/sbml:model/@id',
task=task2,
)
],
parameters=[
data_model.Parameter(id='xDataGenParam7',
name='XDataGenParam1',
value=2.)
],
math='xDataGenVar7 * xDataGenParam7',
),
z_data_generator=data_model.DataGenerator(
id='xDataGen12',
name='XDataGen12',
variables=[
data_model.Variable(
id='xDataGenVar8',
name='XDataGenVar2',
target='/sbml:sbml/sbml:model/@id',
task=task2,
)
],
parameters=[
data_model.Parameter(id='xDataGenParam8',
name='XDataGenParam1',
value=2.)
],
math='xDataGenVar8 * xDataGenParam8',
),
),
])
now = datetime.datetime(2020,
1,
2,
1,
2,
3,
tzinfo=dateutil.tz.tzutc())
document = data_model.SedDocument(
level=1,
version=3,
models=[model1],
simulations=[time_course_simulation],
tasks=[task1],
data_generators=([d.data_generator for d in report.data_sets]),
outputs=[report],
metadata=Metadata(
description="description",
tags=['tag-1', 'tag-2'],
authors=[
Person(given_name='first',
other_name='middle',
family_name='last'),
],
references=ExternalReferences(
identifiers=None,
citations=[
Citation(title='creative title',
authors='Author-1 & Author-2',
journal='major journal',
volume='10',
issue='20',
pages='30-40',
year=2020,
identifiers=[
Identifier(
namespace="doi",
id='10.0/1.0',
url="https://doi.org/10.0/1.0"),
])
]),
license=OntologyTerm(
namespace='SPDX',
id='MIT',
url='https://spdx.org/licenses/MIT.html',
),
created=now,
updated=now,
),
)
self._set_target_namespaces(document)
filename = os.path.join(self.tmp_dir, 'test.xml')
io.SedmlSimulationWriter().run(document, filename)
document2 = io.SedmlSimulationReader().run(filename)
self.assertTrue(document.is_equal(document2))
document = data_model.SedDocument(
level=1,
version=3,
models=[model1, model2],
simulations=[
ss_simulation, one_step_simulation, time_course_simulation
],
tasks=[task1, task2, task3],
data_generators=([d.data_generator for d in report.data_sets] +
[c.x_data_generator for c in plot2d.curves] +
[c.y_data_generator for c in plot2d.curves] +
[s.x_data_generator for s in plot3d.surfaces] +
[s.y_data_generator for s in plot3d.surfaces] +
[s.z_data_generator for s in plot3d.surfaces]),
outputs=[report, plot2d, plot3d],
metadata=Metadata(
description="description",
license=OntologyTerm(
namespace='SPDX',
id='MIT',
url='https://spdx.org/licenses/MIT.html',
),
),
)
self._set_target_namespaces(document)
filename = os.path.join(self.tmp_dir, 'test.xml')
io.SedmlSimulationWriter().run(document, filename)
document2 = io.SedmlSimulationReader().run(filename)
self.assertTrue(document.is_equal(document2))
document2.metadata.license.namespace = None
document2.metadata.license.url = None
io.SedmlSimulationWriter().run(document2, filename)
document3 = io.SedmlSimulationReader().run(filename)
self.assertTrue(document2.is_equal(document3))
document3.metadata = None
io.SedmlSimulationWriter().run(document3, filename)
document4 = io.SedmlSimulationReader().run(filename)
self.assertTrue(document4.is_equal(document3))
document.models[0].changes[
2].new_elements = '<parameter id="new_parameter" value="1.0/>'
document.models[0].changes[
4].new_elements = '<parameter id="new_parameter" value="1.0"/>'
with self.assertRaisesRegex(ValueError, 'invalid XML'):
io.SedmlSimulationWriter().run(document, filename)
document.models[0].changes[
2].new_elements = '<parameter id="new_parameter" value="1.0"/>'
document.models[0].changes[
4].new_elements = '<parameter id="new_parameter" value="1.0/>'
with self.assertRaisesRegex(ValueError, 'invalid XML'):
io.SedmlSimulationWriter().run(document, filename)
